Add files using upload-large-folder tool

classification/unipredict/arnavsmayan-vehicle-manufacturing-dataset/train.csv

@@ -0,0 +1,1792 @@
+Car ID,Brand,Model,Year,Color,Mileage,Location,Price
+1334,Hyundai,Santa Fe,2019,Red,55000,Seattle,22000
+1976,Hyundai,Elantra,2017,Black,40000,San Francisco,27000
+734,Chevrolet,Cruze,2019,Red,45000,Miami,26000
+40,Hyundai,Elantra,2020,Red,35000,Seattle,18000
+1781,Chevrolet,Malibu,2016,Blue,25000,Houston,19000
+1839,Chevrolet,Equinox,2019,Black,45000,Miami,26000
+719,Honda,Odyssey,2020,White,55000,New York,16000
+513,Honda,Civic,2020,Blue,60000,New York,25000
+514,Ford,Focus,2020,Silver,55000,Chicago,24000
+1840,Hyundai,Kona,2016,Blue,30000,San Francisco,29000
+754,Chevrolet,Tahoe,2016,Black,35000,Miami,25000
+1817,Hyundai,Palisade,2020,Silver,50000,San Francisco,18000
+259,Hyundai,Elantra,2018,Red,55000,Seattle,19000
+491,Ford,Escape,2016,Blue,30000,Chicago,29000
+21,Toyota,4Runner,2015,Silver,70000,Los Angeles,27000
+233,Chevrolet,Equinox,2020,Black,40000,Miami,25000
+1656,Hyundai,Sonata,2016,Red,50000,Seattle,21000
+1620,Chevrolet,Equinox,2016,Black,45000,Miami,23000
+751,Toyota,4Runner,2019,Silver,60000,Los Angeles,19000
+32,Honda,HR-V,2018,White,50000,New York,15000
+1527,Honda,Fit,2017,Gray,55000,Atlanta,15000
+845,Honda,Accord,2016,White,45000,New York,26000
+1687,Ford,Mustang,2016,Yellow,35000,Phoenix,24000
+807,Chevrolet,Cruze,2016,Red,35000,Miami,27000
+1692,Ford,Escape,2018,White,40000,Chicago,27000
+1742,Ford,Edge,2016,Blue,40000,Chicago,14000
+1129,Chevrolet,Spark,2017,Blue,45000,Miami,26000
+1288,Honda,Civic,2019,Gray,50000,Atlanta,14000
+942,Toyota,Sienna,2020,Red,35000,Dallas,28000
+928,Honda,CR-V,2020,White,40000,New York,15000
+1464,Chevrolet,Cruze,2019,Red,35000,Miami,25000
+1201,Ford,EcoSport,2020,Red,30000,Chicago,29000
+1232,Chevrolet,Tahoe,2019,Black,30000,Miami,29000
+1121,Toyota,Sienna,2018,Red,55000,Dallas,22000
+841,Ford,Fiesta,2016,Blue,35000,Phoenix,25000
+1085,Ford,Edge,2020,Blue,40000,Chicago,25000
+77,Chevrolet,Cruze,2015,Red,25000,Miami,19000
+1448,Toyota,4Runner,2016,Silver,50000,Los Angeles,15000
+968,Chevrolet,Traverse,2018,Black,70000,Houston,18000
+591,Honda,Accord,2017,White,50000,Atlanta,21000
+479,Toyota,Camry,2020,Silver,40000,Los Angeles,21000
+1648,Honda,Accord,2019,White,35000,New York,20000
+1716,Honda,Civic,2019,White,30000,Atlanta,23000
+355,Ford,Edge,2019,Blue,55000,Chicago,12000
+1683,Chevrolet,Cruze,2017,Red,35000,Miami,20000
+1836,Toyota,Rav29,2015,Gray,70000,Los Angeles,28000
+1936,Ford,Fiesta,2018,Blue,35000,Phoenix,28000
+350,Ford,Explorer,2017,Blue,35000,Phoenix,27000
+1711,Honda,HR-V,2016,White,30000,New York,18000
+820,Honda,Pilot,2015,Gray,45000,Atlanta,16000
+80,Honda,Accord,2015,White,55000,Atlanta,16000
+496,Ford,Explorer,2020,Blue,60000,Phoenix,14000
+1660,Chevrolet,Equinox,2019,Black,55000,Miami,12000
+13,Ford,Escape,2019,White,40000,Chicago,21000
+11,Toyota,Rav4,2017,Gray,55000,Los Angeles,19000
+599,Hyundai,Kona,2018,Blue,55000,San Francisco,12000
+655,Toyota,Yaris,2016,Black,60000,Los Angeles,25000
+728,Toyota,Yaris,2017,Black,50000,Los Angeles,23000
+1249,Ford,Mustang,2015,Yellow,50000,Phoenix,18000
+665,Ford,Mustang,2015,Yellow,35000,Phoenix,27000
+1709,Hyundai,Venue,2019,Silver,40000,Seattle,15000
+486,Ford,Fusion,2020,White,35000,Phoenix,25000
+594,Hyundai,Sonata,2018,Blue,35000,Seattle,27000
+191,Hyundai,Genesis,2020,Black,40000,San Francisco,18000
+460,Honda,Odyssey,2018,White,35000,New York,14000
+1517,Honda,Pilot,2018,White,35000,Atlanta,27000
+534,Ford,Edge,2018,Blue,50000,Chicago,17000
+999,Hyundai,Sonata,2020,Red,40000,Seattle,15000
+1088,Toyota,Sienna,2017,Red,50000,Dallas,21000
+1982,Toyota,Rav31,2018,Gray,30000,Los Angeles,29000
+1602,Chevrolet,Malibu,2017,Blue,35000,Houston,18000
+949,Honda,Civic,2018,Blue,35000,New York,27000
+1807,Hyundai,Tucson,2017,Red,50000,San Francisco,15000
+68,Ford,Fusion,2018,White,50000,Phoenix,15000
+1167,Toyota,Camry,2016,White,55000,Los Angeles,12000
+1803,Toyota,Corolla,2016,Silver,50000,Los Angeles,15000
+1875,Hyundai,Sonata,2017,Red,40000,Seattle,17000
+178,Honda,HR-V,2017,White,55000,New York,15000
+1769,Honda,Pilot,2015,Gray,30000,Atlanta,29000
+1718,Chevrolet,Cruze,2020,Black,45000,Houston,18000
+1161,Toyota,Sienna,2019,Red,30000,Dallas,29000
+333,Toyota,Camry,2018,Silver,40000,Los Angeles,18000
+1352,Ford,Fiesta,2017,Blue,55000,Phoenix,14000
+740,Hyundai,Sonata,2018,Blue,60000,Seattle,14000
+1241,Honda,Civic,2020,Blue,35000,New York,14000
+104,Toyota,Yaris,2018,Black,40000,Los Angeles,17000
+750,Hyundai,Santa Fe,2018,Red,35000,Seattle,18000
+877,Toyota,Camry,2018,White,30000,Los Angeles,29000
+741,Toyota,Rav14,2017,Gray,55000,Los Angeles,12000
+1526,Toyota,Sienna,2017,Red,70000,Dallas,12000
+1259,Ford,Explorer,2016,White,50000,Phoenix,23000
+686,Chevrolet,Malibu,2016,Blue,50000,Houston,17000
+81,Ford,Mustang,2020,Yellow,50000,Phoenix,14000
+1010,Toyota,4Runner,2015,Silver,60000,Los Angeles,25000
+901,Hyundai,Palisade,2016,Silver,40000,San Francisco,18000
+1824,Toyota,Camry,2015,White,40000,Los Angeles,18000
+1877,Honda,CR-V,2020,White,55000,New York,12000
+1664,Ford,Explorer,2020,Blue,55000,Phoenix,12000
+262,Ford,Mustang,2015,Blue,40000,Chicago,18000
+1064,Honda,Accord,2018,White,60000,New York,14000
+106,Ford,EcoSport,2017,Red,70000,Chicago,12000
+1601,Ford,Fusion,2019,White,45000,Phoenix,16000
+1626,Hyundai,Santa Fe,2017,Red,45000,Seattle,26000
+792,Honda,Odyssey,2016,White,55000,New York,19000
+1402,Hyundai,Kona,2019,Blue,40000,San Francisco,21000
+1300,Chevrolet,Traverse,2020,Black,55000,Houston,22000
+1868,Ford,Mustang,2019,Blue,55000,Chicago,22000
+1893,Ford,Fusion,2020,White,50000,Phoenix,17000
+1036,Chevrolet,Equinox,2018,Black,50000,Miami,18000
+349,Honda,Pilot,2018,White,30000,Atlanta,29000
+65,Hyundai,Palisade,2019,Silver,30000,San Francisco,29000
+945,Chevrolet,Malibu,2015,Blue,55000,Houston,22000
+539,Ford,Fusion,2019,White,50000,Phoenix,18000
+544,Ford,EcoSport,2016,Red,50000,Chicago,17000
+64,Chevrolet,Tahoe,2018,Black,45000,Miami,26000
+624,Hyundai,Elantra,2018,Red,55000,Seattle,22000
+1891,Toyota,Sienna,2018,Red,70000,Dallas,18000
+1165,Hyundai,Venue,2017,Silver,40000,Seattle,17000
+1186,Ford,Explorer,2017,White,35000,Phoenix,20000
+1491,Toyota,Yaris,2015,Black,55000,Los Angeles,12000
+1017,Ford,Fusion,2016,White,50000,Phoenix,21000
+1765,Ford,Escape,2020,White,70000,Chicago,28000
+1025,Ford,Focus,2018,Silver,55000,Chicago,12000
+296,Chevrolet,Cruze,2015,Red,30000,Miami,23000
+1388,Toyota,Camry,2015,White,45000,Los Angeles,16000
+586,Honda,Civic,2016,Blue,50000,New York,23000
+1441,Chevrolet,Equinox,2020,Black,70000,Miami,27000
+53,Ford,Escape,2019,Blue,40000,Chicago,21000
+1191,Ford,Edge,2018,Blue,70000,Chicago,20000
+1898,Honda,Civic,2018,Blue,50000,New York,23000
+806,Ford,Focus,2018,Silver,30000,Chicago,29000
+120,Honda,Civic,2015,Gray,40000,Atlanta,18000
+1805,Ford,Escape,2015,Blue,60000,Chicago,14000
+909,Ford,EcoSport,2020,Red,50000,Chicago,24000
+1040,Ford,Explorer,2017,White,55000,Phoenix,19000
+1942,Chevrolet,Camaro,2018,Red,30000,Miami,29000
+987,Ford,Fiesta,2017,Blue,45000,Phoenix,26000
+51,Toyota,Corolla,2017,Silver,55000,Los Angeles,19000
+765,Hyundai,Accent,2018,Silver,70000,San Francisco,20000
+121,Ford,Fusion,2015,White,35000,Phoenix,20000
+86,Ford,Escape,2020,White,35000,Chicago,20000
+231,Honda,CR-V,2019,Red,50000,New York,23000
+575,Chevrolet,Tahoe,2015,Black,30000,Miami,18000
+503,Hyundai,Palisade,2017,Silver,25000,San Francisco,19000
+1994,Ford,Edge,2020,Blue,25000,Chicago,19000
+1881,Toyota,Rav29,2017,Gray,70000,Dallas,12000
+698,Toyota,Camry,2019,Silver,40000,Los Angeles,27000
+1669,Ford,Edge,2015,Blue,55000,Chicago,15000
+1828,Ford,Focus,2016,Silver,40000,Chicago,21000
+440,Honda,Civic,2016,Blue,45000,New York,18000
+1032,Hyundai,Sonata,2016,Blue,40000,Seattle,14000
+198,Honda,CR-V,2015,White,55000,New York,22000
+344,Honda,CR-V,2020,White,35000,New York,25000
+1112,Honda,Pilot,2015,Gray,50000,Atlanta,17000
+225,Toyota,Corolla,2018,Gray,30000,Dallas,23000
+1004,Hyundai,Tucson,2020,Red,50000,San Francisco,14000
+141,Ford,Fusion,2018,White,60000,Phoenix,14000
+281,Honda,Odyssey,2015,White,50000,New York,15000
+167,Toyota,4Runner,2019,Silver,30000,Los Angeles,29000
+1041,Chevrolet,Traverse,2018,Black,50000,Houston,17000
+1685,Toyota,Corolla,2017,Gray,50000,Dallas,23000
+299,Honda,Accord,2020,White,35000,Atlanta,20000
+905,Chevrolet,Malibu,2019,Blue,40000,Houston,21000
+1761,Chevrolet,Impala,2015,Black,50000,Houston,24000
+1502,Honda,Accord,2015,White,55000,New York,19000
+28,Ford,Fusion,2018,White,50000,Phoenix,15000
+1618,Honda,CR-V,2016,Red,55000,New York,22000
+59,Chevrolet,Traverse,2019,Black,40000,Houston,27000
+1287,Toyota,Avalon,2020,Silver,55000,Dallas,16000
+1080,Ford,Explorer,2018,Blue,35000,Phoenix,20000
+875,Toyota,Camry,2017,White,50000,Los Angeles,21000
+563,Honda,CR-V,2016,White,35000,New York,27000
+1759,Honda,Accord,2015,White,70000,Atlanta,20000
+1301,Hyundai,Santa Fe,2018,Red,50000,Seattle,21000
+1738,Chevrolet,Traverse,2017,Black,35000,Houston,14000
+1605,Toyota,Camry,2020,White,40000,Los Angeles,22000
+148,Honda,Civic,2016,Blue,25000,New York,19000
+420,Hyundai,Tucson,2016,Red,30000,San Francisco,29000
+1253,Honda,CR-V,2018,Red,55000,New York,19000
+1390,Ford,Focus,2017,Silver,60000,Chicago,19000
+295,Ford,Focus,2016,Silver,55000,Chicago,19000
+1204,Toyota,Prius,2020,Gray,50000,Dallas,15000
+716,Chevrolet,Traverse,2017,Black,25000,Houston,19000
+274,Hyundai,Tucson,2015,Red,70000,San Francisco,28000
+1021,Toyota,Camry,2019,White,55000,Los Angeles,12000
+1062,Hyundai,Elantra,2018,Red,50000,Seattle,15000
+1047,Hyundai,Palisade,2016,Silver,40000,San Francisco,21000
+1142,Honda,Civic,2016,Gray,25000,Atlanta,19000
+1835,Hyundai,Sonata,2018,Blue,35000,Seattle,25000
+1925,Honda,Fit,2020,Gray,65000,Atlanta,22000
+1629,Ford,Edge,2020,Blue,55000,Chicago,12000
+1227,Chevrolet,Traverse,2020,Black,40000,Houston,25000
+1663,Honda,Pilot,2016,White,60000,Atlanta,14000
+570,Chevrolet,Traverse,2019,Black,35000,Houston,16000
+1798,Toyota,Avalon,2016,Silver,50000,Dallas,21000
+1411,Chevrolet,Tahoe,2020,Black,55000,Miami,22000
+1271,Hyundai,Venue,2020,Silver,45000,Seattle,26000
+564,Ford,Escape,2019,Blue,55000,Chicago,12000
+1292,Toyota,Corolla,2018,Silver,45000,Los Angeles,18000
+485,Honda,Civic,2018,Gray,40000,Atlanta,27000
+173,Honda,Fit,2020,Gray,55000,Atlanta,12000
+1806,Chevrolet,Equinox,2017,Black,55000,Miami,12000
+1437,Hyundai,Sonata,2019,Red,55000,Seattle,24000
+1418,Toyota,Yaris,2019,Black,40000,Los Angeles,17000
+441,Ford,Focus,2019,Silver,35000,Chicago,20000
+634,Hyundai,Sonata,2017,Red,35000,Seattle,27000
+363,Toyota,Yaris,2015,Black,65000,Los Angeles,22000
+339,Honda,Civic,2016,Gray,70000,Atlanta,20000
+459,Toyota,4Runner,2016,Silver,40000,Los Angeles,17000
+1574,Toyota,Camry,2018,Silver,30000,Los Angeles,23000
+873,Hyundai,Venue,2019,Silver,70000,Seattle,27000
+1220,Honda,CR-V,2018,White,40000,New York,21000
+1002,Ford,Escape,2016,Blue,65000,Chicago,22000
+214,Ford,Fusion,2017,White,45000,Phoenix,18000
+1914,Toyota,Highlander,2016,Silver,50000,Dallas,15000
+1270,Chevrolet,Malibu,2019,Blue,50000,Houston,21000
+1500,Hyundai,Elantra,2018,Red,55000,Seattle,16000
+1505,Hyundai,Genesis,2016,Black,45000,San Francisco,18000
+1453,Toyota,Sienna,2018,Red,40000,Dallas,17000
+815,Honda,CR-V,2015,Red,35000,New York,14000
+973,Chevrolet,Tahoe,2016,Black,35000,Miami,20000
+583,Toyota,Camry,2019,White,35000,Los Angeles,20000
+1843,Ford,Explorer,2016,White,50000,Phoenix,15000
+553,Honda,Accord,2017,White,55000,New York,22000
+1878,Ford,Escape,2015,Blue,50000,Chicago,15000
+865,Honda,Odyssey,2016,White,40000,New York,21000
+72,Toyota,Camry,2017,White,45000,Los Angeles,18000
+1569,Toyota,Prius,2017,Gray,30000,Dallas,18000
+1723,Chevrolet,Camaro,2015,Red,35000,Miami,28000
+535,Chevrolet,Tahoe,2016,Black,40000,Miami,14000
+1856,Toyota,Yaris,2017,Black,50000,Los Angeles,14000
+423,Ford,Explorer,2019,Blue,50000,Phoenix,15000
+1027,Hyundai,Elantra,2015,Black,40000,San Francisco,17000
+410,Hyundai,Genesis,2015,Black,70000,San Francisco,20000
+1410,Ford,Edge,2018,Blue,70000,Chicago,28000
+969,Hyundai,Santa Fe,2018,Red,55000,Seattle,19000
+1611,Hyundai,Elantra,2018,Black,40000,San Francisco,18000
+1760,Ford,Mustang,2019,Yellow,55000,Phoenix,22000
+211,Hyundai,Palisade,2016,Silver,40000,San Francisco,17000
+1262,Toyota,4Runner,2016,Silver,70000,Los Angeles,20000
+967,Ford,Explorer,2019,White,35000,Phoenix,25000
+1011,Honda,Odyssey,2016,White,55000,New York,24000
+392,Honda,Fit,2020,Gray,50000,Atlanta,17000
+1475,Hyundai,Kona,2018,Blue,70000,San Francisco,20000
+1059,Honda,Civic,2018,White,30000,Atlanta,29000
+1066,Chevrolet,Camaro,2015,Red,45000,Miami,18000
+99,Toyota,Sienna,2019,Red,50000,Dallas,15000
+1775,Ford,Edge,2016,Blue,55000,Chicago,12000
+616,Honda,HR-V,2018,White,45000,New York,16000
+1325,Toyota,Rav22,2015,Gray,50000,Los Angeles,17000
+1042,Hyundai,Santa Fe,2019,Red,45000,Seattle,16000
+605,Toyota,4Runner,2020,Silver,50000,Los Angeles,17000
+356,Chevrolet,Tahoe,2016,Black,45000,Miami,18000
+658,Toyota,Camry,2016,White,35000,Los Angeles,28000
+1078,Toyota,Rav18,2015,Gray,40000,Dallas,21000
+739,Chevrolet,Impala,2016,Black,40000,Houston,17000
+1844,Chevrolet,Traverse,2019,Black,40000,Houston,17000
+1315,Toyota,Camry,2020,White,50000,Los Angeles,17000
+192,Toyota,Avalon,2019,Silver,35000,Dallas,20000
+157,Toyota,Rav6,2015,Gray,35000,Los Angeles,20000
+431,Toyota,Sienna,2017,Red,40000,Dallas,15000
+1417,Hyundai,Venue,2020,Silver,50000,Seattle,15000
+1737,Ford,Explorer,2017,Blue,40000,Phoenix,17000
+1194,Toyota,Sienna,2020,Red,45000,Dallas,23000
+537,Toyota,Sienna,2017,Red,35000,Dallas,18000
+357,Hyundai,Palisade,2015,Silver,35000,San Francisco,16000
+1639,Ford,EcoSport,2018,Red,25000,Chicago,19000
+1426,Chevrolet,Cruze,2020,Black,25000,Houston,19000
+732,Honda,Civic,2016,Blue,55000,New York,22000
+448,Hyundai,Sonata,2020,Blue,55000,Seattle,22000
+771,Toyota,Camry,2015,Silver,70000,Los Angeles,28000
+679,Honda,Odyssey,2020,White,35000,New York,18000
+903,Honda,Fit,2015,Gray,55000,Atlanta,19000
+1432,Hyundai,Genesis,2019,Black,30000,San Francisco,23000
+1380,Toyota,Sienna,2015,Red,55000,Dallas,12000
+164,Ford,Explorer,2015,White,55000,Phoenix,22000
+382,Honda,Pilot,2017,Gray,55000,Atlanta,12000
+842,Chevrolet,Cruze,2020,Black,70000,Houston,28000
+206,Hyundai,Santa Fe,2017,Red,45000,Seattle,26000
+14,Chevrolet,Equinox,2018,Black,45000,Miami,18000
+1572,Chevrolet,Cruze,2020,Black,50000,Houston,14000
+1006,Honda,Pilot,2015,White,30000,Atlanta,23000
+978,Chevrolet,Malibu,2018,Blue,70000,Houston,20000
+626,Honda,Accord,2015,White,45000,New York,23000
+801,Toyota,Yaris,2020,Black,40000,Los Angeles,25000
+461,Ford,Edge,2015,Blue,70000,Chicago,12000
+6,Toyota,Corolla,2020,Gray,25000,Dallas,19000
+210,Chevrolet,Tahoe,2018,Black,50000,Miami,15000
+1510,Hyundai,Sonata,2015,Red,35000,Seattle,28000
+1216,Ford,Fusion,2018,White,55000,Phoenix,16000
+861,Ford,Explorer,2016,Blue,55000,Phoenix,16000
+703,Toyota,Avalon,2015,Silver,45000,Dallas,26000
+826,Ford,Edge,2020,Blue,70000,Chicago,18000
+84,Toyota,Rav5,2017,Gray,40000,Los Angeles,21000
+83,Hyundai,Sonata,2020,Blue,30000,Seattle,23000
+1105,Hyundai,Sonata,2019,Blue,35000,Seattle,18000
+1819,Honda,Fit,2019,Gray,35000,Atlanta,25000
+223,Chevrolet,Cruze,2018,Red,50000,Miami,14000
+1320,Toyota,Corolla,2020,Gray,50000,Dallas,18000
+1171,Ford,Focus,2017,Silver,70000,Chicago,12000
+1240,Toyota,Camry,2017,White,40000,Los Angeles,17000
+1427,Hyundai,Elantra,2017,Red,30000,Seattle,18000
+208,Honda,Odyssey,2015,White,35000,New York,27000
+1003,Chevrolet,Equinox,2018,Black,55000,Miami,16000
+267,Ford,Fusion,2017,White,35000,Phoenix,24000
+1617,Toyota,Rav26,2017,Gray,70000,Los Angeles,20000
+1682,Ford,Focus,2016,Silver,40000,Chicago,18000
+1870,Hyundai,Genesis,2017,Black,45000,San Francisco,26000
+1172,Chevrolet,Cruze,2020,Red,55000,Miami,15000
+699,Honda,Accord,2017,White,35000,New York,25000
+930,Chevrolet,Equinox,2015,Black,30000,Miami,18000
+929,Ford,Escape,2018,Blue,25000,Chicago,19000
+501,Ford,Edge,2015,Blue,60000,Chicago,12000
+94,Toyota,4Runner,2017,Silver,50000,Los Angeles,21000
+1563,Hyundai,Venue,2019,Silver,45000,Seattle,18000
+1488,Ford,Fusion,2019,White,50000,Phoenix,15000
+958,Chevrolet,Impala,2017,Black,70000,Houston,12000
+16,Toyota,Highlander,2016,Silver,60000,Dallas,25000
+1755,Ford,Focus,2019,Silver,55000,Chicago,19000
+1173,Hyundai,Elantra,2016,Black,50000,San Francisco,17000
+1671,Hyundai,Palisade,2017,Silver,40000,San Francisco,14000
+542,Toyota,Yaris,2020,Black,70000,Los Angeles,18000
+1087,Hyundai,Palisade,2018,Silver,55000,San Francisco,22000
+763,Ford,EcoSport,2018,Red,40000,Chicago,21000
+169,Ford,Edge,2019,Blue,55000,Chicago,12000
+628,Chevrolet,Camaro,2016,Red,35000,Miami,25000
+717,Hyundai,Santa Fe,2019,Red,30000,Seattle,18000
+73,Honda,Civic,2015,Blue,35000,New York,16000
+1615,Chevrolet,Impala,2015,Black,40000,Houston,21000
+798,Ford,Fusion,2017,White,55000,Phoenix,24000
+1132,Honda,Civic,2019,White,55000,Atlanta,12000
+926,Hyundai,Sonata,2019,Red,55000,Seattle,14000
+1658,Honda,CR-V,2016,White,30000,New York,29000
+1867,Honda,Accord,2018,White,70000,New York,27000
+1904,Toyota,Corolla,2020,Gray,45000,Dallas,23000
+269,Hyundai,Sonata,2015,Red,55000,Seattle,22000
+1184,Toyota,Highlander,2017,Silver,45000,Dallas,16000
+662,Hyundai,Elantra,2019,Black,50000,San Francisco,21000
+1525,Hyundai,Palisade,2019,Silver,35000,San Francisco,14000
+1327,Ford,Escape,2018,White,40000,Chicago,18000
+10,Hyundai,Sonata,2016,Blue,50000,Seattle,14000
+1193,Hyundai,Palisade,2016,Silver,50000,San Francisco,24000
+1595,Honda,Odyssey,2015,White,40000,New York,17000
+1681,Honda,Civic,2020,Blue,45000,New York,16000
+1281,Hyundai,Elantra,2016,Red,55000,Seattle,14000
+1539,Toyota,Corolla,2015,Gray,45000,Dallas,16000
+1953,Hyundai,Tucson,2020,Red,55000,San Francisco,15000
+1636,Hyundai,Venue,2019,Silver,55000,Seattle,14000
+1211,Ford,Mustang,2020,Blue,60000,Chicago,12000
+1988,Honda,Pilot,2020,Gray,55000,Atlanta,12000
+1603,Hyundai,Venue,2016,Silver,60000,Seattle,19000
+1993,Honda,Odyssey,2018,White,40000,New York,15000
+536,Hyundai,Palisade,2016,Silver,45000,San Francisco,16000
+1612,Toyota,Corolla,2020,Gray,35000,Dallas,20000
+1700,Toyota,4Runner,2018,Silver,55000,Los Angeles,12000
+1873,Ford,Fusion,2020,White,55000,Phoenix,12000
+1884,Chevrolet,Traverse,2020,Black,40000,Houston,14000
+1239,Toyota,Yaris,2018,Black,50000,Los Angeles,15000
+1214,Toyota,Avalon,2016,Silver,30000,Dallas,18000
+1638,Honda,HR-V,2019,White,40000,New York,15000
+67,Honda,Fit,2017,Gray,55000,Atlanta,12000
+358,Toyota,Sienna,2016,Red,55000,Dallas,14000
+82,Chevrolet,Impala,2015,Black,55000,Houston,19000
+1074,Honda,CR-V,2019,White,55000,New York,16000
+1238,Hyundai,Venue,2017,Silver,55000,Seattle,12000
+242,Ford,Edge,2015,Blue,40000,Chicago,17000
+637,Ford,Escape,2015,Blue,40000,Chicago,17000
+1888,Ford,Edge,2017,Blue,50000,Chicago,18000
+1571,Ford,Fiesta,2017,Blue,55000,Phoenix,16000
+1106,Toyota,Rav19,2018,Gray,60000,Los Angeles,19000
+1926,Ford,Fusion,2018,White,55000,Phoenix,16000
+416,Toyota,Corolla,2016,Silver,70000,Los Angeles,28000
+168,Honda,Odyssey,2016,White,35000,New York,27000
+1923,Hyundai,Palisade,2016,Silver,25000,San Francisco,19000
+1212,Chevrolet,Camaro,2020,Red,40000,Miami,15000
+878,Honda,Civic,2015,Blue,35000,New York,27000
+305,Ford,Escape,2016,White,70000,Chicago,27000
+1282,Toyota,Camry,2015,Silver,60000,Los Angeles,12000
+1221,Ford,Escape,2017,Blue,45000,Chicago,18000
+597,Ford,Escape,2016,White,40000,Chicago,17000
+1880,Hyundai,Tucson,2020,Red,35000,San Francisco,14000
+1894,Chevrolet,Malibu,2018,Blue,45000,Houston,16000
+464,Toyota,Sienna,2015,Red,40000,Dallas,14000
+54,Chevrolet,Equinox,2020,Black,35000,Miami,24000
+1247,Toyota,Corolla,2015,Gray,35000,Dallas,18000
+1851,Toyota,Sienna,2019,Red,40000,Dallas,15000
+1981,Hyundai,Sonata,2016,Blue,45000,Seattle,26000
+951,Honda,Civic,2016,Blue,50000,New York,15000
+424,Chevrolet,Traverse,2020,Black,40000,Houston,17000
+468,Hyundai,Venue,2016,Silver,50000,Seattle,18000
+767,Honda,Civic,2015,White,50000,Atlanta,24000
+1181,Ford,Escape,2015,White,70000,Chicago,18000
+1609,Ford,Focus,2020,Silver,50000,Chicago,17000
+723,Toyota,Sienna,2017,Red,40000,Dallas,21000
+770,Hyundai,Elantra,2019,Red,35000,Seattle,25000
+609,Hyundai,Palisade,2015,Silver,60000,San Francisco,19000
+1024,Honda,Civic,2017,Blue,60000,New York,14000
+1515,Hyundai,Tucson,2018,Red,45000,San Francisco,26000
+737,Honda,Accord,2017,White,55000,Atlanta,12000
+328,Toyota,Prius,2016,Gray,35000,Dallas,25000
+1055,Ford,EcoSport,2016,Red,70000,Chicago,28000
+327,Hyundai,Accent,2017,Silver,40000,San Francisco,22000
+1217,Chevrolet,Impala,2018,Blue,50000,Houston,14000
+1503,Ford,Mustang,2015,Blue,30000,Chicago,23000
+1213,Hyundai,Genesis,2020,Black,25000,San Francisco,19000
+617,Ford,EcoSport,2019,Red,40000,Chicago,18000
+584,Honda,Civic,2020,Blue,60000,New York,25000
+871,Ford,Fusion,2015,White,35000,Phoenix,28000
+669,Honda,CR-V,2017,Red,60000,New York,14000
+895,Chevrolet,Traverse,2018,Black,40000,Houston,22000
+1921,Ford,Edge,2019,Blue,60000,Chicago,12000
+791,Toyota,4Runner,2015,Silver,50000,Los Angeles,14000
+1097,Honda,Civic,2020,Blue,50000,New York,15000
+1107,Honda,CR-V,2017,Red,50000,New York,18000
+204,Ford,Explorer,2019,Blue,55000,Phoenix,22000
+839,Toyota,Prius,2018,Gray,45000,Dallas,23000
+343,Toyota,Corolla,2020,Silver,40000,Los Angeles,27000
+548,Honda,Civic,2016,White,55000,Atlanta,19000
+1460,Honda,Civic,2017,Blue,35000,New York,18000
+1598,Hyundai,Palisade,2015,Silver,55000,San Francisco,15000
+643,Chevrolet,Traverse,2017,Black,60000,Houston,12000
+364,Toyota,Camry,2020,White,55000,Los Angeles,16000
+863,Hyundai,Santa Fe,2018,Red,55000,Seattle,19000
+981,Honda,HR-V,2017,White,45000,New York,23000
+555,Chevrolet,Camaro,2017,Red,45000,Miami,23000
+285,Toyota,Sienna,2016,Red,45000,Dallas,18000
+348,Toyota,Rav8,2016,Gray,45000,Dallas,26000
+1419,Honda,HR-V,2018,White,60000,New York,14000
+280,Toyota,4Runner,2017,Silver,55000,Los Angeles,12000
+7,Honda,Accord,2019,White,30000,Atlanta,18000
+22,Honda,Odyssey,2016,White,55000,New York,22000
+1732,Ford,Escape,2015,Blue,50000,Chicago,15000
+857,Chevrolet,Equinox,2017,Black,40000,Miami,15000
+1351,Honda,Civic,2015,White,35000,Atlanta,16000
+623,Chevrolet,Cruze,2018,Black,70000,Houston,20000
+1342,Ford,Fusion,2017,White,45000,Phoenix,26000
+860,Honda,Pilot,2019,White,65000,Atlanta,22000
+882,Toyota,Corolla,2019,Gray,60000,Dallas,14000
+1998,Honda,Fit,2018,Gray,50000,Atlanta,14000
+275,Toyota,Rav7,2019,Gray,55000,Dallas,22000
+1422,Hyundai,Accent,2019,Silver,35000,San Francisco,16000
+944,Ford,Fusion,2020,White,70000,Phoenix,27000
+162,Toyota,Highlander,2020,Silver,40000,Dallas,25000
+850,Honda,Civic,2018,Gray,40000,Atlanta,17000
+887,Toyota,Rav16,2020,Gray,70000,Los Angeles,12000
+747,Honda,Pilot,2020,Gray,50000,Atlanta,17000
+979,Hyundai,Venue,2015,Silver,55000,Seattle,22000
+1154,Chevrolet,Traverse,2020,Black,50000,Houston,23000
+1236,Ford,Fusion,2020,White,40000,Phoenix,17000
+1771,Chevrolet,Traverse,2019,Black,55000,Houston,12000
+318,Toyota,Sienna,2019,Red,35000,Dallas,14000
+1912,Chevrolet,Equinox,2016,Black,35000,Miami,27000
+569,Ford,Explorer,2020,Blue,45000,Phoenix,18000
+667,Hyundai,Sonata,2020,Blue,50000,Seattle,15000
+1637,Toyota,Yaris,2018,Black,60000,Los Angeles,12000
+375,Hyundai,Sonata,2018,Blue,40000,Seattle,25000
+76,Ford,Focus,2019,Silver,40000,Chicago,15000
+215,Chevrolet,Malibu,2015,Blue,35000,Houston,16000
+1520,Hyundai,Santa Fe,2017,Red,40000,Seattle,17000
+1726,Honda,Civic,2017,Gray,55000,Atlanta,22000
+502,Chevrolet,Tahoe,2018,Black,40000,Miami,15000
+1673,Honda,Fit,2015,Gray,35000,Atlanta,18000
+159,Ford,Escape,2019,White,55000,Chicago,24000
+111,Ford,Fiesta,2019,Blue,35000,Phoenix,18000
+1471,Toyota,Rav24,2016,Gray,55000,Los Angeles,19000
+315,Ford,Edge,2020,Blue,55000,Chicago,12000
+45,Hyundai,Genesis,2015,Black,70000,San Francisco,18000
+1834,Chevrolet,Impala,2015,Black,40000,Houston,27000
+417,Honda,CR-V,2015,White,55000,New York,22000
+1501,Toyota,Camry,2018,Silver,50000,Los Angeles,14000
+1948,Hyundai,Sonata,2015,Red,55000,Seattle,12000
+1813,Toyota,4Runner,2019,Silver,40000,Los Angeles,14000
+1674,Ford,Fusion,2018,White,60000,Phoenix,19000
+1323,Chevrolet,Impala,2020,Black,70000,Houston,18000
+1531,Toyota,Yaris,2019,Black,35000,Los Angeles,18000
+293,Toyota,Camry,2015,White,55000,Los Angeles,16000
+883,Honda,Accord,2019,White,55000,Atlanta,12000
+1558,Hyundai,Palisade,2018,Silver,55000,San Francisco,12000
+1028,Toyota,Corolla,2015,Gray,35000,Dallas,14000
+1590,Honda,Pilot,2020,White,50000,Atlanta,15000
+41,Toyota,Camry,2017,Silver,60000,Los Angeles,19000
+114,Toyota,Camry,2019,Silver,40000,Los Angeles,22000
+1678,Toyota,Camry,2017,White,70000,Los Angeles,18000
+1736,Honda,Pilot,2019,White,50000,Atlanta,15000
+398,Ford,EcoSport,2016,Red,40000,Chicago,22000
+1933,Hyundai,Accent,2019,Silver,60000,San Francisco,25000
+1748,Chevrolet,Malibu,2015,Blue,35000,Houston,25000
+1400,Ford,Escape,2015,White,55000,Chicago,19000
+1720,Toyota,Camry,2016,Silver,60000,Los Angeles,25000
+1820,Ford,Fusion,2018,White,70000,Phoenix,18000
+115,Honda,Accord,2020,White,35000,New York,25000
+733,Ford,Focus,2019,Silver,50000,Chicago,21000
+784,Chevrolet,Equinox,2019,Black,55000,Miami,14000
+675,Ford,Explorer,2015,White,55000,Phoenix,15000
+445,Honda,Accord,2015,White,35000,Atlanta,28000
+1568,Hyundai,Accent,2016,Silver,25000,San Francisco,19000
+788,Ford,Explorer,2016,Blue,30000,Phoenix,18000
+1770,Ford,Explorer,2016,White,35000,Phoenix,27000
+107,Chevrolet,Spark,2017,Blue,55000,Miami,15000
+498,Hyundai,Santa Fe,2017,Red,45000,Seattle,18000
+849,Toyota,Avalon,2020,Silver,50000,Dallas,15000
+1434,Honda,Civic,2020,Gray,45000,Atlanta,18000
+1350,Toyota,Prius,2018,Gray,45000,Dallas,18000
+1608,Honda,Civic,2017,Blue,55000,New York,19000
+1984,Ford,Escape,2019,White,55000,Chicago,12000
+1268,Honda,Fit,2016,Gray,70000,Atlanta,28000
+1385,Toyota,Yaris,2018,Black,55000,Los Angeles,15000
+467,Chevrolet,Malibu,2018,Blue,60000,Houston,19000
+1285,Chevrolet,Camaro,2015,Red,30000,Miami,18000
+744,Chevrolet,Equinox,2020,Black,35000,Miami,14000
+224,Hyundai,Elantra,2016,Black,55000,San Francisco,19000
+593,Chevrolet,Impala,2016,Black,30000,Houston,29000
+1286,Hyundai,Genesis,2019,Black,65000,San Francisco,22000
+1745,Toyota,Sienna,2015,Red,60000,Dallas,19000
+785,Hyundai,Tucson,2015,Red,60000,San Francisco,12000
+1192,Chevrolet,Tahoe,2017,Black,55000,Miami,22000
+1298,Honda,Pilot,2015,White,40000,Atlanta,25000
+1686,Honda,Accord,2019,White,40000,Atlanta,21000
+38,Ford,Fiesta,2018,Blue,40000,Phoenix,14000
+213,Honda,Fit,2019,Gray,55000,Atlanta,12000
+966,Honda,Pilot,2019,Gray,40000,Atlanta,22000
+725,Ford,Fusion,2015,White,35000,Phoenix,20000
+477,Chevrolet,Cruze,2019,Black,55000,Houston,19000
+962,Ford,Escape,2019,White,45000,Chicago,16000
+1278,Honda,Civic,2016,White,55000,Atlanta,12000
+640,Toyota,Rav12,2020,Gray,45000,Dallas,18000
+702,Hyundai,Genesis,2020,Black,50000,San Francisco,21000
+451,Ford,Escape,2020,White,30000,Chicago,29000
+1233,Hyundai,Palisade,2019,Silver,35000,San Francisco,27000
+508,Hyundai,Venue,2015,Silver,55000,Seattle,19000
+1294,Ford,Escape,2020,Blue,60000,Chicago,25000
+1604,Toyota,Yaris,2018,Black,50000,Los Angeles,18000
+384,Chevrolet,Traverse,2020,Black,40000,Houston,17000
+1053,Toyota,Yaris,2019,Black,40000,Los Angeles,27000
+487,Chevrolet,Impala,2016,Blue,70000,Houston,28000
+851,Ford,Fusion,2019,White,60000,Phoenix,14000
+1691,Honda,CR-V,2017,Red,45000,New York,23000
+1224,Toyota,Rav20,2015,Gray,55000,Dallas,24000
+469,Toyota,Yaris,2017,Black,40000,Los Angeles,22000
+1570,Honda,Civic,2016,White,65000,Atlanta,22000
+1151,Toyota,Rav19,2019,Gray,35000,Dallas,20000
+391,Toyota,Sienna,2016,Red,55000,Dallas,15000
+1205,Honda,Civic,2018,White,40000,Atlanta,17000
+92,Chevrolet,Traverse,2016,Black,70000,Houston,27000
+378,Ford,Escape,2015,White,50000,Chicago,21000
+139,Toyota,Sienna,2020,Red,50000,Dallas,15000
+1397,Hyundai,Sonata,2020,Blue,45000,Seattle,16000
+1599,Toyota,Sienna,2019,Red,50000,Dallas,17000
+2000,Chevrolet,Malibu,2016,Blue,30000,Houston,23000
+823,Hyundai,Santa Fe,2017,Red,50000,Seattle,18000
+1045,Ford,Edge,2015,Blue,55000,Chicago,19000
+1555,Honda,Odyssey,2018,White,45000,New York,26000
+1641,Hyundai,Accent,2016,Silver,65000,San Francisco,22000
+694,Honda,Civic,2020,White,70000,Atlanta,20000
+1816,Chevrolet,Tahoe,2015,Black,60000,Miami,19000
+556,Hyundai,Genesis,2020,Black,40000,San Francisco,27000
+1375,Toyota,4Runner,2020,Silver,35000,Los Angeles,27000
+682,Hyundai,Palisade,2016,Silver,40000,San Francisco,22000
+830,Honda,Fit,2016,Gray,40000,Atlanta,18000
+47,Honda,Civic,2017,Gray,50000,Atlanta,17000
+685,Ford,Fusion,2015,White,55000,Phoenix,19000
+653,Chevrolet,Malibu,2018,Blue,45000,Houston,18000
+1490,Hyundai,Venue,2015,Silver,60000,Seattle,14000
+289,Hyundai,Venue,2018,Silver,40000,Seattle,15000
+1374,Hyundai,Santa Fe,2018,Red,30000,Seattle,29000
+1378,Chevrolet,Tahoe,2016,Black,40000,Miami,17000
+258,Chevrolet,Cruze,2020,Black,70000,Houston,18000
+526,Hyundai,Kona,2018,Blue,40000,San Francisco,17000
+321,Chevrolet,Malibu,2019,Blue,50000,Houston,17000
+1235,Honda,Fit,2015,Gray,50000,Atlanta,15000
+664,Honda,Accord,2019,White,30000,Atlanta,29000
+43,Ford,Mustang,2019,Blue,40000,Chicago,22000
+1321,Honda,Accord,2016,White,40000,Atlanta,22000
+1964,Toyota,Sienna,2016,Red,50000,Dallas,17000
+897,Toyota,4Runner,2020,Silver,70000,Los Angeles,18000
+1450,Ford,Edge,2017,Blue,60000,Chicago,14000
+834,Toyota,Yaris,2016,Black,40000,Los Angeles,21000
+935,Chevrolet,Traverse,2019,Black,30000,Houston,23000
+1222,Chevrolet,Equinox,2018,Black,35000,Miami,20000
+263,Chevrolet,Camaro,2016,Red,35000,Miami,20000
+500,Honda,Odyssey,2018,White,55000,New York,14000
+1141,Toyota,Avalon,2017,Silver,40000,Dallas,15000
+1754,Honda,Civic,2020,Blue,35000,New York,20000
+237,Ford,Explorer,2019,White,45000,Phoenix,26000
+1266,Hyundai,Palisade,2019,Silver,40000,San Francisco,27000
+565,Chevrolet,Equinox,2015,Black,50000,Miami,15000
+254,Hyundai,Accent,2015,Silver,60000,San Francisco,19000
+450,Honda,CR-V,2018,Red,45000,New York,26000
+1789,Honda,Civic,2017,White,45000,Atlanta,18000
+907,Toyota,Yaris,2016,Black,70000,Los Angeles,20000
+1909,Toyota,Rav30,2016,Gray,50000,Los Angeles,21000
+1174,Toyota,Corolla,2019,Gray,40000,Dallas,14000
+33,Ford,EcoSport,2019,Red,40000,Chicago,17000
+1665,Chevrolet,Traverse,2018,Black,50000,Houston,15000
+629,Hyundai,Genesis,2016,Black,70000,San Francisco,28000
+934,Ford,Explorer,2020,Blue,55000,Phoenix,19000
+463,Hyundai,Palisade,2020,Silver,50000,San Francisco,17000
+34,Chevrolet,Spark,2020,Blue,35000,Miami,14000
+1987,Toyota,Highlander,2017,Silver,60000,Dallas,14000
+894,Ford,Explorer,2020,White,50000,Phoenix,18000
+430,Hyundai,Palisade,2015,Silver,60000,San Francisco,12000
+179,Ford,EcoSport,2016,Red,50000,Chicago,17000
+721,Chevrolet,Tahoe,2015,Black,55000,Miami,19000
+413,Ford,Fusion,2019,White,45000,Phoenix,23000
+1135,Hyundai,Elantra,2020,Red,60000,Seattle,14000
+908,Honda,HR-V,2019,White,55000,New York,22000
+1482,Honda,Odyssey,2017,White,55000,New York,22000
+1413,Toyota,Sienna,2017,Red,45000,Dallas,26000
+589,Hyundai,Elantra,2015,Black,70000,San Francisco,27000
+1489,Chevrolet,Malibu,2016,Blue,40000,Houston,17000
+1009,Hyundai,Santa Fe,2017,Red,35000,Seattle,20000
+331,Chevrolet,Cruze,2015,Black,50000,Houston,17000
+480,Honda,Accord,2019,White,35000,New York,24000
+1420,Ford,EcoSport,2020,Red,55000,Chicago,12000
+1125,Hyundai,Venue,2018,Silver,35000,Seattle,25000
+505,Honda,Fit,2018,Gray,65000,Atlanta,22000
+1649,Ford,Mustang,2018,Blue,60000,Chicago,25000
+1902,Chevrolet,Cruze,2016,Red,55000,Miami,22000
+18,Ford,Explorer,2018,White,50000,Phoenix,23000
+715,Ford,Explorer,2020,Blue,40000,Phoenix,15000
+31,Toyota,Yaris,2017,Black,55000,Los Angeles,12000
+174,Ford,Fusion,2019,White,50000,Phoenix,15000
+1940,Honda,Accord,2015,White,50000,New York,21000
+1906,Ford,Mustang,2016,Yellow,35000,Phoenix,25000
+449,Toyota,Rav10,2019,Gray,50000,Los Angeles,21000
+1357,Ford,Mustang,2019,Blue,65000,Chicago,22000
+1079,Honda,Pilot,2020,White,45000,Atlanta,18000
+812,Chevrolet,Impala,2016,Black,55000,Houston,12000
+134,Toyota,4Runner,2017,Silver,50000,Los Angeles,21000
+1980,Chevrolet,Impala,2019,Black,50000,Houston,21000
+1206,Ford,Fiesta,2015,Blue,60000,Phoenix,14000
+676,Chevrolet,Traverse,2019,Black,50000,Houston,17000
+1425,Ford,Fiesta,2020,Blue,40000,Phoenix,15000
+810,Honda,Accord,2018,White,40000,Atlanta,17000
+1415,Ford,Fusion,2015,White,35000,Phoenix,27000
+1408,Toyota,4Runner,2016,Silver,40000,Los Angeles,27000
+602,Ford,Explorer,2017,White,35000,Phoenix,14000
+78,Hyundai,Elantra,2020,Black,30000,San Francisco,18000
+1646,Hyundai,Elantra,2016,Red,40000,Seattle,21000
+196,Hyundai,Sonata,2016,Red,35000,Seattle,24000
+1110,Hyundai,Kona,2016,Blue,70000,San Francisco,18000
+520,Chevrolet,Impala,2015,Black,50000,Houston,21000
+1554,Toyota,4Runner,2020,Silver,50000,Los Angeles,21000
+603,Chevrolet,Traverse,2020,Black,70000,Houston,12000
+1947,Chevrolet,Impala,2019,Blue,60000,Houston,14000
+1081,Chevrolet,Traverse,2020,Black,60000,Houston,25000
+244,Hyundai,Palisade,2015,Silver,55000,San Francisco,12000
+1788,Toyota,Prius,2016,Gray,40000,Dallas,21000
+227,Ford,Mustang,2015,Yellow,45000,Phoenix,18000
+1971,Honda,Civic,2020,Blue,35000,New York,24000
+1156,Toyota,4Runner,2017,Silver,40000,Los Angeles,25000
+1063,Toyota,Camry,2015,Silver,40000,Los Angeles,17000
+1908,Hyundai,Sonata,2020,Blue,55000,Seattle,22000
+1421,Chevrolet,Spark,2015,Blue,45000,Miami,18000
+1207,Chevrolet,Cruze,2020,Black,55000,Houston,12000
+57,Honda,Pilot,2017,White,50000,Atlanta,24000
+913,Honda,Civic,2019,White,70000,Atlanta,28000
+338,Toyota,Avalon,2019,Silver,35000,Dallas,24000
+17,Honda,Pilot,2017,Gray,55000,Atlanta,24000
+415,Hyundai,Sonata,2015,Red,35000,Seattle,25000
+1749,Hyundai,Venue,2018,Silver,70000,Seattle,18000
+1219,Toyota,Corolla,2018,Silver,30000,Los Angeles,23000
+1782,Hyundai,Venue,2019,Silver,30000,Seattle,18000
+19,Chevrolet,Traverse,2020,Black,35000,Houston,28000
+1126,Toyota,Yaris,2017,Black,70000,Los Angeles,28000
+924,Ford,Fusion,2015,White,45000,Phoenix,18000
+1484,Chevrolet,Tahoe,2015,Black,45000,Miami,26000
+181,Hyundai,Accent,2016,Silver,45000,San Francisco,16000
+1689,Hyundai,Sonata,2017,Blue,55000,Seattle,22000
+1979,Ford,Mustang,2019,Yellow,55000,Phoenix,22000
+158,Honda,CR-V,2020,Red,60000,New York,25000
+1895,Hyundai,Venue,2019,Silver,40000,Seattle,18000
+571,Hyundai,Santa Fe,2015,Red,55000,Seattle,14000
+1922,Chevrolet,Tahoe,2015,Black,40000,Miami,15000
+119,Toyota,Avalon,2015,Silver,45000,Dallas,16000
+1966,Ford,Fusion,2018,White,40000,Phoenix,18000
+1990,Chevrolet,Traverse,2018,Black,35000,Houston,16000
+995,Toyota,Avalon,2016,Silver,45000,Dallas,18000
+278,Chevrolet,Traverse,2017,Black,30000,Houston,29000
+1353,Chevrolet,Cruze,2020,Black,60000,Houston,12000
+95,Honda,Odyssey,2017,White,45000,New York,26000
+920,Chevrolet,Camaro,2017,Red,50000,Miami,15000
+610,Toyota,Sienna,2017,Red,50000,Dallas,18000
+705,Ford,Fusion,2018,White,35000,Phoenix,27000
+1928,Hyundai,Venue,2016,Silver,55000,Seattle,19000
+421,Toyota,Rav9,2018,Gray,35000,Dallas,27000
+412,Honda,Civic,2020,Gray,50000,Atlanta,24000
+731,Toyota,Camry,2018,White,70000,Los Angeles,27000
+800,Hyundai,Venue,2020,Silver,35000,Seattle,28000
+1098,Ford,Focus,2019,Silver,40000,Chicago,17000
+904,Ford,Fusion,2017,White,50000,Phoenix,23000
+66,Toyota,Sienna,2020,Red,35000,Dallas,27000
+1535,Honda,Civic,2015,Blue,35000,New York,25000
+1307,Toyota,Sienna,2019,Red,40000,Dallas,17000
+915,Chevrolet,Cruze,2017,Black,50000,Houston,21000
+1796,Chevrolet,Camaro,2017,Red,70000,Miami,27000
+1496,Toyota,Prius,2020,Gray,40000,Dallas,15000
+1560,Honda,Fit,2020,Gray,40000,Atlanta,17000
+1147,Honda,CR-V,2020,White,55000,New York,19000
+1666,Hyundai,Santa Fe,2016,Red,40000,Seattle,17000
+1133,Ford,Fiesta,2019,Blue,50000,Phoenix,15000
+385,Hyundai,Santa Fe,2019,Red,60000,Seattle,14000
+1018,Chevrolet,Malibu,2019,Blue,45000,Houston,26000
+1872,Honda,Civic,2015,Gray,35000,Atlanta,27000
+1588,Hyundai,Tucson,2015,Red,35000,San Francisco,27000
+235,Toyota,Highlander,2020,Silver,55000,Dallas,22000
+1382,Ford,Fusion,2020,White,40000,Phoenix,17000
+117,Chevrolet,Camaro,2019,Red,55000,Miami,19000
+1962,Chevrolet,Tahoe,2020,Black,70000,Miami,18000
+1946,Ford,Fusion,2015,White,40000,Phoenix,17000
+789,Chevrolet,Traverse,2018,Black,65000,Houston,22000
+1344,Hyundai,Venue,2018,Silver,35000,Seattle,27000
+1999,Ford,Fusion,2017,White,55000,Phoenix,19000
+910,Chevrolet,Spark,2019,Blue,45000,Miami,23000
+1528,Ford,Fusion,2020,White,50000,Phoenix,17000
+1715,Toyota,Prius,2015,Gray,55000,Dallas,19000
+1384,Hyundai,Venue,2020,Silver,70000,Seattle,12000
+133,Hyundai,Santa Fe,2018,Red,55000,Seattle,22000
+444,Toyota,Corolla,2015,Gray,50000,Dallas,23000
+1935,Honda,Civic,2020,White,50000,Atlanta,23000
+131,Ford,Explorer,2015,Blue,35000,Phoenix,25000
+1625,Chevrolet,Traverse,2020,Black,50000,Houston,21000
+982,Ford,EcoSport,2015,Red,40000,Chicago,27000
+833,Hyundai,Venue,2019,Silver,50000,Seattle,23000
+853,Hyundai,Sonata,2019,Red,45000,Seattle,18000
+1258,Honda,Pilot,2017,Gray,55000,Atlanta,19000
+1958,Hyundai,Santa Fe,2019,Red,60000,Seattle,19000
+1814,Honda,Odyssey,2017,White,45000,New York,16000
+906,Hyundai,Venue,2020,Silver,35000,Seattle,24000
+1118,Ford,Edge,2017,Blue,40000,Chicago,21000
+1791,Chevrolet,Cruze,2020,Black,60000,Houston,25000
+1072,Hyundai,Sonata,2017,Red,30000,Seattle,18000
+368,Ford,Focus,2016,Silver,40000,Chicago,21000
+138,Hyundai,Palisade,2020,Silver,55000,San Francisco,12000
+963,Chevrolet,Equinox,2017,Black,35000,Miami,18000
+250,Toyota,Yaris,2017,Black,50000,Los Angeles,17000
+1200,Honda,HR-V,2019,White,45000,New York,26000
+1349,Hyundai,Accent,2016,Silver,55000,San Francisco,12000
+238,Chevrolet,Traverse,2018,Black,30000,Houston,29000
+1071,Chevrolet,Impala,2015,Blue,25000,Houston,19000
+52,Honda,CR-V,2018,White,50000,New York,23000
+1913,Hyundai,Kona,2019,Blue,55000,San Francisco,12000
+1672,Toyota,Sienna,2017,Red,45000,Dallas,16000
+1313,Toyota,Camry,2015,White,70000,Los Angeles,12000
+1659,Ford,Escape,2017,Blue,35000,Chicago,27000
+1837,Honda,CR-V,2015,Red,55000,New York,22000
+1653,Honda,Civic,2017,Gray,40000,Atlanta,25000
+180,Chevrolet,Spark,2016,Blue,40000,Miami,14000
+541,Hyundai,Venue,2019,Silver,35000,Seattle,25000
+512,Toyota,Camry,2018,White,35000,Los Angeles,20000
+827,Chevrolet,Tahoe,2018,Black,55000,Miami,19000
+252,Ford,EcoSport,2017,Red,45000,Chicago,16000
+1218,Hyundai,Sonata,2017,Red,55000,Seattle,19000
+802,Toyota,Camry,2016,White,70000,Los Angeles,27000
+1566,Ford,EcoSport,2017,Red,60000,Chicago,12000
+530,Chevrolet,Traverse,2017,Black,40000,Houston,17000
+1160,Hyundai,Palisade,2017,Silver,45000,San Francisco,26000
+876,Honda,Civic,2018,Blue,45000,New York,26000
+730,Honda,Civic,2017,Blue,40000,New York,25000
+848,Hyundai,Genesis,2017,Black,55000,San Francisco,12000
+1314,Honda,Civic,2019,Blue,55000,New York,15000
+1435,Ford,Fusion,2018,White,35000,Phoenix,20000
+1579,Toyota,Avalon,2018,Silver,55000,Dallas,24000
+112,Chevrolet,Cruze,2018,Black,60000,Houston,19000
+201,Hyundai,Tucson,2015,Red,40000,San Francisco,27000
+684,Honda,Fit,2020,Gray,70000,Atlanta,18000
+661,Chevrolet,Cruze,2018,Red,55000,Miami,22000
+152,Toyota,Corolla,2019,Gray,50000,Dallas,14000
+651,Honda,Fit,2016,Gray,30000,Atlanta,23000
+454,Toyota,Highlander,2015,Silver,50000,Dallas,15000
+546,Hyundai,Accent,2019,Silver,40000,San Francisco,18000
+867,Chevrolet,Tahoe,2016,Black,35000,Miami,20000
+1622,Toyota,Highlander,2018,Silver,35000,Dallas,25000
+1955,Honda,Pilot,2018,White,40000,Atlanta,14000
+1996,Hyundai,Palisade,2019,Silver,65000,San Francisco,22000
+1185,Honda,Pilot,2019,Gray,40000,Atlanta,18000
+325,Ford,EcoSport,2020,Red,60000,Chicago,19000
+1430,Ford,Mustang,2019,Blue,50000,Chicago,14000
+1369,Hyundai,Tucson,2020,Red,40000,San Francisco,25000
+1291,Hyundai,Sonata,2018,Red,40000,Seattle,21000
+956,Honda,Accord,2015,White,40000,Atlanta,17000
+1766,Chevrolet,Equinox,2015,Black,55000,Miami,22000
+762,Honda,HR-V,2016,White,50000,New York,23000
+310,Ford,Explorer,2016,White,35000,Phoenix,27000
+818,Hyundai,Kona,2015,Blue,50000,San Francisco,17000
+1333,Chevrolet,Traverse,2020,Black,70000,Houston,20000
+1168,Honda,Civic,2020,Blue,50000,New York,15000
+279,Hyundai,Santa Fe,2018,Red,35000,Seattle,27000
+1486,Toyota,Sienna,2017,Red,35000,Dallas,27000
+366,Toyota,Camry,2020,White,55000,Los Angeles,19000
+775,Hyundai,Genesis,2016,Black,30000,San Francisco,29000
+1753,Toyota,Camry,2019,White,40000,Los Angeles,18000
+854,Toyota,Corolla,2019,Silver,35000,Los Angeles,16000
+997,Ford,Fusion,2018,White,55000,Phoenix,14000
+852,Chevrolet,Impala,2016,Blue,55000,Houston,12000
+1506,Toyota,Avalon,2018,Silver,35000,Dallas,20000
+1246,Hyundai,Elantra,2020,Black,45000,San Francisco,16000
+777,Honda,Civic,2015,Gray,55000,Atlanta,12000
+881,Hyundai,Elantra,2020,Black,40000,San Francisco,17000
+153,Honda,Accord,2017,White,55000,Atlanta,19000
+1597,Chevrolet,Tahoe,2018,Black,70000,Miami,12000
+1892,Honda,Fit,2016,Gray,55000,Atlanta,19000
+1697,Ford,Explorer,2015,White,45000,Phoenix,26000
+1509,Chevrolet,Impala,2017,Blue,50000,Houston,23000
+326,Chevrolet,Spark,2017,Blue,50000,Miami,18000
+418,Ford,Escape,2015,Blue,50000,Chicago,21000
+680,Ford,Edge,2018,Blue,60000,Chicago,19000
+547,Toyota,Prius,2020,Gray,35000,Dallas,20000
+1275,Chevrolet,Spark,2015,Blue,50000,Miami,15000
+1119,Chevrolet,Tahoe,2019,Black,35000,Miami,24000
+1712,Ford,EcoSport,2016,Red,65000,Chicago,22000
+176,Hyundai,Venue,2015,Silver,35000,Seattle,14000
+604,Hyundai,Santa Fe,2017,Red,55000,Seattle,15000
+1698,Chevrolet,Traverse,2015,Black,30000,Houston,29000
+917,Toyota,Camry,2019,Silver,30000,Los Angeles,29000
+1780,Ford,Fusion,2015,White,40000,Phoenix,15000
+540,Chevrolet,Malibu,2017,Blue,40000,Houston,22000
+742,Honda,CR-V,2016,Red,50000,New York,15000
+1284,Ford,Mustang,2015,Blue,25000,Chicago,19000
+1704,Hyundai,Palisade,2015,Silver,55000,San Francisco,12000
+809,Toyota,Corolla,2017,Gray,50000,Dallas,15000
+1866,Toyota,Camry,2018,Silver,40000,Los Angeles,25000
+288,Chevrolet,Malibu,2020,Blue,60000,Houston,12000
+832,Chevrolet,Malibu,2016,Blue,55000,Houston,19000
+319,Honda,Fit,2020,Gray,70000,Atlanta,12000
+1169,Toyota,Camry,2019,White,40000,Los Angeles,17000
+1487,Honda,Fit,2017,Gray,55000,Atlanta,12000
+1903,Hyundai,Elantra,2015,Black,50000,San Francisco,24000
+562,Toyota,Corolla,2017,Silver,30000,Los Angeles,29000
+1613,Honda,Accord,2017,White,55000,Atlanta,19000
+1348,Chevrolet,Spark,2015,Blue,60000,Miami,14000
+394,Chevrolet,Malibu,2015,Blue,45000,Houston,16000
+1255,Chevrolet,Equinox,2015,Black,45000,Miami,16000
+132,Chevrolet,Traverse,2017,Black,70000,Houston,28000
+1530,Hyundai,Venue,2018,Silver,45000,Seattle,16000
+1364,Hyundai,Sonata,2020,Red,35000,Seattle,20000
+1366,Honda,CR-V,2017,White,55000,New York,24000
+1944,Toyota,Avalon,2016,Silver,55000,Dallas,12000
+1915,Honda,Pilot,2015,Gray,40000,Atlanta,17000
+1573,Hyundai,Elantra,2020,Red,55000,Seattle,19000
+1058,Toyota,Prius,2018,Gray,45000,Dallas,26000
+1099,Chevrolet,Cruze,2017,Red,35000,Miami,14000
+241,Honda,Odyssey,2017,White,50000,New York,15000
+582,Toyota,Yaris,2016,Black,45000,Los Angeles,18000
+1952,Chevrolet,Equinox,2016,Black,70000,Miami,12000
+1575,Honda,Accord,2016,White,40000,New York,21000
+931,Hyundai,Tucson,2019,Red,65000,San Francisco,22000
+615,Toyota,Yaris,2020,Black,50000,Los Angeles,17000
+1406,Chevrolet,Traverse,2017,Black,50000,Houston,24000
+307,Hyundai,Kona,2016,Blue,50000,San Francisco,21000
+1117,Honda,Odyssey,2015,White,50000,New York,23000
+200,Chevrolet,Equinox,2019,Black,45000,Miami,23000
+1365,Toyota,Corolla,2017,Silver,60000,Los Angeles,25000
+1197,Chevrolet,Malibu,2017,Blue,70000,Houston,28000
+1582,Chevrolet,Impala,2017,Blue,40000,Houston,25000
+552,Toyota,Camry,2020,Silver,70000,Los Angeles,20000
+220,Toyota,Camry,2020,White,30000,Los Angeles,18000
+1542,Chevrolet,Impala,2016,Black,55000,Houston,19000
+1038,Toyota,Highlander,2020,Silver,35000,Dallas,25000
+1182,Chevrolet,Equinox,2020,Black,55000,Miami,19000
+308,Toyota,Highlander,2017,Silver,45000,Dallas,26000
+442,Chevrolet,Cruze,2017,Red,60000,Miami,25000
+458,Hyundai,Santa Fe,2018,Red,50000,Seattle,15000
+521,Hyundai,Sonata,2016,Blue,45000,Seattle,26000
+1158,Ford,Edge,2020,Blue,55000,Chicago,22000
+1297,Toyota,Rav21,2019,Gray,35000,Dallas,28000
+819,Toyota,Highlander,2019,Silver,40000,Dallas,14000
+1370,Toyota,Rav22,2019,Gray,70000,Dallas,27000
+1138,Ford,Mustang,2016,Blue,35000,Chicago,16000
+1970,Toyota,Camry,2017,White,40000,Los Angeles,21000
+163,Honda,Pilot,2018,Gray,70000,Atlanta,27000
+1401,Chevrolet,Equinox,2017,Black,50000,Miami,23000
+1075,Ford,Escape,2017,Blue,50000,Chicago,14000
+515,Chevrolet,Cruze,2020,Red,50000,Miami,23000
+1801,Chevrolet,Impala,2016,Blue,35000,Houston,27000
+1409,Honda,Odyssey,2020,White,35000,New York,25000
+1651,Hyundai,Genesis,2018,Black,50000,San Francisco,23000
+494,Toyota,Rav10,2019,Gray,50000,Dallas,15000
+1827,Honda,Civic,2019,Blue,50000,New York,23000
+531,Hyundai,Santa Fe,2018,Red,35000,Seattle,14000
+1343,Chevrolet,Malibu,2016,Blue,30000,Houston,29000
+12,Honda,CR-V,2020,Red,30000,New York,23000
+88,Hyundai,Kona,2017,Blue,55000,San Francisco,24000
+1189,Toyota,4Runner,2020,Silver,40000,Los Angeles,21000
+365,Honda,Civic,2015,Blue,50000,New York,14000
+1532,Toyota,Camry,2016,White,60000,Los Angeles,19000
+868,Hyundai,Palisade,2016,Silver,60000,San Francisco,25000
+353,Toyota,4Runner,2016,Silver,40000,Los Angeles,17000
+1456,Chevrolet,Malibu,2017,Blue,55000,Houston,15000
+1499,Chevrolet,Cruze,2018,Black,65000,Houston,22000
+426,Toyota,4Runner,2016,Silver,55000,Los Angeles,12000
+888,Honda,CR-V,2016,Red,55000,New York,15000
+1596,Ford,Edge,2017,Blue,35000,Chicago,14000
+1338,Chevrolet,Tahoe,2018,Black,35000,Miami,25000
+727,Hyundai,Venue,2020,Silver,55000,Seattle,24000
+1392,Hyundai,Elantra,2015,Black,40000,San Francisco,22000
+566,Hyundai,Tucson,2016,Red,40000,San Francisco,17000
+1989,Ford,Explorer,2016,White,45000,Phoenix,18000
+811,Ford,Mustang,2020,Yellow,60000,Phoenix,14000
+1102,Honda,Accord,2015,White,50000,Atlanta,17000
+470,Honda,HR-V,2018,White,35000,New York,25000
+452,Chevrolet,Equinox,2015,Black,35000,Miami,27000
+129,Toyota,Rav5,2015,Gray,45000,Dallas,23000
+1688,Chevrolet,Impala,2017,Black,70000,Houston,20000
+510,Toyota,Camry,2019,White,40000,Los Angeles,21000
+1772,Hyundai,Santa Fe,2020,Red,50000,Seattle,15000
+831,Ford,Fusion,2017,White,35000,Phoenix,20000
+1261,Hyundai,Santa Fe,2018,Red,35000,Seattle,24000
+902,Toyota,Sienna,2020,Red,35000,Dallas,20000
+351,Chevrolet,Traverse,2019,Black,55000,Houston,12000
+230,Toyota,Rav7,2016,Gray,55000,Los Angeles,24000
+85,Honda,CR-V,2019,Red,45000,New York,18000
+50,Hyundai,Sonata,2020,Red,35000,Seattle,20000
+1335,Toyota,4Runner,2015,Silver,50000,Los Angeles,24000
+991,Honda,Accord,2019,White,50000,New York,15000
+473,Hyundai,Accent,2018,Silver,50000,San Francisco,17000
+1825,Honda,Civic,2016,Blue,35000,New York,20000
+3,Ford,Focus,2017,Silver,55000,Chicago,14000
+1039,Honda,Pilot,2016,Gray,70000,Atlanta,18000
+360,Ford,Fusion,2020,White,40000,Phoenix,15000
+1865,Hyundai,Elantra,2017,Red,35000,Seattle,28000
+266,Honda,Civic,2019,Gray,40000,Atlanta,21000
+1862,Honda,Civic,2018,White,60000,Atlanta,25000
+1677,Toyota,Yaris,2018,Black,35000,Los Angeles,25000
+687,Hyundai,Venue,2016,Silver,45000,Seattle,16000
+957,Ford,Mustang,2020,Yellow,35000,Phoenix,14000
+455,Honda,Pilot,2020,Gray,40000,Atlanta,17000
+1657,Toyota,Corolla,2017,Silver,45000,Los Angeles,26000
+782,Honda,CR-V,2017,White,45000,New York,18000
+620,Toyota,Prius,2020,Gray,50000,Dallas,23000
+1355,Toyota,Camry,2018,Silver,25000,Los Angeles,19000
+1008,Chevrolet,Traverse,2018,Black,45000,Houston,18000
+428,Ford,Edge,2015,Blue,35000,Chicago,16000
+601,Honda,Pilot,2019,Gray,40000,Atlanta,17000
+1480,Hyundai,Santa Fe,2017,Red,35000,Seattle,25000
+1250,Chevrolet,Impala,2018,Black,40000,Houston,22000
+652,Ford,Fusion,2020,White,40000,Phoenix,21000
+1096,Toyota,Camry,2018,White,55000,Los Angeles,12000
+796,Toyota,Sienna,2018,Red,35000,Dallas,20000
+1642,Toyota,Prius,2017,Gray,55000,Dallas,16000
+560,Chevrolet,Impala,2019,Blue,50000,Houston,21000
+475,Honda,Civic,2016,White,40000,Atlanta,18000
+1758,Toyota,Corolla,2016,Gray,35000,Dallas,24000
+856,Ford,Escape,2020,Blue,60000,Chicago,12000
+1037,Hyundai,Kona,2015,Blue,40000,San Francisco,22000
+1963,Hyundai,Palisade,2020,Silver,55000,San Francisco,19000
+1082,Hyundai,Santa Fe,2019,Red,55000,Seattle,24000
+1109,Chevrolet,Equinox,2019,Black,35000,Miami,25000
+240,Toyota,4Runner,2018,Silver,55000,Los Angeles,12000
+1734,Hyundai,Tucson,2015,Red,60000,San Francisco,14000
+1954,Toyota,Rav30,2018,Gray,50000,Dallas,17000
+1,Toyota,Camry,2018,White,45000,Los Angeles,18000
+435,Hyundai,Venue,2015,Silver,55000,Seattle,16000
+1557,Chevrolet,Tahoe,2016,Black,35000,Miami,27000
+1693,Chevrolet,Equinox,2017,Black,35000,Miami,25000
+390,Hyundai,Palisade,2020,Silver,70000,San Francisco,12000
+1743,Chevrolet,Tahoe,2017,Black,45000,Miami,16000
+1444,Honda,Pilot,2019,White,45000,Atlanta,26000
+773,Ford,Mustang,2018,Blue,50000,Chicago,21000
+37,Honda,Civic,2017,White,50000,Atlanta,17000
+226,Honda,Accord,2016,White,40000,Atlanta,21000
+306,Chevrolet,Equinox,2016,Black,55000,Miami,22000
+932,Toyota,Rav16,2020,Gray,55000,Dallas,16000
+1576,Ford,Mustang,2017,Blue,45000,Chicago,18000
+1043,Toyota,4Runner,2020,Silver,40000,Los Angeles,18000
+1812,Hyundai,Santa Fe,2020,Red,50000,Seattle,17000
+1974,Ford,Focus,2016,Silver,50000,Chicago,24000
+1013,Chevrolet,Tahoe,2020,Black,35000,Miami,28000
+1809,Honda,Pilot,2019,White,35000,Atlanta,14000
+1394,Honda,Accord,2018,White,70000,Atlanta,18000
+585,Toyota,Camry,2016,White,55000,Los Angeles,24000
+1455,Ford,Fusion,2020,White,70000,Phoenix,12000
+758,Ford,Fusion,2015,White,45000,Phoenix,16000
+1725,Toyota,Avalon,2019,Silver,70000,Dallas,27000
+462,Chevrolet,Tahoe,2015,Black,55000,Miami,15000
+1833,Ford,Mustang,2017,Yellow,45000,Phoenix,23000
+1741,Honda,Odyssey,2020,White,50000,New York,17000
+646,Honda,Odyssey,2017,White,30000,New York,18000
+26,Toyota,Sienna,2020,Red,35000,Dallas,27000
+49,Chevrolet,Impala,2019,Blue,40000,Houston,18000
+961,Honda,CR-V,2015,Red,40000,New York,14000
+130,Honda,Pilot,2016,White,40000,Atlanta,27000
+922,Toyota,Avalon,2019,Silver,60000,Dallas,14000
+1919,Toyota,4Runner,2016,Silver,35000,Los Angeles,16000
+90,Honda,Pilot,2018,Gray,35000,Atlanta,28000
+912,Toyota,Prius,2018,Gray,35000,Dallas,25000
+1939,Toyota,Camry,2018,Silver,55000,Los Angeles,22000
+1695,Toyota,Highlander,2017,Silver,55000,Dallas,22000
+1614,Ford,Mustang,2016,Yellow,50000,Phoenix,23000
+488,Hyundai,Sonata,2020,Red,55000,Seattle,22000
+286,Honda,Fit,2017,Gray,35000,Atlanta,16000
+654,Hyundai,Venue,2019,Silver,35000,Seattle,20000
+376,Toyota,Rav9,2015,Gray,70000,Los Angeles,27000
+1587,Chevrolet,Equinox,2019,Black,30000,Miami,29000
+145,Toyota,Camry,2018,White,55000,Los Angeles,14000
+581,Hyundai,Venue,2015,Silver,40000,Seattle,21000
+1264,Ford,Edge,2018,Blue,50000,Chicago,24000
+1478,Ford,Explorer,2020,White,45000,Phoenix,23000
+105,Honda,HR-V,2016,White,35000,New York,14000
+136,Ford,Edge,2019,Blue,30000,Chicago,29000
+724,Honda,Fit,2019,Gray,45000,Atlanta,18000
+97,Chevrolet,Tahoe,2018,Black,35000,Miami,27000
+870,Honda,Fit,2020,Gray,50000,Atlanta,23000
+1991,Hyundai,Santa Fe,2016,Red,55000,Seattle,14000
+947,Toyota,Yaris,2015,Black,45000,Los Angeles,26000
+1451,Chevrolet,Tahoe,2020,Black,55000,Miami,12000
+1190,Honda,Odyssey,2015,White,35000,New York,24000
+264,Hyundai,Genesis,2020,Black,55000,San Francisco,19000
+1177,Chevrolet,Impala,2015,Black,60000,Houston,19000
+1469,Chevrolet,Impala,2018,Black,40000,Houston,18000
+243,Chevrolet,Tahoe,2018,Black,60000,Miami,14000
+835,Honda,HR-V,2020,White,35000,New York,24000
+5,Hyundai,Elantra,2018,Black,40000,San Francisco,15000
+187,Toyota,Camry,2020,Silver,70000,Los Angeles,18000
+1159,Chevrolet,Tahoe,2019,Black,50000,Miami,21000
+123,Hyundai,Sonata,2017,Red,50000,Seattle,23000
+1152,Honda,Pilot,2018,White,60000,Atlanta,25000
+1783,Toyota,Yaris,2015,Black,65000,Los Angeles,22000
+1339,Hyundai,Palisade,2015,Silver,70000,San Francisco,28000
+155,Chevrolet,Impala,2015,Black,40000,Houston,21000
+976,Honda,Fit,2019,Gray,40000,Atlanta,21000
+1140,Hyundai,Genesis,2018,Black,60000,San Francisco,12000
+1778,Toyota,Sienna,2016,Red,55000,Dallas,14000
+60,Hyundai,Santa Fe,2020,Red,35000,Seattle,25000
+1329,Hyundai,Kona,2017,Blue,55000,San Francisco,19000
+154,Ford,Mustang,2017,Yellow,30000,Phoenix,23000
+1552,Chevrolet,Traverse,2020,Black,70000,Houston,28000
+234,Hyundai,Kona,2018,Blue,70000,San Francisco,27000
+422,Honda,Pilot,2019,White,55000,Atlanta,12000
+497,Chevrolet,Traverse,2015,Black,55000,Houston,12000
+118,Hyundai,Genesis,2017,Black,50000,San Francisco,17000
+268,Chevrolet,Impala,2019,Blue,70000,Houston,20000
+523,Honda,CR-V,2015,Red,35000,New York,27000
+1280,Chevrolet,Cruze,2016,Black,35000,Houston,16000
+253,Chevrolet,Spark,2015,Blue,35000,Miami,18000
+1731,Honda,CR-V,2020,White,55000,New York,12000
+1511,Toyota,Corolla,2019,Silver,40000,Los Angeles,25000
+567,Toyota,Rav11,2019,Gray,60000,Dallas,14000
+965,Toyota,Highlander,2017,Silver,50000,Dallas,18000
+1143,Ford,Fusion,2017,White,30000,Phoenix,18000
+964,Hyundai,Kona,2018,Blue,60000,San Francisco,19000
+985,Toyota,Prius,2019,Gray,55000,Dallas,22000
+1724,Hyundai,Genesis,2019,Black,40000,San Francisco,25000
+1069,Honda,Civic,2015,Gray,60000,Atlanta,12000
+272,Ford,Escape,2019,Blue,40000,Chicago,27000
+1763,Toyota,Rav28,2015,Gray,40000,Los Angeles,27000
+1463,Ford,Focus,2018,Silver,40000,Chicago,22000
+574,Ford,Edge,2015,Blue,25000,Chicago,19000
+772,Honda,Accord,2018,White,55000,New York,22000
+1607,Toyota,Camry,2017,White,70000,Los Angeles,18000
+1234,Toyota,Sienna,2016,Red,55000,Dallas,12000
+1977,Toyota,Corolla,2018,Gray,35000,Dallas,25000
+182,Toyota,Prius,2019,Gray,35000,Dallas,18000
+983,Chevrolet,Spark,2018,Blue,35000,Miami,25000
+1559,Toyota,Sienna,2017,Red,50000,Dallas,15000
+1277,Toyota,Prius,2016,Gray,60000,Dallas,14000
+1157,Honda,Odyssey,2015,White,70000,New York,27000
+1818,Toyota,Sienna,2019,Red,40000,Dallas,22000
+1056,Chevrolet,Spark,2016,Blue,55000,Miami,22000
+937,Toyota,4Runner,2017,Silver,45000,Los Angeles,18000
+1273,Honda,HR-V,2020,White,35000,New York,27000
+372,Honda,Accord,2018,White,55000,Atlanta,24000
+1111,Toyota,Highlander,2015,Silver,55000,Dallas,19000
+568,Honda,Pilot,2016,White,55000,Atlanta,12000
+1187,Chevrolet,Traverse,2019,Black,55000,Houston,19000
+1362,Ford,Fusion,2015,White,40000,Phoenix,21000
+1907,Chevrolet,Impala,2015,Black,70000,Houston,28000
+143,Hyundai,Venue,2015,Silver,45000,Seattle,18000
+1792,Hyundai,Elantra,2017,Red,55000,Seattle,24000
+446,Ford,Mustang,2016,Yellow,40000,Phoenix,25000
+1593,Hyundai,Santa Fe,2015,Red,55000,Seattle,12000
+438,Honda,Civic,2017,Blue,30000,New York,23000
+229,Hyundai,Sonata,2015,Blue,60000,Seattle,25000
+386,Toyota,4Runner,2015,Silver,55000,Los Angeles,12000
+1198,Hyundai,Venue,2018,Silver,55000,Seattle,22000
+824,Toyota,4Runner,2017,Silver,40000,Los Angeles,22000
+294,Honda,Civic,2019,Blue,50000,New York,14000
+671,Chevrolet,Equinox,2016,Black,50000,Miami,15000
+550,Chevrolet,Cruze,2015,Black,40000,Houston,21000
+1494,Chevrolet,Spark,2018,Blue,55000,Miami,14000
+1562,Chevrolet,Malibu,2019,Blue,55000,Houston,12000
+1857,Honda,HR-V,2016,White,55000,New York,19000
+1136,Toyota,Camry,2018,Silver,55000,Los Angeles,12000
+1920,Honda,Odyssey,2015,White,55000,New York,14000
+1785,Ford,EcoSport,2018,Red,50000,Chicago,14000
+247,Ford,Fusion,2019,White,35000,Phoenix,14000
+282,Ford,Edge,2020,Blue,40000,Chicago,17000
+681,Chevrolet,Tahoe,2020,Black,50000,Miami,18000
+891,Hyundai,Kona,2015,Blue,45000,San Francisco,16000
+590,Toyota,Corolla,2018,Gray,55000,Dallas,22000
+1086,Chevrolet,Tahoe,2020,Black,70000,Miami,27000
+1750,Toyota,Yaris,2018,Black,55000,Los Angeles,19000
+436,Toyota,Yaris,2018,Black,50000,Los Angeles,14000
+1302,Toyota,4Runner,2015,Silver,45000,Los Angeles,26000
+1180,Honda,CR-V,2016,Red,35000,New York,25000
+766,Toyota,Prius,2019,Gray,55000,Dallas,22000
+1100,Hyundai,Elantra,2018,Black,70000,San Francisco,12000
+1932,Chevrolet,Spark,2015,Blue,35000,Miami,20000
+1845,Hyundai,Santa Fe,2015,Red,60000,Seattle,14000
+1183,Hyundai,Kona,2016,Blue,50000,San Francisco,17000
+504,Toyota,Sienna,2020,Red,30000,Dallas,18000
+898,Honda,Odyssey,2017,White,55000,New York,19000
+787,Honda,Pilot,2015,White,25000,Atlanta,19000
+1051,Chevrolet,Malibu,2017,Blue,50000,Houston,24000
+1680,Toyota,Camry,2016,White,50000,Los Angeles,17000
+557,Toyota,Avalon,2016,Silver,35000,Dallas,25000
+1403,Toyota,Highlander,2020,Silver,35000,Dallas,24000
+1630,Chevrolet,Tahoe,2017,Black,50000,Miami,15000
+260,Toyota,Camry,2015,Silver,50000,Los Angeles,17000
+1304,Ford,Edge,2015,Blue,35000,Chicago,27000
+948,Toyota,Camry,2017,White,30000,Los Angeles,29000
+1583,Hyundai,Sonata,2019,Red,70000,Seattle,27000
+1512,Honda,CR-V,2017,White,70000,New York,27000
+943,Honda,Fit,2019,Gray,40000,Atlanta,25000
+668,Toyota,Rav13,2016,Gray,40000,Los Angeles,17000
+743,Ford,Escape,2020,White,40000,Chicago,17000
+400,Hyundai,Accent,2017,Silver,70000,San Francisco,18000
+1956,Ford,Explorer,2019,Blue,45000,Phoenix,16000
+1101,Toyota,Corolla,2017,Gray,55000,Dallas,15000
+1652,Toyota,Avalon,2015,Silver,35000,Dallas,28000
+397,Honda,HR-V,2018,White,50000,New York,18000
+1779,Honda,Fit,2016,Gray,60000,Atlanta,12000
+1012,Ford,Edge,2020,Blue,50000,Chicago,23000
+383,Ford,Explorer,2016,White,50000,Phoenix,15000
+1874,Chevrolet,Impala,2015,Blue,50000,Houston,15000
+354,Honda,Odyssey,2015,White,60000,New York,14000
+859,Toyota,Rav15,2017,Gray,30000,Dallas,18000
+1443,Toyota,Rav23,2016,Gray,50000,Dallas,21000
+558,Honda,Civic,2020,Gray,70000,Atlanta,28000
+1014,Hyundai,Palisade,2017,Silver,40000,San Francisco,25000
+291,Toyota,Camry,2018,White,30000,Los Angeles,18000
+171,Hyundai,Palisade,2016,Silver,40000,San Francisco,17000
+1696,Honda,Pilot,2020,Gray,50000,Atlanta,21000
+1150,Hyundai,Tucson,2019,Red,45000,San Francisco,18000
+1230,Honda,Odyssey,2017,White,50000,New York,21000
+335,Ford,Mustang,2019,Blue,55000,Chicago,19000
+147,Toyota,Camry,2016,White,40000,Los Angeles,15000
+1581,Ford,Fusion,2019,White,35000,Phoenix,28000
+427,Honda,Odyssey,2019,White,45000,New York,18000
+838,Hyundai,Accent,2020,Silver,50000,San Francisco,24000
+1727,Ford,Fusion,2016,White,50000,Phoenix,21000
+1399,Honda,CR-V,2019,Red,35000,New York,20000
+1647,Toyota,Camry,2017,Silver,45000,Los Angeles,18000
+939,Ford,Edge,2018,Blue,60000,Chicago,25000
+1439,Honda,CR-V,2015,White,35000,New York,28000
+1223,Hyundai,Tucson,2017,Red,60000,San Francisco,25000
+1283,Honda,Accord,2018,White,40000,New York,15000
+302,Hyundai,Sonata,2019,Blue,50000,Seattle,23000
+631,Honda,Civic,2020,Gray,50000,Atlanta,21000
+1538,Hyundai,Elantra,2020,Black,50000,San Francisco,17000
+1170,Honda,Civic,2016,Blue,35000,New York,14000
+1029,Honda,Accord,2017,White,70000,Atlanta,12000
+1627,Toyota,4Runner,2018,Silver,30000,Los Angeles,29000
+270,Toyota,Corolla,2018,Silver,50000,Los Angeles,24000
+1209,Toyota,Camry,2016,Silver,35000,Los Angeles,16000
+803,Honda,Civic,2015,Blue,55000,New York,22000
+1368,Chevrolet,Equinox,2015,Black,35000,Miami,28000
+402,Honda,Civic,2017,White,50000,Atlanta,17000
+55,Hyundai,Tucson,2015,Red,70000,San Francisco,20000
+1544,Toyota,Rav25,2017,Gray,40000,Los Angeles,21000
+374,Chevrolet,Impala,2017,Black,35000,Houston,28000
+712,Hyundai,Tucson,2019,Red,35000,San Francisco,16000
+746,Toyota,Highlander,2016,Silver,55000,Dallas,15000
+393,Ford,Fusion,2019,White,40000,Phoenix,14000
+1918,Hyundai,Santa Fe,2016,Red,45000,Seattle,18000
+554,Ford,Mustang,2017,Blue,50000,Chicago,24000
+648,Chevrolet,Tahoe,2017,Black,55000,Miami,16000
+1387,Honda,Civic,2016,Blue,40000,New York,14000
+377,Honda,CR-V,2018,Red,55000,New York,22000
+1871,Toyota,Avalon,2020,Silver,30000,Dallas,29000
+633,Chevrolet,Impala,2019,Blue,30000,Houston,29000
+1073,Toyota,Corolla,2015,Silver,65000,Los Angeles,22000
+1514,Chevrolet,Equinox,2019,Black,50000,Miami,21000
+297,Hyundai,Elantra,2019,Black,40000,San Francisco,21000
+336,Chevrolet,Camaro,2019,Red,50000,Miami,23000
+1822,Hyundai,Venue,2017,Silver,50000,Seattle,17000
+199,Ford,Escape,2019,Blue,50000,Chicago,24000
+673,Toyota,Highlander,2017,Silver,35000,Dallas,14000
+890,Chevrolet,Equinox,2020,Black,40000,Miami,14000
+990,Toyota,Camry,2017,Silver,55000,Los Angeles,12000
+1986,Hyundai,Kona,2019,Blue,40000,San Francisco,17000
+1248,Honda,Accord,2017,White,60000,Atlanta,19000
+1838,Ford,Escape,2016,White,50000,Chicago,21000
+1340,Toyota,Sienna,2019,Red,55000,Dallas,22000
+1943,Hyundai,Genesis,2020,Black,35000,San Francisco,27000
+1703,Chevrolet,Tahoe,2019,Black,60000,Miami,14000
+1445,Ford,Explorer,2015,Blue,30000,Phoenix,29000
+287,Ford,Fusion,2019,White,55000,Phoenix,14000
+613,Chevrolet,Malibu,2020,Blue,70000,Houston,18000
+1061,Chevrolet,Cruze,2018,Black,55000,Houston,12000
+1556,Ford,Edge,2019,Blue,30000,Chicago,29000
+529,Ford,Explorer,2017,White,50000,Phoenix,15000
+474,Toyota,Prius,2015,Gray,45000,Dallas,16000
+1762,Hyundai,Sonata,2019,Blue,45000,Seattle,23000
+843,Hyundai,Elantra,2016,Red,55000,Seattle,22000
+1436,Chevrolet,Impala,2017,Blue,60000,Houston,25000
+1965,Honda,Fit,2019,Gray,45000,Atlanta,16000
+866,Ford,Edge,2019,Blue,45000,Chicago,18000
+738,Ford,Mustang,2015,Yellow,50000,Phoenix,15000
+795,Hyundai,Palisade,2015,Silver,45000,San Francisco,18000
+1997,Toyota,Sienna,2018,Red,55000,Dallas,16000
+1821,Chevrolet,Malibu,2015,Blue,55000,Houston,19000
+330,Ford,Fiesta,2020,Blue,55000,Phoenix,19000
+1005,Toyota,Rav17,2016,Gray,55000,Dallas,19000
+184,Ford,Fiesta,2018,Blue,50000,Phoenix,18000
+506,Ford,Fusion,2017,White,55000,Phoenix,16000
+1225,Honda,Pilot,2018,White,50000,Atlanta,23000
+840,Honda,Civic,2018,White,40000,Atlanta,27000
+75,Honda,Civic,2016,Blue,60000,New York,12000
+203,Honda,Pilot,2016,White,70000,Atlanta,28000
+1957,Chevrolet,Traverse,2017,Black,35000,Houston,18000
+142,Chevrolet,Malibu,2019,Blue,55000,Houston,12000
+693,Toyota,Prius,2018,Gray,35000,Dallas,24000
+1786,Chevrolet,Spark,2017,Blue,55000,Miami,19000
+759,Chevrolet,Malibu,2015,Blue,40000,Houston,18000
+1407,Hyundai,Santa Fe,2015,Red,45000,Seattle,23000
+1889,Chevrolet,Tahoe,2019,Black,40000,Miami,22000
+342,Hyundai,Sonata,2020,Red,45000,Seattle,23000
+989,Hyundai,Elantra,2019,Red,35000,Seattle,27000
+314,Honda,Odyssey,2016,White,60000,New York,14000
+1176,Ford,Mustang,2018,Yellow,35000,Phoenix,18000
+993,Chevrolet,Camaro,2019,Red,60000,Miami,14000
+1537,Chevrolet,Cruze,2020,Red,55000,Miami,19000
+1175,Honda,Accord,2015,White,45000,Atlanta,16000
+1941,Ford,Mustang,2017,Blue,45000,Chicago,26000
+1728,Chevrolet,Impala,2016,Blue,45000,Houston,26000
+632,Ford,Fusion,2015,White,45000,Phoenix,26000
+346,Chevrolet,Equinox,2020,Black,55000,Miami,22000
+20,Hyundai,Santa Fe,2019,Red,40000,Seattle,25000
+166,Hyundai,Santa Fe,2019,Red,45000,Seattle,26000
+381,Toyota,Highlander,2019,Silver,35000,Dallas,27000
+1744,Hyundai,Palisade,2019,Silver,35000,San Francisco,18000
+1237,Chevrolet,Malibu,2015,Blue,60000,Houston,14000
+600,Toyota,Highlander,2020,Silver,50000,Dallas,15000
+822,Chevrolet,Traverse,2020,Black,60000,Houston,19000
+950,Toyota,Camry,2019,White,55000,Los Angeles,12000
+1026,Chevrolet,Cruze,2016,Red,50000,Miami,15000
+1462,Honda,Civic,2016,Blue,50000,New York,18000
+380,Hyundai,Kona,2019,Blue,30000,San Francisco,29000
+1551,Ford,Explorer,2020,White,35000,Phoenix,25000
+518,Honda,Accord,2015,White,70000,Atlanta,27000
+150,Chevrolet,Cruze,2018,Red,65000,Miami,22000
+207,Toyota,4Runner,2018,Silver,30000,Los Angeles,29000
+1810,Ford,Explorer,2015,Blue,70000,Phoenix,12000
+972,Ford,Edge,2017,Blue,40000,Chicago,18000
+532,Toyota,4Runner,2019,Silver,70000,Los Angeles,12000
+1706,Honda,Fit,2018,Gray,35000,Atlanta,16000
+1580,Honda,Civic,2016,Gray,50000,Atlanta,23000
+836,Ford,EcoSport,2016,Red,70000,Chicago,20000
+519,Ford,Mustang,2016,Yellow,55000,Phoenix,22000
+1090,Ford,Fusion,2015,White,30000,Phoenix,29000
+122,Chevrolet,Impala,2020,Blue,55000,Houston,19000
+1449,Honda,Odyssey,2016,White,40000,New York,17000
+1592,Chevrolet,Traverse,2016,Black,60000,Houston,14000
+337,Hyundai,Genesis,2015,Black,40000,San Francisco,21000
+1068,Toyota,Avalon,2020,Silver,55000,Dallas,14000
+1428,Toyota,Camry,2019,Silver,65000,Los Angeles,22000
+837,Chevrolet,Spark,2019,Blue,55000,Miami,22000
+1869,Chevrolet,Camaro,2017,Red,50000,Miami,21000
+1550,Honda,Pilot,2016,Gray,40000,Atlanta,27000
+156,Hyundai,Sonata,2017,Blue,45000,Seattle,18000
+1166,Toyota,Yaris,2016,Black,60000,Los Angeles,14000
+1714,Hyundai,Accent,2016,Silver,50000,San Francisco,14000
+1481,Toyota,4Runner,2018,Silver,70000,Los Angeles,28000
+580,Chevrolet,Malibu,2019,Blue,30000,Houston,23000
+862,Chevrolet,Traverse,2017,Black,50000,Houston,14000
+1265,Chevrolet,Tahoe,2015,Black,45000,Miami,23000
+1985,Chevrolet,Equinox,2020,Black,50000,Miami,15000
+1103,Ford,Mustang,2019,Yellow,40000,Phoenix,14000
+100,Honda,Fit,2017,Gray,40000,Atlanta,17000
+246,Honda,Fit,2020,Gray,40000,Atlanta,17000
+212,Toyota,Sienna,2018,Red,60000,Dallas,14000
+1048,Toyota,Sienna,2015,Red,35000,Dallas,24000
+1373,Chevrolet,Traverse,2018,Black,45000,Houston,26000
+1467,Honda,Accord,2017,White,50000,Atlanta,17000
+1719,Hyundai,Elantra,2020,Red,35000,Seattle,20000
+1317,Ford,Focus,2017,Silver,45000,Chicago,16000
+1717,Ford,Fiesta,2019,Blue,40000,Phoenix,21000
+977,Ford,Fusion,2019,White,35000,Phoenix,24000
+1553,Hyundai,Santa Fe,2016,Red,55000,Seattle,22000
+1308,Honda,Fit,2018,Gray,60000,Atlanta,14000
+1707,Ford,Fusion,2019,White,55000,Phoenix,14000
+1084,Honda,Odyssey,2016,White,35000,New York,28000
+614,Hyundai,Venue,2015,Silver,55000,Seattle,19000
+146,Honda,Civic,2016,Blue,60000,New York,12000
+1452,Hyundai,Palisade,2016,Silver,50000,San Francisco,15000
+411,Toyota,Avalon,2018,Silver,55000,Dallas,22000
+533,Honda,Odyssey,2017,White,55000,New York,15000
+472,Chevrolet,Spark,2018,Blue,55000,Miami,19000
+1784,Honda,HR-V,2018,White,55000,New York,16000
+1610,Chevrolet,Cruze,2020,Red,45000,Miami,16000
+1245,Chevrolet,Cruze,2020,Red,40000,Miami,14000
+701,Chevrolet,Camaro,2015,Red,55000,Miami,22000
+984,Hyundai,Accent,2020,Silver,70000,San Francisco,28000
+1341,Honda,Fit,2016,Gray,50000,Atlanta,21000
+1859,Chevrolet,Spark,2016,Blue,40000,Miami,21000
+1930,Honda,HR-V,2015,White,40000,New York,21000
+1306,Hyundai,Palisade,2018,Silver,50000,San Francisco,15000
+320,Ford,Fusion,2020,White,55000,Phoenix,15000
+1438,Toyota,Corolla,2020,Silver,50000,Los Angeles,23000
+127,Chevrolet,Equinox,2015,Black,55000,Miami,22000
+102,Chevrolet,Malibu,2016,Blue,55000,Houston,12000
+44,Chevrolet,Camaro,2020,Red,35000,Miami,25000
+1776,Chevrolet,Tahoe,2018,Black,45000,Miami,18000
+1937,Chevrolet,Cruze,2018,Black,40000,Houston,25000
+695,Ford,Fiesta,2016,Blue,55000,Phoenix,22000
+790,Hyundai,Santa Fe,2018,Red,55000,Seattle,16000
+674,Honda,Pilot,2019,Gray,70000,Atlanta,12000
+1823,Toyota,Yaris,2018,Black,45000,Los Angeles,16000
+805,Honda,Civic,2018,Blue,45000,New York,26000
+1516,Toyota,Rav24,2017,Gray,30000,Dallas,29000
+42,Honda,Accord,2018,White,50000,New York,18000
+185,Chevrolet,Cruze,2015,Black,40000,Houston,22000
+872,Chevrolet,Malibu,2015,Blue,40000,Houston,25000
+1790,Ford,Fiesta,2016,Blue,35000,Phoenix,20000
+1848,Ford,Edge,2019,Blue,35000,Chicago,16000
+1163,Ford,Fusion,2016,White,55000,Phoenix,12000
+1203,Hyundai,Accent,2017,Silver,55000,San Francisco,12000
+369,Chevrolet,Cruze,2019,Red,45000,Miami,18000
+195,Chevrolet,Impala,2016,Blue,40000,Houston,21000
+1795,Ford,Mustang,2016,Blue,40000,Chicago,25000
+1440,Ford,Escape,2020,Blue,40000,Chicago,25000
+373,Ford,Mustang,2015,Yellow,50000,Phoenix,23000
+911,Hyundai,Accent,2019,Silver,40000,San Francisco,27000
+992,Ford,Mustang,2016,Blue,40000,Chicago,17000
+1148,Ford,Escape,2018,Blue,30000,Chicago,23000
+1243,Honda,Civic,2018,Blue,55000,New York,15000
+1070,Ford,Fusion,2017,White,40000,Phoenix,15000
+1330,Toyota,Highlander,2020,Silver,50000,Dallas,23000
+925,Chevrolet,Impala,2016,Blue,35000,Houston,16000
+1260,Chevrolet,Traverse,2015,Black,40000,Houston,21000
+1842,Honda,Pilot,2015,Gray,55000,Atlanta,12000
+1684,Hyundai,Elantra,2016,Black,55000,San Francisco,19000
+1577,Chevrolet,Camaro,2015,Red,35000,Miami,20000
+74,Toyota,Camry,2018,White,55000,Los Angeles,14000
+128,Hyundai,Tucson,2019,Red,50000,San Francisco,24000
+1459,Toyota,Camry,2017,White,45000,Los Angeles,16000
+561,Hyundai,Sonata,2020,Red,45000,Seattle,26000
+808,Hyundai,Elantra,2020,Black,55000,San Francisco,12000
+592,Ford,Mustang,2015,Yellow,45000,Phoenix,26000
+1951,Ford,Escape,2019,Blue,35000,Chicago,14000
+1319,Hyundai,Elantra,2019,Black,60000,San Francisco,19000
+844,Toyota,Camry,2015,Silver,50000,Los Angeles,21000
+1360,Toyota,Avalon,2016,Silver,55000,Dallas,19000
+711,Chevrolet,Equinox,2015,Black,45000,Miami,18000
+1702,Ford,Edge,2016,Blue,40000,Chicago,17000
+177,Toyota,Yaris,2018,Black,70000,Los Angeles,12000
+30,Hyundai,Venue,2016,Silver,60000,Seattle,14000
+755,Hyundai,Palisade,2019,Silver,70000,San Francisco,18000
+1897,Toyota,Camry,2020,White,55000,Los Angeles,19000
+1023,Toyota,Camry,2016,White,40000,Los Angeles,17000
+611,Honda,Fit,2018,Gray,40000,Atlanta,22000
+433,Ford,Fusion,2016,White,30000,Phoenix,18000
+1377,Ford,Edge,2016,Blue,50000,Chicago,15000
+1938,Hyundai,Elantra,2018,Red,70000,Seattle,27000
+779,Chevrolet,Impala,2020,Blue,40000,Houston,17000
+886,Hyundai,Sonata,2017,Blue,35000,Seattle,14000
+1479,Chevrolet,Traverse,2020,Black,40000,Houston,27000
+1623,Honda,Pilot,2020,Gray,70000,Atlanta,28000
+347,Hyundai,Tucson,2017,Red,50000,San Francisco,21000
+938,Honda,Odyssey,2018,White,35000,New York,20000
+478,Hyundai,Elantra,2017,Red,50000,Seattle,23000
+1969,Toyota,Yaris,2017,Black,50000,Los Angeles,23000
+1675,Chevrolet,Malibu,2016,Blue,50000,Houston,18000
+1713,Chevrolet,Spark,2017,Blue,55000,Miami,16000
+62,Honda,Odyssey,2016,White,55000,New York,22000
+1381,Honda,Fit,2018,Gray,50000,Atlanta,15000
+953,Chevrolet,Cruze,2019,Red,60000,Miami,14000
+1774,Honda,Odyssey,2016,White,60000,New York,14000
+1916,Ford,Explorer,2018,White,60000,Phoenix,14000
+573,Honda,Odyssey,2015,White,40000,New York,15000
+1740,Toyota,4Runner,2019,Silver,55000,Los Angeles,15000
+892,Toyota,Highlander,2015,Silver,35000,Dallas,18000
+1513,Ford,Escape,2018,Blue,55000,Chicago,22000
+1483,Ford,Edge,2015,Blue,50000,Chicago,21000
+1967,Chevrolet,Malibu,2018,Blue,35000,Houston,20000
+1662,Toyota,Rav26,2020,Gray,40000,Dallas,17000
+718,Toyota,4Runner,2016,Silver,65000,Los Angeles,22000
+1229,Toyota,4Runner,2017,Silver,55000,Los Angeles,22000
+578,Honda,Fit,2020,Gray,50000,Atlanta,14000
+988,Chevrolet,Cruze,2018,Black,30000,Houston,29000
+1497,Honda,Civic,2018,White,25000,Atlanta,19000
+757,Honda,Fit,2019,Gray,50000,Atlanta,17000
+551,Hyundai,Elantra,2018,Red,35000,Seattle,24000
+1395,Ford,Mustang,2017,Yellow,55000,Phoenix,19000
+298,Toyota,Corolla,2016,Gray,45000,Dallas,18000
+1886,Toyota,4Runner,2015,Silver,35000,Los Angeles,18000
+1632,Toyota,Sienna,2020,Red,60000,Dallas,14000
+1447,Hyundai,Santa Fe,2017,Red,55000,Seattle,12000
+885,Chevrolet,Impala,2020,Black,40000,Houston,17000
+1130,Hyundai,Accent,2016,Silver,30000,San Francisco,29000
+1076,Chevrolet,Equinox,2019,Black,55000,Miami,19000
+517,Toyota,Corolla,2019,Gray,40000,Dallas,25000
+657,Honda,Civic,2015,Blue,50000,New York,23000
+1354,Hyundai,Elantra,2017,Red,40000,Seattle,15000
+714,Honda,Pilot,2017,White,60000,Atlanta,12000
+23,Ford,Edge,2017,Blue,50000,Chicago,21000
+572,Toyota,4Runner,2020,Silver,60000,Los Angeles,12000
+1326,Honda,CR-V,2020,Red,45000,New York,16000
+1855,Hyundai,Venue,2016,Silver,55000,Seattle,16000
+1900,Honda,Civic,2017,Blue,35000,New York,24000
+1811,Chevrolet,Traverse,2016,Black,55000,Houston,15000
+1257,Toyota,Highlander,2017,Silver,35000,Dallas,20000
+109,Toyota,Prius,2017,Gray,40000,Dallas,14000
+140,Honda,Fit,2018,Gray,40000,Atlanta,17000
+1633,Honda,Fit,2020,Gray,55000,Atlanta,12000
+1311,Hyundai,Venue,2020,Silver,40000,Seattle,17000
+1458,Toyota,Yaris,2015,Black,40000,Los Angeles,14000
+1643,Honda,Civic,2016,White,50000,Atlanta,14000
+93,Hyundai,Santa Fe,2020,Red,55000,Seattle,22000
+407,Honda,Accord,2016,White,50000,New York,23000
+1269,Ford,Fusion,2018,White,55000,Phoenix,22000
+996,Honda,Civic,2020,Gray,35000,Atlanta,16000
+1123,Ford,Fusion,2016,White,45000,Phoenix,23000
+1000,Toyota,Corolla,2017,Silver,25000,Los Angeles,19000
+780,Hyundai,Sonata,2015,Red,60000,Seattle,14000
+261,Honda,Accord,2020,White,45000,New York,16000
+607,Ford,Edge,2015,Blue,45000,Chicago,16000
+202,Toyota,Rav6,2016,Gray,35000,Dallas,25000
+1846,Toyota,4Runner,2019,Silver,55000,Los Angeles,12000
+1540,Honda,Accord,2015,White,40000,Atlanta,18000
+1549,Toyota,Highlander,2016,Silver,45000,Dallas,23000
+821,Ford,Explorer,2019,White,35000,Phoenix,18000
+71,Toyota,Yaris,2017,Black,55000,Los Angeles,12000
+707,Hyundai,Sonata,2015,Red,50000,Seattle,15000
+940,Chevrolet,Tahoe,2020,Black,55000,Miami,24000
+255,Toyota,Prius,2017,Gray,50000,Dallas,18000
+283,Chevrolet,Tahoe,2019,Black,60000,Miami,14000
+1690,Toyota,Rav27,2018,Gray,50000,Los Angeles,24000
+1676,Hyundai,Venue,2019,Silver,40000,Seattle,22000
+735,Hyundai,Elantra,2016,Black,30000,San Francisco,29000
+1361,Honda,Civic,2015,Gray,30000,Atlanta,23000
+194,Ford,Fusion,2020,White,50000,Phoenix,23000
+1149,Chevrolet,Equinox,2015,Black,40000,Miami,21000
+48,Ford,Fusion,2018,White,45000,Phoenix,16000
+1924,Toyota,Sienna,2018,Red,30000,Dallas,18000
+1975,Chevrolet,Cruze,2020,Red,45000,Miami,23000
+708,Toyota,Corolla,2020,Silver,40000,Los Angeles,17000
+1404,Honda,Pilot,2019,Gray,70000,Atlanta,20000
+980,Toyota,Yaris,2019,Black,50000,Los Angeles,24000
+749,Chevrolet,Traverse,2020,Black,45000,Houston,16000
+1616,Hyundai,Sonata,2016,Blue,35000,Seattle,24000
+271,Honda,CR-V,2018,White,45000,New York,23000
+257,Ford,Fiesta,2017,Blue,35000,Phoenix,25000
+1929,Toyota,Yaris,2019,Black,30000,Los Angeles,23000
+577,Toyota,Sienna,2017,Red,55000,Dallas,16000
+1668,Honda,Odyssey,2019,White,70000,New York,12000
+1721,Honda,Accord,2015,White,55000,New York,24000
+277,Ford,Explorer,2017,Blue,45000,Phoenix,26000
+1887,Honda,Odyssey,2019,White,60000,New York,19000
+516,Hyundai,Elantra,2017,Black,35000,San Francisco,28000
+900,Chevrolet,Tahoe,2015,Black,45000,Miami,16000
+1767,Hyundai,Kona,2020,Blue,50000,San Francisco,21000
+414,Chevrolet,Impala,2015,Blue,40000,Houston,27000
+29,Chevrolet,Malibu,2019,Blue,40000,Houston,17000
+946,Hyundai,Venue,2018,Silver,50000,Seattle,21000
+1466,Toyota,Corolla,2020,Gray,55000,Dallas,19000
+1188,Hyundai,Santa Fe,2019,Red,50000,Seattle,23000
+1519,Chevrolet,Traverse,2020,Black,50000,Houston,15000
+794,Chevrolet,Tahoe,2019,Black,40000,Miami,21000
+587,Ford,Focus,2016,Silver,35000,Chicago,28000
+1104,Chevrolet,Impala,2016,Black,45000,Houston,16000
+1108,Ford,Escape,2020,White,40000,Chicago,22000
+1124,Chevrolet,Malibu,2015,Blue,40000,Houston,27000
+793,Ford,Edge,2016,Blue,30000,Chicago,23000
+1522,Honda,Odyssey,2017,White,55000,New York,12000
+1493,Ford,EcoSport,2016,Red,35000,Chicago,16000
+1756,Chevrolet,Cruze,2016,Red,50000,Miami,23000
+447,Chevrolet,Impala,2018,Black,70000,Houston,27000
+1882,Honda,Pilot,2017,White,55000,Atlanta,15000
+188,Honda,Accord,2016,White,55000,New York,19000
+644,Hyundai,Santa Fe,2020,Red,40000,Seattle,15000
+276,Honda,Pilot,2019,White,50000,Atlanta,21000
+1533,Honda,Civic,2017,Blue,50000,New York,18000
+709,Honda,CR-V,2020,White,60000,New York,14000
+748,Ford,Explorer,2020,White,40000,Phoenix,14000
+1274,Ford,EcoSport,2017,Red,55000,Chicago,12000
+1296,Hyundai,Tucson,2019,Red,50000,San Francisco,23000
+1529,Chevrolet,Malibu,2019,Blue,40000,Houston,14000
+1968,Hyundai,Venue,2016,Silver,55000,Seattle,19000
+608,Chevrolet,Tahoe,2015,Black,35000,Miami,18000
+1746,Honda,Fit,2019,Gray,50000,Atlanta,18000
+642,Ford,Explorer,2018,Blue,55000,Phoenix,14000
+1876,Toyota,Corolla,2017,Silver,60000,Los Angeles,14000
+370,Hyundai,Elantra,2017,Black,35000,San Francisco,20000
+1210,Honda,Accord,2016,White,55000,New York,14000
+1442,Hyundai,Tucson,2018,Red,55000,San Francisco,22000
+189,Ford,Mustang,2016,Blue,50000,Chicago,17000
+509,Toyota,Yaris,2019,Black,30000,Los Angeles,23000
+91,Ford,Explorer,2018,White,40000,Phoenix,25000
+232,Ford,Escape,2015,White,35000,Chicago,28000
+1661,Hyundai,Tucson,2018,Red,50000,San Francisco,15000
+1917,Chevrolet,Traverse,2015,Black,55000,Houston,12000
+432,Honda,Fit,2016,Gray,25000,Atlanta,19000
+190,Chevrolet,Camaro,2020,Red,45000,Miami,16000
+1476,Toyota,Highlander,2015,Silver,55000,Dallas,22000
+970,Toyota,4Runner,2020,Silver,50000,Los Angeles,17000
+884,Ford,Mustang,2015,Yellow,50000,Phoenix,15000
+161,Hyundai,Kona,2020,Blue,35000,San Francisco,28000
+1624,Ford,Explorer,2016,White,55000,Phoenix,22000
+1267,Toyota,Sienna,2018,Red,35000,Dallas,25000
+248,Chevrolet,Malibu,2017,Blue,70000,Houston,12000
+332,Hyundai,Elantra,2016,Red,45000,Seattle,16000
+729,Toyota,Camry,2019,White,35000,Los Angeles,28000
+1654,Ford,Fusion,2016,White,70000,Phoenix,27000
+1127,Honda,HR-V,2015,White,55000,New York,22000
+528,Honda,Pilot,2018,Gray,55000,Atlanta,12000
+324,Honda,HR-V,2017,White,35000,New York,18000
+1347,Ford,EcoSport,2015,Red,40000,Chicago,17000
+1089,Honda,Fit,2019,Gray,45000,Atlanta,26000
+2,Honda,Civic,2019,Blue,35000,New York,16000
+1826,Toyota,Camry,2016,White,55000,Los Angeles,19000
+456,Ford,Explorer,2015,White,60000,Phoenix,14000
+660,Ford,Focus,2018,Silver,70000,Chicago,27000
+1465,Hyundai,Elantra,2020,Black,70000,San Francisco,18000
+1242,Toyota,Camry,2020,White,70000,Los Angeles,12000
+1057,Hyundai,Accent,2015,Silver,50000,San Francisco,21000
+797,Honda,Fit,2015,Gray,60000,Atlanta,25000
+1536,Ford,Focus,2019,Silver,70000,Chicago,18000
+205,Chevrolet,Traverse,2017,Black,50000,Houston,21000
+659,Honda,Civic,2020,Blue,40000,New York,25000
+1305,Chevrolet,Tahoe,2020,Black,55000,Miami,12000
+678,Toyota,4Runner,2020,Silver,45000,Los Angeles,16000
+186,Hyundai,Elantra,2015,Red,35000,Seattle,25000
+1565,Honda,HR-V,2017,White,55000,New York,14000
+1800,Ford,Fusion,2019,White,30000,Phoenix,29000
+726,Chevrolet,Malibu,2019,Blue,60000,Houston,25000
+855,Honda,CR-V,2017,White,55000,New York,14000
+1541,Ford,Mustang,2020,Yellow,35000,Phoenix,20000
+543,Honda,HR-V,2016,White,55000,New York,19000
+193,Honda,Civic,2015,Gray,55000,Atlanta,19000
+598,Chevrolet,Equinox,2017,Black,60000,Miami,14000
+1829,Chevrolet,Cruze,2015,Red,35000,Miami,24000
+1477,Honda,Pilot,2015,Gray,50000,Atlanta,24000
+209,Ford,Edge,2019,Blue,55000,Chicago,12000
+1960,Honda,Odyssey,2018,White,40000,New York,22000
+103,Hyundai,Venue,2018,Silver,50000,Seattle,15000
+401,Toyota,Prius,2015,Gray,55000,Dallas,19000
+172,Toyota,Sienna,2019,Red,60000,Dallas,14000
+923,Honda,Civic,2018,Gray,55000,Atlanta,12000
+8,Ford,Mustang,2015,Yellow,65000,Phoenix,22000
+696,Chevrolet,Cruze,2020,Black,50000,Houston,24000
+1393,Toyota,Corolla,2019,Gray,35000,Dallas,25000
+1830,Hyundai,Elantra,2016,Black,70000,San Francisco,20000
+1208,Hyundai,Elantra,2017,Red,45000,Seattle,18000
+399,Chevrolet,Spark,2015,Blue,35000,Miami,25000
+645,Toyota,4Runner,2020,Silver,25000,Los Angeles,19000
+1044,Honda,Odyssey,2015,White,35000,New York,20000
+403,Ford,Fiesta,2017,Blue,45000,Phoenix,16000
+683,Toyota,Sienna,2019,Red,35000,Dallas,25000
+1309,Ford,Fusion,2019,White,55000,Phoenix,12000
+1290,Chevrolet,Impala,2019,Blue,30000,Houston,23000
+1033,Toyota,Rav18,2015,Gray,45000,Los Angeles,16000
+638,Chevrolet,Equinox,2016,Black,60000,Miami,14000
+1367,Ford,Escape,2017,Blue,50000,Chicago,23000
+893,Honda,Pilot,2017,Gray,60000,Atlanta,19000
+273,Chevrolet,Equinox,2018,Black,35000,Miami,25000
+239,Hyundai,Santa Fe,2016,Red,35000,Seattle,27000
+1973,Honda,Civic,2019,Blue,55000,New York,22000
+954,Hyundai,Elantra,2015,Black,55000,San Francisco,12000
+1832,Honda,Accord,2017,White,50000,Atlanta,24000
+1934,Toyota,Prius,2018,Gray,55000,Dallas,24000
+483,Hyundai,Genesis,2015,Black,50000,San Francisco,24000
+706,Chevrolet,Impala,2017,Blue,55000,Houston,12000
+1927,Chevrolet,Malibu,2015,Blue,50000,Houston,14000
+1799,Honda,Civic,2015,Gray,45000,Atlanta,26000
+635,Toyota,Corolla,2020,Silver,55000,Los Angeles,12000
+1295,Chevrolet,Equinox,2020,Black,55000,Miami,24000
+666,Chevrolet,Impala,2018,Black,55000,Houston,12000
+691,Chevrolet,Spark,2016,Blue,50000,Miami,23000
+362,Hyundai,Venue,2016,Silver,30000,Seattle,18000
+434,Chevrolet,Malibu,2018,Blue,65000,Houston,22000
+1764,Honda,CR-V,2020,Red,35000,New York,25000
+1640,Chevrolet,Spark,2015,Blue,30000,Miami,18000
+1226,Ford,Explorer,2015,Blue,35000,Phoenix,28000
+1276,Hyundai,Accent,2018,Silver,40000,San Francisco,17000
+1386,Toyota,Camry,2020,White,50000,Los Angeles,17000
+768,Ford,Fiesta,2016,Blue,45000,Phoenix,23000
+1567,Chevrolet,Spark,2017,Blue,40000,Miami,15000
+761,Toyota,Yaris,2015,Black,55000,Los Angeles,19000
+1116,Toyota,4Runner,2016,Silver,55000,Los Angeles,19000
+437,Toyota,Camry,2016,White,55000,Los Angeles,19000
+919,Ford,Mustang,2015,Blue,55000,Chicago,12000
+1564,Toyota,Yaris,2019,Black,35000,Los Angeles,16000
+1020,Toyota,Yaris,2017,Black,35000,Los Angeles,27000
+409,Chevrolet,Camaro,2017,Red,35000,Miami,24000
+79,Toyota,Corolla,2016,Gray,65000,Dallas,22000
+317,Hyundai,Palisade,2015,Silver,40000,San Francisco,17000
+476,Ford,Fiesta,2017,Blue,35000,Phoenix,20000
+1768,Toyota,Highlander,2020,Silver,45000,Dallas,26000
+677,Hyundai,Santa Fe,2016,Red,40000,Seattle,14000
+309,Honda,Pilot,2018,Gray,30000,Atlanta,29000
+1498,Ford,Fiesta,2017,Blue,30000,Phoenix,18000
+627,Ford,Mustang,2015,Blue,40000,Chicago,27000
+379,Chevrolet,Equinox,2018,Black,45000,Miami,26000
+1474,Chevrolet,Equinox,2019,Black,35000,Miami,24000
+1164,Chevrolet,Malibu,2016,Blue,50000,Houston,15000
+690,Ford,EcoSport,2016,Red,55000,Chicago,19000
+482,Chevrolet,Camaro,2016,Red,55000,Miami,22000
+1747,Ford,Fusion,2020,White,40000,Phoenix,22000
+588,Chevrolet,Cruze,2017,Red,40000,Miami,25000
+1619,Ford,Escape,2018,White,50000,Chicago,24000
+1585,Honda,CR-V,2017,White,50000,New York,21000
+1001,Honda,CR-V,2020,White,30000,New York,18000
+545,Chevrolet,Spark,2018,Blue,45000,Miami,16000
+1899,Toyota,Camry,2018,White,40000,Los Angeles,21000
+323,Toyota,Yaris,2019,Black,45000,Los Angeles,16000
+1793,Toyota,Camry,2019,Silver,50000,Los Angeles,23000
+1337,Ford,Edge,2019,Blue,40000,Chicago,27000
+1879,Chevrolet,Equinox,2017,Black,40000,Miami,17000
+880,Chevrolet,Cruze,2018,Red,50000,Miami,15000
+1052,Hyundai,Venue,2019,Silver,45000,Seattle,23000
+1083,Toyota,4Runner,2017,Silver,50000,Los Angeles,23000
+1710,Toyota,Yaris,2019,Black,25000,Los Angeles,19000
+1007,Ford,Explorer,2017,Blue,40000,Phoenix,21000
+1854,Chevrolet,Malibu,2018,Blue,65000,Houston,22000
+786,Toyota,Rav14,2018,Gray,40000,Dallas,15000
+525,Chevrolet,Equinox,2016,Black,50000,Miami,15000
+35,Hyundai,Accent,2015,Silver,70000,San Francisco,12000
+151,Hyundai,Elantra,2017,Black,55000,San Francisco,16000
+425,Hyundai,Santa Fe,2020,Red,60000,Seattle,14000
+1153,Ford,Explorer,2015,Blue,55000,Phoenix,24000
+864,Toyota,4Runner,2018,Silver,30000,Los Angeles,23000
+1379,Hyundai,Palisade,2018,Silver,60000,San Francisco,14000
+612,Ford,Fusion,2015,White,35000,Phoenix,25000
+70,Hyundai,Venue,2016,Silver,60000,Seattle,14000
+722,Hyundai,Palisade,2019,Silver,30000,San Francisco,23000
+126,Ford,Escape,2019,Blue,70000,Chicago,20000
+869,Toyota,Sienna,2015,Red,55000,Dallas,24000
+1359,Hyundai,Genesis,2019,Black,50000,San Francisco,14000
+1645,Chevrolet,Cruze,2015,Black,30000,Houston,23000
+1254,Ford,Escape,2016,White,50000,Chicago,17000
+1139,Chevrolet,Camaro,2019,Red,55000,Miami,14000
+361,Chevrolet,Malibu,2015,Blue,25000,Houston,19000
+1802,Hyundai,Sonata,2020,Red,55000,Seattle,12000
+1228,Hyundai,Santa Fe,2020,Red,70000,Seattle,27000
+1472,Honda,CR-V,2017,Red,50000,New York,23000
+538,Honda,Fit,2015,Gray,60000,Atlanta,19000
+814,Toyota,Rav15,2016,Gray,40000,Los Angeles,17000
+1145,Hyundai,Sonata,2018,Red,55000,Seattle,16000
+1883,Ford,Explorer,2017,Blue,50000,Phoenix,17000
+1858,Ford,EcoSport,2020,Red,30000,Chicago,23000
+1945,Honda,Civic,2017,Gray,50000,Atlanta,15000
+636,Honda,CR-V,2020,White,50000,New York,15000
+927,Toyota,Corolla,2019,Silver,60000,Los Angeles,12000
+1293,Honda,CR-V,2019,White,35000,New York,20000
+1272,Toyota,Yaris,2015,Black,30000,Los Angeles,29000
+58,Ford,Explorer,2018,Blue,45000,Phoenix,23000
+481,Ford,Mustang,2016,Blue,70000,Chicago,20000
+236,Honda,Pilot,2020,Gray,50000,Atlanta,21000
+1808,Toyota,Rav28,2017,Gray,40000,Dallas,17000
+619,Hyundai,Accent,2015,Silver,55000,San Francisco,19000
+1446,Chevrolet,Traverse,2017,Black,35000,Houston,27000
+921,Hyundai,Genesis,2020,Black,40000,San Francisco,17000
+1861,Toyota,Prius,2015,Gray,35000,Dallas,20000
+688,Toyota,Yaris,2016,Black,40000,Los Angeles,18000
+170,Chevrolet,Tahoe,2015,Black,50000,Miami,15000
+1995,Chevrolet,Tahoe,2020,Black,30000,Miami,18000
+1910,Honda,CR-V,2016,Red,45000,New York,26000
+1310,Chevrolet,Malibu,2017,Blue,50000,Houston,15000
+301,Chevrolet,Impala,2017,Black,55000,Houston,24000
+367,Honda,Civic,2015,Blue,30000,New York,23000
+1644,Ford,Fiesta,2020,Blue,55000,Phoenix,19000
+874,Toyota,Yaris,2015,Black,55000,Los Angeles,22000
+994,Hyundai,Genesis,2019,Black,55000,San Francisco,12000
+1757,Hyundai,Elantra,2016,Black,40000,San Francisco,21000
+1667,Toyota,4Runner,2020,Silver,35000,Los Angeles,14000
+1777,Hyundai,Palisade,2020,Silver,35000,San Francisco,16000
+1730,Toyota,Corolla,2016,Silver,35000,Los Angeles,27000
+918,Honda,Accord,2020,White,35000,New York,27000
+941,Hyundai,Palisade,2018,Silver,50000,San Francisco,23000
+405,Hyundai,Elantra,2020,Red,35000,Seattle,20000
+1751,Toyota,Camry,2016,White,50000,Los Angeles,17000
+1016,Honda,Fit,2018,Gray,55000,Atlanta,22000
+1589,Toyota,Rav25,2015,Gray,55000,Dallas,12000
+1122,Honda,Fit,2019,Gray,50000,Atlanta,24000
+1534,Toyota,Camry,2019,White,40000,Los Angeles,22000
+1144,Chevrolet,Impala,2019,Blue,65000,Houston,22000
+1992,Toyota,4Runner,2015,Silver,60000,Los Angeles,12000
+15,Hyundai,Kona,2019,Blue,35000,San Francisco,20000
+1423,Toyota,Prius,2017,Gray,55000,Dallas,14000
+453,Hyundai,Kona,2016,Blue,55000,San Francisco,12000
+618,Chevrolet,Spark,2016,Blue,35000,Miami,20000
+858,Hyundai,Tucson,2016,Red,25000,San Francisco,19000
+25,Hyundai,Palisade,2019,Silver,30000,San Francisco,29000
+1263,Honda,Odyssey,2020,White,55000,New York,22000
+388,Ford,Edge,2018,Blue,40000,Chicago,17000
+769,Chevrolet,Cruze,2019,Black,40000,Houston,27000
+522,Toyota,Rav11,2017,Gray,30000,Los Angeles,29000
+1634,Ford,Fusion,2017,White,45000,Phoenix,18000
+625,Toyota,Camry,2018,Silver,50000,Los Angeles,24000
+1358,Chevrolet,Camaro,2018,Red,55000,Miami,16000
+251,Honda,HR-V,2020,White,40000,New York,14000
+960,Toyota,Rav17,2015,Gray,50000,Los Angeles,17000
+889,Ford,Escape,2019,White,50000,Chicago,17000
+124,Toyota,Corolla,2017,Silver,40000,Los Angeles,21000
+125,Honda,CR-V,2018,White,35000,New York,24000
+846,Ford,Mustang,2017,Blue,30000,Chicago,29000
+1256,Hyundai,Kona,2017,Blue,40000,San Francisco,18000
+1804,Honda,CR-V,2018,White,40000,New York,17000
+1470,Hyundai,Sonata,2016,Blue,35000,Seattle,20000
+39,Chevrolet,Cruze,2019,Black,45000,Houston,16000
+24,Chevrolet,Tahoe,2018,Black,45000,Miami,26000
+720,Ford,Edge,2017,Blue,50000,Chicago,14000
+303,Toyota,Rav8,2019,Gray,35000,Los Angeles,28000
+1584,Toyota,Corolla,2016,Silver,55000,Los Angeles,22000
+1331,Honda,Pilot,2015,Gray,40000,Atlanta,21000
+1461,Toyota,Camry,2015,White,60000,Los Angeles,19000
+1433,Toyota,Avalon,2019,Silver,40000,Dallas,21000
+817,Chevrolet,Equinox,2019,Black,55000,Miami,15000
+1060,Ford,Fiesta,2015,Blue,35000,Phoenix,27000
+511,Honda,Civic,2019,Blue,45000,New York,18000
+1034,Honda,CR-V,2015,Red,35000,New York,18000
+621,Honda,Civic,2018,White,40000,Atlanta,21000
+1594,Toyota,4Runner,2015,Silver,50000,Los Angeles,15000
+1244,Ford,Focus,2018,Silver,50000,Chicago,17000
+322,Hyundai,Venue,2018,Silver,40000,Seattle,14000
+1495,Hyundai,Accent,2018,Silver,60000,San Francisco,12000
+389,Chevrolet,Tahoe,2017,Black,35000,Miami,14000
+1054,Honda,HR-V,2019,White,35000,New York,25000
+896,Hyundai,Santa Fe,2017,Red,35000,Seattle,25000
+69,Chevrolet,Malibu,2019,Blue,40000,Houston,17000
+1850,Hyundai,Palisade,2016,Silver,60000,San Francisco,12000
+387,Honda,Odyssey,2015,White,50000,New York,15000
+1631,Hyundai,Palisade,2016,Silver,40000,San Francisco,17000
+135,Honda,Odyssey,2016,White,45000,New York,26000
+56,Toyota,Rav4,2016,Gray,55000,Dallas,22000
+955,Toyota,Corolla,2019,Gray,50000,Dallas,15000
+1735,Toyota,Rav27,2017,Gray,55000,Dallas,12000
+1739,Hyundai,Santa Fe,2019,Red,70000,Seattle,12000
+1202,Chevrolet,Spark,2017,Blue,35000,Miami,27000
+217,Toyota,Yaris,2015,Black,60000,Los Angeles,12000
+1504,Chevrolet,Camaro,2019,Red,40000,Miami,21000
+1019,Hyundai,Venue,2017,Silver,30000,Seattle,29000
+663,Toyota,Corolla,2017,Gray,45000,Dallas,26000
+829,Toyota,Sienna,2016,Red,45000,Dallas,16000
+1431,Chevrolet,Camaro,2018,Red,55000,Miami,19000
+1860,Hyundai,Accent,2016,Silver,45000,San Francisco,18000
+1635,Chevrolet,Malibu,2018,Blue,35000,Houston,16000
+1547,Chevrolet,Equinox,2019,Black,55000,Miami,22000
+1092,Hyundai,Venue,2018,Silver,55000,Seattle,12000
+465,Honda,Fit,2017,Gray,45000,Atlanta,16000
+975,Toyota,Sienna,2017,Red,50000,Dallas,23000
+1978,Honda,Accord,2018,White,70000,Atlanta,28000
+1733,Chevrolet,Equinox,2015,Black,40000,Miami,17000
+745,Hyundai,Kona,2020,Blue,70000,San Francisco,12000
+1178,Hyundai,Sonata,2018,Blue,50000,Seattle,18000
+408,Ford,Mustang,2015,Blue,40000,Chicago,21000
+1398,Toyota,Rav23,2016,Gray,40000,Los Angeles,18000
+27,Honda,Fit,2017,Gray,55000,Atlanta,12000
+1950,Honda,CR-V,2019,White,40000,New York,17000
+776,Toyota,Avalon,2020,Silver,35000,Dallas,27000
+1896,Toyota,Yaris,2016,Black,35000,Los Angeles,20000
+1849,Chevrolet,Tahoe,2017,Black,55000,Miami,14000
+1863,Ford,Fiesta,2019,Blue,55000,Phoenix,24000
+813,Hyundai,Sonata,2019,Blue,50000,Seattle,15000
+1621,Hyundai,Kona,2015,Blue,40000,San Francisco,27000
+639,Hyundai,Tucson,2016,Red,55000,San Francisco,12000
+108,Hyundai,Accent,2019,Silver,50000,San Francisco,17000
+959,Hyundai,Sonata,2020,Blue,55000,Seattle,15000
+1911,Ford,Escape,2015,White,30000,Chicago,29000
+1847,Honda,Odyssey,2020,White,45000,New York,18000
+1328,Chevrolet,Equinox,2017,Black,35000,Miami,20000
+576,Hyundai,Palisade,2015,Silver,65000,San Francisco,22000
+710,Ford,Escape,2020,Blue,55000,Chicago,12000
+799,Chevrolet,Malibu,2017,Blue,50000,Houston,23000
+1067,Hyundai,Genesis,2019,Black,35000,San Francisco,16000
+1546,Ford,Escape,2016,White,70000,Chicago,20000
+622,Ford,Fiesta,2018,Blue,35000,Phoenix,24000
+471,Ford,EcoSport,2020,Red,70000,Chicago,18000
+1015,Toyota,Sienna,2016,Red,70000,Dallas,27000
+879,Ford,Focus,2017,Silver,55000,Chicago,12000
+9,Chevrolet,Impala,2017,Black,55000,Houston,16000
+340,Ford,Fusion,2018,White,55000,Phoenix,22000
+1252,Toyota,Rav21,2019,Gray,70000,Los Angeles,18000
+429,Chevrolet,Tahoe,2016,Black,55000,Miami,14000
+312,Hyundai,Santa Fe,2019,Red,50000,Seattle,15000
+1815,Ford,Edge,2017,Blue,35000,Chicago,18000
+1114,Chevrolet,Traverse,2016,Black,40000,Houston,18000
+1031,Chevrolet,Impala,2017,Black,50000,Houston,17000
+1050,Ford,Fusion,2016,White,55000,Phoenix,22000
+760,Hyundai,Venue,2018,Silver,35000,Seattle,20000
+1722,Ford,Mustang,2019,Blue,50000,Chicago,23000
+1708,Chevrolet,Malibu,2017,Blue,60000,Houston,12000
+165,Chevrolet,Traverse,2017,Black,50000,Houston,21000
+1591,Ford,Explorer,2016,Blue,40000,Phoenix,17000
+524,Ford,Escape,2020,White,55000,Chicago,12000
+816,Ford,Escape,2016,White,70000,Chicago,12000
+304,Honda,CR-V,2017,Red,40000,New York,25000
+1195,Honda,Fit,2019,Gray,40000,Atlanta,27000
+1628,Honda,Odyssey,2020,White,35000,New York,27000

classification/unipredict/arslanr369-bitcoin-price-2014-2023/metadata.json

@@ -0,0 +1,29 @@
+{
+  "dataset": "arslanr369-bitcoin-price-2014-2023",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Close",
+  "label_values": [
+    "between 764.11325075 and 7697.924072",
+    "greater than 20297.0288085",
+    "between 7697.924072 and 20297.0288085",
+    "less than 764.11325075"
+  ],
+  "num_labels": 4,
+  "train_samples": 2904,
+  "test_samples": 324,
+  "train_label_distribution": {
+    "less than 764.11325075": 726,
+    "between 764.11325075 and 7697.924072": 726,
+    "greater than 20297.0288085": 726,
+    "between 7697.924072 and 20297.0288085": 726
+  },
+  "test_label_distribution": {
+    "between 7697.924072 and 20297.0288085": 81,
+    "less than 764.11325075": 81,
+    "between 764.11325075 and 7697.924072": 81,
+    "greater than 20297.0288085": 81
+  }
+}

classification/unipredict/arslanr369-bitcoin-price-2014-2023/test.csv

@@ -0,0 +1,325 @@
+Date,Open,High,Low,Adj Close,Volume,Close
+2018-07-27,7950.4,8262.66,7839.76,8165.01,5195879936,between 7697.924072 and 20297.0288085
+2015-06-07,225.6,226.19,222.65,222.88,13318400,less than 764.11325075
+2018-07-19,7378.2,7494.46,7295.46,7466.86,5111629824,between 764.11325075 and 7697.924072
+2015-02-05,227.66,239.4,214.73,217.11,22516400,less than 764.11325075
+2015-03-15,281.42,286.53,281.0,286.39,11970100,less than 764.11325075
+2017-07-03,2498.56,2595.0,2480.47,2564.06,964112000,between 764.11325075 and 7697.924072
+2020-01-15,8825.34,8890.12,8657.19,8807.01,40102834650,between 7697.924072 and 20297.0288085
+2021-04-15,63075.2,63821.67,62208.96,63314.01,60954381579,greater than 20297.0288085
+2015-02-19,236.41,242.67,235.59,240.28,18270500,less than 764.11325075
+2018-09-06,6755.14,6755.14,6404.72,6529.17,5523470000,between 764.11325075 and 7697.924072
+2020-12-07,19343.13,19411.83,18931.14,19191.63,26896357742,between 7697.924072 and 20297.0288085
+2018-05-30,7469.73,7573.77,7313.6,7406.52,4922540032,between 764.11325075 and 7697.924072
+2021-10-07,55338.62,55338.62,53525.47,53805.98,36807860413,greater than 20297.0288085
+2022-02-28,37706.0,43760.46,37518.21,43193.23,35690014104,greater than 20297.0288085
+2017-03-30,1042.21,1049.29,1020.04,1026.43,352968992,between 764.11325075 and 7697.924072
+2022-09-11,21678.54,21770.55,21406.95,21769.26,34493951963,greater than 20297.0288085
+2022-06-01,31792.55,31957.29,29501.59,29799.08,41135817341,greater than 20297.0288085
+2022-11-17,16670.43,16726.44,16460.68,16687.52,27868914022,between 7697.924072 and 20297.0288085
+2020-06-26,9261.0,9310.52,9101.74,9162.92,18341465837,between 7697.924072 and 20297.0288085
+2017-02-27,1163.78,1181.98,1163.38,1179.97,131570000,between 764.11325075 and 7697.924072
+2022-11-28,16440.22,16482.93,16054.53,16217.32,27743025156,between 7697.924072 and 20297.0288085
+2021-03-27,55137.57,56568.21,54242.91,55973.51,47266542233,greater than 20297.0288085
+2016-09-30,605.72,609.73,604.14,609.73,56122400,less than 764.11325075
+2022-11-21,16291.22,16291.22,15599.05,15787.28,37429485518,between 7697.924072 and 20297.0288085
+2017-03-14,1232.16,1244.81,1220.72,1240.0,245306000,between 764.11325075 and 7697.924072
+2022-11-26,16521.58,16666.86,16416.23,16464.28,18000008764,between 7697.924072 and 20297.0288085
+2018-07-11,6330.77,6444.96,6330.47,6394.71,3644859904,between 764.11325075 and 7697.924072
+2018-08-08,6746.85,6746.85,6226.22,6305.8,5064430000,between 764.11325075 and 7697.924072
+2022-01-21,40699.61,41060.53,35791.43,36457.32,43011992031,greater than 20297.0288085
+2020-07-20,9187.22,9214.27,9137.51,9164.23,13755604146,between 7697.924072 and 20297.0288085
+2018-04-14,7874.67,8140.71,7846.0,7986.24,5191430144,between 7697.924072 and 20297.0288085
+2019-12-24,7354.39,7535.72,7269.53,7322.53,22991622105,between 764.11325075 and 7697.924072
+2015-09-08,239.85,245.78,239.68,243.61,26879200,less than 764.11325075
+2016-07-20,672.81,672.93,663.36,665.68,94636400,less than 764.11325075
+2015-07-20,273.5,278.98,272.96,278.98,22711400,less than 764.11325075
+2020-09-23,10535.49,10537.83,10197.87,10246.19,23788661867,between 7697.924072 and 20297.0288085
+2020-01-19,8941.45,9164.36,8620.08,8706.25,34217320471,between 7697.924072 and 20297.0288085
+2014-10-21,382.42,392.65,380.83,386.48,14188900,less than 764.11325075
+2018-12-04,3886.29,4075.63,3832.75,3956.89,5028069239,between 764.11325075 and 7697.924072
+2019-05-31,8320.29,8586.66,8172.55,8574.5,25365190957,between 7697.924072 and 20297.0288085
+2017-05-20,1984.24,2084.73,1974.92,2084.73,961336000,between 764.11325075 and 7697.924072
+2023-05-01,29227.1,29329.94,27680.79,28091.57,18655599976,greater than 20297.0288085
+2016-10-30,714.12,714.12,696.47,701.86,100665000,less than 764.11325075
+2019-07-20,10525.82,11048.66,10451.28,10767.14,20206615155,between 7697.924072 and 20297.0288085
+2021-11-29,57291.91,58872.88,56792.53,57806.57,32370840356,greater than 20297.0288085
+2018-02-11,8616.13,8616.13,7931.1,8129.97,6122189824,between 7697.924072 and 20297.0288085
+2021-06-11,36697.03,37608.7,36044.45,37334.4,38699736985,greater than 20297.0288085
+2018-09-25,6603.64,6603.64,6381.86,6446.47,4726180000,between 764.11325075 and 7697.924072
+2017-05-04,1490.72,1608.91,1490.72,1537.67,933548992,between 764.11325075 and 7697.924072
+2019-01-06,3836.52,4093.3,3826.51,4076.63,5597027440,between 764.11325075 and 7697.924072
+2016-08-09,591.04,591.09,584.79,587.8,92228096,less than 764.11325075
+2019-09-27,8113.1,8271.52,7965.92,8251.85,16408941156,between 7697.924072 and 20297.0288085
+2022-12-25,16847.51,16860.55,16755.25,16841.99,11656379938,between 7697.924072 and 20297.0288085
+2015-02-04,227.51,230.06,221.11,226.85,26594300,less than 764.11325075
+2017-09-07,4589.14,4655.04,4491.33,4599.88,1844620032,between 764.11325075 and 7697.924072
+2018-10-13,6278.08,6308.51,6259.81,6285.99,3064030000,between 764.11325075 and 7697.924072
+2016-08-22,581.31,588.45,580.59,586.75,72844000,less than 764.11325075
+2020-09-25,10761.11,10777.7,10578.91,10692.72,39348590957,between 7697.924072 and 20297.0288085
+2016-02-29,433.44,441.51,431.69,437.7,60694700,less than 764.11325075
+2023-04-23,27816.14,27820.24,27400.31,27591.38,12785446832,greater than 20297.0288085
+2021-10-13,56038.26,57688.66,54370.97,57401.1,41684252783,greater than 20297.0288085
+2019-12-01,7571.62,7571.62,7291.34,7424.29,18720708479,between 764.11325075 and 7697.924072
+2016-02-25,425.04,427.72,420.42,424.54,70798000,less than 764.11325075
+2022-12-30,16641.33,16643.43,16408.47,16602.59,15929162910,between 7697.924072 and 20297.0288085
+2018-07-17,6739.65,7387.24,6684.17,7321.04,5961950208,between 764.11325075 and 7697.924072
+2022-11-29,16217.64,16522.26,16139.4,16444.98,23581685468,between 7697.924072 and 20297.0288085
+2020-08-07,11778.89,11898.04,11408.59,11601.47,23132312867,between 7697.924072 and 20297.0288085
+2018-08-20,6500.51,6536.92,6297.93,6308.53,3665100000,between 764.11325075 and 7697.924072
+2021-09-15,47098.0,48450.47,46773.33,48176.35,30484496466,greater than 20297.0288085
+2018-04-03,7102.26,7530.94,7072.49,7456.11,5499700224,between 764.11325075 and 7697.924072
+2021-07-08,33889.61,33907.91,32133.18,32877.37,29910396946,greater than 20297.0288085
+2022-03-23,42364.38,42893.51,41877.51,42892.96,25242943069,greater than 20297.0288085
+2017-11-25,8241.71,8790.92,8191.15,8790.92,4342060032,between 7697.924072 and 20297.0288085
+2019-06-23,10696.69,11246.14,10556.1,10855.37,20998326502,between 7697.924072 and 20297.0288085
+2019-08-20,10916.35,10947.04,10618.96,10763.23,15053082175,between 7697.924072 and 20297.0288085
+2016-10-21,630.83,634.09,630.69,632.83,55951000,less than 764.11325075
+2016-02-02,372.92,375.88,372.92,374.45,40378700,less than 764.11325075
+2019-07-29,9548.18,9681.65,9472.95,9519.15,13791445323,between 7697.924072 and 20297.0288085
+2016-02-11,382.11,383.13,376.4,379.65,74375600,less than 764.11325075
+2021-10-06,51486.66,55568.46,50488.19,55361.45,49034730168,greater than 20297.0288085
+2016-08-27,579.45,579.84,568.63,569.95,59698300,less than 764.11325075
+2015-12-03,359.33,370.27,357.41,361.05,50714900,less than 764.11325075
+2021-12-19,46853.87,48089.66,46502.95,46707.02,25154053861,greater than 20297.0288085
+2021-12-21,46886.08,49300.92,46698.77,48936.61,27055803928,greater than 20297.0288085
+2023-07-14,31474.72,31582.25,29966.39,30334.07,20917902660,greater than 20297.0288085
+2023-01-09,17093.99,17389.96,17093.99,17196.55,18624736866,between 7697.924072 and 20297.0288085
+2014-10-09,352.75,382.73,347.69,365.03,83641104,less than 764.11325075
+2015-01-11,274.61,279.64,265.04,265.66,18200800,less than 764.11325075
+2014-12-10,352.2,352.38,346.36,346.36,16427700,less than 764.11325075
+2021-09-12,45206.63,46364.88,44790.46,46063.27,27881980161,greater than 20297.0288085
+2018-04-17,8071.66,8285.96,7881.72,7902.09,6900879872,between 7697.924072 and 20297.0288085
+2019-08-12,11528.19,11528.19,11320.95,11382.62,13647198229,between 7697.924072 and 20297.0288085
+2020-08-21,11878.03,11899.26,11564.98,11592.49,23762425999,between 7697.924072 and 20297.0288085
+2023-03-01,23150.93,23880.63,23088.63,23646.55,24662841200,greater than 20297.0288085
+2015-03-18,285.07,285.34,249.87,256.3,57008000,less than 764.11325075
+2022-05-31,31723.87,32249.86,31286.15,31792.31,33538210634,greater than 20297.0288085
+2022-04-21,41371.52,42893.58,40063.83,40527.36,35372786395,greater than 20297.0288085
+2022-10-13,19156.97,19453.33,18319.82,19382.9,44219840004,between 7697.924072 and 20297.0288085
+2022-05-13,29030.91,30924.8,28782.33,29283.1,42841124537,greater than 20297.0288085
+2021-04-26,49077.79,54288.0,48852.8,54021.75,58284039825,greater than 20297.0288085
+2015-01-04,281.15,287.23,257.61,264.2,55629100,less than 764.11325075
+2018-03-27,8200.0,8232.78,7797.28,7833.04,5378250240,between 7697.924072 and 20297.0288085
+2020-03-05,8760.29,9142.05,8757.25,9078.76,39698054597,between 7697.924072 and 20297.0288085
+2020-01-30,9316.02,9553.13,9230.9,9508.99,32378792851,between 7697.924072 and 20297.0288085
+2022-04-18,39721.2,40986.32,38696.19,40826.21,33705182072,greater than 20297.0288085
+2020-03-13,5017.83,5838.11,4106.98,5563.71,74156772075,between 764.11325075 and 7697.924072
+2022-03-22,41074.11,43124.71,40948.28,42358.81,32004652376,greater than 20297.0288085
+2018-02-27,10393.9,10878.5,10246.1,10725.6,6966179840,between 7697.924072 and 20297.0288085
+2023-04-25,27514.87,28371.08,27207.93,28307.6,17733373139,greater than 20297.0288085
+2016-08-14,585.59,585.67,564.78,570.47,60851100,less than 764.11325075
+2020-12-03,19205.93,19566.19,18925.79,19445.4,31930317405,between 7697.924072 and 20297.0288085
+2017-08-22,3998.35,4128.76,3674.58,4100.52,3764239872,between 764.11325075 and 7697.924072
+2020-05-31,9700.11,9700.34,9432.3,9461.06,27773290299,between 7697.924072 and 20297.0288085
+2021-03-10,54824.01,57258.25,53290.89,56008.55,57295577614,greater than 20297.0288085
+2023-01-15,20977.48,20993.75,20606.99,20880.8,19298407543,greater than 20297.0288085
+2015-11-10,379.98,381.39,329.11,336.82,95797904,less than 764.11325075
+2014-11-22,351.6,364.84,350.88,352.92,15273000,less than 764.11325075
+2018-11-09,6442.6,6456.46,6373.37,6385.62,4346820000,between 764.11325075 and 7697.924072
+2015-07-12,293.14,314.39,292.51,310.87,56405000,less than 764.11325075
+2020-10-07,10603.36,10680.51,10562.51,10668.97,37799458436,between 7697.924072 and 20297.0288085
+2015-07-23,277.34,278.11,275.72,276.05,18531300,less than 764.11325075
+2018-07-18,7315.32,7534.99,7280.47,7370.78,6103410176,between 764.11325075 and 7697.924072
+2015-04-13,235.95,236.93,222.0,224.59,31181800,less than 764.11325075
+2017-03-19,976.73,1069.91,976.73,1036.74,406648000,between 764.11325075 and 7697.924072
+2015-03-08,276.43,277.86,272.57,274.35,22067900,less than 764.11325075
+2019-06-24,10853.74,11065.9,10610.43,11011.1,19271652365,between 7697.924072 and 20297.0288085
+2021-09-11,44869.84,45969.29,44818.27,45201.46,34499835245,greater than 20297.0288085
+2018-05-01,9251.47,9255.88,8891.05,9119.01,7713019904,between 7697.924072 and 20297.0288085
+2016-01-30,378.86,380.92,376.49,378.26,30284400,less than 764.11325075
+2016-12-04,771.64,773.87,768.16,773.87,60557900,between 764.11325075 and 7697.924072
+2018-11-08,6522.27,6536.92,6438.53,6453.72,4665260000,between 764.11325075 and 7697.924072
+2018-06-20,6770.76,6821.56,6611.88,6776.55,3888640000,between 764.11325075 and 7697.924072
+2021-09-20,47261.41,47328.2,42598.91,42843.8,43909845642,greater than 20297.0288085
+2021-04-17,61529.92,62572.18,60361.35,60683.82,66138759198,greater than 20297.0288085
+2017-03-02,1224.68,1262.13,1215.62,1251.01,368275008,between 764.11325075 and 7697.924072
+2023-01-25,22639.27,23722.1,22406.08,23117.86,30685366709,greater than 20297.0288085
+2017-02-28,1180.72,1193.25,1171.82,1179.97,184956000,between 764.11325075 and 7697.924072
+2018-07-05,6599.71,6749.54,6546.65,6639.14,4999240192,between 764.11325075 and 7697.924072
+2014-09-26,411.43,414.94,400.01,404.42,21460800,less than 764.11325075
+2015-07-05,260.8,274.51,258.7,271.91,44156100,less than 764.11325075
+2018-07-12,6396.78,6397.1,6136.42,6228.81,3770170112,between 764.11325075 and 7697.924072
+2017-01-10,902.44,914.87,901.06,907.68,115808000,between 764.11325075 and 7697.924072
+2021-06-25,34659.11,35487.25,31350.88,31637.78,40230904226,greater than 20297.0288085
+2016-01-06,431.86,431.86,426.34,429.11,34042500,less than 764.11325075
+2015-02-25,238.89,239.34,235.53,237.47,11496200,less than 764.11325075
+2022-02-20,40118.1,40119.89,38112.81,38431.38,18340576452,greater than 20297.0288085
+2020-11-14,16317.81,16317.81,15749.19,16068.14,27481710135,between 7697.924072 and 20297.0288085
+2019-01-13,3658.87,3674.76,3544.93,3552.95,4681302466,between 764.11325075 and 7697.924072
+2018-06-29,5898.13,6261.66,5835.75,6218.3,3966230016,between 764.11325075 and 7697.924072
+2018-11-29,4269.0,4413.02,4145.77,4278.85,6503347767,between 764.11325075 and 7697.924072
+2016-04-05,421.02,424.26,420.61,424.03,60718000,less than 764.11325075
+2016-10-17,641.82,642.33,638.66,639.19,58063600,less than 764.11325075
+2018-12-15,3244.0,3275.38,3191.3,3236.76,3551763561,between 764.11325075 and 7697.924072
+2015-03-05,272.74,281.67,264.77,276.18,41302400,less than 764.11325075
+2019-11-17,8549.47,8727.79,8500.97,8577.98,18668638897,between 7697.924072 and 20297.0288085
+2022-10-07,19957.56,20041.09,19395.79,19546.85,29227315390,between 7697.924072 and 20297.0288085
+2016-03-25,416.51,418.08,415.56,417.18,52560000,less than 764.11325075
+2016-05-01,448.48,452.48,447.93,451.88,40660100,less than 764.11325075
+2017-11-13,5938.25,6811.19,5844.29,6559.49,6263249920,between 764.11325075 and 7697.924072
+2019-05-09,5982.32,6183.04,5982.32,6174.53,16784645411,between 764.11325075 and 7697.924072
+2021-02-21,56068.57,58330.57,55672.61,57539.95,51897585191,greater than 20297.0288085
+2023-02-11,21651.84,21891.41,21618.45,21870.88,16356226232,greater than 20297.0288085
+2019-02-17,3633.36,3680.54,3619.18,3673.84,7039512503,between 764.11325075 and 7697.924072
+2021-10-15,57345.9,62757.13,56868.14,61593.95,51780081801,greater than 20297.0288085
+2015-02-09,223.39,223.98,217.02,220.11,27791300,less than 764.11325075
+2017-09-24,3796.15,3796.15,3666.9,3682.84,768014976,between 764.11325075 and 7697.924072
+2016-04-23,445.86,450.28,444.33,450.28,50485400,less than 764.11325075
+2014-10-04,359.89,364.49,325.89,328.87,47236500,less than 764.11325075
+2019-01-02,3849.22,3947.98,3817.41,3943.41,5244856836,between 764.11325075 and 7697.924072
+2022-07-31,23652.07,24121.64,23275.7,23336.9,23553591896,greater than 20297.0288085
+2020-02-15,10313.86,10341.56,9874.43,9889.42,43865054831,between 7697.924072 and 20297.0288085
+2015-12-29,422.1,432.98,420.63,432.98,51596500,less than 764.11325075
+2022-10-15,19185.44,19212.54,19019.25,19067.63,16192235532,between 7697.924072 and 20297.0288085
+2016-07-13,664.8,668.7,654.47,654.47,131449000,less than 764.11325075
+2021-06-08,33589.52,34017.39,31114.44,33472.63,49902050442,greater than 20297.0288085
+2017-03-29,1046.08,1055.13,1015.88,1039.97,298457984,between 764.11325075 and 7697.924072
+2018-12-01,4024.46,4309.38,3969.71,4214.67,5375314093,between 764.11325075 and 7697.924072
+2020-05-29,9528.36,9573.67,9379.34,9439.12,32896642044,between 7697.924072 and 20297.0288085
+2017-03-06,1267.47,1276.0,1264.6,1272.83,153656992,between 764.11325075 and 7697.924072
+2021-03-01,45159.5,49784.02,45115.09,49631.24,53891300112,greater than 20297.0288085
+2018-09-08,6460.17,6534.25,6197.52,6225.98,3835060000,between 764.11325075 and 7697.924072
+2019-07-04,11972.72,12006.08,11166.57,11215.44,25920294033,between 7697.924072 and 20297.0288085
+2016-01-23,382.43,394.54,381.98,387.49,56247400,less than 764.11325075
+2017-03-20,1037.24,1063.03,1036.68,1054.23,286529984,between 764.11325075 and 7697.924072
+2016-01-29,380.11,384.38,365.45,379.47,86125296,less than 764.11325075
+2019-09-24,9729.32,9804.32,8370.8,8620.57,25002886689,between 7697.924072 and 20297.0288085
+2023-07-11,30417.63,30788.31,30358.1,30620.95,12151839152,greater than 20297.0288085
+2022-07-13,19325.97,20223.05,18999.95,20212.07,33042430345,between 7697.924072 and 20297.0288085
+2017-07-14,2360.59,2363.25,2183.22,2233.34,882502976,between 764.11325075 and 7697.924072
+2022-02-18,40552.13,40929.15,39637.62,40030.98,23310007704,greater than 20297.0288085
+2018-01-13,13952.4,14659.5,13952.4,14360.2,12763599872,between 7697.924072 and 20297.0288085
+2016-06-17,768.49,775.36,716.56,748.91,363320992,less than 764.11325075
+2015-01-21,211.38,227.79,211.21,226.9,29924600,less than 764.11325075
+2023-07-15,30331.78,30407.78,30263.46,30295.81,8011667756,greater than 20297.0288085
+2020-05-21,9522.74,9555.24,8869.93,9081.76,39326160532,between 7697.924072 and 20297.0288085
+2021-01-30,34295.93,34834.71,32940.19,34269.52,65141828798,greater than 20297.0288085
+2020-07-02,9231.14,9274.96,9036.62,9123.41,16338916796,between 7697.924072 and 20297.0288085
+2023-07-12,30622.25,30959.96,30228.84,30391.65,14805659717,greater than 20297.0288085
+2022-09-29,19427.78,19589.27,18924.35,19573.05,41037843771,between 7697.924072 and 20297.0288085
+2021-05-06,57441.31,58363.32,55382.51,56396.52,69523285106,greater than 20297.0288085
+2019-01-19,3652.38,3758.53,3652.38,3728.57,5955691380,between 764.11325075 and 7697.924072
+2019-10-11,8585.26,8721.78,8316.18,8321.76,19604381101,between 7697.924072 and 20297.0288085
+2017-07-26,2577.77,2610.76,2450.8,2529.45,937404032,between 764.11325075 and 7697.924072
+2016-05-22,443.22,443.43,439.04,439.32,39657600,less than 764.11325075
+2018-11-03,6387.24,6400.07,6342.37,6361.26,3658640000,between 764.11325075 and 7697.924072
+2020-11-24,18365.02,19348.27,18128.66,19107.46,51469565009,between 7697.924072 and 20297.0288085
+2017-05-07,1579.47,1596.72,1559.76,1596.71,1080029952,between 764.11325075 and 7697.924072
+2023-03-05,22354.14,22613.69,22307.14,22435.51,13317001733,greater than 20297.0288085
+2015-02-11,219.73,223.41,218.07,219.18,17201900,less than 764.11325075
+2017-10-20,5708.11,6060.11,5627.23,6011.45,2354429952,between 764.11325075 and 7697.924072
+2018-12-17,3253.12,3597.92,3253.12,3545.86,5409247918,between 764.11325075 and 7697.924072
+2021-07-23,32305.96,33581.55,32057.89,33581.55,22552046192,greater than 20297.0288085
+2019-04-10,5204.11,5421.65,5193.38,5324.55,15504590933,between 764.11325075 and 7697.924072
+2018-05-06,9845.31,9940.14,9465.25,9654.8,7222280192,between 7697.924072 and 20297.0288085
+2014-10-30,335.71,350.91,335.07,345.3,30177900,less than 764.11325075
+2018-05-15,8705.19,8836.19,8456.45,8510.38,6705710080,between 7697.924072 and 20297.0288085
+2022-06-21,20594.29,21620.63,20415.06,20710.6,28970212744,greater than 20297.0288085
+2016-02-16,401.43,408.95,401.43,407.49,73093104,less than 764.11325075
+2019-02-27,3857.48,3888.8,3787.06,3851.05,8301309684,between 764.11325075 and 7697.924072
+2014-12-28,316.16,320.03,311.08,317.24,11676600,less than 764.11325075
+2020-08-11,11881.65,11932.71,11195.71,11410.53,27039782640,between 7697.924072 and 20297.0288085
+2017-02-17,1026.12,1053.17,1025.64,1046.21,136474000,between 764.11325075 and 7697.924072
+2015-07-31,287.7,288.96,282.34,284.65,23629100,less than 764.11325075
+2015-10-06,240.36,246.93,240.14,246.06,27535100,less than 764.11325075
+2017-02-25,1170.41,1174.85,1124.59,1143.84,139960992,between 764.11325075 and 7697.924072
+2021-12-15,48379.75,49473.96,46671.96,48896.72,36541828520,greater than 20297.0288085
+2019-02-13,3653.6,3669.75,3617.25,3632.07,6438903823,between 764.11325075 and 7697.924072
+2017-07-23,2808.1,2832.18,2653.94,2730.4,1072840000,between 764.11325075 and 7697.924072
+2020-12-18,22806.8,23238.6,22399.81,23137.96,40387896275,greater than 20297.0288085
+2016-03-23,418.16,419.27,417.36,418.04,61444200,less than 764.11325075
+2020-12-02,18801.74,19308.33,18347.72,19201.09,37387697139,between 7697.924072 and 20297.0288085
+2019-06-16,8841.44,9335.87,8814.56,8994.49,23348550311,between 7697.924072 and 20297.0288085
+2022-09-22,18534.65,19456.91,18415.59,19413.55,41135767926,between 7697.924072 and 20297.0288085
+2020-06-29,9140.03,9237.57,9041.88,9190.85,16460547078,between 7697.924072 and 20297.0288085
+2017-06-20,2591.26,2763.45,2589.82,2721.79,1854189952,between 764.11325075 and 7697.924072
+2020-04-01,6437.32,6612.57,6202.37,6606.78,40346426266,between 764.11325075 and 7697.924072
+2022-07-10,21591.08,21591.08,20727.12,20860.45,28688807249,greater than 20297.0288085
+2015-04-02,247.09,254.46,245.42,253.01,26272600,less than 764.11325075
+2017-12-08,17802.9,18353.4,14336.9,16569.4,21135998976,between 7697.924072 and 20297.0288085
+2022-01-16,43172.04,43436.81,42691.02,43113.88,17902097845,greater than 20297.0288085
+2015-01-23,233.52,234.85,225.2,232.88,24621700,less than 764.11325075
+2014-10-13,377.92,397.23,368.9,390.41,35221400,less than 764.11325075
+2020-12-27,26439.37,28288.84,25922.77,26272.29,66479895605,greater than 20297.0288085
+2022-12-11,17129.71,17245.63,17091.82,17104.19,14122486832,between 7697.924072 and 20297.0288085
+2014-12-07,374.84,376.29,373.27,375.1,6491650,less than 764.11325075
+2016-11-14,702.0,706.28,699.81,705.02,62993000,less than 764.11325075
+2019-01-11,3674.02,3713.88,3653.07,3687.37,5538712865,between 764.11325075 and 7697.924072
+2017-06-09,2807.44,2901.71,2795.62,2823.81,1348950016,between 764.11325075 and 7697.924072
+2020-07-13,9277.21,9306.41,9224.29,9243.61,17519821266,between 7697.924072 and 20297.0288085
+2021-08-19,44741.88,46970.76,43998.32,46717.58,37204312299,greater than 20297.0288085
+2015-01-09,282.38,291.11,280.53,290.41,18718600,less than 764.11325075
+2022-01-24,36275.73,37247.52,33184.06,36654.33,41856658597,greater than 20297.0288085
+2022-08-05,22626.83,23422.83,22612.18,23289.31,28881249043,greater than 20297.0288085
+2015-11-24,323.01,323.06,318.12,320.05,29362600,less than 764.11325075
+2018-11-12,6411.76,6434.21,6360.47,6371.27,4295770000,between 764.11325075 and 7697.924072
+2014-11-08,342.15,347.03,342.15,345.49,8535470,less than 764.11325075
+2020-02-06,9617.82,9824.62,9539.82,9729.8,37628823716,between 7697.924072 and 20297.0288085
+2018-10-01,6619.85,6653.3,6549.08,6589.62,4000970000,between 764.11325075 and 7697.924072
+2021-02-11,44898.71,48463.47,44187.76,47909.33,81388911810,greater than 20297.0288085
+2022-05-24,29101.12,29774.36,28786.59,29655.59,26616506245,greater than 20297.0288085
+2018-01-09,15123.7,15497.5,14424.0,14595.4,16659999744,between 7697.924072 and 20297.0288085
+2022-05-22,29432.47,30425.86,29275.18,30323.72,21631532270,greater than 20297.0288085
+2023-06-30,30441.35,31256.86,29600.28,30477.25,26387306197,greater than 20297.0288085
+2015-12-20,462.23,462.64,434.34,442.68,75409400,less than 764.11325075
+2022-05-20,30311.12,30664.98,28793.61,29200.74,30749382605,greater than 20297.0288085
+2015-09-21,231.22,231.22,226.52,227.09,19678800,less than 764.11325075
+2022-01-22,36471.59,36688.81,34349.25,35030.25,39714385405,greater than 20297.0288085
+2016-05-31,534.19,546.62,520.66,531.39,138450000,less than 764.11325075
+2021-01-24,32064.38,32944.01,31106.69,32289.38,48643830599,greater than 20297.0288085
+2018-11-13,6373.19,6395.27,6342.67,6359.49,4503800000,between 764.11325075 and 7697.924072
+2017-04-03,1102.95,1151.74,1102.95,1143.81,580444032,between 764.11325075 and 7697.924072
+2018-05-31,7406.15,7608.9,7361.13,7494.17,5127130112,between 764.11325075 and 7697.924072
+2019-05-17,7886.93,7929.15,7038.12,7343.9,30066644905,between 764.11325075 and 7697.924072
+2019-01-12,3686.97,3698.98,3653.81,3661.3,4778170883,between 764.11325075 and 7697.924072
+2017-02-08,1062.32,1078.97,1037.49,1063.07,201855008,between 764.11325075 and 7697.924072
+2020-06-16,9454.27,9579.43,9400.45,9538.02,21565537209,between 7697.924072 and 20297.0288085
+2020-05-19,9727.06,9836.05,9539.62,9729.04,39254288955,between 7697.924072 and 20297.0288085
+2020-05-04,8895.75,8956.91,8645.02,8912.65,45718796276,between 7697.924072 and 20297.0288085
+2023-05-13,26807.77,27030.48,26710.87,26784.08,9999171605,greater than 20297.0288085
+2021-10-29,60624.87,62927.61,60329.96,62227.96,36856881767,greater than 20297.0288085
+2016-09-11,623.42,628.82,600.51,606.72,73610800,less than 764.11325075
+2017-08-10,3341.84,3453.45,3319.47,3381.28,1515110016,between 764.11325075 and 7697.924072
+2020-07-07,9349.16,9360.62,9201.82,9252.28,13839652595,between 7697.924072 and 20297.0288085
+2021-11-06,61068.88,61590.68,60163.78,61527.48,29094934221,greater than 20297.0288085
+2020-09-13,10452.4,10577.21,10224.33,10323.76,36506852789,between 7697.924072 and 20297.0288085
+2020-07-03,9124.84,9202.34,9058.79,9087.3,13078970999,between 7697.924072 and 20297.0288085
+2019-04-23,5399.37,5633.8,5389.41,5572.36,15867308108,between 764.11325075 and 7697.924072
+2020-01-12,8033.26,8200.06,8009.06,8192.49,22903438381,between 7697.924072 and 20297.0288085
+2017-12-28,15864.1,15888.4,13937.3,14606.5,12336499712,between 7697.924072 and 20297.0288085
+2021-05-12,56714.53,57939.36,49150.54,49150.54,75215403907,greater than 20297.0288085
+2020-04-20,7186.87,7240.29,6835.5,6881.96,37747113936,between 764.11325075 and 7697.924072
+2021-08-05,39744.52,41341.93,37458.0,40869.55,35185031017,greater than 20297.0288085
+2023-01-28,23079.96,23165.9,22908.85,23031.09,14712928379,greater than 20297.0288085
+2020-11-09,15479.6,15785.14,14865.53,15332.32,34149115566,between 7697.924072 and 20297.0288085
+2015-02-10,220.28,221.81,215.33,219.84,21115100,less than 764.11325075
+2018-05-13,8515.49,8773.55,8395.12,8723.94,5866379776,between 7697.924072 and 20297.0288085
+2015-11-07,374.27,390.59,372.43,386.48,56625100,less than 764.11325075
+2015-06-05,224.15,225.97,223.18,224.95,18056500,less than 764.11325075
+2017-04-21,1229.42,1235.94,1215.56,1222.05,272167008,between 764.11325075 and 7697.924072
+2021-03-20,58332.26,60031.29,58213.3,58313.64,50361731222,greater than 20297.0288085
+2016-03-07,407.76,415.92,406.31,414.32,85762400,less than 764.11325075
+2018-03-20,8619.67,9051.02,8389.89,8913.47,6361789952,between 7697.924072 and 20297.0288085
+2019-03-23,4022.71,4049.88,4015.96,4035.83,9578850549,between 764.11325075 and 7697.924072
+2023-06-13,25902.94,26376.35,25728.37,25918.73,14143474486,greater than 20297.0288085
+2021-12-04,53727.88,53904.68,42874.62,49200.7,61385677469,greater than 20297.0288085
+2020-05-25,8786.11,8951.01,8719.67,8906.93,31288157264,between 7697.924072 and 20297.0288085
+2019-12-28,7289.03,7399.04,7286.91,7317.99,21365673026,between 764.11325075 and 7697.924072
+2014-12-02,379.25,384.04,377.86,381.32,12364100,less than 764.11325075
+2015-03-06,275.6,277.61,270.02,272.72,28918900,less than 764.11325075
+2021-08-15,47096.67,47357.11,45579.59,47047.0,30988958446,greater than 20297.0288085
+2023-06-03,27252.32,27317.05,26958.0,27075.13,8385597470,greater than 20297.0288085
+2022-09-07,18837.68,19427.17,18644.47,19290.32,35239757134,between 7697.924072 and 20297.0288085
+2020-07-12,9241.05,9319.42,9197.45,9276.5,14452361907,between 7697.924072 and 20297.0288085
+2016-12-01,746.05,758.28,746.05,756.77,80461904,less than 764.11325075
+2016-03-08,414.46,416.24,411.09,413.97,70311696,less than 764.11325075
+2016-01-12,448.18,448.18,435.69,435.69,115607000,less than 764.11325075
+2016-03-17,417.89,421.0,417.89,420.62,83528600,less than 764.11325075
+2023-01-06,16836.47,16991.99,16716.42,16951.97,14413662913,between 7697.924072 and 20297.0288085
+2021-07-24,33593.73,34490.39,33424.86,34292.45,21664706865,greater than 20297.0288085
+2021-03-18,58893.08,60116.25,54253.58,57858.92,55746041000,greater than 20297.0288085
+2022-06-03,30467.81,30633.04,29375.69,29704.39,26175547452,greater than 20297.0288085
+2017-11-09,7446.83,7446.83,7101.52,7143.58,3226249984,between 764.11325075 and 7697.924072
+2021-06-09,33416.98,37537.37,32475.87,37345.12,53972919008,greater than 20297.0288085
+2017-05-18,1818.7,1904.48,1807.12,1888.65,894321024,between 764.11325075 and 7697.924072

classification/unipredict/arslanr369-bitcoin-price-2014-2023/test.jsonl

@@ -0,0 +1,324 @@
+{"text": "The Date is 2018-07-27. The Open is 7950.4. The High is 8262.66. The Low is 7839.76. The Adj Close is 8165.01. The Volume is 5195879936.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-06-07. The Open is 225.6. The High is 226.19. The Low is 222.65. The Adj Close is 222.88. The Volume is 13318400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-07-19. The Open is 7378.2. The High is 7494.46. The Low is 7295.46. The Adj Close is 7466.86. The Volume is 5111629824.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-02-05. The Open is 227.66. The High is 239.4. The Low is 214.73. The Adj Close is 217.11. The Volume is 22516400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-03-15. The Open is 281.42. The High is 286.53. The Low is 281.0. The Adj Close is 286.39. The Volume is 11970100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-07-03. The Open is 2498.56. The High is 2595.0. The Low is 2480.47. The Adj Close is 2564.06. The Volume is 964112000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-01-15. The Open is 8825.34. The High is 8890.12. The Low is 8657.19. The Adj Close is 8807.01. The Volume is 40102834650.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-04-15. The Open is 63075.2. The High is 63821.67. The Low is 62208.96. The Adj Close is 63314.01. The Volume is 60954381579.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-02-19. The Open is 236.41. The High is 242.67. The Low is 235.59. The Adj Close is 240.28. The Volume is 18270500.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-09-06. The Open is 6755.14. The High is 6755.14. The Low is 6404.72. The Adj Close is 6529.17. The Volume is 5523470000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-12-07. The Open is 19343.13. The High is 19411.83. The Low is 18931.14. The Adj Close is 19191.63. The Volume is 26896357742.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-05-30. The Open is 7469.73. The High is 7573.77. The Low is 7313.6. The Adj Close is 7406.52. The Volume is 4922540032.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-10-07. The Open is 55338.62. The High is 55338.62. The Low is 53525.47. The Adj Close is 53805.98. The Volume is 36807860413.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-02-28. The Open is 37706.0. The High is 43760.46. The Low is 37518.21. The Adj Close is 43193.23. The Volume is 35690014104.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-03-30. The Open is 1042.21. The High is 1049.29. The Low is 1020.04. The Adj Close is 1026.43. The Volume is 352968992.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-09-11. The Open is 21678.54. The High is 21770.55. The Low is 21406.95. The Adj Close is 21769.26. The Volume is 34493951963.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-06-01. The Open is 31792.55. The High is 31957.29. The Low is 29501.59. The Adj Close is 29799.08. The Volume is 41135817341.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-11-17. The Open is 16670.43. The High is 16726.44. The Low is 16460.68. The Adj Close is 16687.52. The Volume is 27868914022.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-06-26. The Open is 9261.0. The High is 9310.52. The Low is 9101.74. The Adj Close is 9162.92. The Volume is 18341465837.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-02-27. The Open is 1163.78. The High is 1181.98. The Low is 1163.38. The Adj Close is 1179.97. The Volume is 131570000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-11-28. The Open is 16440.22. The High is 16482.93. The Low is 16054.53. The Adj Close is 16217.32. The Volume is 27743025156.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-03-27. The Open is 55137.57. The High is 56568.21. The Low is 54242.91. The Adj Close is 55973.51. The Volume is 47266542233.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-09-30. The Open is 605.72. The High is 609.73. The Low is 604.14. The Adj Close is 609.73. The Volume is 56122400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-11-21. The Open is 16291.22. The High is 16291.22. The Low is 15599.05. The Adj Close is 15787.28. The Volume is 37429485518.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-03-14. The Open is 1232.16. The High is 1244.81. The Low is 1220.72. The Adj Close is 1240.0. The Volume is 245306000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-11-26. The Open is 16521.58. The High is 16666.86. The Low is 16416.23. The Adj Close is 16464.28. The Volume is 18000008764.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-07-11. The Open is 6330.77. The High is 6444.96. The Low is 6330.47. The Adj Close is 6394.71. The Volume is 3644859904.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-08-08. The Open is 6746.85. The High is 6746.85. The Low is 6226.22. The Adj Close is 6305.8. The Volume is 5064430000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-01-21. The Open is 40699.61. The High is 41060.53. The Low is 35791.43. The Adj Close is 36457.32. The Volume is 43011992031.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-07-20. The Open is 9187.22. The High is 9214.27. The Low is 9137.51. The Adj Close is 9164.23. The Volume is 13755604146.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-04-14. The Open is 7874.67. The High is 8140.71. The Low is 7846.0. The Adj Close is 7986.24. The Volume is 5191430144.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-12-24. The Open is 7354.39. The High is 7535.72. The Low is 7269.53. The Adj Close is 7322.53. The Volume is 22991622105.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-09-08. The Open is 239.85. The High is 245.78. The Low is 239.68. The Adj Close is 243.61. The Volume is 26879200.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-07-20. The Open is 672.81. The High is 672.93. The Low is 663.36. The Adj Close is 665.68. The Volume is 94636400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-07-20. The Open is 273.5. The High is 278.98. The Low is 272.96. The Adj Close is 278.98. The Volume is 22711400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-09-23. The Open is 10535.49. The High is 10537.83. The Low is 10197.87. The Adj Close is 10246.19. The Volume is 23788661867.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-01-19. The Open is 8941.45. The High is 9164.36. The Low is 8620.08. The Adj Close is 8706.25. The Volume is 34217320471.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-10-21. The Open is 382.42. The High is 392.65. The Low is 380.83. The Adj Close is 386.48. The Volume is 14188900.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-12-04. The Open is 3886.29. The High is 4075.63. The Low is 3832.75. The Adj Close is 3956.89. The Volume is 5028069239.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-05-31. The Open is 8320.29. The High is 8586.66. The Low is 8172.55. The Adj Close is 8574.5. The Volume is 25365190957.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-05-20. The Open is 1984.24. The High is 2084.73. The Low is 1974.92. The Adj Close is 2084.73. The Volume is 961336000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-05-01. The Open is 29227.1. The High is 29329.94. The Low is 27680.79. The Adj Close is 28091.57. The Volume is 18655599976.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-10-30. The Open is 714.12. The High is 714.12. The Low is 696.47. The Adj Close is 701.86. The Volume is 100665000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-07-20. The Open is 10525.82. The High is 11048.66. The Low is 10451.28. The Adj Close is 10767.14. The Volume is 20206615155.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-11-29. The Open is 57291.91. The High is 58872.88. The Low is 56792.53. The Adj Close is 57806.57. The Volume is 32370840356.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-02-11. The Open is 8616.13. The High is 8616.13. The Low is 7931.1. The Adj Close is 8129.97. The Volume is 6122189824.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-06-11. The Open is 36697.03. The High is 37608.7. The Low is 36044.45. The Adj Close is 37334.4. The Volume is 38699736985.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-09-25. The Open is 6603.64. The High is 6603.64. The Low is 6381.86. The Adj Close is 6446.47. The Volume is 4726180000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-05-04. The Open is 1490.72. The High is 1608.91. The Low is 1490.72. The Adj Close is 1537.67. The Volume is 933548992.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-01-06. The Open is 3836.52. The High is 4093.3. The Low is 3826.51. The Adj Close is 4076.63. The Volume is 5597027440.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-08-09. The Open is 591.04. The High is 591.09. The Low is 584.79. The Adj Close is 587.8. The Volume is 92228096.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-09-27. The Open is 8113.1. The High is 8271.52. The Low is 7965.92. The Adj Close is 8251.85. The Volume is 16408941156.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-12-25. The Open is 16847.51. The High is 16860.55. The Low is 16755.25. The Adj Close is 16841.99. The Volume is 11656379938.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-02-04. The Open is 227.51. The High is 230.06. The Low is 221.11. The Adj Close is 226.85. The Volume is 26594300.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-09-07. The Open is 4589.14. The High is 4655.04. The Low is 4491.33. The Adj Close is 4599.88. The Volume is 1844620032.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-10-13. The Open is 6278.08. The High is 6308.51. The Low is 6259.81. The Adj Close is 6285.99. The Volume is 3064030000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-08-22. The Open is 581.31. The High is 588.45. The Low is 580.59. The Adj Close is 586.75. The Volume is 72844000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-09-25. The Open is 10761.11. The High is 10777.7. The Low is 10578.91. The Adj Close is 10692.72. The Volume is 39348590957.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-02-29. The Open is 433.44. The High is 441.51. The Low is 431.69. The Adj Close is 437.7. The Volume is 60694700.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-04-23. The Open is 27816.14. The High is 27820.24. The Low is 27400.31. The Adj Close is 27591.38. The Volume is 12785446832.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-10-13. The Open is 56038.26. The High is 57688.66. The Low is 54370.97. The Adj Close is 57401.1. The Volume is 41684252783.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-12-01. The Open is 7571.62. The High is 7571.62. The Low is 7291.34. The Adj Close is 7424.29. The Volume is 18720708479.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-02-25. The Open is 425.04. The High is 427.72. The Low is 420.42. The Adj Close is 424.54. The Volume is 70798000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-12-30. The Open is 16641.33. The High is 16643.43. The Low is 16408.47. The Adj Close is 16602.59. The Volume is 15929162910.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-07-17. The Open is 6739.65. The High is 7387.24. The Low is 6684.17. The Adj Close is 7321.04. The Volume is 5961950208.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-11-29. The Open is 16217.64. The High is 16522.26. The Low is 16139.4. The Adj Close is 16444.98. The Volume is 23581685468.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-08-07. The Open is 11778.89. The High is 11898.04. The Low is 11408.59. The Adj Close is 11601.47. The Volume is 23132312867.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-08-20. The Open is 6500.51. The High is 6536.92. The Low is 6297.93. The Adj Close is 6308.53. The Volume is 3665100000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-09-15. The Open is 47098.0. The High is 48450.47. The Low is 46773.33. The Adj Close is 48176.35. The Volume is 30484496466.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-04-03. The Open is 7102.26. The High is 7530.94. The Low is 7072.49. The Adj Close is 7456.11. The Volume is 5499700224.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-07-08. The Open is 33889.61. The High is 33907.91. The Low is 32133.18. The Adj Close is 32877.37. The Volume is 29910396946.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-03-23. The Open is 42364.38. The High is 42893.51. The Low is 41877.51. The Adj Close is 42892.96. The Volume is 25242943069.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-11-25. The Open is 8241.71. The High is 8790.92. The Low is 8191.15. The Adj Close is 8790.92. The Volume is 4342060032.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-06-23. The Open is 10696.69. The High is 11246.14. The Low is 10556.1. The Adj Close is 10855.37. The Volume is 20998326502.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-08-20. The Open is 10916.35. The High is 10947.04. The Low is 10618.96. The Adj Close is 10763.23. The Volume is 15053082175.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-10-21. The Open is 630.83. The High is 634.09. The Low is 630.69. The Adj Close is 632.83. The Volume is 55951000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-02-02. The Open is 372.92. The High is 375.88. The Low is 372.92. The Adj Close is 374.45. The Volume is 40378700.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-07-29. The Open is 9548.18. The High is 9681.65. The Low is 9472.95. The Adj Close is 9519.15. The Volume is 13791445323.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-02-11. The Open is 382.11. The High is 383.13. The Low is 376.4. The Adj Close is 379.65. The Volume is 74375600.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-10-06. The Open is 51486.66. The High is 55568.46. The Low is 50488.19. The Adj Close is 55361.45. The Volume is 49034730168.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-08-27. The Open is 579.45. The High is 579.84. The Low is 568.63. The Adj Close is 569.95. The Volume is 59698300.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-12-03. The Open is 359.33. The High is 370.27. The Low is 357.41. The Adj Close is 361.05. The Volume is 50714900.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-12-19. The Open is 46853.87. The High is 48089.66. The Low is 46502.95. The Adj Close is 46707.02. The Volume is 25154053861.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-12-21. The Open is 46886.08. The High is 49300.92. The Low is 46698.77. The Adj Close is 48936.61. The Volume is 27055803928.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-07-14. The Open is 31474.72. The High is 31582.25. The Low is 29966.39. The Adj Close is 30334.07. The Volume is 20917902660.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-01-09. The Open is 17093.99. The High is 17389.96. The Low is 17093.99. The Adj Close is 17196.55. The Volume is 18624736866.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-10-09. The Open is 352.75. The High is 382.73. The Low is 347.69. The Adj Close is 365.03. The Volume is 83641104.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-01-11. The Open is 274.61. The High is 279.64. The Low is 265.04. The Adj Close is 265.66. The Volume is 18200800.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-12-10. The Open is 352.2. The High is 352.38. The Low is 346.36. The Adj Close is 346.36. The Volume is 16427700.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-09-12. The Open is 45206.63. The High is 46364.88. The Low is 44790.46. The Adj Close is 46063.27. The Volume is 27881980161.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-04-17. The Open is 8071.66. The High is 8285.96. The Low is 7881.72. The Adj Close is 7902.09. The Volume is 6900879872.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-08-12. The Open is 11528.19. The High is 11528.19. The Low is 11320.95. The Adj Close is 11382.62. The Volume is 13647198229.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-08-21. The Open is 11878.03. The High is 11899.26. The Low is 11564.98. The Adj Close is 11592.49. The Volume is 23762425999.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-03-01. The Open is 23150.93. The High is 23880.63. The Low is 23088.63. The Adj Close is 23646.55. The Volume is 24662841200.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-03-18. The Open is 285.07. The High is 285.34. The Low is 249.87. The Adj Close is 256.3. The Volume is 57008000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-05-31. The Open is 31723.87. The High is 32249.86. The Low is 31286.15. The Adj Close is 31792.31. The Volume is 33538210634.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-04-21. The Open is 41371.52. The High is 42893.58. The Low is 40063.83. The Adj Close is 40527.36. The Volume is 35372786395.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-10-13. The Open is 19156.97. The High is 19453.33. The Low is 18319.82. The Adj Close is 19382.9. The Volume is 44219840004.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-05-13. The Open is 29030.91. The High is 30924.8. The Low is 28782.33. The Adj Close is 29283.1. The Volume is 42841124537.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-04-26. The Open is 49077.79. The High is 54288.0. The Low is 48852.8. The Adj Close is 54021.75. The Volume is 58284039825.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-01-04. The Open is 281.15. The High is 287.23. The Low is 257.61. The Adj Close is 264.2. The Volume is 55629100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-03-27. The Open is 8200.0. The High is 8232.78. The Low is 7797.28. The Adj Close is 7833.04. The Volume is 5378250240.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-03-05. The Open is 8760.29. The High is 9142.05. The Low is 8757.25. The Adj Close is 9078.76. The Volume is 39698054597.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-01-30. The Open is 9316.02. The High is 9553.13. The Low is 9230.9. The Adj Close is 9508.99. The Volume is 32378792851.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-04-18. The Open is 39721.2. The High is 40986.32. The Low is 38696.19. The Adj Close is 40826.21. The Volume is 33705182072.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-03-13. The Open is 5017.83. The High is 5838.11. The Low is 4106.98. The Adj Close is 5563.71. The Volume is 74156772075.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-03-22. The Open is 41074.11. The High is 43124.71. The Low is 40948.28. The Adj Close is 42358.81. The Volume is 32004652376.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-02-27. The Open is 10393.9. The High is 10878.5. The Low is 10246.1. The Adj Close is 10725.6. The Volume is 6966179840.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-04-25. The Open is 27514.87. The High is 28371.08. The Low is 27207.93. The Adj Close is 28307.6. The Volume is 17733373139.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-08-14. The Open is 585.59. The High is 585.67. The Low is 564.78. The Adj Close is 570.47. The Volume is 60851100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-12-03. The Open is 19205.93. The High is 19566.19. The Low is 18925.79. The Adj Close is 19445.4. The Volume is 31930317405.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-08-22. The Open is 3998.35. The High is 4128.76. The Low is 3674.58. The Adj Close is 4100.52. The Volume is 3764239872.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-05-31. The Open is 9700.11. The High is 9700.34. The Low is 9432.3. The Adj Close is 9461.06. The Volume is 27773290299.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-03-10. The Open is 54824.01. The High is 57258.25. The Low is 53290.89. The Adj Close is 56008.55. The Volume is 57295577614.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-01-15. The Open is 20977.48. The High is 20993.75. The Low is 20606.99. The Adj Close is 20880.8. The Volume is 19298407543.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-11-10. The Open is 379.98. The High is 381.39. The Low is 329.11. The Adj Close is 336.82. The Volume is 95797904.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-11-22. The Open is 351.6. The High is 364.84. The Low is 350.88. The Adj Close is 352.92. The Volume is 15273000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-11-09. The Open is 6442.6. The High is 6456.46. The Low is 6373.37. The Adj Close is 6385.62. The Volume is 4346820000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-07-12. The Open is 293.14. The High is 314.39. The Low is 292.51. The Adj Close is 310.87. The Volume is 56405000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-10-07. The Open is 10603.36. The High is 10680.51. The Low is 10562.51. The Adj Close is 10668.97. The Volume is 37799458436.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-07-23. The Open is 277.34. The High is 278.11. The Low is 275.72. The Adj Close is 276.05. The Volume is 18531300.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-07-18. The Open is 7315.32. The High is 7534.99. The Low is 7280.47. The Adj Close is 7370.78. The Volume is 6103410176.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-04-13. The Open is 235.95. The High is 236.93. The Low is 222.0. The Adj Close is 224.59. The Volume is 31181800.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-03-19. The Open is 976.73. The High is 1069.91. The Low is 976.73. The Adj Close is 1036.74. The Volume is 406648000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-03-08. The Open is 276.43. The High is 277.86. The Low is 272.57. The Adj Close is 274.35. The Volume is 22067900.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-06-24. The Open is 10853.74. The High is 11065.9. The Low is 10610.43. The Adj Close is 11011.1. The Volume is 19271652365.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-09-11. The Open is 44869.84. The High is 45969.29. The Low is 44818.27. The Adj Close is 45201.46. The Volume is 34499835245.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-05-01. The Open is 9251.47. The High is 9255.88. The Low is 8891.05. The Adj Close is 9119.01. The Volume is 7713019904.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-01-30. The Open is 378.86. The High is 380.92. The Low is 376.49. The Adj Close is 378.26. The Volume is 30284400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-12-04. The Open is 771.64. The High is 773.87. The Low is 768.16. The Adj Close is 773.87. The Volume is 60557900.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-11-08. The Open is 6522.27. The High is 6536.92. The Low is 6438.53. The Adj Close is 6453.72. The Volume is 4665260000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-06-20. The Open is 6770.76. The High is 6821.56. The Low is 6611.88. The Adj Close is 6776.55. The Volume is 3888640000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-09-20. The Open is 47261.41. The High is 47328.2. The Low is 42598.91. The Adj Close is 42843.8. The Volume is 43909845642.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-04-17. The Open is 61529.92. The High is 62572.18. The Low is 60361.35. The Adj Close is 60683.82. The Volume is 66138759198.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-03-02. The Open is 1224.68. The High is 1262.13. The Low is 1215.62. The Adj Close is 1251.01. The Volume is 368275008.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-01-25. The Open is 22639.27. The High is 23722.1. The Low is 22406.08. The Adj Close is 23117.86. The Volume is 30685366709.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-02-28. The Open is 1180.72. The High is 1193.25. The Low is 1171.82. The Adj Close is 1179.97. The Volume is 184956000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-07-05. The Open is 6599.71. The High is 6749.54. The Low is 6546.65. The Adj Close is 6639.14. The Volume is 4999240192.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-09-26. The Open is 411.43. The High is 414.94. The Low is 400.01. The Adj Close is 404.42. The Volume is 21460800.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-07-05. The Open is 260.8. The High is 274.51. The Low is 258.7. The Adj Close is 271.91. The Volume is 44156100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-07-12. The Open is 6396.78. The High is 6397.1. The Low is 6136.42. The Adj Close is 6228.81. The Volume is 3770170112.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-01-10. The Open is 902.44. The High is 914.87. The Low is 901.06. The Adj Close is 907.68. The Volume is 115808000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-06-25. The Open is 34659.11. The High is 35487.25. The Low is 31350.88. The Adj Close is 31637.78. The Volume is 40230904226.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-01-06. The Open is 431.86. The High is 431.86. The Low is 426.34. The Adj Close is 429.11. The Volume is 34042500.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-02-25. The Open is 238.89. The High is 239.34. The Low is 235.53. The Adj Close is 237.47. The Volume is 11496200.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-02-20. The Open is 40118.1. The High is 40119.89. The Low is 38112.81. The Adj Close is 38431.38. The Volume is 18340576452.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-11-14. The Open is 16317.81. The High is 16317.81. The Low is 15749.19. The Adj Close is 16068.14. The Volume is 27481710135.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-01-13. The Open is 3658.87. The High is 3674.76. The Low is 3544.93. The Adj Close is 3552.95. The Volume is 4681302466.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-06-29. The Open is 5898.13. The High is 6261.66. The Low is 5835.75. The Adj Close is 6218.3. The Volume is 3966230016.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-11-29. The Open is 4269.0. The High is 4413.02. The Low is 4145.77. The Adj Close is 4278.85. The Volume is 6503347767.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-04-05. The Open is 421.02. The High is 424.26. The Low is 420.61. The Adj Close is 424.03. The Volume is 60718000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-10-17. The Open is 641.82. The High is 642.33. The Low is 638.66. The Adj Close is 639.19. The Volume is 58063600.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-12-15. The Open is 3244.0. The High is 3275.38. The Low is 3191.3. The Adj Close is 3236.76. The Volume is 3551763561.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-03-05. The Open is 272.74. The High is 281.67. The Low is 264.77. The Adj Close is 276.18. The Volume is 41302400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-11-17. The Open is 8549.47. The High is 8727.79. The Low is 8500.97. The Adj Close is 8577.98. The Volume is 18668638897.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-10-07. The Open is 19957.56. The High is 20041.09. The Low is 19395.79. The Adj Close is 19546.85. The Volume is 29227315390.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-03-25. The Open is 416.51. The High is 418.08. The Low is 415.56. The Adj Close is 417.18. The Volume is 52560000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-05-01. The Open is 448.48. The High is 452.48. The Low is 447.93. The Adj Close is 451.88. The Volume is 40660100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-11-13. The Open is 5938.25. The High is 6811.19. The Low is 5844.29. The Adj Close is 6559.49. The Volume is 6263249920.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-05-09. The Open is 5982.32. The High is 6183.04. The Low is 5982.32. The Adj Close is 6174.53. The Volume is 16784645411.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-02-21. The Open is 56068.57. The High is 58330.57. The Low is 55672.61. The Adj Close is 57539.95. The Volume is 51897585191.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-02-11. The Open is 21651.84. The High is 21891.41. The Low is 21618.45. The Adj Close is 21870.88. The Volume is 16356226232.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-02-17. The Open is 3633.36. The High is 3680.54. The Low is 3619.18. The Adj Close is 3673.84. The Volume is 7039512503.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-10-15. The Open is 57345.9. The High is 62757.13. The Low is 56868.14. The Adj Close is 61593.95. The Volume is 51780081801.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-02-09. The Open is 223.39. The High is 223.98. The Low is 217.02. The Adj Close is 220.11. The Volume is 27791300.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-09-24. The Open is 3796.15. The High is 3796.15. The Low is 3666.9. The Adj Close is 3682.84. The Volume is 768014976.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-04-23. The Open is 445.86. The High is 450.28. The Low is 444.33. The Adj Close is 450.28. The Volume is 50485400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-10-04. The Open is 359.89. The High is 364.49. The Low is 325.89. The Adj Close is 328.87. The Volume is 47236500.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-01-02. The Open is 3849.22. The High is 3947.98. The Low is 3817.41. The Adj Close is 3943.41. The Volume is 5244856836.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-07-31. The Open is 23652.07. The High is 24121.64. The Low is 23275.7. The Adj Close is 23336.9. The Volume is 23553591896.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-02-15. The Open is 10313.86. The High is 10341.56. The Low is 9874.43. The Adj Close is 9889.42. The Volume is 43865054831.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-12-29. The Open is 422.1. The High is 432.98. The Low is 420.63. The Adj Close is 432.98. The Volume is 51596500.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-10-15. The Open is 19185.44. The High is 19212.54. The Low is 19019.25. The Adj Close is 19067.63. The Volume is 16192235532.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-07-13. The Open is 664.8. The High is 668.7. The Low is 654.47. The Adj Close is 654.47. The Volume is 131449000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-06-08. The Open is 33589.52. The High is 34017.39. The Low is 31114.44. The Adj Close is 33472.63. The Volume is 49902050442.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-03-29. The Open is 1046.08. The High is 1055.13. The Low is 1015.88. The Adj Close is 1039.97. The Volume is 298457984.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-12-01. The Open is 4024.46. The High is 4309.38. The Low is 3969.71. The Adj Close is 4214.67. The Volume is 5375314093.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-05-29. The Open is 9528.36. The High is 9573.67. The Low is 9379.34. The Adj Close is 9439.12. The Volume is 32896642044.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-03-06. The Open is 1267.47. The High is 1276.0. The Low is 1264.6. The Adj Close is 1272.83. The Volume is 153656992.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-03-01. The Open is 45159.5. The High is 49784.02. The Low is 45115.09. The Adj Close is 49631.24. The Volume is 53891300112.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-09-08. The Open is 6460.17. The High is 6534.25. The Low is 6197.52. The Adj Close is 6225.98. The Volume is 3835060000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-07-04. The Open is 11972.72. The High is 12006.08. The Low is 11166.57. The Adj Close is 11215.44. The Volume is 25920294033.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-01-23. The Open is 382.43. The High is 394.54. The Low is 381.98. The Adj Close is 387.49. The Volume is 56247400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-03-20. The Open is 1037.24. The High is 1063.03. The Low is 1036.68. The Adj Close is 1054.23. The Volume is 286529984.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-01-29. The Open is 380.11. The High is 384.38. The Low is 365.45. The Adj Close is 379.47. The Volume is 86125296.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-09-24. The Open is 9729.32. The High is 9804.32. The Low is 8370.8. The Adj Close is 8620.57. The Volume is 25002886689.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-07-11. The Open is 30417.63. The High is 30788.31. The Low is 30358.1. The Adj Close is 30620.95. The Volume is 12151839152.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-07-13. The Open is 19325.97. The High is 20223.05. The Low is 18999.95. The Adj Close is 20212.07. The Volume is 33042430345.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-07-14. The Open is 2360.59. The High is 2363.25. The Low is 2183.22. The Adj Close is 2233.34. The Volume is 882502976.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-02-18. The Open is 40552.13. The High is 40929.15. The Low is 39637.62. The Adj Close is 40030.98. The Volume is 23310007704.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-01-13. The Open is 13952.4. The High is 14659.5. The Low is 13952.4. The Adj Close is 14360.2. The Volume is 12763599872.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-06-17. The Open is 768.49. The High is 775.36. The Low is 716.56. The Adj Close is 748.91. The Volume is 363320992.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-01-21. The Open is 211.38. The High is 227.79. The Low is 211.21. The Adj Close is 226.9. The Volume is 29924600.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-07-15. The Open is 30331.78. The High is 30407.78. The Low is 30263.46. The Adj Close is 30295.81. The Volume is 8011667756.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-05-21. The Open is 9522.74. The High is 9555.24. The Low is 8869.93. The Adj Close is 9081.76. The Volume is 39326160532.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-01-30. The Open is 34295.93. The High is 34834.71. The Low is 32940.19. The Adj Close is 34269.52. The Volume is 65141828798.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-07-02. The Open is 9231.14. The High is 9274.96. The Low is 9036.62. The Adj Close is 9123.41. The Volume is 16338916796.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-07-12. The Open is 30622.25. The High is 30959.96. The Low is 30228.84. The Adj Close is 30391.65. The Volume is 14805659717.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-09-29. The Open is 19427.78. The High is 19589.27. The Low is 18924.35. The Adj Close is 19573.05. The Volume is 41037843771.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-05-06. The Open is 57441.31. The High is 58363.32. The Low is 55382.51. The Adj Close is 56396.52. The Volume is 69523285106.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-01-19. The Open is 3652.38. The High is 3758.53. The Low is 3652.38. The Adj Close is 3728.57. The Volume is 5955691380.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-10-11. The Open is 8585.26. The High is 8721.78. The Low is 8316.18. The Adj Close is 8321.76. The Volume is 19604381101.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-07-26. The Open is 2577.77. The High is 2610.76. The Low is 2450.8. The Adj Close is 2529.45. The Volume is 937404032.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-05-22. The Open is 443.22. The High is 443.43. The Low is 439.04. The Adj Close is 439.32. The Volume is 39657600.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-11-03. The Open is 6387.24. The High is 6400.07. The Low is 6342.37. The Adj Close is 6361.26. The Volume is 3658640000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-11-24. The Open is 18365.02. The High is 19348.27. The Low is 18128.66. The Adj Close is 19107.46. The Volume is 51469565009.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-05-07. The Open is 1579.47. The High is 1596.72. The Low is 1559.76. The Adj Close is 1596.71. The Volume is 1080029952.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-03-05. The Open is 22354.14. The High is 22613.69. The Low is 22307.14. The Adj Close is 22435.51. The Volume is 13317001733.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-02-11. The Open is 219.73. The High is 223.41. The Low is 218.07. The Adj Close is 219.18. The Volume is 17201900.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-10-20. The Open is 5708.11. The High is 6060.11. The Low is 5627.23. The Adj Close is 6011.45. The Volume is 2354429952.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-12-17. The Open is 3253.12. The High is 3597.92. The Low is 3253.12. The Adj Close is 3545.86. The Volume is 5409247918.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-07-23. The Open is 32305.96. The High is 33581.55. The Low is 32057.89. The Adj Close is 33581.55. The Volume is 22552046192.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-04-10. The Open is 5204.11. The High is 5421.65. The Low is 5193.38. The Adj Close is 5324.55. The Volume is 15504590933.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-05-06. The Open is 9845.31. The High is 9940.14. The Low is 9465.25. The Adj Close is 9654.8. The Volume is 7222280192.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-10-30. The Open is 335.71. The High is 350.91. The Low is 335.07. The Adj Close is 345.3. The Volume is 30177900.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-05-15. The Open is 8705.19. The High is 8836.19. The Low is 8456.45. The Adj Close is 8510.38. The Volume is 6705710080.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-06-21. The Open is 20594.29. The High is 21620.63. The Low is 20415.06. The Adj Close is 20710.6. The Volume is 28970212744.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-02-16. The Open is 401.43. The High is 408.95. The Low is 401.43. The Adj Close is 407.49. The Volume is 73093104.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-02-27. The Open is 3857.48. The High is 3888.8. The Low is 3787.06. The Adj Close is 3851.05. The Volume is 8301309684.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-12-28. The Open is 316.16. The High is 320.03. The Low is 311.08. The Adj Close is 317.24. The Volume is 11676600.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-08-11. The Open is 11881.65. The High is 11932.71. The Low is 11195.71. The Adj Close is 11410.53. The Volume is 27039782640.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-02-17. The Open is 1026.12. The High is 1053.17. The Low is 1025.64. The Adj Close is 1046.21. The Volume is 136474000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-07-31. The Open is 287.7. The High is 288.96. The Low is 282.34. The Adj Close is 284.65. The Volume is 23629100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-10-06. The Open is 240.36. The High is 246.93. The Low is 240.14. The Adj Close is 246.06. The Volume is 27535100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-02-25. The Open is 1170.41. The High is 1174.85. The Low is 1124.59. The Adj Close is 1143.84. The Volume is 139960992.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-12-15. The Open is 48379.75. The High is 49473.96. The Low is 46671.96. The Adj Close is 48896.72. The Volume is 36541828520.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-02-13. The Open is 3653.6. The High is 3669.75. The Low is 3617.25. The Adj Close is 3632.07. The Volume is 6438903823.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-07-23. The Open is 2808.1. The High is 2832.18. The Low is 2653.94. The Adj Close is 2730.4. The Volume is 1072840000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-12-18. The Open is 22806.8. The High is 23238.6. The Low is 22399.81. The Adj Close is 23137.96. The Volume is 40387896275.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-03-23. The Open is 418.16. The High is 419.27. The Low is 417.36. The Adj Close is 418.04. The Volume is 61444200.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-12-02. The Open is 18801.74. The High is 19308.33. The Low is 18347.72. The Adj Close is 19201.09. The Volume is 37387697139.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-06-16. The Open is 8841.44. The High is 9335.87. The Low is 8814.56. The Adj Close is 8994.49. The Volume is 23348550311.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-09-22. The Open is 18534.65. The High is 19456.91. The Low is 18415.59. The Adj Close is 19413.55. The Volume is 41135767926.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-06-29. The Open is 9140.03. The High is 9237.57. The Low is 9041.88. The Adj Close is 9190.85. The Volume is 16460547078.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-06-20. The Open is 2591.26. The High is 2763.45. The Low is 2589.82. The Adj Close is 2721.79. The Volume is 1854189952.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-04-01. The Open is 6437.32. The High is 6612.57. The Low is 6202.37. The Adj Close is 6606.78. The Volume is 40346426266.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-07-10. The Open is 21591.08. The High is 21591.08. The Low is 20727.12. The Adj Close is 20860.45. The Volume is 28688807249.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-04-02. The Open is 247.09. The High is 254.46. The Low is 245.42. The Adj Close is 253.01. The Volume is 26272600.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-12-08. The Open is 17802.9. The High is 18353.4. The Low is 14336.9. The Adj Close is 16569.4. The Volume is 21135998976.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-01-16. The Open is 43172.04. The High is 43436.81. The Low is 42691.02. The Adj Close is 43113.88. The Volume is 17902097845.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-01-23. The Open is 233.52. The High is 234.85. The Low is 225.2. The Adj Close is 232.88. The Volume is 24621700.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-10-13. The Open is 377.92. The High is 397.23. The Low is 368.9. The Adj Close is 390.41. The Volume is 35221400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-12-27. The Open is 26439.37. The High is 28288.84. The Low is 25922.77. The Adj Close is 26272.29. The Volume is 66479895605.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-12-11. The Open is 17129.71. The High is 17245.63. The Low is 17091.82. The Adj Close is 17104.19. The Volume is 14122486832.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-12-07. The Open is 374.84. The High is 376.29. The Low is 373.27. The Adj Close is 375.1. The Volume is 6491650.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-11-14. The Open is 702.0. The High is 706.28. The Low is 699.81. The Adj Close is 705.02. The Volume is 62993000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-01-11. The Open is 3674.02. The High is 3713.88. The Low is 3653.07. The Adj Close is 3687.37. The Volume is 5538712865.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-06-09. The Open is 2807.44. The High is 2901.71. The Low is 2795.62. The Adj Close is 2823.81. The Volume is 1348950016.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-07-13. The Open is 9277.21. The High is 9306.41. The Low is 9224.29. The Adj Close is 9243.61. The Volume is 17519821266.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-08-19. The Open is 44741.88. The High is 46970.76. The Low is 43998.32. The Adj Close is 46717.58. The Volume is 37204312299.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-01-09. The Open is 282.38. The High is 291.11. The Low is 280.53. The Adj Close is 290.41. The Volume is 18718600.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-01-24. The Open is 36275.73. The High is 37247.52. The Low is 33184.06. The Adj Close is 36654.33. The Volume is 41856658597.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-08-05. The Open is 22626.83. The High is 23422.83. The Low is 22612.18. The Adj Close is 23289.31. The Volume is 28881249043.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-11-24. The Open is 323.01. The High is 323.06. The Low is 318.12. The Adj Close is 320.05. The Volume is 29362600.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-11-12. The Open is 6411.76. The High is 6434.21. The Low is 6360.47. The Adj Close is 6371.27. The Volume is 4295770000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-11-08. The Open is 342.15. The High is 347.03. The Low is 342.15. The Adj Close is 345.49. The Volume is 8535470.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-02-06. The Open is 9617.82. The High is 9824.62. The Low is 9539.82. The Adj Close is 9729.8. The Volume is 37628823716.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-10-01. The Open is 6619.85. The High is 6653.3. The Low is 6549.08. The Adj Close is 6589.62. The Volume is 4000970000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-02-11. The Open is 44898.71. The High is 48463.47. The Low is 44187.76. The Adj Close is 47909.33. The Volume is 81388911810.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-05-24. The Open is 29101.12. The High is 29774.36. The Low is 28786.59. The Adj Close is 29655.59. The Volume is 26616506245.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-01-09. The Open is 15123.7. The High is 15497.5. The Low is 14424.0. The Adj Close is 14595.4. The Volume is 16659999744.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-05-22. The Open is 29432.47. The High is 30425.86. The Low is 29275.18. The Adj Close is 30323.72. The Volume is 21631532270.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-06-30. The Open is 30441.35. The High is 31256.86. The Low is 29600.28. The Adj Close is 30477.25. The Volume is 26387306197.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-12-20. The Open is 462.23. The High is 462.64. The Low is 434.34. The Adj Close is 442.68. The Volume is 75409400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-05-20. The Open is 30311.12. The High is 30664.98. The Low is 28793.61. The Adj Close is 29200.74. The Volume is 30749382605.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-09-21. The Open is 231.22. The High is 231.22. The Low is 226.52. The Adj Close is 227.09. The Volume is 19678800.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-01-22. The Open is 36471.59. The High is 36688.81. The Low is 34349.25. The Adj Close is 35030.25. The Volume is 39714385405.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-05-31. The Open is 534.19. The High is 546.62. The Low is 520.66. The Adj Close is 531.39. The Volume is 138450000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-01-24. The Open is 32064.38. The High is 32944.01. The Low is 31106.69. The Adj Close is 32289.38. The Volume is 48643830599.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-11-13. The Open is 6373.19. The High is 6395.27. The Low is 6342.67. The Adj Close is 6359.49. The Volume is 4503800000.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-04-03. The Open is 1102.95. The High is 1151.74. The Low is 1102.95. The Adj Close is 1143.81. The Volume is 580444032.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-05-31. The Open is 7406.15. The High is 7608.9. The Low is 7361.13. The Adj Close is 7494.17. The Volume is 5127130112.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-05-17. The Open is 7886.93. The High is 7929.15. The Low is 7038.12. The Adj Close is 7343.9. The Volume is 30066644905.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-01-12. The Open is 3686.97. The High is 3698.98. The Low is 3653.81. The Adj Close is 3661.3. The Volume is 4778170883.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-02-08. The Open is 1062.32. The High is 1078.97. The Low is 1037.49. The Adj Close is 1063.07. The Volume is 201855008.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-06-16. The Open is 9454.27. The High is 9579.43. The Low is 9400.45. The Adj Close is 9538.02. The Volume is 21565537209.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-05-19. The Open is 9727.06. The High is 9836.05. The Low is 9539.62. The Adj Close is 9729.04. The Volume is 39254288955.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-05-04. The Open is 8895.75. The High is 8956.91. The Low is 8645.02. The Adj Close is 8912.65. The Volume is 45718796276.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-05-13. The Open is 26807.77. The High is 27030.48. The Low is 26710.87. The Adj Close is 26784.08. The Volume is 9999171605.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-10-29. The Open is 60624.87. The High is 62927.61. The Low is 60329.96. The Adj Close is 62227.96. The Volume is 36856881767.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-09-11. The Open is 623.42. The High is 628.82. The Low is 600.51. The Adj Close is 606.72. The Volume is 73610800.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-08-10. The Open is 3341.84. The High is 3453.45. The Low is 3319.47. The Adj Close is 3381.28. The Volume is 1515110016.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-07-07. The Open is 9349.16. The High is 9360.62. The Low is 9201.82. The Adj Close is 9252.28. The Volume is 13839652595.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-11-06. The Open is 61068.88. The High is 61590.68. The Low is 60163.78. The Adj Close is 61527.48. The Volume is 29094934221.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-09-13. The Open is 10452.4. The High is 10577.21. The Low is 10224.33. The Adj Close is 10323.76. The Volume is 36506852789.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-07-03. The Open is 9124.84. The High is 9202.34. The Low is 9058.79. The Adj Close is 9087.3. The Volume is 13078970999.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-04-23. The Open is 5399.37. The High is 5633.8. The Low is 5389.41. The Adj Close is 5572.36. The Volume is 15867308108.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-01-12. The Open is 8033.26. The High is 8200.06. The Low is 8009.06. The Adj Close is 8192.49. The Volume is 22903438381.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-12-28. The Open is 15864.1. The High is 15888.4. The Low is 13937.3. The Adj Close is 14606.5. The Volume is 12336499712.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-05-12. The Open is 56714.53. The High is 57939.36. The Low is 49150.54. The Adj Close is 49150.54. The Volume is 75215403907.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-04-20. The Open is 7186.87. The High is 7240.29. The Low is 6835.5. The Adj Close is 6881.96. The Volume is 37747113936.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-08-05. The Open is 39744.52. The High is 41341.93. The Low is 37458.0. The Adj Close is 40869.55. The Volume is 35185031017.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-01-28. The Open is 23079.96. The High is 23165.9. The Low is 22908.85. The Adj Close is 23031.09. The Volume is 14712928379.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-11-09. The Open is 15479.6. The High is 15785.14. The Low is 14865.53. The Adj Close is 15332.32. The Volume is 34149115566.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-02-10. The Open is 220.28. The High is 221.81. The Low is 215.33. The Adj Close is 219.84. The Volume is 21115100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-05-13. The Open is 8515.49. The High is 8773.55. The Low is 8395.12. The Adj Close is 8723.94. The Volume is 5866379776.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-11-07. The Open is 374.27. The High is 390.59. The Low is 372.43. The Adj Close is 386.48. The Volume is 56625100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-06-05. The Open is 224.15. The High is 225.97. The Low is 223.18. The Adj Close is 224.95. The Volume is 18056500.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-04-21. The Open is 1229.42. The High is 1235.94. The Low is 1215.56. The Adj Close is 1222.05. The Volume is 272167008.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-03-20. The Open is 58332.26. The High is 60031.29. The Low is 58213.3. The Adj Close is 58313.64. The Volume is 50361731222.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-03-07. The Open is 407.76. The High is 415.92. The Low is 406.31. The Adj Close is 414.32. The Volume is 85762400.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2018-03-20. The Open is 8619.67. The High is 9051.02. The Low is 8389.89. The Adj Close is 8913.47. The Volume is 6361789952.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-03-23. The Open is 4022.71. The High is 4049.88. The Low is 4015.96. The Adj Close is 4035.83. The Volume is 9578850549.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-06-13. The Open is 25902.94. The High is 26376.35. The Low is 25728.37. The Adj Close is 25918.73. The Volume is 14143474486.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-12-04. The Open is 53727.88. The High is 53904.68. The Low is 42874.62. The Adj Close is 49200.7. The Volume is 61385677469.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-05-25. The Open is 8786.11. The High is 8951.01. The Low is 8719.67. The Adj Close is 8906.93. The Volume is 31288157264.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2019-12-28. The Open is 7289.03. The High is 7399.04. The Low is 7286.91. The Adj Close is 7317.99. The Volume is 21365673026.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2014-12-02. The Open is 379.25. The High is 384.04. The Low is 377.86. The Adj Close is 381.32. The Volume is 12364100.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2015-03-06. The Open is 275.6. The High is 277.61. The Low is 270.02. The Adj Close is 272.72. The Volume is 28918900.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-08-15. The Open is 47096.67. The High is 47357.11. The Low is 45579.59. The Adj Close is 47047.0. The Volume is 30988958446.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-06-03. The Open is 27252.32. The High is 27317.05. The Low is 26958.0. The Adj Close is 27075.13. The Volume is 8385597470.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-09-07. The Open is 18837.68. The High is 19427.17. The Low is 18644.47. The Adj Close is 19290.32. The Volume is 35239757134.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2020-07-12. The Open is 9241.05. The High is 9319.42. The Low is 9197.45. The Adj Close is 9276.5. The Volume is 14452361907.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-12-01. The Open is 746.05. The High is 758.28. The Low is 746.05. The Adj Close is 756.77. The Volume is 80461904.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-03-08. The Open is 414.46. The High is 416.24. The Low is 411.09. The Adj Close is 413.97. The Volume is 70311696.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-01-12. The Open is 448.18. The High is 448.18. The Low is 435.69. The Adj Close is 435.69. The Volume is 115607000.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2016-03-17. The Open is 417.89. The High is 421.0. The Low is 417.89. The Adj Close is 420.62. The Volume is 83528600.", "label": "less than 764.11325075", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-01-06. The Open is 16836.47. The High is 16991.99. The Low is 16716.42. The Adj Close is 16951.97. The Volume is 14413662913.", "label": "between 7697.924072 and 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-07-24. The Open is 33593.73. The High is 34490.39. The Low is 33424.86. The Adj Close is 34292.45. The Volume is 21664706865.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-03-18. The Open is 58893.08. The High is 60116.25. The Low is 54253.58. The Adj Close is 57858.92. The Volume is 55746041000.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-06-03. The Open is 30467.81. The High is 30633.04. The Low is 29375.69. The Adj Close is 29704.39. The Volume is 26175547452.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-11-09. The Open is 7446.83. The High is 7446.83. The Low is 7101.52. The Adj Close is 7143.58. The Volume is 3226249984.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-06-09. The Open is 33416.98. The High is 37537.37. The Low is 32475.87. The Adj Close is 37345.12. The Volume is 53972919008.", "label": "greater than 20297.0288085", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2017-05-18. The Open is 1818.7. The High is 1904.48. The Low is 1807.12. The Adj Close is 1888.65. The Volume is 894321024.", "label": "between 764.11325075 and 7697.924072", "dataset": "arslanr369-bitcoin-price-2014-2023", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/arslanr369-roblox-stock-pricing-2021-2023/metadata.json

@@ -0,0 +1,29 @@
+{
+  "dataset": "arslanr369-roblox-stock-pricing-2021-2023",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Close",
+  "label_values": [
+    "less than 37.504999",
+    "greater than 77.937498",
+    "between 37.504999 and 45.4449995",
+    "between 45.4449995 and 77.937498"
+  ],
+  "num_labels": 4,
+  "train_samples": 512,
+  "test_samples": 60,
+  "train_label_distribution": {
+    "between 45.4449995 and 77.937498": 128,
+    "greater than 77.937498": 128,
+    "less than 37.504999": 128,
+    "between 37.504999 and 45.4449995": 128
+  },
+  "test_label_distribution": {
+    "greater than 77.937498": 15,
+    "less than 37.504999": 15,
+    "between 37.504999 and 45.4449995": 15,
+    "between 45.4449995 and 77.937498": 15
+  }
+}

classification/unipredict/arslanr369-roblox-stock-pricing-2021-2023/test.csv

@@ -0,0 +1,61 @@
+Date,Open,High,Low,Adj Close,Volume,Close
+2021-06-08,93.61,94.22,88.89,91.41,14403900,greater than 77.937498
+2023-02-14,34.3,35.77,33.68,35.67,11224500,less than 37.504999
+2023-02-08,38.46,39.63,37.47,37.51,8696200,between 37.504999 and 45.4449995
+2021-07-13,86.34,86.45,83.15,84.02,9196200,greater than 77.937498
+2022-10-28,46.17,46.73,45.22,45.78,9221300,between 45.4449995 and 77.937498
+2021-12-22,102.28,106.98,101.54,102.77,12469700,greater than 77.937498
+2021-08-12,84.04,84.71,81.21,82.65,3992400,greater than 77.937498
+2022-04-11,42.48,44.7,40.86,44.02,17180300,between 37.504999 and 45.4449995
+2023-06-12,38.82,39.37,38.22,38.95,6832800,between 37.504999 and 45.4449995
+2022-08-05,46.98,49.91,46.41,49.24,21143700,between 45.4449995 and 77.937498
+2021-03-31,65.5,67.19,64.57,64.83,4094900,between 45.4449995 and 77.937498
+2021-11-08,79.02,79.02,76.83,77.0,16708400,between 45.4449995 and 77.937498
+2022-06-22,28.82,31.94,28.76,31.12,32828800,less than 37.504999
+2022-12-15,29.35,29.8,26.86,27.91,40121100,less than 37.504999
+2022-05-02,30.28,32.74,30.0,32.68,17155000,less than 37.504999
+2021-06-03,98.83,102.05,96.27,96.5,10907500,greater than 77.937498
+2021-06-28,86.99,94.39,86.99,93.04,14046500,greater than 77.937498
+2022-02-08,62.62,64.7,60.91,64.43,13816200,between 45.4449995 and 77.937498
+2022-07-14,37.8,38.83,36.7,37.49,26820400,less than 37.504999
+2022-05-27,31.0,31.99,29.89,31.81,22641500,less than 37.504999
+2022-02-09,65.46,71.42,64.66,70.48,27292500,between 45.4449995 and 77.937498
+2023-04-26,38.0,38.03,35.49,35.76,10959500,less than 37.504999
+2022-06-02,29.03,33.9,28.96,33.48,31343300,less than 37.504999
+2023-06-15,39.69,40.88,39.23,40.63,7161700,between 37.504999 and 45.4449995
+2022-07-07,37.95,39.81,37.78,39.52,31467300,between 37.504999 and 45.4449995
+2021-04-09,70.5,74.99,70.21,71.83,9245500,between 45.4449995 and 77.937498
+2021-04-29,76.71,77.6,72.83,76.12,4374900,between 45.4449995 and 77.937498
+2022-02-14,68.21,71.82,66.8,68.32,26296100,between 45.4449995 and 77.937498
+2022-08-16,48.53,48.59,45.7,47.76,26070400,between 45.4449995 and 77.937498
+2021-12-17,95.99,103.5,93.8,102.4,20705600,greater than 77.937498
+2021-09-20,78.15,79.94,76.77,77.77,5663100,between 45.4449995 and 77.937498
+2022-09-15,44.46,47.05,42.01,43.5,32722100,between 37.504999 and 45.4449995
+2021-08-10,85.44,85.67,83.1,85.08,8272900,greater than 77.937498
+2022-08-22,41.84,43.5,41.23,41.5,14859600,between 37.504999 and 45.4449995
+2023-05-15,39.17,39.17,37.95,38.97,7637600,between 37.504999 and 45.4449995
+2022-02-01,66.31,69.25,63.3,67.75,33738200,between 45.4449995 and 77.937498
+2021-03-19,69.47,72.7,68.08,70.5,6776500,between 45.4449995 and 77.937498
+2021-11-11,96.26,100.41,93.03,98.12,27837100,greater than 77.937498
+2022-09-20,36.85,37.59,36.26,36.41,15256000,less than 37.504999
+2023-03-01,37.27,37.7,36.51,37.48,9360300,less than 37.504999
+2021-12-08,116.04,125.5,113.85,124.78,18413300,greater than 77.937498
+2021-05-06,66.5,67.0,63.83,65.06,6004200,between 45.4449995 and 77.937498
+2021-04-14,79.11,80.57,74.05,75.35,14785800,between 45.4449995 and 77.937498
+2021-12-16,101.85,102.32,93.27,95.21,17835700,greater than 77.937498
+2022-11-16,36.07,36.2,34.31,34.41,13179300,less than 37.504999
+2022-06-15,25.81,29.29,25.36,28.9,39872500,less than 37.504999
+2022-07-13,36.96,37.74,35.78,37.08,31184000,less than 37.504999
+2023-01-23,35.5,36.75,35.1,36.51,9872500,less than 37.504999
+2022-09-22,35.44,36.14,34.9,35.26,14895500,less than 37.504999
+2021-08-16,83.7,84.19,79.14,79.57,12998900,greater than 77.937498
+2022-04-08,43.82,45.44,42.77,43.1,18296500,between 37.504999 and 45.4449995
+2021-10-19,78.26,79.42,78.2,79.17,3666700,greater than 77.937498
+2021-11-24,116.68,126.0,115.81,124.23,28221200,greater than 77.937498
+2021-07-22,80.67,82.18,80.11,82.03,4362500,greater than 77.937498
+2023-03-08,41.21,42.0,40.69,41.35,7274000,between 37.504999 and 45.4449995
+2023-03-22,44.81,44.95,42.8,42.85,7304300,between 37.504999 and 45.4449995
+2022-08-23,41.73,42.64,40.91,41.07,14042300,between 37.504999 and 45.4449995
+2022-10-04,37.75,39.15,37.36,38.68,18832000,between 37.504999 and 45.4449995
+2022-10-17,41.2,43.66,40.51,42.61,71231000,between 37.504999 and 45.4449995
+2022-09-07,37.73,39.95,37.52,39.94,14116200,between 37.504999 and 45.4449995

classification/unipredict/arslanr369-roblox-stock-pricing-2021-2023/test.jsonl

@@ -0,0 +1,60 @@
+{"text": "The Date is 2021-06-08. The Open is 93.61. The High is 94.22. The Low is 88.89. The Adj Close is 91.41. The Volume is 14403900.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-02-14. The Open is 34.3. The High is 35.77. The Low is 33.68. The Adj Close is 35.67. The Volume is 11224500.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-02-08. The Open is 38.46. The High is 39.63. The Low is 37.47. The Adj Close is 37.51. The Volume is 8696200.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-07-13. The Open is 86.34. The High is 86.45. The Low is 83.15. The Adj Close is 84.02. The Volume is 9196200.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-10-28. The Open is 46.17. The High is 46.73. The Low is 45.22. The Adj Close is 45.78. The Volume is 9221300.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-12-22. The Open is 102.28. The High is 106.98. The Low is 101.54. The Adj Close is 102.77. The Volume is 12469700.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-08-12. The Open is 84.04. The High is 84.71. The Low is 81.21. The Adj Close is 82.65. The Volume is 3992400.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-04-11. The Open is 42.48. The High is 44.7. The Low is 40.86. The Adj Close is 44.02. The Volume is 17180300.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-06-12. The Open is 38.82. The High is 39.37. The Low is 38.22. The Adj Close is 38.95. The Volume is 6832800.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-08-05. The Open is 46.98. The High is 49.91. The Low is 46.41. The Adj Close is 49.24. The Volume is 21143700.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-03-31. The Open is 65.5. The High is 67.19. The Low is 64.57. The Adj Close is 64.83. The Volume is 4094900.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-11-08. The Open is 79.02. The High is 79.02. The Low is 76.83. The Adj Close is 77.0. The Volume is 16708400.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-06-22. The Open is 28.82. The High is 31.94. The Low is 28.76. The Adj Close is 31.12. The Volume is 32828800.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-12-15. The Open is 29.35. The High is 29.8. The Low is 26.86. The Adj Close is 27.91. The Volume is 40121100.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-05-02. The Open is 30.28. The High is 32.74. The Low is 30.0. The Adj Close is 32.68. The Volume is 17155000.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-06-03. The Open is 98.83. The High is 102.05. The Low is 96.27. The Adj Close is 96.5. The Volume is 10907500.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-06-28. The Open is 86.99. The High is 94.39. The Low is 86.99. The Adj Close is 93.04. The Volume is 14046500.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-02-08. The Open is 62.62. The High is 64.7. The Low is 60.91. The Adj Close is 64.43. The Volume is 13816200.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-07-14. The Open is 37.8. The High is 38.83. The Low is 36.7. The Adj Close is 37.49. The Volume is 26820400.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-05-27. The Open is 31.0. The High is 31.99. The Low is 29.89. The Adj Close is 31.81. The Volume is 22641500.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-02-09. The Open is 65.46. The High is 71.42. The Low is 64.66. The Adj Close is 70.48. The Volume is 27292500.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-04-26. The Open is 38.0. The High is 38.03. The Low is 35.49. The Adj Close is 35.76. The Volume is 10959500.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-06-02. The Open is 29.03. The High is 33.9. The Low is 28.96. The Adj Close is 33.48. The Volume is 31343300.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-06-15. The Open is 39.69. The High is 40.88. The Low is 39.23. The Adj Close is 40.63. The Volume is 7161700.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-07-07. The Open is 37.95. The High is 39.81. The Low is 37.78. The Adj Close is 39.52. The Volume is 31467300.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-04-09. The Open is 70.5. The High is 74.99. The Low is 70.21. The Adj Close is 71.83. The Volume is 9245500.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-04-29. The Open is 76.71. The High is 77.6. The Low is 72.83. The Adj Close is 76.12. The Volume is 4374900.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-02-14. The Open is 68.21. The High is 71.82. The Low is 66.8. The Adj Close is 68.32. The Volume is 26296100.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-08-16. The Open is 48.53. The High is 48.59. The Low is 45.7. The Adj Close is 47.76. The Volume is 26070400.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-12-17. The Open is 95.99. The High is 103.5. The Low is 93.8. The Adj Close is 102.4. The Volume is 20705600.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-09-20. The Open is 78.15. The High is 79.94. The Low is 76.77. The Adj Close is 77.77. The Volume is 5663100.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-09-15. The Open is 44.46. The High is 47.05. The Low is 42.01. The Adj Close is 43.5. The Volume is 32722100.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-08-10. The Open is 85.44. The High is 85.67. The Low is 83.1. The Adj Close is 85.08. The Volume is 8272900.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-08-22. The Open is 41.84. The High is 43.5. The Low is 41.23. The Adj Close is 41.5. The Volume is 14859600.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-05-15. The Open is 39.17. The High is 39.17. The Low is 37.95. The Adj Close is 38.97. The Volume is 7637600.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-02-01. The Open is 66.31. The High is 69.25. The Low is 63.3. The Adj Close is 67.75. The Volume is 33738200.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-03-19. The Open is 69.47. The High is 72.7. The Low is 68.08. The Adj Close is 70.5. The Volume is 6776500.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-11-11. The Open is 96.26. The High is 100.41. The Low is 93.03. The Adj Close is 98.12. The Volume is 27837100.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-09-20. The Open is 36.85. The High is 37.59. The Low is 36.26. The Adj Close is 36.41. The Volume is 15256000.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-03-01. The Open is 37.27. The High is 37.7. The Low is 36.51. The Adj Close is 37.48. The Volume is 9360300.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-12-08. The Open is 116.04. The High is 125.5. The Low is 113.85. The Adj Close is 124.78. The Volume is 18413300.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-05-06. The Open is 66.5. The High is 67.0. The Low is 63.83. The Adj Close is 65.06. The Volume is 6004200.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-04-14. The Open is 79.11. The High is 80.57. The Low is 74.05. The Adj Close is 75.35. The Volume is 14785800.", "label": "between 45.4449995 and 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-12-16. The Open is 101.85. The High is 102.32. The Low is 93.27. The Adj Close is 95.21. The Volume is 17835700.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-11-16. The Open is 36.07. The High is 36.2. The Low is 34.31. The Adj Close is 34.41. The Volume is 13179300.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-06-15. The Open is 25.81. The High is 29.29. The Low is 25.36. The Adj Close is 28.9. The Volume is 39872500.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-07-13. The Open is 36.96. The High is 37.74. The Low is 35.78. The Adj Close is 37.08. The Volume is 31184000.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-01-23. The Open is 35.5. The High is 36.75. The Low is 35.1. The Adj Close is 36.51. The Volume is 9872500.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-09-22. The Open is 35.44. The High is 36.14. The Low is 34.9. The Adj Close is 35.26. The Volume is 14895500.", "label": "less than 37.504999", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-08-16. The Open is 83.7. The High is 84.19. The Low is 79.14. The Adj Close is 79.57. The Volume is 12998900.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-04-08. The Open is 43.82. The High is 45.44. The Low is 42.77. The Adj Close is 43.1. The Volume is 18296500.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-10-19. The Open is 78.26. The High is 79.42. The Low is 78.2. The Adj Close is 79.17. The Volume is 3666700.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-11-24. The Open is 116.68. The High is 126.0. The Low is 115.81. The Adj Close is 124.23. The Volume is 28221200.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2021-07-22. The Open is 80.67. The High is 82.18. The Low is 80.11. The Adj Close is 82.03. The Volume is 4362500.", "label": "greater than 77.937498", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-03-08. The Open is 41.21. The High is 42.0. The Low is 40.69. The Adj Close is 41.35. The Volume is 7274000.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2023-03-22. The Open is 44.81. The High is 44.95. The Low is 42.8. The Adj Close is 42.85. The Volume is 7304300.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-08-23. The Open is 41.73. The High is 42.64. The Low is 40.91. The Adj Close is 41.07. The Volume is 14042300.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-10-04. The Open is 37.75. The High is 39.15. The Low is 37.36. The Adj Close is 38.68. The Volume is 18832000.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-10-17. The Open is 41.2. The High is 43.66. The Low is 40.51. The Adj Close is 42.61. The Volume is 71231000.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Date is 2022-09-07. The Open is 37.73. The High is 39.95. The Low is 37.52. The Adj Close is 39.94. The Volume is 14116200.", "label": "between 37.504999 and 45.4449995", "dataset": "arslanr369-roblox-stock-pricing-2021-2023", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/arslanr369-roblox-stock-pricing-2021-2023/train.csv

@@ -0,0 +1,513 @@
+Date,Open,High,Low,Adj Close,Volume,Close
+2022-01-26,68.1,70.13,61.19,63.02,25741900,between 45.4449995 and 77.937498
+2021-11-26,129.5,131.0,120.24,122.65,11288400,greater than 77.937498
+2021-10-22,81.85,84.78,80.71,83.98,5746400,greater than 77.937498
+2022-05-23,31.0,32.09,29.67,30.06,30376000,less than 37.504999
+2021-11-23,119.87,121.29,113.33,114.87,25930900,greater than 77.937498
+2022-03-11,42.4,42.99,39.2,39.24,15212700,between 37.504999 and 45.4449995
+2023-03-20,43.37,43.92,42.26,43.36,8261300,between 37.504999 and 45.4449995
+2022-06-16,27.21,27.64,23.88,24.69,37915100,less than 37.504999
+2021-04-01,66.8,69.67,65.9,67.34,4063100,between 45.4449995 and 77.937498
+2022-05-05,34.19,34.19,29.93,30.44,21665800,less than 37.504999
+2022-03-14,38.06,39.42,36.33,36.68,23358300,less than 37.504999
+2021-05-03,74.86,75.1,70.61,71.08,3212200,between 45.4449995 and 77.937498
+2021-12-30,96.4,101.35,95.66,100.53,10013300,greater than 77.937498
+2022-01-28,57.36,59.9,53.63,58.18,30682200,between 45.4449995 and 77.937498
+2022-03-03,48.52,48.52,45.0,45.24,18977400,between 37.504999 and 45.4449995
+2021-05-24,82.6,89.64,82.5,89.23,23117700,greater than 77.937498
+2022-05-12,23.35,31.11,22.61,28.58,95005400,less than 37.504999
+2023-06-07,40.18,40.98,38.35,38.64,9867700,between 37.504999 and 45.4449995
+2022-12-12,31.84,33.61,31.65,33.32,13501400,less than 37.504999
+2023-05-08,36.14,37.07,35.76,36.28,9509300,less than 37.504999
+2021-06-04,100.68,103.87,98.52,99.57,14672000,greater than 77.937498
+2022-12-14,32.1,33.75,31.78,33.13,13499500,less than 37.504999
+2022-04-28,30.83,32.67,29.52,32.11,24732000,less than 37.504999
+2023-05-05,35.14,35.23,34.51,35.05,5388900,less than 37.504999
+2023-05-23,39.17,40.92,39.17,39.66,7681000,between 37.504999 and 45.4449995
+2022-01-13,89.55,89.59,80.11,80.18,20964300,greater than 77.937498
+2023-04-10,45.33,46.48,44.44,46.43,9889600,between 45.4449995 and 77.937498
+2021-05-10,68.84,69.29,64.0,64.0,8124100,between 45.4449995 and 77.937498
+2022-07-01,34.06,36.0,33.96,35.07,32754600,less than 37.504999
+2021-11-05,82.05,82.08,77.05,77.99,6717100,greater than 77.937498
+2021-04-16,78.0,79.36,75.12,75.85,5156200,between 45.4449995 and 77.937498
+2022-01-03,101.91,103.79,97.62,98.81,16964300,greater than 77.937498
+2022-12-07,30.46,31.04,30.14,30.77,8244900,less than 37.504999
+2023-01-19,33.0,34.12,32.74,33.41,20781200,less than 37.504999
+2022-01-07,86.38,88.69,82.58,84.37,25003600,greater than 77.937498
+2021-09-21,78.38,79.17,77.11,78.85,5328100,greater than 77.937498
+2021-04-26,72.0,75.92,71.31,74.9,4192700,between 45.4449995 and 77.937498
+2022-11-03,42.1,45.83,42.07,43.3,13460800,between 37.504999 and 45.4449995
+2023-01-10,30.41,32.03,30.39,32.01,10156900,less than 37.504999
+2021-10-28,80.92,83.5,79.11,82.75,6321000,greater than 77.937498
+2022-08-09,47.98,49.71,46.72,47.35,33978800,between 45.4449995 and 77.937498
+2022-11-23,31.22,32.19,31.16,32.0,11847900,less than 37.504999
+2022-10-25,43.54,45.85,43.21,45.71,19444400,between 45.4449995 and 77.937498
+2023-01-03,28.91,29.62,27.24,27.85,13439400,less than 37.504999
+2023-05-31,39.34,42.0,39.25,41.86,12351500,between 37.504999 and 45.4449995
+2021-05-17,70.95,76.97,70.91,76.93,16797600,between 45.4449995 and 77.937498
+2021-06-07,99.34,100.95,93.28,93.44,15214600,greater than 77.937498
+2021-11-30,128.53,137.71,124.97,126.1,37000900,greater than 77.937498
+2023-05-12,39.41,40.23,38.58,39.36,8570400,between 37.504999 and 45.4449995
+2023-03-03,39.56,41.94,39.56,41.37,16508500,between 37.504999 and 45.4449995
+2023-03-13,38.94,42.05,38.81,41.41,15779900,between 37.504999 and 45.4449995
+2022-08-15,50.47,52.15,48.73,48.96,17188100,between 45.4449995 and 77.937498
+2022-02-11,68.97,72.21,65.75,66.81,21780400,between 45.4449995 and 77.937498
+2022-03-18,45.89,50.24,45.61,49.62,33933300,between 45.4449995 and 77.937498
+2021-09-23,81.5,83.25,80.95,82.28,4262600,greater than 77.937498
+2022-05-19,33.0,35.69,31.57,34.35,45346800,less than 37.504999
+2023-06-13,40.22,40.49,38.76,40.28,10229800,between 37.504999 and 45.4449995
+2021-12-07,118.36,120.16,115.1,115.99,14766600,greater than 77.937498
+2022-09-02,39.01,39.37,37.52,37.94,12155700,between 37.504999 and 45.4449995
+2022-10-11,33.7,35.6,33.2,34.56,21176500,less than 37.504999
+2022-02-04,61.67,64.3,59.91,63.74,16078300,between 45.4449995 and 77.937498
+2022-01-06,87.99,92.08,84.69,89.2,17213300,greater than 77.937498
+2021-07-27,77.63,78.28,71.96,76.19,12323800,between 45.4449995 and 77.937498
+2022-06-21,27.57,29.98,27.4,29.64,27383300,less than 37.504999
+2021-03-29,69.0,69.09,66.51,67.03,4351600,between 45.4449995 and 77.937498
+2023-02-27,37.19,37.6,36.39,36.65,9384800,less than 37.504999
+2022-08-17,47.19,47.95,45.17,46.1,20183900,between 45.4449995 and 77.937498
+2022-11-10,33.33,33.8,31.28,33.73,25224500,less than 37.504999
+2021-06-29,91.55,93.31,89.8,92.6,7806500,greater than 77.937498
+2022-12-16,27.6,27.89,26.6,27.62,21720600,less than 37.504999
+2022-02-25,49.57,50.2,47.25,50.05,21783600,between 45.4449995 and 77.937498
+2021-08-02,77.0,79.0,76.11,78.31,3971300,greater than 77.937498
+2023-04-21,41.2,41.81,40.62,40.7,7482000,between 37.504999 and 45.4449995
+2023-03-17,45.35,45.47,43.53,43.69,13615300,between 37.504999 and 45.4449995
+2022-10-19,42.09,43.15,41.1,41.44,16168300,between 37.504999 and 45.4449995
+2022-12-21,27.04,28.39,26.78,28.09,12505000,less than 37.504999
+2022-09-29,37.26,37.41,35.31,35.38,14100200,less than 37.504999
+2022-01-19,78.24,80.11,75.17,76.16,16892600,between 45.4449995 and 77.937498
+2023-06-06,40.7,41.63,40.6,41.33,7236200,between 37.504999 and 45.4449995
+2022-12-06,31.36,31.59,30.29,30.65,10188300,less than 37.504999
+2021-06-30,92.56,92.9,89.91,89.98,4366900,greater than 77.937498
+2021-08-19,81.67,82.5,79.85,81.35,7561800,greater than 77.937498
+2023-02-24,36.55,37.07,35.86,36.96,10343400,less than 37.504999
+2022-11-11,34.0,36.9,33.06,36.74,21854500,less than 37.504999
+2022-05-13,30.36,33.19,29.87,32.97,57201800,less than 37.504999
+2021-12-27,101.6,108.78,101.6,105.01,16094500,greater than 77.937498
+2021-04-30,74.81,76.99,73.91,74.55,2420200,between 45.4449995 and 77.937498
+2022-08-03,45.9,47.51,45.7,47.0,15025100,between 45.4449995 and 77.937498
+2021-08-05,79.74,81.04,78.7,80.57,4354500,greater than 77.937498
+2022-03-31,48.47,48.55,46.11,46.24,13789500,between 45.4449995 and 77.937498
+2021-04-08,69.47,71.49,68.84,70.76,4244000,between 45.4449995 and 77.937498
+2022-05-26,28.27,30.83,27.89,30.59,19608100,less than 37.504999
+2023-06-14,40.0,40.08,38.88,39.91,8265100,between 37.504999 and 45.4449995
+2021-03-15,70.02,74.06,66.25,72.15,19549800,between 45.4449995 and 77.937498
+2021-05-05,70.6,70.88,66.46,66.59,4584800,between 45.4449995 and 77.937498
+2023-05-10,37.33,39.77,36.11,38.87,26827900,between 37.504999 and 45.4449995
+2022-04-07,46.42,47.97,42.55,44.73,29139000,between 37.504999 and 45.4449995
+2022-04-27,30.64,31.93,29.9,30.36,27858300,less than 37.504999
+2022-07-27,40.1,42.72,39.39,41.95,21235700,between 37.504999 and 45.4449995
+2021-03-18,76.0,77.0,66.8,67.3,9627400,between 45.4449995 and 77.937498
+2021-05-26,89.95,95.0,88.75,89.71,17543500,greater than 77.937498
+2021-10-11,70.63,71.85,69.77,70.22,5036700,between 45.4449995 and 77.937498
+2022-06-01,29.84,31.13,28.19,28.94,23093100,less than 37.504999
+2021-10-27,81.33,82.95,80.46,80.68,3668400,greater than 77.937498
+2022-08-18,45.81,45.83,44.28,45.09,15175400,between 37.504999 and 45.4449995
+2022-06-08,31.93,34.25,31.68,32.83,26455400,less than 37.504999
+2022-10-20,41.24,43.32,41.24,42.57,16379000,between 37.504999 and 45.4449995
+2022-07-19,39.9,40.08,38.16,39.85,20644000,between 37.504999 and 45.4449995
+2023-03-24,44.28,44.84,42.89,43.43,14536900,between 37.504999 and 45.4449995
+2021-10-01,75.99,75.99,73.48,75.59,3896000,between 45.4449995 and 77.937498
+2021-04-19,75.87,78.3,71.43,72.0,6637700,between 45.4449995 and 77.937498
+2023-04-14,45.4,46.12,44.94,45.7,5824700,between 45.4449995 and 77.937498
+2021-10-20,79.4,80.17,78.4,78.58,3565200,greater than 77.937498
+2023-02-09,38.33,38.97,36.38,36.4,10531700,less than 37.504999
+2023-01-18,37.19,37.35,35.41,35.76,17099900,less than 37.504999
+2022-10-05,37.96,38.49,36.76,38.0,14085200,between 37.504999 and 45.4449995
+2022-11-15,37.34,38.68,36.37,36.73,16314300,less than 37.504999
+2021-06-18,82.97,83.4,80.08,81.14,11130600,greater than 77.937498
+2022-03-17,40.15,46.55,40.01,46.35,31971500,between 45.4449995 and 77.937498
+2022-01-31,60.66,66.38,60.04,65.86,27942900,between 45.4449995 and 77.937498
+2021-09-03,83.2,84.04,81.58,82.87,5449700,greater than 77.937498
+2023-01-13,32.77,33.43,32.59,33.21,10184800,less than 37.504999
+2021-07-29,79.15,79.19,76.73,76.85,3941200,between 45.4449995 and 77.937498
+2022-01-18,80.44,82.45,76.75,77.21,23959200,between 45.4449995 and 77.937498
+2021-12-31,100.75,104.68,100.65,103.16,16934900,greater than 77.937498
+2022-09-28,36.93,38.59,36.55,38.04,15877100,between 37.504999 and 45.4449995
+2021-06-21,80.57,83.36,78.56,82.51,11289400,greater than 77.937498
+2021-07-15,79.06,80.29,75.59,77.28,10502400,between 45.4449995 and 77.937498
+2022-11-17,33.37,33.45,31.91,32.53,14114100,less than 37.504999
+2021-08-03,77.11,77.5,73.46,77.49,9686300,between 45.4449995 and 77.937498
+2021-05-11,64.88,77.79,64.7,77.65,30995000,between 45.4449995 and 77.937498
+2021-10-15,74.92,77.21,74.1,76.58,5094800,between 45.4449995 and 77.937498
+2022-01-12,90.31,90.31,86.7,89.06,14057900,greater than 77.937498
+2023-01-17,38.07,38.3,36.01,37.12,33311200,less than 37.504999
+2021-05-19,70.33,76.27,70.33,75.22,10886300,between 45.4449995 and 77.937498
+2022-04-04,46.81,50.72,46.41,50.02,17863100,between 45.4449995 and 77.937498
+2021-12-23,102.36,103.21,98.08,101.82,9023900,greater than 77.937498
+2021-05-04,71.0,71.69,67.66,69.62,5379900,between 45.4449995 and 77.937498
+2022-02-16,56.08,59.0,53.08,53.87,94625800,between 45.4449995 and 77.937498
+2022-01-11,84.83,90.09,84.25,89.04,13972200,greater than 77.937498
+2021-11-03,79.86,80.59,78.02,78.36,4730900,greater than 77.937498
+2023-04-27,36.28,36.36,35.16,35.54,8534800,less than 37.504999
+2022-01-27,63.99,64.53,56.77,57.06,33439600,between 45.4449995 and 77.937498
+2021-07-08,83.28,88.05,82.51,86.88,6729500,greater than 77.937498
+2023-03-10,41.58,41.58,39.15,40.05,13852300,between 37.504999 and 45.4449995
+2022-03-04,46.03,46.1,41.95,42.29,19359100,between 37.504999 and 45.4449995
+2022-03-08,40.88,43.37,39.51,41.91,17363100,between 37.504999 and 45.4449995
+2022-06-14,26.97,26.97,25.18,26.12,18507500,less than 37.504999
+2022-10-26,45.82,47.67,45.27,45.5,17120000,between 45.4449995 and 77.937498
+2022-01-20,77.66,80.91,75.08,75.39,16838300,between 45.4449995 and 77.937498
+2022-11-07,39.96,40.54,37.85,39.82,16341500,between 37.504999 and 45.4449995
+2022-12-22,27.61,27.89,25.7,26.43,12842900,less than 37.504999
+2021-09-30,76.66,76.77,74.05,75.55,6928600,between 45.4449995 and 77.937498
+2021-07-30,76.32,77.55,75.83,76.98,3262400,between 45.4449995 and 77.937498
+2021-10-14,75.52,75.84,74.23,74.35,4540800,between 45.4449995 and 77.937498
+2022-10-06,38.4,39.81,37.57,39.15,15304500,between 37.504999 and 45.4449995
+2022-10-14,38.7,39.19,35.48,35.56,14748400,less than 37.504999
+2021-10-12,70.6,72.0,70.2,71.9,4368500,between 45.4449995 and 77.937498
+2021-08-23,83.05,85.17,80.85,85.06,9943800,greater than 77.937498
+2023-06-09,39.13,40.08,38.4,38.69,5941300,between 37.504999 and 45.4449995
+2022-09-30,34.98,36.81,34.81,35.84,12784200,less than 37.504999
+2023-03-16,43.05,45.38,42.4,45.33,13368900,between 37.504999 and 45.4449995
+2022-12-23,26.31,26.78,25.32,26.75,10774500,less than 37.504999
+2022-08-30,40.19,40.68,38.22,39.03,13348400,between 37.504999 and 45.4449995
+2023-01-20,33.68,35.62,33.29,35.38,14017100,less than 37.504999
+2022-04-25,33.99,35.13,33.67,34.61,20815400,less than 37.504999
+2021-04-12,71.82,77.0,71.51,75.0,8004100,between 45.4449995 and 77.937498
+2022-11-09,35.31,36.75,30.68,30.92,46247300,less than 37.504999
+2021-05-25,90.0,90.11,85.51,89.32,14314600,greater than 77.937498
+2022-08-11,49.0,53.88,48.6,49.56,51720000,between 45.4449995 and 77.937498
+2021-08-04,77.04,81.93,76.55,81.65,9308900,greater than 77.937498
+2021-07-28,77.0,79.2,76.15,79.13,4977600,greater than 77.937498
+2022-09-14,43.45,45.92,42.54,45.07,18469700,between 37.504999 and 45.4449995
+2023-03-07,40.55,42.19,40.33,41.57,12693500,between 37.504999 and 45.4449995
+2021-06-22,83.07,83.86,80.5,82.44,10138600,greater than 77.937498
+2022-03-30,50.09,52.52,47.81,48.12,20247000,between 45.4449995 and 77.937498
+2021-06-01,94.77,97.93,93.22,96.89,13632300,greater than 77.937498
+2023-03-31,43.17,45.1,42.78,44.98,7965900,between 37.504999 and 45.4449995
+2022-04-13,43.34,45.93,42.16,44.97,17132500,between 37.504999 and 45.4449995
+2023-02-07,38.86,39.0,37.42,38.67,8935300,between 37.504999 and 45.4449995
+2022-11-21,31.37,31.48,29.46,30.77,16315500,less than 37.504999
+2021-04-20,71.5,71.74,67.3,69.24,8393600,between 45.4449995 and 77.937498
+2022-05-09,27.15,27.49,24.39,24.61,28258400,less than 37.504999
+2022-02-23,47.6,48.85,45.63,45.68,20266900,between 45.4449995 and 77.937498
+2021-07-06,87.32,88.75,85.32,86.98,6301400,greater than 77.937498
+2021-10-21,78.09,83.77,77.01,83.19,10928400,greater than 77.937498
+2021-03-17,76.03,79.1,74.89,76.79,10054100,between 45.4449995 and 77.937498
+2022-09-27,36.66,38.08,36.03,36.59,21989600,less than 37.504999
+2023-03-02,37.01,39.28,36.77,39.23,13820600,between 37.504999 and 45.4449995
+2022-06-29,34.21,35.01,33.34,34.02,20325700,less than 37.504999
+2021-06-24,83.5,87.7,83.36,87.34,8836200,greater than 77.937498
+2022-03-23,49.62,53.0,48.15,50.71,21607000,between 45.4449995 and 77.937498
+2021-09-16,82.13,83.35,81.04,81.42,5055000,greater than 77.937498
+2023-02-01,37.08,38.65,36.71,38.24,11213100,between 37.504999 and 45.4449995
+2022-04-06,48.02,48.02,44.63,45.89,18574200,between 45.4449995 and 77.937498
+2022-07-22,41.93,42.74,38.91,39.4,22493800,between 37.504999 and 45.4449995
+2021-08-20,81.13,84.56,81.13,82.77,5696800,greater than 77.937498
+2022-11-08,39.64,40.95,38.7,39.14,15155600,between 37.504999 and 45.4449995
+2023-05-30,40.38,40.95,39.3,39.59,6908600,between 37.504999 and 45.4449995
+2021-10-13,72.34,75.07,72.2,74.78,5108300,between 45.4449995 and 77.937498
+2022-01-04,99.02,99.25,91.77,95.15,23034600,greater than 77.937498
+2023-01-24,37.97,37.97,34.38,35.59,5685800,less than 37.504999
+2021-05-28,96.85,98.95,92.53,93.77,13462600,greater than 77.937498
+2022-06-07,30.96,32.02,30.09,31.44,17808800,less than 37.504999
+2023-04-17,40.04,41.0,39.1,40.21,33375300,between 37.504999 and 45.4449995
+2021-06-25,88.0,88.94,85.07,87.0,7594500,greater than 77.937498
+2023-04-19,40.4,41.58,40.02,41.09,8712300,between 37.504999 and 45.4449995
+2021-06-10,91.2,91.82,86.83,91.0,12269200,greater than 77.937498
+2022-07-18,40.42,41.44,39.15,39.75,27096300,between 37.504999 and 45.4449995
+2021-08-09,79.09,86.74,78.47,85.51,16103400,greater than 77.937498
+2021-03-22,71.81,72.49,69.54,70.0,4238500,between 45.4449995 and 77.937498
+2022-06-27,36.0,36.04,33.78,35.93,29741400,less than 37.504999
+2021-03-23,70.1,71.75,67.63,68.0,4456000,between 45.4449995 and 77.937498
+2022-03-07,43.17,44.06,41.15,41.3,17648200,between 37.504999 and 45.4449995
+2023-04-06,44.86,46.41,44.42,46.2,7228600,between 45.4449995 and 77.937498
+2023-05-24,38.76,40.62,38.66,40.51,6601900,between 37.504999 and 45.4449995
+2023-05-19,40.98,41.04,39.22,40.01,10859000,between 37.504999 and 45.4449995
+2021-05-18,76.7,77.59,73.48,74.99,11688200,between 45.4449995 and 77.937498
+2023-01-04,28.59,29.05,27.76,29.04,11799100,less than 37.504999
+2021-11-09,99.6,109.97,94.38,109.52,93507300,greater than 77.937498
+2021-07-26,80.1,81.25,77.0,77.63,6524100,between 45.4449995 and 77.937498
+2022-11-22,30.61,31.12,29.72,30.84,11601500,less than 37.504999
+2022-05-20,34.5,35.21,29.5,31.6,42991900,less than 37.504999
+2021-12-02,114.28,119.91,113.22,116.92,18703700,greater than 77.937498
+2023-05-11,40.52,41.62,39.55,39.88,14781200,between 37.504999 and 45.4449995
+2022-03-28,47.67,49.29,46.12,48.4,18599800,between 45.4449995 and 77.937498
+2021-10-29,82.89,86.15,82.07,84.02,8722300,greater than 77.937498
+2023-06-02,42.31,42.99,40.26,40.34,9276200,between 37.504999 and 45.4449995
+2023-04-24,40.39,40.79,38.51,39.06,11186000,between 37.504999 and 45.4449995
+2022-04-29,31.75,33.53,30.61,30.65,19191000,less than 37.504999
+2022-08-10,44.9,48.36,42.71,48.01,64849700,between 45.4449995 and 77.937498
+2022-12-13,35.0,35.37,31.27,32.25,16276800,less than 37.504999
+2021-06-15,87.71,90.76,86.84,89.8,10831800,greater than 77.937498
+2021-12-10,117.54,121.8,113.57,115.89,11778200,greater than 77.937498
+2022-03-25,50.31,50.36,46.15,47.07,21109900,between 45.4449995 and 77.937498
+2022-03-02,49.9,50.07,46.43,48.45,20016900,between 45.4449995 and 77.937498
+2023-05-17,39.89,42.18,39.66,41.83,12555000,between 37.504999 and 45.4449995
+2023-02-17,42.71,42.71,40.6,40.88,22918100,between 37.504999 and 45.4449995
+2023-04-03,44.45,46.5,44.27,46.42,11268500,between 45.4449995 and 77.937498
+2023-02-28,36.48,37.43,36.34,36.64,10020300,less than 37.504999
+2022-10-18,43.03,44.3,42.31,43.38,34742000,between 37.504999 and 45.4449995
+2021-10-18,77.46,78.37,76.23,77.8,3793400,between 45.4449995 and 77.937498
+2021-08-17,74.89,81.3,73.9,78.68,21772300,greater than 77.937498
+2021-08-06,79.92,82.48,77.65,77.92,4681600,between 45.4449995 and 77.937498
+2022-07-08,38.97,44.5,38.53,41.25,64027700,between 37.504999 and 45.4449995
+2022-11-14,36.15,36.93,34.84,35.6,13073100,less than 37.504999
+2022-11-02,44.08,45.55,42.71,42.87,10591000,between 37.504999 and 45.4449995
+2021-12-21,99.93,103.38,97.0,102.59,10005100,greater than 77.937498
+2021-04-13,74.69,83.41,73.51,82.05,17893700,greater than 77.937498
+2023-04-05,45.52,46.06,44.33,45.39,8102500,between 37.504999 and 45.4449995
+2023-01-09,29.41,31.33,29.41,30.77,18246700,less than 37.504999
+2022-01-25,67.96,69.95,64.66,65.29,20959500,between 45.4449995 and 77.937498
+2022-03-01,51.84,52.43,49.82,50.22,16097900,between 45.4449995 and 77.937498
+2023-02-13,34.61,35.23,34.22,34.43,8016600,less than 37.504999
+2021-12-09,123.74,125.99,115.35,116.3,13753600,greater than 77.937498
+2022-02-22,48.23,49.07,45.72,48.21,38780300,between 45.4449995 and 77.937498
+2023-03-23,43.62,44.0,41.1,42.07,11859900,between 37.504999 and 45.4449995
+2022-01-24,65.41,71.32,60.58,70.74,33280600,between 45.4449995 and 77.937498
+2021-08-25,89.64,90.95,87.64,90.34,7444200,greater than 77.937498
+2023-05-16,38.9,39.67,38.14,39.25,6911700,between 37.504999 and 45.4449995
+2022-05-17,32.88,34.08,29.89,31.94,48152300,less than 37.504999
+2023-02-03,38.32,40.29,37.91,38.54,14357300,between 37.504999 and 45.4449995
+2021-08-31,81.88,83.05,81.41,82.05,4759400,greater than 77.937498
+2022-04-22,35.0,35.83,33.54,34.35,18453300,less than 37.504999
+2021-09-07,83.82,85.29,82.45,84.97,5911600,greater than 77.937498
+2022-04-14,44.53,44.82,42.34,42.36,13482300,between 37.504999 and 45.4449995
+2022-04-21,37.28,37.55,34.34,34.61,22536700,less than 37.504999
+2021-11-17,117.1,125.88,116.55,124.7,44293300,greater than 77.937498
+2022-11-30,29.84,31.83,29.81,31.77,15477400,less than 37.504999
+2021-07-19,75.85,79.98,74.78,79.24,5407200,greater than 77.937498
+2021-05-20,75.72,76.91,73.01,76.33,10951000,between 45.4449995 and 77.937498
+2021-11-19,129.87,138.2,128.54,134.72,38313200,greater than 77.937498
+2022-04-05,49.7,50.67,47.6,49.03,16789600,between 45.4449995 and 77.937498
+2021-11-01,83.95,84.09,80.57,81.14,6274200,greater than 77.937498
+2022-07-28,41.68,42.99,40.16,42.98,17714900,between 37.504999 and 45.4449995
+2021-07-12,87.1,89.87,84.85,86.54,6762000,greater than 77.937498
+2021-06-23,83.2,85.45,82.0,84.86,8015500,greater than 77.937498
+2021-11-22,140.74,141.6,117.32,120.22,57760200,greater than 77.937498
+2023-03-14,42.3,43.73,42.14,43.19,15495300,between 37.504999 and 45.4449995
+2021-12-06,108.52,114.27,104.21,113.25,15186900,greater than 77.937498
+2021-11-16,107.01,120.83,104.2,116.18,53305900,greater than 77.937498
+2023-01-12,32.9,33.24,32.07,33.18,10674800,less than 37.504999
+2022-12-30,26.46,28.54,26.46,28.46,15884700,less than 37.504999
+2022-07-12,39.03,40.46,37.04,38.22,33116500,between 37.504999 and 45.4449995
+2022-01-21,74.19,74.92,68.33,68.93,26043700,between 45.4449995 and 77.937498
+2022-08-29,38.64,39.98,37.79,39.22,14127200,between 37.504999 and 45.4449995
+2022-02-18,54.4,54.41,48.13,49.72,51159600,between 45.4449995 and 77.937498
+2021-10-25,81.52,85.13,81.28,83.21,5674200,greater than 77.937498
+2022-09-01,38.58,38.78,36.56,38.51,14721100,between 37.504999 and 45.4449995
+2023-02-06,37.7,39.35,37.66,38.99,7781500,between 37.504999 and 45.4449995
+2023-03-29,42.51,43.35,41.99,43.01,7406000,between 37.504999 and 45.4449995
+2021-11-04,79.23,82.64,78.85,82.53,6702200,greater than 77.937498
+2022-10-13,35.42,38.11,34.11,37.92,28731600,between 37.504999 and 45.4449995
+2021-09-13,87.47,88.88,84.25,84.65,7188100,greater than 77.937498
+2022-10-31,45.48,45.9,43.97,44.74,10325500,between 37.504999 and 45.4449995
+2021-04-05,70.14,72.86,68.56,70.76,8609500,between 45.4449995 and 77.937498
+2022-07-29,42.98,43.8,41.67,42.93,16524700,between 37.504999 and 45.4449995
+2021-06-16,82.88,86.42,79.88,82.59,43922800,greater than 77.937498
+2021-07-16,78.46,79.11,76.99,77.56,4641500,between 45.4449995 and 77.937498
+2022-07-20,40.02,42.35,39.95,40.76,28015700,between 37.504999 and 45.4449995
+2021-03-25,62.01,69.85,60.69,67.85,6283000,between 45.4449995 and 77.937498
+2021-08-24,85.24,89.95,85.09,89.26,12816900,greater than 77.937498
+2022-12-28,26.17,26.58,25.59,26.16,9948900,less than 37.504999
+2022-11-29,31.45,31.59,29.67,29.83,11417500,less than 37.504999
+2022-08-04,46.86,48.24,46.52,47.96,14673000,between 45.4449995 and 77.937498
+2022-09-26,36.28,37.95,35.48,35.55,25436700,less than 37.504999
+2022-12-01,32.13,33.58,31.53,33.39,15946100,less than 37.504999
+2022-03-22,46.44,51.61,46.21,50.51,25528300,between 45.4449995 and 77.937498
+2022-09-21,36.66,37.77,35.77,35.81,15884700,less than 37.504999
+2021-03-16,73.73,78.0,73.18,77.0,30274400,between 45.4449995 and 77.937498
+2021-04-23,70.04,72.38,69.59,71.78,2729300,between 45.4449995 and 77.937498
+2023-05-18,41.97,42.68,41.39,42.07,8249600,between 37.504999 and 45.4449995
+2023-06-01,41.62,42.9,41.3,42.13,6743800,between 37.504999 and 45.4449995
+2021-10-07,74.1,76.4,73.02,74.8,6987200,between 45.4449995 and 77.937498
+2021-09-28,79.05,79.58,77.0,77.05,6492600,between 45.4449995 and 77.937498
+2022-06-10,29.11,29.92,27.67,27.76,25118100,less than 37.504999
+2023-01-11,33.0,33.5,32.24,32.9,12662600,less than 37.504999
+2021-12-13,115.68,115.8,107.37,111.77,13091400,greater than 77.937498
+2021-09-22,79.29,81.5,78.91,81.18,7989000,greater than 77.937498
+2022-07-05,34.55,40.35,33.9,39.99,59571100,between 37.504999 and 45.4449995
+2021-07-01,89.89,89.99,84.21,85.82,10272300,greater than 77.937498
+2022-11-04,45.06,45.59,39.54,40.58,16634900,between 37.504999 and 45.4449995
+2022-01-14,79.35,81.74,77.42,79.05,18642200,greater than 77.937498
+2022-03-09,42.76,45.75,42.01,44.38,18344800,between 37.504999 and 45.4449995
+2021-12-14,108.73,110.7,105.35,107.67,10989100,greater than 77.937498
+2022-12-09,31.85,32.51,31.44,31.65,7860200,less than 37.504999
+2022-05-18,31.65,34.66,30.93,32.55,42974700,less than 37.504999
+2022-12-29,26.61,27.44,26.19,27.09,11301200,less than 37.504999
+2023-01-25,34.45,35.95,33.88,35.7,9114100,less than 37.504999
+2023-03-21,43.58,45.49,43.58,44.74,8229500,between 37.504999 and 45.4449995
+2023-03-30,43.64,44.09,42.7,43.11,7363100,between 37.504999 and 45.4449995
+2022-09-09,42.79,45.77,42.66,45.53,23394400,between 45.4449995 and 77.937498
+2022-05-03,32.09,33.84,31.8,33.1,16264500,less than 37.504999
+2022-08-19,44.56,44.58,41.94,42.68,15983700,between 37.504999 and 45.4449995
+2022-12-08,30.91,31.99,30.05,31.93,8847300,less than 37.504999
+2022-02-03,62.3,64.97,59.63,60.67,32647600,between 45.4449995 and 77.937498
+2021-09-24,82.1,84.5,82.0,83.22,4962200,greater than 77.937498
+2022-02-24,43.1,50.5,43.1,50.1,31971800,between 45.4449995 and 77.937498
+2022-06-30,34.0,34.05,31.83,32.86,29942300,less than 37.504999
+2021-03-11,74.93,77.78,70.13,73.9,59629300,between 45.4449995 and 77.937498
+2022-04-12,44.61,46.79,42.8,43.0,18299000,between 37.504999 and 45.4449995
+2022-01-05,93.52,95.83,88.06,88.54,15510000,greater than 77.937498
+2022-11-18,33.08,33.13,31.08,31.72,11037100,less than 37.504999
+2023-04-18,40.51,41.09,39.89,40.81,11294600,between 37.504999 and 45.4449995
+2021-12-03,117.89,120.84,109.36,113.79,15529200,greater than 77.937498
+2021-06-14,93.26,93.26,87.57,87.8,11659500,greater than 77.937498
+2022-02-07,63.95,66.95,62.55,63.06,17694100,between 45.4449995 and 77.937498
+2022-02-10,68.25,73.39,67.1,69.92,28605700,between 45.4449995 and 77.937498
+2022-04-20,40.54,40.76,36.05,36.75,37932100,less than 37.504999
+2023-04-13,45.76,46.9,45.57,45.7,7538400,between 45.4449995 and 77.937498
+2021-06-02,96.88,103.29,95.39,99.86,16728900,greater than 77.937498
+2023-01-06,30.21,30.68,28.77,28.88,14444400,less than 37.504999
+2023-01-30,36.74,37.16,35.35,35.53,8977700,less than 37.504999
+2021-04-07,70.7,71.14,67.88,68.73,4424800,between 45.4449995 and 77.937498
+2022-04-26,34.25,34.35,31.62,31.76,24176700,less than 37.504999
+2022-06-23,31.38,34.68,30.5,34.48,39179500,less than 37.504999
+2022-04-01,46.97,47.29,44.7,46.02,15782200,between 45.4449995 and 77.937498
+2022-08-26,42.65,42.98,39.44,39.56,16646000,between 37.504999 and 45.4449995
+2022-09-06,37.57,38.14,35.65,37.91,16687200,between 37.504999 and 45.4449995
+2022-03-24,50.25,50.81,47.23,50.79,17030700,between 45.4449995 and 77.937498
+2021-11-15,108.44,110.35,103.82,108.06,24749500,greater than 77.937498
+2022-11-28,31.31,31.72,30.81,30.88,12423300,less than 37.504999
+2022-08-01,42.37,45.83,41.68,45.24,24181300,between 37.504999 and 45.4449995
+2023-05-03,34.55,35.31,34.1,34.23,6598700,less than 37.504999
+2021-09-17,81.19,81.43,79.05,80.81,5927400,greater than 77.937498
+2021-07-23,82.32,83.25,80.35,81.83,3151300,greater than 77.937498
+2023-03-27,43.7,44.44,42.24,42.27,6444900,between 37.504999 and 45.4449995
+2022-09-13,44.0,45.21,43.23,43.87,16857600,between 37.504999 and 45.4449995
+2023-05-01,35.51,36.51,35.33,36.0,6996600,less than 37.504999
+2023-06-16,40.88,42.86,40.74,41.8,15375200,between 37.504999 and 45.4449995
+2023-05-25,40.79,40.88,39.03,39.51,5852100,between 37.504999 and 45.4449995
+2022-10-24,41.8,42.3,40.41,41.85,11460600,between 37.504999 and 45.4449995
+2022-03-21,49.01,49.34,45.51,46.62,22342000,between 45.4449995 and 77.937498
+2021-10-08,75.1,75.2,70.19,70.44,11768800,between 45.4449995 and 77.937498
+2021-05-13,75.15,75.15,67.51,69.68,17769500,between 45.4449995 and 77.937498
+2022-08-02,44.51,47.2,44.25,45.29,20201300,between 37.504999 and 45.4449995
+2022-06-17,24.74,27.27,24.57,26.87,27819700,less than 37.504999
+2021-08-26,88.65,90.38,85.75,85.81,5740900,greater than 77.937498
+2022-12-19,27.59,27.64,26.23,27.19,13155600,less than 37.504999
+2023-04-04,46.73,47.65,45.66,46.29,14233800,between 45.4449995 and 77.937498
+2022-01-10,83.69,85.6,79.02,85.52,19182100,greater than 77.937498
+2023-05-02,35.79,35.79,34.38,34.45,7173400,less than 37.504999
+2021-08-13,83.02,84.18,81.5,83.96,4218800,greater than 77.937498
+2021-04-21,68.81,71.99,67.6,71.76,4525000,between 45.4449995 and 77.937498
+2022-02-15,69.06,73.71,66.34,73.3,50879500,between 45.4449995 and 77.937498
+2022-09-19,38.96,39.04,36.84,37.11,17794500,less than 37.504999
+2021-07-14,84.04,84.24,79.07,79.17,10466700,greater than 77.937498
+2021-10-06,72.06,74.4,71.1,73.7,6988900,between 45.4449995 and 77.937498
+2021-05-12,74.21,76.0,71.09,75.53,20823900,between 45.4449995 and 77.937498
+2021-07-21,79.66,80.57,78.48,79.91,4077300,greater than 77.937498
+2022-05-31,32.56,33.37,29.45,29.94,44581300,less than 37.504999
+2021-05-14,71.2,73.43,69.25,70.95,7676300,between 45.4449995 and 77.937498
+2021-08-27,86.69,87.01,84.8,85.4,5235600,greater than 77.937498
+2022-08-12,50.0,51.44,48.38,51.15,24482400,between 45.4449995 and 77.937498
+2022-12-20,27.01,28.21,26.8,27.58,11905700,less than 37.504999
+2023-05-22,40.38,40.66,39.22,39.49,9733100,between 37.504999 and 45.4449995
+2023-04-11,46.66,46.99,45.57,46.06,6452300,between 45.4449995 and 77.937498
+2021-10-26,81.19,84.75,80.83,80.89,6833100,greater than 77.937498
+2022-05-04,32.79,35.06,30.77,34.88,20852400,less than 37.504999
+2021-05-07,67.01,68.48,66.2,67.9,4808200,between 45.4449995 and 77.937498
+2022-05-24,29.08,29.08,26.31,27.07,35394300,less than 37.504999
+2022-10-10,34.71,35.89,33.91,35.4,12815100,less than 37.504999
+2021-09-02,85.44,86.74,83.7,84.6,3728500,greater than 77.937498
+2021-11-29,125.14,129.9,121.05,129.36,17703000,greater than 77.937498
+2021-06-17,84.76,86.33,82.0,82.98,12307700,greater than 77.937498
+2023-05-09,35.72,36.77,35.7,36.19,12307900,less than 37.504999
+2023-02-23,37.84,37.95,35.81,37.38,11424300,less than 37.504999
+2021-04-06,71.24,72.39,70.76,71.32,3915500,between 45.4449995 and 77.937498
+2023-04-12,47.05,47.65,45.07,45.2,8812500,between 37.504999 and 45.4449995
+2022-10-03,34.89,36.67,33.6,36.41,21713000,less than 37.504999
+2023-03-06,41.62,42.12,40.54,40.57,8580100,between 37.504999 and 45.4449995
+2021-12-01,128.14,130.74,111.0,113.41,35002100,greater than 77.937498
+2021-07-09,87.29,87.3,85.12,86.26,3936700,greater than 77.937498
+2023-03-15,42.72,43.1,40.81,42.72,16993100,between 37.504999 and 45.4449995
+2023-03-28,42.0,42.58,41.46,41.85,6145900,between 37.504999 and 45.4449995
+2022-09-12,44.33,46.59,44.03,46.57,18639600,between 45.4449995 and 77.937498
+2022-03-29,49.0,51.53,48.0,50.92,20011200,between 45.4449995 and 77.937498
+2023-04-25,38.61,38.97,37.44,37.57,8867600,between 37.504999 and 45.4449995
+2022-04-19,39.28,42.87,38.65,42.0,20957500,between 37.504999 and 45.4449995
+2023-02-22,37.82,38.15,36.73,37.55,12573400,between 37.504999 and 45.4449995
+2022-02-17,55.14,56.9,53.55,54.49,53402200,between 45.4449995 and 77.937498
+2022-07-11,39.95,40.35,37.48,38.41,30345400,between 37.504999 and 45.4449995
+2022-09-23,34.55,35.58,33.56,35.54,15492500,less than 37.504999
+2021-09-27,83.18,83.3,78.66,79.99,8049800,greater than 77.937498
+2022-02-28,50.69,52.7,48.22,51.57,20385400,between 45.4449995 and 77.937498
+2022-06-13,26.11,26.72,25.15,26.46,26198800,less than 37.504999
+2021-09-14,83.16,85.13,81.69,82.33,7644000,greater than 77.937498
+2022-02-02,67.32,68.76,64.18,66.17,17257900,between 45.4449995 and 77.937498
+2021-11-12,97.32,107.97,96.85,107.58,36418600,greater than 77.937498
+2022-11-25,31.5,31.93,31.29,31.76,4046800,less than 37.504999
+2021-09-09,81.66,87.74,81.66,86.35,10906900,greater than 77.937498
+2021-10-04,75.38,78.21,74.5,77.8,7651500,between 45.4449995 and 77.937498
+2023-02-15,42.07,45.34,41.9,45.08,50423700,between 37.504999 and 45.4449995
+2021-04-22,71.5,73.79,69.1,69.85,3372200,between 45.4449995 and 77.937498
+2022-04-18,41.9,41.94,39.27,40.85,19366100,between 37.504999 and 45.4449995
+2022-05-06,30.91,31.0,27.33,27.81,30794900,less than 37.504999
+2021-11-02,81.2,81.94,79.39,79.59,4551300,greater than 77.937498
+2022-05-10,25.1,25.67,21.65,23.19,53591400,less than 37.504999
+2023-01-31,35.96,37.29,35.74,37.21,9242900,less than 37.504999
+2023-01-05,28.83,30.1,28.5,29.98,13696500,less than 37.504999
+2022-08-08,48.13,50.9,48.02,48.9,21346300,between 45.4449995 and 77.937498
+2021-03-30,66.98,66.99,64.47,65.17,3394500,between 45.4449995 and 77.937498
+2022-03-15,36.17,38.62,36.04,37.87,23447200,between 37.504999 and 45.4449995
+2022-11-01,45.51,46.94,44.24,44.31,8403600,between 37.504999 and 45.4449995
+2023-04-28,35.05,35.74,34.52,35.6,6750200,less than 37.504999
+2022-09-16,41.92,41.92,38.81,39.5,50198200,between 37.504999 and 45.4449995
+2021-04-15,75.5,80.75,74.56,79.66,8841100,greater than 77.937498
+2022-10-12,34.45,37.7,34.45,37.19,24981200,less than 37.504999
+2021-07-02,87.16,87.24,84.89,86.2,5807700,greater than 77.937498
+2022-12-27,26.22,26.73,25.46,26.33,11084000,less than 37.504999
+2022-06-09,32.46,32.74,30.47,30.5,18179100,less than 37.504999
+2021-07-07,87.85,89.35,85.65,86.4,6153000,greater than 77.937498
+2021-03-12,72.47,72.96,69.11,69.7,19714700,between 45.4449995 and 77.937498
+2022-07-15,38.41,40.24,37.35,39.77,28323400,between 37.504999 and 45.4449995
+2021-12-20,97.18,101.06,96.59,98.69,12550600,greater than 77.937498
+2022-08-25,41.62,42.26,40.14,41.47,12204500,between 37.504999 and 45.4449995
+2022-07-25,38.85,40.19,37.74,39.84,17454600,between 37.504999 and 45.4449995
+2023-02-02,39.67,41.58,39.37,40.48,17277900,between 37.504999 and 45.4449995
+2022-07-26,39.44,39.54,37.86,39.0,15668500,between 37.504999 and 45.4449995
+2021-04-27,74.77,75.37,72.33,74.35,3200600,between 45.4449995 and 77.937498
+2021-08-30,84.6,84.8,80.0,81.87,9742800,greater than 77.937498
+2023-05-26,39.4,40.49,39.38,40.12,4398900,between 37.504999 and 45.4449995
+2023-02-21,39.31,40.03,37.44,37.56,16504300,between 37.504999 and 45.4449995
+2021-10-05,76.15,76.34,72.32,72.57,16184300,between 45.4449995 and 77.937498
+2021-06-09,91.79,95.99,90.65,91.04,12739300,greater than 77.937498
+2022-10-27,45.55,46.9,44.73,46.5,11431900,between 45.4449995 and 77.937498
+2021-07-20,80.78,80.98,77.45,79.86,4816100,greater than 77.937498
+2023-01-27,35.79,38.13,35.79,37.75,13095400,between 37.504999 and 45.4449995
+2022-07-06,39.06,40.0,37.41,37.95,33629400,between 37.504999 and 45.4449995
+2021-05-21,77.48,84.68,77.31,82.5,31672600,greater than 77.937498
+2022-06-28,35.7,37.48,34.06,34.49,23948700,less than 37.504999
+2022-10-21,41.51,43.39,40.91,42.81,16932900,between 37.504999 and 45.4449995
+2021-11-18,129.28,138.77,120.81,126.12,59193300,greater than 77.937498
+2023-06-08,38.19,38.91,37.62,38.87,6349600,between 37.504999 and 45.4449995
+2023-04-20,40.5,41.99,40.48,41.31,9667600,between 37.504999 and 45.4449995
+2021-09-10,86.75,90.43,84.67,87.88,17466000,greater than 77.937498
+2021-09-29,77.32,79.07,76.26,76.31,5488500,between 45.4449995 and 77.937498
+2021-08-18,79.22,84.4,79.0,83.46,13036300,greater than 77.937498
+2022-05-11,21.92,28.37,21.89,23.97,105794300,less than 37.504999
+2023-02-10,35.1,35.9,34.57,34.82,10077600,less than 37.504999
+2022-08-24,41.28,42.65,41.18,41.18,11233000,between 37.504999 and 45.4449995
+2021-03-26,67.89,71.2,64.77,70.97,4531200,between 45.4449995 and 77.937498
+2022-12-05,33.69,34.7,31.13,31.24,11567500,less than 37.504999
+2022-10-07,37.95,38.06,34.42,34.82,20549600,less than 37.504999
+2021-12-29,98.7,99.54,94.28,97.34,10005100,greater than 77.937498
+2023-02-16,44.29,46.05,43.38,43.58,22342500,between 37.504999 and 45.4449995
+2022-08-31,39.95,40.79,38.71,39.11,11534000,between 37.504999 and 45.4449995
+2023-05-04,34.28,34.6,33.7,34.48,6308000,less than 37.504999
+2021-05-27,89.26,97.5,87.59,97.47,19850300,greater than 77.937498
+2021-09-08,84.12,84.85,81.91,83.51,4387100,greater than 77.937498
+2021-04-28,74.77,76.93,73.64,75.64,3084300,between 45.4449995 and 77.937498
+2022-03-10,43.69,43.69,39.74,41.47,23528800,between 37.504999 and 45.4449995
+2022-12-02,31.83,34.01,31.71,33.99,15831100,less than 37.504999
+2021-08-11,85.78,87.51,82.89,84.69,6531500,greater than 77.937498
+2023-03-09,41.46,42.65,39.76,39.94,8050300,between 37.504999 and 45.4449995
+2022-05-25,27.46,29.37,27.42,28.93,28428000,less than 37.504999
+2021-11-10,103.98,104.46,93.02,95.26,43838100,greater than 77.937498
+2021-12-15,98.01,99.75,93.52,97.95,43177300,greater than 77.937498
+2023-01-26,37.31,37.78,35.2,35.97,9882700,less than 37.504999
+2022-09-08,39.23,41.88,39.06,41.86,16315700,between 37.504999 and 45.4449995
+2021-06-11,91.97,94.77,91.15,92.82,8673800,greater than 77.937498
+2022-06-24,35.07,36.89,34.73,36.42,68215900,less than 37.504999
+2023-06-05,40.27,41.14,39.5,40.79,6519700,between 37.504999 and 45.4449995
+2022-05-16,32.23,36.43,31.43,32.14,68469900,less than 37.504999
+2022-07-21,40.55,42.45,40.29,41.92,21827200,between 37.504999 and 45.4449995
+2021-09-15,80.87,82.6,78.77,82.15,7501200,greater than 77.937498
+2021-12-28,104.79,106.69,97.92,98.74,16707100,greater than 77.937498
+2022-03-16,38.57,41.79,38.46,41.51,21427100,between 37.504999 and 45.4449995
+2021-09-01,81.05,85.33,80.91,84.45,4600500,greater than 77.937498
+2022-06-06,30.95,31.77,29.77,31.16,19262000,less than 37.504999
+2021-03-24,69.97,70.0,64.01,64.5,5460000,between 45.4449995 and 77.937498
+2022-06-03,32.39,32.39,29.86,30.0,24899800,less than 37.504999

classification/unipredict/ashishkumarjayswal-diabetes-dataset/metadata.json

@@ -0,0 +1,23 @@
+{
+  "dataset": "ashishkumarjayswal-diabetes-dataset",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Outcome",
+  "label_values": [
+    "0.0",
+    "1.0"
+  ],
+  "num_labels": 2,
+  "train_samples": 691,
+  "test_samples": 77,
+  "train_label_distribution": {
+    "0.0": 450,
+    "1.0": 241
+  },
+  "test_label_distribution": {
+    "0.0": 50,
+    "1.0": 27
+  }
+}

classification/unipredict/ashishkumarjayswal-diabetes-dataset/test.csv

@@ -0,0 +1,78 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+4,128,70,0,0,34.3,0.3,24,0.0
+11,85,74,0,0,30.1,0.3,35,0.0
+1,107,68,19,0,26.5,0.17,24,0.0
+13,153,88,37,140,40.6,1.17,39,0.0
+6,165,68,26,168,33.6,0.63,49,0.0
+4,125,70,18,122,28.9,1.14,45,1.0
+8,133,72,0,0,32.9,0.27,39,1.0
+7,62,78,0,0,32.6,0.39,41,0.0
+6,190,92,0,0,35.5,0.28,66,1.0
+7,179,95,31,0,34.2,0.16,60,0.0
+11,120,80,37,150,42.3,0.79,48,1.0
+7,114,76,17,110,23.8,0.47,31,0.0
+3,173,84,33,474,35.7,0.26,22,1.0
+1,108,60,46,178,35.5,0.41,24,0.0
+1,113,64,35,0,33.6,0.54,21,1.0
+0,141,84,26,0,32.4,0.43,22,0.0
+0,74,52,10,36,27.8,0.27,22,0.0
+0,119,0,0,0,32.4,0.14,24,1.0
+3,130,64,0,0,23.1,0.31,22,0.0
+5,88,78,30,0,27.6,0.26,37,0.0
+0,129,110,46,130,67.1,0.32,26,1.0
+4,111,72,47,207,37.1,1.39,56,1.0
+5,166,72,19,175,25.8,0.59,51,1.0
+1,106,76,0,0,37.5,0.2,26,0.0
+5,158,84,41,210,39.4,0.4,29,1.0
+0,117,66,31,188,30.8,0.49,22,0.0
+4,115,72,0,0,28.9,0.38,46,1.0
+10,133,68,0,0,27.0,0.24,36,0.0
+2,155,52,27,540,38.7,0.24,25,1.0
+2,93,64,32,160,38.0,0.67,23,1.0
+7,161,86,0,0,30.4,0.17,47,1.0
+2,90,80,14,55,24.4,0.25,24,0.0
+6,102,90,39,0,35.7,0.67,28,0.0
+2,112,86,42,160,38.4,0.25,28,0.0
+3,148,66,25,0,32.5,0.26,22,0.0
+6,114,88,0,0,27.8,0.25,66,0.0
+10,101,86,37,0,45.6,1.14,38,1.0
+0,101,64,17,0,21.0,0.25,21,0.0
+4,112,78,40,0,39.4,0.24,38,0.0
+6,124,72,0,0,27.6,0.37,29,1.0
+8,167,106,46,231,37.6,0.17,43,1.0
+2,100,70,52,57,40.5,0.68,25,0.0
+1,85,66,29,0,26.6,0.35,31,0.0
+1,122,90,51,220,49.7,0.33,31,1.0
+6,115,60,39,0,33.7,0.24,40,1.0
+7,124,70,33,215,25.5,0.16,37,0.0
+1,89,76,34,37,31.2,0.19,23,0.0
+4,76,62,0,0,34.0,0.39,25,0.0
+7,178,84,0,0,39.9,0.33,41,1.0
+1,84,64,23,115,36.9,0.47,28,0.0
+0,73,0,0,0,21.1,0.34,25,0.0
+8,110,76,0,0,27.8,0.24,58,0.0
+1,0,74,20,23,27.7,0.3,21,0.0
+1,91,54,25,100,25.2,0.23,23,0.0
+12,92,62,7,258,27.6,0.93,44,1.0
+9,152,78,34,171,34.2,0.89,33,1.0
+0,161,50,0,0,21.9,0.25,65,0.0
+6,183,94,0,0,40.8,1.46,45,0.0
+10,115,98,0,0,24.0,1.02,34,0.0
+8,126,88,36,108,38.5,0.35,49,0.0
+5,111,72,28,0,23.9,0.41,27,0.0
+3,88,58,11,54,24.8,0.27,22,0.0
+1,71,62,0,0,21.8,0.42,26,0.0
+3,96,78,39,0,37.3,0.24,40,0.0
+0,126,84,29,215,30.7,0.52,24,0.0
+3,122,78,0,0,23.0,0.25,40,0.0
+5,99,74,27,0,29.0,0.2,32,0.0
+1,87,78,27,32,34.6,0.1,22,0.0
+8,124,76,24,600,28.7,0.69,52,1.0
+6,0,68,41,0,39.0,0.73,41,1.0
+1,96,64,27,87,33.2,0.29,21,0.0
+0,131,0,0,0,43.2,0.27,26,1.0
+4,99,68,38,0,32.8,0.14,33,0.0
+1,135,54,0,0,26.7,0.69,62,0.0
+1,124,60,32,0,35.8,0.51,21,0.0
+1,119,88,41,170,45.3,0.51,26,0.0
+3,80,82,31,70,34.2,1.29,27,1.0

classification/unipredict/ashishkumarjayswal-diabetes-dataset/test.jsonl

@@ -0,0 +1,77 @@
+{"text": "The Pregnancies is 4.0. The Glucose is 128.0. The BloodPressure is 70.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 34.3. The DiabetesPedigreeFunction is 0.3. The Age is 24.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 11.0. The Glucose is 85.0. The BloodPressure is 74.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 30.1. The DiabetesPedigreeFunction is 0.3. The Age is 35.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 107.0. The BloodPressure is 68.0. The SkinThickness is 19.0. The Insulin is 0.0. The BMI is 26.5. The DiabetesPedigreeFunction is 0.17. The Age is 24.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 13.0. The Glucose is 153.0. The BloodPressure is 88.0. The SkinThickness is 37.0. The Insulin is 140.0. The BMI is 40.6. The DiabetesPedigreeFunction is 1.17. The Age is 39.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 165.0. The BloodPressure is 68.0. The SkinThickness is 26.0. The Insulin is 168.0. The BMI is 33.6. The DiabetesPedigreeFunction is 0.63. The Age is 49.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 125.0. The BloodPressure is 70.0. The SkinThickness is 18.0. The Insulin is 122.0. The BMI is 28.9. The DiabetesPedigreeFunction is 1.14. The Age is 45.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 133.0. The BloodPressure is 72.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 32.9. The DiabetesPedigreeFunction is 0.27. The Age is 39.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 62.0. The BloodPressure is 78.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 32.6. The DiabetesPedigreeFunction is 0.39. The Age is 41.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 190.0. The BloodPressure is 92.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 35.5. The DiabetesPedigreeFunction is 0.28. The Age is 66.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 179.0. The BloodPressure is 95.0. The SkinThickness is 31.0. The Insulin is 0.0. The BMI is 34.2. The DiabetesPedigreeFunction is 0.16. The Age is 60.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 11.0. The Glucose is 120.0. The BloodPressure is 80.0. The SkinThickness is 37.0. The Insulin is 150.0. The BMI is 42.3. The DiabetesPedigreeFunction is 0.79. The Age is 48.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 114.0. The BloodPressure is 76.0. The SkinThickness is 17.0. The Insulin is 110.0. The BMI is 23.8. The DiabetesPedigreeFunction is 0.47. The Age is 31.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 173.0. The BloodPressure is 84.0. The SkinThickness is 33.0. The Insulin is 474.0. The BMI is 35.7. The DiabetesPedigreeFunction is 0.26. The Age is 22.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 108.0. The BloodPressure is 60.0. The SkinThickness is 46.0. The Insulin is 178.0. The BMI is 35.5. The DiabetesPedigreeFunction is 0.41. The Age is 24.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 113.0. The BloodPressure is 64.0. The SkinThickness is 35.0. The Insulin is 0.0. The BMI is 33.6. The DiabetesPedigreeFunction is 0.54. The Age is 21.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 141.0. The BloodPressure is 84.0. The SkinThickness is 26.0. The Insulin is 0.0. The BMI is 32.4. The DiabetesPedigreeFunction is 0.43. The Age is 22.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 74.0. The BloodPressure is 52.0. The SkinThickness is 10.0. The Insulin is 36.0. The BMI is 27.8. The DiabetesPedigreeFunction is 0.27. The Age is 22.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 119.0. The BloodPressure is 0.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 32.4. The DiabetesPedigreeFunction is 0.14. The Age is 24.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 130.0. The BloodPressure is 64.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 23.1. The DiabetesPedigreeFunction is 0.31. The Age is 22.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 5.0. The Glucose is 88.0. The BloodPressure is 78.0. The SkinThickness is 30.0. The Insulin is 0.0. The BMI is 27.6. The DiabetesPedigreeFunction is 0.26. The Age is 37.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 129.0. The BloodPressure is 110.0. The SkinThickness is 46.0. The Insulin is 130.0. The BMI is 67.1. The DiabetesPedigreeFunction is 0.32. The Age is 26.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 111.0. The BloodPressure is 72.0. The SkinThickness is 47.0. The Insulin is 207.0. The BMI is 37.1. The DiabetesPedigreeFunction is 1.39. The Age is 56.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 5.0. The Glucose is 166.0. The BloodPressure is 72.0. The SkinThickness is 19.0. The Insulin is 175.0. The BMI is 25.8. The DiabetesPedigreeFunction is 0.59. The Age is 51.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 106.0. The BloodPressure is 76.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 37.5. The DiabetesPedigreeFunction is 0.2. The Age is 26.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 5.0. The Glucose is 158.0. The BloodPressure is 84.0. The SkinThickness is 41.0. The Insulin is 210.0. The BMI is 39.4. The DiabetesPedigreeFunction is 0.4. The Age is 29.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 117.0. The BloodPressure is 66.0. The SkinThickness is 31.0. The Insulin is 188.0. The BMI is 30.8. The DiabetesPedigreeFunction is 0.49. The Age is 22.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 115.0. The BloodPressure is 72.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 28.9. The DiabetesPedigreeFunction is 0.38. The Age is 46.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 10.0. The Glucose is 133.0. The BloodPressure is 68.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 27.0. The DiabetesPedigreeFunction is 0.24. The Age is 36.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 155.0. The BloodPressure is 52.0. The SkinThickness is 27.0. The Insulin is 540.0. The BMI is 38.7. The DiabetesPedigreeFunction is 0.24. The Age is 25.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 93.0. The BloodPressure is 64.0. The SkinThickness is 32.0. The Insulin is 160.0. The BMI is 38.0. The DiabetesPedigreeFunction is 0.67. The Age is 23.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 161.0. The BloodPressure is 86.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 30.4. The DiabetesPedigreeFunction is 0.17. The Age is 47.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 90.0. The BloodPressure is 80.0. The SkinThickness is 14.0. The Insulin is 55.0. The BMI is 24.4. The DiabetesPedigreeFunction is 0.25. The Age is 24.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 102.0. The BloodPressure is 90.0. The SkinThickness is 39.0. The Insulin is 0.0. The BMI is 35.7. The DiabetesPedigreeFunction is 0.67. The Age is 28.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 112.0. The BloodPressure is 86.0. The SkinThickness is 42.0. The Insulin is 160.0. The BMI is 38.4. The DiabetesPedigreeFunction is 0.25. The Age is 28.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 148.0. The BloodPressure is 66.0. The SkinThickness is 25.0. The Insulin is 0.0. The BMI is 32.5. The DiabetesPedigreeFunction is 0.26. The Age is 22.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 114.0. The BloodPressure is 88.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 27.8. The DiabetesPedigreeFunction is 0.25. The Age is 66.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 10.0. The Glucose is 101.0. The BloodPressure is 86.0. The SkinThickness is 37.0. The Insulin is 0.0. The BMI is 45.6. The DiabetesPedigreeFunction is 1.14. The Age is 38.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 101.0. The BloodPressure is 64.0. The SkinThickness is 17.0. The Insulin is 0.0. The BMI is 21.0. The DiabetesPedigreeFunction is 0.25. The Age is 21.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 112.0. The BloodPressure is 78.0. The SkinThickness is 40.0. The Insulin is 0.0. The BMI is 39.4. The DiabetesPedigreeFunction is 0.24. The Age is 38.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 124.0. The BloodPressure is 72.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 27.6. The DiabetesPedigreeFunction is 0.37. The Age is 29.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 167.0. The BloodPressure is 106.0. The SkinThickness is 46.0. The Insulin is 231.0. The BMI is 37.6. The DiabetesPedigreeFunction is 0.17. The Age is 43.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 100.0. The BloodPressure is 70.0. The SkinThickness is 52.0. The Insulin is 57.0. The BMI is 40.5. The DiabetesPedigreeFunction is 0.68. The Age is 25.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 85.0. The BloodPressure is 66.0. The SkinThickness is 29.0. The Insulin is 0.0. The BMI is 26.6. The DiabetesPedigreeFunction is 0.35. The Age is 31.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 122.0. The BloodPressure is 90.0. The SkinThickness is 51.0. The Insulin is 220.0. The BMI is 49.7. The DiabetesPedigreeFunction is 0.33. The Age is 31.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 115.0. The BloodPressure is 60.0. The SkinThickness is 39.0. The Insulin is 0.0. The BMI is 33.7. The DiabetesPedigreeFunction is 0.24. The Age is 40.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 124.0. The BloodPressure is 70.0. The SkinThickness is 33.0. The Insulin is 215.0. The BMI is 25.5. The DiabetesPedigreeFunction is 0.16. The Age is 37.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 89.0. The BloodPressure is 76.0. The SkinThickness is 34.0. The Insulin is 37.0. The BMI is 31.2. The DiabetesPedigreeFunction is 0.19. The Age is 23.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 76.0. The BloodPressure is 62.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 34.0. The DiabetesPedigreeFunction is 0.39. The Age is 25.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 178.0. The BloodPressure is 84.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 39.9. The DiabetesPedigreeFunction is 0.33. The Age is 41.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 84.0. The BloodPressure is 64.0. The SkinThickness is 23.0. The Insulin is 115.0. The BMI is 36.9. The DiabetesPedigreeFunction is 0.47. The Age is 28.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 73.0. The BloodPressure is 0.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 21.1. The DiabetesPedigreeFunction is 0.34. The Age is 25.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 110.0. The BloodPressure is 76.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 27.8. The DiabetesPedigreeFunction is 0.24. The Age is 58.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 0.0. The BloodPressure is 74.0. The SkinThickness is 20.0. The Insulin is 23.0. The BMI is 27.7. The DiabetesPedigreeFunction is 0.3. The Age is 21.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 91.0. The BloodPressure is 54.0. The SkinThickness is 25.0. The Insulin is 100.0. The BMI is 25.2. The DiabetesPedigreeFunction is 0.23. The Age is 23.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 12.0. The Glucose is 92.0. The BloodPressure is 62.0. The SkinThickness is 7.0. The Insulin is 258.0. The BMI is 27.6. The DiabetesPedigreeFunction is 0.93. The Age is 44.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 9.0. The Glucose is 152.0. The BloodPressure is 78.0. The SkinThickness is 34.0. The Insulin is 171.0. The BMI is 34.2. The DiabetesPedigreeFunction is 0.89. The Age is 33.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 161.0. The BloodPressure is 50.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 21.9. The DiabetesPedigreeFunction is 0.25. The Age is 65.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 183.0. The BloodPressure is 94.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 40.8. The DiabetesPedigreeFunction is 1.46. The Age is 45.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 10.0. The Glucose is 115.0. The BloodPressure is 98.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 24.0. The DiabetesPedigreeFunction is 1.02. The Age is 34.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 126.0. The BloodPressure is 88.0. The SkinThickness is 36.0. The Insulin is 108.0. The BMI is 38.5. The DiabetesPedigreeFunction is 0.35. The Age is 49.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 5.0. The Glucose is 111.0. The BloodPressure is 72.0. The SkinThickness is 28.0. The Insulin is 0.0. The BMI is 23.9. The DiabetesPedigreeFunction is 0.41. The Age is 27.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 88.0. The BloodPressure is 58.0. The SkinThickness is 11.0. The Insulin is 54.0. The BMI is 24.8. The DiabetesPedigreeFunction is 0.27. The Age is 22.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 71.0. The BloodPressure is 62.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 21.8. The DiabetesPedigreeFunction is 0.42. The Age is 26.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 96.0. The BloodPressure is 78.0. The SkinThickness is 39.0. The Insulin is 0.0. The BMI is 37.3. The DiabetesPedigreeFunction is 0.24. The Age is 40.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 126.0. The BloodPressure is 84.0. The SkinThickness is 29.0. The Insulin is 215.0. The BMI is 30.7. The DiabetesPedigreeFunction is 0.52. The Age is 24.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 122.0. The BloodPressure is 78.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 23.0. The DiabetesPedigreeFunction is 0.25. The Age is 40.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 5.0. The Glucose is 99.0. The BloodPressure is 74.0. The SkinThickness is 27.0. The Insulin is 0.0. The BMI is 29.0. The DiabetesPedigreeFunction is 0.2. The Age is 32.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 87.0. The BloodPressure is 78.0. The SkinThickness is 27.0. The Insulin is 32.0. The BMI is 34.6. The DiabetesPedigreeFunction is 0.1. The Age is 22.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 124.0. The BloodPressure is 76.0. The SkinThickness is 24.0. The Insulin is 600.0. The BMI is 28.7. The DiabetesPedigreeFunction is 0.69. The Age is 52.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 0.0. The BloodPressure is 68.0. The SkinThickness is 41.0. The Insulin is 0.0. The BMI is 39.0. The DiabetesPedigreeFunction is 0.73. The Age is 41.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 96.0. The BloodPressure is 64.0. The SkinThickness is 27.0. The Insulin is 87.0. The BMI is 33.2. The DiabetesPedigreeFunction is 0.29. The Age is 21.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 131.0. The BloodPressure is 0.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 43.2. The DiabetesPedigreeFunction is 0.27. The Age is 26.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 99.0. The BloodPressure is 68.0. The SkinThickness is 38.0. The Insulin is 0.0. The BMI is 32.8. The DiabetesPedigreeFunction is 0.14. The Age is 33.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 135.0. The BloodPressure is 54.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 26.7. The DiabetesPedigreeFunction is 0.69. The Age is 62.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 124.0. The BloodPressure is 60.0. The SkinThickness is 32.0. The Insulin is 0.0. The BMI is 35.8. The DiabetesPedigreeFunction is 0.51. The Age is 21.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 119.0. The BloodPressure is 88.0. The SkinThickness is 41.0. The Insulin is 170.0. The BMI is 45.3. The DiabetesPedigreeFunction is 0.51. The Age is 26.0.", "label": "0.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 80.0. The BloodPressure is 82.0. The SkinThickness is 31.0. The Insulin is 70.0. The BMI is 34.2. The DiabetesPedigreeFunction is 1.29. The Age is 27.0.", "label": "1.0", "dataset": "ashishkumarjayswal-diabetes-dataset", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/ashishkumarjayswal-diabetes-dataset/train.csv

@@ -0,0 +1,692 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+0,102,78,40,90,34.5,0.24,24,0.0
+0,86,68,32,0,35.8,0.24,25,0.0
+7,181,84,21,192,35.9,0.59,51,1.0
+2,56,56,28,45,24.2,0.33,22,0.0
+7,100,0,0,0,30.0,0.48,32,1.0
+4,99,76,15,51,23.2,0.22,21,0.0
+1,103,30,38,83,43.3,0.18,33,0.0
+0,91,68,32,210,39.9,0.38,25,0.0
+5,158,70,0,0,29.8,0.21,63,0.0
+0,105,68,22,0,20.0,0.24,22,0.0
+1,121,78,39,74,39.0,0.26,28,0.0
+7,114,66,0,0,32.8,0.26,42,1.0
+2,92,52,0,0,30.1,0.14,22,0.0
+3,113,50,10,85,29.5,0.63,25,0.0
+0,188,82,14,185,32.0,0.68,22,1.0
+5,108,72,43,75,36.1,0.26,33,0.0
+4,184,78,39,277,37.0,0.26,31,1.0
+3,132,80,0,0,34.4,0.4,44,1.0
+4,116,72,12,87,22.1,0.46,37,0.0
+8,186,90,35,225,34.5,0.42,37,1.0
+1,97,66,15,140,23.2,0.49,22,0.0
+3,129,92,49,155,36.4,0.97,32,1.0
+10,94,72,18,0,23.1,0.59,56,0.0
+3,102,74,0,0,29.5,0.12,32,0.0
+1,77,56,30,56,33.3,1.25,24,0.0
+4,84,90,23,56,39.5,0.16,25,0.0
+10,68,106,23,49,35.5,0.28,47,0.0
+2,82,52,22,115,28.5,1.7,25,0.0
+6,103,72,32,190,37.7,0.32,55,0.0
+0,181,88,44,510,43.3,0.22,26,1.0
+11,143,94,33,146,36.6,0.25,51,1.0
+9,184,85,15,0,30.0,1.21,49,1.0
+4,134,72,0,0,23.8,0.28,60,1.0
+1,90,68,8,0,24.5,1.14,36,0.0
+10,129,62,36,0,41.2,0.44,38,1.0
+0,179,90,27,0,44.1,0.69,23,1.0
+3,182,74,0,0,30.5,0.34,29,1.0
+7,83,78,26,71,29.3,0.77,36,0.0
+9,140,94,0,0,32.7,0.73,45,1.0
+2,129,74,26,205,33.2,0.59,25,0.0
+2,87,58,16,52,32.7,0.17,25,0.0
+1,71,78,50,45,33.2,0.42,21,0.0
+6,92,62,32,126,32.0,0.09,46,0.0
+3,116,0,0,0,23.5,0.19,23,0.0
+8,196,76,29,280,37.5,0.6,57,1.0
+0,93,100,39,72,43.4,1.02,35,0.0
+2,146,70,38,360,28.0,0.34,29,1.0
+2,112,78,50,140,39.4,0.17,24,0.0
+2,119,0,0,0,19.6,0.83,72,0.0
+1,99,58,10,0,25.4,0.55,21,0.0
+6,103,66,0,0,24.3,0.25,29,0.0
+13,152,90,33,29,26.8,0.73,43,1.0
+3,150,76,0,0,21.0,0.21,37,0.0
+1,131,64,14,415,23.7,0.39,21,0.0
+3,121,52,0,0,36.0,0.13,25,1.0
+10,75,82,0,0,33.3,0.26,38,0.0
+4,132,86,31,0,28.0,0.42,63,0.0
+3,99,62,19,74,21.8,0.28,26,0.0
+8,100,76,0,0,38.7,0.19,42,0.0
+4,85,58,22,49,27.8,0.31,28,0.0
+11,103,68,40,0,46.2,0.13,42,0.0
+4,129,86,20,270,35.1,0.23,23,0.0
+4,154,72,29,126,31.3,0.34,37,0.0
+10,122,68,0,0,31.2,0.26,41,0.0
+1,138,82,0,0,40.1,0.24,28,0.0
+9,106,52,0,0,31.2,0.38,42,0.0
+12,88,74,40,54,35.3,0.38,48,0.0
+8,120,78,0,0,25.0,0.41,64,0.0
+0,123,88,37,0,35.2,0.2,29,0.0
+2,144,58,33,135,31.6,0.42,25,1.0
+5,168,64,0,0,32.9,0.14,41,1.0
+1,168,88,29,0,35.0,0.91,52,1.0
+4,117,64,27,120,33.2,0.23,24,0.0
+0,107,62,30,74,36.6,0.76,25,1.0
+13,145,82,19,110,22.2,0.24,57,0.0
+3,111,62,0,0,22.6,0.14,21,0.0
+2,68,62,13,15,20.1,0.26,23,0.0
+1,130,70,13,105,25.9,0.47,22,0.0
+1,96,122,0,0,22.4,0.21,27,0.0
+2,85,65,0,0,39.6,0.93,27,0.0
+7,159,64,0,0,27.4,0.29,40,0.0
+6,125,68,30,120,30.0,0.46,32,0.0
+1,0,68,35,0,32.0,0.39,22,0.0
+1,115,70,30,96,34.6,0.53,32,1.0
+7,160,54,32,175,30.5,0.59,39,1.0
+10,125,70,26,115,31.1,0.2,41,1.0
+6,109,60,27,0,25.0,0.21,27,0.0
+0,151,90,46,0,42.1,0.37,21,1.0
+0,107,60,25,0,26.4,0.13,23,0.0
+8,84,74,31,0,38.3,0.46,39,0.0
+8,112,72,0,0,23.6,0.84,58,0.0
+0,113,76,0,0,33.3,0.28,23,1.0
+3,84,72,32,0,37.2,0.27,28,0.0
+7,106,60,24,0,26.5,0.3,29,1.0
+2,106,56,27,165,29.0,0.43,22,0.0
+0,180,90,26,90,36.5,0.31,35,1.0
+4,90,88,47,54,37.7,0.36,29,0.0
+2,125,60,20,140,33.8,0.09,31,0.0
+1,147,94,41,0,49.3,0.36,27,1.0
+2,197,70,45,543,30.5,0.16,53,1.0
+2,122,60,18,106,29.8,0.72,22,0.0
+1,157,72,21,168,25.6,0.12,24,0.0
+3,111,90,12,78,28.4,0.49,29,0.0
+3,107,62,13,48,22.9,0.68,23,1.0
+3,123,100,35,240,57.3,0.88,22,0.0
+4,96,56,17,49,20.8,0.34,26,0.0
+1,108,88,19,0,27.1,0.4,24,0.0
+3,113,44,13,0,22.4,0.14,22,0.0
+5,109,62,41,129,35.8,0.51,25,1.0
+3,173,78,39,185,33.8,0.97,31,1.0
+1,124,74,36,0,27.8,0.1,30,0.0
+2,96,68,13,49,21.1,0.65,26,0.0
+1,128,82,17,183,27.5,0.12,22,0.0
+1,125,50,40,167,33.3,0.96,28,1.0
+2,146,0,0,0,27.5,0.24,28,1.0
+0,94,70,27,115,43.5,0.35,21,0.0
+2,110,74,29,125,32.4,0.7,27,0.0
+3,124,80,33,130,33.2,0.3,26,0.0
+9,119,80,35,0,29.0,0.26,29,1.0
+5,187,76,27,207,43.6,1.03,53,1.0
+1,114,66,36,200,38.1,0.29,21,0.0
+7,136,74,26,135,26.0,0.65,51,0.0
+6,80,66,30,0,26.2,0.31,41,0.0
+7,125,86,0,0,37.6,0.3,51,0.0
+7,168,88,42,321,38.2,0.79,40,1.0
+1,100,66,15,56,23.6,0.67,26,0.0
+0,109,88,30,0,32.5,0.85,38,1.0
+7,142,60,33,190,28.8,0.69,61,0.0
+4,131,68,21,166,33.1,0.16,28,0.0
+2,127,46,21,335,34.4,0.18,22,0.0
+1,128,98,41,58,32.0,1.32,33,1.0
+0,57,60,0,0,21.7,0.73,67,0.0
+0,98,82,15,84,25.2,0.3,22,0.0
+4,110,76,20,100,28.4,0.12,27,0.0
+5,115,76,0,0,31.2,0.34,44,1.0
+2,87,0,23,0,28.9,0.77,25,0.0
+2,88,74,19,53,29.0,0.23,22,0.0
+2,88,58,26,16,28.4,0.77,22,0.0
+9,123,70,44,94,33.1,0.37,40,0.0
+0,121,66,30,165,34.3,0.2,33,1.0
+4,94,65,22,0,24.7,0.15,21,0.0
+0,134,58,20,291,26.4,0.35,21,0.0
+1,92,62,25,41,19.5,0.48,25,0.0
+0,106,70,37,148,39.4,0.6,22,0.0
+0,139,62,17,210,22.1,0.21,21,0.0
+2,105,80,45,191,33.7,0.71,29,1.0
+8,179,72,42,130,32.7,0.72,36,1.0
+2,120,54,0,0,26.8,0.46,27,0.0
+10,148,84,48,237,37.6,1.0,51,1.0
+8,197,74,0,0,25.9,1.19,39,1.0
+5,73,60,0,0,26.8,0.27,27,0.0
+0,101,76,0,0,35.7,0.2,26,0.0
+6,151,62,31,120,35.5,0.69,28,0.0
+1,130,60,23,170,28.6,0.69,21,0.0
+5,147,78,0,0,33.7,0.22,65,0.0
+7,106,92,18,0,22.7,0.23,48,0.0
+2,99,70,16,44,20.4,0.23,27,0.0
+1,112,72,30,176,34.4,0.53,25,0.0
+5,106,82,30,0,39.5,0.29,38,0.0
+0,105,84,0,0,27.9,0.74,62,1.0
+0,118,64,23,89,0.0,1.73,21,0.0
+6,96,0,0,0,23.7,0.19,28,0.0
+1,126,60,0,0,30.1,0.35,47,1.0
+7,152,88,44,0,50.0,0.34,36,1.0
+1,144,82,40,0,41.3,0.61,28,0.0
+2,114,68,22,0,28.7,0.09,25,0.0
+4,123,62,0,0,32.0,0.23,35,1.0
+3,102,44,20,94,30.8,0.4,26,0.0
+8,181,68,36,495,30.1,0.61,60,1.0
+2,129,0,0,0,38.5,0.3,41,0.0
+5,97,76,27,0,35.6,0.38,52,1.0
+13,104,72,0,0,31.2,0.47,38,1.0
+8,99,84,0,0,35.4,0.39,50,0.0
+0,145,0,0,0,44.2,0.63,31,1.0
+0,147,85,54,0,42.8,0.38,24,0.0
+4,83,86,19,0,29.3,0.32,34,0.0
+1,89,66,23,94,28.1,0.17,21,0.0
+1,93,70,31,0,30.4,0.32,23,0.0
+2,84,50,23,76,30.4,0.97,21,0.0
+1,112,80,45,132,34.8,0.22,24,0.0
+10,129,76,28,122,35.9,0.28,39,0.0
+8,85,55,20,0,24.4,0.14,42,0.0
+4,144,58,28,140,29.5,0.29,37,0.0
+2,99,60,17,160,36.6,0.45,21,0.0
+0,113,80,16,0,31.0,0.87,21,0.0
+2,127,58,24,275,27.7,1.6,25,0.0
+7,107,74,0,0,29.6,0.25,31,1.0
+2,91,62,0,0,27.3,0.53,22,0.0
+2,158,90,0,0,31.6,0.81,66,1.0
+4,109,64,44,99,34.8,0.91,26,1.0
+0,117,80,31,53,45.2,0.09,24,0.0
+1,189,60,23,846,30.1,0.4,59,1.0
+0,119,66,27,0,38.8,0.26,22,0.0
+5,95,72,33,0,37.7,0.37,27,0.0
+2,90,60,0,0,23.5,0.19,25,0.0
+2,89,90,30,0,33.5,0.29,42,0.0
+4,129,60,12,231,27.5,0.53,31,0.0
+8,188,78,0,0,47.9,0.14,43,1.0
+0,91,80,0,0,32.4,0.6,27,0.0
+0,124,70,20,0,27.4,0.25,36,1.0
+1,111,86,19,0,30.1,0.14,23,0.0
+5,115,98,0,0,52.9,0.21,28,1.0
+0,129,80,0,0,31.2,0.7,29,0.0
+0,198,66,32,274,41.3,0.5,28,1.0
+4,158,78,0,0,32.9,0.8,31,1.0
+2,128,78,37,182,43.3,1.22,31,1.0
+1,95,82,25,180,35.0,0.23,43,1.0
+3,187,70,22,200,36.4,0.41,36,1.0
+1,89,24,19,25,27.8,0.56,21,0.0
+5,166,76,0,0,45.7,0.34,27,1.0
+1,111,94,0,0,32.8,0.27,45,0.0
+2,124,68,28,205,32.9,0.88,30,1.0
+9,91,68,0,0,24.2,0.2,58,0.0
+2,122,70,27,0,36.8,0.34,27,0.0
+5,128,80,0,0,34.6,0.14,45,0.0
+5,117,86,30,105,39.1,0.25,42,0.0
+10,162,84,0,0,27.7,0.18,54,0.0
+1,128,88,39,110,36.5,1.06,37,1.0
+13,76,60,0,0,32.8,0.18,41,0.0
+0,94,0,0,0,0.0,0.26,25,0.0
+10,115,0,0,0,35.3,0.13,29,0.0
+5,77,82,41,42,35.8,0.16,35,0.0
+6,162,62,0,0,24.3,0.18,50,1.0
+3,108,62,24,0,26.0,0.22,25,0.0
+3,115,66,39,140,38.1,0.15,28,0.0
+2,94,68,18,76,26.0,0.56,21,0.0
+2,100,64,23,0,29.7,0.37,21,0.0
+0,95,80,45,92,36.5,0.33,26,0.0
+8,154,78,32,0,32.4,0.44,45,1.0
+7,187,68,39,304,37.7,0.25,41,1.0
+4,99,72,17,0,25.6,0.29,28,0.0
+4,97,60,23,0,28.2,0.44,22,0.0
+3,80,0,0,0,0.0,0.17,22,0.0
+1,117,88,24,145,34.5,0.4,40,1.0
+1,95,60,18,58,23.9,0.26,22,0.0
+5,147,75,0,0,29.9,0.43,28,0.0
+2,141,58,34,128,25.4,0.7,24,0.0
+5,117,92,0,0,34.1,0.34,38,0.0
+4,141,74,0,0,27.6,0.24,40,0.0
+10,115,0,0,0,0.0,0.26,30,1.0
+5,139,80,35,160,31.6,0.36,25,1.0
+4,136,70,0,0,31.2,1.18,22,1.0
+5,189,64,33,325,31.2,0.58,29,1.0
+5,123,74,40,77,34.1,0.27,28,0.0
+6,137,61,0,0,24.2,0.15,55,0.0
+5,162,104,0,0,37.7,0.15,52,1.0
+9,154,78,30,100,30.9,0.16,45,0.0
+9,112,82,24,0,28.2,1.28,50,1.0
+7,150,66,42,342,34.7,0.72,42,0.0
+1,80,55,0,0,19.1,0.26,21,0.0
+3,120,70,30,135,42.9,0.45,30,0.0
+0,127,80,37,210,36.3,0.8,23,0.0
+7,119,0,0,0,25.2,0.21,37,0.0
+9,72,78,25,0,31.6,0.28,38,0.0
+3,158,64,13,387,31.2,0.29,24,0.0
+0,105,64,41,142,41.5,0.17,22,0.0
+0,138,60,35,167,34.6,0.53,21,1.0
+1,109,60,8,182,25.4,0.95,21,0.0
+6,144,72,27,228,33.9,0.26,40,0.0
+6,154,74,32,193,29.3,0.84,39,0.0
+4,156,75,0,0,48.3,0.24,32,1.0
+1,103,80,11,82,19.4,0.49,22,0.0
+8,109,76,39,114,27.9,0.64,31,1.0
+1,172,68,49,579,42.4,0.7,28,1.0
+1,102,74,0,0,39.5,0.29,42,1.0
+6,195,70,0,0,30.9,0.33,31,1.0
+0,104,64,23,116,27.8,0.45,23,0.0
+11,136,84,35,130,28.3,0.26,42,1.0
+0,165,90,33,680,52.3,0.43,23,0.0
+4,154,62,31,284,32.8,0.24,23,0.0
+7,133,84,0,0,40.2,0.7,37,0.0
+3,99,54,19,86,25.6,0.15,24,0.0
+2,174,88,37,120,44.5,0.65,24,1.0
+4,90,0,0,0,28.0,0.61,31,0.0
+1,100,74,12,46,19.5,0.15,28,0.0
+1,151,60,0,0,26.1,0.18,22,0.0
+0,167,0,0,0,32.3,0.84,30,1.0
+0,162,76,36,0,49.6,0.36,26,1.0
+2,108,80,0,0,27.0,0.26,52,1.0
+5,137,108,0,0,48.8,0.23,37,1.0
+0,124,56,13,105,21.8,0.45,21,0.0
+3,87,60,18,0,21.8,0.44,21,0.0
+4,146,85,27,100,28.9,0.19,27,0.0
+4,114,64,0,0,28.9,0.13,24,0.0
+8,183,64,0,0,23.3,0.67,32,1.0
+3,193,70,31,0,34.9,0.24,25,1.0
+9,156,86,0,0,24.8,0.23,53,1.0
+2,94,76,18,66,31.6,0.65,23,0.0
+10,161,68,23,132,25.5,0.33,47,1.0
+6,125,76,0,0,33.8,0.12,54,1.0
+0,100,88,60,110,46.8,0.96,31,0.0
+0,141,0,0,0,42.4,0.2,29,1.0
+10,139,80,0,0,27.1,1.44,57,0.0
+0,177,60,29,478,34.6,1.07,21,1.0
+1,88,30,42,99,55.0,0.5,26,1.0
+0,119,64,18,92,34.9,0.72,23,0.0
+1,120,80,48,200,38.9,1.16,41,0.0
+1,163,72,0,0,39.0,1.22,33,1.0
+9,156,86,28,155,34.3,1.19,42,1.0
+3,125,58,0,0,31.6,0.15,24,0.0
+1,97,64,19,82,18.2,0.3,21,0.0
+1,122,64,32,156,35.1,0.69,30,1.0
+0,105,90,0,0,29.6,0.2,46,0.0
+1,181,64,30,180,34.1,0.33,38,1.0
+7,196,90,0,0,39.8,0.45,41,1.0
+3,111,58,31,44,29.5,0.43,22,0.0
+2,139,75,0,0,25.6,0.17,29,0.0
+4,189,110,31,0,28.5,0.68,37,0.0
+2,99,52,15,94,24.6,0.64,21,0.0
+2,101,58,17,265,24.2,0.61,23,0.0
+2,100,54,28,105,37.8,0.5,24,0.0
+0,137,68,14,148,24.8,0.14,21,0.0
+5,104,74,0,0,28.8,0.15,48,0.0
+2,117,90,19,71,25.2,0.31,21,0.0
+6,104,74,18,156,29.9,0.72,41,1.0
+0,131,66,40,0,34.3,0.2,22,1.0
+6,87,80,0,0,23.2,0.08,32,0.0
+0,162,76,56,100,53.2,0.76,25,1.0
+6,102,82,0,0,30.8,0.18,36,1.0
+2,100,66,20,90,32.9,0.87,28,1.0
+11,155,76,28,150,33.3,1.35,51,1.0
+3,158,70,30,328,35.5,0.34,35,1.0
+0,165,76,43,255,47.9,0.26,26,0.0
+1,0,48,20,0,24.7,0.14,22,0.0
+1,116,78,29,180,36.1,0.5,25,0.0
+4,146,78,0,0,38.5,0.52,67,1.0
+1,100,66,29,196,32.0,0.44,42,0.0
+1,79,80,25,37,25.4,0.58,22,0.0
+1,83,68,0,0,18.2,0.62,27,0.0
+3,158,76,36,245,31.6,0.85,28,1.0
+4,146,92,0,0,31.2,0.54,61,1.0
+3,78,50,32,88,31.0,0.25,26,1.0
+0,152,82,39,272,41.5,0.27,27,0.0
+5,139,64,35,140,28.6,0.41,26,0.0
+9,89,62,0,0,22.5,0.14,33,0.0
+4,197,70,39,744,36.7,2.33,31,0.0
+1,80,74,11,60,30.0,0.53,22,0.0
+4,122,68,0,0,35.0,0.39,29,0.0
+9,170,74,31,0,44.0,0.4,43,1.0
+5,130,82,0,0,39.1,0.96,37,1.0
+1,180,0,0,0,43.3,0.28,41,1.0
+10,122,78,31,0,27.6,0.51,45,0.0
+0,93,60,25,92,28.7,0.53,22,0.0
+2,122,76,27,200,35.9,0.48,26,0.0
+3,103,72,30,152,27.6,0.73,27,0.0
+6,166,74,0,0,26.6,0.3,66,0.0
+3,81,86,16,66,27.5,0.31,22,0.0
+0,125,96,0,0,22.5,0.26,21,0.0
+5,155,84,44,545,38.7,0.62,34,0.0
+2,105,58,40,94,34.9,0.23,25,0.0
+3,141,0,0,0,30.0,0.76,27,1.0
+6,125,78,31,0,27.6,0.56,49,1.0
+9,164,84,21,0,30.8,0.83,32,1.0
+4,151,90,38,0,29.7,0.29,36,0.0
+0,173,78,32,265,46.5,1.16,58,0.0
+8,126,74,38,75,25.9,0.16,39,0.0
+6,92,92,0,0,19.9,0.19,28,0.0
+5,144,82,26,285,32.0,0.45,58,1.0
+0,146,70,0,0,37.9,0.33,28,1.0
+1,97,68,21,0,27.2,1.09,22,0.0
+6,134,80,37,370,46.2,0.24,46,1.0
+3,173,82,48,465,38.4,2.14,25,1.0
+5,44,62,0,0,25.0,0.59,36,0.0
+9,145,88,34,165,30.3,0.77,53,1.0
+3,89,74,16,85,30.4,0.55,38,0.0
+0,179,50,36,159,37.8,0.46,22,1.0
+8,194,80,0,0,26.1,0.55,67,0.0
+3,116,74,15,105,26.3,0.11,24,0.0
+1,106,70,28,135,34.2,0.14,22,0.0
+5,85,74,22,0,29.0,1.22,32,1.0
+7,114,64,0,0,27.4,0.73,34,1.0
+0,84,64,22,66,35.8,0.55,21,0.0
+0,104,76,0,0,18.4,0.58,27,0.0
+5,96,74,18,67,33.6,1.0,43,0.0
+1,193,50,16,375,25.9,0.66,24,0.0
+2,108,52,26,63,32.5,0.32,22,0.0
+13,106,72,54,0,36.6,0.18,45,0.0
+0,107,76,0,0,45.3,0.69,24,0.0
+1,116,70,28,0,27.4,0.2,21,0.0
+2,99,0,0,0,22.2,0.11,23,0.0
+3,90,78,0,0,42.7,0.56,21,0.0
+5,112,66,0,0,37.8,0.26,41,1.0
+1,107,50,19,0,28.3,0.18,29,0.0
+17,163,72,41,114,40.9,0.82,47,1.0
+2,197,70,99,0,34.7,0.57,62,1.0
+1,111,62,13,182,24.0,0.14,23,0.0
+4,125,80,0,0,32.3,0.54,27,1.0
+4,142,86,0,0,44.0,0.65,22,1.0
+7,142,90,24,480,30.4,0.13,43,1.0
+0,123,72,0,0,36.3,0.26,52,1.0
+6,105,80,28,0,32.5,0.88,26,0.0
+1,87,60,37,75,37.2,0.51,22,0.0
+2,81,72,15,76,30.1,0.55,25,0.0
+2,95,54,14,88,26.1,0.75,22,0.0
+0,95,85,25,36,37.4,0.25,24,1.0
+5,124,74,0,0,34.0,0.22,38,1.0
+1,105,58,0,0,24.3,0.19,21,0.0
+7,136,90,0,0,29.9,0.21,50,0.0
+7,94,64,25,79,33.3,0.74,41,0.0
+6,85,78,0,0,31.2,0.38,42,0.0
+1,100,72,12,70,25.3,0.66,28,0.0
+0,132,78,0,0,32.4,0.39,21,0.0
+10,111,70,27,0,27.5,0.14,40,1.0
+8,143,66,0,0,34.9,0.13,41,1.0
+5,109,75,26,0,36.0,0.55,60,0.0
+1,109,58,18,116,28.5,0.22,22,0.0
+2,130,96,0,0,22.6,0.27,21,0.0
+3,106,72,0,0,25.8,0.21,27,0.0
+0,101,65,28,0,24.6,0.24,22,0.0
+6,148,72,35,0,33.6,0.63,50,1.0
+1,126,56,29,152,28.7,0.8,21,0.0
+3,128,72,25,190,32.4,0.55,27,1.0
+2,128,64,42,0,40.0,1.1,24,0.0
+2,90,70,17,0,27.3,0.09,22,0.0
+1,144,82,46,180,46.1,0.34,46,1.0
+1,139,46,19,83,28.7,0.65,22,0.0
+1,93,56,11,0,22.5,0.42,22,0.0
+2,71,70,27,0,28.0,0.59,22,0.0
+0,126,86,27,120,27.4,0.52,21,0.0
+1,95,66,13,38,19.6,0.33,25,0.0
+7,147,76,0,0,39.4,0.26,43,1.0
+9,112,82,32,175,34.2,0.26,36,1.0
+1,173,74,0,0,36.8,0.09,38,1.0
+0,93,60,0,0,35.3,0.26,25,0.0
+5,143,78,0,0,45.0,0.19,47,0.0
+10,90,85,32,0,34.9,0.82,56,1.0
+2,108,62,32,56,25.2,0.13,21,0.0
+0,102,52,0,0,25.1,0.08,21,0.0
+0,125,68,0,0,24.7,0.21,21,0.0
+7,137,90,41,0,32.0,0.39,39,0.0
+1,164,82,43,67,32.8,0.34,50,0.0
+0,95,64,39,105,44.6,0.37,22,0.0
+11,135,0,0,0,52.3,0.58,40,1.0
+6,108,44,20,130,24.0,0.81,35,0.0
+2,115,64,22,0,30.8,0.42,21,0.0
+3,111,56,39,0,30.1,0.56,30,0.0
+6,147,80,0,0,29.5,0.18,50,1.0
+3,112,74,30,0,31.6,0.2,25,1.0
+2,75,64,24,55,29.7,0.37,33,0.0
+8,107,80,0,0,24.6,0.86,34,0.0
+2,155,74,17,96,26.6,0.43,27,1.0
+2,122,52,43,158,36.2,0.82,28,0.0
+8,155,62,26,495,34.0,0.54,46,1.0
+3,171,72,33,135,33.3,0.2,24,1.0
+7,184,84,33,0,35.5,0.35,41,1.0
+0,140,65,26,130,42.6,0.43,24,1.0
+5,88,66,21,23,24.4,0.34,30,0.0
+7,187,50,33,392,33.9,0.83,34,1.0
+9,145,80,46,130,37.9,0.64,40,1.0
+5,121,72,23,112,26.2,0.24,30,0.0
+2,92,62,28,0,31.6,0.13,24,0.0
+14,100,78,25,184,36.6,0.41,46,1.0
+0,120,74,18,63,30.5,0.28,26,0.0
+1,81,74,41,57,46.3,1.1,32,0.0
+0,128,68,19,180,30.5,1.39,25,1.0
+1,109,38,18,120,23.1,0.41,26,0.0
+7,150,78,29,126,35.2,0.69,54,1.0
+2,129,84,0,0,28.0,0.28,27,0.0
+9,134,74,33,60,25.9,0.46,81,0.0
+7,102,74,40,105,37.2,0.2,45,0.0
+13,129,0,30,0,39.9,0.57,44,1.0
+11,127,106,0,0,39.0,0.19,51,0.0
+12,121,78,17,0,26.5,0.26,62,0.0
+1,118,58,36,94,33.3,0.26,23,0.0
+3,174,58,22,194,32.9,0.59,36,1.0
+15,136,70,32,110,37.1,0.15,43,1.0
+9,122,56,0,0,33.3,1.11,33,1.0
+12,84,72,31,0,29.7,0.3,46,1.0
+9,102,76,37,0,32.9,0.67,46,1.0
+4,132,0,0,0,32.9,0.3,23,1.0
+1,136,74,50,204,37.4,0.4,24,0.0
+0,117,0,0,0,33.8,0.93,44,0.0
+2,98,60,17,120,34.7,0.2,22,0.0
+0,138,0,0,0,36.3,0.93,25,1.0
+9,171,110,24,240,45.4,0.72,54,1.0
+2,74,0,0,0,0.0,0.1,22,0.0
+4,92,80,0,0,42.2,0.24,29,0.0
+3,84,68,30,106,31.9,0.59,25,0.0
+5,116,74,0,0,25.6,0.2,30,0.0
+9,130,70,0,0,34.2,0.65,45,1.0
+6,93,50,30,64,28.7,0.36,23,0.0
+8,65,72,23,0,32.0,0.6,42,0.0
+4,95,70,32,0,32.1,0.61,24,0.0
+1,119,44,47,63,35.5,0.28,25,0.0
+3,142,80,15,0,32.4,0.2,63,0.0
+1,128,48,45,194,40.5,0.61,24,1.0
+10,92,62,0,0,25.9,0.17,31,0.0
+3,162,52,38,0,37.2,0.65,24,1.0
+4,144,82,32,0,38.5,0.55,37,1.0
+9,124,70,33,402,35.4,0.28,34,0.0
+5,110,68,0,0,26.0,0.29,30,0.0
+8,74,70,40,49,35.3,0.7,39,0.0
+4,127,88,11,155,34.5,0.6,28,0.0
+6,107,88,0,0,36.8,0.73,31,0.0
+2,121,70,32,95,39.1,0.89,23,0.0
+0,101,62,0,0,21.9,0.34,25,0.0
+3,106,54,21,158,30.9,0.29,24,0.0
+0,67,76,0,0,45.3,0.19,46,0.0
+3,169,74,19,125,29.9,0.27,31,1.0
+1,82,64,13,95,21.2,0.41,23,0.0
+1,139,62,41,480,40.7,0.54,21,0.0
+1,86,66,52,65,41.3,0.92,29,0.0
+9,164,78,0,0,32.8,0.15,45,1.0
+6,98,58,33,190,34.0,0.43,43,0.0
+2,83,65,28,66,36.8,0.63,24,0.0
+0,135,94,46,145,40.6,0.28,26,0.0
+6,99,60,19,54,26.9,0.5,32,0.0
+2,109,92,0,0,42.7,0.84,54,0.0
+4,118,70,0,0,44.5,0.9,26,0.0
+0,78,88,29,40,36.9,0.43,21,0.0
+1,107,72,30,82,30.8,0.82,24,0.0
+8,125,96,0,0,0.0,0.23,54,1.0
+1,71,48,18,76,20.4,0.32,22,0.0
+1,95,74,21,73,25.9,0.67,36,0.0
+4,95,64,0,0,32.0,0.16,31,1.0
+4,171,72,0,0,43.6,0.48,26,1.0
+2,90,68,42,0,38.2,0.5,27,1.0
+1,90,62,12,43,27.2,0.58,24,0.0
+0,118,84,47,230,45.8,0.55,31,1.0
+7,195,70,33,145,25.1,0.16,55,1.0
+2,105,75,0,0,23.3,0.56,53,0.0
+3,74,68,28,45,29.7,0.29,23,0.0
+1,97,70,15,0,18.2,0.15,21,0.0
+1,125,70,24,110,24.3,0.22,25,0.0
+2,108,64,0,0,30.8,0.16,21,0.0
+11,138,74,26,144,36.1,0.56,50,1.0
+8,176,90,34,300,33.7,0.47,58,1.0
+4,147,74,25,293,34.9,0.39,30,0.0
+6,117,96,0,0,28.7,0.16,30,0.0
+1,79,60,42,48,43.5,0.68,23,0.0
+1,87,68,34,77,37.6,0.4,24,0.0
+8,95,72,0,0,36.8,0.48,57,0.0
+3,96,56,34,115,24.7,0.94,39,0.0
+10,179,70,0,0,35.1,0.2,37,0.0
+4,145,82,18,0,32.5,0.23,70,1.0
+1,97,70,40,0,38.1,0.22,30,0.0
+1,91,64,24,0,29.2,0.19,21,0.0
+10,168,74,0,0,38.0,0.54,34,1.0
+6,91,0,0,0,29.8,0.5,31,0.0
+1,119,54,13,50,22.3,0.2,24,0.0
+0,100,70,26,50,30.8,0.6,21,0.0
+2,112,75,32,0,35.7,0.15,21,0.0
+0,97,64,36,100,36.8,0.6,25,0.0
+3,61,82,28,0,34.4,0.24,46,0.0
+2,68,70,32,66,25.0,0.19,25,0.0
+1,196,76,36,249,36.5,0.88,29,1.0
+2,84,0,0,0,0.0,0.3,21,0.0
+6,123,72,45,230,33.6,0.73,34,0.0
+5,103,108,37,0,39.2,0.3,65,0.0
+3,163,70,18,105,31.6,0.27,28,1.0
+0,131,88,0,0,31.6,0.74,32,1.0
+4,110,92,0,0,37.6,0.19,30,0.0
+1,143,84,23,310,42.4,1.08,22,0.0
+4,183,0,0,0,28.4,0.21,36,1.0
+9,165,88,0,0,30.4,0.3,49,1.0
+8,151,78,32,210,42.9,0.52,36,1.0
+7,194,68,28,0,35.9,0.74,41,1.0
+4,103,60,33,192,24.0,0.97,33,0.0
+0,137,70,38,0,33.2,0.17,22,0.0
+0,102,86,17,105,29.3,0.69,27,0.0
+13,106,70,0,0,34.2,0.25,52,0.0
+3,126,88,41,235,39.3,0.7,27,0.0
+1,167,74,17,144,23.4,0.45,33,1.0
+2,134,70,0,0,28.9,0.54,23,1.0
+5,136,82,0,0,0.0,0.64,69,0.0
+2,108,62,10,278,25.3,0.88,22,0.0
+0,137,84,27,0,27.3,0.23,59,0.0
+0,102,75,23,0,0.0,0.57,21,0.0
+6,114,0,0,0,0.0,0.19,26,0.0
+7,97,76,32,91,40.9,0.87,32,1.0
+4,110,66,0,0,31.9,0.47,29,0.0
+0,84,82,31,125,38.2,0.23,23,0.0
+0,108,68,20,0,27.3,0.79,32,0.0
+2,83,66,23,50,32.2,0.5,22,0.0
+4,95,60,32,0,35.4,0.28,28,0.0
+0,180,66,39,0,42.0,1.89,25,1.0
+5,86,68,28,71,30.2,0.36,24,0.0
+4,148,60,27,318,30.9,0.15,29,1.0
+7,109,80,31,0,35.9,1.13,43,1.0
+5,114,74,0,0,24.9,0.74,57,0.0
+3,82,70,0,0,21.1,0.39,25,0.0
+13,126,90,0,0,43.4,0.58,42,1.0
+1,90,62,18,59,25.1,1.27,25,0.0
+12,106,80,0,0,23.6,0.14,44,0.0
+2,157,74,35,440,39.4,0.13,30,0.0
+1,117,60,23,106,33.8,0.47,27,0.0
+8,120,86,0,0,28.4,0.26,22,1.0
+3,129,64,29,115,26.4,0.22,28,1.0
+12,140,82,43,325,39.2,0.53,58,1.0
+3,170,64,37,225,34.5,0.36,30,1.0
+3,83,58,31,18,34.3,0.34,25,0.0
+0,104,64,37,64,33.6,0.51,22,1.0
+8,118,72,19,0,23.1,1.48,46,0.0
+1,88,62,24,44,29.9,0.42,23,0.0
+8,100,74,40,215,39.4,0.66,43,1.0
+3,139,54,0,0,25.6,0.4,22,1.0
+5,78,48,0,0,33.7,0.65,25,0.0
+7,103,66,32,0,39.1,0.34,31,1.0
+1,149,68,29,127,29.3,0.35,42,1.0
+1,133,102,28,140,32.8,0.23,45,1.0
+12,151,70,40,271,41.8,0.74,38,1.0
+4,117,62,12,0,29.7,0.38,30,1.0
+9,57,80,37,0,32.8,0.1,41,0.0
+7,105,0,0,0,0.0,0.3,24,0.0
+5,132,80,0,0,26.8,0.19,69,0.0
+6,129,90,7,326,19.6,0.58,60,0.0
+4,137,84,0,0,31.2,0.25,30,0.0
+2,146,76,35,194,38.2,0.33,29,0.0
+2,81,60,22,0,27.7,0.29,25,0.0
+10,108,66,0,0,32.4,0.27,42,1.0
+13,158,114,0,0,42.3,0.26,44,1.0
+3,180,64,25,70,34.0,0.27,26,0.0
+0,114,80,34,285,44.2,0.17,27,0.0
+7,129,68,49,125,38.5,0.44,43,1.0
+1,73,50,10,0,23.0,0.25,21,0.0
+5,122,86,0,0,34.7,0.29,33,0.0
+8,91,82,0,0,35.6,0.59,68,0.0
+1,181,78,42,293,40.0,1.26,22,1.0
+8,108,70,0,0,30.5,0.95,33,1.0
+2,120,76,37,105,39.7,0.21,29,0.0
+2,142,82,18,64,24.7,0.76,21,0.0
+0,135,68,42,250,42.3,0.36,24,1.0
+5,105,72,29,325,36.9,0.16,28,0.0
+2,112,68,22,94,34.1,0.32,26,0.0
+0,102,64,46,78,40.6,0.5,21,0.0
+8,105,100,36,0,43.3,0.24,45,1.0
+0,180,78,63,14,59.4,2.42,25,1.0
+2,100,68,25,71,38.5,0.32,26,0.0
+1,81,72,18,40,26.6,0.28,24,0.0
+5,136,84,41,88,35.0,0.29,35,1.0
+5,99,54,28,83,34.0,0.5,30,0.0
+0,189,104,25,0,34.3,0.43,41,1.0
+7,133,88,15,155,32.4,0.26,37,0.0
+1,146,56,0,0,29.7,0.56,29,0.0
+6,105,70,32,68,30.8,0.12,37,0.0
+1,88,78,29,76,32.0,0.36,29,0.0
+5,126,78,27,22,29.6,0.44,40,0.0
+2,107,74,30,100,33.6,0.4,23,0.0
+14,175,62,30,0,33.6,0.21,38,1.0
+2,102,86,36,120,45.5,0.13,23,1.0
+1,79,75,30,0,32.0,0.4,22,0.0
+2,106,64,35,119,30.5,1.4,34,0.0
+1,199,76,43,0,42.9,1.39,22,1.0
+2,123,48,32,165,42.1,0.52,26,0.0
+2,101,58,35,90,21.8,0.15,22,0.0
+1,140,74,26,180,24.1,0.83,23,0.0
+2,175,88,0,0,22.9,0.33,22,0.0
+6,119,50,22,176,27.1,1.32,33,1.0
+3,176,86,27,156,33.3,1.15,52,1.0
+1,99,72,30,18,38.6,0.41,21,0.0
+12,140,85,33,0,37.4,0.24,41,0.0
+7,81,78,40,48,46.7,0.26,42,0.0
+5,0,80,32,0,41.0,0.35,37,1.0
+4,91,70,32,88,33.1,0.45,22,0.0
+0,99,0,0,0,25.0,0.25,22,0.0
+3,128,78,0,0,21.1,0.27,55,0.0
+0,137,40,35,168,43.1,2.29,33,1.0
+0,111,65,0,0,24.6,0.66,31,0.0
+2,112,66,22,0,25.0,0.31,24,0.0
+1,119,86,39,220,45.6,0.81,29,1.0
+2,92,76,20,0,24.2,1.7,28,0.0
+6,111,64,39,0,34.2,0.26,24,0.0
+4,120,68,0,0,29.6,0.71,34,0.0
+4,123,80,15,176,32.0,0.44,34,0.0
+1,153,82,42,485,40.6,0.69,23,0.0
+4,114,65,0,0,21.9,0.43,37,0.0
+11,111,84,40,0,46.8,0.93,45,1.0
+6,194,78,0,0,23.5,0.13,59,1.0
+9,120,72,22,56,20.8,0.73,48,0.0
+8,120,0,0,0,30.0,0.18,38,1.0
+3,99,80,11,64,19.3,0.28,30,0.0
+3,130,78,23,79,28.4,0.32,34,1.0
+0,146,82,0,0,40.5,1.78,44,0.0
+5,116,74,29,0,32.3,0.66,35,1.0
+4,173,70,14,168,29.7,0.36,33,1.0
+11,138,76,0,0,33.2,0.42,35,0.0
+12,100,84,33,105,30.0,0.49,46,0.0
+1,109,56,21,135,25.2,0.83,23,0.0
+3,78,70,0,0,32.5,0.27,39,0.0
+1,143,86,30,330,30.1,0.89,23,0.0
+6,134,70,23,130,35.4,0.54,29,1.0
+1,143,74,22,61,26.2,0.26,21,0.0
+2,118,80,0,0,42.9,0.69,21,1.0
+7,159,66,0,0,30.4,0.38,36,1.0
+3,100,68,23,81,31.6,0.95,28,0.0
+1,101,50,15,36,24.2,0.53,26,0.0
+6,154,78,41,140,46.1,0.57,27,0.0
+10,101,76,48,180,32.9,0.17,63,0.0
+3,191,68,15,130,30.9,0.3,34,0.0
+2,111,60,0,0,26.2,0.34,23,0.0
+6,80,80,36,0,39.8,0.18,28,0.0

classification/unipredict/ashishkumarjayswal-loanamount-approval/metadata.json

@@ -0,0 +1,23 @@
+{
+  "dataset": "ashishkumarjayswal-loanamount-approval",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Loan_Status",
+  "label_values": [
+    "N",
+    "Y"
+  ],
+  "num_labels": 2,
+  "train_samples": 551,
+  "test_samples": 63,
+  "train_label_distribution": {
+    "Y": 379,
+    "N": 172
+  },
+  "test_label_distribution": {
+    "Y": 43,
+    "N": 20
+  }
+}

classification/unipredict/ashishkumarjayswal-loanamount-approval/test.csv

@@ -0,0 +1,64 @@
+Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
+LP001507,Male,Yes,0,Graduate,No,2698,2034,122.0,360.0,1.0,Semiurban,Y
+LP001917,Female,No,0,Graduate,No,1811,1666,54.0,360.0,1.0,Urban,Y
+LP001882,Male,Yes,3+,Graduate,No,4333,1811,160.0,360.0,0.0,Urban,Y
+LP001279,Male,No,0,Graduate,No,2366,2531,136.0,360.0,1.0,Semiurban,Y
+LP002446,Male,Yes,2,Not Graduate,No,2309,1255,125.0,360.0,0.0,Rural,N
+LP002624,Male,Yes,0,Graduate,No,20833,6667,480.0,360.0,,Urban,Y
+LP001643,Male,Yes,0,Graduate,No,2383,2138,58.0,360.0,,Rural,Y
+LP002219,Male,Yes,3+,Graduate,No,8750,4996,130.0,360.0,1.0,Rural,Y
+LP001250,Male,Yes,3+,Not Graduate,No,4755,0,95.0,,0.0,Semiurban,N
+LP002541,Male,Yes,0,Graduate,No,10833,0,234.0,360.0,1.0,Semiurban,Y
+LP001109,Male,Yes,0,Graduate,No,1828,1330,100.0,,0.0,Urban,N
+LP002043,Female,No,1,Graduate,No,3541,0,112.0,360.0,,Semiurban,Y
+LP001603,Male,Yes,0,Not Graduate,Yes,4344,736,87.0,360.0,1.0,Semiurban,N
+LP002467,Male,Yes,0,Graduate,No,3708,2569,173.0,360.0,1.0,Urban,N
+LP001492,Male,No,0,Graduate,No,14999,0,242.0,360.0,0.0,Semiurban,N
+LP001574,Male,Yes,0,Graduate,No,3707,3166,182.0,,1.0,Rural,Y
+LP002804,Female,Yes,0,Graduate,No,4180,2306,182.0,360.0,1.0,Semiurban,Y
+LP002767,Male,Yes,0,Graduate,No,2768,1950,155.0,360.0,1.0,Rural,Y
+LP002489,Female,No,1,Not Graduate,,5191,0,132.0,360.0,1.0,Semiurban,Y
+LP001006,Male,Yes,0,Not Graduate,No,2583,2358,120.0,360.0,1.0,Urban,Y
+LP002739,Male,Yes,0,Not Graduate,No,2917,536,66.0,360.0,1.0,Rural,N
+LP002229,Male,No,0,Graduate,No,5941,4232,296.0,360.0,1.0,Semiurban,Y
+LP002272,Male,Yes,2,Graduate,No,3276,484,135.0,360.0,,Semiurban,Y
+LP002534,Female,No,0,Not Graduate,No,4350,0,154.0,360.0,1.0,Rural,Y
+LP001241,Female,No,0,Graduate,No,4300,0,136.0,360.0,0.0,Semiurban,N
+LP001914,Male,Yes,0,Graduate,No,3927,800,112.0,360.0,1.0,Semiurban,Y
+LP001273,Male,Yes,0,Graduate,No,6000,2250,265.0,360.0,,Semiurban,N
+LP001431,Female,No,0,Graduate,No,2137,8980,137.0,360.0,0.0,Semiurban,Y
+LP002087,Female,No,0,Graduate,No,2500,0,67.0,360.0,1.0,Urban,Y
+LP002560,Male,No,0,Not Graduate,No,2699,2785,96.0,360.0,,Semiurban,Y
+LP001633,Male,Yes,1,Graduate,No,6400,7250,180.0,360.0,0.0,Urban,N
+LP002794,Female,No,0,Graduate,No,2667,1625,84.0,360.0,,Urban,Y
+LP002697,Male,No,0,Graduate,No,4680,2087,,360.0,1.0,Semiurban,N
+LP002128,Male,Yes,2,Graduate,,2583,2330,125.0,360.0,1.0,Rural,Y
+LP001514,Female,Yes,0,Graduate,No,2330,4486,100.0,360.0,1.0,Semiurban,Y
+LP002131,Male,Yes,2,Not Graduate,No,3083,2168,126.0,360.0,1.0,Urban,Y
+LP001910,Male,No,1,Not Graduate,Yes,4053,2426,158.0,360.0,0.0,Urban,N
+LP001585,,Yes,3+,Graduate,No,51763,0,700.0,300.0,1.0,Urban,Y
+LP001790,Female,No,1,Graduate,No,3812,0,112.0,360.0,1.0,Rural,Y
+LP001630,Male,No,0,Not Graduate,No,2333,1451,102.0,480.0,0.0,Urban,N
+LP001849,Male,No,0,Not Graduate,No,6045,0,115.0,360.0,0.0,Rural,N
+LP002723,Male,No,2,Graduate,No,3588,0,110.0,360.0,0.0,Rural,N
+LP001565,Male,Yes,1,Graduate,No,3089,1280,121.0,360.0,0.0,Semiurban,N
+LP002024,,Yes,0,Graduate,No,2473,1843,159.0,360.0,1.0,Rural,N
+LP002484,Male,Yes,3+,Graduate,No,7740,0,128.0,180.0,1.0,Urban,Y
+LP002459,Male,Yes,0,Graduate,No,4301,0,118.0,360.0,1.0,Urban,Y
+LP001907,Male,Yes,0,Graduate,No,14583,0,436.0,360.0,1.0,Semiurban,Y
+LP002478,,Yes,0,Graduate,Yes,2083,4083,160.0,360.0,,Semiurban,Y
+LP002958,Male,No,0,Graduate,No,3676,4301,172.0,360.0,1.0,Rural,Y
+LP002224,Male,No,0,Graduate,No,3069,0,71.0,480.0,1.0,Urban,N
+LP002255,Male,No,3+,Graduate,No,9167,0,185.0,360.0,1.0,Rural,Y
+LP002928,Male,Yes,0,Graduate,No,3000,3416,56.0,180.0,1.0,Semiurban,Y
+LP002714,Male,No,1,Not Graduate,No,2679,1302,94.0,360.0,1.0,Semiurban,Y
+LP001926,Male,Yes,0,Graduate,No,3704,2000,120.0,360.0,1.0,Rural,Y
+LP001289,Male,No,0,Graduate,No,8566,0,210.0,360.0,1.0,Urban,Y
+LP002562,Male,Yes,1,Not Graduate,No,5333,1131,186.0,360.0,,Urban,Y
+LP002068,Male,No,0,Graduate,No,4917,0,130.0,360.0,0.0,Rural,Y
+LP001392,Female,No,1,Graduate,Yes,7451,0,,360.0,1.0,Semiurban,Y
+LP002622,Male,Yes,2,Graduate,No,3510,4416,243.0,360.0,1.0,Rural,Y
+LP002187,Male,No,0,Graduate,No,2500,0,96.0,480.0,1.0,Semiurban,N
+LP001541,Male,Yes,1,Graduate,No,6000,0,160.0,360.0,,Rural,Y
+LP001038,Male,Yes,0,Not Graduate,No,4887,0,133.0,360.0,1.0,Rural,N
+LP002487,Male,Yes,0,Graduate,No,3015,2188,153.0,360.0,1.0,Rural,Y

classification/unipredict/ashishkumarjayswal-loanamount-approval/test.jsonl

@@ -0,0 +1,63 @@
+{"text": "The Loan_ID is LP001507. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2698. The CoapplicantIncome is 2034.0. The LoanAmount is 122.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001917. The Gender is Female. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 1811. The CoapplicantIncome is 1666.0. The LoanAmount is 54.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001882. The Gender is Male. The Married is Yes. The Dependents is 3+. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 4333. The CoapplicantIncome is 1811.0. The LoanAmount is 160.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001279. The Gender is Male. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2366. The CoapplicantIncome is 2531.0. The LoanAmount is 136.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002446. The Gender is Male. The Married is Yes. The Dependents is 2. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 2309. The CoapplicantIncome is 1255.0. The LoanAmount is 125.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Rural.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002624. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 20833. The CoapplicantIncome is 6667.0. The LoanAmount is 480.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001643. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2383. The CoapplicantIncome is 2138.0. The LoanAmount is 58.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002219. The Gender is Male. The Married is Yes. The Dependents is 3+. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 8750. The CoapplicantIncome is 4996.0. The LoanAmount is 130.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001250. The Gender is Male. The Married is Yes. The Dependents is 3+. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 4755. The CoapplicantIncome is 0.0. The LoanAmount is 95.0. The Loan_Amount_Term is unknown. The Credit_History is 0.0. The Property_Area is Semiurban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002541. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 10833. The CoapplicantIncome is 0.0. The LoanAmount is 234.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001109. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 1828. The CoapplicantIncome is 1330.0. The LoanAmount is 100.0. The Loan_Amount_Term is unknown. The Credit_History is 0.0. The Property_Area is Urban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002043. The Gender is Female. The Married is No. The Dependents is 1. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3541. The CoapplicantIncome is 0.0. The LoanAmount is 112.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001603. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Not Graduate. The Self_Employed is Yes. The ApplicantIncome is 4344. The CoapplicantIncome is 736.0. The LoanAmount is 87.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002467. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3708. The CoapplicantIncome is 2569.0. The LoanAmount is 173.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001492. The Gender is Male. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 14999. The CoapplicantIncome is 0.0. The LoanAmount is 242.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Semiurban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001574. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3707. The CoapplicantIncome is 3166.0. The LoanAmount is 182.0. The Loan_Amount_Term is unknown. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002804. The Gender is Female. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 4180. The CoapplicantIncome is 2306.0. The LoanAmount is 182.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002767. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2768. The CoapplicantIncome is 1950.0. The LoanAmount is 155.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002489. The Gender is Female. The Married is No. The Dependents is 1. The Education is Not Graduate. The Self_Employed is unknown. The ApplicantIncome is 5191. The CoapplicantIncome is 0.0. The LoanAmount is 132.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001006. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 2583. The CoapplicantIncome is 2358.0. The LoanAmount is 120.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002739. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 2917. The CoapplicantIncome is 536.0. The LoanAmount is 66.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002229. The Gender is Male. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 5941. The CoapplicantIncome is 4232.0. The LoanAmount is 296.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002272. The Gender is Male. The Married is Yes. The Dependents is 2. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3276. The CoapplicantIncome is 484.0. The LoanAmount is 135.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002534. The Gender is Female. The Married is No. The Dependents is 0. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 4350. The CoapplicantIncome is 0.0. The LoanAmount is 154.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001241. The Gender is Female. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 4300. The CoapplicantIncome is 0.0. The LoanAmount is 136.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Semiurban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001914. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3927. The CoapplicantIncome is 800.0. The LoanAmount is 112.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001273. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 6000. The CoapplicantIncome is 2250.0. The LoanAmount is 265.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Semiurban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001431. The Gender is Female. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2137. The CoapplicantIncome is 8980.0. The LoanAmount is 137.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002087. The Gender is Female. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2500. The CoapplicantIncome is 0.0. The LoanAmount is 67.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002560. The Gender is Male. The Married is No. The Dependents is 0. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 2699. The CoapplicantIncome is 2785.0. The LoanAmount is 96.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001633. The Gender is Male. The Married is Yes. The Dependents is 1. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 6400. The CoapplicantIncome is 7250.0. The LoanAmount is 180.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Urban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002794. The Gender is Female. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2667. The CoapplicantIncome is 1625.0. The LoanAmount is 84.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002697. The Gender is Male. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 4680. The CoapplicantIncome is 2087.0. The LoanAmount is unknown. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002128. The Gender is Male. The Married is Yes. The Dependents is 2. The Education is Graduate. The Self_Employed is unknown. The ApplicantIncome is 2583. The CoapplicantIncome is 2330.0. The LoanAmount is 125.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001514. The Gender is Female. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2330. The CoapplicantIncome is 4486.0. The LoanAmount is 100.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002131. The Gender is Male. The Married is Yes. The Dependents is 2. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 3083. The CoapplicantIncome is 2168.0. The LoanAmount is 126.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001910. The Gender is Male. The Married is No. The Dependents is 1. The Education is Not Graduate. The Self_Employed is Yes. The ApplicantIncome is 4053. The CoapplicantIncome is 2426.0. The LoanAmount is 158.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Urban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001585. The Gender is unknown. The Married is Yes. The Dependents is 3+. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 51763. The CoapplicantIncome is 0.0. The LoanAmount is 700.0. The Loan_Amount_Term is 300.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001790. The Gender is Female. The Married is No. The Dependents is 1. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3812. The CoapplicantIncome is 0.0. The LoanAmount is 112.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001630. The Gender is Male. The Married is No. The Dependents is 0. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 2333. The CoapplicantIncome is 1451.0. The LoanAmount is 102.0. The Loan_Amount_Term is 480.0. The Credit_History is 0.0. The Property_Area is Urban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001849. The Gender is Male. The Married is No. The Dependents is 0. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 6045. The CoapplicantIncome is 0.0. The LoanAmount is 115.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Rural.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002723. The Gender is Male. The Married is No. The Dependents is 2. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3588. The CoapplicantIncome is 0.0. The LoanAmount is 110.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Rural.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001565. The Gender is Male. The Married is Yes. The Dependents is 1. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3089. The CoapplicantIncome is 1280.0. The LoanAmount is 121.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Semiurban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002024. The Gender is unknown. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2473. The CoapplicantIncome is 1843.0. The LoanAmount is 159.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002484. The Gender is Male. The Married is Yes. The Dependents is 3+. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 7740. The CoapplicantIncome is 0.0. The LoanAmount is 128.0. The Loan_Amount_Term is 180.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002459. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 4301. The CoapplicantIncome is 0.0. The LoanAmount is 118.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001907. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 14583. The CoapplicantIncome is 0.0. The LoanAmount is 436.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002478. The Gender is unknown. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is Yes. The ApplicantIncome is 2083. The CoapplicantIncome is 4083.0. The LoanAmount is 160.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002958. The Gender is Male. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3676. The CoapplicantIncome is 4301.0. The LoanAmount is 172.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002224. The Gender is Male. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3069. The CoapplicantIncome is 0.0. The LoanAmount is 71.0. The Loan_Amount_Term is 480.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002255. The Gender is Male. The Married is No. The Dependents is 3+. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 9167. The CoapplicantIncome is 0.0. The LoanAmount is 185.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002928. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3000. The CoapplicantIncome is 3416.0. The LoanAmount is 56.0. The Loan_Amount_Term is 180.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002714. The Gender is Male. The Married is No. The Dependents is 1. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 2679. The CoapplicantIncome is 1302.0. The LoanAmount is 94.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001926. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3704. The CoapplicantIncome is 2000.0. The LoanAmount is 120.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001289. The Gender is Male. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 8566. The CoapplicantIncome is 0.0. The LoanAmount is 210.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002562. The Gender is Male. The Married is Yes. The Dependents is 1. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 5333. The CoapplicantIncome is 1131.0. The LoanAmount is 186.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Urban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002068. The Gender is Male. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 4917. The CoapplicantIncome is 0.0. The LoanAmount is 130.0. The Loan_Amount_Term is 360.0. The Credit_History is 0.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001392. The Gender is Female. The Married is No. The Dependents is 1. The Education is Graduate. The Self_Employed is Yes. The ApplicantIncome is 7451. The CoapplicantIncome is 0.0. The LoanAmount is unknown. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002622. The Gender is Male. The Married is Yes. The Dependents is 2. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3510. The CoapplicantIncome is 4416.0. The LoanAmount is 243.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002187. The Gender is Male. The Married is No. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 2500. The CoapplicantIncome is 0.0. The LoanAmount is 96.0. The Loan_Amount_Term is 480.0. The Credit_History is 1.0. The Property_Area is Semiurban.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001541. The Gender is Male. The Married is Yes. The Dependents is 1. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 6000. The CoapplicantIncome is 0.0. The LoanAmount is 160.0. The Loan_Amount_Term is 360.0. The Credit_History is unknown. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP001038. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Not Graduate. The Self_Employed is No. The ApplicantIncome is 4887. The CoapplicantIncome is 0.0. The LoanAmount is 133.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "N", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Loan_ID is LP002487. The Gender is Male. The Married is Yes. The Dependents is 0. The Education is Graduate. The Self_Employed is No. The ApplicantIncome is 3015. The CoapplicantIncome is 2188.0. The LoanAmount is 153.0. The Loan_Amount_Term is 360.0. The Credit_History is 1.0. The Property_Area is Rural.", "label": "Y", "dataset": "ashishkumarjayswal-loanamount-approval", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/ashishkumarjayswal-loanamount-approval/train.csv

@@ -0,0 +1,552 @@
+Loan_ID,Gender,Married,Dependents,Education,Self_Employed,ApplicantIncome,CoapplicantIncome,LoanAmount,Loan_Amount_Term,Credit_History,Property_Area,Loan_Status
+LP002544,Male,Yes,1,Not Graduate,No,1958,2436.0,131.0,360.0,1.0,Rural,Y
+LP002408,Male,No,0,Graduate,No,3660,5064.0,187.0,360.0,1.0,Semiurban,Y
+LP001319,Male,Yes,2,Not Graduate,No,3273,1820.0,81.0,360.0,1.0,Urban,Y
+LP002531,Male,Yes,1,Graduate,Yes,16667,2250.0,86.0,360.0,1.0,Semiurban,Y
+LP001669,Female,No,0,Not Graduate,No,1907,2365.0,120.0,,1.0,Urban,Y
+LP001349,Male,No,0,Graduate,No,4843,3806.0,151.0,360.0,1.0,Semiurban,Y
+LP001875,Male,No,0,Graduate,No,4095,3447.0,151.0,360.0,1.0,Rural,Y
+LP001955,Female,No,0,Graduate,No,5000,2541.0,151.0,480.0,1.0,Rural,N
+LP001954,Female,Yes,1,Graduate,No,4666,0.0,135.0,360.0,1.0,Urban,Y
+LP001164,Female,No,0,Graduate,No,4230,0.0,112.0,360.0,1.0,Semiurban,N
+LP002119,Male,Yes,1,Not Graduate,No,4554,1229.0,158.0,360.0,1.0,Urban,Y
+LP002158,Male,Yes,0,Not Graduate,No,3000,1666.0,100.0,480.0,0.0,Urban,N
+LP002585,Male,Yes,0,Graduate,No,3597,2157.0,119.0,360.0,0.0,Rural,N
+LP002888,Male,No,0,Graduate,,3182,2917.0,161.0,360.0,1.0,Urban,Y
+LP001256,Male,No,0,Graduate,No,3750,4750.0,176.0,360.0,1.0,Urban,N
+LP002547,Male,Yes,1,Graduate,No,18333,0.0,500.0,360.0,1.0,Urban,N
+LP001546,Male,No,0,Graduate,,2980,2083.0,120.0,360.0,1.0,Rural,Y
+LP002170,Male,Yes,2,Graduate,No,5000,3667.0,236.0,360.0,1.0,Semiurban,Y
+LP002281,Male,Yes,0,Graduate,No,3033,1459.0,95.0,360.0,1.0,Urban,Y
+LP001343,Male,Yes,0,Graduate,No,1759,3541.0,131.0,360.0,1.0,Semiurban,Y
+LP002160,Male,Yes,3+,Graduate,No,5167,3167.0,200.0,360.0,1.0,Semiurban,Y
+LP001868,Male,No,0,Graduate,No,2060,2209.0,134.0,360.0,1.0,Semiurban,Y
+LP001770,Male,No,0,Not Graduate,No,3189,2598.0,120.0,,1.0,Rural,Y
+LP001529,Male,Yes,0,Graduate,Yes,2577,3750.0,152.0,360.0,1.0,Rural,Y
+LP002368,Male,Yes,2,Graduate,No,5935,0.0,133.0,360.0,1.0,Semiurban,Y
+LP002740,Male,Yes,3+,Graduate,No,6417,0.0,157.0,180.0,1.0,Rural,Y
+LP002652,Male,No,0,Graduate,No,5815,3666.0,311.0,360.0,1.0,Rural,N
+LP001870,Female,No,1,Graduate,No,3481,0.0,155.0,36.0,1.0,Semiurban,N
+LP002524,Male,No,2,Graduate,No,5532,4648.0,162.0,360.0,1.0,Rural,Y
+LP001720,Male,Yes,3+,Not Graduate,No,3850,983.0,100.0,360.0,1.0,Semiurban,Y
+LP002530,,Yes,2,Graduate,No,2873,1872.0,132.0,360.0,0.0,Semiurban,N
+LP001404,Female,Yes,0,Graduate,No,3167,2283.0,154.0,360.0,1.0,Semiurban,Y
+LP002101,Male,Yes,0,Graduate,,63337,0.0,490.0,180.0,1.0,Urban,Y
+LP002847,Male,Yes,,Graduate,No,5116,1451.0,165.0,360.0,0.0,Urban,N
+LP002449,Male,Yes,0,Graduate,No,2483,2466.0,90.0,180.0,0.0,Rural,Y
+LP002693,Male,Yes,2,Graduate,Yes,7948,7166.0,480.0,360.0,1.0,Rural,Y
+LP001877,Male,Yes,2,Graduate,No,4708,1387.0,150.0,360.0,1.0,Semiurban,Y
+LP001398,Male,No,0,Graduate,,5050,0.0,118.0,360.0,1.0,Semiurban,Y
+LP001691,Male,Yes,2,Not Graduate,No,3917,0.0,124.0,360.0,1.0,Semiurban,Y
+LP002191,Male,Yes,0,Graduate,No,19730,5266.0,570.0,360.0,1.0,Rural,N
+LP001925,Female,No,0,Graduate,Yes,2600,1717.0,99.0,300.0,1.0,Semiurban,N
+LP002055,Female,No,0,Graduate,No,3166,2985.0,132.0,360.0,,Rural,Y
+LP001947,Male,Yes,0,Graduate,No,2383,3334.0,172.0,360.0,1.0,Semiurban,Y
+LP001275,Male,Yes,1,Graduate,No,3988,0.0,50.0,240.0,1.0,Urban,Y
+LP002296,Male,No,0,Not Graduate,No,2755,0.0,65.0,300.0,1.0,Rural,N
+LP002149,Male,Yes,2,Graduate,No,8333,3167.0,165.0,360.0,1.0,Rural,Y
+LP002690,Male,No,0,Graduate,No,2500,0.0,55.0,360.0,1.0,Semiurban,Y
+LP001900,Male,Yes,1,Graduate,No,2750,1842.0,115.0,360.0,1.0,Semiurban,Y
+LP001205,Male,Yes,0,Graduate,No,2500,3796.0,120.0,360.0,1.0,Urban,Y
+LP001488,Male,Yes,3+,Graduate,No,4000,7750.0,290.0,360.0,1.0,Semiurban,N
+LP002443,Male,Yes,2,Graduate,No,3340,1710.0,150.0,360.0,0.0,Rural,N
+LP001819,Male,Yes,1,Not Graduate,No,6608,0.0,137.0,180.0,1.0,Urban,Y
+LP001586,Male,Yes,3+,Not Graduate,No,3522,0.0,81.0,180.0,1.0,Rural,N
+LP002933,,No,3+,Graduate,Yes,9357,0.0,292.0,360.0,1.0,Semiurban,Y
+LP001387,Female,Yes,0,Graduate,,2929,2333.0,139.0,360.0,1.0,Semiurban,Y
+LP002699,Male,Yes,2,Graduate,Yes,17500,0.0,400.0,360.0,1.0,Rural,Y
+LP001938,Male,Yes,2,Graduate,No,4400,0.0,127.0,360.0,0.0,Semiurban,N
+LP002974,Male,Yes,0,Graduate,No,3232,1950.0,108.0,360.0,1.0,Rural,Y
+LP002626,Male,Yes,0,Graduate,Yes,2479,3013.0,188.0,360.0,1.0,Urban,Y
+LP001155,Female,Yes,0,Not Graduate,No,1928,1644.0,100.0,360.0,1.0,Semiurban,Y
+LP002051,Male,Yes,0,Graduate,No,2400,2167.0,115.0,360.0,1.0,Semiurban,Y
+LP001836,Female,No,2,Graduate,No,3427,0.0,138.0,360.0,1.0,Urban,N
+LP001532,Male,Yes,2,Not Graduate,No,2281,0.0,113.0,360.0,1.0,Rural,N
+LP001443,Female,No,0,Graduate,No,3692,0.0,93.0,360.0,,Rural,Y
+LP001972,Male,Yes,,Not Graduate,No,2875,1750.0,105.0,360.0,1.0,Semiurban,Y
+LP002692,Male,Yes,3+,Graduate,Yes,5677,1424.0,100.0,360.0,1.0,Rural,Y
+LP001716,Male,Yes,0,Graduate,No,3173,3021.0,137.0,360.0,1.0,Urban,Y
+LP001047,Male,Yes,0,Not Graduate,No,2600,1911.0,116.0,360.0,0.0,Semiurban,N
+LP002768,Male,No,0,Not Graduate,No,3358,0.0,80.0,36.0,1.0,Semiurban,N
+LP002798,Male,Yes,0,Graduate,No,3887,2669.0,162.0,360.0,1.0,Semiurban,Y
+LP001027,Male,Yes,2,Graduate,,2500,1840.0,109.0,360.0,1.0,Urban,Y
+LP001327,Female,Yes,0,Graduate,No,2484,2302.0,137.0,360.0,1.0,Semiurban,Y
+LP001073,Male,Yes,2,Not Graduate,No,4226,1040.0,110.0,360.0,1.0,Urban,Y
+LP001114,Male,No,0,Graduate,No,4166,7210.0,184.0,360.0,1.0,Urban,Y
+LP002178,Male,Yes,0,Graduate,No,3013,3033.0,95.0,300.0,,Urban,Y
+LP001640,Male,Yes,0,Graduate,Yes,39147,4750.0,120.0,360.0,1.0,Semiurban,Y
+LP002863,Male,Yes,3+,Graduate,No,6406,0.0,150.0,360.0,1.0,Semiurban,N
+LP001577,Female,Yes,0,Graduate,No,4583,0.0,112.0,360.0,1.0,Rural,N
+LP001119,Male,No,0,Graduate,No,3600,0.0,80.0,360.0,1.0,Urban,N
+LP001888,Female,No,0,Graduate,No,3237,0.0,30.0,360.0,1.0,Urban,Y
+LP001699,Male,No,0,Graduate,No,2479,0.0,59.0,360.0,1.0,Urban,Y
+LP001097,Male,No,1,Graduate,Yes,4692,0.0,106.0,360.0,1.0,Rural,N
+LP002836,Male,No,0,Graduate,No,3333,0.0,70.0,360.0,1.0,Urban,Y
+LP002447,Male,Yes,2,Not Graduate,No,1958,1456.0,60.0,300.0,,Urban,Y
+LP001225,Male,Yes,0,Graduate,No,5726,4595.0,258.0,360.0,1.0,Semiurban,N
+LP001977,Male,Yes,1,Graduate,No,1625,1803.0,96.0,360.0,1.0,Urban,Y
+LP001068,Male,Yes,0,Graduate,No,2799,2253.0,122.0,360.0,1.0,Semiurban,Y
+LP002493,Male,No,0,Graduate,No,4166,0.0,98.0,360.0,0.0,Semiurban,N
+LP002112,Male,Yes,2,Graduate,Yes,2500,4600.0,176.0,360.0,1.0,Rural,Y
+LP002006,Female,No,0,Graduate,No,2507,0.0,56.0,360.0,1.0,Rural,Y
+LP001066,Male,Yes,0,Graduate,Yes,9560,0.0,191.0,360.0,1.0,Semiurban,Y
+LP001610,Male,Yes,3+,Graduate,No,5516,11300.0,495.0,360.0,0.0,Semiurban,N
+LP002840,Female,No,0,Graduate,No,2378,0.0,9.0,360.0,1.0,Urban,N
+LP002941,Male,Yes,2,Not Graduate,Yes,6383,1000.0,187.0,360.0,1.0,Rural,N
+LP002586,Female,Yes,1,Graduate,No,3326,913.0,105.0,84.0,1.0,Semiurban,Y
+LP001003,Male,Yes,1,Graduate,No,4583,1508.0,128.0,360.0,1.0,Rural,N
+LP001798,Male,Yes,2,Graduate,No,5819,5000.0,120.0,360.0,1.0,Rural,Y
+LP002424,Male,Yes,0,Graduate,No,7333,8333.0,175.0,300.0,,Rural,Y
+LP001518,Male,Yes,1,Graduate,No,1538,1425.0,30.0,360.0,1.0,Urban,Y
+LP002139,Male,Yes,0,Graduate,No,9083,0.0,228.0,360.0,1.0,Semiurban,Y
+LP002194,Female,No,0,Graduate,Yes,15759,0.0,55.0,360.0,1.0,Semiurban,Y
+LP001846,Female,No,3+,Graduate,No,3083,0.0,255.0,360.0,1.0,Rural,Y
+LP002161,Female,No,1,Graduate,No,4723,0.0,81.0,360.0,1.0,Semiurban,N
+LP002308,Male,Yes,0,Not Graduate,No,2167,2400.0,115.0,360.0,1.0,Urban,Y
+LP001788,Female,No,0,Graduate,Yes,3463,0.0,122.0,360.0,,Urban,Y
+LP001222,Female,No,0,Graduate,No,4166,0.0,116.0,360.0,0.0,Semiurban,N
+LP001936,Male,Yes,0,Graduate,No,3075,2416.0,139.0,360.0,1.0,Rural,Y
+LP001391,Male,Yes,0,Not Graduate,No,3572,4114.0,152.0,,0.0,Rural,N
+LP002180,Male,No,0,Graduate,Yes,6822,0.0,141.0,360.0,1.0,Rural,Y
+LP002141,Male,Yes,3+,Graduate,No,2666,2083.0,95.0,360.0,1.0,Rural,Y
+LP001005,Male,Yes,0,Graduate,Yes,3000,0.0,66.0,360.0,1.0,Urban,Y
+LP001580,Male,Yes,2,Graduate,No,8000,0.0,200.0,360.0,1.0,Semiurban,Y
+LP001894,Male,Yes,0,Graduate,No,2620,2223.0,150.0,360.0,1.0,Semiurban,Y
+LP002434,Male,Yes,2,Not Graduate,No,4652,0.0,110.0,360.0,1.0,Rural,Y
+LP001146,Female,Yes,0,Graduate,No,2645,3440.0,120.0,360.0,0.0,Urban,N
+LP002537,Male,Yes,0,Graduate,No,2083,3150.0,128.0,360.0,1.0,Semiurban,Y
+LP001715,Male,Yes,3+,Not Graduate,Yes,5703,0.0,130.0,360.0,1.0,Rural,Y
+LP002515,Male,Yes,1,Graduate,Yes,3450,2079.0,162.0,360.0,1.0,Semiurban,Y
+LP002501,,Yes,0,Graduate,No,16692,0.0,110.0,360.0,1.0,Semiurban,Y
+LP001198,Male,Yes,1,Graduate,No,8080,2250.0,180.0,360.0,1.0,Urban,Y
+LP001422,Female,No,0,Graduate,No,10408,0.0,259.0,360.0,1.0,Urban,Y
+LP001136,Male,Yes,0,Not Graduate,Yes,4695,0.0,96.0,,1.0,Urban,Y
+LP002036,Male,Yes,0,Graduate,No,2058,2134.0,88.0,360.0,,Urban,Y
+LP001137,Female,No,0,Graduate,No,3410,0.0,88.0,,1.0,Urban,Y
+LP002137,Male,Yes,0,Graduate,No,6333,4583.0,259.0,360.0,,Semiurban,Y
+LP002953,Male,Yes,3+,Graduate,No,5703,0.0,128.0,360.0,1.0,Urban,Y
+LP002556,Male,No,0,Graduate,No,2435,0.0,75.0,360.0,1.0,Urban,N
+LP001367,Male,Yes,1,Graduate,No,3052,1030.0,100.0,360.0,1.0,Urban,Y
+LP001280,Male,Yes,2,Not Graduate,No,3333,2000.0,99.0,360.0,,Semiurban,Y
+LP002367,Female,No,1,Not Graduate,No,4606,0.0,81.0,360.0,1.0,Rural,N
+LP001357,Male,,,Graduate,No,3816,754.0,160.0,360.0,1.0,Urban,Y
+LP001637,Male,Yes,1,Graduate,No,33846,0.0,260.0,360.0,1.0,Semiurban,N
+LP002361,Male,Yes,0,Graduate,No,1820,1719.0,100.0,360.0,1.0,Urban,Y
+LP001264,Male,Yes,3+,Not Graduate,Yes,3333,2166.0,130.0,360.0,,Semiurban,Y
+LP001964,Male,Yes,0,Not Graduate,No,1800,2934.0,93.0,360.0,0.0,Urban,N
+LP001908,Female,Yes,0,Not Graduate,No,4100,0.0,124.0,360.0,,Rural,Y
+LP002098,Male,No,0,Graduate,No,2935,0.0,98.0,360.0,1.0,Semiurban,Y
+LP001594,Male,Yes,0,Graduate,No,5708,5625.0,187.0,360.0,1.0,Semiurban,Y
+LP001904,Male,Yes,0,Graduate,No,3103,1300.0,80.0,360.0,1.0,Urban,Y
+LP001095,Male,No,0,Graduate,No,3167,0.0,74.0,360.0,1.0,Urban,N
+LP001572,Male,Yes,0,Graduate,No,9323,0.0,75.0,180.0,1.0,Urban,Y
+LP002115,Male,Yes,3+,Not Graduate,No,2647,1587.0,173.0,360.0,1.0,Rural,N
+LP002795,Male,Yes,3+,Graduate,Yes,10139,0.0,260.0,360.0,1.0,Semiurban,Y
+LP002097,Male,No,1,Graduate,No,4384,1793.0,117.0,360.0,1.0,Urban,Y
+LP001041,Male,Yes,0,Graduate,,2600,3500.0,115.0,,1.0,Urban,Y
+LP002945,Male,Yes,0,Graduate,Yes,9963,0.0,180.0,360.0,1.0,Rural,Y
+LP001478,Male,No,0,Graduate,No,2718,0.0,70.0,360.0,1.0,Semiurban,Y
+LP002842,Male,Yes,1,Graduate,No,3417,1750.0,186.0,360.0,1.0,Urban,Y
+LP002297,Male,No,0,Graduate,No,2500,20000.0,103.0,360.0,1.0,Semiurban,Y
+LP002720,Male,Yes,3+,Graduate,No,4281,0.0,100.0,360.0,1.0,Urban,Y
+LP001884,Female,No,1,Graduate,No,2876,1560.0,90.0,360.0,1.0,Urban,Y
+LP001350,Male,Yes,,Graduate,No,13650,0.0,,360.0,1.0,Urban,Y
+LP002337,Female,No,0,Graduate,No,2995,0.0,60.0,360.0,1.0,Urban,Y
+LP002103,,Yes,1,Graduate,Yes,9833,1833.0,182.0,180.0,1.0,Urban,Y
+LP001562,Male,Yes,0,Graduate,No,7933,0.0,275.0,360.0,1.0,Urban,N
+LP002705,Male,Yes,0,Graduate,No,3775,0.0,110.0,360.0,1.0,Semiurban,Y
+LP002615,Male,Yes,2,Graduate,No,4865,5624.0,208.0,360.0,1.0,Semiurban,Y
+LP001473,Male,No,0,Graduate,No,2014,1929.0,74.0,360.0,1.0,Urban,Y
+LP001835,Male,Yes,0,Not Graduate,No,1668,3890.0,201.0,360.0,0.0,Semiurban,N
+LP001990,Male,No,0,Not Graduate,No,2000,0.0,,360.0,1.0,Urban,N
+LP001698,Male,No,0,Not Graduate,No,3975,2531.0,55.0,360.0,1.0,Rural,Y
+LP001825,Male,Yes,0,Graduate,No,1809,1868.0,90.0,360.0,1.0,Urban,Y
+LP001768,Male,Yes,0,Graduate,,3716,0.0,42.0,180.0,1.0,Rural,Y
+LP001138,Male,Yes,1,Graduate,No,5649,0.0,44.0,360.0,1.0,Urban,Y
+LP002319,Male,Yes,0,Graduate,,6256,0.0,160.0,360.0,,Urban,Y
+LP001854,Male,Yes,3+,Graduate,No,5250,0.0,94.0,360.0,1.0,Urban,N
+LP001014,Male,Yes,3+,Graduate,No,3036,2504.0,158.0,360.0,0.0,Semiurban,N
+LP002643,Male,Yes,2,Graduate,No,3283,2035.0,148.0,360.0,1.0,Urban,Y
+LP002949,Female,No,3+,Graduate,,416,41667.0,350.0,180.0,,Urban,N
+LP002777,Male,Yes,0,Graduate,No,2785,2016.0,110.0,360.0,1.0,Rural,Y
+LP001673,Male,No,0,Graduate,Yes,11000,0.0,83.0,360.0,1.0,Urban,N
+LP002772,Male,No,0,Graduate,No,2526,1783.0,145.0,360.0,1.0,Rural,Y
+LP001465,Male,Yes,0,Graduate,No,6080,2569.0,182.0,360.0,,Rural,N
+LP001677,Male,No,2,Graduate,No,4923,0.0,166.0,360.0,0.0,Semiurban,Y
+LP001993,Female,No,0,Graduate,No,3762,1666.0,135.0,360.0,1.0,Rural,Y
+LP002684,Female,No,0,Not Graduate,No,3400,0.0,95.0,360.0,1.0,Rural,N
+LP002505,Male,Yes,0,Graduate,No,4333,2451.0,110.0,360.0,1.0,Urban,N
+LP002050,Male,Yes,1,Graduate,Yes,10000,0.0,155.0,360.0,1.0,Rural,N
+LP002788,Male,Yes,0,Not Graduate,No,2454,2333.0,181.0,360.0,0.0,Urban,N
+LP002031,Male,Yes,1,Not Graduate,No,3399,1640.0,111.0,180.0,1.0,Urban,Y
+LP002345,Male,Yes,0,Graduate,No,1025,2773.0,112.0,360.0,1.0,Rural,Y
+LP002250,Male,Yes,0,Graduate,No,5488,0.0,125.0,360.0,1.0,Rural,Y
+LP001519,Female,No,0,Graduate,No,10000,1666.0,225.0,360.0,1.0,Rural,N
+LP001813,Male,No,0,Graduate,Yes,6050,4333.0,120.0,180.0,1.0,Urban,N
+LP002931,Male,Yes,2,Graduate,Yes,6000,0.0,205.0,240.0,1.0,Semiurban,N
+LP002114,Female,No,0,Graduate,No,4160,0.0,71.0,360.0,1.0,Semiurban,Y
+LP001841,Male,No,0,Not Graduate,Yes,2583,2167.0,104.0,360.0,1.0,Rural,Y
+LP001865,Male,Yes,1,Graduate,No,6083,4250.0,330.0,360.0,,Urban,Y
+LP001872,Male,No,0,Graduate,Yes,5166,0.0,128.0,360.0,1.0,Semiurban,Y
+LP002328,Male,Yes,0,Not Graduate,No,6096,0.0,218.0,360.0,0.0,Rural,N
+LP002008,Male,Yes,2,Graduate,Yes,5746,0.0,144.0,84.0,,Rural,Y
+LP001658,Male,No,0,Graduate,No,3858,0.0,76.0,360.0,1.0,Semiurban,Y
+LP001379,Male,Yes,2,Graduate,No,3800,3600.0,216.0,360.0,0.0,Urban,N
+LP001998,Male,Yes,2,Not Graduate,No,7667,0.0,185.0,360.0,,Rural,Y
+LP002243,Male,Yes,0,Not Graduate,No,3010,3136.0,,360.0,0.0,Urban,N
+LP002738,Male,No,2,Graduate,No,3617,0.0,107.0,360.0,1.0,Semiurban,Y
+LP002190,Male,Yes,1,Graduate,No,6325,0.0,175.0,360.0,1.0,Semiurban,Y
+LP001963,Male,Yes,1,Graduate,No,2014,2925.0,113.0,360.0,1.0,Urban,N
+LP002734,Male,Yes,0,Graduate,No,6133,3906.0,324.0,360.0,1.0,Urban,Y
+LP001002,Male,No,0,Graduate,No,5849,0.0,,360.0,1.0,Urban,Y
+LP001578,Male,Yes,0,Graduate,No,2439,3333.0,129.0,360.0,1.0,Rural,Y
+LP001030,Male,Yes,2,Graduate,No,1299,1086.0,17.0,120.0,1.0,Urban,Y
+LP002288,Male,Yes,2,Not Graduate,No,2889,0.0,45.0,180.0,0.0,Urban,N
+LP001765,Male,Yes,1,Graduate,No,2491,2054.0,104.0,360.0,1.0,Semiurban,Y
+LP001807,Male,Yes,2,Graduate,Yes,6250,1300.0,108.0,360.0,1.0,Rural,Y
+LP001091,Male,Yes,1,Graduate,,4166,3369.0,201.0,360.0,,Urban,N
+LP002317,Male,Yes,3+,Graduate,No,81000,0.0,360.0,360.0,0.0,Rural,N
+LP002387,Male,Yes,0,Graduate,No,2425,2340.0,143.0,360.0,1.0,Semiurban,Y
+LP001751,Male,Yes,0,Graduate,No,3250,0.0,170.0,360.0,1.0,Rural,N
+LP001228,Male,No,0,Not Graduate,No,3200,2254.0,126.0,180.0,0.0,Urban,N
+LP002792,Male,Yes,1,Graduate,No,5468,1032.0,26.0,360.0,1.0,Semiurban,Y
+LP001726,Male,Yes,0,Graduate,No,3727,1775.0,131.0,360.0,1.0,Semiurban,Y
+LP002683,Male,No,0,Graduate,No,4683,1915.0,185.0,360.0,1.0,Semiurban,N
+LP002335,Female,Yes,0,Not Graduate,No,2149,3237.0,178.0,360.0,0.0,Semiurban,N
+LP001112,Female,Yes,0,Graduate,No,3667,1459.0,144.0,360.0,1.0,Semiurban,Y
+LP001482,Male,Yes,0,Graduate,Yes,3459,0.0,25.0,120.0,1.0,Semiurban,Y
+LP002237,Male,No,1,Graduate,,3667,0.0,113.0,180.0,1.0,Urban,Y
+LP001922,Male,Yes,0,Graduate,No,20667,0.0,,360.0,1.0,Rural,N
+LP001233,Male,Yes,1,Graduate,No,10750,0.0,312.0,360.0,1.0,Urban,Y
+LP001046,Male,Yes,1,Graduate,No,5955,5625.0,315.0,360.0,1.0,Urban,Y
+LP001238,Male,Yes,3+,Not Graduate,Yes,7100,0.0,125.0,60.0,1.0,Urban,Y
+LP001318,Male,Yes,2,Graduate,No,6250,5654.0,188.0,180.0,1.0,Semiurban,Y
+LP002731,Female,No,0,Not Graduate,Yes,18165,0.0,125.0,360.0,1.0,Urban,Y
+LP002403,Male,No,0,Graduate,Yes,10416,0.0,187.0,360.0,0.0,Urban,N
+LP001761,Male,No,0,Graduate,Yes,6400,0.0,200.0,360.0,1.0,Rural,Y
+LP001784,Male,Yes,1,Graduate,No,5500,1260.0,170.0,360.0,1.0,Rural,Y
+LP001266,Male,Yes,1,Graduate,Yes,2395,0.0,,360.0,1.0,Semiurban,Y
+LP002300,Female,No,0,Not Graduate,No,1963,0.0,53.0,360.0,1.0,Semiurban,Y
+LP001497,Male,Yes,2,Graduate,No,5042,2083.0,185.0,360.0,1.0,Rural,N
+LP001491,Male,Yes,2,Graduate,Yes,3316,3500.0,88.0,360.0,1.0,Urban,Y
+LP001489,Female,Yes,0,Graduate,No,4583,0.0,84.0,360.0,1.0,Rural,N
+LP001426,Male,Yes,,Graduate,No,5667,2667.0,180.0,360.0,1.0,Rural,Y
+LP002201,Male,Yes,2,Graduate,Yes,9323,7873.0,380.0,300.0,1.0,Rural,Y
+LP001248,Male,No,0,Graduate,No,3500,0.0,81.0,300.0,1.0,Semiurban,Y
+LP002868,Male,Yes,2,Graduate,No,3159,461.0,108.0,84.0,1.0,Urban,Y
+LP001144,Male,Yes,0,Graduate,No,5821,0.0,144.0,360.0,1.0,Urban,Y
+LP001267,Female,Yes,2,Graduate,No,1378,1881.0,167.0,360.0,1.0,Urban,N
+LP002225,Male,Yes,2,Graduate,No,5391,0.0,130.0,360.0,1.0,Urban,Y
+LP001050,,Yes,2,Not Graduate,No,3365,1917.0,112.0,360.0,0.0,Rural,N
+LP002959,Female,Yes,1,Graduate,No,12000,0.0,496.0,360.0,1.0,Semiurban,Y
+LP001385,Male,No,0,Graduate,No,5316,0.0,136.0,360.0,1.0,Urban,Y
+LP002950,Male,Yes,0,Not Graduate,,2894,2792.0,155.0,360.0,1.0,Rural,Y
+LP002082,Male,Yes,0,Graduate,Yes,5818,2160.0,184.0,360.0,1.0,Semiurban,Y
+LP002741,Female,Yes,1,Graduate,No,4608,2845.0,140.0,180.0,1.0,Semiurban,Y
+LP002948,Male,Yes,2,Graduate,No,5780,0.0,192.0,360.0,1.0,Urban,Y
+LP002912,Male,Yes,1,Graduate,No,4283,3000.0,172.0,84.0,1.0,Rural,N
+LP002841,Male,Yes,0,Graduate,No,3166,2064.0,104.0,360.0,0.0,Urban,N
+LP002209,Female,No,0,Graduate,,2764,1459.0,110.0,360.0,1.0,Urban,Y
+LP002244,Male,Yes,0,Graduate,No,2333,2417.0,136.0,360.0,1.0,Urban,Y
+LP002453,Male,No,0,Graduate,Yes,7085,0.0,84.0,360.0,1.0,Semiurban,Y
+LP001326,Male,No,0,Graduate,,6782,0.0,,360.0,,Urban,N
+LP002110,Male,Yes,1,Graduate,,5250,688.0,160.0,360.0,1.0,Rural,Y
+LP001653,Male,No,0,Not Graduate,No,4885,0.0,48.0,360.0,1.0,Rural,Y
+LP002964,Male,Yes,2,Not Graduate,No,3987,1411.0,157.0,360.0,1.0,Rural,Y
+LP001688,Male,Yes,1,Not Graduate,No,3500,1083.0,135.0,360.0,1.0,Urban,Y
+LP001702,Male,No,0,Graduate,No,3418,0.0,127.0,360.0,1.0,Semiurban,N
+LP002287,Female,No,0,Graduate,No,1500,1800.0,103.0,360.0,0.0,Semiurban,N
+LP001666,Male,No,0,Graduate,No,8333,3750.0,187.0,360.0,1.0,Rural,Y
+LP002785,Male,Yes,1,Graduate,No,3333,3250.0,158.0,360.0,1.0,Urban,Y
+LP001325,Male,No,0,Not Graduate,No,3620,0.0,25.0,120.0,1.0,Semiurban,Y
+LP001195,Male,Yes,0,Graduate,No,2132,1591.0,96.0,360.0,1.0,Semiurban,Y
+LP002716,Male,No,0,Not Graduate,No,6783,0.0,130.0,360.0,1.0,Semiurban,Y
+LP002640,Male,Yes,1,Graduate,No,6065,2004.0,250.0,360.0,1.0,Semiurban,Y
+LP002529,Male,Yes,2,Graduate,No,6700,1750.0,230.0,300.0,1.0,Semiurban,Y
+LP002778,Male,Yes,2,Graduate,Yes,6633,0.0,,360.0,0.0,Rural,N
+LP001369,Male,Yes,2,Graduate,No,11417,1126.0,225.0,360.0,1.0,Urban,Y
+LP002893,Male,No,0,Graduate,No,1836,33837.0,90.0,360.0,1.0,Urban,N
+LP002126,Male,Yes,3+,Not Graduate,No,3173,0.0,74.0,360.0,1.0,Semiurban,Y
+LP002743,Female,No,0,Graduate,No,2138,0.0,99.0,360.0,0.0,Semiurban,N
+LP002555,Male,Yes,2,Graduate,Yes,4583,2083.0,160.0,360.0,1.0,Semiurban,Y
+LP002435,Male,Yes,0,Graduate,,3539,1376.0,55.0,360.0,1.0,Rural,N
+LP001100,Male,No,3+,Graduate,No,12500,3000.0,320.0,360.0,1.0,Rural,N
+LP002342,Male,Yes,2,Graduate,Yes,1600,20000.0,239.0,360.0,1.0,Urban,N
+LP002926,Male,Yes,2,Graduate,Yes,2726,0.0,106.0,360.0,0.0,Semiurban,N
+LP001754,Male,Yes,,Not Graduate,Yes,4735,0.0,138.0,360.0,1.0,Urban,N
+LP001243,Male,Yes,0,Graduate,No,3208,3066.0,172.0,360.0,1.0,Urban,Y
+LP001608,Male,Yes,2,Graduate,No,2045,1619.0,101.0,360.0,1.0,Rural,Y
+LP002188,Male,No,0,Graduate,No,5124,0.0,124.0,,0.0,Rural,N
+LP001316,Male,Yes,0,Graduate,No,2958,2900.0,131.0,360.0,1.0,Semiurban,Y
+LP001401,Male,Yes,1,Graduate,No,14583,0.0,185.0,180.0,1.0,Rural,Y
+LP001778,Male,Yes,1,Graduate,No,3155,1779.0,140.0,360.0,1.0,Semiurban,Y
+LP002455,Male,Yes,2,Graduate,No,3859,0.0,96.0,360.0,1.0,Semiurban,Y
+LP002603,Female,No,0,Graduate,No,645,3683.0,113.0,480.0,1.0,Rural,Y
+LP002129,Male,Yes,0,Graduate,No,2499,2458.0,160.0,360.0,1.0,Semiurban,Y
+LP001008,Male,No,0,Graduate,No,6000,0.0,141.0,360.0,1.0,Urban,Y
+LP002398,Male,No,0,Graduate,No,1926,1851.0,50.0,360.0,1.0,Semiurban,Y
+LP001647,Male,Yes,0,Graduate,No,9328,0.0,188.0,180.0,1.0,Rural,Y
+LP002239,Male,No,0,Not Graduate,No,2346,1600.0,132.0,360.0,1.0,Semiurban,Y
+LP002418,Male,No,3+,Not Graduate,No,4707,1993.0,148.0,360.0,1.0,Semiurban,Y
+LP002409,Male,Yes,0,Graduate,No,7901,1833.0,180.0,360.0,1.0,Rural,Y
+LP001844,Male,No,0,Graduate,Yes,16250,0.0,192.0,360.0,0.0,Urban,N
+LP001693,Female,No,0,Graduate,No,3244,0.0,80.0,360.0,1.0,Urban,Y
+LP001708,Female,No,0,Graduate,No,10000,0.0,214.0,360.0,1.0,Semiurban,N
+LP002984,Male,Yes,2,Graduate,No,7583,0.0,187.0,360.0,1.0,Urban,Y
+LP001206,Male,Yes,3+,Graduate,No,3029,0.0,99.0,360.0,1.0,Urban,Y
+LP002366,Male,Yes,0,Graduate,No,2666,4300.0,121.0,360.0,1.0,Rural,Y
+LP002519,Male,Yes,3+,Graduate,No,4691,0.0,100.0,360.0,1.0,Semiurban,Y
+LP001915,Male,Yes,2,Graduate,No,2301,985.8,78.0,180.0,1.0,Urban,Y
+LP002729,Male,No,1,Graduate,No,11250,0.0,196.0,360.0,,Semiurban,N
+LP001924,Male,No,0,Graduate,No,3158,3053.0,89.0,360.0,1.0,Rural,Y
+LP002862,Male,Yes,2,Not Graduate,No,6125,1625.0,187.0,480.0,1.0,Semiurban,N
+LP002341,Female,No,1,Graduate,No,2600,0.0,160.0,360.0,1.0,Urban,N
+LP002473,Male,Yes,0,Graduate,No,8334,0.0,160.0,360.0,1.0,Semiurban,N
+LP002833,Male,Yes,0,Not Graduate,No,4467,0.0,120.0,360.0,,Rural,Y
+LP001843,Male,Yes,1,Not Graduate,No,2661,7101.0,279.0,180.0,1.0,Semiurban,Y
+LP001978,Male,No,0,Graduate,No,4000,2500.0,140.0,360.0,1.0,Rural,Y
+LP001792,Male,Yes,1,Graduate,No,3315,0.0,96.0,360.0,1.0,Semiurban,Y
+LP001883,Female,No,0,Graduate,,3418,0.0,135.0,360.0,1.0,Rural,N
+LP001255,Male,No,0,Graduate,No,3750,0.0,113.0,480.0,1.0,Urban,N
+LP002670,Female,Yes,2,Graduate,No,2031,1632.0,113.0,480.0,1.0,Semiurban,Y
+LP001776,Female,No,0,Graduate,No,8333,0.0,280.0,360.0,1.0,Semiurban,Y
+LP001028,Male,Yes,2,Graduate,No,3073,8106.0,200.0,360.0,1.0,Urban,Y
+LP002448,Male,Yes,0,Graduate,No,3948,1733.0,149.0,360.0,0.0,Rural,N
+LP002502,Female,Yes,2,Not Graduate,,210,2917.0,98.0,360.0,1.0,Semiurban,Y
+LP001570,Male,Yes,2,Graduate,No,4167,1447.0,158.0,360.0,1.0,Rural,Y
+LP002379,Male,No,0,Graduate,No,6500,0.0,105.0,360.0,0.0,Rural,N
+LP002386,Male,No,0,Graduate,,12876,0.0,405.0,360.0,1.0,Semiurban,Y
+LP001535,Male,No,0,Graduate,No,3254,0.0,50.0,360.0,1.0,Urban,Y
+LP001052,Male,Yes,1,Graduate,,3717,2925.0,151.0,360.0,,Semiurban,N
+LP002600,Male,Yes,1,Graduate,Yes,2895,0.0,95.0,360.0,1.0,Semiurban,Y
+LP002877,Male,Yes,1,Graduate,No,1782,2232.0,107.0,360.0,1.0,Rural,Y
+LP002113,Female,No,3+,Not Graduate,No,1830,0.0,,360.0,0.0,Urban,N
+LP001194,Male,Yes,2,Graduate,No,2708,1167.0,97.0,360.0,1.0,Semiurban,Y
+LP002753,Female,No,1,Graduate,,3652,0.0,95.0,360.0,1.0,Semiurban,Y
+LP002648,Male,Yes,0,Graduate,No,2130,6666.0,70.0,180.0,1.0,Semiurban,N
+LP002757,Female,Yes,0,Not Graduate,No,3017,663.0,102.0,360.0,,Semiurban,Y
+LP001750,Male,Yes,0,Graduate,No,6250,0.0,128.0,360.0,1.0,Semiurban,Y
+LP001213,Male,Yes,1,Graduate,No,4945,0.0,,360.0,0.0,Rural,N
+LP001758,Male,Yes,2,Graduate,No,6250,1695.0,210.0,360.0,1.0,Semiurban,Y
+LP001579,Male,No,0,Graduate,No,2237,0.0,63.0,480.0,0.0,Semiurban,N
+LP001945,Female,No,,Graduate,No,5417,0.0,143.0,480.0,0.0,Urban,N
+LP002035,Male,Yes,2,Graduate,No,3717,0.0,120.0,360.0,1.0,Semiurban,Y
+LP002938,Male,Yes,0,Graduate,Yes,16120,0.0,260.0,360.0,1.0,Urban,Y
+LP001657,Male,Yes,0,Not Graduate,No,6033,0.0,160.0,360.0,1.0,Urban,N
+LP002422,Male,No,1,Graduate,No,37719,0.0,152.0,360.0,1.0,Semiurban,Y
+LP001131,Male,Yes,0,Graduate,No,3941,2336.0,134.0,360.0,1.0,Semiurban,Y
+LP002911,Male,Yes,1,Graduate,No,2787,1917.0,146.0,360.0,0.0,Rural,N
+LP002500,Male,Yes,3+,Not Graduate,No,2947,1664.0,70.0,180.0,0.0,Urban,N
+LP002961,Male,Yes,1,Graduate,No,3400,2500.0,173.0,360.0,1.0,Semiurban,Y
+LP002369,Male,Yes,0,Graduate,No,2920,16.12,87.0,360.0,1.0,Rural,Y
+LP002377,Female,No,1,Graduate,Yes,8624,0.0,150.0,360.0,1.0,Semiurban,Y
+LP002138,Male,Yes,0,Graduate,No,2625,6250.0,187.0,360.0,1.0,Rural,Y
+LP002983,Male,Yes,1,Graduate,No,8072,240.0,253.0,360.0,1.0,Urban,Y
+LP001123,Male,Yes,0,Graduate,No,2400,0.0,75.0,360.0,,Urban,Y
+LP001179,Male,Yes,2,Graduate,No,4616,0.0,134.0,360.0,1.0,Urban,N
+LP001245,Male,Yes,2,Not Graduate,Yes,1875,1875.0,97.0,360.0,1.0,Semiurban,Y
+LP002821,Male,No,0,Not Graduate,Yes,5800,0.0,132.0,360.0,1.0,Semiurban,Y
+LP001020,Male,Yes,1,Graduate,No,12841,10968.0,349.0,360.0,1.0,Semiurban,N
+LP001656,Male,No,0,Graduate,No,12000,0.0,164.0,360.0,1.0,Semiurban,N
+LP002472,Male,No,2,Graduate,No,4354,0.0,136.0,360.0,1.0,Rural,Y
+LP002332,Male,Yes,0,Not Graduate,No,2253,2033.0,110.0,360.0,1.0,Rural,Y
+LP002205,Male,No,1,Graduate,No,3062,1987.0,111.0,180.0,0.0,Urban,N
+LP002429,Male,Yes,1,Graduate,Yes,3466,1210.0,130.0,360.0,1.0,Rural,Y
+LP001106,Male,Yes,0,Graduate,No,2275,2067.0,,360.0,1.0,Urban,Y
+LP002065,Male,Yes,3+,Graduate,No,15000,0.0,300.0,360.0,1.0,Rural,Y
+LP001449,Male,No,0,Graduate,No,3865,1640.0,,360.0,1.0,Rural,Y
+LP001430,Female,No,0,Graduate,No,4166,0.0,44.0,360.0,1.0,Semiurban,Y
+LP001333,Male,Yes,0,Graduate,No,1977,997.0,50.0,360.0,1.0,Semiurban,Y
+LP001508,Male,Yes,2,Graduate,No,11757,0.0,187.0,180.0,1.0,Urban,Y
+LP001903,Male,Yes,0,Graduate,No,3993,3274.0,207.0,360.0,1.0,Semiurban,Y
+LP001811,Male,Yes,0,Not Graduate,No,3406,4417.0,123.0,360.0,1.0,Semiurban,Y
+LP001516,Female,Yes,2,Graduate,No,14866,0.0,70.0,360.0,1.0,Urban,Y
+LP002543,Male,Yes,2,Graduate,No,8333,0.0,246.0,360.0,1.0,Semiurban,Y
+LP002362,Male,Yes,1,Graduate,No,7250,1667.0,110.0,,0.0,Urban,N
+LP002755,Male,Yes,1,Not Graduate,No,2239,2524.0,128.0,360.0,1.0,Urban,Y
+LP002637,Male,No,0,Not Graduate,No,3598,1287.0,100.0,360.0,1.0,Rural,N
+LP001606,Male,Yes,0,Graduate,No,3497,1964.0,116.0,360.0,1.0,Rural,Y
+LP002807,Male,Yes,2,Not Graduate,No,3675,242.0,108.0,360.0,1.0,Semiurban,Y
+LP001439,Male,Yes,0,Not Graduate,No,4300,2014.0,194.0,360.0,1.0,Rural,Y
+LP002588,Male,Yes,0,Graduate,No,4625,2857.0,111.0,12.0,,Urban,Y
+LP002314,Female,No,0,Not Graduate,No,2213,0.0,66.0,360.0,1.0,Rural,Y
+LP001994,Female,No,0,Graduate,No,2400,1863.0,104.0,360.0,0.0,Urban,N
+LP002619,Male,Yes,0,Not Graduate,No,3814,1483.0,124.0,300.0,1.0,Semiurban,Y
+LP002067,Male,Yes,1,Graduate,Yes,8666,4983.0,376.0,360.0,0.0,Rural,N
+LP002837,Male,Yes,3+,Graduate,No,3400,2500.0,123.0,360.0,0.0,Rural,N
+LP002100,Male,No,,Graduate,No,2833,0.0,71.0,360.0,1.0,Urban,Y
+LP002571,Male,No,0,Not Graduate,No,3691,0.0,110.0,360.0,1.0,Rural,Y
+LP002364,Male,Yes,0,Graduate,No,14880,0.0,96.0,360.0,1.0,Semiurban,Y
+LP001036,Female,No,0,Graduate,No,3510,0.0,76.0,360.0,0.0,Urban,N
+LP002086,Female,Yes,0,Graduate,No,4333,2451.0,110.0,360.0,1.0,Urban,N
+LP002263,Male,Yes,0,Graduate,No,2583,2115.0,120.0,360.0,,Urban,Y
+LP002659,Male,Yes,3+,Graduate,No,3466,3428.0,150.0,360.0,1.0,Rural,Y
+LP001871,Female,No,0,Graduate,No,7200,0.0,120.0,360.0,1.0,Rural,Y
+LP001265,Female,No,0,Graduate,No,3846,0.0,111.0,360.0,1.0,Semiurban,Y
+LP001641,Male,Yes,1,Graduate,Yes,2178,0.0,66.0,300.0,0.0,Rural,N
+LP001322,Male,No,0,Graduate,No,4133,0.0,122.0,360.0,1.0,Semiurban,Y
+LP002527,Male,Yes,2,Graduate,Yes,16525,1014.0,150.0,360.0,1.0,Rural,Y
+LP002357,Female,No,0,Not Graduate,No,2720,0.0,80.0,,0.0,Urban,N
+LP001282,Male,Yes,0,Graduate,No,2500,2118.0,104.0,360.0,1.0,Semiurban,Y
+LP002347,Male,Yes,0,Graduate,No,3246,1417.0,138.0,360.0,1.0,Semiurban,Y
+LP001151,Female,No,0,Graduate,No,4000,2275.0,144.0,360.0,1.0,Semiurban,Y
+LP001560,Male,Yes,0,Not Graduate,No,1863,1041.0,98.0,360.0,1.0,Semiurban,Y
+LP002301,Female,No,0,Graduate,Yes,7441,0.0,194.0,360.0,1.0,Rural,N
+LP002545,Male,No,2,Graduate,No,3547,0.0,80.0,360.0,0.0,Rural,N
+LP001636,Male,Yes,0,Graduate,No,4600,0.0,73.0,180.0,1.0,Semiurban,Y
+LP001345,Male,Yes,2,Not Graduate,No,4288,3263.0,133.0,180.0,1.0,Urban,Y
+LP001259,Male,Yes,1,Graduate,Yes,1000,3022.0,110.0,360.0,1.0,Urban,N
+LP001528,Male,No,0,Graduate,No,6277,0.0,118.0,360.0,0.0,Rural,N
+LP002348,Male,Yes,0,Graduate,No,5829,0.0,138.0,360.0,1.0,Rural,Y
+LP001310,Male,Yes,0,Graduate,No,5695,4167.0,175.0,360.0,1.0,Semiurban,Y
+LP002262,Male,Yes,3+,Graduate,No,9504,0.0,275.0,360.0,1.0,Rural,Y
+LP001581,Male,Yes,0,Not Graduate,,1820,1769.0,95.0,360.0,1.0,Rural,Y
+LP002390,Male,No,0,Graduate,No,3750,0.0,100.0,360.0,1.0,Urban,Y
+LP002318,Female,No,1,Not Graduate,Yes,3867,0.0,62.0,360.0,1.0,Semiurban,N
+LP002517,Male,Yes,1,Not Graduate,No,2653,1500.0,113.0,180.0,0.0,Rural,N
+LP002130,Male,Yes,,Not Graduate,No,3523,3230.0,152.0,360.0,0.0,Rural,N
+LP001029,Male,No,0,Graduate,No,1853,2840.0,114.0,360.0,1.0,Rural,N
+LP002587,Male,Yes,0,Not Graduate,No,2600,1700.0,107.0,360.0,1.0,Rural,Y
+LP002894,Female,Yes,0,Graduate,No,3166,0.0,36.0,360.0,1.0,Semiurban,Y
+LP001996,Male,No,0,Graduate,No,20233,0.0,480.0,360.0,1.0,Rural,N
+LP001356,Male,Yes,0,Graduate,No,4652,3583.0,,360.0,1.0,Semiurban,Y
+LP002602,Male,No,0,Graduate,No,6283,4416.0,209.0,360.0,0.0,Rural,N
+LP001800,Male,Yes,1,Not Graduate,No,2510,1983.0,140.0,180.0,1.0,Urban,N
+LP002682,Male,Yes,,Not Graduate,No,3074,1800.0,123.0,360.0,0.0,Semiurban,N
+LP002990,Female,No,0,Graduate,Yes,4583,0.0,133.0,360.0,0.0,Semiurban,N
+LP001199,Male,Yes,2,Not Graduate,No,3357,2859.0,144.0,360.0,1.0,Urban,Y
+LP001722,Male,Yes,0,Graduate,No,150,1800.0,135.0,360.0,1.0,Rural,N
+LP001891,Male,Yes,0,Graduate,No,11146,0.0,136.0,360.0,1.0,Urban,Y
+LP001498,Male,No,0,Graduate,No,5417,0.0,168.0,360.0,1.0,Urban,Y
+LP001671,Female,Yes,0,Graduate,No,3416,2816.0,113.0,360.0,,Semiurban,Y
+LP002370,Male,No,0,Not Graduate,No,2717,0.0,60.0,180.0,1.0,Urban,Y
+LP002494,Male,No,0,Graduate,No,6000,0.0,140.0,360.0,1.0,Rural,Y
+LP002004,Male,No,0,Not Graduate,No,2927,2405.0,111.0,360.0,1.0,Semiurban,Y
+LP002140,Male,No,0,Graduate,No,8750,4167.0,308.0,360.0,1.0,Rural,N
+LP002776,Female,No,0,Graduate,No,5000,0.0,103.0,360.0,0.0,Semiurban,N
+LP002706,Male,Yes,1,Not Graduate,No,5285,1430.0,161.0,360.0,0.0,Semiurban,Y
+LP001674,Male,Yes,1,Not Graduate,No,2600,2500.0,90.0,360.0,1.0,Semiurban,Y
+LP002732,Male,No,0,Not Graduate,,2550,2042.0,126.0,360.0,1.0,Rural,Y
+LP001421,Male,Yes,0,Graduate,No,5568,2142.0,175.0,360.0,1.0,Rural,N
+LP001011,Male,Yes,2,Graduate,Yes,5417,4196.0,267.0,360.0,1.0,Urban,Y
+LP001940,Male,Yes,2,Graduate,No,3153,1560.0,134.0,360.0,1.0,Urban,Y
+LP001086,Male,No,0,Not Graduate,No,1442,0.0,35.0,360.0,1.0,Urban,N
+LP001732,Male,Yes,2,Graduate,,5000,0.0,72.0,360.0,0.0,Semiurban,N
+LP002143,Female,Yes,0,Graduate,No,2423,505.0,130.0,360.0,1.0,Semiurban,Y
+LP002872,,Yes,0,Graduate,No,3087,2210.0,136.0,360.0,0.0,Semiurban,N
+LP002625,,No,0,Graduate,No,3583,0.0,96.0,360.0,1.0,Urban,N
+LP002144,Female,No,,Graduate,No,3813,0.0,116.0,180.0,1.0,Urban,Y
+LP002315,Male,Yes,1,Graduate,No,8300,0.0,152.0,300.0,0.0,Semiurban,N
+LP001736,Male,Yes,0,Graduate,No,2221,0.0,60.0,360.0,0.0,Urban,N
+LP002618,Male,Yes,1,Not Graduate,No,4050,5302.0,138.0,360.0,,Rural,N
+LP002226,Male,Yes,0,Graduate,,3333,2500.0,128.0,360.0,1.0,Semiurban,Y
+LP001157,Female,No,0,Graduate,No,3086,0.0,120.0,360.0,1.0,Semiurban,Y
+LP002106,Male,Yes,,Graduate,Yes,5503,4490.0,70.0,,1.0,Semiurban,Y
+LP001263,Male,Yes,3+,Graduate,No,3167,4000.0,180.0,300.0,0.0,Semiurban,N
+LP002634,Female,No,1,Graduate,No,13262,0.0,40.0,360.0,1.0,Urban,Y
+LP002943,Male,No,,Graduate,No,2987,0.0,88.0,360.0,0.0,Semiurban,N
+LP001493,Male,Yes,2,Not Graduate,No,4200,1430.0,129.0,360.0,1.0,Rural,N
+LP001384,Male,Yes,3+,Not Graduate,No,2071,754.0,94.0,480.0,1.0,Semiurban,Y
+LP002277,Female,No,0,Graduate,No,3180,0.0,71.0,360.0,0.0,Urban,N
+LP001713,Male,Yes,1,Graduate,Yes,7787,0.0,240.0,360.0,1.0,Urban,Y
+LP002717,Male,Yes,0,Graduate,No,1025,5500.0,216.0,360.0,,Rural,Y
+LP002960,Male,Yes,0,Not Graduate,No,2400,3800.0,,180.0,1.0,Urban,N
+LP002582,Female,No,0,Not Graduate,Yes,17263,0.0,225.0,360.0,1.0,Semiurban,Y
+LP002855,Male,Yes,2,Graduate,No,16666,0.0,275.0,360.0,1.0,Urban,Y
+LP002925,,No,0,Graduate,No,4750,0.0,94.0,360.0,1.0,Semiurban,Y
+LP001469,Male,No,0,Graduate,Yes,20166,0.0,650.0,480.0,,Urban,Y
+LP002002,Female,No,0,Graduate,No,2917,0.0,84.0,360.0,1.0,Semiurban,Y
+LP002053,Male,Yes,3+,Graduate,No,4342,189.0,124.0,360.0,1.0,Semiurban,Y
+LP002265,Male,Yes,2,Not Graduate,No,1993,1625.0,113.0,180.0,1.0,Semiurban,Y
+LP001639,Female,Yes,0,Graduate,No,3625,0.0,108.0,360.0,1.0,Semiurban,Y
+LP002536,Male,Yes,3+,Not Graduate,No,3095,0.0,113.0,360.0,1.0,Rural,Y
+LP001892,Male,No,0,Graduate,No,2833,1857.0,126.0,360.0,1.0,Rural,Y
+LP002789,Male,Yes,0,Graduate,No,3593,4266.0,132.0,180.0,0.0,Rural,N
+LP002054,Male,Yes,2,Not Graduate,No,3601,1590.0,,360.0,1.0,Rural,Y
+LP001786,Male,Yes,0,Graduate,,5746,0.0,255.0,360.0,,Urban,N
+LP002231,Female,No,0,Graduate,No,6000,0.0,156.0,360.0,1.0,Urban,Y
+LP001814,Male,Yes,2,Graduate,No,9703,0.0,112.0,360.0,1.0,Urban,Y
+LP001253,Male,Yes,3+,Graduate,Yes,5266,1774.0,187.0,360.0,1.0,Semiurban,Y
+LP001334,Male,Yes,0,Not Graduate,No,4188,0.0,115.0,180.0,1.0,Semiurban,Y
+LP002533,Male,Yes,2,Graduate,No,2947,1603.0,,360.0,1.0,Urban,N
+LP002522,Female,No,0,Graduate,Yes,2500,0.0,93.0,360.0,,Urban,Y
+LP001806,Male,No,0,Graduate,No,2965,5701.0,155.0,60.0,1.0,Urban,Y
+LP001197,Male,Yes,0,Graduate,No,3366,2200.0,135.0,360.0,1.0,Rural,N
+LP001207,Male,Yes,0,Not Graduate,Yes,2609,3449.0,165.0,180.0,0.0,Rural,N
+LP001034,Male,No,1,Not Graduate,No,3596,0.0,100.0,240.0,,Urban,Y
+LP001953,Male,Yes,1,Graduate,No,6875,0.0,200.0,360.0,1.0,Semiurban,Y
+LP002936,Male,Yes,0,Graduate,No,3859,3300.0,142.0,180.0,1.0,Rural,Y
+LP002832,Male,Yes,2,Graduate,No,8799,0.0,258.0,360.0,0.0,Urban,N
+LP001665,Male,Yes,1,Graduate,No,3125,2583.0,170.0,360.0,1.0,Semiurban,N
+LP001664,Male,No,0,Graduate,No,4191,0.0,120.0,360.0,1.0,Rural,Y
+LP001682,Male,Yes,3+,Not Graduate,No,3992,0.0,,180.0,1.0,Urban,N
+LP001087,Female,No,2,Graduate,,3750,2083.0,120.0,360.0,1.0,Semiurban,Y
+LP002917,Female,No,0,Not Graduate,No,2165,0.0,70.0,360.0,1.0,Semiurban,Y
+LP001616,Male,Yes,1,Graduate,No,3750,0.0,116.0,360.0,1.0,Semiurban,Y
+LP002813,Female,Yes,1,Graduate,Yes,19484,0.0,600.0,360.0,1.0,Semiurban,Y
+LP001536,Male,Yes,3+,Graduate,No,39999,0.0,600.0,180.0,0.0,Semiurban,Y
+LP001552,Male,Yes,0,Graduate,No,4583,5625.0,255.0,360.0,1.0,Semiurban,Y
+LP002978,Female,No,0,Graduate,No,2900,0.0,71.0,360.0,1.0,Rural,Y
+LP001432,Male,Yes,2,Graduate,No,2957,0.0,81.0,360.0,1.0,Semiurban,Y
+LP002181,Male,No,0,Not Graduate,No,6216,0.0,133.0,360.0,1.0,Rural,N
+LP002892,Male,Yes,2,Graduate,No,6540,0.0,205.0,360.0,1.0,Semiurban,Y
+LP001120,Male,No,0,Graduate,No,1800,1213.0,47.0,360.0,1.0,Urban,Y
+LP001974,Female,No,0,Graduate,No,5000,0.0,132.0,360.0,1.0,Rural,Y
+LP002874,Male,No,0,Graduate,No,3229,2739.0,110.0,360.0,1.0,Urban,Y
+LP001692,Female,No,0,Not Graduate,No,4408,0.0,120.0,360.0,1.0,Semiurban,Y
+LP002211,Male,Yes,0,Graduate,No,4817,923.0,120.0,180.0,1.0,Urban,Y
+LP001743,Male,Yes,2,Graduate,No,4009,1717.0,116.0,360.0,1.0,Semiurban,Y
+LP002284,Male,No,0,Not Graduate,No,3902,1666.0,109.0,360.0,1.0,Rural,Y
+LP002898,Male,Yes,1,Graduate,No,1880,0.0,61.0,360.0,,Rural,N
+LP001098,Male,Yes,0,Graduate,No,3500,1667.0,114.0,360.0,1.0,Semiurban,Y
+LP001032,Male,No,0,Graduate,No,4950,0.0,125.0,360.0,1.0,Urban,Y
+LP002305,Female,No,0,Graduate,No,4547,0.0,115.0,360.0,1.0,Semiurban,Y
+LP002689,Male,Yes,2,Not Graduate,No,2192,1742.0,45.0,360.0,1.0,Semiurban,Y
+LP001520,Male,Yes,0,Graduate,No,4860,830.0,125.0,360.0,1.0,Semiurban,Y
+LP002116,Female,No,0,Graduate,No,2378,0.0,46.0,360.0,1.0,Rural,N
+LP001935,Male,No,0,Graduate,No,9508,0.0,187.0,360.0,1.0,Rural,Y
+LP001711,Male,Yes,3+,Graduate,No,3430,1250.0,128.0,360.0,0.0,Semiurban,N
+LP001634,Male,No,0,Graduate,No,1916,5063.0,67.0,360.0,,Rural,N
+LP001931,Female,No,0,Graduate,No,4124,0.0,115.0,360.0,1.0,Semiurban,Y
+LP002401,Male,Yes,0,Graduate,No,2213,1125.0,,360.0,1.0,Urban,Y
+LP001186,Female,Yes,1,Graduate,Yes,11500,0.0,286.0,360.0,0.0,Urban,N
+LP001949,Male,Yes,3+,Graduate,,4416,1250.0,110.0,360.0,1.0,Urban,Y
+LP002236,Male,Yes,2,Graduate,No,4566,0.0,100.0,360.0,1.0,Urban,N
+LP002820,Male,Yes,0,Graduate,No,5923,2054.0,211.0,360.0,1.0,Rural,Y
+LP001448,,Yes,3+,Graduate,No,23803,0.0,370.0,360.0,1.0,Rural,Y
+LP001116,Male,No,0,Not Graduate,No,3748,1668.0,110.0,360.0,1.0,Semiurban,Y
+LP001018,Male,Yes,2,Graduate,No,4006,1526.0,168.0,360.0,1.0,Urban,Y
+LP002142,Female,Yes,0,Graduate,Yes,5500,0.0,105.0,360.0,0.0,Rural,N
+LP001043,Male,Yes,0,Not Graduate,No,7660,0.0,104.0,360.0,0.0,Urban,N
+LP001644,,Yes,0,Graduate,Yes,674,5296.0,168.0,360.0,1.0,Rural,Y
+LP002234,Male,No,0,Graduate,Yes,7167,0.0,128.0,360.0,1.0,Urban,Y
+LP001896,Male,Yes,2,Graduate,No,3900,0.0,90.0,360.0,1.0,Semiurban,Y
+LP002784,Male,Yes,1,Not Graduate,No,2492,2375.0,,360.0,1.0,Rural,Y
+LP001824,Male,Yes,1,Graduate,No,2882,1843.0,123.0,480.0,1.0,Semiurban,Y
+LP001504,Male,No,0,Graduate,Yes,6950,0.0,175.0,180.0,1.0,Semiurban,Y
+LP002151,Male,Yes,1,Graduate,No,3875,0.0,67.0,360.0,1.0,Urban,N
+LP002266,Male,Yes,2,Graduate,No,3100,1400.0,113.0,360.0,1.0,Urban,Y
+LP002393,Female,,,Graduate,No,10047,0.0,,240.0,1.0,Semiurban,Y
+LP001370,Male,No,0,Not Graduate,,7333,0.0,120.0,360.0,1.0,Rural,N
+LP001487,Male,No,0,Graduate,No,4895,0.0,102.0,360.0,1.0,Semiurban,Y
+LP001405,Male,Yes,1,Graduate,No,2214,1398.0,85.0,360.0,,Urban,Y
+LP002444,Male,No,1,Not Graduate,Yes,2769,1542.0,190.0,360.0,,Semiurban,N
+LP001543,Male,Yes,1,Graduate,No,9538,0.0,187.0,360.0,1.0,Urban,Y
+LP002223,Male,Yes,0,Graduate,No,4310,0.0,130.0,360.0,,Semiurban,Y
+LP002979,Male,Yes,3+,Graduate,No,4106,0.0,40.0,180.0,1.0,Rural,Y
+LP002407,Female,Yes,0,Not Graduate,Yes,7142,0.0,138.0,360.0,1.0,Rural,Y
+LP001749,Male,Yes,0,Graduate,No,7578,1010.0,175.0,,1.0,Semiurban,Y
+LP001760,Male,,,Graduate,No,4758,0.0,158.0,480.0,1.0,Semiurban,Y
+LP001744,Male,No,0,Graduate,No,2971,2791.0,144.0,360.0,1.0,Semiurban,Y
+LP001734,Female,Yes,2,Graduate,No,4283,2383.0,127.0,360.0,,Semiurban,Y
+LP002940,Male,No,0,Not Graduate,No,3833,0.0,110.0,360.0,1.0,Rural,Y
+LP001531,Male,No,0,Graduate,No,9166,0.0,244.0,360.0,1.0,Urban,N
+LP001864,Male,Yes,3+,Not Graduate,No,4931,0.0,128.0,360.0,,Semiurban,N
+LP001859,Male,Yes,0,Graduate,No,14683,2100.0,304.0,360.0,1.0,Rural,N
+LP001451,Male,Yes,1,Graduate,Yes,10513,3850.0,160.0,180.0,0.0,Urban,N
+LP002197,Male,Yes,2,Graduate,No,5185,0.0,155.0,360.0,1.0,Semiurban,Y
+LP002175,Male,Yes,0,Graduate,No,4750,2333.0,130.0,360.0,1.0,Urban,Y
+LP001024,Male,Yes,2,Graduate,No,3200,700.0,70.0,360.0,1.0,Urban,Y
+LP002606,Female,No,0,Graduate,No,3159,0.0,100.0,360.0,1.0,Semiurban,Y
+LP002916,Male,Yes,0,Graduate,No,2297,1522.0,104.0,360.0,1.0,Urban,Y
+LP001013,Male,Yes,0,Not Graduate,No,2333,1516.0,95.0,360.0,1.0,Urban,Y

classification/unipredict/atharvaingle-crop-recommendation-dataset/metadata.json

@@ -0,0 +1,83 @@
+{
+  "dataset": "atharvaingle-crop-recommendation-dataset",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "label",
+  "label_values": [
+    "orange",
+    "watermelon",
+    "maize",
+    "muskmelon",
+    "cotton",
+    "rice",
+    "blackgram",
+    "pigeonpeas",
+    "apple",
+    "mungbean",
+    "grapes",
+    "banana",
+    "mothbeans",
+    "papaya",
+    "coffee",
+    "chickpea",
+    "lentil",
+    "kidneybeans",
+    "pomegranate",
+    "mango",
+    "jute",
+    "coconut"
+  ],
+  "num_labels": 22,
+  "train_samples": 1980,
+  "test_samples": 220,
+  "train_label_distribution": {
+    "papaya": 90,
+    "watermelon": 90,
+    "coffee": 90,
+    "pomegranate": 90,
+    "jute": 90,
+    "cotton": 90,
+    "banana": 90,
+    "mango": 90,
+    "orange": 90,
+    "mungbean": 90,
+    "blackgram": 90,
+    "maize": 90,
+    "chickpea": 90,
+    "grapes": 90,
+    "mothbeans": 90,
+    "coconut": 90,
+    "lentil": 90,
+    "pigeonpeas": 90,
+    "rice": 90,
+    "apple": 90,
+    "muskmelon": 90,
+    "kidneybeans": 90
+  },
+  "test_label_distribution": {
+    "watermelon": 10,
+    "coffee": 10,
+    "cotton": 10,
+    "grapes": 10,
+    "lentil": 10,
+    "blackgram": 10,
+    "maize": 10,
+    "jute": 10,
+    "orange": 10,
+    "mungbean": 10,
+    "mothbeans": 10,
+    "pomegranate": 10,
+    "mango": 10,
+    "papaya": 10,
+    "kidneybeans": 10,
+    "coconut": 10,
+    "chickpea": 10,
+    "apple": 10,
+    "banana": 10,
+    "muskmelon": 10,
+    "rice": 10,
+    "pigeonpeas": 10
+  }
+}

classification/unipredict/atharvaingle-crop-recommendation-dataset/test.csv

@@ -0,0 +1,221 @@
+N,P,K,temperature,humidity,ph,rainfall,label
+113,30,50,26.04,83.99,6.28,43.88,watermelon
+80,18,31,24.03,58.85,7.3,134.68,coffee
+131,60,17,25.32,81.79,7.43,83.47,cotton
+98,29,30,25.64,61.03,6.22,199.47,coffee
+29,142,203,29.67,83.71,5.89,66.48,grapes
+116,36,25,27.58,58.53,6.17,156.68,coffee
+19,79,19,20.06,67.76,6.68,42.9,lentil
+21,62,24,33.49,62.73,6.85,65.45,blackgram
+33,77,15,23.9,66.32,7.8,40.75,lentil
+66,54,21,25.19,60.2,5.92,72.12,maize
+84,38,43,26.57,73.82,7.26,159.32,jute
+34,11,10,31.75,94.6,7.36,115.2,orange
+25,48,21,28.44,83.49,6.27,52.55,mungbean
+7,45,22,25.51,44.83,9.93,74.33,mothbeans
+37,11,36,24.25,85.56,6.71,106.92,pomegranate
+34,38,31,35.38,45.58,6.45,97.42,mango
+3,136,205,17.59,80.85,6.33,71.41,grapes
+5,16,31,35.96,48.7,4.56,98.01,mango
+24,33,35,29.26,54.82,5.34,100.76,mango
+21,29,12,22.3,92.16,6.44,117.37,orange
+90,57,24,18.93,72.8,6.16,82.34,maize
+32,56,21,27.39,88.67,6.7,58.3,mungbean
+10,5,42,20.24,91.09,6.89,109.25,pomegranate
+50,46,52,31.18,90.22,6.73,54.02,papaya
+38,51,52,32.66,90.79,6.93,78.85,papaya
+29,68,23,24.16,19.28,5.83,116.73,kidneybeans
+101,13,54,25.43,82.91,6.83,56.34,watermelon
+1,12,30,27.75,95.95,5.56,131.09,coconut
+7,63,24,22.95,24.04,5.86,107.73,kidneybeans
+4,80,16,29.2,68.02,7.44,44.93,lentil
+40,17,15,21.35,90.95,7.87,107.09,orange
+35,64,78,17.93,14.27,7.5,85.37,chickpea
+0,23,15,22.57,93.37,7.6,109.86,orange
+35,68,45,42.94,90.09,6.61,234.85,papaya
+70,44,19,23.32,73.45,5.85,94.3,maize
+13,7,43,18.2,91.12,7.01,109.66,pomegranate
+36,128,204,25.24,80.69,5.7,67.04,grapes
+100,24,28,25.6,57.73,7.1,195.77,coffee
+16,139,203,17.83,80.96,6.28,65.85,grapes
+103,51,20,22.8,84.15,7.05,91.64,cotton
+8,139,199,29.37,81.54,6.34,66.13,grapes
+10,56,18,28.0,68.64,7.33,46.11,lentil
+120,48,16,22.46,75.41,7.46,71.85,cotton
+32,145,203,23.83,90.84,6.41,109.6,apple
+37,72,18,18.88,24.54,5.72,105.41,kidneybeans
+95,74,50,25.9,80.47,6.0,110.1,banana
+39,30,38,20.13,87.6,6.97,108.07,pomegranate
+81,45,23,19.33,68.03,6.19,84.23,maize
+110,25,54,28.91,90.78,6.43,23.44,muskmelon
+106,40,30,23.43,64.11,6.78,122.68,coffee
+117,37,32,23.11,67.06,6.79,162.58,coffee
+33,29,34,31.41,49.22,6.83,93.0,mango
+71,35,24,22.27,59.52,5.83,67.97,maize
+115,12,52,27.51,94.96,6.69,21.02,muskmelon
+79,45,43,25.72,79.16,7.17,187.17,jute
+59,55,19,31.74,62.51,7.33,68.97,blackgram
+60,36,43,23.43,83.06,5.29,219.9,rice
+16,130,201,29.12,82.79,5.68,68.85,grapes
+96,18,50,25.33,84.31,6.9,41.53,watermelon
+74,40,40,25.14,83.12,6.39,169.34,jute
+100,10,53,24.54,84.61,6.21,42.01,watermelon
+82,48,36,25.79,81.77,6.35,193.24,jute
+98,22,47,29.07,91.92,6.34,28.84,muskmelon
+30,13,25,27.15,91.49,6.41,164.92,coconut
+84,57,25,22.54,67.99,6.49,64.41,maize
+131,56,20,22.01,81.84,7.76,92.24,cotton
+1,6,35,27.02,95.72,6.23,147.17,coconut
+3,72,24,36.51,57.93,6.03,122.65,pigeonpeas
+99,73,53,26.29,81.06,5.87,118.67,banana
+59,69,80,19.08,17.87,8.17,69.41,chickpea
+22,17,5,24.12,90.72,6.95,102.84,orange
+28,27,32,28.94,93.0,5.76,191.77,coconut
+15,36,27,27.79,53.97,5.64,91.01,mango
+17,64,18,36.75,58.26,6.08,124.6,pigeonpeas
+111,88,55,29.45,78.35,5.51,96.45,banana
+10,24,27,27.57,94.9,5.71,145.93,coconut
+28,57,17,30.48,61.58,9.42,61.87,mothbeans
+21,38,21,29.76,86.45,6.64,37.55,mungbean
+26,126,195,21.41,92.99,5.88,118.4,apple
+0,29,32,28.06,98.37,5.87,171.65,coconut
+24,27,34,28.88,95.11,6.2,145.06,coconut
+117,79,49,25.41,82.36,6.18,112.98,banana
+39,24,39,23.65,93.33,6.43,109.81,pomegranate
+47,63,16,27.44,67.1,6.66,72.51,blackgram
+4,19,43,18.07,93.15,5.78,106.36,pomegranate
+27,73,79,19.16,15.84,7.35,82.7,chickpea
+47,46,52,23.19,91.4,6.5,206.4,papaya
+39,42,20,29.35,61.25,8.06,40.83,mothbeans
+29,25,14,30.49,90.46,7.78,113.33,orange
+19,35,24,27.11,83.64,6.88,49.12,mungbean
+102,71,48,28.65,79.29,5.7,102.46,banana
+65,39,45,23.67,70.89,6.77,184.46,jute
+77,36,23,24.71,56.73,6.65,88.45,maize
+17,16,14,16.4,92.18,6.63,102.94,orange
+15,133,199,24.0,91.61,5.82,117.61,apple
+37,18,39,24.15,94.51,6.42,110.23,pomegranate
+27,60,17,26.42,63.65,7.03,64.42,blackgram
+40,16,35,34.16,54.16,4.95,98.33,mango
+14,5,36,24.93,85.19,5.83,104.77,pomegranate
+70,68,45,33.84,92.85,6.99,203.4,papaya
+25,60,22,21.63,21.18,5.89,134.36,kidneybeans
+27,64,21,32.84,68.68,7.54,73.67,blackgram
+81,49,20,18.04,60.61,5.51,104.23,maize
+30,127,204,22.5,92.46,6.13,100.93,apple
+90,44,38,23.84,83.88,7.47,241.2,rice
+0,65,15,23.46,23.22,5.65,95.84,kidneybeans
+89,45,36,21.33,80.47,6.44,185.5,rice
+14,128,205,22.61,94.59,6.23,116.04,apple
+25,65,21,33.86,68.59,6.88,69.24,blackgram
+19,72,15,28.84,69.76,6.89,44.09,lentil
+32,129,201,16.36,83.0,6.49,71.56,grapes
+75,41,35,24.97,78.63,6.86,166.64,jute
+31,79,25,23.19,22.31,5.9,63.38,kidneybeans
+6,77,25,20.61,24.36,5.79,69.64,kidneybeans
+99,6,46,28.61,94.22,6.4,28.99,muskmelon
+89,28,33,26.44,53.84,6.99,175.37,coffee
+39,5,31,27.1,93.7,5.55,150.95,coconut
+29,145,205,22.81,92.13,6.21,109.34,apple
+39,138,203,21.19,82.33,6.4,74.63,grapes
+4,40,21,28.8,80.46,6.73,44.3,mungbean
+81,40,45,25.76,80.76,6.43,174.51,jute
+14,140,197,23.35,90.9,6.07,113.04,apple
+8,120,196,24.07,82.66,6.05,69.82,grapes
+13,61,22,19.44,63.28,7.73,46.83,lentil
+34,60,22,17.66,18.15,5.64,100.67,kidneybeans
+22,44,24,24.31,56.33,6.03,59.0,mothbeans
+4,40,26,27.58,48.57,6.72,95.84,mango
+30,79,22,18.29,69.49,6.25,48.6,lentil
+22,60,85,18.84,14.74,7.81,94.78,chickpea
+3,68,16,18.32,34.7,4.96,107.47,pigeonpeas
+89,52,42,23.09,81.45,6.14,196.66,jute
+36,67,77,18.37,19.56,7.15,79.26,chickpea
+6,37,17,28.09,80.35,6.76,38.14,mungbean
+101,92,45,28.23,80.64,5.76,98.0,banana
+116,40,33,24.91,54.15,7.04,129.55,coffee
+108,23,51,26.84,83.85,6.11,40.23,watermelon
+6,48,24,28.64,84.61,6.79,48.48,mungbean
+55,67,16,34.37,69.69,6.6,70.27,blackgram
+40,55,18,30.38,40.59,7.12,47.95,mothbeans
+29,77,75,17.5,15.48,7.78,72.94,chickpea
+7,28,35,30.02,46.78,4.67,96.64,mango
+113,19,46,25.42,81.12,6.29,49.52,watermelon
+67,59,41,21.95,80.97,6.01,213.36,rice
+0,55,25,28.17,43.67,4.52,45.78,mothbeans
+12,71,19,24.91,60.71,7.14,42.2,lentil
+81,30,31,24.65,51.94,7.03,135.14,coffee
+42,67,77,18.99,15.94,7.11,78.7,chickpea
+21,44,18,27.07,86.9,7.13,50.47,mungbean
+36,67,20,20.39,60.48,6.92,53.32,lentil
+38,61,52,31.23,94.94,6.62,46.44,papaya
+27,61,18,33.31,67.08,5.27,108.51,pigeonpeas
+91,75,55,27.49,76.11,6.21,109.28,banana
+84,50,44,25.49,81.41,5.94,182.65,rice
+111,29,31,26.06,52.31,6.14,161.34,coffee
+133,50,25,25.72,81.2,7.57,99.93,cotton
+21,20,31,25.6,99.72,5.86,165.82,coconut
+89,25,50,27.05,91.35,6.38,25.08,muskmelon
+36,58,25,28.66,59.32,8.4,36.93,mothbeans
+78,42,42,20.13,81.6,7.63,262.72,rice
+1,135,203,22.78,92.7,5.62,113.78,apple
+105,77,52,29.16,76.16,5.82,100.01,banana
+27,71,23,23.45,46.49,7.11,150.87,pigeonpeas
+21,74,15,29.49,67.11,6.47,153.25,pigeonpeas
+13,121,196,22.21,93.51,6.44,120.16,apple
+20,40,15,29.57,88.08,7.2,45.04,mungbean
+34,76,80,20.66,15.85,7.99,65.24,chickpea
+9,51,19,27.04,49.33,5.49,48.25,mothbeans
+78,50,43,25.12,85.73,6.35,159.57,jute
+102,28,54,25.16,80.28,6.86,55.5,watermelon
+21,31,32,35.39,51.43,5.25,90.3,mango
+77,58,19,22.81,56.51,5.79,101.6,maize
+8,28,38,23.23,94.43,6.84,105.69,pomegranate
+0,19,33,27.13,95.24,6.23,204.72,coconut
+71,60,22,26.07,59.37,6.2,85.76,maize
+78,35,44,26.54,84.67,7.07,183.62,rice
+69,60,54,36.32,93.06,6.99,141.17,papaya
+125,39,21,25.03,82.21,7.95,95.02,cotton
+67,41,40,25.85,87.82,7.33,152.62,jute
+22,67,78,17.17,14.42,6.2,72.33,chickpea
+11,74,17,21.36,69.92,6.63,46.64,lentil
+6,69,19,26.89,41.7,4.75,94.47,pigeonpeas
+28,58,24,19.73,18.28,5.75,143.76,kidneybeans
+94,5,55,28.59,91.89,6.09,26.88,muskmelon
+10,71,18,19.54,66.35,6.15,173.11,pigeonpeas
+40,64,47,32.5,93.48,6.89,71.74,papaya
+9,76,25,28.88,50.12,5.71,179.22,pigeonpeas
+34,34,35,27.27,47.17,6.42,95.26,mango
+24,18,6,26.57,94.45,6.29,116.38,orange
+82,26,47,28.5,93.47,6.57,24.2,muskmelon
+73,35,38,24.89,81.98,5.01,185.95,rice
+82,78,46,25.06,84.97,5.74,110.44,banana
+39,52,53,32.51,94.66,6.7,51.07,papaya
+29,76,15,28.54,64.2,7.03,69.69,blackgram
+98,53,38,20.27,81.64,5.01,270.44,rice
+30,60,21,28.88,62.49,5.46,182.27,pigeonpeas
+20,8,12,25.3,94.96,7.26,117.97,orange
+108,22,47,28.54,91.73,6.16,25.13,muskmelon
+92,20,55,25.1,87.53,6.59,59.27,watermelon
+104,25,51,28.96,93.88,6.47,23.56,muskmelon
+119,19,55,25.19,83.45,6.82,46.87,watermelon
+69,46,41,23.64,80.29,5.01,263.11,rice
+108,38,24,23.41,76.44,7.44,78.82,cotton
+4,20,41,24.27,93.8,6.54,104.54,pomegranate
+131,38,19,23.87,75.68,6.81,90.45,cotton
+129,43,16,25.55,77.85,6.73,78.58,cotton
+24,42,23,28.22,82.36,6.43,44.01,mungbean
+49,55,53,38.44,93.64,6.54,77.72,papaya
+40,63,18,30.42,67.66,6.74,63.02,blackgram
+110,28,46,24.29,88.05,6.5,51.26,watermelon
+24,55,78,17.3,15.15,6.65,75.58,chickpea
+32,57,18,15.54,23.76,5.7,107.39,kidneybeans
+97,74,45,26.48,78.52,5.68,113.12,banana
+20,68,17,30.12,60.12,6.58,71.73,blackgram
+29,44,20,30.04,63.56,8.62,31.83,mothbeans
+111,5,50,27.59,91.8,6.4,24.84,muskmelon
+7,16,9,18.88,92.04,7.81,114.67,orange
+20,122,204,11.8,80.86,6.49,65.07,grapes
+35,38,19,25.33,63.18,9.11,32.71,mothbeans
+15,123,204,22.53,92.55,6.37,115.38,apple
+100,48,17,23.78,83.04,7.83,66.27,cotton

classification/unipredict/atharvaingle-crop-recommendation-dataset/test.jsonl

@@ -0,0 +1,220 @@
+{"text": "The N is 113. The P is 30. The K is 50. The temperature is 26.04. The humidity is 83.99. The ph is 6.28. The rainfall is 43.88.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 80. The P is 18. The K is 31. The temperature is 24.03. The humidity is 58.85. The ph is 7.3. The rainfall is 134.68.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 131. The P is 60. The K is 17. The temperature is 25.32. The humidity is 81.79. The ph is 7.43. The rainfall is 83.47.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 98. The P is 29. The K is 30. The temperature is 25.64. The humidity is 61.03. The ph is 6.22. The rainfall is 199.47.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 29. The P is 142. The K is 203. The temperature is 29.67. The humidity is 83.71. The ph is 5.89. The rainfall is 66.48.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 116. The P is 36. The K is 25. The temperature is 27.58. The humidity is 58.53. The ph is 6.17. The rainfall is 156.68.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 19. The P is 79. The K is 19. The temperature is 20.06. The humidity is 67.76. The ph is 6.68. The rainfall is 42.9.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 21. The P is 62. The K is 24. The temperature is 33.49. The humidity is 62.73. The ph is 6.85. The rainfall is 65.45.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 33. The P is 77. The K is 15. The temperature is 23.9. The humidity is 66.32. The ph is 7.8. The rainfall is 40.75.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 66. The P is 54. The K is 21. The temperature is 25.19. The humidity is 60.2. The ph is 5.92. The rainfall is 72.12.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 84. The P is 38. The K is 43. The temperature is 26.57. The humidity is 73.82. The ph is 7.26. The rainfall is 159.32.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 34. The P is 11. The K is 10. The temperature is 31.75. The humidity is 94.6. The ph is 7.36. The rainfall is 115.2.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 25. The P is 48. The K is 21. The temperature is 28.44. The humidity is 83.49. The ph is 6.27. The rainfall is 52.55.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 7. The P is 45. The K is 22. The temperature is 25.51. The humidity is 44.83. The ph is 9.93. The rainfall is 74.33.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 37. The P is 11. The K is 36. The temperature is 24.25. The humidity is 85.56. The ph is 6.71. The rainfall is 106.92.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 34. The P is 38. The K is 31. The temperature is 35.38. The humidity is 45.58. The ph is 6.45. The rainfall is 97.42.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 3. The P is 136. The K is 205. The temperature is 17.59. The humidity is 80.85. The ph is 6.33. The rainfall is 71.41.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 5. The P is 16. The K is 31. The temperature is 35.96. The humidity is 48.7. The ph is 4.56. The rainfall is 98.01.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 24. The P is 33. The K is 35. The temperature is 29.26. The humidity is 54.82. The ph is 5.34. The rainfall is 100.76.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 21. The P is 29. The K is 12. The temperature is 22.3. The humidity is 92.16. The ph is 6.44. The rainfall is 117.37.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 90. The P is 57. The K is 24. The temperature is 18.93. The humidity is 72.8. The ph is 6.16. The rainfall is 82.34.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 32. The P is 56. The K is 21. The temperature is 27.39. The humidity is 88.67. The ph is 6.7. The rainfall is 58.3.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 10. The P is 5. The K is 42. The temperature is 20.24. The humidity is 91.09. The ph is 6.89. The rainfall is 109.25.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 50. The P is 46. The K is 52. The temperature is 31.18. The humidity is 90.22. The ph is 6.73. The rainfall is 54.02.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 38. The P is 51. The K is 52. The temperature is 32.66. The humidity is 90.79. The ph is 6.93. The rainfall is 78.85.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 29. The P is 68. The K is 23. The temperature is 24.16. The humidity is 19.28. The ph is 5.83. The rainfall is 116.73.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 101. The P is 13. The K is 54. The temperature is 25.43. The humidity is 82.91. The ph is 6.83. The rainfall is 56.34.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 1. The P is 12. The K is 30. The temperature is 27.75. The humidity is 95.95. The ph is 5.56. The rainfall is 131.09.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 7. The P is 63. The K is 24. The temperature is 22.95. The humidity is 24.04. The ph is 5.86. The rainfall is 107.73.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 4. The P is 80. The K is 16. The temperature is 29.2. The humidity is 68.02. The ph is 7.44. The rainfall is 44.93.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 40. The P is 17. The K is 15. The temperature is 21.35. The humidity is 90.95. The ph is 7.87. The rainfall is 107.09.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 35. The P is 64. The K is 78. The temperature is 17.93. The humidity is 14.27. The ph is 7.5. The rainfall is 85.37.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 0. The P is 23. The K is 15. The temperature is 22.57. The humidity is 93.37. The ph is 7.6. The rainfall is 109.86.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 35. The P is 68. The K is 45. The temperature is 42.94. The humidity is 90.09. The ph is 6.61. The rainfall is 234.85.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 70. The P is 44. The K is 19. The temperature is 23.32. The humidity is 73.45. The ph is 5.85. The rainfall is 94.3.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 13. The P is 7. The K is 43. The temperature is 18.2. The humidity is 91.12. The ph is 7.01. The rainfall is 109.66.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 36. The P is 128. The K is 204. The temperature is 25.24. The humidity is 80.69. The ph is 5.7. The rainfall is 67.04.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 100. The P is 24. The K is 28. The temperature is 25.6. The humidity is 57.73. The ph is 7.1. The rainfall is 195.77.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 16. The P is 139. The K is 203. The temperature is 17.83. The humidity is 80.96. The ph is 6.28. The rainfall is 65.85.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 103. The P is 51. The K is 20. The temperature is 22.8. The humidity is 84.15. The ph is 7.05. The rainfall is 91.64.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 8. The P is 139. The K is 199. The temperature is 29.37. The humidity is 81.54. The ph is 6.34. The rainfall is 66.13.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 10. The P is 56. The K is 18. The temperature is 28.0. The humidity is 68.64. The ph is 7.33. The rainfall is 46.11.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 120. The P is 48. The K is 16. The temperature is 22.46. The humidity is 75.41. The ph is 7.46. The rainfall is 71.85.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 32. The P is 145. The K is 203. The temperature is 23.83. The humidity is 90.84. The ph is 6.41. The rainfall is 109.6.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 37. The P is 72. The K is 18. The temperature is 18.88. The humidity is 24.54. The ph is 5.72. The rainfall is 105.41.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 95. The P is 74. The K is 50. The temperature is 25.9. The humidity is 80.47. The ph is 6.0. The rainfall is 110.1.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 39. The P is 30. The K is 38. The temperature is 20.13. The humidity is 87.6. The ph is 6.97. The rainfall is 108.07.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 81. The P is 45. The K is 23. The temperature is 19.33. The humidity is 68.03. The ph is 6.19. The rainfall is 84.23.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 110. The P is 25. The K is 54. The temperature is 28.91. The humidity is 90.78. The ph is 6.43. The rainfall is 23.44.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 106. The P is 40. The K is 30. The temperature is 23.43. The humidity is 64.11. The ph is 6.78. The rainfall is 122.68.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 117. The P is 37. The K is 32. The temperature is 23.11. The humidity is 67.06. The ph is 6.79. The rainfall is 162.58.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 33. The P is 29. The K is 34. The temperature is 31.41. The humidity is 49.22. The ph is 6.83. The rainfall is 93.0.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 71. The P is 35. The K is 24. The temperature is 22.27. The humidity is 59.52. The ph is 5.83. The rainfall is 67.97.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 115. The P is 12. The K is 52. The temperature is 27.51. The humidity is 94.96. The ph is 6.69. The rainfall is 21.02.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 79. The P is 45. The K is 43. The temperature is 25.72. The humidity is 79.16. The ph is 7.17. The rainfall is 187.17.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 59. The P is 55. The K is 19. The temperature is 31.74. The humidity is 62.51. The ph is 7.33. The rainfall is 68.97.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 60. The P is 36. The K is 43. The temperature is 23.43. The humidity is 83.06. The ph is 5.29. The rainfall is 219.9.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 16. The P is 130. The K is 201. The temperature is 29.12. The humidity is 82.79. The ph is 5.68. The rainfall is 68.85.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 96. The P is 18. The K is 50. The temperature is 25.33. The humidity is 84.31. The ph is 6.9. The rainfall is 41.53.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 74. The P is 40. The K is 40. The temperature is 25.14. The humidity is 83.12. The ph is 6.39. The rainfall is 169.34.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 100. The P is 10. The K is 53. The temperature is 24.54. The humidity is 84.61. The ph is 6.21. The rainfall is 42.01.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 82. The P is 48. The K is 36. The temperature is 25.79. The humidity is 81.77. The ph is 6.35. The rainfall is 193.24.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 98. The P is 22. The K is 47. The temperature is 29.07. The humidity is 91.92. The ph is 6.34. The rainfall is 28.84.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 30. The P is 13. The K is 25. The temperature is 27.15. The humidity is 91.49. The ph is 6.41. The rainfall is 164.92.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 84. The P is 57. The K is 25. The temperature is 22.54. The humidity is 67.99. The ph is 6.49. The rainfall is 64.41.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 131. The P is 56. The K is 20. The temperature is 22.01. The humidity is 81.84. The ph is 7.76. The rainfall is 92.24.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 1. The P is 6. The K is 35. The temperature is 27.02. The humidity is 95.72. The ph is 6.23. The rainfall is 147.17.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 3. The P is 72. The K is 24. The temperature is 36.51. The humidity is 57.93. The ph is 6.03. The rainfall is 122.65.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 99. The P is 73. The K is 53. The temperature is 26.29. The humidity is 81.06. The ph is 5.87. The rainfall is 118.67.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 59. The P is 69. The K is 80. The temperature is 19.08. The humidity is 17.87. The ph is 8.17. The rainfall is 69.41.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 22. The P is 17. The K is 5. The temperature is 24.12. The humidity is 90.72. The ph is 6.95. The rainfall is 102.84.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 28. The P is 27. The K is 32. The temperature is 28.94. The humidity is 93.0. The ph is 5.76. The rainfall is 191.77.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 15. The P is 36. The K is 27. The temperature is 27.79. The humidity is 53.97. The ph is 5.64. The rainfall is 91.01.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 17. The P is 64. The K is 18. The temperature is 36.75. The humidity is 58.26. The ph is 6.08. The rainfall is 124.6.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 111. The P is 88. The K is 55. The temperature is 29.45. The humidity is 78.35. The ph is 5.51. The rainfall is 96.45.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 10. The P is 24. The K is 27. The temperature is 27.57. The humidity is 94.9. The ph is 5.71. The rainfall is 145.93.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 28. The P is 57. The K is 17. The temperature is 30.48. The humidity is 61.58. The ph is 9.42. The rainfall is 61.87.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 21. The P is 38. The K is 21. The temperature is 29.76. The humidity is 86.45. The ph is 6.64. The rainfall is 37.55.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 26. The P is 126. The K is 195. The temperature is 21.41. The humidity is 92.99. The ph is 5.88. The rainfall is 118.4.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 0. The P is 29. The K is 32. The temperature is 28.06. The humidity is 98.37. The ph is 5.87. The rainfall is 171.65.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 24. The P is 27. The K is 34. The temperature is 28.88. The humidity is 95.11. The ph is 6.2. The rainfall is 145.06.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 117. The P is 79. The K is 49. The temperature is 25.41. The humidity is 82.36. The ph is 6.18. The rainfall is 112.98.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 39. The P is 24. The K is 39. The temperature is 23.65. The humidity is 93.33. The ph is 6.43. The rainfall is 109.81.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 47. The P is 63. The K is 16. The temperature is 27.44. The humidity is 67.1. The ph is 6.66. The rainfall is 72.51.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 4. The P is 19. The K is 43. The temperature is 18.07. The humidity is 93.15. The ph is 5.78. The rainfall is 106.36.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 27. The P is 73. The K is 79. The temperature is 19.16. The humidity is 15.84. The ph is 7.35. The rainfall is 82.7.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 47. The P is 46. The K is 52. The temperature is 23.19. The humidity is 91.4. The ph is 6.5. The rainfall is 206.4.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 39. The P is 42. The K is 20. The temperature is 29.35. The humidity is 61.25. The ph is 8.06. The rainfall is 40.83.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 29. The P is 25. The K is 14. The temperature is 30.49. The humidity is 90.46. The ph is 7.78. The rainfall is 113.33.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 19. The P is 35. The K is 24. The temperature is 27.11. The humidity is 83.64. The ph is 6.88. The rainfall is 49.12.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 102. The P is 71. The K is 48. The temperature is 28.65. The humidity is 79.29. The ph is 5.7. The rainfall is 102.46.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 65. The P is 39. The K is 45. The temperature is 23.67. The humidity is 70.89. The ph is 6.77. The rainfall is 184.46.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 77. The P is 36. The K is 23. The temperature is 24.71. The humidity is 56.73. The ph is 6.65. The rainfall is 88.45.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 17. The P is 16. The K is 14. The temperature is 16.4. The humidity is 92.18. The ph is 6.63. The rainfall is 102.94.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 15. The P is 133. The K is 199. The temperature is 24.0. The humidity is 91.61. The ph is 5.82. The rainfall is 117.61.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 37. The P is 18. The K is 39. The temperature is 24.15. The humidity is 94.51. The ph is 6.42. The rainfall is 110.23.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 27. The P is 60. The K is 17. The temperature is 26.42. The humidity is 63.65. The ph is 7.03. The rainfall is 64.42.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 40. The P is 16. The K is 35. The temperature is 34.16. The humidity is 54.16. The ph is 4.95. The rainfall is 98.33.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 14. The P is 5. The K is 36. The temperature is 24.93. The humidity is 85.19. The ph is 5.83. The rainfall is 104.77.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 70. The P is 68. The K is 45. The temperature is 33.84. The humidity is 92.85. The ph is 6.99. The rainfall is 203.4.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 25. The P is 60. The K is 22. The temperature is 21.63. The humidity is 21.18. The ph is 5.89. The rainfall is 134.36.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 27. The P is 64. The K is 21. The temperature is 32.84. The humidity is 68.68. The ph is 7.54. The rainfall is 73.67.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 81. The P is 49. The K is 20. The temperature is 18.04. The humidity is 60.61. The ph is 5.51. The rainfall is 104.23.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 30. The P is 127. The K is 204. The temperature is 22.5. The humidity is 92.46. The ph is 6.13. The rainfall is 100.93.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 90. The P is 44. The K is 38. The temperature is 23.84. The humidity is 83.88. The ph is 7.47. The rainfall is 241.2.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 0. The P is 65. The K is 15. The temperature is 23.46. The humidity is 23.22. The ph is 5.65. The rainfall is 95.84.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 89. The P is 45. The K is 36. The temperature is 21.33. The humidity is 80.47. The ph is 6.44. The rainfall is 185.5.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 14. The P is 128. The K is 205. The temperature is 22.61. The humidity is 94.59. The ph is 6.23. The rainfall is 116.04.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 25. The P is 65. The K is 21. The temperature is 33.86. The humidity is 68.59. The ph is 6.88. The rainfall is 69.24.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 19. The P is 72. The K is 15. The temperature is 28.84. The humidity is 69.76. The ph is 6.89. The rainfall is 44.09.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 32. The P is 129. The K is 201. The temperature is 16.36. The humidity is 83.0. The ph is 6.49. The rainfall is 71.56.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 75. The P is 41. The K is 35. The temperature is 24.97. The humidity is 78.63. The ph is 6.86. The rainfall is 166.64.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 31. The P is 79. The K is 25. The temperature is 23.19. The humidity is 22.31. The ph is 5.9. The rainfall is 63.38.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 6. The P is 77. The K is 25. The temperature is 20.61. The humidity is 24.36. The ph is 5.79. The rainfall is 69.64.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 99. The P is 6. The K is 46. The temperature is 28.61. The humidity is 94.22. The ph is 6.4. The rainfall is 28.99.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 89. The P is 28. The K is 33. The temperature is 26.44. The humidity is 53.84. The ph is 6.99. The rainfall is 175.37.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 39. The P is 5. The K is 31. The temperature is 27.1. The humidity is 93.7. The ph is 5.55. The rainfall is 150.95.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 29. The P is 145. The K is 205. The temperature is 22.81. The humidity is 92.13. The ph is 6.21. The rainfall is 109.34.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 39. The P is 138. The K is 203. The temperature is 21.19. The humidity is 82.33. The ph is 6.4. The rainfall is 74.63.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 4. The P is 40. The K is 21. The temperature is 28.8. The humidity is 80.46. The ph is 6.73. The rainfall is 44.3.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 81. The P is 40. The K is 45. The temperature is 25.76. The humidity is 80.76. The ph is 6.43. The rainfall is 174.51.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 14. The P is 140. The K is 197. The temperature is 23.35. The humidity is 90.9. The ph is 6.07. The rainfall is 113.04.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 8. The P is 120. The K is 196. The temperature is 24.07. The humidity is 82.66. The ph is 6.05. The rainfall is 69.82.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 13. The P is 61. The K is 22. The temperature is 19.44. The humidity is 63.28. The ph is 7.73. The rainfall is 46.83.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 34. The P is 60. The K is 22. The temperature is 17.66. The humidity is 18.15. The ph is 5.64. The rainfall is 100.67.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 22. The P is 44. The K is 24. The temperature is 24.31. The humidity is 56.33. The ph is 6.03. The rainfall is 59.0.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 4. The P is 40. The K is 26. The temperature is 27.58. The humidity is 48.57. The ph is 6.72. The rainfall is 95.84.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 30. The P is 79. The K is 22. The temperature is 18.29. The humidity is 69.49. The ph is 6.25. The rainfall is 48.6.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 22. The P is 60. The K is 85. The temperature is 18.84. The humidity is 14.74. The ph is 7.81. The rainfall is 94.78.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 3. The P is 68. The K is 16. The temperature is 18.32. The humidity is 34.7. The ph is 4.96. The rainfall is 107.47.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 89. The P is 52. The K is 42. The temperature is 23.09. The humidity is 81.45. The ph is 6.14. The rainfall is 196.66.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 36. The P is 67. The K is 77. The temperature is 18.37. The humidity is 19.56. The ph is 7.15. The rainfall is 79.26.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 6. The P is 37. The K is 17. The temperature is 28.09. The humidity is 80.35. The ph is 6.76. The rainfall is 38.14.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 101. The P is 92. The K is 45. The temperature is 28.23. The humidity is 80.64. The ph is 5.76. The rainfall is 98.0.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 116. The P is 40. The K is 33. The temperature is 24.91. The humidity is 54.15. The ph is 7.04. The rainfall is 129.55.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 108. The P is 23. The K is 51. The temperature is 26.84. The humidity is 83.85. The ph is 6.11. The rainfall is 40.23.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 6. The P is 48. The K is 24. The temperature is 28.64. The humidity is 84.61. The ph is 6.79. The rainfall is 48.48.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 55. The P is 67. The K is 16. The temperature is 34.37. The humidity is 69.69. The ph is 6.6. The rainfall is 70.27.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 40. The P is 55. The K is 18. The temperature is 30.38. The humidity is 40.59. The ph is 7.12. The rainfall is 47.95.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 29. The P is 77. The K is 75. The temperature is 17.5. The humidity is 15.48. The ph is 7.78. The rainfall is 72.94.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 7. The P is 28. The K is 35. The temperature is 30.02. The humidity is 46.78. The ph is 4.67. The rainfall is 96.64.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 113. The P is 19. The K is 46. The temperature is 25.42. The humidity is 81.12. The ph is 6.29. The rainfall is 49.52.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 67. The P is 59. The K is 41. The temperature is 21.95. The humidity is 80.97. The ph is 6.01. The rainfall is 213.36.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 0. The P is 55. The K is 25. The temperature is 28.17. The humidity is 43.67. The ph is 4.52. The rainfall is 45.78.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 12. The P is 71. The K is 19. The temperature is 24.91. The humidity is 60.71. The ph is 7.14. The rainfall is 42.2.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 81. The P is 30. The K is 31. The temperature is 24.65. The humidity is 51.94. The ph is 7.03. The rainfall is 135.14.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 42. The P is 67. The K is 77. The temperature is 18.99. The humidity is 15.94. The ph is 7.11. The rainfall is 78.7.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 21. The P is 44. The K is 18. The temperature is 27.07. The humidity is 86.9. The ph is 7.13. The rainfall is 50.47.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 36. The P is 67. The K is 20. The temperature is 20.39. The humidity is 60.48. The ph is 6.92. The rainfall is 53.32.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 38. The P is 61. The K is 52. The temperature is 31.23. The humidity is 94.94. The ph is 6.62. The rainfall is 46.44.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 27. The P is 61. The K is 18. The temperature is 33.31. The humidity is 67.08. The ph is 5.27. The rainfall is 108.51.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 91. The P is 75. The K is 55. The temperature is 27.49. The humidity is 76.11. The ph is 6.21. The rainfall is 109.28.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 84. The P is 50. The K is 44. The temperature is 25.49. The humidity is 81.41. The ph is 5.94. The rainfall is 182.65.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 111. The P is 29. The K is 31. The temperature is 26.06. The humidity is 52.31. The ph is 6.14. The rainfall is 161.34.", "label": "coffee", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 133. The P is 50. The K is 25. The temperature is 25.72. The humidity is 81.2. The ph is 7.57. The rainfall is 99.93.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 21. The P is 20. The K is 31. The temperature is 25.6. The humidity is 99.72. The ph is 5.86. The rainfall is 165.82.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 89. The P is 25. The K is 50. The temperature is 27.05. The humidity is 91.35. The ph is 6.38. The rainfall is 25.08.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 36. The P is 58. The K is 25. The temperature is 28.66. The humidity is 59.32. The ph is 8.4. The rainfall is 36.93.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 78. The P is 42. The K is 42. The temperature is 20.13. The humidity is 81.6. The ph is 7.63. The rainfall is 262.72.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 1. The P is 135. The K is 203. The temperature is 22.78. The humidity is 92.7. The ph is 5.62. The rainfall is 113.78.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 105. The P is 77. The K is 52. The temperature is 29.16. The humidity is 76.16. The ph is 5.82. The rainfall is 100.01.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 27. The P is 71. The K is 23. The temperature is 23.45. The humidity is 46.49. The ph is 7.11. The rainfall is 150.87.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 21. The P is 74. The K is 15. The temperature is 29.49. The humidity is 67.11. The ph is 6.47. The rainfall is 153.25.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 13. The P is 121. The K is 196. The temperature is 22.21. The humidity is 93.51. The ph is 6.44. The rainfall is 120.16.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 20. The P is 40. The K is 15. The temperature is 29.57. The humidity is 88.08. The ph is 7.2. The rainfall is 45.04.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 34. The P is 76. The K is 80. The temperature is 20.66. The humidity is 15.85. The ph is 7.99. The rainfall is 65.24.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 9. The P is 51. The K is 19. The temperature is 27.04. The humidity is 49.33. The ph is 5.49. The rainfall is 48.25.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 78. The P is 50. The K is 43. The temperature is 25.12. The humidity is 85.73. The ph is 6.35. The rainfall is 159.57.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 102. The P is 28. The K is 54. The temperature is 25.16. The humidity is 80.28. The ph is 6.86. The rainfall is 55.5.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 21. The P is 31. The K is 32. The temperature is 35.39. The humidity is 51.43. The ph is 5.25. The rainfall is 90.3.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 77. The P is 58. The K is 19. The temperature is 22.81. The humidity is 56.51. The ph is 5.79. The rainfall is 101.6.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 8. The P is 28. The K is 38. The temperature is 23.23. The humidity is 94.43. The ph is 6.84. The rainfall is 105.69.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 0. The P is 19. The K is 33. The temperature is 27.13. The humidity is 95.24. The ph is 6.23. The rainfall is 204.72.", "label": "coconut", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 71. The P is 60. The K is 22. The temperature is 26.07. The humidity is 59.37. The ph is 6.2. The rainfall is 85.76.", "label": "maize", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 78. The P is 35. The K is 44. The temperature is 26.54. The humidity is 84.67. The ph is 7.07. The rainfall is 183.62.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 69. The P is 60. The K is 54. The temperature is 36.32. The humidity is 93.06. The ph is 6.99. The rainfall is 141.17.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 125. The P is 39. The K is 21. The temperature is 25.03. The humidity is 82.21. The ph is 7.95. The rainfall is 95.02.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 67. The P is 41. The K is 40. The temperature is 25.85. The humidity is 87.82. The ph is 7.33. The rainfall is 152.62.", "label": "jute", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 22. The P is 67. The K is 78. The temperature is 17.17. The humidity is 14.42. The ph is 6.2. The rainfall is 72.33.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 11. The P is 74. The K is 17. The temperature is 21.36. The humidity is 69.92. The ph is 6.63. The rainfall is 46.64.", "label": "lentil", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 6. The P is 69. The K is 19. The temperature is 26.89. The humidity is 41.7. The ph is 4.75. The rainfall is 94.47.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 28. The P is 58. The K is 24. The temperature is 19.73. The humidity is 18.28. The ph is 5.75. The rainfall is 143.76.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 94. The P is 5. The K is 55. The temperature is 28.59. The humidity is 91.89. The ph is 6.09. The rainfall is 26.88.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 10. The P is 71. The K is 18. The temperature is 19.54. The humidity is 66.35. The ph is 6.15. The rainfall is 173.11.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 40. The P is 64. The K is 47. The temperature is 32.5. The humidity is 93.48. The ph is 6.89. The rainfall is 71.74.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 9. The P is 76. The K is 25. The temperature is 28.88. The humidity is 50.12. The ph is 5.71. The rainfall is 179.22.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 34. The P is 34. The K is 35. The temperature is 27.27. The humidity is 47.17. The ph is 6.42. The rainfall is 95.26.", "label": "mango", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 24. The P is 18. The K is 6. The temperature is 26.57. The humidity is 94.45. The ph is 6.29. The rainfall is 116.38.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 82. The P is 26. The K is 47. The temperature is 28.5. The humidity is 93.47. The ph is 6.57. The rainfall is 24.2.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 73. The P is 35. The K is 38. The temperature is 24.89. The humidity is 81.98. The ph is 5.01. The rainfall is 185.95.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 82. The P is 78. The K is 46. The temperature is 25.06. The humidity is 84.97. The ph is 5.74. The rainfall is 110.44.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 39. The P is 52. The K is 53. The temperature is 32.51. The humidity is 94.66. The ph is 6.7. The rainfall is 51.07.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 29. The P is 76. The K is 15. The temperature is 28.54. The humidity is 64.2. The ph is 7.03. The rainfall is 69.69.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 98. The P is 53. The K is 38. The temperature is 20.27. The humidity is 81.64. The ph is 5.01. The rainfall is 270.44.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 30. The P is 60. The K is 21. The temperature is 28.88. The humidity is 62.49. The ph is 5.46. The rainfall is 182.27.", "label": "pigeonpeas", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 20. The P is 8. The K is 12. The temperature is 25.3. The humidity is 94.96. The ph is 7.26. The rainfall is 117.97.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 108. The P is 22. The K is 47. The temperature is 28.54. The humidity is 91.73. The ph is 6.16. The rainfall is 25.13.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 92. The P is 20. The K is 55. The temperature is 25.1. The humidity is 87.53. The ph is 6.59. The rainfall is 59.27.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 104. The P is 25. The K is 51. The temperature is 28.96. The humidity is 93.88. The ph is 6.47. The rainfall is 23.56.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 119. The P is 19. The K is 55. The temperature is 25.19. The humidity is 83.45. The ph is 6.82. The rainfall is 46.87.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 69. The P is 46. The K is 41. The temperature is 23.64. The humidity is 80.29. The ph is 5.01. The rainfall is 263.11.", "label": "rice", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 108. The P is 38. The K is 24. The temperature is 23.41. The humidity is 76.44. The ph is 7.44. The rainfall is 78.82.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 4. The P is 20. The K is 41. The temperature is 24.27. The humidity is 93.8. The ph is 6.54. The rainfall is 104.54.", "label": "pomegranate", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 131. The P is 38. The K is 19. The temperature is 23.87. The humidity is 75.68. The ph is 6.81. The rainfall is 90.45.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 129. The P is 43. The K is 16. The temperature is 25.55. The humidity is 77.85. The ph is 6.73. The rainfall is 78.58.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 24. The P is 42. The K is 23. The temperature is 28.22. The humidity is 82.36. The ph is 6.43. The rainfall is 44.01.", "label": "mungbean", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 49. The P is 55. The K is 53. The temperature is 38.44. The humidity is 93.64. The ph is 6.54. The rainfall is 77.72.", "label": "papaya", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 40. The P is 63. The K is 18. The temperature is 30.42. The humidity is 67.66. The ph is 6.74. The rainfall is 63.02.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 110. The P is 28. The K is 46. The temperature is 24.29. The humidity is 88.05. The ph is 6.5. The rainfall is 51.26.", "label": "watermelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 24. The P is 55. The K is 78. The temperature is 17.3. The humidity is 15.15. The ph is 6.65. The rainfall is 75.58.", "label": "chickpea", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 32. The P is 57. The K is 18. The temperature is 15.54. The humidity is 23.76. The ph is 5.7. The rainfall is 107.39.", "label": "kidneybeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 97. The P is 74. The K is 45. The temperature is 26.48. The humidity is 78.52. The ph is 5.68. The rainfall is 113.12.", "label": "banana", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 20. The P is 68. The K is 17. The temperature is 30.12. The humidity is 60.12. The ph is 6.58. The rainfall is 71.73.", "label": "blackgram", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 29. The P is 44. The K is 20. The temperature is 30.04. The humidity is 63.56. The ph is 8.62. The rainfall is 31.83.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 111. The P is 5. The K is 50. The temperature is 27.59. The humidity is 91.8. The ph is 6.4. The rainfall is 24.84.", "label": "muskmelon", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 7. The P is 16. The K is 9. The temperature is 18.88. The humidity is 92.04. The ph is 7.81. The rainfall is 114.67.", "label": "orange", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 20. The P is 122. The K is 204. The temperature is 11.8. The humidity is 80.86. The ph is 6.49. The rainfall is 65.07.", "label": "grapes", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 35. The P is 38. The K is 19. The temperature is 25.33. The humidity is 63.18. The ph is 9.11. The rainfall is 32.71.", "label": "mothbeans", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 15. The P is 123. The K is 204. The temperature is 22.53. The humidity is 92.55. The ph is 6.37. The rainfall is 115.38.", "label": "apple", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The N is 100. The P is 48. The K is 17. The temperature is 23.78. The humidity is 83.04. The ph is 7.83. The rainfall is 66.27.", "label": "cotton", "dataset": "atharvaingle-crop-recommendation-dataset", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/atharvaingle-crop-recommendation-dataset/train.csv

@@ -0,0 +1,1981 @@
+N,P,K,temperature,humidity,ph,rainfall,label
+61,68,50,35.21,91.5,6.79,243.07,papaya
+85,27,45,26.07,88.73,6.47,57.8,watermelon
+87,38,29,25.2,57.88,6.65,156.15,coffee
+20,7,45,18.91,89.24,6.08,112.48,pomegranate
+67,43,38,25.22,70.88,7.3,195.86,jute
+116,52,19,22.94,75.37,6.11,67.08,cotton
+85,95,47,25.94,78.34,6.21,119.85,banana
+33,31,34,31.33,50.22,5.42,89.78,mango
+39,21,9,13.21,94.03,6.35,106.27,orange
+21,39,20,28.14,82.12,7.06,46.76,mungbean
+36,66,15,30.09,69.35,6.67,67.14,blackgram
+40,65,49,35.33,91.06,6.68,163.91,papaya
+100,35,36,25.31,72.01,6.35,190.56,jute
+117,32,34,26.27,52.13,6.76,127.18,coffee
+32,68,52,32.68,92.62,6.8,248.86,papaya
+74,48,17,21.63,60.28,6.43,69.22,maize
+83,10,53,24.93,85.01,6.2,48.76,watermelon
+26,24,34,31.27,52.24,6.81,89.74,mango
+41,69,82,20.02,16.63,6.72,68.98,chickpea
+39,139,201,41.19,81.02,5.54,68.69,grapes
+22,123,205,32.45,83.89,5.9,68.74,grapes
+13,144,204,30.73,82.43,6.09,68.38,grapes
+21,9,40,24.51,90.64,5.96,105.62,pomegranate
+32,48,18,26.46,56.4,5.99,64.16,mothbeans
+17,29,26,26.14,93.28,6.07,195.41,coconut
+107,21,26,26.45,55.32,7.24,144.69,coffee
+18,66,22,25.88,67.55,6.35,47.9,lentil
+78,48,22,23.09,63.1,5.59,70.43,maize
+11,40,23,29.61,63.05,5.8,50.2,mothbeans
+104,80,54,27.09,81.34,5.88,110.13,banana
+20,77,23,34.87,38.84,5.18,148.25,pigeonpeas
+85,52,45,26.31,82.37,7.22,265.54,rice
+18,125,204,22.36,94.48,6.05,116.74,apple
+36,144,196,23.65,94.51,6.5,115.36,apple
+54,77,85,17.14,17.07,7.83,83.75,chickpea
+11,14,5,11.5,94.89,6.95,115.57,orange
+82,22,45,26.22,85.35,6.51,54.6,watermelon
+91,24,55,26.27,83.09,6.26,46.77,watermelon
+86,31,35,27.01,60.77,6.49,191.45,coffee
+92,7,48,26.28,86.63,6.96,54.39,watermelon
+95,7,45,27.3,90.8,6.03,25.09,muskmelon
+87,23,28,26.22,62.27,6.98,193.75,coffee
+117,51,15,22.95,78.72,6.04,99.75,cotton
+15,11,38,23.13,92.68,6.63,109.39,pomegranate
+8,45,15,28.1,60.98,4.61,33.84,mothbeans
+83,23,55,26.9,83.89,6.46,43.97,watermelon
+9,137,200,21.12,90.69,5.64,102.8,apple
+98,44,21,25.77,74.09,6.52,107.49,maize
+36,54,46,42.55,94.94,6.66,214.41,papaya
+98,25,52,25.28,83.15,6.22,49.29,watermelon
+104,35,28,27.51,50.67,6.98,144.0,coffee
+126,50,19,24.69,81.74,6.63,78.58,cotton
+17,134,204,39.04,80.18,6.5,73.88,grapes
+84,40,42,26.28,73.36,6.7,186.69,jute
+28,48,15,25.16,55.25,9.25,40.9,mothbeans
+2,38,18,27.54,89.93,6.62,45.49,mungbean
+25,12,26,28.57,95.68,6.44,134.84,coconut
+44,77,21,32.64,61.3,7.33,61.84,blackgram
+39,60,15,35.09,30.99,5.0,116.91,pigeonpeas
+17,136,196,23.87,90.5,5.88,103.05,apple
+106,46,20,23.44,78.63,6.2,81.15,cotton
+0,123,205,22.03,92.96,5.79,121.13,apple
+49,69,82,18.32,15.36,7.26,81.79,chickpea
+73,58,21,19.97,57.68,6.6,60.65,maize
+104,25,55,29.81,90.37,6.12,22.69,muskmelon
+4,47,20,25.98,64.96,4.19,72.19,mothbeans
+40,22,6,24.54,91.91,6.49,115.98,orange
+32,55,52,37.59,92.0,6.97,159.66,papaya
+67,51,24,23.5,61.32,5.58,64.78,maize
+14,75,24,24.55,57.34,6.44,118.36,pigeonpeas
+55,60,15,32.8,68.78,7.16,64.11,blackgram
+26,18,27,27.46,92.91,5.84,142.14,coconut
+112,87,48,27.2,77.4,6.2,99.47,banana
+38,60,76,18.65,17.81,8.87,77.93,chickpea
+29,41,21,31.49,62.85,8.87,64.57,mothbeans
+95,30,52,29.48,90.34,6.64,26.04,muskmelon
+7,20,12,16.53,94.77,6.48,110.04,orange
+40,5,32,26.07,96.7,5.98,143.53,coconut
+89,85,55,26.67,76.49,6.28,91.73,banana
+14,18,30,29.81,52.14,5.19,95.75,mango
+40,144,196,22.72,92.25,5.99,107.03,apple
+5,23,15,25.67,92.05,7.41,112.54,orange
+87,60,23,20.27,63.91,6.44,62.5,maize
+113,28,48,28.88,92.49,6.17,24.44,muskmelon
+17,52,17,27.88,86.45,6.36,44.64,mungbean
+89,41,38,23.13,74.68,6.34,199.84,jute
+24,70,21,19.15,45.37,5.52,132.77,pigeonpeas
+29,122,196,41.95,81.16,5.64,73.07,grapes
+120,40,33,24.24,54.3,6.73,115.16,coffee
+35,57,83,19.48,17.45,7.48,80.5,chickpea
+91,36,45,24.44,82.45,5.95,267.98,rice
+74,58,18,20.04,56.36,6.73,109.02,maize
+52,65,20,32.82,66.16,6.81,68.84,blackgram
+19,122,202,23.34,90.38,5.81,112.9,apple
+6,9,12,31.08,90.14,7.03,109.69,orange
+24,53,17,28.95,89.08,6.42,57.66,mungbean
+34,140,198,21.7,93.44,5.75,115.18,apple
+13,57,25,28.3,86.21,6.86,50.47,mungbean
+74,46,45,25.76,88.37,6.03,189.43,jute
+87,25,46,27.43,90.03,6.38,21.75,muskmelon
+5,24,40,24.69,93.87,6.3,104.67,pomegranate
+27,61,15,25.27,67.1,6.96,48.34,lentil
+43,68,81,17.48,17.93,6.76,78.92,chickpea
+13,69,19,17.31,20.02,5.86,115.2,kidneybeans
+29,22,43,19.66,87.95,5.56,106.04,pomegranate
+131,49,22,25.5,79.98,7.31,67.06,cotton
+28,10,30,29.87,91.15,6.31,192.77,coconut
+52,68,78,17.49,16.96,6.9,86.05,chickpea
+118,12,47,27.97,92.17,6.01,28.95,muskmelon
+22,138,195,27.83,83.51,6.21,73.03,grapes
+7,40,17,31.21,40.93,8.53,53.79,mothbeans
+58,79,17,27.25,66.1,7.04,62.32,blackgram
+19,57,23,23.67,47.29,7.34,141.13,pigeonpeas
+96,86,51,29.91,76.99,6.26,92.0,banana
+104,47,18,23.97,76.98,7.63,90.76,cotton
+40,51,17,28.66,86.12,6.86,50.02,mungbean
+77,38,36,21.87,80.19,5.95,224.56,rice
+83,57,19,25.73,70.75,6.88,98.74,maize
+35,74,22,26.72,62.97,6.9,42.87,lentil
+25,62,21,26.73,68.14,7.04,67.15,blackgram
+127,53,24,22.22,76.18,6.13,70.41,cotton
+78,37,22,25.34,63.32,6.33,74.52,maize
+21,72,17,31.52,66.56,7.58,61.71,blackgram
+32,25,35,18.1,85.71,5.89,107.01,pomegranate
+39,16,27,35.54,52.95,4.93,91.55,mango
+25,121,201,30.51,82.72,5.59,70.08,grapes
+77,55,43,25.5,76.0,6.66,193.71,jute
+12,39,21,28.99,62.86,8.18,70.47,mothbeans
+30,75,81,19.42,16.8,6.41,68.5,chickpea
+118,18,32,27.65,51.11,6.35,122.84,coffee
+40,45,18,30.44,55.21,5.26,30.92,mothbeans
+92,81,52,27.39,81.47,6.44,94.31,banana
+27,120,200,21.45,90.75,6.11,116.7,apple
+39,28,10,31.35,91.48,7.18,109.15,orange
+42,79,85,17.22,15.82,6.13,76.58,chickpea
+39,77,21,23.0,60.24,4.6,159.69,pigeonpeas
+7,11,32,29.26,95.11,5.54,184.76,coconut
+83,58,45,25.76,83.52,5.88,245.66,rice
+90,92,55,27.01,80.19,6.13,97.33,banana
+27,24,41,24.33,90.88,6.61,110.46,pomegranate
+69,55,38,22.71,82.64,5.7,271.32,rice
+112,54,15,25.46,81.57,6.18,76.89,cotton
+25,143,198,22.81,91.52,6.03,107.86,apple
+62,52,16,22.28,58.84,6.97,63.87,maize
+88,35,35,27.56,58.46,6.78,117.94,coffee
+4,136,204,29.94,81.78,5.9,65.52,grapes
+6,47,18,29.16,80.28,6.72,40.17,mungbean
+29,140,195,23.64,90.95,5.56,116.74,apple
+33,57,17,27.9,88.72,6.78,57.8,mungbean
+74,39,38,23.24,84.59,7.78,233.05,rice
+20,60,78,18.17,14.7,6.36,90.78,chickpea
+27,40,24,27.84,90.0,7.06,52.85,mungbean
+14,58,17,30.54,59.97,4.61,33.49,mothbeans
+0,133,200,23.67,90.49,5.71,104.23,apple
+26,80,83,17.08,16.15,7.53,71.31,chickpea
+34,66,17,18.81,21.28,5.89,125.08,kidneybeans
+77,52,41,23.89,83.46,6.1,167.72,jute
+34,47,19,27.31,85.45,6.57,53.15,mungbean
+25,70,16,19.63,18.91,5.76,106.36,kidneybeans
+68,62,50,33.2,92.76,6.98,197.53,papaya
+24,70,16,25.18,68.93,6.55,35.03,lentil
+36,44,21,25.13,51.33,4.52,38.49,mothbeans
+2,40,27,29.74,47.55,5.95,90.1,mango
+8,6,33,28.28,93.65,6.1,171.95,coconut
+49,54,50,25.62,93.18,6.76,97.26,papaya
+29,144,204,22.43,92.49,5.8,119.1,apple
+117,24,53,29.17,92.21,6.29,21.3,muskmelon
+21,39,20,27.06,52.3,7.39,60.75,mothbeans
+69,67,52,27.72,94.44,6.83,82.83,papaya
+10,56,16,28.17,81.05,6.83,36.36,mungbean
+17,18,43,24.49,90.84,5.84,103.2,pomegranate
+68,70,54,31.3,92.76,6.99,54.78,papaya
+14,67,25,25.29,60.86,7.24,49.37,lentil
+88,78,45,29.1,79.2,6.32,92.08,banana
+8,37,33,28.08,54.96,6.13,97.45,mango
+28,74,81,18.01,18.31,8.75,81.99,chickpea
+91,21,26,26.33,57.36,7.26,191.65,coffee
+117,26,30,27.92,67.97,7.08,115.23,coffee
+32,60,83,19.69,19.44,8.83,91.76,chickpea
+24,44,17,29.86,80.03,6.67,50.66,mungbean
+2,79,15,21.54,65.47,7.51,35.75,lentil
+60,48,39,24.28,80.3,7.04,231.09,rice
+28,27,34,32.45,50.7,6.53,95.05,mango
+12,6,8,30.85,92.87,6.39,107.41,orange
+44,57,53,42.3,90.51,6.93,74.88,papaya
+62,56,35,25.98,81.66,6.24,163.35,jute
+84,29,49,29.94,93.91,6.25,20.39,muskmelon
+73,45,21,24.61,73.59,6.64,96.59,maize
+39,65,53,35.33,92.12,6.56,235.61,papaya
+25,51,24,25.5,61.67,9.39,65.08,mothbeans
+115,48,16,25.54,84.09,7.18,88.94,cotton
+2,129,201,22.78,94.37,5.68,122.14,apple
+1,66,23,19.54,56.93,4.8,173.17,pigeonpeas
+9,56,17,26.14,66.77,6.26,46.48,lentil
+26,72,22,28.77,37.58,4.67,91.72,pigeonpeas
+31,37,21,27.24,86.4,6.71,37.31,mungbean
+16,75,21,18.51,23.62,5.68,87.05,kidneybeans
+14,18,35,31.09,47.02,4.79,91.47,mango
+5,13,37,22.34,89.79,5.65,103.32,pomegranate
+3,18,31,31.65,48.21,6.39,91.1,mango
+42,73,25,34.04,67.21,6.5,73.24,blackgram
+77,48,36,25.87,84.1,7.36,154.84,jute
+7,56,18,18.31,24.33,5.7,76.14,kidneybeans
+36,65,80,18.29,16.68,6.05,74.87,chickpea
+25,132,198,22.32,90.85,5.73,100.12,apple
+56,79,15,29.48,63.2,7.45,71.89,blackgram
+87,28,35,26.56,57.16,6.76,152.06,coffee
+118,18,52,28.05,90.83,6.56,20.76,muskmelon
+17,64,16,30.98,32.25,7.16,180.72,pigeonpeas
+27,69,22,17.92,24.91,5.93,69.15,kidneybeans
+102,73,52,27.91,83.36,6.36,90.24,banana
+49,61,45,32.77,94.57,6.76,240.48,papaya
+31,76,82,20.82,17.85,7.6,79.21,chickpea
+87,28,30,25.6,68.66,6.54,168.84,coffee
+31,58,15,28.32,60.19,6.17,45.37,lentil
+8,33,29,29.98,49.49,6.44,91.82,mango
+70,38,35,24.4,79.27,7.01,164.27,jute
+80,30,25,26.24,65.64,7.49,148.38,coffee
+118,15,45,24.21,84.21,6.54,48.01,watermelon
+95,16,55,25.27,87.55,6.61,40.13,watermelon
+111,40,25,24.48,84.45,6.19,90.94,cotton
+32,138,197,9.54,80.73,5.91,69.44,grapes
+120,20,45,25.67,88.7,6.11,54.23,watermelon
+93,91,47,27.85,83.31,6.1,117.29,banana
+74,56,22,18.28,66.66,6.83,80.98,maize
+109,36,18,25.4,76.53,7.52,62.51,cotton
+14,139,197,21.72,92.84,6.06,121.7,apple
+22,133,201,23.82,80.12,6.0,67.27,grapes
+99,57,38,24.81,82.09,6.36,156.36,jute
+20,30,27,27.81,51.59,4.75,95.9,mango
+23,75,25,31.08,47.2,7.08,91.31,pigeonpeas
+3,141,197,21.98,91.13,6.14,115.48,apple
+47,79,78,17.48,14.76,6.61,65.11,chickpea
+104,20,26,27.23,52.95,7.49,175.73,coffee
+32,56,18,20.05,65.84,7.14,46.05,lentil
+36,26,26,30.17,51.08,6.81,95.23,mango
+10,78,18,18.54,62.71,6.3,44.08,lentil
+104,17,46,25.71,80.23,6.19,43.09,watermelon
+10,70,19,24.85,68.98,7.27,41.61,lentil
+32,137,204,22.86,93.13,5.82,117.73,apple
+33,47,17,24.87,48.28,8.62,63.92,mothbeans
+30,25,31,26.31,98.62,5.8,208.12,coconut
+3,78,18,20.21,68.65,6.89,50.9,lentil
+60,43,44,21.02,82.95,7.42,298.4,rice
+12,78,23,16.07,18.72,6.0,88.07,kidneybeans
+78,40,38,26.46,83.86,7.55,248.23,rice
+111,5,55,26.28,84.42,6.52,50.79,watermelon
+110,39,25,22.61,77.34,7.21,75.14,cotton
+36,43,21,28.36,84.86,7.14,52.93,mungbean
+86,95,49,28.05,78.05,6.46,108.4,banana
+71,46,40,20.28,82.12,7.24,191.95,rice
+34,6,27,25.85,90.93,5.86,147.89,coconut
+89,60,17,25.38,57.21,5.98,101.7,maize
+16,35,31,32.28,50.19,5.32,95.99,mango
+101,17,47,29.49,94.73,6.19,26.31,muskmelon
+34,10,14,34.05,92.06,6.73,116.8,orange
+33,60,15,28.95,81.67,6.51,56.51,mungbean
+120,60,15,22.32,83.86,7.29,65.36,cotton
+0,17,30,35.47,47.97,6.28,97.79,mango
+4,24,43,22.4,88.15,7.2,109.87,pomegranate
+22,28,26,27.67,45.42,4.95,92.85,mango
+110,71,54,28.67,82.21,5.73,94.38,banana
+68,41,16,21.78,57.81,6.16,102.09,maize
+44,56,49,39.23,91.26,6.52,64.45,papaya
+14,25,40,20.07,90.98,6.41,103.71,pomegranate
+31,20,30,32.18,54.01,6.21,91.89,mango
+18,26,31,32.61,47.75,5.42,91.1,mango
+70,42,43,23.17,76.67,6.51,157.12,jute
+88,17,52,29.9,90.75,6.65,25.38,muskmelon
+15,45,23,24.2,61.43,7.22,46.02,mothbeans
+51,72,75,18.89,14.99,7.1,80.11,chickpea
+13,22,5,19.67,90.5,7.76,100.17,orange
+25,130,197,39.71,82.69,5.55,74.92,grapes
+18,27,41,22.37,92.31,7.18,104.82,pomegranate
+82,40,45,26.21,81.7,6.67,180.12,jute
+34,65,47,23.49,93.71,6.83,191.78,papaya
+17,73,18,19.5,34.51,5.63,197.38,pigeonpeas
+27,43,23,31.7,56.85,5.88,44.94,mothbeans
+30,17,31,31.2,54.5,6.8,94.63,mango
+32,141,204,8.83,82.9,5.54,67.24,grapes
+21,38,20,27.11,63.57,5.79,62.2,mothbeans
+35,41,18,28.71,81.59,6.71,59.87,mungbean
+108,22,46,26.18,86.73,6.12,53.33,watermelon
+2,56,23,26.65,59.79,7.55,36.92,mothbeans
+101,10,47,25.54,83.32,6.94,57.57,watermelon
+109,91,53,29.67,83.51,6.01,110.25,banana
+74,43,23,25.95,61.89,6.33,99.58,maize
+26,27,10,28.07,92.91,6.08,114.13,orange
+81,41,38,22.68,83.73,7.52,200.91,rice
+11,143,197,22.98,93.32,5.88,122.2,apple
+60,55,45,21.41,83.33,5.94,287.58,rice
+8,16,6,12.23,90.26,7.11,108.42,orange
+67,45,38,22.73,82.17,7.3,260.89,rice
+27,10,33,27.81,97.48,6.47,154.06,coconut
+31,48,17,28.88,86.94,6.59,53.8,mungbean
+83,7,45,29.08,90.74,6.7,25.33,muskmelon
+92,60,23,18.67,71.52,5.72,69.93,maize
+23,72,84,19.02,17.13,6.92,79.93,chickpea
+28,59,22,30.91,52.8,7.05,170.99,pigeonpeas
+25,21,11,32.24,90.15,6.46,104.71,orange
+13,30,37,20.86,91.62,6.28,106.87,pomegranate
+87,48,38,23.82,80.94,7.16,190.31,jute
+21,63,17,25.09,68.18,6.56,41.45,lentil
+22,26,38,22.92,85.13,6.99,110.24,pomegranate
+2,143,196,22.71,90.45,5.67,109.89,apple
+30,28,30,31.87,52.19,5.06,98.47,mango
+58,51,47,42.13,91.7,6.76,197.4,papaya
+99,19,33,27.54,55.52,6.27,130.64,coffee
+34,66,19,32.97,60.18,7.59,73.45,blackgram
+30,61,18,27.15,67.03,6.16,52.51,lentil
+22,67,22,29.03,64.49,7.48,54.94,lentil
+11,124,204,13.43,80.07,6.36,71.4,grapes
+30,64,20,33.86,61.57,6.57,68.02,blackgram
+39,71,84,20.28,16.4,8.14,82.52,chickpea
+86,40,39,25.72,88.17,6.21,175.61,jute
+26,51,25,28.76,52.63,7.79,55.22,mothbeans
+32,76,15,28.05,63.5,7.6,43.36,lentil
+97,50,41,24.53,80.54,7.07,260.26,rice
+66,53,19,23.09,60.12,6.03,65.5,maize
+8,120,201,21.19,91.13,6.32,122.23,apple
+117,56,15,25.99,77.05,7.37,89.12,cotton
+35,72,21,34.04,64.29,7.74,66.86,blackgram
+90,40,39,25.73,81.86,6.63,191.96,jute
+4,14,41,19.85,89.81,6.43,102.82,pomegranate
+111,6,53,26.49,88.59,6.31,46.06,watermelon
+37,30,34,27.54,53.64,6.8,99.35,mango
+35,52,15,28.7,61.15,9.94,65.68,mothbeans
+7,17,26,34.89,48.76,6.41,91.63,mango
+113,6,52,27.76,90.36,6.74,25.22,muskmelon
+84,55,38,26.87,79.79,6.96,173.1,jute
+8,45,18,27.93,85.42,7.01,43.25,mungbean
+102,73,54,26.4,84.41,5.72,111.02,banana
+36,43,24,27.09,43.65,3.51,41.54,mothbeans
+31,20,26,25.57,97.61,6.44,199.79,coconut
+23,23,30,32.82,47.46,4.76,90.89,mango
+39,140,203,21.12,80.63,6.35,69.28,grapes
+25,40,21,27.73,81.14,6.25,44.18,mungbean
+95,23,45,27.82,90.57,6.27,21.19,muskmelon
+23,59,25,27.83,88.73,6.32,56.69,mungbean
+100,74,52,25.43,81.54,5.84,96.48,banana
+58,71,15,27.83,67.59,6.92,74.01,blackgram
+69,51,23,22.22,72.85,6.8,106.62,maize
+46,76,77,18.24,19.69,6.97,83.75,chickpea
+21,63,22,25.1,67.73,6.86,74.62,blackgram
+28,37,28,32.13,50.53,6.1,98.63,mango
+71,54,16,22.61,63.69,5.75,87.76,maize
+61,53,43,26.4,81.06,6.35,223.37,rice
+45,57,47,23.17,90.79,6.66,161.69,papaya
+122,40,17,24.96,81.32,6.85,80.04,cotton
+28,6,40,22.11,91.34,6.77,106.87,pomegranate
+23,24,32,28.12,46.17,5.63,93.3,mango
+113,41,20,25.0,80.54,7.26,96.33,cotton
+92,40,30,23.36,55.19,6.03,171.7,coffee
+27,71,24,31.46,48.18,7.06,165.41,pigeonpeas
+14,74,19,18.4,36.83,6.62,93.12,pigeonpeas
+23,6,33,29.18,92.73,6.03,204.96,coconut
+35,125,204,19.65,80.15,6.11,73.7,grapes
+130,59,19,25.07,82.5,6.52,93.51,cotton
+8,133,195,20.47,80.98,6.46,71.3,grapes
+19,38,26,31.48,48.78,4.53,93.17,mango
+139,35,15,25.25,83.46,5.9,86.56,cotton
+19,53,22,27.86,80.45,6.85,42.83,mungbean
+87,14,48,29.69,92.59,6.61,29.11,muskmelon
+8,38,32,29.75,46.74,4.98,91.41,mango
+60,46,53,24.49,92.98,6.76,183.49,papaya
+39,17,45,18.1,90.42,6.92,104.88,pomegranate
+23,7,34,26.11,91.52,5.85,134.13,coconut
+53,65,76,20.19,16.42,8.72,77.34,chickpea
+65,54,39,23.75,71.15,7.12,160.09,jute
+8,26,36,18.78,87.4,6.8,102.52,pomegranate
+36,56,83,18.9,19.76,7.45,69.1,chickpea
+109,12,48,29.46,92.13,6.71,20.76,muskmelon
+108,46,17,24.3,84.88,6.93,65.02,cotton
+23,30,44,20.94,85.43,6.12,103.03,pomegranate
+42,74,83,19.26,14.28,7.55,65.78,chickpea
+13,144,197,22.92,94.9,6.28,105.69,apple
+8,23,44,18.47,89.69,7.13,108.48,pomegranate
+13,64,20,19.13,62.58,6.59,36.47,lentil
+6,124,200,22.98,93.85,5.97,109.59,apple
+101,20,48,24.68,82.75,6.21,57.06,watermelon
+14,48,21,29.25,84.8,6.99,53.43,mungbean
+26,64,22,25.95,40.58,5.17,109.18,pigeonpeas
+22,51,16,27.97,61.35,8.64,70.1,mothbeans
+75,54,36,26.29,84.57,7.02,257.49,rice
+17,59,17,18.42,23.43,5.69,132.98,kidneybeans
+56,59,55,37.04,91.79,6.55,188.52,papaya
+11,44,17,26.34,55.59,8.02,35.11,mothbeans
+11,27,30,27.7,48.56,6.39,89.86,mango
+112,17,28,27.63,61.26,6.78,196.65,coffee
+37,23,28,25.61,94.31,5.74,224.32,coconut
+28,35,22,29.53,86.73,7.16,59.87,mungbean
+108,94,47,27.36,84.55,6.39,90.81,banana
+93,47,37,21.53,82.14,6.5,295.92,rice
+40,140,195,14.98,80.5,6.29,71.63,grapes
+94,89,48,28.56,84.52,5.65,111.08,banana
+25,78,76,17.48,15.76,7.23,66.97,chickpea
+36,65,16,25.71,64.11,7.69,50.17,lentil
+57,68,81,17.17,17.3,8.08,72.79,chickpea
+35,128,205,21.07,93.57,6.04,107.87,apple
+16,145,199,26.92,80.77,5.95,69.31,grapes
+34,15,34,27.06,91.11,5.68,224.7,coconut
+25,35,20,28.9,43.35,8.92,71.9,mothbeans
+5,65,19,18.28,68.1,6.98,48.8,lentil
+48,57,54,29.02,90.2,6.62,126.81,papaya
+39,76,76,19.97,15.57,8.14,69.16,chickpea
+7,144,197,23.85,94.35,6.13,114.05,apple
+18,74,15,24.9,22.28,5.71,146.47,kidneybeans
+91,53,40,26.53,81.42,5.39,264.61,rice
+40,121,199,26.18,81.04,6.32,66.06,grapes
+14,67,15,19.56,24.67,5.69,139.29,kidneybeans
+99,54,37,21.14,80.34,5.59,198.67,rice
+27,62,24,28.63,66.77,7.35,62.27,blackgram
+27,24,29,26.61,96.97,6.14,191.01,coconut
+20,45,22,29.59,89.99,6.9,54.96,mungbean
+30,120,200,38.06,82.25,6.23,65.7,grapes
+89,91,55,25.08,80.26,6.28,94.33,banana
+7,15,32,25.04,95.9,6.18,174.8,coconut
+40,68,17,34.13,65.15,7.73,70.41,blackgram
+81,36,38,23.77,87.98,6.33,150.32,jute
+3,69,23,28.67,63.19,7.3,42.96,lentil
+126,46,25,24.44,81.7,6.76,60.8,cotton
+28,76,82,20.57,14.26,6.65,83.76,chickpea
+2,30,30,26.0,94.8,6.33,209.54,coconut
+25,68,19,29.4,64.26,7.11,67.48,blackgram
+92,21,48,25.82,82.04,6.38,54.83,watermelon
+11,41,19,26.86,41.81,5.13,44.14,mothbeans
+7,79,23,19.64,19.69,5.82,96.66,kidneybeans
+71,52,43,26.48,73.96,6.73,180.25,jute
+5,144,205,21.42,92.63,6.18,102.8,apple
+13,23,6,23.96,90.26,7.37,102.7,orange
+92,75,45,29.01,77.95,5.67,90.43,banana
+133,57,19,23.54,75.98,7.95,84.13,cotton
+54,61,77,18.81,15.22,6.21,77.54,chickpea
+85,21,52,29.63,90.1,6.08,23.7,muskmelon
+18,14,11,28.05,90.01,6.55,117.13,orange
+23,39,22,29.26,81.98,6.86,42.02,mungbean
+28,80,17,19.62,18.67,5.81,144.16,kidneybeans
+5,74,21,16.24,21.36,5.59,66.97,kidneybeans
+4,61,21,24.84,60.09,6.75,48.78,lentil
+18,19,29,27.59,92.49,6.21,162.84,coconut
+133,47,24,24.4,79.2,7.23,90.8,cotton
+38,21,35,20.34,89.38,5.84,110.97,pomegranate
+29,21,45,23.41,93.13,6.75,105.22,pomegranate
+38,62,25,32.75,67.78,7.45,63.38,blackgram
+8,15,33,28.97,98.1,5.5,213.9,coconut
+106,70,55,25.87,78.52,5.74,116.3,banana
+114,8,50,24.75,88.31,6.58,57.96,watermelon
+123,39,24,25.01,78.18,7.45,86.06,cotton
+80,16,46,25.5,81.4,6.94,48.48,watermelon
+56,50,52,33.09,92.25,6.77,88.13,papaya
+82,40,40,23.83,84.81,6.27,298.56,rice
+22,62,16,34.65,54.32,4.83,180.9,pigeonpeas
+99,38,21,22.88,71.6,6.35,67.73,maize
+86,59,35,25.79,82.11,6.95,243.51,rice
+100,40,35,27.56,54.41,6.96,177.82,coffee
+95,75,50,28.08,75.26,5.62,118.28,banana
+29,54,16,27.78,54.65,8.15,32.05,mothbeans
+108,26,52,28.83,94.27,6.2,26.24,muskmelon
+28,69,16,29.77,66.29,6.55,35.7,lentil
+26,135,203,33.78,81.16,5.69,74.54,grapes
+11,122,195,12.14,83.57,5.65,69.63,grapes
+17,67,18,31.22,56.47,5.61,129.2,pigeonpeas
+126,37,21,25.85,84.17,6.61,77.03,cotton
+126,38,23,25.36,83.63,6.18,88.44,cotton
+5,62,23,27.93,66.45,4.72,145.37,pigeonpeas
+50,56,76,21.0,19.86,7.97,73.51,chickpea
+32,130,196,40.66,81.25,6.37,74.03,grapes
+33,139,203,33.34,82.51,5.69,70.68,grapes
+36,57,16,28.61,57.14,8.29,57.03,mothbeans
+11,132,197,15.99,81.24,5.73,74.4,grapes
+9,59,24,20.44,39.37,4.75,137.23,pigeonpeas
+6,30,40,22.77,91.45,6.36,106.97,pomegranate
+0,55,22,22.99,20.58,5.92,143.86,kidneybeans
+0,26,31,25.07,95.02,5.55,192.9,coconut
+107,11,54,28.59,91.34,6.09,29.44,muskmelon
+104,23,47,26.98,86.7,6.77,42.91,watermelon
+83,94,47,27.4,81.11,6.47,112.14,banana
+82,20,54,29.34,90.02,6.54,21.45,muskmelon
+37,144,197,11.19,80.81,6.42,66.34,grapes
+78,58,15,25.01,67.82,6.53,62.91,maize
+95,57,41,23.25,73.65,6.43,184.77,jute
+123,50,16,23.05,75.54,6.5,70.66,cotton
+36,140,198,23.34,91.48,6.28,104.43,apple
+4,19,42,23.83,87.84,6.31,111.22,pomegranate
+82,25,51,24.31,87.47,6.07,48.11,watermelon
+107,71,55,29.42,83.97,6.09,117.23,banana
+81,56,36,23.4,72.61,7.1,174.79,jute
+6,144,198,21.11,90.32,5.56,104.51,apple
+117,82,45,25.29,79.29,5.61,105.42,banana
+14,76,20,29.06,62.11,7.04,36.5,lentil
+82,18,48,29.1,94.17,6.16,26.71,muskmelon
+42,59,55,40.1,94.35,6.98,149.12,papaya
+31,55,22,22.91,21.34,5.87,109.23,kidneybeans
+18,21,35,23.28,94.94,6.37,111.14,pomegranate
+61,51,51,39.3,94.16,6.57,120.95,papaya
+109,79,45,27.67,79.69,6.49,108.66,banana
+24,140,205,12.09,83.59,5.93,68.67,grapes
+14,57,15,29.88,83.15,6.62,40.12,mungbean
+83,22,54,25.9,81.97,6.28,54.5,watermelon
+84,25,52,24.37,81.25,6.13,44.21,watermelon
+68,52,49,24.43,92.28,6.58,63.35,papaya
+34,80,19,31.49,63.06,6.52,71.48,blackgram
+21,78,19,27.16,66.76,6.92,69.85,blackgram
+12,129,205,22.36,91.16,6.12,118.68,apple
+66,69,47,23.69,93.61,6.91,87.53,papaya
+34,56,17,33.41,35.43,4.55,139.67,pigeonpeas
+29,78,25,19.96,59.33,5.98,195.79,pigeonpeas
+99,57,35,26.76,81.18,5.96,272.3,rice
+94,54,17,23.39,61.74,5.87,107.32,maize
+29,71,18,22.17,62.14,6.41,53.47,lentil
+12,34,28,33.36,45.02,6.14,98.82,mango
+97,29,27,27.75,54.37,7.21,139.86,coffee
+92,7,45,26.71,81.14,6.94,51.51,watermelon
+74,55,19,18.05,62.89,6.29,84.24,maize
+49,72,15,31.56,67.84,7.14,74.87,blackgram
+13,72,21,24.32,21.03,5.82,60.28,kidneybeans
+16,80,20,31.24,56.67,7.34,122.01,pigeonpeas
+6,59,21,26.59,66.14,6.14,50.91,lentil
+26,9,32,25.95,94.74,6.47,144.16,coconut
+98,8,51,26.18,86.52,6.26,49.43,watermelon
+107,34,32,26.77,66.41,6.78,177.77,coffee
+39,145,201,36.73,80.59,5.78,72.24,grapes
+83,95,50,26.52,77.8,5.51,108.85,banana
+31,26,9,11.7,93.26,7.57,103.2,orange
+98,43,35,25.41,76.44,7.32,188.64,jute
+0,70,21,36.3,56.03,4.67,101.61,pigeonpeas
+77,52,17,24.86,65.74,5.71,75.82,maize
+99,15,27,27.42,56.64,6.09,127.92,coffee
+27,30,5,32.72,90.55,7.66,113.33,orange
+99,55,35,21.72,80.24,6.5,277.96,rice
+99,36,20,20.58,65.35,6.67,78.35,maize
+89,25,34,23.08,63.66,7.18,129.88,coffee
+14,55,15,27.34,55.28,8.05,73.45,mothbeans
+11,6,25,28.69,96.65,6.08,178.96,coconut
+49,71,76,19.71,17.64,6.61,85.58,chickpea
+24,61,17,22.64,65.45,6.23,38.3,lentil
+36,59,46,34.29,93.61,6.72,127.25,papaya
+107,43,18,22.43,81.53,6.75,65.54,cotton
+44,58,18,28.04,65.07,6.81,72.5,blackgram
+86,39,43,26.15,71.24,6.43,193.1,jute
+9,69,20,19.31,23.96,5.59,129.34,kidneybeans
+10,75,17,18.44,68.05,7.73,39.01,lentil
+27,138,201,23.67,93.9,5.95,105.4,apple
+24,80,22,16.71,19.18,5.64,96.77,kidneybeans
+32,71,85,20.63,14.44,6.4,92.07,chickpea
+10,55,23,21.19,19.63,5.73,137.19,kidneybeans
+84,40,43,25.01,88.33,7.23,169.42,jute
+93,83,46,29.38,83.5,5.77,109.25,banana
+36,80,21,33.65,48.41,7.07,100.47,pigeonpeas
+28,45,23,29.65,80.3,6.49,56.76,mungbean
+38,60,20,29.85,60.64,7.49,46.8,lentil
+20,45,16,29.94,54.62,4.63,45.44,mothbeans
+122,59,18,23.5,83.63,6.22,79.81,cotton
+29,138,197,22.19,92.44,5.83,121.66,apple
+30,75,25,30.33,42.35,6.45,149.3,pigeonpeas
+0,21,32,35.9,54.26,6.43,92.2,mango
+9,77,17,20.12,24.45,5.78,106.16,kidneybeans
+116,28,34,23.44,60.4,6.42,122.21,coffee
+90,15,52,27.05,91.38,6.45,23.66,muskmelon
+17,77,23,24.51,20.82,5.67,64.19,kidneybeans
+19,24,15,20.49,93.72,7.14,111.84,orange
+48,62,15,25.37,66.64,7.54,65.82,blackgram
+9,49,16,30.88,41.37,7.66,55.05,mothbeans
+5,8,5,11.03,92.23,6.56,112.77,orange
+33,59,22,22.64,21.59,5.95,122.39,kidneybeans
+11,36,33,35.99,52.23,5.98,95.37,mango
+72,40,38,20.41,82.21,7.59,245.15,rice
+32,139,198,35.89,82.67,6.36,66.54,grapes
+28,130,196,22.13,94.68,6.06,112.92,apple
+117,30,50,24.9,87.21,6.74,46.59,watermelon
+119,7,55,26.04,84.64,6.03,44.4,watermelon
+101,75,50,26.59,81.41,6.24,109.98,banana
+18,79,20,20.28,23.24,5.88,139.75,kidneybeans
+37,56,25,22.06,19.6,5.77,126.73,kidneybeans
+75,36,44,23.28,74.28,6.61,153.74,jute
+79,59,17,20.38,63.74,6.64,108.51,maize
+31,137,196,22.14,93.83,6.4,120.63,apple
+30,63,16,23.61,21.91,5.53,100.6,kidneybeans
+5,35,20,28.93,53.57,9.68,66.36,mothbeans
+13,28,33,28.13,95.65,5.69,151.08,coconut
+107,22,54,28.0,90.85,6.63,21.62,muskmelon
+118,88,51,25.45,79.49,6.2,100.66,banana
+29,57,20,25.61,50.73,5.88,53.39,mothbeans
+95,55,42,26.8,82.15,5.95,193.35,rice
+12,63,17,18.36,19.38,5.72,138.41,kidneybeans
+8,23,38,19.3,87.18,7.01,105.48,pomegranate
+90,48,45,24.06,71.31,6.51,153.64,jute
+34,71,79,17.93,15.86,7.73,74.64,chickpea
+99,12,52,28.7,94.31,6.0,22.22,muskmelon
+35,71,17,29.89,66.35,6.93,198.14,pigeonpeas
+82,35,44,26.97,78.21,6.24,169.84,jute
+24,30,11,32.4,94.52,6.6,113.25,orange
+110,21,54,26.74,87.82,6.75,47.46,watermelon
+99,56,17,24.11,73.13,6.23,71.08,maize
+11,46,24,27.65,89.81,6.46,56.53,mungbean
+22,55,20,33.95,69.96,7.42,61.16,blackgram
+108,35,25,23.98,61.11,6.97,161.53,coffee
+101,33,33,26.97,62.02,6.91,142.86,coffee
+19,65,25,18.1,18.29,5.63,144.79,kidneybeans
+106,49,24,23.04,76.47,6.98,90.65,cotton
+113,85,45,27.95,76.64,6.04,109.09,banana
+87,48,25,18.65,61.38,6.66,93.62,maize
+69,64,47,40.21,94.51,6.99,186.68,papaya
+101,70,48,25.36,75.03,6.01,116.55,banana
+103,72,51,26.13,81.81,6.1,104.48,banana
+24,131,196,22.03,83.74,5.73,65.34,grapes
+11,78,22,23.9,22.74,5.94,112.66,kidneybeans
+36,61,21,34.54,39.04,5.62,168.59,pigeonpeas
+17,136,195,41.21,81.61,6.39,65.9,grapes
+88,54,44,25.74,83.88,6.15,233.13,rice
+35,66,47,31.7,91.66,6.95,48.84,papaya
+85,9,53,28.21,92.87,6.45,28.79,muskmelon
+23,59,19,21.99,24.87,5.85,129.57,kidneybeans
+119,30,49,25.36,80.46,6.9,47.72,watermelon
+90,52,25,25.97,69.36,6.82,103.22,maize
+12,8,10,16.15,91.44,8.0,107.43,orange
+9,48,20,29.66,84.28,6.38,56.1,mungbean
+115,18,53,29.17,94.2,6.01,22.07,muskmelon
+0,18,14,29.77,92.01,7.21,114.42,orange
+105,74,45,25.15,81.38,6.1,119.22,banana
+0,27,38,22.45,89.9,6.74,109.39,pomegranate
+4,18,37,22.92,85.41,7.13,106.28,pomegranate
+35,67,49,41.31,91.15,6.62,239.74,papaya
+22,23,44,20.13,89.32,6.14,107.34,pomegranate
+117,43,25,24.69,78.51,7.84,69.31,cotton
+40,143,201,24.97,82.73,6.48,66.7,grapes
+123,44,21,25.79,75.01,7.64,91.4,cotton
+39,78,15,21.35,62.6,5.93,41.78,lentil
+57,67,25,32.35,66.61,7.55,64.56,blackgram
+4,44,19,27.96,83.53,6.92,43.26,mungbean
+28,75,21,24.77,50.55,6.01,114.28,pigeonpeas
+22,37,20,27.63,86.49,6.61,39.26,mungbean
+40,72,77,17.02,16.99,7.49,88.55,chickpea
+28,70,21,25.39,60.5,7.44,39.18,lentil
+30,122,197,21.38,92.72,5.57,106.14,apple
+16,10,41,24.77,85.64,6.74,105.76,pomegranate
+29,72,24,23.17,36.68,6.96,162.59,pigeonpeas
+87,35,25,21.45,63.16,6.18,65.89,maize
+102,25,50,28.2,92.91,6.1,20.36,muskmelon
+54,66,52,36.57,93.8,6.87,104.42,papaya
+69,47,40,25.37,76.24,6.13,183.83,jute
+140,45,15,25.53,80.05,5.8,99.4,cotton
+34,21,42,18.76,89.93,6.65,111.02,pomegranate
+29,70,15,30.33,63.55,6.87,74.17,blackgram
+111,39,22,22.6,80.35,6.14,88.57,cotton
+45,58,49,30.11,90.35,6.83,75.25,papaya
+38,135,203,23.76,93.66,5.97,100.83,apple
+27,72,23,19.93,21.8,5.96,64.03,kidneybeans
+67,46,44,26.82,78.2,7.09,153.92,jute
+40,45,20,29.38,57.7,6.88,38.34,mothbeans
+34,35,21,28.45,82.68,6.68,58.19,mungbean
+83,15,49,28.93,91.39,6.44,23.2,muskmelon
+91,7,53,25.14,89.28,6.46,43.53,watermelon
+58,75,25,25.26,61.37,7.26,68.65,blackgram
+0,49,18,29.68,87.94,6.99,41.82,mungbean
+7,31,27,31.33,47.59,6.52,94.67,mango
+24,27,9,18.87,93.25,6.16,119.39,orange
+120,16,51,28.0,91.64,6.55,23.29,muskmelon
+86,40,33,26.14,52.26,7.43,136.3,coffee
+1,30,29,28.33,51.4,6.43,91.67,mango
+90,14,52,24.85,89.2,6.39,59.68,watermelon
+26,56,22,23.05,60.42,7.01,52.6,lentil
+33,134,205,21.04,94.34,6.09,114.74,apple
+94,70,48,25.14,84.88,6.2,91.46,banana
+37,126,196,23.6,90.98,5.6,107.17,apple
+98,47,37,23.48,81.33,7.38,224.06,rice
+14,8,43,21.93,94.46,7.05,111.72,pomegranate
+2,140,197,22.7,92.82,5.53,105.05,apple
+32,70,20,20.89,46.25,6.21,195.57,pigeonpeas
+4,59,19,26.25,67.63,7.62,40.81,lentil
+7,74,17,22.47,62.57,5.67,96.75,pigeonpeas
+26,63,17,29.88,65.73,6.95,44.96,lentil
+25,71,24,28.5,60.45,7.19,74.92,blackgram
+7,144,195,22.96,93.58,5.86,104.65,apple
+29,139,205,23.64,93.74,6.16,116.69,apple
+84,7,51,26.82,87.66,6.4,55.74,watermelon
+38,15,30,28.92,48.14,5.08,97.01,mango
+60,38,17,18.42,64.24,6.47,76.41,maize
+14,22,9,17.25,91.14,6.54,112.51,orange
+37,52,47,43.08,93.9,6.54,211.85,papaya
+108,89,53,29.12,80.18,5.91,112.4,banana
+93,53,38,26.93,81.91,7.07,290.68,rice
+74,54,38,25.66,83.47,7.12,217.38,rice
+70,36,42,21.84,80.73,6.95,202.38,rice
+2,75,22,23.89,61.79,6.66,52.56,lentil
+23,57,19,32.84,68.0,7.25,73.4,blackgram
+32,11,31,25.07,93.31,6.21,134.84,coconut
+52,56,85,20.12,14.44,6.82,88.68,chickpea
+8,28,37,23.88,86.21,6.08,108.31,pomegranate
+107,10,49,25.83,89.0,6.76,45.25,watermelon
+22,16,27,29.18,90.27,6.01,188.93,coconut
+90,39,43,24.45,82.29,6.77,190.97,jute
+20,27,41,20.51,92.52,5.7,110.58,pomegranate
+36,125,196,37.47,80.66,6.16,66.84,grapes
+120,25,50,28.05,94.82,6.33,21.85,muskmelon
+110,78,50,25.94,78.9,5.92,98.22,banana
+9,16,39,18.41,91.12,6.1,105.18,pomegranate
+39,7,29,27.54,94.59,6.36,150.2,coconut
+102,37,25,25.31,77.92,5.91,72.83,cotton
+93,20,50,29.93,93.23,6.45,24.35,muskmelon
+60,61,78,20.71,19.84,6.32,94.04,chickpea
+88,52,39,23.93,88.07,6.88,154.66,jute
+3,49,18,27.91,64.71,3.69,32.68,mothbeans
+27,76,83,19.13,14.92,6.29,89.62,chickpea
+6,7,7,27.68,94.47,7.2,114.0,orange
+67,47,44,26.73,81.79,7.87,280.4,rice
+76,56,39,24.39,89.89,6.55,197.12,jute
+32,55,51,29.61,93.16,6.57,62.69,papaya
+101,87,54,29.07,76.5,6.38,100.17,banana
+109,10,53,26.82,87.83,6.55,46.06,watermelon
+18,9,40,19.45,89.02,5.63,106.16,pomegranate
+13,17,45,21.25,92.65,7.16,106.28,pomegranate
+116,23,25,23.41,52.27,6.87,139.37,coffee
+25,59,19,29.07,83.69,6.63,43.95,mungbean
+30,137,200,22.91,90.7,5.6,118.6,apple
+44,74,85,20.19,19.64,7.15,78.26,chickpea
+81,16,45,26.9,86.25,6.73,59.76,watermelon
+68,69,52,25.65,92.75,6.81,52.95,papaya
+76,60,39,20.05,80.35,6.77,208.58,rice
+33,121,203,22.46,94.76,5.61,114.84,apple
+70,68,55,42.85,94.64,6.69,78.81,papaya
+1,48,24,29.35,85.6,6.23,59.04,mungbean
+9,11,8,24.86,94.39,6.56,111.78,orange
+120,23,28,25.67,51.29,6.88,196.27,coffee
+58,63,81,19.81,14.7,6.52,78.97,chickpea
+17,11,32,28.74,93.4,5.62,156.77,coconut
+39,132,196,35.83,83.33,5.78,73.68,grapes
+5,25,6,30.72,94.01,6.01,106.81,orange
+31,56,23,31.47,35.39,5.66,174.57,pigeonpeas
+1,76,19,24.19,46.69,6.67,177.34,pigeonpeas
+28,123,202,22.77,92.12,6.44,120.44,apple
+3,67,24,17.0,19.91,5.52,103.29,kidneybeans
+24,67,22,20.12,22.9,5.62,104.63,kidneybeans
+26,50,19,27.32,51.67,6.01,32.56,mothbeans
+12,19,31,27.25,52.66,5.57,91.87,mango
+59,60,84,19.03,18.67,7.69,94.71,chickpea
+116,5,54,25.38,80.99,6.65,57.23,watermelon
+90,16,45,24.92,80.62,6.29,50.56,watermelon
+79,42,37,24.87,82.84,6.59,295.61,rice
+1,15,9,29.98,94.55,7.53,115.36,orange
+39,9,15,25.35,91.81,7.99,116.76,orange
+2,21,35,25.03,91.54,6.29,179.82,coconut
+39,64,53,23.01,91.07,6.6,208.34,papaya
+21,21,30,27.7,51.42,5.4,100.77,mango
+7,23,35,19.75,88.72,7.05,102.55,pomegranate
+21,139,201,19.36,83.36,5.98,67.15,grapes
+29,16,36,19.81,88.93,5.74,102.86,pomegranate
+9,143,197,23.75,92.88,5.57,117.66,apple
+25,57,24,27.65,58.6,6.97,36.94,mothbeans
+39,37,15,29.0,83.79,6.82,59.84,mungbean
+57,57,75,17.09,18.25,7.79,87.27,chickpea
+40,59,23,36.89,62.73,5.27,163.73,pigeonpeas
+85,21,47,29.87,90.61,6.19,24.7,muskmelon
+26,54,17,28.55,88.96,6.27,49.49,mungbean
+68,40,19,26.14,66.21,6.66,107.24,maize
+32,79,15,23.91,20.75,5.71,81.6,kidneybeans
+60,46,22,24.89,65.61,6.63,87.93,maize
+31,62,52,33.8,93.01,6.99,182.03,papaya
+37,70,25,19.73,24.89,5.82,84.06,kidneybeans
+9,8,40,22.49,89.92,6.55,111.66,pomegranate
+107,58,15,23.74,75.78,7.56,76.64,cotton
+33,61,24,20.05,48.94,4.57,122.46,pigeonpeas
+111,28,26,27.77,64.48,6.94,192.71,coffee
+8,58,17,28.75,69.16,7.29,35.15,lentil
+18,23,44,23.71,89.62,6.18,105.65,pomegranate
+61,41,35,24.97,79.48,6.84,195.76,jute
+9,122,201,29.59,80.92,5.57,68.06,grapes
+30,20,38,22.6,93.16,7.06,110.09,pomegranate
+131,35,18,24.49,82.24,7.06,64.03,cotton
+17,43,22,30.06,45.9,5.5,41.06,mothbeans
+28,58,81,17.48,16.54,6.18,93.35,chickpea
+7,63,24,19.56,64.45,6.82,53.05,lentil
+32,121,199,39.37,81.25,6.13,74.08,grapes
+31,6,26,29.13,91.31,5.74,157.24,coconut
+16,77,22,31.48,35.64,6.57,100.55,pigeonpeas
+50,64,25,28.84,63.37,6.73,70.25,blackgram
+19,21,34,30.02,53.19,5.07,97.73,mango
+58,46,45,42.39,90.79,6.58,88.47,papaya
+87,44,43,23.87,86.79,6.72,177.51,jute
+114,94,53,26.34,76.85,6.19,118.69,banana
+1,17,6,10.79,91.38,6.82,117.53,orange
+96,41,40,23.58,72.0,6.09,190.42,jute
+52,63,19,29.59,68.32,6.93,67.53,blackgram
+40,120,197,23.81,92.49,5.89,119.63,apple
+16,18,26,28.44,91.81,5.57,145.54,coconut
+56,67,78,17.57,16.72,8.26,77.82,chickpea
+50,58,23,27.81,62.5,7.6,69.76,blackgram
+53,67,17,31.78,69.02,7.3,61.47,blackgram
+116,56,17,24.71,77.73,7.98,85.25,cotton
+53,73,77,19.71,18.1,7.33,73.64,chickpea
+94,50,37,25.67,80.66,6.95,209.59,rice
+61,41,17,25.14,65.26,6.02,76.68,maize
+98,26,52,27.34,90.7,6.15,28.69,muskmelon
+1,29,29,27.33,49.3,6.05,93.53,mango
+9,80,19,21.81,18.57,5.95,125.1,kidneybeans
+33,80,22,28.57,65.72,6.59,70.09,blackgram
+9,60,21,29.94,67.31,7.52,40.37,lentil
+49,55,51,24.87,93.91,6.68,135.17,papaya
+25,76,17,31.74,68.64,7.24,62.31,blackgram
+91,50,40,20.82,84.13,6.46,230.22,rice
+121,53,19,23.51,76.73,7.98,80.11,cotton
+115,9,52,29.07,90.98,6.02,29.12,muskmelon
+25,64,20,33.15,32.46,4.81,105.04,pigeonpeas
+5,77,19,31.09,66.69,6.24,175.93,pigeonpeas
+23,62,85,18.97,19.52,8.49,80.71,chickpea
+9,21,32,32.27,53.56,5.87,95.94,mango
+64,35,23,23.02,61.89,5.68,63.04,maize
+112,49,25,25.69,77.91,6.47,66.19,cotton
+38,19,31,34.74,49.09,5.86,90.65,mango
+0,25,14,19.34,91.98,6.36,116.45,orange
+5,126,197,12.8,81.21,6.42,67.1,grapes
+56,57,48,31.56,93.05,6.51,63.62,papaya
+4,69,19,19.25,47.7,5.37,149.06,pigeonpeas
+73,43,42,26.58,78.01,6.31,154.82,jute
+16,71,24,18.33,38.41,4.95,139.65,pigeonpeas
+80,18,51,28.05,91.82,6.71,20.77,muskmelon
+9,38,25,34.59,50.34,5.5,100.31,mango
+111,79,53,28.31,75.77,6.17,119.7,banana
+84,51,35,22.3,80.64,6.04,197.98,rice
+96,46,22,20.58,69.0,6.5,66.29,maize
+28,65,23,28.39,61.89,7.41,74.24,blackgram
+40,126,201,11.36,80.03,6.12,71.18,grapes
+114,27,48,27.82,93.04,6.53,26.32,muskmelon
+22,43,24,25.43,53.22,4.52,46.19,mothbeans
+18,5,11,20.88,90.94,6.25,102.46,orange
+73,57,41,21.45,84.94,5.82,272.2,rice
+80,26,55,24.53,88.99,6.14,49.12,watermelon
+62,48,20,21.7,60.47,6.71,95.71,maize
+35,63,76,17.82,17.61,7.71,90.82,chickpea
+48,65,78,17.44,14.34,7.86,73.09,chickpea
+33,47,46,29.2,93.97,6.84,209.41,papaya
+30,7,15,33.23,91.06,7.83,115.77,orange
+59,47,53,32.86,91.46,6.85,47.27,papaya
+33,37,19,27.93,86.55,7.18,43.48,mungbean
+37,137,199,22.64,90.18,5.7,108.34,apple
+77,51,44,23.26,82.7,7.12,166.22,jute
+82,43,38,23.29,81.43,5.11,242.32,rice
+75,56,18,19.4,62.36,5.7,60.95,maize
+27,80,24,28.42,61.77,7.82,49.02,lentil
+93,22,48,29.13,91.52,6.78,21.9,muskmelon
+100,46,18,24.19,76.04,6.43,69.08,cotton
+74,49,38,23.31,71.45,7.49,164.5,jute
+111,32,34,25.47,69.35,6.39,171.38,coffee
+40,27,45,21.66,94.79,5.89,112.43,pomegranate
+28,72,84,18.73,19.18,6.48,71.58,chickpea
+63,58,47,26.83,90.75,6.86,144.67,papaya
+12,20,10,24.45,93.11,6.53,109.47,orange
+36,68,20,17.06,23.77,5.86,81.83,kidneybeans
+82,29,35,26.67,52.24,6.25,156.15,coffee
+37,23,12,31.53,90.51,6.4,113.12,orange
+13,8,12,25.16,92.55,7.11,114.31,orange
+2,36,31,30.9,49.96,5.73,91.78,mango
+19,36,22,25.45,58.55,6.16,57.05,mothbeans
+34,59,18,23.38,21.99,5.74,87.67,kidneybeans
+53,74,15,29.43,64.94,7.52,72.18,blackgram
+37,10,32,28.96,95.16,6.17,222.8,coconut
+60,37,39,26.59,82.94,6.03,161.25,jute
+52,60,79,19.45,18.23,8.38,75.63,chickpea
+58,67,45,38.72,91.73,6.7,62.62,papaya
+39,70,52,26.27,90.8,6.65,59.49,papaya
+29,34,26,33.88,54.39,6.27,89.29,mango
+36,19,32,27.11,50.71,4.94,92.37,mango
+1,124,199,23.71,93.27,5.66,112.67,apple
+37,64,22,17.48,18.83,5.95,121.94,kidneybeans
+35,131,203,22.43,93.92,5.89,102.72,apple
+88,38,15,25.08,65.92,6.46,62.49,maize
+38,120,197,17.54,82.95,6.32,73.77,grapes
+81,18,50,26.44,80.92,6.51,47.82,watermelon
+132,52,19,24.16,76.74,6.44,61.95,cotton
+31,70,77,20.89,14.32,6.49,90.46,chickpea
+62,42,36,22.78,82.07,6.43,248.72,rice
+131,52,16,23.66,84.48,6.49,88.54,cotton
+129,47,20,24.41,80.8,6.28,98.6,cotton
+32,11,31,29.52,92.56,6.46,131.21,coconut
+50,60,47,32.58,92.75,6.93,93.79,papaya
+83,41,43,21.05,82.68,6.25,233.11,rice
+40,70,20,31.8,45.03,5.62,147.04,pigeonpeas
+31,29,26,28.22,47.41,5.02,97.77,mango
+85,25,47,26.11,87.64,6.3,58.48,watermelon
+31,53,16,28.74,85.82,6.45,48.55,mungbean
+101,11,51,25.51,84.24,6.79,44.21,watermelon
+72,41,36,24.1,80.57,6.19,176.86,jute
+20,60,22,29.65,42.9,6.88,186.92,pigeonpeas
+26,68,24,28.05,64.08,7.5,37.16,lentil
+92,81,52,28.01,76.53,5.89,103.7,banana
+20,50,22,31.0,46.43,9.41,38.32,mothbeans
+1,67,21,27.52,60.54,6.55,48.06,lentil
+50,59,47,40.77,92.09,6.75,209.87,papaya
+95,88,52,28.0,78.9,6.24,94.68,banana
+90,46,42,23.98,81.45,7.5,250.08,rice
+29,25,35,28.36,91.65,5.54,160.73,coconut
+34,54,24,31.21,41.56,5.03,68.8,mothbeans
+26,15,6,17.22,94.79,6.91,108.01,orange
+7,17,10,10.16,91.22,6.47,106.36,orange
+114,40,17,24.33,80.13,6.36,69.45,cotton
+37,24,13,19.14,90.71,7.85,108.02,orange
+38,24,33,28.29,97.0,5.97,142.94,coconut
+97,36,45,22.23,81.86,6.94,278.08,rice
+35,64,15,28.47,63.54,6.5,69.53,blackgram
+99,70,46,26.6,83.0,5.73,100.51,banana
+64,53,38,26.24,78.51,6.86,183.41,jute
+26,18,30,32.06,51.08,6.34,96.6,mango
+68,58,38,23.22,83.03,6.34,221.21,rice
+10,79,18,21.06,55.47,5.62,184.62,pigeonpeas
+77,57,21,24.93,73.8,6.55,79.74,maize
+20,19,35,34.18,50.62,6.11,98.01,mango
+58,55,47,26.05,93.69,6.74,240.69,papaya
+4,67,25,23.79,24.36,5.95,119.64,kidneybeans
+111,53,19,23.96,78.03,6.42,84.63,cotton
+33,75,21,33.05,68.94,6.69,62.3,blackgram
+15,6,41,19.01,88.84,6.9,108.68,pomegranate
+37,50,23,29.65,88.49,6.53,56.02,mungbean
+17,58,25,31.13,43.59,6.46,32.77,mothbeans
+9,16,36,23.78,92.93,5.89,106.98,pomegranate
+85,53,38,24.9,73.84,6.59,153.9,jute
+95,39,36,23.86,83.15,5.56,285.25,rice
+42,60,47,33.47,92.13,6.83,136.83,papaya
+27,8,32,27.01,96.46,5.63,144.33,coconut
+117,86,53,25.2,83.56,5.7,115.86,banana
+29,132,204,23.09,90.23,6.1,108.22,apple
+50,74,17,27.1,63.36,6.54,73.85,blackgram
+19,7,9,27.26,91.71,6.97,101.14,orange
+1,35,34,30.79,46.7,6.27,92.21,mango
+76,44,45,25.49,84.48,6.74,168.78,jute
+15,28,32,28.84,99.64,6.22,224.4,coconut
+37,5,34,25.79,93.84,5.78,152.42,coconut
+5,68,20,18.73,61.33,5.0,139.87,pigeonpeas
+94,37,41,24.76,87.06,6.46,179.16,jute
+28,79,16,24.71,60.27,6.05,53.12,lentil
+27,22,29,28.83,92.17,6.0,145.42,coconut
+90,23,54,28.56,90.46,6.16,27.27,muskmelon
+35,80,25,28.09,44.93,4.9,197.11,pigeonpeas
+93,58,38,20.62,83.77,6.93,279.55,rice
+135,43,16,23.48,81.73,6.72,86.76,cotton
+76,54,45,24.29,77.63,6.18,184.98,jute
+36,24,41,24.94,94.26,7.01,103.88,pomegranate
+113,38,20,22.11,78.58,6.36,74.94,cotton
+84,18,46,27.09,93.42,6.78,25.32,muskmelon
+6,128,200,25.96,82.58,5.84,70.32,grapes
+32,73,81,20.45,15.4,5.99,92.68,chickpea
+120,20,34,23.57,50.56,6.91,130.38,coffee
+57,58,77,18.73,17.58,7.98,81.2,chickpea
+105,95,50,27.33,83.68,5.85,101.05,banana
+13,74,25,24.12,61.1,6.46,44.24,lentil
+73,45,37,23.7,74.64,6.74,181.28,jute
+21,134,202,10.72,80.02,6.43,65.3,grapes
+116,38,34,23.29,50.05,6.02,183.47,coffee
+2,78,23,21.32,66.44,7.32,45.43,lentil
+57,73,85,18.49,14.72,7.36,91.95,chickpea
+103,33,25,27.1,55.75,6.91,139.5,coffee
+22,42,22,25.54,56.97,7.89,48.47,mothbeans
+12,66,20,27.41,63.42,7.34,44.43,lentil
+41,78,21,25.2,60.37,6.58,70.89,blackgram
+83,60,36,25.6,80.15,6.9,200.83,rice
+31,35,23,30.3,47.18,7.71,68.04,mothbeans
+80,43,43,23.79,74.37,6.01,172.64,jute
+60,44,23,24.79,70.05,5.72,76.73,maize
+32,41,16,28.64,61.39,7.7,68.55,mothbeans
+16,73,19,18.42,34.81,4.68,163.27,pigeonpeas
+121,36,24,23.66,81.69,7.35,99.37,cotton
+85,22,53,25.97,89.77,6.85,59.46,watermelon
+1,27,36,23.99,93.34,5.68,104.99,pomegranate
+40,29,42,24.63,89.02,7.1,110.7,pomegranate
+136,36,24,22.74,80.41,7.6,90.07,cotton
+14,59,22,23.83,67.9,6.77,46.91,lentil
+95,21,47,27.93,93.56,6.43,20.66,muskmelon
+36,67,25,35.95,36.53,6.42,136.05,pigeonpeas
+89,57,43,26.92,73.2,7.0,177.22,jute
+37,135,205,11.83,80.28,5.51,74.1,grapes
+17,64,17,21.02,24.94,5.66,124.61,kidneybeans
+98,18,27,27.56,68.49,6.52,167.44,coffee
+31,29,35,27.19,92.2,6.14,141.32,coconut
+5,68,20,19.04,33.11,6.12,155.37,pigeonpeas
+3,56,17,28.2,53.51,8.71,52.14,mothbeans
+40,22,29,27.56,99.98,5.74,174.63,coconut
+76,51,18,26.17,71.96,6.25,79.85,maize
+63,35,16,22.03,65.36,6.27,83.73,maize
+109,23,25,25.12,68.48,7.01,194.88,coffee
+132,41,22,24.29,81.02,7.81,90.42,cotton
+2,39,15,28.07,82.91,6.48,49.62,mungbean
+100,58,41,23.17,87.88,6.66,160.62,jute
+19,26,29,26.93,98.8,5.67,166.57,coconut
+58,61,15,30.95,64.23,7.4,62.79,blackgram
+22,30,12,15.78,92.51,6.35,119.04,orange
+36,11,13,17.34,93.05,7.19,112.72,orange
+7,126,203,16.76,82.0,5.66,73.29,grapes
+90,50,44,26.92,73.49,6.25,171.47,jute
+35,57,25,27.1,42.26,8.34,71.13,mothbeans
+36,43,22,27.83,87.17,6.39,58.37,mungbean
+11,18,42,21.58,94.88,5.94,102.86,pomegranate
+107,45,25,23.09,83.56,7.23,71.84,cotton
+9,141,202,21.01,81.18,6.12,66.38,grapes
+26,10,33,28.27,96.94,6.07,198.82,coconut
+44,47,45,38.73,94.74,6.58,218.14,papaya
+21,23,42,19.54,90.3,6.9,104.37,pomegranate
+37,77,20,25.93,68.71,7.08,51.02,lentil
+6,64,23,23.34,67.4,7.07,36.19,lentil
+60,59,22,31.87,66.74,7.19,74.22,blackgram
+102,49,21,24.69,84.84,6.25,89.8,cotton
+22,49,22,28.23,61.56,3.71,72.67,mothbeans
+8,26,26,25.55,91.64,5.7,212.87,coconut
+12,37,30,31.1,47.41,4.55,90.29,mango
+55,66,22,30.91,68.79,7.75,66.64,blackgram
+35,142,203,21.17,90.24,5.9,123.65,apple
+49,76,18,27.05,67.7,7.39,60.47,blackgram
+61,60,15,24.88,68.74,6.27,91.26,maize
+93,85,49,27.97,79.29,5.69,119.48,banana
+0,74,17,23.33,64.51,7.24,47.02,lentil
+72,41,36,26.51,86.84,6.07,152.98,jute
+37,36,26,32.89,52.61,4.65,94.49,mango
+26,67,16,29.11,67.91,7.18,67.83,blackgram
+31,30,29,26.59,90.99,5.56,178.81,coconut
+17,58,20,28.07,85.92,6.43,39.24,mungbean
+94,50,19,23.3,73.63,5.87,97.59,maize
+31,8,7,34.51,93.64,7.16,103.57,orange
+45,47,55,38.42,91.14,6.75,119.27,papaya
+35,48,15,27.11,87.45,6.98,55.04,mungbean
+86,26,27,27.13,52.89,6.08,192.43,coffee
+89,11,47,29.79,94.65,6.33,27.87,muskmelon
+114,79,51,26.21,82.34,6.31,112.07,banana
+66,44,20,19.08,69.02,6.74,80.73,maize
+18,19,27,27.76,52.35,4.77,94.11,mango
+14,74,15,28.0,65.58,6.49,49.94,lentil
+35,66,81,19.37,15.77,6.14,85.25,chickpea
+59,55,79,20.37,16.9,8.77,82.25,chickpea
+82,23,49,26.81,87.22,6.87,51.7,watermelon
+33,75,84,19.46,18.73,7.22,68.81,chickpea
+57,56,78,17.34,18.76,8.86,67.95,chickpea
+27,145,205,9.47,82.29,5.8,66.03,grapes
+95,39,29,27.35,55.99,7.13,148.98,coffee
+22,144,196,21.91,91.69,6.5,117.08,apple
+19,30,30,29.57,91.41,5.83,224.83,coconut
+52,70,16,33.67,66.6,7.53,67.33,blackgram
+10,9,28,29.01,94.01,6.28,150.05,coconut
+83,25,53,26.49,80.05,6.06,57.73,watermelon
+20,67,19,19.24,50.54,5.67,180.65,pigeonpeas
+59,62,83,18.58,19.22,8.1,72.95,chickpea
+60,58,51,42.07,92.92,6.84,165.74,papaya
+7,60,25,28.28,82.76,6.4,56.05,mungbean
+23,75,17,24.87,64.0,7.2,48.28,lentil
+33,15,7,15.83,91.68,7.65,109.76,orange
+3,23,30,29.7,95.66,6.08,215.2,coconut
+62,49,37,24.22,82.85,7.48,166.14,jute
+32,43,22,32.0,54.11,5.27,71.63,mothbeans
+106,20,51,29.73,90.97,6.34,20.49,muskmelon
+74,39,23,22.63,65.77,6.78,88.17,maize
+18,30,29,26.76,92.86,6.42,224.59,coconut
+1,132,200,16.28,82.94,5.62,66.57,grapes
+22,64,82,19.49,17.17,6.47,87.51,chickpea
+37,18,30,27.64,99.35,6.38,157.92,coconut
+86,76,54,29.32,80.12,5.93,90.11,banana
+38,6,25,25.55,96.93,6.16,191.3,coconut
+103,27,31,27.16,51.59,6.69,126.18,coffee
+89,54,38,24.52,83.54,6.69,230.45,rice
+22,54,20,28.56,83.64,6.69,41.01,mungbean
+39,24,14,30.55,90.9,7.19,106.07,orange
+119,44,15,22.15,82.86,7.09,60.65,cotton
+26,80,18,19.33,23.33,5.58,104.78,kidneybeans
+114,27,28,24.99,57.93,7.16,192.87,coffee
+99,5,47,24.13,84.84,6.65,51.19,watermelon
+59,58,17,28.55,66.31,7.37,62.83,blackgram
+6,125,204,27.92,82.93,5.73,69.92,grapes
+29,35,28,28.35,53.54,6.97,90.4,mango
+105,60,23,23.53,77.22,6.21,87.54,cotton
+94,91,51,29.16,76.67,5.62,109.58,banana
+102,11,45,29.03,93.13,6.36,24.16,muskmelon
+6,140,205,17.67,82.93,6.31,69.87,grapes
+66,60,38,22.09,83.47,6.37,231.74,rice
+105,30,50,25.3,81.78,6.38,57.04,watermelon
+6,72,15,22.99,66.71,7.67,54.49,lentil
+100,32,26,25.23,57.53,6.04,124.23,coffee
+16,70,20,24.8,40.12,5.61,121.56,pigeonpeas
+76,57,18,18.98,74.53,6.09,94.26,maize
+40,73,20,21.59,20.32,5.81,61.14,kidneybeans
+60,58,37,26.14,79.12,6.07,171.49,jute
+36,133,198,25.52,83.98,6.23,69.17,grapes
+20,20,10,11.87,93.68,6.98,106.06,orange
+37,77,17,36.21,31.95,5.62,191.07,pigeonpeas
+38,132,197,20.42,81.54,5.93,66.93,grapes
+95,27,55,28.47,91.21,6.16,20.89,muskmelon
+37,78,79,19.95,14.83,7.79,88.68,chickpea
+27,59,20,28.01,52.61,4.4,36.01,mothbeans
+40,18,43,19.39,86.79,5.77,109.91,pomegranate
+52,51,53,38.38,93.1,6.99,210.27,papaya
+6,68,18,24.39,62.5,6.71,47.26,lentil
+91,25,26,24.53,67.0,7.48,180.51,coffee
+28,58,25,27.48,62.05,6.86,37.81,lentil
+7,77,18,20.56,60.55,6.66,191.09,pigeonpeas
+14,131,198,33.46,83.87,5.56,67.92,grapes
+39,58,85,17.89,15.41,6.0,68.55,chickpea
+85,58,41,21.77,80.32,7.04,226.66,rice
+11,71,24,21.14,22.72,5.61,141.61,kidneybeans
+59,70,84,17.33,18.75,7.55,82.62,chickpea
+28,122,197,19.89,82.73,5.86,69.66,grapes
+29,63,17,30.03,67.89,7.26,66.47,blackgram
+24,37,21,30.57,58.23,5.82,62.75,mothbeans
+4,23,5,22.68,93.36,7.48,110.33,orange
+17,74,17,26.03,69.56,7.39,37.11,lentil
+102,46,19,22.77,82.6,6.63,81.5,cotton
+112,73,48,29.24,77.32,5.71,90.67,banana
+50,47,48,24.64,90.62,6.71,218.23,papaya
+13,126,204,23.11,92.8,6.38,108.18,apple
+72,45,35,25.43,82.95,5.76,195.36,rice
+78,46,42,23.09,78.46,7.1,155.39,jute
+82,24,26,24.31,53.57,6.09,184.41,coffee
+20,28,26,26.38,91.5,5.55,167.05,coconut
+9,60,23,31.97,57.17,6.28,64.26,mothbeans
+99,40,32,24.18,69.95,7.05,163.27,coffee
+39,64,52,28.92,94.64,6.68,63.69,papaya
+8,7,10,28.26,91.98,6.93,105.21,orange
+14,59,15,21.35,22.91,5.78,146.45,kidneybeans
+110,15,48,28.58,92.87,6.21,27.6,muskmelon
+107,51,22,24.87,78.22,5.98,79.57,cotton
+23,45,23,28.78,86.69,6.98,56.12,mungbean
+36,76,75,18.38,16.64,8.74,70.52,chickpea
+25,68,77,20.09,15.11,7.7,85.75,chickpea
+17,57,20,28.51,45.2,3.79,66.18,mothbeans
+30,65,25,32.89,64.59,7.71,71.51,blackgram
+70,47,17,24.61,70.42,6.6,104.16,maize
+30,37,25,29.89,80.14,7.12,54.8,mungbean
+106,21,52,28.9,94.79,6.29,23.04,muskmelon
+32,66,17,34.95,65.27,7.16,70.14,blackgram
+106,16,54,28.96,91.7,6.59,24.75,muskmelon
+114,21,55,25.44,87.94,6.47,57.52,watermelon
+27,80,15,19.07,21.21,5.79,86.22,kidneybeans
+11,61,21,18.62,23.02,5.53,135.34,kidneybeans
+40,79,17,21.13,63.19,6.4,38.72,lentil
+31,79,75,18.82,16.11,8.2,89.73,chickpea
+10,79,20,24.98,66.9,6.38,38.21,lentil
+22,68,16,27.7,63.21,7.75,37.46,lentil
+39,29,29,26.51,94.48,6.14,199.88,coconut
+10,59,15,29.83,89.3,6.32,58.87,mungbean
+18,23,8,21.49,93.44,6.41,101.48,orange
+105,18,35,23.53,68.44,6.74,171.88,coffee
+112,28,54,24.86,85.05,6.74,55.3,watermelon
+36,25,33,27.98,53.33,5.55,99.61,mango
+63,44,41,24.17,83.73,5.58,257.03,rice
+117,27,48,26.53,82.39,6.84,54.31,watermelon
+100,52,19,23.46,82.45,7.9,93.5,cotton
+55,77,22,31.43,62.99,7.76,64.78,blackgram
+20,72,15,36.0,56.01,7.31,134.86,pigeonpeas
+88,50,40,25.63,79.95,7.05,182.26,jute
+16,14,30,29.71,96.3,6.37,209.85,coconut
+96,54,22,25.7,61.33,6.96,83.21,maize
+19,123,200,34.76,81.04,6.17,65.7,grapes
+14,19,14,17.68,94.36,6.7,108.06,orange
+35,135,199,21.77,80.55,6.4,69.4,grapes
+30,143,199,23.77,90.6,5.8,102.26,apple
+114,8,52,29.34,94.55,6.42,28.23,muskmelon
+14,67,22,23.83,24.75,5.62,84.64,kidneybeans
+39,129,203,34.39,83.18,5.86,71.03,grapes
+93,41,17,25.62,66.5,6.05,105.47,maize
+99,41,36,24.46,82.75,6.74,182.56,rice
+111,5,52,29.88,94.04,6.14,21.0,muskmelon
+10,5,5,21.21,91.35,7.82,112.98,orange
+29,36,25,28.29,88.44,7.13,48.57,mungbean
+57,60,17,26.24,67.89,7.5,73.59,blackgram
+60,49,44,20.78,84.5,6.24,240.08,rice
+91,94,46,29.37,76.25,6.15,92.83,banana
+17,57,21,19.88,20.32,5.79,60.92,kidneybeans
+9,25,41,24.82,91.91,5.97,109.29,pomegranate
+65,37,40,23.36,83.6,5.33,188.41,rice
+117,25,53,29.12,92.13,6.41,24.52,muskmelon
+58,70,84,20.65,16.61,6.23,74.66,chickpea
+38,68,54,29.34,90.82,6.74,202.06,papaya
+62,59,41,24.22,74.89,7.18,192.49,jute
+105,88,54,25.79,84.51,6.02,114.2,banana
+5,18,14,33.11,93.48,7.43,119.17,orange
+11,72,20,19.52,24.93,5.95,113.33,kidneybeans
+81,34,30,25.18,62.26,6.65,135.01,coffee
+12,31,26,35.79,51.94,5.4,100.22,mango
+97,12,47,25.29,89.64,6.77,58.29,watermelon
+31,48,45,40.79,92.91,6.56,132.79,papaya
+109,26,45,28.28,90.39,6.22,21.59,muskmelon
+17,77,24,20.77,18.93,5.57,109.02,kidneybeans
+12,29,13,22.46,91.53,7.57,118.01,orange
+0,17,42,23.2,91.19,6.86,109.09,pomegranate
+18,57,21,27.38,63.94,6.16,49.47,lentil
+39,60,21,34.9,63.6,6.97,64.73,blackgram
+76,39,24,24.25,55.65,7.0,64.24,maize
+24,73,20,19.64,32.32,4.61,176.41,pigeonpeas
+50,55,16,28.81,65.34,7.58,62.26,blackgram
+29,11,5,23.13,91.95,7.64,104.42,orange
+63,58,50,43.04,94.64,6.72,41.59,papaya
+4,20,25,28.93,47.94,5.66,99.98,mango
+140,40,17,22.73,77.08,6.01,77.55,cotton
+38,15,27,33.75,48.5,6.78,92.26,mango
+109,29,28,23.26,60.52,6.72,194.18,coffee
+20,74,16,36.04,43.61,4.76,159.89,pigeonpeas
+31,58,15,27.11,84.97,7.12,51.53,mungbean
+61,52,41,24.98,83.89,6.88,204.8,rice
+37,38,32,31.86,45.53,5.42,91.56,mango
+13,61,24,18.3,69.69,7.63,49.39,lentil
+107,12,46,29.57,93.62,6.56,27.57,muskmelon
+10,140,197,22.17,90.27,6.23,124.47,apple
+48,61,21,30.28,61.69,6.63,65.63,blackgram
+100,17,48,29.73,94.3,6.37,26.52,muskmelon
+83,11,53,29.54,92.92,6.16,21.97,muskmelon
+28,67,21,21.8,63.73,6.25,46.62,lentil
+40,58,75,18.59,14.78,7.17,89.61,chickpea
+18,55,23,22.0,56.31,6.99,136.83,pigeonpeas
+115,31,30,24.23,67.38,6.84,122.41,coffee
+106,21,35,25.63,57.04,7.43,188.55,coffee
+56,76,16,28.27,61.19,7.51,63.3,blackgram
+14,23,25,26.19,96.97,5.61,135.42,coconut
+23,35,18,26.49,47.37,5.41,36.99,mothbeans
+33,58,24,35.46,68.76,5.27,108.63,pigeonpeas
+25,76,24,15.33,24.92,5.57,135.33,kidneybeans
+108,72,46,25.16,84.98,6.11,90.95,banana
+102,24,54,27.72,90.94,6.7,22.82,muskmelon
+38,14,37,21.81,94.64,6.66,102.65,pomegranate
+101,58,18,25.67,81.38,6.65,78.6,cotton
+34,73,15,20.97,63.83,7.63,53.1,lentil
+6,62,22,20.53,18.09,5.82,120.45,kidneybeans
+100,14,49,29.49,91.08,6.37,26.02,muskmelon
+39,25,36,18.9,95.0,5.57,107.61,pomegranate
+107,31,31,23.17,52.98,6.77,153.12,coffee
+70,50,53,37.46,90.45,6.93,172.35,papaya
+33,12,8,25.26,90.31,6.82,117.37,orange
+43,64,47,38.59,91.58,6.83,102.27,papaya
+120,19,49,25.79,84.27,6.76,56.45,watermelon
+100,40,20,22.45,76.26,7.43,86.85,cotton
+117,34,25,24.84,56.77,7.21,124.41,coffee
+102,14,52,26.79,89.65,6.51,57.74,watermelon
+16,7,8,22.79,90.61,6.42,116.51,orange
+118,31,34,27.55,62.88,6.12,181.42,coffee
+103,16,49,24.07,81.64,6.92,51.75,watermelon
+23,55,16,21.01,69.69,5.11,185.2,pigeonpeas
+63,37,43,23.42,85.09,6.66,185.74,jute
+23,138,200,9.85,80.23,5.97,68.43,grapes
+44,55,25,29.63,65.91,7.42,71.16,blackgram
+5,136,195,22.36,91.92,6.26,107.77,apple
+67,43,39,26.04,84.97,6.0,186.75,rice
+12,27,26,29.09,45.57,5.32,96.24,mango
+37,49,25,29.91,85.85,6.42,41.39,mungbean
+37,71,16,26.29,68.52,7.32,46.14,lentil
+19,51,25,26.8,48.24,3.53,43.88,mothbeans
+66,58,35,23.56,79.46,7.32,185.26,jute
+15,54,15,29.98,57.03,8.35,44.86,mothbeans
+34,9,36,22.81,86.34,6.28,110.44,pomegranate
+105,93,46,25.01,78.76,5.76,108.37,banana
+4,41,20,28.15,83.8,6.65,37.45,mungbean
+95,16,46,27.08,90.14,6.75,24.45,muskmelon
+119,30,28,26.36,64.58,6.51,163.63,coffee
+88,29,51,24.72,88.95,6.1,48.46,watermelon
+15,125,199,18.43,80.56,5.57,69.76,grapes
+85,38,41,21.59,82.79,6.25,276.66,rice
+78,58,44,26.8,80.89,5.11,284.44,rice
+82,35,35,25.49,86.97,7.3,176.53,jute
+2,38,33,32.39,53.23,4.69,90.22,mango
+71,52,18,25.11,55.98,5.79,78.16,maize
+20,41,20,29.27,89.49,7.07,50.92,mungbean
+15,14,8,10.01,90.22,6.22,119.39,orange
+40,24,25,28.71,50.44,5.45,95.89,mango
+40,5,29,28.48,97.77,5.82,160.39,coconut
+57,64,55,26.68,92.96,6.58,62.51,papaya
+60,68,83,19.12,18.43,6.62,85.53,chickpea
+65,60,22,25.37,72.52,6.61,107.91,maize
+48,62,47,25.35,93.03,6.8,174.4,papaya
+45,61,78,19.49,16.06,6.49,81.53,chickpea
+113,33,34,26.0,62.14,6.56,153.48,coffee
+56,65,45,38.2,93.97,6.75,218.09,papaya
+18,17,31,31.75,45.16,5.67,93.75,mango
+71,56,37,23.19,86.21,6.49,176.1,jute
+25,143,200,23.8,92.8,6.02,100.62,apple
+89,53,44,24.89,71.92,7.32,150.25,jute
+34,60,23,20.13,24.97,5.66,100.05,kidneybeans
+89,60,19,25.19,66.69,5.91,78.07,maize
+107,36,21,25.29,75.67,6.21,62.64,cotton
+89,40,43,26.25,72.97,7.12,189.97,jute
+37,73,21,29.5,63.47,5.56,189.52,pigeonpeas
+117,81,53,29.51,78.21,5.51,98.13,banana
+13,60,25,17.14,20.6,5.69,128.26,kidneybeans
+18,20,26,31.67,51.99,5.44,89.98,mango
+39,73,24,25.66,61.18,7.22,69.29,blackgram
+59,62,49,43.36,93.35,6.94,114.78,papaya
+39,36,22,29.34,60.5,9.07,34.03,mothbeans
+72,60,25,18.53,69.03,5.77,88.1,maize
+120,24,47,26.99,89.41,6.26,58.55,watermelon
+22,36,16,30.58,50.77,8.18,64.59,mothbeans
+86,55,21,21.54,59.64,6.8,109.75,maize
+38,138,204,25.11,83.25,6.33,73.01,grapes
+29,17,29,29.2,95.67,5.96,211.25,coconut
+102,11,47,27.99,92.78,6.5,27.15,muskmelon
+119,9,50,26.75,83.92,6.25,40.79,watermelon
+82,77,46,28.95,82.19,5.9,95.83,banana
+66,53,41,25.08,80.52,7.78,257.0,rice
+37,124,195,18.71,83.48,6.21,66.6,grapes
+31,51,25,27.54,85.57,7.2,53.02,mungbean
+24,56,85,18.2,17.41,6.55,80.64,chickpea
+11,45,19,28.7,44.36,3.83,44.12,mothbeans
+12,67,23,25.63,63.15,6.59,45.5,lentil
+29,128,198,22.44,92.71,5.69,121.5,apple
+53,55,55,33.32,91.25,6.71,234.5,papaya
+57,60,84,19.1,17.26,6.59,75.49,chickpea
+28,68,19,34.64,61.39,7.7,72.43,blackgram
+19,17,39,24.72,85.56,6.73,111.28,pomegranate
+9,139,199,23.25,94.54,5.87,105.36,apple
+76,49,42,24.96,84.48,5.21,196.96,rice
+108,60,17,22.76,76.76,6.56,97.77,cotton
+8,50,21,28.63,89.11,6.22,50.5,mungbean
+74,35,40,26.49,80.16,6.98,242.86,rice
+118,50,19,22.96,82.34,6.36,66.48,cotton
+118,21,51,24.43,86.34,6.68,48.58,watermelon
+129,60,22,24.58,79.12,5.95,71.95,cotton
+19,15,34,26.3,99.66,5.69,215.92,coconut
+89,9,47,29.47,90.77,6.67,28.75,muskmelon
+97,22,26,23.61,59.69,6.07,185.16,coffee
+5,21,38,22.43,90.34,6.11,112.46,pomegranate
+91,35,38,24.9,80.53,6.13,183.68,rice
+5,15,38,18.26,88.17,5.71,108.08,pomegranate
+27,21,30,35.39,52.49,5.06,91.23,mango
+107,5,52,26.66,89.98,6.88,57.41,watermelon
+21,6,41,24.88,89.4,7.09,107.2,pomegranate
+2,21,44,18.92,87.31,6.57,102.8,pomegranate
+102,45,16,23.66,77.52,7.29,74.9,cotton
+119,72,55,25.99,83.34,6.22,112.08,banana
+80,90,47,26.6,79.36,6.21,107.39,banana
+42,58,25,27.46,62.9,6.51,69.46,blackgram
+2,51,17,25.88,45.96,5.84,38.53,mothbeans
+39,57,19,29.32,45.93,6.42,165.41,pigeonpeas
+22,9,44,24.72,88.88,5.74,112.19,pomegranate
+86,18,45,28.97,90.72,6.57,22.26,muskmelon
+75,49,15,21.54,71.51,5.92,102.49,maize
+18,125,203,22.44,91.59,6.16,102.56,apple
+8,136,201,41.66,82.22,5.61,74.2,grapes
+38,56,25,25.74,45.38,7.88,67.43,mothbeans
+60,55,36,26.13,80.49,7.13,150.63,jute
+61,44,17,26.1,71.57,6.93,102.27,maize
+27,13,6,13.36,91.36,7.34,111.23,orange
+20,69,15,23.44,22.77,5.93,107.41,kidneybeans
+51,57,55,24.71,90.15,6.68,108.41,papaya
+3,28,33,30.34,48.89,5.76,94.43,mango
+63,43,17,19.29,65.47,6.81,71.32,maize
+113,38,25,22.0,79.47,7.39,90.42,cotton
+94,53,40,20.28,82.89,5.72,241.97,rice
+10,59,22,28.61,86.99,7.16,36.95,mungbean
+117,76,47,25.56,77.38,6.12,93.1,banana
+42,61,22,26.27,62.29,7.42,70.23,blackgram
+20,129,201,23.41,91.7,5.59,116.08,apple
+29,8,28,26.87,91.73,6.1,214.41,coconut
+20,45,17,28.17,83.7,6.77,37.25,mungbean
+122,48,16,24.65,75.64,6.31,61.83,cotton
+25,51,18,27.78,54.82,9.46,50.28,mothbeans
+107,38,29,26.65,57.57,6.35,145.11,coffee
+97,22,50,26.26,86.15,6.77,58.98,watermelon
+36,38,15,28.36,87.6,6.32,58.0,mungbean
+21,80,20,28.21,68.27,7.35,64.33,blackgram
+118,21,34,24.39,64.73,7.23,119.63,coffee
+14,21,35,29.53,91.91,6.12,194.31,coconut
+33,120,205,35.12,82.27,5.55,69.72,grapes
+37,65,16,22.84,18.97,5.68,63.59,kidneybeans
+60,55,44,23.0,82.32,7.84,263.96,rice
+25,129,195,17.99,81.18,5.78,72.37,grapes
+118,40,35,26.35,58.51,7.46,121.56,coffee
+18,12,35,26.14,96.39,6.34,131.34,coconut
+16,55,19,19.54,47.19,6.41,192.44,pigeonpeas
+32,20,35,26.52,98.38,5.59,144.63,coconut
+75,56,44,25.27,73.75,6.11,168.04,jute
+2,67,18,34.52,47.53,5.92,129.01,pigeonpeas
+58,73,16,33.37,65.68,6.87,64.9,blackgram
+134,56,18,23.81,83.92,6.69,70.97,cotton
+29,67,21,29.79,63.39,6.62,63.02,blackgram
+76,47,42,20.08,83.29,5.74,263.64,rice
+5,45,21,28.36,88.01,6.49,43.05,mungbean
+72,42,43,26.57,80.9,6.35,181.29,jute
+20,7,9,29.48,91.58,7.13,111.17,orange
+2,47,15,29.87,85.99,6.4,58.41,mungbean
+95,12,46,26.22,81.01,6.32,54.65,watermelon
+17,30,27,29.03,90.79,5.89,205.57,coconut
+37,62,22,24.02,61.62,7.4,49.78,lentil
+22,60,18,19.59,61.29,6.74,41.77,lentil
+20,62,18,29.36,64.99,7.37,61.91,blackgram
+4,59,25,27.69,81.94,6.23,54.62,mungbean
+84,36,28,26.74,55.55,6.12,140.63,coffee
+119,25,51,26.47,80.92,6.28,53.66,watermelon
+96,13,55,29.53,94.57,6.7,21.14,muskmelon
+120,23,55,27.84,91.61,6.73,26.48,muskmelon
+80,18,52,27.87,91.15,6.48,24.05,muskmelon
+37,66,85,20.93,18.91,6.46,78.07,chickpea
+114,20,26,25.56,62.67,7.28,193.59,coffee
+95,26,45,29.92,94.56,6.12,28.16,muskmelon
+85,37,39,24.53,82.74,6.36,224.68,rice
+27,62,77,18.2,14.71,6.58,70.18,chickpea
+65,60,43,21.97,81.9,5.66,227.36,rice
+27,72,17,28.98,57.23,6.35,120.74,pigeonpeas
+31,136,197,31.11,83.34,5.65,71.43,grapes
+21,26,27,27.0,47.68,5.7,95.85,mango
+88,21,27,24.43,66.02,7.23,181.64,coffee
+37,74,15,24.92,18.23,5.58,62.71,kidneybeans
+25,22,25,33.56,45.54,5.98,95.71,mango
+40,16,35,31.89,49.02,6.48,89.59,mango
+44,59,78,20.68,19.85,7.6,84.78,chickpea
+40,78,20,19.19,20.83,5.67,80.15,kidneybeans
+84,37,42,25.5,81.13,6.69,169.93,jute
+91,13,47,29.11,92.44,6.14,27.96,muskmelon
+85,33,25,26.21,52.51,6.91,189.09,coffee
+23,142,197,39.07,82.04,6.0,69.31,grapes
+32,78,22,23.97,62.36,7.01,53.41,lentil
+58,66,79,20.99,19.33,8.72,93.55,chickpea
+36,7,37,19.87,86.36,5.78,108.32,pomegranate
+40,136,202,22.85,94.58,5.94,117.53,apple
+26,18,42,19.73,89.65,6.91,108.23,pomegranate
+21,51,15,29.36,89.19,6.68,48.3,mungbean
+24,6,32,28.11,90.02,6.39,172.48,coconut
+2,123,205,22.37,90.79,5.74,124.98,apple
+106,14,45,24.47,84.16,6.42,57.27,watermelon
+37,62,17,25.69,69.84,7.12,74.62,blackgram
+4,6,7,23.01,91.12,6.71,112.67,orange
+8,127,196,27.03,83.17,5.83,70.96,grapes
+86,36,24,26.55,72.89,5.79,73.34,maize
+78,43,42,21.32,83.0,7.28,192.32,rice
+13,75,20,30.56,35.29,6.98,178.9,pigeonpeas
+40,132,202,24.58,80.71,5.97,69.71,grapes
+9,8,15,14.34,94.36,7.99,110.22,orange
+18,36,23,24.02,53.77,7.21,35.03,mothbeans
+38,141,198,13.06,80.28,5.76,70.76,grapes
+111,27,31,23.59,55.28,6.04,191.4,coffee
+23,37,24,28.78,44.23,7.99,33.96,mothbeans
+84,44,21,21.87,61.91,5.85,107.27,maize
+11,72,22,29.38,44.82,6.84,172.4,pigeonpeas
+110,7,45,26.64,84.7,6.19,48.32,watermelon
+54,65,47,27.93,91.56,6.72,149.91,papaya
+83,29,52,25.76,87.59,6.7,46.05,watermelon
+118,13,54,24.41,89.82,6.04,44.08,watermelon
+90,49,21,24.84,68.36,6.47,74.05,maize
+13,132,203,23.6,82.48,6.42,73.24,grapes
+35,18,26,31.99,50.85,5.28,97.39,mango
+80,52,39,26.42,76.86,7.17,197.21,jute
+41,74,18,28.76,61.03,6.6,73.38,blackgram
+6,18,37,19.66,89.94,5.94,108.05,pomegranate
+20,29,27,25.1,92.36,6.05,157.76,coconut
+4,59,22,29.34,49.0,8.91,42.44,mothbeans
+20,79,77,18.55,16.03,7.65,76.33,chickpea
+63,41,45,25.3,86.89,7.12,196.62,jute
+111,41,18,23.64,78.13,6.11,80.96,cotton
+76,44,17,20.42,62.55,5.86,65.28,maize
+11,71,17,19.92,21.47,5.75,82.69,kidneybeans
+40,49,17,31.02,45.89,6.69,53.57,mothbeans
+22,17,26,28.7,47.72,4.75,99.64,mango
+44,75,22,30.03,64.15,7.57,71.21,blackgram
+35,51,17,28.8,49.84,3.56,40.86,mothbeans
+34,61,49,28.13,93.32,6.5,117.82,papaya
+26,121,201,22.19,90.03,6.16,112.31,apple
+39,127,202,15.32,81.67,6.48,71.6,grapes
+0,137,195,22.44,80.19,6.33,65.4,grapes
+51,56,18,28.13,64.21,6.71,70.86,blackgram
+28,145,202,19.21,82.9,6.48,66.83,grapes
+104,26,30,24.41,62.66,6.41,148.7,coffee
+67,55,44,26.28,75.15,7.25,182.27,jute
+95,82,48,27.39,83.31,5.72,92.78,banana
+37,18,12,10.27,90.19,7.4,106.7,orange
+75,53,18,20.69,59.44,6.86,103.65,maize
+11,36,31,27.92,51.78,6.48,100.26,mango
+34,60,25,29.78,85.17,6.79,40.78,mungbean
+16,65,19,27.61,69.3,7.04,42.72,lentil
+19,55,20,27.43,87.81,7.19,54.73,mungbean
+100,56,40,26.39,83.31,7.43,176.15,jute
+38,77,22,28.23,69.32,6.31,35.37,lentil
+93,94,53,25.87,84.42,6.08,114.54,banana
+33,14,35,27.15,96.66,6.03,149.24,coconut
+14,69,19,20.96,63.68,7.24,52.4,lentil
+29,75,75,19.62,18.71,7.06,88.46,chickpea
+61,59,17,23.34,59.25,6.47,105.01,maize
+34,65,48,41.42,90.04,6.67,199.31,papaya
+16,8,9,24.6,91.28,7.6,111.29,orange
+85,89,51,29.21,84.7,6.16,108.55,banana
+0,65,24,28.5,62.45,7.84,53.15,lentil
+34,60,16,31.36,64.25,7.32,63.86,blackgram
+119,5,55,29.69,94.3,6.17,26.84,muskmelon
+92,85,51,29.22,81.08,5.74,108.86,banana
+36,56,20,25.41,49.66,7.44,31.87,mothbeans
+101,25,52,29.1,94.22,6.75,22.52,muskmelon
+21,17,15,23.98,91.55,7.46,118.49,orange
+86,79,45,27.81,82.69,5.81,99.21,banana
+32,120,204,10.38,83.45,6.14,67.39,grapes
+16,143,197,22.62,93.52,5.9,116.93,apple
+40,62,19,27.32,34.14,4.7,96.52,pigeonpeas
+43,50,48,28.28,91.37,6.63,179.27,papaya
+79,45,20,23.81,59.25,5.72,89.96,maize
+70,65,52,30.42,93.13,6.58,75.95,papaya
+19,78,16,20.65,23.11,5.97,67.72,kidneybeans
+101,17,55,24.37,87.13,6.45,44.64,watermelon
+8,60,18,31.22,46.02,3.81,53.12,mothbeans
+15,77,20,25.13,66.93,7.4,49.04,lentil
+89,25,54,24.69,85.57,6.35,48.99,watermelon
+2,123,198,39.65,82.21,6.25,70.4,grapes
+109,31,27,23.06,50.41,6.97,164.5,coffee
+70,54,46,39.73,91.12,6.92,122.76,papaya
+116,71,47,27.57,82.06,6.44,91.34,banana
+67,58,39,25.28,80.54,5.45,220.12,rice
+30,41,15,24.83,44.17,5.89,52.08,mothbeans
+80,43,16,23.56,71.59,6.66,66.72,maize
+28,23,28,30.02,50.1,5.68,96.09,mango
+30,70,79,20.27,19.97,7.31,69.64,chickpea
+12,29,40,19.68,89.75,6.59,111.28,pomegranate
+99,15,27,27.04,57.28,6.5,165.69,coffee
+118,44,23,22.08,82.83,6.69,67.06,cotton
+30,44,16,29.73,82.89,6.44,50.92,mungbean
+17,56,17,27.94,45.41,5.96,69.66,mothbeans
+91,38,36,26.52,77.17,7.29,157.85,jute
+99,29,55,29.19,91.46,6.66,26.48,muskmelon
+49,68,22,28.57,61.53,7.13,63.5,blackgram
+1,59,23,27.47,87.18,7.18,43.78,mungbean
+25,7,35,28.39,99.19,5.56,189.67,coconut
+21,21,38,22.55,89.33,6.33,104.9,pomegranate
+94,26,27,26.37,52.26,7.46,177.32,coffee
+9,17,32,25.95,93.41,5.84,172.05,coconut
+26,73,21,31.33,57.97,4.95,161.78,pigeonpeas
+19,39,17,29.28,81.8,6.89,44.47,mungbean
+3,60,19,25.75,40.72,4.82,100.78,pigeonpeas
+118,88,52,28.65,82.69,5.84,98.75,banana
+15,27,28,33.8,46.13,4.51,90.83,mango
+98,79,50,25.34,84.47,6.44,91.06,banana
+76,48,18,19.3,69.63,5.78,83.21,maize
+100,80,52,27.54,77.26,6.05,110.33,banana
+4,36,22,27.61,86.13,7.01,43.8,mungbean
+66,47,36,24.85,74.44,6.57,175.57,jute
+40,49,47,42.93,91.18,6.5,246.36,papaya
+90,86,55,27.96,84.15,5.64,97.56,banana
+114,30,51,29.25,90.07,6.07,25.93,muskmelon
+10,44,24,30.99,43.02,8.03,58.28,mothbeans
+40,30,35,20.89,91.08,6.27,104.44,pomegranate
+4,46,15,31.01,62.4,3.5,63.77,mothbeans
+0,69,21,25.87,61.88,7.07,36.68,lentil
+35,58,20,29.39,63.48,5.76,90.05,pigeonpeas
+60,51,36,22.7,82.81,6.03,257.0,rice
+7,73,25,27.52,63.13,7.29,45.21,lentil
+95,14,50,26.63,84.32,6.56,56.32,watermelon
+91,41,37,24.49,83.21,6.13,192.23,jute
+110,39,18,24.55,75.4,7.77,63.88,cotton
+38,14,30,26.92,91.2,5.57,194.9,coconut
+11,34,32,29.14,49.41,6.83,97.55,mango
+100,76,45,25.57,75.94,5.59,102.79,banana
+60,57,24,18.66,61.55,6.12,75.03,maize
+105,14,50,26.21,87.69,6.42,59.66,watermelon
+31,60,24,25.4,65.86,7.72,51.92,lentil
+2,131,199,22.47,91.23,6.02,124.22,apple
+119,90,48,28.67,79.59,5.99,118.26,banana
+18,58,16,21.48,38.8,4.96,180.38,pigeonpeas
+100,48,16,25.72,67.22,5.55,74.51,maize
+36,55,20,27.01,84.34,6.64,55.3,mungbean
+121,45,22,22.46,81.31,6.44,64.23,cotton
+25,27,41,19.2,94.28,6.92,108.04,pomegranate
+105,56,15,25.97,81.98,7.27,74.14,cotton
+32,13,42,23.5,92.98,5.79,106.62,pomegranate
+8,59,18,29.51,35.72,6.22,187.9,pigeonpeas
+98,35,18,23.8,74.83,6.25,91.76,maize
+69,37,42,23.06,83.37,7.07,251.05,rice
+27,63,19,20.93,21.19,5.56,133.19,kidneybeans
+3,134,199,20.28,81.32,5.82,71.07,grapes
+82,24,33,26.54,67.1,6.81,120.65,coffee
+31,144,202,11.02,80.56,5.87,68.24,grapes
+31,40,22,29.41,86.16,6.37,53.35,mungbean
+21,135,198,23.86,94.92,5.77,105.02,apple
+13,73,20,30.5,35.49,5.39,162.59,pigeonpeas
+40,9,41,24.38,85.4,5.78,106.13,pomegranate
+43,79,79,19.41,18.98,7.81,80.25,chickpea
+20,60,25,27.33,69.09,6.73,61.19,blackgram
+27,79,82,17.07,17.54,6.31,70.87,chickpea
+16,65,16,18.13,62.46,6.08,50.61,lentil
+1,76,17,28.43,52.1,6.01,147.04,pigeonpeas
+94,39,18,23.89,57.49,5.89,102.83,maize
+1,30,10,11.9,91.35,7.29,103.58,orange
+54,67,52,35.68,93.31,6.59,141.34,papaya
+23,58,19,24.17,58.25,5.24,59.19,mothbeans
+34,59,23,28.56,83.25,6.94,56.48,mungbean
+109,21,55,24.9,89.74,6.77,57.45,watermelon
+29,121,196,22.85,94.32,6.08,123.6,apple
+5,32,33,32.32,52.59,5.84,93.37,mango
+6,63,23,26.02,49.95,5.91,160.33,pigeonpeas
+103,17,51,25.11,80.03,6.21,44.21,watermelon
+86,15,47,24.04,84.18,6.42,53.79,watermelon
+116,25,50,29.26,92.92,6.09,28.71,muskmelon
+22,60,24,18.78,20.25,5.63,104.26,kidneybeans
+88,35,40,23.58,83.59,5.85,291.3,rice
+43,66,79,19.46,15.23,7.98,74.59,chickpea
+0,36,26,34.13,51.26,5.1,96.39,mango
+0,145,205,21.23,90.1,5.52,113.98,apple
+136,36,20,23.1,84.86,6.93,71.3,cotton
+34,62,55,27.59,90.73,6.59,238.5,papaya
+91,12,46,24.64,85.5,6.34,48.31,watermelon
+89,58,35,23.99,82.09,6.1,167.06,jute
+67,60,38,24.8,78.53,7.16,162.28,jute
+88,46,42,22.68,83.46,6.6,194.27,rice
+61,38,20,18.48,62.7,5.97,65.44,maize
+26,35,31,33.45,53.06,5.34,98.05,mango
+8,72,17,20.57,19.75,5.71,87.88,kidneybeans
+35,145,195,22.04,94.58,6.23,110.98,apple
+44,63,15,26.42,64.51,7.34,63.47,blackgram
+7,141,195,23.88,93.45,5.51,104.91,apple
+52,58,16,30.64,61.15,7.17,71.37,blackgram
+26,52,23,29.99,49.6,4.93,52.93,mothbeans
+27,56,20,19.26,20.51,5.54,94.95,kidneybeans
+34,133,202,15.31,80.1,5.8,74.82,grapes
+13,5,8,23.85,90.11,7.47,103.92,orange
+25,63,20,15.79,21.15,5.5,95.17,kidneybeans
+82,13,52,27.12,94.87,6.44,26.52,muskmelon
+117,19,55,28.8,91.78,6.12,25.16,muskmelon
+5,55,18,33.51,45.71,7.32,126.67,pigeonpeas
+26,37,30,35.4,49.46,6.17,97.41,mango
+26,67,24,36.98,37.74,5.64,161.48,pigeonpeas
+95,52,36,26.23,83.84,5.54,286.51,rice
+63,47,35,26.99,89.06,7.43,193.88,jute
+113,15,29,27.1,63.55,6.78,190.24,coffee
+56,75,15,30.2,60.07,7.15,66.37,blackgram
+43,68,20,29.58,66.18,7.5,69.44,blackgram
+0,67,22,29.82,69.41,6.59,51.56,lentil
+42,53,48,23.11,94.32,6.76,231.52,papaya
+31,68,45,42.92,90.08,6.94,196.24,papaya
+24,21,42,20.82,87.23,7.0,109.44,pomegranate
+38,38,18,26.31,61.19,6.29,35.73,mothbeans
+39,65,23,25.43,69.13,7.69,41.03,lentil
+84,21,55,28.47,94.79,6.49,21.08,muskmelon
+91,55,15,18.09,72.61,6.38,78.96,maize
+5,59,15,18.87,20.18,5.97,134.18,kidneybeans
+87,6,45,29.83,90.79,6.4,22.84,muskmelon
+34,45,21,28.19,82.61,6.29,37.01,mungbean
+9,29,34,29.38,45.89,5.73,100.81,mango
+140,38,15,24.15,75.88,6.02,69.92,cotton
+31,75,18,15.47,21.44,5.82,88.89,kidneybeans
+3,27,44,24.57,92.03,6.59,110.96,pomegranate
+49,55,78,18.66,16.18,7.86,81.71,chickpea
+56,58,49,37.13,94.61,6.69,172.48,papaya
+33,73,23,29.24,59.39,5.99,103.33,pigeonpeas
+31,13,33,27.64,95.49,5.86,205.55,coconut
+32,141,203,21.26,92.84,5.82,109.07,apple
+93,81,50,27.72,76.58,6.04,102.21,banana
+14,37,15,27.96,83.98,6.58,48.94,mungbean
+95,38,22,19.85,61.25,5.73,100.77,maize
+0,12,7,20.18,90.65,6.97,116.81,orange
+111,87,48,26.4,81.36,5.57,98.17,banana
+80,45,42,23.14,75.0,7.38,151.9,jute
+3,9,45,23.89,89.62,6.54,104.62,pomegranate
+1,8,26,27.51,94.19,5.56,156.67,coconut
+22,18,31,30.76,47.94,5.96,90.39,mango
+33,12,15,30.26,92.03,6.05,116.72,orange
+108,92,53,27.4,82.96,6.28,104.94,banana
+87,54,20,25.62,63.47,6.58,108.83,maize
+83,38,35,25.71,52.89,7.19,136.73,coffee
+108,89,53,29.55,78.07,5.81,99.34,banana
+31,36,29,33.94,52.72,6.46,97.46,mango
+26,32,32,30.91,49.93,6.81,90.14,mango
+35,134,204,9.95,82.55,5.84,66.01,grapes
+39,70,15,20.77,63.9,6.37,47.93,lentil
+11,60,23,27.34,88.5,7.03,51.1,mungbean
+9,10,10,22.36,93.52,6.01,101.52,orange
+93,26,27,24.59,56.47,7.29,137.7,coffee
+102,53,21,23.04,76.11,6.91,91.5,cotton
+3,77,25,24.85,22.89,5.61,62.21,kidneybeans
+20,139,202,23.5,92.21,5.67,107.99,apple
+2,61,20,22.14,23.02,5.96,76.64,kidneybeans
+22,18,33,30.41,52.48,6.62,93.92,mango
+35,30,34,28.3,95.41,6.14,182.45,coconut
+6,77,20,25.79,60.28,6.06,49.14,lentil
+11,10,45,22.63,88.46,6.4,109.04,pomegranate
+31,25,12,18.05,90.04,7.02,111.78,orange
+9,48,22,27.77,87.1,6.4,49.51,mungbean
+60,54,19,18.75,62.5,6.42,70.23,maize
+0,19,31,25.52,94.38,6.27,178.73,coconut
+10,136,204,21.2,92.16,6.28,105.86,apple
+103,42,17,24.29,84.62,6.53,81.06,cotton
+120,87,52,28.08,76.06,5.91,118.99,banana
+81,25,49,29.87,93.25,6.08,26.26,muskmelon
+81,18,50,26.81,88.23,6.43,58.8,watermelon
+27,8,30,26.45,98.3,6.01,221.23,coconut
+31,5,14,17.67,91.7,6.58,110.69,orange
+93,22,52,26.59,81.33,6.93,41.88,watermelon
+101,31,26,26.71,69.71,6.86,158.86,coffee
+16,6,29,29.29,91.96,5.87,132.15,coconut
+80,77,49,26.05,79.4,5.52,113.23,banana
+1,62,23,15.44,18.37,5.61,139.03,kidneybeans
+99,39,18,19.2,68.31,6.11,87.85,maize
+8,54,20,28.33,80.77,7.03,38.8,mungbean
+95,37,35,27.31,68.42,6.35,192.43,coffee
+70,43,40,24.36,88.8,6.18,169.12,jute
+61,41,44,24.37,82.11,6.54,159.92,jute
+81,30,48,28.52,92.1,6.04,29.87,muskmelon
+27,74,20,24.69,59.97,5.86,91.96,pigeonpeas
+55,78,21,33.39,62.94,6.6,63.57,blackgram
+12,58,23,21.75,63.4,6.77,50.43,lentil
+9,66,21,30.12,34.13,5.72,157.09,pigeonpeas
+69,57,35,24.31,78.54,6.19,186.23,jute
+89,83,47,28.1,77.8,5.63,109.54,banana
+8,28,30,25.52,94.33,6.02,135.13,coconut
+30,30,35,25.01,95.59,6.0,165.81,coconut
+67,35,22,23.31,63.25,6.39,108.76,maize
+101,37,18,22.92,82.69,7.64,92.92,cotton
+120,8,46,29.56,90.71,6.73,28.37,muskmelon
+6,139,199,25.67,81.62,6.29,74.11,grapes
+44,64,54,29.81,91.38,6.74,232.7,papaya
+23,70,15,34.6,63.11,7.4,60.42,blackgram
+42,62,75,18.18,18.9,7.01,81.85,chickpea
+14,29,32,35.64,48.97,6.94,97.52,mango
+39,37,25,33.33,45.61,6.95,98.29,mango
+7,21,35,25.76,94.66,5.76,131.25,coconut
+39,24,31,33.56,53.73,4.76,98.68,mango
+24,63,19,19.35,55.97,4.68,194.59,pigeonpeas
+112,39,29,26.12,63.37,6.73,147.8,coffee
+90,59,35,24.25,89.86,7.1,175.17,jute
+4,13,6,15.63,94.26,7.56,101.47,orange
+3,58,21,25.36,46.83,9.16,55.61,mothbeans
+26,11,11,13.7,90.96,7.61,106.29,orange
+20,23,11,31.85,90.12,6.41,109.95,orange
+90,39,37,24.81,81.69,6.86,190.79,jute
+22,8,33,28.44,95.88,5.67,203.93,coconut
+39,69,53,25.93,93.02,6.96,241.82,papaya
+24,14,33,29.38,93.28,6.37,218.52,coconut
+22,80,20,23.01,18.87,5.67,100.12,kidneybeans
+31,11,45,24.84,86.89,6.03,107.64,pomegranate
+5,56,24,24.81,45.01,5.02,188.49,pigeonpeas
+27,65,18,20.11,23.22,5.6,73.36,kidneybeans
+22,38,31,31.53,53.06,5.82,98.57,mango
+23,45,21,31.47,51.8,8.99,74.44,mothbeans
+34,68,51,27.35,94.18,6.69,40.35,papaya
+103,33,33,26.72,50.5,7.13,126.81,coffee
+100,41,22,22.42,84.56,7.32,93.47,cotton
+24,80,19,29.68,69.09,6.81,65.66,blackgram
+43,61,20,26.87,61.61,6.8,63.52,blackgram
+2,72,18,26.58,60.98,7.84,50.89,lentil
+33,143,204,21.13,91.96,5.81,122.54,apple
+114,40,23,25.54,81.14,6.75,95.43,cotton
+37,57,20,31.1,44.82,7.35,70.8,mothbeans
+86,37,16,20.52,59.21,5.56,67.61,maize
+2,24,34,28.89,54.81,6.47,94.76,mango
+111,5,47,28.03,91.47,6.27,21.18,muskmelon
+37,36,27,27.55,47.91,5.91,90.4,mango
+27,64,15,20.16,24.84,5.51,138.24,kidneybeans
+31,121,201,23.16,90.34,5.73,110.71,apple
+108,33,31,23.69,66.76,7.39,144.66,coffee
+93,56,42,23.86,82.23,7.38,195.09,rice
+37,6,13,26.03,91.51,7.51,101.28,orange
+108,24,31,24.13,56.18,6.43,147.28,coffee
+29,45,16,28.44,87.91,6.58,43.12,mungbean
+32,14,37,22.73,88.49,6.83,104.68,pomegranate
+59,62,52,43.68,93.11,6.61,103.82,papaya
+27,59,22,21.81,23.21,5.79,130.06,kidneybeans
+20,29,10,29.07,93.27,7.37,100.79,orange
+80,15,28,23.11,68.0,6.7,161.89,coffee
+2,24,38,24.56,91.64,5.92,111.97,pomegranate
+36,29,13,20.68,90.92,7.83,109.75,orange
+121,47,16,23.61,79.3,7.72,72.5,cotton
+15,140,195,13.29,83.54,5.7,65.8,grapes
+81,53,42,23.68,81.04,5.18,233.7,rice
+32,68,19,24.63,18.18,5.51,149.74,kidneybeans
+33,14,8,21.03,92.96,7.68,110.68,orange
+80,71,47,27.51,80.8,6.16,105.08,banana
+91,7,52,25.08,83.46,6.41,56.4,watermelon
+98,7,45,27.79,92.51,6.16,26.85,muskmelon
+30,65,82,20.71,15.28,7.1,76.78,chickpea
+65,63,50,31.88,91.33,6.52,79.27,papaya
+40,61,22,20.95,65.81,7.0,44.24,lentil
+87,21,52,27.35,94.29,6.07,27.21,muskmelon
+120,7,47,24.25,83.04,6.65,54.77,watermelon
+95,12,51,25.76,84.17,6.68,44.22,watermelon
+10,125,196,22.31,90.04,5.73,113.07,apple
+24,38,22,24.48,58.52,8.2,34.97,mothbeans
+60,55,40,24.99,88.96,7.03,151.49,jute
+63,43,19,18.52,55.53,6.64,90.99,maize
+93,43,38,23.61,86.14,6.99,150.24,jute
+12,142,203,31.31,82.56,5.97,65.01,grapes
+35,69,23,16.79,24.97,5.58,75.45,kidneybeans
+76,40,43,25.16,83.12,5.07,231.38,rice
+59,63,18,31.66,60.13,6.53,66.69,blackgram
+38,55,19,33.18,38.23,5.86,198.83,pigeonpeas
+98,27,27,24.71,51.29,7.24,197.64,coffee
+23,21,26,26.45,93.45,5.9,149.22,coconut
+36,27,26,26.58,95.79,6.25,171.63,coconut
+83,21,28,25.57,60.49,7.47,190.23,coffee
+22,59,23,27.32,51.28,4.37,36.5,mothbeans
+33,77,21,30.33,65.63,7.01,71.65,blackgram
+67,68,49,35.27,92.38,6.82,149.85,papaya
+52,71,16,27.74,68.54,7.08,71.79,blackgram
+35,55,22,30.89,52.63,8.63,55.52,mothbeans
+34,16,25,30.07,50.96,6.11,92.1,mango
+33,23,45,20.0,85.84,7.12,112.34,pomegranate
+44,76,22,27.26,68.01,7.78,68.92,blackgram
+82,46,41,23.33,79.8,6.58,187.31,jute
+32,18,13,13.84,91.75,6.04,107.99,orange
+22,78,76,17.85,19.09,8.62,76.32,chickpea
+34,48,48,41.04,91.37,6.81,181.53,papaya
+91,35,39,23.79,80.42,6.97,206.26,rice
+10,37,22,28.73,89.13,7.07,58.53,mungbean
+19,7,10,14.78,91.22,6.12,100.2,orange
+3,63,16,24.38,61.18,6.87,53.14,lentil
+79,43,39,21.67,80.71,7.06,210.81,rice
+6,13,29,27.31,99.97,5.83,201.83,coconut
+54,62,80,17.49,16.39,7.49,79.46,chickpea
+23,62,19,16.52,20.46,5.61,98.78,kidneybeans
+83,58,23,19.74,59.66,6.38,65.51,maize
+29,22,40,23.63,89.73,6.15,107.68,pomegranate
+40,11,44,24.46,86.11,6.32,111.38,pomegranate
+104,73,46,29.14,80.12,6.28,90.45,banana
+9,35,20,27.42,80.98,6.91,40.53,mungbean
+110,14,51,27.02,91.67,6.09,21.26,muskmelon
+6,8,11,24.36,92.4,6.6,119.69,orange
+115,17,55,27.58,94.12,6.78,28.08,muskmelon
+22,72,85,18.87,15.66,6.39,88.51,chickpea
+14,121,203,9.72,83.75,6.16,74.46,grapes
+4,134,200,28.58,80.96,5.84,73.34,grapes
+22,55,16,23.79,68.03,6.52,49.74,lentil
+117,25,54,28.68,92.51,6.15,29.11,muskmelon
+40,58,15,29.46,87.61,6.98,43.15,mungbean
+68,57,43,26.09,80.38,5.71,182.9,rice
+28,123,198,23.46,91.46,5.68,111.78,apple
+113,20,48,27.47,94.88,6.44,27.28,muskmelon
+5,19,25,27.35,54.44,6.44,96.28,mango
+106,10,49,27.73,92.01,6.35,20.21,muskmelon
+93,56,36,24.01,82.06,6.98,185.28,rice
+27,139,205,22.48,93.41,5.77,105.55,apple
+32,25,9,10.36,93.76,7.8,101.15,orange
+23,65,20,23.04,22.43,5.83,108.37,kidneybeans
+35,52,19,27.11,89.9,6.7,37.46,mungbean
+97,25,50,26.22,80.9,6.09,49.09,watermelon
+91,84,52,29.15,78.71,6.39,117.54,banana
+26,65,22,17.85,18.78,5.95,143.1,kidneybeans
+84,39,35,23.18,52.14,6.96,117.31,coffee
+21,137,196,23.61,91.7,5.81,123.59,apple
+16,143,204,23.71,91.53,5.63,121.9,apple
+22,55,24,28.57,57.31,8.66,64.53,mothbeans
+24,45,19,26.86,48.82,5.95,34.74,mothbeans
+13,67,18,30.58,34.76,5.38,177.58,pigeonpeas
+31,130,198,21.8,92.73,5.55,120.06,apple
+81,45,35,26.53,80.12,6.16,218.92,rice
+3,9,35,26.92,99.85,6.32,225.63,coconut
+28,46,16,29.01,84.96,6.66,45.91,mungbean
+14,41,17,29.13,88.48,7.09,36.45,mungbean
+18,12,8,12.59,91.82,6.21,119.39,orange
+97,8,52,24.91,86.97,6.24,49.49,watermelon
+88,5,47,25.86,86.67,6.66,41.17,watermelon
+4,43,18,29.03,61.09,8.84,72.98,mothbeans
+9,57,24,29.89,89.72,7.17,42.99,mungbean
+0,5,36,24.35,90.89,6.15,105.53,pomegranate
+25,57,19,17.15,19.87,5.57,88.0,kidneybeans
+6,66,15,34.93,30.4,6.35,159.26,pigeonpeas
+47,80,77,17.18,16.43,7.56,72.85,chickpea
+10,141,201,22.13,90.98,6.39,104.54,apple
+16,15,42,19.68,89.09,6.89,108.55,pomegranate
+34,65,19,23.44,63.22,5.94,45.4,lentil
+34,75,24,23.5,51.29,4.76,192.3,pigeonpeas
+63,58,22,18.25,55.28,6.2,63.72,maize
+111,50,15,25.17,80.3,7.88,84.62,cotton
+57,57,51,39.02,91.49,6.99,105.88,papaya
+83,79,55,25.15,83.35,5.57,98.67,banana
+6,36,22,24.22,59.79,8.87,42.25,mothbeans
+108,88,55,26.29,83.39,5.89,113.87,banana
+81,6,55,24.89,85.87,6.11,51.71,watermelon
+106,85,53,27.2,78.81,5.92,99.72,banana
+79,51,16,25.34,68.5,6.59,96.46,maize
+20,73,22,16.04,22.33,5.98,130.39,kidneybeans
+32,12,30,25.39,98.09,5.58,218.08,coconut
+95,75,45,28.98,82.96,5.83,109.02,banana
+22,79,17,21.42,20.4,5.91,116.52,kidneybeans
+33,59,19,23.19,62.75,7.64,49.55,lentil
+113,21,33,26.02,55.83,7.28,176.9,coffee
+77,36,37,26.88,81.46,6.14,194.58,rice
+8,25,36,19.91,94.95,6.83,104.03,pomegranate
+15,9,11,11.55,94.15,7.91,108.83,orange
+133,47,23,24.89,75.62,6.83,89.76,cotton
+16,56,17,33.8,40.03,7.45,176.62,pigeonpeas
+90,42,43,20.88,82.0,6.5,202.94,rice
+9,77,17,21.66,63.58,6.28,38.08,lentil
+125,60,17,24.14,84.52,6.79,80.36,cotton
+16,51,21,31.02,49.98,3.53,32.81,mothbeans
+113,37,20,25.03,79.04,7.39,97.1,cotton
+22,71,17,18.15,19.39,5.51,107.69,kidneybeans
+41,62,15,29.38,64.15,7.36,65.24,blackgram
+72,51,40,23.21,74.1,7.42,199.48,jute
+52,73,79,17.26,18.75,7.84,94.0,chickpea
+27,56,20,29.21,87.11,6.42,51.54,mungbean
+69,66,49,40.0,90.17,6.53,92.12,papaya
+25,17,40,18.91,87.75,6.61,111.28,pomegranate
+27,56,22,19.92,20.7,5.83,108.64,kidneybeans
+28,7,9,34.59,92.13,6.73,115.57,orange
+44,60,55,34.28,90.56,6.83,98.54,papaya
+116,81,55,26.42,83.7,5.92,95.12,banana
+28,66,23,21.54,24.25,6.0,120.69,kidneybeans
+31,25,38,24.96,92.41,6.5,109.42,pomegranate
+100,6,53,29.05,93.92,6.11,23.67,muskmelon
+21,63,17,15.77,19.23,5.98,108.34,kidneybeans
+38,36,21,28.03,84.88,6.56,36.12,mungbean
+24,128,196,22.75,90.69,5.52,110.43,apple
+118,33,30,24.13,67.23,6.36,173.32,coffee
+98,26,49,27.29,90.53,6.13,23.5,muskmelon
+83,9,45,25.85,89.13,6.05,46.85,watermelon
+95,13,46,29.84,93.76,6.13,23.28,muskmelon
+104,18,30,23.6,60.4,6.78,140.94,coffee
+117,86,48,28.7,82.54,6.23,116.16,banana
+5,29,44,21.02,93.06,5.58,104.78,pomegranate
+32,79,22,27.6,63.46,5.92,54.38,lentil
+88,55,45,24.64,80.41,7.73,253.72,rice
+103,40,30,27.31,55.2,6.35,141.48,coffee
+13,47,20,29.22,87.94,6.54,43.14,mungbean
+85,35,32,26.25,54.29,6.85,133.11,coffee
+20,72,19,32.48,64.35,7.4,65.82,blackgram
+3,26,39,24.38,91.19,7.08,103.6,pomegranate
+83,45,21,18.83,58.75,5.72,79.75,maize
+23,23,27,34.72,51.43,5.16,97.31,mango
+92,35,40,22.18,80.33,6.36,200.09,rice
+134,52,18,23.96,76.59,7.99,76.13,cotton
+86,6,53,25.92,83.47,6.92,42.11,watermelon
+38,61,21,30.27,67.39,4.7,127.78,pigeonpeas
+32,57,22,28.69,87.5,6.77,44.57,mungbean
+2,120,203,23.13,94.71,5.89,108.62,apple
+111,15,54,27.71,92.91,6.19,22.06,muskmelon
+63,42,21,23.26,72.33,5.8,67.1,maize
+11,53,24,28.52,55.77,7.39,61.33,mothbeans
+16,29,13,32.32,93.68,6.2,117.62,orange
+65,62,51,31.53,90.87,6.51,207.07,papaya
+20,68,23,25.55,63.95,7.71,63.18,blackgram
+112,25,51,25.05,85.57,6.93,56.72,watermelon
+37,55,82,19.46,18.02,8.42,78.45,chickpea
+6,142,202,27.24,82.95,6.22,70.43,grapes
+31,13,33,29.7,95.21,6.34,148.3,coconut
+27,57,24,27.34,43.36,6.09,142.33,pigeonpeas
+61,64,52,43.3,92.83,6.64,110.56,papaya
+90,86,52,25.85,81.96,5.79,119.09,banana
+89,22,52,24.9,86.11,6.22,53.15,watermelon
+42,79,23,27.72,63.29,6.78,68.57,blackgram
+115,11,46,24.42,89.4,6.62,40.32,watermelon
+22,56,17,29.88,87.33,6.89,44.75,mungbean
+97,35,26,24.91,53.74,6.33,166.25,coffee
+71,54,35,26.64,70.96,7.31,199.34,jute
+91,56,37,23.43,80.57,6.36,269.5,rice
+35,140,197,16.78,82.75,6.11,66.76,grapes
+31,78,76,17.57,15.0,8.52,89.31,chickpea
+35,138,200,21.2,90.81,5.67,103.68,apple
+24,142,202,22.54,91.48,5.71,101.85,apple
+12,80,19,21.91,65.22,5.96,36.1,lentil
+67,60,25,24.92,66.79,5.75,109.22,maize
+99,16,30,23.53,65.44,6.39,186.17,coffee
+38,135,203,41.36,82.8,6.44,69.92,grapes
+5,65,16,21.33,18.49,5.87,109.1,kidneybeans
+26,122,202,22.45,94.74,5.62,107.18,apple
+99,50,15,18.15,71.09,5.57,88.08,maize
+12,20,39,19.86,86.2,6.03,111.02,pomegranate
+13,16,8,34.74,93.12,6.95,100.2,orange
+110,22,47,29.03,91.82,6.24,24.94,muskmelon
+91,21,50,24.34,81.44,6.76,48.32,watermelon
+33,138,198,22.29,90.69,6.22,122.74,apple
+99,6,45,26.13,86.55,6.0,40.71,watermelon
+20,142,196,10.9,80.02,6.21,68.69,grapes
+31,72,17,28.69,49.47,5.83,96.36,pigeonpeas
+75,38,39,23.45,84.79,6.22,283.93,rice
+84,27,29,23.32,53.0,7.17,168.26,coffee
+34,29,8,31.88,91.15,6.45,105.34,orange
+107,72,45,28.15,81.54,5.79,91.41,banana
+6,13,9,34.51,90.56,7.79,118.33,orange
+40,64,16,16.43,24.24,5.93,140.37,kidneybeans
+38,72,21,28.23,49.44,5.9,186.5,pigeonpeas
+63,50,52,28.65,93.23,6.75,115.82,papaya
+127,37,18,24.88,76.3,7.04,91.92,cotton
+118,45,23,23.37,77.43,7.98,71.68,cotton
+40,21,8,34.91,92.88,7.42,102.19,orange
+97,59,43,26.36,84.04,6.29,271.36,rice
+107,42,24,22.05,84.63,6.14,86.01,cotton
+100,18,52,26.2,80.38,6.88,56.48,watermelon
+108,36,19,22.78,77.51,7.24,64.61,cotton
+6,123,203,12.76,81.62,6.13,66.78,grapes
+34,6,30,27.08,97.0,5.95,171.76,coconut
+24,130,195,30.0,81.54,6.11,67.13,grapes
+12,61,19,19.33,24.14,5.66,68.51,kidneybeans
+92,44,16,18.88,65.77,6.08,94.76,maize
+7,56,23,26.34,40.01,5.55,55.5,mothbeans
+82,75,55,27.35,78.49,6.28,92.16,banana
+22,11,29,28.03,95.02,5.96,218.01,coconut
+89,52,45,24.89,77.01,7.21,196.47,jute
+99,92,47,28.13,77.48,6.32,103.5,banana
+27,30,31,28.99,90.74,5.72,148.84,coconut
+23,138,195,22.49,91.7,5.8,124.39,apple
+22,70,19,18.24,21.08,5.52,69.45,kidneybeans
+37,79,19,27.54,69.35,7.14,69.41,blackgram
+89,47,38,25.52,72.25,6.0,151.89,jute
+28,136,200,23.06,92.4,6.25,114.74,apple
+23,5,44,21.21,94.26,7.16,107.57,pomegranate
+19,120,195,18.74,81.12,5.93,73.56,grapes
+9,59,25,30.39,60.16,7.7,35.37,mothbeans
+10,18,35,27.8,99.65,6.38,181.69,coconut
+26,66,22,18.06,65.1,6.3,51.55,lentil
+64,45,43,25.63,83.53,5.53,209.9,rice

classification/unipredict/awaiskaggler-insurance-csv/metadata.json

@@ -0,0 +1,29 @@
+{
+  "dataset": "awaiskaggler-insurance-csv",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "age",
+  "label_values": [
+    "between 39.0 and 51.0",
+    "between 27.0 and 39.0",
+    "greater than 51.0",
+    "less than 27.0"
+  ],
+  "num_labels": 4,
+  "train_samples": 1202,
+  "test_samples": 136,
+  "train_label_distribution": {
+    "less than 27.0": 300,
+    "between 27.0 and 39.0": 283,
+    "greater than 51.0": 320,
+    "between 39.0 and 51.0": 299
+  },
+  "test_label_distribution": {
+    "greater than 51.0": 36,
+    "less than 27.0": 34,
+    "between 39.0 and 51.0": 34,
+    "between 27.0 and 39.0": 32
+  }
+}

classification/unipredict/awaiskaggler-insurance-csv/test.csv

@@ -0,0 +1,137 @@
+sex,bmi,children,smoker,region,expenses,age
+female,35.8,3,no,northwest,12495.29,greater than 51.0
+male,27.5,1,no,southwest,12333.83,greater than 51.0
+male,29.5,0,no,southeast,9487.64,greater than 51.0
+male,23.9,5,no,southwest,5080.1,less than 27.0
+male,45.4,2,no,southeast,6356.27,between 39.0 and 51.0
+male,32.1,0,no,northeast,13555.0,greater than 51.0
+female,24.5,1,no,northwest,2709.11,less than 27.0
+male,37.0,2,yes,southeast,49577.66,greater than 51.0
+female,34.8,1,no,northwest,9583.89,between 39.0 and 51.0
+female,33.1,0,yes,southeast,40974.16,between 39.0 and 51.0
+female,20.8,0,no,southeast,1607.51,less than 27.0
+male,28.5,5,no,northeast,6799.46,between 27.0 and 39.0
+female,33.1,0,no,southeast,3171.61,between 27.0 and 39.0
+male,33.8,1,no,southeast,1725.55,less than 27.0
+female,21.9,1,yes,northeast,15359.1,less than 27.0
+female,33.2,3,no,northeast,8538.29,between 39.0 and 51.0
+female,27.3,3,yes,southeast,18223.45,less than 27.0
+female,38.1,0,no,southeast,12648.7,greater than 51.0
+female,39.5,1,no,southwest,9880.07,greater than 51.0
+female,36.2,1,no,northwest,7443.64,between 39.0 and 51.0
+female,33.4,0,no,southwest,10795.94,less than 27.0
+female,27.5,2,no,southwest,20177.67,between 27.0 and 39.0
+male,20.4,0,no,southwest,3260.2,between 27.0 and 39.0
+female,26.9,0,yes,northwest,29330.98,greater than 51.0
+male,39.9,0,yes,southwest,48173.36,greater than 51.0
+female,36.0,1,no,southwest,8556.91,between 39.0 and 51.0
+female,17.3,2,no,northeast,6877.98,between 27.0 and 39.0
+female,24.6,1,no,northwest,2709.24,less than 27.0
+female,19.8,1,no,southwest,3378.91,less than 27.0
+male,34.4,0,no,northwest,11743.93,greater than 51.0
+male,24.8,0,yes,northeast,17904.53,between 27.0 and 39.0
+female,28.9,1,no,northwest,9249.5,between 39.0 and 51.0
+male,28.7,3,yes,northwest,20745.99,between 27.0 and 39.0
+male,23.2,0,no,southwest,6250.44,between 39.0 and 51.0
+male,36.0,1,no,southeast,9386.16,greater than 51.0
+female,39.7,0,no,southwest,14319.03,greater than 51.0
+male,29.9,0,no,southwest,10214.64,greater than 51.0
+male,31.5,1,no,southeast,27000.98,greater than 51.0
+female,20.5,0,yes,northeast,14571.89,less than 27.0
+female,30.7,1,no,southeast,5976.83,between 27.0 and 39.0
+female,30.1,1,no,northwest,9910.36,between 39.0 and 51.0
+male,30.4,0,yes,southeast,62592.87,between 39.0 and 51.0
+male,34.1,0,no,southwest,1261.44,less than 27.0
+female,25.8,1,no,southwest,7624.63,between 39.0 and 51.0
+female,36.5,0,no,northeast,12797.21,between 39.0 and 51.0
+female,27.6,0,no,northwest,7421.19,between 39.0 and 51.0
+male,35.2,2,no,southwest,4670.64,between 27.0 and 39.0
+female,27.0,0,no,northwest,11082.58,greater than 51.0
+male,30.6,0,no,northeast,2727.4,less than 27.0
+male,35.2,0,no,northeast,12404.88,between 27.0 and 39.0
+female,41.3,0,no,northeast,17878.9,less than 27.0
+female,35.2,0,no,northwest,2134.9,less than 27.0
+male,34.2,2,yes,southwest,42856.84,between 39.0 and 51.0
+male,45.9,2,no,southwest,3693.43,between 27.0 and 39.0
+male,35.5,0,yes,southeast,36950.26,between 27.0 and 39.0
+female,18.5,1,no,southwest,4766.02,between 27.0 and 39.0
+male,24.3,0,no,northwest,12523.6,greater than 51.0
+female,26.5,2,no,southeast,4340.44,between 27.0 and 39.0
+female,31.4,0,no,southeast,1622.19,less than 27.0
+male,39.9,0,no,southeast,12982.87,greater than 51.0
+male,25.2,0,no,northeast,11931.13,greater than 51.0
+male,27.4,2,no,southwest,7726.85,between 39.0 and 51.0
+male,25.8,5,no,southwest,10096.97,between 39.0 and 51.0
+male,34.4,0,no,southwest,1261.86,less than 27.0
+male,37.3,2,no,southeast,4058.12,between 27.0 and 39.0
+female,36.0,0,no,southwest,2166.73,less than 27.0
+male,35.8,2,no,southeast,4890.0,between 27.0 and 39.0
+male,25.2,0,yes,northeast,15518.18,less than 27.0
+female,32.7,0,no,northwest,13844.8,greater than 51.0
+male,20.3,0,no,southwest,1242.26,less than 27.0
+female,36.4,3,no,northwest,11436.74,greater than 51.0
+male,34.5,3,yes,northwest,60021.4,greater than 51.0
+female,28.9,0,no,southwest,8277.52,between 39.0 and 51.0
+female,40.7,0,no,northeast,9875.68,greater than 51.0
+male,34.8,2,no,northwest,5729.01,between 27.0 and 39.0
+female,38.1,2,no,northeast,24915.05,between 27.0 and 39.0
+male,33.8,0,no,southeast,1674.63,less than 27.0
+male,23.2,0,no,southeast,1515.34,less than 27.0
+male,28.6,3,no,northwest,6548.2,between 27.0 and 39.0
+female,36.7,2,no,northwest,10848.13,greater than 51.0
+female,32.7,2,no,northwest,26018.95,less than 27.0
+male,33.8,1,no,northwest,4462.72,between 27.0 and 39.0
+male,36.3,2,yes,southwest,38711.0,between 27.0 and 39.0
+female,31.9,1,no,southeast,10928.85,greater than 51.0
+male,26.4,0,no,northeast,14394.56,greater than 51.0
+female,32.5,0,yes,southeast,45008.96,greater than 51.0
+female,39.3,0,no,northeast,14901.52,greater than 51.0
+female,30.8,1,no,northeast,9778.35,between 39.0 and 51.0
+female,38.9,3,no,southwest,5972.38,between 27.0 and 39.0
+female,25.3,2,yes,southeast,24667.42,greater than 51.0
+female,27.7,0,yes,northeast,29523.17,greater than 51.0
+female,34.9,0,no,northeast,2899.49,less than 27.0
+female,38.2,0,no,southeast,1631.67,less than 27.0
+female,28.2,3,no,southeast,10702.64,between 39.0 and 51.0
+female,32.3,1,no,northeast,11512.41,greater than 51.0
+female,23.4,3,no,northeast,8252.28,between 39.0 and 51.0
+male,39.4,2,yes,southwest,38344.57,less than 27.0
+male,25.6,0,no,northwest,1632.56,less than 27.0
+female,31.1,0,no,southeast,1621.88,less than 27.0
+female,30.9,2,no,southwest,8520.03,between 39.0 and 51.0
+female,22.8,3,no,northeast,7985.82,between 39.0 and 51.0
+male,25.1,0,no,southeast,5415.66,between 39.0 and 51.0
+male,28.4,1,no,northwest,6664.69,between 39.0 and 51.0
+female,31.9,5,no,southwest,11552.9,between 39.0 and 51.0
+male,34.7,2,no,southwest,6082.41,between 27.0 and 39.0
+female,33.9,3,no,northwest,10115.01,between 39.0 and 51.0
+male,32.3,2,no,southeast,6338.08,between 39.0 and 51.0
+male,28.6,0,no,northwest,11735.88,greater than 51.0
+female,24.3,3,no,southwest,4391.65,less than 27.0
+male,22.9,0,yes,northeast,35069.37,between 39.0 and 51.0
+female,17.4,1,no,southwest,2585.27,less than 27.0
+female,29.4,2,no,northeast,4564.19,less than 27.0
+female,34.6,1,yes,southwest,41661.6,between 39.0 and 51.0
+male,23.0,2,yes,northwest,17361.77,between 27.0 and 39.0
+male,36.0,3,yes,southeast,42124.52,between 39.0 and 51.0
+female,31.2,0,no,southwest,9625.92,greater than 51.0
+female,25.8,0,no,northwest,5266.37,between 27.0 and 39.0
+male,37.1,1,yes,southeast,39871.7,between 27.0 and 39.0
+male,23.4,0,no,southwest,1969.61,less than 27.0
+male,29.6,0,no,northeast,12731.0,greater than 51.0
+female,25.0,2,no,northwest,8017.06,between 39.0 and 51.0
+male,32.8,3,no,northwest,11289.11,greater than 51.0
+male,35.2,1,no,northeast,11394.07,greater than 51.0
+male,44.2,2,no,southeast,4266.17,between 27.0 and 39.0
+female,29.4,1,no,southeast,8547.69,between 39.0 and 51.0
+female,27.7,0,no,northeast,5469.01,between 27.0 and 39.0
+female,31.0,0,no,southeast,6185.32,between 39.0 and 51.0
+female,40.8,3,no,southeast,12485.8,greater than 51.0
+female,37.1,2,no,southwest,7371.77,between 39.0 and 51.0
+male,31.7,2,no,northwest,4433.39,between 27.0 and 39.0
+male,24.4,3,yes,southwest,18259.22,between 27.0 and 39.0
+female,20.2,2,no,northwest,4906.41,between 27.0 and 39.0
+female,39.5,0,no,southeast,2480.98,less than 27.0
+female,26.7,0,no,northwest,4571.41,between 27.0 and 39.0
+male,40.5,0,no,northeast,1984.45,less than 27.0
+male,29.0,0,no,northwest,1906.36,less than 27.0

classification/unipredict/awaiskaggler-insurance-csv/test.jsonl

@@ -0,0 +1,136 @@
+{"text": "The sex is female. The bmi is 35.8. The children is 3. The smoker is no. The region is northwest. The expenses is 12495.29.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 27.5. The children is 1. The smoker is no. The region is southwest. The expenses is 12333.83.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 29.5. The children is 0. The smoker is no. The region is southeast. The expenses is 9487.64.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 23.9. The children is 5. The smoker is no. The region is southwest. The expenses is 5080.1.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 45.4. The children is 2. The smoker is no. The region is southeast. The expenses is 6356.27.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 32.1. The children is 0. The smoker is no. The region is northeast. The expenses is 13555.0.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 24.5. The children is 1. The smoker is no. The region is northwest. The expenses is 2709.11.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 37.0. The children is 2. The smoker is yes. The region is southeast. The expenses is 49577.66.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 34.8. The children is 1. The smoker is no. The region is northwest. The expenses is 9583.89.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 33.1. The children is 0. The smoker is yes. The region is southeast. The expenses is 40974.16.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 20.8. The children is 0. The smoker is no. The region is southeast. The expenses is 1607.51.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 28.5. The children is 5. The smoker is no. The region is northeast. The expenses is 6799.46.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 33.1. The children is 0. The smoker is no. The region is southeast. The expenses is 3171.61.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 33.8. The children is 1. The smoker is no. The region is southeast. The expenses is 1725.55.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 21.9. The children is 1. The smoker is yes. The region is northeast. The expenses is 15359.1.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 33.2. The children is 3. The smoker is no. The region is northeast. The expenses is 8538.29.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 27.3. The children is 3. The smoker is yes. The region is southeast. The expenses is 18223.45.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 38.1. The children is 0. The smoker is no. The region is southeast. The expenses is 12648.7.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 39.5. The children is 1. The smoker is no. The region is southwest. The expenses is 9880.07.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 36.2. The children is 1. The smoker is no. The region is northwest. The expenses is 7443.64.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 33.4. The children is 0. The smoker is no. The region is southwest. The expenses is 10795.94.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 27.5. The children is 2. The smoker is no. The region is southwest. The expenses is 20177.67.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 20.4. The children is 0. The smoker is no. The region is southwest. The expenses is 3260.2.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 26.9. The children is 0. The smoker is yes. The region is northwest. The expenses is 29330.98.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 39.9. The children is 0. The smoker is yes. The region is southwest. The expenses is 48173.36.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 36.0. The children is 1. The smoker is no. The region is southwest. The expenses is 8556.91.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 17.3. The children is 2. The smoker is no. The region is northeast. The expenses is 6877.98.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 24.6. The children is 1. The smoker is no. The region is northwest. The expenses is 2709.24.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 19.8. The children is 1. The smoker is no. The region is southwest. The expenses is 3378.91.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 34.4. The children is 0. The smoker is no. The region is northwest. The expenses is 11743.93.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 24.8. The children is 0. The smoker is yes. The region is northeast. The expenses is 17904.53.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 28.9. The children is 1. The smoker is no. The region is northwest. The expenses is 9249.5.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 28.7. The children is 3. The smoker is yes. The region is northwest. The expenses is 20745.99.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 23.2. The children is 0. The smoker is no. The region is southwest. The expenses is 6250.44.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 36.0. The children is 1. The smoker is no. The region is southeast. The expenses is 9386.16.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 39.7. The children is 0. The smoker is no. The region is southwest. The expenses is 14319.03.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 29.9. The children is 0. The smoker is no. The region is southwest. The expenses is 10214.64.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 31.5. The children is 1. The smoker is no. The region is southeast. The expenses is 27000.98.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 20.5. The children is 0. The smoker is yes. The region is northeast. The expenses is 14571.89.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 30.7. The children is 1. The smoker is no. The region is southeast. The expenses is 5976.83.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 30.1. The children is 1. The smoker is no. The region is northwest. The expenses is 9910.36.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 30.4. The children is 0. The smoker is yes. The region is southeast. The expenses is 62592.87.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 34.1. The children is 0. The smoker is no. The region is southwest. The expenses is 1261.44.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 25.8. The children is 1. The smoker is no. The region is southwest. The expenses is 7624.63.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 36.5. The children is 0. The smoker is no. The region is northeast. The expenses is 12797.21.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 27.6. The children is 0. The smoker is no. The region is northwest. The expenses is 7421.19.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 35.2. The children is 2. The smoker is no. The region is southwest. The expenses is 4670.64.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 27.0. The children is 0. The smoker is no. The region is northwest. The expenses is 11082.58.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 30.6. The children is 0. The smoker is no. The region is northeast. The expenses is 2727.4.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 35.2. The children is 0. The smoker is no. The region is northeast. The expenses is 12404.88.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 41.3. The children is 0. The smoker is no. The region is northeast. The expenses is 17878.9.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 35.2. The children is 0. The smoker is no. The region is northwest. The expenses is 2134.9.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 34.2. The children is 2. The smoker is yes. The region is southwest. The expenses is 42856.84.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 45.9. The children is 2. The smoker is no. The region is southwest. The expenses is 3693.43.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 35.5. The children is 0. The smoker is yes. The region is southeast. The expenses is 36950.26.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 18.5. The children is 1. The smoker is no. The region is southwest. The expenses is 4766.02.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 24.3. The children is 0. The smoker is no. The region is northwest. The expenses is 12523.6.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 26.5. The children is 2. The smoker is no. The region is southeast. The expenses is 4340.44.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 31.4. The children is 0. The smoker is no. The region is southeast. The expenses is 1622.19.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 39.9. The children is 0. The smoker is no. The region is southeast. The expenses is 12982.87.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 25.2. The children is 0. The smoker is no. The region is northeast. The expenses is 11931.13.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 27.4. The children is 2. The smoker is no. The region is southwest. The expenses is 7726.85.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 25.8. The children is 5. The smoker is no. The region is southwest. The expenses is 10096.97.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 34.4. The children is 0. The smoker is no. The region is southwest. The expenses is 1261.86.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 37.3. The children is 2. The smoker is no. The region is southeast. The expenses is 4058.12.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 36.0. The children is 0. The smoker is no. The region is southwest. The expenses is 2166.73.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 35.8. The children is 2. The smoker is no. The region is southeast. The expenses is 4890.0.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 25.2. The children is 0. The smoker is yes. The region is northeast. The expenses is 15518.18.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 32.7. The children is 0. The smoker is no. The region is northwest. The expenses is 13844.8.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 20.3. The children is 0. The smoker is no. The region is southwest. The expenses is 1242.26.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 36.4. The children is 3. The smoker is no. The region is northwest. The expenses is 11436.74.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 34.5. The children is 3. The smoker is yes. The region is northwest. The expenses is 60021.4.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 28.9. The children is 0. The smoker is no. The region is southwest. The expenses is 8277.52.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 40.7. The children is 0. The smoker is no. The region is northeast. The expenses is 9875.68.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 34.8. The children is 2. The smoker is no. The region is northwest. The expenses is 5729.01.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 38.1. The children is 2. The smoker is no. The region is northeast. The expenses is 24915.05.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 33.8. The children is 0. The smoker is no. The region is southeast. The expenses is 1674.63.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 23.2. The children is 0. The smoker is no. The region is southeast. The expenses is 1515.34.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 28.6. The children is 3. The smoker is no. The region is northwest. The expenses is 6548.2.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 36.7. The children is 2. The smoker is no. The region is northwest. The expenses is 10848.13.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 32.7. The children is 2. The smoker is no. The region is northwest. The expenses is 26018.95.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 33.8. The children is 1. The smoker is no. The region is northwest. The expenses is 4462.72.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 36.3. The children is 2. The smoker is yes. The region is southwest. The expenses is 38711.0.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 31.9. The children is 1. The smoker is no. The region is southeast. The expenses is 10928.85.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 26.4. The children is 0. The smoker is no. The region is northeast. The expenses is 14394.56.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 32.5. The children is 0. The smoker is yes. The region is southeast. The expenses is 45008.96.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 39.3. The children is 0. The smoker is no. The region is northeast. The expenses is 14901.52.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 30.8. The children is 1. The smoker is no. The region is northeast. The expenses is 9778.35.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 38.9. The children is 3. The smoker is no. The region is southwest. The expenses is 5972.38.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 25.3. The children is 2. The smoker is yes. The region is southeast. The expenses is 24667.42.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 27.7. The children is 0. The smoker is yes. The region is northeast. The expenses is 29523.17.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 34.9. The children is 0. The smoker is no. The region is northeast. The expenses is 2899.49.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 38.2. The children is 0. The smoker is no. The region is southeast. The expenses is 1631.67.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 28.2. The children is 3. The smoker is no. The region is southeast. The expenses is 10702.64.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 32.3. The children is 1. The smoker is no. The region is northeast. The expenses is 11512.41.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 23.4. The children is 3. The smoker is no. The region is northeast. The expenses is 8252.28.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 39.4. The children is 2. The smoker is yes. The region is southwest. The expenses is 38344.57.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 25.6. The children is 0. The smoker is no. The region is northwest. The expenses is 1632.56.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 31.1. The children is 0. The smoker is no. The region is southeast. The expenses is 1621.88.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 30.9. The children is 2. The smoker is no. The region is southwest. The expenses is 8520.03.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 22.8. The children is 3. The smoker is no. The region is northeast. The expenses is 7985.82.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 25.1. The children is 0. The smoker is no. The region is southeast. The expenses is 5415.66.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 28.4. The children is 1. The smoker is no. The region is northwest. The expenses is 6664.69.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 31.9. The children is 5. The smoker is no. The region is southwest. The expenses is 11552.9.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 34.7. The children is 2. The smoker is no. The region is southwest. The expenses is 6082.41.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 33.9. The children is 3. The smoker is no. The region is northwest. The expenses is 10115.01.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 32.3. The children is 2. The smoker is no. The region is southeast. The expenses is 6338.08.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 28.6. The children is 0. The smoker is no. The region is northwest. The expenses is 11735.88.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 24.3. The children is 3. The smoker is no. The region is southwest. The expenses is 4391.65.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 22.9. The children is 0. The smoker is yes. The region is northeast. The expenses is 35069.37.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 17.4. The children is 1. The smoker is no. The region is southwest. The expenses is 2585.27.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 29.4. The children is 2. The smoker is no. The region is northeast. The expenses is 4564.19.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 34.6. The children is 1. The smoker is yes. The region is southwest. The expenses is 41661.6.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 23.0. The children is 2. The smoker is yes. The region is northwest. The expenses is 17361.77.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 36.0. The children is 3. The smoker is yes. The region is southeast. The expenses is 42124.52.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 31.2. The children is 0. The smoker is no. The region is southwest. The expenses is 9625.92.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 25.8. The children is 0. The smoker is no. The region is northwest. The expenses is 5266.37.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 37.1. The children is 1. The smoker is yes. The region is southeast. The expenses is 39871.7.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 23.4. The children is 0. The smoker is no. The region is southwest. The expenses is 1969.61.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 29.6. The children is 0. The smoker is no. The region is northeast. The expenses is 12731.0.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 25.0. The children is 2. The smoker is no. The region is northwest. The expenses is 8017.06.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 32.8. The children is 3. The smoker is no. The region is northwest. The expenses is 11289.11.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 35.2. The children is 1. The smoker is no. The region is northeast. The expenses is 11394.07.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 44.2. The children is 2. The smoker is no. The region is southeast. The expenses is 4266.17.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 29.4. The children is 1. The smoker is no. The region is southeast. The expenses is 8547.69.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 27.7. The children is 0. The smoker is no. The region is northeast. The expenses is 5469.01.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 31.0. The children is 0. The smoker is no. The region is southeast. The expenses is 6185.32.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 40.8. The children is 3. The smoker is no. The region is southeast. The expenses is 12485.8.", "label": "greater than 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 37.1. The children is 2. The smoker is no. The region is southwest. The expenses is 7371.77.", "label": "between 39.0 and 51.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 31.7. The children is 2. The smoker is no. The region is northwest. The expenses is 4433.39.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 24.4. The children is 3. The smoker is yes. The region is southwest. The expenses is 18259.22.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 20.2. The children is 2. The smoker is no. The region is northwest. The expenses is 4906.41.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 39.5. The children is 0. The smoker is no. The region is southeast. The expenses is 2480.98.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is female. The bmi is 26.7. The children is 0. The smoker is no. The region is northwest. The expenses is 4571.41.", "label": "between 27.0 and 39.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 40.5. The children is 0. The smoker is no. The region is northeast. The expenses is 1984.45.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The sex is male. The bmi is 29.0. The children is 0. The smoker is no. The region is northwest. The expenses is 1906.36.", "label": "less than 27.0", "dataset": "awaiskaggler-insurance-csv", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/awaiskaggler-insurance-csv/train.csv

@@ -0,0 +1,1203 @@
+sex,bmi,children,smoker,region,expenses,age
+male,21.5,0,no,northeast,1702.46,less than 27.0
+female,27.6,0,no,southwest,5383.54,between 27.0 and 39.0
+female,30.8,3,no,southwest,12105.32,greater than 51.0
+male,33.0,1,no,southwest,1980.07,less than 27.0
+female,34.8,0,no,northwest,3556.92,between 27.0 and 39.0
+male,30.6,0,no,northwest,1639.56,less than 27.0
+male,36.2,0,yes,southeast,41676.08,between 39.0 and 51.0
+female,24.3,0,no,northeast,8534.67,between 39.0 and 51.0
+female,39.1,3,yes,southeast,40932.43,between 27.0 and 39.0
+male,30.9,4,no,northwest,8162.72,between 39.0 and 51.0
+male,31.5,1,no,southwest,4076.5,between 27.0 and 39.0
+male,31.4,1,yes,northeast,39556.49,between 39.0 and 51.0
+female,26.7,3,no,northwest,14382.71,greater than 51.0
+male,33.7,0,no,southeast,2498.41,between 27.0 and 39.0
+male,26.5,0,no,southeast,1815.88,less than 27.0
+male,46.5,2,no,southeast,4686.39,between 27.0 and 39.0
+female,30.4,0,yes,northwest,33907.55,less than 27.0
+female,46.8,5,no,southeast,12592.53,greater than 51.0
+female,29.9,2,no,southeast,3981.98,less than 27.0
+male,34.9,0,yes,southwest,34828.65,less than 27.0
+male,42.1,0,yes,southeast,39611.76,between 27.0 and 39.0
+male,39.8,3,no,southwest,15170.07,greater than 51.0
+female,23.1,0,no,northeast,14451.84,greater than 51.0
+female,32.8,2,no,northwest,5327.4,between 27.0 and 39.0
+male,34.4,0,yes,southwest,36197.7,between 27.0 and 39.0
+female,27.6,2,yes,northwest,24535.7,between 39.0 and 51.0
+female,33.3,0,no,southeast,8283.68,between 39.0 and 51.0
+male,30.8,0,yes,southwest,35491.64,between 27.0 and 39.0
+female,29.0,0,no,northwest,2257.48,less than 27.0
+female,33.5,0,yes,southwest,37079.37,between 27.0 and 39.0
+female,31.4,4,no,northeast,4561.19,less than 27.0
+male,38.2,0,no,northeast,14410.93,greater than 51.0
+female,30.5,0,no,southwest,10704.47,greater than 51.0
+female,32.8,2,yes,northwest,40003.33,between 39.0 and 51.0
+female,30.4,3,no,northwest,18804.75,between 27.0 and 39.0
+female,33.8,1,yes,southwest,47928.03,greater than 51.0
+female,34.3,2,no,northeast,13224.06,greater than 51.0
+male,42.4,5,no,southwest,6666.24,between 27.0 and 39.0
+female,31.7,2,no,northwest,11187.66,greater than 51.0
+female,31.0,3,yes,southeast,35595.59,less than 27.0
+male,37.6,1,yes,southeast,37165.16,less than 27.0
+female,28.2,0,no,northwest,12224.35,greater than 51.0
+female,33.2,0,no,northeast,2207.7,less than 27.0
+male,16.8,2,no,northeast,6640.54,between 27.0 and 39.0
+female,41.8,0,no,southeast,5662.23,between 39.0 and 51.0
+male,37.4,0,no,southwest,21797.0,greater than 51.0
+female,28.9,0,yes,northwest,17748.51,less than 27.0
+female,27.6,0,no,northeast,13217.09,greater than 51.0
+male,35.4,0,no,southwest,1263.25,less than 27.0
+male,33.6,4,no,northeast,17128.43,less than 27.0
+female,24.6,0,yes,southwest,17496.31,between 27.0 and 39.0
+female,28.6,2,no,southeast,8516.83,between 39.0 and 51.0
+male,26.3,1,no,northwest,6389.38,between 39.0 and 51.0
+female,28.4,1,no,southwest,2331.52,less than 27.0
+female,37.4,0,no,northwest,2138.07,less than 27.0
+male,35.8,1,yes,southeast,40273.65,between 39.0 and 51.0
+male,25.5,0,no,northeast,3645.09,between 27.0 and 39.0
+male,21.9,3,no,northeast,8891.14,between 39.0 and 51.0
+male,20.9,0,yes,southeast,21195.82,greater than 51.0
+male,32.0,2,no,northwest,8116.27,between 39.0 and 51.0
+female,32.4,1,no,northeast,11879.1,greater than 51.0
+female,34.2,2,no,southwest,3987.93,less than 27.0
+female,25.8,2,no,southwest,4934.71,between 27.0 and 39.0
+male,31.9,0,yes,northwest,33750.29,less than 27.0
+female,35.6,0,no,northeast,2211.13,less than 27.0
+male,30.2,1,no,southwest,7441.05,between 39.0 and 51.0
+male,34.1,0,no,southwest,5979.73,between 39.0 and 51.0
+male,25.3,2,yes,southeast,18972.5,between 27.0 and 39.0
+male,32.6,0,no,southeast,1824.29,less than 27.0
+male,30.2,2,yes,southwest,38998.55,between 39.0 and 51.0
+female,42.7,2,no,southeast,9800.89,between 39.0 and 51.0
+male,36.0,2,no,southeast,7160.33,between 39.0 and 51.0
+male,33.9,3,no,southeast,11987.17,greater than 51.0
+male,47.7,1,no,southeast,9748.91,greater than 51.0
+female,23.8,2,no,northeast,11729.68,greater than 51.0
+male,38.4,3,yes,southeast,41949.24,between 27.0 and 39.0
+male,34.8,3,no,southwest,3443.06,less than 27.0
+female,32.2,2,yes,southwest,47305.31,greater than 51.0
+female,30.2,3,no,northwest,7537.16,between 27.0 and 39.0
+female,31.1,0,no,southeast,8280.62,between 39.0 and 51.0
+female,24.2,2,no,northeast,22395.74,less than 27.0
+female,40.0,3,no,northeast,9704.67,between 39.0 and 51.0
+male,30.0,1,no,southeast,4074.45,between 27.0 and 39.0
+female,32.1,3,no,southwest,14007.22,greater than 51.0
+male,35.5,2,yes,southwest,44585.46,between 27.0 and 39.0
+female,25.9,1,no,southwest,5472.45,between 27.0 and 39.0
+female,26.2,2,no,northwest,10493.95,between 39.0 and 51.0
+male,21.9,1,no,northwest,6117.49,between 39.0 and 51.0
+female,26.7,0,no,southeast,1615.77,less than 27.0
+female,31.5,1,no,northeast,5148.55,between 27.0 and 39.0
+male,35.2,1,yes,southeast,38709.18,between 27.0 and 39.0
+male,24.3,0,no,northwest,9863.47,greater than 51.0
+male,29.6,1,no,northeast,20277.81,between 27.0 and 39.0
+male,43.4,0,no,southwest,12574.05,greater than 51.0
+female,25.8,0,no,northwest,28923.14,greater than 51.0
+male,28.2,4,no,northeast,10407.09,between 39.0 and 51.0
+female,25.0,1,no,southwest,7623.52,between 39.0 and 51.0
+female,20.0,2,yes,northeast,19798.05,between 39.0 and 51.0
+female,29.8,0,yes,southeast,27533.91,greater than 51.0
+male,25.7,0,no,southeast,2137.65,less than 27.0
+male,29.0,0,no,northeast,10796.35,greater than 51.0
+female,28.6,0,no,northeast,11658.12,greater than 51.0
+male,35.1,0,yes,southeast,47055.53,greater than 51.0
+female,25.8,0,no,southwest,2007.95,less than 27.0
+female,27.8,0,yes,southeast,23065.42,between 39.0 and 51.0
+female,42.4,1,no,southwest,3410.32,less than 27.0
+female,42.1,1,no,southeast,3238.44,less than 27.0
+male,37.3,0,no,southeast,1141.45,less than 27.0
+female,19.9,0,no,northeast,5458.05,between 27.0 and 39.0
+male,33.6,1,no,northwest,10825.25,greater than 51.0
+male,19.6,1,no,northwest,8428.07,between 39.0 and 51.0
+male,31.7,2,yes,southeast,34303.17,less than 27.0
+female,37.7,0,yes,southwest,48824.45,greater than 51.0
+male,25.5,0,no,northwest,12124.99,greater than 51.0
+female,33.1,0,no,southwest,11848.14,greater than 51.0
+male,25.7,3,no,northwest,9301.89,between 39.0 and 51.0
+male,39.7,1,no,southwest,9391.35,greater than 51.0
+male,33.8,0,no,northwest,19673.34,between 27.0 and 39.0
+male,27.5,3,no,southwest,8615.3,between 39.0 and 51.0
+male,38.8,1,no,southeast,18963.17,between 27.0 and 39.0
+male,39.2,1,no,southeast,14418.28,greater than 51.0
+female,27.2,0,no,southwest,11073.18,greater than 51.0
+male,32.0,0,yes,northeast,45710.21,greater than 51.0
+male,20.8,0,no,southwest,2302.3,less than 27.0
+female,24.0,0,no,northwest,13012.21,greater than 51.0
+female,27.6,1,no,northwest,28340.19,between 39.0 and 51.0
+male,34.7,0,no,northeast,4518.83,between 27.0 and 39.0
+male,17.7,0,no,northwest,2680.95,less than 27.0
+male,32.7,0,no,southeast,2497.04,between 27.0 and 39.0
+female,26.2,1,no,southeast,7046.72,between 39.0 and 51.0
+female,30.5,0,no,northwest,2128.43,less than 27.0
+male,31.5,0,no,southwest,4402.23,between 27.0 and 39.0
+male,30.3,0,yes,southeast,32548.34,less than 27.0
+female,33.4,0,no,southwest,3172.02,between 27.0 and 39.0
+male,28.9,3,no,northeast,6748.59,between 27.0 and 39.0
+female,38.7,2,no,northeast,3393.36,less than 27.0
+female,29.5,2,no,southeast,7640.31,between 39.0 and 51.0
+female,21.9,0,yes,northeast,16115.3,between 27.0 and 39.0
+male,39.5,0,no,northwest,6948.7,between 39.0 and 51.0
+female,30.2,1,yes,northwest,43943.88,between 27.0 and 39.0
+male,24.0,2,no,northeast,8604.48,between 39.0 and 51.0
+female,34.8,1,no,southwest,3578.0,between 27.0 and 39.0
+male,36.1,1,yes,southeast,42211.14,between 39.0 and 51.0
+female,28.7,0,no,southwest,11455.28,greater than 51.0
+male,37.0,0,no,northeast,12741.17,greater than 51.0
+female,35.0,3,no,northwest,4466.62,less than 27.0
+female,36.9,3,yes,northwest,46661.44,greater than 51.0
+female,39.6,1,no,northwest,2730.11,less than 27.0
+female,39.3,2,no,southeast,3500.61,less than 27.0
+male,40.6,3,yes,northeast,48549.18,greater than 51.0
+female,28.2,0,no,northeast,2200.83,less than 27.0
+male,34.3,1,no,southeast,7147.47,between 39.0 and 51.0
+female,36.1,0,yes,southeast,37133.9,between 27.0 and 39.0
+male,27.0,2,no,southwest,11737.85,between 27.0 and 39.0
+male,30.8,3,no,northeast,10141.14,between 39.0 and 51.0
+male,24.3,5,no,southeast,9788.87,between 39.0 and 51.0
+male,31.6,0,no,northwest,9174.14,greater than 51.0
+female,28.1,0,no,southeast,2155.68,less than 27.0
+male,37.1,2,no,southwest,7740.34,between 39.0 and 51.0
+female,22.5,0,no,northwest,2117.34,less than 27.0
+female,23.6,2,no,southwest,4931.65,between 27.0 and 39.0
+female,31.9,1,yes,northeast,37701.88,between 27.0 and 39.0
+male,19.3,0,yes,southwest,15820.7,between 27.0 and 39.0
+female,32.7,0,no,northeast,10923.93,greater than 51.0
+female,33.0,0,no,southeast,1880.07,less than 27.0
+male,34.2,0,no,southeast,3935.18,between 27.0 and 39.0
+male,23.8,2,no,northwest,3077.1,less than 27.0
+male,41.1,1,yes,southeast,48970.25,greater than 51.0
+female,44.0,0,no,southwest,13063.88,greater than 51.0
+female,35.8,1,no,northwest,5630.46,between 27.0 and 39.0
+male,26.6,0,no,southwest,8444.47,between 39.0 and 51.0
+female,27.6,0,no,southwest,18955.22,less than 27.0
+female,31.2,0,yes,northwest,43578.94,greater than 51.0
+male,24.6,2,no,northwest,5257.51,between 27.0 and 39.0
+female,41.5,4,no,southeast,10977.21,between 39.0 and 51.0
+male,34.5,0,no,southwest,13822.8,greater than 51.0
+male,22.6,0,no,northwest,1628.47,less than 27.0
+male,34.1,1,no,northeast,6600.21,between 39.0 and 51.0
+female,39.1,2,no,southwest,14235.07,greater than 51.0
+female,45.3,1,no,southeast,8569.86,between 39.0 and 51.0
+male,20.6,2,no,northwest,2803.7,less than 27.0
+male,29.7,2,no,northwest,18157.88,between 27.0 and 39.0
+female,22.2,0,no,northwest,3176.29,less than 27.0
+female,24.5,0,no,southeast,12629.9,greater than 51.0
+male,28.6,3,no,southwest,11253.42,greater than 51.0
+female,31.4,0,yes,southwest,34166.27,less than 27.0
+male,23.1,0,no,southeast,2483.74,between 27.0 and 39.0
+female,23.2,2,no,northwest,14426.07,less than 27.0
+female,27.7,0,no,southeast,2464.62,less than 27.0
+male,27.6,0,no,southwest,1252.41,less than 27.0
+female,24.6,3,no,northwest,12479.71,greater than 51.0
+male,31.3,0,no,northwest,1909.53,less than 27.0
+female,33.6,2,no,northwest,3579.83,less than 27.0
+male,27.8,1,yes,northwest,20009.63,between 27.0 and 39.0
+male,32.3,2,no,northwest,6986.7,between 39.0 and 51.0
+female,36.2,0,no,southeast,5920.1,between 39.0 and 51.0
+female,29.9,3,yes,southeast,30942.19,greater than 51.0
+male,25.6,2,yes,southwest,23306.55,between 39.0 and 51.0
+female,25.3,1,no,southwest,7045.5,between 39.0 and 51.0
+female,25.7,0,no,southeast,3756.62,between 27.0 and 39.0
+male,36.2,0,no,southwest,8457.82,between 39.0 and 51.0
+male,27.4,1,no,northwest,2789.06,less than 27.0
+male,18.3,0,no,northeast,11534.87,greater than 51.0
+female,36.7,2,yes,northeast,38511.63,less than 27.0
+male,22.7,0,no,northwest,21984.47,between 27.0 and 39.0
+male,22.5,2,no,northeast,4428.89,between 27.0 and 39.0
+male,26.0,0,no,northeast,3070.81,between 27.0 and 39.0
+female,25.6,4,no,southwest,5708.87,between 27.0 and 39.0
+male,33.7,3,no,southeast,15161.53,greater than 51.0
+male,33.5,0,yes,northeast,34617.84,less than 27.0
+male,32.5,1,no,northeast,3490.55,less than 27.0
+male,34.3,3,no,southwest,9563.03,between 39.0 and 51.0
+female,28.3,0,yes,northwest,18033.97,less than 27.0
+female,27.4,1,no,southwest,6496.89,between 39.0 and 51.0
+female,23.4,0,yes,northeast,19964.75,between 39.0 and 51.0
+female,29.3,4,no,southwest,15828.82,between 39.0 and 51.0
+male,33.9,0,no,northeast,13143.86,greater than 51.0
+male,28.3,0,no,northwest,13770.1,greater than 51.0
+female,17.3,0,no,northeast,3732.63,between 27.0 and 39.0
+male,26.8,0,no,southeast,1665.0,less than 27.0
+male,21.5,1,no,southwest,10791.96,greater than 51.0
+female,26.3,2,no,northwest,7201.7,between 39.0 and 51.0
+female,36.5,0,no,northwest,12235.84,greater than 51.0
+male,27.6,1,no,northeast,4237.13,between 27.0 and 39.0
+male,38.6,2,no,southwest,10325.21,greater than 51.0
+male,25.7,4,yes,southwest,17942.11,less than 27.0
+female,37.4,0,no,southwest,9634.54,greater than 51.0
+male,26.0,1,yes,northwest,16450.89,less than 27.0
+male,30.0,0,no,northwest,24476.48,greater than 51.0
+female,33.2,0,no,southwest,13462.52,greater than 51.0
+female,37.5,2,no,southeast,12265.51,greater than 51.0
+female,31.3,1,no,northwest,3956.07,between 27.0 and 39.0
+female,27.5,0,no,southwest,12233.83,greater than 51.0
+male,27.2,0,no,southwest,2866.09,between 27.0 and 39.0
+male,28.1,4,yes,northwest,21472.48,between 27.0 and 39.0
+female,30.0,0,yes,northwest,33307.55,less than 27.0
+female,36.6,1,yes,southeast,42969.85,between 39.0 and 51.0
+male,27.3,2,no,northwest,22493.66,less than 27.0
+female,30.1,0,no,northeast,21344.85,less than 27.0
+male,36.1,1,no,southwest,10085.85,greater than 51.0
+male,33.3,2,no,northwest,19442.35,between 27.0 and 39.0
+female,23.8,3,yes,northeast,24106.91,between 39.0 and 51.0
+female,24.1,1,no,southeast,2201.1,less than 27.0
+female,38.0,2,no,southwest,12646.21,greater than 51.0
+male,33.1,0,no,southwest,23082.96,less than 27.0
+male,33.3,0,no,southeast,1391.53,less than 27.0
+male,37.0,0,yes,northwest,36219.41,less than 27.0
+male,34.4,2,no,southeast,5584.31,between 27.0 and 39.0
+female,35.1,0,no,southwest,12644.59,greater than 51.0
+male,41.3,3,no,northwest,15555.19,greater than 51.0
+male,24.5,0,no,northeast,2396.1,less than 27.0
+female,36.0,0,no,northeast,13228.85,greater than 51.0
+male,30.0,1,no,southwest,2904.09,less than 27.0
+female,23.2,2,no,northwest,10156.78,between 39.0 and 51.0
+female,38.1,1,yes,northeast,58571.07,between 27.0 and 39.0
+male,30.8,0,no,southwest,4646.76,between 27.0 and 39.0
+male,32.3,2,no,northwest,14119.62,greater than 51.0
+female,31.4,0,yes,southwest,34838.87,between 27.0 and 39.0
+male,23.7,0,no,southwest,10959.33,greater than 51.0
+male,31.4,0,no,northeast,13974.46,greater than 51.0
+male,29.8,2,no,northeast,6406.41,between 27.0 and 39.0
+male,24.1,1,no,northwest,5125.22,between 27.0 and 39.0
+male,33.6,1,no,northwest,11945.13,greater than 51.0
+male,30.8,1,yes,southeast,41999.52,greater than 51.0
+male,40.3,0,no,southeast,5709.16,between 39.0 and 51.0
+male,25.9,0,no,northeast,11165.42,greater than 51.0
+male,21.4,0,no,southwest,12957.12,greater than 51.0
+male,21.8,0,no,northwest,1627.28,less than 27.0
+male,23.8,0,no,northeast,1705.62,less than 27.0
+female,27.8,3,no,southeast,19749.38,between 39.0 and 51.0
+female,29.7,0,no,northwest,4357.04,between 27.0 and 39.0
+male,24.4,3,yes,southeast,19362.0,between 27.0 and 39.0
+male,24.4,0,yes,southeast,21223.68,between 39.0 and 51.0
+male,31.0,1,no,southwest,5488.26,between 27.0 and 39.0
+male,30.5,1,no,southwest,4751.07,between 27.0 and 39.0
+male,31.6,3,no,southeast,4837.58,between 27.0 and 39.0
+male,31.6,0,no,southwest,9850.43,greater than 51.0
+female,27.1,0,no,southwest,2154.36,less than 27.0
+female,37.1,0,no,southwest,10713.64,greater than 51.0
+female,47.4,0,yes,southeast,63770.43,greater than 51.0
+male,31.4,3,yes,northwest,46130.53,greater than 51.0
+male,52.6,1,yes,southeast,44501.4,less than 27.0
+female,31.8,2,no,northeast,16069.08,greater than 51.0
+male,26.4,3,no,southeast,25992.82,greater than 51.0
+female,32.5,1,no,southwest,6238.3,between 39.0 and 51.0
+female,28.1,3,no,northwest,11085.59,between 39.0 and 51.0
+female,35.9,0,no,northeast,13635.64,greater than 51.0
+female,21.9,2,no,southeast,3180.51,less than 27.0
+female,37.3,1,no,southeast,2219.45,less than 27.0
+female,33.1,0,yes,southeast,34439.86,less than 27.0
+male,32.2,0,no,northwest,8835.26,between 39.0 and 51.0
+male,35.3,2,yes,southwest,40103.89,between 39.0 and 51.0
+female,23.6,1,no,southwest,8539.67,between 39.0 and 51.0
+female,18.3,0,no,northwest,9991.04,greater than 51.0
+female,25.6,0,no,southwest,8932.08,between 39.0 and 51.0
+male,27.0,2,no,northeast,4435.09,between 27.0 and 39.0
+female,32.3,2,no,northeast,9411.01,between 39.0 and 51.0
+male,29.7,2,no,northeast,32108.66,between 39.0 and 51.0
+female,37.6,1,no,southeast,3766.88,between 27.0 and 39.0
+male,31.7,3,yes,northeast,36189.1,less than 27.0
+male,40.2,0,no,southeast,7804.16,between 39.0 and 51.0
+female,30.3,0,no,southwest,2632.99,less than 27.0
+male,39.8,0,no,northeast,7448.4,between 39.0 and 51.0
+female,18.3,0,no,northeast,13204.29,greater than 51.0
+female,24.9,0,no,southeast,27117.99,greater than 51.0
+male,29.3,0,no,southwest,1977.82,less than 27.0
+male,25.7,0,no,southeast,12142.58,greater than 51.0
+female,29.5,1,no,northwest,8930.93,between 39.0 and 51.0
+male,21.4,0,no,northwest,7222.79,between 39.0 and 51.0
+male,20.0,0,yes,northeast,22412.65,greater than 51.0
+male,31.4,1,no,northeast,9290.14,between 39.0 and 51.0
+female,38.3,0,no,northeast,7935.29,between 39.0 and 51.0
+female,31.8,2,no,northeast,13607.37,greater than 51.0
+male,34.1,2,no,southeast,23563.02,between 39.0 and 51.0
+female,29.8,2,no,southwest,5152.13,between 27.0 and 39.0
+female,33.3,1,no,southeast,4151.03,between 27.0 and 39.0
+female,30.5,2,no,northwest,15019.76,greater than 51.0
+male,27.1,1,no,southwest,4746.34,between 27.0 and 39.0
+male,29.5,0,no,northeast,2897.32,less than 27.0
+male,35.3,1,no,southeast,27724.29,less than 27.0
+female,30.5,1,yes,northwest,39725.52,between 39.0 and 51.0
+female,23.5,0,no,northeast,3206.49,less than 27.0
+male,42.1,2,no,southeast,5124.19,between 27.0 and 39.0
+female,32.8,2,no,northwest,12268.63,greater than 51.0
+male,25.5,0,no,northeast,1708.0,less than 27.0
+female,32.6,3,no,southwest,7954.52,between 39.0 and 51.0
+male,37.1,1,no,southwest,3277.16,between 27.0 and 39.0
+female,22.2,0,no,northeast,12029.29,greater than 51.0
+male,27.5,1,no,northeast,9617.66,between 39.0 and 51.0
+male,29.6,5,no,northeast,9222.4,between 39.0 and 51.0
+female,26.7,2,yes,southwest,22478.6,between 39.0 and 51.0
+male,32.7,1,no,southeast,10807.49,greater than 51.0
+female,25.3,0,no,northeast,3044.21,less than 27.0
+female,29.9,1,no,southeast,3392.98,less than 27.0
+male,30.6,0,no,northwest,1639.56,less than 27.0
+male,35.9,0,yes,southeast,46599.11,greater than 51.0
+female,33.2,3,no,northwest,6128.8,between 27.0 and 39.0
+male,34.4,0,yes,southeast,37742.58,between 27.0 and 39.0
+female,28.6,5,no,southwest,4687.8,less than 27.0
+male,36.5,2,yes,northwest,42760.5,between 39.0 and 51.0
+female,32.3,1,no,southeast,7633.72,between 39.0 and 51.0
+female,41.4,1,no,northwest,28476.73,between 39.0 and 51.0
+female,27.5,1,no,southwest,5003.85,between 27.0 and 39.0
+male,31.4,1,no,southwest,3659.35,between 27.0 and 39.0
+female,25.3,1,yes,northeast,21771.34,between 39.0 and 51.0
+male,33.3,3,no,southeast,10560.49,greater than 51.0
+female,29.3,3,no,southeast,6184.3,between 27.0 and 39.0
+female,29.9,0,no,northwest,8988.16,between 39.0 and 51.0
+female,39.8,0,no,southeast,11090.72,greater than 51.0
+male,30.9,0,no,northeast,3857.76,between 27.0 and 39.0
+male,32.7,0,yes,southwest,34472.84,less than 27.0
+male,33.6,1,yes,northeast,37607.53,between 27.0 and 39.0
+male,33.1,0,no,southwest,13393.76,greater than 51.0
+female,34.8,1,no,southwest,5246.05,between 27.0 and 39.0
+male,23.8,0,yes,southeast,26926.51,greater than 51.0
+female,27.8,3,no,southeast,14001.29,greater than 51.0
+female,41.9,0,no,southeast,24227.34,greater than 51.0
+male,36.7,1,yes,northeast,39774.28,between 27.0 and 39.0
+female,27.7,3,no,southwest,6414.18,between 27.0 and 39.0
+female,26.6,0,yes,northwest,21348.71,between 39.0 and 51.0
+female,35.5,0,no,southeast,3366.67,between 27.0 and 39.0
+male,28.7,1,no,southwest,8703.46,between 39.0 and 51.0
+female,33.0,0,no,northwest,6571.02,between 39.0 and 51.0
+female,18.3,5,yes,southwest,19023.26,between 39.0 and 51.0
+male,39.5,1,no,southeast,3875.73,between 27.0 and 39.0
+male,29.7,0,no,southeast,7789.64,between 39.0 and 51.0
+male,35.6,0,no,northwest,2534.39,less than 27.0
+male,28.0,1,yes,northwest,17560.38,less than 27.0
+female,19.1,2,yes,northeast,16776.3,between 27.0 and 39.0
+female,25.8,0,no,southwest,3161.45,between 27.0 and 39.0
+female,19.0,3,no,northeast,6753.04,between 27.0 and 39.0
+female,32.3,3,no,northeast,13430.27,greater than 51.0
+male,24.3,1,no,northwest,13112.6,greater than 51.0
+male,34.9,0,no,northeast,11944.59,greater than 51.0
+male,25.5,2,no,northeast,9225.26,between 39.0 and 51.0
+male,32.0,1,no,southeast,11946.63,greater than 51.0
+female,27.6,1,yes,southwest,24520.26,between 39.0 and 51.0
+male,30.5,0,no,southwest,2494.02,between 27.0 and 39.0
+male,23.7,2,no,southwest,3484.33,less than 27.0
+male,31.8,2,no,southeast,12928.79,greater than 51.0
+female,29.6,1,no,southeast,4562.84,between 27.0 and 39.0
+female,23.0,3,no,southwest,12094.48,greater than 51.0
+male,35.3,0,yes,southwest,36837.47,between 27.0 and 39.0
+female,38.8,3,no,southeast,5138.26,between 27.0 and 39.0
+female,25.0,0,no,southwest,13451.12,greater than 51.0
+male,27.5,2,no,northwest,5261.47,between 27.0 and 39.0
+female,20.6,0,no,southwest,1731.68,less than 27.0
+male,30.4,3,no,northeast,3481.87,less than 27.0
+male,41.1,0,no,southeast,1146.8,less than 27.0
+female,32.3,2,no,northeast,10043.25,between 39.0 and 51.0
+male,39.7,4,no,northeast,19496.72,between 27.0 and 39.0
+female,36.9,0,no,southeast,13887.97,greater than 51.0
+male,30.7,0,yes,northeast,42303.69,greater than 51.0
+female,31.5,0,no,southeast,1877.93,less than 27.0
+female,30.8,3,no,southwest,9414.92,between 39.0 and 51.0
+male,28.9,0,no,northeast,2250.84,less than 27.0
+female,23.6,0,no,northeast,4992.38,between 27.0 and 39.0
+female,22.0,0,no,northeast,13616.36,greater than 51.0
+male,36.7,1,no,northwest,28468.92,between 39.0 and 51.0
+male,33.6,0,no,southeast,5699.84,between 39.0 and 51.0
+male,40.9,0,no,northeast,11566.3,greater than 51.0
+male,32.1,2,no,northeast,25333.33,between 39.0 and 51.0
+female,46.7,2,no,southwest,11538.42,greater than 51.0
+male,31.1,0,no,northwest,2699.57,less than 27.0
+male,38.3,0,no,southeast,10226.28,greater than 51.0
+male,36.2,1,no,southwest,8068.19,between 39.0 and 51.0
+male,30.2,2,no,southwest,8968.33,between 39.0 and 51.0
+female,40.2,0,no,southeast,5400.98,between 27.0 and 39.0
+female,32.4,1,no,northeast,13019.16,greater than 51.0
+male,24.7,0,no,northeast,12323.94,greater than 51.0
+male,31.8,0,yes,northeast,41097.16,between 39.0 and 51.0
+male,29.6,4,no,southwest,7512.27,between 39.0 and 51.0
+female,26.7,1,no,southeast,5002.78,between 27.0 and 39.0
+female,32.8,0,no,southwest,5649.72,between 39.0 and 51.0
+female,30.4,0,no,northeast,2741.95,less than 27.0
+male,34.2,2,no,northwest,7261.74,between 39.0 and 51.0
+female,31.8,1,no,northwest,2719.28,less than 27.0
+male,33.1,3,no,southeast,13919.82,greater than 51.0
+male,28.5,0,yes,northeast,35147.53,less than 27.0
+male,25.4,1,yes,southeast,21978.68,between 39.0 and 51.0
+male,31.3,0,no,northwest,6358.78,between 39.0 and 51.0
+female,31.9,3,no,southeast,27322.73,greater than 51.0
+female,22.6,0,no,northwest,3176.82,less than 27.0
+male,46.5,3,no,southeast,6435.62,between 27.0 and 39.0
+female,29.0,1,no,southwest,7050.64,between 39.0 and 51.0
+female,22.4,0,yes,northwest,14711.74,less than 27.0
+female,21.5,3,no,northwest,12475.35,greater than 51.0
+female,34.7,2,yes,southwest,36397.58,less than 27.0
+male,33.9,1,no,northwest,3292.53,less than 27.0
+male,31.1,1,yes,southeast,34806.47,between 27.0 and 39.0
+female,36.6,0,no,northwest,2136.88,less than 27.0
+female,35.2,0,no,southeast,12244.53,greater than 51.0
+female,26.4,1,no,northwest,5385.34,between 27.0 and 39.0
+male,38.9,2,yes,southeast,44202.65,between 39.0 and 51.0
+male,27.4,3,no,northeast,8606.22,between 39.0 and 51.0
+female,26.0,0,no,northwest,3736.46,between 27.0 and 39.0
+female,28.9,2,no,southwest,8823.28,between 39.0 and 51.0
+female,26.6,0,no,southeast,3757.84,between 27.0 and 39.0
+male,22.1,2,no,northeast,8302.54,between 39.0 and 51.0
+female,20.6,0,no,southwest,9264.8,greater than 51.0
+male,35.3,2,no,southeast,18806.15,between 39.0 and 51.0
+male,25.9,3,yes,southwest,19199.94,between 27.0 and 39.0
+male,50.4,1,no,southeast,2438.06,less than 27.0
+female,33.7,1,no,southwest,5012.47,between 27.0 and 39.0
+male,35.4,0,no,northeast,3268.85,between 27.0 and 39.0
+female,28.1,3,no,southwest,11741.73,greater than 51.0
+female,31.8,0,no,southeast,17929.3,between 39.0 and 51.0
+male,25.9,1,no,northwest,4239.89,between 27.0 and 39.0
+female,39.8,0,no,southeast,1633.96,less than 27.0
+female,41.1,0,no,southwest,3989.84,between 27.0 and 39.0
+female,29.9,0,no,northwest,2850.68,less than 27.0
+male,28.6,1,no,northwest,4243.59,between 27.0 and 39.0
+male,31.1,2,no,southeast,2566.47,less than 27.0
+male,40.5,0,no,southeast,13831.12,greater than 51.0
+male,32.3,1,no,northwest,8765.25,between 39.0 and 51.0
+female,35.9,2,no,southeast,5836.52,between 27.0 and 39.0
+female,28.7,1,no,southwest,13224.69,greater than 51.0
+male,26.1,0,no,northeast,1708.93,less than 27.0
+female,29.5,2,no,southwest,6311.95,between 27.0 and 39.0
+female,25.1,0,no,southeast,24513.09,greater than 51.0
+male,38.0,0,no,southwest,11365.95,greater than 51.0
+female,31.9,2,no,northwest,7209.49,between 39.0 and 51.0
+male,31.0,0,no,southeast,16586.5,less than 27.0
+female,22.9,1,no,northeast,4719.52,between 27.0 and 39.0
+female,27.7,1,no,southeast,8232.64,between 39.0 and 51.0
+male,24.6,2,yes,northeast,21259.38,between 39.0 and 51.0
+female,34.6,0,no,southwest,2020.18,less than 27.0
+female,32.4,3,no,northeast,14590.63,greater than 51.0
+male,31.8,2,yes,southeast,43813.87,greater than 51.0
+male,28.7,0,no,southwest,1253.94,less than 27.0
+male,31.1,0,yes,northeast,34254.05,less than 27.0
+female,35.7,2,no,northeast,19144.58,between 39.0 and 51.0
+male,32.9,2,yes,southwest,36085.22,less than 27.0
+male,31.2,1,no,northwest,10461.98,greater than 51.0
+female,29.1,0,yes,northwest,29141.36,greater than 51.0
+male,32.3,3,no,northwest,10269.46,between 39.0 and 51.0
+female,23.5,2,no,southeast,10107.22,between 39.0 and 51.0
+male,22.5,3,no,northeast,5209.58,between 27.0 and 39.0
+female,16.8,1,no,northeast,3167.46,less than 27.0
+female,35.8,1,no,southwest,11674.13,greater than 51.0
+male,27.1,0,yes,southeast,17043.34,less than 27.0
+female,32.1,2,no,southeast,2801.26,less than 27.0
+female,24.1,1,no,southwest,26236.58,between 39.0 and 51.0
+male,32.8,1,yes,northeast,39125.33,between 39.0 and 51.0
+male,20.9,1,no,southwest,1832.09,less than 27.0
+female,32.2,1,no,southwest,6775.96,between 39.0 and 51.0
+female,28.8,0,no,southeast,2156.75,less than 27.0
+female,29.5,1,no,southeast,3392.37,less than 27.0
+female,28.1,1,yes,northeast,22331.57,between 39.0 and 51.0
+female,37.7,1,no,southeast,9877.61,greater than 51.0
+male,36.8,0,no,northeast,13981.85,greater than 51.0
+male,39.1,0,no,northeast,12890.06,less than 27.0
+female,24.4,0,yes,southeast,26125.67,less than 27.0
+male,29.6,0,no,southwest,21232.18,between 39.0 and 51.0
+female,33.8,2,no,northwest,12643.38,greater than 51.0
+female,28.3,0,yes,southwest,17081.08,less than 27.0
+female,31.9,0,no,northeast,2205.98,less than 27.0
+female,33.1,0,no,southwest,7345.08,between 39.0 and 51.0
+female,32.0,1,no,southwest,8551.35,between 39.0 and 51.0
+female,39.1,3,no,southeast,16085.13,greater than 51.0
+female,27.9,4,no,northwest,11015.17,between 39.0 and 51.0
+male,23.1,0,no,northeast,1704.7,less than 27.0
+male,27.7,1,no,northeast,9957.72,greater than 51.0
+male,37.1,2,no,northwest,7265.7,between 39.0 and 51.0
+male,33.3,1,no,northeast,8334.46,between 39.0 and 51.0
+male,27.6,0,no,southwest,24603.05,between 39.0 and 51.0
+male,29.7,0,no,northwest,1769.53,less than 27.0
+female,40.6,1,no,northwest,6373.56,between 27.0 and 39.0
+female,40.3,0,no,northeast,2217.6,less than 27.0
+male,53.1,0,no,southeast,1163.46,less than 27.0
+female,31.1,0,no,northeast,4347.02,between 27.0 and 39.0
+female,23.4,2,no,northwest,6686.43,between 27.0 and 39.0
+male,36.1,3,no,southwest,27941.29,greater than 51.0
+male,36.9,0,no,southeast,1534.3,less than 27.0
+female,35.0,2,yes,northeast,44641.2,greater than 51.0
+female,35.7,0,no,northwest,2404.73,less than 27.0
+female,30.8,0,yes,northeast,37270.15,between 27.0 and 39.0
+female,21.9,1,no,northeast,4718.2,between 27.0 and 39.0
+female,28.1,1,no,southeast,8233.1,between 39.0 and 51.0
+female,23.0,0,yes,southeast,27037.91,greater than 51.0
+male,28.7,2,no,southwest,8027.97,between 39.0 and 51.0
+male,35.3,3,no,southwest,7196.87,between 39.0 and 51.0
+female,23.2,0,no,southeast,25081.77,less than 27.0
+female,35.2,1,no,southeast,14474.68,greater than 51.0
+male,42.5,1,no,southeast,11326.71,between 27.0 and 39.0
+male,35.6,4,no,northeast,10736.87,between 39.0 and 51.0
+female,26.7,2,no,southwest,11150.78,greater than 51.0
+male,23.6,2,no,northeast,8603.82,between 39.0 and 51.0
+female,23.7,3,yes,northwest,18765.88,between 27.0 and 39.0
+male,35.2,1,no,southwest,2416.96,less than 27.0
+male,33.4,2,yes,southwest,38415.47,between 27.0 and 39.0
+female,37.1,3,no,northeast,6334.34,between 27.0 and 39.0
+male,26.1,2,no,northeast,7729.65,between 39.0 and 51.0
+female,40.5,0,no,southwest,1759.34,less than 27.0
+male,33.0,0,no,southeast,20781.49,greater than 51.0
+female,34.1,3,no,southwest,7418.52,between 39.0 and 51.0
+male,41.9,3,yes,northeast,43753.34,between 27.0 and 39.0
+male,26.8,1,no,northwest,12609.89,less than 27.0
+male,29.8,0,no,southwest,20420.6,between 27.0 and 39.0
+female,29.8,0,no,northeast,11286.54,greater than 51.0
+female,31.3,2,yes,southwest,47291.06,greater than 51.0
+male,21.0,2,no,southeast,11013.71,greater than 51.0
+female,23.2,0,no,northeast,2731.91,less than 27.0
+male,25.5,5,no,southeast,14478.33,between 39.0 and 51.0
+female,33.3,0,no,northwest,2855.44,less than 27.0
+female,24.6,2,no,southwest,4529.48,between 27.0 and 39.0
+male,29.8,3,no,northwest,9620.33,between 39.0 and 51.0
+male,33.4,1,no,northeast,8334.59,between 39.0 and 51.0
+female,39.1,0,no,southeast,11856.41,greater than 51.0
+male,29.8,1,no,northeast,9288.03,between 39.0 and 51.0
+male,36.1,0,no,southeast,11363.28,greater than 51.0
+male,33.9,0,yes,southeast,46889.26,greater than 51.0
+female,33.3,4,no,southeast,36580.28,greater than 51.0
+male,26.4,0,no,southeast,11743.3,greater than 51.0
+male,28.0,1,yes,northeast,20773.63,between 27.0 and 39.0
+female,28.5,1,yes,southeast,18328.24,less than 27.0
+female,24.3,1,no,northeast,23288.93,between 27.0 and 39.0
+female,25.7,1,no,northwest,2710.83,less than 27.0
+female,32.1,0,no,northwest,2130.68,less than 27.0
+female,28.8,4,no,northeast,14394.4,greater than 51.0
+female,21.3,3,no,northwest,4296.27,less than 27.0
+female,25.8,1,no,southwest,9861.03,greater than 51.0
+female,18.0,2,yes,northeast,15006.58,between 27.0 and 39.0
+male,21.6,0,yes,northeast,13747.87,less than 27.0
+female,26.6,2,no,northeast,9715.84,between 39.0 and 51.0
+male,25.2,0,no,northwest,1632.04,less than 27.0
+female,30.5,0,no,northwest,11840.78,greater than 51.0
+male,28.3,1,no,southeast,5484.47,between 27.0 and 39.0
+male,37.3,1,no,northeast,4667.61,between 27.0 and 39.0
+female,24.2,5,no,northwest,8965.8,between 39.0 and 51.0
+female,33.3,0,no,northeast,10564.88,greater than 51.0
+male,27.4,1,yes,northeast,17178.68,less than 27.0
+female,46.1,1,no,southeast,9549.57,between 39.0 and 51.0
+female,36.9,0,yes,southeast,36149.48,less than 27.0
+male,28.9,3,no,southwest,5926.85,between 27.0 and 39.0
+female,31.6,1,no,northeast,7358.18,between 39.0 and 51.0
+female,21.8,0,yes,southwest,20167.34,less than 27.0
+male,24.5,2,no,northwest,6710.19,between 39.0 and 51.0
+male,33.7,4,no,southeast,4504.66,less than 27.0
+female,22.5,1,no,northwest,3594.17,less than 27.0
+male,23.3,0,no,southwest,11345.52,greater than 51.0
+female,22.1,0,no,northeast,2585.85,less than 27.0
+female,31.8,2,no,southeast,3056.39,less than 27.0
+female,33.4,1,no,southeast,8240.59,between 39.0 and 51.0
+male,40.3,0,no,northeast,20709.02,greater than 51.0
+male,28.3,3,yes,northwest,32787.46,between 39.0 and 51.0
+male,27.6,0,no,northwest,2523.17,less than 27.0
+male,32.1,2,no,northwest,4433.92,between 27.0 and 39.0
+male,30.1,5,no,northeast,4915.06,less than 27.0
+male,30.6,2,no,northwest,7256.72,between 39.0 and 51.0
+female,37.1,3,yes,northeast,46255.11,greater than 51.0
+male,26.4,2,no,northeast,11244.38,greater than 51.0
+female,37.7,0,no,southeast,5397.62,between 27.0 and 39.0
+male,27.8,0,no,northwest,1635.73,less than 27.0
+male,30.9,2,no,northwest,3877.3,less than 27.0
+male,30.9,1,no,northwest,5373.36,between 27.0 and 39.0
+female,28.8,0,no,northeast,3385.4,less than 27.0
+female,38.1,2,no,northeast,15230.32,greater than 51.0
+female,36.6,2,no,southeast,4949.76,between 27.0 and 39.0
+female,34.1,0,no,southeast,9283.56,greater than 51.0
+male,19.8,1,yes,southeast,17179.52,between 39.0 and 51.0
+female,40.4,2,yes,southeast,43896.38,between 39.0 and 51.0
+female,24.9,3,yes,northeast,21659.93,between 39.0 and 51.0
+male,35.4,0,no,southeast,2322.62,less than 27.0
+female,36.3,0,no,southeast,13887.2,greater than 51.0
+male,36.9,0,no,northwest,1917.32,less than 27.0
+male,29.3,2,no,northwest,6457.84,between 27.0 and 39.0
+female,27.7,0,no,southwest,3554.2,between 27.0 and 39.0
+male,29.4,1,no,southeast,1719.44,less than 27.0
+male,22.0,1,no,southwest,1964.78,less than 27.0
+male,29.8,3,yes,northeast,30184.94,greater than 51.0
+male,36.8,2,no,northwest,26467.1,greater than 51.0
+male,33.7,1,no,southwest,7445.92,between 39.0 and 51.0
+male,44.9,0,yes,southeast,39722.75,less than 27.0
+male,30.8,0,no,southwest,13390.56,greater than 51.0
+female,25.5,1,no,northeast,7077.19,between 39.0 and 51.0
+male,25.0,2,no,northeast,23241.47,less than 27.0
+male,27.7,0,yes,southwest,16297.85,less than 27.0
+female,21.7,0,yes,northeast,14283.46,less than 27.0
+male,21.4,1,no,southwest,10065.41,greater than 51.0
+male,35.8,0,no,northwest,4320.41,between 27.0 and 39.0
+male,29.7,3,yes,southwest,19933.46,less than 27.0
+male,43.9,3,no,southeast,8944.12,between 39.0 and 51.0
+male,43.7,1,no,southwest,11576.13,greater than 51.0
+male,28.9,0,no,northwest,9869.81,greater than 51.0
+male,32.4,1,no,northwest,2362.23,less than 27.0
+male,29.1,0,yes,northwest,17352.68,less than 27.0
+female,26.3,3,no,northwest,5312.17,between 27.0 and 39.0
+male,23.7,0,no,northeast,13129.6,greater than 51.0
+female,31.6,2,no,southwest,10118.42,between 39.0 and 51.0
+female,20.1,1,no,southwest,12032.33,greater than 51.0
+male,25.6,1,no,northwest,2221.56,less than 27.0
+male,42.7,0,no,northeast,5757.41,between 39.0 and 51.0
+female,36.8,1,yes,northeast,47896.79,greater than 51.0
+male,35.6,0,yes,southwest,35585.58,less than 27.0
+female,40.2,0,no,northeast,2217.47,less than 27.0
+female,33.7,4,no,southwest,11299.34,between 39.0 and 51.0
+female,28.8,0,no,northeast,11658.38,greater than 51.0
+female,31.2,0,no,northeast,3943.6,between 27.0 and 39.0
+female,30.1,3,no,northwest,16455.71,greater than 51.0
+male,35.5,0,no,northwest,1646.43,less than 27.0
+male,38.2,2,no,southeast,8347.16,between 39.0 and 51.0
+male,25.6,2,no,southwest,14988.43,greater than 51.0
+female,30.1,2,no,southeast,11881.97,greater than 51.0
+female,32.9,0,no,northeast,7050.02,between 39.0 and 51.0
+female,44.7,0,no,northeast,9541.7,between 39.0 and 51.0
+female,42.9,3,no,northwest,6360.99,between 27.0 and 39.0
+male,31.0,3,no,northwest,10600.55,between 39.0 and 51.0
+female,32.2,1,no,southeast,8871.15,between 39.0 and 51.0
+male,32.8,0,no,northwest,10601.63,greater than 51.0
+male,40.3,0,no,southwest,10602.39,greater than 51.0
+male,41.2,1,no,northeast,6610.11,between 39.0 and 51.0
+male,27.8,1,no,northwest,4454.4,between 27.0 and 39.0
+female,30.6,0,no,northeast,2459.72,less than 27.0
+female,22.1,3,no,northeast,7228.22,between 27.0 and 39.0
+male,25.2,0,no,northwest,2045.69,less than 27.0
+female,40.2,0,no,northwest,3201.25,less than 27.0
+female,28.9,1,no,northeast,4337.74,between 27.0 and 39.0
+female,19.5,2,no,northwest,6933.24,between 27.0 and 39.0
+female,23.8,2,no,northwest,4719.74,between 27.0 and 39.0
+male,30.3,0,no,southeast,3704.35,between 27.0 and 39.0
+male,33.3,0,no,northeast,9722.77,greater than 51.0
+female,33.1,2,no,northwest,7749.16,between 39.0 and 51.0
+male,31.4,1,no,northeast,8964.06,between 39.0 and 51.0
+male,37.5,2,no,southeast,9304.7,between 39.0 and 51.0
+female,17.8,2,yes,northwest,32734.19,between 27.0 and 39.0
+male,37.3,0,no,southwest,20630.28,greater than 51.0
+male,39.6,0,no,southwest,10601.41,greater than 51.0
+female,36.9,0,no,southeast,1629.83,less than 27.0
+female,25.3,0,no,southwest,11070.54,greater than 51.0
+male,33.7,4,no,southeast,12949.16,greater than 51.0
+female,30.5,3,no,northeast,6113.23,between 27.0 and 39.0
+female,20.5,0,no,northeast,4544.23,between 27.0 and 39.0
+male,30.3,0,no,northeast,11938.26,greater than 51.0
+male,36.9,0,no,southeast,8125.78,between 39.0 and 51.0
+male,30.8,3,yes,northeast,39597.41,between 39.0 and 51.0
+male,29.7,2,no,southeast,12925.89,greater than 51.0
+male,36.7,0,no,southwest,9144.57,greater than 51.0
+male,25.3,0,no,southeast,8442.67,between 39.0 and 51.0
+female,34.4,3,no,southwest,8522.0,between 39.0 and 51.0
+male,26.8,3,no,southwest,3906.13,less than 27.0
+male,30.7,0,yes,northeast,33475.82,less than 27.0
+male,30.9,3,yes,northwest,46718.16,greater than 51.0
+female,31.4,0,no,northwest,12622.18,greater than 51.0
+male,25.5,1,no,northeast,25517.11,greater than 51.0
+female,38.0,3,no,southwest,6196.45,between 27.0 and 39.0
+female,32.8,2,yes,southeast,36021.01,less than 27.0
+male,25.8,1,no,northeast,3309.79,less than 27.0
+male,29.2,0,yes,southeast,18246.5,between 27.0 and 39.0
+male,20.1,2,yes,southeast,18767.74,between 39.0 and 51.0
+male,20.0,1,no,northwest,5855.9,between 27.0 and 39.0
+male,30.1,1,no,southwest,6849.03,between 39.0 and 51.0
+female,33.0,0,no,northeast,12430.95,greater than 51.0
+male,32.8,0,no,northeast,10435.07,greater than 51.0
+male,26.6,1,no,southeast,7742.11,between 39.0 and 51.0
+female,34.2,1,no,southwest,9872.7,greater than 51.0
+female,28.4,1,yes,southeast,19521.97,between 27.0 and 39.0
+male,37.8,2,yes,southwest,39241.44,between 27.0 and 39.0
+female,24.7,0,no,southwest,1737.38,less than 27.0
+female,43.1,2,no,southeast,4753.64,between 27.0 and 39.0
+male,38.1,2,yes,southeast,42560.43,between 39.0 and 51.0
+female,38.4,2,no,northeast,11396.9,greater than 51.0
+female,29.8,0,no,southwest,1744.47,less than 27.0
+male,38.1,0,no,southeast,2689.5,between 27.0 and 39.0
+male,32.3,1,no,southwest,8062.76,between 39.0 and 51.0
+female,27.3,1,no,northeast,9447.25,between 39.0 and 51.0
+male,28.5,0,yes,northwest,18310.74,between 27.0 and 39.0
+male,32.7,3,no,southwest,3591.48,less than 27.0
+male,31.4,1,no,northwest,2643.27,less than 27.0
+female,23.7,0,yes,northwest,25678.78,greater than 51.0
+male,21.1,3,no,southeast,6652.53,between 27.0 and 39.0
+female,29.9,0,no,southeast,4889.04,between 27.0 and 39.0
+female,26.3,0,no,northeast,2198.19,less than 27.0
+male,36.3,1,yes,southwest,47403.88,greater than 51.0
+male,35.5,0,no,southeast,1532.47,less than 27.0
+male,32.2,2,no,southwest,6875.96,between 39.0 and 51.0
+female,28.9,0,no,southwest,1743.21,less than 27.0
+female,41.9,0,no,southeast,11093.62,greater than 51.0
+male,26.7,0,yes,northeast,28101.33,greater than 51.0
+male,27.3,0,yes,southwest,16232.85,less than 27.0
+female,27.2,0,no,southeast,8601.33,between 39.0 and 51.0
+female,33.0,2,no,southeast,4349.46,between 27.0 and 39.0
+male,26.4,0,no,northwest,8827.21,between 39.0 and 51.0
+male,34.2,1,yes,northeast,39047.29,between 27.0 and 39.0
+male,30.1,0,no,southeast,1131.51,less than 27.0
+female,29.9,1,no,southeast,5478.04,between 27.0 and 39.0
+male,30.7,2,no,southeast,7731.43,between 39.0 and 51.0
+male,44.8,1,no,southeast,9058.73,between 39.0 and 51.0
+male,37.1,2,yes,southeast,37484.45,less than 27.0
+female,36.0,0,no,southeast,14313.85,greater than 51.0
+female,23.5,2,no,northeast,6402.29,between 27.0 and 39.0
+female,27.8,2,no,northeast,7144.86,between 27.0 and 39.0
+female,27.9,0,no,northeast,4137.52,between 27.0 and 39.0
+female,28.3,0,no,northeast,11657.72,greater than 51.0
+male,42.1,1,yes,southeast,48675.52,greater than 51.0
+male,27.8,0,yes,southwest,37829.72,between 39.0 and 51.0
+female,36.3,2,no,southeast,8527.53,between 39.0 and 51.0
+male,27.6,3,no,northeast,6746.74,between 27.0 and 39.0
+male,20.0,3,no,northeast,4005.42,less than 27.0
+female,27.9,0,yes,southwest,16884.92,less than 27.0
+female,36.6,0,no,northwest,8671.19,between 39.0 and 51.0
+female,34.6,2,no,northeast,3925.76,less than 27.0
+male,34.1,4,yes,southwest,40182.25,between 27.0 and 39.0
+female,33.9,0,no,northeast,9866.3,greater than 51.0
+female,28.2,0,no,southwest,13041.92,greater than 51.0
+male,21.8,1,no,southeast,6272.48,between 39.0 and 51.0
+male,41.7,0,no,southeast,5438.75,between 39.0 and 51.0
+female,20.2,1,yes,northeast,19594.81,between 39.0 and 51.0
+female,27.4,0,no,northeast,25656.58,between 39.0 and 51.0
+male,30.0,0,yes,southwest,22144.03,between 39.0 and 51.0
+male,25.8,0,no,southwest,1972.95,less than 27.0
+male,33.3,2,yes,southeast,36124.57,less than 27.0
+female,26.9,0,no,northwest,5267.82,between 27.0 and 39.0
+male,39.5,0,no,southwest,1682.6,less than 27.0
+female,27.7,3,no,northwest,7281.51,between 27.0 and 39.0
+female,23.3,3,no,northeast,7986.48,between 39.0 and 51.0
+male,33.4,5,no,southeast,6653.79,between 27.0 and 39.0
+male,29.9,1,yes,northeast,22462.04,between 39.0 and 51.0
+female,30.9,0,no,northeast,23045.57,greater than 51.0
+female,33.4,0,no,northwest,12231.61,greater than 51.0
+female,26.6,0,no,northeast,3046.06,less than 27.0
+female,30.7,2,no,northwest,8310.84,between 39.0 and 51.0
+female,28.1,1,no,southeast,6770.19,between 39.0 and 51.0
+female,30.0,0,no,northwest,5272.18,between 27.0 and 39.0
+male,24.4,4,no,northwest,11520.1,greater than 51.0
+female,17.2,2,yes,northeast,14455.64,less than 27.0
+female,47.6,2,yes,southwest,46113.51,between 27.0 and 39.0
+female,35.9,2,no,southwest,11163.57,greater than 51.0
+female,26.6,0,no,northwest,10355.64,greater than 51.0
+female,36.9,1,no,northeast,31620.0,greater than 51.0
+male,35.6,3,yes,northwest,37465.34,less than 27.0
+male,22.3,0,no,southwest,7147.11,between 39.0 and 51.0
+male,18.9,3,no,northeast,4827.9,between 27.0 and 39.0
+female,25.4,3,no,northeast,13047.33,greater than 51.0
+male,30.3,0,no,southwest,8116.68,between 39.0 and 51.0
+male,33.7,0,no,southeast,1136.4,less than 27.0
+female,28.8,0,no,northeast,2457.21,less than 27.0
+male,27.3,3,no,northeast,4661.29,less than 27.0
+male,28.8,1,no,southwest,6282.24,between 39.0 and 51.0
+male,27.7,2,yes,northeast,20984.09,between 27.0 and 39.0
+male,29.6,0,no,northwest,5028.15,between 27.0 and 39.0
+male,35.9,0,no,southeast,8124.41,between 39.0 and 51.0
+male,36.1,3,no,southwest,12363.55,greater than 51.0
+male,28.9,0,no,northwest,3866.86,between 27.0 and 39.0
+male,34.4,0,no,southwest,1826.84,less than 27.0
+male,33.3,0,no,southeast,1135.94,less than 27.0
+female,29.0,0,no,southwest,11842.44,greater than 51.0
+female,38.1,3,no,southeast,20463.0,greater than 51.0
+male,38.2,0,yes,southeast,36307.8,less than 27.0
+female,21.6,1,no,southeast,9855.13,greater than 51.0
+male,25.0,2,no,southeast,6593.51,between 39.0 and 51.0
+female,38.4,0,yes,southeast,40419.02,between 27.0 and 39.0
+male,31.7,0,no,northeast,2254.8,less than 27.0
+female,26.2,0,no,northwest,14256.19,greater than 51.0
+male,22.9,2,yes,northwest,21098.55,between 39.0 and 51.0
+male,27.6,0,no,northwest,10594.5,greater than 51.0
+male,28.2,3,yes,northwest,24915.22,between 39.0 and 51.0
+female,38.3,0,no,southeast,14133.04,less than 27.0
+female,21.8,0,no,northwest,4134.08,between 27.0 and 39.0
+male,32.6,2,no,southwest,7441.5,between 39.0 and 51.0
+male,28.4,1,no,southwest,1842.52,less than 27.0
+female,21.7,0,no,northeast,14449.85,greater than 51.0
+female,29.9,0,no,southeast,13457.96,greater than 51.0
+male,27.8,2,no,northwest,6455.86,between 27.0 and 39.0
+male,25.7,2,no,northeast,3279.87,less than 27.0
+male,30.9,3,no,northwest,6796.86,between 27.0 and 39.0
+male,28.3,1,no,northwest,2639.04,less than 27.0
+male,47.5,1,no,southeast,8083.92,between 39.0 and 51.0
+female,29.6,0,no,southwest,1875.34,less than 27.0
+male,32.3,1,no,northeast,9964.06,greater than 51.0
+male,28.5,0,no,northeast,1712.23,less than 27.0
+female,39.5,0,no,southeast,2026.97,less than 27.0
+male,30.0,1,no,southeast,1720.35,less than 27.0
+male,39.4,1,no,northeast,8342.91,between 39.0 and 51.0
+female,32.5,0,yes,northwest,36898.73,less than 27.0
+male,29.0,1,no,northeast,4040.56,between 27.0 and 39.0
+female,26.2,0,no,southwest,4883.87,between 27.0 and 39.0
+male,32.1,0,no,northwest,2055.32,less than 27.0
+male,26.3,1,no,northwest,6940.91,between 39.0 and 51.0
+female,26.6,1,no,northwest,12044.34,greater than 51.0
+male,23.9,1,no,northeast,6858.48,between 39.0 and 51.0
+male,26.9,0,no,southwest,5969.72,between 39.0 and 51.0
+female,29.3,1,no,southeast,4350.51,between 27.0 and 39.0
+female,22.6,0,no,southwest,2457.5,less than 27.0
+female,22.6,2,yes,southwest,18608.26,between 27.0 and 39.0
+male,34.8,0,yes,southwest,34779.62,less than 27.0
+female,29.6,1,no,southeast,3947.41,between 27.0 and 39.0
+male,23.2,0,no,southeast,1121.87,less than 27.0
+female,31.9,0,no,northwest,2261.57,less than 27.0
+female,20.2,0,no,northwest,2527.82,less than 27.0
+male,28.1,0,no,southwest,10965.45,greater than 51.0
+male,24.9,0,no,southeast,5966.89,between 39.0 and 51.0
+female,39.2,0,no,southwest,13470.86,greater than 51.0
+female,48.1,2,no,northeast,9432.93,between 39.0 and 51.0
+female,28.1,0,no,northwest,2690.11,less than 27.0
+female,35.5,0,yes,northwest,55135.4,between 27.0 and 39.0
+male,28.0,2,no,northwest,6203.9,between 27.0 and 39.0
+female,33.3,2,no,northeast,10370.91,between 39.0 and 51.0
+female,37.4,1,no,northwest,10959.69,greater than 51.0
+male,37.1,1,no,northeast,6079.67,between 27.0 and 39.0
+male,24.1,0,yes,northwest,15817.99,less than 27.0
+male,28.3,1,no,northeast,11272.33,less than 27.0
+female,21.1,0,no,northwest,13415.04,greater than 51.0
+female,29.8,2,no,southeast,8219.2,between 39.0 and 51.0
+female,37.5,2,no,northwest,33471.97,greater than 51.0
+female,34.8,2,yes,southwest,39836.52,between 27.0 and 39.0
+female,25.6,1,yes,northeast,20296.86,between 27.0 and 39.0
+male,34.1,0,yes,northeast,43254.42,greater than 51.0
+male,21.7,1,no,northwest,14349.85,greater than 51.0
+female,30.2,0,yes,southwest,33900.65,less than 27.0
+male,30.0,0,no,northwest,13352.1,greater than 51.0
+female,28.3,1,no,northwest,7153.55,between 39.0 and 51.0
+male,33.8,1,no,northwest,5377.46,between 27.0 and 39.0
+female,21.8,1,yes,northeast,16657.72,between 27.0 and 39.0
+male,32.1,1,no,northeast,11763.0,greater than 51.0
+male,30.3,3,no,southwest,4260.74,between 27.0 and 39.0
+male,28.5,2,no,northwest,3537.7,less than 27.0
+male,23.3,1,no,southeast,1711.03,less than 27.0
+male,30.0,1,no,southeast,9377.9,greater than 51.0
+male,32.6,3,no,northeast,4846.92,between 27.0 and 39.0
+male,31.6,0,no,southeast,12557.61,greater than 51.0
+male,29.2,1,no,southeast,2902.91,less than 27.0
+female,39.2,0,no,southeast,1633.04,less than 27.0
+female,26.8,2,no,northwest,4189.11,less than 27.0
+female,23.2,0,no,northwest,11830.61,greater than 51.0
+female,25.1,0,no,northeast,7325.05,between 39.0 and 51.0
+male,35.7,0,no,southwest,11362.76,greater than 51.0
+male,25.8,1,no,northeast,9282.48,between 39.0 and 51.0
+male,29.8,0,yes,southeast,19350.37,between 27.0 and 39.0
+female,23.2,1,no,southeast,3561.89,between 27.0 and 39.0
+female,28.4,1,no,northwest,4527.18,between 27.0 and 39.0
+female,18.6,0,no,southwest,1728.9,less than 27.0
+male,30.5,0,no,northeast,10072.06,greater than 51.0
+male,37.9,0,no,northwest,14210.54,greater than 51.0
+female,32.4,1,no,southwest,4149.74,between 27.0 and 39.0
+male,38.4,2,no,southeast,4463.21,between 27.0 and 39.0
+female,18.1,0,no,northwest,9644.25,greater than 51.0
+female,33.3,0,no,northeast,20878.78,between 39.0 and 51.0
+female,26.8,1,no,southwest,35160.13,greater than 51.0
+male,43.0,0,no,southeast,1149.4,less than 27.0
+male,24.1,1,no,northwest,4032.24,between 27.0 and 39.0
+female,33.3,1,no,northeast,5594.85,between 27.0 and 39.0
+female,28.0,0,no,southwest,13126.68,less than 27.0
+male,40.4,2,no,northwest,8733.23,between 39.0 and 51.0
+male,32.8,1,no,southwest,14358.36,between 27.0 and 39.0
+female,27.9,1,yes,southeast,19107.78,between 27.0 and 39.0
+female,24.3,0,no,southwest,2150.47,less than 27.0
+female,34.2,1,no,southeast,5245.23,between 27.0 and 39.0
+male,34.4,0,no,southeast,1137.47,less than 27.0
+female,34.1,1,no,northwest,6112.35,between 27.0 and 39.0
+male,26.9,1,no,northeast,4441.21,between 27.0 and 39.0
+female,30.8,2,no,southeast,6313.76,between 27.0 and 39.0
+male,17.5,0,no,northwest,1621.34,less than 27.0
+female,41.2,4,no,northwest,11033.66,between 39.0 and 51.0
+male,32.2,3,no,northeast,11488.32,greater than 51.0
+female,34.6,1,no,northwest,7727.25,between 39.0 and 51.0
+male,26.1,1,yes,southeast,38245.59,between 39.0 and 51.0
+female,35.8,0,no,northwest,7731.86,between 39.0 and 51.0
+female,30.3,0,no,northeast,2203.74,less than 27.0
+female,26.4,0,no,northwest,7419.48,between 39.0 and 51.0
+female,25.7,0,no,northwest,11454.02,greater than 51.0
+male,33.5,0,no,northeast,13143.34,greater than 51.0
+male,35.2,1,no,southeast,1727.54,less than 27.0
+female,39.2,0,no,southeast,13470.8,greater than 51.0
+female,44.2,0,no,southeast,3994.18,between 27.0 and 39.0
+female,32.1,2,no,northwest,4922.92,between 27.0 and 39.0
+male,26.4,0,yes,northeast,20149.32,between 39.0 and 51.0
+male,21.8,2,no,southeast,11884.05,less than 27.0
+male,20.4,0,no,northwest,1625.43,less than 27.0
+female,27.1,1,no,southwest,26140.36,between 39.0 and 51.0
+male,34.2,1,no,southeast,6289.75,between 39.0 and 51.0
+male,38.6,1,no,southwest,4762.33,between 27.0 and 39.0
+female,35.5,0,yes,northeast,42111.66,between 39.0 and 51.0
+female,32.9,2,no,southwest,5375.04,between 27.0 and 39.0
+female,28.0,0,yes,northwest,20234.85,between 27.0 and 39.0
+male,28.9,1,yes,southeast,19719.69,between 27.0 and 39.0
+female,34.0,1,no,southeast,3227.12,less than 27.0
+male,23.8,2,no,southwest,3847.67,between 27.0 and 39.0
+male,29.3,2,no,northeast,4438.26,between 27.0 and 39.0
+female,32.6,3,yes,southeast,40941.29,between 39.0 and 51.0
+female,36.5,1,no,southeast,28287.9,greater than 51.0
+female,22.8,0,no,southwest,8269.04,between 39.0 and 51.0
+male,30.2,1,no,southwest,9724.53,greater than 51.0
+female,39.8,1,no,southeast,4795.66,between 27.0 and 39.0
+male,26.0,0,no,northeast,2102.26,less than 27.0
+female,34.8,2,no,southwest,6571.54,between 27.0 and 39.0
+female,29.6,0,no,southwest,5910.94,between 39.0 and 51.0
+female,24.2,0,no,northwest,2842.76,less than 27.0
+female,28.3,0,yes,northwest,17468.98,less than 27.0
+male,37.3,2,no,southeast,8978.19,between 39.0 and 51.0
+female,27.4,1,no,northeast,9447.38,between 39.0 and 51.0
+female,27.9,3,no,northwest,18838.7,less than 27.0
+male,22.7,3,no,northeast,6985.51,between 27.0 and 39.0
+female,28.3,1,no,southeast,4779.6,between 27.0 and 39.0
+male,32.3,1,yes,northeast,41919.1,between 39.0 and 51.0
+female,36.3,3,no,northeast,6551.75,between 27.0 and 39.0
+male,28.0,1,no,northeast,6067.13,between 27.0 and 39.0
+male,40.4,0,no,southeast,10982.5,greater than 51.0
+female,41.3,1,no,northeast,7650.77,between 39.0 and 51.0
+female,31.7,0,no,northeast,14043.48,greater than 51.0
+female,22.6,1,no,northwest,9566.99,between 39.0 and 51.0
+female,20.0,3,no,northwest,5693.43,between 27.0 and 39.0
+male,17.9,1,no,northwest,5116.5,between 27.0 and 39.0
+female,24.0,2,no,southeast,8211.1,between 39.0 and 51.0
+female,30.1,3,no,southwest,4234.93,less than 27.0
+female,30.2,2,no,southwest,8825.09,between 39.0 and 51.0
+male,24.3,2,no,northwest,6198.75,between 27.0 and 39.0
+male,33.7,0,no,northwest,10976.25,greater than 51.0
+female,34.9,0,no,southeast,2020.55,less than 27.0
+male,37.2,2,no,southeast,7162.01,between 39.0 and 51.0
+male,38.4,0,no,northwest,12950.07,greater than 51.0
+male,29.7,0,no,southeast,4399.73,between 27.0 and 39.0
+female,22.8,0,no,southeast,11833.78,greater than 51.0
+female,20.0,2,no,northeast,7133.9,between 27.0 and 39.0
+male,17.4,1,no,northwest,2775.19,less than 27.0
+female,31.6,0,no,southwest,6186.13,between 39.0 and 51.0
+female,25.9,1,no,northwest,4133.64,between 27.0 and 39.0
+female,25.2,2,no,northeast,9095.07,between 39.0 and 51.0
+male,35.8,1,yes,southeast,38282.75,between 27.0 and 39.0
+male,25.3,1,no,northwest,4894.75,between 27.0 and 39.0
+male,26.2,2,no,southeast,2304.0,less than 27.0
+female,35.9,1,no,northeast,26392.26,between 39.0 and 51.0
+female,28.9,1,no,southeast,5974.38,between 27.0 and 39.0
+male,27.6,2,no,northeast,5031.27,between 27.0 and 39.0
+female,36.1,1,no,southeast,6781.35,between 39.0 and 51.0
+female,27.7,0,no,northwest,8026.67,between 39.0 and 51.0
+male,41.5,0,no,southeast,13405.39,greater than 51.0
+male,41.9,0,no,southeast,1837.28,less than 27.0
+male,40.9,0,yes,southeast,48673.56,greater than 51.0
+male,34.1,0,no,southeast,9140.95,greater than 51.0
+male,22.7,2,no,northeast,7173.36,between 39.0 and 51.0
+male,42.9,2,yes,southeast,47462.89,greater than 51.0
+male,28.9,0,no,southwest,12146.97,greater than 51.0
+female,25.1,0,no,northeast,2196.47,less than 27.0
+male,27.1,1,yes,southwest,19040.88,between 27.0 and 39.0
+female,24.1,0,no,southwest,2974.13,between 27.0 and 39.0
+male,34.0,0,no,northwest,11356.66,greater than 51.0
+male,25.1,3,yes,southwest,25382.3,greater than 51.0
+male,21.4,0,no,northeast,4500.34,between 27.0 and 39.0
+male,37.2,2,no,southeast,4673.39,between 27.0 and 39.0
+female,24.0,1,no,southeast,22192.44,greater than 51.0
+female,30.9,3,no,southwest,5325.65,between 27.0 and 39.0
+female,34.8,2,no,southwest,36910.61,greater than 51.0
+female,39.0,0,yes,northwest,42983.46,between 39.0 and 51.0
+female,27.5,1,no,southwest,7626.99,between 39.0 and 51.0
+male,27.4,0,yes,northwest,24393.62,greater than 51.0
+female,21.8,1,no,northeast,13725.47,between 39.0 and 51.0
+male,49.1,0,no,southeast,11381.33,greater than 51.0
+male,37.0,2,yes,northwest,47496.49,greater than 51.0
+male,24.0,0,no,northeast,10422.92,greater than 51.0
+male,34.1,0,no,southeast,1137.01,less than 27.0
+male,34.4,0,no,southeast,10594.23,greater than 51.0
+male,30.8,3,no,southwest,5253.52,between 27.0 and 39.0
+male,37.0,0,no,southwest,8798.59,greater than 51.0
+male,37.1,1,no,southwest,12347.17,greater than 51.0
+female,17.8,0,no,southwest,1727.79,less than 27.0
+female,46.2,0,yes,southeast,45863.21,between 39.0 and 51.0
+male,24.8,3,no,northeast,9500.57,between 39.0 and 51.0
+male,35.8,2,no,southwest,7160.09,between 39.0 and 51.0
+female,32.4,1,no,northeast,18903.49,between 27.0 and 39.0
+male,24.6,1,no,southwest,1837.24,less than 27.0
+male,25.8,2,yes,northwest,23807.24,between 39.0 and 51.0
+female,27.8,2,no,southeast,8515.76,between 39.0 and 51.0
+male,25.4,1,no,northwest,7518.03,between 39.0 and 51.0
+male,36.4,1,yes,southwest,51194.56,between 27.0 and 39.0
+female,38.1,0,yes,southeast,44400.41,greater than 51.0
+female,33.0,3,no,southeast,7682.67,between 39.0 and 51.0
+male,25.4,2,no,northwest,30284.64,between 39.0 and 51.0
+male,29.0,0,yes,northeast,27218.44,greater than 51.0
+female,28.9,0,no,southeast,3972.92,between 27.0 and 39.0
+female,26.4,0,yes,southeast,19539.24,between 27.0 and 39.0
+male,22.9,0,yes,northeast,16138.76,between 27.0 and 39.0
+female,28.6,0,no,northeast,3213.62,less than 27.0
+female,34.1,3,yes,northwest,39983.43,between 27.0 and 39.0
+female,33.0,3,no,northwest,15612.19,greater than 51.0
+female,26.4,1,no,southwest,2597.78,less than 27.0
+male,31.7,0,yes,southeast,34672.15,between 27.0 and 39.0
+male,37.4,0,no,southwest,12979.36,greater than 51.0
+male,24.0,3,yes,southeast,17663.14,between 27.0 and 39.0
+male,39.6,1,no,southwest,10450.55,greater than 51.0
+female,25.9,0,no,southwest,3353.28,between 27.0 and 39.0
+male,23.8,0,no,northeast,2395.17,less than 27.0
+male,27.9,0,no,northeast,1967.02,less than 27.0
+male,25.5,1,no,northeast,12913.99,greater than 51.0
+male,36.2,0,no,southeast,19214.71,between 27.0 and 39.0
+female,29.6,4,no,northeast,24671.66,less than 27.0
+female,43.3,2,no,southeast,5846.92,between 27.0 and 39.0
+female,29.7,2,no,southwest,11881.36,greater than 51.0
+male,30.2,0,no,northwest,10231.5,greater than 51.0
+male,27.6,1,no,southeast,4747.05,between 27.0 and 39.0
+female,27.2,0,no,northwest,12222.9,greater than 51.0
+female,39.6,1,no,southeast,10579.71,greater than 51.0
+female,37.9,0,no,southwest,6474.01,between 39.0 and 51.0
+female,33.3,2,no,southwest,10806.84,greater than 51.0
+female,29.8,1,no,southeast,6500.24,between 39.0 and 51.0
+male,22.4,2,no,northeast,27375.9,between 27.0 and 39.0
+female,31.0,1,no,southwest,5240.77,between 27.0 and 39.0
+male,34.3,3,no,southeast,5934.38,between 27.0 and 39.0
+male,24.7,1,no,northwest,30166.62,greater than 51.0
+male,22.5,0,no,northeast,8688.86,between 39.0 and 51.0
+male,27.4,0,no,northeast,2104.11,less than 27.0
+male,42.9,1,no,southwest,4536.26,between 27.0 and 39.0
+female,32.7,1,no,northwest,4738.27,between 27.0 and 39.0
+female,37.0,5,no,southwest,4830.63,less than 27.0
+female,33.7,1,no,northeast,8823.99,between 39.0 and 51.0
+male,24.3,5,no,southwest,5615.37,between 27.0 and 39.0
+female,26.3,0,yes,southeast,27808.73,greater than 51.0
+female,24.1,1,yes,northwest,23887.66,greater than 51.0
+male,27.6,1,no,northwest,13937.67,greater than 51.0
+female,26.8,1,yes,southeast,17085.27,less than 27.0
+female,34.5,0,no,northwest,3021.81,less than 27.0
+female,33.5,2,no,northwest,12269.69,greater than 51.0
+female,25.9,3,yes,southeast,24180.93,between 39.0 and 51.0
+male,20.2,3,no,northeast,3861.21,less than 27.0
+male,37.1,1,no,southeast,9048.03,between 39.0 and 51.0
+female,42.8,1,yes,northeast,40904.2,less than 27.0
+male,20.7,0,no,southwest,1242.82,less than 27.0
+male,22.4,0,no,northeast,9361.33,greater than 51.0
+male,36.6,3,no,southwest,11264.54,greater than 51.0
+female,22.2,2,yes,southeast,19444.27,between 39.0 and 51.0
+female,26.9,0,yes,northwest,21774.32,between 39.0 and 51.0
+male,23.0,0,no,northeast,1704.57,less than 27.0
+male,30.9,0,yes,southwest,39727.61,between 39.0 and 51.0
+female,26.5,0,no,northeast,12815.44,greater than 51.0
+female,34.0,0,no,southeast,2473.33,less than 27.0
+female,21.3,1,no,southwest,9182.17,between 39.0 and 51.0
+female,26.1,1,yes,northeast,23401.31,between 39.0 and 51.0
+male,27.9,0,no,southeast,2867.12,between 27.0 and 39.0
+male,38.8,0,no,southeast,12981.35,greater than 51.0
+male,29.8,0,yes,northeast,18648.42,less than 27.0
+female,38.3,0,no,southeast,1631.82,less than 27.0
+female,35.3,0,no,southwest,7348.14,between 39.0 and 51.0
+female,29.1,0,no,southwest,3761.29,between 27.0 and 39.0
+female,20.0,3,yes,northwest,16420.49,between 27.0 and 39.0
+female,34.3,5,no,southeast,8596.83,between 39.0 and 51.0
+female,43.9,2,yes,southeast,46200.99,between 39.0 and 51.0
+male,25.4,0,no,southwest,8782.47,greater than 51.0
+female,24.8,1,no,northwest,10942.13,greater than 51.0
+female,27.7,0,no,southeast,4415.16,between 27.0 and 39.0
+female,26.1,0,no,northeast,5227.99,between 27.0 and 39.0
+female,42.2,0,yes,southeast,38792.69,less than 27.0
+male,45.5,2,yes,southeast,42112.24,less than 27.0
+female,21.7,0,yes,southwest,13844.51,less than 27.0
+male,28.7,3,no,northwest,10264.44,between 39.0 and 51.0
+male,29.9,2,no,southwest,6600.36,between 39.0 and 51.0
+female,44.7,3,no,southwest,11411.69,greater than 51.0
+male,22.1,0,no,southwest,10577.09,greater than 51.0
+male,27.9,1,no,southeast,11554.22,greater than 51.0
+female,40.3,0,no,southeast,1634.57,less than 27.0
+male,31.5,0,no,northwest,11353.23,greater than 51.0
+female,25.7,3,no,southwest,9101.8,between 39.0 and 51.0
+male,28.0,1,yes,southwest,23568.27,between 39.0 and 51.0
+male,31.4,0,no,southeast,27346.04,greater than 51.0
+male,26.2,0,no,northeast,2721.32,less than 27.0
+female,31.2,0,no,southeast,10338.93,greater than 51.0
+female,36.4,1,yes,northeast,48517.56,greater than 51.0
+male,18.7,0,no,northwest,21595.38,less than 27.0
+female,30.2,3,no,northwest,4618.08,less than 27.0
+male,30.5,3,yes,northwest,40720.55,between 39.0 and 51.0
+male,20.4,3,no,southeast,8605.36,between 39.0 and 51.0
+female,35.6,1,no,southeast,7345.73,between 39.0 and 51.0
+male,46.5,1,no,southeast,2927.06,less than 27.0
+female,25.2,0,no,southwest,11837.16,greater than 51.0
+female,32.2,1,no,southeast,18218.16,less than 27.0
+male,16.0,0,no,northeast,1694.8,less than 27.0
+male,19.8,0,no,southwest,1241.57,less than 27.0
+male,32.8,0,yes,southwest,52590.83,greater than 51.0
+male,33.2,2,no,northwest,4058.71,between 27.0 and 39.0
+male,24.3,3,yes,northeast,24869.84,greater than 51.0
+female,29.0,4,no,southeast,7243.81,between 27.0 and 39.0
+male,26.7,1,yes,northwest,26109.33,greater than 51.0
+female,31.8,0,no,southwest,13880.95,greater than 51.0
+male,32.3,2,no,southwest,9630.4,between 39.0 and 51.0
+male,23.2,1,yes,southeast,22218.11,greater than 51.0
+female,28.0,3,no,southwest,7151.09,between 27.0 and 39.0
+female,20.8,1,no,southwest,3208.79,less than 27.0
+male,29.8,3,yes,southwest,25309.49,between 39.0 and 51.0
+male,24.6,0,yes,southeast,19515.54,between 39.0 and 51.0
+male,32.0,0,no,southeast,1981.58,less than 27.0
+male,40.2,0,yes,southeast,38126.25,less than 27.0
+female,30.5,0,no,southwest,12638.2,greater than 51.0
+male,35.0,1,yes,northeast,41034.22,between 39.0 and 51.0
+male,31.1,3,no,northwest,5425.02,between 27.0 and 39.0
+female,33.3,0,no,southwest,1880.49,less than 27.0
+female,31.8,0,no,northwest,11842.62,greater than 51.0
+female,25.2,0,no,northeast,3558.62,between 27.0 and 39.0
+male,37.1,3,no,southwest,3597.6,less than 27.0
+male,35.9,0,no,southeast,1986.93,less than 27.0
+female,24.8,0,yes,southeast,16577.78,between 27.0 and 39.0
+female,37.3,2,no,northwest,5989.52,between 27.0 and 39.0
+female,37.0,1,no,northwest,8023.14,between 39.0 and 51.0
+male,29.4,4,no,southwest,6059.17,between 27.0 and 39.0
+female,32.9,0,no,southwest,1748.77,less than 27.0
+male,22.3,1,no,southwest,2103.08,less than 27.0
+female,30.6,1,no,northeast,16796.41,between 27.0 and 39.0
+male,29.4,1,no,northwest,6393.6,between 39.0 and 51.0
+male,30.4,0,no,southwest,1256.3,less than 27.0
+male,37.7,3,no,northwest,30063.58,greater than 51.0
+male,29.2,1,no,southwest,10436.1,greater than 51.0
+female,27.3,1,no,northeast,6555.07,between 27.0 and 39.0
+female,27.0,0,yes,northwest,28950.47,greater than 51.0
+male,41.5,0,no,southeast,9504.31,greater than 51.0
+male,19.9,0,no,northwest,7526.71,between 39.0 and 51.0
+female,31.2,0,no,northwest,13429.04,greater than 51.0
+male,17.3,2,yes,northeast,12829.46,less than 27.0
+female,23.7,1,yes,northwest,21677.28,between 39.0 and 51.0
+male,31.7,0,yes,northeast,33732.69,less than 27.0
+male,26.2,1,no,northwest,6123.57,between 39.0 and 51.0
+male,26.7,4,no,northwest,4877.98,less than 27.0
+male,30.9,0,no,northwest,3062.51,between 27.0 and 39.0
+female,28.9,2,no,northeast,12096.65,greater than 51.0
+female,33.9,0,no,southeast,11482.63,less than 27.0
+female,38.1,0,yes,southeast,48885.14,between 39.0 and 51.0
+male,33.0,3,no,southeast,4449.46,between 27.0 and 39.0
+female,22.9,1,yes,southeast,23244.79,greater than 51.0
+male,24.8,2,yes,northwest,23967.38,greater than 51.0
+male,30.1,3,no,northwest,8410.05,between 39.0 and 51.0
+male,40.6,2,yes,northwest,45702.02,between 39.0 and 51.0
+female,36.6,3,no,southeast,10381.48,between 39.0 and 51.0
+male,26.0,0,no,northeast,6837.37,between 39.0 and 51.0
+male,23.7,0,no,northwest,2352.97,less than 27.0
+female,28.7,3,no,northwest,8059.68,between 39.0 and 51.0
+female,22.1,1,no,northeast,5354.07,between 27.0 and 39.0
+female,22.6,3,yes,northeast,24873.38,greater than 51.0
+female,27.6,1,no,northwest,11305.93,greater than 51.0
+female,24.3,0,no,southeast,4185.1,between 27.0 and 39.0
+female,30.1,0,no,northeast,2203.47,less than 27.0
+male,34.2,2,yes,southeast,44260.75,greater than 51.0
+male,30.5,2,no,northwest,8413.46,between 39.0 and 51.0
+female,29.2,0,no,northeast,7323.73,less than 27.0
+female,20.0,2,no,northwest,9193.84,between 39.0 and 51.0
+male,33.6,0,yes,northwest,43921.18,greater than 51.0
+female,23.4,2,no,southwest,2913.57,less than 27.0
+female,23.9,5,no,southeast,8582.3,between 39.0 and 51.0
+female,39.8,0,no,northeast,2755.02,less than 27.0
+female,25.7,2,no,southeast,12629.17,greater than 51.0
+female,23.7,1,no,southeast,17626.24,between 27.0 and 39.0
+male,37.4,3,no,northeast,5428.73,between 27.0 and 39.0
+female,21.5,0,no,northwest,3353.47,between 27.0 and 39.0
+female,33.0,0,no,northwest,14692.67,greater than 51.0
+female,29.9,1,no,southwest,7337.75,between 39.0 and 51.0
+female,23.2,0,no,northeast,10197.77,greater than 51.0
+male,34.4,3,yes,northwest,38746.36,between 27.0 and 39.0
+female,35.2,0,yes,southeast,44423.8,greater than 51.0
+female,27.1,1,no,northeast,10106.13,between 39.0 and 51.0
+female,32.3,2,no,northeast,29186.48,greater than 51.0
+female,27.7,3,no,southeast,14001.13,greater than 51.0
+male,42.4,3,yes,southeast,46151.12,between 39.0 and 51.0
+female,25.1,0,no,northwest,14254.61,greater than 51.0
+male,31.1,0,no,southwest,1526.31,less than 27.0
+male,41.8,2,yes,southeast,47269.85,greater than 51.0
+female,24.7,2,yes,northwest,21880.82,between 39.0 and 51.0
+male,28.6,0,no,northeast,30260.0,greater than 51.0
+male,28.3,1,yes,southwest,21082.16,between 39.0 and 51.0
+male,28.3,1,yes,northwest,28868.66,greater than 51.0
+male,38.9,1,no,southeast,3471.41,between 27.0 and 39.0
+male,19.2,1,no,northeast,8627.54,between 39.0 and 51.0
+male,38.1,1,no,southeast,7152.67,between 39.0 and 51.0
+female,30.8,1,no,northeast,10797.34,greater than 51.0
+female,33.2,1,no,northeast,7639.42,between 39.0 and 51.0
+female,30.6,2,no,northwest,24059.68,less than 27.0
+male,28.8,0,no,northwest,12129.61,greater than 51.0

classification/unipredict/barun2104-telecom-churn/metadata.json

@@ -0,0 +1,23 @@
+{
+  "dataset": "barun2104-telecom-churn",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Churn",
+  "label_values": [
+    "0.0",
+    "1.0"
+  ],
+  "num_labels": 2,
+  "train_samples": 2999,
+  "test_samples": 334,
+  "train_label_distribution": {
+    "0.0": 2565,
+    "1.0": 434
+  },
+  "test_label_distribution": {
+    "0.0": 285,
+    "1.0": 49
+  }
+}

classification/unipredict/barun2104-telecom-churn/test.csv

@@ -0,0 +1,335 @@
+AccountWeeks,ContractRenewal,DataPlan,DataUsage,CustServCalls,DayMins,DayCalls,MonthlyCharge,OverageFee,RoamMins,Churn
+41,1,0,0.0,3,223.8,67,59.0,12.24,12.3,0.0
+17,1,1,2.7,6,153.1,115,69.0,9.3,10.0,1.0
+107,1,0,0.0,1,294.9,71,67.0,9.64,13.2,1.0
+104,1,0,0.27,1,183.6,133,44.7,6.04,12.7,0.0
+151,1,0,0.0,1,134.5,88,36.0,7.16,15.4,0.0
+107,1,0,0.0,3,146.9,94,35.0,5.72,11.4,0.0
+70,1,0,0.3,2,156.4,108,45.0,8.55,8.6,0.0
+122,0,1,3.43,1,216.4,80,93.3,12.49,12.7,0.0
+130,1,0,0.33,4,271.8,129,70.3,11.86,8.7,1.0
+89,1,0,0.15,0,192.1,83,48.5,8.18,6.1,0.0
+180,1,0,0.0,3,143.5,121,41.0,9.47,8.8,0.0
+98,1,0,0.0,1,165.0,129,46.0,10.13,12.5,1.0
+51,1,0,0.0,1,181.5,108,48.0,9.85,10.3,0.0
+96,1,1,3.62,0,108.6,90,72.2,10.32,13.4,0.0
+141,1,0,0.24,2,160.1,87,52.4,12.84,7.0,0.0
+141,1,1,2.73,1,148.6,91,64.3,6.56,10.1,0.0
+123,1,0,0.0,3,166.9,98,48.0,11.09,12.8,0.0
+143,1,0,0.42,0,119.1,117,49.2,14.39,12.2,0.0
+70,1,0,0.0,0,148.4,110,48.0,13.36,8.9,0.0
+87,1,0,0.0,0,171.6,119,47.0,10.25,13.8,0.0
+78,1,0,0.0,0,225.1,67,56.0,9.96,14.6,0.0
+80,1,0,0.0,3,51.5,90,23.0,8.2,9.5,0.0
+108,1,1,2.35,3,170.7,88,62.5,5.5,8.7,0.0
+137,1,0,0.0,0,243.4,114,52.0,6.06,12.2,0.0
+45,1,1,2.13,2,135.8,104,63.3,11.13,7.9,0.0
+112,1,0,0.0,0,166.0,79,35.0,3.73,6.3,0.0
+162,1,0,0.0,4,70.7,108,26.0,7.88,9.1,1.0
+65,1,0,0.0,2,148.7,80,48.0,12.95,12.7,0.0
+65,1,0,0.33,0,195.4,110,52.3,9.06,8.9,0.0
+63,1,0,0.0,2,142.5,92,42.0,10.42,7.5,0.0
+110,1,0,0.0,2,241.2,105,56.0,8.72,8.5,0.0
+224,0,0,0.26,1,171.5,99,45.6,8.0,5.0,1.0
+123,1,1,4.21,2,197.6,105,83.1,4.0,15.6,0.0
+58,1,0,0.0,1,149.4,145,43.0,9.83,14.9,0.0
+121,1,1,3.08,1,68.7,95,60.8,10.46,11.4,0.0
+136,1,1,2.57,2,179.4,88,71.7,9.06,9.5,0.0
+145,1,0,0.0,1,245.8,116,67.0,14.34,9.0,1.0
+41,1,1,1.97,2,239.8,110,79.7,11.1,7.3,0.0
+57,0,0,0.0,0,115.0,65,30.0,6.12,6.4,1.0
+85,0,0,0.0,3,116.2,86,40.0,11.49,10.1,0.0
+76,1,0,0.0,4,107.3,140,39.0,11.91,10.0,1.0
+80,1,0,0.0,1,239.9,121,53.0,7.12,9.3,0.0
+106,1,1,3.32,3,197.4,125,78.2,6.17,12.3,0.0
+160,1,0,0.38,3,85.8,77,32.8,8.27,9.2,0.0
+151,1,0,0.0,0,194.8,106,58.0,14.64,5.5,0.0
+113,1,0,0.0,3,132.1,72,44.0,12.38,6.9,0.0
+107,1,1,2.78,1,96.3,83,59.8,8.98,10.3,0.0
+80,1,0,0.0,1,197.6,83,48.0,8.23,6.4,0.0
+96,1,1,3.0,2,175.8,96,78.0,10.33,11.1,0.0
+79,1,0,0.0,3,261.7,97,63.0,10.53,6.0,1.0
+104,1,0,0.0,1,156.2,93,43.0,9.65,13.1,0.0
+123,1,0,0.0,4,123.2,104,38.0,9.5,12.9,1.0
+81,1,0,0.0,2,173.2,80,50.0,11.81,11.8,0.0
+52,1,1,2.97,1,142.1,77,70.7,9.65,11.0,0.0
+71,1,0,0.0,0,104.0,92,35.0,9.85,14.6,0.0
+59,1,0,0.51,4,182.5,104,54.1,10.24,11.3,0.0
+79,1,0,0.31,1,268.3,114,65.1,9.28,6.3,1.0
+100,1,0,0.0,1,166.0,102,49.0,11.81,10.9,0.0
+107,1,0,0.0,1,86.8,95,24.0,5.41,13.2,0.0
+27,1,0,0.0,1,72.7,75,31.0,10.43,9.9,0.0
+114,1,0,0.0,2,169.2,96,42.0,7.5,4.6,0.0
+130,1,1,2.35,0,141.9,92,67.5,11.45,8.7,0.0
+87,1,1,2.11,0,262.8,114,85.1,10.79,7.8,0.0
+89,1,0,0.0,1,213.0,63,52.0,8.83,9.1,1.0
+94,1,0,0.0,3,108.0,79,39.0,12.1,10.4,0.0
+128,1,0,0.0,1,227.9,130,65.0,15.13,5.5,1.0
+107,1,0,0.47,3,212.1,95,53.7,7.51,7.7,0.0
+85,1,0,0.0,0,235.8,109,54.0,7.86,12.0,0.0
+209,1,0,0.0,3,255.1,124,63.0,11.53,8.5,1.0
+93,1,0,0.0,0,173.0,131,46.0,9.52,10.4,0.0
+95,1,0,0.0,0,58.2,96,28.0,10.11,10.4,0.0
+84,1,0,0.0,0,191.0,88,60.0,15.94,6.5,0.0
+76,1,0,0.0,0,203.6,61,49.0,8.09,8.4,0.0
+64,1,0,0.43,2,168.0,116,49.3,9.62,10.1,0.0
+43,1,0,0.0,4,177.2,93,43.0,7.13,12.7,0.0
+122,1,1,3.13,1,163.0,107,86.3,15.64,11.6,0.0
+68,1,0,0.0,1,195.4,116,52.0,10.61,15.1,0.0
+133,1,0,0.0,2,295.0,141,70.0,11.18,12.9,1.0
+89,1,0,0.29,1,178.7,81,53.9,11.69,9.1,0.0
+54,1,0,0.0,1,273.8,113,57.0,5.98,11.7,0.0
+84,1,0,0.0,1,203.4,125,51.0,9.15,13.8,0.0
+116,1,1,3.13,2,268.6,83,92.3,8.91,11.6,0.0
+197,1,0,0.0,0,233.9,96,59.0,10.95,9.5,0.0
+28,1,0,0.0,6,121.7,48,32.0,6.29,8.3,1.0
+161,1,0,0.0,1,196.6,73,48.0,8.51,12.5,0.0
+95,1,0,0.0,0,160.0,133,46.0,10.77,9.1,0.0
+143,1,0,0.0,2,194.3,99,44.0,6.18,10.2,0.0
+46,1,0,0.0,1,156.4,105,43.0,9.28,11.8,0.0
+173,1,0,0.0,0,291.8,143,68.0,10.72,9.9,1.0
+74,1,1,2.94,2,154.1,104,66.4,6.17,10.9,0.0
+78,1,0,0.0,1,130.8,64,42.0,11.19,10.0,0.0
+112,0,1,2.81,1,200.3,72,79.1,9.89,10.4,0.0
+84,1,0,0.0,1,217.1,99,57.0,11.8,9.4,0.0
+168,1,1,1.97,2,175.9,70,67.7,10.59,7.3,0.0
+154,1,0,0.0,1,350.8,75,79.0,10.83,10.1,1.0
+93,1,1,2.54,2,218.7,117,72.4,5.75,9.4,0.0
+110,1,1,2.62,1,236.3,102,83.2,9.8,9.7,0.0
+68,1,0,0.0,2,143.6,80,36.0,6.72,15.5,0.0
+80,1,0,0.24,1,197.5,114,54.4,10.35,11.3,0.0
+114,1,0,0.0,5,147.1,119,39.0,8.05,9.0,1.0
+94,1,0,0.0,1,206.1,49,55.0,11.23,13.0,0.0
+115,1,0,0.23,3,268.0,115,61.3,7.68,17.0,0.0
+90,1,0,0.0,1,193.7,83,47.0,7.71,12.7,0.0
+123,1,1,4.02,2,245.0,88,105.2,13.25,14.9,0.0
+116,1,1,2.97,1,182.8,122,79.7,10.64,11.0,0.0
+60,1,0,0.0,2,180.3,67,49.0,10.4,12.8,0.0
+119,1,0,0.0,1,134.9,70,41.0,10.58,11.3,0.0
+81,1,1,2.48,4,167.9,147,69.8,9.54,9.2,1.0
+94,1,0,0.22,0,137.5,118,43.2,10.16,13.4,0.0
+121,1,0,0.0,2,86.1,100,37.0,12.99,9.1,0.0
+87,1,0,0.0,2,198.3,80,50.0,9.35,16.6,0.0
+128,1,0,0.0,1,148.5,105,46.0,12.15,6.8,0.0
+42,1,0,0.0,0,241.2,134,51.0,5.83,10.6,0.0
+180,1,0,0.4,1,143.3,134,44.0,9.03,10.1,0.0
+144,1,0,0.0,1,278.5,95,68.0,12.04,11.6,1.0
+149,1,0,0.0,0,237.6,79,57.0,9.62,9.1,0.0
+171,1,0,0.25,0,189.8,122,50.5,8.69,10.3,0.0
+79,1,1,1.13,2,50.6,62,37.3,10.07,4.2,0.0
+158,1,0,0.0,2,222.8,101,56.0,10.15,6.9,0.0
+167,0,0,0.0,2,244.8,91,47.0,3.04,10.7,0.0
+137,1,0,0.0,0,174.4,120,43.0,7.82,10.2,0.0
+75,1,0,0.0,3,224.7,116,55.0,9.6,11.3,0.0
+61,1,0,0.0,1,197.3,67,57.0,13.23,9.0,0.0
+93,1,1,2.97,0,116.9,120,69.7,11.62,11.0,0.0
+48,1,1,2.05,1,198.0,70,77.5,13.69,7.6,0.0
+64,1,1,3.59,1,154.6,92,69.9,4.17,13.3,0.0
+180,1,0,0.0,2,139.0,96,43.0,11.25,15.7,0.0
+109,0,1,2.51,0,217.2,138,75.1,7.28,9.3,0.0
+196,1,0,0.36,1,133.1,80,44.6,10.33,10.3,0.0
+121,1,1,2.03,0,245.0,95,81.3,10.85,7.5,0.0
+52,0,0,0.0,0,251.4,118,60.0,9.83,11.0,1.0
+123,0,0,0.0,4,125.5,106,33.0,6.45,6.3,1.0
+85,1,0,0.0,0,209.8,82,53.0,9.73,11.3,0.0
+91,1,1,1.78,3,204.6,96,64.8,6.8,6.6,0.0
+131,1,0,0.0,0,112.8,133,37.0,9.97,10.1,0.0
+46,1,1,1.94,1,191.4,102,83.4,18.09,7.2,0.0
+138,0,1,2.89,3,211.2,117,91.9,15.63,10.7,1.0
+43,1,0,0.0,1,84.2,134,22.0,4.04,10.8,0.0
+127,1,1,1.38,0,82.2,95,41.8,8.17,5.1,0.0
+150,1,1,1.94,0,209.8,112,68.4,7.75,7.2,0.0
+127,1,0,0.27,2,218.6,93,52.7,7.5,9.2,0.0
+107,1,1,3.21,1,201.8,79,93.1,15.25,11.9,0.0
+55,1,1,2.62,1,222.5,104,79.2,8.58,9.7,0.0
+61,0,1,3.16,3,128.2,119,68.6,8.59,11.7,0.0
+110,1,0,0.33,1,208.0,69,47.3,4.76,8.0,0.0
+100,1,0,0.0,1,218.8,125,50.0,7.42,9.7,0.0
+172,1,0,0.0,2,270.0,102,68.0,12.83,12.0,1.0
+116,1,0,0.16,1,63.7,101,29.6,9.79,10.0,0.0
+38,1,0,0.0,2,137.8,86,48.0,14.32,14.1,0.0
+76,1,0,0.0,1,204.0,69,54.0,11.26,9.6,0.0
+98,1,0,0.3,3,126.3,102,39.0,8.34,9.4,0.0
+143,1,1,2.08,1,141.4,130,60.8,9.32,7.7,0.0
+66,1,0,0.0,1,201.3,95,48.0,7.64,7.5,0.0
+91,0,0,0.0,2,145.0,89,40.0,8.79,16.7,1.0
+159,1,0,0.0,1,87.7,103,39.0,13.91,10.5,0.0
+107,1,0,0.0,2,167.3,100,43.0,8.2,6.7,0.0
+136,1,0,0.0,3,101.7,105,35.0,10.14,9.4,0.0
+129,1,0,0.0,1,101.4,145,39.0,12.46,7.1,0.0
+67,1,1,2.7,2,95.0,94,68.0,14.56,10.0,0.0
+135,0,0,0.0,1,186.0,107,38.0,3.3,12.9,0.0
+101,1,1,2.57,0,209.2,82,75.7,7.99,9.5,0.0
+137,0,0,0.34,2,174.0,123,47.4,8.07,11.4,0.0
+130,1,0,0.0,3,203.9,63,51.0,9.59,12.1,0.0
+45,1,0,0.0,1,207.6,71,49.0,7.64,12.4,0.0
+100,1,0,0.0,1,191.9,95,50.0,10.05,18.2,0.0
+111,1,1,1.92,1,127.1,94,61.2,11.42,7.1,0.0
+136,1,0,0.0,0,109.4,91,37.0,10.38,11.6,0.0
+113,0,0,0.0,5,122.2,112,32.0,6.59,10.3,1.0
+71,1,0,0.32,3,185.0,84,55.2,11.63,14.9,0.0
+45,1,0,0.0,1,211.3,87,50.0,8.29,13.3,0.0
+91,1,0,0.0,2,134.7,116,48.0,14.77,6.6,0.0
+141,1,0,0.32,1,83.2,74,34.2,9.53,10.7,0.0
+114,1,0,0.0,2,155.3,75,41.0,8.5,12.6,0.0
+146,1,0,0.0,3,195.9,86,53.0,11.43,12.2,0.0
+21,1,0,0.0,0,225.0,110,60.0,12.21,10.7,0.0
+59,1,0,0.19,1,153.6,92,45.9,10.28,12.5,0.0
+137,1,1,2.11,0,175.3,96,72.1,12.07,7.8,0.0
+33,1,0,0.0,2,213.9,88,57.0,11.99,9.8,0.0
+160,1,0,0.0,1,234.9,136,63.0,13.54,13.9,1.0
+103,1,1,2.86,1,153.5,78,75.6,12.1,10.6,0.0
+140,1,0,0.0,3,149.7,71,44.0,10.63,12.6,0.0
+116,1,1,1.89,2,204.7,118,71.9,10.47,7.0,0.0
+145,1,0,0.0,1,224.2,89,65.0,15.75,16.1,1.0
+54,0,0,0.0,1,214.1,77,57.0,12.03,10.1,0.0
+97,1,0,0.0,4,120.8,96,35.0,8.49,11.9,1.0
+145,1,0,0.0,2,129.4,97,38.0,9.27,1.1,0.0
+129,1,1,1.78,1,196.6,89,66.8,9.03,6.6,0.0
+243,1,0,0.0,2,95.5,92,31.0,8.19,6.6,0.0
+77,1,0,0.0,5,239.2,114,54.0,7.5,10.3,0.0
+130,1,0,0.24,0,212.8,102,55.4,9.49,10.6,1.0
+112,1,0,0.28,2,266.0,97,66.8,10.73,14.2,1.0
+78,1,0,0.0,2,190.3,88,49.0,9.73,11.7,0.0
+109,1,0,0.0,1,175.4,125,52.0,12.54,9.8,0.0
+175,1,0,0.0,1,132.0,95,43.0,11.56,8.7,0.0
+70,1,0,0.0,0,232.8,95,66.0,15.17,12.9,1.0
+79,1,1,4.0,2,103.7,100,78.0,11.82,14.8,0.0
+112,1,1,2.62,0,183.2,95,79.2,12.64,9.7,0.0
+30,1,0,0.0,3,137.6,108,38.0,8.1,5.8,0.0
+88,1,0,0.0,5,131.5,99,38.0,8.74,7.9,1.0
+94,1,0,0.0,1,190.6,108,46.0,7.62,7.5,0.0
+89,1,0,0.0,1,303.9,95,74.0,13.05,5.3,1.0
+70,1,0,0.31,0,59.5,103,35.1,12.86,11.1,0.0
+132,1,1,2.78,1,174.5,101,78.8,12.28,10.3,0.0
+68,1,0,0.0,2,219.6,97,50.0,7.06,10.8,0.0
+25,1,0,0.26,2,178.8,90,45.6,7.06,8.4,0.0
+93,1,0,0.0,3,267.9,114,65.0,11.15,11.3,1.0
+91,1,0,0.3,0,159.0,109,52.0,12.76,10.1,0.0
+136,1,0,0.31,1,259.4,99,62.1,8.64,10.7,1.0
+78,1,0,0.17,1,109.5,105,44.7,14.31,4.9,0.0
+73,1,0,0.0,2,241.7,115,56.0,8.43,11.1,0.0
+16,1,0,0.0,1,153.2,65,46.0,11.49,10.7,0.0
+139,1,1,4.0,0,214.6,101,97.0,11.76,14.8,0.0
+135,1,0,0.0,1,173.4,107,49.0,11.1,13.7,0.0
+93,1,0,0.0,0,114.3,100,39.0,11.06,10.9,0.0
+65,1,1,3.24,1,282.3,70,93.4,7.6,12.0,0.0
+65,1,1,2.4,1,158.1,104,79.0,16.11,8.9,0.0
+137,1,0,0.0,3,242.1,118,58.0,9.55,14.7,0.0
+212,1,0,0.0,1,126.0,96,34.0,7.22,7.6,0.0
+155,1,0,0.34,1,184.6,102,52.4,9.8,7.8,0.0
+160,1,0,0.31,0,174.3,105,48.1,8.57,8.3,0.0
+95,1,1,3.05,3,283.1,112,103.5,14.31,11.3,0.0
+125,1,0,0.0,2,126.7,113,35.0,7.78,14.4,0.0
+139,1,0,0.0,0,211.1,103,54.0,10.35,5.6,0.0
+52,1,1,1.43,2,195.7,119,57.3,5.31,5.3,0.0
+101,1,0,0.0,1,174.9,105,52.0,13.1,8.5,0.0
+86,1,1,3.05,0,145.7,88,72.5,9.55,11.3,0.0
+67,1,0,0.35,1,215.5,102,56.5,9.54,8.6,0.0
+120,1,1,2.84,0,200.3,68,81.4,11.02,10.5,0.0
+94,1,0,0.28,3,269.2,104,65.8,9.69,8.9,1.0
+134,1,0,0.0,2,258.8,85,56.0,6.48,10.9,0.0
+75,1,0,0.0,1,203.3,70,55.0,11.45,14.3,0.0
+96,1,0,0.0,1,97.6,98,26.0,5.28,11.6,0.0
+100,1,0,0.0,1,115.9,87,30.0,5.57,7.1,0.0
+103,1,0,0.0,6,174.7,151,43.0,7.4,15.8,1.0
+149,1,0,0.0,1,69.1,117,24.0,6.82,6.3,0.0
+125,1,0,0.0,1,137.1,94,42.0,10.49,8.6,0.0
+73,1,0,0.0,0,94.1,136,40.0,14.02,9.8,0.0
+120,1,0,0.0,0,221.3,106,61.0,13.38,9.3,0.0
+130,1,0,0.0,5,139.1,72,45.0,12.3,11.4,0.0
+79,1,0,0.0,1,152.2,112,41.0,8.86,5.3,0.0
+174,1,0,0.21,2,190.3,98,56.1,12.64,7.2,0.0
+125,1,0,0.0,1,126.7,108,40.0,10.3,13.3,0.0
+72,1,0,0.0,0,207.8,92,52.0,9.79,13.1,0.0
+73,1,0,0.0,0,157.1,109,50.0,13.44,10.0,0.0
+91,0,0,0.31,3,129.9,112,40.1,8.67,11.2,0.0
+148,0,0,0.0,1,218.7,111,51.0,7.78,8.2,0.0
+122,1,1,4.21,2,146.4,104,75.1,4.49,15.6,0.0
+120,1,1,2.43,1,227.5,81,83.3,11.75,9.0,0.0
+152,1,0,0.0,2,141.5,102,47.0,13.15,3.4,0.0
+146,1,0,0.0,1,115.6,77,38.0,10.68,10.7,0.0
+121,1,0,0.0,1,177.2,142,41.0,6.18,8.4,0.0
+106,1,1,3.81,4,165.9,126,85.1,10.83,14.1,0.0
+94,1,0,0.0,1,207.0,109,50.0,8.37,2.6,0.0
+177,1,0,0.0,3,266.1,91,65.0,11.26,8.9,1.0
+36,1,0,0.0,0,117.1,94,40.0,11.77,9.0,0.0
+103,1,0,0.0,1,158.7,90,44.0,9.92,10.5,0.0
+56,1,0,0.33,1,210.4,80,54.3,8.83,15.5,0.0
+129,1,1,2.51,3,177.3,95,74.1,10.59,9.3,0.0
+114,1,0,0.29,1,84.7,118,38.9,12.5,14.5,0.0
+78,1,0,0.21,2,162.3,116,46.1,9.62,10.1,0.0
+144,0,0,0.0,0,203.5,100,56.0,12.38,11.9,0.0
+138,0,0,0.0,1,268.4,81,61.0,8.72,11.6,0.0
+122,1,0,0.23,2,140.1,120,46.3,11.57,11.2,0.0
+49,1,0,0.0,2,266.3,90,63.0,10.39,14.0,1.0
+93,0,1,3.27,1,187.5,110,79.7,8.49,12.1,0.0
+159,1,1,4.56,0,184.1,78,93.6,9.73,16.9,0.0
+63,0,1,2.7,1,151.5,99,66.0,7.39,10.0,0.0
+144,1,0,0.0,1,201.1,99,60.0,15.18,13.2,0.0
+94,1,0,0.0,2,220.8,111,51.0,7.81,10.5,0.0
+159,1,0,0.0,2,275.8,103,63.0,9.48,7.4,1.0
+108,1,0,0.0,1,103.0,129,39.0,12.12,7.9,0.0
+92,0,1,2.03,0,281.1,88,85.3,9.9,7.5,0.0
+140,1,0,0.0,1,160.5,114,48.0,12.03,11.3,0.0
+123,1,1,1.78,3,212.3,77,75.8,12.58,6.6,0.0
+99,1,0,0.0,2,190.4,102,46.0,7.91,11.2,0.0
+162,1,0,0.0,3,177.1,131,40.0,5.74,6.5,0.0
+60,1,0,0.0,1,145.0,133,43.0,10.46,14.6,0.0
+85,0,0,0.0,2,165.4,107,45.0,9.8,9.6,0.0
+141,1,0,0.0,3,260.2,131,60.0,8.96,7.2,0.0
+88,1,0,0.0,2,202.2,86,53.0,10.84,11.8,0.0
+89,1,0,0.0,1,179.7,128,57.0,14.99,7.6,0.0
+191,1,0,0.0,1,162.0,104,49.0,12.06,10.9,0.0
+118,1,0,0.0,1,136.1,120,41.0,10.21,11.3,0.0
+94,1,0,0.0,1,194.1,62,53.0,11.36,15.3,0.0
+123,1,0,0.15,3,211.0,92,56.5,10.85,9.8,0.0
+49,1,0,0.15,1,236.6,91,60.5,11.05,8.9,0.0
+67,1,0,0.0,0,109.1,117,38.0,10.87,12.8,0.0
+158,1,0,0.33,1,175.9,105,49.3,9.42,11.0,0.0
+88,1,0,0.31,1,181.5,116,50.1,9.35,10.5,0.0
+127,0,0,0.26,2,180.9,114,51.6,10.48,7.4,0.0
+189,1,0,0.0,1,219.9,80,50.0,7.17,11.7,0.0
+96,1,0,0.0,1,179.5,125,45.0,8.12,6.6,0.0
+73,1,1,3.11,0,194.4,104,80.1,8.8,11.5,0.0
+61,1,0,0.0,2,197.7,118,47.0,7.61,7.0,0.0
+108,1,0,0.0,0,239.3,102,60.0,11.17,10.6,0.0
+153,1,0,0.0,0,166.8,127,41.0,7.18,11.8,0.0
+117,1,0,0.41,2,144.6,115,51.1,12.94,7.4,0.0
+92,1,0,0.0,2,91.7,90,33.0,9.69,9.2,0.0
+137,1,0,0.23,1,155.5,81,40.3,6.66,9.1,0.0
+146,1,0,0.0,2,169.5,93,49.0,11.55,9.0,0.0
+148,1,0,0.15,1,203.0,92,49.5,7.55,14.6,0.0
+65,0,0,0.31,0,277.9,123,64.1,7.79,9.2,1.0
+126,1,0,0.0,1,211.6,70,55.0,10.85,7.8,0.0
+148,1,1,2.67,1,158.7,91,67.7,8.03,9.9,0.0
+161,1,0,0.0,4,332.9,67,84.0,15.89,5.4,1.0
+147,1,0,0.33,1,212.8,79,57.3,10.21,10.2,0.0
+106,1,0,0.0,1,158.7,74,33.0,3.22,10.2,0.0
+10,1,0,0.42,1,222.2,127,55.2,7.66,12.9,0.0
+128,1,0,0.0,1,158.8,75,50.0,13.24,7.6,0.0
+91,1,1,2.59,1,273.0,78,90.9,10.78,9.6,0.0
+68,1,0,0.21,1,213.9,112,61.1,13.03,8.4,1.0
+138,1,0,0.0,5,194.3,83,50.0,9.5,9.0,0.0
+106,1,0,0.0,2,210.6,96,57.0,12.46,12.4,1.0
+64,1,0,0.0,0,261.9,113,58.0,7.41,13.8,0.0
+105,1,1,2.4,1,186.9,114,78.0,12.82,8.9,0.0
+62,1,0,0.0,4,321.1,105,78.0,13.28,11.5,1.0
+110,1,1,1.7,1,192.3,114,61.0,6.47,6.3,0.0
+36,1,0,0.0,1,178.6,83,49.0,10.66,10.9,0.0
+67,1,1,3.62,0,174.7,86,80.2,8.03,13.4,0.0
+178,1,1,2.84,2,175.4,88,74.4,9.5,10.5,0.0
+95,1,0,0.0,2,167.6,96,44.0,8.8,13.4,0.0
+105,1,0,0.3,1,146.4,81,48.0,11.26,8.5,0.0
+120,1,0,0.0,1,158.0,110,44.0,9.85,10.0,0.0
+149,1,1,1.76,0,148.5,106,52.6,5.73,6.5,0.0
+99,1,0,0.0,1,62.9,81,31.0,11.55,8.5,0.0
+147,1,0,0.25,2,168.6,92,47.5,9.39,14.4,0.0
+147,0,0,0.0,0,157.0,79,36.0,5.16,7.1,0.0
+160,1,0,0.17,1,216.8,77,56.7,10.37,5.6,0.0
+74,1,0,0.0,3,314.1,86,73.0,11.12,12.3,1.0
+115,1,1,3.11,1,222.6,81,86.1,9.52,11.5,0.0
+75,1,1,0.68,1,200.6,96,55.8,8.21,2.5,0.0
+78,1,0,0.0,0,208.9,119,57.0,12.62,12.8,0.0
+73,1,0,0.0,0,203.3,45,47.0,7.1,8.5,0.0
+52,1,0,0.34,2,124.9,131,50.4,15.03,11.6,0.0

classification/unipredict/bhanupratapbiswas-bollywood-actress-name-and-movie-list/metadata.json

@@ -0,0 +1,44 @@
+{
+  "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "hitFlop",
+  "label_values": [
+    "9",
+    "3",
+    "6",
+    "2",
+    "1",
+    "5",
+    "4",
+    "8",
+    "7"
+  ],
+  "num_labels": 9,
+  "train_samples": 1153,
+  "test_samples": 131,
+  "train_label_distribution": {
+    "1": 656,
+    "8": 18,
+    "4": 61,
+    "2": 231,
+    "5": 52,
+    "6": 57,
+    "3": 49,
+    "7": 26,
+    "9": 3
+  },
+  "test_label_distribution": {
+    "1": 73,
+    "4": 7,
+    "9": 1,
+    "5": 6,
+    "2": 26,
+    "3": 6,
+    "6": 7,
+    "8": 2,
+    "7": 3
+  }
+}

classification/unipredict/bhanupratapbiswas-bollywood-actress-name-and-movie-list/test.csv

@@ -0,0 +1,132 @@
+imdbId,title,releaseYear,releaseDate,genre,writers,actors,directors,sequel,hitFlop
+tt0420123,Revati,2005,7-May-05,Drama,Farogh Siddique,Kashmira Shah | Kiran Kumar | Ayub Khan | Javed Khan,Farogh Siddique,0,1
+tt0819810,Traffic Signal,2007,2-Feb-07,Drama,Madhur Bhandarkar (dialogue) | Madhur Bhandarkar (screenplay) | Madhur Bhandarkar (story) | Nishant A Bhuse (story) | Sachin Yardi (dialogue) | Sachin Yardi (screenplay) | Sachin Yardi (story),Kunal Khemu | Neetu Chandra | Upendra Limaye | Ranvir Shorey,Madhur Bhandarkar,0,4
+tt0284137,Gadar: Ek Prem Katha,2001,15-Jun-01,Action | Drama | Romance,Shaktimaan Talwar,Sunny Deol | Ameesha Patel | Amrish Puri | Lillete Dubey,Anil Sharma,0,9
+tt1890513,Ragini MMS,2011,13-May-11,Drama | Horror | Mystery,Vaspar Dandiwala (story) | Pawan Kripalani (story) | Virag Mishra (lyrics) | Agnel Roman (lyrics) | Mayank Tewaari (dialogue) | Mayank Tewaari (screenplay),Kainaz Motivala | Rajkummar Rao | Rajat Kaul | Janice,Pawan Kripalani,0,5
+tt1727496,Dil Toh Baccha Hai Ji,2011,27-Jan-11,Comedy | Drama | Romance,Madhur Bhandarkar (story) | Sanjay Chhel (dialogue) | Kumaar (lyrics) | Neelesh Misra (lyrics) | Anil Pandey (story) | Sayeed Qadri (lyrics) | Neeraj Udhwani (screenplay) | Neeraj Udhwani (story),Ajay Devgn | Emraan Hashmi | Omi Vaidya | Shazahn Padamsee,Madhur Bhandarkar,0,2
+tt0461209,Ek Khiladi Ek Haseena,2005,18-Nov-05,Comedy | Crime | Thriller,Suparn Verma (story),Fardeen Khan | Koena Mitra | Kay Kay Menon | Rakhi Sawant,Suparn Verma,0,2
+tt2122340,Ferrari Ki Sawaari,2012,15-Jun-12,Crime | Family | Sport,Vidhu Vinod Chopra (original story and screenplay) | Rajesh Mapuskar (original story and screenplay) | Rajkumar Hirani (original story and dialogue) | Shekhar Dhavalikar (script associate) | Ranjeet Bahadur (dialogue associate),Sharman Joshi | Boman Irani | Ritwik Sahore | Paresh Rawal,Rajesh Mapuskar,0,5
+tt0346548,Avgat,2001,1-Jun-01,Action | Crime | Drama,,Ajinkya Deo | Nethra Raghuraman | Sayaji Shinde | Rohini Hattangadi,Mohan Sharma,0,1
+tt0486615,London Dreams,2009,30-Oct-09,Musical,Ritesh Shah (dialogue) | Suresh Nair (story),Salman Khan | Om Puri | Ajay Devgn | Dilyana Bouklieva,Vipul Amrutlal Shah,0,2
+tt1999857,Cycle Kick,2011,17-Jun-11,Drama | Sport,Shashi Sudigala,Nishan Nanaiah | Sunny Hinduja | Girija Oak | Ishita Sharma,Shashi Sudigala,0,1
+tt2064852,Lottery,2009,20-Mar-09,Crime | Drama | Mystery,Aadesh K. Arjun (dialogue) | Hemant Prabhu (screenplay),Abhijeet | Rucha Gujrati | Manisha Kelkar | Nassar Abdulla,Hemant Prabhu,0,1
+tt3527144,The Dirty Relation,2013,14-Jun-13,Thriller,,Sanjay Chauhan | Jasleen Matharu | Kanwaljeet Matharu | Kesar Matharu,Kesar Matharu,0,1
+tt3211170,Say Yes to Love,2012,16-Mar-12,Romance,Marukh Mirza Beig,Nazia Hussain | Salim Khan | Aasad Mirza | Saira Mirza,Marukh Mirza Beig,0,1
+tt0439872,Wajahh: A Reason to Kill,2004,8-Oct-04,Thriller,Ghalib Asadbhopali (dialogue) | Tony Mirrcandani (story) | Sandeep Patel (screenplay),Arbaaz Khan | Gracy Singh | Shamita Shetty | Zulfi Sayed,Gautam Adhikari,0,1
+tt1937092,Always Kabhi Kabhi,2011,17-Jun-11,Comedy | Crime | Drama,Roshan Abbas (story) | Kanika Dhillon (additional screenplay) | Ishita Moitra (screenplay) | Ranjit Raina (story),Lillete Dubey | Satyajeet Dubey | Ali Fazal | Manoj Joshi,Roshan Abbas,0,1
+tt2319889,Jannat 2,2012,4-May-12,Crime | Drama | Thriller,Kapil Chopra | Sanjay Masoom (dialogue),Mohammed Zeeshan Ayyub | Viren Basoya | Manish Chaudhary | Esha Gupta,Kunal Deshmukh,1,5
+tt1828289,Shagird,2011,13-May-11,Action | Crime | Drama,Tigmanshu Dhulia (story) | Tigmanshu Dhulia | Kamal Pandey (story),Nana Patekar | Mohit Ahlawat | Rimi Sen | Anurag Kashyap,Tigmanshu Dhulia,0,1
+tt2341766,Nautanki Saala!,2013,12-Apr-13,Comedy,Beno?t Graffin (original story) | Pierre Salvadori (original story) | Nipun Dharmadhikari (screenplay) | Rohan Sippy (screenplay) | Charudutt Acharya (screenplay),Ayushmann Khurrana | Kunaal Roy Kapur | Pooja Salvi | Gaelyn Mendonca,Rohan Sippy,0,3
+tt0995035,Dhol,2007,14-Sep-07,Comedy,Manisha Korde (screenplay),Sharman Joshi | Tusshar Kapoor | Kunal Khemu | Rajpal Yadav,Priyadarshan,0,4
+tt0893585,Detective Naani,2009,22-May-09,Crime | Drama | Family,Romilla Mukherjee,Ava Mukherjee | Zain Khan | Simran Singh | Amrita Raichand,Romilla Mukherjee,0,1
+tt0354538,Ek Aur Ek Gyarah: By Hook or by Crook,2003,28-Mar-03,Action | Comedy | Musical,Aman Jaffery (dialogue) | Shahnawaz Ahmed Kenny (scenario) | Bolu Khan (dialogue) | Yunus Sajawal (screenplay),Sanjay Dutt | Govinda | Amrita Arora | Nandini Singh,David Dhawan,0,3
+tt2138010,3 Nights 4 Days,2009,9-Oct-09,Romance,,Hrishitaa Bhatt | Anuj Sawhney,Devang Dholakia,0,1
+tt0401532,Jaago,2004,6-Feb-04,Drama,K.K. Singh (dialogue) | K.K. Singh (screenplay) | K.K. Singh (story),Sanjay Kapoor | Raveena Tandon | Manoj Bajpayee | Puru Rajkumar,Mehul Kumar,0,1
+tt1060249,Drona,2008,2-Oct-08,Action | Adventure | Drama,Goldie Behl | Rohini Killough (screenplay) | Vaibhav Modi (lyrics) | Jaydeep Sarkar (screenplay),Jayshree Arora | Veer Arya | Abhishek Bachchan | Jaya Bhaduri,Goldie Behl,0,1
+tt2613942,Dehraadun Diary,2013,4-Jan-13,Thriller,Aseem Arora (screenplay) | Aseem Arora (script),Rati Agnihotri | Neelima Azim | Rohit Bakshi | Vishal Bhosle,Milind Ukey,0,1
+tt1183917,Teen Patti,2010,26-Feb-10,Drama | Thriller,Shivkumar Subramaniam (story) | Leena Yadav (story) | Ben Rekhi (english dialogue),Amitabh Bachchan | Madhavan | Shraddha Kapoor | Siddharth Kher,Leena Yadav,0,1
+tt1132595,Maan Gaye Mughall-E-Azam,2008,22-Aug-08,Comedy | Crime | Drama,Sanjay Chhel | Sunil Munshi (screenplay),Mallika Sherawat | Rahul Bose | Paresh Rawal | Kay Kay Menon,Sanjay Chhel,0,1
+tt1512321,Vaada Raha... I Promise,2009,11-Sep-09,Drama,Aseem Arora | Sandeep Chatterjee (lyrics) | Samir Karnik (screenplay) | Babbu Mann (lyrics) | Sandeep Nath (lyrics) | Rahul Seth (lyrics) | A.M. Turaz (lyrics),Bobby Deol | Dwij Yadav | Kangana Ranaut | Mohnish Bahl,Samir Karnik,0,1
+tt0430480,Popcorn Khao! Mast Ho Jao,2004,1-Oct-04,Comedy | Drama | Romance,Vishal Dadlani (lyrics) | Kabir Sadanand | Raghuvir Shekhawat (dialogue),Akshay Kapoor | Tanisha | Yash Tonk | Deepak Tijori,Kabir Sadanand,0,1
+tt2389974,Aatma,2013,22-Mar-13,Drama | Horror | Thriller,Sudarshana Dwivedi (dialogue) | Suparn Verma (dialogue) | Suparn Verma (story) | Suparn Verma,Jaideep Ahlawat | Bipasha Basu | Padam Bhola | Darshan Jariwala,Suparn Verma,0,2
+tt3362728,Ungli,2014,5-Dec-14,Comedy | Drama | Thriller,Renzil D'Silva (story) | Milap Zaveri (dialogue),Emraan Hashmi | Kangana Ranaut | Sanjay Dutt | Randeep Hooda,Renzil D'Silva,0,1
+tt1191118,Hello Darling,2010,27-Aug-10,Comedy | Drama,Shabbir Ahmed (lyrics) | Kumaar (lyrics) | Ashiesh Pandit (lyrics) | Sachin Shah | Pankaj Trivedi,Gul Panag | Celina Jaitly | Isha Koppikar | Chunky Pandey,Manoj Tiwari,0,1
+tt0814014,Apne,2007,29-Jun-07,Drama | Sport,Neeraj Pathak (screenplay) | Neeraj Pathak (story),Dharmendra | Sunny Deol | Bobby Deol | Shilpa Shetty,Anil Sharma,0,4
+tt1740710,Mere Brother Ki Dulhan,2011,9-Sep-11,Comedy | Drama | Romance,Ali Abbas Zafar,Imran Khan | Katrina Kaif | Ali Zafar | Tara D'Souza,Ali Abbas Zafar,0,6
+tt0489028,Double Cross: Ek Dhoka,2005,12-Jul-05,Comedy,,Ayesha Jhulka | Negar Khan | Sahil Khan,Vicky Tejwani,0,1
+tt0483029,Kyon?,2003,,Crime,,Vinay Apte | Ashok Beniwal | Chaitanya Chaudhary | Rahul Dev,Kalpana Lajmi,0,1
+tt2066062,Shortcut Romeo,2013,21-Jun-13,Action | Crime | Romance,Susi Ganesan (story) | Ilashree Goswami (dialogue),Neil Nitin Mukesh | Ameesha Patel | Puja Gupta | Jatin Grewal,Susi Ganesan,0,1
+tt0392625,Pratha,2002,,Action | Drama | Thriller,,Deepak Bandhu | Ashney Shroff | Vicky Ahuja | Ravindra Bundela,Raja Bundela,0,1
+tt0285319,Paagalpan,2001,8-Jun-01,Romance,Joy Augustine (story),Karan Nath | Aarti Agarwal | Vilas Ujawane | Bharat Dabholkar,Joy Augustine,0,1
+tt1605790,Zokkomon,2011,22-Apr-11,Action | Adventure | Drama,Javed Akhtar (lyrics) | Satyajit Bhatkal (story) | Svati Chakravarty Bhatkal (story) | Lancy Fernandes (story) | Divy Nidhi Sharma (dialogues),Anupam Kher | Manjari Phadnis | Tinnu Anand | Sheeba Chaddha,Satyajit Bhatkal,0,1
+tt1729637,Bodyguard,2011,31-Aug-11,Romance,J.P. Chowksey (screenplay) | Kiran Kotrial (screenplay) | Siddique,Salman Khan | Kareena Kapoor | Raj Babbar | Asrani,Siddique,0,8
+tt2378057,?: A Question Mark,2012,17-Feb-12,Horror | Mystery | Thriller,Yash Dave | Allison Patel,Kiran Bhatia | Yaman Chatwal | Maanvi Gagroo | Chirag Jain,Allyson Patel | Yash Dave,0,1
+tt1948150,Singham,2011,22-Jul-11,Action | Crime | Drama,Farhad | Farhad | Hari (original story) | Yunus Sajawal (screenplay) | Sajid (dialogue),Ajay Devgn | Kajal Agarwal | Prakash Raj | Sonali Kulkarni,Rohit Shetty,0,7
+tt3822600,Amit Sahni Ki List,2014,18-Jul-14,Comedy | Romance,Rohit G. Banawlikar (screenplay) | Shiv Singh (screenplay),Vir Das | Anindita Nayar | Natasha Rastogi | Kavi Shastri,Ajay Bhuyan,0,1
+tt0485551,Time Pass,2005,,Romance,Chander Mishra,Sherlyn Chopra | Tanaaz Currim Irani | Adi Irani | Monica Patel,Chander Mishra,0,1
+tt0321067,Ab Ke Baras,2002,10-May-02,Adventure | Fantasy | Thriller,Robin Bhatt (screenplay) | Sutanu Gupta (screenplay) | Ravi Rai (dialogue),Arya Babbar | Amrita Rao | Ashutosh Rana | Shakti Kapoor,Raj Kanwar,0,2
+tt3542028,Chal Bhaag,2014,13-Jun-14,Comedy,Tarun Bajaj,Deepak Dobriyal | Keeya Khanna | Sanjay Mishra | Yashpal Sharma,Prakash Saini,0,1
+tt0272736,Mujhse Dosti Karoge!,2002,9-Aug-02,Musical | Romance | Drama,Aditya Chopra | Kunal Kohli,Rani Mukerji | Hrithik Roshan | Kareena Kapoor | Uday Chopra,Kunal Kohli,0,2
+tt2797242,Bombay Talkies,2013,3-May-13,Drama,,Rani Mukerji | Randeep Hooda | Saqib Saleem | Nawazuddin Siddiqui,Zoya Akhtar | Dibakar Banerjee | Karan Johar | Anurag Kashyap,0,2
+tt1630282,Sahi Dhandhe Galat Bande,2011,19-Aug-11,Action | Comedy | Drama,Parvin Dabas,Anupam Kher | Sharat Saxena | Parvin Dabas | Vansh Bhardwaj,Parvin Dabas,0,1
+tt0779768,Teesri Aankh: The Hidden Camera,2006,3-Mar-06,Action | Thriller,Harry Baweja (story) | Harry Baweja (screenplay) | Pathik Vats (dialogue) | Sameer (lyrics) | Nitin Arora (lyrics) | Earl D'Souza (lyrics) | Karmjeet Kadhowala (lyrics),Sunny Deol | Ameesha Patel | Neha Dhupia | Mukesh Rishi,Harry Baweja,0,1
+tt2016845,Aagaah: The Warning,2011,5-Aug-11,Drama | Horror | Thriller,Karan Razdan (story),Ila Arun | Anang Desai | Zakir Hussain | Satish Kaushik,Karan Razdan,0,1
+tt1224454,Sirf....: Life Looks Greener on the Other Side,2008,25-Apr-08,Drama,Rajatesh Nayyar (story) | Shashikant Verma (story) | Santosh Saroj (dialogue) | Mehboob (lyrics) | Vipul Saini (lyrics),Kay Kay Menon | Manisha Koirala | Ranvir Shorey | Sonali Kulkarni,Rajatesh Nayyar,0,1
+tt2576450,Besharam,2013,2-Oct-13,Comedy | Romance,Rajeev Barnwal | Abhinav Kashyap,Ranbir Kapoor | Pallavi Sharda | Rishi Kapoor | Neetu Singh,Abhinav Kashyap,0,2
+tt0920464,Manorama Six Feet Under,2007,21-Sep-07,Crime | Drama | Mystery,Devika Bhagat (story) | Navdeep Singh (story) | Manoj Tapadia (dialogue) | Abhinav Kashyap (dialogue),Abhay Deol | Gul Panag | Raima Sen | Sarika,Navdeep Singh,0,1
+tt1578116,Atithi Tum Kab Jaoge?,2010,5-Mar-10,Comedy | Drama,Robin Bhatt (screenplay) | Ashwani Dhir | Tushar Hiranandani (screenplay) | Amit Mishra (lyrics),Ajay Devgn | Konkona Sen Sharma | Paresh Rawal | Satish Kaushik,Ashwani Dhir,0,4
+tt1391894,Siddharth: The Prisoner,2009,27-Feb-09,Crime | Drama | Thriller,Anadi (dialogue) | Pryas Gupta (dialogue) | Pryas Gupta (story) | Hitesh Kewalya (dialogue),Rajat Kapoor | Sachin Nayak | Pradip Sagar | Pradeep Kabra,Pryas Gupta,0,1
+tt2343417,Chhodo Kal Ki Baatein,2012,12-Apr-12,Drama,Pramod Joshi (dialogue) | Pramod Joshi (screenplay) | Raz Kazi (dialogue) | Raz Kazi (screenplay),Barkha Bisht | Balaji Iyer | Raghavendra Kadkol | Sachin Khedekar,Pramod Joshi,0,1
+tt1703958,Ek Main Aur Ekk Tu,2012,10-Feb-12,Comedy | Drama | Romance,Shakun Batra | Ayesha DeVitre,Kareena Kapoor | Boman Irani | Imran Khan | Ratna Pathak,Shakun Batra,0,4
+tt0377126,Basti,2003,8-Aug-03,Action | Crime,,Sadashiv Amrapurkar | Liyaqat Bari | Brij Gopal | Rajendra Gupta,,0,1
+tt1562871,Ra.One,2011,26-Oct-11,Action | Adventure | Sci-Fi,David Benullo | Kanika Dhillon (dialogue) | Kanika Dhillon (screenplay) | Niranjan Iyengar (dialogue) | Shah Rukh Khan (screenplay) | Mushtaq Sheikh (screenplay) | Anubhav Sinha (story),Arjun Rampal | Shah Rukh Khan | Kareena Kapoor | Shahana Goswami,Anubhav Sinha,0,6
+tt4010306,Jigariyaa,2014,10-Oct-14,Drama,Vinod Bachchan | Apratim Khare | Raj Purohit,Deepak Chadha | Harshvardhan Deo | Sneha Deori | Vineeta Malik,Raj Purohit,0,1
+tt0495034,Golmaal: Fun Unlimited,2006,14-Jul-06,Comedy,Neeraj Vora,Ajay Devgn | Arshad Warsi | Sharman Joshi | Tusshar Kapoor,Rohit Shetty,0,6
+tt0347779,Pinjar,2003,,Drama,Chandra Prakash Dwivedi (additional dialogue) | Chandra Prakash Dwivedi (screenplay) | Amrita Pritam (dialogue) | Amrita Pritam (novel) | Amrita Pritam (story),Urmila Matondkar | Manoj Bajpayee | Sanjay Suri | Sandali Sinha,Chandra Prakash Dwivedi,0,1
+tt0380337,Ek Din 24 Ghante,2003,7-Nov-03,Thriller,,Rahul Bose | Ahmed Chaudhary | Nandita Das | Vinit Kumar,Anant Balani,0,1
+tt3390572,Haider,2014,2-Oct-14,Crime | Drama | Romance,"William Shakespeare (based on the play ""Hamlet"" by) | Basharat Peer (screenplay) | Vishal Bhardwaj (screenplay) | Vishal Bhardwaj (dialogue)",Tabu | Shahid Kapoor | Shraddha Kapoor | Kay Kay Menon,Vishal Bhardwaj,0,4
+tt0338477,Talaash: The Hunt Begins...,2003,3-Jan-03,Action | Adventure | Crime,Suneel Darshan (story) | Robin Bhatt (screenplay) | K.K. Singh (dialogue) | Ravi Shankar Jaiswal (additional dialogue),Rakhee Gulzar | Akshay Kumar | Kareena Kapoor | Pooja Batra,Suneel Darshan,0,2
+tt0490210,Sarkar Raj,2008,6-Jun-08,Action | Crime | Drama,Prashant Pandey,Amitabh Bachchan | Abhishek Bachchan | Aishwarya Rai Bachchan | Ravi Kale,Ram Gopal Varma,1,3
+tt1242530,What's Your Raashee?,2009,2-Oct-09,Comedy | Drama | Romance,Ashutosh Gowariker (screenplay) | Naushil Mehta (screenplay) | Amit Mistry (dialogue) | Madhu Rye (novel),Harman Baweja | Priyanka Chopra | Anjan Srivastav | Manju Singh,Ashutosh Gowariker,0,1
+tt2429640,Murder 3,2013,15-Feb-13,Thriller,"Mahesh Bhatt | Hatem Khraiche (original film ""La Cara Oculta"") | Amit Masurkar (additional screenplay)",Randeep Hooda | Aditi Rao Hydari | Sara Loren | Rajesh Shringarpore,Vishesh Bhatt,1,2
+tt2574698,Gunday,2014,14-Feb-14,Action | Crime | Drama,Ali Abbas Zafar | Sanjay Masoom (additional dialogue),Ranveer Singh | Arjun Kapoor | Priyanka Chopra | Irrfan Khan,Ali Abbas Zafar,0,5
+tt0483239,Bullet: Ek Dhamaka,2005,4-Feb-05,Action | Drama,Anand Raj Anand (lyrics) | Neha Bhasin (lyrics) | Salim Shahid (story) | Faiz Shaid (story),Benjamin Gilani | Natalya Gudkova | Iqbal Khan | Asseem Merchant,Irfan Khan,0,1
+tt1221142,Mumbai Cutting,2011,1-Mar-11,Drama,Jahnu Barua | Rahul Dholakia | Rituparno Ghosh | Anurag Kashyap | Sudhir Mishra | Kundan Shah | Gaurav Sinha,Raj Singh Arora | Abhisar Bose | Neetu Chandra | Master Chinmay,Jahnu Barua | Rahul Dholakia | Rituparno Ghosh | Shashanka Ghosh | Manish Jha | Anurag Kashyap | Sudhir Mishra | Ruchi Narain | Ayush Raina | Revathy | Kundan Shah,0,1
+tt0265452,Officer,2001,14-Mar-01,Action | Crime | Drama,Naeem Sha (dialogue) | Naeem Sha (screenplay) | Naeem Sha (story),Sunil Shetty | Raveena Tandon | Danny Denzongpa | Sadashiv Amrapurkar,Naeem Sha,0,2
+tt1916728,Shor in the City,2011,28-Apr-11,Crime | Drama,Krishna D.K. (story) | Raj Nidimoru (story) | Sita Menon (story) | Sita Menon (dialogue) | Chintan Gandhi (dialogue) | Sameer (lyrics) | Nishu (lyrics),Sendhil Ramamurthy | Tusshar Kapoor | Nikhil Dwivedi | Preeti Desai,Krishna D.K. | Raj Nidimoru,0,2
+tt0437238,Hulchul,2004,26-Nov-04,Action | Comedy | Drama,K.P. Saxena (dialogue) | Siddique (story) | Neeraj Vora (screenplay),Akshaye Khanna | Kareena Kapoor | Sunil Shetty | Paresh Rawal,Priyadarshan,0,6
+tt0348824,Chalo Ishq Ladaaye,2002,27-Dec-02,Comedy | Romance,Imtiaz Patel | Yunus Sajawal,Govinda | Kader Khan | Rani Mukerji | Zohra Segal,Aziz Sejawal,0,1
+tt3302962,Shaadi Ke Side Effects,2014,28-Feb-14,Comedy | Romance,Saket Chaudhary (screenplay) | Saket Chaudhary (story) | Zeenat Lakhani (screenplay) | Zeenat Lakhani (story) | Arshad Sayed (dialogue) | Arshad Sayed (screenplay),Farhan Akhtar | Vidya Balan | Vir Das | Ram Kapoor,Saket Chaudhary,1,2
+tt3021244,Chaarfutiya Chhokare,2014,26-Sep-14,Drama | Thriller,Manish Harishankar,Soha Ali Khan | Zakir Hussain | Seema Biswas | Mukesh Tiwari,Manish Harishankar,0,1
+tt0435259,Padmashree Laloo Prasad Yadav,2005,28-Jan-05,Comedy,Sanjay Pawar (dialogue) | Vinay | Yash,Sunil Shetty | Masumeh Makhija | Mahesh Manjrekar | Johnny Lever,Mahesh Manjrekar,0,1
+tt3645014,The Xpose,2014,16-May-14,Thriller,Himesh Reshammiya (story) | Jainesh Ejardar (screenplay) | Himesh Reshammiya (screenplay) | Bunty Rathore (dialogue),Himesh Reshammiya | Yo Yo Honey Singh | Irrfan Khan | Zoya Afroz,Anant Mahadevan,0,2
+tt0396057,Hum Pyar Tumhi Se Kar Baithe,2002,8-Nov-02,Musical | Romance,Mohan Singh Rathor (dialogue) | Mohan Singh Rathor (screenplay) | Mohan Singh Rathor (story),Jugal Hansraj | Tina Rana | Sachin Khedekar | Vishnu Sharma,Mohan Singh Rathor,0,1
+tt0495032,Gangster,2006,28-Apr-06,Crime | Drama | Mystery,Mahesh Bhatt (story) | Girish Dhamija (dialogue) | Anurag Basu (screenplay),Kangana Ranaut | Shiney Ahuja | Emraan Hashmi | Gulshan Grover,Anurag Basu,0,5
+tt2401719,Prague,2013,27-Sep-13,Mystery | Romance | Thriller,Rohit Khaitan (conceived by) | Akshendra Mishra (additional screenplay) | Sumit Saxena (screenplay) | Ashish R. Shukla (screenplay) | Ashish R. Shukla (story) | Vijay Verma (additional screenplay),Chandan Roy Sanyal | Arfi Lamba | Kumar Mayank | Sonia Bindra,Ashish R. Shukla,0,1
+tt1105747,Yuvvraaj,2008,21-Nov-08,Comedy | Drama | Romance,Sachin Bhowmick (screenplay) | Subhash Ghai (story) | Kamlesh Pandey (screenplay),Salman Khan | Anil Kapoor | Zayed Khan | Mithun Chakraborty,Subhash Ghai,0,1
+tt0375733,Encounter: The Killing,2002,9-Aug-02,Crime | Drama,Ajay Phansekar,Naseeruddin Shah | Dilip Prabhavalkar | Tara Deshpande | Akash Khurana,Ajay Phansekar,0,1
+tt0367110,Swades,2004,17-Dec-04,Drama,M.G. Sathya (story) | Ashutosh Gowariker (story) | Ashutosh Gowariker (screenplay) | Sameer Sharma (screenplay) | Lalit Marathe (screenplay) | Amin Hajee (screenplay) | Charlotte Whitby-Coles (screenplay) | Yashodeep Nigudkar (screenplay) | Ayan Mukherjee (screenplay) | K.P. Saxena (dialogue),Shah Rukh Khan | Gayatri Joshi | Kishori Balal | Smith Seth,Ashutosh Gowariker,0,2
+tt2344678,Himmatwala,2013,29-Mar-13,Action | Comedy,K. Raghavendra Rao (original story) | Sajid Khan (story) | Sajid Khan (screenplay) | Farhad (screenplay) | Sajid (screenplay),Ajay Devgn | Tamannaah Bhatia | Mahesh Manjrekar | Paresh Rawal,Sajid Khan,0,2
+tt1797548,Yeh Faasley,2011,4-Mar-11,Crime | Drama | Mystery,Arpita Chatterjee (story) | Sameer Kohli (story) | Rajen Makhijani (screenplay) | Rajen Makhijani (story) | Yogesh Mittal (dialogue) | Yogesh Mittal (screenplay) | Yogesh Mittal (story) | Atul Tiwari (dialogue) | Atul Tiwari (screenplay) | Atul Tiwari (story),Rachita Bhattacharya | Seema Biswas | Sudha Chandran | Sanjiv Chopra,Yogesh Mittal,0,1
+tt0449306,Lucky: No Time for Love,2005,8-Apr-05,Musical | Drama | Romance,Radhika Rao | Vinay Sapru | Milap Zaveri (dialogue),Salman Khan | Sneha Ullal | Mithun Chakraborty | Kader Khan,Radhika Rao | Vinay Sapru,0,3
+tt0324951,23rd March 1931: Shaheed,2002,7-Jun-02,Biography | Drama | History,Sutanu Gupta (screenplay) | Sanjay Masoom (dialogue),Bobby Deol | Sunny Deol | Amrita Singh | Rahul Dev,Guddu Dhanoa,0,1
+tt1185412,Veer,2010,22-Jan-10,Action | Adventure | Drama,Shailesh Verma (screenplay) | Shaktimaan Talwar (screenplay) | Salman Khan (story),Salman Khan | Mithun Chakraborty | Jackie Shroff | Sohail Khan,Anil Sharma,0,3
+tt0437182,Family: Ties of Blood,2006,12-Jan-06,Action | Crime | Drama,Rajat Arora (screenwriter) | Tigmanshu Dhulia (dialogue) | Shridhar Raghavan (screenplay) | Ashok Rawat (script & story) | Rajkumar Santoshi (dialogue) | Rajkumar Santoshi (screenplay) | Shaktimaan Talwar (story),Amitabh Bachchan | Akshay Kumar | Bhoomika Chawla | Aryeman Ramsay,Rajkumar Santoshi,0,1
+tt1388424,Three: Love Lies Betrayal,2009,4-Sep-09,Drama | Mystery | Thriller,Vikram Bhatt,Aashish Chaudhary | Akshay Kapoor | Nausheen Ali Sardar | Achint Kaur,Vishal Pandya,0,1
+tt0499041,Kalyug,2005,9-Dec-05,Action | Crime | Drama,Jay Dixit (dialogue) | Anand Sivakumaran (screenplay) | Mohit Suri (story),Kunal Khemu | Deepal Shaw | Smiley Suri | Atul Parchure,Mohit Suri,0,5
+tt3524410,Yeh Hai Bakrapur,2014,9-May-14,Comedy | Drama,Azad Alam (additional screenplay & dialogue) | Janaki Vishwanathan (screenplay) | Janaki Vishwanathan,Asif Basra | Anshuman Jha | Yoshika Verma | Amit Sial,Janaki Vishwanathan,0,1
+tt3257168,Shorts,2013,12-Jul-13,Drama,,Satyakam Anand | Aparajit Bhattacharjee | Richa Chadda | Aditi Khanna,Neeraj Ghaywan | Siddharth Gupt | Rohit Pandey | Anirban Roy | Shlok Sharma,0,1
+tt0346457,The Rising: Ballad of Mangal Pandey,2005,12-Aug-05,Biography | Drama | History,Farrukh Dhondy (screenplay) | Ranjit Kapoor (Hindi dialogue),Aamir Khan | Rani Mukerji | Toby Stephens | Coral Beed,Ketan Mehta,0,2
+tt2998196,Kuku Mathur Ki Jhand Ho Gayi,2014,30-May-14,Comedy | Romance,,Siddharth Gupta | Simran Kaur Mundi | Pallavi Batra | Roopa Ganguly,Aman Sachdeva,0,1
+tt0278522,Jodi No.1,2001,13-Apr-01,Comedy,Rumi Jaffery (dialogue) | Imtiaz Patel (screenplay) | Yunus Sajawal (screenplay),Sanjay Dutt | Govinda | Twinkle Khanna | Monica Bedi,David Dhawan,0,6
+tt0415768,Dus,2005,8-Jul-05,Action | Crime | Thriller,Anubhav Sinha (dialogue) | Vinay | Yash,Sanjay Dutt | Sunil Shetty | Abhishek Bachchan | Zayed Khan,Anubhav Sinha,0,4
+tt0331851,Armaan,2003,16-May-03,Drama | Family | Romance,Javed Akhtar (dialogue) | Javed Akhtar (screenplay) | Honey Irani (screenplay) | Honey Irani (story),Amitabh Bachchan | Anil Kapoor | Preity Zinta | Gracy Singh,Honey Irani,0,2
+tt0995752,Tashan,2008,25-Apr-08,Action | Comedy | Crime,Vijay Krishna Acharya (story) | Vijay Krishna Acharya (screenplay) | Vijay Krishna Acharya (dialogue) | Piyush Mishra (lyrics) | Vishal Dadlani (lyrics) | Kausar Munir (lyrics),Akshay Kumar | Saif Ali Khan | Kareena Kapoor | Anil Kapoor,Vijay Krishna Acharya,0,3
+tt1363363,Chatur Singh Two Star,2011,19-Aug-11,Action | Adventure | Comedy,Rumi Jaffery (screenplay) | Sai Kabir (dialogue),Sanjay Dutt | Ameesha Patel | Anupam Kher | Satish Kaushik,Ajay Chandhok,0,1
+tt0426075,Lakeer - Forbidden Lines,2004,,Action | Drama | Romance,Ahmed Khan (screenplay) | Shahab Khan (screenplay) | Mehboob (dialogue),Sunny Deol | Sunil Shetty | Sohail Khan | John Abraham,Ahmed Khan,0,1
+tt1260689,Summer 2007,2008,13-Jun-08,Crime | Drama | Thriller,Gourov Dasgupta (lyrics) | Bijesh Jayarajan (screenplay) | Bijesh Jayarajan (story) | Ujjaiyinee Roy (lyrics) | Ritesh Shah (dialogues) | Vibha Singh (lyrics),Ahraz Ahmed | Punit Aneja | Arjan Bajwa | Neetu Chandra,Sohail Tatari,0,1
+tt2988272,Shuddh Desi Romance,2013,6-Sep-13,Comedy | Drama | Romance,Jaideep Sahni,Sushant Singh Rajput | Parineeti Chopra | Vaani Kapoor | Rishi Kapoor,Maneesh Sharma,0,6
+tt1095038,Victoria No. 203: Diamonds Are Forever,2007,31-Aug-07,Comedy | Crime | Mystery,Sanjeev Puri (dialogue) | Manoj Tyagi (adaptation),Anupam Kher | Om Puri | Jimmy Shergill | Soniya Mehra,Anant Mahadevan,0,1
+tt1744641,Ramayana: The Epic,2010,15-Oct-10,Animation,Chetan Desai (screenplay) | Riturraj Tripathii (dialogue) | Riturraj Tripathii (screenplay) | Riturraj Tripathii (story) | Riturraj Tripathii,Manoj Bajpayee | Juhi Chawla | Ashutosh Rana | Mukesh Rishi,Chetan Desai,0,1
+tt2112124,Chennai Express,2013,8-Aug-13,Action | Comedy | Romance,K. Subhash (story) | Yunus Sajawal (screenplay) | Robin Bhatt (additional screenplay) | Farhad (dialogue) | Sajid (dialogue),Deepika Padukone | Shah Rukh Khan | Satyaraj | Nikitin Dheer,Rohit Shetty,0,8
+tt1132606,Ugly Aur Pagli,2008,1-Aug-08,Comedy | Drama,Anil Pandey (story) | Amitabh Verma (lyrics) | Suparn Verma (additional screenplay & dialogue),Mallika Sherawat | Ranvir Shorey | Bharati Achrekar | Zeenat Aman,Sachin Kamlakar Khot,0,2
+tt1806740,9 Eleven,2011,,Thriller,Manan Katohora,Kashmira Shah | Devasish Ray | Jyoti Singh | Sonny Chatrath,Manan Katohora,0,1
+tt1629424,Trump Card,2010,12-Mar-10,Action | Drama | Mystery,Arshad Khan (screenplay) | Yawer Rehman (screenplay) | Yawer Rehman (script),Vikrum Kumar | Haidar Ali | Urvashi Chaudhary | Mansi Dovhal,Arshad Khan,0,1
+tt0448206,Bunty Aur Babli,2005,27-May-05,Adventure | Comedy | Crime,Aditya Chopra (story) | Jaideep Sahni (screenplay) | Jaideep Sahni (dialogue),Amitabh Bachchan | Rani Mukerji | Abhishek Bachchan | Kiran Juneja,Shaad Ali,0,7
+tt0378025,Hawayein,2003,22-Aug-03,Drama | Romance,Ammtoje Mann (screenplay) | Harjit Singh (dialogue),Babbu Mann | Ammtoje Mann | Mahie Gill | Mukul Dev,Ammtoje Mann,0,1
+tt1433810,Mumbai Diaries,2010,21-Jan-11,Drama,Kiran Rao,Prateik | Monica Dogra | Kriti Malhotra | Aamir Khan,Kiran Rao,0,1
+tt1170399,C Kkompany,2008,29-Aug-08,Comedy | Drama,Shabbir Ahmed (lyrics) | Anand Raj Anand (lyrics) | Sachin Yardi,Tusshar Kapoor | Anupam Kher | Rajpal Yadav | Raima Sen,Sachin Yardi,0,1
+tt1706317,Tezz,2012,26-Apr-12,Action | Drama,Robin Bhatt | Aditya Dhar (dialogue writer),Anil Kapoor | Ajay Devgn | Mohanlal | Kangana Ranaut,Priyadarshan,0,1
+tt0306840,Koi Mere Dil Se Poochhe,2002,11-Jan-02,Musical | Romance | Thriller,,Jaya Bhaduri | Aftab Shivdasani | Sanjay Kapoor | Juliet Alburque,Vinay Shukla,0,2
+tt1809399,Utt Pataang,2011,1-Feb-11,Comedy | Drama,Arun Kumar (lyrics) | Rohit Sharma (lyrics) | Saurabh Shukla (dialogues) | Saurabh Shukla (screenplay) | Srikanth Velagaleti (screenplay) | Srikanth Velagaleti (story),Vinay Pathak | Saurabh Shukla | Mahie Gill | Mona Singh,Srikanth Velagaleti,0,1
+tt0330217,Dil Ka Rishta,2003,17-Jan-03,Romance,Shabbir Boxwala | Vrinda Rai (story) | Naeem Sha (dialogue),Arjun Rampal | Aishwarya Rai Bachchan | Priyanshu Chatterjee | Rakhee Gulzar,Naresh Malhotra,0,2
+tt1454461,Ek: The Power of One,2009,27-Mar-09,Action | Drama | Thriller,Shabbir Ahmed (lyrics) | Sameer Arora (additional screenplay & dialogue) | Vivek Buddhakoti (additional screenplay & dialogue) | Mayur Puri (lyrics) | Pankaj Trivedi (story),Rana Jung Bahadur | Jaspal Bhatti | Preeti Bhutani | Bobby Deol,Sangeeth Sivan,0,1
+tt1849718,Agneepath,2012,26-Jan-12,Action | Crime | Drama,Ila Bedi Dutta (screenplay) | Karan Malhotra (screenplay) | Piyush Mishra (dialogue),Hrithik Roshan | Priyanka Chopra | Sanjay Dutt | Rishi Kapoor,Karan Malhotra,0,7
+tt0382188,Mumbai Matinee,2003,26-Sep-03,Romance | Comedy,Anant Balani,Rahul Bose | Perizaad Zorabian | Vijay Raaz | Saurabh Shukla,Anant Balani,0,1
+tt1188996,My Name Is Khan,2010,12-Feb-10,Drama | Romance | Thriller,Shibani Bathija (story) | Shibani Bathija (dialogue) | Niranjan Iyengar (dialogue),Shah Rukh Khan | Kajol | Katie A. Keane | Kenton Duty,Karan Johar,0,6
+tt1918927,Luv Ka the End,2011,6-May-11,Comedy | Drama,Amitabh Bhattacharya (lyrics) | Ashish Patil (story) | Ashish Patil | Roye Segal (screenplay) | Shenaz Treasury (screenplay) | Nihkil Vyas (dialogue) | Nikhil Vyas (dialogues),Riya Bamniyal | Bumpy | Sreejita De | Shraddha Kapoor,Bumpy,0,2
+tt0995827,The Train: Some Lines Shoulder Never Be Crossed...,2007,,Thriller,Hriday Lani (screenplay) | Sanjay Masoom (dialogue),Emraan Hashmi | Geeta Basra | Rajat Bedi | Anant Mahadevan,Hasnain Hyderabadwala | Raksha Mistry,0,2
+tt0341549,Rishtey,2002,6-Dec-02,Family,Rajeev Kaul (screenplay) | Rajeev Kaul (story) | Tanveer Khan (dialogue) | Praful Parekh (screenplay) | Praful Parekh (story),Anil Kapoor | Karisma Kapoor | Shilpa Shetty | Kaivalya Chheda,Indra Kumar,0,2
+tt0331256,Gunaah,2002,16-Oct-02,Crime | Drama,Mahesh Bhatt (screenplay) | Pranay Narayan (dialogue),Bipasha Basu | Dino Morea | Ashutosh Rana | Banjara,Amol Shetge,0,2
+tt0872190,Cash,2007,3-Aug-07,Action | Drama | Thriller,Vishal Dadlani (lyrics) | Panchhi Jalonvi (lyrics) | Anubhav Sinha (dialogues) | Vinay (story) | Yash (story),Ajay Devgn | Sunil Shetty | Zayed Khan | Ritesh Deshmukh,Anubhav Sinha,0,2
+tt3169704,Raqt,2013,27-Sep-13,Thriller,Adi Irani | Shiva Rindan | Ranjiv Verma,Shweta Bhardwaj | Gulshan Grover | Adi Irani | Farida Jalal,Adi Irani | Shiva Rindan,0,1

classification/unipredict/bhanupratapbiswas-bollywood-actress-name-and-movie-list/test.jsonl

@@ -0,0 +1,131 @@
+{"text": "The imdbId is tt0420123. The title is Revati. The releaseYear is 2005. The releaseDate is 7-May-05. The genre is Drama. The writers is Farogh Siddique. The actors is Kashmira Shah | Kiran Kumar | Ayub Khan | Javed Khan. The directors is Farogh Siddique. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0819810. The title is Traffic Signal. The releaseYear is 2007. The releaseDate is 2-Feb-07. The genre is Drama. The writers is Madhur Bhandarkar (dialogue) | Madhur Bhandarkar (screenplay) | Madhur Bhandarkar (story) | Nishant A Bhuse (story) | Sachin Yardi (dialogue) | Sachin Yardi (screenplay) | Sachin Yardi (story). The actors is Kunal Khemu | Neetu Chandra | Upendra Limaye | Ranvir Shorey. The directors is Madhur Bhandarkar. The sequel is 0.0.", "label": "4", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0284137. The title is Gadar: Ek Prem Katha. The releaseYear is 2001. The releaseDate is 15-Jun-01. The genre is Action | Drama | Romance. The writers is Shaktimaan Talwar. The actors is Sunny Deol | Ameesha Patel | Amrish Puri | Lillete Dubey. The directors is Anil Sharma. The sequel is 0.0.", "label": "9", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1890513. The title is Ragini MMS. The releaseYear is 2011. The releaseDate is 13-May-11. The genre is Drama | Horror | Mystery. The writers is Vaspar Dandiwala (story) | Pawan Kripalani (story) | Virag Mishra (lyrics) | Agnel Roman (lyrics) | Mayank Tewaari (dialogue) | Mayank Tewaari (screenplay). The actors is Kainaz Motivala | Rajkummar Rao | Rajat Kaul | Janice. The directors is Pawan Kripalani. The sequel is 0.0.", "label": "5", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1727496. The title is Dil Toh Baccha Hai Ji. The releaseYear is 2011. The releaseDate is 27-Jan-11. The genre is Comedy | Drama | Romance. The writers is Madhur Bhandarkar (story) | Sanjay Chhel (dialogue) | Kumaar (lyrics) | Neelesh Misra (lyrics) | Anil Pandey (story) | Sayeed Qadri (lyrics) | Neeraj Udhwani (screenplay) | Neeraj Udhwani (story). The actors is Ajay Devgn | Emraan Hashmi | Omi Vaidya | Shazahn Padamsee. The directors is Madhur Bhandarkar. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0461209. The title is Ek Khiladi Ek Haseena. The releaseYear is 2005. The releaseDate is 18-Nov-05. The genre is Comedy | Crime | Thriller. The writers is Suparn Verma (story). The actors is Fardeen Khan | Koena Mitra | Kay Kay Menon | Rakhi Sawant. The directors is Suparn Verma. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2122340. The title is Ferrari Ki Sawaari. The releaseYear is 2012. The releaseDate is 15-Jun-12. The genre is Crime | Family | Sport. The writers is Vidhu Vinod Chopra (original story and screenplay) | Rajesh Mapuskar (original story and screenplay) | Rajkumar Hirani (original story and dialogue) | Shekhar Dhavalikar (script associate) | Ranjeet Bahadur (dialogue associate). The actors is Sharman Joshi | Boman Irani | Ritwik Sahore | Paresh Rawal. The directors is Rajesh Mapuskar. The sequel is 0.0.", "label": "5", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0346548. The title is Avgat. The releaseYear is 2001. The releaseDate is 1-Jun-01. The genre is Action | Crime | Drama. The writers is unknown. The actors is Ajinkya Deo | Nethra Raghuraman | Sayaji Shinde | Rohini Hattangadi. The directors is Mohan Sharma. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0486615. The title is London Dreams. The releaseYear is 2009. The releaseDate is 30-Oct-09. The genre is Musical. The writers is Ritesh Shah (dialogue) | Suresh Nair (story). The actors is Salman Khan | Om Puri | Ajay Devgn | Dilyana Bouklieva. The directors is Vipul Amrutlal Shah. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1999857. The title is Cycle Kick. The releaseYear is 2011. The releaseDate is 17-Jun-11. The genre is Drama | Sport. The writers is Shashi Sudigala. The actors is Nishan Nanaiah | Sunny Hinduja | Girija Oak | Ishita Sharma. The directors is Shashi Sudigala. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2064852. The title is Lottery. The releaseYear is 2009. The releaseDate is 20-Mar-09. The genre is Crime | Drama | Mystery. The writers is Aadesh K. Arjun (dialogue) | Hemant Prabhu (screenplay). The actors is Abhijeet | Rucha Gujrati | Manisha Kelkar | Nassar Abdulla. The directors is Hemant Prabhu. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3527144. The title is The Dirty Relation. The releaseYear is 2013. The releaseDate is 14-Jun-13. The genre is Thriller. The writers is unknown. The actors is Sanjay Chauhan | Jasleen Matharu | Kanwaljeet Matharu | Kesar Matharu. The directors is Kesar Matharu. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3211170. The title is Say Yes to Love. The releaseYear is 2012. The releaseDate is 16-Mar-12. The genre is Romance. The writers is Marukh Mirza Beig. The actors is Nazia Hussain | Salim Khan | Aasad Mirza | Saira Mirza. The directors is Marukh Mirza Beig. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0439872. The title is Wajahh: A Reason to Kill. The releaseYear is 2004. The releaseDate is 8-Oct-04. The genre is Thriller. The writers is Ghalib Asadbhopali (dialogue) | Tony Mirrcandani (story) | Sandeep Patel (screenplay). The actors is Arbaaz Khan | Gracy Singh | Shamita Shetty | Zulfi Sayed. The directors is Gautam Adhikari. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1937092. The title is Always Kabhi Kabhi. The releaseYear is 2011. The releaseDate is 17-Jun-11. The genre is Comedy | Crime | Drama. The writers is Roshan Abbas (story) | Kanika Dhillon (additional screenplay) | Ishita Moitra (screenplay) | Ranjit Raina (story). The actors is Lillete Dubey | Satyajeet Dubey | Ali Fazal | Manoj Joshi. The directors is Roshan Abbas. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2319889. The title is Jannat 2. The releaseYear is 2012. The releaseDate is 4-May-12. The genre is Crime | Drama | Thriller. The writers is Kapil Chopra | Sanjay Masoom (dialogue). The actors is Mohammed Zeeshan Ayyub | Viren Basoya | Manish Chaudhary | Esha Gupta. The directors is Kunal Deshmukh. The sequel is 1.0.", "label": "5", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1828289. The title is Shagird. The releaseYear is 2011. The releaseDate is 13-May-11. The genre is Action | Crime | Drama. The writers is Tigmanshu Dhulia (story) | Tigmanshu Dhulia | Kamal Pandey (story). The actors is Nana Patekar | Mohit Ahlawat | Rimi Sen | Anurag Kashyap. The directors is Tigmanshu Dhulia. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2341766. The title is Nautanki Saala!. The releaseYear is 2013. The releaseDate is 12-Apr-13. The genre is Comedy. The writers is Beno?t Graffin (original story) | Pierre Salvadori (original story) | Nipun Dharmadhikari (screenplay) | Rohan Sippy (screenplay) | Charudutt Acharya (screenplay). The actors is Ayushmann Khurrana | Kunaal Roy Kapur | Pooja Salvi | Gaelyn Mendonca. The directors is Rohan Sippy. The sequel is 0.0.", "label": "3", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0995035. The title is Dhol. The releaseYear is 2007. The releaseDate is 14-Sep-07. The genre is Comedy. The writers is Manisha Korde (screenplay). The actors is Sharman Joshi | Tusshar Kapoor | Kunal Khemu | Rajpal Yadav. The directors is Priyadarshan. The sequel is 0.0.", "label": "4", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0893585. The title is Detective Naani. The releaseYear is 2009. The releaseDate is 22-May-09. The genre is Crime | Drama | Family. The writers is Romilla Mukherjee. The actors is Ava Mukherjee | Zain Khan | Simran Singh | Amrita Raichand. The directors is Romilla Mukherjee. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0354538. The title is Ek Aur Ek Gyarah: By Hook or by Crook. The releaseYear is 2003. The releaseDate is 28-Mar-03. The genre is Action | Comedy | Musical. The writers is Aman Jaffery (dialogue) | Shahnawaz Ahmed Kenny (scenario) | Bolu Khan (dialogue) | Yunus Sajawal (screenplay). The actors is Sanjay Dutt | Govinda | Amrita Arora | Nandini Singh. The directors is David Dhawan. The sequel is 0.0.", "label": "3", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2138010. The title is 3 Nights 4 Days. The releaseYear is 2009. The releaseDate is 9-Oct-09. The genre is Romance. The writers is unknown. The actors is Hrishitaa Bhatt | Anuj Sawhney. The directors is Devang Dholakia. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0401532. The title is Jaago. The releaseYear is 2004. The releaseDate is 6-Feb-04. The genre is Drama. The writers is K.K. Singh (dialogue) | K.K. Singh (screenplay) | K.K. Singh (story). The actors is Sanjay Kapoor | Raveena Tandon | Manoj Bajpayee | Puru Rajkumar. The directors is Mehul Kumar. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1060249. The title is Drona. The releaseYear is 2008. The releaseDate is 2-Oct-08. The genre is Action | Adventure | Drama. The writers is Goldie Behl | Rohini Killough (screenplay) | Vaibhav Modi (lyrics) | Jaydeep Sarkar (screenplay). The actors is Jayshree Arora | Veer Arya | Abhishek Bachchan | Jaya Bhaduri. The directors is Goldie Behl. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2613942. The title is Dehraadun Diary. The releaseYear is 2013. The releaseDate is 4-Jan-13. The genre is Thriller. The writers is Aseem Arora (screenplay) | Aseem Arora (script). The actors is Rati Agnihotri | Neelima Azim | Rohit Bakshi | Vishal Bhosle. The directors is Milind Ukey. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1183917. The title is Teen Patti. The releaseYear is 2010. The releaseDate is 26-Feb-10. The genre is Drama | Thriller. The writers is Shivkumar Subramaniam (story) | Leena Yadav (story) | Ben Rekhi (english dialogue). The actors is Amitabh Bachchan | Madhavan | Shraddha Kapoor | Siddharth Kher. The directors is Leena Yadav. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1132595. The title is Maan Gaye Mughall-E-Azam. The releaseYear is 2008. The releaseDate is 22-Aug-08. The genre is Comedy | Crime | Drama. The writers is Sanjay Chhel | Sunil Munshi (screenplay). The actors is Mallika Sherawat | Rahul Bose | Paresh Rawal | Kay Kay Menon. The directors is Sanjay Chhel. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1512321. The title is Vaada Raha... I Promise. The releaseYear is 2009. The releaseDate is 11-Sep-09. The genre is Drama. The writers is Aseem Arora | Sandeep Chatterjee (lyrics) | Samir Karnik (screenplay) | Babbu Mann (lyrics) | Sandeep Nath (lyrics) | Rahul Seth (lyrics) | A.M. Turaz (lyrics). The actors is Bobby Deol | Dwij Yadav | Kangana Ranaut | Mohnish Bahl. The directors is Samir Karnik. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0430480. The title is Popcorn Khao! Mast Ho Jao. The releaseYear is 2004. The releaseDate is 1-Oct-04. The genre is Comedy | Drama | Romance. The writers is Vishal Dadlani (lyrics) | Kabir Sadanand | Raghuvir Shekhawat (dialogue). The actors is Akshay Kapoor | Tanisha | Yash Tonk | Deepak Tijori. The directors is Kabir Sadanand. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2389974. The title is Aatma. The releaseYear is 2013. The releaseDate is 22-Mar-13. The genre is Drama | Horror | Thriller. The writers is Sudarshana Dwivedi (dialogue) | Suparn Verma (dialogue) | Suparn Verma (story) | Suparn Verma. The actors is Jaideep Ahlawat | Bipasha Basu | Padam Bhola | Darshan Jariwala. The directors is Suparn Verma. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3362728. The title is Ungli. The releaseYear is 2014. The releaseDate is 5-Dec-14. The genre is Comedy | Drama | Thriller. The writers is Renzil D'Silva (story) | Milap Zaveri (dialogue). The actors is Emraan Hashmi | Kangana Ranaut | Sanjay Dutt | Randeep Hooda. The directors is Renzil D'Silva. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1191118. The title is Hello Darling. The releaseYear is 2010. The releaseDate is 27-Aug-10. The genre is Comedy | Drama. The writers is Shabbir Ahmed (lyrics) | Kumaar (lyrics) | Ashiesh Pandit (lyrics) | Sachin Shah | Pankaj Trivedi. The actors is Gul Panag | Celina Jaitly | Isha Koppikar | Chunky Pandey. The directors is Manoj Tiwari. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0814014. The title is Apne. The releaseYear is 2007. The releaseDate is 29-Jun-07. The genre is Drama | Sport. The writers is Neeraj Pathak (screenplay) | Neeraj Pathak (story). The actors is Dharmendra | Sunny Deol | Bobby Deol | Shilpa Shetty. The directors is Anil Sharma. The sequel is 0.0.", "label": "4", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1740710. The title is Mere Brother Ki Dulhan. The releaseYear is 2011. The releaseDate is 9-Sep-11. The genre is Comedy | Drama | Romance. The writers is Ali Abbas Zafar. The actors is Imran Khan | Katrina Kaif | Ali Zafar | Tara D'Souza. The directors is Ali Abbas Zafar. The sequel is 0.0.", "label": "6", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0489028. The title is Double Cross: Ek Dhoka. The releaseYear is 2005. The releaseDate is 12-Jul-05. The genre is Comedy. The writers is unknown. The actors is Ayesha Jhulka | Negar Khan | Sahil Khan. The directors is Vicky Tejwani. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0483029. The title is Kyon?. The releaseYear is 2003. The releaseDate is unknown. The genre is Crime. The writers is unknown. The actors is Vinay Apte | Ashok Beniwal | Chaitanya Chaudhary | Rahul Dev. The directors is Kalpana Lajmi. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2066062. The title is Shortcut Romeo. The releaseYear is 2013. The releaseDate is 21-Jun-13. The genre is Action | Crime | Romance. The writers is Susi Ganesan (story) | Ilashree Goswami (dialogue). The actors is Neil Nitin Mukesh | Ameesha Patel | Puja Gupta | Jatin Grewal. The directors is Susi Ganesan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0392625. The title is Pratha. The releaseYear is 2002. The releaseDate is unknown. The genre is Action | Drama | Thriller. The writers is unknown. The actors is Deepak Bandhu | Ashney Shroff | Vicky Ahuja | Ravindra Bundela. The directors is Raja Bundela. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0285319. The title is Paagalpan. The releaseYear is 2001. The releaseDate is 8-Jun-01. The genre is Romance. The writers is Joy Augustine (story). The actors is Karan Nath | Aarti Agarwal | Vilas Ujawane | Bharat Dabholkar. The directors is Joy Augustine. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1605790. The title is Zokkomon. The releaseYear is 2011. The releaseDate is 22-Apr-11. The genre is Action | Adventure | Drama. The writers is Javed Akhtar (lyrics) | Satyajit Bhatkal (story) | Svati Chakravarty Bhatkal (story) | Lancy Fernandes (story) | Divy Nidhi Sharma (dialogues). The actors is Anupam Kher | Manjari Phadnis | Tinnu Anand | Sheeba Chaddha. The directors is Satyajit Bhatkal. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1729637. The title is Bodyguard. The releaseYear is 2011. The releaseDate is 31-Aug-11. The genre is Romance. The writers is J.P. Chowksey (screenplay) | Kiran Kotrial (screenplay) | Siddique. The actors is Salman Khan | Kareena Kapoor | Raj Babbar | Asrani. The directors is Siddique. The sequel is 0.0.", "label": "8", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2378057. The title is ?: A Question Mark. The releaseYear is 2012. The releaseDate is 17-Feb-12. The genre is Horror | Mystery | Thriller. The writers is Yash Dave | Allison Patel. The actors is Kiran Bhatia | Yaman Chatwal | Maanvi Gagroo | Chirag Jain. The directors is Allyson Patel | Yash Dave. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1948150. The title is Singham. The releaseYear is 2011. The releaseDate is 22-Jul-11. The genre is Action | Crime | Drama. The writers is Farhad | Farhad | Hari (original story) | Yunus Sajawal (screenplay) | Sajid (dialogue). The actors is Ajay Devgn | Kajal Agarwal | Prakash Raj | Sonali Kulkarni. The directors is Rohit Shetty. The sequel is 0.0.", "label": "7", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3822600. The title is Amit Sahni Ki List. The releaseYear is 2014. The releaseDate is 18-Jul-14. The genre is Comedy | Romance. The writers is Rohit G. Banawlikar (screenplay) | Shiv Singh (screenplay). The actors is Vir Das | Anindita Nayar | Natasha Rastogi | Kavi Shastri. The directors is Ajay Bhuyan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0485551. The title is Time Pass. The releaseYear is 2005. The releaseDate is unknown. The genre is Romance. The writers is Chander Mishra. The actors is Sherlyn Chopra | Tanaaz Currim Irani | Adi Irani | Monica Patel. The directors is Chander Mishra. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0321067. The title is Ab Ke Baras. The releaseYear is 2002. The releaseDate is 10-May-02. The genre is Adventure | Fantasy | Thriller. The writers is Robin Bhatt (screenplay) | Sutanu Gupta (screenplay) | Ravi Rai (dialogue). The actors is Arya Babbar | Amrita Rao | Ashutosh Rana | Shakti Kapoor. The directors is Raj Kanwar. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3542028. The title is Chal Bhaag. The releaseYear is 2014. The releaseDate is 13-Jun-14. The genre is Comedy. The writers is Tarun Bajaj. The actors is Deepak Dobriyal | Keeya Khanna | Sanjay Mishra | Yashpal Sharma. The directors is Prakash Saini. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0272736. The title is Mujhse Dosti Karoge!. The releaseYear is 2002. The releaseDate is 9-Aug-02. The genre is Musical | Romance | Drama. The writers is Aditya Chopra | Kunal Kohli. The actors is Rani Mukerji | Hrithik Roshan | Kareena Kapoor | Uday Chopra. The directors is Kunal Kohli. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2797242. The title is Bombay Talkies. The releaseYear is 2013. The releaseDate is 3-May-13. The genre is Drama. The writers is unknown. The actors is Rani Mukerji | Randeep Hooda | Saqib Saleem | Nawazuddin Siddiqui. The directors is Zoya Akhtar | Dibakar Banerjee | Karan Johar | Anurag Kashyap. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1630282. The title is Sahi Dhandhe Galat Bande. The releaseYear is 2011. The releaseDate is 19-Aug-11. The genre is Action | Comedy | Drama. The writers is Parvin Dabas. The actors is Anupam Kher | Sharat Saxena | Parvin Dabas | Vansh Bhardwaj. The directors is Parvin Dabas. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0779768. The title is Teesri Aankh: The Hidden Camera. The releaseYear is 2006. The releaseDate is 3-Mar-06. The genre is Action | Thriller. The writers is Harry Baweja (story) | Harry Baweja (screenplay) | Pathik Vats (dialogue) | Sameer (lyrics) | Nitin Arora (lyrics) | Earl D'Souza (lyrics) | Karmjeet Kadhowala (lyrics). The actors is Sunny Deol | Ameesha Patel | Neha Dhupia | Mukesh Rishi. The directors is Harry Baweja. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2016845. The title is Aagaah: The Warning. The releaseYear is 2011. The releaseDate is 5-Aug-11. The genre is Drama | Horror | Thriller. The writers is Karan Razdan (story). The actors is Ila Arun | Anang Desai | Zakir Hussain | Satish Kaushik. The directors is Karan Razdan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1224454. The title is Sirf....: Life Looks Greener on the Other Side. The releaseYear is 2008. The releaseDate is 25-Apr-08. The genre is Drama. The writers is Rajatesh Nayyar (story) | Shashikant Verma (story) | Santosh Saroj (dialogue) | Mehboob (lyrics) | Vipul Saini (lyrics). The actors is Kay Kay Menon | Manisha Koirala | Ranvir Shorey | Sonali Kulkarni. The directors is Rajatesh Nayyar. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2576450. The title is Besharam. The releaseYear is 2013. The releaseDate is 2-Oct-13. The genre is Comedy | Romance. The writers is Rajeev Barnwal | Abhinav Kashyap. The actors is Ranbir Kapoor | Pallavi Sharda | Rishi Kapoor | Neetu Singh. The directors is Abhinav Kashyap. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0920464. The title is Manorama Six Feet Under. The releaseYear is 2007. The releaseDate is 21-Sep-07. The genre is Crime | Drama | Mystery. The writers is Devika Bhagat (story) | Navdeep Singh (story) | Manoj Tapadia (dialogue) | Abhinav Kashyap (dialogue). The actors is Abhay Deol | Gul Panag | Raima Sen | Sarika. The directors is Navdeep Singh. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1578116. The title is Atithi Tum Kab Jaoge?. The releaseYear is 2010. The releaseDate is 5-Mar-10. The genre is Comedy | Drama. The writers is Robin Bhatt (screenplay) | Ashwani Dhir | Tushar Hiranandani (screenplay) | Amit Mishra (lyrics). The actors is Ajay Devgn | Konkona Sen Sharma | Paresh Rawal | Satish Kaushik. The directors is Ashwani Dhir. The sequel is 0.0.", "label": "4", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1391894. The title is Siddharth: The Prisoner. The releaseYear is 2009. The releaseDate is 27-Feb-09. The genre is Crime | Drama | Thriller. The writers is Anadi (dialogue) | Pryas Gupta (dialogue) | Pryas Gupta (story) | Hitesh Kewalya (dialogue). The actors is Rajat Kapoor | Sachin Nayak | Pradip Sagar | Pradeep Kabra. The directors is Pryas Gupta. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2343417. The title is Chhodo Kal Ki Baatein. The releaseYear is 2012. The releaseDate is 12-Apr-12. The genre is Drama. The writers is Pramod Joshi (dialogue) | Pramod Joshi (screenplay) | Raz Kazi (dialogue) | Raz Kazi (screenplay). The actors is Barkha Bisht | Balaji Iyer | Raghavendra Kadkol | Sachin Khedekar. The directors is Pramod Joshi. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1703958. The title is Ek Main Aur Ekk Tu. The releaseYear is 2012. The releaseDate is 10-Feb-12. The genre is Comedy | Drama | Romance. The writers is Shakun Batra | Ayesha DeVitre. The actors is Kareena Kapoor | Boman Irani | Imran Khan | Ratna Pathak. The directors is Shakun Batra. The sequel is 0.0.", "label": "4", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0377126. The title is Basti. The releaseYear is 2003. The releaseDate is 8-Aug-03. The genre is Action | Crime. The writers is unknown. The actors is Sadashiv Amrapurkar | Liyaqat Bari | Brij Gopal | Rajendra Gupta. The directors is unknown. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1562871. The title is Ra.One. The releaseYear is 2011. The releaseDate is 26-Oct-11. The genre is Action | Adventure | Sci-Fi. The writers is David Benullo | Kanika Dhillon (dialogue) | Kanika Dhillon (screenplay) | Niranjan Iyengar (dialogue) | Shah Rukh Khan (screenplay) | Mushtaq Sheikh (screenplay) | Anubhav Sinha (story). The actors is Arjun Rampal | Shah Rukh Khan | Kareena Kapoor | Shahana Goswami. The directors is Anubhav Sinha. The sequel is 0.0.", "label": "6", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt4010306. The title is Jigariyaa. The releaseYear is 2014. The releaseDate is 10-Oct-14. The genre is Drama. The writers is Vinod Bachchan | Apratim Khare | Raj Purohit. The actors is Deepak Chadha | Harshvardhan Deo | Sneha Deori | Vineeta Malik. The directors is Raj Purohit. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0495034. The title is Golmaal: Fun Unlimited. The releaseYear is 2006. The releaseDate is 14-Jul-06. The genre is Comedy. The writers is Neeraj Vora. The actors is Ajay Devgn | Arshad Warsi | Sharman Joshi | Tusshar Kapoor. The directors is Rohit Shetty. The sequel is 0.0.", "label": "6", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0347779. The title is Pinjar. The releaseYear is 2003. The releaseDate is unknown. The genre is Drama. The writers is Chandra Prakash Dwivedi (additional dialogue) | Chandra Prakash Dwivedi (screenplay) | Amrita Pritam (dialogue) | Amrita Pritam (novel) | Amrita Pritam (story). The actors is Urmila Matondkar | Manoj Bajpayee | Sanjay Suri | Sandali Sinha. The directors is Chandra Prakash Dwivedi. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0380337. The title is Ek Din 24 Ghante. The releaseYear is 2003. The releaseDate is 7-Nov-03. The genre is Thriller. The writers is unknown. The actors is Rahul Bose | Ahmed Chaudhary | Nandita Das | Vinit Kumar. The directors is Anant Balani. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3390572. The title is Haider. The releaseYear is 2014. The releaseDate is 2-Oct-14. The genre is Crime | Drama | Romance. The writers is William Shakespeare (based on the play \"Hamlet\" by) | Basharat Peer (screenplay) | Vishal Bhardwaj (screenplay) | Vishal Bhardwaj (dialogue). The actors is Tabu | Shahid Kapoor | Shraddha Kapoor | Kay Kay Menon. The directors is Vishal Bhardwaj. The sequel is 0.0.", "label": "4", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0338477. The title is Talaash: The Hunt Begins... The releaseYear is 2003. The releaseDate is 3-Jan-03. The genre is Action | Adventure | Crime. The writers is Suneel Darshan (story) | Robin Bhatt (screenplay) | K.K. Singh (dialogue) | Ravi Shankar Jaiswal (additional dialogue). The actors is Rakhee Gulzar | Akshay Kumar | Kareena Kapoor | Pooja Batra. The directors is Suneel Darshan. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0490210. The title is Sarkar Raj. The releaseYear is 2008. The releaseDate is 6-Jun-08. The genre is Action | Crime | Drama. The writers is Prashant Pandey. The actors is Amitabh Bachchan | Abhishek Bachchan | Aishwarya Rai Bachchan | Ravi Kale. The directors is Ram Gopal Varma. The sequel is 1.0.", "label": "3", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1242530. The title is What's Your Raashee?. The releaseYear is 2009. The releaseDate is 2-Oct-09. The genre is Comedy | Drama | Romance. The writers is Ashutosh Gowariker (screenplay) | Naushil Mehta (screenplay) | Amit Mistry (dialogue) | Madhu Rye (novel). The actors is Harman Baweja | Priyanka Chopra | Anjan Srivastav | Manju Singh. The directors is Ashutosh Gowariker. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2429640. The title is Murder 3. The releaseYear is 2013. The releaseDate is 15-Feb-13. The genre is Thriller. The writers is Mahesh Bhatt | Hatem Khraiche (original film \"La Cara Oculta\") | Amit Masurkar (additional screenplay). The actors is Randeep Hooda | Aditi Rao Hydari | Sara Loren | Rajesh Shringarpore. The directors is Vishesh Bhatt. The sequel is 1.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2574698. The title is Gunday. The releaseYear is 2014. The releaseDate is 14-Feb-14. The genre is Action | Crime | Drama. The writers is Ali Abbas Zafar | Sanjay Masoom (additional dialogue). The actors is Ranveer Singh | Arjun Kapoor | Priyanka Chopra | Irrfan Khan. The directors is Ali Abbas Zafar. The sequel is 0.0.", "label": "5", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0483239. The title is Bullet: Ek Dhamaka. The releaseYear is 2005. The releaseDate is 4-Feb-05. The genre is Action | Drama. The writers is Anand Raj Anand (lyrics) | Neha Bhasin (lyrics) | Salim Shahid (story) | Faiz Shaid (story). The actors is Benjamin Gilani | Natalya Gudkova | Iqbal Khan | Asseem Merchant. The directors is Irfan Khan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1221142. The title is Mumbai Cutting. The releaseYear is 2011. The releaseDate is 1-Mar-11. The genre is Drama. The writers is Jahnu Barua | Rahul Dholakia | Rituparno Ghosh | Anurag Kashyap | Sudhir Mishra | Kundan Shah | Gaurav Sinha. The actors is Raj Singh Arora | Abhisar Bose | Neetu Chandra | Master Chinmay. The directors is Jahnu Barua | Rahul Dholakia | Rituparno Ghosh | Shashanka Ghosh | Manish Jha | Anurag Kashyap | Sudhir Mishra | Ruchi Narain | Ayush Raina | Revathy | Kundan Shah. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0265452. The title is Officer. The releaseYear is 2001. The releaseDate is 14-Mar-01. The genre is Action | Crime | Drama. The writers is Naeem Sha (dialogue) | Naeem Sha (screenplay) | Naeem Sha (story). The actors is Sunil Shetty | Raveena Tandon | Danny Denzongpa | Sadashiv Amrapurkar. The directors is Naeem Sha. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1916728. The title is Shor in the City. The releaseYear is 2011. The releaseDate is 28-Apr-11. The genre is Crime | Drama. The writers is Krishna D.K. (story) | Raj Nidimoru (story) | Sita Menon (story) | Sita Menon (dialogue) | Chintan Gandhi (dialogue) | Sameer (lyrics) | Nishu (lyrics). The actors is Sendhil Ramamurthy | Tusshar Kapoor | Nikhil Dwivedi | Preeti Desai. The directors is Krishna D.K. | Raj Nidimoru. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0437238. The title is Hulchul. The releaseYear is 2004. The releaseDate is 26-Nov-04. The genre is Action | Comedy | Drama. The writers is K.P. Saxena (dialogue) | Siddique (story) | Neeraj Vora (screenplay). The actors is Akshaye Khanna | Kareena Kapoor | Sunil Shetty | Paresh Rawal. The directors is Priyadarshan. The sequel is 0.0.", "label": "6", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0348824. The title is Chalo Ishq Ladaaye. The releaseYear is 2002. The releaseDate is 27-Dec-02. The genre is Comedy | Romance. The writers is Imtiaz Patel | Yunus Sajawal. The actors is Govinda | Kader Khan | Rani Mukerji | Zohra Segal. The directors is Aziz Sejawal. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3302962. The title is Shaadi Ke Side Effects. The releaseYear is 2014. The releaseDate is 28-Feb-14. The genre is Comedy | Romance. The writers is Saket Chaudhary (screenplay) | Saket Chaudhary (story) | Zeenat Lakhani (screenplay) | Zeenat Lakhani (story) | Arshad Sayed (dialogue) | Arshad Sayed (screenplay). The actors is Farhan Akhtar | Vidya Balan | Vir Das | Ram Kapoor. The directors is Saket Chaudhary. The sequel is 1.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3021244. The title is Chaarfutiya Chhokare. The releaseYear is 2014. The releaseDate is 26-Sep-14. The genre is Drama | Thriller. The writers is Manish Harishankar. The actors is Soha Ali Khan | Zakir Hussain | Seema Biswas | Mukesh Tiwari. The directors is Manish Harishankar. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0435259. The title is Padmashree Laloo Prasad Yadav. The releaseYear is 2005. The releaseDate is 28-Jan-05. The genre is Comedy. The writers is Sanjay Pawar (dialogue) | Vinay | Yash. The actors is Sunil Shetty | Masumeh Makhija | Mahesh Manjrekar | Johnny Lever. The directors is Mahesh Manjrekar. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3645014. The title is The Xpose. The releaseYear is 2014. The releaseDate is 16-May-14. The genre is Thriller. The writers is Himesh Reshammiya (story) | Jainesh Ejardar (screenplay) | Himesh Reshammiya (screenplay) | Bunty Rathore (dialogue). The actors is Himesh Reshammiya | Yo Yo Honey Singh | Irrfan Khan | Zoya Afroz. The directors is Anant Mahadevan. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0396057. The title is Hum Pyar Tumhi Se Kar Baithe. The releaseYear is 2002. The releaseDate is 8-Nov-02. The genre is Musical | Romance. The writers is Mohan Singh Rathor (dialogue) | Mohan Singh Rathor (screenplay) | Mohan Singh Rathor (story). The actors is Jugal Hansraj | Tina Rana | Sachin Khedekar | Vishnu Sharma. The directors is Mohan Singh Rathor. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0495032. The title is Gangster. The releaseYear is 2006. The releaseDate is 28-Apr-06. The genre is Crime | Drama | Mystery. The writers is Mahesh Bhatt (story) | Girish Dhamija (dialogue) | Anurag Basu (screenplay). The actors is Kangana Ranaut | Shiney Ahuja | Emraan Hashmi | Gulshan Grover. The directors is Anurag Basu. The sequel is 0.0.", "label": "5", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2401719. The title is Prague. The releaseYear is 2013. The releaseDate is 27-Sep-13. The genre is Mystery | Romance | Thriller. The writers is Rohit Khaitan (conceived by) | Akshendra Mishra (additional screenplay) | Sumit Saxena (screenplay) | Ashish R. Shukla (screenplay) | Ashish R. Shukla (story) | Vijay Verma (additional screenplay). The actors is Chandan Roy Sanyal | Arfi Lamba | Kumar Mayank | Sonia Bindra. The directors is Ashish R. Shukla. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1105747. The title is Yuvvraaj. The releaseYear is 2008. The releaseDate is 21-Nov-08. The genre is Comedy | Drama | Romance. The writers is Sachin Bhowmick (screenplay) | Subhash Ghai (story) | Kamlesh Pandey (screenplay). The actors is Salman Khan | Anil Kapoor | Zayed Khan | Mithun Chakraborty. The directors is Subhash Ghai. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0375733. The title is Encounter: The Killing. The releaseYear is 2002. The releaseDate is 9-Aug-02. The genre is Crime | Drama. The writers is Ajay Phansekar. The actors is Naseeruddin Shah | Dilip Prabhavalkar | Tara Deshpande | Akash Khurana. The directors is Ajay Phansekar. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0367110. The title is Swades. The releaseYear is 2004. The releaseDate is 17-Dec-04. The genre is Drama. The writers is M.G. Sathya (story) | Ashutosh Gowariker (story) | Ashutosh Gowariker (screenplay) | Sameer Sharma (screenplay) | Lalit Marathe (screenplay) | Amin Hajee (screenplay) | Charlotte Whitby-Coles (screenplay) | Yashodeep Nigudkar (screenplay) | Ayan Mukherjee (screenplay) | K.P. Saxena (dialogue). The actors is Shah Rukh Khan | Gayatri Joshi | Kishori Balal | Smith Seth. The directors is Ashutosh Gowariker. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2344678. The title is Himmatwala. The releaseYear is 2013. The releaseDate is 29-Mar-13. The genre is Action | Comedy. The writers is K. Raghavendra Rao (original story) | Sajid Khan (story) | Sajid Khan (screenplay) | Farhad (screenplay) | Sajid (screenplay). The actors is Ajay Devgn | Tamannaah Bhatia | Mahesh Manjrekar | Paresh Rawal. The directors is Sajid Khan. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1797548. The title is Yeh Faasley. The releaseYear is 2011. The releaseDate is 4-Mar-11. The genre is Crime | Drama | Mystery. The writers is Arpita Chatterjee (story) | Sameer Kohli (story) | Rajen Makhijani (screenplay) | Rajen Makhijani (story) | Yogesh Mittal (dialogue) | Yogesh Mittal (screenplay) | Yogesh Mittal (story) | Atul Tiwari (dialogue) | Atul Tiwari (screenplay) | Atul Tiwari (story). The actors is Rachita Bhattacharya | Seema Biswas | Sudha Chandran | Sanjiv Chopra. The directors is Yogesh Mittal. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0449306. The title is Lucky: No Time for Love. The releaseYear is 2005. The releaseDate is 8-Apr-05. The genre is Musical | Drama | Romance. The writers is Radhika Rao | Vinay Sapru | Milap Zaveri (dialogue). The actors is Salman Khan | Sneha Ullal | Mithun Chakraborty | Kader Khan. The directors is Radhika Rao | Vinay Sapru. The sequel is 0.0.", "label": "3", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0324951. The title is 23rd March 1931: Shaheed. The releaseYear is 2002. The releaseDate is 7-Jun-02. The genre is Biography | Drama | History. The writers is Sutanu Gupta (screenplay) | Sanjay Masoom (dialogue). The actors is Bobby Deol | Sunny Deol | Amrita Singh | Rahul Dev. The directors is Guddu Dhanoa. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1185412. The title is Veer. The releaseYear is 2010. The releaseDate is 22-Jan-10. The genre is Action | Adventure | Drama. The writers is Shailesh Verma (screenplay) | Shaktimaan Talwar (screenplay) | Salman Khan (story). The actors is Salman Khan | Mithun Chakraborty | Jackie Shroff | Sohail Khan. The directors is Anil Sharma. The sequel is 0.0.", "label": "3", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0437182. The title is Family: Ties of Blood. The releaseYear is 2006. The releaseDate is 12-Jan-06. The genre is Action | Crime | Drama. The writers is Rajat Arora (screenwriter) | Tigmanshu Dhulia (dialogue) | Shridhar Raghavan (screenplay) | Ashok Rawat (script & story) | Rajkumar Santoshi (dialogue) | Rajkumar Santoshi (screenplay) | Shaktimaan Talwar (story). The actors is Amitabh Bachchan | Akshay Kumar | Bhoomika Chawla | Aryeman Ramsay. The directors is Rajkumar Santoshi. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1388424. The title is Three: Love Lies Betrayal. The releaseYear is 2009. The releaseDate is 4-Sep-09. The genre is Drama | Mystery | Thriller. The writers is Vikram Bhatt. The actors is Aashish Chaudhary | Akshay Kapoor | Nausheen Ali Sardar | Achint Kaur. The directors is Vishal Pandya. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0499041. The title is Kalyug. The releaseYear is 2005. The releaseDate is 9-Dec-05. The genre is Action | Crime | Drama. The writers is Jay Dixit (dialogue) | Anand Sivakumaran (screenplay) | Mohit Suri (story). The actors is Kunal Khemu | Deepal Shaw | Smiley Suri | Atul Parchure. The directors is Mohit Suri. The sequel is 0.0.", "label": "5", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3524410. The title is Yeh Hai Bakrapur. The releaseYear is 2014. The releaseDate is 9-May-14. The genre is Comedy | Drama. The writers is Azad Alam (additional screenplay & dialogue) | Janaki Vishwanathan (screenplay) | Janaki Vishwanathan. The actors is Asif Basra | Anshuman Jha | Yoshika Verma | Amit Sial. The directors is Janaki Vishwanathan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3257168. The title is Shorts. The releaseYear is 2013. The releaseDate is 12-Jul-13. The genre is Drama. The writers is unknown. The actors is Satyakam Anand | Aparajit Bhattacharjee | Richa Chadda | Aditi Khanna. The directors is Neeraj Ghaywan | Siddharth Gupt | Rohit Pandey | Anirban Roy | Shlok Sharma. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0346457. The title is The Rising: Ballad of Mangal Pandey. The releaseYear is 2005. The releaseDate is 12-Aug-05. The genre is Biography | Drama | History. The writers is Farrukh Dhondy (screenplay) | Ranjit Kapoor (Hindi dialogue). The actors is Aamir Khan | Rani Mukerji | Toby Stephens | Coral Beed. The directors is Ketan Mehta. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2998196. The title is Kuku Mathur Ki Jhand Ho Gayi. The releaseYear is 2014. The releaseDate is 30-May-14. The genre is Comedy | Romance. The writers is unknown. The actors is Siddharth Gupta | Simran Kaur Mundi | Pallavi Batra | Roopa Ganguly. The directors is Aman Sachdeva. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0278522. The title is Jodi No.1. The releaseYear is 2001. The releaseDate is 13-Apr-01. The genre is Comedy. The writers is Rumi Jaffery (dialogue) | Imtiaz Patel (screenplay) | Yunus Sajawal (screenplay). The actors is Sanjay Dutt | Govinda | Twinkle Khanna | Monica Bedi. The directors is David Dhawan. The sequel is 0.0.", "label": "6", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0415768. The title is Dus. The releaseYear is 2005. The releaseDate is 8-Jul-05. The genre is Action | Crime | Thriller. The writers is Anubhav Sinha (dialogue) | Vinay | Yash. The actors is Sanjay Dutt | Sunil Shetty | Abhishek Bachchan | Zayed Khan. The directors is Anubhav Sinha. The sequel is 0.0.", "label": "4", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0331851. The title is Armaan. The releaseYear is 2003. The releaseDate is 16-May-03. The genre is Drama | Family | Romance. The writers is Javed Akhtar (dialogue) | Javed Akhtar (screenplay) | Honey Irani (screenplay) | Honey Irani (story). The actors is Amitabh Bachchan | Anil Kapoor | Preity Zinta | Gracy Singh. The directors is Honey Irani. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0995752. The title is Tashan. The releaseYear is 2008. The releaseDate is 25-Apr-08. The genre is Action | Comedy | Crime. The writers is Vijay Krishna Acharya (story) | Vijay Krishna Acharya (screenplay) | Vijay Krishna Acharya (dialogue) | Piyush Mishra (lyrics) | Vishal Dadlani (lyrics) | Kausar Munir (lyrics). The actors is Akshay Kumar | Saif Ali Khan | Kareena Kapoor | Anil Kapoor. The directors is Vijay Krishna Acharya. The sequel is 0.0.", "label": "3", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1363363. The title is Chatur Singh Two Star. The releaseYear is 2011. The releaseDate is 19-Aug-11. The genre is Action | Adventure | Comedy. The writers is Rumi Jaffery (screenplay) | Sai Kabir (dialogue). The actors is Sanjay Dutt | Ameesha Patel | Anupam Kher | Satish Kaushik. The directors is Ajay Chandhok. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0426075. The title is Lakeer - Forbidden Lines. The releaseYear is 2004. The releaseDate is unknown. The genre is Action | Drama | Romance. The writers is Ahmed Khan (screenplay) | Shahab Khan (screenplay) | Mehboob (dialogue). The actors is Sunny Deol | Sunil Shetty | Sohail Khan | John Abraham. The directors is Ahmed Khan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1260689. The title is Summer 2007. The releaseYear is 2008. The releaseDate is 13-Jun-08. The genre is Crime | Drama | Thriller. The writers is Gourov Dasgupta (lyrics) | Bijesh Jayarajan (screenplay) | Bijesh Jayarajan (story) | Ujjaiyinee Roy (lyrics) | Ritesh Shah (dialogues) | Vibha Singh (lyrics). The actors is Ahraz Ahmed | Punit Aneja | Arjan Bajwa | Neetu Chandra. The directors is Sohail Tatari. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2988272. The title is Shuddh Desi Romance. The releaseYear is 2013. The releaseDate is 6-Sep-13. The genre is Comedy | Drama | Romance. The writers is Jaideep Sahni. The actors is Sushant Singh Rajput | Parineeti Chopra | Vaani Kapoor | Rishi Kapoor. The directors is Maneesh Sharma. The sequel is 0.0.", "label": "6", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1095038. The title is Victoria No. 203: Diamonds Are Forever. The releaseYear is 2007. The releaseDate is 31-Aug-07. The genre is Comedy | Crime | Mystery. The writers is Sanjeev Puri (dialogue) | Manoj Tyagi (adaptation). The actors is Anupam Kher | Om Puri | Jimmy Shergill | Soniya Mehra. The directors is Anant Mahadevan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1744641. The title is Ramayana: The Epic. The releaseYear is 2010. The releaseDate is 15-Oct-10. The genre is Animation. The writers is Chetan Desai (screenplay) | Riturraj Tripathii (dialogue) | Riturraj Tripathii (screenplay) | Riturraj Tripathii (story) | Riturraj Tripathii. The actors is Manoj Bajpayee | Juhi Chawla | Ashutosh Rana | Mukesh Rishi. The directors is Chetan Desai. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt2112124. The title is Chennai Express. The releaseYear is 2013. The releaseDate is 8-Aug-13. The genre is Action | Comedy | Romance. The writers is K. Subhash (story) | Yunus Sajawal (screenplay) | Robin Bhatt (additional screenplay) | Farhad (dialogue) | Sajid (dialogue). The actors is Deepika Padukone | Shah Rukh Khan | Satyaraj | Nikitin Dheer. The directors is Rohit Shetty. The sequel is 0.0.", "label": "8", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1132606. The title is Ugly Aur Pagli. The releaseYear is 2008. The releaseDate is 1-Aug-08. The genre is Comedy | Drama. The writers is Anil Pandey (story) | Amitabh Verma (lyrics) | Suparn Verma (additional screenplay & dialogue). The actors is Mallika Sherawat | Ranvir Shorey | Bharati Achrekar | Zeenat Aman. The directors is Sachin Kamlakar Khot. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1806740. The title is 9 Eleven. The releaseYear is 2011. The releaseDate is unknown. The genre is Thriller. The writers is Manan Katohora. The actors is Kashmira Shah | Devasish Ray | Jyoti Singh | Sonny Chatrath. The directors is Manan Katohora. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1629424. The title is Trump Card. The releaseYear is 2010. The releaseDate is 12-Mar-10. The genre is Action | Drama | Mystery. The writers is Arshad Khan (screenplay) | Yawer Rehman (screenplay) | Yawer Rehman (script). The actors is Vikrum Kumar | Haidar Ali | Urvashi Chaudhary | Mansi Dovhal. The directors is Arshad Khan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0448206. The title is Bunty Aur Babli. The releaseYear is 2005. The releaseDate is 27-May-05. The genre is Adventure | Comedy | Crime. The writers is Aditya Chopra (story) | Jaideep Sahni (screenplay) | Jaideep Sahni (dialogue). The actors is Amitabh Bachchan | Rani Mukerji | Abhishek Bachchan | Kiran Juneja. The directors is Shaad Ali. The sequel is 0.0.", "label": "7", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0378025. The title is Hawayein. The releaseYear is 2003. The releaseDate is 22-Aug-03. The genre is Drama | Romance. The writers is Ammtoje Mann (screenplay) | Harjit Singh (dialogue). The actors is Babbu Mann | Ammtoje Mann | Mahie Gill | Mukul Dev. The directors is Ammtoje Mann. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1433810. The title is Mumbai Diaries. The releaseYear is 2010. The releaseDate is 21-Jan-11. The genre is Drama. The writers is Kiran Rao. The actors is Prateik | Monica Dogra | Kriti Malhotra | Aamir Khan. The directors is Kiran Rao. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1170399. The title is C Kkompany. The releaseYear is 2008. The releaseDate is 29-Aug-08. The genre is Comedy | Drama. The writers is Shabbir Ahmed (lyrics) | Anand Raj Anand (lyrics) | Sachin Yardi. The actors is Tusshar Kapoor | Anupam Kher | Rajpal Yadav | Raima Sen. The directors is Sachin Yardi. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1706317. The title is Tezz. The releaseYear is 2012. The releaseDate is 26-Apr-12. The genre is Action | Drama. The writers is Robin Bhatt | Aditya Dhar (dialogue writer). The actors is Anil Kapoor | Ajay Devgn | Mohanlal | Kangana Ranaut. The directors is Priyadarshan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0306840. The title is Koi Mere Dil Se Poochhe. The releaseYear is 2002. The releaseDate is 11-Jan-02. The genre is Musical | Romance | Thriller. The writers is unknown. The actors is Jaya Bhaduri | Aftab Shivdasani | Sanjay Kapoor | Juliet Alburque. The directors is Vinay Shukla. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1809399. The title is Utt Pataang. The releaseYear is 2011. The releaseDate is 1-Feb-11. The genre is Comedy | Drama. The writers is Arun Kumar (lyrics) | Rohit Sharma (lyrics) | Saurabh Shukla (dialogues) | Saurabh Shukla (screenplay) | Srikanth Velagaleti (screenplay) | Srikanth Velagaleti (story). The actors is Vinay Pathak | Saurabh Shukla | Mahie Gill | Mona Singh. The directors is Srikanth Velagaleti. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0330217. The title is Dil Ka Rishta. The releaseYear is 2003. The releaseDate is 17-Jan-03. The genre is Romance. The writers is Shabbir Boxwala | Vrinda Rai (story) | Naeem Sha (dialogue). The actors is Arjun Rampal | Aishwarya Rai Bachchan | Priyanshu Chatterjee | Rakhee Gulzar. The directors is Naresh Malhotra. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1454461. The title is Ek: The Power of One. The releaseYear is 2009. The releaseDate is 27-Mar-09. The genre is Action | Drama | Thriller. The writers is Shabbir Ahmed (lyrics) | Sameer Arora (additional screenplay & dialogue) | Vivek Buddhakoti (additional screenplay & dialogue) | Mayur Puri (lyrics) | Pankaj Trivedi (story). The actors is Rana Jung Bahadur | Jaspal Bhatti | Preeti Bhutani | Bobby Deol. The directors is Sangeeth Sivan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1849718. The title is Agneepath. The releaseYear is 2012. The releaseDate is 26-Jan-12. The genre is Action | Crime | Drama. The writers is Ila Bedi Dutta (screenplay) | Karan Malhotra (screenplay) | Piyush Mishra (dialogue). The actors is Hrithik Roshan | Priyanka Chopra | Sanjay Dutt | Rishi Kapoor. The directors is Karan Malhotra. The sequel is 0.0.", "label": "7", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0382188. The title is Mumbai Matinee. The releaseYear is 2003. The releaseDate is 26-Sep-03. The genre is Romance | Comedy. The writers is Anant Balani. The actors is Rahul Bose | Perizaad Zorabian | Vijay Raaz | Saurabh Shukla. The directors is Anant Balani. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1188996. The title is My Name Is Khan. The releaseYear is 2010. The releaseDate is 12-Feb-10. The genre is Drama | Romance | Thriller. The writers is Shibani Bathija (story) | Shibani Bathija (dialogue) | Niranjan Iyengar (dialogue). The actors is Shah Rukh Khan | Kajol | Katie A. Keane | Kenton Duty. The directors is Karan Johar. The sequel is 0.0.", "label": "6", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt1918927. The title is Luv Ka the End. The releaseYear is 2011. The releaseDate is 6-May-11. The genre is Comedy | Drama. The writers is Amitabh Bhattacharya (lyrics) | Ashish Patil (story) | Ashish Patil | Roye Segal (screenplay) | Shenaz Treasury (screenplay) | Nihkil Vyas (dialogue) | Nikhil Vyas (dialogues). The actors is Riya Bamniyal | Bumpy | Sreejita De | Shraddha Kapoor. The directors is Bumpy. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0995827. The title is The Train: Some Lines Shoulder Never Be Crossed... The releaseYear is 2007. The releaseDate is unknown. The genre is Thriller. The writers is Hriday Lani (screenplay) | Sanjay Masoom (dialogue). The actors is Emraan Hashmi | Geeta Basra | Rajat Bedi | Anant Mahadevan. The directors is Hasnain Hyderabadwala | Raksha Mistry. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0341549. The title is Rishtey. The releaseYear is 2002. The releaseDate is 6-Dec-02. The genre is Family. The writers is Rajeev Kaul (screenplay) | Rajeev Kaul (story) | Tanveer Khan (dialogue) | Praful Parekh (screenplay) | Praful Parekh (story). The actors is Anil Kapoor | Karisma Kapoor | Shilpa Shetty | Kaivalya Chheda. The directors is Indra Kumar. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0331256. The title is Gunaah. The releaseYear is 2002. The releaseDate is 16-Oct-02. The genre is Crime | Drama. The writers is Mahesh Bhatt (screenplay) | Pranay Narayan (dialogue). The actors is Bipasha Basu | Dino Morea | Ashutosh Rana | Banjara. The directors is Amol Shetge. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt0872190. The title is Cash. The releaseYear is 2007. The releaseDate is 3-Aug-07. The genre is Action | Drama | Thriller. The writers is Vishal Dadlani (lyrics) | Panchhi Jalonvi (lyrics) | Anubhav Sinha (dialogues) | Vinay (story) | Yash (story). The actors is Ajay Devgn | Sunil Shetty | Zayed Khan | Ritesh Deshmukh. The directors is Anubhav Sinha. The sequel is 0.0.", "label": "2", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The imdbId is tt3169704. The title is Raqt. The releaseYear is 2013. The releaseDate is 27-Sep-13. The genre is Thriller. The writers is Adi Irani | Shiva Rindan | Ranjiv Verma. The actors is Shweta Bhardwaj | Gulshan Grover | Adi Irani | Farida Jalal. The directors is Adi Irani | Shiva Rindan. The sequel is 0.0.", "label": "1", "dataset": "bhanupratapbiswas-bollywood-actress-name-and-movie-list", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/bhanupratapbiswas-fashion-products/metadata.json

@@ -0,0 +1,26 @@
+{
+  "dataset": "bhanupratapbiswas-fashion-products",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Category",
+  "label_values": [
+    "Kids' Fashion",
+    "Men's Fashion",
+    "Women's Fashion"
+  ],
+  "num_labels": 3,
+  "train_samples": 898,
+  "test_samples": 102,
+  "train_label_distribution": {
+    "Men's Fashion": 289,
+    "Kids' Fashion": 315,
+    "Women's Fashion": 294
+  },
+  "test_label_distribution": {
+    "Kids' Fashion": 36,
+    "Women's Fashion": 33,
+    "Men's Fashion": 33
+  }
+}

classification/unipredict/bhanupratapbiswas-fashion-products/test.csv

@@ -0,0 +1,103 @@
+User ID,Product ID,Product Name,Brand,Price,Rating,Color,Size,Category
+3,279,Shoes,Adidas,11,2.19,Yellow,M,Kids' Fashion
+41,401,Jeans,Gucci,93,4.64,Green,S,Women's Fashion
+44,98,Sweater,Gucci,61,4.28,Black,XL,Kids' Fashion
+65,561,Shoes,Zara,96,3.97,Yellow,M,Men's Fashion
+69,486,Jeans,Adidas,69,1.2,Yellow,L,Women's Fashion
+46,142,Shoes,Nike,38,4.4,White,XL,Men's Fashion
+51,154,Dress,Zara,11,2.3,Blue,L,Men's Fashion
+41,352,Dress,H&M,71,1.5,Green,XL,Men's Fashion
+66,358,Shoes,Adidas,55,1.17,Blue,XL,Men's Fashion
+28,980,T-shirt,H&M,50,4.81,Blue,XL,Kids' Fashion
+76,712,Shoes,Gucci,100,4.61,Green,S,Kids' Fashion
+82,536,Dress,H&M,18,3.35,Yellow,S,Kids' Fashion
+65,124,Sweater,H&M,62,1.09,Black,L,Men's Fashion
+52,599,T-shirt,H&M,80,4.47,Green,S,Men's Fashion
+11,472,Jeans,Adidas,67,3.48,Green,L,Women's Fashion
+79,548,Sweater,Zara,65,1.49,Red,M,Kids' Fashion
+74,677,Shoes,Gucci,68,4.57,Blue,L,Women's Fashion
+35,882,Sweater,Gucci,57,2.41,Yellow,XL,Women's Fashion
+91,820,T-shirt,Nike,49,3.16,White,L,Women's Fashion
+23,374,Jeans,Nike,20,1.47,Black,S,Men's Fashion
+68,221,T-shirt,Adidas,49,2.86,Black,L,Men's Fashion
+11,554,Sweater,Adidas,51,4.1,Blue,L,Men's Fashion
+48,799,Shoes,Adidas,20,2.33,Yellow,XL,Men's Fashion
+37,948,Sweater,Gucci,83,3.55,Yellow,XL,Women's Fashion
+2,558,Jeans,Adidas,23,2.03,Red,XL,Women's Fashion
+66,412,Shoes,Nike,63,3.4,Black,XL,Kids' Fashion
+95,728,Dress,Zara,63,2.41,Yellow,S,Men's Fashion
+92,924,Sweater,Nike,80,3.7,Black,M,Men's Fashion
+4,973,Jeans,H&M,20,3.74,Red,L,Kids' Fashion
+59,355,Sweater,Nike,43,2.49,Red,XL,Kids' Fashion
+5,680,Dress,Nike,55,4.05,Red,XL,Women's Fashion
+29,517,T-shirt,Gucci,74,2.85,Blue,M,Men's Fashion
+83,248,Sweater,Zara,81,3.62,Red,M,Kids' Fashion
+84,19,Shoes,Gucci,54,3.28,White,M,Women's Fashion
+60,991,Shoes,Nike,25,4.97,Red,L,Kids' Fashion
+19,84,Sweater,Adidas,42,2.56,Red,L,Women's Fashion
+49,741,Shoes,Nike,81,3.39,Blue,XL,Women's Fashion
+37,16,Dress,Adidas,27,1.42,Blue,S,Women's Fashion
+7,326,Jeans,Zara,20,3.31,Green,L,Men's Fashion
+67,184,Sweater,Zara,67,3.45,Green,L,Women's Fashion
+33,188,Shoes,Nike,44,4.36,Green,XL,Men's Fashion
+59,837,Shoes,Nike,50,2.41,Black,M,Kids' Fashion
+21,555,T-shirt,Adidas,67,3.94,Blue,L,Women's Fashion
+55,359,Dress,Nike,68,4.45,Red,L,Kids' Fashion
+98,934,T-shirt,Nike,82,2.64,Green,XL,Women's Fashion
+45,489,Dress,Adidas,37,3.56,Black,M,Men's Fashion
+73,276,Jeans,Nike,43,1.39,Yellow,M,Men's Fashion
+56,871,Shoes,H&M,66,2.62,White,XL,Men's Fashion
+30,661,Jeans,Gucci,71,1.53,Blue,XL,Kids' Fashion
+61,697,T-shirt,Nike,99,4.8,Green,L,Women's Fashion
+1,879,T-shirt,Nike,67,4.3,Red,S,Kids' Fashion
+99,679,Jeans,Adidas,19,1.18,Yellow,L,Kids' Fashion
+35,687,Sweater,H&M,74,4.17,Blue,L,Kids' Fashion
+33,256,Jeans,Gucci,86,2.06,Blue,L,Kids' Fashion
+75,146,Shoes,Adidas,53,3.47,Yellow,M,Women's Fashion
+82,649,T-shirt,Gucci,76,2.44,Red,S,Women's Fashion
+19,617,Jeans,Adidas,77,1.7,Black,XL,Kids' Fashion
+53,464,Shoes,Zara,74,1.36,Green,L,Men's Fashion
+66,228,Shoes,H&M,15,1.97,Green,S,Men's Fashion
+65,371,Dress,Nike,33,1.08,White,XL,Women's Fashion
+20,458,T-shirt,Gucci,18,1.18,Black,L,Kids' Fashion
+95,509,Dress,Gucci,28,1.96,Blue,L,Men's Fashion
+6,585,Jeans,Adidas,68,3.59,Yellow,M,Women's Fashion
+13,746,Shoes,Gucci,21,1.25,Black,S,Kids' Fashion
+40,607,T-shirt,Nike,55,1.18,Yellow,S,Women's Fashion
+63,643,T-shirt,Zara,42,4.63,Black,L,Kids' Fashion
+97,720,Shoes,Adidas,27,2.57,Black,S,Men's Fashion
+71,589,T-shirt,Adidas,99,1.02,Red,L,Women's Fashion
+39,888,Sweater,Gucci,73,4.95,Blue,S,Kids' Fashion
+91,43,T-shirt,Adidas,39,3.6,White,M,Men's Fashion
+18,446,Shoes,Adidas,91,1.13,Black,L,Kids' Fashion
+40,130,Shoes,Nike,81,1.02,Green,M,Women's Fashion
+33,664,Shoes,H&M,28,4.46,White,L,Kids' Fashion
+74,875,Jeans,Adidas,52,3.28,Black,L,Kids' Fashion
+6,559,Shoes,Gucci,10,4.23,Red,XL,Women's Fashion
+11,660,Dress,Adidas,41,2.69,Blue,S,Kids' Fashion
+50,454,Shoes,Zara,47,3.67,White,L,Kids' Fashion
+39,460,Jeans,H&M,94,2.58,Black,L,Kids' Fashion
+18,829,Dress,Adidas,19,4.26,Black,XL,Men's Fashion
+88,847,T-shirt,Gucci,10,1.37,Green,S,Women's Fashion
+93,232,Jeans,Zara,49,1.41,Red,S,Men's Fashion
+65,735,Sweater,H&M,28,3.9,Yellow,L,Men's Fashion
+88,143,Shoes,Gucci,81,4.92,White,L,Women's Fashion
+51,51,Jeans,Adidas,67,3.84,Yellow,XL,Women's Fashion
+99,834,Shoes,H&M,98,3.78,Yellow,S,Women's Fashion
+3,300,Shoes,Adidas,57,4.92,White,S,Kids' Fashion
+22,362,Jeans,Adidas,15,1.28,Red,S,Men's Fashion
+84,202,Sweater,Nike,40,3.5,Green,S,Men's Fashion
+98,893,Shoes,H&M,76,2.07,Red,S,Kids' Fashion
+98,528,Shoes,Zara,33,2.71,Black,M,Men's Fashion
+40,468,Dress,Zara,38,1.31,White,S,Women's Fashion
+38,290,Sweater,Zara,13,4.88,White,S,Men's Fashion
+68,620,Sweater,Nike,37,3.46,White,XL,Kids' Fashion
+3,796,Jeans,H&M,86,1.86,Black,M,Women's Fashion
+10,238,Shoes,Adidas,47,2.15,White,XL,Kids' Fashion
+27,514,Dress,Adidas,47,3.43,White,M,Kids' Fashion
+25,3,Dress,Adidas,44,3.34,Yellow,XL,Women's Fashion
+53,595,T-shirt,Gucci,96,3.63,Blue,S,Women's Fashion
+26,540,Jeans,H&M,80,1.17,Yellow,M,Kids' Fashion
+25,881,Shoes,H&M,20,2.76,Red,XL,Men's Fashion
+38,190,Sweater,Zara,32,4.03,Yellow,XL,Kids' Fashion
+24,582,Dress,Zara,70,2.24,White,M,Men's Fashion

classification/unipredict/bhanupratapbiswas-fashion-products/test.jsonl

@@ -0,0 +1,102 @@
+{"text": "The User ID is 3. The Product ID is 279. The Product Name is Shoes. The Brand is Adidas. The Price is 11. The Rating is 2.19. The Color is Yellow. The Size is M.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 41. The Product ID is 401. The Product Name is Jeans. The Brand is Gucci. The Price is 93. The Rating is 4.64. The Color is Green. The Size is S.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 44. The Product ID is 98. The Product Name is Sweater. The Brand is Gucci. The Price is 61. The Rating is 4.28. The Color is Black. The Size is XL.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 65. The Product ID is 561. The Product Name is Shoes. The Brand is Zara. The Price is 96. The Rating is 3.97. The Color is Yellow. The Size is M.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 69. The Product ID is 486. The Product Name is Jeans. The Brand is Adidas. The Price is 69. The Rating is 1.2. The Color is Yellow. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 46. The Product ID is 142. The Product Name is Shoes. The Brand is Nike. The Price is 38. The Rating is 4.4. The Color is White. The Size is XL.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 51. The Product ID is 154. The Product Name is Dress. The Brand is Zara. The Price is 11. The Rating is 2.3. The Color is Blue. The Size is L.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 41. The Product ID is 352. The Product Name is Dress. The Brand is H&M. The Price is 71. The Rating is 1.5. The Color is Green. The Size is XL.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 66. The Product ID is 358. The Product Name is Shoes. The Brand is Adidas. The Price is 55. The Rating is 1.17. The Color is Blue. The Size is XL.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 28. The Product ID is 980. The Product Name is T-shirt. The Brand is H&M. The Price is 50. The Rating is 4.81. The Color is Blue. The Size is XL.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 76. The Product ID is 712. The Product Name is Shoes. The Brand is Gucci. The Price is 100. The Rating is 4.61. The Color is Green. The Size is S.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 82. The Product ID is 536. The Product Name is Dress. The Brand is H&M. The Price is 18. The Rating is 3.35. The Color is Yellow. The Size is S.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 65. The Product ID is 124. The Product Name is Sweater. The Brand is H&M. The Price is 62. The Rating is 1.09. The Color is Black. The Size is L.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 52. The Product ID is 599. The Product Name is T-shirt. The Brand is H&M. The Price is 80. The Rating is 4.47. The Color is Green. The Size is S.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 11. The Product ID is 472. The Product Name is Jeans. The Brand is Adidas. The Price is 67. The Rating is 3.48. The Color is Green. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 79. The Product ID is 548. The Product Name is Sweater. The Brand is Zara. The Price is 65. The Rating is 1.49. The Color is Red. The Size is M.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 74. The Product ID is 677. The Product Name is Shoes. The Brand is Gucci. The Price is 68. The Rating is 4.57. The Color is Blue. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 35. The Product ID is 882. The Product Name is Sweater. The Brand is Gucci. The Price is 57. The Rating is 2.41. The Color is Yellow. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 91. The Product ID is 820. The Product Name is T-shirt. The Brand is Nike. The Price is 49. The Rating is 3.16. The Color is White. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 23. The Product ID is 374. The Product Name is Jeans. The Brand is Nike. The Price is 20. The Rating is 1.47. The Color is Black. The Size is S.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 68. The Product ID is 221. The Product Name is T-shirt. The Brand is Adidas. The Price is 49. The Rating is 2.86. The Color is Black. The Size is L.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 11. The Product ID is 554. The Product Name is Sweater. The Brand is Adidas. The Price is 51. The Rating is 4.1. The Color is Blue. The Size is L.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 48. The Product ID is 799. The Product Name is Shoes. The Brand is Adidas. The Price is 20. The Rating is 2.33. The Color is Yellow. The Size is XL.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 37. The Product ID is 948. The Product Name is Sweater. The Brand is Gucci. The Price is 83. The Rating is 3.55. The Color is Yellow. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 2. The Product ID is 558. The Product Name is Jeans. The Brand is Adidas. The Price is 23. The Rating is 2.03. The Color is Red. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 66. The Product ID is 412. The Product Name is Shoes. The Brand is Nike. The Price is 63. The Rating is 3.4. The Color is Black. The Size is XL.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 95. The Product ID is 728. The Product Name is Dress. The Brand is Zara. The Price is 63. The Rating is 2.41. The Color is Yellow. The Size is S.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 92. The Product ID is 924. The Product Name is Sweater. The Brand is Nike. The Price is 80. The Rating is 3.7. The Color is Black. The Size is M.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 4. The Product ID is 973. The Product Name is Jeans. The Brand is H&M. The Price is 20. The Rating is 3.74. The Color is Red. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 59. The Product ID is 355. The Product Name is Sweater. The Brand is Nike. The Price is 43. The Rating is 2.49. The Color is Red. The Size is XL.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 5. The Product ID is 680. The Product Name is Dress. The Brand is Nike. The Price is 55. The Rating is 4.05. The Color is Red. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 29. The Product ID is 517. The Product Name is T-shirt. The Brand is Gucci. The Price is 74. The Rating is 2.85. The Color is Blue. The Size is M.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 83. The Product ID is 248. The Product Name is Sweater. The Brand is Zara. The Price is 81. The Rating is 3.62. The Color is Red. The Size is M.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 84. The Product ID is 19. The Product Name is Shoes. The Brand is Gucci. The Price is 54. The Rating is 3.28. The Color is White. The Size is M.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 60. The Product ID is 991. The Product Name is Shoes. The Brand is Nike. The Price is 25. The Rating is 4.97. The Color is Red. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 19. The Product ID is 84. The Product Name is Sweater. The Brand is Adidas. The Price is 42. The Rating is 2.56. The Color is Red. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 49. The Product ID is 741. The Product Name is Shoes. The Brand is Nike. The Price is 81. The Rating is 3.39. The Color is Blue. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 37. The Product ID is 16. The Product Name is Dress. The Brand is Adidas. The Price is 27. The Rating is 1.42. The Color is Blue. The Size is S.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 7. The Product ID is 326. The Product Name is Jeans. The Brand is Zara. The Price is 20. The Rating is 3.31. The Color is Green. The Size is L.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 67. The Product ID is 184. The Product Name is Sweater. The Brand is Zara. The Price is 67. The Rating is 3.45. The Color is Green. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 33. The Product ID is 188. The Product Name is Shoes. The Brand is Nike. The Price is 44. The Rating is 4.36. The Color is Green. The Size is XL.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 59. The Product ID is 837. The Product Name is Shoes. The Brand is Nike. The Price is 50. The Rating is 2.41. The Color is Black. The Size is M.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 21. The Product ID is 555. The Product Name is T-shirt. The Brand is Adidas. The Price is 67. The Rating is 3.94. The Color is Blue. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 55. The Product ID is 359. The Product Name is Dress. The Brand is Nike. The Price is 68. The Rating is 4.45. The Color is Red. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 98. The Product ID is 934. The Product Name is T-shirt. The Brand is Nike. The Price is 82. The Rating is 2.64. The Color is Green. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 45. The Product ID is 489. The Product Name is Dress. The Brand is Adidas. The Price is 37. The Rating is 3.56. The Color is Black. The Size is M.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 73. The Product ID is 276. The Product Name is Jeans. The Brand is Nike. The Price is 43. The Rating is 1.39. The Color is Yellow. The Size is M.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 56. The Product ID is 871. The Product Name is Shoes. The Brand is H&M. The Price is 66. The Rating is 2.62. The Color is White. The Size is XL.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 30. The Product ID is 661. The Product Name is Jeans. The Brand is Gucci. The Price is 71. The Rating is 1.53. The Color is Blue. The Size is XL.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 61. The Product ID is 697. The Product Name is T-shirt. The Brand is Nike. The Price is 99. The Rating is 4.8. The Color is Green. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 1. The Product ID is 879. The Product Name is T-shirt. The Brand is Nike. The Price is 67. The Rating is 4.3. The Color is Red. The Size is S.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 99. The Product ID is 679. The Product Name is Jeans. The Brand is Adidas. The Price is 19. The Rating is 1.18. The Color is Yellow. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 35. The Product ID is 687. The Product Name is Sweater. The Brand is H&M. The Price is 74. The Rating is 4.17. The Color is Blue. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 33. The Product ID is 256. The Product Name is Jeans. The Brand is Gucci. The Price is 86. The Rating is 2.06. The Color is Blue. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 75. The Product ID is 146. The Product Name is Shoes. The Brand is Adidas. The Price is 53. The Rating is 3.47. The Color is Yellow. The Size is M.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 82. The Product ID is 649. The Product Name is T-shirt. The Brand is Gucci. The Price is 76. The Rating is 2.44. The Color is Red. The Size is S.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 19. The Product ID is 617. The Product Name is Jeans. The Brand is Adidas. The Price is 77. The Rating is 1.7. The Color is Black. The Size is XL.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 53. The Product ID is 464. The Product Name is Shoes. The Brand is Zara. The Price is 74. The Rating is 1.36. The Color is Green. The Size is L.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 66. The Product ID is 228. The Product Name is Shoes. The Brand is H&M. The Price is 15. The Rating is 1.97. The Color is Green. The Size is S.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 65. The Product ID is 371. The Product Name is Dress. The Brand is Nike. The Price is 33. The Rating is 1.08. The Color is White. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 20. The Product ID is 458. The Product Name is T-shirt. The Brand is Gucci. The Price is 18. The Rating is 1.18. The Color is Black. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 95. The Product ID is 509. The Product Name is Dress. The Brand is Gucci. The Price is 28. The Rating is 1.96. The Color is Blue. The Size is L.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 6. The Product ID is 585. The Product Name is Jeans. The Brand is Adidas. The Price is 68. The Rating is 3.59. The Color is Yellow. The Size is M.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 13. The Product ID is 746. The Product Name is Shoes. The Brand is Gucci. The Price is 21. The Rating is 1.25. The Color is Black. The Size is S.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 40. The Product ID is 607. The Product Name is T-shirt. The Brand is Nike. The Price is 55. The Rating is 1.18. The Color is Yellow. The Size is S.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 63. The Product ID is 643. The Product Name is T-shirt. The Brand is Zara. The Price is 42. The Rating is 4.63. The Color is Black. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 97. The Product ID is 720. The Product Name is Shoes. The Brand is Adidas. The Price is 27. The Rating is 2.57. The Color is Black. The Size is S.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 71. The Product ID is 589. The Product Name is T-shirt. The Brand is Adidas. The Price is 99. The Rating is 1.02. The Color is Red. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 39. The Product ID is 888. The Product Name is Sweater. The Brand is Gucci. The Price is 73. The Rating is 4.95. The Color is Blue. The Size is S.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 91. The Product ID is 43. The Product Name is T-shirt. The Brand is Adidas. The Price is 39. The Rating is 3.6. The Color is White. The Size is M.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 18. The Product ID is 446. The Product Name is Shoes. The Brand is Adidas. The Price is 91. The Rating is 1.13. The Color is Black. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 40. The Product ID is 130. The Product Name is Shoes. The Brand is Nike. The Price is 81. The Rating is 1.02. The Color is Green. The Size is M.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 33. The Product ID is 664. The Product Name is Shoes. The Brand is H&M. The Price is 28. The Rating is 4.46. The Color is White. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 74. The Product ID is 875. The Product Name is Jeans. The Brand is Adidas. The Price is 52. The Rating is 3.28. The Color is Black. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 6. The Product ID is 559. The Product Name is Shoes. The Brand is Gucci. The Price is 10. The Rating is 4.23. The Color is Red. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 11. The Product ID is 660. The Product Name is Dress. The Brand is Adidas. The Price is 41. The Rating is 2.69. The Color is Blue. The Size is S.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 50. The Product ID is 454. The Product Name is Shoes. The Brand is Zara. The Price is 47. The Rating is 3.67. The Color is White. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 39. The Product ID is 460. The Product Name is Jeans. The Brand is H&M. The Price is 94. The Rating is 2.58. The Color is Black. The Size is L.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 18. The Product ID is 829. The Product Name is Dress. The Brand is Adidas. The Price is 19. The Rating is 4.26. The Color is Black. The Size is XL.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 88. The Product ID is 847. The Product Name is T-shirt. The Brand is Gucci. The Price is 10. The Rating is 1.37. The Color is Green. The Size is S.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 93. The Product ID is 232. The Product Name is Jeans. The Brand is Zara. The Price is 49. The Rating is 1.41. The Color is Red. The Size is S.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 65. The Product ID is 735. The Product Name is Sweater. The Brand is H&M. The Price is 28. The Rating is 3.9. The Color is Yellow. The Size is L.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 88. The Product ID is 143. The Product Name is Shoes. The Brand is Gucci. The Price is 81. The Rating is 4.92. The Color is White. The Size is L.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 51. The Product ID is 51. The Product Name is Jeans. The Brand is Adidas. The Price is 67. The Rating is 3.84. The Color is Yellow. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 99. The Product ID is 834. The Product Name is Shoes. The Brand is H&M. The Price is 98. The Rating is 3.78. The Color is Yellow. The Size is S.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 3. The Product ID is 300. The Product Name is Shoes. The Brand is Adidas. The Price is 57. The Rating is 4.92. The Color is White. The Size is S.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 22. The Product ID is 362. The Product Name is Jeans. The Brand is Adidas. The Price is 15. The Rating is 1.28. The Color is Red. The Size is S.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 84. The Product ID is 202. The Product Name is Sweater. The Brand is Nike. The Price is 40. The Rating is 3.5. The Color is Green. The Size is S.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 98. The Product ID is 893. The Product Name is Shoes. The Brand is H&M. The Price is 76. The Rating is 2.07. The Color is Red. The Size is S.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 98. The Product ID is 528. The Product Name is Shoes. The Brand is Zara. The Price is 33. The Rating is 2.71. The Color is Black. The Size is M.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 40. The Product ID is 468. The Product Name is Dress. The Brand is Zara. The Price is 38. The Rating is 1.31. The Color is White. The Size is S.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 38. The Product ID is 290. The Product Name is Sweater. The Brand is Zara. The Price is 13. The Rating is 4.88. The Color is White. The Size is S.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 68. The Product ID is 620. The Product Name is Sweater. The Brand is Nike. The Price is 37. The Rating is 3.46. The Color is White. The Size is XL.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 3. The Product ID is 796. The Product Name is Jeans. The Brand is H&M. The Price is 86. The Rating is 1.86. The Color is Black. The Size is M.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 10. The Product ID is 238. The Product Name is Shoes. The Brand is Adidas. The Price is 47. The Rating is 2.15. The Color is White. The Size is XL.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 27. The Product ID is 514. The Product Name is Dress. The Brand is Adidas. The Price is 47. The Rating is 3.43. The Color is White. The Size is M.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 25. The Product ID is 3. The Product Name is Dress. The Brand is Adidas. The Price is 44. The Rating is 3.34. The Color is Yellow. The Size is XL.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 53. The Product ID is 595. The Product Name is T-shirt. The Brand is Gucci. The Price is 96. The Rating is 3.63. The Color is Blue. The Size is S.", "label": "Women's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 26. The Product ID is 540. The Product Name is Jeans. The Brand is H&M. The Price is 80. The Rating is 1.17. The Color is Yellow. The Size is M.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 25. The Product ID is 881. The Product Name is Shoes. The Brand is H&M. The Price is 20. The Rating is 2.76. The Color is Red. The Size is XL.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 38. The Product ID is 190. The Product Name is Sweater. The Brand is Zara. The Price is 32. The Rating is 4.03. The Color is Yellow. The Size is XL.", "label": "Kids' Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 24. The Product ID is 582. The Product Name is Dress. The Brand is Zara. The Price is 70. The Rating is 2.24. The Color is White. The Size is M.", "label": "Men's Fashion", "dataset": "bhanupratapbiswas-fashion-products", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/bhanupratapbiswas-ipl-dataset-2008-2016/metadata.json

@@ -0,0 +1,59 @@
+{
+  "dataset": "bhanupratapbiswas-ipl-dataset-2008-2016",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "winner",
+  "label_values": [
+    "Kings XI Punjab",
+    "Mumbai Indians",
+    "Kolkata Knight Riders",
+    "nan",
+    "Chennai Super Kings",
+    "Rising Pune Supergiants",
+    "Rajasthan Royals",
+    "Pune Warriors",
+    "Sunrisers Hyderabad",
+    "Deccan Chargers",
+    "Gujarat Lions",
+    "Delhi Daredevils",
+    "Royal Challengers Bangalore",
+    "Kochi Tuskers Kerala"
+  ],
+  "num_labels": 14,
+  "train_samples": 514,
+  "test_samples": 63,
+  "train_label_distribution": {
+    "Kolkata Knight Riders": 61,
+    "Chennai Super Kings": 71,
+    "Sunrisers Hyderabad": 30,
+    "Kings XI Punjab": 56,
+    "Royal Challengers Bangalore": 63,
+    "Pune Warriors": 10,
+    "Delhi Daredevils": 50,
+    "Mumbai Indians": 72,
+    "Kochi Tuskers Kerala": 5,
+    "Rajasthan Royals": 56,
+    "Deccan Chargers": 26,
+    "Gujarat Lions": 8,
+    "Rising Pune Supergiants": 4,
+    "nan": 2
+  },
+  "test_label_distribution": {
+    "Rising Pune Supergiants": 1,
+    "Rajasthan Royals": 7,
+    "Royal Challengers Bangalore": 7,
+    "Delhi Daredevils": 6,
+    "Sunrisers Hyderabad": 4,
+    "Chennai Super Kings": 8,
+    "Mumbai Indians": 8,
+    "Kings XI Punjab": 7,
+    "Kolkata Knight Riders": 7,
+    "Gujarat Lions": 1,
+    "Pune Warriors": 2,
+    "Deccan Chargers": 3,
+    "Kochi Tuskers Kerala": 1,
+    "nan": 1
+  }
+}

classification/unipredict/bhanupratapbiswas-ipl-dataset-2008-2016/train.csv

@@ -0,0 +1,515 @@
+season,city,team1,team2,toss_winner,toss_decision,result,dl_applied,win_by_runs,win_by_wickets,player_of_match,venue,umpire1,umpire2,umpire3,winner
+2012,Pune,Kolkata Knight Riders,Pune Warriors,Kolkata Knight Riders,bat,normal,0,34,0,Shakib Al Hasan,Subrata Roy Sahara Stadium,S Asnani,BR Doctrove,,Kolkata Knight Riders
+2015,Chennai,Chennai Super Kings,Royal Challengers Bangalore,Chennai Super Kings,bat,normal,0,24,0,SK Raina,"MA Chidambaram Stadium, Chepauk",C Shamshuddin,K Srinath,,Chennai Super Kings
+2013,Hyderabad,Sunrisers Hyderabad,Pune Warriors,Pune Warriors,field,normal,0,22,0,A Mishra,"Rajiv Gandhi International Stadium, Uppal",S Ravi,SJA Taufel,,Sunrisers Hyderabad
+2008,Chandigarh,Deccan Chargers,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,6,SE Marsh,"Punjab Cricket Association Stadium, Mohali",Asad Rauf,SJ Davis,,Kings XI Punjab
+2016,Bangalore,Royal Challengers Bangalore,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,45,0,AB de Villiers,M Chinnaswamy Stadium,HDPK Dharmasena,VK Sharma,,Royal Challengers Bangalore
+2012,Chennai,Delhi Daredevils,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,9,BW Hilfenhaus,"MA Chidambaram Stadium, Chepauk",S Das,BR Doctrove,,Chennai Super Kings
+2013,Kolkata,Delhi Daredevils,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,6,SP Narine,Eden Gardens,S Ravi,SJA Taufel,,Kolkata Knight Riders
+2011,Mumbai,Kochi Tuskers Kerala,Pune Warriors,Kochi Tuskers Kerala,bat,normal,0,0,4,MD Mishra,Dr DY Patil Sports Academy,S Asnani,PR Reiffel,,Pune Warriors
+2015,Delhi,Delhi Daredevils,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,10,VR Aaron,Feroz Shah Kotla,M Erasmus,S Ravi,,Royal Challengers Bangalore
+2013,Chennai,Chennai Super Kings,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,14,0,MEK Hussey,"MA Chidambaram Stadium, Chepauk",Aleem Dar,SJA Taufel,,Chennai Super Kings
+2013,Chennai,Chennai Super Kings,Kings XI Punjab,Chennai Super Kings,bat,normal,0,15,0,SK Raina,"MA Chidambaram Stadium, Chepauk",M Erasmus,VA Kulkarni,,Chennai Super Kings
+2012,Kolkata,Kings XI Punjab,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,2,0,SP Narine,Eden Gardens,Asad Rauf,S Asnani,,Kings XI Punjab
+2009,Bloemfontein,Delhi Daredevils,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,6,B Lee,OUTsurance Oval,HDPK Dharmasena,IL Howell,,Kings XI Punjab
+2009,Bloemfontein,Delhi Daredevils,Rajasthan Royals,Delhi Daredevils,bat,normal,0,14,0,AB de Villiers,OUTsurance Oval,SS Hazare,IL Howell,,Delhi Daredevils
+2012,Jaipur,Rajasthan Royals,Mumbai Indians,Rajasthan Royals,bat,normal,0,0,10,DR Smith,Sawai Mansingh Stadium,HDPK Dharmasena,C Shamshuddin,,Mumbai Indians
+2013,Chandigarh,Kings XI Punjab,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,4,0,MS Gony,"Punjab Cricket Association Stadium, Mohali",CK Nandan,SJA Taufel,,Kings XI Punjab
+2009,Centurion,Chennai Super Kings,Kings XI Punjab,Chennai Super Kings,bat,normal,1,12,0,ML Hayden,SuperSport Park,DJ Harper,TH Wijewardene,,Chennai Super Kings
+2011,Chandigarh,Chennai Super Kings,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,6,PC Valthaty,"Punjab Cricket Association Stadium, Mohali",Asad Rauf,SL Shastri,,Kings XI Punjab
+2013,Chandigarh,Kings XI Punjab,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,10,MEK Hussey,"Punjab Cricket Association Stadium, Mohali",Aleem Dar,C Shamshuddin,,Chennai Super Kings
+2013,Hyderabad,Royal Challengers Bangalore,Sunrisers Hyderabad,Royal Challengers Bangalore,bat,tie,0,0,0,GH Vihari,"Rajiv Gandhi International Stadium, Uppal",AK Chowdhary,S Ravi,,Sunrisers Hyderabad
+2011,Kolkata,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,9,CH Gayle,Eden Gardens,SS Hazare,RB Tiffin,,Royal Challengers Bangalore
+2011,Kochi,Kochi Tuskers Kerala,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,17,0,BJ Hodge,Nehru Stadium,S Ravi,RJ Tucker,,Kochi Tuskers Kerala
+2016,Hyderabad,Kings XI Punjab,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,0,5,Mustafizur Rahman,"Rajiv Gandhi International Stadium, Uppal",AK Chaudhary,CK Nandan,,Sunrisers Hyderabad
+2015,Mumbai,Mumbai Indians,Sunrisers Hyderabad,Mumbai Indians,bat,normal,0,20,0,SL Malinga,Wankhede Stadium,HDPK Dharmasena,CB Gaffaney,,Mumbai Indians
+2012,Chennai,Chennai Super Kings,Deccan Chargers,Chennai Super Kings,bat,normal,0,10,0,SK Raina,"MA Chidambaram Stadium, Chepauk",HDPK Dharmasena,BNJ Oxenford,,Chennai Super Kings
+2010,Chandigarh,Rajasthan Royals,Kings XI Punjab,Kings XI Punjab,field,normal,0,31,0,AC Voges,"Punjab Cricket Association Stadium, Mohali",BR Doctrove,SK Tarapore,,Rajasthan Royals
+2011,Jaipur,Chennai Super Kings,Rajasthan Royals,Rajasthan Royals,field,normal,0,63,0,M Vijay,Sawai Mansingh Stadium,K Hariharan,SJA Taufel,,Chennai Super Kings
+2014,Mumbai,Mumbai Indians,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,7,SK Raina,Brabourne Stadium,VA Kulkarni,BNJ Oxenford,,Chennai Super Kings
+2013,Mumbai,Mumbai Indians,Royal Challengers Bangalore,Mumbai Indians,bat,normal,0,58,0,DR Smith,Wankhede Stadium,Asad Rauf,S Asnani,,Mumbai Indians
+2015,Kolkata,Mumbai Indians,Chennai Super Kings,Chennai Super Kings,field,normal,0,41,0,RG Sharma,Eden Gardens,HDPK Dharmasena,RK Illingworth,,Mumbai Indians
+2015,Visakhapatnam,Delhi Daredevils,Sunrisers Hyderabad,Delhi Daredevils,bat,normal,0,4,0,JP Duminy,Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium,PG Pathak,S Ravi,,Delhi Daredevils
+2015,Mumbai,Royal Challengers Bangalore,Mumbai Indians,Royal Challengers Bangalore,bat,normal,0,39,0,AB de Villiers,Wankhede Stadium,JD Cloete,C Shamshuddin,,Royal Challengers Bangalore
+2008,Chandigarh,Chennai Super Kings,Kings XI Punjab,Chennai Super Kings,bat,normal,0,33,0,MEK Hussey,"Punjab Cricket Association Stadium, Mohali",MR Benson,SL Shastri,,Chennai Super Kings
+2015,Delhi,Delhi Daredevils,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,6,UT Yadav,Feroz Shah Kotla,SD Fry,CB Gaffaney,,Kolkata Knight Riders
+2008,Mumbai,Mumbai Indians,Royal Challengers Bangalore,Mumbai Indians,bat,normal,0,0,5,MV Boucher,Wankhede Stadium,SJ Davis,DJ Harper,,Royal Challengers Bangalore
+2008,Chennai,Chennai Super Kings,Delhi Daredevils,Chennai Super Kings,bat,normal,0,0,8,V Sehwag,"MA Chidambaram Stadium, Chepauk",BF Bowden,K Hariharan,,Delhi Daredevils
+2013,Hyderabad,Delhi Daredevils,Sunrisers Hyderabad,Delhi Daredevils,bat,normal,0,0,6,DJG Sammy,"Rajiv Gandhi International Stadium, Uppal",Asad Rauf,S Asnani,,Sunrisers Hyderabad
+2010,Ahmedabad,Rajasthan Royals,Chennai Super Kings,Rajasthan Royals,bat,normal,0,17,0,NV Ojha,"Sardar Patel Stadium, Motera",SS Hazare,SJA Taufel,,Rajasthan Royals
+2016,Kolkata,Rising Pune Supergiants,Kolkata Knight Riders,Rising Pune Supergiants,bat,normal,1,0,8,YK Pathan,Eden Gardens,A Nand Kishore,BNJ Oxenford,,Kolkata Knight Riders
+2012,Jaipur,Rajasthan Royals,Pune Warriors,Rajasthan Royals,bat,normal,0,45,0,A Chandila,Sawai Mansingh Stadium,BF Bowden,SK Tarapore,,Rajasthan Royals
+2015,Hyderabad,Sunrisers Hyderabad,Kings XI Punjab,Sunrisers Hyderabad,bat,normal,0,5,0,DA Warner,"Rajiv Gandhi International Stadium, Uppal",AK Chaudhary,HDPK Dharmasena,,Sunrisers Hyderabad
+2013,Pune,Rajasthan Royals,Pune Warriors,Rajasthan Royals,bat,normal,0,0,7,AJ Finch,Subrata Roy Sahara Stadium,M Erasmus,K Srinath,,Pune Warriors
+2014,Cuttack,Kings XI Punjab,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,9,G Gambhir,Barabati Stadium,NJ Llong,CK Nandan,,Kolkata Knight Riders
+2014,Hyderabad,Sunrisers Hyderabad,Mumbai Indians,Sunrisers Hyderabad,bat,normal,0,0,7,AT Rayudu,"Rajiv Gandhi International Stadium, Uppal",HDPK Dharmasena,VA Kulkarni,,Mumbai Indians
+2012,Chennai,Chennai Super Kings,Kolkata Knight Riders,Chennai Super Kings,bat,normal,0,0,5,MS Bisla,"MA Chidambaram Stadium, Chepauk",BF Bowden,SJA Taufel,,Kolkata Knight Riders
+2010,Mumbai,Mumbai Indians,Royal Challengers Bangalore,Mumbai Indians,bat,normal,0,0,7,JH Kallis,Brabourne Stadium,HDPK Dharmasena,SS Hazare,,Royal Challengers Bangalore
+2014,,Sunrisers Hyderabad,Delhi Daredevils,Sunrisers Hyderabad,bat,normal,0,4,0,AJ Finch,Dubai International Cricket Stadium,M Erasmus,S Ravi,,Sunrisers Hyderabad
+2013,Hyderabad,Sunrisers Hyderabad,Rajasthan Royals,Sunrisers Hyderabad,bat,normal,0,23,0,A Mishra,"Rajiv Gandhi International Stadium, Uppal",Asad Rauf,AK Chaudhary,,Sunrisers Hyderabad
+2008,Jaipur,Deccan Chargers,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,8,YK Pathan,Sawai Mansingh Stadium,MR Benson,AM Saheba,,Rajasthan Royals
+2011,Jaipur,Pune Warriors,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,6,LRPL Taylor,Sawai Mansingh Stadium,SK Tarapore,SJA Taufel,,Rajasthan Royals
+2014,Ranchi,Chennai Super Kings,Kolkata Knight Riders,Chennai Super Kings,bat,normal,0,34,0,RA Jadeja,JSCA International Stadium Complex,AK Chaudhary,NJ Llong,,Chennai Super Kings
+2011,Bangalore,Royal Challengers Bangalore,Kings XI Punjab,Kings XI Punjab,field,normal,0,85,0,CH Gayle,M Chinnaswamy Stadium,Aleem Dar,RB Tiffin,,Royal Challengers Bangalore
+2008,Chandigarh,Kings XI Punjab,Kolkata Knight Riders,Kings XI Punjab,bat,normal,0,9,0,IK Pathan,"Punjab Cricket Association Stadium, Mohali",DJ Harper,I Shivram,,Kings XI Punjab
+2012,Hyderabad,Kings XI Punjab,Deccan Chargers,Deccan Chargers,field,normal,0,25,0,Mandeep Singh,"Rajiv Gandhi International Stadium, Uppal",HDPK Dharmasena,BNJ Oxenford,,Kings XI Punjab
+2013,Pune,Royal Challengers Bangalore,Pune Warriors,Royal Challengers Bangalore,bat,normal,0,17,0,AB de Villiers,Subrata Roy Sahara Stadium,Aleem Dar,C Shamshuddin,,Royal Challengers Bangalore
+2012,Chandigarh,Kings XI Punjab,Kolkata Knight Riders,Kings XI Punjab,bat,normal,0,0,8,G Gambhir,"Punjab Cricket Association Stadium, Mohali",JD Cloete,RJ Tucker,,Kolkata Knight Riders
+2010,Cuttack,Deccan Chargers,Kings XI Punjab,Kings XI Punjab,field,normal,0,6,0,A Symonds,Barabati Stadium,BF Bowden,M Erasmus,,Deccan Chargers
+2011,Delhi,Delhi Daredevils,Kochi Tuskers Kerala,Kochi Tuskers Kerala,field,normal,0,0,7,P Parameswaran,Feroz Shah Kotla,Asad Rauf,SL Shastri,,Kochi Tuskers Kerala
+2009,Cape Town,Kolkata Knight Riders,Deccan Chargers,Kolkata Knight Riders,bat,normal,0,0,8,RP Singh,Newlands,MR Benson,BR Doctrove,,Deccan Chargers
+2014,Ahmedabad,Mumbai Indians,Rajasthan Royals,Mumbai Indians,bat,normal,0,25,0,MEK Hussey,"Sardar Patel Stadium, Motera",S Ravi,RJ Tucker,,Mumbai Indians
+2009,Johannesburg,Kings XI Punjab,Deccan Chargers,Deccan Chargers,field,normal,0,1,0,Yuvraj Singh,New Wanderers Stadium,S Ravi,RB Tiffin,,Kings XI Punjab
+2012,Jaipur,Rajasthan Royals,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,22,0,BJ Hodge,Sawai Mansingh Stadium,BF Bowden,VA Kulkarni,,Rajasthan Royals
+2016,Rajkot,Royal Challengers Bangalore,Gujarat Lions,Royal Challengers Bangalore,bat,normal,0,0,6,V Kohli,Saurashtra Cricket Association Stadium,K Bharatan,BNJ Oxenford,,Gujarat Lions
+2010,Mumbai,Mumbai Indians,Royal Challengers Bangalore,Mumbai Indians,bat,normal,0,35,0,KA Pollard,Dr DY Patil Sports Academy,BR Doctrove,RB Tiffin,,Mumbai Indians
+2014,Hyderabad,Sunrisers Hyderabad,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,6,WP Saha,"Rajiv Gandhi International Stadium, Uppal",VA Kulkarni,PG Pathak,,Kings XI Punjab
+2013,Ranchi,Pune Warriors,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,7,0,MK Pandey,JSCA International Stadium Complex,NJ Llong,K Srinath,,Pune Warriors
+2016,Hyderabad,Sunrisers Hyderabad,Kolkata Knight Riders,Sunrisers Hyderabad,bat,normal,0,0,8,G Gambhir,"Rajiv Gandhi International Stadium, Uppal",AK Chaudhary,CK Nandan,,Kolkata Knight Riders
+2016,Bangalore,Royal Challengers Bangalore,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,5,AD Russell,M Chinnaswamy Stadium,M Erasmus,S Ravi,,Kolkata Knight Riders
+2012,Delhi,Deccan Chargers,Delhi Daredevils,Deccan Chargers,bat,normal,0,0,5,KP Pietersen,Feroz Shah Kotla,BF Bowden,SK Tarapore,,Delhi Daredevils
+2013,Kolkata,Mumbai Indians,Chennai Super Kings,Mumbai Indians,bat,normal,0,23,0,KA Pollard,Eden Gardens,HDPK Dharmasena,SJA Taufel,,Mumbai Indians
+2010,Kolkata,Kolkata Knight Riders,Deccan Chargers,Kolkata Knight Riders,bat,normal,0,24,0,SC Ganguly,Eden Gardens,K Hariharan,DJ Harper,,Kolkata Knight Riders
+2009,Centurion,Kings XI Punjab,Mumbai Indians,Kings XI Punjab,bat,normal,0,0,8,Harbhajan Singh,SuperSport Park,SS Hazare,RE Koertzen,,Mumbai Indians
+2010,Dharamsala,Kings XI Punjab,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,6,MS Dhoni,Himachal Pradesh Cricket Association Stadium,BF Bowden,AM Saheba,,Chennai Super Kings
+2013,Bangalore,Royal Challengers Bangalore,Mumbai Indians,Mumbai Indians,field,normal,0,2,0,CH Gayle,M Chinnaswamy Stadium,VA Kulkarni,C Shamshuddin,,Royal Challengers Bangalore
+2013,Mumbai,Mumbai Indians,Rajasthan Royals,Rajasthan Royals,field,normal,0,14,0,AP Tare,Wankhede Stadium,Asad Rauf,S Asnani,,Mumbai Indians
+2010,Ahmedabad,Rajasthan Royals,Kolkata Knight Riders,Rajasthan Royals,bat,normal,0,34,0,AA Jhunjhunwala,"Sardar Patel Stadium, Motera",RE Koertzen,RB Tiffin,,Rajasthan Royals
+2011,Delhi,Delhi Daredevils,Kings XI Punjab,Kings XI Punjab,field,normal,0,29,0,DA Warner,Feroz Shah Kotla,S Asnani,RE Koertzen,,Delhi Daredevils
+2013,Kolkata,Rajasthan Royals,Kolkata Knight Riders,Rajasthan Royals,bat,normal,0,0,8,YK Pathan,Eden Gardens,HDPK Dharmasena,CK Nandan,,Kolkata Knight Riders
+2016,Bangalore,Gujarat Lions,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,4,AB de Villiers,M Chinnaswamy Stadium,AK Chaudhary,HDPK Dharmasena,,Royal Challengers Bangalore
+2016,Bangalore,Royal Challengers Bangalore,Kings XI Punjab,Kings XI Punjab,field,normal,1,82,0,V Kohli,M Chinnaswamy Stadium,KN Ananthapadmanabhan,M Erasmus,,Royal Challengers Bangalore
+2013,Bangalore,Royal Challengers Bangalore,Chennai Super Kings,Chennai Super Kings,field,normal,0,24,0,V Kohli,M Chinnaswamy Stadium,C Shamshuddin,RJ Tucker,,Royal Challengers Bangalore
+2015,Ahmedabad,Rajasthan Royals,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,9,MA Starc,"Sardar Patel Stadium, Motera",M Erasmus,S Ravi,,Royal Challengers Bangalore
+2012,Hyderabad,Deccan Chargers,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,9,0,DW Steyn,"Rajiv Gandhi International Stadium, Uppal",S Ravi,SJA Taufel,,Deccan Chargers
+2012,Mumbai,Chennai Super Kings,Mumbai Indians,Mumbai Indians,field,normal,0,0,2,DR Smith,Wankhede Stadium,Asad Rauf,S Asnani,,Mumbai Indians
+2013,Hyderabad,Chennai Super Kings,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,77,0,SK Raina,"Rajiv Gandhi International Stadium, Uppal",S Das,NJ Llong,,Chennai Super Kings
+2009,Johannesburg,Delhi Daredevils,Royal Challengers Bangalore,Delhi Daredevils,bat,normal,0,0,7,JH Kallis,New Wanderers Stadium,IL Howell,RB Tiffin,,Royal Challengers Bangalore
+2016,Chandigarh,Royal Challengers Bangalore,Kings XI Punjab,Kings XI Punjab,field,normal,0,1,0,SR Watson,"Punjab Cricket Association IS Bindra Stadium, Mohali",AK Chaudhary,HDPK Dharmasena,,Royal Challengers Bangalore
+2013,Bangalore,Rajasthan Royals,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,7,R Vinay Kumar,M Chinnaswamy Stadium,Aleem Dar,C Shamshuddin,,Royal Challengers Bangalore
+2013,Delhi,Rajasthan Royals,Delhi Daredevils,Rajasthan Royals,bat,normal,0,5,0,R Dravid,Feroz Shah Kotla,S Das,C Shamshuddin,,Rajasthan Royals
+2010,Mumbai,Kolkata Knight Riders,Deccan Chargers,Deccan Chargers,field,normal,0,11,0,AD Mathews,Dr DY Patil Sports Academy,RE Koertzen,RB Tiffin,,Kolkata Knight Riders
+2014,Kolkata,Sunrisers Hyderabad,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,4,YK Pathan,Eden Gardens,RM Deshpande,BNJ Oxenford,,Kolkata Knight Riders
+2016,Bangalore,Royal Challengers Bangalore,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,7,Q de Kock,M Chinnaswamy Stadium,VA Kulkarni,A Nand Kishore,,Delhi Daredevils
+2016,Bangalore,Sunrisers Hyderabad,Royal Challengers Bangalore,Sunrisers Hyderabad,bat,normal,0,8,0,BCJ Cutting,M Chinnaswamy Stadium,HDPK Dharmasena,BNJ Oxenford,,Sunrisers Hyderabad
+2012,Delhi,Chennai Super Kings,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,8,M Morkel,Feroz Shah Kotla,Asad Rauf,SK Tarapore,,Delhi Daredevils
+2010,Bangalore,Mumbai Indians,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,57,0,R McLaren,M Chinnaswamy Stadium,HDPK Dharmasena,SJA Taufel,,Mumbai Indians
+2014,Mumbai,Kings XI Punjab,Mumbai Indians,Kings XI Punjab,bat,normal,0,0,5,CJ Anderson,Wankhede Stadium,BNJ Oxenford,C Shamshuddin,,Mumbai Indians
+2013,Kolkata,Kolkata Knight Riders,Chennai Super Kings,Kolkata Knight Riders,bat,normal,0,0,4,RA Jadeja,Eden Gardens,Asad Rauf,AK Chaudhary,,Chennai Super Kings
+2013,Ranchi,Royal Challengers Bangalore,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,5,JH Kallis,JSCA International Stadium Complex,NJ Llong,K Srinath,,Kolkata Knight Riders
+2010,Bangalore,Royal Challengers Bangalore,Deccan Chargers,Deccan Chargers,field,normal,0,0,7,TL Suman,M Chinnaswamy Stadium,S Asnani,DJ Harper,,Deccan Chargers
+2012,Bangalore,Royal Challengers Bangalore,Mumbai Indians,Mumbai Indians,field,normal,0,0,5,AT Rayudu,M Chinnaswamy Stadium,S Das,BR Doctrove,,Mumbai Indians
+2016,Kolkata,Kolkata Knight Riders,Mumbai Indians,Mumbai Indians,field,normal,0,0,6,RG Sharma,Eden Gardens,Nitin Menon,S Ravi,,Mumbai Indians
+2011,Kolkata,Kolkata Knight Riders,Deccan Chargers,Kolkata Knight Riders,bat,normal,0,9,0,JH Kallis,Eden Gardens,RE Koertzen,SK Tarapore,,Kolkata Knight Riders
+2016,Hyderabad,Sunrisers Hyderabad,Rising Pune Supergiants,Rising Pune Supergiants,field,normal,1,34,0,AB Dinda,"Rajiv Gandhi International Stadium, Uppal",AY Dandekar,CK Nandan,,Rising Pune Supergiants
+2012,Mumbai,Mumbai Indians,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,9,CH Gayle,Wankhede Stadium,BF Bowden,VA Kulkarni,,Royal Challengers Bangalore
+2010,Jaipur,Mumbai Indians,Rajasthan Royals,Rajasthan Royals,field,normal,0,37,0,SR Tendulkar,Sawai Mansingh Stadium,BR Doctrove,SK Tarapore,,Mumbai Indians
+2013,Dharamsala,Kings XI Punjab,Delhi Daredevils,Delhi Daredevils,field,normal,0,7,0,DA Miller,Himachal Pradesh Cricket Association Stadium,HDPK Dharmasena,S Ravi,,Kings XI Punjab
+2013,Chennai,Rajasthan Royals,Chennai Super Kings,Rajasthan Royals,bat,normal,0,0,5,MEK Hussey,"MA Chidambaram Stadium, Chepauk",S Asnani,AK Chaudhary,,Chennai Super Kings
+2008,Hyderabad,Kolkata Knight Riders,Deccan Chargers,Kolkata Knight Riders,bat,normal,0,23,0,SC Ganguly,"Rajiv Gandhi International Stadium, Uppal",IL Howell,AM Saheba,,Kolkata Knight Riders
+2009,Durban,Delhi Daredevils,Deccan Chargers,Deccan Chargers,field,normal,0,12,0,R Bhatia,Kingsmead,DJ Harper,SL Shastri,,Delhi Daredevils
+2013,Jaipur,Royal Challengers Bangalore,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,4,SV Samson,Sawai Mansingh Stadium,M Erasmus,K Srinath,,Rajasthan Royals
+2012,Kolkata,Mumbai Indians,Kolkata Knight Riders,Mumbai Indians,bat,normal,0,27,0,RG Sharma,Eden Gardens,S Ravi,SJA Taufel,,Mumbai Indians
+2009,Port Elizabeth,Chennai Super Kings,Royal Challengers Bangalore,Chennai Super Kings,bat,normal,0,92,0,M Muralitharan,St George's Park,BG Jerling,SJA Taufel,,Chennai Super Kings
+2015,Visakhapatnam,Sunrisers Hyderabad,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,1,16,0,DA Warner,Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium,RK Illingworth,VA Kulkarni,,Sunrisers Hyderabad
+2016,Kolkata,Kolkata Knight Riders,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,22,0,YK Pathan,Eden Gardens,KN Ananthapadmanabhan,M Erasmus,,Kolkata Knight Riders
+2016,Hyderabad,Gujarat Lions,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,0,5,B Kumar,"Rajiv Gandhi International Stadium, Uppal",M Erasmus,S Ravi,,Sunrisers Hyderabad
+2013,Dharamsala,Kings XI Punjab,Mumbai Indians,Mumbai Indians,field,normal,0,50,0,Azhar Mahmood,Himachal Pradesh Cricket Association Stadium,HDPK Dharmasena,CK Nandan,,Kings XI Punjab
+2012,Hyderabad,Rajasthan Royals,Deccan Chargers,Rajasthan Royals,bat,normal,0,0,5,DW Steyn,"Rajiv Gandhi International Stadium, Uppal",S Ravi,SJA Taufel,,Deccan Chargers
+2014,Delhi,Delhi Daredevils,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,4,AR Patel,Feroz Shah Kotla,HDPK Dharmasena,PG Pathak,,Kings XI Punjab
+2011,Mumbai,Deccan Chargers,Mumbai Indians,Deccan Chargers,bat,normal,0,10,0,A Mishra,Wankhede Stadium,S Ravi,SK Tarapore,,Deccan Chargers
+2014,Sharjah,Kings XI Punjab,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,72,0,GJ Maxwell,Sharjah Cricket Stadium,M Erasmus,S Ravi,,Kings XI Punjab
+2016,Visakhapatnam,Mumbai Indians,Kings XI Punjab,Mumbai Indians,bat,normal,0,0,7,MP Stoinis,Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium,HDPK Dharmasena,CK Nandan,,Kings XI Punjab
+2016,Bangalore,Rising Pune Supergiants,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,7,V Kohli,M Chinnaswamy Stadium,CB Gaffaney,BNJ Oxenford,,Royal Challengers Bangalore
+2015,Pune,Rajasthan Royals,Kings XI Punjab,Kings XI Punjab,field,normal,0,26,0,JP Faulkner,Maharashtra Cricket Association Stadium,SD Fry,CB Gaffaney,,Rajasthan Royals
+2010,Chandigarh,Kings XI Punjab,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,5,G Gambhir,"Punjab Cricket Association Stadium, Mohali",BR Doctrove,S Ravi,,Delhi Daredevils
+2008,Jaipur,Mumbai Indians,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,5,Sohail Tanvir,Sawai Mansingh Stadium,BF Bowden,K Hariharan,,Rajasthan Royals
+2008,Chennai,Chennai Super Kings,Deccan Chargers,Deccan Chargers,field,normal,0,0,7,AC Gilchrist,"MA Chidambaram Stadium, Chepauk",MR Benson,RB Tiffin,,Deccan Chargers
+2014,Kolkata,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,30,0,RV Uthappa,Eden Gardens,AK Chaudhary,CK Nandan,,Kolkata Knight Riders
+2013,Kolkata,Kolkata Knight Riders,Mumbai Indians,Kolkata Knight Riders,bat,normal,0,0,5,DR Smith,Eden Gardens,HDPK Dharmasena,S Ravi,,Mumbai Indians
+2015,Mumbai,Mumbai Indians,Chennai Super Kings,Mumbai Indians,bat,normal,0,0,6,A Nehra,Wankhede Stadium,AK Chaudhary,M Erasmus,,Chennai Super Kings
+2016,Kanpur,Kolkata Knight Riders,Gujarat Lions,Gujarat Lions,field,normal,0,0,6,DR Smith,Green Park,AK Chaudhary,CK Nandan,,Gujarat Lions
+2015,Chandigarh,Mumbai Indians,Kings XI Punjab,Mumbai Indians,bat,normal,0,23,0,LMP Simmons,"Punjab Cricket Association Stadium, Mohali",RK Illingworth,VA Kulkarni,,Mumbai Indians
+2009,Cape Town,Kings XI Punjab,Delhi Daredevils,Delhi Daredevils,field,normal,1,0,10,DL Vettori,Newlands,MR Benson,SD Ranade,,Delhi Daredevils
+2014,Abu Dhabi,Kings XI Punjab,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,23,0,Sandeep Sharma,Sheikh Zayed Stadium,HDPK Dharmasena,RK Illingworth,,Kings XI Punjab
+2015,Bangalore,Delhi Daredevils,Royal Challengers Bangalore,Royal Challengers Bangalore,field,no result,0,0,0,,M Chinnaswamy Stadium,HDPK Dharmasena,K Srinivasan,,nan
+2016,Kanpur,Mumbai Indians,Gujarat Lions,Gujarat Lions,field,normal,0,0,6,SK Raina,Green Park,AK Chaudhary,CK Nandan,,Gujarat Lions
+2009,Durban,Rajasthan Royals,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,4,LR Shukla,Kingsmead,BG Jerling,SJA Taufel,,Kolkata Knight Riders
+2008,Mumbai,Rajasthan Royals,Mumbai Indians,Mumbai Indians,field,normal,0,0,7,A Nehra,Dr DY Patil Sports Academy,DJ Harper,RE Koertzen,,Mumbai Indians
+2013,Kolkata,Rajasthan Royals,Mumbai Indians,Rajasthan Royals,bat,normal,0,0,4,Harbhajan Singh,Eden Gardens,C Shamshuddin,SJA Taufel,,Mumbai Indians
+2014,Sharjah,Delhi Daredevils,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,8,YS Chahal,Sharjah Cricket Stadium,Aleem Dar,S Ravi,,Royal Challengers Bangalore
+2013,Raipur,Kolkata Knight Riders,Delhi Daredevils,Kolkata Knight Riders,bat,normal,0,0,7,DA Warner,Shaheed Veer Narayan Singh International Stadium,HDPK Dharmasena,CK Nandan,,Delhi Daredevils
+2016,Chandigarh,Kings XI Punjab,Gujarat Lions,Gujarat Lions,field,normal,0,0,5,AJ Finch,"Punjab Cricket Association IS Bindra Stadium, Mohali",AK Chaudhary,VA Kulkarni,,Gujarat Lions
+2008,Chennai,Chennai Super Kings,Mumbai Indians,Mumbai Indians,field,normal,0,6,0,ML Hayden,"MA Chidambaram Stadium, Chepauk",DJ Harper,GA Pratapkumar,,Chennai Super Kings
+2014,Cuttack,Kings XI Punjab,Chennai Super Kings,Chennai Super Kings,field,normal,0,44,0,GJ Maxwell,Barabati Stadium,HDPK Dharmasena,PG Pathak,,Kings XI Punjab
+2011,Mumbai,Mumbai Indians,Kings XI Punjab,Kings XI Punjab,field,normal,0,23,0,KA Pollard,Wankhede Stadium,HDPK Dharmasena,PR Reiffel,,Mumbai Indians
+2014,Delhi,Delhi Daredevils,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,8,DR Smith,Feroz Shah Kotla,RM Deshpande,BNJ Oxenford,,Chennai Super Kings
+2011,Hyderabad,Kolkata Knight Riders,Deccan Chargers,Deccan Chargers,field,normal,0,20,0,YK Pathan,"Rajiv Gandhi International Stadium, Uppal",S Asnani,RJ Tucker,,Kolkata Knight Riders
+2013,Bangalore,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,8,CH Gayle,M Chinnaswamy Stadium,Asad Rauf,AK Chowdhary,,Royal Challengers Bangalore
+2010,Delhi,Mumbai Indians,Delhi Daredevils,Delhi Daredevils,field,normal,0,98,0,SR Tendulkar,Feroz Shah Kotla,BR Doctrove,SK Tarapore,,Mumbai Indians
+2011,Mumbai,Mumbai Indians,Delhi Daredevils,Delhi Daredevils,field,normal,0,32,0,AT Rayudu,Wankhede Stadium,K Hariharan,SJA Taufel,,Mumbai Indians
+2010,Mumbai,Mumbai Indians,Deccan Chargers,Deccan Chargers,field,normal,0,41,0,Harbhajan Singh,Dr DY Patil Sports Academy,S Das,K Hariharan,,Mumbai Indians
+2013,Delhi,Sunrisers Hyderabad,Rajasthan Royals,Sunrisers Hyderabad,bat,normal,0,0,4,BJ Hodge,Feroz Shah Kotla,S Ravi,RJ Tucker,,Rajasthan Royals
+2011,Kolkata,Rajasthan Royals,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,8,L Balaji,Eden Gardens,Aleem Dar,RB Tiffin,,Kolkata Knight Riders
+2009,Centurion,Chennai Super Kings,Kolkata Knight Riders,Chennai Super Kings,bat,normal,0,0,7,BJ Hodge,SuperSport Park,SJA Taufel,RB Tiffin,,Kolkata Knight Riders
+2008,Delhi,Delhi Daredevils,Deccan Chargers,Deccan Chargers,field,normal,0,12,0,A Mishra,Feroz Shah Kotla,BG Jerling,GA Pratapkumar,,Delhi Daredevils
+2012,Dharamsala,Chennai Super Kings,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,6,AC Gilchrist,Himachal Pradesh Cricket Association Stadium,VA Kulkarni,SK Tarapore,,Kings XI Punjab
+2009,Port Elizabeth,Mumbai Indians,Chennai Super Kings,Mumbai Indians,bat,normal,0,0,7,ML Hayden,St George's Park,SK Tarapore,SJA Taufel,,Chennai Super Kings
+2011,Chennai,Chennai Super Kings,Delhi Daredevils,Chennai Super Kings,bat,normal,0,18,0,MS Dhoni,"MA Chidambaram Stadium, Chepauk",AM Saheba,SL Shastri,,Chennai Super Kings
+2012,Bangalore,Chennai Super Kings,Mumbai Indians,Mumbai Indians,field,normal,0,38,0,MS Dhoni,M Chinnaswamy Stadium,BF Bowden,HDPK Dharmasena,,Chennai Super Kings
+2012,Jaipur,Deccan Chargers,Rajasthan Royals,Deccan Chargers,bat,normal,0,0,5,BJ Hodge,Sawai Mansingh Stadium,Aleem Dar,BNJ Oxenford,,Rajasthan Royals
+2008,Delhi,Delhi Daredevils,Kings XI Punjab,Delhi Daredevils,bat,normal,1,6,0,DPMD Jayawardene,Feroz Shah Kotla,AV Jayaprakash,RE Koertzen,,Kings XI Punjab
+2014,Ranchi,Chennai Super Kings,Royal Challengers Bangalore,Chennai Super Kings,bat,normal,0,0,5,AB de Villiers,JSCA International Stadium Complex,BNJ Oxenford,C Shamshuddin,,Royal Challengers Bangalore
+2016,Chandigarh,Kings XI Punjab,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,6,RV Uthappa,"Punjab Cricket Association IS Bindra Stadium, Mohali",S Ravi,C Shamshuddin,,Kolkata Knight Riders
+2008,Jaipur,Rajasthan Royals,Kolkata Knight Riders,Rajasthan Royals,bat,normal,0,45,0,SA Asnodkar,Sawai Mansingh Stadium,RE Koertzen,GA Pratapkumar,,Rajasthan Royals
+2016,Kolkata,Kolkata Knight Riders,Kings XI Punjab,Kings XI Punjab,field,normal,0,7,0,AD Russell,Eden Gardens,AK Chaudhary,HDPK Dharmasena,,Kolkata Knight Riders
+2013,Mumbai,Mumbai Indians,Delhi Daredevils,Mumbai Indians,bat,normal,0,44,0,KD Karthik,Wankhede Stadium,M Erasmus,VA Kulkarni,,Mumbai Indians
+2014,,Royal Challengers Bangalore,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,5,Sandeep Sharma,Dubai International Cricket Stadium,BF Bowden,S Ravi,,Kings XI Punjab
+2012,Pune,Kolkata Knight Riders,Delhi Daredevils,Kolkata Knight Riders,bat,normal,0,18,0,YK Pathan,Subrata Roy Sahara Stadium,BR Doctrove,SJA Taufel,,Kolkata Knight Riders
+2011,Jaipur,Mumbai Indians,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,7,J Botha,Sawai Mansingh Stadium,Asad Rauf,SK Tarapore,,Rajasthan Royals
+2011,Bangalore,Chennai Super Kings,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,8,CH Gayle,M Chinnaswamy Stadium,K Hariharan,RE Koertzen,,Royal Challengers Bangalore
+2014,Abu Dhabi,Sunrisers Hyderabad,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,4,AM Rahane,Sheikh Zayed Stadium,BF Bowden,RK Illingworth,,Rajasthan Royals
+2015,Chennai,Chennai Super Kings,Sunrisers Hyderabad,Chennai Super Kings,bat,normal,0,45,0,BB McCullum,"MA Chidambaram Stadium, Chepauk",RK Illingworth,VA Kulkarni,,Chennai Super Kings
+2009,Cape Town,Rajasthan Royals,Kolkata Knight Riders,Kolkata Knight Riders,field,tie,0,0,0,YK Pathan,Newlands,MR Benson,M Erasmus,,Rajasthan Royals
+2011,Delhi,Kolkata Knight Riders,Delhi Daredevils,Delhi Daredevils,field,normal,0,17,0,MK Tiwary,Feroz Shah Kotla,PR Reiffel,RJ Tucker,,Kolkata Knight Riders
+2016,Visakhapatnam,Delhi Daredevils,Rising Pune Supergiants,Rising Pune Supergiants,field,normal,1,19,0,AB Dinda,Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium,Nitin Menon,C Shamshuddin,,Rising Pune Supergiants
+2015,Pune,Kings XI Punjab,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,4,AD Russell,Maharashtra Cricket Association Stadium,SD Fry,CK Nandan,,Kolkata Knight Riders
+2011,Jaipur,Kochi Tuskers Kerala,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,8,SK Warne,Sawai Mansingh Stadium,BR Doctrove,SK Tarapore,,Rajasthan Royals
+2008,Bangalore,Chennai Super Kings,Royal Challengers Bangalore,Chennai Super Kings,bat,normal,0,13,0,MS Dhoni,M Chinnaswamy Stadium,BR Doctrove,RB Tiffin,,Chennai Super Kings
+2016,Hyderabad,Mumbai Indians,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,0,7,DA Warner,"Rajiv Gandhi International Stadium, Uppal",HDPK Dharmasena,VK Sharma,,Sunrisers Hyderabad
+2012,Delhi,Pune Warriors,Delhi Daredevils,Delhi Daredevils,field,normal,0,20,0,SC Ganguly,Feroz Shah Kotla,Asad Rauf,S Das,,Pune Warriors
+2015,Ranchi,Royal Challengers Bangalore,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,3,A Nehra,JSCA International Stadium Complex,AK Chaudhary,CB Gaffaney,,Chennai Super Kings
+2009,East London,Mumbai Indians,Delhi Daredevils,Mumbai Indians,bat,normal,0,0,7,A Nehra,Buffalo Park,M Erasmus,SK Tarapore,,Delhi Daredevils
+2009,East London,Chennai Super Kings,Deccan Chargers,Chennai Super Kings,bat,normal,0,78,0,MS Dhoni,Buffalo Park,BR Doctrove,M Erasmus,,Chennai Super Kings
+2011,Jaipur,Rajasthan Royals,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,9,S Aravind,Sawai Mansingh Stadium,HDPK Dharmasena,K Hariharan,,Royal Challengers Bangalore
+2014,Kolkata,Chennai Super Kings,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,8,RV Uthappa,Eden Gardens,RM Deshpande,C Shamshuddin,,Kolkata Knight Riders
+2013,Jaipur,Delhi Daredevils,Rajasthan Royals,Delhi Daredevils,bat,normal,0,0,9,AM Rahane,Sawai Mansingh Stadium,Aleem Dar,RJ Tucker,,Rajasthan Royals
+2015,Mumbai,Mumbai Indians,Rajasthan Royals,Rajasthan Royals,field,normal,0,8,0,AT Rayudu,Wankhede Stadium,HDPK Dharmasena,CK Nandan,,Mumbai Indians
+2009,Durban,Kings XI Punjab,Mumbai Indians,Kings XI Punjab,bat,normal,0,3,0,KC Sangakkara,Kingsmead,MR Benson,SL Shastri,,Kings XI Punjab
+2011,Mumbai,Mumbai Indians,Chennai Super Kings,Chennai Super Kings,field,normal,0,8,0,Harbhajan Singh,Wankhede Stadium,Asad Rauf,AM Saheba,,Mumbai Indians
+2008,Kolkata,Deccan Chargers,Kolkata Knight Riders,Deccan Chargers,bat,normal,0,0,5,DJ Hussey,Eden Gardens,BF Bowden,K Hariharan,,Kolkata Knight Riders
+2013,Mumbai,Mumbai Indians,Pune Warriors,Mumbai Indians,bat,normal,0,41,0,RG Sharma,Wankhede Stadium,S Ravi,SJA Taufel,,Mumbai Indians
+2016,Bangalore,Royal Challengers Bangalore,Mumbai Indians,Mumbai Indians,field,normal,0,0,6,KH Pandya,M Chinnaswamy Stadium,AY Dandekar,C Shamshuddin,,Mumbai Indians
+2015,Bangalore,Chennai Super Kings,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,27,0,SK Raina,M Chinnaswamy Stadium,JD Cloete,C Shamshuddin,,Chennai Super Kings
+2011,Chennai,Chennai Super Kings,Kolkata Knight Riders,Chennai Super Kings,bat,normal,0,2,0,S Anirudha,"MA Chidambaram Stadium, Chepauk",BR Doctrove,PR Reiffel,,Chennai Super Kings
+2008,Mumbai,Kolkata Knight Riders,Mumbai Indians,Mumbai Indians,field,normal,0,0,8,SM Pollock,Wankhede Stadium,BR Doctrove,DJ Harper,,Mumbai Indians
+2012,Cuttack,Deccan Chargers,Pune Warriors,Deccan Chargers,bat,normal,0,13,0,KC Sangakkara,Barabati Stadium,Aleem Dar,AK Chaudhary,,Deccan Chargers
+2008,Jaipur,Kings XI Punjab,Rajasthan Royals,Kings XI Punjab,bat,normal,0,0,6,SR Watson,Sawai Mansingh Stadium,Aleem Dar,RB Tiffin,,Rajasthan Royals
+2016,Raipur,Sunrisers Hyderabad,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,6,KK Nair,Shaheed Veer Narayan Singh International Stadium,A Nand Kishore,BNJ Oxenford,,Delhi Daredevils
+2009,Centurion,Deccan Chargers,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,6,DP Nannes,SuperSport Park,GAV Baxter,AM Saheba,,Delhi Daredevils
+2016,Delhi,Kings XI Punjab,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,8,A Mishra,Feroz Shah Kotla,S Ravi,C Shamshuddin,,Delhi Daredevils
+2013,Chennai,Chennai Super Kings,Delhi Daredevils,Chennai Super Kings,bat,normal,0,33,0,MS Dhoni,"MA Chidambaram Stadium, Chepauk",C Shamshuddin,RJ Tucker,,Chennai Super Kings
+2012,Mumbai,Pune Warriors,Mumbai Indians,Mumbai Indians,field,normal,0,28,0,SPD Smith,Wankhede Stadium,AK Chaudhary,SJA Taufel,,Pune Warriors
+2010,Bangalore,Royal Challengers Bangalore,Chennai Super Kings,Chennai Super Kings,field,normal,0,36,0,RV Uthappa,M Chinnaswamy Stadium,RE Koertzen,RB Tiffin,,Royal Challengers Bangalore
+2011,Hyderabad,Deccan Chargers,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,8,PC Valthaty,"Rajiv Gandhi International Stadium, Uppal",RE Koertzen,S Ravi,,Kings XI Punjab
+2008,Hyderabad,Deccan Chargers,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,7,SE Marsh,"Rajiv Gandhi International Stadium, Uppal",BR Doctrove,RB Tiffin,,Kings XI Punjab
+2016,Visakhapatnam,Sunrisers Hyderabad,Mumbai Indians,Mumbai Indians,field,normal,0,85,0,A Nehra,Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium,S Ravi,C Shamshuddin,,Sunrisers Hyderabad
+2015,Bangalore,Royal Challengers Bangalore,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,0,8,DA Warner,M Chinnaswamy Stadium,RM Deshpande,RK Illingworth,,Sunrisers Hyderabad
+2008,Mumbai,Rajasthan Royals,Delhi Daredevils,Delhi Daredevils,field,normal,0,105,0,SR Watson,Wankhede Stadium,BF Bowden,RE Koertzen,,Rajasthan Royals
+2009,Cape Town,Royal Challengers Bangalore,Rajasthan Royals,Royal Challengers Bangalore,bat,normal,0,75,0,R Dravid,Newlands,BR Doctrove,RB Tiffin,,Royal Challengers Bangalore
+2010,Ahmedabad,Rajasthan Royals,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,6,V Sehwag,"Sardar Patel Stadium, Motera",BG Jerling,RE Koertzen,,Delhi Daredevils
+2011,Mumbai,Kolkata Knight Riders,Mumbai Indians,Mumbai Indians,field,normal,0,0,4,MM Patel,Wankhede Stadium,Asad Rauf,SJA Taufel,,Mumbai Indians
+2010,Delhi,Delhi Daredevils,Kings XI Punjab,Delhi Daredevils,bat,normal,0,0,7,PP Chawla,Feroz Shah Kotla,BF Bowden,AM Saheba,,Kings XI Punjab
+2015,Ahmedabad,Mumbai Indians,Rajasthan Royals,Mumbai Indians,bat,normal,0,0,7,SPD Smith,"Sardar Patel Stadium, Motera",AK Chaudhary,SD Fry,,Rajasthan Royals
+2011,Mumbai,Pune Warriors,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,7,YK Pathan,Dr DY Patil Sports Academy,S Ravi,SJA Taufel,,Kolkata Knight Riders
+2014,Ahmedabad,Rajasthan Royals,Delhi Daredevils,Delhi Daredevils,field,normal,0,62,0,AM Rahane,"Sardar Patel Stadium, Motera",S Ravi,RJ Tucker,,Rajasthan Royals
+2010,Delhi,Deccan Chargers,Delhi Daredevils,Deccan Chargers,bat,normal,0,11,0,A Symonds,Feroz Shah Kotla,BR Doctrove,SK Tarapore,,Deccan Chargers
+2009,Port Elizabeth,Mumbai Indians,Royal Challengers Bangalore,Mumbai Indians,bat,normal,0,16,0,JP Duminy,St George's Park,BR Doctrove,BG Jerling,,Mumbai Indians
+2015,Hyderabad,Sunrisers Hyderabad,Chennai Super Kings,Chennai Super Kings,field,normal,0,22,0,DA Warner,"Rajiv Gandhi International Stadium, Uppal",AK Chaudhary,K Srinivasan,,Sunrisers Hyderabad
+2012,Chandigarh,Kings XI Punjab,Mumbai Indians,Kings XI Punjab,bat,normal,0,0,4,AT Rayudu,"Punjab Cricket Association Stadium, Mohali",Aleem Dar,BNJ Oxenford,,Mumbai Indians
+2010,Chennai,Deccan Chargers,Chennai Super Kings,Deccan Chargers,bat,normal,0,31,0,WPUJC Vaas,"MA Chidambaram Stadium, Chepauk",K Hariharan,DJ Harper,,Deccan Chargers
+2009,Centurion,Delhi Daredevils,Rajasthan Royals,Delhi Daredevils,bat,normal,0,0,5,YK Pathan,SuperSport Park,GAV Baxter,RE Koertzen,,Rajasthan Royals
+2014,Abu Dhabi,Rajasthan Royals,Kolkata Knight Riders,Rajasthan Royals,bat,tie,0,0,0,JP Faulkner,Sheikh Zayed Stadium,Aleem Dar,AK Chaudhary,,Rajasthan Royals
+2013,Chandigarh,Pune Warriors,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,7,DA Miller,"Punjab Cricket Association Stadium, Mohali",M Erasmus,K Srinath,,Kings XI Punjab
+2009,Port Elizabeth,Royal Challengers Bangalore,Delhi Daredevils,Royal Challengers Bangalore,bat,normal,0,0,6,TM Dilshan,St George's Park,S Asnani,BG Jerling,,Delhi Daredevils
+2011,Kolkata,Chennai Super Kings,Kolkata Knight Riders,Chennai Super Kings,bat,normal,1,10,0,Iqbal Abdulla,Eden Gardens,Asad Rauf,PR Reiffel,,Kolkata Knight Riders
+2014,Cuttack,Mumbai Indians,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,6,RV Uthappa,Barabati Stadium,AK Chaudhary,NJ Llong,,Kolkata Knight Riders
+2014,Delhi,Delhi Daredevils,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,1,0,8,DW Steyn,Feroz Shah Kotla,RM Deshpande,BNJ Oxenford,,Sunrisers Hyderabad
+2012,Chennai,Chennai Super Kings,Kolkata Knight Riders,Chennai Super Kings,bat,normal,0,0,5,G Gambhir,"MA Chidambaram Stadium, Chepauk",BF Bowden,C Shamshuddin,,Kolkata Knight Riders
+2010,Chennai,Chennai Super Kings,Mumbai Indians,Chennai Super Kings,bat,normal,0,24,0,SK Raina,"MA Chidambaram Stadium, Chepauk",S Asnani,DJ Harper,,Chennai Super Kings
+2012,Chennai,Rajasthan Royals,Chennai Super Kings,Rajasthan Royals,bat,normal,0,0,7,F du Plessis,"MA Chidambaram Stadium, Chepauk",Aleem Dar,BNJ Oxenford,,Chennai Super Kings
+2014,Mumbai,Mumbai Indians,Delhi Daredevils,Delhi Daredevils,field,normal,0,15,0,MEK Hussey,Wankhede Stadium,S Ravi,RJ Tucker,,Mumbai Indians
+2013,Delhi,Chennai Super Kings,Mumbai Indians,Chennai Super Kings,bat,normal,0,48,0,MEK Hussey,Feroz Shah Kotla,NJ Llong,RJ Tucker,,Chennai Super Kings
+2012,Pune,Pune Warriors,Rajasthan Royals,Pune Warriors,bat,normal,0,0,7,SR Watson,Subrata Roy Sahara Stadium,Asad Rauf,BR Doctrove,,Rajasthan Royals
+2012,Pune,Deccan Chargers,Pune Warriors,Deccan Chargers,bat,normal,0,18,0,CL White,Subrata Roy Sahara Stadium,S Ravi,RJ Tucker,,Deccan Chargers
+2011,Indore,Rajasthan Royals,Kochi Tuskers Kerala,Kochi Tuskers Kerala,field,normal,0,0,8,BJ Hodge,Holkar Cricket Stadium,PR Reiffel,RJ Tucker,,Kochi Tuskers Kerala
+2013,Delhi,Delhi Daredevils,Sunrisers Hyderabad,Delhi Daredevils,bat,normal,0,0,3,A Mishra,Feroz Shah Kotla,Aleem Dar,Subroto Das,,Sunrisers Hyderabad
+2012,Chennai,Royal Challengers Bangalore,Chennai Super Kings,Royal Challengers Bangalore,bat,normal,0,0,5,F du Plessis,"MA Chidambaram Stadium, Chepauk",HDPK Dharmasena,RJ Tucker,,Chennai Super Kings
+2016,Rajkot,Kings XI Punjab,Gujarat Lions,Gujarat Lions,field,normal,0,23,0,AR Patel,Saurashtra Cricket Association Stadium,BNJ Oxenford,VK Sharma,,Kings XI Punjab
+2010,Bangalore,Rajasthan Royals,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,10,JH Kallis,M Chinnaswamy Stadium,K Hariharan,DJ Harper,,Royal Challengers Bangalore
+2010,Bangalore,Delhi Daredevils,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,17,0,KM Jadhav,M Chinnaswamy Stadium,BG Jerling,RE Koertzen,,Delhi Daredevils
+2011,Hyderabad,Mumbai Indians,Deccan Chargers,Deccan Chargers,field,normal,0,37,0,SL Malinga,"Rajiv Gandhi International Stadium, Uppal",HDPK Dharmasena,AL Hill,,Mumbai Indians
+2016,Pune,Royal Challengers Bangalore,Rising Pune Supergiants,Rising Pune Supergiants,field,normal,0,13,0,AB de Villiers,Maharashtra Cricket Association Stadium,CB Gaffaney,VK Sharma,,Royal Challengers Bangalore
+2008,Mumbai,Mumbai Indians,Deccan Chargers,Deccan Chargers,field,normal,0,0,10,AC Gilchrist,Dr DY Patil Sports Academy,Asad Rauf,SL Shastri,,Deccan Chargers
+2010,Kolkata,Royal Challengers Bangalore,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,7,MK Tiwary,Eden Gardens,HDPK Dharmasena,AM Saheba,,Kolkata Knight Riders
+2014,Bangalore,Kings XI Punjab,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,3,MK Pandey,M Chinnaswamy Stadium,HDPK Dharmasena,BNJ Oxenford,,Kolkata Knight Riders
+2009,Durban,Chennai Super Kings,Deccan Chargers,Deccan Chargers,field,normal,0,0,6,HH Gibbs,Kingsmead,IL Howell,TH Wijewardene,,Deccan Chargers
+2009,Johannesburg,Kolkata Knight Riders,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,7,A Mishra,New Wanderers Stadium,SL Shastri,RB Tiffin,,Delhi Daredevils
+2011,Bangalore,Royal Challengers Bangalore,Pune Warriors,Pune Warriors,field,normal,0,26,0,V Kohli,M Chinnaswamy Stadium,Aleem Dar,SS Hazare,,Royal Challengers Bangalore
+2010,Mumbai,Mumbai Indians,Rajasthan Royals,Mumbai Indians,bat,normal,0,4,0,YK Pathan,Brabourne Stadium,RE Koertzen,RB Tiffin,,Mumbai Indians
+2014,Delhi,Delhi Daredevils,Kolkata Knight Riders,Delhi Daredevils,bat,normal,0,0,8,G Gambhir,Feroz Shah Kotla,BNJ Oxenford,C Shamshuddin,,Kolkata Knight Riders
+2012,Cuttack,Deccan Chargers,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,5,B Lee,Barabati Stadium,BF Bowden,SK Tarapore,,Kolkata Knight Riders
+2013,Jaipur,Rajasthan Royals,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,19,0,SK Trivedi,Sawai Mansingh Stadium,Aleem Dar,S Das,,Rajasthan Royals
+2014,Abu Dhabi,Kolkata Knight Riders,Mumbai Indians,Kolkata Knight Riders,bat,normal,0,41,0,JH Kallis,Sheikh Zayed Stadium,M Erasmus,RK Illingworth,,Kolkata Knight Riders
+2013,Pune,Chennai Super Kings,Pune Warriors,Chennai Super Kings,bat,normal,0,37,0,MS Dhoni,Subrata Roy Sahara Stadium,S Das,SJA Taufel,,Chennai Super Kings
+2012,Chennai,Chennai Super Kings,Pune Warriors,Pune Warriors,field,normal,0,13,0,KMDN Kulasekara,"MA Chidambaram Stadium, Chepauk",Asad Rauf,S Das,,Chennai Super Kings
+2008,Delhi,Delhi Daredevils,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,10,0,GD McGrath,Feroz Shah Kotla,Aleem Dar,I Shivram,,Delhi Daredevils
+2013,Mumbai,Mumbai Indians,Kings XI Punjab,Mumbai Indians,bat,normal,0,4,0,RG Sharma,Wankhede Stadium,Asad Rauf,AK Chaudhary,,Mumbai Indians
+2011,Kochi,Kochi Tuskers Kerala,Royal Challengers Bangalore,Kochi Tuskers Kerala,bat,normal,0,0,6,AB de Villiers,Nehru Stadium,HDPK Dharmasena,K Hariharan,,Royal Challengers Bangalore
+2015,Bangalore,Royal Challengers Bangalore,Kings XI Punjab,Kings XI Punjab,field,normal,0,138,0,CH Gayle,M Chinnaswamy Stadium,RK Illingworth,VA Kulkarni,,Royal Challengers Bangalore
+2008,Kolkata,Kolkata Knight Riders,Royal Challengers Bangalore,Kolkata Knight Riders,bat,normal,0,5,0,SC Ganguly,Eden Gardens,Asad Rauf,IL Howell,,Kolkata Knight Riders
+2010,Chennai,Chennai Super Kings,Rajasthan Royals,Chennai Super Kings,bat,normal,0,23,0,M Vijay,"MA Chidambaram Stadium, Chepauk",RE Koertzen,RB Tiffin,,Chennai Super Kings
+2010,Chandigarh,Kolkata Knight Riders,Kings XI Punjab,Kolkata Knight Riders,bat,normal,0,39,0,MK Tiwary,"Punjab Cricket Association Stadium, Mohali",BR Doctrove,S Ravi,,Kolkata Knight Riders
+2008,Bangalore,Royal Challengers Bangalore,Mumbai Indians,Mumbai Indians,field,normal,0,0,9,CRD Fernando,M Chinnaswamy Stadium,BF Bowden,AV Jayaprakash,,Mumbai Indians
+2010,Chandigarh,Mumbai Indians,Kings XI Punjab,Mumbai Indians,bat,normal,0,0,6,KC Sangakkara,"Punjab Cricket Association Stadium, Mohali",M Erasmus,AM Saheba,,Kings XI Punjab
+2013,Kolkata,Kolkata Knight Riders,Sunrisers Hyderabad,Kolkata Knight Riders,bat,normal,0,48,0,G Gambhir,Eden Gardens,M Erasmus,VA Kulkarni,,Kolkata Knight Riders
+2011,Chennai,Rajasthan Royals,Chennai Super Kings,Rajasthan Royals,bat,normal,0,0,8,MEK Hussey,"MA Chidambaram Stadium, Chepauk",SS Hazare,RB Tiffin,,Chennai Super Kings
+2015,Ahmedabad,Chennai Super Kings,Rajasthan Royals,Chennai Super Kings,bat,normal,0,0,8,AM Rahane,"Sardar Patel Stadium, Motera",AK Chaudhary,M Erasmus,,Rajasthan Royals
+2015,Delhi,Kings XI Punjab,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,9,NM Coulter-Nile,Feroz Shah Kotla,RK Illingworth,S Ravi,,Delhi Daredevils
+2008,Chandigarh,Kings XI Punjab,Mumbai Indians,Mumbai Indians,field,normal,0,66,0,KC Sangakkara,"Punjab Cricket Association Stadium, Mohali",Aleem Dar,AM Saheba,,Kings XI Punjab
+2013,Raipur,Delhi Daredevils,Pune Warriors,Pune Warriors,field,normal,0,15,0,DA Warner,Shaheed Veer Narayan Singh International Stadium,CK Nandan,S Ravi,,Delhi Daredevils
+2008,Bangalore,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,140,0,BB McCullum,M Chinnaswamy Stadium,Asad Rauf,RE Koertzen,,Kolkata Knight Riders
+2008,Chennai,Chennai Super Kings,Kings XI Punjab,Kings XI Punjab,field,normal,0,18,0,L Balaji,"MA Chidambaram Stadium, Chepauk",AV Jayaprakash,BG Jerling,,Chennai Super Kings
+2012,Delhi,Royal Challengers Bangalore,Delhi Daredevils,Delhi Daredevils,field,normal,0,21,0,CH Gayle,Feroz Shah Kotla,HDPK Dharmasena,C Shamshuddin,,Royal Challengers Bangalore
+2012,Visakhapatnam,Chennai Super Kings,Deccan Chargers,Deccan Chargers,field,normal,0,74,0,RA Jadeja,Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium,JD Cloete,HDPK Dharmasena,,Chennai Super Kings
+2016,Chandigarh,Mumbai Indians,Kings XI Punjab,Kings XI Punjab,field,normal,0,25,0,PA Patel,"Punjab Cricket Association IS Bindra Stadium, Mohali",Nitin Menon,RJ Tucker,,Mumbai Indians
+2016,Visakhapatnam,Sunrisers Hyderabad,Rising Pune Supergiants,Sunrisers Hyderabad,bat,normal,0,4,0,A Zampa,Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium,CB Gaffaney,VK Sharma,,Sunrisers Hyderabad
+2015,Mumbai,Kings XI Punjab,Mumbai Indians,Mumbai Indians,field,normal,0,18,0,GJ Bailey,Wankhede Stadium,AK Chaudhary,K Srinivasan,,Kings XI Punjab
+2010,Kolkata,Kolkata Knight Riders,Kings XI Punjab,Kolkata Knight Riders,bat,normal,0,0,8,DPMD Jayawardene,Eden Gardens,S Asnani,DJ Harper,,Kings XI Punjab
+2014,Sharjah,Rajasthan Royals,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,7,GJ Maxwell,Sharjah Cricket Stadium,BF Bowden,M Erasmus,,Kings XI Punjab
+2015,Pune,Royal Challengers Bangalore,Rajasthan Royals,Royal Challengers Bangalore,bat,normal,0,71,0,AB de Villiers,Maharashtra Cricket Association Stadium,AK Chaudhary,C Shamshuddin,,Royal Challengers Bangalore
+2012,Mumbai,Mumbai Indians,Rajasthan Royals,Rajasthan Royals,field,normal,0,27,0,KA Pollard,Wankhede Stadium,Aleem Dar,BNJ Oxenford,,Mumbai Indians
+2010,Chennai,Royal Challengers Bangalore,Chennai Super Kings,Royal Challengers Bangalore,bat,normal,0,0,5,M Vijay,"MA Chidambaram Stadium, Chepauk",BG Jerling,RE Koertzen,,Chennai Super Kings
+2012,Jaipur,Royal Challengers Bangalore,Rajasthan Royals,Rajasthan Royals,field,normal,0,46,0,AB de Villiers,Sawai Mansingh Stadium,Asad Rauf,S Asnani,,Royal Challengers Bangalore
+2014,Mumbai,Kings XI Punjab,Chennai Super Kings,Chennai Super Kings,field,normal,0,24,0,V Sehwag,Wankhede Stadium,HDPK Dharmasena,RJ Tucker,,Kings XI Punjab
+2009,Centurion,Royal Challengers Bangalore,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,7,A Singh,SuperSport Park,K Hariharan,DJ Harper,,Rajasthan Royals
+2008,Kolkata,Kolkata Knight Riders,Chennai Super Kings,Kolkata Knight Riders,bat,normal,1,3,0,M Ntini,Eden Gardens,Asad Rauf,K Hariharan,,Chennai Super Kings
+2013,Pune,Pune Warriors,Mumbai Indians,Pune Warriors,bat,normal,0,0,5,MG Johnson,Subrata Roy Sahara Stadium,Asad Rauf,AK Chaudhary,,Mumbai Indians
+2012,Bangalore,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,42,0,L Balaji,M Chinnaswamy Stadium,S Ravi,RJ Tucker,,Kolkata Knight Riders
+2016,Mumbai,Kolkata Knight Riders,Mumbai Indians,Mumbai Indians,field,normal,0,0,6,RG Sharma,Wankhede Stadium,Nitin Menon,RJ Tucker,,Mumbai Indians
+2011,Mumbai,Royal Challengers Bangalore,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,6,SK Raina,Wankhede Stadium,Asad Rauf,SJA Taufel,,Chennai Super Kings
+2010,Delhi,Delhi Daredevils,Chennai Super Kings,Delhi Daredevils,bat,normal,0,0,5,ML Hayden,Feroz Shah Kotla,BR Doctrove,SK Tarapore,,Chennai Super Kings
+2008,Chennai,Kolkata Knight Riders,Chennai Super Kings,Kolkata Knight Riders,bat,normal,0,0,9,JDP Oram,"MA Chidambaram Stadium, Chepauk",BF Bowden,AV Jayaprakash,,Chennai Super Kings
+2016,Delhi,Gujarat Lions,Delhi Daredevils,Delhi Daredevils,field,normal,0,1,0,CH Morris,Feroz Shah Kotla,M Erasmus,S Ravi,,Gujarat Lions
+2016,Rajkot,Gujarat Lions,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,8,RR Pant,Saurashtra Cricket Association Stadium,CB Gaffaney,BNJ Oxenford,,Delhi Daredevils
+2015,Chennai,Chennai Super Kings,Rajasthan Royals,Chennai Super Kings,bat,normal,0,12,0,RA Jadeja,"MA Chidambaram Stadium, Chepauk",M Erasmus,CK Nandan,,Chennai Super Kings
+2014,Chandigarh,Kings XI Punjab,Mumbai Indians,Mumbai Indians,field,normal,0,0,7,LMP Simmons,"Punjab Cricket Association Stadium, Mohali",HDPK Dharmasena,VA Kulkarni,,Mumbai Indians
+2013,Hyderabad,Mumbai Indians,Sunrisers Hyderabad,Mumbai Indians,bat,normal,0,0,7,I Sharma,"Rajiv Gandhi International Stadium, Uppal",Asad Rauf,S Asnani,,Sunrisers Hyderabad
+2009,Durban,Rajasthan Royals,Kings XI Punjab,Kings XI Punjab,field,normal,0,78,0,GC Smith,Kingsmead,SS Hazare,IL Howell,,Rajasthan Royals
+2014,Ranchi,Rajasthan Royals,Chennai Super Kings,Rajasthan Royals,bat,normal,0,0,5,RA Jadeja,JSCA International Stadium Complex,BNJ Oxenford,C Shamshuddin,,Chennai Super Kings
+2008,Mumbai,Mumbai Indians,Delhi Daredevils,Delhi Daredevils,field,normal,0,29,0,SM Pollock,Dr DY Patil Sports Academy,IL Howell,RE Koertzen,,Mumbai Indians
+2015,Chennai,Chennai Super Kings,Delhi Daredevils,Delhi Daredevils,field,normal,0,1,0,A Nehra,"MA Chidambaram Stadium, Chepauk",RK Illingworth,VA Kulkarni,,Chennai Super Kings
+2008,Delhi,Rajasthan Royals,Delhi Daredevils,Rajasthan Royals,bat,normal,0,0,9,MF Maharoof,Feroz Shah Kotla,Aleem Dar,GA Pratapkumar,,Delhi Daredevils
+2016,Kolkata,Kolkata Knight Riders,Gujarat Lions,Gujarat Lions,field,normal,0,0,5,P Kumar,Eden Gardens,M Erasmus,RJ Tucker,,Gujarat Lions
+2015,Visakhapatnam,Sunrisers Hyderabad,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,6,AM Rahane,Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium,PG Pathak,S Ravi,,Rajasthan Royals
+2010,Kolkata,Chennai Super Kings,Kolkata Knight Riders,Chennai Super Kings,bat,normal,0,55,0,MS Dhoni,Eden Gardens,HDPK Dharmasena,AM Saheba,,Chennai Super Kings
+2014,Bangalore,Royal Challengers Bangalore,Delhi Daredevils,Delhi Daredevils,field,normal,0,16,0,Yuvraj Singh,M Chinnaswamy Stadium,K Srinath,RJ Tucker,,Royal Challengers Bangalore
+2011,Indore,Kochi Tuskers Kerala,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,6,KD Karthik,Holkar Cricket Stadium,S Asnani,RJ Tucker,,Kings XI Punjab
+2010,Delhi,Delhi Daredevils,Kolkata Knight Riders,Delhi Daredevils,bat,normal,0,40,0,DA Warner,Feroz Shah Kotla,SS Hazare,SJA Taufel,,Delhi Daredevils
+2011,Bangalore,Royal Challengers Bangalore,Mumbai Indians,Mumbai Indians,field,normal,0,0,9,SR Tendulkar,M Chinnaswamy Stadium,HDPK Dharmasena,AL Hill,,Mumbai Indians
+2008,Chennai,Rajasthan Royals,Chennai Super Kings,Rajasthan Royals,bat,normal,0,10,0,JA Morkel,"MA Chidambaram Stadium, Chepauk",DJ Harper,SL Shastri,,Rajasthan Royals
+2015,Delhi,Delhi Daredevils,Mumbai Indians,Mumbai Indians,field,normal,0,37,0,SS Iyer,Feroz Shah Kotla,SD Fry,CK Nandan,,Delhi Daredevils
+2008,Chandigarh,Delhi Daredevils,Kings XI Punjab,Delhi Daredevils,bat,normal,0,0,4,SM Katich,"Punjab Cricket Association Stadium, Mohali",RE Koertzen,I Shivram,,Kings XI Punjab
+2011,Bangalore,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,1,0,4,CH Gayle,M Chinnaswamy Stadium,RE Koertzen,RB Tiffin,,Royal Challengers Bangalore
+2013,Chennai,Pune Warriors,Chennai Super Kings,Pune Warriors,bat,normal,0,24,0,SPD Smith,"MA Chidambaram Stadium, Chepauk",Asad Rauf,AK Chaudhary,,Pune Warriors
+2014,Ranchi,Chennai Super Kings,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,0,6,DA Warner,JSCA International Stadium Complex,BNJ Oxenford,C Shamshuddin,,Sunrisers Hyderabad
+2014,Sharjah,Mumbai Indians,Delhi Daredevils,Mumbai Indians,bat,normal,0,0,6,M Vijay,Sharjah Cricket Stadium,Aleem Dar,VA Kulkarni,,Delhi Daredevils
+2011,Mumbai,Mumbai Indians,Kochi Tuskers Kerala,Kochi Tuskers Kerala,field,normal,0,0,8,BB McCullum,Wankhede Stadium,BR Doctrove,PR Reiffel,,Kochi Tuskers Kerala
+2014,,Chennai Super Kings,Rajasthan Royals,Rajasthan Royals,field,normal,0,7,0,RA Jadeja,Dubai International Cricket Stadium,HDPK Dharmasena,RK Illingworth,,Chennai Super Kings
+2009,Port Elizabeth,Mumbai Indians,Kolkata Knight Riders,Mumbai Indians,bat,normal,0,92,0,SR Tendulkar,St George's Park,BG Jerling,RB Tiffin,,Mumbai Indians
+2012,Kolkata,Kolkata Knight Riders,Pune Warriors,Kolkata Knight Riders,bat,normal,0,7,0,SP Narine,Eden Gardens,BF Bowden,SK Tarapore,,Kolkata Knight Riders
+2015,Raipur,Chennai Super Kings,Delhi Daredevils,Chennai Super Kings,bat,normal,0,0,6,Z Khan,Shaheed Veer Narayan Singh International Stadium,RK Illingworth,VA Kulkarni,,Delhi Daredevils
+2012,Jaipur,Rajasthan Royals,Kings XI Punjab,Kings XI Punjab,field,normal,0,31,0,AM Rahane,Sawai Mansingh Stadium,BF Bowden,SK Tarapore,,Rajasthan Royals
+2009,Johannesburg,Kolkata Knight Riders,Deccan Chargers,Deccan Chargers,field,normal,0,0,6,RG Sharma,New Wanderers Stadium,RE Koertzen,S Ravi,,Deccan Chargers
+2012,Delhi,Kings XI Punjab,Delhi Daredevils,Kings XI Punjab,bat,normal,0,0,5,UT Yadav,Feroz Shah Kotla,HDPK Dharmasena,BNJ Oxenford,,Delhi Daredevils
+2009,Durban,Deccan Chargers,Mumbai Indians,Deccan Chargers,bat,normal,0,12,0,PP Ojha,Kingsmead,HDPK Dharmasena,SJA Taufel,,Deccan Chargers
+2013,Jaipur,Chennai Super Kings,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,5,SR Watson,Sawai Mansingh Stadium,HDPK Dharmasena,CK Nandan,,Rajasthan Royals
+2010,Kolkata,Mumbai Indians,Kolkata Knight Riders,Mumbai Indians,bat,normal,0,0,9,M Kartik,Eden Gardens,BG Jerling,RE Koertzen,,Kolkata Knight Riders
+2013,Delhi,Delhi Daredevils,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,5,Harmeet Singh,Feroz Shah Kotla,VA Kulkarni,K Srinath,,Kings XI Punjab
+2014,Sharjah,Sunrisers Hyderabad,Chennai Super Kings,Sunrisers Hyderabad,bat,normal,0,0,5,DR Smith,Sharjah Cricket Stadium,AK Chaudhary,VA Kulkarni,,Chennai Super Kings
+2012,Kolkata,Kolkata Knight Riders,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,5,MEK Hussey,Eden Gardens,JD Cloete,SJA Taufel,,Chennai Super Kings
+2014,,Kolkata Knight Riders,Delhi Daredevils,Kolkata Knight Riders,bat,normal,0,0,4,JP Duminy,Dubai International Cricket Stadium,Aleem Dar,VA Kulkarni,,Delhi Daredevils
+2008,Chennai,Royal Challengers Bangalore,Chennai Super Kings,Royal Challengers Bangalore,bat,normal,0,14,0,A Kumble,"MA Chidambaram Stadium, Chepauk",DJ Harper,I Shivram,,Royal Challengers Bangalore
+2008,Jaipur,Rajasthan Royals,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,65,0,GC Smith,Sawai Mansingh Stadium,BF Bowden,SL Shastri,,Rajasthan Royals
+2016,Hyderabad,Sunrisers Hyderabad,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,15,0,DA Warner,"Rajiv Gandhi International Stadium, Uppal",AK Chaudhary,HDPK Dharmasena,,Sunrisers Hyderabad
+2012,Mumbai,Mumbai Indians,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,7,S Nadeem,Wankhede Stadium,BF Bowden,SK Tarapore,,Delhi Daredevils
+2013,Bangalore,Delhi Daredevils,Royal Challengers Bangalore,Royal Challengers Bangalore,field,tie,0,0,0,V Kohli,M Chinnaswamy Stadium,M Erasmus,VA Kulkarni,,Royal Challengers Bangalore
+2010,Mumbai,Chennai Super Kings,Mumbai Indians,Mumbai Indians,field,normal,0,0,5,SR Tendulkar,Brabourne Stadium,BF Bowden,AM Saheba,,Mumbai Indians
+2014,Kolkata,Kolkata Knight Riders,Kings XI Punjab,Kings XI Punjab,field,normal,0,28,0,UT Yadav,Eden Gardens,NJ Llong,S Ravi,,Kolkata Knight Riders
+2009,Centurion,Chennai Super Kings,Rajasthan Royals,Rajasthan Royals,field,normal,0,38,0,SK Raina,SuperSport Park,GAV Baxter,RE Koertzen,,Chennai Super Kings
+2013,Kolkata,Kings XI Punjab,Kolkata Knight Riders,Kings XI Punjab,bat,normal,0,0,6,JH Kallis,Eden Gardens,CK Nandan,S Ravi,,Kolkata Knight Riders
+2010,Delhi,Delhi Daredevils,Royal Challengers Bangalore,Delhi Daredevils,bat,normal,0,37,0,PD Collingwood,Feroz Shah Kotla,BF Bowden,M Erasmus,,Delhi Daredevils
+2013,Chennai,Royal Challengers Bangalore,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,4,RA Jadeja,"MA Chidambaram Stadium, Chepauk",Asad Rauf,AK Chowdhary,,Chennai Super Kings
+2013,Jaipur,Rajasthan Royals,Mumbai Indians,Rajasthan Royals,bat,normal,0,87,0,AM Rahane,Sawai Mansingh Stadium,Aleem Dar,C Shamshuddin,,Rajasthan Royals
+2012,Chandigarh,Rajasthan Royals,Kings XI Punjab,Rajasthan Royals,bat,normal,0,43,0,SR Watson,"Punjab Cricket Association Stadium, Mohali",JD Cloete,SJA Taufel,,Rajasthan Royals
+2009,Kimberley,Rajasthan Royals,Chennai Super Kings,Rajasthan Royals,bat,normal,0,0,7,S Badrinath,De Beers Diamond Oval,GAV Baxter,HDPK Dharmasena,,Chennai Super Kings
+2016,Delhi,Delhi Daredevils,Mumbai Indians,Mumbai Indians,field,normal,0,10,0,SV Samson,Feroz Shah Kotla,S Ravi,C Shamshuddin,,Delhi Daredevils
+2010,Chandigarh,Kings XI Punjab,Royal Challengers Bangalore,Kings XI Punjab,bat,normal,0,0,6,KP Pietersen,"Punjab Cricket Association Stadium, Mohali",BF Bowden,M Erasmus,,Royal Challengers Bangalore
+2008,Bangalore,Royal Challengers Bangalore,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,6,S Sreesanth,M Chinnaswamy Stadium,SJ Davis,BR Doctrove,,Kings XI Punjab
+2010,Mumbai,Kolkata Knight Riders,Mumbai Indians,Kolkata Knight Riders,bat,normal,0,0,7,SR Tendulkar,Brabourne Stadium,SS Hazare,SJA Taufel,,Mumbai Indians
+2014,Delhi,Delhi Daredevils,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,7,KK Nair,Feroz Shah Kotla,SS Hazare,S Ravi,,Rajasthan Royals
+2013,Mumbai,Sunrisers Hyderabad,Mumbai Indians,Sunrisers Hyderabad,bat,normal,0,0,7,KA Pollard,Wankhede Stadium,AK Chaudhary,SJA Taufel,,Mumbai Indians
+2015,Pune,Kings XI Punjab,Delhi Daredevils,Kings XI Punjab,bat,normal,0,0,5,MA Agarwal,Maharashtra Cricket Association Stadium,CB Gaffaney,K Srinath,,Delhi Daredevils
+2012,Bangalore,Pune Warriors,Royal Challengers Bangalore,Pune Warriors,bat,normal,0,0,6,CH Gayle,M Chinnaswamy Stadium,S Asnani,S Das,,Royal Challengers Bangalore
+2008,Hyderabad,Deccan Chargers,Delhi Daredevils,Deccan Chargers,bat,normal,0,0,9,V Sehwag,"Rajiv Gandhi International Stadium, Uppal",IL Howell,AM Saheba,,Delhi Daredevils
+2016,Chandigarh,Kings XI Punjab,Delhi Daredevils,Delhi Daredevils,field,normal,0,9,0,MP Stoinis,"Punjab Cricket Association IS Bindra Stadium, Mohali",HDPK Dharmasena,CK Nandan,,Kings XI Punjab
+2008,Mumbai,Chennai Super Kings,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,3,YK Pathan,Dr DY Patil Sports Academy,BF Bowden,RE Koertzen,,Rajasthan Royals
+2012,Mumbai,Deccan Chargers,Mumbai Indians,Mumbai Indians,field,normal,0,0,5,DW Steyn,Wankhede Stadium,AK Chaudhary,BNJ Oxenford,,Mumbai Indians
+2009,Durban,Rajasthan Royals,Mumbai Indians,Rajasthan Royals,bat,normal,0,2,0,SK Warne,Kingsmead,BR Doctrove,DJ Harper,,Rajasthan Royals
+2015,Mumbai,Sunrisers Hyderabad,Rajasthan Royals,Rajasthan Royals,field,normal,0,7,0,EJG Morgan,Brabourne Stadium,JD Cloete,C Shamshuddin,,Sunrisers Hyderabad
+2015,Chandigarh,Sunrisers Hyderabad,Kings XI Punjab,Kings XI Punjab,field,normal,0,20,0,TA Boult,"Punjab Cricket Association Stadium, Mohali",HDPK Dharmasena,CB Gaffaney,,Sunrisers Hyderabad
+2011,Chennai,Chennai Super Kings,Royal Challengers Bangalore,Chennai Super Kings,bat,normal,0,58,0,M Vijay,"MA Chidambaram Stadium, Chepauk",Asad Rauf,SJA Taufel,,Chennai Super Kings
+2014,Abu Dhabi,Royal Challengers Bangalore,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,6,PV Tambe,Sheikh Zayed Stadium,HDPK Dharmasena,C Shamshuddin,,Rajasthan Royals
+2013,Pune,Pune Warriors,Kings XI Punjab,Pune Warriors,bat,normal,0,0,8,M Vohra,Subrata Roy Sahara Stadium,S Asnani,SJA Taufel,,Kings XI Punjab
+2013,Pune,Pune Warriors,Delhi Daredevils,Pune Warriors,bat,normal,0,38,0,LJ Wright,Subrata Roy Sahara Stadium,NJ Llong,SJA Taufel,,Pune Warriors
+2012,Chandigarh,Kings XI Punjab,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,5,CH Gayle,"Punjab Cricket Association Stadium, Mohali",S Ravi,RJ Tucker,,Royal Challengers Bangalore
+2014,Chandigarh,Kings XI Punjab,Rajasthan Royals,Rajasthan Royals,field,normal,0,16,0,SE Marsh,"Punjab Cricket Association Stadium, Mohali",HDPK Dharmasena,PG Pathak,,Kings XI Punjab
+2008,Jaipur,Chennai Super Kings,Rajasthan Royals,Chennai Super Kings,bat,normal,0,0,8,Sohail Tanvir,Sawai Mansingh Stadium,Asad Rauf,AV Jayaprakash,,Rajasthan Royals
+2011,Dharamsala,Kings XI Punjab,Delhi Daredevils,Delhi Daredevils,field,normal,0,29,0,PP Chawla,Himachal Pradesh Cricket Association Stadium,Asad Rauf,SL Shastri,,Kings XI Punjab
+2010,Cuttack,Deccan Chargers,Delhi Daredevils,Deccan Chargers,bat,normal,0,10,0,A Symonds,Barabati Stadium,BF Bowden,M Erasmus,,Deccan Chargers
+2008,Hyderabad,Deccan Chargers,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,3,YK Pathan,"Rajiv Gandhi International Stadium, Uppal",Asad Rauf,MR Benson,,Rajasthan Royals
+2008,Hyderabad,Deccan Chargers,Chennai Super Kings,Deccan Chargers,bat,normal,0,0,7,SK Raina,"Rajiv Gandhi International Stadium, Uppal",BG Jerling,AM Saheba,,Chennai Super Kings
+2013,Chandigarh,Sunrisers Hyderabad,Kings XI Punjab,Kings XI Punjab,field,normal,0,30,0,PA Patel,"Punjab Cricket Association Stadium, Mohali",S Das,RJ Tucker,,Sunrisers Hyderabad
+2014,Hyderabad,Sunrisers Hyderabad,Kolkata Knight Riders,Sunrisers Hyderabad,bat,normal,0,0,7,UT Yadav,"Rajiv Gandhi International Stadium, Uppal",NJ Llong,CK Nandan,,Kolkata Knight Riders
+2011,Mumbai,Pune Warriors,Chennai Super Kings,Pune Warriors,bat,normal,0,0,8,DE Bollinger,Dr DY Patil Sports Academy,Asad Rauf,SL Shastri,,Chennai Super Kings
+2015,Ahmedabad,Rajasthan Royals,Kings XI Punjab,Kings XI Punjab,field,tie,0,0,0,SE Marsh,"Sardar Patel Stadium, Motera",M Erasmus,S Ravi,,Kings XI Punjab
+2012,Bangalore,Deccan Chargers,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,5,AB de Villiers,M Chinnaswamy Stadium,HDPK Dharmasena,BNJ Oxenford,,Royal Challengers Bangalore
+2015,Hyderabad,Sunrisers Hyderabad,Mumbai Indians,Sunrisers Hyderabad,bat,normal,0,0,9,MJ McClenaghan,"Rajiv Gandhi International Stadium, Uppal",CB Gaffaney,K Srinath,,Mumbai Indians
+2015,Kolkata,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,3,CH Gayle,Eden Gardens,S Ravi,C Shamshuddin,,Royal Challengers Bangalore
+2015,Chandigarh,Kings XI Punjab,Chennai Super Kings,Kings XI Punjab,bat,normal,0,0,7,P Negi,"Punjab Cricket Association Stadium, Mohali",CK Nandan,C Shamshuddin,,Chennai Super Kings
+2012,Pune,Mumbai Indians,Pune Warriors,Mumbai Indians,bat,normal,0,1,0,SL Malinga,Subrata Roy Sahara Stadium,Asad Rauf,S Asnani,,Mumbai Indians
+2011,Chennai,Chennai Super Kings,Pune Warriors,Pune Warriors,field,normal,0,25,0,MEK Hussey,"MA Chidambaram Stadium, Chepauk",Aleem Dar,RB Tiffin,,Chennai Super Kings
+2015,Mumbai,Mumbai Indians,Chennai Super Kings,Mumbai Indians,bat,normal,0,25,0,KA Pollard,Wankhede Stadium,HDPK Dharmasena,RK Illingworth,,Mumbai Indians
+2011,Hyderabad,Deccan Chargers,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,4,V Sehwag,"Rajiv Gandhi International Stadium, Uppal",Asad Rauf,AM Saheba,,Delhi Daredevils
+2009,Johannesburg,Deccan Chargers,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,6,0,A Kumble,New Wanderers Stadium,RE Koertzen,SJA Taufel,,Deccan Chargers
+2011,Kochi,Delhi Daredevils,Kochi Tuskers Kerala,Delhi Daredevils,bat,normal,0,38,0,V Sehwag,Nehru Stadium,HDPK Dharmasena,AL Hill,,Delhi Daredevils
+2012,Mumbai,Mumbai Indians,Kings XI Punjab,Mumbai Indians,bat,normal,0,0,6,SE Marsh,Wankhede Stadium,S Ravi,RJ Tucker,,Kings XI Punjab
+2013,Delhi,Royal Challengers Bangalore,Delhi Daredevils,Delhi Daredevils,field,normal,0,4,0,JD Unadkat,Feroz Shah Kotla,NJ Llong,K Srinath,,Royal Challengers Bangalore
+2015,Kolkata,Kolkata Knight Riders,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,35,0,UT Yadav,Eden Gardens,AK Chaudhary,M Erasmus,,Kolkata Knight Riders
+2011,Delhi,Delhi Daredevils,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,3,V Kohli,Feroz Shah Kotla,S Asnani,RJ Tucker,,Royal Challengers Bangalore
+2008,Mumbai,Chennai Super Kings,Mumbai Indians,Mumbai Indians,field,normal,0,0,9,ST Jayasuriya,Wankhede Stadium,BR Doctrove,AM Saheba,,Mumbai Indians
+2012,Delhi,Delhi Daredevils,Mumbai Indians,Mumbai Indians,field,normal,0,37,0,V Sehwag,Feroz Shah Kotla,Aleem Dar,BNJ Oxenford,,Delhi Daredevils
+2016,Delhi,Delhi Daredevils,Rising Pune Supergiants,Rising Pune Supergiants,field,normal,0,0,7,AM Rahane,Feroz Shah Kotla,C Shamshuddin,RJ Tucker,,Rising Pune Supergiants
+2010,Delhi,Delhi Daredevils,Rajasthan Royals,Delhi Daredevils,bat,normal,0,67,0,KD Karthik,Feroz Shah Kotla,HDPK Dharmasena,SJA Taufel,,Delhi Daredevils
+2009,Port Elizabeth,Deccan Chargers,Rajasthan Royals,Deccan Chargers,bat,normal,0,0,3,YK Pathan,St George's Park,S Asnani,BG Jerling,,Rajasthan Royals
+2013,Bangalore,Royal Challengers Bangalore,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,7,AC Gilchrist,M Chinnaswamy Stadium,HDPK Dharmasena,S Ravi,,Kings XI Punjab
+2011,Chennai,Chennai Super Kings,Royal Challengers Bangalore,Chennai Super Kings,bat,normal,0,21,0,MEK Hussey,"MA Chidambaram Stadium, Chepauk",HDPK Dharmasena,AL Hill,,Chennai Super Kings
+2010,Nagpur,Chennai Super Kings,Deccan Chargers,Chennai Super Kings,bat,normal,0,0,6,RJ Harris,"Vidarbha Cricket Association Stadium, Jamtha",HDPK Dharmasena,SJA Taufel,,Deccan Chargers
+2011,Chennai,Royal Challengers Bangalore,Mumbai Indians,Mumbai Indians,field,normal,0,43,0,CH Gayle,"MA Chidambaram Stadium, Chepauk",Asad Rauf,SJA Taufel,,Royal Challengers Bangalore
+2015,Kolkata,Kings XI Punjab,Kolkata Knight Riders,Kings XI Punjab,bat,normal,0,0,1,AD Russell,Eden Gardens,AK Chaudhary,HDPK Dharmasena,,Kolkata Knight Riders
+2009,Cape Town,Kings XI Punjab,Rajasthan Royals,Kings XI Punjab,bat,normal,0,27,0,KC Sangakkara,Newlands,M Erasmus,K Hariharan,,Kings XI Punjab
+2008,Hyderabad,Deccan Chargers,Royal Challengers Bangalore,Deccan Chargers,bat,normal,0,0,5,R Vinay Kumar,"Rajiv Gandhi International Stadium, Uppal",Asad Rauf,RE Koertzen,,Royal Challengers Bangalore
+2009,Centurion,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,6,LRPL Taylor,SuperSport Park,M Erasmus,SS Hazare,,Royal Challengers Bangalore
+2011,Mumbai,Pune Warriors,Mumbai Indians,Pune Warriors,bat,normal,0,0,7,MM Patel,Wankhede Stadium,Asad Rauf,AM Saheba,,Mumbai Indians
+2009,Centurion,Mumbai Indians,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,4,V Sehwag,SuperSport Park,IL Howell,S Ravi,,Delhi Daredevils
+2012,Chandigarh,Deccan Chargers,Kings XI Punjab,Deccan Chargers,bat,normal,0,0,4,DJ Hussey,"Punjab Cricket Association Stadium, Mohali",HDPK Dharmasena,BNJ Oxenford,,Kings XI Punjab
+2008,Chandigarh,Kings XI Punjab,Rajasthan Royals,Rajasthan Royals,field,normal,0,41,0,SE Marsh,"Punjab Cricket Association Stadium, Mohali",SJ Davis,K Hariharan,,Kings XI Punjab
+2013,Chandigarh,Kings XI Punjab,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,8,KK Cooper,"Punjab Cricket Association Stadium, Mohali",HDPK Dharmasena,S Ravi,,Rajasthan Royals
+2016,Pune,Rising Pune Supergiants,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,2,SA Yadav,Maharashtra Cricket Association Stadium,CB Gaffaney,A Nand Kishore,,Kolkata Knight Riders
+2014,,Mumbai Indians,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,7,PA Patel,Dubai International Cricket Stadium,Aleem Dar,AK Chaudhary,,Royal Challengers Bangalore
+2010,Mumbai,Kings XI Punjab,Mumbai Indians,Mumbai Indians,field,normal,0,0,4,SL Malinga,Brabourne Stadium,BR Doctrove,SK Tarapore,,Mumbai Indians
+2013,Bangalore,Sunrisers Hyderabad,Royal Challengers Bangalore,Sunrisers Hyderabad,bat,normal,0,0,7,V Kohli,M Chinnaswamy Stadium,S Ravi,SJA Taufel,,Royal Challengers Bangalore
+2011,Hyderabad,Deccan Chargers,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,33,0,DW Steyn,"Rajiv Gandhi International Stadium, Uppal",RE Koertzen,S Ravi,,Deccan Chargers
+2012,Pune,Royal Challengers Bangalore,Pune Warriors,Pune Warriors,field,normal,0,35,0,CH Gayle,Subrata Roy Sahara Stadium,BF Bowden,SK Tarapore,,Royal Challengers Bangalore
+2012,Pune,Pune Warriors,Kings XI Punjab,Pune Warriors,bat,normal,0,22,0,MN Samuels,Subrata Roy Sahara Stadium,S Das,SJA Taufel,,Pune Warriors
+2013,Chandigarh,Royal Challengers Bangalore,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,6,DA Miller,"Punjab Cricket Association Stadium, Mohali",VA Kulkarni,NJ Llong,,Kings XI Punjab
+2009,Cape Town,Deccan Chargers,Royal Challengers Bangalore,Deccan Chargers,bat,normal,0,24,0,AC Gilchrist,Newlands,M Erasmus,AM Saheba,,Deccan Chargers
+2012,Kolkata,Rajasthan Royals,Kolkata Knight Riders,Rajasthan Royals,bat,normal,0,0,5,Shakib Al Hasan,Eden Gardens,Asad Rauf,S Asnani,,Kolkata Knight Riders
+2011,Jaipur,Delhi Daredevils,Rajasthan Royals,Delhi Daredevils,bat,normal,0,0,6,SK Warne,Sawai Mansingh Stadium,Aleem Dar,RB Tiffin,,Rajasthan Royals
+2012,Chennai,Kings XI Punjab,Chennai Super Kings,Kings XI Punjab,bat,normal,0,7,0,Mandeep Singh,"MA Chidambaram Stadium, Chepauk",BF Bowden,SK Tarapore,,Kings XI Punjab
+2012,Delhi,Delhi Daredevils,Rajasthan Royals,Delhi Daredevils,bat,normal,0,1,0,V Sehwag,Feroz Shah Kotla,S Ravi,RJ Tucker,,Delhi Daredevils
+2014,Ahmedabad,Rajasthan Royals,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,10,0,PV Tambe,"Sardar Patel Stadium, Motera",NJ Llong,CK Nandan,,Rajasthan Royals
+2009,Durban,Kings XI Punjab,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,1,11,0,CH Gayle,Kingsmead,DJ Harper,SD Ranade,,Kolkata Knight Riders
+2011,Dharamsala,Deccan Chargers,Kings XI Punjab,Kings XI Punjab,field,normal,0,82,0,S Dhawan,Himachal Pradesh Cricket Association Stadium,Asad Rauf,AM Saheba,,Deccan Chargers
+2015,Delhi,Delhi Daredevils,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,3,DJ Hooda,Feroz Shah Kotla,SD Fry,CB Gaffaney,,Rajasthan Royals
+2014,,Sunrisers Hyderabad,Mumbai Indians,Mumbai Indians,field,normal,0,15,0,B Kumar,Dubai International Cricket Stadium,HDPK Dharmasena,M Erasmus,,Sunrisers Hyderabad
+2013,Jaipur,Sunrisers Hyderabad,Rajasthan Royals,Sunrisers Hyderabad,bat,normal,0,0,8,JP Faulkner,Sawai Mansingh Stadium,VA Kulkarni,K Srinath,,Rajasthan Royals
+2010,Bangalore,Kings XI Punjab,Royal Challengers Bangalore,Kings XI Punjab,bat,normal,0,0,8,JH Kallis,M Chinnaswamy Stadium,S Das,DJ Harper,,Royal Challengers Bangalore
+2011,Mumbai,Mumbai Indians,Pune Warriors,Pune Warriors,field,normal,0,21,0,R Sharma,Dr DY Patil Sports Academy,HDPK Dharmasena,SJA Taufel,,Mumbai Indians
+2016,Mumbai,Mumbai Indians,Rising Pune Supergiants,Mumbai Indians,bat,normal,0,0,9,AM Rahane,Wankhede Stadium,HDPK Dharmasena,CK Nandan,,Rising Pune Supergiants
+2015,Bangalore,Mumbai Indians,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,18,0,Harbhajan Singh,M Chinnaswamy Stadium,RK Illingworth,VA Kulkarni,,Mumbai Indians
+2011,Kochi,Deccan Chargers,Kochi Tuskers Kerala,Kochi Tuskers Kerala,field,normal,0,55,0,I Sharma,Nehru Stadium,HDPK Dharmasena,AL Hill,,Deccan Chargers
+2014,,Mumbai Indians,Chennai Super Kings,Mumbai Indians,bat,normal,0,0,7,MM Sharma,Dubai International Cricket Stadium,BF Bowden,M Erasmus,,Chennai Super Kings
+2010,Ahmedabad,Deccan Chargers,Rajasthan Royals,Deccan Chargers,bat,normal,0,0,8,YK Pathan,"Sardar Patel Stadium, Motera",HDPK Dharmasena,SJA Taufel,,Rajasthan Royals
+2009,Durban,Chennai Super Kings,Royal Challengers Bangalore,Chennai Super Kings,bat,normal,0,0,2,LRPL Taylor,Kingsmead,BR Doctrove,DJ Harper,,Royal Challengers Bangalore
+2013,Pune,Kolkata Knight Riders,Pune Warriors,Kolkata Knight Riders,bat,normal,0,46,0,G Gambhir,Subrata Roy Sahara Stadium,Asad Rauf,S Asnani,,Kolkata Knight Riders
+2012,Chandigarh,Pune Warriors,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,7,AD Mascarenhas,"Punjab Cricket Association Stadium, Mohali",VA Kulkarni,SK Tarapore,,Kings XI Punjab
+2016,Hyderabad,Sunrisers Hyderabad,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,7,CH Morris,"Rajiv Gandhi International Stadium, Uppal",K Bharatan,M Erasmus,,Delhi Daredevils
+2011,Chandigarh,Kings XI Punjab,Pune Warriors,Kings XI Punjab,bat,normal,0,0,5,R Sharma,"Punjab Cricket Association Stadium, Mohali",SK Tarapore,RJ Tucker,,Pune Warriors
+2016,Rajkot,Rising Pune Supergiants,Gujarat Lions,Rising Pune Supergiants,bat,normal,0,0,7,AJ Finch,Saurashtra Cricket Association Stadium,VA Kulkarni,CK Nandan,,Gujarat Lions
+2014,Ahmedabad,Sunrisers Hyderabad,Rajasthan Royals,Rajasthan Royals,field,normal,0,32,0,B Kumar,"Sardar Patel Stadium, Motera",AK Chaudhary,NJ Llong,,Sunrisers Hyderabad
+2009,Cape Town,Mumbai Indians,Chennai Super Kings,Chennai Super Kings,field,normal,0,19,0,SR Tendulkar,Newlands,BR Doctrove,K Hariharan,,Mumbai Indians
+2016,Mumbai,Royal Challengers Bangalore,Mumbai Indians,Mumbai Indians,field,normal,0,0,6,RG Sharma,Wankhede Stadium,AK Chaudhary,CK Nandan,,Mumbai Indians
+2009,Kimberley,Deccan Chargers,Rajasthan Royals,Deccan Chargers,bat,normal,0,53,0,DR Smith,De Beers Diamond Oval,GAV Baxter,HDPK Dharmasena,,Deccan Chargers
+2016,Delhi,Gujarat Lions,Sunrisers Hyderabad,Sunrisers Hyderabad,field,normal,0,0,4,DA Warner,Feroz Shah Kotla,M Erasmus,CK Nandan,,Sunrisers Hyderabad
+2014,Sharjah,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,2,0,CA Lynn,Sharjah Cricket Stadium,Aleem Dar,VA Kulkarni,,Kolkata Knight Riders
+2013,Hyderabad,Kings XI Punjab,Sunrisers Hyderabad,Kings XI Punjab,bat,normal,0,0,5,GH Vihari,"Rajiv Gandhi International Stadium, Uppal",HDPK Dharmasena,CK Nandan,,Sunrisers Hyderabad
+2009,Centurion,Deccan Chargers,Mumbai Indians,Deccan Chargers,bat,normal,0,19,0,RG Sharma,SuperSport Park,MR Benson,HDPK Dharmasena,,Deccan Chargers
+2016,Delhi,Sunrisers Hyderabad,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,22,0,MC Henriques,Feroz Shah Kotla,M Erasmus,C Shamshuddin,,Sunrisers Hyderabad
+2014,Mumbai,Rajasthan Royals,Mumbai Indians,Mumbai Indians,field,normal,0,0,5,CJ Anderson,Wankhede Stadium,K Srinath,RJ Tucker,,Mumbai Indians
+2015,Bangalore,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,7,Mandeep Singh,M Chinnaswamy Stadium,JD Cloete,PG Pathak,,Royal Challengers Bangalore
+2010,Jaipur,Kings XI Punjab,Rajasthan Royals,Kings XI Punjab,bat,normal,0,0,9,MJ Lumb,Sawai Mansingh Stadium,S Ravi,SK Tarapore,,Rajasthan Royals
+2008,Bangalore,Royal Challengers Bangalore,Deccan Chargers,Deccan Chargers,field,normal,0,3,0,P Kumar,M Chinnaswamy Stadium,BR Doctrove,SL Shastri,,Royal Challengers Bangalore
+2015,Kolkata,Kolkata Knight Riders,Delhi Daredevils,Kolkata Knight Riders,bat,normal,0,13,0,PP Chawla,Eden Gardens,AK Chaudhary,M Erasmus,,Kolkata Knight Riders
+2016,Raipur,Delhi Daredevils,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,6,V Kohli,Shaheed Veer Narayan Singh International Stadium,A Nand Kishore,BNJ Oxenford,,Royal Challengers Bangalore
+2008,Kolkata,Kings XI Punjab,Kolkata Knight Riders,Kings XI Punjab,bat,normal,0,0,3,Umar Gul,Eden Gardens,SJ Davis,I Shivram,,Kolkata Knight Riders
+2016,Delhi,Delhi Daredevils,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,27,0,CR Brathwaite,Feroz Shah Kotla,KN Ananthapadmanabhan,M Erasmus,,Delhi Daredevils
+2011,Bangalore,Kochi Tuskers Kerala,Royal Challengers Bangalore,Kochi Tuskers Kerala,bat,normal,0,0,9,CH Gayle,M Chinnaswamy Stadium,Aleem Dar,SS Hazare,,Royal Challengers Bangalore
+2011,Chennai,Chennai Super Kings,Deccan Chargers,Chennai Super Kings,bat,normal,0,19,0,JA Morkel,"MA Chidambaram Stadium, Chepauk",Aleem Dar,RB Tiffin,,Chennai Super Kings
+2016,Mumbai,Mumbai Indians,Gujarat Lions,Gujarat Lions,field,normal,0,0,3,AJ Finch,Wankhede Stadium,HDPK Dharmasena,VK Sharma,,Gujarat Lions
+2013,Jaipur,Pune Warriors,Rajasthan Royals,Pune Warriors,bat,normal,0,0,5,AM Rahane,Sawai Mansingh Stadium,C Shamshuddin,RJ Tucker,,Rajasthan Royals
+2013,Mumbai,Mumbai Indians,Chennai Super Kings,Mumbai Indians,bat,normal,0,60,0,MG Johnson,Wankhede Stadium,HDPK Dharmasena,CK Nandan,,Mumbai Indians
+2009,Durban,Kolkata Knight Riders,Royal Challengers Bangalore,Kolkata Knight Riders,bat,normal,0,0,5,MV Boucher,Kingsmead,MR Benson,TH Wijewardene,,Royal Challengers Bangalore
+2015,Chennai,Chennai Super Kings,Mumbai Indians,Chennai Super Kings,bat,normal,0,0,6,HH Pandya,"MA Chidambaram Stadium, Chepauk",CB Gaffaney,CK Nandan,,Mumbai Indians
+2011,Mumbai,Pune Warriors,Deccan Chargers,Deccan Chargers,field,normal,0,0,6,A Mishra,Dr DY Patil Sports Academy,S Ravi,SK Tarapore,,Deccan Chargers
+2009,East London,Mumbai Indians,Kolkata Knight Riders,Mumbai Indians,bat,normal,0,9,0,JP Duminy,Buffalo Park,M Erasmus,SK Tarapore,,Mumbai Indians
+2010,Nagpur,Rajasthan Royals,Deccan Chargers,Rajasthan Royals,bat,normal,0,2,0,SK Warne,"Vidarbha Cricket Association Stadium, Jamtha",HDPK Dharmasena,SJA Taufel,,Rajasthan Royals
+2008,Delhi,Delhi Daredevils,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,4,MS Dhoni,Feroz Shah Kotla,Aleem Dar,RB Tiffin,,Chennai Super Kings
+2012,Kolkata,Kolkata Knight Riders,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,8,IK Pathan,Eden Gardens,S Asnani,HDPK Dharmasena,,Delhi Daredevils
+2008,Bangalore,Royal Challengers Bangalore,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,5,SP Goswami,M Chinnaswamy Stadium,SJ Davis,GA Pratapkumar,,Delhi Daredevils
+2011,Kolkata,Kochi Tuskers Kerala,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,6,0,DPMD Jayawardene,Eden Gardens,Aleem Dar,RB Tiffin,,Kochi Tuskers Kerala
+2016,Chandigarh,Kings XI Punjab,Sunrisers Hyderabad,Kings XI Punjab,bat,normal,0,0,7,HM Amla,"Punjab Cricket Association IS Bindra Stadium, Mohali",KN Ananthapadmanabhan,M Erasmus,,Sunrisers Hyderabad
+2010,Mumbai,Chennai Super Kings,Mumbai Indians,Chennai Super Kings,bat,normal,0,22,0,SK Raina,Dr DY Patil Sports Academy,RE Koertzen,SJA Taufel,,Chennai Super Kings
+2009,Durban,Chennai Super Kings,Kings XI Punjab,Chennai Super Kings,bat,normal,0,24,0,M Muralitharan,Kingsmead,BG Jerling,SJA Taufel,,Chennai Super Kings
+2015,Mumbai,Rajasthan Royals,Kolkata Knight Riders,Rajasthan Royals,bat,normal,0,9,0,SR Watson,Brabourne Stadium,RM Deshpande,RK Illingworth,,Rajasthan Royals
+2011,Kolkata,Kings XI Punjab,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,8,Iqbal Abdulla,Eden Gardens,AM Saheba,SL Shastri,,Kolkata Knight Riders
+2010,Jaipur,Rajasthan Royals,Royal Challengers Bangalore,Rajasthan Royals,bat,normal,0,0,5,KP Pietersen,Sawai Mansingh Stadium,BR Doctrove,S Ravi,,Royal Challengers Bangalore
+2011,Mumbai,Mumbai Indians,Rajasthan Royals,Mumbai Indians,bat,normal,0,0,10,SR Watson,Wankhede Stadium,RE Koertzen,PR Reiffel,,Rajasthan Royals
+2015,Kolkata,Mumbai Indians,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,7,M Morkel,Eden Gardens,S Ravi,C Shamshuddin,,Kolkata Knight Riders
+2010,Chennai,Chennai Super Kings,Delhi Daredevils,Chennai Super Kings,bat,normal,0,0,6,G Gambhir,"MA Chidambaram Stadium, Chepauk",HDPK Dharmasena,SS Hazare,,Delhi Daredevils
+2011,Hyderabad,Deccan Chargers,Pune Warriors,Deccan Chargers,bat,normal,0,0,6,MR Marsh,"Rajiv Gandhi International Stadium, Uppal",Asad Rauf,AM Saheba,,Pune Warriors
+2016,Pune,Rising Pune Supergiants,Mumbai Indians,Mumbai Indians,field,normal,0,0,8,RG Sharma,Maharashtra Cricket Association Stadium,AY Dandekar,RJ Tucker,,Mumbai Indians
+2015,Raipur,Sunrisers Hyderabad,Delhi Daredevils,Sunrisers Hyderabad,bat,normal,0,6,0,MC Henriques,Shaheed Veer Narayan Singh International Stadium,VA Kulkarni,S Ravi,,Sunrisers Hyderabad
+2012,Dharamsala,Kings XI Punjab,Delhi Daredevils,Delhi Daredevils,field,normal,0,0,6,UT Yadav,Himachal Pradesh Cricket Association Stadium,BF Bowden,VA Kulkarni,,Delhi Daredevils
+2008,Kolkata,Kolkata Knight Riders,Mumbai Indians,Kolkata Knight Riders,bat,normal,0,0,7,ST Jayasuriya,Eden Gardens,BF Bowden,AV Jayaprakash,,Mumbai Indians
+2012,Hyderabad,Deccan Chargers,Delhi Daredevils,Deccan Chargers,bat,normal,0,0,9,DA Warner,"Rajiv Gandhi International Stadium, Uppal",JD Cloete,SJA Taufel,,Delhi Daredevils
+2009,Kimberley,Deccan Chargers,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,3,DPMD Jayawardene,De Beers Diamond Oval,GAV Baxter,AM Saheba,,Kings XI Punjab
+2011,Delhi,Delhi Daredevils,Mumbai Indians,Delhi Daredevils,bat,normal,0,0,8,SL Malinga,Feroz Shah Kotla,AM Saheba,RB Tiffin,,Mumbai Indians
+2009,Centurion,Royal Challengers Bangalore,Deccan Chargers,Royal Challengers Bangalore,bat,normal,0,12,0,MK Pandey,SuperSport Park,IL Howell,S Ravi,,Royal Challengers Bangalore
+2009,Durban,Kolkata Knight Riders,Delhi Daredevils,Kolkata Knight Riders,bat,normal,0,0,9,G Gambhir,Kingsmead,GAV Baxter,IL Howell,,Delhi Daredevils
+2010,Mumbai,Mumbai Indians,Deccan Chargers,Mumbai Indians,bat,normal,0,63,0,AT Rayudu,Brabourne Stadium,BR Doctrove,S Ravi,,Mumbai Indians
+2011,Hyderabad,Deccan Chargers,Rajasthan Royals,Rajasthan Royals,field,normal,0,0,8,SK Trivedi,"Rajiv Gandhi International Stadium, Uppal",RE Koertzen,SK Tarapore,,Rajasthan Royals
+2013,Chennai,Sunrisers Hyderabad,Chennai Super Kings,Sunrisers Hyderabad,bat,normal,0,0,5,MS Dhoni,"MA Chidambaram Stadium, Chepauk",Aleem Dar,S Das,,Chennai Super Kings
+2014,Abu Dhabi,Chennai Super Kings,Kings XI Punjab,Chennai Super Kings,bat,normal,0,0,6,GJ Maxwell,Sheikh Zayed Stadium,RK Illingworth,C Shamshuddin,,Kings XI Punjab
+2011,Dharamsala,Kings XI Punjab,Royal Challengers Bangalore,Kings XI Punjab,bat,normal,0,111,0,AC Gilchrist,Himachal Pradesh Cricket Association Stadium,Asad Rauf,AM Saheba,,Kings XI Punjab
+2008,Mumbai,Kings XI Punjab,Mumbai Indians,Mumbai Indians,field,normal,0,1,0,SE Marsh,Wankhede Stadium,BF Bowden,GA Pratapkumar,,Kings XI Punjab
+2012,Chennai,Chennai Super Kings,Delhi Daredevils,Delhi Daredevils,field,normal,0,86,0,M Vijay,"MA Chidambaram Stadium, Chepauk",BR Doctrove,SJA Taufel,,Chennai Super Kings
+2014,Chandigarh,Delhi Daredevils,Kings XI Punjab,Kings XI Punjab,field,normal,0,0,7,M Vohra,"Punjab Cricket Association Stadium, Mohali",HDPK Dharmasena,VA Kulkarni,,Kings XI Punjab
+2010,Chennai,Kolkata Knight Riders,Chennai Super Kings,Kolkata Knight Riders,bat,normal,0,0,9,R Ashwin,"MA Chidambaram Stadium, Chepauk",SS Hazare,SJA Taufel,,Chennai Super Kings
+2015,Bangalore,Royal Challengers Bangalore,Rajasthan Royals,Rajasthan Royals,field,no result,0,0,0,,M Chinnaswamy Stadium,JD Cloete,PG Pathak,,nan
+2012,Visakhapatnam,Deccan Chargers,Mumbai Indians,Deccan Chargers,bat,normal,0,0,5,RG Sharma,Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium,AK Chaudhary,JD Cloete,,Mumbai Indians
+2013,Delhi,Chennai Super Kings,Delhi Daredevils,Chennai Super Kings,bat,normal,0,86,0,MEK Hussey,Feroz Shah Kotla,M Erasmus,VA Kulkarni,,Chennai Super Kings
+2014,Mumbai,Mumbai Indians,Chennai Super Kings,Chennai Super Kings,field,normal,0,0,4,DR Smith,Wankhede Stadium,HDPK Dharmasena,VA Kulkarni,,Chennai Super Kings
+2010,Dharamsala,Kings XI Punjab,Deccan Chargers,Deccan Chargers,field,normal,0,0,5,RG Sharma,Himachal Pradesh Cricket Association Stadium,M Erasmus,AM Saheba,,Deccan Chargers
+2009,Durban,Royal Challengers Bangalore,Kings XI Punjab,Royal Challengers Bangalore,bat,normal,0,0,7,RS Bopara,Kingsmead,BR Doctrove,TH Wijewardene,,Kings XI Punjab
+2008,Mumbai,Kings XI Punjab,Chennai Super Kings,Kings XI Punjab,bat,normal,0,0,9,M Ntini,Wankhede Stadium,Asad Rauf,DJ Harper,,Chennai Super Kings
+2011,Jaipur,Rajasthan Royals,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,9,G Gambhir,Sawai Mansingh Stadium,Aleem Dar,SS Hazare,,Kolkata Knight Riders
+2009,Durban,Royal Challengers Bangalore,Kings XI Punjab,Royal Challengers Bangalore,bat,normal,0,8,0,Yuvraj Singh,Kingsmead,HDPK Dharmasena,S Ravi,,Royal Challengers Bangalore
+2010,Chennai,Kings XI Punjab,Chennai Super Kings,Chennai Super Kings,field,tie,0,0,0,J Theron,"MA Chidambaram Stadium, Chepauk",K Hariharan,DJ Harper,,Kings XI Punjab
+2012,Bangalore,Royal Challengers Bangalore,Delhi Daredevils,Delhi Daredevils,field,normal,0,20,0,AB de Villiers,M Chinnaswamy Stadium,S Asnani,S Ravi,,Royal Challengers Bangalore
+2015,Kolkata,Chennai Super Kings,Kolkata Knight Riders,Kolkata Knight Riders,field,normal,0,0,7,AD Russell,Eden Gardens,AK Chaudhary,M Erasmus,,Kolkata Knight Riders
+2010,Mumbai,Mumbai Indians,Delhi Daredevils,Mumbai Indians,bat,normal,0,39,0,KA Pollard,Brabourne Stadium,S Asnani,DJ Harper,,Mumbai Indians
+2015,Chandigarh,Kings XI Punjab,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,22,0,AR Patel,"Punjab Cricket Association Stadium, Mohali",JD Cloete,C Shamshuddin,,Kings XI Punjab
+2010,Bangalore,Kolkata Knight Riders,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,7,R Vinay Kumar,M Chinnaswamy Stadium,K Hariharan,DJ Harper,,Royal Challengers Bangalore
+2008,Hyderabad,Mumbai Indians,Deccan Chargers,Deccan Chargers,field,normal,0,25,0,DJ Bravo,"Rajiv Gandhi International Stadium, Uppal",BR Doctrove,DJ Harper,,Mumbai Indians
+2009,Johannesburg,Chennai Super Kings,Royal Challengers Bangalore,Royal Challengers Bangalore,field,normal,0,0,6,MK Pandey,New Wanderers Stadium,RE Koertzen,SJA Taufel,,Royal Challengers Bangalore

config.json

@@ -0,0 +1,22 @@
+{
+  "random_seed": 42,
+  "min_samples": 100,
+  "serializer": {
+    "numeric_precision": 2,
+    "model_max_len_tokens": 4096,
+    "appx_chars_per_token": 3.5,
+    "max_chars": 14336
+  },
+  "classification": {
+    "max_samples_per_dataset": 10000,
+    "train_ratio": 0.9,
+    "max_label_ratio": 0.1,
+    "max_num_labels": 50
+  },
+  "retrieval": {
+    "max_samples_per_dataset": 10000,
+    "num_queries_per_type": 10000,
+    "min_matches": 5,
+    "max_conditions": 3
+  }
+}