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MentalHealth_risk_identification.csv

@@ -0,0 +1,2088 @@
+,Age,Gender,Work Hours,Family History,Sleep Hours,Stress Level,Physical Activity,Social Interaction,Diet Quality,Treatment
+0,79,Male,20,Yes,7,7,24,2,Average,Yes
+1,20,Others,31,No,8,7,2,2,Average,No
+2,40,Male,39,No,8,4,7,8,Good,Yes
+3,35,Female,66,Yes,7,10,40,2,Average,Yes
+4,81,Female,42,Yes,6,2,78,2,Good,Yes
+5,72,Male,68,Yes,10,8,55,8,Poor,Yes
+6,53,Male,58,No,7,7,136,6,Poor,Yes
+7,74,Female,68,No,7,2,87,5,Poor,Yes
+8,63,Female,53,No,8,3,45,4,Poor,Yes
+9,61,Male,67,No,4,7,160,1,Poor,Yes
+10,46,Male,68,No,7,10,81,10,Poor,Yes
+11,40,Male,57,No,4,8,159,4,Poor,Yes
+12,88,Female,69,Yes,4,3,59,6,Poor,Yes
+13,68,Female,64,No,5,4,38,6,Poor,Yes
+14,53,Others,55,No,5,2,154,0,Poor,Yes
+15,51,Male,37,Yes,4,2,149,2,Good,Yes
+16,66,Female,58,Yes,6,1,175,0,Poor,Yes
+17,60,Female,22,Yes,7,4,30,9,Good,Yes
+18,67,Male,56,No,8,3,90,3,Poor,Yes
+19,56,Others,43,No,6,2,16,2,Average,Yes
+20,75,Male,33,No,7,1,156,5,Average,No
+21,24,Male,20,No,7,0,68,8,Average,No
+22,49,Female,67,Yes,5,0,176,0,Poor,Yes
+23,72,Male,41,Yes,6,4,150,9,Average,No
+24,45,Male,45,No,6,8,73,1,Average,No
+25,43,Male,51,No,8,9,95,9,Poor,Yes
+26,48,Male,22,No,5,0,84,0,Good,No
+27,95,Female,34,Yes,9,2,97,8,Good,Yes
+28,80,Female,32,Yes,7,10,153,1,Poor,Yes
+29,23,Male,52,Yes,7,10,112,10,Poor,Yes
+30,27,Male,24,No,7,6,179,7,Average,No
+31,76,Female,40,No,5,4,139,3,Good,No
+32,22,Male,34,Yes,6,9,20,6,Poor,Yes
+33,83,Female,29,Yes,5,7,149,0,Average,Yes
+34,74,Male,27,No,4,4,74,10,Good,No
+35,35,Male,46,No,7,6,49,3,Good,No
+36,74,Male,68,No,7,4,20,0,Poor,Yes
+37,99,Male,58,No,5,4,69,0,Poor,Yes
+38,32,Male,44,Yes,9,9,167,1,Poor,Yes
+39,54,Male,22,No,6,6,149,8,Average,No
+40,27,Male,69,No,7,5,123,5,Poor,Yes
+41,62,Female,58,Yes,6,8,133,2,Poor,Yes
+42,71,Male,56,No,6,4,136,7,Average,No
+43,61,Others,49,No,6,10,65,1,Poor,Yes
+44,61,Male,56,Yes,8,2,15,8,Poor,Yes
+45,39,Others,55,No,4,10,27,3,Poor,Yes
+46,82,Female,40,Yes,10,8,150,9,Poor,Yes
+47,92,Male,21,No,6,2,151,9,Good,Yes
+48,83,Male,69,Yes,10,0,165,2,Poor,Yes
+49,84,Female,56,No,4,0,148,5,Average,No
+50,19,Others,61,No,7,5,60,5,Average,Yes
+51,77,Female,41,Yes,8,9,28,0,Poor,Yes
+52,64,Male,55,Yes,5,1,89,1,Poor,Yes
+53,39,Female,23,No,7,1,6,4,Good,Yes
+54,35,Male,36,No,4,7,32,0,Good,No
+55,97,Male,27,Yes,7,5,159,2,Good,Yes
+56,23,Male,64,Yes,6,10,56,3,Poor,Yes
+57,98,Female,46,Yes,5,7,5,2,Average,Yes
+58,63,Male,34,Yes,9,4,9,5,Good,No
+59,47,Female,38,Yes,8,2,72,10,Average,Yes
+60,69,Female,60,No,6,8,77,9,Poor,Yes
+61,28,Male,66,Yes,6,8,92,0,Poor,Yes
+62,54,Male,38,Yes,8,7,63,10,Good,Yes
+63,73,Male,68,Yes,6,3,131,8,Poor,Yes
+64,37,Others,62,No,8,7,171,8,Poor,Yes
+65,42,Male,37,Yes,6,4,177,8,Good,No
+66,76,Female,42,Yes,7,8,139,9,Average,Yes
+67,61,Female,59,Yes,9,10,38,5,Poor,Yes
+68,85,Male,25,No,8,4,56,6,Good,No
+69,65,Male,32,Yes,7,3,43,9,Good,Yes
+70,87,Female,31,Yes,6,10,155,4,Average,Yes
+71,57,Female,44,No,8,3,39,5,Average,No
+72,36,Female,24,No,8,3,82,3,Average,No
+73,45,Male,63,Yes,7,1,146,4,Poor,Yes
+74,93,Male,46,No,6,0,162,2,Good,No
+75,66,Male,65,No,6,1,148,9,Poor,No
+76,75,Male,64,No,4,3,134,5,Poor,Yes
+77,75,Others,46,No,6,7,62,3,Average,Yes
+78,78,Male,42,No,7,6,9,0,Good,Yes
+79,36,Male,51,No,6,2,129,2,Average,Yes
+80,48,Male,46,No,5,9,52,1,Poor,Yes
+81,47,Female,42,No,8,7,10,5,Good,Yes
+82,24,Female,49,Yes,7,8,161,9,Poor,Yes
+83,86,Male,65,No,8,9,88,5,Poor,Yes
+84,67,Male,32,Yes,7,0,126,0,Good,Yes
+85,86,Male,26,Yes,7,1,80,10,Good,No
+86,69,Female,43,No,7,6,50,3,Good,Yes
+87,51,Female,49,No,5,4,109,6,Average,No
+88,98,Female,34,No,8,7,173,6,Good,No
+89,72,Male,51,No,7,10,107,2,Average,Yes
+90,54,Female,53,Yes,6,7,97,1,Average,Yes
+91,23,Female,63,Yes,9,6,155,5,Poor,Yes
+92,42,Female,47,No,6,7,61,10,Average,No
+93,52,Female,39,Yes,5,7,112,10,Good,Yes
+94,39,Female,54,No,7,0,0,1,Poor,Yes
+95,72,Male,45,No,5,2,163,0,Average,Yes
+96,64,Female,31,No,9,9,39,5,Poor,Yes
+97,63,Male,52,No,6,1,37,7,Average,Yes
+98,36,Female,58,No,9,8,112,5,Poor,Yes
+99,94,Female,41,No,5,3,89,4,Average,Yes
+100,21,Male,65,No,5,6,63,3,Poor,Yes
+101,47,Others,63,No,6,0,127,10,Poor,No
+102,99,Male,36,No,4,9,84,8,Poor,Yes
+103,97,Others,54,Yes,6,4,36,6,Poor,Yes
+104,44,Male,36,Yes,4,10,56,9,Poor,Yes
+105,67,Female,34,No,5,4,66,0,Good,Yes
+106,69,Male,65,No,7,7,95,7,Average,Yes
+107,70,Female,47,Yes,5,10,118,7,Average,Yes
+108,86,Male,64,No,4,6,88,0,Poor,Yes
+109,32,Male,34,Yes,7,7,126,1,Average,Yes
+110,59,Female,50,No,6,3,112,3,Average,No
+111,85,Female,40,Yes,7,7,13,10,Average,Yes
+112,32,Female,55,Yes,9,7,41,4,Poor,Yes
+113,19,Male,53,No,4,4,64,1,Poor,Yes
+114,93,Female,23,No,5,7,50,5,Good,Yes
+115,31,Female,28,Yes,9,5,105,1,Average,Yes
+116,80,Female,24,No,7,10,2,0,Poor,Yes
+117,58,Male,27,Yes,6,4,14,9,Good,Yes
+118,22,Female,23,No,6,6,112,8,Average,No
+119,91,Others,67,No,8,10,158,1,Poor,Yes
+120,92,Male,39,No,5,8,157,9,Poor,Yes
+121,36,Female,45,No,6,6,176,9,Average,No
+122,52,Male,55,Yes,7,5,51,0,Poor,Yes
+123,34,Female,65,No,7,3,52,2,Poor,Yes
+124,56,Male,64,Yes,5,8,62,8,Average,Yes
+125,86,Female,38,Yes,6,9,92,4,Poor,Yes
+126,90,Male,30,Yes,8,9,112,0,Poor,Yes
+127,98,Male,54,No,4,8,2,2,Average,Yes
+128,53,Female,37,Yes,6,1,170,0,Good,Yes
+129,83,Male,41,No,5,7,118,1,Average,Yes
+130,49,Female,38,Yes,4,6,92,1,Average,Yes
+131,39,Female,41,No,7,8,29,3,Average,Yes
+132,86,Male,57,Yes,7,9,0,0,Poor,Yes
+133,46,Male,56,Yes,6,5,75,6,Poor,Yes
+134,27,Female,54,Yes,8,3,123,5,Average,Yes
+135,49,Female,43,No,7,8,93,0,Poor,Yes
+136,99,Male,25,Yes,8,2,135,3,Good,Yes
+137,23,Male,60,No,8,3,148,7,Poor,Yes
+138,29,Male,43,Yes,6,7,138,3,Average,Yes
+139,94,Female,20,No,9,1,88,7,Good,No
+140,71,Female,62,Yes,6,8,110,1,Poor,Yes
+141,44,Others,44,Yes,7,6,99,8,Good,Yes
+142,97,Others,40,Yes,9,7,58,5,Average,Yes
+143,62,Female,32,Yes,5,7,65,4,Average,No
+144,64,Male,51,Yes,7,0,121,6,Poor,Yes
+145,50,Male,51,Yes,6,6,113,7,Poor,Yes
+146,29,Female,54,No,8,7,35,3,Poor,Yes
+147,56,Male,41,No,6,2,57,5,Average,No
+148,53,Female,69,Yes,7,9,164,9,Poor,Yes
+149,93,Female,67,Yes,7,10,166,3,Poor,Yes
+150,86,Female,57,No,7,10,98,1,Poor,Yes
+151,74,Female,22,Yes,8,1,2,2,Good,Yes
+152,68,Male,44,Yes,8,0,158,9,Good,No
+153,58,Female,66,Yes,8,1,165,6,Poor,Yes
+154,36,Male,53,No,6,1,168,5,Poor,Yes
+155,45,Female,47,Yes,7,6,21,9,Average,Yes
+156,24,Others,59,No,7,7,131,5,Poor,Yes
+157,93,Female,61,No,5,1,176,10,Average,No
+158,31,Male,33,No,8,2,146,2,Good,No
+159,46,Female,21,Yes,8,4,31,9,Good,No
+160,80,Female,32,No,6,10,27,4,Poor,Yes
+161,37,Female,39,No,6,5,141,5,Good,Yes
+162,54,Male,58,No,7,1,136,2,Poor,No
+163,69,Male,68,Yes,4,4,102,10,Poor,Yes
+164,85,Female,63,Yes,8,5,160,3,Poor,Yes
+165,58,Female,27,Yes,7,8,87,2,Poor,Yes
+166,77,Female,49,Yes,7,10,137,5,Average,Yes
+167,81,Male,57,Yes,8,4,75,4,Poor,Yes
+168,75,Female,63,Yes,6,0,113,6,Poor,Yes
+169,21,Female,65,No,7,6,7,10,Poor,Yes
+170,57,Female,45,Yes,6,1,34,1,Average,Yes
+171,60,Male,24,No,7,8,8,9,Poor,Yes
+172,53,Male,23,Yes,7,0,82,7,Good,No
+173,93,Male,20,Yes,7,6,64,5,Average,Yes
+174,60,Female,59,No,5,10,129,0,Poor,Yes
+175,27,Female,60,No,5,7,147,1,Poor,Yes
+176,18,Male,57,No,4,6,154,3,Average,No
+177,78,Female,34,Yes,6,2,66,9,Average,Yes
+178,86,Female,56,Yes,9,8,80,5,Poor,Yes
+179,41,Female,47,Yes,5,1,77,3,Average,Yes
+180,22,Male,65,No,5,8,89,3,Average,Yes
+181,18,Male,66,No,10,7,64,10,Poor,Yes
+182,88,Female,43,No,9,10,36,8,Average,Yes
+183,44,Female,36,No,6,4,95,10,Good,No
+184,78,Male,31,Yes,5,5,72,4,Good,Yes
+185,24,Female,51,No,7,5,158,7,Poor,No
+186,90,Male,43,No,5,3,18,9,Average,Yes
+187,30,Male,22,No,4,6,34,9,Good,No
+188,62,Female,42,Yes,7,5,140,6,Average,Yes
+189,72,Others,26,Yes,6,9,168,7,Poor,Yes
+190,58,Female,48,Yes,6,7,5,2,Average,Yes
+191,21,Male,64,No,4,7,81,1,Poor,Yes
+192,41,Female,23,No,6,4,174,5,Good,No
+193,49,Male,59,No,9,10,3,7,Average,Yes
+194,62,Female,22,No,6,4,165,9,Average,Yes
+195,36,Female,69,No,6,5,141,5,Average,Yes
+196,22,Female,32,No,7,6,167,7,Good,No
+197,92,Female,44,Yes,4,2,91,9,Average,Yes
+198,55,Male,35,Yes,7,9,37,6,Poor,Yes
+199,80,Male,68,Yes,7,4,51,3,Poor,Yes
+200,37,Female,54,No,6,2,107,2,Poor,Yes
+201,26,Others,42,No,8,4,143,9,Average,Yes
+202,96,Female,53,Yes,4,1,95,1,Poor,Yes
+203,36,Female,49,Yes,8,5,161,8,Average,Yes
+204,18,Others,30,No,5,8,153,2,Poor,Yes
+205,26,Female,52,No,6,4,14,5,Poor,Yes
+206,95,Female,59,No,8,3,15,3,Poor,Yes
+207,96,Male,28,No,6,6,56,8,Good,Yes
+208,91,Female,21,Yes,7,2,58,9,Good,Yes
+209,58,Others,36,No,7,7,3,1,Good,Yes
+210,26,Female,42,No,6,5,171,10,Average,Yes
+211,82,Male,39,No,5,8,107,9,Poor,Yes
+212,74,Female,58,No,6,3,126,2,Poor,Yes
+213,42,Male,26,No,8,7,0,9,Average,Yes
+214,38,Female,42,Yes,6,9,117,1,Average,Yes
+215,68,Male,63,No,6,2,10,1,Poor,Yes
+216,60,Male,53,No,5,4,136,2,Poor,Yes
+217,93,Female,56,No,6,5,143,0,Poor,Yes
+218,39,Female,42,No,4,3,59,7,Good,Yes
+219,29,Male,46,Yes,5,3,65,2,Average,Yes
+220,67,Male,27,Yes,7,2,133,2,Average,No
+221,49,Male,63,Yes,8,2,163,1,Poor,Yes
+222,85,Female,39,No,4,7,93,9,Average,No
+223,23,Female,36,No,4,1,114,2,Average,Yes
+224,38,Female,41,Yes,5,8,62,6,Poor,Yes
+225,39,Female,40,No,5,2,2,8,Good,Yes
+226,39,Male,48,No,7,8,92,7,Average,Yes
+227,45,Male,28,Yes,7,9,80,8,Poor,Yes
+228,50,Female,48,No,8,1,158,10,Good,No
+229,68,Male,33,Yes,4,1,152,1,Good,Yes
+230,85,Male,47,No,7,5,168,10,Good,Yes
+231,78,Male,69,No,7,5,75,10,Average,No
+232,53,Female,45,Yes,8,1,11,0,Average,Yes
+233,78,Male,32,No,9,10,9,1,Poor,Yes
+234,97,Male,68,Yes,7,5,19,5,Poor,Yes
+235,69,Male,21,No,7,2,101,0,Good,Yes
+236,38,Male,58,Yes,7,1,132,7,Average,Yes
+237,99,Female,36,No,8,4,95,8,Average,Yes
+238,40,Female,57,No,8,6,146,7,Average,No
+239,30,Female,41,No,6,5,42,6,Average,Yes
+240,68,Female,40,No,6,0,151,10,Good,No
+241,19,Male,47,No,5,0,22,2,Average,Yes
+242,28,Female,48,No,8,10,86,10,Poor,Yes
+243,21,Female,33,No,7,3,116,4,Average,Yes
+244,97,Male,50,No,7,6,22,7,Average,Yes
+245,88,Male,64,Yes,8,7,59,5,Poor,Yes
+246,37,Female,55,Yes,5,2,174,9,Average,No
+247,30,Male,52,No,7,3,69,10,Poor,Yes
+248,20,Male,20,No,6,5,106,7,Good,No
+249,73,Male,32,Yes,7,9,132,10,Poor,Yes
+250,82,Male,21,No,10,3,177,8,Good,No
+251,26,Female,55,Yes,5,6,173,8,Poor,Yes
+252,53,Female,52,No,8,0,124,10,Average,No
+253,86,Male,62,No,4,0,9,6,Average,Yes
+254,99,Male,29,No,5,8,153,8,Poor,Yes
+255,47,Female,34,No,6,8,120,1,Poor,Yes
+256,26,Others,48,Yes,9,8,76,8,Poor,Yes
+257,71,Male,56,Yes,8,6,163,10,Poor,Yes
+258,63,Female,54,No,4,1,6,5,Poor,Yes
+259,68,Female,68,No,4,3,46,5,Poor,Yes
+260,22,Male,28,No,6,0,175,10,Good,No
+261,68,Female,45,No,9,9,179,2,Average,No
+262,27,Male,37,Yes,7,5,166,5,Good,Yes
+263,78,Male,35,No,9,6,123,8,Good,No
+264,45,Others,61,No,5,3,53,9,Poor,Yes
+265,87,Female,25,No,8,1,9,9,Good,No
+266,77,Male,54,No,5,5,164,1,Poor,Yes
+267,79,Male,37,No,6,5,56,1,Average,No
+268,35,Female,68,No,7,1,29,0,Poor,Yes
+269,70,Male,26,Yes,5,10,37,8,Poor,Yes
+270,71,Others,28,Yes,6,1,84,4,Average,Yes
+271,37,Male,52,Yes,7,4,157,7,Poor,Yes
+272,46,Female,22,No,5,2,17,1,Good,Yes
+273,19,Female,48,No,5,0,75,4,Good,Yes
+274,47,Male,49,Yes,8,3,72,6,Average,No
+275,63,Female,23,No,8,4,138,0,Good,No
+276,39,Female,62,No,8,8,149,5,Poor,Yes
+277,37,Male,60,No,6,6,48,2,Poor,Yes
+278,73,Others,48,No,5,5,11,5,Average,Yes
+279,49,Male,20,Yes,6,10,126,10,Average,Yes
+280,35,Female,25,No,4,7,103,7,Average,Yes
+281,50,Female,26,No,5,0,7,0,Good,Yes
+282,64,Female,24,No,5,0,72,7,Good,No
+283,51,Female,31,Yes,7,3,176,1,Good,Yes
+284,88,Female,38,No,9,10,86,8,Poor,No
+285,69,Male,68,Yes,7,5,150,3,Poor,Yes
+286,75,Female,61,No,6,2,86,6,Poor,Yes
+287,21,Male,52,Yes,4,6,46,8,Poor,Yes
+288,78,Male,28,Yes,6,1,150,1,Average,Yes
+289,20,Male,25,No,5,4,172,7,Good,No
+290,61,Male,40,No,5,3,150,1,Good,No
+291,26,Female,52,No,9,5,35,5,Poor,Yes
+292,52,Male,54,No,8,9,5,7,Poor,Yes
+293,18,Others,26,No,8,1,165,7,Average,No
+294,27,Male,62,Yes,8,5,55,6,Average,No
+295,20,Female,63,Yes,5,0,48,4,Average,Yes
+296,25,Female,34,No,7,7,94,7,Average,No
+297,57,Female,34,Yes,5,2,162,7,Good,Yes
+298,20,Others,44,Yes,6,2,119,10,Average,Yes
+299,60,Female,51,Yes,10,0,5,0,Poor,Yes
+300,54,Others,65,No,6,9,13,1,Poor,Yes
+301,26,Male,35,No,7,10,95,10,Poor,Yes
+302,40,Male,49,No,7,9,150,6,Poor,Yes
+303,72,Male,61,No,8,9,42,10,Poor,Yes
+304,59,Female,25,Yes,6,10,134,5,Poor,Yes
+305,33,Female,56,No,8,8,101,10,Poor,Yes
+306,66,Male,68,No,4,0,10,1,Poor,Yes
+307,39,Others,57,No,6,6,105,5,Poor,Yes
+308,99,Female,35,No,8,4,160,6,Good,Yes
+309,36,Female,47,No,6,2,68,5,Average,Yes
+310,85,Female,35,Yes,4,3,63,9,Good,Yes
+311,51,Others,54,Yes,4,0,92,2,Poor,Yes
+312,84,Male,45,Yes,6,7,106,0,Good,Yes
+313,72,Female,24,No,7,10,152,4,Poor,Yes
+314,62,Female,31,Yes,5,10,159,5,Poor,Yes
+315,78,Male,27,No,6,9,97,2,Average,Yes
+316,97,Female,28,No,10,2,23,8,Good,Yes
+317,71,Male,43,No,6,10,75,7,Poor,Yes
+318,68,Female,44,Yes,5,3,70,6,Average,Yes
+319,83,Male,21,Yes,8,3,31,9,Good,No
+320,76,Male,43,Yes,9,0,72,2,Average,Yes
+321,39,Others,26,Yes,8,8,166,1,Poor,Yes
+322,74,Female,49,No,4,8,64,2,Poor,Yes
+323,65,Male,67,No,5,4,120,6,Poor,No
+324,37,Male,61,Yes,6,2,121,0,Poor,Yes
+325,96,Female,24,No,7,2,78,0,Good,Yes
+326,60,Male,40,No,7,6,118,4,Average,No
+327,54,Male,31,No,8,9,3,4,Average,Yes
+328,58,Female,34,Yes,7,8,142,6,Poor,Yes
+329,95,Female,42,Yes,6,0,85,5,Good,No
+330,83,Male,55,No,6,8,102,9,Average,No
+331,70,Male,68,Yes,6,4,62,5,Average,No
+332,86,Others,60,No,8,10,160,4,Poor,Yes
+333,78,Female,68,No,6,4,121,5,Poor,Yes
+334,77,Male,43,No,6,2,177,2,Average,No
+335,33,Male,66,No,7,6,14,8,Average,Yes
+336,24,Female,36,Yes,6,6,178,2,Average,Yes
+337,20,Male,20,No,6,3,45,9,Good,No
+338,21,Male,40,No,7,5,135,8,Good,No
+339,69,Male,53,No,7,2,70,2,Poor,Yes
+340,58,Female,42,No,7,5,12,2,Average,Yes
+341,76,Male,33,Yes,7,5,153,0,Average,No
+342,73,Male,53,Yes,5,9,77,4,Average,Yes
+343,23,Female,26,No,4,0,81,1,Good,No
+344,96,Female,32,No,7,10,138,2,Poor,Yes
+345,90,Female,32,No,9,6,68,0,Average,No
+346,96,Female,50,No,6,0,27,3,Average,Yes
+347,80,Female,39,No,5,10,169,10,Poor,Yes
+348,77,Male,29,No,7,6,46,1,Average,No
+349,80,Male,37,No,7,6,30,6,Average,No
+350,64,Male,31,Yes,9,9,4,9,Poor,Yes
+351,56,Male,35,Yes,6,3,171,5,Good,Yes
+352,69,Female,34,No,6,1,106,10,Good,No
+353,70,Female,22,Yes,7,0,57,0,Good,Yes
+354,97,Female,43,No,5,5,90,2,Average,Yes
+355,45,Male,24,Yes,7,3,3,9,Good,No
+356,71,Male,42,No,8,0,96,10,Average,No
+357,91,Female,34,Yes,7,5,15,7,Good,Yes
+358,48,Female,46,Yes,4,2,143,0,Average,Yes
+359,43,Female,40,No,4,4,130,10,Good,No
+360,77,Female,44,No,6,6,167,1,Average,Yes
+361,64,Male,63,No,7,6,74,1,Poor,Yes
+362,95,Male,54,No,8,8,176,3,Poor,Yes
+363,91,Female,56,No,9,6,86,8,Poor,Yes
+364,69,Male,36,No,7,10,161,0,Poor,Yes
+365,40,Male,23,No,5,1,117,5,Good,No
+366,63,Male,23,Yes,8,8,25,1,Poor,Yes
+367,38,Male,48,Yes,6,9,75,6,Poor,Yes
+368,86,Male,39,No,6,0,1,8,Good,Yes
+369,51,Male,34,No,7,6,68,0,Average,Yes
+370,44,Female,63,Yes,5,6,29,10,Average,Yes
+371,67,Male,34,No,8,6,62,9,Average,Yes
+372,95,Female,59,Yes,5,0,124,5,Poor,Yes
+373,76,Male,41,No,6,7,45,5,Good,No
+374,93,Male,41,No,6,10,162,10,Poor,Yes
+375,78,Female,42,No,6,5,92,5,Average,Yes
+376,52,Female,34,No,6,6,62,8,Average,Yes
+377,47,Male,44,No,5,0,36,10,Good,No
+378,79,Male,62,No,8,9,56,8,Poor,Yes
+379,25,Male,34,No,7,3,29,1,Good,Yes
+380,73,Female,25,No,5,5,177,9,Good,No
+381,64,Male,23,No,5,4,87,10,Good,No
+382,64,Female,55,Yes,7,5,69,2,Poor,Yes
+383,34,Male,61,Yes,7,6,39,2,Poor,Yes
+384,38,Male,56,Yes,7,3,4,1,Average,Yes
+385,97,Male,46,No,9,2,84,6,Good,No
+386,70,Male,65,Yes,8,2,99,4,Poor,Yes
+387,30,Male,20,Yes,5,10,93,5,Poor,Yes
+388,83,Female,35,No,6,6,154,4,Average,Yes
+389,78,Female,65,Yes,4,8,59,10,Poor,Yes
+390,97,Male,60,No,8,8,55,6,Average,No
+391,35,Female,61,No,5,10,113,0,Poor,Yes
+392,90,Male,44,No,6,7,164,3,Average,Yes
+393,38,Female,69,No,7,4,41,1,Poor,Yes
+394,39,Female,63,Yes,6,6,74,1,Poor,Yes
+395,70,Female,60,Yes,7,4,129,10,Poor,Yes
+396,78,Male,34,No,10,1,90,2,Good,No
+397,43,Male,58,No,6,3,82,8,Poor,Yes
+398,73,Male,61,Yes,6,6,105,1,Average,Yes
+399,47,Female,60,No,5,7,4,2,Poor,Yes
+400,91,Male,30,Yes,7,7,118,10,Average,Yes
+401,79,Female,48,No,5,9,89,2,Average,Yes
+402,99,Female,44,No,7,7,106,2,Average,Yes
+403,23,Female,53,No,6,4,115,4,Poor,Yes
+404,90,Male,34,No,7,5,35,8,Good,No
+405,22,Male,23,Yes,5,2,179,4,Good,Yes
+406,99,Female,41,Yes,5,4,41,4,Average,Yes
+407,32,Male,69,Yes,5,6,41,8,Poor,Yes
+408,92,Male,51,No,5,10,166,7,Poor,Yes
+409,24,Female,50,No,7,0,124,5,Average,No
+410,62,Male,38,Yes,7,8,135,9,Poor,Yes
+411,41,Female,50,No,9,8,170,7,Poor,Yes
+412,21,Male,61,No,7,3,67,1,Average,Yes
+413,20,Female,20,No,7,9,151,0,Average,Yes
+414,59,Male,20,Yes,5,9,95,7,Poor,Yes
+415,97,Male,33,No,6,3,180,10,Good,No
+416,67,Others,54,No,7,3,141,5,Poor,No
+417,25,Female,46,No,8,9,94,1,Poor,Yes
+418,75,Female,45,No,6,4,27,7,Average,Yes
+419,46,Male,33,No,8,8,71,1,Average,Yes
+420,87,Male,34,No,5,0,38,4,Average,Yes
+421,25,Female,67,No,4,3,50,8,Poor,Yes
+422,67,Male,65,Yes,8,5,142,9,Poor,Yes
+423,61,Female,52,No,6,5,157,2,Poor,Yes
+424,89,Male,54,No,6,3,67,10,Average,No
+425,50,Female,43,Yes,4,3,148,2,Average,Yes
+426,20,Others,34,No,8,4,0,1,Average,No
+427,26,Male,63,No,5,10,108,8,Poor,Yes
+428,35,Male,32,No,9,1,74,4,Good,No
+429,65,Male,56,Yes,4,1,167,1,Poor,Yes
+430,33,Female,62,Yes,7,10,74,9,Poor,Yes
+431,76,Male,57,No,8,3,8,2,Poor,Yes
+432,20,Male,38,No,6,1,157,8,Average,No
+433,53,Male,53,Yes,5,2,121,6,Poor,Yes
+434,25,Female,61,Yes,4,2,146,0,Poor,Yes
+435,91,Male,44,Yes,7,3,48,9,Average,Yes
+436,73,Male,43,Yes,7,1,31,0,Good,Yes
+437,38,Female,62,No,5,4,132,0,Average,Yes
+438,34,Male,33,No,6,8,7,6,Poor,Yes
+439,45,Male,67,Yes,4,1,122,5,Poor,Yes
+440,88,Male,35,No,6,2,10,8,Good,Yes
+441,88,Female,57,Yes,9,5,116,6,Poor,Yes
+442,75,Female,31,No,6,7,67,5,Average,No
+443,33,Others,32,Yes,8,7,153,4,Good,No
+444,64,Female,29,No,5,3,161,5,Good,No
+445,77,Male,47,Yes,6,3,120,9,Average,Yes
+446,52,Male,33,Yes,6,0,132,7,Good,Yes
+447,44,Male,52,Yes,8,4,142,0,Poor,Yes
+448,77,Male,28,No,4,6,172,1,Average,Yes
+449,20,Male,26,Yes,4,2,15,5,Good,Yes
+450,23,Female,30,No,4,9,127,8,Average,Yes
+451,34,Female,64,No,4,9,98,8,Poor,Yes
+452,77,Male,64,Yes,7,4,59,7,Poor,Yes
+453,22,Female,42,Yes,10,9,103,0,Poor,Yes
+454,56,Female,55,No,10,10,18,10,Average,No
+455,82,Female,28,No,6,6,132,4,Average,No
+456,21,Female,61,No,8,8,16,9,Poor,Yes
+457,87,Others,40,No,4,3,160,1,Good,Yes
+458,36,Female,56,No,5,5,50,0,Poor,Yes
+459,63,Male,47,No,7,9,139,7,Poor,Yes
+460,69,Male,64,No,7,0,116,8,Poor,Yes
+461,73,Male,24,No,6,2,131,2,Good,Yes
+462,54,Male,57,Yes,6,4,17,4,Poor,Yes
+463,47,Male,45,Yes,7,0,130,0,Average,Yes
+464,55,Female,57,Yes,8,3,161,8,Poor,Yes
+465,26,Male,54,Yes,5,1,40,1,Average,Yes
+466,57,Male,62,No,8,3,75,3,Poor,No
+467,44,Male,41,Yes,7,7,149,3,Average,No
+468,40,Male,35,No,6,9,6,9,Poor,Yes
+469,34,Female,53,Yes,9,6,137,2,Poor,Yes
+470,41,Others,69,Yes,4,8,27,5,Poor,Yes
+471,82,Female,32,Yes,6,8,24,7,Poor,Yes
+472,30,Male,65,No,5,1,76,10,Poor,Yes
+473,57,Male,64,Yes,4,8,173,9,Poor,No
+474,98,Female,41,No,9,8,45,9,Poor,Yes
+475,93,Female,35,Yes,7,1,140,10,Good,No
+476,28,Male,31,No,6,2,54,9,Good,Yes
+477,34,Female,40,No,6,3,174,0,Good,No
+478,68,Male,35,Yes,7,8,31,7,Poor,Yes
+479,18,Female,24,No,7,10,30,8,Poor,Yes
+480,39,Female,59,No,8,10,100,9,Poor,Yes
+481,23,Male,39,No,6,5,14,4,Average,Yes
+482,75,Male,45,No,7,6,125,1,Good,Yes
+483,65,Male,55,Yes,6,5,168,9,Poor,Yes
+484,79,Female,26,No,9,10,154,7,Poor,Yes
+485,43,Male,66,Yes,10,0,3,4,Poor,No
+486,93,Female,45,No,7,5,123,10,Average,Yes
+487,73,Female,34,Yes,7,9,25,10,Average,Yes
+488,70,Male,27,Yes,7,6,14,10,Average,Yes
+489,73,Male,59,Yes,7,2,6,0,Poor,Yes
+490,98,Female,68,No,9,2,30,8,Poor,Yes
+491,91,Male,42,No,6,5,167,3,Good,Yes
+492,51,Male,24,Yes,4,9,172,7,Poor,Yes
+493,62,Female,35,No,6,7,118,0,Average,No
+494,90,Male,39,Yes,8,2,121,3,Good,Yes
+495,25,Male,45,No,7,1,19,10,Average,No
+496,41,Female,53,No,4,1,73,9,Poor,Yes
+497,87,Female,59,No,4,8,127,4,Average,Yes
+498,98,Female,55,Yes,8,5,7,4,Poor,Yes
+499,68,Male,51,No,7,3,94,6,Poor,Yes
+500,38,Female,54,No,5,7,69,3,Poor,Yes
+501,88,Female,68,Yes,8,6,91,7,Average,Yes
+502,99,Female,30,Yes,6,7,65,6,Good,Yes
+503,56,Female,56,No,6,10,146,0,Poor,Yes
+504,62,Female,61,No,5,2,161,6,Poor,Yes
+505,98,Male,24,Yes,5,8,122,3,Poor,Yes
+506,45,Male,43,No,8,10,36,2,Poor,Yes
+507,48,Male,36,Yes,7,4,131,0,Good,No
+508,31,Male,53,Yes,6,0,146,10,Poor,No
+509,19,Male,30,Yes,5,7,73,9,Good,Yes
+510,37,Female,51,No,8,2,95,10,Poor,No
+511,36,Male,67,Yes,5,0,158,2,Poor,Yes
+512,25,Male,34,No,8,4,87,9,Good,No
+513,25,Female,62,Yes,10,9,78,7,Average,Yes
+514,33,Male,67,No,6,6,18,5,Poor,Yes
+515,31,Female,27,Yes,4,0,7,10,Good,Yes
+516,66,Male,22,Yes,8,8,38,6,Poor,Yes
+517,83,Female,22,No,5,1,106,10,Average,No
+518,80,Female,35,Yes,6,2,57,6,Average,Yes
+519,43,Female,44,No,7,3,102,2,Good,No
+520,32,Female,40,No,7,7,51,8,Average,No
+521,24,Others,36,No,8,0,132,9,Good,No
+522,63,Male,44,No,5,0,167,8,Average,Yes
+523,84,Male,28,Yes,4,5,106,2,Good,Yes
+524,77,Female,28,Yes,7,1,60,3,Good,Yes
+525,34,Female,45,No,8,4,136,3,Average,No
+526,42,Female,21,No,6,4,26,9,Good,Yes
+527,69,Female,58,Yes,6,6,175,3,Poor,Yes
+528,44,Female,24,No,8,2,102,0,Good,Yes
+529,56,Male,25,Yes,5,10,112,6,Poor,Yes
+530,75,Male,50,No,4,8,8,2,Average,Yes
+531,87,Male,35,No,5,4,173,6,Good,No
+532,76,Male,43,No,7,8,33,9,Average,Yes
+533,73,Female,29,No,7,10,24,8,Poor,Yes
+534,97,Male,39,No,7,5,4,3,Good,Yes
+535,54,Female,50,No,4,3,179,0,Average,Yes
+536,73,Others,56,Yes,4,1,156,8,Poor,Yes
+537,55,Female,23,No,5,4,166,1,Average,No
+538,23,Female,54,Yes,8,5,111,9,Poor,Yes
+539,68,Female,36,No,4,3,37,3,Average,No
+540,33,Female,64,Yes,7,9,38,3,Poor,Yes
+541,93,Female,61,No,5,4,39,7,Poor,Yes
+542,32,Male,33,Yes,9,2,45,10,Good,No
+543,92,Others,23,Yes,8,4,175,0,Good,Yes
+544,53,Female,43,No,7,8,72,6,Poor,Yes
+545,99,Male,34,No,7,2,6,1,Good,Yes
+546,95,Female,41,No,9,2,9,10,Average,Yes
+547,52,Female,30,Yes,5,5,55,3,Good,Yes
+548,41,Male,38,No,8,3,135,3,Good,No
+549,86,Others,24,No,6,2,67,3,Good,Yes
+550,73,Male,44,Yes,8,0,102,9,Average,No
+551,92,Others,61,Yes,6,9,73,2,Average,Yes
+552,86,Female,46,No,8,2,118,2,Good,No
+553,31,Male,29,No,6,1,124,0,Good,No
+554,54,Others,66,No,5,5,89,1,Poor,Yes
+555,38,Female,45,Yes,8,2,88,8,Average,Yes
+556,98,Female,47,No,6,3,54,7,Average,Yes
+557,33,Male,46,No,5,5,28,7,Good,Yes
+558,41,Male,45,No,5,1,106,7,Average,Yes
+559,19,Female,21,No,7,7,17,4,Average,No
+560,19,Female,58,No,6,4,88,7,Poor,No
+561,87,Male,60,Yes,8,8,164,9,Poor,No
+562,43,Male,26,No,4,2,165,1,Good,Yes
+563,85,Male,51,No,5,3,165,1,Poor,Yes
+564,48,Female,44,No,6,1,85,8,Average,No
+565,46,Others,55,Yes,7,9,11,4,Poor,Yes
+566,30,Female,24,No,6,6,150,6,Average,No
+567,38,Female,24,No,4,4,52,7,Good,No
+568,55,Male,30,Yes,7,7,54,10,Good,Yes
+569,43,Female,23,No,7,0,156,6,Good,No
+570,50,Male,58,Yes,9,0,107,5,Poor,No
+571,88,Others,49,No,5,2,26,7,Average,Yes
+572,80,Male,67,No,8,10,118,10,Poor,Yes
+573,98,Female,27,Yes,7,4,109,2,Good,Yes
+574,96,Female,21,No,9,10,117,0,Poor,Yes
+575,41,Female,31,Yes,9,4,167,0,Good,No
+576,19,Female,43,No,8,2,176,1,Average,No
+577,49,Male,44,No,5,7,54,3,Average,Yes
+578,90,Male,54,Yes,7,10,22,7,Poor,Yes
+579,83,Female,52,No,6,8,51,3,Average,Yes
+580,99,Male,67,No,6,6,44,4,Poor,Yes
+581,70,Female,59,No,9,1,83,1,Poor,Yes
+582,67,Male,60,No,6,7,103,5,Poor,Yes
+583,81,Male,43,No,8,10,158,0,Poor,Yes
+584,75,Male,64,Yes,7,9,138,4,Poor,Yes
+585,85,Others,35,No,6,5,59,9,Average,Yes
+586,40,Male,22,No,4,9,16,9,Poor,Yes
+587,77,Male,45,Yes,4,0,84,2,Good,Yes
+588,38,Male,42,No,7,7,62,9,Average,No
+589,98,Female,54,No,6,6,138,2,Average,Yes
+590,55,Female,23,Yes,4,9,155,8,Average,Yes
+591,21,Male,37,No,9,0,128,7,Average,No
+592,23,Female,44,Yes,8,2,12,8,Average,Yes
+593,19,Female,31,No,6,7,126,9,Average,No
+594,30,Male,62,No,10,0,175,6,Poor,No
+595,22,Male,33,Yes,7,6,41,4,Average,Yes
+596,65,Female,67,No,7,0,116,10,Poor,No
+597,65,Female,54,Yes,9,2,16,5,Poor,Yes
+598,77,Male,45,Yes,9,8,86,5,Poor,Yes
+599,48,Female,31,No,10,7,48,0,Average,No
+600,65,Others,24,No,7,7,3,7,Average,Yes
+601,30,Female,66,No,10,3,37,5,Poor,Yes
+602,94,Male,39,No,8,0,136,0,Average,Yes
+603,43,Male,25,Yes,9,8,172,9,Poor,Yes
+604,50,Male,44,Yes,7,8,21,2,Poor,Yes
+605,30,Male,66,No,5,4,28,0,Poor,Yes
+606,59,Female,55,Yes,6,10,94,8,Poor,Yes
+607,36,Male,48,No,8,0,37,10,Average,Yes
+608,97,Male,44,Yes,4,5,169,9,Average,Yes
+609,88,Female,46,Yes,7,8,53,10,Poor,Yes
+610,75,Female,32,No,7,4,165,0,Good,No
+611,19,Male,42,Yes,6,2,126,2,Good,No
+612,99,Male,62,Yes,6,2,40,3,Average,Yes
+613,54,Male,67,Yes,7,6,159,3,Poor,Yes
+614,83,Others,23,No,7,10,116,10,Average,No
+615,66,Male,48,No,9,5,120,3,Average,Yes
+616,79,Male,30,Yes,8,5,2,9,Good,Yes
+617,94,Male,46,No,4,3,101,1,Average,Yes
+618,23,Male,50,Yes,7,1,62,7,Average,No
+619,78,Female,69,No,6,7,32,4,Poor,Yes
+620,69,Female,58,No,6,8,153,5,Poor,Yes
+621,68,Male,62,No,5,7,23,2,Poor,Yes
+622,51,Male,57,Yes,6,2,107,6,Average,Yes
+623,42,Male,40,No,5,0,161,0,Good,No
+624,28,Male,55,No,5,9,175,1,Poor,Yes
+625,56,Female,51,Yes,7,5,10,0,Poor,Yes
+626,70,Female,45,Yes,8,3,81,5,Average,Yes
+627,72,Male,54,No,8,5,122,6,Average,No
+628,30,Male,32,No,8,0,33,8,Good,Yes
+629,35,Male,56,Yes,10,1,44,0,Poor,Yes
+630,33,Male,58,No,10,10,115,7,Poor,Yes
+631,31,Female,34,Yes,6,7,56,10,Average,No
+632,89,Male,32,Yes,7,10,48,4,Average,Yes
+633,28,Male,31,No,8,8,176,0,Poor,No
+634,90,Others,49,No,7,4,154,3,Good,No
+635,31,Female,30,No,7,0,55,1,Good,No
+636,93,Female,63,Yes,6,8,173,6,Poor,Yes
+637,80,Male,50,No,6,5,16,0,Good,Yes
+638,34,Male,23,No,6,7,22,9,Average,Yes
+639,98,Male,26,No,8,2,50,6,Good,Yes
+640,56,Male,60,Yes,6,1,12,3,Poor,Yes
+641,99,Female,22,Yes,6,4,92,3,Good,Yes
+642,99,Male,62,Yes,5,7,46,0,Average,Yes
+643,58,Female,41,Yes,9,3,31,5,Average,Yes
+644,97,Male,25,No,6,1,118,5,Good,Yes
+645,56,Others,41,Yes,7,1,118,6,Average,No
+646,30,Female,30,No,8,10,119,6,Poor,No
+647,80,Male,24,Yes,6,3,170,2,Good,Yes
+648,30,Female,59,No,9,6,157,0,Poor,No
+649,19,Female,50,No,4,6,162,8,Good,No
+650,59,Female,65,Yes,4,6,13,2,Poor,Yes
+651,54,Female,51,No,9,6,173,5,Poor,Yes
+652,51,Male,42,Yes,4,10,25,3,Poor,Yes
+653,82,Male,20,Yes,5,4,30,0,Average,Yes
+654,79,Male,39,Yes,7,7,3,8,Average,Yes
+655,69,Female,33,No,6,9,65,6,Poor,Yes
+656,68,Female,36,Yes,7,7,165,3,Average,Yes
+657,32,Male,23,Yes,5,10,95,4,Poor,Yes
+658,36,Female,62,Yes,8,4,29,5,Poor,Yes
+659,59,Male,60,Yes,6,6,66,9,Poor,Yes
+660,45,Male,43,No,5,0,153,5,Average,Yes
+661,75,Others,55,No,5,2,18,2,Poor,Yes
+662,25,Female,23,No,4,10,138,10,Average,No
+663,52,Male,66,No,6,9,163,3,Average,Yes
+664,45,Male,48,Yes,6,4,18,3,Average,Yes
+665,29,Male,33,No,8,8,136,10,Poor,No
+666,35,Female,67,No,4,7,19,6,Average,Yes
+667,58,Male,26,No,7,10,56,0,Poor,Yes
+668,35,Male,64,Yes,6,9,137,6,Poor,Yes
+669,45,Male,64,No,6,4,69,7,Poor,Yes
+670,73,Male,59,No,8,1,83,5,Average,No
+671,73,Female,31,Yes,6,9,11,9,Poor,Yes
+672,36,Female,52,Yes,7,6,115,4,Average,Yes
+673,19,Male,68,No,8,1,121,5,Poor,No
+674,79,Female,48,No,6,1,57,8,Good,Yes
+675,84,Male,31,Yes,9,4,47,10,Good,No
+676,91,Female,32,Yes,9,6,19,5,Good,Yes
+677,45,Male,27,No,4,2,85,0,Good,No
+678,33,Female,22,Yes,6,6,125,5,Average,Yes
+679,24,Female,52,No,4,10,34,3,Poor,Yes
+680,18,Male,44,No,8,2,156,10,Good,No
+681,98,Male,40,Yes,5,2,41,3,Good,Yes
+682,36,Female,65,No,5,1,60,3,Poor,Yes
+683,56,Female,47,No,4,10,40,9,Poor,Yes
+684,60,Female,60,Yes,7,8,135,6,Poor,Yes
+685,19,Female,51,No,6,0,49,2,Poor,Yes
+686,40,Female,66,No,7,3,119,2,Poor,Yes
+687,88,Female,61,Yes,4,1,125,2,Average,Yes
+688,95,Male,35,No,6,6,143,4,Average,Yes
+689,53,Female,28,No,6,6,128,10,Average,Yes
+690,30,Male,36,No,9,8,154,8,Poor,Yes
+691,79,Others,47,No,6,2,126,10,Average,No
+692,87,Male,37,Yes,6,2,14,2,Average,Yes
+693,70,Female,40,No,4,7,20,2,Average,Yes
+694,52,Male,46,No,7,9,92,4,Average,No
+695,66,Male,31,No,6,5,158,9,Average,No
+696,58,Female,37,No,8,6,65,1,Average,Yes
+697,38,Others,39,Yes,7,4,46,8,Good,No
+698,88,Female,47,Yes,5,10,152,7,Poor,Yes
+699,50,Female,55,Yes,6,4,130,2,Poor,Yes
+700,95,Male,61,Yes,9,9,122,7,Poor,Yes
+701,51,Female,47,Yes,5,8,132,5,Average,No
+702,79,Female,69,No,6,2,170,3,Average,Yes
+703,57,Male,62,Yes,8,4,27,10,Poor,Yes
+704,84,Female,62,No,6,7,82,0,Poor,Yes
+705,65,Female,51,No,5,8,42,2,Poor,Yes
+706,39,Female,60,No,6,7,70,1,Poor,Yes
+707,35,Others,29,No,6,5,81,0,Good,No
+708,29,Female,25,No,10,2,81,8,Good,No
+709,95,Male,29,No,6,8,114,10,Poor,Yes
+710,25,Female,39,Yes,8,2,124,1,Good,Yes
+711,70,Male,39,Yes,7,3,90,10,Average,Yes
+712,56,Male,21,Yes,9,5,145,7,Good,No
+713,49,Male,59,No,6,4,89,9,Poor,Yes
+714,36,Male,55,No,5,3,8,5,Poor,Yes
+715,38,Female,25,Yes,7,1,89,6,Good,Yes
+716,41,Male,32,Yes,4,9,97,7,Poor,Yes
+717,39,Female,52,No,5,7,49,1,Poor,Yes
+718,67,Female,36,Yes,9,9,26,4,Poor,Yes
+719,72,Male,62,No,6,6,23,10,Poor,Yes
+720,23,Male,34,Yes,7,6,59,5,Average,Yes
+721,72,Male,57,No,6,4,150,2,Poor,Yes
+722,84,Male,37,Yes,7,5,104,6,Average,No
+723,98,Male,56,Yes,10,6,134,2,Poor,Yes
+724,79,Male,67,No,9,7,57,6,Poor,Yes
+725,55,Male,52,No,7,6,62,6,Poor,No
+726,46,Male,65,Yes,6,7,133,0,Poor,Yes
+727,77,Male,39,Yes,6,2,87,7,Average,Yes
+728,77,Female,66,No,5,3,19,2,Poor,Yes
+729,68,Male,23,Yes,6,4,92,2,Good,Yes
+730,53,Others,50,No,7,0,115,3,Good,Yes
+731,64,Male,22,Yes,6,0,157,8,Good,No
+732,94,Female,42,Yes,7,3,40,9,Average,No
+733,99,Female,63,No,6,2,129,4,Poor,Yes
+734,39,Male,20,No,10,4,127,4,Good,No
+735,87,Male,28,No,6,1,174,2,Average,No
+736,71,Male,59,Yes,8,6,162,6,Poor,Yes
+737,67,Male,27,No,5,8,154,0,Poor,Yes
+738,58,Male,69,No,6,0,170,2,Poor,Yes
+739,64,Male,35,Yes,9,6,156,4,Average,Yes
+740,33,Female,21,Yes,4,4,124,3,Good,Yes
+741,80,Male,59,Yes,7,7,115,5,Poor,Yes
+742,45,Female,46,No,6,0,101,7,Average,Yes
+743,54,Female,26,No,5,2,67,5,Good,No
+744,88,Male,32,Yes,8,3,70,10,Good,No
+745,91,Male,56,No,7,2,34,0,Poor,Yes
+746,61,Female,43,Yes,6,6,1,4,Average,Yes
+747,71,Male,33,No,7,6,51,7,Average,Yes
+748,45,Male,60,No,9,3,146,10,Poor,No
+749,33,Others,64,Yes,6,1,19,10,Poor,Yes
+750,75,Female,32,Yes,6,6,131,4,Average,Yes
+751,48,Female,59,No,7,8,123,5,Poor,Yes
+752,56,Female,28,Yes,6,7,3,3,Average,Yes
+753,69,Female,45,Yes,7,9,9,4,Poor,Yes
+754,42,Others,28,No,10,4,60,10,Good,No
+755,68,Female,43,No,5,7,64,4,Good,No
+756,18,Male,45,No,6,4,26,8,Average,No
+757,50,Male,66,No,10,8,23,7,Poor,Yes
+758,35,Female,61,No,8,10,7,6,Poor,Yes
+759,59,Male,65,Yes,7,10,103,8,Poor,Yes
+760,87,Male,58,No,6,6,7,0,Poor,Yes
+761,24,Others,28,No,6,3,33,5,Good,No
+762,54,Female,60,Yes,8,10,84,4,Average,Yes
+763,59,Male,20,Yes,9,1,128,2,Good,No
+764,62,Male,67,No,6,6,3,5,Poor,Yes
+765,34,Male,29,No,6,8,70,7,Average,Yes
+766,43,Male,34,Yes,7,1,130,8,Good,Yes
+767,42,Male,36,Yes,7,5,117,6,Average,Yes
+768,89,Female,56,No,9,0,29,6,Poor,Yes
+769,80,Male,53,No,5,7,10,9,Poor,Yes
+770,96,Female,28,No,4,10,153,2,Average,Yes
+771,60,Female,25,Yes,4,7,122,3,Average,Yes
+772,85,Male,32,Yes,8,0,134,10,Good,No
+773,78,Male,64,No,5,10,40,5,Poor,Yes
+774,21,Female,40,Yes,6,0,20,9,Good,Yes
+775,51,Female,53,No,8,1,139,2,Average,Yes
+776,94,Male,54,No,7,6,100,0,Poor,Yes
+777,85,Male,57,Yes,6,7,62,5,Poor,Yes
+778,93,Female,31,No,8,5,38,2,Good,Yes
+779,96,Female,29,No,8,6,88,2,Average,No
+780,53,Male,49,Yes,7,9,41,2,Poor,Yes
+781,45,Female,40,No,6,10,177,4,Poor,Yes
+782,81,Male,55,No,8,10,110,4,Poor,Yes
+783,18,Male,27,Yes,7,10,102,0,Average,No
+784,28,Female,41,Yes,5,1,1,3,Average,Yes
+785,78,Male,38,No,7,10,49,6,Poor,Yes
+786,59,Male,29,Yes,4,10,35,9,Poor,Yes
+787,49,Male,24,No,8,10,57,6,Poor,Yes
+788,67,Female,32,Yes,7,9,38,8,Poor,Yes
+789,82,Male,27,Yes,10,9,13,7,Poor,Yes
+790,83,Female,22,No,4,0,89,1,Good,No
+791,73,Male,38,No,5,4,24,6,Good,Yes
+792,77,Female,38,Yes,6,7,154,0,Average,Yes
+793,92,Female,48,Yes,6,0,24,7,Average,Yes
+794,50,Male,28,No,8,2,76,6,Average,No
+795,44,Male,49,No,7,7,114,8,Average,No
+796,93,Others,25,No,7,0,109,8,Good,No
+797,32,Female,24,No,5,4,147,7,Good,No
+798,58,Male,64,No,6,2,119,6,Poor,Yes
+799,31,Male,51,No,5,4,91,1,Poor,Yes
+800,46,Male,53,No,8,9,128,1,Average,No
+801,84,Male,28,Yes,7,2,10,0,Average,Yes
+802,19,Male,32,Yes,8,6,158,1,Good,No
+803,88,Male,31,Yes,8,4,153,0,Good,Yes
+804,97,Female,23,No,4,3,101,1,Average,Yes
+805,79,Female,30,No,7,5,165,5,Average,No
+806,82,Female,34,Yes,4,1,173,7,Average,Yes
+807,49,Female,21,No,8,10,89,6,Average,Yes
+808,38,Male,32,Yes,5,8,109,5,Poor,Yes
+809,80,Female,55,No,6,3,55,5,Poor,Yes
+810,20,Male,61,No,7,7,35,7,Average,No
+811,34,Male,31,No,9,3,73,3,Good,Yes
+812,19,Others,69,Yes,6,3,162,9,Poor,No
+813,23,Female,25,No,9,10,4,2,Average,Yes
+814,23,Male,22,No,8,3,12,0,Good,Yes
+815,42,Male,51,Yes,6,9,134,0,Poor,Yes
+816,96,Male,28,Yes,8,10,128,2,Poor,Yes
+817,31,Male,42,Yes,5,4,29,9,Good,Yes
+818,89,Male,68,No,7,7,54,0,Poor,Yes
+819,74,Male,20,Yes,9,3,90,9,Good,No
+820,66,Female,30,Yes,5,8,153,9,Poor,Yes
+821,56,Female,39,No,7,5,159,8,Average,No
+822,71,Male,33,No,7,1,27,0,Good,No
+823,66,Male,45,No,4,6,72,5,Good,No
+824,97,Male,54,Yes,7,8,80,9,Poor,Yes
+825,81,Male,54,No,6,10,60,2,Poor,Yes
+826,83,Others,47,No,6,8,172,4,Poor,Yes
+827,28,Male,51,No,8,9,49,2,Poor,Yes
+828,77,Others,54,Yes,5,3,107,9,Poor,Yes
+829,18,Female,30,No,4,7,45,6,Average,Yes
+830,73,Female,32,No,5,9,104,6,Average,Yes
+831,38,Female,67,No,8,2,27,4,Poor,Yes
+832,36,Male,49,Yes,6,4,62,4,Average,Yes
+833,47,Male,37,Yes,7,1,118,7,Average,No
+834,27,Female,40,Yes,8,7,118,2,Good,Yes
+835,36,Female,57,No,7,8,69,4,Poor,Yes
+836,47,Female,42,Yes,10,8,68,7,Poor,Yes
+837,46,Female,38,No,7,1,45,4,Good,Yes
+838,19,Female,48,No,4,0,64,6,Average,No
+839,59,Female,27,No,6,3,90,10,Good,No
+840,20,Male,60,No,6,2,39,4,Poor,Yes
+841,85,Male,67,No,5,7,116,3,Poor,No
+842,35,Male,53,No,10,1,162,4,Poor,Yes
+843,92,Female,32,No,6,0,91,10,Good,No
+844,20,Female,45,No,8,1,157,9,Good,No
+845,59,Female,22,Yes,5,2,115,0,Good,Yes
+846,61,Female,24,No,8,8,150,7,Average,No
+847,65,Male,63,No,7,7,66,9,Poor,Yes
+848,83,Male,62,No,4,6,112,1,Poor,Yes
+849,75,Male,25,Yes,4,8,70,1,Poor,Yes
+850,20,Female,30,No,6,3,78,8,Good,No
+851,37,Male,45,Yes,7,4,179,1,Average,Yes
+852,29,Others,20,No,6,4,119,9,Good,Yes
+853,78,Male,49,Yes,6,2,33,1,Average,Yes
+854,53,Male,54,No,5,9,110,4,Poor,Yes
+855,35,Male,60,Yes,5,4,108,6,Poor,Yes
+856,65,Others,30,No,5,2,132,6,Average,No
+857,29,Male,47,No,6,10,124,4,Poor,Yes
+858,72,Female,62,Yes,10,5,81,1,Poor,Yes
+859,54,Others,62,No,6,6,91,2,Poor,Yes
+860,36,Male,30,Yes,9,9,125,9,Average,No
+861,71,Male,24,No,7,7,128,0,Good,Yes
+862,68,Female,26,No,8,8,40,8,Poor,Yes
+863,19,Others,60,No,7,3,33,4,Average,No
+864,35,Female,33,Yes,7,9,132,5,Poor,Yes
+865,50,Female,56,No,8,3,84,9,Poor,Yes
+866,52,Female,38,No,6,5,121,10,Good,Yes
+867,39,Female,54,Yes,4,7,83,3,Poor,Yes
+868,55,Female,46,Yes,9,0,63,1,Average,No
+869,80,Male,24,Yes,7,5,105,5,Good,Yes
+870,50,Female,28,No,6,4,123,2,Good,No
+871,47,Male,22,No,8,1,10,10,Average,No
+872,72,Female,22,Yes,8,9,107,3,Poor,Yes
+873,38,Female,67,No,7,8,81,8,Poor,Yes
+874,61,Female,41,Yes,8,9,5,6,Poor,Yes
+875,48,Male,22,Yes,10,5,160,2,Average,No
+876,31,Male,40,Yes,7,5,147,4,Good,Yes
+877,22,Male,55,No,6,4,41,6,Poor,Yes
+878,31,Female,34,No,8,7,92,6,Good,No
+879,34,Female,65,No,5,1,158,9,Poor,Yes
+880,62,Male,20,Yes,6,10,51,3,Poor,Yes
+881,42,Male,56,Yes,6,3,141,1,Poor,Yes
+882,40,Female,36,No,6,5,25,4,Average,No
+883,80,Female,44,No,6,1,173,4,Average,Yes
+884,27,Male,38,Yes,4,0,159,2,Good,Yes
+885,18,Male,28,No,5,0,64,1,Average,Yes
+886,66,Female,37,No,8,10,148,7,Poor,Yes
+887,52,Female,30,No,6,0,78,3,Good,No
+888,31,Female,26,Yes,7,6,2,5,Average,Yes
+889,67,Female,26,No,8,10,53,8,Average,Yes
+890,35,Male,55,No,5,5,41,2,Poor,Yes
+891,62,Female,62,Yes,8,8,24,0,Poor,Yes
+892,93,Male,54,Yes,9,9,149,10,Average,Yes
+893,94,Male,39,Yes,7,1,89,1,Good,Yes
+894,80,Female,27,No,4,5,26,4,Good,Yes
+895,90,Female,46,No,8,1,142,8,Average,No
+896,53,Male,51,No,4,0,91,6,Average,Yes
+897,30,Male,37,No,6,10,1,6,Poor,Yes
+898,67,Male,68,No,7,10,159,9,Poor,Yes
+899,65,Female,54,No,6,1,115,4,Poor,Yes
+900,27,Male,44,No,7,7,42,0,Average,Yes
+901,22,Male,48,No,9,3,53,9,Good,No
+902,51,Male,63,Yes,8,0,76,2,Poor,Yes
+903,52,Male,45,No,6,8,13,6,Poor,Yes
+904,75,Male,63,No,6,2,84,5,Poor,Yes
+905,50,Male,23,No,7,1,82,10,Good,No
+906,68,Female,34,No,5,0,113,8,Good,No
+907,32,Female,39,No,8,3,44,7,Good,No
+908,36,Female,35,Yes,6,9,113,3,Average,Yes
+909,37,Female,67,Yes,5,7,166,2,Poor,Yes
+910,77,Male,22,Yes,6,8,124,2,Average,Yes
+911,31,Male,46,No,7,9,52,0,Poor,Yes
+912,21,Female,68,No,7,4,180,0,Average,No
+913,94,Male,24,No,7,7,147,1,Average,Yes
+914,29,Male,47,No,7,0,156,9,Average,No
+915,96,Female,46,Yes,5,0,30,8,Average,Yes
+916,81,Male,47,No,6,10,137,3,Average,Yes
+917,92,Female,63,Yes,7,7,58,3,Poor,Yes
+918,86,Male,62,No,8,6,3,0,Poor,Yes
+919,34,Male,59,No,9,10,120,7,Average,No
+920,20,Male,47,No,4,4,158,0,Average,No
+921,79,Others,43,No,4,0,4,8,Good,Yes
+922,29,Female,30,No,8,4,25,8,Good,No
+923,86,Others,27,Yes,8,4,15,5,Good,Yes
+924,61,Male,47,Yes,6,7,133,10,Average,Yes
+925,91,Male,59,Yes,7,2,73,5,Poor,Yes
+926,75,Male,20,No,8,9,46,8,Poor,Yes
+927,45,Male,29,No,6,8,94,2,Poor,Yes
+928,96,Male,57,Yes,9,5,32,4,Poor,Yes
+929,39,Female,56,No,8,10,107,1,Poor,Yes
+930,63,Female,59,Yes,7,8,97,9,Poor,Yes
+931,90,Female,51,No,6,5,57,4,Poor,Yes
+932,20,Female,37,No,7,0,107,8,Average,No
+933,73,Male,21,Yes,6,6,129,6,Average,Yes
+934,36,Female,65,No,8,9,155,6,Average,Yes
+935,18,Male,54,No,5,6,114,8,Poor,Yes
+936,70,Male,55,No,6,0,129,4,Poor,Yes
+937,47,Female,27,No,8,5,81,5,Average,Yes
+938,55,Female,41,No,5,4,105,0,Good,No
+939,80,Male,20,No,9,6,68,3,Good,Yes
+940,98,Female,27,No,8,0,180,8,Good,No
+941,59,Male,42,No,9,5,55,3,Average,No
+942,44,Others,38,Yes,7,5,155,5,Good,No
+943,49,Male,65,No,8,6,16,1,Poor,Yes
+944,56,Others,25,No,6,7,7,5,Average,Yes
+945,95,Female,65,No,6,8,164,0,Average,No
+946,42,Male,53,No,6,6,117,1,Poor,Yes
+947,80,Female,59,No,9,4,14,9,Poor,Yes
+948,37,Male,65,Yes,6,1,31,0,Average,Yes
+949,18,Female,51,No,6,6,8,2,Average,Yes
+950,80,Female,26,Yes,5,8,75,5,Poor,Yes
+951,94,Male,65,Yes,7,2,19,3,Poor,Yes
+952,94,Male,33,No,8,0,168,5,Average,No
+953,55,Female,65,No,5,5,16,4,Poor,Yes
+954,66,Female,25,No,6,0,179,2,Good,No
+955,61,Male,46,Yes,4,6,165,8,Average,No
+956,22,Male,42,No,7,6,105,0,Average,Yes
+957,86,Female,48,Yes,9,5,47,3,Average,Yes
+958,76,Male,29,No,4,10,156,7,Average,Yes
+959,74,Male,32,No,7,10,156,3,Poor,No
+960,63,Male,30,No,7,5,11,10,Good,No
+961,21,Female,33,No,8,6,66,4,Average,Yes
+962,35,Male,30,Yes,7,2,48,10,Good,No
+963,19,Female,51,Yes,7,10,101,1,Poor,Yes
+964,40,Male,38,No,4,0,149,4,Good,Yes
+965,19,Male,47,No,5,0,104,2,Average,No
+966,70,Male,32,No,9,9,104,6,Poor,Yes
+967,38,Male,69,No,8,1,172,1,Poor,No
+968,58,Male,61,No,4,0,103,5,Poor,Yes
+969,38,Others,38,No,5,2,108,9,Good,No
+970,97,Male,40,No,8,10,89,10,Poor,Yes
+971,80,Male,66,No,9,2,7,4,Average,No
+972,97,Male,55,No,7,5,140,8,Poor,Yes
+973,50,Female,36,Yes,8,7,100,1,Average,Yes
+974,87,Female,44,Yes,7,9,109,8,Poor,Yes
+975,38,Male,39,No,5,10,45,0,Average,No
+976,84,Male,58,No,8,0,58,5,Poor,Yes
+977,99,Male,54,No,5,1,126,8,Poor,Yes
+978,32,Male,61,No,4,7,107,0,Poor,Yes
+979,86,Male,59,Yes,6,6,168,8,Poor,Yes
+980,41,Male,33,Yes,7,3,90,7,Good,Yes
+981,45,Female,29,No,8,0,106,2,Good,No
+982,21,Female,31,Yes,6,9,75,2,Poor,Yes
+983,28,Male,68,No,8,3,58,3,Poor,Yes
+984,87,Female,33,No,8,4,3,5,Good,Yes
+985,80,Male,26,No,10,9,137,7,Poor,Yes
+986,62,Female,23,No,6,7,142,6,Average,No
+987,30,Female,36,No,6,8,94,2,Poor,Yes
+988,99,Male,40,No,8,6,104,1,Average,Yes
+989,76,Male,25,Yes,6,3,145,0,Good,Yes
+990,95,Others,52,No,10,7,147,5,Poor,Yes
+991,89,Male,60,No,6,6,133,5,Poor,Yes
+992,51,Others,49,Yes,6,6,119,3,Average,Yes
+993,43,Female,27,No,7,9,48,2,Poor,Yes
+994,40,Female,41,No,10,1,62,9,Average,Yes
+995,52,Male,53,No,7,1,61,5,Average,Yes
+996,18,Female,62,No,8,1,144,9,Poor,No
+997,48,Female,24,No,7,6,35,3,Average,No
+998,41,Others,41,No,8,3,46,8,Average,No
+999,68,Male,25,Yes,7,1,178,5,Good,Yes
+1000,69,Female,50,Yes,9,7,133,9,Average,Yes
+1001,84,Female,69,No,6,4,156,0,Poor,Yes
+1002,59,Male,33,No,6,2,80,8,Good,No
+1003,62,Others,45,No,7,5,34,6,Average,No
+1004,26,Others,57,Yes,9,8,98,10,Poor,No
+1005,19,Female,62,No,7,10,117,10,Poor,Yes
+1006,22,Male,57,Yes,8,3,144,6,Poor,Yes
+1007,45,Male,69,No,5,1,153,0,Poor,Yes
+1008,98,Male,22,Yes,7,9,79,2,Poor,Yes
+1009,58,Male,52,No,6,6,147,5,Poor,Yes
+1010,55,Male,62,No,6,6,67,8,Poor,Yes
+1011,25,Male,26,No,5,4,66,9,Good,No
+1012,25,Female,51,No,5,3,141,5,Average,No
+1013,51,Male,52,No,7,10,164,2,Poor,Yes
+1014,97,Male,58,No,5,3,120,1,Poor,Yes
+1015,59,Male,45,No,7,3,115,4,Average,Yes
+1016,61,Female,36,No,6,5,18,5,Good,Yes
+1017,66,Female,63,Yes,7,1,16,2,Average,Yes
+1018,27,Male,28,Yes,9,9,3,7,Poor,Yes
+1019,25,Male,39,Yes,6,4,136,7,Good,No
+1020,55,Male,61,No,5,6,158,10,Poor,Yes
+1021,66,Female,52,No,5,4,169,3,Poor,Yes
+1022,37,Female,57,No,6,5,12,7,Poor,Yes
+1023,41,Female,62,No,6,9,96,1,Poor,Yes
+1024,62,Female,65,Yes,8,2,138,6,Poor,Yes
+1025,64,Others,61,No,8,5,30,10,Poor,Yes
+1026,95,Male,25,Yes,9,5,133,9,Good,Yes
+1027,89,Male,48,Yes,10,6,44,7,Average,Yes
+1028,47,Male,63,No,6,0,56,8,Poor,Yes
+1029,78,Female,40,No,8,7,5,4,Average,Yes
+1030,81,Female,27,No,8,5,82,10,Average,No
+1031,46,Male,46,No,4,7,68,1,Good,Yes
+1032,22,Female,62,No,6,9,94,10,Poor,Yes
+1033,71,Others,39,Yes,8,9,5,10,Poor,Yes
+1034,27,Female,25,No,5,0,163,8,Good,No
+1035,45,Female,32,Yes,7,10,35,6,Poor,Yes
+1036,77,Male,20,Yes,8,1,165,7,Good,No
+1037,79,Female,32,No,4,7,60,4,Average,Yes
+1038,86,Male,57,Yes,8,9,169,3,Poor,Yes
+1039,66,Female,23,Yes,4,4,17,4,Good,Yes
+1040,82,Female,20,No,8,0,148,9,Good,No
+1041,99,Male,40,Yes,7,5,131,5,Good,Yes
+1042,60,Female,38,Yes,6,7,39,7,Average,Yes
+1043,28,Female,43,No,7,10,162,3,Poor,Yes
+1044,89,Male,53,No,7,6,103,2,Poor,Yes
+1045,43,Male,42,No,6,9,160,6,Poor,Yes
+1046,32,Male,34,Yes,8,7,55,9,Average,Yes
+1047,45,Others,38,No,6,8,47,0,Poor,Yes
+1048,30,Male,61,No,8,2,155,5,Poor,Yes
+1049,37,Others,51,No,6,9,63,0,Poor,Yes
+1050,89,Female,31,No,5,5,77,5,Good,No
+1051,78,Male,32,No,7,6,15,4,Average,No
+1052,65,Male,55,Yes,7,1,27,2,Average,Yes
+1053,86,Female,44,No,8,4,70,7,Good,No
+1054,92,Female,68,Yes,8,5,70,0,Poor,Yes
+1055,98,Male,29,No,7,9,126,8,Poor,Yes
+1056,69,Others,26,No,5,3,3,5,Average,Yes
+1057,98,Female,56,No,7,1,18,2,Poor,Yes
+1058,54,Female,40,No,5,0,31,1,Average,No
+1059,65,Female,34,No,10,10,68,0,Poor,Yes
+1060,43,Female,27,No,4,2,75,10,Good,No
+1061,38,Female,34,No,10,10,97,8,Poor,No
+1062,93,Male,44,No,6,9,149,6,Poor,Yes
+1063,79,Female,33,No,8,0,123,3,Good,No
+1064,22,Female,51,No,9,1,25,2,Average,No
+1065,30,Female,54,No,5,10,134,5,Poor,Yes
+1066,66,Male,51,No,6,7,137,9,Poor,Yes
+1067,82,Male,54,No,8,8,37,10,Poor,Yes
+1068,28,Female,35,No,8,0,121,0,Good,Yes
+1069,47,Male,21,No,9,1,41,5,Good,Yes
+1070,95,Male,68,No,9,7,169,3,Poor,Yes
+1071,40,Male,68,No,8,4,97,2,Poor,Yes
+1072,75,Male,57,No,6,2,153,0,Poor,Yes
+1073,36,Male,60,Yes,6,7,169,2,Poor,Yes
+1074,52,Male,50,Yes,7,3,163,2,Average,Yes
+1075,28,Female,63,Yes,9,2,101,0,Poor,Yes
+1076,26,Female,21,No,6,1,35,10,Average,No
+1077,23,Female,61,No,7,0,116,6,Average,No
+1078,69,Male,23,No,4,6,10,7,Good,Yes
+1079,38,Female,27,No,7,9,31,7,Poor,Yes
+1080,30,Female,21,No,8,6,79,1,Average,No
+1081,99,Male,42,Yes,7,8,66,3,Poor,Yes
+1082,26,Male,68,No,7,6,56,7,Average,Yes
+1083,23,Male,43,Yes,4,5,176,3,Average,Yes
+1084,75,Male,62,No,7,1,126,0,Poor,Yes
+1085,34,Male,66,No,4,7,171,6,Poor,Yes
+1086,53,Male,42,Yes,6,0,9,10,Average,Yes
+1087,39,Female,67,Yes,7,6,137,7,Poor,Yes
+1088,77,Male,23,No,5,8,169,8,Poor,Yes
+1089,41,Female,52,Yes,7,7,10,10,Average,Yes
+1090,56,Female,52,No,7,2,8,8,Average,Yes
+1091,33,Female,59,No,8,8,132,6,Poor,No
+1092,76,Male,69,No,6,6,44,0,Average,Yes
+1093,30,Male,23,No,5,1,112,3,Good,Yes
+1094,28,Female,24,No,6,0,111,8,Good,No
+1095,71,Male,37,No,9,0,61,7,Good,No
+1096,65,Female,37,Yes,5,8,76,0,Poor,Yes
+1097,77,Male,69,Yes,7,8,130,6,Poor,Yes
+1098,43,Male,44,Yes,7,2,14,9,Average,Yes
+1099,19,Female,60,Yes,9,8,129,1,Poor,Yes
+1100,93,Male,28,Yes,6,8,163,3,Poor,Yes
+1101,52,Male,52,No,8,5,44,5,Poor,Yes
+1102,94,Female,25,No,7,4,29,2,Good,Yes
+1103,44,Female,64,Yes,7,5,37,8,Poor,Yes
+1104,52,Female,65,No,6,0,49,8,Poor,No
+1105,75,Female,34,Yes,6,4,106,7,Good,No
+1106,43,Male,26,Yes,4,10,122,2,Poor,Yes
+1107,48,Male,51,Yes,9,1,0,8,Average,Yes
+1108,37,Male,56,No,4,3,24,2,Average,Yes
+1109,98,Female,23,Yes,4,6,55,0,Average,Yes
+1110,83,Others,21,No,5,7,21,0,Average,Yes
+1111,43,Female,58,Yes,8,0,63,4,Poor,Yes
+1112,79,Male,53,No,8,9,25,0,Poor,Yes
+1113,78,Female,25,Yes,4,2,43,4,Good,Yes
+1114,87,Female,22,No,6,5,169,9,Good,No
+1115,95,Female,39,Yes,8,0,28,10,Average,No
+1116,60,Female,69,Yes,6,6,160,2,Poor,No
+1117,40,Female,41,No,8,3,4,3,Good,No
+1118,69,Female,33,Yes,6,6,45,1,Average,Yes
+1119,93,Female,64,No,6,4,19,4,Average,Yes
+1120,34,Female,46,No,8,5,169,7,Average,Yes
+1121,76,Male,40,No,7,2,170,2,Good,No
+1122,73,Male,46,No,7,0,15,6,Good,No
+1123,91,Male,27,Yes,4,10,159,7,Poor,Yes
+1124,44,Male,46,Yes,6,5,96,6,Average,Yes
+1125,29,Male,55,Yes,7,8,26,10,Poor,Yes
+1126,63,Female,40,Yes,7,7,5,10,Average,Yes
+1127,72,Male,67,No,8,5,61,10,Poor,Yes
+1128,27,Female,45,Yes,6,9,113,6,Poor,Yes
+1129,97,Male,54,No,6,8,180,1,Poor,Yes
+1130,79,Female,58,No,8,9,57,8,Average,Yes
+1131,78,Female,56,No,7,7,73,0,Poor,Yes
+1132,53,Male,30,Yes,6,3,77,0,Good,Yes
+1133,80,Female,55,No,7,7,19,4,Average,Yes
+1134,87,Male,55,Yes,6,10,76,10,Poor,Yes
+1135,30,Male,21,No,5,2,54,5,Good,No
+1136,68,Others,42,No,8,9,8,10,Average,Yes
+1137,37,Female,60,No,4,2,142,4,Poor,Yes
+1138,64,Female,47,No,5,7,135,7,Average,No
+1139,92,Female,39,Yes,4,0,41,4,Good,Yes
+1140,37,Female,37,No,5,5,170,4,Good,No
+1141,79,Male,43,No,5,10,84,9,Poor,Yes
+1142,74,Female,21,No,6,8,114,9,Poor,Yes
+1143,88,Male,27,No,9,10,104,3,Poor,Yes
+1144,56,Male,35,Yes,4,5,159,8,Good,Yes
+1145,89,Others,29,No,8,10,177,4,Poor,Yes
+1146,96,Male,55,No,8,3,148,9,Poor,Yes
+1147,84,Female,69,Yes,6,3,107,7,Poor,Yes
+1148,95,Male,21,No,6,3,110,5,Good,No
+1149,88,Male,54,No,10,4,122,9,Poor,Yes
+1150,96,Female,53,Yes,9,4,157,1,Poor,Yes
+1151,35,Male,55,No,8,1,161,1,Poor,Yes
+1152,53,Male,39,Yes,8,8,59,10,Poor,Yes
+1153,53,Male,47,No,6,8,143,1,Poor,Yes
+1154,66,Male,62,No,5,0,146,2,Poor,No
+1155,67,Others,67,No,5,7,11,2,Poor,Yes
+1156,82,Male,58,Yes,6,3,93,5,Poor,Yes
+1157,84,Female,67,No,6,4,96,1,Poor,Yes
+1158,49,Female,66,Yes,5,10,113,7,Poor,Yes
+1159,54,Female,44,Yes,6,3,171,2,Average,Yes
+1160,58,Male,67,No,6,0,15,2,Poor,Yes
+1161,80,Female,65,Yes,7,1,25,5,Poor,Yes
+1162,84,Male,42,No,8,7,10,7,Average,No
+1163,50,Female,45,Yes,9,7,108,0,Average,Yes
+1164,40,Female,48,No,6,2,58,0,Average,No
+1165,95,Others,33,Yes,6,6,70,8,Average,Yes
+1166,87,Female,57,No,5,2,120,8,Poor,Yes
+1167,42,Female,33,Yes,5,5,117,10,Good,No
+1168,86,Male,62,No,7,2,64,6,Poor,Yes
+1169,21,Female,24,No,8,8,14,8,Poor,Yes
+1170,36,Male,64,No,7,2,105,9,Poor,No
+1171,65,Others,56,Yes,7,8,26,2,Poor,Yes
+1172,81,Female,39,Yes,6,9,56,5,Poor,Yes
+1173,65,Male,40,No,7,1,159,0,Good,No
+1174,72,Male,38,Yes,9,10,86,10,Poor,Yes
+1175,88,Female,64,No,7,4,29,0,Average,Yes
+1176,91,Male,61,No,5,7,70,3,Poor,Yes
+1177,70,Male,56,Yes,10,0,108,1,Poor,Yes
+1178,68,Male,37,No,6,8,18,8,Poor,Yes
+1179,48,Male,35,No,8,4,169,9,Good,No
+1180,95,Male,64,No,8,2,38,6,Average,No
+1181,43,Female,53,No,6,5,50,8,Average,Yes
+1182,34,Male,67,Yes,7,5,57,1,Poor,Yes
+1183,50,Male,28,Yes,10,8,99,2,Poor,Yes
+1184,45,Others,62,Yes,5,1,175,4,Average,Yes
+1185,35,Male,57,Yes,5,9,61,3,Poor,Yes
+1186,67,Female,63,No,7,1,163,0,Poor,No
+1187,43,Male,43,Yes,5,5,176,3,Average,Yes
+1188,90,Female,61,Yes,6,6,167,8,Poor,Yes
+1189,59,Female,57,No,5,5,18,1,Poor,Yes
+1190,53,Female,36,No,8,6,171,8,Average,No
+1191,71,Male,60,Yes,7,3,167,3,Average,No
+1192,21,Male,55,Yes,9,4,114,7,Poor,Yes
+1193,84,Female,24,Yes,5,9,73,0,Poor,Yes
+1194,39,Female,35,Yes,9,2,35,7,Good,No
+1195,77,Male,60,No,6,2,16,0,Poor,Yes
+1196,64,Female,31,Yes,8,2,57,4,Good,Yes
+1197,58,Male,35,No,5,9,150,8,Poor,Yes
+1198,28,Female,51,No,6,3,51,1,Poor,Yes
+1199,82,Others,21,No,6,5,161,5,Good,No
+1200,76,Male,62,No,8,0,158,7,Average,No
+1201,44,Male,34,Yes,6,7,91,2,Good,Yes
+1202,54,Female,62,Yes,7,8,160,4,Poor,Yes
+1203,58,Female,56,No,9,7,157,5,Poor,Yes
+1204,83,Male,31,No,4,10,89,4,Average,Yes
+1205,62,Others,30,No,6,0,55,8,Good,Yes
+1206,94,Male,49,No,7,8,91,5,Poor,Yes
+1207,92,Female,50,Yes,6,6,78,7,Average,Yes
+1208,34,Male,33,No,6,8,131,4,Poor,Yes
+1209,32,Male,33,Yes,5,7,145,2,Average,Yes
+1210,58,Others,45,No,6,6,37,0,Average,Yes
+1211,67,Female,20,No,4,10,113,8,Poor,Yes
+1212,67,Female,32,No,8,1,81,0,Good,No
+1213,81,Female,58,No,8,10,21,2,Poor,Yes
+1214,83,Female,51,No,7,9,174,7,Poor,Yes
+1215,56,Female,45,No,7,6,45,6,Good,No
+1216,91,Male,48,No,8,7,132,5,Average,Yes
+1217,84,Female,48,Yes,7,2,65,4,Average,Yes
+1218,55,Female,65,No,7,3,129,8,Poor,Yes
+1219,74,Female,47,Yes,8,7,106,1,Average,Yes
+1220,86,Female,49,No,7,4,88,10,Good,Yes
+1221,20,Female,35,Yes,6,7,118,6,Good,Yes
+1222,42,Female,52,No,8,9,93,10,Poor,Yes
+1223,91,Others,37,No,7,10,76,4,Poor,Yes
+1224,33,Male,41,No,7,1,164,4,Average,No
+1225,43,Female,37,No,5,5,166,10,Good,No
+1226,23,Female,60,No,6,8,33,3,Poor,Yes
+1227,72,Female,44,Yes,5,3,166,3,Good,Yes
+1228,89,Female,45,No,6,10,139,10,Poor,No
+1229,59,Female,26,No,7,0,117,5,Good,Yes
+1230,62,Female,26,No,6,8,166,0,Poor,Yes
+1231,38,Female,67,No,6,9,156,5,Poor,Yes
+1232,33,Male,30,No,7,8,71,2,Poor,Yes
+1233,96,Male,28,No,6,7,8,2,Good,Yes
+1234,22,Male,41,No,6,4,47,9,Good,No
+1235,61,Male,60,No,6,8,139,6,Average,Yes
+1236,45,Male,48,Yes,7,9,157,7,Average,Yes
+1237,49,Female,57,No,7,6,148,8,Poor,No
+1238,74,Female,64,Yes,5,3,136,4,Average,Yes
+1239,56,Male,49,No,6,3,92,9,Average,Yes
+1240,85,Female,49,Yes,4,1,159,5,Average,No
+1241,64,Male,44,No,7,1,51,10,Average,No
+1242,82,Male,49,Yes,7,9,116,10,Poor,Yes
+1243,88,Female,30,No,6,7,52,1,Good,Yes
+1244,85,Female,67,No,7,9,38,2,Poor,Yes
+1245,82,Female,23,No,6,10,94,3,Poor,Yes
+1246,20,Male,58,Yes,6,10,47,4,Poor,Yes
+1247,39,Female,23,No,7,3,47,6,Average,Yes
+1248,87,Male,30,No,9,4,99,4,Average,No
+1249,61,Male,46,No,7,10,68,0,Average,Yes
+1250,84,Male,31,No,6,10,118,3,Poor,Yes
+1251,92,Male,63,No,6,6,48,3,Average,Yes
+1252,64,Others,36,No,9,7,151,9,Average,Yes
+1253,81,Male,37,No,9,4,153,5,Good,No
+1254,37,Female,40,No,7,6,154,1,Average,Yes
+1255,29,Male,69,No,8,4,75,1,Poor,No
+1256,79,Male,68,No,7,4,86,1,Average,Yes
+1257,47,Female,37,No,5,4,97,9,Good,No
+1258,76,Male,37,No,7,7,119,5,Average,No
+1259,49,Male,59,No,5,1,118,10,Poor,Yes
+1260,18,Female,53,No,7,9,127,6,Poor,Yes
+1261,83,Male,61,No,7,0,29,1,Poor,Yes
+1262,93,Male,36,No,8,5,175,10,Good,Yes
+1263,61,Female,56,No,7,3,135,1,Poor,Yes
+1264,90,Female,38,No,7,10,87,2,Poor,Yes
+1265,64,Male,66,Yes,6,6,134,0,Poor,Yes
+1266,87,Male,20,No,4,10,151,6,Poor,Yes
+1267,71,Male,39,No,6,3,153,10,Good,Yes
+1268,73,Female,47,Yes,5,4,151,2,Average,Yes
+1269,71,Female,44,No,8,2,131,3,Average,No
+1270,26,Female,64,No,6,5,106,0,Poor,Yes
+1271,97,Male,57,No,7,2,54,4,Poor,Yes
+1272,79,Female,59,No,4,6,83,10,Average,Yes
+1273,57,Male,36,No,8,1,176,4,Average,No
+1274,31,Female,60,No,5,6,59,2,Poor,Yes
+1275,33,Male,50,No,7,0,161,2,Average,No
+1276,82,Female,46,No,9,2,143,10,Average,No
+1277,54,Female,69,No,6,5,96,5,Poor,Yes
+1278,57,Male,53,No,8,8,167,7,Poor,No
+1279,59,Female,28,Yes,7,4,166,7,Average,No
+1280,90,Female,32,No,6,0,72,8,Good,No
+1281,45,Female,37,No,9,2,69,9,Good,No
+1282,38,Male,32,No,7,10,25,4,Average,No
+1283,75,Male,68,Yes,8,3,97,1,Poor,Yes
+1284,61,Female,20,Yes,8,6,127,8,Average,Yes
+1285,57,Male,38,Yes,6,4,126,4,Good,Yes
+1286,91,Male,21,No,9,3,37,8,Average,Yes
+1287,91,Female,29,No,5,8,98,2,Poor,Yes
+1288,89,Male,40,No,7,1,46,9,Good,Yes
+1289,77,Male,67,No,5,3,148,8,Poor,Yes
+1290,74,Male,42,No,6,7,147,5,Average,Yes
+1291,74,Male,60,Yes,8,7,40,10,Poor,Yes
+1292,95,Others,38,Yes,8,4,5,2,Good,Yes
+1293,45,Male,66,Yes,7,7,109,0,Average,Yes
+1294,71,Male,27,Yes,6,10,59,1,Poor,Yes
+1295,39,Male,33,No,5,3,61,6,Good,Yes
+1296,81,Male,31,No,6,10,13,5,Average,Yes
+1297,56,Male,39,No,7,0,55,2,Good,No
+1298,29,Female,55,No,8,6,11,10,Poor,Yes
+1299,98,Male,30,No,5,4,123,8,Good,No
+1300,60,Female,29,No,5,8,164,3,Poor,Yes
+1301,92,Male,22,No,6,0,141,0,Good,No
+1302,25,Female,20,No,5,5,34,2,Good,Yes
+1303,92,Male,27,Yes,4,4,15,9,Good,Yes
+1304,22,Male,60,Yes,6,0,50,4,Poor,Yes
+1305,37,Male,57,Yes,7,0,34,7,Poor,Yes
+1306,26,Others,27,Yes,6,9,74,10,Poor,Yes
+1307,76,Female,45,Yes,8,1,165,7,Good,Yes
+1308,66,Male,43,No,6,3,75,0,Good,Yes
+1309,90,Male,26,Yes,7,1,163,9,Good,No
+1310,35,Male,39,Yes,6,10,122,4,Average,Yes
+1311,21,Male,61,Yes,7,3,14,0,Poor,Yes
+1312,95,Female,23,Yes,5,8,27,9,Average,Yes
+1313,97,Male,49,Yes,4,8,3,0,Poor,Yes
+1314,78,Male,57,No,7,5,141,3,Poor,Yes
+1315,96,Male,37,No,8,2,111,0,Good,Yes
+1316,89,Male,33,Yes,5,2,28,8,Average,Yes
+1317,93,Male,23,No,4,1,53,0,Good,Yes
+1318,97,Male,48,No,7,7,54,5,Average,Yes
+1319,53,Male,64,No,7,9,94,0,Poor,Yes
+1320,95,Male,37,No,7,1,92,3,Good,Yes
+1321,76,Female,49,Yes,8,4,52,7,Average,Yes
+1322,42,Male,49,Yes,6,9,174,10,Poor,Yes
+1323,81,Male,45,Yes,4,9,123,9,Average,Yes
+1324,98,Female,53,No,6,10,141,10,Average,Yes
+1325,68,Female,44,No,7,10,154,5,Poor,Yes
+1326,51,Female,58,No,10,5,27,5,Poor,Yes
+1327,31,Female,48,No,8,5,40,8,Average,No
+1328,42,Female,35,No,8,6,88,2,Average,Yes
+1329,32,Female,52,No,7,4,56,0,Poor,Yes
+1330,46,Female,49,Yes,10,9,169,6,Poor,Yes
+1331,43,Female,66,Yes,6,4,26,3,Average,Yes
+1332,42,Female,68,No,7,3,172,5,Poor,No
+1333,29,Female,45,Yes,4,9,68,9,Poor,Yes
+1334,29,Others,36,No,9,9,15,5,Average,No
+1335,76,Female,46,No,6,4,111,1,Good,Yes
+1336,79,Female,63,Yes,6,8,91,7,Poor,Yes
+1337,39,Male,46,Yes,8,9,10,8,Poor,Yes
+1338,85,Female,61,No,5,3,104,6,Poor,Yes
+1339,49,Female,24,No,8,10,1,6,Poor,Yes
+1340,33,Male,66,No,7,5,0,9,Poor,Yes
+1341,74,Female,43,No,8,10,9,6,Poor,Yes
+1342,45,Male,31,Yes,6,0,146,0,Average,Yes
+1343,91,Male,69,No,7,7,38,2,Poor,Yes
+1344,30,Female,40,No,8,2,116,0,Good,No
+1345,34,Female,61,No,6,10,77,5,Poor,Yes
+1346,92,Others,51,No,7,8,114,6,Poor,Yes
+1347,31,Male,45,Yes,4,10,149,8,Poor,Yes
+1348,36,Male,22,No,4,5,55,0,Good,No
+1349,50,Female,32,No,7,3,23,0,Good,Yes
+1350,48,Male,48,No,6,3,179,4,Average,No
+1351,61,Female,32,No,6,8,72,3,Poor,Yes
+1352,95,Male,52,Yes,9,10,31,10,Poor,Yes
+1353,44,Male,25,No,8,3,129,6,Good,No
+1354,27,Female,32,No,6,10,149,0,Poor,Yes
+1355,96,Male,64,No,7,2,56,4,Poor,Yes
+1356,19,Male,56,Yes,6,2,8,10,Poor,Yes
+1357,43,Female,35,Yes,5,5,96,10,Good,Yes
+1358,33,Female,45,Yes,5,5,24,6,Average,Yes
+1359,52,Male,32,Yes,5,6,81,9,Good,Yes
+1360,29,Male,49,No,7,7,39,0,Average,Yes
+1361,79,Female,26,No,6,7,178,4,Average,No
+1362,94,Male,41,No,10,9,113,9,Poor,Yes
+1363,58,Female,56,No,5,10,66,2,Poor,Yes
+1364,60,Female,22,No,5,2,145,1,Good,No
+1365,92,Male,21,No,7,5,76,1,Good,No
+1366,48,Male,52,No,7,7,27,7,Poor,Yes
+1367,52,Female,32,Yes,7,7,103,4,Average,Yes
+1368,73,Male,59,Yes,4,3,35,5,Poor,Yes
+1369,63,Female,39,No,6,5,53,1,Good,Yes
+1370,61,Female,61,No,9,5,44,4,Poor,No
+1371,94,Male,34,No,8,5,114,4,Average,Yes
+1372,29,Female,45,Yes,6,2,53,6,Average,Yes
+1373,69,Male,34,No,7,4,7,5,Good,No
+1374,57,Male,57,No,5,4,90,1,Poor,Yes
+1375,85,Male,60,No,7,6,127,0,Poor,Yes
+1376,88,Male,61,Yes,8,8,102,3,Average,Yes
+1377,65,Male,45,No,7,8,19,5,Poor,Yes
+1378,72,Male,69,No,7,1,168,2,Poor,Yes
+1379,35,Female,57,Yes,7,6,62,9,Poor,Yes
+1380,32,Male,26,No,7,3,112,0,Average,Yes
+1381,62,Female,35,No,7,9,159,3,Poor,Yes
+1382,86,Male,57,Yes,9,2,53,3,Poor,Yes
+1383,18,Female,24,No,6,0,178,9,Good,No
+1384,73,Female,22,No,6,1,16,3,Good,No
+1385,26,Male,26,No,7,6,175,4,Average,No
+1386,38,Male,36,Yes,5,2,125,6,Good,Yes
+1387,63,Female,41,Yes,9,10,91,5,Poor,Yes
+1388,55,Female,35,No,7,8,23,4,Average,No
+1389,28,Male,43,No,7,0,63,6,Average,No
+1390,84,Male,61,No,5,6,135,7,Average,No
+1391,37,Female,54,No,4,4,58,6,Poor,Yes
+1392,54,Male,23,Yes,7,8,132,1,Poor,Yes
+1393,69,Female,40,No,4,8,31,0,Poor,Yes
+1394,37,Male,37,No,5,2,37,2,Average,Yes
+1395,65,Male,44,No,7,5,15,8,Average,Yes
+1396,87,Male,38,Yes,6,7,0,5,Average,Yes
+1397,33,Male,50,No,5,2,132,0,Average,Yes
+1398,79,Male,21,No,5,5,7,2,Good,Yes
+1399,76,Male,48,Yes,6,9,48,4,Poor,Yes
+1400,61,Male,37,Yes,8,1,43,5,Average,Yes
+1401,75,Male,35,Yes,7,5,69,0,Good,Yes
+1402,34,Male,65,Yes,8,10,114,1,Poor,Yes
+1403,42,Male,49,No,9,3,115,6,Average,Yes
+1404,19,Male,36,No,9,2,42,10,Good,No
+1405,76,Male,30,No,7,5,149,1,Average,Yes
+1406,68,Male,36,No,6,3,140,9,Average,No
+1407,75,Female,62,No,7,5,42,7,Poor,Yes
+1408,28,Female,34,No,6,10,8,1,Poor,Yes
+1409,58,Female,40,Yes,7,3,44,1,Good,Yes
+1410,66,Female,53,No,7,4,0,9,Poor,Yes
+1411,76,Male,24,No,7,5,128,3,Average,No
+1412,45,Female,61,Yes,4,10,41,1,Poor,Yes
+1413,84,Female,69,No,6,6,45,7,Poor,Yes
+1414,86,Female,61,Yes,6,8,60,3,Poor,Yes
+1415,18,Male,48,No,9,1,103,5,Average,No
+1416,62,Male,38,No,7,2,72,4,Good,Yes
+1417,44,Male,52,No,7,9,55,1,Poor,Yes
+1418,43,Female,64,Yes,7,5,86,9,Average,Yes
+1419,65,Male,22,Yes,8,3,21,8,Good,No
+1420,64,Male,67,No,5,3,22,7,Poor,Yes
+1421,64,Male,26,No,5,6,13,8,Good,Yes
+1422,49,Female,49,Yes,6,8,150,0,Poor,Yes
+1423,19,Male,61,No,8,9,141,5,Average,No
+1424,50,Male,58,Yes,7,5,129,10,Poor,Yes
+1425,35,Male,22,No,9,4,68,0,Average,No
+1426,88,Male,64,No,8,0,128,5,Poor,Yes
+1427,70,Female,55,No,4,4,101,5,Poor,Yes
+1428,31,Female,27,No,6,0,157,9,Good,No
+1429,93,Male,44,No,7,9,88,3,Poor,Yes
+1430,62,Male,21,No,5,10,160,7,Poor,Yes
+1431,63,Male,64,No,8,3,104,2,Poor,No
+1432,71,Female,63,No,9,0,165,0,Poor,No
+1433,90,Female,57,Yes,7,4,4,1,Poor,Yes
+1434,40,Female,59,No,10,1,108,8,Poor,No
+1435,70,Others,64,No,7,4,179,9,Poor,No
+1436,85,Male,53,Yes,4,6,19,1,Poor,Yes
+1437,71,Male,46,No,8,5,50,10,Average,Yes
+1438,43,Male,52,No,5,4,28,10,Average,Yes
+1439,36,Male,65,Yes,4,1,72,7,Poor,Yes
+1440,49,Female,33,No,5,0,92,2,Good,No
+1441,44,Male,50,No,10,6,56,3,Average,No
+1442,74,Male,30,Yes,7,2,160,3,Good,No
+1443,19,Male,35,No,8,2,135,3,Good,No
+1444,78,Female,54,Yes,7,10,155,5,Average,Yes
+1445,84,Others,51,No,6,5,108,10,Poor,Yes
+1446,80,Male,40,No,6,8,49,8,Poor,Yes
+1447,30,Male,38,No,6,8,64,9,Poor,Yes
+1448,97,Male,51,No,6,3,97,7,Poor,Yes
+1449,57,Male,34,Yes,7,8,7,3,Average,Yes
+1450,37,Male,46,Yes,6,0,4,1,Average,Yes
+1451,86,Female,32,No,6,8,121,0,Poor,Yes
+1452,88,Male,29,Yes,8,6,156,7,Average,Yes
+1453,26,Male,37,Yes,4,0,84,4,Good,Yes
+1454,66,Others,53,No,6,7,8,3,Poor,Yes
+1455,94,Female,21,No,5,3,68,0,Average,Yes
+1456,66,Female,54,No,5,5,171,0,Poor,Yes
+1457,22,Male,22,No,4,8,122,6,Poor,Yes
+1458,51,Female,33,No,8,2,26,7,Average,No
+1459,93,Female,22,No,8,5,64,4,Average,No
+1460,70,Female,51,Yes,6,9,138,9,Poor,Yes
+1461,74,Male,55,Yes,8,3,78,1,Poor,Yes
+1462,54,Female,39,No,6,10,126,6,Poor,Yes
+1463,59,Female,30,Yes,7,3,125,9,Average,No
+1464,38,Others,31,No,5,4,44,8,Good,No
+1465,54,Others,32,Yes,6,10,96,8,Poor,Yes
+1466,27,Others,30,Yes,7,6,32,0,Average,Yes
+1467,66,Female,36,No,8,0,78,1,Good,No
+1468,20,Male,47,No,5,9,168,0,Poor,Yes
+1469,23,Male,68,No,8,7,61,5,Average,Yes
+1470,62,Others,67,Yes,6,1,25,4,Poor,Yes
+1471,93,Male,39,No,7,3,45,8,Average,No
+1472,76,Male,39,Yes,4,10,162,7,Poor,Yes
+1473,59,Male,49,No,6,8,25,7,Poor,Yes
+1474,46,Female,29,Yes,8,4,169,0,Good,Yes
+1475,97,Male,45,Yes,6,2,75,2,Good,Yes
+1476,81,Female,69,No,8,10,73,4,Average,Yes
+1477,70,Female,32,No,7,8,20,3,Average,No
+1478,35,Female,43,Yes,6,1,153,8,Good,No
+1479,35,Male,57,Yes,6,5,88,6,Poor,Yes
+1480,82,Female,52,Yes,4,8,87,8,Poor,Yes
+1481,24,Male,48,No,9,8,49,0,Average,No
+1482,30,Female,28,No,4,10,19,6,Poor,Yes
+1483,30,Male,41,No,4,8,138,10,Poor,Yes
+1484,29,Female,56,Yes,6,5,137,9,Poor,Yes
+1485,92,Male,23,Yes,6,4,169,7,Good,Yes
+1486,75,Female,46,No,8,7,77,1,Good,Yes
+1487,37,Others,63,No,6,2,96,3,Poor,Yes
+1488,54,Male,22,No,6,5,111,9,Good,No
+1489,44,Male,45,No,7,9,21,8,Poor,Yes
+1490,27,Male,63,Yes,6,3,162,9,Poor,Yes
+1491,89,Female,35,Yes,9,1,46,2,Good,Yes
+1492,21,Male,52,Yes,6,2,4,4,Poor,Yes
+1493,75,Female,56,Yes,4,6,105,4,Poor,Yes
+1494,21,Male,63,Yes,6,8,44,3,Poor,Yes
+1495,89,Female,66,No,6,0,5,0,Poor,Yes
+1496,64,Others,63,Yes,8,7,73,1,Average,Yes
+1497,32,Female,34,Yes,6,7,72,2,Good,Yes
+1498,83,Male,31,No,9,0,20,9,Good,No
+1499,52,Female,22,No,7,9,66,10,Poor,Yes
+1500,33,Male,36,No,7,7,131,7,Average,No
+1501,65,Female,34,Yes,7,6,134,6,Average,No
+1502,87,Male,48,No,7,3,13,1,Good,Yes
+1503,25,Male,39,No,8,2,169,5,Good,No
+1504,64,Female,68,Yes,6,1,154,0,Average,Yes
+1505,40,Female,58,No,7,6,55,3,Poor,Yes
+1506,72,Male,42,Yes,6,7,100,1,Good,Yes
+1507,40,Male,56,Yes,7,2,88,10,Poor,Yes
+1508,75,Female,60,No,7,4,74,5,Poor,Yes
+1509,20,Male,56,No,4,4,6,9,Poor,Yes
+1510,88,Male,26,No,8,0,111,9,Average,No
+1511,69,Male,37,No,4,1,16,10,Good,Yes
+1512,19,Female,42,No,8,0,117,8,Good,No
+1513,56,Female,47,Yes,9,2,21,7,Good,Yes
+1514,18,Male,24,Yes,7,4,18,1,Good,Yes
+1515,34,Female,34,No,5,6,147,6,Average,No
+1516,50,Male,48,Yes,7,7,108,0,Good,Yes
+1517,76,Male,55,No,9,0,124,7,Poor,No
+1518,46,Male,38,Yes,5,6,179,3,Average,No
+1519,71,Female,65,No,7,1,133,10,Poor,No
+1520,89,Others,61,No,8,7,46,0,Poor,Yes
+1521,67,Male,23,No,6,1,153,10,Good,No
+1522,81,Male,34,No,6,0,20,0,Good,No
+1523,38,Male,45,No,7,1,114,0,Average,Yes
+1524,35,Others,34,No,6,9,69,5,Average,Yes
+1525,97,Female,60,Yes,8,8,21,7,Poor,Yes
+1526,42,Female,50,Yes,6,3,131,3,Good,Yes
+1527,20,Female,57,Yes,7,7,108,9,Poor,Yes
+1528,99,Others,49,Yes,6,4,65,3,Average,Yes
+1529,38,Others,32,No,7,3,153,1,Good,Yes
+1530,91,Male,42,No,8,0,8,0,Good,Yes
+1531,20,Female,65,Yes,6,0,105,2,Poor,Yes
+1532,76,Male,20,Yes,7,0,41,5,Good,Yes
+1533,52,Female,22,No,4,1,116,2,Good,Yes
+1534,89,Male,50,Yes,6,1,145,1,Average,Yes
+1535,23,Female,23,Yes,9,4,86,10,Good,No
+1536,74,Female,64,No,7,10,96,4,Poor,Yes
+1537,76,Male,56,No,6,2,106,1,Average,Yes
+1538,71,Male,56,No,7,0,140,10,Poor,No
+1539,18,Female,23,Yes,7,1,38,6,Good,Yes
+1540,99,Male,28,Yes,8,5,176,10,Good,No
+1541,82,Male,31,No,8,6,134,6,Good,Yes
+1542,65,Female,60,No,5,7,107,6,Poor,Yes
+1543,62,Female,51,Yes,6,6,31,3,Poor,Yes
+1544,42,Female,69,No,4,6,178,4,Poor,Yes
+1545,20,Others,32,No,9,0,149,8,Good,No
+1546,64,Female,43,Yes,5,9,46,7,Poor,Yes
+1547,79,Male,40,No,4,4,55,10,Good,Yes
+1548,82,Female,63,No,7,5,60,8,Average,No
+1549,30,Male,65,No,4,2,56,0,Poor,Yes
+1550,38,Female,20,No,7,3,85,9,Good,No
+1551,25,Female,51,No,6,0,167,8,Poor,No
+1552,61,Male,30,No,5,2,80,8,Average,No
+1553,21,Female,42,Yes,7,3,33,0,Good,Yes
+1554,93,Male,54,No,6,9,162,0,Poor,No
+1555,43,Female,35,No,8,1,73,1,Average,Yes
+1556,75,Female,46,Yes,8,9,63,9,Poor,Yes
+1557,88,Female,31,No,6,9,159,5,Poor,Yes
+1558,70,Male,66,No,5,7,110,3,Poor,Yes
+1559,78,Male,43,No,6,6,93,9,Average,No
+1560,61,Female,63,Yes,6,5,171,4,Average,Yes
+1561,98,Male,41,Yes,8,3,138,10,Average,No
+1562,42,Female,35,Yes,8,10,79,8,Average,Yes
+1563,95,Female,28,Yes,8,7,119,2,Average,Yes
+1564,71,Male,43,No,7,2,58,10,Average,No
+1565,59,Male,39,No,6,3,91,6,Good,No
+1566,20,Male,37,No,8,0,91,3,Good,No
+1567,77,Male,41,No,4,10,133,6,Average,No
+1568,47,Male,65,Yes,5,7,29,10,Poor,Yes
+1569,92,Male,56,No,6,3,5,6,Poor,Yes
+1570,89,Male,61,Yes,6,4,1,2,Average,Yes
+1571,39,Female,59,No,5,9,76,2,Average,Yes
+1572,41,Male,26,Yes,5,0,147,9,Average,No
+1573,64,Male,40,Yes,7,6,10,9,Average,Yes
+1574,26,Others,31,Yes,8,5,169,9,Average,No
+1575,49,Others,51,No,6,8,11,9,Poor,Yes
+1576,46,Female,61,No,5,8,129,2,Poor,Yes
+1577,82,Male,67,No,9,7,101,0,Average,Yes
+1578,82,Male,23,No,8,9,135,2,Poor,Yes
+1579,75,Female,51,No,5,9,165,7,Poor,Yes
+1580,34,Female,28,No,7,7,118,4,Average,No
+1581,47,Male,44,No,7,6,152,5,Good,No
+1582,47,Male,61,Yes,7,1,99,5,Poor,Yes
+1583,83,Male,44,No,7,1,2,7,Average,Yes
+1584,44,Male,49,Yes,6,3,10,3,Average,Yes
+1585,33,Female,62,No,4,10,112,3,Poor,Yes
+1586,29,Female,44,Yes,7,1,89,6,Average,Yes
+1587,76,Male,33,No,6,2,9,0,Good,Yes
+1588,78,Male,46,No,8,1,54,9,Average,No
+1589,99,Male,60,No,6,9,72,9,Poor,Yes
+1590,49,Female,50,No,7,4,25,1,Average,No
+1591,42,Male,66,No,9,2,41,3,Poor,Yes
+1592,30,Male,38,Yes,5,0,45,5,Good,Yes
+1593,56,Others,49,No,9,8,6,3,Poor,Yes
+1594,41,Female,24,Yes,7,9,172,10,Poor,Yes
+1595,85,Female,22,Yes,8,9,95,3,Poor,Yes
+1596,80,Male,26,No,6,7,12,3,Good,Yes
+1597,71,Male,32,No,6,9,35,5,Poor,Yes
+1598,88,Female,63,No,8,1,109,1,Poor,Yes
+1599,75,Female,34,Yes,6,6,137,7,Average,Yes
+1600,46,Female,56,No,4,3,142,3,Poor,Yes
+1601,32,Male,68,No,6,1,29,0,Poor,Yes
+1602,80,Male,54,No,6,4,129,8,Poor,Yes
+1603,28,Male,35,No,8,1,176,0,Good,No
+1604,25,Female,66,No,5,9,5,2,Poor,Yes
+1605,92,Male,55,Yes,6,8,42,10,Poor,Yes
+1606,46,Male,66,Yes,8,10,114,3,Poor,Yes
+1607,62,Male,24,Yes,6,2,82,2,Good,Yes
+1608,40,Male,51,Yes,6,4,160,0,Poor,Yes
+1609,76,Female,33,No,7,8,70,2,Poor,Yes
+1610,78,Male,51,Yes,9,9,90,3,Poor,Yes
+1611,37,Female,62,Yes,6,5,94,2,Poor,Yes
+1612,44,Male,23,Yes,8,5,156,6,Good,Yes
+1613,96,Female,64,Yes,6,4,45,7,Poor,Yes
+1614,47,Male,51,Yes,7,1,14,3,Poor,Yes
+1615,92,Female,21,Yes,7,0,96,7,Good,Yes
+1616,22,Male,27,Yes,4,8,104,5,Average,Yes
+1617,52,Female,56,No,5,8,162,9,Poor,Yes
+1618,26,Female,51,No,8,1,102,6,Poor,Yes
+1619,44,Others,36,No,4,7,93,5,Average,Yes
+1620,92,Female,44,Yes,8,8,64,8,Average,Yes
+1621,33,Male,59,No,4,3,124,2,Poor,Yes
+1622,18,Female,45,Yes,6,5,80,4,Average,Yes
+1623,90,Female,61,Yes,5,8,4,7,Average,Yes
+1624,92,Female,36,No,5,6,93,3,Good,Yes
+1625,31,Others,58,No,6,6,26,5,Average,No
+1626,94,Female,42,Yes,6,2,17,10,Average,No
+1627,38,Female,55,No,5,8,3,5,Poor,Yes
+1628,85,Male,67,No,7,10,129,3,Poor,Yes
+1629,69,Male,67,No,7,2,119,3,Poor,Yes
+1630,61,Male,63,Yes,5,9,176,6,Average,Yes
+1631,35,Male,44,Yes,8,3,20,0,Good,Yes
+1632,38,Female,67,Yes,9,0,106,1,Poor,Yes
+1633,40,Female,66,No,4,6,57,0,Poor,Yes
+1634,52,Female,37,No,7,6,96,5,Average,Yes
+1635,50,Male,28,No,7,7,169,9,Average,No
+1636,63,Female,50,Yes,5,0,39,1,Good,Yes
+1637,56,Female,29,Yes,7,6,163,10,Average,Yes
+1638,40,Female,21,Yes,7,5,169,5,Good,Yes
+1639,29,Male,28,Yes,5,8,123,0,Average,Yes
+1640,58,Female,30,No,7,10,141,9,Poor,Yes
+1641,20,Female,65,No,7,7,61,0,Poor,Yes
+1642,24,Others,34,No,6,0,93,3,Average,No
+1643,92,Male,56,Yes,9,0,32,3,Poor,Yes
+1644,94,Others,57,Yes,6,0,109,7,Poor,Yes
+1645,57,Female,24,Yes,5,0,169,5,Average,No
+1646,25,Female,65,No,6,10,39,8,Poor,Yes
+1647,66,Female,44,No,4,7,124,3,Good,Yes
+1648,78,Female,61,No,7,6,10,1,Poor,Yes
+1649,29,Female,64,No,4,2,142,6,Poor,Yes
+1650,79,Male,21,No,8,5,179,5,Average,No
+1651,89,Male,67,No,5,6,56,3,Poor,Yes
+1652,92,Male,21,No,7,0,92,2,Good,Yes
+1653,58,Female,44,No,5,8,166,5,Poor,Yes
+1654,78,Female,24,Yes,6,3,123,5,Good,Yes
+1655,99,Female,43,No,5,0,94,7,Average,No
+1656,22,Male,25,No,9,9,68,0,Poor,Yes
+1657,94,Female,55,No,10,7,146,3,Average,Yes
+1658,83,Male,29,No,6,8,166,1,Poor,Yes
+1659,27,Male,66,Yes,4,8,173,7,Average,No
+1660,31,Female,41,No,7,9,126,4,Poor,Yes
+1661,99,Male,22,No,7,3,146,3,Good,Yes
+1662,42,Others,28,No,7,6,53,10,Average,Yes
+1663,82,Male,57,No,7,10,79,8,Average,Yes
+1664,62,Male,65,Yes,6,10,93,0,Poor,Yes
+1665,84,Male,46,Yes,9,10,158,9,Poor,Yes
+1666,66,Female,50,No,6,8,16,2,Average,Yes
+1667,33,Male,22,No,8,7,6,8,Good,Yes
+1668,86,Others,45,No,6,7,163,4,Average,Yes
+1669,98,Others,27,Yes,8,2,49,6,Good,Yes
+1670,71,Female,66,Yes,7,3,99,2,Poor,Yes
+1671,57,Female,52,No,9,0,96,3,Poor,No
+1672,23,Female,44,No,6,8,78,6,Average,No
+1673,99,Male,37,Yes,6,10,165,1,Poor,Yes
+1674,32,Female,28,No,7,3,78,8,Average,No
+1675,53,Female,44,Yes,8,10,22,9,Poor,Yes
+1676,86,Male,33,No,9,10,14,4,Poor,Yes
+1677,86,Male,57,Yes,4,10,9,2,Poor,Yes
+1678,86,Male,52,No,6,6,92,7,Poor,Yes
+1679,33,Female,35,Yes,4,2,153,7,Good,Yes
+1680,56,Male,36,Yes,5,9,114,7,Poor,Yes
+1681,98,Male,43,Yes,6,4,20,6,Good,Yes
+1682,75,Male,53,No,4,6,98,8,Poor,Yes
+1683,50,Male,49,No,6,7,1,6,Average,Yes
+1684,31,Male,51,No,7,2,60,4,Poor,No
+1685,28,Female,21,No,4,3,98,0,Average,No
+1686,25,Male,60,No,5,1,145,2,Poor,Yes
+1687,34,Female,69,No,7,0,57,10,Poor,No
+1688,74,Female,36,No,6,1,148,2,Good,Yes
+1689,37,Female,60,Yes,6,10,13,6,Poor,Yes
+1690,38,Male,54,No,5,7,30,4,Poor,Yes
+1691,86,Male,28,Yes,8,10,59,5,Poor,Yes
+1692,34,Female,67,No,7,8,166,3,Poor,Yes
+1693,41,Female,45,No,6,4,146,10,Good,No
+1694,49,Female,48,Yes,8,7,104,5,Average,Yes
+1695,78,Others,37,Yes,5,8,31,4,Average,Yes
+1696,71,Female,43,Yes,6,8,68,4,Average,Yes
+1697,37,Male,34,Yes,8,4,26,8,Good,Yes
+1698,72,Male,62,Yes,6,3,140,8,Poor,Yes
+1699,60,Female,50,No,7,0,114,1,Good,Yes
+1700,71,Others,34,No,6,1,120,5,Average,Yes
+1701,30,Female,64,No,7,4,99,2,Poor,Yes
+1702,47,Female,34,No,6,1,145,2,Good,No
+1703,64,Others,46,No,7,1,101,5,Good,Yes
+1704,35,Male,40,No,6,2,95,8,Average,No
+1705,77,Female,26,No,4,3,69,8,Average,Yes
+1706,85,Male,32,No,4,6,33,1,Average,Yes
+1707,75,Male,28,No,5,8,1,7,Poor,Yes
+1708,52,Female,34,No,9,3,126,7,Good,No
+1709,36,Others,23,Yes,8,9,112,9,Poor,Yes
+1710,58,Female,32,No,6,1,29,8,Good,No
+1711,64,Female,56,No,6,2,165,6,Average,No
+1712,31,Male,55,Yes,6,3,63,3,Poor,Yes
+1713,53,Others,23,Yes,8,5,129,10,Good,No
+1714,75,Male,42,Yes,7,0,120,5,Average,Yes
+1715,28,Female,60,No,8,2,60,6,Poor,Yes
+1716,49,Female,42,Yes,4,1,87,3,Average,Yes
+1717,66,Male,69,No,7,9,152,0,Poor,Yes
+1718,18,Female,47,Yes,6,7,32,1,Average,Yes
+1719,60,Male,33,No,5,10,85,9,Average,Yes
+1720,47,Male,28,Yes,8,10,9,2,Average,Yes
+1721,90,Female,43,No,10,1,131,7,Average,No
+1722,36,Female,23,Yes,6,6,88,6,Average,Yes
+1723,74,Female,44,Yes,6,8,100,8,Poor,Yes
+1724,60,Male,60,Yes,6,10,21,8,Poor,Yes
+1725,55,Others,47,Yes,8,4,146,3,Average,Yes
+1726,93,Female,47,No,6,10,51,6,Poor,Yes
+1727,62,Male,62,No,4,5,50,6,Average,No
+1728,99,Male,30,No,8,2,83,1,Good,Yes
+1729,35,Female,47,No,8,8,9,8,Poor,Yes
+1730,72,Male,36,No,7,7,178,10,Average,No
+1731,35,Female,63,Yes,9,3,125,6,Poor,Yes
+1732,43,Male,21,No,5,8,35,9,Poor,Yes
+1733,66,Female,50,No,6,4,40,1,Average,Yes
+1734,65,Male,27,No,5,8,177,6,Poor,Yes
+1735,88,Male,54,No,7,4,54,10,Poor,Yes
+1736,87,Male,67,Yes,7,2,24,3,Poor,Yes
+1737,74,Male,56,No,10,3,35,6,Average,Yes
+1738,56,Male,21,No,6,9,25,10,Poor,Yes
+1739,60,Male,40,Yes,7,2,169,1,Good,Yes
+1740,55,Female,30,Yes,5,7,94,10,Good,Yes
+1741,31,Female,34,Yes,5,2,100,1,Good,Yes
+1742,48,Male,32,No,7,3,111,3,Good,Yes
+1743,80,Female,21,No,6,3,156,6,Good,No
+1744,89,Female,56,Yes,7,2,72,5,Poor,Yes
+1745,22,Others,58,Yes,6,0,75,5,Average,Yes
+1746,18,Male,44,Yes,6,4,96,7,Average,Yes
+1747,34,Female,41,Yes,6,8,173,2,Poor,No
+1748,47,Female,47,No,6,3,165,2,Average,No
+1749,42,Male,22,Yes,7,9,95,4,Poor,Yes
+1750,95,Others,49,No,7,5,175,4,Good,No
+1751,46,Female,49,No,5,5,168,8,Average,No
+1752,27,Male,44,Yes,5,2,172,4,Good,Yes
+1753,35,Female,46,No,5,1,172,0,Average,No
+1754,24,Female,35,Yes,7,2,105,10,Good,No
+1755,74,Male,67,Yes,7,8,134,5,Average,Yes
+1756,42,Female,24,Yes,4,1,64,3,Good,Yes
+1757,71,Male,68,Yes,8,10,16,8,Average,Yes
+1758,24,Male,30,No,7,0,126,5,Good,No
+1759,92,Male,52,Yes,4,6,82,8,Poor,Yes
+1760,30,Male,61,Yes,8,3,32,4,Poor,Yes
+1761,72,Female,57,No,4,8,28,6,Poor,Yes
+1762,65,Male,55,No,5,5,21,0,Poor,Yes
+1763,49,Female,22,Yes,5,0,139,6,Good,No
+1764,96,Female,62,No,7,1,61,4,Poor,Yes
+1765,89,Male,47,Yes,8,5,157,9,Average,Yes
+1766,64,Female,36,Yes,4,5,16,5,Good,Yes
+1767,44,Male,66,Yes,6,0,42,3,Poor,Yes
+1768,65,Male,36,Yes,7,2,168,4,Good,Yes
+1769,78,Female,44,Yes,7,0,56,2,Average,Yes
+1770,99,Female,41,No,7,1,180,5,Good,No
+1771,19,Female,47,Yes,5,2,125,1,Good,Yes
+1772,21,Female,29,Yes,7,8,50,5,Poor,Yes
+1773,28,Male,21,No,7,10,179,7,Poor,No
+1774,92,Female,33,No,7,1,93,1,Good,No
+1775,35,Female,56,Yes,7,8,156,4,Poor,Yes
+1776,25,Male,25,No,5,9,66,9,Poor,Yes
+1777,26,Female,58,Yes,7,8,78,8,Poor,Yes
+1778,49,Female,26,Yes,6,5,59,6,Good,Yes
+1779,46,Female,54,No,6,1,171,0,Poor,Yes
+1780,94,Female,36,Yes,5,3,80,6,Average,Yes
+1781,26,Female,65,Yes,5,1,41,8,Average,Yes
+1782,21,Male,38,No,7,3,86,2,Good,No
+1783,58,Female,59,Yes,7,1,51,9,Poor,Yes
+1784,56,Male,32,No,6,1,101,8,Good,Yes
+1785,78,Male,62,Yes,5,1,4,9,Poor,Yes
+1786,80,Male,67,Yes,7,9,78,5,Poor,Yes
+1787,77,Male,42,Yes,6,7,142,7,Good,Yes
+1788,32,Female,42,Yes,8,0,133,2,Good,No
+1789,60,Male,21,Yes,9,1,173,2,Good,No
+1790,94,Male,28,Yes,5,6,34,3,Average,Yes
+1791,28,Male,47,No,10,2,7,8,Average,No
+1792,76,Female,28,Yes,7,0,27,8,Good,No
+1793,70,Male,61,Yes,6,0,127,8,Poor,Yes
+1794,99,Male,44,No,7,8,103,7,Poor,Yes
+1795,65,Male,40,No,4,0,89,0,Good,Yes
+1796,79,Female,22,Yes,8,3,62,4,Good,Yes
+1797,77,Female,44,No,6,10,138,0,Poor,Yes
+1798,65,Male,50,No,7,10,115,3,Poor,Yes
+1799,33,Female,51,Yes,7,10,18,10,Poor,Yes
+1800,72,Female,57,No,7,6,48,4,Poor,Yes
+1801,88,Male,37,Yes,5,7,12,8,Good,Yes
+1802,68,Others,21,No,8,0,97,8,Good,No
+1803,52,Female,45,No,5,1,52,1,Average,Yes
+1804,97,Female,22,Yes,6,10,19,1,Average,Yes
+1805,90,Male,45,Yes,6,6,124,4,Good,Yes
+1806,20,Others,60,No,8,0,27,4,Average,No
+1807,34,Female,42,No,5,0,0,8,Average,No
+1808,74,Female,43,No,6,5,109,7,Average,Yes
+1809,87,Male,63,Yes,5,8,44,10,Poor,Yes
+1810,36,Male,59,Yes,5,6,106,4,Poor,Yes
+1811,57,Female,31,No,5,9,53,6,Average,Yes
+1812,57,Male,64,Yes,7,4,21,2,Poor,Yes
+1813,24,Female,58,No,9,4,30,1,Poor,Yes
+1814,90,Male,33,Yes,6,2,6,9,Good,Yes
+1815,56,Female,40,No,4,9,47,5,Poor,Yes
+1816,81,Female,44,Yes,6,4,32,10,Good,Yes
+1817,87,Male,65,No,6,4,58,8,Average,Yes
+1818,36,Male,67,No,7,1,96,3,Average,No
+1819,89,Female,28,Yes,7,2,6,7,Good,Yes
+1820,42,Female,61,No,7,4,105,10,Average,Yes
+1821,26,Male,37,No,7,1,121,5,Good,No
+1822,53,Male,39,Yes,7,9,97,2,Poor,Yes
+1823,60,Female,61,No,7,1,101,10,Poor,Yes
+1824,52,Male,50,No,7,9,123,4,Poor,Yes
+1825,36,Male,21,No,6,1,121,5,Good,No
+1826,54,Male,36,No,4,4,14,0,Average,No
+1827,53,Male,57,Yes,8,5,64,1,Poor,Yes
+1828,20,Male,49,Yes,7,9,76,2,Poor,Yes
+1829,34,Female,60,Yes,8,9,49,8,Poor,Yes
+1830,73,Others,67,No,8,0,163,6,Poor,Yes
+1831,41,Female,43,Yes,7,5,178,7,Average,No
+1832,65,Female,42,No,4,5,25,9,Average,Yes
+1833,97,Female,36,No,6,10,37,10,Average,No
+1834,21,Male,37,No,9,6,97,7,Average,No
+1835,81,Male,23,No,6,5,17,6,Average,No
+1836,69,Male,49,No,7,8,146,6,Poor,Yes
+1837,63,Male,29,No,6,9,168,5,Average,No
+1838,32,Male,35,No,5,5,133,1,Good,Yes
+1839,75,Female,45,Yes,8,8,44,3,Poor,Yes
+1840,26,Male,49,Yes,5,7,122,3,Average,Yes
+1841,63,Male,45,No,8,0,147,7,Average,No
+1842,49,Female,46,No,8,3,170,3,Average,No
+1843,80,Female,33,Yes,4,1,11,0,Good,Yes
+1844,18,Male,21,Yes,5,1,146,8,Average,No
+1845,44,Male,63,No,7,6,80,4,Poor,Yes
+1846,81,Male,48,Yes,7,3,166,8,Average,No
+1847,76,Female,55,Yes,8,0,47,6,Average,Yes
+1848,48,Female,56,Yes,8,7,69,10,Average,Yes
+1849,88,Male,48,Yes,7,9,90,9,Poor,Yes
+1850,55,Male,30,Yes,6,6,172,3,Average,Yes
+1851,79,Male,61,Yes,5,2,89,0,Poor,Yes
+1852,41,Male,28,No,7,1,144,8,Average,No
+1853,79,Female,23,No,7,2,17,0,Good,No
+1854,95,Male,23,No,8,4,32,2,Average,Yes
+1855,20,Male,49,Yes,4,5,73,3,Average,Yes
+1856,62,Others,69,No,4,0,49,1,Poor,Yes
+1857,42,Female,63,No,4,10,120,6,Poor,Yes
+1858,94,Female,24,Yes,4,8,143,5,Poor,Yes
+1859,73,Male,42,Yes,6,0,114,10,Average,Yes
+1860,70,Female,36,No,8,1,31,7,Good,No
+1861,48,Female,61,Yes,4,3,170,8,Poor,Yes
+1862,87,Male,32,No,4,2,100,4,Good,No
+1863,74,Female,69,No,8,1,14,8,Poor,Yes
+1864,30,Others,42,Yes,8,3,68,6,Average,No
+1865,65,Male,35,No,7,4,147,2,Good,Yes
+1866,93,Female,63,No,5,8,121,4,Poor,Yes
+1867,29,Male,28,No,6,4,156,6,Good,No
+1868,67,Male,47,Yes,7,5,56,9,Average,No
+1869,46,Male,30,Yes,7,4,104,1,Good,Yes
+1870,26,Female,28,Yes,7,3,68,4,Good,Yes
+1871,92,Male,44,Yes,10,5,47,9,Good,Yes
+1872,79,Male,34,No,6,8,19,8,Poor,Yes
+1873,80,Female,38,Yes,10,6,27,0,Average,Yes
+1874,60,Male,45,No,5,3,80,2,Average,Yes
+1875,64,Male,20,No,8,8,144,2,Average,Yes
+1876,43,Female,46,No,5,5,122,5,Average,No
+1877,28,Female,42,Yes,7,9,157,1,Poor,Yes
+1878,69,Female,37,No,7,9,150,0,Poor,No
+1879,58,Male,21,Yes,5,6,52,0,Average,Yes
+1880,97,Others,59,Yes,5,3,153,8,Poor,Yes
+1881,57,Female,58,No,6,6,90,3,Poor,Yes
+1882,57,Female,47,No,7,1,109,6,Average,Yes
+1883,57,Male,45,No,8,4,121,6,Average,No
+1884,64,Female,40,Yes,6,2,92,9,Good,Yes
+1885,53,Male,44,No,8,8,87,10,Poor,Yes
+1886,95,Others,30,No,6,8,85,4,Average,No
+1887,31,Male,30,No,7,8,52,3,Poor,Yes
+1888,86,Others,44,Yes,7,3,112,7,Average,Yes
+1889,80,Female,34,No,4,6,121,10,Good,Yes
+1890,44,Female,65,Yes,7,7,122,0,Average,Yes
+1891,46,Female,20,Yes,7,4,101,10,Good,No
+1892,49,Male,37,Yes,6,5,126,0,Average,Yes
+1893,28,Male,60,Yes,4,1,34,8,Poor,Yes
+1894,43,Male,33,No,4,0,97,4,Good,Yes
+1895,35,Male,34,Yes,8,9,96,9,Average,Yes
+1896,46,Male,57,Yes,7,4,35,8,Poor,Yes
+1897,24,Male,67,No,8,3,5,8,Poor,Yes
+1898,77,Male,45,Yes,7,0,87,6,Average,Yes
+1899,61,Male,43,No,7,1,55,4,Good,No
+1900,64,Male,60,No,5,5,3,8,Poor,Yes
+1901,58,Male,69,No,8,0,19,1,Poor,Yes
+1902,23,Female,54,No,6,0,106,6,Poor,No
+1903,71,Male,36,No,7,7,76,2,Average,Yes
+1904,67,Female,35,Yes,4,2,65,5,Good,Yes
+1905,46,Male,26,No,9,6,63,9,Average,No
+1906,63,Female,67,No,5,3,173,4,Poor,Yes
+1907,54,Female,57,No,7,2,60,5,Poor,Yes
+1908,91,Female,39,Yes,7,5,51,6,Good,Yes
+1909,34,Female,49,No,8,0,1,2,Average,No
+1910,65,Female,43,Yes,7,7,99,10,Average,Yes
+1911,36,Male,40,Yes,7,7,71,10,Good,Yes
+1912,54,Male,54,Yes,7,0,130,6,Poor,Yes
+1913,27,Male,63,No,6,3,84,4,Poor,Yes
+1914,81,Female,66,No,7,2,158,8,Poor,No
+1915,73,Male,60,Yes,7,6,2,6,Average,Yes
+1916,25,Male,46,Yes,5,1,121,0,Average,Yes
+1917,82,Female,26,No,6,10,148,7,Poor,Yes
+1918,50,Female,61,Yes,5,10,170,2,Poor,Yes
+1919,60,Female,50,Yes,6,7,32,3,Good,Yes
+1920,55,Male,64,Yes,6,9,49,7,Average,Yes
+1921,85,Male,25,Yes,7,10,32,1,Poor,Yes
+1922,98,Male,20,Yes,10,9,44,10,Poor,Yes
+1923,49,Male,59,No,8,3,133,4,Poor,Yes
+1924,23,Male,33,Yes,8,4,123,1,Average,Yes
+1925,70,Male,22,No,7,2,78,0,Average,Yes
+1926,92,Male,60,No,6,2,168,10,Poor,No
+1927,49,Male,30,No,4,8,143,5,Average,No
+1928,55,Male,21,Yes,7,2,57,6,Good,No
+1929,33,Male,57,No,8,2,38,2,Poor,Yes
+1930,25,Others,63,No,5,9,66,10,Poor,Yes
+1931,75,Female,68,Yes,7,4,68,8,Poor,Yes
+1932,49,Female,57,No,4,0,102,2,Poor,Yes
+1933,90,Male,58,No,7,2,137,8,Poor,Yes
+1934,97,Female,28,Yes,6,7,105,8,Average,Yes
+1935,34,Male,61,No,8,5,168,7,Poor,Yes
+1936,23,Male,27,Yes,6,8,18,5,Poor,Yes
+1937,87,Female,46,No,6,8,171,10,Poor,Yes
+1938,37,Male,60,No,6,1,34,9,Average,No
+1939,45,Female,60,No,9,8,103,9,Average,No
+1940,57,Female,54,No,5,6,164,9,Poor,Yes
+1941,58,Male,62,No,6,3,152,7,Poor,Yes
+1942,92,Female,31,Yes,8,4,19,2,Good,Yes
+1943,58,Male,60,No,6,0,149,1,Poor,Yes
+1944,74,Male,26,No,5,10,25,10,Poor,Yes
+1945,30,Male,56,No,6,2,142,9,Poor,Yes
+1946,22,Male,51,No,8,4,47,2,Average,No
+1947,48,Male,38,No,7,9,111,0,Poor,Yes
+1948,62,Male,21,No,6,5,130,3,Good,Yes
+1949,80,Male,43,No,5,6,17,6,Good,Yes
+1950,87,Male,32,Yes,6,5,102,2,Good,Yes
+1951,39,Female,49,Yes,4,8,7,10,Poor,Yes
+1952,44,Male,56,No,6,6,78,10,Poor,Yes
+1953,64,Male,51,Yes,7,7,172,0,Poor,Yes
+1954,22,Male,40,Yes,8,4,43,2,Good,No
+1955,62,Female,54,No,5,10,28,0,Poor,Yes
+1956,63,Female,20,Yes,8,6,153,7,Average,No
+1957,53,Male,40,Yes,8,8,105,6,Poor,Yes
+1958,52,Male,46,No,6,5,40,2,Average,Yes
+1959,46,Male,51,No,5,0,37,1,Poor,Yes
+1960,59,Male,58,No,7,10,164,6,Poor,No
+1961,52,Male,31,Yes,6,3,41,9,Good,No
+1962,64,Female,29,Yes,7,6,100,6,Average,Yes
+1963,22,Male,63,Yes,5,5,168,7,Poor,Yes
+1964,93,Male,57,Yes,7,7,161,7,Poor,Yes
+1965,90,Male,49,No,7,1,60,4,Average,No
+1966,83,Female,45,No,5,9,41,0,Poor,Yes
+1967,63,Male,45,Yes,6,9,177,0,Poor,Yes
+1968,46,Male,45,Yes,6,1,63,6,Good,No
+1969,89,Male,27,No,9,8,0,7,Average,Yes
+1970,81,Female,48,No,7,7,58,7,Average,No
+1971,59,Female,24,No,7,8,9,4,Average,Yes
+1972,95,Male,69,Yes,5,1,5,1,Poor,Yes
+1973,98,Male,60,No,9,0,58,5,Poor,Yes
+1974,95,Female,37,Yes,8,4,137,10,Good,Yes
+1975,65,Male,56,Yes,8,0,47,6,Poor,Yes
+1976,42,Female,56,Yes,7,8,81,0,Poor,Yes
+1977,74,Female,35,Yes,4,0,10,5,Average,Yes
+1978,48,Female,42,No,6,5,126,0,Average,Yes
+1979,34,Female,59,No,6,9,10,10,Poor,Yes
+1980,72,Others,26,Yes,9,0,49,4,Average,Yes
+1981,20,Male,62,Yes,5,2,2,1,Poor,Yes
+1982,45,Male,20,No,7,10,93,7,Poor,Yes
+1983,86,Female,50,No,7,8,35,3,Poor,Yes
+1984,54,Female,43,Yes,7,2,41,4,Average,Yes
+1985,78,Male,33,No,6,0,68,10,Average,Yes
+1986,89,Female,42,No,6,9,131,5,Poor,Yes
+1987,72,Female,50,No,5,9,9,1,Poor,Yes
+1988,90,Male,28,No,8,8,47,5,Poor,Yes
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deply.py

@@ -0,0 +1,141 @@
+import streamlit as st
+import pickle
+import pandas as pd
+
+# Background and styling
+st.markdown(
+    """
+    <style>
+    .stApp::before {
+        content: "";
+        position: fixed;
+        top: 0;
+        left: 0;
+        height: 100%;
+        width: 100%;
+        background-image: url("https://images.unsplash.com/photo-1516574187841-cb9cc2ca948b");
+        background-size: cover;
+        background-position: center;
+        background-attachment: fixed;
+        opacity: 0.15;
+        z-index: -1;
+    }
+
+    .stApp {
+        background-color: #1e1e2f;
+        color: #ffffff;
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    }
+
+    h1, h2, h3, h4, h5 {
+        color: #ffd700 !important;
+    }
+
+    .block-container {
+        padding: 2rem;
+    }
+
+    p, li {
+        text-align: justify;
+        color: #e0e0e0;
+        font-size: 16px;
+    }
+
+    .center-button {
+        display: flex;
+        justify-content: center;
+        margin-top: 30px;
+    }
+
+    .stButton>button {
+        background-color: #ff7f50;
+        color: white;
+        border-radius: 12px;
+        padding: 0.5em 2em;
+        font-size: 16px;
+        transition: 0.3s;
+    }
+
+    .stButton>button:hover {
+        background-color: #ff5722;
+        font-weight: bold;
+    }
+
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Title and Form
+st.title("🧠 Mental Health Detection")
+st.markdown("#### Please fill in your daily activity details to get a mental wellness insight.")
+
+# Input Form
+with st.form("mental_health_form"):
+    col1, col2 = st.columns(2)
+
+    with col1:
+        work_hours = st.number_input("🕒 Working hours per week", min_value=20, max_value=70)
+        Family_history = st.selectbox("👨‍👩‍👧 Family history of mental health issues?", ("Yes", "No"))
+        sleep = st.number_input("😴 Sleep hours per day", min_value=4, max_value=10)
+        Diet = st.selectbox("🍽️ Diet quality", ("Good", "Average", "Poor"))
+
+    with col2:
+        stress = st.slider("😰 Stress levels (1-10)", min_value=0, max_value=10)
+        physical_activity = st.number_input("🏃 Physical activity (minutes/day)", min_value=0, max_value=180)
+        social_interaction = st.slider("🗣️ Social interaction level (1-10)", min_value=0, max_value=10)
+
+    # Center the submit button
+    st.markdown('<div class="center-button">', unsafe_allow_html=True)
+    submitted = st.form_submit_button("🔍 Predict")
+    st.markdown('</div>', unsafe_allow_html=True)
+
+    
+df = pd.DataFrame([[work_hours, Family_history, sleep, stress,
+                    physical_activity, social_interaction, Diet]],
+                  columns=['Work Hours', 'Family History', 'Sleep Hours',
+                           'Stress Level', 'Physical Activity',
+                           'Social Interaction', 'Diet Quality'])
+# Model prediction
+if submitted:
+    with open("encoding.pkl", 'rb') as file:
+        encode = pickle.load(file)
+    with open("model.pkl", "rb") as file:
+        model = pickle.load(file)
+    with open("pip.pkl",'rb') as file:
+        pipe=pickle.load(file)
+
+    transformed_df = pipe.transform(df)
+    prediction = model.predict(transformed_df)[0]
+
+    # Output results
+    if prediction == 1:
+        st.warning("⚠️ You may be at risk for mental health issues. It's recommended to take some self-care actions.")
+    else:
+        st.success("✅ You're doing well! Keep up the good habits.")
+
+    # Personalized Tips
+    st.markdown("### 💡 Personalized Tips to Boost Your Mental Health")
+    tips = []
+
+    if Diet != "Good":
+        tips.append("🍎 **Eat a Nutrient-Rich Snack Daily**  \nSupports brain health with omega-3s, vitamins, and minerals.")
+
+    if stress > 6:
+        tips.append("🧘 **Meditate or Stretch for 15 Minutes**  \nEases anxiety and improves focus.")
+        tips.append("🌬️ **Practice Deep Breathing for 10 Minutes**  \nLowers stress and anxiety levels.")
+        tips.append("🚰 **Drink Water & Take 5-Minute Breaks**  \nHydration supports mental clarity.")
+        tips.append("🎧 **Walk 30 Minutes Daily with Music**  \nReduces stress and uplifts mood.")
+
+    if sleep < 7:
+        tips.append("🛌 **Set a Consistent Sleep Schedule (7–8 Hours)**  \nImproves energy and stabilizes mood.")
+
+    if work_hours > 50:
+        tips.append("📅 **Limit Work to 40–45 Hours Per Week**  \nPrevents burnout and enhances productivity.")
+
+    if social_interaction < 5:
+        tips.append("👥 **Spend at Least 1 Hour Socializing Daily**  \nBoosts emotional well-being and reduces isolation.")
+
+    for tip in tips:
+        st.markdown(f"<p>{tip}</p>", unsafe_allow_html=True)
+

encoding.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3c55a370262a989a8ba1149ca0bd67d1846ffabe2be8405c3ff6a40e55008ea
+size 632

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f7df1888742544b1758d6c95936ef0e612c5290bc947b677d88423a6a9ff6172
+size 40873

neuron.ipynb

@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","source":[],"metadata":{"id":"_HeKT-ajBWXh","executionInfo":{"status":"ok","timestamp":1747299154667,"user_tz":-330,"elapsed":2,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"_HeKT-ajBWXh","execution_count":82,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4i_t5qxQBYMv","executionInfo":{"status":"ok","timestamp":1747299156431,"user_tz":-330,"elapsed":1762,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"a38f6c33-89f2-4b52-c22a-0dcc797f849d"},"id":"4i_t5qxQBYMv","execution_count":83,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","source":["import os\n","# os.get_dir('/content/drive')\n","path='/content/drive/MyDrive/Deep Learning/mini_sample_project/'\n","os.listdir('/content/drive/MyDrive/Deep Learning/mini_sample_project')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"u8nAtqyhBWUx","executionInfo":{"status":"ok","timestamp":1747299156432,"user_tz":-330,"elapsed":15,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"6e6db817-9dc0-4578-f38c-ccd96101de8f"},"id":"u8nAtqyhBWUx","execution_count":84,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['neuron.ipynb', 'MentalHealth_risk_identification.csv']"]},"metadata":{},"execution_count":84}]},{"cell_type":"code","execution_count":85,"id":"c6de1db9","metadata":{"id":"c6de1db9","executionInfo":{"status":"ok","timestamp":1747299156432,"user_tz":-330,"elapsed":5,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["import pandas as pd\n","import seaborn as sns\n","import matplotlib.pyplot as plt"]},{"cell_type":"code","execution_count":86,"id":"f6557186","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":441},"id":"f6557186","executionInfo":{"status":"ok","timestamp":1747299156480,"user_tz":-330,"elapsed":51,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"570a5141-d1aa-4888-d25a-0234ef17944b"},"outputs":[{"output_type":"stream","name":"stdout","text":["2087\n"]},{"output_type":"execute_result","data":{"text/plain":["      Age  Gender  Work Hours Family History  Sleep Hours  Stress Level  \\\n","0      79    Male          20            Yes            7             7   \n","1      20  Others          31             No            8             7   \n","2      40    Male          39             No            8             4   \n","3      35  Female          66            Yes            7            10   \n","4      81  Female          42            Yes            6             2   \n","...   ...     ...         ...            ...          ...           ...   \n","2082   63    Male          38             No            7             0   \n","2083   96  Female          34             No            6             9   \n","2084   25    Male          62            Yes            7             7   \n","2085   96  Female          65            Yes            4             9   \n","2086   50    Male          46            Yes            7             6   \n","\n","      Physical Activity  Social Interaction Diet Quality Treatment  \n","0                    24                   2      Average       Yes  \n","1                     2                   2      Average        No  \n","2                     7                   8         Good       Yes  \n","3                    40                   2      Average       Yes  \n","4                    78                   2         Good       Yes  \n","...                 ...                 ...          ...       ...  \n","2082                 61                   5         Good        No  \n","2083                 97                   1      Average       Yes  \n","2084                138                   2         Poor       Yes  \n","2085                 76                   7         Poor       Yes  \n","2086                 51                   3      Average        No  \n","\n","[2087 rows x 10 columns]"],"text/html":["\n","  <div id=\"df-9b3bb592-0efd-431e-8268-0f52ba52c002\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Age</th>\n","      <th>Gender</th>\n","      <th>Work Hours</th>\n","      <th>Family History</th>\n","      <th>Sleep 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\"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stress Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Physical Activity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 0,\n        \"max\": 180,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          16,\n          151\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Social Interaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        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0})\n","\n","df.head()\n","df"]},{"cell_type":"code","execution_count":87,"id":"531f8999","metadata":{"id":"531f8999","executionInfo":{"status":"ok","timestamp":1747299156483,"user_tz":-330,"elapsed":2,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import StandardScaler,OrdinalEncoder,OneHotEncoder\n","from sklearn.pipeline import Pipeline\n","from sklearn.compose import ColumnTransformer"]},{"cell_type":"code","source":["en=OneHotEncoder(sparse_output=False, drop='first')\n","df['Treatment']=en.fit_transform(df[['Treatment']])#.toarray()"],"metadata":{"id":"i7qmLmP-Hqm8","executionInfo":{"status":"ok","timestamp":1747299156505,"user_tz":-330,"elapsed":3,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"i7qmLmP-Hqm8","execution_count":88,"outputs":[]},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"RejiKiE6H0pd","executionInfo":{"status":"ok","timestamp":1747299156547,"user_tz":-330,"elapsed":37,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"8dec80a7-1598-4b57-c064-518c49960a34"},"id":"RejiKiE6H0pd","execution_count":89,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Age  Gender  Work Hours Family History  Sleep Hours  Stress Level  \\\n","0      79    Male          20            Yes            7             7   \n","1      20  Others          31             No            8             7   \n","2      40    Male          39             No            8             4   \n","3      35  Female          66            Yes            7            10   \n","4      81  Female          42            Yes            6             2   \n","...   ...     ...         ...            ...          ...           ...   \n","2082   63    Male          38             No            7             0   \n","2083   96  Female          34             No            6             9   \n","2084   25    Male          62            Yes            7             7   \n","2085   96  Female          65            Yes            4             9   \n","2086   50    Male          46            Yes            7             6   \n","\n","      Physical Activity  Social Interaction Diet Quality  Treatment  \n","0                    24                   2      Average        1.0  \n","1                     2                   2      Average        0.0  \n","2                     7                   8         Good        1.0  \n","3                    40                   2      Average        1.0  \n","4                    78                   2         Good        1.0  \n","...                 ...                 ...          ...        ...  \n","2082                 61                   5         Good        0.0  \n","2083                 97                   1      Average        1.0  \n","2084                138                   2         Poor        1.0  \n","2085                 76                   7         Poor        1.0  \n","2086                 51                   3      Average        0.0  \n","\n","[2087 rows x 10 columns]"],"text/html":["\n","  <div id=\"df-8c6f2ab2-aa98-4e3a-bb52-58a529044a53\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        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<td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2085</th>\n","      <td>96</td>\n","      <td>Female</td>\n","      <td>65</td>\n","      <td>Yes</td>\n","      <td>4</td>\n","      <td>9</td>\n","      <td>76</td>\n","      <td>7</td>\n","      <td>Poor</td>\n","      <td>1.0</td>\n","    </tr>\n","    <tr>\n","      <th>2086</th>\n","      <td>50</td>\n","      <td>Male</td>\n","      <td>46</td>\n","      <td>Yes</td>\n","      <td>7</td>\n","      <td>6</td>\n","      <td>51</td>\n","      <td>3</td>\n","      <td>Average</td>\n","      <td>0.0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>2087 rows × 10 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8c6f2ab2-aa98-4e3a-bb52-58a529044a53')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg 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\"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Work Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 20,\n        \"max\": 69,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          37,\n          25,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Family History\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"No\",\n          \"Yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sleep Hours\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 10,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          7,\n          8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stress Level\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          3,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Physical Activity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 0,\n        \"max\": 180,\n        \"num_unique_values\": 181,\n        \"samples\": [\n          16,\n          151\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Social Interaction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 10,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Diet Quality\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Average\",\n          \"Good\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Treatment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43318563444454833,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":89}]},{"cell_type":"code","execution_count":90,"id":"c5ba1a82","metadata":{"id":"c5ba1a82","executionInfo":{"status":"ok","timestamp":1747299156567,"user_tz":-330,"elapsed":17,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["std=StandardScaler()"]},{"cell_type":"code","execution_count":91,"id":"7fa99037","metadata":{"id":"7fa99037","executionInfo":{"status":"ok","timestamp":1747299156576,"user_tz":-330,"elapsed":4,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["nominal=['Gender','Family History']\n","ordinal=['Diet Quality']\n","s=['Age','Work Hours','Sleep Hours','Stress Level','Physical Activity','Social Interaction']\n",""]},{"cell_type":"code","source":["from sklearn.preprocessing import OneHotEncoder"],"metadata":{"id":"6wU03PY8CV-Q","executionInfo":{"status":"ok","timestamp":1747299156595,"user_tz":-330,"elapsed":17,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"6wU03PY8CV-Q","execution_count":92,"outputs":[]},{"cell_type":"code","execution_count":93,"id":"b0e87e39","metadata":{"id":"b0e87e39","executionInfo":{"status":"ok","timestamp":1747299156598,"user_tz":-330,"elapsed":1,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["ord_p=Pipeline([(\"ordnal\",OrdinalEncoder())])\n","nom_p=Pipeline([(\"nomianl\",OneHotEncoder(sparse_output=False, drop='first'))])\n","scale=Pipeline([(\"s\",StandardScaler())])"]},{"cell_type":"code","execution_count":94,"id":"586f583b","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":148},"id":"586f583b","executionInfo":{"status":"ok","timestamp":1747299156687,"user_tz":-330,"elapsed":88,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"dc1442cb-f99f-4c27-ed1e-4d9ff1dac379"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["ColumnTransformer(remainder='passthrough',\n","                  transformers=[('ord',\n","                                 Pipeline(steps=[('ordnal', OrdinalEncoder())]),\n","                                 ['Diet Quality']),\n","                                ('nominal',\n","                                 Pipeline(steps=[('nomianl',\n","                                                  OneHotEncoder(drop='first',\n","                                                                sparse_output=False))]),\n","                                 ['Gender', 'Family History']),\n","                                ('scaling',\n","                                 Pipeline(steps=[('s', StandardScaler())]),\n","                                 ['Age', 'Work Hours', 'Sleep Hours',\n","                                  'Stress Level', 'Physical Activity',\n","                                  'Social Interaction'])])"],"text/html":["<style>#sk-container-id-2 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: #000;\n","  --sklearn-color-text-muted: #666;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  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border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-2 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-2 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-2 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-2 div.sk-toggleable {\n","  /* Default theme specific background. 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var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-2 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-2 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-2 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-2 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-2 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-2 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-2 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 0.5em;\n","  text-align: center;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-2 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-2 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-2 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n","                  transformers=[(&#x27;ord&#x27;,\n","                                 Pipeline(steps=[(&#x27;ordnal&#x27;, OrdinalEncoder())]),\n","                                 [&#x27;Diet Quality&#x27;]),\n","                                (&#x27;nominal&#x27;,\n","                                 Pipeline(steps=[(&#x27;nomianl&#x27;,\n","                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n","                                                                sparse_output=False))]),\n","                                 [&#x27;Gender&#x27;, &#x27;Family History&#x27;]),\n","                                (&#x27;scaling&#x27;,\n","                                 Pipeline(steps=[(&#x27;s&#x27;, StandardScaler())]),\n","                                 [&#x27;Age&#x27;, &#x27;Work Hours&#x27;, &#x27;Sleep Hours&#x27;,\n","                                  &#x27;Stress Level&#x27;, &#x27;Physical Activity&#x27;,\n","                                  &#x27;Social Interaction&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n","                  transformers=[(&#x27;ord&#x27;,\n","                                 Pipeline(steps=[(&#x27;ordnal&#x27;, OrdinalEncoder())]),\n","                                 [&#x27;Diet Quality&#x27;]),\n","                                (&#x27;nominal&#x27;,\n","                                 Pipeline(steps=[(&#x27;nomianl&#x27;,\n","                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n","                                                                sparse_output=False))]),\n","                                 [&#x27;Gender&#x27;, &#x27;Family History&#x27;]),\n","                                (&#x27;scaling&#x27;,\n","                                 Pipeline(steps=[(&#x27;s&#x27;, StandardScaler())]),\n","                                 [&#x27;Age&#x27;, &#x27;Work Hours&#x27;, &#x27;Sleep Hours&#x27;,\n","                                  &#x27;Stress Level&#x27;, &#x27;Physical Activity&#x27;,\n","                                  &#x27;Social Interaction&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ord</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Diet Quality&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OrdinalEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OrdinalEncoder()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>nominal</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Gender&#x27;, &#x27;Family History&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(drop=&#x27;first&#x27;, sparse_output=False)</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>scaling</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Age&#x27;, &#x27;Work Hours&#x27;, &#x27;Sleep Hours&#x27;, &#x27;Stress Level&#x27;, &#x27;Physical Activity&#x27;, &#x27;Social Interaction&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>remainder</div></div></label><div class=\"sk-toggleable__content \"><pre></pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>passthrough</div></div></label><div class=\"sk-toggleable__content \"><pre>passthrough</pre></div> </div></div></div></div></div></div></div></div></div>"]},"metadata":{},"execution_count":94}],"source":["pip=ColumnTransformer([(\"ord\",ord_p,ordinal),('nominal',nom_p,nominal),('scaling',scale,s)],remainder=\"passthrough\")\n","pip"]},{"cell_type":"code","execution_count":95,"id":"8d49a873","metadata":{"id":"8d49a873","executionInfo":{"status":"ok","timestamp":1747299156698,"user_tz":-330,"elapsed":9,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["x=df.drop(\"Treatment\",axis=1)\n","y=df['Treatment']"]},{"cell_type":"code","execution_count":96,"id":"12ce1044","metadata":{"id":"12ce1044","executionInfo":{"status":"ok","timestamp":1747299156744,"user_tz":-330,"elapsed":42,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["x=pip.fit_transform(x)"]},{"cell_type":"code","execution_count":97,"id":"7f58f1d5","metadata":{"id":"7f58f1d5","executionInfo":{"status":"ok","timestamp":1747299156747,"user_tz":-330,"elapsed":29,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=27)"]},{"cell_type":"code","execution_count":98,"id":"a3d1ba4d","metadata":{"id":"a3d1ba4d","executionInfo":{"status":"ok","timestamp":1747299156748,"user_tz":-330,"elapsed":20,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["import keras"]},{"cell_type":"code","execution_count":99,"id":"012633cb","metadata":{"id":"012633cb","executionInfo":{"status":"ok","timestamp":1747299156750,"user_tz":-330,"elapsed":20,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"outputs":[],"source":["from keras.layers import Input, Dense, BatchNormalization"]},{"cell_type":"code","source":["from keras.models import Sequential"],"metadata":{"id":"UFPMNun_Cxp7","executionInfo":{"status":"ok","timestamp":1747299156753,"user_tz":-330,"elapsed":21,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"UFPMNun_Cxp7","execution_count":100,"outputs":[]},{"cell_type":"code","source":["model = Sequential()"],"metadata":{"id":"Z84dxzW_C4Zq","executionInfo":{"status":"ok","timestamp":1747299156765,"user_tz":-330,"elapsed":11,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"Z84dxzW_C4Zq","execution_count":101,"outputs":[]},{"cell_type":"code","source":["from keras.initializers import HeNormal, HeUniform, GlorotUniform, GlorotNormal"],"metadata":{"id":"tT4QtidkC7Ke","executionInfo":{"status":"ok","timestamp":1747299156770,"user_tz":-330,"elapsed":3,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"tT4QtidkC7Ke","execution_count":102,"outputs":[]},{"cell_type":"code","source":["h = HeNormal()"],"metadata":{"id":"nGqaZbC8C9Dv","executionInfo":{"status":"ok","timestamp":1747299156774,"user_tz":-330,"elapsed":2,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"nGqaZbC8C9Dv","execution_count":103,"outputs":[]},{"cell_type":"code","source":["model.add(Input(shape = (10,)))\n","model.add(BatchNormalization())\n","model.add(Dense(10, activation = 'relu', kernel_initializer = h))\n","model.add(BatchNormalization())\n","model.add(Dense(10, activation = 'relu', kernel_initializer = h))\n","# model\n","model.add(Dense(1,activation=\"sigmoid\",kernel_initializer=h))"],"metadata":{"id":"aDQLrl2jC--X","executionInfo":{"status":"ok","timestamp":1747299156776,"user_tz":-330,"elapsed":1,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"aDQLrl2jC--X","execution_count":104,"outputs":[]},{"cell_type":"code","source":["model.summary()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":321},"id":"15I7WO7SD94d","executionInfo":{"status":"ok","timestamp":1747299156832,"user_tz":-330,"elapsed":55,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"f0c26576-f7f6-454d-f34a-8f9e14e9f635"},"id":"15I7WO7SD94d","execution_count":105,"outputs":[{"output_type":"display_data","data":{"text/plain":["\u001b[1mModel: \"sequential_1\"\u001b[0m\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │            \u001b[38;5;34m40\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m110\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │            \u001b[38;5;34m40\u001b[0m │\n","│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m110\u001b[0m │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m11\u001b[0m │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n","┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n","┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n","│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │            <span style=\"color: #00af00; text-decoration-color: #00af00\">40</span> │\n","│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">110</span> │\n","├─────────────────────────────────┼────────────────────────┼───────────────┤\n","│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span> │\n","└─────────────────────────────────┴────────────────────────┴───────────────┘\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Total params: \u001b[0m\u001b[38;5;34m311\u001b[0m (1.21 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">311</span> (1.21 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m271\u001b[0m (1.06 KB)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">271</span> (1.06 KB)\n","</pre>\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m40\u001b[0m (160.00 B)\n"],"text/html":["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">40</span> (160.00 B)\n","</pre>\n"]},"metadata":{}}]},{"cell_type":"code","source":["model.compile(optimizer='adam',loss=\"binary_crossentropy\",metrics=['accuracy'])"],"metadata":{"id":"ZlGao5vnE7-D","executionInfo":{"status":"ok","timestamp":1747299156833,"user_tz":-330,"elapsed":11,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"ZlGao5vnE7-D","execution_count":106,"outputs":[]},{"cell_type":"code","source":["model.fit(x_train,y_train,epochs=30,validation_split=0.2,batch_size=30)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TEn1hVh4FGow","executionInfo":{"status":"ok","timestamp":1747299170222,"user_tz":-330,"elapsed":13396,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"2dcb523c-e15c-477f-bd98-844446cab1f7"},"id":"TEn1hVh4FGow","execution_count":107,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 14ms/step - accuracy: 0.6232 - loss: 0.6828 - val_accuracy: 0.7844 - val_loss: 0.4737\n","Epoch 2/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.6873 - loss: 0.5785 - val_accuracy: 0.7784 - val_loss: 0.4527\n","Epoch 3/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 6ms/step - accuracy: 0.7258 - loss: 0.5342 - val_accuracy: 0.7754 - val_loss: 0.4334\n","Epoch 4/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 7ms/step - accuracy: 0.7735 - loss: 0.5094 - val_accuracy: 0.7964 - val_loss: 0.4145\n","Epoch 5/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 8ms/step - accuracy: 0.7566 - loss: 0.5060 - val_accuracy: 0.7934 - val_loss: 0.4003\n","Epoch 6/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.7718 - loss: 0.4710 - val_accuracy: 0.8054 - val_loss: 0.3871\n","Epoch 7/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 5ms/step - accuracy: 0.7742 - loss: 0.4832 - val_accuracy: 0.8084 - val_loss: 0.3771\n","Epoch 8/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8003 - loss: 0.4416 - val_accuracy: 0.8204 - val_loss: 0.3655\n","Epoch 9/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7815 - loss: 0.4451 - val_accuracy: 0.8293 - val_loss: 0.3586\n","Epoch 10/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━���━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8084 - loss: 0.4219 - val_accuracy: 0.8323 - val_loss: 0.3506\n","Epoch 11/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7794 - loss: 0.4470 - val_accuracy: 0.8323 - val_loss: 0.3466\n","Epoch 12/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7999 - loss: 0.4202 - val_accuracy: 0.8323 - val_loss: 0.3424\n","Epoch 13/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8004 - loss: 0.4146 - val_accuracy: 0.8293 - val_loss: 0.3396\n","Epoch 14/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8137 - loss: 0.4199 - val_accuracy: 0.8323 - val_loss: 0.3353\n","Epoch 15/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8026 - loss: 0.4003 - val_accuracy: 0.8353 - val_loss: 0.3311\n","Epoch 16/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8048 - loss: 0.4034 - val_accuracy: 0.8383 - val_loss: 0.3284\n","Epoch 17/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8126 - loss: 0.3755 - val_accuracy: 0.8413 - val_loss: 0.3254\n","Epoch 18/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8069 - loss: 0.4014 - val_accuracy: 0.8443 - val_loss: 0.3248\n","Epoch 19/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8253 - loss: 0.3790 - val_accuracy: 0.8413 - val_loss: 0.3235\n","Epoch 20/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8247 - loss: 0.3805 - val_accuracy: 0.8383 - val_loss: 0.3230\n","Epoch 21/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8185 - loss: 0.3970 - val_accuracy: 0.8383 - val_loss: 0.3222\n","Epoch 22/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8063 - loss: 0.4218 - val_accuracy: 0.8413 - val_loss: 0.3190\n","Epoch 23/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8066 - loss: 0.3963 - val_accuracy: 0.8413 - val_loss: 0.3185\n","Epoch 24/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7945 - loss: 0.3924 - val_accuracy: 0.8383 - val_loss: 0.3161\n","Epoch 25/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8165 - loss: 0.3983 - val_accuracy: 0.8353 - val_loss: 0.3181\n","Epoch 26/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8058 - loss: 0.3890 - val_accuracy: 0.8323 - val_loss: 0.3155\n","Epoch 27/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7941 - loss: 0.4248 - val_accuracy: 0.8353 - val_loss: 0.3152\n","Epoch 28/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8023 - loss: 0.3983 - val_accuracy: 0.8383 - val_loss: 0.3151\n","Epoch 29/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8038 - loss: 0.4004 - val_accuracy: 0.8323 - val_loss: 0.3143\n","Epoch 30/30\n","\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8205 - loss: 0.3855 - val_accuracy: 0.8383 - val_loss: 0.3142\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.src.callbacks.history.History at 0x7db25d815050>"]},"metadata":{},"execution_count":107}]},{"cell_type":"code","source":["model.evaluate(x_test,y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6gDAuGr6FfbG","executionInfo":{"status":"ok","timestamp":1747299170470,"user_tz":-330,"elapsed":245,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"0ae407a1-89f7-4423-f336-82eec67fbc32"},"id":"6gDAuGr6FfbG","execution_count":108,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m14/14\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8193 - loss: 0.3716 \n"]},{"output_type":"execute_result","data":{"text/plain":["[0.37067076563835144, 0.8253588676452637]"]},"metadata":{},"execution_count":108}]},{"cell_type":"code","source":["x_test[0].reshape(1,-1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IruIsuC9G2aG","executionInfo":{"status":"ok","timestamp":1747299170482,"user_tz":-330,"elapsed":13,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"9296c698-5e72-4a7f-f235-75a8b8758858"},"id":"IruIsuC9G2aG","execution_count":109,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 0.        ,  1.        ,  0.        ,  0.        , -1.10039259,\n","        -0.61261705,  0.31432239,  0.64045774,  0.79635556,  0.64158901]])"]},"metadata":{},"execution_count":109}]},{"cell_type":"code","source":["model.predict(x_test[0].reshape(1,-1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"h-VAIOg8Gs7L","executionInfo":{"status":"ok","timestamp":1747299170701,"user_tz":-330,"elapsed":206,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"44672033-de6f-4279-a10c-e1b405f7a4ff"},"id":"h-VAIOg8Gs7L","execution_count":110,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["array([[0.1687277]], dtype=float32)"]},"metadata":{},"execution_count":110}]},{"cell_type":"code","source":["import numpy as np"],"metadata":{"id":"bFbHTw6cHUU0","executionInfo":{"status":"ok","timestamp":1747299170754,"user_tz":-330,"elapsed":52,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"bFbHTw6cHUU0","execution_count":111,"outputs":[]},{"cell_type":"code","source":["np.argmax(model.predict(x_test[0].reshape(1,-1)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mMerNJ1kHNkx","executionInfo":{"status":"ok","timestamp":1747299170875,"user_tz":-330,"elapsed":158,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"27c02805-9a6f-4cea-eb2b-9eac3df3a6e4"},"id":"mMerNJ1kHNkx","execution_count":112,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step\n"]},{"output_type":"execute_result","data":{"text/plain":["np.int64(0)"]},"metadata":{},"execution_count":112}]},{"cell_type":"code","source":["en.inverse_transform(np.array(y_test)[1].reshape(1,-1))\n","# en.inverse_transform(np.array([[1]]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"0XC0TjuHHakJ","executionInfo":{"status":"ok","timestamp":1747299795173,"user_tz":-330,"elapsed":54,"user":{"displayName":"Surendra","userId":"17580320614144699442"}},"outputId":"d2fa44d2-f69c-4ca5-923b-e46b1636d5a2"},"id":"0XC0TjuHHakJ","execution_count":128,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([['No']], dtype=object)"]},"metadata":{},"execution_count":128}]},{"cell_type":"code","source":["import pickle as pkl"],"metadata":{"id":"9vP3zNFMJrwr","executionInfo":{"status":"ok","timestamp":1747299416783,"user_tz":-330,"elapsed":5,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"9vP3zNFMJrwr","execution_count":120,"outputs":[]},{"cell_type":"code","source":["with open(path+\"model.pkl\",\"wb\") as f:\n","  pkl.dump(model,f)\n","with open(path+\"pip.pkl\",\"wb\") as f:\n","  pkl.dump(pip,f)\n","with open(path+\"encoding.pkl\",\"wb\") as f:\n","  pkl.dump(en,f)"],"metadata":{"id":"bhBheETTKUG0","executionInfo":{"status":"ok","timestamp":1747299501836,"user_tz":-330,"elapsed":120,"user":{"displayName":"Surendra","userId":"17580320614144699442"}}},"id":"bhBheETTKUG0","execution_count":121,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.12.4"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":5}

pip.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cbd155b7bec8e91d870b83a330be1c17ad7ae16325a4962bebbbdfeaf073e80a
+size 2774

requirements.txt

@@ -0,0 +1,5 @@
+pandas
+seaborn
+matplotlib
+scikit-learn
+tensorflow