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classification/unipredict/deependraverma13-diabetes-healthcare-comprehensive-dataset/test.csv

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+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
+8,186,90,35,225,34.5,0.42,37,1.0
+2,175,88,0,0,22.9,0.33,22,0.0
+3,111,58,31,44,29.5,0.43,22,0.0
+0,165,90,33,680,52.3,0.43,23,0.0
+4,76,62,0,0,34.0,0.39,25,0.0
+8,151,78,32,210,42.9,0.52,36,1.0
+12,88,74,40,54,35.3,0.38,48,0.0
+12,151,70,40,271,41.8,0.74,38,1.0
+5,136,82,0,0,0.0,0.64,69,0.0
+3,96,56,34,115,24.7,0.94,39,0.0
+4,116,72,12,87,22.1,0.46,37,0.0
+7,119,0,0,0,25.2,0.21,37,0.0
+2,109,92,0,0,42.7,0.84,54,0.0
+1,88,30,42,99,55.0,0.5,26,1.0
+2,158,90,0,0,31.6,0.81,66,1.0
+12,140,82,43,325,39.2,0.53,58,1.0
+0,137,84,27,0,27.3,0.23,59,0.0
+4,156,75,0,0,48.3,0.24,32,1.0
+7,124,70,33,215,25.5,0.16,37,0.0
+6,162,62,0,0,24.3,0.18,50,1.0
+2,112,86,42,160,38.4,0.25,28,0.0
+9,112,82,32,175,34.2,0.26,36,1.0
+1,71,48,18,76,20.4,0.32,22,0.0
+1,121,78,39,74,39.0,0.26,28,0.0
+2,90,70,17,0,27.3,0.09,22,0.0
+10,129,62,36,0,41.2,0.44,38,1.0
+8,120,86,0,0,28.4,0.26,22,1.0
+5,99,74,27,0,29.0,0.2,32,0.0
+2,125,60,20,140,33.8,0.09,31,0.0
+2,90,60,0,0,23.5,0.19,25,0.0
+2,84,50,23,76,30.4,0.97,21,0.0
+5,105,72,29,325,36.9,0.16,28,0.0
+0,125,96,0,0,22.5,0.26,21,0.0
+1,86,66,52,65,41.3,0.92,29,0.0
+1,97,68,21,0,27.2,1.09,22,0.0
+3,158,76,36,245,31.6,0.85,28,1.0
+1,138,82,0,0,40.1,0.24,28,0.0
+9,164,84,21,0,30.8,0.83,32,1.0
+4,118,70,0,0,44.5,0.9,26,0.0
+6,165,68,26,168,33.6,0.63,49,0.0
+4,129,60,12,231,27.5,0.53,31,0.0
+11,111,84,40,0,46.8,0.93,45,1.0
+7,137,90,41,0,32.0,0.39,39,0.0
+7,109,80,31,0,35.9,1.13,43,1.0
+1,119,86,39,220,45.6,0.81,29,1.0
+0,124,70,20,0,27.4,0.25,36,1.0
+2,88,58,26,16,28.4,0.77,22,0.0
+0,124,56,13,105,21.8,0.45,21,0.0
+3,141,0,0,0,30.0,0.76,27,1.0
+12,121,78,17,0,26.5,0.26,62,0.0
+0,138,60,35,167,34.6,0.53,21,1.0
+2,90,80,14,55,24.4,0.25,24,0.0
+0,177,60,29,478,34.6,1.07,21,1.0
+0,100,88,60,110,46.8,0.96,31,0.0
+3,89,74,16,85,30.4,0.55,38,0.0
+0,189,104,25,0,34.3,0.43,41,1.0
+0,86,68,32,0,35.8,0.24,25,0.0
+4,142,86,0,0,44.0,0.65,22,1.0
+8,91,82,0,0,35.6,0.59,68,0.0
+8,85,55,20,0,24.4,0.14,42,0.0
+3,113,44,13,0,22.4,0.14,22,0.0
+9,72,78,25,0,31.6,0.28,38,0.0
+2,90,68,42,0,38.2,0.5,27,1.0
+9,91,68,0,0,24.2,0.2,58,0.0
+10,68,106,23,49,35.5,0.28,47,0.0
+8,108,70,0,0,30.5,0.95,33,1.0
+2,129,74,26,205,33.2,0.59,25,0.0
+3,128,78,0,0,21.1,0.27,55,0.0
+6,166,74,0,0,26.6,0.3,66,0.0
+0,104,76,0,0,18.4,0.58,27,0.0
+0,128,68,19,180,30.5,1.39,25,1.0
+11,120,80,37,150,42.3,0.79,48,1.0
+1,91,54,25,100,25.2,0.23,23,0.0
+0,152,82,39,272,41.5,0.27,27,0.0
+8,100,74,40,215,39.4,0.66,43,1.0
+5,139,64,35,140,28.6,0.41,26,0.0
+2,84,0,0,0,0.0,0.3,21,0.0

classification/unipredict/deependraverma13-diabetes-healthcare-comprehensive-dataset/test.jsonl

@@ -0,0 +1,77 @@
+{"text": "The Pregnancies is 8.0. The Glucose is 186.0. The BloodPressure is 90.0. The SkinThickness is 35.0. The Insulin is 225.0. The BMI is 34.5. The DiabetesPedigreeFunction is 0.42. The Age is 37.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 175.0. The BloodPressure is 88.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 22.9. The DiabetesPedigreeFunction is 0.33. The Age is 22.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 111.0. The BloodPressure is 58.0. The SkinThickness is 31.0. The Insulin is 44.0. The BMI is 29.5. The DiabetesPedigreeFunction is 0.43. The Age is 22.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 165.0. The BloodPressure is 90.0. The SkinThickness is 33.0. The Insulin is 680.0. The BMI is 52.3. The DiabetesPedigreeFunction is 0.43. The Age is 23.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 76.0. The BloodPressure is 62.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 34.0. The DiabetesPedigreeFunction is 0.39. The Age is 25.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 151.0. The BloodPressure is 78.0. The SkinThickness is 32.0. The Insulin is 210.0. The BMI is 42.9. The DiabetesPedigreeFunction is 0.52. The Age is 36.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 12.0. The Glucose is 88.0. The BloodPressure is 74.0. The SkinThickness is 40.0. The Insulin is 54.0. The BMI is 35.3. The DiabetesPedigreeFunction is 0.38. The Age is 48.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 12.0. The Glucose is 151.0. The BloodPressure is 70.0. The SkinThickness is 40.0. The Insulin is 271.0. The BMI is 41.8. The DiabetesPedigreeFunction is 0.74. The Age is 38.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 5.0. The Glucose is 136.0. The BloodPressure is 82.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 0.0. The DiabetesPedigreeFunction is 0.64. The Age is 69.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 96.0. The BloodPressure is 56.0. The SkinThickness is 34.0. The Insulin is 115.0. The BMI is 24.7. The DiabetesPedigreeFunction is 0.94. The Age is 39.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 116.0. The BloodPressure is 72.0. The SkinThickness is 12.0. The Insulin is 87.0. The BMI is 22.1. The DiabetesPedigreeFunction is 0.46. The Age is 37.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 119.0. The BloodPressure is 0.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 25.2. The DiabetesPedigreeFunction is 0.21. The Age is 37.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 109.0. The BloodPressure is 92.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 42.7. The DiabetesPedigreeFunction is 0.84. The Age is 54.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 88.0. The BloodPressure is 30.0. The SkinThickness is 42.0. The Insulin is 99.0. The BMI is 55.0. The DiabetesPedigreeFunction is 0.5. The Age is 26.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 158.0. The BloodPressure is 90.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 31.6. The DiabetesPedigreeFunction is 0.81. The Age is 66.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 12.0. The Glucose is 140.0. The BloodPressure is 82.0. The SkinThickness is 43.0. The Insulin is 325.0. The BMI is 39.2. The DiabetesPedigreeFunction is 0.53. The Age is 58.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 137.0. The BloodPressure is 84.0. The SkinThickness is 27.0. The Insulin is 0.0. The BMI is 27.3. The DiabetesPedigreeFunction is 0.23. The Age is 59.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 156.0. The BloodPressure is 75.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 48.3. The DiabetesPedigreeFunction is 0.24. The Age is 32.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 124.0. The BloodPressure is 70.0. The SkinThickness is 33.0. The Insulin is 215.0. The BMI is 25.5. The DiabetesPedigreeFunction is 0.16. The Age is 37.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 162.0. The BloodPressure is 62.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 24.3. The DiabetesPedigreeFunction is 0.18. The Age is 50.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 112.0. The BloodPressure is 86.0. The SkinThickness is 42.0. The Insulin is 160.0. The BMI is 38.4. The DiabetesPedigreeFunction is 0.25. The Age is 28.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 9.0. The Glucose is 112.0. The BloodPressure is 82.0. The SkinThickness is 32.0. The Insulin is 175.0. The BMI is 34.2. The DiabetesPedigreeFunction is 0.26. The Age is 36.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 71.0. The BloodPressure is 48.0. The SkinThickness is 18.0. The Insulin is 76.0. The BMI is 20.4. The DiabetesPedigreeFunction is 0.32. The Age is 22.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 121.0. The BloodPressure is 78.0. The SkinThickness is 39.0. The Insulin is 74.0. The BMI is 39.0. The DiabetesPedigreeFunction is 0.26. The Age is 28.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 90.0. The BloodPressure is 70.0. The SkinThickness is 17.0. The Insulin is 0.0. The BMI is 27.3. The DiabetesPedigreeFunction is 0.09. The Age is 22.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 10.0. The Glucose is 129.0. The BloodPressure is 62.0. The SkinThickness is 36.0. The Insulin is 0.0. The BMI is 41.2. The DiabetesPedigreeFunction is 0.44. The Age is 38.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 120.0. The BloodPressure is 86.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 28.4. The DiabetesPedigreeFunction is 0.26. The Age is 22.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 5.0. The Glucose is 99.0. The BloodPressure is 74.0. The SkinThickness is 27.0. The Insulin is 0.0. The BMI is 29.0. The DiabetesPedigreeFunction is 0.2. The Age is 32.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 125.0. The BloodPressure is 60.0. The SkinThickness is 20.0. The Insulin is 140.0. The BMI is 33.8. The DiabetesPedigreeFunction is 0.09. The Age is 31.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 90.0. The BloodPressure is 60.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 23.5. The DiabetesPedigreeFunction is 0.19. The Age is 25.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 84.0. The BloodPressure is 50.0. The SkinThickness is 23.0. The Insulin is 76.0. The BMI is 30.4. The DiabetesPedigreeFunction is 0.97. The Age is 21.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 5.0. The Glucose is 105.0. The BloodPressure is 72.0. The SkinThickness is 29.0. The Insulin is 325.0. The BMI is 36.9. The DiabetesPedigreeFunction is 0.16. The Age is 28.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 125.0. The BloodPressure is 96.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 22.5. The DiabetesPedigreeFunction is 0.26. The Age is 21.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 86.0. The BloodPressure is 66.0. The SkinThickness is 52.0. The Insulin is 65.0. The BMI is 41.3. The DiabetesPedigreeFunction is 0.92. The Age is 29.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 97.0. The BloodPressure is 68.0. The SkinThickness is 21.0. The Insulin is 0.0. The BMI is 27.2. The DiabetesPedigreeFunction is 1.09. The Age is 22.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 158.0. The BloodPressure is 76.0. The SkinThickness is 36.0. The Insulin is 245.0. The BMI is 31.6. The DiabetesPedigreeFunction is 0.85. The Age is 28.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 138.0. The BloodPressure is 82.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 40.1. The DiabetesPedigreeFunction is 0.24. The Age is 28.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 9.0. The Glucose is 164.0. The BloodPressure is 84.0. The SkinThickness is 21.0. The Insulin is 0.0. The BMI is 30.8. The DiabetesPedigreeFunction is 0.83. The Age is 32.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 118.0. The BloodPressure is 70.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 44.5. The DiabetesPedigreeFunction is 0.9. The Age is 26.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 165.0. The BloodPressure is 68.0. The SkinThickness is 26.0. The Insulin is 168.0. The BMI is 33.6. The DiabetesPedigreeFunction is 0.63. The Age is 49.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 129.0. The BloodPressure is 60.0. The SkinThickness is 12.0. The Insulin is 231.0. The BMI is 27.5. The DiabetesPedigreeFunction is 0.53. The Age is 31.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 11.0. The Glucose is 111.0. The BloodPressure is 84.0. The SkinThickness is 40.0. The Insulin is 0.0. The BMI is 46.8. The DiabetesPedigreeFunction is 0.93. The Age is 45.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 137.0. The BloodPressure is 90.0. The SkinThickness is 41.0. The Insulin is 0.0. The BMI is 32.0. The DiabetesPedigreeFunction is 0.39. The Age is 39.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 7.0. The Glucose is 109.0. The BloodPressure is 80.0. The SkinThickness is 31.0. The Insulin is 0.0. The BMI is 35.9. The DiabetesPedigreeFunction is 1.13. The Age is 43.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 119.0. The BloodPressure is 86.0. The SkinThickness is 39.0. The Insulin is 220.0. The BMI is 45.6. The DiabetesPedigreeFunction is 0.81. The Age is 29.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 124.0. The BloodPressure is 70.0. The SkinThickness is 20.0. The Insulin is 0.0. The BMI is 27.4. The DiabetesPedigreeFunction is 0.25. The Age is 36.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 88.0. The BloodPressure is 58.0. The SkinThickness is 26.0. The Insulin is 16.0. The BMI is 28.4. The DiabetesPedigreeFunction is 0.77. The Age is 22.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 124.0. The BloodPressure is 56.0. The SkinThickness is 13.0. The Insulin is 105.0. The BMI is 21.8. The DiabetesPedigreeFunction is 0.45. The Age is 21.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 141.0. The BloodPressure is 0.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 30.0. The DiabetesPedigreeFunction is 0.76. The Age is 27.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 12.0. The Glucose is 121.0. The BloodPressure is 78.0. The SkinThickness is 17.0. The Insulin is 0.0. The BMI is 26.5. The DiabetesPedigreeFunction is 0.26. The Age is 62.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 138.0. The BloodPressure is 60.0. The SkinThickness is 35.0. The Insulin is 167.0. The BMI is 34.6. The DiabetesPedigreeFunction is 0.53. The Age is 21.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 90.0. The BloodPressure is 80.0. The SkinThickness is 14.0. The Insulin is 55.0. The BMI is 24.4. The DiabetesPedigreeFunction is 0.25. The Age is 24.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 177.0. The BloodPressure is 60.0. The SkinThickness is 29.0. The Insulin is 478.0. The BMI is 34.6. The DiabetesPedigreeFunction is 1.07. The Age is 21.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 100.0. The BloodPressure is 88.0. The SkinThickness is 60.0. The Insulin is 110.0. The BMI is 46.8. The DiabetesPedigreeFunction is 0.96. The Age is 31.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 89.0. The BloodPressure is 74.0. The SkinThickness is 16.0. The Insulin is 85.0. The BMI is 30.4. The DiabetesPedigreeFunction is 0.55. The Age is 38.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 189.0. The BloodPressure is 104.0. The SkinThickness is 25.0. The Insulin is 0.0. The BMI is 34.3. The DiabetesPedigreeFunction is 0.43. The Age is 41.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 86.0. The BloodPressure is 68.0. The SkinThickness is 32.0. The Insulin is 0.0. The BMI is 35.8. The DiabetesPedigreeFunction is 0.24. The Age is 25.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 4.0. The Glucose is 142.0. The BloodPressure is 86.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 44.0. The DiabetesPedigreeFunction is 0.65. The Age is 22.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 91.0. The BloodPressure is 82.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 35.6. The DiabetesPedigreeFunction is 0.59. The Age is 68.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 85.0. The BloodPressure is 55.0. The SkinThickness is 20.0. The Insulin is 0.0. The BMI is 24.4. The DiabetesPedigreeFunction is 0.14. The Age is 42.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 113.0. The BloodPressure is 44.0. The SkinThickness is 13.0. The Insulin is 0.0. The BMI is 22.4. The DiabetesPedigreeFunction is 0.14. The Age is 22.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 9.0. The Glucose is 72.0. The BloodPressure is 78.0. The SkinThickness is 25.0. The Insulin is 0.0. The BMI is 31.6. The DiabetesPedigreeFunction is 0.28. The Age is 38.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 90.0. The BloodPressure is 68.0. The SkinThickness is 42.0. The Insulin is 0.0. The BMI is 38.2. The DiabetesPedigreeFunction is 0.5. The Age is 27.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 9.0. The Glucose is 91.0. The BloodPressure is 68.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 24.2. The DiabetesPedigreeFunction is 0.2. The Age is 58.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 10.0. The Glucose is 68.0. The BloodPressure is 106.0. The SkinThickness is 23.0. The Insulin is 49.0. The BMI is 35.5. The DiabetesPedigreeFunction is 0.28. The Age is 47.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 108.0. The BloodPressure is 70.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 30.5. The DiabetesPedigreeFunction is 0.95. The Age is 33.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 129.0. The BloodPressure is 74.0. The SkinThickness is 26.0. The Insulin is 205.0. The BMI is 33.2. The DiabetesPedigreeFunction is 0.59. The Age is 25.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 3.0. The Glucose is 128.0. The BloodPressure is 78.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 21.1. The DiabetesPedigreeFunction is 0.27. The Age is 55.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 6.0. The Glucose is 166.0. The BloodPressure is 74.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 26.6. The DiabetesPedigreeFunction is 0.3. The Age is 66.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 104.0. The BloodPressure is 76.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 18.4. The DiabetesPedigreeFunction is 0.58. The Age is 27.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 128.0. The BloodPressure is 68.0. The SkinThickness is 19.0. The Insulin is 180.0. The BMI is 30.5. The DiabetesPedigreeFunction is 1.39. The Age is 25.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 11.0. The Glucose is 120.0. The BloodPressure is 80.0. The SkinThickness is 37.0. The Insulin is 150.0. The BMI is 42.3. The DiabetesPedigreeFunction is 0.79. The Age is 48.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 1.0. The Glucose is 91.0. The BloodPressure is 54.0. The SkinThickness is 25.0. The Insulin is 100.0. The BMI is 25.2. The DiabetesPedigreeFunction is 0.23. The Age is 23.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 0.0. The Glucose is 152.0. The BloodPressure is 82.0. The SkinThickness is 39.0. The Insulin is 272.0. The BMI is 41.5. The DiabetesPedigreeFunction is 0.27. The Age is 27.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 8.0. The Glucose is 100.0. The BloodPressure is 74.0. The SkinThickness is 40.0. The Insulin is 215.0. The BMI is 39.4. The DiabetesPedigreeFunction is 0.66. The Age is 43.0.", "label": "1.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 5.0. The Glucose is 139.0. The BloodPressure is 64.0. The SkinThickness is 35.0. The Insulin is 140.0. The BMI is 28.6. The DiabetesPedigreeFunction is 0.41. The Age is 26.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Pregnancies is 2.0. The Glucose is 84.0. The BloodPressure is 0.0. The SkinThickness is 0.0. The Insulin is 0.0. The BMI is 0.0. The DiabetesPedigreeFunction is 0.3. The Age is 21.0.", "label": "0.0", "dataset": "deependraverma13-diabetes-healthcare-comprehensive-dataset", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/deependraverma13-diabetes-healthcare-comprehensive-dataset/train.csv

@@ -0,0 +1,692 @@
+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
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classification/unipredict/desalegngeb-german-fintech-companies/metadata.json

@@ -0,0 +1,29 @@
+{
+  "dataset": "desalegngeb-german-fintech-companies",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Segment",
+  "label_values": [
+    "Payments",
+    "Asset Management",
+    "Other FinTechs",
+    "Financing"
+  ],
+  "num_labels": 4,
+  "train_samples": 879,
+  "test_samples": 99,
+  "train_label_distribution": {
+    "Financing": 232,
+    "Other FinTechs": 318,
+    "Payments": 171,
+    "Asset Management": 158
+  },
+  "test_label_distribution": {
+    "Financing": 26,
+    "Payments": 19,
+    "Other FinTechs": 36,
+    "Asset Management": 18
+  }
+}

classification/unipredict/desalegngeb-german-fintech-companies/test.csv

@@ -0,0 +1,100 @@
+ID,Name,Status,Original German,Founding year,Founder,Linkedin-Account Founder,Legal Name,Legal form,Street,Postal code,City,Country,Register Number/ Company ID/ LEI,Subsegment,Bank Cooperation,Homepage,E-Mail,Insolvency,Liquidation,Date of inactivity,Local court,Former name,Segment
+166,Indiegogo,1,0,2008.0,Danae Ringelmann; Slava Rubin; Eric Schell ,https://www.linkedin.com/in/danae/; https://www.linkedin.com/in/indieslava/; https://www.linkedin.com/in/whattheschell/,Indiegogo Inc.,Inc.,965 Mission Street,CA 94103,San Francisco,USA,,Reward-based Crowdfunding,0,https://www.indiegogo.com/,press@indiegogo.com,0,0,,0,,Financing
+876,Smarthotel,1,1,2015.0,Maximilian Waldmann; Frederic Haitz ,https://www.linkedin.com/in/waldmann/; https://www.linkedin.com/in/fhaitz/,Invisible Pay GmbH,GmbH,Ohlauer Straße 43,10999,Berlin,Germany,HRB 167658 B,Alternative Payment Methods,0,https://www.yoursmarthotel.com/,contact@yoursmarthotel.com,0,0,,Berlin (Charlottenburg),Conichi,Payments
+893,Wirecard,1,1,1999.0,Markus Braun,https://www.linkedin.com/in/markus-braun/,Wirecard AG,AG,Einsteinring 35,85609,Aschheim,Germany,HRB 169227,Alternative Payment Methods,0,https://www.wirecard.com/,contact@wirecard.com,1,0,2020-08-25 00:00:00,München,,Payments
+425,Fincite,1,1,2015.0,Ralf Ruben Heim,https://www.linkedin.com/in/ralfheim/,Fincite GmbH,GmbH,Franklinstr. 52,60486,Frankfurt am Main,Germany,HRB 111506,"Technology, IT and Infrastructure",1,https://www.fincite.de/,office@fincite.de,0,0,,Frankfurt am Main,,Other FinTechs
+619,Ownly,1,1,2015.0,Nicholas Ziegert,https://www.linkedin.com/in/dr-nicholas-ziegert-622960108/,W_Z FinTech GmbH,GmbH,Kampstraße 7,20357,Hamburg,Germany,HRB 137731 ,Personal Financial Management,0,https://www.ownly.de/,contact@ownly.de ,0,0,,Hamburg,,Asset Management
+33,green rocket,1,0,2013.0,Wolfgang Deutschmann; Peter Garber,https://www.linkedin.com/in/wolfgangdeutschmann/,GREEN ROCKET Deutschland GmbH,GmbH,Seeholzenstraße 2a,82166,Gräfeling,Germany,HRB 229313,Crowdinvesting,0,https://www.greenrocket.de/,deutschmann@greenrocket.com,0,0,,München,,Financing
+355,Haftpflicht Helden,1,1,,Florian Knörrich; Stefan Herbst; Jan Louis Schmidt,https://www.linkedin.com/in/florian-kn%C3%B6rrich-4909b44/; https://www.linkedin.com/in/stefan-herbst-66a557108/,Insurance Hero GmbH,GmbH,Heidenkampsweg 81,20097,Hamburg,Germany,HRB 139747,Insurance,0,https://haftpflichthelden.de/,help@helden.de,0,0,,Hamburg,,Other FinTechs
+838,Loyal,1,1,2019.0,UMT United Mobility Technology AG,,UMT United Mobility Technology AG,AG,Brienner Str. 7,80333,München,Germany,HRB 167884,Alternative Payment Methods,0,https://loyalapp.de/,loyal@umt.ag,0,0,,München,,Payments
+3,fundflow,1,1,2016.0,Joachim Kaune; Antonio Faralli,https://www.linkedin.com/in/joachim-kaune-26ab8846/; https://www.linkedin.com/in/antoniofaralli/,Fundflow GmbH,GmbH,Prenzlauer Allee 53,10405,Berlin,Germany,HRB 173662 B,Credit and Factoring,1,https://fundflow.de/,info@fundflow.de,0,0,,Berlin (Charlottenburg),,Financing
+653,Diversifikator,1,1,2016.0,Dirk Söhnholz,https://www.linkedin.com/in/dirksoehnholz/,Diversifikator GmbH,GmbH,Kiefernweg 1,61184,Karben,Germany,HRB 104667,Robo-Advice,0,https://diversifikator.com/de/,soehnholz@diversifikator.com,0,0,,Karben,,Asset Management
+627,Klimafonds,1,1,2012.0,Gerd Junker; Carmen Junker,https://www.linkedin.com/in/gerd-junker-5227026/; https://www.linkedin.com/in/carmen-junker-75b419227/,Grünes Geld GmbH,GmbH,Beineweg 18,63864,Glattbach,Germany,HRB 10196,Investment and Banking,1,https://www.klimafonds.de,info@klimafonds.de,0,0,,Glattbach,,Asset Management
+850,paydirekt,1,1,2014.0,,,paydirekt GmbH,GmbH,Stephanstr. 14-16,60313,Frankfurt am Main,Germany,HRB 99538,Alternative Payment Methods,1,https://www.paydirekt.de/,service@paydirekt.de,0,0,,Frankfurt am Main,,Payments
+737,Swiss options trading,1,0,2012.0,BDSwiss Group,,BDS Markets,,Niddastraße 58,60329,Frankfurt am Main,Mauritius,143350,Investment and Banking,1,http://www.swissoptionstrading.com,support@swissoptionstrading.com,0,0,,0,,Asset Management
+872,Sales King,1,1,2008.0,Georg Leciejewski; Ole Wiemeler,https://www.linkedin.com/in/georg-leciejewski-7767202/; https://www.linkedin.com/in/olewiemeler/,Sales King GmbH,GmbH,Vorgebirgsstrasse 18,50354,Hürth,Germany,HRB 44907,Other FinTechs,0,https://www.salesking.eu/,support@salesking.eu,0,0,,Köln,,Payments
+744,Airbank,1,1,2021.0,Christopher Zemina; Patrick de Castro Neuhaus,https://www.linkedin.com/in/christopherzemina; https://www.linkedin.com/in/patrick-de-castro-neuhaus-08a1091b9,Airlabs UG,UG,Chausseestraße 86,10115,Berlin ,Germany,HRB 226357 B,Alternative Payment Methods,0,https://www.joinairbank.com/de,,0,0,,Berlin (Charlottenburg),,Asset Management
+78,Captiq,1,1,2018.0,Soraya Braun; Lorenz Beimler,https://www.linkedin.com/in/soraya-braun-837852155/; https://www.linkedin.com/in/lorenz-beimler-3b1480149/,Captiq GmbH,GmbH,Neuer Mainzer Straße 66-68,60311,Frankfurt am Main,Germany,HRB 113460,Credit and Factoring,0,https://captiq.com,info@captiq.com,0,0,,Frankfurt am Main,,Financing
+640,olb,1,1,,,,Oldenburgische Landesbank AG,AG,Stau 15/17,26122,Oldenburg,Germany,HRB 3003,Robo-Advice,0,https://www.olb.de/landingpages/ww/depots,olb@olb.de,0,0,,Oldenburg,wüstenrot,Asset Management
+807,dban,0,1,2017.0,Michael Scholz,https://www.linkedin.com/in/michael-scholz-880521120/,ementexx GmbH,GmbH,Berner Straße 109,60437,Frankfurt am Main,Germany,HRB 110075,Alternative Payment Methods,0,,,0,0,,Frankfurt am Main,,Payments
+60,Bambus Immobilien,1,1,2018.0,Franz Hoerhager; Patrick Wollner,https://www.linkedin.com/in/franz-hoerhager/; https://www.linkedin.com/in/pwollner/,Bambus Immobilien GmbH,GmbH,Luise-Ulrich-Straße 20,80636,München,Germany,HRB 201351 B,Credit and Factoring,1,https://www.bambus.io,beratung@bambus.io,0,0,,Berlin (Charlottenburg),,Financing
+908,Koala,1,1,2019.0,Christoph Schönfelder; David Scharfschwerdt,https://www.linkedin.com/in/christoph-sch%C3%B6nfelder-74167659/; https://www.linkedin.com/in/david-scharfschwerdt-239591197/,Koala AG,AG,Klostersande 4a,25336,Elmshorn,Germany,HRB 14851,Alternative Payment Methods,0,https://www.koalaapp.de/,info@koalaapp.de,0,0,,Elmshorn,,Payments
+12,aescuvest,1,1,2014.0,Patrick Pfeffer,https://www.linkedin.com/in/patrickpfeffer/,aescuvest GmbH,GmbH,Hanauer Landstr. 328-330,60314,Frankfurt am Main,Germany,HRB 100439,Crowdinvesting,0,https://www.aescuvest.de,kontakt@aescuvest.de,0,0,,Frankfurt am Main,,Financing
+914,UnitedCrowd,1,1,2016.0,Michael Göymen; André Wendt,https://www.linkedin.com/in/michaelgoeymen/; https://www.linkedin.com/in/andrewendt/,UnitedCrowd GmbH,GmbH,Venloerstr. 5 – 7,50672,Köln,Germany,HRB 98686,Blockchain and Cryptocurrencies,0,https://unitedcrowd.com,gmbh@unitedcrowd.com,0,0,,Köln,,Payments
+535,xware42,0,0,2011.0,David Lais,https://www.linkedin.com/in/paymenthippie/,xWare42 GmbH,GmbH,Stefan-George-Ring 2,81929,München,Germany,HRB 194682,"Technology, IT and Infrastructure",0,https://www.xware42.com/,,0,0,,München,,Other FinTechs
+638,Moneymeets,1,1,2012.0,Johannes Cremer; Dieter Fromm,https://www.linkedin.com/in/johannescremer/; https://www.linkedin.com/in/dieter-fromm/,moneymeets Portfolio Management GmbH/moneymeets community GmbH,GmbH,Im Zollhafen 22,50678,Köln,Germany,HRB 76112,Investment and Banking,1,https://www.moneymeets.com/,corporate@moneymeets.com ,0,0,,Köln,,Asset Management
+37,InvestoFolio,0,1,2015.0,Johannes Hillmann; Ralf Schepers; Ralf Köhler,,DikoBa Financing & Consulting GmbH,GmbH,Sontraer Str. 17,60386,Frankfurt am Main,Germany,HRB 101783,Crowdlending,0,https://www.investofolio.de/,ap@investofolio.de,0,0,,Königstein im Taunus,immo-folio,Financing
+407,cybits,0,1,2011.0,,,Cybits Holding AG,AG,Peter-Sander-Straße 41 a,55252,Mainz,Germany,HRB 25974,"Technology, IT and Infrastructure",0,,,1,0,2017-05-15 00:00:00,Mainz-Kastel,,Other FinTechs
+588,moneyfarm,1,0,2016.0,Giovanni Daprà; Paolo Galvani,https://www.linkedin.com/in/daprgio/; https://www.linkedin.com/in/paolo-galvani-b09b23/,MFM Investment GmbH,GmbH,Mainzer Landstraße 250-254,60326,Frankfurt am Main,Germany,HRB 105192,Robo-Advice,1,https://www.moneyfarm.com/de/,service@moneyfarm.de,0,0,,Frankfurt am Main,Vaamo,Asset Management
+436,Fyber,1,0,2009.0,Janis Zech; Jan Beckers; Andreas Bodczek,https://www.linkedin.com/in/janiszech/; https://www.linkedin.com/in/janbeckers/; https://www.linkedin.com/in/andreasbodczek/,Fyber N.V.,N.V.,Wallstraße 9-13,10179,Berlin,Germany,HRB 166541,"Technology, IT and Infrastructure",0,https://www.fyber.com/,info@fyber.com,0,0,,Berlin (Charlottenburg),,Other FinTechs
+593,VisualVest,1,1,2015.0,Olaf Zeitnitz,https://www.linkedin.com/in/dr-olaf-zeitnitz-722b0a1b/,VisualVest GmbH,GmbH,Mainzer Landstraße 50,60325,Frankfurt am Main,Germany,HRB 101346,Robo-Advice,1,https://www.visualvest.de/,kontakt@kundenservice.visualvest.de,0,0,,Frankfurt am Main,,Asset Management
+241,TraFinScout,1,1,2019.0,Eckhard Creutzburg; Christian Etzel; Joachim Reinhardt; Michael Vander,https://www.linkedin.com/in/eckhard-creutzburg-108913128/; https://www.linkedin.com/in/christian-etzel-35193412/; https://www.linkedin.com/in/joachim-reinhardt-4640151/; https://www.linkedin.com/in/michaelvander/,TraFinScout GmbH,GmbH,Solmsstr. 4,60486,Frankfurt am Main,Germany,HRB 114419,Credit and Factoring,1,https://trafinscout.com/,info@trafinscout.com,0,0,,Frankfurt am Main,,Financing
+134,Deutsche Mikroinvest,0,1,2012.0,Carsten Bischof; Knut Haake,https://www.linkedin.com/in/knut-h-a321b5167/,DMI Deutsche Mikroinvest GmbH,GmbH,Am Birkengraben 14,50259,Pulheim,Germany,HRB 75246,Crowdinvesting,0,,,0,0,2019-03-01 00:00:00,Köln,,Financing
+895,Worldremit,1,0,2010.0,Ismail Ahmed,https://www.linkedin.com/in/ismail-ahmed-worldremit/,WorldRemit Ltd.,Ltd.,62 Buckingham Gate,SW1E 6AJ,London,England,213800BO4SXT9R8PHV43,Alternative Payment Methods,0,https://www.worldremit.com/de,CustomerService@WorldRemit.com,0,0,,0,,Payments
+311,wertgarantie,1,1,2013.0,Sebastian Grötsch,https://www.linkedin.com/in/sebastiangroetsch/,WERTGARANTIE SE,SE,Breite Straße 6,30159,Hannover,Germany,HR B 208988,Insurance,0,https://www.wertgarantie.de/,kunde@wertgarantie.com,0,0,,Hannover,sofortschutz; Traumschutz,Other FinTechs
+308,element,1,1,2016.0,Henning Groß; Inna Leontenkova,https://www.linkedin.com/in/denkgross/; https://www.linkedin.com/in/leontenkova/,ELEMENT Insurance AG,AG,Hardenbergstraße 32,10623,Berlin,Germany,HRB 182671 B,Insurance,0,https://www.element.in/de/,info@element.in,0,0,,Berlin (Charlottenburg),,Other FinTechs
+447,Kobil,1,1,1986.0,Ismet Koyun,https://www.linkedin.com/in/ismet-koyun-69b9a136/,KOBIL Systems GmbH,GmbH,Pfortenring 11,67547,Worms,Germany,HRB 10856,"Technology, IT and Infrastructure",0,https://www.kobil.com/,marketing@kobil.com,0,0,,Mainz,,Other FinTechs
+795,cflox,1,1,2013.0,Philipp Tillmanns; Christoph Kaup; Thomas Krings,https://www.linkedin.com/in/philipp-tillmanns-a594205/; https://www.linkedin.com/in/thomas-krings-ab6a4398/,cflox GmbH,GmbH,Große Brunnenstraße 122,22763,Hamburg,Germany,HRB 127858,Other FinTechs,0,https://cflox.com/,info@cflox.com,0,0,,Hamburg,,Payments
+571,Paylobby,1,1,2016.0,Julia Houben; Peter Petridis; Tommy Djoumessy,https://www.linkedin.com/in/julia-houben-315b4442/; https://www.linkedin.com/in/peter-petridis-746359109/,Paylobby GmbH,GmbH,Hirschgartenallee,80639,München,Germany,HRB 229274,Search Engines and Comparison Sites,0,http://www.paylobby.com/en,info@paylobby.com,0,0,,München,,Other FinTechs
+561,Monite,1,1,2020.0,Andrey Korchak,https://www.linkedin.com/in/a-korchak/,Monite GmbH,GmbH,Friedrichstrasse 68,10117,Berlin,Germany,HRB 221153 B,"Technology, IT and Infrastructure",0,https://monite.com/,hello@monite.com,0,0,,Berlin (Charlottenburg),Gemms,Other FinTechs
+385,awamo,1,1,2015.0,Benedikt Kramer; Roland Claussen; Philipp Neub,https://www.linkedin.com/in/benediktkramer/; https://www.linkedin.com/in/rclaussen/; https://www.linkedin.com/in/philippneub/,awamo GmbH,GmbH,Kaiserstraße 61,60329,Frankfurt am Main,Germany,HRB 102394,"Technology, IT and Infrastructure",0,https://awamo.com/,info@awamo.com,0,0,,Frankfurt am Main,,Other FinTechs
+57,DKB Crowdfunding,1,1,2019.0,,,DKB Crowdfunding GmbH,GmbH,Baseler-Str. 10,60329,Frankfurt am Main,Germany,HRB 115401,Crowdfunding,1,https://www.dkb-crowdfunding.de/,kontakt@dkb-crowd.de,0,0,,Frankfurt am Main,,Financing
+299,Palturai,1,1,2014.0,Tilo Walter; Petra Kaul; Thorsten Lau; Hans-Dieter Greb,https://www.linkedin.com/in/tilo-walter-18b90567/; https://www.linkedin.com/in/petra-kaul-122ba9a6/,Palturai GmbH,GmbH,Reifenberger Straße 1,65719,Hogheim,Germany,HRB 99412,"Technology, IT and Infrastructure",0,https://palturai.com/,info@palturai.com,0,0,,Hogheim,,Other FinTechs
+705,flowpilot.io,1,1,2018.0,Bernd Thöne; Sophie Schwalbe,https://www.linkedin.com/in/bernd-th%C3%B6ne/; https://www.linkedin.com/in/sophieschwalbe/,flowpilot UG (haftungsbeschränkt),UG,Friedrichstraße 68,10117,Berlin,Germany,HRB 196599,Investment and Banking,0,https://flowpilot.io/,info@flowpilot.io,0,0,,Berlin (Charlottenburg),,Asset Management
+197,Rhein-Main Crowdfunding,1,1,2014.0,Forum Kiedrich GmbH,,Forum Kiedrich GmbH,GmbH,Biebricher Allee 22,65187,Wiesbaden,Germany,HRB 13012,Crowdinvesting,0,http://www.rm-crowdfunding.de/,crowdfunding@forum-kiedrich.de ,0,0,,Wiesbaden,,Financing
+558,smartlutions,1,1,2015.0,Michael Reusch,https://www.linkedin.com/in/smartlutions/,smartlutions GmbH,GmbH,Robert-Bosch-Str. 2a,50354,Hürth,Germany,HRB 84051,Other FinTechs,0,https://smartlutions.net/,info@smartlutions.net,0,0,,Köln,,Other FinTechs
+466,Mynigma,0,1,2013.0,Gaurav Singh; Lukas Neumann; Roman Priebe,https://www.linkedin.com/in/mrgauravsingh/; https://www.linkedin.com/in/lksnmnn/; https://de.linkedin.com/in/romanpriebe,Mynigma UG,UG,Luisenplatz 3 ,10585,Berlin,Germany,HRB 151727 B,"Technology, IT and Infrastructure",0,https://www.mynigma.org/,info@mynigma.org ,0,1,2018-01-17 00:00:00,Berlin (Charlottenburg),,Other FinTechs
+683,simplefinance,0,1,2014.0,Fabian Schwietal,https://www.linkedin.com/in/fabian-schwietal-68112866/,Simple Finance GmbH,GmbH,Wilhelmstr. 30,80801,München,Germany,HRB 217801,Robo-Advice,0,,,0,1,2019-09-05 00:00:00,München,,Asset Management
+500,sevDesk,1,1,2013.0,Fabian Silberer; Marco Reinbold,https://www.linkedin.com/in/fabian-silberer-8129568/; https://www.linkedin.com/in/marco-reinbold-545317138/,sevDesk GmbH,GmbH,Hauptstraße 115,77652,Offenburg,Germany,HRB 710506,Other FinTechs,0,https://sevdesk.de/,support@sevdesk.com,0,0,,Offenburg,,Other FinTechs
+977,XPAY,1,1,2016.0,Denis Raskopoljac,https://www.linkedin.com/in/denis-raskopoljac/,XPAY Solutions GmbH,GmbH,Stuntzstraße 16,81677,München,Germany,HRB 225853,Alternative Payment Methods,0,https://www.xpay.de/,info@xpay.de,0,0,,München,,Payments
+445,IPO.GO,0,1,2005.0,,,IPO.GO AG,AG,Im Bildösch 17,78476,Allensbach,Germany,HRB 705817,Other FinTechs,0,http://www.ipogo.de/,info@ipogo.de ,0,1,31.12.2018,Allensbach,,Other FinTechs
+130,Crowdstein,0,1,2014.0,,,Crowdstein UG,UG,Nordheimstraße 3,22309,Hamburg,Germany,HRB 131016 ,Crowdinvesting,0,,,0,1,2015-10-05 00:00:00,Hamburg,,Financing
+270,Ico-Lux,1,1,2018.0,Jan Franke; Stefan Brechtken; Lars Winterfeld,https://www.linkedin.com/in/jan-franke-66306b202/; https://www.linkedin.com/in/lars-winterfeld-0781a71a4/,ICO-LUX GmbH,GmbH,Hans-Knöll-Str. 6,07745,Jena,Germany,HRB 514767,"Technology, IT and Infrastructure",0,https://ico-lux.de/,info@ico-lux.de,0,0,,Jena,,Other FinTechs
+609,PayCenter,1,1,2011.0,Ludwig Adam,https://www.linkedin.com/in/ludwigadam/,PayCenter GmbH,GmbH,Max-Lehner-Str. 1a,85354,Freising,Germany,HRB 194018,Investment and Banking,0,https://www.paycenter.de/,info@PayCenter.de,0,0,,München,,Asset Management
+329,Hallokredit.com,0,1,2015.0,,,InnoWeb GmbH,GmbH,Limburgerpark 2,04279,Leipzig,Germany,HRB 32403,Search Engines and Comparison Sites,0,https://www.hallokredit.com/,info@hallokredit.de,0,0,,Leipzig,,Other FinTechs
+233,Planethome Investment,1,1,2019.0,Frank W. Kewitz; Philip Moffat,https://www.linkedin.com/in/frank-w-kewitz-1828619b/,Planethome Investment AG,AG,Uhlandstraße 175,10719,Berlin,Germany,HRB 239061 B,Crowdlending,1,https://www.planethome-invest.com/de/,info@planethome-invest.com,0,0,,Berlin (Charlottenburg),,Financing
+172,KAM on!,1,1,2014.0,Alex Jan Avedikjan; Kristina Kamper,https://www.linkedin.com/in/dr-alex-jan-avedikjan-699917135/; https://www.linkedin.com/in/kristina-kamper-05b3b378/,KAM on! GmbH,GmbH,Bernhard-Wicki-Straße 3,80636,München,Germany,HRB 237423,Reward-based Crowdfunding,0,https://kam-on.de/,info@kam-on.de,0,0,,München,Monaco Funding,Financing
+819,gastrofix,1,1,2011.0,Reinhard Martens,https://www.linkedin.com/in/reinhardmartens/,Gastrofix GmbH,GmbH,Alex-Wedding-Str. 7,10178,Berlin,Germany,HRB 138363 B,Other FinTechs,0,https://www.gastrofix.com/de/,sales@gastrofix.com,0,0,,Berlin (Charlottenburg),,Payments
+204,Socialfunders,1,1,2012.0,Stefan Funk; Stefan Pandorf; Stephanie Henn,https://www.linkedin.com/in/stefan-pandorf/; https://www.linkedin.com/in/stephanie-henn-57730970/,Particulate Solutions GmbH,GmbH,Universitätsstr. 3,56070,Koblenz,Germany,HRB 23180,Donation-based Crowdfunding,0,https://www.socialfunders.org/,info@socialfunders.org,0,0,,Koblenz,,Financing
+10,SEEDRS,1,0,2012.0,Carlos Silva; Jeff Lynn,https://www.linkedin.com/in/cmsilva; https://www.linkedin.com/in/jefflynn/,Seedrs Ltd.,Ltd.,"Churchill House, 142-146 Old Street",EC1V 9BW,London,England,6848016,Crowdinvesting,0,https://www.seedrs.com/,support@seedrs.com,0,0,,0,,Financing
+295,Flexperto,1,1,2012.0,Felix Anthonj; Tobias Krauß,https://www.linkedin.com/in/felix-anthonj-6066a440/; https://www.linkedin.com/in/tobias-krauss/,Flexperto GmbH,GmbH,Neue Grünstraße 27,10179,Verlin,Germany,HRB 165181,"Technology, IT and Infrastructure",0,https://flexperto.com/,info@flexperto.com,0,0,,Verlin,,Other FinTechs
+323,adiume,1,1,2016.0,Lennart Schulze; Mahir Arslan,https://www.linkedin.com/in/lennart-schulze/; https://www.linkedin.com/in/mahir-a-847257120/,Adiume GbR,GbR,Schreinerstr. 30,10247,Berlin,Germany,,Other FinTechs,0,https://adiume.com/,contact@adiume.com,0,0,2016-02-29 00:00:00,Berlin (Charlottenburg),,Other FinTechs
+881,Stripe,1,0,2011.0,Patrick Collison; John Collison,https://www.linkedin.com/in/patrickcollison/; https://www.linkedin.com/in/johnbcollison/,Stripe Inc.,Inc.,510 Townsend Street,CA 94103,San Francisco,USA,4675506,Other FinTechs,0,https://stripe.com/de,support@stripe.com,0,0,,0,,Payments
+446,ivitec,1,1,2007.0,,,ivitec GmbH ,GmbH,Lange Reihe 29,20099,Hamburg,Germany,HRB 141680,Other FinTechs,0,https://ivitec.com/index.html,info@ivitec.com,0,0,,Hamburg,iPharro Media,Other FinTechs
+678,rethink finance,0,1,2013.0,Alexander Decker; Bernhard Flohr; Gibran Watfe,https://www.linkedin.com/in/alexander-decker-2b394654/; https://www.linkedin.com/in/gibranwatfe/,rethink finance UG,UG,Mommsenstraße 22,10629,Berlin,Germany,HRB 150750 B ,Personal Financial Management,0,,,0,1,2016-04-13 00:00:00,Berlin (Charlottenburg),,Asset Management
+709,True wealth,1,0,2013.0,Oliver Herren; Felix Niederer,https://www.linkedin.com/in/oliver-herren-60a6704a/; https://www.linkedin.com/in/niederer/,True Wealth AG,AG,Grubenstrasse 18,8045,Zürich,Switzerland,CHE-489.219.513,Investment and Banking,1,https://www.truewealth.ch/de,info@truewealth.ch,0,0,,0,,Asset Management
+46,transvendo,1,1,2016.0,Sven Kirchberg; Cirino Marino,https://www.linkedin.com/in/sven-kirchberg-65a78b132/; https://www.linkedin.com/in/cirino-marino-2499a37a/,Transvendo GmbH & Co. KG,GmbH & Co. KG,Würmstraße 55,82166,München,Germany,HRA 105939,Crowdinvesting,0,http://www.transvendo.investments/,kontakt@transvendo.de,0,0,,München,,Financing
+856,paymill,0,1,2012.0,Mark Henkel; Stefan Sambol,https://www.linkedin.com/in/mahenkel/; https://www.linkedin.com/in/dsambol/,PAYMILL GmbH,GmbH,St.-Martin-Straße 63,81669,München,Germany,HRB 226526,Alternative Payment Methods,0,https://www.paymill.com/de/,support@paymill.com,0,1,,München,Klik&Pay,Payments
+707,Trade Republic,1,1,2015.0,Christian Hecker; Thomas Pischke; Marco Cancellieri,https://www.linkedin.com/in/christianhe/; https://www.linkedin.com/in/tpischke/; https://www.linkedin.com/in/marco-cancellieri-5a48b2a2/,Trade Republic Bank GmbH,GmbH,Kastanienallee 32,10435,Berlin,Germany,HRB 85864,Investment and Banking,1,https://traderepublic.com/de-de,service@traderepublic.com,0,0,,Düsseldorf,,Asset Management
+27,Econeers,1,1,2013.0,Jens Uwe Sauer,https://www.linkedin.com/in/juwes,OneCrowd Loans GmbH,GmbH,Käthe-Kollwitz-Ufer 79,01309,Dresden,Germany,HRB 27674,Crowdinvesting,0,https://www.econeers.de/,info@econeers.de,0,0,,Dresden,,Financing
+443,Insurgram,0,1,2015.0,Antonia Ermacora; Matthias Nannt,https://www.linkedin.com/in/antoniaermacora/; https://www.linkedin.com/in/matthiasnannt/,chatShopper GmbH,GmbH,Rheinsberger Straße 76/77,10115,Berlin,Germany,HRB 172753 B,Insurance,0,,,0,1,2016/2017,Berlin (Charlottenburg),ChatDichSicher,Other FinTechs
+755,ratepay,1,1,2009.0,Miriam Wohlfarth,https://www.linkedin.com/in/miriam-wohlfarth/,RatePAY GmbH,GmbH,Franklinstraße 28-29,10587,Berlin,Germany,HRB 124156B,Alternative Payment Methods,1,https://www.ratepay.com/,info@ratepay.com,0,0,,Berlin (Charlottenburg),,Payments
+870,Ripple,1,0,2012.0,Ryan Fugger; Chris Larsen; Jed McCaleb,https://www.linkedin.com/in/larsen-chris/; https://www.linkedin.com/in/jed-mccaleb-4052a4/,Ripple Labs Inc. ,Inc.,300 Montgomery St,CA 94104,San Francisco,USA,,Blockchain and Cryptocurrencies,1,https://ripple.com/,,0,0,,0,,Payments
+697,zinspilot,1,1,2011.0,Tim Sievers,https://www.linkedin.com/in/tim-sievers/,Deposit Solutions GmbH,GmbH,Drehbahn 7-11,20354,Hamburg,Germany,HRB 118186,Investment and Banking,1,https://www.zinspilot.de/de/start/,service@zinspilot.de,0,0,,Hamburg,,Asset Management
+240,StartMark,1,1,2019.0,"Guido Stefan, Frank Schmidt",https://www.linkedin.com/in/guido-stefan-p-3b9850182/; https://www.linkedin.com/in/frank-schmidt-0832aa86/,StartMark GmbH,GmbH,Heinrichstr. 155,40239,Düsseldorf,Germany,HRB 85090,Crowdinvesting,0,https://www.startmark.de/,info@startmark.de,0,0,,Düsseldorf,,Financing
+300,360T,1,1,2000.0,Carlo Kölzer,,360 Treasury Systems AG,AG,Grüneburgweg 16-18,60322,Frankfurt am Main,Germany,HRB 49874,"Technology, IT and Infrastructure",0,https://www.360t.com/,info@360t.com,0,0,,Frankfurt am Main,,Other FinTechs
+141,Entrafin,1,1,2015.0,Christoph Bauer; Stefan Fenner,https://www.linkedin.com/in/christoph-bauer-22764a1b5/; https://www.linkedin.com/in/dr-stefan-fenner-7a99701a7/,entrafin GmbH,GmbH,Kölner Str. 30,50859,Köln,Germany,HRB 93445,Credit and Factoring,1,https://www.entrafin.de/,christoph.bauer@entrafin.de,0,0,,Köln,,Financing
+304,solarisBank,1,1,2016.0,Peter Grosskopf; Andreas Bittner,https://www.linkedin.com/in/petergrosskopf/; https://www.linkedin.com/in/andreas-bittner-67775645/,solarisBank AG,AG,Anna-Louisa-Karsch-Straße 2,10178,Berlin,Germany,HRB 168180 B,"Technology, IT and Infrastructure",0,https://www.solarisbank.com/de/,support@solarisbank.de ,0,0,,Berlin (Charlottenburg),,Other FinTechs
+884,Tabbt,1,1,2014.0,Jan Michaelis; Lucas Romero,https://www.linkedin.com/in/jan-michaelis-9b5695b5/,Tabbt GmbH,GmbH,Waterloohain 5,22769,Hamburg,Germany,HRB 143301,Alternative Payment Methods,0,https://www.tabbt.com/,info@tabbt.com,0,0,,Hamburg,,Payments
+411,picsure,0,1,2017.0,Enrico Bolloni; Ole Roel; Florian Bischof,https://www.linkedin.com/in/enrico-bolloni/; https://www.linkedin.com/in/oleroel/; https://www.linkedin.com/in/florian-bischof/,Picsure GmbH,GmbH,Atelierstr. 29,81671,München,Germany,HRB 235272,Insurance,0,https://picsure.ai/,,0,1,2017-12-29 00:00:00,München,dibis,Other FinTechs
+279,Neodigital,1,1,2017.0,Dirk Wittling; Stephen Voss,https://www.linkedin.com/in/dirk-wittling-711177165/; https://www.linkedin.com/in/stephen-voss-712b107/,Neodigital Versicherung AG,AG,Untere Bliesstr. 13-15,66538,Neunkirchen,Germany,HRB 103769,Insurance,1,https://neodigital.de/, info@neodigital.de,0,0,,Neunkirchen,,Other FinTechs
+542,bendesk,1,1,2019.0,,,tridion benefits GmbH,GmbH,Rurstr. 42,50935,Köln,Germany,HRB 68751,Other FinTechs,0,https://www.bendesk.de/,info@bendesk.de,0,0,,Köln,,Other FinTechs
+955,Smartificate,1,1,2021.0,Lenz Zuber; Yannic Neubauer,https://www.linkedin.com/in/lenz-zuber/; https://www.linkedin.com/in/yannic-neubauer/,smartificate GmbH,GmbH,Lasbeker Str. 9,22967,Tremsbüttel,Germany,HRB 21788 HL,Other FinTechs,0,https://smartificate.de/,presse@smartificate.de,0,0,,Lübeck,,Other FinTechs
+261,Insurninja,1,1,2018.0,Tim Schlawinsky,https://www.linkedin.com/in/timheckhausen/,insurninja GmbH,GmbH,Breite Str. 6-8,30159,Hannover,Germany,HRB 85922,Insurance,0,https://insurninja.com/,dojo@insurninja.com,0,0,,Hannover,,Other FinTechs
+151,flexpayment,0,1,2011.0,Aimé Ndaysiaba; Cemil Arslan,https://www.linkedin.com/in/aim%C3%A9-ndayisaba-9ba8b44/; https://www.linkedin.com/in/cemil-arslan-114722131/,FLEX Financial Solutions GmbH,GmbH,Erste Brunnenstr. 12,20459,Hamburg,Germany,HRB 118283,Credit and Factoring,1,https://www.flexpayment.de/,info@flexpayment.de,0,0,,Hamburg,,Financing
+256,Fintiba,1,1,2016.0,Christian Becker,https://www.linkedin.com/in/cbecker93/,Fintiba GmbH,GmbH,Baseler Straße 35-37,60329,Frankfurt am Main,Germany,HRB 106751,Insurance,0,https://www.fintiba.com/,info@fintiba.com,0,0,,Frankfurt am Main,,Other FinTechs
+31,Geldwerk1,1,1,2015.0,Ralf Beck,https://www.linkedin.com/in/ralf-beck-6603259b/,Geldwerk1 GmbH,GmbH,An der Palmweide 55,44227,Dortmund,Germany,HRB 27442,Crowdinvesting,0,https://www.geldwerk1.de/,service@geldwerk1.de,0,0,,Dortmund,,Financing
+545,cyberversicherungssumme,1,1,2018.0,Nikolaus Stapels,https://www.linkedin.com/in/nikolaus-stapels-0251ba121/,Vertriebssoftware24 GmbH,GmbH,Timmerbarg 5,23795,Klein Rönnau,Germany,HRB 19677 KI,Insurance,0,https://cyberversicherungssumme.de/index.html,,0,0,,Kiel,,Other FinTechs
+221,vaidoo,1,1,,,,,,Dolberger Straße 59,59229,Ahlen,Germany,,Credit and Factoring,0,https://vaidoo.de/,info@vaidoo.de,0,0,,0,,Financing
+752,epay,1,1,,,,transact Elektronische Zahlungssysteme GmbH,GmbH,Fraunhoferstr. 10,82152,Martinsried ,Germany,HRB 114 439,Alternative Payment Methods,0,https://epay.de/de/,info@epay.de,0,0,,München,,Payments
+338,safe.me,1,1,2015.0,Michael Stock,https://www.linkedin.com/in/michael-stock-b5938234/,safe.me GmbH,GmbH,Minderheideweg 63,32425,Minden,Germany,HRB 14791,Insurance,0,https://safe.me/,service@safe.me,0,0,,Bad Oeynhausen,,Other FinTechs
+886,Tradeshift,1,0,2010.0,Christian Lanng; Gert Sylvest; Mikkel Brun,https://www.linkedin.com/in/christianlanng/; https://www.linkedin.com/in/gert-sylvest-a2720b/; https://www.linkedin.com/in/hippe/,Tradeshift Inc.,Inc.,612 Howard Street,CA 94105,San Francisco,USA,,Other FinTechs,1,https://tradeshift.com/de,,0,0,,0,,Payments
+131,CrowdTrader,0,1,2015.0,,,CrowdTrader GmbH,GmbH,Pfatthaagäcker 5,88048,Friedrichshafen,Germany,HRB 731868,Crowdinvesting,0,,,0,1,2018-07-23 00:00:00,Ulm,,Financing
+255,FinList,1,1,2021.0,Sandra Olschewski; Florian Hollm,https://www.linkedin.com/in/sandra-olschewski-29686a8a/; https://www.linkedin.com/in/florianhollm/,FinList GmbH,GmbH,Wilhelm-Külz-Straße 25,16540,Hohen Neuendorf,Germany,HRB 13335 NP,Search Engines and Comparison Sites,0,https://www.finlist.de/,https://www.finlist.de/contact,0,0,,Neuruppin,,Financing
+556,riskdatascience,1,1,2017.0,Dr. Dimitrios Geromichalos,,RiskDataScience GmbH,GmbH,Nördliche Münchner Straße 47,82031,Grünwald,Germany,HRB 232912,Other FinTechs,1,http://riskdatascience.net/,riskdatascience@web.de,0,0,,München,,Other FinTechs
+140,Ecocrowd,1,1,2014.0,Jörg Sommer,https://www.linkedin.com/in/joerg-sommer,Deutsche Umweltstiftung,Stiftung,Greifswalder Straße 4,10405,Berlin,Germany,,Reward-based Crowdfunding,0,https://www.ecocrowd.de/,team@ecocrowd.de,0,0,,0,,Financing
+534,WhoFinance,1,1,2007.0,Klaus-Jürgen Baum; Mustafa Behan,https://www.linkedin.com/in/klaus-j%C3%BCrgen-baum-489411146/; https://www.linkedin.com/in/mustafa-behan-ab920a39/,WhoFinance GmbH,GmbH,Teerofendamm 1,14532,Kleinmachnow ,Germany,HRB 110212B ,Other FinTechs,0,https://www.whofinance.de/,bjoern.pommeranz@whofinance.de,0,0,,Kleinmachnow,,Other FinTechs
+515,taxbutler,0,1,2014.0,Matthias Raisch,https://www.linkedin.com/in/matthias-raisch-4556188a/,pareton GmbH,GmbH,Reitschulstraße 18,74379,Ingersheim,Germany,HRB 749615,Other FinTechs,0,,,1,0,,Ingersheim,pareton,Other FinTechs
+669,moneygarden,1,1,2011.0,Thomas Kirst; Kosta Kampouridis,https://www.linkedin.com/in/thomaskirst/,moneygarden Thomas Kirst,personally liable,Barlowstr. 36,81927 ,München,Germany,,Personal Financial Management,0,https://moneygarden.de/,info@moneygarden.de,0,0,,0,,Asset Management
+672,novofina,0,0,2014.0,Harald Helnwein,https://www.linkedin.com/in/novofina,Ing. Harald Helnwein,personally liable,Baumgasse 42,1030,Wien,Austria,,Robo-Advice,0,,info@HelnweinTrading.com,0,0,,0,,Asset Management
+480,payfit,1,0,2015.0,Firmin Zocchetto; Ghislain de Fontenay; Florian Fournier ,https://www.linkedin.com/in/firmin-zocchetto/; https://www.linkedin.com/in/ghislain-de-fontenay-458297b8/; https://www.linkedin.com/in/florian-fournier-b093a2aa/,PayFit GmbH ,GmbH,Ohlauer Straße 43 ,10999,Berlin,Germany,HRB 198627B,Other FinTechs,0,https://payfit.com/de/,kontakt@payfit.com,0,0,,Berlin (Charlottenburg),,Other FinTechs

classification/unipredict/desalegngeb-german-fintech-companies/test.jsonl

@@ -0,0 +1,99 @@
+{"text": "The ID is 166. The Name is Indiegogo. The Status is 1. The Original German is 0.0. The Founding year is 2008.0. The Founder is Danae Ringelmann; Slava Rubin; Eric Schell. The Linkedin-Account Founder is https://www.linkedin.com/in/danae/; https://www.linkedin.com/in/indieslava/; https://www.linkedin.com/in/whattheschell/. The Legal Name is Indiegogo Inc. The Legal form is Inc. The Street is 965 Mission Street. The Postal code is CA 94103. The City is San Francisco. The Country is USA. The Register Number/ Company ID/ LEI is unknown. The Subsegment is Reward-based Crowdfunding. The Bank Cooperation is 0. The Homepage is https://www.indiegogo.com/. The E-Mail is press@indiegogo.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 876. The Name is Smarthotel. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Maximilian Waldmann; Frederic Haitz. The Linkedin-Account Founder is https://www.linkedin.com/in/waldmann/; https://www.linkedin.com/in/fhaitz/. The Legal Name is Invisible Pay GmbH. The Legal form is GmbH. The Street is Ohlauer Straße 43. The Postal code is 10999. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 167658 B. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://www.yoursmarthotel.com/. The E-Mail is contact@yoursmarthotel.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is Conichi.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 893. The Name is Wirecard. The Status is 1. The Original German is 1.0. The Founding year is 1999.0. The Founder is Markus Braun. The Linkedin-Account Founder is https://www.linkedin.com/in/markus-braun/. The Legal Name is Wirecard AG. The Legal form is AG. The Street is Einsteinring 35. The Postal code is 85609. The City is Aschheim. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 169227. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://www.wirecard.com/. The E-Mail is contact@wirecard.com. The Insolvency is 1. The Liquidation is 0. The Date of inactivity is 2020-08-25 00:00:00. The Local court is München. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 425. The Name is Fincite. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Ralf Ruben Heim. The Linkedin-Account Founder is https://www.linkedin.com/in/ralfheim/. The Legal Name is Fincite GmbH. The Legal form is GmbH. The Street is Franklinstr. 52. The Postal code is 60486. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 111506. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 1. The Homepage is https://www.fincite.de/. The E-Mail is office@fincite.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 619. The Name is Ownly. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Nicholas Ziegert. The Linkedin-Account Founder is https://www.linkedin.com/in/dr-nicholas-ziegert-622960108/. The Legal Name is W_Z FinTech GmbH. The Legal form is GmbH. The Street is Kampstraße 7. The Postal code is 20357. The City is Hamburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 137731. The Subsegment is Personal Financial Management. The Bank Cooperation is 0. The Homepage is https://www.ownly.de/. The E-Mail is contact@ownly.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hamburg. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 33. The Name is green rocket. The Status is 1. The Original German is 0.0. The Founding year is 2013.0. The Founder is Wolfgang Deutschmann; Peter Garber. The Linkedin-Account Founder is https://www.linkedin.com/in/wolfgangdeutschmann/. The Legal Name is GREEN ROCKET Deutschland GmbH. The Legal form is GmbH. The Street is Seeholzenstraße 2a. The Postal code is 82166. The City is Gräfeling. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 229313. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is https://www.greenrocket.de/. The E-Mail is deutschmann@greenrocket.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 355. The Name is Haftpflicht Helden. The Status is 1. The Original German is 1.0. The Founding year is unknown. The Founder is Florian Knörrich; Stefan Herbst; Jan Louis Schmidt. The Linkedin-Account Founder is https://www.linkedin.com/in/florian-kn%C3%B6rrich-4909b44/; https://www.linkedin.com/in/stefan-herbst-66a557108/. The Legal Name is Insurance Hero GmbH. The Legal form is GmbH. The Street is Heidenkampsweg 81. The Postal code is 20097. The City is Hamburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 139747. The Subsegment is Insurance. The Bank Cooperation is 0. The Homepage is https://haftpflichthelden.de/. The E-Mail is help@helden.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hamburg. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 838. The Name is Loyal. The Status is 1. The Original German is 1.0. The Founding year is 2019.0. The Founder is UMT United Mobility Technology AG. The Linkedin-Account Founder is unknown. The Legal Name is UMT United Mobility Technology AG. The Legal form is AG. The Street is Brienner Str. 7. The Postal code is 80333. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 167884. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://loyalapp.de/. The E-Mail is loyal@umt.ag. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 3. The Name is fundflow. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Joachim Kaune; Antonio Faralli. The Linkedin-Account Founder is https://www.linkedin.com/in/joachim-kaune-26ab8846/; https://www.linkedin.com/in/antoniofaralli/. The Legal Name is Fundflow GmbH. The Legal form is GmbH. The Street is Prenzlauer Allee 53. The Postal code is 10405. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 173662 B. The Subsegment is Credit and Factoring. The Bank Cooperation is 1. The Homepage is https://fundflow.de/. The E-Mail is info@fundflow.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 653. The Name is Diversifikator. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Dirk Söhnholz. The Linkedin-Account Founder is https://www.linkedin.com/in/dirksoehnholz/. The Legal Name is Diversifikator GmbH. The Legal form is GmbH. The Street is Kiefernweg 1. The Postal code is 61184. The City is Karben. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 104667. The Subsegment is Robo-Advice. The Bank Cooperation is 0. The Homepage is https://diversifikator.com/de/. The E-Mail is soehnholz@diversifikator.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Karben. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 627. The Name is Klimafonds. The Status is 1. The Original German is 1.0. The Founding year is 2012.0. The Founder is Gerd Junker; Carmen Junker. The Linkedin-Account Founder is https://www.linkedin.com/in/gerd-junker-5227026/; https://www.linkedin.com/in/carmen-junker-75b419227/. The Legal Name is Grünes Geld GmbH. The Legal form is GmbH. The Street is Beineweg 18. The Postal code is 63864. The City is Glattbach. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 10196. The Subsegment is Investment and Banking. The Bank Cooperation is 1. The Homepage is https://www.klimafonds.de. The E-Mail is info@klimafonds.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Glattbach. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 850. The Name is paydirekt. The Status is 1. The Original German is 1.0. The Founding year is 2014.0. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is paydirekt GmbH. The Legal form is GmbH. The Street is Stephanstr. 14-16. The Postal code is 60313. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 99538. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 1. The Homepage is https://www.paydirekt.de/. The E-Mail is service@paydirekt.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 737. The Name is Swiss options trading. The Status is 1. The Original German is 0.0. The Founding year is 2012.0. The Founder is BDSwiss Group. The Linkedin-Account Founder is unknown. The Legal Name is BDS Markets. The Legal form is unknown. The Street is Niddastraße 58. The Postal code is 60329. The City is Frankfurt am Main. The Country is Mauritius. The Register Number/ Company ID/ LEI is 143350. The Subsegment is Investment and Banking. The Bank Cooperation is 1. The Homepage is http://www.swissoptionstrading.com. The E-Mail is support@swissoptionstrading.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 872. The Name is Sales King. The Status is 1. The Original German is 1.0. The Founding year is 2008.0. The Founder is Georg Leciejewski; Ole Wiemeler. The Linkedin-Account Founder is https://www.linkedin.com/in/georg-leciejewski-7767202/; https://www.linkedin.com/in/olewiemeler/. The Legal Name is Sales King GmbH. The Legal form is GmbH. The Street is Vorgebirgsstrasse 18. The Postal code is 50354. The City is Hürth. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 44907. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://www.salesking.eu/. The E-Mail is support@salesking.eu. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Köln. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 744. The Name is Airbank. The Status is 1. The Original German is 1.0. The Founding year is 2021.0. The Founder is Christopher Zemina; Patrick de Castro Neuhaus. The Linkedin-Account Founder is https://www.linkedin.com/in/christopherzemina; https://www.linkedin.com/in/patrick-de-castro-neuhaus-08a1091b9. The Legal Name is Airlabs UG. The Legal form is UG. The Street is Chausseestraße 86. The Postal code is 10115. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 226357 B. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://www.joinairbank.com/de. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 78. The Name is Captiq. The Status is 1. The Original German is 1.0. The Founding year is 2018.0. The Founder is Soraya Braun; Lorenz Beimler. The Linkedin-Account Founder is https://www.linkedin.com/in/soraya-braun-837852155/; https://www.linkedin.com/in/lorenz-beimler-3b1480149/. The Legal Name is Captiq GmbH. The Legal form is GmbH. The Street is Neuer Mainzer Straße 66-68. The Postal code is 60311. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 113460. The Subsegment is Credit and Factoring. The Bank Cooperation is 0. The Homepage is https://captiq.com. The E-Mail is info@captiq.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 640. The Name is olb. The Status is 1. The Original German is 1.0. The Founding year is unknown. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is Oldenburgische Landesbank AG. The Legal form is AG. The Street is Stau 15/17. The Postal code is 26122. The City is Oldenburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 3003. The Subsegment is Robo-Advice. The Bank Cooperation is 0. The Homepage is https://www.olb.de/landingpages/ww/depots. The E-Mail is olb@olb.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Oldenburg. The Former name is wüstenrot.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 807. The Name is dban. The Status is 0. The Original German is 1.0. The Founding year is 2017.0. The Founder is Michael Scholz. The Linkedin-Account Founder is https://www.linkedin.com/in/michael-scholz-880521120/. The Legal Name is ementexx GmbH. The Legal form is GmbH. The Street is Berner Straße 109. The Postal code is 60437. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 110075. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 60. The Name is Bambus Immobilien. The Status is 1. The Original German is 1.0. The Founding year is 2018.0. The Founder is Franz Hoerhager; Patrick Wollner. The Linkedin-Account Founder is https://www.linkedin.com/in/franz-hoerhager/; https://www.linkedin.com/in/pwollner/. The Legal Name is Bambus Immobilien GmbH. The Legal form is GmbH. The Street is Luise-Ulrich-Straße 20. The Postal code is 80636. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 201351 B. The Subsegment is Credit and Factoring. The Bank Cooperation is 1. The Homepage is https://www.bambus.io. The E-Mail is beratung@bambus.io. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 908. The Name is Koala. The Status is 1. The Original German is 1.0. The Founding year is 2019.0. The Founder is Christoph Schönfelder; David Scharfschwerdt. The Linkedin-Account Founder is https://www.linkedin.com/in/christoph-sch%C3%B6nfelder-74167659/; https://www.linkedin.com/in/david-scharfschwerdt-239591197/. The Legal Name is Koala AG. The Legal form is AG. The Street is Klostersande 4a. The Postal code is 25336. The City is Elmshorn. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 14851. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://www.koalaapp.de/. The E-Mail is info@koalaapp.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Elmshorn. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 12. The Name is aescuvest. The Status is 1. The Original German is 1.0. The Founding year is 2014.0. The Founder is Patrick Pfeffer. The Linkedin-Account Founder is https://www.linkedin.com/in/patrickpfeffer/. The Legal Name is aescuvest GmbH. The Legal form is GmbH. The Street is Hanauer Landstr. 328-330. The Postal code is 60314. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 100439. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is https://www.aescuvest.de. The E-Mail is kontakt@aescuvest.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 914. The Name is UnitedCrowd. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Michael Göymen; André Wendt. The Linkedin-Account Founder is https://www.linkedin.com/in/michaelgoeymen/; https://www.linkedin.com/in/andrewendt/. The Legal Name is UnitedCrowd GmbH. The Legal form is GmbH. The Street is Venloerstr. 5 – 7. The Postal code is 50672. The City is Köln. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 98686. The Subsegment is Blockchain and Cryptocurrencies. The Bank Cooperation is 0. The Homepage is https://unitedcrowd.com. The E-Mail is gmbh@unitedcrowd.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Köln. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 535. The Name is xware42. The Status is 0. The Original German is 0.0. The Founding year is 2011.0. The Founder is David Lais. The Linkedin-Account Founder is https://www.linkedin.com/in/paymenthippie/. The Legal Name is xWare42 GmbH. The Legal form is GmbH. The Street is Stefan-George-Ring 2. The Postal code is 81929. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 194682. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://www.xware42.com/. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 638. The Name is Moneymeets. The Status is 1. The Original German is 1.0. The Founding year is 2012.0. The Founder is Johannes Cremer; Dieter Fromm. The Linkedin-Account Founder is https://www.linkedin.com/in/johannescremer/; https://www.linkedin.com/in/dieter-fromm/. The Legal Name is moneymeets Portfolio Management GmbH/moneymeets community GmbH. The Legal form is GmbH. The Street is Im Zollhafen 22. The Postal code is 50678. The City is Köln. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 76112. The Subsegment is Investment and Banking. The Bank Cooperation is 1. The Homepage is https://www.moneymeets.com/. The E-Mail is corporate@moneymeets.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Köln. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 37. The Name is InvestoFolio. The Status is 0. The Original German is 1.0. The Founding year is 2015.0. The Founder is Johannes Hillmann; Ralf Schepers; Ralf Köhler. The Linkedin-Account Founder is unknown. The Legal Name is DikoBa Financing & Consulting GmbH. The Legal form is GmbH. The Street is Sontraer Str. 17. The Postal code is 60386. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 101783. The Subsegment is Crowdlending. The Bank Cooperation is 0. The Homepage is https://www.investofolio.de/. The E-Mail is ap@investofolio.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Königstein im Taunus. The Former name is immo-folio.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 407. The Name is cybits. The Status is 0. The Original German is 1.0. The Founding year is 2011.0. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is Cybits Holding AG. The Legal form is AG. The Street is Peter-Sander-Straße 41 a. The Postal code is 55252. The City is Mainz. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 25974. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is unknown. The Insolvency is 1. The Liquidation is 0. The Date of inactivity is 2017-05-15 00:00:00. The Local court is Mainz-Kastel. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 588. The Name is moneyfarm. The Status is 1. The Original German is 0.0. The Founding year is 2016.0. The Founder is Giovanni Daprà; Paolo Galvani. The Linkedin-Account Founder is https://www.linkedin.com/in/daprgio/; https://www.linkedin.com/in/paolo-galvani-b09b23/. The Legal Name is MFM Investment GmbH. The Legal form is GmbH. The Street is Mainzer Landstraße 250-254. The Postal code is 60326. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 105192. The Subsegment is Robo-Advice. The Bank Cooperation is 1. The Homepage is https://www.moneyfarm.com/de/. The E-Mail is service@moneyfarm.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is Vaamo.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 436. The Name is Fyber. The Status is 1. The Original German is 0.0. The Founding year is 2009.0. The Founder is Janis Zech; Jan Beckers; Andreas Bodczek. The Linkedin-Account Founder is https://www.linkedin.com/in/janiszech/; https://www.linkedin.com/in/janbeckers/; https://www.linkedin.com/in/andreasbodczek/. The Legal Name is Fyber N.V. The Legal form is N.V. The Street is Wallstraße 9-13. The Postal code is 10179. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 166541. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://www.fyber.com/. The E-Mail is info@fyber.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 593. The Name is VisualVest. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Olaf Zeitnitz. The Linkedin-Account Founder is https://www.linkedin.com/in/dr-olaf-zeitnitz-722b0a1b/. The Legal Name is VisualVest GmbH. The Legal form is GmbH. The Street is Mainzer Landstraße 50. The Postal code is 60325. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 101346. The Subsegment is Robo-Advice. The Bank Cooperation is 1. The Homepage is https://www.visualvest.de/. The E-Mail is kontakt@kundenservice.visualvest.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 241. The Name is TraFinScout. The Status is 1. The Original German is 1.0. The Founding year is 2019.0. The Founder is Eckhard Creutzburg; Christian Etzel; Joachim Reinhardt; Michael Vander. The Linkedin-Account Founder is https://www.linkedin.com/in/eckhard-creutzburg-108913128/; https://www.linkedin.com/in/christian-etzel-35193412/; https://www.linkedin.com/in/joachim-reinhardt-4640151/; https://www.linkedin.com/in/michaelvander/. The Legal Name is TraFinScout GmbH. The Legal form is GmbH. The Street is Solmsstr. 4. The Postal code is 60486. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 114419. The Subsegment is Credit and Factoring. The Bank Cooperation is 1. The Homepage is https://trafinscout.com/. The E-Mail is info@trafinscout.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 134. The Name is Deutsche Mikroinvest. The Status is 0. The Original German is 1.0. The Founding year is 2012.0. The Founder is Carsten Bischof; Knut Haake. The Linkedin-Account Founder is https://www.linkedin.com/in/knut-h-a321b5167/. The Legal Name is DMI Deutsche Mikroinvest GmbH. The Legal form is GmbH. The Street is Am Birkengraben 14. The Postal code is 50259. The City is Pulheim. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 75246. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is 2019-03-01 00:00:00. The Local court is Köln. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 895. The Name is Worldremit. The Status is 1. The Original German is 0.0. The Founding year is 2010.0. The Founder is Ismail Ahmed. The Linkedin-Account Founder is https://www.linkedin.com/in/ismail-ahmed-worldremit/. The Legal Name is WorldRemit Ltd. The Legal form is Ltd. The Street is 62 Buckingham Gate. The Postal code is SW1E 6AJ. The City is London. The Country is England. The Register Number/ Company ID/ LEI is 213800BO4SXT9R8PHV43. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://www.worldremit.com/de. The E-Mail is CustomerService@WorldRemit.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 311. The Name is wertgarantie. The Status is 1. The Original German is 1.0. The Founding year is 2013.0. The Founder is Sebastian Grötsch. The Linkedin-Account Founder is https://www.linkedin.com/in/sebastiangroetsch/. The Legal Name is WERTGARANTIE SE. The Legal form is SE. The Street is Breite Straße 6. The Postal code is 30159. The City is Hannover. The Country is Germany. The Register Number/ Company ID/ LEI is HR B 208988. The Subsegment is Insurance. The Bank Cooperation is 0. The Homepage is https://www.wertgarantie.de/. The E-Mail is kunde@wertgarantie.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hannover. The Former name is sofortschutz; Traumschutz.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 308. The Name is element. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Henning Groß; Inna Leontenkova. The Linkedin-Account Founder is https://www.linkedin.com/in/denkgross/; https://www.linkedin.com/in/leontenkova/. The Legal Name is ELEMENT Insurance AG. The Legal form is AG. The Street is Hardenbergstraße 32. The Postal code is 10623. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 182671 B. The Subsegment is Insurance. The Bank Cooperation is 0. The Homepage is https://www.element.in/de/. The E-Mail is info@element.in. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 447. The Name is Kobil. The Status is 1. The Original German is 1.0. The Founding year is 1986.0. The Founder is Ismet Koyun. The Linkedin-Account Founder is https://www.linkedin.com/in/ismet-koyun-69b9a136/. The Legal Name is KOBIL Systems GmbH. The Legal form is GmbH. The Street is Pfortenring 11. The Postal code is 67547. The City is Worms. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 10856. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://www.kobil.com/. The E-Mail is marketing@kobil.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Mainz. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 795. The Name is cflox. The Status is 1. The Original German is 1.0. The Founding year is 2013.0. The Founder is Philipp Tillmanns; Christoph Kaup; Thomas Krings. The Linkedin-Account Founder is https://www.linkedin.com/in/philipp-tillmanns-a594205/; https://www.linkedin.com/in/thomas-krings-ab6a4398/. The Legal Name is cflox GmbH. The Legal form is GmbH. The Street is Große Brunnenstraße 122. The Postal code is 22763. The City is Hamburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 127858. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://cflox.com/. The E-Mail is info@cflox.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hamburg. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 571. The Name is Paylobby. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Julia Houben; Peter Petridis; Tommy Djoumessy. The Linkedin-Account Founder is https://www.linkedin.com/in/julia-houben-315b4442/; https://www.linkedin.com/in/peter-petridis-746359109/. The Legal Name is Paylobby GmbH. The Legal form is GmbH. The Street is Hirschgartenallee. The Postal code is 80639. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 229274. The Subsegment is Search Engines and Comparison Sites. The Bank Cooperation is 0. The Homepage is http://www.paylobby.com/en. The E-Mail is info@paylobby.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 561. The Name is Monite. The Status is 1. The Original German is 1.0. The Founding year is 2020.0. The Founder is Andrey Korchak. The Linkedin-Account Founder is https://www.linkedin.com/in/a-korchak/. The Legal Name is Monite GmbH. The Legal form is GmbH. The Street is Friedrichstrasse 68. The Postal code is 10117. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 221153 B. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://monite.com/. The E-Mail is hello@monite.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is Gemms.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 385. The Name is awamo. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Benedikt Kramer; Roland Claussen; Philipp Neub. The Linkedin-Account Founder is https://www.linkedin.com/in/benediktkramer/; https://www.linkedin.com/in/rclaussen/; https://www.linkedin.com/in/philippneub/. The Legal Name is awamo GmbH. The Legal form is GmbH. The Street is Kaiserstraße 61. The Postal code is 60329. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 102394. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://awamo.com/. The E-Mail is info@awamo.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 57. The Name is DKB Crowdfunding. The Status is 1. The Original German is 1.0. The Founding year is 2019.0. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is DKB Crowdfunding GmbH. The Legal form is GmbH. The Street is Baseler-Str. 10. The Postal code is 60329. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 115401. The Subsegment is Crowdfunding. The Bank Cooperation is 1. The Homepage is https://www.dkb-crowdfunding.de/. The E-Mail is kontakt@dkb-crowd.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 299. The Name is Palturai. The Status is 1. The Original German is 1.0. The Founding year is 2014.0. The Founder is Tilo Walter; Petra Kaul; Thorsten Lau; Hans-Dieter Greb. The Linkedin-Account Founder is https://www.linkedin.com/in/tilo-walter-18b90567/; https://www.linkedin.com/in/petra-kaul-122ba9a6/. The Legal Name is Palturai GmbH. The Legal form is GmbH. The Street is Reifenberger Straße 1. The Postal code is 65719. The City is Hogheim. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 99412. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://palturai.com/. The E-Mail is info@palturai.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hogheim. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 705. The Name is flowpilot.io. The Status is 1. The Original German is 1.0. The Founding year is 2018.0. The Founder is Bernd Thöne; Sophie Schwalbe. The Linkedin-Account Founder is https://www.linkedin.com/in/bernd-th%C3%B6ne/; https://www.linkedin.com/in/sophieschwalbe/. The Legal Name is flowpilot UG (haftungsbeschränkt). The Legal form is UG. The Street is Friedrichstraße 68. The Postal code is 10117. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 196599. The Subsegment is Investment and Banking. The Bank Cooperation is 0. The Homepage is https://flowpilot.io/. The E-Mail is info@flowpilot.io. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 197. The Name is Rhein-Main Crowdfunding. The Status is 1. The Original German is 1.0. The Founding year is 2014.0. The Founder is Forum Kiedrich GmbH. The Linkedin-Account Founder is unknown. The Legal Name is Forum Kiedrich GmbH. The Legal form is GmbH. The Street is Biebricher Allee 22. The Postal code is 65187. The City is Wiesbaden. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 13012. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is http://www.rm-crowdfunding.de/. The E-Mail is crowdfunding@forum-kiedrich.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Wiesbaden. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 558. The Name is smartlutions. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Michael Reusch. The Linkedin-Account Founder is https://www.linkedin.com/in/smartlutions/. The Legal Name is smartlutions GmbH. The Legal form is GmbH. The Street is Robert-Bosch-Str. 2a. The Postal code is 50354. The City is Hürth. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 84051. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://smartlutions.net/. The E-Mail is info@smartlutions.net. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Köln. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 466. The Name is Mynigma. The Status is 0. The Original German is 1.0. The Founding year is 2013.0. The Founder is Gaurav Singh; Lukas Neumann; Roman Priebe. The Linkedin-Account Founder is https://www.linkedin.com/in/mrgauravsingh/; https://www.linkedin.com/in/lksnmnn/; https://de.linkedin.com/in/romanpriebe. The Legal Name is Mynigma UG. The Legal form is UG. The Street is Luisenplatz 3. The Postal code is 10585. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 151727 B. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://www.mynigma.org/. The E-Mail is info@mynigma.org. The Insolvency is 0. The Liquidation is 1. The Date of inactivity is 2018-01-17 00:00:00. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 683. The Name is simplefinance. The Status is 0. The Original German is 1.0. The Founding year is 2014.0. The Founder is Fabian Schwietal. The Linkedin-Account Founder is https://www.linkedin.com/in/fabian-schwietal-68112866/. The Legal Name is Simple Finance GmbH. The Legal form is GmbH. The Street is Wilhelmstr. 30. The Postal code is 80801. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 217801. The Subsegment is Robo-Advice. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 1. The Date of inactivity is 2019-09-05 00:00:00. The Local court is München. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 500. The Name is sevDesk. The Status is 1. The Original German is 1.0. The Founding year is 2013.0. The Founder is Fabian Silberer; Marco Reinbold. The Linkedin-Account Founder is https://www.linkedin.com/in/fabian-silberer-8129568/; https://www.linkedin.com/in/marco-reinbold-545317138/. The Legal Name is sevDesk GmbH. The Legal form is GmbH. The Street is Hauptstraße 115. The Postal code is 77652. The City is Offenburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 710506. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://sevdesk.de/. The E-Mail is support@sevdesk.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Offenburg. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 977. The Name is XPAY. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Denis Raskopoljac. The Linkedin-Account Founder is https://www.linkedin.com/in/denis-raskopoljac/. The Legal Name is XPAY Solutions GmbH. The Legal form is GmbH. The Street is Stuntzstraße 16. The Postal code is 81677. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 225853. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://www.xpay.de/. The E-Mail is info@xpay.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 445. The Name is IPO.GO. The Status is 0. The Original German is 1.0. The Founding year is 2005.0. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is IPO.GO AG. The Legal form is AG. The Street is Im Bildösch 17. The Postal code is 78476. The City is Allensbach. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 705817. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is http://www.ipogo.de/. The E-Mail is info@ipogo.de. The Insolvency is 0. The Liquidation is 1. The Date of inactivity is 31.12.2018. The Local court is Allensbach. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 130. The Name is Crowdstein. The Status is 0. The Original German is 1.0. The Founding year is 2014.0. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is Crowdstein UG. The Legal form is UG. The Street is Nordheimstraße 3. The Postal code is 22309. The City is Hamburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 131016. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 1. The Date of inactivity is 2015-10-05 00:00:00. The Local court is Hamburg. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 270. The Name is Ico-Lux. The Status is 1. The Original German is 1.0. The Founding year is 2018.0. The Founder is Jan Franke; Stefan Brechtken; Lars Winterfeld. The Linkedin-Account Founder is https://www.linkedin.com/in/jan-franke-66306b202/; https://www.linkedin.com/in/lars-winterfeld-0781a71a4/. The Legal Name is ICO-LUX GmbH. The Legal form is GmbH. The Street is Hans-Knöll-Str. 6. The Postal code is 07745. The City is Jena. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 514767. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://ico-lux.de/. The E-Mail is info@ico-lux.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Jena. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 609. The Name is PayCenter. The Status is 1. The Original German is 1.0. The Founding year is 2011.0. The Founder is Ludwig Adam. The Linkedin-Account Founder is https://www.linkedin.com/in/ludwigadam/. The Legal Name is PayCenter GmbH. The Legal form is GmbH. The Street is Max-Lehner-Str. 1a. The Postal code is 85354. The City is Freising. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 194018. The Subsegment is Investment and Banking. The Bank Cooperation is 0. The Homepage is https://www.paycenter.de/. The E-Mail is info@PayCenter.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 329. The Name is Hallokredit.com. The Status is 0. The Original German is 1.0. The Founding year is 2015.0. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is InnoWeb GmbH. The Legal form is GmbH. The Street is Limburgerpark 2. The Postal code is 04279. The City is Leipzig. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 32403. The Subsegment is Search Engines and Comparison Sites. The Bank Cooperation is 0. The Homepage is https://www.hallokredit.com/. The E-Mail is info@hallokredit.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Leipzig. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 233. The Name is Planethome Investment. The Status is 1. The Original German is 1.0. The Founding year is 2019.0. The Founder is Frank W. Kewitz; Philip Moffat. The Linkedin-Account Founder is https://www.linkedin.com/in/frank-w-kewitz-1828619b/. The Legal Name is Planethome Investment AG. The Legal form is AG. The Street is Uhlandstraße 175. The Postal code is 10719. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 239061 B. The Subsegment is Crowdlending. The Bank Cooperation is 1. The Homepage is https://www.planethome-invest.com/de/. The E-Mail is info@planethome-invest.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 172. The Name is KAM on!. The Status is 1. The Original German is 1.0. The Founding year is 2014.0. The Founder is Alex Jan Avedikjan; Kristina Kamper. The Linkedin-Account Founder is https://www.linkedin.com/in/dr-alex-jan-avedikjan-699917135/; https://www.linkedin.com/in/kristina-kamper-05b3b378/. The Legal Name is KAM on! GmbH. The Legal form is GmbH. The Street is Bernhard-Wicki-Straße 3. The Postal code is 80636. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 237423. The Subsegment is Reward-based Crowdfunding. The Bank Cooperation is 0. The Homepage is https://kam-on.de/. The E-Mail is info@kam-on.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is Monaco Funding.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 819. The Name is gastrofix. The Status is 1. The Original German is 1.0. The Founding year is 2011.0. The Founder is Reinhard Martens. The Linkedin-Account Founder is https://www.linkedin.com/in/reinhardmartens/. The Legal Name is Gastrofix GmbH. The Legal form is GmbH. The Street is Alex-Wedding-Str. 7. The Postal code is 10178. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 138363 B. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://www.gastrofix.com/de/. The E-Mail is sales@gastrofix.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 204. The Name is Socialfunders. The Status is 1. The Original German is 1.0. The Founding year is 2012.0. The Founder is Stefan Funk; Stefan Pandorf; Stephanie Henn. The Linkedin-Account Founder is https://www.linkedin.com/in/stefan-pandorf/; https://www.linkedin.com/in/stephanie-henn-57730970/. The Legal Name is Particulate Solutions GmbH. The Legal form is GmbH. The Street is Universitätsstr. 3. The Postal code is 56070. The City is Koblenz. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 23180. The Subsegment is Donation-based Crowdfunding. The Bank Cooperation is 0. The Homepage is https://www.socialfunders.org/. The E-Mail is info@socialfunders.org. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Koblenz. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 10. The Name is SEEDRS. The Status is 1. The Original German is 0.0. The Founding year is 2012.0. The Founder is Carlos Silva; Jeff Lynn. The Linkedin-Account Founder is https://www.linkedin.com/in/cmsilva; https://www.linkedin.com/in/jefflynn/. The Legal Name is Seedrs Ltd. The Legal form is Ltd. The Street is Churchill House, 142-146 Old Street. The Postal code is EC1V 9BW. The City is London. The Country is England. The Register Number/ Company ID/ LEI is 6848016. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is https://www.seedrs.com/. The E-Mail is support@seedrs.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 295. The Name is Flexperto. The Status is 1. The Original German is 1.0. The Founding year is 2012.0. The Founder is Felix Anthonj; Tobias Krauß. The Linkedin-Account Founder is https://www.linkedin.com/in/felix-anthonj-6066a440/; https://www.linkedin.com/in/tobias-krauss/. The Legal Name is Flexperto GmbH. The Legal form is GmbH. The Street is Neue Grünstraße 27. The Postal code is 10179. The City is Verlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 165181. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://flexperto.com/. The E-Mail is info@flexperto.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Verlin. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 323. The Name is adiume. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Lennart Schulze; Mahir Arslan. The Linkedin-Account Founder is https://www.linkedin.com/in/lennart-schulze/; https://www.linkedin.com/in/mahir-a-847257120/. The Legal Name is Adiume GbR. The Legal form is GbR. The Street is Schreinerstr. 30. The Postal code is 10247. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is unknown. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://adiume.com/. The E-Mail is contact@adiume.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is 2016-02-29 00:00:00. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 881. The Name is Stripe. The Status is 1. The Original German is 0.0. The Founding year is 2011.0. The Founder is Patrick Collison; John Collison. The Linkedin-Account Founder is https://www.linkedin.com/in/patrickcollison/; https://www.linkedin.com/in/johnbcollison/. The Legal Name is Stripe Inc. The Legal form is Inc. The Street is 510 Townsend Street. The Postal code is CA 94103. The City is San Francisco. The Country is USA. The Register Number/ Company ID/ LEI is 4675506. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://stripe.com/de. The E-Mail is support@stripe.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 446. The Name is ivitec. The Status is 1. The Original German is 1.0. The Founding year is 2007.0. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is ivitec GmbH. The Legal form is GmbH. The Street is Lange Reihe 29. The Postal code is 20099. The City is Hamburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 141680. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://ivitec.com/index.html. The E-Mail is info@ivitec.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hamburg. The Former name is iPharro Media.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 678. The Name is rethink finance. The Status is 0. The Original German is 1.0. The Founding year is 2013.0. The Founder is Alexander Decker; Bernhard Flohr; Gibran Watfe. The Linkedin-Account Founder is https://www.linkedin.com/in/alexander-decker-2b394654/; https://www.linkedin.com/in/gibranwatfe/. The Legal Name is rethink finance UG. The Legal form is UG. The Street is Mommsenstraße 22. The Postal code is 10629. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 150750 B. The Subsegment is Personal Financial Management. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 1. The Date of inactivity is 2016-04-13 00:00:00. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 709. The Name is True wealth. The Status is 1. The Original German is 0.0. The Founding year is 2013.0. The Founder is Oliver Herren; Felix Niederer. The Linkedin-Account Founder is https://www.linkedin.com/in/oliver-herren-60a6704a/; https://www.linkedin.com/in/niederer/. The Legal Name is True Wealth AG. The Legal form is AG. The Street is Grubenstrasse 18. The Postal code is 8045. The City is Zürich. The Country is Switzerland. The Register Number/ Company ID/ LEI is CHE-489.219.513. The Subsegment is Investment and Banking. The Bank Cooperation is 1. The Homepage is https://www.truewealth.ch/de. The E-Mail is info@truewealth.ch. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 46. The Name is transvendo. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Sven Kirchberg; Cirino Marino. The Linkedin-Account Founder is https://www.linkedin.com/in/sven-kirchberg-65a78b132/; https://www.linkedin.com/in/cirino-marino-2499a37a/. The Legal Name is Transvendo GmbH & Co. KG. The Legal form is GmbH & Co. KG. The Street is Würmstraße 55. The Postal code is 82166. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRA 105939. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is http://www.transvendo.investments/. The E-Mail is kontakt@transvendo.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 856. The Name is paymill. The Status is 0. The Original German is 1.0. The Founding year is 2012.0. The Founder is Mark Henkel; Stefan Sambol. The Linkedin-Account Founder is https://www.linkedin.com/in/mahenkel/; https://www.linkedin.com/in/dsambol/. The Legal Name is PAYMILL GmbH. The Legal form is GmbH. The Street is St.-Martin-Straße 63. The Postal code is 81669. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 226526. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://www.paymill.com/de/. The E-Mail is support@paymill.com. The Insolvency is 0. The Liquidation is 1. The Date of inactivity is unknown. The Local court is München. The Former name is Klik&Pay.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 707. The Name is Trade Republic. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Christian Hecker; Thomas Pischke; Marco Cancellieri. The Linkedin-Account Founder is https://www.linkedin.com/in/christianhe/; https://www.linkedin.com/in/tpischke/; https://www.linkedin.com/in/marco-cancellieri-5a48b2a2/. The Legal Name is Trade Republic Bank GmbH. The Legal form is GmbH. The Street is Kastanienallee 32. The Postal code is 10435. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 85864. The Subsegment is Investment and Banking. The Bank Cooperation is 1. The Homepage is https://traderepublic.com/de-de. The E-Mail is service@traderepublic.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Düsseldorf. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 27. The Name is Econeers. The Status is 1. The Original German is 1.0. The Founding year is 2013.0. The Founder is Jens Uwe Sauer. The Linkedin-Account Founder is https://www.linkedin.com/in/juwes. The Legal Name is OneCrowd Loans GmbH. The Legal form is GmbH. The Street is Käthe-Kollwitz-Ufer 79. The Postal code is 01309. The City is Dresden. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 27674. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is https://www.econeers.de/. The E-Mail is info@econeers.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Dresden. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 443. The Name is Insurgram. The Status is 0. The Original German is 1.0. The Founding year is 2015.0. The Founder is Antonia Ermacora; Matthias Nannt. The Linkedin-Account Founder is https://www.linkedin.com/in/antoniaermacora/; https://www.linkedin.com/in/matthiasnannt/. The Legal Name is chatShopper GmbH. The Legal form is GmbH. The Street is Rheinsberger Straße 76/77. The Postal code is 10115. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 172753 B. The Subsegment is Insurance. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 1. The Date of inactivity is 2016/2017. The Local court is Berlin (Charlottenburg). The Former name is ChatDichSicher.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 755. The Name is ratepay. The Status is 1. The Original German is 1.0. The Founding year is 2009.0. The Founder is Miriam Wohlfarth. The Linkedin-Account Founder is https://www.linkedin.com/in/miriam-wohlfarth/. The Legal Name is RatePAY GmbH. The Legal form is GmbH. The Street is Franklinstraße 28-29. The Postal code is 10587. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 124156B. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 1. The Homepage is https://www.ratepay.com/. The E-Mail is info@ratepay.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 870. The Name is Ripple. The Status is 1. The Original German is 0.0. The Founding year is 2012.0. The Founder is Ryan Fugger; Chris Larsen; Jed McCaleb. The Linkedin-Account Founder is https://www.linkedin.com/in/larsen-chris/; https://www.linkedin.com/in/jed-mccaleb-4052a4/. The Legal Name is Ripple Labs Inc. The Legal form is Inc. The Street is 300 Montgomery St. The Postal code is CA 94104. The City is San Francisco. The Country is USA. The Register Number/ Company ID/ LEI is unknown. The Subsegment is Blockchain and Cryptocurrencies. The Bank Cooperation is 1. The Homepage is https://ripple.com/. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 697. The Name is zinspilot. The Status is 1. The Original German is 1.0. The Founding year is 2011.0. The Founder is Tim Sievers. The Linkedin-Account Founder is https://www.linkedin.com/in/tim-sievers/. The Legal Name is Deposit Solutions GmbH. The Legal form is GmbH. The Street is Drehbahn 7-11. The Postal code is 20354. The City is Hamburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 118186. The Subsegment is Investment and Banking. The Bank Cooperation is 1. The Homepage is https://www.zinspilot.de/de/start/. The E-Mail is service@zinspilot.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hamburg. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 240. The Name is StartMark. The Status is 1. The Original German is 1.0. The Founding year is 2019.0. The Founder is Guido Stefan, Frank Schmidt. The Linkedin-Account Founder is https://www.linkedin.com/in/guido-stefan-p-3b9850182/; https://www.linkedin.com/in/frank-schmidt-0832aa86/. The Legal Name is StartMark GmbH. The Legal form is GmbH. The Street is Heinrichstr. 155. The Postal code is 40239. The City is Düsseldorf. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 85090. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is https://www.startmark.de/. The E-Mail is info@startmark.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Düsseldorf. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 300. The Name is 360T. The Status is 1. The Original German is 1.0. The Founding year is 2000.0. The Founder is Carlo Kölzer. The Linkedin-Account Founder is unknown. The Legal Name is 360 Treasury Systems AG. The Legal form is AG. The Street is Grüneburgweg 16-18. The Postal code is 60322. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 49874. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://www.360t.com/. The E-Mail is info@360t.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 141. The Name is Entrafin. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Christoph Bauer; Stefan Fenner. The Linkedin-Account Founder is https://www.linkedin.com/in/christoph-bauer-22764a1b5/; https://www.linkedin.com/in/dr-stefan-fenner-7a99701a7/. The Legal Name is entrafin GmbH. The Legal form is GmbH. The Street is Kölner Str. 30. The Postal code is 50859. The City is Köln. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 93445. The Subsegment is Credit and Factoring. The Bank Cooperation is 1. The Homepage is https://www.entrafin.de/. The E-Mail is christoph.bauer@entrafin.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Köln. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 304. The Name is solarisBank. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Peter Grosskopf; Andreas Bittner. The Linkedin-Account Founder is https://www.linkedin.com/in/petergrosskopf/; https://www.linkedin.com/in/andreas-bittner-67775645/. The Legal Name is solarisBank AG. The Legal form is AG. The Street is Anna-Louisa-Karsch-Straße 2. The Postal code is 10178. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 168180 B. The Subsegment is Technology, IT and Infrastructure. The Bank Cooperation is 0. The Homepage is https://www.solarisbank.com/de/. The E-Mail is support@solarisbank.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 884. The Name is Tabbt. The Status is 1. The Original German is 1.0. The Founding year is 2014.0. The Founder is Jan Michaelis; Lucas Romero. The Linkedin-Account Founder is https://www.linkedin.com/in/jan-michaelis-9b5695b5/. The Legal Name is Tabbt GmbH. The Legal form is GmbH. The Street is Waterloohain 5. The Postal code is 22769. The City is Hamburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 143301. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://www.tabbt.com/. The E-Mail is info@tabbt.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hamburg. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 411. The Name is picsure. The Status is 0. The Original German is 1.0. The Founding year is 2017.0. The Founder is Enrico Bolloni; Ole Roel; Florian Bischof. The Linkedin-Account Founder is https://www.linkedin.com/in/enrico-bolloni/; https://www.linkedin.com/in/oleroel/; https://www.linkedin.com/in/florian-bischof/. The Legal Name is Picsure GmbH. The Legal form is GmbH. The Street is Atelierstr. 29. The Postal code is 81671. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 235272. The Subsegment is Insurance. The Bank Cooperation is 0. The Homepage is https://picsure.ai/. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 1. The Date of inactivity is 2017-12-29 00:00:00. The Local court is München. The Former name is dibis.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 279. The Name is Neodigital. The Status is 1. The Original German is 1.0. The Founding year is 2017.0. The Founder is Dirk Wittling; Stephen Voss. The Linkedin-Account Founder is https://www.linkedin.com/in/dirk-wittling-711177165/; https://www.linkedin.com/in/stephen-voss-712b107/. The Legal Name is Neodigital Versicherung AG. The Legal form is AG. The Street is Untere Bliesstr. 13-15. The Postal code is 66538. The City is Neunkirchen. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 103769. The Subsegment is Insurance. The Bank Cooperation is 1. The Homepage is https://neodigital.de/. The E-Mail is info@neodigital.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Neunkirchen. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 542. The Name is bendesk. The Status is 1. The Original German is 1.0. The Founding year is 2019.0. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is tridion benefits GmbH. The Legal form is GmbH. The Street is Rurstr. 42. The Postal code is 50935. The City is Köln. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 68751. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://www.bendesk.de/. The E-Mail is info@bendesk.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Köln. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 955. The Name is Smartificate. The Status is 1. The Original German is 1.0. The Founding year is 2021.0. The Founder is Lenz Zuber; Yannic Neubauer. The Linkedin-Account Founder is https://www.linkedin.com/in/lenz-zuber/; https://www.linkedin.com/in/yannic-neubauer/. The Legal Name is smartificate GmbH. The Legal form is GmbH. The Street is Lasbeker Str. 9. The Postal code is 22967. The City is Tremsbüttel. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 21788 HL. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://smartificate.de/. The E-Mail is presse@smartificate.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Lübeck. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 261. The Name is Insurninja. The Status is 1. The Original German is 1.0. The Founding year is 2018.0. The Founder is Tim Schlawinsky. The Linkedin-Account Founder is https://www.linkedin.com/in/timheckhausen/. The Legal Name is insurninja GmbH. The Legal form is GmbH. The Street is Breite Str. 6-8. The Postal code is 30159. The City is Hannover. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 85922. The Subsegment is Insurance. The Bank Cooperation is 0. The Homepage is https://insurninja.com/. The E-Mail is dojo@insurninja.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hannover. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 151. The Name is flexpayment. The Status is 0. The Original German is 1.0. The Founding year is 2011.0. The Founder is Aimé Ndaysiaba; Cemil Arslan. The Linkedin-Account Founder is https://www.linkedin.com/in/aim%C3%A9-ndayisaba-9ba8b44/; https://www.linkedin.com/in/cemil-arslan-114722131/. The Legal Name is FLEX Financial Solutions GmbH. The Legal form is GmbH. The Street is Erste Brunnenstr. 12. The Postal code is 20459. The City is Hamburg. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 118283. The Subsegment is Credit and Factoring. The Bank Cooperation is 1. The Homepage is https://www.flexpayment.de/. The E-Mail is info@flexpayment.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Hamburg. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 256. The Name is Fintiba. The Status is 1. The Original German is 1.0. The Founding year is 2016.0. The Founder is Christian Becker. The Linkedin-Account Founder is https://www.linkedin.com/in/cbecker93/. The Legal Name is Fintiba GmbH. The Legal form is GmbH. The Street is Baseler Straße 35-37. The Postal code is 60329. The City is Frankfurt am Main. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 106751. The Subsegment is Insurance. The Bank Cooperation is 0. The Homepage is https://www.fintiba.com/. The E-Mail is info@fintiba.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Frankfurt am Main. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 31. The Name is Geldwerk1. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Ralf Beck. The Linkedin-Account Founder is https://www.linkedin.com/in/ralf-beck-6603259b/. The Legal Name is Geldwerk1 GmbH. The Legal form is GmbH. The Street is An der Palmweide 55. The Postal code is 44227. The City is Dortmund. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 27442. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is https://www.geldwerk1.de/. The E-Mail is service@geldwerk1.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Dortmund. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 545. The Name is cyberversicherungssumme. The Status is 1. The Original German is 1.0. The Founding year is 2018.0. The Founder is Nikolaus Stapels. The Linkedin-Account Founder is https://www.linkedin.com/in/nikolaus-stapels-0251ba121/. The Legal Name is Vertriebssoftware24 GmbH. The Legal form is GmbH. The Street is Timmerbarg 5. The Postal code is 23795. The City is Klein Rönnau. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 19677 KI. The Subsegment is Insurance. The Bank Cooperation is 0. The Homepage is https://cyberversicherungssumme.de/index.html. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Kiel. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 221. The Name is vaidoo. The Status is 1. The Original German is 1.0. The Founding year is unknown. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is unknown. The Legal form is unknown. The Street is Dolberger Straße 59. The Postal code is 59229. The City is Ahlen. The Country is Germany. The Register Number/ Company ID/ LEI is unknown. The Subsegment is Credit and Factoring. The Bank Cooperation is 0. The Homepage is https://vaidoo.de/. The E-Mail is info@vaidoo.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 752. The Name is epay. The Status is 1. The Original German is 1.0. The Founding year is unknown. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is transact Elektronische Zahlungssysteme GmbH. The Legal form is GmbH. The Street is Fraunhoferstr. 10. The Postal code is 82152. The City is Martinsried. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 114 439. The Subsegment is Alternative Payment Methods. The Bank Cooperation is 0. The Homepage is https://epay.de/de/. The E-Mail is info@epay.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 338. The Name is safe.me. The Status is 1. The Original German is 1.0. The Founding year is 2015.0. The Founder is Michael Stock. The Linkedin-Account Founder is https://www.linkedin.com/in/michael-stock-b5938234/. The Legal Name is safe.me GmbH. The Legal form is GmbH. The Street is Minderheideweg 63. The Postal code is 32425. The City is Minden. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 14791. The Subsegment is Insurance. The Bank Cooperation is 0. The Homepage is https://safe.me/. The E-Mail is service@safe.me. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Bad Oeynhausen. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 886. The Name is Tradeshift. The Status is 1. The Original German is 0.0. The Founding year is 2010.0. The Founder is Christian Lanng; Gert Sylvest; Mikkel Brun. The Linkedin-Account Founder is https://www.linkedin.com/in/christianlanng/; https://www.linkedin.com/in/gert-sylvest-a2720b/; https://www.linkedin.com/in/hippe/. The Legal Name is Tradeshift Inc. The Legal form is Inc. The Street is 612 Howard Street. The Postal code is CA 94105. The City is San Francisco. The Country is USA. The Register Number/ Company ID/ LEI is unknown. The Subsegment is Other FinTechs. The Bank Cooperation is 1. The Homepage is https://tradeshift.com/de. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Payments", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 131. The Name is CrowdTrader. The Status is 0. The Original German is 1.0. The Founding year is 2015.0. The Founder is unknown. The Linkedin-Account Founder is unknown. The Legal Name is CrowdTrader GmbH. The Legal form is GmbH. The Street is Pfatthaagäcker 5. The Postal code is 88048. The City is Friedrichshafen. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 731868. The Subsegment is Crowdinvesting. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is unknown. The Insolvency is 0. The Liquidation is 1. The Date of inactivity is 2018-07-23 00:00:00. The Local court is Ulm. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 255. The Name is FinList. The Status is 1. The Original German is 1.0. The Founding year is 2021.0. The Founder is Sandra Olschewski; Florian Hollm. The Linkedin-Account Founder is https://www.linkedin.com/in/sandra-olschewski-29686a8a/; https://www.linkedin.com/in/florianhollm/. The Legal Name is FinList GmbH. The Legal form is GmbH. The Street is Wilhelm-Külz-Straße 25. The Postal code is 16540. The City is Hohen Neuendorf. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 13335 NP. The Subsegment is Search Engines and Comparison Sites. The Bank Cooperation is 0. The Homepage is https://www.finlist.de/. The E-Mail is https://www.finlist.de/contact. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Neuruppin. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 556. The Name is riskdatascience. The Status is 1. The Original German is 1.0. The Founding year is 2017.0. The Founder is Dr. Dimitrios Geromichalos. The Linkedin-Account Founder is unknown. The Legal Name is RiskDataScience GmbH. The Legal form is GmbH. The Street is Nördliche Münchner Straße 47. The Postal code is 82031. The City is Grünwald. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 232912. The Subsegment is Other FinTechs. The Bank Cooperation is 1. The Homepage is http://riskdatascience.net/. The E-Mail is riskdatascience@web.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is München. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 140. The Name is Ecocrowd. The Status is 1. The Original German is 1.0. The Founding year is 2014.0. The Founder is Jörg Sommer. The Linkedin-Account Founder is https://www.linkedin.com/in/joerg-sommer. The Legal Name is Deutsche Umweltstiftung. The Legal form is Stiftung. The Street is Greifswalder Straße 4. The Postal code is 10405. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is unknown. The Subsegment is Reward-based Crowdfunding. The Bank Cooperation is 0. The Homepage is https://www.ecocrowd.de/. The E-Mail is team@ecocrowd.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Financing", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 534. The Name is WhoFinance. The Status is 1. The Original German is 1.0. The Founding year is 2007.0. The Founder is Klaus-Jürgen Baum; Mustafa Behan. The Linkedin-Account Founder is https://www.linkedin.com/in/klaus-j%C3%BCrgen-baum-489411146/; https://www.linkedin.com/in/mustafa-behan-ab920a39/. The Legal Name is WhoFinance GmbH. The Legal form is GmbH. The Street is Teerofendamm 1. The Postal code is 14532. The City is Kleinmachnow. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 110212B. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://www.whofinance.de/. The E-Mail is bjoern.pommeranz@whofinance.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Kleinmachnow. The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 515. The Name is taxbutler. The Status is 0. The Original German is 1.0. The Founding year is 2014.0. The Founder is Matthias Raisch. The Linkedin-Account Founder is https://www.linkedin.com/in/matthias-raisch-4556188a/. The Legal Name is pareton GmbH. The Legal form is GmbH. The Street is Reitschulstraße 18. The Postal code is 74379. The City is Ingersheim. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 749615. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is unknown. The Insolvency is 1. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Ingersheim. The Former name is pareton.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 669. The Name is moneygarden. The Status is 1. The Original German is 1.0. The Founding year is 2011.0. The Founder is Thomas Kirst; Kosta Kampouridis. The Linkedin-Account Founder is https://www.linkedin.com/in/thomaskirst/. The Legal Name is moneygarden Thomas Kirst. The Legal form is personally liable. The Street is Barlowstr. 36. The Postal code is 81927. The City is München. The Country is Germany. The Register Number/ Company ID/ LEI is unknown. The Subsegment is Personal Financial Management. The Bank Cooperation is 0. The Homepage is https://moneygarden.de/. The E-Mail is info@moneygarden.de. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 672. The Name is novofina. The Status is 0. The Original German is 0.0. The Founding year is 2014.0. The Founder is Harald Helnwein. The Linkedin-Account Founder is https://www.linkedin.com/in/novofina. The Legal Name is Ing. Harald Helnwein. The Legal form is personally liable. The Street is Baumgasse 42. The Postal code is 1030. The City is Wien. The Country is Austria. The Register Number/ Company ID/ LEI is unknown. The Subsegment is Robo-Advice. The Bank Cooperation is 0. The Homepage is unknown. The E-Mail is info@HelnweinTrading.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is 0. The Former name is unknown.", "label": "Asset Management", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The ID is 480. The Name is payfit. The Status is 1. The Original German is 0.0. The Founding year is 2015.0. The Founder is Firmin Zocchetto; Ghislain de Fontenay; Florian Fournier. The Linkedin-Account Founder is https://www.linkedin.com/in/firmin-zocchetto/; https://www.linkedin.com/in/ghislain-de-fontenay-458297b8/; https://www.linkedin.com/in/florian-fournier-b093a2aa/. The Legal Name is PayFit GmbH. The Legal form is GmbH. The Street is Ohlauer Straße 43. The Postal code is 10999. The City is Berlin. The Country is Germany. The Register Number/ Company ID/ LEI is HRB 198627B. The Subsegment is Other FinTechs. The Bank Cooperation is 0. The Homepage is https://payfit.com/de/. The E-Mail is kontakt@payfit.com. The Insolvency is 0. The Liquidation is 0. The Date of inactivity is unknown. The Local court is Berlin (Charlottenburg). The Former name is unknown.", "label": "Other FinTechs", "dataset": "desalegngeb-german-fintech-companies", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/dileep070-heart-disease-prediction-using-logistic-regression/metadata.json

@@ -0,0 +1,23 @@
+{
+  "dataset": "dileep070-heart-disease-prediction-using-logistic-regression",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "TenYearCHD",
+  "label_values": [
+    "0.0",
+    "1.0"
+  ],
+  "num_labels": 2,
+  "train_samples": 3813,
+  "test_samples": 425,
+  "train_label_distribution": {
+    "0.0": 3234,
+    "1.0": 579
+  },
+  "test_label_distribution": {
+    "1.0": 65,
+    "0.0": 360
+  }
+}

classification/unipredict/dileep070-heart-disease-prediction-using-logistic-regression/test.csv

@@ -0,0 +1,426 @@
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classification/unipredict/dsfelix-us-stores-sales/metadata.json

@@ -0,0 +1,29 @@
+{
+  "dataset": "dsfelix-us-stores-sales",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Sales",
+  "label_values": [
+    "between 138.0 and 230.0",
+    "less than 100.0",
+    "between 100.0 and 138.0",
+    "greater than 230.0"
+  ],
+  "num_labels": 4,
+  "train_samples": 3822,
+  "test_samples": 426,
+  "train_label_distribution": {
+    "greater than 230.0": 970,
+    "between 138.0 and 230.0": 944,
+    "less than 100.0": 953,
+    "between 100.0 and 138.0": 955
+  },
+  "test_label_distribution": {
+    "between 100.0 and 138.0": 107,
+    "less than 100.0": 106,
+    "between 138.0 and 230.0": 105,
+    "greater than 230.0": 108
+  }
+}

classification/unipredict/dsfelix-us-stores-sales/test.csv

@@ -0,0 +1,427 @@
+Area Code,State,Market,Market Size,Profit,Margin,COGS,Total Expenses,Marketing,Inventory,Budget Profit,Budget COGS,Budget Margin,Budget Sales,ProductId,Date,Product Type,Product,Type,Sales
+435,Utah,West,Small Market,26,62,46,40,15,367,50,50,80,130,2,06/01/11 00:00:00,Coffee,Columbian,Regular,between 100.0 and 138.0
+959,Connecticut,East,Small Market,39,49,34,20,9,890,40,30,50,80,11,08/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+959,Connecticut,East,Small Market,24,60,50,36,15,1064,30,30,50,80,10,08/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 100.0 and 138.0
+603,New Hampshire,East,Small Market,-4,43,34,46,12,240,20,10,30,40,9,10/01/11 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+541,Oregon,West,Small Market,-6,75,54,79,49,427,40,60,100,160,1,02/01/11 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+262,Wisconsin,Central,Small Market,2,90,64,88,58,551,10,50,80,130,12,05/01/10 00:00:00,Tea,Earl Grey,Regular,between 138.0 and 230.0
+505,New Mexico,South,Small Market,-10,62,44,69,40,325,0,30,50,80,5,11/01/11 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+816,Missouri,Central,Small Market,24,60,50,36,15,1064,30,50,60,110,3,08/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 100.0 and 138.0
+503,Oregon,West,Small Market,18,73,59,55,22,331,20,50,70,120,4,02/01/10 00:00:00,Espresso,Caffe Latte,Regular,between 100.0 and 138.0
+712,Iowa,Central,Small Market,31,43,0,12,0,430,60,0,60,60,6,10/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,less than 100.0
+580,Oklahoma,South,Small Market,25,154,110,129,100,665,40,100,150,250,9,06/01/10 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+314,Missouri,Central,Small Market,27,46,31,19,8,844,50,30,60,90,6,12/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,less than 100.0
+509,Washington,West,Small Market,-4,76,55,79,49,627,-10,40,50,90,13,12/01/11 00:00:00,Tea,Green Tea,Regular,between 138.0 and 230.0
+206,Washington,West,Small Market,24,154,110,130,100,665,90,130,200,330,3,06/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+503,Oregon,West,Small Market,37,53,41,24,13,592,30,30,50,80,9,07/01/11 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+541,Oregon,West,Small Market,28,38,25,19,7,823,20,20,30,50,8,11/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+719,Colorado,Central,Major Market,145,145,100,37,28,598,130,110,170,280,5,08/01/11 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+708,Illinois,Central,Major Market,115,176,122,61,39,801,140,120,190,310,3,09/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+213,California,West,Major Market,230,266,266,92,74,1819,170,250,260,510,4,07/01/11 00:00:00,Espresso,Caffe Latte,Regular,greater than 230.0
+262,Wisconsin,Central,Small Market,86,112,83,54,27,433,80,80,120,200,3,04/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+775,Nevada,West,Small Market,141,228,228,87,63,1459,100,150,160,310,12,01/01/10 00:00:00,Tea,Earl Grey,Regular,greater than 230.0
+414,Wisconsin,Central,Small Market,58,112,83,54,27,433,80,80,120,200,3,04/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+503,Oregon,West,Small Market,-7,71,51,76,46,503,50,70,100,170,1,10/01/11 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+210,Texas,South,Major Market,26,51,34,30,10,428,30,30,50,80,9,08/01/11 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+702,Nevada,West,Small Market,333,362,272,113,89,1616,190,170,260,430,11,06/01/11 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+513,Ohio,Central,Major Market,99,167,111,68,36,767,90,90,150,240,11,06/01/10 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+971,Oregon,West,Small Market,17,36,25,19,7,820,30,10,30,40,8,10/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+630,Illinois,Central,Major Market,211,229,228,87,63,1436,180,260,270,530,6,02/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,greater than 230.0
+775,Nevada,West,Small Market,169,176,122,62,39,789,130,90,150,240,10,10/01/11 00:00:00,Herbal Tea,Mint,Decaf,greater than 230.0
+505,New Mexico,South,Small Market,5,35,28,30,8,958,20,20,40,60,9,03/01/10 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+339,Massachusetts,East,Major Market,23,43,29,20,8,862,30,30,40,70,11,09/01/10 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+435,Utah,West,Small Market,44,96,64,52,21,460,60,50,90,140,9,08/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 138.0 and 230.0
+504,Louisiana,South,Small Market,71,72,49,24,13,779,70,60,90,150,3,04/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 100.0 and 138.0
+774,Massachusetts,East,Major Market,690,516,60,51,19,-2572,460,50,490,540,2,09/01/11 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+847,Illinois,Central,Major Market,157,165,113,59,36,803,110,110,160,270,3,11/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+425,Washington,West,Small Market,4,90,65,87,58,513,10,40,60,100,13,02/01/11 00:00:00,Tea,Green Tea,Regular,between 138.0 and 230.0
+573,Missouri,Central,Small Market,-26,23,86,49,26,1698,10,60,20,80,9,10/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+959,Connecticut,East,Small Market,46,72,60,36,18,1070,40,40,60,100,10,07/01/11 00:00:00,Herbal Tea,Mint,Decaf,between 100.0 and 138.0
+314,Missouri,Central,Small Market,-3,43,34,46,12,240,10,10,30,40,13,10/01/10 00:00:00,Tea,Green Tea,Regular,less than 100.0
+641,Iowa,Central,Small Market,-2,13,10,15,3,598,10,0,10,10,3,10/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+206,Washington,West,Small Market,71,123,103,52,31,1073,90,90,120,210,6,08/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+920,Wisconsin,Central,Small Market,91,116,86,55,28,547,100,80,120,200,3,10/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+312,Illinois,Central,Major Market,33,81,54,48,17,641,30,40,70,110,12,06/01/10 00:00:00,Tea,Earl Grey,Regular,between 100.0 and 138.0
+951,California,West,Major Market,69,239,173,176,156,927,60,160,230,390,5,08/01/11 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+314,Missouri,Central,Small Market,19,53,39,40,12,244,30,20,40,60,11,10/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+630,Illinois,Central,Major Market,88,124,86,36,24,1003,80,80,110,190,10,12/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+775,Nevada,West,Small Market,163,271,196,108,64,1167,170,160,250,410,9,08/01/10 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+425,Washington,West,Small Market,70,71,48,24,13,834,50,40,60,100,9,09/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+636,Missouri,Central,Small Market,-18,40,93,52,28,840,10,70,40,110,9,04/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 138.0 and 230.0
+786,Florida,East,Major Market,68,96,81,44,25,1046,40,60,70,130,10,06/01/11 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+774,Massachusetts,East,Major Market,340,395,75,55,24,235,330,70,370,440,2,02/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+650,California,West,Major Market,-174,-24,154,93,50,3654,-100,220,-30,190,3,10/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+435,Utah,West,Small Market,43,93,76,58,28,613,70,90,120,210,3,07/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+918,Oklahoma,South,Small Market,139,159,114,54,35,-387,100,90,140,230,4,05/01/11 00:00:00,Espresso,Caffe Latte,Regular,greater than 230.0
+646,New York,East,Major Market,-269,-52,121,133,109,1673,-180,110,-50,60,5,05/01/11 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+435,Utah,West,Small Market,34,55,37,21,10,876,40,30,50,80,6,06/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,less than 100.0
+339,Massachusetts,East,Major Market,19,66,54,53,20,404,20,30,30,60,9,10/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+330,Ohio,Central,Major Market,33,79,59,46,19,411,50,50,80,130,1,11/01/10 00:00:00,Coffee,Amaretto,Regular,between 138.0 and 230.0
+775,Nevada,West,Small Market,9,35,28,29,8,961,20,30,40,70,3,02/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+773,Illinois,Central,Major Market,177,213,154,94,50,829,140,160,220,380,2,03/01/11 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+561,Florida,East,Major Market,51,103,77,52,25,557,60,70,100,170,6,11/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+603,New Hampshire,East,Small Market,16,27,18,16,5,816,20,10,30,40,11,03/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+325,Texas,South,Major Market,56,76,52,38,16,357,50,40,80,120,9,02/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+916,California,West,Major Market,-141,-32,191,109,63,1564,-150,240,-50,190,3,04/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+573,Missouri,Central,Small Market,61,64,44,23,12,886,50,50,70,120,5,09/01/11 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+319,Iowa,Central,Small Market,115,178,123,63,39,611,120,100,160,260,9,04/01/10 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+815,Illinois,Central,Major Market,216,329,247,113,81,1744,320,310,420,730,5,12/01/10 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+314,Missouri,Central,Small Market,16,51,38,40,12,256,10,30,40,70,11,12/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+325,Texas,South,Major Market,39,71,52,45,17,405,70,30,80,110,8,10/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+941,Florida,East,Major Market,35,62,48,27,15,593,30,30,50,80,8,06/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+419,Ohio,Central,Major Market,18,66,54,54,20,404,70,70,100,170,6,10/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+509,Washington,West,Small Market,30,51,35,21,9,776,40,30,40,70,9,04/01/10 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+303,Colorado,Central,Major Market,94,163,162,69,45,1091,100,130,150,280,8,04/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+515,Iowa,Central,Small Market,167,266,266,99,74,1819,150,220,240,460,11,07/01/10 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+646,New York,East,Major Market,94,130,89,36,24,777,110,90,140,230,12,01/01/10 00:00:00,Tea,Earl Grey,Regular,between 138.0 and 230.0
+505,New Mexico,South,Small Market,-4,42,34,46,12,-104,10,20,40,60,4,05/01/10 00:00:00,Espresso,Caffe Latte,Regular,less than 100.0
+262,Wisconsin,Central,Small Market,19,66,54,53,20,301,30,50,70,120,1,03/01/11 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+360,Washington,West,Small Market,-3,76,55,79,49,627,-10,40,50,90,13,12/01/10 00:00:00,Tea,Green Tea,Regular,between 100.0 and 138.0
+719,Colorado,Central,Major Market,26,71,52,45,17,361,30,40,60,100,10,05/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 100.0 and 138.0
+775,Nevada,West,Small Market,-6,41,33,45,12,243,10,30,50,80,2,11/01/11 00:00:00,Coffee,Columbian,Regular,less than 100.0
+435,Utah,West,Small Market,-33,25,116,58,35,1142,-10,90,30,120,10,06/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+203,Connecticut,East,Small Market,0,87,63,87,57,521,10,60,80,140,5,04/01/11 00:00:00,Espresso,Caffe Mocha,Regular,between 138.0 and 230.0
+253,Washington,West,Small Market,55,64,43,22,12,822,50,30,60,90,9,06/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+617,Massachusetts,East,Major Market,547,595,54,48,17,-1006,530,50,560,610,2,06/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+515,Iowa,Central,Small Market,3,33,28,30,8,964,20,20,40,60,2,04/01/10 00:00:00,Coffee,Columbian,Regular,less than 100.0
+337,Louisiana,South,Small Market,50,59,40,21,11,843,50,50,70,120,2,07/01/11 00:00:00,Coffee,Columbian,Regular,less than 100.0
+407,Florida,East,Major Market,51,103,77,52,25,423,60,70,100,170,6,02/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+504,Louisiana,South,Small Market,38,77,52,39,16,376,50,40,70,110,6,03/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+415,California,West,Major Market,326,335,252,115,83,1316,220,240,320,560,6,04/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,greater than 230.0
+857,Massachusetts,East,Major Market,113,146,145,60,40,1292,100,130,150,280,7,08/01/11 00:00:00,Espresso,Regular Espresso,Regular,greater than 230.0
+801,Utah,West,Small Market,16,57,43,41,14,388,40,50,70,120,2,07/01/10 00:00:00,Coffee,Columbian,Regular,between 100.0 and 138.0
+806,Texas,South,Major Market,88,114,75,55,24,-1050,40,50,90,140,4,11/01/11 00:00:00,Espresso,Caffe Latte,Regular,between 138.0 and 230.0
+775,Nevada,West,Small Market,12,38,31,30,9,1009,30,30,50,80,3,12/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+920,Wisconsin,Central,Small Market,20,54,36,34,11,458,10,30,40,70,11,08/01/10 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+702,Nevada,West,Small Market,-1,45,36,46,13,212,20,40,60,100,2,05/01/10 00:00:00,Coffee,Columbian,Regular,less than 100.0
+636,Missouri,Central,Small Market,-32,18,88,50,27,715,-10,70,20,90,9,03/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+959,Connecticut,East,Small Market,31,39,27,18,7,772,40,20,50,70,13,02/01/11 00:00:00,Tea,Green Tea,Regular,less than 100.0
+716,New York,East,Major Market,184,178,123,42,34,941,150,130,190,320,12,08/01/11 00:00:00,Tea,Earl Grey,Regular,greater than 230.0
+515,Iowa,Central,Small Market,15,32,21,17,5,798,30,20,40,60,5,06/01/10 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+773,Illinois,Central,Major Market,317,349,263,111,86,1433,300,300,420,720,5,05/01/11 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+515,Iowa,Central,Small Market,25,35,23,18,6,807,40,20,50,70,5,12/01/11 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+214,Texas,South,Major Market,52,79,54,27,15,601,40,40,60,100,6,12/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+773,Illinois,Central,Major Market,112,152,104,40,29,871,120,80,140,220,10,05/01/10 00:00:00,Herbal Tea,Mint,Decaf,greater than 230.0
+603,New Hampshire,East,Small Market,25,60,41,35,13,236,40,30,60,90,1,02/01/10 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+956,Texas,South,Major Market,86,114,75,56,24,378,60,60,100,160,4,02/01/11 00:00:00,Espresso,Caffe Latte,Regular,between 138.0 and 230.0
+203,Connecticut,East,Small Market,27,36,24,18,6,806,40,20,40,60,13,10/01/11 00:00:00,Tea,Green Tea,Regular,less than 100.0
+206,Washington,West,Small Market,57,99,60,42,18,329,40,20,50,70,11,10/01/10 00:00:00,Tea,Darjeeling,Regular,between 138.0 and 230.0
+603,New Hampshire,East,Small Market,-10,61,43,63,39,282,10,40,60,100,5,05/01/11 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+213,California,West,Major Market,230,266,266,92,74,1797,170,250,260,510,4,08/01/11 00:00:00,Espresso,Caffe Latte,Regular,greater than 230.0
+435,Utah,West,Small Market,-4,69,49,73,44,335,0,30,50,80,11,12/01/10 00:00:00,Tea,Darjeeling,Regular,between 100.0 and 138.0
+435,Utah,West,Small Market,36,60,41,36,13,236,30,30,50,80,8,02/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+956,Texas,South,Major Market,174,265,225,91,69,1272,230,260,320,580,2,11/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+559,California,West,Major Market,253,277,235,87,72,1060,180,200,250,450,9,05/01/11 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+619,California,West,Major Market,155,152,104,37,29,952,100,60,110,170,11,07/01/11 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+573,Missouri,Central,Small Market,26,60,45,38,14,253,30,30,60,90,11,08/01/11 00:00:00,Tea,Darjeeling,Regular,between 100.0 and 138.0
+916,California,West,Major Market,-2,50,95,52,30,608,30,110,70,180,1,01/01/10 00:00:00,Coffee,Amaretto,Regular,between 138.0 and 230.0
+715,Wisconsin,Central,Small Market,19,74,59,55,22,377,20,60,70,130,1,06/01/10 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+210,Texas,South,Major Market,112,152,104,40,29,952,120,80,140,220,5,07/01/10 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+603,New Hampshire,East,Small Market,-7,64,45,71,41,320,30,30,60,90,5,10/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+918,Oklahoma,South,Small Market,107,133,88,61,29,817,60,60,110,170,6,12/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,greater than 230.0
+435,Utah,West,Small Market,41,107,86,66,32,484,80,100,140,240,3,02/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+971,Oregon,West,Small Market,11,67,47,55,15,474,40,50,90,140,2,08/01/11 00:00:00,Coffee,Columbian,Regular,between 100.0 and 138.0
+303,Colorado,Central,Major Market,137,136,94,34,26,562,140,100,170,270,5,06/01/11 00:00:00,Espresso,Caffe Mocha,Regular,between 138.0 and 230.0
+650,California,West,Major Market,-117,-26,144,91,47,862,-120,180,-40,140,3,01/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 100.0 and 138.0
+413,Massachusetts,East,Major Market,-16,61,53,77,48,491,0,50,60,110,5,05/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+775,Nevada,West,Small Market,-605,-294,294,145,111,8252,-320,210,-210,0,13,12/01/11 00:00:00,Tea,Green Tea,Regular,less than 100.0
+209,California,West,Major Market,88,114,75,55,24,659,40,50,80,130,13,11/01/11 00:00:00,Tea,Green Tea,Regular,between 138.0 and 230.0
+715,Wisconsin,Central,Small Market,11,30,22,19,7,570,30,20,40,60,6,11/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,less than 100.0
+682,Texas,South,Major Market,30,56,43,26,14,531,40,50,60,110,3,12/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+603,New Hampshire,East,Small Market,1,88,63,81,57,332,10,60,80,140,5,08/01/11 00:00:00,Espresso,Caffe Mocha,Regular,between 138.0 and 230.0
+509,Washington,West,Small Market,70,71,48,24,13,829,60,30,60,90,9,10/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+775,Nevada,West,Small Market,13,24,15,15,4,848,10,10,20,30,5,11/01/11 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+541,Oregon,West,Small Market,83,149,149,66,41,1245,90,140,140,280,6,06/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,greater than 230.0
+513,Ohio,Central,Major Market,21,30,20,16,5,482,30,10,30,40,8,09/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+608,Wisconsin,Central,Small Market,-6,66,47,72,42,622,0,40,50,90,12,07/01/10 00:00:00,Tea,Earl Grey,Regular,between 100.0 and 138.0
+713,Texas,South,Major Market,26,71,52,45,17,405,70,30,80,110,8,10/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+435,Utah,West,Small Market,-6,38,30,44,11,165,10,10,30,40,13,02/01/10 00:00:00,Tea,Green Tea,Regular,less than 100.0
+970,Colorado,Central,Major Market,47,79,59,47,19,411,40,50,70,120,10,11/01/11 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+985,Louisiana,South,Small Market,41,81,56,40,17,382,50,40,70,110,6,02/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+775,Nevada,West,Small Market,31,43,0,12,0,731,30,0,40,40,4,03/01/10 00:00:00,Espresso,Caffe Latte,Regular,less than 100.0
+920,Wisconsin,Central,Small Market,24,59,40,35,12,443,20,30,50,80,11,06/01/10 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+505,New Mexico,South,Small Market,-3,42,33,45,12,36,10,20,40,60,4,03/01/10 00:00:00,Espresso,Caffe Latte,Regular,less than 100.0
+970,Colorado,Central,Major Market,83,110,72,54,23,650,130,100,160,260,6,10/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+330,Ohio,Central,Major Market,27,71,52,44,17,361,40,50,70,120,1,05/01/10 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+573,Missouri,Central,Small Market,47,72,49,25,13,809,70,50,90,140,5,03/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+702,Nevada,West,Small Market,1,48,38,47,14,248,20,40,60,100,2,08/01/10 00:00:00,Coffee,Columbian,Regular,less than 100.0
+985,Louisiana,South,Small Market,31,67,46,36,14,454,40,30,60,90,6,09/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+435,Utah,West,Small Market,17,68,92,51,28,1898,20,80,60,140,10,12/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+781,Massachusetts,East,Major Market,-23,63,63,86,57,435,-10,60,60,120,5,01/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+435,Utah,West,Small Market,39,46,31,20,8,844,30,30,40,70,4,12/01/11 00:00:00,Espresso,Caffe Latte,Regular,less than 100.0
+360,Washington,West,Small Market,16,130,93,114,84,692,60,110,160,270,3,09/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+512,Texas,South,Major Market,47,101,67,54,22,-1239,30,50,70,120,4,12/01/10 00:00:00,Espresso,Caffe Latte,Regular,between 138.0 and 230.0
+918,Oklahoma,South,Small Market,13,66,54,53,20,312,20,50,60,110,8,01/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+567,Ohio,Central,Major Market,145,164,118,55,36,636,90,100,140,240,12,06/01/11 00:00:00,Tea,Earl Grey,Regular,greater than 230.0
+847,Illinois,Central,Major Market,167,266,266,99,74,1797,220,300,320,620,6,08/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,greater than 230.0
+435,Utah,West,Small Market,-9,36,29,45,11,169,10,10,30,40,13,01/01/10 00:00:00,Tea,Green Tea,Regular,less than 100.0
+630,Illinois,Central,Major Market,59,114,75,55,24,549,50,60,100,160,12,02/01/10 00:00:00,Tea,Earl Grey,Regular,between 138.0 and 230.0
+573,Missouri,Central,Small Market,-6,64,45,70,41,320,30,20,60,80,8,10/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+513,Ohio,Central,Major Market,12,25,16,15,4,484,20,10,20,30,8,08/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+682,Texas,South,Major Market,40,72,53,45,17,410,40,50,70,120,8,09/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+847,Illinois,Central,Major Market,168,185,127,59,40,671,140,130,190,320,3,05/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+405,Oklahoma,South,Small Market,27,47,32,20,8,813,40,40,50,90,3,03/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+607,New York,East,Major Market,350,374,249,138,87,2580,230,220,340,560,2,11/01/11 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+641,Iowa,Central,Small Market,13,29,20,16,5,808,20,20,30,50,5,07/01/10 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+608,Wisconsin,Central,Small Market,34,82,67,59,25,391,30,70,80,150,1,09/01/11 00:00:00,Coffee,Amaretto,Regular,between 138.0 and 230.0
+715,Wisconsin,Central,Small Market,15,70,56,55,21,385,50,40,80,120,1,10/01/10 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+641,Iowa,Central,Small Market,356,387,291,120,96,1742,240,250,340,590,8,07/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+254,Texas,South,Major Market,112,152,104,40,29,821,120,80,140,220,5,05/01/10 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+425,Washington,West,Small Market,40,84,71,44,22,950,50,60,80,140,6,03/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+719,Colorado,Central,Major Market,144,191,132,47,36,994,160,130,200,330,3,08/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+314,Missouri,Central,Small Market,25,83,67,58,25,599,30,70,80,150,2,09/01/10 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+315,New York,East,Major Market,98,136,94,38,26,608,120,90,150,240,13,07/01/10 00:00:00,Tea,Green Tea,Regular,greater than 230.0
+509,Washington,West,Small Market,30,66,41,36,12,320,20,30,40,70,11,12/01/10 00:00:00,Tea,Darjeeling,Regular,between 100.0 and 138.0
+505,New Mexico,South,Small Market,-8,40,82,48,25,1804,10,90,50,140,3,11/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 100.0 and 138.0
+505,New Mexico,South,Small Market,17,36,25,19,7,854,20,20,30,50,8,07/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+971,Oregon,West,Small Market,-3,73,52,76,47,433,30,60,90,150,1,04/01/10 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+505,New Mexico,South,Small Market,-16,40,93,51,28,840,10,110,50,160,3,04/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+936,Texas,South,Major Market,185,281,239,96,74,1246,340,350,420,770,2,10/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+775,Nevada,West,Small Market,47,43,0,11,0,688,30,0,40,40,4,04/01/11 00:00:00,Espresso,Caffe Latte,Regular,less than 100.0
+971,Oregon,West,Small Market,65,83,57,43,18,364,70,70,100,170,3,01/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+715,Wisconsin,Central,Small Market,17,132,95,115,86,554,30,100,130,230,2,04/01/10 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+631,New York,East,Major Market,251,280,266,91,74,1355,190,250,280,530,7,07/01/11 00:00:00,Espresso,Regular Espresso,Regular,greater than 230.0
+716,New York,East,Major Market,367,390,260,143,91,2548,230,210,330,540,2,10/01/11 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+505,New Mexico,South,Small Market,39,72,47,46,15,361,50,50,90,140,2,03/01/11 00:00:00,Coffee,Columbian,Regular,between 100.0 and 138.0
+505,New Mexico,South,Small Market,-7,61,43,68,39,256,0,30,50,80,5,02/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+702,Nevada,West,Small Market,17,35,23,18,6,800,30,10,30,40,6,10/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,less than 100.0
+408,California,West,Major Market,106,145,100,39,28,822,110,80,130,210,8,04/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+303,Colorado,Central,Major Market,27,66,56,39,17,992,30,40,60,100,12,05/01/10 00:00:00,Tea,Earl Grey,Regular,between 100.0 and 138.0
+239,Florida,East,Major Market,44,48,32,21,8,821,40,30,50,80,11,01/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+630,Illinois,Central,Major Market,232,265,191,109,63,865,170,200,270,470,2,04/01/11 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+318,Louisiana,South,Small Market,23,59,40,36,12,443,30,30,50,80,6,06/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,less than 100.0
+325,Texas,South,Major Market,71,112,87,41,28,564,110,100,140,240,3,07/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+309,Illinois,Central,Major Market,143,191,132,48,36,994,140,110,170,280,10,08/01/10 00:00:00,Herbal Tea,Mint,Decaf,greater than 230.0
+847,Illinois,Central,Major Market,48,73,50,25,14,589,40,40,60,100,11,11/01/10 00:00:00,Tea,Darjeeling,Regular,between 100.0 and 138.0
+815,Illinois,Central,Major Market,186,204,141,64,45,764,160,140,220,360,3,06/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+573,Missouri,Central,Small Market,-12,61,43,69,39,256,10,30,60,90,8,02/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+971,Oregon,West,Small Market,64,123,82,59,27,788,50,30,60,90,12,10/01/10 00:00:00,Tea,Earl Grey,Regular,between 138.0 and 230.0
+314,Missouri,Central,Small Market,61,105,85,69,32,494,40,90,100,190,2,01/01/11 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+719,Colorado,Central,Major Market,24,68,50,44,16,397,30,40,60,100,10,06/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 100.0 and 138.0
+430,Texas,South,Major Market,206,307,260,101,80,1319,290,320,380,700,2,07/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+702,Nevada,West,Small Market,16,28,19,17,5,820,20,10,30,40,5,04/01/11 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+541,Oregon,West,Small Market,28,48,32,20,8,482,30,20,30,50,11,11/01/10 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+206,Washington,West,Small Market,39,82,69,43,21,965,60,60,80,140,6,04/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+719,Colorado,Central,Major Market,92,161,161,69,45,1267,110,120,140,260,8,10/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+206,Washington,West,Small Market,20,54,36,34,11,458,30,20,40,60,12,08/01/10 00:00:00,Tea,Earl Grey,Regular,less than 100.0
+818,California,West,Major Market,26,168,121,142,109,912,40,110,170,280,5,11/01/10 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+206,Washington,West,Small Market,65,92,78,42,24,995,60,70,90,160,6,05/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+860,Connecticut,East,Small Market,23,65,48,42,15,380,40,40,70,110,6,09/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+262,Wisconsin,Central,Small Market,70,72,49,25,13,779,50,40,60,100,9,04/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+860,Connecticut,East,Small Market,26,71,53,45,17,299,40,50,70,120,6,03/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+413,Massachusetts,East,Major Market,367,422,72,55,23,558,360,60,400,460,2,01/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+775,Nevada,West,Small Market,-552,-245,245,132,93,1419,-240,160,-160,0,13,01/01/11 00:00:00,Tea,Green Tea,Regular,less than 100.0
+319,Iowa,Central,Small Market,9,28,21,19,6,614,20,20,30,50,3,04/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+978,Massachusetts,East,Major Market,43,48,32,19,8,482,40,30,50,80,13,11/01/11 00:00:00,Tea,Green Tea,Regular,less than 100.0
+972,Texas,South,Major Market,164,252,213,88,66,942,240,260,320,580,2,02/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+530,California,West,Major Market,326,336,253,116,83,1686,220,240,320,560,6,09/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,greater than 230.0
+918,Oklahoma,South,Small Market,30,42,29,19,8,835,40,30,50,80,3,05/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+513,Ohio,Central,Major Market,81,146,145,65,40,1292,120,160,180,340,5,08/01/10 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+712,Iowa,Central,Small Market,141,150,104,55,33,596,110,80,140,220,9,02/01/11 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+425,Washington,West,Small Market,92,105,62,43,19,346,40,40,70,110,11,03/01/11 00:00:00,Tea,Darjeeling,Regular,between 138.0 and 230.0
+505,New Mexico,South,Small Market,-4,43,35,43,13,-180,0,30,30,60,4,06/01/11 00:00:00,Espresso,Caffe Latte,Regular,less than 100.0
+971,Oregon,West,Small Market,24,32,21,16,5,480,20,0,20,20,11,10/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+909,California,West,Major Market,-217,-36,238,113,78,2797,-150,290,-40,250,3,07/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+860,Connecticut,East,Small Market,28,88,72,60,27,606,30,50,70,120,9,08/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 138.0 and 230.0
+801,Utah,West,Small Market,-17,49,120,57,37,1334,-10,100,40,140,10,07/01/11 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+603,New Hampshire,East,Small Market,21,31,21,17,5,846,30,20,30,50,11,10/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+607,New York,East,Major Market,-120,-33,213,87,66,1214,-80,160,-20,140,10,02/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+702,Nevada,West,Small Market,-380,-265,265,133,100,3742,-250,170,-170,0,13,05/01/10 00:00:00,Tea,Green Tea,Regular,less than 100.0
+580,Oklahoma,South,Small Market,13,30,20,17,5,482,30,10,30,40,5,09/01/10 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+425,Washington,West,Small Market,18,74,59,56,22,377,20,50,70,120,5,06/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+505,New Mexico,South,Small Market,-10,61,43,68,39,256,0,30,50,80,5,02/01/11 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+916,California,West,Major Market,134,179,123,45,34,959,110,90,130,220,11,12/01/10 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+959,Connecticut,East,Small Market,2,87,62,85,56,612,20,50,90,140,5,09/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 138.0 and 230.0
+325,Texas,South,Major Market,95,130,89,35,24,777,90,70,110,180,5,01/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 138.0 and 230.0
+580,Oklahoma,South,Small Market,104,120,86,50,26,300,70,70,100,170,4,02/01/11 00:00:00,Espresso,Caffe Latte,Regular,between 138.0 and 230.0
+216,Ohio,Central,Major Market,42,72,53,44,17,410,50,50,80,130,1,09/01/11 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+775,Nevada,West,Small Market,44,43,0,10,0,516,30,0,40,40,4,08/01/11 00:00:00,Espresso,Caffe Latte,Regular,less than 100.0
+305,Florida,East,Major Market,20,77,63,57,23,397,30,40,60,100,9,08/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 138.0 and 230.0
+217,Illinois,Central,Major Market,119,213,154,94,50,829,140,160,220,380,2,03/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+775,Nevada,West,Small Market,241,362,272,121,89,1616,190,170,260,430,11,06/01/10 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+816,Missouri,Central,Small Market,-1,82,59,83,53,338,10,50,70,120,8,07/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 138.0 and 230.0
+440,Ohio,Central,Major Market,-2,75,53,77,48,458,20,50,80,130,3,05/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 100.0 and 138.0
+720,Colorado,Central,Major Market,105,145,100,40,28,598,130,110,170,280,5,08/01/10 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+206,Washington,West,Small Market,1,86,61,85,55,613,0,40,60,100,13,11/01/10 00:00:00,Tea,Green Tea,Regular,between 138.0 and 230.0
+419,Ohio,Central,Major Market,83,149,149,66,41,1245,120,170,180,350,5,06/01/10 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+971,Oregon,West,Small Market,26,37,25,17,7,826,30,20,30,50,8,08/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+754,Florida,East,Major Market,86,112,83,54,27,433,80,70,120,190,6,04/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+405,Oklahoma,South,Small Market,16,111,79,106,72,551,30,70,110,180,9,01/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 138.0 and 230.0
+425,Washington,West,Small Market,83,99,68,43,21,445,80,80,120,200,2,09/01/11 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+425,Washington,West,Small Market,75,135,90,60,29,1148,80,70,120,190,8,07/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 138.0 and 230.0
+541,Oregon,West,Small Market,17,36,25,19,7,823,30,20,30,50,8,09/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+312,Illinois,Central,Major Market,181,185,127,63,40,830,130,120,180,300,3,12/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+347,New York,East,Major Market,288,284,239,90,66,1260,210,220,290,510,7,03/01/11 00:00:00,Espresso,Regular Espresso,Regular,greater than 230.0
+253,Washington,West,Small Market,65,68,47,24,13,834,40,40,60,100,9,11/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+435,Utah,West,Small Market,37,59,40,22,11,881,60,20,60,80,6,10/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,less than 100.0
+573,Missouri,Central,Small Market,39,85,68,59,25,619,40,60,90,150,2,12/01/11 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+409,Texas,South,Major Market,159,245,207,86,64,965,220,260,300,560,2,01/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+206,Washington,West,Small Market,56,77,52,39,16,376,30,30,50,80,12,03/01/11 00:00:00,Tea,Earl Grey,Regular,between 100.0 and 138.0
+718,New York,East,Major Market,312,407,250,95,70,723,320,240,400,640,7,12/01/10 00:00:00,Espresso,Regular Espresso,Regular,greater than 230.0
+985,Louisiana,South,Small Market,126,130,101,45,33,552,80,90,120,210,8,11/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+918,Oklahoma,South,Small Market,121,186,134,65,41,-2248,90,100,140,240,4,12/01/10 00:00:00,Espresso,Caffe Latte,Regular,greater than 230.0
+206,Washington,West,Small Market,122,195,130,73,42,1134,120,120,180,300,8,11/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+650,California,West,Major Market,125,118,81,34,22,588,70,50,80,130,12,09/01/11 00:00:00,Tea,Earl Grey,Regular,between 138.0 and 230.0
+504,Louisiana,South,Small Market,46,68,46,37,14,388,40,30,60,90,6,04/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+212,New York,East,Major Market,138,185,127,47,35,1007,160,130,200,330,11,07/01/10 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+971,Oregon,West,Small Market,-4,73,52,76,47,421,30,60,90,150,1,03/01/11 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+414,Wisconsin,Central,Small Market,27,134,96,116,87,683,60,90,140,230,2,10/01/11 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+954,Florida,East,Major Market,81,134,96,53,29,666,80,90,120,210,3,09/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+719,Colorado,Central,Major Market,34,75,63,41,19,965,40,50,70,120,12,04/01/10 00:00:00,Tea,Earl Grey,Regular,between 138.0 and 230.0
+775,Nevada,West,Small Market,18,29,20,15,5,829,20,10,30,40,5,05/01/11 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+216,Ohio,Central,Major Market,112,146,145,60,40,1304,120,160,180,340,5,07/01/11 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+262,Wisconsin,Central,Small Market,13,66,54,53,20,301,30,50,70,120,1,03/01/10 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+773,Illinois,Central,Major Market,230,349,263,119,86,1433,300,300,420,720,5,05/01/10 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+435,Utah,West,Small Market,-19,30,24,43,9,212,0,10,20,30,13,09/01/11 00:00:00,Tea,Green Tea,Regular,less than 100.0
+801,Utah,West,Small Market,-3,42,34,45,12,211,0,20,30,50,13,11/01/10 00:00:00,Tea,Green Tea,Regular,less than 100.0
+956,Texas,South,Major Market,71,102,68,54,22,9,50,50,90,140,4,04/01/11 00:00:00,Espresso,Caffe Latte,Regular,between 138.0 and 230.0
+949,California,West,Major Market,41,79,52,46,17,668,20,30,50,80,13,07/01/11 00:00:00,Tea,Green Tea,Regular,between 100.0 and 138.0
+660,Missouri,Central,Small Market,-44,11,104,55,32,1574,-10,80,20,100,9,09/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+920,Wisconsin,Central,Small Market,30,66,41,36,12,320,30,30,60,90,8,12/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+515,Iowa,Central,Small Market,262,324,265,125,100,1538,170,220,290,510,12,05/01/11 00:00:00,Tea,Earl Grey,Regular,greater than 230.0
+303,Colorado,Central,Major Market,87,153,153,66,42,1319,80,140,140,280,8,12/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+254,Texas,South,Major Market,135,136,94,35,26,608,90,80,110,190,6,07/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+515,Iowa,Central,Small Market,15,30,22,18,7,611,20,20,30,50,3,05/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+660,Missouri,Central,Small Market,35,77,65,42,20,1053,70,50,80,130,3,10/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+636,Missouri,Central,Small Market,-7,62,44,69,40,325,10,30,60,90,8,09/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+216,Ohio,Central,Major Market,67,116,84,49,26,500,60,70,100,170,12,01/01/10 00:00:00,Tea,Earl Grey,Regular,between 138.0 and 230.0
+860,Connecticut,East,Small Market,33,96,77,63,29,529,40,50,80,130,9,05/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 138.0 and 230.0
+718,New York,East,Major Market,384,405,270,146,94,2041,270,250,390,640,2,03/01/11 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+971,Oregon,West,Small Market,33,54,36,21,10,777,40,30,40,70,8,01/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+508,Massachusetts,East,Major Market,564,613,52,49,17,-1493,560,40,590,630,2,07/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+970,Colorado,Central,Major Market,46,77,57,46,18,323,40,40,70,110,10,03/01/11 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+715,Wisconsin,Central,Small Market,50,65,50,27,16,587,60,50,80,130,6,07/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+815,Illinois,Central,Major Market,82,110,72,55,23,650,50,50,80,130,12,10/01/11 00:00:00,Tea,Earl Grey,Regular,between 138.0 and 230.0
+435,Utah,West,Small Market,70,72,49,25,13,809,50,40,70,110,6,03/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 100.0 and 138.0
+971,Oregon,West,Small Market,99,171,170,72,47,1091,100,160,160,320,6,01/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,greater than 230.0
+503,Oregon,West,Small Market,-4,73,52,76,47,433,30,60,90,150,1,04/01/11 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+337,Louisiana,South,Small Market,70,113,67,43,20,400,80,50,100,150,5,01/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 138.0 and 230.0
+503,Oregon,West,Small Market,37,90,65,65,21,392,50,80,110,190,2,02/01/11 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+959,Connecticut,East,Small Market,70,73,50,26,14,898,50,50,70,120,11,12/01/11 00:00:00,Tea,Darjeeling,Regular,between 100.0 and 138.0
+312,Illinois,Central,Major Market,155,249,249,94,69,1775,210,280,300,580,6,09/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,greater than 230.0
+845,New York,East,Major Market,-202,-56,125,146,113,3142,-170,110,-60,50,5,10/01/10 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+712,Iowa,Central,Small Market,188,204,141,63,45,764,140,120,180,300,9,06/01/11 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+505,New Mexico,South,Small Market,30,66,43,46,14,452,30,50,70,120,2,11/01/11 00:00:00,Coffee,Columbian,Regular,between 100.0 and 138.0
+435,Utah,West,Small Market,-16,40,93,51,28,840,0,70,40,110,10,04/01/11 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+339,Massachusetts,East,Major Market,112,146,145,60,40,1304,100,130,150,280,7,07/01/11 00:00:00,Espresso,Regular Espresso,Regular,greater than 230.0
+775,Nevada,West,Small Market,21,31,21,17,5,846,20,10,20,30,5,10/01/11 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+863,Florida,East,Major Market,62,117,87,55,28,472,70,80,110,190,6,05/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+504,Louisiana,South,Small Market,42,47,31,19,8,856,50,30,60,90,3,11/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+515,Iowa,Central,Small Market,15,26,17,16,4,776,30,10,40,50,5,04/01/11 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+918,Oklahoma,South,Small Market,20,66,54,56,20,312,20,50,60,110,8,01/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+312,Illinois,Central,Major Market,288,310,224,116,73,1191,190,220,300,520,2,12/01/11 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+970,Colorado,Central,Major Market,80,108,72,54,23,541,80,80,130,210,6,03/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+319,Iowa,Central,Small Market,17,29,20,16,5,829,20,20,30,50,1,05/01/11 00:00:00,Coffee,Amaretto,Regular,less than 100.0
+970,Colorado,Central,Major Market,45,56,44,26,14,618,30,30,50,80,13,02/01/11 00:00:00,Tea,Green Tea,Regular,between 100.0 and 138.0
+719,Colorado,Central,Major Market,157,139,95,40,26,821,110,100,140,240,3,01/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+503,Oregon,West,Small Market,19,38,25,19,7,823,20,20,30,50,8,11/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+563,Iowa,Central,Small Market,197,332,271,135,102,1714,180,230,300,530,12,06/01/10 00:00:00,Tea,Earl Grey,Regular,greater than 230.0
+959,Connecticut,East,Small Market,50,77,65,38,20,1042,50,40,70,110,10,06/01/11 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+720,Colorado,Central,Major Market,53,108,72,55,23,558,80,80,130,210,6,01/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+351,Massachusetts,East,Major Market,39,45,30,19,8,820,40,30,50,80,11,04/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+978,Massachusetts,East,Major Market,7,72,39,65,36,560,20,30,70,100,5,07/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+860,Connecticut,East,Small Market,15,31,20,16,5,814,20,20,30,50,13,07/01/10 00:00:00,Tea,Green Tea,Regular,less than 100.0
+541,Oregon,West,Small Market,44,48,32,21,8,456,30,20,30,50,11,01/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+818,California,West,Major Market,344,367,311,110,96,1296,240,260,330,590,9,08/01/11 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+303,Colorado,Central,Major Market,13,67,54,54,20,337,20,40,60,100,11,04/01/10 00:00:00,Tea,Darjeeling,Regular,between 100.0 and 138.0
+630,Illinois,Central,Major Market,291,304,228,108,75,1691,290,290,390,680,5,11/01/11 00:00:00,Espresso,Caffe Mocha,Regular,greater than 230.0
+515,Iowa,Central,Small Market,7,37,31,30,9,1000,30,20,40,60,2,10/01/10 00:00:00,Coffee,Columbian,Regular,less than 100.0
+405,Oklahoma,South,Small Market,132,143,102,54,31,-2003,60,80,100,180,4,11/01/11 00:00:00,Espresso,Caffe Latte,Regular,greater than 230.0
+505,New Mexico,South,Small Market,17,29,20,16,5,829,20,10,30,40,8,05/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+206,Washington,West,Small Market,15,128,92,113,83,541,60,110,160,270,3,02/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+580,Oklahoma,South,Small Market,13,66,54,53,20,301,20,50,60,110,8,03/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+541,Oregon,West,Small Market,25,51,34,31,10,432,40,40,60,100,3,07/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+904,Florida,East,Major Market,55,103,86,48,26,1081,50,60,80,140,10,07/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+206,Washington,West,Small Market,36,65,50,29,16,587,50,40,70,110,4,07/01/10 00:00:00,Espresso,Caffe Latte,Regular,between 100.0 and 138.0
+505,New Mexico,South,Small Market,-7,62,44,69,40,325,0,30,50,80,5,11/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+505,New Mexico,South,Small Market,14,55,41,41,13,239,20,30,50,80,6,06/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,less than 100.0
+314,Missouri,Central,Small Market,-39,11,88,50,27,525,-20,70,10,80,9,01/01/10 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+505,New Mexico,South,Small Market,14,41,34,29,10,1010,20,30,40,70,9,07/01/11 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+409,Texas,South,Major Market,28,64,43,36,13,419,50,30,60,90,9,10/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+815,Illinois,Central,Major Market,79,108,72,55,23,541,40,60,90,150,12,03/01/11 00:00:00,Tea,Earl Grey,Regular,between 138.0 and 230.0
+360,Washington,West,Small Market,62,70,48,23,13,839,50,40,60,100,9,08/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+212,New York,East,Major Market,316,369,284,130,107,1565,180,210,290,500,9,08/01/11 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+614,Ohio,Central,Major Market,9,26,18,17,5,478,20,10,20,30,8,06/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+702,Nevada,West,Small Market,316,312,234,114,77,1310,150,150,220,370,11,01/01/11 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+509,Washington,West,Small Market,41,84,70,43,21,957,60,60,80,140,6,02/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+970,Colorado,Central,Major Market,54,68,52,27,17,554,50,50,70,120,2,08/01/11 00:00:00,Coffee,Columbian,Regular,between 100.0 and 138.0
+505,New Mexico,South,Small Market,27,74,48,47,15,462,50,50,90,140,2,12/01/10 00:00:00,Coffee,Columbian,Regular,between 100.0 and 138.0
+832,Texas,South,Major Market,98,136,94,38,26,608,90,80,110,190,6,07/01/10 00:00:00,Espresso,Decaf Espresso,Decaf,greater than 230.0
+360,Washington,West,Small Market,31,67,46,36,14,454,30,30,40,70,12,09/01/10 00:00:00,Tea,Earl Grey,Regular,between 100.0 and 138.0
+720,Colorado,Central,Major Market,125,117,81,33,22,441,110,90,140,230,5,03/01/11 00:00:00,Espresso,Caffe Mocha,Regular,between 138.0 and 230.0
+712,Iowa,Central,Small Market,10,27,18,17,5,818,20,10,30,40,1,02/01/10 00:00:00,Coffee,Amaretto,Regular,less than 100.0
+405,Oklahoma,South,Small Market,96,123,82,58,27,788,60,50,80,130,6,10/01/11 00:00:00,Espresso,Decaf Espresso,Decaf,between 138.0 and 230.0
+573,Missouri,Central,Small Market,56,102,82,64,31,601,40,80,100,180,2,11/01/11 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+567,Ohio,Central,Major Market,-12,56,39,60,36,517,0,40,50,90,3,07/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+775,Nevada,West,Small Market,4,33,28,29,8,964,20,30,40,70,3,04/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+305,Florida,East,Major Market,73,134,88,61,29,653,90,80,130,210,2,04/01/10 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+435,Utah,West,Small Market,-9,38,30,44,11,165,10,10,30,40,13,02/01/11 00:00:00,Tea,Green Tea,Regular,less than 100.0
+503,Oregon,West,Small Market,28,93,67,65,22,400,60,80,120,200,2,01/01/10 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+435,Utah,West,Small Market,39,72,47,46,15,361,30,40,60,100,9,03/01/11 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+954,Florida,East,Major Market,44,89,75,45,23,1063,30,50,60,110,10,11/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+603,New Hampshire,East,Small Market,32,43,0,11,0,344,30,0,40,40,13,12/01/10 00:00:00,Tea,Green Tea,Regular,less than 100.0
+641,Iowa,Central,Small Market,315,312,234,116,77,1310,200,200,280,480,8,01/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+970,Colorado,Central,Major Market,99,170,170,71,47,1073,100,140,150,290,8,02/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+262,Wisconsin,Central,Small Market,30,67,46,37,14,449,40,20,50,70,11,10/01/10 00:00:00,Tea,Darjeeling,Regular,between 100.0 and 138.0
+805,California,West,Major Market,67,107,83,40,27,584,60,70,90,160,10,05/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+503,Oregon,West,Small Market,25,60,41,35,13,435,50,40,80,120,3,12/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 100.0 and 138.0
+516,New York,East,Major Market,115,157,108,42,30,971,140,110,170,280,11,10/01/10 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+435,Utah,West,Small Market,-67,11,104,56,32,1574,-20,80,20,100,10,09/01/11 00:00:00,Herbal Tea,Mint,Decaf,between 100.0 and 138.0
+305,Florida,East,Major Market,31,58,45,27,14,605,30,30,50,80,8,04/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+641,Iowa,Central,Small Market,291,304,228,108,75,1691,180,210,280,490,8,11/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+715,Wisconsin,Central,Small Market,22,80,65,58,24,403,30,60,80,140,1,12/01/10 00:00:00,Coffee,Amaretto,Regular,between 138.0 and 230.0
+805,California,West,Major Market,62,100,78,38,25,593,60,60,90,150,10,04/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+775,Nevada,West,Small Market,149,239,239,90,66,1464,110,150,170,320,12,04/01/10 00:00:00,Tea,Earl Grey,Regular,greater than 230.0
+435,Utah,West,Small Market,50,63,48,25,15,599,40,40,60,100,5,05/01/11 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0
+801,Utah,West,Small Market,39,105,85,66,32,494,70,100,130,230,3,01/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+513,Ohio,Central,Major Market,18,36,24,18,6,856,30,20,30,50,9,06/01/10 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+603,New Hampshire,East,Small Market,23,36,25,18,7,854,30,20,40,60,11,07/01/11 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+614,Ohio,Central,Major Market,30,52,36,22,10,862,40,30,50,80,9,11/01/10 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+234,Ohio,Central,Major Market,-8,63,44,64,40,496,20,40,70,110,3,06/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 100.0 and 138.0
+720,Colorado,Central,Major Market,137,161,161,69,45,1267,110,120,140,260,8,10/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+503,Oregon,West,Small Market,-7,65,47,72,42,521,20,50,80,130,1,12/01/10 00:00:00,Coffee,Amaretto,Regular,between 100.0 and 138.0
+815,Illinois,Central,Major Market,106,165,113,59,36,803,110,110,160,270,3,11/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,greater than 230.0
+936,Texas,South,Major Market,47,79,59,47,19,411,40,50,70,120,8,11/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,between 138.0 and 230.0
+435,Utah,West,Small Market,14,53,39,39,12,212,20,20,40,60,12,05/01/10 00:00:00,Tea,Earl Grey,Regular,less than 100.0
+435,Utah,West,Small Market,11,51,37,40,12,195,10,20,30,50,12,04/01/10 00:00:00,Tea,Earl Grey,Regular,less than 100.0
+310,California,West,Major Market,59,96,64,49,21,593,30,40,60,100,13,05/01/11 00:00:00,Tea,Green Tea,Regular,between 138.0 and 230.0
+985,Louisiana,South,Small Market,4,90,64,81,58,-113,10,50,80,130,4,05/01/11 00:00:00,Espresso,Caffe Latte,Regular,between 138.0 and 230.0
+518,New York,East,Major Market,111,152,104,41,29,952,120,110,160,270,12,07/01/10 00:00:00,Tea,Earl Grey,Regular,greater than 230.0
+563,Iowa,Central,Small Market,11,26,17,15,4,777,30,10,40,50,5,01/01/10 00:00:00,Espresso,Caffe Mocha,Regular,less than 100.0
+617,Massachusetts,East,Major Market,29,49,33,20,9,870,40,30,50,80,11,12/01/10 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+315,New York,East,Major Market,247,390,260,143,91,2548,230,210,330,540,2,10/01/10 00:00:00,Coffee,Columbian,Regular,greater than 230.0
+509,Washington,West,Small Market,44,68,47,24,13,834,40,40,60,100,9,11/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 100.0 and 138.0
+541,Oregon,West,Small Market,53,87,68,34,22,587,60,50,80,130,9,08/01/10 00:00:00,Herbal Tea,Lemon,Decaf,between 138.0 and 230.0
+904,Florida,East,Major Market,15,29,22,19,7,573,20,0,20,20,8,10/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,less than 100.0
+920,Wisconsin,Central,Small Market,48,88,53,40,16,321,50,50,80,130,8,11/01/10 00:00:00,Herbal Tea,Chamomile,Decaf,between 138.0 and 230.0
+775,Nevada,West,Small Market,291,304,228,108,75,1691,140,160,220,380,11,11/01/11 00:00:00,Tea,Darjeeling,Regular,greater than 230.0
+775,Nevada,West,Small Market,211,229,228,87,63,1436,100,150,160,310,12,02/01/11 00:00:00,Tea,Earl Grey,Regular,greater than 230.0
+971,Oregon,West,Small Market,8,68,48,60,15,422,30,60,80,140,2,05/01/10 00:00:00,Coffee,Columbian,Regular,between 100.0 and 138.0
+918,Oklahoma,South,Small Market,24,132,95,116,86,554,30,90,130,220,9,04/01/11 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+712,Iowa,Central,Small Market,11,30,22,19,7,608,20,20,30,50,3,06/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+352,Florida,East,Major Market,105,120,86,49,26,490,70,80,110,190,3,02/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+801,Utah,West,Small Market,50,71,49,37,15,315,40,40,60,100,8,09/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+225,Louisiana,South,Small Market,-4,76,55,79,49,-1053,0,40,60,100,4,12/01/11 00:00:00,Espresso,Caffe Latte,Regular,between 138.0 and 230.0
+702,Nevada,West,Small Market,-363,-255,255,129,96,7058,-170,110,-110,0,13,10/01/10 00:00:00,Tea,Green Tea,Regular,less than 100.0
+715,Wisconsin,Central,Small Market,39,82,69,43,21,965,50,80,90,170,5,04/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 138.0 and 230.0
+860,Connecticut,East,Small Market,61,64,44,23,12,886,50,40,70,110,11,09/01/11 00:00:00,Tea,Darjeeling,Regular,between 100.0 and 138.0
+775,Nevada,West,Small Market,8,38,32,30,9,996,20,40,40,80,3,06/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,less than 100.0
+254,Texas,South,Major Market,26,51,34,30,10,432,30,30,50,80,9,07/01/11 00:00:00,Herbal Tea,Lemon,Decaf,less than 100.0
+860,Connecticut,East,Small Market,17,34,23,17,6,809,30,20,40,60,13,09/01/10 00:00:00,Tea,Green Tea,Regular,less than 100.0
+801,Utah,West,Small Market,49,71,49,38,15,310,60,30,60,90,8,10/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,between 100.0 and 138.0
+541,Oregon,West,Small Market,17,34,22,17,6,452,20,10,20,30,11,02/01/10 00:00:00,Tea,Darjeeling,Regular,less than 100.0
+801,Utah,West,Small Market,-41,19,120,60,37,1439,-30,100,20,120,10,08/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 138.0 and 230.0
+970,Colorado,Central,Major Market,30,56,44,26,14,618,30,30,50,80,13,02/01/10 00:00:00,Tea,Green Tea,Regular,between 100.0 and 138.0
+360,Washington,West,Small Market,86,104,72,46,23,461,80,80,120,200,2,12/01/11 00:00:00,Coffee,Columbian,Regular,between 138.0 and 230.0
+206,Washington,West,Small Market,181,195,130,73,42,1134,120,120,180,300,8,11/01/11 00:00:00,Herbal Tea,Chamomile,Decaf,greater than 230.0
+847,Illinois,Central,Major Market,99,137,95,38,26,809,100,80,120,200,10,02/01/10 00:00:00,Herbal Tea,Mint,Decaf,greater than 230.0
+720,Colorado,Central,Major Market,31,77,57,46,18,323,40,40,70,110,10,03/01/10 00:00:00,Herbal Tea,Mint,Decaf,between 100.0 and 138.0
+430,Texas,South,Major Market,101,107,83,39,27,613,100,100,130,230,3,02/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+702,Nevada,West,Small Market,221,270,195,102,64,956,160,160,240,400,9,05/01/11 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+801,Utah,West,Small Market,70,91,76,44,23,965,70,90,110,200,1,04/01/11 00:00:00,Coffee,Amaretto,Regular,between 138.0 and 230.0
+504,Louisiana,South,Small Market,65,68,47,24,13,772,70,50,90,140,3,02/01/11 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 100.0 and 138.0
+971,Oregon,West,Small Market,132,143,102,54,31,666,70,70,100,170,13,11/01/11 00:00:00,Tea,Green Tea,Regular,greater than 230.0
+563,Iowa,Central,Small Market,95,150,104,55,33,596,110,80,140,220,9,02/01/10 00:00:00,Herbal Tea,Lemon,Decaf,greater than 230.0
+573,Missouri,Central,Small Market,47,92,78,45,24,965,50,80,90,170,3,01/01/10 00:00:00,Coffee,Decaf Irish Cream,Decaf,between 138.0 and 230.0
+585,New York,East,Major Market,77,79,54,27,15,601,60,50,80,130,13,12/01/11 00:00:00,Tea,Green Tea,Regular,between 138.0 and 230.0
+801,Utah,West,Small Market,34,62,48,28,15,611,40,40,60,100,5,03/01/10 00:00:00,Espresso,Caffe Mocha,Regular,between 100.0 and 138.0

classification/unipredict/eishkaran-heart-disease/metadata.json

@@ -0,0 +1,23 @@
+{
+  "dataset": "eishkaran-heart-disease",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "target",
+  "label_values": [
+    "0.0",
+    "1.0"
+  ],
+  "num_labels": 2,
+  "train_samples": 1070,
+  "test_samples": 120,
+  "train_label_distribution": {
+    "1.0": 566,
+    "0.0": 504
+  },
+  "test_label_distribution": {
+    "0.0": 57,
+    "1.0": 63
+  }
+}

classification/unipredict/eishkaran-heart-disease/test.csv

@@ -0,0 +1,121 @@
+age,sex,chest pain type,resting bp s,cholesterol,fasting blood sugar,resting ecg,max heart rate,exercise angina,oldpeak,ST slope,target
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+60,1,3,115,0,1,0,143,0,2.4,1,1.0
+58,1,4,125,300,0,2,171,0,0.0,1,1.0
+54,1,3,150,195,0,0,122,0,0.0,1,0.0
+62,0,4,140,394,0,2,157,0,1.2,2,0.0
+64,0,4,180,325,0,0,154,1,0.0,1,0.0
+53,1,2,120,181,0,0,132,0,0.0,1,0.0
+54,0,3,110,214,0,0,158,0,1.6,2,0.0
+43,1,4,140,288,0,0,135,1,2.0,2,1.0
+59,1,3,126,218,1,0,134,0,2.2,2,1.0
+35,1,2,120,308,0,2,180,0,0.0,1,0.0
+58,1,3,105,240,0,2,154,1,0.6,2,0.0
+54,1,4,140,239,0,0,160,0,1.2,1,0.0
+66,0,4,155,0,1,0,90,0,0.0,2,1.0
+58,1,4,130,263,0,0,140,1,2.0,2,1.0
+61,1,4,134,0,1,1,86,0,1.5,2,1.0
+50,1,2,120,168,0,0,160,0,0.0,1,0.0
+55,1,2,130,262,0,0,155,0,0.0,1,0.0
+48,0,4,150,227,0,0,130,1,1.0,2,0.0
+61,1,4,150,0,0,0,105,1,0.0,2,1.0
+60,1,4,130,253,0,0,144,1,1.4,1,1.0
+54,1,4,110,206,0,2,108,1,0.0,2,1.0
+46,1,3,150,163,0,0,116,0,0.0,1,0.0
+53,0,2,113,468,0,0,127,0,0.0,1,0.0
+60,0,3,120,178,1,0,96,0,0.0,1,0.0
+44,1,2,130,219,0,2,188,0,0.0,1,0.0
+41,1,2,120,291,0,1,160,0,0.0,1,0.0
+59,1,3,125,0,1,0,175,0,2.6,2,1.0
+39,1,3,120,339,0,0,170,0,0.0,1,0.0
+57,0,4,180,347,0,1,126,1,0.8,2,0.0
+49,0,3,130,207,0,1,135,0,0.0,1,0.0
+62,1,4,115,0,1,0,128,1,2.5,3,1.0
+33,0,4,100,246,0,0,150,1,1.0,2,1.0
+56,1,4,130,0,0,2,122,1,1.0,2,1.0
+52,1,2,120,325,0,0,172,0,0.2,1,0.0
+51,1,4,95,0,1,0,126,0,2.2,2,1.0
+46,1,4,130,222,0,0,112,0,0.0,2,1.0
+40,1,4,125,0,1,0,165,0,0.0,2,1.0
+52,1,2,120,284,0,0,118,0,0.0,1,0.0
+59,1,4,122,233,0,0,117,1,1.3,3,1.0
+47,0,2,140,257,0,0,135,0,1.0,1,0.0
+50,1,4,115,0,0,0,120,1,0.5,2,1.0
+57,1,4,122,264,0,2,100,0,0.0,2,1.0
+58,1,2,126,0,1,0,110,1,2.0,2,1.0
+52,1,4,112,230,0,0,160,0,0.0,1,1.0
+56,1,3,130,276,0,0,128,1,1.0,1,0.0
+38,1,3,100,0,0,0,179,0,-1.1,1,0.0
+53,1,4,140,203,1,2,155,1,3.1,3,1.0
+54,1,2,120,238,0,0,154,0,0.0,1,0.0
+60,1,4,130,186,1,2,140,1,0.5,2,1.0
+45,1,4,115,260,0,2,185,0,0.0,1,0.0
+63,1,4,96,305,0,1,121,1,1.0,1,1.0
+44,1,4,135,491,0,0,135,0,0.0,2,1.0
+53,1,3,155,175,1,1,160,0,0.3,1,0.0
+49,1,4,150,222,0,0,122,0,2.0,2,1.0
+41,0,2,126,306,0,0,163,0,0.0,1,0.0
+60,1,4,130,206,0,2,132,1,2.4,2,1.0
+66,0,3,146,278,0,2,152,0,0.0,2,0.0
+34,1,2,150,214,0,1,168,0,0.0,1,0.0
+59,1,4,135,234,0,0,161,0,0.5,2,0.0
+61,1,4,140,207,0,2,138,1,1.9,1,1.0
+72,1,4,160,123,1,2,130,0,1.5,2,1.0
+43,1,4,132,247,1,2,143,1,0.1,2,1.0
+58,1,4,135,222,0,0,100,0,0.0,1,0.0
+54,1,4,120,0,0,0,155,0,0.0,2,1.0
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+64,1,4,120,0,1,1,106,0,2.0,2,1.0
+63,0,4,150,407,0,2,154,0,4.0,2,1.0
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+47,1,3,110,0,1,0,120,1,0.0,2,1.0
+41,1,4,150,171,0,0,128,1,1.5,2,0.0
+45,1,3,110,0,0,0,138,0,-0.1,1,0.0
+59,1,1,170,288,0,2,159,0,0.2,2,1.0
+43,1,4,150,247,0,0,171,0,1.5,1,0.0
+47,1,3,138,257,0,2,156,0,0.0,1,0.0
+43,1,3,130,315,0,0,162,0,1.9,1,0.0
+43,1,4,120,177,0,2,120,1,2.5,2,1.0
+49,1,4,130,206,0,0,170,0,0.0,2,1.0
+42,1,2,120,295,0,0,162,0,0.0,1,0.0
+75,1,4,170,203,1,1,108,0,0.0,2,1.0
+60,1,4,125,258,0,2,141,1,2.8,2,1.0
+48,1,4,115,0,1,0,128,0,0.0,2,1.0
+66,1,4,150,0,0,0,108,1,2.0,2,1.0
+59,1,4,178,0,1,2,120,1,0.0,2,1.0
+70,1,2,156,245,0,2,143,0,0.0,1,0.0
+53,1,2,120,0,0,0,95,0,0.0,2,1.0
+46,1,2,101,197,1,0,156,0,0.0,1,0.0
+59,1,4,110,239,0,2,142,1,1.2,2,1.0
+52,1,4,112,230,0,0,160,0,0.0,1,1.0
+64,0,4,130,303,0,0,122,0,2.0,2,0.0
+59,1,4,110,239,0,2,142,1,1.2,2,1.0
+43,0,3,150,254,0,0,175,0,0.0,1,0.0
+54,1,2,192,283,0,2,195,0,0.0,1,1.0
+47,1,3,108,243,0,0,152,0,0.0,1,1.0
+58,1,3,140,179,0,0,160,0,0.0,1,0.0
+55,1,2,130,262,0,0,155,0,0.0,1,0.0
+57,1,3,128,229,0,2,150,0,0.4,2,1.0
+37,1,4,140,207,0,0,130,1,1.5,2,1.0
+47,1,4,150,226,0,0,98,1,1.5,2,1.0
+48,1,4,130,256,1,2,150,1,0.0,1,1.0
+51,1,4,130,0,1,0,170,0,-0.7,1,1.0
+67,0,4,106,223,0,0,142,0,0.3,1,0.0
+68,1,4,135,0,0,1,120,1,0.0,1,1.0
+52,1,2,128,205,1,0,184,0,0.0,1,0.0
+57,1,4,150,276,0,2,112,1,0.6,2,1.0
+50,1,4,150,243,0,2,128,0,2.6,2,1.0
+53,1,3,130,246,1,2,173,0,0.0,1,0.0
+55,1,2,110,214,1,1,180,0,0.4,1,0.0
+51,1,4,140,298,0,0,122,1,4.2,2,1.0
+54,0,3,108,267,0,2,167,0,0.0,1,0.0
+50,1,4,145,0,1,0,139,1,0.7,2,1.0
+62,0,4,124,209,0,0,163,0,0.0,1,0.0
+49,0,3,160,180,0,0,156,0,1.0,2,1.0
+65,0,4,150,225,0,2,114,0,1.0,2,1.0

classification/unipredict/eishkaran-heart-disease/test.jsonl

@@ -0,0 +1,120 @@
+{"text": "The age is 54.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 140.0. The cholesterol is 239.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 160.0. The exercise angina is 0.0. The oldpeak is 1.2. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 65.0. The sex is 0.0. The chest pain type is 3.0. The resting bp s is 140.0. The cholesterol is 417.0. The fasting blood sugar is 1.0. The resting ecg is 2.0. The max heart rate is 157.0. The exercise angina is 0.0. The oldpeak is 0.8. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 38.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 115.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 128.0. The exercise angina is 1.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 29.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 130.0. The cholesterol is 204.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 202.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 63.0. The sex is 1.0. The chest pain type is 1.0. The resting bp s is 145.0. The cholesterol is 233.0. The fasting blood sugar is 1.0. The resting ecg is 2.0. The max heart rate is 150.0. The exercise angina is 0.0. The oldpeak is 2.3. The ST slope is 3.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 60.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 115.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 143.0. The exercise angina is 0.0. The oldpeak is 2.4. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 58.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 125.0. The cholesterol is 300.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 171.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 54.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 150.0. The cholesterol is 195.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 122.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 62.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 140.0. The cholesterol is 394.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 157.0. The exercise angina is 0.0. The oldpeak is 1.2. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 64.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 180.0. The cholesterol is 325.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 154.0. The exercise angina is 1.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 53.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 181.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 132.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 54.0. The sex is 0.0. The chest pain type is 3.0. The resting bp s is 110.0. The cholesterol is 214.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 158.0. The exercise angina is 0.0. The oldpeak is 1.6. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 43.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 140.0. The cholesterol is 288.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 135.0. The exercise angina is 1.0. The oldpeak is 2.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 59.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 126.0. The cholesterol is 218.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 134.0. The exercise angina is 0.0. The oldpeak is 2.2. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 35.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 308.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 180.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 58.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 105.0. The cholesterol is 240.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 154.0. The exercise angina is 1.0. The oldpeak is 0.6. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 54.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 140.0. The cholesterol is 239.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 160.0. The exercise angina is 0.0. The oldpeak is 1.2. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 66.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 155.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 90.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 58.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 263.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 140.0. The exercise angina is 1.0. The oldpeak is 2.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 61.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 134.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 1.0. The max heart rate is 86.0. The exercise angina is 0.0. The oldpeak is 1.5. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 50.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 168.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 160.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 55.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 130.0. The cholesterol is 262.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 155.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 48.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 227.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 130.0. The exercise angina is 1.0. The oldpeak is 1.0. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 61.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 105.0. The exercise angina is 1.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 60.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 253.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 144.0. The exercise angina is 1.0. The oldpeak is 1.4. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 54.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 110.0. The cholesterol is 206.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 108.0. The exercise angina is 1.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 46.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 150.0. The cholesterol is 163.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 116.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 53.0. The sex is 0.0. The chest pain type is 2.0. The resting bp s is 113.0. The cholesterol is 468.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 127.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 60.0. The sex is 0.0. The chest pain type is 3.0. The resting bp s is 120.0. The cholesterol is 178.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 96.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 44.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 130.0. The cholesterol is 219.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 188.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 41.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 291.0. The fasting blood sugar is 0.0. The resting ecg is 1.0. The max heart rate is 160.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 59.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 125.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 175.0. The exercise angina is 0.0. The oldpeak is 2.6. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 39.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 120.0. The cholesterol is 339.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 170.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 57.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 180.0. The cholesterol is 347.0. The fasting blood sugar is 0.0. The resting ecg is 1.0. The max heart rate is 126.0. The exercise angina is 1.0. The oldpeak is 0.8. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 49.0. The sex is 0.0. The chest pain type is 3.0. The resting bp s is 130.0. The cholesterol is 207.0. The fasting blood sugar is 0.0. The resting ecg is 1.0. The max heart rate is 135.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 62.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 115.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 128.0. The exercise angina is 1.0. The oldpeak is 2.5. The ST slope is 3.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 33.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 100.0. The cholesterol is 246.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 150.0. The exercise angina is 1.0. The oldpeak is 1.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 56.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 122.0. The exercise angina is 1.0. The oldpeak is 1.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 52.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 325.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 172.0. The exercise angina is 0.0. The oldpeak is 0.2. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 51.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 95.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 126.0. The exercise angina is 0.0. The oldpeak is 2.2. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 46.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 222.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 112.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 40.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 125.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 165.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 52.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 284.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 118.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 59.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 122.0. The cholesterol is 233.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 117.0. The exercise angina is 1.0. The oldpeak is 1.3. The ST slope is 3.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 47.0. The sex is 0.0. The chest pain type is 2.0. The resting bp s is 140.0. The cholesterol is 257.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 135.0. The exercise angina is 0.0. The oldpeak is 1.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 50.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 115.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 120.0. The exercise angina is 1.0. The oldpeak is 0.5. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 57.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 122.0. The cholesterol is 264.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 100.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 58.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 126.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 110.0. The exercise angina is 1.0. The oldpeak is 2.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 52.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 112.0. The cholesterol is 230.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 160.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 56.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 130.0. The cholesterol is 276.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 128.0. The exercise angina is 1.0. The oldpeak is 1.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 38.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 100.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 179.0. The exercise angina is 0.0. The oldpeak is -1.1. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 53.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 140.0. The cholesterol is 203.0. The fasting blood sugar is 1.0. The resting ecg is 2.0. The max heart rate is 155.0. The exercise angina is 1.0. The oldpeak is 3.1. The ST slope is 3.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 54.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 238.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 154.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 60.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 186.0. The fasting blood sugar is 1.0. The resting ecg is 2.0. The max heart rate is 140.0. The exercise angina is 1.0. The oldpeak is 0.5. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 45.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 115.0. The cholesterol is 260.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 185.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 63.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 96.0. The cholesterol is 305.0. The fasting blood sugar is 0.0. The resting ecg is 1.0. The max heart rate is 121.0. The exercise angina is 1.0. The oldpeak is 1.0. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 44.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 135.0. The cholesterol is 491.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 135.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 53.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 155.0. The cholesterol is 175.0. The fasting blood sugar is 1.0. The resting ecg is 1.0. The max heart rate is 160.0. The exercise angina is 0.0. The oldpeak is 0.3. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 49.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 222.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 122.0. The exercise angina is 0.0. The oldpeak is 2.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 41.0. The sex is 0.0. The chest pain type is 2.0. The resting bp s is 126.0. The cholesterol is 306.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 163.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 60.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 206.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 132.0. The exercise angina is 1.0. The oldpeak is 2.4. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 66.0. The sex is 0.0. The chest pain type is 3.0. The resting bp s is 146.0. The cholesterol is 278.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 152.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 34.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 150.0. The cholesterol is 214.0. The fasting blood sugar is 0.0. The resting ecg is 1.0. The max heart rate is 168.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 59.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 135.0. The cholesterol is 234.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 161.0. The exercise angina is 0.0. The oldpeak is 0.5. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 61.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 140.0. The cholesterol is 207.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 138.0. The exercise angina is 1.0. The oldpeak is 1.9. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 72.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 160.0. The cholesterol is 123.0. The fasting blood sugar is 1.0. The resting ecg is 2.0. The max heart rate is 130.0. The exercise angina is 0.0. The oldpeak is 1.5. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 43.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 132.0. The cholesterol is 247.0. The fasting blood sugar is 1.0. The resting ecg is 2.0. The max heart rate is 143.0. The exercise angina is 1.0. The oldpeak is 0.1. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 58.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 135.0. The cholesterol is 222.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 100.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 54.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 120.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 155.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 29.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 130.0. The cholesterol is 204.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 202.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 49.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 120.0. The cholesterol is 297.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 132.0. The exercise angina is 0.0. The oldpeak is 1.0. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 64.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 120.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 1.0. The max heart rate is 106.0. The exercise angina is 0.0. The oldpeak is 2.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 63.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 407.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 154.0. The exercise angina is 0.0. The oldpeak is 4.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 50.0. The sex is 0.0. The chest pain type is 3.0. The resting bp s is 140.0. The cholesterol is 288.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 140.0. The exercise angina is 1.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 44.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 184.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 142.0. The exercise angina is 0.0. The oldpeak is 1.0. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 47.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 110.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 120.0. The exercise angina is 1.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 41.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 171.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 128.0. The exercise angina is 1.0. The oldpeak is 1.5. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 45.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 110.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 138.0. The exercise angina is 0.0. The oldpeak is -0.1. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 59.0. The sex is 1.0. The chest pain type is 1.0. The resting bp s is 170.0. The cholesterol is 288.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 159.0. The exercise angina is 0.0. The oldpeak is 0.2. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 43.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 247.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 171.0. The exercise angina is 0.0. The oldpeak is 1.5. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 47.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 138.0. The cholesterol is 257.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 156.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 43.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 130.0. The cholesterol is 315.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 162.0. The exercise angina is 0.0. The oldpeak is 1.9. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 43.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 120.0. The cholesterol is 177.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 120.0. The exercise angina is 1.0. The oldpeak is 2.5. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 49.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 206.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 170.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 42.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 295.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 162.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 75.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 170.0. The cholesterol is 203.0. The fasting blood sugar is 1.0. The resting ecg is 1.0. The max heart rate is 108.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 60.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 125.0. The cholesterol is 258.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 141.0. The exercise angina is 1.0. The oldpeak is 2.8. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 48.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 115.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 128.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 66.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 108.0. The exercise angina is 1.0. The oldpeak is 2.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 59.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 178.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 2.0. The max heart rate is 120.0. The exercise angina is 1.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 70.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 156.0. The cholesterol is 245.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 143.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 53.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 120.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 95.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 46.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 101.0. The cholesterol is 197.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 156.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 59.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 110.0. The cholesterol is 239.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 142.0. The exercise angina is 1.0. The oldpeak is 1.2. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 52.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 112.0. The cholesterol is 230.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 160.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 64.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 303.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 122.0. The exercise angina is 0.0. The oldpeak is 2.0. The ST slope is 2.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 59.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 110.0. The cholesterol is 239.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 142.0. The exercise angina is 1.0. The oldpeak is 1.2. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 43.0. The sex is 0.0. The chest pain type is 3.0. The resting bp s is 150.0. The cholesterol is 254.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 175.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 54.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 192.0. The cholesterol is 283.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 195.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 47.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 108.0. The cholesterol is 243.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 152.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 58.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 140.0. The cholesterol is 179.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 160.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 55.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 130.0. The cholesterol is 262.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 155.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 57.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 128.0. The cholesterol is 229.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 150.0. The exercise angina is 0.0. The oldpeak is 0.4. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 37.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 140.0. The cholesterol is 207.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 130.0. The exercise angina is 1.0. The oldpeak is 1.5. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 47.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 226.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 98.0. The exercise angina is 1.0. The oldpeak is 1.5. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 48.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 256.0. The fasting blood sugar is 1.0. The resting ecg is 2.0. The max heart rate is 150.0. The exercise angina is 1.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 51.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 130.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 170.0. The exercise angina is 0.0. The oldpeak is -0.7. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 67.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 106.0. The cholesterol is 223.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 142.0. The exercise angina is 0.0. The oldpeak is 0.3. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 68.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 135.0. The cholesterol is 0.0. The fasting blood sugar is 0.0. The resting ecg is 1.0. The max heart rate is 120.0. The exercise angina is 1.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 52.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 128.0. The cholesterol is 205.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 184.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 57.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 276.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 112.0. The exercise angina is 1.0. The oldpeak is 0.6. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 50.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 243.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 128.0. The exercise angina is 0.0. The oldpeak is 2.6. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 53.0. The sex is 1.0. The chest pain type is 3.0. The resting bp s is 130.0. The cholesterol is 246.0. The fasting blood sugar is 1.0. The resting ecg is 2.0. The max heart rate is 173.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 55.0. The sex is 1.0. The chest pain type is 2.0. The resting bp s is 110.0. The cholesterol is 214.0. The fasting blood sugar is 1.0. The resting ecg is 1.0. The max heart rate is 180.0. The exercise angina is 0.0. The oldpeak is 0.4. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 51.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 140.0. The cholesterol is 298.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 122.0. The exercise angina is 1.0. The oldpeak is 4.2. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 54.0. The sex is 0.0. The chest pain type is 3.0. The resting bp s is 108.0. The cholesterol is 267.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 167.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 50.0. The sex is 1.0. The chest pain type is 4.0. The resting bp s is 145.0. The cholesterol is 0.0. The fasting blood sugar is 1.0. The resting ecg is 0.0. The max heart rate is 139.0. The exercise angina is 1.0. The oldpeak is 0.7. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 62.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 124.0. The cholesterol is 209.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 163.0. The exercise angina is 0.0. The oldpeak is 0.0. The ST slope is 1.0.", "label": "0.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 49.0. The sex is 0.0. The chest pain type is 3.0. The resting bp s is 160.0. The cholesterol is 180.0. The fasting blood sugar is 0.0. The resting ecg is 0.0. The max heart rate is 156.0. The exercise angina is 0.0. The oldpeak is 1.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The age is 65.0. The sex is 0.0. The chest pain type is 4.0. The resting bp s is 150.0. The cholesterol is 225.0. The fasting blood sugar is 0.0. The resting ecg is 2.0. The max heart rate is 114.0. The exercise angina is 0.0. The oldpeak is 1.0. The ST slope is 2.0.", "label": "1.0", "dataset": "eishkaran-heart-disease", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/eishkaran-heart-disease/train.csv

@@ -0,0 +1,1071 @@
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classification/unipredict/elakiricoder-gender-classification-dataset/metadata.json

@@ -0,0 +1,23 @@
+{
+  "dataset": "elakiricoder-gender-classification-dataset",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "gender",
+  "label_values": [
+    "Male",
+    "Female"
+  ],
+  "num_labels": 2,
+  "train_samples": 4500,
+  "test_samples": 501,
+  "train_label_distribution": {
+    "Female": 2250,
+    "Male": 2250
+  },
+  "test_label_distribution": {
+    "Female": 251,
+    "Male": 250
+  }
+}

classification/unipredict/elakiricoder-gender-classification-dataset/test.csv

@@ -0,0 +1,502 @@
+long_hair,forehead_width_cm,forehead_height_cm,nose_wide,nose_long,lips_thin,distance_nose_to_lip_long,gender
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classification/unipredict/fedesoriano-hepatitis-c-dataset/metadata.json

@@ -0,0 +1,32 @@
+{
+  "dataset": "fedesoriano-hepatitis-c-dataset",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Category",
+  "label_values": [
+    "0=Blood Donor",
+    "1=Hepatitis",
+    "2=Fibrosis",
+    "0s=suspect Blood Donor",
+    "3=Cirrhosis"
+  ],
+  "num_labels": 5,
+  "train_samples": 551,
+  "test_samples": 64,
+  "train_label_distribution": {
+    "3=Cirrhosis": 27,
+    "0=Blood Donor": 479,
+    "2=Fibrosis": 18,
+    "1=Hepatitis": 21,
+    "0s=suspect Blood Donor": 6
+  },
+  "test_label_distribution": {
+    "0=Blood Donor": 54,
+    "3=Cirrhosis": 3,
+    "2=Fibrosis": 3,
+    "1=Hepatitis": 3,
+    "0s=suspect Blood Donor": 1
+  }
+}

classification/unipredict/fedesoriano-hepatitis-c-dataset/test.csv

@@ -0,0 +1,65 @@
+Unnamed: 0,Age,Sex,ALB,ALP,ALT,AST,BIL,CHE,CHOL,CREA,GGT,PROT,Category
+206,50,m,42.2,145.0,27.5,37.9,4.5,13.71,8.8,103.0,239.0,73.1,0=Blood Donor
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+506,57,f,48.0,56.9,8.6,20.1,4.4,8.14,6.9,63.0,14.8,73.1,0=Blood Donor
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+333,33,f,38.5,82.2,11.9,17.0,7.3,7.23,3.92,50.0,7.0,73.3,0=Blood Donor
+288,62,m,46.6,98.0,36.7,29.4,7.0,7.56,5.52,70.0,23.1,86.5,0=Blood Donor
+580,57,f,43.0,52.1,8.3,35.8,18.0,8.61,6.19,71.4,27.9,82.0,2=Fibrosis
+437,48,f,44.4,64.5,17.2,21.1,17.3,4.93,4.05,67.0,12.1,71.2,0=Blood Donor
+421,47,f,38.7,43.4,12.1,19.4,3.0,5.35,4.6,56.0,15.7,67.8,0=Blood Donor
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+137,44,m,47.3,43.9,16.2,19.3,4.7,10.02,5.85,100.0,14.9,71.2,0=Blood Donor
+236,53,m,49.2,71.8,42.8,29.4,6.8,15.1,6.24,107.0,48.3,77.8,0=Blood Donor
+26,34,m,44.8,77.7,36.9,31.0,19.5,10.51,5.59,80.0,23.7,78.9,0=Blood Donor
+519,60,f,45.4,51.4,16.4,19.0,6.8,10.78,6.79,78.0,12.5,72.8,0=Blood Donor
+98,40,m,45.1,63.4,39.6,31.4,19.7,11.31,4.74,91.0,18.2,81.1,0=Blood Donor
+77,38,m,42.0,42.7,34.8,42.2,3.3,6.1,4.74,96.0,14.6,66.7,0=Blood Donor
+357,37,f,39.2,58.1,12.0,21.0,5.3,5.96,5.8,72.0,15.6,70.7,0=Blood Donor
+211,51,m,42.0,84.3,14.7,19.2,3.2,8.19,4.68,81.0,20.9,77.1,0=Blood Donor
+194,49,m,47.8,67.9,44.2,29.9,6.9,8.76,6.23,99.0,26.9,79.5,0=Blood Donor
+219,52,m,46.8,85.5,15.2,31.9,10.3,9.09,6.56,89.0,21.8,73.3,0=Blood Donor
+551,35,m,47.0,37.9,13.3,48.4,8.0,10.3,4.14,69.2,68.2,76.0,1=Hepatitis
+542,19,m,41.0,,87.0,67.0,12.0,7.55,3.9,62.0,65.0,75.0,1=Hepatitis
+540,59,f,19.3,208.2,325.3,146.6,6.9,5.33,4.72,32.0,295.6,53.1,0s=suspect Blood Donor
+396,44,f,36.3,76.4,17.1,20.8,4.6,6.8,4.27,74.0,14.3,68.0,0=Blood Donor
+594,51,m,39.0,66.0,29.6,185.0,19.0,2.0,3.6,58.3,399.5,79.4,3=Cirrhosis
+69,37,m,46.4,53.3,20.2,24.9,8.7,8.63,5.9,86.0,23.3,78.9,0=Blood Donor
+427,48,f,39.2,51.0,15.3,18.4,7.6,9.17,6.66,70.0,16.2,71.7,0=Blood Donor
+390,44,f,45.6,57.6,21.0,19.1,3.7,10.36,6.68,74.0,20.8,74.4,0=Blood Donor
+491,55,f,39.9,83.6,18.4,27.4,8.0,8.43,7.67,73.0,13.6,73.3,0=Blood Donor
+482,53,f,51.3,84.1,40.6,43.6,9.2,7.1,5.62,62.0,74.9,77.1,0=Blood Donor
+147,45,m,45.7,75.4,41.4,28.6,7.7,10.88,6.25,99.0,85.4,78.8,0=Blood Donor
+41,35,m,47.4,54.5,18.6,21.6,10.3,8.1,6.23,66.0,28.1,74.0,0=Blood Donor
+33,34,m,43.6,58.9,47.1,31.1,18.5,9.14,4.99,95.0,22.2,69.3,0=Blood Donor
+246,55,m,40.0,44.9,10.2,14.1,2.6,5.98,4.55,71.0,8.8,66.3,0=Blood Donor
+62,37,m,41.9,77.5,24.9,25.8,4.1,8.7,4.36,84.0,16.0,71.5,0=Blood Donor
+168,47,m,42.2,52.9,20.7,27.1,13.9,10.15,5.15,79.0,28.8,69.5,0=Blood Donor
+183,48,m,45.7,88.5,59.1,34.0,6.5,10.81,6.25,87.0,38.3,74.1,0=Blood Donor
+488,55,f,41.9,86.1,19.3,22.3,7.0,8.21,5.05,71.0,32.5,75.2,0=Blood Donor
+571,50,m,42.0,,258.0,106.0,15.0,8.74,4.7,77.0,80.0,84.0,2=Fibrosis
+72,38,m,39.9,62.9,71.7,43.9,10.4,10.9,7.01,99.0,88.3,73.1,0=Blood Donor
+462,51,f,39.6,43.0,16.3,19.6,7.5,5.57,4.96,83.0,62.1,72.9,0=Blood Donor
+54,37,m,31.4,106.0,16.6,17.0,2.4,5.95,5.3,68.0,22.9,72.3,0=Blood Donor
+185,48,m,46.4,64.1,29.3,27.6,13.2,10.07,8.28,98.0,28.9,83.3,0=Blood Donor
+125,43,m,41.5,75.6,15.3,21.2,5.2,7.36,4.71,84.0,12.9,72.2,0=Blood Donor
+524,62,f,35.4,59.7,21.2,24.7,3.3,9.0,7.45,59.0,17.7,65.4,0=Blood Donor
+401,45,f,41.7,62.4,15.9,16.8,3.7,4.38,5.05,90.0,10.0,70.5,0=Blood Donor
+128,43,m,41.2,59.4,34.3,24.2,4.1,9.64,4.93,84.0,20.8,74.5,0=Blood Donor
+67,37,m,40.8,118.9,17.2,19.2,3.2,9.17,4.26,88.0,13.5,72.0,0=Blood Donor
+150,46,m,39.7,40.3,14.6,22.3,5.3,6.3,4.66,71.0,11.7,67.2,0=Blood Donor
+34,35,m,37.5,69.8,37.1,25.0,7.8,11.66,5.73,84.0,27.3,71.0,0=Blood Donor
+97,40,m,39.1,66.5,33.3,32.9,14.8,7.87,4.91,88.0,18.5,68.8,0=Blood Donor
+122,43,m,48.6,45.0,10.5,40.5,5.3,7.09,,63.0,25.1,70.0,0=Blood Donor
+263,57,m,46.4,80.1,20.2,23.9,19.2,8.02,5.31,74.0,17.1,77.5,0=Blood Donor
+116,42,m,37.8,83.7,25.3,20.0,18.6,7.52,5.07,108.0,17.4,64.1,0=Blood Donor
+278,60,m,40.4,46.8,17.7,25.7,13.5,5.79,5.42,92.0,19.2,70.0,0=Blood Donor
+264,58,m,44.0,56.0,30.6,33.0,4.8,12.37,6.33,74.0,58.7,75.8,0=Blood Donor
+576,64,m,38.0,35.7,7.1,41.3,13.0,7.1,4.52,70.0,53.0,66.8,2=Fibrosis
+274,59,m,43.8,46.6,28.3,27.4,6.1,10.56,5.47,83.0,20.3,78.5,0=Blood Donor
+297,64,m,44.5,87.8,15.1,23.2,12.3,9.49,7.7,78.0,20.0,74.3,0=Blood Donor
+153,46,m,47.7,51.6,19.7,21.8,6.3,7.25,5.19,84.0,20.9,75.2,0=Blood Donor
+230,53,m,49.0,86.0,28.9,23.9,11.8,6.09,4.0,74.0,33.6,75.8,0=Blood Donor
+589,42,m,36.0,69.6,14.9,263.1,40.0,3.61,3.93,49.6,61.0,68.6,3=Cirrhosis
+443,49,f,40.5,31.3,16.2,19.4,11.2,4.95,5.1,75.0,14.9,73.2,0=Blood Donor
+545,27,m,45.0,27.5,10.5,37.8,10.0,8.77,3.2,55.2,35.9,74.5,1=Hepatitis

classification/unipredict/fedesoriano-hepatitis-c-dataset/test.jsonl

@@ -0,0 +1,64 @@
+{"text": "The Unnamed: 0 is 206. The Age is 50. The Sex is m. The ALB is 42.2. The ALP is 145.0. The ALT is 27.5. The AST is 37.9. The BIL is 4.5. The CHE is 13.71. The CHOL is 8.8. The CREA is 103.0. The GGT is 239.0. The PROT is 73.1.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 606. The Age is 42. The Sex is f. The ALB is 33.0. The ALP is 79.0. The ALT is 3.7. The AST is 55.7. The BIL is 200.0. The CHE is 1.72. The CHOL is 5.16. The CREA is 89.1. The GGT is 146.3. The PROT is 69.9.", "label": "3=Cirrhosis", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 506. The Age is 57. The Sex is f. The ALB is 48.0. The ALP is 56.9. The ALT is 8.6. The AST is 20.1. The BIL is 4.4. The CHE is 8.14. The CHOL is 6.9. The CREA is 63.0. The GGT is 14.8. The PROT is 73.1.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 490. The Age is 55. The Sex is f. The ALB is 35.2. The ALP is 69.6. The ALT is 23.6. The AST is 26.0. The BIL is 3.8. The CHE is 4.97. The CHOL is 4.43. The CREA is 69.0. The GGT is 29.4. The PROT is 60.5.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 333. The Age is 33. The Sex is f. The ALB is 38.5. The ALP is 82.2. The ALT is 11.9. The AST is 17.0. The BIL is 7.3. The CHE is 7.23. The CHOL is 3.92. The CREA is 50.0. The GGT is 7.0. The PROT is 73.3.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 288. The Age is 62. The Sex is m. The ALB is 46.6. The ALP is 98.0. The ALT is 36.7. The AST is 29.4. The BIL is 7.0. The CHE is 7.56. The CHOL is 5.52. The CREA is 70.0. The GGT is 23.1. The PROT is 86.5.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 580. The Age is 57. The Sex is f. The ALB is 43.0. The ALP is 52.1. The ALT is 8.3. The AST is 35.8. The BIL is 18.0. The CHE is 8.61. The CHOL is 6.19. The CREA is 71.4. The GGT is 27.9. The PROT is 82.0.", "label": "2=Fibrosis", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 437. The Age is 48. The Sex is f. The ALB is 44.4. The ALP is 64.5. The ALT is 17.2. The AST is 21.1. The BIL is 17.3. The CHE is 4.93. The CHOL is 4.05. The CREA is 67.0. The GGT is 12.1. The PROT is 71.2.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 421. The Age is 47. The Sex is f. The ALB is 38.7. The ALP is 43.4. The ALT is 12.1. The AST is 19.4. The BIL is 3.0. The CHE is 5.35. The CHOL is 4.6. The CREA is 56.0. The GGT is 15.7. The PROT is 67.8.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 202. The Age is 50. The Sex is m. The ALB is 40.3. The ALP is 70.5. The ALT is 46.4. The AST is 32.3. The BIL is 6.8. The CHE is 8.11. The CHOL is 4.06. The CREA is 77.0. The GGT is 16.9. The PROT is 69.2.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 137. The Age is 44. The Sex is m. The ALB is 47.3. The ALP is 43.9. The ALT is 16.2. The AST is 19.3. The BIL is 4.7. The CHE is 10.02. The CHOL is 5.85. The CREA is 100.0. The GGT is 14.9. The PROT is 71.2.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 236. The Age is 53. The Sex is m. The ALB is 49.2. The ALP is 71.8. The ALT is 42.8. The AST is 29.4. The BIL is 6.8. The CHE is 15.1. The CHOL is 6.24. The CREA is 107.0. The GGT is 48.3. The PROT is 77.8.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 26. The Age is 34. The Sex is m. The ALB is 44.8. The ALP is 77.7. The ALT is 36.9. The AST is 31.0. The BIL is 19.5. The CHE is 10.51. The CHOL is 5.59. The CREA is 80.0. The GGT is 23.7. The PROT is 78.9.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 519. The Age is 60. The Sex is f. The ALB is 45.4. The ALP is 51.4. The ALT is 16.4. The AST is 19.0. The BIL is 6.8. The CHE is 10.78. The CHOL is 6.79. The CREA is 78.0. The GGT is 12.5. The PROT is 72.8.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 98. The Age is 40. The Sex is m. The ALB is 45.1. The ALP is 63.4. The ALT is 39.6. The AST is 31.4. The BIL is 19.7. The CHE is 11.31. The CHOL is 4.74. The CREA is 91.0. The GGT is 18.2. The PROT is 81.1.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 77. The Age is 38. The Sex is m. The ALB is 42.0. The ALP is 42.7. The ALT is 34.8. The AST is 42.2. The BIL is 3.3. The CHE is 6.1. The CHOL is 4.74. The CREA is 96.0. The GGT is 14.6. The PROT is 66.7.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 357. The Age is 37. The Sex is f. The ALB is 39.2. The ALP is 58.1. The ALT is 12.0. The AST is 21.0. The BIL is 5.3. The CHE is 5.96. The CHOL is 5.8. The CREA is 72.0. The GGT is 15.6. The PROT is 70.7.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 211. The Age is 51. The Sex is m. The ALB is 42.0. The ALP is 84.3. The ALT is 14.7. The AST is 19.2. The BIL is 3.2. The CHE is 8.19. The CHOL is 4.68. The CREA is 81.0. The GGT is 20.9. The PROT is 77.1.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 194. The Age is 49. The Sex is m. The ALB is 47.8. The ALP is 67.9. The ALT is 44.2. The AST is 29.9. The BIL is 6.9. The CHE is 8.76. The CHOL is 6.23. The CREA is 99.0. The GGT is 26.9. The PROT is 79.5.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 219. The Age is 52. The Sex is m. The ALB is 46.8. The ALP is 85.5. The ALT is 15.2. The AST is 31.9. The BIL is 10.3. The CHE is 9.09. The CHOL is 6.56. The CREA is 89.0. The GGT is 21.8. The PROT is 73.3.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 551. The Age is 35. The Sex is m. The ALB is 47.0. The ALP is 37.9. The ALT is 13.3. The AST is 48.4. The BIL is 8.0. The CHE is 10.3. The CHOL is 4.14. The CREA is 69.2. The GGT is 68.2. The PROT is 76.0.", "label": "1=Hepatitis", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 542. The Age is 19. The Sex is m. The ALB is 41.0. The ALP is unknown. The ALT is 87.0. The AST is 67.0. The BIL is 12.0. The CHE is 7.55. The CHOL is 3.9. The CREA is 62.0. The GGT is 65.0. The PROT is 75.0.", "label": "1=Hepatitis", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 540. The Age is 59. The Sex is f. The ALB is 19.3. The ALP is 208.2. The ALT is 325.3. The AST is 146.6. The BIL is 6.9. The CHE is 5.33. The CHOL is 4.72. The CREA is 32.0. The GGT is 295.6. The PROT is 53.1.", "label": "0s=suspect Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 396. The Age is 44. The Sex is f. The ALB is 36.3. The ALP is 76.4. The ALT is 17.1. The AST is 20.8. The BIL is 4.6. The CHE is 6.8. The CHOL is 4.27. The CREA is 74.0. The GGT is 14.3. The PROT is 68.0.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 594. The Age is 51. The Sex is m. The ALB is 39.0. The ALP is 66.0. The ALT is 29.6. The AST is 185.0. The BIL is 19.0. The CHE is 2.0. The CHOL is 3.6. The CREA is 58.3. The GGT is 399.5. The PROT is 79.4.", "label": "3=Cirrhosis", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 69. The Age is 37. The Sex is m. The ALB is 46.4. The ALP is 53.3. The ALT is 20.2. The AST is 24.9. The BIL is 8.7. The CHE is 8.63. The CHOL is 5.9. The CREA is 86.0. The GGT is 23.3. The PROT is 78.9.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 427. The Age is 48. The Sex is f. The ALB is 39.2. The ALP is 51.0. The ALT is 15.3. The AST is 18.4. The BIL is 7.6. The CHE is 9.17. The CHOL is 6.66. The CREA is 70.0. The GGT is 16.2. The PROT is 71.7.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 390. The Age is 44. The Sex is f. The ALB is 45.6. The ALP is 57.6. The ALT is 21.0. The AST is 19.1. The BIL is 3.7. The CHE is 10.36. The CHOL is 6.68. The CREA is 74.0. The GGT is 20.8. The PROT is 74.4.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 491. The Age is 55. The Sex is f. The ALB is 39.9. The ALP is 83.6. The ALT is 18.4. The AST is 27.4. The BIL is 8.0. The CHE is 8.43. The CHOL is 7.67. The CREA is 73.0. The GGT is 13.6. The PROT is 73.3.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 482. The Age is 53. The Sex is f. The ALB is 51.3. The ALP is 84.1. The ALT is 40.6. The AST is 43.6. The BIL is 9.2. The CHE is 7.1. The CHOL is 5.62. The CREA is 62.0. The GGT is 74.9. The PROT is 77.1.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 147. The Age is 45. The Sex is m. The ALB is 45.7. The ALP is 75.4. The ALT is 41.4. The AST is 28.6. The BIL is 7.7. The CHE is 10.88. The CHOL is 6.25. The CREA is 99.0. The GGT is 85.4. The PROT is 78.8.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 41. The Age is 35. The Sex is m. The ALB is 47.4. The ALP is 54.5. The ALT is 18.6. The AST is 21.6. The BIL is 10.3. The CHE is 8.1. The CHOL is 6.23. The CREA is 66.0. The GGT is 28.1. The PROT is 74.0.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 33. The Age is 34. The Sex is m. The ALB is 43.6. The ALP is 58.9. The ALT is 47.1. The AST is 31.1. The BIL is 18.5. The CHE is 9.14. The CHOL is 4.99. The CREA is 95.0. The GGT is 22.2. The PROT is 69.3.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 246. The Age is 55. The Sex is m. The ALB is 40.0. The ALP is 44.9. The ALT is 10.2. The AST is 14.1. The BIL is 2.6. The CHE is 5.98. The CHOL is 4.55. The CREA is 71.0. The GGT is 8.8. The PROT is 66.3.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 62. The Age is 37. The Sex is m. The ALB is 41.9. The ALP is 77.5. The ALT is 24.9. The AST is 25.8. The BIL is 4.1. The CHE is 8.7. The CHOL is 4.36. The CREA is 84.0. The GGT is 16.0. The PROT is 71.5.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 168. The Age is 47. The Sex is m. The ALB is 42.2. The ALP is 52.9. The ALT is 20.7. The AST is 27.1. The BIL is 13.9. The CHE is 10.15. The CHOL is 5.15. The CREA is 79.0. The GGT is 28.8. The PROT is 69.5.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 183. The Age is 48. The Sex is m. The ALB is 45.7. The ALP is 88.5. The ALT is 59.1. The AST is 34.0. The BIL is 6.5. The CHE is 10.81. The CHOL is 6.25. The CREA is 87.0. The GGT is 38.3. The PROT is 74.1.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 488. The Age is 55. The Sex is f. The ALB is 41.9. The ALP is 86.1. The ALT is 19.3. The AST is 22.3. The BIL is 7.0. The CHE is 8.21. The CHOL is 5.05. The CREA is 71.0. The GGT is 32.5. The PROT is 75.2.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 571. The Age is 50. The Sex is m. The ALB is 42.0. The ALP is unknown. The ALT is 258.0. The AST is 106.0. The BIL is 15.0. The CHE is 8.74. The CHOL is 4.7. The CREA is 77.0. The GGT is 80.0. The PROT is 84.0.", "label": "2=Fibrosis", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 72. The Age is 38. The Sex is m. The ALB is 39.9. The ALP is 62.9. The ALT is 71.7. The AST is 43.9. The BIL is 10.4. The CHE is 10.9. The CHOL is 7.01. The CREA is 99.0. The GGT is 88.3. The PROT is 73.1.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 462. The Age is 51. The Sex is f. The ALB is 39.6. The ALP is 43.0. The ALT is 16.3. The AST is 19.6. The BIL is 7.5. The CHE is 5.57. The CHOL is 4.96. The CREA is 83.0. The GGT is 62.1. The PROT is 72.9.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 54. The Age is 37. The Sex is m. The ALB is 31.4. The ALP is 106.0. The ALT is 16.6. The AST is 17.0. The BIL is 2.4. The CHE is 5.95. The CHOL is 5.3. The CREA is 68.0. The GGT is 22.9. The PROT is 72.3.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 185. The Age is 48. The Sex is m. The ALB is 46.4. The ALP is 64.1. The ALT is 29.3. The AST is 27.6. The BIL is 13.2. The CHE is 10.07. The CHOL is 8.28. The CREA is 98.0. The GGT is 28.9. The PROT is 83.3.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 125. The Age is 43. The Sex is m. The ALB is 41.5. The ALP is 75.6. The ALT is 15.3. The AST is 21.2. The BIL is 5.2. The CHE is 7.36. The CHOL is 4.71. The CREA is 84.0. The GGT is 12.9. The PROT is 72.2.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 524. The Age is 62. The Sex is f. The ALB is 35.4. The ALP is 59.7. The ALT is 21.2. The AST is 24.7. The BIL is 3.3. The CHE is 9.0. The CHOL is 7.45. The CREA is 59.0. The GGT is 17.7. The PROT is 65.4.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 401. The Age is 45. The Sex is f. The ALB is 41.7. The ALP is 62.4. The ALT is 15.9. The AST is 16.8. The BIL is 3.7. The CHE is 4.38. The CHOL is 5.05. The CREA is 90.0. The GGT is 10.0. The PROT is 70.5.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 128. The Age is 43. The Sex is m. The ALB is 41.2. The ALP is 59.4. The ALT is 34.3. The AST is 24.2. The BIL is 4.1. The CHE is 9.64. The CHOL is 4.93. The CREA is 84.0. The GGT is 20.8. The PROT is 74.5.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 67. The Age is 37. The Sex is m. The ALB is 40.8. The ALP is 118.9. The ALT is 17.2. The AST is 19.2. The BIL is 3.2. The CHE is 9.17. The CHOL is 4.26. The CREA is 88.0. The GGT is 13.5. The PROT is 72.0.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 150. The Age is 46. The Sex is m. The ALB is 39.7. The ALP is 40.3. The ALT is 14.6. The AST is 22.3. The BIL is 5.3. The CHE is 6.3. The CHOL is 4.66. The CREA is 71.0. The GGT is 11.7. The PROT is 67.2.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 34. The Age is 35. The Sex is m. The ALB is 37.5. The ALP is 69.8. The ALT is 37.1. The AST is 25.0. The BIL is 7.8. The CHE is 11.66. The CHOL is 5.73. The CREA is 84.0. The GGT is 27.3. The PROT is 71.0.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 97. The Age is 40. The Sex is m. The ALB is 39.1. The ALP is 66.5. The ALT is 33.3. The AST is 32.9. The BIL is 14.8. The CHE is 7.87. The CHOL is 4.91. The CREA is 88.0. The GGT is 18.5. The PROT is 68.8.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 122. The Age is 43. The Sex is m. The ALB is 48.6. The ALP is 45.0. The ALT is 10.5. The AST is 40.5. The BIL is 5.3. The CHE is 7.09. The CHOL is unknown. The CREA is 63.0. The GGT is 25.1. The PROT is 70.0.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 263. The Age is 57. The Sex is m. The ALB is 46.4. The ALP is 80.1. The ALT is 20.2. The AST is 23.9. The BIL is 19.2. The CHE is 8.02. The CHOL is 5.31. The CREA is 74.0. The GGT is 17.1. The PROT is 77.5.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 116. The Age is 42. The Sex is m. The ALB is 37.8. The ALP is 83.7. The ALT is 25.3. The AST is 20.0. The BIL is 18.6. The CHE is 7.52. The CHOL is 5.07. The CREA is 108.0. The GGT is 17.4. The PROT is 64.1.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 278. The Age is 60. The Sex is m. The ALB is 40.4. The ALP is 46.8. The ALT is 17.7. The AST is 25.7. The BIL is 13.5. The CHE is 5.79. The CHOL is 5.42. The CREA is 92.0. The GGT is 19.2. The PROT is 70.0.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 264. The Age is 58. The Sex is m. The ALB is 44.0. The ALP is 56.0. The ALT is 30.6. The AST is 33.0. The BIL is 4.8. The CHE is 12.37. The CHOL is 6.33. The CREA is 74.0. The GGT is 58.7. The PROT is 75.8.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 576. The Age is 64. The Sex is m. The ALB is 38.0. The ALP is 35.7. The ALT is 7.1. The AST is 41.3. The BIL is 13.0. The CHE is 7.1. The CHOL is 4.52. The CREA is 70.0. The GGT is 53.0. The PROT is 66.8.", "label": "2=Fibrosis", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 274. The Age is 59. The Sex is m. The ALB is 43.8. The ALP is 46.6. The ALT is 28.3. The AST is 27.4. The BIL is 6.1. The CHE is 10.56. The CHOL is 5.47. The CREA is 83.0. The GGT is 20.3. The PROT is 78.5.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 297. The Age is 64. The Sex is m. The ALB is 44.5. The ALP is 87.8. The ALT is 15.1. The AST is 23.2. The BIL is 12.3. The CHE is 9.49. The CHOL is 7.7. The CREA is 78.0. The GGT is 20.0. The PROT is 74.3.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 153. The Age is 46. The Sex is m. The ALB is 47.7. The ALP is 51.6. The ALT is 19.7. The AST is 21.8. The BIL is 6.3. The CHE is 7.25. The CHOL is 5.19. The CREA is 84.0. The GGT is 20.9. The PROT is 75.2.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 230. The Age is 53. The Sex is m. The ALB is 49.0. The ALP is 86.0. The ALT is 28.9. The AST is 23.9. The BIL is 11.8. The CHE is 6.09. The CHOL is 4.0. The CREA is 74.0. The GGT is 33.6. The PROT is 75.8.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 589. The Age is 42. The Sex is m. The ALB is 36.0. The ALP is 69.6. The ALT is 14.9. The AST is 263.1. The BIL is 40.0. The CHE is 3.61. The CHOL is 3.93. The CREA is 49.6. The GGT is 61.0. The PROT is 68.6.", "label": "3=Cirrhosis", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 443. The Age is 49. The Sex is f. The ALB is 40.5. The ALP is 31.3. The ALT is 16.2. The AST is 19.4. The BIL is 11.2. The CHE is 4.95. The CHOL is 5.1. The CREA is 75.0. The GGT is 14.9. The PROT is 73.2.", "label": "0=Blood Donor", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The Unnamed: 0 is 545. The Age is 27. The Sex is m. The ALB is 45.0. The ALP is 27.5. The ALT is 10.5. The AST is 37.8. The BIL is 10.0. The CHE is 8.77. The CHOL is 3.2. The CREA is 55.2. The GGT is 35.9. The PROT is 74.5.", "label": "1=Hepatitis", "dataset": "fedesoriano-hepatitis-c-dataset", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/fedesoriano-hepatitis-c-dataset/train.csv

@@ -0,0 +1,552 @@
+Unnamed: 0,Age,Sex,ALB,ALP,ALT,AST,BIL,CHE,CHOL,CREA,GGT,PROT,Category
+605,74,m,23.0,34.1,2.1,90.4,22.0,2.5,3.29,51.0,46.8,57.1,3=Cirrhosis
+292,63,m,45.3,71.3,16.6,24.1,5.7,8.92,4.69,81.0,19.8,73.7,0=Blood Donor
+280,60,m,41.1,67.2,49.0,34.4,16.4,10.14,4.65,107.0,25.5,71.9,0=Blood Donor
+512,59,f,43.0,82.4,33.1,30.0,7.5,9.2,7.43,61.0,30.7,75.4,0=Blood Donor
+505,57,f,38.7,62.8,21.8,29.2,9.2,6.55,7.08,68.0,13.0,70.7,0=Blood Donor
+149,46,m,35.7,49.2,22.7,23.0,17.4,8.51,5.85,87.0,13.4,62.1,0=Blood Donor
+460,51,f,41.0,81.2,35.1,33.3,5.2,8.9,6.56,70.0,21.7,72.4,0=Blood Donor
+317,76,m,38.0,97.1,28.6,33.1,14.1,4.83,5.56,90.0,117.6,73.8,0=Blood Donor
+578,36,f,46.0,39.3,67.1,161.9,13.0,9.24,4.81,65.3,60.0,73.9,2=Fibrosis
+587,39,m,34.0,137.8,4.8,35.6,9.0,3.65,4.82,519.0,133.4,57.5,3=Cirrhosis
+159,46,m,37.3,91.6,33.4,35.9,2.6,9.74,6.73,78.0,64.2,69.9,0=Blood Donor
+105,41,m,40.2,48.2,37.0,34.9,9.2,11.35,5.56,78.0,26.6,73.3,0=Blood Donor
+58,37,m,47.9,68.8,40.3,46.9,6.0,9.76,6.42,81.0,22.7,80.6,0=Blood Donor
+276,60,m,42.2,48.8,41.0,34.8,3.6,9.6,5.6,98.0,71.4,71.6,0=Blood Donor
+509,58,f,35.3,91.4,14.7,21.0,18.0,6.58,5.51,60.0,10.2,70.4,0=Blood Donor
+164,47,m,49.5,75.2,29.6,29.0,7.3,10.67,5.96,75.0,34.9,72.4,0=Blood Donor
+208,51,m,36.8,98.5,25.5,27.7,5.0,9.49,6.08,89.0,18.2,64.9,0=Blood Donor
+215,52,m,42.3,72.6,47.4,23.8,6.0,8.62,7.2,86.0,36.1,77.6,0=Blood Donor
+184,48,m,42.6,69.1,17.6,28.3,7.7,15.4,5.07,81.0,29.9,77.8,0=Blood Donor
+577,71,m,37.0,,130.0,90.0,15.0,9.92,4.7,79.0,77.0,76.0,2=Fibrosis
+454,51,f,37.8,80.7,32.9,27.3,6.2,7.29,5.26,68.0,27.1,70.6,0=Blood Donor
+117,42,m,46.2,57.7,35.6,22.9,14.8,11.01,8.6,80.0,70.1,77.5,0=Blood Donor
+552,36,m,44.0,32.9,9.4,32.0,14.0,11.42,5.73,68.6,40.6,70.9,1=Hepatitis
+273,59,m,48.9,50.3,23.7,28.1,15.8,8.97,4.33,75.0,19.0,77.6,0=Blood Donor
+103,41,m,42.3,55.9,19.6,18.9,10.9,7.15,3.29,86.0,24.5,76.1,0=Blood Donor
+378,42,f,43.4,54.0,11.3,21.3,1.8,6.43,4.43,54.0,18.6,82.3,0=Blood Donor
+85,39,m,43.9,90.1,87.9,60.6,8.6,9.94,4.64,98.0,99.3,66.2,0=Blood Donor
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+336,34,f,37.3,36.3,19.9,28.7,3.8,3.9,4.94,86.0,4.9,70.7,0=Blood Donor
+442,49,f,39.3,59.4,18.3,15.0,4.8,8.03,4.58,83.0,12.5,74.3,0=Blood Donor
+374,41,f,42.4,51.3,16.3,18.3,4.0,6.68,4.24,65.0,17.1,71.9,0=Blood Donor
+537,55,m,47.3,106.0,208.8,130.6,0.8,14.8,8.08,76.0,71.6,78.3,0s=suspect Blood Donor
+360,38,f,40.0,73.5,16.6,19.2,8.3,5.23,5.52,54.0,24.0,71.0,0=Blood Donor
+595,51,m,33.0,29.6,4.5,66.6,91.0,4.02,4.08,75.9,28.5,62.3,3=Cirrhosis
+382,43,f,41.2,38.2,18.6,20.5,9.3,6.15,5.44,64.0,9.7,70.9,0=Blood Donor
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+231,53,m,37.8,98.1,30.5,21.1,4.0,5.02,4.42,94.0,23.2,65.2,0=Blood Donor
+474,53,f,42.4,55.0,20.9,42.4,7.7,6.6,4.26,67.0,14.2,70.9,0=Blood Donor
+100,40,m,46.9,61.8,27.4,43.0,7.0,7.26,5.09,98.0,14.2,75.3,0=Blood Donor
+407,46,f,48.8,66.3,19.7,23.6,4.3,10.57,5.88,78.0,24.2,72.4,0=Blood Donor
+182,48,m,46.0,58.1,21.4,29.3,6.9,9.36,5.7,83.0,32.3,74.1,0=Blood Donor
+410,46,f,39.9,71.3,15.4,29.5,4.6,5.95,6.94,72.0,13.4,69.5,0=Blood Donor
+275,59,m,37.8,83.7,25.3,20.0,18.6,7.52,5.07,108.0,17.4,64.1,0=Blood Donor
+13,33,m,36.3,78.6,23.6,22.0,7.0,8.56,5.38,78.0,19.4,68.7,0=Blood Donor
+180,48,m,38.8,94.7,28.6,33.4,5.1,7.93,4.27,87.0,14.7,66.8,0=Blood Donor
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+377,41,f,39.8,67.9,19.4,19.5,12.2,7.41,5.02,64.0,27.3,65.5,0=Blood Donor
+554,41,m,42.0,39.6,26.5,77.6,42.0,9.67,9.67,57.7,143.4,75.8,1=Hepatitis
+581,68,f,43.0,22.9,5.0,42.1,12.0,7.29,4.89,80.9,11.9,76.1,2=Fibrosis
+539,74,m,20.3,84.0,22.8,43.0,5.7,4.91,3.19,52.0,218.3,47.8,0s=suspect Blood Donor
+145,45,m,46.4,77.0,60.3,32.9,8.8,9.38,6.27,85.0,81.1,75.5,0=Blood Donor
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+395,44,f,43.0,86.5,18.3,25.0,2.2,8.45,5.9,63.0,13.6,72.7,0=Blood Donor
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+172,47,m,36.7,44.8,29.3,23.5,6.9,7.38,4.44,87.0,24.4,68.3,0=Blood Donor
+40,35,m,44.5,70.3,26.2,25.1,5.1,10.12,4.69,82.0,20.7,67.2,0=Blood Donor
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+135,44,m,48.6,73.5,49.6,32.5,22.6,11.24,6.68,95.0,32.2,74.0,0=Blood Donor
+32,34,m,46.1,70.6,35.8,30.0,7.6,7.7,4.2,93.0,14.3,78.7,0=Blood Donor
+251,55,m,41.5,59.5,15.4,16.2,6.8,6.35,5.22,80.0,12.4,69.9,0=Blood Donor
+197,50,m,44.3,82.5,38.6,32.9,7.3,8.57,5.95,83.0,23.4,66.6,0=Blood Donor
+90,39,m,46.4,102.9,44.4,26.2,4.1,9.29,8.89,103.0,64.0,72.2,0=Blood Donor
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+76,38,m,44.7,69.4,47.4,35.1,16.7,6.9,4.14,67.0,17.3,70.1,0=Blood Donor
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+328,33,f,38.2,54.4,17.3,21.2,7.1,8.67,5.69,68.0,32.1,66.9,0=Blood Donor
+243,55,m,44.1,60.0,26.3,25.9,5.1,7.23,7.3,88.0,41.6,77.7,0=Blood Donor
+109,42,m,45.3,55.3,31.0,50.0,18.5,15.43,5.88,83.0,15.4,72.4,0=Blood Donor
+265,58,m,46.8,79.3,38.0,24.1,4.7,9.51,5.07,99.0,22.9,72.4,0=Blood Donor
+471,52,f,36.7,87.6,34.3,30.8,17.7,10.12,6.98,72.0,24.2,66.3,0=Blood Donor
+74,38,m,43.4,58.9,45.7,45.7,9.2,10.86,5.74,90.0,30.0,73.9,0=Blood Donor
+464,51,f,43.7,61.3,18.0,23.3,4.3,9.57,6.04,70.0,18.6,75.2,0=Blood Donor
+205,50,m,39.7,96.5,54.8,30.8,3.3,8.98,5.66,82.0,52.4,65.8,0=Blood Donor
+268,59,m,45.7,115.4,16.4,23.8,3.7,8.2,4.46,70.0,14.3,78.5,0=Blood Donor
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+256,56,m,42.1,45.6,30.0,26.7,6.1,8.9,6.4,71.0,37.6,70.1,0=Blood Donor
+435,48,f,42.5,62.2,12.1,20.1,23.1,4.01,5.58,67.0,13.0,74.2,0=Blood Donor
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+241,54,m,40.8,72.0,25.9,29.2,8.5,7.1,5.79,89.0,15.6,67.9,0=Blood Donor
+417,46,f,37.9,59.5,33.0,25.0,3.7,6.06,5.3,92.0,43.9,70.0,0=Blood Donor
+420,47,f,35.6,37.5,17.6,18.0,4.7,4.32,5.59,75.0,13.0,66.0,0=Blood Donor
+110,42,m,44.1,46.8,23.8,19.4,7.0,10.83,6.28,95.0,19.7,73.0,0=Blood Donor
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+511,58,f,47.0,74.8,36.1,28.9,5.3,9.82,6.71,70.0,32.0,76.6,0=Blood Donor
+608,52,f,39.0,37.0,1.3,30.4,21.0,6.33,3.78,158.2,142.5,82.7,3=Cirrhosis
+216,52,m,42.2,72.2,47.9,23.7,8.6,11.91,6.29,96.0,62.5,72.9,0=Blood Donor
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+142,45,m,41.7,73.2,43.6,29.4,6.4,8.89,5.31,71.0,67.4,70.3,0=Blood Donor
+133,44,m,35.5,81.7,27.5,29.5,6.4,8.81,6.65,83.0,24.1,68.0,0=Blood Donor
+365,38,f,40.0,79.3,11.9,22.0,6.5,8.33,4.58,60.0,13.7,68.1,0=Blood Donor
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+467,52,f,36.0,47.2,19.6,22.5,5.9,7.85,5.69,85.0,30.4,69.2,0=Blood Donor
+88,39,m,45.7,73.4,45.5,33.8,5.0,9.07,4.41,82.0,20.6,78.9,0=Blood Donor
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+55,37,m,42.9,70.7,16.3,24.1,15.7,9.03,6.8,93.0,70.1,73.4,0=Blood Donor
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+363,38,f,41.2,61.9,19.4,22.9,10.5,7.86,3.61,85.0,19.5,66.6,0=Blood Donor
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+331,33,f,45.9,72.0,37.8,33.5,17.7,7.32,4.25,81.0,21.5,78.3,0=Blood Donor
+134,44,m,44.0,84.3,47.4,31.3,4.1,10.28,5.42,105.0,34.2,75.3,0=Blood Donor
+530,64,f,43.0,52.7,31.6,29.2,5.5,11.69,6.0,63.0,61.9,73.4,0=Blood Donor
+112,42,m,46.3,71.4,30.3,22.5,26.9,7.95,7.3,88.0,21.8,71.8,0=Blood Donor
+573,54,m,41.0,41.8,41.5,187.9,21.0,8.59,5.85,91.0,104.9,79.1,2=Fibrosis
+11,32,m,44.3,52.3,21.7,22.4,17.2,4.15,3.57,78.0,24.1,75.4,0=Blood Donor
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+247,55,m,46.2,87.1,36.9,21.0,4.5,7.55,6.33,80.0,30.2,72.2,0=Blood Donor
+84,39,m,36.6,71.3,28.9,25.9,6.1,5.7,4.66,77.0,22.6,66.3,0=Blood Donor
+322,32,f,43.5,66.2,9.2,17.8,5.7,7.14,4.38,71.0,44.6,76.1,0=Blood Donor
+527,63,f,36.1,67.6,13.8,12.2,5.8,9.44,6.88,66.0,22.4,65.0,0=Blood Donor
+68,37,m,43.6,72.8,51.4,43.7,13.8,8.16,4.88,70.0,94.5,75.2,0=Blood Donor
+429,48,f,46.3,71.8,23.0,25.2,11.1,9.41,5.84,85.0,19.1,77.4,0=Blood Donor
+477,53,f,43.7,84.3,18.8,18.6,8.5,10.22,6.65,56.0,16.5,76.9,0=Blood Donor
+531,65,f,33.0,74.3,13.2,16.8,4.1,7.46,5.76,52.0,9.1,64.2,0=Blood Donor
+179,48,m,44.7,56.4,20.9,24.2,17.5,5.49,5.65,74.0,28.5,75.0,0=Blood Donor
+51,36,m,42.6,65.3,35.8,27.1,15.7,10.66,4.38,96.0,34.7,71.0,0=Blood Donor
+262,57,m,43.3,86.8,21.2,22.2,6.8,7.87,4.91,65.0,19.2,71.3,0=Blood Donor
+408,46,f,36.7,62.3,10.8,17.4,3.7,6.17,4.07,67.0,15.1,69.0,0=Blood Donor
+8,32,m,42.2,41.9,35.8,31.1,16.1,5.82,4.6,109.0,21.5,67.1,0=Blood Donor
+362,38,f,50.3,92.1,40.1,30.9,4.2,10.02,3.97,100.0,30.5,77.3,0=Blood Donor
+22,33,m,46.7,88.3,23.4,23.9,7.8,9.42,4.62,78.0,29.5,74.3,0=Blood Donor
+227,53,m,45.3,53.0,39.6,31.3,9.6,9.96,7.22,73.0,42.9,78.6,0=Blood Donor
+541,38,m,45.0,56.3,,33.1,7.0,9.58,6.0,77.9,18.9,63.0,1=Hepatitis
+139,45,m,41.4,63.2,41.4,31.6,5.8,12.28,7.29,82.0,56.0,70.6,0=Blood Donor
+339,34,f,36.2,70.9,14.3,21.6,3.7,9.82,5.59,65.0,27.4,74.3,0=Blood Donor
+425,48,f,45.6,107.2,24.4,39.0,13.8,9.77,,88.0,38.0,75.1,0=Blood Donor
+181,48,m,43.4,41.2,18.4,21.2,9.8,7.19,6.37,80.0,15.8,68.9,0=Blood Donor
+158,46,m,49.7,48.2,11.5,17.6,11.2,6.74,3.71,106.0,10.6,75.2,0=Blood Donor
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+258,56,m,49.5,56.3,22.1,24.9,11.5,8.56,4.69,91.0,14.7,69.6,0=Blood Donor
+444,49,f,34.9,37.9,15.3,19.4,7.1,5.3,5.88,83.0,7.9,62.5,0=Blood Donor
+144,45,m,40.3,64.4,18.8,27.2,5.2,6.71,4.25,74.0,19.7,67.2,0=Blood Donor
+359,37,f,40.5,47.3,15.3,19.4,4.9,5.4,3.95,76.0,7.1,69.0,0=Blood Donor
+304,66,m,48.4,76.0,31.9,29.6,13.8,8.81,4.17,111.0,26.2,76.7,0=Blood Donor
+284,61,m,34.3,60.5,35.4,26.6,15.2,9.03,7.62,74.0,51.4,76.0,0=Blood Donor
+504,57,f,37.9,50.3,12.2,18.1,3.5,6.72,5.06,71.0,10.0,69.3,0=Blood Donor
+416,46,f,42.3,61.9,20.0,21.8,9.7,8.8,7.09,67.0,35.0,75.2,0=Blood Donor
+575,59,m,44.0,34.5,8.9,74.5,6.0,9.45,4.45,65.0,95.3,69.7,2=Fibrosis
+190,49,m,44.7,60.2,34.6,28.5,7.1,10.14,5.68,98.0,87.0,69.8,0=Blood Donor
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+173,47,m,48.0,56.4,23.0,25.7,6.5,8.32,6.3,89.0,23.3,78.0,0=Blood Donor
+101,41,m,45.1,86.8,24.0,23.9,10.6,7.84,6.23,72.0,35.3,75.1,0=Blood Donor
+141,45,m,36.2,102.6,70.8,40.3,14.5,8.69,5.98,94.0,80.6,66.6,0=Blood Donor
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+61,37,m,51.2,84.5,18.8,24.7,9.9,8.62,6.59,94.0,25.3,76.3,0=Blood Donor
+424,47,f,40.3,65.0,13.5,15.2,6.4,7.16,4.55,70.0,16.5,66.2,0=Blood Donor
+548,32,m,45.0,34.6,44.3,96.2,16.0,10.11,6.28,81.2,48.1,82.1,1=Hepatitis
+9,32,m,50.9,65.5,23.2,21.2,6.9,8.69,4.1,83.0,13.7,71.3,0=Blood Donor
+174,47,m,55.4,61.2,25.4,32.9,28.3,8.39,5.25,96.0,10.3,82.1,0=Blood Donor
+358,37,f,42.1,47.5,18.3,21.7,2.8,8.29,4.36,83.0,28.9,73.4,0=Blood Donor
+389,44,f,37.4,28.9,16.8,25.8,3.1,6.7,4.45,50.0,9.2,66.3,0=Blood Donor
+27,34,m,42.6,27.0,21.4,21.7,7.2,8.15,6.79,85.0,13.9,67.7,0=Blood Donor
+240,54,m,46.4,75.1,54.1,39.6,10.6,6.59,7.43,85.0,73.2,75.2,0=Blood Donor
+544,25,m,42.0,38.2,63.3,187.7,14.0,6.0,4.28,66.9,40.2,70.5,1=Hepatitis
+586,38,m,44.0,,94.0,60.0,12.0,4.37,3.2,61.0,99.0,77.0,3=Cirrhosis
+38,35,m,48.7,72.7,24.1,31.0,45.1,9.4,3.8,90.0,20.0,75.8,0=Blood Donor
+30,34,m,46.8,61.7,24.5,24.2,23.1,10.99,4.6,83.0,23.8,73.1,0=Blood Donor
+307,66,m,41.0,61.4,28.7,34.8,5.4,9.36,6.66,100.0,26.8,73.6,0=Blood Donor
+356,36,f,42.7,55.1,12.4,16.0,6.8,6.63,5.05,75.0,10.4,72.8,0=Blood Donor
+502,57,f,38.6,80.9,33.1,26.7,6.5,6.45,5.1,59.0,11.3,70.9,0=Blood Donor
+470,52,f,51.5,81.8,26.3,20.6,7.8,6.74,5.9,88.0,16.3,82.2,0=Blood Donor
+599,58,m,31.0,143.1,7.0,181.8,58.0,3.29,3.92,66.4,273.7,78.1,3=Cirrhosis
+86,39,m,43.4,48.6,33.4,35.0,23.4,9.68,5.55,73.0,22.1,75.2,0=Blood Donor
+318,77,m,52.2,52.2,12.0,23.5,10.9,5.51,4.41,103.0,25.8,67.2,0=Blood Donor
+565,29,m,41.0,43.1,2.4,83.5,6.0,11.49,5.42,55.2,130.0,66.5,2=Fibrosis
+130,44,m,38.4,110.8,17.2,30.7,4.6,9.51,5.12,74.0,25.2,73.6,0=Blood Donor
+388,43,f,47.8,40.9,12.9,17.2,5.5,4.31,3.96,76.0,10.4,70.7,0=Blood Donor
+220,52,m,44.7,43.5,40.9,47.2,10.4,10.08,5.0,74.0,26.5,75.4,0=Blood Donor
+535,48,m,24.9,116.9,49.2,24.3,4.9,3.44,5.25,29.0,83.0,47.8,0s=suspect Blood Donor
+558,51,m,43.0,37.2,21.4,132.8,5.0,10.12,5.23,76.2,76.4,76.7,1=Hepatitis
+306,66,m,32.6,58.3,20.3,27.3,9.4,9.4,6.8,88.0,24.6,73.1,0=Blood Donor
+341,34,f,45.5,76.9,17.4,18.6,3.2,8.45,5.1,53.0,25.8,79.7,0=Blood Donor
+368,40,f,41.7,55.3,12.4,23.5,8.2,4.8,5.32,75.0,10.6,73.5,0=Blood Donor
+106,41,m,53.3,79.0,29.5,28.5,14.1,7.96,5.9,87.0,33.8,83.4,0=Blood Donor
+267,58,m,40.9,55.8,17.5,23.0,5.3,8.52,6.41,93.0,18.5,70.9,0=Blood Donor
+523,62,f,36.6,82.5,18.2,24.3,10.6,9.82,7.12,54.0,15.6,67.8,0=Blood Donor
+148,46,m,42.6,61.6,18.6,21.4,10.2,7.0,6.13,89.0,19.9,73.7,0=Blood Donor
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+567,46,m,45.0,26.9,23.1,125.0,17.0,6.97,4.01,60.5,72.2,73.0,2=Fibrosis
+508,57,f,42.7,62.9,11.2,16.6,11.6,12.7,6.25,62.0,8.1,71.9,0=Blood Donor
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+169,47,m,48.3,59.3,49.9,31.7,7.1,11.09,5.56,88.0,91.5,73.0,0=Blood Donor
+235,53,m,38.1,82.5,8.0,17.5,2.4,9.13,6.28,103.0,35.8,69.9,0=Blood Donor
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+597,56,m,23.0,105.6,5.1,123.0,43.0,1.8,2.4,62.7,35.9,62.8,3=Cirrhosis
+15,33,m,38.7,39.8,22.5,23.0,4.1,4.63,4.97,63.0,15.2,71.9,0=Blood Donor
+285,61,m,44.8,104.9,15.0,18.8,6.5,9.38,5.42,69.0,26.2,79.6,0=Blood Donor
+526,63,f,47.6,59.9,21.1,27.8,8.4,9.24,6.08,62.0,18.3,74.5,0=Blood Donor
+213,51,m,50.8,56.0,33.2,32.9,6.3,11.33,5.93,93.0,22.4,80.3,0=Blood Donor
+351,35,f,46.1,88.7,23.1,20.0,7.7,8.41,4.79,74.0,28.1,79.3,0=Blood Donor
+57,37,m,41.5,64.6,23.7,29.9,9.3,5.49,3.97,100.0,10.4,69.3,0=Blood Donor
+214,51,m,46.3,69.4,20.1,26.8,4.5,9.99,4.14,81.0,12.3,73.9,0=Blood Donor
+12,33,m,46.4,68.2,10.3,20.0,5.7,7.36,4.3,79.0,18.7,68.6,0=Blood Donor
+223,52,m,41.2,67.4,20.4,18.8,4.3,8.51,4.97,82.0,40.4,68.5,0=Blood Donor
+60,37,m,38.6,61.2,24.6,31.9,7.9,6.02,4.63,72.0,10.3,56.3,0=Blood Donor
+19,33,m,36.6,57.1,38.9,40.3,24.9,9.62,5.5,112.0,27.6,69.3,0=Blood Donor
+345,35,f,43.4,62.6,19.2,22.0,3.0,10.03,5.07,64.0,12.4,72.8,0=Blood Donor
+78,38,m,48.4,44.9,23.4,22.1,7.9,10.53,7.51,87.0,43.2,82.6,0=Blood Donor
+481,53,f,47.4,66.9,24.3,32.6,14.9,10.51,6.03,81.0,25.2,75.0,0=Blood Donor
+591,46,m,20.0,,62.0,113.0,254.0,1.48,,114.0,138.0,,3=Cirrhosis
+397,44,f,35.5,71.0,15.8,30.0,3.7,7.56,4.43,53.0,17.8,67.7,0=Blood Donor
+201,50,m,35.5,59.4,43.9,35.8,7.6,11.23,6.56,106.0,24.9,73.2,0=Blood Donor
+252,56,m,39.9,62.9,27.2,22.8,7.7,5.74,6.75,86.0,30.7,72.5,0=Blood Donor
+207,51,m,38.0,54.8,23.8,28.6,13.5,6.33,4.65,99.0,18.3,66.4,0=Blood Donor
+209,51,m,47.2,78.1,27.4,23.9,8.4,10.14,6.54,86.0,35.3,71.6,0=Blood Donor
+557,50,m,42.0,41.6,10.2,38.1,17.0,9.54,7.04,75.3,92.1,72.3,1=Hepatitis
+426,48,f,35.2,45.4,23.4,27.1,4.9,6.83,4.35,70.0,11.8,72.1,0=Blood Donor
+574,57,m,47.0,29.7,10.2,55.9,12.0,6.6,4.64,70.9,69.6,80.9,2=Fibrosis
+93,40,m,47.8,68.0,17.5,22.3,7.7,9.94,6.09,88.0,23.3,73.9,0=Blood Donor
+330,33,f,42.4,137.2,14.2,13.1,3.4,8.23,,48.0,25.7,74.4,0=Blood Donor
+349,35,f,38.0,51.3,18.3,15.3,3.9,8.63,4.91,61.0,16.2,71.8,0=Blood Donor
+350,35,f,46.9,50.8,35.4,22.1,13.1,8.27,5.7,95.0,37.3,80.7,0=Blood Donor
+186,48,m,50.4,34.6,37.2,28.0,17.3,10.87,6.97,114.0,45.2,72.9,0=Blood Donor
+188,49,m,39.7,77.3,20.2,19.0,8.8,7.26,4.98,84.0,74.5,65.4,0=Blood Donor
+136,44,m,40.8,71.6,17.2,21.0,3.5,7.38,4.46,88.0,11.4,63.4,0=Blood Donor
+171,47,m,45.3,51.6,18.4,16.9,7.1,8.96,4.29,74.0,15.7,69.1,0=Blood Donor
+500,57,f,41.2,83.5,32.6,39.3,4.0,10.67,8.46,69.0,22.4,75.7,0=Blood Donor
+496,56,f,33.2,54.3,15.5,22.8,8.0,5.61,3.87,55.0,19.1,63.1,0=Blood Donor
+517,60,f,52.4,88.2,50.2,31.7,8.8,8.49,5.0,77.0,47.0,77.0,0=Blood Donor
+178,48,m,43.1,83.9,20.8,27.4,18.3,9.82,6.14,90.0,16.4,74.9,0=Blood Donor
+603,61,m,39.0,102.9,27.3,143.2,15.0,5.38,4.88,72.3,400.3,73.4,3=Cirrhosis
+528,63,f,27.8,85.7,25.4,38.9,4.2,6.06,3.96,63.0,46.0,56.9,0=Blood Donor
+370,40,f,43.2,42.4,15.7,23.6,9.7,7.56,6.74,88.0,11.5,73.2,0=Blood Donor
+445,49,f,45.4,45.9,14.3,15.9,5.8,9.05,6.81,69.0,14.5,78.2,0=Blood Donor
+52,36,m,42.4,47.3,23.0,25.5,6.1,9.46,5.29,79.0,17.5,73.8,0=Blood Donor
+170,47,m,46.2,63.0,7.0,17.4,6.5,7.06,5.23,60.0,26.6,75.4,0=Blood Donor
+71,38,m,48.1,63.2,11.7,14.7,5.1,8.83,3.87,85.0,9.5,73.1,0=Blood Donor
+413,46,f,39.9,73.9,14.0,17.2,16.3,6.93,5.11,71.0,12.7,64.7,0=Blood Donor
+195,50,m,40.0,87.8,87.5,52.6,4.8,8.7,6.46,76.0,152.5,71.0,0=Blood Donor
+154,46,m,44.8,98.0,41.0,30.6,4.0,10.92,7.42,91.0,28.8,79.8,0=Blood Donor
+560,58,m,43.0,99.1,12.2,63.2,13.0,5.95,6.15,147.3,491.0,65.6,1=Hepatitis
+6,32,m,41.6,43.3,18.5,19.7,12.3,9.92,6.05,111.0,91.0,74.0,0=Blood Donor
+324,33,f,36.9,51.7,17.4,22.0,8.3,7.0,5.02,52.0,19.1,72.0,0=Blood Donor
+314,70,m,41.0,63.5,16.9,21.6,5.9,6.03,4.74,83.0,13.5,73.7,0=Blood Donor
+45,36,m,45.5,57.6,22.5,19.5,7.5,5.28,4.06,88.0,62.5,71.6,0=Blood Donor
+199,50,m,43.5,76.2,13.1,18.8,5.6,8.11,4.55,57.0,16.5,67.0,0=Blood Donor
+604,65,m,,,40.0,54.0,13.0,7.5,,70.0,107.0,79.0,3=Cirrhosis
+63,37,m,46.1,44.3,42.7,26.5,6.4,10.86,5.05,74.0,22.2,73.1,0=Blood Donor

classification/unipredict/fedesoriano-stroke-prediction-dataset/metadata.json

@@ -0,0 +1,23 @@
+{
+  "dataset": "fedesoriano-stroke-prediction-dataset",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "stroke",
+  "label_values": [
+    "0",
+    "1"
+  ],
+  "num_labels": 2,
+  "train_samples": 4598,
+  "test_samples": 512,
+  "train_label_distribution": {
+    "0": 4374,
+    "1": 224
+  },
+  "test_label_distribution": {
+    "0": 487,
+    "1": 25
+  }
+}

classification/unipredict/fedesoriano-stroke-prediction-dataset/test.csv

@@ -0,0 +1,513 @@
+id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke
+42647,Female,59.0,0,0,Yes,Govt_job,Urban,101.19,29.9,formerly smoked,0
+52978,Female,30.0,0,0,Yes,Private,Urban,84.92,47.8,never smoked,0
+34248,Male,50.0,1,0,No,Private,Urban,81.96,,formerly smoked,0
+57979,Male,8.0,0,0,No,children,Rural,108.06,14.6,Unknown,0
+1681,Female,68.0,0,0,No,Private,Urban,82.85,,smokes,0
+33367,Male,20.0,0,0,No,Private,Rural,87.08,27.1,never smoked,0
+40509,Female,23.0,0,0,No,Private,Urban,91.19,28.3,never smoked,0
+59904,Female,1.8,0,0,No,children,Urban,162.93,15.7,Unknown,0
+20463,Male,81.0,1,1,Yes,Private,Urban,250.89,28.1,smokes,1
+57212,Male,49.0,0,0,No,Private,Urban,144.1,30.7,smokes,0
+47356,Female,42.0,0,0,Yes,Private,Urban,87.4,24.5,formerly smoked,0
+56734,Male,33.0,0,0,Yes,Govt_job,Urban,82.83,25.4,Unknown,0
+64849,Female,42.0,0,0,Yes,Private,Urban,92.2,34.2,Unknown,0
+68074,Male,54.0,0,0,Yes,Private,Rural,100.47,50.2,formerly smoked,0
+35829,Female,33.0,0,0,Yes,Private,Urban,242.84,15.7,smokes,0
+54240,Female,30.0,0,0,Yes,Govt_job,Urban,61.29,24.0,Unknown,0
+23599,Female,30.0,0,0,No,Private,Urban,105.08,25.5,never smoked,0
+54264,Female,81.0,1,0,Yes,Private,Urban,58.71,34.5,never smoked,0
+21653,Male,8.0,0,0,No,children,Rural,104.3,18.5,Unknown,0
+44965,Female,14.0,0,0,No,Self-employed,Urban,124.39,34.0,Unknown,0
+65411,Female,51.0,0,0,Yes,Private,Urban,152.56,21.8,Unknown,0
+61868,Female,62.0,0,0,Yes,Private,Urban,74.12,21.8,formerly smoked,0
+29487,Male,0.72,0,0,No,children,Urban,80.08,16.4,Unknown,0
+11726,Female,49.0,0,0,Yes,Govt_job,Rural,83.84,19.3,formerly smoked,0
+34001,Female,6.0,0,0,No,children,Urban,78.26,19.4,Unknown,0
+18192,Male,10.0,0,0,No,children,Rural,93.11,14.6,Unknown,0
+46474,Male,26.0,0,0,Yes,Private,Rural,100.09,27.4,never smoked,0
+49279,Male,57.0,0,1,Yes,Private,Urban,76.5,29.2,formerly smoked,0
+65895,Female,52.0,0,0,Yes,Private,Urban,98.27,61.2,Unknown,0
+32452,Female,82.0,0,1,Yes,Self-employed,Rural,211.88,28.7,never smoked,0
+23238,Male,53.0,0,1,Yes,Private,Rural,95.23,35.2,smokes,0
+7745,Female,35.0,0,0,Yes,Private,Urban,109.03,19.5,formerly smoked,0
+37526,Female,68.0,1,1,Yes,Private,Rural,233.3,,Unknown,0
+31590,Male,22.0,0,0,No,Private,Urban,111.1,26.6,never smoked,0
+41148,Male,71.0,0,1,Yes,Private,Urban,70.71,30.1,never smoked,0
+6295,Female,57.0,0,0,Yes,Govt_job,Urban,104.36,19.2,smokes,0
+26242,Male,6.0,0,0,No,children,Urban,83.28,20.0,Unknown,0
+46797,Female,31.0,0,0,Yes,Private,Rural,75.82,29.1,never smoked,0
+35941,Male,38.0,0,0,Yes,Private,Urban,167.16,18.3,never smoked,0
+48127,Male,53.0,0,0,Yes,Self-employed,Urban,109.09,26.3,smokes,0
+61699,Male,80.0,0,0,Yes,Private,Rural,94.96,22.1,formerly smoked,0
+32126,Female,56.0,0,1,Yes,Private,Urban,91.89,23.3,smokes,0
+22001,Male,80.0,0,1,Yes,Govt_job,Rural,181.23,32.2,formerly smoked,0
+10710,Female,56.0,0,0,Yes,Private,Urban,185.17,40.4,formerly smoked,1
+26084,Female,77.0,1,0,Yes,Self-employed,Urban,109.51,,never smoked,0
+60603,Female,51.0,0,0,No,Private,Rural,66.67,29.5,never smoked,0
+43035,Male,35.0,0,0,Yes,Private,Rural,145.18,32.6,smokes,0
+69285,Female,45.0,0,0,Yes,Private,Urban,73.27,22.2,smokes,0
+61843,Male,58.0,0,0,Yes,Private,Rural,189.84,,Unknown,1
+42047,Female,55.0,0,0,Yes,Self-employed,Urban,59.2,43.8,never smoked,0
+66287,Male,33.0,0,0,Yes,Private,Rural,88.04,30.3,formerly smoked,0
+43698,Female,27.0,0,0,No,Govt_job,Rural,65.43,27.2,Unknown,0
+42329,Female,77.0,0,0,Yes,Private,Rural,75.06,22.0,Unknown,0
+10460,Female,79.0,0,0,Yes,Govt_job,Urban,77.08,35.0,Unknown,0
+64931,Male,37.0,0,0,Yes,Private,Rural,131.05,27.2,never smoked,0
+29552,Female,55.0,1,1,Yes,Private,Urban,210.4,40.0,smokes,1
+68371,Male,57.0,0,0,Yes,Private,Urban,134.76,29.1,Unknown,0
+66051,Male,43.0,0,0,Yes,Self-employed,Rural,115.79,31.8,Unknown,0
+18943,Male,26.0,0,0,No,Govt_job,Rural,76.74,29.8,Unknown,0
+8579,Female,2.0,0,0,No,children,Rural,89.72,17.8,Unknown,0
+61830,Male,51.0,0,0,Yes,Private,Rural,78.05,31.4,never smoked,0
+60981,Female,26.0,0,0,No,Private,Rural,130.07,33.1,never smoked,0
+49421,Female,66.0,1,0,Yes,Private,Rural,205.23,39.5,never smoked,0
+56799,Male,76.0,0,0,Yes,Govt_job,Urban,82.35,38.9,never smoked,0
+1506,Female,48.0,0,0,No,Govt_job,Urban,101.41,20.7,smokes,0
+54918,Female,18.0,0,0,No,Private,Rural,111.38,38.4,smokes,0
+23551,Male,28.0,0,0,Yes,Private,Urban,87.43,55.7,Unknown,0
+27479,Male,63.0,0,0,Yes,Self-employed,Rural,104.7,21.0,formerly smoked,0
+13859,Female,31.0,0,0,No,Private,Urban,102.39,22.9,smokes,0
+47585,Female,31.0,0,0,No,Self-employed,Urban,62.68,35.8,never smoked,0
+15018,Female,23.0,0,0,No,Govt_job,Urban,84.46,28.4,formerly smoked,0
+37629,Female,55.0,0,0,No,Private,Rural,93.36,28.4,never smoked,0
+51020,Female,55.0,0,0,Yes,Private,Rural,87.78,25.2,formerly smoked,0
+51149,Male,70.0,0,0,Yes,Private,Urban,66.85,29.3,Unknown,0
+11447,Female,41.0,0,0,Yes,Govt_job,Urban,80.28,37.3,never smoked,0
+43806,Male,44.0,0,0,Yes,Private,Urban,142.31,29.1,smokes,0
+11692,Female,53.0,0,0,No,Govt_job,Urban,101.81,29.4,smokes,0
+8831,Female,58.0,0,0,Yes,Private,Rural,94.3,29.1,Unknown,0
+39393,Female,63.0,0,0,Yes,Private,Urban,57.06,37.9,never smoked,0
+5353,Male,52.0,0,1,No,Private,Rural,101.5,31.2,smokes,0
+51285,Female,46.0,0,0,Yes,Private,Urban,61.81,25.5,Unknown,0
+1213,Female,31.0,0,0,Yes,Self-employed,Urban,87.23,,formerly smoked,0
+65379,Male,9.0,0,0,No,children,Urban,69.52,24.2,Unknown,0
+57523,Female,26.0,0,0,Yes,Private,Urban,116.38,21.9,formerly smoked,0
+9079,Female,76.0,0,1,Yes,Self-employed,Urban,202.21,39.3,formerly smoked,0
+14414,Female,34.0,0,0,Yes,Private,Rural,85.79,32.0,never smoked,0
+56584,Female,22.0,0,0,No,Private,Rural,62.0,32.7,smokes,0
+46323,Female,2.0,0,0,No,children,Rural,165.11,18.0,Unknown,0
+39308,Male,62.0,0,0,Yes,Private,Urban,145.37,33.3,Unknown,0
+61336,Female,69.0,0,0,Yes,Self-employed,Urban,126.04,35.9,never smoked,0
+66270,Female,57.0,0,0,Yes,Private,Rural,69.4,24.0,Unknown,0
+60426,Female,69.0,0,0,Yes,Self-employed,Urban,67.55,38.1,Unknown,0
+34436,Female,2.0,0,0,No,children,Rural,109.56,16.4,Unknown,0
+22853,Male,82.0,0,0,No,Self-employed,Rural,106.43,27.0,smokes,0
+58227,Female,64.0,0,0,Yes,Govt_job,Rural,62.41,30.0,never smoked,0
+2893,Female,7.0,0,0,No,children,Rural,72.35,17.0,Unknown,0
+47383,Male,1.8,0,0,No,children,Urban,153.31,17.1,Unknown,0
+41652,Female,31.0,0,0,No,Private,Urban,63.41,25.5,formerly smoked,0
+66690,Female,63.0,0,0,Yes,Self-employed,Urban,69.46,26.6,never smoked,0
+15324,Female,40.0,0,0,No,Private,Urban,86.1,23.9,Unknown,0
+40163,Female,82.0,1,0,Yes,Private,Urban,222.52,,formerly smoked,0
+12465,Female,52.0,0,0,No,Private,Rural,88.04,42.1,never smoked,0
+36850,Male,36.0,0,0,Yes,Govt_job,Urban,57.59,32.8,Unknown,0
+11630,Female,25.0,0,0,No,Private,Urban,92.06,25.3,smokes,0
+7282,Male,44.0,0,0,Yes,Private,Rural,81.84,25.1,never smoked,0
+30927,Male,24.0,0,0,No,Private,Rural,93.76,24.0,formerly smoked,0
+34657,Female,44.0,0,0,Yes,Self-employed,Urban,82.33,24.5,never smoked,0
+70392,Male,34.0,0,0,Yes,Private,Rural,112.72,19.4,Unknown,0
+6319,Female,79.0,0,0,Yes,Private,Urban,97.93,31.2,Unknown,0
+1099,Female,15.0,0,0,No,children,Rural,101.15,22.2,Unknown,0
+71796,Female,70.0,0,1,Yes,Private,Rural,59.35,32.3,formerly smoked,1
+22485,Male,56.0,0,0,Yes,Private,Urban,197.1,43.6,formerly smoked,0
+65894,Female,2.0,0,0,No,children,Urban,82.3,18.8,Unknown,0
+27721,Male,32.0,0,0,Yes,Private,Rural,83.13,32.0,smokes,0
+4128,Female,55.0,0,0,Yes,Private,Rural,76.7,39.7,formerly smoked,0
+67800,Female,13.0,0,0,No,children,Rural,77.55,21.3,Unknown,0
+53646,Female,33.0,1,0,No,Private,Rural,97.87,,smokes,0
+12741,Female,25.0,0,0,Yes,Private,Rural,97.52,45.5,formerly smoked,0
+47181,Female,68.0,0,0,Yes,Private,Urban,103.46,35.9,never smoked,0
+60586,Female,68.0,0,0,Yes,Private,Rural,85.29,27.1,formerly smoked,0
+67758,Male,9.0,0,0,No,children,Urban,114.99,18.8,Unknown,0
+3180,Female,42.0,0,0,Yes,Govt_job,Urban,88.89,33.0,never smoked,0
+28637,Female,14.0,0,0,No,children,Rural,72.36,20.5,Unknown,0
+47330,Male,9.0,0,0,No,children,Rural,60.39,16.4,Unknown,0
+452,Male,48.0,1,0,Yes,Private,Urban,173.14,37.0,smokes,0
+3595,Male,32.0,0,0,Yes,Private,Urban,97.95,40.2,smokes,0
+50072,Female,26.0,0,0,No,Private,Rural,58.55,29.0,never smoked,0
+24665,Female,64.0,1,0,Yes,Private,Rural,93.99,37.8,formerly smoked,0
+25488,Female,46.0,0,0,Yes,Self-employed,Urban,94.63,24.9,never smoked,0
+2244,Male,44.0,0,0,Yes,Private,Urban,80.75,30.9,never smoked,0
+37060,Female,81.0,0,0,Yes,Private,Rural,80.13,23.4,never smoked,1
+49529,Female,1.16,0,0,No,children,Urban,60.98,17.2,Unknown,0
+35927,Male,65.0,0,0,Yes,Private,Urban,88.57,29.0,smokes,0
+15969,Female,41.0,0,0,Yes,Self-employed,Rural,102.89,37.2,formerly smoked,0
+28326,Female,79.0,0,0,Yes,Private,Urban,65.59,28.1,never smoked,0
+13031,Female,15.0,0,0,No,children,Urban,91.16,38.0,never smoked,0
+49003,Male,43.0,0,0,Yes,Private,Urban,146.01,31.5,smokes,0
+23094,Male,65.0,0,0,Yes,Self-employed,Urban,105.61,27.9,Unknown,0
+72435,Female,37.0,0,0,Yes,Private,Urban,217.11,29.1,never smoked,0
+68344,Female,62.0,0,0,Yes,Private,Urban,82.38,27.2,formerly smoked,0
+10449,Female,24.0,0,0,Yes,Private,Urban,75.23,29.0,never smoked,0
+49645,Male,58.0,0,0,No,Private,Rural,76.22,22.2,formerly smoked,0
+25287,Male,54.0,0,0,Yes,Private,Urban,92.95,41.0,never smoked,0
+50975,Male,49.0,0,0,Yes,Private,Rural,62.64,27.0,never smoked,0
+49451,Female,53.0,0,0,Yes,Private,Rural,83.91,36.6,Unknown,0
+72715,Female,50.0,0,1,Yes,Private,Urban,193.8,26.4,never smoked,0
+45961,Female,78.0,0,0,Yes,Private,Urban,79.94,26.7,never smoked,0
+12134,Female,53.0,0,0,Yes,Govt_job,Rural,87.62,33.7,smokes,0
+36355,Male,58.0,0,0,Yes,Govt_job,Rural,111.73,34.6,never smoked,0
+71966,Female,18.0,0,0,No,Never_worked,Urban,81.73,21.6,never smoked,0
+52530,Male,55.0,0,0,Yes,Govt_job,Urban,231.15,22.3,never smoked,0
+54756,Female,59.0,0,0,Yes,Private,Rural,57.47,30.1,formerly smoked,0
+59521,Male,33.0,0,0,Yes,Private,Rural,74.88,31.6,smokes,0
+11111,Female,66.0,1,0,Yes,Govt_job,Urban,205.01,52.7,formerly smoked,0
+41500,Male,0.16,0,0,No,children,Rural,69.79,13.0,Unknown,0
+949,Male,20.0,0,0,No,Private,Rural,75.9,32.2,never smoked,0
+68268,Female,63.0,0,0,Yes,Self-employed,Urban,93.88,34.8,Unknown,0
+48425,Male,21.0,0,0,No,Private,Rural,89.29,23.4,never smoked,0
+28013,Female,38.0,0,0,Yes,Self-employed,Urban,98.37,27.2,never smoked,0
+15752,Male,39.0,0,0,Yes,Private,Urban,90.36,30.8,formerly smoked,0
+24892,Male,64.0,0,0,Yes,Private,Rural,97.08,31.7,Unknown,0
+41244,Female,7.0,0,0,No,children,Urban,79.58,15.5,Unknown,0
+67099,Male,0.56,0,0,No,children,Rural,57.02,20.7,Unknown,0
+6172,Female,79.0,0,0,Yes,Private,Rural,208.05,,smokes,0
+11762,Female,76.0,0,0,Yes,Private,Urban,207.28,34.9,Unknown,1
+30328,Female,69.0,1,0,Yes,Govt_job,Rural,103.44,43.1,formerly smoked,0
+21202,Female,27.0,0,0,Yes,Private,Urban,80.57,39.8,smokes,0
+31811,Female,52.0,0,1,Yes,Private,Urban,85.66,39.4,never smoked,0
+69482,Female,31.0,0,0,Yes,Govt_job,Urban,81.71,32.7,Unknown,0
+55400,Female,5.0,0,0,No,children,Rural,73.92,17.2,Unknown,0
+11605,Female,26.0,0,0,No,Private,Rural,108.2,26.2,never smoked,0
+10055,Female,37.0,0,0,No,Govt_job,Rural,72.08,,formerly smoked,0
+72823,Female,79.0,0,0,Yes,Private,Urban,70.35,23.0,formerly smoked,0
+22321,Female,44.0,0,0,Yes,Private,Urban,124.06,20.8,never smoked,0
+58037,Male,21.0,0,0,No,Private,Rural,78.52,27.2,never smoked,0
+61103,Female,64.0,1,0,Yes,Self-employed,Urban,190.92,31.4,never smoked,0
+46864,Male,54.0,0,1,Yes,Govt_job,Urban,222.46,35.7,never smoked,0
+33876,Male,10.0,0,0,No,children,Urban,87.09,14.3,Unknown,0
+38549,Female,62.0,0,0,Yes,Private,Urban,212.62,35.8,never smoked,0
+56553,Male,51.0,0,0,Yes,Private,Urban,63.61,42.3,Unknown,0
+31795,Male,61.0,0,0,Yes,Self-employed,Urban,73.24,34.9,never smoked,0
+768,Female,74.0,0,0,Yes,Self-employed,Urban,68.18,27.3,formerly smoked,0
+9489,Female,65.0,0,0,Yes,Private,Urban,84.75,21.4,Unknown,0
+875,Female,34.0,0,0,No,Private,Urban,67.66,22.4,never smoked,0
+71221,Female,42.0,0,0,Yes,Govt_job,Urban,99.94,33.4,never smoked,0
+28493,Male,57.0,0,0,Yes,Private,Urban,86.3,31.7,Unknown,1
+25883,Female,82.0,1,0,Yes,Self-employed,Urban,77.32,24.8,Unknown,0
+28150,Female,65.0,1,0,Yes,Private,Urban,180.76,26.9,Unknown,0
+53422,Male,52.0,0,0,Yes,Private,Rural,191.66,26.1,smokes,0
+13902,Female,42.0,0,0,Yes,Private,Urban,74.8,50.6,Unknown,0
+29224,Male,30.0,0,0,Yes,Private,Urban,91.23,,smokes,0
+68306,Male,17.0,0,0,No,Private,Rural,119.58,25.0,never smoked,0
+68089,Female,44.0,0,0,Yes,Private,Urban,121.46,40.4,Unknown,0
+44993,Female,79.0,1,0,No,Govt_job,Urban,98.02,22.3,formerly smoked,1
+10552,Female,81.0,0,0,Yes,Self-employed,Rural,81.95,16.9,never smoked,1
+18205,Female,1.32,0,0,No,children,Rural,110.17,20.3,Unknown,0
+1656,Male,38.0,0,0,Yes,Private,Urban,92.22,40.8,never smoked,0
+55138,Female,81.0,0,0,No,Self-employed,Urban,71.91,19.2,Unknown,0
+64652,Female,44.0,0,0,Yes,Private,Rural,56.85,24.4,never smoked,0
+20316,Female,75.0,0,0,Yes,Govt_job,Rural,219.39,33.4,smokes,0
+62602,Female,49.0,0,0,Yes,Private,Urban,60.91,29.9,never smoked,1
+18518,Male,66.0,0,0,Yes,Private,Rural,242.3,35.3,smokes,0
+59880,Male,45.0,0,0,Yes,Private,Rural,99.91,30.9,Unknown,0
+36366,Male,77.0,0,0,Yes,Govt_job,Urban,64.4,27.8,never smoked,0
+3442,Female,79.0,0,0,No,Self-employed,Rural,82.07,30.4,Unknown,0
+61376,Male,38.0,0,0,Yes,Private,Urban,215.69,38.6,formerly smoked,0
+12900,Male,11.0,0,0,No,children,Rural,80.08,21.8,never smoked,0
+8752,Female,63.0,0,0,Yes,Govt_job,Urban,197.54,,never smoked,1
+22623,Male,77.0,0,0,Yes,Private,Urban,71.44,24.1,smokes,0
+69370,Male,78.0,0,0,Yes,Govt_job,Urban,59.74,27.0,formerly smoked,0
+62466,Female,80.0,0,0,Yes,Private,Urban,64.44,45.0,never smoked,1
+44510,Female,56.0,0,0,Yes,Private,Rural,131.63,27.6,never smoked,0
+27660,Female,73.0,1,0,No,Self-employed,Rural,198.3,54.3,formerly smoked,0
+35602,Female,52.0,0,0,Yes,Govt_job,Rural,107.84,22.0,formerly smoked,0
+19801,Female,44.0,0,0,Yes,Private,Rural,98.3,25.0,never smoked,0
+56575,Female,51.0,1,0,Yes,Govt_job,Urban,69.94,33.3,smokes,0
+4913,Female,57.0,0,0,Yes,Private,Rural,93.85,29.1,never smoked,0
+11208,Female,2.0,0,0,No,children,Rural,70.25,17.0,Unknown,0
+69959,Female,80.0,1,0,No,Private,Urban,66.03,35.4,never smoked,1
+19849,Female,1.64,0,0,No,children,Urban,90.74,19.9,Unknown,0
+63650,Female,47.0,0,0,Yes,Govt_job,Urban,135.79,32.1,formerly smoked,0
+54383,Male,60.0,0,0,Yes,Private,Rural,101.34,32.8,never smoked,0
+5964,Female,59.0,0,0,Yes,Private,Urban,182.52,30.1,Unknown,0
+59027,Female,12.0,0,0,No,children,Rural,108.63,23.4,never smoked,0
+43433,Female,52.0,0,0,Yes,Self-employed,Rural,59.62,50.8,Unknown,0
+12022,Male,37.0,0,0,Yes,Govt_job,Urban,82.09,35.7,smokes,0
+25454,Female,13.0,0,0,No,children,Rural,93.3,25.9,Unknown,0
+33906,Male,51.0,0,0,Yes,Govt_job,Urban,92.32,34.7,smokes,0
+39450,Male,22.0,0,0,No,Private,Rural,58.96,25.3,Unknown,0
+60211,Male,1.4,0,0,No,children,Urban,90.51,18.9,Unknown,0
+70674,Male,60.0,0,0,Yes,Self-employed,Urban,69.53,26.2,never smoked,0
+49627,Female,12.0,0,0,No,children,Urban,82.39,17.1,never smoked,0
+5464,Male,32.0,0,0,Yes,Private,Rural,70.96,33.1,Unknown,0
+51275,Female,10.0,0,0,No,children,Urban,61.34,19.1,Unknown,0
+53028,Female,39.0,0,0,Yes,Private,Rural,81.31,34.7,never smoked,0
+8168,Female,34.0,0,0,Yes,Private,Rural,112.54,23.4,formerly smoked,0
+49152,Female,40.0,0,0,No,Private,Rural,70.45,23.3,smokes,0
+13062,Male,18.0,0,0,No,Private,Rural,123.79,20.5,Unknown,0
+9648,Female,71.0,0,1,Yes,Private,Urban,170.95,35.2,never smoked,0
+17251,Female,76.0,1,0,Yes,Self-employed,Urban,78.7,27.6,formerly smoked,0
+40878,Male,71.0,0,0,Yes,Self-employed,Rural,56.43,29.2,formerly smoked,0
+47159,Male,68.0,0,0,Yes,Private,Urban,155.17,35.5,never smoked,0
+52236,Female,60.0,0,0,Yes,Private,Rural,230.78,40.2,never smoked,0
+24592,Female,51.0,1,0,Yes,Private,Urban,109.16,28.0,smokes,0
+28291,Female,79.0,0,1,Yes,Private,Urban,226.98,29.8,never smoked,1
+63949,Female,33.0,0,0,Yes,Govt_job,Urban,75.67,44.7,never smoked,0
+39258,Female,59.0,0,0,Yes,Self-employed,Urban,65.82,29.4,never smoked,0
+48303,Male,39.0,0,0,Yes,Private,Rural,71.3,34.7,never smoked,0
+42117,Male,43.0,0,0,Yes,Self-employed,Urban,143.43,45.9,Unknown,1
+49702,Female,81.0,0,0,Yes,Self-employed,Rural,101.32,29.6,formerly smoked,0
+45801,Female,38.0,0,0,No,Private,Rural,97.49,26.9,never smoked,0
+42251,Male,71.0,1,1,Yes,Self-employed,Rural,67.06,26.7,smokes,0
+4861,Female,30.0,0,0,Yes,Private,Urban,70.67,24.6,smokes,0
+16420,Female,45.0,0,0,Yes,Private,Urban,108.03,37.3,never smoked,0
+11861,Male,61.0,0,0,Yes,Self-employed,Rural,81.96,29.9,never smoked,0
+56001,Male,57.0,0,0,Yes,Private,Rural,82.08,24.7,Unknown,0
+57798,Male,12.0,0,0,No,children,Rural,127.25,28.2,Unknown,0
+60276,Male,78.0,1,1,Yes,Self-employed,Rural,106.41,27.3,never smoked,0
+16245,Male,51.0,1,0,Yes,Self-employed,Rural,211.83,56.6,never smoked,0
+48144,Female,20.0,0,0,No,Govt_job,Rural,73.0,20.8,never smoked,0
+14976,Male,80.0,0,1,Yes,Private,Rural,82.41,26.3,smokes,0
+52554,Male,19.0,0,0,No,Private,Rural,64.92,22.5,Unknown,0
+62834,Female,32.0,0,0,Yes,Private,Urban,88.33,20.0,Unknown,0
+19239,Female,50.0,0,0,Yes,Govt_job,Urban,104.24,32.8,Unknown,0
+28873,Female,21.0,0,0,No,Private,Rural,74.24,32.7,never smoked,0
+2005,Male,78.0,0,1,Yes,Self-employed,Urban,169.43,23.5,formerly smoked,0
+32166,Male,47.0,1,0,Yes,Private,Urban,75.64,24.4,never smoked,0
+7700,Female,52.0,0,0,Yes,Private,Urban,106.54,22.4,never smoked,0
+37972,Female,52.0,0,0,Yes,Private,Rural,68.7,16.0,Unknown,0
+72514,Male,18.0,0,0,No,Private,Rural,120.58,21.5,never smoked,0
+7663,Male,20.0,0,0,No,Govt_job,Rural,106.97,27.9,formerly smoked,0
+59540,Female,19.0,0,0,No,Private,Rural,56.85,21.1,never smoked,0
+16446,Male,2.0,0,0,No,children,Rural,76.12,16.8,Unknown,0
+30084,Male,0.8,0,0,No,children,Rural,98.67,17.5,Unknown,0
+48064,Male,11.0,0,0,No,children,Rural,65.07,21.5,never smoked,0
+7841,Female,50.0,0,0,Yes,Private,Urban,91.68,22.4,never smoked,0
+34400,Female,77.0,1,0,Yes,Self-employed,Rural,176.71,33.2,never smoked,0
+16550,Female,69.0,0,1,No,Govt_job,Urban,202.38,34.6,Unknown,0
+10950,Female,2.0,0,0,No,children,Urban,112.75,25.1,Unknown,0
+57854,Male,1.64,0,0,No,children,Urban,56.3,19.7,Unknown,0
+49014,Female,76.0,0,0,Yes,Govt_job,Urban,204.05,23.5,never smoked,0
+25070,Male,62.0,0,0,Yes,Govt_job,Rural,103.0,31.9,Unknown,0
+65144,Female,57.0,0,0,Yes,Self-employed,Urban,98.44,33.6,Unknown,0
+59045,Female,52.0,0,0,Yes,Private,Urban,67.3,36.3,never smoked,0
+47005,Female,47.0,0,0,Yes,Private,Urban,68.48,21.3,never smoked,0
+72398,Female,73.0,1,0,Yes,Private,Urban,110.38,26.3,never smoked,0
+24066,Female,45.0,0,0,Yes,Private,Urban,72.65,25.6,Unknown,0
+12366,Female,35.0,0,0,No,Private,Urban,97.58,24.3,Unknown,0
+13116,Male,49.0,0,0,Yes,Private,Urban,87.06,28.3,never smoked,0
+21678,Male,33.0,0,0,Yes,Private,Urban,90.73,32.8,smokes,0
+11973,Female,10.0,0,0,No,children,Urban,124.6,18.6,Unknown,0
+21796,Male,59.0,0,0,Yes,Private,Urban,66.46,39.6,formerly smoked,0
+1625,Female,13.0,0,0,No,children,Urban,99.13,22.8,Unknown,0
+20546,Female,68.0,0,0,Yes,Private,Urban,79.58,22.2,never smoked,0
+28048,Male,13.0,0,0,No,children,Urban,82.38,24.3,Unknown,0
+37832,Female,14.0,0,0,No,children,Rural,129.53,21.3,never smoked,0
+37830,Female,29.0,0,0,No,Private,Urban,73.67,21.0,Unknown,0
+40870,Female,75.0,0,0,Yes,Govt_job,Urban,73.89,20.9,Unknown,0
+51486,Female,61.0,0,0,Yes,Private,Rural,106.65,35.9,formerly smoked,0
+5731,Female,57.0,1,0,Yes,Private,Urban,108.61,38.1,smokes,0
+53967,Female,80.0,0,0,Yes,Self-employed,Rural,72.61,27.6,never smoked,0
+16590,Male,71.0,0,1,Yes,Private,Urban,81.76,,smokes,1
+42743,Female,20.0,0,0,No,Private,Urban,95.5,31.3,Unknown,0
+2772,Male,55.0,0,0,Yes,Private,Urban,87.72,27.0,Unknown,0
+29539,Male,62.0,1,0,Yes,Self-employed,Rural,95.49,40.2,smokes,0
+52428,Male,25.0,0,0,No,Private,Urban,116.12,20.4,smokes,0
+24630,Male,57.0,0,0,Yes,Private,Rural,230.59,23.2,formerly smoked,0
+54022,Female,78.0,0,0,Yes,Self-employed,Rural,67.9,35.3,never smoked,0
+43282,Male,0.72,0,0,No,children,Rural,159.79,19.9,Unknown,0
+25904,Female,76.0,1,1,Yes,Self-employed,Urban,199.86,,smokes,1
+69643,Male,81.0,0,0,Yes,Private,Rural,59.93,28.9,formerly smoked,0
+16605,Male,57.0,0,0,Yes,Private,Urban,106.24,32.3,never smoked,0
+57210,Female,28.0,0,0,Yes,Private,Rural,131.8,30.3,never smoked,0
+45759,Female,32.0,0,0,Yes,Private,Rural,91.98,27.6,smokes,0
+49789,Female,73.0,0,0,No,Govt_job,Urban,62.99,25.4,formerly smoked,0
+34721,Female,62.0,1,0,Yes,Govt_job,Urban,92.13,33.7,never smoked,0
+15742,Female,3.0,0,0,No,children,Rural,75.41,21.9,Unknown,0
+12593,Female,18.0,0,0,No,Private,Urban,80.33,19.7,never smoked,0
+45010,Female,57.0,0,0,Yes,Private,Rural,77.93,21.7,never smoked,0
+45040,Male,55.0,0,0,Yes,Private,Urban,203.81,33.9,formerly smoked,0
+46385,Female,21.0,0,0,Yes,Private,Urban,59.15,22.6,never smoked,0
+5824,Male,61.0,0,0,Yes,Private,Rural,204.5,35.1,formerly smoked,0
+25495,Male,5.0,0,0,No,children,Urban,112.11,20.1,Unknown,0
+48769,Female,38.0,0,0,Yes,Private,Rural,61.88,29.0,Unknown,0
+35648,Female,74.0,0,0,Yes,Self-employed,Rural,95.94,27.0,never smoked,0
+59178,Female,7.0,0,0,No,children,Urban,86.75,22.3,Unknown,0
+34130,Male,54.0,1,0,Yes,Private,Rural,116.44,24.5,never smoked,0
+48609,Female,81.0,0,1,Yes,Private,Rural,123.49,30.7,smokes,0
+21688,Female,42.0,0,0,Yes,Private,Rural,88.31,24.0,smokes,0
+52340,Male,55.0,0,0,Yes,Private,Urban,67.02,41.1,smokes,0
+21963,Male,31.0,0,0,Yes,Private,Urban,108.51,26.7,Unknown,0
+22590,Male,5.0,0,0,No,children,Urban,83.75,18.1,Unknown,0
+15266,Female,32.0,0,0,Yes,Private,Rural,77.67,32.3,smokes,0
+33526,Female,51.0,0,0,Yes,Self-employed,Rural,91.63,35.3,Unknown,0
+11962,Male,36.0,0,0,Yes,Private,Urban,89.33,30.7,never smoked,0
+22902,Male,41.0,1,0,Yes,Private,Urban,69.52,31.9,never smoked,0
+27119,Female,28.0,0,0,No,Private,Rural,104.16,21.5,never smoked,0
+32840,Female,52.0,0,0,Yes,Private,Urban,97.32,21.8,smokes,0
+60159,Female,29.0,0,0,No,Govt_job,Rural,118.61,26.5,never smoked,0
+43675,Female,7.0,0,0,No,children,Urban,61.42,20.8,Unknown,0
+40732,Female,50.0,0,0,Yes,Self-employed,Rural,126.85,49.5,formerly smoked,0
+1772,Female,64.0,0,0,Yes,Govt_job,Urban,77.68,31.4,never smoked,0
+38541,Male,55.0,0,0,Yes,Private,Urban,84.44,30.5,formerly smoked,0
+35123,Female,1.24,0,0,No,children,Urban,84.2,19.2,Unknown,0
+38207,Female,79.0,1,0,Yes,Self-employed,Rural,76.64,19.5,never smoked,0
+39038,Male,11.0,0,0,No,children,Rural,79.03,16.5,Unknown,0
+54574,Female,20.0,0,0,No,Private,Urban,115.69,29.2,never smoked,0
+6202,Male,4.0,0,0,No,children,Urban,87.0,19.0,Unknown,0
+70031,Female,71.0,1,0,Yes,Private,Rural,195.25,33.3,never smoked,0
+19769,Female,67.0,0,0,Yes,Self-employed,Rural,80.18,22.9,formerly smoked,0
+11250,Male,78.0,0,0,Yes,Self-employed,Rural,93.85,22.7,formerly smoked,0
+50644,Male,37.0,0,0,Yes,Private,Urban,64.07,28.0,Unknown,0
+9565,Female,39.0,0,0,No,Private,Rural,79.0,30.0,never smoked,0
+36471,Male,65.0,0,0,Yes,Private,Urban,145.15,28.9,Unknown,0
+259,Male,79.0,0,0,Yes,Private,Urban,198.79,24.9,never smoked,0
+14872,Male,45.0,1,0,Yes,Self-employed,Rural,239.19,52.5,Unknown,0
+20217,Female,38.0,0,0,Yes,Govt_job,Urban,102.84,22.4,never smoked,0
+70661,Female,28.0,0,0,No,Private,Rural,134.12,28.8,formerly smoked,0
+36896,Male,25.0,0,0,Yes,Private,Rural,66.51,29.2,Unknown,0
+7047,Female,31.0,0,0,Yes,Private,Rural,69.72,39.5,smokes,0
+4062,Male,72.0,0,1,Yes,Private,Rural,238.27,,smokes,0
+14444,Female,37.0,0,0,No,Self-employed,Urban,90.71,45.8,Unknown,0
+48775,Female,78.0,1,0,Yes,Self-employed,Rural,201.07,21.8,Unknown,0
+42412,Female,18.0,0,0,No,Private,Urban,146.59,27.7,Unknown,0
+56716,Female,26.0,0,0,No,Private,Urban,82.59,29.4,never smoked,0
+71539,Male,25.0,0,0,No,Private,Urban,138.29,27.3,Unknown,0
+69461,Female,49.0,0,0,Yes,Govt_job,Urban,90.58,23.2,Unknown,0
+36679,Female,22.0,1,0,No,Private,Urban,71.22,40.0,never smoked,0
+16449,Female,33.0,0,0,Yes,Govt_job,Rural,76.66,24.8,never smoked,0
+70718,Male,33.0,0,0,Yes,Private,Rural,153.34,31.5,never smoked,0
+13328,Female,45.0,0,0,Yes,Private,Rural,106.95,33.4,Unknown,0
+64202,Male,50.0,0,0,Yes,Private,Rural,119.77,23.5,Unknown,0
+9620,Female,43.0,0,0,Yes,Govt_job,Rural,81.77,25.4,never smoked,0
+47730,Female,41.0,0,0,No,Private,Urban,86.03,26.4,never smoked,0
+43615,Female,49.0,0,0,Yes,Self-employed,Urban,75.15,25.0,Unknown,0
+5835,Male,68.0,0,0,Yes,Private,Urban,92.21,27.3,Unknown,0
+1737,Female,16.0,0,0,No,Private,Rural,86.53,42.2,never smoked,0
+66362,Female,61.0,0,0,Yes,Private,Urban,129.31,41.2,Unknown,0
+52220,Female,26.0,0,0,No,Private,Rural,154.08,20.2,formerly smoked,0
+6852,Female,52.0,1,0,Yes,Self-employed,Rural,104.45,,never smoked,0
+37086,Male,17.0,0,0,No,Private,Rural,60.57,34.0,Unknown,0
+59953,Female,15.0,0,0,No,Private,Rural,69.38,28.4,never smoked,0
+91,Female,42.0,0,0,No,Private,Urban,98.53,18.5,never smoked,0
+2138,Male,58.0,0,0,Yes,Govt_job,Urban,84.94,,never smoked,0
+14407,Male,45.0,0,0,No,Self-employed,Urban,104.12,37.7,Unknown,0
+2818,Female,80.0,0,0,No,Self-employed,Rural,230.74,30.2,formerly smoked,0
+59464,Female,18.0,0,0,No,Private,Rural,135.19,23.4,never smoked,0
+66893,Male,49.0,1,0,Yes,Govt_job,Urban,139.43,40.2,formerly smoked,0
+21346,Female,12.0,0,0,No,children,Rural,70.13,17.8,Unknown,0
+53494,Female,9.0,0,0,No,children,Rural,125.09,15.4,Unknown,0
+34133,Female,20.0,0,0,No,Private,Rural,93.74,23.7,Unknown,0
+47037,Female,67.0,0,0,Yes,Private,Urban,102.71,39.9,formerly smoked,0
+6324,Male,51.0,0,0,Yes,Private,Rural,107.42,20.2,formerly smoked,0
+57618,Female,47.0,0,0,Yes,Self-employed,Rural,140.39,25.5,never smoked,0
+59762,Male,61.0,0,0,Yes,Private,Urban,227.98,14.2,Unknown,0
+69835,Female,57.0,0,0,Yes,Private,Rural,131.4,32.3,never smoked,0
+51554,Male,42.0,0,0,Yes,Private,Urban,177.91,,Unknown,0
+29104,Female,19.0,0,0,No,Private,Urban,110.7,38.5,never smoked,0
+11120,Female,78.0,1,0,Yes,Private,Urban,218.46,34.3,never smoked,0
+62791,Male,79.0,1,1,Yes,Self-employed,Rural,205.23,22.0,never smoked,0
+58820,Male,56.0,0,0,Yes,Private,Rural,86.36,27.7,formerly smoked,0
+49661,Male,53.0,0,0,Yes,Govt_job,Urban,85.17,29.2,never smoked,0
+21491,Female,80.0,0,0,Yes,Private,Urban,213.11,34.7,never smoked,0
+11412,Female,59.0,0,0,Yes,Private,Rural,234.82,51.8,never smoked,0
+5077,Male,45.0,0,0,Yes,Private,Urban,76.72,29.1,Unknown,0
+28799,Male,11.0,0,0,No,children,Rural,90.69,18.6,Unknown,0
+11280,Female,28.0,0,0,Yes,Private,Urban,98.05,24.7,never smoked,0
+14688,Female,44.0,0,0,Yes,Private,Urban,73.87,28.8,smokes,0
+27832,Female,51.0,0,0,Yes,Private,Rural,82.93,29.7,smokes,0
+712,Female,82.0,1,1,No,Private,Rural,84.03,26.5,formerly smoked,1
+61987,Female,40.0,0,0,Yes,Private,Urban,101.06,32.3,smokes,0
+44171,Male,62.0,0,0,Yes,Private,Rural,62.56,32.3,never smoked,0
+37025,Female,2.0,0,0,No,children,Urban,114.02,18.1,Unknown,0
+63936,Female,30.0,0,0,No,Private,Urban,69.67,35.8,formerly smoked,0
+58153,Female,18.0,0,0,No,Private,Urban,123.66,22.2,never smoked,0
+71038,Male,34.0,0,0,Yes,Private,Urban,137.96,35.1,Unknown,0
+50118,Female,65.0,0,1,Yes,Private,Rural,196.36,34.5,formerly smoked,0
+53265,Female,33.0,0,0,Yes,Self-employed,Urban,70.59,20.2,Unknown,0
+24735,Female,21.0,0,0,No,Private,Rural,80.84,30.7,Unknown,0
+59642,Female,45.0,0,0,Yes,Private,Urban,107.29,29.6,never smoked,0
+47732,Male,5.0,0,0,No,children,Rural,163.7,18.4,Unknown,0
+15539,Female,41.0,0,0,Yes,Private,Rural,97.41,25.5,never smoked,0
+52668,Female,24.0,0,0,No,Private,Urban,65.44,23.6,never smoked,0
+65842,Female,67.0,1,0,Yes,Self-employed,Rural,61.94,25.3,smokes,1
+24873,Female,81.0,0,0,Yes,Private,Rural,99.48,27.2,never smoked,0
+43268,Female,52.0,1,0,No,Private,Urban,73.0,25.2,smokes,0
+71057,Female,54.0,0,0,Yes,Private,Rural,70.19,39.1,smokes,0
+21408,Female,39.0,0,0,Yes,Self-employed,Rural,89.86,24.4,never smoked,0
+71590,Female,5.0,0,0,No,children,Rural,102.04,18.5,Unknown,0
+31867,Female,49.0,0,0,No,Private,Rural,65.81,32.3,Unknown,0
+2013,Male,14.0,0,0,No,Private,Rural,110.72,,never smoked,0
+51512,Female,19.0,0,0,No,Private,Rural,57.4,22.9,Unknown,0
+49744,Female,59.0,0,0,Yes,Private,Urban,240.71,43.9,formerly smoked,0
+39714,Male,12.0,0,0,No,children,Urban,64.08,18.2,Unknown,0
+39708,Male,55.0,0,0,Yes,Private,Rural,56.87,28.9,formerly smoked,0
+2291,Female,80.0,1,0,Yes,Self-employed,Urban,218.0,33.5,Unknown,0
+13857,Male,0.32,0,0,No,children,Urban,89.04,17.8,Unknown,0
+10245,Female,54.0,0,0,Yes,Self-employed,Rural,77.52,35.8,never smoked,0
+36728,Male,74.0,0,0,Yes,Private,Urban,79.44,32.8,never smoked,0
+28265,Female,42.0,0,0,Yes,Self-employed,Rural,79.14,25.0,formerly smoked,0
+12336,Female,73.0,0,0,Yes,Self-employed,Urban,87.56,24.1,never smoked,0
+47167,Female,77.0,1,0,Yes,Self-employed,Urban,124.13,31.4,never smoked,1
+5951,Male,28.0,1,0,No,Private,Urban,86.61,38.6,smokes,0
+56410,Male,1.88,0,0,No,children,Urban,81.42,13.5,Unknown,0
+3379,Female,61.0,0,0,Yes,Private,Urban,87.52,23.7,Unknown,0
+40899,Female,78.0,0,0,Yes,Self-employed,Rural,60.67,,formerly smoked,1
+46035,Male,1.0,0,0,No,children,Urban,84.85,20.3,Unknown,0
+47345,Male,45.0,0,0,Yes,Private,Rural,97.12,29.2,never smoked,0
+57968,Female,11.0,0,0,No,children,Urban,107.18,27.6,Unknown,0
+52549,Male,59.0,0,0,Yes,Govt_job,Rural,88.81,38.0,formerly smoked,0
+56791,Male,9.0,0,0,No,children,Urban,170.76,20.0,Unknown,0
+67343,Female,57.0,0,0,Yes,Private,Rural,81.42,35.8,never smoked,0
+71387,Female,66.0,0,0,Yes,Govt_job,Rural,59.62,32.4,never smoked,0
+8543,Female,53.0,0,0,Yes,Private,Rural,105.28,23.1,never smoked,0
+42626,Female,76.0,1,0,Yes,Govt_job,Rural,63.28,28.2,never smoked,0
+17398,Male,41.0,0,0,Yes,Private,Rural,101.79,26.7,Unknown,0
+66678,Female,22.0,0,0,No,Private,Urban,73.4,21.6,never smoked,0
+49190,Female,45.0,0,0,Yes,Private,Rural,112.55,32.1,never smoked,0
+58267,Male,70.0,1,0,Yes,Private,Rural,242.52,45.5,formerly smoked,1
+737,Male,10.0,0,0,No,children,Urban,88.69,30.4,Unknown,0
+41081,Male,63.0,0,0,Yes,Private,Rural,137.3,31.7,formerly smoked,1
+16600,Male,9.0,0,0,No,children,Rural,65.52,33.5,Unknown,0
+40240,Male,40.0,1,0,Yes,Self-employed,Urban,93.2,24.8,smokes,0
+11816,Female,46.0,0,0,Yes,Self-employed,Urban,71.12,27.3,never smoked,0
+18414,Female,23.0,0,0,No,Private,Rural,193.22,,smokes,0
+66570,Female,23.0,0,0,No,Private,Rural,69.24,51.0,never smoked,0
+63912,Female,77.0,0,0,Yes,Govt_job,Rural,167.59,34.3,formerly smoked,0
+72474,Female,82.0,0,0,Yes,Govt_job,Rural,58.3,20.4,never smoked,0
+37395,Female,16.0,0,0,No,Private,Urban,63.63,20.0,smokes,0
+71793,Female,21.0,0,0,No,Private,Urban,129.16,34.4,Unknown,0
+35222,Female,75.0,0,0,Yes,Private,Urban,86.4,42.6,never smoked,0
+51897,Male,36.0,0,0,Yes,Private,Rural,161.0,29.0,smokes,0
+26103,Male,36.0,0,0,Yes,Private,Rural,106.85,40.1,never smoked,0
+57159,Male,56.0,0,0,Yes,Self-employed,Rural,125.87,24.6,never smoked,0
+59454,Female,79.0,0,0,Yes,Self-employed,Urban,74.35,28.5,formerly smoked,0
+44426,Female,21.0,0,0,Yes,Private,Urban,126.35,26.9,never smoked,0
+16783,Male,57.0,0,1,Yes,Self-employed,Urban,92.82,27.8,formerly smoked,0
+59368,Female,78.0,0,0,Yes,Private,Urban,243.5,26.1,never smoked,0
+63958,Female,42.0,0,0,Yes,Private,Urban,96.99,34.8,formerly smoked,0
+38432,Female,64.0,0,0,Yes,Private,Urban,63.32,18.7,formerly smoked,0
+19828,Female,56.0,1,0,Yes,Private,Rural,97.37,34.1,smokes,0
+71010,Female,80.0,0,0,No,Self-employed,Urban,57.57,22.8,never smoked,0
+14551,Female,69.0,0,0,No,Private,Urban,102.48,30.2,formerly smoked,0
+55424,Female,64.0,1,0,Yes,Private,Rural,88.53,24.6,never smoked,0
+11312,Female,78.0,0,0,Yes,Self-employed,Rural,208.99,31.4,formerly smoked,0
+54065,Female,45.0,0,0,Yes,Private,Urban,91.04,21.1,never smoked,0
+39852,Male,59.0,1,1,Yes,Govt_job,Rural,81.51,32.6,never smoked,0
+44749,Female,64.0,0,0,No,Govt_job,Rural,81.6,36.3,smokes,0
+49465,Female,13.0,0,0,No,children,Urban,70.16,21.2,never smoked,0
+72082,Female,45.0,0,0,Yes,Self-employed,Rural,69.76,25.3,smokes,0
+31164,Female,43.0,0,0,Yes,Private,Rural,95.93,21.8,Unknown,0
+67217,Female,45.0,0,0,Yes,Private,Urban,92.86,35.1,formerly smoked,0
+11882,Male,34.0,0,0,No,Private,Urban,94.15,28.6,never smoked,0
+50763,Male,42.0,0,0,Yes,Govt_job,Urban,58.35,24.3,never smoked,0
+62075,Female,40.0,0,0,Yes,Private,Urban,65.42,17.4,formerly smoked,0
+47348,Female,61.0,0,0,Yes,Private,Urban,129.31,30.7,formerly smoked,0
+61983,Female,41.0,0,0,Yes,Private,Urban,133.76,43.4,smokes,0
+18827,Male,57.0,0,0,Yes,Self-employed,Rural,84.79,32.8,formerly smoked,0
+17004,Female,70.0,0,0,Yes,Private,Urban,221.58,47.5,never smoked,1
+33976,Male,55.0,0,0,Yes,Private,Urban,68.79,27.0,never smoked,0
+55976,Male,5.0,0,0,No,children,Rural,145.71,18.1,Unknown,0
+35095,Female,17.0,0,0,No,Private,Urban,104.02,26.1,Unknown,0
+45399,Male,60.0,0,0,Yes,Private,Urban,80.74,27.7,Unknown,0
+68483,Female,60.0,0,0,Yes,Private,Urban,65.38,41.2,formerly smoked,0
+954,Male,18.0,0,0,No,Private,Rural,103.94,23.3,never smoked,0
+48836,Female,14.0,0,0,No,children,Urban,91.85,27.8,never smoked,0
+2903,Female,35.0,0,0,No,Private,Rural,123.83,23.8,never smoked,0
+28932,Female,36.0,0,0,Yes,Private,Rural,67.29,36.7,formerly smoked,0
+23659,Female,5.0,0,0,No,children,Urban,75.86,20.0,Unknown,0
+65814,Male,21.0,0,0,No,Private,Urban,138.51,24.3,never smoked,0
+6599,Male,64.0,1,0,Yes,Self-employed,Rural,85.66,28.5,never smoked,0
+11974,Male,11.0,0,0,No,children,Urban,82.58,25.5,Unknown,0

classification/unipredict/gabrielsantello-cars-purchase-decision-dataset/metadata.json

@@ -0,0 +1,23 @@
+{
+  "dataset": "gabrielsantello-cars-purchase-decision-dataset",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Purchased",
+  "label_values": [
+    "0",
+    "1"
+  ],
+  "num_labels": 2,
+  "train_samples": 899,
+  "test_samples": 101,
+  "train_label_distribution": {
+    "0": 538,
+    "1": 361
+  },
+  "test_label_distribution": {
+    "1": 41,
+    "0": 60
+  }
+}

classification/unipredict/gabrielsantello-cars-purchase-decision-dataset/test.csv

@@ -0,0 +1,102 @@
+User ID,Gender,Age,AnnualSalary,Purchased
+574,Female,58,34500,1
+71,Male,42,46500,0
+788,Female,59,42000,0
+355,Female,31,63500,0
+267,Male,52,90500,1
+90,Male,35,75000,0
+796,Male,36,76500,0
+374,Male,60,62500,1
+974,Female,49,36000,1
+792,Female,34,43000,0
+709,Female,41,60000,0
+314,Female,38,138500,1
+231,Female,27,85500,0
+917,Female,50,109500,1
+958,Male,38,54500,0
+20,Male,40,107000,1
+967,Male,25,22000,0
+915,Female,42,70000,0
+126,Male,26,80000,0
+481,Female,62,78500,1
+571,Female,38,112000,0
+11,Male,40,57000,0
+541,Female,21,68000,0
+570,Female,31,80500,0
+346,Female,28,58500,0
+446,Female,50,37500,1
+543,Female,29,83000,0
+128,Male,42,73500,0
+620,Female,40,64500,0
+12,Male,29,90500,0
+103,Male,39,134000,1
+61,Male,59,145500,1
+356,Female,42,64500,0
+339,Male,35,79000,0
+983,Female,33,87500,0
+246,Female,38,50000,0
+751,Male,45,106500,1
+408,Male,47,23000,1
+82,Female,46,23500,1
+113,Male,42,80500,0
+219,Female,46,132500,1
+693,Male,38,78500,0
+728,Male,40,75000,0
+936,Male,40,65000,0
+734,Female,47,113000,1
+839,Female,38,79500,1
+656,Female,48,35000,1
+938,Female,50,36000,1
+272,Female,38,79500,0
+622,Female,45,131000,1
+765,Male,40,57000,0
+10,Male,24,64500,0
+875,Female,44,74500,0
+324,Male,61,43500,1
+178,Male,46,27500,1
+665,Male,48,81500,0
+981,Male,37,34500,0
+508,Male,36,54500,0
+794,Female,31,15000,0
+353,Male,49,74000,0
+493,Female,37,57000,0
+26,Female,47,47000,0
+488,Female,47,49000,1
+23,Male,39,42000,0
+426,Female,41,72000,0
+458,Female,51,89500,1
+358,Male,39,101500,1
+953,Male,60,34000,1
+961,Male,35,72000,0
+109,Male,47,118500,1
+75,Male,23,82500,0
+396,Female,26,15000,0
+4,Female,48,119000,1
+935,Male,45,32000,1
+801,Male,38,73500,0
+569,Male,30,149500,1
+720,Female,61,25500,1
+524,Female,56,36500,1
+766,Male,57,74000,1
+607,Female,37,137000,1
+604,Female,57,28500,1
+34,Male,50,66500,0
+540,Female,39,56500,0
+738,Male,35,59000,0
+428,Female,49,43500,1
+38,Female,43,76500,0
+970,Female,34,70500,0
+201,Female,34,61500,0
+785,Female,51,133500,1
+94,Female,39,134000,1
+891,Female,28,55500,0
+955,Male,33,101500,1
+491,Female,49,135500,1
+602,Female,28,18500,0
+107,Female,47,97500,0
+704,Male,34,44500,0
+470,Female,29,28000,0
+68,Female,35,44500,0
+163,Male,39,146500,1
+754,Female,33,69000,0
+124,Female,39,71000,0

classification/unipredict/gabrielsantello-cars-purchase-decision-dataset/test.jsonl

@@ -0,0 +1,101 @@
+{"text": "The User ID is 574. The Gender is Female. The Age is 58. The AnnualSalary is 34500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 71. The Gender is Male. The Age is 42. The AnnualSalary is 46500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 788. The Gender is Female. The Age is 59. The AnnualSalary is 42000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 355. The Gender is Female. The Age is 31. The AnnualSalary is 63500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 267. The Gender is Male. The Age is 52. The AnnualSalary is 90500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 90. The Gender is Male. The Age is 35. The AnnualSalary is 75000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 796. The Gender is Male. The Age is 36. The AnnualSalary is 76500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 374. The Gender is Male. The Age is 60. The AnnualSalary is 62500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 974. The Gender is Female. The Age is 49. The AnnualSalary is 36000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 792. The Gender is Female. The Age is 34. The AnnualSalary is 43000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 709. The Gender is Female. The Age is 41. The AnnualSalary is 60000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 314. The Gender is Female. The Age is 38. The AnnualSalary is 138500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 231. The Gender is Female. The Age is 27. The AnnualSalary is 85500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 917. The Gender is Female. The Age is 50. The AnnualSalary is 109500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 958. The Gender is Male. The Age is 38. The AnnualSalary is 54500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 20. The Gender is Male. The Age is 40. The AnnualSalary is 107000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 967. The Gender is Male. The Age is 25. The AnnualSalary is 22000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 915. The Gender is Female. The Age is 42. The AnnualSalary is 70000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 126. The Gender is Male. The Age is 26. The AnnualSalary is 80000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 481. The Gender is Female. The Age is 62. The AnnualSalary is 78500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 571. The Gender is Female. The Age is 38. The AnnualSalary is 112000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 11. The Gender is Male. The Age is 40. The AnnualSalary is 57000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 541. The Gender is Female. The Age is 21. The AnnualSalary is 68000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 570. The Gender is Female. The Age is 31. The AnnualSalary is 80500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 346. The Gender is Female. The Age is 28. The AnnualSalary is 58500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 446. The Gender is Female. The Age is 50. The AnnualSalary is 37500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 543. The Gender is Female. The Age is 29. The AnnualSalary is 83000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 128. The Gender is Male. The Age is 42. The AnnualSalary is 73500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 620. The Gender is Female. The Age is 40. The AnnualSalary is 64500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 12. The Gender is Male. The Age is 29. The AnnualSalary is 90500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 103. The Gender is Male. The Age is 39. The AnnualSalary is 134000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 61. The Gender is Male. The Age is 59. The AnnualSalary is 145500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 356. The Gender is Female. The Age is 42. The AnnualSalary is 64500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 339. The Gender is Male. The Age is 35. The AnnualSalary is 79000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 983. The Gender is Female. The Age is 33. The AnnualSalary is 87500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 246. The Gender is Female. The Age is 38. The AnnualSalary is 50000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 751. The Gender is Male. The Age is 45. The AnnualSalary is 106500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 408. The Gender is Male. The Age is 47. The AnnualSalary is 23000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 82. The Gender is Female. The Age is 46. The AnnualSalary is 23500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 113. The Gender is Male. The Age is 42. The AnnualSalary is 80500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 219. The Gender is Female. The Age is 46. The AnnualSalary is 132500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 693. The Gender is Male. The Age is 38. The AnnualSalary is 78500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 728. The Gender is Male. The Age is 40. The AnnualSalary is 75000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 936. The Gender is Male. The Age is 40. The AnnualSalary is 65000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 734. The Gender is Female. The Age is 47. The AnnualSalary is 113000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 839. The Gender is Female. The Age is 38. The AnnualSalary is 79500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 656. The Gender is Female. The Age is 48. The AnnualSalary is 35000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 938. The Gender is Female. The Age is 50. The AnnualSalary is 36000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 272. The Gender is Female. The Age is 38. The AnnualSalary is 79500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 622. The Gender is Female. The Age is 45. The AnnualSalary is 131000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 765. The Gender is Male. The Age is 40. The AnnualSalary is 57000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 10. The Gender is Male. The Age is 24. The AnnualSalary is 64500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 875. The Gender is Female. The Age is 44. The AnnualSalary is 74500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 324. The Gender is Male. The Age is 61. The AnnualSalary is 43500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 178. The Gender is Male. The Age is 46. The AnnualSalary is 27500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 665. The Gender is Male. The Age is 48. The AnnualSalary is 81500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 981. The Gender is Male. The Age is 37. The AnnualSalary is 34500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 508. The Gender is Male. The Age is 36. The AnnualSalary is 54500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 794. The Gender is Female. The Age is 31. The AnnualSalary is 15000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 353. The Gender is Male. The Age is 49. The AnnualSalary is 74000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 493. The Gender is Female. The Age is 37. The AnnualSalary is 57000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 26. The Gender is Female. The Age is 47. The AnnualSalary is 47000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 488. The Gender is Female. The Age is 47. The AnnualSalary is 49000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 23. The Gender is Male. The Age is 39. The AnnualSalary is 42000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 426. The Gender is Female. The Age is 41. The AnnualSalary is 72000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 458. The Gender is Female. The Age is 51. The AnnualSalary is 89500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 358. The Gender is Male. The Age is 39. The AnnualSalary is 101500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 953. The Gender is Male. The Age is 60. The AnnualSalary is 34000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 961. The Gender is Male. The Age is 35. The AnnualSalary is 72000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 109. The Gender is Male. The Age is 47. The AnnualSalary is 118500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 75. The Gender is Male. The Age is 23. The AnnualSalary is 82500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 396. The Gender is Female. The Age is 26. The AnnualSalary is 15000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 4. The Gender is Female. The Age is 48. The AnnualSalary is 119000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 935. The Gender is Male. The Age is 45. The AnnualSalary is 32000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 801. The Gender is Male. The Age is 38. The AnnualSalary is 73500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 569. The Gender is Male. The Age is 30. The AnnualSalary is 149500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 720. The Gender is Female. The Age is 61. The AnnualSalary is 25500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 524. The Gender is Female. The Age is 56. The AnnualSalary is 36500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 766. The Gender is Male. The Age is 57. The AnnualSalary is 74000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 607. The Gender is Female. The Age is 37. The AnnualSalary is 137000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 604. The Gender is Female. The Age is 57. The AnnualSalary is 28500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 34. The Gender is Male. The Age is 50. The AnnualSalary is 66500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 540. The Gender is Female. The Age is 39. The AnnualSalary is 56500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 738. The Gender is Male. The Age is 35. The AnnualSalary is 59000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
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+{"text": "The User ID is 38. The Gender is Female. The Age is 43. The AnnualSalary is 76500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 970. The Gender is Female. The Age is 34. The AnnualSalary is 70500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 201. The Gender is Female. The Age is 34. The AnnualSalary is 61500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 785. The Gender is Female. The Age is 51. The AnnualSalary is 133500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 94. The Gender is Female. The Age is 39. The AnnualSalary is 134000.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 891. The Gender is Female. The Age is 28. The AnnualSalary is 55500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 955. The Gender is Male. The Age is 33. The AnnualSalary is 101500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 491. The Gender is Female. The Age is 49. The AnnualSalary is 135500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 602. The Gender is Female. The Age is 28. The AnnualSalary is 18500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 107. The Gender is Female. The Age is 47. The AnnualSalary is 97500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 704. The Gender is Male. The Age is 34. The AnnualSalary is 44500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 470. The Gender is Female. The Age is 29. The AnnualSalary is 28000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 68. The Gender is Female. The Age is 35. The AnnualSalary is 44500.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 163. The Gender is Male. The Age is 39. The AnnualSalary is 146500.", "label": "1", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 754. The Gender is Female. The Age is 33. The AnnualSalary is 69000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}
+{"text": "The User ID is 124. The Gender is Female. The Age is 39. The AnnualSalary is 71000.", "label": "0", "dataset": "gabrielsantello-cars-purchase-decision-dataset", "benchmark": "unipredict", "task_type": "clf"}

classification/unipredict/gabrielsantello-cars-purchase-decision-dataset/train.csv

@@ -0,0 +1,900 @@
+User ID,Gender,Age,AnnualSalary,Purchased
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+531,Male,35,22000,0
+730,Male,43,59500,0
+379,Female,62,31500,1
+698,Male,42,98500,1
+83,Male,36,144000,1
+259,Male,27,17500,0
+973,Female,63,110500,1
+525,Female,42,72500,0
+212,Female,47,141500,1
+130,Female,38,149500,1
+724,Male,54,73500,1
+964,Female,49,76500,0
+30,Male,27,58000,0
+194,Female,48,131000,1
+749,Male,35,55000,0
+19,Female,48,31500,1
+318,Male,49,42500,1
+111,Male,38,41500,0
+226,Female,50,146500,1
+929,Female,27,81500,0
+586,Female,41,67500,0
+642,Female,58,101000,1
+627,Male,30,81500,0
+317,Female,38,81500,0
+878,Male,41,61500,0
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classification/unipredict/gauravduttakiit-resume-dataset/metadata.json

@@ -0,0 +1,92 @@
+{
+  "dataset": "gauravduttakiit-resume-dataset",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "Category",
+  "label_values": [
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+    "Mechanical Engineer",
+    "Automation Testing",
+    "Testing",
+    "Advocate",
+    "Java Developer",
+    "Arts",
+    "Business Analyst",
+    "Data Science",
+    "Health and fitness",
+    "DevOps Engineer",
+    "Web Designing",
+    "Hadoop",
+    "SAP Developer",
+    "PMO",
+    "Blockchain",
+    "Network Security Engineer",
+    "Operations Manager",
+    "Sales",
+    "Electrical Engineering",
+    "Civil Engineer",
+    "HR",
+    "DotNet Developer",
+    "ETL Developer",
+    "Database"
+  ],
+  "num_labels": 25,
+  "train_samples": 850,
+  "test_samples": 102,
+  "train_label_distribution": {
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+    "Data Science": 36,
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+    "Business Analyst": 25,
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+    "Database": 29,
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+    "Network Security Engineer": 22,
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+    "Advocate": 18
+  },
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+    "Electrical Engineering": 3,
+    "Database": 4,
+    "DotNet Developer": 3,
+    "Advocate": 2
+  }
+}

classification/unipredict/geomack-spotifyclassification/metadata.json

@@ -0,0 +1,23 @@
+{
+  "dataset": "geomack-spotifyclassification",
+  "benchmark": "unipredict",
+  "sub_benchmark": "",
+  "task_type": "clf",
+  "data_type": "mixed",
+  "target_column": "target",
+  "label_values": [
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+    "1"
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+  "num_labels": 2,
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+  "train_label_distribution": {
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+}