diff --git a/.DS_Store b/.DS_Store
index be5fbe0a..93ae7a55 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/INFO_6105/ML_Data_Cleaning_and_Feature_Selection/ObesityClassification_ML_Data_Cleaning_and_feature_selection/ObesityDataSet.csv b/INFO_6105/ML_Data_Cleaning_and_Feature_Selection/ObesityClassification_ML_Data_Cleaning_and_feature_selection/ObesityDataSet.csv
new file mode 100644
index 00000000..b75c5e7b
--- /dev/null
+++ b/INFO_6105/ML_Data_Cleaning_and_Feature_Selection/ObesityClassification_ML_Data_Cleaning_and_feature_selection/ObesityDataSet.csv
@@ -0,0 +1,2112 @@
+Gender,Age,Height,Weight,family_history_with_overweight,FAVC,FCVC,NCP,CAEC,SMOKE,CH2O,SCC,FAF,TUE,CALC,MTRANS,NObeyesdad
+Female,21,1.62,64,yes,no,2,3,Sometimes,no,2,no,0,1,no,Public_Transportation,Normal_Weight
+Female,21,1.52,56,yes,no,3,3,Sometimes,yes,3,yes,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.8,77,yes,no,2,3,Sometimes,no,2,no,2,1,Frequently,Public_Transportation,Normal_Weight
+Male,27,1.8,87,no,no,3,3,Sometimes,no,2,no,2,0,Frequently,Walking,Overweight_Level_I
+Male,22,1.78,89.8,no,no,2,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,29,1.62,53,no,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Automobile,Normal_Weight
+Female,23,1.5,55,yes,yes,3,3,Sometimes,no,2,no,1,0,Sometimes,Motorbike,Normal_Weight
+Male,22,1.64,53,no,no,2,3,Sometimes,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,24,1.78,64,yes,yes,3,3,Sometimes,no,2,no,1,1,Frequently,Public_Transportation,Normal_Weight
+Male,22,1.72,68,yes,yes,2,3,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,26,1.85,105,yes,yes,3,3,Frequently,no,3,no,2,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,21,1.72,80,yes,yes,2,3,Frequently,no,2,yes,2,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22,1.65,56,no,no,3,3,Sometimes,no,3,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,41,1.8,99,no,yes,2,3,Sometimes,no,2,no,2,1,Frequently,Automobile,Obesity_Type_I
+Male,23,1.77,60,yes,yes,3,1,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.7,66,yes,no,3,3,Always,no,2,yes,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,27,1.93,102,yes,yes,2,1,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Female,29,1.53,78,no,yes,2,1,Sometimes,no,2,no,0,0,no,Automobile,Obesity_Type_I
+Female,30,1.71,82,yes,yes,3,4,Frequently,yes,1,no,0,0,no,Automobile,Overweight_Level_II
+Female,23,1.65,70,yes,no,2,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22,1.65,80,yes,no,2,3,Sometimes,no,2,no,3,2,no,Walking,Overweight_Level_II
+Female,52,1.69,87,yes,yes,3,1,Sometimes,yes,2,no,0,0,no,Automobile,Obesity_Type_I
+Female,22,1.65,60,yes,yes,3,3,Sometimes,no,2,no,1,0,Sometimes,Automobile,Normal_Weight
+Female,22,1.6,82,yes,yes,1,1,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21,1.85,68,yes,yes,2,3,Sometimes,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.6,50,yes,no,2,4,Frequently,yes,2,no,3,2,no,Public_Transportation,Normal_Weight
+Male,21,1.7,65,yes,yes,2,1,Frequently,no,2,no,1,2,Always,Walking,Normal_Weight
+Female,23,1.6,52,no,yes,2,4,Frequently,no,2,no,2,1,Sometimes,Automobile,Normal_Weight
+Male,19,1.75,76,yes,yes,3,3,Sometimes,no,2,yes,3,1,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.68,70,no,yes,2,3,Sometimes,no,2,no,2,2,Frequently,Walking,Normal_Weight
+Male,29,1.77,83,no,yes,1,4,Frequently,no,3,no,0,1,no,Motorbike,Overweight_Level_I
+Female,31,1.58,68,yes,no,2,1,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Female,24,1.77,76,no,no,2,3,Sometimes,no,3,no,1,1,Sometimes,Walking,Normal_Weight
+Male,39,1.79,90,no,no,2,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22,1.65,62,no,yes,2,4,Frequently,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,21,1.5,65,yes,no,2,3,Sometimes,no,2,no,2,2,Sometimes,Public_Transportation,Overweight_Level_II
+Female,22,1.56,49,no,yes,2,3,Sometimes,no,1,yes,2,0,no,Walking,Normal_Weight
+Female,21,1.6,48,no,yes,2,3,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.65,67,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,21,1.75,88,yes,yes,2,3,Sometimes,no,3,no,3,0,Sometimes,Public_Transportation,Overweight_Level_II
+Female,21,1.67,75,yes,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23,1.68,60,no,no,2,4,Frequently,no,2,no,0,0,no,Walking,Normal_Weight
+Female,21,1.66,64,yes,yes,1,3,Sometimes,no,1,no,0,0,no,Public_Transportation,Normal_Weight
+Male,21,1.66,62,yes,yes,2,3,Sometimes,yes,2,no,1,1,Frequently,Public_Transportation,Normal_Weight
+Male,21,1.81,80,no,no,1,3,no,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Female,21,1.53,65,yes,no,2,3,Sometimes,no,1,no,0,1,no,Public_Transportation,Overweight_Level_II
+Male,21,1.82,72,yes,yes,1,3,Frequently,no,3,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,21,1.75,72,yes,yes,1,3,Frequently,no,3,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,20,1.66,60,yes,no,3,3,Sometimes,no,2,no,1,0,Sometimes,Walking,Normal_Weight
+Female,21,1.55,50,no,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,21,1.61,54.5,yes,yes,3,3,Sometimes,no,3,no,0,1,Sometimes,Walking,Normal_Weight
+Female,20,1.5,44,no,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Normal_Weight
+Female,23,1.64,52,no,yes,3,1,Sometimes,no,2,no,2,2,no,Public_Transportation,Normal_Weight
+Female,23,1.63,55,yes,no,3,3,no,no,2,yes,2,1,no,Public_Transportation,Normal_Weight
+Female,22,1.6,55,no,no,3,4,Always,no,3,no,2,0,no,Public_Transportation,Normal_Weight
+Male,23,1.68,62,no,no,2,4,Frequently,no,2,no,0,0,Sometimes,Automobile,Normal_Weight
+Male,22,1.7,70,yes,yes,2,3,Sometimes,no,1,no,0,1,Sometimes,Automobile,Normal_Weight
+Male,21,1.64,65,yes,no,2,3,Sometimes,no,1,no,0,1,no,Public_Transportation,Normal_Weight
+Female,17,1.65,67,yes,yes,3,1,Sometimes,no,2,no,1,1,no,Walking,Normal_Weight
+Male,20,1.76,55,yes,yes,2,4,Sometimes,no,3,no,2,2,no,Public_Transportation,Insufficient_Weight
+Female,21,1.55,49,yes,yes,2,3,Sometimes,no,3,no,3,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.65,58,no,yes,2,3,Sometimes,no,2,no,3,1,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.67,62,no,yes,2,1,no,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.68,55,yes,yes,2,3,Sometimes,no,2,no,0,2,Sometimes,Automobile,Normal_Weight
+Female,21,1.66,57,yes,yes,2,3,Frequently,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Female,21,1.62,69,yes,yes,1,3,Frequently,no,2,no,0,1,no,Public_Transportation,Overweight_Level_I
+Male,23,1.8,90,yes,yes,1,3,Always,no,2,no,0,2,Frequently,Public_Transportation,Overweight_Level_II
+Male,23,1.65,95,yes,yes,2,3,Always,no,2,no,0,1,Frequently,Automobile,Obesity_Type_I
+Male,30,1.76,112,yes,yes,1,3,no,yes,2,yes,0,0,Frequently,Automobile,Obesity_Type_II
+Male,23,1.8,60,yes,no,2,3,no,no,3,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.65,80,yes,yes,2,3,Always,no,2,no,0,2,no,Public_Transportation,Overweight_Level_II
+Female,22,1.67,50,yes,no,3,3,no,no,3,yes,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,24,1.65,60,yes,no,2,3,Sometimes,no,3,yes,1,0,no,Public_Transportation,Normal_Weight
+Male,19,1.85,65,yes,no,2,3,Sometimes,no,3,no,2,1,Sometimes,Bike,Normal_Weight
+Male,24,1.7,85,yes,yes,2,3,Frequently,no,3,no,0,1,Frequently,Public_Transportation,Overweight_Level_II
+Female,23,1.63,45,yes,no,3,3,Sometimes,no,3,yes,2,0,no,Public_Transportation,Insufficient_Weight
+Female,24,1.6,45,yes,no,2,3,no,no,2,no,1,0,no,Public_Transportation,Insufficient_Weight
+Female,24,1.7,80,yes,yes,2,3,Sometimes,no,3,no,0,0,no,Public_Transportation,Overweight_Level_II
+Female,23,1.65,90,yes,yes,2,3,Sometimes,no,3,no,0,1,no,Public_Transportation,Obesity_Type_I
+Male,23,1.65,60,yes,no,2,3,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,19,1.63,58,no,no,3,3,Sometimes,no,2,yes,0,0,no,Public_Transportation,Normal_Weight
+Male,30,1.8,91,yes,yes,2,3,Frequently,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.67,85.5,yes,yes,2,3,Always,no,2,no,0,1,no,Public_Transportation,Obesity_Type_I
+Female,19,1.6,45,no,no,3,3,no,no,3,yes,2,0,no,Walking,Insufficient_Weight
+Male,25,1.7,83,yes,yes,2,3,Frequently,no,2,no,0,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.65,58.5,yes,no,2,3,Sometimes,no,2,no,0,0,no,Public_Transportation,Normal_Weight
+Male,21,1.85,83,yes,yes,2,1,Frequently,no,3,no,1,0,no,Public_Transportation,Normal_Weight
+Male,19,1.82,87,yes,yes,2,3,Sometimes,no,2,no,0,0,no,Public_Transportation,Overweight_Level_I
+Female,22,1.65,65,yes,yes,2,3,Frequently,no,2,yes,1,0,no,Automobile,Normal_Weight
+Female,29,1.7,78,yes,yes,3,3,Sometimes,no,1,no,2,1,Frequently,Automobile,Overweight_Level_II
+Female,25,1.63,93,no,no,3,4,Always,no,1,no,2,0,no,Public_Transportation,Obesity_Type_II
+Female,20,1.61,64,yes,no,3,3,Always,no,2,yes,0,1,Frequently,Public_Transportation,Normal_Weight
+Male,55,1.78,84,yes,no,3,4,Frequently,no,3,yes,3,0,Frequently,Walking,Overweight_Level_I
+Female,20,1.6,57,no,no,3,3,Always,no,2,no,1,0,no,Walking,Normal_Weight
+Female,24,1.6,48,no,yes,3,3,Sometimes,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Male,26,1.7,70,yes,no,3,1,Frequently,no,2,no,2,0,Frequently,Public_Transportation,Normal_Weight
+Female,23,1.66,60,yes,no,2,3,Sometimes,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Female,21,1.52,42,no,no,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,no,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Male,23,1.72,70,no,no,2,3,Sometimes,no,2,no,3,1,Frequently,Public_Transportation,Normal_Weight
+Female,21,1.69,63,no,yes,3,1,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.7,66.4,yes,no,2,3,Frequently,no,2,no,0,0,no,Public_Transportation,Normal_Weight
+Female,21,1.55,57,no,yes,2,4,Frequently,no,2,yes,2,0,Sometimes,Automobile,Normal_Weight
+Female,22,1.65,58,yes,yes,3,4,Frequently,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Female,38,1.56,80,yes,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,25,1.57,55,no,yes,2,1,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,25,1.57,55,no,yes,2,1,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.88,90,yes,yes,2,3,Always,no,1,no,0,1,Sometimes,Automobile,Overweight_Level_I
+Male,22,1.75,95,yes,no,2,3,Sometimes,no,3,no,3,2,no,Walking,Obesity_Type_I
+Female,21,1.65,88,yes,yes,3,1,Sometimes,no,3,no,2,1,no,Public_Transportation,Obesity_Type_I
+Male,21,1.75,75,yes,yes,3,3,Sometimes,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.58,58,yes,yes,2,1,Sometimes,no,1,no,0,0,no,Public_Transportation,Normal_Weight
+Female,18,1.56,51,yes,yes,2,4,Frequently,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.5,49,yes,no,2,1,Sometimes,no,2,no,3,0,no,Walking,Normal_Weight
+Female,19,1.61,62,yes,yes,3,1,Frequently,no,2,no,1,0,Frequently,Public_Transportation,Normal_Weight
+Female,17,1.75,57,yes,yes,3,3,Frequently,no,2,no,0,1,no,Public_Transportation,Normal_Weight
+Female,15,1.65,86,yes,yes,3,3,Sometimes,no,1,no,3,2,no,Walking,Obesity_Type_I
+Female,17,1.7,85,yes,no,2,3,Frequently,no,2,no,1,1,no,Public_Transportation,Overweight_Level_II
+Male,23,1.62,53,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,19,1.63,76,yes,no,3,3,Frequently,yes,3,no,2,1,Sometimes,Automobile,Overweight_Level_II
+Female,23,1.67,75,yes,yes,2,3,Frequently,yes,2,no,0,2,no,Walking,Overweight_Level_I
+Male,23,1.87,95,yes,yes,1,3,Frequently,no,2,no,0,2,Frequently,Public_Transportation,Overweight_Level_II
+Male,21,1.75,50,yes,no,3,4,Frequently,no,1,no,1,0,no,Walking,Insufficient_Weight
+Male,24,1.66,67,yes,yes,3,1,Sometimes,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Male,23,1.76,90,no,yes,3,3,Sometimes,no,1,no,0,0,no,Public_Transportation,Overweight_Level_II
+Male,18,1.75,80,yes,yes,2,3,Always,no,2,no,0,0,Frequently,Public_Transportation,Overweight_Level_I
+Male,19,1.67,68,no,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Female,19,1.65,61,no,yes,3,1,Sometimes,no,3,yes,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.72,70,yes,yes,3,3,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,27,1.7,78,no,yes,3,3,Sometimes,no,3,no,1,1,Frequently,Public_Transportation,Overweight_Level_II
+Female,20,1.58,53,yes,yes,2,4,Frequently,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,23,1.62,58,no,no,2,3,Sometimes,no,1,no,1,0,no,Public_Transportation,Normal_Weight
+Female,19,1.65,56,yes,yes,3,3,Frequently,yes,3,yes,1,2,Frequently,Public_Transportation,Normal_Weight
+Female,61,1.65,66,no,yes,3,3,Always,no,2,no,1,1,Frequently,Public_Transportation,Normal_Weight
+Male,30,1.77,109,yes,yes,3,3,Sometimes,no,1,no,2,0,Sometimes,Automobile,Obesity_Type_I
+Male,24,1.7,75,yes,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Automobile,Overweight_Level_I
+Male,25,1.79,72,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,44,1.6,80,yes,no,2,3,Sometimes,yes,3,no,0,0,no,Motorbike,Obesity_Type_I
+Male,31,1.76,75,yes,no,3,3,Always,no,3,yes,3,0,no,Bike,Normal_Weight
+Male,25,1.7,68,no,yes,2,3,Frequently,no,2,no,0,1,no,Public_Transportation,Normal_Weight
+Male,23,1.89,65,yes,yes,3,3,Frequently,no,3,no,1,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,25,1.87,66,no,yes,3,3,Always,no,2,no,2,0,Frequently,Public_Transportation,Normal_Weight
+Male,23,1.74,93.5,no,yes,2,3,Frequently,yes,1,no,1,1,Frequently,Automobile,Obesity_Type_I
+Female,34,1.68,75,no,yes,3,1,Sometimes,no,1,no,0,0,Sometimes,Automobile,Overweight_Level_I
+Male,22,1.61,67,yes,no,2,4,Sometimes,no,3,yes,2,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,24,1.56,51,no,no,3,3,Frequently,no,2,no,2,2,Frequently,Public_Transportation,Normal_Weight
+Female,36,1.63,80,yes,no,3,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,27,1.6,61,no,yes,3,3,Always,no,2,no,0,0,Sometimes,Automobile,Normal_Weight
+Female,32,1.67,90,yes,yes,3,1,Sometimes,no,2,no,2,0,Sometimes,Automobile,Obesity_Type_I
+Male,25,1.78,78,yes,yes,2,1,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Female,30,1.62,59,yes,yes,3,3,Sometimes,no,2,no,2,0,Sometimes,Automobile,Normal_Weight
+Female,38,1.5,60,yes,yes,2,1,Always,yes,2,no,0,0,Sometimes,Automobile,Overweight_Level_I
+Male,34,1.69,84,yes,no,2,3,Sometimes,no,3,no,2,0,no,Automobile,Overweight_Level_II
+Male,22,1.74,94,yes,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,31,1.68,63,yes,yes,3,1,Frequently,no,2,no,1,1,Sometimes,Automobile,Normal_Weight
+Female,35,1.53,45,yes,no,3,3,Frequently,no,1,no,0,1,no,Automobile,Normal_Weight
+Male,21,1.67,60,yes,yes,2,3,Frequently,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Female,40,1.55,62,yes,no,3,3,Sometimes,no,3,no,0,0,Sometimes,Automobile,Overweight_Level_I
+Male,27,1.64,78,yes,yes,2,1,Sometimes,no,2,no,0,0,Frequently,Automobile,Overweight_Level_II
+Male,20,1.83,72,yes,no,3,3,Sometimes,yes,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,55,1.65,80,no,yes,2,3,Sometimes,no,2,no,1,0,no,Automobile,Overweight_Level_II
+Female,21,1.63,60,yes,yes,3,3,Always,yes,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,25,1.89,75,no,no,3,3,Frequently,no,2,yes,3,0,no,Public_Transportation,Normal_Weight
+Male,35,1.77,85,yes,no,3,3,Frequently,no,3,no,2,1,Sometimes,Automobile,Overweight_Level_II
+Male,30,1.92,130,yes,no,2,3,Sometimes,yes,1,no,1,0,Sometimes,Public_Transportation,Obesity_Type_II
+Female,29,1.74,72,yes,no,3,3,Always,no,2,no,0,0,Sometimes,Automobile,Normal_Weight
+Male,20,1.65,80,yes,no,2,3,Sometimes,no,2,no,1,2,no,Walking,Overweight_Level_II
+Female,22,1.73,79,yes,yes,2,1,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,45,1.63,77,yes,yes,2,3,Frequently,no,1,no,0,0,no,Automobile,Overweight_Level_II
+Male,22,1.72,82,no,yes,2,1,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18,1.6,58,yes,yes,2,3,Frequently,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.65,59,no,no,3,1,Sometimes,no,2,no,1,2,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.74,64,no,no,3,3,Sometimes,no,2,yes,0,1,no,Public_Transportation,Normal_Weight
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,38,1.64,59.8,yes,yes,2,3,Sometimes,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Female,18,1.57,48,yes,yes,3,1,Always,no,1,no,1,0,no,Public_Transportation,Normal_Weight
+Male,22,1.84,84,yes,yes,3,3,Frequently,no,2,yes,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,26,1.91,84,yes,yes,3,3,Frequently,yes,2,no,2,2,Frequently,Public_Transportation,Normal_Weight
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18,1.58,48,no,yes,2,3,Sometimes,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Female,23,1.68,67,yes,yes,2,3,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.68,52,no,yes,3,3,Frequently,no,2,no,0,1,no,Public_Transportation,Insufficient_Weight
+Female,23,1.48,60,yes,yes,2,1,Sometimes,yes,1,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,31,1.62,68,no,no,3,3,Sometimes,no,2,no,1,0,Sometimes,Automobile,Overweight_Level_I
+Male,39,1.78,96,yes,no,2,3,Sometimes,no,3,no,1,0,Frequently,Automobile,Obesity_Type_I
+Male,25,1.78,98,yes,no,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,35,1.78,105,yes,yes,3,1,no,no,3,no,3,1,Frequently,Automobile,Obesity_Type_I
+Female,33,1.63,62,yes,yes,2,3,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,20,1.6,56,no,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,26,1.75,80,yes,yes,3,1,Frequently,yes,2,yes,2,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,20,1.83,85,yes,no,3,3,Sometimes,no,3,yes,3,0,Sometimes,Automobile,Overweight_Level_I
+Male,20,1.78,68,no,no,2,1,Frequently,no,3,yes,2,1,no,Public_Transportation,Normal_Weight
+Female,23,1.6,83,yes,yes,3,3,Frequently,no,3,no,3,0,no,Public_Transportation,Obesity_Type_I
+Male,19,1.8,85,yes,yes,3,3,Sometimes,no,2,no,1,1,Sometimes,Walking,Overweight_Level_I
+Male,22,1.75,74,yes,no,2,3,Sometimes,no,2,no,1,2,Sometimes,Bike,Normal_Weight
+Male,41,1.75,118,yes,yes,2,3,Sometimes,no,2,no,0,0,Sometimes,Bike,Obesity_Type_II
+Female,18,1.59,40,yes,yes,2,1,Frequently,no,1,no,0,2,no,Public_Transportation,Insufficient_Weight
+Female,23,1.66,60,yes,yes,2,1,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.63,83,yes,no,3,1,Sometimes,yes,3,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,41,1.54,80,yes,yes,2,3,Always,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,26,1.56,102,yes,yes,3,3,Sometimes,yes,1,no,0,1,Sometimes,Public_Transportation,Obesity_Type_III
+Male,29,1.69,90,yes,no,2,3,Sometimes,no,3,no,1,0,Sometimes,Automobile,Obesity_Type_I
+Male,27,1.83,71,yes,yes,2,3,Sometimes,no,2,no,3,2,no,Automobile,Normal_Weight
+Female,23,1.6,78,yes,yes,2,1,Sometimes,yes,2,no,1,0,Frequently,Public_Transportation,Obesity_Type_I
+Male,19,1.75,100,yes,yes,2,3,Frequently,no,2,no,2,0,no,Public_Transportation,Obesity_Type_I
+Male,30,1.75,73,yes,no,2,3,no,no,2,yes,1,0,Sometimes,Walking,Normal_Weight
+Female,22,1.69,65,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.69,65,yes,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.8,114,yes,yes,2,3,Frequently,no,2,no,0,1,no,Public_Transportation,Obesity_Type_II
+Female,21,1.63,51,no,yes,2,1,Sometimes,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Female,24,1.5,63,yes,no,2,1,Sometimes,no,2,no,0,0,no,Public_Transportation,Overweight_Level_II
+Male,21,1.8,62,yes,yes,3,3,Sometimes,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Male,21,1.65,53,yes,yes,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,21,1.68,70,yes,yes,3,3,Sometimes,no,3,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.6,63,yes,no,3,3,Frequently,no,2,no,3,0,Sometimes,Walking,Normal_Weight
+Male,21,1.71,75,no,no,2,3,Frequently,no,3,yes,3,1,Frequently,Automobile,Overweight_Level_I
+Female,21,1.5,42.3,yes,no,1,1,Sometimes,no,2,no,3,0,no,Public_Transportation,Normal_Weight
+Female,21,1.6,68,yes,yes,2,3,Sometimes,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.75,78,yes,no,2,3,Frequently,no,2,yes,0,2,Frequently,Public_Transportation,Overweight_Level_I
+Male,23,1.72,82,yes,yes,2,1,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Overweight_Level_II
+Female,21,1.72,66.5,yes,yes,3,4,Always,no,3,no,0,2,no,Public_Transportation,Normal_Weight
+Female,22,1.61,63,yes,yes,3,3,Frequently,no,3,no,1,0,no,Public_Transportation,Normal_Weight
+Female,23,1.63,82,yes,yes,2,1,Sometimes,no,2,no,0,0,no,Public_Transportation,Obesity_Type_I
+Male,25,1.83,121,yes,no,3,3,Sometimes,no,3,no,2,0,Sometimes,Walking,Obesity_Type_II
+Female,20,1.6,50,no,yes,2,3,Sometimes,no,1,no,0,1,no,Automobile,Normal_Weight
+Female,24,1.62,58,no,yes,3,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,40,1.68,80,no,no,3,3,Sometimes,no,3,no,0,0,no,Automobile,Overweight_Level_II
+Male,32,1.75,120,yes,no,3,3,Sometimes,no,3,no,0,2,no,Automobile,Obesity_Type_II
+Male,20,1.9,91,yes,yes,3,4,Sometimes,no,3,no,2,0,Sometimes,Walking,Overweight_Level_I
+Female,21,1.63,66,yes,yes,3,1,Sometimes,yes,3,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,51,1.59,50,yes,no,3,3,Sometimes,yes,3,yes,2,0,no,Public_Transportation,Normal_Weight
+Female,34,1.68,77,yes,no,3,1,Sometimes,no,2,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,19,1.59,49,yes,yes,2,1,no,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Female,19,1.69,70,yes,no,2,1,no,no,2,no,2,0,no,Public_Transportation,Normal_Weight
+Female,21,1.66,59,no,yes,1,3,Always,no,2,yes,3,0,no,Automobile,Normal_Weight
+Female,19,1.64,53,yes,yes,3,3,Sometimes,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Female,20,1.62,53,no,yes,3,1,Sometimes,no,3,no,1,1,no,Public_Transportation,Normal_Weight
+Female,19,1.7,64,yes,yes,3,3,no,no,3,yes,3,1,no,Public_Transportation,Normal_Weight
+Female,17,1.63,65,no,yes,2,1,Sometimes,no,3,no,1,1,no,Public_Transportation,Normal_Weight
+Male,22,1.6,66,no,yes,3,3,Sometimes,no,2,no,3,0,no,Bike,Overweight_Level_I
+Female,20,1.63,64,yes,yes,1,3,Always,no,2,no,0,2,no,Automobile,Normal_Weight
+Male,33,1.85,99,yes,yes,2,3,Sometimes,yes,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,21,1.54,49,yes,no,2,1,Sometimes,no,2,yes,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,20,1.64,49,no,no,3,3,Sometimes,yes,2,no,2,1,Sometimes,Automobile,Insufficient_Weight
+Female,20,1.57,60,no,yes,3,3,Sometimes,no,3,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Female,20,1.62,52,no,yes,3,3,Frequently,no,1,no,3,1,no,Automobile,Normal_Weight
+Male,21,1.72,72,yes,yes,3,3,Always,no,3,yes,3,0,Sometimes,Automobile,Normal_Weight
+Male,21,1.76,78,yes,yes,3,1,Sometimes,no,2,no,3,0,Sometimes,Walking,Overweight_Level_I
+Female,20,1.65,75,yes,yes,3,1,Sometimes,no,2,no,1,1,no,Public_Transportation,Overweight_Level_II
+Male,20,1.67,78,yes,no,2,1,Sometimes,no,3,no,0,0,Frequently,Automobile,Overweight_Level_II
+Male,56,1.79,90,yes,no,2,3,Sometimes,yes,2,no,1,0,Frequently,Automobile,Overweight_Level_II
+Female,26,1.59,47,yes,yes,2,1,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Female,22,1.64,56,yes,no,3,3,Sometimes,no,2,no,0,0,no,Public_Transportation,Normal_Weight
+Male,19,1.78,81,yes,no,1,3,Sometimes,no,2,no,3,0,no,Bike,Overweight_Level_I
+Male,18,1.75,85,yes,no,2,3,Sometimes,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19,1.85,115,no,no,2,3,Sometimes,no,3,yes,1,2,no,Public_Transportation,Obesity_Type_I
+Male,18,1.7,80,no,no,3,1,Sometimes,no,1,no,1,0,no,Public_Transportation,Overweight_Level_II
+Female,18,1.67,91,yes,yes,1,3,Frequently,no,1,no,0,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21,1.72,75,no,yes,2,3,Sometimes,no,1,no,1,0,no,Public_Transportation,Overweight_Level_I
+Female,28,1.7,73,yes,no,2,3,Frequently,no,2,yes,2,0,Sometimes,Walking,Overweight_Level_I
+Male,18,1.74,70,no,yes,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.74,53,no,yes,2,3,Frequently,no,1,no,3,2,no,Public_Transportation,Insufficient_Weight
+Male,18,1.87,67,yes,yes,3,3,Frequently,no,3,yes,1,1,Sometimes,Automobile,Normal_Weight
+Male,18,1.7,50,no,yes,1,3,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,39,1.65,50,no,yes,3,4,Frequently,no,2,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Male,38,1.7,78,no,yes,3,3,Frequently,no,2,no,0,0,Frequently,Automobile,Overweight_Level_II
+Male,17,1.67,60,no,no,2,4,Always,no,2,no,0,2,no,Public_Transportation,Normal_Weight
+Male,23,1.72,66,yes,yes,2,3,Sometimes,no,2,no,0,1,Sometimes,Walking,Normal_Weight
+Male,23,1.82,107,no,yes,2,3,Sometimes,no,3,no,0,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,19,1.5,50,no,yes,2,3,Frequently,no,1,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.7,60,no,yes,2,3,Sometimes,no,2,no,2,1,no,Public_Transportation,Normal_Weight
+Male,25,1.71,71,yes,yes,2,3,Frequently,no,2,no,0,0,Sometimes,Motorbike,Normal_Weight
+Female,25,1.61,61,no,no,2,3,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.5,50,yes,yes,3,3,Frequently,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,16,1.67,50,yes,yes,2,1,Frequently,no,3,no,1,0,no,Public_Transportation,Insufficient_Weight
+Male,21,1.82,67,no,yes,2,4,Always,yes,3,no,3,2,Sometimes,Walking,Normal_Weight
+Female,32,1.57,57,yes,yes,3,3,Sometimes,no,2,no,0,0,Sometimes,Automobile,Normal_Weight
+Male,18,1.79,52,no,no,3,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,21,1.75,62,no,yes,3,4,Frequently,yes,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.7,55,yes,yes,2,3,Frequently,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.62,55,yes,yes,2,3,Frequently,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Male,17,1.69,60,no,no,2,3,Sometimes,no,3,no,2,1,Sometimes,Walking,Normal_Weight
+Male,20,1.77,70,yes,yes,1,1,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,21,1.79,105,yes,yes,2,3,Always,no,1,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,21,1.6,61,no,yes,2,3,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.6,58,yes,yes,2,3,Sometimes,no,3,no,3,0,no,Public_Transportation,Normal_Weight
+Female,17,1.56,51,no,yes,3,3,Sometimes,no,1,no,0,2,no,Public_Transportation,Normal_Weight
+Male,19,1.88,79,no,no,2,3,Sometimes,no,3,no,3,0,Sometimes,Automobile,Normal_Weight
+Male,16,1.82,71,yes,yes,2,3,Frequently,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,17,1.8,58,no,yes,2,3,Frequently,no,2,no,2,1,no,Walking,Insufficient_Weight
+Male,21,1.7,65,yes,yes,2,3,Frequently,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Male,19,1.82,75,yes,no,2,3,Frequently,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Male,18,1.86,110,yes,yes,2,1,Sometimes,yes,2,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,16,1.66,58,no,no,2,1,Sometimes,no,1,no,0,1,no,Walking,Normal_Weight
+Female,21,1.53,53,no,yes,3,3,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,26,1.74,80,no,no,2,3,Sometimes,no,2,no,2,0,no,Automobile,Overweight_Level_I
+Male,18,1.8,80,yes,yes,2,3,Frequently,no,1,no,0,1,Frequently,Public_Transportation,Normal_Weight
+Male,23,1.7,75,no,yes,3,3,Sometimes,no,3,no,1,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26,1.7,70,no,yes,2,3,Sometimes,yes,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.72,55,no,yes,2,4,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,16,1.84,45,yes,yes,3,3,Always,no,3,no,3,2,Sometimes,Walking,Insufficient_Weight
+Female,16,1.57,49,no,yes,2,4,Always,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.8,85,yes,no,2,3,Sometimes,no,3,no,2,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23,1.75,120,yes,yes,2,3,Sometimes,yes,2,no,2,1,Sometimes,Public_Transportation,Obesity_Type_II
+Female,24,1.56,60,yes,yes,1,3,Always,no,2,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Female,23,1.59,48,yes,yes,2,1,Frequently,no,1,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.8,75,no,yes,3,4,Always,no,3,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,16,1.66,58,no,no,2,1,Sometimes,no,1,no,0,1,no,Walking,Normal_Weight
+Male,17,1.79,57,yes,yes,2,4,Frequently,no,2,no,2,1,no,Public_Transportation,Insufficient_Weight
+Male,17,1.72,62,no,yes,2,3,Always,no,2,no,3,1,no,Public_Transportation,Normal_Weight
+Female,16,1.6,57,no,yes,3,3,Sometimes,no,1,no,3,0,no,Public_Transportation,Normal_Weight
+Male,17,1.66,56,yes,yes,1,3,Always,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,26,1.65,63,no,yes,3,3,Sometimes,no,1,no,1,0,no,Walking,Normal_Weight
+Male,26,1.7,72,no,yes,2,3,Always,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,38,1.75,75,yes,no,3,3,Frequently,no,1,no,2,1,no,Automobile,Normal_Weight
+Male,18,1.75,70,no,yes,3,4,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Female,25,1.56,45,no,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,27,1.55,63,no,yes,2,3,Sometimes,no,1,no,0,1,Sometimes,Automobile,Overweight_Level_I
+Male,21,1.67,67,yes,yes,3,1,Always,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,38,1.75,75,yes,no,3,3,Frequently,no,1,no,3,1,no,Automobile,Normal_Weight
+Female,23,1.75,56,no,no,3,3,Frequently,no,2,no,2,0,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.8,72,yes,yes,2,3,Sometimes,no,2,no,3,0,Sometimes,Automobile,Normal_Weight
+Female,30,1.65,71,yes,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.55,58,no,yes,2,1,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.7,55.3,yes,yes,3,3,Sometimes,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.72,76,yes,no,3,4,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19,1.74,74,yes,no,3,1,Sometimes,no,3,no,3,1,Sometimes,Walking,Normal_Weight
+Female,19,1.65,82,yes,yes,3,3,Sometimes,no,1,no,0,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,17,1.8,50,no,yes,3,4,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17,1.74,56,yes,yes,2,3,Sometimes,no,2,no,2,1,no,Public_Transportation,Normal_Weight
+Male,27,1.85,75,yes,yes,2,1,Sometimes,no,2,no,1,0,no,Walking,Normal_Weight
+Female,23,1.7,56,no,no,3,4,Always,no,3,yes,3,0,no,Automobile,Normal_Weight
+Female,18,1.45,53,no,yes,2,3,Frequently,no,2,yes,1,2,Frequently,Public_Transportation,Overweight_Level_I
+Male,19,1.7,50,no,yes,1,4,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,20,1.7,65,no,yes,2,3,Sometimes,no,2,no,0,1,no,Public_Transportation,Normal_Weight
+Male,18,1.78,64.4,yes,yes,3,3,Frequently,no,2,no,3,2,no,Walking,Normal_Weight
+Female,17,1.6,65,no,yes,3,1,Sometimes,no,2,yes,1,2,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19,1.53,42,no,no,2,3,Sometimes,no,1,yes,2,0,Frequently,Public_Transportation,Insufficient_Weight
+Male,21,1.8,72,no,yes,3,3,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Male,20,1.6,50,no,no,2,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.74,105,yes,yes,3,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.65,66,no,no,3,3,Sometimes,no,2,no,3,0,no,Public_Transportation,Normal_Weight
+Male,18,1.87,173,yes,yes,3,3,Frequently,no,2,no,2,1,Sometimes,Public_Transportation,Obesity_Type_III
+Male,17,1.7,55,no,yes,3,3,Always,no,2,no,3,1,Sometimes,Public_Transportation,Normal_Weight
+Female,21,1.54,47,yes,no,3,3,Always,no,1,no,2,0,no,Public_Transportation,Normal_Weight
+Male,17,1.8,97,yes,yes,2,3,Sometimes,no,3,no,1,1,Sometimes,Walking,Overweight_Level_II
+Male,18,1.82,80,yes,yes,2,3,Sometimes,no,2,no,3,2,no,Walking,Normal_Weight
+Male,20,1.98,125,yes,yes,2,3,Always,no,3,no,1,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,17,1.75,70,yes,no,2,3,Sometimes,no,1,no,3,2,Sometimes,Walking,Normal_Weight
+Female,26,1.65,60,no,no,3,4,Always,no,2,no,2,0,no,Walking,Normal_Weight
+Female,17,1.6,53,no,yes,1,3,Sometimes,no,1,no,2,2,Sometimes,Walking,Normal_Weight
+Female,24,1.6,51,yes,yes,1,3,Sometimes,no,2,no,0,1,Sometimes,Automobile,Normal_Weight
+Female,17,1.6,59,no,yes,2,3,Sometimes,no,2,no,1,2,no,Public_Transportation,Normal_Weight
+Female,27,1.55,62,no,yes,3,1,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,17,1.9,60,no,no,3,3,Sometimes,no,2,no,3,1,no,Walking,Insufficient_Weight
+Female,17,1.7,56,yes,yes,1,3,Sometimes,no,1,no,1,1,no,Automobile,Normal_Weight
+Male,41,1.75,110,yes,no,2,1,Sometimes,no,1,no,1,0,Frequently,Automobile,Obesity_Type_II
+Female,33,1.56,48,yes,no,2,3,Sometimes,no,2,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.87,75,no,yes,2,3,Frequently,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,40,1.56,80,yes,yes,2,1,Sometimes,no,2,no,2,0,no,Public_Transportation,Obesity_Type_I
+Female,37,1.65,73,yes,no,3,3,Sometimes,no,2,no,1,0,Sometimes,Automobile,Overweight_Level_I
+Male,19,1.8,80,no,yes,2,1,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,24,1.84,86,yes,yes,2,1,Always,no,2,no,2,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,24,1.7,68,no,yes,3,3,Sometimes,no,2,no,0,0,Sometimes,Motorbike,Normal_Weight
+Male,33,1.72,83,yes,yes,2,1,Sometimes,no,2,no,3,0,Sometimes,Automobile,Overweight_Level_II
+Female,40,1.58,63,no,yes,2,3,Frequently,no,2,no,3,1,Sometimes,Public_Transportation,Overweight_Level_I
+Female,37,1.68,83,yes,yes,2,1,Sometimes,no,2,no,0,0,no,Automobile,Overweight_Level_II
+Male,20,1.58,74,no,no,3,3,Frequently,no,2,no,3,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19,1.8,60,no,yes,2,3,Sometimes,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Male,17,1.62,69,yes,yes,3,1,Always,no,2,yes,1,2,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18,1.62,58,no,yes,3,3,Sometimes,no,1,no,0,2,no,Automobile,Normal_Weight
+Female,21,1.54,56,no,yes,2,1,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.76,70,yes,yes,2,4,Frequently,no,2,no,2,1,Sometimes,Automobile,Normal_Weight
+Male,41,1.8,92,yes,yes,2,1,Frequently,no,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,36,1.58,60,yes,no,3,3,Sometimes,no,1,no,2,0,Sometimes,Automobile,Normal_Weight
+Male,18,1.76,68,no,no,2,4,Frequently,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.73,70,no,yes,3,3,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,17,1.7,70,yes,yes,3,3,Sometimes,no,2,no,0,2,Sometimes,Walking,Normal_Weight
+Male,25,1.7,83,no,yes,3,3,Sometimes,no,3,yes,3,0,no,Motorbike,Overweight_Level_II
+Male,21,1.8,75,yes,yes,3,3,Frequently,no,1,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,29,1.6,56,yes,no,2,3,Frequently,no,1,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,17,1.7,98,yes,no,2,3,Sometimes,no,2,no,0,1,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.6,60,yes,yes,3,1,Sometimes,no,1,yes,0,2,no,Walking,Normal_Weight
+Female,16,1.55,45,no,yes,2,3,Frequently,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Female,18,1.59,53,no,no,1,3,Sometimes,no,1,no,1,2,no,Public_Transportation,Normal_Weight
+Female,37,1.5,75,yes,yes,2,3,Frequently,no,1,no,0,0,Sometimes,Motorbike,Obesity_Type_I
+Male,18,1.78,108,yes,no,2,3,Sometimes,no,3,no,1,0,no,Public_Transportation,Obesity_Type_I
+Female,16,1.61,65,yes,yes,1,1,Sometimes,no,2,no,0,0,no,Public_Transportation,Overweight_Level_I
+Male,23,1.71,50,yes,yes,2,3,Always,no,3,no,0,2,no,Public_Transportation,Insufficient_Weight
+Female,18,1.7,50,no,no,2,3,Sometimes,no,2,no,0,1,Sometimes,Walking,Insufficient_Weight
+Male,18,1.76,69.5,no,no,3,3,Frequently,no,3,no,2,2,no,Public_Transportation,Normal_Weight
+Male,18,1.7,78,no,yes,1,3,Sometimes,no,2,no,0,1,Sometimes,Public_Transportation,Overweight_Level_II
+Female,17,1.53,55,yes,yes,2,3,Sometimes,no,1,no,0,1,no,Public_Transportation,Normal_Weight
+Female,20,1.54,39,yes,yes,1,3,Sometimes,no,2,no,3,2,Sometimes,Public_Transportation,Insufficient_Weight
+Female,38,1.55,59,yes,no,3,3,Sometimes,no,1,no,2,1,Frequently,Automobile,Normal_Weight
+Male,20,1.66,60,no,yes,2,4,Frequently,no,3,no,0,0,no,Public_Transportation,Normal_Weight
+Male,21,1.85,125,yes,yes,3,1,Always,no,1,no,0,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,21,1.65,60,no,no,3,1,Frequently,no,1,no,0,0,Sometimes,Motorbike,Normal_Weight
+Male,18,1.65,70,yes,no,2,3,Sometimes,no,2,no,0,0,no,Public_Transportation,Overweight_Level_I
+Male,26,1.83,82,no,yes,2,3,Always,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,27,1.83,99,yes,yes,3,1,Frequently,no,2,no,2,1,Sometimes,Walking,Overweight_Level_II
+Female,26,1.66,112,yes,no,3,3,Sometimes,no,3,no,0,0,no,Automobile,Obesity_Type_III
+Male,34,1.78,73,no,yes,2,3,Sometimes,no,2,no,3,1,Sometimes,Automobile,Normal_Weight
+Male,18,1.74,86,no,no,3,3,Sometimes,no,2,no,3,0,no,Public_Transportation,Overweight_Level_II
+Male,33,1.76,66.5,no,no,2,3,Sometimes,no,2,no,3,1,Sometimes,Automobile,Normal_Weight
+Female,19,1.51,59,yes,yes,3,3,Frequently,no,1,no,1,2,Sometimes,Walking,Overweight_Level_I
+Male,20,1.81,79,yes,no,3,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Female,33,1.55,55,yes,yes,3,1,Sometimes,no,3,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,20,1.83,66,no,no,2,3,Sometimes,no,2,no,1,1,no,Public_Transportation,Normal_Weight
+Female,20,1.6,65,no,no,3,3,Sometimes,no,2,yes,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,33,1.85,85,no,yes,2,3,Frequently,no,2,no,1,0,Sometimes,Automobile,Normal_Weight
+Male,33,1.75,85,no,no,2,3,Sometimes,no,2,yes,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33,1.83,113,yes,yes,2,1,Always,no,2,no,1,2,Frequently,Automobile,Obesity_Type_I
+Male,14,1.71,72,yes,yes,3,3,Sometimes,no,3,no,2,1,no,Walking,Normal_Weight
+Male,34,1.84,88,no,yes,3,3,Sometimes,no,1,yes,2,0,no,Automobile,Overweight_Level_I
+Male,18,1.77,87,yes,yes,3,3,Sometimes,no,2,no,1,1,Frequently,Public_Transportation,Overweight_Level_II
+Male,18,1.7,90,no,yes,3,3,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,29,1.62,89,yes,yes,1,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18,1.85,60,yes,yes,3,4,Sometimes,no,2,yes,2,0,Sometimes,Automobile,Insufficient_Weight
+Male,18,1.84,60,yes,yes,3,4,Sometimes,no,2,yes,2,0,Sometimes,Automobile,Insufficient_Weight
+Male,19,1.75,58,no,yes,2,3,Sometimes,no,2,no,2,0,Sometimes,Bike,Normal_Weight
+Male,33,1.85,93,yes,yes,2,3,Sometimes,no,1,no,1,1,Sometimes,Walking,Overweight_Level_II
+Male,33,1.74,76,no,no,2,3,Sometimes,no,1,no,0,2,Sometimes,Walking,Overweight_Level_I
+Female,19,1.61,53.8,yes,yes,2,3,Always,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.75,70,no,no,2,3,Sometimes,no,3,no,1,1,no,Public_Transportation,Normal_Weight
+Female,20,1.67,60,no,yes,3,3,no,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.7,69,no,yes,3,1,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,26,1.9,80,yes,yes,2,3,Frequently,no,1,yes,1,0,Sometimes,Automobile,Normal_Weight
+Male,18,1.65,85,no,yes,2,3,Sometimes,no,1,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18,1.6,55,no,yes,2,4,Frequently,no,2,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.8,70,no,yes,3,3,Frequently,no,1,no,2,0,no,Public_Transportation,Normal_Weight
+Female,18,1.64,59,yes,yes,2,3,Sometimes,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.89,87,no,yes,2,4,Frequently,no,2,no,3,1,Frequently,Automobile,Normal_Weight
+Female,19,1.76,80,yes,yes,2,1,Sometimes,no,3,no,3,1,no,Public_Transportation,Overweight_Level_I
+Female,18,1.56,55,no,yes,2,3,Sometimes,no,1,no,0,0,Frequently,Automobile,Normal_Weight
+Female,18,1.6,56,yes,yes,2,1,Always,no,2,no,1,0,Sometimes,Walking,Normal_Weight
+Female,19,1.67,64,no,yes,3,3,Sometimes,no,2,no,2,1,Sometimes,Automobile,Normal_Weight
+Female,19,1.6,60,no,yes,2,3,Frequently,no,2,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.55,56,no,yes,2,3,Sometimes,no,1,no,0,0,no,Automobile,Normal_Weight
+Female,18,1.55,50,yes,yes,3,3,Frequently,no,1,no,1,2,no,Public_Transportation,Normal_Weight
+Male,26,1.72,65,yes,yes,2,3,Sometimes,no,2,no,0,1,Sometimes,Walking,Normal_Weight
+Male,18,1.72,53,yes,yes,2,3,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19,1.7,60,yes,yes,2,1,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,19,1.51,45,no,yes,2,4,Sometimes,no,1,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.83,82,yes,yes,3,3,Frequently,no,2,no,2,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.8,87,yes,yes,2,4,Sometimes,no,2,no,2,1,Sometimes,Public_Transportation,Overweight_Level_I
+Female,24,1.6,100.5,yes,yes,3,1,Sometimes,no,1,no,0,2,Sometimes,Public_Transportation,Obesity_Type_II
+Female,18,1.63,63,yes,yes,1,3,Sometimes,no,2,no,2,2,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.71,71,no,yes,3,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.7,65,yes,no,2,3,Frequently,no,2,no,1,0,no,Public_Transportation,Normal_Weight
+Male,23,1.75,69,no,no,3,3,no,no,2,no,2,1,Sometimes,Automobile,Normal_Weight
+Female,18,1.62,50,no,yes,3,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Normal_Weight
+Female,20,1.68,68,no,yes,3,1,Sometimes,no,1,no,1,0,no,Public_Transportation,Normal_Weight
+Male,18,1.85,66,no,yes,2,3,Frequently,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,33,1.59,60,no,yes,3,1,Frequently,no,2,no,0,0,no,Public_Transportation,Normal_Weight
+Female,19,1.5,45,no,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.69,60,no,yes,2,3,Always,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.76,79,yes,yes,2,3,Frequently,no,3,no,1,2,Frequently,Public_Transportation,Overweight_Level_I
+Female,18,1.62,55,yes,yes,2,3,Frequently,no,1,no,1,1,no,Public_Transportation,Normal_Weight
+Male,21,1.71,100,yes,yes,2,1,Sometimes,no,2,no,0,2,no,Public_Transportation,Obesity_Type_I
+Male,27,1.72,88,yes,yes,2,1,Always,no,2,no,0,0,Sometimes,Automobile,Overweight_Level_II
+Male,17,1.8,68,yes,no,2,3,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.93,86,no,no,3,4,Always,no,2,no,2,0,Sometimes,Walking,Normal_Weight
+Female,18,1.6,51,yes,yes,2,3,Frequently,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,22,1.74,75,yes,yes,3,3,Frequently,no,1,no,1,0,no,Automobile,Normal_Weight
+Male,22,1.74,75,yes,yes,3,3,Frequently,no,1,no,1,0,no,Automobile,Normal_Weight
+Female,20,1.62,45,no,yes,3,3,Frequently,no,1,no,1,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.54,42,no,yes,3,1,Sometimes,no,2,no,0,1,no,Public_Transportation,Insufficient_Weight
+Female,20,1.56,51.5,no,yes,2,3,Frequently,no,2,no,3,0,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.6,83,yes,yes,2,3,Sometimes,no,2,yes,1,0,no,Public_Transportation,Obesity_Type_I
+Female,18,1.54,71,no,no,3,4,Frequently,no,2,no,1,1,no,Public_Transportation,Overweight_Level_II
+Female,18,1.63,51,yes,yes,1,3,Frequently,no,1,no,1,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.78,64,no,yes,2,3,Sometimes,no,1,no,1,0,no,Public_Transportation,Normal_Weight
+Female,18,1.62,68,no,no,2,1,Sometimes,no,1,no,0,2,no,Public_Transportation,Overweight_Level_I
+Female,18,1.71,75,yes,yes,3,3,Sometimes,no,2,no,1,0,no,Public_Transportation,Overweight_Level_I
+Female,18,1.64,56,yes,yes,3,3,Frequently,no,1,yes,1,1,no,Public_Transportation,Normal_Weight
+Male,19,1.69,65,no,yes,2,3,Frequently,no,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,17,1.58,50,no,yes,1,4,Frequently,no,2,no,1,2,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.57,50,no,yes,2,3,Sometimes,no,1,no,0,1,Sometimes,Public_Transportation,Normal_Weight
+Male,18,1.74,64,yes,yes,3,4,Sometimes,no,1,yes,2,0,Sometimes,Public_Transportation,Normal_Weight
+Female,20,1.58,53.5,yes,yes,2,1,Frequently,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,18,1.5,58,no,yes,2,3,Sometimes,no,1,no,0,0,no,Public_Transportation,Overweight_Level_I
+Female,36,1.65,80,yes,yes,2,3,Sometimes,no,1,no,0,2,no,Automobile,Overweight_Level_II
+Male,21,1.8,73,yes,yes,1,3,Always,no,2,no,3,1,Sometimes,Public_Transportation,Normal_Weight
+Male,23,1.75,75,no,yes,2,3,Sometimes,no,2,no,1,0,Frequently,Public_Transportation,Normal_Weight
+Male,20,1.84,104,yes,no,2,3,Sometimes,no,3,no,3,0,no,Motorbike,Obesity_Type_I
+Male,21,1.88,84,yes,yes,3,3,Sometimes,no,3,no,2,1,Sometimes,Walking,Normal_Weight
+Female,19,1.56,50,no,yes,2,1,Sometimes,no,1,no,0,2,no,Public_Transportation,Normal_Weight
+Male,24,1.75,84,no,yes,3,3,no,no,2,yes,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,25,1.66,68,no,yes,2,3,Sometimes,yes,1,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Male,45,1.7,86,no,yes,3,3,Frequently,no,1,no,0,0,no,Automobile,Overweight_Level_II
+Male,20,1.8,65,no,yes,2,3,Frequently,no,1,no,2,0,Sometimes,Motorbike,Normal_Weight
+Female,18,1.67,66,no,yes,3,3,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Normal_Weight
+Male,19,1.8,60,yes,yes,3,1,Always,no,1,yes,0,0,no,Motorbike,Normal_Weight
+Male,18,1.72,53,yes,yes,2,3,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,20,1.56,45,no,no,2,3,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Normal_Weight
+Female,25.196214,1.686306,104.572712,yes,yes,3,3,Sometimes,no,1.152736,no,0.319156,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.503343,1.683124,126.67378,yes,yes,3,3,Sometimes,no,1.115967,no,1.541072,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622397,110.79263,yes,yes,3,3,Sometimes,no,2.704507,no,0,0.29499,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.853826,1.755643,137.796884,yes,yes,3,3,Sometimes,no,2.184707,no,1.978631,0.838957,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.90012,1.843419,165.057269,yes,yes,3,3,Sometimes,no,2.406541,no,0.10032,0.479221,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.306615,1.7456,133.03441,yes,yes,3,3,Sometimes,no,2.984323,no,1.586525,0.62535,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.630927,111.485516,yes,yes,3,3,Sometimes,no,2.444125,no,0,0.26579,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.629191,104.826776,yes,yes,3,3,Sometimes,no,2.654702,no,0,0.555468,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.849705,1.770612,133.963349,yes,yes,3,3,Sometimes,no,2.825629,no,1.399183,0.928972,Sometimes,Public_Transportation,Obesity_Type_III
+Male,19.799054,1.743702,54.927529,yes,yes,2,3.28926,Sometimes,no,2.847264,no,1.680844,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.188754,1.771915,55.695036,yes,yes,2,4,Sometimes,no,2.884033,no,2,1.340107,Sometimes,Public_Transportation,Insufficient_Weight
+Male,22.285024,1.75376,55.879263,yes,yes,2.450218,3.995147,Sometimes,no,2.147746,no,2,0.58998,Sometimes,Public_Transportation,Insufficient_Weight
+Female,22,1.675446,51.154201,yes,yes,3,3,Frequently,no,2.815293,no,1.978631,1,no,Public_Transportation,Insufficient_Weight
+Female,21.02497,1.666203,49.869791,yes,yes,3,3,Frequently,no,2.593459,no,2,1,no,Public_Transportation,Insufficient_Weight
+Female,22.038327,1.711467,51.965521,yes,yes,2.880161,3,Frequently,no,1.031354,no,2.206738,1.37465,no,Public_Transportation,Insufficient_Weight
+Female,21.243142,1.598019,44.845655,no,no,3,1.72626,Frequently,no,2.444125,no,1.31817,0,no,Public_Transportation,Insufficient_Weight
+Female,22.142432,1.59611,42.848033,no,no,3,2.581015,Frequently,no,2.654702,no,0.902095,0,no,Public_Transportation,Insufficient_Weight
+Female,21.962426,1.57206,43.919835,no,no,3,1.600812,Frequently,no,2.651258,no,0.600817,0,no,Public_Transportation,Insufficient_Weight
+Female,21.491055,1.586952,43.087508,no,no,2.00876,1.73762,Frequently,no,1.792022,no,0.119643,0,no,Public_Transportation,Insufficient_Weight
+Female,22.717943,1.59559,44.581159,no,no,2.596579,1.10548,Frequently,no,1.490613,no,0.345684,0,no,Public_Transportation,Insufficient_Weight
+Female,23.501249,1.6,45,no,no,2.591439,3,Frequently,no,2.074048,no,1.679935,0,no,Public_Transportation,Insufficient_Weight
+Female,18.535075,1.688025,45,no,yes,3,3,Sometimes,no,3,yes,2.539762,1.283673,no,Public_Transportation,Insufficient_Weight
+Female,19,1.556211,42.339767,no,yes,3,2.0846,Sometimes,no,2.13755,yes,0.196152,0.062488,no,Public_Transportation,Insufficient_Weight
+Female,19,1.564199,42.096062,no,yes,3,1.894384,Sometimes,no,2.456581,yes,1.596576,0.9974,no,Public_Transportation,Insufficient_Weight
+Female,20.254534,1.56948,41.324558,no,yes,2.392665,1,Frequently,no,1,no,0,0.738269,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,yes,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.225396,1.550648,44.641796,no,yes,3,2.857787,Frequently,no,1,no,0.754646,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.86997,1.520997,42,no,yes,3,1,Frequently,no,1.527036,no,0,0.860497,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.147114,1.528011,42,no,yes,3,1,Frequently,no,1.322669,no,0,0.478676,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,yes,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21.996523,1.733263,50.890363,no,no,3,3.765526,Frequently,no,1.656082,no,0.42777,0.555967,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21.376426,1.722527,50.833029,no,no,3,3.285167,Frequently,no,1.569082,no,0.545931,0.469735,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.724106,1.734832,50,no,no,1.123939,4,Frequently,no,1,no,1.303976,0.371941,Sometimes,Public_Transportation,Insufficient_Weight
+Male,23,1.882779,64.106108,yes,yes,3,3,Sometimes,no,2.843777,no,1.488843,0.009254,Sometimes,Automobile,Insufficient_Weight
+Male,22.851834,1.854592,63.611109,yes,yes,3,3.691226,Sometimes,no,2.650989,no,1.228136,0.8324,Sometimes,Automobile,Insufficient_Weight
+Male,23,1.835643,58.854416,yes,yes,3,3,Sometimes,no,2.027984,no,1.661556,0.114716,Sometimes,Automobile,Insufficient_Weight
+Male,18.216032,1.755507,52,yes,yes,3,3,Frequently,no,2,no,0.658894,1,no,Public_Transportation,Insufficient_Weight
+Male,21.81119,1.71266,51.598593,yes,yes,3,3.156153,Frequently,no,1.253803,no,0.544784,0.256382,no,Public_Transportation,Insufficient_Weight
+Male,18.164768,1.694265,52,yes,yes,3,3,Frequently,no,2,no,0.819682,1,no,Public_Transportation,Insufficient_Weight
+Female,18.871794,1.586895,41.452385,no,yes,2,1.07976,Sometimes,no,1,no,0.194745,1.547086,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.766033,1.579561,41.890204,no,yes,2.027574,1,Sometimes,no,1.636326,no,0,1.906141,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.540535,1.564568,41.397378,no,yes,2.658112,1,Sometimes,no,1.298165,no,0,1.639326,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.88036,1.670635,49.742931,no,no,2.88626,3.559841,Frequently,no,1.632004,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.717249,1.688426,49.660995,no,no,2.714447,3,Frequently,no,2,no,1.903182,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.633898,1.66084,49.039794,no,no,2.750715,3,Frequently,no,2,no,1.067817,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,23,1.710182,50.287967,yes,yes,2,3,Frequently,no,2.099863,no,1.63581,2,no,Public_Transportation,Insufficient_Weight
+Female,20.406871,1.755978,53.699561,yes,yes,2,3.891994,Frequently,no,1.86393,no,2.870127,2,no,Public_Transportation,Insufficient_Weight
+Female,23,1.728834,51.442293,yes,yes,2,3,Frequently,no,1.229915,no,0.619533,2,no,Public_Transportation,Insufficient_Weight
+Female,19.058511,1.662017,49.838965,no,yes,1.4925,3,Sometimes,no,1.049134,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.827008,1.7,50,no,yes,1,3.240578,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.314964,1.661403,49.932199,no,yes,2.205439,3,Sometimes,no,1.530531,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,29.970445,1.610863,49.516027,yes,yes,2.059138,3.904858,Frequently,no,2,no,0.821977,0,no,Public_Transportation,Insufficient_Weight
+Female,32.383858,1.640688,46.655622,yes,yes,3,3.11158,Frequently,no,2.721769,no,1.44287,0,no,Public_Transportation,Insufficient_Weight
+Female,24.163526,1.64603,49.839685,yes,yes,3,3.590039,Frequently,no,2.7305,no,0.107078,0,no,Public_Transportation,Insufficient_Weight
+Male,17.038222,1.710564,51.588874,no,yes,2,2.057935,Sometimes,no,2.371015,no,0.288032,0.714627,Sometimes,Public_Transportation,Insufficient_Weight
+Male,16.30687,1.752755,50,no,yes,2.310423,3.558637,Sometimes,no,1.787843,no,1.926592,0.828549,Sometimes,Public_Transportation,Insufficient_Weight
+Male,16.198153,1.691007,52.629374,no,yes,2,2.000986,Sometimes,no,2.673835,no,0.99295,0.474836,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.753349,51.457226,no,yes,2.823179,3,Sometimes,no,1.773236,no,1.252472,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.781543,50.869704,no,yes,2.052932,3,Sometimes,no,2,no,0.520408,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.755823,52.331172,no,yes,2.596364,3,Sometimes,no,2,no,0.281734,1.488372,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.486869,1.836669,58.943347,yes,yes,2.767731,3.821168,Sometimes,no,2,no,2,0.556245,no,Automobile,Insufficient_Weight
+Male,19.920629,1.768493,57.790381,yes,yes,2,3.897078,Sometimes,no,2.622638,no,2,1.894105,no,Automobile,Insufficient_Weight
+Male,18.426619,1.777108,57.145917,yes,yes,2,3.092116,Sometimes,no,2.426465,no,2,1.839862,no,Automobile,Insufficient_Weight
+Female,17.082867,1.640824,43.365005,no,yes,2.815157,3,Sometimes,no,2.911187,no,2.595128,1.380204,Sometimes,Public_Transportation,Insufficient_Weight
+Female,17.000433,1.584951,44.411801,no,yes,2.737762,3,Sometimes,no,2.310921,no,2.240714,1.59257,Sometimes,Public_Transportation,Insufficient_Weight
+Female,16.270434,1.818268,47.124717,no,yes,3,3.286431,Sometimes,no,2.148146,no,2.458237,1.273333,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.908114,1.793926,59.682591,yes,yes,2.568063,4,Sometimes,no,2,no,2,0.220029,no,Automobile,Insufficient_Weight
+Male,17.120699,1.809251,58.968994,yes,yes,2.524428,4,Sometimes,no,2,no,2,0.03838,no,Automobile,Insufficient_Weight
+Male,19.329542,1.767335,55.700497,yes,yes,2,4,Sometimes,no,2.157395,no,2,1.679149,no,Automobile,Insufficient_Weight
+Female,22.377998,1.699568,54.98774,yes,yes,3,3,Frequently,no,2,no,0.139808,0.875464,no,Public_Transportation,Insufficient_Weight
+Female,20.954955,1.759358,55.01045,yes,yes,2.971574,3.592415,Frequently,no,2.894678,no,2,1.009632,no,Public_Transportation,Insufficient_Weight
+Female,22.991668,1.740295,54.166453,yes,yes,3,3,Frequently,no,2.025279,no,2,0.152985,no,Public_Transportation,Insufficient_Weight
+Female,19.300435,1.739354,49.649613,no,yes,3,3.754599,Sometimes,no,1.692112,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,17.065445,1.647811,49.603807,no,yes,3,3.566082,Sometimes,no,1.438398,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.084967,1.768435,49.597765,no,yes,3,3.725797,Sometimes,no,1.191401,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.024853,1.7,50,no,yes,1.0816,3.520555,Frequently,no,1.016968,no,0.651412,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.833682,1.699464,49.676046,no,yes,1.270448,3.731212,Frequently,no,1.876915,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,17.767432,1.74379,50,no,yes,1.344854,4,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.504696,1.590317,42.367615,no,no,2.959658,3,Frequently,no,1,no,1.522399,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.950605,1.527133,42,no,no,2.725282,1.259803,Frequently,no,1,no,1.37467,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.530875,42,no,no,2.844607,1.273128,Frequently,no,1.69551,no,0.260079,0.635867,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17,1.848294,59.409018,yes,yes,2.44004,3,Sometimes,no,2,no,2.784471,1,no,Automobile,Insufficient_Weight
+Male,17,1.824414,59.295172,yes,yes,2.432302,3,Sometimes,no,2,no,2.349495,1,no,Automobile,Insufficient_Weight
+Male,18.525525,1.856633,59.258372,yes,yes,2.592247,3.304123,Sometimes,no,2.036764,no,2.038653,1.119877,no,Automobile,Insufficient_Weight
+Female,22.926352,1.715597,50,yes,yes,2.449267,3.647154,Frequently,no,1.266018,no,0.866045,0.097234,no,Public_Transportation,Insufficient_Weight
+Female,21.785351,1.675054,49.945233,yes,yes,2.929889,3,Frequently,no,2.792074,no,0.630944,1.366355,no,Public_Transportation,Insufficient_Weight
+Female,23,1.717601,51.073918,yes,yes,2,3,Frequently,no,1.426874,no,2.20208,2,no,Public_Transportation,Insufficient_Weight
+Female,17.402028,1.710756,50,no,yes,2.015258,3.300666,Sometimes,no,1.315608,no,0.069802,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18,1.7,50,no,yes,1.031149,3,Sometimes,no,1.963628,no,0.028202,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.48207,1.7,50,no,yes,1.592183,3.535016,Sometimes,no,1.086391,no,0.618913,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.53084,1.573816,39.850137,no,yes,1.21498,1.717608,Sometimes,no,1.179942,no,0.684739,2,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.94814,1.530884,39.371523,no,yes,1.522001,3,Sometimes,no,1.98126,no,2.306844,0.720454,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.400053,1.529724,40.343463,no,yes,2.703436,2.884479,Sometimes,no,1.439962,no,0.144627,0.322307,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19.556729,1.767563,56.307019,yes,yes,2.362918,4,Sometimes,no,2.358172,no,2,0.939819,no,Automobile,Insufficient_Weight
+Male,17.70368,1.883364,60,yes,yes,3,3.626815,Sometimes,no,2,no,2.011519,0.456462,no,Automobile,Insufficient_Weight
+Male,18.274358,1.824655,58.621349,yes,yes,2.14084,4,Sometimes,no,2.931438,no,2,1.164457,no,Automobile,Insufficient_Weight
+Male,18,1.840138,60,yes,yes,3,4,Sometimes,no,2,no,2,0,no,Automobile,Insufficient_Weight
+Male,17.210933,1.819557,58.325122,yes,yes,2.5596,4,Sometimes,no,2,no,2,0.331483,no,Automobile,Insufficient_Weight
+Male,17.469417,1.798645,59.612717,yes,yes,2.336044,4,Sometimes,no,2,no,2,0.133005,no,Automobile,Insufficient_Weight
+Male,18,1.7108,50.92538,yes,yes,2,3,Sometimes,no,2,no,0,1.593704,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.70653,51.121749,yes,yes,2,3,Sometimes,no,2,no,0,1.329237,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.716691,51.149283,yes,yes,1.813234,3,Sometimes,no,1.274815,no,0.520407,1.673584,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.010211,1.555431,44.188767,no,no,2.724285,3,Frequently,no,1,yes,1.070331,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.091346,1.603923,45,no,no,3,3,Frequently,no,1.28246,yes,1.60195,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.773303,1.602082,45,no,no,3,3,Frequently,no,2.875583,yes,1.258504,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.483036,1.53777,42,no,yes,3,1,Frequently,no,1.387531,no,0,0.466169,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.538398,42,no,yes,2.71897,1.473088,Frequently,no,1.164644,no,1.235675,0.930926,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.407204,1.520862,42,no,yes,3,1,Frequently,no,1.848703,no,0,0.744555,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.717826,51.7325,yes,yes,1.133844,3,Sometimes,no,1.325326,no,0.227985,1.708309,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18.424941,1.753389,54.121925,yes,yes,2,3.16645,Sometimes,no,2.278578,no,1.838881,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.70165,50.157707,yes,yes,1.757466,3,Sometimes,no,1.153559,no,0.833976,1.616045,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19.97981,1.75336,54.997374,yes,yes,2,3.494849,Sometimes,no,2.976672,no,1.94907,2,no,Public_Transportation,Insufficient_Weight
+Female,21.798856,1.672007,49.980968,yes,yes,2.979383,3,Frequently,no,2.975887,no,0.945093,1.241755,no,Public_Transportation,Insufficient_Weight
+Female,22.209706,1.593847,44.050251,no,no,3,2.99321,Frequently,no,2.702783,no,1.967234,0,no,Public_Transportation,Insufficient_Weight
+Female,23.018443,1.584785,44.376637,no,no,2.204914,2.127797,Frequently,no,2.120292,no,0.995735,0,no,Public_Transportation,Insufficient_Weight
+Female,19.376997,1.600264,45,no,no,3,3,Frequently,no,2.949419,yes,1.295697,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.971429,1.549311,41.557468,no,yes,2.927218,1,Frequently,no,1,no,0,0.276592,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.345161,1.534385,41.96525,no,yes,2.88853,1,Frequently,no,1,no,0,0.196224,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.519916,1.725548,49.815599,yes,no,2.890535,3.90779,Frequently,no,1.109115,no,1.548953,0.555468,no,Public_Transportation,Insufficient_Weight
+Male,20.406066,1.868931,63.199726,yes,yes,3,3.699594,Sometimes,no,2.825629,no,1.699592,0.071028,Sometimes,Automobile,Insufficient_Weight
+Female,21.478496,1.686936,51.256059,yes,yes,3,3.179995,Frequently,no,1.910378,no,0.480614,0.625079,no,Public_Transportation,Insufficient_Weight
+Female,18.372563,1.589829,40.202773,no,yes,2,1.075553,Sometimes,no,1,no,0.127425,1.679442,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.931667,1.653209,49.026214,no,no,2.530066,3,Sometimes,no,1.965237,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,22.998709,1.740108,53.65727,yes,yes,2.241606,3,Frequently,no,1.846626,no,2.460238,0.814518,no,Public_Transportation,Insufficient_Weight
+Female,18.006742,1.7,50,no,yes,1.003566,3.238258,Frequently,no,1.014634,no,0.783676,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,34.799519,1.689141,50,yes,yes,2.652779,3.804944,Frequently,no,1.60114,no,0.174692,0.096982,no,Public_Transportation,Insufficient_Weight
+Male,16.496978,1.691206,50,no,yes,2,1.630846,Sometimes,no,2.975528,no,0.548991,0.369134,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.788459,51.552595,no,yes,2.897899,3,Sometimes,no,1.835545,no,1.162519,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.377131,1.811238,58.83071,yes,yes,2.483979,3.762778,Sometimes,no,2,no,2,0.930051,no,Automobile,Insufficient_Weight
+Female,16.611837,1.830068,43.534531,no,yes,2.945967,3,Sometimes,no,2.953192,no,2.830911,1.466667,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.080493,1.782756,56.029418,yes,yes,2,4,Sometimes,no,2.28525,no,2,1.162801,no,Automobile,Insufficient_Weight
+Female,22.970655,1.751691,55.967655,yes,yes,2.478891,3.371832,Frequently,no,2.004126,no,2,0.327376,no,Public_Transportation,Insufficient_Weight
+Female,17.764764,1.78656,50,no,yes,2.784464,4,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.272573,1.71367,50,no,yes,1.005578,4,Frequently,no,1,no,1.683957,0.704978,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.540375,42.006282,no,no,2.938031,2.705445,Frequently,no,1.333807,yes,1.877369,0.370973,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.06713,1.896734,59.895052,yes,yes,2.842102,3.34175,Sometimes,no,2,no,2.511157,0.560351,no,Automobile,Insufficient_Weight
+Female,23,1.710129,50.079991,yes,yes,2,3,Frequently,no,2.685842,no,0.373186,2,no,Public_Transportation,Insufficient_Weight
+Female,18,1.704193,50.631889,no,yes,2,3,Sometimes,no,2,no,0,1.525597,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.054008,1.556611,39.695295,no,yes,1.889199,2.217651,Sometimes,no,2.013605,no,2.075293,1.683261,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.845399,60,yes,yes,3,4,Sometimes,no,2,no,2,0,no,Automobile,Insufficient_Weight
+Male,17.491272,1.834637,59.990861,yes,yes,2.943749,4,Sometimes,no,2,no,2,0.128895,no,Automobile,Insufficient_Weight
+Male,18,1.721854,52.514302,yes,yes,2.33998,3,Sometimes,no,2,no,0.027433,1.884138,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.172661,1.605521,44.661461,no,no,3,2.893778,Frequently,no,1,yes,0.769726,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.220108,1.530266,42,no,yes,3,1,Frequently,no,1.954889,no,0,0.907868,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.71889,52.058335,yes,yes,1.950742,3,Sometimes,no,1.751723,no,0.201136,1.743319,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19.96247,1.756338,54.98234,yes,yes,2.277436,3.502604,Sometimes,no,2.891713,no,1.696294,1.894902,no,Public_Transportation,Insufficient_Weight
+Male,17.580627,1.770324,55.695253,yes,yes,2,4,Sometimes,no,2.369627,no,2,1.612466,no,Automobile,Insufficient_Weight
+Female,21.125098,1.767479,56.265959,yes,yes,2.371338,3.998766,Frequently,no,2.263466,no,2,0.844004,no,Public_Transportation,Insufficient_Weight
+Female,22.033129,1.704223,51.437985,yes,yes,2.984425,3,Frequently,no,2.044694,no,2.008256,1.250871,no,Public_Transportation,Insufficient_Weight
+Female,20.744839,1.667852,49.803921,yes,yes,2.977018,3.193671,Frequently,no,2.482933,no,2,1,no,Public_Transportation,Insufficient_Weight
+Female,22.547298,1.722461,51.881263,yes,yes,2.663421,3,Frequently,no,1.04111,no,0.794402,1.391948,no,Public_Transportation,Insufficient_Weight
+Female,21.837996,1.588046,44.236067,no,no,3,1.69608,Frequently,no,2.550307,no,1.098862,0,no,Public_Transportation,Insufficient_Weight
+Female,23.035829,1.598612,42.993937,no,no,2.753752,2.812377,Frequently,no,2.346647,no,1.612248,0,no,Public_Transportation,Insufficient_Weight
+Female,21.529439,1.592379,44.00945,no,no,3,1.612747,Frequently,no,2.566629,no,1.190465,0,no,Public_Transportation,Insufficient_Weight
+Female,21.287999,1.555778,42.3601,no,no,2.318355,1.082304,Frequently,no,1.220365,no,0.033328,0,no,Public_Transportation,Insufficient_Weight
+Female,23.444286,1.596466,44.594588,no,no,2.594653,1.882158,Frequently,no,1.916812,no,0.417119,0,no,Public_Transportation,Insufficient_Weight
+Female,22.329041,1.598393,44.918255,no,no,2.886157,2.326233,Frequently,no,2.306821,no,1.42237,0,no,Public_Transportation,Insufficient_Weight
+Female,18.91505,1.633316,45,no,yes,3,3,Sometimes,no,2.924594,yes,1.352558,0.220087,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.559567,42.12617,no,yes,3,1.989398,Sometimes,no,2.241607,no,0.206025,0.284453,no,Public_Transportation,Insufficient_Weight
+Female,19.052833,1.546551,42.069992,no,yes,3,1.735493,Sometimes,no,2.318736,no,1.193486,0.74568,no,Public_Transportation,Insufficient_Weight
+Female,19.59904,1.566501,41.706283,no,yes,2.967853,1,Frequently,no,1.131185,no,0,0.504176,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,yes,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.673262,1.599486,44.810751,no,no,3,2.974568,Frequently,no,1.145761,yes,0.87967,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.244359,1.559186,41.952805,no,yes,2.619835,1,Frequently,no,1.198883,no,0,0.751212,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.552695,1.523426,42,no,yes,3,1,Frequently,no,1.185062,no,0,0.076654,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21,1.52,42,no,yes,3,1,Frequently,no,1,no,0,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21.997683,1.689441,51.107925,yes,yes,3,3.715118,Frequently,no,1.774576,no,0.10297,0.646423,no,Public_Transportation,Insufficient_Weight
+Female,21.274628,1.737453,50.479039,yes,yes,3,3.489918,Frequently,no,1.326694,no,0.791929,0.128394,no,Public_Transportation,Insufficient_Weight
+Female,18.909439,1.732096,50,no,yes,1.053534,3.378859,Sometimes,no,1,no,1.853425,0.861809,Sometimes,Public_Transportation,Insufficient_Weight
+Male,22.396504,1.869098,61.411141,yes,yes,3,3.263201,Sometimes,no,2.233274,no,1.557737,0.000355,Sometimes,Automobile,Insufficient_Weight
+Male,19.084317,1.851123,61.264785,yes,yes,3,3.994588,Sometimes,no,2.548527,no,1.285976,0.267076,Sometimes,Automobile,Insufficient_Weight
+Male,21.084625,1.787264,58.585146,yes,yes,2.530233,3.24934,Sometimes,no,2.387945,no,1.976341,1.672508,no,Automobile,Insufficient_Weight
+Male,18.068767,1.787787,52,yes,yes,3,3,Sometimes,no,2,no,0.854337,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,21.813083,1.712515,51.710723,yes,yes,2.8813,3.087544,Frequently,no,1.24818,no,1.952427,0.427461,no,Public_Transportation,Insufficient_Weight
+Male,18.038422,1.698914,51.692272,yes,yes,2.824559,3,Sometimes,no,2,no,0.533309,1.099764,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.874591,1.533609,41.669346,no,yes,2.762325,1.163666,Sometimes,no,1.30491,no,0.25289,1.001405,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.21164,1.567981,41.934368,no,yes,2.070964,1,Sometimes,no,1.676975,no,0,1.718513,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.988581,1.544263,41.535047,no,yes,2.68601,1,Sometimes,no,1.310074,no,0,1.0647,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.408999,1.670552,49.804245,no,yes,2.794197,3.409363,Sometimes,no,1.543021,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.665881,1.676346,49.105025,no,no,2.720701,3,Sometimes,no,2,no,1.862235,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.758286,1.667404,49.125955,no,no,2.880792,3.281391,Sometimes,no,1.960131,no,1.513029,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,22.422674,1.70011,50.173425,yes,yes,2.674431,3,Frequently,no,2.453384,no,1.321624,1.990617,no,Public_Transportation,Insufficient_Weight
+Male,20.242237,1.75633,54.567343,yes,yes,2,3.98525,Sometimes,no,2.654078,no,2.113151,2,no,Public_Transportation,Insufficient_Weight
+Female,22.637018,1.703584,51.607091,yes,yes,2.55996,3,Frequently,no,1.990788,no,0.486006,1.561272,no,Public_Transportation,Insufficient_Weight
+Female,19.007177,1.690727,49.895716,no,yes,1.212908,3.207071,Sometimes,no,1.029703,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.288205,1.713564,50,no,yes,1.140615,3.471536,Sometimes,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.314429,1.67231,49.713944,no,yes,2.562409,3.488342,Sometimes,no,1.67062,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,27.14823,1.660446,49.558304,yes,yes,2.004146,3.443456,Frequently,no,2.020764,no,1.078719,1.26729,no,Public_Transportation,Insufficient_Weight
+Female,25.380557,1.630379,46.062954,yes,yes,2.690754,3.03779,Frequently,no,2.212296,no,1.616882,0,no,Public_Transportation,Insufficient_Weight
+Female,22.867719,1.655413,50.424661,yes,yes,3,3.642802,Frequently,no,1.995177,no,0.118271,0.065515,no,Public_Transportation,Insufficient_Weight
+Male,17.729923,1.732862,51.216463,no,yes,2.051283,2.645858,Sometimes,no,2.35752,no,0.291309,0.897942,Sometimes,Public_Transportation,Insufficient_Weight
+Female,16.834813,1.74402,50,no,yes,2.19005,3.420618,Sometimes,no,1.356405,no,1.351996,0.98468,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.521754,1.757958,52.09432,no,yes,2.21498,2.64155,Sometimes,no,2.121251,no,0.998391,0.85882,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.786758,51.524444,no,yes,2.91548,3,Sometimes,no,1.777486,no,1.077469,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.767058,51.132809,yes,yes,2.708965,3,Sometimes,no,1.873004,no,1.21718,1,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18.128249,1.699437,52.08657,yes,yes,2.853513,3,Sometimes,no,2,no,0.680464,1.258881,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.405104,1.82525,58.913579,yes,yes,2.580872,3.887906,Sometimes,no,2,no,2,0.453649,no,Automobile,Insufficient_Weight
+Male,19.72925,1.793315,58.19515,yes,yes,2.508835,3.435905,Sometimes,no,2.076933,no,2.026668,1.443328,no,Automobile,Insufficient_Weight
+Male,18.525834,1.776989,57.275946,yes,yes,2,3.747163,Sometimes,no,2.575535,no,2,1.84383,no,Automobile,Insufficient_Weight
+Female,18.595614,1.609495,42.656563,no,yes,2.896562,2.625475,Sometimes,no,2.839558,yes,1.949667,1.253311,no,Public_Transportation,Insufficient_Weight
+Female,16.613108,1.777929,44.762023,no,yes,2.911877,3.098399,Sometimes,no,2.196405,no,2.328147,1.55011,Sometimes,Public_Transportation,Insufficient_Weight
+Female,16.928791,1.710948,45.248627,no,yes,2.910733,3.12544,Sometimes,no,2.204263,no,2.407906,1.403037,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17.758315,1.854162,59.881316,yes,yes,2.966126,3.96981,Sometimes,no,2,no,2.000088,0.378619,no,Automobile,Insufficient_Weight
+Male,17.282945,1.821514,59.605028,yes,yes,2.613249,3.712183,Sometimes,no,2,no,2.002997,0.174848,no,Automobile,Insufficient_Weight
+Male,19.993154,1.762073,55.511075,yes,yes,2,4,Sometimes,no,2.542415,no,2,1.807465,no,Automobile,Insufficient_Weight
+Female,22.935612,1.732307,54.835576,yes,yes,3,3,Frequently,no,2.00357,no,1.25955,0.417116,no,Public_Transportation,Insufficient_Weight
+Male,20.738469,1.759933,55.003417,yes,yes,2.627031,3.832911,Sometimes,no,2.993448,no,2,1.425903,no,Public_Transportation,Insufficient_Weight
+Female,22.99368,1.741377,54.877111,yes,yes,3,3,Frequently,no,2.009796,no,2,0.071317,no,Public_Transportation,Insufficient_Weight
+Female,19.317148,1.731195,49.650897,no,yes,2.919751,3.576103,Sometimes,no,1.949308,no,1.940182,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,16.910997,1.74823,49.928447,no,yes,2.494451,3.56544,Sometimes,no,1.491268,no,1.951027,0.956204,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.071027,1.756865,49.699672,no,yes,1.69427,3.266644,Sometimes,no,1.095417,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.019572,1.701378,50.088468,no,yes,1.601236,3.433908,Sometimes,no,1.055019,no,0.819269,1.030848,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.735968,1.699956,49.98297,no,yes,1.204855,3.531038,Sometimes,no,1.134658,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.094079,1.723328,50,no,yes,1.052699,3.998618,Frequently,no,1,no,2,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.054938,1.585886,42.541794,no,no,2.910345,3,Frequently,no,1,yes,1.461005,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.850119,1.524926,42,no,yes,2.866383,1.226342,Frequently,no,1,no,0.14495,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.349258,1.52337,42,no,yes,2.913486,1.060796,Frequently,no,1.596212,no,0.108948,0.773087,Sometimes,Public_Transportation,Insufficient_Weight
+Male,17,1.844749,59.313525,yes,yes,2.432886,3,Sometimes,no,2,no,2.697949,1,no,Automobile,Insufficient_Weight
+Male,17.203917,1.853325,59.619485,yes,yes,2.883745,3.595761,Sometimes,no,2,no,2.077653,0.714701,no,Automobile,Insufficient_Weight
+Male,17.671064,1.854706,59.20945,yes,yes,2.707666,3.737914,Sometimes,no,2.026547,no,2.009397,0.613971,no,Automobile,Insufficient_Weight
+Female,21.310907,1.72064,50,yes,yes,2.919584,3.697831,Frequently,no,1.147121,no,0.993058,0.08922,no,Public_Transportation,Insufficient_Weight
+Female,21.708354,1.704167,50.165754,yes,yes,2.969205,3.21043,Frequently,no,2.765876,no,0.611037,1.300692,no,Public_Transportation,Insufficient_Weight
+Female,22.176922,1.71746,51.491957,yes,yes,2.486189,3,Frequently,no,1.064378,no,2.206055,1.958089,no,Public_Transportation,Insufficient_Weight
+Female,17.823438,1.708406,50,no,yes,1.642241,3.45259,Sometimes,no,1.099231,no,0.418875,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18,1.763465,50.279053,no,yes,1.567101,3,Sometimes,no,1.994139,no,0.107981,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.281092,1.7,50,no,yes,1.036414,3.205587,Sometimes,no,1.745959,no,0.115974,1,Sometimes,Public_Transportation,Insufficient_Weight
+Female,18.656912,1.574017,41.220175,no,yes,1.649974,1.513835,Sometimes,no,1.549974,no,0.227802,1.972926,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.994543,1.537739,39.101805,no,yes,1.118436,3,Sometimes,no,1.997744,no,2.432443,1.626194,Sometimes,Public_Transportation,Insufficient_Weight
+Female,20.255616,1.534223,41.268597,no,yes,2.673638,2.779379,Frequently,no,1.249074,no,0.043412,0.403694,Sometimes,Public_Transportation,Insufficient_Weight
+Male,19.865895,1.76033,55.3707,yes,yes,2.120185,4,Sometimes,no,2.582165,no,2,1.510609,no,Automobile,Insufficient_Weight
+Male,17.888073,1.841879,60,yes,yes,3,3.732126,Sometimes,no,2,no,2.000934,0.384662,no,Automobile,Insufficient_Weight
+Male,18.470562,1.856406,58.673963,yes,yes,2.34222,3.937099,Sometimes,no,2.311791,no,2.013377,1.128355,no,Automobile,Insufficient_Weight
+Male,17.925497,1.829142,59.933015,yes,yes,2.86099,4,Sometimes,no,2,no,2,0.007872,no,Automobile,Insufficient_Weight
+Male,17.000752,1.822084,58.443049,yes,yes,2.559571,3.047959,Sometimes,no,2,no,2.011646,0.588994,no,Automobile,Insufficient_Weight
+Male,17.362129,1.80671,59.243506,yes,yes,2.424977,4,Sometimes,no,2,no,2,0.03921,no,Automobile,Insufficient_Weight
+Male,18,1.707259,50.664459,yes,yes,1.786841,3,Sometimes,no,1.546856,no,0.512094,1.608265,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.708107,51.314659,yes,yes,1.303878,3,Sometimes,no,1.755497,no,0.062932,1.672532,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.739344,50.951444,yes,yes,1.889883,3,Sometimes,no,1.959531,no,0.520407,1.151166,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.029494,1.573987,44.316254,no,no,2.984004,3,Frequently,no,1.015249,yes,1.327833,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.269079,1.58092,44.753943,no,no,3,2.975362,Frequently,no,1.246822,yes,0.979701,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.946244,1.603435,45,no,no,3,3,Frequently,no,2.487919,yes,1.230441,0,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.639431,1.53535,42,no,yes,3,1,Frequently,no,1.49681,no,0,0.513427,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19,1.53161,42,no,yes,2.749268,1.394539,Frequently,no,1.322048,no,0.463949,0.800993,Sometimes,Public_Transportation,Insufficient_Weight
+Female,19.434709,1.525691,42,no,yes,3,1,Frequently,no,1.764055,no,0,0.560887,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.719827,52.289828,yes,yes,1.202075,3,Sometimes,no,1.927976,no,0.023574,1.747256,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18.381382,1.722547,53.783977,yes,yes,2,3.131032,Sometimes,no,2.072194,no,1.487987,2,Sometimes,Public_Transportation,Insufficient_Weight
+Male,18,1.738702,50.248677,yes,yes,1.871213,3,Sometimes,no,1.283738,no,0.684879,1.487223,Sometimes,Public_Transportation,Insufficient_Weight
+Male,26.69858,1.816298,86.963765,no,yes,2.341133,1.578521,Sometimes,no,2,no,2,0.554072,no,Automobile,Overweight_Level_I
+Male,21.125836,1.638085,70,no,yes,2,1,no,no,2.115967,no,0.770536,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,25.191627,1.813678,85.637789,no,yes,1.450218,3.985442,Sometimes,no,2.147746,no,0.046836,1,no,Automobile,Overweight_Level_I
+Male,21.963457,1.697228,75.5771,yes,yes,2.204914,3.623364,Sometimes,no,1.815293,no,0.989316,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.617469,68.869791,no,yes,1.206276,3,no,no,2.406541,no,0.94984,0.479221,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.114981,1.855543,88.965521,yes,yes,2,3,Sometimes,no,1.015677,no,0,0.37465,Sometimes,Public_Transportation,Overweight_Level_I
+Female,41.823567,1.721854,82.919584,no,yes,2.81646,3.36313,Sometimes,no,2.722063,no,3,0.26579,Sometimes,Automobile,Overweight_Level_I
+Male,21.142432,1.855353,86.413388,yes,yes,2,3,Sometimes,no,1.345298,no,1.097905,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.962426,1.696336,75,yes,yes,2,3,Sometimes,no,1.825629,no,0.699592,0.142056,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18.836315,1.751631,80,yes,yes,2,1.73762,Sometimes,no,2.207978,no,2.641072,0.707044,Frequently,Public_Transportation,Overweight_Level_I
+Male,23.572648,1.698346,75,yes,yes,2,3,Sometimes,no,2,no,0.345684,1.415536,Sometimes,Public_Transportation,Overweight_Level_I
+Female,33.700749,1.642971,74.803157,yes,no,3,1.146052,Sometimes,no,1.074048,no,0.679935,0,Sometimes,Automobile,Overweight_Level_I
+Female,21.845025,1.613668,68.126955,yes,yes,1.758394,3.981997,Sometimes,no,2.174248,no,0.920476,1.358163,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.605404,68.226511,no,yes,2,1.9154,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,35.125401,1.529834,62.903938,no,yes,2,2.105616,Sometimes,no,1.456581,no,0,0.9974,Sometimes,Automobile,Overweight_Level_I
+Female,36.769646,1.55,62.337721,yes,yes,2.392665,3,Sometimes,no,2.951056,no,0,0.369134,Sometimes,Automobile,Overweight_Level_I
+Female,21.868932,1.731261,78.175706,yes,yes,2.577427,1,Sometimes,no,2,no,2.287423,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.549208,1.629194,70,no,yes,2,1,no,no,2.803311,no,0.245354,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,30.958957,1.633491,68.803694,yes,yes,2.052152,3,Sometimes,no,1.972074,no,0.228307,0,Sometimes,Automobile,Overweight_Level_I
+Male,21.017383,1.752391,79.109637,yes,yes,3,1,Sometimes,no,2,no,2.57223,0,Sometimes,Automobile,Overweight_Level_I
+Male,19.623574,1.818226,85.833029,yes,yes,2.954996,3.714833,Sometimes,no,2.430918,no,2.545931,0.469735,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.637947,1.809101,85,yes,yes,3,3,Sometimes,no,2.229171,no,1.607953,0.628059,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.413642,1.709484,75,yes,yes,2,3,Sometimes,no,2.843777,no,2.022315,0.009254,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.029633,1.607082,67.722222,yes,yes,2,3.691226,no,no,3,no,1.228136,0.3352,Sometimes,Public_Transportation,Overweight_Level_I
+Female,22.667596,1.718939,75.951472,yes,yes,2,3,Frequently,no,2,no,0,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,20,1.831357,89.652557,yes,yes,2.555401,3.292386,Sometimes,no,3,no,2,0.315918,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.81119,1.6,66.401407,yes,yes,2.108711,3,Sometimes,no,2.746197,no,1.910432,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.754813,77.929204,yes,yes,2.915279,1.104642,Sometimes,no,1.530046,no,1.360635,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,18.128206,1.778447,80.273807,yes,yes,1.570089,3,Sometimes,no,2,no,2.707882,0,no,Public_Transportation,Overweight_Level_I
+Male,21,1.709561,75,yes,yes,2,3,Sometimes,no,1.636326,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,29.08107,1.674568,71.602622,yes,yes,2,3,Sometimes,no,1.701835,no,1.682271,0,Sometimes,Automobile,Overweight_Level_I
+Male,26.358919,1.755317,82.228794,yes,yes,1.94313,3.559841,Sometimes,no,2.367996,no,0.23553,0.879977,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.717249,1.708071,75,yes,yes,2.714447,3,Sometimes,no,3,no,1.096818,1.89388,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.816949,1.806947,85.079589,yes,yes,2,3,Sometimes,no,2.292073,no,1.067817,0.19668,Sometimes,Public_Transportation,Overweight_Level_I
+Female,24.023972,1.599697,64.808022,no,yes,2.903545,3,Sometimes,no,1.549931,no,0,0.273882,Sometimes,Public_Transportation,Overweight_Level_I
+Female,32.593129,1.721903,72.748903,yes,yes,2,3,Sometimes,no,1,no,0,1.339232,Sometimes,Automobile,Overweight_Level_I
+Male,23,1.712556,75.480764,yes,yes,3,3.654061,Sometimes,no,1.229915,no,0.793489,1.597608,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.941489,1.551288,54.93242,no,yes,1.75375,1.296156,Sometimes,no,2,yes,0.110174,1.944675,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.172992,1.603842,65,yes,yes,2.543563,1,Sometimes,no,2,yes,0.694281,1.056911,Sometimes,Public_Transportation,Overweight_Level_I
+Female,23.71259,1.588597,62.339003,no,yes,2.39728,2.656588,Sometimes,no,2.061062,no,1.912981,1.887386,Sometimes,Public_Transportation,Overweight_Level_I
+Female,35.194089,1.673482,73.193589,yes,no,3,2.809716,Sometimes,no,1.591425,no,0.178023,0,Sometimes,Automobile,Overweight_Level_I
+Male,23.172982,1.858624,88.675503,yes,yes,2,2.77684,Sometimes,no,1.278231,no,0.55713,1,Sometimes,Automobile,Overweight_Level_I
+Female,37.218161,1.593894,63.320629,yes,yes,2.37464,3,Sometimes,no,2,no,2.892922,0.480813,Sometimes,Automobile,Overweight_Level_I
+Male,19.076443,1.62,69.529625,no,yes,2.278644,1,Sometimes,no,2.628985,no,1,1.285373,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.30687,1.616366,67.183128,yes,yes,1.620845,1,Sometimes,no,2,yes,0.926592,1.657098,no,Public_Transportation,Overweight_Level_I
+Female,18.297229,1.637396,70,yes,yes,2,1.999014,Sometimes,no,2.326165,no,0.00705,0,no,Public_Transportation,Overweight_Level_I
+Female,18.610761,1.550723,60.628321,no,yes,2.823179,2.644692,Sometimes,no,1.226764,yes,0.747528,0.008899,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.451085,1.6,65,yes,yes,3,2.449723,Sometimes,no,2,yes,0.479592,1.720642,Sometimes,Public_Transportation,Overweight_Level_I
+Male,29.740496,1.820471,87.668828,no,yes,3,3,Sometimes,no,1.174611,no,2,0,no,Automobile,Overweight_Level_I
+Female,31.539393,1.657496,73.641633,yes,yes,2,3,Sometimes,no,1,no,0,1.112489,Sometimes,Automobile,Overweight_Level_I
+Male,18.026457,1.752123,80,yes,yes,2,2.794156,Sometimes,no,2.377362,no,0.101236,0.105895,no,Public_Transportation,Overweight_Level_I
+Male,19.47554,1.857231,88.138777,yes,yes,2.061952,4,Sometimes,no,2.426465,no,2,0.160138,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19,1.76,79.544998,yes,yes,2,1.146794,Sometimes,no,3,no,1.809745,1.690102,Frequently,Public_Transportation,Overweight_Level_I
+Female,18,1.644682,68.392133,yes,yes,2,1.131695,Sometimes,no,1.344539,no,0,1.59257,no,Public_Transportation,Overweight_Level_I
+Female,18.270434,1.737165,76.699774,yes,yes,2.838969,3,Sometimes,no,2.425927,no,1,1.453335,Frequently,Public_Transportation,Overweight_Level_I
+Female,19.816228,1.507853,64.259379,no,yes,2.568063,3,Sometimes,no,1.817983,yes,0,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,28.39124,1.81006,87.286783,no,yes,3,3,Sometimes,no,1.205633,no,2,0,no,Automobile,Overweight_Level_I
+Male,26.758516,1.80179,86.981202,no,yes,2.652958,2.488189,Sometimes,no,2,no,2,0.096614,no,Automobile,Overweight_Level_I
+Male,21.033794,1.625891,70,no,yes,2,1,no,no,2.00876,no,0.63119,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.429812,1.64037,70,no,yes,2,1,no,no,2.069184,no,0.729898,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26.650419,1.791047,83.26312,no,yes,1.27785,3.999591,Sometimes,no,2.495944,no,0.00342,1,no,Automobile,Overweight_Level_I
+Male,21.618246,1.679306,75,yes,yes,2,3,Sometimes,no,1.957871,no,0.994592,0.117303,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.012542,1.615145,68.653347,yes,yes,1.729824,3.612941,no,no,2.043703,no,0.562118,0.426643,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.071195,1.616467,68.77185,yes,yes,1.452524,3.950553,no,no,2.109858,no,0.508847,1.194633,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.741202,1.816783,86.522799,yes,yes,2,3,Sometimes,no,2.177386,no,0.336814,0.004813,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.21638,1.812472,86.748295,yes,yes,2,3,Sometimes,no,2.168651,no,0.977998,0.142638,Sometimes,Public_Transportation,Overweight_Level_I
+Female,42.24475,1.768231,75.62931,yes,yes,3,2.951837,Sometimes,no,2.112032,no,0.378683,0,Sometimes,Automobile,Overweight_Level_I
+Male,22.283082,1.870931,89.251639,yes,yes,2,2.799979,Sometimes,no,1.081333,no,0.444347,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.086512,1.863685,89.558854,yes,yes,2,3,Sometimes,no,1.005727,no,0,0.798219,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23.451595,1.670227,75,yes,yes,2,3,Sometimes,no,2,no,0.129163,1.983678,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.740275,1.717288,75.948164,yes,yes,2,3,Sometimes,no,2,no,0,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,18.900253,1.750359,79.828725,yes,yes,2,2.228113,Sometimes,no,2.045004,no,1.293665,0.961806,Frequently,Public_Transportation,Overweight_Level_I
+Male,23.170309,1.707557,75.306702,yes,yes,2.303367,3.042774,Sometimes,no,1.277636,no,0.944982,0.366126,Sometimes,Public_Transportation,Overweight_Level_I
+Female,32.997118,1.672446,74.812707,yes,yes,2.948425,1.198643,Sometimes,no,1,no,0,0.173796,Sometimes,Automobile,Overweight_Level_I
+Female,32.501143,1.675979,74.959747,yes,yes,2.291846,1.555557,Sometimes,no,1,no,0,0.388271,Sometimes,Automobile,Overweight_Level_I
+Female,21.938831,1.611239,67.939653,yes,yes,1.906194,3.987707,Sometimes,no,2.597746,no,1.922234,1.376124,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.909534,1.611356,68.06609,yes,yes,1.834155,3.995957,Sometimes,no,2.632871,no,1.236114,1.980875,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,37.455752,1.508908,63.183846,yes,yes,2.048582,1.047197,Sometimes,no,2,no,0.15136,0.22596,Sometimes,Automobile,Overweight_Level_I
+Female,40,1.561109,62.871794,yes,yes,2.948248,3,Sometimes,no,2.429911,no,0.11964,0.360193,Sometimes,Automobile,Overweight_Level_I
+Female,38.943282,1.554728,63.011645,yes,yes,2.869436,3,Sometimes,no,2.972426,no,2.16079,0.305954,Sometimes,Automobile,Overweight_Level_I
+Female,21.987341,1.730182,78.55444,yes,yes,2.293705,1,Sometimes,no,2,no,2.063943,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.198217,1.743841,78.88036,yes,yes,2.510583,1,Sometimes,no,2,no,2.119682,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,29.32038,1.642506,69.906708,yes,yes,2.366949,3,Sometimes,no,1.926577,no,1.581242,0,Sometimes,Automobile,Overweight_Level_I
+Female,31.255587,1.69308,71.927379,yes,yes,2.615788,3,Sometimes,no,1.771781,no,0.759422,0.686529,Sometimes,Automobile,Overweight_Level_I
+Male,22.851773,1.752115,79.414603,yes,yes,3,1,Sometimes,no,2,no,2.315983,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26,1.745033,80,yes,yes,2.217267,1.193589,Sometimes,no,2,no,2,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.789291,1.820643,85,yes,yes,3,3,Sometimes,no,2.759246,no,1.763277,0.453314,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.895877,1.80733,85.073801,yes,yes,2.801514,3,Sometimes,no,2.734782,no,2.207881,0.143675,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.693804,1.8,85,yes,yes,2.188722,3,Sometimes,no,2.721356,no,1.528968,1,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.226314,1.814255,85.255631,yes,yes,2.971351,3.390143,Sometimes,no,2.133767,no,2.318492,0.678618,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.652229,1.700181,75.057177,yes,yes,2.086093,3.546352,Sometimes,no,2.081121,no,1.993666,0.673835,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.687409,1.604697,68.016472,yes,yes,1.901611,3.266501,no,no,2.926995,no,0.985872,0.307982,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.455463,1.611,68.107866,yes,yes,1.977298,3.554974,no,no,2.217957,no,0.97812,0.459759,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.754497,77.956921,yes,yes,2.446872,2.372311,Sometimes,no,1.81031,no,0.094893,1.650778,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.752944,77.965532,yes,yes,2.839048,2.10601,Sometimes,no,1.639202,no,1.117311,0.967919,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.337404,1.859927,87.105828,yes,yes,2.21232,4,Sometimes,no,2.374044,no,2,0.622638,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.701643,1.896073,90.52446,yes,yes,2.427689,3.715306,Sometimes,no,2.876096,no,1.815768,0.426465,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.620245,1.605314,66.621646,yes,yes,2.357496,3.376717,Sometimes,no,2.184843,no,1.358185,0.014885,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.012569,1.758628,78.370039,yes,yes,3,1,Sometimes,no,2,no,2.971832,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.016623,1.755427,78.300084,yes,yes,3,1,Sometimes,no,2,no,2.877473,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19,1.779882,80.091886,yes,yes,1.078529,1.211606,Sometimes,no,2.568063,no,3,0.817983,no,Public_Transportation,Overweight_Level_I
+Male,21,1.712061,75,yes,yes,2,3,Sometimes,no,1.333707,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.676014,75,yes,yes,2,3,Sometimes,no,1.164062,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,27.899784,1.7,74.244004,yes,yes,2,3,Sometimes,no,2,no,1.722053,0,Sometimes,Automobile,Overweight_Level_I
+Male,28.825223,1.765874,82.045045,yes,yes,1.064162,3.98955,Sometimes,no,2.028426,no,0.81517,0.894678,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26.047077,1.74595,80.018571,yes,yes,1.993101,3.171082,Sometimes,no,2.364498,no,1.224743,0.022245,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23,1.690196,75,yes,yes,2.620963,3,Sometimes,no,2.824559,no,0.754599,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,20,1.81748,85,yes,yes,2.95118,3,Sometimes,no,3,no,2.433918,0.561602,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.832995,1.580964,65.363941,no,yes,2.021446,3,Sometimes,no,1.077917,no,0.523847,0.808599,Sometimes,Public_Transportation,Overweight_Level_I
+Female,30.255744,1.652084,73.575981,yes,yes,2,3,Sometimes,no,1,no,0,0.018877,Sometimes,Automobile,Overweight_Level_I
+Female,30.613586,1.645511,69.168978,yes,yes,2.000466,3,Sometimes,no,1.131448,no,0.061366,0,Sometimes,Automobile,Overweight_Level_I
+Male,23.245408,1.707968,75.383716,yes,yes,2.5621,3.105007,Sometimes,no,1.172427,no,0.184917,0.923082,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18,1.456346,55.523481,no,yes,2,3,Sometimes,no,1.040342,yes,0.497373,1.783319,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18,1.498561,55.376512,no,yes,2,3,Sometimes,no,1.274718,yes,0.129902,0.978574,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.950499,1.603501,65,yes,yes,2.96008,1,Sometimes,no,2,yes,0.736032,1.344072,no,Public_Transportation,Overweight_Level_I
+Female,21.837058,1.558045,63.597633,no,yes,2.53915,1.590982,Sometimes,no,1.977801,no,1.264616,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,27,1.55,62.877347,no,yes,2.244142,1.704828,Sometimes,no,1,no,0.792929,0.395468,Sometimes,Public_Transportation,Overweight_Level_I
+Female,35.483601,1.647514,73.91692,yes,no,3,2.725012,Sometimes,no,1.62244,no,0.902661,0,Sometimes,Automobile,Overweight_Level_I
+Male,23.801436,1.855779,86.098523,yes,yes,2,2.372339,Sometimes,no,1.675733,no,1.066241,1,Sometimes,Automobile,Overweight_Level_I
+Male,23.367212,1.853223,87.083266,yes,yes,2,1.097312,Sometimes,no,1.328835,no,1.264257,1,Sometimes,Automobile,Overweight_Level_I
+Female,40,1.570811,62.211572,yes,yes,2.253371,3,Sometimes,no,2.756916,no,1.016042,0.213437,Sometimes,Automobile,Overweight_Level_I
+Female,16.38009,1.617124,67.913561,yes,yes,2.851664,1,Sometimes,no,2,no,0.977929,1.765321,no,Public_Transportation,Overweight_Level_I
+Female,16.865984,1.644053,67.439589,yes,yes,1.31415,1.068196,Sometimes,no,1.364957,yes,0,0.057926,no,Public_Transportation,Overweight_Level_I
+Female,16.093234,1.608914,65,yes,yes,1.321028,1,Sometimes,no,2,yes,0.371452,0.965604,no,Public_Transportation,Overweight_Level_I
+Female,18,1.647971,68.818893,yes,yes,2,1.411685,Sometimes,no,1.859089,no,0,1.306,no,Public_Transportation,Overweight_Level_I
+Female,18.83919,1.624831,69.975607,yes,yes,2.253998,2.752318,Sometimes,no,2.328331,no,0.815509,0.024088,no,Public_Transportation,Overweight_Level_I
+Female,19.726522,1.508267,61.10403,no,yes,2.778079,3,Sometimes,no,1.696691,yes,0.94224,0.879925,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18.947102,1.518917,60.267427,no,yes,2.838037,2.737571,Sometimes,no,1.144467,yes,0.753782,1.286844,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.781183,1.6,65,yes,yes,3,2.973504,Sometimes,no,2,yes,0.178976,0.166076,Sometimes,Public_Transportation,Overweight_Level_I
+Male,33.100581,1.838791,87.85785,no,yes,2.814453,2.608055,Sometimes,no,1.85916,no,2,0.516084,no,Automobile,Overweight_Level_I
+Female,33.251015,1.730379,75.920519,yes,yes,2.013782,2.625942,Sometimes,no,1.045586,no,0.553305,0.480214,Sometimes,Automobile,Overweight_Level_I
+Female,18.985119,1.759117,80,yes,yes,2,1.411808,Sometimes,no,2.651194,no,2.878414,0.953841,no,Public_Transportation,Overweight_Level_I
+Female,18.871917,1.755254,80,yes,yes,2,1.095223,Sometimes,no,2.474132,no,2.876696,0.7939,no,Public_Transportation,Overweight_Level_I
+Male,19.478533,1.804099,85.196279,yes,yes,2.459976,3.30846,Sometimes,no,2.078011,no,1.779646,0.871772,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.429723,1.813567,86.144904,yes,yes,2.643183,3.821461,Sometimes,no,2.397124,no,2.044165,0.471663,Sometimes,Public_Transportation,Overweight_Level_I
+Female,20.979254,1.75655,78.721696,yes,yes,2,3,Sometimes,no,2.813234,no,0.228753,2,Frequently,Public_Transportation,Overweight_Level_I
+Female,18.869151,1.756774,79.989789,yes,yes,2,2.658639,Sometimes,no,2.781628,no,1.955992,1.409198,Frequently,Public_Transportation,Overweight_Level_I
+Female,18.236087,1.62,69.3899,yes,yes,2.22399,1,Sometimes,no,2.20312,no,0.104451,1.899073,no,Public_Transportation,Overweight_Level_I
+Female,20.901393,1.709585,75,yes,yes,2.104105,3,Sometimes,no,1.871033,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.08525,1.535618,57.259124,no,yes,1.972545,2.339614,Sometimes,no,1.711074,yes,0.095517,1.191053,Sometimes,Public_Transportation,Overweight_Level_I
+Male,25.785925,1.818848,87.032398,no,yes,2.286481,1.713762,Sometimes,no,1.797161,no,1.658698,0.926581,no,Automobile,Overweight_Level_I
+Male,26.787842,1.817641,87.107317,no,yes,2.971588,2.743277,Sometimes,no,1.394883,no,2,0.470243,no,Automobile,Overweight_Level_I
+Male,21.037514,1.636592,70,no,yes,2,1,no,no,2.881677,no,0.915699,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.00829,1.621412,70,no,yes,2,1,no,no,2.795651,no,0.80873,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,26.70371,1.802871,85.770013,yes,yes,2.872121,3.051804,Sometimes,no,2.111913,no,0.843709,0.153559,Sometimes,Automobile,Overweight_Level_I
+Male,21.731497,1.696201,75.399191,yes,yes,2.109162,3.245148,Sometimes,no,1.971774,no,0.989335,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.052894,1.694633,75.0522,yes,yes,2.178889,3.563744,Sometimes,no,1.853991,no,0.997731,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.618148,68.981403,yes,yes,1.142468,3,no,no,2.197732,no,0.827506,0.572877,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.241058,1.856811,88.633616,yes,yes,2.047069,3.829101,Sometimes,no,1.641022,no,1.554817,0.248218,Sometimes,Public_Transportation,Overweight_Level_I
+Female,38.939448,1.738321,86.934846,no,yes,2.843709,3.058539,Sometimes,no,1.130079,no,2.834373,0.044954,Sometimes,Automobile,Overweight_Level_I
+Female,39.965474,1.739293,80.914382,no,yes,2.416044,3.196043,Sometimes,no,1.352649,no,0.148628,1.08266,Sometimes,Automobile,Overweight_Level_I
+Male,20.261925,1.807538,85.316125,yes,yes,2,3,Sometimes,no,1.968011,no,1.072662,0.776758,Sometimes,Public_Transportation,Overweight_Level_I
+Male,20.31094,1.849425,85.228116,yes,yes,2.146598,3,Sometimes,no,2.100112,no,1.17116,0.833761,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.420537,1.712473,75,yes,yes,2,3,Sometimes,no,1.362642,no,0.921268,0.139013,Sometimes,Public_Transportation,Overweight_Level_I
+Female,18.845518,1.753471,80,yes,yes,2,1.391778,Sometimes,no,2.64548,no,2.685087,0.97554,no,Public_Transportation,Overweight_Level_I
+Male,23.562135,1.717432,75.371244,yes,yes,2,3,Sometimes,no,2,no,0.121585,1.967259,Sometimes,Public_Transportation,Overweight_Level_I
+Male,23.118327,1.677573,75,yes,yes,2,3,Sometimes,no,2,no,0.334579,1.905826,Sometimes,Public_Transportation,Overweight_Level_I
+Female,33.732714,1.679725,74.885222,yes,yes,3,1.13715,Sometimes,no,1.000695,no,0.261274,0,Sometimes,Automobile,Overweight_Level_I
+Female,21.571288,1.600914,68.058902,yes,yes,1.766849,3.322522,Sometimes,no,2.616285,no,0.943058,0.256977,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.165574,1.617749,68.908136,yes,yes,1.188089,3.269088,Sometimes,no,2.039535,no,0.779686,1.043108,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.610209,68.865008,no,yes,1.910176,2.938135,no,no,2.943097,no,0.997202,0.207922,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.612556,69.463458,no,yes,2,1.259628,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,38.692265,1.548178,62.341438,yes,yes,2.956671,2.965494,Sometimes,no,2.868132,no,0,0.54925,Sometimes,Automobile,Overweight_Level_I
+Female,38.952866,1.568441,62.855073,yes,yes,2.002796,3,Sometimes,no,2.526775,no,0.271174,0.806069,Sometimes,Automobile,Overweight_Level_I
+Female,36.631456,1.532322,62.417228,yes,yes,2.288604,2.845307,Sometimes,no,2.638164,no,0,0.990741,Sometimes,Automobile,Overweight_Level_I
+Female,21.996118,1.730199,78.997062,yes,yes,2.277077,1,Sometimes,no,2,no,1.061743,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.951309,1.730167,78.429312,yes,yes,2.138334,1,Sometimes,no,2,no,2.269058,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.018228,1.627396,70,no,yes,2,1,no,no,2.976177,no,0.780117,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.62,70,no,yes,2,1,no,no,3,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,29.934465,1.638744,69.242354,yes,yes,2.029634,3,Sometimes,no,1.827515,no,0.480206,0,Sometimes,Automobile,Overweight_Level_I
+Female,31.166572,1.680858,71.81338,yes,yes,2.048216,3,Sometimes,no,1.305816,no,0.170452,0.276806,Sometimes,Automobile,Overweight_Level_I
+Female,20.534606,1.758372,79.469513,yes,yes,2.8557,1,Sometimes,no,2.663861,no,2.762711,0.783336,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.97166,1.822573,85.084364,yes,yes,2.995599,3.095663,Sometimes,no,2.568157,no,2.7243,0.330457,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.627,1.815409,85.302146,yes,yes,2.987148,3.483449,Sometimes,no,2.359246,no,2.060415,0.598999,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.929387,1.698049,75,yes,yes,2,3,Sometimes,no,2.763573,no,1.502711,0.127879,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.009437,1.60681,67.773914,yes,yes,2,3.156309,no,no,3,no,1.179592,0.086868,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.538225,1.610327,67.999778,yes,yes,1.887951,3.728377,no,no,2.682107,no,1.123931,0.492528,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.828435,1.710415,75.142858,yes,yes,2.786008,3,Sometimes,no,2.526193,no,0.925118,2,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.814657,1.716289,75.688433,yes,yes,2,3,Sometimes,no,2,no,0.092344,1.466496,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.576682,1.834715,89.429199,yes,yes,2.342323,3.60885,Sometimes,no,2.878866,no,2,0.208323,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.814653,1.611822,67.140367,yes,yes,1.874935,3.370362,Sometimes,no,2.558773,no,1.206121,0.271024,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.979769,1.6,66.126964,yes,yes,2.213135,3,Sometimes,no,2.046766,no,2.172546,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21,1.753578,77.97917,yes,yes,2.273548,2.39007,Sometimes,no,1.648404,no,0.874643,1.102696,Sometimes,Public_Transportation,Overweight_Level_I
+Female,21.0285,1.742364,78.664618,yes,yes,2.780699,1.101404,Sometimes,no,1.579207,no,1.153775,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,18.721041,1.775626,80.113493,yes,yes,1.687569,2.756405,Sometimes,no,2.079131,no,2.931527,0.68173,no,Public_Transportation,Overweight_Level_I
+Male,21,1.714253,75,yes,yes,2,3,Sometimes,no,1.081597,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21,1.693628,75,yes,yes,2,3,Sometimes,no,1.833055,no,1,0,Sometimes,Public_Transportation,Overweight_Level_I
+Female,29.463168,1.659172,71.170001,yes,yes,2,3,Sometimes,no,1.281683,no,1.609801,0,Sometimes,Automobile,Overweight_Level_I
+Male,24.284861,1.776347,82.329047,yes,yes,1.989905,3.054899,Sometimes,no,2.080968,no,1.107543,0.939726,no,Public_Transportation,Overweight_Level_I
+Male,26.288417,1.767806,82.694689,yes,yes,1.947405,3.118013,Sometimes,no,2.136159,no,0.783963,0.88868,no,Public_Transportation,Overweight_Level_I
+Male,23.455324,1.702516,75,yes,yes,2.162519,3,Sometimes,no,2.152666,no,1.078074,1.502839,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.637203,1.814182,85.301029,yes,yes,2.923916,3.335876,Sometimes,no,2.372444,no,1.305633,0.311436,Sometimes,Public_Transportation,Overweight_Level_I
+Female,22.239836,1.59994,65.423942,no,yes,2.99448,3,Sometimes,no,1.706568,no,0.784216,0.150969,Sometimes,Public_Transportation,Overweight_Level_I
+Female,31.62638,1.666023,72.906186,yes,yes,2,3,Sometimes,no,1,no,0,1.301385,Sometimes,Automobile,Overweight_Level_I
+Male,23,1.701584,75.093569,yes,yes,3,3.205009,Sometimes,no,2.643468,no,0.998039,1.898139,Sometimes,Public_Transportation,Overweight_Level_I
+Male,22.874658,1.713343,75.828119,yes,yes,2.507841,3.648194,Sometimes,no,1.279756,no,0.132629,1.82753,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.420269,1.489409,53.620604,no,yes,1.836554,1.865238,Sometimes,no,2,yes,0.320209,1.969507,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.807828,1.518067,55.822119,no,yes,1.773265,2.118153,Sometimes,no,1.075239,yes,0.085388,1.915191,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.240576,1.616533,65.062945,yes,yes,2.388168,1,Sometimes,no,1.438018,yes,0.110887,1.165817,no,Public_Transportation,Overweight_Level_I
+Female,23.252457,1.589616,65.127324,no,yes,2.286146,2.961113,Sometimes,no,2.657303,no,1.91108,1.618227,Sometimes,Public_Transportation,Overweight_Level_I
+Female,34.772902,1.675612,73.501233,yes,yes,3,2.400943,Sometimes,no,1.509734,no,0.167125,0,Sometimes,Automobile,Overweight_Level_I
+Male,22.766227,1.874061,89.754302,yes,yes,2,2.870005,Sometimes,no,1.15031,no,0.417724,1,Sometimes,Automobile,Overweight_Level_I
+Female,39.214514,1.580765,62.631382,yes,yes,2.487167,3,Sometimes,no,2.396977,no,0.577063,0.435072,Sometimes,Automobile,Overweight_Level_I
+Male,19.858973,1.62,69.575315,no,yes,2.185938,1,no,no,2.81353,no,1,1.110222,Sometimes,Public_Transportation,Overweight_Level_I
+Female,16.370009,1.613921,66.738769,yes,yes,2.206399,1,Sometimes,no,2,yes,0.94893,1.772463,no,Public_Transportation,Overweight_Level_I
+Female,17.992717,1.618683,67.193585,yes,yes,1.952987,1,Sometimes,no,1.334856,yes,0.732276,1.890214,no,Public_Transportation,Overweight_Level_I
+Male,19.179932,1.637537,70,yes,yes,2,1.030416,Sometimes,no,2.118716,no,0.529259,0,no,Public_Transportation,Overweight_Level_I
+Female,19.621545,1.566524,61.616,no,yes,2.908757,2.696051,Sometimes,no,1.622827,yes,0.539952,0.001096,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19.755797,1.542328,63.856193,no,yes,2.628791,2.762883,Sometimes,no,1.512035,yes,0.303722,0.003229,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.099015,1.6,65,yes,yes,3,2.401341,Sometimes,no,2,yes,0.663896,1.753745,Sometimes,Public_Transportation,Overweight_Level_I
+Female,17.25813,1.600184,65,yes,yes,2.749629,2.298612,Sometimes,no,2,yes,0.547515,1.619473,Sometimes,Public_Transportation,Overweight_Level_I
+Male,29.153907,1.773656,87.070234,no,yes,1.595746,3.618722,Sometimes,no,1.274389,no,1.504003,0.370067,no,Automobile,Overweight_Level_I
+Female,32.278869,1.64602,74.147443,yes,yes,2.885178,2.562895,Sometimes,no,1.017006,no,0.588673,0.916291,Sometimes,Automobile,Overweight_Level_I
+Female,31.793937,1.65015,73.810728,yes,yes,2.372494,2.849848,Sometimes,no,1.028538,no,0.675983,0.303025,Sometimes,Automobile,Overweight_Level_I
+Male,18.014333,1.751029,80,yes,yes,2,2.805436,Sometimes,no,2.122884,no,0.045651,0.017225,Frequently,Public_Transportation,Overweight_Level_I
+Male,19.685,1.838266,89.496905,yes,yes,2.153639,3.715148,Sometimes,no,2.703337,no,2,0.226097,Sometimes,Public_Transportation,Overweight_Level_I
+Male,19.506389,1.824449,87.656029,yes,yes,2.793561,3.788602,Sometimes,no,2.429059,no,2.094542,0.393358,Sometimes,Public_Transportation,Overweight_Level_I
+Female,19,1.76,79.349221,yes,yes,2,1.893811,Sometimes,no,3,no,1.376229,1.801322,no,Public_Transportation,Overweight_Level_I
+Female,18,1.622241,68.250249,yes,yes,2,1.010319,Sometimes,no,1.319437,no,0,1.626456,no,Public_Transportation,Overweight_Level_I
+Female,18.003153,1.729996,75.816864,yes,yes,2.992329,3,Sometimes,no,2.004908,no,1,1.405086,Frequently,Public_Transportation,Overweight_Level_I
+Female,18.052394,1.725233,75.970712,yes,yes,2.927409,3,Sometimes,no,2.209991,no,1,0.797215,Frequently,Public_Transportation,Overweight_Level_I
+Female,19.774317,1.541039,64.726948,no,yes,2.706134,2.669766,Sometimes,no,1.979848,yes,0.445238,0.908836,Sometimes,Public_Transportation,Overweight_Level_I
+Male,21.180346,1.773766,89.28282,yes,yes,2.010684,1.322087,Sometimes,no,2,no,0.540397,0.973834,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.793724,1.75463,89.068227,yes,yes,2,2.041558,Sometimes,no,2.38725,no,0.813917,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,20.880161,1.674327,80,yes,yes,2,3,Sometimes,no,2,no,1.66639,1.443212,no,Public_Transportation,Overweight_Level_II
+Male,21.18354,1.72,80.555875,yes,yes,2,2.468421,Sometimes,no,2,no,2,1,no,Public_Transportation,Overweight_Level_II
+Male,32.774488,1.913241,101.482054,yes,yes,2,2.110937,Sometimes,no,2.175632,no,1,0,Sometimes,Walking,Overweight_Level_II
+Male,31.327609,1.737984,82.523113,yes,yes,2.300408,3.071028,Sometimes,no,1.738959,no,0.090147,0,Sometimes,Automobile,Overweight_Level_II
+Male,29.956198,1.703688,82.207978,yes,yes,2.119643,3.292956,Sometimes,no,1.723372,no,0,0.987102,Sometimes,Automobile,Overweight_Level_II
+Male,21.403421,1.716308,80,yes,yes,2,3,Sometimes,no,2,no,2.936551,1.40913,no,Public_Transportation,Overweight_Level_II
+Male,22.591439,1.65,80,yes,yes,2,3,Sometimes,no,2,no,0.451078,2,no,Public_Transportation,Overweight_Level_II
+Female,28.583944,1.57856,65.522744,yes,no,2,2.283673,Sometimes,no,1.970622,no,1.863258,1.581703,Sometimes,Public_Transportation,Overweight_Level_II
+Male,38.825189,1.780846,85.687751,yes,yes,2.901924,1.124977,Sometimes,no,2.76382,no,0.8554,0.999183,Sometimes,Automobile,Overweight_Level_II
+Male,35.217173,1.823168,91.630257,yes,yes,2,2.9948,Sometimes,no,1.290979,no,0.87599,0.85405,Sometimes,Automobile,Overweight_Level_II
+Female,20.392665,1.525234,65.220249,yes,no,2.451009,3,Sometimes,no,2,no,2.177243,1.323877,no,Public_Transportation,Overweight_Level_II
+Female,22.154854,1.481682,61.373868,yes,no,2,2.983297,Sometimes,no,1.453626,no,0.32896,0.509396,no,Public_Transportation,Overweight_Level_II
+Male,19.108796,1.768578,87.803311,yes,yes,2.754646,3,Sometimes,no,2.957821,no,2.242754,0.786609,Sometimes,Public_Transportation,Overweight_Level_II
+Female,20.70768,1.569878,69.743323,yes,no,2.417635,3,Sometimes,no,1.937674,no,1.417035,1,no,Public_Transportation,Overweight_Level_II
+Female,20.634694,1.568188,67.904023,yes,no,2.512719,3,Sometimes,no,1.131169,no,0.095389,1,no,Public_Transportation,Overweight_Level_II
+Male,22.052152,1.792527,89.994415,yes,yes,1.771693,1.890213,Sometimes,no,2,no,0,1.36595,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.869778,1.795311,89.868784,yes,yes,1.57223,1.888067,Sometimes,no,2,no,0,0.552498,Sometimes,Public_Transportation,Overweight_Level_II
+Female,22.909992,1.700038,80,yes,yes,2,3,Sometimes,no,2,no,0.973114,1.540298,no,Public_Transportation,Overweight_Level_II
+Female,23,1.668649,80.458343,yes,yes,2,2.256119,Sometimes,no,1.142873,no,0.807076,1.611271,no,Public_Transportation,Overweight_Level_II
+Male,24.679807,1.7,84.687554,yes,yes,2,3,Sometimes,no,2.020424,no,0,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23.884212,1.713825,83.952968,yes,yes,2,2.6648,Sometimes,no,1.810738,no,0.80989,1,Sometimes,Public_Transportation,Overweight_Level_II
+Female,25.461411,1.7,78.055968,yes,yes,2.661556,3,Sometimes,no,3,no,0.55181,0.658783,Sometimes,Public_Transportation,Overweight_Level_II
+Male,31.333798,1.835381,91.059595,yes,yes,2,3,Sometimes,no,1.18423,no,0.155579,0.453404,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.108711,1.7,80.761409,yes,yes,2,3,Sometimes,no,2.879402,no,0,0.322405,Sometimes,Public_Transportation,Overweight_Level_II
+Female,28.830558,1.7,78,yes,yes,3,3,Sometimes,no,1.699971,no,1.727114,1,Frequently,Automobile,Overweight_Level_II
+Male,17.570089,1.7,83.199034,yes,no,2.097373,2.547086,Sometimes,no,1.538626,no,1,0.761898,no,Public_Transportation,Overweight_Level_II
+Female,19.027574,1.659877,77.272652,yes,yes,2.061461,2.812283,Sometimes,no,3,no,0.362441,0.78519,Sometimes,Public_Transportation,Overweight_Level_II
+Male,25.632447,1.836943,96.192662,yes,yes,1.317729,2.278652,Sometimes,no,2,no,1.80674,1.93977,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.94313,1.819614,93.086419,yes,yes,1.882235,1.240046,Sometimes,no,2,no,0,1.541493,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23.285553,1.717775,85.312639,yes,yes,2.951591,3,Sometimes,no,2.971557,no,0,0.947192,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.791867,90,yes,yes,2.067817,3,Sometimes,no,1.082084,no,0,1.922364,Sometimes,Public_Transportation,Overweight_Level_II
+Female,25.19291,1.7,80.749657,yes,yes,2.54527,3,Sometimes,no,2.269564,no,0.503122,1,Frequently,Public_Transportation,Overweight_Level_II
+Male,34.389906,1.68108,83.568035,yes,yes,2,1.660768,Sometimes,no,2.482294,no,0.891205,0,no,Automobile,Overweight_Level_II
+Female,26.595893,1.660756,78.574786,yes,yes,2,2.597608,Sometimes,no,2,no,0,0.434198,Frequently,Public_Transportation,Overweight_Level_II
+Male,55.24625,1.769269,80.491339,no,yes,2,3,Sometimes,no,2,no,1,0,no,Automobile,Overweight_Level_II
+Male,34.543563,1.765188,85,yes,yes,2.694281,3,Sometimes,no,2.653831,no,1.96582,0.891189,no,Automobile,Overweight_Level_II
+Male,21.808159,1.65,80,yes,yes,2,3,Sometimes,no,2,no,0.826609,2,no,Public_Transportation,Overweight_Level_II
+Male,18.118277,1.654757,80,yes,yes,2.821977,2.044035,Sometimes,no,1.954968,no,1,0.854536,no,Public_Transportation,Overweight_Level_II
+Female,42.189023,1.647768,79.165306,yes,yes,2,3,Sometimes,no,1,no,0,1.48189,no,Automobile,Overweight_Level_II
+Male,22,1.691303,80.539,yes,yes,2,2.038373,Sometimes,no,2,no,2.70825,1.506576,Sometimes,Public_Transportation,Overweight_Level_II
+Female,28.770852,1.532897,65.031879,yes,no,2,1,Sometimes,no,1,no,0.262171,0,no,Public_Transportation,Overweight_Level_II
+Female,25.483381,1.565288,64.848627,yes,no,2,1,Sometimes,no,1,no,0.740633,0,no,Public_Transportation,Overweight_Level_II
+Female,21.67315,1.5,63.65233,yes,no,2,1.474836,Sometimes,no,2,no,0.274838,0.613902,no,Public_Transportation,Overweight_Level_II
+Female,23.469538,1.507106,64.814109,yes,no,2.252472,3.986652,Sometimes,no,2,no,0.934286,0.890626,no,Public_Transportation,Overweight_Level_II
+Male,23,1.70074,81.32297,yes,yes,2,2.720642,Sometimes,no,1.5197,no,0.279375,1.043435,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.715118,81.650778,yes,yes,2,1.976744,Sometimes,no,1.628997,no,0.880584,1.127675,Sometimes,Public_Transportation,Overweight_Level_II
+Female,38.464538,1.696423,78.967919,yes,yes,3,3,Sometimes,no,2.981604,no,0,0,no,Automobile,Overweight_Level_II
+Female,37.496175,1.653088,80,yes,yes,2.033745,3,Sometimes,no,1.517225,no,0,1.517897,no,Automobile,Overweight_Level_II
+Female,33.690239,1.681842,77.426465,yes,yes,3,2.679724,Sometimes,no,1.999737,no,1.747347,0.681773,no,Automobile,Overweight_Level_II
+Female,34.369686,1.652202,77.13322,yes,yes,2.595128,1.619796,Sometimes,no,1.642251,no,0.662831,0.999268,no,Automobile,Overweight_Level_II
+Male,31.426573,1.848683,99,yes,yes,2.759286,2.59257,Sometimes,no,2.549617,no,1.427233,0.519395,Sometimes,Automobile,Overweight_Level_II
+Male,34.288249,1.835678,96.01851,yes,yes,2,1.546665,Sometimes,no,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,18.863875,1.560029,71.728069,yes,no,3,3.339914,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Overweight_Level_II
+Female,18.951144,1.621048,72.105856,yes,no,3,3.884861,Sometimes,no,2,no,1,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.671876,1.699474,78,yes,no,1.925064,2.358298,Sometimes,no,2.774043,no,0,0.133566,no,Public_Transportation,Overweight_Level_II
+Male,50.832559,1.745528,82.130728,yes,yes,2,3,Sometimes,no,1.774778,no,0.943266,0,no,Automobile,Overweight_Level_II
+Male,17.971574,1.720379,85,yes,yes,2,3,Sometimes,no,2.802498,no,1,0.41758,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18,1.758787,85.050558,yes,yes,2.846981,3,Sometimes,no,2.87781,no,1,0.429081,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18.701766,1.704908,81.384224,yes,yes,2.650629,1,Sometimes,no,1.708083,no,1.876051,0.938791,Sometimes,Public_Transportation,Overweight_Level_II
+Female,34.389679,1.691322,77.561602,yes,yes,3,1.802305,Sometimes,no,2,no,0.390877,0,no,Automobile,Overweight_Level_II
+Male,17.178483,1.78629,95.277392,yes,yes,2,3,Sometimes,no,3,no,1.072318,0.651904,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.0816,1.705617,83.016968,yes,yes,2,2.7976,Sometimes,no,2.991671,no,2.148738,0,no,Automobile,Overweight_Level_II
+Male,33.270448,1.733439,84.75383,yes,yes,2.631565,2.627173,Sometimes,no,2.253422,no,2.690756,0.845774,no,Automobile,Overweight_Level_II
+Female,36.310292,1.701397,83,yes,yes,2,1,Sometimes,no,2,no,1.472172,0,no,Automobile,Overweight_Level_II
+Female,20,1.625236,74.433362,yes,no,2.522399,1.032887,Sometimes,no,2.397653,no,2.358699,0.440087,Sometimes,Public_Transportation,Overweight_Level_II
+Female,18.549437,1.545196,72.467862,yes,no,3,3.014808,Sometimes,no,2,no,1.997529,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,34.243146,1.843172,92.69551,yes,yes,2,2.271734,Sometimes,no,2.629043,no,1,0.075823,Sometimes,Automobile,Overweight_Level_II
+Male,23.32012,1.705813,82.011962,yes,yes,2.784471,2.122545,Sometimes,no,2.984153,no,2.164472,0.203978,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.703095,1.704141,78.790936,yes,no,1.650505,3,Sometimes,no,2,no,1.73234,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.223259,1.706082,78.073528,yes,no,1.961347,3,Sometimes,no,2,no,1.655488,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,29.695603,1.842943,93.798055,yes,yes,2.133955,2.902766,Sometimes,no,1.753464,no,1.729987,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,27,1.880992,99.623778,yes,yes,2.684528,1,Sometimes,no,1.05668,no,1.516147,0.084006,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.027662,1.726774,82.853749,yes,yes,2.265973,2.687502,Sometimes,no,2,no,2.21722,0.648522,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.015258,1.73196,84.315608,yes,yes,2,3,Sometimes,no,2.205911,no,1.293396,0,no,Automobile,Overweight_Level_II
+Male,18,1.751278,86.963628,yes,no,3,3,Sometimes,no,2,no,1.271667,0.155278,Sometimes,Public_Transportation,Overweight_Level_II
+Male,30.553097,1.780448,88.431954,yes,yes,2,1.178708,Sometimes,no,1.791286,no,0.893362,0.276364,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33,1.85,97.92035,yes,yes,2,3,Sometimes,no,1.117464,no,1,0.663649,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.911994,1.785241,88.03748,yes,yes,1.306844,2.279546,Sometimes,no,2,no,0,0.739609,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.444846,1.718845,86.319887,yes,yes,2,2.677693,Sometimes,no,2.693978,no,0,0.688013,Sometimes,Public_Transportation,Overweight_Level_II
+Female,19.911246,1.530248,68.85097,yes,no,2.258795,3.53009,Sometimes,no,1.172186,no,0.678943,1,no,Public_Transportation,Overweight_Level_II
+Female,34.204408,1.664927,80.386078,yes,yes,2,3,Sometimes,no,2.641642,no,0.285889,1.50301,no,Automobile,Overweight_Level_II
+Female,34.281681,1.673333,77.205685,yes,yes,2.689929,1.835543,Sometimes,no,1.718569,no,0.674348,0.707246,no,Automobile,Overweight_Level_II
+Male,23.384374,1.725587,82.480214,yes,yes,2.712747,2.853676,Sometimes,no,1.526313,no,1,0.173665,Sometimes,Public_Transportation,Overweight_Level_II
+Female,43.238402,1.733875,86.94538,yes,yes,2.353603,2.337035,Sometimes,no,1.830614,no,0.706287,0,no,Automobile,Overweight_Level_II
+Female,45,1.675953,79.66832,yes,yes,2.598051,3,Sometimes,no,1,no,0,0,no,Automobile,Overweight_Level_II
+Male,22.087389,1.786238,89.802492,yes,yes,1.718156,1.631184,Sometimes,no,2,no,0,0.306124,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.378039,1.718534,80,yes,yes,2,3,Sometimes,no,2,no,2.721646,1.269982,no,Public_Transportation,Overweight_Level_II
+Male,31.398281,1.919543,101.544589,yes,yes,2,1.005391,Sometimes,no,2.001936,no,1,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,31.484494,1.707613,82.586893,yes,yes,2.795086,3.250467,Sometimes,no,1.367876,no,0.022958,0,no,Automobile,Overweight_Level_II
+Male,21.709159,1.658393,80,yes,yes,2,3,Sometimes,no,2,no,2.939733,1.692287,no,Public_Transportation,Overweight_Level_II
+Female,30.711381,1.536819,65.912688,yes,no,2.030256,2.948721,Sometimes,no,1.984323,no,0.986414,0.333673,no,Public_Transportation,Overweight_Level_II
+Male,34.821442,1.805459,90.13868,yes,yes,2,1.80993,Sometimes,no,2.174835,no,0.340915,0.020153,Sometimes,Automobile,Overweight_Level_II
+Female,20.843363,1.521008,67.083121,yes,no,2.493448,3,Sometimes,no,1.849997,no,0.954459,1.444532,no,Public_Transportation,Overweight_Level_II
+Male,19.443639,1.744733,87.27989,yes,yes,2.442536,3,Sometimes,no,2.825629,no,3,0,Sometimes,Public_Transportation,Overweight_Level_II
+Female,20.492077,1.529223,65.140408,yes,no,2.003951,3,Sometimes,no,1.207978,no,1.916751,1.228995,no,Public_Transportation,Overweight_Level_II
+Male,22.944349,1.799742,89.98168,yes,yes,1.34138,1.94671,Sometimes,no,2,no,0,0.975504,Sometimes,Public_Transportation,Overweight_Level_II
+Female,23,1.681314,80.037202,yes,yes,2,2.9796,Sometimes,no,1.964435,no,0.189957,1.73216,no,Public_Transportation,Overweight_Level_II
+Male,24.06894,1.706912,85.743727,yes,yes,2,2.994198,Sometimes,no,2.747302,no,0,0.799756,Sometimes,Public_Transportation,Overweight_Level_II
+Female,23.728707,1.663509,80,yes,yes,2,3,Sometimes,no,2.13755,no,0,0.124977,no,Public_Transportation,Overweight_Level_II
+Male,29.216019,1.752265,88.096062,yes,yes,2,1.894384,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.751511,1.735343,83.337721,yes,yes,2.607335,3,Sometimes,no,2,no,0.451009,0.630866,Sometimes,Public_Transportation,Overweight_Level_II
+Female,28.977792,1.7,78,yes,yes,3,3,Sometimes,no,1.507638,no,1.82434,1,Frequently,Automobile,Overweight_Level_II
+Male,17.441593,1.7,83.414072,yes,no,2.061384,2.579291,Sometimes,no,1.909253,no,1,0.962468,Sometimes,Public_Transportation,Overweight_Level_II
+Female,19.126145,1.633794,77.858532,yes,no,2.696381,3,Sometimes,no,2.472964,no,1.856119,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.122589,1.856759,95.887056,yes,yes,1.116068,2.449067,Sometimes,no,2,no,0.92635,1.97117,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23,1.77433,90,yes,yes,2.116432,3,Sometimes,no,1.002292,no,0,1.066708,Sometimes,Public_Transportation,Overweight_Level_II
+Female,28.824194,1.7,78,yes,yes,3,3,Sometimes,no,2.147074,no,1.416076,1,Frequently,Automobile,Overweight_Level_II
+Male,34.204611,1.719509,84.416515,yes,yes,2.031246,3,Sometimes,no,2.803002,no,1.98448,0.418623,no,Automobile,Overweight_Level_II
+Female,28.167799,1.658202,78,yes,yes,2.938031,1.532833,Sometimes,no,1.931242,no,0.525002,0.371941,Frequently,Automobile,Overweight_Level_II
+Male,55.137881,1.657221,80.993213,yes,yes,2,3,Sometimes,no,2,no,1,0,no,Automobile,Overweight_Level_II
+Male,34.970367,1.713348,84.722222,yes,yes,2.884212,3,Sometimes,no,3,no,2,0.8324,no,Automobile,Overweight_Level_II
+Male,20.986834,1.677178,80.379575,yes,yes,2,2.961706,Sometimes,no,2,no,1.661556,1.114716,no,Public_Transportation,Overweight_Level_II
+Female,38.378056,1.67805,77.224574,yes,yes,2.444599,3,Sometimes,no,1.029798,no,0,0,no,Automobile,Overweight_Level_II
+Male,22.18881,1.717722,81.92991,yes,yes,2,1.152521,Sometimes,no,1.723159,no,1.39016,1.094941,Sometimes,Public_Transportation,Overweight_Level_II
+Female,21.082384,1.486484,60.117993,yes,no,2,1.104642,Sometimes,no,1,no,0,0.969097,no,Public_Transportation,Overweight_Level_II
+Female,25.293202,1.503379,64.342459,yes,no,2,1,Sometimes,no,1.639807,no,0.072117,0,no,Public_Transportation,Overweight_Level_II
+Male,23,1.718981,81.66995,yes,yes,2,1.729553,Sometimes,no,1.400247,no,0.887923,1.011983,Sometimes,Public_Transportation,Overweight_Level_II
+Female,39.170029,1.688354,79.278896,yes,yes,3,3,Sometimes,no,2.994515,no,0,0,no,Automobile,Overweight_Level_II
+Female,34.044229,1.665807,77.098973,yes,yes,2.976975,1.346987,Sometimes,no,1.868349,no,0.702538,0.879333,no,Automobile,Overweight_Level_II
+Male,33.182127,1.838441,97.029249,yes,yes,2,1.977221,Sometimes,no,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,19.821996,1.653431,75.090439,yes,no,2.766036,2.443812,Sometimes,no,2.707927,no,0.702839,0.827439,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.149706,1.699818,78,yes,no,1.096455,2.491315,Sometimes,no,2.450069,no,0,0.726118,Sometimes,Public_Transportation,Overweight_Level_II
+Male,46.491859,1.718097,88.600878,yes,yes,2.129969,3,Sometimes,no,1.568035,no,0.870127,0,no,Automobile,Overweight_Level_II
+Male,17.894784,1.731389,84.064875,yes,no,2.019674,2.843319,Sometimes,no,2.832004,no,1,0.608607,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18.88485,1.699667,79.67793,yes,no,2.735297,2.157164,Sometimes,no,1.087166,no,0.110174,0.003695,no,Public_Transportation,Overweight_Level_II
+Female,38.384177,1.698626,78.637307,yes,yes,3,3,Sometimes,no,2.503198,no,0,0,no,Automobile,Overweight_Level_II
+Male,22.675679,1.823765,96.945262,yes,yes,1.588782,2.601675,Sometimes,no,2.469469,no,1.736538,1.886855,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.049121,1.708742,83.001642,yes,yes,2,1.171027,Sometimes,no,2.405172,no,2.300282,0,no,Automobile,Overweight_Level_II
+Female,37.205173,1.667469,80.993373,yes,yes,2.01054,2.77684,Sometimes,no,1.651548,no,0,0.790967,no,Automobile,Overweight_Level_II
+Female,19.027417,1.588147,73.939268,yes,no,3,3.362758,Sometimes,no,2,no,2.892922,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,37.492444,1.835024,92.368359,yes,yes,2,1.672706,Sometimes,no,2.766674,no,1,0.027093,Sometimes,Automobile,Overweight_Level_II
+Male,24.657598,1.7088,83.520113,yes,yes,2.689577,2.714115,Sometimes,no,2.279214,no,0.970661,0.828549,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18.011718,1.680991,79.752916,yes,yes,2.413156,2.521546,Sometimes,no,1.985312,no,0.00705,0.965464,Sometimes,Public_Transportation,Overweight_Level_II
+Male,27.349745,1.835271,97.58826,yes,yes,2.923433,1.338033,Sometimes,no,1.944095,no,1.931829,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,18,1.742819,86.565148,yes,yes,3,3,Sometimes,no,2,no,2.040816,0.860321,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.009285,1.741192,84.773349,yes,yes,2,3,Sometimes,no,2.035954,no,1.210736,0,no,Automobile,Overweight_Level_II
+Male,18,1.759721,86.0805,yes,yes,2.882522,3,Sometimes,no,2.452986,no,1,0.746651,Sometimes,Public_Transportation,Overweight_Level_II
+Male,30.079371,1.810616,92.860254,yes,yes,2,3,Sometimes,no,1.622638,no,0.033745,0.105895,Sometimes,Public_Transportation,Overweight_Level_II
+Male,25.035915,1.763101,88.532032,yes,yes,1.973499,1.081805,Sometimes,no,2,no,0,0.464022,Sometimes,Public_Transportation,Overweight_Level_II
+Female,18.198322,1.543338,71.799982,yes,no,3,3.087119,Sometimes,no,2,no,1.403872,1,no,Public_Transportation,Overweight_Level_II
+Female,35.456326,1.651812,79.437921,yes,yes,2.156065,2.909117,Sometimes,no,1.221281,no,0.503279,1.796136,no,Automobile,Overweight_Level_II
+Male,23.806789,1.732492,84.557797,yes,yes,2.992205,3,Sometimes,no,2.413813,no,0.458237,0.688293,Sometimes,Public_Transportation,Overweight_Level_II
+Female,39.392569,1.706349,85.720788,yes,yes,2.944287,1.466393,Sometimes,no,2.442775,no,0.675262,0.779334,no,Automobile,Overweight_Level_II
+Male,21.384259,1.780679,89.667406,yes,yes,1.780746,1.471053,Sometimes,no,2,no,0.467562,0.568668,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.730414,1.758687,89.673648,yes,yes,2.164062,2.983201,Sometimes,no,2.168904,no,0.752926,0,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.20563,1.704573,80,yes,yes,2,3,Sometimes,no,2,no,2.847761,1.413375,no,Public_Transportation,Overweight_Level_II
+Male,21.333429,1.716545,80.005809,yes,yes,2,2.783336,Sometimes,no,2,no,2.536615,1.206535,no,Public_Transportation,Overweight_Level_II
+Male,32.787101,1.903832,99.812443,yes,yes,2,1.863012,Sometimes,no,2.196471,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Male,32.610018,1.742538,83.390983,yes,yes,2.247704,3.053598,Sometimes,no,1.919629,no,0.408023,0,no,Automobile,Overweight_Level_II
+Male,30.022598,1.747739,83.314157,yes,yes,2.011656,3.165837,Sometimes,no,1.844645,no,0.889963,0.395979,no,Automobile,Overweight_Level_II
+Male,21.123048,1.717037,80,yes,yes,2,3,Sometimes,no,2,no,2.270555,1.343044,no,Public_Transportation,Overweight_Level_II
+Female,22.989846,1.65,80,yes,yes,2,3,Sometimes,no,2,no,0.146919,2,no,Public_Transportation,Overweight_Level_II
+Female,24.320154,1.577921,65.293179,yes,no,2.03414,2.741413,Sometimes,no,1.996384,no,1.276552,1.019452,no,Public_Transportation,Overweight_Level_II
+Male,37.275298,1.746055,83.282232,yes,yes,2.746408,1.058123,Sometimes,no,2.265727,no,0.885633,0.673408,no,Automobile,Overweight_Level_II
+Male,34.098174,1.841151,91.798103,yes,yes,2,2.997414,Sometimes,no,1.259454,no,0.935217,0.9976,Sometimes,Automobile,Overweight_Level_II
+Female,21.001282,1.517998,64.696248,yes,no,2.1239,1.672958,Sometimes,no,2,no,1.582428,1.313363,no,Public_Transportation,Overweight_Level_II
+Female,21.95994,1.483284,62.894283,yes,no,2,1.680838,Sometimes,no,1.833635,no,0.281876,0.575848,no,Public_Transportation,Overweight_Level_II
+Male,19.795269,1.758972,87.861431,yes,yes,2.332074,3,Sometimes,no,2.999495,no,2.405963,0.441502,Sometimes,Public_Transportation,Overweight_Level_II
+Female,19.090023,1.562434,70.442775,yes,no,2.748243,3.070386,Sometimes,no,1.962322,no,1.145752,1,no,Public_Transportation,Overweight_Level_II
+Female,20.820455,1.547066,67.473282,yes,no,2.303656,3,Sometimes,no,1.163111,no,1.926381,1.119877,no,Public_Transportation,Overweight_Level_II
+Male,22.087056,1.792435,89.998729,yes,yes,1.904732,2.608416,Sometimes,no,1.204175,no,0,1.895312,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.185756,1.784555,89.836692,yes,yes,1.979944,1.599464,Sometimes,no,2,no,0.17048,0.819475,Sometimes,Public_Transportation,Overweight_Level_II
+Female,22.980957,1.700216,80.473587,yes,yes,2,2.815255,Sometimes,no,1.622214,no,0.463891,1.462664,no,Public_Transportation,Overweight_Level_II
+Male,23,1.672101,80.939733,yes,yes,2,2.395785,Sometimes,no,1.40077,no,0.788659,1.544357,no,Public_Transportation,Overweight_Level_II
+Male,24.190896,1.7,84.849349,yes,yes,2,3,Sometimes,no,2.056053,no,0,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23.94003,1.721348,83.986714,yes,yes,2.407817,2.844138,Sometimes,no,1.983649,no,0.868721,0.089354,Sometimes,Public_Transportation,Overweight_Level_II
+Female,26.591628,1.7,78.008388,yes,yes,2.734314,3,Sometimes,no,3,no,0.897702,0.908271,Frequently,Public_Transportation,Overweight_Level_II
+Male,31.264628,1.803129,91.052215,yes,yes,2,3,Sometimes,no,1.199517,no,0.047738,0.163329,Sometimes,Public_Transportation,Overweight_Level_II
+Female,23.629159,1.70002,80.420434,yes,yes,2,3,Sometimes,no,2.386904,no,0.926201,1.34403,Sometimes,Public_Transportation,Overweight_Level_II
+Female,26.208134,1.7,78.041338,yes,yes,2.784383,3,Sometimes,no,2.165605,no,0.855973,0.839659,Frequently,Automobile,Overweight_Level_II
+Male,17.689057,1.713599,83.285753,yes,yes,2.009952,2.716106,Sometimes,no,1.660614,no,1,0.60473,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.16097,1.662728,77.473204,yes,no,2.008656,1.402771,Sometimes,no,3,no,0.112383,0.32803,Sometimes,Public_Transportation,Overweight_Level_II
+Male,25.45763,1.837399,95.952027,yes,yes,1.122127,2.865657,Sometimes,no,2,no,1.28775,1.944177,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.988004,1.80827,91.363293,yes,yes,1.963965,1.836226,Sometimes,no,1.85537,no,0,1.667745,Sometimes,Public_Transportation,Overweight_Level_II
+Male,23.603191,1.714508,85.137113,yes,yes,2.319776,2.884848,Sometimes,no,2.154898,no,0.325534,0.954216,Sometimes,Public_Transportation,Overweight_Level_II
+Male,22.882558,1.793451,89.909259,yes,yes,1.899116,2.375026,Sometimes,no,1.39854,no,0,1.365793,Sometimes,Public_Transportation,Overweight_Level_II
+Female,23.586058,1.700499,81.108599,yes,yes,2.042762,2.9774,Sometimes,no,1.522426,no,0.501417,1.029135,Sometimes,Public_Transportation,Overweight_Level_II
+Male,34.432669,1.694997,84.451423,yes,yes,2.129668,2.693646,Sometimes,no,2.60669,no,1.170823,0.290898,no,Automobile,Overweight_Level_II
+Female,25.472995,1.688911,78.434492,yes,yes,2.182401,2.832018,Sometimes,no,2.456939,no,0.038809,0.524015,Frequently,Public_Transportation,Overweight_Level_II
+Male,55.022494,1.673394,80.400306,yes,yes,2,3,Sometimes,no,2,no,1,0,no,Automobile,Overweight_Level_II
+Male,34.647036,1.769499,85,yes,yes,2.802696,3,Sometimes,no,2.978465,no,1.974656,0.936925,no,Automobile,Overweight_Level_II
+Male,21.997335,1.65,80,yes,yes,2,3,Sometimes,no,2,no,0.527341,2,no,Public_Transportation,Overweight_Level_II
+Male,18.181821,1.662669,79.863546,yes,no,2.492758,2.270163,Sometimes,no,1.992586,no,1.452467,0.864583,no,Public_Transportation,Overweight_Level_II
+Female,41.743333,1.67861,79.849252,yes,yes,2.733129,3,Sometimes,no,1.302594,no,0,1.103349,no,Automobile,Overweight_Level_II
+Male,21.473078,1.694423,80.311273,yes,yes,2,2.646717,Sometimes,no,2,no,2.627515,1.358812,no,Public_Transportation,Overweight_Level_II
+Female,27.757187,1.505437,63.892071,yes,no,2,1,Sometimes,no,1.278578,no,0.02112,0,no,Public_Transportation,Overweight_Level_II
+Female,25.113537,1.505387,63.715219,yes,no,2,1.193729,Sometimes,no,1.846441,no,0.546402,0.235711,no,Public_Transportation,Overweight_Level_II
+Female,21.82165,1.491441,62.269912,yes,no,2,1.477581,Sometimes,no,1.995035,no,0.324676,0.51743,no,Public_Transportation,Overweight_Level_II
+Female,20.670975,1.509408,64.852953,yes,no,2.294067,3.209508,Sometimes,no,2,no,1.103088,1.261043,no,Public_Transportation,Overweight_Level_II
+Male,22.649792,1.685045,81.022119,yes,yes,2,2.756622,Sometimes,no,1.606076,no,0.432973,1.749586,no,Public_Transportation,Overweight_Level_II
+Male,23.154311,1.723072,82.338464,yes,yes,2.315932,2.658478,Sometimes,no,1.568476,no,0.949976,0.544899,Sometimes,Public_Transportation,Overweight_Level_II
+Female,38.097395,1.696954,78.156035,yes,yes,3,3,Sometimes,no,2.166024,no,0,0,no,Automobile,Overweight_Level_II
+Female,37.642177,1.676095,79.079738,yes,yes,2.802128,3,Sometimes,no,1.744628,no,0,0.721167,no,Automobile,Overweight_Level_II
+Female,34.176795,1.681021,77.392179,yes,yes,2.79606,1.971472,Sometimes,no,1.921601,no,0.935217,0.704637,no,Automobile,Overweight_Level_II
+Female,34.231083,1.654067,79.697278,yes,yes,2.086898,1.818026,Sometimes,no,2.088929,no,0.305422,1.040787,no,Automobile,Overweight_Level_II
+Male,31.965402,1.840708,98.259423,yes,yes,2.333503,1.820779,Sometimes,no,2.559265,no,1.327193,0.481357,Sometimes,Automobile,Overweight_Level_II
+Male,33.185661,1.836007,97.416417,yes,yes,2,1.724887,Sometimes,no,3,no,1,0,Sometimes,Automobile,Overweight_Level_II
+Female,18.741188,1.556789,71.788181,yes,no,3,3.129155,Sometimes,no,2,no,1.634134,1,no,Public_Transportation,Overweight_Level_II
+Female,19.955257,1.5891,72.713611,yes,no,3,3.856434,Sometimes,no,2,no,1.32417,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.684891,1.700627,78.048207,yes,no,1.653081,2.753418,Sometimes,no,2.192063,no,1.474937,0.518542,Sometimes,Public_Transportation,Overweight_Level_II
+Male,47.7061,1.743935,84.729197,yes,yes,2.535315,3,Sometimes,no,1.146595,no,0.31381,0,no,Automobile,Overweight_Level_II
+Male,17.997009,1.742654,85,yes,no,2,3,Sometimes,no,2.976229,no,1,0.121992,Sometimes,Public_Transportation,Overweight_Level_II
+Male,17.971786,1.754441,85.002884,yes,yes,2.765769,3,Sometimes,no,2.845134,no,1,0.42683,Sometimes,Public_Transportation,Overweight_Level_II
+Male,20.432717,1.719342,80.790813,yes,yes,2.215464,2.116195,Sometimes,no,1.80996,no,1.910577,0.971746,no,Public_Transportation,Overweight_Level_II
+Female,33.954433,1.682253,77.431678,yes,yes,3,2.049908,Sometimes,no,1.999882,no,1.637368,0.10724,no,Automobile,Overweight_Level_II
+Male,17.039058,1.799902,95.419668,yes,yes,2,3,Sometimes,no,3,no,1.04729,0.933802,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.070142,1.709428,83.014033,yes,yes,2,1.044628,Sometimes,no,2.01051,no,2.805801,0,no,Automobile,Overweight_Level_II
+Male,34.993835,1.752456,84.783756,yes,yes,2.63165,2.98212,Sometimes,no,2.778587,no,2.667739,0.905181,no,Automobile,Overweight_Level_II
+Male,35.719457,1.685947,83.3258,yes,yes,2,1.009426,Sometimes,no,2.136398,no,1.060349,0,no,Automobile,Overweight_Level_II
+Female,19.005725,1.599999,73.513873,yes,no,2.942154,2.667711,Sometimes,no,2.184768,no,1.524405,0.805008,Sometimes,Public_Transportation,Overweight_Level_II
+Female,18.850466,1.550053,72.9518,yes,no,3,3.000974,Sometimes,no,2,no,2.274248,1,Sometimes,Public_Transportation,Overweight_Level_II
+Male,31.540751,1.828092,91.495718,yes,yes,2,2.698883,Sometimes,no,2.035104,no,0.598438,0.017016,Sometimes,Automobile,Overweight_Level_II
+Male,23.335391,1.71338,82.085549,yes,yes,2.716909,2.598079,Sometimes,no,2.90545,no,1.600431,0.176892,Sometimes,Public_Transportation,Overweight_Level_II
+Male,19.749627,1.680764,79.621463,yes,no,1.83746,3,Sometimes,no,2,no,1.194519,1.212762,no,Public_Transportation,Overweight_Level_II
+Male,19.565496,1.705584,78.025625,yes,no,1.936479,2.837388,Sometimes,no,2.159987,no,1.475772,0.646514,Sometimes,Public_Transportation,Overweight_Level_II
+Male,30.357745,1.841852,93.614246,yes,yes,2.045027,2.946063,Sometimes,no,1.307563,no,1.442485,0.934493,Sometimes,Public_Transportation,Overweight_Level_II
+Male,27,1.834986,99.083478,yes,yes,2.760607,1,Sometimes,no,1.336378,no,1.898889,0.487375,Sometimes,Public_Transportation,Overweight_Level_II
+Male,21.380336,1.724839,82.27635,yes,yes,2.09449,1.873484,Sometimes,no,2,no,2.079709,0.880414,Sometimes,Public_Transportation,Overweight_Level_II
+Male,33.151905,1.685127,83.986895,yes,yes,2,2.473911,Sometimes,no,2.452789,no,0.932792,0,no,Automobile,Overweight_Level_II
+Male,18,1.750097,86.372141,yes,yes,2.907062,3,Sometimes,no,2.740848,no,1.219827,0.037634,Sometimes,Public_Transportation,Overweight_Level_II
+Male,32.241587,1.757961,87.906019,yes,yes,2,1.79558,Sometimes,no,1.855054,no,1.083572,0.218574,Sometimes,Public_Transportation,Overweight_Level_II
+Male,31.662814,1.848965,98.912261,yes,yes,2.399531,2.623079,Sometimes,no,2.535127,no,1.030752,0.606264,Sometimes,Automobile,Overweight_Level_II
+Male,24.347414,1.789193,89.393589,yes,yes,1.521604,2.174968,Sometimes,no,2,no,0,0.632467,Sometimes,Public_Transportation,Overweight_Level_II
+Male,24.362124,1.716677,85.261339,yes,yes,2,2.676148,Sometimes,no,2.083939,no,0.632164,0.993786,Sometimes,Public_Transportation,Overweight_Level_II
+Female,19.462713,1.550122,69.936073,yes,no,2.501683,3.394788,Sometimes,no,1.337378,no,0.932888,1,no,Public_Transportation,Overweight_Level_II
+Female,35.432059,1.663178,80.135167,yes,yes,2,3,Sometimes,no,1.992548,no,0.039207,1.528714,no,Automobile,Overweight_Level_II
+Female,33.864257,1.679299,77.355417,yes,yes,2.76802,2.137068,Sometimes,no,1.873591,no,0.966973,0.691944,no,Automobile,Overweight_Level_II
+Male,23.807181,1.729177,82.52724,yes,yes,2.742796,2.937607,Sometimes,no,1.99467,no,1,0.023564,Sometimes,Public_Transportation,Overweight_Level_II
+Male,39.585811,1.719153,86.464843,yes,yes,2.325623,1.313403,Sometimes,no,1.94609,no,0.604259,0,no,Automobile,Overweight_Level_II
+Female,45.821267,1.687326,80.413997,yes,yes,2.076689,3,Sometimes,no,1.026729,no,0.647798,0,no,Automobile,Overweight_Level_II
+Male,25.929746,1.772449,105,yes,yes,3,3,Sometimes,no,2.045069,no,1.725931,1.619596,Sometimes,Public_Transportation,Obesity_Type_I
+Male,26.042738,1.837117,105.712219,yes,yes,3,3,Sometimes,no,2.509147,no,2,1.564796,Sometimes,Public_Transportation,Obesity_Type_I
+Male,30.551762,1.784377,102.872505,yes,yes,2.271306,3,Sometimes,no,1.771198,no,2,0.413752,Sometimes,Automobile,Obesity_Type_I
+Male,39.759575,1.792507,101.780099,yes,yes,2.33361,2.113575,Sometimes,no,2.504136,no,2.998981,1,Sometimes,Automobile,Obesity_Type_I
+Female,31.386405,1.556579,78.233341,yes,yes,2.13683,2.092179,Sometimes,no,1.505381,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,25.706285,1.585547,80.351263,yes,yes,2,1,Sometimes,no,2,no,0,0,Sometimes,Public_Transportation,Obesity_Type_I
+Female,43.604901,1.569234,81.827288,yes,yes,2.909853,1,Sometimes,no,2,no,1.308852,0,no,Automobile,Obesity_Type_I
+Female,42.31607,1.583943,81.936398,yes,yes,2.490507,2.974204,Sometimes,no,1.846754,no,0,0,no,Automobile,Obesity_Type_I
+Female,22.654316,1.621233,82,yes,yes,1.063449,1,Sometimes,no,2,no,0,1.482016,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.679935,1.6084,82.603984,yes,yes,2.699282,1,Sometimes,no,2.598632,no,0.292093,2,Sometimes,Public_Transportation,Obesity_Type_I
+Male,24.079524,1.733439,97.911865,yes,yes,2,3,Sometimes,no,2.843675,no,1.309304,1.338655,no,Public_Transportation,Obesity_Type_I
+Male,21.196152,1.65,88.472359,yes,yes,2.4277,1.001633,Sometimes,no,3,no,1.172641,1,no,Public_Transportation,Obesity_Type_I
+Female,22.596576,1.650052,87.272552,yes,yes,2.87599,1.2919,Sometimes,no,2.777712,no,0.432813,1,no,Public_Transportation,Obesity_Type_I
+Female,23,1.644161,84.340406,yes,yes,2.177243,3,Sometimes,no,2.715572,no,2.230109,0.070897,no,Public_Transportation,Obesity_Type_I
+Female,37.356288,1.559499,77.26813,yes,yes,2,3,Sometimes,no,1.630528,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,22.754646,1.734237,95,yes,yes,2,3,Sometimes,no,2.287248,no,2.669179,1.237867,no,Public_Transportation,Obesity_Type_I
+Male,23.252906,1.775815,97.813023,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Female,22.025438,1.640426,87.344156,yes,yes,3,1,Sometimes,no,3,no,1.280924,1.77795,no,Public_Transportation,Obesity_Type_I
+Female,18.086772,1.65,82.13682,yes,yes,3,3,Sometimes,no,1,no,1.02975,1.350099,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.994393,1.753321,107.998815,yes,yes,3,3,Sometimes,no,1.776991,no,1.757724,0,Sometimes,Automobile,Obesity_Type_I
+Female,40.821515,1.553026,77.74518,yes,yes,2,3,Sometimes,no,1.406115,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,23,1.706525,90.500055,yes,yes,2,3,Sometimes,no,1.530493,no,0.967627,1,no,Public_Transportation,Obesity_Type_I
+Female,37.955371,1.560648,80,yes,yes,2.846452,1.139317,Sometimes,no,1.459511,no,1.71807,0,Sometimes,Automobile,Obesity_Type_I
+Male,31.315593,1.673352,90,yes,yes,2.19011,2.029858,Sometimes,no,2.348981,no,1.946907,0,Sometimes,Automobile,Obesity_Type_I
+Male,22.661556,1.660324,94.189167,yes,yes,2,3,Sometimes,no,2,no,0,0.105936,no,Public_Transportation,Obesity_Type_I
+Male,40.317787,1.786318,98.447311,yes,yes,2,3,Sometimes,no,2.680292,no,1.549693,0.522259,Sometimes,Automobile,Obesity_Type_I
+Male,23.365649,1.757691,95.361795,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,21.72127,1.712163,98.699971,yes,yes,2,2.977543,Sometimes,no,2.205977,no,2.698874,2,no,Public_Transportation,Obesity_Type_I
+Male,35.389491,1.78,100.84763,yes,yes,2.069267,1.476204,Sometimes,no,3,no,2.05252,0.092736,Frequently,Automobile,Obesity_Type_I
+Female,22.061461,1.6,82.040318,yes,yes,1.362441,2.57038,Sometimes,no,2.166228,no,2.232851,1.281141,no,Public_Transportation,Obesity_Type_I
+Female,23,1.61082,82.532994,yes,yes,2.09663,2.879541,Sometimes,no,2.432967,no,1.887012,0,no,Public_Transportation,Obesity_Type_I
+Female,23,1.665199,83.15115,yes,yes,2.928234,1.458507,Sometimes,no,2.777379,no,0.354541,1.707018,no,Public_Transportation,Obesity_Type_I
+Female,40.951591,1.542122,80,yes,yes,2,1.105617,Sometimes,no,1.372811,no,1.629432,0,Sometimes,Automobile,Obesity_Type_I
+Female,39.135634,1.507867,79.58958,yes,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,26.271621,1.660955,90,yes,yes,2,3,Sometimes,no,3,no,0.669278,0.505266,Sometimes,Automobile,Obesity_Type_I
+Female,23.779235,1.553127,78,yes,yes,2,1,Sometimes,no,2,no,0.827502,0,no,Public_Transportation,Obesity_Type_I
+Male,20.586978,1.781952,102.910613,yes,yes,2,3,Sometimes,no,1.689793,no,1.464204,0,no,Public_Transportation,Obesity_Type_I
+Male,21.669478,1.75,96.940255,yes,yes,2,3,Sometimes,no,2.771469,no,2.359339,1.206251,no,Public_Transportation,Obesity_Type_I
+Female,23,1.615854,80.615325,yes,yes,2,1,Sometimes,no,2,no,0.026142,0,no,Public_Transportation,Obesity_Type_I
+Male,20.825962,1.793378,105.655037,yes,yes,2,3,Sometimes,no,1.155384,no,0.003938,0.526999,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18.178023,1.85478,114.77484,yes,yes,2,1.854536,Sometimes,no,2.160169,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18,1.685633,90.032671,yes,yes,2.496455,3,Sometimes,no,1.850344,no,1.066101,0.732265,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18.267696,1.706343,93.348843,yes,yes,1.70825,3,Sometimes,no,1,no,0.899864,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.791415,105.138073,yes,yes,2.262171,3,Sometimes,no,2.03415,no,0.317363,0.508663,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.753716,106.49141,yes,yes,2.740633,3,Sometimes,no,2.081005,no,0.650235,0.76906,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.9859,1.780503,103.189532,yes,yes,2,3,Sometimes,no,1.608128,no,0.478134,0,no,Public_Transportation,Obesity_Type_I
+Male,21.504943,1.819867,106.038468,yes,yes,2,3,Sometimes,no,2.715252,no,0,0.621605,no,Public_Transportation,Obesity_Type_I
+Male,18,1.791174,108.9606,yes,yes,2,1.08687,Sometimes,no,2.279124,no,1,1.609773,no,Public_Transportation,Obesity_Type_I
+Male,18,1.82093,108.742005,yes,yes,2,1.25535,Sometimes,no,2.497065,no,1,1.441605,no,Public_Transportation,Obesity_Type_I
+Female,19.442663,1.627812,82,yes,yes,1.081585,2.870661,Sometimes,no,1.11756,no,0,1.616826,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.865018,1.632118,82,yes,yes,2.587789,1.482103,Sometimes,no,1.911664,no,0,0.128681,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.948577,1.773594,105.000789,yes,yes,2.252653,3,Sometimes,no,2.962371,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.214617,1.930416,118.560509,yes,yes,2,3,Sometimes,no,3,no,0.732186,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,22.277859,1.947406,116.893105,yes,yes,2,3,Sometimes,no,3,no,0.975187,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,37.832949,1.610867,80,yes,yes,2.036613,1.81698,Sometimes,no,1.93059,no,1.851404,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.631769,1.513202,75.410647,yes,yes,2,2.582591,Sometimes,no,1.535134,no,1.88452,0,Sometimes,Automobile,Obesity_Type_I
+Male,17.6739,1.738465,97.185474,yes,yes,2,3,Sometimes,no,2,no,0,0.233314,no,Public_Transportation,Obesity_Type_I
+Female,37.524551,1.567915,76.129784,yes,yes,2,3,Sometimes,no,2.102976,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,43.510672,1.587546,76.126112,yes,yes,2,3,Sometimes,no,2.091934,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,18,1.820385,108.395005,yes,yes,2,2.164839,Sometimes,no,2.094479,no,1,0.676557,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18,1.792239,108.244379,yes,yes,2,2.141839,Sometimes,no,2.939847,no,1,0.103056,Sometimes,Public_Transportation,Obesity_Type_I
+Male,29.506287,1.82697,108.751502,yes,yes,2,2.877583,Sometimes,no,2.358038,no,0.877295,1.817146,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.670058,86.242679,yes,yes,2.609123,3,Sometimes,no,1.955053,no,1.186013,0,no,Public_Transportation,Obesity_Type_I
+Male,24.733308,1.639383,91.267087,yes,yes,1.036159,3,Sometimes,no,1,no,0.344013,0.329367,Sometimes,Automobile,Obesity_Type_I
+Male,29,1.682916,89.991671,yes,yes,1.851262,3,Sometimes,no,2.11267,no,0.388269,0,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.609321,84.493156,yes,yes,2,3,Sometimes,no,1.051735,no,1,0,no,Public_Transportation,Obesity_Type_I
+Male,21.098035,1.690437,96.366777,yes,yes,2,2.95833,Sometimes,no,2,no,0,1.595306,no,Public_Transportation,Obesity_Type_I
+Male,19.089595,1.700164,99.204695,yes,yes,2,2.119826,Sometimes,no,2,no,0,1.882539,no,Public_Transportation,Obesity_Type_I
+Female,18.312665,1.60074,82.249831,yes,yes,2.501236,3,Sometimes,no,1.220331,no,0.245003,0.537202,no,Public_Transportation,Obesity_Type_I
+Male,18.260079,1.801848,104.741914,yes,yes,2,3,Sometimes,no,3,no,1.783858,0,no,Public_Transportation,Obesity_Type_I
+Male,26.004294,1.844751,105.025808,yes,yes,3,3,Sometimes,no,2.925029,no,2,1.758865,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.994746,1.811602,106.042142,yes,yes,3,3,Sometimes,no,2.858171,no,1.813318,0.680215,Sometimes,Public_Transportation,Obesity_Type_I
+Male,36.726617,1.787521,100.624553,yes,yes,2.031185,2.992606,Sometimes,no,2.852254,no,2.00123,0.36037,Frequently,Automobile,Obesity_Type_I
+Male,38.297259,1.789109,100.066268,yes,yes,2.014194,2.050121,Sometimes,no,2.184707,no,2.000561,0.853891,Frequently,Automobile,Obesity_Type_I
+Female,30.163408,1.533364,78.030383,yes,yes,2.028225,1.923607,Sometimes,no,1.798917,no,0,0,no,Public_Transportation,Obesity_Type_I
+Female,28.770039,1.580858,79.713492,yes,yes,2,1,Sometimes,no,2,no,0.005405,0,no,Public_Transportation,Obesity_Type_I
+Female,42.337283,1.64639,86.639861,yes,yes,2.81646,2.27374,Sometimes,no,1.722063,no,0,0,no,Automobile,Obesity_Type_I
+Female,47.283374,1.643786,81.978743,yes,yes,2.037585,1.418985,Sometimes,no,1.827351,no,0,0,no,Automobile,Obesity_Type_I
+Female,22.518787,1.634342,82.414477,yes,yes,1.853314,1.320768,Sometimes,no,2.135552,no,0.248034,1.727828,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.836315,1.604893,82,yes,yes,1.00876,1,Sometimes,no,2,no,0,0.585912,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.654961,94.790579,yes,yes,2,3,Sometimes,no,1.490613,no,0.654316,1,no,Public_Transportation,Obesity_Type_I
+Male,22.975526,1.701986,95,yes,yes,2,3,Sometimes,no,2.02127,no,1.814869,1.066603,no,Public_Transportation,Obesity_Type_I
+Male,22.720449,1.65,89.139209,yes,yes,2.103335,2.964024,Sometimes,no,3,no,0.632947,1,no,Public_Transportation,Obesity_Type_I
+Male,23.887569,1.657995,90,yes,yes,2,3,Sometimes,no,3,no,0.603638,0.969085,no,Public_Transportation,Obesity_Type_I
+Female,23,1.634688,82.628,yes,yes,2.060922,2.933409,Sometimes,no,2.235282,no,0.380633,0.0026,no,Public_Transportation,Obesity_Type_I
+Female,23,1.628168,84.49798,yes,yes,2.058687,2.962004,Sometimes,no,2.010596,no,0.851059,0.630866,no,Public_Transportation,Obesity_Type_I
+Female,38.148845,1.557808,79.661693,yes,yes,2,3,Sometimes,no,1.274774,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.96543,1.560199,80,yes,yes,2.533605,1.271624,Sometimes,no,1.893691,no,1.296535,0,Sometimes,Automobile,Obesity_Type_I
+Male,22.771612,1.750384,95.324282,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,22.582371,1.753081,95.269089,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Female,21.008051,1.65,88.126544,yes,yes,2.457547,1.00061,Sometimes,no,3,no,1.361533,1,no,Public_Transportation,Obesity_Type_I
+Female,22.591026,1.650012,87.676154,yes,yes,2.983851,1.068443,Sometimes,no,2.854161,no,1.103209,1,no,Public_Transportation,Obesity_Type_I
+Female,16.129279,1.65,85.583485,yes,yes,2.954996,3,Sometimes,no,1,no,2.091862,1.06053,no,Public_Transportation,Obesity_Type_I
+Female,16.913841,1.634832,85.803809,yes,yes,2.061969,3,Sometimes,no,1.229171,no,2.392047,1.256119,no,Public_Transportation,Obesity_Type_I
+Male,29.43879,1.770126,108.602715,yes,yes,3,3,Sometimes,no,1.163264,no,1.866023,1.604608,Sometimes,Automobile,Obesity_Type_I
+Male,29.881301,1.758189,108.721893,yes,yes,3,3,Sometimes,no,1.271178,no,1.944728,0,Sometimes,Automobile,Obesity_Type_I
+Female,43.591999,1.595165,77.354744,yes,yes,2,3,Sometimes,no,2.117346,no,0,0,no,Automobile,Obesity_Type_I
+Female,40.993179,1.567756,79.493626,yes,yes,2,3,Sometimes,no,2.952506,no,0,0,no,Automobile,Obesity_Type_I
+Male,22.936098,1.702825,93.638318,yes,yes,2,3,Sometimes,no,1.746197,no,0.455216,0.335158,no,Public_Transportation,Obesity_Type_I
+Male,23,1.735659,93.429205,yes,yes,2,3,Sometimes,no,1.249307,no,0.973465,1,no,Public_Transportation,Obesity_Type_I
+Female,37.597953,1.62901,80,yes,yes,2.450784,2.952821,Sometimes,no,1.335192,no,0.180276,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.532066,1.615385,80,yes,yes,2.972426,3,Sometimes,no,1.636326,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,31.630054,1.671705,90,yes,yes,2.467002,1.851088,Sometimes,no,2.104054,no,1.991565,0,Sometimes,Automobile,Obesity_Type_I
+Male,31.641081,1.676595,89.993812,yes,yes,2.934671,2.119682,Sometimes,no,2.041462,no,0.578074,0,Sometimes,Automobile,Obesity_Type_I
+Male,21.872484,1.699998,95.564428,yes,yes,2,2.970675,Sometimes,no,2,no,0,0.169294,no,Public_Transportation,Obesity_Type_I
+Male,21.834894,1.722785,94.094184,yes,yes,2,2.966803,Sometimes,no,2,no,0,1.281541,no,Public_Transportation,Obesity_Type_I
+Male,36.023972,1.670667,90.575934,yes,yes,2.903545,1.508685,Sometimes,no,2.450069,no,1.45473,0,Sometimes,Automobile,Obesity_Type_I
+Male,39.656559,1.789992,98.021766,yes,yes,2.043359,2.209314,Sometimes,no,2.785804,no,1.259613,0.669616,Sometimes,Automobile,Obesity_Type_I
+Male,24.184891,1.768834,97.449743,yes,yes,2,3,Sometimes,no,2.973729,no,2.491642,1.36595,no,Public_Transportation,Obesity_Type_I
+Male,23.237302,1.761008,97.829344,yes,yes,2,3,Sometimes,no,2.988771,no,2.429923,1.978043,no,Public_Transportation,Obesity_Type_I
+Male,35.322112,1.78,102.265955,yes,yes,2.787589,1.114564,Sometimes,no,3,no,2.710338,0.572185,Sometimes,Automobile,Obesity_Type_I
+Male,31.387982,1.810215,105.254354,yes,yes,2.39728,2.036794,Sometimes,no,2.659419,no,1.062011,1.771135,Sometimes,Automobile,Obesity_Type_I
+Female,23,1.608469,82.954796,yes,yes,2.150054,2.988539,Sometimes,no,2.768325,no,2.08515,0,no,Public_Transportation,Obesity_Type_I
+Female,23,1.630357,83.1011,yes,yes,2.977585,1.630506,Sometimes,no,2.839319,no,1.091474,0.889517,no,Public_Transportation,Obesity_Type_I
+Female,22.679454,1.62826,82.967937,yes,yes,2.274491,1,Sometimes,no,2.2695,no,0.946461,1.73107,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.480889,1.605662,82.470375,yes,yes,1.557287,1,Sometimes,no,2.371015,no,0.288032,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,41.403862,1.567973,81.056851,yes,yes,2.152264,2.977999,Sometimes,no,1.513199,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,40.702771,1.548403,80,yes,yes,2,3,Sometimes,no,1.326165,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,29.389239,1.681855,90,yes,yes,2.08841,2.644692,Sometimes,no,2.773236,no,1.252472,0,Sometimes,Automobile,Obesity_Type_I
+Male,30.967417,1.688436,90,yes,yes,2.180047,2.733077,Sometimes,no,2.77962,no,1.454129,0,Sometimes,Automobile,Obesity_Type_I
+Female,23.474165,1.577115,78,yes,yes,2,1,Sometimes,no,2,no,0.876101,0,no,Public_Transportation,Obesity_Type_I
+Female,24.317607,1.586751,79.109029,yes,yes,2,1,Sometimes,no,2,no,0.852446,0,no,Public_Transportation,Obesity_Type_I
+Male,17.052914,1.710616,99.860254,yes,yes,2,3,Sometimes,no,2,no,0.067491,0.894105,no,Public_Transportation,Obesity_Type_I
+Male,18.04892,1.721384,99.430612,yes,yes,2,3,Sometimes,no,2,no,1.579431,0.839862,no,Public_Transportation,Obesity_Type_I
+Female,22.875223,1.624367,82,yes,yes,1.826885,1,Sometimes,no,2,no,0,0.459274,no,Public_Transportation,Obesity_Type_I
+Female,22.884722,1.622787,82,yes,yes,1.754401,1,Sometimes,no,2,no,0,0.301909,no,Public_Transportation,Obesity_Type_I
+Male,18.729566,1.855433,112.875283,yes,yes,2,2.427137,Sometimes,no,2.574073,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.011124,1.856315,118.183797,yes,yes,2,3,Sometimes,no,3,no,0.788585,1.220029,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18.603496,1.681719,90.671871,yes,yes,1.524428,3,Sometimes,no,1.383831,no,0.130417,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,21.106056,1.722884,92.949254,yes,yes,1.164062,3,Sometimes,no,1.157395,no,0,0.320851,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.799406,106.528811,yes,yes,2.123159,3,Sometimes,no,2.415343,no,0.295178,0.569852,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.980847,1.819875,106.048516,yes,yes,2,3,Sometimes,no,2.745242,no,0,0.80898,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.016665,1.790172,105.042194,yes,yes,2.259679,3,Sometimes,no,1.026143,no,0.268801,0.077818,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.72738,1.783782,105.000276,yes,yes,2.044326,3,Sometimes,no,2.35818,no,0.349371,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18,1.783906,109.207614,yes,yes,2,1.867836,Sometimes,no,2.438398,no,1,0.802305,no,Public_Transportation,Obesity_Type_I
+Male,18,1.844218,109.195529,yes,yes,2,1.548407,Sometimes,no,2.191401,no,1,1.676944,no,Public_Transportation,Obesity_Type_I
+Female,19.431662,1.631662,82,yes,yes,2.843456,2.937989,Sometimes,no,1.001995,no,0,1.554404,no,Public_Transportation,Obesity_Type_I
+Female,18.166318,1.649553,82.323954,yes,yes,2.864776,3,Sometimes,no,1.876915,no,0.631565,0.186414,no,Public_Transportation,Obesity_Type_I
+Male,22.352025,1.754711,105.000706,yes,yes,2.871137,3,Sometimes,no,2.627569,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,22.469351,1.762515,105.000097,yes,yes,2.282803,3,Sometimes,no,2.104264,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,19.524698,1.942725,121.657979,yes,yes,2,3,Sometimes,no,3,no,1,1.014808,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.491475,1.975663,120.702935,yes,yes,2,3,Sometimes,no,3,no,0.767013,1,Sometimes,Public_Transportation,Obesity_Type_I
+Female,39.213399,1.586301,80,yes,yes,2.020502,1.237454,Sometimes,no,1.93142,no,1.967973,0,Sometimes,Automobile,Obesity_Type_I
+Female,38.098745,1.598448,80,yes,yes,2.020785,1.169173,Sometimes,no,1.972551,no,1.903338,0,Sometimes,Automobile,Obesity_Type_I
+Male,19.034033,1.703098,98.296651,yes,yes,2,2.391753,Sometimes,no,2,no,0,1.119877,no,Public_Transportation,Obesity_Type_I
+Male,17.073648,1.706996,99.544696,yes,yes,2,3,Sometimes,no,2,no,1.73209,0.048617,no,Public_Transportation,Obesity_Type_I
+Female,38.547267,1.526472,75.150345,yes,yes,2,3,Sometimes,no,1.084442,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,38.683794,1.501993,76.642944,yes,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,18,1.784402,108.413119,yes,yes,2,2.475444,Sometimes,no,2.655795,no,1,0.169879,no,Public_Transportation,Obesity_Type_I
+Male,18,1.792687,108.204547,yes,yes,2,2.478794,Sometimes,no,2.967064,no,1,0.4346,no,Public_Transportation,Obesity_Type_I
+Male,29.622804,1.814052,109.411622,yes,yes,2.407817,2.070033,Sometimes,no,1.796257,no,1.234483,0.178708,Sometimes,Automobile,Obesity_Type_I
+Male,30.79626,1.789421,109.599453,yes,yes,2.21498,2.282392,Sometimes,no,1.179942,no,1.771754,0.537659,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.686904,90.004046,yes,yes,2.737149,3,Sometimes,no,1.997195,no,1.352663,0.468483,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18.107092,1.703259,91.499683,yes,yes,1.899793,3,Sometimes,no,1.439962,no,0.9529,0.838847,Sometimes,Public_Transportation,Obesity_Type_I
+Male,31.335093,1.665798,89.738596,yes,yes,2.274164,1.049534,Sometimes,no,1.358172,no,1.482411,0,Sometimes,Automobile,Obesity_Type_I
+Male,29,1.66671,89.048151,yes,yes,1.897796,3,Sometimes,no,1.193039,no,0.011519,0,Sometimes,Automobile,Obesity_Type_I
+Female,18.011903,1.65,84.210535,yes,yes,2.85916,3,Sometimes,no,1,no,1.020525,0.786066,no,Public_Transportation,Obesity_Type_I
+Female,18,1.649439,84.897738,yes,yes,2,3,Sometimes,no,1.039313,no,1,0,no,Public_Transportation,Obesity_Type_I
+Male,21.077356,1.702002,97.971598,yes,yes,2,1.66338,Sometimes,no,2,no,0,1.865851,no,Public_Transportation,Obesity_Type_I
+Male,21.052016,1.69382,99.530971,yes,yes,2,1.672751,Sometimes,no,2,no,0,1.946173,no,Public_Transportation,Obesity_Type_I
+Female,18.078256,1.622999,82.403076,yes,yes,2.340405,3,Sometimes,no,1.655245,no,1.006884,0.548539,no,Public_Transportation,Obesity_Type_I
+Female,18.063582,1.633675,82.459577,yes,yes,2.921576,3,Sometimes,no,1.003636,no,1.000227,0.905596,no,Public_Transportation,Obesity_Type_I
+Male,20.418832,1.836669,105.257543,yes,yes,2,3,Sometimes,no,2.793505,no,2.21939,0.202902,no,Public_Transportation,Obesity_Type_I
+Male,20.580984,1.798354,103.705418,yes,yes,2,3,Sometimes,no,2.401315,no,2.891986,0,no,Public_Transportation,Obesity_Type_I
+Male,26.032416,1.824432,105.131956,yes,yes,3,3,Sometimes,no,2.110611,no,1.890907,1.58483,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.955361,1.830384,105.479313,yes,yes,3,3,Sometimes,no,2.480277,no,1.796779,1.596562,Sometimes,Public_Transportation,Obesity_Type_I
+Male,26.015448,1.829907,105.436173,yes,yes,3,3,Sometimes,no,2.224914,no,1.781589,1.59405,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.920738,1.823755,105.800158,yes,yes,2.92711,3,Sometimes,no,2.151496,no,1.166064,1.509831,Sometimes,Public_Transportation,Obesity_Type_I
+Male,29.633715,1.834842,105.19936,yes,yes,2.805533,3,Sometimes,no,1.882847,no,2,0.70778,Sometimes,Automobile,Obesity_Type_I
+Male,32.895637,1.783901,103.771371,yes,yes,2.902469,1.734762,Sometimes,no,2.600238,no,2.113992,0.584755,Sometimes,Automobile,Obesity_Type_I
+Male,38.748307,1.782067,103.586342,yes,yes,2.845961,1.92822,Sometimes,no,2.642273,no,2.999918,1,Frequently,Automobile,Obesity_Type_I
+Male,39.685846,1.781032,96.303855,yes,yes,2.252698,2.475228,Sometimes,no,2.923856,no,2.165429,0.616045,Frequently,Automobile,Obesity_Type_I
+Female,33.226808,1.557943,77.647716,yes,yes,2.02091,2.093831,Sometimes,no,1.506518,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,26.220065,1.560258,78.435904,yes,yes,2.108163,1.231915,Sometimes,no,1.808127,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,23.090215,1.596586,80.726195,yes,yes,1.518966,1,Sometimes,no,2,no,0,1.094046,no,Public_Transportation,Obesity_Type_I
+Female,24.565628,1.62093,81.718232,yes,yes,2.260543,1.374791,Sometimes,no,1.947935,no,0,0.078607,no,Public_Transportation,Obesity_Type_I
+Female,42.586285,1.571417,81.918809,yes,yes,2.522183,1.326982,Sometimes,no,1.872673,no,1.048507,0,no,Automobile,Obesity_Type_I
+Female,43.719395,1.584322,80.986496,yes,yes,2.186322,2.900915,Sometimes,no,2.471033,no,0.256113,0,no,Automobile,Obesity_Type_I
+Female,43.37634,1.582523,81.919454,yes,yes,2.76632,1.317884,Sometimes,no,1.889341,no,0.990642,0,no,Automobile,Obesity_Type_I
+Female,39.648946,1.572791,80.086524,yes,yes,2.071622,2.977909,Sometimes,no,1.468297,no,0,0,no,Automobile,Obesity_Type_I
+Female,22.693989,1.627908,82,yes,yes,1.918251,1.355752,Sometimes,no,1.998108,no,0,1.382906,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.676243,1.620938,82.283185,yes,yes,1.967061,1,Sometimes,no,2.548651,no,0.249264,1.516731,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.804818,1.613119,82.636162,yes,yes,2.964419,1,Sometimes,no,2.977516,no,0.742113,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.857123,1.62686,82.410189,yes,yes,2.640801,1.015488,Sometimes,no,2.022355,no,0.244749,0.289218,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23.096353,1.728183,97.959899,yes,yes,2,2.986172,Sometimes,no,2.364208,no,2.492402,1.632506,no,Public_Transportation,Obesity_Type_I
+Male,22.815416,1.732694,98.44113,yes,yes,2,2.993623,Sometimes,no,2.326635,no,2.236586,1.529423,no,Public_Transportation,Obesity_Type_I
+Female,21.02064,1.65,88.129437,yes,yes,2.870152,1.001383,Sometimes,no,3,no,1.322558,1,no,Public_Transportation,Obesity_Type_I
+Female,21.001458,1.65,88.026943,yes,yes,2.482575,1.001542,Sometimes,no,3,no,1.751656,1,no,Public_Transportation,Obesity_Type_I
+Female,22.87795,1.66913,86.002736,yes,yes,2.290095,2.590283,Sometimes,no,2.506296,no,0.312254,1,no,Public_Transportation,Obesity_Type_I
+Female,22.847618,1.669136,85.568385,yes,yes,2.074843,1.773916,Sometimes,no,2.428271,no,0.035091,1,no,Public_Transportation,Obesity_Type_I
+Female,23,1.611058,84.191125,yes,yes,2.14128,2.989112,Sometimes,no,2.519841,no,2.11137,0.013483,no,Public_Transportation,Obesity_Type_I
+Female,23,1.649736,84.134712,yes,yes,2.334474,1.496776,Sometimes,no,2.776724,no,0.782416,0.167728,no,Public_Transportation,Obesity_Type_I
+Female,39.129291,1.532643,77.550345,yes,yes,2,3,Sometimes,no,1.186996,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.441044,1.524293,76.202761,yes,yes,2,2.994046,Sometimes,no,1.603549,no,1.335857,0,Sometimes,Automobile,Obesity_Type_I
+Male,22.969366,1.701634,95,yes,yes,2,3,Sometimes,no,2.133469,no,1.030199,1.082838,no,Public_Transportation,Obesity_Type_I
+Male,22.735328,1.723921,94.743892,yes,yes,2,3,Sometimes,no,2.183149,no,1.932386,0.212767,no,Public_Transportation,Obesity_Type_I
+Male,23.32471,1.769484,96.078462,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,23.668137,1.777416,97.842406,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Female,21.895468,1.64456,88.119139,yes,yes,2.693859,1.000283,Sometimes,no,3,no,1.201403,1.612432,no,Public_Transportation,Obesity_Type_I
+Female,21.3928,1.641149,88.079177,yes,yes,2.607747,1.000414,Sometimes,no,3,no,1.269169,1.460564,no,Public_Transportation,Obesity_Type_I
+Female,18.178076,1.642892,82.144405,yes,yes,2.66889,3,Sometimes,no,1.172593,no,0.886448,1.252677,no,Public_Transportation,Obesity_Type_I
+Female,18.907514,1.65,82.01831,yes,yes,3,3,Sometimes,no,1,no,0.215187,1.195929,no,Public_Transportation,Obesity_Type_I
+Male,26.011928,1.777251,106.452367,yes,yes,3,3,Sometimes,no,1.911379,no,1.911096,1.032392,Sometimes,Public_Transportation,Obesity_Type_I
+Male,25.696736,1.814696,107.55963,yes,yes,2.921225,3,Sometimes,no,2.869439,no,0.181753,0.133523,Sometimes,Public_Transportation,Obesity_Type_I
+Female,40.466313,1.559005,77.601483,yes,yes,2,3,Sometimes,no,1.572371,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,38.445148,1.556028,77.684229,yes,yes,2,3,Sometimes,no,1.440629,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,24.473062,1.653751,91.204753,yes,yes,1.492834,3,Sometimes,no,1.005367,no,0.922739,0.733221,no,Automobile,Obesity_Type_I
+Male,23.479181,1.680171,91.068054,yes,yes,1.220024,3,Sometimes,no,1.046254,no,0.520989,0.61599,no,Automobile,Obesity_Type_I
+Female,38.397463,1.552648,80,yes,yes,2.667676,1.135278,Sometimes,no,1.399629,no,1.648883,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.974483,1.560215,80,yes,yes,2.569075,2.463113,Sometimes,no,1.567366,no,0.359134,0,Sometimes,Automobile,Obesity_Type_I
+Male,31.783524,1.672959,90,yes,yes,2.949242,1.782109,Sometimes,no,2.210997,no,1.992719,0,Sometimes,Automobile,Obesity_Type_I
+Male,29.681308,1.680058,89.994351,yes,yes,2.104772,2.376374,Sometimes,no,2.218691,no,1.52184,0,Sometimes,Automobile,Obesity_Type_I
+Male,21.587743,1.664752,94.256547,yes,yes,2,2.976098,Sometimes,no,2,no,0,1.39322,no,Public_Transportation,Obesity_Type_I
+Male,21.746113,1.668553,94.454394,yes,yes,2,2.968098,Sometimes,no,2,no,0,1.292476,no,Public_Transportation,Obesity_Type_I
+Male,39.569004,1.785286,100.431625,yes,yes,2.871768,1.792695,Sometimes,no,2.691322,no,2.545707,0.903903,Frequently,Automobile,Obesity_Type_I
+Male,36.67933,1.780725,98.790167,yes,yes,2.540949,1.116401,Sometimes,no,2.929123,no,2.787319,0.732881,Frequently,Automobile,Obesity_Type_I
+Male,22.927011,1.751046,95.285898,yes,yes,2,3,Sometimes,no,2.530301,no,2.89118,1.287117,no,Public_Transportation,Obesity_Type_I
+Male,23.329344,1.751365,95.290429,yes,yes,2,3,Sometimes,no,3,no,3,2,no,Public_Transportation,Obesity_Type_I
+Male,22.126325,1.717262,98.579156,yes,yes,2,2.984523,Sometimes,no,2.263551,no,2.819661,2,no,Public_Transportation,Obesity_Type_I
+Male,21.679158,1.741393,98.182498,yes,yes,2,2.978103,Sometimes,no,2.668261,no,2.462269,1.402414,no,Public_Transportation,Obesity_Type_I
+Male,36.673882,1.7921,101.285765,yes,yes,2.222282,1.578751,Sometimes,no,2.791604,no,2.352091,0.317846,Sometimes,Automobile,Obesity_Type_I
+Male,37.936044,1.785957,99.199287,yes,yes,2.04516,1.874532,Sometimes,no,2.856521,no,2.005286,0.105408,Sometimes,Automobile,Obesity_Type_I
+Female,22.226815,1.609068,83.785312,yes,yes,2.094184,2.735706,Sometimes,no,2.257922,no,2.230547,0.195339,no,Public_Transportation,Obesity_Type_I
+Female,22.538932,1.603963,82.012637,yes,yes,1.123672,1.014916,Sometimes,no,2.042078,no,1.846666,1.426103,no,Public_Transportation,Obesity_Type_I
+Female,22.307413,1.605495,82.528575,yes,yes,2.049112,2.622055,Sometimes,no,2.280555,no,2.052896,0.896185,no,Public_Transportation,Obesity_Type_I
+Female,22.362877,1.607182,82.368441,yes,yes,1.880534,2.806566,Sometimes,no,2.313245,no,2.146778,0.196084,no,Public_Transportation,Obesity_Type_I
+Female,22.89974,1.661715,82.595793,yes,yes,1.203754,1.355354,Sometimes,no,2.765593,no,0.128342,1.659476,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.997168,1.655742,82.46376,yes,yes,2.73691,1.478334,Sometimes,no,2.677409,no,0.065264,0.40699,Sometimes,Public_Transportation,Obesity_Type_I
+Female,38.895069,1.549257,80,yes,yes,2.736628,1.130751,Sometimes,no,1.385175,no,1.666965,0,Sometimes,Automobile,Obesity_Type_I
+Female,40.789529,1.549748,80,yes,yes,2,1.099151,Sometimes,no,1.687611,no,1.874662,0,Sometimes,Automobile,Obesity_Type_I
+Female,40.654155,1.52906,79.760922,yes,yes,2,3,Sometimes,no,1,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,38.542937,1.517248,79.843221,yes,yes,2,3,Sometimes,no,1.095134,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,25.95383,1.65731,90,yes,yes,2,3,Sometimes,no,3,no,0.413643,0.920858,Sometimes,Automobile,Obesity_Type_I
+Male,26.826961,1.673287,90,yes,yes,2,3,Sometimes,no,3,no,0.884935,0.12228,Sometimes,Automobile,Obesity_Type_I
+Female,23.803904,1.581527,78.089575,yes,yes,2,1,Sometimes,no,2,no,0.057758,0,no,Public_Transportation,Obesity_Type_I
+Female,23.652435,1.562724,80.535698,yes,yes,2,1,Sometimes,no,2,no,0.389717,0,no,Public_Transportation,Obesity_Type_I
+Male,20.677105,1.781251,102.950929,yes,yes,2,3,Sometimes,no,1.618189,no,0.571648,0,no,Public_Transportation,Obesity_Type_I
+Male,21.797388,1.775584,103.605896,yes,yes,2.121909,3,Sometimes,no,2.500556,no,1.20758,0,no,Public_Transportation,Obesity_Type_I
+Male,22.719385,1.746645,96.875502,yes,yes,2,3,Sometimes,no,2.714091,no,2.64102,1.224737,no,Public_Transportation,Obesity_Type_I
+Male,22.177099,1.741353,96.738014,yes,yes,2,3,Sometimes,no,2.697661,no,2.614909,1.229474,no,Public_Transportation,Obesity_Type_I
+Female,23.099906,1.571812,78.997166,yes,yes,2,1,Sometimes,no,2,no,0.402614,0,no,Public_Transportation,Obesity_Type_I
+Female,24.17851,1.589027,80.553067,yes,yes,2,1,Sometimes,no,2,no,0.011477,0,no,Public_Transportation,Obesity_Type_I
+Male,20.924808,1.803245,105.686091,yes,yes,2,3,Sometimes,no,2.290368,no,0.001086,0.544128,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.975973,1.792646,105.619143,yes,yes,2,3,Sometimes,no,1.025275,no,0.00203,0.175587,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.856301,1.87199,115.627554,yes,yes,2,2.358455,Sometimes,no,2.515765,no,0.98232,1.157466,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18.140751,1.859056,111.235188,yes,yes,2,1.706551,Sometimes,no,2.039514,no,1,2,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18,1.692242,90.019501,yes,yes,2.642744,3,Sometimes,no,1.904054,no,1.645654,0.363549,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18,1.683,90.924208,yes,yes,2.387426,3,Sometimes,no,1.77962,no,0.743005,0.920162,Sometimes,Public_Transportation,Obesity_Type_I
+Female,20.206358,1.738397,93.890682,yes,yes,1.996638,3,Sometimes,no,1.033199,no,0.584793,0.518297,Sometimes,Public_Transportation,Obesity_Type_I
+Female,18.152876,1.694969,92.508122,yes,yes,2.107854,3,Sometimes,no,1.383894,no,1.030526,0.831412,Sometimes,Public_Transportation,Obesity_Type_I
+Male,22.336216,1.785718,105.055686,yes,yes,2.253707,3,Sometimes,no,2.524432,no,0.58216,0.175694,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.7425,105.028665,yes,yes,2.393837,3,Sometimes,no,2.01499,no,0.978815,0.41322,Sometimes,Public_Transportation,Obesity_Type_I
+Male,22.088059,1.803132,106.329569,yes,yes,2.348745,3,Sometimes,no,2.096751,no,0.544564,0.67688,Sometimes,Public_Transportation,Obesity_Type_I
+Male,23,1.774644,105.966894,yes,yes,2.312825,3,Sometimes,no,2.073497,no,0.614466,0.579541,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.993067,1.782269,104.97003,yes,yes,2,3,Sometimes,no,1.234943,no,0.008368,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.654752,1.780791,102.921218,yes,yes,2,3,Sometimes,no,1.613829,no,1.251665,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.624552,1.790151,106.320686,yes,yes,2.490776,3,Sometimes,no,2.654517,no,0.112454,0.756339,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.682636,1.818641,105.496975,yes,yes,2.078082,3,Sometimes,no,2.895614,no,0.06275,0.261793,Sometimes,Public_Transportation,Obesity_Type_I
+Male,18,1.806827,108.829395,yes,yes,2,1.24884,Sometimes,no,2.416213,no,1,1.546738,no,Public_Transportation,Obesity_Type_I
+Male,18,1.811189,108.800964,yes,yes,2,1.250548,Sometimes,no,2.121176,no,1,0.741069,no,Public_Transportation,Obesity_Type_I
+Male,18,1.811738,108.897324,yes,yes,2,1.202179,Sometimes,no,2.36293,no,1,1.47574,no,Public_Transportation,Obesity_Type_I
+Male,18,1.799779,108.934574,yes,yes,2,1.194815,Sometimes,no,2.434832,no,1,1.541455,no,Public_Transportation,Obesity_Type_I
+Female,19.374207,1.632605,82,yes,yes,2.805512,2.880817,Sometimes,no,1.001307,no,0,1.376597,Sometimes,Public_Transportation,Obesity_Type_I
+Female,19.275687,1.623377,82.851318,yes,yes,1.276858,2.918124,Sometimes,no,1.403784,no,0.552511,1.560402,Sometimes,Public_Transportation,Obesity_Type_I
+Female,22.956845,1.618686,81.281578,yes,yes,2.396265,1.073421,Sometimes,no,1.979833,no,0.022598,0.061282,no,Public_Transportation,Obesity_Type_I
+Female,22.829681,1.616085,82.582954,yes,yes,2.663866,1.416309,Sometimes,no,2.379275,no,0.256323,0.598244,no,Public_Transportation,Obesity_Type_I
+Male,22.200779,1.769328,105.000576,yes,yes,2.685484,3,Sometimes,no,2.649459,no,1,0,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.688557,1.807029,105.696358,yes,yes,2.055209,3,Sometimes,no,2.885852,no,0.903369,0.14451,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.803186,1.882533,117.468516,yes,yes,2,2.18162,Sometimes,no,2.325091,no,0.891444,1.223661,Sometimes,Public_Transportation,Obesity_Type_I
+Male,19.515324,1.879144,112.932984,yes,yes,2,2.152733,Sometimes,no,2.53369,no,0.917563,1.285838,Sometimes,Public_Transportation,Obesity_Type_I
+Male,21.962219,1.931263,118.20313,yes,yes,2,3,Sometimes,no,3,no,0.743593,1,Sometimes,Public_Transportation,Obesity_Type_I
+Male,20.534089,1.861358,115.39792,yes,yes,2,3,Sometimes,no,3,no,0.987591,1.914531,Sometimes,Public_Transportation,Obesity_Type_I
+Female,39.29266,1.567131,80,yes,yes,2.025479,1.046144,Sometimes,no,1.941224,no,1.983265,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.872971,1.565366,80,yes,yes,2.002564,1.974233,Sometimes,no,1.935398,no,1.649299,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.613378,1.516007,77.033049,yes,yes,2,2.880794,Sometimes,no,1.61837,no,0.120165,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.471683,1.549812,76.082517,yes,yes,2,2.765213,Sometimes,no,1.551266,no,1.350001,0,Sometimes,Automobile,Obesity_Type_I
+Male,18.611197,1.714143,96.568814,yes,yes,2,2.967089,Sometimes,no,2,no,0,0.467048,no,Public_Transportation,Obesity_Type_I
+Male,17.412629,1.723191,97.350366,yes,yes,2,3,Sometimes,no,2,no,0,0.931721,no,Public_Transportation,Obesity_Type_I
+Female,39.12631,1.562889,76.65949,yes,yes,2,3,Sometimes,no,1.440526,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,37.063599,1.502609,75.279605,yes,yes,2,3,Sometimes,no,1.759803,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,41.318302,1.544937,77.053948,yes,yes,2,3,Sometimes,no,2.090413,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Female,43.726081,1.592316,77.00103,yes,yes,2,3,Sometimes,no,2.806922,no,0,0,Sometimes,Automobile,Obesity_Type_I
+Male,18,1.787733,108.019211,yes,yes,2,2.37985,Sometimes,no,2.616138,no,1,0.652485,no,Public_Transportation,Obesity_Type_I
+Male,18,1.782722,108.044313,yes,yes,2,2.655265,Sometimes,no,2.297896,no,1,0.552665,no,Public_Transportation,Obesity_Type_I
+Male,18,1.797523,108.316462,yes,yes,2,1.941307,Sometimes,no,2.708168,no,1,1.425852,no,Public_Transportation,Obesity_Type_I
+Male,18,1.803527,108.251044,yes,yes,2,1.709546,Sometimes,no,2.530157,no,1,0.6454,no,Public_Transportation,Obesity_Type_I
+Male,29.409825,1.801224,108.15619,yes,yes,2.385502,2.883984,Sometimes,no,2.140544,no,1.144876,1.767468,Sometimes,Automobile,Obesity_Type_I
+Male,28.421533,1.829239,107.10819,yes,yes,2.465575,2.935381,Sometimes,no,2.480555,no,1.00183,1.670313,Sometimes,Automobile,Obesity_Type_I
+Female,18,1.692913,89.93889,yes,yes,2.818502,3,Sometimes,no,1.991251,no,1.492967,0,no,Public_Transportation,Obesity_Type_I
+Female,18,1.668555,85.607466,yes,yes,2.081238,3,Sometimes,no,1.18363,no,1.17177,0,no,Public_Transportation,Obesity_Type_I
+Male,23.246702,1.652971,93.310284,yes,yes,1.368978,3,Sometimes,no,1.953152,no,0.104707,0.22183,no,Automobile,Obesity_Type_I
+Male,24.481032,1.661277,90.744965,yes,yes,1.712848,3,Sometimes,no,1.162153,no,0.458981,0.885532,no,Automobile,Obesity_Type_I
+Male,29,1.641784,89.424947,yes,yes,1.3307,3,Sometimes,no,1.880967,no,0.322013,0,Sometimes,Automobile,Obesity_Type_I
+Male,27.968765,1.673767,89.995034,yes,yes,1.961069,3,Sometimes,no,2.652327,no,0.56931,0.319008,Sometimes,Automobile,Obesity_Type_I
+Female,18.024744,1.617192,83.121811,yes,yes,2.976509,3,Sometimes,no,1.022705,no,1.02569,0.313016,no,Public_Transportation,Obesity_Type_I
+Female,18.10682,1.602129,82.412665,yes,yes,2.319648,3,Sometimes,no,1.107164,no,0.692123,0.30402,no,Public_Transportation,Obesity_Type_I
+Male,21.379676,1.701413,96.710735,yes,yes,2,2.961192,Sometimes,no,2.623344,no,0.981686,1.35537,no,Public_Transportation,Obesity_Type_I
+Male,21.353237,1.709731,95.032177,yes,yes,2,2.973476,Sometimes,no,2,no,0,1.331527,no,Public_Transportation,Obesity_Type_I
+Male,20.338815,1.698829,98.572753,yes,yes,2,2.392811,Sometimes,no,2,no,0,1.80931,no,Public_Transportation,Obesity_Type_I
+Male,20.720639,1.705304,99.873716,yes,yes,2,1.293342,Sometimes,no,2,no,0,1.917679,no,Public_Transportation,Obesity_Type_I
+Female,19.045357,1.61291,82.193405,yes,yes,1.261288,2.930044,Sometimes,no,1.166655,no,0.133398,0.95174,no,Public_Transportation,Obesity_Type_I
+Female,18.945961,1.605469,82.039,yes,yes,2.76533,3,Sometimes,no,1.048584,no,0.192559,0.720411,no,Public_Transportation,Obesity_Type_I
+Male,18.88061,1.80416,104.40682,yes,yes,2,3,Sometimes,no,3,no,2.2405,0,no,Public_Transportation,Obesity_Type_I
+Male,19.850524,1.785062,104.187314,yes,yes,2,3,Sometimes,no,2.15157,no,1.605983,0,no,Public_Transportation,Obesity_Type_I
+Male,30.200946,1.755926,112.289883,yes,yes,2.317734,3,Sometimes,no,2.152736,no,0,1.108145,no,Automobile,Obesity_Type_II
+Male,25.314589,1.787802,115.127662,yes,yes,1.588114,3,Sometimes,no,2.115967,no,1.541072,0,no,Automobile,Obesity_Type_II
+Male,37.186795,1.704877,107.94747,yes,yes,2.549782,3.985442,Sometimes,no,1,no,1.976582,0,no,Public_Transportation,Obesity_Type_II
+Male,32.707653,1.695347,102.810704,yes,yes,2.795086,2.129909,Sometimes,no,1,no,1.989316,0,no,Public_Transportation,Obesity_Type_II
+Male,27.562425,1.908608,128.828122,yes,yes,2.206276,3,Sometimes,no,1.813082,no,1.94984,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.421596,1.819296,122.137917,yes,yes,2,3,Sometimes,no,1.984323,no,0.793262,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.945139,1.773259,118.154345,yes,no,2.18354,3,Sometimes,no,2.277937,no,0.68183,0,Sometimes,Automobile,Obesity_Type_II
+Male,34.139453,1.774647,120.151967,yes,no,2.962415,3,Sometimes,no,2.172649,no,1.097905,0,Sometimes,Automobile,Obesity_Type_II
+Male,26.225442,1.773664,116.160329,yes,yes,2.442536,3,Sometimes,no,2.174371,no,0,1.928972,Sometimes,Public_Transportation,Obesity_Type_II
+Male,28.363149,1.793476,112.725005,yes,yes,1.99124,3,Sometimes,no,2,no,0,0.292956,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.991467,1.82559,120.860386,yes,yes,3,3,Sometimes,no,3,no,0.691369,1.415536,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.498751,1.885543,121.253083,yes,yes,2.408561,3,Sometimes,no,2.851904,no,1.320065,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.605225,1.75,120,yes,yes,2.758394,3,Sometimes,yes,2.174248,no,1.079524,1.358163,Sometimes,Automobile,Obesity_Type_II
+Male,31.457413,1.87407,128.867444,yes,yes,2.956297,3,Sometimes,yes,1.2751,no,0.901924,1.875023,Sometimes,Automobile,Obesity_Type_II
+Male,24.82929,1.755967,112.256165,yes,yes,1.369529,3,Sometimes,no,2,no,1.596576,0.0026,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.739421,1.757069,117.298233,yes,yes,1.392665,3,Sometimes,no,2,no,1.097983,0.630866,Sometimes,Public_Transportation,Obesity_Type_II
+Male,41,1.75,116.594351,yes,yes,2,2.831771,Sometimes,no,1.725226,no,0.356288,0,Sometimes,Automobile,Obesity_Type_II
+Male,27.83173,1.704028,101.634313,yes,yes,2.630401,1,Sometimes,no,1,no,0.245354,0.315261,Sometimes,Automobile,Obesity_Type_II
+Male,23.260061,1.849003,121.414744,yes,yes,3,2.595126,Sometimes,no,2.054072,no,0.835271,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.705772,1.841989,122.024954,yes,yes,3,2.52751,Sometimes,no,1.645338,no,1.025438,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.041043,1.613491,98.490766,yes,yes,3,2.120936,Sometimes,no,1,no,1.543386,0.890213,no,Public_Transportation,Obesity_Type_II
+Male,20.013906,1.647821,106.509947,yes,yes,2.869778,1.468948,Sometimes,no,1.656082,no,0,1.444033,no,Public_Transportation,Obesity_Type_II
+Male,30.02019,1.75834,112.010504,yes,yes,1.868212,3,Sometimes,no,2.023328,no,0,0.613992,Sometimes,Automobile,Obesity_Type_II
+Male,25.15462,1.758398,112.089022,yes,yes,1.108663,3,Sometimes,no,2,no,1.230219,0.001716,Sometimes,Automobile,Obesity_Type_II
+Male,30.870724,1.670774,101.626189,yes,yes,2.907744,3.990925,Sometimes,no,1,no,1.99975,0,no,Public_Transportation,Obesity_Type_II
+Male,28.712995,1.665585,98.66176,yes,yes,2.95801,2.834253,Sometimes,no,1,no,1.999886,0,no,Public_Transportation,Obesity_Type_II
+Male,30.710511,1.914186,129.852531,yes,yes,2.197261,3,Sometimes,no,1.11184,no,0.906843,0.976473,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.827481,1.899588,124.269251,yes,yes,2.048962,3,Sometimes,no,2.822871,no,1.066169,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.86893,1.75,118.102897,yes,yes,2.139196,3,Sometimes,no,2.04843,no,0.368026,0.360986,Sometimes,Automobile,Obesity_Type_II
+Male,36.542885,1.75,119.434645,yes,yes,2.72989,3,Sometimes,no,2.030084,no,0.592607,0.754417,Sometimes,Automobile,Obesity_Type_II
+Male,21.556361,1.773664,116.160329,yes,yes,2,3,Sometimes,no,2,no,1.399183,1,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.963649,1.792998,116.102615,yes,yes,1.994679,3,Sometimes,no,2,no,0.966617,0.739006,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.145295,1.82559,120.860386,yes,yes,2.403421,3,Sometimes,no,2.490613,no,2,0.707768,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.2984,1.827279,120.996074,yes,yes,3,3,Sometimes,no,3,no,1.110215,0.396352,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.755387,1.775416,121.204668,yes,yes,2.758394,3,Sometimes,no,2.161303,no,0.428173,0.716327,Sometimes,Automobile,Obesity_Type_II
+Male,30.62865,1.766975,118.363376,yes,yes,2.964319,3,Sometimes,no,2.377257,no,0.614959,1.875023,Sometimes,Automobile,Obesity_Type_II
+Male,23.454558,1.754218,117.384745,yes,yes,1.617093,3,Sometimes,no,2,no,1.818052,0.631825,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.432227,1.754996,119.087557,yes,yes,1.631144,3,Sometimes,no,2,no,1.593183,0.86374,Sometimes,Public_Transportation,Obesity_Type_II
+Male,40.50021,1.748103,108.308112,yes,yes,2.317459,3.734323,Sometimes,no,1,no,1.628637,0,no,Automobile,Obesity_Type_II
+Male,34.576714,1.73325,103.669116,yes,yes,2.501224,2.049565,Sometimes,no,1,no,1.746583,0,no,Automobile,Obesity_Type_II
+Male,22.276969,1.84995,121.786485,yes,yes,3,2.272214,Sometimes,no,1.555534,no,0.348839,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,21.963787,1.849601,122.333425,yes,yes,3,2.218285,Sometimes,no,1.340117,no,0.428259,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.001685,1.606066,99.961731,yes,yes,3,1.418833,Sometimes,no,1,no,1.19102,1.384186,no,Public_Transportation,Obesity_Type_II
+Male,20.027764,1.611434,103.175516,yes,yes,2.983042,1.109956,Sometimes,no,1.430444,no,0,1.690901,no,Public_Transportation,Obesity_Type_II
+Male,28.704462,1.762887,113.90198,yes,yes,2.323351,3,Sometimes,no,2.165048,no,0,1.493716,Sometimes,Automobile,Obesity_Type_II
+Male,26.774115,1.755938,112.287678,yes,yes,1.428289,3,Sometimes,no,2.117733,no,0.485322,0.696948,Sometimes,Automobile,Obesity_Type_II
+Male,25.539411,1.787052,115.428276,yes,yes,1.992889,3,Sometimes,no,2.141271,no,1.121038,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.300208,1.765258,114.330023,yes,yes,1.562804,3,Sometimes,no,2.075493,no,1.553734,0.000436,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.194458,1.726279,110.714711,yes,yes,1.794825,3.914454,Sometimes,no,1.972016,no,0.668963,0,no,Automobile,Obesity_Type_II
+Male,40.794057,1.735851,109.889334,yes,yes,2.305349,3.169089,Sometimes,no,1.02161,no,0.908981,0,no,Automobile,Obesity_Type_II
+Male,33.55336,1.699793,103.841672,yes,yes,2.576449,2.419656,Sometimes,no,1,no,1.982379,0,no,Public_Transportation,Obesity_Type_II
+Male,30.304203,1.703202,103.0344,yes,yes,2.754645,2.175432,Sometimes,no,1.541733,no,0.358709,1.0739,no,Public_Transportation,Obesity_Type_II
+Male,30.958051,1.906821,128.856677,yes,yes,2.633855,3,Sometimes,no,1.619305,no,1.847802,0.424612,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.429687,1.910987,129.935666,yes,yes,2.200588,3,Sometimes,no,1.295697,no,1.058378,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.520854,1.784049,120.644178,yes,yes,2.499108,3,Sometimes,no,2.040952,no,0.838739,0.489854,Sometimes,Automobile,Obesity_Type_II
+Male,30.610436,1.783914,120.549592,yes,yes,2.05687,3,Sometimes,no,2.358088,no,0.093418,1.759954,Sometimes,Automobile,Obesity_Type_II
+Male,26.740655,1.863883,120.202596,yes,yes,2.247795,3,Sometimes,no,2.816015,no,0.712726,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.680376,1.812259,118.277657,yes,yes,2.239634,3,Sometimes,no,2.684264,no,0.979306,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,37.056193,1.75015,118.206565,yes,yes,2.09283,3,Sometimes,no,2.077704,no,0.598655,0,Sometimes,Automobile,Obesity_Type_II
+Male,28.825279,1.815347,120.399758,yes,yes,2.9673,3,Sometimes,no,2.530035,no,1.045107,0.947866,Sometimes,Automobile,Obesity_Type_II
+Male,25.883749,1.767077,114.133149,yes,yes,2.225731,3,Sometimes,no,2.154326,no,1.266866,0.390178,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.486521,1.762461,115.531622,yes,yes,2.178308,3,Sometimes,no,2.165804,no,1.420675,1.875683,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.883021,1.779049,112.438508,yes,yes,2.065752,3,Sometimes,no,2.078297,no,0,0.677354,Sometimes,Automobile,Obesity_Type_II
+Male,26.957645,1.780731,112.957922,yes,yes,2.263245,3,Sometimes,no,2.092509,no,0,1.836853,Sometimes,Automobile,Obesity_Type_II
+Male,26.490926,1.872517,120.898397,yes,yes,2.443538,3,Sometimes,no,2.939492,no,1.208142,0.738935,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.558801,1.801224,119.050381,yes,yes,2.744994,3,Sometimes,no,2.478838,no,0.684487,0.677909,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.908879,1.876898,121.277832,yes,yes,2.630137,2.806298,Sometimes,no,1.970508,no,1.304291,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.201622,1.77558,120.589419,yes,yes,2.113843,3,Sometimes,no,2.316069,no,1.804157,0.357314,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.689773,1.76569,120.082941,yes,yes,2.821727,3,Sometimes,no,2.173279,no,1.096556,0.232858,Sometimes,Automobile,Obesity_Type_II
+Male,30.242597,1.759772,118.382361,yes,yes,2.312528,3,Sometimes,no,2.208068,no,1.07672,1.035711,Sometimes,Automobile,Obesity_Type_II
+Male,31.346845,1.823545,126.460936,yes,yes,2.938801,3,Sometimes,yes,1.478994,no,0.946763,1.360463,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.577343,1.868923,125.064264,yes,yes,2.050619,3,Sometimes,yes,1.515183,no,0.849811,0.261901,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.124595,1.77151,113.207124,yes,yes,1.457758,3,Sometimes,no,2.020249,no,1.556709,0.00133,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.509034,1.77219,114.097656,yes,yes,2.19331,3,Sometimes,no,2.089983,no,1.360994,0.857438,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.478662,1.858265,117.57457,yes,yes,2.028571,3,Sometimes,no,2.530428,no,1.31257,0.066805,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.100513,1.830596,118.424156,yes,yes,1.455602,3,Sometimes,no,2.363307,no,1.144683,0.101026,Sometimes,Public_Transportation,Obesity_Type_II
+Male,38.523646,1.765836,118.533246,yes,yes,2.177896,2.987652,Sometimes,no,1.745095,no,0.656548,0,Sometimes,Automobile,Obesity_Type_II
+Male,38.71216,1.770278,117.29188,yes,yes,2.252382,2.842848,Sometimes,no,1.879381,no,0.919388,0,Sometimes,Automobile,Obesity_Type_II
+Male,25.717522,1.673491,103.706181,yes,yes,2.668949,1.134321,Sometimes,no,1.279443,no,0.11243,1.135503,no,Public_Transportation,Obesity_Type_II
+Male,32.259623,1.703346,102.686239,yes,yes,2.723953,1.924168,Sometimes,no,1,no,1.622055,0.069367,no,Public_Transportation,Obesity_Type_II
+Male,23.47007,1.842906,121.142535,yes,yes,3,2.701689,Sometimes,no,2.738485,no,0.992253,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.408805,1.779547,118.740035,yes,yes,2.736298,2.992903,Sometimes,no,2.045561,no,0.854957,0.428452,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.443011,1.873368,121.82962,yes,yes,2.722161,2.658837,Sometimes,no,2.375707,no,1.299469,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.906342,1.755902,120.021161,yes,yes,2.971574,2.80742,Sometimes,no,1.962646,no,1.583832,0.504816,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.007488,1.617655,100.941357,yes,yes,2.871016,1.834472,Sometimes,no,1.016585,no,0.236168,0.974939,no,Public_Transportation,Obesity_Type_II
+Male,30.686701,1.644517,100.004418,yes,yes,2.96405,2.123138,Sometimes,no,1,no,1.699181,0.620465,no,Public_Transportation,Obesity_Type_II
+Male,20.184451,1.701284,104.578255,yes,yes,2.653721,1.265463,Sometimes,no,1.368457,no,0.218356,0.768071,no,Public_Transportation,Obesity_Type_II
+Male,25.447208,1.65891,104.548794,yes,yes,2.859097,1.340361,Sometimes,no,1.530508,no,0.174475,1.261705,no,Public_Transportation,Obesity_Type_II
+Male,30.002029,1.759324,112.000381,yes,yes,1.572036,3,Sometimes,no,2.003563,no,0,0.340196,Sometimes,Automobile,Obesity_Type_II
+Male,30.188303,1.758382,112.10074,yes,yes,1.387489,3,Sometimes,no,2.017712,no,0,0.731257,Sometimes,Automobile,Obesity_Type_II
+Male,24.244029,1.622297,99.982541,yes,yes,2.941929,3.989492,Sometimes,no,1.014135,no,1.958694,0.687342,no,Public_Transportation,Obesity_Type_II
+Male,24.521879,1.623278,97.582959,yes,yes,2.973569,2.650088,Sometimes,no,1.003063,no,1.981154,0.157007,no,Public_Transportation,Obesity_Type_II
+Male,29.721964,1.918859,129.991623,yes,yes,2.041376,3,Sometimes,no,1.120213,no,1.05545,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.906575,1.913252,129.232216,yes,yes,2.02472,3,Sometimes,no,1.800335,no,1.196368,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,37.084742,1.75,118.07381,yes,yes,2.133964,3,Sometimes,no,2.008361,no,0.202029,0.200516,Sometimes,Automobile,Obesity_Type_II
+Male,39.08886,1.75,119.029103,yes,yes,2.702457,3,Sometimes,no,2.005194,no,0.325313,0.419055,Sometimes,Automobile,Obesity_Type_II
+Male,22.658572,1.784994,113.714521,yes,yes,1.760038,3,Sometimes,no,2.003531,no,1.089061,0.072264,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.889099,1.792533,116.157766,yes,yes,1.996646,3,Sometimes,no,2,no,1.600536,0.739684,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.63474,1.829757,120.980508,yes,yes,2.644094,3,Sometimes,no,2.740525,no,2,0.500936,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.920362,1.796415,120.988454,yes,yes,2.475892,3,Sometimes,no,2.949356,no,1.866839,0.100048,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.783845,1.75,120,yes,yes,2.941627,3,Sometimes,no,2.318134,no,0.582686,1.588046,Sometimes,Automobile,Obesity_Type_II
+Male,31.627962,1.762389,118.548733,yes,yes,2.998441,3,Sometimes,no,2.462916,no,0.554646,1.99219,Sometimes,Automobile,Obesity_Type_II
+Male,22.976188,1.825718,121.236915,yes,yes,2.397284,2.89292,Sometimes,no,1.983973,no,1.859667,0.0026,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.977357,1.839699,120.431551,yes,yes,2.382705,2.938902,Sometimes,no,1.999278,no,1.686229,0.630866,Sometimes,Public_Transportation,Obesity_Type_II
+Male,40.973007,1.749405,109.908779,yes,yes,2.176317,2.986637,Sometimes,no,1.015672,no,0.94141,0,no,Automobile,Obesity_Type_II
+Male,41,1.75,115.806977,yes,yes,2,2.701521,Sometimes,no,1.142644,no,0.514225,0,no,Automobile,Obesity_Type_II
+Male,21.721507,1.849998,122.119682,yes,yes,3,2.014671,Sometimes,no,1.292786,no,0.145687,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,21.544554,1.84998,122.609911,yes,yes,3,1.971659,Sometimes,no,1.179254,no,0.178856,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.000307,1.607939,100.618239,yes,yes,2.877743,1.311797,Sometimes,no,1.000463,no,0.182249,1.4312,no,Public_Transportation,Obesity_Type_II
+Male,24.0409,1.603226,99.605527,yes,yes,3,1.262831,Sometimes,no,1,no,0.883171,1.382995,no,Public_Transportation,Obesity_Type_II
+Male,29.509151,1.770663,112.471118,yes,yes,2.303041,3,Sometimes,no,2.065817,no,0,0.725222,Sometimes,Automobile,Obesity_Type_II
+Male,29.246269,1.758038,112.395309,yes,yes,2.323003,3,Sometimes,no,2.155558,no,0,1.251554,Sometimes,Automobile,Obesity_Type_II
+Male,25.341399,1.786997,115.025361,yes,yes,1.99953,3,Sometimes,no,2.111908,no,1.453042,0.849503,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.314163,1.771837,114.906095,yes,yes,1.585183,3,Sometimes,no,2.101841,no,1.543961,0.000073,Sometimes,Public_Transportation,Obesity_Type_II
+Male,40.366238,1.722396,109.349025,yes,yes,2.281963,3.770379,Sometimes,no,1,no,1.330519,0,no,Automobile,Obesity_Type_II
+Male,33.749594,1.701387,107.025415,yes,yes,2.561638,2.87747,Sometimes,no,1,no,1.980401,0,no,Automobile,Obesity_Type_II
+Male,32.867331,1.697421,103.017623,yes,yes,2.600217,2.175153,Sometimes,no,1,no,1.985536,0,no,Public_Transportation,Obesity_Type_II
+Male,30.403205,1.696365,102.815983,yes,yes,2.79166,2.13229,Sometimes,no,1.254589,no,0.652736,1.040713,no,Public_Transportation,Obesity_Type_II
+Male,29.687488,1.909198,129.679131,yes,yes,2.08868,3,Sometimes,no,1.520215,no,1.857351,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,28.992809,1.909105,129.874864,yes,yes,2.206119,3,Sometimes,no,1.483856,no,1.113169,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.475248,1.801368,121.094257,yes,yes,2.328469,3,Sometimes,no,2.001208,no,0.800487,0.176678,Sometimes,Automobile,Obesity_Type_II
+Male,30.350516,1.860292,123.721352,yes,yes,2.002784,3,Sometimes,no,2.292906,no,1.034031,0,Sometimes,Automobile,Obesity_Type_II
+Male,26.684354,1.819535,118.332689,yes,yes,1.975675,3,Sometimes,no,2.357969,no,0.704236,0.010721,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.607478,1.793174,118.165082,yes,yes,2.002076,3,Sometimes,no,2.338373,no,0.897562,0.081156,Sometimes,Public_Transportation,Obesity_Type_II
+Male,33.174147,1.75015,118.299585,yes,yes,2.218599,3,Sometimes,no,2.104337,no,0.766007,0.262118,Sometimes,Automobile,Obesity_Type_II
+Male,32.29016,1.754956,120.098812,yes,yes,2.9673,3,Sometimes,no,2.530035,no,0.955317,1.339232,Sometimes,Automobile,Obesity_Type_II
+Male,26.050109,1.773115,115.089316,yes,yes,2.392179,3,Sometimes,no,2.16467,no,1.079934,1.073026,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.846279,1.772731,115.828167,yes,yes,2.381164,3,Sometimes,no,2.170225,no,1.211048,1.89933,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.620095,1.787933,112.536367,yes,yes,2.008245,3,Sometimes,no,2.040137,no,0,0.474216,Sometimes,Automobile,Obesity_Type_II
+Male,27.439056,1.785277,112.740797,yes,yes,2.155182,3,Sometimes,no,2.049079,no,0,1.74992,Sometimes,Automobile,Obesity_Type_II
+Male,28.493397,1.817572,120.8158,yes,yes,2.969233,3,Sometimes,no,2.807984,no,0.982134,1.191998,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.474245,1.84342,120.420406,yes,yes,2.765063,3,Sometimes,no,2.867206,no,0.706777,0.677909,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.036269,1.874519,121.244969,yes,yes,2.630137,3,Sometimes,no,2.960088,no,1.356468,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.825398,1.796332,120.901591,yes,yes,2.195964,3,Sometimes,no,2.514872,no,1.664722,0.127673,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.493946,1.756221,119.117122,yes,yes,2.619987,3,Sometimes,no,2.194746,no,1.076926,1.090995,Sometimes,Automobile,Obesity_Type_II
+Male,30.607546,1.757132,118.565568,yes,yes,2.918113,3,Sometimes,no,2.240463,no,1.076248,1.480875,Sometimes,Automobile,Obesity_Type_II
+Male,31.194323,1.889104,129.157346,yes,yes,2.889485,3,Sometimes,yes,1.279771,no,0.941424,0.008343,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.36347,1.869323,127.507411,yes,yes,2.939727,3,Sometimes,yes,1.344122,no,0.923428,1.432336,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.027254,1.757154,112.200812,yes,yes,1.264234,3,Sometimes,no,2,no,1.333435,0.002168,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.058566,1.764484,113.234349,yes,yes,1.517912,3,Sometimes,no,2.038958,no,1.590255,0.00164,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.417552,1.774775,117.398976,yes,yes,2.23372,2.993634,Sometimes,no,2.028368,no,0.863158,0.449886,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.5822,1.769933,117.708705,yes,yes,1.475906,2.999346,Sometimes,no,2.01943,no,1.046878,0.460866,Sometimes,Public_Transportation,Obesity_Type_II
+Male,40.106145,1.760175,117.651046,yes,yes,2.032883,2.976211,Sometimes,no,1.726109,no,0.477855,0,Sometimes,Automobile,Obesity_Type_II
+Male,40.174191,1.763029,116.974504,yes,yes,2.046651,2.842035,Sometimes,no,1.732072,no,0.584272,0,Sometimes,Automobile,Obesity_Type_II
+Male,27.186873,1.679515,102.872802,yes,yes,2.667229,1.09749,Sometimes,no,1.225958,no,0.206954,1.003011,no,Public_Transportation,Obesity_Type_II
+Male,30.424369,1.699354,100.176866,yes,yes,2.819934,1.91863,Sometimes,no,1,no,1.39302,0.553311,no,Public_Transportation,Obesity_Type_II
+Male,23.141402,1.849307,121.658729,yes,yes,3,2.510135,Sometimes,no,1.693362,no,0.769709,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.178638,1.86741,121.684311,yes,yes,2.954417,2.65772,Sometimes,no,2.104696,no,0.870056,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.832105,1.86714,121.835883,yes,yes,2.826251,2.604998,Sometimes,no,1.842173,no,1.284798,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.083621,1.848553,121.421121,yes,yes,3,2.567567,Sometimes,no,2.011023,no,0.916478,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.079971,1.61981,98.54302,yes,yes,2.95841,2.434347,Sometimes,no,1,no,1.930033,0.754023,no,Public_Transportation,Obesity_Type_II
+Male,26.947786,1.647807,99.592225,yes,yes,2.935157,1.845858,Sometimes,no,1,no,1.089891,0.715993,no,Public_Transportation,Obesity_Type_II
+Male,20.101026,1.619128,104.303711,yes,yes,2.870895,1.627555,Sometimes,no,1.375728,no,0.210181,1.163117,no,Public_Transportation,Obesity_Type_II
+Male,22.789402,1.64187,104.270062,yes,yes,2.869833,1.569176,Sometimes,no,1.533682,no,0.167943,1.368262,no,Public_Transportation,Obesity_Type_II
+Male,28.404332,1.787379,112.173731,yes,yes,1.878251,3,Sometimes,no,2.022933,no,0,0.325445,Sometimes,Automobile,Obesity_Type_II
+Male,30.003358,1.758355,112.007101,yes,yes,1.750809,3,Sometimes,no,2.002871,no,0,0.114457,Sometimes,Automobile,Obesity_Type_II
+Male,25.136116,1.76414,113.089716,yes,yes,1.168856,3,Sometimes,no,2.013205,no,1.246223,0.00159,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.139466,1.767186,112.226032,yes,yes,1.443674,3,Sometimes,no,2.002194,no,1.386151,0.001337,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.743821,1.67782,102.022057,yes,yes,2.826036,2.371658,Sometimes,no,1,no,1.996487,0,no,Public_Transportation,Obesity_Type_II
+Male,30.796262,1.668478,100.543983,yes,yes,2.9553,2.378211,Sometimes,no,1,no,1.738267,0.394533,no,Public_Transportation,Obesity_Type_II
+Male,28.393111,1.685462,99.540122,yes,yes,2.774562,2.301129,Sometimes,no,1,no,1.621733,0.138314,no,Public_Transportation,Obesity_Type_II
+Male,27.93982,1.694642,99.709329,yes,yes,2.772027,2.454432,Sometimes,no,1,no,0.858554,0.051268,no,Public_Transportation,Obesity_Type_II
+Male,31.034092,1.886109,129.349471,yes,yes,2.499626,3,Sometimes,yes,1.125338,no,0.94522,1.022505,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.684347,1.915,129.966428,yes,yes,2.108638,3,Sometimes,yes,1.014876,no,0.987521,0.047473,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.108216,1.841908,124.070717,yes,yes,2.467548,3,Sometimes,no,2.66029,no,0.994422,0.310394,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.347497,1.868127,123.172214,yes,yes,2.585942,3,Sometimes,no,2.302506,no,0.597863,0.111925,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.761799,1.751688,119.205308,yes,yes,2.14961,3,Sometimes,no,2.133876,no,0.393452,0.345887,Sometimes,Automobile,Obesity_Type_II
+Male,30.976932,1.755333,118.237782,yes,yes,2.938616,3,Sometimes,no,2.06039,no,0.371508,1.333559,Sometimes,Automobile,Obesity_Type_II
+Male,37.588628,1.754217,117.8972,yes,yes,2.535154,2.915921,Sometimes,no,1.8924,no,0.693732,0.06741,Sometimes,Automobile,Obesity_Type_II
+Male,37.997912,1.76071,118.668332,yes,yes,2.611222,2.992083,Sometimes,no,1.796376,no,0.641954,0.20281,Sometimes,Automobile,Obesity_Type_II
+Male,21.6548,1.755938,116.311962,yes,yes,1.800122,3,Sometimes,no,2,no,1.689525,0.764452,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.829753,1.765137,116.669906,yes,yes,1.482722,3,Sometimes,no,2,no,1.112504,0.690353,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.015173,1.788239,115.382519,yes,yes,1.735664,3,Sometimes,no,2.041536,no,1.392406,0.39174,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.812795,1.767121,116.164351,yes,yes,1.655684,3,Sometimes,no,2,no,0.976425,0.690082,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.975685,1.840039,120.975489,yes,yes,2.598207,2.849347,Sometimes,no,2.006167,no,1.52001,0.295685,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.149036,1.824901,120.805715,yes,yes,2.225149,3,Sometimes,no,2.357978,no,1.943743,0.682128,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.062942,1.828391,120.998266,yes,yes,3,3,Sometimes,no,3,no,1.685369,0.264969,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.140074,1.829529,120.996581,yes,yes,3,3,Sometimes,no,3,no,1.467863,0.343635,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.722801,1.779325,120.751656,yes,yes,2.519592,3,Sometimes,no,2.229145,no,0.350717,1.140348,Sometimes,Automobile,Obesity_Type_II
+Male,30.916426,1.781139,120.794535,yes,yes,2.111887,3,Sometimes,no,2.357373,no,0.425621,1.416353,Sometimes,Automobile,Obesity_Type_II
+Male,31.010302,1.764166,119.373019,yes,yes,2.970983,3,Sometimes,no,2.828861,no,0.454944,1.915818,Sometimes,Automobile,Obesity_Type_II
+Male,30.605464,1.754796,118.805937,yes,yes,2.907542,3,Sometimes,no,2.284494,no,0.647632,1.668318,Sometimes,Automobile,Obesity_Type_II
+Male,24.622054,1.756509,117.368716,yes,yes,1.451337,3,Sometimes,no,2,no,1.384607,0.631217,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.745833,1.756772,117.326523,yes,yes,1.537505,3,Sometimes,no,2,no,1.631912,0.631422,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.327836,1.754439,119.441207,yes,yes,1.893428,3,Sometimes,no,2,no,1.917383,0.93648,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.411141,1.755142,119.192922,yes,yes,2.007845,2.954446,Sometimes,no,1.968796,no,1.587406,0.838947,Sometimes,Public_Transportation,Obesity_Type_II
+Male,39.825592,1.706741,108.012603,yes,yes,2.487781,3.755976,Sometimes,no,1,no,1.860765,0,no,Automobile,Obesity_Type_II
+Male,36.839761,1.74285,106.421042,yes,yes,2.541785,2.902639,Sometimes,no,1,no,1.668961,0,no,Automobile,Obesity_Type_II
+Male,33.789301,1.6811,102.523111,yes,yes,2.813775,2.372705,Sometimes,no,1,no,1.979355,0,no,Public_Transportation,Obesity_Type_II
+Male,34.161998,1.705682,103.067766,yes,yes,2.562687,2.100918,Sometimes,no,1.458545,no,1.167856,0.412329,no,Public_Transportation,Obesity_Type_II
+Male,22.580038,1.849507,121.560938,yes,yes,3,2.272801,Sometimes,no,1.560064,no,0.79677,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.851721,1.853373,121.737836,yes,yes,2.922511,2.692889,Sometimes,no,1.809531,no,0.478595,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.254934,1.84753,122.06261,yes,yes,3,2.280545,Sometimes,no,1.591597,no,0.932783,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.343204,1.87034,121.458232,yes,yes,2.836055,2.675411,Sometimes,no,1.711666,no,0.93732,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.006271,1.607787,100.783434,yes,yes,2.880759,1.487674,Sometimes,no,1.01378,no,1.00109,1.09838,no,Public_Transportation,Obesity_Type_II
+Male,24.001196,1.603091,100.209405,yes,yes,3,1.02075,Sometimes,no,1,no,0.233056,1.707421,no,Public_Transportation,Obesity_Type_II
+Male,20.156664,1.620109,103.393354,yes,yes,2.766441,1.240424,Sometimes,no,1.413218,no,0.165269,0.862587,no,Public_Transportation,Obesity_Type_II
+Male,23.360307,1.610647,100.642257,yes,yes,2.997524,1.154318,Sometimes,no,1.238057,no,1.1293,1.665621,no,Public_Transportation,Obesity_Type_II
+Male,28.986237,1.758618,113.501549,yes,yes,2.320201,3,Sometimes,no,2.164784,no,0,1.465479,Sometimes,Automobile,Obesity_Type_II
+Male,29.713169,1.763259,113.215814,yes,yes,2.181057,3,Sometimes,no,2.08554,no,0,1.439004,Sometimes,Automobile,Obesity_Type_II
+Male,27.394123,1.764138,112.323213,yes,yes,1.924632,3,Sometimes,no,2.006595,no,0.1768,0.511694,Sometimes,Automobile,Obesity_Type_II
+Male,24.912254,1.75596,112.277567,yes,yes,1.397468,3,Sometimes,no,2.01897,no,0.665439,0.062167,Sometimes,Automobile,Obesity_Type_II
+Male,25.526746,1.783381,115.347176,yes,yes,2.191429,3,Sometimes,no,2.102709,no,1.32534,0.380979,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.523127,1.786532,114.534678,yes,yes,2.100177,3,Sometimes,no,2.099687,no,1.281165,0.247332,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.822348,1.766626,114.187096,yes,yes,2.075321,3,Sometimes,no,2.144838,no,1.501754,0.276319,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.879411,1.765464,114.144378,yes,yes,1.626369,3,Sometimes,no,2.109697,no,1.352973,0.076693,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.361365,1.75853,111.635463,yes,yes,1.263216,3.219347,Sometimes,no,1.981782,no,0.482625,0,Sometimes,Automobile,Obesity_Type_II
+Male,30.451174,1.758539,111.950413,yes,yes,1.067909,3.656401,Sometimes,no,1.98459,no,0.054238,0,Sometimes,Automobile,Obesity_Type_II
+Male,40.564513,1.748015,109.758736,yes,yes,2.310751,3.220181,Sometimes,no,1.006643,no,1.15804,0,no,Automobile,Obesity_Type_II
+Male,40.501722,1.744974,111.169678,yes,yes,2.294259,2.850948,Sometimes,no,1.87029,no,0.917014,0,no,Automobile,Obesity_Type_II
+Male,30.315784,1.701566,103.743534,yes,yes,2.57649,2.187145,Sometimes,no,1.38107,no,1.928275,0.413663,no,Public_Transportation,Obesity_Type_II
+Male,33.293166,1.696412,103.250355,yes,yes,2.679664,2.415522,Sometimes,no,1,no,1.987296,0,no,Public_Transportation,Obesity_Type_II
+Male,30.638944,1.680489,102.004554,yes,yes,2.938687,2.138375,Sometimes,no,1.251715,no,1.181811,0.778375,no,Public_Transportation,Obesity_Type_II
+Male,29.791101,1.703317,102.592171,yes,yes,2.654792,1.077331,Sometimes,no,1.345407,no,0.327418,0.386861,no,Public_Transportation,Obesity_Type_II
+Male,31.205668,1.877732,127.161381,yes,yes,2.731368,3,Sometimes,yes,1.486824,no,1.485978,1.150439,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.899219,1.909639,129.013178,yes,yes,2.22259,3,Sometimes,yes,1.591909,no,1.392026,0.917727,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.669219,1.909188,129.19449,yes,yes,2.432355,3,Sometimes,no,1.336526,no,1.63812,0.090341,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.595632,1.910672,129.232708,yes,yes,2.497548,3,Sometimes,no,1.362583,no,1.144076,0.173232,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.554956,1.779136,120.60094,yes,yes,2.671238,3,Sometimes,no,2.145368,no,0.882709,0.593917,Sometimes,Automobile,Obesity_Type_II
+Male,31.571392,1.767485,120.158049,yes,yes,2.57691,3,Sometimes,no,2.080187,no,1.04268,0.376683,Sometimes,Automobile,Obesity_Type_II
+Male,30.577944,1.783953,120.613561,yes,yes,2.341999,3,Sometimes,no,2.299878,no,0.565242,0.921991,Sometimes,Automobile,Obesity_Type_II
+Male,30.717727,1.76714,120.344402,yes,yes,2.117121,3,Sometimes,no,2.193008,no,0.992829,1.556052,Sometimes,Automobile,Obesity_Type_II
+Male,26.734476,1.816197,119.622764,yes,yes,2.247037,3,Sometimes,no,2.718408,no,0.763595,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.699317,1.839941,119.956651,yes,yes,2.244654,3,Sometimes,no,2.795752,no,0.839481,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.659092,1.84842,117.631707,yes,yes,2.128574,3,Sometimes,no,2.531984,no,1.003294,0.026575,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.348156,1.830317,117.75701,yes,yes,2.068834,3,Sometimes,no,2.543841,no,1.217993,0.038253,Sometimes,Public_Transportation,Obesity_Type_II
+Male,37.638102,1.750085,118.114184,yes,yes,2.073224,3,Sometimes,no,2.024035,no,0.131371,0,Sometimes,Automobile,Obesity_Type_II
+Male,37.765356,1.763582,117.86159,yes,yes,2.145114,2.888193,Sometimes,no,2.038128,no,0.852344,0,Sometimes,Automobile,Obesity_Type_II
+Male,28.255199,1.816547,120.699119,yes,yes,2.997951,3,Sometimes,no,2.715856,no,0.739881,0.972054,Sometimes,Automobile,Obesity_Type_II
+Male,27.931432,1.805445,119.484614,yes,yes,2.911312,3,Sometimes,no,2.501808,no,0.94676,0.785701,Sometimes,Automobile,Obesity_Type_II
+Male,25.492855,1.770124,114.163921,yes,yes,2.159033,3,Sometimes,no,2.116399,no,1.331526,0.052942,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.550506,1.77274,114.254278,yes,yes,2.175276,3,Sometimes,no,2.128176,no,1.306476,0.079334,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.542454,1.763215,115.10813,yes,yes,2.21965,3,Sometimes,no,2.165408,no,1.315045,0.688985,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.758307,1.766547,115.45845,yes,yes,2.203962,3,Sometimes,no,2.15687,no,1.289421,1.220767,Sometimes,Public_Transportation,Obesity_Type_II
+Male,29.981494,1.773181,112.348722,yes,yes,1.947495,3,Sometimes,no,2.042073,no,0,0.618087,Sometimes,Automobile,Obesity_Type_II
+Male,29.891473,1.76003,112.409191,yes,yes,2.262292,3,Sometimes,no,2.115354,no,0,0.847587,Sometimes,Automobile,Obesity_Type_II
+Male,26.778684,1.780089,113.15464,yes,yes,2.219186,3,Sometimes,no,2.092326,no,0.545919,1.74288,Sometimes,Automobile,Obesity_Type_II
+Male,28.377958,1.766946,113.235538,yes,yes,2.314175,3,Sometimes,no,2.151809,no,0,1.578517,Sometimes,Automobile,Obesity_Type_II
+Male,26.199321,1.885119,121.065052,yes,yes,2.423291,3,Sometimes,no,2.902682,no,1.243567,0.555591,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.266287,1.828276,120.872294,yes,yes,2.637202,3,Sometimes,no,2.966647,no,1.159598,0.758897,Sometimes,Public_Transportation,Obesity_Type_II
+Male,27.635029,1.806227,120.423567,yes,yes,2.974006,3,Sometimes,no,2.565828,no,0.690269,1.339691,Sometimes,Public_Transportation,Obesity_Type_II
+Male,26.899886,1.812919,119.841446,yes,yes,2.347942,3,Sometimes,no,2.730663,no,0.689371,0.188693,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.758998,1.859717,121.284533,yes,yes,2.654076,2.574108,Sometimes,no,1.78471,no,1.170537,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.771001,1.86516,121.527369,yes,yes,2.739,2.740492,Sometimes,no,1.824561,no,1.094839,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.826684,1.783609,120.921535,yes,yes,2.591292,2.956422,Sometimes,no,2.31083,no,1.48524,0.281815,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.186273,1.794827,120.919703,yes,yes,2.611847,2.749334,Sometimes,no,2.364849,no,1.141708,0.063005,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.945369,1.759647,120.009392,yes,yes,2.766612,3,Sometimes,no,2.173417,no,1.089344,1.198439,Sometimes,Automobile,Obesity_Type_II
+Male,31.490699,1.773521,120.209711,yes,yes,2.777165,3,Sometimes,no,2.106861,no,0.925941,0.2916,Sometimes,Automobile,Obesity_Type_II
+Male,31.56724,1.753327,118.26569,yes,yes,2.232836,3,Sometimes,no,2.146398,no,0.886817,0.47787,Sometimes,Automobile,Obesity_Type_II
+Male,30.444081,1.761068,118.370628,yes,yes,2.571274,3,Sometimes,no,2.224164,no,1.018158,1.152899,Sometimes,Automobile,Obesity_Type_II
+Male,31.199261,1.848845,125.077863,yes,yes,2.49619,3,Sometimes,yes,1.662117,no,0.992371,0.217632,Sometimes,Public_Transportation,Obesity_Type_II
+Male,31.190219,1.842812,125.973927,yes,yes,2.151335,3,Sometimes,yes,1.491169,no,0.883542,0.527766,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.575349,1.825449,124.95278,yes,yes,2.01695,3,Sometimes,no,1.83412,no,0.797209,0.185499,Sometimes,Public_Transportation,Obesity_Type_II
+Male,30.702559,1.86198,126.418413,yes,yes,2.927187,3,Sometimes,no,1.508796,no,0.902776,1.015467,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.057878,1.763987,113.069667,yes,yes,1.412566,3,Sometimes,no,2.002495,no,1.592795,0.001867,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.151286,1.761519,112.835195,yes,yes,1.289315,3,Sometimes,no,2.007348,no,1.376217,0.001518,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.137087,1.772045,114.067936,yes,yes,1.624366,3,Sometimes,no,2.081719,no,1.538922,0.356868,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.3292,1.771817,114.004832,yes,yes,1.528331,3,Sometimes,no,2.079353,no,1.522429,0.228598,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.66668,1.79858,117.93329,yes,yes,2.037042,3,Sometimes,no,2.43699,no,1.016254,0.020044,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.01277,1.788586,117.849351,yes,yes,2.191108,2.993856,Sometimes,no,2.444917,no,1.055854,0.145284,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.047945,1.803207,118.332966,yes,yes,1.431346,3,Sometimes,no,2.098959,no,1.110868,0.196952,Sometimes,Public_Transportation,Obesity_Type_II
+Male,25.426457,1.836592,118.377601,yes,yes,1.84199,3,Sometimes,no,2.503244,no,1.226019,0.078207,Sometimes,Public_Transportation,Obesity_Type_II
+Male,37.207082,1.762921,118.40174,yes,yes,2.13683,2.993084,Sometimes,no,1.885926,no,0.615298,0,Sometimes,Automobile,Obesity_Type_II
+Male,38.10894,1.752863,119.201465,yes,yes,2.499388,2.989791,Sometimes,no,1.959777,no,0.6081,0.64676,Sometimes,Automobile,Obesity_Type_II
+Male,38.644441,1.768235,117.792268,yes,yes,2.230742,2.920373,Sometimes,no,1.831187,no,0.756277,0,Sometimes,Automobile,Obesity_Type_II
+Male,38.112989,1.766888,118.134898,yes,yes,2.240757,2.911568,Sometimes,no,1.895876,no,0.822186,0,Sometimes,Automobile,Obesity_Type_II
+Male,24.825393,1.603501,101.038263,yes,yes,2.996186,1.134042,Sometimes,no,1.270166,no,0.073065,1.551934,no,Public_Transportation,Obesity_Type_II
+Male,26.624342,1.690262,103.180918,yes,yes,2.649406,1.120102,Sometimes,no,1.153286,no,0.216908,0.619012,no,Public_Transportation,Obesity_Type_II
+Male,33.722449,1.712905,103.276087,yes,yes,2.525884,2.040582,Sometimes,no,1,no,1.67036,0.023959,no,Public_Transportation,Obesity_Type_II
+Male,32.516469,1.695735,102.784864,yes,yes,2.736647,2.015675,Sometimes,no,1,no,1.977918,0.056351,no,Public_Transportation,Obesity_Type_II
+Male,23.881938,1.829971,121.04172,yes,yes,2.684335,2.711238,Sometimes,no,2.732331,no,1.156024,0.442456,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.319635,1.84629,121.248035,yes,yes,3,2.695396,Sometimes,no,2.628816,no,0.975384,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.912387,1.774983,119.081804,yes,yes,2.425503,2.996834,Sometimes,no,2.017602,no,1.580263,0.531769,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.982997,1.78968,118.436166,yes,yes,1.469384,2.996444,Sometimes,no,2.067741,no,1.082236,0.150544,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.511445,1.852664,121.310257,yes,yes,2.906269,2.894142,Sometimes,no,2.435489,no,1.266739,0.36216,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.05331,1.872561,121.471077,yes,yes,2.808027,2.683061,Sometimes,no,2.640483,no,1.280191,0,Sometimes,Public_Transportation,Obesity_Type_II
+Male,22.906886,1.81955,120.775439,yes,yes,2.636719,2.806341,Sometimes,no,1.967591,no,1.525384,0.315595,Sometimes,Public_Transportation,Obesity_Type_II
+Male,23.147644,1.815514,120.337664,yes,yes,2.996717,2.791366,Sometimes,no,2.626309,no,1.194898,0.034897,Sometimes,Public_Transportation,Obesity_Type_II
+Male,24.001889,1.614075,100.245302,yes,yes,2.880483,1.703299,Sometimes,no,1.006378,no,1.076729,1.058007,no,Public_Transportation,Obesity_Type_II
+Male,24.002404,1.609418,100.078367,yes,yes,2.885693,1.685134,Sometimes,no,1.011849,no,0.503105,1.217929,no,Public_Transportation,Obesity_Type_II
+Male,30.71516,1.650189,101.141277,yes,yes,2.913452,2.269799,Sometimes,no,1,no,1.889937,0.378818,no,Public_Transportation,Obesity_Type_II
+Male,30.64243,1.653876,102.583895,yes,yes,2.919526,2.142328,Sometimes,no,1.175714,no,0.958555,0.636289,no,Public_Transportation,Obesity_Type_II
+Male,20.068432,1.657132,105.580491,yes,yes,2.724121,1.437959,Sometimes,no,1.590418,no,0.029603,1.122118,no,Public_Transportation,Obesity_Type_II
+Male,20.914366,1.644751,101.067988,yes,yes,2.801992,1.343117,Sometimes,no,1.128942,no,0.233987,0.81998,no,Public_Transportation,Obesity_Type_II
+Male,25.512048,1.660761,104.321463,yes,yes,2.748971,1.213431,Sometimes,no,1.448875,no,0.128548,1.239038,no,Public_Transportation,Obesity_Type_II
+Male,26.844812,1.69151,102.59518,yes,yes,2.680375,1.089048,Sometimes,no,1.366238,no,0.181324,1.041677,no,Public_Transportation,Obesity_Type_II
+Female,25.919241,1.611462,102.093223,yes,yes,3,3,Sometimes,no,1.023328,no,0.05093,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.584782,105.055597,yes,yes,3,3,Sometimes,no,1.197667,no,0,0.534769,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.233541,1.792378,137.859737,yes,yes,3,3,Sometimes,no,2.838893,no,1.990317,0.735868,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.147705,1.802257,149.935848,yes,yes,3,3,Sometimes,no,2.181811,no,1.995582,0.939665,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.65632,111.93301,yes,yes,3,3,Sometimes,no,2.774014,no,0,0.138418,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.600762,105.448264,yes,yes,3,3,Sometimes,no,1.031354,no,0,0.37465,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.902283,1.678658,104.954834,yes,yes,3,3,Sometimes,no,1.66616,no,0.210351,0.73421,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.540865,1.678201,109.900472,yes,yes,3,3,Sometimes,no,1.471664,no,0.143955,0.444532,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.39251,1.65563,121.205171,yes,yes,3,3,Sometimes,no,1.347559,no,0.462951,0.31794,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.305402,1.694952,133.76473,yes,yes,3,3,Sometimes,no,1.338241,no,1.92628,0.886135,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622771,109.959714,yes,yes,3,3,Sometimes,no,2.679137,no,0,0.479348,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.583889,110.545378,yes,yes,3,3,Sometimes,no,2.578292,no,0,0.492394,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.334585,1.729045,131.529267,yes,yes,3,3,Sometimes,no,1.302193,no,1.742453,0.94232,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.978655,1.664622,114.465425,yes,yes,3,3,Sometimes,no,2.408442,no,0.194056,0.803141,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.01245,1.747773,127.902844,yes,yes,3,3,Sometimes,no,1.705218,no,0.390937,0.998646,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.056059,1.730113,152.094362,yes,yes,3,3,Sometimes,no,2.374958,no,0.750111,0.671458,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.771001,1.746652,133.800129,yes,yes,3,3,Sometimes,no,2.869234,no,1.465931,0.627886,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.265943,1.719365,114.511537,yes,yes,3,3,Sometimes,no,2.987406,no,0.38926,0.526776,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.632377,111.946655,yes,yes,3,3,Sometimes,no,2.737091,no,0,0.037078,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.642572,111.868169,yes,yes,3,3,Sometimes,no,2.623489,no,0,0.138563,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.96701,1.654875,104.758713,yes,yes,3,3,Sometimes,no,2.612758,no,0.24629,0.802136,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.627567,107.482662,yes,yes,3,3,Sometimes,no,2.687378,no,0,0.410651,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.590046,1.736276,130.927138,yes,yes,3,3,Sometimes,no,1.852692,no,1.46361,0.962336,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.497373,1.737056,132.527011,yes,yes,3,3,Sometimes,no,2.86559,no,0.973864,0.58345,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.991886,1.580968,102.003378,yes,yes,3,3,Sometimes,no,1.003563,no,0.008127,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.908431,1.607128,103.026858,yes,yes,3,3,Sometimes,no,1.077253,no,0.162083,0.824607,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.177882,1.821566,142.102468,yes,yes,3,3,Sometimes,no,2.714949,no,1.999773,0.813784,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.112503,1.82773,152.720545,yes,yes,3,3,Sometimes,no,2.154949,no,1.999897,0.957463,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.656504,111.993054,yes,yes,3,3,Sometimes,no,2.893117,no,0,0.01931,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.637725,111.208963,yes,yes,3,3,Sometimes,no,2.70914,no,0,0.110518,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.902283,1.669701,104.585783,yes,yes,3,3,Sometimes,no,1.570188,no,0.210351,0.881848,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.540865,1.668709,104.754958,yes,yes,3,3,Sometimes,no,1.412049,no,0.143955,0.753077,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.520992,1.668642,124.704781,yes,yes,3,3,Sometimes,no,1.15635,no,0.786828,0.366385,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.871153,1.690614,129.769141,yes,yes,3,3,Sometimes,no,1.154698,no,1.71836,0.959218,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622418,110.676343,yes,yes,3,3,Sometimes,no,2.691584,no,0,0.425473,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643332,110.824698,yes,yes,3,3,Sometimes,no,2.709654,no,0,0.25115,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.768834,1.733383,135.524857,yes,yes,3,3,Sometimes,no,1.485736,no,1.950374,0.869238,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.891491,1.748313,133.573787,yes,yes,3,3,Sometimes,no,2.874336,no,1.624981,0.825609,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.941943,1.812963,138.730619,yes,yes,3,3,Sometimes,no,2.641489,no,0.481555,0.735201,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.989016,1.80734,155.872093,yes,yes,3,3,Sometimes,no,2.417122,no,0.952725,0.573958,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.367481,1.745644,133.665587,yes,yes,3,3,Sometimes,no,2.900857,no,1.508897,0.625371,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.051107,1.753266,133.852432,yes,yes,3,3,Sometimes,no,2.95311,no,1.445148,0.67321,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.632193,111.886611,yes,yes,3,3,Sometimes,no,2.617988,no,0,0.156187,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641098,111.818345,yes,yes,3,3,Sometimes,no,2.55057,no,0,0.20482,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.998646,1.640741,104.808542,yes,yes,3,3,Sometimes,no,2.653531,no,0.190061,0.692343,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.618573,104.928643,yes,yes,3,3,Sometimes,no,1.698767,no,0,0.54396,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.846357,1.757442,133.365094,yes,yes,3,3,Sometimes,no,2.84837,no,1.206059,0.766668,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.885655,1.763343,133.952675,yes,yes,3,3,Sometimes,no,2.835622,no,1.419473,0.816986,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.930376,1.610086,102.38745,yes,yes,3,3,Sometimes,no,1.050564,no,0.026033,0.539074,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.897815,1.664463,102.781971,yes,yes,3,3,Sometimes,no,1.068493,no,0.112122,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.602025,104.899348,yes,yes,3,3,Sometimes,no,2.613928,no,0,0.553093,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.968792,1.632896,104.988925,yes,yes,3,3,Sometimes,no,1.239833,no,0.162279,0.619235,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.335019,1.771043,137.044959,yes,yes,3,3,Sometimes,no,2.861533,no,1.70464,0.655571,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.700748,1.789555,137.767787,yes,yes,3,3,Sometimes,no,2.832659,no,1.505775,0.923005,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.222536,1.801746,141.166265,yes,yes,3,3,Sometimes,no,2.418488,no,1.99507,0.893514,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.3756,1.787195,151.975864,yes,yes,3,3,Sometimes,no,2.304716,no,0.82666,0.914492,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.644141,111.942544,yes,yes,3,3,Sometimes,no,2.763005,no,0,0.101867,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643355,111.600553,yes,yes,3,3,Sometimes,no,2.652616,no,0,0.250502,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.623707,105.037463,yes,yes,3,3,Sometimes,no,2.553198,no,0,0.393838,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.610636,105.423532,yes,yes,3,3,Sometimes,no,2.180566,no,0,0.519905,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.943827,1.629491,104.839068,yes,yes,3,3,Sometimes,no,2.20979,no,0.114698,0.604422,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.291974,1.684768,104.821175,yes,yes,3,3,Sometimes,no,1.444379,no,0.224482,0.912187,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.653233,1.66494,110.92217,yes,yes,3,3,Sometimes,no,1.604075,no,0.029728,0.200122,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.783865,1.655646,110.21734,yes,yes,3,3,Sometimes,no,1.528258,no,0.01586,0.436068,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.17614,1.666194,124.805868,yes,yes,3,3,Sometimes,no,1.228838,no,0.792553,0.639561,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.207423,1.707508,121.864326,yes,yes,3,3,Sometimes,no,1.615548,no,1.376534,0.926052,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.633056,1.754174,133.783955,yes,yes,3,3,Sometimes,no,1.94595,no,1.493018,0.906611,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.009596,1.714193,131.866734,yes,yes,3,3,Sometimes,no,1.709556,no,1.592494,0.925843,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.62439,110.008636,yes,yes,3,3,Sometimes,no,2.507461,no,0,0.368472,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.638836,110.970479,yes,yes,3,3,Sometimes,no,2.644135,no,0,0.357581,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.621245,111.267334,yes,yes,3,3,Sometimes,no,2.605685,no,0,0.199227,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.954511,1.623514,109.980145,yes,yes,3,3,Sometimes,no,2.217348,no,0.001015,0.481031,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.001969,1.736215,132.145549,yes,yes,3,3,Sometimes,no,1.657541,no,1.672639,0.629285,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.772251,1.732951,132.904884,yes,yes,3,3,Sometimes,no,1.817899,no,1.577824,0.930836,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.76197,1.69135,114.480696,yes,yes,3,3,Sometimes,no,2.509535,no,0.334264,0.668172,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.449655,1.635062,113.277388,yes,yes,3,3,Sometimes,no,2.578038,no,0.165422,0.463196,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.378203,1.746061,128.261402,yes,yes,3,3,Sometimes,no,2.501638,no,1.546179,0.66488,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.725718,1.746529,129.363771,yes,yes,3,3,Sometimes,no,2.250711,no,0.64235,0.685129,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.344018,1.746516,144.302261,yes,yes,3,3,Sometimes,no,2.36651,no,1.247505,0.723366,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.322097,1.751118,149.291106,yes,yes,3,3,Sometimes,no,2.309409,no,1.68291,0.70558,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.262934,1.741014,132.57927,yes,yes,3,3,Sometimes,no,2.436261,no,1.464674,0.87092,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.566815,1.748533,133.94608,yes,yes,3,3,Sometimes,no,2.833566,no,1.413239,0.862724,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.475242,1.694726,112.776612,yes,yes,3,3,Sometimes,no,2.724099,no,0.336795,0.153462,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.612462,1.674515,112.879662,yes,yes,3,3,Sometimes,no,2.989389,no,0.36009,0.169016,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.631332,111.829957,yes,yes,3,3,Sometimes,no,2.55975,no,0,0.237307,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641918,111.86899,yes,yes,3,3,Sometimes,no,2.635454,no,0,0.088309,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.640125,111.539494,yes,yes,3,3,Sometimes,no,2.625537,no,0,0.162494,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.649178,111.914361,yes,yes,3,3,Sometimes,no,2.884077,no,0,0.096576,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.96773,1.603404,105.031908,yes,yes,3,3,Sometimes,no,1.919473,no,0.027101,0.546137,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.989938,1.644199,105.036075,yes,yes,3,3,Sometimes,no,2.310076,no,0.07115,0.733085,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.628909,106.875927,yes,yes,3,3,Sometimes,no,2.686824,no,0,0.540812,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.617227,1.628019,108.265922,yes,yes,3,3,Sometimes,no,1.6213,no,0.090917,0.438216,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.030909,1.71818,133.466763,yes,yes,3,3,Sometimes,no,1.517215,no,1.486289,0.937492,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.951084,1.708581,131.274851,yes,yes,3,3,Sometimes,no,1.796956,no,1.684582,0.887388,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.980221,1.724983,132.94066,yes,yes,3,3,Sometimes,no,2.085553,no,1.271651,0.587932,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.426036,1.739991,133.485478,yes,yes,3,3,Sometimes,no,2.837797,no,1.343875,0.803156,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.999185,1.568543,102.000122,yes,yes,3,3,Sometimes,no,1.000544,no,0.001297,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.934757,1.579893,102.134646,yes,yes,3,3,Sometimes,no,1.005864,no,0.02006,0.695838,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.327723,1.782714,154.618446,yes,yes,3,3,Sometimes,no,2.319559,no,1.97073,0.768375,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.47219,1.793824,160.935351,yes,yes,3,3,Sometimes,no,2.069257,no,1.986646,0.947091,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.659557,111.999096,yes,yes,3,3,Sometimes,no,2.956548,no,0,0.010056,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.65782,111.95611,yes,yes,3,3,Sometimes,no,2.777557,no,0,0.051858,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.816445,1.684082,104.592372,yes,yes,3,3,Sometimes,no,1.295436,no,0.282063,0.929356,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.498965,1.68395,104.846817,yes,yes,3,3,Sometimes,no,1.241378,no,0.259427,0.852362,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.137495,1.716521,127.642324,yes,yes,3,3,Sometimes,no,1.313834,no,0.912334,0.707494,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.190733,1.680762,125.418548,yes,yes,3,3,Sometimes,no,1.124366,no,0.877067,0.552006,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.623303,110.81746,yes,yes,3,3,Sometimes,no,2.673754,no,0,0.33929,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.631856,110.804337,yes,yes,3,3,Sometimes,no,2.70485,no,0,0.243338,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.840654,1.747479,136.516648,yes,yes,3,3,Sometimes,no,1.607531,no,1.963379,0.858392,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.871667,1.782453,137.852618,yes,yes,3,3,Sometimes,no,2.748909,no,1.989171,0.832515,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.501721,1.809871,152.394739,yes,yes,3,3,Sometimes,no,2.351207,no,0.246831,0.91336,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.521294,1.803677,160.639405,yes,yes,3,3,Sometimes,no,2.404049,no,0.427905,0.639894,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.314593,1.745602,133.554686,yes,yes,3,3,Sometimes,no,2.923792,no,1.536555,0.62535,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.529746,1.751038,133.843033,yes,yes,3,3,Sometimes,no,2.955075,no,1.46046,0.655558,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.631547,111.588625,yes,yes,3,3,Sometimes,no,2.554007,no,0,0.252635,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.635905,111.571076,yes,yes,3,3,Sometimes,no,2.511399,no,0,0.258472,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.616975,104.846218,yes,yes,3,3,Sometimes,no,2.653563,no,0,0.55415,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.62788,104.92057,yes,yes,3,3,Sometimes,no,2.588107,no,0,0.465607,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.768153,1.76416,133.888629,yes,yes,3,3,Sometimes,no,2.32502,no,1.441791,0.918468,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.238416,1.763847,133.937873,yes,yes,3,3,Sometimes,no,2.836791,no,1.46782,0.890527,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.930376,1.608808,102.083964,yes,yes,3,3,Sometimes,no,1.019684,no,0.026033,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.919571,1.610488,102.174953,yes,yes,3,3,Sometimes,no,1.032834,no,0.04525,0.922749,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.918524,1.621231,104.986792,yes,yes,3,3,Sometimes,no,1.653049,no,0.139159,0.711331,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.565662,1.642392,104.988082,yes,yes,3,3,Sometimes,no,1.204055,no,0.094851,0.603736,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.744914,1.801983,138.034526,yes,yes,3,3,Sometimes,no,2.691591,no,1.168368,0.735372,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.704699,1.787614,137.858254,yes,yes,3,3,Sometimes,no,2.531456,no,1.980738,0.835771,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.089969,1.801478,151.417292,yes,yes,3,3,Sometimes,no,2.226081,no,1.881761,0.933964,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.120739,1.807576,152.567671,yes,yes,3,3,Sometimes,no,2.164718,no,1.999631,0.941336,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.650125,111.939671,yes,yes,3,3,Sometimes,no,2.770732,no,0,0.125235,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.652674,111.919155,yes,yes,3,3,Sometimes,no,2.814517,no,0,0.101598,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.986185,1.663632,105.122109,yes,yes,3,3,Sometimes,no,1.626467,no,0.010183,0.412806,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.982113,1.627818,105.428628,yes,yes,3,3,Sometimes,no,1.48075,no,0.098043,0.663492,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.748627,1.66877,104.774144,yes,yes,3,3,Sometimes,no,1.526416,no,0.180158,0.747909,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.959772,1.642098,104.966758,yes,yes,3,3,Sometimes,no,1.482002,no,0.204108,0.657221,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.653233,1.65757,109.931233,yes,yes,3,3,Sometimes,no,1.610472,no,0.029728,0.472343,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.783865,1.643111,109.910012,yes,yes,3,3,Sometimes,no,1.530992,no,0.01586,0.445495,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.412434,1.675562,121.639178,yes,yes,3,3,Sometimes,no,1.484938,no,0.74225,0.604692,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.77789,1.661415,120.748213,yes,yes,3,3,Sometimes,no,1.91039,no,0.217455,0.77582,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.140165,1.713133,133.735889,yes,yes,3,3,Sometimes,no,1.411365,no,1.564618,0.910683,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.413498,1.7199,133.886031,yes,yes,3,3,Sometimes,no,2.41752,no,1.556155,0.873936,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622701,109.982692,yes,yes,3,3,Sometimes,no,2.688229,no,0,0.451377,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622468,110.400847,yes,yes,3,3,Sometimes,no,2.695094,no,0,0.413474,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.618867,110.777391,yes,yes,3,3,Sometimes,no,2.618198,no,0,0.380695,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.600905,110.074946,yes,yes,3,3,Sometimes,no,2.555189,no,0,0.462973,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.391371,1.730636,131.902591,yes,yes,3,3,Sometimes,no,1.419136,no,1.700889,0.930888,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.051982,1.729719,131.877558,yes,yes,3,3,Sometimes,no,1.422483,no,1.708971,0.673009,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.455303,1.677672,114.470482,yes,yes,3,3,Sometimes,no,2.426094,no,0.294763,0.737226,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.69484,1.637524,113.90506,yes,yes,3,3,Sometimes,no,2.495961,no,0.189831,0.652289,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.601222,1.738717,128.114161,yes,yes,3,3,Sometimes,no,1.797041,no,1.427413,0.966181,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.81158,1.741193,128.763843,yes,yes,3,3,Sometimes,no,1.768111,no,0.616503,0.968151,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.993565,1.792833,152.43563,yes,yes,3,3,Sometimes,no,2.365188,no,1.256115,0.760091,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.074449,1.810427,152.217135,yes,yes,3,3,Sometimes,no,2.299156,no,1.699056,0.729722,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.904037,1.743589,133.281333,yes,yes,3,3,Sometimes,no,2.684819,no,1.46525,0.804491,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.783234,1.747962,133.936535,yes,yes,3,3,Sometimes,no,2.84174,no,1.429256,0.775378,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.291205,1.71146,113.372851,yes,yes,3,3,Sometimes,no,2.796894,no,0.382189,0.16779,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.311534,1.685482,113.451224,yes,yes,3,3,Sometimes,no,2.987718,no,0.387074,0.283804,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.639251,111.927001,yes,yes,3,3,Sometimes,no,2.675567,no,0,0.081929,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.654784,111.933152,yes,yes,3,3,Sometimes,no,2.770125,no,0,0.088236,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641209,111.856492,yes,yes,3,3,Sometimes,no,2.621877,no,0,0.153669,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643167,111.894229,yes,yes,3,3,Sometimes,no,2.72005,no,0,0.127443,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.966504,1.63073,104.790549,yes,yes,3,3,Sometimes,no,2.436097,no,0.129178,0.683736,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.950898,1.649867,104.791035,yes,yes,3,3,Sometimes,no,2.535629,no,0.152713,0.7702,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.613574,107.012256,yes,yes,3,3,Sometimes,no,2.678779,no,0,0.508848,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.627532,106.69053,yes,yes,3,3,Sometimes,no,2.569713,no,0,0.396972,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.848608,1.726606,131.76807,yes,yes,3,3,Sometimes,no,1.759352,no,1.469928,0.950438,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.801791,1.721476,131.042274,yes,yes,3,3,Sometimes,no,1.837184,no,1.525165,0.926443,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.712641,1.742901,132.8071,yes,yes,3,3,Sometimes,no,2.856795,no,1.046463,0.586163,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.063874,1.737313,133.166595,yes,yes,3,3,Sometimes,no,2.846259,no,1.295759,0.723154,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.95774,1.62414,102.233445,yes,yes,3,3,Sometimes,no,1.067716,no,0.030541,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.909353,1.644078,102.277765,yes,yes,3,3,Sometimes,no,1.029241,no,0.075776,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.954995,1.592529,102.874549,yes,yes,3,3,Sometimes,no,1.074412,no,0.006265,0.845633,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.908829,1.607734,102.305767,yes,yes,3,3,Sometimes,no,1.030501,no,0.06582,0.991473,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.297004,1.817271,141.917802,yes,yes,3,3,Sometimes,no,2.699675,no,1.520818,0.76399,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.301773,1.808765,140.292018,yes,yes,3,3,Sometimes,no,2.830247,no,1.783138,0.789064,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.872667,1.817231,152.371911,yes,yes,3,3,Sometimes,no,2.305521,no,1.956278,0.92376,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.13782,1.819728,151.278532,yes,yes,3,3,Sometimes,no,2.155926,no,1.999836,0.94603,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.656465,111.949972,yes,yes,3,3,Sometimes,no,2.784303,no,0,0.127775,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.648143,111.950113,yes,yes,3,3,Sometimes,no,2.786417,no,0,0.079673,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.622703,111.216192,yes,yes,3,3,Sometimes,no,2.707201,no,0,0.167272,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.640606,111.036881,yes,yes,3,3,Sometimes,no,2.709428,no,0,0.228486,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.42724,1.683502,104.759643,yes,yes,3,3,Sometimes,no,1.525127,no,0.220825,0.896105,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.795187,1.669039,104.593929,yes,yes,3,3,Sometimes,no,1.554925,no,0.209586,0.823069,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.493586,1.673665,104.704707,yes,yes,3,3,Sometimes,no,1.170515,no,0.264831,0.896842,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.524336,1.668931,104.768318,yes,yes,3,3,Sometimes,no,1.436616,no,0.167086,0.764717,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.908785,1.700996,126.490236,yes,yes,3,3,Sometimes,no,1.242832,no,0.530925,0.575969,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.781751,1.734092,125.117633,yes,yes,3,3,Sometimes,no,1.54249,no,0.6277,0.450479,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.289104,1.708291,130.986338,yes,yes,3,3,Sometimes,no,1.205548,no,1.723934,0.952352,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.210732,1.716497,130.871127,yes,yes,3,3,Sometimes,no,1.301657,no,1.718543,0.947884,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.62495,111.00492,yes,yes,3,3,Sometimes,no,2.704315,no,0,0.322666,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.594776,110.640929,yes,yes,3,3,Sometimes,no,2.639816,no,0,0.452236,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.626503,110.818757,yes,yes,3,3,Sometimes,no,2.708927,no,0,0.278962,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.624576,110.803117,yes,yes,3,3,Sometimes,no,2.704827,no,0,0.269577,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.656907,1.729099,134.842656,yes,yes,3,3,Sometimes,no,1.3954,no,1.931173,0.878258,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.760734,1.73581,135.346677,yes,yes,3,3,Sometimes,no,2.611654,no,1.618512,0.868788,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.028989,1.748524,133.878843,yes,yes,3,3,Sometimes,no,2.868167,no,1.48372,0.83509,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.282238,1.748951,133.662583,yes,yes,3,3,Sometimes,no,2.247979,no,1.609938,0.849236,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.388049,1.777971,137.785027,yes,yes,3,3,Sometimes,no,2.702789,no,1.606109,0.731213,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.022206,1.73995,135.693381,yes,yes,3,3,Sometimes,no,1.663213,no,1.094035,0.786665,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.102241,1.816868,153.959945,yes,yes,3,3,Sometimes,no,2.414739,no,1.917014,0.927982,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.291969,1.8002,155.242672,yes,yes,3,3,Sometimes,no,2.351193,no,1.051889,0.686491,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.530998,1.74647,133.644711,yes,yes,3,3,Sometimes,no,2.896088,no,1.612741,0.77319,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.976968,1.759091,133.903612,yes,yes,3,3,Sometimes,no,2.862408,no,1.456933,0.742423,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.924956,1.752531,133.618706,yes,yes,3,3,Sometimes,no,2.887659,no,1.480919,0.779641,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.28253,1.761773,133.903469,yes,yes,3,3,Sometimes,no,2.893062,no,1.408177,0.807457,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.633195,111.883747,yes,yes,3,3,Sometimes,no,2.619517,no,0,0.140368,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.633945,111.9307,yes,yes,3,3,Sometimes,no,2.682804,no,0,0.15171,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641601,111.830924,yes,yes,3,3,Sometimes,no,2.552388,no,0,0.196288,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641132,111.841706,yes,yes,3,3,Sometimes,no,2.617401,no,0,0.200379,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.999174,1.638218,104.810024,yes,yes,3,3,Sometimes,no,2.654636,no,0.069238,0.629578,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.999942,1.627483,104.881994,yes,yes,3,3,Sometimes,no,2.569364,no,0.159255,0.419446,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.610225,104.936381,yes,yes,3,3,Sometimes,no,1.322004,no,0,0.539876,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.61739,105.013901,yes,yes,3,3,Sometimes,no,1.292479,no,0,0.541309,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.365041,1.744319,133.45249,yes,yes,3,3,Sometimes,no,2.839069,no,1.231031,0.792496,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.421726,1.755467,133.478611,yes,yes,3,3,Sometimes,no,2.843782,no,1.302507,0.773807,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.826782,1.746416,133.747012,yes,yes,3,3,Sometimes,no,2.858389,no,1.444382,0.713823,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.94093,1.746411,133.676663,yes,yes,3,3,Sometimes,no,2.864933,no,1.501647,0.786846,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.921678,1.611452,102.363149,yes,yes,3,3,Sometimes,no,1.031701,no,0.03465,0.912345,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.940153,1.596813,102.320437,yes,yes,3,3,Sometimes,no,1.000536,no,0.005939,0.566353,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.999636,1.610126,102.686908,yes,yes,3,3,Sometimes,no,1.020313,no,0.108386,1,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.907833,1.623113,102.555691,yes,yes,3,3,Sometimes,no,1.063422,no,0.051097,0.543118,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.991194,1.618348,104.94582,yes,yes,3,3,Sometimes,no,2.45126,no,0.043689,0.670402,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.993154,1.609401,104.854928,yes,yes,3,3,Sometimes,no,2.613504,no,0.067985,0.778632,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.988668,1.621671,105.313967,yes,yes,3,3,Sometimes,no,1.042989,no,0.097114,0.42954,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.976209,1.614484,104.999403,yes,yes,3,3,Sometimes,no,1.237557,no,0.083675,0.543761,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.743587,1.789143,138.202869,yes,yes,3,3,Sometimes,no,2.83377,no,0.806422,0.672513,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.323767,1.774207,138.143162,yes,yes,3,3,Sometimes,no,2.816052,no,1.571865,0.688058,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.394047,1.792933,137.832414,yes,yes,3,3,Sometimes,no,2.682909,no,1.318743,0.900497,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.838323,1.758959,137.79299,yes,yes,3,3,Sometimes,no,2.640539,no,1.879818,0.885993,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.20634,1.807406,141.799429,yes,yes,3,3,Sometimes,no,2.472903,no,1.998047,0.840911,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.532826,1.75091,142.545183,yes,yes,3,3,Sometimes,no,2.372058,no,1.324805,0.870795,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.438478,1.805803,153.149491,yes,yes,3,3,Sometimes,no,2.387991,no,0.850715,0.656491,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.796266,1.796538,152.473675,yes,yes,3,3,Sometimes,no,2.322003,no,0.886602,0.843283,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.634894,111.946321,yes,yes,3,3,Sometimes,no,2.737353,no,0,0.076094,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.639524,111.945588,yes,yes,3,3,Sometimes,no,2.739351,no,0,0.064769,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643017,111.720238,yes,yes,3,3,Sometimes,no,2.632498,no,0,0.140792,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.641849,111.682693,yes,yes,3,3,Sometimes,no,2.632253,no,0,0.244205,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.621167,104.947703,yes,yes,3,3,Sometimes,no,2.57721,no,0,0.402075,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.992898,1.638075,105.036522,yes,yes,3,3,Sometimes,no,2.419153,no,0.019404,0.597626,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.60937,105.407313,yes,yes,3,3,Sometimes,no,2.609052,no,0,0.54859,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.608283,105.359688,yes,yes,3,3,Sometimes,no,2.476002,no,0,0.546345,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.951737,1.629442,104.835346,yes,yes,3,3,Sometimes,no,2.225139,no,0.035928,0.565315,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.982261,1.629225,104.838425,yes,yes,3,3,Sometimes,no,2.556068,no,0.01682,0.58284,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.470652,1.680218,104.807284,yes,yes,3,3,Sometimes,no,1.423073,no,0.197993,0.763359,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.289428,1.686033,104.772164,yes,yes,3,3,Sometimes,no,1.299194,no,0.234303,0.946888,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.696073,1.662978,110.930509,yes,yes,3,3,Sometimes,no,1.679489,no,0.017804,0.21523,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.561868,1.675185,110.621723,yes,yes,3,3,Sometimes,no,1.49583,no,0.109327,0.384129,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.834018,1.62456,110.10589,yes,yes,3,3,Sometimes,no,1.81786,no,0.011161,0.447224,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.895546,1.626179,110.074019,yes,yes,3,3,Sometimes,no,1.967707,no,0.01437,0.434073,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.035557,1.682594,127.427458,yes,yes,3,3,Sometimes,no,1.441289,no,1.425712,0.662277,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.634286,1.669354,126.088301,yes,yes,3,3,Sometimes,no,1.144539,no,0.922014,0.899673,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.700876,1.68838,121.889803,yes,yes,3,3,Sometimes,no,1.353167,no,1.093679,0.534553,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.741442,1.694439,122.813033,yes,yes,3,3,Sometimes,no,1.409444,no,0.933595,0.840393,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.695892,1.755476,133.870501,yes,yes,3,3,Sometimes,no,1.959287,no,1.433151,0.911335,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.635977,1.748106,133.259033,yes,yes,3,3,Sometimes,no,1.931163,no,1.562213,0.926565,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.232659,1.719913,131.567481,yes,yes,3,3,Sometimes,no,1.651462,no,1.655993,0.939982,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.008297,1.723587,131.929712,yes,yes,3,3,Sometimes,no,1.683448,no,1.645532,0.858059,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.962949,1.623812,109.996742,yes,yes,3,3,Sometimes,no,2.395387,no,0.000272,0.461532,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.624099,109.978402,yes,yes,3,3,Sometimes,no,2.523793,no,0,0.383953,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.632983,111.157811,yes,yes,3,3,Sometimes,no,2.638896,no,0,0.224559,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.640745,110.919646,yes,yes,3,3,Sometimes,no,2.666178,no,0,0.276907,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.629727,111.275646,yes,yes,3,3,Sometimes,no,2.495851,no,0,0.218645,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.624134,111.531208,yes,yes,3,3,Sometimes,no,2.609188,no,0,0.17403,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.964788,1.623938,109.984263,yes,yes,3,3,Sometimes,no,2.471721,no,0.000096,0.433463,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.994949,1.593321,110.168166,yes,yes,3,3,Sometimes,no,2.447306,no,0.000454,0.486558,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.952737,1.730333,132.116491,yes,yes,3,3,Sometimes,no,1.674061,no,1.683497,0.780199,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.978166,1.721057,132.054793,yes,yes,3,3,Sometimes,no,1.678791,no,1.68249,0.818871,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.67447,1.71978,132.262558,yes,yes,3,3,Sometimes,no,1.777805,no,1.584716,0.927341,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.568951,1.699315,133.10761,yes,yes,3,3,Sometimes,no,1.733306,no,1.773304,0.921136,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.647935,1.681394,114.479459,yes,yes,3,3,Sometimes,no,2.435978,no,0.232742,0.692608,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.196367,1.697421,114.482386,yes,yes,3,3,Sometimes,no,2.909675,no,0.360908,0.573887,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.284833,1.650726,113.774198,yes,yes,3,3,Sometimes,no,2.75137,no,0.324913,0.516764,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.693108,1.667383,112.982549,yes,yes,3,3,Sometimes,no,2.887909,no,0.312923,0.210997,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.862264,1.746277,128.705761,yes,yes,3,3,Sometimes,no,2.411582,no,0.985287,0.675076,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.423482,1.735461,126.798173,yes,yes,3,3,Sometimes,no,2.38639,no,1.544632,0.900067,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.394082,1.747714,128.148108,yes,yes,3,3,Sometimes,no,2.232601,no,0.554323,0.836151,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.217015,1.71582,129.466541,yes,yes,3,3,Sometimes,no,1.755907,no,1.48809,0.857718,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.330178,1.747987,147.296186,yes,yes,3,3,Sometimes,no,2.336349,no,1.4164,0.711724,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.528936,1.817917,142.559161,yes,yes,3,3,Sometimes,no,2.562002,no,1.976427,0.740331,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.364339,1.80835,150.51648,yes,yes,3,3,Sometimes,no,2.305574,no,1.734424,0.863043,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.131526,1.739457,150.37757,yes,yes,3,3,Sometimes,no,2.319912,no,1.582675,0.680746,Sometimes,Public_Transportation,Obesity_Type_III
+Female,19.012872,1.742062,133.779919,yes,yes,3,3,Sometimes,no,2.70196,no,1.465909,0.813235,Sometimes,Public_Transportation,Obesity_Type_III
+Female,18.469086,1.741925,133.017105,yes,yes,3,3,Sometimes,no,2.474518,no,1.560261,0.662489,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.572114,1.751067,133.845064,yes,yes,3,3,Sometimes,no,2.748569,no,1.427037,0.902825,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.680123,1.749118,133.955091,yes,yes,3,3,Sometimes,no,2.827773,no,1.412357,0.901071,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.469756,1.663341,113.077187,yes,yes,3,3,Sometimes,no,2.632224,no,0.300964,0.26956,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.12791,1.668537,112.555456,yes,yes,3,3,Sometimes,no,2.868679,no,0.115369,0.028583,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.986368,1.668951,112.249699,yes,yes,3,3,Sometimes,no,2.930137,no,0.043101,0.138629,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.951979,1.661712,112.098616,yes,yes,3,3,Sometimes,no,2.961899,no,0.259424,0.080128,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.633442,111.821817,yes,yes,3,3,Sometimes,no,2.550672,no,0,0.224655,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.63302,111.863186,yes,yes,3,3,Sometimes,no,2.584305,no,0,0.232108,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.633887,111.878132,yes,yes,3,3,Sometimes,no,2.621976,no,0,0.123861,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643421,111.939983,yes,yes,3,3,Sometimes,no,2.722276,no,0,0.091711,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.640535,111.555967,yes,yes,3,3,Sometimes,no,2.634342,no,0,0.178301,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.626483,111.357062,yes,yes,3,3,Sometimes,no,2.61939,no,0,0.171034,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.64599,111.922491,yes,yes,3,3,Sometimes,no,2.78678,no,0,0.09776,Sometimes,Public_Transportation,Obesity_Type_III
+Female,26,1.643892,111.884535,yes,yes,3,3,Sometimes,no,2.768141,no,0,0.094213,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.97731,1.617817,104.950776,yes,yes,3,3,Sometimes,no,1.71659,no,0.001272,0.545993,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.955014,1.626449,104.879602,yes,yes,3,3,Sometimes,no,2.094901,no,0.07089,0.599441,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.996716,1.62658,105.037203,yes,yes,3,3,Sometimes,no,2.347322,no,0.008013,0.503896,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.992348,1.606474,104.954291,yes,yes,3,3,Sometimes,no,2.331123,no,0.063383,0.561661,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.974446,1.628855,108.090006,yes,yes,3,3,Sometimes,no,1.757105,no,0.085119,0.465444,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.777565,1.628205,107.378702,yes,yes,3,3,Sometimes,no,2.506631,no,0.025787,0.484165,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.722004,1.62847,107.218949,yes,yes,3,3,Sometimes,no,2.48707,no,0.067329,0.455823,Sometimes,Public_Transportation,Obesity_Type_III
+Female,25.765628,1.627839,108.10736,yes,yes,3,3,Sometimes,no,2.320068,no,0.045246,0.413106,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.016849,1.724268,133.033523,yes,yes,3,3,Sometimes,no,1.650612,no,1.537639,0.912457,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.682367,1.732383,133.043941,yes,yes,3,3,Sometimes,no,1.610768,no,1.510398,0.931455,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.285965,1.72692,131.335786,yes,yes,3,3,Sometimes,no,1.796267,no,1.728332,0.897924,Sometimes,Public_Transportation,Obesity_Type_III
+Female,20.976842,1.71073,131.408528,yes,yes,3,3,Sometimes,no,1.728139,no,1.676269,0.906247,Sometimes,Public_Transportation,Obesity_Type_III
+Female,21.982942,1.748584,133.742943,yes,yes,3,3,Sometimes,no,2.00513,no,1.34139,0.59927,Sometimes,Public_Transportation,Obesity_Type_III
+Female,22.524036,1.752206,133.689352,yes,yes,3,3,Sometimes,no,2.054193,no,1.414209,0.646288,Sometimes,Public_Transportation,Obesity_Type_III
+Female,24.361936,1.73945,133.346641,yes,yes,3,3,Sometimes,no,2.852339,no,1.139107,0.586035,Sometimes,Public_Transportation,Obesity_Type_III
+Female,23.664709,1.738836,133.472641,yes,yes,3,3,Sometimes,no,2.863513,no,1.026452,0.714137,Sometimes,Public_Transportation,Obesity_Type_III
diff --git a/INFO_6105/ML_Data_Cleaning_and_Feature_Selection/ObesityClassification_ML_Data_Cleaning_and_feature_selection/Obesity_levels.ipynb b/INFO_6105/ML_Data_Cleaning_and_Feature_Selection/ObesityClassification_ML_Data_Cleaning_and_feature_selection/Obesity_levels.ipynb
new file mode 100644
index 00000000..52f2714c
--- /dev/null
+++ b/INFO_6105/ML_Data_Cleaning_and_Feature_Selection/ObesityClassification_ML_Data_Cleaning_and_feature_selection/Obesity_levels.ipynb
@@ -0,0 +1,3153 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "collapsed_sections": [
+        "OlRi3tI4MZii",
+        "P7uukpzDk_1W",
+        "gDvBG_dRKVGZ",
+        "dTdfZ62UV-mE",
+        "xpzVqdLiaNs8",
+        "3p8bPcm3lVNy",
+        "m0tiI0aqleXO",
+        "ZidWDrJmcm_J",
+        "scobhWyNv17-"
+      ]
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Introduction \n",
+        "\n",
+        "The purpose of this data is for the estimation of obesity levels in individuals from the countries of Mexico, Peru and Colombia, based on their eating habits and physical condition. \n",
+        "\n",
+        "## More about the data      \n",
+        "\n",
+        "Number of attributes : 17 \n",
+        "\n",
+        "Number of rows : 2111\n",
+        "\n",
+        "**Independent Variables**\n",
+        "\n",
+        "1.   ***Gender*** - (Male/Female) \n",
+        "2.   ***Age*** - In years\n",
+        "3.   ***Height*** - In meters\n",
+        "4.   ***Weight*** - In Kgs\n",
+        "4.   ***family_history_with_overweight*** - Family history in obesity - Yes or No\n",
+        "5.   ***FAVC*** - Frequent consumption of high caloric food  - Yes/No\n",
+        "6.   ***FCVC*** - Frequency of consumption of vegetables - 1 = never, 2 = sometimes, 3 = always\n",
+        "\n",
+        "7.   ***NCP*** - Number of main meals - 1, 2, 3 or 4 meals\n",
+        "8.   ***CAEC*** - Consumption of food between meals - No, Sometimes, Frequently, Always\n",
+        "9.   ***Smoke*** - Does the person smoke - Yes/No\n",
+        "10.  ***CH20*** - Consumption of water daily - 1 = less than a liter, 2 = 1–2 liters, 3 = more than 2 liters\n",
+        "11.  ***SCC*** - Calories consumption monitoring - Yes/No\n",
+        "12.  ***FAF*** - Physical activity frequency - 0 = none, 1 = 1 to 2 days, 2= 2 to 4 days, 3 = 4 to 5 days\n",
+        "13.  ***TUE*** - Time using technology devices - 0 = 0–2 hours, 1 = 3–5 hours, 2 = more than 5 hours\n",
+        "14.  ***CALC*** - Consumption of alcohol - No, Sometimes, Frequently and Always\n",
+        "15.  ***MTRANS*** - Transportation used - Public Transportation, Motorbike, Bike, Automobile and Walking\n",
+        "\n",
+        "**Dependent Variables**\n",
+        "1.   ***NObeyesdad*** - Obesity level - Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II and Obesity Type III  \n",
+        "\n",
+        "# What are the data types?\n",
+        "\n",
+        "Gender - Categorical<br>\n",
+        "Age - Numeric<br>\n",
+        "Height - Numeric<br>\n",
+        "Weight - Numeric<br>\n",
+        "family_history_with_overweight - Categorical<br>\n",
+        "FAVC - Categorical<br>\n",
+        "FCVC - Categorical<br>\n",
+        "NCP - Categorical <br>\n",
+        "CAEC - Categorical<br>\n",
+        "SMOKE - Categorical<br>\n",
+        "CH2O - Categorical<br>\n",
+        "SCC - Categorical<br>\n",
+        "FAF - Categorical<br>\n",
+        "TUE - Categorical<br>\n",
+        "CALC - Categorical<br>\n",
+        "MTRANS - Categorical<br>\n",
+        "NObeyesdad - Categorical<br>\n",
+        "\n",
+        "\n",
+        "Source - [Table 1](https://www.sciencedirect.com/science/article/pii/S2352340919306985?via%3Dihub)"
+      ],
+      "metadata": {
+        "id": "OlRi3tI4MZii"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Are there missing values?\n",
+        "\n",
+        "## Setting up the data\n"
+      ],
+      "metadata": {
+        "id": "3g8zISI5h4yv"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Importing the dependencies\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import seaborn as sns\n",
+        "import matplotlib.pylab as plt\n",
+        "import math\n",
+        "import matplotlib\n",
+        "from matplotlib import pyplot\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import statsmodels.api as sm\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn import svm\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn import preprocessing\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.decomposition import PCA\n",
+        "\n",
+        "#installing dependencies\n",
+        "!pip install eli5\n",
+        "\n",
+        "# Creating a instance of label Encoder.\n",
+        "le = LabelEncoder()\n",
+        "svc = SVC()\n"
+      ],
+      "metadata": {
+        "id": "6QP5asYKlpGL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b66ddbbf-2b09-4e66-d522-3b0e74c5ee74"
+      },
+      "execution_count": 167,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: eli5 in /usr/local/lib/python3.7/dist-packages (0.13.0)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from eli5) (1.15.0)\n",
+            "Requirement already satisfied: jinja2>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from eli5) (3.1.2)\n",
+            "Requirement already satisfied: scikit-learn>=0.20 in /usr/local/lib/python3.7/dist-packages (from eli5) (1.0.2)\n",
+            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from eli5) (0.10.1)\n",
+            "Requirement already satisfied: attrs>17.1.0 in /usr/local/lib/python3.7/dist-packages (from eli5) (22.1.0)\n",
+            "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from eli5) (1.21.6)\n",
+            "Requirement already satisfied: tabulate>=0.7.7 in /usr/local/lib/python3.7/dist-packages (from eli5) (0.8.10)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from eli5) (1.7.3)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.7/dist-packages (from jinja2>=3.0.0->eli5) (2.0.1)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20->eli5) (1.2.0)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20->eli5) (3.1.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Author - @Sai Dutt\n",
+        "# Reading the file and storing columns seperately\n",
+        "data = pd.read_csv(\"https://raw.githubusercontent.com/Apurva-Nehru/Skunks_Skool/4e9f3d8a9b24ae9dcbd7f17b3ce7a16cc0501b54/INFO_6105/ML_Data_Cleaning_and_Feature_Selection/ObesityClassification_ML_Data_Cleaning_and_feature_selection/ObesityDataSet.csv\")\n"
+      ],
+      "metadata": {
+        "id": "lGlD-J2Tm1Y7"
+      },
+      "execution_count": 168,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let us check if our data has been loaded"
+      ],
+      "metadata": {
+        "id": "CYiFC5zGupbk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Check the dimensions of the data \n",
+        "print(data.shape)\n",
+        "\n",
+        "# First few rows of the dataset \n",
+        "data.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "6aeSGhv0TIPk",
+        "outputId": "52981f6d-4228-489e-c821-1a7d928226bf"
+      },
+      "execution_count": 169,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2111, 17)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Gender   Age  Height  Weight family_history_with_overweight FAVC  FCVC  \\\n",
+              "0  Female  21.0    1.62    64.0                            yes   no   2.0   \n",
+              "1  Female  21.0    1.52    56.0                            yes   no   3.0   \n",
+              "2    Male  23.0    1.80    77.0                            yes   no   2.0   \n",
+              "3    Male  27.0    1.80    87.0                             no   no   3.0   \n",
+              "4    Male  22.0    1.78    89.8                             no   no   2.0   \n",
+              "\n",
+              "   NCP       CAEC SMOKE  CH2O  SCC  FAF  TUE        CALC  \\\n",
+              "0  3.0  Sometimes    no   2.0   no  0.0  1.0          no   \n",
+              "1  3.0  Sometimes   yes   3.0  yes  3.0  0.0   Sometimes   \n",
+              "2  3.0  Sometimes    no   2.0   no  2.0  1.0  Frequently   \n",
+              "3  3.0  Sometimes    no   2.0   no  2.0  0.0  Frequently   \n",
+              "4  1.0  Sometimes    no   2.0   no  0.0  0.0   Sometimes   \n",
+              "\n",
+              "                  MTRANS           NObeyesdad  \n",
+              "0  Public_Transportation        Normal_Weight  \n",
+              "1  Public_Transportation        Normal_Weight  \n",
+              "2  Public_Transportation        Normal_Weight  \n",
+              "3                Walking   Overweight_Level_I  \n",
+              "4  Public_Transportation  Overweight_Level_II  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-df6a8009-45e5-4727-8f7a-8de123779960\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>family_history_with_overweight</th>\n",
+              "      <th>FAVC</th>\n",
+              "      <th>FCVC</th>\n",
+              "      <th>NCP</th>\n",
+              "      <th>CAEC</th>\n",
+              "      <th>SMOKE</th>\n",
+              "      <th>CH2O</th>\n",
+              "      <th>SCC</th>\n",
+              "      <th>FAF</th>\n",
+              "      <th>TUE</th>\n",
+              "      <th>CALC</th>\n",
+              "      <th>MTRANS</th>\n",
+              "      <th>NObeyesdad</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>1.62</td>\n",
+              "      <td>64.0</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>Sometimes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>no</td>\n",
+              "      <td>Public_Transportation</td>\n",
+              "      <td>Normal_Weight</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>1.52</td>\n",
+              "      <td>56.0</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>Sometimes</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Sometimes</td>\n",
+              "      <td>Public_Transportation</td>\n",
+              "      <td>Normal_Weight</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>1.80</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>Sometimes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>no</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Frequently</td>\n",
+              "      <td>Public_Transportation</td>\n",
+              "      <td>Normal_Weight</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>1.80</td>\n",
+              "      <td>87.0</td>\n",
+              "      <td>no</td>\n",
+              "      <td>no</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>Sometimes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>no</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Frequently</td>\n",
+              "      <td>Walking</td>\n",
+              "      <td>Overweight_Level_I</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1.78</td>\n",
+              "      <td>89.8</td>\n",
+              "      <td>no</td>\n",
+              "      <td>no</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Sometimes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Sometimes</td>\n",
+              "      <td>Public_Transportation</td>\n",
+              "      <td>Overweight_Level_II</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-df6a8009-45e5-4727-8f7a-8de123779960')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-df6a8009-45e5-4727-8f7a-8de123779960 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-df6a8009-45e5-4727-8f7a-8de123779960');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 169
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#checking if the any data is missing\n",
+        "data.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EuWF0suC32re",
+        "outputId": "1cbf56ad-5560-4615-e4aa-9218459b2197"
+      },
+      "execution_count": 170,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Gender                            0\n",
+              "Age                               0\n",
+              "Height                            0\n",
+              "Weight                            0\n",
+              "family_history_with_overweight    0\n",
+              "FAVC                              0\n",
+              "FCVC                              0\n",
+              "NCP                               0\n",
+              "CAEC                              0\n",
+              "SMOKE                             0\n",
+              "CH2O                              0\n",
+              "SCC                               0\n",
+              "FAF                               0\n",
+              "TUE                               0\n",
+              "CALC                              0\n",
+              "MTRANS                            0\n",
+              "NObeyesdad                        0\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 170
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Check if there are any missing values \n",
+        "sns.heatmap(data.isnull(), cbar=False, yticklabels=False, cmap='plasma')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "CegN87gLbmTJ",
+        "outputId": "4685713f-13e3-4ba3-fae9-609ef85cbc09"
+      },
+      "execution_count": 171,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb79eadc190>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 171
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 842.4x595.44 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We see from the above result that there is no missing data.\n",
+        "\n",
+        "Let us check for duplicate data. "
+      ],
+      "metadata": {
+        "id": "ezj6J6P8-qdc"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Check how many duplicate rows there are \n",
+        "dup_data = data[data.duplicated()]\n",
+        "print(dup_data.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "U_cy-SJEd615",
+        "outputId": "a0d31332-0758-49fb-9ec9-0a0bec969a80"
+      },
+      "execution_count": 172,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(24, 17)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Oops, we see that there are 24 rows of duplicate data. Lets remove them."
+      ],
+      "metadata": {
+        "id": "VkCcb3lXeI7c"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Drop duplicates \n",
+        "df = data.drop_duplicates(keep='last')"
+      ],
+      "metadata": {
+        "id": "SpLHWgFZeRz7"
+      },
+      "execution_count": 173,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Before we proceed lets validate the data further. We see that there are no empty cells in our data but let us check the validity of the data. Let us validate the categorical data"
+      ],
+      "metadata": {
+        "id": "XBt9mg2NNN_q"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Checking unique values of categorical variables\n",
+        "# Author - Sai Dutt\n",
+        "cat_col = ['Gender', 'family_history_with_overweight', 'FAVC', 'CAEC', 'SMOKE', 'SCC', 'CALC', 'MTRANS', 'NObeyesdad']\n",
+        "\n",
+        "for cat in cat_col:\n",
+        "  print(cat)\n",
+        "  print(data[cat].unique())\n",
+        "  print(\"\\n\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nHYv-uLYPjWp",
+        "outputId": "ec19955f-79bc-4752-e162-394cf4661c31"
+      },
+      "execution_count": 174,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Gender\n",
+            "['Female' 'Male']\n",
+            "\n",
+            "\n",
+            "family_history_with_overweight\n",
+            "['yes' 'no']\n",
+            "\n",
+            "\n",
+            "FAVC\n",
+            "['no' 'yes']\n",
+            "\n",
+            "\n",
+            "CAEC\n",
+            "['Sometimes' 'Frequently' 'Always' 'no']\n",
+            "\n",
+            "\n",
+            "SMOKE\n",
+            "['no' 'yes']\n",
+            "\n",
+            "\n",
+            "SCC\n",
+            "['no' 'yes']\n",
+            "\n",
+            "\n",
+            "CALC\n",
+            "['no' 'Sometimes' 'Frequently' 'Always']\n",
+            "\n",
+            "\n",
+            "MTRANS\n",
+            "['Public_Transportation' 'Walking' 'Automobile' 'Motorbike' 'Bike']\n",
+            "\n",
+            "\n",
+            "NObeyesdad\n",
+            "['Normal_Weight' 'Overweight_Level_I' 'Overweight_Level_II'\n",
+            " 'Obesity_Type_I' 'Insufficient_Weight' 'Obesity_Type_II'\n",
+            " 'Obesity_Type_III']\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let us check the validity of the data"
+      ],
+      "metadata": {
+        "id": "DRNqOwCV7c8H"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "JAVmPRJi7i4x",
+        "outputId": "bfef76ad-ff60-4d2e-e560-ac302acfbf78"
+      },
+      "execution_count": 175,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               Age       Height       Weight         FCVC          NCP  \\\n",
+              "count  2111.000000  2111.000000  2111.000000  2111.000000  2111.000000   \n",
+              "mean     24.312600     1.701677    86.586058     2.419043     2.685628   \n",
+              "std       6.345968     0.093305    26.191172     0.533927     0.778039   \n",
+              "min      14.000000     1.450000    39.000000     1.000000     1.000000   \n",
+              "25%      19.947192     1.630000    65.473343     2.000000     2.658738   \n",
+              "50%      22.777890     1.700499    83.000000     2.385502     3.000000   \n",
+              "75%      26.000000     1.768464   107.430682     3.000000     3.000000   \n",
+              "max      61.000000     1.980000   173.000000     3.000000     4.000000   \n",
+              "\n",
+              "              CH2O          FAF          TUE  \n",
+              "count  2111.000000  2111.000000  2111.000000  \n",
+              "mean      2.008011     1.010298     0.657866  \n",
+              "std       0.612953     0.850592     0.608927  \n",
+              "min       1.000000     0.000000     0.000000  \n",
+              "25%       1.584812     0.124505     0.000000  \n",
+              "50%       2.000000     1.000000     0.625350  \n",
+              "75%       2.477420     1.666678     1.000000  \n",
+              "max       3.000000     3.000000     2.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-364f3c1c-46c5-424c-879e-76ead4934261\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>FCVC</th>\n",
+              "      <th>NCP</th>\n",
+              "      <th>CH2O</th>\n",
+              "      <th>FAF</th>\n",
+              "      <th>TUE</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>2111.000000</td>\n",
+              "      <td>2111.000000</td>\n",
+              "      <td>2111.000000</td>\n",
+              "      <td>2111.000000</td>\n",
+              "      <td>2111.000000</td>\n",
+              "      <td>2111.000000</td>\n",
+              "      <td>2111.000000</td>\n",
+              "      <td>2111.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>24.312600</td>\n",
+              "      <td>1.701677</td>\n",
+              "      <td>86.586058</td>\n",
+              "      <td>2.419043</td>\n",
+              "      <td>2.685628</td>\n",
+              "      <td>2.008011</td>\n",
+              "      <td>1.010298</td>\n",
+              "      <td>0.657866</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>6.345968</td>\n",
+              "      <td>0.093305</td>\n",
+              "      <td>26.191172</td>\n",
+              "      <td>0.533927</td>\n",
+              "      <td>0.778039</td>\n",
+              "      <td>0.612953</td>\n",
+              "      <td>0.850592</td>\n",
+              "      <td>0.608927</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>14.000000</td>\n",
+              "      <td>1.450000</td>\n",
+              "      <td>39.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>19.947192</td>\n",
+              "      <td>1.630000</td>\n",
+              "      <td>65.473343</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>2.658738</td>\n",
+              "      <td>1.584812</td>\n",
+              "      <td>0.124505</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>22.777890</td>\n",
+              "      <td>1.700499</td>\n",
+              "      <td>83.000000</td>\n",
+              "      <td>2.385502</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.625350</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>26.000000</td>\n",
+              "      <td>1.768464</td>\n",
+              "      <td>107.430682</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>2.477420</td>\n",
+              "      <td>1.666678</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>61.000000</td>\n",
+              "      <td>1.980000</td>\n",
+              "      <td>173.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>4.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-364f3c1c-46c5-424c-879e-76ead4934261')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-364f3c1c-46c5-424c-879e-76ead4934261 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-364f3c1c-46c5-424c-879e-76ead4934261');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 175
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Data transformation\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "PnMpfnA5UnYN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Author - Sai Dutt\n",
+        "# Storing colum names in a list for later use\n",
+        "cols = []\n",
+        "for col in data.head(1) :  \n",
+        "  cols.append(col)\n"
+      ],
+      "metadata": {
+        "id": "PGIrcgp1wm9q"
+      },
+      "execution_count": 176,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let us take a closer look at the independent variable"
+      ],
+      "metadata": {
+        "id": "LRn14xIZv_6S"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['NObeyesdad'].unique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j-xH3d_CsN2U",
+        "outputId": "2945428d-b6fa-436a-f8fc-7ce3104095c3"
+      },
+      "execution_count": 177,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Normal_Weight', 'Overweight_Level_I', 'Overweight_Level_II',\n",
+              "       'Obesity_Type_I', 'Insufficient_Weight', 'Obesity_Type_II',\n",
+              "       'Obesity_Type_III'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 177
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Thats a lot of categories. Let us encode them with numbers. Since, there is a natural heirarchy among the classes this makes sense. "
+      ],
+      "metadata": {
+        "id": "h-nnr-GqxXPO"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['NObeyesdad'] = data['NObeyesdad'].map({'Normal_Weight' : 0, 'Overweight_Level_I': 1, 'Overweight_Level_II': 1, 'Insufficient_Weight': 0, \\\n",
+        " 'Obesity_Type_I' : 2  , 'Obesity_Type_II' : 2, \\\n",
+        " 'Obesity_Type_III' : 2 })\n"
+      ],
+      "metadata": {
+        "id": "jmcKIo1KxWCG"
+      },
+      "execution_count": 178,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We convert the categorical features into numerical data. Categories such as CAEC, CALC, family_history and MTRANS  follow a set order so this makes sense. The rest are have two classes.  \n"
+      ],
+      "metadata": {
+        "id": "yr2FPxLEv-8m"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Encoding the categories with numeric values\n",
+        "# Author - Sai Dutt\n",
+        "\n",
+        "data['CAEC'] = data['CAEC'].apply(lambda x: ['no', 'Sometimes', 'Frequently', 'Always'].index(x))\n",
+        "data['SMOKE'] = data['SMOKE'].apply(lambda x: ['yes', 'no'].index(x))\n",
+        "data['SCC'] = data['SCC'].apply(lambda x: ['yes', 'no'].index(x))\n",
+        "data['CALC'] = data['CALC'].apply(lambda x:['no', 'Sometimes', 'Frequently', 'Always'].index(x))\n",
+        "data['Gender'] = data['Gender'].map({'Male' : 0, 'Female': 1})\n",
+        "data['family_history_with_overweight'] = data['family_history_with_overweight'].map({'no' : 0, 'yes': 1})\n",
+        "data['FAVC'] = data['FAVC'].map({'no' : 0, 'yes': 1})\n",
+        "data['MTRANS'] = data['MTRANS'].map({'Public_Transportation': 1, 'Walking':2, 'Automobile':3, 'Motorbike':4, 'Bike':5})\n",
+        "data.head()\n"
+      ],
+      "metadata": {
+        "id": "g3g9DrY_f3BT",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "outputId": "dbe0b97f-e6b1-4585-c1f9-e69f098e7d00"
+      },
+      "execution_count": 179,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Gender   Age  Height  Weight  family_history_with_overweight  FAVC  FCVC  \\\n",
+              "0       1  21.0    1.62    64.0                               1     0   2.0   \n",
+              "1       1  21.0    1.52    56.0                               1     0   3.0   \n",
+              "2       0  23.0    1.80    77.0                               1     0   2.0   \n",
+              "3       0  27.0    1.80    87.0                               0     0   3.0   \n",
+              "4       0  22.0    1.78    89.8                               0     0   2.0   \n",
+              "\n",
+              "   NCP  CAEC  SMOKE  CH2O  SCC  FAF  TUE  CALC  MTRANS  NObeyesdad  \n",
+              "0  3.0     1      1   2.0    1  0.0  1.0     0       1           0  \n",
+              "1  3.0     1      0   3.0    0  3.0  0.0     1       1           0  \n",
+              "2  3.0     1      1   2.0    1  2.0  1.0     2       1           0  \n",
+              "3  3.0     1      1   2.0    1  2.0  0.0     2       2           1  \n",
+              "4  1.0     1      1   2.0    1  0.0  0.0     1       1           1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-72f9a2fd-277b-4f53-9e51-f50d03cb711a\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>family_history_with_overweight</th>\n",
+              "      <th>FAVC</th>\n",
+              "      <th>FCVC</th>\n",
+              "      <th>NCP</th>\n",
+              "      <th>CAEC</th>\n",
+              "      <th>SMOKE</th>\n",
+              "      <th>CH2O</th>\n",
+              "      <th>SCC</th>\n",
+              "      <th>FAF</th>\n",
+              "      <th>TUE</th>\n",
+              "      <th>CALC</th>\n",
+              "      <th>MTRANS</th>\n",
+              "      <th>NObeyesdad</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>1.62</td>\n",
+              "      <td>64.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>1.52</td>\n",
+              "      <td>56.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>0</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>1.80</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>1.80</td>\n",
+              "      <td>87.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>1.78</td>\n",
+              "      <td>89.8</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72f9a2fd-277b-4f53-9e51-f50d03cb711a')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-72f9a2fd-277b-4f53-9e51-f50d03cb711a button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-72f9a2fd-277b-4f53-9e51-f50d03cb711a');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 179
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Let us split the data into training and test sets (70 and 30 respectively) "
+      ],
+      "metadata": {
+        "id": "bXYvDZDi1cih"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Store the feature and target variable \n",
+        "X = data.iloc[:, :-1]\n",
+        "y = data.iloc[:, -1]\n",
+        "\n",
+        "print(data.shape)\n",
+        "print(X.shape)\n",
+        "print(y.shape)\n",
+        "\n",
+        "#Partition the data into training and test sets (70/30)\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.30,random_state=42)\n",
+        "\n",
+        "print(X_train.shape)\n",
+        "print(X_test.shape)\n",
+        "print(y_train.shape)\n",
+        "print(y_test.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UAJBrKN81cOd",
+        "outputId": "6db4af94-8b25-42b3-9c77-e06777dd1460"
+      },
+      "execution_count": 180,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2111, 17)\n",
+            "(2111, 16)\n",
+            "(2111,)\n",
+            "(1477, 16)\n",
+            "(634, 16)\n",
+            "(1477,)\n",
+            "(634,)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# What are the likely distributions of the numeric variables?"
+      ],
+      "metadata": {
+        "id": "P7uukpzDk_1W"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Most of our data is categorical with the exception of age, height and weight. We can look at their distribution in our dataset. "
+      ],
+      "metadata": {
+        "id": "aPqcy-TOipoR"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#checking the distribution of independent variables\n",
+        "from statsmodels.graphics.gofplots import qqplot\n",
+        "data_norm=data[['Age', 'Weight', 'Height']]\n",
+        "\n",
+        "for c in data_norm.columns[:]:\n",
+        "  plt.figure(figsize=(8,5))\n",
+        "  fig=qqplot(data_norm[c],line='45',fit='True')\n",
+        "  plt.xticks(fontsize=13)\n",
+        "  plt.yticks(fontsize=13)\n",
+        "  plt.xlabel(\"Theoretical quantiles\",fontsize=15)\n",
+        "  plt.ylabel(\"Sample quantiles\",fontsize=15)\n",
+        "  plt.title(\"Q-Q plot of {}\".format(c),fontsize=16)\n",
+        "  plt.grid(True)\n",
+        "  plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "mtvxVR0iZJKW",
+        "outputId": "a4792811-f552-4e10-c72a-f17ca5803801"
+      },
+      "execution_count": 181,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 842.4x595.44 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 842.4x595.44 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x360 with 0 Axes>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 842.4x595.44 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Hmm, age doesn't seem to fit the line well. Let us check the histogram"
+      ],
+      "metadata": {
+        "id": "lWTQQi_hn7pF"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.histplot(data=data, x=\"Age\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "M110b24hnhWi",
+        "outputId": "b2c766bf-2ae5-413b-94aa-36ca0b2d1262"
+      },
+      "execution_count": 182,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb79d99d390>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 182
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 842.4x595.44 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "As we can see from the above graphs, Height seems to follow the normal distribution and Weight is bimodal but Height seems to be skewed positively i.e the tail is on the right side. \n"
+      ],
+      "metadata": {
+        "id": "TRZqqfqfnlIa"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Which independent variables are useful to predict a target (dependent variable)? (Use at least three methods)"
+      ],
+      "metadata": {
+        "id": "B-cLs6cdlJMh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Identifying the important features using Random forest classifiers"
+      ],
+      "metadata": {
+        "id": "gDvBG_dRKVGZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Source - [6]\n",
+        "model = RandomForestClassifier()\n",
+        "model.fit(X_train, y_train)\n",
+        "\n",
+        "# Importance rankings\n",
+        "importances = pd.DataFrame(data={\n",
+        "    'Attribute': X_train.columns,\n",
+        "    'Importance': model.feature_importances_\n",
+        "})\n",
+        "\n",
+        "importances = importances.sort_values(by='Importance', ascending=False)\n",
+        "\n",
+        "plt.bar(x=importances['Attribute'], height=importances['Importance'], color='#087E8B')\n",
+        "plt.title('Feature importances obtained from coefficients', size=20)\n",
+        "plt.xticks(rotation='vertical')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "vgVY2acT30te",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "outputId": "04e03ad0-9798-4300-bf24-cfae4e82dc3e"
+      },
+      "execution_count": 183,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 842.4x595.44 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Identifying the important features using Logistic Regression"
+      ],
+      "metadata": {
+        "id": "dTdfZ62UV-mE"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Source - [6]\n",
+        "\n",
+        "model = LogisticRegression()\n",
+        "model.fit(X_train, y_train)\n",
+        "\n",
+        "importances = pd.DataFrame(data={\n",
+        "    'Attribute': X_train.columns,\n",
+        "    'Importance': model.coef_[0]\n",
+        "})\n",
+        "\n",
+        "importances = importances.sort_values(by='Importance', ascending=False)\n",
+        "\n",
+        "plt.bar(x=importances['Attribute'], height=importances['Importance'], color='#087E8B')\n",
+        "plt.title('Feature importances obtained from coefficients', size=20)\n",
+        "plt.xticks(rotation='vertical')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "ToaTlwWrV-Rh",
+        "outputId": "ee0c079e-b94e-4445-b806-a33023afadff"
+      },
+      "execution_count": 184,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+            "\n",
+            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+            "Please also refer to the documentation for alternative solver options:\n",
+            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 842.4x595.44 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Hmm, this seems to contradict the output of the random forest classifier. Moreover, it seems to show that weight has a negative importance which can't be right (judging from the heat map) "
+      ],
+      "metadata": {
+        "id": "KDx1HXw-8sHx"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Identifying the important features XGBoost"
+      ],
+      "metadata": {
+        "id": "xpzVqdLiaNs8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Source - [6]\n",
+        "from xgboost import XGBClassifier\n",
+        "\n",
+        "model = XGBClassifier()\n",
+        "model.fit(X_train, y_train)\n",
+        "\n",
+        "\n",
+        "importances = pd.DataFrame(data={\n",
+        "    'Attribute': X_train.columns,\n",
+        "    'Importance': model.feature_importances_\n",
+        "})\n",
+        "\n",
+        "\n",
+        "importances = importances.sort_values(by='Importance', ascending=False)"
+      ],
+      "metadata": {
+        "id": "EvURwLdwbFzW"
+      },
+      "execution_count": 185,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Source - [6]\n",
+        "plt.bar(x=importances['Attribute'], height=importances['Importance'], color='#087E8B')\n",
+        "plt.title('Feature importances obtained from coefficients', size=20)\n",
+        "plt.xticks(rotation='vertical')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "RnvjDF9EoPy-",
+        "outputId": "cc3b9ada-902b-45a2-8a2b-298aad4a54e7"
+      },
+      "execution_count": 186,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 842.4x595.44 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Do the training and test sets have the same data?\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "Cxt6Yi9nlQi1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Source - [3]\n",
+        "\n",
+        "X_test_plot = X_test[cols[:-1]]\n",
+        "X_train_plot = X_train[cols[:-1]]\n",
+        "\n",
+        "# Plotting the data to see the histogram\n",
+        "for col in X_test_plot.columns[:]:\n",
+        "  plt.figure(figsize=(8,6))  \n",
+        "  plt.hist(X_test_plot[c], bins=20, alpha=0.5, label=\"Test\")\n",
+        "  plt.hist(X_train_plot[c], bins=20, alpha=0.5, label=\"Train\")\n",
+        "  plt.xlabel(c, size=14)\n",
+        "  plt.ylabel(\"Count\", size=14)\n",
+        "  plt.legend(loc='upper right')\n",
+        "  plt.title(\"{} distribution\".format(c))\n",
+        "  plt.show()\n",
+        " \n"
+      ],
+      "metadata": {
+        "id": "wULFyYcQ64_f",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "outputId": "293fa2c9-5943-4257-f209-26e3c2ebadb1"
+      },
+      "execution_count": 187,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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sxuuuca375ojxGpbViqxwq8yi721zXmNsrXsBhEpf9bGyt+bg5zJw/oyZYRjyeIwayzyKlByR1jfklSSjsa2Cqvp4OZ0Ov9/0Bj0AjB8/XuPHj/c9zsrK0sKFC9WjRw8ZhqGKigpt2rRJmZmZWrp0qYYNGxbEbgEAaBtaNQDMnDlTa9as0eHDh3X77bcrISFBK1asaHB7p9Op2bNna+rUqaqsrFR6errmzJnTih0DANA2tWoAmDx5siZPnnzGbdauXVvjcd++fbVs2bKWbAsAEFYcMk1DDj/n5tuq02PQ9P2DPgUAAFaLcDkU4z1mUTFuxxxqoqJiVFZ2WPHxHeVyRcjRnL+CYcg0TXm9Hh0/fkRxcXFNrkMAANDmmJ4qHd2yzpJa3I459HTsmKwTJ46qtPSgDMOe19xwOl1q1669unZNV0lJeZNqEAAAAGHF4XAoPj5B8fFcD8bpbPo0iL0nUAAAsCkCAAAANkQAAADAhggAAADYEAEAAAAbIgAAAGBDBAAAAGyIAAAAgA0RAAAAsCECAAAANkQAAADAhggAAADYEAEAAAAbIgAAAGBDBAAAAGyIAAAAgA0RAAAAsCECAAAANkQAAADAhggAAADYEAEAAAAbIgAAAGBDBAAAAGyIAAAAgA0RAAAAsCECAAAANkQAAADAhggAAADYEAEAAAAbIgAAAGBDrRoACgoKlJWVpYyMDO3cuVOSdOTIEY0bN05Dhw7V8OHDNWHCBJWWlvr22bJli3JycjR06FCNGTNGJSUlrdkyAABtUqsGgCFDhuiVV15Renq6b5nD4dDYsWO1evVqLVu2TF27dtXjjz8uSTIMQxMnTlR+fr5Wr16tzMxM3zoAANB0rRoAMjMzlZqaWmNZQkKC+vfv73vcu3dvFRYWSpK2bt2q6OhoZWZmSpJGjhypVatWtV7DAAC0USF1DoBhGHr11VeVlZUlSSoqKlJaWppvfWJiogzDUFlZWbBaBACgTYgIdgPVzZgxQ7GxsbrlllssrZuU1N7SeqEqOTk+2C2Enepj5i47JXdctCV1XS6nYi2qFRMTqfgEa763zX2N1V9TKPVVWyiNPz+XgWPMAtPU8QqZAFBQUKC9e/dq4cKFcjpPH5hITU31TQdIUmlpqZxOpxISEgKqXVJyQoZhWtpvqElOjtehQ8eD3UZYqT1mMV63TpZXWlI7xmtYViuywq0yi763zXmNsXHRNfYNlb7qrxca48/PZeAYs8DUHi+n0+H3m96QmAJ44okntHXrVs2fP19RUVG+5b169VJFRYU2bdokSVq6dKmGDRsWrDYBAGgzWvUIwMyZM7VmzRodPnxYt99+uxISEvSXv/xFzz77rLp166aRI0dKkrp06aL58+fL6XRq9uzZmjp1qiorK5Wenq45c+a0ZssAALRJrRoAJk+erMmTJ9dZvmPHjgb36du3r5YtW9aSbQFAgyJcDsV4jzVpX3fZKcV43dWKRavCtO5cB6A5QuYcAAAIRaanSke3rGvSvu5a502c1WeI5CIAIDSExDkAAACgdREAAACwIQIAAAA2RAAAAMCGCAAAANgQAQAAABsiAAAAYEMEAAAAbIgAAACADXElQABN0pxL5NbmcngtqQPAfwQAAE3SnEvk1pbYe5AldQD4jykAAABsiAAAAIANEQAAALAhAgAAADZEAAAAwIYIAAAA2BABAAAAGyIAAABgQwQAAABsiAAAAIANEQAAALAhAgAAADZEAAAAwIYIAAAA2BABAAAAGyIAAABgQwQAAABsiAAAAIANEQAAALAhAgAAADZEAAAAwIZaLQAUFBQoKytLGRkZ2rlzp2/5nj17NGLECA0dOlQjRozQd99959c6AADQdK0WAIYMGaJXXnlF6enpNZZPnTpVo0aN0urVqzVq1Cjl5+f7tQ4AADRdqwWAzMxMpaam1lhWUlKibdu2KTs7W5KUnZ2tbdu2qbS09IzrAABA80QE88mLiorUqVMnuVwuSZLL5VJKSoqKiopkmmaD6xITE4PZNgAAYS+oAaC1JCW1D3YLrSI5OT7YLYSd6mPmLjsld1y0JXVdLqdiLaoVExOp+ARrvrfNfY3VX5OVr9HKWlbXa26t6vta+b1sy/hdFpimjldQA0BqaqoOHjwor9crl8slr9er4uJipaamyjTNBtcFqqTkhAzDbIFXEDqSk+N16NDxYLcRVmqPWYzXrZPllZbUjvEaltWKrHCrzKLvbXNeY2xcdI19rXyNVtayul5zatUeMyu/l20Vv8sCU3u8nE6H3296gxoAkpKS1LNnTy1fvly5ublavny5evbs6TvEf6Z1gF1EuByK8R6zpJbL4bWkDoDw12oBYObMmVqzZo0OHz6s22+/XQkJCVqxYoWmTZumvLw8LViwQB06dFBBQYFvnzOtA+zC9FTp6JZ1ltRK7D3IkjoAwl+rBYDJkydr8uTJdZZ3795dr7/+er37nGkdAABoOq4ECACADREAAACwIQIAAAA2RAAAAMCGCAAAANgQAQAAABsiAAAAYEMEAAAAbIgAAACADREAAACwIQIAAAA2RAAAAMCGCAAAANgQAQAAABsiAAAAYEMEAAAAbIgAAACADREAAACwIQIAAAA2RAAAAMCGCAAAANiQ3wFg48aN8ng8dZZ7PB5t3LjR0qYAAEDL8jsA3HrrrTp69Gid5cePH9ett95qaVMAAKBl+R0ATNOUw+Gos7ysrEzt2rWztCkAANCyIhrb4K677pIkORwOTZw4UZGRkb51hmHo22+/VZ8+fVquQwAAYLlGA0DHjh0lnT4C0KFDB8XExPjWRUZGql+/fvr1r3/dch0CAADLNRoA/vznP0uS0tPTNWbMGMXGxrZ4UwAAoGU1GgB+NGHChJbsAwAAtCK/A0BZWZmefPJJffrppyopKZFhGDXWf/HFF5Y3BwAAWobfAWDSpEnavn27fvOb3yglJaXeTwQAAIDw4HcA2LBhgxYtWqRLL720JfsBAACtwO/rACQlJXECIAAAbYTfAeAPf/iDnnrqKZWXl7dkPwAAoBX4PQXwzDPP6IcfftAVV1yhtLQ0RUTU3HXZsmXNauSDDz7Q3LlzZZqmTNPUhAkT9LOf/Ux79uxRXl6eysrKlJCQoIKCAnXr1q1ZzwUAgN35HQCGDh3aYk2YpqmHHnpIr7zyinr06KFvvvlGN910k6699lpNnTpVo0aNUm5urt555x3l5+dryZIlLdYLwkuMo1LyVDZpX3fZKcV43b7HLofXqrYAIOSFzHUAnE6njh8/Lun0DYZSUlJ05MgRbdu2TYsWLZIkZWdna8aMGSotLVViYmKL9oMw4anU0c3vN2lXd1y0Tpb/Ozwk9h5kVVcAEPL8DgAtyeFw6C9/+YvuuecexcbGqry8XM8995yKiorUqVMnuVwuSZLL5VJKSoqKiooIAAAANIPfAaBPnz5n/Ox/cy4E5PF49Oyzz2rBggXq16+fPv/8c/3+97/X7Nmzm1yzuqSk9pbUCXXJyfHBbqHVuctOyR0X3eT9Y6vt63I5azxujrZaKxzGy+p6Vo5ZTEyk4hPs93MaKDv+LmuOpo6X3wEgPz+/xmOPx6Nt27ZpzZo1vjsGNtX27dtVXFysfv36SZL69eundu3aKTo6WgcPHpTX65XL5ZLX61VxcbFSU1MDql9SckKGYTarx1CXnByvQ4eOB7uNVhfjddc4jB+I2FpTADFeo8m16vbV9mqFy3hZXc/KMYuscKvMhj+ngbDr77Kmqj1eTqfD7ze9fgeAG2+8sd7lP/nJT/Tpp59q9OjR/paqo3Pnzjpw4IB2796t8847T7t27VJJSYnOOecc9ezZU8uXL1dubq6WL1+unj17cvgfAIBmavY5AAMGDNCsWbOaVSM5OVnTpk3T/fff75tmmDVrlhISEjRt2jTl5eVpwYIF6tChgwoKCprbMgAAttfsALBixQp17Nix2Y3k5OQoJyenzvLu3bvr9ddfb3Z9AADwb34HgOHDh9dZdvjwYR09elTTpk2zsicAANDCmnwhIIfDocTERF1++eXq3r275Y0BAICWEzIXAgIAAK0n4HMANmzYoF27dsnhcOj8889X//79W6IvAADQgvwOAAcPHtS9996rr7/+WikpKZKk4uJi9erVS/PmzVOnTp1arEkAAGAtv28HPHPmTLlcLq1Zs0YffvihPvzwQ61Zs0Yul0uPPvpoS/YIwMYMSW7DDOifadZdZgT7hQAhxu8jAB9//LFefvllde3a1besa9eumjRpkn7729+2RG8AIK9hatcPZQHt076np84+3bskyOls+HLmgN34fQRAUr33AjjT/QEAAEBo8jsADBw4UDNmzFBRUZFvWWFhoWbNmqWBAwe2SHMAAKBl+D0FMHnyZN1999269tpra5wE2KNHD02ePLnFGgTQegydPuRen/JTbnmqrftxnr0+LqcjsMOLAFqd3wEgNTVVb731lj755BPt3r1b0unL9F5xxRUt1hyA1nWm+faoyAhVuT2+x/XNs/+I+XYg9DUa0j/88ENlZWXpxIkTcjgcuvLKKzV69GiNHj1aF198sbKysvTxxx+3Rq8AAMAijQaAV155RXfccYfat697f+H4+HiNHTtWixcvbpHmAABAy2g0AOzYseOMJ/kNGDBA33zzjaVNAQCAltVoACgtLZXT2fBmDodDZWWBfUYXAAAEV6MBoHPnztqxY0eD63fs2MFlgAEACDONBoDBgwdr7ty5qqioqLPu1KlTeuqppzR48OAWaQ4AALSMRj8GePfdd2v16tUaOnSobr75Zp133nmSpN27d+tvf/ubTNPUXXfd1eKNAkC4i3A5FOM9ZlGxaFWY0dbUgi01GgCSkpK0dOlSTZs2TU8++aRM8/SFPxwOh6666irl5+fr7LPPbvFGASDcmZ4qHd2yzpJaZ/UZIrkIAGg6vy4ElJ6erueff15Hjx7V3r17JUnnnHOOzjrrrBZtDgAAtAy/rwQoSWeddZYuueSSluoFAAC0Ei7XDQCADQV0BAAAEBo4oRDNRQAAgDDECYVoLqYAAACwIQIAAAA2RAAAAMCGCAAAANgQAQAAABsiAAAAYEMEAAAAbIgAAACADYXMhYAqKys1a9YsbdiwQdHR0erdu7dmzJihPXv2KC8vT2VlZUpISFBBQYG6desW7HYBAAhrIRMA5syZo+joaK1evVoOh0OHDx+WJE2dOlWjRo1Sbm6u3nnnHeXn52vJkiVB7hYAgPAWElMA5eXlevvtt3X//ffL4XBIks4++2yVlJRo27Ztys7OliRlZ2dr27ZtKi0tDWa7AACEvZA4ArBv3z4lJCRo3rx5+uyzzxQXF6f7779fMTEx6tSpk1wulyTJ5XIpJSVFRUVFSkxMDHLXAACEr5AIAF6vV/v27dNPfvITPfzww/rnP/+pu+66S3PnzrWkflJSe0vqhLrk5Phgt9Dq3GWn5I5r+k1MYqvt63I5azxujnCtVX7KrajIhn8tVF/ndDoa3DYiwqXYdpGW9NVYT/Wpr7dAe/KnN3+Ew/+xmJhIxSeEzu8PO/4ua46mjldIBIDU1FRFRET4DvVfeuml6tixo2JiYnTw4EF5vV65XC55vV4VFxcrNTU1oPolJSdkGGZLtB4ykpPjdejQ8WC30epivG6dLK9s0r6xcdE19o3xGk2uVbev8KzlMUxVuT31rouKjKixzjjDth6PVyfLDUv6OlNPDamvt0B78qe3xoTL/7HICrfKQuT3h11/lzVV7fFyOh1+v+kNiXMAEhMT1b9/f3388ceSpD179qikpETdunVTz549tXz5cknS8uXL1bNnTw7/AwDQTCFxBECSHnnkEf3pT39SQUGBIiIiNHv2bHXo0EHTpk1TXl6eFixYoA4dOqigoCDYrQIAEPZCJgB07dpVL7/8cp3l3bt31+uvvx6EjgAAaLtCYgoAAAC0LgIAAAA2RAAAAMCGCAAAANhQyJwECHuIcVRKHms+uyxJLofXsloAYCcEALQuT6WObn7fsnKJvQdZVgsA7IQAAATIkORt5MqSpmnK3cg2LqeDOTgAQUMAAALkNUzt+qHsjNu07+lpdJvuXRLkdDqsbA0A/MYbEAAAbIgAAACADREAAACwIQIAAAA2RNxs7kMAABQgSURBVAAAAMCGCAAAANgQAQAAABsiAAAAYEMEAAAAbIgAAACADXEpYDTKyjv4cfc+6/lzb4IfNXaPAv+qAGgLCABonIV38OPufdbz594EP2rsHgXnpidY1RaAEMcUAAAANkQAAADAhpgCgC3UN09efsotT7Vljc2P+7azujkACAICAGyhvnnyqMgIVbk9vseNzY//iHlyAG0BAQAAAhDIpy4aO8rkcjqYh0XQEAAAIACBfOqisaNM3bskyOl0WN4j4A8CABAkDofDv3MO+Oy+Jfwd79pqjz/jjbaCAAAEidcwtWd/4+8k+ey+Nfwd79pqjz/jjbaC6ScAAGyIAAAAgA0RAAAAsCECAAAANkQAAADAhkIuAMybN08ZGRnauXOnJGnLli3KycnR0KFDNWbMGJWUlAS5QwAAwl9IBYCvv/5aW7ZsUXp6uiTJMAxNnDhR+fn5Wr16tTIzM/X4448HuUsAAMJfyASAqqoqTZ8+XdOmTfMt27p1q6Kjo5WZmSlJGjlypFatWhWkDgEAaDtCJgDMnTtXOTk56tKli29ZUVGR0tLSfI8TExNlGIbKygK/mAcAAPi3kLgS4ObNm7V161Y9+OCDLVI/Kal9i9QNNcnJ8S1S1112Su64aEtquVxOxVpUK5B65afcioqs+9+9+jKn01HvNrU5HI1v508tf+r4U8vfOlbU8ne8IiJcim0X6VdP0pm/jw19786kvt4CGacz1Qq0zpnGLNBxqs7Kn6WYmEjFJ7TM74+maKnfZW1VU8crJALAxo0btWvXLg0ZMkSSdODAAd1xxx0aPXq0CgsLfduVlpbK6XQqISGwS3GWlJyQ0YRrgIeT5OR4HTp0vEVqx3jdOlleaVEtw7JagdTzGGaNm7JIdW/UYtSzTX1Ms/Ht/KnlTx1/avlbp7m1Ahkvj8erk+WGXz1JZ/4+1ve9a0x9vQUyTmeqFUidxsYs0HGqzsqfpcgKt8pa6PdHoFryd1lbVHu8nE6H3296Q2IKYPz48Vq/fr3Wrl2rtWvXqnPnznrhhRc0duxYVVRUaNOmTZKkpUuXatiwYUHuFgCA8BcSRwAa4nQ6NXv2bE2dOlWVlZVKT0/XnDlzgt0WAABhLyQDwNq1a31f9+3bV8uWLQtiNwAAtD0hMQUAAABaFwEAAAAbIgAAAGBDBAAAAGyIAAAAgA0RAAAAsCECAAAANkQAAADAhggAAADYEAEAAAAbCslLAQOAHTgcDrmbeKdS0zR9+7qcjma9m4twORTjPdaMCtWLRavCtO6W32g5BAAACBKvYWrP/rIm7du+p0e7fji9b/cuCXI6HU3uw/RU6eiWdU3ev7qz+gyRXASAcEAAAGC5QN/ZVn83W2edVU0BqIEAAMBygb6zrf5utrZz0xOsagtANZwECACADREAAACwIaYAACDMNefTBNK/z8Fo7qcJEF4IAAAQ5przaQLp3+dgNPfTBAgvhD0AAGyIAAAAgA0RAAAAsCECAAAANsRJgAhphk6f4NSQM11BrsZ2FvYEAG0BAQAhzWuYDV4hTjrzFeSq42pyAFATAaCNinFUSp5KS2q5HF5L6gAAQgcBoK3yVOro5vctKZXYe5AldQAAoYOTAAEAsCECAAAANkQAAADAhggAAADYEAEAAAAbIgAAAGBDBAAAAGwoJK4DcOTIET300EP6/vvvFRUVpXPOOUfTp09XYmKitmzZovz8fFVWVio9PV1z5sxRUlJSsFsGACCshcQRAIfDobFjx2r16tVatmyZunbtqscff1yGYWjixInKz8/X6tWrlZmZqccffzzY7QIAEPZCIgAkJCSof//+vse9e/dWYWGhtm7dqujoaGVmZkqSRo4cqVWrVgWrTQAA2oyQCADVGYahV199VVlZWSoqKlJaWppvXWJiogzDUFlZ4zd/AQAADQuJcwCqmzFjhmJjY3XLLbfo73//uyU1k5LaW1In1CUnx/u+dpedkjsu2pK6LpdTsQHUqqryyu01GlxvOv3PnQ4Ziops+L+p0+k443pfHUf921Vf1txagfblTx1/avlbx4pa/o5XID1ZXauhek2pU1+tQOucacya2lPtWs2pU71WRIRLse0im1xHkmJiIhWfEN/4hmdQ/XcZGtfU8QqpAFBQUKC9e/dq4cKFcjqdSk1NVWFhoW99aWmpnE6nEhICu7VrSckJGX7cMz6cJSfH69Ch477HMV63TpZbczfAGK8RUC33GW7he+kFbu34rsTvWuemJ6jK7WlwvWGYZ1z/I9Osu11UZESNZc2p1ZS+/KnjTy1/6zS3ViDjFUhPVtdqqF5T6tRXK5A6jY1ZU3uqXas5darX8ni8OlnecHj3R2SFW2XVfhcFqvbvMpxZ7fFyOh1+v+kNmSmAJ554Qlu3btX8+fMVFRUlSerVq5cqKiq0adMmSdLSpUs1bNiwYLYJAECbEBJHAL799ls9++yz6tatm0aOHClJ6tKli+bPn6/Zs2dr6tSpNT4GCAAAmickAsAFF1ygHTt21Luub9++WrZsWSt3ZE+GJG89UyWmacodwBRK255sAYC2ISQCAEKDt4G5+/Y9PQ3O6dfn3PTAztEAALS+kDkHAAAAtB4CAAAANsQUQBvglXSw9KQq3P/++E6kApu3l5i7BwA7IQC0AZVuQx9/sV/l1T6rf0X3aBUFMG8vMXcPAHbCFAAAADZEAAAAwIaYAgAAWCbC5VCM91iT93eXnVKM1326VmSEPM24xHEdEdGqMK25R0pbQAAAAFjG9FTp6JZ1Td7fHRftu/dIYu9BzapV21l9hkguAsCPmAIAAMCGCAAAANgQUwAhJMZRKXkCv4VvpExldnHJ7fn3oa3E9hEqsrI5AG2ew+EI+Pohtf147xCX08E7zBBHAAglnkod3fx+wLu5DVMHDp6ocT/wlOt+bmVnAGzAa5jasz+w64fU9uO9Q7p3SZDT6bCoM7QEAhoAADZEAAAAwIYIAAAA2BABAAAAGyIAAABgQwQAAABsiAAAAIANEQAAALAhAgAAADZEAAAAwIYIAAAA2BABAAAAGyIAAABgQ9wNEABguabeWrj8lFue/93PNE0Zsu6daoTLoRjvMYuKRavCjG58uxBGAAAAWK6ptxaOiozw3dq8fU+PvIZp2W2FTU+Vjm5ZZ0mts/oMkVzhHQCYAgAAwIYIAAAA2BBTAACAkNXUcwnQOAIAACBkNfVcgvqk9CFIVBcWAWDPnj3Ky8tTWVmZEhISVFBQoG7dugWlF6+kSrfR7DrRkU65mt8OACAI2sInCsIiAEydOlWjRo1Sbm6u3nnnHeXn52vJkiVB6aXSbWjNp981u87PBnRTbCSnYABAOGoLnygI+b9AJSUl2rZtm7KzsyVJ2dnZ2rZtm0pLS4PcWfM4nQ6ddBs1/nkNU+4m/OOgFgAgUCF/BKCoqEidOnWSy3X6gLnL5VJKSoqKioqUmJjoVw2rPkN6+vkdah8b2ew6hil98uX+Gsv6/Z9oFZd5A67VtXMHRbd3yPm/n52VJGdEhGLaxwdUJ6JdnGLa133+QGs1VMfqWoHUq69OZGREk8assZ78reVPHX9q+VunubUCGa9AerK6VkP1mlKnvlqB1GlszJraU+1azalTvVZz61hRq/qYOSMiLOnpRw5XhFzt4kKwlqtZf6eq7xtIHYdpmiH9BnLr1q16+OGHtWLFCt+yn//855ozZ44uuuiiIHYGAED4CvkpgNTUVB08eFBe7+kE6PV6VVxcrNTU1CB3BgBA+Ar5AJCUlKSePXtq+fLlkqTly5erZ8+efh/+BwAAdYX8FIAk7dq1S3l5eTp27Jg6dOiggoICnXfeecFuCwCAsBUWAQAAAFgr5KcAAACA9QgAAADYEAEAAAAbIgAAAGBDBAAAAGyIABBGCgoKlJWVpYyMDO3cubPebZ5++mkNHDhQubm5ys3N1SOPPNLKXYYOf8ZLklauXKnhw4crOztbw4cP1+HDh1uxy9Diz5g99NBDvv9fubm5uvDCC/X++++3cqehwZ/xKikp0fjx4zV8+HBdf/31mjZtmjweT73b2oE/Y3bo0CHdfffdvjF75513WrnL0HHkyBGNGzdOQ4cO1fDhwzVhwoR674Vz6tQp/f73v9d1112nYcOG6YMPPmi8uImwsXHjRrOwsNC85pprzB07dtS7zVNPPWU+9thjrdxZaPJnvL788kvz+uuvN4uLi03TNM1jx46ZFRUVrdlmSPFnzKrbvn27efnll5uVlZWt0F3o8We8Zs6c6fuZrKqqMn/1q1+ZK1asaM02Q4o/Y/bAAw+Y8+bNM03TNEtKSszBgwebhYWFrdlmyDhy5Ij56aef+h4/9thj5h//+Mc62z399NPmpEmTTNM0zT179phXXHGFeeLEiTPW5ghAGMnMzOQSyAHwZ7xeeukljRkzRsnJyZKk+Ph4RUe3/m05Q0Wg/8feeOMNDR8+XFFRUS3YVejyZ7wcDofKy8tlGIaqqqrkdrvVqVOnVuow9PgzZt98842uvvpqSVJiYqIuvPBCvffee63RXshJSEhQ//79fY979+6twsLCOtu99957GjFihCSpW7du6tWrl9atO/PtigkAbdCKFSs0fPhwjRkzRps3bw52OyFt165d2rdvn26++WbdeOONWrBggUyujeWXqqoqLVu2TL/85S+D3UpIu+eee7Rnzx5dddVVvn/9+vULdlsh7aKLLtLKlStlmqb27dunzZs31/tHz24Mw9Crr76qrKysOusKCwuVnp7ue5yamqoDBw6csR4BoI0ZOXKk3n//fS1btkx33HGH7rnnHh05ciTYbYUsr9erHTt2aNGiRXr55Ze1bt06W883BuIf//iH0tLS1LNnz2C3EtJWrVqljIwMrV+/XuvWrdOmTZu0atWqYLcV0vLy8nT48GHl5ubq0Ucf1cCBA323hLezGTNmKDY2Vrfccosl9QgAbUxycrIiIyMlSVdeeaVSU1P17bffBrmr0JWWlqZhw4YpKipK7du315AhQ/Tll18Gu62w8F//9V+8+/fD3/72N+Xk5MjpdCo+Pl5ZWVn67LPPgt1WSEtMTNTjjz+ud999VwsXLlR5ebnOP//8YLcVVAUFBdq7d6/+8pe/yOms+6c7LS1N+/fv9z0uKipS586dz1iTANDGHDx40Pf19u3btX//fp177rlB7Ci0ZWdna/369TJNU263W59++qkuvPDCYLcV8g4cOKDPP/9cw4cPD3YrIa9Lly6+udiqqipt2LBBF1xwQZC7Cm1HjhzxfVJiw4YN2rlzp7Kzs4PcVfA88cQT2rp1q+bPn9/g+TbDhg3Ta6+9Jkn67rvv9NVXX/nOo2gINwMKIzNnztSaNWt0+PBhdezYUQkJCVqxYoXGjRun++67TxdffLEefvhhff3113I6nYqMjNR9992nwYMHB7v1oPBnvAzDUEFBgdatWyen06mrrrpKDz/8cL0J2w78GTNJeuaZZ7Rz5049+eSTQe44uPwZr++//15Tp07V4cOH5fV61b9/f02aNEkRERHBbj8o/BmzDz/8UI8++qicTqc6duyo/Px82041ffvtt8rOzla3bt0UExMj6XSonD9/vnJzc/Xcc8+pU6dOOnnypPLy8rR9+3Y5nU5NnDhR11577RlrEwAAALAhe77NAQDA5ggAAADYEAEAAAAbIgAAAGBDBAAAAGyIAADAEk8//XTAn9UePXq0pk+f3kIdATgTAgBgc3l5ebrzzjvrLP/qq6+UkZGhH374wa86Y8aM0csvv2x1e8rKytILL7xgeV3A7ux5JQoAlouLi1NcXFyw2wDgJ44AAPDLv/71L40fP159+vTRwIED9cADD+jQoUO+9bWnADwej2bNmqXLLrtMl112mWbNmqWpU6dq9OjRNeoahqEnnnhC/fv318CBA1VQUCDDMCSdniLYv3+/Zs+erYyMDGVkZLTOiwVsgAAAoFHFxcW6+eabdcEFF+iNN97QokWLdPLkSd1zzz2+P9a1vfjii3rrrbc0c+ZMvfbaazIMQ8uXL6+z3bJly+RyubR06VJNmTJFixcv1sqVKyWdDhWdO3fWvffeq/Xr12v9+vUt+joBO2EKAIA++ugj9enTp8ay6n/YX331VV144YWaOHGib1lBQYEuv/xybd26VZdcckmdmkuWLNG4ceM0dOhQSdKkSZP00Ucf1dnu/PPP1/333y9JOvfcc/X6669rw4YNys7OVkJCglwul+Li4pScnGzJawVwGgEAgDIzMzVjxoway7799lvde++9kqSvv/5amzZtqhMSJOn777+vEwCOHz+uQ4cO+W4eJEkOh0OXXHKJDhw4UGPb2of1U1JSVFJS0qzXA6BxBAAAateunc4555way44dO+b72jAMDR48WA8//HCdfZOSkpr13LXviudwOMQ9yoCWRwAA0KiLLrpI7733ntLS0hQZGdno9vHx8UpOTtZXX32lgQMHSpJM09RXX30V8KH8yMhIeb3eJvUNoGGcBAigUaNGjdLx48f1hz/8Qf/85z+1b98+ffLJJ5oyZYpOnDhR7z633nqrXnjhBf3973/X7t279dhjj9X41IC/0tPT9fnnn+vgwYMqLS1t7ksB8L8IAAAa1alTJ7366qtyOp0aO3asbrjhBj3yyCOKiopSVFRUvfuMGTNGOTk5+uMf/6gRI0ZIkq677jpFR0cH9Nz33XefioqKdO211/qOJgBoPofJZBuAVvKLX/xC/fr105QpU4LdCmB7nAMAoEXs379f69ev12WXXSaPx6P//M//1I4dO+p82gBAcBAAALQIp9Opt99+W7Nnz5ZhGDr//PP1/PPP1/hoIIDgYQoAAAAb4iRAAABsiAAAAIANEQAAALAhAgAAADZEAAAAwIYIAAAA2ND/B9eyDFVW0uecAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 576x432 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Apart from a few boundary values the data split between training set and test set is fine"
+      ],
+      "metadata": {
+        "id": "C7eP7vVgjVqW"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Are the predictor variables independent of all the other predictor variables?\n"
+      ],
+      "metadata": {
+        "id": "2C9TyI2JlTAY"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Correlation matrix\n",
+        "data.corr()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "Hc0UrXb77EYq",
+        "outputId": "c4841b51-9280-4367-e19b-90e90cabe9d0"
+      },
+      "execution_count": 188,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                  Gender       Age    Height    Weight  \\\n",
+              "Gender                          1.000000 -0.048394 -0.618466 -0.161668   \n",
+              "Age                            -0.048394  1.000000 -0.025958  0.202560   \n",
+              "Height                         -0.618466 -0.025958  1.000000  0.463136   \n",
+              "Weight                         -0.161668  0.202560  0.463136  1.000000   \n",
+              "family_history_with_overweight -0.102512  0.205725  0.247684  0.496820   \n",
+              "FAVC                           -0.064934  0.063902  0.178364  0.272300   \n",
+              "FCVC                            0.274505  0.016291 -0.038121  0.216125   \n",
+              "NCP                            -0.067600 -0.043944  0.243672  0.107469   \n",
+              "CAEC                            0.091543 -0.083739 -0.048818 -0.287493   \n",
+              "SMOKE                           0.044698 -0.091987 -0.055499 -0.025746   \n",
+              "CH2O                           -0.107930 -0.045304  0.213376  0.200575   \n",
+              "SCC                            -0.102633  0.116283  0.133753  0.201906   \n",
+              "FAF                            -0.189607 -0.144938  0.294709 -0.051436   \n",
+              "TUE                            -0.017269 -0.296931  0.051912 -0.071561   \n",
+              "CALC                            0.007616  0.044487  0.129732  0.206677   \n",
+              "MTRANS                         -0.164116  0.567983  0.085768 -0.046615   \n",
+              "NObeyesdad                     -0.042359  0.308309  0.144356  0.848079   \n",
+              "\n",
+              "                                family_history_with_overweight      FAVC  \\\n",
+              "Gender                                               -0.102512 -0.064934   \n",
+              "Age                                                   0.205725  0.063902   \n",
+              "Height                                                0.247684  0.178364   \n",
+              "Weight                                                0.496820  0.272300   \n",
+              "family_history_with_overweight                        1.000000  0.208036   \n",
+              "FAVC                                                  0.208036  1.000000   \n",
+              "FCVC                                                  0.040372 -0.027283   \n",
+              "NCP                                                   0.071370 -0.007000   \n",
+              "CAEC                                                 -0.169787 -0.150068   \n",
+              "SMOKE                                                -0.017385  0.050660   \n",
+              "CH2O                                                  0.147437  0.009719   \n",
+              "SCC                                                   0.185422  0.190658   \n",
+              "FAF                                                  -0.056673 -0.107995   \n",
+              "TUE                                                   0.022943  0.068417   \n",
+              "CALC                                                 -0.036676  0.089520   \n",
+              "MTRANS                                                0.065036 -0.009102   \n",
+              "NObeyesdad                                            0.511098  0.283598   \n",
+              "\n",
+              "                                    FCVC       NCP      CAEC     SMOKE  \\\n",
+              "Gender                          0.274505 -0.067600  0.091543  0.044698   \n",
+              "Age                             0.016291 -0.043944 -0.083739 -0.091987   \n",
+              "Height                         -0.038121  0.243672 -0.048818 -0.055499   \n",
+              "Weight                          0.216125  0.107469 -0.287493 -0.025746   \n",
+              "family_history_with_overweight  0.040372  0.071370 -0.169787 -0.017385   \n",
+              "FAVC                           -0.027283 -0.007000 -0.150068  0.050660   \n",
+              "FCVC                            1.000000  0.042216  0.054670 -0.014320   \n",
+              "NCP                             0.042216  1.000000  0.097801 -0.007811   \n",
+              "CAEC                            0.054670  0.097801  1.000000 -0.055282   \n",
+              "SMOKE                          -0.014320 -0.007811 -0.055282  1.000000   \n",
+              "CH2O                            0.068461  0.057088 -0.144995  0.031995   \n",
+              "SCC                            -0.071852  0.015624 -0.109179  0.047731   \n",
+              "FAF                             0.019939  0.129504  0.030110 -0.011216   \n",
+              "TUE                            -0.101135  0.036326  0.048567 -0.017613   \n",
+              "CALC                            0.060781  0.071747 -0.047540 -0.082471   \n",
+              "MTRANS                         -0.065098  0.059022 -0.003556 -0.021045   \n",
+              "NObeyesdad                      0.113146 -0.034655 -0.367379  0.002433   \n",
+              "\n",
+              "                                    CH2O       SCC       FAF       TUE  \\\n",
+              "Gender                         -0.107930 -0.102633 -0.189607 -0.017269   \n",
+              "Age                            -0.045304  0.116283 -0.144938 -0.296931   \n",
+              "Height                          0.213376  0.133753  0.294709  0.051912   \n",
+              "Weight                          0.200575  0.201906 -0.051436 -0.071561   \n",
+              "family_history_with_overweight  0.147437  0.185422 -0.056673  0.022943   \n",
+              "FAVC                            0.009719  0.190658 -0.107995  0.068417   \n",
+              "FCVC                            0.068461 -0.071852  0.019939 -0.101135   \n",
+              "NCP                             0.057088  0.015624  0.129504  0.036326   \n",
+              "CAEC                           -0.144995 -0.109179  0.030110  0.048567   \n",
+              "SMOKE                           0.031995  0.047731 -0.011216 -0.017613   \n",
+              "CH2O                            1.000000 -0.008036  0.167236  0.011965   \n",
+              "SCC                            -0.008036  1.000000 -0.074221  0.010928   \n",
+              "FAF                             0.167236 -0.074221  1.000000  0.058562   \n",
+              "TUE                             0.011965  0.010928  0.058562  1.000000   \n",
+              "CALC                            0.091386 -0.003463 -0.086799 -0.045864   \n",
+              "MTRANS                         -0.035638  0.012794  0.036098 -0.165571   \n",
+              "NObeyesdad                      0.135521  0.185938 -0.178033 -0.101004   \n",
+              "\n",
+              "                                    CALC    MTRANS  NObeyesdad  \n",
+              "Gender                          0.007616 -0.164116   -0.042359  \n",
+              "Age                             0.044487  0.567983    0.308309  \n",
+              "Height                          0.129732  0.085768    0.144356  \n",
+              "Weight                          0.206677 -0.046615    0.848079  \n",
+              "family_history_with_overweight -0.036676  0.065036    0.511098  \n",
+              "FAVC                            0.089520 -0.009102    0.283598  \n",
+              "FCVC                            0.060781 -0.065098    0.113146  \n",
+              "NCP                             0.071747  0.059022   -0.034655  \n",
+              "CAEC                           -0.047540 -0.003556   -0.367379  \n",
+              "SMOKE                          -0.082471 -0.021045    0.002433  \n",
+              "CH2O                            0.091386 -0.035638    0.135521  \n",
+              "SCC                            -0.003463  0.012794    0.185938  \n",
+              "FAF                            -0.086799  0.036098   -0.178033  \n",
+              "TUE                            -0.045864 -0.165571   -0.101004  \n",
+              "CALC                            1.000000 -0.025492    0.103055  \n",
+              "MTRANS                         -0.025492  1.000000   -0.018238  \n",
+              "NObeyesdad                      0.103055 -0.018238    1.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7cd3fcef-9949-4f08-abfa-ba5966720979\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>family_history_with_overweight</th>\n",
+              "      <th>FAVC</th>\n",
+              "      <th>FCVC</th>\n",
+              "      <th>NCP</th>\n",
+              "      <th>CAEC</th>\n",
+              "      <th>SMOKE</th>\n",
+              "      <th>CH2O</th>\n",
+              "      <th>SCC</th>\n",
+              "      <th>FAF</th>\n",
+              "      <th>TUE</th>\n",
+              "      <th>CALC</th>\n",
+              "      <th>MTRANS</th>\n",
+              "      <th>NObeyesdad</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.048394</td>\n",
+              "      <td>-0.618466</td>\n",
+              "      <td>-0.161668</td>\n",
+              "      <td>-0.102512</td>\n",
+              "      <td>-0.064934</td>\n",
+              "      <td>0.274505</td>\n",
+              "      <td>-0.067600</td>\n",
+              "      <td>0.091543</td>\n",
+              "      <td>0.044698</td>\n",
+              "      <td>-0.107930</td>\n",
+              "      <td>-0.102633</td>\n",
+              "      <td>-0.189607</td>\n",
+              "      <td>-0.017269</td>\n",
+              "      <td>0.007616</td>\n",
+              "      <td>-0.164116</td>\n",
+              "      <td>-0.042359</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Age</th>\n",
+              "      <td>-0.048394</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.025958</td>\n",
+              "      <td>0.202560</td>\n",
+              "      <td>0.205725</td>\n",
+              "      <td>0.063902</td>\n",
+              "      <td>0.016291</td>\n",
+              "      <td>-0.043944</td>\n",
+              "      <td>-0.083739</td>\n",
+              "      <td>-0.091987</td>\n",
+              "      <td>-0.045304</td>\n",
+              "      <td>0.116283</td>\n",
+              "      <td>-0.144938</td>\n",
+              "      <td>-0.296931</td>\n",
+              "      <td>0.044487</td>\n",
+              "      <td>0.567983</td>\n",
+              "      <td>0.308309</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Height</th>\n",
+              "      <td>-0.618466</td>\n",
+              "      <td>-0.025958</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.463136</td>\n",
+              "      <td>0.247684</td>\n",
+              "      <td>0.178364</td>\n",
+              "      <td>-0.038121</td>\n",
+              "      <td>0.243672</td>\n",
+              "      <td>-0.048818</td>\n",
+              "      <td>-0.055499</td>\n",
+              "      <td>0.213376</td>\n",
+              "      <td>0.133753</td>\n",
+              "      <td>0.294709</td>\n",
+              "      <td>0.051912</td>\n",
+              "      <td>0.129732</td>\n",
+              "      <td>0.085768</td>\n",
+              "      <td>0.144356</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Weight</th>\n",
+              "      <td>-0.161668</td>\n",
+              "      <td>0.202560</td>\n",
+              "      <td>0.463136</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.496820</td>\n",
+              "      <td>0.272300</td>\n",
+              "      <td>0.216125</td>\n",
+              "      <td>0.107469</td>\n",
+              "      <td>-0.287493</td>\n",
+              "      <td>-0.025746</td>\n",
+              "      <td>0.200575</td>\n",
+              "      <td>0.201906</td>\n",
+              "      <td>-0.051436</td>\n",
+              "      <td>-0.071561</td>\n",
+              "      <td>0.206677</td>\n",
+              "      <td>-0.046615</td>\n",
+              "      <td>0.848079</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>family_history_with_overweight</th>\n",
+              "      <td>-0.102512</td>\n",
+              "      <td>0.205725</td>\n",
+              "      <td>0.247684</td>\n",
+              "      <td>0.496820</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.208036</td>\n",
+              "      <td>0.040372</td>\n",
+              "      <td>0.071370</td>\n",
+              "      <td>-0.169787</td>\n",
+              "      <td>-0.017385</td>\n",
+              "      <td>0.147437</td>\n",
+              "      <td>0.185422</td>\n",
+              "      <td>-0.056673</td>\n",
+              "      <td>0.022943</td>\n",
+              "      <td>-0.036676</td>\n",
+              "      <td>0.065036</td>\n",
+              "      <td>0.511098</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>FAVC</th>\n",
+              "      <td>-0.064934</td>\n",
+              "      <td>0.063902</td>\n",
+              "      <td>0.178364</td>\n",
+              "      <td>0.272300</td>\n",
+              "      <td>0.208036</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.027283</td>\n",
+              "      <td>-0.007000</td>\n",
+              "      <td>-0.150068</td>\n",
+              "      <td>0.050660</td>\n",
+              "      <td>0.009719</td>\n",
+              "      <td>0.190658</td>\n",
+              "      <td>-0.107995</td>\n",
+              "      <td>0.068417</td>\n",
+              "      <td>0.089520</td>\n",
+              "      <td>-0.009102</td>\n",
+              "      <td>0.283598</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>FCVC</th>\n",
+              "      <td>0.274505</td>\n",
+              "      <td>0.016291</td>\n",
+              "      <td>-0.038121</td>\n",
+              "      <td>0.216125</td>\n",
+              "      <td>0.040372</td>\n",
+              "      <td>-0.027283</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.042216</td>\n",
+              "      <td>0.054670</td>\n",
+              "      <td>-0.014320</td>\n",
+              "      <td>0.068461</td>\n",
+              "      <td>-0.071852</td>\n",
+              "      <td>0.019939</td>\n",
+              "      <td>-0.101135</td>\n",
+              "      <td>0.060781</td>\n",
+              "      <td>-0.065098</td>\n",
+              "      <td>0.113146</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>NCP</th>\n",
+              "      <td>-0.067600</td>\n",
+              "      <td>-0.043944</td>\n",
+              "      <td>0.243672</td>\n",
+              "      <td>0.107469</td>\n",
+              "      <td>0.071370</td>\n",
+              "      <td>-0.007000</td>\n",
+              "      <td>0.042216</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.097801</td>\n",
+              "      <td>-0.007811</td>\n",
+              "      <td>0.057088</td>\n",
+              "      <td>0.015624</td>\n",
+              "      <td>0.129504</td>\n",
+              "      <td>0.036326</td>\n",
+              "      <td>0.071747</td>\n",
+              "      <td>0.059022</td>\n",
+              "      <td>-0.034655</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>CAEC</th>\n",
+              "      <td>0.091543</td>\n",
+              "      <td>-0.083739</td>\n",
+              "      <td>-0.048818</td>\n",
+              "      <td>-0.287493</td>\n",
+              "      <td>-0.169787</td>\n",
+              "      <td>-0.150068</td>\n",
+              "      <td>0.054670</td>\n",
+              "      <td>0.097801</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.055282</td>\n",
+              "      <td>-0.144995</td>\n",
+              "      <td>-0.109179</td>\n",
+              "      <td>0.030110</td>\n",
+              "      <td>0.048567</td>\n",
+              "      <td>-0.047540</td>\n",
+              "      <td>-0.003556</td>\n",
+              "      <td>-0.367379</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SMOKE</th>\n",
+              "      <td>0.044698</td>\n",
+              "      <td>-0.091987</td>\n",
+              "      <td>-0.055499</td>\n",
+              "      <td>-0.025746</td>\n",
+              "      <td>-0.017385</td>\n",
+              "      <td>0.050660</td>\n",
+              "      <td>-0.014320</td>\n",
+              "      <td>-0.007811</td>\n",
+              "      <td>-0.055282</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.031995</td>\n",
+              "      <td>0.047731</td>\n",
+              "      <td>-0.011216</td>\n",
+              "      <td>-0.017613</td>\n",
+              "      <td>-0.082471</td>\n",
+              "      <td>-0.021045</td>\n",
+              "      <td>0.002433</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>CH2O</th>\n",
+              "      <td>-0.107930</td>\n",
+              "      <td>-0.045304</td>\n",
+              "      <td>0.213376</td>\n",
+              "      <td>0.200575</td>\n",
+              "      <td>0.147437</td>\n",
+              "      <td>0.009719</td>\n",
+              "      <td>0.068461</td>\n",
+              "      <td>0.057088</td>\n",
+              "      <td>-0.144995</td>\n",
+              "      <td>0.031995</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.008036</td>\n",
+              "      <td>0.167236</td>\n",
+              "      <td>0.011965</td>\n",
+              "      <td>0.091386</td>\n",
+              "      <td>-0.035638</td>\n",
+              "      <td>0.135521</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>SCC</th>\n",
+              "      <td>-0.102633</td>\n",
+              "      <td>0.116283</td>\n",
+              "      <td>0.133753</td>\n",
+              "      <td>0.201906</td>\n",
+              "      <td>0.185422</td>\n",
+              "      <td>0.190658</td>\n",
+              "      <td>-0.071852</td>\n",
+              "      <td>0.015624</td>\n",
+              "      <td>-0.109179</td>\n",
+              "      <td>0.047731</td>\n",
+              "      <td>-0.008036</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.074221</td>\n",
+              "      <td>0.010928</td>\n",
+              "      <td>-0.003463</td>\n",
+              "      <td>0.012794</td>\n",
+              "      <td>0.185938</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>FAF</th>\n",
+              "      <td>-0.189607</td>\n",
+              "      <td>-0.144938</td>\n",
+              "      <td>0.294709</td>\n",
+              "      <td>-0.051436</td>\n",
+              "      <td>-0.056673</td>\n",
+              "      <td>-0.107995</td>\n",
+              "      <td>0.019939</td>\n",
+              "      <td>0.129504</td>\n",
+              "      <td>0.030110</td>\n",
+              "      <td>-0.011216</td>\n",
+              "      <td>0.167236</td>\n",
+              "      <td>-0.074221</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.058562</td>\n",
+              "      <td>-0.086799</td>\n",
+              "      <td>0.036098</td>\n",
+              "      <td>-0.178033</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>TUE</th>\n",
+              "      <td>-0.017269</td>\n",
+              "      <td>-0.296931</td>\n",
+              "      <td>0.051912</td>\n",
+              "      <td>-0.071561</td>\n",
+              "      <td>0.022943</td>\n",
+              "      <td>0.068417</td>\n",
+              "      <td>-0.101135</td>\n",
+              "      <td>0.036326</td>\n",
+              "      <td>0.048567</td>\n",
+              "      <td>-0.017613</td>\n",
+              "      <td>0.011965</td>\n",
+              "      <td>0.010928</td>\n",
+              "      <td>0.058562</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.045864</td>\n",
+              "      <td>-0.165571</td>\n",
+              "      <td>-0.101004</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>CALC</th>\n",
+              "      <td>0.007616</td>\n",
+              "      <td>0.044487</td>\n",
+              "      <td>0.129732</td>\n",
+              "      <td>0.206677</td>\n",
+              "      <td>-0.036676</td>\n",
+              "      <td>0.089520</td>\n",
+              "      <td>0.060781</td>\n",
+              "      <td>0.071747</td>\n",
+              "      <td>-0.047540</td>\n",
+              "      <td>-0.082471</td>\n",
+              "      <td>0.091386</td>\n",
+              "      <td>-0.003463</td>\n",
+              "      <td>-0.086799</td>\n",
+              "      <td>-0.045864</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.025492</td>\n",
+              "      <td>0.103055</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>MTRANS</th>\n",
+              "      <td>-0.164116</td>\n",
+              "      <td>0.567983</td>\n",
+              "      <td>0.085768</td>\n",
+              "      <td>-0.046615</td>\n",
+              "      <td>0.065036</td>\n",
+              "      <td>-0.009102</td>\n",
+              "      <td>-0.065098</td>\n",
+              "      <td>0.059022</td>\n",
+              "      <td>-0.003556</td>\n",
+              "      <td>-0.021045</td>\n",
+              "      <td>-0.035638</td>\n",
+              "      <td>0.012794</td>\n",
+              "      <td>0.036098</td>\n",
+              "      <td>-0.165571</td>\n",
+              "      <td>-0.025492</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>-0.018238</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>NObeyesdad</th>\n",
+              "      <td>-0.042359</td>\n",
+              "      <td>0.308309</td>\n",
+              "      <td>0.144356</td>\n",
+              "      <td>0.848079</td>\n",
+              "      <td>0.511098</td>\n",
+              "      <td>0.283598</td>\n",
+              "      <td>0.113146</td>\n",
+              "      <td>-0.034655</td>\n",
+              "      <td>-0.367379</td>\n",
+              "      <td>0.002433</td>\n",
+              "      <td>0.135521</td>\n",
+              "      <td>0.185938</td>\n",
+              "      <td>-0.178033</td>\n",
+              "      <td>-0.101004</td>\n",
+              "      <td>0.103055</td>\n",
+              "      <td>-0.018238</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7cd3fcef-9949-4f08-abfa-ba5966720979')\"\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
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+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-7cd3fcef-9949-4f08-abfa-ba5966720979 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-7cd3fcef-9949-4f08-abfa-ba5966720979');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 188
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Wow, thats a lot. Lets use the heatmap instead. "
+      ],
+      "metadata": {
+        "id": "dywO1kPJYvg5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Source - [2]\n",
+        "plt.figure(figsize=(15,10))\n",
+        "sns.heatmap(data.corr(), annot = True, cmap='RdYlGn') "
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "It8JMPe7Y17R",
+        "outputId": "e1850e5e-7f8a-45ec-9c71-5d97dcfaf8bb"
+      },
+      "execution_count": 189,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb79d394210>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 189
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x720 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "From the heat map we see that there seems to be a strong correlation between Weight and Obesity level which is to be expected. Moreover there is an inverse correlation between height and gender. \n"
+      ],
+      "metadata": {
+        "id": "xzSmhhRvyxJb"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Which predictor variables are the most important?\n"
+      ],
+      "metadata": {
+        "id": "3p8bPcm3lVNy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Using OLS for finding the p value to check the significant features [3]\n",
+        "\n",
+        "model = sm.OLS(data['NObeyesdad'], data[cols]).fit()\n",
+        "\n",
+        "# Print out the statistics\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "VJXZbYMxw9Ms",
+        "outputId": "4efdab29-b74c-4604-bf57-b213c1916b3e"
+      },
+      "execution_count": 190,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<class 'statsmodels.iolib.summary.Summary'>\n",
+              "\"\"\"\n",
+              "                                 OLS Regression Results                                \n",
+              "=======================================================================================\n",
+              "Dep. Variable:             NObeyesdad   R-squared (uncentered):                   1.000\n",
+              "Model:                            OLS   Adj. R-squared (uncentered):              1.000\n",
+              "Method:                 Least Squares   F-statistic:                          2.628e+30\n",
+              "Date:                Mon, 10 Oct 2022   Prob (F-statistic):                        0.00\n",
+              "Time:                        03:40:39   Log-Likelihood:                          65064.\n",
+              "No. Observations:                2111   AIC:                                 -1.301e+05\n",
+              "Df Residuals:                    2094   BIC:                                 -1.300e+05\n",
+              "Df Model:                          17                                                  \n",
+              "Covariance Type:            nonrobust                                                  \n",
+              "==================================================================================================\n",
+              "                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
+              "--------------------------------------------------------------------------------------------------\n",
+              "Gender                         -2.758e-15   5.06e-16     -5.455      0.000   -3.75e-15   -1.77e-15\n",
+              "Age                              2.98e-16   4.69e-17      6.360      0.000    2.06e-16     3.9e-16\n",
+              "Height                         -3.227e-16   1.68e-15     -0.192      0.848   -3.62e-15    2.98e-15\n",
+              "Weight                          3.621e-17   2.01e-17      1.800      0.072   -3.23e-18    7.57e-17\n",
+              "family_history_with_overweight  4.306e-16   6.86e-16      0.628      0.530   -9.14e-16    1.78e-15\n",
+              "FAVC                            3.398e-16   7.35e-16      0.462      0.644    -1.1e-15    1.78e-15\n",
+              "FCVC                             1.48e-16   4.53e-16      0.327      0.744    -7.4e-16    1.04e-15\n",
+              "NCP                             6.449e-16   2.96e-16      2.181      0.029    6.49e-17    1.22e-15\n",
+              "CAEC                            7.318e-17   5.12e-16      0.143      0.886   -9.31e-16    1.08e-15\n",
+              "SMOKE                          -4.783e-16    1.5e-15     -0.319      0.750   -3.42e-15    2.46e-15\n",
+              "CH2O                           -5.265e-16   3.76e-16     -1.399      0.162   -1.26e-15    2.12e-16\n",
+              "SCC                            -2.089e-16   1.09e-15     -0.191      0.849   -2.36e-15    1.94e-15\n",
+              "FAF                             2.888e-16   2.81e-16      1.027      0.305   -2.63e-16     8.4e-16\n",
+              "TUE                            -4.121e-16    3.8e-16     -1.083      0.279   -1.16e-15    3.34e-16\n",
+              "CALC                            1.081e-15    4.5e-16      2.403      0.016    1.99e-16    1.96e-15\n",
+              "MTRANS                         -5.719e-16   3.23e-16     -1.769      0.077   -1.21e-15    6.21e-17\n",
+              "NObeyesdad                         1.0000   6.19e-16   1.61e+15      0.000       1.000       1.000\n",
+              "==============================================================================\n",
+              "Omnibus:                        7.977   Durbin-Watson:                   0.070\n",
+              "Prob(Omnibus):                  0.019   Jarque-Bera (JB):                7.999\n",
+              "Skew:                          -0.151   Prob(JB):                       0.0183\n",
+              "Kurtosis:                       3.003   Cond. No.                         868.\n",
+              "==============================================================================\n",
+              "\n",
+              "Notes:\n",
+              "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
+              "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+              "\"\"\""
+            ],
+            "text/html": [
+              "<table class=\"simpletable\">\n",
+              "<caption>OLS Regression Results</caption>\n",
+              "<tr>\n",
+              "  <th>Dep. Variable:</th>       <td>NObeyesdad</td>    <th>  R-squared (uncentered):</th>       <td>   1.000</td> \n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th>  <td>   1.000</td> \n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>           <td>2.628e+30</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Date:</th>             <td>Mon, 10 Oct 2022</td> <th>  Prob (F-statistic):</th>            <td>  0.00</td>  \n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Time:</th>                 <td>03:40:39</td>     <th>  Log-Likelihood:    </th>           <td>  65064.</td> \n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>No. Observations:</th>      <td>  2111</td>      <th>  AIC:               </th>          <td>-1.301e+05</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Df Residuals:</th>          <td>  2094</td>      <th>  BIC:               </th>          <td>-1.300e+05</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Df Model:</th>              <td>    17</td>      <th>                     </th>               <td> </td>    \n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>               <td> </td>    \n",
+              "</tr>\n",
+              "</table>\n",
+              "<table class=\"simpletable\">\n",
+              "<tr>\n",
+              "                 <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Gender</th>                         <td>-2.758e-15</td> <td> 5.06e-16</td> <td>   -5.455</td> <td> 0.000</td> <td>-3.75e-15</td> <td>-1.77e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Age</th>                            <td>  2.98e-16</td> <td> 4.69e-17</td> <td>    6.360</td> <td> 0.000</td> <td> 2.06e-16</td> <td>  3.9e-16</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Height</th>                         <td>-3.227e-16</td> <td> 1.68e-15</td> <td>   -0.192</td> <td> 0.848</td> <td>-3.62e-15</td> <td> 2.98e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Weight</th>                         <td> 3.621e-17</td> <td> 2.01e-17</td> <td>    1.800</td> <td> 0.072</td> <td>-3.23e-18</td> <td> 7.57e-17</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>family_history_with_overweight</th> <td> 4.306e-16</td> <td> 6.86e-16</td> <td>    0.628</td> <td> 0.530</td> <td>-9.14e-16</td> <td> 1.78e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>FAVC</th>                           <td> 3.398e-16</td> <td> 7.35e-16</td> <td>    0.462</td> <td> 0.644</td> <td> -1.1e-15</td> <td> 1.78e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>FCVC</th>                           <td>  1.48e-16</td> <td> 4.53e-16</td> <td>    0.327</td> <td> 0.744</td> <td> -7.4e-16</td> <td> 1.04e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>NCP</th>                            <td> 6.449e-16</td> <td> 2.96e-16</td> <td>    2.181</td> <td> 0.029</td> <td> 6.49e-17</td> <td> 1.22e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>CAEC</th>                           <td> 7.318e-17</td> <td> 5.12e-16</td> <td>    0.143</td> <td> 0.886</td> <td>-9.31e-16</td> <td> 1.08e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>SMOKE</th>                          <td>-4.783e-16</td> <td>  1.5e-15</td> <td>   -0.319</td> <td> 0.750</td> <td>-3.42e-15</td> <td> 2.46e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>CH2O</th>                           <td>-5.265e-16</td> <td> 3.76e-16</td> <td>   -1.399</td> <td> 0.162</td> <td>-1.26e-15</td> <td> 2.12e-16</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>SCC</th>                            <td>-2.089e-16</td> <td> 1.09e-15</td> <td>   -0.191</td> <td> 0.849</td> <td>-2.36e-15</td> <td> 1.94e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>FAF</th>                            <td> 2.888e-16</td> <td> 2.81e-16</td> <td>    1.027</td> <td> 0.305</td> <td>-2.63e-16</td> <td>  8.4e-16</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>TUE</th>                            <td>-4.121e-16</td> <td>  3.8e-16</td> <td>   -1.083</td> <td> 0.279</td> <td>-1.16e-15</td> <td> 3.34e-16</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>CALC</th>                           <td> 1.081e-15</td> <td>  4.5e-16</td> <td>    2.403</td> <td> 0.016</td> <td> 1.99e-16</td> <td> 1.96e-15</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>MTRANS</th>                         <td>-5.719e-16</td> <td> 3.23e-16</td> <td>   -1.769</td> <td> 0.077</td> <td>-1.21e-15</td> <td> 6.21e-17</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>NObeyesdad</th>                     <td>    1.0000</td> <td> 6.19e-16</td> <td> 1.61e+15</td> <td> 0.000</td> <td>    1.000</td> <td>    1.000</td>\n",
+              "</tr>\n",
+              "</table>\n",
+              "<table class=\"simpletable\">\n",
+              "<tr>\n",
+              "  <th>Omnibus:</th>       <td> 7.977</td> <th>  Durbin-Watson:     </th> <td>   0.070</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Prob(Omnibus):</th> <td> 0.019</td> <th>  Jarque-Bera (JB):  </th> <td>   7.999</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Skew:</th>          <td>-0.151</td> <th>  Prob(JB):          </th> <td>  0.0183</td>\n",
+              "</tr>\n",
+              "<tr>\n",
+              "  <th>Kurtosis:</th>      <td> 3.003</td> <th>  Cond. No.          </th> <td>    868.</td>\n",
+              "</tr>\n",
+              "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+            ]
+          },
+          "metadata": {},
+          "execution_count": 190
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Gender, Age, NCP and CALC have a p value < 0.05. They might not follow the null hypothesis. It can be noted that weight has a p value > 0.05. "
+      ],
+      "metadata": {
+        "id": "4xG26XBa0Czv"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Do the ranges of the predictor variables make sense?\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "9LhfWVvNlb5Y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Checking the Ranges of the predictor variables and dependent variable\n",
+        "data.iloc[:, 1:4].describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "udsAuCU-Yovg",
+        "outputId": "60972234-9696-4276-fddd-9636a60f1b60"
+      },
+      "execution_count": 191,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "               Age       Height       Weight\n",
+              "count  2111.000000  2111.000000  2111.000000\n",
+              "mean     24.312600     1.701677    86.586058\n",
+              "std       6.345968     0.093305    26.191172\n",
+              "min      14.000000     1.450000    39.000000\n",
+              "25%      19.947192     1.630000    65.473343\n",
+              "50%      22.777890     1.700499    83.000000\n",
+              "75%      26.000000     1.768464   107.430682\n",
+              "max      61.000000     1.980000   173.000000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fc257941-4fab-4b5f-8777-038aad307755\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>2111.000000</td>\n",
+              "      <td>2111.000000</td>\n",
+              "      <td>2111.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>24.312600</td>\n",
+              "      <td>1.701677</td>\n",
+              "      <td>86.586058</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>6.345968</td>\n",
+              "      <td>0.093305</td>\n",
+              "      <td>26.191172</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>14.000000</td>\n",
+              "      <td>1.450000</td>\n",
+              "      <td>39.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>19.947192</td>\n",
+              "      <td>1.630000</td>\n",
+              "      <td>65.473343</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>22.777890</td>\n",
+              "      <td>1.700499</td>\n",
+              "      <td>83.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>26.000000</td>\n",
+              "      <td>1.768464</td>\n",
+              "      <td>107.430682</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>61.000000</td>\n",
+              "      <td>1.980000</td>\n",
+              "      <td>173.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc257941-4fab-4b5f-8777-038aad307755')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-fc257941-4fab-4b5f-8777-038aad307755 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-fc257941-4fab-4b5f-8777-038aad307755');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
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+              "          element.appendChild(docLink);\n",
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+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 191
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The above ranges of age height and weight make sense. "
+      ],
+      "metadata": {
+        "id": "IC7s6DzC-K9p"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# What are the distributions of the predictor variables?\n",
+        "We see from the QQ-Plots that roughly the numeric varibles follow the normal distribution. "
+      ],
+      "metadata": {
+        "id": "m0tiI0aqleXO"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Remove outliers and keep outliers (does if have an effect of the final predictive model)?\n",
+        "\n",
+        "Let us look at the box-plot of the numerical variables for a better understanding. "
+      ],
+      "metadata": {
+        "id": "T2Y8MsoIlgj1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Checking the Ranges of the predictor variables and dependent variable after normalizing[3]\n",
+        "plt.figure(figsize=(20,7))\n",
+        "sns.boxplot(data=data.iloc[:, 1:4])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "id": "O5DfKowt4-RR",
+        "outputId": "a96c9e03-34a1-45ad-b889-3322bb411f33"
+      },
+      "execution_count": 192,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb79d45aad0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 192
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1440x504 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "When it comes to age there are a lot of outliers, mostly older people. Hence removing the outliers will affect the final predictive model."
+      ],
+      "metadata": {
+        "id": "9IEj8nVE5gW0"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Remove 1%, 5%, and 10% of your data randomly and impute the values back using at least 3 imputation methods. How well did the methods recover the missing values? That is remove some data, check the % error on residuals for numeric data and check for bias and variance of the error.\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "SNwYCmu2licH"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data1 = data.mask(np.random.random(data.shape) < .01)"
+      ],
+      "metadata": {
+        "id": "28ju-72f-WJo"
+      },
+      "execution_count": 193,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data10 = data.mask(np.random.random(data.shape) < .1)"
+      ],
+      "metadata": {
+        "id": "nvJ5Ogq5ynDs"
+      },
+      "execution_count": 194,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data5 = data.mask(np.random.random(data.shape) < .05)"
+      ],
+      "metadata": {
+        "id": "Q574m9aFLiIO"
+      },
+      "execution_count": 195,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Imputing missing values using KNN Imputer"
+      ],
+      "metadata": {
+        "id": "FnTaBlk-OTVX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Author -Sai Dutt\n",
+        "from sklearn.impute import KNNImputer\n",
+        "\n",
+        "imputer = KNNImputer(n_neighbors=5)\n",
+        "new_data1 = pd.DataFrame(imputer.fit_transform(data1),columns = data1.columns)\n",
+        "new_data10 = pd.DataFrame(imputer.fit_transform(data10),columns = data10.columns)\n",
+        "new_data5 = pd.DataFrame(imputer.fit_transform(data5),columns = data5.columns)\n"
+      ],
+      "metadata": {
+        "id": "nvVSsasYLv-m"
+      },
+      "execution_count": 196,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "#References\n",
+        "\n",
+        "[1] - [Comprehensive data exploration with python](https://www.kaggle.com/code/pmarcelino/comprehensive-data-exploration-with-python/notebook)\n",
+        "\n",
+        "[2] - [Obesity classification and data analysis](https://medium.com/geekculture/obesity-classification-and-data-analysis-via-machine-learning-6635682f0f87)\n",
+        "\n",
+        "[3] - [Abalone EDA](https://github.com/aiskunks/Skunks_Skool/blob/e662b7d0be79e471e1e0e08ce820678de509b694/INFO_6105/ML_Data_Cleaning_and_Feature_Selection/6105_ML_Data_Cleaning_and_Feature_Selection_Abalone_Example/ML_Data_Cleaning_and_Feature_Selection_Abalone.ipynb)\n",
+        "\n",
+        "[4] - [Outlier detection](https://medium.datadriveninvestor.com/finding-outliers-in-dataset-using-python-efc3fce6ce32)\n",
+        "\n",
+        "[5] - [Outlier calculation](https://stackoverflow.com/questions/39068214/how-to-count-outliers-for-all-columns-in-python)\n",
+        "\n",
+        "[6] - [Feature importance](https://python-bloggers.com/2021/01/3-essential-ways-to-calculate-feature-importance-in-python/)"
+      ],
+      "metadata": {
+        "id": "ZidWDrJmcm_J"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Licensing\n",
+        "\n",
+        "Copyright 2022 Sai Dutt\n",
+        "\n",
+        "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
+        "\n",
+        "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
+        "\n",
+        "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
+      ],
+      "metadata": {
+        "id": "scobhWyNv17-"
+      }
+    }
+  ]
+}
\ No newline at end of file