From 03fa59ed7b2da6dc8b195d1927ee7c4fab4f1308 Mon Sep 17 00:00:00 2001 From: Sepehr Heydarian Date: Sun, 8 Dec 2024 02:31:51 -0800 Subject: [PATCH] added report html, updated readme, added figures and tables from scripts. --- README.md | 1 - .../ObesityDataSet_processed_data.csv | 2088 ++++++++++++++ report/obesity_level_predictor_report.html | 650 +++++ report/obesity_level_predictor_report.ipynb | 2412 ----------------- results/figures/Data_vali_targ_varib_dist.png | Bin 0 -> 144136 bytes results/figures/Obesity_test.png | Bin 115123 -> 115461 bytes .../categorical_feat_target_relationship.png | Bin 254153 -> 270457 bytes results/figures/correlation_heatmap_chart.png | Bin 92081 -> 92463 bytes .../figures/numeric_feature_distribution.png | Bin 264042 -> 265210 bytes results/figures/target_distribution_plot.png | Bin 86970 -> 87817 bytes .../htmls/feature_feature_correlation.html | 635 +++++ results/htmls/feature_label_correlation.html | 11 +- results/models/trained_pipelines.pkl | Bin 3273640 -> 3057718 bytes results/tables/best_hyper_parameters.csv | 2 +- results/tables/cross_validation_scores.csv | 4 +- 15 files changed, 3378 insertions(+), 2425 deletions(-) create mode 100644 data/processed/ObesityDataSet_processed_data.csv create mode 100644 report/obesity_level_predictor_report.html delete mode 100644 report/obesity_level_predictor_report.ipynb create mode 100644 results/figures/Data_vali_targ_varib_dist.png create mode 100644 results/htmls/feature_feature_correlation.html diff --git a/README.md b/README.md index a4582af..1f316de 100644 --- a/README.md +++ b/README.md @@ -83,7 +83,6 @@ python scripts/evaluate_models.py --test-data=data/processed/obesity_test_target ``` quarto render report/obesity_level_predictor_report.qmd --to html -quarto render report/obesity_level_predictor_report.qmd --to pdf ``` ## Dependencies diff --git a/data/processed/ObesityDataSet_processed_data.csv b/data/processed/ObesityDataSet_processed_data.csv new file mode 100644 index 0000000..c48cd01 --- /dev/null +++ b/data/processed/ObesityDataSet_processed_data.csv @@ -0,0 +1,2088 @@ +,gender,age,height,weight,family_history_with_overweight,frequent_high_calorie_intake,vegetable_intake_in_meals,meals_per_day,food_intake_between_meals,smoker,daily_water_intake,monitor_calories,days_per_week_with_physical_activity,daily_screen_time,frequent_alcohol_intake,mode_of_transportation,obesity_level +0,Female,21.0,1.62,64.0,yes,no,2.0,3.0,Sometimes,no,2.0,no,0.0,1.0,no,Public_Transportation,Normal_Weight +1,Female,21.0,1.52,56.0,yes,no,3.0,3.0,Sometimes,yes,3.0,yes,3.0,0.0,Sometimes,Public_Transportation,Normal_Weight +2,Male,23.0,1.8,77.0,yes,no,2.0,3.0,Sometimes,no,2.0,no,2.0,1.0,Frequently,Public_Transportation,Normal_Weight +3,Male,27.0,1.8,87.0,no,no,3.0,3.0,Sometimes,no,2.0,no,2.0,0.0,Frequently,Walking,Overweight_Level_I +4,Male,22.0,1.78,89.8,no,no,2.0,1.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II +5,Male,29.0,1.62,53.0,no,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Automobile,Normal_Weight +6,Female,23.0,1.5,55.0,yes,yes,3.0,3.0,Sometimes,no,2.0,no,1.0,0.0,Sometimes,Motorbike,Normal_Weight +7,Male,22.0,1.64,53.0,no,no,2.0,3.0,Sometimes,no,2.0,no,3.0,0.0,Sometimes,Public_Transportation,Normal_Weight +8,Male,24.0,1.78,64.0,yes,yes,3.0,3.0,Sometimes,no,2.0,no,1.0,1.0,Frequently,Public_Transportation,Normal_Weight +9,Male,22.0,1.72,68.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,1.0,no,Public_Transportation,Normal_Weight +10,Male,26.0,1.85,105.0,yes,yes,3.0,3.0,Frequently,no,3.0,no,2.0,2.0,Sometimes,Public_Transportation,Obesity_Type_I +11,Female,21.0,1.72,80.0,yes,yes,2.0,3.0,Frequently,no,2.0,yes,2.0,1.0,Sometimes,Public_Transportation,Overweight_Level_II +12,Male,22.0,1.65,56.0,no,no,3.0,3.0,Sometimes,no,3.0,no,2.0,0.0,Sometimes,Public_Transportation,Normal_Weight +13,Male,41.0,1.8,99.0,no,yes,2.0,3.0,Sometimes,no,2.0,no,2.0,1.0,Frequently,Automobile,Obesity_Type_I +14,Male,23.0,1.77,60.0,yes,yes,3.0,1.0,Sometimes,no,1.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight +15,Female,22.0,1.7,66.0,yes,no,3.0,3.0,Always,no,2.0,yes,2.0,1.0,Sometimes,Public_Transportation,Normal_Weight +16,Male,27.0,1.93,102.0,yes,yes,2.0,1.0,Sometimes,no,1.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II +17,Female,29.0,1.53,78.0,no,yes,2.0,1.0,Sometimes,no,2.0,no,0.0,0.0,no,Automobile,Obesity_Type_I +18,Female,30.0,1.71,82.0,yes,yes,3.0,4.0,Frequently,yes,1.0,no,0.0,0.0,no,Automobile,Overweight_Level_II +19,Female,23.0,1.65,70.0,yes,no,2.0,1.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +20,Male,22.0,1.65,80.0,yes,no,2.0,3.0,Sometimes,no,2.0,no,3.0,2.0,no,Walking,Overweight_Level_II +21,Female,52.0,1.69,87.0,yes,yes,3.0,1.0,Sometimes,yes,2.0,no,0.0,0.0,no,Automobile,Obesity_Type_I +22,Female,22.0,1.65,60.0,yes,yes,3.0,3.0,Sometimes,no,2.0,no,1.0,0.0,Sometimes,Automobile,Normal_Weight +23,Female,22.0,1.6,82.0,yes,yes,1.0,1.0,Sometimes,no,2.0,no,0.0,2.0,Sometimes,Public_Transportation,Obesity_Type_I +24,Male,21.0,1.85,68.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,1.0,Sometimes,Public_Transportation,Normal_Weight +25,Male,20.0,1.6,50.0,yes,no,2.0,4.0,Frequently,yes,2.0,no,3.0,2.0,no,Public_Transportation,Normal_Weight +26,Male,21.0,1.7,65.0,yes,yes,2.0,1.0,Frequently,no,2.0,no,1.0,2.0,Always,Walking,Normal_Weight +27,Female,23.0,1.6,52.0,no,yes,2.0,4.0,Frequently,no,2.0,no,2.0,1.0,Sometimes,Automobile,Normal_Weight +28,Male,19.0,1.75,76.0,yes,yes,3.0,3.0,Sometimes,no,2.0,yes,3.0,1.0,Sometimes,Public_Transportation,Normal_Weight +29,Male,23.0,1.68,70.0,no,yes,2.0,3.0,Sometimes,no,2.0,no,2.0,2.0,Frequently,Walking,Normal_Weight +30,Male,29.0,1.77,83.0,no,yes,1.0,4.0,Frequently,no,3.0,no,0.0,1.0,no,Motorbike,Overweight_Level_I +31,Female,31.0,1.58,68.0,yes,no,2.0,1.0,Sometimes,no,1.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II +32,Female,24.0,1.77,76.0,no,no,2.0,3.0,Sometimes,no,3.0,no,1.0,1.0,Sometimes,Walking,Normal_Weight +33,Male,39.0,1.79,90.0,no,no,2.0,1.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II +34,Male,22.0,1.65,62.0,no,yes,2.0,4.0,Frequently,no,2.0,no,2.0,0.0,Sometimes,Public_Transportation,Normal_Weight +35,Female,21.0,1.5,65.0,yes,no,2.0,3.0,Sometimes,no,2.0,no,2.0,2.0,Sometimes,Public_Transportation,Overweight_Level_II +36,Female,22.0,1.56,49.0,no,yes,2.0,3.0,Sometimes,no,1.0,yes,2.0,0.0,no,Walking,Normal_Weight +37,Female,21.0,1.6,48.0,no,yes,2.0,3.0,Sometimes,no,1.0,no,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight +38,Male,23.0,1.65,67.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight +39,Female,21.0,1.75,88.0,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II 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+103,Female,22.0,1.65,58.0,yes,yes,3.0,4.0,Frequently,no,2.0,no,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight +104,Female,38.0,1.56,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +105,Female,25.0,1.57,55.0,no,yes,2.0,1.0,Sometimes,no,2.0,no,2.0,0.0,Sometimes,Public_Transportation,Normal_Weight +107,Male,22.0,1.88,90.0,yes,yes,2.0,3.0,Always,no,1.0,no,0.0,1.0,Sometimes,Automobile,Overweight_Level_I +108,Male,22.0,1.75,95.0,yes,no,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Walking,Obesity_Type_I +109,Female,21.0,1.65,88.0,yes,yes,3.0,1.0,Sometimes,no,3.0,no,2.0,1.0,no,Public_Transportation,Obesity_Type_I +110,Male,21.0,1.75,75.0,yes,yes,3.0,3.0,Sometimes,no,2.0,no,0.0,1.0,Sometimes,Public_Transportation,Normal_Weight +111,Female,22.0,1.58,58.0,yes,yes,2.0,1.0,Sometimes,no,1.0,no,0.0,0.0,no,Public_Transportation,Normal_Weight +112,Female,18.0,1.56,51.0,yes,yes,2.0,4.0,Frequently,no,2.0,no,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight +113,Female,22.0,1.5,49.0,yes,no,2.0,1.0,Sometimes,no,2.0,no,3.0,0.0,no,Walking,Normal_Weight +114,Female,19.0,1.61,62.0,yes,yes,3.0,1.0,Frequently,no,2.0,no,1.0,0.0,Frequently,Public_Transportation,Normal_Weight +115,Female,17.0,1.75,57.0,yes,yes,3.0,3.0,Frequently,no,2.0,no,0.0,1.0,no,Public_Transportation,Normal_Weight +116,Female,15.0,1.65,86.0,yes,yes,3.0,3.0,Sometimes,no,1.0,no,3.0,2.0,no,Walking,Obesity_Type_I +117,Female,17.0,1.7,85.0,yes,no,2.0,3.0,Frequently,no,2.0,no,1.0,1.0,no,Public_Transportation,Overweight_Level_II +118,Male,23.0,1.62,53.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight +119,Female,19.0,1.63,76.0,yes,no,3.0,3.0,Frequently,yes,3.0,no,2.0,1.0,Sometimes,Automobile,Overweight_Level_II +120,Female,23.0,1.67,75.0,yes,yes,2.0,3.0,Frequently,yes,2.0,no,0.0,2.0,no,Walking,Overweight_Level_I +121,Male,23.0,1.87,95.0,yes,yes,1.0,3.0,Frequently,no,2.0,no,0.0,2.0,Frequently,Public_Transportation,Overweight_Level_II +122,Male,21.0,1.75,50.0,yes,no,3.0,4.0,Frequently,no,1.0,no,1.0,0.0,no,Walking,Insufficient_Weight +123,Male,24.0,1.66,67.0,yes,yes,3.0,1.0,Sometimes,no,2.0,no,1.0,0.0,no,Public_Transportation,Normal_Weight +124,Male,23.0,1.76,90.0,no,yes,3.0,3.0,Sometimes,no,1.0,no,0.0,0.0,no,Public_Transportation,Overweight_Level_II +125,Male,18.0,1.75,80.0,yes,yes,2.0,3.0,Always,no,2.0,no,0.0,0.0,Frequently,Public_Transportation,Overweight_Level_I +126,Male,19.0,1.67,68.0,no,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight +127,Female,19.0,1.65,61.0,no,yes,3.0,1.0,Sometimes,no,3.0,yes,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight +128,Male,20.0,1.72,70.0,yes,yes,3.0,3.0,Frequently,no,1.0,no,0.0,0.0,Sometimes,Public_Transportation,Normal_Weight +129,Male,27.0,1.7,78.0,no,yes,3.0,3.0,Sometimes,no,3.0,no,1.0,1.0,Frequently,Public_Transportation,Overweight_Level_II +130,Female,20.0,1.58,53.0,yes,yes,2.0,4.0,Frequently,no,2.0,no,1.0,1.0,no,Public_Transportation,Normal_Weight +131,Male,23.0,1.62,58.0,no,no,2.0,3.0,Sometimes,no,1.0,no,1.0,0.0,no,Public_Transportation,Normal_Weight +132,Female,19.0,1.65,56.0,yes,yes,3.0,3.0,Frequently,yes,3.0,yes,1.0,2.0,Frequently,Public_Transportation,Normal_Weight +133,Female,61.0,1.65,66.0,no,yes,3.0,3.0,Always,no,2.0,no,1.0,1.0,Frequently,Public_Transportation,Normal_Weight +134,Male,30.0,1.77,109.0,yes,yes,3.0,3.0,Sometimes,no,1.0,no,2.0,0.0,Sometimes,Automobile,Obesity_Type_I +135,Male,24.0,1.7,75.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,0.0,Sometimes,Automobile,Overweight_Level_I +136,Male,25.0,1.79,72.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight +137,Male,44.0,1.6,80.0,yes,no,2.0,3.0,Sometimes,yes,3.0,no,0.0,0.0,no,Motorbike,Obesity_Type_I +138,Male,31.0,1.76,75.0,yes,no,3.0,3.0,Always,no,3.0,yes,3.0,0.0,no,Bike,Normal_Weight +139,Male,25.0,1.7,68.0,no,yes,2.0,3.0,Frequently,no,2.0,no,0.0,1.0,no,Public_Transportation,Normal_Weight +140,Male,23.0,1.89,65.0,yes,yes,3.0,3.0,Frequently,no,3.0,no,1.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +141,Male,25.0,1.87,66.0,no,yes,3.0,3.0,Always,no,2.0,no,2.0,0.0,Frequently,Public_Transportation,Normal_Weight +142,Male,23.0,1.74,93.5,no,yes,2.0,3.0,Frequently,yes,1.0,no,1.0,1.0,Frequently,Automobile,Obesity_Type_I +143,Female,34.0,1.68,75.0,no,yes,3.0,1.0,Sometimes,no,1.0,no,0.0,0.0,Sometimes,Automobile,Overweight_Level_I +144,Male,22.0,1.61,67.0,yes,no,2.0,4.0,Sometimes,no,3.0,yes,2.0,2.0,Sometimes,Public_Transportation,Overweight_Level_I +145,Male,21.0,1.62,70.0,no,yes,2.0,1.0,no,no,3.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +146,Female,24.0,1.56,51.0,no,no,3.0,3.0,Frequently,no,2.0,no,2.0,2.0,Frequently,Public_Transportation,Normal_Weight +147,Female,36.0,1.63,80.0,yes,no,3.0,3.0,Sometimes,no,1.0,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +148,Female,27.0,1.6,61.0,no,yes,3.0,3.0,Always,no,2.0,no,0.0,0.0,Sometimes,Automobile,Normal_Weight +149,Female,32.0,1.67,90.0,yes,yes,3.0,1.0,Sometimes,no,2.0,no,2.0,0.0,Sometimes,Automobile,Obesity_Type_I +150,Male,25.0,1.78,78.0,yes,yes,2.0,1.0,Sometimes,no,2.0,no,2.0,1.0,Sometimes,Public_Transportation,Normal_Weight +151,Female,30.0,1.62,59.0,yes,yes,3.0,3.0,Sometimes,no,2.0,no,2.0,0.0,Sometimes,Automobile,Normal_Weight +152,Female,38.0,1.5,60.0,yes,yes,2.0,1.0,Always,yes,2.0,no,0.0,0.0,Sometimes,Automobile,Overweight_Level_I +153,Male,34.0,1.69,84.0,yes,no,2.0,3.0,Sometimes,no,3.0,no,2.0,0.0,no,Automobile,Overweight_Level_II +154,Male,22.0,1.74,94.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I +155,Female,31.0,1.68,63.0,yes,yes,3.0,1.0,Frequently,no,2.0,no,1.0,1.0,Sometimes,Automobile,Normal_Weight 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+165,Male,30.0,1.92,130.0,yes,no,2.0,3.0,Sometimes,yes,1.0,no,1.0,0.0,Sometimes,Public_Transportation,Obesity_Type_II +166,Female,29.0,1.74,72.0,yes,no,3.0,3.0,Always,no,2.0,no,0.0,0.0,Sometimes,Automobile,Normal_Weight +167,Male,20.0,1.65,80.0,yes,no,2.0,3.0,Sometimes,no,2.0,no,1.0,2.0,no,Walking,Overweight_Level_II +168,Female,22.0,1.73,79.0,yes,yes,2.0,1.0,Sometimes,no,2.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +169,Female,45.0,1.63,77.0,yes,yes,2.0,3.0,Frequently,no,1.0,no,0.0,0.0,no,Automobile,Overweight_Level_II +170,Male,22.0,1.72,82.0,no,yes,2.0,1.0,Sometimes,no,2.0,no,2.0,1.0,Sometimes,Public_Transportation,Overweight_Level_II +171,Male,18.0,1.6,58.0,yes,yes,2.0,3.0,Frequently,no,2.0,no,1.0,0.0,Sometimes,Public_Transportation,Normal_Weight +172,Female,23.0,1.65,59.0,no,no,3.0,1.0,Sometimes,no,2.0,no,1.0,2.0,Sometimes,Public_Transportation,Normal_Weight 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+476,Female,18.0,1.71,75.0,yes,yes,3.0,3.0,Sometimes,no,2.0,no,1.0,0.0,no,Public_Transportation,Overweight_Level_I +477,Female,18.0,1.64,56.0,yes,yes,3.0,3.0,Frequently,no,1.0,yes,1.0,1.0,no,Public_Transportation,Normal_Weight +478,Male,19.0,1.69,65.0,no,yes,2.0,3.0,Frequently,no,1.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight +479,Female,17.0,1.58,50.0,no,yes,1.0,4.0,Frequently,no,2.0,no,1.0,2.0,Sometimes,Public_Transportation,Normal_Weight +480,Female,18.0,1.57,50.0,no,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,1.0,Sometimes,Public_Transportation,Normal_Weight +481,Male,18.0,1.74,64.0,yes,yes,3.0,4.0,Sometimes,no,1.0,yes,2.0,0.0,Sometimes,Public_Transportation,Normal_Weight +482,Female,20.0,1.58,53.5,yes,yes,2.0,1.0,Frequently,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight +483,Female,18.0,1.5,58.0,no,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,0.0,no,Public_Transportation,Overweight_Level_I +484,Female,36.0,1.65,80.0,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,2.0,no,Automobile,Overweight_Level_II +485,Male,21.0,1.8,73.0,yes,yes,1.0,3.0,Always,no,2.0,no,3.0,1.0,Sometimes,Public_Transportation,Normal_Weight +486,Male,23.0,1.75,75.0,no,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,0.0,Frequently,Public_Transportation,Normal_Weight +487,Male,20.0,1.84,104.0,yes,no,2.0,3.0,Sometimes,no,3.0,no,3.0,0.0,no,Motorbike,Obesity_Type_I +488,Male,21.0,1.88,84.0,yes,yes,3.0,3.0,Sometimes,no,3.0,no,2.0,1.0,Sometimes,Walking,Normal_Weight +489,Female,19.0,1.56,50.0,no,yes,2.0,1.0,Sometimes,no,1.0,no,0.0,2.0,no,Public_Transportation,Normal_Weight +490,Male,24.0,1.75,84.0,no,yes,3.0,3.0,no,no,2.0,yes,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II +491,Male,25.0,1.66,68.0,no,yes,2.0,3.0,Sometimes,yes,1.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight +492,Male,45.0,1.7,86.0,no,yes,3.0,3.0,Frequently,no,1.0,no,0.0,0.0,no,Automobile,Overweight_Level_II +493,Male,20.0,1.8,65.0,no,yes,2.0,3.0,Frequently,no,1.0,no,2.0,0.0,Sometimes,Motorbike,Normal_Weight +494,Female,18.0,1.67,66.0,no,yes,3.0,3.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Normal_Weight +495,Male,19.0,1.8,60.0,yes,yes,3.0,1.0,Always,no,1.0,yes,0.0,0.0,no,Motorbike,Normal_Weight +497,Male,20.0,1.56,45.0,no,no,2.0,3.0,Sometimes,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Normal_Weight +498,Female,25.196214,1.686306,104.572712,yes,yes,3.0,3.0,Sometimes,no,1.152736,no,0.319156,1.0,Sometimes,Public_Transportation,Obesity_Type_III +499,Female,18.503343,1.683124,126.67378,yes,yes,3.0,3.0,Sometimes,no,1.115967,no,1.541072,1.0,Sometimes,Public_Transportation,Obesity_Type_III +500,Female,26.0,1.622397,110.79263,yes,yes,3.0,3.0,Sometimes,no,2.704507,no,0.0,0.29499,Sometimes,Public_Transportation,Obesity_Type_III +501,Female,21.853826,1.755643,137.796884,yes,yes,3.0,3.0,Sometimes,no,2.184707,no,1.978631,0.838957,Sometimes,Public_Transportation,Obesity_Type_III +502,Female,21.90012,1.843419,165.057269,yes,yes,3.0,3.0,Sometimes,no,2.406541,no,0.10032,0.479221,Sometimes,Public_Transportation,Obesity_Type_III +503,Female,18.306615,1.7456,133.03441,yes,yes,3.0,3.0,Sometimes,no,2.984323,no,1.586525,0.62535,Sometimes,Public_Transportation,Obesity_Type_III +504,Female,26.0,1.630927,111.485516,yes,yes,3.0,3.0,Sometimes,no,2.444125,no,0.0,0.26579,Sometimes,Public_Transportation,Obesity_Type_III +505,Female,26.0,1.629191,104.826776,yes,yes,3.0,3.0,Sometimes,no,2.654702,no,0.0,0.555468,Sometimes,Public_Transportation,Obesity_Type_III +506,Female,21.849705,1.770612,133.963349,yes,yes,3.0,3.0,Sometimes,no,2.825629,no,1.399183,0.928972,Sometimes,Public_Transportation,Obesity_Type_III +507,Male,19.799054,1.743702,54.927529,yes,yes,2.0,3.28926,Sometimes,no,2.847264,no,1.680844,2.0,Sometimes,Public_Transportation,Insufficient_Weight +508,Male,17.188754,1.771915,55.695036,yes,yes,2.0,4.0,Sometimes,no,2.884033,no,2.0,1.340107,Sometimes,Public_Transportation,Insufficient_Weight +509,Male,22.285024,1.75376,55.879263,yes,yes,2.450218,3.995147,Sometimes,no,2.147746,no,2.0,0.58998,Sometimes,Public_Transportation,Insufficient_Weight +510,Female,22.0,1.675446,51.154201,yes,yes,3.0,3.0,Frequently,no,2.815293,no,1.978631,1.0,no,Public_Transportation,Insufficient_Weight +511,Female,21.02497,1.666203,49.869791,yes,yes,3.0,3.0,Frequently,no,2.593459,no,2.0,1.0,no,Public_Transportation,Insufficient_Weight +512,Female,22.038327,1.711467,51.965521,yes,yes,2.880161,3.0,Frequently,no,1.031354,no,2.206738,1.37465,no,Public_Transportation,Insufficient_Weight +513,Female,21.243142,1.598019,44.845655,no,no,3.0,1.72626,Frequently,no,2.444125,no,1.31817,0.0,no,Public_Transportation,Insufficient_Weight +514,Female,22.142432,1.59611,42.848033,no,no,3.0,2.581015,Frequently,no,2.654702,no,0.902095,0.0,no,Public_Transportation,Insufficient_Weight +515,Female,21.962426,1.57206,43.919835,no,no,3.0,1.600812,Frequently,no,2.651258,no,0.600817,0.0,no,Public_Transportation,Insufficient_Weight +516,Female,21.491055,1.586952,43.087508,no,no,2.00876,1.73762,Frequently,no,1.792022,no,0.119643,0.0,no,Public_Transportation,Insufficient_Weight +517,Female,22.717943,1.59559,44.581159,no,no,2.596579,1.10548,Frequently,no,1.490613,no,0.345684,0.0,no,Public_Transportation,Insufficient_Weight +518,Female,23.501249,1.6,45.0,no,no,2.591439,3.0,Frequently,no,2.074048,no,1.679935,0.0,no,Public_Transportation,Insufficient_Weight +519,Female,18.535075,1.688025,45.0,no,yes,3.0,3.0,Sometimes,no,3.0,yes,2.539762,1.283673,no,Public_Transportation,Insufficient_Weight +520,Female,19.0,1.556211,42.339767,no,yes,3.0,2.0846,Sometimes,no,2.13755,yes,0.196152,0.062488,no,Public_Transportation,Insufficient_Weight +521,Female,19.0,1.564199,42.096062,no,yes,3.0,1.894384,Sometimes,no,2.456581,yes,1.596576,0.9974,no,Public_Transportation,Insufficient_Weight +522,Female,20.254534,1.56948,41.324558,no,yes,2.392665,1.0,Frequently,no,1.0,no,0.0,0.738269,Sometimes,Public_Transportation,Insufficient_Weight +523,Female,21.0,1.52,42.0,no,yes,3.0,1.0,Frequently,no,1.0,no,0.0,0.0,Sometimes,Public_Transportation,Insufficient_Weight +524,Female,20.225396,1.550648,44.641796,no,yes,3.0,2.857787,Frequently,no,1.0,no,0.754646,0.0,Sometimes,Public_Transportation,Insufficient_Weight +525,Female,19.86997,1.520997,42.0,no,yes,3.0,1.0,Frequently,no,1.527036,no,0.0,0.860497,Sometimes,Public_Transportation,Insufficient_Weight +526,Female,20.147114,1.528011,42.0,no,yes,3.0,1.0,Frequently,no,1.322669,no,0.0,0.478676,Sometimes,Public_Transportation,Insufficient_Weight +528,Female,21.996523,1.733263,50.890363,no,no,3.0,3.765526,Frequently,no,1.656082,no,0.42777,0.555967,Sometimes,Public_Transportation,Insufficient_Weight +529,Female,21.376426,1.722527,50.833029,no,no,3.0,3.285167,Frequently,no,1.569082,no,0.545931,0.469735,Sometimes,Public_Transportation,Insufficient_Weight +530,Female,19.724106,1.734832,50.0,no,no,1.123939,4.0,Frequently,no,1.0,no,1.303976,0.371941,Sometimes,Public_Transportation,Insufficient_Weight +531,Male,23.0,1.882779,64.106108,yes,yes,3.0,3.0,Sometimes,no,2.843777,no,1.488843,0.009254,Sometimes,Automobile,Insufficient_Weight +532,Male,22.851834,1.854592,63.611109,yes,yes,3.0,3.691226,Sometimes,no,2.650989,no,1.228136,0.8324,Sometimes,Automobile,Insufficient_Weight +533,Male,23.0,1.835643,58.854416,yes,yes,3.0,3.0,Sometimes,no,2.027984,no,1.661556,0.114716,Sometimes,Automobile,Insufficient_Weight +534,Male,18.216032,1.755507,52.0,yes,yes,3.0,3.0,Frequently,no,2.0,no,0.658894,1.0,no,Public_Transportation,Insufficient_Weight +535,Male,21.81119,1.71266,51.598593,yes,yes,3.0,3.156153,Frequently,no,1.253803,no,0.544784,0.256382,no,Public_Transportation,Insufficient_Weight +536,Male,18.164768,1.694265,52.0,yes,yes,3.0,3.0,Frequently,no,2.0,no,0.819682,1.0,no,Public_Transportation,Insufficient_Weight +537,Female,18.871794,1.586895,41.452385,no,yes,2.0,1.07976,Sometimes,no,1.0,no,0.194745,1.547086,Sometimes,Public_Transportation,Insufficient_Weight +538,Female,18.766033,1.579561,41.890204,no,yes,2.027574,1.0,Sometimes,no,1.636326,no,0.0,1.906141,Sometimes,Public_Transportation,Insufficient_Weight +539,Female,18.540535,1.564568,41.397378,no,yes,2.658112,1.0,Sometimes,no,1.298165,no,0.0,1.639326,Sometimes,Public_Transportation,Insufficient_Weight +540,Female,19.88036,1.670635,49.742931,no,no,2.88626,3.559841,Frequently,no,1.632004,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +541,Female,19.717249,1.688426,49.660995,no,no,2.714447,3.0,Frequently,no,2.0,no,1.903182,1.0,Sometimes,Public_Transportation,Insufficient_Weight +542,Female,19.633898,1.66084,49.039794,no,no,2.750715,3.0,Frequently,no,2.0,no,1.067817,1.0,Sometimes,Public_Transportation,Insufficient_Weight +543,Female,23.0,1.710182,50.287967,yes,yes,2.0,3.0,Frequently,no,2.099863,no,1.63581,2.0,no,Public_Transportation,Insufficient_Weight +544,Female,20.406871,1.755978,53.699561,yes,yes,2.0,3.891994,Frequently,no,1.86393,no,2.870127,2.0,no,Public_Transportation,Insufficient_Weight +545,Female,23.0,1.728834,51.442293,yes,yes,2.0,3.0,Frequently,no,1.229915,no,0.619533,2.0,no,Public_Transportation,Insufficient_Weight +546,Female,19.058511,1.662017,49.838965,no,yes,1.4925,3.0,Sometimes,no,1.049134,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +547,Female,18.827008,1.7,50.0,no,yes,1.0,3.240578,Sometimes,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +548,Female,19.314964,1.661403,49.932199,no,yes,2.205439,3.0,Sometimes,no,1.530531,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +549,Female,29.970445,1.610863,49.516027,yes,yes,2.059138,3.904858,Frequently,no,2.0,no,0.821977,0.0,no,Public_Transportation,Insufficient_Weight +550,Female,32.383858,1.640688,46.655622,yes,yes,3.0,3.11158,Frequently,no,2.721769,no,1.44287,0.0,no,Public_Transportation,Insufficient_Weight +551,Female,24.163526,1.64603,49.839685,yes,yes,3.0,3.590039,Frequently,no,2.7305,no,0.107078,0.0,no,Public_Transportation,Insufficient_Weight +552,Male,17.038222,1.710564,51.588874,no,yes,2.0,2.057935,Sometimes,no,2.371015,no,0.288032,0.714627,Sometimes,Public_Transportation,Insufficient_Weight +553,Male,16.30687,1.752755,50.0,no,yes,2.310423,3.558637,Sometimes,no,1.787843,no,1.926592,0.828549,Sometimes,Public_Transportation,Insufficient_Weight +554,Male,16.198153,1.691007,52.629374,no,yes,2.0,2.000986,Sometimes,no,2.673835,no,0.99295,0.474836,Sometimes,Public_Transportation,Insufficient_Weight +555,Male,18.0,1.753349,51.457226,no,yes,2.823179,3.0,Sometimes,no,1.773236,no,1.252472,1.0,Sometimes,Public_Transportation,Insufficient_Weight +556,Male,18.0,1.781543,50.869704,no,yes,2.052932,3.0,Sometimes,no,2.0,no,0.520408,1.0,Sometimes,Public_Transportation,Insufficient_Weight +557,Male,18.0,1.755823,52.331172,no,yes,2.596364,3.0,Sometimes,no,2.0,no,0.281734,1.488372,Sometimes,Public_Transportation,Insufficient_Weight +558,Male,17.486869,1.836669,58.943347,yes,yes,2.767731,3.821168,Sometimes,no,2.0,no,2.0,0.556245,no,Automobile,Insufficient_Weight +559,Male,19.920629,1.768493,57.790381,yes,yes,2.0,3.897078,Sometimes,no,2.622638,no,2.0,1.894105,no,Automobile,Insufficient_Weight +560,Male,18.426619,1.777108,57.145917,yes,yes,2.0,3.092116,Sometimes,no,2.426465,no,2.0,1.839862,no,Automobile,Insufficient_Weight +561,Female,17.082867,1.640824,43.365005,no,yes,2.815157,3.0,Sometimes,no,2.911187,no,2.595128,1.380204,Sometimes,Public_Transportation,Insufficient_Weight +562,Female,17.000433,1.584951,44.411801,no,yes,2.737762,3.0,Sometimes,no,2.310921,no,2.240714,1.59257,Sometimes,Public_Transportation,Insufficient_Weight +563,Female,16.270434,1.818268,47.124717,no,yes,3.0,3.286431,Sometimes,no,2.148146,no,2.458237,1.273333,Sometimes,Public_Transportation,Insufficient_Weight +564,Male,17.908114,1.793926,59.682591,yes,yes,2.568063,4.0,Sometimes,no,2.0,no,2.0,0.220029,no,Automobile,Insufficient_Weight +565,Male,17.120699,1.809251,58.968994,yes,yes,2.524428,4.0,Sometimes,no,2.0,no,2.0,0.03838,no,Automobile,Insufficient_Weight +566,Male,19.329542,1.767335,55.700497,yes,yes,2.0,4.0,Sometimes,no,2.157395,no,2.0,1.679149,no,Automobile,Insufficient_Weight +567,Female,22.377998,1.699568,54.98774,yes,yes,3.0,3.0,Frequently,no,2.0,no,0.139808,0.875464,no,Public_Transportation,Insufficient_Weight +568,Female,20.954955,1.759358,55.01045,yes,yes,2.971574,3.592415,Frequently,no,2.894678,no,2.0,1.009632,no,Public_Transportation,Insufficient_Weight +569,Female,22.991668,1.740295,54.166453,yes,yes,3.0,3.0,Frequently,no,2.025279,no,2.0,0.152985,no,Public_Transportation,Insufficient_Weight +570,Female,19.300435,1.739354,49.649613,no,yes,3.0,3.754599,Sometimes,no,1.692112,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +571,Female,17.065445,1.647811,49.603807,no,yes,3.0,3.566082,Sometimes,no,1.438398,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +572,Female,19.084967,1.768435,49.597765,no,yes,3.0,3.725797,Sometimes,no,1.191401,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +573,Female,18.024853,1.7,50.0,no,yes,1.0816,3.520555,Frequently,no,1.016968,no,0.651412,1.0,Sometimes,Public_Transportation,Insufficient_Weight +574,Female,19.833682,1.699464,49.676046,no,yes,1.270448,3.731212,Frequently,no,1.876915,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +575,Female,17.767432,1.74379,50.0,no,yes,1.344854,4.0,Frequently,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +576,Female,19.504696,1.590317,42.367615,no,no,2.959658,3.0,Frequently,no,1.0,no,1.522399,0.0,Sometimes,Public_Transportation,Insufficient_Weight +577,Female,19.950605,1.527133,42.0,no,no,2.725282,1.259803,Frequently,no,1.0,no,1.37467,0.0,Sometimes,Public_Transportation,Insufficient_Weight +578,Female,19.0,1.530875,42.0,no,no,2.844607,1.273128,Frequently,no,1.69551,no,0.260079,0.635867,Sometimes,Public_Transportation,Insufficient_Weight +579,Male,17.0,1.848294,59.409018,yes,yes,2.44004,3.0,Sometimes,no,2.0,no,2.784471,1.0,no,Automobile,Insufficient_Weight +580,Male,17.0,1.824414,59.295172,yes,yes,2.432302,3.0,Sometimes,no,2.0,no,2.349495,1.0,no,Automobile,Insufficient_Weight +581,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 +582,Female,22.926352,1.715597,50.0,yes,yes,2.449267,3.647154,Frequently,no,1.266018,no,0.866045,0.097234,no,Public_Transportation,Insufficient_Weight +583,Female,21.785351,1.675054,49.945233,yes,yes,2.929889,3.0,Frequently,no,2.792074,no,0.630944,1.366355,no,Public_Transportation,Insufficient_Weight +584,Female,23.0,1.717601,51.073918,yes,yes,2.0,3.0,Frequently,no,1.426874,no,2.20208,2.0,no,Public_Transportation,Insufficient_Weight +585,Female,17.402028,1.710756,50.0,no,yes,2.015258,3.300666,Sometimes,no,1.315608,no,0.069802,1.0,Sometimes,Public_Transportation,Insufficient_Weight +586,Female,18.0,1.7,50.0,no,yes,1.031149,3.0,Sometimes,no,1.963628,no,0.028202,1.0,Sometimes,Public_Transportation,Insufficient_Weight +587,Female,18.48207,1.7,50.0,no,yes,1.592183,3.535016,Sometimes,no,1.086391,no,0.618913,1.0,Sometimes,Public_Transportation,Insufficient_Weight +588,Female,18.53084,1.573816,39.850137,no,yes,1.21498,1.717608,Sometimes,no,1.179942,no,0.684739,2.0,Sometimes,Public_Transportation,Insufficient_Weight +589,Female,19.94814,1.530884,39.371523,no,yes,1.522001,3.0,Sometimes,no,1.98126,no,2.306844,0.720454,Sometimes,Public_Transportation,Insufficient_Weight +590,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 +591,Male,19.556729,1.767563,56.307019,yes,yes,2.362918,4.0,Sometimes,no,2.358172,no,2.0,0.939819,no,Automobile,Insufficient_Weight +592,Male,17.70368,1.883364,60.0,yes,yes,3.0,3.626815,Sometimes,no,2.0,no,2.011519,0.456462,no,Automobile,Insufficient_Weight +593,Male,18.274358,1.824655,58.621349,yes,yes,2.14084,4.0,Sometimes,no,2.931438,no,2.0,1.164457,no,Automobile,Insufficient_Weight +594,Male,18.0,1.840138,60.0,yes,yes,3.0,4.0,Sometimes,no,2.0,no,2.0,0.0,no,Automobile,Insufficient_Weight +595,Male,17.210933,1.819557,58.325122,yes,yes,2.5596,4.0,Sometimes,no,2.0,no,2.0,0.331483,no,Automobile,Insufficient_Weight +596,Male,17.469417,1.798645,59.612717,yes,yes,2.336044,4.0,Sometimes,no,2.0,no,2.0,0.133005,no,Automobile,Insufficient_Weight +597,Male,18.0,1.7108,50.92538,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,1.593704,Sometimes,Public_Transportation,Insufficient_Weight +598,Male,18.0,1.70653,51.121749,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,1.329237,Sometimes,Public_Transportation,Insufficient_Weight +599,Male,18.0,1.716691,51.149283,yes,yes,1.813234,3.0,Sometimes,no,1.274815,no,0.520407,1.673584,Sometimes,Public_Transportation,Insufficient_Weight +600,Female,19.010211,1.555431,44.188767,no,no,2.724285,3.0,Frequently,no,1.0,yes,1.070331,0.0,Sometimes,Public_Transportation,Insufficient_Weight +601,Female,19.091346,1.603923,45.0,no,no,3.0,3.0,Frequently,no,1.28246,yes,1.60195,0.0,Sometimes,Public_Transportation,Insufficient_Weight +602,Female,19.773303,1.602082,45.0,no,no,3.0,3.0,Frequently,no,2.875583,yes,1.258504,0.0,Sometimes,Public_Transportation,Insufficient_Weight +603,Female,19.483036,1.53777,42.0,no,yes,3.0,1.0,Frequently,no,1.387531,no,0.0,0.466169,Sometimes,Public_Transportation,Insufficient_Weight +604,Female,19.0,1.538398,42.0,no,yes,2.71897,1.473088,Frequently,no,1.164644,no,1.235675,0.930926,Sometimes,Public_Transportation,Insufficient_Weight +605,Female,19.407204,1.520862,42.0,no,yes,3.0,1.0,Frequently,no,1.848703,no,0.0,0.744555,Sometimes,Public_Transportation,Insufficient_Weight +606,Male,18.0,1.717826,51.7325,yes,yes,1.133844,3.0,Sometimes,no,1.325326,no,0.227985,1.708309,Sometimes,Public_Transportation,Insufficient_Weight +607,Male,18.424941,1.753389,54.121925,yes,yes,2.0,3.16645,Sometimes,no,2.278578,no,1.838881,2.0,Sometimes,Public_Transportation,Insufficient_Weight +608,Male,18.0,1.70165,50.157707,yes,yes,1.757466,3.0,Sometimes,no,1.153559,no,0.833976,1.616045,Sometimes,Public_Transportation,Insufficient_Weight +609,Male,19.97981,1.75336,54.997374,yes,yes,2.0,3.494849,Sometimes,no,2.976672,no,1.94907,2.0,no,Public_Transportation,Insufficient_Weight +610,Female,21.798856,1.672007,49.980968,yes,yes,2.979383,3.0,Frequently,no,2.975887,no,0.945093,1.241755,no,Public_Transportation,Insufficient_Weight +611,Female,22.209706,1.593847,44.050251,no,no,3.0,2.99321,Frequently,no,2.702783,no,1.967234,0.0,no,Public_Transportation,Insufficient_Weight +612,Female,23.018443,1.584785,44.376637,no,no,2.204914,2.127797,Frequently,no,2.120292,no,0.995735,0.0,no,Public_Transportation,Insufficient_Weight +613,Female,19.376997,1.600264,45.0,no,no,3.0,3.0,Frequently,no,2.949419,yes,1.295697,0.0,Sometimes,Public_Transportation,Insufficient_Weight +614,Female,20.971429,1.549311,41.557468,no,yes,2.927218,1.0,Frequently,no,1.0,no,0.0,0.276592,Sometimes,Public_Transportation,Insufficient_Weight +615,Female,20.345161,1.534385,41.96525,no,yes,2.88853,1.0,Frequently,no,1.0,no,0.0,0.196224,Sometimes,Public_Transportation,Insufficient_Weight +616,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 +617,Male,20.406066,1.868931,63.199726,yes,yes,3.0,3.699594,Sometimes,no,2.825629,no,1.699592,0.071028,Sometimes,Automobile,Insufficient_Weight +618,Female,21.478496,1.686936,51.256059,yes,yes,3.0,3.179995,Frequently,no,1.910378,no,0.480614,0.625079,no,Public_Transportation,Insufficient_Weight +619,Female,18.372563,1.589829,40.202773,no,yes,2.0,1.075553,Sometimes,no,1.0,no,0.127425,1.679442,Sometimes,Public_Transportation,Insufficient_Weight +620,Female,19.931667,1.653209,49.026214,no,no,2.530066,3.0,Sometimes,no,1.965237,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +621,Female,22.998709,1.740108,53.65727,yes,yes,2.241606,3.0,Frequently,no,1.846626,no,2.460238,0.814518,no,Public_Transportation,Insufficient_Weight +622,Female,18.006742,1.7,50.0,no,yes,1.003566,3.238258,Frequently,no,1.014634,no,0.783676,1.0,Sometimes,Public_Transportation,Insufficient_Weight +623,Female,34.799519,1.689141,50.0,yes,yes,2.652779,3.804944,Frequently,no,1.60114,no,0.174692,0.096982,no,Public_Transportation,Insufficient_Weight +624,Male,16.496978,1.691206,50.0,no,yes,2.0,1.630846,Sometimes,no,2.975528,no,0.548991,0.369134,Sometimes,Public_Transportation,Insufficient_Weight +625,Male,18.0,1.788459,51.552595,no,yes,2.897899,3.0,Sometimes,no,1.835545,no,1.162519,1.0,Sometimes,Public_Transportation,Insufficient_Weight +626,Male,17.377131,1.811238,58.83071,yes,yes,2.483979,3.762778,Sometimes,no,2.0,no,2.0,0.930051,no,Automobile,Insufficient_Weight +627,Female,16.611837,1.830068,43.534531,no,yes,2.945967,3.0,Sometimes,no,2.953192,no,2.830911,1.466667,Sometimes,Public_Transportation,Insufficient_Weight +628,Male,17.080493,1.782756,56.029418,yes,yes,2.0,4.0,Sometimes,no,2.28525,no,2.0,1.162801,no,Automobile,Insufficient_Weight +629,Female,22.970655,1.751691,55.967655,yes,yes,2.478891,3.371832,Frequently,no,2.004126,no,2.0,0.327376,no,Public_Transportation,Insufficient_Weight +630,Female,17.764764,1.78656,50.0,no,yes,2.784464,4.0,Sometimes,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +631,Female,19.272573,1.71367,50.0,no,yes,1.005578,4.0,Frequently,no,1.0,no,1.683957,0.704978,Sometimes,Public_Transportation,Insufficient_Weight +632,Female,19.0,1.540375,42.006282,no,no,2.938031,2.705445,Frequently,no,1.333807,yes,1.877369,0.370973,Sometimes,Public_Transportation,Insufficient_Weight +633,Male,17.06713,1.896734,59.895052,yes,yes,2.842102,3.34175,Sometimes,no,2.0,no,2.511157,0.560351,no,Automobile,Insufficient_Weight +634,Female,23.0,1.710129,50.079991,yes,yes,2.0,3.0,Frequently,no,2.685842,no,0.373186,2.0,no,Public_Transportation,Insufficient_Weight +635,Female,18.0,1.704193,50.631889,no,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,1.525597,Sometimes,Public_Transportation,Insufficient_Weight +636,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 +637,Male,18.0,1.845399,60.0,yes,yes,3.0,4.0,Sometimes,no,2.0,no,2.0,0.0,no,Automobile,Insufficient_Weight +638,Male,17.491272,1.834637,59.990861,yes,yes,2.943749,4.0,Sometimes,no,2.0,no,2.0,0.128895,no,Automobile,Insufficient_Weight +639,Male,18.0,1.721854,52.514302,yes,yes,2.33998,3.0,Sometimes,no,2.0,no,0.027433,1.884138,Sometimes,Public_Transportation,Insufficient_Weight +640,Female,20.172661,1.605521,44.661461,no,no,3.0,2.893778,Frequently,no,1.0,yes,0.769726,0.0,Sometimes,Public_Transportation,Insufficient_Weight +641,Female,19.220108,1.530266,42.0,no,yes,3.0,1.0,Frequently,no,1.954889,no,0.0,0.907868,Sometimes,Public_Transportation,Insufficient_Weight +642,Male,18.0,1.71889,52.058335,yes,yes,1.950742,3.0,Sometimes,no,1.751723,no,0.201136,1.743319,Sometimes,Public_Transportation,Insufficient_Weight +643,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 +644,Male,17.580627,1.770324,55.695253,yes,yes,2.0,4.0,Sometimes,no,2.369627,no,2.0,1.612466,no,Automobile,Insufficient_Weight +645,Female,21.125098,1.767479,56.265959,yes,yes,2.371338,3.998766,Frequently,no,2.263466,no,2.0,0.844004,no,Public_Transportation,Insufficient_Weight +646,Female,22.033129,1.704223,51.437985,yes,yes,2.984425,3.0,Frequently,no,2.044694,no,2.008256,1.250871,no,Public_Transportation,Insufficient_Weight +647,Female,20.744839,1.667852,49.803921,yes,yes,2.977018,3.193671,Frequently,no,2.482933,no,2.0,1.0,no,Public_Transportation,Insufficient_Weight +648,Female,22.547298,1.722461,51.881263,yes,yes,2.663421,3.0,Frequently,no,1.04111,no,0.794402,1.391948,no,Public_Transportation,Insufficient_Weight +649,Female,21.837996,1.588046,44.236067,no,no,3.0,1.69608,Frequently,no,2.550307,no,1.098862,0.0,no,Public_Transportation,Insufficient_Weight +650,Female,23.035829,1.598612,42.993937,no,no,2.753752,2.812377,Frequently,no,2.346647,no,1.612248,0.0,no,Public_Transportation,Insufficient_Weight +651,Female,21.529439,1.592379,44.00945,no,no,3.0,1.612747,Frequently,no,2.566629,no,1.190465,0.0,no,Public_Transportation,Insufficient_Weight +652,Female,21.287999,1.555778,42.3601,no,no,2.318355,1.082304,Frequently,no,1.220365,no,0.033328,0.0,no,Public_Transportation,Insufficient_Weight +653,Female,23.444286,1.596466,44.594588,no,no,2.594653,1.882158,Frequently,no,1.916812,no,0.417119,0.0,no,Public_Transportation,Insufficient_Weight +654,Female,22.329041,1.598393,44.918255,no,no,2.886157,2.326233,Frequently,no,2.306821,no,1.42237,0.0,no,Public_Transportation,Insufficient_Weight +655,Female,18.91505,1.633316,45.0,no,yes,3.0,3.0,Sometimes,no,2.924594,yes,1.352558,0.220087,Sometimes,Public_Transportation,Insufficient_Weight +656,Female,19.0,1.559567,42.12617,no,yes,3.0,1.989398,Sometimes,no,2.241607,no,0.206025,0.284453,no,Public_Transportation,Insufficient_Weight +657,Female,19.052833,1.546551,42.069992,no,yes,3.0,1.735493,Sometimes,no,2.318736,no,1.193486,0.74568,no,Public_Transportation,Insufficient_Weight +658,Female,19.59904,1.566501,41.706283,no,yes,2.967853,1.0,Frequently,no,1.131185,no,0.0,0.504176,Sometimes,Public_Transportation,Insufficient_Weight +660,Female,19.673262,1.599486,44.810751,no,no,3.0,2.974568,Frequently,no,1.145761,yes,0.87967,0.0,Sometimes,Public_Transportation,Insufficient_Weight +661,Female,20.244359,1.559186,41.952805,no,yes,2.619835,1.0,Frequently,no,1.198883,no,0.0,0.751212,Sometimes,Public_Transportation,Insufficient_Weight +662,Female,20.552695,1.523426,42.0,no,yes,3.0,1.0,Frequently,no,1.185062,no,0.0,0.076654,Sometimes,Public_Transportation,Insufficient_Weight +664,Female,21.997683,1.689441,51.107925,yes,yes,3.0,3.715118,Frequently,no,1.774576,no,0.10297,0.646423,no,Public_Transportation,Insufficient_Weight +665,Female,21.274628,1.737453,50.479039,yes,yes,3.0,3.489918,Frequently,no,1.326694,no,0.791929,0.128394,no,Public_Transportation,Insufficient_Weight +666,Female,18.909439,1.732096,50.0,no,yes,1.053534,3.378859,Sometimes,no,1.0,no,1.853425,0.861809,Sometimes,Public_Transportation,Insufficient_Weight +667,Male,22.396504,1.869098,61.411141,yes,yes,3.0,3.263201,Sometimes,no,2.233274,no,1.557737,0.000355,Sometimes,Automobile,Insufficient_Weight +668,Male,19.084317,1.851123,61.264785,yes,yes,3.0,3.994588,Sometimes,no,2.548527,no,1.285976,0.267076,Sometimes,Automobile,Insufficient_Weight +669,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 +670,Male,18.068767,1.787787,52.0,yes,yes,3.0,3.0,Sometimes,no,2.0,no,0.854337,1.0,Sometimes,Public_Transportation,Insufficient_Weight +671,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 +672,Male,18.038422,1.698914,51.692272,yes,yes,2.824559,3.0,Sometimes,no,2.0,no,0.533309,1.099764,Sometimes,Public_Transportation,Insufficient_Weight +673,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 +674,Female,19.21164,1.567981,41.934368,no,yes,2.070964,1.0,Sometimes,no,1.676975,no,0.0,1.718513,Sometimes,Public_Transportation,Insufficient_Weight +675,Female,18.988581,1.544263,41.535047,no,yes,2.68601,1.0,Sometimes,no,1.310074,no,0.0,1.0647,Sometimes,Public_Transportation,Insufficient_Weight +676,Female,19.408999,1.670552,49.804245,no,yes,2.794197,3.409363,Sometimes,no,1.543021,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +677,Female,19.665881,1.676346,49.105025,no,no,2.720701,3.0,Sometimes,no,2.0,no,1.862235,1.0,Sometimes,Public_Transportation,Insufficient_Weight +678,Female,19.758286,1.667404,49.125955,no,no,2.880792,3.281391,Sometimes,no,1.960131,no,1.513029,1.0,Sometimes,Public_Transportation,Insufficient_Weight +679,Female,22.422674,1.70011,50.173425,yes,yes,2.674431,3.0,Frequently,no,2.453384,no,1.321624,1.990617,no,Public_Transportation,Insufficient_Weight +680,Male,20.242237,1.75633,54.567343,yes,yes,2.0,3.98525,Sometimes,no,2.654078,no,2.113151,2.0,no,Public_Transportation,Insufficient_Weight +681,Female,22.637018,1.703584,51.607091,yes,yes,2.55996,3.0,Frequently,no,1.990788,no,0.486006,1.561272,no,Public_Transportation,Insufficient_Weight +682,Female,19.007177,1.690727,49.895716,no,yes,1.212908,3.207071,Sometimes,no,1.029703,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +683,Female,18.288205,1.713564,50.0,no,yes,1.140615,3.471536,Sometimes,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +684,Female,19.314429,1.67231,49.713944,no,yes,2.562409,3.488342,Sometimes,no,1.67062,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +685,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 +686,Female,25.380557,1.630379,46.062954,yes,yes,2.690754,3.03779,Frequently,no,2.212296,no,1.616882,0.0,no,Public_Transportation,Insufficient_Weight +687,Female,22.867719,1.655413,50.424661,yes,yes,3.0,3.642802,Frequently,no,1.995177,no,0.118271,0.065515,no,Public_Transportation,Insufficient_Weight +688,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 +689,Female,16.834813,1.74402,50.0,no,yes,2.19005,3.420618,Sometimes,no,1.356405,no,1.351996,0.98468,Sometimes,Public_Transportation,Insufficient_Weight +690,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 +691,Male,18.0,1.786758,51.524444,no,yes,2.91548,3.0,Sometimes,no,1.777486,no,1.077469,1.0,Sometimes,Public_Transportation,Insufficient_Weight +692,Male,18.0,1.767058,51.132809,yes,yes,2.708965,3.0,Sometimes,no,1.873004,no,1.21718,1.0,Sometimes,Public_Transportation,Insufficient_Weight +693,Male,18.128249,1.699437,52.08657,yes,yes,2.853513,3.0,Sometimes,no,2.0,no,0.680464,1.258881,Sometimes,Public_Transportation,Insufficient_Weight +694,Male,17.405104,1.82525,58.913579,yes,yes,2.580872,3.887906,Sometimes,no,2.0,no,2.0,0.453649,no,Automobile,Insufficient_Weight +695,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 +696,Male,18.525834,1.776989,57.275946,yes,yes,2.0,3.747163,Sometimes,no,2.575535,no,2.0,1.84383,no,Automobile,Insufficient_Weight +697,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 +698,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 +699,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 +700,Male,17.758315,1.854162,59.881316,yes,yes,2.966126,3.96981,Sometimes,no,2.0,no,2.000088,0.378619,no,Automobile,Insufficient_Weight +701,Male,17.282945,1.821514,59.605028,yes,yes,2.613249,3.712183,Sometimes,no,2.0,no,2.002997,0.174848,no,Automobile,Insufficient_Weight +702,Male,19.993154,1.762073,55.511075,yes,yes,2.0,4.0,Sometimes,no,2.542415,no,2.0,1.807465,no,Automobile,Insufficient_Weight +703,Female,22.935612,1.732307,54.835576,yes,yes,3.0,3.0,Frequently,no,2.00357,no,1.25955,0.417116,no,Public_Transportation,Insufficient_Weight +704,Male,20.738469,1.759933,55.003417,yes,yes,2.627031,3.832911,Sometimes,no,2.993448,no,2.0,1.425903,no,Public_Transportation,Insufficient_Weight +705,Female,22.99368,1.741377,54.877111,yes,yes,3.0,3.0,Frequently,no,2.009796,no,2.0,0.071317,no,Public_Transportation,Insufficient_Weight +706,Female,19.317148,1.731195,49.650897,no,yes,2.919751,3.576103,Sometimes,no,1.949308,no,1.940182,1.0,Sometimes,Public_Transportation,Insufficient_Weight +707,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 +708,Female,19.071027,1.756865,49.699672,no,yes,1.69427,3.266644,Sometimes,no,1.095417,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +709,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 +710,Female,19.735968,1.699956,49.98297,no,yes,1.204855,3.531038,Sometimes,no,1.134658,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +711,Female,18.094079,1.723328,50.0,no,yes,1.052699,3.998618,Frequently,no,1.0,no,2.0,1.0,Sometimes,Public_Transportation,Insufficient_Weight +712,Female,19.054938,1.585886,42.541794,no,no,2.910345,3.0,Frequently,no,1.0,yes,1.461005,0.0,Sometimes,Public_Transportation,Insufficient_Weight +713,Female,20.850119,1.524926,42.0,no,yes,2.866383,1.226342,Frequently,no,1.0,no,0.14495,0.0,Sometimes,Public_Transportation,Insufficient_Weight +714,Female,19.349258,1.52337,42.0,no,yes,2.913486,1.060796,Frequently,no,1.596212,no,0.108948,0.773087,Sometimes,Public_Transportation,Insufficient_Weight +715,Male,17.0,1.844749,59.313525,yes,yes,2.432886,3.0,Sometimes,no,2.0,no,2.697949,1.0,no,Automobile,Insufficient_Weight +716,Male,17.203917,1.853325,59.619485,yes,yes,2.883745,3.595761,Sometimes,no,2.0,no,2.077653,0.714701,no,Automobile,Insufficient_Weight +717,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 +718,Female,21.310907,1.72064,50.0,yes,yes,2.919584,3.697831,Frequently,no,1.147121,no,0.993058,0.08922,no,Public_Transportation,Insufficient_Weight +719,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 +720,Female,22.176922,1.71746,51.491957,yes,yes,2.486189,3.0,Frequently,no,1.064378,no,2.206055,1.958089,no,Public_Transportation,Insufficient_Weight +721,Female,17.823438,1.708406,50.0,no,yes,1.642241,3.45259,Sometimes,no,1.099231,no,0.418875,1.0,Sometimes,Public_Transportation,Insufficient_Weight +722,Female,18.0,1.763465,50.279053,no,yes,1.567101,3.0,Sometimes,no,1.994139,no,0.107981,1.0,Sometimes,Public_Transportation,Insufficient_Weight +723,Female,18.281092,1.7,50.0,no,yes,1.036414,3.205587,Sometimes,no,1.745959,no,0.115974,1.0,Sometimes,Public_Transportation,Insufficient_Weight +724,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 +725,Female,19.994543,1.537739,39.101805,no,yes,1.118436,3.0,Sometimes,no,1.997744,no,2.432443,1.626194,Sometimes,Public_Transportation,Insufficient_Weight +726,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 +727,Male,19.865895,1.76033,55.3707,yes,yes,2.120185,4.0,Sometimes,no,2.582165,no,2.0,1.510609,no,Automobile,Insufficient_Weight +728,Male,17.888073,1.841879,60.0,yes,yes,3.0,3.732126,Sometimes,no,2.0,no,2.000934,0.384662,no,Automobile,Insufficient_Weight +729,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 +730,Male,17.925497,1.829142,59.933015,yes,yes,2.86099,4.0,Sometimes,no,2.0,no,2.0,0.007872,no,Automobile,Insufficient_Weight +731,Male,17.000752,1.822084,58.443049,yes,yes,2.559571,3.047959,Sometimes,no,2.0,no,2.011646,0.588994,no,Automobile,Insufficient_Weight +732,Male,17.362129,1.80671,59.243506,yes,yes,2.424977,4.0,Sometimes,no,2.0,no,2.0,0.03921,no,Automobile,Insufficient_Weight +733,Male,18.0,1.707259,50.664459,yes,yes,1.786841,3.0,Sometimes,no,1.546856,no,0.512094,1.608265,Sometimes,Public_Transportation,Insufficient_Weight +734,Male,18.0,1.708107,51.314659,yes,yes,1.303878,3.0,Sometimes,no,1.755497,no,0.062932,1.672532,Sometimes,Public_Transportation,Insufficient_Weight +735,Male,18.0,1.739344,50.951444,yes,yes,1.889883,3.0,Sometimes,no,1.959531,no,0.520407,1.151166,Sometimes,Public_Transportation,Insufficient_Weight +736,Female,19.029494,1.573987,44.316254,no,no,2.984004,3.0,Frequently,no,1.015249,yes,1.327833,0.0,Sometimes,Public_Transportation,Insufficient_Weight +737,Female,19.269079,1.58092,44.753943,no,no,3.0,2.975362,Frequently,no,1.246822,yes,0.979701,0.0,Sometimes,Public_Transportation,Insufficient_Weight +738,Female,19.946244,1.603435,45.0,no,no,3.0,3.0,Frequently,no,2.487919,yes,1.230441,0.0,Sometimes,Public_Transportation,Insufficient_Weight +739,Female,19.639431,1.53535,42.0,no,yes,3.0,1.0,Frequently,no,1.49681,no,0.0,0.513427,Sometimes,Public_Transportation,Insufficient_Weight +740,Female,19.0,1.53161,42.0,no,yes,2.749268,1.394539,Frequently,no,1.322048,no,0.463949,0.800993,Sometimes,Public_Transportation,Insufficient_Weight +741,Female,19.434709,1.525691,42.0,no,yes,3.0,1.0,Frequently,no,1.764055,no,0.0,0.560887,Sometimes,Public_Transportation,Insufficient_Weight +742,Male,18.0,1.719827,52.289828,yes,yes,1.202075,3.0,Sometimes,no,1.927976,no,0.023574,1.747256,Sometimes,Public_Transportation,Insufficient_Weight +743,Male,18.381382,1.722547,53.783977,yes,yes,2.0,3.131032,Sometimes,no,2.072194,no,1.487987,2.0,Sometimes,Public_Transportation,Insufficient_Weight +744,Male,18.0,1.738702,50.248677,yes,yes,1.871213,3.0,Sometimes,no,1.283738,no,0.684879,1.487223,Sometimes,Public_Transportation,Insufficient_Weight +745,Male,26.69858,1.816298,86.963765,no,yes,2.341133,1.578521,Sometimes,no,2.0,no,2.0,0.554072,no,Automobile,Overweight_Level_I +746,Male,21.125836,1.638085,70.0,no,yes,2.0,1.0,no,no,2.115967,no,0.770536,0.0,Sometimes,Public_Transportation,Overweight_Level_I +747,Male,25.191627,1.813678,85.637789,no,yes,1.450218,3.985442,Sometimes,no,2.147746,no,0.046836,1.0,no,Automobile,Overweight_Level_I +748,Male,21.963457,1.697228,75.5771,yes,yes,2.204914,3.623364,Sometimes,no,1.815293,no,0.989316,0.0,Sometimes,Public_Transportation,Overweight_Level_I +749,Male,21.0,1.617469,68.869791,no,yes,1.206276,3.0,no,no,2.406541,no,0.94984,0.479221,Sometimes,Public_Transportation,Overweight_Level_I +750,Male,19.114981,1.855543,88.965521,yes,yes,2.0,3.0,Sometimes,no,1.015677,no,0.0,0.37465,Sometimes,Public_Transportation,Overweight_Level_I +751,Female,41.823567,1.721854,82.919584,no,yes,2.81646,3.36313,Sometimes,no,2.722063,no,3.0,0.26579,Sometimes,Automobile,Overweight_Level_I +752,Male,21.142432,1.855353,86.413388,yes,yes,2.0,3.0,Sometimes,no,1.345298,no,1.097905,1.0,Sometimes,Public_Transportation,Overweight_Level_I +753,Male,21.962426,1.696336,75.0,yes,yes,2.0,3.0,Sometimes,no,1.825629,no,0.699592,0.142056,Sometimes,Public_Transportation,Overweight_Level_I +754,Female,18.836315,1.751631,80.0,yes,yes,2.0,1.73762,Sometimes,no,2.207978,no,2.641072,0.707044,Frequently,Public_Transportation,Overweight_Level_I +755,Male,23.572648,1.698346,75.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.345684,1.415536,Sometimes,Public_Transportation,Overweight_Level_I +756,Female,33.700749,1.642971,74.803157,yes,no,3.0,1.146052,Sometimes,no,1.074048,no,0.679935,0.0,Sometimes,Automobile,Overweight_Level_I +757,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 +758,Male,21.0,1.605404,68.226511,no,yes,2.0,1.9154,no,no,3.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +759,Female,35.125401,1.529834,62.903938,no,yes,2.0,2.105616,Sometimes,no,1.456581,no,0.0,0.9974,Sometimes,Automobile,Overweight_Level_I +760,Female,36.769646,1.55,62.337721,yes,yes,2.392665,3.0,Sometimes,no,2.951056,no,0.0,0.369134,Sometimes,Automobile,Overweight_Level_I +761,Female,21.868932,1.731261,78.175706,yes,yes,2.577427,1.0,Sometimes,no,2.0,no,2.287423,0.0,Sometimes,Public_Transportation,Overweight_Level_I +762,Male,22.549208,1.629194,70.0,no,yes,2.0,1.0,no,no,2.803311,no,0.245354,0.0,Sometimes,Public_Transportation,Overweight_Level_I +765,Female,30.958957,1.633491,68.803694,yes,yes,2.052152,3.0,Sometimes,no,1.972074,no,0.228307,0.0,Sometimes,Automobile,Overweight_Level_I +766,Male,21.017383,1.752391,79.109637,yes,yes,3.0,1.0,Sometimes,no,2.0,no,2.57223,0.0,Sometimes,Automobile,Overweight_Level_I +767,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 +768,Male,19.637947,1.809101,85.0,yes,yes,3.0,3.0,Sometimes,no,2.229171,no,1.607953,0.628059,Sometimes,Public_Transportation,Overweight_Level_I +769,Male,21.413642,1.709484,75.0,yes,yes,2.0,3.0,Sometimes,no,2.843777,no,2.022315,0.009254,Sometimes,Public_Transportation,Overweight_Level_I +770,Male,21.029633,1.607082,67.722222,yes,yes,2.0,3.691226,no,no,3.0,no,1.228136,0.3352,Sometimes,Public_Transportation,Overweight_Level_I +771,Female,22.667596,1.718939,75.951472,yes,yes,2.0,3.0,Frequently,no,2.0,no,0.0,2.0,Sometimes,Public_Transportation,Overweight_Level_I +772,Male,20.0,1.831357,89.652557,yes,yes,2.555401,3.292386,Sometimes,no,3.0,no,2.0,0.315918,Sometimes,Public_Transportation,Overweight_Level_I +773,Female,21.81119,1.6,66.401407,yes,yes,2.108711,3.0,Sometimes,no,2.746197,no,1.910432,0.0,Sometimes,Public_Transportation,Overweight_Level_I +774,Female,21.0,1.754813,77.929204,yes,yes,2.915279,1.104642,Sometimes,no,1.530046,no,1.360635,0.0,Sometimes,Public_Transportation,Overweight_Level_I +775,Male,18.128206,1.778447,80.273807,yes,yes,1.570089,3.0,Sometimes,no,2.0,no,2.707882,0.0,no,Public_Transportation,Overweight_Level_I +776,Male,21.0,1.709561,75.0,yes,yes,2.0,3.0,Sometimes,no,1.636326,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +777,Female,29.08107,1.674568,71.602622,yes,yes,2.0,3.0,Sometimes,no,1.701835,no,1.682271,0.0,Sometimes,Automobile,Overweight_Level_I +778,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 +779,Male,22.717249,1.708071,75.0,yes,yes,2.714447,3.0,Sometimes,no,3.0,no,1.096818,1.89388,Sometimes,Public_Transportation,Overweight_Level_I +780,Male,19.816949,1.806947,85.079589,yes,yes,2.0,3.0,Sometimes,no,2.292073,no,1.067817,0.19668,Sometimes,Public_Transportation,Overweight_Level_I +781,Female,24.023972,1.599697,64.808022,no,yes,2.903545,3.0,Sometimes,no,1.549931,no,0.0,0.273882,Sometimes,Public_Transportation,Overweight_Level_I +782,Female,32.593129,1.721903,72.748903,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,1.339232,Sometimes,Automobile,Overweight_Level_I +783,Male,23.0,1.712556,75.480764,yes,yes,3.0,3.654061,Sometimes,no,1.229915,no,0.793489,1.597608,Sometimes,Public_Transportation,Overweight_Level_I +784,Female,16.941489,1.551288,54.93242,no,yes,1.75375,1.296156,Sometimes,no,2.0,yes,0.110174,1.944675,Sometimes,Public_Transportation,Overweight_Level_I +785,Female,16.172992,1.603842,65.0,yes,yes,2.543563,1.0,Sometimes,no,2.0,yes,0.694281,1.056911,Sometimes,Public_Transportation,Overweight_Level_I +786,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 +787,Female,35.194089,1.673482,73.193589,yes,no,3.0,2.809716,Sometimes,no,1.591425,no,0.178023,0.0,Sometimes,Automobile,Overweight_Level_I +788,Male,23.172982,1.858624,88.675503,yes,yes,2.0,2.77684,Sometimes,no,1.278231,no,0.55713,1.0,Sometimes,Automobile,Overweight_Level_I +789,Female,37.218161,1.593894,63.320629,yes,yes,2.37464,3.0,Sometimes,no,2.0,no,2.892922,0.480813,Sometimes,Automobile,Overweight_Level_I +790,Male,19.076443,1.62,69.529625,no,yes,2.278644,1.0,Sometimes,no,2.628985,no,1.0,1.285373,Sometimes,Public_Transportation,Overweight_Level_I +791,Female,16.30687,1.616366,67.183128,yes,yes,1.620845,1.0,Sometimes,no,2.0,yes,0.926592,1.657098,no,Public_Transportation,Overweight_Level_I +792,Female,18.297229,1.637396,70.0,yes,yes,2.0,1.999014,Sometimes,no,2.326165,no,0.00705,0.0,no,Public_Transportation,Overweight_Level_I +793,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 +794,Female,17.451085,1.6,65.0,yes,yes,3.0,2.449723,Sometimes,no,2.0,yes,0.479592,1.720642,Sometimes,Public_Transportation,Overweight_Level_I +795,Male,29.740496,1.820471,87.668828,no,yes,3.0,3.0,Sometimes,no,1.174611,no,2.0,0.0,no,Automobile,Overweight_Level_I +796,Female,31.539393,1.657496,73.641633,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,1.112489,Sometimes,Automobile,Overweight_Level_I +797,Male,18.026457,1.752123,80.0,yes,yes,2.0,2.794156,Sometimes,no,2.377362,no,0.101236,0.105895,no,Public_Transportation,Overweight_Level_I +798,Male,19.47554,1.857231,88.138777,yes,yes,2.061952,4.0,Sometimes,no,2.426465,no,2.0,0.160138,Sometimes,Public_Transportation,Overweight_Level_I +799,Female,19.0,1.76,79.544998,yes,yes,2.0,1.146794,Sometimes,no,3.0,no,1.809745,1.690102,Frequently,Public_Transportation,Overweight_Level_I +800,Female,18.0,1.644682,68.392133,yes,yes,2.0,1.131695,Sometimes,no,1.344539,no,0.0,1.59257,no,Public_Transportation,Overweight_Level_I +801,Female,18.270434,1.737165,76.699774,yes,yes,2.838969,3.0,Sometimes,no,2.425927,no,1.0,1.453335,Frequently,Public_Transportation,Overweight_Level_I +802,Female,19.816228,1.507853,64.259379,no,yes,2.568063,3.0,Sometimes,no,1.817983,yes,0.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +803,Male,28.39124,1.81006,87.286783,no,yes,3.0,3.0,Sometimes,no,1.205633,no,2.0,0.0,no,Automobile,Overweight_Level_I +804,Male,26.758516,1.80179,86.981202,no,yes,2.652958,2.488189,Sometimes,no,2.0,no,2.0,0.096614,no,Automobile,Overweight_Level_I +805,Male,21.033794,1.625891,70.0,no,yes,2.0,1.0,no,no,2.00876,no,0.63119,0.0,Sometimes,Public_Transportation,Overweight_Level_I +806,Male,22.429812,1.64037,70.0,no,yes,2.0,1.0,no,no,2.069184,no,0.729898,0.0,Sometimes,Public_Transportation,Overweight_Level_I +807,Male,26.650419,1.791047,83.26312,no,yes,1.27785,3.999591,Sometimes,no,2.495944,no,0.00342,1.0,no,Automobile,Overweight_Level_I +808,Male,21.618246,1.679306,75.0,yes,yes,2.0,3.0,Sometimes,no,1.957871,no,0.994592,0.117303,Sometimes,Public_Transportation,Overweight_Level_I +809,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 +810,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 +811,Male,19.741202,1.816783,86.522799,yes,yes,2.0,3.0,Sometimes,no,2.177386,no,0.336814,0.004813,Sometimes,Public_Transportation,Overweight_Level_I +812,Male,19.21638,1.812472,86.748295,yes,yes,2.0,3.0,Sometimes,no,2.168651,no,0.977998,0.142638,Sometimes,Public_Transportation,Overweight_Level_I +813,Female,42.24475,1.768231,75.62931,yes,yes,3.0,2.951837,Sometimes,no,2.112032,no,0.378683,0.0,Sometimes,Automobile,Overweight_Level_I +814,Male,22.283082,1.870931,89.251639,yes,yes,2.0,2.799979,Sometimes,no,1.081333,no,0.444347,1.0,Sometimes,Public_Transportation,Overweight_Level_I +815,Male,21.086512,1.863685,89.558854,yes,yes,2.0,3.0,Sometimes,no,1.005727,no,0.0,0.798219,Sometimes,Public_Transportation,Overweight_Level_I +816,Male,23.451595,1.670227,75.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.129163,1.983678,Sometimes,Public_Transportation,Overweight_Level_I +817,Male,22.740275,1.717288,75.948164,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,2.0,Sometimes,Public_Transportation,Overweight_Level_I +818,Male,18.900253,1.750359,79.828725,yes,yes,2.0,2.228113,Sometimes,no,2.045004,no,1.293665,0.961806,Frequently,Public_Transportation,Overweight_Level_I +819,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 +820,Female,32.997118,1.672446,74.812707,yes,yes,2.948425,1.198643,Sometimes,no,1.0,no,0.0,0.173796,Sometimes,Automobile,Overweight_Level_I +821,Female,32.501143,1.675979,74.959747,yes,yes,2.291846,1.555557,Sometimes,no,1.0,no,0.0,0.388271,Sometimes,Automobile,Overweight_Level_I +822,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 +823,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 +825,Female,37.455752,1.508908,63.183846,yes,yes,2.048582,1.047197,Sometimes,no,2.0,no,0.15136,0.22596,Sometimes,Automobile,Overweight_Level_I +826,Female,40.0,1.561109,62.871794,yes,yes,2.948248,3.0,Sometimes,no,2.429911,no,0.11964,0.360193,Sometimes,Automobile,Overweight_Level_I +827,Female,38.943282,1.554728,63.011645,yes,yes,2.869436,3.0,Sometimes,no,2.972426,no,2.16079,0.305954,Sometimes,Automobile,Overweight_Level_I +828,Female,21.987341,1.730182,78.55444,yes,yes,2.293705,1.0,Sometimes,no,2.0,no,2.063943,0.0,Sometimes,Public_Transportation,Overweight_Level_I +829,Female,21.198217,1.743841,78.88036,yes,yes,2.510583,1.0,Sometimes,no,2.0,no,2.119682,0.0,Sometimes,Public_Transportation,Overweight_Level_I +835,Female,29.32038,1.642506,69.906708,yes,yes,2.366949,3.0,Sometimes,no,1.926577,no,1.581242,0.0,Sometimes,Automobile,Overweight_Level_I +836,Female,31.255587,1.69308,71.927379,yes,yes,2.615788,3.0,Sometimes,no,1.771781,no,0.759422,0.686529,Sometimes,Automobile,Overweight_Level_I +837,Male,22.851773,1.752115,79.414603,yes,yes,3.0,1.0,Sometimes,no,2.0,no,2.315983,0.0,Sometimes,Public_Transportation,Overweight_Level_I +838,Male,26.0,1.745033,80.0,yes,yes,2.217267,1.193589,Sometimes,no,2.0,no,2.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +839,Male,19.789291,1.820643,85.0,yes,yes,3.0,3.0,Sometimes,no,2.759246,no,1.763277,0.453314,Sometimes,Public_Transportation,Overweight_Level_I +840,Male,19.895877,1.80733,85.073801,yes,yes,2.801514,3.0,Sometimes,no,2.734782,no,2.207881,0.143675,Sometimes,Public_Transportation,Overweight_Level_I +841,Male,19.693804,1.8,85.0,yes,yes,2.188722,3.0,Sometimes,no,2.721356,no,1.528968,1.0,Sometimes,Public_Transportation,Overweight_Level_I +842,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 +843,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 +844,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 +845,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 +846,Female,21.0,1.754497,77.956921,yes,yes,2.446872,2.372311,Sometimes,no,1.81031,no,0.094893,1.650778,Sometimes,Public_Transportation,Overweight_Level_I +847,Female,21.0,1.752944,77.965532,yes,yes,2.839048,2.10601,Sometimes,no,1.639202,no,1.117311,0.967919,Sometimes,Public_Transportation,Overweight_Level_I +848,Male,19.337404,1.859927,87.105828,yes,yes,2.21232,4.0,Sometimes,no,2.374044,no,2.0,0.622638,Sometimes,Public_Transportation,Overweight_Level_I +849,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 +850,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 +851,Female,21.012569,1.758628,78.370039,yes,yes,3.0,1.0,Sometimes,no,2.0,no,2.971832,0.0,Sometimes,Public_Transportation,Overweight_Level_I +852,Female,21.016623,1.755427,78.300084,yes,yes,3.0,1.0,Sometimes,no,2.0,no,2.877473,0.0,Sometimes,Public_Transportation,Overweight_Level_I +853,Female,19.0,1.779882,80.091886,yes,yes,1.078529,1.211606,Sometimes,no,2.568063,no,3.0,0.817983,no,Public_Transportation,Overweight_Level_I +854,Male,21.0,1.712061,75.0,yes,yes,2.0,3.0,Sometimes,no,1.333707,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +855,Male,21.0,1.676014,75.0,yes,yes,2.0,3.0,Sometimes,no,1.164062,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +856,Female,27.899784,1.7,74.244004,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.722053,0.0,Sometimes,Automobile,Overweight_Level_I +857,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 +858,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 +859,Male,23.0,1.690196,75.0,yes,yes,2.620963,3.0,Sometimes,no,2.824559,no,0.754599,2.0,Sometimes,Public_Transportation,Overweight_Level_I +860,Male,20.0,1.81748,85.0,yes,yes,2.95118,3.0,Sometimes,no,3.0,no,2.433918,0.561602,Sometimes,Public_Transportation,Overweight_Level_I +861,Female,21.832995,1.580964,65.363941,no,yes,2.021446,3.0,Sometimes,no,1.077917,no,0.523847,0.808599,Sometimes,Public_Transportation,Overweight_Level_I +862,Female,30.255744,1.652084,73.575981,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,0.018877,Sometimes,Automobile,Overweight_Level_I +863,Female,30.613586,1.645511,69.168978,yes,yes,2.000466,3.0,Sometimes,no,1.131448,no,0.061366,0.0,Sometimes,Automobile,Overweight_Level_I +864,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 +865,Female,18.0,1.456346,55.523481,no,yes,2.0,3.0,Sometimes,no,1.040342,yes,0.497373,1.783319,Sometimes,Public_Transportation,Overweight_Level_I +866,Female,18.0,1.498561,55.376512,no,yes,2.0,3.0,Sometimes,no,1.274718,yes,0.129902,0.978574,Sometimes,Public_Transportation,Overweight_Level_I +867,Female,16.950499,1.603501,65.0,yes,yes,2.96008,1.0,Sometimes,no,2.0,yes,0.736032,1.344072,no,Public_Transportation,Overweight_Level_I +868,Female,21.837058,1.558045,63.597633,no,yes,2.53915,1.590982,Sometimes,no,1.977801,no,1.264616,0.0,Sometimes,Public_Transportation,Overweight_Level_I +869,Female,27.0,1.55,62.877347,no,yes,2.244142,1.704828,Sometimes,no,1.0,no,0.792929,0.395468,Sometimes,Public_Transportation,Overweight_Level_I +870,Female,35.483601,1.647514,73.91692,yes,no,3.0,2.725012,Sometimes,no,1.62244,no,0.902661,0.0,Sometimes,Automobile,Overweight_Level_I +871,Male,23.801436,1.855779,86.098523,yes,yes,2.0,2.372339,Sometimes,no,1.675733,no,1.066241,1.0,Sometimes,Automobile,Overweight_Level_I +872,Male,23.367212,1.853223,87.083266,yes,yes,2.0,1.097312,Sometimes,no,1.328835,no,1.264257,1.0,Sometimes,Automobile,Overweight_Level_I +873,Female,40.0,1.570811,62.211572,yes,yes,2.253371,3.0,Sometimes,no,2.756916,no,1.016042,0.213437,Sometimes,Automobile,Overweight_Level_I +874,Female,16.38009,1.617124,67.913561,yes,yes,2.851664,1.0,Sometimes,no,2.0,no,0.977929,1.765321,no,Public_Transportation,Overweight_Level_I +875,Female,16.865984,1.644053,67.439589,yes,yes,1.31415,1.068196,Sometimes,no,1.364957,yes,0.0,0.057926,no,Public_Transportation,Overweight_Level_I +876,Female,16.093234,1.608914,65.0,yes,yes,1.321028,1.0,Sometimes,no,2.0,yes,0.371452,0.965604,no,Public_Transportation,Overweight_Level_I +877,Female,18.0,1.647971,68.818893,yes,yes,2.0,1.411685,Sometimes,no,1.859089,no,0.0,1.306,no,Public_Transportation,Overweight_Level_I +878,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 +879,Female,19.726522,1.508267,61.10403,no,yes,2.778079,3.0,Sometimes,no,1.696691,yes,0.94224,0.879925,Sometimes,Public_Transportation,Overweight_Level_I +880,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 +881,Female,17.781183,1.6,65.0,yes,yes,3.0,2.973504,Sometimes,no,2.0,yes,0.178976,0.166076,Sometimes,Public_Transportation,Overweight_Level_I +882,Male,33.100581,1.838791,87.85785,no,yes,2.814453,2.608055,Sometimes,no,1.85916,no,2.0,0.516084,no,Automobile,Overweight_Level_I +883,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 +884,Female,18.985119,1.759117,80.0,yes,yes,2.0,1.411808,Sometimes,no,2.651194,no,2.878414,0.953841,no,Public_Transportation,Overweight_Level_I +885,Female,18.871917,1.755254,80.0,yes,yes,2.0,1.095223,Sometimes,no,2.474132,no,2.876696,0.7939,no,Public_Transportation,Overweight_Level_I +886,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 +887,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 +888,Female,20.979254,1.75655,78.721696,yes,yes,2.0,3.0,Sometimes,no,2.813234,no,0.228753,2.0,Frequently,Public_Transportation,Overweight_Level_I +889,Female,18.869151,1.756774,79.989789,yes,yes,2.0,2.658639,Sometimes,no,2.781628,no,1.955992,1.409198,Frequently,Public_Transportation,Overweight_Level_I +890,Female,18.236087,1.62,69.3899,yes,yes,2.22399,1.0,Sometimes,no,2.20312,no,0.104451,1.899073,no,Public_Transportation,Overweight_Level_I +891,Female,20.901393,1.709585,75.0,yes,yes,2.104105,3.0,Sometimes,no,1.871033,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +892,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 +893,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 +894,Male,26.787842,1.817641,87.107317,no,yes,2.971588,2.743277,Sometimes,no,1.394883,no,2.0,0.470243,no,Automobile,Overweight_Level_I +895,Male,21.037514,1.636592,70.0,no,yes,2.0,1.0,no,no,2.881677,no,0.915699,0.0,Sometimes,Public_Transportation,Overweight_Level_I +896,Male,21.00829,1.621412,70.0,no,yes,2.0,1.0,no,no,2.795651,no,0.80873,0.0,Sometimes,Public_Transportation,Overweight_Level_I +897,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 +898,Male,21.731497,1.696201,75.399191,yes,yes,2.109162,3.245148,Sometimes,no,1.971774,no,0.989335,0.0,Sometimes,Public_Transportation,Overweight_Level_I +899,Male,21.052894,1.694633,75.0522,yes,yes,2.178889,3.563744,Sometimes,no,1.853991,no,0.997731,0.0,Sometimes,Public_Transportation,Overweight_Level_I +900,Female,21.0,1.618148,68.981403,yes,yes,1.142468,3.0,no,no,2.197732,no,0.827506,0.572877,Sometimes,Public_Transportation,Overweight_Level_I +901,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 +902,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 +903,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 +904,Male,20.261925,1.807538,85.316125,yes,yes,2.0,3.0,Sometimes,no,1.968011,no,1.072662,0.776758,Sometimes,Public_Transportation,Overweight_Level_I +905,Male,20.31094,1.849425,85.228116,yes,yes,2.146598,3.0,Sometimes,no,2.100112,no,1.17116,0.833761,Sometimes,Public_Transportation,Overweight_Level_I +906,Male,21.420537,1.712473,75.0,yes,yes,2.0,3.0,Sometimes,no,1.362642,no,0.921268,0.139013,Sometimes,Public_Transportation,Overweight_Level_I +907,Female,18.845518,1.753471,80.0,yes,yes,2.0,1.391778,Sometimes,no,2.64548,no,2.685087,0.97554,no,Public_Transportation,Overweight_Level_I +908,Male,23.562135,1.717432,75.371244,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.121585,1.967259,Sometimes,Public_Transportation,Overweight_Level_I +909,Male,23.118327,1.677573,75.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.334579,1.905826,Sometimes,Public_Transportation,Overweight_Level_I +910,Female,33.732714,1.679725,74.885222,yes,yes,3.0,1.13715,Sometimes,no,1.000695,no,0.261274,0.0,Sometimes,Automobile,Overweight_Level_I +911,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 +912,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 +913,Male,21.0,1.610209,68.865008,no,yes,1.910176,2.938135,no,no,2.943097,no,0.997202,0.207922,Sometimes,Public_Transportation,Overweight_Level_I +914,Male,21.0,1.612556,69.463458,no,yes,2.0,1.259628,no,no,3.0,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +915,Female,38.692265,1.548178,62.341438,yes,yes,2.956671,2.965494,Sometimes,no,2.868132,no,0.0,0.54925,Sometimes,Automobile,Overweight_Level_I +916,Female,38.952866,1.568441,62.855073,yes,yes,2.002796,3.0,Sometimes,no,2.526775,no,0.271174,0.806069,Sometimes,Automobile,Overweight_Level_I +917,Female,36.631456,1.532322,62.417228,yes,yes,2.288604,2.845307,Sometimes,no,2.638164,no,0.0,0.990741,Sometimes,Automobile,Overweight_Level_I +918,Female,21.996118,1.730199,78.997062,yes,yes,2.277077,1.0,Sometimes,no,2.0,no,1.061743,0.0,Sometimes,Public_Transportation,Overweight_Level_I +919,Female,21.951309,1.730167,78.429312,yes,yes,2.138334,1.0,Sometimes,no,2.0,no,2.269058,0.0,Sometimes,Public_Transportation,Overweight_Level_I +920,Male,22.018228,1.627396,70.0,no,yes,2.0,1.0,no,no,2.976177,no,0.780117,0.0,Sometimes,Public_Transportation,Overweight_Level_I +924,Female,29.934465,1.638744,69.242354,yes,yes,2.029634,3.0,Sometimes,no,1.827515,no,0.480206,0.0,Sometimes,Automobile,Overweight_Level_I +925,Female,31.166572,1.680858,71.81338,yes,yes,2.048216,3.0,Sometimes,no,1.305816,no,0.170452,0.276806,Sometimes,Automobile,Overweight_Level_I +926,Female,20.534606,1.758372,79.469513,yes,yes,2.8557,1.0,Sometimes,no,2.663861,no,2.762711,0.783336,Sometimes,Public_Transportation,Overweight_Level_I +927,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 +928,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 +929,Male,21.929387,1.698049,75.0,yes,yes,2.0,3.0,Sometimes,no,2.763573,no,1.502711,0.127879,Sometimes,Public_Transportation,Overweight_Level_I +930,Female,21.009437,1.60681,67.773914,yes,yes,2.0,3.156309,no,no,3.0,no,1.179592,0.086868,Sometimes,Public_Transportation,Overweight_Level_I +931,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 +932,Male,22.828435,1.710415,75.142858,yes,yes,2.786008,3.0,Sometimes,no,2.526193,no,0.925118,2.0,Sometimes,Public_Transportation,Overweight_Level_I +933,Male,22.814657,1.716289,75.688433,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.092344,1.466496,Sometimes,Public_Transportation,Overweight_Level_I +934,Male,19.576682,1.834715,89.429199,yes,yes,2.342323,3.60885,Sometimes,no,2.878866,no,2.0,0.208323,Sometimes,Public_Transportation,Overweight_Level_I +935,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 +936,Female,21.979769,1.6,66.126964,yes,yes,2.213135,3.0,Sometimes,no,2.046766,no,2.172546,0.0,Sometimes,Public_Transportation,Overweight_Level_I +937,Female,21.0,1.753578,77.97917,yes,yes,2.273548,2.39007,Sometimes,no,1.648404,no,0.874643,1.102696,Sometimes,Public_Transportation,Overweight_Level_I +938,Female,21.0285,1.742364,78.664618,yes,yes,2.780699,1.101404,Sometimes,no,1.579207,no,1.153775,0.0,Sometimes,Public_Transportation,Overweight_Level_I +939,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 +940,Male,21.0,1.714253,75.0,yes,yes,2.0,3.0,Sometimes,no,1.081597,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +941,Male,21.0,1.693628,75.0,yes,yes,2.0,3.0,Sometimes,no,1.833055,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_I +942,Female,29.463168,1.659172,71.170001,yes,yes,2.0,3.0,Sometimes,no,1.281683,no,1.609801,0.0,Sometimes,Automobile,Overweight_Level_I +943,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 +944,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 +945,Male,23.455324,1.702516,75.0,yes,yes,2.162519,3.0,Sometimes,no,2.152666,no,1.078074,1.502839,Sometimes,Public_Transportation,Overweight_Level_I +946,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 +947,Female,22.239836,1.59994,65.423942,no,yes,2.99448,3.0,Sometimes,no,1.706568,no,0.784216,0.150969,Sometimes,Public_Transportation,Overweight_Level_I +948,Female,31.62638,1.666023,72.906186,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,1.301385,Sometimes,Automobile,Overweight_Level_I +949,Male,23.0,1.701584,75.093569,yes,yes,3.0,3.205009,Sometimes,no,2.643468,no,0.998039,1.898139,Sometimes,Public_Transportation,Overweight_Level_I +950,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 +951,Female,17.420269,1.489409,53.620604,no,yes,1.836554,1.865238,Sometimes,no,2.0,yes,0.320209,1.969507,Sometimes,Public_Transportation,Overweight_Level_I +952,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 +953,Female,16.240576,1.616533,65.062945,yes,yes,2.388168,1.0,Sometimes,no,1.438018,yes,0.110887,1.165817,no,Public_Transportation,Overweight_Level_I +954,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 +955,Female,34.772902,1.675612,73.501233,yes,yes,3.0,2.400943,Sometimes,no,1.509734,no,0.167125,0.0,Sometimes,Automobile,Overweight_Level_I +956,Male,22.766227,1.874061,89.754302,yes,yes,2.0,2.870005,Sometimes,no,1.15031,no,0.417724,1.0,Sometimes,Automobile,Overweight_Level_I +957,Female,39.214514,1.580765,62.631382,yes,yes,2.487167,3.0,Sometimes,no,2.396977,no,0.577063,0.435072,Sometimes,Automobile,Overweight_Level_I +958,Male,19.858973,1.62,69.575315,no,yes,2.185938,1.0,no,no,2.81353,no,1.0,1.110222,Sometimes,Public_Transportation,Overweight_Level_I +959,Female,16.370009,1.613921,66.738769,yes,yes,2.206399,1.0,Sometimes,no,2.0,yes,0.94893,1.772463,no,Public_Transportation,Overweight_Level_I +960,Female,17.992717,1.618683,67.193585,yes,yes,1.952987,1.0,Sometimes,no,1.334856,yes,0.732276,1.890214,no,Public_Transportation,Overweight_Level_I +961,Male,19.179932,1.637537,70.0,yes,yes,2.0,1.030416,Sometimes,no,2.118716,no,0.529259,0.0,no,Public_Transportation,Overweight_Level_I +962,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 +963,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 +964,Female,17.099015,1.6,65.0,yes,yes,3.0,2.401341,Sometimes,no,2.0,yes,0.663896,1.753745,Sometimes,Public_Transportation,Overweight_Level_I +965,Female,17.25813,1.600184,65.0,yes,yes,2.749629,2.298612,Sometimes,no,2.0,yes,0.547515,1.619473,Sometimes,Public_Transportation,Overweight_Level_I +966,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 +967,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 +968,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 +969,Male,18.014333,1.751029,80.0,yes,yes,2.0,2.805436,Sometimes,no,2.122884,no,0.045651,0.017225,Frequently,Public_Transportation,Overweight_Level_I +970,Male,19.685,1.838266,89.496905,yes,yes,2.153639,3.715148,Sometimes,no,2.703337,no,2.0,0.226097,Sometimes,Public_Transportation,Overweight_Level_I +971,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 +972,Female,19.0,1.76,79.349221,yes,yes,2.0,1.893811,Sometimes,no,3.0,no,1.376229,1.801322,no,Public_Transportation,Overweight_Level_I +973,Female,18.0,1.622241,68.250249,yes,yes,2.0,1.010319,Sometimes,no,1.319437,no,0.0,1.626456,no,Public_Transportation,Overweight_Level_I +974,Female,18.003153,1.729996,75.816864,yes,yes,2.992329,3.0,Sometimes,no,2.004908,no,1.0,1.405086,Frequently,Public_Transportation,Overweight_Level_I +975,Female,18.052394,1.725233,75.970712,yes,yes,2.927409,3.0,Sometimes,no,2.209991,no,1.0,0.797215,Frequently,Public_Transportation,Overweight_Level_I +976,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 +977,Male,21.180346,1.773766,89.28282,yes,yes,2.010684,1.322087,Sometimes,no,2.0,no,0.540397,0.973834,Sometimes,Public_Transportation,Overweight_Level_II +978,Male,21.793724,1.75463,89.068227,yes,yes,2.0,2.041558,Sometimes,no,2.38725,no,0.813917,0.0,Sometimes,Public_Transportation,Overweight_Level_II +979,Male,20.880161,1.674327,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.66639,1.443212,no,Public_Transportation,Overweight_Level_II +980,Male,21.18354,1.72,80.555875,yes,yes,2.0,2.468421,Sometimes,no,2.0,no,2.0,1.0,no,Public_Transportation,Overweight_Level_II +981,Male,32.774488,1.913241,101.482054,yes,yes,2.0,2.110937,Sometimes,no,2.175632,no,1.0,0.0,Sometimes,Walking,Overweight_Level_II +982,Male,31.327609,1.737984,82.523113,yes,yes,2.300408,3.071028,Sometimes,no,1.738959,no,0.090147,0.0,Sometimes,Automobile,Overweight_Level_II +983,Male,29.956198,1.703688,82.207978,yes,yes,2.119643,3.292956,Sometimes,no,1.723372,no,0.0,0.987102,Sometimes,Automobile,Overweight_Level_II +984,Male,21.403421,1.716308,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.936551,1.40913,no,Public_Transportation,Overweight_Level_II +985,Male,22.591439,1.65,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.451078,2.0,no,Public_Transportation,Overweight_Level_II +986,Female,28.583944,1.57856,65.522744,yes,no,2.0,2.283673,Sometimes,no,1.970622,no,1.863258,1.581703,Sometimes,Public_Transportation,Overweight_Level_II +987,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 +988,Male,35.217173,1.823168,91.630257,yes,yes,2.0,2.9948,Sometimes,no,1.290979,no,0.87599,0.85405,Sometimes,Automobile,Overweight_Level_II +989,Female,20.392665,1.525234,65.220249,yes,no,2.451009,3.0,Sometimes,no,2.0,no,2.177243,1.323877,no,Public_Transportation,Overweight_Level_II +990,Female,22.154854,1.481682,61.373868,yes,no,2.0,2.983297,Sometimes,no,1.453626,no,0.32896,0.509396,no,Public_Transportation,Overweight_Level_II +991,Male,19.108796,1.768578,87.803311,yes,yes,2.754646,3.0,Sometimes,no,2.957821,no,2.242754,0.786609,Sometimes,Public_Transportation,Overweight_Level_II +992,Female,20.70768,1.569878,69.743323,yes,no,2.417635,3.0,Sometimes,no,1.937674,no,1.417035,1.0,no,Public_Transportation,Overweight_Level_II +993,Female,20.634694,1.568188,67.904023,yes,no,2.512719,3.0,Sometimes,no,1.131169,no,0.095389,1.0,no,Public_Transportation,Overweight_Level_II +994,Male,22.052152,1.792527,89.994415,yes,yes,1.771693,1.890213,Sometimes,no,2.0,no,0.0,1.36595,Sometimes,Public_Transportation,Overweight_Level_II +995,Male,22.869778,1.795311,89.868784,yes,yes,1.57223,1.888067,Sometimes,no,2.0,no,0.0,0.552498,Sometimes,Public_Transportation,Overweight_Level_II +996,Female,22.909992,1.700038,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.973114,1.540298,no,Public_Transportation,Overweight_Level_II +997,Female,23.0,1.668649,80.458343,yes,yes,2.0,2.256119,Sometimes,no,1.142873,no,0.807076,1.611271,no,Public_Transportation,Overweight_Level_II +998,Male,24.679807,1.7,84.687554,yes,yes,2.0,3.0,Sometimes,no,2.020424,no,0.0,1.0,Sometimes,Public_Transportation,Overweight_Level_II +999,Male,23.884212,1.713825,83.952968,yes,yes,2.0,2.6648,Sometimes,no,1.810738,no,0.80989,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1000,Female,25.461411,1.7,78.055968,yes,yes,2.661556,3.0,Sometimes,no,3.0,no,0.55181,0.658783,Sometimes,Public_Transportation,Overweight_Level_II +1001,Male,31.333798,1.835381,91.059595,yes,yes,2.0,3.0,Sometimes,no,1.18423,no,0.155579,0.453404,Sometimes,Public_Transportation,Overweight_Level_II +1002,Male,24.108711,1.7,80.761409,yes,yes,2.0,3.0,Sometimes,no,2.879402,no,0.0,0.322405,Sometimes,Public_Transportation,Overweight_Level_II +1003,Female,28.830558,1.7,78.0,yes,yes,3.0,3.0,Sometimes,no,1.699971,no,1.727114,1.0,Frequently,Automobile,Overweight_Level_II +1004,Male,17.570089,1.7,83.199034,yes,no,2.097373,2.547086,Sometimes,no,1.538626,no,1.0,0.761898,no,Public_Transportation,Overweight_Level_II +1005,Female,19.027574,1.659877,77.272652,yes,yes,2.061461,2.812283,Sometimes,no,3.0,no,0.362441,0.78519,Sometimes,Public_Transportation,Overweight_Level_II +1006,Male,25.632447,1.836943,96.192662,yes,yes,1.317729,2.278652,Sometimes,no,2.0,no,1.80674,1.93977,Sometimes,Public_Transportation,Overweight_Level_II +1007,Male,22.94313,1.819614,93.086419,yes,yes,1.882235,1.240046,Sometimes,no,2.0,no,0.0,1.541493,Sometimes,Public_Transportation,Overweight_Level_II +1008,Male,23.285553,1.717775,85.312639,yes,yes,2.951591,3.0,Sometimes,no,2.971557,no,0.0,0.947192,Sometimes,Public_Transportation,Overweight_Level_II +1009,Male,23.0,1.791867,90.0,yes,yes,2.067817,3.0,Sometimes,no,1.082084,no,0.0,1.922364,Sometimes,Public_Transportation,Overweight_Level_II +1010,Female,25.19291,1.7,80.749657,yes,yes,2.54527,3.0,Sometimes,no,2.269564,no,0.503122,1.0,Frequently,Public_Transportation,Overweight_Level_II +1011,Male,34.389906,1.68108,83.568035,yes,yes,2.0,1.660768,Sometimes,no,2.482294,no,0.891205,0.0,no,Automobile,Overweight_Level_II +1012,Female,26.595893,1.660756,78.574786,yes,yes,2.0,2.597608,Sometimes,no,2.0,no,0.0,0.434198,Frequently,Public_Transportation,Overweight_Level_II +1013,Male,55.24625,1.769269,80.491339,no,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,0.0,no,Automobile,Overweight_Level_II +1014,Male,34.543563,1.765188,85.0,yes,yes,2.694281,3.0,Sometimes,no,2.653831,no,1.96582,0.891189,no,Automobile,Overweight_Level_II +1015,Male,21.808159,1.65,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.826609,2.0,no,Public_Transportation,Overweight_Level_II +1016,Male,18.118277,1.654757,80.0,yes,yes,2.821977,2.044035,Sometimes,no,1.954968,no,1.0,0.854536,no,Public_Transportation,Overweight_Level_II +1017,Female,42.189023,1.647768,79.165306,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,1.48189,no,Automobile,Overweight_Level_II +1018,Male,22.0,1.691303,80.539,yes,yes,2.0,2.038373,Sometimes,no,2.0,no,2.70825,1.506576,Sometimes,Public_Transportation,Overweight_Level_II +1019,Female,28.770852,1.532897,65.031879,yes,no,2.0,1.0,Sometimes,no,1.0,no,0.262171,0.0,no,Public_Transportation,Overweight_Level_II +1020,Female,25.483381,1.565288,64.848627,yes,no,2.0,1.0,Sometimes,no,1.0,no,0.740633,0.0,no,Public_Transportation,Overweight_Level_II +1021,Female,21.67315,1.5,63.65233,yes,no,2.0,1.474836,Sometimes,no,2.0,no,0.274838,0.613902,no,Public_Transportation,Overweight_Level_II +1022,Female,23.469538,1.507106,64.814109,yes,no,2.252472,3.986652,Sometimes,no,2.0,no,0.934286,0.890626,no,Public_Transportation,Overweight_Level_II +1023,Male,23.0,1.70074,81.32297,yes,yes,2.0,2.720642,Sometimes,no,1.5197,no,0.279375,1.043435,Sometimes,Public_Transportation,Overweight_Level_II +1024,Male,23.0,1.715118,81.650778,yes,yes,2.0,1.976744,Sometimes,no,1.628997,no,0.880584,1.127675,Sometimes,Public_Transportation,Overweight_Level_II +1025,Female,38.464538,1.696423,78.967919,yes,yes,3.0,3.0,Sometimes,no,2.981604,no,0.0,0.0,no,Automobile,Overweight_Level_II +1026,Female,37.496175,1.653088,80.0,yes,yes,2.033745,3.0,Sometimes,no,1.517225,no,0.0,1.517897,no,Automobile,Overweight_Level_II +1027,Female,33.690239,1.681842,77.426465,yes,yes,3.0,2.679724,Sometimes,no,1.999737,no,1.747347,0.681773,no,Automobile,Overweight_Level_II +1028,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 +1029,Male,31.426573,1.848683,99.0,yes,yes,2.759286,2.59257,Sometimes,no,2.549617,no,1.427233,0.519395,Sometimes,Automobile,Overweight_Level_II +1030,Male,34.288249,1.835678,96.01851,yes,yes,2.0,1.546665,Sometimes,no,3.0,no,1.0,0.0,Sometimes,Automobile,Overweight_Level_II +1031,Female,18.863875,1.560029,71.728069,yes,no,3.0,3.339914,Sometimes,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1032,Female,18.951144,1.621048,72.105856,yes,no,3.0,3.884861,Sometimes,no,2.0,no,1.0,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1033,Male,19.671876,1.699474,78.0,yes,no,1.925064,2.358298,Sometimes,no,2.774043,no,0.0,0.133566,no,Public_Transportation,Overweight_Level_II +1034,Male,50.832559,1.745528,82.130728,yes,yes,2.0,3.0,Sometimes,no,1.774778,no,0.943266,0.0,no,Automobile,Overweight_Level_II +1035,Male,17.971574,1.720379,85.0,yes,yes,2.0,3.0,Sometimes,no,2.802498,no,1.0,0.41758,Sometimes,Public_Transportation,Overweight_Level_II +1036,Male,18.0,1.758787,85.050558,yes,yes,2.846981,3.0,Sometimes,no,2.87781,no,1.0,0.429081,Sometimes,Public_Transportation,Overweight_Level_II +1037,Male,18.701766,1.704908,81.384224,yes,yes,2.650629,1.0,Sometimes,no,1.708083,no,1.876051,0.938791,Sometimes,Public_Transportation,Overweight_Level_II +1038,Female,34.389679,1.691322,77.561602,yes,yes,3.0,1.802305,Sometimes,no,2.0,no,0.390877,0.0,no,Automobile,Overweight_Level_II +1039,Male,17.178483,1.78629,95.277392,yes,yes,2.0,3.0,Sometimes,no,3.0,no,1.072318,0.651904,Sometimes,Public_Transportation,Overweight_Level_II +1040,Male,33.0816,1.705617,83.016968,yes,yes,2.0,2.7976,Sometimes,no,2.991671,no,2.148738,0.0,no,Automobile,Overweight_Level_II +1041,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 +1042,Female,36.310292,1.701397,83.0,yes,yes,2.0,1.0,Sometimes,no,2.0,no,1.472172,0.0,no,Automobile,Overweight_Level_II +1043,Female,20.0,1.625236,74.433362,yes,no,2.522399,1.032887,Sometimes,no,2.397653,no,2.358699,0.440087,Sometimes,Public_Transportation,Overweight_Level_II +1044,Female,18.549437,1.545196,72.467862,yes,no,3.0,3.014808,Sometimes,no,2.0,no,1.997529,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1045,Male,34.243146,1.843172,92.69551,yes,yes,2.0,2.271734,Sometimes,no,2.629043,no,1.0,0.075823,Sometimes,Automobile,Overweight_Level_II +1046,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 +1047,Male,19.703095,1.704141,78.790936,yes,no,1.650505,3.0,Sometimes,no,2.0,no,1.73234,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1048,Male,19.223259,1.706082,78.073528,yes,no,1.961347,3.0,Sometimes,no,2.0,no,1.655488,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1049,Male,29.695603,1.842943,93.798055,yes,yes,2.133955,2.902766,Sometimes,no,1.753464,no,1.729987,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1050,Male,27.0,1.880992,99.623778,yes,yes,2.684528,1.0,Sometimes,no,1.05668,no,1.516147,0.084006,Sometimes,Public_Transportation,Overweight_Level_II +1051,Male,21.027662,1.726774,82.853749,yes,yes,2.265973,2.687502,Sometimes,no,2.0,no,2.21722,0.648522,Sometimes,Public_Transportation,Overweight_Level_II +1052,Male,33.015258,1.73196,84.315608,yes,yes,2.0,3.0,Sometimes,no,2.205911,no,1.293396,0.0,no,Automobile,Overweight_Level_II +1053,Male,18.0,1.751278,86.963628,yes,no,3.0,3.0,Sometimes,no,2.0,no,1.271667,0.155278,Sometimes,Public_Transportation,Overweight_Level_II +1054,Male,30.553097,1.780448,88.431954,yes,yes,2.0,1.178708,Sometimes,no,1.791286,no,0.893362,0.276364,Sometimes,Public_Transportation,Overweight_Level_II +1055,Male,33.0,1.85,97.92035,yes,yes,2.0,3.0,Sometimes,no,1.117464,no,1.0,0.663649,Sometimes,Public_Transportation,Overweight_Level_II +1056,Male,24.911994,1.785241,88.03748,yes,yes,1.306844,2.279546,Sometimes,no,2.0,no,0.0,0.739609,Sometimes,Public_Transportation,Overweight_Level_II +1057,Male,24.444846,1.718845,86.319887,yes,yes,2.0,2.677693,Sometimes,no,2.693978,no,0.0,0.688013,Sometimes,Public_Transportation,Overweight_Level_II +1058,Female,19.911246,1.530248,68.85097,yes,no,2.258795,3.53009,Sometimes,no,1.172186,no,0.678943,1.0,no,Public_Transportation,Overweight_Level_II +1059,Female,34.204408,1.664927,80.386078,yes,yes,2.0,3.0,Sometimes,no,2.641642,no,0.285889,1.50301,no,Automobile,Overweight_Level_II +1060,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 +1061,Male,23.384374,1.725587,82.480214,yes,yes,2.712747,2.853676,Sometimes,no,1.526313,no,1.0,0.173665,Sometimes,Public_Transportation,Overweight_Level_II +1062,Female,43.238402,1.733875,86.94538,yes,yes,2.353603,2.337035,Sometimes,no,1.830614,no,0.706287,0.0,no,Automobile,Overweight_Level_II +1063,Female,45.0,1.675953,79.66832,yes,yes,2.598051,3.0,Sometimes,no,1.0,no,0.0,0.0,no,Automobile,Overweight_Level_II +1064,Male,22.087389,1.786238,89.802492,yes,yes,1.718156,1.631184,Sometimes,no,2.0,no,0.0,0.306124,Sometimes,Public_Transportation,Overweight_Level_II +1065,Male,21.378039,1.718534,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.721646,1.269982,no,Public_Transportation,Overweight_Level_II +1066,Male,31.398281,1.919543,101.544589,yes,yes,2.0,1.005391,Sometimes,no,2.001936,no,1.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II +1067,Male,31.484494,1.707613,82.586893,yes,yes,2.795086,3.250467,Sometimes,no,1.367876,no,0.022958,0.0,no,Automobile,Overweight_Level_II +1068,Male,21.709159,1.658393,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.939733,1.692287,no,Public_Transportation,Overweight_Level_II +1069,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 +1070,Male,34.821442,1.805459,90.13868,yes,yes,2.0,1.80993,Sometimes,no,2.174835,no,0.340915,0.020153,Sometimes,Automobile,Overweight_Level_II +1071,Female,20.843363,1.521008,67.083121,yes,no,2.493448,3.0,Sometimes,no,1.849997,no,0.954459,1.444532,no,Public_Transportation,Overweight_Level_II +1072,Male,19.443639,1.744733,87.27989,yes,yes,2.442536,3.0,Sometimes,no,2.825629,no,3.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II +1073,Female,20.492077,1.529223,65.140408,yes,no,2.003951,3.0,Sometimes,no,1.207978,no,1.916751,1.228995,no,Public_Transportation,Overweight_Level_II +1074,Male,22.944349,1.799742,89.98168,yes,yes,1.34138,1.94671,Sometimes,no,2.0,no,0.0,0.975504,Sometimes,Public_Transportation,Overweight_Level_II +1075,Female,23.0,1.681314,80.037202,yes,yes,2.0,2.9796,Sometimes,no,1.964435,no,0.189957,1.73216,no,Public_Transportation,Overweight_Level_II +1076,Male,24.06894,1.706912,85.743727,yes,yes,2.0,2.994198,Sometimes,no,2.747302,no,0.0,0.799756,Sometimes,Public_Transportation,Overweight_Level_II +1077,Female,23.728707,1.663509,80.0,yes,yes,2.0,3.0,Sometimes,no,2.13755,no,0.0,0.124977,no,Public_Transportation,Overweight_Level_II +1078,Male,29.216019,1.752265,88.096062,yes,yes,2.0,1.894384,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Overweight_Level_II +1079,Male,24.751511,1.735343,83.337721,yes,yes,2.607335,3.0,Sometimes,no,2.0,no,0.451009,0.630866,Sometimes,Public_Transportation,Overweight_Level_II +1080,Female,28.977792,1.7,78.0,yes,yes,3.0,3.0,Sometimes,no,1.507638,no,1.82434,1.0,Frequently,Automobile,Overweight_Level_II +1081,Male,17.441593,1.7,83.414072,yes,no,2.061384,2.579291,Sometimes,no,1.909253,no,1.0,0.962468,Sometimes,Public_Transportation,Overweight_Level_II +1082,Female,19.126145,1.633794,77.858532,yes,no,2.696381,3.0,Sometimes,no,2.472964,no,1.856119,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1083,Male,24.122589,1.856759,95.887056,yes,yes,1.116068,2.449067,Sometimes,no,2.0,no,0.92635,1.97117,Sometimes,Public_Transportation,Overweight_Level_II +1084,Male,23.0,1.77433,90.0,yes,yes,2.116432,3.0,Sometimes,no,1.002292,no,0.0,1.066708,Sometimes,Public_Transportation,Overweight_Level_II +1085,Female,28.824194,1.7,78.0,yes,yes,3.0,3.0,Sometimes,no,2.147074,no,1.416076,1.0,Frequently,Automobile,Overweight_Level_II +1086,Male,34.204611,1.719509,84.416515,yes,yes,2.031246,3.0,Sometimes,no,2.803002,no,1.98448,0.418623,no,Automobile,Overweight_Level_II +1087,Female,28.167799,1.658202,78.0,yes,yes,2.938031,1.532833,Sometimes,no,1.931242,no,0.525002,0.371941,Frequently,Automobile,Overweight_Level_II +1088,Male,55.137881,1.657221,80.993213,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,0.0,no,Automobile,Overweight_Level_II +1089,Male,34.970367,1.713348,84.722222,yes,yes,2.884212,3.0,Sometimes,no,3.0,no,2.0,0.8324,no,Automobile,Overweight_Level_II +1090,Male,20.986834,1.677178,80.379575,yes,yes,2.0,2.961706,Sometimes,no,2.0,no,1.661556,1.114716,no,Public_Transportation,Overweight_Level_II +1091,Female,38.378056,1.67805,77.224574,yes,yes,2.444599,3.0,Sometimes,no,1.029798,no,0.0,0.0,no,Automobile,Overweight_Level_II +1092,Male,22.18881,1.717722,81.92991,yes,yes,2.0,1.152521,Sometimes,no,1.723159,no,1.39016,1.094941,Sometimes,Public_Transportation,Overweight_Level_II +1093,Female,21.082384,1.486484,60.117993,yes,no,2.0,1.104642,Sometimes,no,1.0,no,0.0,0.969097,no,Public_Transportation,Overweight_Level_II +1094,Female,25.293202,1.503379,64.342459,yes,no,2.0,1.0,Sometimes,no,1.639807,no,0.072117,0.0,no,Public_Transportation,Overweight_Level_II +1095,Male,23.0,1.718981,81.66995,yes,yes,2.0,1.729553,Sometimes,no,1.400247,no,0.887923,1.011983,Sometimes,Public_Transportation,Overweight_Level_II +1096,Female,39.170029,1.688354,79.278896,yes,yes,3.0,3.0,Sometimes,no,2.994515,no,0.0,0.0,no,Automobile,Overweight_Level_II +1097,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 +1098,Male,33.182127,1.838441,97.029249,yes,yes,2.0,1.977221,Sometimes,no,3.0,no,1.0,0.0,Sometimes,Automobile,Overweight_Level_II +1099,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 +1100,Male,19.149706,1.699818,78.0,yes,no,1.096455,2.491315,Sometimes,no,2.450069,no,0.0,0.726118,Sometimes,Public_Transportation,Overweight_Level_II +1101,Male,46.491859,1.718097,88.600878,yes,yes,2.129969,3.0,Sometimes,no,1.568035,no,0.870127,0.0,no,Automobile,Overweight_Level_II +1102,Male,17.894784,1.731389,84.064875,yes,no,2.019674,2.843319,Sometimes,no,2.832004,no,1.0,0.608607,Sometimes,Public_Transportation,Overweight_Level_II +1103,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 +1104,Female,38.384177,1.698626,78.637307,yes,yes,3.0,3.0,Sometimes,no,2.503198,no,0.0,0.0,no,Automobile,Overweight_Level_II +1105,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 +1106,Male,33.049121,1.708742,83.001642,yes,yes,2.0,1.171027,Sometimes,no,2.405172,no,2.300282,0.0,no,Automobile,Overweight_Level_II +1107,Female,37.205173,1.667469,80.993373,yes,yes,2.01054,2.77684,Sometimes,no,1.651548,no,0.0,0.790967,no,Automobile,Overweight_Level_II +1108,Female,19.027417,1.588147,73.939268,yes,no,3.0,3.362758,Sometimes,no,2.0,no,2.892922,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1109,Male,37.492444,1.835024,92.368359,yes,yes,2.0,1.672706,Sometimes,no,2.766674,no,1.0,0.027093,Sometimes,Automobile,Overweight_Level_II +1110,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 +1111,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 +1112,Male,27.349745,1.835271,97.58826,yes,yes,2.923433,1.338033,Sometimes,no,1.944095,no,1.931829,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1113,Male,18.0,1.742819,86.565148,yes,yes,3.0,3.0,Sometimes,no,2.0,no,2.040816,0.860321,Sometimes,Public_Transportation,Overweight_Level_II +1114,Male,33.009285,1.741192,84.773349,yes,yes,2.0,3.0,Sometimes,no,2.035954,no,1.210736,0.0,no,Automobile,Overweight_Level_II +1115,Male,18.0,1.759721,86.0805,yes,yes,2.882522,3.0,Sometimes,no,2.452986,no,1.0,0.746651,Sometimes,Public_Transportation,Overweight_Level_II +1116,Male,30.079371,1.810616,92.860254,yes,yes,2.0,3.0,Sometimes,no,1.622638,no,0.033745,0.105895,Sometimes,Public_Transportation,Overweight_Level_II +1117,Male,25.035915,1.763101,88.532032,yes,yes,1.973499,1.081805,Sometimes,no,2.0,no,0.0,0.464022,Sometimes,Public_Transportation,Overweight_Level_II +1118,Female,18.198322,1.543338,71.799982,yes,no,3.0,3.087119,Sometimes,no,2.0,no,1.403872,1.0,no,Public_Transportation,Overweight_Level_II +1119,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 +1120,Male,23.806789,1.732492,84.557797,yes,yes,2.992205,3.0,Sometimes,no,2.413813,no,0.458237,0.688293,Sometimes,Public_Transportation,Overweight_Level_II +1121,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 +1122,Male,21.384259,1.780679,89.667406,yes,yes,1.780746,1.471053,Sometimes,no,2.0,no,0.467562,0.568668,Sometimes,Public_Transportation,Overweight_Level_II +1123,Male,22.730414,1.758687,89.673648,yes,yes,2.164062,2.983201,Sometimes,no,2.168904,no,0.752926,0.0,Sometimes,Public_Transportation,Overweight_Level_II +1124,Male,21.20563,1.704573,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.847761,1.413375,no,Public_Transportation,Overweight_Level_II +1125,Male,21.333429,1.716545,80.005809,yes,yes,2.0,2.783336,Sometimes,no,2.0,no,2.536615,1.206535,no,Public_Transportation,Overweight_Level_II +1126,Male,32.787101,1.903832,99.812443,yes,yes,2.0,1.863012,Sometimes,no,2.196471,no,1.0,0.0,Sometimes,Automobile,Overweight_Level_II +1127,Male,32.610018,1.742538,83.390983,yes,yes,2.247704,3.053598,Sometimes,no,1.919629,no,0.408023,0.0,no,Automobile,Overweight_Level_II +1128,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 +1129,Male,21.123048,1.717037,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,2.270555,1.343044,no,Public_Transportation,Overweight_Level_II +1130,Female,22.989846,1.65,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.146919,2.0,no,Public_Transportation,Overweight_Level_II +1131,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 +1132,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 +1133,Male,34.098174,1.841151,91.798103,yes,yes,2.0,2.997414,Sometimes,no,1.259454,no,0.935217,0.9976,Sometimes,Automobile,Overweight_Level_II +1134,Female,21.001282,1.517998,64.696248,yes,no,2.1239,1.672958,Sometimes,no,2.0,no,1.582428,1.313363,no,Public_Transportation,Overweight_Level_II +1135,Female,21.95994,1.483284,62.894283,yes,no,2.0,1.680838,Sometimes,no,1.833635,no,0.281876,0.575848,no,Public_Transportation,Overweight_Level_II +1136,Male,19.795269,1.758972,87.861431,yes,yes,2.332074,3.0,Sometimes,no,2.999495,no,2.405963,0.441502,Sometimes,Public_Transportation,Overweight_Level_II +1137,Female,19.090023,1.562434,70.442775,yes,no,2.748243,3.070386,Sometimes,no,1.962322,no,1.145752,1.0,no,Public_Transportation,Overweight_Level_II +1138,Female,20.820455,1.547066,67.473282,yes,no,2.303656,3.0,Sometimes,no,1.163111,no,1.926381,1.119877,no,Public_Transportation,Overweight_Level_II +1139,Male,22.087056,1.792435,89.998729,yes,yes,1.904732,2.608416,Sometimes,no,1.204175,no,0.0,1.895312,Sometimes,Public_Transportation,Overweight_Level_II +1140,Male,22.185756,1.784555,89.836692,yes,yes,1.979944,1.599464,Sometimes,no,2.0,no,0.17048,0.819475,Sometimes,Public_Transportation,Overweight_Level_II +1141,Female,22.980957,1.700216,80.473587,yes,yes,2.0,2.815255,Sometimes,no,1.622214,no,0.463891,1.462664,no,Public_Transportation,Overweight_Level_II +1142,Male,23.0,1.672101,80.939733,yes,yes,2.0,2.395785,Sometimes,no,1.40077,no,0.788659,1.544357,no,Public_Transportation,Overweight_Level_II +1143,Male,24.190896,1.7,84.849349,yes,yes,2.0,3.0,Sometimes,no,2.056053,no,0.0,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1144,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 +1145,Female,26.591628,1.7,78.008388,yes,yes,2.734314,3.0,Sometimes,no,3.0,no,0.897702,0.908271,Frequently,Public_Transportation,Overweight_Level_II +1146,Male,31.264628,1.803129,91.052215,yes,yes,2.0,3.0,Sometimes,no,1.199517,no,0.047738,0.163329,Sometimes,Public_Transportation,Overweight_Level_II +1147,Female,23.629159,1.70002,80.420434,yes,yes,2.0,3.0,Sometimes,no,2.386904,no,0.926201,1.34403,Sometimes,Public_Transportation,Overweight_Level_II +1148,Female,26.208134,1.7,78.041338,yes,yes,2.784383,3.0,Sometimes,no,2.165605,no,0.855973,0.839659,Frequently,Automobile,Overweight_Level_II +1149,Male,17.689057,1.713599,83.285753,yes,yes,2.009952,2.716106,Sometimes,no,1.660614,no,1.0,0.60473,Sometimes,Public_Transportation,Overweight_Level_II +1150,Male,19.16097,1.662728,77.473204,yes,no,2.008656,1.402771,Sometimes,no,3.0,no,0.112383,0.32803,Sometimes,Public_Transportation,Overweight_Level_II +1151,Male,25.45763,1.837399,95.952027,yes,yes,1.122127,2.865657,Sometimes,no,2.0,no,1.28775,1.944177,Sometimes,Public_Transportation,Overweight_Level_II +1152,Male,22.988004,1.80827,91.363293,yes,yes,1.963965,1.836226,Sometimes,no,1.85537,no,0.0,1.667745,Sometimes,Public_Transportation,Overweight_Level_II +1153,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 +1154,Male,22.882558,1.793451,89.909259,yes,yes,1.899116,2.375026,Sometimes,no,1.39854,no,0.0,1.365793,Sometimes,Public_Transportation,Overweight_Level_II +1155,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 +1156,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 +1157,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 +1158,Male,55.022494,1.673394,80.400306,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.0,0.0,no,Automobile,Overweight_Level_II +1159,Male,34.647036,1.769499,85.0,yes,yes,2.802696,3.0,Sometimes,no,2.978465,no,1.974656,0.936925,no,Automobile,Overweight_Level_II +1160,Male,21.997335,1.65,80.0,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.527341,2.0,no,Public_Transportation,Overweight_Level_II +1161,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 +1162,Female,41.743333,1.67861,79.849252,yes,yes,2.733129,3.0,Sometimes,no,1.302594,no,0.0,1.103349,no,Automobile,Overweight_Level_II +1163,Male,21.473078,1.694423,80.311273,yes,yes,2.0,2.646717,Sometimes,no,2.0,no,2.627515,1.358812,no,Public_Transportation,Overweight_Level_II +1164,Female,27.757187,1.505437,63.892071,yes,no,2.0,1.0,Sometimes,no,1.278578,no,0.02112,0.0,no,Public_Transportation,Overweight_Level_II +1165,Female,25.113537,1.505387,63.715219,yes,no,2.0,1.193729,Sometimes,no,1.846441,no,0.546402,0.235711,no,Public_Transportation,Overweight_Level_II +1166,Female,21.82165,1.491441,62.269912,yes,no,2.0,1.477581,Sometimes,no,1.995035,no,0.324676,0.51743,no,Public_Transportation,Overweight_Level_II +1167,Female,20.670975,1.509408,64.852953,yes,no,2.294067,3.209508,Sometimes,no,2.0,no,1.103088,1.261043,no,Public_Transportation,Overweight_Level_II +1168,Male,22.649792,1.685045,81.022119,yes,yes,2.0,2.756622,Sometimes,no,1.606076,no,0.432973,1.749586,no,Public_Transportation,Overweight_Level_II +1169,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 +1170,Female,38.097395,1.696954,78.156035,yes,yes,3.0,3.0,Sometimes,no,2.166024,no,0.0,0.0,no,Automobile,Overweight_Level_II +1171,Female,37.642177,1.676095,79.079738,yes,yes,2.802128,3.0,Sometimes,no,1.744628,no,0.0,0.721167,no,Automobile,Overweight_Level_II +1172,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 +1173,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 +1174,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 +1175,Male,33.185661,1.836007,97.416417,yes,yes,2.0,1.724887,Sometimes,no,3.0,no,1.0,0.0,Sometimes,Automobile,Overweight_Level_II +1176,Female,18.741188,1.556789,71.788181,yes,no,3.0,3.129155,Sometimes,no,2.0,no,1.634134,1.0,no,Public_Transportation,Overweight_Level_II +1177,Female,19.955257,1.5891,72.713611,yes,no,3.0,3.856434,Sometimes,no,2.0,no,1.32417,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1178,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 +1179,Male,47.7061,1.743935,84.729197,yes,yes,2.535315,3.0,Sometimes,no,1.146595,no,0.31381,0.0,no,Automobile,Overweight_Level_II +1180,Male,17.997009,1.742654,85.0,yes,no,2.0,3.0,Sometimes,no,2.976229,no,1.0,0.121992,Sometimes,Public_Transportation,Overweight_Level_II +1181,Male,17.971786,1.754441,85.002884,yes,yes,2.765769,3.0,Sometimes,no,2.845134,no,1.0,0.42683,Sometimes,Public_Transportation,Overweight_Level_II +1182,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 +1183,Female,33.954433,1.682253,77.431678,yes,yes,3.0,2.049908,Sometimes,no,1.999882,no,1.637368,0.10724,no,Automobile,Overweight_Level_II +1184,Male,17.039058,1.799902,95.419668,yes,yes,2.0,3.0,Sometimes,no,3.0,no,1.04729,0.933802,Sometimes,Public_Transportation,Overweight_Level_II +1185,Male,33.070142,1.709428,83.014033,yes,yes,2.0,1.044628,Sometimes,no,2.01051,no,2.805801,0.0,no,Automobile,Overweight_Level_II +1186,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 +1187,Male,35.719457,1.685947,83.3258,yes,yes,2.0,1.009426,Sometimes,no,2.136398,no,1.060349,0.0,no,Automobile,Overweight_Level_II +1188,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 +1189,Female,18.850466,1.550053,72.9518,yes,no,3.0,3.000974,Sometimes,no,2.0,no,2.274248,1.0,Sometimes,Public_Transportation,Overweight_Level_II +1190,Male,31.540751,1.828092,91.495718,yes,yes,2.0,2.698883,Sometimes,no,2.035104,no,0.598438,0.017016,Sometimes,Automobile,Overweight_Level_II +1191,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 +1192,Male,19.749627,1.680764,79.621463,yes,no,1.83746,3.0,Sometimes,no,2.0,no,1.194519,1.212762,no,Public_Transportation,Overweight_Level_II +1193,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 +1194,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 +1195,Male,27.0,1.834986,99.083478,yes,yes,2.760607,1.0,Sometimes,no,1.336378,no,1.898889,0.487375,Sometimes,Public_Transportation,Overweight_Level_II +1196,Male,21.380336,1.724839,82.27635,yes,yes,2.09449,1.873484,Sometimes,no,2.0,no,2.079709,0.880414,Sometimes,Public_Transportation,Overweight_Level_II +1197,Male,33.151905,1.685127,83.986895,yes,yes,2.0,2.473911,Sometimes,no,2.452789,no,0.932792,0.0,no,Automobile,Overweight_Level_II +1198,Male,18.0,1.750097,86.372141,yes,yes,2.907062,3.0,Sometimes,no,2.740848,no,1.219827,0.037634,Sometimes,Public_Transportation,Overweight_Level_II +1199,Male,32.241587,1.757961,87.906019,yes,yes,2.0,1.79558,Sometimes,no,1.855054,no,1.083572,0.218574,Sometimes,Public_Transportation,Overweight_Level_II +1200,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 +1201,Male,24.347414,1.789193,89.393589,yes,yes,1.521604,2.174968,Sometimes,no,2.0,no,0.0,0.632467,Sometimes,Public_Transportation,Overweight_Level_II +1202,Male,24.362124,1.716677,85.261339,yes,yes,2.0,2.676148,Sometimes,no,2.083939,no,0.632164,0.993786,Sometimes,Public_Transportation,Overweight_Level_II +1203,Female,19.462713,1.550122,69.936073,yes,no,2.501683,3.394788,Sometimes,no,1.337378,no,0.932888,1.0,no,Public_Transportation,Overweight_Level_II +1204,Female,35.432059,1.663178,80.135167,yes,yes,2.0,3.0,Sometimes,no,1.992548,no,0.039207,1.528714,no,Automobile,Overweight_Level_II +1205,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 +1206,Male,23.807181,1.729177,82.52724,yes,yes,2.742796,2.937607,Sometimes,no,1.99467,no,1.0,0.023564,Sometimes,Public_Transportation,Overweight_Level_II +1207,Male,39.585811,1.719153,86.464843,yes,yes,2.325623,1.313403,Sometimes,no,1.94609,no,0.604259,0.0,no,Automobile,Overweight_Level_II +1208,Female,45.821267,1.687326,80.413997,yes,yes,2.076689,3.0,Sometimes,no,1.026729,no,0.647798,0.0,no,Automobile,Overweight_Level_II +1209,Male,25.929746,1.772449,105.0,yes,yes,3.0,3.0,Sometimes,no,2.045069,no,1.725931,1.619596,Sometimes,Public_Transportation,Obesity_Type_I +1210,Male,26.042738,1.837117,105.712219,yes,yes,3.0,3.0,Sometimes,no,2.509147,no,2.0,1.564796,Sometimes,Public_Transportation,Obesity_Type_I +1211,Male,30.551762,1.784377,102.872505,yes,yes,2.271306,3.0,Sometimes,no,1.771198,no,2.0,0.413752,Sometimes,Automobile,Obesity_Type_I +1212,Male,39.759575,1.792507,101.780099,yes,yes,2.33361,2.113575,Sometimes,no,2.504136,no,2.998981,1.0,Sometimes,Automobile,Obesity_Type_I +1213,Female,31.386405,1.556579,78.233341,yes,yes,2.13683,2.092179,Sometimes,no,1.505381,no,0.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I +1214,Female,25.706285,1.585547,80.351263,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I +1215,Female,43.604901,1.569234,81.827288,yes,yes,2.909853,1.0,Sometimes,no,2.0,no,1.308852,0.0,no,Automobile,Obesity_Type_I +1216,Female,42.31607,1.583943,81.936398,yes,yes,2.490507,2.974204,Sometimes,no,1.846754,no,0.0,0.0,no,Automobile,Obesity_Type_I +1217,Female,22.654316,1.621233,82.0,yes,yes,1.063449,1.0,Sometimes,no,2.0,no,0.0,1.482016,Sometimes,Public_Transportation,Obesity_Type_I +1218,Female,22.679935,1.6084,82.603984,yes,yes,2.699282,1.0,Sometimes,no,2.598632,no,0.292093,2.0,Sometimes,Public_Transportation,Obesity_Type_I +1219,Male,24.079524,1.733439,97.911865,yes,yes,2.0,3.0,Sometimes,no,2.843675,no,1.309304,1.338655,no,Public_Transportation,Obesity_Type_I +1220,Male,21.196152,1.65,88.472359,yes,yes,2.4277,1.001633,Sometimes,no,3.0,no,1.172641,1.0,no,Public_Transportation,Obesity_Type_I +1221,Female,22.596576,1.650052,87.272552,yes,yes,2.87599,1.2919,Sometimes,no,2.777712,no,0.432813,1.0,no,Public_Transportation,Obesity_Type_I +1222,Female,23.0,1.644161,84.340406,yes,yes,2.177243,3.0,Sometimes,no,2.715572,no,2.230109,0.070897,no,Public_Transportation,Obesity_Type_I +1223,Female,37.356288,1.559499,77.26813,yes,yes,2.0,3.0,Sometimes,no,1.630528,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1224,Male,22.754646,1.734237,95.0,yes,yes,2.0,3.0,Sometimes,no,2.287248,no,2.669179,1.237867,no,Public_Transportation,Obesity_Type_I +1225,Male,23.252906,1.775815,97.813023,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I +1226,Female,22.025438,1.640426,87.344156,yes,yes,3.0,1.0,Sometimes,no,3.0,no,1.280924,1.77795,no,Public_Transportation,Obesity_Type_I +1227,Female,18.086772,1.65,82.13682,yes,yes,3.0,3.0,Sometimes,no,1.0,no,1.02975,1.350099,Sometimes,Public_Transportation,Obesity_Type_I +1228,Male,25.994393,1.753321,107.998815,yes,yes,3.0,3.0,Sometimes,no,1.776991,no,1.757724,0.0,Sometimes,Automobile,Obesity_Type_I +1229,Female,40.821515,1.553026,77.74518,yes,yes,2.0,3.0,Sometimes,no,1.406115,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1230,Male,23.0,1.706525,90.500055,yes,yes,2.0,3.0,Sometimes,no,1.530493,no,0.967627,1.0,no,Public_Transportation,Obesity_Type_I +1231,Female,37.955371,1.560648,80.0,yes,yes,2.846452,1.139317,Sometimes,no,1.459511,no,1.71807,0.0,Sometimes,Automobile,Obesity_Type_I +1232,Male,31.315593,1.673352,90.0,yes,yes,2.19011,2.029858,Sometimes,no,2.348981,no,1.946907,0.0,Sometimes,Automobile,Obesity_Type_I +1233,Male,22.661556,1.660324,94.189167,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.105936,no,Public_Transportation,Obesity_Type_I +1234,Male,40.317787,1.786318,98.447311,yes,yes,2.0,3.0,Sometimes,no,2.680292,no,1.549693,0.522259,Sometimes,Automobile,Obesity_Type_I +1235,Male,23.365649,1.757691,95.361795,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I +1236,Male,21.72127,1.712163,98.699971,yes,yes,2.0,2.977543,Sometimes,no,2.205977,no,2.698874,2.0,no,Public_Transportation,Obesity_Type_I +1237,Male,35.389491,1.78,100.84763,yes,yes,2.069267,1.476204,Sometimes,no,3.0,no,2.05252,0.092736,Frequently,Automobile,Obesity_Type_I +1238,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 +1239,Female,23.0,1.61082,82.532994,yes,yes,2.09663,2.879541,Sometimes,no,2.432967,no,1.887012,0.0,no,Public_Transportation,Obesity_Type_I +1240,Female,23.0,1.665199,83.15115,yes,yes,2.928234,1.458507,Sometimes,no,2.777379,no,0.354541,1.707018,no,Public_Transportation,Obesity_Type_I +1241,Female,40.951591,1.542122,80.0,yes,yes,2.0,1.105617,Sometimes,no,1.372811,no,1.629432,0.0,Sometimes,Automobile,Obesity_Type_I +1242,Female,39.135634,1.507867,79.58958,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1243,Male,26.271621,1.660955,90.0,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.669278,0.505266,Sometimes,Automobile,Obesity_Type_I +1244,Female,23.779235,1.553127,78.0,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.827502,0.0,no,Public_Transportation,Obesity_Type_I +1245,Male,20.586978,1.781952,102.910613,yes,yes,2.0,3.0,Sometimes,no,1.689793,no,1.464204,0.0,no,Public_Transportation,Obesity_Type_I +1246,Male,21.669478,1.75,96.940255,yes,yes,2.0,3.0,Sometimes,no,2.771469,no,2.359339,1.206251,no,Public_Transportation,Obesity_Type_I +1247,Female,23.0,1.615854,80.615325,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.026142,0.0,no,Public_Transportation,Obesity_Type_I +1248,Male,20.825962,1.793378,105.655037,yes,yes,2.0,3.0,Sometimes,no,1.155384,no,0.003938,0.526999,Sometimes,Public_Transportation,Obesity_Type_I +1249,Male,18.178023,1.85478,114.77484,yes,yes,2.0,1.854536,Sometimes,no,2.160169,no,1.0,2.0,Sometimes,Public_Transportation,Obesity_Type_I +1250,Female,18.0,1.685633,90.032671,yes,yes,2.496455,3.0,Sometimes,no,1.850344,no,1.066101,0.732265,Sometimes,Public_Transportation,Obesity_Type_I +1251,Female,18.267696,1.706343,93.348843,yes,yes,1.70825,3.0,Sometimes,no,1.0,no,0.899864,1.0,Sometimes,Public_Transportation,Obesity_Type_I +1252,Male,23.0,1.791415,105.138073,yes,yes,2.262171,3.0,Sometimes,no,2.03415,no,0.317363,0.508663,Sometimes,Public_Transportation,Obesity_Type_I +1253,Male,23.0,1.753716,106.49141,yes,yes,2.740633,3.0,Sometimes,no,2.081005,no,0.650235,0.76906,Sometimes,Public_Transportation,Obesity_Type_I +1254,Male,20.9859,1.780503,103.189532,yes,yes,2.0,3.0,Sometimes,no,1.608128,no,0.478134,0.0,no,Public_Transportation,Obesity_Type_I +1255,Male,21.504943,1.819867,106.038468,yes,yes,2.0,3.0,Sometimes,no,2.715252,no,0.0,0.621605,no,Public_Transportation,Obesity_Type_I +1256,Male,18.0,1.791174,108.9606,yes,yes,2.0,1.08687,Sometimes,no,2.279124,no,1.0,1.609773,no,Public_Transportation,Obesity_Type_I +1257,Male,18.0,1.82093,108.742005,yes,yes,2.0,1.25535,Sometimes,no,2.497065,no,1.0,1.441605,no,Public_Transportation,Obesity_Type_I +1258,Female,19.442663,1.627812,82.0,yes,yes,1.081585,2.870661,Sometimes,no,1.11756,no,0.0,1.616826,Sometimes,Public_Transportation,Obesity_Type_I +1259,Female,22.865018,1.632118,82.0,yes,yes,2.587789,1.482103,Sometimes,no,1.911664,no,0.0,0.128681,Sometimes,Public_Transportation,Obesity_Type_I +1260,Male,21.948577,1.773594,105.000789,yes,yes,2.252653,3.0,Sometimes,no,2.962371,no,1.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I +1261,Male,21.214617,1.930416,118.560509,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.732186,1.0,Sometimes,Public_Transportation,Obesity_Type_I +1262,Male,22.277859,1.947406,116.893105,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.975187,1.0,Sometimes,Public_Transportation,Obesity_Type_I +1263,Female,37.832949,1.610867,80.0,yes,yes,2.036613,1.81698,Sometimes,no,1.93059,no,1.851404,0.0,Sometimes,Automobile,Obesity_Type_I +1264,Female,37.631769,1.513202,75.410647,yes,yes,2.0,2.582591,Sometimes,no,1.535134,no,1.88452,0.0,Sometimes,Automobile,Obesity_Type_I +1265,Male,17.6739,1.738465,97.185474,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.233314,no,Public_Transportation,Obesity_Type_I +1266,Female,37.524551,1.567915,76.129784,yes,yes,2.0,3.0,Sometimes,no,2.102976,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1267,Female,43.510672,1.587546,76.126112,yes,yes,2.0,3.0,Sometimes,no,2.091934,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1268,Male,18.0,1.820385,108.395005,yes,yes,2.0,2.164839,Sometimes,no,2.094479,no,1.0,0.676557,Sometimes,Public_Transportation,Obesity_Type_I +1269,Male,18.0,1.792239,108.244379,yes,yes,2.0,2.141839,Sometimes,no,2.939847,no,1.0,0.103056,Sometimes,Public_Transportation,Obesity_Type_I +1270,Male,29.506287,1.82697,108.751502,yes,yes,2.0,2.877583,Sometimes,no,2.358038,no,0.877295,1.817146,Sometimes,Automobile,Obesity_Type_I +1271,Female,18.0,1.670058,86.242679,yes,yes,2.609123,3.0,Sometimes,no,1.955053,no,1.186013,0.0,no,Public_Transportation,Obesity_Type_I +1272,Male,24.733308,1.639383,91.267087,yes,yes,1.036159,3.0,Sometimes,no,1.0,no,0.344013,0.329367,Sometimes,Automobile,Obesity_Type_I +1273,Male,29.0,1.682916,89.991671,yes,yes,1.851262,3.0,Sometimes,no,2.11267,no,0.388269,0.0,Sometimes,Automobile,Obesity_Type_I +1274,Female,18.0,1.609321,84.493156,yes,yes,2.0,3.0,Sometimes,no,1.051735,no,1.0,0.0,no,Public_Transportation,Obesity_Type_I +1275,Male,21.098035,1.690437,96.366777,yes,yes,2.0,2.95833,Sometimes,no,2.0,no,0.0,1.595306,no,Public_Transportation,Obesity_Type_I +1276,Male,19.089595,1.700164,99.204695,yes,yes,2.0,2.119826,Sometimes,no,2.0,no,0.0,1.882539,no,Public_Transportation,Obesity_Type_I +1277,Female,18.312665,1.60074,82.249831,yes,yes,2.501236,3.0,Sometimes,no,1.220331,no,0.245003,0.537202,no,Public_Transportation,Obesity_Type_I +1278,Male,18.260079,1.801848,104.741914,yes,yes,2.0,3.0,Sometimes,no,3.0,no,1.783858,0.0,no,Public_Transportation,Obesity_Type_I +1279,Male,26.004294,1.844751,105.025808,yes,yes,3.0,3.0,Sometimes,no,2.925029,no,2.0,1.758865,Sometimes,Public_Transportation,Obesity_Type_I +1280,Male,25.994746,1.811602,106.042142,yes,yes,3.0,3.0,Sometimes,no,2.858171,no,1.813318,0.680215,Sometimes,Public_Transportation,Obesity_Type_I +1281,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 +1282,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 +1283,Female,30.163408,1.533364,78.030383,yes,yes,2.028225,1.923607,Sometimes,no,1.798917,no,0.0,0.0,no,Public_Transportation,Obesity_Type_I +1284,Female,28.770039,1.580858,79.713492,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.005405,0.0,no,Public_Transportation,Obesity_Type_I +1285,Female,42.337283,1.64639,86.639861,yes,yes,2.81646,2.27374,Sometimes,no,1.722063,no,0.0,0.0,no,Automobile,Obesity_Type_I +1286,Female,47.283374,1.643786,81.978743,yes,yes,2.037585,1.418985,Sometimes,no,1.827351,no,0.0,0.0,no,Automobile,Obesity_Type_I +1287,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 +1288,Female,22.836315,1.604893,82.0,yes,yes,1.00876,1.0,Sometimes,no,2.0,no,0.0,0.585912,Sometimes,Public_Transportation,Obesity_Type_I +1289,Male,23.0,1.654961,94.790579,yes,yes,2.0,3.0,Sometimes,no,1.490613,no,0.654316,1.0,no,Public_Transportation,Obesity_Type_I +1290,Male,22.975526,1.701986,95.0,yes,yes,2.0,3.0,Sometimes,no,2.02127,no,1.814869,1.066603,no,Public_Transportation,Obesity_Type_I +1291,Male,22.720449,1.65,89.139209,yes,yes,2.103335,2.964024,Sometimes,no,3.0,no,0.632947,1.0,no,Public_Transportation,Obesity_Type_I +1292,Male,23.887569,1.657995,90.0,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.603638,0.969085,no,Public_Transportation,Obesity_Type_I +1293,Female,23.0,1.634688,82.628,yes,yes,2.060922,2.933409,Sometimes,no,2.235282,no,0.380633,0.0026,no,Public_Transportation,Obesity_Type_I +1294,Female,23.0,1.628168,84.49798,yes,yes,2.058687,2.962004,Sometimes,no,2.010596,no,0.851059,0.630866,no,Public_Transportation,Obesity_Type_I +1295,Female,38.148845,1.557808,79.661693,yes,yes,2.0,3.0,Sometimes,no,1.274774,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1296,Female,37.96543,1.560199,80.0,yes,yes,2.533605,1.271624,Sometimes,no,1.893691,no,1.296535,0.0,Sometimes,Automobile,Obesity_Type_I +1297,Male,22.771612,1.750384,95.324282,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I +1298,Male,22.582371,1.753081,95.269089,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I +1299,Female,21.008051,1.65,88.126544,yes,yes,2.457547,1.00061,Sometimes,no,3.0,no,1.361533,1.0,no,Public_Transportation,Obesity_Type_I +1300,Female,22.591026,1.650012,87.676154,yes,yes,2.983851,1.068443,Sometimes,no,2.854161,no,1.103209,1.0,no,Public_Transportation,Obesity_Type_I +1301,Female,16.129279,1.65,85.583485,yes,yes,2.954996,3.0,Sometimes,no,1.0,no,2.091862,1.06053,no,Public_Transportation,Obesity_Type_I +1302,Female,16.913841,1.634832,85.803809,yes,yes,2.061969,3.0,Sometimes,no,1.229171,no,2.392047,1.256119,no,Public_Transportation,Obesity_Type_I +1303,Male,29.43879,1.770126,108.602715,yes,yes,3.0,3.0,Sometimes,no,1.163264,no,1.866023,1.604608,Sometimes,Automobile,Obesity_Type_I +1304,Male,29.881301,1.758189,108.721893,yes,yes,3.0,3.0,Sometimes,no,1.271178,no,1.944728,0.0,Sometimes,Automobile,Obesity_Type_I +1305,Female,43.591999,1.595165,77.354744,yes,yes,2.0,3.0,Sometimes,no,2.117346,no,0.0,0.0,no,Automobile,Obesity_Type_I +1306,Female,40.993179,1.567756,79.493626,yes,yes,2.0,3.0,Sometimes,no,2.952506,no,0.0,0.0,no,Automobile,Obesity_Type_I +1307,Male,22.936098,1.702825,93.638318,yes,yes,2.0,3.0,Sometimes,no,1.746197,no,0.455216,0.335158,no,Public_Transportation,Obesity_Type_I +1308,Male,23.0,1.735659,93.429205,yes,yes,2.0,3.0,Sometimes,no,1.249307,no,0.973465,1.0,no,Public_Transportation,Obesity_Type_I +1309,Female,37.597953,1.62901,80.0,yes,yes,2.450784,2.952821,Sometimes,no,1.335192,no,0.180276,0.0,Sometimes,Automobile,Obesity_Type_I +1310,Female,37.532066,1.615385,80.0,yes,yes,2.972426,3.0,Sometimes,no,1.636326,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1311,Male,31.630054,1.671705,90.0,yes,yes,2.467002,1.851088,Sometimes,no,2.104054,no,1.991565,0.0,Sometimes,Automobile,Obesity_Type_I +1312,Male,31.641081,1.676595,89.993812,yes,yes,2.934671,2.119682,Sometimes,no,2.041462,no,0.578074,0.0,Sometimes,Automobile,Obesity_Type_I +1313,Male,21.872484,1.699998,95.564428,yes,yes,2.0,2.970675,Sometimes,no,2.0,no,0.0,0.169294,no,Public_Transportation,Obesity_Type_I +1314,Male,21.834894,1.722785,94.094184,yes,yes,2.0,2.966803,Sometimes,no,2.0,no,0.0,1.281541,no,Public_Transportation,Obesity_Type_I +1315,Male,36.023972,1.670667,90.575934,yes,yes,2.903545,1.508685,Sometimes,no,2.450069,no,1.45473,0.0,Sometimes,Automobile,Obesity_Type_I +1316,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 +1317,Male,24.184891,1.768834,97.449743,yes,yes,2.0,3.0,Sometimes,no,2.973729,no,2.491642,1.36595,no,Public_Transportation,Obesity_Type_I +1318,Male,23.237302,1.761008,97.829344,yes,yes,2.0,3.0,Sometimes,no,2.988771,no,2.429923,1.978043,no,Public_Transportation,Obesity_Type_I +1319,Male,35.322112,1.78,102.265955,yes,yes,2.787589,1.114564,Sometimes,no,3.0,no,2.710338,0.572185,Sometimes,Automobile,Obesity_Type_I +1320,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 +1321,Female,23.0,1.608469,82.954796,yes,yes,2.150054,2.988539,Sometimes,no,2.768325,no,2.08515,0.0,no,Public_Transportation,Obesity_Type_I +1322,Female,23.0,1.630357,83.1011,yes,yes,2.977585,1.630506,Sometimes,no,2.839319,no,1.091474,0.889517,no,Public_Transportation,Obesity_Type_I +1323,Female,22.679454,1.62826,82.967937,yes,yes,2.274491,1.0,Sometimes,no,2.2695,no,0.946461,1.73107,Sometimes,Public_Transportation,Obesity_Type_I +1324,Female,22.480889,1.605662,82.470375,yes,yes,1.557287,1.0,Sometimes,no,2.371015,no,0.288032,2.0,Sometimes,Public_Transportation,Obesity_Type_I +1325,Female,41.403862,1.567973,81.056851,yes,yes,2.152264,2.977999,Sometimes,no,1.513199,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1326,Female,40.702771,1.548403,80.0,yes,yes,2.0,3.0,Sometimes,no,1.326165,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1327,Male,29.389239,1.681855,90.0,yes,yes,2.08841,2.644692,Sometimes,no,2.773236,no,1.252472,0.0,Sometimes,Automobile,Obesity_Type_I +1328,Male,30.967417,1.688436,90.0,yes,yes,2.180047,2.733077,Sometimes,no,2.77962,no,1.454129,0.0,Sometimes,Automobile,Obesity_Type_I +1329,Female,23.474165,1.577115,78.0,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.876101,0.0,no,Public_Transportation,Obesity_Type_I +1330,Female,24.317607,1.586751,79.109029,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.852446,0.0,no,Public_Transportation,Obesity_Type_I +1331,Male,17.052914,1.710616,99.860254,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.067491,0.894105,no,Public_Transportation,Obesity_Type_I +1332,Male,18.04892,1.721384,99.430612,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.579431,0.839862,no,Public_Transportation,Obesity_Type_I +1333,Female,22.875223,1.624367,82.0,yes,yes,1.826885,1.0,Sometimes,no,2.0,no,0.0,0.459274,no,Public_Transportation,Obesity_Type_I +1334,Female,22.884722,1.622787,82.0,yes,yes,1.754401,1.0,Sometimes,no,2.0,no,0.0,0.301909,no,Public_Transportation,Obesity_Type_I +1335,Male,18.729566,1.855433,112.875283,yes,yes,2.0,2.427137,Sometimes,no,2.574073,no,1.0,2.0,Sometimes,Public_Transportation,Obesity_Type_I +1336,Male,21.011124,1.856315,118.183797,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.788585,1.220029,Sometimes,Public_Transportation,Obesity_Type_I +1337,Female,18.603496,1.681719,90.671871,yes,yes,1.524428,3.0,Sometimes,no,1.383831,no,0.130417,1.0,Sometimes,Public_Transportation,Obesity_Type_I +1338,Female,21.106056,1.722884,92.949254,yes,yes,1.164062,3.0,Sometimes,no,1.157395,no,0.0,0.320851,Sometimes,Public_Transportation,Obesity_Type_I +1339,Male,23.0,1.799406,106.528811,yes,yes,2.123159,3.0,Sometimes,no,2.415343,no,0.295178,0.569852,Sometimes,Public_Transportation,Obesity_Type_I +1340,Male,21.980847,1.819875,106.048516,yes,yes,2.0,3.0,Sometimes,no,2.745242,no,0.0,0.80898,Sometimes,Public_Transportation,Obesity_Type_I +1341,Male,21.016665,1.790172,105.042194,yes,yes,2.259679,3.0,Sometimes,no,1.026143,no,0.268801,0.077818,Sometimes,Public_Transportation,Obesity_Type_I +1342,Male,21.72738,1.783782,105.000276,yes,yes,2.044326,3.0,Sometimes,no,2.35818,no,0.349371,0.0,Sometimes,Public_Transportation,Obesity_Type_I +1343,Male,18.0,1.783906,109.207614,yes,yes,2.0,1.867836,Sometimes,no,2.438398,no,1.0,0.802305,no,Public_Transportation,Obesity_Type_I +1344,Male,18.0,1.844218,109.195529,yes,yes,2.0,1.548407,Sometimes,no,2.191401,no,1.0,1.676944,no,Public_Transportation,Obesity_Type_I +1345,Female,19.431662,1.631662,82.0,yes,yes,2.843456,2.937989,Sometimes,no,1.001995,no,0.0,1.554404,no,Public_Transportation,Obesity_Type_I +1346,Female,18.166318,1.649553,82.323954,yes,yes,2.864776,3.0,Sometimes,no,1.876915,no,0.631565,0.186414,no,Public_Transportation,Obesity_Type_I +1347,Male,22.352025,1.754711,105.000706,yes,yes,2.871137,3.0,Sometimes,no,2.627569,no,1.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I +1348,Male,22.469351,1.762515,105.000097,yes,yes,2.282803,3.0,Sometimes,no,2.104264,no,1.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I +1349,Male,19.524698,1.942725,121.657979,yes,yes,2.0,3.0,Sometimes,no,3.0,no,1.0,1.014808,Sometimes,Public_Transportation,Obesity_Type_I +1350,Male,20.491475,1.975663,120.702935,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.767013,1.0,Sometimes,Public_Transportation,Obesity_Type_I +1351,Female,39.213399,1.586301,80.0,yes,yes,2.020502,1.237454,Sometimes,no,1.93142,no,1.967973,0.0,Sometimes,Automobile,Obesity_Type_I +1352,Female,38.098745,1.598448,80.0,yes,yes,2.020785,1.169173,Sometimes,no,1.972551,no,1.903338,0.0,Sometimes,Automobile,Obesity_Type_I +1353,Male,19.034033,1.703098,98.296651,yes,yes,2.0,2.391753,Sometimes,no,2.0,no,0.0,1.119877,no,Public_Transportation,Obesity_Type_I +1354,Male,17.073648,1.706996,99.544696,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.73209,0.048617,no,Public_Transportation,Obesity_Type_I +1355,Female,38.547267,1.526472,75.150345,yes,yes,2.0,3.0,Sometimes,no,1.084442,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1356,Female,38.683794,1.501993,76.642944,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1357,Male,18.0,1.784402,108.413119,yes,yes,2.0,2.475444,Sometimes,no,2.655795,no,1.0,0.169879,no,Public_Transportation,Obesity_Type_I +1358,Male,18.0,1.792687,108.204547,yes,yes,2.0,2.478794,Sometimes,no,2.967064,no,1.0,0.4346,no,Public_Transportation,Obesity_Type_I +1359,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 +1360,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 +1361,Female,18.0,1.686904,90.004046,yes,yes,2.737149,3.0,Sometimes,no,1.997195,no,1.352663,0.468483,Sometimes,Public_Transportation,Obesity_Type_I +1362,Female,18.107092,1.703259,91.499683,yes,yes,1.899793,3.0,Sometimes,no,1.439962,no,0.9529,0.838847,Sometimes,Public_Transportation,Obesity_Type_I +1363,Male,31.335093,1.665798,89.738596,yes,yes,2.274164,1.049534,Sometimes,no,1.358172,no,1.482411,0.0,Sometimes,Automobile,Obesity_Type_I +1364,Male,29.0,1.66671,89.048151,yes,yes,1.897796,3.0,Sometimes,no,1.193039,no,0.011519,0.0,Sometimes,Automobile,Obesity_Type_I +1365,Female,18.011903,1.65,84.210535,yes,yes,2.85916,3.0,Sometimes,no,1.0,no,1.020525,0.786066,no,Public_Transportation,Obesity_Type_I +1366,Female,18.0,1.649439,84.897738,yes,yes,2.0,3.0,Sometimes,no,1.039313,no,1.0,0.0,no,Public_Transportation,Obesity_Type_I +1367,Male,21.077356,1.702002,97.971598,yes,yes,2.0,1.66338,Sometimes,no,2.0,no,0.0,1.865851,no,Public_Transportation,Obesity_Type_I +1368,Male,21.052016,1.69382,99.530971,yes,yes,2.0,1.672751,Sometimes,no,2.0,no,0.0,1.946173,no,Public_Transportation,Obesity_Type_I +1369,Female,18.078256,1.622999,82.403076,yes,yes,2.340405,3.0,Sometimes,no,1.655245,no,1.006884,0.548539,no,Public_Transportation,Obesity_Type_I +1370,Female,18.063582,1.633675,82.459577,yes,yes,2.921576,3.0,Sometimes,no,1.003636,no,1.000227,0.905596,no,Public_Transportation,Obesity_Type_I +1371,Male,20.418832,1.836669,105.257543,yes,yes,2.0,3.0,Sometimes,no,2.793505,no,2.21939,0.202902,no,Public_Transportation,Obesity_Type_I +1372,Male,20.580984,1.798354,103.705418,yes,yes,2.0,3.0,Sometimes,no,2.401315,no,2.891986,0.0,no,Public_Transportation,Obesity_Type_I +1373,Male,26.032416,1.824432,105.131956,yes,yes,3.0,3.0,Sometimes,no,2.110611,no,1.890907,1.58483,Sometimes,Public_Transportation,Obesity_Type_I +1374,Male,25.955361,1.830384,105.479313,yes,yes,3.0,3.0,Sometimes,no,2.480277,no,1.796779,1.596562,Sometimes,Public_Transportation,Obesity_Type_I +1375,Male,26.015448,1.829907,105.436173,yes,yes,3.0,3.0,Sometimes,no,2.224914,no,1.781589,1.59405,Sometimes,Public_Transportation,Obesity_Type_I +1376,Male,25.920738,1.823755,105.800158,yes,yes,2.92711,3.0,Sometimes,no,2.151496,no,1.166064,1.509831,Sometimes,Public_Transportation,Obesity_Type_I +1377,Male,29.633715,1.834842,105.19936,yes,yes,2.805533,3.0,Sometimes,no,1.882847,no,2.0,0.70778,Sometimes,Automobile,Obesity_Type_I +1378,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 +1379,Male,38.748307,1.782067,103.586342,yes,yes,2.845961,1.92822,Sometimes,no,2.642273,no,2.999918,1.0,Frequently,Automobile,Obesity_Type_I +1380,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 +1381,Female,33.226808,1.557943,77.647716,yes,yes,2.02091,2.093831,Sometimes,no,1.506518,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1382,Female,26.220065,1.560258,78.435904,yes,yes,2.108163,1.231915,Sometimes,no,1.808127,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1383,Female,23.090215,1.596586,80.726195,yes,yes,1.518966,1.0,Sometimes,no,2.0,no,0.0,1.094046,no,Public_Transportation,Obesity_Type_I +1384,Female,24.565628,1.62093,81.718232,yes,yes,2.260543,1.374791,Sometimes,no,1.947935,no,0.0,0.078607,no,Public_Transportation,Obesity_Type_I +1385,Female,42.586285,1.571417,81.918809,yes,yes,2.522183,1.326982,Sometimes,no,1.872673,no,1.048507,0.0,no,Automobile,Obesity_Type_I +1386,Female,43.719395,1.584322,80.986496,yes,yes,2.186322,2.900915,Sometimes,no,2.471033,no,0.256113,0.0,no,Automobile,Obesity_Type_I +1387,Female,43.37634,1.582523,81.919454,yes,yes,2.76632,1.317884,Sometimes,no,1.889341,no,0.990642,0.0,no,Automobile,Obesity_Type_I +1388,Female,39.648946,1.572791,80.086524,yes,yes,2.071622,2.977909,Sometimes,no,1.468297,no,0.0,0.0,no,Automobile,Obesity_Type_I +1389,Female,22.693989,1.627908,82.0,yes,yes,1.918251,1.355752,Sometimes,no,1.998108,no,0.0,1.382906,Sometimes,Public_Transportation,Obesity_Type_I +1390,Female,22.676243,1.620938,82.283185,yes,yes,1.967061,1.0,Sometimes,no,2.548651,no,0.249264,1.516731,Sometimes,Public_Transportation,Obesity_Type_I +1391,Female,22.804818,1.613119,82.636162,yes,yes,2.964419,1.0,Sometimes,no,2.977516,no,0.742113,2.0,Sometimes,Public_Transportation,Obesity_Type_I +1392,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 +1393,Male,23.096353,1.728183,97.959899,yes,yes,2.0,2.986172,Sometimes,no,2.364208,no,2.492402,1.632506,no,Public_Transportation,Obesity_Type_I +1394,Male,22.815416,1.732694,98.44113,yes,yes,2.0,2.993623,Sometimes,no,2.326635,no,2.236586,1.529423,no,Public_Transportation,Obesity_Type_I +1395,Female,21.02064,1.65,88.129437,yes,yes,2.870152,1.001383,Sometimes,no,3.0,no,1.322558,1.0,no,Public_Transportation,Obesity_Type_I +1396,Female,21.001458,1.65,88.026943,yes,yes,2.482575,1.001542,Sometimes,no,3.0,no,1.751656,1.0,no,Public_Transportation,Obesity_Type_I +1397,Female,22.87795,1.66913,86.002736,yes,yes,2.290095,2.590283,Sometimes,no,2.506296,no,0.312254,1.0,no,Public_Transportation,Obesity_Type_I +1398,Female,22.847618,1.669136,85.568385,yes,yes,2.074843,1.773916,Sometimes,no,2.428271,no,0.035091,1.0,no,Public_Transportation,Obesity_Type_I +1399,Female,23.0,1.611058,84.191125,yes,yes,2.14128,2.989112,Sometimes,no,2.519841,no,2.11137,0.013483,no,Public_Transportation,Obesity_Type_I +1400,Female,23.0,1.649736,84.134712,yes,yes,2.334474,1.496776,Sometimes,no,2.776724,no,0.782416,0.167728,no,Public_Transportation,Obesity_Type_I +1401,Female,39.129291,1.532643,77.550345,yes,yes,2.0,3.0,Sometimes,no,1.186996,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1402,Female,37.441044,1.524293,76.202761,yes,yes,2.0,2.994046,Sometimes,no,1.603549,no,1.335857,0.0,Sometimes,Automobile,Obesity_Type_I +1403,Male,22.969366,1.701634,95.0,yes,yes,2.0,3.0,Sometimes,no,2.133469,no,1.030199,1.082838,no,Public_Transportation,Obesity_Type_I +1404,Male,22.735328,1.723921,94.743892,yes,yes,2.0,3.0,Sometimes,no,2.183149,no,1.932386,0.212767,no,Public_Transportation,Obesity_Type_I +1405,Male,23.32471,1.769484,96.078462,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I +1406,Male,23.668137,1.777416,97.842406,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I +1407,Female,21.895468,1.64456,88.119139,yes,yes,2.693859,1.000283,Sometimes,no,3.0,no,1.201403,1.612432,no,Public_Transportation,Obesity_Type_I +1408,Female,21.3928,1.641149,88.079177,yes,yes,2.607747,1.000414,Sometimes,no,3.0,no,1.269169,1.460564,no,Public_Transportation,Obesity_Type_I +1409,Female,18.178076,1.642892,82.144405,yes,yes,2.66889,3.0,Sometimes,no,1.172593,no,0.886448,1.252677,no,Public_Transportation,Obesity_Type_I +1410,Female,18.907514,1.65,82.01831,yes,yes,3.0,3.0,Sometimes,no,1.0,no,0.215187,1.195929,no,Public_Transportation,Obesity_Type_I +1411,Male,26.011928,1.777251,106.452367,yes,yes,3.0,3.0,Sometimes,no,1.911379,no,1.911096,1.032392,Sometimes,Public_Transportation,Obesity_Type_I +1412,Male,25.696736,1.814696,107.55963,yes,yes,2.921225,3.0,Sometimes,no,2.869439,no,0.181753,0.133523,Sometimes,Public_Transportation,Obesity_Type_I +1413,Female,40.466313,1.559005,77.601483,yes,yes,2.0,3.0,Sometimes,no,1.572371,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1414,Female,38.445148,1.556028,77.684229,yes,yes,2.0,3.0,Sometimes,no,1.440629,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1415,Male,24.473062,1.653751,91.204753,yes,yes,1.492834,3.0,Sometimes,no,1.005367,no,0.922739,0.733221,no,Automobile,Obesity_Type_I +1416,Male,23.479181,1.680171,91.068054,yes,yes,1.220024,3.0,Sometimes,no,1.046254,no,0.520989,0.61599,no,Automobile,Obesity_Type_I +1417,Female,38.397463,1.552648,80.0,yes,yes,2.667676,1.135278,Sometimes,no,1.399629,no,1.648883,0.0,Sometimes,Automobile,Obesity_Type_I +1418,Female,37.974483,1.560215,80.0,yes,yes,2.569075,2.463113,Sometimes,no,1.567366,no,0.359134,0.0,Sometimes,Automobile,Obesity_Type_I +1419,Male,31.783524,1.672959,90.0,yes,yes,2.949242,1.782109,Sometimes,no,2.210997,no,1.992719,0.0,Sometimes,Automobile,Obesity_Type_I +1420,Male,29.681308,1.680058,89.994351,yes,yes,2.104772,2.376374,Sometimes,no,2.218691,no,1.52184,0.0,Sometimes,Automobile,Obesity_Type_I +1421,Male,21.587743,1.664752,94.256547,yes,yes,2.0,2.976098,Sometimes,no,2.0,no,0.0,1.39322,no,Public_Transportation,Obesity_Type_I +1422,Male,21.746113,1.668553,94.454394,yes,yes,2.0,2.968098,Sometimes,no,2.0,no,0.0,1.292476,no,Public_Transportation,Obesity_Type_I +1423,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 +1424,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 +1425,Male,22.927011,1.751046,95.285898,yes,yes,2.0,3.0,Sometimes,no,2.530301,no,2.89118,1.287117,no,Public_Transportation,Obesity_Type_I +1426,Male,23.329344,1.751365,95.290429,yes,yes,2.0,3.0,Sometimes,no,3.0,no,3.0,2.0,no,Public_Transportation,Obesity_Type_I +1427,Male,22.126325,1.717262,98.579156,yes,yes,2.0,2.984523,Sometimes,no,2.263551,no,2.819661,2.0,no,Public_Transportation,Obesity_Type_I +1428,Male,21.679158,1.741393,98.182498,yes,yes,2.0,2.978103,Sometimes,no,2.668261,no,2.462269,1.402414,no,Public_Transportation,Obesity_Type_I +1429,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 +1430,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 +1431,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 +1432,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 +1433,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 +1434,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 +1435,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 +1436,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 +1437,Female,38.895069,1.549257,80.0,yes,yes,2.736628,1.130751,Sometimes,no,1.385175,no,1.666965,0.0,Sometimes,Automobile,Obesity_Type_I +1438,Female,40.789529,1.549748,80.0,yes,yes,2.0,1.099151,Sometimes,no,1.687611,no,1.874662,0.0,Sometimes,Automobile,Obesity_Type_I +1439,Female,40.654155,1.52906,79.760922,yes,yes,2.0,3.0,Sometimes,no,1.0,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1440,Female,38.542937,1.517248,79.843221,yes,yes,2.0,3.0,Sometimes,no,1.095134,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1441,Male,25.95383,1.65731,90.0,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.413643,0.920858,Sometimes,Automobile,Obesity_Type_I +1442,Male,26.826961,1.673287,90.0,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.884935,0.12228,Sometimes,Automobile,Obesity_Type_I +1443,Female,23.803904,1.581527,78.089575,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.057758,0.0,no,Public_Transportation,Obesity_Type_I +1444,Female,23.652435,1.562724,80.535698,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.389717,0.0,no,Public_Transportation,Obesity_Type_I +1445,Male,20.677105,1.781251,102.950929,yes,yes,2.0,3.0,Sometimes,no,1.618189,no,0.571648,0.0,no,Public_Transportation,Obesity_Type_I +1446,Male,21.797388,1.775584,103.605896,yes,yes,2.121909,3.0,Sometimes,no,2.500556,no,1.20758,0.0,no,Public_Transportation,Obesity_Type_I +1447,Male,22.719385,1.746645,96.875502,yes,yes,2.0,3.0,Sometimes,no,2.714091,no,2.64102,1.224737,no,Public_Transportation,Obesity_Type_I +1448,Male,22.177099,1.741353,96.738014,yes,yes,2.0,3.0,Sometimes,no,2.697661,no,2.614909,1.229474,no,Public_Transportation,Obesity_Type_I +1449,Female,23.099906,1.571812,78.997166,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.402614,0.0,no,Public_Transportation,Obesity_Type_I +1450,Female,24.17851,1.589027,80.553067,yes,yes,2.0,1.0,Sometimes,no,2.0,no,0.011477,0.0,no,Public_Transportation,Obesity_Type_I +1451,Male,20.924808,1.803245,105.686091,yes,yes,2.0,3.0,Sometimes,no,2.290368,no,0.001086,0.544128,Sometimes,Public_Transportation,Obesity_Type_I +1452,Male,20.975973,1.792646,105.619143,yes,yes,2.0,3.0,Sometimes,no,1.025275,no,0.00203,0.175587,Sometimes,Public_Transportation,Obesity_Type_I +1453,Male,21.856301,1.87199,115.627554,yes,yes,2.0,2.358455,Sometimes,no,2.515765,no,0.98232,1.157466,Sometimes,Public_Transportation,Obesity_Type_I +1454,Male,18.140751,1.859056,111.235188,yes,yes,2.0,1.706551,Sometimes,no,2.039514,no,1.0,2.0,Sometimes,Public_Transportation,Obesity_Type_I +1455,Female,18.0,1.692242,90.019501,yes,yes,2.642744,3.0,Sometimes,no,1.904054,no,1.645654,0.363549,Sometimes,Public_Transportation,Obesity_Type_I +1456,Female,18.0,1.683,90.924208,yes,yes,2.387426,3.0,Sometimes,no,1.77962,no,0.743005,0.920162,Sometimes,Public_Transportation,Obesity_Type_I +1457,Female,20.206358,1.738397,93.890682,yes,yes,1.996638,3.0,Sometimes,no,1.033199,no,0.584793,0.518297,Sometimes,Public_Transportation,Obesity_Type_I +1458,Female,18.152876,1.694969,92.508122,yes,yes,2.107854,3.0,Sometimes,no,1.383894,no,1.030526,0.831412,Sometimes,Public_Transportation,Obesity_Type_I +1459,Male,22.336216,1.785718,105.055686,yes,yes,2.253707,3.0,Sometimes,no,2.524432,no,0.58216,0.175694,Sometimes,Public_Transportation,Obesity_Type_I +1460,Male,23.0,1.7425,105.028665,yes,yes,2.393837,3.0,Sometimes,no,2.01499,no,0.978815,0.41322,Sometimes,Public_Transportation,Obesity_Type_I +1461,Male,22.088059,1.803132,106.329569,yes,yes,2.348745,3.0,Sometimes,no,2.096751,no,0.544564,0.67688,Sometimes,Public_Transportation,Obesity_Type_I +1462,Male,23.0,1.774644,105.966894,yes,yes,2.312825,3.0,Sometimes,no,2.073497,no,0.614466,0.579541,Sometimes,Public_Transportation,Obesity_Type_I +1463,Male,20.993067,1.782269,104.97003,yes,yes,2.0,3.0,Sometimes,no,1.234943,no,0.008368,0.0,Sometimes,Public_Transportation,Obesity_Type_I +1464,Male,20.654752,1.780791,102.921218,yes,yes,2.0,3.0,Sometimes,no,1.613829,no,1.251665,0.0,Sometimes,Public_Transportation,Obesity_Type_I +1465,Male,21.624552,1.790151,106.320686,yes,yes,2.490776,3.0,Sometimes,no,2.654517,no,0.112454,0.756339,Sometimes,Public_Transportation,Obesity_Type_I +1466,Male,21.682636,1.818641,105.496975,yes,yes,2.078082,3.0,Sometimes,no,2.895614,no,0.06275,0.261793,Sometimes,Public_Transportation,Obesity_Type_I +1467,Male,18.0,1.806827,108.829395,yes,yes,2.0,1.24884,Sometimes,no,2.416213,no,1.0,1.546738,no,Public_Transportation,Obesity_Type_I +1468,Male,18.0,1.811189,108.800964,yes,yes,2.0,1.250548,Sometimes,no,2.121176,no,1.0,0.741069,no,Public_Transportation,Obesity_Type_I +1469,Male,18.0,1.811738,108.897324,yes,yes,2.0,1.202179,Sometimes,no,2.36293,no,1.0,1.47574,no,Public_Transportation,Obesity_Type_I +1470,Male,18.0,1.799779,108.934574,yes,yes,2.0,1.194815,Sometimes,no,2.434832,no,1.0,1.541455,no,Public_Transportation,Obesity_Type_I +1471,Female,19.374207,1.632605,82.0,yes,yes,2.805512,2.880817,Sometimes,no,1.001307,no,0.0,1.376597,Sometimes,Public_Transportation,Obesity_Type_I +1472,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 +1473,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 +1474,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 +1475,Male,22.200779,1.769328,105.000576,yes,yes,2.685484,3.0,Sometimes,no,2.649459,no,1.0,0.0,Sometimes,Public_Transportation,Obesity_Type_I +1476,Male,21.688557,1.807029,105.696358,yes,yes,2.055209,3.0,Sometimes,no,2.885852,no,0.903369,0.14451,Sometimes,Public_Transportation,Obesity_Type_I +1477,Male,20.803186,1.882533,117.468516,yes,yes,2.0,2.18162,Sometimes,no,2.325091,no,0.891444,1.223661,Sometimes,Public_Transportation,Obesity_Type_I +1478,Male,19.515324,1.879144,112.932984,yes,yes,2.0,2.152733,Sometimes,no,2.53369,no,0.917563,1.285838,Sometimes,Public_Transportation,Obesity_Type_I +1479,Male,21.962219,1.931263,118.20313,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.743593,1.0,Sometimes,Public_Transportation,Obesity_Type_I +1480,Male,20.534089,1.861358,115.39792,yes,yes,2.0,3.0,Sometimes,no,3.0,no,0.987591,1.914531,Sometimes,Public_Transportation,Obesity_Type_I +1481,Female,39.29266,1.567131,80.0,yes,yes,2.025479,1.046144,Sometimes,no,1.941224,no,1.983265,0.0,Sometimes,Automobile,Obesity_Type_I +1482,Female,37.872971,1.565366,80.0,yes,yes,2.002564,1.974233,Sometimes,no,1.935398,no,1.649299,0.0,Sometimes,Automobile,Obesity_Type_I +1483,Female,37.613378,1.516007,77.033049,yes,yes,2.0,2.880794,Sometimes,no,1.61837,no,0.120165,0.0,Sometimes,Automobile,Obesity_Type_I +1484,Female,37.471683,1.549812,76.082517,yes,yes,2.0,2.765213,Sometimes,no,1.551266,no,1.350001,0.0,Sometimes,Automobile,Obesity_Type_I +1485,Male,18.611197,1.714143,96.568814,yes,yes,2.0,2.967089,Sometimes,no,2.0,no,0.0,0.467048,no,Public_Transportation,Obesity_Type_I +1486,Male,17.412629,1.723191,97.350366,yes,yes,2.0,3.0,Sometimes,no,2.0,no,0.0,0.931721,no,Public_Transportation,Obesity_Type_I +1487,Female,39.12631,1.562889,76.65949,yes,yes,2.0,3.0,Sometimes,no,1.440526,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1488,Female,37.063599,1.502609,75.279605,yes,yes,2.0,3.0,Sometimes,no,1.759803,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1489,Female,41.318302,1.544937,77.053948,yes,yes,2.0,3.0,Sometimes,no,2.090413,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1490,Female,43.726081,1.592316,77.00103,yes,yes,2.0,3.0,Sometimes,no,2.806922,no,0.0,0.0,Sometimes,Automobile,Obesity_Type_I +1491,Male,18.0,1.787733,108.019211,yes,yes,2.0,2.37985,Sometimes,no,2.616138,no,1.0,0.652485,no,Public_Transportation,Obesity_Type_I +1492,Male,18.0,1.782722,108.044313,yes,yes,2.0,2.655265,Sometimes,no,2.297896,no,1.0,0.552665,no,Public_Transportation,Obesity_Type_I +1493,Male,18.0,1.797523,108.316462,yes,yes,2.0,1.941307,Sometimes,no,2.708168,no,1.0,1.425852,no,Public_Transportation,Obesity_Type_I +1494,Male,18.0,1.803527,108.251044,yes,yes,2.0,1.709546,Sometimes,no,2.530157,no,1.0,0.6454,no,Public_Transportation,Obesity_Type_I +1495,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 +1496,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 +1497,Female,18.0,1.692913,89.93889,yes,yes,2.818502,3.0,Sometimes,no,1.991251,no,1.492967,0.0,no,Public_Transportation,Obesity_Type_I +1498,Female,18.0,1.668555,85.607466,yes,yes,2.081238,3.0,Sometimes,no,1.18363,no,1.17177,0.0,no,Public_Transportation,Obesity_Type_I +1499,Male,23.246702,1.652971,93.310284,yes,yes,1.368978,3.0,Sometimes,no,1.953152,no,0.104707,0.22183,no,Automobile,Obesity_Type_I +1500,Male,24.481032,1.661277,90.744965,yes,yes,1.712848,3.0,Sometimes,no,1.162153,no,0.458981,0.885532,no,Automobile,Obesity_Type_I +1501,Male,29.0,1.641784,89.424947,yes,yes,1.3307,3.0,Sometimes,no,1.880967,no,0.322013,0.0,Sometimes,Automobile,Obesity_Type_I +1502,Male,27.968765,1.673767,89.995034,yes,yes,1.961069,3.0,Sometimes,no,2.652327,no,0.56931,0.319008,Sometimes,Automobile,Obesity_Type_I +1503,Female,18.024744,1.617192,83.121811,yes,yes,2.976509,3.0,Sometimes,no,1.022705,no,1.02569,0.313016,no,Public_Transportation,Obesity_Type_I +1504,Female,18.10682,1.602129,82.412665,yes,yes,2.319648,3.0,Sometimes,no,1.107164,no,0.692123,0.30402,no,Public_Transportation,Obesity_Type_I +1505,Male,21.379676,1.701413,96.710735,yes,yes,2.0,2.961192,Sometimes,no,2.623344,no,0.981686,1.35537,no,Public_Transportation,Obesity_Type_I +1506,Male,21.353237,1.709731,95.032177,yes,yes,2.0,2.973476,Sometimes,no,2.0,no,0.0,1.331527,no,Public_Transportation,Obesity_Type_I +1507,Male,20.338815,1.698829,98.572753,yes,yes,2.0,2.392811,Sometimes,no,2.0,no,0.0,1.80931,no,Public_Transportation,Obesity_Type_I +1508,Male,20.720639,1.705304,99.873716,yes,yes,2.0,1.293342,Sometimes,no,2.0,no,0.0,1.917679,no,Public_Transportation,Obesity_Type_I +1509,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 +1510,Female,18.945961,1.605469,82.039,yes,yes,2.76533,3.0,Sometimes,no,1.048584,no,0.192559,0.720411,no,Public_Transportation,Obesity_Type_I +1511,Male,18.88061,1.80416,104.40682,yes,yes,2.0,3.0,Sometimes,no,3.0,no,2.2405,0.0,no,Public_Transportation,Obesity_Type_I +1512,Male,19.850524,1.785062,104.187314,yes,yes,2.0,3.0,Sometimes,no,2.15157,no,1.605983,0.0,no,Public_Transportation,Obesity_Type_I +1513,Male,30.200946,1.755926,112.289883,yes,yes,2.317734,3.0,Sometimes,no,2.152736,no,0.0,1.108145,no,Automobile,Obesity_Type_II +1514,Male,25.314589,1.787802,115.127662,yes,yes,1.588114,3.0,Sometimes,no,2.115967,no,1.541072,0.0,no,Automobile,Obesity_Type_II +1515,Male,37.186795,1.704877,107.94747,yes,yes,2.549782,3.985442,Sometimes,no,1.0,no,1.976582,0.0,no,Public_Transportation,Obesity_Type_II +1516,Male,32.707653,1.695347,102.810704,yes,yes,2.795086,2.129909,Sometimes,no,1.0,no,1.989316,0.0,no,Public_Transportation,Obesity_Type_II +1517,Male,27.562425,1.908608,128.828122,yes,yes,2.206276,3.0,Sometimes,no,1.813082,no,1.94984,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1518,Male,30.421596,1.819296,122.137917,yes,yes,2.0,3.0,Sometimes,no,1.984323,no,0.793262,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1519,Male,26.945139,1.773259,118.154345,yes,no,2.18354,3.0,Sometimes,no,2.277937,no,0.68183,0.0,Sometimes,Automobile,Obesity_Type_II +1520,Male,34.139453,1.774647,120.151967,yes,no,2.962415,3.0,Sometimes,no,2.172649,no,1.097905,0.0,Sometimes,Automobile,Obesity_Type_II +1521,Male,26.225442,1.773664,116.160329,yes,yes,2.442536,3.0,Sometimes,no,2.174371,no,0.0,1.928972,Sometimes,Public_Transportation,Obesity_Type_II +1522,Male,28.363149,1.793476,112.725005,yes,yes,1.99124,3.0,Sometimes,no,2.0,no,0.0,0.292956,Sometimes,Public_Transportation,Obesity_Type_II +1523,Male,27.991467,1.82559,120.860386,yes,yes,3.0,3.0,Sometimes,no,3.0,no,0.691369,1.415536,Sometimes,Public_Transportation,Obesity_Type_II +1524,Male,25.498751,1.885543,121.253083,yes,yes,2.408561,3.0,Sometimes,no,2.851904,no,1.320065,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1525,Male,30.605225,1.75,120.0,yes,yes,2.758394,3.0,Sometimes,yes,2.174248,no,1.079524,1.358163,Sometimes,Automobile,Obesity_Type_II +1526,Male,31.457413,1.87407,128.867444,yes,yes,2.956297,3.0,Sometimes,yes,1.2751,no,0.901924,1.875023,Sometimes,Automobile,Obesity_Type_II +1527,Male,24.82929,1.755967,112.256165,yes,yes,1.369529,3.0,Sometimes,no,2.0,no,1.596576,0.0026,Sometimes,Public_Transportation,Obesity_Type_II +1528,Male,24.739421,1.757069,117.298233,yes,yes,1.392665,3.0,Sometimes,no,2.0,no,1.097983,0.630866,Sometimes,Public_Transportation,Obesity_Type_II +1529,Male,41.0,1.75,116.594351,yes,yes,2.0,2.831771,Sometimes,no,1.725226,no,0.356288,0.0,Sometimes,Automobile,Obesity_Type_II +1530,Male,27.83173,1.704028,101.634313,yes,yes,2.630401,1.0,Sometimes,no,1.0,no,0.245354,0.315261,Sometimes,Automobile,Obesity_Type_II +1531,Male,23.260061,1.849003,121.414744,yes,yes,3.0,2.595126,Sometimes,no,2.054072,no,0.835271,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1532,Male,22.705772,1.841989,122.024954,yes,yes,3.0,2.52751,Sometimes,no,1.645338,no,1.025438,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1533,Male,24.041043,1.613491,98.490766,yes,yes,3.0,2.120936,Sometimes,no,1.0,no,1.543386,0.890213,no,Public_Transportation,Obesity_Type_II +1534,Male,20.013906,1.647821,106.509947,yes,yes,2.869778,1.468948,Sometimes,no,1.656082,no,0.0,1.444033,no,Public_Transportation,Obesity_Type_II +1535,Male,30.02019,1.75834,112.010504,yes,yes,1.868212,3.0,Sometimes,no,2.023328,no,0.0,0.613992,Sometimes,Automobile,Obesity_Type_II +1536,Male,25.15462,1.758398,112.089022,yes,yes,1.108663,3.0,Sometimes,no,2.0,no,1.230219,0.001716,Sometimes,Automobile,Obesity_Type_II +1537,Male,30.870724,1.670774,101.626189,yes,yes,2.907744,3.990925,Sometimes,no,1.0,no,1.99975,0.0,no,Public_Transportation,Obesity_Type_II +1538,Male,28.712995,1.665585,98.66176,yes,yes,2.95801,2.834253,Sometimes,no,1.0,no,1.999886,0.0,no,Public_Transportation,Obesity_Type_II +1539,Male,30.710511,1.914186,129.852531,yes,yes,2.197261,3.0,Sometimes,no,1.11184,no,0.906843,0.976473,Sometimes,Public_Transportation,Obesity_Type_II +1540,Male,29.827481,1.899588,124.269251,yes,yes,2.048962,3.0,Sometimes,no,2.822871,no,1.066169,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1541,Male,31.86893,1.75,118.102897,yes,yes,2.139196,3.0,Sometimes,no,2.04843,no,0.368026,0.360986,Sometimes,Automobile,Obesity_Type_II +1542,Male,36.542885,1.75,119.434645,yes,yes,2.72989,3.0,Sometimes,no,2.030084,no,0.592607,0.754417,Sometimes,Automobile,Obesity_Type_II +1543,Male,21.556361,1.773664,116.160329,yes,yes,2.0,3.0,Sometimes,no,2.0,no,1.399183,1.0,Sometimes,Public_Transportation,Obesity_Type_II +1544,Male,23.963649,1.792998,116.102615,yes,yes,1.994679,3.0,Sometimes,no,2.0,no,0.966617,0.739006,Sometimes,Public_Transportation,Obesity_Type_II +1545,Male,24.145295,1.82559,120.860386,yes,yes,2.403421,3.0,Sometimes,no,2.490613,no,2.0,0.707768,Sometimes,Public_Transportation,Obesity_Type_II +1546,Male,25.2984,1.827279,120.996074,yes,yes,3.0,3.0,Sometimes,no,3.0,no,1.110215,0.396352,Sometimes,Public_Transportation,Obesity_Type_II +1547,Male,31.755387,1.775416,121.204668,yes,yes,2.758394,3.0,Sometimes,no,2.161303,no,0.428173,0.716327,Sometimes,Automobile,Obesity_Type_II +1548,Male,30.62865,1.766975,118.363376,yes,yes,2.964319,3.0,Sometimes,no,2.377257,no,0.614959,1.875023,Sometimes,Automobile,Obesity_Type_II +1549,Male,23.454558,1.754218,117.384745,yes,yes,1.617093,3.0,Sometimes,no,2.0,no,1.818052,0.631825,Sometimes,Public_Transportation,Obesity_Type_II +1550,Male,23.432227,1.754996,119.087557,yes,yes,1.631144,3.0,Sometimes,no,2.0,no,1.593183,0.86374,Sometimes,Public_Transportation,Obesity_Type_II +1551,Male,40.50021,1.748103,108.308112,yes,yes,2.317459,3.734323,Sometimes,no,1.0,no,1.628637,0.0,no,Automobile,Obesity_Type_II +1552,Male,34.576714,1.73325,103.669116,yes,yes,2.501224,2.049565,Sometimes,no,1.0,no,1.746583,0.0,no,Automobile,Obesity_Type_II +1553,Male,22.276969,1.84995,121.786485,yes,yes,3.0,2.272214,Sometimes,no,1.555534,no,0.348839,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1554,Male,21.963787,1.849601,122.333425,yes,yes,3.0,2.218285,Sometimes,no,1.340117,no,0.428259,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1555,Male,24.001685,1.606066,99.961731,yes,yes,3.0,1.418833,Sometimes,no,1.0,no,1.19102,1.384186,no,Public_Transportation,Obesity_Type_II +1556,Male,20.027764,1.611434,103.175516,yes,yes,2.983042,1.109956,Sometimes,no,1.430444,no,0.0,1.690901,no,Public_Transportation,Obesity_Type_II +1557,Male,28.704462,1.762887,113.90198,yes,yes,2.323351,3.0,Sometimes,no,2.165048,no,0.0,1.493716,Sometimes,Automobile,Obesity_Type_II +1558,Male,26.774115,1.755938,112.287678,yes,yes,1.428289,3.0,Sometimes,no,2.117733,no,0.485322,0.696948,Sometimes,Automobile,Obesity_Type_II +1559,Male,25.539411,1.787052,115.428276,yes,yes,1.992889,3.0,Sometimes,no,2.141271,no,1.121038,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1560,Male,25.300208,1.765258,114.330023,yes,yes,1.562804,3.0,Sometimes,no,2.075493,no,1.553734,0.000436,Sometimes,Public_Transportation,Obesity_Type_II +1561,Male,31.194458,1.726279,110.714711,yes,yes,1.794825,3.914454,Sometimes,no,1.972016,no,0.668963,0.0,no,Automobile,Obesity_Type_II +1562,Male,40.794057,1.735851,109.889334,yes,yes,2.305349,3.169089,Sometimes,no,1.02161,no,0.908981,0.0,no,Automobile,Obesity_Type_II +1563,Male,33.55336,1.699793,103.841672,yes,yes,2.576449,2.419656,Sometimes,no,1.0,no,1.982379,0.0,no,Public_Transportation,Obesity_Type_II +1564,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 +1565,Male,30.958051,1.906821,128.856677,yes,yes,2.633855,3.0,Sometimes,no,1.619305,no,1.847802,0.424612,Sometimes,Public_Transportation,Obesity_Type_II +1566,Male,29.429687,1.910987,129.935666,yes,yes,2.200588,3.0,Sometimes,no,1.295697,no,1.058378,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1567,Male,30.520854,1.784049,120.644178,yes,yes,2.499108,3.0,Sometimes,no,2.040952,no,0.838739,0.489854,Sometimes,Automobile,Obesity_Type_II +1568,Male,30.610436,1.783914,120.549592,yes,yes,2.05687,3.0,Sometimes,no,2.358088,no,0.093418,1.759954,Sometimes,Automobile,Obesity_Type_II +1569,Male,26.740655,1.863883,120.202596,yes,yes,2.247795,3.0,Sometimes,no,2.816015,no,0.712726,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1570,Male,26.680376,1.812259,118.277657,yes,yes,2.239634,3.0,Sometimes,no,2.684264,no,0.979306,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1571,Male,37.056193,1.75015,118.206565,yes,yes,2.09283,3.0,Sometimes,no,2.077704,no,0.598655,0.0,Sometimes,Automobile,Obesity_Type_II +1572,Male,28.825279,1.815347,120.399758,yes,yes,2.9673,3.0,Sometimes,no,2.530035,no,1.045107,0.947866,Sometimes,Automobile,Obesity_Type_II +1573,Male,25.883749,1.767077,114.133149,yes,yes,2.225731,3.0,Sometimes,no,2.154326,no,1.266866,0.390178,Sometimes,Public_Transportation,Obesity_Type_II +1574,Male,25.486521,1.762461,115.531622,yes,yes,2.178308,3.0,Sometimes,no,2.165804,no,1.420675,1.875683,Sometimes,Public_Transportation,Obesity_Type_II +1575,Male,29.883021,1.779049,112.438508,yes,yes,2.065752,3.0,Sometimes,no,2.078297,no,0.0,0.677354,Sometimes,Automobile,Obesity_Type_II +1576,Male,26.957645,1.780731,112.957922,yes,yes,2.263245,3.0,Sometimes,no,2.092509,no,0.0,1.836853,Sometimes,Automobile,Obesity_Type_II +1577,Male,26.490926,1.872517,120.898397,yes,yes,2.443538,3.0,Sometimes,no,2.939492,no,1.208142,0.738935,Sometimes,Public_Transportation,Obesity_Type_II +1578,Male,27.558801,1.801224,119.050381,yes,yes,2.744994,3.0,Sometimes,no,2.478838,no,0.684487,0.677909,Sometimes,Public_Transportation,Obesity_Type_II +1579,Male,22.908879,1.876898,121.277832,yes,yes,2.630137,2.806298,Sometimes,no,1.970508,no,1.304291,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1580,Male,24.201622,1.77558,120.589419,yes,yes,2.113843,3.0,Sometimes,no,2.316069,no,1.804157,0.357314,Sometimes,Public_Transportation,Obesity_Type_II +1581,Male,31.689773,1.76569,120.082941,yes,yes,2.821727,3.0,Sometimes,no,2.173279,no,1.096556,0.232858,Sometimes,Automobile,Obesity_Type_II +1582,Male,30.242597,1.759772,118.382361,yes,yes,2.312528,3.0,Sometimes,no,2.208068,no,1.07672,1.035711,Sometimes,Automobile,Obesity_Type_II +1583,Male,31.346845,1.823545,126.460936,yes,yes,2.938801,3.0,Sometimes,yes,1.478994,no,0.946763,1.360463,Sometimes,Public_Transportation,Obesity_Type_II +1584,Male,30.577343,1.868923,125.064264,yes,yes,2.050619,3.0,Sometimes,yes,1.515183,no,0.849811,0.261901,Sometimes,Public_Transportation,Obesity_Type_II +1585,Male,25.124595,1.77151,113.207124,yes,yes,1.457758,3.0,Sometimes,no,2.020249,no,1.556709,0.00133,Sometimes,Public_Transportation,Obesity_Type_II +1586,Male,25.509034,1.77219,114.097656,yes,yes,2.19331,3.0,Sometimes,no,2.089983,no,1.360994,0.857438,Sometimes,Public_Transportation,Obesity_Type_II +1587,Male,25.478662,1.858265,117.57457,yes,yes,2.028571,3.0,Sometimes,no,2.530428,no,1.31257,0.066805,Sometimes,Public_Transportation,Obesity_Type_II +1588,Male,25.100513,1.830596,118.424156,yes,yes,1.455602,3.0,Sometimes,no,2.363307,no,1.144683,0.101026,Sometimes,Public_Transportation,Obesity_Type_II +1589,Male,38.523646,1.765836,118.533246,yes,yes,2.177896,2.987652,Sometimes,no,1.745095,no,0.656548,0.0,Sometimes,Automobile,Obesity_Type_II +1590,Male,38.71216,1.770278,117.29188,yes,yes,2.252382,2.842848,Sometimes,no,1.879381,no,0.919388,0.0,Sometimes,Automobile,Obesity_Type_II +1591,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 +1592,Male,32.259623,1.703346,102.686239,yes,yes,2.723953,1.924168,Sometimes,no,1.0,no,1.622055,0.069367,no,Public_Transportation,Obesity_Type_II +1593,Male,23.47007,1.842906,121.142535,yes,yes,3.0,2.701689,Sometimes,no,2.738485,no,0.992253,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1594,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 +1595,Male,24.443011,1.873368,121.82962,yes,yes,2.722161,2.658837,Sometimes,no,2.375707,no,1.299469,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1596,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 +1597,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 +1598,Male,30.686701,1.644517,100.004418,yes,yes,2.96405,2.123138,Sometimes,no,1.0,no,1.699181,0.620465,no,Public_Transportation,Obesity_Type_II +1599,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 +1600,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 +1601,Male,30.002029,1.759324,112.000381,yes,yes,1.572036,3.0,Sometimes,no,2.003563,no,0.0,0.340196,Sometimes,Automobile,Obesity_Type_II +1602,Male,30.188303,1.758382,112.10074,yes,yes,1.387489,3.0,Sometimes,no,2.017712,no,0.0,0.731257,Sometimes,Automobile,Obesity_Type_II +1603,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 +1604,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 +1605,Male,29.721964,1.918859,129.991623,yes,yes,2.041376,3.0,Sometimes,no,1.120213,no,1.05545,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1606,Male,29.906575,1.913252,129.232216,yes,yes,2.02472,3.0,Sometimes,no,1.800335,no,1.196368,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1607,Male,37.084742,1.75,118.07381,yes,yes,2.133964,3.0,Sometimes,no,2.008361,no,0.202029,0.200516,Sometimes,Automobile,Obesity_Type_II +1608,Male,39.08886,1.75,119.029103,yes,yes,2.702457,3.0,Sometimes,no,2.005194,no,0.325313,0.419055,Sometimes,Automobile,Obesity_Type_II +1609,Male,22.658572,1.784994,113.714521,yes,yes,1.760038,3.0,Sometimes,no,2.003531,no,1.089061,0.072264,Sometimes,Public_Transportation,Obesity_Type_II +1610,Male,22.889099,1.792533,116.157766,yes,yes,1.996646,3.0,Sometimes,no,2.0,no,1.600536,0.739684,Sometimes,Public_Transportation,Obesity_Type_II +1611,Male,24.63474,1.829757,120.980508,yes,yes,2.644094,3.0,Sometimes,no,2.740525,no,2.0,0.500936,Sometimes,Public_Transportation,Obesity_Type_II +1612,Male,24.920362,1.796415,120.988454,yes,yes,2.475892,3.0,Sometimes,no,2.949356,no,1.866839,0.100048,Sometimes,Public_Transportation,Obesity_Type_II +1613,Male,31.783845,1.75,120.0,yes,yes,2.941627,3.0,Sometimes,no,2.318134,no,0.582686,1.588046,Sometimes,Automobile,Obesity_Type_II +1614,Male,31.627962,1.762389,118.548733,yes,yes,2.998441,3.0,Sometimes,no,2.462916,no,0.554646,1.99219,Sometimes,Automobile,Obesity_Type_II +1615,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 +1616,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 +1617,Male,40.973007,1.749405,109.908779,yes,yes,2.176317,2.986637,Sometimes,no,1.015672,no,0.94141,0.0,no,Automobile,Obesity_Type_II +1618,Male,41.0,1.75,115.806977,yes,yes,2.0,2.701521,Sometimes,no,1.142644,no,0.514225,0.0,no,Automobile,Obesity_Type_II +1619,Male,21.721507,1.849998,122.119682,yes,yes,3.0,2.014671,Sometimes,no,1.292786,no,0.145687,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1620,Male,21.544554,1.84998,122.609911,yes,yes,3.0,1.971659,Sometimes,no,1.179254,no,0.178856,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1621,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 +1622,Male,24.0409,1.603226,99.605527,yes,yes,3.0,1.262831,Sometimes,no,1.0,no,0.883171,1.382995,no,Public_Transportation,Obesity_Type_II +1623,Male,29.509151,1.770663,112.471118,yes,yes,2.303041,3.0,Sometimes,no,2.065817,no,0.0,0.725222,Sometimes,Automobile,Obesity_Type_II +1624,Male,29.246269,1.758038,112.395309,yes,yes,2.323003,3.0,Sometimes,no,2.155558,no,0.0,1.251554,Sometimes,Automobile,Obesity_Type_II +1625,Male,25.341399,1.786997,115.025361,yes,yes,1.99953,3.0,Sometimes,no,2.111908,no,1.453042,0.849503,Sometimes,Public_Transportation,Obesity_Type_II +1626,Male,25.314163,1.771837,114.906095,yes,yes,1.585183,3.0,Sometimes,no,2.101841,no,1.543961,7.3e-05,Sometimes,Public_Transportation,Obesity_Type_II +1627,Male,40.366238,1.722396,109.349025,yes,yes,2.281963,3.770379,Sometimes,no,1.0,no,1.330519,0.0,no,Automobile,Obesity_Type_II +1628,Male,33.749594,1.701387,107.025415,yes,yes,2.561638,2.87747,Sometimes,no,1.0,no,1.980401,0.0,no,Automobile,Obesity_Type_II +1629,Male,32.867331,1.697421,103.017623,yes,yes,2.600217,2.175153,Sometimes,no,1.0,no,1.985536,0.0,no,Public_Transportation,Obesity_Type_II +1630,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 +1631,Male,29.687488,1.909198,129.679131,yes,yes,2.08868,3.0,Sometimes,no,1.520215,no,1.857351,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1632,Male,28.992809,1.909105,129.874864,yes,yes,2.206119,3.0,Sometimes,no,1.483856,no,1.113169,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1633,Male,30.475248,1.801368,121.094257,yes,yes,2.328469,3.0,Sometimes,no,2.001208,no,0.800487,0.176678,Sometimes,Automobile,Obesity_Type_II +1634,Male,30.350516,1.860292,123.721352,yes,yes,2.002784,3.0,Sometimes,no,2.292906,no,1.034031,0.0,Sometimes,Automobile,Obesity_Type_II +1635,Male,26.684354,1.819535,118.332689,yes,yes,1.975675,3.0,Sometimes,no,2.357969,no,0.704236,0.010721,Sometimes,Public_Transportation,Obesity_Type_II +1636,Male,26.607478,1.793174,118.165082,yes,yes,2.002076,3.0,Sometimes,no,2.338373,no,0.897562,0.081156,Sometimes,Public_Transportation,Obesity_Type_II +1637,Male,33.174147,1.75015,118.299585,yes,yes,2.218599,3.0,Sometimes,no,2.104337,no,0.766007,0.262118,Sometimes,Automobile,Obesity_Type_II +1638,Male,32.29016,1.754956,120.098812,yes,yes,2.9673,3.0,Sometimes,no,2.530035,no,0.955317,1.339232,Sometimes,Automobile,Obesity_Type_II +1639,Male,26.050109,1.773115,115.089316,yes,yes,2.392179,3.0,Sometimes,no,2.16467,no,1.079934,1.073026,Sometimes,Public_Transportation,Obesity_Type_II +1640,Male,25.846279,1.772731,115.828167,yes,yes,2.381164,3.0,Sometimes,no,2.170225,no,1.211048,1.89933,Sometimes,Public_Transportation,Obesity_Type_II +1641,Male,29.620095,1.787933,112.536367,yes,yes,2.008245,3.0,Sometimes,no,2.040137,no,0.0,0.474216,Sometimes,Automobile,Obesity_Type_II +1642,Male,27.439056,1.785277,112.740797,yes,yes,2.155182,3.0,Sometimes,no,2.049079,no,0.0,1.74992,Sometimes,Automobile,Obesity_Type_II +1643,Male,28.493397,1.817572,120.8158,yes,yes,2.969233,3.0,Sometimes,no,2.807984,no,0.982134,1.191998,Sometimes,Public_Transportation,Obesity_Type_II +1644,Male,27.474245,1.84342,120.420406,yes,yes,2.765063,3.0,Sometimes,no,2.867206,no,0.706777,0.677909,Sometimes,Public_Transportation,Obesity_Type_II +1645,Male,25.036269,1.874519,121.244969,yes,yes,2.630137,3.0,Sometimes,no,2.960088,no,1.356468,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1646,Male,24.825398,1.796332,120.901591,yes,yes,2.195964,3.0,Sometimes,no,2.514872,no,1.664722,0.127673,Sometimes,Public_Transportation,Obesity_Type_II +1647,Male,30.493946,1.756221,119.117122,yes,yes,2.619987,3.0,Sometimes,no,2.194746,no,1.076926,1.090995,Sometimes,Automobile,Obesity_Type_II +1648,Male,30.607546,1.757132,118.565568,yes,yes,2.918113,3.0,Sometimes,no,2.240463,no,1.076248,1.480875,Sometimes,Automobile,Obesity_Type_II +1649,Male,31.194323,1.889104,129.157346,yes,yes,2.889485,3.0,Sometimes,yes,1.279771,no,0.941424,0.008343,Sometimes,Public_Transportation,Obesity_Type_II +1650,Male,31.36347,1.869323,127.507411,yes,yes,2.939727,3.0,Sometimes,yes,1.344122,no,0.923428,1.432336,Sometimes,Public_Transportation,Obesity_Type_II +1651,Male,25.027254,1.757154,112.200812,yes,yes,1.264234,3.0,Sometimes,no,2.0,no,1.333435,0.002168,Sometimes,Public_Transportation,Obesity_Type_II +1652,Male,25.058566,1.764484,113.234349,yes,yes,1.517912,3.0,Sometimes,no,2.038958,no,1.590255,0.00164,Sometimes,Public_Transportation,Obesity_Type_II +1653,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 +1654,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 +1655,Male,40.106145,1.760175,117.651046,yes,yes,2.032883,2.976211,Sometimes,no,1.726109,no,0.477855,0.0,Sometimes,Automobile,Obesity_Type_II +1656,Male,40.174191,1.763029,116.974504,yes,yes,2.046651,2.842035,Sometimes,no,1.732072,no,0.584272,0.0,Sometimes,Automobile,Obesity_Type_II +1657,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 +1658,Male,30.424369,1.699354,100.176866,yes,yes,2.819934,1.91863,Sometimes,no,1.0,no,1.39302,0.553311,no,Public_Transportation,Obesity_Type_II +1659,Male,23.141402,1.849307,121.658729,yes,yes,3.0,2.510135,Sometimes,no,1.693362,no,0.769709,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1660,Male,24.178638,1.86741,121.684311,yes,yes,2.954417,2.65772,Sometimes,no,2.104696,no,0.870056,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1661,Male,22.832105,1.86714,121.835883,yes,yes,2.826251,2.604998,Sometimes,no,1.842173,no,1.284798,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1662,Male,23.083621,1.848553,121.421121,yes,yes,3.0,2.567567,Sometimes,no,2.011023,no,0.916478,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1663,Male,24.079971,1.61981,98.54302,yes,yes,2.95841,2.434347,Sometimes,no,1.0,no,1.930033,0.754023,no,Public_Transportation,Obesity_Type_II +1664,Male,26.947786,1.647807,99.592225,yes,yes,2.935157,1.845858,Sometimes,no,1.0,no,1.089891,0.715993,no,Public_Transportation,Obesity_Type_II +1665,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 +1666,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 +1667,Male,28.404332,1.787379,112.173731,yes,yes,1.878251,3.0,Sometimes,no,2.022933,no,0.0,0.325445,Sometimes,Automobile,Obesity_Type_II +1668,Male,30.003358,1.758355,112.007101,yes,yes,1.750809,3.0,Sometimes,no,2.002871,no,0.0,0.114457,Sometimes,Automobile,Obesity_Type_II +1669,Male,25.136116,1.76414,113.089716,yes,yes,1.168856,3.0,Sometimes,no,2.013205,no,1.246223,0.00159,Sometimes,Public_Transportation,Obesity_Type_II +1670,Male,25.139466,1.767186,112.226032,yes,yes,1.443674,3.0,Sometimes,no,2.002194,no,1.386151,0.001337,Sometimes,Public_Transportation,Obesity_Type_II +1671,Male,31.743821,1.67782,102.022057,yes,yes,2.826036,2.371658,Sometimes,no,1.0,no,1.996487,0.0,no,Public_Transportation,Obesity_Type_II +1672,Male,30.796262,1.668478,100.543983,yes,yes,2.9553,2.378211,Sometimes,no,1.0,no,1.738267,0.394533,no,Public_Transportation,Obesity_Type_II +1673,Male,28.393111,1.685462,99.540122,yes,yes,2.774562,2.301129,Sometimes,no,1.0,no,1.621733,0.138314,no,Public_Transportation,Obesity_Type_II +1674,Male,27.93982,1.694642,99.709329,yes,yes,2.772027,2.454432,Sometimes,no,1.0,no,0.858554,0.051268,no,Public_Transportation,Obesity_Type_II +1675,Male,31.034092,1.886109,129.349471,yes,yes,2.499626,3.0,Sometimes,yes,1.125338,no,0.94522,1.022505,Sometimes,Public_Transportation,Obesity_Type_II +1676,Male,30.684347,1.915,129.966428,yes,yes,2.108638,3.0,Sometimes,yes,1.014876,no,0.987521,0.047473,Sometimes,Public_Transportation,Obesity_Type_II +1677,Male,30.108216,1.841908,124.070717,yes,yes,2.467548,3.0,Sometimes,no,2.66029,no,0.994422,0.310394,Sometimes,Public_Transportation,Obesity_Type_II +1678,Male,31.347497,1.868127,123.172214,yes,yes,2.585942,3.0,Sometimes,no,2.302506,no,0.597863,0.111925,Sometimes,Public_Transportation,Obesity_Type_II +1679,Male,31.761799,1.751688,119.205308,yes,yes,2.14961,3.0,Sometimes,no,2.133876,no,0.393452,0.345887,Sometimes,Automobile,Obesity_Type_II +1680,Male,30.976932,1.755333,118.237782,yes,yes,2.938616,3.0,Sometimes,no,2.06039,no,0.371508,1.333559,Sometimes,Automobile,Obesity_Type_II +1681,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 +1682,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 +1683,Male,21.6548,1.755938,116.311962,yes,yes,1.800122,3.0,Sometimes,no,2.0,no,1.689525,0.764452,Sometimes,Public_Transportation,Obesity_Type_II +1684,Male,22.829753,1.765137,116.669906,yes,yes,1.482722,3.0,Sometimes,no,2.0,no,1.112504,0.690353,Sometimes,Public_Transportation,Obesity_Type_II +1685,Male,25.015173,1.788239,115.382519,yes,yes,1.735664,3.0,Sometimes,no,2.041536,no,1.392406,0.39174,Sometimes,Public_Transportation,Obesity_Type_II +1686,Male,23.812795,1.767121,116.164351,yes,yes,1.655684,3.0,Sometimes,no,2.0,no,0.976425,0.690082,Sometimes,Public_Transportation,Obesity_Type_II +1687,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 +1688,Male,24.149036,1.824901,120.805715,yes,yes,2.225149,3.0,Sometimes,no,2.357978,no,1.943743,0.682128,Sometimes,Public_Transportation,Obesity_Type_II +1689,Male,25.062942,1.828391,120.998266,yes,yes,3.0,3.0,Sometimes,no,3.0,no,1.685369,0.264969,Sometimes,Public_Transportation,Obesity_Type_II +1690,Male,25.140074,1.829529,120.996581,yes,yes,3.0,3.0,Sometimes,no,3.0,no,1.467863,0.343635,Sometimes,Public_Transportation,Obesity_Type_II +1691,Male,30.722801,1.779325,120.751656,yes,yes,2.519592,3.0,Sometimes,no,2.229145,no,0.350717,1.140348,Sometimes,Automobile,Obesity_Type_II +1692,Male,30.916426,1.781139,120.794535,yes,yes,2.111887,3.0,Sometimes,no,2.357373,no,0.425621,1.416353,Sometimes,Automobile,Obesity_Type_II +1693,Male,31.010302,1.764166,119.373019,yes,yes,2.970983,3.0,Sometimes,no,2.828861,no,0.454944,1.915818,Sometimes,Automobile,Obesity_Type_II +1694,Male,30.605464,1.754796,118.805937,yes,yes,2.907542,3.0,Sometimes,no,2.284494,no,0.647632,1.668318,Sometimes,Automobile,Obesity_Type_II +1695,Male,24.622054,1.756509,117.368716,yes,yes,1.451337,3.0,Sometimes,no,2.0,no,1.384607,0.631217,Sometimes,Public_Transportation,Obesity_Type_II +1696,Male,23.745833,1.756772,117.326523,yes,yes,1.537505,3.0,Sometimes,no,2.0,no,1.631912,0.631422,Sometimes,Public_Transportation,Obesity_Type_II +1697,Male,23.327836,1.754439,119.441207,yes,yes,1.893428,3.0,Sometimes,no,2.0,no,1.917383,0.93648,Sometimes,Public_Transportation,Obesity_Type_II +1698,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 +1699,Male,39.825592,1.706741,108.012603,yes,yes,2.487781,3.755976,Sometimes,no,1.0,no,1.860765,0.0,no,Automobile,Obesity_Type_II +1700,Male,36.839761,1.74285,106.421042,yes,yes,2.541785,2.902639,Sometimes,no,1.0,no,1.668961,0.0,no,Automobile,Obesity_Type_II +1701,Male,33.789301,1.6811,102.523111,yes,yes,2.813775,2.372705,Sometimes,no,1.0,no,1.979355,0.0,no,Public_Transportation,Obesity_Type_II +1702,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 +1703,Male,22.580038,1.849507,121.560938,yes,yes,3.0,2.272801,Sometimes,no,1.560064,no,0.79677,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1704,Male,22.851721,1.853373,121.737836,yes,yes,2.922511,2.692889,Sometimes,no,1.809531,no,0.478595,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1705,Male,23.254934,1.84753,122.06261,yes,yes,3.0,2.280545,Sometimes,no,1.591597,no,0.932783,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1706,Male,22.343204,1.87034,121.458232,yes,yes,2.836055,2.675411,Sometimes,no,1.711666,no,0.93732,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1707,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 +1708,Male,24.001196,1.603091,100.209405,yes,yes,3.0,1.02075,Sometimes,no,1.0,no,0.233056,1.707421,no,Public_Transportation,Obesity_Type_II +1709,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 +1710,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 +1711,Male,28.986237,1.758618,113.501549,yes,yes,2.320201,3.0,Sometimes,no,2.164784,no,0.0,1.465479,Sometimes,Automobile,Obesity_Type_II +1712,Male,29.713169,1.763259,113.215814,yes,yes,2.181057,3.0,Sometimes,no,2.08554,no,0.0,1.439004,Sometimes,Automobile,Obesity_Type_II +1713,Male,27.394123,1.764138,112.323213,yes,yes,1.924632,3.0,Sometimes,no,2.006595,no,0.1768,0.511694,Sometimes,Automobile,Obesity_Type_II +1714,Male,24.912254,1.75596,112.277567,yes,yes,1.397468,3.0,Sometimes,no,2.01897,no,0.665439,0.062167,Sometimes,Automobile,Obesity_Type_II +1715,Male,25.526746,1.783381,115.347176,yes,yes,2.191429,3.0,Sometimes,no,2.102709,no,1.32534,0.380979,Sometimes,Public_Transportation,Obesity_Type_II +1716,Male,25.523127,1.786532,114.534678,yes,yes,2.100177,3.0,Sometimes,no,2.099687,no,1.281165,0.247332,Sometimes,Public_Transportation,Obesity_Type_II +1717,Male,25.822348,1.766626,114.187096,yes,yes,2.075321,3.0,Sometimes,no,2.144838,no,1.501754,0.276319,Sometimes,Public_Transportation,Obesity_Type_II +1718,Male,25.879411,1.765464,114.144378,yes,yes,1.626369,3.0,Sometimes,no,2.109697,no,1.352973,0.076693,Sometimes,Public_Transportation,Obesity_Type_II +1719,Male,30.361365,1.75853,111.635463,yes,yes,1.263216,3.219347,Sometimes,no,1.981782,no,0.482625,0.0,Sometimes,Automobile,Obesity_Type_II +1720,Male,30.451174,1.758539,111.950413,yes,yes,1.067909,3.656401,Sometimes,no,1.98459,no,0.054238,0.0,Sometimes,Automobile,Obesity_Type_II +1721,Male,40.564513,1.748015,109.758736,yes,yes,2.310751,3.220181,Sometimes,no,1.006643,no,1.15804,0.0,no,Automobile,Obesity_Type_II +1722,Male,40.501722,1.744974,111.169678,yes,yes,2.294259,2.850948,Sometimes,no,1.87029,no,0.917014,0.0,no,Automobile,Obesity_Type_II +1723,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 +1724,Male,33.293166,1.696412,103.250355,yes,yes,2.679664,2.415522,Sometimes,no,1.0,no,1.987296,0.0,no,Public_Transportation,Obesity_Type_II +1725,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 +1726,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 +1727,Male,31.205668,1.877732,127.161381,yes,yes,2.731368,3.0,Sometimes,yes,1.486824,no,1.485978,1.150439,Sometimes,Public_Transportation,Obesity_Type_II +1728,Male,30.899219,1.909639,129.013178,yes,yes,2.22259,3.0,Sometimes,yes,1.591909,no,1.392026,0.917727,Sometimes,Public_Transportation,Obesity_Type_II +1729,Male,29.669219,1.909188,129.19449,yes,yes,2.432355,3.0,Sometimes,no,1.336526,no,1.63812,0.090341,Sometimes,Public_Transportation,Obesity_Type_II +1730,Male,30.595632,1.910672,129.232708,yes,yes,2.497548,3.0,Sometimes,no,1.362583,no,1.144076,0.173232,Sometimes,Public_Transportation,Obesity_Type_II +1731,Male,30.554956,1.779136,120.60094,yes,yes,2.671238,3.0,Sometimes,no,2.145368,no,0.882709,0.593917,Sometimes,Automobile,Obesity_Type_II +1732,Male,31.571392,1.767485,120.158049,yes,yes,2.57691,3.0,Sometimes,no,2.080187,no,1.04268,0.376683,Sometimes,Automobile,Obesity_Type_II +1733,Male,30.577944,1.783953,120.613561,yes,yes,2.341999,3.0,Sometimes,no,2.299878,no,0.565242,0.921991,Sometimes,Automobile,Obesity_Type_II +1734,Male,30.717727,1.76714,120.344402,yes,yes,2.117121,3.0,Sometimes,no,2.193008,no,0.992829,1.556052,Sometimes,Automobile,Obesity_Type_II +1735,Male,26.734476,1.816197,119.622764,yes,yes,2.247037,3.0,Sometimes,no,2.718408,no,0.763595,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1736,Male,26.699317,1.839941,119.956651,yes,yes,2.244654,3.0,Sometimes,no,2.795752,no,0.839481,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1737,Male,25.659092,1.84842,117.631707,yes,yes,2.128574,3.0,Sometimes,no,2.531984,no,1.003294,0.026575,Sometimes,Public_Transportation,Obesity_Type_II +1738,Male,26.348156,1.830317,117.75701,yes,yes,2.068834,3.0,Sometimes,no,2.543841,no,1.217993,0.038253,Sometimes,Public_Transportation,Obesity_Type_II +1739,Male,37.638102,1.750085,118.114184,yes,yes,2.073224,3.0,Sometimes,no,2.024035,no,0.131371,0.0,Sometimes,Automobile,Obesity_Type_II +1740,Male,37.765356,1.763582,117.86159,yes,yes,2.145114,2.888193,Sometimes,no,2.038128,no,0.852344,0.0,Sometimes,Automobile,Obesity_Type_II +1741,Male,28.255199,1.816547,120.699119,yes,yes,2.997951,3.0,Sometimes,no,2.715856,no,0.739881,0.972054,Sometimes,Automobile,Obesity_Type_II +1742,Male,27.931432,1.805445,119.484614,yes,yes,2.911312,3.0,Sometimes,no,2.501808,no,0.94676,0.785701,Sometimes,Automobile,Obesity_Type_II +1743,Male,25.492855,1.770124,114.163921,yes,yes,2.159033,3.0,Sometimes,no,2.116399,no,1.331526,0.052942,Sometimes,Public_Transportation,Obesity_Type_II +1744,Male,25.550506,1.77274,114.254278,yes,yes,2.175276,3.0,Sometimes,no,2.128176,no,1.306476,0.079334,Sometimes,Public_Transportation,Obesity_Type_II +1745,Male,25.542454,1.763215,115.10813,yes,yes,2.21965,3.0,Sometimes,no,2.165408,no,1.315045,0.688985,Sometimes,Public_Transportation,Obesity_Type_II +1746,Male,25.758307,1.766547,115.45845,yes,yes,2.203962,3.0,Sometimes,no,2.15687,no,1.289421,1.220767,Sometimes,Public_Transportation,Obesity_Type_II +1747,Male,29.981494,1.773181,112.348722,yes,yes,1.947495,3.0,Sometimes,no,2.042073,no,0.0,0.618087,Sometimes,Automobile,Obesity_Type_II +1748,Male,29.891473,1.76003,112.409191,yes,yes,2.262292,3.0,Sometimes,no,2.115354,no,0.0,0.847587,Sometimes,Automobile,Obesity_Type_II +1749,Male,26.778684,1.780089,113.15464,yes,yes,2.219186,3.0,Sometimes,no,2.092326,no,0.545919,1.74288,Sometimes,Automobile,Obesity_Type_II +1750,Male,28.377958,1.766946,113.235538,yes,yes,2.314175,3.0,Sometimes,no,2.151809,no,0.0,1.578517,Sometimes,Automobile,Obesity_Type_II +1751,Male,26.199321,1.885119,121.065052,yes,yes,2.423291,3.0,Sometimes,no,2.902682,no,1.243567,0.555591,Sometimes,Public_Transportation,Obesity_Type_II +1752,Male,27.266287,1.828276,120.872294,yes,yes,2.637202,3.0,Sometimes,no,2.966647,no,1.159598,0.758897,Sometimes,Public_Transportation,Obesity_Type_II +1753,Male,27.635029,1.806227,120.423567,yes,yes,2.974006,3.0,Sometimes,no,2.565828,no,0.690269,1.339691,Sometimes,Public_Transportation,Obesity_Type_II +1754,Male,26.899886,1.812919,119.841446,yes,yes,2.347942,3.0,Sometimes,no,2.730663,no,0.689371,0.188693,Sometimes,Public_Transportation,Obesity_Type_II +1755,Male,22.758998,1.859717,121.284533,yes,yes,2.654076,2.574108,Sometimes,no,1.78471,no,1.170537,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1756,Male,22.771001,1.86516,121.527369,yes,yes,2.739,2.740492,Sometimes,no,1.824561,no,1.094839,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1757,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 +1758,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 +1759,Male,30.945369,1.759647,120.009392,yes,yes,2.766612,3.0,Sometimes,no,2.173417,no,1.089344,1.198439,Sometimes,Automobile,Obesity_Type_II +1760,Male,31.490699,1.773521,120.209711,yes,yes,2.777165,3.0,Sometimes,no,2.106861,no,0.925941,0.2916,Sometimes,Automobile,Obesity_Type_II +1761,Male,31.56724,1.753327,118.26569,yes,yes,2.232836,3.0,Sometimes,no,2.146398,no,0.886817,0.47787,Sometimes,Automobile,Obesity_Type_II +1762,Male,30.444081,1.761068,118.370628,yes,yes,2.571274,3.0,Sometimes,no,2.224164,no,1.018158,1.152899,Sometimes,Automobile,Obesity_Type_II +1763,Male,31.199261,1.848845,125.077863,yes,yes,2.49619,3.0,Sometimes,yes,1.662117,no,0.992371,0.217632,Sometimes,Public_Transportation,Obesity_Type_II +1764,Male,31.190219,1.842812,125.973927,yes,yes,2.151335,3.0,Sometimes,yes,1.491169,no,0.883542,0.527766,Sometimes,Public_Transportation,Obesity_Type_II +1765,Male,30.575349,1.825449,124.95278,yes,yes,2.01695,3.0,Sometimes,no,1.83412,no,0.797209,0.185499,Sometimes,Public_Transportation,Obesity_Type_II +1766,Male,30.702559,1.86198,126.418413,yes,yes,2.927187,3.0,Sometimes,no,1.508796,no,0.902776,1.015467,Sometimes,Public_Transportation,Obesity_Type_II +1767,Male,25.057878,1.763987,113.069667,yes,yes,1.412566,3.0,Sometimes,no,2.002495,no,1.592795,0.001867,Sometimes,Public_Transportation,Obesity_Type_II +1768,Male,25.151286,1.761519,112.835195,yes,yes,1.289315,3.0,Sometimes,no,2.007348,no,1.376217,0.001518,Sometimes,Public_Transportation,Obesity_Type_II +1769,Male,25.137087,1.772045,114.067936,yes,yes,1.624366,3.0,Sometimes,no,2.081719,no,1.538922,0.356868,Sometimes,Public_Transportation,Obesity_Type_II +1770,Male,25.3292,1.771817,114.004832,yes,yes,1.528331,3.0,Sometimes,no,2.079353,no,1.522429,0.228598,Sometimes,Public_Transportation,Obesity_Type_II +1771,Male,25.66668,1.79858,117.93329,yes,yes,2.037042,3.0,Sometimes,no,2.43699,no,1.016254,0.020044,Sometimes,Public_Transportation,Obesity_Type_II +1772,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 +1773,Male,25.047945,1.803207,118.332966,yes,yes,1.431346,3.0,Sometimes,no,2.098959,no,1.110868,0.196952,Sometimes,Public_Transportation,Obesity_Type_II +1774,Male,25.426457,1.836592,118.377601,yes,yes,1.84199,3.0,Sometimes,no,2.503244,no,1.226019,0.078207,Sometimes,Public_Transportation,Obesity_Type_II +1775,Male,37.207082,1.762921,118.40174,yes,yes,2.13683,2.993084,Sometimes,no,1.885926,no,0.615298,0.0,Sometimes,Automobile,Obesity_Type_II +1776,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 +1777,Male,38.644441,1.768235,117.792268,yes,yes,2.230742,2.920373,Sometimes,no,1.831187,no,0.756277,0.0,Sometimes,Automobile,Obesity_Type_II +1778,Male,38.112989,1.766888,118.134898,yes,yes,2.240757,2.911568,Sometimes,no,1.895876,no,0.822186,0.0,Sometimes,Automobile,Obesity_Type_II +1779,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 +1780,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 +1781,Male,33.722449,1.712905,103.276087,yes,yes,2.525884,2.040582,Sometimes,no,1.0,no,1.67036,0.023959,no,Public_Transportation,Obesity_Type_II +1782,Male,32.516469,1.695735,102.784864,yes,yes,2.736647,2.015675,Sometimes,no,1.0,no,1.977918,0.056351,no,Public_Transportation,Obesity_Type_II +1783,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 +1784,Male,23.319635,1.84629,121.248035,yes,yes,3.0,2.695396,Sometimes,no,2.628816,no,0.975384,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1785,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 +1786,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 +1787,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 +1788,Male,24.05331,1.872561,121.471077,yes,yes,2.808027,2.683061,Sometimes,no,2.640483,no,1.280191,0.0,Sometimes,Public_Transportation,Obesity_Type_II +1789,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 +1790,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 +1791,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 +1792,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 +1793,Male,30.71516,1.650189,101.141277,yes,yes,2.913452,2.269799,Sometimes,no,1.0,no,1.889937,0.378818,no,Public_Transportation,Obesity_Type_II +1794,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 +1795,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 +1796,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 +1797,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 +1798,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 +1799,Female,25.919241,1.611462,102.093223,yes,yes,3.0,3.0,Sometimes,no,1.023328,no,0.05093,1.0,Sometimes,Public_Transportation,Obesity_Type_III +1800,Female,26.0,1.584782,105.055597,yes,yes,3.0,3.0,Sometimes,no,1.197667,no,0.0,0.534769,Sometimes,Public_Transportation,Obesity_Type_III +1801,Female,18.233541,1.792378,137.859737,yes,yes,3.0,3.0,Sometimes,no,2.838893,no,1.990317,0.735868,Sometimes,Public_Transportation,Obesity_Type_III +1802,Female,18.147705,1.802257,149.935848,yes,yes,3.0,3.0,Sometimes,no,2.181811,no,1.995582,0.939665,Sometimes,Public_Transportation,Obesity_Type_III +1803,Female,26.0,1.65632,111.93301,yes,yes,3.0,3.0,Sometimes,no,2.774014,no,0.0,0.138418,Sometimes,Public_Transportation,Obesity_Type_III +1804,Female,26.0,1.600762,105.448264,yes,yes,3.0,3.0,Sometimes,no,1.031354,no,0.0,0.37465,Sometimes,Public_Transportation,Obesity_Type_III +1805,Female,25.902283,1.678658,104.954834,yes,yes,3.0,3.0,Sometimes,no,1.66616,no,0.210351,0.73421,Sometimes,Public_Transportation,Obesity_Type_III +1806,Female,25.540865,1.678201,109.900472,yes,yes,3.0,3.0,Sometimes,no,1.471664,no,0.143955,0.444532,Sometimes,Public_Transportation,Obesity_Type_III +1807,Female,22.39251,1.65563,121.205171,yes,yes,3.0,3.0,Sometimes,no,1.347559,no,0.462951,0.31794,Sometimes,Public_Transportation,Obesity_Type_III +1808,Female,21.305402,1.694952,133.76473,yes,yes,3.0,3.0,Sometimes,no,1.338241,no,1.92628,0.886135,Sometimes,Public_Transportation,Obesity_Type_III +1809,Female,26.0,1.622771,109.959714,yes,yes,3.0,3.0,Sometimes,no,2.679137,no,0.0,0.479348,Sometimes,Public_Transportation,Obesity_Type_III +1810,Female,26.0,1.583889,110.545378,yes,yes,3.0,3.0,Sometimes,no,2.578292,no,0.0,0.492394,Sometimes,Public_Transportation,Obesity_Type_III +1811,Female,21.334585,1.729045,131.529267,yes,yes,3.0,3.0,Sometimes,no,1.302193,no,1.742453,0.94232,Sometimes,Public_Transportation,Obesity_Type_III +1812,Female,22.978655,1.664622,114.465425,yes,yes,3.0,3.0,Sometimes,no,2.408442,no,0.194056,0.803141,Sometimes,Public_Transportation,Obesity_Type_III +1813,Female,21.01245,1.747773,127.902844,yes,yes,3.0,3.0,Sometimes,no,1.705218,no,0.390937,0.998646,Sometimes,Public_Transportation,Obesity_Type_III +1814,Female,21.056059,1.730113,152.094362,yes,yes,3.0,3.0,Sometimes,no,2.374958,no,0.750111,0.671458,Sometimes,Public_Transportation,Obesity_Type_III +1815,Female,18.771001,1.746652,133.800129,yes,yes,3.0,3.0,Sometimes,no,2.869234,no,1.465931,0.627886,Sometimes,Public_Transportation,Obesity_Type_III +1816,Female,24.265943,1.719365,114.511537,yes,yes,3.0,3.0,Sometimes,no,2.987406,no,0.38926,0.526776,Sometimes,Public_Transportation,Obesity_Type_III +1817,Female,26.0,1.632377,111.946655,yes,yes,3.0,3.0,Sometimes,no,2.737091,no,0.0,0.037078,Sometimes,Public_Transportation,Obesity_Type_III +1818,Female,26.0,1.642572,111.868169,yes,yes,3.0,3.0,Sometimes,no,2.623489,no,0.0,0.138563,Sometimes,Public_Transportation,Obesity_Type_III +1819,Female,25.96701,1.654875,104.758713,yes,yes,3.0,3.0,Sometimes,no,2.612758,no,0.24629,0.802136,Sometimes,Public_Transportation,Obesity_Type_III +1820,Female,26.0,1.627567,107.482662,yes,yes,3.0,3.0,Sometimes,no,2.687378,no,0.0,0.410651,Sometimes,Public_Transportation,Obesity_Type_III +1821,Female,20.590046,1.736276,130.927138,yes,yes,3.0,3.0,Sometimes,no,1.852692,no,1.46361,0.962336,Sometimes,Public_Transportation,Obesity_Type_III +1822,Female,24.497373,1.737056,132.527011,yes,yes,3.0,3.0,Sometimes,no,2.86559,no,0.973864,0.58345,Sometimes,Public_Transportation,Obesity_Type_III +1823,Female,25.991886,1.580968,102.003378,yes,yes,3.0,3.0,Sometimes,no,1.003563,no,0.008127,1.0,Sometimes,Public_Transportation,Obesity_Type_III +1824,Female,25.908431,1.607128,103.026858,yes,yes,3.0,3.0,Sometimes,no,1.077253,no,0.162083,0.824607,Sometimes,Public_Transportation,Obesity_Type_III +1825,Female,18.177882,1.821566,142.102468,yes,yes,3.0,3.0,Sometimes,no,2.714949,no,1.999773,0.813784,Sometimes,Public_Transportation,Obesity_Type_III +1826,Female,18.112503,1.82773,152.720545,yes,yes,3.0,3.0,Sometimes,no,2.154949,no,1.999897,0.957463,Sometimes,Public_Transportation,Obesity_Type_III +1827,Female,26.0,1.656504,111.993054,yes,yes,3.0,3.0,Sometimes,no,2.893117,no,0.0,0.01931,Sometimes,Public_Transportation,Obesity_Type_III +1828,Female,26.0,1.637725,111.208963,yes,yes,3.0,3.0,Sometimes,no,2.70914,no,0.0,0.110518,Sometimes,Public_Transportation,Obesity_Type_III +1829,Female,25.902283,1.669701,104.585783,yes,yes,3.0,3.0,Sometimes,no,1.570188,no,0.210351,0.881848,Sometimes,Public_Transportation,Obesity_Type_III +1830,Female,25.540865,1.668709,104.754958,yes,yes,3.0,3.0,Sometimes,no,1.412049,no,0.143955,0.753077,Sometimes,Public_Transportation,Obesity_Type_III +1831,Female,20.520992,1.668642,124.704781,yes,yes,3.0,3.0,Sometimes,no,1.15635,no,0.786828,0.366385,Sometimes,Public_Transportation,Obesity_Type_III +1832,Female,20.871153,1.690614,129.769141,yes,yes,3.0,3.0,Sometimes,no,1.154698,no,1.71836,0.959218,Sometimes,Public_Transportation,Obesity_Type_III +1833,Female,26.0,1.622418,110.676343,yes,yes,3.0,3.0,Sometimes,no,2.691584,no,0.0,0.425473,Sometimes,Public_Transportation,Obesity_Type_III +1834,Female,26.0,1.643332,110.824698,yes,yes,3.0,3.0,Sometimes,no,2.709654,no,0.0,0.25115,Sometimes,Public_Transportation,Obesity_Type_III +1835,Female,21.768834,1.733383,135.524857,yes,yes,3.0,3.0,Sometimes,no,1.485736,no,1.950374,0.869238,Sometimes,Public_Transportation,Obesity_Type_III +1836,Female,20.891491,1.748313,133.573787,yes,yes,3.0,3.0,Sometimes,no,2.874336,no,1.624981,0.825609,Sometimes,Public_Transportation,Obesity_Type_III +1837,Female,20.941943,1.812963,138.730619,yes,yes,3.0,3.0,Sometimes,no,2.641489,no,0.481555,0.735201,Sometimes,Public_Transportation,Obesity_Type_III +1838,Female,20.989016,1.80734,155.872093,yes,yes,3.0,3.0,Sometimes,no,2.417122,no,0.952725,0.573958,Sometimes,Public_Transportation,Obesity_Type_III +1839,Female,18.367481,1.745644,133.665587,yes,yes,3.0,3.0,Sometimes,no,2.900857,no,1.508897,0.625371,Sometimes,Public_Transportation,Obesity_Type_III +1840,Female,21.051107,1.753266,133.852432,yes,yes,3.0,3.0,Sometimes,no,2.95311,no,1.445148,0.67321,Sometimes,Public_Transportation,Obesity_Type_III +1841,Female,26.0,1.632193,111.886611,yes,yes,3.0,3.0,Sometimes,no,2.617988,no,0.0,0.156187,Sometimes,Public_Transportation,Obesity_Type_III +1842,Female,26.0,1.641098,111.818345,yes,yes,3.0,3.0,Sometimes,no,2.55057,no,0.0,0.20482,Sometimes,Public_Transportation,Obesity_Type_III +1843,Female,25.998646,1.640741,104.808542,yes,yes,3.0,3.0,Sometimes,no,2.653531,no,0.190061,0.692343,Sometimes,Public_Transportation,Obesity_Type_III +1844,Female,26.0,1.618573,104.928643,yes,yes,3.0,3.0,Sometimes,no,1.698767,no,0.0,0.54396,Sometimes,Public_Transportation,Obesity_Type_III +1845,Female,22.846357,1.757442,133.365094,yes,yes,3.0,3.0,Sometimes,no,2.84837,no,1.206059,0.766668,Sometimes,Public_Transportation,Obesity_Type_III +1846,Female,19.885655,1.763343,133.952675,yes,yes,3.0,3.0,Sometimes,no,2.835622,no,1.419473,0.816986,Sometimes,Public_Transportation,Obesity_Type_III +1847,Female,25.930376,1.610086,102.38745,yes,yes,3.0,3.0,Sometimes,no,1.050564,no,0.026033,0.539074,Sometimes,Public_Transportation,Obesity_Type_III +1848,Female,25.897815,1.664463,102.781971,yes,yes,3.0,3.0,Sometimes,no,1.068493,no,0.112122,1.0,Sometimes,Public_Transportation,Obesity_Type_III +1849,Female,26.0,1.602025,104.899348,yes,yes,3.0,3.0,Sometimes,no,2.613928,no,0.0,0.553093,Sometimes,Public_Transportation,Obesity_Type_III +1850,Female,25.968792,1.632896,104.988925,yes,yes,3.0,3.0,Sometimes,no,1.239833,no,0.162279,0.619235,Sometimes,Public_Transportation,Obesity_Type_III +1851,Female,18.335019,1.771043,137.044959,yes,yes,3.0,3.0,Sometimes,no,2.861533,no,1.70464,0.655571,Sometimes,Public_Transportation,Obesity_Type_III +1852,Female,21.700748,1.789555,137.767787,yes,yes,3.0,3.0,Sometimes,no,2.832659,no,1.505775,0.923005,Sometimes,Public_Transportation,Obesity_Type_III +1853,Female,18.222536,1.801746,141.166265,yes,yes,3.0,3.0,Sometimes,no,2.418488,no,1.99507,0.893514,Sometimes,Public_Transportation,Obesity_Type_III +1854,Female,20.3756,1.787195,151.975864,yes,yes,3.0,3.0,Sometimes,no,2.304716,no,0.82666,0.914492,Sometimes,Public_Transportation,Obesity_Type_III +1855,Female,26.0,1.644141,111.942544,yes,yes,3.0,3.0,Sometimes,no,2.763005,no,0.0,0.101867,Sometimes,Public_Transportation,Obesity_Type_III +1856,Female,26.0,1.643355,111.600553,yes,yes,3.0,3.0,Sometimes,no,2.652616,no,0.0,0.250502,Sometimes,Public_Transportation,Obesity_Type_III +1857,Female,26.0,1.623707,105.037463,yes,yes,3.0,3.0,Sometimes,no,2.553198,no,0.0,0.393838,Sometimes,Public_Transportation,Obesity_Type_III +1858,Female,26.0,1.610636,105.423532,yes,yes,3.0,3.0,Sometimes,no,2.180566,no,0.0,0.519905,Sometimes,Public_Transportation,Obesity_Type_III +1859,Female,25.943827,1.629491,104.839068,yes,yes,3.0,3.0,Sometimes,no,2.20979,no,0.114698,0.604422,Sometimes,Public_Transportation,Obesity_Type_III +1860,Female,25.291974,1.684768,104.821175,yes,yes,3.0,3.0,Sometimes,no,1.444379,no,0.224482,0.912187,Sometimes,Public_Transportation,Obesity_Type_III +1861,Female,25.653233,1.66494,110.92217,yes,yes,3.0,3.0,Sometimes,no,1.604075,no,0.029728,0.200122,Sometimes,Public_Transportation,Obesity_Type_III +1862,Female,25.783865,1.655646,110.21734,yes,yes,3.0,3.0,Sometimes,no,1.528258,no,0.01586,0.436068,Sometimes,Public_Transportation,Obesity_Type_III +1863,Female,19.17614,1.666194,124.805868,yes,yes,3.0,3.0,Sometimes,no,1.228838,no,0.792553,0.639561,Sometimes,Public_Transportation,Obesity_Type_III +1864,Female,21.207423,1.707508,121.864326,yes,yes,3.0,3.0,Sometimes,no,1.615548,no,1.376534,0.926052,Sometimes,Public_Transportation,Obesity_Type_III +1865,Female,21.633056,1.754174,133.783955,yes,yes,3.0,3.0,Sometimes,no,1.94595,no,1.493018,0.906611,Sometimes,Public_Transportation,Obesity_Type_III +1866,Female,21.009596,1.714193,131.866734,yes,yes,3.0,3.0,Sometimes,no,1.709556,no,1.592494,0.925843,Sometimes,Public_Transportation,Obesity_Type_III +1867,Female,26.0,1.62439,110.008636,yes,yes,3.0,3.0,Sometimes,no,2.507461,no,0.0,0.368472,Sometimes,Public_Transportation,Obesity_Type_III +1868,Female,26.0,1.638836,110.970479,yes,yes,3.0,3.0,Sometimes,no,2.644135,no,0.0,0.357581,Sometimes,Public_Transportation,Obesity_Type_III +1869,Female,26.0,1.621245,111.267334,yes,yes,3.0,3.0,Sometimes,no,2.605685,no,0.0,0.199227,Sometimes,Public_Transportation,Obesity_Type_III +1870,Female,25.954511,1.623514,109.980145,yes,yes,3.0,3.0,Sometimes,no,2.217348,no,0.001015,0.481031,Sometimes,Public_Transportation,Obesity_Type_III +1871,Female,21.001969,1.736215,132.145549,yes,yes,3.0,3.0,Sometimes,no,1.657541,no,1.672639,0.629285,Sometimes,Public_Transportation,Obesity_Type_III +1872,Female,21.772251,1.732951,132.904884,yes,yes,3.0,3.0,Sometimes,no,1.817899,no,1.577824,0.930836,Sometimes,Public_Transportation,Obesity_Type_III +1873,Female,23.76197,1.69135,114.480696,yes,yes,3.0,3.0,Sometimes,no,2.509535,no,0.334264,0.668172,Sometimes,Public_Transportation,Obesity_Type_III +1874,Female,24.449655,1.635062,113.277388,yes,yes,3.0,3.0,Sometimes,no,2.578038,no,0.165422,0.463196,Sometimes,Public_Transportation,Obesity_Type_III +1875,Female,18.378203,1.746061,128.261402,yes,yes,3.0,3.0,Sometimes,no,2.501638,no,1.546179,0.66488,Sometimes,Public_Transportation,Obesity_Type_III +1876,Female,19.725718,1.746529,129.363771,yes,yes,3.0,3.0,Sometimes,no,2.250711,no,0.64235,0.685129,Sometimes,Public_Transportation,Obesity_Type_III +1877,Female,21.344018,1.746516,144.302261,yes,yes,3.0,3.0,Sometimes,no,2.36651,no,1.247505,0.723366,Sometimes,Public_Transportation,Obesity_Type_III +1878,Female,21.322097,1.751118,149.291106,yes,yes,3.0,3.0,Sometimes,no,2.309409,no,1.68291,0.70558,Sometimes,Public_Transportation,Obesity_Type_III +1879,Female,19.262934,1.741014,132.57927,yes,yes,3.0,3.0,Sometimes,no,2.436261,no,1.464674,0.87092,Sometimes,Public_Transportation,Obesity_Type_III +1880,Female,21.566815,1.748533,133.94608,yes,yes,3.0,3.0,Sometimes,no,2.833566,no,1.413239,0.862724,Sometimes,Public_Transportation,Obesity_Type_III +1881,Female,24.475242,1.694726,112.776612,yes,yes,3.0,3.0,Sometimes,no,2.724099,no,0.336795,0.153462,Sometimes,Public_Transportation,Obesity_Type_III +1882,Female,25.612462,1.674515,112.879662,yes,yes,3.0,3.0,Sometimes,no,2.989389,no,0.36009,0.169016,Sometimes,Public_Transportation,Obesity_Type_III +1883,Female,26.0,1.631332,111.829957,yes,yes,3.0,3.0,Sometimes,no,2.55975,no,0.0,0.237307,Sometimes,Public_Transportation,Obesity_Type_III +1884,Female,26.0,1.641918,111.86899,yes,yes,3.0,3.0,Sometimes,no,2.635454,no,0.0,0.088309,Sometimes,Public_Transportation,Obesity_Type_III +1885,Female,26.0,1.640125,111.539494,yes,yes,3.0,3.0,Sometimes,no,2.625537,no,0.0,0.162494,Sometimes,Public_Transportation,Obesity_Type_III +1886,Female,26.0,1.649178,111.914361,yes,yes,3.0,3.0,Sometimes,no,2.884077,no,0.0,0.096576,Sometimes,Public_Transportation,Obesity_Type_III +1887,Female,25.96773,1.603404,105.031908,yes,yes,3.0,3.0,Sometimes,no,1.919473,no,0.027101,0.546137,Sometimes,Public_Transportation,Obesity_Type_III +1888,Female,25.989938,1.644199,105.036075,yes,yes,3.0,3.0,Sometimes,no,2.310076,no,0.07115,0.733085,Sometimes,Public_Transportation,Obesity_Type_III +1889,Female,26.0,1.628909,106.875927,yes,yes,3.0,3.0,Sometimes,no,2.686824,no,0.0,0.540812,Sometimes,Public_Transportation,Obesity_Type_III +1890,Female,25.617227,1.628019,108.265922,yes,yes,3.0,3.0,Sometimes,no,1.6213,no,0.090917,0.438216,Sometimes,Public_Transportation,Obesity_Type_III +1891,Female,21.030909,1.71818,133.466763,yes,yes,3.0,3.0,Sometimes,no,1.517215,no,1.486289,0.937492,Sometimes,Public_Transportation,Obesity_Type_III +1892,Female,20.951084,1.708581,131.274851,yes,yes,3.0,3.0,Sometimes,no,1.796956,no,1.684582,0.887388,Sometimes,Public_Transportation,Obesity_Type_III +1893,Female,22.980221,1.724983,132.94066,yes,yes,3.0,3.0,Sometimes,no,2.085553,no,1.271651,0.587932,Sometimes,Public_Transportation,Obesity_Type_III +1894,Female,23.426036,1.739991,133.485478,yes,yes,3.0,3.0,Sometimes,no,2.837797,no,1.343875,0.803156,Sometimes,Public_Transportation,Obesity_Type_III +1895,Female,25.999185,1.568543,102.000122,yes,yes,3.0,3.0,Sometimes,no,1.000544,no,0.001297,1.0,Sometimes,Public_Transportation,Obesity_Type_III +1896,Female,25.934757,1.579893,102.134646,yes,yes,3.0,3.0,Sometimes,no,1.005864,no,0.02006,0.695838,Sometimes,Public_Transportation,Obesity_Type_III +1897,Female,20.327723,1.782714,154.618446,yes,yes,3.0,3.0,Sometimes,no,2.319559,no,1.97073,0.768375,Sometimes,Public_Transportation,Obesity_Type_III +1898,Female,19.47219,1.793824,160.935351,yes,yes,3.0,3.0,Sometimes,no,2.069257,no,1.986646,0.947091,Sometimes,Public_Transportation,Obesity_Type_III +1899,Female,26.0,1.659557,111.999096,yes,yes,3.0,3.0,Sometimes,no,2.956548,no,0.0,0.010056,Sometimes,Public_Transportation,Obesity_Type_III +1900,Female,26.0,1.65782,111.95611,yes,yes,3.0,3.0,Sometimes,no,2.777557,no,0.0,0.051858,Sometimes,Public_Transportation,Obesity_Type_III +1901,Female,25.816445,1.684082,104.592372,yes,yes,3.0,3.0,Sometimes,no,1.295436,no,0.282063,0.929356,Sometimes,Public_Transportation,Obesity_Type_III +1902,Female,25.498965,1.68395,104.846817,yes,yes,3.0,3.0,Sometimes,no,1.241378,no,0.259427,0.852362,Sometimes,Public_Transportation,Obesity_Type_III +1903,Female,19.137495,1.716521,127.642324,yes,yes,3.0,3.0,Sometimes,no,1.313834,no,0.912334,0.707494,Sometimes,Public_Transportation,Obesity_Type_III +1904,Female,20.190733,1.680762,125.418548,yes,yes,3.0,3.0,Sometimes,no,1.124366,no,0.877067,0.552006,Sometimes,Public_Transportation,Obesity_Type_III +1905,Female,26.0,1.623303,110.81746,yes,yes,3.0,3.0,Sometimes,no,2.673754,no,0.0,0.33929,Sometimes,Public_Transportation,Obesity_Type_III +1906,Female,26.0,1.631856,110.804337,yes,yes,3.0,3.0,Sometimes,no,2.70485,no,0.0,0.243338,Sometimes,Public_Transportation,Obesity_Type_III +1907,Female,21.840654,1.747479,136.516648,yes,yes,3.0,3.0,Sometimes,no,1.607531,no,1.963379,0.858392,Sometimes,Public_Transportation,Obesity_Type_III +1908,Female,20.871667,1.782453,137.852618,yes,yes,3.0,3.0,Sometimes,no,2.748909,no,1.989171,0.832515,Sometimes,Public_Transportation,Obesity_Type_III +1909,Female,21.501721,1.809871,152.394739,yes,yes,3.0,3.0,Sometimes,no,2.351207,no,0.246831,0.91336,Sometimes,Public_Transportation,Obesity_Type_III +1910,Female,21.521294,1.803677,160.639405,yes,yes,3.0,3.0,Sometimes,no,2.404049,no,0.427905,0.639894,Sometimes,Public_Transportation,Obesity_Type_III +1911,Female,18.314593,1.745602,133.554686,yes,yes,3.0,3.0,Sometimes,no,2.923792,no,1.536555,0.62535,Sometimes,Public_Transportation,Obesity_Type_III +1912,Female,19.529746,1.751038,133.843033,yes,yes,3.0,3.0,Sometimes,no,2.955075,no,1.46046,0.655558,Sometimes,Public_Transportation,Obesity_Type_III +1913,Female,26.0,1.631547,111.588625,yes,yes,3.0,3.0,Sometimes,no,2.554007,no,0.0,0.252635,Sometimes,Public_Transportation,Obesity_Type_III +1914,Female,26.0,1.635905,111.571076,yes,yes,3.0,3.0,Sometimes,no,2.511399,no,0.0,0.258472,Sometimes,Public_Transportation,Obesity_Type_III +1915,Female,26.0,1.616975,104.846218,yes,yes,3.0,3.0,Sometimes,no,2.653563,no,0.0,0.55415,Sometimes,Public_Transportation,Obesity_Type_III +1916,Female,26.0,1.62788,104.92057,yes,yes,3.0,3.0,Sometimes,no,2.588107,no,0.0,0.465607,Sometimes,Public_Transportation,Obesity_Type_III +1917,Female,21.768153,1.76416,133.888629,yes,yes,3.0,3.0,Sometimes,no,2.32502,no,1.441791,0.918468,Sometimes,Public_Transportation,Obesity_Type_III +1918,Female,21.238416,1.763847,133.937873,yes,yes,3.0,3.0,Sometimes,no,2.836791,no,1.46782,0.890527,Sometimes,Public_Transportation,Obesity_Type_III +1919,Female,25.930376,1.608808,102.083964,yes,yes,3.0,3.0,Sometimes,no,1.019684,no,0.026033,1.0,Sometimes,Public_Transportation,Obesity_Type_III +1920,Female,25.919571,1.610488,102.174953,yes,yes,3.0,3.0,Sometimes,no,1.032834,no,0.04525,0.922749,Sometimes,Public_Transportation,Obesity_Type_III +1921,Female,25.918524,1.621231,104.986792,yes,yes,3.0,3.0,Sometimes,no,1.653049,no,0.139159,0.711331,Sometimes,Public_Transportation,Obesity_Type_III +1922,Female,25.565662,1.642392,104.988082,yes,yes,3.0,3.0,Sometimes,no,1.204055,no,0.094851,0.603736,Sometimes,Public_Transportation,Obesity_Type_III +1923,Female,18.744914,1.801983,138.034526,yes,yes,3.0,3.0,Sometimes,no,2.691591,no,1.168368,0.735372,Sometimes,Public_Transportation,Obesity_Type_III +1924,Female,21.704699,1.787614,137.858254,yes,yes,3.0,3.0,Sometimes,no,2.531456,no,1.980738,0.835771,Sometimes,Public_Transportation,Obesity_Type_III +1925,Female,20.089969,1.801478,151.417292,yes,yes,3.0,3.0,Sometimes,no,2.226081,no,1.881761,0.933964,Sometimes,Public_Transportation,Obesity_Type_III +1926,Female,18.120739,1.807576,152.567671,yes,yes,3.0,3.0,Sometimes,no,2.164718,no,1.999631,0.941336,Sometimes,Public_Transportation,Obesity_Type_III +1927,Female,26.0,1.650125,111.939671,yes,yes,3.0,3.0,Sometimes,no,2.770732,no,0.0,0.125235,Sometimes,Public_Transportation,Obesity_Type_III +1928,Female,26.0,1.652674,111.919155,yes,yes,3.0,3.0,Sometimes,no,2.814517,no,0.0,0.101598,Sometimes,Public_Transportation,Obesity_Type_III +1929,Female,25.986185,1.663632,105.122109,yes,yes,3.0,3.0,Sometimes,no,1.626467,no,0.010183,0.412806,Sometimes,Public_Transportation,Obesity_Type_III +1930,Female,25.982113,1.627818,105.428628,yes,yes,3.0,3.0,Sometimes,no,1.48075,no,0.098043,0.663492,Sometimes,Public_Transportation,Obesity_Type_III +1931,Female,25.748627,1.66877,104.774144,yes,yes,3.0,3.0,Sometimes,no,1.526416,no,0.180158,0.747909,Sometimes,Public_Transportation,Obesity_Type_III +1932,Female,25.959772,1.642098,104.966758,yes,yes,3.0,3.0,Sometimes,no,1.482002,no,0.204108,0.657221,Sometimes,Public_Transportation,Obesity_Type_III +1933,Female,25.653233,1.65757,109.931233,yes,yes,3.0,3.0,Sometimes,no,1.610472,no,0.029728,0.472343,Sometimes,Public_Transportation,Obesity_Type_III +1934,Female,25.783865,1.643111,109.910012,yes,yes,3.0,3.0,Sometimes,no,1.530992,no,0.01586,0.445495,Sometimes,Public_Transportation,Obesity_Type_III +1935,Female,21.412434,1.675562,121.639178,yes,yes,3.0,3.0,Sometimes,no,1.484938,no,0.74225,0.604692,Sometimes,Public_Transportation,Obesity_Type_III +1936,Female,22.77789,1.661415,120.748213,yes,yes,3.0,3.0,Sometimes,no,1.91039,no,0.217455,0.77582,Sometimes,Public_Transportation,Obesity_Type_III +1937,Female,21.140165,1.713133,133.735889,yes,yes,3.0,3.0,Sometimes,no,1.411365,no,1.564618,0.910683,Sometimes,Public_Transportation,Obesity_Type_III +1938,Female,21.413498,1.7199,133.886031,yes,yes,3.0,3.0,Sometimes,no,2.41752,no,1.556155,0.873936,Sometimes,Public_Transportation,Obesity_Type_III +1939,Female,26.0,1.622701,109.982692,yes,yes,3.0,3.0,Sometimes,no,2.688229,no,0.0,0.451377,Sometimes,Public_Transportation,Obesity_Type_III +1940,Female,26.0,1.622468,110.400847,yes,yes,3.0,3.0,Sometimes,no,2.695094,no,0.0,0.413474,Sometimes,Public_Transportation,Obesity_Type_III +1941,Female,26.0,1.618867,110.777391,yes,yes,3.0,3.0,Sometimes,no,2.618198,no,0.0,0.380695,Sometimes,Public_Transportation,Obesity_Type_III +1942,Female,26.0,1.600905,110.074946,yes,yes,3.0,3.0,Sometimes,no,2.555189,no,0.0,0.462973,Sometimes,Public_Transportation,Obesity_Type_III +1943,Female,21.391371,1.730636,131.902591,yes,yes,3.0,3.0,Sometimes,no,1.419136,no,1.700889,0.930888,Sometimes,Public_Transportation,Obesity_Type_III +1944,Female,21.051982,1.729719,131.877558,yes,yes,3.0,3.0,Sometimes,no,1.422483,no,1.708971,0.673009,Sometimes,Public_Transportation,Obesity_Type_III +1945,Female,23.455303,1.677672,114.470482,yes,yes,3.0,3.0,Sometimes,no,2.426094,no,0.294763,0.737226,Sometimes,Public_Transportation,Obesity_Type_III +1946,Female,23.69484,1.637524,113.90506,yes,yes,3.0,3.0,Sometimes,no,2.495961,no,0.189831,0.652289,Sometimes,Public_Transportation,Obesity_Type_III +1947,Female,20.601222,1.738717,128.114161,yes,yes,3.0,3.0,Sometimes,no,1.797041,no,1.427413,0.966181,Sometimes,Public_Transportation,Obesity_Type_III +1948,Female,20.81158,1.741193,128.763843,yes,yes,3.0,3.0,Sometimes,no,1.768111,no,0.616503,0.968151,Sometimes,Public_Transportation,Obesity_Type_III +1949,Female,19.993565,1.792833,152.43563,yes,yes,3.0,3.0,Sometimes,no,2.365188,no,1.256115,0.760091,Sometimes,Public_Transportation,Obesity_Type_III +1950,Female,20.074449,1.810427,152.217135,yes,yes,3.0,3.0,Sometimes,no,2.299156,no,1.699056,0.729722,Sometimes,Public_Transportation,Obesity_Type_III +1951,Female,18.904037,1.743589,133.281333,yes,yes,3.0,3.0,Sometimes,no,2.684819,no,1.46525,0.804491,Sometimes,Public_Transportation,Obesity_Type_III +1952,Female,19.783234,1.747962,133.936535,yes,yes,3.0,3.0,Sometimes,no,2.84174,no,1.429256,0.775378,Sometimes,Public_Transportation,Obesity_Type_III +1953,Female,24.291205,1.71146,113.372851,yes,yes,3.0,3.0,Sometimes,no,2.796894,no,0.382189,0.16779,Sometimes,Public_Transportation,Obesity_Type_III +1954,Female,25.311534,1.685482,113.451224,yes,yes,3.0,3.0,Sometimes,no,2.987718,no,0.387074,0.283804,Sometimes,Public_Transportation,Obesity_Type_III +1955,Female,26.0,1.639251,111.927001,yes,yes,3.0,3.0,Sometimes,no,2.675567,no,0.0,0.081929,Sometimes,Public_Transportation,Obesity_Type_III +1956,Female,26.0,1.654784,111.933152,yes,yes,3.0,3.0,Sometimes,no,2.770125,no,0.0,0.088236,Sometimes,Public_Transportation,Obesity_Type_III +1957,Female,26.0,1.641209,111.856492,yes,yes,3.0,3.0,Sometimes,no,2.621877,no,0.0,0.153669,Sometimes,Public_Transportation,Obesity_Type_III +1958,Female,26.0,1.643167,111.894229,yes,yes,3.0,3.0,Sometimes,no,2.72005,no,0.0,0.127443,Sometimes,Public_Transportation,Obesity_Type_III +1959,Female,25.966504,1.63073,104.790549,yes,yes,3.0,3.0,Sometimes,no,2.436097,no,0.129178,0.683736,Sometimes,Public_Transportation,Obesity_Type_III +1960,Female,25.950898,1.649867,104.791035,yes,yes,3.0,3.0,Sometimes,no,2.535629,no,0.152713,0.7702,Sometimes,Public_Transportation,Obesity_Type_III +1961,Female,26.0,1.613574,107.012256,yes,yes,3.0,3.0,Sometimes,no,2.678779,no,0.0,0.508848,Sometimes,Public_Transportation,Obesity_Type_III +1962,Female,26.0,1.627532,106.69053,yes,yes,3.0,3.0,Sometimes,no,2.569713,no,0.0,0.396972,Sometimes,Public_Transportation,Obesity_Type_III +1963,Female,20.848608,1.726606,131.76807,yes,yes,3.0,3.0,Sometimes,no,1.759352,no,1.469928,0.950438,Sometimes,Public_Transportation,Obesity_Type_III +1964,Female,20.801791,1.721476,131.042274,yes,yes,3.0,3.0,Sometimes,no,1.837184,no,1.525165,0.926443,Sometimes,Public_Transportation,Obesity_Type_III +1965,Female,23.712641,1.742901,132.8071,yes,yes,3.0,3.0,Sometimes,no,2.856795,no,1.046463,0.586163,Sometimes,Public_Transportation,Obesity_Type_III +1966,Female,24.063874,1.737313,133.166595,yes,yes,3.0,3.0,Sometimes,no,2.846259,no,1.295759,0.723154,Sometimes,Public_Transportation,Obesity_Type_III +1967,Female,25.95774,1.62414,102.233445,yes,yes,3.0,3.0,Sometimes,no,1.067716,no,0.030541,1.0,Sometimes,Public_Transportation,Obesity_Type_III +1968,Female,25.909353,1.644078,102.277765,yes,yes,3.0,3.0,Sometimes,no,1.029241,no,0.075776,1.0,Sometimes,Public_Transportation,Obesity_Type_III +1969,Female,25.954995,1.592529,102.874549,yes,yes,3.0,3.0,Sometimes,no,1.074412,no,0.006265,0.845633,Sometimes,Public_Transportation,Obesity_Type_III +1970,Female,25.908829,1.607734,102.305767,yes,yes,3.0,3.0,Sometimes,no,1.030501,no,0.06582,0.991473,Sometimes,Public_Transportation,Obesity_Type_III +1971,Female,19.297004,1.817271,141.917802,yes,yes,3.0,3.0,Sometimes,no,2.699675,no,1.520818,0.76399,Sometimes,Public_Transportation,Obesity_Type_III +1972,Female,18.301773,1.808765,140.292018,yes,yes,3.0,3.0,Sometimes,no,2.830247,no,1.783138,0.789064,Sometimes,Public_Transportation,Obesity_Type_III +1973,Female,19.872667,1.817231,152.371911,yes,yes,3.0,3.0,Sometimes,no,2.305521,no,1.956278,0.92376,Sometimes,Public_Transportation,Obesity_Type_III +1974,Female,18.13782,1.819728,151.278532,yes,yes,3.0,3.0,Sometimes,no,2.155926,no,1.999836,0.94603,Sometimes,Public_Transportation,Obesity_Type_III +1975,Female,26.0,1.656465,111.949972,yes,yes,3.0,3.0,Sometimes,no,2.784303,no,0.0,0.127775,Sometimes,Public_Transportation,Obesity_Type_III +1976,Female,26.0,1.648143,111.950113,yes,yes,3.0,3.0,Sometimes,no,2.786417,no,0.0,0.079673,Sometimes,Public_Transportation,Obesity_Type_III +1977,Female,26.0,1.622703,111.216192,yes,yes,3.0,3.0,Sometimes,no,2.707201,no,0.0,0.167272,Sometimes,Public_Transportation,Obesity_Type_III +1978,Female,26.0,1.640606,111.036881,yes,yes,3.0,3.0,Sometimes,no,2.709428,no,0.0,0.228486,Sometimes,Public_Transportation,Obesity_Type_III +1979,Female,25.42724,1.683502,104.759643,yes,yes,3.0,3.0,Sometimes,no,1.525127,no,0.220825,0.896105,Sometimes,Public_Transportation,Obesity_Type_III +1980,Female,25.795187,1.669039,104.593929,yes,yes,3.0,3.0,Sometimes,no,1.554925,no,0.209586,0.823069,Sometimes,Public_Transportation,Obesity_Type_III +1981,Female,25.493586,1.673665,104.704707,yes,yes,3.0,3.0,Sometimes,no,1.170515,no,0.264831,0.896842,Sometimes,Public_Transportation,Obesity_Type_III +1982,Female,25.524336,1.668931,104.768318,yes,yes,3.0,3.0,Sometimes,no,1.436616,no,0.167086,0.764717,Sometimes,Public_Transportation,Obesity_Type_III +1983,Female,20.908785,1.700996,126.490236,yes,yes,3.0,3.0,Sometimes,no,1.242832,no,0.530925,0.575969,Sometimes,Public_Transportation,Obesity_Type_III +1984,Female,20.781751,1.734092,125.117633,yes,yes,3.0,3.0,Sometimes,no,1.54249,no,0.6277,0.450479,Sometimes,Public_Transportation,Obesity_Type_III +1985,Female,21.289104,1.708291,130.986338,yes,yes,3.0,3.0,Sometimes,no,1.205548,no,1.723934,0.952352,Sometimes,Public_Transportation,Obesity_Type_III +1986,Female,21.210732,1.716497,130.871127,yes,yes,3.0,3.0,Sometimes,no,1.301657,no,1.718543,0.947884,Sometimes,Public_Transportation,Obesity_Type_III +1987,Female,26.0,1.62495,111.00492,yes,yes,3.0,3.0,Sometimes,no,2.704315,no,0.0,0.322666,Sometimes,Public_Transportation,Obesity_Type_III +1988,Female,26.0,1.594776,110.640929,yes,yes,3.0,3.0,Sometimes,no,2.639816,no,0.0,0.452236,Sometimes,Public_Transportation,Obesity_Type_III +1989,Female,26.0,1.626503,110.818757,yes,yes,3.0,3.0,Sometimes,no,2.708927,no,0.0,0.278962,Sometimes,Public_Transportation,Obesity_Type_III +1990,Female,26.0,1.624576,110.803117,yes,yes,3.0,3.0,Sometimes,no,2.704827,no,0.0,0.269577,Sometimes,Public_Transportation,Obesity_Type_III +1991,Female,21.656907,1.729099,134.842656,yes,yes,3.0,3.0,Sometimes,no,1.3954,no,1.931173,0.878258,Sometimes,Public_Transportation,Obesity_Type_III +1992,Female,21.760734,1.73581,135.346677,yes,yes,3.0,3.0,Sometimes,no,2.611654,no,1.618512,0.868788,Sometimes,Public_Transportation,Obesity_Type_III +1993,Female,21.028989,1.748524,133.878843,yes,yes,3.0,3.0,Sometimes,no,2.868167,no,1.48372,0.83509,Sometimes,Public_Transportation,Obesity_Type_III +1994,Female,21.282238,1.748951,133.662583,yes,yes,3.0,3.0,Sometimes,no,2.247979,no,1.609938,0.849236,Sometimes,Public_Transportation,Obesity_Type_III +1995,Female,20.388049,1.777971,137.785027,yes,yes,3.0,3.0,Sometimes,no,2.702789,no,1.606109,0.731213,Sometimes,Public_Transportation,Obesity_Type_III +1996,Female,21.022206,1.73995,135.693381,yes,yes,3.0,3.0,Sometimes,no,1.663213,no,1.094035,0.786665,Sometimes,Public_Transportation,Obesity_Type_III +1997,Female,20.102241,1.816868,153.959945,yes,yes,3.0,3.0,Sometimes,no,2.414739,no,1.917014,0.927982,Sometimes,Public_Transportation,Obesity_Type_III +1998,Female,21.291969,1.8002,155.242672,yes,yes,3.0,3.0,Sometimes,no,2.351193,no,1.051889,0.686491,Sometimes,Public_Transportation,Obesity_Type_III +1999,Female,20.530998,1.74647,133.644711,yes,yes,3.0,3.0,Sometimes,no,2.896088,no,1.612741,0.77319,Sometimes,Public_Transportation,Obesity_Type_III +2000,Female,18.976968,1.759091,133.903612,yes,yes,3.0,3.0,Sometimes,no,2.862408,no,1.456933,0.742423,Sometimes,Public_Transportation,Obesity_Type_III +2001,Female,20.924956,1.752531,133.618706,yes,yes,3.0,3.0,Sometimes,no,2.887659,no,1.480919,0.779641,Sometimes,Public_Transportation,Obesity_Type_III +2002,Female,21.28253,1.761773,133.903469,yes,yes,3.0,3.0,Sometimes,no,2.893062,no,1.408177,0.807457,Sometimes,Public_Transportation,Obesity_Type_III +2003,Female,26.0,1.633195,111.883747,yes,yes,3.0,3.0,Sometimes,no,2.619517,no,0.0,0.140368,Sometimes,Public_Transportation,Obesity_Type_III +2004,Female,26.0,1.633945,111.9307,yes,yes,3.0,3.0,Sometimes,no,2.682804,no,0.0,0.15171,Sometimes,Public_Transportation,Obesity_Type_III +2005,Female,26.0,1.641601,111.830924,yes,yes,3.0,3.0,Sometimes,no,2.552388,no,0.0,0.196288,Sometimes,Public_Transportation,Obesity_Type_III +2006,Female,26.0,1.641132,111.841706,yes,yes,3.0,3.0,Sometimes,no,2.617401,no,0.0,0.200379,Sometimes,Public_Transportation,Obesity_Type_III +2007,Female,25.999174,1.638218,104.810024,yes,yes,3.0,3.0,Sometimes,no,2.654636,no,0.069238,0.629578,Sometimes,Public_Transportation,Obesity_Type_III +2008,Female,25.999942,1.627483,104.881994,yes,yes,3.0,3.0,Sometimes,no,2.569364,no,0.159255,0.419446,Sometimes,Public_Transportation,Obesity_Type_III +2009,Female,26.0,1.610225,104.936381,yes,yes,3.0,3.0,Sometimes,no,1.322004,no,0.0,0.539876,Sometimes,Public_Transportation,Obesity_Type_III +2010,Female,26.0,1.61739,105.013901,yes,yes,3.0,3.0,Sometimes,no,1.292479,no,0.0,0.541309,Sometimes,Public_Transportation,Obesity_Type_III +2011,Female,23.365041,1.744319,133.45249,yes,yes,3.0,3.0,Sometimes,no,2.839069,no,1.231031,0.792496,Sometimes,Public_Transportation,Obesity_Type_III +2012,Female,23.421726,1.755467,133.478611,yes,yes,3.0,3.0,Sometimes,no,2.843782,no,1.302507,0.773807,Sometimes,Public_Transportation,Obesity_Type_III +2013,Female,18.826782,1.746416,133.747012,yes,yes,3.0,3.0,Sometimes,no,2.858389,no,1.444382,0.713823,Sometimes,Public_Transportation,Obesity_Type_III +2014,Female,18.94093,1.746411,133.676663,yes,yes,3.0,3.0,Sometimes,no,2.864933,no,1.501647,0.786846,Sometimes,Public_Transportation,Obesity_Type_III +2015,Female,25.921678,1.611452,102.363149,yes,yes,3.0,3.0,Sometimes,no,1.031701,no,0.03465,0.912345,Sometimes,Public_Transportation,Obesity_Type_III +2016,Female,25.940153,1.596813,102.320437,yes,yes,3.0,3.0,Sometimes,no,1.000536,no,0.005939,0.566353,Sometimes,Public_Transportation,Obesity_Type_III +2017,Female,25.999636,1.610126,102.686908,yes,yes,3.0,3.0,Sometimes,no,1.020313,no,0.108386,1.0,Sometimes,Public_Transportation,Obesity_Type_III +2018,Female,25.907833,1.623113,102.555691,yes,yes,3.0,3.0,Sometimes,no,1.063422,no,0.051097,0.543118,Sometimes,Public_Transportation,Obesity_Type_III +2019,Female,25.991194,1.618348,104.94582,yes,yes,3.0,3.0,Sometimes,no,2.45126,no,0.043689,0.670402,Sometimes,Public_Transportation,Obesity_Type_III +2020,Female,25.993154,1.609401,104.854928,yes,yes,3.0,3.0,Sometimes,no,2.613504,no,0.067985,0.778632,Sometimes,Public_Transportation,Obesity_Type_III +2021,Female,25.988668,1.621671,105.313967,yes,yes,3.0,3.0,Sometimes,no,1.042989,no,0.097114,0.42954,Sometimes,Public_Transportation,Obesity_Type_III +2022,Female,25.976209,1.614484,104.999403,yes,yes,3.0,3.0,Sometimes,no,1.237557,no,0.083675,0.543761,Sometimes,Public_Transportation,Obesity_Type_III +2023,Female,18.743587,1.789143,138.202869,yes,yes,3.0,3.0,Sometimes,no,2.83377,no,0.806422,0.672513,Sometimes,Public_Transportation,Obesity_Type_III +2024,Female,20.323767,1.774207,138.143162,yes,yes,3.0,3.0,Sometimes,no,2.816052,no,1.571865,0.688058,Sometimes,Public_Transportation,Obesity_Type_III +2025,Female,21.394047,1.792933,137.832414,yes,yes,3.0,3.0,Sometimes,no,2.682909,no,1.318743,0.900497,Sometimes,Public_Transportation,Obesity_Type_III +2026,Female,21.838323,1.758959,137.79299,yes,yes,3.0,3.0,Sometimes,no,2.640539,no,1.879818,0.885993,Sometimes,Public_Transportation,Obesity_Type_III +2027,Female,18.20634,1.807406,141.799429,yes,yes,3.0,3.0,Sometimes,no,2.472903,no,1.998047,0.840911,Sometimes,Public_Transportation,Obesity_Type_III +2028,Female,18.532826,1.75091,142.545183,yes,yes,3.0,3.0,Sometimes,no,2.372058,no,1.324805,0.870795,Sometimes,Public_Transportation,Obesity_Type_III +2029,Female,20.438478,1.805803,153.149491,yes,yes,3.0,3.0,Sometimes,no,2.387991,no,0.850715,0.656491,Sometimes,Public_Transportation,Obesity_Type_III +2030,Female,20.796266,1.796538,152.473675,yes,yes,3.0,3.0,Sometimes,no,2.322003,no,0.886602,0.843283,Sometimes,Public_Transportation,Obesity_Type_III +2031,Female,26.0,1.634894,111.946321,yes,yes,3.0,3.0,Sometimes,no,2.737353,no,0.0,0.076094,Sometimes,Public_Transportation,Obesity_Type_III +2032,Female,26.0,1.639524,111.945588,yes,yes,3.0,3.0,Sometimes,no,2.739351,no,0.0,0.064769,Sometimes,Public_Transportation,Obesity_Type_III +2033,Female,26.0,1.643017,111.720238,yes,yes,3.0,3.0,Sometimes,no,2.632498,no,0.0,0.140792,Sometimes,Public_Transportation,Obesity_Type_III +2034,Female,26.0,1.641849,111.682693,yes,yes,3.0,3.0,Sometimes,no,2.632253,no,0.0,0.244205,Sometimes,Public_Transportation,Obesity_Type_III +2035,Female,26.0,1.621167,104.947703,yes,yes,3.0,3.0,Sometimes,no,2.57721,no,0.0,0.402075,Sometimes,Public_Transportation,Obesity_Type_III +2036,Female,25.992898,1.638075,105.036522,yes,yes,3.0,3.0,Sometimes,no,2.419153,no,0.019404,0.597626,Sometimes,Public_Transportation,Obesity_Type_III +2037,Female,26.0,1.60937,105.407313,yes,yes,3.0,3.0,Sometimes,no,2.609052,no,0.0,0.54859,Sometimes,Public_Transportation,Obesity_Type_III +2038,Female,26.0,1.608283,105.359688,yes,yes,3.0,3.0,Sometimes,no,2.476002,no,0.0,0.546345,Sometimes,Public_Transportation,Obesity_Type_III +2039,Female,25.951737,1.629442,104.835346,yes,yes,3.0,3.0,Sometimes,no,2.225139,no,0.035928,0.565315,Sometimes,Public_Transportation,Obesity_Type_III +2040,Female,25.982261,1.629225,104.838425,yes,yes,3.0,3.0,Sometimes,no,2.556068,no,0.01682,0.58284,Sometimes,Public_Transportation,Obesity_Type_III +2041,Female,25.470652,1.680218,104.807284,yes,yes,3.0,3.0,Sometimes,no,1.423073,no,0.197993,0.763359,Sometimes,Public_Transportation,Obesity_Type_III +2042,Female,25.289428,1.686033,104.772164,yes,yes,3.0,3.0,Sometimes,no,1.299194,no,0.234303,0.946888,Sometimes,Public_Transportation,Obesity_Type_III +2043,Female,25.696073,1.662978,110.930509,yes,yes,3.0,3.0,Sometimes,no,1.679489,no,0.017804,0.21523,Sometimes,Public_Transportation,Obesity_Type_III +2044,Female,25.561868,1.675185,110.621723,yes,yes,3.0,3.0,Sometimes,no,1.49583,no,0.109327,0.384129,Sometimes,Public_Transportation,Obesity_Type_III +2045,Female,25.834018,1.62456,110.10589,yes,yes,3.0,3.0,Sometimes,no,1.81786,no,0.011161,0.447224,Sometimes,Public_Transportation,Obesity_Type_III +2046,Female,25.895546,1.626179,110.074019,yes,yes,3.0,3.0,Sometimes,no,1.967707,no,0.01437,0.434073,Sometimes,Public_Transportation,Obesity_Type_III +2047,Female,19.035557,1.682594,127.427458,yes,yes,3.0,3.0,Sometimes,no,1.441289,no,1.425712,0.662277,Sometimes,Public_Transportation,Obesity_Type_III +2048,Female,18.634286,1.669354,126.088301,yes,yes,3.0,3.0,Sometimes,no,1.144539,no,0.922014,0.899673,Sometimes,Public_Transportation,Obesity_Type_III +2049,Female,20.700876,1.68838,121.889803,yes,yes,3.0,3.0,Sometimes,no,1.353167,no,1.093679,0.534553,Sometimes,Public_Transportation,Obesity_Type_III +2050,Female,20.741442,1.694439,122.813033,yes,yes,3.0,3.0,Sometimes,no,1.409444,no,0.933595,0.840393,Sometimes,Public_Transportation,Obesity_Type_III +2051,Female,21.695892,1.755476,133.870501,yes,yes,3.0,3.0,Sometimes,no,1.959287,no,1.433151,0.911335,Sometimes,Public_Transportation,Obesity_Type_III +2052,Female,21.635977,1.748106,133.259033,yes,yes,3.0,3.0,Sometimes,no,1.931163,no,1.562213,0.926565,Sometimes,Public_Transportation,Obesity_Type_III +2053,Female,21.232659,1.719913,131.567481,yes,yes,3.0,3.0,Sometimes,no,1.651462,no,1.655993,0.939982,Sometimes,Public_Transportation,Obesity_Type_III +2054,Female,21.008297,1.723587,131.929712,yes,yes,3.0,3.0,Sometimes,no,1.683448,no,1.645532,0.858059,Sometimes,Public_Transportation,Obesity_Type_III +2055,Female,25.962949,1.623812,109.996742,yes,yes,3.0,3.0,Sometimes,no,2.395387,no,0.000272,0.461532,Sometimes,Public_Transportation,Obesity_Type_III +2056,Female,26.0,1.624099,109.978402,yes,yes,3.0,3.0,Sometimes,no,2.523793,no,0.0,0.383953,Sometimes,Public_Transportation,Obesity_Type_III +2057,Female,26.0,1.632983,111.157811,yes,yes,3.0,3.0,Sometimes,no,2.638896,no,0.0,0.224559,Sometimes,Public_Transportation,Obesity_Type_III +2058,Female,26.0,1.640745,110.919646,yes,yes,3.0,3.0,Sometimes,no,2.666178,no,0.0,0.276907,Sometimes,Public_Transportation,Obesity_Type_III +2059,Female,26.0,1.629727,111.275646,yes,yes,3.0,3.0,Sometimes,no,2.495851,no,0.0,0.218645,Sometimes,Public_Transportation,Obesity_Type_III +2060,Female,26.0,1.624134,111.531208,yes,yes,3.0,3.0,Sometimes,no,2.609188,no,0.0,0.17403,Sometimes,Public_Transportation,Obesity_Type_III +2061,Female,25.964788,1.623938,109.984263,yes,yes,3.0,3.0,Sometimes,no,2.471721,no,9.6e-05,0.433463,Sometimes,Public_Transportation,Obesity_Type_III +2062,Female,25.994949,1.593321,110.168166,yes,yes,3.0,3.0,Sometimes,no,2.447306,no,0.000454,0.486558,Sometimes,Public_Transportation,Obesity_Type_III +2063,Female,20.952737,1.730333,132.116491,yes,yes,3.0,3.0,Sometimes,no,1.674061,no,1.683497,0.780199,Sometimes,Public_Transportation,Obesity_Type_III +2064,Female,20.978166,1.721057,132.054793,yes,yes,3.0,3.0,Sometimes,no,1.678791,no,1.68249,0.818871,Sometimes,Public_Transportation,Obesity_Type_III +2065,Female,21.67447,1.71978,132.262558,yes,yes,3.0,3.0,Sometimes,no,1.777805,no,1.584716,0.927341,Sometimes,Public_Transportation,Obesity_Type_III +2066,Female,21.568951,1.699315,133.10761,yes,yes,3.0,3.0,Sometimes,no,1.733306,no,1.773304,0.921136,Sometimes,Public_Transportation,Obesity_Type_III +2067,Female,23.647935,1.681394,114.479459,yes,yes,3.0,3.0,Sometimes,no,2.435978,no,0.232742,0.692608,Sometimes,Public_Transportation,Obesity_Type_III +2068,Female,24.196367,1.697421,114.482386,yes,yes,3.0,3.0,Sometimes,no,2.909675,no,0.360908,0.573887,Sometimes,Public_Transportation,Obesity_Type_III +2069,Female,24.284833,1.650726,113.774198,yes,yes,3.0,3.0,Sometimes,no,2.75137,no,0.324913,0.516764,Sometimes,Public_Transportation,Obesity_Type_III +2070,Female,24.693108,1.667383,112.982549,yes,yes,3.0,3.0,Sometimes,no,2.887909,no,0.312923,0.210997,Sometimes,Public_Transportation,Obesity_Type_III +2071,Female,18.862264,1.746277,128.705761,yes,yes,3.0,3.0,Sometimes,no,2.411582,no,0.985287,0.675076,Sometimes,Public_Transportation,Obesity_Type_III +2072,Female,18.423482,1.735461,126.798173,yes,yes,3.0,3.0,Sometimes,no,2.38639,no,1.544632,0.900067,Sometimes,Public_Transportation,Obesity_Type_III +2073,Female,20.394082,1.747714,128.148108,yes,yes,3.0,3.0,Sometimes,no,2.232601,no,0.554323,0.836151,Sometimes,Public_Transportation,Obesity_Type_III +2074,Female,20.217015,1.71582,129.466541,yes,yes,3.0,3.0,Sometimes,no,1.755907,no,1.48809,0.857718,Sometimes,Public_Transportation,Obesity_Type_III +2075,Female,21.330178,1.747987,147.296186,yes,yes,3.0,3.0,Sometimes,no,2.336349,no,1.4164,0.711724,Sometimes,Public_Transportation,Obesity_Type_III +2076,Female,19.528936,1.817917,142.559161,yes,yes,3.0,3.0,Sometimes,no,2.562002,no,1.976427,0.740331,Sometimes,Public_Transportation,Obesity_Type_III +2077,Female,19.364339,1.80835,150.51648,yes,yes,3.0,3.0,Sometimes,no,2.305574,no,1.734424,0.863043,Sometimes,Public_Transportation,Obesity_Type_III +2078,Female,21.131526,1.739457,150.37757,yes,yes,3.0,3.0,Sometimes,no,2.319912,no,1.582675,0.680746,Sometimes,Public_Transportation,Obesity_Type_III +2079,Female,19.012872,1.742062,133.779919,yes,yes,3.0,3.0,Sometimes,no,2.70196,no,1.465909,0.813235,Sometimes,Public_Transportation,Obesity_Type_III +2080,Female,18.469086,1.741925,133.017105,yes,yes,3.0,3.0,Sometimes,no,2.474518,no,1.560261,0.662489,Sometimes,Public_Transportation,Obesity_Type_III +2081,Female,21.572114,1.751067,133.845064,yes,yes,3.0,3.0,Sometimes,no,2.748569,no,1.427037,0.902825,Sometimes,Public_Transportation,Obesity_Type_III +2082,Female,21.680123,1.749118,133.955091,yes,yes,3.0,3.0,Sometimes,no,2.827773,no,1.412357,0.901071,Sometimes,Public_Transportation,Obesity_Type_III +2083,Female,24.469756,1.663341,113.077187,yes,yes,3.0,3.0,Sometimes,no,2.632224,no,0.300964,0.26956,Sometimes,Public_Transportation,Obesity_Type_III +2084,Female,25.12791,1.668537,112.555456,yes,yes,3.0,3.0,Sometimes,no,2.868679,no,0.115369,0.028583,Sometimes,Public_Transportation,Obesity_Type_III +2085,Female,25.986368,1.668951,112.249699,yes,yes,3.0,3.0,Sometimes,no,2.930137,no,0.043101,0.138629,Sometimes,Public_Transportation,Obesity_Type_III +2086,Female,25.951979,1.661712,112.098616,yes,yes,3.0,3.0,Sometimes,no,2.961899,no,0.259424,0.080128,Sometimes,Public_Transportation,Obesity_Type_III +2087,Female,26.0,1.633442,111.821817,yes,yes,3.0,3.0,Sometimes,no,2.550672,no,0.0,0.224655,Sometimes,Public_Transportation,Obesity_Type_III +2088,Female,26.0,1.63302,111.863186,yes,yes,3.0,3.0,Sometimes,no,2.584305,no,0.0,0.232108,Sometimes,Public_Transportation,Obesity_Type_III +2089,Female,26.0,1.633887,111.878132,yes,yes,3.0,3.0,Sometimes,no,2.621976,no,0.0,0.123861,Sometimes,Public_Transportation,Obesity_Type_III +2090,Female,26.0,1.643421,111.939983,yes,yes,3.0,3.0,Sometimes,no,2.722276,no,0.0,0.091711,Sometimes,Public_Transportation,Obesity_Type_III +2091,Female,26.0,1.640535,111.555967,yes,yes,3.0,3.0,Sometimes,no,2.634342,no,0.0,0.178301,Sometimes,Public_Transportation,Obesity_Type_III +2092,Female,26.0,1.626483,111.357062,yes,yes,3.0,3.0,Sometimes,no,2.61939,no,0.0,0.171034,Sometimes,Public_Transportation,Obesity_Type_III +2093,Female,26.0,1.64599,111.922491,yes,yes,3.0,3.0,Sometimes,no,2.78678,no,0.0,0.09776,Sometimes,Public_Transportation,Obesity_Type_III +2094,Female,26.0,1.643892,111.884535,yes,yes,3.0,3.0,Sometimes,no,2.768141,no,0.0,0.094213,Sometimes,Public_Transportation,Obesity_Type_III +2095,Female,25.97731,1.617817,104.950776,yes,yes,3.0,3.0,Sometimes,no,1.71659,no,0.001272,0.545993,Sometimes,Public_Transportation,Obesity_Type_III +2096,Female,25.955014,1.626449,104.879602,yes,yes,3.0,3.0,Sometimes,no,2.094901,no,0.07089,0.599441,Sometimes,Public_Transportation,Obesity_Type_III +2097,Female,25.996716,1.62658,105.037203,yes,yes,3.0,3.0,Sometimes,no,2.347322,no,0.008013,0.503896,Sometimes,Public_Transportation,Obesity_Type_III +2098,Female,25.992348,1.606474,104.954291,yes,yes,3.0,3.0,Sometimes,no,2.331123,no,0.063383,0.561661,Sometimes,Public_Transportation,Obesity_Type_III +2099,Female,25.974446,1.628855,108.090006,yes,yes,3.0,3.0,Sometimes,no,1.757105,no,0.085119,0.465444,Sometimes,Public_Transportation,Obesity_Type_III +2100,Female,25.777565,1.628205,107.378702,yes,yes,3.0,3.0,Sometimes,no,2.506631,no,0.025787,0.484165,Sometimes,Public_Transportation,Obesity_Type_III +2101,Female,25.722004,1.62847,107.218949,yes,yes,3.0,3.0,Sometimes,no,2.48707,no,0.067329,0.455823,Sometimes,Public_Transportation,Obesity_Type_III +2102,Female,25.765628,1.627839,108.10736,yes,yes,3.0,3.0,Sometimes,no,2.320068,no,0.045246,0.413106,Sometimes,Public_Transportation,Obesity_Type_III +2103,Female,21.016849,1.724268,133.033523,yes,yes,3.0,3.0,Sometimes,no,1.650612,no,1.537639,0.912457,Sometimes,Public_Transportation,Obesity_Type_III +2104,Female,21.682367,1.732383,133.043941,yes,yes,3.0,3.0,Sometimes,no,1.610768,no,1.510398,0.931455,Sometimes,Public_Transportation,Obesity_Type_III +2105,Female,21.285965,1.72692,131.335786,yes,yes,3.0,3.0,Sometimes,no,1.796267,no,1.728332,0.897924,Sometimes,Public_Transportation,Obesity_Type_III +2106,Female,20.976842,1.71073,131.408528,yes,yes,3.0,3.0,Sometimes,no,1.728139,no,1.676269,0.906247,Sometimes,Public_Transportation,Obesity_Type_III +2107,Female,21.982942,1.748584,133.742943,yes,yes,3.0,3.0,Sometimes,no,2.00513,no,1.34139,0.59927,Sometimes,Public_Transportation,Obesity_Type_III +2108,Female,22.524036,1.752206,133.689352,yes,yes,3.0,3.0,Sometimes,no,2.054193,no,1.414209,0.646288,Sometimes,Public_Transportation,Obesity_Type_III +2109,Female,24.361936,1.73945,133.346641,yes,yes,3.0,3.0,Sometimes,no,2.852339,no,1.139107,0.586035,Sometimes,Public_Transportation,Obesity_Type_III +2110,Female,23.664709,1.738836,133.472641,yes,yes,3.0,3.0,Sometimes,no,2.863513,no,1.026452,0.714137,Sometimes,Public_Transportation,Obesity_Type_III diff --git a/report/obesity_level_predictor_report.html b/report/obesity_level_predictor_report.html new file mode 100644 index 0000000..1c822f4 --- /dev/null +++ b/report/obesity_level_predictor_report.html @@ -0,0 +1,650 @@ + + + + + + + + + + + +Predicting Obesity Level Based on Eating Habits and Physical Condition + + + + + + + + + + + + + + + + + + + +
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Predicting Obesity Level Based on Eating Habits and Physical Condition

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Author
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Yun Zhou, Zanan Pech, Sepehr Heydarian

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Published
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December 6, 2024

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Summary

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In this study, we aim to develop a classification model to determine whether an individual is obese and, if so, categorize the level of obesity. This analysis seeks to use supervised machine learning to predict obesity levels in individuals based on features related to lifestyle habits and physical condition. Three machine learning models — K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree enhanced with AdaBoost — were trained and evaluated for their performance. The results indicate that SVM and the Decision Tree with AdaBoost achieved high predictive accuracy (0.9713 and 0.9793 respectively) making them the most effective models for this classification task. In contrast, KNN exhibited comparatively lower performance, achieving an accuracy of 0.8804, demonstrating its inferiority relative to the other two models in this context. However, large portion of the dataset used in our analysis was synthetically created, while ensuring a balance dataset, this may introduce potential biases. Additionally, the data was collected from only three countries and would benefit to have data from more a diverse global population for a broader application. Despite these limitation, our results show promising potential for application of machine learning in obesity diagnosis to aid healthcare professionals.

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Introduction

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Obesity, a complex and seemingly insurmountable public health and medical challenge, has become a global issue with severe negative impacts on both health and the economy (2024). This condition is associated with various medical and psychological complications, significantly affecting individuals’ health and social well-being. The World Health Organization (WHO) defines obesity as an excessive accumulation of body fat that poses a risk to health (2024). To implement this definition in practice, body mass index (BMI) — a widely used indicator of body fat — is used as a diagnostic measure to classify obesity (2024). Those living with obesity often face persistent stigma and discrimination, which further heightens their risk of disease and mortality (Westbury et al. 2023).

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The dataset we used for our analysis contains 2111 observations from individuals from Mexico, Peru and Colombia, with 16 features related to lifestyle habits, diet, physical activity along with obesity level as the target variable (“Estimation of Obesity Levels Based On Eating Habits and Physical Condition ” 2019; Palechor and De la Hoz Manotas 2019). The benefit of using this dataset lies in its abundant features which takes many lifestyle factors into account that can be used for classification models. Traditional methods for identifying and managing obesity often rely on clinical measurements like BMI, which, while effective, can be time-consuming and resource-intensive (Han, Sattar, and Lean 2006). Additionally, while BMI is a common diagnostic tool, it has inconsistencies as many factors affect it (Callahan et al. 2023). This highlights the need for additional tools and approaches for obesity diagnosis. Machine learning, a subset of artificial intelligence, has emerged as a promising tool in healthcare, capable of analyzing complex patterns in large datasets (Zhou, Chen, and Liu 2022). This brings forth the focus of our research as to why use machine learning as a diagnostic tool for obesity? By leveraging predictive models, machine learning can enhance the detection and management of obesity by identifying at-risk individuals, uncovering hidden risk factors, and enabling personalized interventions; this approach not only streamlines the diagnostic process but also opens the door to more accurate and scalable solutions for tackling obesity (Zhou, Chen, and Liu 2022).

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Methods

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Data

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The dataset used is obtained from UC Irvine Machine Learning Repository (link here). This dataset was used in work by Fabio Mendoza Palechor and Alexis de la Hoz Manotas (Palechor and De la Hoz Manotas 2019). This work can be found here. The dataset contains 2111 observations with 16 features (and one target - obesity level) from individuals from Mexico, Peru, and Colombia (“Estimation of Obesity Levels Based On Eating Habits and Physical Condition ” 2019). This dataset was chosen because it includes wide range of features including lifestyle habits, physical activity and other metrics including age, weight and sex. The obesity level categories are as follows: Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II, and Obesity Type III. Additionally, 77% of this dataset was synthetically generated with SMOTE filter to balance the target classes (“Estimation of Obesity Levels Based On Eating Habits and Physical Condition ” 2019; Palechor and De la Hoz Manotas 2019) which is one of the limitation as it could introduce bias.

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As part of data validation process, we followed the guidelines outlined in the data validation chapter of work by Chen (n.d.). We checked for missing values, duplicated rows, outliers, and ensured data types for each feature are correct. We verified that the categorical features contain valid categories. We identified 24 duplicated rows which were dropped subsequently. We reviewed the distribution of the target variable and found that all classes are within threshold of 20% of the average observations, shown in (Figure 1). This is in line with the fact that the original dataset was balanced by synthetic generation and SMOTE filter (Palechor and De la Hoz Manotas 2019). These confirm balance in the target classes, ensuring our data is suited for classification modeling.

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+Figure 1: Target Variable Expected Distribution +
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Analysis

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In this study, we trained three machine learning models — Decision Tree enhanced with AdaBoost, Support Vector Machine (SVM) with an RBF kernel, and K-Nearest Neighbors (KNN) — to predict obesity outcomes. The dataset was divided into training (70%) and testing (30%) sets to ensure reliable evaluation of model performance. Each model underwent hyperparameter tuning to optimize its predictive capabilities, utilizing a grid search approach to explore various combinations of the hyperparameters. Importantly, our analysis does not focus on predicting a single category of obesity, but considers multiple level for a more comprehensive approach. Additionally, our dataset is balanced and, therefore, our metrics for hyperparameter tuning is accuracy.

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In our analysis, we adapted AdaBoost to mitigate potential overfitting issues in a decision tree model(Freund and Schapire 1995). As a comparison we implemented SVM with an RBF kernel and KNN to establish a baseline for our model accuracy.

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For KNN, key hyperparameters such as the number of neighbors (n_neighbors), which were varied from 3 to 9, the weight function (uniform or distance), and the distance metric (euclidean or manhattan) were tested. These adjustments aimed to optimize how KNN classifies data points based on their proximity to others. The SVM model utilized a range of values for the regularization parameter (C), with values of 0.1, 1, 10, and 100 to balance classification error and margin maximization. Additionally, the kernel coefficient (gamma) was adjusted using the options ‘scale’, ‘auto’, and specific numeric values such as 0.01, 0.1, and 1 to control the influence of individual data points. Finally, for the AdaBoost-enhanced Decision Tree, the number of estimators (n_estimators) was varied between 100, 150, and 200, and the learning rate was optimized at 0.3, 0.5, and 0.7. The depth of the base estimator (estimator__max_depth) was tested between 5 and 9 to improve the model’s capacity to capture complex patterns in the data.

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Result & Discussions

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After tuning the hyperparameters, both the SVM and AdaBoost-enhanced Decision Tree models performed exceptionally well, achieving an accuracy of 0.9713 for SVM and 0.9793 for AdaBoost-enhanced Decision Tree. These results from test scores are shown in Table 1. In contrast, KNN, despite its adjustments, achieved a lower accuracy of 0.8804. This performance difference suggests that ensemble methods like AdaBoost, which combine the predictions of multiple models, and kernel-based methods like SVM, which use a non-linear approach to classify data, are more effective in handling the complexities of obesity classification compared to KNN, which relies on simpler distance-based logic. In relation to our research question, effective models have highest accuracy for predicting obesity levels as in healthcare misdiagnosis has severed consequences. KNN performed relatively poor compared our other models as its not well suited for high-dimensional data.

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+Table 1: Test result of model performance on test set +
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ModelAccuracy
0AdaBoost + Decision Tree0.979266
1KNN0.880383
2SVM (RBF Kernel)0.971292
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Given our high scores in AdaBoost and SVM models, using machine learning shows promising potential in enhancing obesity diagnosis. Unlike traditional methods, such as BMI, which may not always lead to the most accurate diagnosis, the use of tools that consider multiple features can be of great use for healthcare professionals. These machine learning models offer a more comprehensive approach as they can identify underlying patterns from various features that traditional diagnosis methods can miss.

+

Although these results yield high test scores which indicate that the model generalizes well, limitations in our dataset should be acknowledged. The dataset we used for our analysis had majority of it data synthetically generated. While this technique could address class imbalance, it could lead to potential biases as there may be patterns that do not exist in the actual population. This may result in a less effective performance when applied to real-world unseen data. Another limitation is related to the lack of representation in the dataset as it collected information from three countries only - Mexico, Peru and Colombia. Certain patterns could exist in lifestyle and diet within each of these countries, and considering that all these countries are located in the Americas, the data is not representative of a diverse global population. This geographical limitation may hinder our model’s applicability as a worldwide healthcare tool.

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Future work should focus on validating these models using larger datasets from variety of regions and populations that include features related to lifestyle habits, diet and physical condition. These efforts would ensure broader applicability of machine learning tools in healthcare, specifically in diagnosing levels of obesity.

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References

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+2024. World Health Organization. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight. +
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+Callahan, Emily A, National Academies of Sciences Engineering, Medicine, et al. 2023. “The Science, Strengths, and Limitations of Body Mass Index.” In Translating Knowledge of Foundational Drivers of Obesity into Practice: Proceedings of a Workshop Series. National Academies Press (US). +
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+Chen, Florencia D’Andrea, Tiffany a. Timbers Joel Ostblom. n.d. “Reproducible and Trustworthy Workflows for Data Science.” https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/. +
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+“Estimation of Obesity Levels Based On Eating Habits and Physical Condition .” 2019. UCI Machine Learning Repository. +
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+Freund, Yoav, and Robert E Schapire. 1995. “A Desicion-Theoretic Generalization of on-Line Learning and an Application to Boosting.” In European Conference on Computational Learning Theory, 23–37. Springer. +
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+Han, Thang S, Naveed Sattar, and Mike Lean. 2006. “Assessment of Obesity and Its Clinical Implications.” Bmj 333 (7570): 695–98. +
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+Palechor, Fabio Mendoza, and Alexis De la Hoz Manotas. 2019. “Dataset for Estimation of Obesity Levels Based on Eating Habits and Physical Condition in Individuals from Colombia, Peru and Mexico.” Data in Brief 25: 104344. +
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+Westbury, Susannah, Oyinlola Oyebode, Thijs Van Rens, and Thomas M Barber. 2023. “Obesity Stigma: Causes, Consequences, and Potential Solutions.” Current Obesity Reports 12 (1): 10–23. +
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+Zhou, Xiaobei, Lei Chen, and Hui-Xin Liu. 2022. “Applications of Machine Learning Models to Predict and Prevent Obesity: A Mini-Review.” Frontiers in Nutrition 9: 933130. +
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+ + + + + \ No newline at end of file diff --git a/report/obesity_level_predictor_report.ipynb b/report/obesity_level_predictor_report.ipynb deleted file mode 100644 index d20c391..0000000 --- a/report/obesity_level_predictor_report.ipynb +++ /dev/null @@ -1,2412 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "ba702beb", - "metadata": {}, - "source": [ - "# Predicting Obesity Level Based on Eating Habits and Physical Condition" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d9e31ec2-875e-491b-9c0a-63590bd76d91", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "deepchecks - WARNING - You are using deepchecks version 0.18.1, however a newer version is available. Deepchecks is frequently updated with major improvements. You should consider upgrading via the \"python -m pip install --upgrade deepchecks\" command.\n" - ] - } - ], - "source": [ - "# import libraries\n", - "import numpy as np\n", - "import pandas as pd\n", - "import pandera as pa\n", - "import warnings\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.preprocessing import StandardScaler\n", - "from sklearn.compose import make_column_transformer, make_column_selector\n", - "from sklearn.pipeline import make_pipeline\n", - "from sklearn.model_selection import StratifiedKFold\n", - "from deepchecks.tabular import Dataset\n", - "from deepchecks.tabular.checks import FeatureLabelCorrelation\n", - "from deepchecks.tabular.checks.data_integrity import FeatureFeatureCorrelation" - ] - }, - { - "cell_type": "markdown", - "id": "192434ec", - "metadata": {}, - "source": [ - "# Summary\n", - "\n", - "In this study, we aim to develop a classification model to determine whether an individual is obese and, if so, categorize the level of obesity. Three machine learning models—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree enhanced with AdaBoost—were trained and evaluated for their performance. The results indicate that SVM and the Decision Tree with AdaBoost achieved high predictive accuracy, both around 97%, making them the most effective models for this classification task. In contrast, KNN exhibited comparatively lower performance, achieving an accuracy of 89%, demonstrating its inferiority relative to the other two models in this context.\n" - ] - }, - { - "cell_type": "markdown", - "id": "946324af", - "metadata": {}, - "source": [ - "# Introduction\n", - "\n", - "Obesity, a complex and seemingly insurmountable public health and medical challenge, has become a global issue with severe negative impacts on both health and the economy (World Health Organization, 2023). This condition is associated with various medical and psychological complications, significantly affecting individuals' health and social well-being. The World Health Organization (WHO) defines obesity as an excessive accumulation of body fat that poses a risk to health (World Health Organization, 2023). To implement this definition in practice, body mass index (BMI)—a widely used indicator of body fat—is employed to classify obesity. Specifically, under WHO guidelines, individuals with a BMI exceeding 30 are categorized as obese (World Health Organization, 2023). Those living with obesity often face persistent stigma and discrimination, which further heightens their risk of disease and mortality (Westbury et al., 2023).\n", - "\n", - "Traditional methods for identifying and managing obesity often rely on clinical measurements like body mass index (BMI), which, while effective, can be time-consuming and resource-intensive (Han et al., 2006). Machine learning (ML), a subset of artificial intelligence, has emerged as a promising tool in healthcare, capable of analyzing complex patterns in large datasets (Frontiers in Endocrinology, 2022). By leveraging predictive models, ML can enhance the detection and management of obesity by identifying at-risk individuals, uncovering hidden risk factors, and enabling personalized interventions; this approach not only streamlines the diagnostic process but also opens the door to more accurate and scalable solutions for tackling obesity (Zhou et al., 2022)." - ] - }, - { - "cell_type": "markdown", - "id": "9fa7d4cc", - "metadata": {}, - "source": [ - "# Methods\n", - "\n", - "### Data\n", - "\n", - "The dataset used is obtained from UC Irvine Machine Learning Repository ([link here](https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition)). This dataset was used in work by Fabio Mendoza Palechor and Alexis de la Hoz Manotas (Palechor, F. M., & De La Hoz Manotas, A., 2019). Find work [here](https://doi.org/10.1016/j.dib.2019.104344). The dataset contains 2111 observations with 16 features (and one target - obesity level) from individuals from Mexico, Peru, and Colombia (Estimation of Obesity Levels Based On Eating Habits and Physical Condition, 2019). The obesity level categories are as follows: Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II, and Obesity Type III. This dataset contains 24 duplicate rows which were dropped after data validation process.\n", - "\n", - "\n", - "### Analysis\n", - "\n", - "In this study, we trained three machine learning models—Decision Tree enhanced with AdaBoost, Support Vector Machine (SVM) with an RBF kernel, and K-Nearest Neighbors (KNN)—to predict obesity outcomes. The dataset was divided into training (70%) and testing (30%) sets to ensure reliable evaluation of model performance. Each model underwent hyperparameter tuning to optimize its predictive capabilities, utilizing a grid search approach to explore various combinations of hyperparameters.\n", - "\n", - "For KNN, key hyperparameters such as the number of neighbors (n_neighbors), which were varied from 3 to 9, the weight function (uniform or distance), and the distance metric (euclidean or manhattan) were tested. These adjustments aimed to optimize how KNN classifies data points based on their proximity to others. The SVM model utilized a range of values for the regularization parameter (C), with values of 0.1, 1, 10, and 100 to balance classification error and margin maximization. Additionally, the kernel coefficient (gamma) was adjusted using options like 'scale', 'auto', and specific numeric values such as 0.01, 0.1, and 1 to control the influence of individual data points. Finally, for the AdaBoost-enhanced Decision Tree, the number of estimators (n_estimators) was varied between 100, 150, and 200, and the learning rate was optimized at 0.3, 0.5, and 0.7. The depth of the base estimator (estimator__max_depth) was tested between 5 and 9 to improve the model's capacity to capture complex patterns in the data.\n", - "\n", - "### Result & Discussions\n", - "\n", - "After tuning the hyperparameters, both the SVM and AdaBoost-enhanced Decision Tree models performed exceptionally well, achieving an accuracy of around 97%. In contrast, KNN, despite its adjustments, achieved a lower accuracy of 89%. This performance difference suggests that ensemble methods like AdaBoost, which combine the predictions of multiple models, and kernel-based methods like SVM, which use a non-linear approach to classify data, are more effective in handling the complexities of obesity classification compared to KNN, which relies on simpler distance-based logic.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "18135c1c-ce1e-4636-9fe5-d9105f42e070", - "metadata": {}, - "outputs": [], - "source": [ - "# Code below is adapted from work by uci machine learning repository\n", - "# https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition\n", - "\n", - "# import dataset\n", - "from ucimlrepo import fetch_ucirepo \n", - " \n", - "# fetch dataset \n", - "result = fetch_ucirepo(id=544) \n", - " \n", - "# data (as pandas dataframes) \n", - "features = result.data.features\n", - "target = result.data.targets \n", - "\n", - "# create a merged dataframe\n", - "merged_df = pd.concat([features, target], axis = 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "3cabb1aa-5f80-4a07-bb2e-216b76f25f80", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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GenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
0Female21.0000001.62000064.000000yesno2.03.0Sometimesno2.000000no0.0000001.000000noPublic_TransportationNormal_Weight
1Female21.0000001.52000056.000000yesno3.03.0Sometimesyes3.000000yes3.0000000.000000SometimesPublic_TransportationNormal_Weight
2Male23.0000001.80000077.000000yesno2.03.0Sometimesno2.000000no2.0000001.000000FrequentlyPublic_TransportationNormal_Weight
3Male27.0000001.80000087.000000nono3.03.0Sometimesno2.000000no2.0000000.000000FrequentlyWalkingOverweight_Level_I
4Male22.0000001.78000089.800000nono2.01.0Sometimesno2.000000no0.0000000.000000SometimesPublic_TransportationOverweight_Level_II
......................................................
2106Female20.9768421.710730131.408528yesyes3.03.0Sometimesno1.728139no1.6762690.906247SometimesPublic_TransportationObesity_Type_III
2107Female21.9829421.748584133.742943yesyes3.03.0Sometimesno2.005130no1.3413900.599270SometimesPublic_TransportationObesity_Type_III
2108Female22.5240361.752206133.689352yesyes3.03.0Sometimesno2.054193no1.4142090.646288SometimesPublic_TransportationObesity_Type_III
2109Female24.3619361.739450133.346641yesyes3.03.0Sometimesno2.852339no1.1391070.586035SometimesPublic_TransportationObesity_Type_III
2110Female23.6647091.738836133.472641yesyes3.03.0Sometimesno2.863513no1.0264520.714137SometimesPublic_TransportationObesity_Type_III
\n", - "

2111 rows × 17 columns

\n", - "
" - ], - "text/plain": [ - " Gender Age Height Weight family_history_with_overweight \\\n", - "0 Female 21.000000 1.620000 64.000000 yes \n", - "1 Female 21.000000 1.520000 56.000000 yes \n", - "2 Male 23.000000 1.800000 77.000000 yes \n", - "3 Male 27.000000 1.800000 87.000000 no \n", - "4 Male 22.000000 1.780000 89.800000 no \n", - "... ... ... ... ... ... \n", - "2106 Female 20.976842 1.710730 131.408528 yes \n", - "2107 Female 21.982942 1.748584 133.742943 yes \n", - "2108 Female 22.524036 1.752206 133.689352 yes \n", - "2109 Female 24.361936 1.739450 133.346641 yes \n", - "2110 Female 23.664709 1.738836 133.472641 yes \n", - "\n", - " FAVC FCVC NCP CAEC SMOKE CH2O SCC FAF TUE \\\n", - "0 no 2.0 3.0 Sometimes no 2.000000 no 0.000000 1.000000 \n", - "1 no 3.0 3.0 Sometimes yes 3.000000 yes 3.000000 0.000000 \n", - "2 no 2.0 3.0 Sometimes no 2.000000 no 2.000000 1.000000 \n", - "3 no 3.0 3.0 Sometimes no 2.000000 no 2.000000 0.000000 \n", - "4 no 2.0 1.0 Sometimes no 2.000000 no 0.000000 0.000000 \n", - "... ... ... ... ... ... ... ... ... ... \n", - "2106 yes 3.0 3.0 Sometimes no 1.728139 no 1.676269 0.906247 \n", - "2107 yes 3.0 3.0 Sometimes no 2.005130 no 1.341390 0.599270 \n", - "2108 yes 3.0 3.0 Sometimes no 2.054193 no 1.414209 0.646288 \n", - "2109 yes 3.0 3.0 Sometimes no 2.852339 no 1.139107 0.586035 \n", - "2110 yes 3.0 3.0 Sometimes no 2.863513 no 1.026452 0.714137 \n", - "\n", - " CALC MTRANS NObeyesdad \n", - "0 no Public_Transportation Normal_Weight \n", - "1 Sometimes Public_Transportation Normal_Weight \n", - "2 Frequently Public_Transportation Normal_Weight \n", - "3 Frequently Walking Overweight_Level_I \n", - "4 Sometimes Public_Transportation Overweight_Level_II \n", - "... ... ... ... \n", - "2106 Sometimes Public_Transportation Obesity_Type_III \n", - "2107 Sometimes Public_Transportation Obesity_Type_III \n", - "2108 Sometimes Public_Transportation Obesity_Type_III \n", - "2109 Sometimes Public_Transportation Obesity_Type_III \n", - "2110 Sometimes Public_Transportation Obesity_Type_III \n", - "\n", - "[2111 rows x 17 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# View dataframe\n", - "merged_df" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "5e50378e-609a-424b-95a6-7eed679c3efe", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The dataframe has 2111 instances and 17 features.\n" - ] - } - ], - "source": [ - "# View shape of dataframe \n", - "print(f\"The dataframe has {merged_df.shape[0]} instances and {merged_df.shape[1]} features.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "43dd2e95-34e3-42b8-a7ea-56d29255bfe4", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 2111 entries, 0 to 2110\n", - "Data columns (total 17 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 Gender 2111 non-null object \n", - " 1 Age 2111 non-null float64\n", - " 2 Height 2111 non-null float64\n", - " 3 Weight 2111 non-null float64\n", - " 4 family_history_with_overweight 2111 non-null object \n", - " 5 FAVC 2111 non-null object \n", - " 6 FCVC 2111 non-null float64\n", - " 7 NCP 2111 non-null float64\n", - " 8 CAEC 2111 non-null object \n", - " 9 SMOKE 2111 non-null object \n", - " 10 CH2O 2111 non-null float64\n", - " 11 SCC 2111 non-null object \n", - " 12 FAF 2111 non-null float64\n", - " 13 TUE 2111 non-null float64\n", - " 14 CALC 2111 non-null object \n", - " 15 MTRANS 2111 non-null object \n", - " 16 NObeyesdad 2111 non-null object \n", - "dtypes: float64(8), object(9)\n", - "memory usage: 280.5+ KB\n" - ] - } - ], - "source": [ - "# Review summary information of dataframe, dtypes, column names \n", - "merged_df.info()" - ] - }, - { - "cell_type": "markdown", - "id": "1cc394e5-9582-4e9b-b127-cde071aecfb8", - "metadata": {}, - "source": [ - "# Data Validation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "23feaba5-3a8b-4c3c-83f3-e69e99056d62", - "metadata": {}, - "outputs": [], - "source": [ - "# 1. Check correct data file format\n", - "# The number of instances and features are retrieved from the dataset information in the documentation linked below\n", - "# https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition\n", - "\n", - "# Check if number of instances are the same as documentation \n", - "assert merged_df.shape[0] == 2111, \"merged_df does not have number of instances mentioned in the documentation\"\n", - "\n", - "# check if number of features are the same as documentation (17 including the target)\n", - "assert merged_df.shape[1] == 17, \"merged_df does not have number of features mentioned in the documentation\"\n", - "\n", - "# check if DataFrame is correct type\n", - "assert isinstance(merged_df, pd.DataFrame), \"merged_df is not a pandas DataFrame\" " - ] - }, - { - "cell_type": "markdown", - "id": "04671dce-4f14-4fc3-9575-f6d4b4e6f678", - "metadata": {}, - "source": [ - "Orginal column names are retrieved from\\\n", - "https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+eating+habits+and+physical+condition" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "06f97e99-0950-400a-81d0-82ad1e32aad1", - "metadata": {}, - "outputs": [], - "source": [ - "# Create variable with features listed in the documentation cited in cell above \n", - "original_column_names = ['Gender', 'Age', 'Height', 'Weight', 'family_history_with_overweight',\n", - " 'FAVC', 'FCVC', 'NCP', 'CAEC', 'SMOKE', 'CH2O', 'SCC', 'FAF', 'TUE',\n", - " 'CALC', 'MTRANS', 'NObeyesdad']" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "0b31a6c1-79b1-4df0-b11f-6ff0fbbcb926", - "metadata": {}, - "outputs": [], - "source": [ - "# 2. Check columns names (Data Validation)\n", - "assert sorted(original_column_names) == sorted(merged_df.columns), \"merged_df does not have the same column names as original_column_names\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b7660379-2edd-4eb5-a33d-69fc68286efe", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 2111 entries, 0 to 2110\n", - "Data columns (total 17 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 gender 2111 non-null object \n", - " 1 age 2111 non-null float64\n", - " 2 height 2111 non-null float64\n", - " 3 weight 2111 non-null float64\n", - " 4 family_history_with_overweight 2111 non-null object \n", - " 5 frequent_high_calorie_intake 2111 non-null object \n", - " 6 vegetable_intake_in_meals 2111 non-null float64\n", - " 7 meals_per_day 2111 non-null float64\n", - " 8 food_intake_between_meals 2111 non-null object \n", - " 9 smoker 2111 non-null object \n", - " 10 daily_water_intake 2111 non-null float64\n", - " 11 monitor_calories 2111 non-null object \n", - " 12 days_per_week_with_physical_activity 2111 non-null float64\n", - " 13 daily_screen_time 2111 non-null float64\n", - " 14 frequent_alcohol_intake 2111 non-null object \n", - " 15 mode_of_transportation 2111 non-null object \n", - " 16 obesity_level 2111 non-null object \n", - "dtypes: float64(8), object(9)\n", - "memory usage: 280.5+ KB\n" - ] - } - ], - "source": [ - "# Rename columns into meaningful names\n", - "# create dictionary for renaming\n", - "\n", - "rename_dict = {\n", - " 'Gender': 'gender',\n", - " 'Age': 'age',\n", - " 'Height': 'height',\n", - " 'Weight': 'weight',\n", - " 'FAVC': 'frequent_high_calorie_intake',\n", - " 'FCVC': 'vegetable_intake_in_meals',\n", - " 'NCP': 'meals_per_day',\n", - " 'CAEC': 'food_intake_between_meals',\n", - " 'SMOKE': 'smoker',\n", - " 'CH2O': 'daily_water_intake',\n", - " 'SCC': 'monitor_calories',\n", - " 'FAF': 'days_per_week_with_physical_activity',\n", - " 'TUE': 'daily_screen_time',\n", - " 'CALC': 'frequent_alcohol_intake',\n", - " 'MTRANS': 'mode_of_transportation',\n", - " 'NObeyesdad': 'obesity_level'\n", - "}\n", - "\n", - "merged_df = merged_df.rename(columns = rename_dict)\n", - "merged_df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "eddbd6a1-f79c-405f-908a-84fabee1241a", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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genderageheightweightfamily_history_with_overweightfrequent_high_calorie_intakevegetable_intake_in_mealsmeals_per_dayfood_intake_between_mealssmokerdaily_water_intakemonitor_caloriesdays_per_week_with_physical_activitydaily_screen_timefrequent_alcohol_intakemode_of_transportationobesity_level
0Female21.0000001.62000064.000000yesno2.03.0Sometimesno2.000000no0.0000001.000000noPublic_TransportationNormal_Weight
1Female21.0000001.52000056.000000yesno3.03.0Sometimesyes3.000000yes3.0000000.000000SometimesPublic_TransportationNormal_Weight
2Male23.0000001.80000077.000000yesno2.03.0Sometimesno2.000000no2.0000001.000000FrequentlyPublic_TransportationNormal_Weight
3Male27.0000001.80000087.000000nono3.03.0Sometimesno2.000000no2.0000000.000000FrequentlyWalkingOverweight_Level_I
4Male22.0000001.78000089.800000nono2.01.0Sometimesno2.000000no0.0000000.000000SometimesPublic_TransportationOverweight_Level_II
......................................................
2106Female20.9768421.710730131.408528yesyes3.03.0Sometimesno1.728139no1.6762690.906247SometimesPublic_TransportationObesity_Type_III
2107Female21.9829421.748584133.742943yesyes3.03.0Sometimesno2.005130no1.3413900.599270SometimesPublic_TransportationObesity_Type_III
2108Female22.5240361.752206133.689352yesyes3.03.0Sometimesno2.054193no1.4142090.646288SometimesPublic_TransportationObesity_Type_III
2109Female24.3619361.739450133.346641yesyes3.03.0Sometimesno2.852339no1.1391070.586035SometimesPublic_TransportationObesity_Type_III
2110Female23.6647091.738836133.472641yesyes3.03.0Sometimesno2.863513no1.0264520.714137SometimesPublic_TransportationObesity_Type_III
\n", - "

2087 rows × 17 columns

\n", - "
" - ], - "text/plain": [ - " gender age height weight family_history_with_overweight \\\n", - "0 Female 21.000000 1.620000 64.000000 yes \n", - "1 Female 21.000000 1.520000 56.000000 yes \n", - "2 Male 23.000000 1.800000 77.000000 yes \n", - "3 Male 27.000000 1.800000 87.000000 no \n", - "4 Male 22.000000 1.780000 89.800000 no \n", - "... ... ... ... ... ... \n", - "2106 Female 20.976842 1.710730 131.408528 yes \n", - "2107 Female 21.982942 1.748584 133.742943 yes \n", - "2108 Female 22.524036 1.752206 133.689352 yes \n", - "2109 Female 24.361936 1.739450 133.346641 yes \n", - "2110 Female 23.664709 1.738836 133.472641 yes \n", - "\n", - " frequent_high_calorie_intake vegetable_intake_in_meals meals_per_day \\\n", - "0 no 2.0 3.0 \n", - "1 no 3.0 3.0 \n", - "2 no 2.0 3.0 \n", - "3 no 3.0 3.0 \n", - "4 no 2.0 1.0 \n", - "... ... ... ... \n", - "2106 yes 3.0 3.0 \n", - "2107 yes 3.0 3.0 \n", - "2108 yes 3.0 3.0 \n", - "2109 yes 3.0 3.0 \n", - "2110 yes 3.0 3.0 \n", - "\n", - " food_intake_between_meals smoker daily_water_intake monitor_calories \\\n", - "0 Sometimes no 2.000000 no \n", - "1 Sometimes yes 3.000000 yes \n", - "2 Sometimes no 2.000000 no \n", - "3 Sometimes no 2.000000 no \n", - "4 Sometimes no 2.000000 no \n", - "... ... ... ... ... \n", - "2106 Sometimes no 1.728139 no \n", - "2107 Sometimes no 2.005130 no \n", - "2108 Sometimes no 2.054193 no \n", - "2109 Sometimes no 2.852339 no \n", - "2110 Sometimes no 2.863513 no \n", - "\n", - " days_per_week_with_physical_activity daily_screen_time \\\n", - "0 0.000000 1.000000 \n", - "1 3.000000 0.000000 \n", - "2 2.000000 1.000000 \n", - "3 2.000000 0.000000 \n", - "4 0.000000 0.000000 \n", - "... ... ... \n", - "2106 1.676269 0.906247 \n", - "2107 1.341390 0.599270 \n", - "2108 1.414209 0.646288 \n", - "2109 1.139107 0.586035 \n", - "2110 1.026452 0.714137 \n", - "\n", - " frequent_alcohol_intake mode_of_transportation obesity_level \n", - "0 no Public_Transportation Normal_Weight \n", - "1 Sometimes Public_Transportation Normal_Weight \n", - "2 Frequently Public_Transportation Normal_Weight \n", - "3 Frequently Walking Overweight_Level_I \n", - "4 Sometimes Public_Transportation Overweight_Level_II \n", - "... ... ... ... \n", - "2106 Sometimes Public_Transportation Obesity_Type_III \n", - "2107 Sometimes Public_Transportation Obesity_Type_III \n", - "2108 Sometimes Public_Transportation Obesity_Type_III \n", - "2109 Sometimes Public_Transportation Obesity_Type_III \n", - "2110 Sometimes Public_Transportation Obesity_Type_III \n", - "\n", - "[2087 rows x 17 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Code below adapted from https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/lectures/130-data-validation.html\n", - "\n", - "# 4. Check missingness not beyond expected threshold with \"nullable\"\n", - "# 5. Check correct data types in each column with \"first parameter in pa.Column\"\n", - "# 7. Check the outlier or anomalous values with \"pa.Check.between\"\n", - "# 8. Check category levels with \"pa.Check.isin\"\n", - "schema = pa.DataFrameSchema({\n", - " \"gender\": pa.Column(str, pa.Check.isin([\"Female\", \"Male\"])),\n", - " \"age\": pa.Column(float, pa.Check.between(10, 99), nullable=True),\n", - " \"height\": pa.Column(float, pa.Check.between(1.0, 2.5), nullable=True),\n", - " \"weight\": pa.Column(float, pa.Check.between(30, 200), nullable=True),\n", - " \"family_history_with_overweight\": pa.Column(str, pa.Check.isin([\"yes\", \"no\"])),\n", - " \"frequent_high_calorie_intake\": pa.Column(str, pa.Check.isin([\"yes\", \"no\"])),\n", - " \"vegetable_intake_in_meals\": pa.Column(float, pa.Check.between(1.0, 3.0), nullable=True),\n", - " \"meals_per_day\": pa.Column(float, pa.Check.between(0.0, 10.0), nullable=True),\n", - " \"food_intake_between_meals\": pa.Column(str, pa.Check.isin(['Sometimes', 'Frequently', 'Always', 'no'])),\n", - " \"smoker\": pa.Column(str, pa.Check.isin(['no', 'yes'])),\n", - " \"daily_water_intake\": pa.Column(float, pa.Check.between(0.0, 5.0), nullable=True),\n", - " \"monitor_calories\": pa.Column(str, pa.Check.isin([\"yes\", \"no\"])),\n", - " \"days_per_week_with_physical_activity\": pa.Column(float, pa.Check.between(0.0, 7.0), nullable=True),\n", - " \"daily_screen_time\": pa.Column(float, pa.Check.between(0.0, 24.0), nullable=True),\n", - " \"frequent_alcohol_intake\": pa.Column(str, pa.Check.isin(['no', 'Sometimes', 'Frequently', 'Always'])),\n", - " \"mode_of_transportation\": pa.Column(str, pa.Check.isin(['Public_Transportation', 'Walking', 'Automobile', 'Motorbike',\n", - " 'Bike'])),\n", - " \"obesity_level\": pa.Column(str, pa.Check.isin(['Normal_Weight', 'Overweight_Level_I', 'Overweight_Level_II',\n", - " 'Obesity_Type_I', 'Insufficient_Weight', 'Obesity_Type_II','Obesity_Type_III'])), \n", - " },\n", - "\n", - " # 6. Check no duplicated observations \n", - " # 3. Check the empty observations \n", - " checks=[\n", - " pa.Check(lambda df: ~df.duplicated().any(), error=\"Duplicate rows found.\"),\n", - " pa.Check(lambda df: ~(df.isna().all(axis=1)).any(), error=\"Empty rows found.\")\n", - " ],\n", - " # Drop duplicate row\n", - " drop_invalid_rows=True\n", - ")\n", - "\n", - "schema.validate(merged_df, lazy=True).drop_duplicates().dropna(how=\"all\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "c66eaf22", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2087, 17)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "merged_df = merged_df.drop_duplicates().dropna(how=\"all\")\n", - "merged_df.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "6d3fb8d9-e491-4081-adfd-5eb790d66ddb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.HConcatChart(...)" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# 9.Target/response variable follows expected distribution\n", - "# Use Altair for visualization\n", - "import altair as alt\n", - "\n", - "actual_counts = merged_df['obesity_level'].value_counts().reset_index()\n", - "actual_counts.columns = ['obesity_level', 'count']\n", - "expect_counts = merged_df.shape[0] / merged_df['obesity_level'].nunique()\n", - "actual_counts['expected'] = expect_counts\n", - "\n", - "# Add threshold ±60\n", - "actual_counts['expected_lower'] = actual_counts['expected'] - 60\n", - "actual_counts['expected_upper'] = actual_counts['expected'] + 60\n", - "\n", - "actual_dist = alt.Chart(actual_counts).mark_bar(color='steelblue', opacity=0.6).encode(\n", - " x=alt.X('obesity_level:N', title='Obesity Level'),\n", - " y=alt.Y('count:Q', title='Count'),\n", - " tooltip=['obesity_level', 'count']\n", - ")\n", - "\n", - "expected_dist = alt.Chart(actual_counts).mark_bar(color='orange', opacity=0.3).encode(\n", - " x=alt.X('obesity_level:N', title='Obesity Level'),\n", - " y=alt.Y('expected:Q'),\n", - " tooltip=['obesity_level', 'expected']\n", - ")\n", - "\n", - "error_bar = alt.Chart(actual_counts).mark_errorbar(color='black').encode(\n", - " x=alt.X('obesity_level:N'),\n", - " y=alt.Y('expected_lower:Q', title='Error Bar'),\n", - " y2='expected_upper:Q'\n", - ")\n", - "\n", - "ticks = alt.Chart(actual_counts).mark_tick(\n", - " color='black',\n", - " thickness=2,\n", - " size=20 \n", - ").encode(\n", - " x=alt.X('obesity_level:N'),\n", - " y=alt.Y('expected_lower:Q') \n", - ") + alt.Chart(actual_counts).mark_tick(\n", - " color='black',\n", - " thickness=2,\n", - " size=20 \n", - ").encode(\n", - " x=alt.X('obesity_level:N'),\n", - " y=alt.Y('expected_upper:Q') \n", - ")\n", - "\n", - "target_dist = alt.layer(\n", - " expected_dist, actual_dist, error_bar, ticks\n", - ").properties(\n", - " title='Target Variable vs Expected Distribution',\n", - " width=400,\n", - " height=300\n", - ")\n", - "\n", - "legend_data = pd.DataFrame({\n", - " 'Category': ['Actual', 'Expected'],\n", - " 'Color': ['steelblue', 'orange']\n", - "})\n", - "\n", - "manual_legend = alt.Chart(legend_data).mark_rect().encode(\n", - " y=alt.Y('Category:N', axis=alt.Axis(title='Legend')),\n", - " color=alt.Color('Color:N', scale=None)\n", - ").properties(\n", - " width=20,\n", - " height=100\n", - ") + alt.Chart(legend_data).mark_text(align='left', dx=25).encode(\n", - " y=alt.Y('Category:N')\n", - ")\n", - "\n", - "final_dist = alt.hconcat(target_dist, manual_legend).resolve_legend(color=\"independent\")\n", - "\n", - "final_dist" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "cfc019f1-1754-4ad3-88b0-56c80acb674d", - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "857c1de0153d4c2eb041c4b3517656dd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value='

Feature Label Correlation

'), HTML(value='

Return the PPS (Predict…" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e0f4bfed741044f7a6eed794069d2323", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "VBox(children=(HTML(value='

Feature-Feature Correlation

'), HTML(value='

Checks for pairwi…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import warnings\n", - "# To hide the warnings about \"is_categorical_dtype is deprecated \n", - "# and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\"\n", - "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n", - "\n", - "# 10. No anomalous correlations between target/response variable and features/explanatory variables\n", - "# Code adapted from deepchecks \n", - "# https://docs.deepchecks.com/stable/tabular/auto_checks/data_integrity/plot_feature_label_correlation.html\n", - "ds = Dataset(merged_df, \n", - " label='obesity_level', \n", - " cat_features=['gender', \n", - " 'family_history_with_overweight',\n", - " 'frequent_high_calorie_intake',\n", - " 'food_intake_between_meals',\n", - " 'smoker',\n", - " 'monitor_calories',\n", - " 'frequent_alcohol_intake', \n", - " 'mode_of_transportation'])\n", - "\n", - "check_feat_lab_corr = FeatureLabelCorrelation().add_condition_feature_pps_less_than(0.9)\n", - "check_feat_lab_corr_result = check_feat_lab_corr.run(dataset=ds)\n", - "check_feat_lab_corr_result.show()\n", - "\n", - "if not check_feat_lab_corr_result.passed_conditions():\n", - " raise ValueError(\"Feature-Label correlation exceeds the maximum acceptable threshold.\")\n", - "\n", - "# 11. No anomalous correlations between features/explanatory variables1\n", - "# Code adapted from deepchecks \n", - "# https://docs.deepchecks.com/stable/tabular/auto_checks/data_integrity/plot_feature_feature_correlation.html\n", - "check_feat_feat_corr = FeatureFeatureCorrelation(threshold=0.8)\n", - "check_feat_feat_corr_result = check_feat_feat_corr.run(dataset=ds)\n", - "check_feat_feat_corr_result.show()\n", - "\n", - "if not check_feat_feat_corr_result.passed_conditions():\n", - " raise ValueError(\"Feature-Feature correlation exceeds the maximum acceptable threshold.\")" - ] - }, - { - "cell_type": "markdown", - "id": "fb537412-daf4-48a0-a8f4-65e28a420b3f", - "metadata": {}, - "source": [ - "## EDA" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "fad7329f-c475-442a-9a0f-84210407d985", - "metadata": {}, - "outputs": [], - "source": [ - "# Splitting data\n", - "from sklearn.model_selection import train_test_split\n", - "# Separate features and target\n", - "X = merged_df.drop('obesity_level', axis=1)\n", - "y = merged_df['obesity_level']\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=522, stratify=y)\n", - "X_train_df = pd.DataFrame(X_train, columns=X.columns) \n", - "y_train_df = pd.DataFrame(y_train, columns=['obesity_level'])\n", - "train_df = pd.concat([X_train_df, y_train_df], axis=1)\n", - "\n", - "X_test_df = pd.DataFrame(X_test, columns=X.columns) \n", - "y_test_df = pd.DataFrame(y_test, columns=['obesity_level'])\n", - "test_df = pd.concat([X_test_df, y_test_df], axis=1)\n", - "\n", - "# Encode target variable\n", - "from sklearn.preprocessing import LabelEncoder\n", - "label_encoder = LabelEncoder()\n", - "label_encoder.fit(y_train)\n", - "\n", - "train_df['obesity_level'] = label_encoder.transform(train_df['obesity_level'])\n", - "test_df['obesity_level'] = label_encoder.transform(test_df['obesity_level'])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "d416b184-e522-4de3-9a6d-65db73f44e26", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "

\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Plot 1: Target variable distribution \n", - "target_distribution = alt.Chart(train_df).mark_bar().encode(\n", - " x=alt.X('obesity_level:N', title='Obesity Level'),\n", - " y=alt.Y('count():Q', title='Count'),\n", - " color=alt.Color('obesity_level')\n", - ").properties(\n", - " title='Figure 1. Distribution of Target: Obesity Level',\n", - " width=400,\n", - " height=300\n", - ")\n", - "target_distribution" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "a55eac6c-3942-410f-90ac-446cc8ef0f45", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Plot 2: Correlation between numeric features\n", - "\n", - "# Keep numeric features\n", - "numeric_data = train_df.select_dtypes(include=['float64', 'int64'])\n", - "\n", - "# Calculate the correlation between features\n", - "correlation_data = numeric_data.corr().reset_index().melt('index')\n", - "\n", - "# Plot heatmap\n", - "correlation_chart = alt.Chart(correlation_data).mark_rect().encode(\n", - " x=alt.X('variable:N', title='Features'),\n", - " y=alt.Y('index:N', title='Features'),\n", - " color=alt.Color(\n", - " 'value:Q',\n", - " scale=alt.Scale(\n", - " domain=[0, 1], \n", - " range=['#E6F7FF', '#0050B3'] \n", - " ),\n", - " title='Correlation'\n", - " ),\n", - " tooltip=['index', 'variable', 'value'] \n", - ").properties(\n", - " title='Figure 2. Numeric Feature Correlation Heatmap',\n", - " width=400,\n", - " height=400\n", - ")\n", - "\n", - "correlation_chart" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "dbe743a6-3739-46eb-bf9a-931975706fd2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.Chart(...)" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Plot 3: Relationship between continuous features and target\n", - "\n", - "alt.data_transformers.enable('vegafusion')\n", - "\n", - "numeric_features_facet = alt.Chart(\n", - " train_df.melt(id_vars=['obesity_level'], value_vars=numeric_data)\n", - ").mark_boxplot().encode(\n", - " x=alt.X('obesity_level:N', title='Obesity Level'),\n", - " y=alt.Y(\n", - " 'value:Q',\n", - " title='Value',\n", - " scale=alt.Scale(padding=5) \n", - " ),\n", - " color=alt.Color('obesity_level'),\n", - " facet=alt.Facet('variable:N', title='Features', columns=2) \n", - ").properties(\n", - " title='Figure 3. Distribution of Numeric Features by Obesity Level',\n", - " width=200,\n", - " height=200\n", - ").resolve_scale(\n", - " y='independent' \n", - ")\n", - "\n", - "numeric_features_facet\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "cadb5463-0ecc-41ae-81dd-321045ea6ce0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.VConcatChart(...)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Plot 4: Relationship between calegorical features and target\n", - "\n", - "# Select binary/categorical features (excluding numeric features)\n", - "categorical_features = train_df.select_dtypes(exclude=['float64', 'int64']).columns.difference(['obesity_level'])\n", - "\n", - "# Create a list to store individual charts\n", - "charts = []\n", - "\n", - "# Loop through each categorical feature\n", - "for feature in categorical_features:\n", - " # Create a bar chart for the current feature\n", - " chart = alt.Chart(merged_df).mark_bar().encode(\n", - " x=alt.X(f\"{feature}:N\", title=feature, sort='-y'), # Sort categories by count (descending)\n", - " y=alt.Y('count():Q', title='Count'),\n", - " color=alt.Color('obesity_level:N', title='Obesity Level'),\n", - " order=alt.Order('count():Q', sort='descending') # Sort stack order by count, ascending\n", - " ).properties(\n", - " title=f'{feature} by Obesity Level',\n", - " width=200,\n", - " height=200\n", - " )\n", - " charts.append(chart)\n", - "\n", - "# Arrange charts in rows of 2 columns\n", - "rows = [alt.hconcat(*charts[i:i+2]) for i in range(0, len(charts), 2)]\n", - "\n", - "# Combine all rows into a vertical concatenation\n", - "combined_chart = alt.vconcat(*rows).properties(\n", - " title=\"Figure 4. Relationship between Categorical Features and Target\"\n", - ").configure_title(\n", - " fontSize=16,\n", - " anchor='middle', \n", - " font='Arial'\n", - ")\n", - "\n", - "# Display the combined chart\n", - "combined_chart" - ] - }, - { - "cell_type": "markdown", - "id": "71e7e6cf-f51a-400e-9fa6-a2a73c76f537", - "metadata": {}, - "source": [ - "## Classification Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "778bbd87-db44-438f-9398-3225cda7e25b", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.model_selection import cross_val_score, cross_validate\n", - "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n", - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.pipeline import Pipeline\n", - "from sklearn.neighbors import KNeighborsClassifier\n", - "from sklearn.svm import SVC\n", - "from sklearn.tree import DecisionTreeClassifier\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.ensemble import AdaBoostClassifier\n", - "from sklearn.model_selection import GridSearchCV\n", - "from sklearn.metrics import accuracy_score\n", - "\n", - "# Identify categorical and numerical columns\n", - "categorical_cols = X.select_dtypes(include=['object']).columns\n", - "numerical_cols = X.select_dtypes(include=['float64']).columns" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "f3788b54-cb41-41d4-96da-3aaac2e0032d", - "metadata": {}, - "outputs": [], - "source": [ - "# Preprocessing for numeric and categorical data\n", - "numeric_transformer = StandardScaler()\n", - "categorical_transformer = OneHotEncoder(handle_unknown='ignore')\n", - "\n", - "preprocessor = ColumnTransformer(\n", - " transformers=[\n", - " ('num', numeric_transformer, numerical_cols),\n", - " ('cat', categorical_transformer, categorical_cols)\n", - " ]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "c1d694e2-b372-47dd-9702-66b416359af5", - "metadata": {}, - "outputs": [], - "source": [ - "# Models to evaluate\n", - "models = {\n", - " 'KNN': KNeighborsClassifier(),\n", - " 'SVM (RBF Kernel)': SVC(kernel='rbf', random_state=522),\n", - " 'AdaBoost + Decision Tree': AdaBoostClassifier(\n", - " estimator=DecisionTreeClassifier(max_depth=5, random_state=522), \n", - " n_estimators=50, \n", - " learning_rate=0.5,\n", - " algorithm=\"SAMME\",\n", - " random_state=522\n", - " )\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "d730d63a-685a-4012-aa02-9f2e2443d841", - "metadata": {}, - "outputs": [], - "source": [ - "# Code adapted from DSCI 571 Lecture 4\n", - "# https://pages.github.ubc.ca/MDS-2024-25/DSCI_571_sup-learn-1_students/lectures/notes/04_preprocessing-pipelines-column-transformer.html\n", - "\n", - "def mean_std_cross_val_scores(model, X_train, y_train, **kwargs):\n", - " \"\"\"\n", - " Returns mean and std of cross validation\n", - "\n", - " Parameters\n", - " ----------\n", - " model :\n", - " scikit-learn model\n", - " X_train : numpy array or pandas DataFrame\n", - " X in the training data\n", - " y_train :\n", - " y in the training data\n", - "\n", - " Returns\n", - " ----------\n", - " pandas Series with mean scores from cross_validation\n", - " \"\"\"\n", - " scores = cross_validate(model, X_train, y_train, return_train_score=True, **kwargs)\n", - "\n", - " mean_scores = pd.DataFrame(scores).mean()\n", - " std_scores = pd.DataFrame(scores).std()\n", - " out_col = []\n", - "\n", - " for i in range(len(mean_scores)):\n", - " out_col.append((f\"%0.3f (+/- %0.3f)\" % (mean_scores.iloc[i], std_scores.iloc[i])))\n", - "\n", - " return pd.Series(data=out_col, index=mean_scores.index)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "7e254019-4c59-4e86-8246-8ad8d2e7dbad", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Cross-Validation Results for Models:\n" - ] - }, - { - "data": { - "text/html": [ - "
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KNNSVM (RBF Kernel)AdaBoost + Decision Tree
fit_time0.006 (+/- 0.003)0.049 (+/- 0.003)0.222 (+/- 0.002)
score_time0.026 (+/- 0.046)0.019 (+/- 0.001)0.006 (+/- 0.000)
test_score0.812 (+/- 0.035)0.898 (+/- 0.020)0.965 (+/- 0.007)
train_score0.880 (+/- 0.007)0.956 (+/- 0.003)1.000 (+/- 0.000)
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" - ], - "text/plain": [ - " KNN SVM (RBF Kernel) AdaBoost + Decision Tree\n", - "fit_time 0.006 (+/- 0.003) 0.049 (+/- 0.003) 0.222 (+/- 0.002)\n", - "score_time 0.026 (+/- 0.046) 0.019 (+/- 0.001) 0.006 (+/- 0.000)\n", - "test_score 0.812 (+/- 0.035) 0.898 (+/- 0.020) 0.965 (+/- 0.007)\n", - "train_score 0.880 (+/- 0.007) 0.956 (+/- 0.003) 1.000 (+/- 0.000)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "warnings.filterwarnings(\"ignore\", category=UserWarning, module=\"sklearn.preprocessing\")\n", - "\n", - "# Evaluate models with detailed cross-validation results\n", - "results = {}\n", - "\n", - "for name, model in models.items():\n", - " pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', model)])\n", - " \n", - " results[name] = mean_std_cross_val_scores(\n", - " pipeline, \n", - " train_df.drop('obesity_level', axis=1), \n", - " train_df['obesity_level'], \n", - " cv=5, \n", - " scoring='accuracy'\n", - " )\n", - "\n", - "# Combine results into a DataFrame for comparison\n", - "cv_results_df = pd.DataFrame(results)\n", - "\n", - "# Display the results\n", - "print(\"Cross-Validation Results for Models:\")\n", - "cv_results_df" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "71f88ac5-9336-4e48-b815-1e028c7c6abb", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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Best ParamsBest CV Accuracy
KNN{'classifier__metric': 'manhattan', 'classifie...0.870548
SVM (RBF Kernel){'classifier__C': 100, 'classifier__gamma': 0.01}0.954795
AdaBoost + Decision Tree{'classifier__estimator__max_depth': 7, 'class...0.972603
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" - ], - "text/plain": [ - " Best Params \\\n", - "KNN {'classifier__metric': 'manhattan', 'classifie... \n", - "SVM (RBF Kernel) {'classifier__C': 100, 'classifier__gamma': 0.01} \n", - "AdaBoost + Decision Tree {'classifier__estimator__max_depth': 7, 'class... \n", - "\n", - " Best CV Accuracy \n", - "KNN 0.870548 \n", - "SVM (RBF Kernel) 0.954795 \n", - "AdaBoost + Decision Tree 0.972603 " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Hyper Parameter Optimization\n", - "param_grids = {\n", - " 'KNN': {\n", - " 'n_neighbors': [3, 5, 7, 9], \n", - " 'weights': ['uniform', 'distance'], \n", - " 'metric': ['euclidean', 'manhattan'] \n", - " },\n", - " 'SVM (RBF Kernel)': {\n", - " 'C': [0.1, 1, 10, 100], \n", - " 'gamma': ['scale', 'auto', 0.01, 0.1, 1] \n", - " },\n", - " 'AdaBoost + Decision Tree': {\n", - " 'n_estimators': [100, 150, 200], \n", - " 'learning_rate': [0.3, 0.5, 0.7], \n", - " 'estimator__max_depth': [5, 6, 7, 8, 9] \n", - " }\n", - "}\n", - "\n", - "# To store best params and scores\n", - "best_params = {}\n", - "best_scores = {}\n", - "\n", - "# Hyper paramaters optimization for each model\n", - "cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=522)\n", - "for name, model in models.items():\n", - " pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', model)])\n", - " param_grid = {f'classifier__{key}': value for key, value in param_grids[name].items()}\n", - " \n", - " grid_search = GridSearchCV(\n", - " pipeline,\n", - " param_grid=param_grid,\n", - " cv=cv,\n", - " scoring='accuracy',\n", - " n_jobs=-1\n", - " )\n", - " grid_search.fit(\n", - " train_df.drop('obesity_level', axis=1), \n", - " train_df['obesity_level']\n", - " )\n", - " \n", - " best_params[name] = grid_search.best_params_\n", - " best_scores[name] = grid_search.best_score_\n", - "\n", - "# Print result\n", - "hy_results_df = pd.DataFrame({\n", - " 'Best Params': best_params,\n", - " 'Best CV Accuracy': best_scores\n", - "})\n", - "hy_results_df" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "3a27964e-b192-41dc-9e4b-feeaed011c17", - "metadata": {}, - "outputs": [], - "source": [ - "# Auto generating parameters\n", - "auto_best_params = {}\n", - "\n", - "for name, params in hy_results_df['Best Params'].items():\n", - " if name == 'AdaBoost + Decision Tree':\n", - " # Capture DecisionTreeClassifier Parameters\n", - " base_estimator_params = {\n", - " key.split('__')[2]: value for key, value in params.items() if key.startswith('classifier__estimator__')\n", - " }\n", - " # Capture AdaBoostClassifier parameters\n", - " ada_params = {\n", - " key.split('__')[1]: value for key, value in params.items() if not key.startswith('classifier__estimator__')\n", - " }\n", - " # Create nested model parameters\n", - " auto_best_params[name] = {\n", - " **ada_params,\n", - " 'estimator': DecisionTreeClassifier(**base_estimator_params)\n", - " }\n", - " else:\n", - " # For other models\n", - " auto_best_params[name] = {\n", - " key.split('__')[1]: value for key, value in params.items()\n", - " }\n", - "\n", - "# Test by the dynamic genertating parameters\n", - "trained_pipelines = {} \n", - "\n", - "for name, model in models.items():\n", - " params = auto_best_params[name]\n", - " \n", - " # Dynamic Set AdaBoost + Decision Tree\n", - " if name == 'AdaBoost + Decision Tree':\n", - " model.set_params(**{k: v for k, v in params.items() if k != 'estimator'})\n", - " model.estimator = params['estimator']\n", - " else:\n", - " model.set_params(**params)\n", - " \n", - " pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('classifier', model)])\n", - " \n", - " pipeline.fit(\n", - " train_df.drop('obesity_level', axis=1), \n", - " train_df['obesity_level']\n", - " )\n", - "\n", - " # Save trained_pipeline to a dict\n", - " trained_pipelines[name] = pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "e1d79acc-a2ea-440c-9138-f8b0dfb35be5", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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ModelAccuracy
0AdaBoost + Decision Tree0.979266
1KNN0.880383
2SVM (RBF Kernel)0.971292
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" - ], - "text/plain": [ - " Model Accuracy\n", - "0 AdaBoost + Decision Tree 0.979266\n", - "1 KNN 0.880383\n", - "2 SVM (RBF Kernel) 0.971292" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Evaluate by test set\n", - "test_results = {} \n", - "for name, pipeline in trained_pipelines.items():\n", - " y_pred = pipeline.predict(test_df.drop('obesity_level', axis=1))\n", - " \n", - " # Evaluate performance\n", - " accuracy = accuracy_score(test_df['obesity_level'], y_pred)\n", - " test_results[name] = {\n", - " \"Accuracy\": accuracy\n", - " }\n", - "\n", - "test_results_df = pd.DataFrame.from_dict(test_results, orient='index').reset_index()\n", - "test_results_df.columns = ['Model', 'Accuracy']\n", - "test_results_df" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "54824c3f-40fa-4924-af06-7e248600af5d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
\n", - "" - ], - "text/plain": [ - "alt.LayerChart(...)" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Result Visualization of model comparison\n", - "results_data = pd.DataFrame({\n", - " 'Model': list(test_results.keys()),\n", - " 'Accuracy': [result['Accuracy'] for result in test_results.values()]\n", - "})\n", - "\n", - "# Create bar chart for comparison\n", - "bar_chart = alt.Chart(results_data).mark_bar().encode(\n", - " x=alt.X('Model:N', title='Model', sort=None, axis=alt.Axis(labelAngle=0)),\n", - " y=alt.Y('Accuracy:Q', title='Accuracy', scale=alt.Scale(domain=[0, 1])),\n", - " tooltip=['Model', 'Accuracy']\n", - ")\n", - "\n", - "# Add text labels \n", - "text = bar_chart.mark_text(\n", - " align='center',\n", - " baseline='middle',\n", - " dy=-10, \n", - " fontSize=12\n", - ").encode(\n", - " text=alt.Text('Accuracy:Q', format='.2f') \n", - ")\n", - "\n", - "# Combine the bar chart with the text labels\n", - "final_chart_with_text = (bar_chart + text).properties(\n", - " title='Figure 5. Model Performance Comparison',\n", - " width=600,\n", - " height=400\n", - ").configure_axis(\n", - " labelFontSize=12,\n", - " titleFontSize=14\n", - ").configure_title(\n", - " fontSize=16\n", - ").configure_mark(\n", - " color='skyblue'\n", - ")\n", - "\n", - "final_chart_with_text" - ] - }, - { - "cell_type": "markdown", - "id": "1e1974ee", - "metadata": {}, - "source": [ - "# References\n", - "\n", - "Estimation of Obesity Levels Based On Eating Habits and Physical Condition. (2019). UCI Machine Learning Repository. Retrieved from https://doi.org/10.24432/C5H31Z\n", - "\n", - "Han, T. S., Sattar, N., & Lean, M. (2006). Assessment of obesity and its clinical implications. BMJ, 333(7570), 695–698. Retrieved from https://doi.org/10.1136/bmj.333.7570.695\n", - "\n", - "Palechor, F. M., & De La Hoz Manotas, A. (2019). Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico. Retrieved from Data in Brief, 25, 104344. https://doi.org/10.1016/j.dib.2019.104344\n", - "\n", - "Westbury, S., Oyebode, O., Van Rens, T., & Barber, T. M. (2023). Obesity stigma: causes, consequences, and potential solutions. Current Obesity Reports, 12(1), 10–23. Retrieved from https://doi.org/10.1007/s13679-023-00495-3\n", - "\n", - "World Health Organization. (2023). Obesity and overweight. Retrieved from https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight\n", - "\n", - "Zhou, X., Chen, L., & Liu, H. (2022). Applications of machine learning models to predict and prevent obesity: A mini-review. Frontiers in Nutrition, 9. Retrieved from https://doi.org/10.3389/fnut.2022.933130\n", - "\n", - "Feature label correlation — DeepChecks documentation. (n.d.). Retrieved from https://docs.deepchecks.com/stable/tabular/auto_checks/data_integrity/plot_feature_label_correlation.html\n", - "\n", - "Tiffany A. Timbers, Joel Ostblom, Florencia D’Andrea, Rodolfo Lourenzutti, Daniel Chen (2024). Reproducible and Trustworthy Workflows for Data Science. Retrieved from https://ubc-dsci.github.io/reproducible-and-trustworthy-workflows-for-data-science/\n", - "\n", - "Lecture 4: Preprocessing, sklearn Pipeline, sklearn ColumnTrasnsformer — DSCI 571 Supervised Learning I. UBC Master of Data Science program (2023). Retrieved from https://pages.github.ubc.ca/mds-2024-25/DSCI_571_sup-learn-1_students/lectures/notes/04_preprocessing-pipelines-column-transformer.html\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/results/figures/Data_vali_targ_varib_dist.png b/results/figures/Data_vali_targ_varib_dist.png new file mode 100644 index 0000000000000000000000000000000000000000..b3193791750aed80949d533b22bf7a1c42c7e659 GIT binary patch literal 144136 zcmeEv3tW_Sy8jRvCFkrv9Dsw5F(J+~*}- zW0e#yrzlZp%k@~gO64&`1_twjCWvK(3NqZBQ4nT^0p>pM|9PI5`Moa)UUqGl>CApU zZDqsozQ4UOcweFne3Xs{9riz$>O>jro-Q^ 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