add polyreg and linreg

LinearRegressionInsuranceModel.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e0f8838048b9663a0b4bb8a8112ae978a1fe6ae5c449dec20ce7530f2d442d2
+size 80881

PolynomialRegressionInsuranceModel.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d96fdfb6052b82ace3702c19602b89f42010a1025b165bdb744b14044f53ea8f
+size 82968

app.py

@@ -6,6 +6,8 @@ from models.svr import NuSVRInsuranceModel
 from models.randomforest import RandomForestInsuranceModel
 from models.XGBoost import XGBoostInsuranceModel
 from models.knn import KNNInsuranceModel
+from models.linreg import LinearRegressionInsuranceModel
+from models.polyreg import PolynomialRegressionInsuranceModel
 import os
 # -------------------------------------------------------
 # Placeholder code to load pre-trained models 
@@ -15,13 +17,17 @@ model_nusvr = joblib.load(os.path.join('.', 'NuSVRInsuranceModel.joblib'))
 model_xgb = joblib.load(os.path.join('.', 'XGBoostInsuranceModel.joblib'))
 model_knn   = joblib.load(os.path.join('.', 'KNNInsuranceModel.joblib'))
 model_rf    = joblib.load(os.path.join('.', 'RandomForestInsuranceModel.joblib'))
+model_lr = joblib.load(os.path.join('.', 'LinearRegressionInsuranceModel.joblib'))
+model_pr = joblib.load(os.path.join('.', 'PolynomialRegressionInsuranceModel.joblib'))
 
 # # Dictionary to map model choice to the actual loaded model
 models_dict = {
      "NuSVR": model_nusvr,
-     "XGBoost": model_xgb,
+     "Gradient Boost Regressor": model_xgb,
      "KNN": model_knn,
-     "Random Forest": model_rf
+     "Random Forest": model_rf,
+     "Linear Regression": model_lr,
+     "Polynomial Regression": model_pr
  }
 
 # -------------------------------------------------------
@@ -85,7 +91,7 @@ def build_interface():
     # Dropdown to choose model
     with gr.Row():
         model_dropdown = gr.Dropdown(
-            choices=["NuSVR", "Gradient Boost Regressor", "KNN", "Random Forest"], 
+            choices=["NuSVR", "Gradient Boost Regressor", "KNN", "Random Forest", 'Linear Regression', 'Polynomial Regression'], 
             value="NuSVR", 
             label="Select Model"
         )

cleaned_insurance_data.csv

@@ -0,0 +1,1341 @@
+index,PatientID,age,gender,bmi,bloodpressure,diabetic,children,smoker,region,claim
+0.0,1.0,39.0,male,23.2,91.0,Yes,0.0,No,southeast,1121.87
+1.0,2.0,24.0,male,30.1,87.0,No,0.0,No,southeast,1131.51
+2.0,3.0,38.0,male,33.3,82.0,Yes,0.0,No,southeast,1135.94
+3.0,4.0,38.0,male,33.7,80.0,No,0.0,No,northwest,1136.4
+4.0,5.0,38.0,male,34.1,100.0,No,0.0,No,northwest,1137.01
+5.0,6.0,38.0,male,34.4,96.0,Yes,0.0,No,northwest,1137.47
+6.0,7.0,38.0,male,37.3,86.0,Yes,0.0,No,northwest,1141.45
+7.0,8.0,19.0,male,41.1,100.0,No,0.0,No,northwest,1146.8
+8.0,9.0,20.0,male,43.0,86.0,No,0.0,No,northwest,1149.4
+9.0,10.0,30.0,male,53.1,97.0,No,0.0,No,northwest,1163.46
+10.0,11.0,36.0,male,19.8,88.0,Yes,0.0,No,northwest,1241.57
+11.0,12.0,37.0,male,20.3,90.0,Yes,0.0,No,northwest,1242.26
+12.0,13.0,19.0,male,20.7,81.0,No,0.0,No,northwest,1242.82
+13.0,14.0,32.0,male,27.6,100.0,No,0.0,No,southeast,1252.41
+14.0,15.0,40.0,male,28.7,81.0,Yes,0.0,No,southeast,1253.94
+15.0,16.0,32.0,male,30.4,86.0,Yes,0.0,No,southeast,1256.3
+16.0,17.0,35.0,male,34.1,90.0,No,0.0,No,southwest,1261.44
+17.0,18.0,41.0,male,34.4,84.0,No,0.0,No,southwest,1261.86
+18.0,19.0,49.0,male,35.4,97.0,Yes,0.0,No,southwest,1263.25
+19.0,20.0,48.0,male,33.3,91.0,Yes,0.0,No,southeast,1391.53
+20.0,21.0,45.0,male,23.2,85.0,Yes,0.0,No,southeast,1515.34
+21.0,22.0,34.0,male,31.1,96.0,No,0.0,No,southwest,1526.31
+22.0,23.0,18.0,male,35.5,100.0,Yes,0.0,No,southeast,1532.47
+23.0,24.0,42.0,male,36.9,93.0,No,0.0,No,southeast,1534.3
+24.0,25.0,50.0,female,20.8,85.0,Yes,0.0,No,southeast,1607.51
+25.0,26.0,23.0,male,43.0,86.0,No,0.0,No,northwest,1149.4
+26.0,27.0,36.0,female,26.7,97.0,Yes,0.0,No,southeast,1615.77
+27.0,28.0,30.0,male,17.5,93.0,Yes,0.0,No,northwest,1621.34
+28.0,29.0,58.0,female,31.1,87.0,No,0.0,No,southeast,1621.88
+29.0,30.0,35.0,female,31.4,93.0,No,0.0,No,southeast,1622.19
+30.0,31.0,29.0,male,20.4,80.0,Yes,0.0,No,northwest,1625.43
+31.0,32.0,49.0,male,21.8,80.0,No,0.0,No,northwest,1627.28
+32.0,33.0,21.0,male,22.6,96.0,Yes,0.0,No,northwest,1628.47
+33.0,34.0,52.0,female,36.9,81.0,No,0.0,No,southeast,1629.83
+34.0,35.0,43.0,female,38.2,86.0,Yes,0.0,No,southeast,1631.67
+35.0,36.0,47.0,female,38.3,93.0,No,0.0,No,southeast,1631.82
+36.0,37.0,48.0,male,25.2,98.0,No,0.0,No,northwest,1632.04
+37.0,38.0,34.0,male,25.6,83.0,No,0.0,No,northwest,1632.56
+38.0,39.0,28.0,female,39.2,87.0,No,0.0,No,southeast,1633.04
+39.0,40.0,49.0,female,39.8,100.0,Yes,0.0,No,southeast,1633.96
+40.0,41.0,29.0,female,40.3,94.0,No,0.0,No,southeast,1634.57
+41.0,42.0,18.0,male,27.8,93.0,No,0.0,No,northwest,1635.73
+42.0,43.0,32.0,male,30.6,93.0,No,0.0,No,northwest,1639.56
+43.0,44.0,44.0,male,30.6,82.0,No,0.0,No,northwest,1639.56
+44.0,45.0,37.0,male,35.5,91.0,No,0.0,No,northwest,1646.43
+45.0,46.0,29.0,male,26.8,94.0,Yes,0.0,No,southeast,1665.0
+46.0,47.0,24.0,male,33.8,89.0,No,0.0,No,southeast,1674.63
+47.0,48.0,31.0,male,39.5,80.0,Yes,0.0,No,southwest,1682.6
+48.0,49.0,34.0,male,16.0,83.0,No,0.0,No,northeast,1694.8
+49.0,50.0,30.0,male,21.5,83.0,Yes,0.0,No,northeast,1702.46
+50.0,51.0,45.0,male,23.0,81.0,Yes,0.0,No,northeast,1704.57
+51.0,52.0,31.0,male,23.1,81.0,Yes,0.0,No,northwest,1704.7
+52.0,53.0,30.0,male,23.8,91.0,Yes,0.0,No,northwest,1705.62
+53.0,54.0,34.0,male,25.5,82.0,Yes,0.0,No,northwest,1708.0
+54.0,55.0,21.0,male,26.1,87.0,Yes,0.0,No,northwest,1708.93
+55.0,56.0,21.0,male,23.3,80.0,Yes,1.0,No,northwest,1711.03
+56.0,57.0,23.0,male,28.5,88.0,Yes,0.0,No,northwest,1712.23
+57.0,58.0,32.0,male,29.4,97.0,No,1.0,No,southeast,1719.44
+58.0,59.0,50.0,male,30.0,88.0,No,1.0,No,southeast,1720.35
+59.0,60.0,47.0,male,33.8,90.0,No,1.0,No,southeast,1725.55
+60.0,61.0,49.0,male,35.2,80.0,Yes,1.0,No,southeast,1727.54
+61.0,62.0,32.0,female,17.8,90.0,No,0.0,No,southwest,1727.79
+62.0,63.0,51.0,female,18.6,98.0,Yes,0.0,No,southwest,1728.9
+63.0,64.0,60.0,female,20.6,90.0,No,0.0,No,southwest,1731.68
+64.0,65.0,32.0,female,24.7,89.0,No,0.0,No,southwest,1737.38
+65.0,66.0,51.0,female,28.9,87.0,Yes,0.0,No,southwest,1743.21
+66.0,67.0,27.0,female,29.8,100.0,No,0.0,No,southwest,1744.47
+67.0,68.0,39.0,female,32.9,99.0,Yes,0.0,No,southwest,1748.77
+68.0,69.0,26.0,female,40.5,87.0,Yes,0.0,No,southwest,1759.34
+69.0,70.0,32.0,male,29.7,80.0,Yes,0.0,No,northwest,1769.53
+70.0,71.0,28.0,male,26.5,88.0,Yes,0.0,No,southeast,1815.88
+71.0,72.0,48.0,male,32.6,80.0,Yes,0.0,No,southeast,1824.29
+72.0,73.0,22.0,male,34.4,98.0,Yes,0.0,No,southwest,1826.84
+73.0,74.0,20.0,male,20.9,98.0,No,1.0,No,southwest,1832.09
+74.0,75.0,50.0,male,24.6,90.0,No,1.0,No,southwest,1837.24
+75.0,76.0,30.0,male,41.9,95.0,Yes,0.0,No,southeast,1837.28
+76.0,77.0,38.0,male,28.4,87.0,No,1.0,No,southwest,1842.52
+77.0,78.0,50.0,female,29.6,95.0,No,0.0,No,southwest,1875.34
+78.0,79.0,42.0,female,31.5,100.0,No,0.0,No,southeast,1877.93
+79.0,80.0,53.0,female,33.0,83.0,Yes,0.0,No,southeast,1880.07
+80.0,81.0,26.0,female,33.3,83.0,Yes,0.0,No,southwest,1880.49
+81.0,82.0,43.0,male,29.0,91.0,Yes,0.0,No,northwest,1906.36
+82.0,83.0,42.0,male,31.3,98.0,No,0.0,No,northwest,1909.53
+83.0,84.0,43.0,male,36.9,91.0,Yes,0.0,No,northwest,1917.32
+84.0,85.0,36.0,male,22.0,88.0,Yes,1.0,No,southwest,1964.78
+85.0,86.0,19.0,male,27.9,94.0,Yes,0.0,No,northeast,1967.02
+86.0,87.0,19.0,male,23.4,97.0,No,0.0,No,northwest,1969.61
+87.0,88.0,37.0,male,25.8,86.0,No,0.0,No,northwest,1972.95
+88.0,89.0,40.0,male,29.3,97.0,Yes,0.0,No,northwest,1977.82
+89.0,90.0,23.0,male,33.0,80.0,Yes,1.0,No,northwest,1980.07
+90.0,91.0,19.0,male,32.0,100.0,Yes,0.0,No,southeast,1981.58
+91.0,92.0,20.0,male,40.5,95.0,No,0.0,No,southeast,1984.45
+92.0,93.0,41.0,male,35.9,97.0,No,0.0,No,southeast,1986.93
+93.0,94.0,26.0,female,25.8,100.0,Yes,0.0,No,southwest,2007.95
+94.0,95.0,48.0,female,34.6,99.0,No,0.0,No,southwest,2020.18
+95.0,96.0,54.0,female,34.9,90.0,No,0.0,No,southeast,2020.55
+96.0,97.0,44.0,female,39.5,93.0,Yes,0.0,No,southeast,2026.97
+97.0,98.0,44.0,male,25.2,91.0,No,0.0,No,northwest,2045.69
+98.0,99.0,47.0,male,32.1,81.0,No,0.0,No,northwest,2055.32
+99.0,100.0,50.0,male,26.0,82.0,No,0.0,No,northeast,2102.26
+100.0,101.0,30.0,male,22.3,83.0,Yes,1.0,No,southwest,2103.08
+101.0,102.0,33.0,male,27.4,100.0,No,0.0,No,northeast,2104.11
+102.0,103.0,59.0,female,22.5,87.0,No,0.0,No,northwest,2117.34
+103.0,104.0,42.0,female,30.5,99.0,No,0.0,No,northwest,2128.43
+104.0,105.0,52.0,female,32.1,94.0,Yes,0.0,No,northwest,2130.68
+105.0,106.0,43.0,female,35.2,80.0,No,0.0,No,northwest,2134.9
+106.0,107.0,42.0,female,36.6,90.0,No,0.0,No,northwest,2136.88
+107.0,108.0,33.0,male,25.7,100.0,Yes,0.0,No,southeast,2137.65
+108.0,109.0,26.0,female,37.4,83.0,Yes,0.0,No,northwest,2138.07
+109.0,110.0,41.0,female,24.3,91.0,Yes,0.0,No,southwest,2150.47
+110.0,111.0,27.0,female,27.1,88.0,Yes,0.0,No,southwest,2154.36
+111.0,112.0,37.0,female,28.1,80.0,No,0.0,No,southeast,2155.68
+112.0,113.0,43.0,female,28.8,88.0,Yes,0.0,No,southeast,2156.75
+113.0,114.0,30.0,female,36.0,80.0,No,0.0,No,southwest,2166.73
+114.0,115.0,47.0,female,25.1,80.0,No,0.0,No,southeast,2196.47
+115.0,116.0,26.0,female,26.3,87.0,Yes,0.0,No,southeast,2198.19
+116.0,117.0,49.0,female,28.2,98.0,Yes,0.0,No,southeast,2200.83
+117.0,118.0,59.0,female,24.1,82.0,No,1.0,No,southeast,2201.1
+118.0,119.0,58.0,female,30.1,81.0,Yes,0.0,No,southeast,2203.47
+119.0,120.0,32.0,female,30.3,94.0,Yes,0.0,No,southeast,2203.74
+120.0,121.0,37.0,female,31.9,89.0,No,0.0,No,southeast,2205.98
+121.0,122.0,48.0,female,33.2,97.0,No,0.0,No,southeast,2207.7
+122.0,123.0,52.0,female,35.6,89.0,No,0.0,No,southeast,2211.13
+123.0,124.0,55.0,female,40.2,82.0,No,0.0,No,southeast,2217.47
+124.0,125.0,33.0,female,40.3,94.0,Yes,0.0,No,southeast,2217.6
+125.0,126.0,53.0,female,37.3,88.0,Yes,1.0,No,southeast,2219.45
+126.0,127.0,26.0,male,25.6,86.0,No,1.0,No,northwest,2221.56
+127.0,128.0,49.0,male,28.9,81.0,No,0.0,No,northwest,2250.84
+128.0,129.0,31.0,male,31.7,90.0,No,0.0,No,northwest,2254.8
+129.0,130.0,42.0,female,29.0,81.0,Yes,0.0,No,northwest,2257.48
+130.0,131.0,30.0,female,31.9,89.0,No,0.0,No,northwest,2261.57
+131.0,132.0,45.0,male,20.8,88.0,No,0.0,No,northwest,2302.3
+132.0,133.0,49.0,male,26.2,91.0,No,2.0,No,southeast,2304.0
+133.0,134.0,47.0,male,35.4,95.0,No,0.0,No,southeast,2322.62
+134.0,135.0,47.0,female,28.4,87.0,Yes,1.0,No,southwest,2331.52
+135.0,136.0,29.0,male,23.7,89.0,Yes,0.0,No,northwest,2352.97
+136.0,137.0,40.0,male,32.4,81.0,No,1.0,No,northwest,2362.23
+137.0,138.0,21.0,male,23.8,95.0,Yes,0.0,No,southeast,2395.17
+138.0,139.0,32.0,male,24.5,90.0,Yes,0.0,No,southeast,2396.1
+139.0,140.0,26.0,female,35.7,90.0,Yes,0.0,No,northwest,2404.73
+140.0,141.0,47.0,male,35.2,80.0,No,1.0,No,southwest,2416.96
+141.0,142.0,46.0,male,50.4,89.0,Yes,1.0,No,southeast,2438.06
+142.0,143.0,44.0,female,28.8,94.0,Yes,0.0,No,southeast,2457.21
+143.0,144.0,41.0,female,22.6,91.0,Yes,0.0,No,southwest,2457.5
+144.0,145.0,37.0,female,30.6,96.0,Yes,0.0,No,southeast,2459.72
+145.0,146.0,29.0,female,27.7,80.0,Yes,0.0,No,southeast,2464.62
+146.0,147.0,47.0,female,34.0,84.0,No,0.0,No,southeast,2473.33
+147.0,148.0,35.0,female,39.5,82.0,Yes,0.0,No,southeast,2480.98
+148.0,149.0,48.0,male,23.1,91.0,Yes,0.0,No,southeast,2483.74
+149.0,150.0,22.0,male,30.5,82.0,No,0.0,No,southwest,2494.02
+150.0,151.0,41.0,male,32.7,93.0,No,0.0,No,southeast,2497.04
+151.0,152.0,26.0,male,33.7,99.0,No,0.0,No,southeast,2498.41
+152.0,153.0,18.0,male,27.6,94.0,Yes,0.0,No,northwest,2523.17
+153.0,154.0,28.0,female,20.2,82.0,No,0.0,No,northwest,2527.82
+154.0,155.0,33.0,male,35.6,98.0,No,0.0,No,northwest,2534.39
+155.0,156.0,49.0,male,31.1,88.0,Yes,2.0,No,southeast,2566.47
+156.0,157.0,33.0,female,17.4,87.0,No,1.0,No,southwest,2585.27
+157.0,158.0,43.0,female,22.1,82.0,Yes,0.0,No,southeast,2585.85
+158.0,159.0,57.0,female,26.4,86.0,No,1.0,No,southwest,2597.78
+159.0,160.0,30.0,female,30.3,94.0,Yes,0.0,No,southwest,2632.99
+160.0,161.0,42.0,male,28.3,86.0,Yes,1.0,No,northwest,2639.04
+161.0,162.0,21.0,male,31.4,91.0,No,1.0,No,northwest,2643.27
+162.0,163.0,47.0,male,17.7,92.0,Yes,0.0,No,northwest,2680.95
+163.0,164.0,28.0,male,38.1,81.0,Yes,0.0,No,southeast,2689.5
+164.0,165.0,52.0,female,28.1,90.0,Yes,0.0,No,northwest,2690.11
+165.0,166.0,30.0,male,31.1,91.0,No,0.0,No,northwest,2699.57
+166.0,167.0,51.0,female,24.5,80.0,Yes,1.0,No,northwest,2709.11
+167.0,168.0,58.0,female,24.6,87.0,Yes,1.0,No,northwest,2709.24
+168.0,169.0,26.0,female,25.7,88.0,No,1.0,No,northwest,2710.83
+169.0,170.0,41.0,female,31.8,88.0,Yes,1.0,No,northwest,2719.28
+170.0,171.0,46.0,male,26.2,84.0,No,0.0,No,southeast,2721.32
+171.0,172.0,23.0,male,30.6,96.0,No,0.0,No,southeast,2727.4
+172.0,173.0,34.0,female,39.6,91.0,Yes,1.0,No,northwest,2730.11
+173.0,174.0,29.0,female,23.2,80.0,No,0.0,No,southeast,2731.91
+174.0,175.0,58.0,female,30.4,85.0,Yes,0.0,No,southeast,2741.95
+175.0,176.0,59.0,female,39.8,81.0,Yes,0.0,No,northeast,2755.02
+176.0,177.0,30.0,male,17.4,89.0,No,1.0,No,northwest,2775.19
+177.0,178.0,49.0,male,27.4,91.0,No,1.0,No,northwest,2789.06
+178.0,179.0,60.0,female,32.1,98.0,No,2.0,No,southeast,2801.26
+179.0,180.0,49.0,male,20.6,92.0,No,2.0,No,northwest,2803.7
+180.0,181.0,45.0,female,24.2,88.0,No,0.0,No,northwest,2842.76
+181.0,182.0,54.0,female,29.9,90.0,No,0.0,No,northwest,2850.68
+182.0,183.0,27.0,female,33.3,92.0,Yes,0.0,No,northwest,2855.44
+183.0,184.0,37.0,male,27.2,87.0,Yes,0.0,No,southwest,2866.09
+184.0,185.0,48.0,male,27.9,82.0,Yes,0.0,No,southeast,2867.12
+185.0,186.0,49.0,male,29.5,92.0,No,0.0,No,northeast,2897.32
+186.0,187.0,25.0,female,34.9,100.0,No,0.0,No,northeast,2899.49
+187.0,188.0,20.0,male,29.2,95.0,No,1.0,No,southeast,2902.91
+188.0,189.0,35.0,male,30.0,86.0,No,1.0,No,southwest,2904.09
+189.0,190.0,37.0,female,23.4,96.0,No,2.0,No,southwest,2913.57
+190.0,191.0,28.0,male,46.5,94.0,No,1.0,No,southeast,2927.06
+191.0,192.0,28.0,female,24.1,80.0,No,0.0,No,southwest,2974.13
+192.0,193.0,55.0,female,34.5,87.0,Yes,0.0,No,northwest,3021.81
+193.0,194.0,54.0,female,25.3,100.0,No,0.0,No,northeast,3044.21
+194.0,195.0,57.0,female,26.6,83.0,No,0.0,No,northeast,3046.06
+195.0,196.0,33.0,female,31.8,98.0,Yes,2.0,No,southeast,3056.39
+196.0,197.0,30.0,male,30.9,89.0,Yes,0.0,No,northwest,3062.51
+197.0,198.0,49.0,male,26.0,98.0,Yes,0.0,No,northeast,3070.81
+198.0,199.0,25.0,male,23.0,81.0,Yes,0.0,No,northeast,1704.57
+199.0,200.0,38.0,male,23.8,99.0,Yes,2.0,No,northwest,3077.1
+200.0,201.0,60.0,female,25.8,100.0,Yes,0.0,No,southwest,3161.45
+201.0,202.0,59.0,female,16.8,96.0,Yes,1.0,No,northeast,3167.46
+202.0,203.0,52.0,female,33.1,87.0,No,0.0,No,southeast,3171.61
+203.0,204.0,35.0,female,33.4,99.0,No,0.0,No,southwest,3172.02
+204.0,205.0,35.0,female,22.2,93.0,No,0.0,No,northwest,3176.29
+205.0,206.0,48.0,female,22.6,94.0,No,0.0,No,northwest,3176.82
+206.0,207.0,27.0,female,21.9,80.0,No,2.0,No,southeast,3180.51
+207.0,208.0,48.0,female,40.2,82.0,No,0.0,No,northwest,3201.25
+208.0,209.0,45.0,female,23.5,96.0,No,0.0,No,northeast,3206.49
+209.0,210.0,33.0,female,20.8,87.0,Yes,1.0,No,southwest,3208.79
+210.0,211.0,59.0,female,28.6,82.0,No,0.0,No,northeast,3213.62
+211.0,212.0,36.0,female,34.0,88.0,Yes,1.0,No,southeast,3227.12
+212.0,213.0,46.0,female,42.1,89.0,No,1.0,No,southeast,3238.44
+213.0,214.0,35.0,male,20.4,88.0,No,0.0,No,southwest,3260.2
+214.0,215.0,43.0,male,35.4,86.0,Yes,0.0,No,northeast,3268.85
+215.0,216.0,28.0,male,37.1,90.0,Yes,1.0,No,southwest,3277.16
+216.0,217.0,26.0,male,25.7,91.0,No,2.0,No,northeast,3279.87
+217.0,218.0,23.0,male,33.9,81.0,No,1.0,No,northwest,3292.53
+218.0,219.0,48.0,male,25.8,82.0,Yes,1.0,No,northeast,3309.79
+219.0,220.0,49.0,female,25.9,97.0,No,0.0,No,southwest,3353.28
+220.0,221.0,41.0,female,21.5,92.0,No,0.0,No,northwest,3353.47
+221.0,222.0,28.0,female,35.5,95.0,Yes,0.0,No,southeast,3366.67
+222.0,223.0,53.0,female,19.8,93.0,Yes,1.0,No,southwest,3378.91
+223.0,224.0,28.0,female,28.8,99.0,Yes,0.0,No,northeast,3385.4
+224.0,225.0,40.0,female,29.5,100.0,No,1.0,No,southeast,3392.37
+225.0,226.0,60.0,female,29.9,82.0,Yes,1.0,No,southeast,3392.98
+226.0,227.0,35.0,female,38.7,85.0,No,2.0,No,southeast,3393.36
+227.0,228.0,34.0,female,42.4,92.0,No,1.0,No,southwest,3410.32
+228.0,229.0,40.0,male,34.8,81.0,No,3.0,No,southwest,3443.06
+229.0,230.0,42.0,male,38.9,94.0,No,1.0,No,southeast,3471.41
+230.0,231.0,22.0,male,30.4,95.0,Yes,3.0,No,southeast,3481.87
+231.0,232.0,35.0,male,23.7,88.0,No,2.0,No,southwest,3484.33
+232.0,233.0,49.0,male,32.5,93.0,Yes,1.0,No,southeast,3490.55
+233.0,234.0,30.0,female,39.3,92.0,No,2.0,No,southeast,3500.61
+234.0,235.0,21.0,male,28.5,98.0,Yes,2.0,No,northwest,3537.7
+235.0,236.0,58.0,female,27.7,82.0,No,0.0,No,southwest,3554.2
+236.0,237.0,59.0,female,34.8,85.0,Yes,0.0,No,northwest,3556.92
+237.0,238.0,48.0,female,25.2,82.0,No,0.0,No,southeast,3558.62
+238.0,239.0,32.0,female,23.2,89.0,Yes,1.0,No,southeast,3561.89
+239.0,240.0,31.0,female,34.8,88.0,No,1.0,No,southwest,3578.0
+240.0,241.0,47.0,female,33.6,84.0,Yes,2.0,No,northwest,3579.83
+241.0,242.0,23.0,male,32.7,82.0,Yes,3.0,No,southwest,3591.48
+242.0,243.0,36.0,female,22.5,91.0,Yes,1.0,No,northwest,3594.17
+243.0,244.0,48.0,male,37.1,97.0,No,3.0,No,southwest,3597.6
+244.0,245.0,18.0,male,25.5,99.0,Yes,0.0,No,northeast,3645.09
+245.0,246.0,23.0,male,31.4,88.0,No,1.0,No,southwest,3659.35
+246.0,247.0,20.0,male,45.9,97.0,No,2.0,No,southwest,3693.43
+247.0,248.0,31.0,male,30.3,92.0,No,0.0,No,southeast,3704.35
+248.0,249.0,35.0,female,17.3,82.0,Yes,0.0,No,northeast,3732.63
+249.0,250.0,49.0,female,26.0,88.0,No,0.0,No,northwest,3736.46
+250.0,251.0,45.0,female,25.7,80.0,No,0.0,No,southeast,3756.62
+251.0,252.0,30.0,female,26.6,89.0,Yes,0.0,No,southeast,3757.84
+252.0,253.0,52.0,female,29.1,90.0,No,0.0,No,southwest,3761.29
+253.0,254.0,44.0,female,37.6,84.0,No,1.0,No,southeast,3766.88
+254.0,255.0,31.0,male,23.8,83.0,No,2.0,No,southwest,3847.67
+255.0,256.0,25.0,male,30.9,86.0,Yes,0.0,No,northeast,3857.76
+256.0,257.0,34.0,male,20.2,80.0,No,3.0,No,northeast,3861.21
+257.0,258.0,33.0,male,28.9,87.0,No,0.0,No,northwest,3866.86
+258.0,259.0,32.0,male,39.5,88.0,Yes,1.0,No,southeast,3875.73
+259.0,260.0,18.0,male,30.9,92.0,No,2.0,No,northwest,3877.3
+260.0,261.0,28.0,male,26.8,92.0,No,3.0,No,southwest,3906.13
+261.0,262.0,42.0,female,34.6,80.0,No,2.0,No,northeast,3925.76
+262.0,263.0,36.0,male,34.2,96.0,Yes,0.0,No,southeast,3935.18
+263.0,264.0,46.0,female,31.2,88.0,Yes,0.0,No,northeast,3943.6
+264.0,265.0,46.0,female,29.6,86.0,Yes,1.0,No,southeast,3947.41
+265.0,266.0,38.0,female,31.3,86.0,Yes,1.0,No,northwest,3956.07
+266.0,267.0,53.0,female,28.9,93.0,No,0.0,No,southeast,3972.92
+267.0,268.0,35.0,female,29.9,93.0,Yes,2.0,No,southeast,3981.98
+268.0,269.0,48.0,female,34.2,99.0,Yes,2.0,No,southwest,3987.93
+269.0,270.0,49.0,female,41.1,88.0,Yes,0.0,No,southwest,3989.84
+270.0,271.0,46.0,female,44.2,91.0,Yes,0.0,No,southeast,3994.18
+271.0,272.0,48.0,male,20.0,82.0,No,3.0,No,northeast,4005.42
+272.0,273.0,44.0,male,24.1,86.0,No,1.0,No,northwest,4032.24
+273.0,274.0,43.0,male,29.0,91.0,No,1.0,No,northeast,4040.56
+274.0,275.0,24.0,male,37.3,99.0,Yes,2.0,No,southeast,4058.12
+275.0,276.0,37.0,male,33.2,90.0,Yes,2.0,No,northwest,4058.71
+276.0,277.0,23.0,male,30.0,95.0,Yes,1.0,No,southeast,4074.45
+277.0,278.0,26.0,male,31.5,95.0,Yes,1.0,No,southwest,4076.5
+278.0,279.0,49.0,female,25.9,80.0,Yes,1.0,No,northwest,4133.64
+279.0,280.0,47.0,female,21.8,98.0,No,0.0,No,northwest,4134.08
+280.0,281.0,25.0,female,27.9,88.0,Yes,0.0,No,southeast,4137.52
+281.0,282.0,59.0,female,32.4,94.0,No,1.0,No,southwest,4149.74
+282.0,283.0,48.0,female,33.3,100.0,Yes,1.0,No,southeast,4151.03
+283.0,284.0,34.0,female,24.3,90.0,Yes,0.0,No,southeast,4185.1
+284.0,285.0,25.0,female,26.8,98.0,No,2.0,No,northwest,4189.11
+285.0,286.0,27.0,female,30.1,81.0,No,3.0,No,southwest,4234.93
+286.0,287.0,30.0,male,27.6,90.0,No,1.0,No,southeast,4237.13
+287.0,288.0,35.0,male,25.9,84.0,Yes,1.0,No,northwest,4239.89
+288.0,289.0,37.0,male,28.6,93.0,Yes,1.0,No,northwest,4243.59
+289.0,290.0,35.0,male,30.3,80.0,No,3.0,No,southwest,4260.74
+290.0,291.0,49.0,male,44.2,80.0,No,2.0,No,southeast,4266.17
+291.0,292.0,25.0,female,21.3,95.0,Yes,3.0,No,northwest,4296.27
+292.0,293.0,21.0,male,35.8,88.0,No,0.0,No,northwest,4320.41
+293.0,294.0,37.0,female,28.9,87.0,No,1.0,No,southeast,4337.74
+294.0,295.0,57.0,female,26.5,90.0,Yes,2.0,No,southeast,4340.44
+295.0,296.0,32.0,female,31.1,88.0,No,0.0,No,southeast,4347.02
+296.0,297.0,25.0,female,33.0,89.0,Yes,2.0,No,southeast,4349.46
+297.0,298.0,40.0,female,29.3,87.0,Yes,1.0,No,southeast,4350.51
+298.0,299.0,30.0,female,29.7,93.0,No,0.0,No,northwest,4357.04
+299.0,300.0,31.0,female,24.3,90.0,Yes,3.0,No,southwest,4391.65
+300.0,301.0,37.0,male,29.7,81.0,No,0.0,No,southeast,4399.73
+301.0,302.0,26.0,male,31.5,88.0,Yes,0.0,No,southwest,4402.23
+302.0,303.0,41.0,female,27.7,92.0,Yes,0.0,No,southeast,4415.16
+303.0,304.0,46.0,male,22.5,88.0,Yes,2.0,No,southeast,4428.89
+304.0,305.0,32.0,male,31.7,83.0,Yes,2.0,No,northwest,4433.39
+305.0,306.0,34.0,male,32.1,83.0,Yes,2.0,No,northwest,4433.92
+306.0,307.0,44.0,male,27.0,100.0,Yes,2.0,No,southeast,4435.09
+307.0,308.0,46.0,male,29.3,80.0,Yes,2.0,No,southeast,4438.26
+308.0,309.0,41.0,male,26.9,90.0,No,1.0,No,southeast,4441.21
+309.0,310.0,32.0,male,33.0,94.0,Yes,3.0,No,southeast,4449.46
+310.0,311.0,43.0,male,27.8,81.0,No,1.0,No,northwest,4454.4
+311.0,312.0,37.0,male,33.8,90.0,No,1.0,No,northwest,4462.72
+312.0,313.0,21.0,male,38.4,87.0,Yes,2.0,No,southeast,4463.21
+313.0,314.0,45.0,female,35.0,91.0,Yes,3.0,No,northwest,4466.62
+314.0,315.0,36.0,male,21.4,89.0,No,0.0,No,southeast,4500.34
+315.0,316.0,44.0,male,33.7,96.0,No,4.0,No,southeast,4504.66
+316.0,317.0,35.0,male,34.7,83.0,Yes,0.0,No,southeast,4518.83
+317.0,318.0,51.0,female,28.4,93.0,No,1.0,No,northwest,4527.18
+318.0,319.0,47.0,female,24.6,90.0,Yes,2.0,No,southwest,4529.48
+319.0,320.0,26.0,male,42.9,95.0,Yes,1.0,No,southwest,4536.26
+320.0,321.0,50.0,female,20.5,89.0,Yes,0.0,No,southeast,4544.23
+321.0,322.0,43.0,female,31.4,99.0,No,4.0,No,southeast,4561.19
+322.0,323.0,55.0,female,29.6,93.0,Yes,1.0,No,southeast,4562.84
+323.0,324.0,26.0,female,29.4,96.0,Yes,2.0,No,southeast,4564.19
+324.0,325.0,60.0,female,26.7,94.0,No,0.0,No,northwest,4571.41
+325.0,326.0,31.0,female,30.2,83.0,No,3.0,No,northwest,4618.08
+326.0,327.0,18.0,male,30.8,97.0,Yes,0.0,No,southwest,4646.76
+327.0,328.0,47.0,male,27.3,85.0,Yes,3.0,No,southeast,4661.29
+328.0,329.0,26.0,male,37.3,86.0,Yes,1.0,No,southeast,4667.61
+329.0,330.0,22.0,male,35.2,87.0,Yes,2.0,No,southwest,4670.64
+330.0,331.0,32.0,male,37.2,82.0,No,2.0,No,southeast,4673.39
+331.0,332.0,43.0,male,46.5,83.0,Yes,2.0,No,southeast,4686.39
+332.0,333.0,46.0,female,28.6,80.0,No,5.0,No,southwest,4687.8
+333.0,334.0,56.0,female,21.9,100.0,Yes,1.0,No,southeast,4718.2
+334.0,335.0,45.0,female,22.9,94.0,No,1.0,No,southeast,4719.52
+335.0,336.0,42.0,female,23.8,80.0,Yes,2.0,No,northwest,4719.74
+336.0,337.0,48.0,female,32.7,95.0,No,1.0,No,northwest,4738.27
+337.0,338.0,29.0,male,27.1,82.0,Yes,1.0,No,southwest,4746.34
+338.0,339.0,44.0,male,27.6,86.0,No,1.0,No,southeast,4747.05
+339.0,340.0,19.0,male,30.5,92.0,Yes,1.0,No,southwest,4751.07
+340.0,341.0,50.0,female,43.1,98.0,No,2.0,No,southeast,4753.64
+341.0,342.0,46.0,male,38.6,83.0,Yes,1.0,No,southwest,4762.33
+342.0,343.0,33.0,female,18.5,94.0,Yes,1.0,No,southwest,4766.02
+343.0,344.0,56.0,female,28.3,100.0,Yes,1.0,No,southeast,4779.6
+344.0,345.0,38.0,female,39.8,85.0,Yes,1.0,No,southeast,4795.66
+345.0,346.0,33.0,male,18.9,87.0,Yes,3.0,No,southeast,4827.9
+346.0,347.0,55.0,female,37.0,83.0,Yes,5.0,No,southwest,4830.63
+347.0,348.0,43.0,male,31.6,89.0,No,3.0,No,southeast,4837.58
+348.0,349.0,46.0,male,32.6,82.0,Yes,3.0,No,southeast,4846.92
+349.0,350.0,47.0,male,26.7,93.0,No,4.0,No,northwest,4877.98
+350.0,351.0,60.0,female,26.2,99.0,No,0.0,No,southwest,4883.87
+351.0,352.0,31.0,female,29.9,85.0,No,0.0,No,southeast,4889.04
+352.0,353.0,28.0,male,35.8,97.0,No,2.0,No,southeast,4890.0
+353.0,354.0,39.0,male,25.3,91.0,No,1.0,No,northwest,4894.75
+354.0,355.0,48.0,female,20.2,97.0,No,2.0,No,northwest,4906.41
+355.0,356.0,38.0,male,30.1,87.0,No,5.0,No,southeast,4915.06
+356.0,357.0,40.0,female,32.1,95.0,Yes,2.0,No,northwest,4922.92
+357.0,358.0,31.0,female,23.6,95.0,No,2.0,No,southwest,4931.65
+358.0,359.0,25.0,female,25.8,97.0,Yes,2.0,No,southwest,4934.71
+359.0,360.0,46.0,female,36.6,84.0,Yes,2.0,No,southeast,4949.76
+360.0,361.0,29.0,female,23.6,87.0,Yes,0.0,No,southeast,4992.38
+361.0,362.0,27.0,female,26.7,99.0,No,1.0,No,southeast,5002.78
+362.0,363.0,40.0,female,27.5,98.0,Yes,1.0,No,southwest,5003.85
+363.0,364.0,25.0,female,33.7,95.0,No,1.0,No,southwest,5012.47
+364.0,365.0,35.0,male,29.6,94.0,Yes,0.0,No,northwest,5028.15
+365.0,366.0,35.0,male,27.6,100.0,No,2.0,No,southeast,5031.27
+366.0,367.0,24.0,male,23.9,84.0,No,5.0,No,southwest,5080.1
+367.0,368.0,19.0,male,17.9,90.0,No,1.0,No,northwest,5116.5
+368.0,369.0,39.0,male,42.1,100.0,No,2.0,No,southeast,5124.19
+369.0,370.0,31.0,male,24.1,88.0,Yes,1.0,No,northwest,5125.22
+370.0,371.0,55.0,female,38.8,86.0,No,3.0,No,southeast,5138.26
+371.0,372.0,33.0,female,31.5,86.0,No,1.0,No,southeast,5148.55
+372.0,373.0,34.0,female,29.8,100.0,Yes,2.0,No,southwest,5152.13
+373.0,374.0,32.0,male,22.5,93.0,No,3.0,No,southeast,5209.58
+374.0,375.0,38.0,female,26.1,81.0,Yes,0.0,No,southeast,5227.99
+375.0,376.0,46.0,female,31.0,84.0,No,1.0,No,southwest,5240.77
+376.0,377.0,57.0,female,34.2,93.0,Yes,1.0,No,southeast,5245.23
+377.0,378.0,57.0,female,34.8,96.0,No,1.0,No,southwest,5246.05
+378.0,379.0,23.0,male,30.8,89.0,Yes,3.0,No,southwest,5253.52
+379.0,380.0,20.0,male,24.6,92.0,No,2.0,No,northwest,5257.51
+380.0,381.0,19.0,male,27.5,83.0,No,2.0,No,northwest,5261.47
+381.0,382.0,44.0,female,25.8,90.0,No,0.0,No,northwest,5266.37
+382.0,383.0,52.0,female,26.9,96.0,No,0.0,No,northwest,5267.82
+383.0,384.0,32.0,female,30.0,88.0,Yes,0.0,No,northwest,5272.18
+384.0,385.0,41.0,female,26.3,82.0,Yes,3.0,No,northwest,5312.17
+385.0,386.0,49.0,female,30.9,90.0,Yes,3.0,No,southwest,5325.65
+386.0,387.0,25.0,female,32.8,81.0,No,2.0,No,northwest,5327.4
+387.0,388.0,31.0,female,22.1,97.0,Yes,1.0,No,southeast,5354.07
+388.0,389.0,32.0,male,30.9,91.0,No,1.0,No,northwest,5373.36
+389.0,390.0,54.0,female,32.9,91.0,Yes,2.0,No,southwest,5375.04
+390.0,391.0,44.0,male,33.8,96.0,No,1.0,No,northwest,5377.46
+391.0,392.0,40.0,female,27.6,92.0,Yes,0.0,No,southwest,5383.54
+392.0,393.0,58.0,female,26.4,88.0,Yes,1.0,No,northwest,5385.34
+393.0,394.0,32.0,female,37.7,96.0,No,0.0,No,southeast,5397.62
+394.0,395.0,29.0,female,40.2,85.0,Yes,0.0,No,southeast,5400.98
+395.0,396.0,30.0,male,25.1,100.0,Yes,0.0,No,southeast,5415.66
+396.0,397.0,41.0,male,31.1,86.0,Yes,3.0,No,northwest,5425.02
+397.0,398.0,27.0,male,37.4,83.0,No,3.0,No,southeast,5428.73
+398.0,399.0,29.0,male,41.7,86.0,Yes,0.0,No,southeast,5438.75
+399.0,400.0,27.0,female,19.9,81.0,No,0.0,No,southeast,5458.05
+400.0,401.0,57.0,female,27.7,97.0,Yes,0.0,No,southeast,5469.01
+401.0,402.0,43.0,female,25.9,99.0,No,1.0,No,southwest,5472.45
+402.0,403.0,55.0,female,29.9,89.0,No,1.0,No,southeast,5478.04
+403.0,404.0,47.0,male,28.3,91.0,Yes,1.0,No,southeast,5484.47
+404.0,405.0,23.0,male,31.0,90.0,No,1.0,No,southwest,5488.26
+405.0,406.0,20.0,male,34.4,93.0,No,2.0,No,southeast,5584.31
+406.0,407.0,43.0,female,33.3,94.0,Yes,1.0,No,southeast,5594.85
+407.0,408.0,31.0,male,24.3,83.0,Yes,5.0,No,southwest,5615.37
+408.0,409.0,57.0,female,35.8,85.0,Yes,1.0,No,northwest,5630.46
+409.0,410.0,54.0,female,32.8,86.0,Yes,0.0,No,southwest,5649.72
+410.0,411.0,25.0,female,41.8,95.0,Yes,0.0,No,southeast,5662.23
+411.0,412.0,26.0,female,20.0,94.0,Yes,3.0,No,northwest,5693.43
+412.0,413.0,38.0,male,33.6,100.0,Yes,0.0,No,southeast,5699.84
+413.0,414.0,43.0,female,25.6,82.0,No,4.0,No,southwest,5708.87
+414.0,415.0,20.0,male,40.3,81.0,Yes,0.0,No,southeast,5709.16
+415.0,416.0,22.0,male,34.8,89.0,No,2.0,No,northwest,5729.01
+416.0,417.0,23.0,male,42.7,82.0,No,0.0,No,southeast,5757.41
+417.0,418.0,41.0,female,35.9,82.0,No,2.0,No,southeast,5836.52
+418.0,419.0,28.0,female,43.3,90.0,No,2.0,No,southeast,5846.92
+419.0,420.0,37.0,male,20.0,91.0,No,1.0,No,northwest,5855.9
+420.0,421.0,58.0,female,29.6,95.0,Yes,0.0,No,southwest,5910.94
+421.0,422.0,51.0,female,36.2,90.0,Yes,0.0,No,southeast,5920.1
+422.0,423.0,27.0,male,28.9,94.0,Yes,3.0,No,southwest,5926.85
+423.0,424.0,46.0,male,34.3,89.0,Yes,3.0,No,southeast,5934.38
+424.0,425.0,25.0,male,24.9,94.0,No,0.0,No,southeast,5966.89
+425.0,426.0,46.0,male,26.9,90.0,No,0.0,No,southwest,5969.72
+426.0,427.0,40.0,female,38.9,100.0,Yes,3.0,No,southwest,5972.38
+427.0,428.0,35.0,female,28.9,97.0,Yes,1.0,No,southeast,5974.38
+428.0,429.0,34.0,female,30.7,90.0,No,1.0,No,southeast,5976.83
+429.0,430.0,38.0,male,34.1,90.0,No,0.0,No,southwest,5979.73
+430.0,431.0,33.0,female,37.3,95.0,Yes,2.0,No,northwest,5989.52
+431.0,432.0,38.0,male,29.4,96.0,No,4.0,No,southwest,6059.17
+432.0,433.0,26.0,male,28.0,100.0,No,1.0,No,southeast,6067.13
+433.0,434.0,36.0,male,37.1,88.0,No,1.0,No,southeast,6079.67
+434.0,435.0,42.0,male,34.7,97.0,Yes,2.0,No,southwest,6082.41
+435.0,436.0,38.0,female,34.1,93.0,No,1.0,No,northwest,6112.35
+436.0,437.0,28.0,female,30.5,82.0,Yes,3.0,No,southeast,6113.23
+437.0,438.0,29.0,male,21.9,91.0,Yes,1.0,No,northwest,6117.49
+438.0,439.0,46.0,male,26.2,84.0,Yes,1.0,No,northwest,6123.57
+439.0,440.0,55.0,female,33.2,89.0,Yes,3.0,No,northwest,6128.8
+440.0,441.0,42.0,female,29.3,84.0,No,3.0,No,southeast,6184.3
+441.0,442.0,39.0,female,31.0,97.0,No,0.0,No,southeast,6185.32
+442.0,443.0,32.0,female,31.6,91.0,Yes,0.0,No,southwest,6186.13
+443.0,444.0,32.0,female,38.0,92.0,No,3.0,No,southwest,6196.45
+444.0,445.0,27.0,male,24.3,82.0,No,2.0,No,northwest,6198.75
+445.0,446.0,33.0,male,28.0,93.0,No,2.0,No,northwest,6203.9
+446.0,447.0,25.0,female,32.5,100.0,No,1.0,No,southwest,6238.3
+447.0,448.0,45.0,male,23.2,92.0,No,0.0,No,southwest,6250.44
+448.0,449.0,24.0,male,21.8,83.0,Yes,1.0,No,southeast,6272.48
+449.0,450.0,43.0,male,28.8,93.0,No,1.0,No,southwest,6282.24
+450.0,451.0,20.0,male,34.2,89.0,Yes,1.0,No,southeast,6289.75
+451.0,452.0,48.0,female,29.5,95.0,No,2.0,No,southwest,6311.95
+452.0,453.0,56.0,female,30.8,92.0,Yes,2.0,No,southeast,6313.76
+453.0,454.0,58.0,female,37.1,97.0,No,3.0,No,southeast,6334.34
+454.0,455.0,24.0,male,32.3,92.0,Yes,2.0,No,southeast,6338.08
+455.0,456.0,40.0,male,45.4,91.0,No,2.0,No,southeast,6356.27
+456.0,457.0,28.0,male,31.3,91.0,No,0.0,No,northwest,6358.78
+457.0,458.0,50.0,female,42.9,98.0,No,3.0,No,northwest,6360.99
+458.0,459.0,34.0,female,40.6,98.0,No,1.0,No,northwest,6373.56
+459.0,460.0,21.0,male,26.3,85.0,Yes,1.0,No,northwest,6389.38
+460.0,461.0,31.0,male,29.4,99.0,No,1.0,No,northwest,6393.6
+461.0,462.0,42.0,female,23.5,94.0,No,2.0,No,southeast,6402.29
+462.0,463.0,18.0,male,29.8,97.0,No,2.0,No,southeast,6406.41
+463.0,464.0,51.0,female,27.7,81.0,No,3.0,No,southwest,6414.18
+464.0,465.0,20.0,male,46.5,89.0,No,3.0,No,southeast,6435.62
+465.0,466.0,28.0,male,27.8,93.0,Yes,2.0,No,northwest,6455.86
+466.0,467.0,24.0,male,29.3,93.0,No,2.0,No,northwest,6457.84
+467.0,468.0,59.0,female,37.9,91.0,No,0.0,No,southwest,6474.01
+468.0,469.0,38.0,female,27.4,81.0,No,1.0,No,southwest,6496.89
+469.0,470.0,53.0,female,29.8,99.0,No,1.0,No,southeast,6500.24
+470.0,471.0,42.0,male,28.6,85.0,Yes,3.0,No,northwest,6548.2
+471.0,472.0,29.0,female,36.3,83.0,No,3.0,No,southeast,6551.75
+472.0,473.0,31.0,female,27.3,86.0,Yes,1.0,No,southeast,6555.07
+473.0,474.0,48.0,female,33.0,99.0,No,0.0,No,northwest,6571.02
+474.0,475.0,55.0,female,34.8,80.0,Yes,2.0,No,southwest,6571.54
+475.0,476.0,46.0,male,25.0,91.0,No,2.0,No,southeast,6593.51
+476.0,477.0,45.0,male,34.1,96.0,No,1.0,No,southeast,6600.21
+477.0,478.0,31.0,male,29.9,99.0,Yes,2.0,No,southwest,6600.36
+478.0,479.0,38.0,male,41.2,81.0,Yes,1.0,No,southeast,6610.11
+479.0,480.0,35.0,male,16.8,98.0,No,2.0,No,southeast,6640.54
+480.0,481.0,48.0,male,21.1,82.0,No,3.0,No,southeast,6652.53
+481.0,482.0,20.0,male,33.4,89.0,Yes,5.0,No,southeast,6653.79
+482.0,483.0,29.0,male,28.4,87.0,No,1.0,No,northwest,6664.69
+483.0,484.0,48.0,male,42.4,92.0,Yes,5.0,No,southwest,6666.24
+484.0,485.0,55.0,female,23.4,85.0,Yes,2.0,No,northwest,6686.43
+485.0,486.0,37.0,male,24.5,86.0,No,2.0,No,northwest,6710.19
+486.0,487.0,48.0,male,27.6,90.0,Yes,3.0,No,southeast,6746.74
+487.0,488.0,37.0,male,28.9,91.0,No,3.0,No,southeast,6748.59
+488.0,489.0,54.0,female,19.0,84.0,Yes,3.0,No,southeast,6753.04
+489.0,490.0,48.0,female,28.1,95.0,Yes,1.0,No,southeast,6770.19
+490.0,491.0,54.0,female,32.2,89.0,No,1.0,No,southwest,6775.96
+491.0,492.0,40.0,female,36.1,80.0,No,1.0,No,southeast,6781.35
+492.0,493.0,27.0,male,30.9,91.0,No,3.0,No,northwest,6796.86
+493.0,494.0,37.0,male,28.5,81.0,No,5.0,No,southeast,6799.46
+494.0,495.0,32.0,male,26.0,90.0,No,0.0,No,southeast,6837.37
+495.0,496.0,46.0,male,30.1,85.0,Yes,1.0,No,southwest,6849.03
+496.0,497.0,29.0,male,23.9,84.0,Yes,1.0,No,southeast,6858.48
+497.0,498.0,45.0,male,32.2,100.0,Yes,2.0,No,southwest,6875.96
+498.0,499.0,31.0,female,17.3,100.0,No,2.0,No,southeast,6877.98
+499.0,500.0,60.0,female,19.5,95.0,No,2.0,No,northwest,6933.24
+500.0,501.0,39.0,male,26.3,97.0,No,1.0,No,northwest,6940.91
+501.0,502.0,40.0,male,39.5,88.0,No,0.0,No,northwest,6948.7
+502.0,503.0,19.0,male,22.7,82.0,No,3.0,No,southeast,6985.51
+503.0,504.0,40.0,male,32.3,87.0,Yes,2.0,No,northwest,6986.7
+504.0,505.0,55.0,female,25.3,91.0,Yes,1.0,No,southwest,7045.5
+505.0,506.0,34.0,female,26.2,88.0,Yes,1.0,No,southeast,7046.72
+506.0,507.0,56.0,female,32.9,84.0,Yes,0.0,No,southeast,7050.02
+507.0,508.0,26.0,female,29.0,93.0,No,1.0,No,southwest,7050.64
+508.0,509.0,33.0,female,25.5,87.0,No,1.0,No,southeast,7077.19
+509.0,510.0,50.0,female,20.0,89.0,Yes,2.0,No,southeast,7133.9
+510.0,511.0,55.0,female,27.8,80.0,Yes,2.0,No,southeast,7144.86
+511.0,512.0,19.0,male,22.3,81.0,No,0.0,No,southwest,7147.11
+512.0,513.0,46.0,male,34.3,89.0,No,1.0,No,southeast,7147.47
+513.0,514.0,49.0,female,28.0,92.0,No,3.0,No,southwest,7151.09
+514.0,515.0,40.0,male,38.1,91.0,No,1.0,No,southeast,7152.67
+515.0,516.0,27.0,female,28.3,88.0,No,1.0,No,northwest,7153.55
+516.0,517.0,48.0,male,35.8,82.0,Yes,2.0,No,southwest,7160.09
+517.0,518.0,18.0,male,36.0,93.0,No,2.0,No,southeast,7160.33
+518.0,519.0,19.0,male,37.2,96.0,Yes,2.0,No,southeast,7162.01
+519.0,520.0,24.0,male,22.7,88.0,Yes,2.0,No,southeast,7173.36
+520.0,521.0,21.0,male,35.3,96.0,No,3.0,No,southwest,7196.87
+521.0,522.0,54.0,female,26.3,89.0,No,2.0,No,northwest,7201.7
+522.0,523.0,34.0,female,31.9,94.0,No,2.0,No,northwest,7209.49
+523.0,524.0,43.0,male,21.4,88.0,No,0.0,No,northwest,7222.79
+524.0,525.0,29.0,female,22.1,83.0,Yes,3.0,No,southeast,7228.22
+525.0,526.0,55.0,female,29.0,84.0,Yes,4.0,No,southeast,7243.81
+526.0,527.0,20.0,male,30.6,90.0,No,2.0,No,northwest,7256.72
+527.0,528.0,39.0,male,34.2,81.0,Yes,2.0,No,northwest,7261.74
+528.0,529.0,48.0,male,37.1,81.0,Yes,2.0,No,northwest,7265.7
+529.0,530.0,46.0,female,27.7,83.0,No,3.0,No,northwest,7281.51
+530.0,531.0,37.0,female,29.2,92.0,No,0.0,No,southeast,7323.73
+531.0,532.0,25.0,female,25.1,94.0,Yes,0.0,No,southeast,7325.05
+532.0,533.0,56.0,female,29.9,81.0,Yes,1.0,No,southwest,7337.75
+533.0,534.0,32.0,female,33.1,94.0,Yes,0.0,No,southwest,7345.08
+534.0,535.0,60.0,female,35.6,80.0,No,1.0,No,southeast,7345.73
+535.0,536.0,37.0,female,35.3,100.0,No,0.0,No,southwest,7348.14
+536.0,537.0,55.0,female,31.6,91.0,No,1.0,No,southeast,7358.18
+537.0,538.0,26.0,female,37.1,88.0,No,2.0,No,southwest,7371.77
+538.0,539.0,49.0,female,34.1,82.0,No,3.0,No,southwest,7418.52
+539.0,540.0,51.0,female,26.4,97.0,No,0.0,No,northwest,7419.48
+540.0,541.0,55.0,female,27.6,89.0,No,0.0,No,northwest,7421.19
+541.0,542.0,46.0,male,30.2,80.0,No,1.0,No,southwest,7441.05
+542.0,543.0,19.0,male,32.6,94.0,Yes,2.0,No,southwest,7441.5
+543.0,544.0,31.0,female,36.2,96.0,No,1.0,No,northwest,7443.64
+544.0,545.0,26.0,male,33.7,92.0,No,1.0,No,southwest,7445.92
+545.0,546.0,28.0,male,39.8,88.0,No,0.0,No,southeast,7448.4
+546.0,547.0,31.0,male,29.6,85.0,No,4.0,No,southwest,7512.27
+547.0,548.0,46.0,male,25.4,98.0,No,1.0,No,northwest,7518.03
+548.0,549.0,33.0,male,19.9,95.0,No,0.0,No,northwest,7526.71
+549.0,550.0,31.0,female,30.2,95.0,No,3.0,No,northwest,7537.16
+550.0,551.0,28.0,female,25.0,97.0,No,1.0,No,southwest,7623.52
+551.0,552.0,37.0,female,25.8,87.0,No,1.0,No,southwest,7624.63
+552.0,553.0,34.0,female,27.5,96.0,Yes,1.0,No,southwest,7626.99
+553.0,554.0,56.0,female,32.3,99.0,Yes,1.0,No,southeast,7633.72
+554.0,555.0,34.0,female,33.2,83.0,No,1.0,No,southeast,7639.42
+555.0,556.0,55.0,female,29.5,81.0,No,2.0,No,southeast,7640.31
+556.0,557.0,52.0,female,41.3,95.0,No,1.0,No,southeast,7650.77
+557.0,558.0,45.0,female,33.0,87.0,No,3.0,No,southeast,7682.67
+558.0,559.0,46.0,male,27.4,88.0,No,2.0,No,southwest,7726.85
+559.0,560.0,54.0,female,34.6,83.0,No,1.0,No,northwest,7727.25
+560.0,561.0,50.0,male,26.1,96.0,Yes,2.0,No,southeast,7729.65
+561.0,562.0,48.0,male,30.7,84.0,No,2.0,No,southeast,7731.43
+562.0,563.0,26.0,female,35.8,100.0,Yes,0.0,No,northwest,7731.86
+563.0,564.0,36.0,male,37.1,90.0,No,2.0,No,southwest,7740.34
+564.0,565.0,18.0,male,26.6,95.0,No,1.0,No,southeast,7742.11
+565.0,566.0,56.0,female,33.1,91.0,No,2.0,No,northwest,7749.16
+566.0,567.0,29.0,male,29.7,100.0,Yes,0.0,No,southeast,7789.64
+567.0,568.0,50.0,male,40.2,91.0,Yes,0.0,No,southeast,7804.16
+568.0,569.0,58.0,female,38.3,87.0,Yes,0.0,No,northeast,7935.29
+569.0,570.0,49.0,female,32.6,97.0,No,3.0,No,southwest,7954.52
+570.0,571.0,51.0,female,22.8,86.0,Yes,3.0,No,northeast,7985.82
+571.0,572.0,60.0,female,23.3,87.0,Yes,3.0,No,northeast,7986.48
+572.0,573.0,54.0,female,25.0,81.0,Yes,2.0,No,northwest,8017.06
+573.0,574.0,42.0,female,37.0,80.0,No,1.0,No,northwest,8023.14
+574.0,575.0,53.0,female,27.7,88.0,Yes,0.0,No,northwest,8026.67
+575.0,576.0,42.0,male,28.7,88.0,No,2.0,No,southwest,8027.97
+576.0,577.0,44.0,female,28.7,88.0,No,3.0,No,northwest,8059.68
+577.0,578.0,25.0,male,32.3,98.0,No,1.0,No,southwest,8062.76
+578.0,579.0,22.0,male,36.2,100.0,Yes,1.0,No,southwest,8068.19
+579.0,580.0,25.0,male,47.5,84.0,Yes,1.0,No,southeast,8083.92
+580.0,581.0,18.0,male,32.0,81.0,Yes,2.0,No,northwest,8116.27
+581.0,582.0,40.0,male,30.3,83.0,Yes,0.0,No,southwest,8116.68
+582.0,583.0,27.0,male,35.9,90.0,No,0.0,No,southeast,8124.41
+583.0,584.0,44.0,male,36.9,94.0,No,0.0,No,southeast,8125.78
+584.0,585.0,29.0,male,30.9,89.0,Yes,4.0,No,northwest,8162.72
+585.0,586.0,29.0,female,24.0,92.0,No,2.0,No,southeast,8211.1
+586.0,587.0,26.0,female,29.8,84.0,No,2.0,No,southeast,8219.2
+587.0,588.0,54.0,female,27.7,100.0,No,1.0,No,southeast,8232.64
+588.0,589.0,33.0,female,28.1,92.0,No,1.0,No,southeast,8233.1
+589.0,590.0,37.0,female,33.4,100.0,No,1.0,No,southeast,8240.59
+590.0,591.0,38.0,female,23.4,96.0,Yes,3.0,No,northeast,8252.28
+591.0,592.0,31.0,female,22.8,82.0,Yes,0.0,No,southwest,8269.04
+592.0,593.0,41.0,female,28.9,80.0,Yes,0.0,No,southwest,8277.52
+593.0,594.0,41.0,female,31.1,93.0,No,0.0,No,southeast,8280.62
+594.0,595.0,33.0,female,33.3,93.0,Yes,0.0,No,southeast,8283.68
+595.0,596.0,27.0,male,22.1,81.0,No,2.0,No,northeast,8302.54
+596.0,597.0,38.0,female,30.7,92.0,Yes,2.0,No,northwest,8310.84
+597.0,598.0,20.0,male,33.3,93.0,Yes,1.0,No,northeast,8334.46
+598.0,599.0,40.0,male,33.4,94.0,No,1.0,No,northeast,8334.59
+599.0,600.0,34.0,male,39.4,94.0,Yes,1.0,No,northeast,8342.91
+600.0,601.0,48.0,male,38.2,81.0,Yes,2.0,No,southeast,8347.16
+601.0,602.0,44.0,male,30.1,86.0,No,3.0,No,northwest,8410.05
+602.0,603.0,30.0,male,30.5,87.0,Yes,2.0,No,northwest,8413.46
+603.0,604.0,39.0,male,19.6,91.0,Yes,1.0,No,northwest,8428.07
+604.0,605.0,34.0,male,25.3,100.0,No,0.0,No,southeast,8442.67
+605.0,606.0,45.0,male,26.6,99.0,No,0.0,No,southwest,8444.47
+606.0,607.0,46.0,male,36.2,85.0,No,0.0,No,southwest,8457.82
+607.0,608.0,50.0,female,27.8,80.0,No,2.0,No,southeast,8515.76
+608.0,609.0,59.0,female,28.6,80.0,Yes,2.0,No,southeast,8516.83
+609.0,610.0,44.0,female,30.9,80.0,Yes,2.0,No,southwest,8520.03
+610.0,611.0,27.0,female,34.4,85.0,No,3.0,No,southwest,8522.0
+611.0,612.0,29.0,female,36.3,91.0,Yes,2.0,No,southeast,8527.53
+612.0,613.0,28.0,female,24.3,93.0,Yes,0.0,No,northeast,8534.67
+613.0,614.0,36.0,female,33.2,98.0,No,3.0,No,northeast,8538.29
+614.0,615.0,26.0,female,23.6,92.0,Yes,1.0,No,southwest,8539.67
+615.0,616.0,54.0,female,29.4,96.0,Yes,1.0,No,southeast,8547.69
+616.0,617.0,36.0,female,32.0,81.0,No,1.0,No,southwest,8551.35
+617.0,618.0,60.0,female,36.0,90.0,No,1.0,No,southwest,8556.91
+618.0,619.0,36.0,female,45.3,96.0,Yes,1.0,No,southeast,8569.86
+619.0,620.0,46.0,female,23.9,96.0,No,5.0,No,southeast,8582.3
+620.0,621.0,43.0,female,34.3,96.0,No,5.0,No,southeast,8596.83
+621.0,622.0,39.0,female,27.2,95.0,Yes,0.0,No,southeast,8601.33
+622.0,623.0,36.0,male,23.6,86.0,No,2.0,No,northeast,8603.82
+623.0,624.0,28.0,male,24.0,94.0,Yes,2.0,No,northeast,8604.48
+624.0,625.0,42.0,male,20.4,97.0,No,3.0,No,southeast,8605.36
+625.0,626.0,26.0,male,27.4,92.0,No,3.0,No,northeast,8606.22
+626.0,627.0,23.0,male,27.5,97.0,No,3.0,No,southwest,8615.3
+627.0,628.0,43.0,male,19.2,99.0,Yes,1.0,No,northeast,8627.54
+628.0,629.0,56.0,female,36.6,100.0,No,0.0,No,northwest,8671.19
+629.0,630.0,30.0,male,22.5,97.0,Yes,0.0,No,northeast,8688.86
+630.0,631.0,26.0,male,28.7,93.0,No,1.0,No,southwest,8703.46
+631.0,632.0,21.0,male,40.4,98.0,No,2.0,No,northwest,8733.23
+632.0,633.0,18.0,male,32.3,98.0,Yes,1.0,No,northwest,8765.25
+633.0,634.0,29.0,male,25.4,87.0,Yes,0.0,No,southwest,8782.47
+634.0,635.0,29.0,male,37.0,84.0,Yes,0.0,No,southwest,8798.59
+635.0,636.0,43.0,female,28.9,95.0,No,2.0,No,southwest,8823.28
+636.0,637.0,33.0,female,33.7,81.0,Yes,1.0,No,northeast,8823.99
+637.0,638.0,46.0,female,30.2,99.0,Yes,2.0,No,southwest,8825.09
+638.0,639.0,31.0,male,26.4,100.0,Yes,0.0,No,northwest,8827.21
+639.0,640.0,22.0,male,32.2,95.0,Yes,0.0,No,northwest,8835.26
+640.0,641.0,34.0,female,32.2,96.0,Yes,1.0,No,southeast,8871.15
+641.0,642.0,35.0,male,21.9,99.0,No,3.0,No,northeast,8891.14
+642.0,643.0,54.0,female,29.5,99.0,No,1.0,No,northwest,8930.93
+643.0,644.0,43.0,female,25.6,100.0,Yes,0.0,No,southwest,8932.08
+644.0,645.0,43.0,male,43.9,89.0,Yes,3.0,No,southeast,8944.12
+645.0,646.0,38.0,male,31.4,98.0,Yes,1.0,No,northeast,8964.06
+646.0,647.0,45.0,female,24.2,98.0,No,5.0,No,northwest,8965.8
+647.0,648.0,23.0,male,30.2,95.0,No,2.0,No,southwest,8968.33
+648.0,649.0,32.0,male,37.3,85.0,No,2.0,No,southeast,8978.19
+649.0,650.0,37.0,female,29.9,85.0,No,0.0,No,northwest,8988.16
+650.0,651.0,50.0,male,37.1,96.0,No,1.0,No,southeast,9048.03
+651.0,652.0,22.0,male,44.8,92.0,Yes,1.0,No,southeast,9058.73
+652.0,653.0,55.0,female,25.2,81.0,Yes,2.0,No,northeast,9095.07
+653.0,654.0,48.0,female,25.7,93.0,No,3.0,No,southwest,9101.8
+654.0,655.0,43.0,male,34.1,89.0,Yes,0.0,No,southeast,9140.95
+655.0,656.0,34.0,male,36.7,92.0,No,0.0,No,southwest,9144.57
+656.0,657.0,46.0,male,31.6,91.0,No,0.0,No,northwest,9174.14
+657.0,658.0,56.0,female,21.3,100.0,No,1.0,No,southwest,9182.17
+658.0,659.0,34.0,female,20.0,91.0,Yes,2.0,No,northwest,9193.84
+659.0,660.0,19.0,male,29.6,94.0,No,5.0,No,northeast,9222.4
+660.0,661.0,50.0,male,25.5,86.0,Yes,2.0,No,northeast,9225.26
+661.0,662.0,43.0,female,28.9,84.0,Yes,1.0,No,northwest,9249.5
+662.0,663.0,57.0,female,20.6,98.0,No,0.0,No,southwest,9264.8
+663.0,664.0,23.0,male,25.8,88.0,Yes,1.0,No,northeast,9282.48
+664.0,665.0,43.0,female,34.1,81.0,No,0.0,No,southeast,9283.56
+665.0,666.0,44.0,male,29.8,82.0,Yes,1.0,No,northeast,9288.03
+666.0,667.0,23.0,male,31.4,94.0,Yes,1.0,No,northeast,9290.14
+667.0,668.0,33.0,male,25.7,80.0,No,3.0,No,northwest,9301.89
+668.0,669.0,39.0,male,37.5,90.0,Yes,2.0,No,southeast,9304.7
+669.0,670.0,45.0,male,22.4,83.0,Yes,0.0,No,northeast,9361.33
+670.0,671.0,37.0,male,30.0,95.0,No,1.0,No,southeast,9377.9
+671.0,672.0,34.0,male,36.0,100.0,Yes,1.0,No,southeast,9386.16
+672.0,673.0,20.0,male,39.7,90.0,No,1.0,No,southwest,9391.35
+673.0,674.0,31.0,female,32.3,82.0,Yes,2.0,No,northeast,9411.01
+674.0,675.0,58.0,female,30.8,93.0,No,3.0,No,southwest,9414.92
+675.0,676.0,49.0,female,48.1,81.0,Yes,2.0,No,northeast,9432.93
+676.0,677.0,40.0,female,27.3,100.0,No,1.0,No,northeast,9447.25
+677.0,678.0,41.0,female,27.4,86.0,No,1.0,No,northeast,9447.38
+678.0,679.0,39.0,male,29.5,98.0,No,0.0,No,southeast,9487.64
+679.0,680.0,37.0,male,24.8,85.0,No,3.0,No,northeast,9500.57
+680.0,681.0,43.0,male,41.5,82.0,No,0.0,No,southeast,9504.31
+681.0,682.0,27.0,female,44.7,98.0,No,0.0,No,northeast,9541.7
+682.0,683.0,27.0,female,46.1,106.0,No,1.0,No,southeast,9549.57
+683.0,684.0,34.0,male,34.3,105.0,No,3.0,No,southwest,9563.03
+684.0,685.0,56.0,female,22.6,97.0,No,1.0,No,northwest,9566.99
+685.0,686.0,57.0,female,34.8,87.0,No,1.0,No,northwest,9583.89
+686.0,687.0,40.0,male,27.5,81.0,Yes,1.0,No,northeast,9617.66
+687.0,688.0,50.0,male,29.8,86.0,Yes,3.0,No,northwest,9620.33
+688.0,689.0,60.0,female,31.2,88.0,Yes,0.0,No,southwest,9625.92
+689.0,690.0,48.0,male,32.3,88.0,Yes,2.0,No,southwest,9630.4
+690.0,691.0,26.0,female,37.4,103.0,Yes,0.0,No,southwest,9634.54
+691.0,692.0,42.0,female,18.1,84.0,Yes,0.0,No,northwest,9644.25
+692.0,693.0,44.0,female,40.0,96.0,No,3.0,No,northeast,9704.67
+693.0,694.0,59.0,female,26.6,83.0,No,2.0,No,northeast,9715.84
+694.0,695.0,32.0,male,33.3,102.0,Yes,0.0,No,northeast,9722.77
+695.0,696.0,26.0,male,30.2,99.0,No,1.0,No,southwest,9724.53
+696.0,697.0,49.0,male,47.7,88.0,No,1.0,No,southeast,9748.91
+697.0,698.0,53.0,female,30.8,105.0,No,1.0,No,northeast,9778.35
+698.0,699.0,25.0,male,24.3,89.0,No,5.0,No,southeast,9788.87
+699.0,700.0,41.0,female,42.7,105.0,No,2.0,No,southeast,9800.89
+700.0,701.0,46.0,male,31.6,106.0,No,0.0,No,southwest,9850.43
+701.0,702.0,36.0,female,21.6,109.0,Yes,1.0,No,southeast,9855.13
+702.0,703.0,26.0,female,25.8,110.0,No,1.0,No,southwest,9861.03
+703.0,704.0,19.0,male,24.3,89.0,Yes,0.0,No,northwest,9863.47
+704.0,705.0,34.0,female,33.9,84.0,No,0.0,No,northeast,9866.3
+705.0,706.0,39.0,male,28.9,82.0,Yes,0.0,No,northwest,9869.81
+706.0,707.0,28.0,female,34.2,86.0,No,1.0,No,southwest,9872.7
+707.0,708.0,27.0,female,40.7,100.0,No,0.0,No,northeast,9875.68
+708.0,709.0,30.0,female,37.7,80.0,No,1.0,No,southeast,9877.61
+709.0,710.0,27.0,female,39.5,90.0,No,1.0,No,southwest,9880.07
+710.0,711.0,49.0,female,30.1,100.0,Yes,1.0,No,northwest,9910.36
+711.0,712.0,24.0,male,27.7,109.0,Yes,1.0,No,northeast,9957.72
+712.0,713.0,24.0,male,32.3,105.0,No,1.0,No,northeast,9964.06
+713.0,714.0,45.0,female,18.3,105.0,No,0.0,No,northwest,9991.04
+714.0,715.0,42.0,female,32.3,101.0,Yes,2.0,No,northeast,10043.25
+715.0,716.0,42.0,male,21.4,104.0,No,1.0,No,southwest,10065.41
+716.0,717.0,40.0,male,30.5,91.0,No,0.0,No,northeast,10072.06
+717.0,718.0,27.0,male,36.1,104.0,Yes,1.0,No,southwest,10085.85
+718.0,719.0,34.0,male,25.8,100.0,Yes,5.0,No,southwest,10096.97
+719.0,720.0,27.0,female,27.1,103.0,No,1.0,No,northeast,10106.13
+720.0,721.0,52.0,female,23.5,93.0,No,2.0,No,southeast,10107.22
+721.0,722.0,44.0,female,33.9,100.0,Yes,3.0,No,northwest,10115.01
+722.0,723.0,36.0,female,31.6,99.0,No,2.0,No,southwest,10118.42
+723.0,724.0,18.0,male,30.8,94.0,No,3.0,No,northeast,10141.14
+724.0,725.0,29.0,female,23.2,109.0,No,2.0,No,northwest,10156.78
+725.0,726.0,36.0,female,23.2,104.0,Yes,0.0,No,northeast,10197.77
+726.0,727.0,30.0,male,29.9,90.0,No,0.0,No,southwest,10214.64
+727.0,728.0,32.0,male,38.3,92.0,Yes,0.0,No,southeast,10226.28
+728.0,729.0,45.0,male,30.2,89.0,No,0.0,No,northwest,10231.5
+729.0,730.0,31.0,male,28.7,107.0,Yes,3.0,No,northwest,10264.44
+730.0,731.0,19.0,male,32.3,93.0,Yes,3.0,No,northwest,10269.46
+731.0,732.0,29.0,male,38.6,82.0,Yes,2.0,No,southwest,10325.21
+732.0,733.0,53.0,female,31.2,86.0,Yes,0.0,No,southeast,10338.93
+733.0,734.0,29.0,female,26.6,105.0,Yes,0.0,No,northwest,10355.64
+734.0,735.0,25.0,female,33.3,80.0,Yes,2.0,No,northeast,10370.91
+735.0,736.0,51.0,female,36.6,104.0,No,3.0,No,southeast,10381.48
+736.0,737.0,23.0,male,28.2,95.0,No,4.0,No,northeast,10407.09
+737.0,738.0,30.0,male,24.0,86.0,No,0.0,No,northeast,10422.92
+738.0,739.0,27.0,male,32.8,86.0,No,0.0,No,northeast,10435.07
+739.0,740.0,37.0,male,29.2,109.0,No,1.0,No,southwest,10436.1
+740.0,741.0,27.0,male,39.6,101.0,Yes,1.0,No,southwest,10450.55
+741.0,742.0,19.0,male,31.2,98.0,No,1.0,No,northwest,10461.98
+742.0,743.0,35.0,female,26.2,109.0,Yes,2.0,No,northwest,10493.95
+743.0,744.0,42.0,male,33.3,101.0,Yes,3.0,No,southeast,10560.49
+744.0,745.0,56.0,female,33.3,81.0,Yes,0.0,No,northeast,10564.88
+745.0,746.0,40.0,male,22.1,83.0,Yes,0.0,No,southwest,10577.09
+746.0,747.0,55.0,female,39.6,91.0,No,1.0,No,southeast,10579.71
+747.0,748.0,34.0,male,34.4,106.0,No,0.0,No,southeast,10594.23
+748.0,749.0,25.0,male,27.6,110.0,Yes,0.0,No,northwest,10594.5
+749.0,750.0,40.0,male,31.0,100.0,No,3.0,No,northwest,10600.55
+750.0,751.0,29.0,male,39.6,92.0,No,0.0,No,southwest,10601.41
+751.0,752.0,49.0,male,32.8,89.0,No,0.0,No,northwest,10601.63
+752.0,753.0,40.0,male,40.3,96.0,No,0.0,No,southwest,10602.39
+753.0,754.0,38.0,female,28.2,86.0,No,3.0,No,southeast,10702.64
+754.0,755.0,59.0,female,30.5,82.0,No,0.0,No,southwest,10704.47
+755.0,756.0,47.0,female,37.1,101.0,Yes,0.0,No,southwest,10713.64
+756.0,757.0,26.0,male,35.6,106.0,Yes,4.0,No,northeast,10736.87
+757.0,758.0,30.0,male,21.5,87.0,No,1.0,No,southwest,10791.96
+758.0,759.0,57.0,female,33.4,84.0,Yes,0.0,No,southwest,10795.94
+759.0,760.0,34.0,male,29.0,110.0,Yes,0.0,No,northeast,10796.35
+760.0,761.0,43.0,female,30.8,106.0,Yes,1.0,No,northeast,10797.34
+761.0,762.0,42.0,female,33.3,103.0,Yes,2.0,No,southwest,10806.84
+762.0,763.0,28.0,male,32.7,103.0,No,1.0,No,southeast,10807.49
+763.0,764.0,38.0,male,33.6,80.0,Yes,1.0,No,northwest,10825.25
+764.0,765.0,31.0,female,36.7,103.0,No,2.0,No,northwest,10848.13
+765.0,766.0,26.0,female,32.7,90.0,Yes,0.0,No,northeast,10923.93
+766.0,767.0,51.0,female,31.9,102.0,Yes,1.0,No,southeast,10928.85
+767.0,768.0,39.0,female,24.8,89.0,Yes,1.0,No,northwest,10942.13
+768.0,769.0,37.0,male,23.7,91.0,Yes,0.0,No,southwest,10959.33
+769.0,770.0,48.0,female,37.4,80.0,Yes,1.0,No,northwest,10959.69
+770.0,771.0,39.0,male,28.1,106.0,Yes,0.0,No,southwest,10965.45
+771.0,772.0,30.0,male,33.7,103.0,Yes,0.0,No,northwest,10976.25
+772.0,773.0,60.0,female,41.5,85.0,No,4.0,No,southeast,10977.21
+773.0,774.0,24.0,male,40.4,89.0,Yes,0.0,No,southeast,10982.5
+774.0,775.0,45.0,male,21.0,85.0,Yes,2.0,No,southeast,11013.71
+775.0,776.0,50.0,female,27.9,83.0,Yes,4.0,No,northwest,11015.17
+776.0,777.0,49.0,female,41.2,92.0,Yes,4.0,No,northwest,11033.66
+777.0,778.0,53.0,female,25.3,104.0,Yes,0.0,No,southwest,11070.54
+778.0,779.0,57.0,female,27.2,90.0,Yes,0.0,No,southwest,11073.18
+779.0,780.0,38.0,female,27.0,84.0,No,0.0,No,northwest,11082.58
+780.0,781.0,36.0,female,28.1,106.0,Yes,3.0,No,northwest,11085.59
+781.0,782.0,60.0,female,39.8,93.0,Yes,0.0,No,southeast,11090.72
+782.0,783.0,48.0,female,41.9,99.0,Yes,0.0,No,southeast,11093.62
+783.0,784.0,46.0,female,26.7,102.0,Yes,2.0,No,southwest,11150.78
+784.0,785.0,47.0,female,35.9,104.0,No,2.0,No,southwest,11163.57
+785.0,786.0,25.0,male,25.9,82.0,No,0.0,No,northeast,11165.42
+786.0,787.0,26.0,female,31.7,106.0,Yes,2.0,No,northwest,11187.66
+787.0,788.0,47.0,male,26.4,84.0,No,2.0,No,northeast,11244.38
+788.0,789.0,31.0,male,28.6,86.0,No,3.0,No,southwest,11253.42
+789.0,790.0,49.0,male,36.6,84.0,No,3.0,No,southwest,11264.54
+790.0,791.0,43.0,male,28.3,91.0,No,1.0,No,northeast,11272.33
+791.0,792.0,51.0,female,29.8,101.0,Yes,0.0,No,northeast,11286.54
+792.0,793.0,47.0,male,32.8,83.0,No,3.0,No,northwest,11289.11
+793.0,794.0,37.0,female,33.7,91.0,Yes,4.0,No,southwest,11299.34
+794.0,795.0,48.0,female,27.6,94.0,No,1.0,No,northwest,11305.93
+795.0,796.0,47.0,male,42.5,96.0,No,1.0,No,southeast,11326.71
+796.0,797.0,38.0,male,23.3,98.0,No,0.0,No,southwest,11345.52
+797.0,798.0,33.0,male,31.5,108.0,No,0.0,No,northwest,11353.23
+798.0,799.0,47.0,male,34.0,105.0,No,0.0,No,northwest,11356.66
+799.0,800.0,30.0,male,35.7,91.0,No,0.0,No,southwest,11362.76
+800.0,801.0,32.0,male,36.1,80.0,No,0.0,No,southeast,11363.28
+801.0,802.0,43.0,male,38.0,109.0,Yes,0.0,No,southwest,11365.95
+802.0,803.0,42.0,male,49.1,109.0,Yes,0.0,No,southeast,11381.33
+803.0,804.0,20.0,male,35.2,92.0,No,1.0,No,northeast,11394.07
+804.0,805.0,36.0,female,38.4,98.0,Yes,2.0,No,northeast,11396.9
+805.0,806.0,37.0,female,44.7,81.0,No,3.0,No,southwest,11411.69
+806.0,807.0,59.0,female,36.4,91.0,No,3.0,No,northwest,11436.74
+807.0,808.0,51.0,female,25.7,83.0,Yes,0.0,No,northwest,11454.02
+808.0,809.0,44.0,female,28.7,95.0,No,0.0,No,southwest,11455.28
+809.0,810.0,40.0,female,33.9,93.0,Yes,0.0,No,southeast,11482.63
+810.0,811.0,33.0,male,32.2,85.0,No,3.0,No,northeast,11488.32
+811.0,812.0,59.0,female,32.3,97.0,Yes,1.0,No,northeast,11512.41
+812.0,813.0,45.0,male,24.4,103.0,Yes,4.0,No,northwest,11520.1
+813.0,814.0,35.0,male,18.3,95.0,No,0.0,No,northeast,11534.87
+814.0,815.0,57.0,female,46.7,101.0,Yes,2.0,No,southwest,11538.42
+815.0,816.0,29.0,female,31.9,90.0,Yes,5.0,No,southwest,11552.9
+816.0,817.0,26.0,male,27.9,97.0,Yes,1.0,No,southeast,11554.22
+817.0,818.0,35.0,male,40.9,80.0,Yes,0.0,No,northeast,11566.3
+818.0,819.0,18.0,male,43.7,101.0,No,1.0,No,southwest,11576.13
+819.0,820.0,42.0,female,28.3,85.0,No,0.0,No,northeast,11657.72
+820.0,821.0,43.0,female,28.6,110.0,No,0.0,No,northeast,11658.12
+821.0,822.0,40.0,female,28.8,83.0,No,0.0,No,northeast,11658.38
+822.0,823.0,31.0,female,35.8,109.0,No,1.0,No,southwest,11674.13
+823.0,824.0,50.0,female,23.8,100.0,No,2.0,No,northeast,11729.68
+824.0,825.0,29.0,male,28.6,85.0,Yes,0.0,No,northwest,11735.88
+825.0,826.0,37.0,male,27.0,83.0,Yes,2.0,No,southwest,11737.85
+826.0,827.0,54.0,female,28.1,83.0,No,3.0,No,southwest,11741.73
+827.0,828.0,34.0,male,26.4,105.0,Yes,0.0,No,southeast,11743.3
+828.0,829.0,29.0,male,34.4,108.0,No,0.0,No,northwest,11743.93
+829.0,830.0,25.0,male,32.1,97.0,Yes,1.0,No,northeast,11763.0
+830.0,831.0,50.0,female,23.2,89.0,No,0.0,No,northwest,11830.61
+831.0,832.0,37.0,female,22.8,97.0,Yes,0.0,No,southeast,11833.78
+832.0,833.0,43.0,female,25.2,91.0,Yes,0.0,No,southwest,11837.16
+833.0,834.0,25.0,female,30.5,110.0,No,0.0,No,northwest,11840.78
+834.0,835.0,45.0,female,29.0,87.0,No,0.0,No,southwest,11842.44
+835.0,836.0,33.0,female,31.8,95.0,No,0.0,No,northwest,11842.62
+836.0,837.0,26.0,female,33.1,110.0,No,0.0,No,southwest,11848.14
+837.0,838.0,50.0,female,39.1,100.0,No,0.0,No,southeast,11856.41
+838.0,839.0,52.0,female,32.4,83.0,No,1.0,No,northeast,11879.1
+839.0,840.0,39.0,female,29.7,103.0,No,2.0,No,southwest,11881.36
+840.0,841.0,38.0,female,30.1,92.0,Yes,2.0,No,southeast,11881.97
+841.0,842.0,23.0,male,21.8,92.0,No,2.0,No,southeast,11884.05
+842.0,843.0,41.0,male,25.2,101.0,No,0.0,No,northeast,11931.13
+843.0,844.0,44.0,male,30.3,102.0,Yes,0.0,No,northeast,11938.26
+844.0,845.0,36.0,male,34.9,88.0,Yes,0.0,No,northeast,11944.59
+845.0,846.0,48.0,male,33.6,97.0,No,1.0,No,northwest,11945.13
+846.0,847.0,45.0,male,32.0,83.0,No,1.0,No,southeast,11946.63
+847.0,848.0,24.0,male,33.9,81.0,Yes,3.0,No,southeast,11987.17
+848.0,849.0,42.0,female,22.2,99.0,Yes,0.0,No,northeast,12029.29
+849.0,850.0,47.0,female,20.1,99.0,No,1.0,No,southwest,12032.33
+850.0,851.0,31.0,female,26.6,90.0,Yes,1.0,No,northwest,12044.34
+851.0,852.0,42.0,female,23.0,80.0,Yes,3.0,No,southwest,12094.48
+852.0,853.0,30.0,female,28.9,94.0,No,2.0,No,northeast,12096.65
+853.0,854.0,27.0,female,30.8,97.0,Yes,3.0,No,southwest,12105.32
+854.0,855.0,50.0,male,25.5,87.0,No,0.0,No,northwest,12124.99
+855.0,856.0,23.0,male,28.8,86.0,No,0.0,No,northwest,12129.61
+856.0,857.0,35.0,male,25.7,90.0,Yes,0.0,No,southeast,12142.58
+857.0,858.0,23.0,male,28.9,83.0,Yes,0.0,No,southwest,12146.97
+858.0,859.0,34.0,female,27.2,96.0,No,0.0,No,northwest,12222.9
+859.0,860.0,33.0,female,28.2,96.0,Yes,0.0,No,northwest,12224.35
+860.0,861.0,37.0,female,33.4,84.0,Yes,0.0,No,northwest,12231.61
+861.0,862.0,44.0,female,27.5,86.0,Yes,0.0,No,southwest,12233.83
+862.0,863.0,26.0,female,36.5,80.0,Yes,0.0,No,northwest,12235.84
+863.0,864.0,27.0,female,35.2,83.0,Yes,0.0,No,southeast,12244.53
+864.0,865.0,60.0,female,37.5,97.0,Yes,2.0,No,southeast,12265.51
+865.0,866.0,50.0,female,32.8,86.0,No,2.0,No,northwest,12268.63
+866.0,867.0,59.0,female,33.5,97.0,No,2.0,No,northwest,12269.69
+867.0,868.0,46.0,male,24.7,84.0,No,0.0,No,northeast,12323.94
+868.0,869.0,44.0,male,27.5,104.0,Yes,1.0,No,southwest,12333.83
+869.0,870.0,40.0,male,37.1,84.0,Yes,1.0,No,southwest,12347.17
+870.0,871.0,33.0,male,36.1,80.0,Yes,3.0,No,southwest,12363.55
+871.0,872.0,43.0,male,35.2,107.0,Yes,0.0,No,northeast,12404.88
+872.0,873.0,36.0,female,33.0,82.0,Yes,0.0,No,northeast,12430.95
+873.0,874.0,39.0,female,21.5,86.0,No,3.0,No,northwest,12475.35
+874.0,875.0,25.0,female,24.6,80.0,Yes,3.0,No,northwest,12479.71
+875.0,876.0,28.0,female,40.8,87.0,Yes,3.0,No,southeast,12485.8
+876.0,877.0,33.0,female,35.8,109.0,No,3.0,No,northwest,12495.29
+877.0,878.0,27.0,male,24.3,102.0,Yes,0.0,No,northwest,12523.6
+878.0,879.0,35.0,male,31.6,89.0,Yes,0.0,No,southeast,12557.61
+879.0,880.0,36.0,male,43.4,96.0,No,0.0,No,southwest,12574.05
+880.0,881.0,28.0,female,46.8,106.0,No,5.0,No,southeast,12592.53
+881.0,882.0,36.0,male,26.8,88.0,Yes,1.0,No,northwest,12609.89
+882.0,883.0,55.0,female,31.4,85.0,No,0.0,No,northwest,12622.18
+883.0,884.0,57.0,female,25.7,94.0,No,2.0,No,southeast,12629.17
+884.0,885.0,57.0,female,24.5,88.0,Yes,0.0,No,southeast,12629.9
+885.0,886.0,28.0,female,30.5,96.0,No,0.0,No,southwest,12638.2
+886.0,887.0,31.0,female,33.8,86.0,Yes,2.0,No,northwest,12643.38
+887.0,888.0,59.0,female,35.1,84.0,Yes,0.0,No,southwest,12644.59
+888.0,889.0,28.0,female,38.0,110.0,Yes,2.0,No,southwest,12646.21
+889.0,890.0,54.0,female,38.1,82.0,No,0.0,No,southeast,12648.7
+890.0,891.0,18.0,male,29.6,81.0,Yes,0.0,No,northeast,12731.0
+891.0,892.0,34.0,male,37.0,92.0,Yes,0.0,No,northeast,12741.17
+892.0,893.0,31.0,female,36.5,99.0,No,0.0,No,northeast,12797.21
+893.0,894.0,39.0,female,26.5,94.0,No,0.0,No,northeast,12815.44
+894.0,895.0,38.0,male,17.3,82.0,Yes,2.0,Yes,northeast,12829.46
+895.0,896.0,49.0,male,39.1,102.0,Yes,0.0,No,northeast,12890.06
+896.0,897.0,36.0,male,25.5,85.0,No,1.0,No,northeast,12913.99
+897.0,898.0,40.0,male,29.7,87.0,Yes,2.0,No,southeast,12925.89
+898.0,899.0,45.0,male,31.8,95.0,Yes,2.0,No,southeast,12928.79
+899.0,900.0,33.0,male,33.7,98.0,No,4.0,No,southeast,12949.16
+900.0,901.0,40.0,male,38.4,82.0,No,0.0,No,northwest,12950.07
+901.0,902.0,19.0,male,21.4,85.0,Yes,0.0,No,southwest,12957.12
+902.0,903.0,43.0,male,37.4,81.0,No,0.0,No,southwest,12979.36
+903.0,904.0,35.0,male,38.8,95.0,Yes,0.0,No,southeast,12981.35
+904.0,905.0,25.0,male,39.9,88.0,No,0.0,No,southeast,12982.87
+905.0,906.0,58.0,female,24.0,109.0,No,0.0,No,northwest,13012.21
+906.0,907.0,46.0,female,32.4,108.0,No,1.0,No,northeast,13019.16
+907.0,908.0,35.0,female,28.2,105.0,Yes,0.0,No,southwest,13041.92
+908.0,909.0,31.0,female,25.4,100.0,Yes,3.0,No,northeast,13047.33
+909.0,910.0,25.0,female,44.0,88.0,Yes,0.0,No,southwest,13063.88
+910.0,911.0,23.0,male,24.3,90.0,No,1.0,No,northwest,13112.6
+911.0,912.0,55.0,female,28.0,105.0,Yes,0.0,No,southwest,13126.68
+912.0,913.0,20.0,male,23.7,89.0,Yes,0.0,No,northeast,13129.6
+913.0,914.0,22.0,male,33.5,101.0,Yes,0.0,No,northeast,13143.34
+914.0,915.0,19.0,male,33.9,80.0,Yes,0.0,No,northeast,13143.86
+915.0,916.0,49.0,female,18.3,82.0,Yes,0.0,No,northeast,13204.29
+916.0,917.0,43.0,female,27.6,105.0,Yes,0.0,No,northeast,13217.09
+917.0,918.0,43.0,female,34.3,91.0,Yes,2.0,No,northeast,13224.06
+918.0,919.0,56.0,female,28.7,95.0,No,1.0,No,southwest,13224.69
+919.0,920.0,48.0,female,36.0,83.0,No,0.0,No,northeast,13228.85
+920.0,921.0,38.0,male,30.0,110.0,Yes,0.0,No,northwest,13352.1
+921.0,922.0,27.0,male,30.8,98.0,No,0.0,No,southwest,13390.56
+922.0,923.0,28.0,male,33.1,84.0,No,0.0,No,southwest,13393.76
+923.0,924.0,45.0,male,41.5,103.0,Yes,0.0,No,southeast,13405.39
+924.0,925.0,45.0,female,21.1,94.0,No,0.0,No,northwest,13415.04
+925.0,926.0,42.0,female,31.2,92.0,Yes,0.0,No,northwest,13429.04
+926.0,927.0,51.0,female,32.3,83.0,Yes,3.0,No,northeast,13430.27
+927.0,928.0,31.0,female,25.0,93.0,Yes,0.0,No,southwest,13451.12
+928.0,929.0,53.0,female,29.9,87.0,No,0.0,No,southeast,13457.96
+929.0,930.0,26.0,female,33.2,106.0,No,0.0,No,southwest,13462.52
+930.0,931.0,32.0,female,39.2,96.0,Yes,0.0,No,southeast,13470.8
+931.0,932.0,40.0,female,39.2,84.0,No,0.0,No,southwest,13470.86
+932.0,933.0,47.0,male,32.1,91.0,No,0.0,No,northeast,13555.0
+933.0,934.0,28.0,female,31.8,110.0,Yes,2.0,No,northeast,13607.37
+934.0,935.0,41.0,female,22.0,89.0,No,0.0,No,northeast,13616.36
+935.0,936.0,59.0,female,35.9,89.0,No,0.0,No,northeast,13635.64
+936.0,937.0,55.0,female,21.8,93.0,No,1.0,No,northeast,13725.47
+937.0,938.0,33.0,male,21.6,106.0,Yes,0.0,Yes,northeast,13747.87
+938.0,939.0,30.0,male,28.3,85.0,No,0.0,No,northwest,13770.1
+939.0,940.0,24.0,male,34.5,95.0,No,0.0,No,southwest,13822.8
+940.0,941.0,43.0,male,40.5,99.0,Yes,0.0,No,southeast,13831.12
+941.0,942.0,52.0,female,21.7,81.0,Yes,0.0,Yes,southwest,13844.51
+942.0,943.0,34.0,female,32.7,110.0,Yes,0.0,No,northwest,13844.8
+943.0,944.0,35.0,female,31.8,95.0,No,0.0,No,southwest,13880.95
+944.0,945.0,57.0,female,36.3,98.0,Yes,0.0,No,southeast,13887.2
+945.0,946.0,57.0,female,36.9,88.0,Yes,0.0,No,southeast,13887.97
+946.0,947.0,20.0,male,33.1,99.0,No,3.0,No,southeast,13919.82
+947.0,948.0,33.0,male,27.6,109.0,No,1.0,No,northwest,13937.67
+948.0,949.0,31.0,male,31.4,82.0,No,0.0,No,northeast,13974.46
+949.0,950.0,43.0,male,36.8,85.0,No,0.0,No,northeast,13981.85
+950.0,951.0,32.0,female,27.7,86.0,Yes,3.0,No,southeast,14001.13
+951.0,952.0,50.0,female,27.8,80.0,Yes,3.0,No,southeast,14001.29
+952.0,953.0,41.0,female,32.1,109.0,Yes,3.0,No,southwest,14007.22
+953.0,954.0,42.0,female,31.7,83.0,No,0.0,No,northeast,14043.48
+954.0,955.0,24.0,male,32.3,97.0,No,2.0,No,northwest,14119.62
+955.0,956.0,30.0,female,38.3,98.0,No,0.0,No,southeast,14133.04
+956.0,957.0,47.0,male,37.9,103.0,No,0.0,No,northwest,14210.54
+957.0,958.0,56.0,female,39.1,83.0,No,2.0,No,southwest,14235.07
+958.0,959.0,28.0,female,25.1,103.0,No,0.0,No,northwest,14254.61
+959.0,960.0,51.0,female,26.2,106.0,Yes,0.0,No,northwest,14256.19
+960.0,961.0,48.0,female,21.7,90.0,Yes,0.0,Yes,northeast,14283.46
+961.0,962.0,38.0,female,36.0,83.0,No,0.0,No,southeast,14313.85
+962.0,963.0,38.0,female,39.7,98.0,No,0.0,No,southwest,14319.03
+963.0,964.0,40.0,male,21.7,107.0,Yes,1.0,No,northwest,14349.85
+964.0,965.0,44.0,male,32.8,89.0,Yes,1.0,No,southwest,14358.36
+965.0,966.0,41.0,female,26.7,94.0,Yes,3.0,No,northwest,14382.71
+966.0,967.0,45.0,female,28.8,101.0,No,4.0,No,northeast,14394.4
+967.0,968.0,45.0,male,26.4,103.0,Yes,0.0,No,northeast,14394.56
+968.0,969.0,37.0,male,38.2,97.0,Yes,0.0,No,northeast,14410.93
+969.0,970.0,31.0,male,39.2,91.0,No,1.0,No,southeast,14418.28
+970.0,971.0,41.0,female,23.2,98.0,No,2.0,No,northwest,14426.07
+971.0,972.0,56.0,female,21.7,89.0,Yes,0.0,No,northeast,14449.85
+972.0,973.0,47.0,female,23.1,92.0,No,0.0,No,northeast,14451.84
+973.0,974.0,52.0,female,17.2,80.0,Yes,2.0,Yes,northeast,14455.64
+974.0,975.0,57.0,female,35.2,94.0,No,1.0,No,southeast,14474.68
+975.0,976.0,50.0,male,25.5,84.0,No,5.0,No,southeast,14478.33
+976.0,977.0,40.0,female,20.5,87.0,Yes,0.0,Yes,northeast,14571.89
+977.0,978.0,53.0,female,32.4,102.0,Yes,3.0,No,northeast,14590.63
+978.0,979.0,35.0,female,33.0,105.0,No,0.0,No,northwest,14692.67
+979.0,980.0,26.0,female,22.4,100.0,Yes,0.0,Yes,northwest,14711.74
+980.0,981.0,36.0,female,39.3,82.0,No,0.0,No,northeast,14901.52
+981.0,982.0,21.0,male,25.6,95.0,No,2.0,No,southwest,14988.43
+982.0,983.0,39.0,female,18.0,100.0,Yes,2.0,Yes,northeast,15006.58
+983.0,984.0,27.0,female,30.5,100.0,Yes,2.0,No,northwest,15019.76
+984.0,985.0,24.0,male,33.7,101.0,No,3.0,No,southeast,15161.53
+985.0,986.0,24.0,male,39.8,95.0,Yes,3.0,No,southwest,15170.07
+986.0,987.0,55.0,female,38.1,91.0,No,2.0,No,northeast,15230.32
+987.0,988.0,37.0,female,21.9,89.0,Yes,1.0,Yes,northeast,15359.1
+988.0,989.0,46.0,male,25.2,104.0,Yes,0.0,Yes,northeast,15518.18
+989.0,990.0,21.0,male,41.3,85.0,Yes,3.0,No,northwest,15555.19
+990.0,991.0,36.0,female,33.0,94.0,No,3.0,No,northwest,15612.19
+991.0,992.0,27.0,male,24.1,95.0,No,0.0,Yes,northwest,15817.99
+992.0,993.0,30.0,male,19.3,84.0,Yes,0.0,Yes,southwest,15820.7
+993.0,994.0,34.0,female,29.3,87.0,Yes,4.0,No,southwest,15828.82
+994.0,995.0,53.0,female,31.8,96.0,No,2.0,No,northeast,16069.08
+995.0,996.0,55.0,female,39.1,83.0,Yes,3.0,No,southeast,16085.13
+996.0,997.0,29.0,female,21.9,92.0,Yes,0.0,Yes,northeast,16115.3
+997.0,998.0,30.0,male,22.9,108.0,No,0.0,Yes,northeast,16138.76
+998.0,999.0,34.0,male,27.3,89.0,Yes,0.0,Yes,southwest,16232.85
+999.0,1000.0,33.0,male,27.7,94.0,No,0.0,Yes,southwest,16297.85
+1000.0,1001.0,45.0,female,20.0,91.0,No,3.0,Yes,northwest,16420.49
+1001.0,1002.0,50.0,male,26.0,81.0,Yes,1.0,Yes,northwest,16450.89
+1002.0,1003.0,44.0,female,30.1,105.0,Yes,3.0,No,northwest,16455.71
+1003.0,1004.0,42.0,female,24.8,83.0,No,0.0,Yes,southeast,16577.78
+1004.0,1005.0,44.0,male,31.0,88.0,No,0.0,No,southeast,16586.5
+1005.0,1006.0,56.0,female,21.8,105.0,No,1.0,Yes,northeast,16657.72
+1006.0,1007.0,50.0,female,19.1,108.0,No,2.0,Yes,northeast,16776.3
+1007.0,1008.0,45.0,female,30.6,85.0,No,1.0,No,northeast,16796.41
+1008.0,1009.0,60.0,female,27.9,97.0,No,0.0,Yes,southwest,16884.92
+1009.0,1010.0,22.0,male,27.1,86.0,Yes,0.0,Yes,southeast,17043.34
+1010.0,1011.0,25.0,female,28.3,107.0,Yes,0.0,Yes,southwest,17081.08
+1011.0,1012.0,32.0,female,26.8,109.0,No,1.0,Yes,southeast,17085.27
+1012.0,1013.0,35.0,male,33.6,90.0,Yes,4.0,No,northeast,17128.43
+1013.0,1014.0,45.0,male,27.4,83.0,No,1.0,Yes,northeast,17178.68
+1014.0,1015.0,44.0,male,19.8,106.0,No,1.0,Yes,southeast,17179.52
+1015.0,1016.0,47.0,male,29.1,97.0,No,0.0,Yes,northwest,17352.68
+1016.0,1017.0,19.0,male,23.0,99.0,Yes,2.0,Yes,northwest,17361.77
+1017.0,1018.0,32.0,female,28.3,92.0,No,0.0,Yes,northwest,17468.98
+1018.0,1019.0,45.0,female,24.6,96.0,Yes,0.0,Yes,southwest,17496.31
+1019.0,1020.0,23.0,male,28.0,86.0,Yes,1.0,Yes,northwest,17560.38
+1020.0,1021.0,60.0,female,23.7,91.0,Yes,1.0,No,southeast,17626.24
+1021.0,1022.0,19.0,male,24.0,80.0,No,3.0,Yes,southeast,17663.14
+1022.0,1023.0,58.0,female,28.9,83.0,Yes,0.0,Yes,northwest,17748.51
+1023.0,1024.0,51.0,female,41.3,98.0,No,0.0,No,northeast,17878.9
+1024.0,1025.0,22.0,male,24.8,100.0,No,0.0,Yes,northeast,17904.53
+1025.0,1026.0,59.0,female,31.8,103.0,Yes,0.0,No,southeast,17929.3
+1026.0,1027.0,36.0,male,25.7,96.0,Yes,4.0,Yes,southwest,17942.11
+1027.0,1028.0,29.0,female,28.3,103.0,No,0.0,Yes,northwest,18033.97
+1028.0,1029.0,33.0,male,29.7,102.0,Yes,2.0,No,northwest,18157.88
+1029.0,1030.0,35.0,female,32.2,107.0,Yes,1.0,No,southeast,18218.16
+1030.0,1031.0,52.0,female,27.3,104.0,No,3.0,Yes,southeast,18223.45
+1031.0,1032.0,44.0,male,29.2,88.0,Yes,0.0,Yes,southeast,18246.5
+1032.0,1033.0,22.0,male,24.4,83.0,Yes,3.0,Yes,southwest,18259.22
+1033.0,1034.0,23.0,male,28.5,97.0,No,0.0,Yes,northwest,18310.74
+1034.0,1035.0,36.0,female,28.5,87.0,No,1.0,Yes,southeast,18328.24
+1035.0,1036.0,45.0,female,22.6,96.0,No,2.0,Yes,southwest,18608.26
+1036.0,1037.0,38.0,male,29.8,87.0,Yes,0.0,Yes,northeast,18648.42
+1037.0,1038.0,58.0,female,23.7,95.0,No,3.0,Yes,northwest,18765.88
+1038.0,1039.0,21.0,male,20.1,108.0,No,2.0,Yes,southeast,18767.74
+1039.0,1040.0,39.0,female,30.4,92.0,Yes,3.0,No,northwest,18804.75
+1040.0,1041.0,46.0,male,35.3,89.0,No,2.0,No,southeast,18806.15
+1041.0,1042.0,30.0,female,27.9,84.0,No,3.0,No,northwest,18838.7
+1042.0,1043.0,26.0,female,32.4,81.0,No,1.0,No,northeast,18903.49
+1043.0,1044.0,41.0,female,27.6,89.0,No,0.0,No,southwest,18955.22
+1044.0,1045.0,36.0,male,38.8,99.0,No,1.0,No,southeast,18963.17
+1045.0,1046.0,19.0,male,25.3,95.0,No,2.0,Yes,southeast,18972.5
+1046.0,1047.0,60.0,female,18.3,82.0,Yes,5.0,Yes,southwest,19023.26
+1047.0,1048.0,27.0,male,27.1,104.0,No,1.0,Yes,southwest,19040.88
+1048.0,1049.0,50.0,female,27.9,83.0,No,1.0,Yes,southeast,19107.78
+1049.0,1050.0,27.0,female,35.7,96.0,Yes,2.0,No,northeast,19144.58
+1050.0,1051.0,43.0,male,25.9,81.0,No,3.0,Yes,southwest,19199.94
+1051.0,1052.0,33.0,male,36.2,100.0,No,0.0,No,southeast,19214.71
+1052.0,1053.0,49.0,male,29.8,98.0,No,0.0,Yes,southeast,19350.37
+1053.0,1054.0,35.0,male,24.4,101.0,Yes,3.0,Yes,southeast,19362.0
+1054.0,1055.0,34.0,male,33.3,93.0,Yes,2.0,No,northwest,19442.35
+1055.0,1056.0,25.0,female,22.2,100.0,Yes,2.0,Yes,southeast,19444.27
+1056.0,1057.0,21.0,male,39.7,107.0,Yes,4.0,No,northeast,19496.72
+1057.0,1058.0,21.0,male,24.6,87.0,No,0.0,Yes,southeast,19515.54
+1058.0,1059.0,40.0,female,28.4,109.0,Yes,1.0,Yes,southeast,19521.97
+1059.0,1060.0,34.0,female,26.4,105.0,No,0.0,Yes,southeast,19539.24
+1060.0,1061.0,53.0,female,20.2,87.0,No,1.0,Yes,northeast,19594.81
+1061.0,1062.0,47.0,male,33.8,98.0,No,0.0,No,northwest,19673.34
+1062.0,1063.0,38.0,male,28.9,106.0,No,1.0,Yes,southeast,19719.69
+1063.0,1064.0,30.0,female,27.8,97.0,No,3.0,No,southeast,19749.38
+1064.0,1065.0,28.0,female,20.0,96.0,Yes,2.0,Yes,northeast,19798.05
+1065.0,1066.0,23.0,male,29.7,99.0,Yes,3.0,Yes,southwest,19933.46
+1066.0,1067.0,43.0,female,23.4,82.0,Yes,0.0,Yes,northeast,19964.75
+1067.0,1068.0,40.0,male,27.8,93.0,No,1.0,Yes,northwest,20009.63
+1068.0,1069.0,19.0,male,26.4,87.0,Yes,0.0,Yes,northeast,20149.32
+1069.0,1070.0,28.0,female,21.8,81.0,No,0.0,Yes,southwest,20167.34
+1070.0,1071.0,42.0,female,27.5,110.0,Yes,2.0,No,southwest,20177.67
+1071.0,1072.0,44.0,female,28.0,82.0,Yes,0.0,Yes,northwest,20234.85
+1072.0,1073.0,20.0,male,29.6,91.0,Yes,1.0,No,northeast,20277.81
+1073.0,1074.0,46.0,female,25.6,100.0,Yes,1.0,Yes,northeast,20296.86
+1074.0,1075.0,47.0,male,29.8,89.0,Yes,0.0,No,southwest,20420.6
+1075.0,1076.0,44.0,female,38.1,98.0,Yes,3.0,No,southeast,20463.0
+1076.0,1077.0,19.0,male,37.3,102.0,Yes,0.0,No,southwest,20630.28
+1077.0,1078.0,19.0,male,40.3,110.0,No,0.0,No,northeast,20709.02
+1078.0,1079.0,39.0,male,28.7,81.0,No,3.0,Yes,northwest,20745.99
+1079.0,1080.0,49.0,male,28.0,106.0,No,1.0,Yes,northeast,20773.63
+1080.0,1081.0,43.0,male,33.0,85.0,No,0.0,No,southeast,20781.49
+1081.0,1082.0,42.0,female,33.3,107.0,No,0.0,No,northeast,20878.78
+1082.0,1083.0,30.0,male,27.7,89.0,Yes,2.0,Yes,northeast,20984.09
+1083.0,1084.0,37.0,male,28.3,109.0,Yes,1.0,Yes,southwest,21082.16
+1084.0,1085.0,43.0,male,22.9,106.0,Yes,2.0,Yes,northwest,21098.55
+1085.0,1086.0,38.0,male,20.9,98.0,No,0.0,Yes,southeast,21195.82
+1086.0,1087.0,44.0,male,24.4,110.0,No,0.0,Yes,southeast,21223.68
+1087.0,1088.0,44.0,male,29.6,91.0,Yes,0.0,No,southwest,21232.18
+1088.0,1089.0,33.0,male,24.6,87.0,Yes,2.0,Yes,northeast,21259.38
+1089.0,1090.0,45.0,female,30.1,83.0,Yes,0.0,No,northeast,21344.85
+1090.0,1091.0,54.0,female,26.6,99.0,No,0.0,Yes,northwest,21348.71
+1091.0,1092.0,22.0,male,28.1,95.0,Yes,4.0,Yes,northwest,21472.48
+1092.0,1093.0,50.0,male,18.7,89.0,No,0.0,No,northwest,21595.38
+1093.0,1094.0,59.0,female,24.9,108.0,No,3.0,Yes,northeast,21659.93
+1094.0,1095.0,26.0,female,23.7,94.0,Yes,1.0,Yes,northwest,21677.28
+1095.0,1096.0,38.0,female,25.3,104.0,Yes,1.0,Yes,northeast,21771.34
+1096.0,1097.0,34.0,female,26.9,105.0,No,0.0,Yes,northwest,21774.32
+1097.0,1098.0,39.0,male,37.4,99.0,Yes,0.0,No,southwest,21797.0
+1098.0,1099.0,58.0,female,24.7,82.0,No,2.0,Yes,northwest,21880.82
+1099.0,1100.0,48.0,male,25.4,99.0,No,1.0,Yes,southeast,21978.68
+1100.0,1101.0,34.0,male,22.7,107.0,Yes,0.0,No,northwest,21984.47
+1101.0,1102.0,40.0,male,30.0,106.0,No,0.0,Yes,southwest,22144.03
+1102.0,1103.0,40.0,female,24.0,87.0,Yes,1.0,No,southeast,22192.44
+1103.0,1104.0,36.0,male,23.2,80.0,Yes,1.0,Yes,southeast,22218.11
+1104.0,1105.0,60.0,female,28.1,84.0,No,1.0,Yes,northeast,22331.57
+1105.0,1106.0,45.0,female,24.2,103.0,No,2.0,No,northeast,22395.74
+1106.0,1107.0,40.0,male,20.0,110.0,Yes,0.0,Yes,northeast,22412.65
+1107.0,1108.0,32.0,male,29.9,85.0,Yes,1.0,Yes,northeast,22462.04
+1108.0,1109.0,49.0,female,26.7,88.0,No,2.0,Yes,southwest,22478.6
+1109.0,1110.0,32.0,male,27.3,94.0,No,2.0,No,northwest,22493.66
+1110.0,1111.0,57.0,female,30.9,83.0,No,0.0,No,northeast,23045.57
+1111.0,1112.0,27.0,female,27.8,87.0,Yes,0.0,Yes,southeast,23065.42
+1112.0,1113.0,22.0,male,33.1,91.0,No,0.0,No,southwest,23082.96
+1113.0,1114.0,23.0,male,25.0,80.0,Yes,2.0,No,northeast,23241.47
+1114.0,1115.0,43.0,female,22.9,84.0,Yes,1.0,Yes,southeast,23244.79
+1115.0,1116.0,59.0,female,24.3,104.0,No,1.0,No,northeast,23288.93
+1116.0,1117.0,42.0,male,25.6,106.0,Yes,2.0,Yes,southwest,23306.55
+1117.0,1118.0,26.0,female,26.1,99.0,Yes,1.0,Yes,northeast,23401.31
+1118.0,1119.0,36.0,male,34.1,89.0,No,2.0,No,southeast,23563.02
+1119.0,1120.0,44.0,male,28.0,93.0,No,1.0,Yes,southwest,23568.27
+1120.0,1121.0,47.0,male,25.8,101.0,Yes,2.0,Yes,northwest,23807.24
+1121.0,1122.0,59.0,female,24.1,91.0,Yes,1.0,Yes,northwest,23887.66
+1122.0,1123.0,28.0,male,24.8,92.0,Yes,2.0,Yes,northwest,23967.38
+1123.0,1124.0,60.0,female,30.6,85.0,No,2.0,No,northwest,24059.68
+1124.0,1125.0,46.0,female,23.8,86.0,Yes,3.0,Yes,northeast,24106.91
+1125.0,1126.0,58.0,female,25.9,102.0,No,3.0,Yes,southeast,24180.93
+1126.0,1127.0,51.0,female,41.9,106.0,No,0.0,No,southeast,24227.34
+1127.0,1128.0,48.0,male,27.4,106.0,No,0.0,Yes,northwest,24393.62
+1128.0,1129.0,45.0,male,30.0,84.0,No,0.0,No,northwest,24476.48
+1129.0,1130.0,53.0,female,25.1,104.0,No,0.0,No,southeast,24513.09
+1130.0,1131.0,39.0,female,27.6,104.0,Yes,1.0,Yes,southwest,24520.26
+1131.0,1132.0,43.0,female,27.6,107.0,No,2.0,Yes,northwest,24535.7
+1132.0,1133.0,46.0,male,27.6,84.0,No,0.0,No,southwest,24603.05
+1133.0,1134.0,27.0,female,25.3,93.0,Yes,2.0,Yes,southeast,24667.42
+1134.0,1135.0,36.0,female,29.6,99.0,No,4.0,No,northeast,24671.66
+1135.0,1136.0,23.0,male,24.3,97.0,No,3.0,Yes,northeast,24869.84
+1136.0,1137.0,32.0,female,22.6,84.0,Yes,3.0,Yes,northeast,24873.38
+1137.0,1138.0,58.0,female,38.1,94.0,No,2.0,No,northeast,24915.05
+1138.0,1139.0,20.0,male,28.2,107.0,Yes,3.0,Yes,northwest,24915.22
+1139.0,1140.0,35.0,female,23.2,86.0,No,0.0,No,southeast,25081.77
+1140.0,1141.0,19.0,male,29.8,89.0,No,3.0,Yes,southwest,25309.49
+1141.0,1142.0,29.0,male,32.1,85.0,No,2.0,No,northeast,25333.33
+1142.0,1143.0,26.0,male,25.1,85.0,No,3.0,Yes,southwest,25382.3
+1143.0,1144.0,19.0,male,25.5,85.0,Yes,1.0,No,northeast,25517.11
+1144.0,1145.0,47.0,female,27.4,80.0,No,0.0,No,northeast,25656.58
+1145.0,1146.0,56.0,female,23.7,84.0,No,0.0,Yes,northwest,25678.78
+1146.0,1147.0,32.0,male,26.4,95.0,Yes,3.0,No,southeast,25992.82
+1147.0,1148.0,29.0,female,32.7,128.0,Yes,2.0,No,northwest,26018.95
+1148.0,1149.0,32.0,male,26.7,115.0,Yes,1.0,Yes,northwest,26109.33
+1149.0,1150.0,57.0,female,24.4,104.0,Yes,0.0,Yes,southeast,26125.67
+1150.0,1151.0,55.0,female,27.1,135.0,No,1.0,No,southwest,26140.36
+1151.0,1152.0,52.0,female,24.1,84.0,Yes,1.0,No,southwest,26236.58
+1152.0,1153.0,38.0,female,35.9,128.0,No,1.0,No,northeast,26392.26
+1153.0,1154.0,43.0,male,36.8,126.0,No,2.0,No,northwest,26467.1
+1154.0,1155.0,31.0,male,23.8,126.0,Yes,0.0,Yes,southeast,26926.51
+1155.0,1156.0,28.0,male,31.5,94.0,Yes,1.0,No,southeast,27000.98
+1156.0,1157.0,46.0,female,23.0,137.0,Yes,0.0,Yes,southeast,27037.91
+1157.0,1158.0,30.0,female,24.9,91.0,Yes,0.0,No,southeast,27117.99
+1158.0,1159.0,20.0,male,29.0,101.0,No,0.0,Yes,northeast,27218.44
+1159.0,1160.0,30.0,female,31.9,87.0,Yes,3.0,No,southeast,27322.73
+1160.0,1161.0,36.0,male,31.4,81.0,Yes,0.0,No,southeast,27346.04
+1161.0,1162.0,22.0,male,22.4,137.0,Yes,2.0,No,northeast,27375.9
+1162.0,1163.0,49.0,female,29.8,92.0,Yes,0.0,Yes,southeast,27533.91
+1163.0,1164.0,37.0,male,35.3,81.0,No,1.0,No,southeast,27724.29
+1164.0,1165.0,53.0,female,26.3,119.0,No,0.0,Yes,southeast,27808.73
+1165.0,1166.0,22.0,male,36.1,80.0,No,3.0,No,southwest,27941.29
+1166.0,1167.0,23.0,male,26.7,123.0,Yes,0.0,Yes,northeast,28101.33
+1167.0,1168.0,33.0,female,36.5,118.0,No,1.0,No,southeast,28287.9
+1168.0,1169.0,36.0,female,27.6,118.0,No,1.0,No,northwest,28340.19
+1169.0,1170.0,27.0,male,36.7,130.0,No,1.0,No,northwest,28468.92
+1170.0,1171.0,27.0,female,41.4,95.0,No,1.0,No,northwest,28476.73
+1171.0,1172.0,38.0,male,28.3,93.0,No,1.0,Yes,northwest,28868.66
+1172.0,1173.0,57.0,female,25.8,98.0,Yes,0.0,No,northwest,28923.14
+1173.0,1174.0,30.0,female,27.0,113.0,No,0.0,Yes,northwest,28950.47
+1174.0,1175.0,44.0,female,29.1,139.0,No,0.0,Yes,northwest,29141.36
+1175.0,1176.0,52.0,female,32.3,99.0,Yes,2.0,No,northeast,29186.48
+1176.0,1177.0,47.0,female,26.9,114.0,Yes,0.0,Yes,northwest,29330.98
+1177.0,1178.0,55.0,female,27.7,111.0,Yes,0.0,Yes,northeast,29523.17
+1178.0,1179.0,27.0,male,37.7,95.0,No,3.0,No,northwest,30063.58
+1179.0,1180.0,19.0,male,24.7,97.0,Yes,1.0,No,northwest,30166.62
+1180.0,1181.0,39.0,male,29.8,110.0,No,3.0,Yes,northeast,30184.94
+1181.0,1182.0,32.0,male,28.6,132.0,No,0.0,No,northeast,30260.0
+1182.0,1183.0,36.0,male,25.4,84.0,No,2.0,No,northwest,30284.64
+1183.0,1184.0,30.0,female,29.9,95.0,Yes,3.0,Yes,southeast,30942.19
+1184.0,1185.0,45.0,female,36.9,122.0,Yes,1.0,No,northeast,31620.0
+1185.0,1186.0,18.0,male,29.7,103.0,No,2.0,No,northeast,32108.66
+1186.0,1187.0,46.0,male,30.3,114.0,Yes,0.0,Yes,southeast,32548.34
+1187.0,1188.0,25.0,female,17.8,83.0,No,2.0,Yes,northwest,32734.19
+1188.0,1189.0,37.0,male,28.3,88.0,No,3.0,Yes,northwest,32787.46
+1189.0,1190.0,33.0,female,30.0,81.0,Yes,0.0,Yes,northwest,33307.55
+1190.0,1191.0,29.0,female,37.5,106.0,Yes,2.0,No,northwest,33471.97
+1191.0,1192.0,29.0,male,30.7,94.0,No,0.0,Yes,northeast,33475.82
+1192.0,1193.0,47.0,male,31.7,129.0,Yes,0.0,Yes,northeast,33732.69
+1193.0,1194.0,46.0,male,31.9,82.0,Yes,0.0,Yes,northwest,33750.29
+1194.0,1195.0,48.0,female,30.2,129.0,Yes,0.0,Yes,southwest,33900.65
+1195.0,1196.0,59.0,female,30.4,80.0,No,0.0,Yes,northwest,33907.55
+1196.0,1197.0,36.0,female,31.4,136.0,Yes,0.0,Yes,southwest,34166.27
+1197.0,1198.0,30.0,male,31.1,136.0,No,0.0,Yes,northeast,34254.05
+1198.0,1199.0,22.0,male,31.7,115.0,No,2.0,Yes,southeast,34303.17
+1199.0,1200.0,30.0,female,33.1,93.0,Yes,0.0,Yes,southeast,34439.86
+1200.0,1201.0,40.0,male,32.7,98.0,No,0.0,Yes,southwest,34472.84
+1201.0,1202.0,45.0,male,33.5,81.0,No,0.0,Yes,northeast,34617.84
+1202.0,1203.0,32.0,male,31.7,125.0,No,0.0,Yes,southeast,34672.15
+1203.0,1204.0,26.0,male,34.8,94.0,Yes,0.0,Yes,southwest,34779.62
+1204.0,1205.0,32.0,male,31.1,114.0,Yes,1.0,Yes,southeast,34806.47
+1205.0,1206.0,20.0,male,34.9,124.0,Yes,0.0,Yes,southwest,34828.65
+1206.0,1207.0,46.0,female,31.4,111.0,No,0.0,Yes,southwest,34838.87
+1207.0,1208.0,22.0,male,22.9,80.0,No,0.0,Yes,northeast,35069.37
+1208.0,1209.0,23.0,male,28.5,106.0,No,0.0,Yes,northeast,35147.53
+1209.0,1210.0,32.0,female,26.8,107.0,No,1.0,No,southwest,35160.13
+1210.0,1211.0,25.0,male,30.8,140.0,Yes,0.0,Yes,southwest,35491.64
+1211.0,1212.0,22.0,male,35.6,97.0,Yes,0.0,Yes,southwest,35585.58
+1212.0,1213.0,56.0,female,31.0,129.0,Yes,3.0,Yes,southeast,35595.59
+1213.0,1214.0,30.0,female,32.8,98.0,No,2.0,Yes,southeast,36021.01
+1214.0,1215.0,40.0,male,32.9,87.0,Yes,2.0,Yes,southwest,36085.22
+1215.0,1216.0,22.0,male,33.3,117.0,No,2.0,Yes,southeast,36124.57
+1216.0,1217.0,34.0,female,36.9,131.0,No,0.0,Yes,southeast,36149.48
+1217.0,1218.0,28.0,male,31.7,105.0,Yes,3.0,Yes,northeast,36189.1
+1218.0,1219.0,37.0,male,34.4,126.0,No,0.0,Yes,southwest,36197.7
+1219.0,1220.0,35.0,male,37.0,108.0,Yes,0.0,Yes,northwest,36219.41
+1220.0,1221.0,32.0,male,38.2,135.0,Yes,0.0,Yes,southeast,36307.8
+1221.0,1222.0,31.0,female,34.7,96.0,No,2.0,Yes,southwest,36397.58
+1222.0,1223.0,55.0,female,33.3,86.0,No,4.0,No,southeast,36580.28
+1223.0,1224.0,20.0,male,35.3,86.0,No,0.0,Yes,southwest,36837.47
+1224.0,1225.0,60.0,female,32.5,97.0,No,0.0,Yes,northwest,36898.73
+1225.0,1226.0,50.0,female,34.8,140.0,Yes,2.0,No,southwest,36910.61
+1226.0,1227.0,46.0,male,35.5,140.0,Yes,0.0,Yes,southeast,36950.26
+1227.0,1228.0,48.0,female,33.5,124.0,No,0.0,Yes,southwest,37079.37
+1228.0,1229.0,32.0,female,36.1,97.0,Yes,0.0,Yes,southeast,37133.9
+1229.0,1230.0,41.0,male,37.6,127.0,Yes,1.0,Yes,southeast,37165.16
+1230.0,1231.0,58.0,female,30.8,139.0,No,0.0,Yes,northeast,37270.15
+1231.0,1232.0,31.0,male,35.6,106.0,No,3.0,Yes,northwest,37465.34
+1232.0,1233.0,40.0,male,37.1,119.0,No,2.0,Yes,southeast,37484.45
+1233.0,1234.0,20.0,male,33.6,103.0,No,1.0,Yes,northeast,37607.53
+1234.0,1235.0,47.0,female,31.9,89.0,Yes,1.0,Yes,northeast,37701.88
+1235.0,1236.0,19.0,male,34.4,106.0,No,0.0,Yes,southeast,37742.58
+1236.0,1237.0,48.0,male,27.8,81.0,Yes,0.0,Yes,southwest,37829.72
+1237.0,1238.0,46.0,male,40.2,89.0,No,0.0,Yes,southeast,38126.25
+1238.0,1239.0,42.0,male,26.1,131.0,Yes,1.0,Yes,southeast,38245.59
+1239.0,1240.0,29.0,male,35.8,124.0,No,1.0,Yes,southeast,38282.75
+1240.0,1241.0,34.0,male,39.4,85.0,No,2.0,Yes,southwest,38344.57
+1241.0,1242.0,32.0,male,33.4,112.0,Yes,2.0,Yes,southwest,38415.47
+1242.0,1243.0,27.0,female,36.7,140.0,No,2.0,Yes,northeast,38511.63
+1243.0,1244.0,29.0,male,35.2,85.0,No,1.0,Yes,southeast,38709.18
+1244.0,1245.0,22.0,male,36.3,123.0,No,2.0,Yes,southwest,38711.0
+1245.0,1246.0,28.0,male,34.4,137.0,No,3.0,Yes,northwest,38746.36
+1246.0,1247.0,25.0,female,42.2,131.0,Yes,0.0,Yes,southeast,38792.69
+1247.0,1248.0,26.0,male,30.2,88.0,No,2.0,Yes,southwest,38998.55
+1248.0,1249.0,29.0,male,34.2,101.0,Yes,1.0,Yes,northeast,39047.29
+1249.0,1250.0,20.0,male,32.8,99.0,Yes,1.0,Yes,northeast,39125.33
+1250.0,1251.0,42.0,male,37.8,106.0,No,2.0,Yes,southwest,39241.44
+1251.0,1252.0,21.0,male,31.4,89.0,Yes,1.0,Yes,northeast,39556.49
+1252.0,1253.0,37.0,male,30.8,125.0,Yes,3.0,Yes,northeast,39597.41
+1253.0,1254.0,22.0,male,42.1,119.0,No,0.0,Yes,southeast,39611.76
+1254.0,1255.0,46.0,male,44.9,138.0,Yes,0.0,Yes,southeast,39722.75
+1255.0,1256.0,40.0,female,30.5,93.0,Yes,1.0,Yes,northwest,39725.52
+1256.0,1257.0,27.0,male,30.9,139.0,No,0.0,Yes,southwest,39727.61
+1257.0,1258.0,43.0,male,36.7,139.0,Yes,1.0,Yes,northeast,39774.28
+1258.0,1259.0,38.0,female,34.8,132.0,No,2.0,Yes,southwest,39836.52
+1259.0,1260.0,47.0,male,37.1,114.0,Yes,1.0,Yes,southeast,39871.7
+1260.0,1261.0,50.0,female,34.1,82.0,Yes,3.0,Yes,northwest,39983.43
+1261.0,1262.0,40.0,female,32.8,107.0,Yes,2.0,Yes,northwest,40003.33
+1262.0,1263.0,30.0,male,35.3,89.0,No,2.0,Yes,southwest,40103.89
+1263.0,1264.0,50.0,male,34.1,86.0,No,4.0,Yes,southwest,40182.25
+1264.0,1265.0,21.0,male,35.8,116.0,No,1.0,Yes,southeast,40273.65
+1265.0,1266.0,54.0,female,38.4,85.0,Yes,0.0,Yes,southeast,40419.02
+1266.0,1267.0,43.0,male,30.5,96.0,Yes,3.0,Yes,northwest,40720.55
+1267.0,1268.0,43.0,female,42.8,105.0,Yes,1.0,Yes,northeast,40904.2
+1268.0,1269.0,42.0,female,39.1,131.0,Yes,3.0,Yes,southeast,40932.43
+1269.0,1270.0,45.0,female,32.6,108.0,Yes,3.0,Yes,southeast,40941.29
+1270.0,1271.0,27.0,female,33.1,128.0,No,0.0,Yes,southeast,40974.16
+1271.0,1272.0,35.0,male,35.0,109.0,No,1.0,Yes,northeast,41034.22
+1272.0,1273.0,31.0,male,31.8,95.0,No,0.0,Yes,northeast,41097.16
+1273.0,1274.0,51.0,female,34.6,86.0,No,1.0,Yes,southwest,41661.6
+1274.0,1275.0,27.0,male,36.2,98.0,No,0.0,Yes,southeast,41676.08
+1275.0,1276.0,37.0,male,32.3,115.0,No,1.0,Yes,northeast,41919.1
+1276.0,1277.0,20.0,male,38.4,124.0,Yes,3.0,Yes,southeast,41949.24
+1277.0,1278.0,46.0,male,30.8,120.0,No,1.0,Yes,southeast,41999.52
+1278.0,1279.0,35.0,female,35.5,135.0,No,0.0,Yes,northeast,42111.66
+1279.0,1280.0,37.0,male,45.5,124.0,Yes,2.0,Yes,southeast,42112.24
+1280.0,1281.0,39.0,male,36.0,98.0,Yes,3.0,Yes,southeast,42124.52
+1281.0,1282.0,39.0,male,36.1,109.0,Yes,1.0,Yes,southeast,42211.14
+1282.0,1283.0,42.0,male,30.7,117.0,Yes,0.0,Yes,northeast,42303.69
+1283.0,1284.0,28.0,male,38.1,125.0,Yes,2.0,Yes,southeast,42560.43
+1284.0,1285.0,25.0,male,36.5,104.0,Yes,2.0,Yes,northwest,42760.5
+1285.0,1286.0,49.0,male,34.2,98.0,Yes,2.0,Yes,southwest,42856.84
+1286.0,1287.0,55.0,female,36.6,120.0,No,1.0,Yes,southeast,42969.85
+1287.0,1288.0,32.0,female,39.0,96.0,Yes,0.0,Yes,northwest,42983.46
+1288.0,1289.0,22.0,male,34.1,108.0,No,0.0,Yes,northeast,43254.42
+1289.0,1290.0,52.0,female,31.2,127.0,No,0.0,Yes,northwest,43578.94
+1290.0,1291.0,47.0,male,41.9,140.0,Yes,3.0,Yes,northeast,43753.34
+1291.0,1292.0,36.0,male,31.8,94.0,Yes,2.0,Yes,southeast,43813.87
+1292.0,1293.0,45.0,female,40.4,123.0,No,2.0,Yes,southeast,43896.38
+1293.0,1294.0,28.0,male,33.6,101.0,Yes,0.0,Yes,northwest,43921.18
+1294.0,1295.0,36.0,female,30.2,130.0,No,1.0,Yes,northwest,43943.88
+1295.0,1296.0,50.0,male,38.9,102.0,Yes,2.0,Yes,southeast,44202.65
+1296.0,1297.0,26.0,male,34.2,99.0,No,2.0,Yes,southeast,44260.75
+1297.0,1298.0,45.0,female,38.1,115.0,Yes,0.0,Yes,southeast,44400.41
+1298.0,1299.0,30.0,female,35.2,95.0,Yes,0.0,Yes,southeast,44423.8
+1299.0,1300.0,50.0,male,52.6,110.0,No,1.0,Yes,southeast,44501.4
+1300.0,1301.0,29.0,male,35.5,98.0,No,2.0,Yes,southwest,44585.46
+1301.0,1302.0,60.0,female,35.0,92.0,Yes,2.0,Yes,northeast,44641.2
+1302.0,1303.0,35.0,female,32.5,134.0,Yes,0.0,Yes,southeast,45008.96
+1303.0,1304.0,43.0,male,40.6,93.0,No,2.0,Yes,northwest,45702.02
+1304.0,1305.0,42.0,male,32.0,83.0,Yes,0.0,Yes,northeast,45710.21
+1305.0,1306.0,57.0,female,46.2,96.0,No,0.0,Yes,southeast,45863.21
+1306.0,1307.0,43.0,female,47.6,112.0,Yes,2.0,Yes,southwest,46113.51
+1307.0,1308.0,47.0,male,31.4,137.0,No,3.0,Yes,northwest,46130.53
+1308.0,1309.0,20.0,male,42.4,104.0,No,3.0,Yes,southeast,46151.12
+1309.0,1310.0,47.0,female,43.9,104.0,No,2.0,Yes,southeast,46200.99
+1310.0,1311.0,26.0,female,37.1,95.0,No,3.0,Yes,northeast,46255.11
+1311.0,1312.0,38.0,male,35.9,117.0,Yes,0.0,Yes,southeast,46599.11
+1312.0,1313.0,56.0,female,36.9,110.0,No,3.0,Yes,northwest,46661.44
+1313.0,1314.0,29.0,male,30.9,114.0,Yes,3.0,Yes,northwest,46718.16
+1314.0,1315.0,22.0,male,33.9,97.0,No,0.0,Yes,southeast,46889.26
+1315.0,1316.0,23.0,male,35.1,121.0,No,0.0,Yes,southeast,47055.53
+1316.0,1317.0,41.0,male,41.8,109.0,Yes,2.0,Yes,southeast,47269.85
+1317.0,1318.0,49.0,female,31.3,111.0,No,2.0,Yes,southwest,47291.06
+1318.0,1319.0,27.0,female,32.2,115.0,Yes,2.0,Yes,southwest,47305.31
+1319.0,1320.0,40.0,male,36.3,94.0,Yes,1.0,Yes,southwest,47403.88
+1320.0,1321.0,19.0,male,42.9,104.0,Yes,2.0,Yes,southeast,47462.89
+1321.0,1322.0,26.0,male,37.0,81.0,No,2.0,Yes,northwest,47496.49
+1322.0,1323.0,33.0,female,36.8,117.0,Yes,1.0,Yes,northeast,47896.79
+1323.0,1324.0,49.0,female,33.8,107.0,No,1.0,Yes,southwest,47928.03
+1324.0,1325.0,39.0,male,39.9,115.0,No,0.0,Yes,southwest,48173.36
+1325.0,1326.0,52.0,female,36.4,133.0,Yes,1.0,Yes,northeast,48517.56
+1326.0,1327.0,26.0,male,40.6,113.0,Yes,3.0,Yes,northeast,48549.18
+1327.0,1328.0,49.0,male,40.9,107.0,No,0.0,Yes,southeast,48673.56
+1328.0,1329.0,45.0,male,42.1,117.0,No,1.0,Yes,southeast,48675.52
+1329.0,1330.0,52.0,female,37.7,109.0,Yes,0.0,Yes,southwest,48824.45
+1330.0,1331.0,25.0,female,38.1,111.0,No,0.0,Yes,southeast,48885.14
+1331.0,1332.0,18.0,male,41.1,104.0,No,1.0,Yes,southeast,48970.25
+1332.0,1333.0,26.0,male,37.0,120.0,No,2.0,Yes,southeast,49577.66
+1333.0,1334.0,44.0,male,36.4,127.0,No,1.0,Yes,southwest,51194.56
+1334.0,1335.0,43.0,male,32.8,125.0,No,0.0,Yes,southwest,52590.83
+1335.0,1336.0,44.0,female,35.5,88.0,Yes,0.0,Yes,northwest,55135.4
+1336.0,1337.0,59.0,female,38.1,120.0,No,1.0,Yes,northeast,58571.07
+1337.0,1338.0,30.0,male,34.5,91.0,Yes,3.0,Yes,northwest,60021.4
+1338.0,1339.0,37.0,male,30.4,106.0,No,0.0,Yes,southeast,62592.87
+1339.0,1340.0,30.0,female,47.4,101.0,No,0.0,Yes,southeast,63770.43

models/linreg.py

@@ -0,0 +1,194 @@
+# linreg_model.py
+
+import pandas as pd
+import numpy as np
+import joblib
+
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, r2_score
+
+
+class LinearRegressionInsuranceModel:
+    """
+    A Linear Regression-based insurance claim prediction model with:
+      1. Data loading & cleaning
+      2. Preprocessing (categorical encoding, numerical scaling)
+      3. Model training and evaluation
+      4. Consistent API: preprocessing, predict, postprocessing
+    """
+
+    def __init__(self, csv_path):
+        """
+        Initializes the model by loading data, preprocessing, training, and evaluating.
+        
+        Parameters:
+            csv_path (str): Path to the cleaned insurance data CSV file.
+        """
+        # -----------------------------------------------------
+        # 1. Load and clean the data
+        # -----------------------------------------------------
+        df = pd.read_csv(csv_path)
+        # Drop irrelevant columns and handle missing values
+        df = df.drop(columns=['index', 'PatientID'], errors='ignore').dropna()
+
+        # -----------------------------------------------------
+        # 2. Handle outliers in the target variable 'claim'
+        # -----------------------------------------------------
+        target_column = 'claim'
+        mean_y = df[target_column].mean()
+        std_y = df[target_column].std()
+        threshold_low = mean_y - 3.5 * std_y
+        threshold_high = mean_y + 3.5 * std_y
+        df = df[(df[target_column] >= threshold_low) & (df[target_column] <= threshold_high)]
+
+        # -----------------------------------------------------
+        # 3. Define features and target
+        # -----------------------------------------------------
+        self.features = df.drop(columns=[target_column])
+        self.target = df[target_column].values  # or df['claim'].to_numpy()
+
+        # -----------------------------------------------------
+        # 4. Define preprocessing pipelines
+        # -----------------------------------------------------
+        categorical_columns = ['gender', 'smoker', 'region', 'diabetic']
+        numerical_columns = ['bmi', 'bloodpressure', 'children', 'age']
+
+        # Pipeline for categorical features
+        categorical_pipeline = Pipeline([
+            ('onehot', OneHotEncoder(handle_unknown='ignore'))
+        ])
+
+        # Pipeline for numerical features
+        numerical_pipeline = Pipeline([
+            ('scaler', StandardScaler())
+        ])
+
+        # Combine pipelines using ColumnTransformer
+        self.preprocessor = ColumnTransformer([
+            ('categorical', categorical_pipeline, categorical_columns),
+            ('numerical', numerical_pipeline, numerical_columns)
+        ])
+
+        # -----------------------------------------------------
+        # 5. Fit the preprocessor
+        # -----------------------------------------------------
+        self.preprocessor.fit(self.features)
+
+        # -----------------------------------------------------
+        # 6. Transform the features
+        # -----------------------------------------------------
+        X_preprocessed = self.preprocessor.transform(self.features)
+
+        # -----------------------------------------------------
+        # 7. Train-test split
+        # -----------------------------------------------------
+        X_train, X_test, y_train, y_test = train_test_split(
+            X_preprocessed,
+            self.target,
+            test_size=0.2,
+            random_state=42
+        )
+
+        # -----------------------------------------------------
+        # 8. Initialize and train the Linear Regression model
+        # -----------------------------------------------------
+        self.model = LinearRegression()
+        self.model.fit(X_train, y_train)
+
+        # -----------------------------------------------------
+        # 9. Evaluate the model on the test set
+        # -----------------------------------------------------
+        y_pred = self.model.predict(X_test)
+        mae = mean_absolute_error(y_test, y_pred)
+        r2 = r2_score(y_test, y_pred)
+        self.__scores = {
+            'MAE': mae,
+            'R2': r2
+        }
+
+        print(f"[Linear Regression] Test MAE: {mae:.3f}")
+        print(f"[Linear Regression] Test R^2: {r2:.3f}")
+
+    def preprocessing(self, raw_df):
+        """
+        Preprocesses new raw data by applying the same transformations as training.
+        
+        Parameters:
+            raw_df (pd.DataFrame): New data with the same feature columns as training (excluding 'claim').
+        
+        Returns:
+            np.ndarray: Transformed feature matrix ready for prediction.
+        """
+        return self.preprocessor.transform(raw_df)
+
+    def predict(self, preprocessed_data):
+        """
+        Makes predictions on preprocessed data.
+        
+        Parameters:
+            preprocessed_data (np.ndarray): Transformed feature matrix.
+        
+        Returns:
+            np.ndarray: Predicted claim amounts.
+        """
+        preds = self.model.predict(preprocessed_data)
+        return self.postprocessing(preds)
+
+    def postprocessing(self, preds):
+        """
+        Postprocesses predictions. Currently a pass-through, but can be extended if needed.
+        
+        Parameters:
+            preds (np.ndarray): Raw predictions from the model.
+        
+        Returns:
+            np.ndarray: Final predictions.
+        """
+        return preds
+
+    def get_scores(self):
+        """
+        Retrieves the evaluation metrics.
+        
+        Returns:
+            dict: Dictionary containing MAE and R2.
+        """
+        return self.__scores
+
+    def get_coefficients(self):
+        """
+        Retrieves the model's coefficients.
+        
+        Returns:
+            pd.DataFrame: DataFrame of feature coefficients.
+        """
+        # Extract feature names after preprocessing
+        categorical_features = self.preprocessor.named_transformers_['categorical'].named_steps['onehot'].get_feature_names_out(['gender', 'smoker', 'region', 'diabetic'])
+        numerical_features = self.preprocessor.named_transformers_['numerical'].named_steps['scaler'].get_feature_names_out(['bmi', 'bloodpressure', 'children', 'age'])
+        all_features = np.concatenate([categorical_features, numerical_features])
+
+        # Retrieve coefficients from the Linear Regression model
+        coefficients = pd.DataFrame({
+            'Feature': all_features,
+            'Coefficient': self.model.coef_
+        }).sort_values(by='Coefficient', ascending=False).reset_index(drop=True)
+
+        return coefficients
+
+
+if __name__ == "__main__":
+    # -----------------------------------------------------
+    # 10. Instantiate and train the model
+    # -----------------------------------------------------
+    # Replace the CSV path with your actual path
+    model = LinearRegressionInsuranceModel("cleaned_insurance_data.csv")
+
+    # -----------------------------------------------------
+    # 11. Export the entire model class instance
+    # -----------------------------------------------------
+    joblib.dump(model, "LinearRegressionInsuranceModel.joblib")
+    print("Exported LinearRegressionInsuranceModel to LinearRegressionInsuranceModel.joblib")

models/polyreg.py

@@ -0,0 +1,207 @@
+# polynomial_regression_model.py
+
+import pandas as pd
+import numpy as np
+import joblib
+
+from sklearn.preprocessing import OneHotEncoder, StandardScaler, PolynomialFeatures
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_absolute_error, r2_score
+
+class PolynomialRegressionInsuranceModel:
+    """
+    A Polynomial Regression-based insurance claim prediction model with:
+      1. Data loading & cleaning
+      2. Preprocessing (categorical encoding, numerical scaling, polynomial features)
+      3. Model training and evaluation
+      4. Consistent API: preprocessing, predict, postprocessing
+    """
+
+    def __init__(self, csv_path):
+        """
+        Initializes the model by loading data, preprocessing, training, and evaluating.
+        
+        Parameters:
+            csv_path (str): Path to the cleaned insurance data CSV file.
+        """
+        # -----------------------------------------------------
+        # 1. Load and clean the data
+        # -----------------------------------------------------
+        df = pd.read_csv(csv_path)
+        # Drop irrelevant columns and handle missing values
+        df = df.drop(columns=['index', 'PatientID'], errors='ignore').dropna()
+
+        # -----------------------------------------------------
+        # 2. Handle outliers in the target variable 'claim'
+        # -----------------------------------------------------
+        target_column = 'claim'
+        mean_y = df[target_column].mean()
+        std_y = df[target_column].std()
+        threshold_low = mean_y - 3.5 * std_y
+        threshold_high = mean_y + 3.5 * std_y
+        df = df[(df[target_column] >= threshold_low) & (df[target_column] <= threshold_high)]
+
+        # -----------------------------------------------------
+        # 3. Define features and target
+        # -----------------------------------------------------
+        self.features = df.drop(columns=[target_column])
+        self.target = df[target_column].values  # or df['claim'].to_numpy()
+
+        # -----------------------------------------------------
+        # 4. Define preprocessing pipelines
+        # -----------------------------------------------------
+        categorical_columns = ['gender', 'smoker', 'region', 'diabetic']
+        numerical_columns = ['bmi', 'bloodpressure', 'children', 'age']
+
+        # Pipeline for categorical features
+        categorical_pipeline = Pipeline([
+            ('onehot', OneHotEncoder(handle_unknown='ignore'))
+        ])
+
+        # Pipeline for numerical features
+        numerical_pipeline = Pipeline([
+            ('scaler', StandardScaler())
+        ])
+
+        # Combine pipelines using ColumnTransformer
+        self.preprocessor = ColumnTransformer([
+            ('categorical', categorical_pipeline, categorical_columns),
+            ('numerical', numerical_pipeline, numerical_columns)
+        ])
+
+        # Pipeline for polynomial features
+        self.poly = PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)
+
+        # -----------------------------------------------------
+        # 5. Combine preprocessing and polynomial features
+        # -----------------------------------------------------
+        self.full_preprocessor = Pipeline([
+            ('preprocessor', self.preprocessor),
+            ('poly', self.poly)
+        ])
+
+        # Transform the features
+        X_preprocessed = self.full_preprocessor.fit_transform(self.features)
+
+        # -----------------------------------------------------
+        # 6. Train-test split
+        # -----------------------------------------------------
+        X_train, X_test, y_train, y_test = train_test_split(
+            X_preprocessed,
+            self.target,
+            test_size=0.2,
+            random_state=42
+        )
+
+        # -----------------------------------------------------
+        # 7. Initialize and train the Linear Regression model
+        # -----------------------------------------------------
+        self.model = LinearRegression()
+
+        # Perform 5-fold cross-validation on training data
+        cv_scores = cross_val_score(self.model, X_train, y_train, cv=5, scoring='r2')
+        print(f"[Polynomial Regression] Cross-Validation R2 Scores: {cv_scores}")
+        print(f"[Polynomial Regression] Average CV R2 Score: {cv_scores.mean():.3f}")
+
+        # Train the model on the full training data
+        self.model.fit(X_train, y_train)
+
+        # -----------------------------------------------------
+        # 8. Evaluate the model on the test set
+        # -----------------------------------------------------
+        y_pred = self.model.predict(X_test)
+        mae = mean_absolute_error(y_test, y_pred)
+        r2 = r2_score(y_test, y_pred)
+        self.__scores = {
+            'MAE': mae,
+            'R2': r2,
+            'Cross-Validation R2 Scores': cv_scores,
+            'Average CV R2': cv_scores.mean()
+        }
+
+        print(f"[Polynomial Regression] Test MAE: {mae:.3f}")
+        print(f"[Polynomial Regression] Test R^2: {r2:.3f}")
+
+    def preprocessing(self, raw_df):
+        """
+        Preprocesses new raw data by applying the same transformations as training.
+        
+        Parameters:
+            raw_df (pd.DataFrame): New data with the same feature columns as training (excluding 'claim').
+        
+        Returns:
+            np.ndarray: Transformed feature matrix ready for prediction.
+        """
+        return self.full_preprocessor.transform(raw_df)
+
+    def predict(self, preprocessed_data):
+        """
+        Makes predictions on preprocessed data.
+        
+        Parameters:
+            preprocessed_data (np.ndarray): Transformed feature matrix.
+        
+        Returns:
+            np.ndarray: Predicted claim amounts.
+        """
+        preds = self.model.predict(preprocessed_data)
+        return self.postprocessing(preds)
+
+    def postprocessing(self, preds):
+        """
+        Postprocesses predictions. Currently a pass-through, but can be extended.
+        
+        Parameters:
+            preds (np.ndarray): Raw predictions from the model.
+        
+        Returns:
+            np.ndarray: Final predictions.
+        """
+        return preds
+
+    def get_scores(self):
+        """
+        Retrieves the evaluation metrics.
+        
+        Returns:
+            dict: Dictionary containing MAE, R2, and cross-validation scores.
+        """
+        return self.__scores
+
+    def get_coefficients(self):
+        """
+        Retrieves the model's coefficients.
+        
+        Returns:
+            pd.DataFrame: DataFrame of feature coefficients.
+        """
+        # Extract feature names after preprocessing and polynomial transformation
+        categorical_features = self.preprocessor.named_transformers_['categorical'].named_steps['onehot'].get_feature_names_out(['gender', 'smoker', 'region', 'diabetic'])
+        numerical_features = self.preprocessor.named_transformers_['numerical'].named_steps['scaler'].get_feature_names_out(['bmi', 'bloodpressure', 'children', 'age'])
+        all_features = np.concatenate([categorical_features, numerical_features])
+
+        # Get feature names after polynomial transformation
+        poly_feature_names = self.poly.get_feature_names_out(all_features)
+
+        # Create DataFrame of coefficients
+        coefficients = pd.DataFrame({
+            'Feature': poly_feature_names,
+            'Coefficient': self.model.coef_
+        }).sort_values(by='Coefficient', ascending=False)
+
+        return coefficients
+
+if __name__ == "__main__":
+    # -----------------------------------------------------
+    # 9. Instantiate and train the model
+    # -----------------------------------------------------
+    model = PolynomialRegressionInsuranceModel("cleaned_insurance_data.csv")
+
+    # -----------------------------------------------------
+    # 10. Export the entire model class instance
+    # -----------------------------------------------------
+    joblib.dump(model, "PolynomialRegressionInsuranceModel.joblib")
+    print("Exported PolynomialRegressionInsuranceModel to PolynomialRegressionInsuranceModel.joblib")