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22_23-J 52
Commit 3ec0eded authored by Dinuka Nawanjana's avatar Dinuka Nawanjana
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project/DATASETS/Kidney/kidney.csv 0 → 100644
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Bp,Sg,Al,Su,Rbc,Bu,Sc,Sod,Pot,Hemo,Wbcc,Rbcc,Htn,Class
80.0,1.02,1.0,0.0,1.0,36.0,1.2,137.53,4.63,15.4,7800.0,5.2,1.0,1
50.0,1.02,4.0,0.0,1.0,18.0,0.8,137.53,4.63,11.3,6000.0,4.71,0.0,1
80.0,1.01,2.0,3.0,1.0,53.0,1.8,137.53,4.63,9.6,7500.0,4.71,0.0,1
70.0,1.005,4.0,0.0,1.0,56.0,3.8,111.0,2.5,11.2,6700.0,3.9,1.0,1
80.0,1.01,2.0,0.0,1.0,26.0,1.4,137.53,4.63,11.6,7300.0,4.6,0.0,1
90.0,1.015,3.0,0.0,1.0,25.0,1.1,142.0,3.2,12.2,7800.0,4.4,1.0,1
70.0,1.01,0.0,0.0,1.0,54.0,24.0,104.0,4.0,12.4,8406.0,4.71,0.0,1
76.0,1.015,2.0,4.0,1.0,31.0,1.1,137.53,4.63,12.4,6900.0,5.0,0.0,1
100.0,1.015,3.0,0.0,1.0,60.0,1.9,137.53,4.63,10.8,9600.0,4.0,1.0,1
90.0,1.02,2.0,0.0,0.0,107.0,7.2,114.0,3.7,9.5,12100.0,3.7,1.0,1
60.0,1.01,2.0,4.0,1.0,55.0,4.0,137.53,4.63,9.4,8406.0,4.71,1.0,1
70.0,1.01,3.0,0.0,0.0,60.0,2.7,131.0,4.2,10.8,4500.0,3.8,1.0,1
70.0,1.015,3.0,1.0,1.0,72.0,2.1,138.0,5.8,9.7,12200.0,3.4,1.0,1
70.0,1.02,1.0,0.0,1.0,86.0,4.6,135.0,3.4,9.8,8406.0,4.71,1.0,1
80.0,1.01,3.0,2.0,1.0,90.0,4.1,130.0,6.4,5.6,11000.0,2.6,1.0,1
80.0,1.015,3.0,0.0,1.0,162.0,9.6,141.0,4.9,7.6,3800.0,2.8,1.0,1
70.0,1.015,2.0,0.0,1.0,46.0,2.2,138.0,4.1,12.6,8406.0,4.71,0.0,1
80.0,1.02,1.0,0.0,1.0,87.0,5.2,139.0,3.7,12.1,8406.0,4.71,1.0,1
100.0,1.025,0.0,3.0,1.0,27.0,1.3,135.0,4.3,12.7,11400.0,4.3,1.0,1
60.0,1.015,1.0,0.0,1.0,31.0,1.6,137.53,4.63,10.3,5300.0,3.7,1.0,1
80.0,1.015,2.0,0.0,0.0,148.0,3.9,135.0,5.2,7.7,9200.0,3.2,1.0,1
90.0,1.02,1.0,0.0,1.0,180.0,76.0,4.5,4.63,10.9,6200.0,3.6,1.0,1
80.0,1.025,4.0,0.0,1.0,163.0,7.7,136.0,3.8,9.8,6900.0,3.4,1.0,1
70.0,1.01,0.0,0.0,1.0,57.0,3.07,137.53,4.63,12.53,8406.0,4.71,0.0,1
100.0,1.015,4.0,0.0,1.0,50.0,1.4,129.0,4.0,11.1,8300.0,4.6,1.0,1
60.0,1.025,0.0,0.0,1.0,75.0,1.9,141.0,5.2,9.9,8400.0,3.7,1.0,1
80.0,1.015,0.0,0.0,1.0,45.0,2.4,140.0,3.4,11.6,10300.0,4.0,1.0,1
70.0,1.01,3.0,4.0,1.0,87.0,2.7,130.0,4.0,12.5,9600.0,4.1,1.0,1
70.0,1.02,1.0,3.0,1.0,31.0,1.4,137.53,4.63,12.53,8406.0,4.71,0.0,1
70.0,1.005,1.0,0.0,0.0,28.0,1.4,137.53,4.63,12.9,8406.0,4.71,0.0,1
70.0,1.02,1.0,0.0,1.0,155.0,7.3,132.0,4.9,12.53,8406.0,4.71,1.0,1
90.0,1.015,3.0,0.0,1.0,33.0,1.5,141.0,4.6,10.1,7800.0,4.0,0.0,1
90.0,1.01,1.0,1.0,1.0,39.0,1.5,133.0,4.9,11.3,9600.0,4.0,1.0,1
100.0,1.02,2.0,0.0,0.0,55.0,2.5,137.53,4.63,10.1,8406.0,4.71,1.0,1
70.0,1.01,1.0,0.0,1.0,153.0,5.2,137.53,4.63,12.53,8406.0,4.71,0.0,1
90.0,1.02,2.0,1.0,0.0,39.0,2.0,137.53,4.63,12.0,9800.0,4.9,1.0,1
70.0,1.015,1.0,0.0,1.0,29.0,1.8,133.0,3.9,10.3,8406.0,4.71,1.0,1
80.0,1.02,1.0,0.0,1.0,65.0,3.4,141.0,4.7,9.7,6900.0,2.5,1.0,1
80.0,1.02,3.0,0.0,0.0,103.0,4.1,132.0,5.9,12.5,8406.0,4.71,1.0,1
80.0,1.01,2.0,2.0,1.0,70.0,3.4,136.0,4.2,13.0,9800.0,4.2,1.0,1
90.0,1.01,2.0,0.0,1.0,80.0,2.1,137.53,4.63,11.1,9100.0,4.1,1.0,1
70.0,1.01,0.0,0.0,1.0,20.0,0.7,137.53,4.63,12.53,8406.0,4.71,0.0,1
100.0,1.01,0.0,0.0,1.0,29.0,1.0,139.0,4.2,9.7,9200.0,4.5,1.0,1
80.0,1.01,1.0,0.0,0.0,202.0,10.8,134.0,3.4,7.9,7900.0,3.1,0.0,1
80.0,1.01,3.0,0.0,0.0,77.0,6.3,134.0,4.8,9.7,8406.0,4.71,1.0,1
80.0,1.02,3.0,0.0,1.0,89.0,5.9,130.0,4.9,9.3,8406.0,4.71,1.0,1
70.0,1.015,0.0,0.0,1.0,24.0,1.2,142.0,4.2,12.4,6400.0,4.7,0.0,1
80.0,1.01,3.0,0.0,1.0,17.0,0.8,137.53,4.63,15.0,8600.0,4.71,0.0,1
70.0,1.005,0.0,0.0,1.0,32.0,0.9,125.0,4.0,10.0,18900.0,3.5,1.0,1
70.0,1.01,2.0,0.0,1.0,72.0,3.0,137.53,4.63,9.7,21600.0,3.5,1.0,1
60.0,1.02,1.0,0.0,1.0,114.0,3.25,142.0,4.3,8.6,11000.0,3.8,1.0,1
100.0,1.015,3.0,0.0,1.0,66.0,1.6,136.0,4.4,10.3,8406.0,4.71,1.0,1
90.0,1.015,0.0,0.0,1.0,38.0,2.2,137.53,4.63,10.9,4300.0,3.7,0.0,1
80.0,1.015,0.0,5.0,1.0,24.0,1.0,137.53,4.63,13.6,8500.0,4.7,1.0,1
80.0,1.01,2.0,2.0,1.0,57.0,3.4,136.0,4.2,13.0,9800.0,4.2,1.0,1
80.0,1.005,3.0,0.0,0.0,57.0,3.07,137.53,4.63,9.5,8406.0,4.71,0.0,1
70.0,1.015,3.0,4.0,1.0,164.0,9.7,131.0,4.4,10.2,11300.0,3.4,1.0,1
90.0,1.02,1.0,0.0,1.0,155.0,7.3,132.0,4.9,12.53,8406.0,4.71,1.0,1
80.0,1.02,2.0,0.0,0.0,142.0,4.6,138.0,5.8,10.5,7200.0,4.3,1.0,1
100.0,1.02,1.0,0.0,1.0,96.0,6.4,137.53,4.63,6.6,8406.0,4.71,1.0,1
90.0,1.02,1.0,0.0,1.0,66.0,3.2,138.0,6.6,12.53,8406.0,4.71,1.0,1
80.0,1.01,1.0,3.0,1.0,391.0,32.0,163.0,39.0,12.53,8406.0,4.71,0.0,1
60.0,1.02,3.0,0.0,1.0,15.0,0.6,138.0,4.0,11.0,7700.0,3.8,1.0,1
70.0,1.015,1.0,0.0,0.0,111.0,6.1,131.0,3.7,7.5,8406.0,4.71,0.0,1
80.0,1.01,0.0,0.0,1.0,57.0,3.07,137.53,4.63,9.8,8406.0,4.71,0.0,1
90.0,1.01,1.0,0.0,1.0,20.0,1.1,137.53,4.63,15.0,8406.0,4.71,0.0,1
70.0,1.02,2.0,0.0,0.0,55.0,1.6,131.0,4.8,12.53,8406.0,4.71,1.0,1
80.0,1.02,3.0,0.0,1.0,57.0,3.07,137.53,4.63,12.53,8406.0,4.71,0.0,1
70.0,1.01,2.0,0.0,1.0,73.0,3.3,137.53,4.63,10.9,8406.0,4.71,0.0,1
70.0,1.015,0.0,4.0,1.0,20.0,1.1,137.53,4.63,15.6,6900.0,6.0,0.0,1
80.0,1.015,0.0,4.0,1.0,19.0,0.7,137.0,4.4,15.2,8300.0,5.2,1.0,1
60.0,1.01,1.0,0.0,1.0,92.0,3.3,141.0,4.0,9.8,14600.0,3.2,1.0,1
90.0,1.01,3.0,3.0,1.0,35.0,1.3,137.53,4.63,10.3,8406.0,4.71,1.0,1
100.0,1.015,2.0,0.0,0.0,107.0,6.7,132.0,4.4,4.8,6300.0,4.71,1.0,1
90.0,1.015,2.0,0.0,0.0,107.0,6.7,131.0,4.8,9.1,6400.0,3.4,1.0,1
76.0,1.015,1.0,0.0,1.0,16.0,0.7,138.0,3.2,8.1,8406.0,4.71,0.0,1
80.0,1.005,4.0,0.0,0.0,139.0,8.5,132.0,5.5,10.3,6200.0,4.0,0.0,1
70.0,1.01,1.0,0.0,1.0,48.0,3.2,137.0,5.0,11.9,7100.0,3.7,1.0,1
80.0,1.02,1.0,0.0,1.0,85.0,3.2,141.0,3.5,10.1,8406.0,4.71,1.0,1
80.0,1.01,1.0,0.0,1.0,55.0,1.8,137.53,4.63,13.5,11800.0,5.0,1.0,1
80.0,1.01,0.0,0.0,1.0,98.0,2.8,133.0,5.0,10.8,9400.0,3.8,1.0,1
90.0,1.02,1.0,0.0,1.0,45.0,2.4,128.0,4.4,8.3,5500.0,3.7,1.0,1
70.0,1.02,1.0,0.0,1.0,77.0,1.9,140.0,3.9,12.53,8406.0,4.71,1.0,1
70.0,1.015,1.0,0.0,1.0,19.0,1.0,134.0,3.6,12.53,8406.0,4.71,1.0,1
70.0,1.01,3.0,0.0,1.0,186.0,15.0,135.0,7.6,7.1,3800.0,2.1,1.0,1
70.0,1.015,2.0,0.0,1.0,46.0,1.5,137.53,4.63,9.9,8406.0,4.71,0.0,1
80.0,1.02,1.0,0.0,1.0,37.0,1.9,137.53,4.63,12.53,8406.0,4.71,0.0,1
100.0,1.005,1.0,0.0,1.0,47.0,2.9,137.53,4.63,11.1,5800.0,5.0,1.0,1
110.0,1.01,4.0,0.0,1.0,52.0,2.2,137.53,4.63,12.53,13200.0,4.7,1.0,1
70.0,1.02,0.0,0.0,1.0,32.0,1.4,139.0,4.7,12.53,8406.0,4.71,0.0,1
100.0,1.01,2.0,2.0,1.0,35.0,3.2,143.0,3.5,13.0,9800.0,4.2,1.0,1
70.0,1.015,4.0,1.0,0.0,26.0,1.7,136.0,3.8,16.1,12500.0,5.6,0.0,1
70.0,1.01,3.0,0.0,1.0,82.0,3.6,133.0,4.4,10.4,5600.0,3.6,1.0,1
100.0,1.01,3.0,2.0,0.0,90.0,5.6,140.0,2.9,9.2,7000.0,3.2,1.0,1
70.0,1.01,0.0,0.0,1.0,66.0,1.6,137.0,4.5,11.6,11900.0,3.9,0.0,1
90.0,1.015,1.0,0.0,1.0,25.0,1.1,131.0,3.7,12.53,8406.0,4.71,1.0,1
80.0,1.01,1.0,1.0,1.0,32.0,2.7,137.53,4.63,11.2,8406.0,4.71,0.0,1
60.0,1.015,1.0,0.0,1.0,51.0,2.2,132.0,3.8,10.0,9100.0,4.0,1.0,1
140.0,1.02,1.0,0.0,1.0,106.0,6.5,135.0,4.3,6.2,5800.0,2.3,1.0,1
180.0,1.02,0.0,4.0,1.0,24.0,1.2,139.0,3.9,11.2,10400.0,4.2,1.0,1
70.0,1.015,4.0,0.0,0.0,22.0,0.9,133.0,3.8,12.53,8406.0,4.71,0.0,1
90.0,1.015,2.0,0.0,1.0,80.0,4.4,139.0,5.7,11.3,10700.0,3.9,0.0,1
60.0,1.01,0.0,0.0,1.0,32.0,2.1,141.0,4.2,13.9,7000.0,4.71,0.0,1
70.0,1.015,2.0,0.0,1.0,217.0,10.2,137.53,4.63,10.2,12700.0,4.2,1.0,1
90.0,1.02,1.0,0.0,1.0,88.0,2.0,137.53,4.63,12.53,8406.0,4.71,1.0,1
80.0,1.015,0.0,0.0,1.0,32.0,11.5,139.0,4.0,14.1,6800.0,5.2,0.0,1
90.0,1.02,1.0,0.0,1.0,118.0,6.1,127.0,4.4,6.0,6500.0,4.71,1.0,1
100.0,1.015,1.0,4.0,1.0,53.0,2.8,139.0,4.5,11.2,13600.0,4.4,1.0,1
80.0,1.015,0.0,0.0,1.0,15.0,1.0,141.0,4.2,11.8,10200.0,4.2,0.0,1
70.0,1.02,1.0,0.0,1.0,50.1,1.9,137.53,4.63,11.7,8406.0,4.71,0.0,1
90.0,1.015,0.0,0.0,1.0,19.0,2.0,142.0,3.8,11.7,11400.0,4.7,0.0,1
80.0,1.01,3.0,3.0,1.0,71.0,4.4,128.0,5.4,10.0,9000.0,3.9,1.0,1
60.0,1.015,3.0,0.0,0.0,34.0,1.2,137.53,4.63,10.8,8406.0,4.71,0.0,1
90.0,1.015,0.0,2.0,1.0,57.0,3.07,137.53,4.63,12.53,9800.0,4.71,0.0,1
60.0,1.015,3.0,0.0,0.0,51.0,1.8,137.53,4.63,12.1,10300.0,4.71,0.0,1
80.0,1.01,0.0,0.0,1.0,28.0,0.9,137.53,4.63,12.4,5600.0,4.3,0.0,1
70.0,1.015,4.0,0.0,0.0,16.0,0.5,137.53,4.63,12.53,8406.0,4.71,0.0,1
70.0,1.02,0.0,0.0,1.0,36.0,1.3,139.0,3.7,12.5,9800.0,4.4,0.0,1
70.0,1.01,3.0,0.0,1.0,25.0,1.2,137.53,4.63,11.4,8406.0,4.71,0.0,1
70.0,1.01,0.0,0.0,1.0,27.0,1.2,137.53,4.63,12.53,8406.0,4.71,0.0,1
90.0,1.025,1.0,3.0,1.0,40.0,2.2,137.0,5.3,12.6,8406.0,4.71,0.0,1
60.0,1.02,3.0,0.0,1.0,21.0,1.3,137.0,3.4,15.0,8406.0,4.71,1.0,1
70.0,1.02,1.0,0.0,1.0,219.0,12.2,130.0,3.8,6.0,8406.0,4.71,1.0,1
80.0,1.015,2.0,3.0,1.0,30.0,1.1,137.53,4.63,14.0,14900.0,4.71,0.0,1
100.0,1.015,0.0,0.0,1.0,98.0,2.5,137.53,4.63,9.1,5500.0,3.6,1.0,1
90.0,1.02,1.0,0.0,1.0,36.0,2.5,131.0,4.3,12.53,8406.0,4.71,1.0,1
90.0,1.015,0.0,0.0,1.0,125.0,4.0,136.0,4.6,12.0,8200.0,4.5,1.0,1
60.0,1.015,4.0,0.0,1.0,125.0,5.3,136.0,4.9,11.4,15200.0,4.3,1.0,1
90.0,1.015,4.0,3.0,1.0,166.0,5.6,133.0,47.0,8.1,5000.0,2.9,1.0,1
70.0,1.025,1.0,0.0,1.0,49.0,1.4,135.0,4.7,11.1,8406.0,4.71,1.0,1
90.0,1.01,2.0,0.0,1.0,208.0,9.2,134.0,4.8,8.2,16300.0,2.7,0.0,1
50.0,1.01,0.0,0.0,1.0,25.0,0.6,137.53,4.63,11.8,12400.0,4.71,0.0,1
76.0,1.02,1.0,0.0,1.0,176.0,13.8,136.0,4.5,8.6,13200.0,2.7,1.0,1
100.0,1.015,4.0,0.0,1.0,125.0,5.3,136.0,4.9,12.0,8400.0,8.0,1.0,1
100.0,1.01,1.0,0.0,1.0,57.0,16.9,138.0,5.2,10.8,10200.0,3.8,0.0,1
80.0,1.015,0.0,2.0,1.0,24.0,1.3,140.0,4.0,13.2,8406.0,4.71,0.0,1
90.0,1.02,1.0,0.0,1.0,68.0,2.8,146.0,6.3,9.3,8406.0,4.71,1.0,1
60.0,1.01,2.0,0.0,1.0,86.0,4.0,134.0,5.1,10.0,9200.0,4.71,1.0,1
76.0,1.01,1.0,0.0,1.0,51.0,1.6,142.0,3.5,12.53,8406.0,4.71,0.0,1
70.0,1.015,2.0,0.0,1.0,68.0,2.8,132.0,4.1,11.1,8406.0,4.71,1.0,1
70.0,1.01,0.0,4.0,1.0,40.0,1.2,142.0,5.6,12.53,8406.0,4.71,0.0,1
70.0,1.01,1.0,0.0,1.0,106.0,6.0,137.0,4.9,6.1,6500.0,4.71,1.0,1
90.0,1.02,1.0,0.0,1.0,145.0,7.1,135.0,5.3,12.53,8406.0,4.71,0.0,1
80.0,1.015,1.0,4.0,0.0,165.0,18.0,135.0,4.7,12.53,8406.0,4.71,0.0,1
90.0,1.01,2.0,0.0,0.0,53.0,2.3,136.0,5.2,11.1,10500.0,4.1,0.0,1
90.0,1.015,5.0,0.0,0.0,322.0,13.0,126.0,4.8,8.0,4200.0,3.3,1.0,1
100.0,1.01,1.0,3.0,0.0,23.0,1.0,139.0,4.0,12.53,8406.0,4.71,0.0,1
60.0,1.01,3.0,1.0,1.0,36.0,1.7,130.0,3.0,7.9,15200.0,3.0,1.0,1
60.0,1.02,1.0,0.0,1.0,26.0,48.1,137.53,4.63,12.53,8406.0,4.71,1.0,1
70.0,1.02,1.0,0.0,0.0,29.0,1.0,137.53,4.63,10.5,8406.0,4.71,1.0,1
60.0,1.025,3.0,0.0,1.0,27.0,0.9,137.53,4.63,12.3,6700.0,4.71,0.0,1
90.0,1.02,1.0,0.0,1.0,46.0,1.7,141.0,5.5,9.6,8406.0,4.71,1.0,1
70.0,1.01,0.0,0.0,1.0,20.0,0.8,133.0,3.5,10.9,8406.0,4.71,0.0,1
90.0,1.01,2.0,1.0,0.0,235.0,14.2,132.0,3.4,8.3,14600.0,2.9,1.0,1
90.0,1.005,4.0,3.0,0.0,132.0,16.4,140.0,4.2,8.4,8406.0,3.0,1.0,1
70.0,1.02,3.0,0.0,0.0,40.0,1.8,137.53,4.63,11.1,4700.0,4.71,0.0,1
90.0,1.015,2.0,0.0,1.0,76.0,3.3,137.53,4.63,12.53,8406.0,4.71,0.0,1
70.0,1.025,3.0,0.0,1.0,42.0,1.7,136.0,4.7,12.6,7900.0,3.9,1.0,1
60.0,1.02,3.0,2.0,1.0,48.0,1.5,132.0,4.0,10.9,8406.0,4.71,1.0,1
80.0,1.01,1.0,0.0,0.0,35.0,1.3,122.0,3.5,10.4,10900.0,4.3,0.0,1
60.0,1.02,1.0,0.0,1.0,39.0,2.1,147.0,4.2,10.9,9400.0,2.4,1.0,1
76.0,1.015,3.0,0.0,0.0,57.0,3.07,137.53,4.63,14.3,10200.0,4.8,1.0,1
70.0,1.02,1.0,0.0,1.0,34.0,1.5,124.0,4.1,9.8,6000.0,4.71,0.0,1
80.0,1.01,0.0,0.0,1.0,40.0,2.0,140.0,4.1,9.0,8100.0,3.2,1.0,1
76.0,1.015,0.0,0.0,1.0,15.0,0.8,137.0,4.2,14.3,9500.0,5.4,0.0,1
80.0,1.02,0.0,2.0,1.0,57.0,3.07,137.53,4.63,12.53,8406.0,4.71,0.0,1
60.0,1.02,1.0,0.0,1.0,44.0,3.9,127.0,4.3,12.53,8406.0,4.71,0.0,1
70.0,1.02,0.0,0.0,0.0,19.0,0.9,137.53,4.63,12.7,2200.0,4.71,0.0,1
70.0,1.015,4.0,4.0,1.0,28.0,1.5,137.53,4.63,11.0,6700.0,4.71,1.0,1
70.0,1.01,0.0,2.0,1.0,68.0,2.8,137.53,4.63,8.7,8406.0,4.71,1.0,1
70.0,1.015,2.0,5.0,1.0,41.0,1.7,131.0,3.9,12.5,9600.0,4.4,1.0,1
70.0,1.02,3.0,0.0,1.0,60.0,2.6,115.0,5.7,8.7,12800.0,3.1,1.0,1
80.0,1.01,1.0,2.0,1.0,113.0,2.9,130.0,2.5,10.6,12800.0,4.9,0.0,1
70.0,1.015,1.0,0.0,0.0,1.5,7.3,145.0,2.8,13.1,11200.0,4.71,0.0,1
70.0,1.02,1.0,0.0,1.0,146.0,7.5,141.0,4.7,11.0,8600.0,4.6,0.0,1
50.0,1.01,0.0,0.0,1.0,58.0,2.2,113.0,3.0,12.53,4200.0,3.4,1.0,1
90.0,1.01,4.0,0.0,1.0,40.0,1.7,125.0,3.5,8.3,12400.0,3.9,0.0,1
80.0,1.015,2.0,1.0,1.0,133.0,2.5,137.53,4.63,13.2,8406.0,4.71,0.0,1
90.0,1.02,2.0,0.0,0.0,153.0,2.7,139.0,4.3,9.8,9800.0,4.71,0.0,1
90.0,1.01,2.0,0.0,1.0,53.0,2.3,137.53,4.63,11.9,8406.0,4.71,0.0,1
90.0,1.01,1.0,4.0,0.0,56.0,1.9,137.53,4.63,10.3,8406.0,4.71,0.0,1
70.0,1.025,2.0,0.0,1.0,52.0,2.2,136.0,3.8,10.0,19100.0,3.7,0.0,1
80.0,1.02,0.0,0.0,1.0,23.0,0.8,140.0,4.1,11.3,8406.0,4.71,0.0,1
70.0,1.015,0.0,0.0,1.0,106.0,6.5,135.0,4.3,12.53,8406.0,4.71,0.0,1
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76.0,1.02,1.0,0.0,1.0,23.0,0.6,138.0,4.4,12.0,8406.0,4.71,0.0,1
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76.0,1.01,2.0,0.0,1.0,22.0,0.7,137.53,4.63,10.7,12300.0,4.71,0.0,1
76.0,1.02,1.0,0.0,1.0,66.0,2.5,142.0,3.6,12.2,8406.0,4.71,0.0,1
60.0,1.01,4.0,1.0,0.0,58.0,4.3,137.0,5.4,9.5,7500.0,3.4,1.0,1
60.0,1.01,4.0,0.0,0.0,67.0,1.0,135.0,4.9,9.9,16700.0,4.8,0.0,1
70.0,1.01,3.0,0.0,1.0,115.0,6.0,134.0,2.7,9.1,9200.0,3.4,1.0,1
110.0,1.015,0.0,0.0,1.0,16.0,0.9,137.53,4.63,12.53,8406.0,4.71,0.0,1
90.0,1.025,1.0,0.0,0.0,223.0,18.1,113.0,6.5,5.5,2600.0,2.8,1.0,1
70.0,1.01,2.0,0.0,1.0,49.0,1.2,137.53,4.63,12.53,8406.0,4.71,1.0,1
90.0,1.02,2.0,1.0,0.0,98.6,3.3,138.0,3.9,5.8,8406.0,4.71,1.0,1
100.0,1.01,3.0,0.0,0.0,158.0,11.8,122.0,3.2,8.1,9600.0,3.5,1.0,1
80.0,1.02,1.0,0.0,1.0,111.0,9.3,124.0,5.3,6.8,4300.0,3.0,1.0,1
100.0,1.02,4.0,2.0,1.0,40.0,3.2,137.0,4.7,11.2,26400.0,3.9,1.0,1
80.0,1.015,0.0,0.0,1.0,37.0,1.5,140.0,5.2,8.8,10700.0,3.2,1.0,1
90.0,1.025,1.0,0.0,1.0,89.0,3.0,140.0,4.1,12.0,7900.0,3.9,1.0,1
70.0,1.02,1.0,0.0,1.0,94.0,7.3,137.0,4.3,7.9,8406.0,4.71,1.0,1
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90.0,1.01,4.0,2.0,1.0,82.0,13.5,145.0,6.3,8.8,8406.0,4.71,1.0,1
70.0,1.02,1.0,0.0,1.0,28.0,2.1,137.53,4.63,12.6,8406.0,4.71,1.0,1
70.0,1.01,1.0,0.0,1.0,96.0,3.9,135.0,4.0,13.8,8406.0,4.71,1.0,1
70.0,1.01,0.0,0.0,1.0,50.0,2.2,137.53,4.63,12.0,10400.0,4.6,1.0,1
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100.0,1.015,4.0,2.0,1.0,132.0,12.8,135.0,5.7,7.3,9800.0,3.9,1.0,1
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70.0,1.015,3.0,4.0,1.0,150.0,11.9,132.0,5.6,10.9,8800.0,3.4,1.0,1
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80.0,1.015,0.0,0.0,1.0,30.0,1.0,137.53,4.63,13.7,4900.0,5.2,0.0,1
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100.0,1.01,1.0,0.0,1.0,61.0,1.8,141.0,4.4,12.2,10500.0,4.3,0.0,1
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90.0,1.01,0.0,3.0,1.0,30.0,1.3,136.0,4.1,13.0,9200.0,4.6,1.0,1
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100.0,1.015,4.0,2.0,0.0,54.0,7.2,140.0,4.6,7.9,7500.0,3.4,1.0,1
80.0,1.015,0.0,0.0,1.0,48.0,1.6,137.53,4.63,11.3,7200.0,3.8,1.0,1
70.0,1.02,1.0,0.0,1.0,52.0,2.5,137.53,4.63,12.53,8406.0,4.71,1.0,1
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80.0,1.02,1.0,0.0,1.0,66.0,2.0,136.0,5.4,9.1,8406.0,4.71,1.0,1
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100.0,1.02,1.0,0.0,1.0,241.0,13.4,127.0,4.8,9.4,8406.0,4.71,1.0,1
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70.0,1.01,4.0,3.0,1.0,96.0,6.3,120.0,3.9,9.4,11500.0,3.3,1.0,1
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70.0,1.02,0.0,0.0,1.0,19.0,0.7,135.0,3.9,16.0,5300.0,5.9,0.0,0
70.0,1.02,0.0,0.0,1.0,18.0,0.8,145.0,5.0,14.7,9800.0,6.0,0.0,0
70.0,1.025,0.0,0.0,1.0,22.0,0.6,137.0,3.8,13.4,8406.0,4.71,0.0,0
70.0,1.025,0.0,0.0,1.0,46.0,1.2,135.0,4.9,15.9,11000.0,5.1,0.37,0
70.0,1.02,0.0,0.0,1.0,32.0,0.9,143.0,4.7,16.6,7100.0,5.3,0.0,0
70.0,1.02,0.0,0.0,1.0,28.0,0.6,146.0,3.5,14.8,8400.0,5.9,0.0,0
80.0,1.025,0.0,0.0,1.0,44.0,1.0,140.0,4.9,14.9,7000.0,5.7,0.0,0
80.0,1.02,0.0,0.0,1.0,42.0,0.5,139.0,5.0,16.7,10200.0,5.0,0.0,0
76.0,1.02,0.0,0.0,1.0,19.0,1.2,150.0,4.8,14.9,4700.0,5.4,0.0,0
60.0,1.02,0.0,0.0,1.0,50.0,0.7,135.0,5.0,14.3,8300.0,5.8,0.0,0
70.0,1.02,1.0,0.0,1.0,25.0,0.9,150.0,3.6,15.0,9600.0,6.5,0.0,0
70.0,1.02,0.0,0.0,1.0,38.0,0.6,140.0,5.0,16.8,6300.0,5.9,0.0,0
60.0,1.025,0.0,0.0,1.0,26.0,1.0,146.0,4.9,15.8,7700.0,5.2,0.37,0
60.0,1.02,0.0,0.0,1.0,49.0,1.2,135.0,4.5,13.5,8600.0,4.9,0.0,0
60.0,1.02,0.0,0.0,1.0,48.0,0.5,150.0,3.5,15.1,11000.0,4.7,0.0,0
60.0,1.02,0.0,0.0,1.0,26.0,0.7,141.0,4.2,15.0,9200.0,5.8,0.0,0
60.0,1.025,0.0,0.0,1.0,33.0,0.9,147.0,4.5,16.9,7200.0,5.0,0.0,0
70.0,1.02,0.0,0.0,1.0,44.0,1.2,145.0,5.0,14.8,8406.0,4.71,0.0,0
70.0,1.02,0.0,0.0,1.0,26.0,1.1,137.53,4.63,17.0,6700.0,6.1,0.0,0
80.0,1.025,0.0,0.0,1.0,38.0,0.6,135.0,3.9,13.1,6200.0,4.5,0.0,0
80.0,1.02,0.0,0.0,1.0,25.0,0.8,138.0,5.0,17.1,9100.0,5.2,0.0,0
80.0,1.02,0.0,0.0,1.0,30.0,1.2,140.0,4.5,15.2,4300.0,5.7,0.0,0
60.0,1.02,0.0,0.0,1.0,17.0,0.5,150.0,3.5,13.6,7900.0,4.5,0.0,0
80.0,1.025,0.0,0.0,1.0,46.0,0.6,135.0,4.9,13.9,6900.0,4.9,0.0,0
60.0,1.02,0.0,0.0,1.0,25.0,1.2,139.0,5.0,17.2,8100.0,5.9,0.0,0
60.0,1.02,0.0,0.0,1.0,27.0,0.7,142.0,4.9,13.2,11000.0,5.4,0.0,0
60.0,1.025,0.0,0.0,1.0,18.0,1.1,147.0,4.7,13.7,7500.0,5.6,0.0,0
70.0,1.02,0.0,0.0,1.0,57.0,3.07,135.0,4.1,15.3,6300.0,6.1,0.0,0
80.0,1.02,0.0,0.0,1.0,28.0,0.9,142.0,4.8,17.3,8200.0,4.8,0.0,0
70.0,1.025,0.0,0.0,1.0,46.0,0.6,145.0,5.0,15.6,9400.0,4.7,0.0,0
70.0,1.025,0.0,0.0,1.0,57.0,3.07,137.53,4.63,13.8,7800.0,4.4,0.0,0
76.0,1.02,0.0,0.0,1.0,30.0,0.5,135.0,4.9,15.4,5000.0,5.2,0.0,0
70.0,1.02,0.0,0.0,1.0,48.0,1.2,139.0,4.3,15.0,8100.0,4.9,0.0,0
70.0,1.025,0.0,0.0,1.0,29.0,0.7,137.0,3.5,17.4,7000.0,5.3,0.0,0
60.0,1.02,0.0,0.0,1.0,15.0,1.1,135.0,4.4,12.53,8406.0,4.71,0.0,0
60.0,1.02,0.0,0.0,1.0,49.0,1.2,150.0,4.7,15.7,10400.0,6.2,0.0,0
60.0,1.02,0.0,0.0,1.0,39.0,1.0,144.0,3.5,13.9,9600.0,4.8,0.0,0
60.0,1.02,1.0,0.0,1.0,40.0,0.5,140.0,4.6,16.0,4500.0,4.9,0.0,0
80.0,1.025,0.0,0.0,1.0,30.0,1.1,143.0,5.0,15.9,7800.0,4.5,0.0,0
80.0,1.02,0.0,0.0,1.0,15.0,0.7,150.0,4.9,12.53,8406.0,4.71,0.0,0
80.0,1.02,0.0,0.0,1.0,50.0,1.2,140.0,3.5,14.0,6700.0,6.5,0.0,0
60.0,1.02,0.0,0.0,1.0,25.0,1.1,141.0,4.7,15.8,8300.0,5.2,0.0,0
60.0,1.025,0.0,0.0,1.0,31.0,0.8,150.0,4.6,13.4,10700.0,5.8,0.0,0
70.0,1.02,0.0,0.0,1.0,29.0,0.6,145.0,4.9,12.53,8600.0,6.5,0.0,0
60.0,1.025,0.0,0.0,1.0,25.0,0.9,146.0,3.5,14.1,7800.0,5.1,0.0,0
80.0,1.02,0.0,0.0,1.0,32.0,1.1,135.0,3.9,12.53,8406.0,4.71,0.0,0
70.0,1.025,0.0,0.0,1.0,39.0,0.7,147.0,4.7,13.5,6700.0,4.5,0.0,0
70.0,1.025,0.0,0.0,1.0,33.0,1.0,150.0,5.0,15.3,10500.0,6.1,0.0,0
80.0,1.02,0.0,0.0,1.0,46.0,1.2,142.0,4.0,17.7,4300.0,5.5,0.0,0
80.0,1.025,0.0,0.0,1.0,57.0,3.07,136.0,3.5,15.4,5600.0,4.5,0.0,0
60.0,1.02,0.0,0.0,1.0,45.0,0.5,139.0,4.8,14.2,10700.0,5.6,0.0,0
60.0,1.02,0.0,0.0,1.0,27.0,0.5,137.53,4.63,15.2,9200.0,5.2,0.0,0
70.0,1.025,0.0,0.0,1.0,40.0,0.9,141.0,4.9,14.0,7500.0,6.2,0.0,0
80.0,1.02,0.0,0.0,1.0,34.0,0.8,147.0,3.5,17.8,4700.0,4.5,0.0,0
70.0,1.02,0.0,0.0,1.0,42.0,0.5,136.0,3.5,13.3,7000.0,4.9,0.0,0
70.0,1.025,0.0,0.0,1.0,29.0,1.1,142.0,4.5,14.3,6700.0,5.9,0.0,0
70.0,1.025,0.0,0.0,1.0,37.0,0.9,150.0,5.0,13.4,7300.0,4.7,0.0,0
60.0,1.02,0.0,0.0,1.0,46.0,0.5,138.0,4.2,15.0,7700.0,6.3,0.0,0
60.0,1.025,0.0,0.0,1.0,35.0,0.8,135.0,4.1,16.2,5500.0,5.7,0.0,0
60.0,1.02,0.0,0.0,1.0,27.0,0.7,150.0,3.3,14.4,8100.0,4.7,0.0,0
60.0,1.025,0.0,0.0,1.0,18.0,1.2,138.0,4.3,13.5,7900.0,6.4,0.0,0
60.0,1.02,1.0,0.0,1.0,41.0,0.9,141.0,4.4,15.5,4300.0,5.8,0.0,0
60.0,1.025,0.0,0.0,1.0,25.0,1.0,144.0,5.0,17.8,7200.0,5.5,0.0,0
80.0,1.02,0.0,0.0,1.0,19.0,0.5,147.0,3.5,13.6,7300.0,6.4,0.0,0
70.0,1.025,0.0,0.0,1.0,36.0,1.1,150.0,3.5,14.5,9400.0,6.1,0.0,0
70.0,1.025,0.0,0.0,1.0,20.0,1.0,142.0,4.8,16.1,9600.0,4.5,0.0,0
80.0,1.02,0.0,0.0,1.0,49.0,0.9,139.0,3.3,17.5,9900.0,4.7,0.0,0
60.0,1.02,0.0,0.0,1.0,47.0,1.1,141.0,4.9,15.0,7000.0,5.2,0.0,0
60.0,1.02,0.0,0.0,1.0,37.0,0.6,150.0,5.0,13.6,5800.0,4.5,0.0,0
60.0,1.025,0.0,0.0,1.0,17.0,0.4,147.0,4.7,14.6,6800.0,5.1,0.0,0
60.0,1.02,0.0,0.0,1.0,24.0,0.8,145.0,5.0,15.0,6300.0,4.6,0.0,0
70.0,1.025,0.0,0.0,1.0,38.0,0.5,144.0,4.8,17.1,7400.0,6.1,0.0,0
70.0,1.025,0.0,0.0,1.0,16.0,1.1,140.0,3.6,13.6,11000.0,4.9,0.0,0
60.0,1.02,0.0,0.0,1.0,22.0,1.2,138.0,3.5,13.0,5200.0,5.6,0.0,0
60.0,1.02,0.0,0.0,1.0,50.0,0.6,147.0,3.7,17.2,6000.0,4.5,0.0,0
60.0,1.025,0.0,0.0,1.0,39.0,0.5,135.0,3.9,14.7,5800.0,6.2,0.0,0
80.0,1.02,0.0,0.0,1.0,16.0,0.7,138.0,3.5,13.7,5400.0,5.8,0.0,0
80.0,1.025,0.0,0.0,1.0,19.0,1.1,144.0,5.0,15.0,10300.0,4.8,0.0,0
80.0,1.025,0.0,0.0,1.0,40.0,0.5,137.53,4.63,17.8,5900.0,5.2,0.0,0
80.0,1.025,0.0,0.0,1.0,44.0,0.7,137.0,3.5,14.8,9300.0,4.7,0.0,0
80.0,1.02,0.0,0.0,1.0,46.0,1.0,145.0,3.5,12.53,10700.0,6.3,0.0,0
80.0,1.025,0.0,0.0,1.0,15.0,0.5,141.0,3.6,15.0,10500.0,5.3,0.0,0
60.0,1.025,0.0,0.0,1.0,41.0,1.1,139.0,3.8,17.4,6700.0,6.1,0.0,0
80.0,1.025,0.0,0.0,1.0,42.0,0.7,146.0,5.0,14.9,9400.0,5.9,0.0,0
70.0,1.02,0.0,0.0,1.0,48.0,0.8,144.0,3.5,13.6,10300.0,4.8,0.0,0
70.0,1.02,0.0,0.0,1.0,42.0,1.2,139.0,3.7,16.2,9300.0,5.4,0.0,0
60.0,1.025,0.0,0.0,1.0,50.0,0.5,145.0,5.0,17.6,6500.0,5.0,0.0,0
60.0,1.02,0.0,0.0,1.0,26.0,0.9,150.0,4.9,15.0,10500.0,5.5,0.0,0
70.0,1.025,0.0,0.0,1.0,38.0,1.0,142.0,3.6,13.7,9200.0,4.9,0.0,0
80.0,1.025,0.0,0.0,1.0,44.0,1.2,146.0,3.6,16.3,8000.0,6.4,0.0,0
80.0,1.02,0.0,0.0,1.0,41.0,0.5,143.0,4.5,15.1,9700.0,5.6,0.0,0
70.0,1.025,0.0,0.0,1.0,16.0,1.1,147.0,3.5,16.4,9100.0,5.2,0.0,0
70.0,1.02,0.0,0.0,1.0,27.0,0.7,145.0,4.8,13.8,6400.0,4.8,0.0,0
60.0,1.025,0.0,0.0,1.0,57.0,0.9,140.0,4.8,15.2,7700.0,5.5,0.0,0
80.0,1.025,0.0,0.0,1.0,45.0,0.6,138.0,3.5,16.1,5400.0,5.7,0.0,0
60.0,1.02,0.0,0.0,1.0,23.0,1.1,139.0,3.5,15.3,6500.0,4.9,0.0,0
70.0,1.025,0.0,0.0,1.0,47.0,0.5,142.0,4.8,16.6,5800.0,5.9,0.0,0
80.0,1.025,0.0,0.0,1.0,22.0,0.8,137.0,5.0,16.8,6000.0,6.5,0.0,0
80.0,1.025,0.0,0.0,1.0,46.0,0.7,141.0,4.9,13.9,5100.0,5.0,0.0,0
60.0,1.02,0.0,0.0,1.0,18.0,1.1,150.0,4.7,15.4,11000.0,4.5,0.0,0
70.0,1.02,0.0,0.0,1.0,25.0,0.6,146.0,4.9,16.5,8000.0,5.1,0.0,0
70.0,1.025,0.0,0.0,1.0,47.0,0.5,142.0,3.5,16.4,5700.0,6.5,0.0,0
80.0,1.025,0.0,0.0,1.0,17.0,0.9,136.0,3.9,16.7,6200.0,5.2,0.0,0
80.0,1.02,0.0,0.0,1.0,15.0,1.2,144.0,3.7,15.5,9500.0,6.4,0.0,0
80.0,1.025,0.0,0.0,1.0,48.0,0.7,140.0,5.0,17.0,7200.0,5.8,0.0,0
80.0,1.025,0.0,0.0,1.0,25.0,0.8,135.0,3.7,15.0,6300.0,5.3,0.0,0
80.0,1.025,0.0,0.0,1.0,16.0,1.1,142.0,4.1,15.6,5800.0,6.3,0.0,0
80.0,1.02,0.0,0.0,1.0,48.0,1.2,147.0,4.3,14.8,6600.0,5.5,0.0,0
60.0,1.025,0.0,0.0,1.0,45.0,0.7,141.0,4.4,13.0,7400.0,5.4,0.0,0
80.0,1.02,0.0,0.0,1.0,46.0,0.8,139.0,5.0,14.1,9500.0,4.6,0.0,0
80.0,1.02,0.0,0.0,1.0,49.0,0.5,150.0,4.9,15.7,6700.0,4.9,0.0,0
70.0,1.025,0.0,0.0,1.0,31.0,1.2,141.0,3.5,16.5,7800.0,6.2,0.0,0
80.0,1.02,0.0,0.0,1.0,26.0,0.6,137.0,4.4,15.8,6600.0,5.4,0.0,0
60.0,1.025,0.0,0.0,1.0,50.0,1.0,135.0,4.9,14.2,7200.0,5.9,0.0,0
80.0,1.025,0.0,0.0,1.0,18.0,1.1,141.0,3.5,15.8,6800.0,6.1,0.0,0
project/DATASETS/Kidney/new_model.csv 0 → 100644
View file @ 3ec0eded
Bp,Sg,Al,Su,Rbc,Bu,Sc,Sod,Pot,Hemo,Wbcc,Rbcc,Htn,Class
80.0,1.02,1.0,0.0,1.0,36.0,1.2,137.53,4.63,15.4,7800.0,5.2,1.0,1
50.0,1.02,4.0,0.0,1.0,18.0,0.8,137.53,4.63,11.3,6000.0,4.71,0.0,1
80.0,1.01,2.0,3.0,1.0,53.0,1.8,137.53,4.63,9.6,7500.0,4.71,0.0,1
70.0,1.005,4.0,0.0,1.0,56.0,3.8,111.0,2.5,11.2,6700.0,3.9,1.0,1
80.0,1.01,2.0,0.0,1.0,26.0,1.4,137.53,4.63,11.6,7300.0,4.6,0.0,1
90.0,1.015,3.0,0.0,1.0,25.0,1.1,142.0,3.2,12.2,7800.0,4.4,1.0,1
70.0,1.01,0.0,0.0,1.0,54.0,24.0,104.0,4.0,12.4,8406.0,4.71,0.0,1
76.0,1.015,2.0,4.0,1.0,31.0,1.1,137.53,4.63,12.4,6900.0,5.0,0.0,1
100.0,1.015,3.0,0.0,1.0,60.0,1.9,137.53,4.63,10.8,9600.0,4.0,1.0,1
90.0,1.02,2.0,0.0,0.0,107.0,7.2,114.0,3.7,9.5,12100.0,3.7,1.0,1
60.0,1.01,2.0,4.0,1.0,55.0,4.0,137.53,4.63,9.4,8406.0,4.71,1.0,1
70.0,1.01,3.0,0.0,0.0,60.0,2.7,131.0,4.2,10.8,4500.0,3.8,1.0,1
70.0,1.015,3.0,1.0,1.0,72.0,2.1,138.0,5.8,9.7,12200.0,3.4,1.0,1
70.0,1.02,1.0,0.0,1.0,86.0,4.6,135.0,3.4,9.8,8406.0,4.71,1.0,1
80.0,1.01,3.0,2.0,1.0,90.0,4.1,130.0,6.4,5.6,11000.0,2.6,1.0,1
80.0,1.015,3.0,0.0,1.0,162.0,9.6,141.0,4.9,7.6,3800.0,2.8,1.0,1
70.0,1.015,2.0,0.0,1.0,46.0,2.2,138.0,4.1,12.6,8406.0,4.71,0.0,1
80.0,1.02,1.0,0.0,1.0,87.0,5.2,139.0,3.7,12.1,8406.0,4.71,1.0,1
100.0,1.025,0.0,3.0,1.0,27.0,1.3,135.0,4.3,12.7,11400.0,4.3,1.0,1
60.0,1.015,1.0,0.0,1.0,31.0,1.6,137.53,4.63,10.3,5300.0,3.7,1.0,1
80.0,1.015,2.0,0.0,0.0,148.0,3.9,135.0,5.2,7.7,9200.0,3.2,1.0,1
90.0,1.02,1.0,0.0,1.0,180.0,76.0,4.5,4.63,10.9,6200.0,3.6,1.0,1
80.0,1.025,4.0,0.0,1.0,163.0,7.7,136.0,3.8,9.8,6900.0,3.4,1.0,1
70.0,1.01,0.0,0.0,1.0,57.0,3.07,137.53,4.63,12.53,8406.0,4.71,0.0,1
100.0,1.015,4.0,0.0,1.0,50.0,1.4,129.0,4.0,11.1,8300.0,4.6,1.0,1
60.0,1.025,0.0,0.0,1.0,75.0,1.9,141.0,5.2,9.9,8400.0,3.7,1.0,1
80.0,1.015,0.0,0.0,1.0,45.0,2.4,140.0,3.4,11.6,10300.0,4.0,1.0,1
70.0,1.01,3.0,4.0,1.0,87.0,2.7,130.0,4.0,12.5,9600.0,4.1,1.0,1
70.0,1.02,1.0,3.0,1.0,31.0,1.4,137.53,4.63,12.53,8406.0,4.71,0.0,1
70.0,1.005,1.0,0.0,0.0,28.0,1.4,137.53,4.63,12.9,8406.0,4.71,0.0,1
70.0,1.02,1.0,0.0,1.0,155.0,7.3,132.0,4.9,12.53,8406.0,4.71,1.0,1
90.0,1.015,3.0,0.0,1.0,33.0,1.5,141.0,4.6,10.1,7800.0,4.0,0.0,1
90.0,1.01,1.0,1.0,1.0,39.0,1.5,133.0,4.9,11.3,9600.0,4.0,1.0,1
100.0,1.02,2.0,0.0,0.0,55.0,2.5,137.53,4.63,10.1,8406.0,4.71,1.0,1
70.0,1.01,1.0,0.0,1.0,153.0,5.2,137.53,4.63,12.53,8406.0,4.71,0.0,1
90.0,1.02,2.0,1.0,0.0,39.0,2.0,137.53,4.63,12.0,9800.0,4.9,1.0,1
70.0,1.015,1.0,0.0,1.0,29.0,1.8,133.0,3.9,10.3,8406.0,4.71,1.0,1
80.0,1.02,1.0,0.0,1.0,65.0,3.4,141.0,4.7,9.7,6900.0,2.5,1.0,1
80.0,1.02,3.0,0.0,0.0,103.0,4.1,132.0,5.9,12.5,8406.0,4.71,1.0,1
80.0,1.01,2.0,2.0,1.0,70.0,3.4,136.0,4.2,13.0,9800.0,4.2,1.0,1
90.0,1.01,2.0,0.0,1.0,80.0,2.1,137.53,4.63,11.1,9100.0,4.1,1.0,1
70.0,1.01,0.0,0.0,1.0,20.0,0.7,137.53,4.63,12.53,8406.0,4.71,0.0,1
100.0,1.01,0.0,0.0,1.0,29.0,1.0,139.0,4.2,9.7,9200.0,4.5,1.0,1
80.0,1.01,1.0,0.0,0.0,202.0,10.8,134.0,3.4,7.9,7900.0,3.1,0.0,1
80.0,1.01,3.0,0.0,0.0,77.0,6.3,134.0,4.8,9.7,8406.0,4.71,1.0,1
80.0,1.02,3.0,0.0,1.0,89.0,5.9,130.0,4.9,9.3,8406.0,4.71,1.0,1
70.0,1.015,0.0,0.0,1.0,24.0,1.2,142.0,4.2,12.4,6400.0,4.7,0.0,1
80.0,1.01,3.0,0.0,1.0,17.0,0.8,137.53,4.63,15.0,8600.0,4.71,0.0,1
70.0,1.005,0.0,0.0,1.0,32.0,0.9,125.0,4.0,10.0,18900.0,3.5,1.0,1
70.0,1.01,2.0,0.0,1.0,72.0,3.0,137.53,4.63,9.7,21600.0,3.5,1.0,1
60.0,1.02,1.0,0.0,1.0,114.0,3.25,142.0,4.3,8.6,11000.0,3.8,1.0,1
100.0,1.015,3.0,0.0,1.0,66.0,1.6,136.0,4.4,10.3,8406.0,4.71,1.0,1
90.0,1.015,0.0,0.0,1.0,38.0,2.2,137.53,4.63,10.9,4300.0,3.7,0.0,1
80.0,1.015,0.0,5.0,1.0,24.0,1.0,137.53,4.63,13.6,8500.0,4.7,1.0,1
80.0,1.01,2.0,2.0,1.0,57.0,3.4,136.0,4.2,13.0,9800.0,4.2,1.0,1
80.0,1.005,3.0,0.0,0.0,57.0,3.07,137.53,4.63,9.5,8406.0,4.71,0.0,1
70.0,1.015,3.0,4.0,1.0,164.0,9.7,131.0,4.4,10.2,11300.0,3.4,1.0,1
90.0,1.02,1.0,0.0,1.0,155.0,7.3,132.0,4.9,12.53,8406.0,4.71,1.0,1
80.0,1.02,2.0,0.0,0.0,142.0,4.6,138.0,5.8,10.5,7200.0,4.3,1.0,1
100.0,1.02,1.0,0.0,1.0,96.0,6.4,137.53,4.63,6.6,8406.0,4.71,1.0,1
90.0,1.02,1.0,0.0,1.0,66.0,3.2,138.0,6.6,12.53,8406.0,4.71,1.0,1
80.0,1.01,1.0,3.0,1.0,391.0,32.0,163.0,39.0,12.53,8406.0,4.71,0.0,1
60.0,1.02,3.0,0.0,1.0,15.0,0.6,138.0,4.0,11.0,7700.0,3.8,1.0,1
70.0,1.015,1.0,0.0,0.0,111.0,6.1,131.0,3.7,7.5,8406.0,4.71,0.0,1
80.0,1.01,0.0,0.0,1.0,57.0,3.07,137.53,4.63,9.8,8406.0,4.71,0.0,1
90.0,1.01,1.0,0.0,1.0,20.0,1.1,137.53,4.63,15.0,8406.0,4.71,0.0,1
70.0,1.02,2.0,0.0,0.0,55.0,1.6,131.0,4.8,12.53,8406.0,4.71,1.0,1
80.0,1.02,3.0,0.0,1.0,57.0,3.07,137.53,4.63,12.53,8406.0,4.71,0.0,1
70.0,1.01,2.0,0.0,1.0,73.0,3.3,137.53,4.63,10.9,8406.0,4.71,0.0,1
70.0,1.015,0.0,4.0,1.0,20.0,1.1,137.53,4.63,15.6,6900.0,6.0,0.0,1
80.0,1.015,0.0,4.0,1.0,19.0,0.7,137.0,4.4,15.2,8300.0,5.2,1.0,1
60.0,1.01,1.0,0.0,1.0,92.0,3.3,141.0,4.0,9.8,14600.0,3.2,1.0,1
90.0,1.01,3.0,3.0,1.0,35.0,1.3,137.53,4.63,10.3,8406.0,4.71,1.0,1
100.0,1.015,2.0,0.0,0.0,107.0,6.7,132.0,4.4,4.8,6300.0,4.71,1.0,1
90.0,1.015,2.0,0.0,0.0,107.0,6.7,131.0,4.8,9.1,6400.0,3.4,1.0,1
76.0,1.015,1.0,0.0,1.0,16.0,0.7,138.0,3.2,8.1,8406.0,4.71,0.0,1
80.0,1.005,4.0,0.0,0.0,139.0,8.5,132.0,5.5,10.3,6200.0,4.0,0.0,1
70.0,1.01,1.0,0.0,1.0,48.0,3.2,137.0,5.0,11.9,7100.0,3.7,1.0,1
80.0,1.02,1.0,0.0,1.0,85.0,3.2,141.0,3.5,10.1,8406.0,4.71,1.0,1
80.0,1.01,1.0,0.0,1.0,55.0,1.8,137.53,4.63,13.5,11800.0,5.0,1.0,1
80.0,1.01,0.0,0.0,1.0,98.0,2.8,133.0,5.0,10.8,9400.0,3.8,1.0,1
90.0,1.02,1.0,0.0,1.0,45.0,2.4,128.0,4.4,8.3,5500.0,3.7,1.0,1
70.0,1.02,1.0,0.0,1.0,77.0,1.9,140.0,3.9,12.53,8406.0,4.71,1.0,1
70.0,1.015,1.0,0.0,1.0,19.0,1.0,134.0,3.6,12.53,8406.0,4.71,1.0,1
70.0,1.01,3.0,0.0,1.0,186.0,15.0,135.0,7.6,7.1,3800.0,2.1,1.0,1
70.0,1.015,2.0,0.0,1.0,46.0,1.5,137.53,4.63,9.9,8406.0,4.71,0.0,1
80.0,1.02,1.0,0.0,1.0,37.0,1.9,137.53,4.63,12.53,8406.0,4.71,0.0,1
100.0,1.005,1.0,0.0,1.0,47.0,2.9,137.53,4.63,11.1,5800.0,5.0,1.0,1
110.0,1.01,4.0,0.0,1.0,52.0,2.2,137.53,4.63,12.53,13200.0,4.7,1.0,1
70.0,1.02,0.0,0.0,1.0,32.0,1.4,139.0,4.7,12.53,8406.0,4.71,0.0,1
100.0,1.01,2.0,2.0,1.0,35.0,3.2,143.0,3.5,13.0,9800.0,4.2,1.0,1
70.0,1.015,4.0,1.0,0.0,26.0,1.7,136.0,3.8,16.1,12500.0,5.6,0.0,1
70.0,1.01,3.0,0.0,1.0,82.0,3.6,133.0,4.4,10.4,5600.0,3.6,1.0,1
100.0,1.01,3.0,2.0,0.0,90.0,5.6,140.0,2.9,9.2,7000.0,3.2,1.0,1
70.0,1.01,0.0,0.0,1.0,66.0,1.6,137.0,4.5,11.6,11900.0,3.9,0.0,1
90.0,1.015,1.0,0.0,1.0,25.0,1.1,131.0,3.7,12.53,8406.0,4.71,1.0,1
80.0,1.01,1.0,1.0,1.0,32.0,2.7,137.53,4.63,11.2,8406.0,4.71,0.0,1
60.0,1.015,1.0,0.0,1.0,51.0,2.2,132.0,3.8,10.0,9100.0,4.0,1.0,1
140.0,1.02,1.0,0.0,1.0,106.0,6.5,135.0,4.3,6.2,5800.0,2.3,1.0,1
180.0,1.02,0.0,4.0,1.0,24.0,1.2,139.0,3.9,11.2,10400.0,4.2,1.0,1
70.0,1.015,4.0,0.0,0.0,22.0,0.9,133.0,3.8,12.53,8406.0,4.71,0.0,1
90.0,1.015,2.0,0.0,1.0,80.0,4.4,139.0,5.7,11.3,10700.0,3.9,0.0,1
60.0,1.01,0.0,0.0,1.0,32.0,2.1,141.0,4.2,13.9,7000.0,4.71,0.0,1
70.0,1.015,2.0,0.0,1.0,217.0,10.2,137.53,4.63,10.2,12700.0,4.2,1.0,1
90.0,1.02,1.0,0.0,1.0,88.0,2.0,137.53,4.63,12.53,8406.0,4.71,1.0,1
80.0,1.015,0.0,0.0,1.0,32.0,11.5,139.0,4.0,14.1,6800.0,5.2,0.0,1
90.0,1.02,1.0,0.0,1.0,118.0,6.1,127.0,4.4,6.0,6500.0,4.71,1.0,1
100.0,1.015,1.0,4.0,1.0,53.0,2.8,139.0,4.5,11.2,13600.0,4.4,1.0,1
80.0,1.015,0.0,0.0,1.0,15.0,1.0,141.0,4.2,11.8,10200.0,4.2,0.0,1
70.0,1.02,1.0,0.0,1.0,50.1,1.9,137.53,4.63,11.7,8406.0,4.71,0.0,1
90.0,1.015,0.0,0.0,1.0,19.0,2.0,142.0,3.8,11.7,11400.0,4.7,0.0,1
80.0,1.01,3.0,3.0,1.0,71.0,4.4,128.0,5.4,10.0,9000.0,3.9,1.0,1
60.0,1.015,3.0,0.0,0.0,34.0,1.2,137.53,4.63,10.8,8406.0,4.71,0.0,1
90.0,1.015,0.0,2.0,1.0,57.0,3.07,137.53,4.63,12.53,9800.0,4.71,0.0,1
60.0,1.015,3.0,0.0,0.0,51.0,1.8,137.53,4.63,12.1,10300.0,4.71,0.0,1
80.0,1.01,0.0,0.0,1.0,28.0,0.9,137.53,4.63,12.4,5600.0,4.3,0.0,1
70.0,1.015,4.0,0.0,0.0,16.0,0.5,137.53,4.63,12.53,8406.0,4.71,0.0,1
70.0,1.02,0.0,0.0,1.0,36.0,1.3,139.0,3.7,12.5,9800.0,4.4,0.0,1
70.0,1.01,3.0,0.0,1.0,25.0,1.2,137.53,4.63,11.4,8406.0,4.71,0.0,1
70.0,1.01,0.0,0.0,1.0,27.0,1.2,137.53,4.63,12.53,8406.0,4.71,0.0,1
90.0,1.025,1.0,3.0,1.0,40.0,2.2,137.0,5.3,12.6,8406.0,4.71,0.0,1
60.0,1.02,3.0,0.0,1.0,21.0,1.3,137.0,3.4,15.0,8406.0,4.71,1.0,1
70.0,1.02,1.0,0.0,1.0,219.0,12.2,130.0,3.8,6.0,8406.0,4.71,1.0,1
80.0,1.015,2.0,3.0,1.0,30.0,1.1,137.53,4.63,14.0,14900.0,4.71,0.0,1
100.0,1.015,0.0,0.0,1.0,98.0,2.5,137.53,4.63,9.1,5500.0,3.6,1.0,1
90.0,1.02,1.0,0.0,1.0,36.0,2.5,131.0,4.3,12.53,8406.0,4.71,1.0,1
90.0,1.015,0.0,0.0,1.0,125.0,4.0,136.0,4.6,12.0,8200.0,4.5,1.0,1
60.0,1.015,4.0,0.0,1.0,125.0,5.3,136.0,4.9,11.4,15200.0,4.3,1.0,1
90.0,1.015,4.0,3.0,1.0,166.0,5.6,133.0,47.0,8.1,5000.0,2.9,1.0,1
70.0,1.025,1.0,0.0,1.0,49.0,1.4,135.0,4.7,11.1,8406.0,4.71,1.0,1
90.0,1.01,2.0,0.0,1.0,208.0,9.2,134.0,4.8,8.2,16300.0,2.7,0.0,1
50.0,1.01,0.0,0.0,1.0,25.0,0.6,137.53,4.63,11.8,12400.0,4.71,0.0,1
76.0,1.02,1.0,0.0,1.0,176.0,13.8,136.0,4.5,8.6,13200.0,2.7,1.0,1
100.0,1.015,4.0,0.0,1.0,125.0,5.3,136.0,4.9,12.0,8400.0,8.0,1.0,1
100.0,1.01,1.0,0.0,1.0,57.0,16.9,138.0,5.2,10.8,10200.0,3.8,0.0,1
80.0,1.015,0.0,2.0,1.0,24.0,1.3,140.0,4.0,13.2,8406.0,4.71,0.0,1
90.0,1.02,1.0,0.0,1.0,68.0,2.8,146.0,6.3,9.3,8406.0,4.71,1.0,1
60.0,1.01,2.0,0.0,1.0,86.0,4.0,134.0,5.1,10.0,9200.0,4.71,1.0,1
76.0,1.01,1.0,0.0,1.0,51.0,1.6,142.0,3.5,12.53,8406.0,4.71,0.0,1
70.0,1.015,2.0,0.0,1.0,68.0,2.8,132.0,4.1,11.1,8406.0,4.71,1.0,1
70.0,1.01,0.0,4.0,1.0,40.0,1.2,142.0,5.6,12.53,8406.0,4.71,0.0,1
70.0,1.01,1.0,0.0,1.0,106.0,6.0,137.0,4.9,6.1,6500.0,4.71,1.0,1
90.0,1.02,1.0,0.0,1.0,145.0,7.1,135.0,5.3,12.53,8406.0,4.71,0.0,1
80.0,1.015,1.0,4.0,0.0,165.0,18.0,135.0,4.7,12.53,8406.0,4.71,0.0,1
90.0,1.01,2.0,0.0,0.0,53.0,2.3,136.0,5.2,11.1,10500.0,4.1,0.0,1
90.0,1.015,5.0,0.0,0.0,322.0,13.0,126.0,4.8,8.0,4200.0,3.3,1.0,1
100.0,1.01,1.0,3.0,0.0,23.0,1.0,139.0,4.0,12.53,8406.0,4.71,0.0,1
60.0,1.01,3.0,1.0,1.0,36.0,1.7,130.0,3.0,7.9,15200.0,3.0,1.0,1
60.0,1.02,1.0,0.0,1.0,26.0,48.1,137.53,4.63,12.53,8406.0,4.71,1.0,1
70.0,1.02,1.0,0.0,0.0,29.0,1.0,137.53,4.63,10.5,8406.0,4.71,1.0,1
60.0,1.025,3.0,0.0,1.0,27.0,0.9,137.53,4.63,12.3,6700.0,4.71,0.0,1
90.0,1.02,1.0,0.0,1.0,46.0,1.7,141.0,5.5,9.6,8406.0,4.71,1.0,1
70.0,1.01,0.0,0.0,1.0,20.0,0.8,133.0,3.5,10.9,8406.0,4.71,0.0,1
90.0,1.01,2.0,1.0,0.0,235.0,14.2,132.0,3.4,8.3,14600.0,2.9,1.0,1
90.0,1.005,4.0,3.0,0.0,132.0,16.4,140.0,4.2,8.4,8406.0,3.0,1.0,1
70.0,1.02,3.0,0.0,0.0,40.0,1.8,137.53,4.63,11.1,4700.0,4.71,0.0,1
90.0,1.015,2.0,0.0,1.0,76.0,3.3,137.53,4.63,12.53,8406.0,4.71,0.0,1
70.0,1.025,3.0,0.0,1.0,42.0,1.7,136.0,4.7,12.6,7900.0,3.9,1.0,1
60.0,1.02,3.0,2.0,1.0,48.0,1.5,132.0,4.0,10.9,8406.0,4.71,1.0,1
80.0,1.01,1.0,0.0,0.0,35.0,1.3,122.0,3.5,10.4,10900.0,4.3,0.0,1
60.0,1.02,1.0,0.0,1.0,39.0,2.1,147.0,4.2,10.9,9400.0,2.4,1.0,1
76.0,1.015,3.0,0.0,0.0,57.0,3.07,137.53,4.63,14.3,10200.0,4.8,1.0,1
70.0,1.02,1.0,0.0,1.0,34.0,1.5,124.0,4.1,9.8,6000.0,4.71,0.0,1
80.0,1.01,0.0,0.0,1.0,40.0,2.0,140.0,4.1,9.0,8100.0,3.2,1.0,1
76.0,1.015,0.0,0.0,1.0,15.0,0.8,137.0,4.2,14.3,9500.0,5.4,0.0,1
80.0,1.02,0.0,2.0,1.0,57.0,3.07,137.53,4.63,12.53,8406.0,4.71,0.0,1
60.0,1.02,1.0,0.0,1.0,44.0,3.9,127.0,4.3,12.53,8406.0,4.71,0.0,1
70.0,1.02,0.0,0.0,0.0,19.0,0.9,137.53,4.63,12.7,2200.0,4.71,0.0,1
70.0,1.015,4.0,4.0,1.0,28.0,1.5,137.53,4.63,11.0,6700.0,4.71,1.0,1
70.0,1.01,0.0,2.0,1.0,68.0,2.8,137.53,4.63,8.7,8406.0,4.71,1.0,1
70.0,1.015,2.0,5.0,1.0,41.0,1.7,131.0,3.9,12.5,9600.0,4.4,1.0,1
70.0,1.02,3.0,0.0,1.0,60.0,2.6,115.0,5.7,8.7,12800.0,3.1,1.0,1
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70.0,1.015,1.0,0.0,0.0,1.5,7.3,145.0,2.8,13.1,11200.0,4.71,0.0,1
70.0,1.02,1.0,0.0,1.0,146.0,7.5,141.0,4.7,11.0,8600.0,4.6,0.0,1
50.0,1.01,0.0,0.0,1.0,58.0,2.2,113.0,3.0,12.53,4200.0,3.4,1.0,1
90.0,1.01,4.0,0.0,1.0,40.0,1.7,125.0,3.5,8.3,12400.0,3.9,0.0,1
80.0,1.015,2.0,1.0,1.0,133.0,2.5,137.53,4.63,13.2,8406.0,4.71,0.0,1
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90.0,1.01,1.0,4.0,0.0,56.0,1.9,137.53,4.63,10.3,8406.0,4.71,0.0,1
70.0,1.025,2.0,0.0,1.0,52.0,2.2,136.0,3.8,10.0,19100.0,3.7,0.0,1
80.0,1.02,0.0,0.0,1.0,23.0,0.8,140.0,4.1,11.3,8406.0,4.71,0.0,1
70.0,1.015,0.0,0.0,1.0,106.0,6.5,135.0,4.3,12.53,8406.0,4.71,0.0,1
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76.0,1.02,1.0,0.0,1.0,23.0,0.6,138.0,4.4,12.0,8406.0,4.71,0.0,1
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70.0,1.01,2.0,0.0,1.0,49.0,1.2,137.53,4.63,12.53,8406.0,4.71,1.0,1
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80.0,1.02,1.0,0.0,1.0,111.0,9.3,124.0,5.3,6.8,4300.0,3.0,1.0,1
100.0,1.02,4.0,2.0,1.0,40.0,3.2,137.0,4.7,11.2,26400.0,3.9,1.0,1
80.0,1.015,0.0,0.0,1.0,37.0,1.5,140.0,5.2,8.8,10700.0,3.2,1.0,1
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70.0,1.02,1.0,0.0,1.0,94.0,7.3,137.0,4.3,7.9,8406.0,4.71,1.0,1
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90.0,1.01,4.0,2.0,1.0,82.0,13.5,145.0,6.3,8.8,8406.0,4.71,1.0,1
70.0,1.02,1.0,0.0,1.0,28.0,2.1,137.53,4.63,12.6,8406.0,4.71,1.0,1
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120.0,1.015,0.0,0.0,1.0,18.0,1.2,137.53,4.63,12.53,8406.0,4.71,0.0,1
70.0,1.015,3.0,4.0,1.0,150.0,11.9,132.0,5.6,10.9,8800.0,3.4,1.0,1
80.0,1.01,3.0,1.0,1.0,73.0,3.9,137.0,4.9,10.9,7400.0,3.7,1.0,1
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100.0,1.015,4.0,2.0,0.0,54.0,7.2,140.0,4.6,7.9,7500.0,3.4,1.0,1
80.0,1.015,0.0,0.0,1.0,48.0,1.6,137.53,4.63,11.3,7200.0,3.8,1.0,1
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76.0,1.02,0.0,0.0,1.0,30.0,0.5,135.0,4.9,15.4,5000.0,5.2,0.0,0
70.0,1.02,0.0,0.0,1.0,48.0,1.2,139.0,4.3,15.0,8100.0,4.9,0.0,0
70.0,1.025,0.0,0.0,1.0,29.0,0.7,137.0,3.5,17.4,7000.0,5.3,0.0,0
60.0,1.02,0.0,0.0,1.0,15.0,1.1,135.0,4.4,12.53,8406.0,4.71,0.0,0
60.0,1.02,0.0,0.0,1.0,49.0,1.2,150.0,4.7,15.7,10400.0,6.2,0.0,0
60.0,1.02,0.0,0.0,1.0,39.0,1.0,144.0,3.5,13.9,9600.0,4.8,0.0,0
60.0,1.02,1.0,0.0,1.0,40.0,0.5,140.0,4.6,16.0,4500.0,4.9,0.0,0
80.0,1.025,0.0,0.0,1.0,30.0,1.1,143.0,5.0,15.9,7800.0,4.5,0.0,0
80.0,1.02,0.0,0.0,1.0,15.0,0.7,150.0,4.9,12.53,8406.0,4.71,0.0,0
80.0,1.02,0.0,0.0,1.0,50.0,1.2,140.0,3.5,14.0,6700.0,6.5,0.0,0
60.0,1.02,0.0,0.0,1.0,25.0,1.1,141.0,4.7,15.8,8300.0,5.2,0.0,0
60.0,1.025,0.0,0.0,1.0,31.0,0.8,150.0,4.6,13.4,10700.0,5.8,0.0,0
70.0,1.02,0.0,0.0,1.0,29.0,0.6,145.0,4.9,12.53,8600.0,6.5,0.0,0
60.0,1.025,0.0,0.0,1.0,25.0,0.9,146.0,3.5,14.1,7800.0,5.1,0.0,0
80.0,1.02,0.0,0.0,1.0,32.0,1.1,135.0,3.9,12.53,8406.0,4.71,0.0,0
70.0,1.025,0.0,0.0,1.0,39.0,0.7,147.0,4.7,13.5,6700.0,4.5,0.0,0
70.0,1.025,0.0,0.0,1.0,33.0,1.0,150.0,5.0,15.3,10500.0,6.1,0.0,0
80.0,1.02,0.0,0.0,1.0,46.0,1.2,142.0,4.0,17.7,4300.0,5.5,0.0,0
80.0,1.025,0.0,0.0,1.0,57.0,3.07,136.0,3.5,15.4,5600.0,4.5,0.0,0
60.0,1.02,0.0,0.0,1.0,45.0,0.5,139.0,4.8,14.2,10700.0,5.6,0.0,0
60.0,1.02,0.0,0.0,1.0,27.0,0.5,137.53,4.63,15.2,9200.0,5.2,0.0,0
70.0,1.025,0.0,0.0,1.0,40.0,0.9,141.0,4.9,14.0,7500.0,6.2,0.0,0
80.0,1.02,0.0,0.0,1.0,34.0,0.8,147.0,3.5,17.8,4700.0,4.5,0.0,0
70.0,1.02,0.0,0.0,1.0,42.0,0.5,136.0,3.5,13.3,7000.0,4.9,0.0,0
70.0,1.025,0.0,0.0,1.0,29.0,1.1,142.0,4.5,14.3,6700.0,5.9,0.0,0
70.0,1.025,0.0,0.0,1.0,37.0,0.9,150.0,5.0,13.4,7300.0,4.7,0.0,0
60.0,1.02,0.0,0.0,1.0,46.0,0.5,138.0,4.2,15.0,7700.0,6.3,0.0,0
60.0,1.025,0.0,0.0,1.0,35.0,0.8,135.0,4.1,16.2,5500.0,5.7,0.0,0
60.0,1.02,0.0,0.0,1.0,27.0,0.7,150.0,3.3,14.4,8100.0,4.7,0.0,0
60.0,1.025,0.0,0.0,1.0,18.0,1.2,138.0,4.3,13.5,7900.0,6.4,0.0,0
60.0,1.02,1.0,0.0,1.0,41.0,0.9,141.0,4.4,15.5,4300.0,5.8,0.0,0
60.0,1.025,0.0,0.0,1.0,25.0,1.0,144.0,5.0,17.8,7200.0,5.5,0.0,0
80.0,1.02,0.0,0.0,1.0,19.0,0.5,147.0,3.5,13.6,7300.0,6.4,0.0,0
70.0,1.025,0.0,0.0,1.0,36.0,1.1,150.0,3.5,14.5,9400.0,6.1,0.0,0
70.0,1.025,0.0,0.0,1.0,20.0,1.0,142.0,4.8,16.1,9600.0,4.5,0.0,0
80.0,1.02,0.0,0.0,1.0,49.0,0.9,139.0,3.3,17.5,9900.0,4.7,0.0,0
60.0,1.02,0.0,0.0,1.0,47.0,1.1,141.0,4.9,15.0,7000.0,5.2,0.0,0
60.0,1.02,0.0,0.0,1.0,37.0,0.6,150.0,5.0,13.6,5800.0,4.5,0.0,0
60.0,1.025,0.0,0.0,1.0,17.0,0.4,147.0,4.7,14.6,6800.0,5.1,0.0,0
60.0,1.02,0.0,0.0,1.0,24.0,0.8,145.0,5.0,15.0,6300.0,4.6,0.0,0
70.0,1.025,0.0,0.0,1.0,38.0,0.5,144.0,4.8,17.1,7400.0,6.1,0.0,0
70.0,1.025,0.0,0.0,1.0,16.0,1.1,140.0,3.6,13.6,11000.0,4.9,0.0,0
60.0,1.02,0.0,0.0,1.0,22.0,1.2,138.0,3.5,13.0,5200.0,5.6,0.0,0
60.0,1.02,0.0,0.0,1.0,50.0,0.6,147.0,3.7,17.2,6000.0,4.5,0.0,0
60.0,1.025,0.0,0.0,1.0,39.0,0.5,135.0,3.9,14.7,5800.0,6.2,0.0,0
80.0,1.02,0.0,0.0,1.0,16.0,0.7,138.0,3.5,13.7,5400.0,5.8,0.0,0
80.0,1.025,0.0,0.0,1.0,19.0,1.1,144.0,5.0,15.0,10300.0,4.8,0.0,0
80.0,1.025,0.0,0.0,1.0,40.0,0.5,137.53,4.63,17.8,5900.0,5.2,0.0,0
80.0,1.025,0.0,0.0,1.0,44.0,0.7,137.0,3.5,14.8,9300.0,4.7,0.0,0
80.0,1.02,0.0,0.0,1.0,46.0,1.0,145.0,3.5,12.53,10700.0,6.3,0.0,0
80.0,1.025,0.0,0.0,1.0,15.0,0.5,141.0,3.6,15.0,10500.0,5.3,0.0,0
60.0,1.025,0.0,0.0,1.0,41.0,1.1,139.0,3.8,17.4,6700.0,6.1,0.0,0
80.0,1.025,0.0,0.0,1.0,42.0,0.7,146.0,5.0,14.9,9400.0,5.9,0.0,0
70.0,1.02,0.0,0.0,1.0,48.0,0.8,144.0,3.5,13.6,10300.0,4.8,0.0,0
70.0,1.02,0.0,0.0,1.0,42.0,1.2,139.0,3.7,16.2,9300.0,5.4,0.0,0
60.0,1.025,0.0,0.0,1.0,50.0,0.5,145.0,5.0,17.6,6500.0,5.0,0.0,0
60.0,1.02,0.0,0.0,1.0,26.0,0.9,150.0,4.9,15.0,10500.0,5.5,0.0,0
70.0,1.025,0.0,0.0,1.0,38.0,1.0,142.0,3.6,13.7,9200.0,4.9,0.0,0
80.0,1.025,0.0,0.0,1.0,44.0,1.2,146.0,3.6,16.3,8000.0,6.4,0.0,0
80.0,1.02,0.0,0.0,1.0,41.0,0.5,143.0,4.5,15.1,9700.0,5.6,0.0,0
70.0,1.025,0.0,0.0,1.0,16.0,1.1,147.0,3.5,16.4,9100.0,5.2,0.0,0
70.0,1.02,0.0,0.0,1.0,27.0,0.7,145.0,4.8,13.8,6400.0,4.8,0.0,0
60.0,1.025,0.0,0.0,1.0,57.0,0.9,140.0,4.8,15.2,7700.0,5.5,0.0,0
80.0,1.025,0.0,0.0,1.0,45.0,0.6,138.0,3.5,16.1,5400.0,5.7,0.0,0
60.0,1.02,0.0,0.0,1.0,23.0,1.1,139.0,3.5,15.3,6500.0,4.9,0.0,0
70.0,1.025,0.0,0.0,1.0,47.0,0.5,142.0,4.8,16.6,5800.0,5.9,0.0,0
80.0,1.025,0.0,0.0,1.0,22.0,0.8,137.0,5.0,16.8,6000.0,6.5,0.0,0
80.0,1.025,0.0,0.0,1.0,46.0,0.7,141.0,4.9,13.9,5100.0,5.0,0.0,0
60.0,1.02,0.0,0.0,1.0,18.0,1.1,150.0,4.7,15.4,11000.0,4.5,0.0,0
70.0,1.02,0.0,0.0,1.0,25.0,0.6,146.0,4.9,16.5,8000.0,5.1,0.0,0
70.0,1.025,0.0,0.0,1.0,47.0,0.5,142.0,3.5,16.4,5700.0,6.5,0.0,0
80.0,1.025,0.0,0.0,1.0,17.0,0.9,136.0,3.9,16.7,6200.0,5.2,0.0,0
80.0,1.02,0.0,0.0,1.0,15.0,1.2,144.0,3.7,15.5,9500.0,6.4,0.0,0
80.0,1.025,0.0,0.0,1.0,48.0,0.7,140.0,5.0,17.0,7200.0,5.8,0.0,0
80.0,1.025,0.0,0.0,1.0,25.0,0.8,135.0,3.7,15.0,6300.0,5.3,0.0,0
80.0,1.025,0.0,0.0,1.0,16.0,1.1,142.0,4.1,15.6,5800.0,6.3,0.0,0
80.0,1.02,0.0,0.0,1.0,48.0,1.2,147.0,4.3,14.8,6600.0,5.5,0.0,0
60.0,1.025,0.0,0.0,1.0,45.0,0.7,141.0,4.4,13.0,7400.0,5.4,0.0,0
80.0,1.02,0.0,0.0,1.0,46.0,0.8,139.0,5.0,14.1,9500.0,4.6,0.0,0
80.0,1.02,0.0,0.0,1.0,49.0,0.5,150.0,4.9,15.7,6700.0,4.9,0.0,0
70.0,1.025,0.0,0.0,1.0,31.0,1.2,141.0,3.5,16.5,7800.0,6.2,0.0,0
80.0,1.02,0.0,0.0,1.0,26.0,0.6,137.0,4.4,15.8,6600.0,5.4,0.0,0
60.0,1.025,0.0,0.0,1.0,50.0,1.0,135.0,4.9,14.2,7200.0,5.9,0.0,0
80.0,1.025,0.0,0.0,1.0,18.0,1.1,141.0,3.5,15.8,6800.0,6.1,0.0,0
project/DATASETS/Lung/cancer patient data sets new.csv 0 → 100644
View file @ 3ec0eded
Age,Gender,Alcohol use,Dust Allergy,Genetic Risk,Smoking,Chest Pain,Coughing of Blood,Fatigue,Shortness of Breath,Wheezing,Swallowing Difficulty,Dry Cough,Level
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
35,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
46,1,8,7,7,8,7,9,3,4,1,4,2,High
35,1,5,6,5,2,4,8,8,9,2,1,7,High
52,2,4,5,3,3,2,4,3,2,2,3,3,Low
28,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,2,5,6,6,6,6,5,1,3,2,4,4,Medium
46,1,3,4,4,2,4,4,1,4,6,5,1,Medium
44,1,7,7,7,7,7,7,5,2,7,8,5,High
64,2,8,7,7,7,7,7,9,5,7,2,1,High
39,2,5,6,5,6,6,6,5,2,4,3,5,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
14,1,4,5,5,5,6,5,5,2,1,4,1,Medium
24,1,8,7,6,8,9,6,5,5,2,3,7,High
53,2,5,6,5,2,4,8,8,9,2,1,7,High
62,1,8,7,7,8,7,9,3,4,1,4,2,High
29,2,7,7,7,7,7,7,2,6,7,6,3,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
28,2,6,7,3,3,2,2,3,7,7,4,7,Medium
35,2,6,2,6,6,7,6,5,4,6,5,5,High
42,1,4,5,5,7,2,3,8,7,3,8,6,High
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
14,1,4,5,5,5,6,5,5,2,1,4,1,Medium
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
45,2,2,4,6,6,7,2,3,7,3,8,3,Low
27,2,1,4,2,1,3,1,3,2,4,2,4,Low
26,2,3,1,3,3,4,3,1,3,4,5,3,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
22,1,1,3,3,6,1,2,3,1,3,2,1,Low
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
24,1,8,7,6,8,9,6,5,5,2,3,7,High
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
38,2,2,3,3,3,1,1,1,3,2,5,1,Low
18,2,2,1,2,1,2,2,2,1,3,4,4,Low
23,2,2,3,2,2,2,4,1,4,2,4,3,Low
24,2,2,2,1,4,3,6,2,2,3,4,1,Low
35,2,1,2,2,2,2,1,3,5,1,3,2,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
52,2,2,1,3,7,1,1,1,1,3,2,3,Low
53,2,5,6,5,2,4,8,8,9,2,1,7,High
44,2,3,2,3,7,2,2,2,3,2,1,2,Low
38,2,3,5,1,4,2,4,2,3,4,6,2,Low
62,2,2,1,2,6,3,2,4,2,1,2,3,Low
61,2,3,4,1,3,1,5,2,3,2,1,1,Low
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
38,1,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
45,2,2,4,3,2,3,3,4,6,5,5,5,Medium
33,2,6,7,7,3,8,7,3,6,4,2,2,Medium
62,1,8,7,7,8,7,9,3,4,1,4,2,High
32,2,6,7,3,3,2,2,3,7,7,4,7,Medium
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
22,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
48,1,6,7,7,3,8,7,3,6,4,2,2,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
29,2,7,7,7,7,7,7,2,6,7,6,3,High
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
36,2,1,5,2,1,2,4,6,2,5,8,2,Medium
31,2,2,4,3,2,3,3,4,6,5,5,5,Medium
38,2,2,3,2,3,3,4,4,2,4,6,2,Medium
35,1,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,7,7,7,7,7,7,5,2,7,8,5,High
33,1,8,7,7,7,7,7,9,5,7,2,1,High
45,1,7,7,7,4,7,7,4,5,6,5,6,High
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
53,1,8,7,6,8,9,6,5,5,2,3,7,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
46,2,8,7,7,8,7,9,3,4,1,4,2,High
27,1,7,7,7,7,7,7,2,6,7,6,3,High
26,1,7,7,6,7,7,8,4,3,1,4,7,High
37,1,7,7,7,7,7,7,8,7,6,7,6,High
28,1,7,7,7,7,7,7,5,2,7,8,5,High
19,1,8,7,7,7,7,7,9,5,7,2,1,High
29,2,5,6,5,2,4,8,8,9,2,1,7,High
39,2,8,7,7,8,7,9,3,4,1,4,2,High
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
49,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
26,2,7,7,7,7,7,7,2,6,7,6,3,High
37,2,7,7,6,7,7,8,4,3,1,4,7,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
44,1,7,7,7,7,7,7,5,2,7,8,5,High
37,2,8,7,7,7,7,7,9,5,7,2,1,High
25,2,5,6,5,2,4,8,8,9,2,1,7,High
18,2,8,7,7,8,7,9,3,4,1,4,2,High
47,1,5,6,5,2,4,8,8,9,2,1,7,High
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
26,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,2,5,6,6,6,6,5,1,3,2,4,4,Medium
27,2,3,4,4,2,4,4,1,4,6,5,1,Medium
48,1,7,7,7,7,7,7,5,2,7,8,5,High
64,1,8,7,7,7,7,7,9,5,7,2,1,High
39,1,5,6,5,6,6,6,5,2,4,3,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
14,1,4,5,5,5,6,5,5,2,1,4,1,Medium
24,1,8,7,6,8,9,6,5,5,2,3,7,High
53,2,5,6,5,2,4,8,8,9,2,1,7,High
62,1,8,7,7,8,7,9,3,4,1,4,2,High
29,2,7,7,7,7,7,7,2,6,7,6,3,High
28,2,6,7,3,3,2,2,3,7,7,4,7,Medium
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
28,2,6,7,3,3,2,2,3,7,7,4,7,Medium
35,2,6,2,6,6,7,6,5,4,6,5,5,High
42,1,4,5,5,7,2,3,8,7,3,8,6,High
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,2,6,2,6,6,7,6,5,4,6,5,5,High
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
45,2,2,4,6,6,7,2,3,7,3,8,3,Low
27,1,1,4,2,1,3,1,3,2,4,2,4,Low
26,1,3,1,3,3,4,3,1,3,4,5,3,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
22,2,1,3,3,6,1,2,3,1,3,2,1,Low
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
42,1,4,5,5,7,2,3,8,7,3,8,6,High
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
38,2,2,3,3,3,1,1,1,3,2,5,1,Low
18,2,2,1,2,1,2,2,2,1,3,4,4,Low
23,2,2,3,2,2,2,4,1,4,2,4,3,Low
24,2,2,2,1,4,3,6,2,2,3,4,1,Low
35,2,1,2,2,2,2,1,3,5,1,3,2,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
52,1,2,1,3,7,1,1,1,1,3,2,3,Low
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
44,1,3,2,3,7,2,2,2,3,2,1,2,Low
38,1,3,5,1,4,2,4,2,3,4,6,2,Low
62,1,2,1,2,6,3,2,4,2,1,2,3,Low
61,1,3,4,1,3,1,5,2,3,2,1,1,Low
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
38,2,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
45,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
32,1,6,7,3,3,2,2,3,7,7,4,7,Medium
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
22,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
48,2,6,7,7,3,8,7,3,6,4,2,2,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
36,2,1,5,2,1,2,4,6,2,5,8,2,Medium
31,2,2,4,3,2,3,3,4,6,5,5,5,Medium
38,2,2,3,2,3,3,4,4,2,4,6,2,Medium
35,1,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,7,7,7,7,7,7,5,2,7,8,5,High
33,1,8,7,7,7,7,7,9,5,7,2,1,High
45,1,7,7,7,4,7,7,4,5,6,5,6,High
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
53,1,8,7,6,8,9,6,5,5,2,3,7,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
46,1,8,7,7,8,7,9,3,4,1,4,2,High
27,1,7,7,7,7,7,7,2,6,7,6,3,High
26,1,7,7,6,7,7,8,4,3,1,4,7,High
37,1,7,7,7,7,7,7,8,7,6,7,6,High
28,1,7,7,7,7,7,7,5,2,7,8,5,High
19,1,8,7,7,7,7,7,9,5,7,2,1,High
29,2,5,6,5,2,4,8,8,9,2,1,7,High
39,2,8,7,7,8,7,9,3,4,1,4,2,High
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
49,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
26,2,7,7,7,7,7,7,2,6,7,6,3,High
37,2,7,7,6,7,7,8,4,3,1,4,7,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
44,1,7,7,7,7,7,7,5,2,7,8,5,High
37,2,8,7,7,7,7,7,9,5,7,2,1,High
25,2,5,6,5,2,4,8,8,9,2,1,7,High
18,2,8,7,7,8,7,9,3,4,1,4,2,High
47,1,5,6,5,2,4,8,8,9,2,1,7,High
35,1,5,6,6,6,6,5,1,3,2,4,4,Medium
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
26,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
35,1,5,6,5,2,4,8,8,9,2,1,7,High
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,1,5,6,6,6,6,5,1,3,2,4,4,Medium
27,1,3,4,4,2,4,4,1,4,6,5,1,Medium
48,1,7,7,7,7,7,7,5,2,7,8,5,High
64,1,8,7,7,7,7,7,9,5,7,2,1,High
39,1,5,6,5,6,6,6,5,2,4,3,5,Medium
45,2,2,4,6,6,7,2,3,7,3,8,3,Low
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
14,1,4,5,5,5,6,5,5,2,1,4,1,Medium
24,1,8,7,6,8,9,6,5,5,2,3,7,High
53,2,5,6,5,2,4,8,8,9,2,1,7,High
62,1,8,7,7,8,7,9,3,4,1,4,2,High
29,2,7,7,7,7,7,7,2,6,7,6,3,High
27,1,1,4,2,1,3,1,3,2,4,2,4,Low
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
28,2,6,7,3,3,2,2,3,7,7,4,7,Medium
35,2,6,2,6,6,7,6,5,4,6,5,5,High
42,1,4,5,5,7,2,3,8,7,3,8,6,High
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
26,1,3,1,3,3,4,3,1,3,4,5,3,Low
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
45,2,2,4,6,6,7,2,3,7,3,8,3,Low
27,1,1,4,2,1,3,1,3,2,4,2,4,Low
26,1,3,1,3,3,4,3,1,3,4,5,3,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
22,2,1,3,3,6,1,2,3,1,3,2,1,Low
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
38,2,2,3,3,3,1,1,1,3,2,5,1,Low
18,2,2,1,2,1,2,2,2,1,3,4,4,Low
23,2,2,3,2,2,2,4,1,4,2,4,3,Low
24,2,2,2,1,4,3,6,2,2,3,4,1,Low
35,2,1,2,2,2,2,1,3,5,1,3,2,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
52,1,2,1,3,7,1,1,1,1,3,2,3,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
44,1,3,2,3,7,2,2,2,3,2,1,2,Low
38,1,3,5,1,4,2,4,2,3,4,6,2,Low
62,1,2,1,2,6,3,2,4,2,1,2,3,Low
61,1,3,4,1,3,1,5,2,3,2,1,1,Low
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
38,2,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
45,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,6,7,7,3,8,7,3,6,4,2,2,Medium
22,2,1,3,3,6,1,2,3,1,3,2,1,Low
32,1,6,7,3,3,2,2,3,7,7,4,7,Medium
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
22,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
48,2,6,7,7,3,8,7,3,6,4,2,2,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
36,2,1,5,2,1,2,4,6,2,5,8,2,Medium
31,2,2,4,3,2,3,3,4,6,5,5,5,Medium
38,2,2,3,2,3,3,4,4,2,4,6,2,Medium
35,1,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,7,7,7,7,7,7,5,2,7,8,5,High
33,1,8,7,7,7,7,7,9,5,7,2,1,High
45,1,7,7,7,4,7,7,4,5,6,5,6,High
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
53,1,8,7,6,8,9,6,5,5,2,3,7,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
46,1,8,7,7,8,7,9,3,4,1,4,2,High
27,1,7,7,7,7,7,7,2,6,7,6,3,High
26,1,7,7,6,7,7,8,4,3,1,4,7,High
37,1,7,7,7,7,7,7,8,7,6,7,6,High
28,1,7,7,7,7,7,7,5,2,7,8,5,High
19,1,8,7,7,7,7,7,9,5,7,2,1,High
29,2,5,6,5,2,4,8,8,9,2,1,7,High
39,2,8,7,7,8,7,9,3,4,1,4,2,High
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
49,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
26,2,7,7,7,7,7,7,2,6,7,6,3,High
37,2,7,7,6,7,7,8,4,3,1,4,7,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
44,1,7,7,7,7,7,7,5,2,7,8,5,High
37,2,8,7,7,7,7,7,9,5,7,2,1,High
25,2,5,6,5,2,4,8,8,9,2,1,7,High
18,2,8,7,7,8,7,9,3,4,1,4,2,High
47,1,5,6,5,2,4,8,8,9,2,1,7,High
27,1,3,4,4,2,4,4,1,4,6,5,1,Medium
38,2,2,3,3,3,1,1,1,3,2,5,1,Low
26,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
35,1,5,6,5,2,4,8,8,9,2,1,7,High
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,1,5,6,6,6,6,5,1,3,2,4,4,Medium
27,1,3,4,4,2,4,4,1,4,6,5,1,Medium
48,1,7,7,7,7,7,7,5,2,7,8,5,High
64,1,8,7,7,7,7,7,9,5,7,2,1,High
39,1,5,6,5,6,6,6,5,2,4,3,5,Medium
18,2,2,1,2,1,2,2,2,1,3,4,4,Low
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
14,1,4,5,5,5,6,5,5,2,1,4,1,Medium
24,1,8,7,6,8,9,6,5,5,2,3,7,High
53,2,5,6,5,2,4,8,8,9,2,1,7,High
62,1,8,7,7,8,7,9,3,4,1,4,2,High
29,2,7,7,7,7,7,7,2,6,7,6,3,High
23,2,2,3,2,2,2,4,1,4,2,4,3,Low
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
28,2,6,7,3,3,2,2,3,7,7,4,7,Medium
35,2,6,2,6,6,7,6,5,4,6,5,5,High
42,1,4,5,5,7,2,3,8,7,3,8,6,High
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
24,2,2,2,1,4,3,6,2,2,3,4,1,Low
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
45,1,2,4,6,6,7,2,3,7,3,8,3,Low
27,1,1,4,2,1,3,1,3,2,4,2,4,Low
26,1,3,1,3,3,4,3,1,3,4,5,3,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
22,2,1,3,3,6,1,2,3,1,3,2,1,Low
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
35,2,1,2,2,2,2,1,3,5,1,3,2,Low
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
25,2,2,3,3,3,1,1,1,3,2,5,1,Low
38,2,2,1,2,1,2,2,2,1,3,4,4,Low
43,2,2,3,2,2,2,4,1,4,2,4,3,Low
44,2,2,2,1,4,3,6,2,2,3,4,1,Low
55,2,1,2,2,2,2,1,3,5,1,3,2,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
52,1,2,1,3,7,1,1,1,1,3,2,3,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
44,1,3,2,3,7,2,2,2,3,2,1,2,Low
38,1,3,5,1,4,2,4,2,3,4,6,2,Low
62,1,2,1,2,6,3,2,4,2,1,2,3,Low
61,1,3,4,1,3,1,5,2,3,2,1,1,Low
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
38,2,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
45,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,6,7,7,3,8,7,3,6,4,2,2,Medium
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
32,2,6,7,3,3,2,2,3,7,7,4,7,Medium
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,1,5,2,1,2,4,6,2,5,8,2,Medium
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
32,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
48,2,6,7,7,3,8,7,3,6,4,2,2,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
52,1,2,1,3,7,1,1,1,1,3,2,3,Low
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
46,2,1,5,2,1,2,4,6,2,5,8,2,Medium
51,2,2,4,3,2,3,3,4,6,5,5,5,Medium
38,1,2,3,2,3,3,4,4,2,4,6,2,Medium
45,1,8,7,7,1,4,3,2,6,5,1,4,Medium
54,1,7,7,7,7,7,7,5,2,7,8,5,High
33,1,8,7,7,7,7,7,9,5,7,2,1,High
45,1,7,7,7,4,7,7,4,5,6,5,6,High
44,1,3,2,3,7,2,2,2,3,2,1,2,Low
63,1,8,7,6,8,9,6,5,5,2,3,7,High
45,1,5,6,5,2,4,8,8,9,2,1,7,High
46,1,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,7,7,7,7,2,6,7,6,3,High
36,1,7,7,6,7,7,8,4,3,1,4,7,High
37,1,7,7,7,7,7,7,8,7,6,7,6,High
28,1,7,7,7,7,7,7,5,2,7,8,5,High
19,1,8,7,7,7,7,7,9,5,7,2,1,High
39,1,5,6,5,2,4,8,8,9,2,1,7,High
39,1,8,7,7,8,7,9,3,4,1,4,2,High
38,1,3,5,1,4,2,4,2,3,4,6,2,Low
49,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
36,1,7,7,7,7,7,7,2,6,7,6,3,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
44,1,7,7,7,7,7,7,5,2,7,8,5,High
37,1,8,7,7,7,7,7,9,5,7,2,1,High
25,1,5,6,5,2,4,8,8,9,2,1,7,High
28,1,8,7,7,8,7,9,3,4,1,4,2,High
47,1,5,6,5,2,4,8,8,9,2,1,7,High
48,1,7,7,7,7,7,7,5,2,7,8,5,High
62,1,2,1,2,6,3,2,4,2,1,2,3,Low
26,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
35,1,5,6,5,2,4,8,8,9,2,1,7,High
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,2,5,6,6,6,6,5,1,3,2,4,4,Medium
27,2,3,4,4,2,4,4,1,4,6,5,1,Medium
48,2,7,7,7,7,7,7,5,2,7,8,5,High
64,1,8,7,7,7,7,7,9,5,7,2,1,High
39,1,5,6,5,6,6,6,5,2,4,3,5,Medium
61,1,3,4,1,3,1,5,2,3,2,1,1,Low
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
24,1,4,5,5,5,6,5,5,2,1,4,1,Medium
24,1,8,7,6,8,9,6,5,5,2,3,7,High
53,1,5,6,5,2,4,8,8,9,2,1,7,High
62,1,8,7,7,8,7,9,3,4,1,4,2,High
29,1,7,7,7,7,7,7,2,6,7,6,3,High
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,1,1,5,2,1,2,4,6,2,5,8,2,Medium
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
28,1,6,7,3,3,2,2,3,7,7,4,7,Medium
35,1,6,2,6,6,7,6,5,4,6,5,5,High
42,1,4,5,5,7,2,3,8,7,3,8,6,High
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
45,2,2,4,6,6,7,2,3,7,3,8,3,Low
27,1,1,4,2,1,3,1,3,2,4,2,4,Low
26,1,3,1,3,3,4,3,1,3,4,5,3,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
22,2,1,3,3,6,1,2,3,1,3,2,1,Low
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
38,2,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
38,2,2,3,3,3,1,1,1,3,2,5,1,Low
18,2,2,1,2,1,2,2,2,1,3,4,4,Low
23,2,2,3,2,2,2,4,1,4,2,4,3,Low
24,1,2,2,1,4,3,6,2,2,3,4,1,Low
35,1,1,2,2,2,2,1,3,5,1,3,2,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
52,1,2,1,3,7,1,1,1,1,3,2,3,Low
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
44,1,3,2,3,7,2,2,2,3,2,1,2,Low
38,1,3,5,1,4,2,4,2,3,4,6,2,Low
62,1,2,1,2,6,3,2,4,2,1,2,3,Low
61,1,3,4,1,3,1,5,2,3,2,1,1,Low
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
38,2,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
45,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,6,7,7,3,8,7,3,6,4,2,2,Medium
45,1,2,4,3,2,3,3,4,6,5,5,5,Medium
32,1,6,7,3,3,2,2,3,7,7,4,7,Medium
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
22,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
48,2,6,7,7,3,8,7,3,6,4,2,2,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
33,1,6,7,7,3,8,7,3,6,4,2,2,Medium
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
36,2,1,5,2,1,2,4,6,2,5,8,2,Medium
31,2,2,4,3,2,3,3,4,6,5,5,5,Medium
38,2,2,3,2,3,3,4,4,2,4,6,2,Medium
35,1,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,7,7,7,7,7,7,5,2,7,8,5,High
33,1,8,7,7,7,7,7,9,5,7,2,1,High
45,1,7,7,7,4,7,7,4,5,6,5,6,High
32,1,6,7,3,3,2,2,3,7,7,4,7,Medium
53,1,8,7,6,8,9,6,5,5,2,3,7,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
46,1,8,7,7,8,7,9,3,4,1,4,2,High
27,1,7,7,7,7,7,7,2,6,7,6,3,High
26,1,7,7,6,7,7,8,4,3,1,4,7,High
37,1,7,7,7,7,7,7,8,7,6,7,6,High
28,1,7,7,7,7,7,7,5,2,7,8,5,High
19,1,8,7,7,7,7,7,9,5,7,2,1,High
29,2,5,6,5,2,4,8,8,9,2,1,7,High
39,2,8,7,7,8,7,9,3,4,1,4,2,High
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
49,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
26,2,7,7,7,7,7,7,2,6,7,6,3,High
37,2,7,7,6,7,7,8,4,3,1,4,7,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
44,1,7,7,7,7,7,7,5,2,7,8,5,High
37,2,8,7,7,7,7,7,9,5,7,2,1,High
25,2,5,6,5,2,4,8,8,9,2,1,7,High
18,2,8,7,7,8,7,9,3,4,1,4,2,High
47,1,5,6,5,2,4,8,8,9,2,1,7,High
64,1,8,7,7,7,7,7,9,5,7,2,1,High
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
26,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
35,1,5,6,5,2,4,8,8,9,2,1,7,High
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,1,5,6,6,6,6,5,1,3,2,4,4,Medium
27,1,3,4,4,2,4,4,1,4,6,5,1,Medium
48,1,7,7,7,7,7,7,5,2,7,8,5,High
64,1,8,7,7,7,7,7,9,5,7,2,1,High
39,1,5,6,5,6,6,6,5,2,4,3,5,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
14,1,4,5,5,5,6,5,5,2,1,4,1,Medium
24,1,8,7,6,8,9,6,5,5,2,3,7,High
53,2,5,6,5,2,4,8,8,9,2,1,7,High
62,1,8,7,7,8,7,9,3,4,1,4,2,High
29,2,7,7,7,7,7,7,2,6,7,6,3,High
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
28,2,6,7,3,3,2,2,3,7,7,4,7,Medium
35,2,6,2,6,6,7,6,5,4,6,5,5,High
42,1,4,5,5,7,2,3,8,7,3,8,6,High
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
22,1,8,7,7,1,4,3,2,6,5,1,4,Medium
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
45,2,2,4,6,6,7,2,3,7,3,8,3,Low
27,1,1,4,2,1,3,1,3,2,4,2,4,Low
26,1,3,1,3,3,4,3,1,3,4,5,3,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
22,2,1,3,3,6,1,2,3,1,3,2,1,Low
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
38,2,2,3,3,3,1,1,1,3,2,5,1,Low
18,2,2,1,2,1,2,2,2,1,3,4,4,Low
23,2,2,3,2,2,2,4,1,4,2,4,3,Low
24,2,2,2,1,4,3,6,2,2,3,4,1,Low
35,2,1,2,2,2,2,1,3,5,1,3,2,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
52,1,2,1,3,7,1,1,1,1,3,2,3,Low
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
44,1,3,2,3,7,2,2,2,3,2,1,2,Low
38,1,3,5,1,4,2,4,2,3,4,6,2,Low
62,1,2,1,2,6,3,2,4,2,1,2,3,Low
61,1,3,4,1,3,1,5,2,3,2,1,1,Low
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
38,2,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
45,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,6,7,7,3,8,7,3,6,4,2,2,Medium
48,2,6,7,7,3,8,7,3,6,4,2,2,Medium
32,1,6,7,3,3,2,2,3,7,7,4,7,Medium
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
22,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
48,2,6,7,7,3,8,7,3,6,4,2,2,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
36,2,1,5,2,1,2,4,6,2,5,8,2,Medium
31,2,2,4,3,2,3,3,4,6,5,5,5,Medium
38,2,2,3,2,3,3,4,4,2,4,6,2,Medium
35,1,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,7,7,7,7,7,7,5,2,7,8,5,High
33,1,8,7,7,7,7,7,9,5,7,2,1,High
45,1,7,7,7,4,7,7,4,5,6,5,6,High
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
53,1,8,7,6,8,9,6,5,5,2,3,7,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
46,1,8,7,7,8,7,9,3,4,1,4,2,High
27,1,7,7,7,7,7,7,2,6,7,6,3,High
26,1,7,7,6,7,7,8,4,3,1,4,7,High
37,1,7,7,7,7,7,7,8,7,6,7,6,High
28,1,7,7,7,7,7,7,5,2,7,8,5,High
19,1,8,7,7,7,7,7,9,5,7,2,1,High
29,2,5,6,5,2,4,8,8,9,2,1,7,High
39,2,8,7,7,8,7,9,3,4,1,4,2,High
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
49,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
26,2,7,7,7,7,7,7,2,6,7,6,3,High
37,2,7,7,6,7,7,8,4,3,1,4,7,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
44,1,7,7,7,7,7,7,5,2,7,8,5,High
37,2,8,7,7,7,7,7,9,5,7,2,1,High
25,2,5,6,5,2,4,8,8,9,2,1,7,High
18,2,8,7,7,8,7,9,3,4,1,4,2,High
47,1,5,6,5,2,4,8,8,9,2,1,7,High
39,1,5,6,5,6,6,6,5,2,4,3,5,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
26,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
35,1,5,6,5,2,4,8,8,9,2,1,7,High
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,1,5,6,6,6,6,5,1,3,2,4,4,Medium
27,1,3,4,4,2,4,4,1,4,6,5,1,Medium
48,1,7,7,7,7,7,7,5,2,7,8,5,High
64,1,8,7,7,7,7,7,9,5,7,2,1,High
39,1,5,6,5,6,6,6,5,2,4,3,5,Medium
36,2,1,5,2,1,2,4,6,2,5,8,2,Medium
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
14,1,4,5,5,5,6,5,5,2,1,4,1,Medium
24,1,8,7,6,8,9,6,5,5,2,3,7,High
53,2,5,6,5,2,4,8,8,9,2,1,7,High
62,1,8,7,7,8,7,9,3,4,1,4,2,High
29,2,7,7,7,7,7,7,2,6,7,6,3,High
31,2,2,4,3,2,3,3,4,6,5,5,5,Medium
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
28,2,6,7,3,3,2,2,3,7,7,4,7,Medium
35,2,6,2,6,6,7,6,5,4,6,5,5,High
42,1,4,5,5,7,2,3,8,7,3,8,6,High
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
38,2,2,3,2,3,3,4,4,2,4,6,2,Medium
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
45,2,2,4,6,6,7,2,3,7,3,8,3,Low
27,1,1,4,2,1,3,1,3,2,4,2,4,Low
26,1,3,1,3,3,4,3,1,3,4,5,3,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
22,2,1,3,3,6,1,2,3,1,3,2,1,Low
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
35,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
38,2,2,3,3,3,1,1,1,3,2,5,1,Low
18,2,2,1,2,1,2,2,2,1,3,4,4,Low
23,2,2,3,2,2,2,4,1,4,2,4,3,Low
24,2,2,2,1,4,3,6,2,2,3,4,1,Low
35,2,1,2,2,2,2,1,3,5,1,3,2,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
52,1,2,1,3,7,1,1,1,1,3,2,3,Low
44,1,7,7,7,7,7,7,5,2,7,8,5,High
44,1,3,2,3,7,2,2,2,3,2,1,2,Low
38,1,3,5,1,4,2,4,2,3,4,6,2,Low
62,1,2,1,2,6,3,2,4,2,1,2,3,Low
61,1,3,4,1,3,1,5,2,3,2,1,1,Low
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
38,2,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
45,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,8,7,7,7,7,7,9,5,7,2,1,High
32,1,6,7,3,3,2,2,3,7,7,4,7,Medium
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
22,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
48,2,6,7,7,3,8,7,3,6,4,2,2,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
45,1,7,7,7,4,7,7,4,5,6,5,6,High
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
36,2,1,5,2,1,2,4,6,2,5,8,2,Medium
31,2,2,4,3,2,3,3,4,6,5,5,5,Medium
38,2,2,3,2,3,3,4,4,2,4,6,2,Medium
35,1,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,7,7,7,7,7,7,5,2,7,8,5,High
33,1,8,7,7,7,7,7,9,5,7,2,1,High
45,1,7,7,7,4,7,7,4,5,6,5,6,High
53,1,8,7,6,8,9,6,5,5,2,3,7,High
53,1,8,7,6,8,9,6,5,5,2,3,7,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
46,1,8,7,7,8,7,9,3,4,1,4,2,High
27,1,7,7,7,7,7,7,2,6,7,6,3,High
26,1,7,7,6,7,7,8,4,3,1,4,7,High
37,1,7,7,7,7,7,7,8,7,6,7,6,High
28,1,7,7,7,7,7,7,5,2,7,8,5,High
19,1,8,7,7,7,7,7,9,5,7,2,1,High
29,2,5,6,5,2,4,8,8,9,2,1,7,High
39,2,8,7,7,8,7,9,3,4,1,4,2,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
49,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
26,2,7,7,7,7,7,7,2,6,7,6,3,High
37,2,7,7,6,7,7,8,4,3,1,4,7,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
44,1,7,7,7,7,7,7,5,2,7,8,5,High
37,2,8,7,7,7,7,7,9,5,7,2,1,High
25,2,5,6,5,2,4,8,8,9,2,1,7,High
18,2,8,7,7,8,7,9,3,4,1,4,2,High
47,1,5,6,5,2,4,8,8,9,2,1,7,High
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
46,1,8,7,7,8,7,9,3,4,1,4,2,High
26,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
35,1,5,6,5,2,4,8,8,9,2,1,7,High
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,1,5,6,6,6,6,5,1,3,2,4,4,Medium
27,1,3,4,4,2,4,4,1,4,6,5,1,Medium
48,1,7,7,7,7,7,7,5,2,7,8,5,High
64,1,8,7,7,7,7,7,9,5,7,2,1,High
39,1,5,6,5,6,6,6,5,2,4,3,5,Medium
27,1,7,7,7,7,7,7,2,6,7,6,3,High
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
14,1,4,5,5,5,6,5,5,2,1,4,1,Medium
24,1,8,7,6,8,9,6,5,5,2,3,7,High
53,2,5,6,5,2,4,8,8,9,2,1,7,High
62,1,8,7,7,8,7,9,3,4,1,4,2,High
29,2,7,7,7,7,7,7,2,6,7,6,3,High
26,1,7,7,6,7,7,8,4,3,1,4,7,High
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
28,2,6,7,3,3,2,2,3,7,7,4,7,Medium
35,2,6,2,6,6,7,6,5,4,6,5,5,High
42,1,4,5,5,7,2,3,8,7,3,8,6,High
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
37,1,7,7,7,7,7,7,8,7,6,7,6,High
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
45,2,2,4,6,6,7,2,3,7,3,8,3,Low
27,1,1,4,2,1,3,1,3,2,4,2,4,Low
26,1,3,1,3,3,4,3,1,3,4,5,3,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
22,2,1,3,3,6,1,2,3,1,3,2,1,Low
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
28,1,7,7,7,7,7,7,5,2,7,8,5,High
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
38,2,2,3,3,3,1,1,1,3,2,5,1,Low
18,2,2,1,2,1,2,2,2,1,3,4,4,Low
23,2,2,3,2,2,2,4,1,4,2,4,3,Low
24,2,2,2,1,4,3,6,2,2,3,4,1,Low
35,2,1,2,2,2,2,1,3,5,1,3,2,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
52,1,2,1,3,7,1,1,1,1,3,2,3,Low
19,1,8,7,7,7,7,7,9,5,7,2,1,High
44,1,3,2,3,7,2,2,2,3,2,1,2,Low
38,1,3,5,1,4,2,4,2,3,4,6,2,Low
62,1,2,1,2,6,3,2,4,2,1,2,3,Low
61,1,3,4,1,3,1,5,2,3,2,1,1,Low
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
38,2,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
45,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,6,7,7,3,8,7,3,6,4,2,2,Medium
29,2,5,6,5,2,4,8,8,9,2,1,7,High
32,1,6,7,3,3,2,2,3,7,7,4,7,Medium
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
22,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
48,2,6,7,7,3,8,7,3,6,4,2,2,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
39,2,8,7,7,8,7,9,3,4,1,4,2,High
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
36,2,1,5,2,1,2,4,6,2,5,8,2,Medium
31,2,2,4,3,2,3,3,4,6,5,5,5,Medium
38,2,2,3,2,3,3,4,4,2,4,6,2,Medium
35,1,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,7,7,7,7,7,7,5,2,7,8,5,High
33,1,8,7,7,7,7,7,9,5,7,2,1,High
45,1,7,7,7,4,7,7,4,5,6,5,6,High
49,1,5,6,5,2,4,8,8,9,2,1,7,High
53,1,8,7,6,8,9,6,5,5,2,3,7,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
46,1,8,7,7,8,7,9,3,4,1,4,2,High
27,1,7,7,7,7,7,7,2,6,7,6,3,High
26,1,7,7,6,7,7,8,4,3,1,4,7,High
37,1,7,7,7,7,7,7,8,7,6,7,6,High
28,1,7,7,7,7,7,7,5,2,7,8,5,High
19,1,8,7,7,7,7,7,9,5,7,2,1,High
29,2,5,6,5,2,4,8,8,9,2,1,7,High
39,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
49,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
26,2,7,7,7,7,7,7,2,6,7,6,3,High
37,2,7,7,6,7,7,8,4,3,1,4,7,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
44,1,7,7,7,7,7,7,5,2,7,8,5,High
37,2,8,7,7,7,7,7,9,5,7,2,1,High
25,2,5,6,5,2,4,8,8,9,2,1,7,High
18,2,8,7,7,8,7,9,3,4,1,4,2,High
47,1,5,6,5,2,4,8,8,9,2,1,7,High
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
26,2,7,7,7,7,7,7,2,6,7,6,3,High
26,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
35,2,5,6,5,2,4,8,8,9,2,1,7,High
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
35,1,5,6,6,6,6,5,1,3,2,4,4,Medium
27,1,3,4,4,2,4,4,1,4,6,5,1,Medium
48,1,7,7,7,7,7,7,5,2,7,8,5,High
64,1,8,7,7,7,7,7,9,5,7,2,1,High
39,1,5,6,5,6,6,6,5,2,4,3,5,Medium
37,2,7,7,6,7,7,8,4,3,1,4,7,High
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
73,1,6,6,6,8,5,5,4,6,2,1,6,Medium
17,1,1,5,4,2,2,3,1,7,8,6,7,Medium
34,1,7,7,6,7,7,8,4,3,1,4,7,High
36,1,7,7,7,7,7,7,8,7,6,7,6,High
14,1,4,5,5,5,6,5,5,2,1,4,1,Medium
24,1,8,7,6,8,9,6,5,5,2,3,7,High
53,2,5,6,5,2,4,8,8,9,2,1,7,High
62,1,8,7,7,8,7,9,3,4,1,4,2,High
29,2,7,7,7,7,7,7,2,6,7,6,3,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
65,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
19,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,7,7,7,4,7,7,4,5,6,5,6,High
28,2,6,7,3,3,2,2,3,7,7,4,7,Medium
35,2,6,2,6,6,7,6,5,4,6,5,5,High
42,1,4,5,5,7,2,3,8,7,3,8,6,High
32,2,6,7,7,3,8,7,3,6,4,2,2,Medium
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
25,2,1,4,2,1,3,1,3,2,4,2,4,Low
44,1,7,7,7,7,7,7,5,2,7,8,5,High
27,2,1,4,3,2,4,2,2,3,4,1,6,Low
28,1,7,8,6,4,7,8,2,6,4,2,2,Low
32,1,3,6,7,4,7,4,2,3,2,2,5,Low
45,2,2,4,6,6,7,2,3,7,3,8,3,Low
27,1,1,4,2,1,3,1,3,2,4,2,4,Low
26,1,3,1,3,3,4,3,1,3,4,5,3,Low
48,1,2,3,1,1,1,4,2,1,2,4,2,Low
17,2,2,3,4,3,1,2,1,3,2,1,1,Low
22,2,1,3,3,6,1,2,3,1,3,2,1,Low
42,1,1,2,4,1,6,2,1,1,2,1,1,Low
37,2,8,7,7,7,7,7,9,5,7,2,1,High
35,1,3,2,2,2,3,4,4,2,2,3,2,Low
24,2,2,2,2,2,1,1,1,1,2,3,2,Low
38,2,2,3,3,3,1,1,1,3,2,5,1,Low
18,2,2,1,2,1,2,2,2,1,3,4,4,Low
23,2,2,3,2,2,2,4,1,4,2,4,3,Low
24,2,2,2,1,4,3,6,2,2,3,4,1,Low
35,2,1,2,2,2,2,1,3,5,1,3,2,Low
38,2,2,3,2,2,1,3,1,1,1,1,4,Low
47,2,3,1,2,2,2,5,3,1,2,3,4,Low
52,1,2,1,3,7,1,1,1,1,3,2,3,Low
25,2,5,6,5,2,4,8,8,9,2,1,7,High
44,1,3,2,3,7,2,2,2,3,2,1,2,Low
38,1,3,5,1,4,2,4,2,3,4,6,2,Low
62,1,2,1,2,6,3,2,4,2,1,2,3,Low
61,1,3,4,1,3,1,5,2,3,2,1,1,Low
55,1,1,1,2,3,4,3,2,2,1,2,5,Low
45,2,2,3,2,3,3,4,4,2,4,6,2,Medium
38,2,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,1,5,2,1,2,4,6,2,5,8,2,Medium
45,1,2,4,3,2,3,3,4,6,5,5,5,Medium
33,1,6,7,7,3,8,7,3,6,4,2,2,Medium
18,2,8,7,7,8,7,9,3,4,1,4,2,High
32,1,6,7,3,3,2,2,3,7,7,4,7,Medium
44,1,2,3,2,3,3,4,4,2,4,6,2,Medium
62,1,8,7,7,1,4,3,2,6,5,1,4,Medium
38,2,1,5,2,1,2,4,6,2,5,8,2,Medium
33,1,2,4,3,2,3,3,4,6,5,5,5,Medium
22,1,8,7,7,1,4,3,2,6,5,1,4,Medium
35,1,1,5,2,1,2,4,6,2,5,8,2,Medium
23,1,2,4,3,2,3,3,4,6,5,5,5,Medium
48,2,6,7,7,3,8,7,3,6,4,2,2,Medium
46,2,6,7,3,3,2,2,3,7,7,4,7,Medium
47,1,5,6,5,2,4,8,8,9,2,1,7,High
52,2,2,3,2,3,3,4,4,2,4,6,2,Medium
52,1,8,7,7,1,4,3,2,6,5,1,4,Medium
48,2,8,7,7,1,4,3,2,6,5,1,4,Medium
36,2,1,5,2,1,2,4,6,2,5,8,2,Medium
31,2,2,4,3,2,3,3,4,6,5,5,5,Medium
38,2,2,3,2,3,3,4,4,2,4,6,2,Medium
35,1,8,7,7,1,4,3,2,6,5,1,4,Medium
44,1,7,7,7,7,7,7,5,2,7,8,5,High
33,1,4,5,3,3,2,4,3,2,2,3,3,Low
45,1,1,4,2,1,3,1,3,2,4,2,4,Low
26,2,8,7,7,8,7,9,3,4,1,4,2,High
53,1,1,4,3,2,4,2,2,3,4,1,6,Low
35,2,5,6,5,2,4,8,8,9,2,1,7,High
46,1,8,7,7,8,7,9,3,4,1,4,2,High
27,1,7,7,7,7,7,7,2,6,7,6,3,High
26,1,2,4,3,2,3,3,4,6,5,5,5,Medium
37,1,2,3,2,3,3,4,4,2,4,6,2,Medium
28,1,7,7,7,7,7,7,5,2,7,8,5,High
19,1,8,7,7,7,7,7,9,5,7,2,1,High
29,2,5,6,5,2,4,8,8,9,2,1,7,High
39,2,8,7,7,8,7,9,3,4,1,4,2,High
37,1,7,7,6,7,7,8,4,3,1,4,7,High
49,1,5,6,5,2,4,8,8,9,2,1,7,High
37,1,8,7,7,8,7,9,3,4,1,4,2,High
26,2,7,7,7,7,7,7,2,6,7,6,3,High
37,2,7,7,6,7,7,8,4,3,1,4,7,High
33,1,7,7,7,7,7,7,8,7,6,7,6,High
44,1,7,7,7,7,7,7,5,2,7,8,5,High
37,2,8,7,7,7,7,7,9,5,7,2,1,High
25,2,5,6,5,2,4,8,8,9,2,1,7,High
18,2,8,7,7,8,7,9,3,4,1,4,2,High
47,1,5,6,5,2,4,8,8,9,2,1,7,High
project/DATASETS/Lung/survey lung cancer1.csv 0 → 100644
View file @ 3ec0eded
GENDER,AGE,SMOKING,YELLOW_FINGERS,ANXIETY,PEER_PRESSURE,CHRONIC DISEASE,FATIGUE ,ALLERGY ,WHEEZING,ALCOHOL CONSU1ING,COUGHING,SHORTNESS OF BREATH,SWALLOWING DIFFICULTY,CHEST PAIN,LUNG_CANCER
1,69,0,1,1,0,0,1,0,1,1,1,1,1,1,1
1,74,1,0,0,0,1,1,1,0,0,0,1,1,1,1
0,59,0,0,0,1,0,1,0,1,0,1,1,0,1,0
1,63,1,1,1,0,0,0,0,0,1,0,0,1,1,0
0,63,0,1,0,0,0,0,0,1,0,1,1,0,0,0
0,75,0,1,0,0,1,1,1,1,0,1,1,0,0,1
1,52,1,0,0,0,0,1,0,1,1,1,1,0,1,1
0,51,1,1,1,1,0,1,1,0,0,0,1,1,0,1
0,68,1,0,1,0,0,1,0,0,0,0,0,0,0,0
1,53,1,1,1,1,1,0,1,0,1,0,0,1,1,1
0,61,1,1,1,1,1,1,0,1,0,1,1,1,0,1
1,72,0,0,0,0,1,1,1,1,1,1,1,0,1,1
0,60,1,0,0,0,0,1,0,0,0,0,1,0,0,0
1,58,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,69,1,0,0,0,0,0,1,1,1,1,0,0,1,0
0,48,0,1,1,1,1,1,1,1,0,1,1,1,0,1
1,75,1,0,0,0,1,0,1,1,1,1,1,0,1,1
1,57,1,1,1,1,1,0,0,0,1,0,0,1,1,1
0,68,1,1,1,1,1,1,0,0,0,1,1,0,0,1
0,61,0,0,0,0,1,1,0,0,0,0,1,0,0,0
0,44,1,1,1,1,1,1,0,0,0,0,1,1,0,1
0,64,0,1,1,1,0,0,1,1,0,1,0,1,0,1
0,21,1,0,0,0,1,1,1,0,0,0,1,0,0,0
1,60,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,72,1,1,1,1,1,0,1,1,1,1,0,1,1,1
1,65,0,1,1,0,0,1,0,1,1,1,1,1,1,1
0,61,1,1,1,0,0,1,1,0,1,0,1,1,1,1
1,69,0,0,0,1,0,1,0,1,0,1,1,0,1,0
0,53,1,1,1,0,1,0,0,1,1,0,1,1,1,1
1,55,0,1,0,0,0,1,0,1,1,1,1,0,0,0
0,57,1,1,0,0,0,0,0,0,0,0,1,0,0,0
1,62,1,0,1,0,0,0,1,1,1,0,1,1,1,1
1,56,1,1,1,0,0,0,0,0,0,0,1,1,0,0
0,67,1,1,1,0,1,0,0,0,0,0,1,1,1,1
1,59,0,1,1,0,0,0,0,0,0,0,0,1,1,0
0,59,1,1,1,0,1,0,0,0,0,0,1,1,0,1
1,60,0,1,0,0,1,0,0,1,0,1,1,0,1,1
0,56,0,0,0,0,1,0,0,1,0,0,1,1,0,0
1,56,1,0,0,0,1,0,0,1,0,0,1,0,1,1
1,60,1,0,0,0,1,0,1,1,1,1,0,0,1,1
1,68,1,0,1,0,0,1,1,0,1,1,1,0,1,1
1,63,0,0,0,1,0,1,1,1,1,0,0,1,0,1
0,77,0,1,1,1,1,1,0,1,1,0,0,0,0,1
1,52,1,0,0,1,0,1,1,1,1,0,1,0,1,1
0,70,1,1,0,1,1,0,0,0,1,1,0,1,0,1
1,72,1,1,1,1,1,1,0,1,1,1,1,1,1,1
1,62,1,1,0,0,1,0,1,0,0,1,1,1,1,1
0,64,1,1,0,1,0,1,0,1,1,1,0,1,1,1
0,70,0,0,1,1,1,1,1,1,1,0,1,1,1,1
1,60,0,0,1,1,1,0,0,0,1,0,0,0,0,0
0,56,0,0,0,1,1,1,1,1,1,0,0,0,1,1
1,63,1,1,1,0,1,1,1,1,0,0,1,0,0,1
0,54,1,0,0,1,0,1,1,1,1,1,0,1,1,1
1,49,1,0,0,1,1,1,1,1,1,1,1,1,1,1
0,57,0,1,0,1,1,1,1,0,1,1,0,0,0,1
1,52,0,1,1,0,1,0,1,1,1,1,0,1,0,1
0,63,0,1,0,1,0,1,0,0,0,1,1,0,1,1
1,73,0,0,0,0,1,0,1,0,1,1,1,1,1,1
1,47,0,1,0,1,1,1,0,1,0,0,1,1,1,1
1,69,1,1,1,1,0,1,1,0,1,1,1,0,1,1
1,70,0,1,0,1,1,1,1,1,1,1,0,1,1,1
0,60,0,1,1,0,0,0,0,0,0,1,0,0,0,0
1,70,0,1,0,1,0,1,1,1,1,1,0,0,0,1
0,68,0,0,1,0,1,0,1,1,1,0,0,1,0,1
1,74,0,1,0,1,0,1,1,1,1,1,1,0,1,1
0,71,1,1,1,1,1,1,0,1,0,1,0,1,1,1
0,56,0,1,0,0,1,1,1,1,0,1,1,0,1,1
1,66,1,0,0,0,0,1,0,1,1,1,1,0,0,1
0,76,1,1,1,1,0,1,1,0,0,0,1,1,1,1
0,78,1,1,1,1,0,1,0,1,0,1,1,1,0,1
1,68,1,1,1,1,0,0,1,0,1,0,0,1,1,1
0,66,1,1,1,1,0,1,0,1,0,1,1,1,0,1
1,67,0,0,0,0,1,1,1,1,1,1,1,0,1,1
0,60,1,0,0,0,1,1,0,0,0,0,1,0,0,1
1,61,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,58,1,0,0,0,0,0,1,1,1,1,0,0,0,1
0,76,0,1,1,1,1,1,1,1,0,1,1,1,1,1
1,56,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,67,1,1,1,1,1,0,0,0,1,0,0,1,1,1
0,73,1,1,1,1,0,1,0,0,0,1,1,1,1,1
0,58,0,0,0,0,0,1,0,0,0,0,1,0,0,0
0,54,1,1,1,1,1,1,0,0,0,0,1,1,0,1
0,62,1,1,1,1,1,0,1,1,1,0,0,1,1,1
0,81,0,0,0,1,1,0,1,0,1,1,1,0,0,1
1,56,0,0,0,0,1,1,1,0,1,1,1,0,1,1
1,60,0,1,1,0,0,0,0,1,1,1,1,1,0,1
1,66,0,1,1,0,1,0,1,0,1,1,1,0,1,1
1,62,0,1,1,0,0,1,0,1,0,0,0,1,1,1
0,62,1,1,1,0,1,0,1,0,1,0,0,0,0,1
0,55,1,0,0,1,1,1,1,1,1,0,0,1,1,1
0,62,0,0,0,1,0,0,0,1,1,0,0,1,1,1
0,71,0,0,0,0,1,1,1,0,0,1,1,0,1,1
1,52,1,0,0,0,1,1,1,1,1,0,0,1,1,1
0,59,0,1,1,1,1,0,1,1,1,1,1,1,0,1
1,48,1,0,0,0,1,1,1,0,1,1,1,1,1,1
1,60,0,1,1,1,0,1,0,0,0,0,0,1,1,1
0,61,1,1,1,0,0,0,1,0,1,1,1,0,1,1
1,59,1,0,0,1,0,0,0,0,1,1,1,0,0,1
1,64,0,1,1,1,0,1,1,0,0,1,0,1,0,1
1,56,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,58,1,0,0,0,0,0,1,1,1,1,0,0,0,1
0,81,0,1,1,1,1,1,1,1,0,1,1,1,1,1
1,64,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,62,1,1,1,1,1,0,0,0,1,0,0,1,1,1
0,72,1,1,1,1,0,1,0,0,0,1,1,1,1,1
0,60,0,0,0,0,1,1,0,0,0,0,1,0,0,1
0,61,1,1,1,1,1,1,0,0,0,0,1,1,0,1
0,60,1,1,1,1,1,0,1,1,1,0,0,1,1,1
0,49,0,0,0,1,1,0,1,0,1,1,1,0,0,1
1,53,0,0,0,0,1,1,1,0,1,0,1,0,1,1
1,58,0,1,1,0,0,1,0,1,1,1,1,1,1,1
1,61,1,1,1,0,0,1,1,0,1,0,1,1,1,1
0,68,0,0,0,1,0,1,0,1,0,1,1,0,1,1
1,60,1,1,1,0,0,0,0,0,1,0,0,1,1,1
0,72,0,1,0,0,0,1,0,1,1,1,1,0,0,1
0,72,0,1,0,0,1,1,1,1,0,1,1,0,0,1
1,57,1,0,0,0,0,1,0,1,1,1,1,0,1,1
0,51,1,1,1,1,0,1,1,0,0,0,1,1,0,1
0,54,1,1,1,1,0,1,0,1,0,1,1,1,0,1
0,56,0,1,1,1,0,0,1,0,1,0,0,1,1,1
1,77,1,1,1,1,0,1,0,1,0,1,1,1,0,1
1,64,0,0,0,0,1,1,1,1,1,1,1,0,1,1
1,57,1,0,1,0,1,1,0,0,0,0,1,0,0,1
0,66,1,1,1,0,1,1,1,1,1,1,1,0,0,1
1,70,1,0,0,0,0,0,1,0,1,1,0,0,1,1
0,53,0,1,1,1,1,1,1,0,0,1,1,0,0,1
1,51,1,0,0,0,0,1,0,1,1,1,1,0,1,1
1,58,1,1,1,1,1,0,0,0,1,0,0,1,1,1
0,58,1,1,1,1,0,1,0,0,0,1,1,1,0,1
0,63,0,0,0,0,1,1,0,0,0,0,1,0,0,0
0,51,1,1,1,1,0,1,0,0,0,0,1,1,0,1
0,61,0,1,1,1,0,0,1,1,0,1,0,1,0,1
0,61,1,0,0,0,1,1,1,0,0,0,1,0,0,1
1,76,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,71,1,1,1,0,1,0,1,1,1,1,0,1,1,1
1,69,0,0,1,0,0,1,0,1,1,1,1,1,0,1
0,56,1,1,1,0,0,1,1,0,0,0,1,0,1,1
1,67,0,0,0,1,0,1,0,1,0,1,1,0,1,1
0,54,1,1,1,0,1,0,0,1,1,0,1,1,1,1
1,63,0,1,0,0,0,1,0,1,1,1,1,0,0,1
0,47,1,1,0,1,1,1,1,1,0,1,1,0,0,1
1,62,1,0,1,0,0,1,0,1,1,1,1,0,1,1
1,65,1,1,1,1,0,1,1,0,0,0,1,1,0,1
0,63,1,1,1,1,1,1,1,1,0,1,1,1,1,1
1,64,0,1,1,1,0,0,1,0,1,0,0,1,1,1
0,65,1,1,1,1,0,1,0,1,0,1,1,1,0,1
1,51,0,1,0,0,1,1,1,1,1,1,1,0,1,1
0,56,0,0,0,1,1,1,0,0,1,1,1,1,0,1
1,70,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,58,1,0,0,0,0,0,1,1,1,1,0,0,1,1
1,67,1,0,1,0,0,1,1,0,1,1,1,0,1,1
1,62,0,0,0,1,0,1,1,1,1,0,0,1,0,1
0,74,0,1,1,1,1,1,0,1,1,0,0,0,0,1
1,69,1,0,0,1,0,0,0,0,0,0,0,0,1,0
0,64,1,1,0,1,1,0,0,0,0,0,0,0,0,0
1,75,1,1,1,1,1,0,0,0,0,0,0,0,1,1
1,47,1,1,0,0,1,0,0,0,0,0,0,0,1,0
0,57,1,1,0,1,0,0,0,0,0,0,0,0,1,0
0,56,0,0,1,1,1,1,1,1,1,0,1,1,1,1
1,68,0,0,1,1,1,0,0,0,1,0,0,0,0,0
0,55,0,0,0,1,1,1,1,1,1,0,0,0,1,1
1,62,1,1,1,0,1,1,1,1,0,0,1,0,0,1
0,73,1,0,0,1,0,1,1,1,1,1,0,1,1,1
1,68,1,0,0,1,1,1,1,1,1,1,1,1,1,1
0,75,0,1,0,1,1,1,1,0,1,1,0,0,0,1
1,63,0,1,1,0,1,0,1,1,1,1,0,1,0,1
0,61,0,1,0,1,0,1,0,0,0,1,1,0,1,1
1,62,0,0,0,0,1,0,1,0,1,1,1,1,1,1
1,44,0,1,0,1,1,1,0,1,0,0,1,1,1,1
1,56,1,1,1,1,0,1,1,0,1,1,1,0,1,1
1,54,0,1,0,1,1,1,1,1,1,1,0,1,1,1
0,57,0,1,1,0,0,0,0,0,0,1,0,0,0,0
1,56,0,1,0,1,0,1,1,1,1,1,0,0,0,1
0,69,0,0,1,0,1,0,1,1,1,0,0,1,0,1
1,72,0,1,0,1,0,1,1,1,1,1,1,0,1,1
0,59,1,1,1,1,1,1,0,1,0,1,0,1,1,1
0,70,0,1,0,0,1,1,1,1,0,1,1,0,1,1
1,64,1,0,0,0,0,1,0,1,1,1,1,0,0,1
0,61,1,1,1,1,0,1,1,0,0,0,1,1,1,1
0,72,1,1,1,1,0,1,0,1,0,1,1,1,0,1
1,63,1,1,1,1,0,0,1,0,1,0,0,1,1,1
0,74,1,1,1,1,0,1,0,1,0,1,1,1,0,1
1,71,0,0,0,0,1,1,1,1,1,1,1,0,1,1
0,71,1,0,0,0,1,1,0,0,0,0,1,0,0,0
1,72,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,77,1,0,0,0,0,0,1,1,1,1,0,0,0,1
0,72,0,1,1,1,1,1,0,0,0,0,0,0,0,1
1,55,1,0,0,0,0,1,0,0,0,0,0,0,0,1
1,65,1,1,1,1,1,0,0,0,0,0,0,0,0,1
0,67,1,1,1,1,0,1,0,0,0,0,0,0,0,1
0,69,0,0,0,0,1,1,0,0,0,0,0,0,0,1
0,55,1,1,1,1,1,1,0,0,0,0,0,0,0,1
0,51,1,1,1,1,1,0,0,0,0,0,0,0,0,1
0,64,0,0,0,1,1,0,0,0,0,0,0,0,0,1
1,63,0,0,0,0,1,1,1,0,1,1,1,0,1,1
1,69,0,1,1,0,0,0,0,1,1,1,1,1,0,1
1,64,0,1,1,0,1,0,1,0,1,1,1,0,1,1
1,59,0,1,1,0,0,1,0,1,0,0,0,1,1,1
0,73,1,1,1,0,1,0,1,0,1,0,0,0,0,1
0,55,1,0,0,1,1,1,1,1,1,0,0,1,1,1
0,63,0,0,0,1,0,0,0,1,1,0,0,1,1,1
0,60,0,0,0,0,1,1,1,0,0,1,1,0,1,1
1,74,1,0,0,0,1,1,1,1,1,0,0,1,1,1
0,65,0,1,1,1,1,0,1,1,1,1,1,1,0,1
1,79,1,0,0,0,1,1,1,0,1,1,1,1,1,1
1,62,0,1,1,1,0,1,0,0,0,0,0,1,1,1
0,71,1,1,1,0,0,0,1,0,1,1,1,0,1,1
1,63,1,0,0,1,0,0,0,0,0,1,1,0,0,0
1,67,0,1,1,1,0,1,1,0,0,1,0,1,0,1
1,55,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,54,1,0,0,0,0,0,1,1,1,1,0,0,0,1
0,77,0,1,1,1,1,1,1,1,0,1,1,1,1,1
1,58,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,64,1,1,1,1,1,0,0,0,1,0,0,1,1,1
0,61,1,1,1,1,0,1,0,0,0,1,1,1,1,1
0,62,0,0,0,0,1,1,0,0,0,0,1,0,0,0
0,67,1,1,1,1,1,1,0,0,0,0,1,1,0,1
0,56,1,1,1,1,1,0,1,1,1,0,0,1,1,1
0,70,0,0,0,1,1,0,1,0,1,1,1,0,0,1
1,70,0,0,0,0,1,1,1,0,1,1,1,0,1,1
0,57,0,0,1,1,1,1,1,1,1,0,1,1,1,1
1,61,0,0,1,1,1,0,0,0,1,0,0,0,0,0
0,77,0,0,0,1,1,1,1,1,1,0,0,0,1,1
1,63,1,1,1,0,1,1,1,1,0,0,1,0,0,1
0,62,1,0,0,1,0,1,1,1,1,1,0,1,1,1
1,59,1,0,0,1,1,1,1,1,1,1,1,1,1,1
0,70,0,1,0,1,1,1,1,0,1,1,0,0,0,1
1,71,0,1,1,0,1,0,1,1,1,1,0,1,0,1
0,56,0,1,0,1,0,1,0,0,0,1,1,0,1,1
1,57,0,0,0,0,1,0,1,0,1,1,1,1,1,1
1,78,0,1,0,1,1,1,0,1,0,0,1,1,1,1
1,64,1,1,1,1,0,1,1,0,1,1,1,0,1,1
1,62,0,1,0,1,1,1,1,1,1,1,0,1,1,1
0,49,0,1,1,0,0,0,0,0,0,1,0,0,0,1
1,77,0,1,0,1,0,1,1,1,1,1,0,0,0,1
0,64,0,0,1,0,1,0,1,1,1,0,0,1,0,1
1,63,0,1,0,1,0,1,1,1,1,1,1,0,1,1
0,54,1,1,1,1,1,1,0,1,0,1,0,1,1,1
0,38,0,1,0,0,1,1,1,1,0,1,1,0,1,1
0,75,0,1,1,1,0,0,1,1,0,1,0,1,0,1
0,70,1,0,0,1,1,0,1,0,0,0,1,0,0,1
1,59,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,77,1,1,1,0,1,0,1,1,0,0,0,1,1,1
1,61,0,0,1,0,1,1,0,1,1,1,1,1,0,1
0,64,1,1,1,0,0,1,1,0,0,0,1,0,1,1
1,59,0,0,0,1,0,1,0,1,0,0,1,0,1,0
0,71,1,1,1,0,1,0,0,1,1,0,1,1,1,1
1,67,0,1,0,0,0,1,0,1,1,1,1,0,0,1
0,64,1,1,0,1,1,1,1,1,0,1,1,0,0,1
1,68,1,0,1,0,0,1,0,0,0,0,0,0,0,0
1,69,1,1,1,1,0,1,1,0,0,0,1,1,0,1
0,64,1,1,1,1,1,1,1,1,0,1,1,1,1,1
1,59,0,1,1,1,1,0,1,0,1,0,0,1,1,1
0,67,1,1,1,1,0,1,0,1,0,1,1,1,0,1
1,74,0,1,0,0,1,1,1,1,1,1,1,0,1,1
0,77,0,0,0,1,1,1,0,0,1,1,1,1,0,1
1,60,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,64,1,0,0,0,0,0,1,1,1,1,0,0,1,1
1,70,1,0,1,0,0,1,1,0,1,1,1,0,1,1
1,58,0,0,0,1,0,1,1,1,1,0,0,1,0,1
0,59,0,1,1,1,1,1,0,1,1,0,0,0,0,1
1,39,1,0,0,1,0,1,1,1,1,0,1,0,1,1
0,67,0,1,0,0,0,0,0,1,0,1,1,0,0,0
0,71,0,1,0,0,1,1,1,1,0,1,1,0,0,1
1,70,1,0,0,0,0,1,0,1,1,1,1,0,1,1
0,60,1,1,1,1,0,1,1,0,0,0,1,1,0,1
0,55,1,0,1,0,0,1,0,0,0,0,0,0,0,0
1,60,1,1,1,1,1,0,1,0,1,0,0,1,1,1
0,55,1,1,1,1,1,1,0,1,0,1,1,1,0,1
1,55,0,0,0,0,1,1,1,1,1,1,1,0,1,1
0,70,1,0,0,0,0,1,0,0,0,0,1,0,0,0
1,63,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,64,1,0,0,0,0,0,1,1,1,1,0,0,1,0
0,59,0,1,1,1,1,1,1,1,0,1,1,1,0,1
1,56,1,0,0,0,1,0,1,1,1,1,1,0,1,1
1,64,1,1,1,1,1,0,0,0,1,0,0,1,1,1
0,62,1,1,1,1,1,1,0,0,0,1,1,0,0,1
0,87,0,0,0,0,1,1,0,0,0,0,1,0,0,0
0,77,1,1,1,1,1,1,0,0,0,0,1,1,0,1
0,59,0,1,1,1,0,0,1,1,0,1,0,1,0,1
0,59,1,0,0,0,1,1,1,0,0,0,1,0,0,0
1,55,1,0,0,0,0,1,1,0,0,0,1,0,1,0
1,46,0,1,1,0,0,0,0,0,0,0,0,1,1,0
1,60,0,1,1,0,0,1,0,1,1,1,1,1,1,1
1,58,1,1,1,1,1,0,0,0,1,0,0,1,1,1
0,58,1,1,1,1,0,1,0,0,0,1,1,1,0,1
0,63,0,0,0,0,1,1,0,0,0,0,1,0,0,0
0,51,1,1,1,1,0,1,0,0,0,0,1,1,0,1
0,61,0,1,1,1,0,0,1,1,0,1,0,1,0,1
0,61,1,0,0,0,1,1,1,0,0,0,1,0,0,1
1,76,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,71,1,1,1,0,1,0,1,1,1,1,0,1,1,1
1,69,0,0,1,0,0,1,0,1,1,1,1,1,0,1
0,56,1,1,1,0,0,1,1,0,0,0,1,0,1,1
1,67,0,0,0,1,0,1,0,1,0,1,1,0,1,1
0,54,1,1,1,0,1,0,0,1,1,0,1,1,1,1
1,63,0,1,0,0,0,1,0,1,1,1,1,0,0,1
0,47,1,1,0,1,1,1,1,1,0,1,1,0,0,1
1,62,1,0,1,0,0,1,0,1,1,1,1,0,1,1
1,65,1,1,1,1,0,1,1,0,0,0,1,1,0,1
0,63,1,1,1,1,1,1,1,1,0,1,1,1,1,1
1,64,0,1,1,1,0,0,1,0,1,0,0,1,1,1
0,65,1,1,1,1,0,1,0,1,0,1,1,1,0,1
1,51,0,1,0,0,1,1,1,1,1,1,1,0,1,1
0,56,0,0,0,1,1,1,0,0,1,1,1,1,0,1
1,70,1,0,0,0,0,1,1,1,1,1,1,0,1,1
1,58,1,0,0,0,0,0,1,1,1,1,0,0,1,1
1,67,1,0,1,0,0,1,1,0,1,1,1,0,1,1
1,62,0,0,0,1,0,1,1,1,1,0,0,1,0,1
project/DATASETS/Skin/data/sample_submission.csv 0 → 100644
View file @ 3ec0eded
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project/DATASETS/Skin/data/test.csv 0 → 100644
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This source diff could not be displayed because it is too large. You can view the blob instead.
project/DATASETS/Skin/data/train.csv 0 → 100644
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project/DATASETS/Stroke/healthcare-dataset-stroke-data.csv 0 → 100644
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project/app.py 0 → 100644
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from flask import Flask
app = Flask(__name__)
app.config['SECRET_KEY'] = 'secret-key'
from routes import *
if __name__ == '__main__':
app.run(debug=True)
project/model train/Kidney Disease/.ipynb_checkpoints/kidney_prediction_RF_model-checkpoint.ipynb 0 → 100644
View file @ 3ec0eded
{
"cells": [
{
"cell_type": "markdown",
"id": "e555e9d8",
"metadata": {},
"source": [
"### Importing the dependancies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1123a658",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"id": "4e1ec49f",
"metadata": {},
"source": [
"### Data collecting process"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "111d33c0",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'C:/Users/Mishane/Downloads/new_model.csv'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-2-b85f22f1af58>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'C://Users//Mishane//Downloads//new_model.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 608\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 461\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 817\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 821\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1048\u001b[0m )\n\u001b[0;32m 1049\u001b[0m \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1052\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1866\u001b[0m \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1868\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1869\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m 1360\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1361\u001b[0m \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m self.handles = get_handle(\n\u001b[0m\u001b[0;32m 1363\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1364\u001b[0m \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 556\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 557\u001b[0m \u001b[1;31m# open URLs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 558\u001b[1;33m ioargs = _get_filepath_or_buffer(\n\u001b[0m\u001b[0;32m 559\u001b[0m \u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 560\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencoding\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36m_get_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode, storage_options)\u001b[0m\n\u001b[0;32m 331\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 332\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 333\u001b[1;33m file_obj = fsspec.open(\n\u001b[0m\u001b[0;32m 334\u001b[0m \u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfsspec_mode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstorage_options\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 335\u001b[0m ).open()\n",
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"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\fsspec\\spec.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, path, mode, block_size, cache_options, **kwargs)\u001b[0m\n\u001b[0;32m 938\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 939\u001b[0m \u001b[0mac\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"autocommit\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_intrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 940\u001b[1;33m f = self._open(\n\u001b[0m\u001b[0;32m 941\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 942\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\fsspec\\implementations\\local.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self, path, mode, block_size, **kwargs)\u001b[0m\n\u001b[0;32m 116\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_mkdir\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;34m\"w\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 117\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexist_ok\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 118\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mLocalFileOpener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 120\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mtouch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\fsspec\\implementations\\local.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, path, mode, autocommit, fs, **kwargs)\u001b[0m\n\u001b[0;32m 198\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautocommit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mautocommit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 199\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mblocksize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDEFAULT_BUFFER_SIZE\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 200\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 201\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 202\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_open\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\fsspec\\implementations\\local.py\u001b[0m in \u001b[0;36m_open\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 203\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclosed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 204\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mautocommit\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;34m\"w\"\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 205\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 206\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 207\u001b[0m \u001b[1;31m# TODO: check if path is writable?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:/Users/Mishane/Downloads/new_model.csv'"
]
}
],
"source": [
"import pandas as pd\n",
"data = pd.read_csv('C://Users//Mishane//Downloads//new_model.csv')"
]
},
{
"cell_type": "markdown",
"id": "5da5cf75",
"metadata": {},
"source": [
"### Explore the dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "740f14ff",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Bp', 'Sg', 'Al', 'Su', 'Rbc', 'Bu', 'Sc', 'Sod', 'Pot', 'Hemo', 'Wbcc',\n",
" 'Rbcc', 'Htn', 'Class'],\n",
" dtype='object')\n"
]
},
{
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" <th></th>\n",
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" <th>Sg</th>\n",
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" <th>Su</th>\n",
" <th>Rbc</th>\n",
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" <td>1</td>\n",
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" <tr>\n",
" <th>3</th>\n",
" <td>70.0</td>\n",
" <td>1.005</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>56.0</td>\n",
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" <th>4</th>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Bp Sg Al Su Rbc Bu Sc Sod Pot Hemo Wbcc Rbcc \\\n",
"0 80.0 1.020 1.0 0.0 1.0 36.0 1.2 137.53 4.63 15.4 7800.0 5.20 \n",
"1 50.0 1.020 4.0 0.0 1.0 18.0 0.8 137.53 4.63 11.3 6000.0 4.71 \n",
"2 80.0 1.010 2.0 3.0 1.0 53.0 1.8 137.53 4.63 9.6 7500.0 4.71 \n",
"3 70.0 1.005 4.0 0.0 1.0 56.0 3.8 111.00 2.50 11.2 6700.0 3.90 \n",
"4 80.0 1.010 2.0 0.0 1.0 26.0 1.4 137.53 4.63 11.6 7300.0 4.60 \n",
"\n",
" Htn Class \n",
"0 1.0 1 \n",
"1 0.0 1 \n",
"2 0.0 1 \n",
"3 1.0 1 \n",
"4 0.0 1 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(data.columns)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7c8d2381",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400, 14)\n",
" Bp Sg Al Su Rbc Bu \\\n",
"count 400.000000 400.000000 400.000000 400.000000 400.000000 400.00000 \n",
"mean 76.455000 1.017712 1.015000 0.395000 0.882500 57.40550 \n",
"std 13.476536 0.005434 1.272329 1.040038 0.322418 49.28597 \n",
"min 50.000000 1.005000 0.000000 0.000000 0.000000 1.50000 \n",
"25% 70.000000 1.015000 0.000000 0.000000 1.000000 27.00000 \n",
"50% 78.000000 1.020000 1.000000 0.000000 1.000000 44.00000 \n",
"75% 80.000000 1.020000 2.000000 0.000000 1.000000 61.75000 \n",
"max 180.000000 1.025000 5.000000 5.000000 1.000000 391.00000 \n",
"\n",
" Sc Sod Pot Hemo Wbcc \\\n",
"count 400.00000 400.000000 400.000000 400.000000 400.000000 \n",
"mean 3.07235 137.529025 4.627850 12.526900 8406.090000 \n",
"std 5.61749 9.204273 2.819783 2.716171 2523.219976 \n",
"min 0.40000 4.500000 2.500000 3.100000 2200.000000 \n",
"25% 0.90000 135.000000 4.000000 10.875000 6975.000000 \n",
"50% 1.40000 137.530000 4.630000 12.530000 8406.000000 \n",
"75% 3.07000 141.000000 4.800000 14.625000 9400.000000 \n",
"max 76.00000 163.000000 47.000000 17.800000 26400.000000 \n",
"\n",
" Rbcc Htn Class \n",
"count 400.000000 400.000000 400.000000 \n",
"mean 4.708275 0.369350 0.625000 \n",
"std 0.840315 0.482023 0.484729 \n",
"min 2.100000 0.000000 0.000000 \n",
"25% 4.500000 0.000000 0.000000 \n",
"50% 4.710000 0.000000 1.000000 \n",
"75% 5.100000 1.000000 1.000000 \n",
"max 8.000000 1.000000 1.000000 \n"
]
}
],
"source": [
"# Print the shape of the data\n",
"# data = data.sample(frac=0.1, random_state = 48)\n",
"print(data.shape)\n",
"print(data.describe())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e535ee6f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bp 0\n",
"Sg 0\n",
"Al 0\n",
"Su 0\n",
"Rbc 0\n",
"Bu 0\n",
"Sc 0\n",
"Sod 0\n",
"Pot 0\n",
"Hemo 0\n",
"Wbcc 0\n",
"Rbcc 0\n",
"Htn 0\n",
"Class 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(data.isnull().sum())\n",
"\n",
"# Check for null values in a specific column\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d21cf5f8",
"metadata": {},
"outputs": [
{
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>56.0</td>\n",
" <td>3.8</td>\n",
" <td>111.00</td>\n",
" <td>2.50</td>\n",
" <td>11.2</td>\n",
" <td>6700.0</td>\n",
" <td>3.90</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>80.0</td>\n",
" <td>1.010</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>26.0</td>\n",
" <td>1.4</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>11.6</td>\n",
" <td>7300.0</td>\n",
" <td>4.60</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>49.0</td>\n",
" <td>0.5</td>\n",
" <td>150.00</td>\n",
" <td>4.90</td>\n",
" <td>15.7</td>\n",
" <td>6700.0</td>\n",
" <td>4.90</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td>70.0</td>\n",
" <td>1.025</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>31.0</td>\n",
" <td>1.2</td>\n",
" <td>141.00</td>\n",
" <td>3.50</td>\n",
" <td>16.5</td>\n",
" <td>7800.0</td>\n",
" <td>6.20</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>26.0</td>\n",
" <td>0.6</td>\n",
" <td>137.00</td>\n",
" <td>4.40</td>\n",
" <td>15.8</td>\n",
" <td>6600.0</td>\n",
" <td>5.40</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398</th>\n",
" <td>60.0</td>\n",
" <td>1.025</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>50.0</td>\n",
" <td>1.0</td>\n",
" <td>135.00</td>\n",
" <td>4.90</td>\n",
" <td>14.2</td>\n",
" <td>7200.0</td>\n",
" <td>5.90</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>80.0</td>\n",
" <td>1.025</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>18.0</td>\n",
" <td>1.1</td>\n",
" <td>141.00</td>\n",
" <td>3.50</td>\n",
" <td>15.8</td>\n",
" <td>6800.0</td>\n",
" <td>6.10</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>400 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" Bp Sg Al Su Rbc Bu Sc Sod Pot Hemo Wbcc Rbcc \\\n",
"0 80.0 1.020 1.0 0.0 1.0 36.0 1.2 137.53 4.63 15.4 7800.0 5.20 \n",
"1 50.0 1.020 4.0 0.0 1.0 18.0 0.8 137.53 4.63 11.3 6000.0 4.71 \n",
"2 80.0 1.010 2.0 3.0 1.0 53.0 1.8 137.53 4.63 9.6 7500.0 4.71 \n",
"3 70.0 1.005 4.0 0.0 1.0 56.0 3.8 111.00 2.50 11.2 6700.0 3.90 \n",
"4 80.0 1.010 2.0 0.0 1.0 26.0 1.4 137.53 4.63 11.6 7300.0 4.60 \n",
".. ... ... ... ... ... ... ... ... ... ... ... ... \n",
"395 80.0 1.020 0.0 0.0 1.0 49.0 0.5 150.00 4.90 15.7 6700.0 4.90 \n",
"396 70.0 1.025 0.0 0.0 1.0 31.0 1.2 141.00 3.50 16.5 7800.0 6.20 \n",
"397 80.0 1.020 0.0 0.0 1.0 26.0 0.6 137.00 4.40 15.8 6600.0 5.40 \n",
"398 60.0 1.025 0.0 0.0 1.0 50.0 1.0 135.00 4.90 14.2 7200.0 5.90 \n",
"399 80.0 1.025 0.0 0.0 1.0 18.0 1.1 141.00 3.50 15.8 6800.0 6.10 \n",
"\n",
" Htn Class \n",
"0 1.0 1 \n",
"1 0.0 1 \n",
"2 0.0 1 \n",
"3 1.0 1 \n",
"4 0.0 1 \n",
".. ... ... \n",
"395 0.0 0 \n",
"396 0.0 0 \n",
"397 0.0 0 \n",
"398 0.0 0 \n",
"399 0.0 0 \n",
"\n",
"[400 rows x 14 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"id": "f0e0e01a",
"metadata": {},
"source": [
"### Checking the distribution of target variable\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "dd942b3b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Outlier percentile: 100.0\n",
"Having disease: 250\n",
"Not having disease: 150\n"
]
}
],
"source": [
"# Determine number of fraud cases in dataset\n",
"\n",
"have = data[data['Class'] == 1]\n",
"not_have = data[data['Class'] == 0]\n",
"\n",
"outlier_fraction = 1.6666666666666667\n",
"outlier_fraction = outlier_fraction / np.max(outlier_fraction) * 100\n",
"print(\"Outlier percentile:\", outlier_fraction)\n",
"\n",
"print('Having disease: {}'.format(len(data[data['Class'] == 1])))\n",
"print('Not having disease: {}'.format(len(data[data['Class'] == 0])))"
]
},
{
"cell_type": "markdown",
"id": "4a85a5bb",
"metadata": {},
"source": [
"### Visualizing the dataset"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cf5c2e23",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Correlation matrix\n",
"corrmat = data.corr()\n",
"fig = plt.figure(figsize = (12, 9))\n",
"\n",
"sns.heatmap(corrmat, vmax = .8, square = True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "38ffa726",
"metadata": {},
"source": [
"### Splitting the features and target"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ad95615d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400, 13)\n",
"(400,)\n"
]
}
],
"source": [
"#seperating the X and the Y from the dataset\n",
"X=data.drop(['Class'], axis=1)\n",
"Y=data[\"Class\"]\n",
"print(X.shape)\n",
"print(Y.shape)\n",
"#getting just the values for the sake of processing (its a numpy array with no columns)\n",
"X_data=X.values\n",
"Y_data=Y.values"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "987d85cc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[8.000e+01, 1.020e+00, 1.000e+00, ..., 7.800e+03, 5.200e+00,\n",
" 1.000e+00],\n",
" [5.000e+01, 1.020e+00, 4.000e+00, ..., 6.000e+03, 4.710e+00,\n",
" 0.000e+00],\n",
" [8.000e+01, 1.010e+00, 2.000e+00, ..., 7.500e+03, 4.710e+00,\n",
" 0.000e+00],\n",
" ...,\n",
" [8.000e+01, 1.020e+00, 0.000e+00, ..., 6.600e+03, 5.400e+00,\n",
" 0.000e+00],\n",
" [6.000e+01, 1.025e+00, 0.000e+00, ..., 7.200e+03, 5.900e+00,\n",
" 0.000e+00],\n",
" [8.000e+01, 1.025e+00, 0.000e+00, ..., 6.800e+03, 6.100e+00,\n",
" 0.000e+00]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_data"
]
},
{
"cell_type": "markdown",
"id": "ef0afcab",
"metadata": {},
"source": [
"### Splittng data into train and test"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "5c0ce1f4",
"metadata": {},
"outputs": [],
"source": [
"# Using Skicit-learn to split data into training and testing sets\n",
"from sklearn.model_selection import train_test_split\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data, test_size = 0.2, random_state = 42)"
]
},
{
"cell_type": "markdown",
"id": "26033386",
"metadata": {},
"source": [
"### Model training - Random Forest"
]
},
{
"cell_type": "markdown",
"id": "d98c855a",
"metadata": {},
"source": [
"#### ISOLATION FOREST"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "b41d16ee",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report, accuracy_score,precision_score,recall_score,f1_score,matthews_corrcoef\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "53a3cd66",
"metadata": {},
"outputs": [],
"source": [
"#Building another model/classifier ISOLATION FOREST\n",
"from sklearn.ensemble import IsolationForest\n",
"ifc=IsolationForest(max_samples=len(X_train),\n",
" contamination=outlier_fraction/100,random_state=1)\n",
"ifc.fit(X_train)\n",
"scores_pred = ifc.decision_function(X_train)\n",
"y_pred = ifc.predict(X_test)\n",
"\n",
"\n",
"# Reshape the prediction values to 0 for valid, 1 for fraud. \n",
"y_pred[y_pred == 1] = 0\n",
"y_pred[y_pred == -1] = 1\n",
"\n",
"n_errors = (y_pred != Y_test).sum()"
]
},
{
"cell_type": "markdown",
"id": "e8dd7a8f",
"metadata": {},
"source": [
"### Model eveuation"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "60c5a8a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the Model used is Isolation Forest\n",
"The accuracy is 0.9875\n",
"The precision is 1.0\n",
"The recall is 0.9807692307692307\n",
"The F1-Score is 0.9902912621359222\n",
"The Matthews correlation coefficient is0.9731133552274452\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Isolation Forest: 1\n",
"0.9875\n",
" precision recall f1-score support\n",
"\n",
" 0 0.97 1.00 0.98 28\n",
" 1 1.00 0.98 0.99 52\n",
"\n",
" accuracy 0.99 80\n",
" macro avg 0.98 0.99 0.99 80\n",
"weighted avg 0.99 0.99 0.99 80\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 648x504 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#evaluation of the model\n",
"#printing every score of the classifier\n",
"#scoring in any thing\n",
"\n",
"from sklearn.metrics import confusion_matrix\n",
"n_outliers = len(Stroke)\n",
"print(\"the Model used is {}\".format(\"Isolation Forest\"))\n",
"acc= accuracy_score(Y_test,y_pred)\n",
"print(\"The accuracy is {}\".format(acc))\n",
"prec= precision_score(Y_test,y_pred)\n",
"print(\"The precision is {}\".format(prec))\n",
"rec= recall_score(Y_test,y_pred)\n",
"print(\"The recall is {}\".format(rec))\n",
"f1= f1_score(Y_test,y_pred)\n",
"print(\"The F1-Score is {}\".format(f1))\n",
"MCC=matthews_corrcoef(Y_test,y_pred)\n",
"print(\"The Matthews correlation coefficient is{}\".format(MCC))\n",
"\n",
"#printing the confusion matrix\n",
"LABELS = ['No CKD', 'CKD']\n",
"conf_matrix = confusion_matrix(Y_test, y_pred)\n",
"plt.figure(figsize=(12, 12))\n",
"sns.heatmap(conf_matrix, xticklabels=LABELS,\n",
" yticklabels=LABELS, annot=True, fmt=\"d\");\n",
"plt.title(\"Confusion matrix\")\n",
"plt.ylabel('True class')\n",
"plt.xlabel('Predicted class')\n",
"plt.show()\n",
"\n",
"# Run classification metrics\n",
"plt.figure(figsize=(9, 7))\n",
"print('{}: {}'.format(\"Isolation Forest\", n_errors))\n",
"print(accuracy_score(Y_test, y_pred))\n",
"print(classification_report(Y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "5d548aa6",
"metadata": {},
"source": [
"#### Random Forest Classifier"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "c41d3b76",
"metadata": {},
"outputs": [],
"source": [
"# Building the Random Forest Classifier (RANDOM FOREST)\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"# random forest model creation\n",
"rfc = RandomForestClassifier()\n",
"rfc.fit(X_train,Y_train)\n",
"# predictions\n",
"y_pred = rfc.predict(X_test)"
]
},
{
"cell_type": "markdown",
"id": "5821ec3c",
"metadata": {},
"source": [
"### Model eveuation"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "6284b00a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The model used is Random Forest classifier\n",
"The accuracy is 0.9875\n",
"The precision is 1.0\n",
"The recall is 0.9807692307692307\n",
"The F1-Score is 0.9902912621359222\n",
"The Matthews correlation coefficient is 0.9731133552274452\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random Forest: 1\n",
"0.9875\n",
" precision recall f1-score support\n",
"\n",
" 0 0.97 1.00 0.98 28\n",
" 1 1.00 0.98 0.99 52\n",
"\n",
" accuracy 0.99 80\n",
" macro avg 0.98 0.99 0.99 80\n",
"weighted avg 0.99 0.99 0.99 80\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 648x504 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Evaluating the classifier\n",
"#printing every score of the classifier\n",
"#scoring in any thing\n",
"from sklearn.metrics import classification_report, accuracy_score,precision_score,recall_score,f1_score,matthews_corrcoef\n",
"from sklearn.metrics import confusion_matrix\n",
"n_outliers = len(Stroke)\n",
"n_errors = (y_pred != Y_test).sum()\n",
"print(\"The model used is Random Forest classifier\")\n",
"acc= accuracy_score(Y_test,y_pred)\n",
"print(\"The accuracy is {}\".format(acc))\n",
"prec= precision_score(Y_test,y_pred)\n",
"print(\"The precision is {}\".format(prec))\n",
"rec= recall_score(Y_test,y_pred)\n",
"print(\"The recall is {}\".format(rec))\n",
"f1= f1_score(Y_test,y_pred)\n",
"print(\"The F1-Score is {}\".format(f1))\n",
"MCC=matthews_corrcoef(Y_test,y_pred)\n",
"print(\"The Matthews correlation coefficient is {}\".format(MCC))\n",
"\n",
"\n",
"#printing the confusion matrix\n",
"LABELS = ['No CKD', 'CKD']\n",
"conf_matrix = confusion_matrix(Y_test, y_pred)\n",
"plt.figure(figsize=(12, 12))\n",
"sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
"plt.title(\"Confusion matrix\")\n",
"plt.ylabel('True class')\n",
"plt.xlabel('Predicted class')\n",
"plt.show()\n",
"\n",
"# Run classification metrics\n",
"plt.figure(figsize=(9, 7))\n",
"print('{}: {}'.format(\"Random Forest\", n_errors))\n",
"print(accuracy_score(Y_test, y_pred))\n",
"print(classification_report(Y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "5df8ea16",
"metadata": {},
"source": [
"### Saving the model"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "02faf4e6",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'rfc' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-eff0c19026d6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'finalized_model_kidney_prediction_RF.sav'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrfc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'rfc' is not defined"
]
}
],
"source": [
"import pickle\n",
"filename = 'finalized_model_kidney_prediction_RF.sav'\n",
"pickle.dump(rfc, open(filename, 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "896159c4",
"metadata": {},
"outputs": [],
"source": [
"with open('finalized_model_kidney_prediction_RF.sav', 'rb') as f:\n",
" model = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "03d78684",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\n"
]
}
],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"#input_data1 = (70,1.02,0,0,1,44,1.2,145,5,14.8,8406,4.71,0) #0\n",
"input_data2 = (80, 1.02, 1, 0, 1, 36, 1.2, 137.53, 4.63, 15.4, 7800, 5.2, 1) #1\n",
"\n",
"#change input data to numpy array\n",
"input_data_as_numpy_array = np.asarray(input_data2)\n",
"\n",
"#reshape the numpy array as we are predicting for only on instance\n",
"\n",
"input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
"\n",
"prediction = model.predict(input_data_reshaped)\n",
"print(prediction)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41a76901",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"vscode": {
"interpreter": {
"hash": "d9a6414fa631c028c434667d182c0b79dc634ffcd06f52fc304061a2c0b9ef26"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
project/model train/Kidney Disease/kidney_disease_CT.ipynb 0 → 100644
View file @ 3ec0eded
This source diff could not be displayed because it is too large. You can view the blob instead.
project/model train/Kidney Disease/kidney_prediction_RF_model.ipynb 0 → 100644
View file @ 3ec0eded
{
"cells": [
{
"cell_type": "markdown",
"id": "e555e9d8",
"metadata": {},
"source": [
"### Importing the dependancies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1123a658",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"id": "4e1ec49f",
"metadata": {},
"source": [
"### Data collecting process"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "111d33c0",
"metadata": {},
"outputs": [
{
"ename": "FileNotFoundError",
"evalue": "[Errno 2] No such file or directory: 'new_model.csv'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-8021350b35bc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'new_model.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m 608\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m 460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 461\u001b[0m \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 464\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m 817\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 821\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m 1048\u001b[0m )\n\u001b[0;32m 1049\u001b[0m \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1052\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m 1865\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1866\u001b[0m \u001b[1;31m# open handles\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1867\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_open_handles\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1868\u001b[0m \u001b[1;32massert\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1869\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m\"storage_options\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"encoding\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"memory_map\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"compression\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_open_handles\u001b[1;34m(self, src, kwds)\u001b[0m\n\u001b[0;32m 1360\u001b[0m \u001b[0mLet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mreaders\u001b[0m \u001b[0mopen\u001b[0m \u001b[0mIOHanldes\u001b[0m \u001b[0mafter\u001b[0m \u001b[0mthey\u001b[0m \u001b[0mare\u001b[0m \u001b[0mdone\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mtheir\u001b[0m \u001b[0mpotential\u001b[0m \u001b[0mraises\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1361\u001b[0m \"\"\"\n\u001b[1;32m-> 1362\u001b[1;33m self.handles = get_handle(\n\u001b[0m\u001b[0;32m 1363\u001b[0m \u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1364\u001b[0m \u001b[1;34m\"r\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_handle\u001b[1;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[0;32m 640\u001b[0m \u001b[0merrors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"replace\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;31m# Encoding\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m handle = open(\n\u001b[0m\u001b[0;32m 643\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[0mioargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'new_model.csv'"
]
}
],
"source": [
"import pandas as pd\n",
"data = pd.read_csv('new_model.csv')"
]
},
{
"cell_type": "markdown",
"id": "5da5cf75",
"metadata": {},
"source": [
"### Explore the dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "740f14ff",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Bp', 'Sg', 'Al', 'Su', 'Rbc', 'Bu', 'Sc', 'Sod', 'Pot', 'Hemo', 'Wbcc',\n",
" 'Rbcc', 'Htn', 'Class'],\n",
" dtype='object')\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Bp</th>\n",
" <th>Sg</th>\n",
" <th>Al</th>\n",
" <th>Su</th>\n",
" <th>Rbc</th>\n",
" <th>Bu</th>\n",
" <th>Sc</th>\n",
" <th>Sod</th>\n",
" <th>Pot</th>\n",
" <th>Hemo</th>\n",
" <th>Wbcc</th>\n",
" <th>Rbcc</th>\n",
" <th>Htn</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>36.0</td>\n",
" <td>1.2</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>15.4</td>\n",
" <td>7800.0</td>\n",
" <td>5.20</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50.0</td>\n",
" <td>1.020</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>18.0</td>\n",
" <td>0.8</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>11.3</td>\n",
" <td>6000.0</td>\n",
" <td>4.71</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80.0</td>\n",
" <td>1.010</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>53.0</td>\n",
" <td>1.8</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>9.6</td>\n",
" <td>7500.0</td>\n",
" <td>4.71</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>70.0</td>\n",
" <td>1.005</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>56.0</td>\n",
" <td>3.8</td>\n",
" <td>111.00</td>\n",
" <td>2.50</td>\n",
" <td>11.2</td>\n",
" <td>6700.0</td>\n",
" <td>3.90</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>80.0</td>\n",
" <td>1.010</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>26.0</td>\n",
" <td>1.4</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>11.6</td>\n",
" <td>7300.0</td>\n",
" <td>4.60</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Bp Sg Al Su Rbc Bu Sc Sod Pot Hemo Wbcc Rbcc \\\n",
"0 80.0 1.020 1.0 0.0 1.0 36.0 1.2 137.53 4.63 15.4 7800.0 5.20 \n",
"1 50.0 1.020 4.0 0.0 1.0 18.0 0.8 137.53 4.63 11.3 6000.0 4.71 \n",
"2 80.0 1.010 2.0 3.0 1.0 53.0 1.8 137.53 4.63 9.6 7500.0 4.71 \n",
"3 70.0 1.005 4.0 0.0 1.0 56.0 3.8 111.00 2.50 11.2 6700.0 3.90 \n",
"4 80.0 1.010 2.0 0.0 1.0 26.0 1.4 137.53 4.63 11.6 7300.0 4.60 \n",
"\n",
" Htn Class \n",
"0 1.0 1 \n",
"1 0.0 1 \n",
"2 0.0 1 \n",
"3 1.0 1 \n",
"4 0.0 1 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(data.columns)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7c8d2381",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400, 14)\n",
" Bp Sg Al Su Rbc Bu \\\n",
"count 400.000000 400.000000 400.000000 400.000000 400.000000 400.00000 \n",
"mean 76.455000 1.017712 1.015000 0.395000 0.882500 57.40550 \n",
"std 13.476536 0.005434 1.272329 1.040038 0.322418 49.28597 \n",
"min 50.000000 1.005000 0.000000 0.000000 0.000000 1.50000 \n",
"25% 70.000000 1.015000 0.000000 0.000000 1.000000 27.00000 \n",
"50% 78.000000 1.020000 1.000000 0.000000 1.000000 44.00000 \n",
"75% 80.000000 1.020000 2.000000 0.000000 1.000000 61.75000 \n",
"max 180.000000 1.025000 5.000000 5.000000 1.000000 391.00000 \n",
"\n",
" Sc Sod Pot Hemo Wbcc \\\n",
"count 400.00000 400.000000 400.000000 400.000000 400.000000 \n",
"mean 3.07235 137.529025 4.627850 12.526900 8406.090000 \n",
"std 5.61749 9.204273 2.819783 2.716171 2523.219976 \n",
"min 0.40000 4.500000 2.500000 3.100000 2200.000000 \n",
"25% 0.90000 135.000000 4.000000 10.875000 6975.000000 \n",
"50% 1.40000 137.530000 4.630000 12.530000 8406.000000 \n",
"75% 3.07000 141.000000 4.800000 14.625000 9400.000000 \n",
"max 76.00000 163.000000 47.000000 17.800000 26400.000000 \n",
"\n",
" Rbcc Htn Class \n",
"count 400.000000 400.000000 400.000000 \n",
"mean 4.708275 0.369350 0.625000 \n",
"std 0.840315 0.482023 0.484729 \n",
"min 2.100000 0.000000 0.000000 \n",
"25% 4.500000 0.000000 0.000000 \n",
"50% 4.710000 0.000000 1.000000 \n",
"75% 5.100000 1.000000 1.000000 \n",
"max 8.000000 1.000000 1.000000 \n"
]
}
],
"source": [
"# Print the shape of the data\n",
"# data = data.sample(frac=0.1, random_state = 48)\n",
"print(data.shape)\n",
"print(data.describe())"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e535ee6f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bp 0\n",
"Sg 0\n",
"Al 0\n",
"Su 0\n",
"Rbc 0\n",
"Bu 0\n",
"Sc 0\n",
"Sod 0\n",
"Pot 0\n",
"Hemo 0\n",
"Wbcc 0\n",
"Rbcc 0\n",
"Htn 0\n",
"Class 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(data.isnull().sum())\n",
"\n",
"# Check for null values in a specific column\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d21cf5f8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Bp</th>\n",
" <th>Sg</th>\n",
" <th>Al</th>\n",
" <th>Su</th>\n",
" <th>Rbc</th>\n",
" <th>Bu</th>\n",
" <th>Sc</th>\n",
" <th>Sod</th>\n",
" <th>Pot</th>\n",
" <th>Hemo</th>\n",
" <th>Wbcc</th>\n",
" <th>Rbcc</th>\n",
" <th>Htn</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>36.0</td>\n",
" <td>1.2</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>15.4</td>\n",
" <td>7800.0</td>\n",
" <td>5.20</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50.0</td>\n",
" <td>1.020</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>18.0</td>\n",
" <td>0.8</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>11.3</td>\n",
" <td>6000.0</td>\n",
" <td>4.71</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80.0</td>\n",
" <td>1.010</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>53.0</td>\n",
" <td>1.8</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>9.6</td>\n",
" <td>7500.0</td>\n",
" <td>4.71</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>70.0</td>\n",
" <td>1.005</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>56.0</td>\n",
" <td>3.8</td>\n",
" <td>111.00</td>\n",
" <td>2.50</td>\n",
" <td>11.2</td>\n",
" <td>6700.0</td>\n",
" <td>3.90</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>80.0</td>\n",
" <td>1.010</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>26.0</td>\n",
" <td>1.4</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>11.6</td>\n",
" <td>7300.0</td>\n",
" <td>4.60</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>49.0</td>\n",
" <td>0.5</td>\n",
" <td>150.00</td>\n",
" <td>4.90</td>\n",
" <td>15.7</td>\n",
" <td>6700.0</td>\n",
" <td>4.90</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td>70.0</td>\n",
" <td>1.025</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>31.0</td>\n",
" <td>1.2</td>\n",
" <td>141.00</td>\n",
" <td>3.50</td>\n",
" <td>16.5</td>\n",
" <td>7800.0</td>\n",
" <td>6.20</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>26.0</td>\n",
" <td>0.6</td>\n",
" <td>137.00</td>\n",
" <td>4.40</td>\n",
" <td>15.8</td>\n",
" <td>6600.0</td>\n",
" <td>5.40</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398</th>\n",
" <td>60.0</td>\n",
" <td>1.025</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>50.0</td>\n",
" <td>1.0</td>\n",
" <td>135.00</td>\n",
" <td>4.90</td>\n",
" <td>14.2</td>\n",
" <td>7200.0</td>\n",
" <td>5.90</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>80.0</td>\n",
" <td>1.025</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>18.0</td>\n",
" <td>1.1</td>\n",
" <td>141.00</td>\n",
" <td>3.50</td>\n",
" <td>15.8</td>\n",
" <td>6800.0</td>\n",
" <td>6.10</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>400 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" Bp Sg Al Su Rbc Bu Sc Sod Pot Hemo Wbcc Rbcc \\\n",
"0 80.0 1.020 1.0 0.0 1.0 36.0 1.2 137.53 4.63 15.4 7800.0 5.20 \n",
"1 50.0 1.020 4.0 0.0 1.0 18.0 0.8 137.53 4.63 11.3 6000.0 4.71 \n",
"2 80.0 1.010 2.0 3.0 1.0 53.0 1.8 137.53 4.63 9.6 7500.0 4.71 \n",
"3 70.0 1.005 4.0 0.0 1.0 56.0 3.8 111.00 2.50 11.2 6700.0 3.90 \n",
"4 80.0 1.010 2.0 0.0 1.0 26.0 1.4 137.53 4.63 11.6 7300.0 4.60 \n",
".. ... ... ... ... ... ... ... ... ... ... ... ... \n",
"395 80.0 1.020 0.0 0.0 1.0 49.0 0.5 150.00 4.90 15.7 6700.0 4.90 \n",
"396 70.0 1.025 0.0 0.0 1.0 31.0 1.2 141.00 3.50 16.5 7800.0 6.20 \n",
"397 80.0 1.020 0.0 0.0 1.0 26.0 0.6 137.00 4.40 15.8 6600.0 5.40 \n",
"398 60.0 1.025 0.0 0.0 1.0 50.0 1.0 135.00 4.90 14.2 7200.0 5.90 \n",
"399 80.0 1.025 0.0 0.0 1.0 18.0 1.1 141.00 3.50 15.8 6800.0 6.10 \n",
"\n",
" Htn Class \n",
"0 1.0 1 \n",
"1 0.0 1 \n",
"2 0.0 1 \n",
"3 1.0 1 \n",
"4 0.0 1 \n",
".. ... ... \n",
"395 0.0 0 \n",
"396 0.0 0 \n",
"397 0.0 0 \n",
"398 0.0 0 \n",
"399 0.0 0 \n",
"\n",
"[400 rows x 14 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"id": "f0e0e01a",
"metadata": {},
"source": [
"### Checking the distribution of target variable\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "dd942b3b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Outlier percentile: 100.0\n",
"Having disease: 250\n",
"Not having disease: 150\n"
]
}
],
"source": [
"# Determine number of fraud cases in dataset\n",
"\n",
"have = data[data['Class'] == 1]\n",
"not_have = data[data['Class'] == 0]\n",
"\n",
"outlier_fraction = 1.6666666666666667\n",
"outlier_fraction = outlier_fraction / np.max(outlier_fraction) * 100\n",
"print(\"Outlier percentile:\", outlier_fraction)\n",
"\n",
"print('Having disease: {}'.format(len(data[data['Class'] == 1])))\n",
"print('Not having disease: {}'.format(len(data[data['Class'] == 0])))"
]
},
{
"cell_type": "markdown",
"id": "4a85a5bb",
"metadata": {},
"source": [
"### Visualizing the dataset"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cf5c2e23",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Correlation matrix\n",
"corrmat = data.corr()\n",
"fig = plt.figure(figsize = (12, 9))\n",
"\n",
"sns.heatmap(corrmat, vmax = .8, square = True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "38ffa726",
"metadata": {},
"source": [
"### Splitting the features and target"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ad95615d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400, 13)\n",
"(400,)\n"
]
}
],
"source": [
"#seperating the X and the Y from the dataset\n",
"X=data.drop(['Class'], axis=1)\n",
"Y=data[\"Class\"]\n",
"print(X.shape)\n",
"print(Y.shape)\n",
"#getting just the values for the sake of processing (its a numpy array with no columns)\n",
"X_data=X.values\n",
"Y_data=Y.values"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "987d85cc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[8.000e+01, 1.020e+00, 1.000e+00, ..., 7.800e+03, 5.200e+00,\n",
" 1.000e+00],\n",
" [5.000e+01, 1.020e+00, 4.000e+00, ..., 6.000e+03, 4.710e+00,\n",
" 0.000e+00],\n",
" [8.000e+01, 1.010e+00, 2.000e+00, ..., 7.500e+03, 4.710e+00,\n",
" 0.000e+00],\n",
" ...,\n",
" [8.000e+01, 1.020e+00, 0.000e+00, ..., 6.600e+03, 5.400e+00,\n",
" 0.000e+00],\n",
" [6.000e+01, 1.025e+00, 0.000e+00, ..., 7.200e+03, 5.900e+00,\n",
" 0.000e+00],\n",
" [8.000e+01, 1.025e+00, 0.000e+00, ..., 6.800e+03, 6.100e+00,\n",
" 0.000e+00]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_data"
]
},
{
"cell_type": "markdown",
"id": "ef0afcab",
"metadata": {},
"source": [
"### Splittng data into train and test"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "5c0ce1f4",
"metadata": {},
"outputs": [],
"source": [
"# Using Skicit-learn to split data into training and testing sets\n",
"from sklearn.model_selection import train_test_split\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data, test_size = 0.2, random_state = 42)"
]
},
{
"cell_type": "markdown",
"id": "26033386",
"metadata": {},
"source": [
"### Model training - Random Forest"
]
},
{
"cell_type": "markdown",
"id": "d98c855a",
"metadata": {},
"source": [
"#### ISOLATION FOREST"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "b41d16ee",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report, accuracy_score,precision_score,recall_score,f1_score,matthews_corrcoef\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "53a3cd66",
"metadata": {},
"outputs": [],
"source": [
"#Building another model/classifier ISOLATION FOREST\n",
"from sklearn.ensemble import IsolationForest\n",
"ifc=IsolationForest(max_samples=len(X_train),\n",
" contamination=outlier_fraction/100,random_state=1)\n",
"ifc.fit(X_train)\n",
"scores_pred = ifc.decision_function(X_train)\n",
"y_pred = ifc.predict(X_test)\n",
"\n",
"\n",
"# Reshape the prediction values to 0 for valid, 1 for fraud. \n",
"y_pred[y_pred == 1] = 0\n",
"y_pred[y_pred == -1] = 1\n",
"\n",
"n_errors = (y_pred != Y_test).sum()"
]
},
{
"cell_type": "markdown",
"id": "e8dd7a8f",
"metadata": {},
"source": [
"### Model eveuation"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "60c5a8a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the Model used is Isolation Forest\n",
"The accuracy is 0.9875\n",
"The precision is 1.0\n",
"The recall is 0.9807692307692307\n",
"The F1-Score is 0.9902912621359222\n",
"The Matthews correlation coefficient is0.9731133552274452\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Isolation Forest: 1\n",
"0.9875\n",
" precision recall f1-score support\n",
"\n",
" 0 0.97 1.00 0.98 28\n",
" 1 1.00 0.98 0.99 52\n",
"\n",
" accuracy 0.99 80\n",
" macro avg 0.98 0.99 0.99 80\n",
"weighted avg 0.99 0.99 0.99 80\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 648x504 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#evaluation of the model\n",
"#printing every score of the classifier\n",
"#scoring in any thing\n",
"\n",
"from sklearn.metrics import confusion_matrix\n",
"n_outliers = len(Stroke)\n",
"print(\"the Model used is {}\".format(\"Isolation Forest\"))\n",
"acc= accuracy_score(Y_test,y_pred)\n",
"print(\"The accuracy is {}\".format(acc))\n",
"prec= precision_score(Y_test,y_pred)\n",
"print(\"The precision is {}\".format(prec))\n",
"rec= recall_score(Y_test,y_pred)\n",
"print(\"The recall is {}\".format(rec))\n",
"f1= f1_score(Y_test,y_pred)\n",
"print(\"The F1-Score is {}\".format(f1))\n",
"MCC=matthews_corrcoef(Y_test,y_pred)\n",
"print(\"The Matthews correlation coefficient is{}\".format(MCC))\n",
"\n",
"#printing the confusion matrix\n",
"LABELS = ['No CKD', 'CKD']\n",
"conf_matrix = confusion_matrix(Y_test, y_pred)\n",
"plt.figure(figsize=(12, 12))\n",
"sns.heatmap(conf_matrix, xticklabels=LABELS,\n",
" yticklabels=LABELS, annot=True, fmt=\"d\");\n",
"plt.title(\"Confusion matrix\")\n",
"plt.ylabel('True class')\n",
"plt.xlabel('Predicted class')\n",
"plt.show()\n",
"\n",
"# Run classification metrics\n",
"plt.figure(figsize=(9, 7))\n",
"print('{}: {}'.format(\"Isolation Forest\", n_errors))\n",
"print(accuracy_score(Y_test, y_pred))\n",
"print(classification_report(Y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "5d548aa6",
"metadata": {},
"source": [
"#### Random Forest Classifier"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "c41d3b76",
"metadata": {},
"outputs": [],
"source": [
"# Building the Random Forest Classifier (RANDOM FOREST)\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"# random forest model creation\n",
"rfc = RandomForestClassifier()\n",
"rfc.fit(X_train,Y_train)\n",
"# predictions\n",
"y_pred = rfc.predict(X_test)"
]
},
{
"cell_type": "markdown",
"id": "5821ec3c",
"metadata": {},
"source": [
"### Model eveuation"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "6284b00a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The model used is Random Forest classifier\n",
"The accuracy is 0.9875\n",
"The precision is 1.0\n",
"The recall is 0.9807692307692307\n",
"The F1-Score is 0.9902912621359222\n",
"The Matthews correlation coefficient is 0.9731133552274452\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random Forest: 1\n",
"0.9875\n",
" precision recall f1-score support\n",
"\n",
" 0 0.97 1.00 0.98 28\n",
" 1 1.00 0.98 0.99 52\n",
"\n",
" accuracy 0.99 80\n",
" macro avg 0.98 0.99 0.99 80\n",
"weighted avg 0.99 0.99 0.99 80\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 648x504 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Evaluating the classifier\n",
"#printing every score of the classifier\n",
"#scoring in any thing\n",
"from sklearn.metrics import classification_report, accuracy_score,precision_score,recall_score,f1_score,matthews_corrcoef\n",
"from sklearn.metrics import confusion_matrix\n",
"n_outliers = len(Stroke)\n",
"n_errors = (y_pred != Y_test).sum()\n",
"print(\"The model used is Random Forest classifier\")\n",
"acc= accuracy_score(Y_test,y_pred)\n",
"print(\"The accuracy is {}\".format(acc))\n",
"prec= precision_score(Y_test,y_pred)\n",
"print(\"The precision is {}\".format(prec))\n",
"rec= recall_score(Y_test,y_pred)\n",
"print(\"The recall is {}\".format(rec))\n",
"f1= f1_score(Y_test,y_pred)\n",
"print(\"The F1-Score is {}\".format(f1))\n",
"MCC=matthews_corrcoef(Y_test,y_pred)\n",
"print(\"The Matthews correlation coefficient is {}\".format(MCC))\n",
"\n",
"\n",
"#printing the confusion matrix\n",
"LABELS = ['No CKD', 'CKD']\n",
"conf_matrix = confusion_matrix(Y_test, y_pred)\n",
"plt.figure(figsize=(12, 12))\n",
"sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
"plt.title(\"Confusion matrix\")\n",
"plt.ylabel('True class')\n",
"plt.xlabel('Predicted class')\n",
"plt.show()\n",
"\n",
"# Run classification metrics\n",
"plt.figure(figsize=(9, 7))\n",
"print('{}: {}'.format(\"Random Forest\", n_errors))\n",
"print(accuracy_score(Y_test, y_pred))\n",
"print(classification_report(Y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "5df8ea16",
"metadata": {},
"source": [
"### Saving the model"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "02faf4e6",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'rfc' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-eff0c19026d6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'finalized_model_kidney_prediction_RF.sav'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrfc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wb'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'rfc' is not defined"
]
}
],
"source": [
"import pickle\n",
"filename = 'finalized_model_kidney_prediction_RF.sav'\n",
"pickle.dump(rfc, open(filename, 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "896159c4",
"metadata": {},
"outputs": [],
"source": [
"with open('finalized_model_kidney_prediction_RF.sav', 'rb') as f:\n",
" model = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "03d78684",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\n"
]
}
],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"#input_data1 = (70,1.02,0,0,1,44,1.2,145,5,14.8,8406,4.71,0) #0\n",
"input_data2 = (80, 1.02, 1, 0, 1, 36, 1.2, 137.53, 4.63, 15.4, 7800, 5.2, 1) #1\n",
"\n",
"#change input data to numpy array\n",
"input_data_as_numpy_array = np.asarray(input_data2)\n",
"\n",
"#reshape the numpy array as we are predicting for only on instance\n",
"\n",
"input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
"\n",
"prediction = model.predict(input_data_reshaped)\n",
"print(prediction)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41a76901",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"vscode": {
"interpreter": {
"hash": "d9a6414fa631c028c434667d182c0b79dc634ffcd06f52fc304061a2c0b9ef26"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
project/model train/Kidney Disease/kidney_prediction_RF_model_new.ipynb 0 → 100644
View file @ 3ec0eded
{
"cells": [
{
"cell_type": "markdown",
"id": "e555e9d8",
"metadata": {},
"source": [
"### Importing the dependancies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1123a658",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"id": "4e1ec49f",
"metadata": {},
"source": [
"### Data collecting process"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "111d33c0",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"data = pd.read_csv('new_model.csv')"
]
},
{
"cell_type": "markdown",
"id": "5da5cf75",
"metadata": {},
"source": [
"### Explore the dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "740f14ff",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['Bp', 'Sg', 'Al', 'Su', 'Rbc', 'Bu', 'Sc', 'Sod', 'Pot', 'Hemo', 'Wbcc',\n",
" 'Rbcc', 'Htn', 'Class'],\n",
" dtype='object')\n"
]
},
{
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"<div>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>Bp</th>\n",
" <th>Sg</th>\n",
" <th>Al</th>\n",
" <th>Su</th>\n",
" <th>Rbc</th>\n",
" <th>Bu</th>\n",
" <th>Sc</th>\n",
" <th>Sod</th>\n",
" <th>Pot</th>\n",
" <th>Hemo</th>\n",
" <th>Wbcc</th>\n",
" <th>Rbcc</th>\n",
" <th>Htn</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>36.0</td>\n",
" <td>1.2</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>15.4</td>\n",
" <td>7800.0</td>\n",
" <td>5.20</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50.0</td>\n",
" <td>1.020</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>18.0</td>\n",
" <td>0.8</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>11.3</td>\n",
" <td>6000.0</td>\n",
" <td>4.71</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80.0</td>\n",
" <td>1.010</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
" <td>53.0</td>\n",
" <td>1.8</td>\n",
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" <td>4.71</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>70.0</td>\n",
" <td>1.005</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>56.0</td>\n",
" <td>3.8</td>\n",
" <td>111.00</td>\n",
" <td>2.50</td>\n",
" <td>11.2</td>\n",
" <td>6700.0</td>\n",
" <td>3.90</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>80.0</td>\n",
" <td>1.010</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>26.0</td>\n",
" <td>1.4</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>11.6</td>\n",
" <td>7300.0</td>\n",
" <td>4.60</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Bp Sg Al Su Rbc Bu Sc Sod Pot Hemo Wbcc Rbcc \\\n",
"0 80.0 1.020 1.0 0.0 1.0 36.0 1.2 137.53 4.63 15.4 7800.0 5.20 \n",
"1 50.0 1.020 4.0 0.0 1.0 18.0 0.8 137.53 4.63 11.3 6000.0 4.71 \n",
"2 80.0 1.010 2.0 3.0 1.0 53.0 1.8 137.53 4.63 9.6 7500.0 4.71 \n",
"3 70.0 1.005 4.0 0.0 1.0 56.0 3.8 111.00 2.50 11.2 6700.0 3.90 \n",
"4 80.0 1.010 2.0 0.0 1.0 26.0 1.4 137.53 4.63 11.6 7300.0 4.60 \n",
"\n",
" Htn Class \n",
"0 1.0 1 \n",
"1 0.0 1 \n",
"2 0.0 1 \n",
"3 1.0 1 \n",
"4 0.0 1 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(data.columns)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7c8d2381",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400, 14)\n",
" Bp Sg Al Su Rbc Bu \\\n",
"count 400.000000 400.000000 400.000000 400.000000 400.000000 400.00000 \n",
"mean 76.455000 1.017712 1.015000 0.395000 0.882500 57.40550 \n",
"std 13.476536 0.005434 1.272329 1.040038 0.322418 49.28597 \n",
"min 50.000000 1.005000 0.000000 0.000000 0.000000 1.50000 \n",
"25% 70.000000 1.015000 0.000000 0.000000 1.000000 27.00000 \n",
"50% 78.000000 1.020000 1.000000 0.000000 1.000000 44.00000 \n",
"75% 80.000000 1.020000 2.000000 0.000000 1.000000 61.75000 \n",
"max 180.000000 1.025000 5.000000 5.000000 1.000000 391.00000 \n",
"\n",
" Sc Sod Pot Hemo Wbcc \\\n",
"count 400.00000 400.000000 400.000000 400.000000 400.000000 \n",
"mean 3.07235 137.529025 4.627850 12.526900 8406.090000 \n",
"std 5.61749 9.204273 2.819783 2.716171 2523.219976 \n",
"min 0.40000 4.500000 2.500000 3.100000 2200.000000 \n",
"25% 0.90000 135.000000 4.000000 10.875000 6975.000000 \n",
"50% 1.40000 137.530000 4.630000 12.530000 8406.000000 \n",
"75% 3.07000 141.000000 4.800000 14.625000 9400.000000 \n",
"max 76.00000 163.000000 47.000000 17.800000 26400.000000 \n",
"\n",
" Rbcc Htn Class \n",
"count 400.000000 400.000000 400.000000 \n",
"mean 4.708275 0.369350 0.625000 \n",
"std 0.840315 0.482023 0.484729 \n",
"min 2.100000 0.000000 0.000000 \n",
"25% 4.500000 0.000000 0.000000 \n",
"50% 4.710000 0.000000 1.000000 \n",
"75% 5.100000 1.000000 1.000000 \n",
"max 8.000000 1.000000 1.000000 \n"
]
}
],
"source": [
"# Print the shape of the data\n",
"# data = data.sample(frac=0.1, random_state = 48)\n",
"print(data.shape)\n",
"print(data.describe())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e535ee6f",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Bp 0\n",
"Sg 0\n",
"Al 0\n",
"Su 0\n",
"Rbc 0\n",
"Bu 0\n",
"Sc 0\n",
"Sod 0\n",
"Pot 0\n",
"Hemo 0\n",
"Wbcc 0\n",
"Rbcc 0\n",
"Htn 0\n",
"Class 0\n",
"dtype: int64\n"
]
}
],
"source": [
"print(data.isnull().sum())\n",
"\n",
"# Check for null values in a specific column\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d21cf5f8",
"metadata": {},
"outputs": [
{
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" <th>Sg</th>\n",
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" <th>0</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
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" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>36.0</td>\n",
" <td>1.2</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>15.4</td>\n",
" <td>7800.0</td>\n",
" <td>5.20</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>50.0</td>\n",
" <td>1.020</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
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" <td>137.53</td>\n",
" <td>4.63</td>\n",
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" <td>6000.0</td>\n",
" <td>4.71</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>80.0</td>\n",
" <td>1.010</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>1.0</td>\n",
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" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>70.0</td>\n",
" <td>1.005</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>56.0</td>\n",
" <td>3.8</td>\n",
" <td>111.00</td>\n",
" <td>2.50</td>\n",
" <td>11.2</td>\n",
" <td>6700.0</td>\n",
" <td>3.90</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>80.0</td>\n",
" <td>1.010</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>26.0</td>\n",
" <td>1.4</td>\n",
" <td>137.53</td>\n",
" <td>4.63</td>\n",
" <td>11.6</td>\n",
" <td>7300.0</td>\n",
" <td>4.60</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <tr>\n",
" <th>395</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>49.0</td>\n",
" <td>0.5</td>\n",
" <td>150.00</td>\n",
" <td>4.90</td>\n",
" <td>15.7</td>\n",
" <td>6700.0</td>\n",
" <td>4.90</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td>70.0</td>\n",
" <td>1.025</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>31.0</td>\n",
" <td>1.2</td>\n",
" <td>141.00</td>\n",
" <td>3.50</td>\n",
" <td>16.5</td>\n",
" <td>7800.0</td>\n",
" <td>6.20</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td>80.0</td>\n",
" <td>1.020</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>26.0</td>\n",
" <td>0.6</td>\n",
" <td>137.00</td>\n",
" <td>4.40</td>\n",
" <td>15.8</td>\n",
" <td>6600.0</td>\n",
" <td>5.40</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398</th>\n",
" <td>60.0</td>\n",
" <td>1.025</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>50.0</td>\n",
" <td>1.0</td>\n",
" <td>135.00</td>\n",
" <td>4.90</td>\n",
" <td>14.2</td>\n",
" <td>7200.0</td>\n",
" <td>5.90</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td>80.0</td>\n",
" <td>1.025</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>18.0</td>\n",
" <td>1.1</td>\n",
" <td>141.00</td>\n",
" <td>3.50</td>\n",
" <td>15.8</td>\n",
" <td>6800.0</td>\n",
" <td>6.10</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>400 rows × 14 columns</p>\n",
"</div>"
],
"text/plain": [
" Bp Sg Al Su Rbc Bu Sc Sod Pot Hemo Wbcc Rbcc \\\n",
"0 80.0 1.020 1.0 0.0 1.0 36.0 1.2 137.53 4.63 15.4 7800.0 5.20 \n",
"1 50.0 1.020 4.0 0.0 1.0 18.0 0.8 137.53 4.63 11.3 6000.0 4.71 \n",
"2 80.0 1.010 2.0 3.0 1.0 53.0 1.8 137.53 4.63 9.6 7500.0 4.71 \n",
"3 70.0 1.005 4.0 0.0 1.0 56.0 3.8 111.00 2.50 11.2 6700.0 3.90 \n",
"4 80.0 1.010 2.0 0.0 1.0 26.0 1.4 137.53 4.63 11.6 7300.0 4.60 \n",
".. ... ... ... ... ... ... ... ... ... ... ... ... \n",
"395 80.0 1.020 0.0 0.0 1.0 49.0 0.5 150.00 4.90 15.7 6700.0 4.90 \n",
"396 70.0 1.025 0.0 0.0 1.0 31.0 1.2 141.00 3.50 16.5 7800.0 6.20 \n",
"397 80.0 1.020 0.0 0.0 1.0 26.0 0.6 137.00 4.40 15.8 6600.0 5.40 \n",
"398 60.0 1.025 0.0 0.0 1.0 50.0 1.0 135.00 4.90 14.2 7200.0 5.90 \n",
"399 80.0 1.025 0.0 0.0 1.0 18.0 1.1 141.00 3.50 15.8 6800.0 6.10 \n",
"\n",
" Htn Class \n",
"0 1.0 1 \n",
"1 0.0 1 \n",
"2 0.0 1 \n",
"3 1.0 1 \n",
"4 0.0 1 \n",
".. ... ... \n",
"395 0.0 0 \n",
"396 0.0 0 \n",
"397 0.0 0 \n",
"398 0.0 0 \n",
"399 0.0 0 \n",
"\n",
"[400 rows x 14 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"id": "f0e0e01a",
"metadata": {},
"source": [
"### Checking the distribution of target variable\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dd942b3b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Outlier percentile: 100.0\n",
"Having disease: 250\n",
"Not having disease: 150\n"
]
}
],
"source": [
"# Determine number of fraud cases in dataset\n",
"\n",
"have = data[data['Class'] == 1]\n",
"not_have = data[data['Class'] == 0]\n",
"\n",
"outlier_fraction = 1.6666666666666667\n",
"outlier_fraction = outlier_fraction / np.max(outlier_fraction) * 100\n",
"print(\"Outlier percentile:\", outlier_fraction)\n",
"\n",
"print('Having disease: {}'.format(len(data[data['Class'] == 1])))\n",
"print('Not having disease: {}'.format(len(data[data['Class'] == 0])))"
]
},
{
"cell_type": "markdown",
"id": "4a85a5bb",
"metadata": {},
"source": [
"### Visualizing the dataset"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "cf5c2e23",
"metadata": {},
"outputs": [
{
"data": {
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feq+lZpQkSVpxRh0T9jrgG0lOa9YPAFa3E0mSJE2VKR0TNlIjrKpOSvIQ4BHNpiOr6pr2YkmSJK1sG3J25AHAY+h1Q24GHNdKIkmSNF2mtBI26tmR7wcOB84DzgdemuR9bQaTJElayUathD0WeGA1pxgkOYZeg0ySJGlJakorYaM2wi4Cdgd+0qzvBpzbSiJJkjRdbIT9riQn0BsDth1wYZJvN+sPB77ZfjxJkqSVaVgl7B/HkkKSJE2vma4DdGPRRlhVnTbf9iSrgMNaSSRJkjQFFj07Msm2SV6T5L1JnpCelwOXAM8YT0RJkrSS1Uy1ukyqYd2R/wr8EjgDeAnwamBz4NCqOqfdaJIkSSvXsEbYParqQQBJPgRcA+xeVb9uPZkkSZoOE1ytatOwyVpvnb1RVeuBS22ASZIkLd2wStg+Sa5rbgfYslkPUFW1bavpJEnSyufZkb+rqlaNK4gkSdI02ZALeG+Up+x5eduHGNk7ztq56wh9H/zzkS7bORY/+8zVXUcA4K0PfX3XEfpe9523dB2hb923ju86Qt+rX3ZG1xH6/u4ZN3Ydoe8xn7yp6wh9H93slq4j9D3rojt0HaHv+Bet6zrCbdav7zrBxJnkMxjbNDktAUmSpCnSeiVMkiRpUVM6JsxKmCRJUgeshEmSpE45JkySJEljYyVMkiR1yzFhkiRJGhcrYZIkqVNlJUySJEnjYiVMkiR1a0orYTbCJElSp+yOlCRJ0thYCZMkSd2yEiZJkjSdkhyc5KIkFyc5ap77n5Pk3Gb5ZpJ9lnpMK2GSJKlTXY8JS7IKeB9wEHA5cFaS46vqewO7XQo8tqp+meSJwBrg4Us5rpUwSZI07R4GXFxVl1TVLcAngUMHd6iqb1bVL5vVM4Fdl3rQkSphSf5svu1V9fGlBpAkSdOt7UpYktXA6oFNa6pqzcD6LsBlA+uXs3iV68XAfyw116jdkfsP3N4COBD4L2DeRtjgF/sP99qL5+2081IySpIkbbSmwbVmkV0y38Pm3TH5A3qNsMcsNddIjbCqOmJOgO2Af11k//4Xe9UBj5v3i5AkSYLux4TRq3ztNrC+K3DF3J2S7A18CHhiVf18qQfd2DFhNwJ7LfXgkiRJE+AsYK8keybZHDgMOH5whyS7A58DnldVP1iOg446JuwEbivLbQLcH/j0cgSQJElTrubrDRzj4avWJXk58CVgFfCRqrogyeHN/UcDbwDuArw/CcC6qtpvKccddUzYPw7cXgf8pKouX8qBJUmSJkVVnQicOGfb0QO3XwK8ZDmPOeqYsNNmbyfZAVhyP6gkSRJMxJiwTiw6JizJI5KcmuRzSR6c5HzgfODqJAePJ6IkSdLKM6wS9l7gtcB2wFfpnQ1wZpL7Ap8ATmo5nyRJWuFqptsxYV0ZdnbkplV1clV9Briqqs4EqKrvtx9NkiRp5RpWCRvspb1pzn3O/yVJkpZsWseEDWuE7ZPkOnozyW7Z3KZZ36LVZJIkSSvYoo2wqlo1riCSJGk6VcfzhHVlY2fMlyRJ0hKMOlmrJElSKxwTJkmS1AGnqJAkSdLYWAmTJEmdqimd9Kr1RtgtN07OCZY7zkxO4e8337qs6wh9N9+0VdcRANhl/eSUo9d96/iuI/Rt+vAndx2h77hrP9h1hL43XrBD1xH6HrrFzl1H6Lv+N5PzObr3FnfoOkLfrT/6WdcR+jbZYnJ+L6pbVsIkSVKnHBMmSZKksbESJkmSOmUlTJIkSWNjJUySJHVqWs+OtBImSZLUASthkiSpU44JkyRJ0thYCZMkSZ2qshImSZKkMbESJkmSOlUzXSfohpUwSZKkDlgJkyRJnZpxTJgkSZLGxUqYJEnqlGdHLiLJVkk2GVjfJMkd24slSZK0so3aHXkKMNjouiPwleWPI0mSpk3NpNVlUo3aCNuiqq6fXWluL1gJS7I6ydoka//9//73UjNKkqQVrKrdZVKN2gi7IclDZleS7AfctNDOVbWmqvarqv2eveMuS80oSZK04ow6MP+VwGeSXAEUsDPwzLZCSZKk6THJXYZtWrQSlmT/JHerqrOA+wKfAtYBJwGXjiGfJEnSijSsO/IDwC3N7UcCrwXeB/wSWNNiLkmSNCVmKq0uk2pYd+SqqvpFc/uZwJqqOhY4Nsk5rSaTJElawYY2wpJsWlXrgAOB1RvwWEmSpKGmdbLWYQ2pTwCnJbmG3tmQpwMkuRdwbcvZJEmSVqxFG2FV9dYkpwA7ASdX9Wfb2AQ4ou1wkiRp5ZvkubzaNLRLsarOnGfbD9qJI0mSNB0c1yVJkjo1yWcwtmnUGfMlSZK0jKyESZKkTk3r2ZFWwiRJkjpgJUySJHVqWs+OtBImSZLUASthkiSpU9N6dmTrjbDTr9ip7UOM7Dn7XNZ1hL6/P2/nriP0PfamrhP0PGGXq7qO0Pfql53RdYS+4679YNcR+n78wxO6jtD3/f3/susIfe9+8m+6jtB37qe36DpC3z8ccn3XEfpec+KOXUfou5H1XUfo+0jXAaaclTBJktQpz46UJEnS2FgJkyRJnZrWMWFWwiRJkjpgJUySJHVqSqcJsxEmSZK6ZXekJEmSxsZGmCRJ6lRVWl1GkeTgJBcluTjJUfPcnyT/3Nx/bpKHLPXrthEmSZKmWpJVwPuAJwL3B56V5P5zdnsisFezrAb+ZanHtREmSZI6NdPyMoKHARdX1SVVdQvwSeDQOfscCny8es4E7pRkSZcFshEmSZJWtCSrk6wdWFbP2WUXYPDahpc32zZ0nw3i2ZGSJKlTRbtnR1bVGmDNIrvMF2DuzBmj7LNBrIRJkqRpdzmw28D6rsAVG7HPBrERJkmSOjVT7S4jOAvYK8meSTYHDgOOn7PP8cCfNWdJPgK4tqquXMrXbXekJEmaalW1LsnLgS8Bq4CPVNUFSQ5v7j8aOBE4BLgYuBF44VKPayNMkiR1aqblMWGjqKoT6TW0BrcdPXC7gJct5zEXbYQlOYFFBp1V1ZOXM4wkSdK0GDYm7B+BdwCXAjcBH2yW64HzF3rQ4KmgX73xh8uVVZIkrUBFWl0m1aKVsKo6DSDJW6rqgIG7Tkjy9UUe1z8V9N92fu60XhxdkiRpQaOOCdsxyT2q6hKAJHsCO7YXS5IkTYsRZ7VfcUZthB0JnJrkkmZ9D3rXTZIkSdJGGKkRVlUnJdkLuG+z6ftVdXN7sSRJ0rSY5HFbbRqpEZZkM+ClwOy4sFOTfKCqbm0tmSRJ0go2anfkvwCbAe9v1p/XbHtJG6EkSdL0cEzY4vavqn0G1r+a5LttBJIkSZoGozbC1ie5Z1X9CCDJPYD17cWSJEnTwkrY4l4NfG3O2ZFLvmaSJEnStA7MX3TG/CT7J7lbVZ0C7AV8DrgOOBmwO1KSJGkjDbts0QeAW5rbDweOAo4BrqaZEV+SJGkpZtLuMqmGdUeuqqpfNLefCaypqmOBY5Oc02oySZKkFWxoIyzJplW1DjiQ354lf9TxZJIkSQuamdIxYcMaUp8ATktyDXATcDpAknsB17acTZIkacVatBFWVW9NcgqwE3ByVVVz1ybAEW2HkyRJK18N32VFGtqlWFVnzrPtB+3EkSRJmg65rbjVjvP2/OOJaeBectM2XUfoe+AOP+86Qt9W20/Gtdh/fPH2XUfoe8AzJueyqL+54LquI/RddfG2XUfou+9Z7+46Qt9n93591xH67jAzMT9yuW7VsBPwx2ffTSfnc7T5ppMz1/n9f/R/JmIw1ufu9uxW37hPu+rfJ+LrnGtyPiGSJElTxDMcJUlSp2YykYWq1lkJkyRJ6oCVMEmS1KnJGck4XlbCJEmSOmAlTJIkdWqm6wAdsRImSZLUASthkiSpUzPTeXKklTBJkqQuWAmTJEmdmmE6S2FWwiRJkjpgJUySJHVqWucJsxEmSZI65cB8SZIkjY2VMEmS1Ckna11EkkePsk2SJEmjGbU78j0jbpMkSdog1fIyqRbtjkzySOBRwI5J/mrgrm2BVYs8bjWwGuANd3kQT9/m7ssQVZIkaeUYNiZsc2DrZr9tBrZfBzx9oQdV1RpgDcB5e/7xJDdCJUlSx6b17MhFG2FVdRpwWpKPVdVPkmzT21zXjyeeJEnSyjTq2ZHbJDkb2B4gyTXA86vq/NaSSZKkqeDZkYtbA/xVVd29qu4OvKrZJkmSpI0waiVsq6r62uxKVZ2aZKuWMkmSpCkyrZWwURthlyR5PfCvzfpzgUvbiSRJkrTyjdod+SJgR+BzzbID8MK2QkmSpOlRaXeZVMPmCdsCOBy4F3Ae8KqqunUcwSRJklayYd2RxwC3AqcDTwTuB7yy5UySJGmKOCZsfvevqgcBJPkw8O32I0mSJK18wxph/a7HqlqXTHDHqiRJul2yEja/fZJc19wOsGWzHnoz52/bajpJkqQVathlixa8SLckSdJymNaLTI86RYUkSZKW0aiTtUqSJLViZkqHnNsIkyRJnXJgfkuOqa3bPsTI3vLWnbuO0Pfw103OVZ8Ov/aeXUcA4DkHXdl1hL7HfPKmriP0PXSLyXnfvvvJv+k6Qt9n93591xH6nn7uW7qO0PfhB7+h6wh9LzjhsK4j9O190Ju6jtB3ybWT87NuXdcBppyVMEmS1KlprYQ5MF+SJGkBSbZP8uUkP2z+v/M8++yW5GtJLkxyQZK/HOW5bYRJkqROVcvLEh0FnFJVewGnNOtzraN3fe37AY8AXpbk/sOe2EaYJEnSwg6ldy1tmv+fMneHqrqyqv6ruf1r4EJgl2FP7JgwSZLUqbanqEiyGlg9sGlNVa0Z8eF3raorodfYSvJ7Q461B/Bg4FvDnthGmCRJWtGaBteCja4kXwHuNs9dr9uQ4yTZGjgWeGVVXTdsfxthkiSpU12fHVlVj1/oviRXJ9mpqYLtBPxsgf02o9cA+7eq+twox3VMmCRJ0sKOB57f3H4+8IW5OyQJ8GHgwqp656hPbCNMkiR1asLPjnwbcFCSHwIHNesk2TnJic0+jwaeB/xhknOa5ZBhT2x3pCRJ0gKq6ufAgfNsvwI4pLn9DWCDTy+wESZJkjo1sxz1qtshuyMlSZI6YCVMkiR1quuzI7tiJUySJKkDVsIkSVKnpnNE2IiNsGYCsv8JHNBsOg04uqpubSuYJEnSSjZqJexfgM2A9zfrz2u2vaSNUJIkaXpM65iwURth+1fVPgPrX03y3YV2HrxQ5kHb78fe29xrCRElSZJWnlEH5q9Pcs/ZlST3ANYvtHNVramq/apqPxtgkiRpMTNpd5lUo1bCXg18Lckl9GaEvTvwwtZSSZKkqTGtk7WO1AirqlOS7AXch14j7PtVdXOrySRJklawUc+OXAX8EbBH85gDk7AhVwqXJEmaz3TWwUbvjjwB+A1wHtN7EoMkSdKyGbURtmtV7d1qEkmSNJWmtboz6tmR/5HkCa0mkSRJmiKjVsLOBI5LsglwK73B+VVV27aWTJIkTQXPjlzcO4BHAudV1XS+UpIkScto1EbYD4HzbYBJkqTlNq2Ni1EbYVcCpyb5D6A/P5hTVEiSJG2cURthlzbL5s0iSZK0LKb17MhRZ8x/M0CSrarqhnYjSZIkrXwjTVGR5JFJvgdc2Kzvk+T9rSaTJElTYYZqdZlUo84T9i56ly36OUBVfRc4oKVMkiRJK96oY8KoqsuSDG5av/xxJEnStJncWlW7Rm2EXZbkUUAl2Rx4BU3X5DB/esvNw3cak3f/7VVdR+j7ygMm5/yGW66/ousIAHz3S3fpOkLfRze7pesIfdf/JsN3GpNzP71F1xH67jBBP7Y//OA3dB2h78Vn/23XEfom6XU56W7bdx2hb5t9t+o6gibEqI2ww4F3A7sAlwMnAy9rK5QkSZoenh25iKq6BnhOy1kkSZKmxqKNsCTvYZGu2qp6xbInkiRJU6UmaHjBOA2rhK0duP1m4I0tZpEkSZoaizbCquqY2dtJXjm4LkmStBymdUzYqPOEwfSeQSpJkrTsRp4nTJIkqQ2TPKt9m4YNzP81t1XA7pjkutm7gKqqbdsMJ0mSVr7pbIINHxO2zbiCSJIkTRO7IyVJUqemtTtyQwbmS5IkaZlYCZMkSZ1yigpJkiSNjZUwSZLUqWm9bJGVMEmSpA6M1AhL8tQk2w2s3ynJU1pLJUmSpsZMy8ukGrUS9saqunZ2pap+xSIX806yOsnaJGs/f+OlS4woSZK08ow6Jmy+xtqCj62qNcAagG/t/LTp7OiVJEkjcUzY4tYmeWeSeya5R5J/Ar7TZjBJkqSVbNRG2BHALcCngE8DNwEvayuUJEmaHtM6Jmyk7siqugE4quUskiRJU2PUsyO/nOROA+t3TvKl1lJJkqSpMVPV6jKpRu2O3KE5IxKAqvol8HutJJIkSZoCozbCZpLsPruS5O4wpacySJKkZVUtL5Nq1CkqXgd8I8lpzfoBwOp2IkmSJK18ow7MPynJQ4BHNJuOrKpr2oslSZKmxcxE16vasyEX8D4AeAy9yt5mwHGtJJIkSZoCIzXCkrwfuBfwiWbTS5M8vqqcK0ySJC3JtM6YP2ol7LHAA6t653kmOQY4r7VUkiRpakzyhKptGvXsyIuA3QfWdwPOXf44kiRJ02HRSliSE+iNAdsO+H6SbzV3PQz4ZsvZJEnSFJjkgflJtqd32cY9gB8Dz2jmS51v31XAWuC/q+pJw557WHfkV5t9zgZuHT2yJEnSinAUcEpVvS3JUc363yyw718CFwLbjvLEw7ojdwGeCnwGeDPwR8DWwHlVddpiD5QkSRpFtfxviQ4FjmluHwM8Zb6dkuwK/A/gQ6M+8aKVsKr66+aJNwf2Ax4FvAj4YJJfVdX9hx3gK5ttOWqW1h155FZdR+h7yjsm53V5Mjt0HaFnM3jGvS/rOkXfsy66Q9cRALj3FpORA+AfDrm+6wh9x35hi64j9L3ghMO6jtD34Qe/oesIfS8++2+7jtD38Af9WdcR+q698sauI/T9qOsAY5JkNb89yfyaqloz4sPvWlVXAlTVlUkWumzju4D/BWwzaq5Rz47ckl5pbbtmuQLPjtQyswEmSdOp7bMjmwbXgo2uJF8B7jbPXa8b5fmTPAn4WVV9J8njRs01bGD+GuABwK+Bb9EbjP/OhQakSZIk3d5U1eMXui/J1Ul2aqpgOwE/m2e3RwNPTnIIsAWwbZL/XVXPXey4w8aE7Q7cAbgK+G/gcuBXQx4jSZI0sqpqdVmi44HnN7efD3xhnvyvqapdq2oP4DDgq8MaYDCkEVZVBwP7A//YbHoVcFaSk5O8efT8kiRJt0tvAw5K8kPgoGadJDsnOXEpTzx0TFgzS/75SX4FXNssT6I3V9gbl3JwSZKkSZ4nrKp+Dhw4z/YrgEPm2X4qcOoozz1sTNgr6J0R+Wh684T9J3AG8BEcmC9JkrTRhlXC9gA+Cxw5e3qmJEnScprWa0cOmyfsr8YVRJIkaZqMOk+YJElSK5ZhVvvbpWFTVEiSJKkFVsIkSVKnJvnsyDZZCZMkSeqAlTBJktSpZZjV/nbJSpgkSVIHrIRJkqROOU+YJElSB5yiYhFJnppku4H1OyV5SmupJEmSVrhRx4S9saqunV2pql+xyMW7k6xOsjbJ2rOuv3iJESVJ0ko2Q7W6TKpRG2Hz7bdgV2ZVramq/apqv/23vtfGJZMkSVrBRh0TtjbJO4H3AQUcAXyntVSSJGlqOEXF4o4AbgE+BXwauAl4WVuhJEmSVrqRKmFVdQNwVMtZJEnSFJrkcVttGvXsyC8nudPA+p2TfKm1VJIkSSvcqGPCdmjOiASgqn6Z5PfaiSRJkqaJ84QtbibJ7rMrSe4OU/qKSZIkLYNRK2GvA76R5LRm/QBgdTuRJEnSNJmZ0rMjRx2Yf1KShwCPaDYdWVXXtBdLkiRpZduQa0ceADyGXjfkZsBxrSSSJElTZTrrYKOfHfl+4HDgPOB84KVJ3tdmMEmSpJVs1ErYY4EHVjOlbZJj6DXIJEmSlsR5whZ3EbD7wPpuwLnLH0eSJGk6LFoJS3ICva7a7YALk3y7WX848M3240mSpJVuWithw7oj/3EsKSRJkqbMoo2wqjptvu1JVgGHtZJIkiRNlXKesN+VZFvgZcAuwPHAl5v1VwPnAP827AAve9QVSw65XF71rh27jtD3+dffr+sIfTd/+eyuIzTCZ8/YtesQABz/onVdR+i79Uc/6zpC32tOnJzP0Es2va7rCH17H/SmriP0nXS37buO0PfwB/1Z1xH6vnXex7uO0LfurC92HUETYlh35L8CvwTOAF5Cr/G1OXBoVZ3TbjRNm0lpgEmSxssxYfO7R1U9CCDJh4BrgN2r6tetJ5MkSVPBC3jP79bZG1W1HrjUBpgkSdLSDauE7ZNkduBFgC2b9QBVVdu2mk6SJK14DsyfR1WtGlcQSZKkabIhF/CWJEladtM6MH/UyxZJkiRpGVkJkyRJnZrWMWFWwiRJkjpgJUySJHXKMWGSJEkaGythkiSpU86YL0mSpLGxEiZJkjo149mRkiRJGhcrYZIkqVOOCZMkSdLYjNQIS/LoJF9O8oMklyS5NMkli+y/OsnaJGs/dvEVy5dWkiStODNVrS6TatTuyA8DRwLfAdYP27mq1gBrAH71rD+Y3K9ekiSpI6M2wq6tqv9oNYkkSZpK0zombNRG2NeSvB34HHDz7Maq+q9WUkmSJK1wozbCHt78v9/AtgL+cHnjSJKkaTPJ47baNGoj7MVV9VsD8ZPco4U8kiRJU2HUKSo+O8+2zyxnEEmSNJ2q5X+TatFKWJL7Ag8AtkvytIG7tgW2aDOYJEmaDnZHzu8+wJOAOwF/PLD918Cft5RJkiRpIiTZHvgUsAfwY+AZVfXLefa7E/Ah4IH0xs2/qKrOWOy5F22EVdUXgC8keeSwJ5IkSdoYk9xlCBwFnFJVb0tyVLP+N/Ps927gpKp6epLNgTsOe+Jh3ZHvodeaI8mz5t5fVa8YIbwkSdLt1aHA45rbxwCnMqcRlmRb4ADgBQBVdQtwy7AnHtYduXbg9puBN44QVpIkaWRVM60+f5LVwOqBTWuaq/uM4q5VdSVAVV2Z5Pfm2ecewP8FPppkH3pXGPrLqrphsSce1h15zMAX8MrBdUmSpNuDwcspzifJV4C7zXPX60Y8xKbAQ4AjqupbSd5Nr9vy9cMeNKqJ7rCVJEm3TzMdNzGq6vEL3Zfk6iQ7NVWwnYCfzbPb5cDlVfWtZv2z9Bphixp1njBJkqRpdDzw/Ob284EvzN2hqq4CLktyn2bTgcD3hj3xsIH5v+a2Ctgdk1w3e1fvmLXt8OySJEkLq8meJ+xtwKeTvBj4KfCnAEl2Bj5UVYc0+x0B/FtzZuQlwAuHPfGwMWHbLCW1JEnS7VlV/ZxeZWvu9iuAQwbWz+G3r7E91IaMCdso2WJV24cY2ad+fnbXEfreucfjuo7Qt2rb87uOAMAz/+hq3nTKXbqO0bN+fdcJ+jaZoM/QjUzO67L5ppOT5ZJrr+w6Qt82+27VdYS+a6+8sesIfevO+mLXEfo23f9JXUeYOF2PCeuKY8I0MSamASZJ0hi0XgmTJElazISPCWuNlTBJkqQOWAmTJEmdmrESJkmSpHGxEiZJkjpVnh0pSZKkcbESJkmSOuXZkZIkSRobK2GSJKlT0zpjvo0wSZLUKbsjJUmSNDZWwiRJUqecrFWSJEljYyVMkiR1yjFhkiRJGpuRGmFJ/jLJtun5cJL/SvKEtsNJkqSVb4ZqdZlUo1bCXlRV1wFPAHYEXgi8baGdk6xOsjbJ2o9d9N/LEFOSJGllGXVMWJr/DwE+WlXfTZKFdq6qNcAagGtf+PjJbYJKkqTOOSZscd9JcjK9RtiXkmwDzLQXS5IkaWUbtRL2YmBf4JKqujHJ9vS6JCVJkpbEecIW90jgoqr6VZLnAv8fcG17sSRJkla2URth/wLcmGQf4H8BPwE+3loqSZI0Narlf5Nq1EbYuuqNmjsUeHdVvRvYpr1YkiRJK9uoY8J+neQ1wHOBA5KsAjZrL5YkSZoWjglb3DOBm4EXV9VVwC7A21tLJUmStMKNVAlrGl7vHFj/KY4JkyRJy8B5whaR5BFJzkpyfZJbkqxP4tmRkiRJG2nUMWHvBQ4DPgPsB/wZsFdboSRJ0vSY5DMY2zRqI4yqujjJqqpaD3w0yTdbzCVJkrSijdoIuzHJ5sA5Sf4BuBLYqr1YkiRpWjgmbHHPA1YBLwduAHYD/qStUJIkaXpUVavLpBr17MifNDdvAt7cXhxJkqTpsGgjLMl5sPBouarae9kTSZKkqTK5tap2DauEPQ24K3DZnO13B65oJZEkSdI0GNKH+kVg73m27wec0HYf7pxjrh7n8cxy+80yKTnMYhazmGVasrhs3DJsYP4eVXXuPA23tcAeG9HmW4rVYz7eYswyv0nJMik5wCwLMcv8zDI/s8xvkrJoIwxrhG2xyH1bLmcQSZKkaTKsEXZWkj+fuzHJi4HvtBNJkiRp5Rs2MP+VwHFJnsNtja79gM2Bp7aYaz5rxny8xZhlfpOSZVJygFkWYpb5mWV+ZpnfJGXRRkgzuG/xnZI/AB7YrF5QVV9tNZUkSdIKN1IjTJIkSctr1MsWjU2S9UnOSfLdJP+V5FEd53ldkguSnNvkeniXeZpMT01SSe7brO+R5PwxZ+jsdRl4j5yf5IQkd2q2Py7JF8eVY5FcvneXcPw2vo9z3jOfSXLHIcdf8vcuyfVz1l+Q5L1Lfd6NzPJPSV45sP6lJB8aWH9Hkr8a5+dngj/HC37fkjwlyf1bPPbdknwyyY+SfC/JiUnuPe6f7xqfiWuEATdV1b5VtQ/wGuDvugqS5JHAk4CHVO/qAI/ndyeu7cKzgG8Ah3Vx8Al4XWbfIw8EfgG8bIzHXozv3Qk5/jwG3zO3AIcvsu/jgE4b0C34Js3XlGQTYAfgAQP3PwrYbMyZJvVzvJinAK00wpIEOA44taruWVX3B15Lb8J0rVCT2AgbtC3wS+j/dfT1JMc1fyEc3fwwadNOwDVVdTNAVV1TVVckOSTJ95N8I8k/j/mvx62BRwMvpqNGGAu/Lj9OskOTc78kp44hyxnALgPr2873HklycFOd+m6SU8aQa+57t/8eSfLeJC9o+fgLfY/2T/LN5nX4dpJtxnz8A5OcneS8JB9Jcgfof3++n+Qb9K7U0abTgXsl2T7J55tK3ZlJ9k6yB70G2pFNleb32wiQZMckxyY5q1ke3Wx/U5JjkpzcfJ6eluQfmtfrpCSbNfvN+zou4j+5rWH5AOB84NdJ7tw89n7A2WzA5yfJ1kk+2mQ4N8mfLOEl2ejP8TLnWFB61dEnA29v3hv3THJqkr9vPks/WOL75Q+AW6vq6NkNVXUOA3+8pNfrcXrzGvSr7Ul2an4/zlYWfz/JqiQfa9bPS3LkErKpLV3PFjt3AdYD5wDfB64FHtpsfxzwG+AewCrgy8DTW86ydZPlB8D7gcfSmzvtMmDPZp9PAF8c4+vzXODDze1vAg+hN3Hu+WPM8DuvS7P9x8AOze396P1F18bxr2/+XwV8Bjh4sfcIsOOc79n2Hbx3vziw33uBF3Tw3t0cuATYv9lnW2DTMR5/9rNz72afj9M7A3t2+15AgE8v92dq4D2zKfAF4H8C7wHe2Gz/Q+Cc5vabgL9exvfD7PJT4L3Nff8OPKa5vTtw4cCxv0GvKrUPcCPwxOa+4+hVYuZ9HUfI8+PmWC+l19B8C3AIvT/qvr6hnx/g74F3DTz/nTfye7Kkz/FSc2zg9+1jDPzeAU4F3tHcPgT4yhKO+wrgn+bZvgfNz3fgjsAWze29gLXN7VcBrxt4PbcBHgp8eeB57rScnymX5VkmsRI2W6K+L3Aw8PGmTAvw7aq6pKrW02v8PKbNIFV1Pb038mrg/wKfovfD65KqurTZ7RNtZpjHs4BPNrc/2ayP1XyvyxgqO4O2THIO8HNge3o/pGfN9x55BPD12e9ZVf2ipVyLvXfHaoH37kuBK6vqrGaf66pq3ZiPf2lV/aDZ7RjgAOC+zfYfVu+3xf9uIdLse2YtvV+qH6b33vjXJu9Xgbsk2W4Zjzn7fti3qvYF3jBw3+OB9zaZjqdX+ZmtSv5HVd0KnEfvF+pJzfbz6P1Cvg/zv47DzFbDHkWv8nTGwPo3m3025PPzeOB9s09eVb8cIcOg5focLzXHXIt93+bzueb/79D+lWQ2Az6Y5Dx6DdfZrtGzgBcmeRPwoKr6Nb0/uO6R5D1JDgauazmbNsKwecI6VVVnpNe9tePsprm7jCHDenp/7ZzavPGf3/YxF5LkLvT+Yn9gkqL3A7roVRrGaoHXZR23dXEvdrWFpbqpqvZtfmF+kd5Ykn+ejTY3Kr3qylhPA57z3h18XaDd12Yww9zv0csY4+uwgZ+dtnPd1PxC7VuggTyu12cT4JFVddPgxibSbBfuTJJbm4YpwAy9n9kb27CfHRf2IHrdkZfRq6BcB3yk2WdDPj9L/Vwt1+d47J/vOW5u/l/P0n6nXkCv4reYI4Gr6VVJN6FXMaSqvp7kAOB/AP+a5O1V9fEk+wB/RO+1fQbwoiXkUwsmsRLWl97Zf6vo/aUE8LAkezbjA55Jr2zf5vHvk2SvgU37Aj+j99fFHs22Z7aZYY6nAx+vqrtX1R5VtRtwKbDrGDMs9Lr8hF53x0Obba2MyxhUVdfSK+H/9exYGeZ/j5wBPDbJnk3+7dvONue9+xPg/knu0PzCOXAMx5/ve3QhsHOS/Zt9tknSyh9iCxz/amCPJPdqtj0POI1e9+2eSe7ZbB9XdffrwHOgN26P3hi264Bf0+vOadPJwMtnV5LsuwGP/T7zv47D/Ce9kyV+UVXrm0rSnYBH0vuMwIZ9fuZ+DXfegK+hbxk+x8uSY0Rtvje+CtwhA1epaT6rdx/YZzt61ewZet/3Vc1+dwd+VlUfpFflfUjzR+AmVXUs8Hp6Q1c0YSaxEjZboobeXzjPr6r1zV+IZwBvo/eX3NfpjZFo09bAe9I7dXodcDG97pXPAycluQb4dssZBj2L3tc/6Fh6Z9CM00Kvy/2ADyd5LfCtcQSpqrOTfJfeSQqXMc97pKkorAY+1/xQ/xlwUAtx5n3vApcl+TRwLvBDegOg27bQ9+ijzfYtgZvodeVcv9CTtHD8TwCfaRp/ZwFHV9XNzffn/zSfqW9w2+TQbXoT8NEk59IbezVbqTsB+GySQ4Ejqur0Fo79CuB9zbE3pfdeXeyMzb6q+k2SFzLndRzhoefROyvy3+ds27qqrlnoZ+win5//v/kazqdXBXozt3XNbZAlfo6XLccIPkmvO/AVDK9abZCqqiRPBd6V5Ch6Va4f0xs3Oev9wLFJ/hT4GnBDs/1xwKuT3Erv8/xn9E50+GhuO4HtNcuZV8vjdjNZa/OX6l9X1ZM6jkKSravq+qY7433AD6vqn7rOJUmSbj8mujtygv15U/G4gF55+APdxpEkSbc3t5tKmCRJ0kpiJUySJKkDNsIkSZI6YCNMkiSpAzbCJEmSOmAjTJIkqQM2wiRJkjrw/wCgI4L+muqB0gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 864x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Correlation matrix\n",
"corrmat = data.corr()\n",
"fig = plt.figure(figsize = (12, 9))\n",
"\n",
"sns.heatmap(corrmat, vmax = .8, square = True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "38ffa726",
"metadata": {},
"source": [
"### Splitting the features and target"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ad95615d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(400, 11)\n",
"(400,)\n"
]
}
],
"source": [
"#seperating the X and the Y from the dataset\n",
"X=data.drop(['Class','Al','Su'], axis=1)\n",
"Y=data[\"Class\"]\n",
"print(X.shape)\n",
"print(Y.shape)\n",
"#getting just the values for the sake of processing (its a numpy array with no columns)\n",
"X_data=X.values\n",
"Y_data=Y.values"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "987d85cc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[8.000e+01, 1.020e+00, 1.000e+00, ..., 7.800e+03, 5.200e+00,\n",
" 1.000e+00],\n",
" [5.000e+01, 1.020e+00, 1.000e+00, ..., 6.000e+03, 4.710e+00,\n",
" 0.000e+00],\n",
" [8.000e+01, 1.010e+00, 1.000e+00, ..., 7.500e+03, 4.710e+00,\n",
" 0.000e+00],\n",
" ...,\n",
" [8.000e+01, 1.020e+00, 1.000e+00, ..., 6.600e+03, 5.400e+00,\n",
" 0.000e+00],\n",
" [6.000e+01, 1.025e+00, 1.000e+00, ..., 7.200e+03, 5.900e+00,\n",
" 0.000e+00],\n",
" [8.000e+01, 1.025e+00, 1.000e+00, ..., 6.800e+03, 6.100e+00,\n",
" 0.000e+00]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_data"
]
},
{
"cell_type": "markdown",
"id": "ef0afcab",
"metadata": {},
"source": [
"### Splittng data into train and test"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "5c0ce1f4",
"metadata": {},
"outputs": [],
"source": [
"# Using Skicit-learn to split data into training and testing sets\n",
"from sklearn.model_selection import train_test_split\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data, test_size = 0.2, random_state = 42)"
]
},
{
"cell_type": "markdown",
"id": "26033386",
"metadata": {},
"source": [
"### Model training - Random Forest"
]
},
{
"cell_type": "markdown",
"id": "d98c855a",
"metadata": {},
"source": [
"#### ISOLATION FOREST"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b41d16ee",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report, accuracy_score,precision_score,recall_score,f1_score,matthews_corrcoef\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "53a3cd66",
"metadata": {},
"outputs": [],
"source": [
"#Building another model/classifier ISOLATION FOREST\n",
"from sklearn.ensemble import IsolationForest\n",
"ifc=IsolationForest(max_samples=len(X_train),\n",
" contamination=outlier_fraction/100,random_state=1)\n",
"ifc.fit(X_train)\n",
"scores_pred = ifc.decision_function(X_train)\n",
"y_pred = ifc.predict(X_test)\n",
"\n",
"\n",
"# Reshape the prediction values to 0 for valid, 1 for fraud. \n",
"y_pred[y_pred == 1] = 0\n",
"y_pred[y_pred == -1] = 1\n",
"\n",
"n_errors = (y_pred != Y_test).sum()"
]
},
{
"cell_type": "markdown",
"id": "e8dd7a8f",
"metadata": {},
"source": [
"### Model eveuation"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "60c5a8a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the Model used is Isolation Forest\n",
"The accuracy is 0.65\n",
"The precision is 0.65\n",
"The recall is 1.0\n",
"The F1-Score is 0.787878787878788\n",
"The Matthews correlation coefficient is0.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Mishane\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:873: RuntimeWarning: invalid value encountered in scalar divide\n",
" mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Isolation Forest: 28\n",
"0.65\n",
" precision recall f1-score support\n",
"\n",
" 0 0.00 0.00 0.00 28\n",
" 1 0.65 1.00 0.79 52\n",
"\n",
" accuracy 0.65 80\n",
" macro avg 0.33 0.50 0.39 80\n",
"weighted avg 0.42 0.65 0.51 80\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Mishane\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\Mishane\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\Mishane\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 648x504 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#evaluation of the model\n",
"#printing every score of the classifier\n",
"#scoring in any thing\n",
"\n",
"from sklearn.metrics import confusion_matrix\n",
"n_outliers = len(have)\n",
"print(\"the Model used is {}\".format(\"Isolation Forest\"))\n",
"acc= accuracy_score(Y_test,y_pred)\n",
"print(\"The accuracy is {}\".format(acc))\n",
"prec= precision_score(Y_test,y_pred)\n",
"print(\"The precision is {}\".format(prec))\n",
"rec= recall_score(Y_test,y_pred)\n",
"print(\"The recall is {}\".format(rec))\n",
"f1= f1_score(Y_test,y_pred)\n",
"print(\"The F1-Score is {}\".format(f1))\n",
"MCC=matthews_corrcoef(Y_test,y_pred)\n",
"print(\"The Matthews correlation coefficient is{}\".format(MCC))\n",
"\n",
"#printing the confusion matrix\n",
"LABELS = ['No CKD', 'CKD']\n",
"conf_matrix = confusion_matrix(Y_test, y_pred)\n",
"plt.figure(figsize=(12, 12))\n",
"sns.heatmap(conf_matrix, xticklabels=LABELS,\n",
" yticklabels=LABELS, annot=True, fmt=\"d\");\n",
"plt.title(\"Confusion matrix\")\n",
"plt.ylabel('True class')\n",
"plt.xlabel('Predicted class')\n",
"plt.show()\n",
"\n",
"# Run classification metrics\n",
"plt.figure(figsize=(9, 7))\n",
"print('{}: {}'.format(\"Isolation Forest\", n_errors))\n",
"print(accuracy_score(Y_test, y_pred))\n",
"print(classification_report(Y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "5d548aa6",
"metadata": {},
"source": [
"#### Random Forest Classifier"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "c41d3b76",
"metadata": {},
"outputs": [],
"source": [
"# Building the Random Forest Classifier (RANDOM FOREST)\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"# random forest model creation\n",
"rfc = RandomForestClassifier()\n",
"rfc.fit(X_train,Y_train)\n",
"# predictions\n",
"y_pred = rfc.predict(X_test)"
]
},
{
"cell_type": "markdown",
"id": "5821ec3c",
"metadata": {},
"source": [
"### Model eveuation"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "6284b00a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The model used is Random Forest classifier\n",
"The accuracy is 0.9875\n",
"The precision is 1.0\n",
"The recall is 0.9807692307692307\n",
"The F1-Score is 0.9902912621359222\n",
"The Matthews correlation coefficient is 0.9731133552274452\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random Forest: 1\n",
"0.9875\n",
" precision recall f1-score support\n",
"\n",
" 0 0.97 1.00 0.98 28\n",
" 1 1.00 0.98 0.99 52\n",
"\n",
" accuracy 0.99 80\n",
" macro avg 0.98 0.99 0.99 80\n",
"weighted avg 0.99 0.99 0.99 80\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 648x504 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Evaluating the classifier\n",
"#printing every score of the classifier\n",
"#scoring in any thing\n",
"from sklearn.metrics import classification_report, accuracy_score,precision_score,recall_score,f1_score,matthews_corrcoef\n",
"from sklearn.metrics import confusion_matrix\n",
"n_outliers = len(have)\n",
"n_errors = (y_pred != Y_test).sum()\n",
"print(\"The model used is Random Forest classifier\")\n",
"acc= accuracy_score(Y_test,y_pred)\n",
"print(\"The accuracy is {}\".format(acc))\n",
"prec= precision_score(Y_test,y_pred)\n",
"print(\"The precision is {}\".format(prec))\n",
"rec= recall_score(Y_test,y_pred)\n",
"print(\"The recall is {}\".format(rec))\n",
"f1= f1_score(Y_test,y_pred)\n",
"print(\"The F1-Score is {}\".format(f1))\n",
"MCC=matthews_corrcoef(Y_test,y_pred)\n",
"print(\"The Matthews correlation coefficient is {}\".format(MCC))\n",
"\n",
"\n",
"#printing the confusion matrix\n",
"LABELS = ['No CKD', 'CKD']\n",
"conf_matrix = confusion_matrix(Y_test, y_pred)\n",
"plt.figure(figsize=(12, 12))\n",
"sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
"plt.title(\"Confusion matrix\")\n",
"plt.ylabel('True class')\n",
"plt.xlabel('Predicted class')\n",
"plt.show()\n",
"\n",
"# Run classification metrics\n",
"plt.figure(figsize=(9, 7))\n",
"print('{}: {}'.format(\"Random Forest\", n_errors))\n",
"print(accuracy_score(Y_test, y_pred))\n",
"print(classification_report(Y_test, y_pred))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "70d77af8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random forest model accuracy: 0.9875\n"
]
}
],
"source": [
"# calculate and print the accuracy of the model on the test set\n",
"accuracy = rfc.score(X_test, Y_test)\n",
"print(\"Random forest model accuracy:\", accuracy)"
]
},
{
"cell_type": "markdown",
"id": "5df8ea16",
"metadata": {},
"source": [
"### Saving the model"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "02faf4e6",
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"filename = 'finalized_model_kidney_prediction_RF.sav'\n",
"pickle.dump(rfc, open(filename, 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "896159c4",
"metadata": {},
"outputs": [],
"source": [
"with open('finalized_model_kidney_prediction_RF.sav', 'rb') as f:\n",
" model = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "03d78684",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0]\n"
]
}
],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"#input_data1 = (70,1.02,0,0,1,44,1.2,145,5,14.8,8406,4.71,0) #0\n",
"input_data2 = (80, 1.02, 1, 36, 1.2, 137.53, 4.63, 15.4, 7800, 5.2, 1) #1\n",
"\n",
"#change input data to numpy array\n",
"input_data_as_numpy_array = np.asarray(input_data2)\n",
"\n",
"#reshape the numpy array as we are predicting for only on instance\n",
"\n",
"input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
"\n",
"prediction = model.predict(input_data_reshaped)\n",
"print(prediction)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41a76901",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"vscode": {
"interpreter": {
"hash": "d9a6414fa631c028c434667d182c0b79dc634ffcd06f52fc304061a2c0b9ef26"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
project/model train/Lung Cancer/Lung_cancer_classification_NB_model _training.ipynb 0 → 100644
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project/model train/Lung Cancer/lung_cancer_level_LR_model.ipynb 0 → 100644
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project/model train/Lung Cancer/lung_cancer_xray.ipynb 0 → 100644
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project/model train/Skin Cancer/.ipynb_checkpoints/Skin_cancer_Detection-checkpoint.ipynb 0 → 100644
View file @ 3ec0eded
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NvgOAuTNHnFv",
"outputId": "9af20872-9b69-4105-c9b0-a5189e93f11f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "e5VoXOheOrbn"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import shutil\n",
"import random\n",
"metadata = pd.read_csv('/content/drive/MyDrive/Dataset_skin/HAM10000_metadata.csv')\n",
"\n",
"# Define source and destination directories\n",
"src_dir = '/content/drive/MyDrive/Dataset_skin/HAM10000_images_part_1/'\n",
"dst_dir = '/content/newData'\n",
"\n",
"# Create destination directories for each class\n",
"for class_name in metadata['dx'].unique():\n",
" os.makedirs(os.path.join(dst_dir, class_name), exist_ok=True)\n",
"\n",
"# Copy images to their respective class directories\n",
"for index, row in metadata.iterrows():\n",
" img_name = row['image_id'] + '.jpg'\n",
" src_path = os.path.join(src_dir, img_name)\n",
" dst_path = os.path.join(dst_dir, row['dx'], img_name)\n",
" shutil.copy(src_path, dst_path)\n",
" \n",
"print('Dataset splitting complete.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wuoNOi_-B0mN"
},
"outputs": [],
"source": [
"!zip -r newData.zip newData/\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ekUMBoOJB9FL"
},
"outputs": [],
"source": [
"!mv newData.zip /content/drive/MyDrive\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MNEtNMmmQK6n"
},
"outputs": [],
"source": [
"\n",
"data_dir = '/content/newData'\n",
"train_dir = '/content/train_dir'\n",
"test_dir = '/content/test_dir'\n",
"\n",
"\n",
"# Define list of class names\n",
"class_names = [\"akiec\", \"bcc\", \"bkl\", \"df\", \"mel\", \"nv\", \"vasc\"]\n",
"\n",
"# Create train and test directories if they don't exist\n",
"if not os.path.exists(train_dir):\n",
" os.makedirs(train_dir)\n",
"if not os.path.exists(test_dir):\n",
" os.makedirs(test_dir)\n",
"\n",
"# Split data into train and test sets\n",
"for class_name in class_names:\n",
" class_dir = os.path.join(data_dir, class_name)\n",
" train_class_dir = os.path.join(train_dir, class_name)\n",
" test_class_dir = os.path.join(test_dir, class_name)\n",
" if not os.path.exists(train_class_dir):\n",
" os.makedirs(train_class_dir)\n",
" if not os.path.exists(test_class_dir):\n",
" os.makedirs(test_class_dir)\n",
" files = os.listdir(class_dir)\n",
" random.shuffle(files)\n",
" train_files = files[:int(len(files)*0.8)]\n",
" test_files = files[int(len(files)*0.8):]\n",
" for file in train_files:\n",
" src_path = os.path.join(class_dir, file)\n",
" dst_path = os.path.join(train_class_dir, file)\n",
" shutil.copy(src_path, dst_path)\n",
" for file in test_files:\n",
" src_path = os.path.join(class_dir, file)\n",
" dst_path = os.path.join(test_class_dir, file)\n",
" shutil.copy(src_path, dst_path)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "35cL5WbTA_Cr",
"outputId": "fff6d9c7-767d-40bb-da34-ee32b4ba51ec"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 8010 images belonging to 7 classes.\n",
"Found 2005 images belonging to 7 classes.\n"
]
}
],
"source": [
"import os\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
"\n",
"# Define data paths and parameters\n",
"train_dir = '/content/train_dir/'\n",
"test_dir = '/content/test_dir/'\n",
"input_shape = (224, 224, 3)\n",
"batch_size = 32\n",
"epochs = 50\n",
"num_classes = 7\n",
"\n",
"# Create data generators with data augmentation\n",
"train_datagen = ImageDataGenerator(rescale=1./255,\n",
" rotation_range=40,\n",
" width_shift_range=0.2,\n",
" height_shift_range=0.2,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True,\n",
" fill_mode='nearest')\n",
"test_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
"train_generator = train_datagen.flow_from_directory(train_dir,\n",
" target_size=input_shape[:2],\n",
" batch_size=batch_size,\n",
" class_mode='categorical')\n",
"\n",
"test_generator = test_datagen.flow_from_directory(test_dir,\n",
" target_size=input_shape[:2],\n",
" batch_size=batch_size,\n",
" class_mode='categorical')\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UTqAmayWFPCF"
},
"outputs": [],
"source": [
"# Define the model architecture\n",
"model = Sequential()\n",
"model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Conv2D(128, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Conv2D(128, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Flatten())\n",
"model.add(Dense(512, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(num_classes, activation='softmax'))\n",
"\n",
"# Compile the model\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])\n",
"\n",
"# Train the model\n",
"history = model.fit(train_generator,\n",
" steps_per_epoch=train_generator.samples//batch_size,\n",
" epochs=epochs,\n",
" validation_data=test_generator,\n",
" validation_steps=test_generator.samples//batch_size)\n",
"\n",
"# Save the model\n",
"model.save('skin_cancer_model.h5')\n"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
project/model train/Skin Cancer/SkinTumor_prediction_LiR_model.ipynb 0 → 100644
View file @ 3ec0eded
{
"cells": [
{
"cell_type": "markdown",
"id": "d4f44a2e",
"metadata": {
"id": "d4f44a2e"
},
"source": [
"## Title - **Melanoma Tumor Size Prediction**\n",
"## Used Algorithm - **Liner Regression**\n",
"\n",
"\n",
"## Accuracy\n",
"* Accuracy on Isolation Forest : 90%\n",
"* Accuracy on Random Forest : 93%"
]
},
{
"cell_type": "markdown",
"id": "e555e9d8",
"metadata": {
"id": "e555e9d8"
},
"source": [
"### Importing the dependancies"
]
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Lr8KJqCxamQm",
"outputId": "3610dc7a-6a5b-489a-af1e-2f9af9f9afbc"
},
"id": "Lr8KJqCxamQm",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"files.upload()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 90
},
"id": "x73tDO5fanhd",
"outputId": "20d2a8fa-66ff-4df5-990d-de9d4c1418cf"
},
"id": "x73tDO5fanhd",
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <input type=\"file\" id=\"files-0551e200-59b6-43e1-9f54-d379d1013939\" name=\"files[]\" multiple disabled\n",
" style=\"border:none\" />\n",
" <output id=\"result-0551e200-59b6-43e1-9f54-d379d1013939\">\n",
" Upload widget is only available when the cell has been executed in the\n",
" current browser session. Please rerun this cell to enable.\n",
" </output>\n",
" <script>// Copyright 2017 Google LLC\n",
"//\n",
"// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"// you may not use this file except in compliance with the License.\n",
"// You may obtain a copy of the License at\n",
"//\n",
"// http://www.apache.org/licenses/LICENSE-2.0\n",
"//\n",
"// Unless required by applicable law or agreed to in writing, software\n",
"// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"// See the License for the specific language governing permissions and\n",
"// limitations under the License.\n",
"\n",
"/**\n",
" * @fileoverview Helpers for google.colab Python module.\n",
" */\n",
"(function(scope) {\n",
"function span(text, styleAttributes = {}) {\n",
" const element = document.createElement('span');\n",
" element.textContent = text;\n",
" for (const key of Object.keys(styleAttributes)) {\n",
" element.style[key] = styleAttributes[key];\n",
" }\n",
" return element;\n",
"}\n",
"\n",
"// Max number of bytes which will be uploaded at a time.\n",
"const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
"\n",
"function _uploadFiles(inputId, outputId) {\n",
" const steps = uploadFilesStep(inputId, outputId);\n",
" const outputElement = document.getElementById(outputId);\n",
" // Cache steps on the outputElement to make it available for the next call\n",
" // to uploadFilesContinue from Python.\n",
" outputElement.steps = steps;\n",
"\n",
" return _uploadFilesContinue(outputId);\n",
"}\n",
"\n",
"// This is roughly an async generator (not supported in the browser yet),\n",
"// where there are multiple asynchronous steps and the Python side is going\n",
"// to poll for completion of each step.\n",
"// This uses a Promise to block the python side on completion of each step,\n",
"// then passes the result of the previous step as the input to the next step.\n",
"function _uploadFilesContinue(outputId) {\n",
" const outputElement = document.getElementById(outputId);\n",
" const steps = outputElement.steps;\n",
"\n",
" const next = steps.next(outputElement.lastPromiseValue);\n",
" return Promise.resolve(next.value.promise).then((value) => {\n",
" // Cache the last promise value to make it available to the next\n",
" // step of the generator.\n",
" outputElement.lastPromiseValue = value;\n",
" return next.value.response;\n",
" });\n",
"}\n",
"\n",
"/**\n",
" * Generator function which is called between each async step of the upload\n",
" * process.\n",
" * @param {string} inputId Element ID of the input file picker element.\n",
" * @param {string} outputId Element ID of the output display.\n",
" * @return {!Iterable<!Object>} Iterable of next steps.\n",
" */\n",
"function* uploadFilesStep(inputId, outputId) {\n",
" const inputElement = document.getElementById(inputId);\n",
" inputElement.disabled = false;\n",
"\n",
" const outputElement = document.getElementById(outputId);\n",
" outputElement.innerHTML = '';\n",
"\n",
" const pickedPromise = new Promise((resolve) => {\n",
" inputElement.addEventListener('change', (e) => {\n",
" resolve(e.target.files);\n",
" });\n",
" });\n",
"\n",
" const cancel = document.createElement('button');\n",
" inputElement.parentElement.appendChild(cancel);\n",
" cancel.textContent = 'Cancel upload';\n",
" const cancelPromise = new Promise((resolve) => {\n",
" cancel.onclick = () => {\n",
" resolve(null);\n",
" };\n",
" });\n",
"\n",
" // Wait for the user to pick the files.\n",
" const files = yield {\n",
" promise: Promise.race([pickedPromise, cancelPromise]),\n",
" response: {\n",
" action: 'starting',\n",
" }\n",
" };\n",
"\n",
" cancel.remove();\n",
"\n",
" // Disable the input element since further picks are not allowed.\n",
" inputElement.disabled = true;\n",
"\n",
" if (!files) {\n",
" return {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
" }\n",
"\n",
" for (const file of files) {\n",
" const li = document.createElement('li');\n",
" li.append(span(file.name, {fontWeight: 'bold'}));\n",
" li.append(span(\n",
" `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
" `last modified: ${\n",
" file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
" 'n/a'} - `));\n",
" const percent = span('0% done');\n",
" li.appendChild(percent);\n",
"\n",
" outputElement.appendChild(li);\n",
"\n",
" const fileDataPromise = new Promise((resolve) => {\n",
" const reader = new FileReader();\n",
" reader.onload = (e) => {\n",
" resolve(e.target.result);\n",
" };\n",
" reader.readAsArrayBuffer(file);\n",
" });\n",
" // Wait for the data to be ready.\n",
" let fileData = yield {\n",
" promise: fileDataPromise,\n",
" response: {\n",
" action: 'continue',\n",
" }\n",
" };\n",
"\n",
" // Use a chunked sending to avoid message size limits. See b/62115660.\n",
" let position = 0;\n",
" do {\n",
" const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
" const chunk = new Uint8Array(fileData, position, length);\n",
" position += length;\n",
"\n",
" const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
" yield {\n",
" response: {\n",
" action: 'append',\n",
" file: file.name,\n",
" data: base64,\n",
" },\n",
" };\n",
"\n",
" let percentDone = fileData.byteLength === 0 ?\n",
" 100 :\n",
" Math.round((position / fileData.byteLength) * 100);\n",
" percent.textContent = `${percentDone}% done`;\n",
"\n",
" } while (position < fileData.byteLength);\n",
" }\n",
"\n",
" // All done.\n",
" yield {\n",
" response: {\n",
" action: 'complete',\n",
" }\n",
" };\n",
"}\n",
"\n",
"scope.google = scope.google || {};\n",
"scope.google.colab = scope.google.colab || {};\n",
"scope.google.colab._files = {\n",
" _uploadFiles,\n",
" _uploadFilesContinue,\n",
"};\n",
"})(self);\n",
"</script> "
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Saving kaggle.json to kaggle (1).json\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'kaggle.json': b'{\"username\":\"mishaneperera\",\"key\":\"fb1e9a2fcad19f92a1d3ebd6e8c8165f\"}'}"
]
},
"metadata": {},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"source": [
"!mkdir -p ~/.kaggle\n",
"!cp kaggle.json ~/.kaggle/"
],
"metadata": {
"id": "g-QxxXqKap_8"
},
"id": "g-QxxXqKap_8",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!chmod 600 ~/.kaggle/kaggle.json"
],
"metadata": {
"id": "HSge_gifar5N"
},
"id": "HSge_gifar5N",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"!unzip '/content/drive/MyDrive/Skin tumor/machine-hack-melanoma-tumor-size-prediction.zip'"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "u1IPVt6ZavPc",
"outputId": "64d364dd-ce34-47dc-c74c-778cadcff92a"
},
"id": "u1IPVt6ZavPc",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Archive: /content/drive/MyDrive/Skin tumor/machine-hack-melanoma-tumor-size-prediction.zip\n",
"replace sample_submission.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
" inflating: sample_submission.csv \n",
"replace test.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
" inflating: test.csv \n",
"replace train.csv? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
" inflating: train.csv \n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1123a658",
"metadata": {
"id": "1123a658"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "markdown",
"id": "4e1ec49f",
"metadata": {
"id": "4e1ec49f"
},
"source": [
"### Data collecting process"
]
},
{
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"execution_count": null,
"id": "111d33c0",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
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"id": "111d33c0",
"outputId": "a8a28ca2-0714-4c35-b92d-26ff08273e7e"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" mass_npea size_npear malign_ratio damage_size exposed_area \\\n",
"0 6930.90 2919.02 0.42116 51.8298 9.888294e+05 \n",
"1 15635.70 4879.36 0.31206 223.5500 2.058426e+06 \n",
"2 10376.20 2613.88 0.25191 127.3370 1.434676e+06 \n",
"3 13093.80 4510.06 0.34444 155.4400 1.812195e+06 \n",
"4 7545.21 2882.36 0.38201 85.1237 1.043918e+06 \n",
"\n",
" std_dev_malign err_malign malign_penalty damage_ratio tumor_size \n",
"0 109.487 2758.76 72 39.3620 14.103 \n",
"1 248.881 5952.53 240 22.0253 2.648 \n",
"2 160.093 4635.26 73 29.9963 1.688 \n",
"3 173.015 5273.87 32 28.1354 3.796 \n",
"4 124.414 3263.35 57 35.0200 18.023 "
],
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mass_npea</th>\n",
" <th>size_npear</th>\n",
" <th>malign_ratio</th>\n",
" <th>damage_size</th>\n",
" <th>exposed_area</th>\n",
" <th>std_dev_malign</th>\n",
" <th>err_malign</th>\n",
" <th>malign_penalty</th>\n",
" <th>damage_ratio</th>\n",
" <th>tumor_size</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>6930.90</td>\n",
" <td>2919.02</td>\n",
" <td>0.42116</td>\n",
" <td>51.8298</td>\n",
" <td>9.888294e+05</td>\n",
" <td>109.487</td>\n",
" <td>2758.76</td>\n",
" <td>72</td>\n",
" <td>39.3620</td>\n",
" <td>14.103</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>15635.70</td>\n",
" <td>4879.36</td>\n",
" <td>0.31206</td>\n",
" <td>223.5500</td>\n",
" <td>2.058426e+06</td>\n",
" <td>248.881</td>\n",
" <td>5952.53</td>\n",
" <td>240</td>\n",
" <td>22.0253</td>\n",
" <td>2.648</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10376.20</td>\n",
" <td>2613.88</td>\n",
" <td>0.25191</td>\n",
" <td>127.3370</td>\n",
" <td>1.434676e+06</td>\n",
" <td>160.093</td>\n",
" <td>4635.26</td>\n",
" <td>73</td>\n",
" <td>29.9963</td>\n",
" <td>1.688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>13093.80</td>\n",
" <td>4510.06</td>\n",
" <td>0.34444</td>\n",
" <td>155.4400</td>\n",
" <td>1.812195e+06</td>\n",
" <td>173.015</td>\n",
" <td>5273.87</td>\n",
" <td>32</td>\n",
" <td>28.1354</td>\n",
" <td>3.796</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>7545.21</td>\n",
" <td>2882.36</td>\n",
" <td>0.38201</td>\n",
" <td>85.1237</td>\n",
" <td>1.043918e+06</td>\n",
" <td>124.414</td>\n",
" <td>3263.35</td>\n",
" <td>57</td>\n",
" <td>35.0200</td>\n",
" <td>18.023</td>\n",
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]
},
"metadata": {},
"execution_count": 50
}
],
"source": [
"dataset = pd.read_csv('/content/train.csv')\n",
"dataset.head()"
]
},
{
"cell_type": "markdown",
"id": "5da5cf75",
"metadata": {
"id": "5da5cf75"
},
"source": [
"### Explore the dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "740f14ff",
"metadata": {
"id": "740f14ff"
},
"outputs": [],
"source": [
"X = dataset.iloc[:,:-1].values #independent variable array\n",
"y = dataset.iloc[:,1].values #dependent variable vector"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7c8d2381",
"metadata": {
"id": "7c8d2381"
},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=1/3,random_state=0)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e535ee6f",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 75
},
"id": "e535ee6f",
"outputId": "8b64e058-1606-4284-82dd-de77f9a70f70"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LinearRegression()"
],
"text/html": [
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]
},
"metadata": {},
"execution_count": 54
}
],
"source": [
"from sklearn.linear_model import LinearRegression\n",
"regressor = LinearRegression()\n",
"regressor.fit(X_train,y_train) #actually produces the linear eqn for the data\n",
"\n"
]
},
{
"cell_type": "code",
"source": [
"accuracy=regressor.score(X_train,y_train)\n",
"print(accuracy)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5BGEhbFxpjxB",
"outputId": "47340ca9-a7ad-484a-9dc7-61cace4b5c4b"
},
"id": "5BGEhbFxpjxB",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.0\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d21cf5f8",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "d21cf5f8",
"outputId": "19c58cf8-d471-46fa-8380-6827494e6042"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([3564.2 , 2081.8 , 3567.24, ..., 2344.44, 4253.25, 2881.7 ])"
]
},
"metadata": {},
"execution_count": 55
}
],
"source": [
"y_pred = regressor.predict(X_test) \n",
"y_pred"
]
},
{
"cell_type": "markdown",
"id": "f0e0e01a",
"metadata": {
"id": "f0e0e01a"
},
"source": [
"### Checking the distribution of target variable\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf5c2e23",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 853
},
"id": "cf5c2e23",
"outputId": "21f84128-1406-40a8-cec7-31ad93d9474b"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1200x900 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"import seaborn as sns\n",
"# Correlation matrix\n",
"corrmat = dataset.corr()\n",
"fig = plt.figure(figsize = (12, 9))\n",
"\n",
"sns.heatmap(corrmat, vmax = .8, square = True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "38ffa726",
"metadata": {
"id": "38ffa726"
},
"source": [
"### Splitting the features and target"
]
},
{
"cell_type": "markdown",
"id": "5df8ea16",
"metadata": {
"id": "5df8ea16"
},
"source": [
"### Saving the model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "02faf4e6",
"metadata": {
"id": "02faf4e6"
},
"outputs": [],
"source": [
"import pickle\n",
"filename = 'finalized_model_skinT_prediction_RF.sav'\n",
"pickle.dump(regressor, open(filename, 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "896159c4",
"metadata": {
"id": "896159c4"
},
"outputs": [],
"source": [
"with open('finalized_model_skinT_prediction_RF.sav', 'rb') as f:\n",
" model = pickle.load(f)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "03d78684",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "03d78684",
"outputId": "39dc528d-b12e-4716-e42b-40879e4f7698"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[4510.06]\n"
]
}
],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"#input_data1 = (70,1.02,0,0,1,44,1.2,145,5,14.8,8406,4.71,0) #0\n",
"input_data2 = (13093.8,4510.06,0.34444,155.44,1812195.1833,173.015,5273.87,32,28.1354) #1\n",
"\n",
"#change input data to numpy array\n",
"input_data_as_numpy_array = np.asarray(input_data2)\n",
"\n",
"#reshape the numpy array as we are predicting for only on instance\n",
"\n",
"input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)\n",
"\n",
"prediction = model.predict(input_data_reshaped)\n",
"print(prediction)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "41a76901",
"metadata": {
"id": "41a76901"
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"vscode": {
"interpreter": {
"hash": "d9a6414fa631c028c434667d182c0b79dc634ffcd06f52fc304061a2c0b9ef26"
}
},
"colab": {
"provenance": [],
"collapsed_sections": [
"e8dd7a8f",
"5821ec3c"
]
}
},
"nbformat": 4,
"nbformat_minor": 5
}
\ No newline at end of file
project/model train/Skin Cancer/Skin_cancer_Detection.ipynb 0 → 100644
View file @ 3ec0eded
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NvgOAuTNHnFv",
"outputId": "9af20872-9b69-4105-c9b0-a5189e93f11f"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mounted at /content/drive\n"
]
}
],
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "e5VoXOheOrbn"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os\n",
"import shutil\n",
"import random\n",
"metadata = pd.read_csv('/content/drive/MyDrive/Dataset_skin/HAM10000_metadata.csv')\n",
"\n",
"# Define source and destination directories\n",
"src_dir = '/content/drive/MyDrive/Dataset_skin/HAM10000_images_part_1/'\n",
"dst_dir = '/content/newData'\n",
"\n",
"# Create destination directories for each class\n",
"for class_name in metadata['dx'].unique():\n",
" os.makedirs(os.path.join(dst_dir, class_name), exist_ok=True)\n",
"\n",
"# Copy images to their respective class directories\n",
"for index, row in metadata.iterrows():\n",
" img_name = row['image_id'] + '.jpg'\n",
" src_path = os.path.join(src_dir, img_name)\n",
" dst_path = os.path.join(dst_dir, row['dx'], img_name)\n",
" shutil.copy(src_path, dst_path)\n",
" \n",
"print('Dataset splitting complete.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "wuoNOi_-B0mN"
},
"outputs": [],
"source": [
"!zip -r newData.zip newData/\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ekUMBoOJB9FL"
},
"outputs": [],
"source": [
"!mv newData.zip /content/drive/MyDrive\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "MNEtNMmmQK6n"
},
"outputs": [],
"source": [
"\n",
"data_dir = '/content/newData'\n",
"train_dir = '/content/train_dir'\n",
"test_dir = '/content/test_dir'\n",
"\n",
"\n",
"# Define list of class names\n",
"class_names = [\"akiec\", \"bcc\", \"bkl\", \"df\", \"mel\", \"nv\", \"vasc\"]\n",
"\n",
"# Create train and test directories if they don't exist\n",
"if not os.path.exists(train_dir):\n",
" os.makedirs(train_dir)\n",
"if not os.path.exists(test_dir):\n",
" os.makedirs(test_dir)\n",
"\n",
"# Split data into train and test sets\n",
"for class_name in class_names:\n",
" class_dir = os.path.join(data_dir, class_name)\n",
" train_class_dir = os.path.join(train_dir, class_name)\n",
" test_class_dir = os.path.join(test_dir, class_name)\n",
" if not os.path.exists(train_class_dir):\n",
" os.makedirs(train_class_dir)\n",
" if not os.path.exists(test_class_dir):\n",
" os.makedirs(test_class_dir)\n",
" files = os.listdir(class_dir)\n",
" random.shuffle(files)\n",
" train_files = files[:int(len(files)*0.8)]\n",
" test_files = files[int(len(files)*0.8):]\n",
" for file in train_files:\n",
" src_path = os.path.join(class_dir, file)\n",
" dst_path = os.path.join(train_class_dir, file)\n",
" shutil.copy(src_path, dst_path)\n",
" for file in test_files:\n",
" src_path = os.path.join(class_dir, file)\n",
" dst_path = os.path.join(test_class_dir, file)\n",
" shutil.copy(src_path, dst_path)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "35cL5WbTA_Cr",
"outputId": "fff6d9c7-767d-40bb-da34-ee32b4ba51ec"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 8010 images belonging to 7 classes.\n",
"Found 2005 images belonging to 7 classes.\n"
]
}
],
"source": [
"import os\n",
"import numpy as np\n",
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.models import Sequential\n",
"from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
"\n",
"# Define data paths and parameters\n",
"train_dir = '/content/train_dir/'\n",
"test_dir = '/content/test_dir/'\n",
"input_shape = (224, 224, 3)\n",
"batch_size = 32\n",
"epochs = 50\n",
"num_classes = 7\n",
"\n",
"# Create data generators with data augmentation\n",
"train_datagen = ImageDataGenerator(rescale=1./255,\n",
" rotation_range=40,\n",
" width_shift_range=0.2,\n",
" height_shift_range=0.2,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True,\n",
" fill_mode='nearest')\n",
"test_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
"train_generator = train_datagen.flow_from_directory(train_dir,\n",
" target_size=input_shape[:2],\n",
" batch_size=batch_size,\n",
" class_mode='categorical')\n",
"\n",
"test_generator = test_datagen.flow_from_directory(test_dir,\n",
" target_size=input_shape[:2],\n",
" batch_size=batch_size,\n",
" class_mode='categorical')\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "UTqAmayWFPCF"
},
"outputs": [],
"source": [
"# Define the model architecture\n",
"model = Sequential()\n",
"model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Conv2D(128, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Conv2D(128, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"model.add(Flatten())\n",
"model.add(Dense(512, activation='relu'))\n",
"model.add(Dropout(0.5))\n",
"model.add(Dense(num_classes, activation='softmax'))\n",
"\n",
"# Compile the model\n",
"model.compile(loss='categorical_crossentropy',\n",
" optimizer='rmsprop',\n",
" metrics=['accuracy'])\n",
"\n",
"# Train the model\n",
"history = model.fit(train_generator,\n",
" steps_per_epoch=train_generator.samples//batch_size,\n",
" epochs=epochs,\n",
" validation_data=test_generator,\n",
" validation_steps=test_generator.samples//batch_size)\n",
"\n",
"# Save the model\n",
"model.save('skin_cancer_model.h5')\n"
]
}
],
"metadata": {
"colab": {
"provenance": []
},
"gpuClass": "standard",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
project/model train/Stroke/Stroke_prediction_CT.ipynb 0 → 100644
View file @ 3ec0eded
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project/model train/Stroke/Stroke_prediction_RF_model.ipynb 0 → 100644
View file @ 3ec0eded
{
"cells": [
{
"cell_type": "markdown",
"id": "b1456aec",
"metadata": {},
"source": [
"## Title - **Stroke Prediction**\n",
"## Used Algorithm - **Random Forest Classification**\n",
"## Dataset - \n",
"* The dataset link is [Stroke Prediction Dataset](https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset) and it is from kaggle online website\n",
"* The usability of the dataset is 10.00\n",
"* This dataset has 11 parameters and 5110 instances\n",
"\n",
"## Accuracy\n",
"* Accuracy on Isolation Forest : 90%\n",
"* Accuracy on Random Forest : 93%"
]
},
{
"cell_type": "markdown",
"id": "8a19db0e",
"metadata": {},
"source": [
"### Importing the dependancies"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "1123a658",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"id": "2a902969",
"metadata": {},
"source": [
"### Data collecting process"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "111d33c0",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"data = pd.read_csv('..//Trained models//healthcare-dataset-stroke-data.csv')"
]
},
{
"cell_type": "markdown",
"id": "9fc14b95",
"metadata": {},
"source": [
"### Explore the dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "740f14ff",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['id', 'gender', 'age', 'hypertension', 'heart_disease', 'ever_married',\n",
" 'work_type', 'Residence_type', 'avg_glucose_level', 'bmi',\n",
" 'smoking_status', 'stroke'],\n",
" dtype='object')\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>ever_married</th>\n",
" <th>work_type</th>\n",
" <th>Residence_type</th>\n",
" <th>avg_glucose_level</th>\n",
" <th>bmi</th>\n",
" <th>smoking_status</th>\n",
" <th>stroke</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9046</td>\n",
" <td>Male</td>\n",
" <td>67.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>228.69</td>\n",
" <td>36.6</td>\n",
" <td>formerly smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>51676</td>\n",
" <td>Female</td>\n",
" <td>61.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Self-employed</td>\n",
" <td>Rural</td>\n",
" <td>202.21</td>\n",
" <td>NaN</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>31112</td>\n",
" <td>Male</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Rural</td>\n",
" <td>105.92</td>\n",
" <td>32.5</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60182</td>\n",
" <td>Female</td>\n",
" <td>49.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>171.23</td>\n",
" <td>34.4</td>\n",
" <td>smokes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1665</td>\n",
" <td>Female</td>\n",
" <td>79.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Self-employed</td>\n",
" <td>Rural</td>\n",
" <td>174.12</td>\n",
" <td>24.0</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id gender age hypertension heart_disease ever_married \\\n",
"0 9046 Male 67.0 0 1 Yes \n",
"1 51676 Female 61.0 0 0 Yes \n",
"2 31112 Male 80.0 0 1 Yes \n",
"3 60182 Female 49.0 0 0 Yes \n",
"4 1665 Female 79.0 1 0 Yes \n",
"\n",
" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
"0 Private Urban 228.69 36.6 formerly smoked \n",
"1 Self-employed Rural 202.21 NaN never smoked \n",
"2 Private Rural 105.92 32.5 never smoked \n",
"3 Private Urban 171.23 34.4 smokes \n",
"4 Self-employed Rural 174.12 24.0 never smoked \n",
"\n",
" stroke \n",
"0 1 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print(data.columns)\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7c8d2381",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5110, 12)\n",
" id age hypertension heart_disease \\\n",
"count 5110.000000 5110.000000 5110.000000 5110.000000 \n",
"mean 36517.829354 43.226614 0.097456 0.054012 \n",
"std 21161.721625 22.612647 0.296607 0.226063 \n",
"min 67.000000 0.080000 0.000000 0.000000 \n",
"25% 17741.250000 25.000000 0.000000 0.000000 \n",
"50% 36932.000000 45.000000 0.000000 0.000000 \n",
"75% 54682.000000 61.000000 0.000000 0.000000 \n",
"max 72940.000000 82.000000 1.000000 1.000000 \n",
"\n",
" avg_glucose_level bmi stroke \n",
"count 5110.000000 4909.000000 5110.000000 \n",
"mean 106.147677 28.893237 0.048728 \n",
"std 45.283560 7.854067 0.215320 \n",
"min 55.120000 10.300000 0.000000 \n",
"25% 77.245000 23.500000 0.000000 \n",
"50% 91.885000 28.100000 0.000000 \n",
"75% 114.090000 33.100000 0.000000 \n",
"max 271.740000 97.600000 1.000000 \n"
]
}
],
"source": [
"# Print the shape of the data\n",
"# data = data.sample(frac=0.1, random_state = 48)\n",
"print(data.shape)\n",
"print(data.describe())"
]
},
{
"cell_type": "markdown",
"id": "401acdab",
"metadata": {},
"source": [
"#### update null values"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "016ecaa0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 5110 entries, 0 to 5109\n",
"Data columns (total 12 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 id 5110 non-null int64 \n",
" 1 gender 5110 non-null object \n",
" 2 age 5110 non-null float64\n",
" 3 hypertension 5110 non-null int64 \n",
" 4 heart_disease 5110 non-null int64 \n",
" 5 ever_married 5110 non-null object \n",
" 6 work_type 5110 non-null object \n",
" 7 Residence_type 5110 non-null object \n",
" 8 avg_glucose_level 5110 non-null float64\n",
" 9 bmi 5110 non-null float64\n",
" 10 smoking_status 5110 non-null object \n",
" 11 stroke 5110 non-null int64 \n",
"dtypes: float64(3), int64(4), object(5)\n",
"memory usage: 479.2+ KB\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>ever_married</th>\n",
" <th>work_type</th>\n",
" <th>Residence_type</th>\n",
" <th>avg_glucose_level</th>\n",
" <th>bmi</th>\n",
" <th>smoking_status</th>\n",
" <th>stroke</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>9046</td>\n",
" <td>Male</td>\n",
" <td>67.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>228.69</td>\n",
" <td>36.600000</td>\n",
" <td>formerly smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>51676</td>\n",
" <td>Female</td>\n",
" <td>61.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Self-employed</td>\n",
" <td>Rural</td>\n",
" <td>202.21</td>\n",
" <td>28.893237</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>31112</td>\n",
" <td>Male</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Rural</td>\n",
" <td>105.92</td>\n",
" <td>32.500000</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60182</td>\n",
" <td>Female</td>\n",
" <td>49.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Private</td>\n",
" <td>Urban</td>\n",
" <td>171.23</td>\n",
" <td>34.400000</td>\n",
" <td>smokes</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1665</td>\n",
" <td>Female</td>\n",
" <td>79.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>Yes</td>\n",
" <td>Self-employed</td>\n",
" <td>Rural</td>\n",
" <td>174.12</td>\n",
" <td>24.000000</td>\n",
" <td>never smoked</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id gender age hypertension heart_disease ever_married \\\n",
"0 9046 Male 67.0 0 1 Yes \n",
"1 51676 Female 61.0 0 0 Yes \n",
"2 31112 Male 80.0 0 1 Yes \n",
"3 60182 Female 49.0 0 0 Yes \n",
"4 1665 Female 79.0 1 0 Yes \n",
"\n",
" work_type Residence_type avg_glucose_level bmi \\\n",
"0 Private Urban 228.69 36.600000 \n",
"1 Self-employed Rural 202.21 28.893237 \n",
"2 Private Rural 105.92 32.500000 \n",
"3 Private Urban 171.23 34.400000 \n",
"4 Self-employed Rural 174.12 24.000000 \n",
"\n",
" smoking_status stroke \n",
"0 formerly smoked 1 \n",
"1 never smoked 1 \n",
"2 never smoked 1 \n",
"3 smokes 1 \n",
"4 never smoked 1 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = data\n",
"df['bmi']=df['bmi'].fillna(df['bmi'].mean())\n",
"df.info()\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"id": "67f97afc",
"metadata": {},
"source": [
"### Update string data into integer data"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "74074e6f",
"metadata": {},
"outputs": [],
"source": [
"############Gender###############################\n",
"\n",
"data = df.replace(to_replace =\"Male\",\n",
" value =\"0\")\n",
"data = data.replace(to_replace =\"Female\",\n",
" value =\"1\")\n",
"data = data.replace(to_replace =\"Other\",\n",
" value =\"2\")\n",
"\n",
"############ever married#########################\n",
"\n",
"data = data.replace(to_replace =\"Yes\",\n",
" value =\"1\")\n",
"data = data.replace(to_replace =\"No\",\n",
" value =\"0\")\n",
"\n",
"############work type############################\n",
"\n",
"data = data.replace(to_replace =\"Private\",\n",
" value =\"1\")\n",
"data = data.replace(to_replace =\"Self-employed\",\n",
" value =\"0\")\n",
"data = data.replace(to_replace =\"Govt_job\",\n",
" value =\"2\")\n",
"data = data.replace(to_replace =\"children\",\n",
" value =\"3\")\n",
"data = data.replace(to_replace =\"Never_worked\",\n",
" value =\"4\")\n",
"\n",
"############Residence type#######################\n",
"\n",
"data = data.replace(to_replace =\"Urban\",\n",
" value =\"1\")\n",
"data = data.replace(to_replace =\"Rural\",\n",
" value =\"0\")\n",
"\n",
"############smoking status#######################\n",
"\n",
"data = data.replace(to_replace =\"smokes\",\n",
" value =\"0\")\n",
"data = data.replace(to_replace =\"never smoked\",\n",
" value =\"1\")\n",
"data = data.replace(to_replace =\"formerly smoked\",\n",
" value =\"2\")\n",
"data = data.replace(to_replace =\"Unknown\",\n",
" value =\"3\")"
]
},
{
"cell_type": "markdown",
"id": "791343e1",
"metadata": {},
"source": [
"##### Updated dataset"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d21cf5f8",
"metadata": {},
"outputs": [
{
"data": {
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"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>hypertension</th>\n",
" <th>heart_disease</th>\n",
" <th>ever_married</th>\n",
" <th>work_type</th>\n",
" <th>Residence_type</th>\n",
" <th>avg_glucose_level</th>\n",
" <th>bmi</th>\n",
" <th>smoking_status</th>\n",
" <th>stroke</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
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" <td>2</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>202.21</td>\n",
" <td>28.893237</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>31112</td>\n",
" <td>0</td>\n",
" <td>80.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>105.92</td>\n",
" <td>32.500000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>60182</td>\n",
" <td>1</td>\n",
" <td>49.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>171.23</td>\n",
" <td>34.400000</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1665</td>\n",
" <td>1</td>\n",
" <td>79.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>174.12</td>\n",
" <td>24.000000</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5105</th>\n",
" <td>18234</td>\n",
" <td>1</td>\n",
" <td>80.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>83.75</td>\n",
" <td>28.893237</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5106</th>\n",
" <td>44873</td>\n",
" <td>1</td>\n",
" <td>81.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>125.20</td>\n",
" <td>40.000000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5107</th>\n",
" <td>19723</td>\n",
" <td>1</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>82.99</td>\n",
" <td>30.600000</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5108</th>\n",
" <td>37544</td>\n",
" <td>0</td>\n",
" <td>51.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>166.29</td>\n",
" <td>25.600000</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5109</th>\n",
" <td>44679</td>\n",
" <td>1</td>\n",
" <td>44.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>85.28</td>\n",
" <td>26.200000</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5110 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" id gender age hypertension heart_disease ever_married work_type \\\n",
"0 9046 0 67.0 0 1 1 1 \n",
"1 51676 1 61.0 0 0 1 0 \n",
"2 31112 0 80.0 0 1 1 1 \n",
"3 60182 1 49.0 0 0 1 1 \n",
"4 1665 1 79.0 1 0 1 0 \n",
"... ... ... ... ... ... ... ... \n",
"5105 18234 1 80.0 1 0 1 1 \n",
"5106 44873 1 81.0 0 0 1 0 \n",
"5107 19723 1 35.0 0 0 1 0 \n",
"5108 37544 0 51.0 0 0 1 1 \n",
"5109 44679 1 44.0 0 0 1 2 \n",
"\n",
" Residence_type avg_glucose_level bmi smoking_status stroke \n",
"0 1 228.69 36.600000 2 1 \n",
"1 0 202.21 28.893237 1 1 \n",
"2 0 105.92 32.500000 1 1 \n",
"3 1 171.23 34.400000 0 1 \n",
"4 0 174.12 24.000000 1 1 \n",
"... ... ... ... ... ... \n",
"5105 1 83.75 28.893237 1 0 \n",
"5106 1 125.20 40.000000 1 0 \n",
"5107 0 82.99 30.600000 1 0 \n",
"5108 0 166.29 25.600000 2 0 \n",
"5109 1 85.28 26.200000 3 0 \n",
"\n",
"[5110 rows x 12 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "markdown",
"id": "988e3c0e",
"metadata": {},
"source": [
"### Checking the distribution of target variable\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "dd942b3b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.051224027977782347\n",
"Stroke Cases: 249\n",
"Not_Stroke: 4861\n"
]
}
],
"source": [
"# Determine number of fraud cases in dataset\n",
"\n",
"Stroke = data[data['stroke'] == 1]\n",
"Not_Stroke = data[data['stroke'] == 0]\n",
"\n",
"outlier_fraction = len(Stroke)/float(len(Not_Stroke))\n",
"print(outlier_fraction)\n",
"\n",
"print('Stroke Cases: {}'.format(len(data[data['stroke'] == 1])))\n",
"print('Not_Stroke: {}'.format(len(data[data['stroke'] == 0])))"
]
},
{
"cell_type": "markdown",
"id": "6e5ac5b6",
"metadata": {},
"source": [
"### Visualizing the dataset"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "cf5c2e23",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Correlation matrix\n",
"corrmat = data.corr()\n",
"fig = plt.figure(figsize = (12, 9))\n",
"\n",
"sns.heatmap(corrmat, vmax = .8, square = True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "b9ba5dd2",
"metadata": {},
"source": [
"### Splitting the features and target"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ad95615d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(5110, 10)\n",
"(5110,)\n"
]
}
],
"source": [
"#seperating the X and the Y from the dataset\n",
"X=data.drop(['stroke','id'], axis=1)\n",
"Y=data[\"stroke\"]\n",
"print(X.shape)\n",
"print(Y.shape)\n",
"#getting just the values for the sake of processing (its a numpy array with no columns)\n",
"X_data=X.values\n",
"Y_data=Y.values"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "987d85cc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([['0', 67.0, 0, ..., 228.69, 36.6, '2'],\n",
" ['1', 61.0, 0, ..., 202.21, 28.893236911794673, '1'],\n",
" ['0', 80.0, 0, ..., 105.92, 32.5, '1'],\n",
" ...,\n",
" ['1', 35.0, 0, ..., 82.99, 30.6, '1'],\n",
" ['0', 51.0, 0, ..., 166.29, 25.6, '2'],\n",
" ['1', 44.0, 0, ..., 85.28, 26.2, '3']], dtype=object)"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_data"
]
},
{
"cell_type": "markdown",
"id": "acdf7977",
"metadata": {},
"source": [
"### Splittng data into train and test"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "5c0ce1f4",
"metadata": {},
"outputs": [],
"source": [
"# Using Skicit-learn to split data into training and testing sets\n",
"from sklearn.model_selection import train_test_split\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, Y_train, Y_test = train_test_split(X_data, Y_data, test_size = 0.2, random_state = 42)"
]
},
{
"cell_type": "markdown",
"id": "967d0e29",
"metadata": {},
"source": [
"### Model training - Random Forest"
]
},
{
"cell_type": "markdown",
"id": "b036fd27",
"metadata": {},
"source": [
"#### ISOLATION FOREST"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b41d16ee",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import classification_report, accuracy_score,precision_score,recall_score,f1_score,matthews_corrcoef\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "53a3cd66",
"metadata": {},
"outputs": [],
"source": [
"#Building another model/classifier ISOLATION FOREST\n",
"from sklearn.ensemble import IsolationForest\n",
"ifc=IsolationForest(max_samples=len(X_train),\n",
" contamination=outlier_fraction,random_state=1)\n",
"ifc.fit(X_train)\n",
"scores_pred = ifc.decision_function(X_train)\n",
"y_pred = ifc.predict(X_test)\n",
"\n",
"\n",
"# Reshape the prediction values to 0 for valid, 1 for fraud. \n",
"y_pred[y_pred == 1] = 0\n",
"y_pred[y_pred == -1] = 1\n",
"\n",
"n_errors = (y_pred != Y_test).sum()"
]
},
{
"cell_type": "markdown",
"id": "ab450ea4",
"metadata": {},
"source": [
"### Model eveuation"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "60c5a8a9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"the Model used is Isolation Forest\n",
"The accuracy is 0.9090019569471625\n",
"The precision is 0.2459016393442623\n",
"The recall is 0.24193548387096775\n",
"The F1-Score is 0.24390243902439024\n",
"The Matthews correlation coefficient is0.19550087369423888\n"
]
},
{
"data": {
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7T09yVJIzknw0yVO7++rFrm86nmXjrK9/Ix849qN5z5tfk+ttcb388bOfnwfsc6/c/ra3zmv+9gV5yT+8bsHzXv66Nbn/fe4x8miB5eZPnnpwvnbm/2TbbbdJktz/AffJIx75sOx970fkiiuuyI433WHGI4SVpbv/N8kO67RdnMlq+YWOPyzJYUu9/lST0Kq6X1UdPLy/6RDPwoK+/o1v5y573jE32GqrbLHF5rnHXnfOCZ/8TG53m1tlt1vvuuA5J3zyM9n1FjfP7Xa79cijBZaTW9zi5tl3vwfnyLe972dth/zhE/LqV74hV1xxRZLkogsvntXwgAVMrQitqhcl+Yv8fDXV9ZK8c1r9sfLd/ra3zue/dFp++KNL8pOf/jQnfvbkfPd7F673+P/9yU/z1ncenac86QkjjhJYjl729y/IC//qZZmbt//h7XffLfvsc8/8x8c/mP/30ffkbne7ywxHCPPM9exfy8A0p+N/K8mvJflCknT3eVW17RT7Y4W73W1ulSc94XH5o2f8Zba+wQ3yK7e/bTbffP0PW3j9W96R33v8b2XrrW8w4iiB5Wa//X49F114cU499bTc7/73/ln7Fltsnpvc5Eb59Qc9Jne/+13ytnf8Y+6y5wNnOFJgvmkWoVd0d1dVJ0lV3XBDJ8zfs+rwV/5N/vD3f2eKw2M5+u1H7ZvfftTkcbOvecPbcvOddlzvsV85/cwc/5+fyqsOf0su/fFlqapsef3r53cf++ixhgssA/fe++55+G8+JA/b90HZaqsts+222+RNb3lVzvvOd3PssR9Lknz+819Oz81lhx23z8UXfX/GI2a162v/xKJN0jSL0KOq6o1JblJVf5TkSUnevNgJ8/esuvKiry+PrJhRXfyDH2aH7W6S8797QU74xKfzzje+ar3Hvv2fX/Gz969/yzuz9Q22UoDCKvSSF/1DXvKif0iS3O/+986fPv2P8keHPCtPOuR388AH7p1Pnfi53P72u+V617+eAhSWkakVod39iqp6WJJLktwhyQuTfHJa/bFpeOZf/k1+eMkl2WKLLfJXz35KbnyjbfPvn/h0/u7V/5zv//BHecpzXpQ77n7brHn1khffAavUO95+dA5/w8vzXyf/W6644sr88aHPmfWQgHmqezqBY1W9tbufNO/zNkmO6e4Fl/WvSxIKLNUOt37orIcArBCXXPb1hR4vOarLDvv9mdc4N/yrt8/87zDNLZq+U1X/nCRVtV2S42J1PAAAme50/Auq6uVV9YYkd0/ysu7+wLT6AwBYEZb4xKJN3UYvQqvqMfM+npTkBcPPrqrHdPcHFz4TAIDVYhpJ6KPW+fzFTDaqf1SSzjWfPwoAwCq00YvQ7j54Y18TAGCTsUyeWDRr03xs565V9aGquqCqvldVH6iqhR8ADgDAqjLN1fFHJDk2yS2S7JLkw0MbAMDqNTc3+9cyMM0i9KbdfUR3XzW83pbkplPsDwCAFWKaRehFVfXEqtp8eD0xycVT7A8AgBVims+Of1KSf0ry6kxWxX9maAMAWL0sTEoypSK0qjZP8rfd/ehpXB8AgJVtKkVod19dVTetqut39xXT6AMAYEXyxKQk052O/0aST1fVsUkuW9vY3a+aYp8AAKwA0yxCzxtemyXZdmhzEwQAAFMtQs/o7qPnN1TV46bYHwDA8mdhUpLpbtH0vCW2AQCwymz0JLSqHp7kEUl2qarXzfvqRkmu2tj9AQCsJL1Mnlg0a9OYjj8vySlJHp3k8/PaL03yzCn0BwDACrPRi9Du/lKSL1XVu7v7yqq6XpI7JflOd/9gY/cHAMDKs9HvCa2qN1TVnkMBeuMkX0ry9iRfrKrf2dj9AQCsKHM9+9cyMI2FSffv7tOH9wcn+Vp33znJ3ZP8+RT6AwBghZlGETr/CUkPS/IvSdLd351CXwAArEDTWJj0w6p6ZJLvJLlvkkOSpKq2SHKDKfQHALByLJPp8FmbRhH65CSvS3LzJM+Yl4A+JMlHptAfAAArzDRWx38tyX4LtH8sycc2dn8AACtK2yc0me4TkwAAYEGKUAAARjeNe0KTJFW1W3efs6E2AIBVxcKkJNNNQj+wQNv7p9gfAAArxEZPQqvqjkn2THLjqnrMvK9ulGSrjd0fAMBK0pLQJNOZjr9DkkcmuUmSR81rvzTJH02hPwAAVphpbNF0TJJjqmrv7v7sxr4+AAAr39QWJiX5dlV9KJOnJnWSTyV5enefO8U+AQCWN9PxSaa7MOmIJMcmuUWSXZJ8eGgDAGCVm2YSulN3zy8631ZVz5hifwAAy9+cJyYl001CL6yqJ1bV5sPriUkunmJ/AACsENMsQp+U5P8k+W6S85M8dmgDAGCVm9p0fHd/K8mjp3V9AIAVycKkJNPZrP6Fi3zd3f3XG7tPAABWlmkkoZct0HbDJIck2SGJIhQAWL0koUmms1n9K9e+r6ptkzw9ycFJ3pvkles7DwCA1WMq94RW1fZJnpXkCUmOTHK37v7BNPoCAGDlmcY9of+Q5DFJ1iS5c3f/eGP3AQCwUnWbjk+ms0XTszN5StLzk5xXVZcMr0ur6pIp9AcAwAozjXtCp7n3KADAymZhUpLpblYPAAALUoQCADC6qT0xCQCABZiOTyIJBQBgBhShAACMznQ8AMCI2nR8EkkoAAAzIAkFABiTJDSJJBQAgBlQhAIAMDrT8QAAY5qb9QCWB0koAACjU4QCAIyo53rmr6WoqptU1fur6r+r6qtVtXdVbV9Vx1fVWcPP7eYd/7yqOruqzqyqfTd0fUUoAAALeW2Sj3b3HZPcNclXkzw3yQndvXuSE4bPqao9khyYZM8k+yU5vKo2X+ziilAAAK6hqm6U5AFJ3pIk3X1Fd/8wyf5JjhwOOzLJAcP7/ZO8t7sv7+5zkpyd5F6L9aEIBQAY01zP/FVVh1bVKfNeh64zytsmuTDJEVX1xap6c1XdMMnNuvv8JBl+7jQcv0uSb887/9yhbb2sjgcAWGW6e02SNYscskWSuyV5Wnd/rqpem2HqfT1qoW4WG4MkFABgTHPL4LVh5yY5t7s/N3x+fyZF6feqauckGX5eMO/4W847f9ck5y3WgSIUAIBr6O7vJvl2Vd1haHpIkjOSHJvkoKHtoCTHDO+PTXJgVW1ZVbsl2T3JSYv1YToeAICFPC3Ju6rq+km+nuTgTALMo6rqkCTfSvK4JOnu06vqqEwK1auSPLW7r17s4opQAIARLXWfzlnr7lOT3GOBrx6ynuMPS3LYUq9vOh4AgNFJQgEAxuTZ8UkkoQAAzIAiFACA0ZmOBwAY0UpZmDRtklAAAEanCAUAYHSm4wEAxmR1fBJJKAAAMyAJBQAYUUtCk0hCAQCYAUUoAACjMx0PADAm0/FJJKEAAMyAJBQAYEQWJk1IQgEAGJ0iFACA0ZmOBwAYk+n4JJJQAABmQBIKADAiC5MmJKEAAIxOEQoAwOhMxwMAjMh0/IQkFACA0UlCAQBGJAmdkIQCADA6RSgAAKMzHQ8AMKauWY9gWZCEAgAwOkkoAMCILEyakIQCADA6RSgAAKMzHQ8AMKKeszApkYQCADADilAAAEZnOh4AYERWx09IQgEAGJ0kFABgRO2JSUkkoQAAzIAiFACA0ZmOBwAYkYVJE5JQAABGJwkFABiRJyZNSEIBABidIhQAgNGZjgcAGFH3rEewPEhCAQAYnSQUAGBEFiZNSEIBABidIhQAgNGZjgcAGJHp+AlJKAAAo5OEAgCMyBZNE5JQAABGpwgFAGB0puMBAEZkYdKEJBQAgNFJQgEARtQtCU0koQAAzIAiFACA0ZmOBwAYUc/NegTLgyQUAIDRKUIBABid6XgAgBHNWR2fRBIKAMACquobVfWVqjq1qk4Z2ravquOr6qzh53bzjn9eVZ1dVWdW1b4bur4iFABgRN0189e18ODu3qu77zF8fm6SE7p79yQnDJ9TVXskOTDJnkn2S3J4VW2+2IUVoQAALNX+SY4c3h+Z5IB57e/t7su7+5wkZye512IXUoQCAKwyVXVoVZ0y73XoAod1kuOq6vPzvr9Zd5+fJMPPnYb2XZJ8e9655w5t62VhEgDAiHpu9guTuntNkjUbOOy+3X1eVe2U5Piq+u9Fjl3ol+rFLi4JBQDgF3T3ecPPC5J8KJPp9e9V1c5JMvy8YDj83CS3nHf6rknOW+z6GyxCq+pxVbXt8P75VfXBqrrbtf1FAABIumf/2pCquuG8+u+GSX4jyWlJjk1y0HDYQUmOGd4fm+TAqtqyqnZLsnuSkxbrYynT8S/o7qOr6n5J9k3yiiT/nOTeSzgXAICV52ZJPlRVyaRefHd3f7SqTk5yVFUdkuRbSR6XJN19elUdleSMJFcleWp3X71YB0spQtde4DeT/HN3H1NVL74uvw0AAMtfd389yV0XaL84yUPWc85hSQ5bah9LKUK/U1VvTPLQJC+vqi3jXlIAgOtkOSxMWg6WUkz+nyQfS7Jfd/8wyfZJnjPNQQEAsGlbShK6c5KPdPflVfWgJHdJ8vZpDgoAYFPl2fETS0lCP5Dk6qq6fZK3JNktybunOioAADZpSylC57r7qiSPSfKa7n5mJukoAABcJ0uZjr+yqn4nye8nedTQdr3pDQkAYNPVpuOTLC0JPTjJ3kkO6+5zhg1I3zndYQEAsCnbYBLa3Wck+dN5n89J8rJpDgoAYFO1lCcWrQYbLEKravckf5dkjyRbrW3v7ttOcVwAAGzCljIdf0Qmj+m8KsmDM9me6R3THBQAAJu2pSxMukF3n1BV1d3fTPLiqjoxyYumPDYAgE2OfUInllKE/rSqNktyVlX93yTfSbLTdIcFAMCmbClF6DOSbJ3J4qS/TvLrSQ6a4pgAADZZtmiaWMrq+JOHtz/OZLsmAAD4pay3CK2qDydZ7yYC3f3oqYwIAIBN3mJJ6CtGGwUAwCphn9CJ9Rah3f2JJKmqGyb5SXfPDZ83T7LlOMMDAGBTtJR9Qk/IZGHSWjdI8u/TGQ4AAKvBUlbHb9XdP177obt/XFVbL3YCAAALs0/oxFKK0Muq6m7d/YUkqaq7J/nJdIeV7Hibh027C2AT8b9XXj7rIQBwLS11n9Cjq+q84fPOSR4/tREBAGzC7BM6saR9QqvqjknukKSS/Hd3Xzn1kQEAsMlaShKaoeg8bcpjAQBglVhSEQoAwMZhYdLEUrZoAgCAjWqDRWhNPLGqXjh8vlVV3Wv6QwMA2PT0MngtB0tJQg9PsneS3xk+X5rk9VMbEQAAm7yl3BN67+6+W1V9MUm6+wdVdf0pjwsAgE3YUorQK4fnxXeSVNVNk8xNdVQAAJsoC5MmljId/7okH0qyU1UdluRTSf52qqMCAGCTtpTN6t9VVZ9P8pBMNqs/oLu/OvWRAQBsgjwxaWKDRWhV3SrJ/yb58Py27v7WNAcGAMCmayn3hH4kk/tBK8lWSXZLcmaSPac4LgAANmFLmY6/8/zPVXW3JE+e2ogAADZhVndPXOsnJnX3F5LccwpjAQBglVjKPaHPmvdxsyR3S3Lh1EYEALAJ61iYlCztntBt572/KpN7RD8wneEAALAaLFqEDpvUb9PdzxlpPAAArALrLUKraovuvmpYiAQAwEYw17MewfKwWBJ6Uib3f55aVccmOTrJZWu/7O4PTnlsAABsopZyT+j2SS5O8uv5+X6hnUQRCgDAdbJYEbrTsDL+tPy8+FxLkAwAcB3MWR2fZPEidPMk2yQL/qUUoQAAXGeLFaHnd/dLRxsJAMAqYJ/QicWemOQvBADAVCxWhD5ktFEAALCqrHc6vru/P+ZAAABWg7lZD2CZWCwJBQCAqVjKPqEAAGwkFiZNSEIBABidIhQAgNGZjgcAGJGFSROSUAAARicJBQAYkSR0QhIKAMDoFKEAAIzOdDwAwIjsEzohCQUAYHSSUACAEc0JQpNIQgEAmAFFKAAAozMdDwAwojkLk5JIQgEAmAFFKADAiHoZvJaiqjavqi9W1b8On7evquOr6qzh53bzjn1eVZ1dVWdW1b5Lub4iFACAhTw9yVfnfX5ukhO6e/ckJwyfU1V7JDkwyZ5J9ktyeFVtvqGLK0IBALiGqto1yW8mefO85v2THDm8PzLJAfPa39vdl3f3OUnOTnKvDfWhCAUAGNHcMnhV1aFVdcq816HrDPM1Sf58OHytm3X3+Uky/NxpaN8lybfnHXfu0LYoq+MBAFaZ7l6TZM1C31XVI5Nc0N2fr6oHLeFyCy333+Ctp4pQAADmu2+SR1fVI5JsleRGVfXOJN+rqp27+/yq2jnJBcPx5ya55bzzd01y3oY6MR0PADCiuaqZvxbT3c/r7l27+zaZLDj6j+5+YpJjkxw0HHZQkmOG98cmObCqtqyq3ZLsnuSkDf0dJKEAACzFy5IcVVWHJPlWksclSXefXlVHJTkjyVVJntrdV2/oYopQAIARLXWfzuWguz+e5OPD+4uTPGQ9xx2W5LBrc23T8QAAjE4RCgDA6EzHAwCMaG7Dh6wKklAAAEYnCQUAGNHc4jskrRqSUAAARqcIBQBgdKbjAQBGNLfgo9ZXH0koAACjk4QCAIxoJT0xaZokoQAAjE4RCgDA6EzHAwCMyD6hE5JQAABGJwkFABiRZ8dPSEIBABidIhQAgNGZjgcAGJF9QickoQAAjE4SCgAwIls0TUhCAQAYnSIUAIDRmY4HABiRfUInJKEAAIxOEQoAwOhMxwMAjMh0/IQkFACA0UlCAQBG1PYJTSIJBQBgBhShAACMznQ8AMCILEyakIQCADA6SSgAwIgkoROSUAAARqcIBQBgdKbjAQBG1LMewDIhCQUAYHSSUACAEc15YlISSSgAADOgCAUAYHSm4wEARmSf0AlJKAAAo5OEAgCMSBI6IQkFAGB0ilAAAEZnOh4AYESemDQhCQUAYHSKUAAARmc6HgBgRB7bOSEJBQBgdJJQAIAR2Sd0QhIKAMDoFKEAAIzOdDwAwIjsEzohCQUAYHSSUACAEc3JQpNIQgEAmAFFKAAAozMdDwAwIvuETkhCAQAYnSQUAGBEliVNSEIBABidIhQAgNGZjgcAGJGFSROSUAAArqGqtqqqk6rqS1V1elW9ZGjfvqqOr6qzhp/bzTvneVV1dlWdWVX7bqgPRSgAwIjmavavJbg8ya93912T7JVkv6q6T5LnJjmhu3dPcsLwOVW1R5IDk+yZZL8kh1fV5ot1oAgFAOAaeuLHw8frDa9Osn+SI4f2I5McMLzfP8l7u/vy7j4nydlJ7rVYH4pQAIBVpqoOrapT5r0OXeCYzavq1CQXJDm+uz+X5GbdfX6SDD93Gg7fJcm3551+7tC2XhYmAQCMaG4Z7BTa3WuSrNnAMVcn2auqbpLkQ1V1p0UOX2iSf9FfVBIKAMB6dfcPk3w8k3s9v1dVOyfJ8POC4bBzk9xy3mm7JjlvsesqQgEARtTL4LUhVXXTIQFNVd0gyUOT/HeSY5McNBx2UJJjhvfHJjmwqrasqt2S7J7kpMX6MB0PAMC6dk5y5LDCfbMkR3X3v1bVZ5McVVWHJPlWksclSXefXlVHJTkjyVVJnjpM56+XIhQAgGvo7i8n+bUF2i9O8pD1nHNYksOW2ociFABgRJ6YNOGeUAAARqcIBQBgdKbjAQBGtBz2CV0OJKEAAIxOEgoAMCI56IQkFACA0SlCAQAYnel4AIAR2Sd0QhIKAMDoJKEAACOyRdOEJBQAgNEpQgEAGJ3peACAEZmMn5CEAgAwOkkoAMCIbNE0IQkFAGB0ilAAAEZnOh4AYERtaVISSSgAADMgCQUAGJGFSROSUAAARqcIBQBgdKbjAQBGNGdhUhJJKAAAMyAJBQAYkRx0QhIKAMDoFKEAAIzOdDwAwIgsTJqQhAIAMDpFKAAAozMdDwAwIo/tnJCEAgAwOkUoy9pmm22WEz99bN539JuSJEcc+bqc+JkP58TPfDhfPv0TOfEzH57xCIHl4E1rXpnzzv1STv3iCT9re+ELnpVvnnNKTjn5uJxy8nF5+H6/PsMRws/1MvjfcmA6nmXtT57yBznzzP/JtttukyQ5+KA//dl3f/O3z8sll1w6q6EBy8jb335UDj/8iBxxxGuv0f7a170pr3r1G2c0KmAxklCWrVvc4ubZd78H5+1HHrXg97/1mN/M+4/+15FHBSxHJ37qc/n+D34462EA14IilGXrZX///Lzw+S/P3Nwv3sK9z33vmQsvuChf/59vjD8wYMV4yp8cnC98/vi8ac0rc5Ob3HjWw4Ekk4VJs34tB1MtQqvqV6rqhKo6bfh8l6p6/jT7ZNOw734PzoUXXpxTTz1twe8f+7hH5f1Hux8UWL83vPHt+ZU77pO73+M38t3vXpB/+PsXznpIwDzTTkLflOR5Sa5Mku7+cpID13dwVR1aVadU1SlXXHnJlIfGcnaf+9w9D3/EQ/Ll0z+Rt77ttXnAA/fOmje/Mkmy+eab51GP3jcf/MBHZjxKYDm74IKLMjc3l+7Om9/yrtzznnvNekiQxMKktaZdhG7d3Set03bV+g7u7jXdfY/uvsf1r3ejKQ+N5ewlL35F9rjD/XKXPR+YJ/3B0/PJT3w2h/7hs5MkD3rwffO1r/1PzjvvuzMeJbCc3fzmO/3s/QH7Pzynn37mDEcDrGvaq+MvqqrbJZOSu6oem+T8KffJJu63H/vIfMBUPDDPO9/x+jzwAXtnxx23zze+fkpe8tJX5IEP3Cd3vese6e5885vn5k+e8hezHiYwT3VPL5KtqtsmWZNknyQ/SHJOkid09zc3dO6Nt7nd8siKgWXvsit+OushACvEVVd8p2Y9hoNu89szr3GO/MYHZv53mHYSul13P7Sqbphks+6+tKoelWSDRSgAAJuuqS9Mqqo7d/dlQwF6YBKr4wGAVWuue+av5WDaSehjk7y/qp6Q5H5Jfj/Jb0y5TwAAlrmpFqHd/fUh/fyXJN9O8hvd/ZNp9gkAwPI3lSK0qr6SXGMTqu2TbJ7kc1WV7r7LNPoFAFjulsdk+OxNKwl95JSuCwDAJmAqRej8LZiq6q5J7j98PLG7vzSNPgEAVoI5WWiS6T87/ulJ3pVkp+H1zqp62jT7BABg+Zv26vhDkty7uy9Lkqp6eZLPJvnHKfcLAMAyNu0itJJcPe/z1UMbAMCq1Kbjk0y/CH1rJiviPzR8PiDJW6bcJwAAy9zUitCq2izJ55J8IpON6ivJwd39xWn1CQDAyjC1IrS756rqld29d5IvTKsfAICVZG7WA1gmpv3s+OOq6reryn2gAAD8zLTvCX1WkhsmuaqqfprJlHx3942m3C8AwLJkn9CJaT87fttpXh8AgJVp2pvVn7CUNgAAVpepJKFVtVWSrZPsWFXb5ed7g94oyS2m0ScAwEpgn9CJaU3HPznJMzIpOD8/r/3SJK+fUp8AAKwQ0ypCP5PkqCSP7e5/rKqDkvx2km8kefeU+gQAWPZs0TQxrXtC35jk8qEAfUCSv0tyZJIfJVkzpT4BAFghplWEbt7d3x/ePz7Jmu7+QHe/IMntp9QnAAAbQVXdsqr+s6q+WlWnV9XTh/btq+r4qjpr+LndvHOeV1VnV9WZVbXvhvqYWhFaVWun+h+S5D/mfTftvUkBAJat7p75awmuSvLs7v7VJPdJ8tSq2iPJc5Oc0N27Jzlh+JzhuwOT7JlkvySHV9Xmi3UwrSL0PUk+UVXHJPlJkhOHAd4+kyl5AACWqe4+v7u/MLy/NMlXk+ySZP9MbrHM8POA4f3+Sd7b3Zd39zlJzk5yr8X6mEoq2d2HDfuB7pzkuP55yb1ZkqdNo08AgJVgpT0xqapuk+TXknwuyc26+/xkUqhW1U7DYbsk+a95p507tK3X1KbGu/u/Fmj72rT6AwBgaarq0CSHzmta092/sHi8qrZJ8oEkz+juS6pq3UN+dugCbYtW2+7PBABYZYaCc9Edi6rqepkUoO/q7g8Ozd+rqp2HFHTnJBcM7ecmueW803dNct5i15/qYzsBALimuWXw2pCaRJ5vSfLV7n7VvK+OTXLQ8P6gJMfMaz+wqrasqt2S7J7kpMX6kIQCALCu+yb5vSRfqapTh7a/TPKyJEdV1SFJvpXkcUnS3adX1VFJzshkZf1Tu/vqxTpQhAIAjGglPDu+uz+Vhe/zTCbbby50zmFJDltqH6bjAQAYnSIUAIDRmY4HABjRStsndFokoQAAjE4SCgAwoiU+u32TJwkFAGB0ilAAAEZnOh4AYERLeWLRaiAJBQBgdIpQAABGZzoeAGBEK+GxnWOQhAIAMDpJKADAiDwxaUISCgDA6BShAACMznQ8AMCIPLZzQhIKAMDoJKEAACOyMGlCEgoAwOgUoQAAjM50PADAiDwxaUISCgDA6CShAAAjmrNFUxJJKAAAM6AIBQBgdKbjAQBGZDJ+QhIKAMDoJKEAACPyxKQJSSgAAKNThAIAMDrT8QAAIzIdPyEJBQBgdJJQAIARtScmJZGEAgAwA4pQAABGZzoeAGBEFiZNSEIBABidIhQAgNGZjgcAGFGbjk8iCQUAYAYkoQAAI7JP6IQkFACA0SlCAQAYnel4AIAR2Sd0QhIKAMDoJKEAACOyMGlCEgoAwOgUoQAAjM50PADAiCxMmpCEAgAwOkkoAMCIPDt+QhIKAMDoFKEAAIzOdDwAwIjm7BOaRBIKAMAMSEIBAEZkYdKEJBQAgNEpQgEAGJ3peACAEVmYNCEJBQBgdJJQAIARWZg0IQkFAGB0ilAAAEZnOh4AYEQWJk1IQgEAuIaqemtVXVBVp81r276qjq+qs4af28377nlVdXZVnVlV+y6lD0UoAADreluS/dZpe26SE7p79yQnDJ9TVXskOTDJnsM5h1fV5hvqQBEKADCiXgb/2+AYuz+Z5PvrNO+f5Mjh/ZFJDpjX/t7uvry7z0lydpJ7bagPRSgAwCpTVYdW1SnzXocu4bSbdff5STL83Glo3yXJt+cdd+7QtigLkwAARrQcFiZ195okazbS5WqhLjZ0kiQUAICl+F5V7Zwkw88LhvZzk9xy3nG7JjlvQxdThAIAsBTHJjloeH9QkmPmtR9YVVtW1W5Jdk9y0oYuZjoeAGBEK+GxnVX1niQPSrJjVZ2b5EVJXpbkqKo6JMm3kjwuSbr79Ko6KskZSa5K8tTuvnqDffQyuC9hITfe5nbLc2DAsnPZFT+d9RCAFeKqK76z0P2Lo7rtjr828xrn6xd9ceZ/B0koAMCIuudmPYRlwT2hAACMThEKAMDoTMcDAIxobgUsTBqDJBQAgNFJQgEARrRcdyYamyQUAIDRKUIBABid6XgAgBFZmDQhCQUAYHSSUACAEVmYNCEJBQBgdIpQAABGZzoeAGBEc6bjk0hCAQCYAUUoAACjMx0PADCitk9oEkkoAAAzIAkFABiRfUInJKEAAIxOEQoAwOhMxwMAjGjOwqQkklAAAGZAEgoAMCILkyYkoQAAjE4RCgDA6EzHAwCMaM50fBJJKAAAMyAJBQAYkYVJE5JQAABGpwgFAGB0puMBAEbkiUkTklAAAEYnCQUAGJGFSROSUAAARqcIBQBgdKbjAQBG5IlJE5JQAABGJwkFABhR26IpiSQUAIAZUIQCADA60/EAACOyMGlCEgoAwOgUoQAAjM50PADAiDy2c0ISCgDA6CShAAAjsk/ohCQUAIDRKUIBABid6XgAgBFZmDQhCQUAYHSSUACAEUlCJyShAACMThEKAMDoTMcDAIzIZPyEJBQAgNGVm2NZSarq0O5eM+txAMuff1/A8iYJZaU5dNYDAFYM/76AZUwRCgDA6BShAACMThHKSuP+LmCp/PsCljELkwAAGJ0kFACA0SlCAQAYnSKUjaaquqpeOe/zn1XVizdwzgFVtcd6vrtDVX28qk6tqq9W1Zqhfa+qesR1GN/bquqx1/Y8YLaq6q+q6vSq+vLw74N7V9Uzqmrra3md21TVadMaJ3DtKELZmC5P8piq2vFanHNAkgWL0CSvS/Lq7t6ru381yT8O7XslWbAIrSqPooVNSFXtneSRSe7W3XdJ8tAk307yjCQLFqFVtfloAwSuM0UoG9NVmaxGfea6X1TVravqhCHJOKGqblVV+yR5dJJ/GNKN261z2s5Jzl37obu/UlXXT/LSJI8fznl8Vb24qtZU1XFJ3r5QXwuM56+HZHSzqnpOVZ08HP+Sjfj3AH55Oye5qLsvT5LuvijJY5PcIsl/VtV/JklV/biqXlpVn0uyd1U9q6pOG17PWPeiVXXbqvpiVd2zqm5XVR+tqs9X1YlVdcfxfj1YvRShbGyvT/KEqrrxOu3/lOTtQ5LxriSv6+7PJDk2yXOGtPN/1jnn1Un+o6r+raqeWVU36e4rkrwwyfuGc943HHv3JPt39+8u1Nf8i1bV3yfZKcnBmaQquye5VyYJ692r6gEb4e8AbBzHJbllVX2tqg6vqgd29+uSnJfkwd394OG4GyY5rbvvneQnmfzzfe8k90nyR1X1a2svWFV3SPKBJAd398mZ/Mfz07r77kn+LMnhY/1ysJopQtmouvuSJG9P8qfrfLV3kncP79+R5H5LuNYRSX41ydFJHpTkv6pqy/Ucfmx3/2QJfb0gyU26+8k92Z/sN4bXF5N8IckdMylKgWWgu3+cyX9kHprkwiTvq6o/WODQqzMpLJPJP/Mf6u7LhvM/mOT+w3c3TXJMkid296lVtU2SfZIcXVWnJnljJukrMGXun2MaXpNJQXfEIscsaYPa7j4vyVuTvHVYUHCn9Rx62RL7OjmTtHP77v5+kkryd939xqWMBxhfd1+d5ONJPl5VX0ly0AKH/XQ4Lpn8c70+P8rkntL7Jjk9kzDmh92910YbMLAkklA2uqG4OyrJIfOaP5PkwOH9E5J8anh/aZJtF7pOVe1XVdcb3t88yQ5JvrPYORvoK0k+muRlST5SVdsm+ViSJw1pSKpql6raaQm/JjCCYZeM+bMTeyX5Zhb/98AnkxxQVVtX1Q2T/FaSE4fvrshkQeTvV9XvDrM351TV44b+qqruuvF/E2BdilCm5ZVJ5q+S/9MkB1fVl5P8XpKnD+3vTfKcYYHAuguTfiPJaVX1pUyKxed093eT/GeSPdYuTFqg7/X1lSTp7qOTvCmT+1FPzGTq/rNDwvL+LF7gAuPaJsmRVXXG8M/0HklenMl9nP+2dmHSfN39hSRvS3JSks8leXN3f3He95dlsuL+mVW1fyb/sXrI8O+a05PsP9XfCEjisZ0AAMyAJBQAgNEpQgEAGJ0iFACA0SlCAQAYnSIUAIDRKUKBa6Wqrh62xzqtqo6uqq1/iWu9raoeO7x/c1XtscixD6qqfa5DH9+oqh03fGRSVX9QVf90bfsA4NpThALX1k+6e6/uvlMmG3//8fwvq2rz63LR7v7D7j5jkUMelMnjFQHYBChCgV/GiUluP6SU/1lV707ylaravKr+oapOrqovV9WTk589jeafho3HP5LkZ0+nqqqPV9U9hvf7VdUXqupLVXVCVd0mk2L3mUMKe/+qumlVfWDo4+Squu9w7g5VddzwAIQ3Zj2PcFy3jwW+f1RVfW64zr9X1c2G9gcOYzh1+G7bqtq5qj45LyG+/y/2CMB8nh0PXCdVtUWSh2fyKNQkuVeSO3X3OVV1aJIfdfc9q2rLJJ+uquOS/FqSOyS5c5KbJTkjyVvXue5NM3mi1QOGa23f3d+vqjck+XF3v2I47t1JXt3dn6qqW2XyVK1fTfKiJJ/q7pdW1W8mOXSBsf9CHwv8ip9Kcp/u7qr6wyR/nuTZSf4syVO7+9PD415/OvTxse4+bEiCr/MtCgCrhSIUuLZuUFWnDu9PTPKWTKbJT+ruc4b230hyl7X3eya5cZLdkzwgyXu6++ok51XVfyxw/fsk+eTaa3X399czjodm8vjWtZ9vVFXbDn08Zjj3I1X1g+vYx65J3ldVOye5fpK1v9unk7yqqt6V5IPdfW5VnZzkrVV1vST/0t2nLnA9AOYxHQ9cW2vvCd2ru5/W3VcM7ZfNO6aSPG3ecbt193HDdxt6VnAt4Zhk8u+vvef1sUt3X7oR+/jHJP/U3XdO8uQkWyVJd78syR8muUGS/6qqO3b3JzMpfr+T5B1V9ftLGD/AqqYIBabhY0n+ZEgGU1W/UlU3TPLJJAcO94zunOTBC5z72SQPrKrdhnPXTpVfmmTbeccdl+T/rv1QVXsNbz+Z5AlD28OTbHct+pjvxpkUlUly0Lx+btfdX+nulyc5Jckdq+rWSS7o7jdlkgzfbYHrATCPIhSYhjdncr/nF6rqtCRvzOT2nw8lOSvJV5L8c5JPrHtid1+YyT2WH6yqLyV53/DVh5P81tqFSUn+NMk9hoVPZ+Tnq/RfkuQBVfWFTG4L+Na16GO+Fyc5uqpOTHLRvPZnDIuPvpTkJ0n+LZOV+6dW1ReT/HaS1274TwSwulX3Uma9AABg45GEAgAwOkUoAACjU4QCADA6RSgAAKNThAIAMDpFKAAAo1OEAgAwuv8PPKxSJCWVeTsAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Isolation Forest: 93\n",
"0.9090019569471625\n",
" precision recall f1-score support\n",
"\n",
" 0 0.95 0.95 0.95 960\n",
" 1 0.25 0.24 0.24 62\n",
"\n",
" accuracy 0.91 1022\n",
" macro avg 0.60 0.60 0.60 1022\n",
"weighted avg 0.91 0.91 0.91 1022\n",
"\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 648x504 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#evaluation of the model\n",
"#printing every score of the classifier\n",
"#scoring in any thing\n",
"\n",
"from sklearn.metrics import confusion_matrix\n",
"n_outliers = len(Stroke)\n",
"print(\"the Model used is {}\".format(\"Isolation Forest\"))\n",
"acc= accuracy_score(Y_test,y_pred)\n",
"print(\"The accuracy is {}\".format(acc))\n",
"prec= precision_score(Y_test,y_pred)\n",
"print(\"The precision is {}\".format(prec))\n",
"rec= recall_score(Y_test,y_pred)\n",
"print(\"The recall is {}\".format(rec))\n",
"f1= f1_score(Y_test,y_pred)\n",
"print(\"The F1-Score is {}\".format(f1))\n",
"MCC=matthews_corrcoef(Y_test,y_pred)\n",
"print(\"The Matthews correlation coefficient is{}\".format(MCC))\n",
"\n",
"#printing the confusion matrix\n",
"LABELS = ['Not Stroke', 'Stroke']\n",
"conf_matrix = confusion_matrix(Y_test, y_pred)\n",
"plt.figure(figsize=(12, 12))\n",
"sns.heatmap(conf_matrix, xticklabels=LABELS,\n",
" yticklabels=LABELS, annot=True, fmt=\"d\");\n",
"plt.title(\"Confusion matrix\")\n",
"plt.ylabel('True class')\n",
"plt.xlabel('Predicted class')\n",
"plt.show()\n",
"\n",
"# Run classification metrics\n",
"plt.figure(figsize=(9, 7))\n",
"print('{}: {}'.format(\"Isolation Forest\", n_errors))\n",
"print(accuracy_score(Y_test, y_pred))\n",
"print(classification_report(Y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "084eda9a",
"metadata": {},
"source": [
"#### Random Forest Classifier"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "c41d3b76",
"metadata": {},
"outputs": [],
"source": [
"# Building the Random Forest Classifier (RANDOM FOREST)\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"# random forest model creation\n",
"rfc = RandomForestClassifier()\n",
"rfc.fit(X_train,Y_train)\n",
"# predictions\n",
"y_pred = rfc.predict(X_test)"
]
},
{
"cell_type": "markdown",
"id": "7db12efc",
"metadata": {},
"source": [
"### Model eveuation"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "6284b00a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The model used is Random Forest classifier\n",
"The accuracy is 0.9393346379647749\n",
"The precision is 0.0\n",
"The recall is 0.0\n",
"The F1-Score is 0.0\n",
"The Matthews correlation coefficient is 0.0\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Mishane\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\Mishane\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:870: RuntimeWarning: invalid value encountered in double_scalars\n",
" mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Random Forest: 62\n",
"0.9393346379647749\n",
" precision recall f1-score support\n",
"\n",
" 0 0.94 1.00 0.97 960\n",
" 1 0.00 0.00 0.00 62\n",
"\n",
" accuracy 0.94 1022\n",
" macro avg 0.47 0.50 0.48 1022\n",
"weighted avg 0.88 0.94 0.91 1022\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Mishane\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\Mishane\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\Mishane\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 648x504 with 0 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Evaluating the classifier\n",
"#printing every score of the classifier\n",
"#scoring in any thing\n",
"from sklearn.metrics import classification_report, accuracy_score,precision_score,recall_score,f1_score,matthews_corrcoef\n",
"from sklearn.metrics import confusion_matrix\n",
"n_outliers = len(Stroke)\n",
"n_errors = (y_pred != Y_test).sum()\n",
"print(\"The model used is Random Forest classifier\")\n",
"acc= accuracy_score(Y_test,y_pred)\n",
"print(\"The accuracy is {}\".format(acc))\n",
"prec= precision_score(Y_test,y_pred)\n",
"print(\"The precision is {}\".format(prec))\n",
"rec= recall_score(Y_test,y_pred)\n",
"print(\"The recall is {}\".format(rec))\n",
"f1= f1_score(Y_test,y_pred)\n",
"print(\"The F1-Score is {}\".format(f1))\n",
"MCC=matthews_corrcoef(Y_test,y_pred)\n",
"print(\"The Matthews correlation coefficient is {}\".format(MCC))\n",
"\n",
"\n",
"#printing the confusion matrix\n",
"LABELS = ['Not Stroke', 'Stroke']\n",
"conf_matrix = confusion_matrix(Y_test, y_pred)\n",
"plt.figure(figsize=(12, 12))\n",
"sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
"plt.title(\"Confusion matrix\")\n",
"plt.ylabel('True class')\n",
"plt.xlabel('Predicted class')\n",
"plt.show()\n",
"\n",
"# Run classification metrics\n",
"plt.figure(figsize=(9, 7))\n",
"print('{}: {}'.format(\"Random Forest\", n_errors))\n",
"print(accuracy_score(Y_test, y_pred))\n",
"print(classification_report(Y_test, y_pred))"
]
},
{
"cell_type": "markdown",
"id": "b07d65d8",
"metadata": {},
"source": [
"### Saving the model"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "02faf4e6",
"metadata": {},
"outputs": [],
"source": [
"import pickle\n",
"filename = 'finalized_model_disease_classification_LR.sav'\n",
"pickle.dump(rfc, open(filename, 'wb'))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.8"
},
"vscode": {
"interpreter": {
"hash": "d9a6414fa631c028c434667d182c0b79dc634ffcd06f52fc304061a2c0b9ef26"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
project/requirements.txt 0 → 100644
View file @ 3ec0eded
Babsl-py==1.4.0
project/routes.py 0 → 100644
View file @ 3ec0eded
from keras.models import load_model
from flask import render_template, request
from app import app
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import cv2
from pathlib import Path
import pickle
import tensorflow as tf
import sklearn
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
#### Home Page ####
@app.route('/')
@app.route('/home')
def home():
return render_template('home.html')
@app.route('/about')
def about():
return render_template('about.html')
#### Lung Cancer Disease ####
@app.route('/lungcancer')
def lungcancer():
return render_template('lungCancer.html')
@app.route('/lungpradict')
def lungpradict():
return render_template('lung_cancer_detection.html')
@app.route('/predictLungCancer', methods=['POST'])
def predictLungCancer():
GENDER = request.form['GENDER']
AGE = request.form['AGE']
SMOKING = request.form['SMOKING']
YELLOW_FINGERS = request.form['YELLOW_FINGERS']
ANXIETY = request.form['ANXIETY']
PEER_PRESSURE = request.form['PEER_PRESSURE']
CHRONICDISEASE = request.form['CHRONICDISEASE']
FATIGUE = request.form['FATIGUE']
ALLERGY = request.form['ALLERGY']
WHEEZING = request.form['WHEEZING']
ALCOHOL = request.form['ALCOHOL']
COUGHING = request.form['COUGHING']
SHORTBREATH = request.form['SHORTBREATH']
SWALLOW = request.form['SWALLOW']
CHESTPAIN = request.form['CHESTPAIN']
with open('models/finalized_model_lung_classification_LR.sav', 'rb') as f:
model = pickle.load(f)
input_data = (GENDER, AGE, SMOKING, YELLOW_FINGERS, ANXIETY, PEER_PRESSURE, CHRONICDISEASE,
FATIGUE, ALLERGY, WHEEZING, ALCOHOL, COUGHING, SHORTBREATH, SWALLOW, CHESTPAIN)
input_data3 = np.array(input_data).astype(int)
input_data_as_numpy_array = np.asarray(input_data3)
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
prediction = model.predict(input_data_reshaped)
if (prediction == 1):
result = "You have a lung disease"
disease = 'lung'
return docRec(disease, result, value="lung")
else:
result = "You don't have a lung disease"
return render_template('results.html', result=result)
@app.route('/lungcancerlevel')
def lungcancerlevel():
return render_template('lung_cancer_level_detection.html')
@app.route('/predictLungCancerLevel', methods=['POST'])
def predictLungCancerLevel():
GENDER = request.form['GENDER']
AGE = request.form['AGE']
SMOKING = request.form['SMOKING']
FATIGUE = request.form['FATIGUE']
ALLERGY = request.form['ALLERGY']
WHEEZING = request.form['WHEEZING']
ALCOHOL = request.form['ALCOHOL']
COUGHING = request.form['COUGHING']
SHORTBREATH = request.form['SHORTBREATH']
SWALLOW = request.form['SWALLOW']
CHESTPAIN = request.form['CHESTPAIN']
GENETICRISK = request.form['GENETICRISK']
COUGHINGBLOOD = request.form['COUGHINGBLOOD']
with open('models/finalized_model_lung_cancer_LR_new_level.sav', 'rb') as f:
model = pickle.load(f)
input_data = (AGE, GENDER, ALCOHOL, ALLERGY, GENETICRISK, SMOKING, CHESTPAIN,
COUGHINGBLOOD, FATIGUE, SHORTBREATH, WHEEZING, SWALLOW, COUGHING)
# input_data2 = (1, 69, 0, 1, 0, 1, 1, 1, 1, 1)
# change input data to numpy array
input_data_as_numpy_array = np.asarray(input_data)
# reshape the numpy array as we are predicting for only on instance
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
prediction = model.predict(input_data_reshaped)
if (prediction == ['1']):
result = "Your risk is low"
elif (prediction == ['2']):
result = "Your risk is medium"
else:
result = "Your risk is high"
disease = 'lung'
return docRec(disease, result, value="lung2")
@app.route('/lungxray')
def lungxray():
return render_template('lungXray.html')
def predictxray(img):
model = tf.keras.models.load_model('models/lung_disease_model.h5')
pic = []
img = cv2.resize(img, (224, 224))
if img.shape[2] == 1:
img = np.dstack([img, img, img])
img = np.array(img)
img = img/255
# label = to_categorical(0, num_classes=2)
pic.append(img)
# pic_labels.append(pneu)
pic1 = np.array(pic)
a = model.predict(pic1)
print(a.argmax())
predicts = a.argmax()
predicts
if predicts == 0:
return "Anatomia Normal"
elif predicts == 1:
return "Inflammatory processes (pneumonia)"
elif predicts == 2:
return "Higher density (pleural effusion, atelectatic consolidation, hydrothorax, empyema)"
elif predicts == 3:
return "Lower density (pneumotorax, pneumomediastinum, pneumoperitoneum)"
elif predicts == 4:
return "Obstructive pulmonary diseases (emphysema, bronchopneumonia, bronchiectasis, embolism)"
elif predicts == 5:
return "Degenerative infectious diseases (tuberculosis , sarcoidosis, proteinosis, fibrosis)"
elif predicts == 6:
return "Encapsulated lesions (abscesses, nodules, cysts, tumor masses, metastases)"
elif predicts == 7:
return "Mediastinal changes (pericarditis, arteriovenous malformations, lymph node enlargement)"
else:
return "Chest changes (atelectasis, malformations, agenesis, hypoplasia)"
@app.route('/process_image_lung', methods=['POST'])
def process_image_lung():
if request.method == 'POST':
file = request.files['file']
img_bytes = file.read() # Read image data as bytes
# Convert bytes to OpenCV image
img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert BGR to RGB
result = predictxray(img) # Make prediction using img
disease = 'lung'
return docRec(disease, result, value="lung")
return 'File uploaded successfully!'
######################### Kidney Disease #########################################
@app.route('/predictKidneypage')
def predictKidneypage():
return render_template('kidney_prediction.html')
@app.route('/kidney')
def kidney():
return render_template('kidney.html')
@app.route('/predictKidney', methods=['POST'])
def predictKidney():
Bp = request.form['bp']
Sg = request.form['sg']
Rbc = request.form['rbc']
Bu = request.form['bu']
Sc = request.form['sc']
Sod = request.form['sod']
Pot = request.form['pot']
Hemo = request.form['hemo']
Wbcc = request.form['wbcc']
Rbcc = request.form['rbcc']
Htn = request.form['htn']
with open('models/finalized_model_kidney_prediction_RF.sav', 'rb') as f:
model = pickle.load(f)
input_data = (Bp, Sg, Rbc, Bu, Sc, Sod, Pot, Hemo, Wbcc, Rbcc, Htn)
# input_data3 = np.array(input_data).astype(int)
input_data_as_numpy_array = np.asarray(input_data)
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
prediction = model.predict(input_data_reshaped)
print(input_data)
if (prediction == 1):
disease = "kidney"
result = "You have a chronic kidney disease"
return docRec(disease, result, value="kidney")
else:
result = "You don't have a chronic kidney disease"
return render_template('results.html', result=result)
@app.route('/kidneyDisease')
def kidneyDisease():
return render_template('kidney_detection.html')
def predict(img):
model = tf.keras.models.load_model('models/kidney_detect_model.h5')
pic = []
img = cv2.resize(img, (28, 28))
if img.shape[2] == 1:
img = np.dstack([img, img, img])
img = np.array(img)
img = img/255
# label = to_categorical(0, num_classes=2)
pic.append(img)
# pic_labels.append(pneu)
pic1 = np.array(pic)
a = model.predict(pic1)
print(a.argmax())
predicts = a.argmax()
predicts
if predicts == 0:
return "There is a cyst in the uploaded picture. You are not well"
elif predicts == 1:
return "There is nothing unnatural in the uploaded image. you are fine"
elif predicts == 2:
return "There is a stone in the uploaded picture. You are not well"
else:
return "There is a Tumor in the uploaded picture. You are not well"
@app.route('/process_image_kidney', methods=['POST'])
def process_image_kidney():
if request.method == 'POST':
file = request.files['file']
img_bytes = file.read() # Read image data as bytes
# Convert bytes to OpenCV image
img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert BGR to RGB
prediction = predict(img) # Make prediction using img
disease = "kidney"
return docRec(disease, result=prediction, value="kidney")
return 'File uploaded successfully!'
################################ Skin Cancer ##########################################
@app.route('/skinCancerPage')
def skinCancerPage():
return render_template('skinCancerPage.html')
@app.route('/skincancer')
def skincancer():
return render_template('skincancer.html')
def predict2(img):
model = tf.keras.models.load_model('models/skinmodel.h5')
pic = []
img = cv2.resize(img, (224, 224))
if img.shape[2] == 1:
img = np.dstack([img, img, img])
img = np.array(img)
img = img/255
# label = to_categorical(0, num_classes=2)
pic.append(img)
# pic_labels.append(pneu)
pic1 = np.array(pic)
a = model.predict(pic1)
print(a.argmax())
predicts = a.argmax()
predicts
if predicts == 0:
return "Melanocytic nevi"
elif predicts == 1:
return "Melanoma"
elif predicts == 2:
return "Benign keratosis-like lesions"
elif predicts == 3:
return "Basal cell carcinoma"
elif predicts == 4:
return "Actinic keratoses"
elif predicts == 5:
return "Vascular lesions"
else:
return "Dermatofibroma"
@app.route('/process_image_skin', methods=['POST'])
def process_image_skin():
if request.method == 'POST':
file = request.files['file']
img_bytes = file.read() # Read image data as bytes
# Convert bytes to OpenCV image
img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert BGR to RGB
result = predict2(img) # Make prediction using img
disease = 'skin'
return docRec(disease, result, value="skin")
return 'File uploaded successfully!'
@app.route('/skinTumor')
def skinTumor():
return render_template('skinTumor.html')
@app.route('/tumor', methods=['POST'])
def tumor():
mass_npea = request.form['mass_npea']
size_npear = request.form['size_npear']
malign_ratio = request.form['malign_ratio']
damage_size = request.form['damage_size']
exposed_area = request.form['exposed_area']
std_dev_malign = request.form['std_dev_malign']
err_malign = request.form['err_malign']
malign_penalty = request.form['malign_penalty']
damage_ratio = request.form['damage_ratio']
with open('models/finalized_model_skinT_prediction_RF.sav', 'rb') as f:
model = pickle.load(f)
input_data = (mass_npea, size_npear, malign_ratio, damage_size, exposed_area, std_dev_malign, err_malign,
malign_penalty, damage_ratio)
# nput_data3 = np.array(input_data).astype(int)
input_data_as_numpy_array = np.asarray(input_data)
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
result = model.predict(input_data_reshaped)
# convert to string with comma-separated values
# convert the numpy array to a string
result = np.round(result, 2)
print(result)
result = str(result)
# remove the brackets from the string
result = result.replace('[', '').replace(']', '')
result = "Tumor size is "+result+"mm"
# result = ("Predicted result is"+result+"(mm)")
disease = 'skin'
return docRec(disease, result, value="skinT")
######################## Stroke ############################
@app.route('/stroke')
def stroke():
return render_template('strokePage.html')
@app.route('/predictStrokepage')
def predictStrokepage():
return render_template('stroke_predict.html')
# predict stroke
@app.route('/predictStroke', methods=['POST'])
def predictStroke():
gender = request.form['gender']
age = request.form['age']
hypertension = request.form['hypertension']
heart_disease = request.form['heart_disease']
ever_married = request.form['ever_married']
work_type = request.form['work_type']
Residence_type = request.form['Residence_type']
avg_glucose_level = request.form['avg_glucose_level']
bmi = request.form['bmi']
smoking_status = request.form['smoking_status']
with open('models/finalized_model_stroke_prediction_RF.sav', 'rb') as f:
model = pickle.load(f)
input_data = (gender, age, hypertension, heart_disease, ever_married,
work_type, Residence_type, avg_glucose_level, bmi, smoking_status)
# input_data3 = np.array(input_data).astype(int)
input_data_as_numpy_array = np.asarray(input_data)
input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
prediction = model.predict(input_data_reshaped)
print(input_data)
if (prediction == 1):
result = "You are at risk of having a stroke"
disease = 'stroke'
return docRec(disease, result, value="stroke")
else:
result = "You are not at risk of having a stroke"
return render_template('results.html', result=result)
# CT image detection
@app.route('/predictStrokeCt')
def predictStrokeCt():
return render_template('stroke_detection_CT.html')
def predict4(img):
model = tf.keras.models.load_model('models/finalized_model_strokeCT.h5')
# model = load_model('finalized_model_strokeCT.h5')
model = load_model('models/finalized_model_strokeCT.h5')
pic = []
img = cv2.resize(img, (224, 224))
# Convert the image to a numpy array and preprocess it
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x /= 255
# Make a prediction using the model
prediction = model.predict(x)
# Print the predicted class (0 = non-stroke, 1 = stroke)
if prediction < 0.5:
return ("You don't have a stroke")
else:
return ("You have a Stroke")
@app.route('/process_image_stroke', methods=['POST'])
def process_image_stroke():
if request.method == 'POST':
file = request.files['file']
img_bytes = file.read() # Read image data as bytes
# Convert bytes to OpenCV image
img = cv2.imdecode(np.frombuffer(img_bytes, np.uint8), -1)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # Convert BGR to RGB
prediction = predict4(img) # Make prediction using img
disease = 'stroke'
return docRec(disease, prediction, value="stroke")
return 'File uploaded successfully!'
def docRec(disease, result, value):
df = pd.read_excel('docs\RELEVANTDOCTORS.xlsx')
# find rows that contain a keyword in a specific column
keyword = str(disease)
column_name = 'Disease'
rec = df[df[column_name].str.contains(keyword)]
rec = rec.iloc[:, 1:]
table_classes = 'table table-striped table-bordered table-hover'
if value =="skinT":
textshow= True
else:
textshow=False
if value == "lung":
show_button = True
# render the output in a table format
return render_template('results.html', rec=rec.to_html(index=False, classes=table_classes), result=result, show_button=show_button,textshow=textshow)
else:
show_button = False
return render_template('results.html', rec=rec.to_html(index=False, classes=table_classes), result=result, show_button=show_button,textshow=textshow)
project/static/risk.svg 0 → 100644
View file @ 3ec0eded
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d="M872.42,462a1,1,0,0,1,.31-.26.44.44,0,0,1,.47,0,1.24,1.24,0,0,1,.17.27.86.86,0,0,0,.62.41l-.15,1Z" transform="translate(-47.24 -58.63)" fill="#727a9c"/><path d="M861.13,441.7s17.37-.1,13.28,7.29-10.48,8.28-10.48,8.28Z" transform="translate(-47.24 -58.63)" fill="#ffb0b1"/><path d="M753.1,110.05h-.75c-29.84,0-56.23,15.79-71.41,39.09-15.18-23.3-41.57-39.09-71.41-39.09h-.75A85,85,0,0,0,524.59,195c0,27.81,12.18,67.27,35.93,99.74,45.25,61.87,120.42,116,120.42,116s75.17-54.12,120.41-116c23.76-32.47,35.93-71.93,35.93-99.74A85,85,0,0,0,753.1,110.05ZM742.37,299.5a16.85,16.85,0,0,1-15.67-10.82H708.86a5.64,5.64,0,0,1-5.31-3.84l-5.48-16.51-17.34,60.59a5.61,5.61,0,0,1-5.38,4.05H675a5.54,5.54,0,0,1-5.2-4.64L651.93,220.85l-13.06,63.6a5.57,5.57,0,0,1-5.41,4.23H608.33a5.59,5.59,0,1,1,0-11.17h20.73L647.56,192a5.61,5.61,0,0,1,11,.46l18.25,109.64,15.54-54.21a5.5,5.5,0,0,1,5.23-4h.11a5.58,5.58,0,0,1,5.3,3.84l9.91,29.81h13.58a16.76,16.76,0,1,1,15.92,22Z" transform="translate(-47.24 -58.63)" fill="#090450"/></svg>
\ No newline at end of file
project/static/style.css 0 → 100644
View file @ 3ec0eded
/* Body */
body {
font-family: Arial, sans-serif;
margin: 0;
padding: 0;
background-color: #ffffff;
}
/* Main */
main {
max-width: 100%;
margin: 0 auto;
padding: 20px;
}
section {
margin-bottom: 30px;
}
h1,
h2 {
color: #333;
}
h1 {
margin-top: 0;
}
ul {
margin: 0;
padding: 0;
list-style: none;
}
.img1 {
display: block;
max-width: 30%;
height: auto;
float: right;
}
/* Footer */
footer {
background-color: #333;
color: #fff;
text-align: center;
padding: 20px;
margin-top: 40px;
}
\ No newline at end of file
project/templates/about.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<head>
<title>Smart E-Healthcare Application</title>
<link rel="stylesheet" href="/static/style.css">
</head>
<body>
<main>
<section>
<h1>Welcome to VALEO LAB</h1>
<p>This is an online platform that provides
real-time predictions and detection of chronic diseases, such as lung disease, kidney disease, stroke,
and skin cancer. The website offers an easy-to-use interface and accurate analysis of medical data to
promote early detection and prevention of chronic diseases for better health outcomes.</p>
<img class="img1" src="/static/image_predc2.png" alt="Smart E-Healthcare Application">
</section>
<section>
<h2>How it Works</h2>
<p>Our application uses advanced algorithms to analyze medical data and provide accurate predictions and
detection for chronic diseases. Users can easily upload their medical data and receive real-time output.
</p>
</section>
<section>
<h2>Features</h2>
<ul>
<li>Real-time predictions and detection</li>
<li>Easy-to-use interface</li>
<li>Accurate analysis of medical data</li>
<li>Accessible from anywhere</li>
</ul>
</section>
</main>
</body>
{% endblock %}
\ No newline at end of file
project/templates/base.html 0 → 100644
View file @ 3ec0eded
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>My Home Page</title>
<!-- Bootstrap CSS -->
<link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" />
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css" />
<script src="https://code.jquery.com/jquery-3.2.1.slim.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.12.9/umd/popper.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/js/bootstrap.min.js"></script>
</head>
<body>
<header>
<nav class="navbar navbar-expand-lg navbar-light bg-light">
<a class="navbar-brand" href="#"><img src="/static/logo.png" alt="Description of your image" /></a>
<button class="navbar-toggler" type="button" data-toggle="collapse" data-target="#navbarNav"
aria-controls="navbarNav" aria-expanded="false" aria-label="Toggle navigation"><span
class="navbar-toggler-icon"></span></button>
<div class="collapse navbar-collapse" id="navbarNav">
<ul class="navbar-nav">
<li class="nav-item">
<a class="nav-link" href="{{ url_for('home') }}">Home</a>
</li>
<li class="nav-item">
<a class="nav-link" href="{{ url_for('about') }}">About</a>
</li>
<li class="nav-item">
<div class="dropdown">
<a class="nav-link" href="#" role="button" id="dropdownMenuLink" data-toggle="dropdown"
aria-haspopup="true" aria-expanded="false">Services</a>
<div class="dropdown-menu" aria-labelledby="dropdownMenuLink">
<a class="dropdown-item" href="{{ url_for('kidney') }}">Kidney Disease Detection</a>
<a class="dropdown-item" href="{{ url_for('lungcancer') }}">Lung Disease Detection</a>
<a class="dropdown-item" href="{{ url_for('stroke') }}">Stroke prediction</a>
<a class="dropdown-item" href="{{ url_for('skinCancerPage') }}">Skin Cancer Detection</a>
</div>
</div>
</li>
</ul>
</div>
</nav>
</header>
</body>
</html>
{% block main %}
{% endblock %}
\ No newline at end of file
project/templates/home.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<head>
<title>Images with Buttons</title>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link rel="stylesheet" href="/static/style.css">
<!-- Bootstrap CSS -->
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" />
<style>
body {
background-image: linear-gradient(to right, #cad3d5, #90bcd4);
}
.img2 {
max-width: 100%;
height: 200px;
/* adjust the height to your desired size */
object-fit: cover;
}
.btn {
margin-top: 10px;
}
</style>
</head>
<body>
<div class="container" style="padding-top: 2%;">
<div class="row">
<div class="col-md-6 text-center" style="padding-bottom: 1%;">
<img class="img2" src="/static/kidney.jpg" alt="Image 1" /><br />
<a href="{{ url_for('kidney') }}"> <button class="btn btn-primary">Kidney Disease</button></a>
</div>
<div class="col-md-6 text-center" style="padding-bottom: 1%;">
<img class="img2" src="/static/lung.jpg" alt="Image 2"><br />
<a href="{{ url_for('lungcancer') }}"> <button class="btn btn-primary">Lung
Disease</button></a>
</div>
</div>
<div class="row">
<div class="col-md-6 text-center">
<img class="img2" src="/static/stroke.jpg" alt="Image 3"><br />
<a href="{{ url_for('stroke') }}"><button class="btn btn-primary">Stroke</button></a>
</div>
<div class="col-md-6 text-center">
<img class="img2" src="/static/skin.jpg" alt="Image 4"><br />
<a href="{{ url_for('skinCancerPage') }}"><button class="btn btn-primary">Skin Cancer</button></a>
</div>
</div>
</div>
<!-- Bootstrap JS -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.16.0/umd/popper.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/4.5.2/js/bootstrap.min.js"></script>
</body>
{% endblock %}
\ No newline at end of file
project/templates/kidney.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div class="container">
<div class="row">
<div class="col-md-4" style="margin-top: 5%;margin-left: 10%;">
<div class="card">
<img src="/static/text.png" class="card-img-top" alt="..." />
<div class="card-body" style="padding-top: 30%;">
<a href="{{ url_for('predictKidneypage') }}" class="btn btn-primary">Chronic Kidney Disease
Diagnosis</a>
<p class="card-text">Our web-based diagnostic tool enables easy and accurate diagnosis of chronic
kidney disease, empowering patients to take control of their health.</p>
</div>
</div>
</div>
<div class="col-md-4" style="margin-top: 5%;margin-left: 10%;">
<div class="card" style="padding-bottom: 8%;">
<img src="/static/ct.png" class="card-img-top" alt="..." />
<div class="card-body">
<a href="{{ url_for('kidneyDisease') }}" class="btn btn-primary">Detect Chronic Kidney
Disease</a>
<p class="card-text">This website can easily diagnose Chronic Kidney Disease using CT images,
providing a quick and accurate diagnosis for patients.</p>
</div>
</div>
</div>
</div>
</div>
{% endblock %}
\ No newline at end of file
project/templates/kidney_detection.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div style="margin-left: 5%;margin-top: 2%;margin-right: 5%;">
<h2>Detect Kidney Disease</h2>
<div class="alert alert-primary" role="alert">Please input image</div>
</div>
<form action="{{ url_for('process_image_kidney') }}" method="post" enctype="multipart/form-data" style="margin-left: 5%;margin-right: 40%;margin-bottom: 5%;">
<div class="form-group">
<input type="file" name="file" class="form-control" />
<input type="submit" class="btn btn-primary" value="Upload" style="margin-top:2%" />
</div>
</form>
{% endblock %}
project/templates/kidney_prediction.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div style="margin-left: 5%;margin-top: 2%;margin-right: 5%;">
<h2>Predict Kidney Disease </h2>
<div class="alert alert-primary" role="alert">
Please enter your symptoms
</div>
</div>
<form method="post" action="{{ url_for('predictKidney') }}" style="margin-left: 5%;margin-right: 5%;margin-bottom: 5%;">
<div class="form-group">
<label>Blood Pressure(80 mm Hg - 120 mm Hg)</label>
<input type="number" step="0.01" name="bp" class="form-control" />
</div>
<div class="form-group">
<label>Specific Gravity(1.005 - 1.030)</label>
<input type="number" step="0.01" name="sg" class="form-control" />
</div>
<div class="form-group">
<label for="rbc">Red Blood Cells</label>
<select class="form-control" id="rbc" name="rbc">
<option value="0">Normal</option>
<option value="1">Abnormal</option>
</select>
</div>
<div class="form-group">
<label>Blood Urea(1.5 mmol - 300 mmol)</label>
<input type="number" step="0.01" name="bu" class="form-control" />
</div>
<div class="form-group">
<label>Serum Creatinine(1 mg/dl - 10 mg/dl)</label>
<input type="number" step="0.01" name="sc" class="form-control" />
</div>
<div class="form-group">
<label>Sodium(99 mEq/L - 160 mEq/L)</label>
<input type="number" step="0.01" name="sod" class="form-control" />
</div>
<div class="form-group">
<label>Pottasium(3.0 mEq/L - 6.0 mEq/L)</label>
<input type="number" step="0.01" name="pot" class="form-control" />
</div>
<div class="form-group">
<label>Hemoglobin(11.0 g/dL - 18.0 g/dL)</label>
<input type="number" step="0.01" name="hemo" class="form-control" />
</div>
<div class="form-group">
<label>White Blood Cell Count(4000 - 12 000)</label>
<input type="number" step="0.01" name="wbcc" class="form-control" />
</div>
<div class="form-group">
<label>Red Blood Cell Count(2.0 cells/mcL - 5.0 cells/mcL)</label>
<input type="number" step="0.01" name="rbcc" class="form-control" />
</div>
<div class="form-group">
<label for="htn">Hypertension (1 / 0)</label>
<select class="form-control" id="htn" name="htn">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<button type="submit" class="btn btn-primary">Submit</button>
</form>
{% endblock %}
\ No newline at end of file
project/templates/lungCancer.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div class="container">
<div class="row">
<div class="col-md-4" style="margin-top: 5%;margin-left: 10%;">
<div class="card">
<img src="/static/text.png" class="card-img-top" alt="..." />
<div class="card-body" style="padding-top: 30%;">
<a href="{{ url_for('lungpradict') }}" class="btn btn-primary">Predict Lung Disease</a>
<p class="card-text">Get a lung cancer prediction test easily on this website, with just a few
clicks and without leaving your home.</p>
</div>
</div>
</div>
<div class="col-md-4" style="margin-top: 5%;margin-left: 10%;">
<div class="card" style="padding-bottom: 8%;">
<img src="/static/ct.png" class="card-img-top" alt="..." />
<div class="card-body">
<a href="{{ url_for('lungxray') }}" class="btn btn-primary">Detect Lung Disease Using X-ray</a>
<p class="card-text">CT imaging can generate high-resolution X-ray images that can be used to
identify lung diseases. You can visit our website for convenient testing and precise results. We
strongly advise you not to postpone your health.</p>
</div>
</div>
</div>
</div>
</div>
{% endblock %}
\ No newline at end of file
project/templates/lungXray.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div style="margin-left: 5%;margin-top: 2%;margin-right: 5%;">
<h2>Detect X-ray Lung Diseases</h2>
<div class="alert alert-primary" role="alert">Please input image</div>
</div>
<form action="{{ url_for('process_image_lung') }}" method="post" enctype="multipart/form-data"
style="margin-left: 5%;margin-right: 40%;margin-bottom: 5%;">
<div class="form-group">
<input type="file" name="file" class="form-control" />
<input type="submit" class="btn btn-primary" value="Upload" style="margin-top:2%" />
</div>
</form>
{% endblock %}
\ No newline at end of file
project/templates/lung_cancer_detection.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div style="margin-left: 5%;margin-top: 2%;margin-right: 5%;">
<h2>Lung Disease Prediction</h2>
<div class="alert alert-primary" role="alert">
Please enter your symptoms
</div>
</div>
<form method="post" action="{{ url_for('predictLungCancer') }}"
style="margin-left: 5%;margin-right: 5%;margin-bottom: 5%;">
<div class="form-group">
<label for="GENDER">Select gender</label>
<select class="form-control" id="GENDER" name="GENDER">
<option value="0">Male</option>
<option value="1">Female</option>
</select>
</div>
<div class="form-group">
<label for="AGE">Select age</label>
<select class="form-control" id="AGE" name="AGE">
{% for i in range(1, 101) %}
<option value="{{ i }}">{{ i }}</option>
{% endfor %}
</select>
</div>
<div class="form-group">
<label for="SMOKING">Smoking status</label>
<select class="form-control" id="SMOKING" name="SMOKING">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="YELLOW_FINGERS">Is fingers yellow?</label>
<select class="form-control" id="YELLOW_FINGERS" name="YELLOW_FINGERS">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="ANXIETY">Anxiety status</label>
<select class="form-control" id="ANXIETY" name="ANXIETY">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="PEER_PRESSURE">Pressure from outside?</label>
<select class="form-control" id="PEER_PRESSURE" name="PEER_PRESSURE">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="CHRONICDISEASE">Suffer from chronic disease?</label>
<select class="form-control" id="CHRONICDISEASE" name="CHRONICDISEASE">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="FATIGUE">Are you fatigue?</label>
<select class="form-control" id="FATIGUE" name="FATIGUE">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="ALLERGY">Do you have allergy?</label>
<select class="form-control" id="ALLERGY" name="ALLERGY">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="WHEEZING">Do you have wheeze?</label>
<select class="form-control" id="WHEEZING" name="WHEEZING">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="ALCOHOL">Do you use alcohol?</label>
<select class="form-control" id="ALCOHOL" name="ALCOHOL">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="COUGHING">Do you have cough?</label>
<select class="form-control" id="COUGHING" name="COUGHING">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="SHORTBREATH">Are you having short breath?</label>
<select class="form-control" id="SHORTBREATH" name="SHORTBREATH">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="SWALLOW">Any swallow difficulty?</label>
<select class="form-control" id="SWALLOW" name="SWALLOW">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label for="CHESTPAIN">Any chest pain?</label>
<select class="form-control" id="CHESTPAIN" name="CHESTPAIN">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<button type="submit" class="btn btn-primary">Submit</button>
</form>
{% endblock %}
\ No newline at end of file
project/templates/lung_cancer_level_detection.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div style="margin-left: 5%;margin-top: 2%;margin-right: 5%;">
<h2>Calculating The Risk Of Lung Disease </h2>
<div class="alert alert-primary" role="alert">
Please enter your symptoms
</div>
</div>
<form method="post" action="{{ url_for('predictLungCancerLevel') }}"
style="margin-left: 5%;margin-right: 5%;margin-bottom: 5%;">
<div class="form-group">
<label for="GENDER">Select gender</label>
<select class="form-control" id="GENDER" name="GENDER">
<option value="0">Male</option>
<option value="1">Female</option>
</select>
</div>
<div class="form-group">
<label for="AGE">Select age</label>
<select class="form-control" id="AGE" name="AGE">
{% for i in range(1, 101) %}
<option value="{{ i }}">{{ i }}</option>
{% endfor %}
</select>
</div>
<div class="form-group">
<label for="SMOKING">Smoking status</label>
<select class="form-control" id="SMOKING" name="SMOKING">
<option value="1">Minimal dependence</option>
<option value="2">Mild dependence</option>
<option value="3">Moderate dependence</option>
<option value="4">Moderate to high dependence</option>
<option value="5">High dependence</option>
<option value="6">Very high dependence</option>
<option value="7">Severe dependence</option>
<option value="8">Extreme dependence</option>
</select>
</div>
<div class="form-group">
<label for="FATIGUE">Are you fatigue?</label>
<select class="form-control" id="FATIGUE" name="FATIGUE">
<option value="1">Feeling alert and well-rested</option>
<option value="2">Feeling a little tired, but able to function normally</option>
<option value="3">Feeling somewhat tired, with a noticeable decrease in energy levels</option>
<option value="4">Feeling quite tired, with difficulty focusing and a strong desire to rest</option>
<option value="5">Feeling very tired, with significant difficulty concentrating and a strong urge to sleep
</option>
<option value="6">Feeling extremely tired, with severe difficulty staying awake and a strong need to rest
immediately</option>
<option value="7">Feeling completely exhausted, with a complete inability to stay awake and a strong need to
sleep immediately</option>
</select>
</div>
<div class="form-group">
<label for="ALLERGY">Do you have allergy?</label>
<select class="form-control" id="ALLERGY" name="ALLERGY">
<option value="1">Mild</option>
<option value="2">Mild to Moderate</option>
<option value="3">Moderate</option>
<option value="4">Moderate to to Severe</option>
<option value="5">Severe</option>
<option value="6">Very Severe</option>
<option value="7">Dangerous</option>
<option value="8">Fatal</option>
</select>
</div>
<div class="form-group">
<label for="WHEEZING">Do you have wheeze?</label>
<select class="form-control" id="WHEEZING" name="WHEEZING">
<option value="1">Occasional wheezing, heard only with a stethoscope</option>
<option value="2">Wheezing is audible without a stethoscope, but only during expiration (breathing out)
</option>
<option value="3"> Wheezing is audible both during inspiration (breathing in) and expiration (breathing
out), but still relatively mild </option>
<option value="4">Wheezing is louder and more intense, and may be accompanied by a feeling of tightness in
the chest</option>
<option value="5">Wheezing is even louder and more intense, and breathing may be more difficult </option>
<option value="6"> Wheezing is extremely loud and may be heard from a distance, breathing is difficult and
may require medical intervention </option>
<option value="7">Wheezing is very severe, and the person may be in respiratory distress</option>
<option value="8">Complete respiratory failure, requiring immediate medical attention</option>
</select>
</div>
<div class="form-group">
<label for="ALCOHOL">Do you use alcohol?</label>
<select class="form-control" id="ALCOHOL" name="ALCOHOL">
<option value="1">No alcohol intake</option>
<option value="2">Light alcohol intake</option>
<option value="3">Moderate alcohol intake</option>
<option value="4">Heavy alcohol intake</option>
<option value="5">Binge drinking</option>
<option value="6">Alcohol abuse</option>
<option value="7">Alcohol dependence</option>
<option value="8">Alcohol withdrawal</option>
</select>
</div>
<div class="form-group">
<label for="COUGHING">Do you have cough?</label>
<select class="form-control" id="COUGHING" name="COUGHING">
<option value="1">No coughing</option>
<option value="2">Mild coughing</option>
<option value="3">Mild to moderate coughing</option>
<option value="4">Moderate coughing</option>
<option value="5">Moderate to severe coughing</option>
<option value="6">Severe coughing</option>
<option value="7">Very severe coughing</option>
</select>
</div>
<div class="form-group">
<label for="SHORTBREATH">Are you having short breath?</label>
<select class="form-control" id="SHORTBREATH" name="SHORTBREATH">
<option value="1">Mild</option>
<option value="2">Mild to moderate</option>
<option value="3">Moderate</option>
<option value="4">Moderate to severe</option>
<option value="5">Severe</option>
<option value="6">Very Severe</option>
<option value="7">Panic</option>
<option value="8">Respiratory arrest</option>
<option value="9">Cardiac arrest</option>
</select>
</div>
<div class="form-group">
<label for="SWALLOW">Any swallow difficulty?</label>
<select class="form-control" id="SWALLOW" name="SWALLOW">
<option value="1">Normal swallow function</option>
<option value="2">Mild difficulty with swallowing, but able to eat a regular diet</option>
<option value="3">Moderate difficulty with swallowing, requiring modifications to the diet</option>
<option value="4">Severe difficulty with swallowing, requiring a soft or pureed diet and thickened liquids
</option>
<option value="5">Unable to swallow solid foods, requiring a pureed diet and thickened liquids</option>
<option value="6">Unable to swallow, requiring non-oral feeding such as a feeding tube</option>
<option value="7">Non-oral feeding is not possible, and the person is at risk of aspiration</option>
<option value="8">Complete dysphagia, with no oral feeding possible</option>
</select>
</div>
<div class="form-group">
<label for="CHESTPAIN">Any chest pain?</label>
<select class="form-control" id="CHESTPAIN" name="CHESTPAIN">
<option value="1">Chest pain is barely noticeable or not present at all</option>
<option value="2">Mild chest pain</option>
<option value="3">Mild to moderate chest pain</option>
<option value="4">Moderate chest pain</option>
<option value="5">Moderate to severe chest pain</option>
<option value="6">Severe chest pain</option>
<option value="7">Very severe chest pain</option>
<option value="8">Intense chest pain</option>
<option value="9">Excruciating chest pain</option>
</select>
</div>
<div class="form-group">
<label for="GENETICRISK">Does anyone in your household have this disease?</label>
<select class="form-control" id="GENETICRISK" name="GENETICRISK">
<option value="1">No family history of lung cancer, and no known genetic mutations associated with lung
cancer</option>
<option value="2">No family history of lung cancer, but has one or more environmental risk factors (e.g.
smoking, exposure to radon, air pollution)</option>
<option value="3">Has a family history of lung cancer, but no known genetic mutations associated with the
disease</option>
<option value="4">Has a family history of lung cancer and a known genetic mutation associated with the
disease, but does not smoke</option>
<option value="5">Has a family history of lung cancer and a known genetic mutation associated with the
disease, and smokes</option>
<option value="6">Has multiple family members with lung cancer and/or a strong family history of other
smoking-related cancers (e.g. bladder, throat, or esophageal cancer), and may have a genetic
predisposition to smoking addiction</option>
<option value="7">Has a known genetic syndrome associated with lung cancer, such as Li-Fraumeni syndrome or
BRCA2 mutation</option>
</select>
</div>
<div class="form-group">
<label for="COUGHINGBLOOD">Coughing up blood?</label>
<select class="form-control" id="COUGHINGBLOOD" name="COUGHINGBLOOD">
<option value="1">Minimal or trace amounts of blood in sputum</option>
<option value="2">Blood streaks in sputum</option>
<option value="3">Small blood clots in sputum</option>
<option value="4">Moderate amount of blood in sputum</option>
<option value="5">Large blood clots in sputum</option>
<option value="6">Profuse bleeding with constant coughing of blood</option>
<option value="7">Blood pouring from the mouth in a steady stream</option>
<option value="8">Blood vomiting</option>
<option value="8">Massive bleeding leading to death</option>
</select>
</div>
<button type="submit" class="btn btn-primary">Submit</button>
</form>
{% endblock %}
\ No newline at end of file
project/templates/results.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div class="card" style="width: 25rem;margin-left: 35%;margin-top: 5%;">
<img class="card-img-top" src="/static/results.png" alt="Card image cap" />
<div class="card-body">
{% if textshow %}
<h3 class="card-title">Results</h3>
<p class="card-text">{{ result }}<sup>2</sup></p>
<a href="{{ url_for('home') }}" class="btn btn-primary" style="text-align:right;">Home</a>
{% if show_button %}
<a href="{{ url_for('lungcancerlevel') }}" class="btn btn-primary">Predict Lung Disease Risk</a>
{% endif %}>
{%else%}
<h3 class="card-title">Results</h3>
<p class="card-text">{{ result }}</p>
<a href="{{ url_for('home') }}" class="btn btn-primary" style="text-align:right;">Home</a>
{% if show_button %}
<a href="{{ url_for('lungcancerlevel') }}" class="btn btn-primary">Predict Lung Disease Risk</a>
{% endif %}
{% endif %}
</div>
</div>
<div class="card" style="width: 90%;margin-top: 5%;margin-left: 5%;">
<div class="card-body">
<h3 class="card-title">Recommoned Doctors</h3>
{% if rec %}
{{ rec|safe }}
{% else %}
<p>No results found.</p>
{% endif %}
<a href="{{ url_for('home') }}" class="btn btn-primary" style="text-align:right;">Home</a>
</div>
</div>
{% endblock %}
\ No newline at end of file
project/templates/skinCancerPage.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div class="container">
<div class="row">
<div class="col-md-4" style="margin-top: 5%;margin-left: 10%;">
<div class="card">
<img src="/static/text.png" class="card-img-top" alt="..." />
<div class="card-body" style="padding-top: 30%;">
<a href="{{ url_for('skincancer') }}" class="btn btn-primary">Detect Skin Diseases
Diagnosis</a>
<p class="card-text">Our website provides accurate detection of various skin diseases. Get quick
results and take the necessary steps towards treatment.</p>
</div>
</div>
</div>
<div class="col-md-4" style="margin-top: 5%;margin-left: 10%;">
<div class="card" style="padding-bottom: 8%;">
<img src="/static/ct.png" class="card-img-top" alt="..." />
<div class="card-body">
<a href="{{ url_for('skinTumor') }}" class="btn btn-primary">Melanoma Tumor Size Prediction
Disease</a>
<p class="card-text">Our website offers Melanoma Tumor Size Prediction with accuracy. Get quick
results and early detection in just a few clicks.</p>
</div>
</div>
</div>
</div>
</div>
{% endblock %}
\ No newline at end of file
project/templates/skinTumor.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div style="margin-left: 5%;margin-top: 2%;margin-right: 5%;">
<h2>Melanoma Tumor Size Prediction</h2>
<div class="alert alert-primary" role="alert">
Please enter tumor shape
</div>
</div>
<form method="post" action="{{ url_for('tumor') }}" style="margin-left: 5%;margin-right: 5%;margin-bottom: 5%;">
<div class="form-group">
<label>The mass of the area understudy for melanoma tumor (mg)</label>
<input type="number" step="0.01" name="mass_npea" class="form-control" />
</div>
<div class="form-group">
<label>The size of the area understudy for melanoma tumor (mm)<sup>2</sup></label>
<input type="number" step="0.01" name="size_npear" class="form-control" />
</div>
<div class="form-group">
<label>Ration of normal to malign surface understudy (mm)<sup>2</sup></label>
<input type="number" step="0.01" name="malign_ratio" class="form-control" />
</div>
<div class="form-group">
<label>Unrecoverable area of skin damaged by the tumor (mm)<sup>2</sup></label>
<input type="number" step="0.01" name="damage_size" class="form-control" />
</div>
<div class="form-group">
<label>Total area exposed to the tumor (mm)<sup>2</sup></label>
<input type="number" step="0.01" name="exposed_area" class="form-control" />
</div>
<div class="form-group">
<label>Sandard deviation of malign skin measurements (mm)<sup>2</sup></label>
<input type="number" step="0.01" name="std_dev_malign" class="form-control" />
</div>
<div class="form-group">
<label>Error in malign skin measurements (mm)<sup>2</sup></label>
<input type="number" step="0.01" name="err_malign" class="form-control" />
</div>
<div class="form-group">
<label>Penalty applied due to measurement error in the lab (mm)<sup>2</sup></label>
<input type="number" step="0.01" name="malign_penalty" class="form-control" />
</div>
<div class="form-group">
<label>The ratio of damage to total spread on the skin</label>
<input type="number" step="0.01" name="damage_ratio" class="form-control" />
</div>
<button type="submit" class="btn btn-primary">Submit</button>
</form>
{% endblock %}
\ No newline at end of file
project/templates/skincancer.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div style="margin-left: 5%;margin-top: 2%;margin-right: 5%;">
<h2>Detect Skin Cancer</h2>
<div class="alert alert-primary" role="alert">Please input image</div>
</div>
<form action="{{ url_for('process_image_skin') }}" method="post" enctype="multipart/form-data"
style="margin-left: 5%;margin-right: 40%;margin-bottom: 5%;">
<div class="form-group">
<input type="file" name="file" class="form-control" />
<input type="submit" class="btn btn-primary" value="Upload" style="margin-top:2%" />
</div>
</form>
{% endblock %}
\ No newline at end of file
project/templates/strokePage.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div class="container">
<div class="row">
<div class="col-md-4" style="margin-top: 5%;margin-left: 10%;">
<div class="card">
<img src="/static/text.png" class="card-img-top" alt="..." />
<div class="card-body" style="padding-top: 30%;">
<a href="{{ url_for('predictStrokepage') }}" class="btn btn-primary">Predict Stroke
Diagnosis</a>
<p class="card-text">Our website provides predictive models for stroke risk assessment based on
various risk factors. These models can help individuals and healthcare providers make informed
decisions regarding stroke prevention and management.</p>
</div>
</div>
</div>
<div class="col-md-4" style="margin-top: 5%;margin-left: 10%;">
<div class="card" style="padding-bottom: 8%;">
<img src="/static/ct.png" class="card-img-top" alt="..." />
<div class="card-body">
<a href="{{ url_for('predictStrokeCt') }}" class="btn btn-primary">Brain Stroke Detection</a>
<p class="card-text">Our website offers Brain Stroke Detection using advanced AI algorithms and
state-of-the-art medical imaging technology for accurate diagnosis and timely treatment of
strokes.</p>
</div>
</div>
</div>
</div>
</div>
{% endblock %}
\ No newline at end of file
project/templates/stroke_detection_CT.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div style="margin-left: 5%;margin-top: 2%;margin-right: 5%;">
<h2>Detect Stroke</h2>
<div class="alert alert-primary" role="alert">Please input image</div>
</div>
<form action="{{ url_for('process_image_stroke') }}" method="post" enctype="multipart/form-data"
style="margin-left: 5%;margin-right: 40%;margin-bottom: 5%;">
<div class="form-group">
<input type="file" name="file" class="form-control" />
<input type="submit" class="btn btn-primary" value="Upload" style="margin-top:2%" />
</div>
</form>
{% endblock %}
\ No newline at end of file
project/templates/stroke_predict.html 0 → 100644
View file @ 3ec0eded
{% extends 'base.html' %}
{% block main %}
<div style="margin-left: 5%;margin-top: 2%;margin-right: 5%;">
<h2>Predict Stroke </h2>
</div>
<form method="post" action="{{ url_for('predictStroke') }}" style="margin-left: 5%;margin-right: 5%;margin-bottom: 5%;">
<div class="form-group">
<label>Gender</label>
<select class="form-control" id="gender" name="gender">
<option value="0">Male</option>
<option value="1">Female</option>
<option value="2">Other</option>
</select>
</div>
<div class="form-group">
<label>Age</label>
<select class="form-control" id="age" name="age">
{% for i in range(1, 101) %}
<option value="{{ i }}">{{ i }}</option>
{% endfor %}
</select>
</div>
<div class="form-group">
<label>Hypertension</label>
<select class="form-control" id="hypertension" name="hypertension">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label>Previous Heart Disease?</label>
<select class="form-control" id="heart_disease" name="heart_disease">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label>Marital Status</label>
<select class="form-control" id="ever_married" name="ever_married">
<option value="0">No</option>
<option value="1">Yes</option>
</select>
</div>
<div class="form-group">
<label>What is you work type?</label>
<select class="form-control" id="work_type" name="work_type">
<option value="0">Self-employed</option>
<option value="1">Private</option>
<option value="2">Government </option>
<option value="3">Child</option>
<option value="4">Not employed</option>
</select>
</div>
<div class="form-group">
<label>What is your recidance type?</label>
<select class="form-control" id="Residence_type" name="Residence_type">
<option value="0">Rural</option>
<option value="1">Urban</option>
</select>
</div>
<div class="form-group">
<label>Avarage lucose level</label>
<input type="number" step="0.01" name="avg_glucose_level" class="form-control" />
</div>
<div class="form-group">
<label>BMI value</label>
<input type="number" step="0.01" name="bmi" class="form-control" />
</div>
<div class="form-group">
<label>Do you smoke?</label>
<select class="form-control" id="smoking_status" name="smoking_status">
<option value="0">Smokes</option>
<option value="1">Never smoked</option>
<option value="2">Formely smoked</option>
<option value="3">Unknown</option>
</select>
</div>
<button type="submit" class="btn btn-primary">Submit</button>
</form>
{% endblock %}
\ No newline at end of file
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