Upload 3 files

kidney.csv

@@ -0,0 +1,401 @@
+id,age,bp,sg,al,su,rbc,pc,pcc,ba,bgr,bu,sc,sod,pot,hemo,pcv,wc,rc,htn,dm,cad,appet,pe,ane,classification
+0,48.0,80.0,1.02,1.0,0.0,,normal,notpresent,notpresent,121.0,36.0,1.2,,,15.4,44,7800,5.2,yes,yes,no,good,no,no,ckd
+1,7.0,50.0,1.02,4.0,0.0,,normal,notpresent,notpresent,,18.0,0.8,,,11.3,38,6000,,no,no,no,good,no,no,ckd
+2,62.0,80.0,1.01,2.0,3.0,normal,normal,notpresent,notpresent,423.0,53.0,1.8,,,9.6,31,7500,,no,yes,no,poor,no,yes,ckd
+3,48.0,70.0,1.005,4.0,0.0,normal,abnormal,present,notpresent,117.0,56.0,3.8,111.0,2.5,11.2,32,6700,3.9,yes,no,no,poor,yes,yes,ckd
+4,51.0,80.0,1.01,2.0,0.0,normal,normal,notpresent,notpresent,106.0,26.0,1.4,,,11.6,35,7300,4.6,no,no,no,good,no,no,ckd
+5,60.0,90.0,1.015,3.0,0.0,,,notpresent,notpresent,74.0,25.0,1.1,142.0,3.2,12.2,39,7800,4.4,yes,yes,no,good,yes,no,ckd
+6,68.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,100.0,54.0,24.0,104.0,4.0,12.4,36,,,no,no,no,good,no,no,ckd
+7,24.0,,1.015,2.0,4.0,normal,abnormal,notpresent,notpresent,410.0,31.0,1.1,,,12.4,44,6900,5,no,yes,no,good,yes,no,ckd
+8,52.0,100.0,1.015,3.0,0.0,normal,abnormal,present,notpresent,138.0,60.0,1.9,,,10.8,33,9600,4.0,yes,yes,no,good,no,yes,ckd
+9,53.0,90.0,1.02,2.0,0.0,abnormal,abnormal,present,notpresent,70.0,107.0,7.2,114.0,3.7,9.5,29,12100,3.7,yes,yes,no,poor,no,yes,ckd
+10,50.0,60.0,1.01,2.0,4.0,,abnormal,present,notpresent,490.0,55.0,4.0,,,9.4,28,,,yes,yes,no,good,no,yes,ckd
+11,63.0,70.0,1.01,3.0,0.0,abnormal,abnormal,present,notpresent,380.0,60.0,2.7,131.0,4.2,10.8,32,4500,3.8,yes,yes,no,poor,yes,no,ckd
+12,68.0,70.0,1.015,3.0,1.0,,normal,present,notpresent,208.0,72.0,2.1,138.0,5.8,9.7,28,12200,3.4,yes,yes,yes,poor,yes,no,ckd
+13,68.0,70.0,,,,,,notpresent,notpresent,98.0,86.0,4.6,135.0,3.4,9.8,,,,yes,yes,yes,poor,yes,no,ckd
+14,68.0,80.0,1.01,3.0,2.0,normal,abnormal,present,present,157.0,90.0,4.1,130.0,6.4,5.6,16,11000,2.6,yes,yes,yes,poor,yes,no,ckd
+15,40.0,80.0,1.015,3.0,0.0,,normal,notpresent,notpresent,76.0,162.0,9.6,141.0,4.9,7.6,24,3800,2.8,yes,no,no,good,no,yes,ckd
+16,47.0,70.0,1.015,2.0,0.0,,normal,notpresent,notpresent,99.0,46.0,2.2,138.0,4.1,12.6,,,,no,no,no,good,no,no,ckd
+17,47.0,80.0,,,,,,notpresent,notpresent,114.0,87.0,5.2,139.0,3.7,12.1,,,,yes,no,no,poor,no,no,ckd
+18,60.0,100.0,1.025,0.0,3.0,,normal,notpresent,notpresent,263.0,27.0,1.3,135.0,4.3,12.7,37,11400,4.3,yes,yes,yes,good,no,no,ckd
+19,62.0,60.0,1.015,1.0,0.0,,abnormal,present,notpresent,100.0,31.0,1.6,,,10.3,30,5300,3.7,yes,no,yes,good,no,no,ckd
+20,61.0,80.0,1.015,2.0,0.0,abnormal,abnormal,notpresent,notpresent,173.0,148.0,3.9,135.0,5.2,7.7,24,9200,3.2,yes,yes,yes,poor,yes,yes,ckd
+21,60.0,90.0,,,,,,notpresent,notpresent,,180.0,76.0,4.5,,10.9,32,6200,3.6,yes,yes,yes,good,no,no,ckd
+22,48.0,80.0,1.025,4.0,0.0,normal,abnormal,notpresent,notpresent,95.0,163.0,7.7,136.0,3.8,9.8,32,6900,3.4,yes,no,no,good,no,yes,ckd
+23,21.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,,,,,,,,,,no,no,no,poor,no,yes,ckd
+24,42.0,100.0,1.015,4.0,0.0,normal,abnormal,notpresent,present,,50.0,1.4,129.0,4.0,11.1,39,8300,4.6,yes,no,no,poor,no,no,ckd
+25,61.0,60.0,1.025,0.0,0.0,,normal,notpresent,notpresent,108.0,75.0,1.9,141.0,5.2,9.9,29,8400,3.7,yes,yes,no,good,no,yes,ckd
+26,75.0,80.0,1.015,0.0,0.0,,normal,notpresent,notpresent,156.0,45.0,2.4,140.0,3.4,11.6,35,10300,4,yes,yes,no,poor,no,no,ckd
+27,69.0,70.0,1.01,3.0,4.0,normal,abnormal,notpresent,notpresent,264.0,87.0,2.7,130.0,4.0,12.5,37,9600,4.1,yes,yes,yes,good,yes,no,ckd
+28,75.0,70.0,,1.0,3.0,,,notpresent,notpresent,123.0,31.0,1.4,,,,,,,no,yes,no,good,no,no,ckd
+29,68.0,70.0,1.005,1.0,0.0,abnormal,abnormal,present,notpresent,,28.0,1.4,,,12.9,38,,,no,no,yes,good,no,no,ckd
+30,,70.0,,,,,,notpresent,notpresent,93.0,155.0,7.3,132.0,4.9,,,,,yes, yes,no,good,no,no,ckd
+31,73.0,90.0,1.015,3.0,0.0,,abnormal,present,notpresent,107.0,33.0,1.5,141.0,4.6,10.1,30,7800,4,no,no,no,poor,no,no,ckd
+32,61.0,90.0,1.01,1.0,1.0,,normal,notpresent,notpresent,159.0,39.0,1.5,133.0,4.9,11.3,34,9600,4.0,yes,yes,no,poor,no,no,ckd
+33,60.0,100.0,1.02,2.0,0.0,abnormal,abnormal,notpresent,notpresent,140.0,55.0,2.5,,,10.1,29,,,yes,no,no,poor,no,no,ckd
+34,70.0,70.0,1.01,1.0,0.0,normal,,present,present,171.0,153.0,5.2,,,,,,,no,yes,no,poor,no,no,ckd
+35,65.0,90.0,1.02,2.0,1.0,abnormal,normal,notpresent,notpresent,270.0,39.0,2.0,,,12.0,36,9800,4.9,yes,yes,no,poor,no,yes,ckd
+36,76.0,70.0,1.015,1.0,0.0,normal,normal,notpresent,notpresent,92.0,29.0,1.8,133.0,3.9,10.3,32,,,yes,no,no,good,no,no,ckd
+37,72.0,80.0,,,,,,notpresent,notpresent,137.0,65.0,3.4,141.0,4.7,9.7,28,6900,2.5,yes,yes,no,poor,no,yes,ckd	
+38,69.0,80.0,1.02,3.0,0.0,abnormal,normal,notpresent,notpresent,,103.0,4.1,132.0,5.9,12.5,,,,yes,no,no,good,no,no,ckd
+39,82.0,80.0,1.01,2.0,2.0,normal,,notpresent,notpresent,140.0,70.0,3.4,136.0,4.2,13.0,40,9800,4.2,yes,yes,no,good,no,no,ckd
+40,46.0,90.0,1.01,2.0,0.0,normal,abnormal,notpresent,notpresent,99.0,80.0,2.1,,,11.1,32,9100,4.1,yes,no,	no,good,no,no,ckd
+41,45.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,,20.0,0.7,,,,,,,no,no,no,good,yes,no,ckd
+42,47.0,100.0,1.01,0.0,0.0,,normal,notpresent,notpresent,204.0,29.0,1.0,139.0,4.2,9.7,33,9200,4.5,yes,no,no,good,no,yes,ckd
+43,35.0,80.0,1.01,1.0,0.0,abnormal,,notpresent,notpresent,79.0,202.0,10.8,134.0,3.4,7.9,24,7900,3.1,no,yes,no,good,no,no,ckd
+44,54.0,80.0,1.01,3.0,0.0,abnormal,abnormal,notpresent,notpresent,207.0,77.0,6.3,134.0,4.8,9.7,28,,,yes,yes,no,poor,yes,no,ckd
+45,54.0,80.0,1.02,3.0,0.0,,abnormal,notpresent,notpresent,208.0,89.0,5.9,130.0,4.9,9.3,,,,yes,yes,no,poor,yes,no,ckd
+46,48.0,70.0,1.015,0.0,0.0,,normal,notpresent,notpresent,124.0,24.0,1.2,142.0,4.2,12.4,37,6400,4.7,no,yes,no,good,no,no,ckd
+47,11.0,80.0,1.01,3.0,0.0,,normal,notpresent,notpresent,,17.0,0.8,,,15.0,45,8600,,no,no,no,good,no,no,ckd
+48,73.0,70.0,1.005,0.0,0.0,normal,normal,notpresent,notpresent,70.0,32.0,0.9,125.0,4.0,10.0,29,18900,3.5,yes,yes,no,good,yes,no,ckd
+49,60.0,70.0,1.01,2.0,0.0,normal,abnormal,present,notpresent,144.0,72.0,3.0,,,9.7,29,21600,3.5,yes,yes,no,poor,no,yes,ckd
+50,53.0,60.0,,,,,,notpresent,notpresent,91.0,114.0,3.25,142.0,4.3,8.6,28,11000,3.8,yes,yes,no,poor,yes,yes,ckd
+51,54.0,100.0,1.015,3.0,0.0,,normal,present,notpresent,162.0,66.0,1.6,136.0,4.4,10.3,33,,,yes,yes,no,poor,yes,no,ckd
+52,53.0,90.0,1.015,0.0,0.0,,normal,notpresent,notpresent,,38.0,2.2,,,10.9,34,4300,3.7,no,no,no,poor,no,yes,ckd
+53,62.0,80.0,1.015,0.0,5.0,,,notpresent,notpresent,246.0,24.0,1.0,,,13.6,40,8500,4.7,yes,yes,no,good,no,no,ckd
+54,63.0,80.0,1.01,2.0,2.0,normal,,notpresent,notpresent,,,3.4,136.0,4.2,13.0,40,9800,4.2,yes,no,yes,good,no,no,ckd
+55,35.0,80.0,1.005,3.0,0.0,abnormal,normal,notpresent,notpresent,,,,,,9.5,28,,,no,no,no,good,yes,no,ckd
+56,76.0,70.0,1.015,3.0,4.0,normal,abnormal,present,notpresent,,164.0,9.7,131.0,4.4,10.2,30,11300,3.4,yes,yes,yes,poor,yes,no,ckd
+57,76.0,90.0,,,,,normal,notpresent,notpresent,93.0,155.0,7.3,132.0,4.9,,,,,yes,yes,yes,poor,no,no,ckd
+58,73.0,80.0,1.02,2.0,0.0,abnormal,abnormal,notpresent,notpresent,253.0,142.0,4.6,138.0,5.8,10.5,33,7200,4.3,yes,yes,yes,good,no,no,ckd
+59,59.0,100.0,,,,,,notpresent,notpresent,,96.0,6.4,,,6.6,,,,yes,yes,no,good,no,yes,ckd
+60,67.0,90.0,1.02,1.0,0.0,,abnormal,present,notpresent,141.0,66.0,3.2,138.0,6.6,,,,,yes,no,no,good,no,no,ckd
+61,67.0,80.0,1.01,1.0,3.0,normal,abnormal,notpresent,notpresent,182.0,391.0,32.0,163.0,39.0,,,,,no,no,no,good,yes,no,ckd
+62,15.0,60.0,1.02,3.0,0.0,,normal,notpresent,notpresent,86.0,15.0,0.6,138.0,4.0,11.0,33,7700,3.8,yes,yes,no,good,no,no,ckd
+63,46.0,70.0,1.015,1.0,0.0,abnormal,normal,notpresent,notpresent,150.0,111.0,6.1,131.0,3.7,7.5,27,,,no,no,no,good,no,yes,ckd
+64,55.0,80.0,1.01,0.0,0.0,,normal,notpresent,notpresent,146.0,,,,,9.8,,,,no,no,	no,good,no,no,ckd
+65,44.0,90.0,1.01,1.0,0.0,,normal,notpresent,notpresent,,20.0,1.1,,,15.0,48,,,no,	no,no,good,no,no,ckd
+66,67.0,70.0,1.02,2.0,0.0,abnormal,normal,notpresent,notpresent,150.0,55.0,1.6,131.0,4.8,,	?,,,yes,yes,no,good,yes,no,ckd
+67,45.0,80.0,1.02,3.0,0.0,normal,abnormal,notpresent,notpresent,425.0,,,,,,,,,no,no,no,poor,no,no,ckd
+68,65.0,70.0,1.01,2.0,0.0,,normal,present,notpresent,112.0,73.0,3.3,,,10.9,37,,,no,no,no,good,no,no,ckd
+69,26.0,70.0,1.015,0.0,4.0,,normal,notpresent,notpresent,250.0,20.0,1.1,,,15.6,52,6900,6.0,no,yes,no,good,no,no,ckd
+70,61.0,80.0,1.015,0.0,4.0,,normal,notpresent,notpresent,360.0,19.0,0.7,137.0,4.4,15.2,44,8300,5.2,yes,yes,no,good,no,no,ckd
+71,46.0,60.0,1.01,1.0,0.0,normal,normal,notpresent,notpresent,163.0,92.0,3.3,141.0,4.0,9.8,28,14600,3.2,yes,yes,no,good,no,no,ckd
+72,64.0,90.0,1.01,3.0,3.0,,abnormal,present,notpresent,,35.0,1.3,,,10.3,,,,yes,yes,no,good,yes,no,ckd
+73,,100.0,1.015,2.0,0.0,abnormal,abnormal,notpresent,notpresent,129.0,107.0,6.7,132.0,4.4,4.8,14,6300,,yes,no,no,good,yes,yes,ckd
+74,56.0,90.0,1.015,2.0,0.0,abnormal,abnormal,notpresent,notpresent,129.0,107.0,6.7,131.0,4.8,9.1,29,6400,3.4,yes,no,no,good,no,no,ckd
+75,5.0,,1.015,1.0,0.0,,normal,notpresent,notpresent,,16.0,0.7,138.0,3.2,8.1,,,,no,no,no,good,no,yes,ckd
+76,48.0,80.0,1.005,4.0,0.0,abnormal,abnormal,notpresent,present,133.0,139.0,8.5,132.0,5.5,10.3,36,	6200,4,no,yes,no,good,yes,no,ckd
+77,67.0,70.0,1.01,1.0,0.0,,normal,notpresent,notpresent,102.0,48.0,3.2,137.0,5.0,11.9,34,7100,3.7,yes,yes,no,good,yes,no,ckd
+78,70.0,80.0,,,,,,notpresent,notpresent,158.0,85.0,3.2,141.0,3.5,10.1,30,,,yes,no,no,good,yes,no,ckd
+79,56.0,80.0,1.01,1.0,0.0,,normal,notpresent,notpresent,165.0,55.0,1.8,,,13.5,40,11800,5.0,yes,yes,no,poor,yes,no,ckd
+80,74.0,80.0,1.01,0.0,0.0,,normal,notpresent,notpresent,132.0,98.0,2.8,133.0,5.0,10.8,31,9400,3.8,yes,yes,no,good,no,no,ckd
+81,45.0,90.0,,,,,,notpresent,notpresent,360.0,45.0,2.4,128.0,4.4,8.3,29,5500,3.7,yes,yes,no,good,no,no,ckd
+82,38.0,70.0,,,,,,notpresent,notpresent,104.0,77.0,1.9,140.0,3.9,,,,,yes,no,no,poor,yes,no,ckd
+83,48.0,70.0,1.015,1.0,0.0,normal,normal,notpresent,notpresent,127.0,19.0,1.0,134.0,3.6,,,,,yes,yes,no,good,no,no,ckd
+84,59.0,70.0,1.01,3.0,0.0,normal,abnormal,notpresent,notpresent,76.0,186.0,15.0,135.0,7.6,7.1,22,3800,2.1,yes,no,no,poor,yes,yes,ckd
+85,70.0,70.0,1.015,2.0,,,,notpresent,notpresent,,46.0,1.5,,,9.9,,,,no,yes,no,poor,yes,no,ckd
+86,56.0,80.0,,,,,,notpresent,notpresent,415.0,37.0,1.9,,,,,,,no,yes,no,good,no,no,ckd
+87,70.0,100.0,1.005,1.0,0.0,normal,abnormal,present,notpresent,169.0,47.0,2.9,,,11.1,32,5800,5,yes,yes,no,poor,no,no,ckd
+88,58.0,110.0,1.01,4.0,0.0,,normal,notpresent,notpresent,251.0,52.0,2.2,,,,,13200,4.7,yes,	yes,no,good,no,no,ckd
+89,50.0,70.0,1.02,0.0,0.0,,normal,notpresent,notpresent,109.0,32.0,1.4,139.0,4.7,,,,,no,no,no,poor,no,no,ckd
+90,63.0,100.0,1.01,2.0,2.0,normal,normal,notpresent,present,280.0,35.0,3.2,143.0,3.5,13.0,40,9800,4.2,yes,no,yes,good,no,no,ckd
+91,56.0,70.0,1.015,4.0,1.0,abnormal,normal,notpresent,notpresent,210.0,26.0,1.7,136.0,3.8,16.1,52,12500,5.6,no,no,no,good,no,no,ckd
+92,71.0,70.0,1.01,3.0,0.0,normal,abnormal,present,present,219.0,82.0,3.6,133.0,4.4,10.4,33,5600,3.6,yes,yes,yes,good,no,no,ckd
+93,73.0,100.0,1.01,3.0,2.0,abnormal,abnormal,present,notpresent,295.0,90.0,5.6,140.0,2.9,9.2,30,7000,3.2,yes,yes,yes,poor,no,no,ckd
+94,65.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,93.0,66.0,1.6,137.0,4.5,11.6,36,11900,3.9,no,yes,no,good,no,no,ckd
+95,62.0,90.0,1.015,1.0,0.0,,normal,notpresent,notpresent,94.0,25.0,1.1,131.0,3.7,,,,,yes,no,no,good,yes,yes,ckd
+96,60.0,80.0,1.01,1.0,1.0,,normal,notpresent,notpresent,172.0,32.0,2.7,,,11.2,36,,,no,yes,yes,poor,no,no,ckd
+97,65.0,60.0,1.015,1.0,0.0,,normal,notpresent,notpresent,91.0,51.0,2.2,132.0,3.8,10.0,32,9100,4.0,yes,yes,no,poor,yes,no,ckd
+98,50.0,140.0,,,,,,notpresent,notpresent,101.0,106.0,6.5,135.0,4.3,6.2,18,5800,2.3,yes,yes,no,poor,no,yes,ckd
+99,56.0,180.0,,0.0,4.0,,abnormal,notpresent,notpresent,298.0,24.0,1.2,139.0,3.9,11.2,32,10400,4.2,yes,yes,no,poor,yes,no,ckd
+100,34.0,70.0,1.015,4.0,0.0,abnormal,abnormal,notpresent,notpresent,153.0,22.0,0.9,133.0,3.8,,,,,no,no,no,good,yes,no,ckd
+101,71.0,90.0,1.015,2.0,0.0,,abnormal,present,present,88.0,80.0,4.4,139.0,5.7,11.3,33,10700,3.9,no,no,no,good,no,no,ckd
+102,17.0,60.0,1.01,0.0,0.0,,normal,notpresent,notpresent,92.0,32.0,2.1,141.0,4.2,13.9,52,7000,,no,no,no,good,no,no,ckd
+103,76.0,70.0,1.015,2.0,0.0,normal,abnormal,present,notpresent,226.0,217.0,10.2,,,10.2,36,12700,4.2,yes,no,no,poor,yes,yes,ckd
+104,55.0,90.0,,,,,,notpresent,notpresent,143.0,88.0,2.0,,,,,,,yes,yes,no,poor,yes,no,ckd
+105,65.0,80.0,1.015,0.0,0.0,,normal,notpresent,notpresent,115.0,32.0,11.5,139.0,4.0,14.1,42,6800,5.2,no,no,no,good,no,no,ckd
+106,50.0,90.0,,,,,,notpresent,notpresent,89.0,118.0,6.1,127.0,4.4,6.0,17,6500,,yes,yes,no,good,yes,yes,ckd
+107,55.0,100.0,1.015,1.0,4.0,normal,,notpresent,notpresent,297.0,53.0,2.8,139.0,4.5,11.2,34,13600,4.4,yes,yes,no,good,no,no,ckd
+108,45.0,80.0,1.015,0.0,0.0,,abnormal,notpresent,notpresent,107.0,15.0,1.0,141.0,4.2,11.8,37,10200,4.2,no,no,no,good,no,no,ckd
+109,54.0,70.0,,,,,,notpresent,notpresent,233.0,50.1,1.9,,,11.7,,,,no,yes,no,good,no,no,ckd
+110,63.0,90.0,1.015,0.0,0.0,,normal,notpresent,notpresent,123.0,19.0,2.0,142.0,3.8,11.7,34,11400,4.7,no,no,no,good,no,no,ckd
+111,65.0,80.0,1.01,3.0,3.0,,normal,notpresent,notpresent,294.0,71.0,4.4,128.0,5.4,10.0,32,9000,3.9,yes,yes,yes,good,no,no,ckd
+112,,60.0,1.015,3.0,0.0,abnormal,abnormal,notpresent,notpresent,,34.0,1.2,,,10.8,33,,,no,no,no,good,no,no,ckd
+113,61.0,90.0,1.015,0.0,2.0,,normal,notpresent,notpresent,,,,,,,,9800,,no,yes,no,poor,no,yes,ckd
+114,12.0,60.0,1.015,3.0,0.0,abnormal,abnormal,present,notpresent,,51.0,1.8,,,12.1,,10300,,no,no,no,good,no,no,ckd
+115,47.0,80.0,1.01,0.0,0.0,,abnormal,notpresent,notpresent,,28.0,0.9,,,12.4,44,5600,4.3,no,no,no,good,no,yes,ckd
+116,,70.0,1.015,4.0,0.0,abnormal,normal,notpresent,notpresent,104.0,16.0,0.5,,,,,,,no,no,no,good,yes,no,ckd
+117,,70.0,1.02,0.0,0.0,,,notpresent,notpresent,219.0,36.0,1.3,139.0,3.7,12.5,37,9800,4.4,no,no,no,good,no,no,ckd
+118,55.0,70.0,1.01,3.0,0.0,,normal,notpresent,notpresent,99.0,25.0,1.2,,,11.4,,,,no,no,no,poor,yes,no,ckd
+119,60.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,140.0,27.0,1.2,,,,,,,no,no,no,good,no,no,ckd
+120,72.0,90.0,1.025,1.0,3.0,,normal,notpresent,notpresent,323.0,40.0,2.2,137.0,5.3,12.6,,,,no,yes,yes,poor,no,no,ckd
+121,54.0,60.0,,3.0,,,,notpresent,notpresent,125.0,21.0,1.3,137.0,3.4,15.0,46,,,yes,yes,no,good,yes,no,ckd
+122,34.0,70.0,,,,,,notpresent,notpresent,,219.0,12.2,130.0,3.8,6.0,,,,yes,no,no,good,no,yes,ckd
+123,43.0,80.0,1.015,2.0,3.0,,abnormal,present,present,,30.0,1.1,,,14.0,42,14900,,no,no,no,good,no,no,ckd
+124,65.0,100.0,1.015,0.0,0.0,,normal,notpresent,notpresent,90.0,98.0,2.5,,,9.1,28,5500,3.6,yes,no,no,good,no,no,ckd
+125,72.0,90.0,,,,,,notpresent,notpresent,308.0,36.0,2.5,131.0,4.3,,,,,yes,yes,no,poor,no,no,ckd
+126,70.0,90.0,1.015,0.0,0.0,,normal,notpresent,notpresent,144.0,125.0,4.0,136.0,4.6,12.0,37,8200,4.5,yes,yes,no,poor,yes,no,ckd
+127,71.0,60.0,1.015,4.0,0.0,normal,normal,notpresent,notpresent,118.0,125.0,5.3,136.0,4.9,11.4,35,15200,4.3,yes,yes,no,poor,yes,no,ckd
+128,52.0,90.0,1.015,4.0,3.0,normal,abnormal,notpresent,notpresent,224.0,166.0,5.6,133.0,47.0,8.1,23,5000,2.9,yes,yes,no,good,no,yes,ckd
+129,75.0,70.0,1.025,1.0,0.0,,normal,notpresent,notpresent,158.0,49.0,1.4,135.0,4.7,11.1,,,,yes,no,no,poor,yes,no,ckd
+130,50.0,90.0,1.01,2.0,0.0,normal,abnormal,present,present,128.0,208.0,9.2,134.0,4.8,8.2,22,16300,2.7,no,no,no,poor,yes,yes,ckd
+131,5.0,50.0,1.01,0.0,0.0,,normal,notpresent,notpresent,,25.0,0.6,,,11.8,36,12400,,no,no,no,good,no,no,ckd
+132,50.0,,,,,normal,,notpresent,notpresent,219.0,176.0,13.8,136.0,4.5,8.6,24,13200,2.7,yes,no,no,good,yes,yes,ckd
+133,70.0,100.0,1.015,4.0,0.0,normal,normal,notpresent,notpresent,118.0,125.0,5.3,136.0,4.9,12.0,37,	8400,8.0,yes,no,no,good,no,no,ckd
+134,47.0,100.0,1.01,,,normal,,notpresent,notpresent,122.0,,16.9,138.0,5.2,10.8,33,10200,3.8,no,yes,no,good,no,no,ckd
+135,48.0,80.0,1.015,0.0,2.0,,normal,notpresent,notpresent,214.0,24.0,1.3,140.0,4.0,13.2,39,,,no,yes,no,poor,no,no,ckd
+136,46.0,90.0,1.02,,,,normal,notpresent,notpresent,213.0,68.0,2.8,146.0,6.3,9.3,,,,yes,yes,no,good,no,no,ckd
+137,45.0,60.0,1.01,2.0,0.0,normal,abnormal,present,notpresent,268.0,86.0,4.0,134.0,5.1,10.0,29,9200,,yes,yes,no,good,no,no,ckd
+138,73.0,,1.01,1.0,0.0,,,notpresent,notpresent,95.0,51.0,1.6,142.0,3.5,,,,,no,	no,no,good,no,no,ckd
+139,41.0,70.0,1.015,2.0,0.0,,abnormal,notpresent,present,,68.0,2.8,132.0,4.1,11.1,33,,,yes,no,no,good,yes,yes,ckd
+140,69.0,70.0,1.01,0.0,4.0,,normal,notpresent,notpresent,256.0,40.0,1.2,142.0,5.6,,,,,no,no,no,good,no,no,ckd
+141,67.0,70.0,1.01,1.0,0.0,normal,normal,notpresent,notpresent,,106.0,6.0,137.0,4.9,6.1,19,6500,,yes,no,no,good,no,yes,ckd
+142,72.0,90.0,,,,,,notpresent,notpresent,84.0,145.0,7.1,135.0,5.3,,,,,no,yes,no,good,no,no,ckd
+143,41.0,80.0,1.015,1.0,4.0,abnormal,normal,notpresent,notpresent,210.0,165.0,18.0,135.0,4.7,,,,,no,yes,no,good,no,no,ckd
+144,60.0,90.0,1.01,2.0,0.0,abnormal,normal,notpresent,notpresent,105.0,53.0,2.3,136.0,5.2,11.1,33,10500,4.1,no,no,no,good,no,no,ckd
+145,57.0,90.0,1.015,5.0,0.0,abnormal,abnormal,notpresent,present,,322.0,13.0,126.0,4.8,8.0,24,4200,3.3,yes,yes,yes,poor,yes,yes,ckd
+146,53.0,100.0,1.01,1.0,3.0,abnormal,normal,notpresent,notpresent,213.0,23.0,1.0,139.0,4.0,,,,,no,yes,no,good,no,no,ckd
+147,60.0,60.0,1.01,3.0,1.0,normal,abnormal,present,notpresent,288.0,36.0,1.7,130.0,3.0,7.9,25,15200,3.0,yes,no,no,poor,no,yes,ckd
+148,69.0,60.0,,,,,,notpresent,notpresent,171.0,26.0,48.1,,,,,,,yes,no,no,poor,no,no,ckd
+149,65.0,70.0,1.02,1.0,0.0,abnormal,abnormal,notpresent,notpresent,139.0,29.0,1.0,,,10.5,32,,,yes,no,no,good,yes,no,ckd
+150,8.0,60.0,1.025,3.0,0.0,normal,normal,notpresent,notpresent,78.0,27.0,0.9,,,12.3,41,6700,,no,no,no,poor,yes,no,ckd
+151,76.0,90.0,,,,,,notpresent,notpresent,172.0,46.0,1.7,141.0,5.5,9.6,30,,,yes,yes,no,good,no,yes,ckd
+152,39.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,121.0,20.0,0.8,133.0,3.5,10.9,32,,,no,yes,no,good,no,no,ckd
+153,55.0,90.0,1.01,2.0,1.0,abnormal,abnormal,notpresent,notpresent,273.0,235.0,14.2,132.0,3.4,8.3,22,14600,2.9,yes,yes,no,poor,yes,yes,ckd
+154,56.0,90.0,1.005,4.0,3.0,abnormal,abnormal,notpresent,notpresent,242.0,132.0,16.4,140.0,4.2,8.4,26,,3,yes,yes,no,poor,yes,yes,ckd
+155,50.0,70.0,1.02,3.0,0.0,abnormal,normal,present,present,123.0,40.0,1.8,,,11.1,36,4700,,no,no,no,good,no,no,ckd
+156,66.0,90.0,1.015,2.0,0.0,,normal,notpresent,present,153.0,76.0,3.3,,,,,,,no,no,no,poor,no,no,ckd
+157,62.0,70.0,1.025,3.0,0.0,normal,abnormal,notpresent,notpresent,122.0,42.0,1.7,136.0,4.7,12.6,39,7900,3.9,yes,yes,no,good,no,no,ckd
+158,71.0,60.0,1.02,3.0,2.0,normal,normal,present,notpresent,424.0,48.0,1.5,132.0,4.0,10.9,31,,,yes,yes,yes,good,no,no,ckd
+159,59.0,80.0,1.01,1.0,0.0,abnormal,normal,notpresent,notpresent,303.0,35.0,1.3,122.0,3.5,10.4,35,10900,4.3,no,yes,no,poor,no,no,ckd
+160,81.0,60.0,,,,,,notpresent,notpresent,148.0,39.0,2.1,147.0,4.2,10.9,35,9400,2.4,yes,yes,yes,poor,yes,no,ckd
+161,62.0,,1.015,3.0,0.0,abnormal,,notpresent,notpresent,,,,,,14.3,42,10200,4.8,yes,yes,no,good,no,no,ckd
+162,59.0,70.0,,,,,,notpresent,notpresent,204.0,34.0,1.5,124.0,4.1,9.8,37,6000,	?,no,yes,no,good,no,no,ckd
+163,46.0,80.0,1.01,0.0,0.0,,normal,notpresent,notpresent,160.0,40.0,2.0,140.0,4.1,9.0,27,8100,3.2,yes,no,no,poor,no,yes,ckd
+164,14.0,,1.015,0.0,0.0,,,notpresent,notpresent,192.0,15.0,0.8,137.0,4.2,14.3,40,9500,5.4,no,yes,no,poor,yes,no,ckd
+165,60.0,80.0,1.02,0.0,2.0,,,notpresent,notpresent,,,,,,,,,,no,yes,no,good,no,no,ckd
+166,27.0,60.0,,,,,,notpresent,notpresent,76.0,44.0,3.9,127.0,4.3,,,,,no,no,no,poor,yes,yes,ckd
+167,34.0,70.0,1.02,0.0,0.0,abnormal,normal,notpresent,notpresent,139.0,19.0,0.9,,,12.7,42,2200,,no,no,no,poor,no,no,ckd
+168,65.0,70.0,1.015,4.0,4.0,,normal,present,notpresent,307.0,28.0,1.5,,,11.0,39,6700,,yes,yes,no,good,no,no,ckd
+169,,70.0,1.01,0.0,2.0,,normal,notpresent,notpresent,220.0,68.0,2.8,,,8.7,27,,,yes,yes,no,good,no,yes,ckd
+170,66.0,70.0,1.015,2.0,5.0,,normal,notpresent,notpresent,447.0,41.0,1.7,131.0,3.9,12.5,33,9600,4.4,yes,yes,no,good,no,no,ckd
+171,83.0,70.0,1.02,3.0,0.0,normal,normal,notpresent,notpresent,102.0,60.0,2.6,115.0,5.7,8.7,26,12800,3.1,yes,no,no,poor,no,yes,ckd
+172,62.0,80.0,1.01,1.0,2.0,,,notpresent,notpresent,309.0,113.0,2.9,130.0,2.5,10.6,34,12800,4.9,no,no,no,good,no,no,ckd
+173,17.0,70.0,1.015,1.0,0.0,abnormal,normal,notpresent,notpresent,22.0,1.5,7.3,145.0,2.8,13.1,41,11200,,no,no,no,good,no,no,ckd
+174,54.0,70.0,,,,,,notpresent,notpresent,111.0,146.0,7.5,141.0,4.7,11.0,35,8600,4.6,no,no,no,good,no,no,ckd
+175,60.0,50.0,1.01,0.0,0.0,,normal,notpresent,notpresent,261.0,58.0,2.2,113.0,3.0,,,4200,3.4,yes,no,no,good,no,no,ckd
+176,21.0,90.0,1.01,4.0,0.0,normal,abnormal,present,present,107.0,40.0,1.7,125.0,3.5,8.3,23,12400,3.9,no,no,no,good,no,yes,ckd
+177,65.0,80.0,1.015,2.0,1.0,normal,normal,present,notpresent,215.0,133.0,2.5,,,13.2,41,,,no,yes,no,good,no,no,ckd
+178,42.0,90.0,1.02,2.0,0.0,abnormal,abnormal,present,notpresent,93.0,153.0,2.7,139.0,4.3,9.8,34,9800,,no,no,no,poor,yes,yes,ckd
+179,72.0,90.0,1.01,2.0,0.0,,abnormal,present,notpresent,124.0,53.0,2.3,,,11.9,39,,,no,no,no,good,no,no,ckd
+180,73.0,90.0,1.01,1.0,4.0,abnormal,abnormal,present,notpresent,234.0,56.0,1.9,,,10.3,28,,,no,yes,no,good,no,no,ckd
+181,45.0,70.0,1.025,2.0,0.0,normal,abnormal,present,notpresent,117.0,52.0,2.2,136.0,3.8,10.0,30,19100,3.7,no,no,no,good,no,no,ckd
+182,61.0,80.0,1.02,0.0,0.0,,normal,notpresent,notpresent,131.0,23.0,0.8,140.0,4.1,11.3,35,,,no,no,no,good,no,no,ckd
+183,30.0,70.0,1.015,0.0,0.0,,normal,notpresent,notpresent,101.0,106.0,6.5,135.0,4.3,,,,,no,no,no,poor,no,no,ckd
+184,54.0,60.0,1.015,3.0,2.0,,abnormal,notpresent,notpresent,352.0,137.0,3.3,133.0,4.5,11.3,31,5800,3.6,yes,yes,yes,poor,yes,no,ckd
+185,4.0,,1.02,1.0,0.0,,normal,notpresent,notpresent,99.0,23.0,0.6,138.0,4.4,12.0,34,	?,,no,no,no,good,no,no,ckd
+186,8.0,50.0,1.02,4.0,0.0,normal,normal,notpresent,notpresent,,46.0,1.0,135.0,3.8,,,,,no,no,no,good,yes,no,ckd
+187,3.0,,1.01,2.0,0.0,normal,normal,notpresent,notpresent,,22.0,0.7,,,10.7,34,12300,,no,no,no,good,no,no,ckd
+188,8.0,,,,,,,notpresent,notpresent,80.0,66.0,2.5,142.0,3.6,12.2,38,,,no,	no,no,good,no,no,ckd
+189,64.0,60.0,1.01,4.0,1.0,abnormal,abnormal,notpresent,present,239.0,58.0,4.3,137.0,5.4,9.5,29,7500,3.4,yes,yes,no,poor,yes,no,ckd
+190,6.0,60.0,1.01,4.0,0.0,abnormal,abnormal,notpresent,present,94.0,67.0,1.0,135.0,4.9,9.9,30,16700,4.8,no,no,no,poor,no,no,ckd
+191,,70.0,1.01,3.0,0.0,normal,normal,notpresent,notpresent,110.0,115.0,6.0,134.0,2.7,9.1,26,9200,3.4,yes,yes,no,poor,no,no,ckd
+192,46.0,110.0,1.015,0.0,0.0,,normal,notpresent,notpresent,130.0,16.0,0.9,,,,,,,no,no,no,good,no,no,ckd
+193,32.0,90.0,1.025,1.0,0.0,abnormal,abnormal,notpresent,notpresent,,223.0,18.1,113.0,6.5,5.5,15,2600,2.8,yes,yes,no,poor,yes,yes,ckd
+194,80.0,70.0,1.01,2.0,,,abnormal,notpresent,notpresent,,49.0,1.2,,,,,,,yes,	yes,no,good,no,no,ckd
+195,70.0,90.0,1.02,2.0,1.0,abnormal,abnormal,notpresent,present,184.0,98.6,3.3,138.0,3.9,5.8,,,,yes,yes,yes,poor,no,no,ckd
+196,49.0,100.0,1.01,3.0,0.0,abnormal,abnormal,notpresent,notpresent,129.0,158.0,11.8,122.0,3.2,8.1,24,9600,3.5,yes,yes,no,poor,yes,yes,ckd
+197,57.0,80.0,,,,,,notpresent,notpresent,,111.0,9.3,124.0,5.3,6.8,,4300,3.0,yes,yes,no,good,no,yes,ckd
+198,59.0,100.0,1.02,4.0,2.0,normal,normal,notpresent,notpresent,252.0,40.0,3.2,137.0,4.7,11.2,30,26400,3.9,yes,yes,no,poor,yes,no,ckd
+199,65.0,80.0,1.015,0.0,0.0,,normal,notpresent,notpresent,92.0,37.0,1.5,140.0,5.2,8.8,25,10700,3.2,yes,no,yes,good,yes,no,ckd
+200,90.0,90.0,1.025,1.0,0.0,,normal,notpresent,notpresent,139.0,89.0,3.0,140.0,4.1,12.0,37,7900,3.9,yes,yes,no,good,no,no,ckd
+201,64.0,70.0,,,,,,notpresent,notpresent,113.0,94.0,7.3,137.0,4.3,7.9,21,,,yes,yes,yes,good,yes,yes,ckd
+202,78.0,60.0,,,,,,notpresent,notpresent,114.0,74.0,2.9,135.0,5.9,8.0,24,,,no,yes,no,good,no,yes,ckd
+203,,90.0,,,,,,notpresent,notpresent,207.0,80.0,6.8,142.0,5.5,8.5,,,,yes,yes,no,good,no,yes,ckd
+204,65.0,90.0,1.01,4.0,2.0,normal,normal,notpresent,notpresent,172.0,82.0,13.5,145.0,6.3,8.8,31,,,yes,yes,no,good,yes,yes,ckd
+205,61.0,70.0,,,,,,notpresent,notpresent,100.0,28.0,2.1,,,12.6,43,,,yes,yes,no,good,no,no,ckd
+206,60.0,70.0,1.01,1.0,0.0,,normal,notpresent,notpresent,109.0,96.0,3.9,135.0,4.0,13.8,41,,,yes,no,no,good,no,no,ckd
+207,50.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,230.0,50.0,2.2,,,12.0,41,10400,4.6,yes,yes,no,good,no,no,ckd
+208,67.0,80.0,,,,,,notpresent,notpresent,341.0,37.0,1.5,,,12.3,41,6900,4.9,yes,yes,no,good,no,yes,ckd
+209,19.0,70.0,1.02,0.0,0.0,,normal,notpresent,notpresent,,,,,,11.5,,6900,,no,no,no,good,no,no,ckd
+210,59.0,100.0,1.015,4.0,2.0,normal,normal,notpresent,notpresent,255.0,132.0,12.8,135.0,5.7,7.3,20,9800,3.9,yes,yes,yes,good,no,yes,ckd
+211,54.0,120.0,1.015,0.0,0.0,,normal,notpresent,notpresent,103.0,18.0,1.2,,,,,,,no,no,no,good,no,no,ckd
+212,40.0,70.0,1.015,3.0,4.0,normal,normal,notpresent,notpresent,253.0,150.0,11.9,132.0,5.6,10.9,31,8800,3.4,yes,yes,no,poor,yes,no,ckd
+213,55.0,80.0,1.01,3.0,1.0,normal,abnormal,present,present,214.0,73.0,3.9,137.0,4.9,10.9,34,7400,3.7,yes,yes,no,good,yes,no,ckd
+214,68.0,80.0,1.015,0.0,0.0,,abnormal,notpresent,notpresent,171.0,30.0,1.0,,,13.7,	43,4900,5.2,no,yes,no,good,no,no,ckd
+215,2.0,,1.01,3.0,0.0,normal,abnormal,notpresent,notpresent,,,,,,,,,,no,no,no,good,yes,no,ckd
+216,64.0,70.0,1.01,0.0,0.0,,normal,notpresent,notpresent,107.0,15.0,,,,12.8,38,,,no,no,no,good,no,no,ckd
+217,63.0,100.0,1.01,1.0,0.0,,normal,notpresent,notpresent,78.0,61.0,1.8,141.0,4.4,12.2,36,10500,4.3,no,yes,no,good,no,no,ckd
+218,33.0,90.0,1.015,0.0,0.0,,normal,notpresent,notpresent,92.0,19.0,0.8,,,11.8,34,7000,,no,no,no,good,no,no,ckd
+219,68.0,90.0,1.01,0.0,0.0,,normal,notpresent,notpresent,238.0,57.0,2.5,,,9.8,28,8000,3.3,yes,yes,no,poor,no,no,ckd
+220,36.0,80.0,1.01,0.0,0.0,,normal,notpresent,notpresent,103.0,,,,,11.9,36,8800,,no,no,no,good,no,no,ckd
+221,66.0,70.0,1.02,1.0,0.0,normal,,notpresent,notpresent,248.0,30.0,1.7,138.0,5.3,,,,,yes,yes,no,good,no,no,ckd
+222,74.0,60.0,,,,,,notpresent,notpresent,108.0,68.0,1.8,,,,,,,yes,yes,no,good,no,no,ckd
+223,71.0,90.0,1.01,0.0,3.0,,normal,notpresent,notpresent,303.0,30.0,1.3,136.0,4.1,13.0,38,9200,4.6,yes,yes,no,good,no,no,ckd
+224,34.0,60.0,1.02,0.0,0.0,,normal,notpresent,notpresent,117.0,28.0,2.2,138.0,3.8,,,,,no,no,no,good,yes,no,ckd
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+228,60.0,70.0,,,,,,notpresent,notpresent,124.0,52.0,2.5,,,,,,,yes,no,no,good,no,no,ckd
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main.py

@@ -0,0 +1,370 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.ensemble import RandomForestRegressor, VotingRegressor
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.svm import SVR
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+from sklearn.neural_network import MLPRegressor
+from lightgbm import LGBMRegressor
+from xgboost import XGBRegressor
+
+st.title('Kidney Disease Prediction Application')
+st.write('''
+         Please fill in the attributes below, then hit the Predict button
+         to get your results.
+         ''')
+
+st.header('Input Attributes')
+age = st.slider('Your Age (Years)', min_value=0.0, max_value=100.0, value=50.0, step=1.0)
+st.write(''' ''')
+
+bp = st.slider('Blood Pressure (mm/Hg)', min_value=0.0, max_value=200.0, value=150.0, step=1.0)
+st.write(''' ''')
+
+sg = st.slider('Specific Gravity (SG)', min_value=1.005, max_value=1.025, value=1.015, step=0.005)
+st.write(''' ''')
+
+al = st.slider('Albumin Level (g/L)', min_value=0.0, max_value=5.0, value=2.0, step=1.0)
+st.write(''' ''')
+
+sugar = st.slider('Sugar Level', min_value=0.0, max_value=5.0, value=2.0, step=1.0)
+st.write(''' ''')
+
+rbc = st.radio("Red Blood Cell Count", ('Normal', 'Abnormal'))
+st.write(''' ''')
+
+if rbc == "Normal":
+    rbc = 0
+else:
+    rbc = 1
+
+pc = st.radio("Pus Cell Count", ('Normal', 'Abnormal'))
+st.write(''' ''')
+
+if pc == "Normal":
+    pc = 0
+else:
+    pc = 1
+
+pcc = st.radio("Pus Cell Clumps", ('Present', 'Not Present'))
+st.write(''' ''')
+
+if pcc == "Present":
+    pcc = 1
+else:
+    pcc = 0
+
+ba = st.radio("Bacterial Infection", ('Present', 'Not Present'))
+st.write(''' ''')
+
+if ba == "Present":
+    ba = 1
+else:
+    ba = 0
+
+bgr = st.slider('Blood Glucose Random (mgs/dl)', min_value=0.0, max_value=600.0, value=300.0, step=1.0)
+st.write(''' ''')
+
+bu = st.slider('Blood Urea (mgs/dl)', min_value=0.0, max_value=500.0, value=250.0, step=0.1)
+st.write(''' ''')
+
+sc = st.slider('Serum Creatinine (mgs/dl)', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
+st.write(''' ''')
+
+sod = st.slider('Sodium (mEq/L)', min_value=0.0, max_value=200.0, value=100.0, step=0.1)
+st.write(''' ''')
+
+pot = st.slider('Potassium (mEq/L)', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
+st.write(''' ''')
+
+hemo = st.slider('Hemoglobin (gms)', min_value=0.0, max_value=20.0, value=10.0, step=0.1)
+st.write(''' ''')
+
+pcv = st.slider('Packed  Cell Volume', min_value=0.0, max_value=100.0, value=50.0, step=0.1)
+st.write(''' ''')
+
+wbc = st.slider('White Blood Cell Count (cells/cumm)', min_value=0.0, max_value=50000.0, value=25000.0, step=1.0)
+st.write(''' ''')
+
+rbcc = st.slider('Red Blood Cell Count (millions/cmm)', min_value=0.0, max_value=200.0, value=100.0, step=1.0)
+st.write(''' ''')
+
+htn = st.radio("Hypertension", ('Yes', 'No'))
+st.write(''' ''')
+
+if htn == "Yes":
+    htn = 1
+else:
+    htn = 0
+
+dm = st.radio("Diabetes Mellitus", ('Yes', 'No'))
+st.write(''' ''')
+
+if dm == "Yes":
+    dm = 1
+else:
+    dm = 0
+
+cad = st.radio("Coronary Artery Disease", ('Yes', 'No'))
+st.write(''' ''')
+
+if cad == "Yes":
+    cad = 1
+else:
+    cad = 0
+
+appet = st.radio("Appetite", ('Good', 'Poor'))
+st.write(''' ''')
+
+if appet == "Good":
+    appet = 1
+else:
+    appet = 0
+
+pe = st.radio("Pedal Edema", ('Yes', 'No'))
+st.write(''' ''')
+
+if pe == "Yes":
+    pe = 1
+else:
+    pe = 0
+
+ane = st.radio("Anemia", ('Yes', 'No'))
+st.write(''' ''')
+
+if ane == "Yes":
+    ane = 1
+else:
+    ane = 0
+
+selected_models = st.multiselect("Choose Regression Models", ('Random Forest',
+    'Linear Regression',
+    'K-Nearest Neighbors',
+    'Decision Tree',
+    'Gradient Boosting Regression',
+    'XGBoost Regression',
+    'LightGBM Regression'))
+st.write(''' ''')
+
+# Initialize an empty list to store the selected models
+models_to_run = []
+
+# Check which models were selected and add them to the models_to_run list
+if 'Random Forest' in selected_models:
+    models_to_run.append(RandomForestRegressor())
+
+if 'Linear Regression' in selected_models:
+    models_to_run.append(LinearRegression())
+
+if 'K-Nearest Neighbors' in selected_models:
+    models_to_run.append(KNeighborsRegressor())
+
+if 'Decision Tree' in selected_models:
+    models_to_run.append(DecisionTreeRegressor())
+
+if 'Support Vector Machine' in selected_models:
+    models_to_run.append(SVR())
+
+if 'Gradient Boosting Regression' in selected_models:
+    models_to_run.append(GradientBoostingRegressor())
+
+if 'XGBoost Regression' in selected_models:
+    models_to_run.append(XGBRegressor())
+
+if 'LightGBM Regression' in selected_models:
+    models_to_run.append(LGBMRegressor())
+
+if 'Neural Network (MLP) Regression' in selected_models:
+    models_to_run.append(MLPRegressor())
+
+user_input = np.array([age, bp, sg, al, sugar, rbc, pc, pcc, ba, bgr, bu, sc,
+                       sod, pot, hemo, pcv, wbc, rbcc, htn, dm, cad, appet, pe, ane]).reshape(1, -1)
+
+# import dataset
+def get_dataset():
+    data = pd.read_csv('kidney.csv')
+
+    return data
+
+def generate_model_labels(model_names):
+    model_labels = []
+    for name in model_names:
+        words = name.split()
+        if len(words) > 1:
+            # Multiple words, use initials
+            label = "".join(word[0] for word in words)
+        else:
+            # Single word, take the first 3 letters
+            label = name[:3]
+        model_labels.append(label)
+    return model_labels
+
+if st.button('Submit'):
+    df = get_dataset()
+
+    # fix column names
+    df.columns = (["id", "age", "bp", "sg", "al", "su", "rbc", "pc",
+                   "pcc", "ba", "bgr", "bu", "sc", "sod", "pot", "hemo", "pcv",
+                   "wc", "rc", "htn", "dm", "cad", "appet", "pe", "ane", "class"])
+
+    # Transforming classification into numerical format
+    df['class'] = df['class'].apply(lambda x: 1 if x == 'ckd' else 0)
+
+    # Transforming ane into numerical format
+    df['ane'] = df['ane'].apply(lambda x: 1 if x == 'yes' else 0)
+
+    # Transforming pe into numerical format
+    df['pe'] = df['pe'].apply(lambda x: 1 if x == 'yes' else 0)
+
+    # Transforming appet into numerical format
+    df['appet'] = df['appet'].apply(lambda x: 1 if x == 'poor' else 0)
+
+    # Transforming cad into numerical format
+    df['cad'] = df['cad'].apply(lambda x: 1 if x == 'yes' else 0)
+
+    # Transforming dm into numerical format
+    df['dm'] = df['dm'].apply(lambda x: 1 if x == 'yes' else 0)
+
+    # Transforming htn into numerical format
+    df['htn'] = df['htn'].apply(lambda x: 1 if x == 'yes' else 0)
+
+    # Transforming ba into numerical format
+    df['ba'] = df['ba'].apply(lambda x: 1 if x == 'present' else 0)
+
+    # Transforming pcc into numerical format
+    df['pcc'] = df['pcc'].apply(lambda x: 1 if x == 'present' else 0)
+
+    # Transforming pc into numerical format
+    df['pc'] = df['pc'].apply(lambda x: 1 if x == 'abnormal' else 0)
+
+    # Transforming rbc into numerical format
+    df['rbc'] = df['rbc'].apply(lambda x: 1 if x == 'abnormal' else 0)
+
+    # Replace NaN values with median for float columns
+    float_columns = df.select_dtypes(include=['float']).columns
+    df[float_columns] = df[float_columns].fillna(df[float_columns].median())
+
+    # Convert columns to numeric
+    numeric_columns = ['pcv', 'wc', 'rc']
+    df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce')
+
+    # Replace NaN values with median for numeric columns
+    df[numeric_columns] = df[numeric_columns].fillna(df[numeric_columns].median())
+
+    # Split the dataset into train and test
+    X = df.drop(['class','id'], axis=1)
+    y = df['class']
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Create two columns to divide the screen
+    left_column, right_column = st.columns(2)
+
+    # Left column content
+    with left_column:
+        # Create a VotingRegressor with the selected models
+        ensemble = VotingRegressor(
+            estimators=[('rf', LGBMRegressor()), ('gb', GradientBoostingRegressor()), ('xb', XGBRegressor())]
+        )
+
+        # Fit the voting regressor to the training data
+        ensemble.fit(X_train, y_train)
+
+        # Make predictions on the test set
+        model_predictions = ensemble.predict(user_input)
+
+        # Evaluate the model's performance on the test set
+        ensemble_r2 = r2_score(y_test, ensemble.predict(X_test))
+        ensemble_mse = mean_squared_error(y_test, ensemble.predict(X_test))
+        ensemble_mae = mean_absolute_error(y_test, ensemble.predict(X_test))
+        ensemble_rmse = np.sqrt(ensemble_mse)
+
+        st.write(f'According to Ensemble Model, Your Diabetes Risk Score is: {model_predictions[0]:.2f}')
+        st.write('Ensemble Model R-squared (R2) Score:', ensemble_r2)
+        st.write('Ensemble Model Root Mean Squared Error (RMSE):', ensemble_rmse)
+        st.write('Ensemble Model Mean Squared Error (MSE):', ensemble_mse)
+        st.write('Ensemble Model Mean Absolute Error (MAE):', ensemble_mae)
+        st.write('------------------------------------------------------------------------------------------------------')
+
+    # Right column content
+    with right_column:
+        # Initialize lists to store model names and their respective performance metrics
+        model_names = ['Ensemble']
+        r2_scores = [ensemble_r2]
+        rmses = [ensemble_rmse]
+        mses = [ensemble_mse]
+        maes = [ensemble_mae]
+
+        for model in models_to_run:
+            # Train the selected model
+            model.fit(X_train, y_train)
+
+            # Make predictions on the test set
+            model_predictions = model.predict(user_input)
+
+            # Evaluate the model's performance on the test set
+            model_mse = mean_squared_error(y_test, model.predict(X_test))
+            model_mae = mean_absolute_error(y_test, model.predict(X_test))
+            rmse = np.sqrt(model_mse)
+            model_r2 = r2_score(y_test, model.predict(X_test))
+
+            st.write(f'According to {type(model).__name__} Model, Your Diabetes Risk Score is: {model_predictions[0]:.2f}')
+            st.write(f'{type(model).__name__} R-squared (R2) Score:', model_r2)
+            st.write(f'{type(model).__name__} Root Mean Squared Error (RMSE):', rmse)
+            st.write(f'{type(model).__name__} Mean Squared Error (MSE):', model_mse)
+            st.write(f'{type(model).__name__} Mean Absolute Error (MAE):', model_mae)
+            st.write('------------------------------------------------------------------------------------------------------')
+
+            # Append model performance metrics to the lists
+            model_names.append(type(model).__name__)
+            r2_scores.append(model_r2)
+            rmses.append(rmse)
+            mses.append(model_mse)
+            maes.append(model_mae)
+
+    # Create a DataFrame to store the performance metrics
+    metrics_df = pd.DataFrame({
+        'Model': model_names,
+        'R-squared (R2) Score': r2_scores,
+        'Root Mean Squared Error (RMSE)': rmses,
+        'Mean Squared Error (MSE)': mses,
+        'Mean Absolute Error (MAE)': maes
+    })
+
+    # Get the model labels
+    model_labels = generate_model_labels(metrics_df['Model'])
+
+    # Plot the comparison graphs
+    plt.figure(figsize=(12, 10))
+
+    # R2 Score comparison
+    plt.subplot(2, 2, 3)
+    plt.bar(model_labels, metrics_df['R-squared (R2) Score'], color='green')
+    plt.title('R2 Score Comparison')
+
+    # RMSE comparison
+    plt.subplot(2, 2, 4)
+    plt.bar(model_labels, metrics_df['Root Mean Squared Error (RMSE)'], color='blue')
+    plt.title('RMSE Comparison')
+
+    # MSE comparison
+    plt.subplot(2, 2, 1)
+    plt.bar(model_labels, metrics_df['Mean Squared Error (MSE)'], color='orange')
+    plt.title('MSE Comparison')
+
+    # MAE comparison
+    plt.subplot(2, 2, 2)
+    plt.bar(model_labels, metrics_df['Mean Absolute Error (MAE)'], color='purple')
+    plt.title('MAE Comparison')
+
+    # Adjust layout to prevent overlapping of titles
+    plt.tight_layout()
+
+    # Display the graphs in Streamlit
+    st.pyplot()

requirements.txt

@@ -0,0 +1,7 @@
+lightgbm==4.0.0
+matplotlib==3.7.2
+numpy==1.25.1
+pandas==2.0.3
+scikit_learn==1.3.0
+streamlit==1.25.0
+xgboost==1.7.6