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update assignment 4

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ligerfotis 2023-04-27 15:24:51 +02:00
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age,sex,bmi,bp,s1,s2,s3,s4,s5,s6,target
59.0,2.0,32.1,101.0,157.0,93.2,38.0,4.0,4.8598,87.0,151.0
48.0,1.0,21.6,87.0,183.0,103.2,70.0,3.0,3.8918,69.0,75.0
72.0,2.0,30.5,93.0,156.0,93.6,41.0,4.0,4.6728,85.0,141.0
24.0,1.0,25.3,84.0,198.0,131.4,40.0,5.0,4.8903,89.0,206.0
50.0,1.0,23.0,101.0,192.0,125.4,52.0,4.0,4.2905,80.0,135.0
23.0,1.0,22.6,89.0,139.0,64.8,61.0,2.0,4.1897,68.0,97.0
36.0,2.0,22.0,90.0,160.0,99.6,50.0,3.0,3.9512,82.0,138.0
66.0,2.0,26.2,114.0,255.0,185.0,56.0,4.55,4.2485,92.0,63.0
60.0,2.0,32.1,83.0,179.0,119.4,42.0,4.0,4.4773,94.0,110.0
29.0,1.0,30.0,85.0,180.0,93.4,43.0,4.0,5.3845,88.0,310.0
22.0,1.0,18.6,97.0,114.0,57.6,46.0,2.0,3.9512,83.0,101.0
56.0,2.0,28.0,85.0,184.0,144.8,32.0,6.0,3.5835,77.0,69.0
53.0,1.0,23.7,92.0,186.0,109.2,62.0,3.0,4.3041,81.0,179.0
50.0,2.0,26.2,97.0,186.0,105.4,49.0,4.0,5.0626,88.0,185.0
61.0,1.0,24.0,91.0,202.0,115.4,72.0,3.0,4.2905,73.0,118.0
34.0,2.0,24.7,118.0,254.0,184.2,39.0,7.0,5.037,81.0,171.0
47.0,1.0,30.3,109.0,207.0,100.2,70.0,3.0,5.2149,98.0,166.0
68.0,2.0,27.5,111.0,214.0,147.0,39.0,5.0,4.9416,91.0,144.0
38.0,1.0,25.4,84.0,162.0,103.0,42.0,4.0,4.4427,87.0,97.0
41.0,1.0,24.7,83.0,187.0,108.2,60.0,3.0,4.5433,78.0,168.0
35.0,1.0,21.1,82.0,156.0,87.8,50.0,3.0,4.5109,95.0,68.0
25.0,2.0,24.3,95.0,162.0,98.6,54.0,3.0,3.8501,87.0,49.0
25.0,1.0,26.0,92.0,187.0,120.4,56.0,3.0,3.9703,88.0,68.0
61.0,2.0,32.0,103.67,210.0,85.2,35.0,6.0,6.107,124.0,245.0
31.0,1.0,29.7,88.0,167.0,103.4,48.0,4.0,4.3567,78.0,184.0
30.0,2.0,25.2,83.0,178.0,118.4,34.0,5.0,4.852,83.0,202.0
19.0,1.0,19.2,87.0,124.0,54.0,57.0,2.0,4.1744,90.0,137.0
42.0,1.0,31.9,83.0,158.0,87.6,53.0,3.0,4.4659,101.0,85.0
63.0,1.0,24.4,73.0,160.0,91.4,48.0,3.0,4.6347,78.0,131.0
67.0,2.0,25.8,113.0,158.0,54.2,64.0,2.0,5.2933,104.0,283.0
32.0,1.0,30.5,89.0,182.0,110.6,56.0,3.0,4.3438,89.0,129.0
42.0,1.0,20.3,71.0,161.0,81.2,66.0,2.0,4.2341,81.0,59.0
58.0,2.0,38.0,103.0,150.0,107.2,22.0,7.0,4.6444,98.0,341.0
57.0,1.0,21.7,94.0,157.0,58.0,82.0,2.0,4.4427,92.0,87.0
53.0,1.0,20.5,78.0,147.0,84.2,52.0,3.0,3.989,75.0,65.0
62.0,2.0,23.5,80.33,225.0,112.8,86.0,2.62,4.8752,96.0,102.0
52.0,1.0,28.5,110.0,195.0,97.2,60.0,3.0,5.2417,85.0,265.0
46.0,1.0,27.4,78.0,171.0,88.0,58.0,3.0,4.8283,90.0,276.0
48.0,2.0,33.0,123.0,253.0,163.6,44.0,6.0,5.425,97.0,252.0
48.0,2.0,27.7,73.0,191.0,119.4,46.0,4.0,4.852,92.0,90.0
50.0,2.0,25.6,101.0,229.0,162.2,43.0,5.0,4.7791,114.0,100.0
21.0,1.0,20.1,63.0,135.0,69.0,54.0,3.0,4.0943,89.0,55.0
32.0,2.0,25.4,90.33,153.0,100.4,34.0,4.5,4.5326,83.0,61.0
54.0,1.0,24.2,74.0,204.0,109.0,82.0,2.0,4.1744,109.0,92.0
61.0,2.0,32.7,97.0,177.0,118.4,29.0,6.0,4.9972,87.0,259.0
56.0,2.0,23.1,104.0,181.0,116.4,47.0,4.0,4.4773,79.0,53.0
33.0,1.0,25.3,85.0,155.0,85.0,51.0,3.0,4.5539,70.0,190.0
27.0,1.0,19.6,78.0,128.0,68.0,43.0,3.0,4.4427,71.0,142.0
67.0,2.0,22.5,98.0,191.0,119.2,61.0,3.0,3.989,86.0,75.0
37.0,2.0,27.7,93.0,180.0,119.4,30.0,6.0,5.0304,88.0,142.0
58.0,1.0,25.7,99.0,157.0,91.6,49.0,3.0,4.4067,93.0,155.0
65.0,2.0,27.9,103.0,159.0,96.8,42.0,4.0,4.6151,86.0,225.0
34.0,1.0,25.5,93.0,218.0,144.0,57.0,4.0,4.4427,88.0,59.0
46.0,1.0,24.9,115.0,198.0,129.6,54.0,4.0,4.2767,103.0,104.0
35.0,1.0,28.7,97.0,204.0,126.8,64.0,3.0,4.1897,93.0,182.0
37.0,1.0,21.8,84.0,184.0,101.0,73.0,3.0,3.912,93.0,128.0
37.0,1.0,30.2,87.0,166.0,96.0,40.0,4.15,5.0106,87.0,52.0
41.0,1.0,20.5,80.0,124.0,48.8,64.0,2.0,4.0254,75.0,37.0
60.0,1.0,20.4,105.0,198.0,78.4,99.0,2.0,4.6347,79.0,170.0
66.0,2.0,24.0,98.0,236.0,146.4,58.0,4.0,5.0626,96.0,170.0
29.0,1.0,26.0,83.0,141.0,65.2,64.0,2.0,4.0775,83.0,61.0
37.0,2.0,26.8,79.0,157.0,98.0,28.0,6.0,5.0434,96.0,144.0
41.0,2.0,25.7,83.0,181.0,106.6,66.0,3.0,3.7377,85.0,52.0
39.0,1.0,22.9,77.0,204.0,143.2,46.0,4.0,4.3041,74.0,128.0
67.0,2.0,24.0,83.0,143.0,77.2,49.0,3.0,4.4308,94.0,71.0
36.0,2.0,24.1,112.0,193.0,125.0,35.0,6.0,5.1059,95.0,163.0
46.0,2.0,24.7,85.0,174.0,123.2,30.0,6.0,4.6444,96.0,150.0
60.0,2.0,25.0,89.67,185.0,120.8,46.0,4.02,4.5109,92.0,97.0
59.0,2.0,23.6,83.0,165.0,100.0,47.0,4.0,4.4998,92.0,160.0
53.0,1.0,22.1,93.0,134.0,76.2,46.0,3.0,4.0775,96.0,178.0
48.0,1.0,19.9,91.0,189.0,109.6,69.0,3.0,3.9512,101.0,48.0
48.0,1.0,29.5,131.0,207.0,132.2,47.0,4.0,4.9345,106.0,270.0
66.0,2.0,26.0,91.0,264.0,146.6,65.0,4.0,5.5683,87.0,202.0
52.0,2.0,24.5,94.0,217.0,149.4,48.0,5.0,4.585,89.0,111.0
52.0,2.0,26.6,111.0,209.0,126.4,61.0,3.0,4.6821,109.0,85.0
46.0,2.0,23.5,87.0,181.0,114.8,44.0,4.0,4.7095,98.0,42.0
40.0,2.0,29.0,115.0,97.0,47.2,35.0,2.77,4.3041,95.0,170.0
22.0,1.0,23.0,73.0,161.0,97.8,54.0,3.0,3.8286,91.0,200.0
50.0,1.0,21.0,88.0,140.0,71.8,35.0,4.0,5.112,71.0,252.0
20.0,1.0,22.9,87.0,191.0,128.2,53.0,4.0,3.8918,85.0,113.0
68.0,1.0,27.5,107.0,241.0,149.6,64.0,4.0,4.92,90.0,143.0
52.0,2.0,24.3,86.0,197.0,133.6,44.0,5.0,4.5747,91.0,51.0
44.0,1.0,23.1,87.0,213.0,126.4,77.0,3.0,3.8712,72.0,52.0
38.0,1.0,27.3,81.0,146.0,81.6,47.0,3.0,4.4659,81.0,210.0
49.0,1.0,22.7,65.33,168.0,96.2,62.0,2.71,3.8918,60.0,65.0
61.0,1.0,33.0,95.0,182.0,114.8,54.0,3.0,4.1897,74.0,141.0
29.0,2.0,19.4,83.0,152.0,105.8,39.0,4.0,3.5835,83.0,55.0
61.0,1.0,25.8,98.0,235.0,125.8,76.0,3.0,5.112,82.0,134.0
34.0,2.0,22.6,75.0,166.0,91.8,60.0,3.0,4.2627,108.0,42.0
36.0,1.0,21.9,89.0,189.0,105.2,68.0,3.0,4.3694,96.0,111.0
52.0,1.0,24.0,83.0,167.0,86.6,71.0,2.0,3.8501,94.0,98.0
61.0,1.0,31.2,79.0,235.0,156.8,47.0,5.0,5.0499,96.0,164.0
43.0,1.0,26.8,123.0,193.0,102.2,67.0,3.0,4.7791,94.0,48.0
35.0,1.0,20.4,65.0,187.0,105.6,67.0,2.79,4.2767,78.0,96.0
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29.0,1.0,21.0,71.0,156.0,97.0,38.0,4.0,4.654,90.0,162.0
64.0,2.0,27.3,109.0,186.0,107.6,38.0,5.0,5.3083,99.0,150.0
41.0,1.0,34.6,87.33,205.0,142.6,41.0,5.0,4.6728,110.0,279.0
49.0,2.0,25.9,91.0,178.0,106.6,52.0,3.0,4.5747,75.0,92.0
48.0,1.0,20.4,98.0,209.0,139.4,46.0,5.0,4.7707,78.0,83.0
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65.0,2.0,30.2,98.0,219.0,160.6,40.0,5.0,4.5218,84.0,198.0
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57.0,1.0,24.5,93.0,186.0,96.6,71.0,3.0,4.5218,91.0,148.0
49.0,2.0,21.0,82.0,119.0,85.4,23.0,5.0,3.9703,74.0,88.0
41.0,2.0,32.0,126.0,198.0,104.2,49.0,4.0,5.4116,124.0,243.0
25.0,2.0,22.6,85.0,130.0,71.0,48.0,3.0,4.0073,81.0,71.0
52.0,2.0,19.7,81.0,152.0,53.4,82.0,2.0,4.4188,82.0,77.0
34.0,1.0,21.2,84.0,254.0,113.4,52.0,5.0,6.0936,92.0,109.0
42.0,2.0,30.6,101.0,269.0,172.2,50.0,5.0,5.4553,106.0,272.0
28.0,2.0,25.5,99.0,162.0,101.6,46.0,4.0,4.2767,94.0,60.0
47.0,2.0,23.3,90.0,195.0,125.8,54.0,4.0,4.3307,73.0,54.0
32.0,2.0,31.0,100.0,177.0,96.2,45.0,4.0,5.1874,77.0,221.0
43.0,1.0,18.5,87.0,163.0,93.6,61.0,2.67,3.7377,80.0,90.0
59.0,2.0,26.9,104.0,194.0,126.6,43.0,5.0,4.803999999999999,106.0,311.0
53.0,1.0,28.3,101.0,179.0,107.0,48.0,4.0,4.7875,101.0,281.0
60.0,1.0,25.7,103.0,158.0,84.6,64.0,2.0,3.8501,97.0,182.0
54.0,2.0,36.1,115.0,163.0,98.4,43.0,4.0,4.6821,101.0,321.0
35.0,2.0,24.1,94.67,155.0,97.4,32.0,4.84,4.852,94.0,58.0
49.0,2.0,25.8,89.0,182.0,118.6,39.0,5.0,4.803999999999999,115.0,262.0
58.0,1.0,22.8,91.0,196.0,118.8,48.0,4.0,4.9836,115.0,206.0
36.0,2.0,39.1,90.0,219.0,135.8,38.0,6.0,5.4205,103.0,233.0
46.0,2.0,42.2,99.0,211.0,137.0,44.0,5.0,5.0106,99.0,242.0
44.0,2.0,26.6,99.0,205.0,109.0,43.0,5.0,5.5797,111.0,123.0
46.0,1.0,29.9,83.0,171.0,113.0,38.0,4.5,4.585,98.0,167.0
54.0,1.0,21.0,78.0,188.0,107.4,70.0,3.0,3.9703,73.0,63.0
63.0,2.0,25.5,109.0,226.0,103.2,46.0,5.0,5.9506,87.0,197.0
41.0,2.0,24.2,90.0,199.0,123.6,57.0,4.0,4.5218,86.0,71.0
28.0,1.0,25.4,93.0,141.0,79.0,49.0,3.0,4.1744,91.0,168.0
19.0,1.0,23.2,75.0,143.0,70.4,52.0,3.0,4.6347,72.0,140.0
61.0,2.0,26.1,126.0,215.0,129.8,57.0,4.0,4.9488,96.0,217.0
48.0,1.0,32.7,93.0,276.0,198.6,43.0,6.42,5.1475,91.0,121.0
54.0,2.0,27.3,100.0,200.0,144.0,33.0,6.0,4.7449,76.0,235.0
53.0,2.0,26.6,93.0,185.0,122.4,36.0,5.0,4.8903,82.0,245.0
48.0,1.0,22.8,101.0,110.0,41.6,56.0,2.0,4.1271,97.0,40.0
53.0,1.0,28.8,111.67,145.0,87.2,46.0,3.15,4.0775,85.0,52.0
29.0,2.0,18.1,73.0,158.0,99.0,41.0,4.0,4.4998,78.0,104.0
62.0,1.0,32.0,88.0,172.0,69.0,38.0,4.0,5.7838,100.0,132.0
50.0,2.0,23.7,92.0,166.0,97.0,52.0,3.0,4.4427,93.0,88.0
58.0,2.0,23.6,96.0,257.0,171.0,59.0,4.0,4.9053,82.0,69.0
55.0,2.0,24.6,109.0,143.0,76.4,51.0,3.0,4.3567,88.0,219.0
54.0,1.0,22.6,90.0,183.0,104.2,64.0,3.0,4.3041,92.0,72.0
36.0,1.0,27.8,73.0,153.0,104.4,42.0,4.0,3.4965,73.0,201.0
63.0,2.0,24.1,111.0,184.0,112.2,44.0,4.0,4.9345,82.0,110.0
47.0,2.0,26.5,70.0,181.0,104.8,63.0,3.0,4.1897,70.0,51.0
51.0,2.0,32.8,112.0,202.0,100.6,37.0,5.0,5.7746,109.0,277.0
42.0,1.0,19.9,76.0,146.0,83.2,55.0,3.0,3.6636,79.0,63.0
37.0,2.0,23.6,94.0,205.0,138.8,53.0,4.0,4.1897,107.0,118.0
28.0,1.0,22.1,82.0,168.0,100.6,54.0,3.0,4.2047,86.0,69.0
58.0,1.0,28.1,111.0,198.0,80.6,31.0,6.0,6.0684,93.0,273.0
32.0,1.0,26.5,86.0,184.0,101.6,53.0,4.0,4.9904,78.0,258.0
25.0,2.0,23.5,88.0,143.0,80.8,55.0,3.0,3.5835,83.0,43.0
63.0,1.0,26.0,85.67,155.0,78.2,46.0,3.37,5.037,97.0,198.0
52.0,1.0,27.8,85.0,219.0,136.0,49.0,4.0,5.1358,75.0,242.0
65.0,2.0,28.5,109.0,201.0,123.0,46.0,4.0,5.0752,96.0,232.0
42.0,1.0,30.6,121.0,176.0,92.8,69.0,3.0,4.2627,89.0,175.0
53.0,1.0,22.2,78.0,164.0,81.0,70.0,2.0,4.1744,101.0,93.0
79.0,2.0,23.3,88.0,186.0,128.4,33.0,6.0,4.8122,102.0,168.0
43.0,1.0,35.4,93.0,185.0,100.2,44.0,4.0,5.3181,101.0,275.0
44.0,1.0,31.4,115.0,165.0,97.6,52.0,3.0,4.3438,89.0,293.0
62.0,2.0,37.8,119.0,113.0,51.0,31.0,4.0,5.0434,84.0,281.0
33.0,1.0,18.9,70.0,162.0,91.8,59.0,3.0,4.0254,58.0,72.0
56.0,1.0,35.0,79.33,195.0,140.8,42.0,4.64,4.1109,96.0,140.0
66.0,1.0,21.7,126.0,212.0,127.8,45.0,4.71,5.2781,101.0,189.0
34.0,2.0,25.3,111.0,230.0,162.0,39.0,6.0,4.9767,90.0,181.0
46.0,2.0,23.8,97.0,224.0,139.2,42.0,5.0,5.3660000000000005,81.0,209.0
50.0,1.0,31.8,82.0,136.0,69.2,55.0,2.0,4.0775,85.0,136.0
69.0,1.0,34.3,113.0,200.0,123.8,54.0,4.0,4.7095,112.0,261.0
34.0,1.0,26.3,87.0,197.0,120.0,63.0,3.0,4.2485,96.0,113.0
71.0,2.0,27.0,93.33,269.0,190.2,41.0,6.56,5.2417,93.0,131.0
47.0,1.0,27.2,80.0,208.0,145.6,38.0,6.0,4.803999999999999,92.0,174.0
41.0,1.0,33.8,123.33,187.0,127.0,45.0,4.16,4.3175,100.0,257.0
34.0,1.0,33.0,73.0,178.0,114.6,51.0,3.49,4.1271,92.0,55.0
51.0,1.0,24.1,87.0,261.0,175.6,69.0,4.0,4.4067,93.0,84.0
43.0,1.0,21.3,79.0,141.0,78.8,53.0,3.0,3.8286,90.0,42.0
55.0,1.0,23.0,94.67,190.0,137.6,38.0,5.0,4.2767,106.0,146.0
59.0,2.0,27.9,101.0,218.0,144.2,38.0,6.0,5.1874,95.0,212.0
27.0,2.0,33.6,110.0,246.0,156.6,57.0,4.0,5.0876,89.0,233.0
51.0,2.0,22.7,103.0,217.0,162.4,30.0,7.0,4.8122,80.0,91.0
49.0,2.0,27.4,89.0,177.0,113.0,37.0,5.0,4.9053,97.0,111.0
27.0,1.0,22.6,71.0,116.0,43.4,56.0,2.0,4.4188,79.0,152.0
57.0,2.0,23.2,107.33,231.0,159.4,41.0,5.63,5.0304,112.0,120.0
39.0,2.0,26.9,93.0,136.0,75.4,48.0,3.0,4.1431,99.0,67.0
62.0,2.0,34.6,120.0,215.0,129.2,43.0,5.0,5.3660000000000005,123.0,310.0
37.0,1.0,23.3,88.0,223.0,142.0,65.0,3.4,4.3567,82.0,94.0
46.0,1.0,21.1,80.0,205.0,144.4,42.0,5.0,4.5326,87.0,183.0
68.0,2.0,23.5,101.0,162.0,85.4,59.0,3.0,4.4773,91.0,66.0
51.0,1.0,31.5,93.0,231.0,144.0,49.0,4.7,5.2523,117.0,173.0
41.0,1.0,20.8,86.0,223.0,128.2,83.0,3.0,4.0775,89.0,72.0
53.0,1.0,26.5,97.0,193.0,122.4,58.0,3.0,4.1431,99.0,49.0
45.0,1.0,24.2,83.0,177.0,118.4,45.0,4.0,4.2195,82.0,64.0
33.0,1.0,19.5,80.0,171.0,85.4,75.0,2.0,3.9703,80.0,48.0
60.0,2.0,28.2,112.0,185.0,113.8,42.0,4.0,4.9836,93.0,178.0
47.0,2.0,24.9,75.0,225.0,166.0,42.0,5.0,4.4427,102.0,104.0
60.0,2.0,24.9,99.67,162.0,106.6,43.0,3.77,4.1271,95.0,132.0
36.0,1.0,30.0,95.0,201.0,125.2,42.0,4.79,5.1299,85.0,220.0
36.0,1.0,19.6,71.0,250.0,133.2,97.0,3.0,4.5951,92.0,57.0
1 age sex bmi bp s1 s2 s3 s4 s5 s6 target
2 59.0 2.0 32.1 101.0 157.0 93.2 38.0 4.0 4.8598 87.0 151.0
3 48.0 1.0 21.6 87.0 183.0 103.2 70.0 3.0 3.8918 69.0 75.0
4 72.0 2.0 30.5 93.0 156.0 93.6 41.0 4.0 4.6728 85.0 141.0
5 24.0 1.0 25.3 84.0 198.0 131.4 40.0 5.0 4.8903 89.0 206.0
6 50.0 1.0 23.0 101.0 192.0 125.4 52.0 4.0 4.2905 80.0 135.0
7 23.0 1.0 22.6 89.0 139.0 64.8 61.0 2.0 4.1897 68.0 97.0
8 36.0 2.0 22.0 90.0 160.0 99.6 50.0 3.0 3.9512 82.0 138.0
9 66.0 2.0 26.2 114.0 255.0 185.0 56.0 4.55 4.2485 92.0 63.0
10 60.0 2.0 32.1 83.0 179.0 119.4 42.0 4.0 4.4773 94.0 110.0
11 29.0 1.0 30.0 85.0 180.0 93.4 43.0 4.0 5.3845 88.0 310.0
12 22.0 1.0 18.6 97.0 114.0 57.6 46.0 2.0 3.9512 83.0 101.0
13 56.0 2.0 28.0 85.0 184.0 144.8 32.0 6.0 3.5835 77.0 69.0
14 53.0 1.0 23.7 92.0 186.0 109.2 62.0 3.0 4.3041 81.0 179.0
15 50.0 2.0 26.2 97.0 186.0 105.4 49.0 4.0 5.0626 88.0 185.0
16 61.0 1.0 24.0 91.0 202.0 115.4 72.0 3.0 4.2905 73.0 118.0
17 34.0 2.0 24.7 118.0 254.0 184.2 39.0 7.0 5.037 81.0 171.0
18 47.0 1.0 30.3 109.0 207.0 100.2 70.0 3.0 5.2149 98.0 166.0
19 68.0 2.0 27.5 111.0 214.0 147.0 39.0 5.0 4.9416 91.0 144.0
20 38.0 1.0 25.4 84.0 162.0 103.0 42.0 4.0 4.4427 87.0 97.0
21 41.0 1.0 24.7 83.0 187.0 108.2 60.0 3.0 4.5433 78.0 168.0
22 35.0 1.0 21.1 82.0 156.0 87.8 50.0 3.0 4.5109 95.0 68.0
23 25.0 2.0 24.3 95.0 162.0 98.6 54.0 3.0 3.8501 87.0 49.0
24 25.0 1.0 26.0 92.0 187.0 120.4 56.0 3.0 3.9703 88.0 68.0
25 61.0 2.0 32.0 103.67 210.0 85.2 35.0 6.0 6.107 124.0 245.0
26 31.0 1.0 29.7 88.0 167.0 103.4 48.0 4.0 4.3567 78.0 184.0
27 30.0 2.0 25.2 83.0 178.0 118.4 34.0 5.0 4.852 83.0 202.0
28 19.0 1.0 19.2 87.0 124.0 54.0 57.0 2.0 4.1744 90.0 137.0
29 42.0 1.0 31.9 83.0 158.0 87.6 53.0 3.0 4.4659 101.0 85.0
30 63.0 1.0 24.4 73.0 160.0 91.4 48.0 3.0 4.6347 78.0 131.0
31 67.0 2.0 25.8 113.0 158.0 54.2 64.0 2.0 5.2933 104.0 283.0
32 32.0 1.0 30.5 89.0 182.0 110.6 56.0 3.0 4.3438 89.0 129.0
33 42.0 1.0 20.3 71.0 161.0 81.2 66.0 2.0 4.2341 81.0 59.0
34 58.0 2.0 38.0 103.0 150.0 107.2 22.0 7.0 4.6444 98.0 341.0
35 57.0 1.0 21.7 94.0 157.0 58.0 82.0 2.0 4.4427 92.0 87.0
36 53.0 1.0 20.5 78.0 147.0 84.2 52.0 3.0 3.989 75.0 65.0
37 62.0 2.0 23.5 80.33 225.0 112.8 86.0 2.62 4.8752 96.0 102.0
38 52.0 1.0 28.5 110.0 195.0 97.2 60.0 3.0 5.2417 85.0 265.0
39 46.0 1.0 27.4 78.0 171.0 88.0 58.0 3.0 4.8283 90.0 276.0
40 48.0 2.0 33.0 123.0 253.0 163.6 44.0 6.0 5.425 97.0 252.0
41 48.0 2.0 27.7 73.0 191.0 119.4 46.0 4.0 4.852 92.0 90.0
42 50.0 2.0 25.6 101.0 229.0 162.2 43.0 5.0 4.7791 114.0 100.0
43 21.0 1.0 20.1 63.0 135.0 69.0 54.0 3.0 4.0943 89.0 55.0
44 32.0 2.0 25.4 90.33 153.0 100.4 34.0 4.5 4.5326 83.0 61.0
45 54.0 1.0 24.2 74.0 204.0 109.0 82.0 2.0 4.1744 109.0 92.0
46 61.0 2.0 32.7 97.0 177.0 118.4 29.0 6.0 4.9972 87.0 259.0
47 56.0 2.0 23.1 104.0 181.0 116.4 47.0 4.0 4.4773 79.0 53.0
48 33.0 1.0 25.3 85.0 155.0 85.0 51.0 3.0 4.5539 70.0 190.0
49 27.0 1.0 19.6 78.0 128.0 68.0 43.0 3.0 4.4427 71.0 142.0
50 67.0 2.0 22.5 98.0 191.0 119.2 61.0 3.0 3.989 86.0 75.0
51 37.0 2.0 27.7 93.0 180.0 119.4 30.0 6.0 5.0304 88.0 142.0
52 58.0 1.0 25.7 99.0 157.0 91.6 49.0 3.0 4.4067 93.0 155.0
53 65.0 2.0 27.9 103.0 159.0 96.8 42.0 4.0 4.6151 86.0 225.0
54 34.0 1.0 25.5 93.0 218.0 144.0 57.0 4.0 4.4427 88.0 59.0
55 46.0 1.0 24.9 115.0 198.0 129.6 54.0 4.0 4.2767 103.0 104.0
56 35.0 1.0 28.7 97.0 204.0 126.8 64.0 3.0 4.1897 93.0 182.0
57 37.0 1.0 21.8 84.0 184.0 101.0 73.0 3.0 3.912 93.0 128.0
58 37.0 1.0 30.2 87.0 166.0 96.0 40.0 4.15 5.0106 87.0 52.0
59 41.0 1.0 20.5 80.0 124.0 48.8 64.0 2.0 4.0254 75.0 37.0
60 60.0 1.0 20.4 105.0 198.0 78.4 99.0 2.0 4.6347 79.0 170.0
61 66.0 2.0 24.0 98.0 236.0 146.4 58.0 4.0 5.0626 96.0 170.0
62 29.0 1.0 26.0 83.0 141.0 65.2 64.0 2.0 4.0775 83.0 61.0
63 37.0 2.0 26.8 79.0 157.0 98.0 28.0 6.0 5.0434 96.0 144.0
64 41.0 2.0 25.7 83.0 181.0 106.6 66.0 3.0 3.7377 85.0 52.0
65 39.0 1.0 22.9 77.0 204.0 143.2 46.0 4.0 4.3041 74.0 128.0
66 67.0 2.0 24.0 83.0 143.0 77.2 49.0 3.0 4.4308 94.0 71.0
67 36.0 2.0 24.1 112.0 193.0 125.0 35.0 6.0 5.1059 95.0 163.0
68 46.0 2.0 24.7 85.0 174.0 123.2 30.0 6.0 4.6444 96.0 150.0
69 60.0 2.0 25.0 89.67 185.0 120.8 46.0 4.02 4.5109 92.0 97.0
70 59.0 2.0 23.6 83.0 165.0 100.0 47.0 4.0 4.4998 92.0 160.0
71 53.0 1.0 22.1 93.0 134.0 76.2 46.0 3.0 4.0775 96.0 178.0
72 48.0 1.0 19.9 91.0 189.0 109.6 69.0 3.0 3.9512 101.0 48.0
73 48.0 1.0 29.5 131.0 207.0 132.2 47.0 4.0 4.9345 106.0 270.0
74 66.0 2.0 26.0 91.0 264.0 146.6 65.0 4.0 5.5683 87.0 202.0
75 52.0 2.0 24.5 94.0 217.0 149.4 48.0 5.0 4.585 89.0 111.0
76 52.0 2.0 26.6 111.0 209.0 126.4 61.0 3.0 4.6821 109.0 85.0
77 46.0 2.0 23.5 87.0 181.0 114.8 44.0 4.0 4.7095 98.0 42.0
78 40.0 2.0 29.0 115.0 97.0 47.2 35.0 2.77 4.3041 95.0 170.0
79 22.0 1.0 23.0 73.0 161.0 97.8 54.0 3.0 3.8286 91.0 200.0
80 50.0 1.0 21.0 88.0 140.0 71.8 35.0 4.0 5.112 71.0 252.0
81 20.0 1.0 22.9 87.0 191.0 128.2 53.0 4.0 3.8918 85.0 113.0
82 68.0 1.0 27.5 107.0 241.0 149.6 64.0 4.0 4.92 90.0 143.0
83 52.0 2.0 24.3 86.0 197.0 133.6 44.0 5.0 4.5747 91.0 51.0
84 44.0 1.0 23.1 87.0 213.0 126.4 77.0 3.0 3.8712 72.0 52.0
85 38.0 1.0 27.3 81.0 146.0 81.6 47.0 3.0 4.4659 81.0 210.0
86 49.0 1.0 22.7 65.33 168.0 96.2 62.0 2.71 3.8918 60.0 65.0
87 61.0 1.0 33.0 95.0 182.0 114.8 54.0 3.0 4.1897 74.0 141.0
88 29.0 2.0 19.4 83.0 152.0 105.8 39.0 4.0 3.5835 83.0 55.0
89 61.0 1.0 25.8 98.0 235.0 125.8 76.0 3.0 5.112 82.0 134.0
90 34.0 2.0 22.6 75.0 166.0 91.8 60.0 3.0 4.2627 108.0 42.0
91 36.0 1.0 21.9 89.0 189.0 105.2 68.0 3.0 4.3694 96.0 111.0
92 52.0 1.0 24.0 83.0 167.0 86.6 71.0 2.0 3.8501 94.0 98.0
93 61.0 1.0 31.2 79.0 235.0 156.8 47.0 5.0 5.0499 96.0 164.0
94 43.0 1.0 26.8 123.0 193.0 102.2 67.0 3.0 4.7791 94.0 48.0
95 35.0 1.0 20.4 65.0 187.0 105.6 67.0 2.79 4.2767 78.0 96.0
96 27.0 1.0 24.8 91.0 189.0 106.8 69.0 3.0 4.1897 69.0 90.0
97 29.0 1.0 21.0 71.0 156.0 97.0 38.0 4.0 4.654 90.0 162.0
98 64.0 2.0 27.3 109.0 186.0 107.6 38.0 5.0 5.3083 99.0 150.0
99 41.0 1.0 34.6 87.33 205.0 142.6 41.0 5.0 4.6728 110.0 279.0
100 49.0 2.0 25.9 91.0 178.0 106.6 52.0 3.0 4.5747 75.0 92.0
101 48.0 1.0 20.4 98.0 209.0 139.4 46.0 5.0 4.7707 78.0 83.0
102 53.0 1.0 28.0 88.0 233.0 143.8 58.0 4.0 5.0499 91.0 128.0
103 53.0 2.0 22.2 113.0 197.0 115.2 67.0 3.0 4.3041 100.0 102.0
104 23.0 1.0 29.0 90.0 216.0 131.4 65.0 3.0 4.585 91.0 302.0
105 65.0 2.0 30.2 98.0 219.0 160.6 40.0 5.0 4.5218 84.0 198.0
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{
"cells": [
{
"cell_type": "markdown",
"source": [
"Import the libraries"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 1,
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Create the Regression Models"
],
"metadata": {
"collapsed": false
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},
{
"cell_type": "code",
"execution_count": 2,
"outputs": [],
"source": [
"class Predictor:\n",
" def __init__(self):\n",
" self.coefficients = None\n",
"\n",
" def fit(self, X, y):\n",
" pass\n",
"\n",
" def predict(self, X):\n",
" pass\n",
"\n",
"class LinearRegression(Predictor):\n",
" pass\n",
"\n",
"class RidgeRegression(Predictor):\n",
" pass\n",
"\n",
"class LassoRegression(Predictor):\n",
" pass"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Data Preprocessing and Data loading functions"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def preprocess(df):\n",
" pass\n",
"\n",
"def train_test_split(X, y, test_size=0.2):\n",
" pass\n",
"\n",
"def load_data():\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 4,
"outputs": [],
"source": [
"# Load the diabetes dataset\n",
"df = pd.read_csv(\"diabetes.csv\")\n",
"\n",
"# Preprocess the dataset\n",
"df = preprocess(df)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"collapsed": false
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},
{
"cell_type": "markdown",
"source": [
"Load the data"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [
{
"ename": "TypeError",
"evalue": "cannot unpack non-iterable NoneType object",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mTypeError\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[5], line 2\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;66;03m# Load the data\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m X_train, X_test, y_train, y_test \u001B[38;5;241m=\u001B[39m load_data()\n",
"\u001B[0;31mTypeError\u001B[0m: cannot unpack non-iterable NoneType object"
]
}
],
"source": [
"# Load the data\n",
"X_train, X_test, y_train, y_test = load_data()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Fit the models"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"# Fit the linear regression\n",
"linear_regression = LinearRegression()\n",
"linear_regression.fit(X_train, y_train)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"# Fit the ridge regression\n",
"ridge_regression = RidgeRegression(alpha=1)\n",
"ridge_regression.fit(X_train, y_train)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"# Fit the lasso regression\n",
"lasso_regression = LassoRegression(alpha=1, num_iters=10000, lr=0.001)\n",
"lasso_regression.fit(X_train, y_train)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"Evaluate the models"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false
}
}
],
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