├── Association Rule Mining.ipynb ├── AssociationRules_Practicals.ipynb ├── CarPrice.csv ├── Decision Tree.ipynb ├── Ensemble Models - Random Forest.ipynb ├── Ensemble_Models_Practicals.ipynb ├── Housing.csv ├── Linear Regression Housing Case Study.ipynb ├── Linear, Ridge and Lasso Regression Practical.ipynb ├── Machine Learning Model Design Steps.ipynb ├── Machine Learning Pipeline.ipynb ├── Model Selection Principles.ipynb ├── MyCreditData.csv ├── README.md └── supermarket_binarymat.csv /CarPrice.csv: -------------------------------------------------------------------------------- 1 | car_ID,symboling,CarName,fueltype,aspiration,doornumber,carbody,drivewheel,enginelocation,wheelbase,carlength,carwidth,carheight,curbweight,enginetype,cylindernumber,enginesize,fuelsystem,boreratio,stroke,compressionratio,horsepower,peakrpm,citympg,highwaympg,price 2 | 1,3,alfa-romero giulia,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,13495 3 | 2,3,alfa-romero stelvio,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,16500 4 | 3,1,alfa-romero Quadrifoglio,gas,std,two,hatchback,rwd,front,94.5,171.2,65.5,52.4,2823,ohcv,six,152,mpfi,2.68,3.47,9,154,5000,19,26,16500 5 | 4,2,audi 100 ls,gas,std,four,sedan,fwd,front,99.8,176.6,66.2,54.3,2337,ohc,four,109,mpfi,3.19,3.4,10,102,5500,24,30,13950 6 | 5,2,audi 100ls,gas,std,four,sedan,4wd,front,99.4,176.6,66.4,54.3,2824,ohc,five,136,mpfi,3.19,3.4,8,115,5500,18,22,17450 7 | 6,2,audi fox,gas,std,two,sedan,fwd,front,99.8,177.3,66.3,53.1,2507,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,15250 8 | 7,1,audi 100ls,gas,std,four,sedan,fwd,front,105.8,192.7,71.4,55.7,2844,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,17710 9 | 8,1,audi 5000,gas,std,four,wagon,fwd,front,105.8,192.7,71.4,55.7,2954,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,18920 10 | 9,1,audi 4000,gas,turbo,four,sedan,fwd,front,105.8,192.7,71.4,55.9,3086,ohc,five,131,mpfi,3.13,3.4,8.3,140,5500,17,20,23875 11 | 10,0,audi 5000s (diesel),gas,turbo,two,hatchback,4wd,front,99.5,178.2,67.9,52,3053,ohc,five,131,mpfi,3.13,3.4,7,160,5500,16,22,17859.167 12 | 11,2,bmw 320i,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16430 13 | 12,0,bmw 320i,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16925 14 | 13,0,bmw x1,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2710,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,20970 15 | 14,0,bmw x3,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2765,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,21105 16 | 15,1,bmw z4,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3055,ohc,six,164,mpfi,3.31,3.19,9,121,4250,20,25,24565 17 | 16,0,bmw x4,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3230,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,30760 18 | 17,0,bmw x5,gas,std,two,sedan,rwd,front,103.5,193.8,67.9,53.7,3380,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,41315 19 | 18,0,bmw x3,gas,std,four,sedan,rwd,front,110,197,70.9,56.3,3505,ohc,six,209,mpfi,3.62,3.39,8,182,5400,15,20,36880 20 | 19,2,chevrolet impala,gas,std,two,hatchback,fwd,front,88.4,141.1,60.3,53.2,1488,l,three,61,2bbl,2.91,3.03,9.5,48,5100,47,53,5151 21 | 20,1,chevrolet monte carlo,gas,std,two,hatchback,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6295 22 | 21,0,chevrolet vega 2300,gas,std,four,sedan,fwd,front,94.5,158.8,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6575 23 | 22,1,dodge rampage,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572 24 | 23,1,dodge challenger se,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6377 25 | 24,1,dodge d200,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,7957 26 | 25,1,dodge monaco (sw),gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229 27 | 26,1,dodge colt hardtop,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692 28 | 27,1,dodge colt (sw),gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,7609 29 | 28,1,dodge coronet custom,gas,turbo,two,sedan,fwd,front,93.7,157.3,63.8,50.6,2191,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,8558 30 | 29,-1,dodge dart custom,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.34,3.46,8.5,88,5000,24,30,8921 31 | 30,3,dodge coronet custom (sw),gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2811,ohc,four,156,mfi,3.6,3.9,7,145,5000,19,24,12964 32 | 31,2,honda civic,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1713,ohc,four,92,1bbl,2.91,3.41,9.6,58,4800,49,54,6479 33 | 32,2,honda civic cvcc,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1819,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,31,38,6855 34 | 33,1,honda civic,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1837,ohc,four,79,1bbl,2.91,3.07,10.1,60,5500,38,42,5399 35 | 34,1,honda accord cvcc,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1940,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,6529 36 | 35,1,honda civic cvcc,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1956,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7129 37 | 36,0,honda accord lx,gas,std,four,sedan,fwd,front,96.5,163.4,64,54.5,2010,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7295 38 | 37,0,honda civic 1500 gl,gas,std,four,wagon,fwd,front,96.5,157.1,63.9,58.3,2024,ohc,four,92,1bbl,2.92,3.41,9.2,76,6000,30,34,7295 39 | 38,0,honda accord,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2236,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,7895 40 | 39,0,honda civic 1300,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2289,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,9095 41 | 40,0,honda prelude,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2304,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,8845 42 | 41,0,honda accord,gas,std,four,sedan,fwd,front,96.5,175.4,62.5,54.1,2372,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,10295 43 | 42,0,honda civic,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2465,ohc,four,110,mpfi,3.15,3.58,9,101,5800,24,28,12945 44 | 43,1,honda civic (auto),gas,std,two,sedan,fwd,front,96.5,169.1,66,51,2293,ohc,four,110,2bbl,3.15,3.58,9.1,100,5500,25,31,10345 45 | 44,0,isuzu MU-X,gas,std,four,sedan,rwd,front,94.3,170.7,61.8,53.5,2337,ohc,four,111,2bbl,3.31,3.23,8.5,78,4800,24,29,6785 46 | 45,1,isuzu D-Max ,gas,std,two,sedan,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,8916.5 47 | 46,0,isuzu D-Max V-Cross,gas,std,four,sedan,fwd,front,94.5,155.9,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,8916.5 48 | 47,2,isuzu D-Max ,gas,std,two,hatchback,rwd,front,96,172.6,65.2,51.4,2734,ohc,four,119,spfi,3.43,3.23,9.2,90,5000,24,29,11048 49 | 48,0,jaguar xj,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,32250 50 | 49,0,jaguar xf,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,35550 51 | 50,0,jaguar xk,gas,std,two,sedan,rwd,front,102,191.7,70.6,47.8,3950,ohcv,twelve,326,mpfi,3.54,2.76,11.5,262,5000,13,17,36000 52 | 51,1,maxda rx3,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1890,ohc,four,91,2bbl,3.03,3.15,9,68,5000,30,31,5195 53 | 52,1,maxda glc deluxe,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1900,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6095 54 | 53,1,mazda rx2 coupe,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1905,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6795 55 | 54,1,mazda rx-4,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1945,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6695 56 | 55,1,mazda glc deluxe,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1950,ohc,four,91,2bbl,3.08,3.15,9,68,5000,31,38,7395 57 | 56,3,mazda 626,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,10945 58 | 57,3,mazda glc,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,11845 59 | 58,3,mazda rx-7 gs,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2385,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,13645 60 | 59,3,mazda glc 4,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2500,rotor,two,80,mpfi,3.33,3.255,9.4,135,6000,16,23,15645 61 | 60,1,mazda 626,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8845 62 | 61,0,mazda glc custom l,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8495 63 | 62,1,mazda glc custom,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10595 64 | 63,0,mazda rx-4,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10245 65 | 64,0,mazda glc deluxe,diesel,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2443,ohc,four,122,idi,3.39,3.39,22.7,64,4650,36,42,10795 66 | 65,0,mazda 626,gas,std,four,hatchback,fwd,front,98.8,177.8,66.5,55.5,2425,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,11245 67 | 66,0,mazda glc,gas,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2670,ohc,four,140,mpfi,3.76,3.16,8,120,5000,19,27,18280 68 | 67,0,mazda rx-7 gs,diesel,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2700,ohc,four,134,idi,3.43,3.64,22,72,4200,31,39,18344 69 | 68,-1,buick electra 225 custom,diesel,turbo,four,sedan,rwd,front,110,190.9,70.3,56.5,3515,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,25552 70 | 69,-1,buick century luxus (sw),diesel,turbo,four,wagon,rwd,front,110,190.9,70.3,58.7,3750,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28248 71 | 70,0,buick century,diesel,turbo,two,hardtop,rwd,front,106.7,187.5,70.3,54.9,3495,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28176 72 | 71,-1,buick skyhawk,diesel,turbo,four,sedan,rwd,front,115.6,202.6,71.7,56.3,3770,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,31600 73 | 72,-1,buick opel isuzu deluxe,gas,std,four,sedan,rwd,front,115.6,202.6,71.7,56.5,3740,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,34184 74 | 73,3,buick skylark,gas,std,two,convertible,rwd,front,96.6,180.3,70.5,50.8,3685,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,35056 75 | 74,0,buick century special,gas,std,four,sedan,rwd,front,120.9,208.1,71.7,56.7,3900,ohcv,eight,308,mpfi,3.8,3.35,8,184,4500,14,16,40960 76 | 75,1,buick regal sport coupe (turbo),gas,std,two,hardtop,rwd,front,112,199.2,72,55.4,3715,ohcv,eight,304,mpfi,3.8,3.35,8,184,4500,14,16,45400 77 | 76,1,mercury cougar,gas,turbo,two,hatchback,rwd,front,102.7,178.4,68,54.8,2910,ohc,four,140,mpfi,3.78,3.12,8,175,5000,19,24,16503 78 | 77,2,mitsubishi mirage,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1918,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,37,41,5389 79 | 78,2,mitsubishi lancer,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1944,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6189 80 | 79,2,mitsubishi outlander,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,2004,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6669 81 | 80,1,mitsubishi g4,gas,turbo,two,hatchback,fwd,front,93,157.3,63.8,50.8,2145,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7689 82 | 81,3,mitsubishi mirage g4,gas,turbo,two,hatchback,fwd,front,96.3,173,65.4,49.4,2370,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9959 83 | 82,3,mitsubishi g4,gas,std,two,hatchback,fwd,front,96.3,173,65.4,49.4,2328,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8499 84 | 83,3,mitsubishi outlander,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2833,ohc,four,156,spdi,3.58,3.86,7,145,5000,19,24,12629 85 | 84,3,mitsubishi g4,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2921,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14869 86 | 85,3,mitsubishi mirage g4,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2926,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14489 87 | 86,1,mitsubishi montero,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2365,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,6989 88 | 87,1,mitsubishi pajero,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2405,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8189 89 | 88,1,mitsubishi outlander,gas,turbo,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279 90 | 89,-1,mitsubishi mirage g4,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279 91 | 90,1,Nissan versa,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1889,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,5499 92 | 91,1,nissan gt-r,diesel,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,2017,ohc,four,103,idi,2.99,3.47,21.9,55,4800,45,50,7099 93 | 92,1,nissan rogue,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1918,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6649 94 | 93,1,nissan latio,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1938,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6849 95 | 94,1,nissan titan,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2024,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7349 96 | 95,1,nissan leaf,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1951,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7299 97 | 96,1,nissan juke,gas,std,two,hatchback,fwd,front,94.5,165.6,63.8,53.3,2028,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7799 98 | 97,1,nissan latio,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1971,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7499 99 | 98,1,nissan note,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2037,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7999 100 | 99,2,nissan clipper,gas,std,two,hardtop,fwd,front,95.1,162.4,63.8,53.3,2008,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,8249 101 | 100,0,nissan rogue,gas,std,four,hatchback,fwd,front,97.2,173.4,65.2,54.7,2324,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,8949 102 | 101,0,nissan nv200,gas,std,four,sedan,fwd,front,97.2,173.4,65.2,54.7,2302,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,9549 103 | 102,0,nissan dayz,gas,std,four,sedan,fwd,front,100.4,181.7,66.5,55.1,3095,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,13499 104 | 103,0,nissan fuga,gas,std,four,wagon,fwd,front,100.4,184.6,66.5,56.1,3296,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,14399 105 | 104,0,nissan otti,gas,std,four,sedan,fwd,front,100.4,184.6,66.5,55.1,3060,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,19,25,13499 106 | 105,3,nissan teana,gas,std,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3071,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,17199 107 | 106,3,nissan kicks,gas,turbo,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,7.8,200,5200,17,23,19699 108 | 107,1,nissan clipper,gas,std,two,hatchback,rwd,front,99.2,178.5,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,18399 109 | 108,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3020,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,11900 110 | 109,0,peugeot 304,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3197,l,four,152,idi,3.7,3.52,21,95,4150,28,33,13200 111 | 110,0,peugeot 504 (sw),gas,std,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3230,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,12440 112 | 111,0,peugeot 504,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3430,l,four,152,idi,3.7,3.52,21,95,4150,25,25,13860 113 | 112,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,15580 114 | 113,0,peugeot 604sl,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,16900 115 | 114,0,peugeot 504,gas,std,four,wagon,rwd,front,114.2,198.9,68.4,56.7,3285,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,16695 116 | 115,0,peugeot 505s turbo diesel,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3485,l,four,152,idi,3.7,3.52,21,95,4150,25,25,17075 117 | 116,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,16630 118 | 117,0,peugeot 504,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,17950 119 | 118,0,peugeot 604sl,gas,turbo,four,sedan,rwd,front,108,186.7,68.3,56,3130,l,four,134,mpfi,3.61,3.21,7,142,5600,18,24,18150 120 | 119,1,plymouth fury iii,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1918,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,37,41,5572 121 | 120,1,plymouth cricket,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7957 122 | 121,1,plymouth fury iii,gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229 123 | 122,1,plymouth satellite custom (sw),gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692 124 | 123,1,plymouth fury gran sedan,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,2191,ohc,four,98,2bbl,2.97,3.23,9.4,68,5500,31,38,7609 125 | 124,-1,plymouth valiant,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,24,30,8921 126 | 125,3,plymouth duster,gas,turbo,two,hatchback,rwd,front,95.9,173.2,66.3,50.2,2818,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,12764 127 | 126,3,porsche macan,gas,std,two,hatchback,rwd,front,94.5,168.9,68.3,50.2,2778,ohc,four,151,mpfi,3.94,3.11,9.5,143,5500,19,27,22018 128 | 127,3,porcshce panamera,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,32528 129 | 128,3,porsche cayenne,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,34028 130 | 129,3,porsche boxter,gas,std,two,convertible,rwd,rear,89.5,168.9,65,51.6,2800,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,37028 131 | 130,1,porsche cayenne,gas,std,two,hatchback,rwd,front,98.4,175.7,72.3,50.5,3366,dohcv,eight,203,mpfi,3.94,3.11,10,288,5750,17,28,31400.5 132 | 131,0,renault 12tl,gas,std,four,wagon,fwd,front,96.1,181.5,66.5,55.2,2579,ohc,four,132,mpfi,3.46,3.9,8.7,90,5100,23,31,9295 133 | 132,2,renault 5 gtl,gas,std,two,hatchback,fwd,front,96.1,176.8,66.6,50.5,2460,ohc,four,132,mpfi,3.46,3.9,8.7,90,5100,23,31,9895 134 | 133,3,saab 99e,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850 135 | 134,2,saab 99le,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2695,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,12170 136 | 135,3,saab 99le,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2707,ohc,four,121,mpfi,2.54,2.07,9.3,110,5250,21,28,15040 137 | 136,2,saab 99gle,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2758,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,15510 138 | 137,3,saab 99gle,gas,turbo,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2808,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18150 139 | 138,2,saab 99e,gas,turbo,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2847,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18620 140 | 139,2,subaru,gas,std,two,hatchback,fwd,front,93.7,156.9,63.4,53.7,2050,ohcf,four,97,2bbl,3.62,2.36,9,69,4900,31,36,5118 141 | 140,2,subaru dl,gas,std,two,hatchback,fwd,front,93.7,157.9,63.6,53.7,2120,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7053 142 | 141,2,subaru dl,gas,std,two,hatchback,4wd,front,93.3,157.3,63.8,55.7,2240,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7603 143 | 142,0,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2145,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4800,32,37,7126 144 | 143,0,subaru brz,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2190,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4400,28,33,7775 145 | 144,0,subaru baja,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2340,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,26,32,9960 146 | 145,0,subaru r1,gas,std,four,sedan,4wd,front,97,172,65.4,54.3,2385,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,24,25,9233 147 | 146,0,subaru r2,gas,turbo,four,sedan,4wd,front,97,172,65.4,54.3,2510,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,24,29,11259 148 | 147,0,subaru trezia,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2290,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,28,32,7463 149 | 148,0,subaru tribeca,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2455,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,25,31,10198 150 | 149,0,subaru dl,gas,std,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2420,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,23,29,8013 151 | 150,0,subaru dl,gas,turbo,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2650,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,23,23,11694 152 | 151,1,toyota corona mark ii,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,1985,ohc,four,92,2bbl,3.05,3.03,9,62,4800,35,39,5348 153 | 152,1,toyota corona,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,2040,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6338 154 | 153,1,toyota corolla 1200,gas,std,four,hatchback,fwd,front,95.7,158.7,63.6,54.5,2015,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6488 155 | 154,0,toyota corona hardtop,gas,std,four,wagon,fwd,front,95.7,169.7,63.6,59.1,2280,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,37,6918 156 | 155,0,toyota corolla 1600 (sw),gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,2290,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,7898 157 | 156,0,toyota carina,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,3110,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,8778 158 | 157,0,toyota mark ii,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2081,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,6938 159 | 158,0,toyota corolla 1200,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2109,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,7198 160 | 159,0,toyota corona,diesel,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,34,36,7898 161 | 160,0,toyota corolla,diesel,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,38,47,7788 162 | 161,0,toyota corona,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2094,ohc,four,98,2bbl,3.19,3.03,9,70,4800,38,47,7738 163 | 162,0,toyota corolla,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2122,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,8358 164 | 163,0,toyota mark ii,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,52.8,2140,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,9258 165 | 164,1,toyota corolla liftback,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2169,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8058 166 | 165,1,toyota corona,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2204,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8238 167 | 166,1,toyota celica gt liftback,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2265,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9298 168 | 167,1,toyota corolla tercel,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2300,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9538 169 | 168,2,toyota corona liftback,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2540,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,8449 170 | 169,2,toyota corolla,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2536,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9639 171 | 170,2,toyota starlet,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2551,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9989 172 | 171,2,toyota tercel,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2679,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11199 173 | 172,2,toyota corolla,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2714,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11549 174 | 173,2,toyota cressida,gas,std,two,convertible,rwd,front,98.4,176.2,65.6,53,2975,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,17669 175 | 174,-1,toyota corolla,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2326,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,29,34,8948 176 | 175,-1,toyota celica gt,diesel,turbo,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2480,ohc,four,110,idi,3.27,3.35,22.5,73,4500,30,33,10698 177 | 176,-1,toyota corona,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,9988 178 | 177,-1,toyota corolla,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,10898 179 | 178,-1,toyota mark ii,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2458,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,11248 180 | 179,3,toyota corolla liftback,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,2976,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,20,24,16558 181 | 180,3,toyota corona,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,3016,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,19,24,15998 182 | 181,-1,toyota starlet,gas,std,four,sedan,rwd,front,104.5,187.8,66.5,54.1,3131,dohc,six,171,mpfi,3.27,3.35,9.2,156,5200,20,24,15690 183 | 182,-1,toyouta tercel,gas,std,four,wagon,rwd,front,104.5,187.8,66.5,54.1,3151,dohc,six,161,mpfi,3.27,3.35,9.2,156,5200,19,24,15750 184 | 183,2,vokswagen rabbit,diesel,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2261,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7775 185 | 184,2,volkswagen 1131 deluxe sedan,gas,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2209,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,7975 186 | 185,2,volkswagen model 111,diesel,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2264,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7995 187 | 186,2,volkswagen type 3,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2212,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8195 188 | 187,2,volkswagen 411 (sw),gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2275,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8495 189 | 188,2,volkswagen super beetle,diesel,turbo,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2319,ohc,four,97,idi,3.01,3.4,23,68,4500,37,42,9495 190 | 189,2,volkswagen dasher,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2300,ohc,four,109,mpfi,3.19,3.4,10,100,5500,26,32,9995 191 | 190,3,vw dasher,gas,std,two,convertible,fwd,front,94.5,159.3,64.2,55.6,2254,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,11595 192 | 191,3,vw rabbit,gas,std,two,hatchback,fwd,front,94.5,165.7,64,51.4,2221,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,9980 193 | 192,0,volkswagen rabbit,gas,std,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2661,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,24,13295 194 | 193,0,volkswagen rabbit custom,diesel,turbo,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2579,ohc,four,97,idi,3.01,3.4,23,68,4500,33,38,13845 195 | 194,0,volkswagen dasher,gas,std,four,wagon,fwd,front,100.4,183.1,66.9,55.1,2563,ohc,four,109,mpfi,3.19,3.4,9,88,5500,25,31,12290 196 | 195,-2,volvo 145e (sw),gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2912,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,12940 197 | 196,-1,volvo 144ea,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3034,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,13415 198 | 197,-2,volvo 244dl,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2935,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,15985 199 | 198,-1,volvo 245,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3042,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,16515 200 | 199,-2,volvo 264gl,gas,turbo,four,sedan,rwd,front,104.3,188.8,67.2,56.2,3045,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18420 201 | 200,-1,volvo diesel,gas,turbo,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3157,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18950 202 | 201,-1,volvo 145e (sw),gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,2952,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,16845 203 | 202,-1,volvo 144ea,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.8,55.5,3049,ohc,four,141,mpfi,3.78,3.15,8.7,160,5300,19,25,19045 204 | 203,-1,volvo 244dl,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3012,ohcv,six,173,mpfi,3.58,2.87,8.8,134,5500,18,23,21485 205 | 204,-1,volvo 246,diesel,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3217,ohc,six,145,idi,3.01,3.4,23,106,4800,26,27,22470 206 | 205,-1,volvo 264gl,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3062,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,19,25,22625 207 | -------------------------------------------------------------------------------- /Housing.csv: -------------------------------------------------------------------------------- 1 | price,area,bedrooms,bathrooms,stories,mainroad,guestroom,basement,hotwaterheating,airconditioning,parking,prefarea,furnishingstatus 2 | 13300000,7420,4,2,3,yes,no,no,no,yes,2,yes,furnished 3 | 12250000,8960,4,4,4,yes,no,no,no,yes,3,no,furnished 4 | 12250000,9960,3,2,2,yes,no,yes,no,no,2,yes,semi-furnished 5 | 12215000,7500,4,2,2,yes,no,yes,no,yes,3,yes,furnished 6 | 11410000,7420,4,1,2,yes,yes,yes,no,yes,2,no,furnished 7 | 10850000,7500,3,3,1,yes,no,yes,no,yes,2,yes,semi-furnished 8 | 10150000,8580,4,3,4,yes,no,no,no,yes,2,yes,semi-furnished 9 | 10150000,16200,5,3,2,yes,no,no,no,no,0,no,unfurnished 10 | 9870000,8100,4,1,2,yes,yes,yes,no,yes,2,yes,furnished 11 | 9800000,5750,3,2,4,yes,yes,no,no,yes,1,yes,unfurnished 12 | 9800000,13200,3,1,2,yes,no,yes,no,yes,2,yes,furnished 13 | 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the full process of building a Machine Learning model.\n", 9 | "\n", 10 | "**The following steps are involved in the model design:**\n", 11 | "\n", 12 | "1. Import libraries\n", 13 | "2. Import dataset\n", 14 | "3. Exploratory data analysis\n", 15 | "4. Data scrubbing\n", 16 | "5. Pre-model algorithm\n", 17 | "6. Split the data into Training and Testing sets.\n", 18 | "7. Pick the suitable Machine Learning algorithm\n", 19 | "8. Predict\n", 20 | "9. Evaluate\n", 21 | "10. Optimize\n", 22 | "11. Deployment" 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "id": "a4df1717", 28 | "metadata": {}, 29 | "source": [ 30 | "### 1. Importing Libraries\n", 31 | "\n", 32 | "Python code runs line by line from top to bottom. So if we want to use functions or classes from a package we first need to import the package before we use it. \n", 33 | "\n", 34 | "Some people like to import all the required package at the start of the file and on the other hand some people prefer to import the package whenever and wherever they are going to use it within the code." 35 | ] 36 | }, 37 | { 38 | "cell_type": "markdown", 39 | "id": "39bd9dd2", 40 | "metadata": {}, 41 | "source": [ 42 | "### 2. Import and Read the Dataset\n", 43 | "\n", 44 | "Datasets are generally imported from your organization’s records or public repositories such as Kaggle. We can use data from various types of file like `.xlsx, .xls, .csv, .tsv, .sql, .json, .data` etc.\n", 45 | "\n", 46 | "**Some examples to understand how to read the data from different file types:** \n", 47 | "\n", 48 | "- **pd.read_csv(path_of_file)** - to read .csv, .tsv, .data files\n", 49 | "- **pd.read_excel(path_of_file)** - to read .xls, .xlsx files\n", 50 | "- **pd.read_sql(path_of_file)** - to read .sql files.\n", 51 | "- **pd.read_json(path_of_file)** - to read .json files." 52 | ] 53 | }, 54 | { 55 | "cell_type": "markdown", 56 | "id": "75f69682", 57 | "metadata": {}, 58 | "source": [ 59 | "### 3. Exploratory Data Analysis(EDA)\n", 60 | "\n", 61 | "The third step, EDA, provides an opportunity to become familiar with your data including distribution, data-types and the state of missing values. Exploratory data analysis also drives the next stage of data scrubbing and deiciding your choice of machine learning algorithms to be used." 62 | ] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "id": "0c449843", 67 | "metadata": {}, 68 | "source": [ 69 | "### 4. Data Scrubbing/Data Wrangling \n", 70 | "\n", 71 | "The data scrubbing stage usually consumes the most time and effort in developing a prediction model. This includes `cleaning up the data, inspecting its value, making repairs`, and, ultimately, knowing what to throw and when to throw it out." 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "id": "d48bbd5a", 77 | "metadata": {}, 78 | "source": [ 79 | "### 5. Pre-model algorithm (optional)\n", 80 | "\n", 81 | "As an optional extension of the data scrubbing process, unsupervised learning techniques, including k-means clustering analysis and dimensionality reduction algorithms(like PCA, LDA), are sometimes used in preparation for analyzing large and complex datasets.\n", 82 | "\n", 83 | "This step, though, is optional and does not apply to every model, particularly for small datasets with a low number of dimensions (features) or rows." 84 | ] 85 | }, 86 | { 87 | "cell_type": "markdown", 88 | "id": "998a326e", 89 | "metadata": {}, 90 | "source": [ 91 | "### 6. Splitting the data into training and testing sets\n", 92 | "\n", 93 | "We partition the data to train and test sets. It’s also useful to randomize your data at this point using the shuffle feature and to set a random state if you want to replicate the model’s output in the future.\n", 94 | "\n", 95 | "Generally the ratios used for this split into `training:testing is 80:20 / 70:30 / 75:25 / 90:10 / 85:15`. The ratio that we pick totally depends on the size of the data but the most commonly use is 80:20 split." 96 | ] 97 | }, 98 | { 99 | "cell_type": "markdown", 100 | "id": "68cc503a", 101 | "metadata": {}, 102 | "source": [ 103 | "### 7. Pick the suitable algorithm\n", 104 | "\n", 105 | "Choosing a suitable algorithm for the given dataset is very important task and must be done carefully.\n", 106 | "\n", 107 | "The algorithm is a mathematical-based sequence of steps and formulas that learns the patterns from the dataset and represent it as a mathematical equation. By executing these series of steps defined by the algorithm, the model looks at input variables in order to interpret patterns, make calculations, and finally produce the output and decisions that we call as `prediction`.\n", 108 | "\n", 109 | "As input data is variable(changing), algorithms can produce different outputs based on the input data. Algorithms are also malleable in that they have hyperparameters that can be adjusted to create a more customized model.\n", 110 | "\n", 111 | "For context, `the algorithm should not be confused or mistaken for the model. The model is the final state of the algorithm; after hyperparameters are consolidated in response to patterns learned from the data and after a combination of data scrubbing, split validation, and evaluation techniques are completed`.\n", 112 | "\n", 113 | "**Some frequently used algorithms are:**\n", 114 | "\n", 115 | "1. **Linear Regression:** For Regression data. Used when data is less or moderate in size, it has linear patterns and no missing values or outliers.\n", 116 | "\n", 117 | "2. **Logistic Regression:** For Classification data. Used when we have relatively easily separable patterns, limited data with limited outliers.\n", 118 | "\n", 119 | "3. **Decision Tree:** For Classification and Regession both. Used when data is large(but with controlled hyperparameters to aviod overfitting).\n", 120 | "\n", 121 | "4. **Random Forest:** For Classification and Regression both. Used when data is large, messy and complex. Also used to reduce overfitting issue of decision trees by making many small decision trees instead of one large tree.\n", 122 | "\n", 123 | "5. **Gradient Boosting and XGBoost:** For Classification and Regression both. Used when data is large, messy and complex. Used to overcome underfitting issues.\n", 124 | "\n", 125 | "6. **Support Vector Machines(SVMs):** Regression and Classification both. Use when data is moderately large, messy and complex. \n", 126 | "\n", 127 | "7. **K-Means and Hierarchical Clustering:** Unsupervised Learning algorithms.\n", 128 | "\n", 129 | "8. **Multi-layer Perceptrons/Neural Networks:** Both Regression and Classification. When data is very large, too messy and complex. The potential of accuracy in output is very high.\n", 130 | "\n", 131 | "9. **Apriori:** For Association Rule mining." 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "id": "6fd026c0", 137 | "metadata": {}, 138 | "source": [ 139 | "### 8. Predicting the output\n", 140 | "\n", 141 | "After producing an initial model using patterns extracted from the training data, the predict function is called on the test data to validate the performance of the model.\n", 142 | "\n", 143 | "The predict function generates a numeric value such as price in regression and in classification, the predict function generates discrete classes, such as the heart disease patient classification, loan approved or not, fraud or not fraud etc." 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "id": "f084931d", 149 | "metadata": {}, 150 | "source": [ 151 | "### 9. Evaluating the performance or accuracy of prediction made by the model\n", 152 | "\n", 153 | "The next step of the model design process is to evaluate the results. The evaluation method will depend on the scope of your model. Specifically, this depends on whether it is a classification or regression model. \n", 154 | "\n", 155 | "**In classification, the common evaluation methods are the `confusion matrix, classification report, accuracy score and roc-auc score`**.\n", 156 | "\n", 157 | "1. **Accuracy Score:** This is a simple metric measuring how many cases the model classified correctly divided by the full number of cases. If all predictions are correct, the accuracy score is 1.0, and 0 if all cases are mispredicted. But is not always the best method, and it does not give us the whole picture. We use accuracy in the case of balanced data and we check few other measures to know more about the actual performance of the model.\n", 158 | "\n", 159 | "\n", 160 | "2. **Precision:** Precision is the ratio of correctly predicted true positives to the total of predicted positive cases. A high precision score translates to a low occurrence of false positives. `Precision = Number of True Positives / Total predicted positives`. This is the ability of the model not to label a negative case as positive, which is important in the case of drug tests, for example.\n", 161 | "\n", 162 | "\n", 163 | "3. **Recall:** The recall of a model is similar to precision but in this case, represents the ratio of correctly predicted true positives to actual positive cases. In other words, recall addresses the question of how many positive outcomes were rightly classified as positive. This can be understood as the ability of the model to identify all positive cases. Note that the numerator (top) is the same for precision and recall, while the denominators (below) are different. `Recall = No of True Positives / No of actual positive cases.`\n", 164 | "\n", 165 | "\n", 166 | "4. **F1-Score:** F1-score is a weighted average of precision and recall. It’s typically used as a metric for model-to-model comparison rather than for stand-alone model accuracy. In addition, the f1-score is generally lower than the accuracy score due to the way recall and precision are calculated.\n", 167 | "\n", 168 | "5. **Sensitivity and Specificity**\n", 169 | "\n", 170 | "**In regression, the evaluation methods are:**\n", 171 | "\n", 172 | "1. Mean Absolute Error\n", 173 | "2. Root Mean Squared Error\n", 174 | "3. R-squared\n", 175 | "4. Adjusted R-squared" 176 | ] 177 | }, 178 | { 179 | "cell_type": "markdown", 180 | "id": "aa8ff78b", 181 | "metadata": {}, 182 | "source": [ 183 | "### 10. Optimizing the model performance\n", 184 | "\n", 185 | "The final step is to optimize the model. This mean going back to modify the number of clusters or change the hyperparameters of a tree-based learning algorithm like Decision Tree, Random forest etc.\n", 186 | "\n", 187 | "We can find optimal hyperparamters manually with trial and error or we can use `GridSearchCV` method to try a bunch of hyperparameters." 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": null, 193 | "id": "276a7598", 194 | "metadata": {}, 195 | "outputs": [], 196 | "source": [] 197 | } 198 | ], 199 | "metadata": { 200 | "kernelspec": { 201 | "display_name": "Python 3 (ipykernel)", 202 | "language": "python", 203 | "name": "python3" 204 | }, 205 | "language_info": { 206 | "codemirror_mode": { 207 | "name": "ipython", 208 | "version": 3 209 | }, 210 | "file_extension": ".py", 211 | "mimetype": "text/x-python", 212 | "name": "python", 213 | "nbconvert_exporter": "python", 214 | "pygments_lexer": "ipython3", 215 | "version": "3.8.8" 216 | } 217 | }, 218 | "nbformat": 4, 219 | "nbformat_minor": 5 220 | } 221 | -------------------------------------------------------------------------------- /Machine Learning Pipeline.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "5fedfa6c", 6 | "metadata": {}, 7 | "source": [ 8 | "## Machine Learning Workflow\n", 9 | "\n", 10 | "There are many steps in the process of creating, implementing and iterating over a machine learning model for a specific data-driven problem. While there is no single universal way of sequencing the different steps that go into a workflow, there are some general principles that are good to follow for optimal performance of a machine learning algorithm.\n", 11 | "\n", 12 | "A machine learning workflow has the following steps.\n", 13 | "1. ETL (Extract, Transform and Load) data\n", 14 | "2. Data Cleaning\n", 15 | "3. Train-Test-Validation Split\n", 16 | "4. EDA (Exploratory Data Analysis)\n", 17 | "5. Feature Engineering (normalization, removing autocorrelations, discretization, etc.)\n", 18 | "6. Model Selection and Implementation\n", 19 | "7. Model Evaluation\n", 20 | "8. Hyperparameter Tuning\n", 21 | "9. Model Validation\n", 22 | "10. Build ML pipeline" 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "id": "6ed9e57e", 28 | "metadata": {}, 29 | "source": [ 30 | "#### 1. ETL(Extract, Transform and Load) Data\n", 31 | "\n", 32 | "It is often the case that data is stored in a SQL database with a cloud service provider like AWS, Digital Ocean, etc. Depending on the volume of data, an engineer would use a tool like PySpark to extract this data, transform it and load it into a local database." 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "id": "503b1cea", 38 | "metadata": {}, 39 | "source": [ 40 | "#### 2. Data Cleaning and Aggregation\n", 41 | "\n", 42 | "This can involve a range of tasks depending on the form and type of data as well as the problem that the machine learning pipeline is being designed to solve. Some examples include: dealing with null or missing entries, conforming timestamps to a standard, carrying out aggregations like grouping events based on timestamps by the hour or day, grouping IP’s by location, etc. Since Spark is best suited to perform such tasks on big data, this task might very well be the “Transform” part of the above mentioned ETL step. " 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "id": "d213d51f", 48 | "metadata": {}, 49 | "source": [ 50 | "#### 3. Train-Test-Validation Split\n", 51 | "\n", 52 | "Before the modelling phase in Machine Learning, we split our data into Train-Test-Validation sets. The training data is used to train the machine learning models and test data is to test the performance of the trained model. When we take models to productions, then we use the validation data(a part of training data) to tune hyperparameters and/or model validation before we test it on the test data.\n", 53 | "\n", 54 | "All the manipulations like scaling, encoding categorical features etc should be done after the splitting of the data." 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": null, 60 | "id": "3cb1d17f", 61 | "metadata": {}, 62 | "outputs": [], 63 | "source": [ 64 | "from sklearn.model_selection import train_test_split\n", 65 | "# For feature matrix X and target variable y\n", 66 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)" 67 | ] 68 | }, 69 | { 70 | "cell_type": "markdown", 71 | "id": "b45f6b73", 72 | "metadata": {}, 73 | "source": [ 74 | "#### 4. Exploratory Data Analysis\n", 75 | "\n", 76 | "Exploratory Data Analysis or EDA in the context of a machine learning workflow, is the step of inspecting, analyzing and altering your data to get it ready for machine learning modeling." 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "id": "adb90080", 82 | "metadata": {}, 83 | "source": [ 84 | "#### 5. Feature Engineering\n", 85 | "\n", 86 | "Feature engineer refers to prepping, selecting and reducing features in a machine learning problem. This can involve methods that overlap with EDA such as normalization, removing autocorrelations, discretization, etc. Feature engineering can also involve using machine learning algorithms like PCA to reduce dimensionality or methods that are implemented during the model fitting step like regularization." 87 | ] 88 | }, 89 | { 90 | "cell_type": "markdown", 91 | "id": "c97e6f61", 92 | "metadata": {}, 93 | "source": [ 94 | "#### 6. Model Selection and Implementation\n", 95 | "\n", 96 | "Now we’re ready to test out different machine learning models. The choice of the model depends on the attributes of the data one’s working with as well as the type of question we’re trying to answer.\n", 97 | "\n", 98 | "Refer this [cheatsheet](https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html) to choose the right algorithm." 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "id": "87d6c475", 104 | "metadata": {}, 105 | "source": [ 106 | "#### 7. Model Evaluation\n", 107 | "\n", 108 | "We’re now getting into the iterative part of the workflow. Whatever model is built, it must be evaluated on the test data. For classification problems, metrics like accuracy, precision, recall, F1 score and AUROC scores indicate how performant the model is and for regression problems, scores like RMSE and R-squared are some commonly used metrics. \n", 109 | "\n", 110 | "Machine learning engineers iterate over different types of models to figure out the most optimal model for the problem at hand." 111 | ] 112 | }, 113 | { 114 | "cell_type": "markdown", 115 | "id": "3a8359fd", 116 | "metadata": {}, 117 | "source": [ 118 | "#### 8. Hyperparameter Tuning\n", 119 | "\n", 120 | "Once a model has been decided upon, it can be tuned for better performance. Hyperparameter tuning is essential in making sure that the model does not overfit or underfit the data.\n", 121 | "\n", 122 | "This is key to how well the model is fitting known data and how well it’s able to generalize to new data as well. Hence hyperparameter tuning might be done on the validation or holdout dataset." 123 | ] 124 | }, 125 | { 126 | "cell_type": "markdown", 127 | "id": "c3e6201a", 128 | "metadata": {}, 129 | "source": [ 130 | "#### 9. Model Validation\n", 131 | "\n", 132 | "Model validation is the process of making sure that the model is still performant on data that it hasn’t seen at all — neither in the training phase nor in the test phase. This can be done either during the hyperparameter tuning step or after. Typically the same metrics used during the model evaluation phase needs to be used here as well so as to make a reasonable comparison with the former." 133 | ] 134 | }, 135 | { 136 | "cell_type": "markdown", 137 | "id": "52bb4b32", 138 | "metadata": {}, 139 | "source": [ 140 | "#### 10. Build ML pipeline!\n", 141 | "\n", 142 | "When a machine learning workflow is part of a production cycle, it is often the case that a model is tuned and updated based on incoming information. In other words the model that worked well on last month’s data might not be applicable for this month. It is the job of a Machine Learning Engineer or a Pipeline Engineer to make sure that the model deployed into production is thus flexible and alterable without affecting the rest of the codebase. ML pipelines allow one to do the same!\n", 143 | "\n", 144 | "A ML pipeline is a modular sequence of objects that codifies and automates a ML workflow to make it efficient, reproducible and generalizable." 145 | ] 146 | }, 147 | { 148 | "cell_type": "markdown", 149 | "id": "37d79a72", 150 | "metadata": {}, 151 | "source": [ 152 | "## Let's get into the implementation of the ML Pipeline" 153 | ] 154 | }, 155 | { 156 | "cell_type": "markdown", 157 | "id": "7df49db4", 158 | "metadata": {}, 159 | "source": [ 160 | "### Data Cleaning (Numeric)\n", 161 | "\n", 162 | "To introduce pipelines, let’s look at a common set of data cleaning/EDA tasks — dealing with missing values and scaling numeric variables. We’re going to convert an existing code base that performs these tasks to more concise code that uses scikit-learn‘s Pipeline using the following steps.\n", 163 | "\n", 164 | "1. First, to define a pipeline, we pass a list of tuples of the form (name, transform/estimator) into a Pipeline object. For example, if we wanted to perform imputation with a SimpleImputer first, and scale our numerical variables with a StandardScaler next, the code would look as follows:" 165 | ] 166 | }, 167 | { 168 | "cell_type": "code", 169 | "execution_count": null, 170 | "id": "186f3b77", 171 | "metadata": {}, 172 | "outputs": [], 173 | "source": [ 174 | "from sklearn.pipeline import Pipeline\n", 175 | "pipeline = Pipeline([(\"imputer\",SimpleImputer()), (\"scale\",StandardScaler())])" 176 | ] 177 | }, 178 | { 179 | "cell_type": "markdown", 180 | "id": "60946507", 181 | "metadata": {}, 182 | "source": [ 183 | "2. Once a Pipeline object has been instantiated, the methods .fit and .transform can be called like we would with any data transformation object in scikit-learn." 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": null, 189 | "id": "a366bb5f", 190 | "metadata": {}, 191 | "outputs": [], 192 | "source": [ 193 | "from sklearn.model_selection import train_test_split\n", 194 | "x_train, x_test, y_train, y_test = train_test_split(X,y, random_state=0, test_size=0.25)\n", 195 | "pipeline.fit(x_train)\n", 196 | "pipeline.transform(x_test)" 197 | ] 198 | }, 199 | { 200 | "cell_type": "code", 201 | "execution_count": null, 202 | "id": "fdf5a41e", 203 | "metadata": {}, 204 | "outputs": [], 205 | "source": [ 206 | "pipeline.fit(x_train_num)\n", 207 | "x_transform = pipeline.transform(x_test[num_cols])" 208 | ] 209 | }, 210 | { 211 | "cell_type": "markdown", 212 | "id": "3791d1d6", 213 | "metadata": {}, 214 | "source": [ 215 | "If the pipeline includes a machine learning model as well, .predict() can also be called down the line. Each step in the pipeline will be fit in the order provided." 216 | ] 217 | }, 218 | { 219 | "cell_type": "markdown", 220 | "id": "680dec10", 221 | "metadata": {}, 222 | "source": [ 223 | "### Data Cleaning (Categorical)\n", 224 | "\n", 225 | "We’re now going to implement a task similar to the previous exercise with pipeline.Pipeline(), but with categorical variables now. Specifically, we’ll be dealing with missing values in categorical data and one-hot-encoding categorical variables. We will convert an existing codebase to a pipeline like in the previous exercise." 226 | ] 227 | }, 228 | { 229 | "cell_type": "code", 230 | "execution_count": null, 231 | "id": "9155da27", 232 | "metadata": {}, 233 | "outputs": [], 234 | "source": [ 235 | "pipeline = Pipeline([('imputer', SimpleImputer(strategy = 'most_frequent')), \n", 236 | " ('ohe', OneHotEncoder(drop = 'first', sparse = False))])" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": null, 242 | "id": "6ce4afaa", 243 | "metadata": {}, 244 | "outputs": [], 245 | "source": [ 246 | "pipeline.fit(x_train[cat_cols])\n", 247 | "pipeline.transform(x_test[cat_cols])" 248 | ] 249 | }, 250 | { 251 | "cell_type": "markdown", 252 | "id": "937d6357", 253 | "metadata": {}, 254 | "source": [ 255 | "### Column Transformer\n", 256 | "\n", 257 | "Often times, you may not want to simply apply every function to all columns. If our columns are of different types, we may only want to apply certain parts of the pipeline to a subset of columns. This is what we saw in the two previous exercises. One set of transformations are applied to numeric columns and another set to the categorical ones. We can use scikit-learn‘s ColumnTransformer as one way of combining these processes together." 258 | ] 259 | }, 260 | { 261 | "cell_type": "code", 262 | "execution_count": null, 263 | "id": "d6a4ccfe", 264 | "metadata": {}, 265 | "outputs": [], 266 | "source": [ 267 | "num_vals = Pipeline([('imputer', SimpleImputer(strategy = 'mean')), ('scale', StandardScaler())])\n", 268 | "cat_vals = Pipeline([('imputer', SimpleImputer(strategy = 'most_frequent')), \n", 269 | " ('ohe', OneHotEncoder(drop = 'first', sparse = False))])" 270 | ] 271 | }, 272 | { 273 | "cell_type": "code", 274 | "execution_count": null, 275 | "id": "1aed1b24", 276 | "metadata": {}, 277 | "outputs": [], 278 | "source": [ 279 | "#create the column transformer with the categorical and numerical processes\n", 280 | "#num_cols in the numerical columns and cat_cols is the categorical columns\n", 281 | "preprocess = ColumnTransformer(transformers = [('num_preprocess', num_vals, num_cols), \n", 282 | " ('cat_preprocess', cat_vals, cat_cols)])" 283 | ] 284 | }, 285 | { 286 | "cell_type": "code", 287 | "execution_count": null, 288 | "id": "dc975f2a", 289 | "metadata": {}, 290 | "outputs": [], 291 | "source": [ 292 | "preprocess.fit(x_train)\n", 293 | "preprocess.transform(x_test)" 294 | ] 295 | }, 296 | { 297 | "cell_type": "markdown", 298 | "id": "5145ea18", 299 | "metadata": {}, 300 | "source": [ 301 | "### Adding a Model" 302 | ] 303 | }, 304 | { 305 | "cell_type": "code", 306 | "execution_count": null, 307 | "id": "fc872642", 308 | "metadata": {}, 309 | "outputs": [], 310 | "source": [ 311 | "import numpy as np\n", 312 | "import pandas as pd\n", 313 | "\n", 314 | "from sklearn import svm, datasets\n", 315 | "from sklearn.linear_model import LinearRegression\n", 316 | "from sklearn.model_selection import train_test_split\n", 317 | "\n", 318 | "from sklearn.pipeline import Pipeline\n", 319 | "from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder\n", 320 | "from sklearn.compose import ColumnTransformer\n", 321 | "from sklearn.impute import SimpleImputer\n", 322 | "from sklearn.metrics import r2_score" 323 | ] 324 | }, 325 | { 326 | "cell_type": "code", 327 | "execution_count": null, 328 | "id": "9338a95f", 329 | "metadata": {}, 330 | "outputs": [], 331 | "source": [ 332 | "columns = [\"sex\",\"length\",\"diam\",\"height\",\"whole\",\"shucked\",\"viscera\",\"shell\",\"age\"]\n", 333 | "df = pd.read_csv(\"http://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data\",names=columns)\n", 334 | "\n", 335 | "X = df.drop(columns=['age'])\n", 336 | "y = df.age" 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "execution_count": null, 342 | "id": "c55d7068", 343 | "metadata": {}, 344 | "outputs": [], 345 | "source": [ 346 | "num_cols = X.select_dtypes(include=np.number).columns\n", 347 | "cat_cols = X.select_dtypes(include=['object']).columns" 348 | ] 349 | }, 350 | { 351 | "cell_type": "code", 352 | "execution_count": null, 353 | "id": "562ae3c7", 354 | "metadata": {}, 355 | "outputs": [], 356 | "source": [ 357 | "#create some missing values\n", 358 | "for i in range(1000):\n", 359 | " X.loc[np.random.choice(X.index),np.random.choice(X.columns)] = np.nan" 360 | ] 361 | }, 362 | { 363 | "cell_type": "code", 364 | "execution_count": null, 365 | "id": "9ee50baf", 366 | "metadata": {}, 367 | "outputs": [], 368 | "source": [ 369 | "#train-test split\n", 370 | "x_train, x_test, y_train, y_test = train_test_split(X,y, random_state=0, test_size=0.25)" 371 | ] 372 | }, 373 | { 374 | "cell_type": "code", 375 | "execution_count": null, 376 | "id": "a16da663", 377 | "metadata": {}, 378 | "outputs": [], 379 | "source": [ 380 | "#categorical and numerical data processing pipelines\n", 381 | "cat_vals = Pipeline([(\"imputer\",SimpleImputer(strategy='most_frequent')), \n", 382 | " (\"ohe\",OneHotEncoder(sparse=False, drop='first'))])\n", 383 | "\n", 384 | "num_vals = Pipeline([(\"imputer\",SimpleImputer(strategy='mean')), (\"scale\",StandardScaler())])" 385 | ] 386 | }, 387 | { 388 | "cell_type": "code", 389 | "execution_count": null, 390 | "id": "37d66aec", 391 | "metadata": {}, 392 | "outputs": [], 393 | "source": [ 394 | "#combining categorical and numerical pipelines together\n", 395 | "preprocess = ColumnTransformer(\n", 396 | " transformers=[\n", 397 | " (\"cat_process\", cat_vals, cat_cols),\n", 398 | " (\"num_process\", num_vals, num_cols)\n", 399 | " ]\n", 400 | ")" 401 | ] 402 | }, 403 | { 404 | "cell_type": "code", 405 | "execution_count": null, 406 | "id": "dd5fd398", 407 | "metadata": {}, 408 | "outputs": [], 409 | "source": [ 410 | "#Create a pipeline with `preprocess` and a linear regression model, `regr`\n", 411 | "pipeline = Pipeline([('preprocess', preprocess), ('regr', LinearRegression())])\n", 412 | "\n", 413 | "#Fit the pipeline on the training data and predict on the test data\n", 414 | "pipeline.fit(x_train, y_train)\n", 415 | "y_pred = pipeline.predict(x_test)" 416 | ] 417 | }, 418 | { 419 | "cell_type": "code", 420 | "execution_count": null, 421 | "id": "96b61de9", 422 | "metadata": {}, 423 | "outputs": [], 424 | "source": [ 425 | "#Calculate pipeline score and compare to estimator score\n", 426 | "pipeline_score = pipeline.score(x_test, y_test)\n", 427 | "print(pipeline_score)\n", 428 | "\n", 429 | "#r-squared score\n", 430 | "r2_score = r2_score(y_test, y_pred)\n", 431 | "print(r2_score)" 432 | ] 433 | }, 434 | { 435 | "cell_type": "markdown", 436 | "id": "9d6fa7ab", 437 | "metadata": {}, 438 | "source": [ 439 | "### Hyperparameter Tuning" 440 | ] 441 | }, 442 | { 443 | "cell_type": "code", 444 | "execution_count": null, 445 | "id": "85538157", 446 | "metadata": {}, 447 | "outputs": [], 448 | "source": [ 449 | "#Very simple parameter grid, with and without the intercept\n", 450 | "param_grid = {\n", 451 | " \"regr__fit_intercept\": [True,False]\n", 452 | "}" 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": null, 458 | "id": "0e87e88b", 459 | "metadata": {}, 460 | "outputs": [], 461 | "source": [ 462 | "#Grid search using previous pipeline\n", 463 | "gs = GridSearchCV(pipeline, param_grid = param_grid, scoring = 'neg_mean_squared_error', cv = 5)" 464 | ] 465 | }, 466 | { 467 | "cell_type": "code", 468 | "execution_count": null, 469 | "id": "ff7e3012", 470 | "metadata": {}, 471 | "outputs": [], 472 | "source": [ 473 | "#fit grid using training data and print best score\n", 474 | "gs.fit(x_train, y_train)\n", 475 | "best_score = gs.best_score_\n", 476 | "best_params = gs.best_params_\n", 477 | "print(best_score)\n", 478 | "print(best_params)" 479 | ] 480 | }, 481 | { 482 | "cell_type": "markdown", 483 | "id": "f612b303", 484 | "metadata": {}, 485 | "source": [ 486 | "### Final Pipeline\n", 487 | "\n", 488 | "We will now be searching over different types of models, each having their own sets of hyperparameters!" 489 | ] 490 | }, 491 | { 492 | "cell_type": "code", 493 | "execution_count": null, 494 | "id": "ede651ab", 495 | "metadata": {}, 496 | "outputs": [], 497 | "source": [ 498 | "# Update the `search_space` array from the narrative to add a Lasso Regression model as the third dictionary.\n", 499 | "search_space = [{'regr': [LinearRegression()], 'regr__fit_intercept': [True,False]},\n", 500 | " {'regr':[Ridge()],\n", 501 | " 'regr__alpha': [0.01,0.1,1,10,100]},\n", 502 | " {'regr':[Lasso()],\n", 503 | " 'regr__alpha': [0.01,0.1,1,10,100]}]" 504 | ] 505 | }, 506 | { 507 | "cell_type": "code", 508 | "execution_count": null, 509 | "id": "3585e700", 510 | "metadata": {}, 511 | "outputs": [], 512 | "source": [ 513 | "# Initialize a grid search on `search_space`\n", 514 | "gs = GridSearchCV(pipeline, search_space, scoring='neg_mean_squared_error', cv=5)" 515 | ] 516 | }, 517 | { 518 | "cell_type": "code", 519 | "execution_count": null, 520 | "id": "2e005cb0", 521 | "metadata": {}, 522 | "outputs": [], 523 | "source": [ 524 | "# Find the best pipeline, regression model and its hyperparameters\n", 525 | "## Fit to training data\n", 526 | "gs.fit(x_train, y_train)\n", 527 | "\n", 528 | "## Find the best pipeline\n", 529 | "best_pipeline = gs.best_estimator_" 530 | ] 531 | }, 532 | { 533 | "cell_type": "code", 534 | "execution_count": null, 535 | "id": "b793ebe6", 536 | "metadata": {}, 537 | "outputs": [], 538 | "source": [ 539 | "## Find the best regression model\n", 540 | "best_regression_model = best_pipeline.named_steps['regr']\n", 541 | "print('The best regression model is:')\n", 542 | "print(best_regression_model)" 543 | ] 544 | }, 545 | { 546 | "cell_type": "code", 547 | "execution_count": null, 548 | "id": "7f4dd452", 549 | "metadata": {}, 550 | "outputs": [], 551 | "source": [ 552 | "## Find the hyperparameters of the best regression model\n", 553 | "best_model_hyperparameters = best_regression_model.get_params()\n", 554 | "print('The hyperparameters of the regression model are:')\n", 555 | "print(best_model_hyperparameters)" 556 | ] 557 | }, 558 | { 559 | "cell_type": "code", 560 | "execution_count": null, 561 | "id": "f2d7edc3", 562 | "metadata": {}, 563 | "outputs": [], 564 | "source": [ 565 | "# Access the hyperparameters of the categorical preprocessing step\n", 566 | "cat_preprocess_hyperparameters = best_pipeline.named_steps['preprocess'].named_transformers_['cat_preprocess'].named_steps['imputer'].get_params()\n", 567 | "print('The hyperparameters of the imputer are:')\n", 568 | "print(cat_preprocess_hyperparameters)" 569 | ] 570 | }, 571 | { 572 | "cell_type": "markdown", 573 | "id": "8a44bafb", 574 | "metadata": {}, 575 | "source": [ 576 | "### Practical Example combining all the above steps" 577 | ] 578 | }, 579 | { 580 | "cell_type": "code", 581 | "execution_count": null, 582 | "id": "571dc56c", 583 | "metadata": {}, 584 | "outputs": [], 585 | "source": [ 586 | "import numpy as np\n", 587 | "import pandas as pd\n", 588 | "from sklearn.pipeline import Pipeline\n", 589 | "from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV\n", 590 | "from sklearn.linear_model import LogisticRegression\n", 591 | "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n", 592 | "from sklearn.preprocessing import LabelEncoder, StandardScaler, OneHotEncoder\n", 593 | "from sklearn.compose import ColumnTransformer\n", 594 | "from sklearn.impute import SimpleImputer\n", 595 | "from sklearn.decomposition import PCA\n", 596 | "from sklearn.metrics import confusion_matrix\n", 597 | "from scipy.io import arff" 598 | ] 599 | }, 600 | { 601 | "cell_type": "code", 602 | "execution_count": null, 603 | "id": "2f7912b2", 604 | "metadata": {}, 605 | "outputs": [], 606 | "source": [ 607 | "data = arff.loadarff('bone-marrow.arff')\n", 608 | "df = pd.DataFrame(data[0])\n", 609 | "df.drop(columns=['Disease'], inplace=True)" 610 | ] 611 | }, 612 | { 613 | "cell_type": "code", 614 | "execution_count": null, 615 | "id": "3454812d", 616 | "metadata": {}, 617 | "outputs": [], 618 | "source": [ 619 | "#Convert all columns to numeric, coerce errors to null values\n", 620 | "for c in df.columns:\n", 621 | " df[c] = pd.to_numeric(df[c], errors='coerce')\n", 622 | " \n", 623 | "#Make sure binary columns are encoded as 0 and 1\n", 624 | "for c in df.columns[df.nunique()==2]:\n", 625 | " df[c] = (df[c]==1)*1.0" 626 | ] 627 | }, 628 | { 629 | "cell_type": "code", 630 | "execution_count": null, 631 | "id": "311e72be", 632 | "metadata": {}, 633 | "outputs": [], 634 | "source": [ 635 | "# 1. Calculate the number of unique values for each column\n", 636 | "print('Count of unique values in each column:')\n", 637 | "print(df.nunique())\n", 638 | "\n", 639 | "# 2. Set target, survival_status,as y; features (dropping survival status and time) as X\n", 640 | "y = df.survival_status\n", 641 | "X = df.drop(columns=['survival_time','survival_status'])" 642 | ] 643 | }, 644 | { 645 | "cell_type": "code", 646 | "execution_count": null, 647 | "id": "15422abd", 648 | "metadata": {}, 649 | "outputs": [], 650 | "source": [ 651 | "# 3. Define lists of numeric and categorical columns based on number of unique values\n", 652 | "num_cols = X.columns[X.nunique()>7]\n", 653 | "cat_cols = X.columns[X.nunique()<=7]\n", 654 | "\n", 655 | "# 4. Print columns with missing values\n", 656 | "print('Columns with missing values:')\n", 657 | "print(X.columns[X.isnull().sum()>0])" 658 | ] 659 | }, 660 | { 661 | "cell_type": "code", 662 | "execution_count": null, 663 | "id": "43d54e15", 664 | "metadata": {}, 665 | "outputs": [], 666 | "source": [ 667 | "# 5. Split data into train/test split\n", 668 | "x_train, x_test, y_train, y_test = train_test_split(X,y, random_state=0, test_size=0.2)" 669 | ] 670 | }, 671 | { 672 | "cell_type": "markdown", 673 | "id": "50c19705", 674 | "metadata": {}, 675 | "source": [ 676 | "Categorical pipeline will consist of two steps : The first will fill in missing values using the mode and the second will one-hot-encode the variables." 677 | ] 678 | }, 679 | { 680 | "cell_type": "code", 681 | "execution_count": null, 682 | "id": "4fb4a0ea", 683 | "metadata": {}, 684 | "outputs": [], 685 | "source": [ 686 | "# 6. Create categorical preprocessing pipeline\n", 687 | "# Using mode to fill in missing values and OHE\n", 688 | "cat_vals = Pipeline([(\"imputer\",SimpleImputer(strategy='most_frequent')), (\"ohe\",OneHotEncoder(sparse=False, drop='first', handle_unknown = 'ignore'))])" 689 | ] 690 | }, 691 | { 692 | "cell_type": "markdown", 693 | "id": "660df2d2", 694 | "metadata": {}, 695 | "source": [ 696 | "Numerical pipeline will consist of two steps: the first will fill in missing values using the mean and the second will scale features." 697 | ] 698 | }, 699 | { 700 | "cell_type": "code", 701 | "execution_count": null, 702 | "id": "084f3dd4", 703 | "metadata": {}, 704 | "outputs": [], 705 | "source": [ 706 | "# 7. Create numerical preprocessing pipeline\n", 707 | "# Using mean to fill in missing values and standard scaling of features\n", 708 | "num_vals = Pipeline([(\"imputer\",SimpleImputer(strategy='mean')), (\"scale\",StandardScaler())])" 709 | ] 710 | }, 711 | { 712 | "cell_type": "code", 713 | "execution_count": null, 714 | "id": "4d789b22", 715 | "metadata": {}, 716 | "outputs": [], 717 | "source": [ 718 | "# 8. Create column transformer that will preprocess the numerical and categorical features separately\n", 719 | "preprocess = ColumnTransformer(\n", 720 | " transformers=[\n", 721 | " (\"cat_process\", cat_vals, cat_cols),\n", 722 | " (\"num_process\", num_vals, num_cols)\n", 723 | " ]\n", 724 | ")" 725 | ] 726 | }, 727 | { 728 | "cell_type": "code", 729 | "execution_count": null, 730 | "id": "85c993ff", 731 | "metadata": {}, 732 | "outputs": [], 733 | "source": [ 734 | "# 9. Create a pipeline with preprocess, PCA, and a logistic regresssion model\n", 735 | "pipeline = Pipeline([(\"preprocess\",preprocess), \n", 736 | " (\"pca\", PCA()),\n", 737 | " (\"clf\",LogisticRegression())])" 738 | ] 739 | }, 740 | { 741 | "cell_type": "code", 742 | "execution_count": null, 743 | "id": "908c0cc0", 744 | "metadata": {}, 745 | "outputs": [], 746 | "source": [ 747 | "# 10. Fit the pipeline on the training data\n", 748 | "pipeline.fit(x_train, y_train)\n", 749 | "#Predict the pipeline on the test data\n", 750 | "print('Pipeline Accuracy Test Set:')\n", 751 | "print(pipeline.score(x_test,y_test))" 752 | ] 753 | }, 754 | { 755 | "cell_type": "code", 756 | "execution_count": null, 757 | "id": "3a8d21ea", 758 | "metadata": {}, 759 | "outputs": [], 760 | "source": [ 761 | "# 11. Define search space of hyperparameters\n", 762 | "search_space = [{'clf':[LogisticRegression()],\n", 763 | " 'clf__C': np.logspace(-4, 2, 10),\n", 764 | " 'pca__n_components':np.linspace(30,37,3).astype(int)},\n", 765 | " ]" 766 | ] 767 | }, 768 | { 769 | "cell_type": "code", 770 | "execution_count": null, 771 | "id": "7cd67fc4", 772 | "metadata": {}, 773 | "outputs": [], 774 | "source": [ 775 | "#12. Search over hyperparameters abolve to optimize pipeline and fit\n", 776 | "gs = GridSearchCV(pipeline, search_space, cv=5)\n", 777 | "gs.fit(x_train, y_train)" 778 | ] 779 | }, 780 | { 781 | "cell_type": "code", 782 | "execution_count": null, 783 | "id": "395c309d", 784 | "metadata": {}, 785 | "outputs": [], 786 | "source": [ 787 | "# 13. Save the best estimator from the gridsearch and print attributes and final accuracy on test set\n", 788 | "best_model = gs.best_estimator_" 789 | ] 790 | }, 791 | { 792 | "cell_type": "code", 793 | "execution_count": null, 794 | "id": "47db76f8", 795 | "metadata": {}, 796 | "outputs": [], 797 | "source": [ 798 | "# 14. Print attributes of best_model\n", 799 | "print('The best classification model is:')\n", 800 | "print(best_model.named_steps['clf'])\n", 801 | "print('The hyperparameters of the best classification model are:')\n", 802 | "print(best_model.named_steps['clf'].get_params())\n", 803 | "print('The number of components selected in the PCA step are:')\n", 804 | "print(best_model.named_steps['pca'].n_components)\n", 805 | "\n", 806 | "# 15. Print final accuracy score \n", 807 | "print('Best Model Accuracy Test Set:')\n", 808 | "print(best_model.score(x_test,y_test))" 809 | ] 810 | }, 811 | { 812 | "cell_type": "code", 813 | "execution_count": null, 814 | "id": "bdb5d603", 815 | "metadata": {}, 816 | "outputs": [], 817 | "source": [] 818 | } 819 | ], 820 | "metadata": { 821 | "kernelspec": { 822 | "display_name": "Python 3", 823 | "language": "python", 824 | "name": "python3" 825 | }, 826 | "language_info": { 827 | "codemirror_mode": { 828 | "name": "ipython", 829 | "version": 3 830 | }, 831 | "file_extension": ".py", 832 | "mimetype": "text/x-python", 833 | "name": "python", 834 | "nbconvert_exporter": "python", 835 | "pygments_lexer": "ipython3", 836 | "version": "3.8.8" 837 | } 838 | }, 839 | "nbformat": 4, 840 | "nbformat_minor": 5 841 | } 842 | -------------------------------------------------------------------------------- /MyCreditData.csv: -------------------------------------------------------------------------------- 1 | checking_account,duration,credit_history,purpose,amount,savings_account,employment_duration,installment_rate,other_debtors,present_residence,property,age,other_installment_plans,housing,number_credits,job,people_liable,telephone,foreign_worker,gender,profit 2 | 3,18,0,2,1049,4,2,2,2,3,1,21,1,0,0,1,0,0,0,female,242 3 | 3,9,0,5,2799,4,0,1,2,0,3,36,1,0,1,1,1,0,0,male,596 4 | 0,12,4,8,841,0,1,1,2,3,3,23,1,0,0,3,0,0,0,female,25 5 | 3,12,0,5,2122,4,0,0,2,0,3,39,1,0,1,3,1,0,1,male,568 6 | 3,12,0,5,2171,4,0,2,2,3,1,38,0,2,1,3,0,0,1,male,782 7 | 3,10,0,5,2241,4,2,3,2,1,3,48,1,0,1,3,1,0,1,male,737 8 | 3,8,0,5,3398,4,1,3,2,3,3,39,1,2,1,3,0,0,1,male,759 9 | 3,6,0,5,1361,4,2,1,2,3,3,40,1,2,0,3,1,0,1,male,328 10 | 1,18,0,4,1098,4,4,2,2,3,0,65,1,2,1,2,0,0,0,female,223 11 | 0,24,4,4,3758,2,4,3,2,3,2,23,1,0,0,2,0,0,0,female,30 12 | 3,11,0,5,3905,4,0,1,2,0,3,36,1,0,1,1,1,0,0,male,870 13 | 3,30,0,1,6187,0,1,3,2,3,0,24,1,0,1,1,0,0,0,female,1834 14 | 3,6,0,4,1957,4,1,3,2,3,0,31,1,2,0,1,0,0,0,female,31 15 | 0,48,3,0,7582,0,4,1,2,3,2,31,1,2,0,0,0,1,0,male,2053 16 | 3,18,4,4,1936,1,1,1,2,3,0,23,1,0,1,3,0,0,0,female,90 17 | 3,6,4,4,2647,2,0,1,2,1,3,44,1,0,0,1,1,0,0,male,555 18 | 3,11,0,5,3939,4,0,3,2,0,3,40,1,2,1,3,1,0,0,male,381 19 | 0,18,4,4,3213,2,2,3,2,1,3,25,1,0,0,1,0,0,0,female,6 20 | 0,36,0,4,2337,4,3,2,2,3,3,36,1,2,0,1,0,0,0,male,174 21 | 1,11,0,5,7228,4,0,3,2,3,1,39,1,2,1,3,0,0,0,male,78 22 | 3,6,0,5,3676,4,0,3,2,1,3,37,1,0,2,1,1,0,0,male,1361 23 | 0,12,0,5,3124,4,2,3,2,1,3,49,0,2,1,3,1,0,0,male,463 24 | 0,36,4,3,2384,4,2,2,2,2,2,33,1,0,0,3,0,0,0,male,-502 25 | 0,12,0,6,1424,4,1,2,2,1,1,26,1,2,0,1,0,0,0,male,399 26 | 3,6,0,5,4716,1,2,3,2,1,3,44,1,2,1,3,1,0,0,male,771 27 | 0,11,3,4,4771,4,1,1,2,3,1,51,1,2,0,1,0,0,0,nonbinary/notreported,585 28 | 3,12,4,2,652,4,3,2,2,3,1,24,1,0,0,1,0,0,0,female,0 29 | 0,9,0,4,1154,4,3,1,2,3,3,37,1,2,2,3,0,0,0,nonbinary/notreported,229 30 | 1,15,4,5,3556,1,0,0,2,0,2,29,1,2,0,1,0,0,0,male,639 31 | 2,42,0,1,4796,4,3,2,2,3,2,56,1,1,0,1,0,0,0,male,1038 32 | 2,30,0,4,3017,4,3,2,2,3,1,47,1,2,0,1,0,0,0,male,439 33 | 1,36,0,5,3535,4,1,2,2,3,0,37,1,2,1,1,0,1,0,male,640 34 | 1,36,0,5,6614,4,3,2,2,3,0,34,1,2,1,0,0,1,0,male,1666 35 | 1,24,4,4,1376,2,1,2,2,2,0,28,1,2,0,1,0,0,0,female,307 36 | 3,15,4,5,1721,4,2,1,2,1,3,36,1,2,0,1,0,0,0,male,386 37 | 3,6,0,5,860,4,3,3,2,3,2,39,1,2,1,1,0,1,0,female,250 38 | 1,12,0,5,1495,4,3,2,2,2,3,38,1,2,1,3,1,0,0,male,242 39 | 1,12,0,4,1934,4,3,1,2,0,2,26,1,2,1,1,0,0,0,male,460 40 | 1,18,4,1,3378,1,0,1,2,2,1,31,1,2,0,1,0,1,0,male,869 41 | 1,24,0,1,3868,4,3,2,2,0,0,41,1,0,1,0,0,1,0,female,583 42 | 1,12,0,3,996,1,1,2,2,3,3,23,1,2,1,1,0,0,0,female,21 43 | 3,24,4,0,1755,4,3,2,1,3,3,58,1,2,0,3,0,1,0,female,563 44 | 1,18,0,5,1028,4,0,2,2,1,3,36,1,2,1,1,0,0,0,female,327 45 | 0,24,0,8,2825,1,1,2,2,1,2,34,1,2,1,1,1,1,0,male,204 46 | 0,18,4,7,1239,1,0,2,2,3,2,61,1,1,0,1,0,0,0,male,232 47 | 3,18,4,5,1216,4,2,2,2,1,0,23,1,0,0,1,0,1,0,female,-93 48 | 1,24,4,8,1258,4,1,2,2,2,3,25,1,2,0,1,0,1,0,male,307 49 | 1,18,0,7,1864,0,0,2,2,0,3,30,1,2,1,1,0,0,0,female,-978 50 | 1,24,4,5,1474,0,2,2,2,1,3,33,1,2,0,1,0,1,0,female,36 51 | 3,24,0,8,1382,0,1,2,2,2,3,26,1,2,1,1,0,1,0,male,165 52 | 1,12,4,5,640,4,0,2,2,0,3,49,1,2,0,3,0,0,0,male,66 53 | 2,36,4,4,3919,4,0,1,2,0,3,23,1,2,0,1,0,1,0,male,979 54 | 1,9,0,5,1224,4,0,0,2,2,3,30,1,2,1,1,0,0,0,male,370 55 | 1,12,0,4,2331,1,3,3,0,3,3,49,1,2,0,1,0,1,0,male,282 56 | 1,24,4,1,6313,1,3,0,2,3,0,41,1,2,0,0,1,1,0,nonbinary/notreported,1436 57 | 3,12,0,4,385,4,1,2,2,1,3,58,1,2,3,3,0,1,0,female,62 58 | 1,12,0,4,1655,4,3,1,2,3,3,63,1,2,1,3,0,1,0,male,339 59 | 3,15,4,4,1053,4,2,2,2,0,3,27,1,2,0,1,0,0,1,female,87 60 | 1,21,4,4,3160,1,3,2,2,1,1,41,1,2,0,1,0,1,0,male,133 61 | 1,36,4,5,3079,1,0,2,2,3,3,36,1,2,0,1,0,0,0,male,112 62 | 1,12,0,5,1163,2,0,2,2,3,3,44,1,2,0,1,0,1,0,male,63 63 | 1,24,4,1,2679,4,2,2,2,2,2,29,1,2,0,0,0,1,0,female,570 64 | 1,48,0,4,3578,1,3,2,2,2,3,47,1,2,0,1,0,1,0,male,46 65 | 1,36,3,5,10875,4,3,1,2,0,0,45,1,2,1,1,1,1,0,male,2258 66 | 3,12,3,5,1344,4,0,2,2,0,3,43,1,2,1,3,1,0,0,male,130 67 | 1,6,0,4,1237,0,0,3,2,2,1,27,1,2,1,1,0,0,0,female,325 68 | 1,12,4,4,3077,4,0,1,2,3,0,52,1,2,0,1,0,1,0,male,335 69 | 1,24,4,4,2284,4,1,2,2,0,0,28,1,2,0,1,0,1,0,male,137 70 | 0,12,4,4,1567,4,0,3,2,2,0,22,1,2,0,1,0,1,0,female,23 71 | 1,24,3,5,2032,4,3,2,2,3,2,60,1,1,1,1,0,1,0,male,239 72 | 0,21,0,2,2745,3,1,0,2,0,0,32,1,2,1,1,0,1,0,male,484 73 | 1,30,4,4,1867,1,3,2,2,3,0,58,1,2,0,1,0,1,0,male,556 74 | 1,36,4,4,2299,2,3,2,2,3,0,39,1,2,0,1,0,0,0,male,87 75 | 1,24,4,2,929,1,1,2,2,0,0,31,2,2,0,1,0,1,0,male,163 76 | 2,12,4,4,3399,1,3,1,2,1,0,37,1,2,0,0,0,0,0,male,536 77 | 0,9,4,2,2030,1,1,1,2,2,0,24,1,2,0,1,0,1,0,male,254 78 | 1,21,0,1,3275,4,3,3,2,3,0,36,1,2,0,0,0,1,0,male,267 79 | 1,24,0,5,1940,3,3,2,2,3,3,60,1,2,0,1,0,1,0,male,306 80 | 3,21,0,5,1602,4,3,2,2,1,0,30,1,2,1,1,0,1,0,female,475 81 | 1,15,4,4,1979,1,3,2,2,0,0,35,1,2,0,1,0,0,0,male,353 82 | 1,24,0,5,2022,4,0,2,2,3,0,37,1,2,0,1,0,1,0,nonbinary/notreported,355 83 | 1,36,0,4,3342,1,3,2,2,0,0,51,1,2,0,1,0,1,0,male,194 84 | 0,18,4,5,5866,0,0,1,2,0,0,30,1,2,1,1,0,1,0,male,1916 85 | 2,15,0,1,2360,2,0,1,2,0,0,36,1,2,0,1,0,1,0,male,395 86 | 1,15,0,2,1520,1,3,2,2,3,1,63,1,2,0,1,0,0,0,male,626 87 | 3,12,4,5,3651,3,0,3,2,1,1,31,1,2,0,1,1,0,0,male,1537 88 | 1,24,0,1,2346,4,1,2,2,1,0,35,1,2,1,1,0,1,0,male,67 89 | 1,36,3,4,4454,4,0,2,2,3,3,34,1,2,1,1,0,0,0,female,1099 90 | 3,6,0,5,666,3,1,0,2,3,3,39,1,2,1,3,0,1,0,female,14 91 | 0,24,3,5,1965,1,0,2,2,3,0,42,1,0,1,1,0,1,0,female,403 92 | 0,12,0,5,1995,0,2,2,2,2,0,27,1,2,0,1,0,0,0,male,686 93 | 0,30,4,4,2991,1,3,1,2,3,0,25,1,2,0,1,0,0,0,female,636 94 | 0,30,2,8,4221,4,0,1,2,2,0,28,1,2,1,1,0,0,0,female,460 95 | 3,9,4,4,1364,4,1,0,2,3,3,59,1,2,0,1,0,0,0,male,141 96 | 0,18,0,2,6361,4,3,1,2,2,2,41,1,2,0,1,0,1,0,male,742 97 | 1,27,0,2,4526,3,2,2,2,0,3,32,2,2,1,3,1,1,0,nonbinary/notreported,962 98 | 0,12,0,4,3573,4,0,3,2,2,3,23,1,2,0,3,0,0,0,female,196 99 | 0,36,3,8,4455,4,0,1,2,0,3,30,2,2,1,0,0,1,0,male,-2048 100 | 3,9,4,2,2136,4,0,0,2,0,3,25,1,2,0,1,0,0,0,male,538 101 | 0,42,0,8,5954,4,1,1,2,2,3,41,0,2,1,3,0,0,0,female,1025 102 | 1,24,0,2,3777,3,0,2,2,3,3,40,1,2,0,1,0,1,0,male,821 103 | 3,15,4,8,806,4,0,2,2,3,1,22,1,2,0,3,0,0,0,female,122 104 | 0,24,3,8,4712,1,0,2,2,0,1,34,0,2,1,0,0,1,0,male,1392 105 | 0,36,3,5,7432,4,0,1,2,0,1,54,1,0,0,1,0,0,0,female,681 106 | 1,24,0,4,1851,4,1,2,1,0,0,33,1,2,1,1,0,1,0,female,167 107 | 1,24,4,5,1393,4,0,1,1,0,3,31,1,2,0,1,0,1,0,male,360 108 | 1,12,0,8,1412,4,0,2,1,0,3,29,1,2,1,0,0,1,0,female,94 109 | 1,18,4,4,1473,4,2,0,2,3,3,39,1,2,0,1,0,1,0,female,191 110 | 1,24,4,4,1533,4,2,2,2,1,0,38,2,2,0,1,0,1,0,female,172 111 | 1,12,0,7,2012,1,1,2,2,0,0,61,1,2,0,1,0,0,0,female,153 112 | 3,15,4,5,3959,4,0,0,2,0,1,29,1,2,0,1,0,1,0,female,-2317 113 | 3,6,4,2,428,4,3,1,2,2,1,49,0,2,0,1,0,1,0,female,35 114 | 0,12,0,5,2366,2,1,0,2,1,0,36,1,2,0,0,0,1,0,male,55 115 | 1,12,4,2,763,4,0,2,2,2,3,26,1,2,0,1,0,1,0,nonbinary/notreported,59 116 | 0,21,4,2,3976,1,1,1,2,1,0,35,1,2,0,1,0,1,0,male,236 117 | 0,18,4,5,6260,4,1,0,2,1,3,28,1,0,0,3,0,0,0,male,1711 118 | 0,9,0,2,1919,4,1,2,2,1,0,35,1,0,0,1,0,1,0,male,517 119 | 1,24,4,1,2603,3,0,1,2,3,0,28,1,0,0,1,0,1,0,female,262 120 | 1,9,0,7,936,2,3,2,2,0,0,52,1,2,1,1,0,1,0,male,185 121 | 1,24,4,2,3062,2,3,2,2,1,2,32,1,0,0,1,0,1,0,male,625 122 | 0,36,4,4,4795,4,2,2,2,2,2,30,1,2,0,0,0,1,0,female,1399 123 | 1,36,0,1,5842,4,3,1,2,0,1,35,1,2,1,1,1,1,0,male,1053 124 | 0,6,4,4,2063,4,2,2,2,1,0,30,1,0,0,0,0,1,0,female,24 125 | 1,15,0,4,1459,4,0,2,2,0,0,43,1,2,0,3,0,0,0,female,141 126 | 1,15,4,4,1213,2,3,2,2,1,1,47,2,2,0,1,0,1,0,male,254 127 | 1,24,0,4,5103,4,2,0,2,1,2,47,1,1,2,1,0,1,0,female,723 128 | 1,15,4,6,874,1,2,2,2,2,3,24,1,2,0,1,0,0,0,female,161 129 | 1,6,4,2,2978,2,0,3,2,0,0,32,1,2,0,1,0,1,0,male,840 130 | 1,18,4,5,1820,4,0,1,2,0,1,30,1,2,0,0,0,1,0,female,402 131 | 1,24,0,4,2872,0,3,0,2,3,3,36,1,2,0,1,1,1,0,male,306 132 | 2,24,4,2,1925,4,0,1,2,0,3,26,1,2,0,1,0,0,0,nonbinary/notreported,189 133 | 1,18,4,2,2515,4,0,0,2,3,3,43,1,2,0,1,0,1,0,male,340 134 | 2,6,4,2,2116,4,0,1,2,0,3,41,1,2,0,1,0,1,0,male,506 135 | 1,18,4,4,1453,4,2,0,2,2,3,26,1,2,0,1,0,0,0,female,434 136 | 1,10,4,5,1364,4,0,1,2,3,0,64,1,2,0,1,0,1,0,female,242 137 | 1,6,4,2,1543,3,0,2,2,0,3,33,1,2,0,1,0,0,0,male,414 138 | 0,12,4,5,1318,3,3,2,2,3,3,54,1,2,0,1,0,1,0,male,219 139 | 3,24,1,5,2325,0,1,1,2,1,0,32,0,2,0,1,0,0,0,male,486 140 | 0,6,0,9,932,1,1,3,2,1,1,39,1,2,1,3,0,0,0,female,256 141 | 2,24,0,4,3148,1,0,0,2,0,0,31,1,2,1,1,0,1,0,male,569 142 | 1,36,4,4,3835,1,3,1,2,3,3,45,1,2,0,3,0,1,0,female,133 143 | 1,9,4,7,3832,1,3,3,2,3,3,64,1,2,0,3,0,0,0,male,426 144 | 0,24,4,4,5084,1,3,1,2,3,0,42,1,2,0,1,0,1,0,female,1209 145 | 1,9,0,2,2406,4,4,1,2,1,0,31,1,2,0,0,0,0,0,nonbinary/notreported,22 146 | 1,36,4,4,2394,1,0,2,2,3,0,25,1,2,0,1,0,0,0,female,48 147 | 1,21,4,1,2476,1,3,2,2,3,3,46,1,2,0,0,0,1,0,male,483 148 | 3,24,4,1,2964,1,3,2,2,3,2,49,0,1,0,1,1,1,0,male,273 149 | 3,12,4,2,1262,1,3,1,2,3,1,49,1,2,0,3,0,1,0,male,251 150 | 1,12,4,8,1542,4,1,1,2,3,0,36,1,2,0,1,0,1,0,male,76 151 | 0,24,0,4,1743,4,3,2,2,0,1,48,1,2,1,3,0,0,0,male,225 152 | 2,12,1,4,409,3,0,0,2,1,3,42,1,0,1,1,0,0,0,female,98 153 | 1,12,4,4,2171,4,2,1,2,0,0,29,0,2,0,1,0,0,0,female,628 154 | 1,48,0,1,8858,1,1,1,2,2,2,35,1,1,1,1,0,1,0,male,861 155 | 0,24,4,5,3512,0,1,1,2,1,0,38,0,2,1,1,0,1,0,male,340 156 | 0,12,4,4,1158,2,0,0,2,2,0,26,1,2,0,1,0,1,0,male,172 157 | 1,24,0,4,2684,4,0,2,2,0,3,35,1,2,1,3,0,0,0,male,158 158 | 3,12,4,4,1498,4,0,2,2,2,0,23,0,2,0,1,0,0,0,female,310 159 | 1,30,0,4,5954,4,1,0,0,0,0,38,1,2,0,1,0,0,0,male,233 160 | 0,48,1,8,6416,4,3,2,2,1,2,59,1,0,0,1,0,0,0,female,-2157 161 | 0,12,0,2,3617,4,3,3,2,3,0,28,1,0,2,1,0,1,0,male,1314 162 | 1,12,0,4,1291,4,0,2,2,0,1,35,1,2,1,1,0,0,0,female,313 163 | 2,24,0,8,1275,3,0,1,2,3,3,36,1,2,1,1,0,1,0,male,86 164 | 1,24,4,2,3972,4,1,1,2,3,1,25,1,0,0,1,0,1,0,female,485 165 | 1,15,0,2,3343,4,0,2,2,0,2,28,1,1,0,1,0,1,0,male,366 166 | 2,15,4,7,392,4,2,2,2,3,1,23,1,0,0,1,0,1,0,female,61 167 | 1,9,0,5,2134,4,0,2,2,3,0,48,1,2,2,1,0,1,0,male,295 168 | 1,30,0,4,5771,4,1,2,2,0,0,25,1,2,1,1,0,0,0,female,243 169 | 1,24,0,8,4526,4,0,0,2,0,3,74,1,2,0,0,0,1,0,male,580 170 | 1,15,0,2,2788,4,1,1,0,1,0,24,0,2,1,1,0,0,0,female,382 171 | 1,6,0,4,1382,4,0,3,2,2,0,28,1,2,1,1,0,1,0,female,162 172 | 2,36,4,4,5848,4,0,2,2,2,0,24,1,2,0,1,0,0,0,male,1078 173 | 3,12,4,5,1228,4,0,2,2,0,3,24,1,2,0,3,0,0,0,female,-610 174 | 2,12,4,4,1297,4,0,0,2,3,3,23,1,0,0,1,0,0,0,female,439 175 | 1,24,4,4,1552,4,1,0,2,2,0,32,0,2,0,1,1,0,0,male,127 176 | 1,12,4,4,1963,4,1,2,2,0,0,31,1,0,1,0,1,1,0,male,417 177 | 1,24,4,4,3235,2,3,0,2,0,0,36,1,2,0,0,0,1,0,male,277 178 | 1,24,0,8,4139,0,0,0,2,1,1,27,1,2,1,3,0,1,0,male,756 179 | 0,12,0,1,1804,0,2,0,2,3,1,44,1,2,0,1,0,0,0,male,629 180 | 1,18,4,8,1950,4,1,2,2,2,0,34,2,2,1,1,0,1,0,male,454 181 | 1,48,3,4,12749,2,1,2,2,2,0,37,1,2,0,0,0,1,0,male,1667 182 | 1,9,4,6,1236,4,2,3,2,3,3,23,1,0,0,1,0,1,0,female,317 183 | 1,18,0,5,1055,4,2,2,2,2,1,30,1,2,1,1,0,0,0,female,384 184 | 3,30,2,8,8072,1,2,1,2,1,0,25,0,2,2,1,0,0,0,nonbinary/notreported,1323 185 | 1,30,0,4,2831,4,0,2,2,0,0,33,1,2,0,1,0,1,0,female,377 186 | 1,9,4,8,1449,4,1,0,2,0,0,27,1,2,1,1,0,0,0,female,248 187 | 1,36,4,8,5742,0,1,1,2,0,0,31,1,2,1,1,0,1,0,male,1489 188 | 1,12,4,5,2390,1,3,2,2,1,0,50,1,2,0,1,0,1,0,male,459 189 | 1,24,4,4,3430,2,3,0,2,0,0,31,1,2,0,1,1,1,0,male,603 190 | 0,36,4,7,2273,4,1,0,2,2,0,32,1,2,1,1,1,0,0,male,339 191 | 2,21,4,5,2923,0,0,3,2,2,0,28,0,2,0,0,0,1,0,female,133 192 | 1,24,4,4,1901,0,0,2,2,3,0,29,1,0,0,0,0,1,0,male,78 193 | 0,36,4,7,3711,1,0,1,2,0,0,27,1,2,0,1,0,0,0,nonbinary/notreported,777 194 | 0,48,4,5,8487,1,1,3,2,0,0,24,1,2,0,1,0,0,0,female,1644 195 | 1,24,4,5,2255,1,2,2,2,2,1,54,1,2,0,1,0,0,0,male,268 196 | 1,12,4,4,1262,4,0,0,2,0,0,25,1,2,0,1,0,0,0,male,474 197 | 1,33,0,1,7253,4,1,0,2,0,0,35,1,2,1,0,0,1,0,male,1146 198 | 1,6,0,5,6761,4,1,3,2,1,2,45,1,2,1,0,1,1,0,male,135 199 | 1,18,0,2,1817,4,0,2,2,0,2,28,1,2,1,1,0,0,0,female,115 200 | 1,12,4,4,2141,0,1,0,2,2,2,35,1,2,0,1,0,0,0,male,148 201 | 1,48,1,8,3609,4,0,3,2,2,3,27,2,2,0,1,0,0,0,female,586 202 | 1,30,4,4,2333,2,3,2,2,0,0,30,0,2,0,0,0,0,0,male,483 203 | 1,28,1,1,7824,1,2,0,1,3,3,40,0,0,1,1,1,1,0,male,1715 204 | 2,18,1,4,1445,1,1,2,2,3,0,49,0,2,0,3,0,0,0,male,325 205 | 3,24,4,2,7721,1,2,3,2,0,1,30,1,2,0,1,0,1,1,female,624 206 | 3,21,4,5,3763,1,1,1,0,0,3,24,1,2,0,3,0,0,1,male,733 207 | 0,18,4,8,4439,4,3,3,0,2,3,33,0,2,0,0,0,1,0,male,416 208 | 3,12,4,4,1107,4,0,1,2,0,3,20,1,0,0,0,1,1,0,male,335 209 | 0,15,4,4,1444,1,2,2,2,2,1,23,1,2,0,1,0,0,0,male,301 210 | 0,48,1,5,12169,1,4,2,0,3,2,36,1,1,0,0,0,1,0,male,2157 211 | 1,9,4,4,2753,0,3,0,0,3,0,35,1,2,0,1,0,1,0,male,300 212 | 2,4,4,5,1494,1,2,3,2,0,3,29,1,2,0,3,1,0,1,male,226 213 | 3,24,1,2,2828,2,0,2,2,3,3,22,2,2,0,1,0,1,0,male,1113 214 | 3,24,1,2,2483,2,0,2,2,3,3,22,2,2,0,1,0,1,0,male,356 215 | 2,6,0,5,1299,4,0,3,2,2,3,74,1,2,2,2,1,0,1,male,99 216 | 0,9,4,5,1549,1,2,2,2,0,3,35,1,2,0,2,0,0,0,male,400 217 | 2,10,4,5,3949,4,2,3,1,2,1,37,1,2,0,3,1,0,0,male,385 218 | 1,10,4,1,2901,1,2,3,2,3,3,31,1,0,0,1,0,0,0,female,418 219 | 2,6,4,5,709,3,2,1,2,0,3,27,1,2,0,2,0,0,1,female,142 220 | 3,47,4,5,10722,4,2,3,2,2,3,35,1,2,0,3,0,1,0,female,1095 221 | 1,10,4,5,1287,1,3,2,0,0,1,45,1,2,0,3,0,0,1,male,553 222 | 3,18,1,4,1940,4,2,0,0,3,2,36,0,1,0,0,0,1,0,male,161 223 | 2,30,0,4,3656,1,3,2,2,3,1,49,2,2,1,3,0,0,0,male,882 224 | 1,24,3,1,4679,4,1,0,2,1,0,35,1,2,1,3,0,1,0,male,257 225 | 1,27,3,1,8613,3,0,1,2,0,0,27,1,2,1,1,0,0,0,male,2619 226 | 3,18,4,2,2659,3,0,2,2,0,0,28,1,2,0,1,0,0,0,male,286 227 | 1,24,0,4,1516,3,0,2,2,2,3,43,1,2,1,3,0,0,0,female,278 228 | 3,18,4,5,4380,0,0,0,2,3,0,35,1,2,0,3,1,1,0,male,766 229 | 1,14,3,5,802,4,0,2,2,0,0,27,1,2,1,3,0,0,0,male,113 230 | 1,21,4,8,1572,3,3,2,2,3,3,36,0,2,0,3,0,0,0,female,198 231 | 0,48,1,8,3566,0,1,2,2,0,0,30,1,2,0,1,0,0,0,male,310 232 | 1,24,4,4,1278,4,3,2,2,2,3,36,1,2,0,0,0,1,0,nonbinary/notreported,288 233 | 1,6,2,4,426,4,3,2,2,3,0,39,1,2,0,3,0,0,0,female,31 234 | 1,39,4,1,8588,0,3,2,2,0,0,45,1,2,0,0,0,1,0,male,298 235 | 3,30,4,1,3857,4,0,2,2,3,1,40,1,2,0,0,0,1,0,male,240 236 | 0,12,4,5,685,4,1,1,2,1,0,25,0,2,0,3,0,0,0,female,-268 237 | 3,24,4,4,1603,4,3,2,2,3,0,55,1,2,0,1,0,0,0,female,339 238 | 1,21,4,2,2241,4,3,2,2,0,3,50,1,2,1,1,0,0,0,male,86 239 | 3,24,4,4,2384,4,3,2,2,3,3,64,0,0,0,3,0,0,0,male,103 240 | 1,4,4,2,601,4,2,3,2,1,3,23,1,0,0,3,1,0,0,nonbinary/notreported,118 241 | 1,39,4,1,2569,2,0,2,2,3,0,24,1,2,0,1,0,0,0,male,809 242 | 1,15,0,4,1316,2,0,1,2,0,1,47,1,2,1,3,0,0,0,female,305 243 | 1,60,4,5,10366,4,3,1,2,3,1,42,1,2,0,0,0,1,0,male,1943 244 | 1,18,4,8,1568,0,0,0,2,3,1,24,1,0,0,3,0,0,0,female,189 245 | 1,18,0,4,629,2,3,2,2,1,1,32,0,2,1,0,0,1,0,male,141 246 | 1,6,1,4,1750,2,3,1,2,3,1,45,0,2,0,3,1,0,0,male,524 247 | 1,24,4,1,3488,0,1,0,2,3,0,23,1,2,0,1,0,0,0,female,1346 248 | 1,18,0,4,1800,4,0,2,2,0,0,24,1,2,1,1,0,0,0,male,507 249 | 1,24,3,2,4151,0,0,1,2,1,1,35,1,2,1,1,0,0,0,male,397 250 | 0,15,4,3,2631,0,0,0,2,0,3,25,1,2,0,3,0,0,0,female,459 251 | 1,21,4,1,5248,1,0,3,2,1,0,26,1,2,0,1,0,0,0,male,899 252 | 0,18,3,5,2899,1,3,2,2,3,0,43,1,2,0,1,1,0,0,male,246 253 | 0,18,3,3,6204,4,0,1,2,3,3,44,1,2,0,3,1,1,0,male,967 254 | 1,12,4,4,804,4,3,2,2,3,0,38,1,2,0,1,0,0,0,male,200 255 | 1,36,4,4,3595,4,3,2,2,0,0,28,1,2,0,1,0,0,0,nonbinary/notreported,307 256 | 1,36,0,1,5711,3,3,2,2,0,0,38,1,2,1,0,0,1,0,male,155 257 | 2,15,4,8,2687,4,1,1,2,3,1,26,1,0,0,1,0,1,0,male,667 258 | 3,15,3,2,3643,4,3,3,2,3,1,27,1,2,1,3,0,0,0,female,710 259 | 1,10,0,2,2146,4,2,3,2,1,3,23,1,0,1,1,0,0,0,female,749 260 | 3,10,4,4,2315,4,3,0,2,3,3,52,1,2,0,3,0,0,0,male,739 261 | 1,5,4,8,3448,4,1,3,2,3,3,74,1,2,0,3,0,0,0,male,1016 262 | 1,15,4,2,2708,4,2,1,2,1,1,27,0,2,1,3,0,0,0,male,444 263 | 1,11,0,5,1393,4,2,2,2,3,0,35,1,2,1,0,0,0,0,nonbinary/notreported,169 264 | 2,10,4,2,1275,4,2,2,2,0,1,23,1,2,0,1,0,0,0,female,22 265 | 1,9,4,2,1313,4,3,3,2,3,0,20,1,2,0,1,0,0,0,male,421 266 | 1,12,4,4,1493,4,2,2,2,1,0,34,1,2,0,1,1,0,0,female,346 267 | 1,22,4,4,2675,2,3,0,2,3,0,40,1,2,0,1,0,0,0,male,427 268 | 0,9,4,4,2118,4,0,1,2,0,3,37,1,2,0,3,1,0,0,male,422 269 | 1,36,4,5,909,2,3,2,2,3,1,36,1,2,0,1,0,0,0,male,224 270 | 1,12,0,2,1258,4,2,1,2,3,1,22,1,0,1,3,0,0,0,female,316 271 | 1,15,1,4,1569,0,3,2,2,3,0,34,0,2,0,3,1,0,0,male,453 272 | 1,6,4,1,1236,2,0,1,2,3,1,50,1,0,0,1,0,0,0,male,203 273 | 1,36,3,2,7678,2,1,1,2,3,0,40,1,2,1,1,0,1,0,female,1290 274 | 1,6,4,3,660,2,1,1,2,3,3,23,1,0,0,3,0,0,0,female,96 275 | 1,24,4,2,2835,2,3,0,2,3,1,53,1,2,0,1,0,0,0,male,204 276 | 1,24,4,1,2670,4,3,2,2,3,0,35,1,2,0,0,0,1,0,male,810 277 | 1,12,1,9,3447,2,0,2,2,1,3,35,1,2,0,3,1,0,0,nonbinary/notreported,961 278 | 1,15,4,4,3568,4,3,2,2,0,0,54,0,0,0,0,0,1,0,female,177 279 | 0,21,0,8,3652,4,1,1,2,1,1,27,1,2,1,1,0,0,0,male,599 280 | 3,24,4,4,3660,4,0,1,2,3,0,28,1,2,0,1,0,0,0,female,335 281 | 2,9,4,4,1126,0,3,1,2,3,3,49,1,2,0,1,0,0,0,male,167 282 | 2,6,3,4,683,4,2,1,2,2,1,29,0,2,0,1,0,0,0,female,75 283 | 2,12,4,2,2251,4,0,3,2,0,0,46,1,2,0,3,0,0,0,female,283 284 | 1,12,4,1,4675,1,2,3,2,3,0,20,1,0,0,1,0,0,0,female,1091 285 | 0,21,3,5,2353,4,0,3,2,3,1,47,1,2,1,1,0,0,0,male,619 286 | 3,21,4,4,3357,3,2,2,2,0,0,29,0,2,0,1,0,0,0,female,98 287 | 1,6,4,5,672,4,4,3,2,3,3,54,1,2,0,2,0,1,0,female,25 288 | 3,6,0,4,338,2,3,2,2,3,0,52,1,2,1,1,0,0,0,male,62 289 | 1,9,4,4,2697,4,0,3,2,0,3,32,1,2,0,1,1,0,0,male,350 290 | 1,9,4,5,2507,2,3,1,2,3,2,51,1,1,0,3,0,0,0,male,386 291 | 1,15,3,4,1478,4,0,2,2,1,3,33,0,2,1,1,0,0,0,female,99 292 | 1,12,0,7,3565,1,2,1,2,2,1,37,1,2,1,3,1,0,0,male,875 293 | 1,15,4,2,2221,2,0,1,2,3,0,20,1,0,0,1,0,0,0,female,606 294 | 1,6,0,4,1898,1,0,3,2,0,3,34,1,2,1,3,1,0,0,male,126 295 | 0,6,3,8,1449,0,3,3,2,0,0,31,0,2,1,1,1,0,0,male,531 296 | 1,15,3,2,960,3,1,0,2,0,1,30,1,2,1,1,0,0,0,female,66 297 | 1,36,4,1,8133,4,0,3,2,0,1,30,0,2,0,1,0,0,0,female,1777 298 | 1,9,4,2,2301,0,2,1,2,3,1,22,1,0,0,1,0,0,0,female,74 299 | 1,6,3,8,1743,0,0,3,2,0,3,34,1,2,1,3,0,0,0,male,76 300 | 0,12,4,2,983,3,2,3,2,3,3,19,1,0,0,3,0,0,0,female,292 301 | 1,18,3,4,2320,4,4,1,2,1,3,34,1,2,1,1,0,0,0,female,348 302 | 3,12,1,9,339,4,3,2,2,2,0,45,0,2,0,3,0,0,0,female,50 303 | 2,24,4,4,5152,4,1,2,2,0,0,25,0,2,0,1,0,0,0,male,732 304 | 2,24,4,2,3749,4,2,1,2,3,0,26,1,2,0,1,0,0,0,female,558 305 | 1,9,0,4,3074,1,0,3,2,0,3,33,1,2,1,1,1,0,0,male,439 306 | 2,9,4,4,745,4,0,0,2,0,3,28,1,2,0,3,0,0,0,female,-443 307 | 1,24,4,5,1469,0,3,2,2,3,3,41,1,0,0,3,0,0,0,female,285 308 | 3,6,4,2,1374,4,0,3,2,0,3,36,0,2,0,3,0,1,0,male,323 309 | 1,6,1,5,783,1,0,3,1,0,3,26,2,2,0,3,1,0,0,male,143 310 | 3,21,4,4,2606,4,2,2,2,3,1,28,1,0,0,0,0,1,0,female,330 311 | 1,54,2,1,9436,1,0,1,2,0,1,39,1,2,0,3,1,0,0,male,842 312 | 1,12,0,4,930,1,3,2,2,3,3,65,1,2,3,1,0,0,0,male,52 313 | 1,48,0,1,2751,1,3,2,2,1,0,38,1,2,1,1,1,1,0,male,617 314 | 1,6,0,5,250,3,0,1,2,0,3,41,0,2,1,3,0,0,0,female,31 315 | 0,24,4,5,1201,4,2,2,2,2,1,26,1,2,0,1,0,0,0,male,216 316 | 3,6,4,5,662,4,2,0,2,3,3,41,1,2,0,3,1,1,0,male,121 317 | 1,15,4,1,1300,1,3,2,2,3,2,45,0,1,0,1,1,0,0,male,151 318 | 1,24,1,8,1559,4,1,2,2,3,0,40,0,2,0,1,0,1,0,male,501 319 | 2,12,4,4,3016,4,0,0,2,2,0,24,1,2,0,1,0,0,0,female,179 320 | 1,15,0,4,1360,4,0,2,2,0,1,31,1,2,1,1,0,0,0,male,86 321 | 1,6,2,5,1204,0,0,2,2,2,2,35,0,0,0,1,0,0,1,male,363 322 | 1,10,4,5,1597,2,0,0,2,0,2,40,1,0,0,3,1,0,1,nonbinary/notreported,315 323 | 1,12,4,4,2073,0,0,2,0,0,3,28,1,2,0,1,0,0,0,female,794 324 | 1,11,4,8,2142,3,3,3,2,0,3,28,1,2,0,1,0,1,0,male,287 325 | 3,10,0,2,2132,1,2,1,0,1,3,27,1,0,1,1,0,0,1,female,270 326 | 1,10,4,5,1546,4,0,0,2,0,3,31,1,2,0,3,1,0,1,male,351 327 | 1,24,0,5,1287,3,3,2,2,3,3,37,1,2,1,1,0,1,0,female,70 328 | 1,10,4,5,1418,0,0,0,2,0,3,35,1,0,0,3,0,0,1,male,56 329 | 2,6,0,5,1343,4,3,3,2,3,3,46,1,2,1,1,1,0,1,male,114 330 | 1,18,4,5,2662,1,1,2,2,1,1,32,1,2,0,1,0,0,1,male,565 331 | 1,18,0,4,6070,4,3,0,2,3,0,33,1,2,1,1,0,1,0,male,396 332 | 1,24,0,7,1927,1,0,0,2,0,0,33,1,2,1,1,0,1,0,female,221 333 | 1,18,0,4,2404,4,0,1,2,0,0,26,1,2,1,1,0,0,0,female,638 334 | 1,6,0,4,1554,4,1,3,2,0,0,24,1,0,1,1,0,1,0,female,227 335 | 1,22,4,5,1283,1,1,2,2,3,1,25,1,0,0,1,0,0,0,female,241 336 | 1,24,3,5,717,1,3,2,2,3,0,54,1,2,1,1,0,1,0,female,71 337 | 3,24,4,2,1747,4,2,2,0,2,1,24,1,2,0,3,0,0,1,male,229 338 | 3,9,0,3,1288,0,3,0,1,3,3,48,1,2,1,1,1,0,1,male,336 339 | 3,10,0,5,1038,4,1,2,0,1,1,49,1,2,1,1,0,1,0,male,344 340 | 1,10,4,1,2848,0,0,3,0,0,3,32,1,2,0,1,1,0,0,male,638 341 | 1,12,4,1,1413,3,1,0,2,0,1,55,1,2,0,1,0,0,1,male,510 342 | 1,30,0,4,3077,1,3,0,2,0,0,40,1,2,1,1,1,1,0,male,349 343 | 3,24,1,1,3632,4,0,3,1,3,0,22,0,0,0,1,0,0,1,female,635 344 | 1,18,0,1,3229,1,4,1,2,3,2,38,1,2,0,0,0,1,0,male,162 345 | 1,9,4,5,3577,0,0,3,1,0,3,26,1,0,0,1,1,0,1,male,534 346 | 1,12,0,5,682,0,1,2,2,1,0,51,1,2,1,1,0,1,0,female,177 347 | 1,10,4,4,1924,4,0,3,2,3,1,38,1,2,0,1,0,1,1,male,563 348 | 1,10,4,7,727,2,3,2,2,3,2,46,1,1,0,1,0,1,0,male,219 349 | 2,10,0,5,781,4,3,2,2,3,2,63,1,1,1,1,0,1,0,male,208 350 | 3,12,0,5,2121,4,0,2,2,0,1,30,1,2,1,1,0,0,0,male,270 351 | 1,12,0,7,701,4,0,2,2,0,0,32,1,2,1,1,0,0,0,male,144 352 | 1,10,0,2,2069,1,0,1,2,2,0,26,1,2,1,1,0,0,1,female,440 353 | 1,24,4,5,1525,3,1,2,2,1,0,34,1,2,0,1,1,1,0,female,254 354 | 1,48,0,8,7629,1,3,2,2,0,0,46,0,2,1,0,1,0,0,male,1216 355 | 3,12,0,5,3499,4,0,0,0,0,3,29,1,2,1,1,0,0,0,female,-1246 356 | 1,6,4,4,1346,0,3,1,2,3,2,42,0,1,0,1,1,1,0,male,339 357 | 1,36,0,1,10477,1,3,1,2,3,2,42,1,1,1,1,0,0,0,male,4826 358 | 3,24,4,1,2924,4,0,0,1,3,2,63,0,2,0,1,1,1,0,male,797 359 | 1,10,0,5,1231,4,3,0,2,3,3,32,1,2,1,3,1,0,1,male,249 360 | 2,18,4,5,1961,4,3,0,2,0,0,23,1,2,0,0,0,0,0,female,214 361 | 1,15,0,5,5045,1,3,3,2,3,0,59,1,2,0,1,0,1,0,female,352 362 | 1,12,0,5,1255,4,3,2,2,3,3,61,1,2,1,3,0,0,0,male,206 363 | 3,12,4,2,1858,4,2,2,2,2,0,22,1,0,0,1,0,0,0,female,246 364 | 1,6,0,2,1221,1,0,3,2,0,1,27,1,2,1,1,0,0,0,female,216 365 | 1,9,4,2,1388,4,0,2,2,0,3,26,1,0,0,1,0,0,0,female,319 366 | 1,12,4,4,2279,1,0,2,2,3,2,37,1,1,0,1,0,1,0,male,508 367 | 1,12,2,2,2759,4,3,1,2,3,1,34,1,2,1,1,0,0,0,male,691 368 | 2,24,4,4,1258,2,0,0,2,1,0,57,1,2,0,3,0,0,0,female,214 369 | 0,12,2,9,1410,4,0,1,2,0,3,31,1,2,0,3,0,1,0,male,214 370 | 3,15,4,5,1403,4,0,1,2,3,0,28,1,0,0,1,0,0,0,female,130 371 | 3,24,4,2,3021,4,0,1,2,0,3,24,1,0,0,3,0,0,0,nonbinary/notreported,865 372 | 3,24,4,8,6568,4,0,1,2,0,0,21,2,2,0,3,0,0,0,female,625 373 | 1,24,0,4,2578,3,3,1,2,0,0,34,1,2,0,1,0,0,0,male,606 374 | 0,24,0,1,7758,3,3,1,2,3,2,29,1,0,0,1,0,0,0,female,782 375 | 3,6,4,6,343,4,2,2,2,2,3,27,1,2,0,1,0,0,0,female,46 376 | 1,21,3,2,1591,0,1,2,2,1,3,34,1,2,1,0,0,0,0,male,279 377 | 3,27,4,4,3416,4,0,0,2,0,0,27,1,2,0,0,0,0,0,male,916 378 | 3,12,2,3,1108,4,1,2,2,1,3,28,1,2,1,1,0,0,0,male,-479 379 | 0,27,3,1,5965,4,3,3,2,0,0,30,1,2,1,0,0,1,0,male,1683 380 | 0,15,4,3,1514,0,0,2,1,0,3,22,1,2,0,1,0,0,0,male,502 381 | 1,30,0,4,6742,1,1,1,2,1,1,36,1,2,1,1,0,0,0,male,356 382 | 3,18,4,2,3650,4,2,3,2,3,0,22,1,0,0,1,0,0,0,female,557 383 | 3,21,4,2,3599,4,1,3,2,3,0,26,1,0,0,3,0,0,0,female,1081 384 | 1,60,0,5,13756,1,3,1,2,3,2,63,0,1,0,0,0,1,0,male,1886 385 | 0,9,4,5,276,4,0,2,2,3,3,22,1,0,0,3,0,0,0,female,67 386 | 1,42,0,2,4041,2,0,2,2,3,3,36,1,2,1,1,0,1,0,male,789 387 | 0,9,4,4,458,4,0,2,2,1,3,24,1,2,0,1,0,0,0,male,95 388 | 0,9,4,2,918,4,0,2,2,2,1,30,1,2,0,1,0,0,0,female,-664 389 | 1,24,4,5,7393,4,0,3,2,3,1,43,1,2,0,3,1,0,0,male,541 390 | 2,10,4,6,1225,4,0,1,2,0,0,37,1,2,0,1,0,1,0,male,293 391 | 3,24,4,1,2812,1,3,1,2,3,3,26,1,0,0,1,0,0,0,female,297 392 | 1,15,4,1,3029,4,1,1,2,0,0,33,1,2,0,1,0,0,0,male,531 393 | 2,12,0,5,1480,2,4,1,2,3,2,66,0,1,2,2,0,0,0,nonbinary/notreported,295 394 | 2,6,0,7,1047,4,0,1,2,3,1,50,1,2,0,3,0,0,0,female,243 395 | 1,15,0,4,1471,4,0,2,2,3,2,35,1,1,1,1,0,1,0,male,444 396 | 1,24,4,2,5511,0,0,2,2,2,0,25,2,2,0,1,0,0,0,male,947 397 | 0,9,4,4,1206,4,3,2,2,3,3,25,1,2,0,1,0,0,0,female,326 398 | 0,24,3,4,6403,4,2,3,2,0,0,33,1,2,0,1,0,0,0,male,985 399 | 1,12,4,4,707,4,0,2,2,0,3,30,0,2,1,1,0,0,0,male,82 400 | 1,12,3,1,1503,4,0,2,2,3,3,41,1,0,0,1,0,0,0,female,323 401 | 0,12,4,5,6078,4,1,1,2,0,0,32,1,2,0,1,0,0,0,male,908 402 | 0,27,4,8,2528,4,2,2,2,2,1,32,1,2,0,1,1,1,0,female,133 403 | 0,12,4,8,1037,0,1,0,2,3,3,39,1,2,0,3,0,0,0,male,246 404 | 3,6,4,1,1352,2,4,3,2,0,1,23,1,0,0,2,0,1,0,nonbinary/notreported,138 405 | 1,24,4,4,3181,4,2,2,2,3,1,26,1,2,0,1,0,1,0,female,99 406 | 1,18,4,4,4594,4,2,0,2,0,0,32,1,2,0,1,0,1,0,male,573 407 | 0,48,4,0,5381,1,4,0,2,3,2,40,0,1,0,2,0,1,0,male,1039 408 | 1,15,4,1,4657,4,0,0,2,0,0,30,1,2,0,1,0,1,0,male,1216 409 | 0,9,4,8,1391,4,0,1,2,2,3,27,0,2,0,1,0,1,0,female,267 410 | 0,18,4,8,1913,3,2,0,2,1,3,36,0,2,0,1,0,1,0,female,288 411 | 1,42,4,4,7166,1,1,1,2,3,1,29,1,0,0,1,0,1,0,female,1525 412 | 1,13,4,4,1409,0,4,1,2,3,3,64,1,2,0,1,0,0,0,female,31 413 | 1,24,3,8,2978,1,0,2,2,3,3,32,1,2,1,1,1,1,0,male,455 414 | 1,12,0,4,976,1,3,2,2,3,0,35,1,2,1,1,0,0,0,nonbinary/notreported,196 415 | 1,24,3,8,2375,2,0,2,2,0,0,44,1,2,1,1,1,1,0,male,501 416 | 1,12,0,4,522,2,3,2,2,3,1,42,1,2,1,1,1,1,0,male,125 417 | 1,28,0,4,2743,4,3,2,2,0,0,29,1,2,1,1,0,0,0,male,461 418 | 1,11,0,4,1154,0,4,2,2,3,3,57,1,2,2,3,0,0,0,female,206 419 | 1,24,0,1,5804,3,0,2,2,0,3,27,1,2,1,1,0,0,0,male,2195 420 | 1,18,0,4,1169,1,0,2,2,1,1,29,1,2,1,1,0,1,0,male,347 421 | 3,15,0,2,1478,4,3,2,2,3,0,44,1,2,1,1,1,1,0,male,58 422 | 1,12,4,4,776,4,0,2,2,0,3,28,1,2,0,1,0,0,0,female,39 423 | 0,11,0,5,1322,3,0,2,2,3,0,40,1,2,1,1,0,0,0,female,108 424 | 0,16,0,5,1175,4,4,1,2,1,0,68,1,1,2,2,0,1,0,male,321 425 | 1,12,4,5,2133,1,3,2,2,3,2,52,1,1,0,0,0,1,0,female,584 426 | 1,15,0,4,1829,4,3,2,2,3,0,46,1,2,1,1,0,1,0,male,190 427 | 1,12,0,4,717,4,3,2,2,3,3,52,1,2,2,1,0,0,0,male,59 428 | 0,39,3,7,11760,0,1,1,2,1,2,32,1,0,0,1,0,1,0,male,328 429 | 0,9,0,7,1501,4,3,1,2,1,0,34,1,2,1,0,0,1,0,female,-197 430 | 3,12,4,7,1200,1,0,2,2,3,1,23,0,0,0,1,0,1,0,female,67 431 | 0,9,4,5,3195,1,0,3,2,0,3,33,1,2,0,3,0,0,0,female,521 432 | 1,30,0,4,4530,4,1,2,2,3,0,26,1,0,0,0,0,1,0,female,244 433 | 1,12,3,3,1555,3,3,2,2,3,2,55,1,1,1,1,1,0,0,male,-531 434 | 0,15,0,8,2326,2,0,1,2,3,0,27,0,2,0,1,0,0,0,male,300 435 | 0,18,0,8,1887,1,0,2,2,3,3,28,0,2,1,1,0,0,0,female,579 436 | 1,12,0,8,1264,1,3,2,2,3,2,57,1,0,0,3,0,0,0,male,324 437 | 1,7,3,4,846,1,3,0,2,3,2,36,1,1,0,1,0,0,0,male,75 438 | 1,15,0,7,1532,0,0,2,2,1,0,31,1,2,0,1,0,0,0,female,159 439 | 1,6,3,4,935,4,0,0,2,0,3,24,1,2,0,1,0,0,0,female,115 440 | 3,27,0,8,2442,4,3,2,2,3,0,43,2,2,3,0,1,1,0,nonbinary/notreported,407 441 | 0,18,0,8,3590,4,4,0,2,1,0,40,1,2,2,2,1,1,0,female,470 442 | 1,21,0,2,2288,4,2,2,2,3,1,23,1,2,0,1,0,1,0,female,327 443 | 1,27,3,8,5117,4,1,0,2,3,0,26,1,2,1,1,0,0,0,male,307 444 | 3,39,0,2,14179,1,1,2,2,3,1,30,1,2,1,0,0,1,0,male,1906 445 | 1,15,4,4,1386,1,0,2,2,0,3,40,1,0,0,1,0,1,0,female,206 446 | 1,12,0,4,618,4,3,2,2,3,3,56,1,2,0,1,0,0,0,male,166 447 | 1,12,4,2,1574,4,0,2,2,0,3,50,1,2,0,1,0,0,0,male,236 448 | 1,6,0,4,700,1,3,2,2,3,2,36,1,1,1,1,0,0,0,male,203 449 | 1,12,4,4,886,1,0,2,2,0,0,21,1,2,0,1,0,0,0,female,197 450 | 1,36,4,1,4686,4,0,1,2,0,2,32,1,1,0,0,0,1,0,male,1158 451 | 0,9,4,4,790,2,0,2,2,1,3,66,1,2,0,3,0,0,0,female,108 452 | 0,12,4,4,766,2,0,2,2,1,3,66,1,2,0,3,0,0,0,male,-338 453 | 3,20,4,2,2212,1,1,2,2,3,0,39,1,2,0,1,0,1,0,male,617 454 | 0,10,4,5,7308,4,4,1,2,3,2,70,0,1,0,0,0,1,0,male,1871 455 | 0,24,0,7,5743,4,2,1,2,3,2,24,1,1,1,1,0,1,0,female,1400 456 | 3,14,4,5,3973,4,4,3,2,3,2,22,1,1,0,1,0,0,0,nonbinary/notreported,754 457 | 0,60,3,4,7418,1,0,3,2,2,3,27,1,2,0,3,0,0,0,male,446 458 | 0,20,3,0,2629,4,0,1,2,1,0,29,0,2,1,1,0,1,0,male,231 459 | 0,18,4,8,1941,3,0,2,2,0,1,35,1,2,0,3,0,1,0,male,393 460 | 0,24,3,2,2333,1,2,2,2,0,1,29,0,2,0,3,0,0,0,male,687 461 | 1,12,4,1,2445,1,2,1,2,3,0,26,1,0,0,1,0,1,0,female,672 462 | 0,20,4,1,6468,1,4,3,2,3,3,60,1,2,0,0,0,1,0,male,370 463 | 0,18,0,2,7374,4,4,2,2,3,1,40,2,2,1,0,0,1,0,male,2153 464 | 1,15,4,1,3812,0,2,3,2,3,0,23,1,2,0,1,0,1,0,female,1411 465 | 3,28,4,5,4006,4,0,0,2,0,0,45,1,2,0,3,0,0,0,male,-410 466 | 0,12,4,5,7472,1,4,3,2,0,3,24,1,0,0,2,0,0,0,female,498 467 | 2,12,4,2,1424,1,3,0,2,3,3,55,1,2,0,0,0,1,0,female,247 468 | 0,12,4,1,2028,1,0,2,2,0,0,30,1,2,0,1,0,0,0,nonbinary/notreported,434 469 | 1,15,4,5,5324,2,3,3,2,3,2,35,1,1,0,1,0,0,0,female,69 470 | 0,36,4,4,2323,4,1,2,2,3,0,24,1,0,0,1,0,0,0,male,617 471 | 1,12,4,7,1393,4,3,2,2,3,1,47,0,2,2,1,1,1,0,male,87 472 | 1,18,4,2,1984,4,0,2,2,3,2,47,0,1,1,1,0,0,0,male,118 473 | 1,24,4,4,999,1,3,2,2,0,0,25,1,2,1,1,0,0,0,male,217 474 | 1,36,4,8,7409,1,3,0,2,0,1,37,1,2,1,1,0,0,0,male,124 475 | 1,15,4,2,2186,1,1,3,2,3,3,33,0,0,0,3,0,0,0,female,263 476 | 2,36,4,4,4473,4,3,2,2,0,0,31,1,2,0,1,0,0,0,male,756 477 | 1,24,4,9,937,4,2,2,2,1,0,27,1,2,1,3,0,0,0,female,121 478 | 1,18,4,2,3422,4,3,2,2,3,1,47,0,2,2,1,1,1,0,male,426 479 | 1,24,4,4,3105,1,2,2,2,0,0,25,1,2,1,1,0,0,0,male,370 480 | 1,12,0,7,2748,4,3,1,2,3,2,57,0,1,2,3,0,0,0,female,499 481 | 0,18,4,3,3872,4,4,1,2,3,0,67,1,2,0,1,0,1,0,female,147 482 | 1,27,4,3,5190,1,3,2,2,3,1,48,1,2,3,1,1,1,0,male,1134 483 | 0,18,4,2,3001,4,1,1,2,3,3,40,1,0,0,1,0,0,0,female,278 484 | 1,24,3,8,3863,4,0,3,2,0,2,32,1,1,0,1,0,0,0,male,1452 485 | 1,12,0,2,5801,1,3,1,2,3,1,49,1,0,0,1,0,1,1,nonbinary/notreported,990 486 | 1,12,0,2,1592,3,1,0,2,0,1,35,1,2,0,1,0,0,0,female,372 487 | 1,12,0,8,1185,4,0,0,2,0,3,27,1,2,1,1,0,0,0,female,74 488 | 1,18,0,2,3780,4,2,0,2,0,0,35,1,2,1,0,0,1,0,male,462 489 | 0,18,0,2,3612,4,3,0,2,3,1,37,1,2,0,1,0,1,1,female,590 490 | 1,12,4,8,1076,4,0,1,2,0,3,26,1,2,0,1,0,1,0,female,36 491 | 1,12,4,5,3527,1,2,1,2,1,1,45,1,2,0,0,1,1,0,male,543 492 | 1,18,4,4,2051,4,2,2,2,2,3,33,1,2,0,1,0,0,0,male,412 493 | 1,12,0,2,3331,4,3,1,2,3,1,42,2,2,0,1,0,0,0,male,656 494 | 3,18,2,8,3104,4,1,0,2,2,1,31,0,2,0,1,0,1,0,male,430 495 | 1,24,0,4,2611,4,3,2,0,1,3,46,1,2,1,1,0,0,0,nonbinary/notreported,429 496 | 3,12,0,1,1409,4,3,2,2,1,3,54,1,2,0,1,0,0,0,male,202 497 | 1,24,4,4,1311,0,1,2,2,1,1,26,1,2,0,1,0,1,0,female,200 498 | 1,6,4,4,2108,4,1,1,2,0,3,29,1,0,0,1,0,0,0,female,258 499 | 1,24,0,1,4042,1,1,0,2,3,1,43,1,2,1,1,0,1,0,male,484 500 | 1,12,0,5,926,4,4,3,2,0,1,38,1,2,0,2,0,0,0,female,172 501 | 3,12,4,4,1680,2,3,0,2,2,3,35,1,2,0,1,0,0,0,female,312 502 | 1,24,4,5,1249,4,2,2,2,0,3,28,1,2,0,1,0,0,0,female,117 503 | 1,24,0,5,2463,0,1,2,2,1,1,27,1,2,1,1,0,1,0,female,130 504 | 1,6,4,4,1595,4,1,0,2,0,1,51,1,2,0,1,1,0,0,male,239 505 | 1,24,0,3,2058,4,0,2,2,0,3,33,1,2,1,1,0,1,0,male,229 506 | 1,24,4,1,7814,4,1,0,2,1,0,38,1,2,0,0,0,1,0,nonbinary/notreported,2094 507 | 1,6,0,4,1740,4,3,1,2,0,3,30,1,0,1,1,0,0,0,female,164 508 | 1,12,0,4,1240,1,3,2,2,0,3,38,1,2,1,1,0,1,0,female,155 509 | 1,24,0,1,6842,1,0,1,2,3,1,46,1,2,1,0,1,1,0,male,612 510 | 1,24,0,2,5150,4,3,2,2,3,0,33,1,2,0,1,0,1,0,male,1508 511 | 3,6,4,5,1203,0,3,0,2,0,1,43,1,2,0,1,0,1,0,male,297 512 | 1,6,0,5,2080,2,0,3,2,0,0,24,1,2,0,1,0,0,0,female,570 513 | 1,6,4,7,1538,4,2,3,2,0,2,56,1,2,0,1,0,0,0,female,191 514 | 0,24,0,5,3878,0,2,2,2,0,0,37,1,2,0,1,0,1,0,male,395 515 | 0,30,4,2,3832,4,2,1,2,2,1,22,1,2,0,1,0,0,0,female,805 516 | 1,15,4,5,3186,3,1,1,2,1,0,20,1,0,0,1,0,0,0,female,625 517 | 0,24,4,4,2896,0,2,1,2,2,0,29,1,2,0,1,0,0,0,male,251 518 | 0,24,3,8,6967,0,1,2,2,3,0,36,1,0,0,0,0,1,0,male,943 519 | 1,36,4,7,1819,4,0,2,2,3,2,37,2,1,0,1,0,1,0,nonbinary/notreported,-972 520 | 1,24,4,4,5943,1,2,3,2,2,0,44,1,2,1,1,0,1,0,female,-1961 521 | 1,36,0,2,7127,4,2,1,2,3,1,23,1,0,1,1,0,1,0,female,-2523 522 | 1,36,4,2,3349,4,0,2,2,0,0,28,1,2,0,0,0,1,0,female,-1492 523 | 1,36,4,2,10974,4,4,2,2,0,0,26,1,2,1,0,0,1,0,female,-4369 524 | 1,6,4,4,518,4,0,0,2,2,3,29,1,2,0,1,0,0,0,female,50 525 | 1,18,4,4,1126,1,2,2,2,0,3,21,1,0,0,1,0,1,0,female,203 526 | 0,12,0,1,1860,4,4,2,2,0,0,34,1,2,1,0,0,1,0,male,346 527 | 1,36,0,4,9566,4,0,1,2,0,0,31,2,2,1,1,0,0,0,female,2312 528 | 3,12,4,4,701,4,0,2,2,0,3,40,1,2,0,3,0,0,0,female,27 529 | 0,12,4,4,2930,4,1,1,2,2,3,27,1,2,0,1,0,0,0,female,1125 530 | 1,18,4,4,1505,4,0,2,2,0,2,32,1,1,0,0,0,1,0,male,348 531 | 1,18,0,4,2238,4,0,1,2,2,0,25,1,2,1,1,0,0,0,female,629 532 | 1,4,0,4,1503,4,1,1,2,2,3,42,1,2,1,3,1,0,0,male,617 533 | 1,24,0,1,2197,1,1,2,2,3,0,43,1,2,1,1,1,1,0,male,363 534 | 2,12,4,4,1881,4,0,1,2,0,0,44,1,0,0,3,0,1,0,female,367 535 | 3,18,0,4,1880,4,1,2,2,2,1,32,1,2,1,0,0,1,0,female,305 536 | 3,18,4,4,2389,4,2,2,2,2,0,27,2,2,0,1,0,0,0,female,288 537 | 0,24,4,4,1967,4,3,2,2,3,0,20,1,2,0,1,0,1,0,female,82 538 | 1,4,0,5,3380,4,1,3,2,2,3,37,1,2,0,1,1,0,0,female,509 539 | 1,4,0,5,1455,4,1,1,2,2,3,42,1,2,2,3,1,0,0,male,259 540 | 1,7,0,4,730,1,3,2,2,0,1,46,1,0,1,3,0,1,0,male,236 541 | 0,18,2,2,3244,4,0,3,2,3,0,33,0,2,1,1,0,1,0,female,909 542 | 0,9,4,4,1670,4,2,2,2,0,0,22,1,2,0,1,0,1,0,female,-628 543 | 0,48,4,4,3979,1,1,2,2,2,0,41,1,2,1,1,1,1,0,nonbinary/notreported,934 544 | 0,12,4,2,1922,4,0,2,2,0,1,37,1,2,0,3,0,0,0,male,-304 545 | 0,18,0,2,1295,4,2,2,2,2,1,27,1,2,1,1,0,0,0,female,146 546 | 1,4,0,4,1544,4,1,1,2,2,3,42,1,2,2,3,1,0,0,male,290 547 | 0,8,4,8,907,4,2,0,2,0,3,26,1,2,0,1,0,1,0,female,161 548 | 0,30,4,4,1715,1,0,2,2,2,0,26,1,2,0,1,0,0,0,female,248 549 | 2,10,0,4,1347,1,1,2,2,0,1,27,1,2,1,1,0,1,0,male,456 550 | 0,12,4,5,1007,3,0,2,2,2,3,22,1,2,0,1,0,0,0,female,95 551 | 1,12,0,2,1402,2,1,0,2,3,0,37,1,0,0,1,0,1,0,female,250 552 | 0,12,4,5,2002,4,1,0,2,3,1,30,1,0,0,1,1,1,0,male,523 553 | 1,12,0,7,2096,4,1,1,2,1,3,49,1,2,0,3,1,0,0,male,468 554 | 1,12,4,5,1101,4,0,0,2,0,3,27,1,2,1,1,0,1,0,female,145 555 | 1,10,4,9,894,1,1,2,2,1,1,40,1,2,0,1,0,1,0,female,230 556 | 0,11,4,2,1577,3,2,2,2,2,3,20,1,2,0,1,0,0,0,female,325 557 | 1,33,3,8,2764,4,0,1,2,0,0,26,1,2,1,1,0,1,0,female,195 558 | 0,48,2,5,8358,2,2,3,2,2,0,30,1,2,1,1,0,0,0,female,135 559 | 2,12,4,2,1474,4,2,2,2,2,1,33,0,2,0,0,0,1,0,female,162 560 | 1,24,4,1,5433,1,4,1,2,3,1,26,1,0,0,0,0,1,0,female,291 561 | 0,14,4,8,1410,2,3,3,2,0,3,35,1,2,0,1,0,1,0,female,51 562 | 1,20,0,5,3485,1,2,1,2,3,3,44,1,2,1,1,0,1,0,male,860 563 | 1,18,0,1,3850,4,1,0,2,2,0,27,1,2,1,1,0,0,0,nonbinary/notreported,1137 564 | 0,60,4,5,7408,0,2,2,2,0,1,24,1,2,0,0,0,0,0,female,-2527 565 | 2,24,4,4,1377,0,3,2,2,0,2,47,1,1,0,1,0,1,0,female,593 566 | 1,30,3,8,4272,0,0,1,2,0,1,26,1,2,1,3,0,0,0,male,757 567 | 0,24,3,4,1553,0,1,0,2,0,1,23,1,0,1,1,0,1,0,female,402 568 | 0,36,3,8,9857,0,1,3,2,1,1,31,1,2,1,3,1,1,0,male,2481 569 | 1,6,0,5,362,0,0,2,2,3,0,52,1,2,1,3,0,0,0,female,84 570 | 1,12,0,2,1935,4,3,2,2,3,3,43,1,2,2,1,0,1,0,male,50 571 | 1,48,4,4,10222,1,1,2,2,1,0,37,2,2,0,1,0,1,0,male,1506 572 | 2,12,4,5,1330,4,2,2,2,2,3,26,1,2,0,1,0,0,0,male,113 573 | 1,36,4,7,9055,1,0,1,2,3,2,35,1,1,0,3,1,1,0,male,1139 574 | 0,26,4,1,7966,4,2,1,2,1,0,30,1,2,1,1,0,0,0,male,1045 575 | 0,30,1,2,3496,3,0,2,2,0,0,34,2,2,0,1,1,1,0,male,815 576 | 0,36,4,1,6948,4,0,1,2,0,0,35,1,0,0,0,0,1,0,male,1406 577 | 0,48,2,8,12204,1,0,1,2,0,0,48,0,2,0,0,0,1,0,male,512 578 | 3,36,4,2,3446,4,3,2,2,0,0,42,1,2,0,1,1,0,0,male,-212 579 | 3,12,4,7,684,4,0,2,2,3,0,40,1,0,0,3,1,0,0,male,-265 580 | 3,33,0,2,4281,2,0,3,2,3,0,23,1,2,1,1,0,0,0,female,-416 581 | 3,42,4,4,7174,1,1,2,2,1,0,30,1,2,0,0,0,1,0,female,-2163 582 | 3,24,1,4,1546,4,1,2,1,3,0,24,0,0,0,3,0,0,0,male,-76 583 | 3,24,4,2,2359,0,4,3,2,2,1,33,1,2,0,1,0,0,0,male,-1497 584 | 1,24,4,4,3621,0,3,1,2,3,0,31,1,2,1,1,0,0,0,male,-989 585 | 3,12,4,6,741,0,4,2,2,1,1,22,1,2,0,1,0,0,0,female,-376 586 | 3,12,4,2,7865,4,3,2,2,3,2,53,1,1,0,0,0,1,0,male,-3906 587 | 3,24,4,1,2910,4,1,1,2,2,2,34,1,1,0,0,0,1,0,male,510 588 | 3,18,0,5,5302,4,3,1,2,3,2,36,1,1,2,0,0,1,0,nonbinary/notreported,1641 589 | 3,36,4,2,3620,4,0,3,1,0,1,37,1,2,0,1,1,0,0,male,795 590 | 3,18,4,4,3509,4,1,2,1,2,3,25,1,2,0,1,0,0,0,female,651 591 | 0,12,4,2,3017,4,2,0,2,2,3,34,1,0,0,0,0,0,0,female,521 592 | 3,12,4,2,1657,4,0,1,2,0,3,27,1,2,0,1,0,0,0,male,150 593 | 3,8,0,0,1164,4,3,0,2,3,2,51,0,1,1,0,1,1,0,male,377 594 | 3,36,0,2,6229,4,2,2,0,3,2,23,1,0,1,3,0,1,0,female,-2098 595 | 3,24,1,5,1193,4,4,3,0,3,2,29,1,0,1,2,0,0,0,female,-214 596 | 3,30,2,2,4583,4,0,1,1,0,3,32,1,2,1,1,0,0,0,male,834 597 | 3,36,0,2,5371,4,0,0,1,0,1,28,1,2,1,1,0,0,0,male,1735 598 | 3,12,4,2,708,4,0,1,1,1,1,38,1,2,0,3,1,0,0,male,142 599 | 3,21,0,5,571,4,3,2,2,3,3,65,1,2,1,1,0,0,0,male,125 600 | 3,30,4,4,2522,4,3,3,1,1,1,39,1,2,0,1,1,0,0,nonbinary/notreported,595 601 | 3,36,4,2,5179,4,1,2,2,0,1,29,1,2,0,1,0,0,0,male,-4029 602 | 3,36,4,1,8229,4,0,1,2,0,1,26,1,2,0,1,1,0,0,male,-2911 603 | 1,24,0,2,2028,4,1,1,2,0,1,30,1,2,1,3,0,0,0,male,608 604 | 3,6,4,5,1374,1,4,2,2,1,1,75,1,2,0,0,0,1,0,female,273 605 | 3,12,4,2,1289,4,0,2,1,2,1,21,1,2,0,3,0,0,0,male,131 606 | 3,36,4,2,2712,4,3,1,2,0,1,41,0,2,0,1,1,0,0,male,-118 607 | 3,15,0,2,975,4,0,1,2,1,1,25,1,2,1,1,0,0,0,male,253 608 | 0,6,3,2,1050,4,4,2,2,2,1,35,2,2,1,0,0,1,1,male,343 609 | 3,6,0,5,609,4,1,2,2,1,1,37,1,2,1,1,0,0,0,female,47 610 | 3,48,4,1,4788,4,1,2,2,1,1,26,1,2,0,1,1,0,0,male,452 611 | 0,24,4,2,3069,0,3,2,2,3,2,30,1,1,0,1,0,0,0,male,472 612 | 0,12,4,5,836,0,2,2,2,0,1,23,0,2,0,3,0,0,0,female,-208 613 | 3,12,4,2,2577,4,0,1,2,2,0,42,1,2,0,1,0,0,0,male,198 614 | 3,12,4,2,1620,4,0,1,0,1,1,30,1,2,0,1,0,0,0,female,403 615 | 3,15,4,2,1845,4,2,2,1,2,1,46,1,0,0,1,0,0,0,female,16 616 | 3,24,4,1,6579,4,4,2,2,0,2,29,1,1,0,0,0,1,0,male,691 617 | 3,12,4,5,1893,4,0,2,1,3,1,29,1,2,0,1,0,1,0,female,112 618 | 3,30,0,1,10623,4,3,0,2,3,2,38,1,1,2,0,1,1,0,male,690 619 | 3,18,4,5,2249,0,1,2,2,1,0,30,1,2,0,0,1,1,0,male,782 620 | 3,30,4,2,3108,4,2,1,2,3,1,31,1,2,0,3,0,0,0,male,-512 621 | 0,12,0,5,958,4,1,1,2,1,3,47,1,2,1,3,1,0,0,male,436 622 | 1,24,4,1,9277,1,0,1,2,3,2,48,1,1,0,1,0,1,0,male,923 623 | 1,24,0,0,6314,4,4,2,0,0,2,27,0,2,1,0,0,1,0,male,1492 624 | 3,12,0,1,1526,4,3,2,2,3,2,66,1,1,1,0,0,0,0,male,110 625 | 3,12,4,2,3590,4,0,1,0,0,1,29,1,2,0,3,1,0,0,male,453 626 | 3,24,0,1,6615,4,4,1,2,3,2,75,1,1,1,0,0,1,0,male,1168 627 | 3,6,0,2,1872,4,4,2,2,3,2,36,1,1,2,0,0,1,0,male,71 628 | 1,12,4,5,2859,1,4,2,2,3,2,38,1,2,0,0,0,1,0,male,327 629 | 1,18,0,4,1582,3,3,2,2,3,0,46,1,2,1,1,0,0,0,male,460 630 | 1,6,4,9,1238,1,4,2,2,3,1,36,1,2,0,0,1,1,0,male,276 631 | 3,12,4,2,2578,4,4,0,2,3,2,55,1,1,0,0,0,0,0,female,189 632 | 3,15,0,2,1433,4,0,2,2,1,1,25,1,0,1,1,0,0,0,female,2 633 | 3,42,4,2,7882,4,1,1,1,3,1,45,1,1,0,1,1,0,0,male,2991 634 | 3,24,4,2,4169,4,0,2,2,3,1,28,1,2,0,1,0,0,0,male,810 635 | 3,36,4,2,3959,4,4,2,2,1,1,30,1,2,0,0,0,1,0,male,844 636 | 3,36,4,5,3249,4,1,1,2,3,2,39,0,1,0,0,1,1,0,male,1314 637 | 3,24,4,2,3149,4,2,2,2,2,2,22,0,1,0,1,0,0,0,male,237 638 | 3,12,0,2,2246,4,3,0,2,1,1,60,1,2,1,1,0,0,0,male,-549 639 | 3,13,0,8,1797,4,2,0,2,2,1,28,0,2,1,3,0,0,0,male,464 640 | 3,20,0,2,4272,4,3,3,2,3,1,24,1,2,1,1,0,0,0,female,778 641 | 3,24,0,1,2957,4,3,2,2,3,1,63,1,2,1,1,0,1,0,male,429 642 | 3,36,0,2,2348,4,0,0,2,0,1,46,1,2,1,1,0,1,0,female,316 643 | 2,42,2,8,6289,4,2,1,2,2,1,33,1,2,1,1,0,0,0,nonbinary/notreported,577 644 | 3,24,0,1,6419,4,3,1,2,3,2,44,1,1,1,0,1,1,0,female,1372 645 | 3,48,0,1,6143,4,3,2,2,3,2,58,2,1,1,3,0,0,0,female,-3646 646 | 1,24,0,7,1597,4,3,2,2,3,2,54,1,1,1,1,1,0,0,male,211 647 | 3,36,4,0,15857,4,4,1,0,1,0,43,1,2,0,0,0,0,0,male,1798 648 | 1,24,0,4,2223,0,3,2,2,3,1,52,0,2,1,1,0,0,0,male,837 649 | 1,48,3,4,7238,1,3,0,2,1,0,32,0,2,1,1,1,0,0,male,840 650 | 0,30,3,8,2503,0,3,2,2,0,1,41,2,2,1,1,0,0,0,nonbinary/notreported,431 651 | 0,18,4,8,2622,0,0,2,2,3,0,34,1,2,0,1,0,0,0,male,702 652 | 0,24,4,2,4351,1,0,3,2,3,1,48,1,2,0,3,0,1,0,female,216 653 | 0,6,4,4,368,1,3,2,2,3,1,38,1,2,0,1,0,0,0,male,79 654 | 0,12,4,9,754,1,3,2,2,3,1,38,1,2,1,1,0,0,0,male,197 655 | 1,24,0,4,2424,1,3,2,2,3,1,53,1,2,1,1,0,0,0,male,595 656 | 0,48,3,8,6681,1,0,2,2,3,2,38,1,1,0,1,1,1,0,male,1610 657 | 0,18,3,8,2427,1,3,2,2,0,1,42,1,2,1,1,0,0,0,nonbinary/notreported,94 658 | 0,24,0,4,1216,0,2,2,2,3,2,38,0,2,1,1,1,0,0,male,56 659 | 0,6,4,4,753,4,0,1,1,1,3,64,1,2,0,1,0,0,0,female,24 660 | 0,7,4,4,2576,4,0,1,1,0,3,35,1,2,0,1,0,0,1,male,867 661 | 0,6,4,4,590,4,2,0,2,1,3,26,1,2,0,3,0,0,1,female,110 662 | 0,8,4,4,1414,4,0,2,1,0,3,33,1,2,0,1,0,0,1,male,319 663 | 0,12,4,4,1103,4,1,2,1,1,3,29,1,2,1,1,0,0,0,male,161 664 | 0,12,3,4,585,4,0,2,0,3,3,20,1,0,1,1,0,0,0,female,38 665 | 0,6,4,4,1068,4,3,2,2,3,0,28,1,2,0,1,1,0,0,male,131 666 | 3,8,0,5,713,4,3,2,2,3,3,47,1,2,1,3,0,0,0,male,325 667 | 0,12,4,4,1092,4,0,2,1,3,3,49,1,2,1,1,0,1,0,female,84 668 | 0,7,4,4,2329,4,2,3,1,2,3,45,1,2,0,1,0,0,0,female,301 669 | 0,13,0,4,882,4,2,2,1,3,3,23,1,2,1,1,0,0,0,male,250 670 | 0,18,4,4,866,4,0,2,1,0,3,25,1,2,0,3,0,0,0,female,177 671 | 0,7,4,4,2415,4,0,0,1,0,3,34,1,2,0,1,0,0,0,male,872 672 | 0,13,4,4,2101,4,2,1,1,3,1,23,1,2,0,3,0,0,0,female,273 673 | 0,18,4,4,1301,4,3,2,1,0,3,32,1,2,0,3,0,0,0,female,96 674 | 0,18,4,4,1113,4,0,2,1,3,3,26,1,2,0,3,1,0,0,female,217 675 | 0,8,4,4,760,4,1,2,1,0,3,44,1,2,0,3,0,0,0,female,62 676 | 0,12,4,4,625,4,2,2,1,2,3,26,0,2,0,3,0,0,0,female,228 677 | 2,6,0,5,1323,0,3,1,2,3,0,28,1,2,1,1,1,1,0,male,155 678 | 3,9,0,4,1138,4,0,2,2,3,3,25,1,2,1,3,0,0,0,nonbinary/notreported,301 679 | 0,18,0,4,1795,4,3,0,1,3,3,48,0,0,1,3,0,1,0,female,7 680 | 0,15,0,4,2728,1,1,2,1,0,3,35,0,2,2,1,0,1,0,male,685 681 | 0,6,4,4,484,4,1,0,1,1,3,28,0,2,0,3,0,0,0,female,155 682 | 0,10,1,4,1048,4,0,2,2,3,3,23,2,2,0,3,0,0,0,male,310 683 | 0,12,4,4,1155,4,3,0,1,1,3,40,0,2,1,3,0,0,0,female,234 684 | 0,20,3,1,7057,1,1,0,2,3,1,36,0,0,1,0,1,1,0,male,524 685 | 0,15,0,4,1537,1,3,2,1,3,3,50,1,2,1,1,0,1,0,male,352 686 | 3,12,4,4,2214,4,0,2,2,1,1,24,1,2,0,3,0,0,0,male,745 687 | 1,24,0,2,1585,4,1,2,2,1,1,40,1,2,1,1,0,0,0,male,575 688 | 0,10,4,2,1521,4,0,2,2,0,0,31,1,2,0,3,0,0,0,male,374 689 | 0,36,1,6,3990,1,2,0,2,0,2,29,0,2,0,2,0,0,0,female,342 690 | 2,18,4,2,3049,4,2,3,2,2,1,45,2,2,0,3,0,0,0,female,382 691 | 3,24,4,4,1282,0,0,2,2,0,0,32,1,2,0,3,0,0,0,female,-225 692 | 1,60,4,4,10144,0,1,1,2,3,3,21,1,2,0,1,0,1,0,nonbinary/notreported,3613 693 | 3,12,4,5,1168,4,0,2,2,1,3,27,1,2,0,3,0,0,0,female,149 694 | 0,6,4,3,454,4,2,0,2,2,1,22,1,2,0,3,0,0,0,female,82 695 | 1,15,3,1,3594,4,2,3,2,0,1,46,1,2,1,3,0,0,0,female,429 696 | 1,12,4,2,1768,4,0,0,2,0,3,24,1,0,0,3,0,0,0,male,59 697 | 1,60,3,4,15653,4,1,1,2,3,0,21,1,2,1,1,0,1,0,male,4393 698 | 2,12,3,5,2247,4,0,1,2,0,0,36,2,2,1,1,0,1,0,female,227 699 | 1,24,4,4,1413,4,0,2,2,0,1,28,1,2,0,1,0,0,0,female,185 700 | 0,45,0,1,4576,0,4,0,2,3,0,27,1,2,0,1,0,0,0,male,325 701 | 3,24,0,4,1231,3,3,2,2,3,1,57,1,0,1,0,0,1,0,female,237 702 | 3,36,4,1,8335,1,3,0,2,3,2,47,1,1,0,1,0,0,0,male,30 703 | 0,36,0,1,5800,4,0,0,2,3,0,34,1,2,1,1,0,1,0,male,2042 704 | 3,18,3,7,8471,1,0,3,2,0,0,23,1,0,1,1,0,1,0,female,118 705 | 3,30,4,2,3622,3,3,2,2,3,1,57,1,0,1,1,0,1,0,female,245 706 | 3,6,0,4,1169,1,3,2,2,3,3,67,1,2,1,1,0,1,0,male,181 707 | 1,15,4,6,1262,2,1,2,2,1,1,36,1,2,1,1,0,1,0,male,304 708 | 2,24,0,2,3617,1,3,2,0,3,2,20,1,0,1,1,0,0,0,male,728 709 | 0,30,0,5,2181,1,3,2,2,3,3,36,1,2,1,1,0,0,0,male,606 710 | 3,48,1,8,7685,4,1,1,1,3,0,37,1,0,0,1,0,0,0,female,-4881 711 | 1,48,0,7,6110,4,0,3,2,1,2,31,0,1,0,1,0,1,0,nonbinary/notreported,1204 712 | 1,24,4,5,3757,4,3,2,0,3,2,62,1,1,0,1,0,1,0,female,909 713 | 3,42,0,3,3394,4,4,2,0,3,0,65,1,2,1,2,0,0,0,male,493 714 | 1,36,0,8,6304,1,3,2,2,3,3,36,1,2,1,1,0,0,0,male,370 715 | 1,9,0,7,1244,1,3,2,2,3,1,41,1,0,1,3,0,0,0,nonbinary/notreported,185 716 | 1,6,4,5,3518,4,0,1,1,1,1,26,1,0,0,1,0,0,0,male,320 717 | 1,36,2,3,2613,4,0,2,2,0,0,27,1,2,1,1,0,0,0,male,500 718 | 3,48,4,7,7476,4,1,2,2,2,2,50,1,1,0,0,0,1,0,male,913 719 | 1,24,4,8,4591,3,0,1,2,1,1,54,1,2,2,0,0,1,0,male,-184 720 | 0,18,4,2,1924,1,2,2,2,1,3,27,1,0,0,1,0,0,0,female,-193 721 | 0,72,4,4,5595,0,0,1,2,0,0,24,1,2,0,1,0,0,0,female,-767 722 | 0,48,3,7,6224,4,3,2,2,3,2,50,1,1,0,1,0,0,0,male,-570 723 | 2,15,4,7,1905,4,3,2,2,3,0,40,1,0,0,0,0,1,0,male,511 724 | 1,21,3,1,2993,4,0,0,2,0,3,28,2,2,1,3,0,0,0,male,553 725 | 1,36,3,1,8947,1,1,0,2,0,0,31,2,2,0,0,1,1,0,nonbinary/notreported,2486 726 | 3,24,4,2,4020,4,0,1,2,0,0,27,2,2,0,1,0,0,0,male,1008 727 | 0,18,4,1,2779,4,0,3,2,1,0,21,1,0,0,1,0,1,0,nonbinary/notreported,739 728 | 1,21,4,5,2782,2,1,3,2,0,0,31,0,2,0,0,0,0,0,female,131 729 | 1,12,4,5,1884,4,3,2,2,3,0,39,1,2,0,0,0,1,0,male,221 730 | 1,36,0,1,11054,1,0,2,2,0,0,30,1,2,0,0,0,1,0,male,283 731 | 0,60,3,4,9157,1,0,1,2,0,2,27,1,1,0,0,0,0,0,male,1768 732 | 0,42,1,1,9283,4,4,3,2,0,2,55,0,1,0,0,0,1,0,male,414 733 | 1,60,4,5,6527,1,0,2,2,3,2,34,1,1,0,1,1,1,0,male,1260 734 | 1,15,0,1,3368,3,3,0,2,3,2,23,1,0,1,1,0,1,0,male,159 735 | 3,15,4,5,2511,4,4,3,2,3,0,23,1,0,0,1,0,0,0,female,835 736 | 3,36,4,1,5493,4,3,1,2,3,2,42,1,1,0,1,1,0,0,male,843 737 | 1,6,4,6,1338,2,0,3,2,3,3,62,1,2,0,1,0,0,0,male,115 738 | 0,9,4,4,1082,4,3,2,2,3,0,37,1,2,1,3,0,0,0,male,114 739 | 1,18,0,4,1149,3,0,2,2,1,3,46,1,2,1,1,0,0,0,male,316 740 | 0,15,4,3,1308,4,3,2,2,3,0,38,1,2,1,3,0,0,0,male,237 741 | 0,20,2,1,6148,0,3,0,2,3,0,31,0,2,1,1,0,1,0,female,1101 742 | 1,12,4,2,1736,4,1,0,2,3,3,31,1,2,0,3,0,0,0,female,261 743 | 1,12,4,4,3059,3,1,1,2,3,3,61,1,2,0,3,0,0,0,nonbinary/notreported,279 744 | 3,24,4,2,2996,1,0,1,2,3,0,20,1,2,0,1,0,0,0,female,-2054 745 | 1,30,0,1,7596,1,3,3,2,3,0,63,1,2,1,1,0,0,0,male,1886 746 | 1,30,4,1,4811,1,1,1,2,3,1,24,2,0,0,3,0,0,0,female,415 747 | 1,6,4,2,1766,4,0,3,2,0,1,21,1,0,0,1,0,0,0,female,8 748 | 0,24,4,1,2760,1,3,2,2,3,2,36,0,1,0,1,0,1,0,male,955 749 | 1,24,0,3,5507,4,3,0,2,3,2,44,1,1,1,1,0,0,0,male,1336 750 | 0,9,4,7,1199,4,1,2,2,3,1,67,1,2,1,0,0,1,0,female,98 751 | 2,24,4,2,2892,4,3,0,2,3,2,51,1,1,0,1,0,0,0,male,413 752 | 0,36,3,5,2862,0,3,2,2,1,2,30,1,1,0,1,0,0,0,male,842 753 | 3,9,4,5,654,4,0,2,2,1,0,28,1,2,0,3,0,0,0,male,-174 754 | 0,9,0,7,1136,3,3,2,2,1,2,32,1,1,1,1,1,0,0,male,-560 755 | 0,24,4,1,4113,2,2,0,2,3,0,28,1,0,0,1,0,0,0,female,-2996 756 | 0,6,4,5,14555,1,4,3,2,0,1,23,1,2,0,2,0,1,0,male,-4986 757 | 3,15,2,5,950,4,3,2,2,1,0,33,1,0,1,1,1,0,0,male,-71 758 | 3,24,0,5,1199,4,3,2,2,3,0,60,1,2,1,3,0,0,0,male,-305 759 | 3,12,2,5,1082,4,0,2,2,3,0,48,0,2,1,1,0,0,0,male,-383 760 | 0,30,4,5,2150,4,0,2,1,0,2,24,0,2,0,1,0,0,0,female,-630 761 | 0,36,0,5,2820,4,2,2,2,3,0,27,1,2,1,1,0,0,0,male,-1462 762 | 0,48,4,4,3060,4,1,2,2,3,3,28,1,2,1,1,0,0,0,male,8 763 | 3,18,4,4,2600,4,0,2,2,3,2,65,1,1,1,1,0,0,0,male,-1373 764 | 1,21,2,5,5003,1,0,3,2,3,1,29,0,2,1,1,0,1,0,female,-3379 765 | 0,60,4,7,6288,4,0,2,2,3,2,42,1,1,0,1,0,0,0,male,-1572 766 | 1,24,3,5,2538,4,3,2,2,3,0,47,1,2,1,3,1,0,0,male,-6 767 | 1,9,4,4,1478,4,1,2,2,0,0,22,1,2,0,1,0,0,0,male,-536 768 | 0,39,0,4,4933,4,1,1,1,0,3,25,1,2,1,1,0,0,0,male,-610 769 | 1,18,0,5,1530,4,0,0,2,0,1,32,0,2,1,1,0,0,0,male,-676 770 | 0,9,1,5,1437,0,1,1,2,1,2,29,1,2,0,1,0,0,0,male,-193 771 | 3,15,4,6,1275,1,0,2,2,0,0,24,1,0,0,1,0,0,0,female,-160 772 | 3,24,4,4,1823,4,4,2,2,0,0,30,2,2,0,0,1,0,0,male,-424 773 | 3,9,4,5,1422,4,2,0,2,0,2,27,1,1,0,0,0,1,0,male,-360 774 | 3,18,4,6,1217,4,0,2,2,1,3,47,1,2,0,3,0,1,0,female,-206 775 | 3,36,4,5,9271,4,1,1,2,2,0,24,1,2,0,1,0,1,0,male,-3763 776 | 3,36,3,8,2145,4,1,1,2,2,0,24,1,2,1,1,0,1,0,male,-400 777 | 3,36,4,5,1842,4,2,2,2,3,0,34,1,2,0,1,0,1,0,female,-187 778 | 0,18,3,2,4297,4,3,2,2,1,2,40,1,2,0,0,0,1,0,male,-989 779 | 3,6,0,2,3384,4,0,3,2,3,3,44,1,0,0,0,0,1,0,male,-724 780 | 0,18,0,4,1245,4,0,2,2,0,0,33,1,2,0,1,0,0,0,female,-199 781 | 1,15,4,7,4623,0,0,0,2,0,1,40,1,2,0,0,0,1,0,male,-1191 782 | 0,30,0,2,8386,4,1,1,2,0,1,49,1,2,0,1,0,0,0,nonbinary/notreported,-593 783 | 3,24,3,4,1024,4,2,2,2,3,3,48,2,2,0,1,0,0,0,female,-366 784 | 0,36,4,5,14318,4,3,2,2,0,2,57,1,1,0,0,0,1,0,male,-7618 785 | 0,6,1,7,433,3,2,2,2,0,1,24,0,0,0,1,1,0,0,female,-248 786 | 3,12,1,4,2149,4,0,2,2,2,2,29,1,1,0,1,0,0,0,male,-1688 787 | 1,24,4,4,2397,2,3,0,2,0,0,35,0,2,1,1,0,1,0,male,-1080 788 | 0,6,1,5,931,0,2,3,2,2,1,32,2,2,0,3,0,0,0,female,-351 789 | 0,15,3,3,1512,3,0,0,2,1,1,61,2,2,1,1,0,0,0,female,-865 790 | 0,24,2,8,4241,4,0,3,2,3,3,36,1,2,2,3,0,1,0,male,-2542 791 | 0,24,0,2,4736,4,2,1,2,3,0,25,0,2,0,3,0,0,0,female,-2032 792 | 0,15,2,5,1778,4,2,1,2,2,3,26,1,0,1,2,0,0,0,female,-1067 793 | 2,15,4,4,2327,4,2,1,2,1,3,25,1,2,0,3,0,0,0,female,-1350 794 | 3,24,1,2,6872,4,2,1,2,2,1,55,0,2,0,1,0,1,0,male,-2251 795 | 3,12,4,7,795,4,2,2,2,3,1,53,1,2,0,1,0,0,0,female,-25 796 | 2,30,3,8,1908,4,3,2,2,3,3,66,1,2,0,0,0,1,0,male,-711 797 | 0,36,2,8,1953,4,3,2,2,3,2,61,1,1,0,0,0,1,0,male,-422 798 | 2,18,4,2,2864,4,0,1,2,2,3,34,1,2,0,3,1,0,0,male,-1157 799 | 2,21,0,7,2319,4,2,1,2,2,0,33,1,0,0,1,0,0,0,male,-1016 800 | 3,24,4,5,915,1,3,2,2,0,0,29,0,2,0,1,0,0,0,nonbinary/notreported,-39 801 | 2,24,4,5,947,4,1,2,2,1,2,38,0,1,0,1,1,0,0,male,-18 802 | 3,24,4,5,1381,1,0,2,2,0,1,35,1,2,0,1,0,0,0,nonbinary/notreported,-636 803 | 3,24,4,5,1285,1,1,2,2,3,2,32,1,0,0,1,0,0,0,female,-449 804 | 3,24,4,5,1371,1,0,2,2,3,3,25,1,0,0,1,0,0,0,female,-941 805 | 0,18,4,5,1042,1,0,2,2,0,1,33,1,2,0,1,0,0,0,female,-163 806 | 3,12,4,5,900,1,0,2,2,0,0,23,1,2,0,1,0,0,0,female,-239 807 | 3,24,4,5,1207,4,2,2,2,3,1,24,1,0,0,1,0,0,0,female,-79 808 | 0,18,2,5,2278,0,2,0,2,1,0,28,1,2,1,1,0,0,0,female,-836 809 | 3,60,3,8,6836,4,3,0,2,3,2,63,1,2,1,1,0,1,0,male,-2889 810 | 3,24,4,2,3345,4,3,2,2,0,1,39,1,0,0,0,0,1,0,male,-328 811 | 3,6,1,7,1198,4,3,2,2,3,2,35,1,1,0,1,0,0,0,female,-316 812 | 0,48,4,8,15672,4,0,1,2,0,0,23,1,2,0,1,0,1,0,male,-4829 813 | 3,60,4,8,7297,4,3,2,0,3,2,36,1,0,0,1,0,0,0,male,-1055 814 | 1,18,4,3,1943,4,2,2,2,3,3,23,1,2,0,1,0,0,0,female,-119 815 | 3,18,4,4,3190,4,0,1,2,0,3,24,1,2,0,1,0,0,0,female,-2160 816 | 0,9,1,1,5129,4,3,1,2,3,2,74,0,1,0,0,1,1,0,female,-316 817 | 1,18,3,2,1808,4,1,2,2,2,3,22,1,2,0,1,0,0,0,female,-799 818 | 2,10,4,5,1240,0,3,3,2,3,2,48,1,1,0,3,1,0,0,female,-466 819 | 3,12,4,5,759,4,1,2,2,0,3,26,1,2,0,1,0,0,0,male,-58 820 | 0,8,4,2,1237,4,0,0,2,3,3,24,1,2,0,1,0,0,0,female,-206 821 | 1,9,4,2,1980,4,2,1,0,0,0,19,1,0,1,1,0,0,0,female,-616 822 | 0,48,4,4,10961,3,1,3,0,0,2,27,0,2,1,1,0,1,0,male,-4018 823 | 3,36,3,7,6887,4,0,2,2,1,1,29,2,2,0,1,0,1,0,male,-2529 824 | 3,24,4,4,1938,4,2,2,2,1,1,32,1,2,0,1,0,0,0,male,-622 825 | 3,21,4,4,1835,4,0,0,2,0,3,25,1,2,1,1,0,1,0,female,-665 826 | 3,24,3,4,1659,4,2,2,2,0,0,29,1,0,0,3,0,1,0,female,-286 827 | 0,6,3,5,1209,4,4,2,2,3,1,47,1,2,0,0,0,1,0,male,-568 828 | 0,48,2,8,3844,0,1,2,2,3,2,34,1,1,0,3,1,0,0,male,-207 829 | 3,12,0,5,4843,4,3,0,0,3,1,43,1,0,1,1,0,1,0,male,-1092 830 | 0,12,4,3,639,4,0,2,2,0,0,30,1,2,0,1,0,0,0,male,-21 831 | 0,48,4,4,5951,4,0,1,2,0,3,22,1,2,0,1,0,0,0,female,-1352 832 | 0,36,2,4,3804,4,0,2,2,2,0,42,1,2,0,1,0,1,0,female,-668 833 | 1,36,3,4,4463,4,0,2,2,0,0,26,1,2,1,0,0,1,0,male,-709 834 | 1,36,3,8,7980,1,2,2,2,3,0,27,1,0,1,1,0,1,0,male,-1082 835 | 2,36,4,4,4210,4,0,2,2,0,0,26,1,2,0,1,0,0,0,male,-161 836 | 1,6,4,2,4611,4,2,3,2,3,1,32,1,2,0,1,0,0,0,female,-791 837 | 0,24,4,1,11560,4,0,3,2,3,0,23,1,0,1,0,0,0,0,female,-3327 838 | 1,18,2,8,4165,4,0,1,2,0,0,36,2,2,1,1,1,0,0,male,-1599 839 | 0,24,4,2,4057,4,1,0,2,1,0,43,1,2,0,1,0,1,0,male,-849 840 | 1,18,1,5,6458,4,3,1,2,3,2,39,0,2,1,0,1,1,0,male,-3719 841 | 1,12,4,5,1386,2,0,1,2,0,1,26,1,2,0,1,0,0,0,female,-675 842 | 3,36,4,7,1977,1,3,2,2,3,2,40,1,2,0,0,0,1,0,male,-1143 843 | 0,18,0,2,1928,4,2,1,2,0,3,31,1,2,1,3,0,0,0,male,-681 844 | 1,12,4,2,1123,2,0,2,2,3,0,29,1,0,0,3,0,0,0,female,-539 845 | 0,24,4,0,11328,4,0,1,0,1,0,29,0,2,1,0,0,1,0,male,-4619 846 | 0,24,0,0,11938,4,0,1,0,1,0,39,1,2,1,0,1,1,0,male,-3008 847 | 0,27,0,4,2520,2,0,2,2,0,1,23,1,2,1,3,0,0,0,male,-332 848 | 0,30,3,4,1919,0,2,2,2,1,2,30,2,2,1,0,0,0,0,male,-936 849 | 0,60,1,0,14782,0,3,0,2,3,2,60,0,1,1,0,0,1,0,female,-6060 850 | 0,36,4,4,2671,0,0,2,0,3,2,50,1,1,0,1,0,0,0,female,-296 851 | 0,36,4,7,12612,0,0,3,2,3,2,47,1,1,0,1,1,1,0,male,-8130 852 | 0,45,4,4,3031,0,0,2,1,3,1,21,1,0,0,1,0,0,0,male,35 853 | 3,12,1,4,626,4,0,2,2,3,3,24,0,2,0,3,0,0,0,female,-48 854 | 0,24,0,8,1935,4,3,2,2,3,3,31,1,2,1,1,0,1,0,male,-28 855 | 2,24,0,5,1344,1,1,2,2,0,3,37,0,2,1,3,1,0,0,male,54 856 | 1,18,4,2,1533,4,2,2,0,2,1,43,1,2,0,3,1,0,0,female,-717 857 | 3,48,4,5,3931,4,1,2,2,3,2,46,1,1,0,1,1,0,0,male,-435 858 | 3,24,1,2,3349,2,2,2,2,3,2,30,1,1,0,1,1,1,0,male,-1759 859 | 3,36,4,4,2302,4,0,2,2,3,0,31,1,0,0,1,0,0,0,male,-408 860 | 3,42,4,4,3965,4,2,2,2,1,0,34,1,2,0,1,0,0,0,male,-941 861 | 3,12,4,4,727,0,2,2,2,1,2,33,1,2,0,3,0,1,0,female,-3 862 | 1,48,4,8,3914,1,0,2,2,0,3,38,0,2,0,1,0,0,0,male,-113 863 | 3,48,4,8,4308,4,2,0,2,3,1,24,1,0,0,1,0,0,0,female,-1135 864 | 0,12,4,4,1534,4,2,3,2,2,3,23,1,0,0,1,0,0,0,female,-869 865 | 1,18,0,5,2775,4,1,1,2,0,1,31,0,2,1,1,0,0,0,male,-95 866 | 3,40,0,7,5998,4,0,2,2,1,2,27,0,2,0,1,0,1,0,male,-2064 867 | 2,15,0,4,1271,1,0,0,2,3,2,39,1,1,1,1,0,1,0,male,-268 868 | 0,12,4,5,1295,4,2,0,2,2,0,25,1,0,0,1,0,0,0,female,-123 869 | 0,36,4,1,9398,4,2,3,2,3,0,28,1,0,0,0,0,1,0,female,-2807 870 | 0,12,4,2,951,0,2,2,2,3,0,27,0,0,3,1,0,0,0,nonbinary/notreported,-363 871 | 0,24,4,5,1355,4,2,0,2,3,0,25,1,2,0,3,0,1,0,female,-416 872 | 3,48,4,6,3051,4,0,0,2,3,0,54,1,2,0,1,0,0,0,male,-1081 873 | 1,36,0,5,7855,4,0,2,2,0,3,25,2,2,1,1,0,1,0,female,-4878 874 | 1,18,4,4,433,4,4,0,0,3,3,22,1,0,0,1,0,0,0,nonbinary/notreported,-162 875 | 1,36,3,8,9572,4,2,3,2,2,0,28,1,2,1,1,0,0,0,male,-2782 876 | 0,24,1,7,1837,4,1,2,2,3,2,34,0,1,0,3,0,0,0,female,-238 877 | 0,30,0,5,4249,4,4,2,2,0,0,28,1,2,1,0,0,0,0,female,-578 878 | 0,30,0,5,5234,4,4,2,2,0,0,28,1,2,1,0,0,0,0,female,-1318 879 | 3,48,4,4,6758,4,0,0,2,0,0,31,1,2,0,1,0,1,0,female,-2069 880 | 3,9,4,4,1366,4,2,0,2,3,1,22,1,0,0,1,0,0,0,female,-23 881 | 3,24,1,0,1358,1,3,2,2,1,0,40,2,2,0,0,0,1,0,male,-880 882 | 3,18,4,2,2473,4,4,2,2,2,0,25,1,2,0,2,0,0,0,male,-865 883 | 2,9,2,4,1337,4,2,2,2,0,0,34,1,2,1,0,0,1,0,male,-300 884 | 3,48,4,5,7763,4,3,2,2,3,2,42,0,1,0,0,0,0,0,male,-711 885 | 0,15,1,5,1264,0,0,1,2,0,1,25,1,0,0,1,0,0,0,female,-223 886 | 0,15,4,5,2631,0,0,1,2,3,0,28,1,0,1,1,0,1,0,female,-1107 887 | 0,48,4,5,6560,0,1,0,2,0,1,24,1,2,0,1,0,0,0,male,-2209 888 | 3,24,4,5,3123,4,2,2,2,2,1,27,1,2,0,1,0,0,0,female,-150 889 | 3,36,0,7,8065,4,0,0,2,0,2,25,1,2,1,0,0,1,0,female,-2148 890 | 3,24,4,4,2439,4,2,2,2,3,3,35,1,2,0,1,0,1,0,female,-1274 891 | 0,36,4,2,9034,0,2,2,0,2,2,29,1,0,0,0,0,1,0,male,-2793 892 | 0,60,4,5,14027,4,1,2,2,0,2,27,1,2,0,0,0,1,0,male,-5281 893 | 3,36,0,1,9629,4,1,2,2,3,0,24,1,2,1,1,0,1,0,male,-4541 894 | 0,12,4,4,1484,1,0,1,2,2,3,25,1,2,0,1,0,1,0,female,-556 895 | 3,18,4,2,1131,4,4,2,2,0,0,33,1,2,0,1,0,0,0,female,-280 896 | 0,24,3,2,2064,4,4,0,2,0,1,34,1,2,0,0,0,1,0,female,-1044 897 | 0,18,4,1,12976,4,4,0,2,3,2,38,1,1,0,0,0,1,0,female,-3821 898 | 1,21,3,8,2580,2,2,2,2,0,3,41,0,2,0,3,1,0,0,male,-605 899 | 1,27,4,5,2570,4,0,0,2,1,3,21,1,0,0,1,0,0,0,female,-826 900 | 0,27,4,8,3915,4,0,2,2,0,0,36,1,2,0,1,1,1,0,male,-922 901 | 1,10,4,5,1309,1,0,2,1,3,1,27,1,2,0,3,0,0,0,male,-463 902 | 3,24,4,5,4817,4,1,1,0,1,1,31,1,2,0,1,0,1,0,male,-1990 903 | 3,12,4,5,2579,4,2,2,2,2,3,33,1,2,0,3,1,0,0,male,-568 904 | 0,36,3,5,2225,4,3,2,2,3,2,57,0,1,1,1,0,1,0,nonbinary/notreported,-825 905 | 3,18,4,2,4153,4,0,1,0,1,0,42,1,2,0,1,0,0,0,male,-88 906 | 3,18,2,2,3114,4,2,3,2,3,1,26,1,0,0,1,0,0,0,female,-1344 907 | 3,18,0,2,2124,4,0,2,2,3,3,24,1,0,1,1,0,0,0,female,-78 908 | 3,18,1,2,1553,4,0,2,2,1,0,44,0,2,0,1,0,0,0,male,6 909 | 3,30,4,2,2406,4,1,2,2,3,3,23,1,0,0,1,0,0,0,female,-911 910 | 3,24,3,5,1333,4,4,2,2,0,3,43,1,1,1,1,1,0,0,male,-313 911 | 3,48,2,2,7119,4,0,0,2,3,2,53,1,1,1,1,1,0,0,male,-1094 912 | 3,24,3,5,4870,4,0,0,2,3,2,53,1,1,1,1,1,0,0,male,-1904 913 | 3,12,0,5,691,4,3,2,2,1,1,35,1,2,1,1,0,0,0,male,-166 914 | 3,42,3,4,4370,4,1,0,2,0,1,26,0,2,1,1,1,1,0,male,-541 915 | 3,36,1,2,2746,4,3,2,2,3,0,31,0,2,0,1,0,0,0,nonbinary/notreported,-77 916 | 3,24,2,2,4110,4,3,0,2,3,2,23,0,0,1,1,1,0,0,male,-1108 917 | 3,18,4,2,2462,4,0,1,2,0,0,22,1,2,0,1,0,0,0,male,-649 918 | 3,12,4,2,1282,4,0,1,2,3,0,20,1,0,0,1,0,0,0,female,-660 919 | 0,12,2,2,2969,4,2,2,2,1,1,25,1,0,1,1,0,0,0,female,-474 920 | 3,48,2,1,4605,4,3,0,2,3,2,24,1,1,1,1,1,0,0,male,-461 921 | 3,48,0,1,6331,4,3,2,2,3,2,46,1,1,1,1,0,1,0,male,-677 922 | 3,24,1,2,3552,4,1,0,2,3,0,27,0,2,0,1,0,0,0,male,-490 923 | 3,12,1,5,697,4,2,2,2,0,0,46,0,2,1,1,0,1,0,male,2 924 | 3,24,4,5,1442,4,1,2,2,3,0,23,1,0,1,1,0,0,0,nonbinary/notreported,-825 925 | 3,27,2,8,5293,4,4,1,2,3,1,50,1,2,1,1,0,1,0,male,-1901 926 | 3,21,3,7,3414,4,2,1,2,2,1,26,2,2,1,1,0,0,0,male,-726 927 | 3,18,4,2,2039,4,0,3,2,3,3,20,1,0,0,1,0,0,0,female,-1069 928 | 3,24,1,8,3161,4,0,2,2,0,1,31,0,0,0,1,0,1,0,male,-1166 929 | 3,12,4,9,902,4,1,2,2,3,1,21,1,0,0,1,0,0,0,female,-419 930 | 3,48,4,1,10297,4,1,2,2,3,2,39,1,1,2,1,1,1,0,male,-5302 931 | 0,48,2,8,14421,4,0,1,2,0,0,25,2,2,0,1,0,1,0,male,-5788 932 | 0,18,0,5,1056,4,3,0,1,1,3,30,1,2,1,1,0,0,0,male,-503 933 | 3,12,4,5,1274,4,2,0,2,2,3,37,0,2,0,3,0,0,0,female,-373 934 | 0,12,4,5,1223,4,3,3,2,2,3,46,1,0,1,1,0,0,0,male,-421 935 | 3,12,4,5,1372,4,1,1,2,1,0,36,1,2,0,1,0,0,0,male,-378 936 | 3,16,0,5,2625,4,3,1,1,3,1,43,1,0,0,1,0,1,1,male,-1684 937 | 3,20,0,5,2235,4,0,2,1,0,1,33,0,0,1,1,0,0,1,female,-1004 938 | 0,9,4,2,959,4,0,3,2,0,0,29,0,2,0,1,0,0,0,female,-84 939 | 0,18,0,5,884,4,3,2,2,3,0,36,1,2,0,1,1,1,0,male,-155 940 | 0,24,4,5,1246,4,2,2,2,0,3,23,0,2,0,3,0,0,0,male,-282 941 | 0,36,3,5,8086,0,3,1,2,3,0,42,2,2,3,0,0,1,0,male,-4456 942 | 1,48,0,5,10127,2,0,1,2,0,2,44,1,1,0,1,0,0,0,male,-6173 943 | 0,12,4,5,888,4,3,2,2,3,0,41,0,2,0,3,1,0,0,male,-171 944 | 1,12,4,7,719,4,3,2,2,3,0,41,0,2,0,3,1,0,0,male,-117 945 | 0,36,4,5,12389,1,0,3,2,3,2,37,0,1,0,1,0,1,0,male,-3674 946 | 3,12,4,4,709,4,3,2,2,3,3,57,1,2,0,3,0,0,0,male,-171 947 | 0,15,1,5,6850,0,4,3,2,0,1,34,2,2,0,0,1,1,0,male,-621 948 | 1,10,4,2,2210,4,0,1,2,0,3,25,1,0,0,3,0,0,0,male,-215 949 | 1,30,1,1,7485,1,4,2,2,2,3,53,0,2,0,0,0,1,0,female,-1552 950 | 3,18,1,5,1442,4,1,2,2,3,2,32,0,1,1,3,1,0,0,male,-550 951 | 1,12,0,4,797,1,3,2,2,1,1,33,1,2,0,3,1,0,0,female,-302 952 | 0,45,0,4,4746,4,2,2,2,0,1,24,0,2,1,3,0,0,0,male,-1506 953 | 2,12,0,5,939,2,1,2,2,0,3,28,1,2,2,1,0,1,0,female,-125 954 | 0,21,4,8,1188,4,3,1,2,3,1,39,1,2,0,1,1,0,0,female,-470 955 | 1,48,0,1,11590,0,0,1,2,3,0,24,1,0,1,3,0,0,0,female,-3230 956 | 2,12,1,8,609,4,2,2,2,2,3,26,0,2,0,2,0,0,0,female,-58 957 | 3,18,0,3,1190,4,4,1,2,3,2,55,1,1,2,2,1,0,0,nonbinary/notreported,-73 958 | 0,21,4,8,2767,0,3,2,2,0,0,61,1,0,1,3,0,0,0,male,-1110 959 | 0,30,4,2,3441,0,0,1,0,3,0,21,0,0,0,1,0,0,0,female,-1394 960 | 0,30,2,8,4280,0,0,2,2,3,0,26,1,0,1,3,0,0,0,female,-583 961 | 0,24,4,4,3092,0,2,0,2,0,0,22,1,0,0,1,0,1,0,female,-1499 962 | 1,18,4,5,6761,1,0,1,2,3,0,68,2,0,1,1,0,0,0,male,-444 963 | 0,12,4,4,1331,4,2,1,2,2,0,22,1,2,0,1,0,0,0,male,-484 964 | 0,54,2,8,15945,4,2,0,2,3,2,58,1,0,0,1,0,1,0,male,-1348 965 | 3,24,4,2,3234,4,2,2,2,3,3,23,1,0,0,3,0,1,0,female,-1150 966 | 0,15,4,4,802,4,3,2,2,1,0,37,1,2,0,1,1,0,0,male,-58 967 | 0,48,4,2,9960,4,2,3,2,0,0,26,0,2,0,1,0,1,0,female,-243 968 | 1,24,3,8,8648,4,2,1,2,0,0,27,0,2,1,1,0,1,0,male,-1846 969 | 3,18,4,4,1345,4,0,2,2,1,3,26,1,2,0,1,0,0,0,female,-285 970 | 3,45,4,4,1845,4,0,2,2,3,2,23,1,1,0,1,0,1,0,male,-154 971 | 3,21,1,5,1647,1,0,2,2,0,1,40,0,2,1,3,1,0,0,male,-890 972 | 1,48,4,8,4844,4,4,0,2,0,0,33,1,0,0,0,0,1,0,male,-1547 973 | 0,27,2,8,8318,4,3,1,2,3,2,42,2,1,1,0,0,1,0,female,-3488 974 | 2,18,4,4,2100,4,0,2,0,0,3,37,1,2,0,1,0,0,0,male,-1630 975 | 3,45,2,8,11816,4,3,1,2,3,0,29,1,0,1,1,0,0,0,male,-4443 976 | 3,6,4,7,448,4,2,2,2,3,1,23,1,2,0,1,0,0,0,female,-194 977 | 3,30,4,3,11998,4,2,3,2,2,2,34,0,2,0,3,0,1,0,male,-6032 978 | 0,48,2,0,18424,4,0,3,2,0,1,32,0,2,0,0,0,1,1,female,-6118 979 | 3,6,4,5,14896,4,3,3,2,3,2,68,0,2,0,0,0,1,0,male,-4948 980 | 0,12,4,2,2762,1,3,3,2,0,1,25,1,2,0,1,0,1,0,female,-874 981 | 3,12,4,1,3386,4,3,0,2,3,2,35,1,1,0,1,0,1,0,male,-594 982 | 0,24,4,4,2039,4,2,3,2,2,1,22,1,2,0,1,0,1,0,female,-532 983 | 1,18,3,8,2169,4,0,2,2,0,0,28,1,2,0,1,0,1,0,female,-692 984 | 0,48,0,2,5096,4,0,1,2,1,0,30,1,2,0,0,0,1,0,female,-1610 985 | 3,18,4,4,1882,4,0,2,2,3,0,25,0,0,1,1,0,0,0,female,-811 986 | 3,48,4,4,6999,4,1,3,1,2,3,34,1,2,1,1,0,1,0,female,-4947 987 | 1,12,0,8,2292,4,4,2,2,0,0,42,2,2,1,0,0,1,0,male,-113 988 | 3,14,4,5,8978,4,3,3,2,3,1,45,1,2,0,0,0,1,1,male,-2727 989 | 3,12,4,4,674,0,1,2,2,2,1,20,1,2,0,1,0,0,0,female,-206 990 | 3,18,4,5,976,4,2,3,2,0,0,23,1,2,0,3,0,0,0,female,-264 991 | 0,24,4,5,2718,4,0,0,2,3,1,20,1,0,0,3,0,1,0,female,-1242 992 | 3,18,4,7,750,4,4,2,2,2,3,27,1,2,0,2,0,0,0,female,-13 993 | 0,24,4,1,12579,4,3,2,2,0,2,44,1,1,0,0,0,1,0,female,-5025 994 | 3,18,4,1,7511,1,3,3,2,3,1,51,1,1,0,1,1,1,0,male,-89 995 | 3,18,0,5,3966,4,3,3,2,3,3,33,0,0,2,1,0,1,0,female,-653 996 | 3,12,2,4,6199,4,0,2,2,0,1,28,1,0,1,1,0,1,0,male,-2834 997 | 3,24,4,4,1987,4,0,1,2,3,3,21,1,0,0,3,1,0,0,male,-582 998 | 3,24,4,5,2303,4,3,2,0,2,3,45,1,2,0,1,0,0,0,male,-341 999 | 1,21,0,5,12680,1,3,2,2,3,2,30,1,1,0,0,0,1,0,male,-1419 1000 | 0,12,4,4,6468,1,4,1,2,2,2,52,1,2,0,0,0,1,0,male,-1853 1001 | 3,30,4,2,6350,1,3,2,2,3,1,31,1,2,0,1,0,0,0,male,-3086 1002 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning-Algorithm-Implementations 2 | Various Machine Learning Algorithms implemented on variety of data. 3 | --------------------------------------------------------------------------------