├── Code-and-Data-Files ├── Car-price-prediction-linear-regression-Intro-version.ipynb ├── CarPrice_Assignment.csv ├── Car_sales.csv ├── Car_sales.xls ├── Decision_Tree_IRIS.ipynb ├── EDA-Overview-Lending-Club.ipynb ├── Employee-Attrition-using-Naive-Bayes.ipynb ├── Fashion_MNIST_Image_Classification_using_Deep_Learning_tf_Keras.ipynb ├── HR-Employee-Attrition.csv ├── Handwritten-Digit-MNIST-SVM.ipynb ├── Lending-Club-EDA-Project.ipynb ├── Machine-Learning-Foundations.ipynb ├── Mall_Customers.csv ├── PCA-Housing.ipynb ├── Python-Intro-Numpy-Pandas.ipynb ├── Random-Forest-Credit-Default-Prediction.ipynb ├── churn_data.csv ├── credit-card-default.csv ├── customer-segmentation-k-means-analysis.ipynb ├── customer_data.csv ├── gapminderData.csv ├── internet_data.csv ├── iris_csv.csv ├── loan.csv ├── newhousing.csv └── telecom-churn-prediction-logistic-regression.ipynb └── README.md /Code-and-Data-Files/CarPrice_Assignment.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 | -------------------------------------------------------------------------------- /Code-and-Data-Files/Car_sales.csv: -------------------------------------------------------------------------------- 1 | Sales_in_thousands,Resale_value,Vehicle_type,Price_in_thousands,Engine_size,Horsepower,Wheelbase,Width,Length,Curb_weight,Fuel_capacity,Fuel_efficiency,Power_perf_factor 2 | 16.919,16.36,Passenger,21.5,1.8,140,101.2,67.3,172.4,2.639,13.2,28,58.28014952 3 | 39.384,19.875,Passenger,28.4,3.2,225,108.1,70.3,192.9,3.517,17.2,25,91.37077766 4 | 14.114,18.225,Passenger,,3.2,225,106.9,70.6,192,3.47,17.2,26, 5 | 8.588,29.725,Passenger,42,3.5,210,114.6,71.4,196.6,3.85,18,22,91.38977933 6 | 20.397,22.255,Passenger,23.99,1.8,150,102.6,68.2,178,2.998,16.4,27,62.7776392 7 | 18.78,23.555,Passenger,33.95,2.8,200,108.7,76.1,192,3.561,18.5,22,84.56510502 8 | 1.38,39,Passenger,62,4.2,310,113,74,198.2,3.902,23.7,21,134.6568582 9 | 19.747,,Passenger,26.99,2.5,170,107.3,68.4,176,3.179,16.6,26,71.19120671 10 | 9.231,28.675,Passenger,33.4,2.8,193,107.3,68.5,176,3.197,16.6,24,81.87706856 11 | 17.527,36.125,Passenger,38.9,2.8,193,111.4,70.9,188,3.472,18.5,25,83.9987238 12 | 91.561,12.475,Passenger,21.975,3.1,175,109,72.7,194.6,3.368,17.5,25,71.18145132 13 | 39.35,13.74,Passenger,25.3,3.8,240,109,72.7,196.2,3.543,17.5,23,95.63670253 14 | 27.851,20.19,Passenger,31.965,3.8,205,113.8,74.7,206.8,3.778,18.5,24,85.82840825 15 | 83.257,13.36,Passenger,27.885,3.8,205,112.2,73.5,200,3.591,17.5,25,84.25452581 16 | 63.729,22.525,Passenger,39.895,4.6,275,115.3,74.5,207.2,3.978,18.5,22,113.8545976 17 | 15.943,27.1,Passenger,44.475,4.6,275,112.2,75,201,,18.5,22,115.6213578 18 | 6.536,25.725,Passenger,39.665,4.6,275,108,75.5,200.6,3.843,19,22,113.7658739 19 | 11.185,18.225,Passenger,31.01,3,200,107.4,70.3,194.8,3.77,18,22,83.48309358 20 | 14.785,,Car,46.225,5.7,255,117.5,77,201.2,5.572,30,15,109.5091165 21 | 145.519,9.25,Passenger,13.26,2.2,115,104.1,67.9,180.9,2.676,14.3,27,46.36334747 22 | 135.126,11.225,Passenger,16.535,3.1,170,107,69.4,190.4,3.051,15,25,67.31446216 23 | 24.629,10.31,Passenger,18.89,3.1,175,107.5,72.5,200.9,3.33,16.6,25,69.9913956 24 | 42.593,11.525,Passenger,19.39,3.4,180,110.5,72.7,197.9,3.34,17,27,72.03091719 25 | 26.402,13.025,Passenger,24.34,3.8,200,101.1,74.1,193.2,3.5,16.8,25,81.11854333 26 | 17.947,36.225,Passenger,45.705,5.7,345,104.5,73.6,179.7,3.21,19.1,22,141.14115 27 | 32.299,9.125,Passenger,13.96,1.8,120,97.1,66.7,174.3,2.398,13.2,33,48.2976361 28 | 21.855,5.16,Passenger,9.235,1,55,93.1,62.6,149.4,1.895,10.3,45,23.27627233 29 | 107.995,,Passenger,18.89,3.4,180,110.5,73,200,3.389,17,27,71.83803944 30 | 7.854,12.36,Passenger,19.84,2.5,163,103.7,69.7,190.9,2.967,15.9,24,65.95718396 31 | 32.775,14.18,Passenger,24.495,2.5,168,106,69.2,193,3.332,16,24,69.52135505 32 | 31.148,13.725,Passenger,22.245,2.7,200,113,74.4,209.1,3.452,17,26,80.02378204 33 | 32.306,12.64,Passenger,16.48,2,132,108,71,186,2.911,16,27,53.56619987 34 | 13.462,17.325,Passenger,28.34,3.5,253,113,74.4,207.7,3.564,17,23,101.3292807 35 | 53.48,19.54,Car,,,,,,,,,, 36 | 30.696,,Passenger,29.185,3.5,253,113,74.4,197.8,3.567,17,23,101.6552441 37 | 76.034,7.75,Passenger,12.64,2,132,105,74.4,174.4,2.567,12.5,29,52.08489875 38 | 4.734,12.545,Passenger,19.045,2.5,163,103.7,69.1,190.2,2.879,15.9,24,65.65050834 39 | 71.186,10.185,Passenger,20.23,2.5,168,108,71,186,3.058,16,24,67.87610784 40 | 88.028,12.275,Passenger,22.505,2.7,202,113,74.7,203.7,3.489,17,,80.83147017 41 | 0.916,58.47,Passenger,69.725,8,450,96.2,75.7,176.7,3.375,19,16,188.144323 42 | 227.061,15.06,Car,19.46,5.2,230,138.7,79.3,224.2,4.47,26,17,90.21170005 43 | 16.767,15.51,Car,21.315,3.9,175,109.6,78.8,192.6,4.245,32,15,71.13529161 44 | 31.038,13.425,Car,18.575,3.9,175,127.2,78.8,208.5,4.298,32,16,70.07832154 45 | 111.313,11.26,Car,16.98,2.5,120,131,71.5,215,3.557,22,19,49.64500177 46 | 101.323,,Car,26.31,5.2,230,115.7,71.7,193.5,4.394,25,17,92.85412522 47 | 181.749,12.025,Car,19.565,2.4,150,113.3,76.8,186.3,3.533,20,24,61.22700031 48 | 70.227,7.425,Passenger,12.07,2,110,98.4,67,174.7,2.468,12.7,30,44.08370946 49 | 113.369,12.76,Passenger,21.56,3.8,190,101.3,73.1,183.2,3.203,15.7,24,76.50918456 50 | 35.068,8.835,Passenger,17.035,2.5,170,106.5,69.1,184.6,2.769,15,25,67.35101072 51 | 245.815,10.055,Passenger,17.885,3,155,108.5,73,197.6,3.368,16,24,62.5037395 52 | 175.67,,Passenger,12.315,2,107,103,66.9,174.8,2.564,13.2,30,43.11713201 53 | 63.403,14.21,Passenger,22.195,4.6,200,114.7,78.2,212,3.908,19,21,80.49953671 54 | 276.747,16.64,Car,31.93,4,210,111.6,70.2,190.7,3.876,21,19,87.63549578 55 | 155.787,13.175,Car,21.41,3,150,120.7,76.6,200.9,3.761,26,21,62.09504839 56 | 125.338,23.575,Car,36.135,4.6,240,119,78.7,204.6,4.808,26,16,100.0248023 57 | 220.65,7.85,Car,12.05,2.5,119,117.5,69.4,200.7,3.086,20,23,47.38953131 58 | 540.561,15.075,Car,26.935,4.6,220,138.5,79.1,224.5,4.241,25.1,18,89.40193473 59 | 199.685,9.85,Passenger,12.885,1.6,106,103.2,67.1,175.1,2.339,11.9,32,42.87909734 60 | 230.902,13.21,Passenger,15.35,2.3,135,106.9,70.3,188.8,2.932,17.1,27,54.26954829 61 | 73.203,17.71,Car,20.55,2,146,103.2,68.9,177.6,3.219,15.3,24,60.08796662 62 | 12.855,17.525,Car,26.6,3.2,205,106.4,70.4,178.2,3.857,21.1,19,83.6025008 63 | 76.029,19.49,Car,26,3.5,210,118.1,75.6,201.2,4.288,20,23,85.21769134 64 | 41.184,5.86,Passenger,9.699,1.5,92,96.1,65.7,166.7,2.24,11.9,31,36.67228358 65 | 66.692,7.825,Passenger,11.799,2,140,100.4,66.9,174,2.626,14.5,27,54.59004516 66 | 29.45,8.91,Passenger,14.999,2.4,148,106.3,71.6,185.4,3.072,17.2,25,58.758249 67 | 23.713,19.69,Passenger,29.465,3,227,108.3,70.2,193.7,3.342,18.5,25,92.43688923 68 | 15.467,,Passenger,42.8,3,240,114.5,71.6,191.3,3.65,18.4,21,102.1789848 69 | 55.557,13.475,Car,14.46,2.5,120,93.4,66.7,152,3.045,19,17,48.67289791 70 | 80.556,13.775,Car,21.62,4,190,101.4,69.4,167.5,3.194,20,20,76.58443962 71 | 157.04,18.81,Car,26.895,4,195,105.9,72.3,181.5,3.88,20.5,19,80.38777912 72 | 24.072,26.975,Passenger,31.505,3,210,105.1,70.5,190.2,3.373,18.5,23,87.21100104 73 | 12.698,32.075,Passenger,37.805,3,225,110.2,70.9,189.2,3.638,19.8,23,94.9466984 74 | 3.334,,Passenger,46.305,4,300,110.2,70.9,189.2,3.693,19.8,21,125.0133574 75 | 6.375,40.375,Passenger,54.005,4,290,112.2,72,196.7,3.89,22.5,22,124.4467163 76 | 9.126,,Car,60.105,4.7,230,112.2,76.4,192.5,5.401,25.4,15,105.760458 77 | 51.238,,Car,34.605,3,220,103,71.5,180.1,3.9,17.2,21,91.94380156 78 | 13.798,20.525,Passenger,39.08,4.6,275,109,73.6,208.5,3.868,20,22,113.5402069 79 | 48.911,21.725,Passenger,43.33,4.6,215,117.7,78.2,215.3,4.121,19,21,93.9579169 80 | 22.925,,Car,42.66,5.4,300,119,79.9,204.8,5.393,30,15,123.9720467 81 | 26.232,8.325,Passenger,13.987,1.8,113,98.4,66.5,173.6,2.25,13.2,30,45.83218056 82 | 42.541,10.395,Passenger,19.047,2.4,154,100.8,68.9,175.4,2.91,15.9,24,62.44196235 83 | 55.616,10.595,Passenger,17.357,2.4,145,103.7,68.5,187.8,2.945,16.3,25,58.60677292 84 | 5.711,16.575,Passenger,24.997,3.5,210,107.1,70.3,194.1,3.443,19,22,84.83077858 85 | 0.11,20.94,Passenger,25.45,3,161,97.2,72.4,180.3,3.131,19.8,21,67.54415494 86 | 11.337,19.125,Car,31.807,3.5,200,107.3,69.9,186.6,4.52,24.3,18,83.92081504 87 | 39.348,13.88,Car,22.527,3,173,107.3,66.7,178.3,3.51,19.5,20,70.66094179 88 | 14.351,8.8,Passenger,16.24,2,125,106.5,69.1,184.8,2.769,15,28,50.99774761 89 | 26.529,13.89,Passenger,16.54,2,125,106.4,69.6,185,2.892,16,30,51.11347426 90 | 67.956,11.03,Passenger,19.035,3,153,108.5,73,199.7,3.379,16,24,62.23996663 91 | 81.174,14.875,Passenger,22.605,4.6,200,114.7,78.2,212,3.958,19,21,80.65769646 92 | 27.609,20.43,Car,27.56,4,210,111.6,70.2,190.1,3.876,21,18,85.94974425 93 | 20.38,14.795,Car,22.51,3.3,170,112.2,74.9,194.7,3.944,20,21,69.671461 94 | 18.392,26.05,Passenger,31.75,2.3,185,105.9,67.7,177.4,3.25,16.4,26,78.28073088 95 | 27.602,41.45,Passenger,49.9,3.2,221,111.5,70.8,189.4,3.823,21.1,25,98.2497375 96 | 16.774,50.375,Passenger,69.7,4.3,275,121.5,73.1,203.1,4.133,23.2,21,125.2738757 97 | 3.311,58.6,Passenger,82.6,5,302,99,71.3,177.1,4.125,21.1,20,139.9822936 98 | 7.998,,Passenger,38.9,2.3,190,94.5,67.5,157.9,3.055,15.9,26,82.80736193 99 | 1.526,,Passenger,41,2.3,185,94.5,67.5,157.3,2.975,14,27,81.84896924 100 | 11.592,,Passenger,41.6,3.2,215,105.9,67.8,180.3,3.213,16.4,26,92.92579177 101 | 0.954,,Passenger,85.5,5,302,113.6,73.1,196.6,4.115,23.2,20,141.1009845 102 | 28.976,,Car,35.3,3.2,215,111,72.2,180.6,4.387,19,20,90.49553213 103 | 42.643,8.45,Passenger,13.499,1.8,126,99.8,67.3,177.5,2.593,13.2,30,50.24197791 104 | 88.094,11.295,Passenger,20.39,2.4,155,103.1,69.1,183.5,3.012,15.9,25,63.31372783 105 | 79.853,15.125,Passenger,26.249,3,222,108.3,70.3,190.5,3.294,18.5,25,89.42782031 106 | 27.308,15.38,Car,26.399,3.3,170,112.2,74.9,194.8,3.991,20,21,71.17166413 107 | 42.574,17.81,Car,29.299,3.3,170,106.3,71.7,182.6,3.947,21,19,72.29035508 108 | 54.158,,Car,22.799,3.3,170,104.3,70.4,178,3.821,19.4,18,69.78294434 109 | 65.005,,Car,17.89,3.3,170,116.1,66.5,196.1,3.217,19.4,18,67.88927059 110 | 1.112,11.24,Passenger,18.145,3.1,150,107,69.4,192,3.102,15.2,25,60.86161155 111 | 38.554,,Passenger,24.15,3.5,215,109,73.6,195.9,3.455,18,,86.27252291 112 | 80.255,,Passenger,18.27,2.4,150,107,70.1,186.7,2.958,15,27,60.72744693 113 | 14.69,19.89,Passenger,36.229,4,250,113.8,74.4,205.4,3.967,18.5,22,103.4416926 114 | 20.017,19.925,Car,31.598,4.3,190,107,67.8,181.2,4.068,17.5,19,80.51167259 115 | 24.361,15.24,Car,25.345,3.4,185,120,72.2,201.4,3.948,25,22,76.09657042 116 | 32.734,7.75,Passenger,12.64,2,132,105,74.4,174.4,2.559,12.5,29,52.08489875 117 | 5.24,9.8,Passenger,16.08,2,132,108,71,186.3,2.942,16,27,53.41189767 118 | 24.155,12.025,Car,18.85,2.4,150,113.3,76.8,186.3,3.528,20,24,60.95118512 119 | 1.872,,Passenger,43,3.5,253,113.3,76.3,165.4,2.85,12,21,106.9844563 120 | 51.645,13.79,Passenger,21.61,2.4,150,104.1,68.4,181.9,2.906,15,27,62.0158703 121 | 131.097,10.29,Passenger,19.72,3.4,175,107,70.4,186.3,3.091,15.2,25,70.38973726 122 | 19.911,17.805,Passenger,25.31,3.8,200,101.1,74.5,193.4,3.492,16.8,25,81.49272616 123 | 92.364,14.01,Passenger,21.665,3.8,195,110.5,72.7,196.5,3.396,18,25,78.31816813 124 | 35.945,13.225,Passenger,23.755,3.8,205,112.2,72.6,202.5,3.59,17.5,24,82.6613556 125 | 39.572,,Car,25.635,3.4,185,120,72.7,201.3,3.942,25,23,76.20843952 126 | 8.982,41.25,Passenger,41.43,2.7,217,95.2,70.1,171,2.778,17,22,93.4373307 127 | 1.28,60.625,Passenger,71.02,3.4,300,92.6,69.5,174.5,3.032,17,21,134.3909754 128 | 1.866,67.55,Passenger,74.97,3.4,300,92.6,69.5,174.5,3.075,17,23,135.9147096 129 | 9.191,,Passenger,33.12,2.3,170,106.4,70.6,189.2,3.28,18.5,23,73.50377819 130 | 12.115,,Passenger,26.1,2,185,102.6,67.4,182.2,2.99,16.9,23,76.02304771 131 | 80.62,9.2,Passenger,10.685,1.9,100,102.4,66.4,176.9,2.332,12.1,33,39.98642475 132 | 24.546,10.59,Passenger,12.535,1.9,100,102.4,66.4,180,2.367,12.1,33,40.70007242 133 | 5.223,10.79,Passenger,14.29,1.9,124,102.4,66.4,176.9,2.452,12.1,31,49.86577367 134 | 8.472,,Passenger,18.835,2.2,137,106.5,69,190.4,3.075,13.1,27,56.29524304 135 | 49.989,,Passenger,15.01,2.2,137,106.5,69,190.4,2.91,13.1,28,54.81972825 136 | 47.107,,Passenger,22.695,2.5,165,103.5,67.5,185.8,3.415,16.9,25,67.7659076 137 | 33.028,,Car,20.095,2.5,165,99.4,68.3,175.2,3.125,15.9,24,66.76294331 138 | 142.535,10.025,Passenger,13.108,1.8,120,97,66.7,174,2.42,13.2,33,47.96897242 139 | 247.994,13.245,Passenger,17.518,2.2,133,105.2,70.1,188.5,2.998,18.5,27,54.37241965 140 | 63.849,18.14,Passenger,25.545,3,210,107.1,71.7,191.9,3.417,18.5,26,84.91189826 141 | 33.269,15.445,Passenger,16.875,1.8,140,102.4,68.3,170.5,2.425,14.5,31,56.49603034 142 | 84.087,9.575,Car,11.528,2.4,142,103.3,66.5,178.7,2.58,15.1,23,55.29711658 143 | 65.119,,Car,22.368,3,194,114.2,73.4,193.5,3.759,20.9,22,78.02721947 144 | 25.106,13.325,Car,16.888,2,127,94.9,66.7,163.8,2.668,15.3,27,51.95510887 145 | 68.411,19.425,Car,22.288,2.7,150,105.3,66.5,183.3,3.44,18.5,23,62.35557713 146 | 9.835,34.08,Car,51.728,4.7,230,112.2,76.4,192.5,5.115,25.4,15,102.5289842 147 | 9.761,11.425,Passenger,14.9,2,115,98.9,68.3,163.3,2.767,14.5,26,46.94387676 148 | 83.721,13.24,Passenger,16.7,2,115,98.9,68.3,172.3,2.853,14.5,26,47.63823666 149 | 51.102,16.725,Passenger,21.2,1.8,150,106.4,68.5,184.1,3.043,16.4,27,61.70138136 150 | 9.569,16.575,Passenger,19.99,2,115,97.4,66.7,160.4,3.079,13.7,26,48.90737225 151 | 5.596,13.76,Passenger,17.5,2,115,98.9,68.3,163.3,2.762,14.6,26,47.94684106 152 | 49.463,,Passenger,15.9,2,115,98.9,67.9,161.1,2.769,14.5,26,47.32963226 153 | 16.957,,Passenger,23.4,1.9,160,100.5,67.6,176.6,2.998,15.8,25,66.1130568 154 | 3.545,,Passenger,24.4,1.9,160,100.5,67.6,176.6,3.042,15.8,25,66.4988123 155 | 15.245,,Passenger,27.5,2.4,168,104.9,69.3,185.9,3.208,17.9,25,70.65449545 156 | 17.531,,Passenger,28.8,2.4,168,104.9,69.3,186.2,3.259,17.9,25,71.1559776 157 | 3.493,,Passenger,45.5,2.3,236,104.9,71.5,185.7,3.601,18.5,23,101.6233572 158 | 18.969,,Passenger,36,2.9,201,109.9,72.1,189.8,3.6,21.1,24,85.73565451 159 | -------------------------------------------------------------------------------- /Code-and-Data-Files/Car_sales.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Mastering-Machine-Learning-Algorithms-using-Python/6f954531e9a1e8fb31f27323cecdfe71578a87c1/Code-and-Data-Files/Car_sales.xls -------------------------------------------------------------------------------- /Code-and-Data-Files/Employee-Attrition-using-Naive-Bayes.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", 7 | "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5" 8 | }, 9 | "source": [ 10 | "## Employee Attrition Prediction using Naive Bayes " 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | "### Problem Statement:\n", 18 | "We have historical Employee Data with a number of features about each employee. The ask from the organisation is to predict whether or not an Employee, given it's attributes values, will attrite or not. " 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 1, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "import numpy as np\n", 28 | "import pandas as pd\n", 29 | "import matplotlib.pyplot as plt \n", 30 | "import seaborn as sns\n", 31 | "import warnings\n", 32 | "warnings.filterwarnings('ignore')" 33 | ] 34 | }, 35 | { 36 | "cell_type": "markdown", 37 | "metadata": {}, 38 | "source": [ 39 | "### Import Employee Data into a DataFrame" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 2, 45 | "metadata": { 46 | "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", 47 | "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a" 48 | }, 49 | "outputs": [], 50 | "source": [ 51 | "df = pd.read_csv(\"HR-Employee-Attrition.csv\")" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 3, 57 | "metadata": {}, 58 | "outputs": [ 59 | { 60 | "data": { 61 | "text/html": [ 62 | "
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AgeAttritionBusinessTravelDailyRateDepartmentDistanceFromHomeEducationEducationFieldEmployeeCountEmployeeNumber...RelationshipSatisfactionStandardHoursStockOptionLevelTotalWorkingYearsTrainingTimesLastYearWorkLifeBalanceYearsAtCompanyYearsInCurrentRoleYearsSinceLastPromotionYearsWithCurrManager
041YesTravel_Rarely1102Sales12Life Sciences11...18008016405
149NoTravel_Frequently279Research & Development81Life Sciences12...4801103310717
237YesTravel_Rarely1373Research & Development22Other14...28007330000
333NoTravel_Frequently1392Research & Development34Life Sciences15...38008338730
427NoTravel_Rarely591Research & Development21Medical17...48016332222
..................................................................
146536NoTravel_Frequently884Research & Development232Medical12061...380117335203
146639NoTravel_Rarely613Research & Development61Medical12062...18019537717
146727NoTravel_Rarely155Research & Development43Life Sciences12064...28016036203
146849NoTravel_Frequently1023Sales23Medical12065...480017329608
146934NoTravel_Rarely628Research & Development83Medical12068...18006344312
\n", 370 | "

1470 rows × 35 columns

\n", 371 | "
" 372 | ], 373 | "text/plain": [ 374 | " Age Attrition BusinessTravel DailyRate Department \\\n", 375 | "0 41 Yes Travel_Rarely 1102 Sales \n", 376 | "1 49 No Travel_Frequently 279 Research & Development \n", 377 | "2 37 Yes Travel_Rarely 1373 Research & Development \n", 378 | "3 33 No Travel_Frequently 1392 Research & Development \n", 379 | "4 27 No Travel_Rarely 591 Research & Development \n", 380 | "... ... ... ... ... ... \n", 381 | "1465 36 No Travel_Frequently 884 Research & Development \n", 382 | "1466 39 No Travel_Rarely 613 Research & Development \n", 383 | "1467 27 No Travel_Rarely 155 Research & Development \n", 384 | "1468 49 No Travel_Frequently 1023 Sales \n", 385 | "1469 34 No Travel_Rarely 628 Research & Development \n", 386 | "\n", 387 | " DistanceFromHome Education EducationField EmployeeCount \\\n", 388 | "0 1 2 Life Sciences 1 \n", 389 | "1 8 1 Life Sciences 1 \n", 390 | "2 2 2 Other 1 \n", 391 | "3 3 4 Life Sciences 1 \n", 392 | "4 2 1 Medical 1 \n", 393 | "... ... ... ... ... \n", 394 | "1465 23 2 Medical 1 \n", 395 | "1466 6 1 Medical 1 \n", 396 | "1467 4 3 Life Sciences 1 \n", 397 | "1468 2 3 Medical 1 \n", 398 | "1469 8 3 Medical 1 \n", 399 | "\n", 400 | " EmployeeNumber ... RelationshipSatisfaction StandardHours \\\n", 401 | "0 1 ... 1 80 \n", 402 | "1 2 ... 4 80 \n", 403 | "2 4 ... 2 80 \n", 404 | "3 5 ... 3 80 \n", 405 | "4 7 ... 4 80 \n", 406 | "... ... ... ... ... \n", 407 | "1465 2061 ... 3 80 \n", 408 | "1466 2062 ... 1 80 \n", 409 | "1467 2064 ... 2 80 \n", 410 | "1468 2065 ... 4 80 \n", 411 | "1469 2068 ... 1 80 \n", 412 | "\n", 413 | " StockOptionLevel TotalWorkingYears TrainingTimesLastYear \\\n", 414 | "0 0 8 0 \n", 415 | "1 1 10 3 \n", 416 | "2 0 7 3 \n", 417 | "3 0 8 3 \n", 418 | "4 1 6 3 \n", 419 | "... ... ... ... \n", 420 | "1465 1 17 3 \n", 421 | "1466 1 9 5 \n", 422 | "1467 1 6 0 \n", 423 | "1468 0 17 3 \n", 424 | "1469 0 6 3 \n", 425 | "\n", 426 | " WorkLifeBalance YearsAtCompany YearsInCurrentRole \\\n", 427 | "0 1 6 4 \n", 428 | "1 3 10 7 \n", 429 | "2 3 0 0 \n", 430 | "3 3 8 7 \n", 431 | "4 3 2 2 \n", 432 | "... ... ... ... \n", 433 | "1465 3 5 2 \n", 434 | "1466 3 7 7 \n", 435 | "1467 3 6 2 \n", 436 | "1468 2 9 6 \n", 437 | "1469 4 4 3 \n", 438 | "\n", 439 | " YearsSinceLastPromotion YearsWithCurrManager \n", 440 | "0 0 5 \n", 441 | "1 1 7 \n", 442 | "2 0 0 \n", 443 | "3 3 0 \n", 444 | "4 2 2 \n", 445 | "... ... ... \n", 446 | "1465 0 3 \n", 447 | "1466 1 7 \n", 448 | "1467 0 3 \n", 449 | "1468 0 8 \n", 450 | "1469 1 2 \n", 451 | "\n", 452 | "[1470 rows x 35 columns]" 453 | ] 454 | }, 455 | "execution_count": 3, 456 | "metadata": {}, 457 | "output_type": "execute_result" 458 | } 459 | ], 460 | "source": [ 461 | "df" 462 | ] 463 | }, 464 | { 465 | "cell_type": "code", 466 | "execution_count": 4, 467 | "metadata": {}, 468 | "outputs": [ 469 | { 470 | "name": "stdout", 471 | "output_type": "stream", 472 | "text": [ 473 | "\n", 474 | "RangeIndex: 1470 entries, 0 to 1469\n", 475 | "Data columns (total 35 columns):\n", 476 | " # Column Non-Null Count Dtype \n", 477 | "--- ------ -------------- ----- \n", 478 | " 0 Age 1470 non-null int64 \n", 479 | " 1 Attrition 1470 non-null object\n", 480 | " 2 BusinessTravel 1470 non-null object\n", 481 | " 3 DailyRate 1470 non-null int64 \n", 482 | " 4 Department 1470 non-null object\n", 483 | " 5 DistanceFromHome 1470 non-null int64 \n", 484 | " 6 Education 1470 non-null int64 \n", 485 | " 7 EducationField 1470 non-null object\n", 486 | " 8 EmployeeCount 1470 non-null int64 \n", 487 | " 9 EmployeeNumber 1470 non-null int64 \n", 488 | " 10 EnvironmentSatisfaction 1470 non-null int64 \n", 489 | " 11 Gender 1470 non-null object\n", 490 | " 12 HourlyRate 1470 non-null int64 \n", 491 | " 13 JobInvolvement 1470 non-null int64 \n", 492 | " 14 JobLevel 1470 non-null int64 \n", 493 | " 15 JobRole 1470 non-null object\n", 494 | " 16 JobSatisfaction 1470 non-null int64 \n", 495 | " 17 MaritalStatus 1470 non-null object\n", 496 | " 18 MonthlyIncome 1470 non-null int64 \n", 497 | " 19 MonthlyRate 1470 non-null int64 \n", 498 | " 20 NumCompaniesWorked 1470 non-null int64 \n", 499 | " 21 Over18 1470 non-null object\n", 500 | " 22 OverTime 1470 non-null object\n", 501 | " 23 PercentSalaryHike 1470 non-null int64 \n", 502 | " 24 PerformanceRating 1470 non-null int64 \n", 503 | " 25 RelationshipSatisfaction 1470 non-null int64 \n", 504 | " 26 StandardHours 1470 non-null int64 \n", 505 | " 27 StockOptionLevel 1470 non-null int64 \n", 506 | " 28 TotalWorkingYears 1470 non-null int64 \n", 507 | " 29 TrainingTimesLastYear 1470 non-null int64 \n", 508 | " 30 WorkLifeBalance 1470 non-null int64 \n", 509 | " 31 YearsAtCompany 1470 non-null int64 \n", 510 | " 32 YearsInCurrentRole 1470 non-null int64 \n", 511 | " 33 YearsSinceLastPromotion 1470 non-null int64 \n", 512 | " 34 YearsWithCurrManager 1470 non-null int64 \n", 513 | "dtypes: int64(26), object(9)\n", 514 | "memory usage: 402.1+ KB\n" 515 | ] 516 | } 517 | ], 518 | "source": [ 519 | "df.info()" 520 | ] 521 | }, 522 | { 523 | "cell_type": "markdown", 524 | "metadata": {}, 525 | "source": [ 526 | "#### Observation: No Missing Values in the Data" 527 | ] 528 | }, 529 | { 530 | "cell_type": "markdown", 531 | "metadata": {}, 532 | "source": [ 533 | "#### Replace 'Yes' and 'No' in target feature by 1 and 0" 534 | ] 535 | }, 536 | { 537 | "cell_type": "code", 538 | "execution_count": 5, 539 | "metadata": {}, 540 | "outputs": [], 541 | "source": [ 542 | "df.Attrition.replace({\"Yes\":1,\"No\":0}, inplace=True)" 543 | ] 544 | }, 545 | { 546 | "cell_type": "markdown", 547 | "metadata": {}, 548 | "source": [ 549 | "#### Delete the following Features as their values are same across all observations" 550 | ] 551 | }, 552 | { 553 | "cell_type": "code", 554 | "execution_count": 6, 555 | "metadata": {}, 556 | "outputs": [ 557 | { 558 | "data": { 559 | "text/plain": [ 560 | "Index(['Age', 'Attrition', 'BusinessTravel', 'DailyRate', 'Department',\n", 561 | " 'DistanceFromHome', 'Education', 'EducationField', 'EmployeeNumber',\n", 562 | " 'EnvironmentSatisfaction', 'Gender', 'HourlyRate', 'JobInvolvement',\n", 563 | " 'JobLevel', 'JobRole', 'JobSatisfaction', 'MaritalStatus',\n", 564 | " 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked', 'Over18',\n", 565 | " 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n", 566 | " 'RelationshipSatisfaction', 'StockOptionLevel', 'TotalWorkingYears',\n", 567 | " 'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsAtCompany',\n", 568 | " 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n", 569 | " 'YearsWithCurrManager'],\n", 570 | " dtype='object')" 571 | ] 572 | }, 573 | "execution_count": 6, 574 | "metadata": {}, 575 | "output_type": "execute_result" 576 | } 577 | ], 578 | "source": [ 579 | "df.drop(columns=['EmployeeCount','StandardHours'], inplace=True)\n", 580 | "df.columns" 581 | ] 582 | }, 583 | { 584 | "cell_type": "markdown", 585 | "metadata": {}, 586 | "source": [ 587 | "### Create lists of Categorical Features and Numerical Features" 588 | ] 589 | }, 590 | { 591 | "cell_type": "code", 592 | "execution_count": 7, 593 | "metadata": {}, 594 | "outputs": [], 595 | "source": [ 596 | "cat_col = df.select_dtypes(exclude=np.number).columns\n", 597 | "num_col = df.select_dtypes(include=np.number).columns" 598 | ] 599 | }, 600 | { 601 | "cell_type": "code", 602 | "execution_count": 8, 603 | "metadata": {}, 604 | "outputs": [ 605 | { 606 | "name": "stdout", 607 | "output_type": "stream", 608 | "text": [ 609 | "\n", 610 | "=================> BusinessTravel \n", 611 | "\n", 612 | "Travel_Rarely 1043\n", 613 | "Travel_Frequently 277\n", 614 | "Non-Travel 150\n", 615 | "Name: BusinessTravel, dtype: int64\n", 616 | "\n", 617 | "=================> Department \n", 618 | "\n", 619 | "Research & Development 961\n", 620 | "Sales 446\n", 621 | "Human Resources 63\n", 622 | "Name: Department, dtype: int64\n", 623 | "\n", 624 | "=================> EducationField \n", 625 | "\n", 626 | "Life Sciences 606\n", 627 | "Medical 464\n", 628 | "Marketing 159\n", 629 | "Technical Degree 132\n", 630 | "Other 82\n", 631 | "Human Resources 27\n", 632 | "Name: EducationField, dtype: int64\n", 633 | "\n", 634 | "=================> Gender \n", 635 | "\n", 636 | "Male 882\n", 637 | "Female 588\n", 638 | "Name: Gender, dtype: int64\n", 639 | "\n", 640 | "=================> JobRole \n", 641 | "\n", 642 | "Sales Executive 326\n", 643 | "Research Scientist 292\n", 644 | "Laboratory Technician 259\n", 645 | "Manufacturing Director 145\n", 646 | "Healthcare Representative 131\n", 647 | "Manager 102\n", 648 | "Sales Representative 83\n", 649 | "Research Director 80\n", 650 | "Human Resources 52\n", 651 | "Name: JobRole, dtype: int64\n", 652 | "\n", 653 | "=================> MaritalStatus \n", 654 | "\n", 655 | "Married 673\n", 656 | "Single 470\n", 657 | "Divorced 327\n", 658 | "Name: MaritalStatus, dtype: int64\n", 659 | "\n", 660 | "=================> Over18 \n", 661 | "\n", 662 | "Y 1470\n", 663 | "Name: Over18, dtype: int64\n", 664 | "\n", 665 | "=================> OverTime \n", 666 | "\n", 667 | "No 1054\n", 668 | "Yes 416\n", 669 | "Name: OverTime, dtype: int64\n" 670 | ] 671 | } 672 | ], 673 | "source": [ 674 | "for i in cat_col:\n", 675 | " print(f\"\\n=================> {i} \\n\")\n", 676 | " print(df[i].value_counts())" 677 | ] 678 | }, 679 | { 680 | "cell_type": "markdown", 681 | "metadata": {}, 682 | "source": [ 683 | "### Create Dummy Variables for Categorical Variable" 684 | ] 685 | }, 686 | { 687 | "cell_type": "code", 688 | "execution_count": 9, 689 | "metadata": {}, 690 | "outputs": [], 691 | "source": [ 692 | "encoded_cat_col = pd.get_dummies(df[cat_col], drop_first=True)" 693 | ] 694 | }, 695 | { 696 | "cell_type": "code", 697 | "execution_count": 10, 698 | "metadata": {}, 699 | "outputs": [], 700 | "source": [ 701 | "final_model = pd.concat([df[num_col],encoded_cat_col], axis = 1)" 702 | ] 703 | }, 704 | { 705 | "cell_type": "code", 706 | "execution_count": 11, 707 | "metadata": {}, 708 | "outputs": [ 709 | { 710 | "data": { 711 | "text/html": [ 712 | "
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AgeAttritionDailyRateDistanceFromHomeEducationEmployeeNumberEnvironmentSatisfactionHourlyRateJobInvolvementJobLevel...JobRole_Laboratory TechnicianJobRole_ManagerJobRole_Manufacturing DirectorJobRole_Research DirectorJobRole_Research ScientistJobRole_Sales ExecutiveJobRole_Sales RepresentativeMaritalStatus_MarriedMaritalStatus_SingleOverTime_Yes
0411110212129432...0000010011
149027981236122...0000100100
2371137322449221...1000000011
3330139234545631...0000100101
427059121714031...1000000100
..................................................................
1465360884232206134142...1000000100
146639061361206244223...0000000100
146727015543206428742...0010000101
1468490102323206546322...0000010100
146934062883206828242...1000000100
\n", 1020 | "

1470 rows × 46 columns

\n", 1021 | "
" 1022 | ], 1023 | "text/plain": [ 1024 | " Age Attrition DailyRate DistanceFromHome Education EmployeeNumber \\\n", 1025 | "0 41 1 1102 1 2 1 \n", 1026 | "1 49 0 279 8 1 2 \n", 1027 | "2 37 1 1373 2 2 4 \n", 1028 | "3 33 0 1392 3 4 5 \n", 1029 | "4 27 0 591 2 1 7 \n", 1030 | "... ... ... ... ... ... ... \n", 1031 | "1465 36 0 884 23 2 2061 \n", 1032 | "1466 39 0 613 6 1 2062 \n", 1033 | "1467 27 0 155 4 3 2064 \n", 1034 | "1468 49 0 1023 2 3 2065 \n", 1035 | "1469 34 0 628 8 3 2068 \n", 1036 | "\n", 1037 | " EnvironmentSatisfaction HourlyRate JobInvolvement JobLevel ... \\\n", 1038 | "0 2 94 3 2 ... \n", 1039 | "1 3 61 2 2 ... \n", 1040 | "2 4 92 2 1 ... \n", 1041 | "3 4 56 3 1 ... \n", 1042 | "4 1 40 3 1 ... \n", 1043 | "... ... ... ... ... ... \n", 1044 | "1465 3 41 4 2 ... \n", 1045 | "1466 4 42 2 3 ... \n", 1046 | "1467 2 87 4 2 ... \n", 1047 | "1468 4 63 2 2 ... \n", 1048 | "1469 2 82 4 2 ... \n", 1049 | "\n", 1050 | " JobRole_Laboratory Technician JobRole_Manager \\\n", 1051 | "0 0 0 \n", 1052 | "1 0 0 \n", 1053 | "2 1 0 \n", 1054 | "3 0 0 \n", 1055 | "4 1 0 \n", 1056 | "... ... ... \n", 1057 | "1465 1 0 \n", 1058 | "1466 0 0 \n", 1059 | "1467 0 0 \n", 1060 | "1468 0 0 \n", 1061 | "1469 1 0 \n", 1062 | "\n", 1063 | " JobRole_Manufacturing Director JobRole_Research Director \\\n", 1064 | "0 0 0 \n", 1065 | "1 0 0 \n", 1066 | "2 0 0 \n", 1067 | "3 0 0 \n", 1068 | "4 0 0 \n", 1069 | "... ... ... \n", 1070 | "1465 0 0 \n", 1071 | "1466 0 0 \n", 1072 | "1467 1 0 \n", 1073 | "1468 0 0 \n", 1074 | "1469 0 0 \n", 1075 | "\n", 1076 | " JobRole_Research Scientist JobRole_Sales Executive \\\n", 1077 | "0 0 1 \n", 1078 | "1 1 0 \n", 1079 | "2 0 0 \n", 1080 | "3 1 0 \n", 1081 | "4 0 0 \n", 1082 | "... ... ... \n", 1083 | "1465 0 0 \n", 1084 | "1466 0 0 \n", 1085 | "1467 0 0 \n", 1086 | "1468 0 1 \n", 1087 | "1469 0 0 \n", 1088 | "\n", 1089 | " JobRole_Sales Representative MaritalStatus_Married \\\n", 1090 | "0 0 0 \n", 1091 | "1 0 1 \n", 1092 | "2 0 0 \n", 1093 | "3 0 1 \n", 1094 | "4 0 1 \n", 1095 | "... ... ... \n", 1096 | "1465 0 1 \n", 1097 | "1466 0 1 \n", 1098 | "1467 0 1 \n", 1099 | "1468 0 1 \n", 1100 | "1469 0 1 \n", 1101 | "\n", 1102 | " MaritalStatus_Single OverTime_Yes \n", 1103 | "0 1 1 \n", 1104 | "1 0 0 \n", 1105 | "2 1 1 \n", 1106 | "3 0 1 \n", 1107 | "4 0 0 \n", 1108 | "... ... ... \n", 1109 | "1465 0 0 \n", 1110 | "1466 0 0 \n", 1111 | "1467 0 1 \n", 1112 | "1468 0 0 \n", 1113 | "1469 0 0 \n", 1114 | "\n", 1115 | "[1470 rows x 46 columns]" 1116 | ] 1117 | }, 1118 | "execution_count": 11, 1119 | "metadata": {}, 1120 | "output_type": "execute_result" 1121 | } 1122 | ], 1123 | "source": [ 1124 | "final_model" 1125 | ] 1126 | }, 1127 | { 1128 | "cell_type": "code", 1129 | "execution_count": 12, 1130 | "metadata": {}, 1131 | "outputs": [], 1132 | "source": [ 1133 | "from sklearn.model_selection import train_test_split\n", 1134 | "from sklearn import metrics\n", 1135 | "from sklearn.metrics import classification_report" 1136 | ] 1137 | }, 1138 | { 1139 | "cell_type": "markdown", 1140 | "metadata": {}, 1141 | "source": [ 1142 | "### Segrgate X and y Features, Create Train and Test Sets" 1143 | ] 1144 | }, 1145 | { 1146 | "cell_type": "code", 1147 | "execution_count": 13, 1148 | "metadata": {}, 1149 | "outputs": [], 1150 | "source": [ 1151 | "x = final_model.drop(columns=\"Attrition\")\n", 1152 | "y = final_model[\"Attrition\"]\n", 1153 | "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=10)" 1154 | ] 1155 | }, 1156 | { 1157 | "cell_type": "code", 1158 | "execution_count": 14, 1159 | "metadata": {}, 1160 | "outputs": [], 1161 | "source": [ 1162 | "from sklearn.naive_bayes import GaussianNB" 1163 | ] 1164 | }, 1165 | { 1166 | "cell_type": "markdown", 1167 | "metadata": {}, 1168 | "source": [ 1169 | "### Create our Gaussian Naive Bayes Model" 1170 | ] 1171 | }, 1172 | { 1173 | "cell_type": "code", 1174 | "execution_count": 15, 1175 | "metadata": {}, 1176 | "outputs": [], 1177 | "source": [ 1178 | "model = GaussianNB()" 1179 | ] 1180 | }, 1181 | { 1182 | "cell_type": "markdown", 1183 | "metadata": {}, 1184 | "source": [ 1185 | "#### Train and Predict using Training Data" 1186 | ] 1187 | }, 1188 | { 1189 | "cell_type": "code", 1190 | "execution_count": 16, 1191 | "metadata": {}, 1192 | "outputs": [], 1193 | "source": [ 1194 | "model.fit(x_train, y_train)\n", 1195 | "train_Pred = model.predict(x_train)" 1196 | ] 1197 | }, 1198 | { 1199 | "cell_type": "code", 1200 | "execution_count": 17, 1201 | "metadata": {}, 1202 | "outputs": [ 1203 | { 1204 | "data": { 1205 | "text/plain": [ 1206 | "array([[749, 127],\n", 1207 | " [ 58, 95]])" 1208 | ] 1209 | }, 1210 | "execution_count": 17, 1211 | "metadata": {}, 1212 | "output_type": "execute_result" 1213 | } 1214 | ], 1215 | "source": [ 1216 | "metrics.confusion_matrix(y_train,train_Pred)" 1217 | ] 1218 | }, 1219 | { 1220 | "cell_type": "code", 1221 | "execution_count": 23, 1222 | "metadata": {}, 1223 | "outputs": [ 1224 | { 1225 | "data": { 1226 | "text/plain": [ 1227 | "82.02137998056365" 1228 | ] 1229 | }, 1230 | "execution_count": 23, 1231 | "metadata": {}, 1232 | "output_type": "execute_result" 1233 | } 1234 | ], 1235 | "source": [ 1236 | "Accuracy_percent_train = (metrics.accuracy_score(y_train,train_Pred)) * 100\n", 1237 | "Accuracy_percent_train" 1238 | ] 1239 | }, 1240 | { 1241 | "cell_type": "markdown", 1242 | "metadata": {}, 1243 | "source": [ 1244 | "#### Our model is able to predict with 82% accuracy from Training Data" 1245 | ] 1246 | }, 1247 | { 1248 | "cell_type": "markdown", 1249 | "metadata": {}, 1250 | "source": [ 1251 | "### Predict using Test Data" 1252 | ] 1253 | }, 1254 | { 1255 | "cell_type": "code", 1256 | "execution_count": 19, 1257 | "metadata": {}, 1258 | "outputs": [], 1259 | "source": [ 1260 | "test_Pred = model.predict(x_test)" 1261 | ] 1262 | }, 1263 | { 1264 | "cell_type": "code", 1265 | "execution_count": 20, 1266 | "metadata": {}, 1267 | "outputs": [ 1268 | { 1269 | "data": { 1270 | "text/plain": [ 1271 | "array([[300, 57],\n", 1272 | " [ 28, 56]])" 1273 | ] 1274 | }, 1275 | "execution_count": 20, 1276 | "metadata": {}, 1277 | "output_type": "execute_result" 1278 | } 1279 | ], 1280 | "source": [ 1281 | "metrics.confusion_matrix(y_test,test_Pred)" 1282 | ] 1283 | }, 1284 | { 1285 | "cell_type": "code", 1286 | "execution_count": 21, 1287 | "metadata": {}, 1288 | "outputs": [ 1289 | { 1290 | "data": { 1291 | "text/plain": [ 1292 | "80.72562358276643" 1293 | ] 1294 | }, 1295 | "execution_count": 21, 1296 | "metadata": {}, 1297 | "output_type": "execute_result" 1298 | } 1299 | ], 1300 | "source": [ 1301 | "Accuracy_percent_test = (metrics.accuracy_score(y_test,test_Pred))*100\n", 1302 | "Accuracy_percent_test" 1303 | ] 1304 | }, 1305 | { 1306 | "cell_type": "markdown", 1307 | "metadata": {}, 1308 | "source": [ 1309 | "#### Our model is able to predict with 80.7% accuracy from Test Data" 1310 | ] 1311 | }, 1312 | { 1313 | "cell_type": "code", 1314 | "execution_count": 28, 1315 | "metadata": {}, 1316 | "outputs": [ 1317 | { 1318 | "data": { 1319 | "text/plain": [ 1320 | "[(0, 0),\n", 1321 | " (1, 1),\n", 1322 | " (0, 0),\n", 1323 | " (0, 0),\n", 1324 | " (0, 0),\n", 1325 | " (1, 1),\n", 1326 | " (0, 0),\n", 1327 | " (1, 0),\n", 1328 | " (0, 0),\n", 1329 | " (0, 0),\n", 1330 | " (0, 0),\n", 1331 | " (0, 0),\n", 1332 | " (0, 0),\n", 1333 | " (0, 1),\n", 1334 | " (0, 0),\n", 1335 | " (1, 0),\n", 1336 | " (0, 0),\n", 1337 | " (0, 0),\n", 1338 | " (0, 0),\n", 1339 | " (0, 0)]" 1340 | ] 1341 | }, 1342 | "execution_count": 28, 1343 | "metadata": {}, 1344 | "output_type": "execute_result" 1345 | } 1346 | ], 1347 | "source": [ 1348 | "list(zip(y_test, test_Pred))[0:20]" 1349 | ] 1350 | }, 1351 | { 1352 | "cell_type": "code", 1353 | "execution_count": null, 1354 | "metadata": {}, 1355 | "outputs": [], 1356 | "source": [] 1357 | }, 1358 | { 1359 | "cell_type": "code", 1360 | "execution_count": null, 1361 | "metadata": {}, 1362 | "outputs": [], 1363 | "source": [] 1364 | }, 1365 | { 1366 | "cell_type": "code", 1367 | "execution_count": null, 1368 | "metadata": {}, 1369 | "outputs": [], 1370 | "source": [] 1371 | }, 1372 | { 1373 | "cell_type": "code", 1374 | "execution_count": null, 1375 | "metadata": {}, 1376 | "outputs": [], 1377 | "source": [] 1378 | }, 1379 | { 1380 | "cell_type": "code", 1381 | "execution_count": null, 1382 | "metadata": {}, 1383 | "outputs": [], 1384 | "source": [] 1385 | }, 1386 | { 1387 | "cell_type": "code", 1388 | "execution_count": null, 1389 | "metadata": {}, 1390 | "outputs": [], 1391 | "source": [] 1392 | }, 1393 | { 1394 | "cell_type": "code", 1395 | "execution_count": null, 1396 | "metadata": {}, 1397 | "outputs": [], 1398 | "source": [] 1399 | }, 1400 | { 1401 | "cell_type": "code", 1402 | "execution_count": null, 1403 | "metadata": {}, 1404 | "outputs": [], 1405 | "source": [] 1406 | }, 1407 | { 1408 | "cell_type": "code", 1409 | "execution_count": null, 1410 | "metadata": {}, 1411 | "outputs": [], 1412 | "source": [] 1413 | }, 1414 | { 1415 | "cell_type": "code", 1416 | "execution_count": 22, 1417 | "metadata": {}, 1418 | "outputs": [ 1419 | { 1420 | "name": "stdout", 1421 | "output_type": "stream", 1422 | "text": [ 1423 | " precision recall f1-score support\n", 1424 | "\n", 1425 | " 0 0.91 0.84 0.88 357\n", 1426 | " 1 0.50 0.67 0.57 84\n", 1427 | "\n", 1428 | " accuracy 0.81 441\n", 1429 | " macro avg 0.71 0.75 0.72 441\n", 1430 | "weighted avg 0.83 0.81 0.82 441\n", 1431 | "\n" 1432 | ] 1433 | } 1434 | ], 1435 | "source": [ 1436 | "print(classification_report(y_test, test_Pred))" 1437 | ] 1438 | }, 1439 | { 1440 | "cell_type": "code", 1441 | "execution_count": null, 1442 | "metadata": {}, 1443 | "outputs": [], 1444 | "source": [] 1445 | }, 1446 | { 1447 | "cell_type": "code", 1448 | "execution_count": null, 1449 | "metadata": {}, 1450 | "outputs": [], 1451 | "source": [] 1452 | }, 1453 | { 1454 | "cell_type": "code", 1455 | "execution_count": null, 1456 | "metadata": {}, 1457 | "outputs": [], 1458 | "source": [] 1459 | } 1460 | ], 1461 | "metadata": { 1462 | "kernelspec": { 1463 | "display_name": "Python 3", 1464 | "language": "python", 1465 | "name": "python3" 1466 | }, 1467 | "language_info": { 1468 | "codemirror_mode": { 1469 | "name": "ipython", 1470 | "version": 3 1471 | }, 1472 | "file_extension": ".py", 1473 | "mimetype": "text/x-python", 1474 | "name": "python", 1475 | "nbconvert_exporter": "python", 1476 | "pygments_lexer": "ipython3", 1477 | "version": "3.8.5" 1478 | } 1479 | }, 1480 | "nbformat": 4, 1481 | "nbformat_minor": 1 1482 | } 1483 | -------------------------------------------------------------------------------- /Code-and-Data-Files/Fashion_MNIST_Image_Classification_using_Deep_Learning_tf_Keras.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "Fashion MNIST - Image Classification using Deep Learning - tf.Keras.ipynb", 7 | "private_outputs": true, 8 | "provenance": [], 9 | "collapsed_sections": [] 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | }, 15 | "language_info": { 16 | "name": "python" 17 | }, 18 | "accelerator": "GPU" 19 | }, 20 | "cells": [ 21 | { 22 | "cell_type": "markdown", 23 | "metadata": { 24 | "id": "mbjbQpQUxVRZ" 25 | }, 26 | "source": [ 27 | "## Fashion MNIST Image Classification using tf.Keras" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "metadata": { 33 | "id": "f0JUkBqYxGDq" 34 | }, 35 | "source": [ 36 | "import tensorflow as tf\n", 37 | "import numpy as np\n", 38 | "import pandas as pd\n", 39 | "import matplotlib.pyplot as plt" 40 | ], 41 | "execution_count": null, 42 | "outputs": [] 43 | }, 44 | { 45 | "cell_type": "code", 46 | "metadata": { 47 | "id": "s9cH4hfOxNcX" 48 | }, 49 | "source": [ 50 | "from tensorflow import keras\n", 51 | "from keras import optimizers" 52 | ], 53 | "execution_count": null, 54 | "outputs": [] 55 | }, 56 | { 57 | "cell_type": "code", 58 | "metadata": { 59 | "id": "XCtyzYJnxNig" 60 | }, 61 | "source": [ 62 | "keras.__version__" 63 | ], 64 | "execution_count": null, 65 | "outputs": [] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "metadata": { 70 | "id": "liHOa4WBxNlf" 71 | }, 72 | "source": [ 73 | "fashion_mnist = keras.datasets.fashion_mnist" 74 | ], 75 | "execution_count": null, 76 | "outputs": [] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "metadata": { 81 | "id": "4zoJIz3Lxog3" 82 | }, 83 | "source": [ 84 | "(X_train_full, y_train_full), (X_test, y_test) = fashion_mnist.load_data()" 85 | ], 86 | "execution_count": null, 87 | "outputs": [] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "metadata": { 92 | "id": "gFiBnZN9xojx" 93 | }, 94 | "source": [ 95 | "print(X_train_full.shape)\n", 96 | "print(y_train_full.shape)\n", 97 | "print(X_test.shape)\n", 98 | "print(y_test.shape)" 99 | ], 100 | "execution_count": null, 101 | "outputs": [] 102 | }, 103 | { 104 | "cell_type": "markdown", 105 | "metadata": { 106 | "id": "vLU7OMq5yRwu" 107 | }, 108 | "source": [ 109 | "### Create a Validation Set from Train set and scale down the pixel values to 0-1" 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "metadata": { 115 | "id": "lMjUa479xoph" 116 | }, 117 | "source": [ 118 | "X_valid, X_train = X_train_full[:5000] / 255, X_train_full[5000:] / 255 " 119 | ], 120 | "execution_count": null, 121 | "outputs": [] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "metadata": { 126 | "id": "uv3uwl42yM9N" 127 | }, 128 | "source": [ 129 | "y_valid, y_train = y_train_full[:5000], y_train_full[5000:] " 130 | ], 131 | "execution_count": null, 132 | "outputs": [] 133 | }, 134 | { 135 | "cell_type": "code", 136 | "metadata": { 137 | "id": "scyAnVX7yNAE" 138 | }, 139 | "source": [ 140 | "class_names = ['T-shirt/Top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle Boot']" 141 | ], 142 | "execution_count": null, 143 | "outputs": [] 144 | }, 145 | { 146 | "cell_type": "code", 147 | "metadata": { 148 | "id": "ekWayzcSyNCc" 149 | }, 150 | "source": [ 151 | "class_names[y_train[0]]" 152 | ], 153 | "execution_count": null, 154 | "outputs": [] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "metadata": { 159 | "id": "a_HmyB0y75LK" 160 | }, 161 | "source": [ 162 | "y_train[0]" 163 | ], 164 | "execution_count": null, 165 | "outputs": [] 166 | }, 167 | { 168 | "cell_type": "markdown", 169 | "metadata": { 170 | "id": "94HhAIOQbN_j" 171 | }, 172 | "source": [ 173 | "## How an Image looks like as Matrix of Pixel values" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "metadata": { 179 | "id": "eFyfEXmIZuDz" 180 | }, 181 | "source": [ 182 | "X_train[0].shape" 183 | ], 184 | "execution_count": null, 185 | "outputs": [] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "metadata": { 190 | "id": "1FfEfunwbkHE" 191 | }, 192 | "source": [ 193 | "X_train[0]" 194 | ], 195 | "execution_count": null, 196 | "outputs": [] 197 | }, 198 | { 199 | "cell_type": "markdown", 200 | "metadata": { 201 | "id": "qFampMDGbEky" 202 | }, 203 | "source": [ 204 | "## Display the first 8 images from the Dataset" 205 | ] 206 | }, 207 | { 208 | "cell_type": "code", 209 | "metadata": { 210 | "id": "BwxMEQ-wZuHP" 211 | }, 212 | "source": [ 213 | "fig, ax = plt.subplots(4, 4, figsize=(10,10))\n", 214 | "for i, axi in enumerate(ax.flat):\n", 215 | " axi.imshow(X_train[i])\n", 216 | " axi.set(xticks=[], yticks=[])" 217 | ], 218 | "execution_count": null, 219 | "outputs": [] 220 | }, 221 | { 222 | "cell_type": "markdown", 223 | "metadata": { 224 | "id": "NrpvzuIUzay8" 225 | }, 226 | "source": [ 227 | "## Create our Keras Sequential Model" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "metadata": { 233 | "id": "aWTSicPbyNFA" 234 | }, 235 | "source": [ 236 | "model = keras.models.Sequential()\n", 237 | "model.add(keras.layers.Flatten(input_shape=[28, 28]))\n", 238 | "model.add(keras.layers.Dense(300, activation='relu'))\n", 239 | "model.add(keras.layers.Dense(100, activation='relu'))\n", 240 | "model.add(keras.layers.Dense(10, activation='softmax')) " 241 | ], 242 | "execution_count": null, 243 | "outputs": [] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "metadata": { 248 | "id": "66bBvYXMzWub" 249 | }, 250 | "source": [ 251 | "model.summary()" 252 | ], 253 | "execution_count": null, 254 | "outputs": [] 255 | }, 256 | { 257 | "cell_type": "code", 258 | "metadata": { 259 | "id": "F6JSQ0OkzW0T" 260 | }, 261 | "source": [ 262 | "model.layers" 263 | ], 264 | "execution_count": null, 265 | "outputs": [] 266 | }, 267 | { 268 | "cell_type": "code", 269 | "metadata": { 270 | "id": "J4BQV7uI4xPp" 271 | }, 272 | "source": [ 273 | "hidden1 = model.layers[1]\n", 274 | "hidden1.name" 275 | ], 276 | "execution_count": null, 277 | "outputs": [] 278 | }, 279 | { 280 | "cell_type": "code", 281 | "metadata": { 282 | "id": "y8KSDFWMzW2o" 283 | }, 284 | "source": [ 285 | "weights, biases = hidden1.get_weights()\n", 286 | "print(weights)\n", 287 | "print(biases)" 288 | ], 289 | "execution_count": null, 290 | "outputs": [] 291 | }, 292 | { 293 | "cell_type": "markdown", 294 | "metadata": { 295 | "id": "BlEm261n5AmH" 296 | }, 297 | "source": [ 298 | "### Compile the Model" 299 | ] 300 | }, 301 | { 302 | "cell_type": "code", 303 | "metadata": { 304 | "id": "3vmIoabYzW5R" 305 | }, 306 | "source": [ 307 | "model.compile(loss='sparse_categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])\n", 308 | "#model.compile(loss='mse', optimizer=optimizers.RMSprop(lr=0.001), metrics=['accuracy'])" 309 | ], 310 | "execution_count": null, 311 | "outputs": [] 312 | }, 313 | { 314 | "cell_type": "code", 315 | "metadata": { 316 | "id": "JiPBF9-TxosI" 317 | }, 318 | "source": [ 319 | "history = model.fit(X_train, y_train, epochs=10, batch_size=128, validation_data=(X_valid, y_valid))" 320 | ], 321 | "execution_count": null, 322 | "outputs": [] 323 | }, 324 | { 325 | "cell_type": "markdown", 326 | "metadata": { 327 | "id": "SoNSzVIf5nl0" 328 | }, 329 | "source": [ 330 | "### Evaluating our Model" 331 | ] 332 | }, 333 | { 334 | "cell_type": "code", 335 | "metadata": { 336 | "id": "xQnOITJD5gBZ" 337 | }, 338 | "source": [ 339 | "import pandas as pd\n", 340 | "import numpy as np\n", 341 | "import matplotlib.pyplot as plt" 342 | ], 343 | "execution_count": null, 344 | "outputs": [] 345 | }, 346 | { 347 | "cell_type": "code", 348 | "metadata": { 349 | "id": "HXXkCDG25gEz" 350 | }, 351 | "source": [ 352 | "pd.DataFrame(history.history).plot(figsize=(8,5))\n", 353 | "plt.grid=True\n", 354 | "plt.gca().set_ylim(0, 1)\n", 355 | "plt.show()" 356 | ], 357 | "execution_count": null, 358 | "outputs": [] 359 | }, 360 | { 361 | "cell_type": "code", 362 | "metadata": { 363 | "id": "JivQ0gkf5gJD" 364 | }, 365 | "source": [ 366 | "model.evaluate(X_test, y_test)" 367 | ], 368 | "execution_count": null, 369 | "outputs": [] 370 | }, 371 | { 372 | "cell_type": "markdown", 373 | "metadata": { 374 | "id": "N_JJpScx6c6M" 375 | }, 376 | "source": [ 377 | "## Use our Model to make Predictions" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "metadata": { 383 | "id": "u4skATNb6Nmu" 384 | }, 385 | "source": [ 386 | "X_new = X_test[:3]\n", 387 | "y_proba = model.predict(X_new)\n", 388 | "y_proba.round(2)" 389 | ], 390 | "execution_count": null, 391 | "outputs": [] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "metadata": { 396 | "id": "evpIscPH6gmZ" 397 | }, 398 | "source": [ 399 | "predict_x = model.predict(X_test) \n", 400 | "y_pred = np.argmax(predict_x, axis=1)" 401 | ], 402 | "execution_count": null, 403 | "outputs": [] 404 | }, 405 | { 406 | "cell_type": "code", 407 | "metadata": { 408 | "id": "Yqc_cMSP6Npz" 409 | }, 410 | "source": [ 411 | "y_pred" 412 | ], 413 | "execution_count": null, 414 | "outputs": [] 415 | }, 416 | { 417 | "cell_type": "markdown", 418 | "metadata": { 419 | "id": "MNZjI7Ftc9DF" 420 | }, 421 | "source": [ 422 | "## Compare the Actual and Predicted Labels" 423 | ] 424 | }, 425 | { 426 | "cell_type": "code", 427 | "metadata": { 428 | "id": "CciiKrKS6Nsj" 429 | }, 430 | "source": [ 431 | "list(zip(np.array(class_names)[y_test], np.array(class_names)[y_pred]))" 432 | ], 433 | "execution_count": null, 434 | "outputs": [] 435 | }, 436 | { 437 | "cell_type": "code", 438 | "metadata": { 439 | "id": "Yyv5WM2W5gNK" 440 | }, 441 | "source": [ 442 | "y_test" 443 | ], 444 | "execution_count": null, 445 | "outputs": [] 446 | }, 447 | { 448 | "cell_type": "code", 449 | "metadata": { 450 | "id": "cAWDbSUV8nSF" 451 | }, 452 | "source": [ 453 | "from sklearn.metrics import confusion_matrix\n", 454 | "from sklearn.metrics import classification_report" 455 | ], 456 | "execution_count": null, 457 | "outputs": [] 458 | }, 459 | { 460 | "cell_type": "code", 461 | "metadata": { 462 | "id": "Gk82wIdq8eaE" 463 | }, 464 | "source": [ 465 | "matrix = confusion_matrix(y_test, y_pred)\n", 466 | "print('Confusion matrix : \\n', matrix)" 467 | ], 468 | "execution_count": null, 469 | "outputs": [] 470 | }, 471 | { 472 | "cell_type": "code", 473 | "metadata": { 474 | "id": "RVOd16vk8ec6" 475 | }, 476 | "source": [ 477 | "matrix = classification_report(y_test, y_pred,labels=[1,0])\n", 478 | "print('Classification report : \\n', matrix)" 479 | ], 480 | "execution_count": null, 481 | "outputs": [] 482 | }, 483 | { 484 | "cell_type": "code", 485 | "metadata": { 486 | "id": "brx5f6-R8efj" 487 | }, 488 | "source": [ 489 | "from sklearn.metrics import accuracy_score\n", 490 | "accuracy_score(y_test, y_pred)" 491 | ], 492 | "execution_count": null, 493 | "outputs": [] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "metadata": { 498 | "id": "aFxHVXUVZBNa" 499 | }, 500 | "source": [ 501 | "" 502 | ], 503 | "execution_count": null, 504 | "outputs": [] 505 | }, 506 | { 507 | "cell_type": "code", 508 | "metadata": { 509 | "id": "_6JMlvE8ZBYe" 510 | }, 511 | "source": [ 512 | "" 513 | ], 514 | "execution_count": null, 515 | "outputs": [] 516 | }, 517 | { 518 | "cell_type": "code", 519 | "metadata": { 520 | "id": "zMcMXZtcZBcg" 521 | }, 522 | "source": [ 523 | "" 524 | ], 525 | "execution_count": null, 526 | "outputs": [] 527 | }, 528 | { 529 | "cell_type": "code", 530 | "metadata": { 531 | "id": "dLPwf3_BZBgB" 532 | }, 533 | "source": [ 534 | "" 535 | ], 536 | "execution_count": null, 537 | "outputs": [] 538 | }, 539 | { 540 | "cell_type": "code", 541 | "metadata": { 542 | "id": "u_MCOXVSZBjU" 543 | }, 544 | "source": [ 545 | "" 546 | ], 547 | "execution_count": null, 548 | "outputs": [] 549 | }, 550 | { 551 | "cell_type": "code", 552 | "metadata": { 553 | "id": "EcTHGdHLZBmd" 554 | }, 555 | "source": [ 556 | "" 557 | ], 558 | "execution_count": null, 559 | "outputs": [] 560 | }, 561 | { 562 | "cell_type": "code", 563 | "metadata": { 564 | "id": "Pv2vPYhCZBpj" 565 | }, 566 | "source": [ 567 | "" 568 | ], 569 | "execution_count": null, 570 | "outputs": [] 571 | }, 572 | { 573 | "cell_type": "code", 574 | "metadata": { 575 | "id": "zc6phznyZBsr" 576 | }, 577 | "source": [ 578 | "" 579 | ], 580 | "execution_count": null, 581 | "outputs": [] 582 | }, 583 | { 584 | "cell_type": "code", 585 | "metadata": { 586 | "id": "Uykm-FiSZBvo" 587 | }, 588 | "source": [ 589 | "" 590 | ], 591 | "execution_count": null, 592 | "outputs": [] 593 | }, 594 | { 595 | "cell_type": "code", 596 | "metadata": { 597 | "id": "ptgc6FYlZBzO" 598 | }, 599 | "source": [ 600 | "" 601 | ], 602 | "execution_count": null, 603 | "outputs": [] 604 | } 605 | ] 606 | } -------------------------------------------------------------------------------- /Code-and-Data-Files/Mall_Customers.csv: -------------------------------------------------------------------------------- 1 | CustomerID,Gender,Age,Annual Income (k$),Spending Score (1-100) 2 | 1,Male,19,15,39 3 | 2,Male,21,15,81 4 | 3,Female,20,16,6 5 | 4,Female,23,16,77 6 | 5,Female,31,17,40 7 | 6,Female,22,17,76 8 | 7,Female,35,18,6 9 | 8,Female,23,18,94 10 | 9,Male,64,19,3 11 | 10,Female,30,19,72 12 | 11,Male,67,19,14 13 | 12,Female,35,19,99 14 | 13,Female,58,20,15 15 | 14,Female,24,20,77 16 | 15,Male,37,20,13 17 | 16,Male,22,20,79 18 | 17,Female,35,21,35 19 | 18,Male,20,21,66 20 | 19,Male,52,23,29 21 | 20,Female,35,23,98 22 | 21,Male,35,24,35 23 | 22,Male,25,24,73 24 | 23,Female,46,25,5 25 | 24,Male,31,25,73 26 | 25,Female,54,28,14 27 | 26,Male,29,28,82 28 | 27,Female,45,28,32 29 | 28,Male,35,28,61 30 | 29,Female,40,29,31 31 | 30,Female,23,29,87 32 | 31,Male,60,30,4 33 | 32,Female,21,30,73 34 | 33,Male,53,33,4 35 | 34,Male,18,33,92 36 | 35,Female,49,33,14 37 | 36,Female,21,33,81 38 | 37,Female,42,34,17 39 | 38,Female,30,34,73 40 | 39,Female,36,37,26 41 | 40,Female,20,37,75 42 | 41,Female,65,38,35 43 | 42,Male,24,38,92 44 | 43,Male,48,39,36 45 | 44,Female,31,39,61 46 | 45,Female,49,39,28 47 | 46,Female,24,39,65 48 | 47,Female,50,40,55 49 | 48,Female,27,40,47 50 | 49,Female,29,40,42 51 | 50,Female,31,40,42 52 | 51,Female,49,42,52 53 | 52,Male,33,42,60 54 | 53,Female,31,43,54 55 | 54,Male,59,43,60 56 | 55,Female,50,43,45 57 | 56,Male,47,43,41 58 | 57,Female,51,44,50 59 | 58,Male,69,44,46 60 | 59,Female,27,46,51 61 | 60,Male,53,46,46 62 | 61,Male,70,46,56 63 | 62,Male,19,46,55 64 | 63,Female,67,47,52 65 | 64,Female,54,47,59 66 | 65,Male,63,48,51 67 | 66,Male,18,48,59 68 | 67,Female,43,48,50 69 | 68,Female,68,48,48 70 | 69,Male,19,48,59 71 | 70,Female,32,48,47 72 | 71,Male,70,49,55 73 | 72,Female,47,49,42 74 | 73,Female,60,50,49 75 | 74,Female,60,50,56 76 | 75,Male,59,54,47 77 | 76,Male,26,54,54 78 | 77,Female,45,54,53 79 | 78,Male,40,54,48 80 | 79,Female,23,54,52 81 | 80,Female,49,54,42 82 | 81,Male,57,54,51 83 | 82,Male,38,54,55 84 | 83,Male,67,54,41 85 | 84,Female,46,54,44 86 | 85,Female,21,54,57 87 | 86,Male,48,54,46 88 | 87,Female,55,57,58 89 | 88,Female,22,57,55 90 | 89,Female,34,58,60 91 | 90,Female,50,58,46 92 | 91,Female,68,59,55 93 | 92,Male,18,59,41 94 | 93,Male,48,60,49 95 | 94,Female,40,60,40 96 | 95,Female,32,60,42 97 | 96,Male,24,60,52 98 | 97,Female,47,60,47 99 | 98,Female,27,60,50 100 | 99,Male,48,61,42 101 | 100,Male,20,61,49 102 | 101,Female,23,62,41 103 | 102,Female,49,62,48 104 | 103,Male,67,62,59 105 | 104,Male,26,62,55 106 | 105,Male,49,62,56 107 | 106,Female,21,62,42 108 | 107,Female,66,63,50 109 | 108,Male,54,63,46 110 | 109,Male,68,63,43 111 | 110,Male,66,63,48 112 | 111,Male,65,63,52 113 | 112,Female,19,63,54 114 | 113,Female,38,64,42 115 | 114,Male,19,64,46 116 | 115,Female,18,65,48 117 | 116,Female,19,65,50 118 | 117,Female,63,65,43 119 | 118,Female,49,65,59 120 | 119,Female,51,67,43 121 | 120,Female,50,67,57 122 | 121,Male,27,67,56 123 | 122,Female,38,67,40 124 | 123,Female,40,69,58 125 | 124,Male,39,69,91 126 | 125,Female,23,70,29 127 | 126,Female,31,70,77 128 | 127,Male,43,71,35 129 | 128,Male,40,71,95 130 | 129,Male,59,71,11 131 | 130,Male,38,71,75 132 | 131,Male,47,71,9 133 | 132,Male,39,71,75 134 | 133,Female,25,72,34 135 | 134,Female,31,72,71 136 | 135,Male,20,73,5 137 | 136,Female,29,73,88 138 | 137,Female,44,73,7 139 | 138,Male,32,73,73 140 | 139,Male,19,74,10 141 | 140,Female,35,74,72 142 | 141,Female,57,75,5 143 | 142,Male,32,75,93 144 | 143,Female,28,76,40 145 | 144,Female,32,76,87 146 | 145,Male,25,77,12 147 | 146,Male,28,77,97 148 | 147,Male,48,77,36 149 | 148,Female,32,77,74 150 | 149,Female,34,78,22 151 | 150,Male,34,78,90 152 | 151,Male,43,78,17 153 | 152,Male,39,78,88 154 | 153,Female,44,78,20 155 | 154,Female,38,78,76 156 | 155,Female,47,78,16 157 | 156,Female,27,78,89 158 | 157,Male,37,78,1 159 | 158,Female,30,78,78 160 | 159,Male,34,78,1 161 | 160,Female,30,78,73 162 | 161,Female,56,79,35 163 | 162,Female,29,79,83 164 | 163,Male,19,81,5 165 | 164,Female,31,81,93 166 | 165,Male,50,85,26 167 | 166,Female,36,85,75 168 | 167,Male,42,86,20 169 | 168,Female,33,86,95 170 | 169,Female,36,87,27 171 | 170,Male,32,87,63 172 | 171,Male,40,87,13 173 | 172,Male,28,87,75 174 | 173,Male,36,87,10 175 | 174,Male,36,87,92 176 | 175,Female,52,88,13 177 | 176,Female,30,88,86 178 | 177,Male,58,88,15 179 | 178,Male,27,88,69 180 | 179,Male,59,93,14 181 | 180,Male,35,93,90 182 | 181,Female,37,97,32 183 | 182,Female,32,97,86 184 | 183,Male,46,98,15 185 | 184,Female,29,98,88 186 | 185,Female,41,99,39 187 | 186,Male,30,99,97 188 | 187,Female,54,101,24 189 | 188,Male,28,101,68 190 | 189,Female,41,103,17 191 | 190,Female,36,103,85 192 | 191,Female,34,103,23 193 | 192,Female,32,103,69 194 | 193,Male,33,113,8 195 | 194,Female,38,113,91 196 | 195,Female,47,120,16 197 | 196,Female,35,120,79 198 | 197,Female,45,126,28 199 | 198,Male,32,126,74 200 | 199,Male,32,137,18 201 | 200,Male,30,137,83 202 | -------------------------------------------------------------------------------- /Code-and-Data-Files/PCA-Housing.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## PCA Demonstration in Python" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 11, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "# Importing the required libraries" 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 12, 22 | "metadata": {}, 23 | "outputs": [], 24 | "source": [ 25 | "import numpy as np, pandas as pd" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 13, 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "data = pd.read_csv(\"newhousing.csv\")" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 14, 40 | "metadata": {}, 41 | "outputs": [ 42 | { 43 | "data": { 44 | "text/html": [ 45 | "
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priceareabedroomsbathroomsstoriesmainroadguestroombasementhotwaterheatingairconditioningparkingprefareasemi-furnishedunfurnishedareaperbedroombbratio
0525000055003211010000101833.3333330.666667
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4357000045002110000000002250.0000000.500000
...................................................
540440300048803111000021011626.6666670.333333
541266000020002121000000101000.0000000.500000
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5435110000114102121000001005705.0000000.500000
544441000039683120000000101322.6666670.333333
\n", 293 | "

545 rows × 16 columns

\n", 294 | "
" 295 | ], 296 | "text/plain": [ 297 | " price area bedrooms bathrooms stories mainroad guestroom \\\n", 298 | "0 5250000 5500 3 2 1 1 0 \n", 299 | "1 4480000 4040 3 1 2 1 0 \n", 300 | "2 3570000 3640 2 1 1 1 0 \n", 301 | "3 2870000 3040 2 1 1 0 0 \n", 302 | "4 3570000 4500 2 1 1 0 0 \n", 303 | ".. ... ... ... ... ... ... ... \n", 304 | "540 4403000 4880 3 1 1 1 0 \n", 305 | "541 2660000 2000 2 1 2 1 0 \n", 306 | "542 4480000 8250 3 1 1 1 0 \n", 307 | "543 5110000 11410 2 1 2 1 0 \n", 308 | "544 4410000 3968 3 1 2 0 0 \n", 309 | "\n", 310 | " basement hotwaterheating airconditioning parking prefarea \\\n", 311 | "0 1 0 0 0 0 \n", 312 | "1 0 0 0 1 0 \n", 313 | "2 0 0 0 0 0 \n", 314 | "3 0 0 0 0 0 \n", 315 | "4 0 0 0 0 0 \n", 316 | ".. ... ... ... ... ... \n", 317 | "540 0 0 0 2 1 \n", 318 | "541 0 0 0 0 0 \n", 319 | "542 0 0 0 0 0 \n", 320 | "543 0 0 0 0 1 \n", 321 | "544 0 0 0 0 0 \n", 322 | "\n", 323 | " semi-furnished unfurnished areaperbedroom bbratio \n", 324 | "0 1 0 1833.333333 0.666667 \n", 325 | "1 1 0 1346.666667 0.333333 \n", 326 | "2 0 1 1820.000000 0.500000 \n", 327 | "3 0 1 1520.000000 0.500000 \n", 328 | "4 0 0 2250.000000 0.500000 \n", 329 | ".. ... ... ... ... \n", 330 | "540 0 1 1626.666667 0.333333 \n", 331 | "541 1 0 1000.000000 0.500000 \n", 332 | "542 0 0 2750.000000 0.333333 \n", 333 | "543 0 0 5705.000000 0.500000 \n", 334 | "544 1 0 1322.666667 0.333333 \n", 335 | "\n", 336 | "[545 rows x 16 columns]" 337 | ] 338 | }, 339 | "execution_count": 14, 340 | "metadata": {}, 341 | "output_type": "execute_result" 342 | } 343 | ], 344 | "source": [ 345 | "data" 346 | ] 347 | }, 348 | { 349 | "cell_type": "code", 350 | "execution_count": 15, 351 | "metadata": {}, 352 | "outputs": [ 353 | { 354 | "data": { 355 | "text/html": [ 356 | "
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priceareabedroomsbathroomsstoriesmainroadguestroombasementhotwaterheatingairconditioningparkingprefareasemi-furnishedunfurnishedareaperbedroombbratio
count5.450000e+02545.000000545.000000545.000000545.000000545.000000545.000000545.000000545.000000545.000000545.000000545.000000545.000000545.000000545.000000545.000000
mean4.766729e+065150.5412842.9651381.2862391.8055050.8587160.1779820.3504590.0458720.3155960.6935780.2348620.4165140.3266061819.8525990.446361
std1.870440e+062170.1410230.7380640.5024700.8674920.3486350.3828490.4775520.2093990.4651800.8615860.4243020.4934340.469402839.0918250.159492
min1.750000e+061650.0000001.0000001.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000381.0000000.166667
25%3.430000e+063600.0000002.0000001.0000001.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001237.5000000.333333
50%4.340000e+064600.0000003.0000001.0000002.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000001666.6666670.400000
75%5.740000e+066360.0000003.0000002.0000002.0000001.0000000.0000001.0000000.0000001.0000001.0000000.0000001.0000001.0000002183.3333330.500000
max1.330000e+0716200.0000006.0000004.0000004.0000001.0000001.0000001.0000001.0000001.0000003.0000001.0000001.0000001.0000006600.0000001.000000
\n", 547 | "
" 548 | ], 549 | "text/plain": [ 550 | " price area bedrooms bathrooms stories \\\n", 551 | "count 5.450000e+02 545.000000 545.000000 545.000000 545.000000 \n", 552 | "mean 4.766729e+06 5150.541284 2.965138 1.286239 1.805505 \n", 553 | "std 1.870440e+06 2170.141023 0.738064 0.502470 0.867492 \n", 554 | "min 1.750000e+06 1650.000000 1.000000 1.000000 1.000000 \n", 555 | "25% 3.430000e+06 3600.000000 2.000000 1.000000 1.000000 \n", 556 | "50% 4.340000e+06 4600.000000 3.000000 1.000000 2.000000 \n", 557 | "75% 5.740000e+06 6360.000000 3.000000 2.000000 2.000000 \n", 558 | "max 1.330000e+07 16200.000000 6.000000 4.000000 4.000000 \n", 559 | "\n", 560 | " mainroad guestroom basement hotwaterheating airconditioning \\\n", 561 | "count 545.000000 545.000000 545.000000 545.000000 545.000000 \n", 562 | "mean 0.858716 0.177982 0.350459 0.045872 0.315596 \n", 563 | "std 0.348635 0.382849 0.477552 0.209399 0.465180 \n", 564 | "min 0.000000 0.000000 0.000000 0.000000 0.000000 \n", 565 | "25% 1.000000 0.000000 0.000000 0.000000 0.000000 \n", 566 | "50% 1.000000 0.000000 0.000000 0.000000 0.000000 \n", 567 | "75% 1.000000 0.000000 1.000000 0.000000 1.000000 \n", 568 | "max 1.000000 1.000000 1.000000 1.000000 1.000000 \n", 569 | "\n", 570 | " parking prefarea semi-furnished unfurnished areaperbedroom \\\n", 571 | "count 545.000000 545.000000 545.000000 545.000000 545.000000 \n", 572 | "mean 0.693578 0.234862 0.416514 0.326606 1819.852599 \n", 573 | "std 0.861586 0.424302 0.493434 0.469402 839.091825 \n", 574 | "min 0.000000 0.000000 0.000000 0.000000 381.000000 \n", 575 | "25% 0.000000 0.000000 0.000000 0.000000 1237.500000 \n", 576 | "50% 0.000000 0.000000 0.000000 0.000000 1666.666667 \n", 577 | "75% 1.000000 0.000000 1.000000 1.000000 2183.333333 \n", 578 | "max 3.000000 1.000000 1.000000 1.000000 6600.000000 \n", 579 | "\n", 580 | " bbratio \n", 581 | "count 545.000000 \n", 582 | "mean 0.446361 \n", 583 | "std 0.159492 \n", 584 | "min 0.166667 \n", 585 | "25% 0.333333 \n", 586 | "50% 0.400000 \n", 587 | "75% 0.500000 \n", 588 | "max 1.000000 " 589 | ] 590 | }, 591 | "execution_count": 15, 592 | "metadata": {}, 593 | "output_type": "execute_result" 594 | } 595 | ], 596 | "source": [ 597 | "data.describe()" 598 | ] 599 | }, 600 | { 601 | "cell_type": "markdown", 602 | "metadata": {}, 603 | "source": [ 604 | "Scaling the data" 605 | ] 606 | }, 607 | { 608 | "cell_type": "code", 609 | "execution_count": 16, 610 | "metadata": {}, 611 | "outputs": [], 612 | "source": [ 613 | "from sklearn.preprocessing import StandardScaler" 614 | ] 615 | }, 616 | { 617 | "cell_type": "code", 618 | "execution_count": 17, 619 | "metadata": {}, 620 | "outputs": [], 621 | "source": [ 622 | "scaler = StandardScaler()" 623 | ] 624 | }, 625 | { 626 | "cell_type": "code", 627 | "execution_count": 18, 628 | "metadata": {}, 629 | "outputs": [], 630 | "source": [ 631 | "X = data.copy()" 632 | ] 633 | }, 634 | { 635 | "cell_type": "code", 636 | "execution_count": 19, 637 | "metadata": {}, 638 | "outputs": [], 639 | "source": [ 640 | "x = scaler.fit_transform(X)" 641 | ] 642 | }, 643 | { 644 | "cell_type": "code", 645 | "execution_count": 21, 646 | "metadata": {}, 647 | "outputs": [ 648 | { 649 | "data": { 650 | "text/html": [ 651 | "
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priceareabedroomsbathroomsstoriesmainroadguestroombasementhotwaterheatingairconditioningparkingprefareasemi-furnishedunfurnishedareaperbedroombbratio
00.2586100.1611780.0472781.421812-0.9293970.405623-0.4653151.361397-0.219265-0.679063-0.805741-0.5540351.183588-0.6964290.0160811.382568
1-0.153436-0.5122070.047278-0.5701870.2244100.405623-0.465315-0.734539-0.219265-0.6790630.355976-0.5540351.183588-0.696429-0.564444-0.709324
2-0.640400-0.696696-1.308863-0.570187-0.9293970.405623-0.465315-0.734539-0.219265-0.679063-0.805741-0.554035-0.8448881.4358960.0001760.336622
3-1.014987-0.973430-1.308863-0.570187-0.929397-2.465344-0.465315-0.734539-0.219265-0.679063-0.805741-0.554035-0.8448881.435896-0.3576820.336622
4-0.640400-0.300045-1.308863-0.570187-0.929397-2.465344-0.465315-0.734539-0.219265-0.679063-0.805741-0.554035-0.844888-0.6964290.5131050.336622
...................................................
540-0.194641-0.1247800.047278-0.570187-0.9293970.405623-0.465315-0.734539-0.219265-0.6790631.5176921.804941-0.8448881.435896-0.230444-0.709324
541-1.127363-1.453102-1.308863-0.5701870.2244100.405623-0.465315-0.734539-0.219265-0.679063-0.805741-0.5540351.183588-0.696429-0.9779690.336622
542-0.1534361.4295410.047278-0.570187-0.9293970.405623-0.465315-0.734539-0.219265-0.679063-0.805741-0.554035-0.844888-0.6964291.109535-0.709324
5430.1836932.887006-1.308863-0.5701870.2244100.405623-0.465315-0.734539-0.219265-0.679063-0.8057411.804941-0.844888-0.6964294.6344350.336622
544-0.190895-0.5454150.047278-0.5701870.224410-2.465344-0.465315-0.734539-0.219265-0.679063-0.805741-0.5540351.183588-0.696429-0.593073-0.709324
\n", 899 | "

545 rows × 16 columns

\n", 900 | "
" 901 | ], 902 | "text/plain": [ 903 | " price area bedrooms bathrooms stories mainroad guestroom \\\n", 904 | "0 0.258610 0.161178 0.047278 1.421812 -0.929397 0.405623 -0.465315 \n", 905 | "1 -0.153436 -0.512207 0.047278 -0.570187 0.224410 0.405623 -0.465315 \n", 906 | "2 -0.640400 -0.696696 -1.308863 -0.570187 -0.929397 0.405623 -0.465315 \n", 907 | "3 -1.014987 -0.973430 -1.308863 -0.570187 -0.929397 -2.465344 -0.465315 \n", 908 | "4 -0.640400 -0.300045 -1.308863 -0.570187 -0.929397 -2.465344 -0.465315 \n", 909 | ".. ... ... ... ... ... ... ... \n", 910 | "540 -0.194641 -0.124780 0.047278 -0.570187 -0.929397 0.405623 -0.465315 \n", 911 | "541 -1.127363 -1.453102 -1.308863 -0.570187 0.224410 0.405623 -0.465315 \n", 912 | "542 -0.153436 1.429541 0.047278 -0.570187 -0.929397 0.405623 -0.465315 \n", 913 | "543 0.183693 2.887006 -1.308863 -0.570187 0.224410 0.405623 -0.465315 \n", 914 | "544 -0.190895 -0.545415 0.047278 -0.570187 0.224410 -2.465344 -0.465315 \n", 915 | "\n", 916 | " basement hotwaterheating airconditioning parking prefarea \\\n", 917 | "0 1.361397 -0.219265 -0.679063 -0.805741 -0.554035 \n", 918 | "1 -0.734539 -0.219265 -0.679063 0.355976 -0.554035 \n", 919 | "2 -0.734539 -0.219265 -0.679063 -0.805741 -0.554035 \n", 920 | "3 -0.734539 -0.219265 -0.679063 -0.805741 -0.554035 \n", 921 | "4 -0.734539 -0.219265 -0.679063 -0.805741 -0.554035 \n", 922 | ".. ... ... ... ... ... \n", 923 | "540 -0.734539 -0.219265 -0.679063 1.517692 1.804941 \n", 924 | "541 -0.734539 -0.219265 -0.679063 -0.805741 -0.554035 \n", 925 | "542 -0.734539 -0.219265 -0.679063 -0.805741 -0.554035 \n", 926 | "543 -0.734539 -0.219265 -0.679063 -0.805741 1.804941 \n", 927 | "544 -0.734539 -0.219265 -0.679063 -0.805741 -0.554035 \n", 928 | "\n", 929 | " semi-furnished unfurnished areaperbedroom bbratio \n", 930 | "0 1.183588 -0.696429 0.016081 1.382568 \n", 931 | "1 1.183588 -0.696429 -0.564444 -0.709324 \n", 932 | "2 -0.844888 1.435896 0.000176 0.336622 \n", 933 | "3 -0.844888 1.435896 -0.357682 0.336622 \n", 934 | "4 -0.844888 -0.696429 0.513105 0.336622 \n", 935 | ".. ... ... ... ... \n", 936 | "540 -0.844888 1.435896 -0.230444 -0.709324 \n", 937 | "541 1.183588 -0.696429 -0.977969 0.336622 \n", 938 | "542 -0.844888 -0.696429 1.109535 -0.709324 \n", 939 | "543 -0.844888 -0.696429 4.634435 0.336622 \n", 940 | "544 1.183588 -0.696429 -0.593073 -0.709324 \n", 941 | "\n", 942 | "[545 rows x 16 columns]" 943 | ] 944 | }, 945 | "execution_count": 21, 946 | "metadata": {}, 947 | "output_type": "execute_result" 948 | } 949 | ], 950 | "source": [ 951 | "dfx = pd.DataFrame(x, columns=X.columns)\n", 952 | "dfx" 953 | ] 954 | }, 955 | { 956 | "cell_type": "markdown", 957 | "metadata": {}, 958 | "source": [ 959 | "### Applying PCA on the data" 960 | ] 961 | }, 962 | { 963 | "cell_type": "code", 964 | "execution_count": 22, 965 | "metadata": {}, 966 | "outputs": [], 967 | "source": [ 968 | "from sklearn.decomposition import PCA" 969 | ] 970 | }, 971 | { 972 | "cell_type": "code", 973 | "execution_count": 23, 974 | "metadata": {}, 975 | "outputs": [], 976 | "source": [ 977 | "pca = PCA(random_state=100)" 978 | ] 979 | }, 980 | { 981 | "cell_type": "code", 982 | "execution_count": 24, 983 | "metadata": {}, 984 | "outputs": [ 985 | { 986 | "data": { 987 | "text/plain": [ 988 | "PCA(random_state=100)" 989 | ] 990 | }, 991 | "execution_count": 24, 992 | "metadata": {}, 993 | "output_type": "execute_result" 994 | } 995 | ], 996 | "source": [ 997 | "pca.fit(x)" 998 | ] 999 | }, 1000 | { 1001 | "cell_type": "code", 1002 | "execution_count": 25, 1003 | "metadata": {}, 1004 | "outputs": [ 1005 | { 1006 | "data": { 1007 | "text/plain": [ 1008 | "array([[ 4.82691560e-01, 3.79259187e-01, 1.93444345e-01,\n", 1009 | " 3.31592969e-01, 2.20511391e-01, 2.25130078e-01,\n", 1010 | " 1.87658075e-01, 1.32122283e-01, 2.54880647e-02,\n", 1011 | " 2.67834986e-01, 2.71095618e-01, 2.11749047e-01,\n", 1012 | " 7.33740295e-02, -2.09608241e-01, 2.22158739e-01,\n", 1013 | " 1.85543681e-01],\n", 1014 | " [-8.68229361e-02, 3.35184987e-01, -4.89972866e-01,\n", 1015 | " -2.53147243e-01, -3.82621294e-01, 1.76917174e-01,\n", 1016 | " -1.28866383e-02, -5.12208954e-03, -6.59030403e-02,\n", 1017 | " -8.94738334e-02, 9.53693725e-02, 8.53366832e-02,\n", 1018 | " -9.18180644e-02, 8.21599552e-02, 5.88817690e-01,\n", 1019 | " 8.63208692e-02],\n", 1020 | " [ 2.17427216e-02, -5.38681701e-02, -1.52434343e-01,\n", 1021 | " 4.10985855e-01, 1.31641225e-01, -1.15103674e-01,\n", 1022 | " -1.06329298e-01, -2.05081994e-01, -3.46053956e-02,\n", 1023 | " 7.56155215e-02, -7.33137511e-02, -1.67949504e-01,\n", 1024 | " -4.50573699e-01, 4.33716661e-01, 3.41474167e-02,\n", 1025 | " 5.36420574e-01],\n", 1026 | " [-6.57974393e-02, -1.73106669e-01, -2.48021188e-01,\n", 1027 | " 2.89342424e-01, -2.10220065e-01, -1.30250694e-01,\n", 1028 | " 3.87384866e-02, 1.37260547e-01, 1.93311132e-01,\n", 1029 | " -2.36840295e-01, -4.36824452e-02, -1.65679742e-01,\n", 1030 | " 4.70738181e-01, -3.99694175e-01, -1.36766848e-02,\n", 1031 | " 4.84370736e-01],\n", 1032 | " [-8.50283223e-03, -1.51710020e-01, -3.08001831e-02,\n", 1033 | " 3.50375465e-02, -2.30775827e-01, -7.17563428e-02,\n", 1034 | " 5.27924659e-01, 6.25445376e-01, -1.35306027e-01,\n", 1035 | " 3.90419133e-03, -1.80387559e-01, 3.00767754e-01,\n", 1036 | " -2.20755386e-01, 1.81024249e-01, -1.35970157e-01,\n", 1037 | " 7.16113953e-02],\n", 1038 | " [ 4.69380044e-02, 1.00462082e-01, 1.94428858e-01,\n", 1039 | " 4.46273861e-02, -1.33463140e-01, -2.97178131e-02,\n", 1040 | " 2.09151454e-04, 1.28341581e-01, 7.69345992e-01,\n", 1041 | " -3.83823328e-01, 2.78303311e-01, -4.17817800e-02,\n", 1042 | " -2.05638288e-01, 2.03280611e-01, -2.45870009e-03,\n", 1043 | " -1.03511526e-01],\n", 1044 | " [-1.11104302e-02, 2.20342257e-01, 1.75426844e-01,\n", 1045 | " 5.17426312e-03, -2.29403673e-01, -6.66501476e-01,\n", 1046 | " 9.33656143e-02, 1.23154277e-01, -1.74550356e-01,\n", 1047 | " 2.44784647e-01, 2.83537212e-01, -4.49588602e-01,\n", 1048 | " 1.04152190e-02, 1.62521981e-03, 1.28563988e-01,\n", 1049 | " -1.01191363e-01],\n", 1050 | " [ 4.05779082e-02, 1.30459933e-02, -2.09558064e-01,\n", 1051 | " -2.08536780e-01, 2.61163901e-01, 4.28141734e-02,\n", 1052 | " 4.91307935e-01, -1.03437735e-01, 4.11383678e-01,\n", 1053 | " 3.26956862e-01, -3.86120642e-01, -3.76618394e-01,\n", 1054 | " 8.18717045e-03, -3.59961610e-02, 1.29111323e-01,\n", 1055 | " -5.96506748e-02],\n", 1056 | " [ 5.50953696e-02, 2.88097624e-01, 1.21136360e-01,\n", 1057 | " 6.42070971e-02, 8.60641064e-02, -5.10827292e-01,\n", 1058 | " -1.24661639e-01, -1.10750256e-01, 1.15079051e-01,\n", 1059 | " -1.45343273e-01, -5.61363810e-01, 4.38064273e-01,\n", 1060 | " 1.00134161e-01, 3.90802736e-03, 2.15989638e-01,\n", 1061 | " -3.03126705e-02],\n", 1062 | " [-1.00869804e-01, 2.78417358e-01, 2.94743998e-01,\n", 1063 | " 1.51007675e-01, 1.02874361e-01, 2.33198603e-01,\n", 1064 | " 3.02389751e-01, -4.42872756e-02, -3.30118352e-01,\n", 1065 | " -5.86071554e-01, -2.00495889e-01, -3.70511394e-01,\n", 1066 | " 2.02763847e-02, 4.31561923e-02, 1.02802465e-01,\n", 1067 | " -3.85855629e-02],\n", 1068 | " [-3.33026090e-02, -1.19501494e-01, -1.73348961e-01,\n", 1069 | " -8.09963374e-02, 1.89235588e-01, -2.78675287e-01,\n", 1070 | " 5.19973589e-01, -4.78671858e-01, -7.20111491e-02,\n", 1071 | " -2.39033975e-01, 4.17384861e-01, 3.05344313e-01,\n", 1072 | " 3.07868047e-02, -5.42703882e-03, -5.73484784e-02,\n", 1073 | " 3.15675333e-02],\n", 1074 | " [ 6.33211594e-02, -2.75387474e-02, -2.89932547e-01,\n", 1075 | " -2.10262329e-01, 6.66690227e-01, -1.83455445e-01,\n", 1076 | " -2.07080712e-01, 4.81508686e-01, -7.53256653e-02,\n", 1077 | " -2.51613412e-01, 1.48081548e-01, -9.21272150e-02,\n", 1078 | " -5.34174729e-03, 1.82927484e-02, 1.19418669e-01,\n", 1079 | " 8.56225394e-03],\n", 1080 | " [ 3.46883351e-02, -4.17627417e-02, -5.25828008e-03,\n", 1081 | " -3.14981461e-02, -3.33062196e-02, -7.31559738e-02,\n", 1082 | " -3.05316563e-02, -4.35270603e-02, -1.88659861e-02,\n", 1083 | " -1.08534838e-01, -6.73857778e-02, -2.70166828e-02,\n", 1084 | " -6.76575615e-01, -7.15215405e-01, 1.79302909e-03,\n", 1085 | " -6.89847435e-03],\n", 1086 | " [ 8.53259699e-01, -1.50538284e-01, -1.63812402e-01,\n", 1087 | " -1.45508009e-01, -2.27963486e-01, -6.04892976e-02,\n", 1088 | " -3.86963182e-02, -1.03607867e-01, -1.13296915e-01,\n", 1089 | " -2.17732043e-01, -1.20324760e-01, -1.55466153e-01,\n", 1090 | " 2.96512182e-02, 1.08260885e-01, -1.20722871e-01,\n", 1091 | " -8.57665133e-02],\n", 1092 | " [ 5.51246332e-02, -5.12424309e-01, 5.04403322e-01,\n", 1093 | " -3.23982493e-01, 3.38353947e-03, 7.63941481e-03,\n", 1094 | " 1.11038918e-02, -1.36802481e-02, -6.96079056e-03,\n", 1095 | " -6.62619083e-03, 1.08285906e-02, 1.52544790e-02,\n", 1096 | " 1.81466070e-02, 2.33859901e-02, 5.29394361e-01,\n", 1097 | " 3.05021426e-01],\n", 1098 | " [-3.66712251e-04, 4.08137595e-01, 1.65442121e-01,\n", 1099 | " -5.67251865e-01, -1.36758456e-02, 1.49395847e-03,\n", 1100 | " -1.47902853e-02, -8.82464298e-03, 1.46164959e-02,\n", 1101 | " 3.14238838e-03, -7.17888215e-03, 3.31597858e-03,\n", 1102 | " -1.28405285e-03, -3.89805246e-03, -4.25780001e-01,\n", 1103 | " 5.49727574e-01]])" 1104 | ] 1105 | }, 1106 | "execution_count": 25, 1107 | "metadata": {}, 1108 | "output_type": "execute_result" 1109 | } 1110 | ], 1111 | "source": [ 1112 | "pca.components_" 1113 | ] 1114 | }, 1115 | { 1116 | "cell_type": "code", 1117 | "execution_count": 26, 1118 | "metadata": {}, 1119 | "outputs": [ 1120 | { 1121 | "data": { 1122 | "text/plain": [ 1123 | "array([0.20967771, 0.12745495, 0.10099763, 0.09591569, 0.08818216,\n", 1124 | " 0.06718996, 0.0559836 , 0.05203792, 0.04968734, 0.04585248,\n", 1125 | " 0.03694627, 0.02759915, 0.02364306, 0.01534462, 0.00231593,\n", 1126 | " 0.0011715 ])" 1127 | ] 1128 | }, 1129 | "execution_count": 26, 1130 | "metadata": {}, 1131 | "output_type": "execute_result" 1132 | } 1133 | ], 1134 | "source": [ 1135 | "pca.explained_variance_ratio_" 1136 | ] 1137 | }, 1138 | { 1139 | "cell_type": "code", 1140 | "execution_count": 27, 1141 | "metadata": {}, 1142 | "outputs": [], 1143 | "source": [ 1144 | "import matplotlib.pyplot as plt" 1145 | ] 1146 | }, 1147 | { 1148 | "cell_type": "code", 1149 | "execution_count": 28, 1150 | "metadata": {}, 1151 | "outputs": [ 1152 | { 1153 | "data": { 1154 | "text/plain": [ 1155 | "" 1156 | ] 1157 | }, 1158 | "execution_count": 28, 1159 | "metadata": {}, 1160 | "output_type": "execute_result" 1161 | }, 1162 | { 1163 | "data": { 1164 | "image/png": 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\n", 1165 | "text/plain": [ 1166 | "
" 1167 | ] 1168 | }, 1169 | "metadata": { 1170 | "needs_background": "light" 1171 | }, 1172 | "output_type": "display_data" 1173 | } 1174 | ], 1175 | "source": [ 1176 | "plt.bar(range(1,len(pca.explained_variance_ratio_)+1), pca.explained_variance_ratio_)" 1177 | ] 1178 | }, 1179 | { 1180 | "cell_type": "code", 1181 | "execution_count": 29, 1182 | "metadata": {}, 1183 | "outputs": [], 1184 | "source": [ 1185 | "var_cumu = np.cumsum(pca.explained_variance_ratio_)" 1186 | ] 1187 | }, 1188 | { 1189 | "cell_type": "markdown", 1190 | "metadata": {}, 1191 | "source": [ 1192 | "#### Making the scree plot" 1193 | ] 1194 | }, 1195 | { 1196 | "cell_type": "code", 1197 | "execution_count": 30, 1198 | "metadata": {}, 1199 | "outputs": [ 1200 | { 1201 | "data": { 1202 | "text/plain": [ 1203 | "[]" 1204 | ] 1205 | }, 1206 | "execution_count": 30, 1207 | "metadata": {}, 1208 | "output_type": "execute_result" 1209 | }, 1210 | { 1211 | "data": { 1212 | "image/png": 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\n", 1213 | "text/plain": [ 1214 | "
" 1215 | ] 1216 | }, 1217 | "metadata": { 1218 | "needs_background": "light" 1219 | }, 1220 | "output_type": "display_data" 1221 | } 1222 | ], 1223 | "source": [ 1224 | "plt.plot(range(1,len(var_cumu)+1), var_cumu)" 1225 | ] 1226 | }, 1227 | { 1228 | "cell_type": "markdown", 1229 | "metadata": {}, 1230 | "source": [ 1231 | "PCA with 2 components" 1232 | ] 1233 | }, 1234 | { 1235 | "cell_type": "code", 1236 | "execution_count": 31, 1237 | "metadata": {}, 1238 | "outputs": [], 1239 | "source": [ 1240 | "pc2 = PCA(n_components=6, random_state=100)" 1241 | ] 1242 | }, 1243 | { 1244 | "cell_type": "code", 1245 | "execution_count": 32, 1246 | "metadata": {}, 1247 | "outputs": [], 1248 | "source": [ 1249 | "newdata = pc2.fit_transform(x)" 1250 | ] 1251 | }, 1252 | { 1253 | "cell_type": "code", 1254 | "execution_count": 33, 1255 | "metadata": {}, 1256 | "outputs": [ 1257 | { 1258 | "data": { 1259 | "text/plain": [ 1260 | "(545, 6)" 1261 | ] 1262 | }, 1263 | "execution_count": 33, 1264 | "metadata": {}, 1265 | "output_type": "execute_result" 1266 | } 1267 | ], 1268 | "source": [ 1269 | "newdata.shape" 1270 | ] 1271 | }, 1272 | { 1273 | "cell_type": "markdown", 1274 | "metadata": {}, 1275 | "source": [ 1276 | "Making a dataframe out of it for convenience\n", 1277 | "\n" 1278 | ] 1279 | }, 1280 | { 1281 | "cell_type": "code", 1282 | "execution_count": 34, 1283 | "metadata": {}, 1284 | "outputs": [ 1285 | { 1286 | "data": { 1287 | "text/html": [ 1288 | "
\n", 1289 | "\n", 1302 | "\n", 1303 | " \n", 1304 | " \n", 1305 | " \n", 1306 | " \n", 1307 | " \n", 1308 | " \n", 1309 | " \n", 1310 | " \n", 1311 | " \n", 1312 | " \n", 1313 | " \n", 1314 | " \n", 1315 | " \n", 1316 | " \n", 1317 | " \n", 1318 | " \n", 1319 | " \n", 1320 | " \n", 1321 | " \n", 1322 | " \n", 1323 | " \n", 1324 | " \n", 1325 | " \n", 1326 | " \n", 1327 | " \n", 1328 | " \n", 1329 | " \n", 1330 | " \n", 1331 | " \n", 1332 | " \n", 1333 | " \n", 1334 | " \n", 1335 | " \n", 1336 | " \n", 1337 | " \n", 1338 | " \n", 1339 | " \n", 1340 | " \n", 1341 | " \n", 1342 | " \n", 1343 | " \n", 1344 | " \n", 1345 | " \n", 1346 | " \n", 1347 | " \n", 1348 | " \n", 1349 | " \n", 1350 | " \n", 1351 | " \n", 1352 | " \n", 1353 | " \n", 1354 | " \n", 1355 | " \n", 1356 | " \n", 1357 | " \n", 1358 | " \n", 1359 | " \n", 1360 | " \n", 1361 | "
PC1PC2PC3PC4PC5PC6
00.615199-0.0111240.1905232.4165400.528108-0.249667
1-0.724275-0.539129-1.2462450.397548-1.293617-0.307270
2-2.1372861.2206511.312260-0.2170790.088041-0.040716
3-3.1488920.4417811.6372610.2343120.3878780.000099
4-2.0722880.9725100.7140420.933466-0.221870-0.350270
\n", 1362 | "
" 1363 | ], 1364 | "text/plain": [ 1365 | " PC1 PC2 PC3 PC4 PC5 PC6\n", 1366 | "0 0.615199 -0.011124 0.190523 2.416540 0.528108 -0.249667\n", 1367 | "1 -0.724275 -0.539129 -1.246245 0.397548 -1.293617 -0.307270\n", 1368 | "2 -2.137286 1.220651 1.312260 -0.217079 0.088041 -0.040716\n", 1369 | "3 -3.148892 0.441781 1.637261 0.234312 0.387878 0.000099\n", 1370 | "4 -2.072288 0.972510 0.714042 0.933466 -0.221870 -0.350270" 1371 | ] 1372 | }, 1373 | "execution_count": 34, 1374 | "metadata": {}, 1375 | "output_type": "execute_result" 1376 | } 1377 | ], 1378 | "source": [ 1379 | "df = pd.DataFrame(newdata, columns=[\"PC1\", \"PC2\", \"PC3\", \"PC4\", \"PC5\", \"PC6\"])\n", 1380 | "df.head()" 1381 | ] 1382 | }, 1383 | { 1384 | "cell_type": "markdown", 1385 | "metadata": {}, 1386 | "source": [ 1387 | "## Next.....Model building..." 1388 | ] 1389 | }, 1390 | { 1391 | "cell_type": "code", 1392 | "execution_count": null, 1393 | "metadata": {}, 1394 | "outputs": [], 1395 | "source": [] 1396 | }, 1397 | { 1398 | "cell_type": "code", 1399 | "execution_count": null, 1400 | "metadata": {}, 1401 | "outputs": [], 1402 | "source": [] 1403 | }, 1404 | { 1405 | "cell_type": "code", 1406 | "execution_count": null, 1407 | "metadata": {}, 1408 | "outputs": [], 1409 | "source": [] 1410 | }, 1411 | { 1412 | "cell_type": "code", 1413 | "execution_count": null, 1414 | "metadata": {}, 1415 | "outputs": [], 1416 | "source": [] 1417 | }, 1418 | { 1419 | "cell_type": "code", 1420 | "execution_count": null, 1421 | "metadata": {}, 1422 | "outputs": [], 1423 | "source": [] 1424 | }, 1425 | { 1426 | "cell_type": "code", 1427 | "execution_count": null, 1428 | "metadata": {}, 1429 | "outputs": [], 1430 | "source": [] 1431 | }, 1432 | { 1433 | "cell_type": "code", 1434 | "execution_count": null, 1435 | "metadata": {}, 1436 | "outputs": [], 1437 | "source": [] 1438 | }, 1439 | { 1440 | "cell_type": "code", 1441 | "execution_count": null, 1442 | "metadata": {}, 1443 | "outputs": [], 1444 | "source": [] 1445 | }, 1446 | { 1447 | "cell_type": "code", 1448 | "execution_count": null, 1449 | "metadata": {}, 1450 | "outputs": [], 1451 | "source": [] 1452 | }, 1453 | { 1454 | "cell_type": "code", 1455 | "execution_count": null, 1456 | "metadata": {}, 1457 | "outputs": [], 1458 | "source": [] 1459 | }, 1460 | { 1461 | "cell_type": "code", 1462 | "execution_count": null, 1463 | "metadata": {}, 1464 | "outputs": [], 1465 | "source": [] 1466 | }, 1467 | { 1468 | "cell_type": "code", 1469 | "execution_count": null, 1470 | "metadata": {}, 1471 | "outputs": [], 1472 | "source": [] 1473 | }, 1474 | { 1475 | "cell_type": "code", 1476 | "execution_count": null, 1477 | "metadata": {}, 1478 | "outputs": [], 1479 | "source": [] 1480 | }, 1481 | { 1482 | "cell_type": "code", 1483 | "execution_count": null, 1484 | "metadata": {}, 1485 | "outputs": [], 1486 | "source": [] 1487 | }, 1488 | { 1489 | "cell_type": "code", 1490 | "execution_count": null, 1491 | "metadata": {}, 1492 | "outputs": [], 1493 | "source": [] 1494 | }, 1495 | { 1496 | "cell_type": "code", 1497 | "execution_count": null, 1498 | "metadata": {}, 1499 | "outputs": [], 1500 | "source": [] 1501 | }, 1502 | { 1503 | "cell_type": "code", 1504 | "execution_count": null, 1505 | "metadata": {}, 1506 | "outputs": [], 1507 | "source": [] 1508 | }, 1509 | { 1510 | "cell_type": "code", 1511 | "execution_count": null, 1512 | "metadata": {}, 1513 | "outputs": [], 1514 | "source": [] 1515 | }, 1516 | { 1517 | "cell_type": "code", 1518 | "execution_count": null, 1519 | "metadata": {}, 1520 | "outputs": [], 1521 | "source": [] 1522 | }, 1523 | { 1524 | "cell_type": "code", 1525 | "execution_count": null, 1526 | "metadata": {}, 1527 | "outputs": [], 1528 | "source": [] 1529 | }, 1530 | { 1531 | "cell_type": "code", 1532 | "execution_count": null, 1533 | "metadata": {}, 1534 | "outputs": [], 1535 | "source": [] 1536 | }, 1537 | { 1538 | "cell_type": "code", 1539 | "execution_count": null, 1540 | "metadata": {}, 1541 | "outputs": [], 1542 | "source": [] 1543 | } 1544 | ], 1545 | "metadata": { 1546 | "kernelspec": { 1547 | "display_name": "Python 3", 1548 | "language": "python", 1549 | "name": "python3" 1550 | }, 1551 | "language_info": { 1552 | "codemirror_mode": { 1553 | "name": "ipython", 1554 | "version": 3 1555 | }, 1556 | "file_extension": ".py", 1557 | "mimetype": "text/x-python", 1558 | "name": "python", 1559 | "nbconvert_exporter": "python", 1560 | "pygments_lexer": "ipython3", 1561 | "version": "3.8.5" 1562 | } 1563 | }, 1564 | "nbformat": 4, 1565 | "nbformat_minor": 2 1566 | } 1567 | -------------------------------------------------------------------------------- /Code-and-Data-Files/iris_csv.csv: -------------------------------------------------------------------------------- 1 | sepallength,sepalwidth,petallength,petalwidth,class 2 | 5.1,3.5,1.4,0.2,Iris-setosa 3 | 4.9,3.0,1.4,0.2,Iris-setosa 4 | 4.7,3.2,1.3,0.2,Iris-setosa 5 | 4.6,3.1,1.5,0.2,Iris-setosa 6 | 5.0,3.6,1.4,0.2,Iris-setosa 7 | 5.4,3.9,1.7,0.4,Iris-setosa 8 | 4.6,3.4,1.4,0.3,Iris-setosa 9 | 5.0,3.4,1.5,0.2,Iris-setosa 10 | 4.4,2.9,1.4,0.2,Iris-setosa 11 | 4.9,3.1,1.5,0.1,Iris-setosa 12 | 5.4,3.7,1.5,0.2,Iris-setosa 13 | 4.8,3.4,1.6,0.2,Iris-setosa 14 | 4.8,3.0,1.4,0.1,Iris-setosa 15 | 4.3,3.0,1.1,0.1,Iris-setosa 16 | 5.8,4.0,1.2,0.2,Iris-setosa 17 | 5.7,4.4,1.5,0.4,Iris-setosa 18 | 5.4,3.9,1.3,0.4,Iris-setosa 19 | 5.1,3.5,1.4,0.3,Iris-setosa 20 | 5.7,3.8,1.7,0.3,Iris-setosa 21 | 5.1,3.8,1.5,0.3,Iris-setosa 22 | 5.4,3.4,1.7,0.2,Iris-setosa 23 | 5.1,3.7,1.5,0.4,Iris-setosa 24 | 4.6,3.6,1.0,0.2,Iris-setosa 25 | 5.1,3.3,1.7,0.5,Iris-setosa 26 | 4.8,3.4,1.9,0.2,Iris-setosa 27 | 5.0,3.0,1.6,0.2,Iris-setosa 28 | 5.0,3.4,1.6,0.4,Iris-setosa 29 | 5.2,3.5,1.5,0.2,Iris-setosa 30 | 5.2,3.4,1.4,0.2,Iris-setosa 31 | 4.7,3.2,1.6,0.2,Iris-setosa 32 | 4.8,3.1,1.6,0.2,Iris-setosa 33 | 5.4,3.4,1.5,0.4,Iris-setosa 34 | 5.2,4.1,1.5,0.1,Iris-setosa 35 | 5.5,4.2,1.4,0.2,Iris-setosa 36 | 4.9,3.1,1.5,0.1,Iris-setosa 37 | 5.0,3.2,1.2,0.2,Iris-setosa 38 | 5.5,3.5,1.3,0.2,Iris-setosa 39 | 4.9,3.1,1.5,0.1,Iris-setosa 40 | 4.4,3.0,1.3,0.2,Iris-setosa 41 | 5.1,3.4,1.5,0.2,Iris-setosa 42 | 5.0,3.5,1.3,0.3,Iris-setosa 43 | 4.5,2.3,1.3,0.3,Iris-setosa 44 | 4.4,3.2,1.3,0.2,Iris-setosa 45 | 5.0,3.5,1.6,0.6,Iris-setosa 46 | 5.1,3.8,1.9,0.4,Iris-setosa 47 | 4.8,3.0,1.4,0.3,Iris-setosa 48 | 5.1,3.8,1.6,0.2,Iris-setosa 49 | 4.6,3.2,1.4,0.2,Iris-setosa 50 | 5.3,3.7,1.5,0.2,Iris-setosa 51 | 5.0,3.3,1.4,0.2,Iris-setosa 52 | 7.0,3.2,4.7,1.4,Iris-versicolor 53 | 6.4,3.2,4.5,1.5,Iris-versicolor 54 | 6.9,3.1,4.9,1.5,Iris-versicolor 55 | 5.5,2.3,4.0,1.3,Iris-versicolor 56 | 6.5,2.8,4.6,1.5,Iris-versicolor 57 | 5.7,2.8,4.5,1.3,Iris-versicolor 58 | 6.3,3.3,4.7,1.6,Iris-versicolor 59 | 4.9,2.4,3.3,1.0,Iris-versicolor 60 | 6.6,2.9,4.6,1.3,Iris-versicolor 61 | 5.2,2.7,3.9,1.4,Iris-versicolor 62 | 5.0,2.0,3.5,1.0,Iris-versicolor 63 | 5.9,3.0,4.2,1.5,Iris-versicolor 64 | 6.0,2.2,4.0,1.0,Iris-versicolor 65 | 6.1,2.9,4.7,1.4,Iris-versicolor 66 | 5.6,2.9,3.6,1.3,Iris-versicolor 67 | 6.7,3.1,4.4,1.4,Iris-versicolor 68 | 5.6,3.0,4.5,1.5,Iris-versicolor 69 | 5.8,2.7,4.1,1.0,Iris-versicolor 70 | 6.2,2.2,4.5,1.5,Iris-versicolor 71 | 5.6,2.5,3.9,1.1,Iris-versicolor 72 | 5.9,3.2,4.8,1.8,Iris-versicolor 73 | 6.1,2.8,4.0,1.3,Iris-versicolor 74 | 6.3,2.5,4.9,1.5,Iris-versicolor 75 | 6.1,2.8,4.7,1.2,Iris-versicolor 76 | 6.4,2.9,4.3,1.3,Iris-versicolor 77 | 6.6,3.0,4.4,1.4,Iris-versicolor 78 | 6.8,2.8,4.8,1.4,Iris-versicolor 79 | 6.7,3.0,5.0,1.7,Iris-versicolor 80 | 6.0,2.9,4.5,1.5,Iris-versicolor 81 | 5.7,2.6,3.5,1.0,Iris-versicolor 82 | 5.5,2.4,3.8,1.1,Iris-versicolor 83 | 5.5,2.4,3.7,1.0,Iris-versicolor 84 | 5.8,2.7,3.9,1.2,Iris-versicolor 85 | 6.0,2.7,5.1,1.6,Iris-versicolor 86 | 5.4,3.0,4.5,1.5,Iris-versicolor 87 | 6.0,3.4,4.5,1.6,Iris-versicolor 88 | 6.7,3.1,4.7,1.5,Iris-versicolor 89 | 6.3,2.3,4.4,1.3,Iris-versicolor 90 | 5.6,3.0,4.1,1.3,Iris-versicolor 91 | 5.5,2.5,4.0,1.3,Iris-versicolor 92 | 5.5,2.6,4.4,1.2,Iris-versicolor 93 | 6.1,3.0,4.6,1.4,Iris-versicolor 94 | 5.8,2.6,4.0,1.2,Iris-versicolor 95 | 5.0,2.3,3.3,1.0,Iris-versicolor 96 | 5.6,2.7,4.2,1.3,Iris-versicolor 97 | 5.7,3.0,4.2,1.2,Iris-versicolor 98 | 5.7,2.9,4.2,1.3,Iris-versicolor 99 | 6.2,2.9,4.3,1.3,Iris-versicolor 100 | 5.1,2.5,3.0,1.1,Iris-versicolor 101 | 5.7,2.8,4.1,1.3,Iris-versicolor 102 | 6.3,3.3,6.0,2.5,Iris-virginica 103 | 5.8,2.7,5.1,1.9,Iris-virginica 104 | 7.1,3.0,5.9,2.1,Iris-virginica 105 | 6.3,2.9,5.6,1.8,Iris-virginica 106 | 6.5,3.0,5.8,2.2,Iris-virginica 107 | 7.6,3.0,6.6,2.1,Iris-virginica 108 | 4.9,2.5,4.5,1.7,Iris-virginica 109 | 7.3,2.9,6.3,1.8,Iris-virginica 110 | 6.7,2.5,5.8,1.8,Iris-virginica 111 | 7.2,3.6,6.1,2.5,Iris-virginica 112 | 6.5,3.2,5.1,2.0,Iris-virginica 113 | 6.4,2.7,5.3,1.9,Iris-virginica 114 | 6.8,3.0,5.5,2.1,Iris-virginica 115 | 5.7,2.5,5.0,2.0,Iris-virginica 116 | 5.8,2.8,5.1,2.4,Iris-virginica 117 | 6.4,3.2,5.3,2.3,Iris-virginica 118 | 6.5,3.0,5.5,1.8,Iris-virginica 119 | 7.7,3.8,6.7,2.2,Iris-virginica 120 | 7.7,2.6,6.9,2.3,Iris-virginica 121 | 6.0,2.2,5.0,1.5,Iris-virginica 122 | 6.9,3.2,5.7,2.3,Iris-virginica 123 | 5.6,2.8,4.9,2.0,Iris-virginica 124 | 7.7,2.8,6.7,2.0,Iris-virginica 125 | 6.3,2.7,4.9,1.8,Iris-virginica 126 | 6.7,3.3,5.7,2.1,Iris-virginica 127 | 7.2,3.2,6.0,1.8,Iris-virginica 128 | 6.2,2.8,4.8,1.8,Iris-virginica 129 | 6.1,3.0,4.9,1.8,Iris-virginica 130 | 6.4,2.8,5.6,2.1,Iris-virginica 131 | 7.2,3.0,5.8,1.6,Iris-virginica 132 | 7.4,2.8,6.1,1.9,Iris-virginica 133 | 7.9,3.8,6.4,2.0,Iris-virginica 134 | 6.4,2.8,5.6,2.2,Iris-virginica 135 | 6.3,2.8,5.1,1.5,Iris-virginica 136 | 6.1,2.6,5.6,1.4,Iris-virginica 137 | 7.7,3.0,6.1,2.3,Iris-virginica 138 | 6.3,3.4,5.6,2.4,Iris-virginica 139 | 6.4,3.1,5.5,1.8,Iris-virginica 140 | 6.0,3.0,4.8,1.8,Iris-virginica 141 | 6.9,3.1,5.4,2.1,Iris-virginica 142 | 6.7,3.1,5.6,2.4,Iris-virginica 143 | 6.9,3.1,5.1,2.3,Iris-virginica 144 | 5.8,2.7,5.1,1.9,Iris-virginica 145 | 6.8,3.2,5.9,2.3,Iris-virginica 146 | 6.7,3.3,5.7,2.5,Iris-virginica 147 | 6.7,3.0,5.2,2.3,Iris-virginica 148 | 6.3,2.5,5.0,1.9,Iris-virginica 149 | 6.5,3.0,5.2,2.0,Iris-virginica 150 | 6.2,3.4,5.4,2.3,Iris-virginica 151 | 5.9,3.0,5.1,1.8,Iris-virginica 152 | -------------------------------------------------------------------------------- /Code-and-Data-Files/newhousing.csv: -------------------------------------------------------------------------------- 1 | price,area,bedrooms,bathrooms,stories,mainroad,guestroom,basement,hotwaterheating,airconditioning,parking,prefarea,semi-furnished,unfurnished,areaperbedroom,bbratio 2 | 5250000,5500,3,2,1,1,0,1,0,0,0,0,1,0,1833.333333333333,0.6666666666666666 3 | 4480000,4040,3,1,2,1,0,0,0,0,1,0,1,0,1346.6666666666667,0.3333333333333333 4 | 3570000,3640,2,1,1,1,0,0,0,0,0,0,0,1,1820.0,0.5 5 | 2870000,3040,2,1,1,0,0,0,0,0,0,0,0,1,1520.0,0.5 6 | 3570000,4500,2,1,1,0,0,0,0,0,0,0,0,0,2250.0,0.5 7 | 3920000,7260,3,2,1,1,1,1,0,0,3,0,0,0,2420.0,0.6666666666666666 8 | 2450000,3210,3,1,2,1,0,1,0,0,0,0,0,1,1070.0,0.3333333333333333 9 | 3290000,4040,2,1,1,1,0,0,0,0,0,0,0,1,2020.0,0.5 10 | 5460000,6100,3,1,3,1,1,0,0,1,0,1,1,0,2033.333333333333,0.3333333333333333 11 | 4235000,6650,3,1,2,1,1,0,0,0,0,0,1,0,2216.6666666666665,0.3333333333333333 12 | 6300000,6400,3,1,1,1,1,1,0,1,1,1,1,0,2133.333333333333,0.3333333333333333 13 | 1767150,2400,3,1,1,0,0,0,0,0,0,0,1,0,800.0,0.3333333333333333 14 | 2940000,3660,4,1,2,0,0,0,0,0,0,0,0,1,915.0,0.25 15 | 5425000,6825,3,1,1,1,1,1,0,1,0,1,1,0,2275.0,0.3333333333333333 16 | 6265000,6000,4,1,3,1,1,1,0,0,0,1,0,1,1500.0,0.25 17 | 6475000,3960,3,1,1,1,0,1,0,0,2,0,1,0,1320.0,0.3333333333333333 18 | 3850000,5300,5,2,2,1,0,0,0,0,0,0,1,0,1060.0,0.4 19 | 4200000,4079,3,1,3,1,0,0,0,0,0,0,1,0,1359.6666666666667,0.3333333333333333 20 | 3535000,3850,3,1,1,1,0,0,0,0,2,0,0,1,1283.3333333333333,0.3333333333333333 21 | 8680000,7155,3,2,1,1,1,1,0,1,2,0,0,1,2385.0,0.6666666666666666 22 | 3290000,5880,3,1,1,1,0,0,0,0,1,0,0,1,1960.0,0.3333333333333333 23 | 4200000,4000,4,2,2,0,0,0,0,0,0,0,1,0,1000.0,0.5 24 | 3430000,2610,3,1,2,1,0,1,0,0,0,1,0,1,870.0,0.3333333333333333 25 | 5243000,6050,3,1,1,1,0,1,0,0,0,1,1,0,2016.666666666667,0.3333333333333333 26 | 5145000,7980,3,1,1,1,0,0,0,0,1,1,1,0,2660.0,0.3333333333333333 27 | 5880000,6500,3,2,3,1,0,0,0,1,0,0,0,1,2166.6666666666665,0.6666666666666666 28 | 3885000,3180,4,2,2,1,0,0,0,0,0,0,0,0,795.0,0.5 29 | 2940000,5850,3,1,2,1,0,1,0,0,1,0,0,1,1950.0,0.3333333333333333 30 | 6650000,4260,4,2,2,1,0,0,1,0,0,0,1,0,1065.0,0.5 31 | 6090000,6600,3,1,1,1,1,1,0,0,2,1,1,0,2200.0,0.3333333333333333 32 | 6020000,6800,2,1,1,1,1,1,0,0,2,0,0,0,3400.0,0.5 33 | 4200000,5800,3,1,1,1,0,0,1,0,2,0,1,0,1933.333333333333,0.3333333333333333 34 | 3633000,3520,3,1,1,1,0,0,0,0,2,1,0,1,1173.3333333333333,0.3333333333333333 35 | 9870000,8100,4,1,2,1,1,1,0,1,2,1,0,0,2025.0,0.25 36 | 5215000,7200,3,1,2,1,1,1,0,0,1,1,0,0,2400.0,0.3333333333333333 37 | 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Mastering-Machine-Learning-Algorithms-using-Python --------------------------------------------------------------------------------