├── GroupA-Assignment1 ├── Code.ipynb └── autodata.csv ├── GroupA-Assignment10 └── Code.ipynb ├── GroupA-Assignment2 ├── Code.ipynb ├── README.md └── tecdiv.csv ├── GroupA-Assignment3 └── Code.ipynb ├── GroupA-Assignment4 ├── Code.ipynb └── HousingData.csv ├── GroupA-Assignment5 ├── Code.ipynb └── Social_Network_Ads.csv ├── GroupA-Assignment6 └── Code.ipynb ├── GroupA-Assignment7 └── Code.ipynb ├── GroupA-Assignment8 └── Code.ipynb ├── GroupA-Assignment9 └── Code.ipynb ├── GroupC-Mini Project ├── DSBDA Mini Project.ipynb └── covid_vaccine_statewise.csv └── README.md /GroupA-Assignment1/autodata.csv: -------------------------------------------------------------------------------- 1 | ,symboling,normalized-losses,make,aspiration,num-of-doors,body-style,drive-wheels,engine-location,wheel-base,length,width,height,curb-weight,engine-type,num-of-cylinders,engine-size,fuel-system,bore,stroke,compression-ratio,horsepower,peak-rpm,city-mpg,highway-mpg,price,city-L/100km,horsepower-binned,diesel,gas 2 | 0,3,122,alfa-romero,std,two,convertible,rwd,front,88.6,0.8111484863046613,0.8902777777777777,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9.0,111.0,5000.0,21,27,13495.0,11.19047619047619,Low,0,1 3 | 1,3,122,alfa-romero,std,two,convertible,rwd,front,88.6,0.8111484863046613,0.8902777777777777,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9.0,111.0,5000.0,21,27,16500.0,11.19047619047619,Low,0,1 4 | 2,1,122,alfa-romero,std,two,hatchback,rwd,front,94.5,0.8226814031715521,0.9097222222222222,52.4,2823,ohcv,six,152,mpfi,2.68,3.47,9.0,154.0,5000.0,19,26,16500.0,12.368421052631579,Medium,0,1 5 | 3,2,164,audi,std,four,sedan,fwd,front,99.8,0.8486304661220567,0.9194444444444445,54.3,2337,ohc,four,109,mpfi,3.19,3.4,10.0,102.0,5500.0,24,30,13950.0,9.791666666666666,Low,0,1 6 | 4,2,164,audi,std,four,sedan,4wd,front,99.4,0.8486304661220567,0.9222222222222223,54.3,2824,ohc,five,136,mpfi,3.19,3.4,8.0,115.0,5500.0,18,22,17450.0,13.055555555555555,Low,0,1 7 | 5,2,122,audi,std,two,sedan,fwd,front,99.8,0.8519942335415667,0.9208333333333333,53.1,2507,ohc,five,136,mpfi,3.19,3.4,8.5,110.0,5500.0,19,25,15250.0,12.368421052631579,Low,0,1 8 | 6,1,158,audi,std,four,sedan,fwd,front,105.8,0.9259971167707832,0.9916666666666667,55.7,2844,ohc,five,136,mpfi,3.19,3.4,8.5,110.0,5500.0,19,25,17710.0,12.368421052631579,Low,0,1 9 | 7,1,122,audi,std,four,wagon,fwd,front,105.8,0.9259971167707832,0.9916666666666667,55.7,2954,ohc,five,136,mpfi,3.19,3.4,8.5,110.0,5500.0,19,25,18920.0,12.368421052631579,Low,0,1 10 | 8,1,158,audi,turbo,four,sedan,fwd,front,105.8,0.9259971167707832,0.9916666666666667,55.9,3086,ohc,five,131,mpfi,3.13,3.4,8.3,140.0,5500.0,17,20,23875.0,13.823529411764707,Medium,0,1 11 | 9,2,192,bmw,std,two,sedan,rwd,front,101.2,0.849591542527631,0.8999999999999999,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101.0,5800.0,23,29,16430.0,10.217391304347826,Low,0,1 12 | 10,0,192,bmw,std,four,sedan,rwd,front,101.2,0.849591542527631,0.8999999999999999,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101.0,5800.0,23,29,16925.0,10.217391304347826,Low,0,1 13 | 11,0,188,bmw,std,two,sedan,rwd,front,101.2,0.849591542527631,0.8999999999999999,54.3,2710,ohc,six,164,mpfi,3.31,3.19,9.0,121.0,4250.0,21,28,20970.0,11.19047619047619,Medium,0,1 14 | 12,0,188,bmw,std,four,sedan,rwd,front,101.2,0.849591542527631,0.8999999999999999,54.3,2765,ohc,six,164,mpfi,3.31,3.19,9.0,121.0,4250.0,21,28,21105.0,11.19047619047619,Medium,0,1 15 | 13,1,122,bmw,std,four,sedan,rwd,front,103.5,0.9082172032676598,0.9291666666666667,55.7,3055,ohc,six,164,mpfi,3.31,3.19,9.0,121.0,4250.0,20,25,24565.0,11.75,Medium,0,1 16 | 14,0,122,bmw,std,four,sedan,rwd,front,103.5,0.9082172032676598,0.9291666666666667,55.7,3230,ohc,six,209,mpfi,3.62,3.39,8.0,182.0,5400.0,16,22,30760.0,14.6875,Medium,0,1 17 | 15,0,122,bmw,std,two,sedan,rwd,front,103.5,0.9312830370014417,0.9430555555555556,53.7,3380,ohc,six,209,mpfi,3.62,3.39,8.0,182.0,5400.0,16,22,41315.0,14.6875,Medium,0,1 18 | 16,0,122,bmw,std,four,sedan,rwd,front,110.0,0.9466602594906295,0.9847222222222223,56.3,3505,ohc,six,209,mpfi,3.62,3.39,8.0,182.0,5400.0,15,20,36880.0,15.666666666666666,Medium,0,1 19 | 17,2,121,chevrolet,std,two,hatchback,fwd,front,88.4,0.6780394041326285,0.8374999999999999,53.2,1488,l,three,61,2bbl,2.91,3.03,9.5,48.0,5100.0,47,53,5151.0,5.0,Low,0,1 20 | 18,1,98,chevrolet,std,two,hatchback,fwd,front,94.5,0.7491590581451226,0.8833333333333333,52.0,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70.0,5400.0,38,43,6295.0,6.184210526315789,Low,0,1 21 | 19,0,81,chevrolet,std,four,sedan,fwd,front,94.5,0.7630946660259491,0.8833333333333333,52.0,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70.0,5400.0,38,43,6575.0,6.184210526315789,Low,0,1 22 | 20,1,118,dodge,std,two,hatchback,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68.0,5500.0,37,41,5572.0,6.351351351351352,Low,0,1 23 | 21,1,118,dodge,std,two,hatchback,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.4,68.0,5500.0,31,38,6377.0,7.580645161290323,Low,0,1 24 | 22,1,118,dodge,turbo,two,hatchback,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.8,2128,ohc,four,98,mpfi,3.03,3.39,7.6,102.0,5500.0,24,30,7957.0,9.791666666666666,Low,0,1 25 | 23,1,148,dodge,std,four,hatchback,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68.0,5500.0,31,38,6229.0,7.580645161290323,Low,0,1 26 | 24,1,148,dodge,std,four,sedan,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68.0,5500.0,31,38,6692.0,7.580645161290323,Low,0,1 27 | 25,1,148,dodge,std,four,sedan,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68.0,5500.0,31,38,7609.0,7.580645161290323,Low,0,1 28 | 26,1,148,dodge,turbo,four,sedan,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.6,2191,ohc,four,98,mpfi,3.03,3.39,7.6,102.0,5500.0,24,30,8558.0,9.791666666666666,Low,0,1 29 | 27,-1,110,dodge,std,four,wagon,fwd,front,103.3,0.8390197020663143,0.8972222222222221,59.8,2535,ohc,four,122,2bbl,3.34,3.46,8.5,88.0,5000.0,24,30,8921.0,9.791666666666666,Low,0,1 30 | 28,3,145,dodge,turbo,two,hatchback,fwd,front,95.9,0.8322921672272945,0.9208333333333333,50.2,2811,ohc,four,156,mfi,3.6,3.9,7.0,145.0,5000.0,19,24,12964.0,12.368421052631579,Medium,0,1 31 | 29,2,137,honda,std,two,hatchback,fwd,front,86.6,0.6948582412301778,0.8875,50.8,1713,ohc,four,92,1bbl,2.91,3.41,9.6,58.0,4800.0,49,54,6479.0,4.795918367346939,Low,0,1 32 | 30,2,137,honda,std,two,hatchback,fwd,front,86.6,0.6948582412301778,0.8875,50.8,1819,ohc,four,92,1bbl,2.91,3.41,9.2,76.0,6000.0,31,38,6855.0,7.580645161290323,Low,0,1 33 | 31,1,101,honda,std,two,hatchback,fwd,front,93.7,0.7208073041806824,0.8888888888888888,52.6,1837,ohc,four,79,1bbl,2.91,3.07,10.1,60.0,5500.0,38,42,5399.0,6.184210526315789,Low,0,1 34 | 32,1,101,honda,std,two,hatchback,fwd,front,93.7,0.7208073041806824,0.8888888888888888,52.6,1940,ohc,four,92,1bbl,2.91,3.41,9.2,76.0,6000.0,30,34,6529.0,7.833333333333333,Low,0,1 35 | 33,1,101,honda,std,two,hatchback,fwd,front,93.7,0.7208073041806824,0.8888888888888888,52.6,1956,ohc,four,92,1bbl,2.91,3.41,9.2,76.0,6000.0,30,34,7129.0,7.833333333333333,Low,0,1 36 | 34,0,110,honda,std,four,sedan,fwd,front,96.5,0.7851994233541567,0.8888888888888888,54.5,2010,ohc,four,92,1bbl,2.91,3.41,9.2,76.0,6000.0,30,34,7295.0,7.833333333333333,Low,0,1 37 | 35,0,78,honda,std,four,wagon,fwd,front,96.5,0.754925516578568,0.8875,58.3,2024,ohc,four,92,1bbl,2.92,3.41,9.2,76.0,6000.0,30,34,7295.0,7.833333333333333,Low,0,1 38 | 36,0,106,honda,std,two,hatchback,fwd,front,96.5,0.8049014896684287,0.9055555555555556,53.3,2236,ohc,four,110,1bbl,3.15,3.58,9.0,86.0,5800.0,27,33,7895.0,8.703703703703704,Low,0,1 39 | 37,0,106,honda,std,two,hatchback,fwd,front,96.5,0.8049014896684287,0.9055555555555556,53.3,2289,ohc,four,110,1bbl,3.15,3.58,9.0,86.0,5800.0,27,33,9095.0,8.703703703703704,Low,0,1 40 | 38,0,85,honda,std,four,sedan,fwd,front,96.5,0.8428640076886112,0.9055555555555556,54.1,2304,ohc,four,110,1bbl,3.15,3.58,9.0,86.0,5800.0,27,33,8845.0,8.703703703703704,Low,0,1 41 | 39,0,85,honda,std,four,sedan,fwd,front,96.5,0.8428640076886112,0.8680555555555556,54.1,2372,ohc,four,110,1bbl,3.15,3.58,9.0,86.0,5800.0,27,33,10295.0,8.703703703703704,Low,0,1 42 | 40,0,85,honda,std,four,sedan,fwd,front,96.5,0.8428640076886112,0.9055555555555556,54.1,2465,ohc,four,110,mpfi,3.15,3.58,9.0,101.0,5800.0,24,28,12945.0,9.791666666666666,Low,0,1 43 | 41,1,107,honda,std,two,sedan,fwd,front,96.5,0.8125901009130225,0.9166666666666666,51.0,2293,ohc,four,110,2bbl,3.15,3.58,9.1,100.0,5500.0,25,31,10345.0,9.4,Low,0,1 44 | 42,0,122,isuzu,std,four,sedan,rwd,front,94.3,0.8202787121576165,0.8583333333333333,53.5,2337,ohc,four,111,2bbl,3.31,3.23,8.5,78.0,4800.0,24,29,6785.0,9.791666666666666,Low,0,1 45 | 43,2,122,isuzu,std,two,hatchback,rwd,front,96.0,0.8294089380105718,0.9055555555555556,51.4,2734,ohc,four,119,spfi,3.43,3.23,9.2,90.0,5000.0,24,29,11048.0,9.791666666666666,Low,0,1 46 | 44,0,145,jaguar,std,four,sedan,rwd,front,113.0,0.9591542527630946,0.9666666666666666,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176.0,4750.0,15,19,32250.0,15.666666666666666,Medium,0,1 47 | 45,0,122,jaguar,std,four,sedan,rwd,front,113.0,0.9591542527630946,0.9666666666666666,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176.0,4750.0,15,19,35550.0,15.666666666666666,Medium,0,1 48 | 46,0,122,jaguar,std,two,sedan,rwd,front,102.0,0.921191734742912,0.9805555555555555,47.8,3950,ohcv,twelve,326,mpfi,3.54,2.76,11.5,262.0,5000.0,13,17,36000.0,18.076923076923077,High,0,1 49 | 47,1,104,mazda,std,two,hatchback,fwd,front,93.1,0.7645362806343105,0.8916666666666667,54.1,1890,ohc,four,91,2bbl,3.03,3.15,9.0,68.0,5000.0,30,31,5195.0,7.833333333333333,Low,0,1 50 | 48,1,104,mazda,std,two,hatchback,fwd,front,93.1,0.7645362806343105,0.8916666666666667,54.1,1900,ohc,four,91,2bbl,3.03,3.15,9.0,68.0,5000.0,31,38,6095.0,7.580645161290323,Low,0,1 51 | 49,1,104,mazda,std,two,hatchback,fwd,front,93.1,0.7645362806343105,0.8916666666666667,54.1,1905,ohc,four,91,2bbl,3.03,3.15,9.0,68.0,5000.0,31,38,6795.0,7.580645161290323,Low,0,1 52 | 50,1,113,mazda,std,four,sedan,fwd,front,93.1,0.8015377222489188,0.8916666666666667,54.1,1945,ohc,four,91,2bbl,3.03,3.15,9.0,68.0,5000.0,31,38,6695.0,7.580645161290323,Low,0,1 53 | 51,1,113,mazda,std,four,sedan,fwd,front,93.1,0.8015377222489188,0.8916666666666667,54.1,1950,ohc,four,91,2bbl,3.08,3.15,9.0,68.0,5000.0,31,38,7395.0,7.580645161290323,Low,0,1 54 | 52,3,150,mazda,std,two,hatchback,rwd,front,95.3,0.8121095627102355,0.9125000000000001,49.6,2380,rotor,two,70,4bbl,3.3297512437810943,,9.4,101.0,6000.0,17,23,10945.0,13.823529411764707,Low,0,1 55 | 53,3,150,mazda,std,two,hatchback,rwd,front,95.3,0.8121095627102355,0.9125000000000001,49.6,2380,rotor,two,70,4bbl,3.3297512437810943,,9.4,101.0,6000.0,17,23,11845.0,13.823529411764707,Low,0,1 56 | 54,3,150,mazda,std,two,hatchback,rwd,front,95.3,0.8121095627102355,0.9125000000000001,49.6,2385,rotor,two,70,4bbl,3.3297512437810943,,9.4,101.0,6000.0,17,23,13645.0,13.823529411764707,Low,0,1 57 | 55,3,150,mazda,std,two,hatchback,rwd,front,95.3,0.8121095627102355,0.9125000000000001,49.6,2500,rotor,two,80,mpfi,3.3297512437810943,,9.4,135.0,6000.0,16,23,15645.0,14.6875,Medium,0,1 58 | 56,1,129,mazda,std,two,hatchback,fwd,front,98.8,0.8543969245555022,0.9236111111111112,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84.0,4800.0,26,32,8845.0,9.038461538461538,Low,0,1 59 | 57,0,115,mazda,std,four,sedan,fwd,front,98.8,0.8543969245555022,0.9236111111111112,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84.0,4800.0,26,32,8495.0,9.038461538461538,Low,0,1 60 | 58,1,129,mazda,std,two,hatchback,fwd,front,98.8,0.8543969245555022,0.9236111111111112,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84.0,4800.0,26,32,10595.0,9.038461538461538,Low,0,1 61 | 59,0,115,mazda,std,four,sedan,fwd,front,98.8,0.8543969245555022,0.9236111111111112,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84.0,4800.0,26,32,10245.0,9.038461538461538,Low,0,1 62 | 60,0,122,mazda,std,four,sedan,fwd,front,98.8,0.8543969245555022,0.9236111111111112,55.5,2443,ohc,four,122,idi,3.39,3.39,22.7,64.0,4650.0,36,42,10795.0,6.527777777777778,Low,1,0 63 | 61,0,115,mazda,std,four,hatchback,fwd,front,98.8,0.8543969245555022,0.9236111111111112,55.5,2425,ohc,four,122,2bbl,3.39,3.39,8.6,84.0,4800.0,26,32,11245.0,9.038461538461538,Low,0,1 64 | 62,0,118,mazda,std,four,sedan,rwd,front,104.9,0.8409418548774628,0.9180555555555555,54.4,2670,ohc,four,140,mpfi,3.76,3.16,8.0,120.0,5000.0,19,27,18280.0,12.368421052631579,Medium,0,1 65 | 63,0,122,mazda,std,four,sedan,rwd,front,104.9,0.8409418548774628,0.9180555555555555,54.4,2700,ohc,four,134,idi,3.43,3.64,22.0,72.0,4200.0,31,39,18344.0,7.580645161290323,Low,1,0 66 | 64,-1,93,mercedes-benz,turbo,four,sedan,rwd,front,110.0,0.9173474291206152,0.9763888888888889,56.5,3515,ohc,five,183,idi,3.58,3.64,21.5,123.0,4350.0,22,25,25552.0,10.681818181818182,Medium,1,0 67 | 65,-1,93,mercedes-benz,turbo,four,wagon,rwd,front,110.0,0.9173474291206152,0.9763888888888889,58.7,3750,ohc,five,183,idi,3.58,3.64,21.5,123.0,4350.0,22,25,28248.0,10.681818181818182,Medium,1,0 68 | 66,0,93,mercedes-benz,turbo,two,hardtop,rwd,front,106.7,0.9010091302258529,0.9763888888888889,54.9,3495,ohc,five,183,idi,3.58,3.64,21.5,123.0,4350.0,22,25,28176.0,10.681818181818182,Medium,1,0 69 | 67,-1,93,mercedes-benz,turbo,four,sedan,rwd,front,115.6,0.9735703988467083,0.9958333333333333,56.3,3770,ohc,five,183,idi,3.58,3.64,21.5,123.0,4350.0,22,25,31600.0,10.681818181818182,Medium,1,0 70 | 68,-1,122,mercedes-benz,std,four,sedan,rwd,front,115.6,0.9735703988467083,0.9958333333333333,56.5,3740,ohcv,eight,234,mpfi,3.46,3.1,8.3,155.0,4750.0,16,18,34184.0,14.6875,Medium,0,1 71 | 69,3,142,mercedes-benz,std,two,convertible,rwd,front,96.6,0.8664103796251803,0.9791666666666666,50.8,3685,ohcv,eight,234,mpfi,3.46,3.1,8.3,155.0,4750.0,16,18,35056.0,14.6875,Medium,0,1 72 | 70,0,122,mercedes-benz,std,four,sedan,rwd,front,120.9,1.0,0.9958333333333333,56.7,3900,ohcv,eight,308,mpfi,3.8,3.35,8.0,184.0,4500.0,14,16,40960.0,16.785714285714285,Medium,0,1 73 | 71,1,122,mercedes-benz,std,two,hardtop,rwd,front,112.0,0.9572320999519461,1.0,55.4,3715,ohcv,eight,304,mpfi,3.8,3.35,8.0,184.0,4500.0,14,16,45400.0,16.785714285714285,Medium,0,1 74 | 72,1,122,mercury,turbo,two,hatchback,rwd,front,102.7,0.857280153772225,0.9444444444444444,54.8,2910,ohc,four,140,mpfi,3.78,3.12,8.0,175.0,5000.0,19,24,16503.0,12.368421052631579,Medium,0,1 75 | 73,2,161,mitsubishi,std,two,hatchback,fwd,front,93.7,0.7558865929841423,0.8944444444444445,50.8,1918,ohc,four,92,2bbl,2.97,3.23,9.4,68.0,5500.0,37,41,5389.0,6.351351351351352,Low,0,1 76 | 74,2,161,mitsubishi,std,two,hatchback,fwd,front,93.7,0.7558865929841423,0.8944444444444445,50.8,1944,ohc,four,92,2bbl,2.97,3.23,9.4,68.0,5500.0,31,38,6189.0,7.580645161290323,Low,0,1 77 | 75,2,161,mitsubishi,std,two,hatchback,fwd,front,93.7,0.7558865929841423,0.8944444444444445,50.8,2004,ohc,four,92,2bbl,2.97,3.23,9.4,68.0,5500.0,31,38,6669.0,7.580645161290323,Low,0,1 78 | 76,1,161,mitsubishi,turbo,two,hatchback,fwd,front,93.0,0.7558865929841423,0.8861111111111111,50.8,2145,ohc,four,98,spdi,3.03,3.39,7.6,102.0,5500.0,24,30,7689.0,9.791666666666666,Low,0,1 79 | 77,3,153,mitsubishi,turbo,two,hatchback,fwd,front,96.3,0.8313310908217203,0.9083333333333334,49.4,2370,ohc,four,110,spdi,3.17,3.46,7.5,116.0,5500.0,23,30,9959.0,10.217391304347826,Low,0,1 80 | 78,3,153,mitsubishi,std,two,hatchback,fwd,front,96.3,0.8313310908217203,0.9083333333333334,49.4,2328,ohc,four,122,2bbl,3.35,3.46,8.5,88.0,5000.0,25,32,8499.0,9.4,Low,0,1 81 | 79,3,122,mitsubishi,turbo,two,hatchback,fwd,front,95.9,0.8322921672272945,0.9208333333333333,50.2,2833,ohc,four,156,spdi,3.58,3.86,7.0,145.0,5000.0,19,24,12629.0,12.368421052631579,Medium,0,1 82 | 80,3,122,mitsubishi,turbo,two,hatchback,fwd,front,95.9,0.8322921672272945,0.9208333333333333,50.2,2921,ohc,four,156,spdi,3.59,3.86,7.0,145.0,5000.0,19,24,14869.0,12.368421052631579,Medium,0,1 83 | 81,3,122,mitsubishi,turbo,two,hatchback,fwd,front,95.9,0.8322921672272945,0.9208333333333333,50.2,2926,ohc,four,156,spdi,3.59,3.86,7.0,145.0,5000.0,19,24,14489.0,12.368421052631579,Medium,0,1 84 | 82,1,125,mitsubishi,std,four,sedan,fwd,front,96.3,0.8284478616049976,0.9083333333333334,51.6,2365,ohc,four,122,2bbl,3.35,3.46,8.5,88.0,5000.0,25,32,6989.0,9.4,Low,0,1 85 | 83,1,125,mitsubishi,std,four,sedan,fwd,front,96.3,0.8284478616049976,0.9083333333333334,51.6,2405,ohc,four,122,2bbl,3.35,3.46,8.5,88.0,5000.0,25,32,8189.0,9.4,Low,0,1 86 | 84,1,125,mitsubishi,turbo,four,sedan,fwd,front,96.3,0.8284478616049976,0.9083333333333334,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116.0,5500.0,23,30,9279.0,10.217391304347826,Low,0,1 87 | 85,-1,137,mitsubishi,std,four,sedan,fwd,front,96.3,0.8284478616049976,0.9083333333333334,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116.0,5500.0,23,30,9279.0,10.217391304347826,Low,0,1 88 | 86,1,128,nissan,std,two,sedan,fwd,front,94.5,0.794329649207112,0.8861111111111111,54.5,1889,ohc,four,97,2bbl,3.15,3.29,9.4,69.0,5200.0,31,37,5499.0,7.580645161290323,Low,0,1 89 | 87,1,128,nissan,std,two,sedan,fwd,front,94.5,0.794329649207112,0.8861111111111111,54.5,2017,ohc,four,103,idi,2.99,3.47,21.9,55.0,4800.0,45,50,7099.0,5.222222222222222,Low,1,0 90 | 88,1,128,nissan,std,two,sedan,fwd,front,94.5,0.794329649207112,0.8861111111111111,54.5,1918,ohc,four,97,2bbl,3.15,3.29,9.4,69.0,5200.0,31,37,6649.0,7.580645161290323,Low,0,1 91 | 89,1,122,nissan,std,four,sedan,fwd,front,94.5,0.794329649207112,0.8861111111111111,54.5,1938,ohc,four,97,2bbl,3.15,3.29,9.4,69.0,5200.0,31,37,6849.0,7.580645161290323,Low,0,1 92 | 90,1,103,nissan,std,four,wagon,fwd,front,94.5,0.8178760211436809,0.8861111111111111,53.5,2024,ohc,four,97,2bbl,3.15,3.29,9.4,69.0,5200.0,31,37,7349.0,7.580645161290323,Low,0,1 93 | 91,1,128,nissan,std,two,sedan,fwd,front,94.5,0.794329649207112,0.8861111111111111,54.5,1951,ohc,four,97,2bbl,3.15,3.29,9.4,69.0,5200.0,31,37,7299.0,7.580645161290323,Low,0,1 94 | 92,1,128,nissan,std,two,hatchback,fwd,front,94.5,0.7957712638154734,0.8861111111111111,53.3,2028,ohc,four,97,2bbl,3.15,3.29,9.4,69.0,5200.0,31,37,7799.0,7.580645161290323,Low,0,1 95 | 93,1,122,nissan,std,four,sedan,fwd,front,94.5,0.794329649207112,0.8861111111111111,54.5,1971,ohc,four,97,2bbl,3.15,3.29,9.4,69.0,5200.0,31,37,7499.0,7.580645161290323,Low,0,1 96 | 94,1,103,nissan,std,four,wagon,fwd,front,94.5,0.8178760211436809,0.8861111111111111,53.5,2037,ohc,four,97,2bbl,3.15,3.29,9.4,69.0,5200.0,31,37,7999.0,7.580645161290323,Low,0,1 97 | 95,2,168,nissan,std,two,hardtop,fwd,front,95.1,0.7803940413262855,0.8861111111111111,53.3,2008,ohc,four,97,2bbl,3.15,3.29,9.4,69.0,5200.0,31,37,8249.0,7.580645161290323,Low,0,1 98 | 96,0,106,nissan,std,four,hatchback,fwd,front,97.2,0.8332532436328689,0.9055555555555556,54.7,2324,ohc,four,120,2bbl,3.33,3.47,8.5,97.0,5200.0,27,34,8949.0,8.703703703703704,Low,0,1 99 | 97,0,106,nissan,std,four,sedan,fwd,front,97.2,0.8332532436328689,0.9055555555555556,54.7,2302,ohc,four,120,2bbl,3.33,3.47,8.5,97.0,5200.0,27,34,9549.0,8.703703703703704,Low,0,1 100 | 98,0,128,nissan,std,four,sedan,fwd,front,100.4,0.8731379144641999,0.9236111111111112,55.1,3095,ohcv,six,181,mpfi,3.43,3.27,9.0,152.0,5200.0,17,22,13499.0,13.823529411764707,Medium,0,1 101 | 99,0,108,nissan,std,four,wagon,fwd,front,100.4,0.8870735223450265,0.9236111111111112,56.1,3296,ohcv,six,181,mpfi,3.43,3.27,9.0,152.0,5200.0,17,22,14399.0,13.823529411764707,Medium,0,1 102 | 100,0,108,nissan,std,four,sedan,fwd,front,100.4,0.8870735223450265,0.9236111111111112,55.1,3060,ohcv,six,181,mpfi,3.43,3.27,9.0,152.0,5200.0,19,25,13499.0,12.368421052631579,Medium,0,1 103 | 101,3,194,nissan,std,two,hatchback,rwd,front,91.3,0.8202787121576165,0.9430555555555556,49.7,3071,ohcv,six,181,mpfi,3.43,3.27,9.0,160.0,5200.0,19,25,17199.0,12.368421052631579,Medium,0,1 104 | 102,3,194,nissan,turbo,two,hatchback,rwd,front,91.3,0.8202787121576165,0.9430555555555556,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,7.8,200.0,5200.0,17,23,19699.0,13.823529411764707,High,0,1 105 | 103,1,231,nissan,std,two,hatchback,rwd,front,99.2,0.857760691975012,0.9430555555555556,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,9.0,160.0,5200.0,19,25,18399.0,12.368421052631579,Medium,0,1 106 | 104,0,161,peugot,std,four,sedan,rwd,front,107.9,0.897164824603556,0.9500000000000001,56.7,3020,l,four,120,mpfi,3.46,3.19,8.4,97.0,5000.0,19,24,11900.0,12.368421052631579,Low,0,1 107 | 105,0,161,peugot,turbo,four,sedan,rwd,front,107.9,0.897164824603556,0.9500000000000001,56.7,3197,l,four,152,idi,3.7,3.52,21.0,95.0,4150.0,28,33,13200.0,8.392857142857142,Low,1,0 108 | 106,0,122,peugot,std,four,wagon,rwd,front,114.2,0.9557904853435849,0.9500000000000001,58.7,3230,l,four,120,mpfi,3.46,3.19,8.4,97.0,5000.0,19,24,12440.0,12.368421052631579,Low,0,1 109 | 107,0,122,peugot,turbo,four,wagon,rwd,front,114.2,0.9557904853435849,0.9500000000000001,58.7,3430,l,four,152,idi,3.7,3.52,21.0,95.0,4150.0,25,25,13860.0,9.4,Low,1,0 110 | 108,0,161,peugot,std,four,sedan,rwd,front,107.9,0.897164824603556,0.9500000000000001,56.7,3075,l,four,120,mpfi,3.46,2.19,8.4,95.0,5000.0,19,24,15580.0,12.368421052631579,Low,0,1 111 | 109,0,161,peugot,turbo,four,sedan,rwd,front,107.9,0.897164824603556,0.9500000000000001,56.7,3252,l,four,152,idi,3.7,3.52,21.0,95.0,4150.0,28,33,16900.0,8.392857142857142,Low,1,0 112 | 110,0,122,peugot,std,four,wagon,rwd,front,114.2,0.9557904853435849,0.9500000000000001,56.7,3285,l,four,120,mpfi,3.46,2.19,8.4,95.0,5000.0,19,24,16695.0,12.368421052631579,Low,0,1 113 | 111,0,122,peugot,turbo,four,wagon,rwd,front,114.2,0.9557904853435849,0.9500000000000001,58.7,3485,l,four,152,idi,3.7,3.52,21.0,95.0,4150.0,25,25,17075.0,9.4,Low,1,0 114 | 112,0,161,peugot,std,four,sedan,rwd,front,107.9,0.897164824603556,0.9500000000000001,56.7,3075,l,four,120,mpfi,3.46,3.19,8.4,97.0,5000.0,19,24,16630.0,12.368421052631579,Low,0,1 115 | 113,0,161,peugot,turbo,four,sedan,rwd,front,107.9,0.897164824603556,0.9500000000000001,56.7,3252,l,four,152,idi,3.7,3.52,21.0,95.0,4150.0,28,33,17950.0,8.392857142857142,Low,1,0 116 | 114,0,161,peugot,turbo,four,sedan,rwd,front,108.0,0.897164824603556,0.9486111111111111,56.0,3130,l,four,134,mpfi,3.61,3.21,7.0,142.0,5600.0,18,24,18150.0,13.055555555555555,Medium,0,1 117 | 115,1,119,plymouth,std,two,hatchback,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.8,1918,ohc,four,90,2bbl,2.97,3.23,9.4,68.0,5500.0,37,41,5572.0,6.351351351351352,Low,0,1 118 | 116,1,119,plymouth,turbo,two,hatchback,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.8,2128,ohc,four,98,spdi,3.03,3.39,7.6,102.0,5500.0,24,30,7957.0,9.791666666666666,Low,0,1 119 | 117,1,154,plymouth,std,four,hatchback,fwd,front,93.7,0.7558865929841423,0.8861111111111111,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68.0,5500.0,31,38,6229.0,7.580645161290323,Low,0,1 120 | 118,1,154,plymouth,std,four,sedan,fwd,front,93.7,0.8039404132628545,0.8861111111111111,50.8,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68.0,5500.0,31,38,6692.0,7.580645161290323,Low,0,1 121 | 119,1,154,plymouth,std,four,sedan,fwd,front,93.7,0.8039404132628545,0.8861111111111111,50.8,2191,ohc,four,98,2bbl,2.97,3.23,9.4,68.0,5500.0,31,38,7609.0,7.580645161290323,Low,0,1 122 | 120,-1,74,plymouth,std,four,wagon,fwd,front,103.3,0.8390197020663143,0.8972222222222221,59.8,2535,ohc,four,122,2bbl,3.35,3.46,8.5,88.0,5000.0,24,30,8921.0,9.791666666666666,Low,0,1 123 | 121,3,122,plymouth,turbo,two,hatchback,rwd,front,95.9,0.8322921672272945,0.9208333333333333,50.2,2818,ohc,four,156,spdi,3.59,3.86,7.0,145.0,5000.0,19,24,12764.0,12.368421052631579,Medium,0,1 124 | 122,3,186,porsche,std,two,hatchback,rwd,front,94.5,0.8116290245074483,0.9486111111111111,50.2,2778,ohc,four,151,mpfi,3.94,3.11,9.5,143.0,5500.0,19,27,22018.0,12.368421052631579,Medium,0,1 125 | 123,3,122,porsche,std,two,hardtop,rwd,rear,89.5,0.8116290245074483,0.9027777777777778,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207.0,5900.0,17,25,32528.0,13.823529411764707,High,0,1 126 | 124,3,122,porsche,std,two,hardtop,rwd,rear,89.5,0.8116290245074483,0.9027777777777778,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207.0,5900.0,17,25,34028.0,13.823529411764707,High,0,1 127 | 125,3,122,porsche,std,two,convertible,rwd,rear,89.5,0.8116290245074483,0.9027777777777778,51.6,2800,ohcf,six,194,mpfi,3.74,2.9,9.5,207.0,5900.0,17,25,37028.0,13.823529411764707,High,0,1 128 | 126,0,122,renault,std,four,wagon,fwd,front,96.1,0.8721768380586257,0.9236111111111112,55.2,2579,ohc,four,132,mpfi,3.46,3.9,8.7,,,23,31,9295.0,10.217391304347826,,0,1 129 | 127,2,122,renault,std,two,hatchback,fwd,front,96.1,0.849591542527631,0.9249999999999999,50.5,2460,ohc,four,132,mpfi,3.46,3.9,8.7,,,23,31,9895.0,10.217391304347826,,0,1 130 | 128,3,150,saab,std,two,hatchback,fwd,front,99.1,0.8966842864007688,0.9236111111111112,56.1,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110.0,5250.0,21,28,11850.0,11.19047619047619,Low,0,1 131 | 129,2,104,saab,std,four,sedan,fwd,front,99.1,0.8966842864007688,0.9236111111111112,56.1,2695,ohc,four,121,mpfi,3.54,3.07,9.3,110.0,5250.0,21,28,12170.0,11.19047619047619,Low,0,1 132 | 130,3,150,saab,std,two,hatchback,fwd,front,99.1,0.8966842864007688,0.9236111111111112,56.1,2707,ohc,four,121,mpfi,2.54,2.07,9.3,110.0,5250.0,21,28,15040.0,11.19047619047619,Low,0,1 133 | 131,2,104,saab,std,four,sedan,fwd,front,99.1,0.8966842864007688,0.9236111111111112,56.1,2758,ohc,four,121,mpfi,3.54,3.07,9.3,110.0,5250.0,21,28,15510.0,11.19047619047619,Low,0,1 134 | 132,3,150,saab,turbo,two,hatchback,fwd,front,99.1,0.8966842864007688,0.9236111111111112,56.1,2808,dohc,four,121,mpfi,3.54,3.07,9.0,160.0,5500.0,19,26,18150.0,12.368421052631579,Medium,0,1 135 | 133,2,104,saab,turbo,four,sedan,fwd,front,99.1,0.8966842864007688,0.9236111111111112,56.1,2847,dohc,four,121,mpfi,3.54,3.07,9.0,160.0,5500.0,19,26,18620.0,12.368421052631579,Medium,0,1 136 | 134,2,83,subaru,std,two,hatchback,fwd,front,93.7,0.7539644401729938,0.8805555555555555,53.7,2050,ohcf,four,97,2bbl,3.62,2.36,9.0,69.0,4900.0,31,36,5118.0,7.580645161290323,Low,0,1 137 | 135,2,83,subaru,std,two,hatchback,fwd,front,93.7,0.758769822200865,0.8833333333333333,53.7,2120,ohcf,four,108,2bbl,3.62,2.64,8.7,73.0,4400.0,26,31,7053.0,9.038461538461538,Low,0,1 138 | 136,2,83,subaru,std,two,hatchback,4wd,front,93.3,0.7558865929841423,0.8861111111111111,55.7,2240,ohcf,four,108,2bbl,3.62,2.64,8.7,73.0,4400.0,26,31,7603.0,9.038461538461538,Low,0,1 139 | 137,0,102,subaru,std,four,sedan,fwd,front,97.2,0.8265257087938491,0.9083333333333334,52.5,2145,ohcf,four,108,2bbl,3.62,2.64,9.5,82.0,4800.0,32,37,7126.0,7.34375,Low,0,1 140 | 138,0,102,subaru,std,four,sedan,fwd,front,97.2,0.8265257087938491,0.9083333333333334,52.5,2190,ohcf,four,108,2bbl,3.62,2.64,9.5,82.0,4400.0,28,33,7775.0,8.392857142857142,Low,0,1 141 | 139,0,102,subaru,std,four,sedan,fwd,front,97.2,0.8265257087938491,0.9083333333333334,52.5,2340,ohcf,four,108,mpfi,3.62,2.64,9.0,94.0,5200.0,26,32,9960.0,9.038461538461538,Low,0,1 142 | 140,0,102,subaru,std,four,sedan,4wd,front,97.0,0.8265257087938491,0.9083333333333334,54.3,2385,ohcf,four,108,2bbl,3.62,2.64,9.0,82.0,4800.0,24,25,9233.0,9.791666666666666,Low,0,1 143 | 141,0,102,subaru,turbo,four,sedan,4wd,front,97.0,0.8265257087938491,0.9083333333333334,54.3,2510,ohcf,four,108,mpfi,3.62,2.64,7.7,111.0,4800.0,24,29,11259.0,9.791666666666666,Low,0,1 144 | 142,0,89,subaru,std,four,wagon,fwd,front,97.0,0.8337337818356559,0.9083333333333334,53.0,2290,ohcf,four,108,2bbl,3.62,2.64,9.0,82.0,4800.0,28,32,7463.0,8.392857142857142,Low,0,1 145 | 143,0,89,subaru,std,four,wagon,fwd,front,97.0,0.8337337818356559,0.9083333333333334,53.0,2455,ohcf,four,108,mpfi,3.62,2.64,9.0,94.0,5200.0,25,31,10198.0,9.4,Low,0,1 146 | 144,0,85,subaru,std,four,wagon,4wd,front,96.9,0.8342143200384431,0.9083333333333334,54.9,2420,ohcf,four,108,2bbl,3.62,2.64,9.0,82.0,4800.0,23,29,8013.0,10.217391304347826,Low,0,1 147 | 145,0,85,subaru,turbo,four,wagon,4wd,front,96.9,0.8342143200384431,0.9083333333333334,54.9,2650,ohcf,four,108,mpfi,3.62,2.64,7.7,111.0,4800.0,23,23,11694.0,10.217391304347826,Low,0,1 148 | 146,1,87,toyota,std,two,hatchback,fwd,front,95.7,0.762614127823162,0.8833333333333333,54.5,1985,ohc,four,92,2bbl,3.05,3.03,9.0,62.0,4800.0,35,39,5348.0,6.714285714285714,Low,0,1 149 | 147,1,87,toyota,std,two,hatchback,fwd,front,95.7,0.762614127823162,0.8833333333333333,54.5,2040,ohc,four,92,2bbl,3.05,3.03,9.0,62.0,4800.0,31,38,6338.0,7.580645161290323,Low,0,1 150 | 148,1,74,toyota,std,four,hatchback,fwd,front,95.7,0.762614127823162,0.8833333333333333,54.5,2015,ohc,four,92,2bbl,3.05,3.03,9.0,62.0,4800.0,31,38,6488.0,7.580645161290323,Low,0,1 151 | 149,0,77,toyota,std,four,wagon,fwd,front,95.7,0.8154733301297453,0.8833333333333333,59.1,2280,ohc,four,92,2bbl,3.05,3.03,9.0,62.0,4800.0,31,37,6918.0,7.580645161290323,Low,0,1 152 | 150,0,81,toyota,std,four,wagon,4wd,front,95.7,0.8154733301297453,0.8833333333333333,59.1,2290,ohc,four,92,2bbl,3.05,3.03,9.0,62.0,4800.0,27,32,7898.0,8.703703703703704,Low,0,1 153 | 151,0,91,toyota,std,four,wagon,4wd,front,95.7,0.8154733301297453,0.8833333333333333,59.1,3110,ohc,four,92,2bbl,3.05,3.03,9.0,62.0,4800.0,27,32,8778.0,8.703703703703704,Low,0,1 154 | 152,0,91,toyota,std,four,sedan,fwd,front,95.7,0.7991350312349833,0.8944444444444445,53.0,2081,ohc,four,98,2bbl,3.19,3.03,9.0,70.0,4800.0,30,37,6938.0,7.833333333333333,Low,0,1 155 | 153,0,91,toyota,std,four,hatchback,fwd,front,95.7,0.7991350312349833,0.8944444444444445,52.8,2109,ohc,four,98,2bbl,3.19,3.03,9.0,70.0,4800.0,30,37,7198.0,7.833333333333333,Low,0,1 156 | 154,0,91,toyota,std,four,sedan,fwd,front,95.7,0.7991350312349833,0.8944444444444445,53.0,2275,ohc,four,110,idi,3.27,3.35,22.5,56.0,4500.0,34,36,7898.0,6.911764705882353,Low,1,0 157 | 155,0,91,toyota,std,four,hatchback,fwd,front,95.7,0.7991350312349833,0.8944444444444445,52.8,2275,ohc,four,110,idi,3.27,3.35,22.5,56.0,4500.0,38,47,7788.0,6.184210526315789,Low,1,0 158 | 156,0,91,toyota,std,four,sedan,fwd,front,95.7,0.7991350312349833,0.8944444444444445,53.0,2094,ohc,four,98,2bbl,3.19,3.03,9.0,70.0,4800.0,38,47,7738.0,6.184210526315789,Low,0,1 159 | 157,0,91,toyota,std,four,hatchback,fwd,front,95.7,0.7991350312349833,0.8944444444444445,52.8,2122,ohc,four,98,2bbl,3.19,3.03,9.0,70.0,4800.0,28,34,8358.0,8.392857142857142,Low,0,1 160 | 158,0,91,toyota,std,four,sedan,fwd,front,95.7,0.7991350312349833,0.8944444444444445,52.8,2140,ohc,four,98,2bbl,3.19,3.03,9.0,70.0,4800.0,28,34,9258.0,8.392857142857142,Low,0,1 161 | 159,1,168,toyota,std,two,sedan,rwd,front,94.5,0.810667948101874,0.8888888888888888,52.6,2169,ohc,four,98,2bbl,3.19,3.03,9.0,70.0,4800.0,29,34,8058.0,8.10344827586207,Low,0,1 162 | 160,1,168,toyota,std,two,hatchback,rwd,front,94.5,0.810667948101874,0.8888888888888888,52.6,2204,ohc,four,98,2bbl,3.19,3.03,9.0,70.0,4800.0,29,34,8238.0,8.10344827586207,Low,0,1 163 | 161,1,168,toyota,std,two,sedan,rwd,front,94.5,0.810667948101874,0.8888888888888888,52.6,2265,dohc,four,98,mpfi,3.24,3.08,9.4,112.0,6600.0,26,29,9298.0,9.038461538461538,Low,0,1 164 | 162,1,168,toyota,std,two,hatchback,rwd,front,94.5,0.810667948101874,0.8888888888888888,52.6,2300,dohc,four,98,mpfi,3.24,3.08,9.4,112.0,6600.0,26,29,9538.0,9.038461538461538,Low,0,1 165 | 163,2,134,toyota,std,two,hardtop,rwd,front,98.4,0.8467083133109082,0.911111111111111,52.0,2540,ohc,four,146,mpfi,3.62,3.5,9.3,116.0,4800.0,24,30,8449.0,9.791666666666666,Low,0,1 166 | 164,2,134,toyota,std,two,hardtop,rwd,front,98.4,0.8467083133109082,0.911111111111111,52.0,2536,ohc,four,146,mpfi,3.62,3.5,9.3,116.0,4800.0,24,30,9639.0,9.791666666666666,Low,0,1 167 | 165,2,134,toyota,std,two,hatchback,rwd,front,98.4,0.8467083133109082,0.911111111111111,52.0,2551,ohc,four,146,mpfi,3.62,3.5,9.3,116.0,4800.0,24,30,9989.0,9.791666666666666,Low,0,1 168 | 166,2,134,toyota,std,two,hardtop,rwd,front,98.4,0.8467083133109082,0.911111111111111,52.0,2679,ohc,four,146,mpfi,3.62,3.5,9.3,116.0,4800.0,24,30,11199.0,9.791666666666666,Low,0,1 169 | 167,2,134,toyota,std,two,hatchback,rwd,front,98.4,0.8467083133109082,0.911111111111111,52.0,2714,ohc,four,146,mpfi,3.62,3.5,9.3,116.0,4800.0,24,30,11549.0,9.791666666666666,Low,0,1 170 | 168,2,134,toyota,std,two,convertible,rwd,front,98.4,0.8467083133109082,0.911111111111111,53.0,2975,ohc,four,146,mpfi,3.62,3.5,9.3,116.0,4800.0,24,30,17669.0,9.791666666666666,Low,0,1 171 | 169,-1,65,toyota,std,four,sedan,fwd,front,102.4,0.8438250840941854,0.9236111111111112,54.9,2326,ohc,four,122,mpfi,3.31,3.54,8.7,92.0,4200.0,29,34,8948.0,8.10344827586207,Low,0,1 172 | 170,-1,65,toyota,turbo,four,sedan,fwd,front,102.4,0.8438250840941854,0.9236111111111112,54.9,2480,ohc,four,110,idi,3.27,3.35,22.5,73.0,4500.0,30,33,10698.0,7.833333333333333,Low,1,0 173 | 171,-1,65,toyota,std,four,hatchback,fwd,front,102.4,0.8438250840941854,0.9236111111111112,53.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92.0,4200.0,27,32,9988.0,8.703703703703704,Low,0,1 174 | 172,-1,65,toyota,std,four,sedan,fwd,front,102.4,0.8438250840941854,0.9236111111111112,54.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92.0,4200.0,27,32,10898.0,8.703703703703704,Low,0,1 175 | 173,-1,65,toyota,std,four,hatchback,fwd,front,102.4,0.8438250840941854,0.9236111111111112,53.9,2458,ohc,four,122,mpfi,3.31,3.54,8.7,92.0,4200.0,27,32,11248.0,8.703703703703704,Low,0,1 176 | 174,3,197,toyota,std,two,hatchback,rwd,front,102.9,0.8817876021143681,0.9402777777777778,52.0,2976,dohc,six,171,mpfi,3.27,3.35,9.3,161.0,5200.0,20,24,16558.0,11.75,Medium,0,1 177 | 175,3,197,toyota,std,two,hatchback,rwd,front,102.9,0.8817876021143681,0.9402777777777778,52.0,3016,dohc,six,171,mpfi,3.27,3.35,9.3,161.0,5200.0,19,24,15998.0,12.368421052631579,Medium,0,1 178 | 176,-1,90,toyota,std,four,sedan,rwd,front,104.5,0.9024507448342144,0.9236111111111112,54.1,3131,dohc,six,171,mpfi,3.27,3.35,9.2,156.0,5200.0,20,24,15690.0,11.75,Medium,0,1 179 | 177,-1,122,toyota,std,four,wagon,rwd,front,104.5,0.9024507448342144,0.9236111111111112,54.1,3151,dohc,six,161,mpfi,3.27,3.35,9.2,156.0,5200.0,19,24,15750.0,12.368421052631579,Medium,0,1 180 | 178,2,122,volkswagen,std,two,sedan,fwd,front,97.3,0.8250840941854877,0.9097222222222222,55.7,2261,ohc,four,97,idi,3.01,3.4,23.0,52.0,4800.0,37,46,7775.0,6.351351351351352,Low,1,0 181 | 179,2,122,volkswagen,std,two,sedan,fwd,front,97.3,0.8250840941854877,0.9097222222222222,55.7,2209,ohc,four,109,mpfi,3.19,3.4,9.0,85.0,5250.0,27,34,7975.0,8.703703703703704,Low,0,1 182 | 180,2,94,volkswagen,std,four,sedan,fwd,front,97.3,0.8250840941854877,0.9097222222222222,55.7,2264,ohc,four,97,idi,3.01,3.4,23.0,52.0,4800.0,37,46,7995.0,6.351351351351352,Low,1,0 183 | 181,2,94,volkswagen,std,four,sedan,fwd,front,97.3,0.8250840941854877,0.9097222222222222,55.7,2212,ohc,four,109,mpfi,3.19,3.4,9.0,85.0,5250.0,27,34,8195.0,8.703703703703704,Low,0,1 184 | 182,2,94,volkswagen,std,four,sedan,fwd,front,97.3,0.8250840941854877,0.9097222222222222,55.7,2275,ohc,four,109,mpfi,3.19,3.4,9.0,85.0,5250.0,27,34,8495.0,8.703703703703704,Low,0,1 185 | 183,2,94,volkswagen,turbo,four,sedan,fwd,front,97.3,0.8250840941854877,0.9097222222222222,55.7,2319,ohc,four,97,idi,3.01,3.4,23.0,68.0,4500.0,37,42,9495.0,6.351351351351352,Low,1,0 186 | 184,2,94,volkswagen,std,four,sedan,fwd,front,97.3,0.8250840941854877,0.9097222222222222,55.7,2300,ohc,four,109,mpfi,3.19,3.4,10.0,100.0,5500.0,26,32,9995.0,9.038461538461538,Low,0,1 187 | 185,3,122,volkswagen,std,two,convertible,fwd,front,94.5,0.7654973570398848,0.8916666666666667,55.6,2254,ohc,four,109,mpfi,3.19,3.4,8.5,90.0,5500.0,24,29,11595.0,9.791666666666666,Low,0,1 188 | 186,3,256,volkswagen,std,two,hatchback,fwd,front,94.5,0.7962518020182604,0.8888888888888888,51.4,2221,ohc,four,109,mpfi,3.19,3.4,8.5,90.0,5500.0,24,29,9980.0,9.791666666666666,Low,0,1 189 | 187,0,122,volkswagen,std,four,sedan,fwd,front,100.4,0.865929841422393,0.9291666666666667,55.1,2661,ohc,five,136,mpfi,3.19,3.4,8.5,110.0,5500.0,19,24,13295.0,12.368421052631579,Low,0,1 190 | 188,0,122,volkswagen,turbo,four,sedan,fwd,front,100.4,0.865929841422393,0.9291666666666667,55.1,2579,ohc,four,97,idi,3.01,3.4,23.0,68.0,4500.0,33,38,13845.0,7.121212121212121,Low,1,0 191 | 189,0,122,volkswagen,std,four,wagon,fwd,front,100.4,0.8798654493032196,0.9291666666666667,55.1,2563,ohc,four,109,mpfi,3.19,3.4,9.0,88.0,5500.0,25,31,12290.0,9.4,Low,0,1 192 | 190,-2,103,volvo,std,four,sedan,rwd,front,104.3,0.9072561268620856,0.9333333333333333,56.2,2912,ohc,four,141,mpfi,3.78,3.15,9.5,114.0,5400.0,23,28,12940.0,10.217391304347826,Low,0,1 193 | 191,-1,74,volvo,std,four,wagon,rwd,front,104.3,0.9072561268620856,0.9333333333333333,57.5,3034,ohc,four,141,mpfi,3.78,3.15,9.5,114.0,5400.0,23,28,13415.0,10.217391304347826,Low,0,1 194 | 192,-2,103,volvo,std,four,sedan,rwd,front,104.3,0.9072561268620856,0.9333333333333333,56.2,2935,ohc,four,141,mpfi,3.78,3.15,9.5,114.0,5400.0,24,28,15985.0,9.791666666666666,Low,0,1 195 | 193,-1,74,volvo,std,four,wagon,rwd,front,104.3,0.9072561268620856,0.9333333333333333,57.5,3042,ohc,four,141,mpfi,3.78,3.15,9.5,114.0,5400.0,24,28,16515.0,9.791666666666666,Low,0,1 196 | 194,-2,103,volvo,turbo,four,sedan,rwd,front,104.3,0.9072561268620856,0.9333333333333333,56.2,3045,ohc,four,130,mpfi,3.62,3.15,7.5,162.0,5100.0,17,22,18420.0,13.823529411764707,Medium,0,1 197 | 195,-1,74,volvo,turbo,four,wagon,rwd,front,104.3,0.9072561268620856,0.9333333333333333,57.5,3157,ohc,four,130,mpfi,3.62,3.15,7.5,162.0,5100.0,17,22,18950.0,13.823529411764707,Medium,0,1 198 | 196,-1,95,volvo,std,four,sedan,rwd,front,109.1,0.9072561268620856,0.9569444444444445,55.5,2952,ohc,four,141,mpfi,3.78,3.15,9.5,114.0,5400.0,23,28,16845.0,10.217391304347826,Low,0,1 199 | 197,-1,95,volvo,turbo,four,sedan,rwd,front,109.1,0.9072561268620856,0.9555555555555555,55.5,3049,ohc,four,141,mpfi,3.78,3.15,8.7,160.0,5300.0,19,25,19045.0,12.368421052631579,Medium,0,1 200 | 198,-1,95,volvo,std,four,sedan,rwd,front,109.1,0.9072561268620856,0.9569444444444445,55.5,3012,ohcv,six,173,mpfi,3.58,2.87,8.8,134.0,5500.0,18,23,21485.0,13.055555555555555,Medium,0,1 201 | 199,-1,95,volvo,turbo,four,sedan,rwd,front,109.1,0.9072561268620856,0.9569444444444445,55.5,3217,ohc,six,145,idi,3.01,3.4,23.0,106.0,4800.0,26,27,22470.0,9.038461538461538,Low,1,0 202 | 200,-1,95,volvo,turbo,four,sedan,rwd,front,109.1,0.9072561268620856,0.9569444444444445,55.5,3062,ohc,four,141,mpfi,3.78,3.15,9.5,114.0,5400.0,19,25,22625.0,12.368421052631579,Low,0,1 203 | -------------------------------------------------------------------------------- /GroupA-Assignment2/README.md: -------------------------------------------------------------------------------- 1 | **NOTE:** 2 | 3 | The dataset used for this practical was collected by us. You can use a diffrent dataset. 4 | -------------------------------------------------------------------------------- /GroupA-Assignment2/tecdiv.csv: -------------------------------------------------------------------------------- 1 | Timestamp,Email Address,Name,Email,Roll no ,PRN No.,Mobile No.,First year: Sem 1,First year: Sem 2,Second year: Sem 1,Second year: Sem 2 2 | 1/17/2022 12:45:09,sejal.zambare19@pccoepune.org,Sejal Zambare,sejal.zambare19@gmail.com,TECOC359,72026841K,8208217782,8.4,8.6,9.8,9.9 3 | 1/17/2022 12:45:44,rushikesh.thorat19@pccoepune.org,Rushikesh Vilas Thorat,rushikesh.thorat19@pccoepune.org,TECOC347,72026776F,9021261925,8.14,8.14,9.32,9.82 4 | 1/17/2022 12:46:10,atharv.sontakke19@pccoepune.org,Atharv Sontakke,atharv123sontakke@gmail.com,TECOC340,72026742M,9009804629,6.61,6.61,9.14,9.14 5 | 1/17/2022 12:46:21,amisha.sherekar19@pccoepune.org,Amisha Sunil Sherekar,amisha.sherekar19@pccoepune.org,TECOC328,72026696D,8698227548,7.2,7.3,8.9,9.2 6 | 1/17/2022 12:46:31,saurabh.sawardekar19@pccoepune.org,Saurabh Raju Sawardekar,saurabh.sawardekar19@pccoepune.org,TECOC326,72026682D,7774072850,7.05,7.45,9.05,9.4 7 | 1/17/2022 12:48:06,priyanka.kizhekethottam19@pccoepune.org,Priyanka Sunil Kizhekethottam,priyankasunilkpsk@gmail.com,TECOC308,72026604B,8879528852,7.5,8,7.91,7.84 8 | 1/17/2022 12:48:27,janhavi.pimplikar19@pccoepune.org,Janhavi Pimplikar,jpimplikar26@gmail.com,TECOC304,72026588G,7028925097,8.27,8.5,9.68,9.14 9 | 1/17/2022 12:48:36,lalit.shirsath19@pccoepune.org,Lalit vilas shirsath,lalitshirsath1111@gmail.com,TECOC368,72026714F,7498041827,8.68,9,9.59,9.66 10 | 1/17/2022 12:48:47,shishir.singh19@pccoepune.org,Shishir Singh,shishirsingh5742@gmail.com,TECOC336,72026730H,9359146752,7.64,7.64,9.18,9.39 11 | 1/17/2022 12:49:11,vighnesh.pathrikar19@pccoepune.org,Vighnesh Pathrikar,vighnesh.pathrikar19@pccoepune.org,TECOC353,72026808H,8007669855,9.09,9.36,9.91,9.95 12 | 1/17/2022 12:49:35,vedant.nerkar19@pccoepune.org,Vedant Narendra Nerkar,vedantnerkar11@gmail.com,TECOB249,72026501M,9028008196,7.45,7.91,9.41,9.41 13 | 1/17/2022 12:51:15,twinkle.shirsath19@pccoepune.org,Twinkle Shirsath,twinkle.shirsath19@pccoepune.org,TECOC332,72026715D,9307452683,9,9.5,9.77,9.86 14 | 1/17/2022 12:51:43,akshay.siddannavar19@pccoepune.org,Akshay Ajay Siddannavar,akshay.siddannavar19@pccoepune.org,TECOC334,72026725M,9823343665,9,9.27,9.77,9.33 15 | 1/17/2022 12:51:44,avadhut.joshi19@pccoepune.org,Avadhut Joshi,avadhut.joshi19@pccoepune.org,TECOC362,72026290K,8380995879,7.59,7.95,9.05,9.66 16 | 1/17/2022 12:52:01,ritik.bazaz19@pccoepune.org,Ritik Bazaz,ritikbazazrb@gmail.com,TECOC315,72026645K,9086791952,6.36,6.59,8.5,8.59 17 | 1/17/2022 12:52:32,sumedha.zaware19@pccoepune.org,Sumedha Zaware,zawaresumedha@gmail.com,TECOC342,72072651L,8308261661,8.04,8.1,9.59,9.66 18 | 1/17/2022 12:52:38,rushikesh.markad19@pccoepune.org,Rushikesh Karbhari Markad,rushikeshmarkad0@gmail.com,TECOC366,72026443L,9623614171,8.68,8.77,9.68,9.82 19 | 1/17/2022 12:54:06,tejaswini.zalki19@pccoepune.org,Tejaswini Ashok Zalki,zalkiteju1420@gmail.com,TECOC358,72026839H,9689650164,8.71,8.91,9.86,9.84 20 | 1/17/2022 12:55:16,ashootosh.pawar18@pccoepune.org,Ashootosh pawar,ashootoshpawar14@gmail.com,TECOC370,71910603G,8459324811,6.25,6.89,6.9,7.2 21 | 1/17/2022 12:57:12,atharva.sarode19@pccoepune.org,Atharva Sachin Sarode,atharva.sarode19@pccoepune.org,TECOC321,72026673E,8805627267,8.32,9,9.55,9.48 22 | 1/17/2022 12:57:49,rizwan.sayyed19@pccoepune.org,Rizwan Sayyed,rizwan.sayyed19@pccoepune.org,TECOC316,72026646H,9307781701,9.09,9.4,9.64,9.64 23 | 1/17/2022 12:58:20,siddhesh.vharamble19@pccoepune.org,Siddhesh Vharambale,siddhesh.vharamble19@pccoepune.org,TECOC352,72026807K,9767417437,7.8,8.14,9.5,9.71 24 | 1/17/2022 12:58:44,riya.shah19@pccoepune.org,Riya Sameer Shah,riya.shah19@pccoepune.org,TECOC327,72026685J,9518597804,8.82,9.23,9.82,9.82 25 | 1/17/2022 13:03:24,tejas.podutwar19@pccoepune.org,Tejas Podutwar,tejas.podutwar19@pccoepune.org,TECOC305,72026592E,9518505174,8.68,9.42,9.86,9.82 26 | 1/17/2022 13:03:40,aditi.naiknaware19@pccoepune.org,Aditi Hanumant Naiknaware ,aditi.naiknaware@gmail.com,TECOC373 ,72026487B,8080105892,9.42,9.55,9.05,9.18 27 | 1/17/2022 14:26:54,prathamesh.pimparwar19@pccoepune.org,Prathamesh Pimparwar,prathamesh.pimparwar19@pccoepune.org,TECOC303,72026587J,7057421821,7.14,7.14,9.3,9.05 28 | 1/17/2022 14:31:50,sanskruti.raskar19@pccoepune.org,Sanskruti Hanumant Raskar ,sanskruti.raskar19@pccoepune.org,TECOC312,72026629H,8637729749,8.81,9.18,8.86,9.09 29 | 1/17/2022 15:20:24,pratik.athawale19@pccoepune.org,Pratik Athawale,pratik.athawale19@pccoepune.org,TECOC307,72026601H,7448228857,7.31,8.38,9.36,9 30 | 1/17/2022 15:33:45,mahesh.supe19@pccoepune.org,Mahesh Supe,maheshsupe714@gmail.com,TECOC343,72026752J,9921967225,7,7.66,8.6,8.91 31 | 1/17/2022 16:07:20,komal.karkhile19@pccoepune.org,Karkhile Komal Balu ,komal.karkhile19@pccoepune.org,TECOC364 ,72026335,7028948265,8.87,8.77,9.44,9.66 32 | 1/17/2022 17:12:16,nikita.gaikwad19@pccoepune.org,Nikita Sukhadev Gaikwad,nikitagaikwad281@gmail.com,TECOC367,72026507L,7507102142,8.95,9.16,9.59,9.89 33 | 1/18/2022 9:28:00,abhishek.rath19@pccoepune.org,Abhishek Rath,abhishek.rath19@pccoepune.org,TECOC313,72026631K,8459454489,9.13,9.36,9.86,9.78 34 | 1/18/2022 9:48:02,rutuja.patil20@pccoepune.org,Rutuja Shantaram Patil,rutuja.patil20@pccoepune.org,TECOC376,72164282K,9518771019,95,95,9.4,9.9 35 | 1/18/2022 10:29:57,pradnya.thakur19@pccoepune.org,Pradnya Thakur ,pradnya.thakur19@pccoepune.org,TECOC369,72026774K,7620616603,9.02,9.2,9.3,9.45 36 | 1/18/2022 10:30:10,sakshi.tendulkar19@pccoepune.org,Sakshi Tendulkar,sakshi.tendulkar19@pccoepune.org,TECOC346,72026772C,7887560141,8.8,9.16,9.5,9.9 37 | 1/18/2022 10:30:34,akash.satpute19@pccoepune.org,Akash Satpute,akashsatpute244@gmail.com,TECOC323,72026676K,7744018328,8.5,8.75,9.64,9.61 38 | 1/18/2022 10:31:08,jai.suryawanshi19@pccoepune.org,JAI SURYAWANSHI,jai.suryawanshi19@pccoepune.org,TECOC344,72026757K,09172258853,7.6,7.8,9.75,9.77 39 | 1/18/2022 10:32:08,tushar.varkhede19@pccoepune.org,Tushar Damodhar Varkhede,tusharvarkhede363@gmail.com,TECOC350,72026797J,9579224129,8.66,8.66,9.66,9.66 40 | 1/18/2022 10:33:20,rohit.sarode19@pccoepune.org,Rohit Sarde,rohitsarode2627@gmail.com,TECOC322,72026674C,9975810366,6.63,6.82,9.23,9.23 41 | 1/18/2022 10:33:36,pratik.patil20@pccoepune.org,Pratik Kumar Patil,pratik.patil20@pccoepune.org,TECOC382,72164281M,9172604237,0,0,9.86,9.91 42 | 1/18/2022 10:33:45,varunraj.tipugade20@pccoepune.org,Varunraj Vijay Tipugade,varunrajtipugade2001@gmail.com,TECOC380,72164294C,7030212587,0,0,9.59,9.59 43 | 1/18/2022 10:37:46,chinmay.singhania19@pccoepune.org,Chinmay Singhania,chinmay.singhania@gmail.com,TECOC337,72026732D,7028870131,7.54,7.86,9.23,9.45 44 | 1/18/2022 10:44:03,vishwajeet.shinde19@pccoepune.org,Vishwajeet shankar shinde,vishwajeetshinde2001@gmail.com,TECOC329,72026709K,8888547620,8.25,8.8,9,9.25 45 | 1/18/2022 10:54:01,sudeep.pawar19@pccoepune.org,Sudeep Pawar,sudeeppawar19@gmail.com,TECOC302,72026584D,7249702685,8.1,8.1,9.5,9.7 46 | 1/18/2022 11:34:25,diksha.waghchoure19@pccoepune.org,Diksha Waghchoure,diksha.waghchoure19@pccoepune.org,TECOC355,72026815L,9325717326,7.9,8.34,9.7,9.7 47 | 1/18/2022 11:39:40,pratiksha.ganjave20@pccoepune.org,Pratiksha Dattu Ganjave,pratikshaganjave.ggsp@gmail.com,TECOC379,120B20082,9284148739,0,0,8.5,9.14 48 | 1/18/2022 11:42:27,abhishek.dongare19@pccoepune.org,Abhishek Dongare,abhishekdongare2001@gmail.com,TECOC361,72026168E,8788342904,8.95,9.1,9.67,9 49 | 1/18/2022 11:48:29,rutuja.patil19@pccoepune.org,Rutuja Patil,rutupatil2000@gmail.com,TECOC318,72026655G,9327076993,7.25,7.51,8.68,8.98 50 | 1/18/2022 11:59:29,saumya.phadkar19@pccoepune.org,Saumya Phadkar,saumya.phadkar@gmail.com,TECOC324,72026678F,9881724240,7.4,8,9.5,9.1 51 | 1/19/2022 10:18:25,rutvik.nare20@pccoepune.org,Rutvik Nare,rutvik.nare20@pccoepune.org,TECOC375,72164285D,9881439608,0,0,8.3,8.5 52 | 1/19/2022 10:20:23,pratiksha.pawar19@pccoepune.org,Pratiksha Pawar,pratiksha.pawar19@pccoepune.org,TECOC301,72026579H,9660663089,8.98,9.45,9.61,9.61 53 | 1/19/2022 10:21:03,sagar.shirke19@pccoepune.org,Sagar Dattatray Shirke,sagar.shirke19@pccoepune.org,TECOC331,72026713H,9421259558,8.61,9.25,9.21,9.4 54 | 1/19/2022 10:21:28,anushka.shrirao19@pccoepune.org,Anushka,shriraoanushka@gmail.com,TECOC333,72026721J,9309523229,7,8,9,9 55 | 1/19/2022 10:21:34,mohini.shinde20@pccoepune.org,Mohini Anandrao Shinde,mohini.shinde20@pccoepune.org,TECOC378,120B20046,7360120199,8.9,9.1,9.2,9.2 56 | 1/19/2022 10:21:43,saisanjana.prodduturu19@pccoepune.org,Sai Sanjana Prodduturu,saisanjana.prodduturu19@pccoepune.org,TECOC309,72026605L,9075294894,7.13,6.6,9.04,9.45 57 | 1/19/2022 10:23:55,mrunali.yewale19@pccoepune.org,Mrunali Yewale,mrunali.yewale19@pccoepune.org,TECOC357,72026836C,8381099465,8.55,8.64,9.73,9.8 58 | 1/19/2022 10:26:31,aniket.raut19@pccoepune.org,Aniket Raut,aniket.raut19@pccoepune.org,TECOC314,72026640J,8605167820,7.27,7.83,9.55,9.13 59 | 1/19/2022 10:39:32,deepali.javriya19@pccoepune.org,Deepali Javriya ,deepali.javriya19@pccoepune.org,TECOC372 ,72026114H ,7999242308,9.54,10,9.45,9.54 60 | 1/19/2022 11:27:25,gaurav.rasal19@pccoepune.org,Gaurav Rasal,gauravvr77@gmail.com,TECOC311,72026627M,7722082807,8.59,9,9.63,9.56 61 | 1/20/2022 9:24:40,pratik.meshram20@pccoepune.org,Pratik Amrut Meshram,pratik.meshram20@pccoepune.org,TECOC381,72164278M,7666479857,0,0,9.09,9.36 62 | 1/20/2022 9:36:14,prasad.zore19@pccoepune.org,Prasad Zore,prasad.zore@outlook.com,TECOC360,72026843F,7387645749,8.31,8.27,9.45,9.77 63 | 1/20/2022 9:42:34,sudhir.varu19@pccoepune.org,SUDHIR VARU,sudhirvaru01@gmail.com,TECOC351,72026799E,9657017250,8.31,8.42,8.53,8.64 64 | 1/20/2022 10:22:05,bhagyashree.takale19@pccoepune.org,Bhagyashree Gorakh Takale,bbhagyashree002@gmail.com,TECOC345,72026760K,8805813576,8.7,8.5,8.7,8.8 65 | 1/20/2022 10:38:06,sarvesh.waghmare19@pccoepune.org,Waghmare Sarvesh Jitendra,sarvesh.waghmare19@pccoepune.org,TECOC356,72026817G,7218935035,6.9,7.07,9.05,9.2 -------------------------------------------------------------------------------- /GroupA-Assignment4/Code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "NAME: Sumedha Zaware\n", 8 | "\n", 9 | "ROLL No.: TECOC342\n", 10 | "\n", 11 | "**Assignment-4**" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "metadata": {}, 18 | "outputs": [], 19 | "source": [ 20 | "import numpy as np\n", 21 | "import pandas as pd" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 3, 27 | "metadata": {}, 28 | "outputs": [ 29 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" 155 | ], 156 | "text/plain": [ 157 | " crim zn indus chas nox rm age dis rad tax ptratio \\\n", 158 | "0 0.00632 18.0 2.31 0 0.538 6.575 65.2 4.0900 1 296 15.3 \n", 159 | "1 0.02731 0.0 7.07 0 0.469 6.421 78.9 4.9671 2 242 17.8 \n", 160 | "2 0.02729 0.0 7.07 0 0.469 7.185 61.1 4.9671 2 242 17.8 \n", 161 | "3 0.03237 0.0 2.18 0 0.458 6.998 45.8 6.0622 3 222 18.7 \n", 162 | "4 0.06905 0.0 2.18 0 0.458 7.147 54.2 6.0622 3 222 18.7 \n", 163 | "\n", 164 | " b lstat medv \n", 165 | "0 396.90 4.98 24.0 \n", 166 | "1 396.90 9.14 21.6 \n", 167 | "2 392.83 4.03 34.7 \n", 168 | "3 394.63 2.94 33.4 \n", 169 | "4 396.90 5.33 36.2 " 170 | ] 171 | }, 172 | "execution_count": 3, 173 | "metadata": {}, 174 | "output_type": "execute_result" 175 | } 176 | ], 177 | "source": [ 178 | "data = pd.read_csv(\"https://raw.githubusercontent.com/selva86/datasets/master/BostonHousing.csv\")\n", 179 | "data.head()" 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": 4, 185 | "metadata": {}, 186 | "outputs": [ 187 | { 188 | "data": { 189 | "text/html": [ 190 | "
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crimzninduschasnoxrmagedisradtaxptratioblstatmedv
5010.062630.011.9300.5736.59369.12.4786127321.0391.999.6722.4
5020.045270.011.9300.5736.12076.72.2875127321.0396.909.0820.6
5030.060760.011.9300.5736.97691.02.1675127321.0396.905.6423.9
5040.109590.011.9300.5736.79489.32.3889127321.0393.456.4822.0
5050.047410.011.9300.5736.03080.82.5050127321.0396.907.8811.9
\n", 312 | "
" 313 | ], 314 | "text/plain": [ 315 | " crim zn indus chas nox rm age dis rad tax ptratio \\\n", 316 | "501 0.06263 0.0 11.93 0 0.573 6.593 69.1 2.4786 1 273 21.0 \n", 317 | "502 0.04527 0.0 11.93 0 0.573 6.120 76.7 2.2875 1 273 21.0 \n", 318 | "503 0.06076 0.0 11.93 0 0.573 6.976 91.0 2.1675 1 273 21.0 \n", 319 | "504 0.10959 0.0 11.93 0 0.573 6.794 89.3 2.3889 1 273 21.0 \n", 320 | "505 0.04741 0.0 11.93 0 0.573 6.030 80.8 2.5050 1 273 21.0 \n", 321 | "\n", 322 | " b lstat medv \n", 323 | "501 391.99 9.67 22.4 \n", 324 | "502 396.90 9.08 20.6 \n", 325 | "503 396.90 5.64 23.9 \n", 326 | "504 393.45 6.48 22.0 \n", 327 | "505 396.90 7.88 11.9 " 328 | ] 329 | }, 330 | "execution_count": 4, 331 | "metadata": {}, 332 | "output_type": "execute_result" 333 | } 334 | ], 335 | "source": [ 336 | "data.tail()" 337 | ] 338 | }, 339 | { 340 | "cell_type": "code", 341 | "execution_count": 5, 342 | "metadata": {}, 343 | "outputs": [ 344 | { 345 | "name": "stdout", 346 | "output_type": "stream", 347 | "text": [ 348 | "The shape of the data is: \n" 349 | ] 350 | }, 351 | { 352 | "data": { 353 | "text/plain": [ 354 | "(506, 14)" 355 | ] 356 | }, 357 | "execution_count": 5, 358 | "metadata": {}, 359 | "output_type": "execute_result" 360 | } 361 | ], 362 | "source": [ 363 | "print(\"The shape of the data is: \")\n", 364 | "data.shape" 365 | ] 366 | }, 367 | { 368 | "cell_type": "markdown", 369 | "metadata": {}, 370 | "source": [ 371 | "Hence, we can see that there are no NULL values" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": 6, 377 | "metadata": {}, 378 | "outputs": [ 379 | { 380 | "data": { 381 | "text/plain": [ 382 | "crim 0\n", 383 | "zn 0\n", 384 | "indus 0\n", 385 | "chas 0\n", 386 | "nox 0\n", 387 | "rm 0\n", 388 | "age 0\n", 389 | "dis 0\n", 390 | "rad 0\n", 391 | "tax 0\n", 392 | "ptratio 0\n", 393 | "b 0\n", 394 | "lstat 0\n", 395 | "medv 0\n", 396 | "dtype: int64" 397 | ] 398 | }, 399 | "execution_count": 6, 400 | "metadata": {}, 401 | "output_type": "execute_result" 402 | } 403 | ], 404 | "source": [ 405 | "data.isnull().sum()" 406 | ] 407 | }, 408 | { 409 | "cell_type": "markdown", 410 | "metadata": {}, 411 | "source": [ 412 | "Define the independent and dependent variables from the dataset" 413 | ] 414 | }, 415 | { 416 | "cell_type": "code", 417 | "execution_count": 7, 418 | "metadata": {}, 419 | "outputs": [], 420 | "source": [ 421 | "X = data.iloc[:,0:13]\n", 422 | "y = data.iloc[:,-1]" 423 | ] 424 | }, 425 | { 426 | "cell_type": "markdown", 427 | "metadata": {}, 428 | "source": [ 429 | "Splitting data into traing and testing dataset" 430 | ] 431 | }, 432 | { 433 | "cell_type": "code", 434 | "execution_count": 8, 435 | "metadata": {}, 436 | "outputs": [], 437 | "source": [ 438 | "from sklearn.model_selection import train_test_split\n", 439 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20,random_state=42)" 440 | ] 441 | }, 442 | { 443 | "cell_type": "markdown", 444 | "metadata": {}, 445 | "source": [ 446 | "Shapes of the training and testing dataset" 447 | ] 448 | }, 449 | { 450 | "cell_type": "code", 451 | "execution_count": 9, 452 | "metadata": {}, 453 | "outputs": [ 454 | { 455 | "name": "stdout", 456 | "output_type": "stream", 457 | "text": [ 458 | "(404, 13)\n", 459 | "(102, 13)\n", 460 | "(404,)\n", 461 | "(102,)\n" 462 | ] 463 | } 464 | ], 465 | "source": [ 466 | "print(X_train.shape)\n", 467 | "print(X_test.shape)\n", 468 | "print(y_train.shape)\n", 469 | "print(y_test.shape)" 470 | ] 471 | }, 472 | { 473 | "cell_type": "markdown", 474 | "metadata": {}, 475 | "source": [ 476 | "Importing LinearRegression() function" 477 | ] 478 | }, 479 | { 480 | "cell_type": "code", 481 | "execution_count": 10, 482 | "metadata": {}, 483 | "outputs": [], 484 | "source": [ 485 | "from sklearn.linear_model import LinearRegression" 486 | ] 487 | }, 488 | { 489 | "cell_type": "code", 490 | "execution_count": 11, 491 | "metadata": {}, 492 | "outputs": [ 493 | { 494 | "data": { 495 | "text/plain": [ 496 | "Pipeline(steps=[('standardscaler', StandardScaler(with_mean=False)),\n", 497 | " ('linearregression', LinearRegression())])" 498 | ] 499 | }, 500 | "execution_count": 11, 501 | "metadata": {}, 502 | "output_type": "execute_result" 503 | } 504 | ], 505 | "source": [ 506 | "from sklearn.preprocessing import StandardScaler\n", 507 | "from sklearn.pipeline import make_pipeline\n", 508 | "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n", 509 | "model.fit(X_train, y_train)" 510 | ] 511 | }, 512 | { 513 | "cell_type": "code", 514 | "execution_count": 12, 515 | "metadata": {}, 516 | "outputs": [ 517 | { 518 | "data": { 519 | "text/plain": [ 520 | "0.6687594935356321" 521 | ] 522 | }, 523 | "execution_count": 12, 524 | "metadata": {}, 525 | "output_type": "execute_result" 526 | } 527 | ], 528 | "source": [ 529 | "model.score(X_test,y_test)" 530 | ] 531 | } 532 | ], 533 | "metadata": { 534 | "interpreter": { 535 | "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186" 536 | }, 537 | "kernelspec": { 538 | "display_name": "Python 3.8.3 ('base')", 539 | "language": "python", 540 | "name": "python3" 541 | }, 542 | "language_info": { 543 | "codemirror_mode": { 544 | "name": "ipython", 545 | "version": 3 546 | }, 547 | "file_extension": ".py", 548 | "mimetype": "text/x-python", 549 | "name": "python", 550 | "nbconvert_exporter": "python", 551 | "pygments_lexer": "ipython3", 552 | "version": "3.8.3" 553 | } 554 | }, 555 | "nbformat": 4, 556 | "nbformat_minor": 4 557 | } 558 | -------------------------------------------------------------------------------- /GroupA-Assignment4/HousingData.csv: -------------------------------------------------------------------------------- 1 | CRIM,ZN,INDUS,CHAS,NOX,RM,AGE,DIS,RAD,TAX,PTRATIO,B,LSTAT,MEDV 2 | 0.00632,18,2.31,0,0.538,6.575,65.2,4.09,1,296,15.3,396.9,4.98,24 3 | 0.02731,0,7.07,0,0.469,6.421,78.9,4.9671,2,242,17.8,396.9,9.14,21.6 4 | 0.02729,0,7.07,0,0.469,7.185,61.1,4.9671,2,242,17.8,392.83,4.03,34.7 5 | 0.03237,0,2.18,0,0.458,6.998,45.8,6.0622,3,222,18.7,394.63,2.94,33.4 6 | 0.06905,0,2.18,0,0.458,7.147,54.2,6.0622,3,222,18.7,396.9,NA,36.2 7 | 0.02985,0,2.18,0,0.458,6.43,58.7,6.0622,3,222,18.7,394.12,5.21,28.7 8 | 0.08829,12.5,7.87,NA,0.524,6.012,66.6,5.5605,5,311,15.2,395.6,12.43,22.9 9 | 0.14455,12.5,7.87,0,0.524,6.172,96.1,5.9505,5,311,15.2,396.9,19.15,27.1 10 | 0.21124,12.5,7.87,0,0.524,5.631,100,6.0821,5,311,15.2,386.63,29.93,16.5 11 | 0.17004,12.5,7.87,NA,0.524,6.004,85.9,6.5921,5,311,15.2,386.71,17.1,18.9 12 | 0.22489,12.5,7.87,0,0.524,6.377,94.3,6.3467,5,311,15.2,392.52,20.45,15 13 | 0.11747,12.5,7.87,0,0.524,6.009,82.9,6.2267,5,311,15.2,396.9,13.27,18.9 14 | 0.09378,12.5,7.87,0,0.524,5.889,39,5.4509,5,311,15.2,390.5,15.71,21.7 15 | 0.62976,0,8.14,0,0.538,5.949,61.8,4.7075,4,307,21,396.9,8.26,20.4 16 | 0.63796,0,8.14,NA,0.538,6.096,84.5,4.4619,4,307,21,380.02,10.26,18.2 17 | 0.62739,0,8.14,0,0.538,5.834,56.5,4.4986,4,307,21,395.62,8.47,19.9 18 | 1.05393,0,8.14,0,0.538,5.935,29.3,4.4986,4,307,21,386.85,6.58,23.1 19 | 0.7842,0,8.14,0,0.538,5.99,81.7,4.2579,4,307,21,386.75,14.67,17.5 20 | 0.80271,0,8.14,0,0.538,5.456,36.6,3.7965,4,307,21,288.99,11.69,20.2 21 | 0.7258,0,8.14,0,0.538,5.727,69.5,3.7965,4,307,21,390.95,11.28,18.2 22 | 1.25179,0,8.14,0,0.538,5.57,98.1,3.7979,4,307,21,376.57,21.02,13.6 23 | 0.85204,0,8.14,0,0.538,5.965,89.2,4.0123,4,307,21,392.53,13.83,19.6 24 | 1.23247,0,8.14,0,0.538,6.142,91.7,3.9769,4,307,21,396.9,18.72,15.2 25 | 0.98843,0,8.14,0,0.538,5.813,100,4.0952,4,307,21,394.54,19.88,14.5 26 | 0.75026,0,8.14,0,0.538,5.924,94.1,4.3996,4,307,21,394.33,16.3,15.6 27 | 0.84054,0,8.14,0,0.538,5.599,85.7,4.4546,4,307,21,303.42,16.51,13.9 28 | 0.67191,0,8.14,0,0.538,5.813,90.3,4.682,4,307,21,376.88,14.81,16.6 29 | 0.95577,0,8.14,0,0.538,6.047,88.8,4.4534,4,307,21,306.38,17.28,14.8 30 | 0.77299,0,8.14,0,0.538,6.495,94.4,4.4547,4,307,21,387.94,12.8,18.4 31 | 1.00245,0,8.14,0,0.538,6.674,87.3,4.239,4,307,21,380.23,11.98,21 32 | 1.13081,0,8.14,0,0.538,5.713,94.1,4.233,4,307,21,360.17,22.6,12.7 33 | 1.35472,0,8.14,0,0.538,6.072,100,4.175,4,307,21,376.73,13.04,14.5 34 | 1.38799,0,8.14,0,0.538,5.95,82,3.99,4,307,21,232.6,27.71,13.2 35 | 1.15172,0,8.14,0,0.538,5.701,95,3.7872,4,307,21,358.77,18.35,13.1 36 | 1.61282,0,8.14,0,0.538,6.096,96.9,3.7598,4,307,21,248.31,20.34,13.5 37 | 0.06417,0,5.96,0,0.499,5.933,68.2,3.3603,5,279,19.2,396.9,NA,18.9 38 | 0.09744,0,NA,0,0.499,5.841,61.4,3.3779,5,279,19.2,377.56,11.41,20 39 | 0.08014,0,5.96,0,0.499,5.85,41.5,3.9342,5,279,19.2,396.9,8.77,21 40 | 0.17505,0,5.96,0,0.499,5.966,30.2,3.8473,5,279,19.2,393.43,10.13,24.7 41 | 0.02763,75,2.95,0,0.428,6.595,21.8,5.4011,3,252,18.3,395.63,4.32,30.8 42 | 0.03359,75,2.95,0,0.428,7.024,15.8,5.4011,3,252,18.3,395.62,1.98,34.9 43 | 0.12744,0,6.91,0,0.448,6.77,2.9,5.7209,3,233,17.9,385.41,4.84,26.6 44 | 0.1415,0,6.91,0,0.448,6.169,6.6,5.7209,3,233,17.9,383.37,5.81,25.3 45 | 0.15936,0,6.91,NA,0.448,6.211,6.5,5.7209,3,233,17.9,394.46,7.44,24.7 46 | 0.12269,0,6.91,0,0.448,6.069,40,5.7209,3,233,17.9,389.39,9.55,21.2 47 | 0.17142,0,6.91,0,0.448,5.682,33.8,5.1004,3,233,17.9,396.9,10.21,19.3 48 | 0.18836,0,6.91,0,0.448,5.786,33.3,5.1004,3,233,17.9,396.9,14.15,20 49 | 0.22927,0,NA,0,0.448,6.03,85.5,5.6894,3,233,17.9,392.74,18.8,16.6 50 | 0.25387,0,6.91,0,0.448,5.399,95.3,5.87,3,233,17.9,396.9,30.81,14.4 51 | 0.21977,0,6.91,0,0.448,5.602,62,6.0877,3,233,17.9,396.9,16.2,19.4 52 | 0.08873,21,5.64,0,0.439,5.963,45.7,6.8147,4,243,16.8,395.56,13.45,19.7 53 | 0.04337,21,NA,0,0.439,6.115,63,6.8147,4,243,16.8,393.97,9.43,20.5 54 | 0.0536,21,5.64,0,0.439,6.511,21.1,6.8147,4,243,16.8,396.9,5.28,25 55 | NA,21,5.64,0,0.439,5.998,21.4,6.8147,4,243,16.8,396.9,8.43,23.4 56 | 0.0136,75,4,0,0.41,5.888,47.6,7.3197,3,469,21.1,396.9,14.8,18.9 57 | 0.01311,90,1.22,0,0.403,7.249,21.9,8.6966,5,226,17.9,395.93,4.81,35.4 58 | 0.02055,85,0.74,0,0.41,6.383,35.7,9.1876,2,313,17.3,396.9,5.77,24.7 59 | 0.01432,100,1.32,0,0.411,6.816,40.5,8.3248,5,256,15.1,392.9,3.95,31.6 60 | 0.15445,25,5.13,0,0.453,6.145,29.2,7.8148,8,284,19.7,390.68,6.86,23.3 61 | 0.10328,25,5.13,0,0.453,5.927,47.2,6.932,8,284,19.7,396.9,9.22,19.6 62 | 0.14932,25,5.13,0,0.453,5.741,66.2,7.2254,8,284,19.7,395.11,13.15,18.7 63 | 0.17171,25,5.13,0,0.453,5.966,93.4,6.8185,8,284,19.7,378.08,14.44,16 64 | 0.11027,25,5.13,0,0.453,6.456,67.8,7.2255,8,284,19.7,396.9,6.73,22.2 65 | 0.1265,25,5.13,0,0.453,6.762,43.4,7.9809,8,284,19.7,395.58,9.5,25 66 | 0.01951,17.5,1.38,0,0.4161,7.104,59.5,9.2229,3,216,18.6,393.24,8.05,33 67 | 0.03584,80,3.37,0,0.398,6.29,17.8,6.6115,4,337,16.1,396.9,4.67,23.5 68 | 0.04379,80,3.37,0,0.398,5.787,31.1,6.6115,4,337,16.1,396.9,10.24,19.4 69 | 0.05789,12.5,6.07,0,0.409,5.878,21.4,6.498,4,345,18.9,396.21,8.1,22 70 | 0.13554,12.5,6.07,0,0.409,5.594,36.8,6.498,4,345,18.9,396.9,13.09,17.4 71 | 0.12816,12.5,6.07,0,0.409,5.885,33,6.498,4,345,18.9,396.9,8.79,20.9 72 | 0.08826,0,10.81,0,0.413,6.417,6.6,5.2873,4,305,19.2,383.73,6.72,24.2 73 | 0.15876,0,10.81,0,0.413,5.961,17.5,5.2873,4,305,19.2,376.94,9.88,21.7 74 | 0.09164,0,10.81,0,0.413,6.065,7.8,5.2873,4,305,19.2,390.91,5.52,22.8 75 | 0.19539,0,10.81,0,0.413,6.245,6.2,5.2873,4,305,19.2,377.17,NA,23.4 76 | 0.07896,0,12.83,0,0.437,6.273,NA,4.2515,5,398,18.7,394.92,6.78,24.1 77 | 0.09512,0,12.83,0,0.437,6.286,45,4.5026,5,398,18.7,383.23,8.94,21.4 78 | 0.10153,0,12.83,0,0.437,6.279,74.5,4.0522,5,398,18.7,373.66,11.97,20 79 | 0.08707,0,12.83,0,0.437,6.14,45.8,4.0905,5,398,18.7,386.96,10.27,20.8 80 | 0.05646,0,12.83,0,0.437,6.232,53.7,5.0141,5,398,18.7,386.4,12.34,21.2 81 | 0.08387,0,12.83,0,0.437,5.874,36.6,4.5026,5,398,18.7,396.06,NA,20.3 82 | 0.04113,25,4.86,0,0.426,6.727,33.5,5.4007,4,281,19,396.9,5.29,28 83 | 0.04462,25,4.86,0,0.426,6.619,70.4,5.4007,4,281,19,395.63,7.22,23.9 84 | 0.03659,25,4.86,0,0.426,6.302,32.2,5.4007,4,281,19,396.9,6.72,24.8 85 | 0.03551,25,4.86,0,0.426,6.167,46.7,5.4007,4,281,19,390.64,7.51,22.9 86 | 0.05059,0,4.49,0,0.449,6.389,48,4.7794,3,247,18.5,396.9,9.62,23.9 87 | 0.05735,0,4.49,0,0.449,6.63,56.1,4.4377,3,247,18.5,392.3,6.53,26.6 88 | 0.05188,0,4.49,0,0.449,6.015,45.1,4.4272,3,247,18.5,395.99,12.86,22.5 89 | 0.07151,0,4.49,0,0.449,6.121,56.8,3.7476,3,247,18.5,395.15,NA,22.2 90 | 0.0566,0,3.41,0,0.489,7.007,86.3,3.4217,2,270,17.8,396.9,5.5,23.6 91 | 0.05302,0,3.41,0,0.489,7.079,63.1,3.4145,2,270,17.8,396.06,5.7,28.7 92 | 0.04684,0,3.41,0,0.489,6.417,66.1,3.0923,2,270,17.8,392.18,8.81,22.6 93 | 0.03932,0,3.41,0,0.489,6.405,73.9,3.0921,2,270,17.8,393.55,8.2,22 94 | 0.04203,NA,15.04,0,0.464,6.442,53.6,3.6659,4,270,18.2,395.01,8.16,22.9 95 | 0.02875,28,15.04,0,0.464,6.211,28.9,3.6659,4,270,18.2,396.33,6.21,25 96 | 0.04294,28,15.04,0,0.464,6.249,77.3,3.615,4,270,18.2,396.9,10.59,20.6 97 | 0.12204,0,2.89,0,0.445,6.625,57.8,3.4952,2,276,18,357.98,6.65,28.4 98 | 0.11504,0,2.89,0,0.445,6.163,69.6,3.4952,2,276,18,391.83,11.34,21.4 99 | 0.12083,0,2.89,0,0.445,8.069,76,3.4952,2,276,18,396.9,4.21,38.7 100 | 0.08187,0,2.89,0,0.445,7.82,36.9,3.4952,2,276,18,393.53,3.57,43.8 101 | 0.0686,0,2.89,0,0.445,7.416,62.5,3.4952,2,276,18,396.9,6.19,33.2 102 | 0.14866,0,8.56,0,0.52,6.727,79.9,2.7778,5,384,20.9,394.76,9.42,27.5 103 | 0.11432,0,8.56,0,0.52,6.781,71.3,2.8561,5,384,20.9,395.58,7.67,26.5 104 | 0.22876,0,8.56,0,0.52,6.405,85.4,2.7147,5,384,20.9,70.8,10.63,18.6 105 | 0.21161,0,8.56,0,0.52,6.137,NA,2.7147,5,384,20.9,394.47,13.44,19.3 106 | 0.1396,0,8.56,0,0.52,6.167,90,2.421,5,384,20.9,392.69,12.33,20.1 107 | 0.13262,0,8.56,0,0.52,5.851,96.7,2.1069,5,384,20.9,394.05,16.47,19.5 108 | 0.1712,0,8.56,0,0.52,5.836,91.9,2.211,5,384,20.9,395.67,18.66,19.5 109 | 0.13117,0,8.56,0,0.52,6.127,85.2,2.1224,5,384,20.9,387.69,14.09,20.4 110 | 0.12802,0,8.56,0,0.52,6.474,97.1,2.4329,5,384,20.9,395.24,12.27,19.8 111 | 0.26363,0,8.56,0,0.52,6.229,91.2,2.5451,5,384,20.9,391.23,15.55,19.4 112 | 0.10793,0,8.56,0,0.52,6.195,54.4,2.7778,5,384,20.9,393.49,13,21.7 113 | 0.10084,0,10.01,0,0.547,6.715,81.6,2.6775,6,432,17.8,395.59,10.16,22.8 114 | 0.12329,0,10.01,0,0.547,5.913,92.9,2.3534,6,432,17.8,394.95,16.21,18.8 115 | 0.22212,0,10.01,0,0.547,6.092,95.4,2.548,6,432,17.8,396.9,17.09,18.7 116 | 0.14231,0,10.01,0,0.547,6.254,84.2,2.2565,6,432,17.8,388.74,10.45,18.5 117 | NA,0,10.01,0,0.547,5.928,88.2,2.4631,6,432,17.8,344.91,15.76,18.3 118 | 0.13158,0,10.01,0,0.547,6.176,72.5,2.7301,6,432,17.8,393.3,NA,21.2 119 | 0.15098,0,10.01,0,0.547,6.021,82.6,2.7474,6,432,17.8,394.51,10.3,19.2 120 | 0.13058,NA,10.01,0,0.547,5.872,73.1,2.4775,6,432,17.8,338.63,15.37,20.4 121 | 0.14476,0,10.01,NA,0.547,5.731,65.2,2.7592,6,432,17.8,391.5,13.61,19.3 122 | 0.06899,0,25.65,0,0.581,5.87,69.7,2.2577,2,188,19.1,389.15,14.37,22 123 | 0.07165,0,25.65,0,0.581,6.004,84.1,2.1974,2,188,19.1,377.67,14.27,20.3 124 | 0.09299,0,25.65,0,0.581,5.961,92.9,2.0869,2,188,19.1,378.09,17.93,20.5 125 | 0.15038,0,NA,0,0.581,5.856,97,1.9444,2,188,19.1,370.31,25.41,17.3 126 | 0.09849,0,25.65,0,0.581,5.879,95.8,2.0063,2,188,19.1,379.38,17.58,18.8 127 | 0.16902,0,25.65,0,0.581,5.986,88.4,1.9929,2,188,19.1,385.02,14.81,21.4 128 | 0.38735,0,25.65,0,0.581,5.613,NA,1.7572,2,188,19.1,359.29,27.26,15.7 129 | 0.25915,0,21.89,0,0.624,5.693,96,1.7883,4,437,21.2,392.11,17.19,16.2 130 | 0.32543,0,21.89,0,0.624,6.431,98.8,1.8125,4,437,21.2,396.9,15.39,18 131 | 0.88125,0,21.89,0,0.624,5.637,94.7,1.9799,4,437,21.2,396.9,18.34,14.3 132 | 0.34006,0,21.89,0,0.624,6.458,98.9,2.1185,4,437,21.2,395.04,12.6,19.2 133 | 1.19294,0,21.89,0,0.624,6.326,97.7,2.271,4,437,21.2,396.9,12.26,19.6 134 | 0.59005,0,21.89,0,0.624,6.372,97.9,2.3274,4,437,21.2,385.76,11.12,23 135 | 0.32982,NA,NA,0,0.624,5.822,95.4,2.4699,4,437,21.2,388.69,15.03,18.4 136 | 0.97617,0,21.89,0,0.624,5.757,98.4,2.346,4,437,21.2,262.76,17.31,15.6 137 | 0.55778,0,21.89,0,0.624,6.335,98.2,2.1107,4,437,21.2,394.67,16.96,18.1 138 | 0.32264,0,21.89,0,0.624,5.942,93.5,1.9669,4,437,21.2,378.25,16.9,17.4 139 | 0.35233,0,21.89,0,0.624,6.454,98.4,1.8498,4,437,21.2,394.08,14.59,17.1 140 | 0.2498,0,21.89,0,0.624,5.857,NA,1.6686,4,437,21.2,392.04,21.32,13.3 141 | 0.54452,0,21.89,0,0.624,6.151,97.9,1.6687,4,437,21.2,396.9,18.46,17.8 142 | 0.2909,0,21.89,0,0.624,6.174,93.6,1.6119,4,437,21.2,388.08,24.16,14 143 | 1.62864,0,21.89,0,0.624,5.019,100,1.4394,4,437,21.2,396.9,34.41,14.4 144 | 3.32105,0,19.58,1,0.871,5.403,100,1.3216,5,403,14.7,396.9,26.82,13.4 145 | 4.0974,0,19.58,0,0.871,5.468,100,1.4118,5,403,14.7,396.9,26.42,15.6 146 | 2.77974,0,19.58,0,0.871,4.903,97.8,1.3459,5,403,14.7,396.9,29.29,11.8 147 | 2.37934,0,19.58,0,0.871,6.13,100,1.4191,5,403,14.7,172.91,27.8,13.8 148 | 2.15505,NA,19.58,0,0.871,5.628,100,1.5166,5,403,14.7,169.27,16.65,15.6 149 | 2.36862,0,NA,0,0.871,4.926,95.7,1.4608,5,403,14.7,391.71,29.53,14.6 150 | 2.33099,0,NA,0,0.871,5.186,93.8,1.5296,5,403,14.7,356.99,28.32,17.8 151 | 2.73397,0,19.58,0,0.871,5.597,94.9,1.5257,5,403,14.7,351.85,21.45,15.4 152 | 1.6566,0,19.58,0,0.871,6.122,NA,1.618,5,403,14.7,372.8,14.1,21.5 153 | 1.49632,0,19.58,0,0.871,5.404,100,1.5916,5,403,14.7,341.6,13.28,19.6 154 | 1.12658,0,19.58,NA,0.871,5.012,88,1.6102,5,403,14.7,343.28,12.12,15.3 155 | 2.14918,0,19.58,0,0.871,5.709,98.5,1.6232,5,403,14.7,261.95,15.79,19.4 156 | 1.41385,0,19.58,1,0.871,6.129,96,1.7494,5,403,14.7,321.02,15.12,17 157 | 3.53501,0,19.58,1,0.871,6.152,NA,1.7455,5,403,14.7,88.01,15.02,15.6 158 | 2.44668,0,19.58,0,0.871,5.272,94,1.7364,5,403,14.7,88.63,16.14,13.1 159 | 1.22358,NA,19.58,0,0.605,6.943,97.4,1.8773,5,403,14.7,363.43,4.59,41.3 160 | 1.34284,0,19.58,0,0.605,6.066,100,1.7573,5,403,14.7,353.89,6.43,24.3 161 | 1.42502,0,19.58,0,0.871,6.51,100,1.7659,5,403,14.7,364.31,7.39,23.3 162 | 1.27346,0,19.58,1,0.605,6.25,92.6,1.7984,5,403,14.7,338.92,5.5,27 163 | 1.46336,0,19.58,0,0.605,7.489,90.8,1.9709,5,403,14.7,374.43,1.73,50 164 | 1.83377,0,19.58,1,0.605,7.802,98.2,2.0407,5,403,14.7,389.61,1.92,50 165 | 1.51902,0,19.58,1,0.605,8.375,NA,2.162,5,403,14.7,388.45,3.32,50 166 | 2.24236,0,19.58,0,0.605,5.854,91.8,2.422,5,403,14.7,395.11,11.64,22.7 167 | 2.924,0,19.58,0,0.605,6.101,93,2.2834,5,403,14.7,240.16,9.81,25 168 | 2.01019,0,19.58,0,0.605,7.929,96.2,2.0459,5,403,14.7,369.3,3.7,50 169 | 1.80028,NA,19.58,0,0.605,5.877,79.2,2.4259,5,403,14.7,227.61,12.14,23.8 170 | 2.3004,0,19.58,0,0.605,6.319,96.1,2.1,5,403,14.7,297.09,11.1,23.8 171 | 2.44953,0,19.58,0,0.605,6.402,95.2,2.2625,5,403,14.7,330.04,11.32,22.3 172 | 1.20742,0,19.58,0,0.605,5.875,94.6,2.4259,5,403,14.7,292.29,14.43,17.4 173 | 2.3139,0,19.58,0,0.605,5.88,97.3,2.3887,5,403,14.7,348.13,12.03,19.1 174 | 0.13914,0,4.05,0,0.51,5.572,88.5,2.5961,5,296,16.6,396.9,14.69,23.1 175 | 0.09178,0,NA,0,0.51,6.416,NA,2.6463,5,296,16.6,395.5,9.04,23.6 176 | 0.08447,0,4.05,0,0.51,5.859,68.7,2.7019,5,296,16.6,393.23,9.64,22.6 177 | 0.06664,0,4.05,0,0.51,6.546,33.1,3.1323,5,296,16.6,390.96,5.33,29.4 178 | 0.07022,0,4.05,0,0.51,6.02,47.2,3.5549,5,296,16.6,393.23,10.11,23.2 179 | 0.05425,0,NA,0,0.51,6.315,73.4,3.3175,5,296,16.6,395.6,6.29,24.6 180 | 0.06642,0,4.05,0,0.51,6.86,74.4,2.9153,5,296,16.6,391.27,6.92,29.9 181 | 0.0578,0,2.46,0,0.488,6.98,58.4,2.829,3,193,17.8,396.9,5.04,37.2 182 | 0.06588,0,2.46,0,0.488,7.765,83.3,2.741,3,193,17.8,395.56,7.56,39.8 183 | 0.06888,0,2.46,0,0.488,6.144,62.2,2.5979,3,193,17.8,396.9,9.45,36.2 184 | 0.09103,0,2.46,0,0.488,7.155,92.2,2.7006,3,193,17.8,394.12,4.82,37.9 185 | NA,0,2.46,0,0.488,6.563,95.6,2.847,3,193,17.8,396.9,5.68,32.5 186 | 0.08308,0,2.46,0,0.488,5.604,89.8,2.9879,3,193,17.8,391,13.98,26.4 187 | 0.06047,0,2.46,0,0.488,6.153,68.8,3.2797,3,193,17.8,387.11,13.15,29.6 188 | 0.05602,NA,2.46,0,0.488,7.831,53.6,3.1992,3,193,17.8,392.63,4.45,50 189 | 0.07875,45,3.44,0,0.437,6.782,41.1,3.7886,5,398,15.2,393.87,6.68,32 190 | 0.12579,45,3.44,0,0.437,6.556,29.1,4.5667,5,398,15.2,382.84,4.56,29.8 191 | 0.0837,45,3.44,0,0.437,7.185,38.9,4.5667,5,398,15.2,396.9,5.39,34.9 192 | 0.09068,45,3.44,0,0.437,6.951,21.5,6.4798,5,398,15.2,377.68,5.1,37 193 | NA,45,3.44,0,0.437,6.739,30.8,6.4798,5,398,15.2,389.71,4.69,30.5 194 | NA,45,3.44,0,0.437,7.178,26.3,6.4798,5,398,15.2,390.49,2.87,36.4 195 | 0.02187,60,2.93,0,0.401,6.8,NA,6.2196,1,265,15.6,393.37,5.03,31.1 196 | 0.01439,60,2.93,0,0.401,6.604,18.8,6.2196,1,265,15.6,376.7,4.38,29.1 197 | 0.01381,80,0.46,0,0.422,7.875,32,5.6484,4,255,14.4,394.23,2.97,50 198 | NA,80,1.52,0,0.404,7.287,34.1,7.309,2,329,12.6,396.9,4.08,33.3 199 | 0.04666,80,1.52,0,0.404,7.107,36.6,7.309,2,329,12.6,354.31,8.61,30.3 200 | 0.03768,80,1.52,0,0.404,7.274,38.3,7.309,2,329,12.6,392.2,6.62,34.6 201 | 0.0315,95,1.47,0,0.403,6.975,15.3,7.6534,3,402,17,396.9,4.56,34.9 202 | 0.01778,95,1.47,0,0.403,7.135,13.9,7.6534,3,402,17,384.3,4.45,32.9 203 | 0.03445,82.5,2.03,0,0.415,6.162,38.4,6.27,2,348,14.7,393.77,7.43,24.1 204 | 0.02177,82.5,2.03,0,0.415,7.61,15.7,6.27,2,348,14.7,395.38,3.11,42.3 205 | 0.0351,95,2.68,0,0.4161,7.853,33.2,5.118,4,224,14.7,392.78,3.81,48.5 206 | 0.02009,95,2.68,0,0.4161,8.034,31.9,5.118,4,224,14.7,390.55,2.88,50 207 | 0.13642,NA,10.59,0,0.489,5.891,22.3,3.9454,4,277,18.6,396.9,10.87,22.6 208 | 0.22969,0,10.59,NA,0.489,6.326,52.5,4.3549,4,277,18.6,394.87,10.97,24.4 209 | 0.25199,0,10.59,0,0.489,5.783,72.7,4.3549,4,277,18.6,389.43,NA,22.5 210 | 0.13587,0,10.59,1,0.489,6.064,59.1,4.2392,4,277,18.6,381.32,14.66,24.4 211 | 0.43571,0,10.59,1,0.489,5.344,100,3.875,4,277,18.6,396.9,23.09,20 212 | 0.17446,NA,10.59,1,0.489,5.96,92.1,3.8771,4,277,18.6,393.25,17.27,21.7 213 | 0.37578,0,10.59,1,0.489,5.404,88.6,3.665,4,277,18.6,395.24,23.98,19.3 214 | 0.21719,0,10.59,1,0.489,5.807,53.8,3.6526,4,277,18.6,390.94,16.03,22.4 215 | 0.14052,0,10.59,0,0.489,6.375,32.3,3.9454,4,277,18.6,385.81,9.38,28.1 216 | 0.28955,0,10.59,0,0.489,5.412,9.8,3.5875,4,277,18.6,348.93,29.55,23.7 217 | 0.19802,0,10.59,0,0.489,6.182,NA,3.9454,4,277,18.6,393.63,9.47,25 218 | 0.0456,0,13.89,1,0.55,5.888,56,3.1121,5,276,16.4,392.8,13.51,23.3 219 | 0.07013,0,13.89,0,0.55,6.642,85.1,3.4211,5,276,16.4,392.78,9.69,28.7 220 | 0.11069,0,13.89,1,0.55,5.951,93.8,2.8893,5,276,16.4,396.9,17.92,21.5 221 | 0.11425,0,NA,1,0.55,6.373,92.4,3.3633,5,276,16.4,393.74,10.5,23 222 | 0.35809,0,6.2,1,0.507,6.951,88.5,2.8617,8,307,17.4,391.7,9.71,26.7 223 | 0.40771,0,6.2,1,0.507,6.164,91.3,3.048,8,307,17.4,395.24,21.46,21.7 224 | 0.62356,0,6.2,1,0.507,6.879,77.7,3.2721,8,307,17.4,390.39,9.93,27.5 225 | 0.6147,0,6.2,0,0.507,6.618,80.8,3.2721,8,307,17.4,396.9,7.6,30.1 226 | 0.31533,0,6.2,0,0.504,8.266,78.3,2.8944,8,307,17.4,385.05,4.14,44.8 227 | 0.52693,0,6.2,0,0.504,8.725,83,2.8944,8,307,17.4,382,4.63,50 228 | 0.38214,0,6.2,0,0.504,8.04,86.5,3.2157,8,307,17.4,387.38,NA,37.6 229 | 0.41238,0,6.2,0,0.504,7.163,79.9,3.2157,8,307,17.4,372.08,6.36,31.6 230 | 0.29819,0,6.2,0,0.504,7.686,17,3.3751,8,307,17.4,377.51,NA,46.7 231 | NA,0,6.2,0,0.504,6.552,21.4,3.3751,8,307,17.4,380.34,3.76,31.5 232 | 0.537,0,6.2,0,0.504,5.981,68.1,3.6715,8,307,17.4,378.35,11.65,24.3 233 | 0.46296,0,6.2,0,0.504,7.412,76.9,3.6715,8,307,17.4,376.14,5.25,31.7 234 | 0.57529,0,6.2,0,0.507,8.337,73.3,3.8384,8,307,17.4,385.91,2.47,41.7 235 | 0.33147,0,6.2,0,0.507,8.247,NA,3.6519,8,307,17.4,378.95,3.95,48.3 236 | 0.44791,0,6.2,1,0.507,6.726,66.5,3.6519,8,307,17.4,360.2,8.05,29 237 | 0.33045,0,6.2,0,0.507,6.086,61.5,3.6519,8,307,17.4,376.75,10.88,24 238 | NA,0,6.2,1,0.507,6.631,76.5,4.148,8,307,17.4,388.45,9.54,25.1 239 | 0.51183,0,6.2,0,0.507,7.358,71.6,4.148,8,307,17.4,390.07,4.73,31.5 240 | 0.08244,NA,4.93,0,0.428,6.481,18.5,6.1899,6,300,16.6,379.41,6.36,23.7 241 | 0.09252,30,4.93,0,0.428,6.606,42.2,6.1899,6,300,16.6,383.78,7.37,23.3 242 | 0.11329,30,4.93,NA,0.428,6.897,54.3,6.3361,6,300,16.6,391.25,11.38,22 243 | NA,30,4.93,0,0.428,6.095,65.1,6.3361,6,300,16.6,394.62,12.4,20.1 244 | 0.1029,30,4.93,0,0.428,6.358,52.9,7.0355,6,300,16.6,372.75,11.22,22.2 245 | 0.12757,30,4.93,0,0.428,6.393,7.8,7.0355,6,300,16.6,374.71,5.19,23.7 246 | 0.20608,22,5.86,0,0.431,5.593,76.5,7.9549,7,330,19.1,372.49,12.5,17.6 247 | 0.19133,22,NA,NA,0.431,5.605,70.2,7.9549,7,330,19.1,389.13,18.46,18.5 248 | 0.33983,22,5.86,0,0.431,6.108,34.9,8.0555,7,330,19.1,390.18,9.16,24.3 249 | 0.19657,22,5.86,0,0.431,6.226,79.2,8.0555,7,330,19.1,376.14,10.15,20.5 250 | 0.16439,22,5.86,0,0.431,6.433,49.1,7.8265,7,330,19.1,374.71,9.52,24.5 251 | 0.19073,22,5.86,0,0.431,6.718,17.5,7.8265,7,330,19.1,393.74,6.56,26.2 252 | 0.1403,22,5.86,0,0.431,6.487,13,7.3967,7,330,19.1,396.28,5.9,24.4 253 | 0.21409,22,5.86,0,0.431,6.438,8.9,7.3967,7,330,19.1,377.07,3.59,24.8 254 | 0.08221,22,5.86,0,0.431,6.957,6.8,8.9067,7,330,19.1,386.09,3.53,29.6 255 | 0.36894,22,5.86,0,0.431,8.259,8.4,8.9067,7,330,19.1,396.9,3.54,42.8 256 | 0.04819,80,3.64,NA,0.392,6.108,32,9.2203,1,315,16.4,392.89,6.57,21.9 257 | 0.03548,80,3.64,0,0.392,5.876,19.1,9.2203,1,315,16.4,395.18,9.25,20.9 258 | 0.01538,90,3.75,0,0.394,7.454,34.2,6.3361,3,244,15.9,386.34,3.11,44 259 | 0.61154,20,3.97,0,0.647,8.704,86.9,1.801,5,264,13,389.7,5.12,50 260 | 0.66351,20,3.97,0,0.647,7.333,100,1.8946,5,264,13,383.29,7.79,36 261 | 0.65665,20,3.97,0,0.647,6.842,100,2.0107,5,264,13,391.93,6.9,30.1 262 | 0.54011,20,3.97,0,0.647,7.203,81.8,2.1121,5,264,13,392.8,9.59,33.8 263 | 0.53412,20,3.97,0,0.647,7.52,89.4,2.1398,5,264,13,388.37,7.26,43.1 264 | NA,20,3.97,0,0.647,8.398,91.5,2.2885,5,264,13,386.86,5.91,48.8 265 | 0.82526,20,3.97,0,0.647,7.327,94.5,2.0788,5,264,13,393.42,11.25,31 266 | 0.55007,20,3.97,0,0.647,7.206,91.6,1.9301,5,264,13,387.89,8.1,36.5 267 | 0.76162,20,3.97,0,0.647,5.56,62.8,1.9865,5,264,13,392.4,10.45,22.8 268 | 0.7857,NA,3.97,0,0.647,7.014,84.6,2.1329,5,264,13,384.07,14.79,30.7 269 | 0.57834,20,3.97,0,0.575,8.297,67,2.4216,5,264,13,384.54,7.44,50 270 | 0.5405,20,3.97,0,0.575,7.47,52.6,2.872,5,264,13,390.3,3.16,43.5 271 | 0.09065,20,6.96,1,0.464,5.92,61.5,3.9175,3,223,18.6,391.34,13.65,20.7 272 | 0.29916,20,6.96,0,0.464,5.856,42.1,4.429,3,223,18.6,388.65,13,21.1 273 | 0.16211,20,6.96,0,0.464,6.24,16.3,4.429,3,223,18.6,396.9,NA,25.2 274 | 0.1146,20,6.96,0,0.464,6.538,58.7,3.9175,3,223,18.6,394.96,7.73,24.4 275 | 0.22188,20,6.96,1,0.464,7.691,51.8,4.3665,3,223,18.6,390.77,6.58,35.2 276 | 0.05644,40,6.41,1,0.447,6.758,32.9,4.0776,4,254,17.6,396.9,3.53,32.4 277 | 0.09604,40,6.41,0,0.447,6.854,42.8,4.2673,4,254,17.6,396.9,2.98,32 278 | 0.10469,40,6.41,1,0.447,7.267,49,4.7872,4,254,17.6,389.25,6.05,33.2 279 | 0.06127,40,6.41,1,0.447,6.826,27.6,4.8628,4,254,17.6,393.45,NA,33.1 280 | 0.07978,40,6.41,0,0.447,6.482,32.1,4.1403,4,254,17.6,396.9,7.19,29.1 281 | 0.21038,20,3.33,0,0.4429,6.812,32.2,4.1007,5,216,14.9,396.9,4.85,35.1 282 | 0.03578,20,3.33,0,0.4429,7.82,64.5,4.6947,5,216,14.9,387.31,3.76,45.4 283 | 0.03705,20,3.33,0,0.4429,6.968,NA,5.2447,5,216,14.9,392.23,4.59,35.4 284 | 0.06129,20,3.33,1,0.4429,7.645,49.7,5.2119,5,216,14.9,377.07,3.01,46 285 | 0.01501,90,1.21,1,0.401,7.923,24.8,5.885,1,198,13.6,395.52,3.16,50 286 | 0.00906,90,2.97,0,0.4,7.088,20.8,7.3073,1,285,15.3,394.72,7.85,32.2 287 | 0.01096,55,2.25,0,0.389,6.453,31.9,7.3073,1,300,15.3,394.72,8.23,22 288 | 0.01965,80,1.76,0,0.385,6.23,NA,9.0892,1,241,18.2,341.6,12.93,20.1 289 | 0.03871,52.5,5.32,0,0.405,6.209,31.3,7.3172,6,293,16.6,396.9,7.14,23.2 290 | NA,52.5,5.32,0,0.405,6.315,45.6,7.3172,6,293,16.6,396.9,7.6,22.3 291 | 0.04297,52.5,5.32,0,0.405,6.565,22.9,7.3172,6,293,16.6,371.72,9.51,24.8 292 | 0.03502,80,4.95,0,0.411,6.861,27.9,5.1167,4,245,19.2,396.9,3.33,28.5 293 | 0.07886,80,4.95,0,0.411,7.148,27.7,5.1167,4,245,19.2,396.9,3.56,37.3 294 | 0.03615,80,NA,0,0.411,6.63,23.4,5.1167,4,245,19.2,396.9,4.7,27.9 295 | 0.08265,0,13.92,0,0.437,6.127,18.4,5.5027,4,289,16,396.9,8.58,23.9 296 | 0.08199,0,13.92,NA,0.437,6.009,42.3,5.5027,4,289,16,396.9,10.4,21.7 297 | 0.12932,0,13.92,0,0.437,6.678,31.1,5.9604,4,289,16,396.9,6.27,28.6 298 | 0.05372,0,13.92,0,0.437,6.549,51,5.9604,4,289,16,392.85,7.39,27.1 299 | 0.14103,0,NA,0,0.437,5.79,58,6.32,4,289,16,396.9,15.84,20.3 300 | 0.06466,70,2.24,0,0.4,6.345,20.1,7.8278,5,358,14.8,368.24,4.97,22.5 301 | 0.05561,70,2.24,0,0.4,7.041,10,7.8278,5,358,14.8,371.58,4.74,29 302 | 0.04417,70,2.24,0,0.4,6.871,47.4,7.8278,5,358,14.8,390.86,6.07,24.8 303 | 0.03537,NA,6.09,0,0.433,6.59,40.4,5.4917,7,329,16.1,395.75,9.5,22 304 | NA,34,6.09,0,0.433,6.495,18.4,5.4917,7,329,16.1,383.61,8.67,26.4 305 | 0.1,NA,6.09,0,0.433,6.982,17.7,5.4917,7,329,16.1,390.43,4.86,33.1 306 | 0.05515,33,2.18,0,0.472,7.236,41.1,4.022,7,222,18.4,393.68,6.93,36.1 307 | 0.05479,33,NA,0,0.472,6.616,58.1,3.37,7,222,18.4,393.36,8.93,28.4 308 | 0.07503,33,2.18,0,0.472,7.42,71.9,3.0992,7,222,18.4,396.9,6.47,33.4 309 | 0.04932,33,2.18,0,0.472,6.849,70.3,3.1827,7,222,18.4,396.9,7.53,28.2 310 | 0.49298,0,9.9,0,0.544,6.635,82.5,3.3175,4,304,18.4,396.9,4.54,22.8 311 | 0.3494,0,9.9,0,0.544,5.972,76.7,3.1025,4,304,18.4,396.24,9.97,20.3 312 | 2.63548,0,9.9,0,0.544,4.973,37.8,2.5194,4,304,18.4,350.45,12.64,16.1 313 | 0.79041,0,9.9,0,0.544,6.122,52.8,2.6403,4,304,18.4,396.9,5.98,22.1 314 | 0.26169,0,9.9,0,0.544,6.023,90.4,2.834,4,304,18.4,396.3,11.72,19.4 315 | 0.26938,0,9.9,0,0.544,6.266,82.8,3.2628,4,304,18.4,393.39,7.9,21.6 316 | 0.3692,0,9.9,0,0.544,6.567,87.3,3.6023,4,304,18.4,395.69,9.28,23.8 317 | 0.25356,0,9.9,0,0.544,5.705,77.7,3.945,4,304,18.4,396.42,11.5,16.2 318 | 0.31827,0,9.9,0,0.544,5.914,NA,3.9986,4,304,18.4,390.7,18.33,17.8 319 | 0.24522,0,9.9,0,0.544,5.782,71.7,4.0317,4,304,18.4,396.9,15.94,19.8 320 | 0.40202,0,9.9,0,0.544,6.382,67.2,3.5325,4,304,18.4,395.21,10.36,23.1 321 | 0.47547,0,9.9,0,0.544,6.113,58.8,4.0019,4,304,18.4,396.23,12.73,21 322 | 0.1676,0,7.38,0,0.493,6.426,52.3,4.5404,5,287,19.6,396.9,7.2,23.8 323 | 0.18159,0,7.38,0,0.493,6.376,54.3,4.5404,5,287,19.6,396.9,6.87,23.1 324 | 0.35114,0,7.38,0,0.493,6.041,49.9,4.7211,5,287,19.6,396.9,7.7,20.4 325 | 0.28392,0,7.38,0,0.493,5.708,74.3,4.7211,5,287,19.6,391.13,11.74,18.5 326 | 0.34109,0,7.38,0,0.493,6.415,40.1,4.7211,5,287,19.6,396.9,6.12,25 327 | 0.19186,0,7.38,0,0.493,6.431,14.7,5.4159,5,287,19.6,393.68,5.08,24.6 328 | 0.30347,0,7.38,0,0.493,6.312,28.9,5.4159,5,287,19.6,396.9,6.15,23 329 | 0.24103,0,7.38,0,0.493,6.083,43.7,5.4159,5,287,19.6,396.9,12.79,22.2 330 | 0.06617,0,3.24,0,0.46,5.868,25.8,5.2146,4,430,16.9,382.44,9.97,19.3 331 | 0.06724,0,3.24,0,0.46,6.333,17.2,5.2146,4,430,16.9,375.21,7.34,22.6 332 | 0.04544,NA,3.24,0,0.46,6.144,32.2,5.8736,4,430,16.9,368.57,9.09,19.8 333 | 0.05023,35,6.06,0,0.4379,5.706,28.4,6.6407,1,304,16.9,394.02,12.43,17.1 334 | 0.03466,NA,6.06,0,0.4379,6.031,23.3,6.6407,1,304,16.9,362.25,7.83,19.4 335 | 0.05083,0,5.19,0,0.515,6.316,38.1,6.4584,5,224,20.2,389.71,5.68,22.2 336 | 0.03738,0,5.19,0,0.515,6.31,38.5,6.4584,5,224,20.2,389.4,6.75,20.7 337 | 0.03961,0,5.19,0,0.515,6.037,34.5,5.9853,5,224,20.2,396.9,8.01,21.1 338 | 0.03427,0,5.19,0,0.515,5.869,46.3,5.2311,5,224,20.2,396.9,9.8,19.5 339 | 0.03041,0,5.19,0,0.515,5.895,59.6,5.615,5,224,20.2,394.81,10.56,18.5 340 | 0.03306,0,5.19,0,0.515,6.059,37.3,4.8122,5,224,20.2,396.14,8.51,20.6 341 | 0.05497,0,5.19,0,0.515,5.985,45.4,4.8122,5,224,20.2,396.9,9.74,19 342 | 0.06151,0,5.19,0,0.515,5.968,58.5,4.8122,5,224,20.2,396.9,9.29,18.7 343 | 0.01301,35,1.52,0,0.442,7.241,49.3,7.0379,1,284,15.5,394.74,5.49,32.7 344 | 0.02498,0,1.89,0,0.518,6.54,59.7,6.2669,1,422,15.9,389.96,8.65,16.5 345 | 0.02543,55,3.78,0,0.484,6.696,56.4,5.7321,5,370,17.6,396.9,7.18,23.9 346 | 0.03049,55,NA,0,0.484,6.874,28.1,6.4654,5,370,17.6,387.97,4.61,31.2 347 | 0.03113,0,4.39,0,0.442,6.014,48.5,8.0136,3,352,18.8,385.64,10.53,17.5 348 | 0.06162,0,4.39,0,0.442,5.898,52.3,8.0136,3,352,18.8,364.61,12.67,17.2 349 | 0.0187,85,4.15,0,0.429,6.516,27.7,8.5353,4,351,17.9,392.43,6.36,23.1 350 | 0.01501,80,2.01,0,0.435,6.635,29.7,8.344,4,280,17,390.94,5.99,24.5 351 | 0.02899,40,1.25,0,0.429,6.939,34.5,8.7921,1,335,19.7,389.85,NA,26.6 352 | 0.06211,NA,1.25,0,0.429,6.49,44.4,8.7921,1,335,19.7,396.9,NA,22.9 353 | 0.0795,60,1.69,0,0.411,6.579,35.9,10.7103,4,411,18.3,370.78,5.49,24.1 354 | 0.07244,60,1.69,0,0.411,5.884,18.5,10.7103,4,411,18.3,392.33,7.79,18.6 355 | 0.01709,90,2.02,0,0.41,6.728,36.1,12.1265,5,187,17,384.46,4.5,30.1 356 | 0.04301,80,1.91,0,0.413,5.663,21.9,10.5857,4,334,22,382.8,8.05,18.2 357 | 0.10659,NA,1.91,0,0.413,5.936,NA,10.5857,4,334,22,376.04,5.57,20.6 358 | 8.98296,0,18.1,1,0.77,6.212,97.4,2.1222,24,666,20.2,377.73,17.6,17.8 359 | 3.8497,0,18.1,1,0.77,6.395,91,2.5052,24,666,20.2,391.34,13.27,21.7 360 | 5.20177,0,18.1,1,0.77,6.127,83.4,2.7227,24,666,20.2,395.43,11.48,22.7 361 | 4.26131,0,NA,0,0.77,6.112,81.3,2.5091,24,666,20.2,390.74,12.67,22.6 362 | 4.54192,0,18.1,0,0.77,6.398,88,2.5182,24,666,20.2,374.56,7.79,25 363 | 3.83684,0,18.1,0,0.77,6.251,91.1,2.2955,24,666,20.2,350.65,14.19,19.9 364 | 3.67822,0,18.1,0,0.77,5.362,96.2,2.1036,24,666,20.2,380.79,10.19,20.8 365 | 4.22239,0,18.1,1,0.77,5.803,89,1.9047,24,666,20.2,353.04,14.64,16.8 366 | 3.47428,0,18.1,1,0.718,8.78,82.9,1.9047,24,666,20.2,354.55,5.29,21.9 367 | 4.55587,0,18.1,0,0.718,3.561,87.9,1.6132,24,666,20.2,354.7,7.12,27.5 368 | 3.69695,0,18.1,0,0.718,4.963,91.4,1.7523,24,666,20.2,316.03,14,21.9 369 | 13.5222,0,18.1,NA,0.631,3.863,100,1.5106,24,666,20.2,131.42,13.33,23.1 370 | 4.89822,0,18.1,0,0.631,4.97,NA,1.3325,24,666,20.2,375.52,3.26,50 371 | NA,0,18.1,1,0.631,6.683,96.8,1.3567,24,666,20.2,375.33,3.73,50 372 | 6.53876,0,18.1,1,0.631,7.016,97.5,1.2024,24,666,20.2,392.05,2.96,50 373 | 9.2323,0,18.1,0,0.631,6.216,100,1.1691,24,666,20.2,366.15,9.53,50 374 | 8.26725,0,18.1,1,0.668,5.875,89.6,1.1296,24,666,20.2,347.88,8.88,50 375 | 11.1081,0,18.1,0,0.668,4.906,100,1.1742,24,666,20.2,396.9,34.77,13.8 376 | 18.4982,0,18.1,0,0.668,4.138,100,1.137,24,666,20.2,396.9,37.97,13.8 377 | 19.6091,NA,18.1,0,0.671,7.313,97.9,1.3163,24,666,20.2,396.9,13.44,15 378 | 15.288,0,18.1,NA,0.671,6.649,93.3,1.3449,24,666,20.2,363.02,NA,13.9 379 | 9.82349,0,18.1,0,0.671,6.794,98.8,1.358,24,666,20.2,396.9,21.24,13.3 380 | 23.6482,0,18.1,0,0.671,6.38,96.2,1.3861,24,666,20.2,396.9,23.69,13.1 381 | 17.8667,0,18.1,0,0.671,6.223,100,1.3861,24,666,20.2,393.74,21.78,10.2 382 | 88.9762,0,18.1,0,0.671,6.968,91.9,1.4165,24,666,20.2,396.9,17.21,10.4 383 | 15.8744,0,18.1,0,0.671,6.545,99.1,1.5192,24,666,20.2,396.9,21.08,10.9 384 | 9.18702,0,18.1,0,0.7,5.536,100,1.5804,24,666,20.2,396.9,23.6,11.3 385 | 7.99248,0,18.1,0,0.7,5.52,100,1.5331,24,666,20.2,396.9,NA,12.3 386 | 20.0849,0,18.1,0,0.7,4.368,91.2,1.4395,24,666,20.2,285.83,30.63,8.8 387 | 16.8118,0,18.1,0,0.7,5.277,98.1,1.4261,24,666,20.2,396.9,30.81,7.2 388 | 24.3938,0,18.1,0,0.7,4.652,100,1.4672,24,666,20.2,396.9,28.28,10.5 389 | 22.5971,0,18.1,0,0.7,5,89.5,1.5184,24,666,20.2,396.9,31.99,7.4 390 | 14.3337,0,18.1,NA,0.7,4.88,100,1.5895,24,666,20.2,372.92,30.62,10.2 391 | 8.15174,0,18.1,0,0.7,5.39,98.9,1.7281,24,666,20.2,396.9,20.85,11.5 392 | 6.96215,0,18.1,0,0.7,5.713,97,1.9265,24,666,20.2,394.43,17.11,15.1 393 | 5.29305,0,18.1,0,0.7,6.051,82.5,2.1678,24,666,20.2,378.38,18.76,23.2 394 | 11.5779,0,18.1,0,0.7,5.036,97,1.77,24,666,20.2,396.9,25.68,9.7 395 | NA,0,18.1,0,0.693,6.193,92.6,1.7912,24,666,20.2,396.9,15.17,13.8 396 | NA,0,18.1,0,0.693,5.887,94.7,1.7821,24,666,20.2,396.9,16.35,12.7 397 | 8.71675,0,18.1,0,0.693,6.471,98.8,1.7257,24,666,20.2,391.98,17.12,13.1 398 | 5.87205,0,18.1,0,0.693,6.405,96,1.6768,24,666,20.2,396.9,19.37,12.5 399 | 7.67202,0,18.1,0,0.693,5.747,98.9,1.6334,24,666,20.2,393.1,19.92,8.5 400 | 38.3518,0,18.1,0,0.693,5.453,100,1.4896,24,666,20.2,396.9,30.59,5 401 | 9.91655,0,18.1,0,0.693,5.852,77.8,1.5004,24,666,20.2,338.16,29.97,6.3 402 | 25.0461,0,18.1,0,0.693,5.987,100,1.5888,24,666,20.2,396.9,26.77,5.6 403 | 14.2362,0,18.1,NA,0.693,6.343,100,1.5741,24,666,20.2,396.9,20.32,7.2 404 | 9.59571,0,18.1,0,0.693,6.404,100,1.639,24,666,20.2,376.11,20.31,12.1 405 | 24.8017,0,18.1,0,0.693,5.349,96,1.7028,24,666,20.2,396.9,19.77,8.3 406 | 41.5292,0,18.1,0,0.693,5.531,85.4,1.6074,24,666,20.2,329.46,27.38,8.5 407 | 67.9208,0,18.1,0,0.693,5.683,100,1.4254,24,666,20.2,384.97,22.98,5 408 | 20.7162,0,NA,0,0.659,4.138,100,1.1781,24,666,20.2,370.22,23.34,11.9 409 | 11.9511,0,18.1,0,0.659,5.608,100,1.2852,24,666,20.2,332.09,NA,27.9 410 | 7.40389,0,18.1,0,0.597,5.617,97.9,1.4547,24,666,20.2,314.64,26.4,17.2 411 | NA,0,18.1,0,0.597,6.852,100,1.4655,24,666,20.2,179.36,19.78,27.5 412 | 51.1358,0,18.1,0,0.597,5.757,100,1.413,24,666,20.2,2.6,10.11,15 413 | 14.0507,0,18.1,0,0.597,6.657,100,1.5275,24,666,20.2,35.05,21.22,17.2 414 | 18.811,0,18.1,0,0.597,4.628,100,1.5539,24,666,20.2,28.79,34.37,17.9 415 | 28.6558,0,18.1,0,0.597,5.155,100,1.5894,24,666,20.2,210.97,20.08,16.3 416 | 45.7461,0,18.1,0,0.693,4.519,100,1.6582,24,666,20.2,88.27,36.98,7 417 | 18.0846,0,18.1,0,0.679,6.434,100,1.8347,24,666,20.2,27.25,29.05,7.2 418 | 10.8342,0,18.1,0,0.679,6.782,90.8,1.8195,24,666,20.2,21.57,25.79,7.5 419 | 25.9406,0,18.1,0,0.679,5.304,89.1,1.6475,24,666,20.2,127.36,26.64,10.4 420 | 73.5341,0,18.1,0,0.679,5.957,100,1.8026,24,666,20.2,16.45,20.62,8.8 421 | 11.8123,0,18.1,0,0.718,6.824,76.5,1.794,24,666,20.2,48.45,22.74,8.4 422 | 11.0874,0,18.1,0,0.718,6.411,100,1.8589,24,666,20.2,318.75,15.02,16.7 423 | 7.02259,0,18.1,0,0.718,6.006,95.3,1.8746,24,666,20.2,319.98,15.7,14.2 424 | 12.0482,0,18.1,0,0.614,5.648,87.6,1.9512,24,666,20.2,291.55,14.1,20.8 425 | 7.05042,0,18.1,0,0.614,6.103,NA,2.0218,24,666,20.2,2.52,23.29,13.4 426 | 8.79212,0,18.1,0,0.584,5.565,70.6,2.0635,24,666,20.2,3.65,17.16,11.7 427 | 15.8603,0,18.1,0,0.679,5.896,95.4,1.9096,24,666,20.2,7.68,24.39,8.3 428 | NA,0,18.1,0,0.584,5.837,59.7,1.9976,24,666,20.2,24.65,15.69,10.2 429 | 37.6619,NA,18.1,0,0.679,6.202,78.7,1.8629,24,666,20.2,18.82,14.52,10.9 430 | 7.36711,0,18.1,0,0.679,6.193,78.1,1.9356,24,666,20.2,96.73,21.52,11 431 | 9.33889,0,18.1,0,0.679,6.38,NA,1.9682,24,666,20.2,60.72,24.08,9.5 432 | NA,0,18.1,0,0.584,6.348,86.1,2.0527,24,666,20.2,83.45,17.64,14.5 433 | 10.0623,0,18.1,0,0.584,6.833,94.3,2.0882,24,666,20.2,81.33,19.69,14.1 434 | 6.44405,0,18.1,0,0.584,6.425,74.8,2.2004,24,666,20.2,97.95,12.03,16.1 435 | 5.58107,0,18.1,0,0.713,6.436,87.9,2.3158,24,666,20.2,100.19,16.22,14.3 436 | 13.9134,0,18.1,0,0.713,6.208,95,2.2222,24,666,20.2,100.63,15.17,11.7 437 | 11.1604,0,18.1,0,0.74,6.629,94.6,2.1247,24,666,20.2,109.85,23.27,13.4 438 | 14.4208,0,18.1,0,0.74,6.461,93.3,2.0026,24,666,20.2,27.49,18.05,9.6 439 | 15.1772,0,18.1,0,0.74,6.152,100,1.9142,24,666,20.2,9.32,26.45,8.7 440 | 13.6781,0,18.1,0,0.74,5.935,87.9,1.8206,24,666,20.2,68.95,34.02,8.4 441 | 9.39063,0,18.1,0,0.74,5.627,93.9,1.8172,24,666,20.2,396.9,22.88,12.8 442 | 22.0511,0,18.1,0,0.74,5.818,92.4,1.8662,24,666,20.2,391.45,NA,10.5 443 | 9.72418,0,18.1,0,0.74,6.406,97.2,2.0651,24,666,20.2,385.96,NA,17.1 444 | 5.66637,0,18.1,NA,0.74,6.219,100,2.0048,24,666,20.2,395.69,16.59,18.4 445 | 9.96654,0,18.1,0,0.74,6.485,100,1.9784,24,666,20.2,386.73,18.85,15.4 446 | 12.8023,0,18.1,0,0.74,5.854,96.6,1.8956,24,666,20.2,240.52,23.79,10.8 447 | 10.6718,0,18.1,0,0.74,6.459,94.8,1.9879,24,666,20.2,43.06,23.98,11.8 448 | 6.28807,0,18.1,0,0.74,6.341,96.4,2.072,24,666,20.2,318.01,17.79,14.9 449 | 9.92485,0,18.1,0,0.74,6.251,96.6,2.198,24,666,20.2,388.52,16.44,12.6 450 | 9.32909,0,18.1,0,0.713,6.185,98.7,2.2616,24,666,20.2,396.9,18.13,14.1 451 | 7.52601,0,18.1,0,0.713,6.417,98.3,2.185,24,666,20.2,304.21,19.31,13 452 | 6.71772,0,18.1,NA,0.713,6.749,92.6,2.3236,24,666,20.2,0.32,17.44,13.4 453 | 5.44114,0,18.1,0,0.713,6.655,NA,2.3552,24,666,20.2,355.29,17.73,15.2 454 | 5.09017,0,18.1,0,0.713,6.297,91.8,2.3682,24,666,20.2,385.09,17.27,16.1 455 | 8.24809,0,NA,0,0.713,7.393,99.3,2.4527,24,666,20.2,375.87,16.74,17.8 456 | 9.51363,0,18.1,0,0.713,6.728,94.1,2.4961,24,666,20.2,6.68,18.71,14.9 457 | 4.75237,0,18.1,0,0.713,6.525,86.5,2.4358,24,666,20.2,50.92,18.13,14.1 458 | 4.66883,0,18.1,0,0.713,5.976,87.9,2.5806,24,666,20.2,10.48,19.01,12.7 459 | 8.20058,0,18.1,0,0.713,5.936,80.3,2.7792,24,666,20.2,3.5,16.94,13.5 460 | 7.75223,NA,NA,0,0.713,6.301,83.7,2.7831,24,666,20.2,272.21,16.23,14.9 461 | 6.80117,0,18.1,0,0.713,6.081,84.4,2.7175,24,666,20.2,396.9,14.7,20 462 | NA,0,18.1,0,0.713,6.701,90,2.5975,24,666,20.2,255.23,16.42,16.4 463 | 3.69311,0,18.1,0,0.713,6.376,88.4,2.5671,24,666,20.2,391.43,14.65,17.7 464 | 6.65492,0,18.1,0,0.713,6.317,83,2.7344,24,666,20.2,396.9,13.99,19.5 465 | 5.82115,0,18.1,0,0.713,6.513,89.9,2.8016,24,666,20.2,393.82,10.29,20.2 466 | 7.83932,0,18.1,0,0.655,6.209,65.4,2.9634,24,666,20.2,396.9,13.22,21.4 467 | NA,0,18.1,NA,0.655,5.759,48.2,3.0665,24,666,20.2,334.4,14.13,19.9 468 | 3.77498,0,NA,0,0.655,5.952,84.7,2.8715,24,666,20.2,22.01,17.15,19 469 | 4.42228,0,18.1,0,0.584,6.003,94.5,2.5403,24,666,20.2,331.29,21.32,19.1 470 | 15.5757,0,18.1,0,0.58,5.926,71,2.9084,24,666,20.2,368.74,18.13,19.1 471 | 13.0751,0,18.1,0,0.58,5.713,56.7,2.8237,24,666,20.2,396.9,14.76,20.1 472 | 4.34879,0,18.1,0,0.58,6.167,84,3.0334,24,666,20.2,396.9,16.29,19.9 473 | 4.03841,0,18.1,0,0.532,6.229,90.7,3.0993,24,666,20.2,395.33,12.87,19.6 474 | 3.56868,0,18.1,0,0.58,6.437,75,2.8965,24,666,20.2,393.37,14.36,23.2 475 | 4.64689,0,18.1,0,0.614,6.98,67.6,2.5329,24,666,20.2,374.68,NA,29.8 476 | 8.05579,0,18.1,0,0.584,5.427,95.4,2.4298,24,666,20.2,352.58,18.14,13.8 477 | 6.39312,0,18.1,0,0.584,6.162,97.4,2.206,24,666,20.2,302.76,24.1,13.3 478 | 4.87141,0,18.1,0,0.614,6.484,93.6,2.3053,24,666,20.2,396.21,18.68,16.7 479 | 15.0234,0,18.1,0,0.614,5.304,97.3,2.1007,24,666,20.2,349.48,24.91,12 480 | 10.233,0,18.1,0,0.614,6.185,96.7,2.1705,24,666,20.2,379.7,18.03,14.6 481 | 14.3337,0,18.1,NA,0.614,6.229,88,1.9512,24,666,20.2,383.32,13.11,21.4 482 | 5.82401,0,18.1,0,0.532,6.242,64.7,3.4242,24,666,20.2,396.9,10.74,23 483 | 5.70818,0,18.1,0,0.532,6.75,74.9,3.3317,24,666,20.2,393.07,7.74,23.7 484 | 5.73116,0,18.1,NA,0.532,7.061,77,3.4106,24,666,20.2,395.28,7.01,25 485 | 2.81838,0,18.1,0,0.532,5.762,40.3,4.0983,24,666,20.2,392.92,10.42,21.8 486 | 2.37857,0,18.1,0,0.583,5.871,41.9,3.724,24,666,20.2,370.73,13.34,20.6 487 | 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0.23912,0,9.69,0,0.585,6.019,65.3,2.4091,6,391,19.2,396.9,12.92,21.2 501 | 0.17783,0,9.69,0,0.585,5.569,73.5,2.3999,6,391,19.2,395.77,15.1,17.5 502 | 0.22438,0,9.69,0,0.585,6.027,79.7,2.4982,6,391,19.2,396.9,14.33,16.8 503 | 0.06263,0,11.93,0,0.573,6.593,69.1,2.4786,1,273,21,391.99,NA,22.4 504 | 0.04527,0,11.93,0,0.573,6.12,76.7,2.2875,1,273,21,396.9,9.08,20.6 505 | 0.06076,0,11.93,0,0.573,6.976,91,2.1675,1,273,21,396.9,5.64,23.9 506 | 0.10959,0,11.93,0,0.573,6.794,89.3,2.3889,1,273,21,393.45,6.48,22 507 | 0.04741,0,11.93,0,0.573,6.03,NA,2.505,1,273,21,396.9,7.88,11.9 508 | -------------------------------------------------------------------------------- /GroupA-Assignment5/Social_Network_Ads.csv: -------------------------------------------------------------------------------- 1 | User ID,Gender,Age,EstimatedSalary,Purchased 2 | 15624510,Male,19,19000,0 3 | 15810944,Male,35,20000,0 4 | 15668575,Female,26,43000,0 5 | 15603246,Female,27,57000,0 6 | 15804002,Male,19,76000,0 7 | 15728773,Male,27,58000,0 8 | 15598044,Female,27,84000,0 9 | 15694829,Female,32,150000,1 10 | 15600575,Male,25,33000,0 11 | 15727311,Female,35,65000,0 12 | 15570769,Female,26,80000,0 13 | 15606274,Female,26,52000,0 14 | 15746139,Male,20,86000,0 15 | 15704987,Male,32,18000,0 16 | 15628972,Male,18,82000,0 17 | 15697686,Male,29,80000,0 18 | 15733883,Male,47,25000,1 19 | 15617482,Male,45,26000,1 20 | 15704583,Male,46,28000,1 21 | 15621083,Female,48,29000,1 22 | 15649487,Male,45,22000,1 23 | 15736760,Female,47,49000,1 24 | 15714658,Male,48,41000,1 25 | 15599081,Female,45,22000,1 26 | 15705113,Male,46,23000,1 27 | 15631159,Male,47,20000,1 28 | 15792818,Male,49,28000,1 29 | 15633531,Female,47,30000,1 30 | 15744529,Male,29,43000,0 31 | 15669656,Male,31,18000,0 32 | 15581198,Male,31,74000,0 33 | 15729054,Female,27,137000,1 34 | 15573452,Female,21,16000,0 35 | 15776733,Female,28,44000,0 36 | 15724858,Male,27,90000,0 37 | 15713144,Male,35,27000,0 38 | 15690188,Female,33,28000,0 39 | 15689425,Male,30,49000,0 40 | 15671766,Female,26,72000,0 41 | 15782806,Female,27,31000,0 42 | 15764419,Female,27,17000,0 43 | 15591915,Female,33,51000,0 44 | 15772798,Male,35,108000,0 45 | 15792008,Male,30,15000,0 46 | 15715541,Female,28,84000,0 47 | 15639277,Male,23,20000,0 48 | 15798850,Male,25,79000,0 49 | 15776348,Female,27,54000,0 50 | 15727696,Male,30,135000,1 51 | 15793813,Female,31,89000,0 52 | 15694395,Female,24,32000,0 53 | 15764195,Female,18,44000,0 54 | 15744919,Female,29,83000,0 55 | 15671655,Female,35,23000,0 56 | 15654901,Female,27,58000,0 57 | 15649136,Female,24,55000,0 58 | 15775562,Female,23,48000,0 59 | 15807481,Male,28,79000,0 60 | 15642885,Male,22,18000,0 61 | 15789109,Female,32,117000,0 62 | 15814004,Male,27,20000,0 63 | 15673619,Male,25,87000,0 64 | 15595135,Female,23,66000,0 65 | 15583681,Male,32,120000,1 66 | 15605000,Female,59,83000,0 67 | 15718071,Male,24,58000,0 68 | 15679760,Male,24,19000,0 69 | 15654574,Female,23,82000,0 70 | 15577178,Female,22,63000,0 71 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15725660,Male,30,87000,0 135 | 15638963,Female,21,68000,0 136 | 15800061,Female,28,55000,0 137 | 15578006,Male,23,63000,0 138 | 15668504,Female,20,82000,0 139 | 15687491,Male,30,107000,1 140 | 15610403,Female,28,59000,0 141 | 15741094,Male,19,25000,0 142 | 15807909,Male,19,85000,0 143 | 15666141,Female,18,68000,0 144 | 15617134,Male,35,59000,0 145 | 15783029,Male,30,89000,0 146 | 15622833,Female,34,25000,0 147 | 15746422,Female,24,89000,0 148 | 15750839,Female,27,96000,1 149 | 15749130,Female,41,30000,0 150 | 15779862,Male,29,61000,0 151 | 15767871,Male,20,74000,0 152 | 15679651,Female,26,15000,0 153 | 15576219,Male,41,45000,0 154 | 15699247,Male,31,76000,0 155 | 15619087,Female,36,50000,0 156 | 15605327,Male,40,47000,0 157 | 15610140,Female,31,15000,0 158 | 15791174,Male,46,59000,0 159 | 15602373,Male,29,75000,0 160 | 15762605,Male,26,30000,0 161 | 15598840,Female,32,135000,1 162 | 15744279,Male,32,100000,1 163 | 15670619,Male,25,90000,0 164 | 15599533,Female,37,33000,0 165 | 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15667417,Female,54,104000,1 319 | 15684861,Male,35,55000,0 320 | 15742204,Male,45,32000,1 321 | 15623502,Male,36,60000,0 322 | 15774872,Female,52,138000,1 323 | 15611191,Female,53,82000,1 324 | 15674331,Male,41,52000,0 325 | 15619465,Female,48,30000,1 326 | 15575247,Female,48,131000,1 327 | 15695679,Female,41,60000,0 328 | 15713463,Male,41,72000,0 329 | 15785170,Female,42,75000,0 330 | 15796351,Male,36,118000,1 331 | 15639576,Female,47,107000,1 332 | 15693264,Male,38,51000,0 333 | 15589715,Female,48,119000,1 334 | 15769902,Male,42,65000,0 335 | 15587177,Male,40,65000,0 336 | 15814553,Male,57,60000,1 337 | 15601550,Female,36,54000,0 338 | 15664907,Male,58,144000,1 339 | 15612465,Male,35,79000,0 340 | 15810800,Female,38,55000,0 341 | 15665760,Male,39,122000,1 342 | 15588080,Female,53,104000,1 343 | 15776844,Male,35,75000,0 344 | 15717560,Female,38,65000,0 345 | 15629739,Female,47,51000,1 346 | 15729908,Male,47,105000,1 347 | 15716781,Female,41,63000,0 348 | 15646936,Male,53,72000,1 349 | 15768151,Female,54,108000,1 350 | 15579212,Male,39,77000,0 351 | 15721835,Male,38,61000,0 352 | 15800515,Female,38,113000,1 353 | 15591279,Male,37,75000,0 354 | 15587419,Female,42,90000,1 355 | 15750335,Female,37,57000,0 356 | 15699619,Male,36,99000,1 357 | 15606472,Male,60,34000,1 358 | 15778368,Male,54,70000,1 359 | 15671387,Female,41,72000,0 360 | 15573926,Male,40,71000,1 361 | 15709183,Male,42,54000,0 362 | 15577514,Male,43,129000,1 363 | 15778830,Female,53,34000,1 364 | 15768072,Female,47,50000,1 365 | 15768293,Female,42,79000,0 366 | 15654456,Male,42,104000,1 367 | 15807525,Female,59,29000,1 368 | 15574372,Female,58,47000,1 369 | 15671249,Male,46,88000,1 370 | 15779744,Male,38,71000,0 371 | 15624755,Female,54,26000,1 372 | 15611430,Female,60,46000,1 373 | 15774744,Male,60,83000,1 374 | 15629885,Female,39,73000,0 375 | 15708791,Male,59,130000,1 376 | 15793890,Female,37,80000,0 377 | 15646091,Female,46,32000,1 378 | 15596984,Female,46,74000,0 379 | 15800215,Female,42,53000,0 380 | 15577806,Male,41,87000,1 381 | 15749381,Female,58,23000,1 382 | 15683758,Male,42,64000,0 383 | 15670615,Male,48,33000,1 384 | 15715622,Female,44,139000,1 385 | 15707634,Male,49,28000,1 386 | 15806901,Female,57,33000,1 387 | 15775335,Male,56,60000,1 388 | 15724150,Female,49,39000,1 389 | 15627220,Male,39,71000,0 390 | 15672330,Male,47,34000,1 391 | 15668521,Female,48,35000,1 392 | 15807837,Male,48,33000,1 393 | 15592570,Male,47,23000,1 394 | 15748589,Female,45,45000,1 395 | 15635893,Male,60,42000,1 396 | 15757632,Female,39,59000,0 397 | 15691863,Female,46,41000,1 398 | 15706071,Male,51,23000,1 399 | 15654296,Female,50,20000,1 400 | 15755018,Male,36,33000,0 401 | 15594041,Female,49,36000,1 -------------------------------------------------------------------------------- /GroupA-Assignment6/Code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "NAME: Sumedha Zaware\n", 8 | "\n", 9 | "ROLL NO: TECOC342" 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 56, 15 | "metadata": { 16 | "id": "Hsgi7fUhID20" 17 | }, 18 | "outputs": [], 19 | "source": [ 20 | "# Import the required libraries\n", 21 | "import pandas as pd\n", 22 | "import matplotlib.pyplot as plt" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 57, 28 | "metadata": { 29 | "colab": { 30 | "base_uri": "https://localhost:8080/", 31 | "height": 206 32 | }, 33 | "id": "lJgXZa9FJvxG", 34 | "outputId": "84734b4a-d460-4587-90ea-30fd0fa697ea" 35 | }, 36 | "outputs": [ 37 | { 38 | "data": { 39 | "text/html": [ 40 | "
\n", 41 | "\n", 54 | "\n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | "
sepal lengthsepal widthpetal lengthpetal widthclass
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
\n", 108 | "
" 109 | ], 110 | "text/plain": [ 111 | " sepal length sepal width petal length petal width class\n", 112 | "0 5.1 3.5 1.4 0.2 Iris-setosa\n", 113 | "1 4.9 3.0 1.4 0.2 Iris-setosa\n", 114 | "2 4.7 3.2 1.3 0.2 Iris-setosa\n", 115 | "3 4.6 3.1 1.5 0.2 Iris-setosa\n", 116 | "4 5.0 3.6 1.4 0.2 Iris-setosa" 117 | ] 118 | }, 119 | "execution_count": 57, 120 | "metadata": {}, 121 | "output_type": "execute_result" 122 | } 123 | ], 124 | "source": [ 125 | "data = pd.read_csv(\"https://raw.githubusercontent.com/plotly/datasets/master/iris-data.csv\")\n", 126 | "data.head()" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": 58, 132 | "metadata": {}, 133 | "outputs": [ 134 | { 135 | "data": { 136 | "text/plain": [ 137 | "(150, 5)" 138 | ] 139 | }, 140 | "execution_count": 58, 141 | "metadata": {}, 142 | "output_type": "execute_result" 143 | } 144 | ], 145 | "source": [ 146 | "data.shape" 147 | ] 148 | }, 149 | { 150 | "cell_type": "code", 151 | "execution_count": 59, 152 | "metadata": {}, 153 | "outputs": [ 154 | { 155 | "data": { 156 | "text/html": [ 157 | "
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sepal lengthsepal widthpetal lengthpetal widthclass
05.13.51.40.2Iris-setosa
14.93.01.40.2Iris-setosa
24.73.21.30.2Iris-setosa
34.63.11.50.2Iris-setosa
45.03.61.40.2Iris-setosa
\n", 225 | "
" 226 | ], 227 | "text/plain": [ 228 | " sepal length sepal width petal length petal width class\n", 229 | "0 5.1 3.5 1.4 0.2 Iris-setosa\n", 230 | "1 4.9 3.0 1.4 0.2 Iris-setosa\n", 231 | "2 4.7 3.2 1.3 0.2 Iris-setosa\n", 232 | "3 4.6 3.1 1.5 0.2 Iris-setosa\n", 233 | "4 5.0 3.6 1.4 0.2 Iris-setosa" 234 | ] 235 | }, 236 | "execution_count": 59, 237 | "metadata": {}, 238 | "output_type": "execute_result" 239 | } 240 | ], 241 | "source": [ 242 | "data.head()" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 60, 248 | "metadata": {}, 249 | "outputs": [ 250 | { 251 | "data": { 252 | "text/html": [ 253 | "
\n", 254 | "\n", 267 | "\n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | " \n", 315 | " \n", 316 | " \n", 317 | " \n", 318 | " \n", 319 | " \n", 320 | "
sepal lengthsepal widthpetal lengthpetal widthclass
1456.73.05.22.3Iris-virginica
1466.32.55.01.9Iris-virginica
1476.53.05.22.0Iris-virginica
1486.23.45.42.3Iris-virginica
1495.93.05.11.8Iris-virginica
\n", 321 | "
" 322 | ], 323 | "text/plain": [ 324 | " sepal length sepal width petal length petal width class\n", 325 | "145 6.7 3.0 5.2 2.3 Iris-virginica\n", 326 | "146 6.3 2.5 5.0 1.9 Iris-virginica\n", 327 | "147 6.5 3.0 5.2 2.0 Iris-virginica\n", 328 | "148 6.2 3.4 5.4 2.3 Iris-virginica\n", 329 | "149 5.9 3.0 5.1 1.8 Iris-virginica" 330 | ] 331 | }, 332 | "execution_count": 60, 333 | "metadata": {}, 334 | "output_type": "execute_result" 335 | } 336 | ], 337 | "source": [ 338 | "data.tail()" 339 | ] 340 | }, 341 | { 342 | "cell_type": "code", 343 | "execution_count": 61, 344 | "metadata": {}, 345 | "outputs": [ 346 | { 347 | "name": "stdout", 348 | "output_type": "stream", 349 | "text": [ 350 | "\n", 351 | "RangeIndex: 150 entries, 0 to 149\n", 352 | "Data columns (total 5 columns):\n", 353 | " # Column Non-Null Count Dtype \n", 354 | "--- ------ -------------- ----- \n", 355 | " 0 sepal length 150 non-null float64\n", 356 | " 1 sepal width 150 non-null float64\n", 357 | " 2 petal length 150 non-null float64\n", 358 | " 3 petal width 150 non-null float64\n", 359 | " 4 class 150 non-null object \n", 360 | "dtypes: float64(4), object(1)\n", 361 | "memory usage: 6.0+ KB\n" 362 | ] 363 | } 364 | ], 365 | "source": [ 366 | "data.info()" 367 | ] 368 | }, 369 | { 370 | "cell_type": "code", 371 | "execution_count": 62, 372 | "metadata": {}, 373 | "outputs": [ 374 | { 375 | "data": { 376 | "text/html": [ 377 | "
\n", 378 | "\n", 391 | "\n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | "
sepal lengthsepal widthpetal lengthpetal width
count150.000000150.000000150.000000150.000000
mean5.8433333.0540003.7586671.198667
std0.8280660.4335941.7644200.763161
min4.3000002.0000001.0000000.100000
25%5.1000002.8000001.6000000.300000
50%5.8000003.0000004.3500001.300000
75%6.4000003.3000005.1000001.800000
max7.9000004.4000006.9000002.500000
\n", 460 | "
" 461 | ], 462 | "text/plain": [ 463 | " sepal length sepal width petal length petal width\n", 464 | "count 150.000000 150.000000 150.000000 150.000000\n", 465 | "mean 5.843333 3.054000 3.758667 1.198667\n", 466 | "std 0.828066 0.433594 1.764420 0.763161\n", 467 | "min 4.300000 2.000000 1.000000 0.100000\n", 468 | "25% 5.100000 2.800000 1.600000 0.300000\n", 469 | "50% 5.800000 3.000000 4.350000 1.300000\n", 470 | "75% 6.400000 3.300000 5.100000 1.800000\n", 471 | "max 7.900000 4.400000 6.900000 2.500000" 472 | ] 473 | }, 474 | "execution_count": 62, 475 | "metadata": {}, 476 | "output_type": "execute_result" 477 | } 478 | ], 479 | "source": [ 480 | "data.describe()" 481 | ] 482 | }, 483 | { 484 | "cell_type": "markdown", 485 | "metadata": {}, 486 | "source": [ 487 | "Let us check if there are any Null values present" 488 | ] 489 | }, 490 | { 491 | "cell_type": "code", 492 | "execution_count": 63, 493 | "metadata": { 494 | "colab": { 495 | "base_uri": "https://localhost:8080/" 496 | }, 497 | "id": "-BY21zZ_J40K", 498 | "outputId": "1eef2af3-bf21-40b3-99aa-f625b866b752" 499 | }, 500 | "outputs": [ 501 | { 502 | "data": { 503 | "text/plain": [ 504 | "sepal length 0\n", 505 | "sepal width 0\n", 506 | "petal length 0\n", 507 | "petal width 0\n", 508 | "class 0\n", 509 | "dtype: int64" 510 | ] 511 | }, 512 | "execution_count": 63, 513 | "metadata": {}, 514 | "output_type": "execute_result" 515 | } 516 | ], 517 | "source": [ 518 | "data.isnull().sum()" 519 | ] 520 | }, 521 | { 522 | "cell_type": "markdown", 523 | "metadata": {}, 524 | "source": [ 525 | "Defining X and Y for the model" 526 | ] 527 | }, 528 | { 529 | "cell_type": "code", 530 | "execution_count": 64, 531 | "metadata": { 532 | "colab": { 533 | "base_uri": "https://localhost:8080/" 534 | }, 535 | "id": "WCvFKeYwKQiy", 536 | "outputId": "d67bb333-416a-4588-854e-0d18855e854c" 537 | }, 538 | "outputs": [ 539 | { 540 | "name": "stdout", 541 | "output_type": "stream", 542 | "text": [ 543 | " sepal length sepal width petal length petal width\n", 544 | "0 5.1 3.5 1.4 0.2\n", 545 | "1 4.9 3.0 1.4 0.2\n", 546 | "2 4.7 3.2 1.3 0.2\n", 547 | "3 4.6 3.1 1.5 0.2\n", 548 | "4 5.0 3.6 1.4 0.2\n", 549 | ".. ... ... ... ...\n", 550 | "145 6.7 3.0 5.2 2.3\n", 551 | "146 6.3 2.5 5.0 1.9\n", 552 | "147 6.5 3.0 5.2 2.0\n", 553 | "148 6.2 3.4 5.4 2.3\n", 554 | "149 5.9 3.0 5.1 1.8\n", 555 | "\n", 556 | "[150 rows x 4 columns]\n", 557 | " class\n", 558 | "0 Iris-setosa\n", 559 | "1 Iris-setosa\n", 560 | "2 Iris-setosa\n", 561 | "3 Iris-setosa\n", 562 | "4 Iris-setosa\n", 563 | ".. ...\n", 564 | "145 Iris-virginica\n", 565 | "146 Iris-virginica\n", 566 | "147 Iris-virginica\n", 567 | "148 Iris-virginica\n", 568 | "149 Iris-virginica\n", 569 | "\n", 570 | "[150 rows x 1 columns]\n", 571 | "(150, 4)\n", 572 | "(150, 1)\n" 573 | ] 574 | } 575 | ], 576 | "source": [ 577 | "X = data.drop(['class'], axis=1)\n", 578 | "y = data.drop(['sepal length', 'sepal width', 'petal length', 'petal width'], axis=1)\n", 579 | "print(X)\n", 580 | "print(y)\n", 581 | "print(X.shape)\n", 582 | "print(y.shape)" 583 | ] 584 | }, 585 | { 586 | "cell_type": "code", 587 | "execution_count": 74, 588 | "metadata": { 589 | "colab": { 590 | "base_uri": "https://localhost:8080/" 591 | }, 592 | "id": "e0TFwRlSKiYE", 593 | "outputId": "d9833105-c4ce-4231-de81-14a09fe3156a" 594 | }, 595 | "outputs": [ 596 | { 597 | "name": "stdout", 598 | "output_type": "stream", 599 | "text": [ 600 | "(120, 4)\n", 601 | "(30, 4)\n", 602 | "(120, 1)\n", 603 | "(30, 1)\n" 604 | ] 605 | } 606 | ], 607 | "source": [ 608 | "from sklearn.model_selection import train_test_split\n", 609 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True)\n", 610 | "print(X_train.shape)\n", 611 | "print(X_test.shape)\n", 612 | "print(y_train.shape)\n", 613 | "print(y_test.shape)" 614 | ] 615 | }, 616 | { 617 | "cell_type": "code", 618 | "execution_count": 75, 619 | "metadata": { 620 | "colab": { 621 | "base_uri": "https://localhost:8080/" 622 | }, 623 | "id": "ETse-LCALVAd", 624 | "outputId": "0615493c-0379-47c5-bcf2-cf92b598118c" 625 | }, 626 | "outputs": [ 627 | { 628 | "name": "stderr", 629 | "output_type": "stream", 630 | "text": [ 631 | "c:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:73: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", 632 | " return f(**kwargs)\n" 633 | ] 634 | }, 635 | { 636 | "data": { 637 | "text/plain": [ 638 | "GaussianNB()" 639 | ] 640 | }, 641 | "execution_count": 75, 642 | "metadata": {}, 643 | "output_type": "execute_result" 644 | } 645 | ], 646 | "source": [ 647 | "from sklearn.naive_bayes import GaussianNB\n", 648 | "model = GaussianNB()\n", 649 | "model.fit(X_train, y_train)" 650 | ] 651 | }, 652 | { 653 | "cell_type": "code", 654 | "execution_count": 76, 655 | "metadata": { 656 | "id": "RBvohEa0LybS" 657 | }, 658 | "outputs": [ 659 | { 660 | "data": { 661 | "text/plain": [ 662 | "0.9666666666666667" 663 | ] 664 | }, 665 | "execution_count": 76, 666 | "metadata": {}, 667 | "output_type": "execute_result" 668 | } 669 | ], 670 | "source": [ 671 | "y_pred = model.predict(X_test)\n", 672 | "model.score(X_test,y_test)" 673 | ] 674 | }, 675 | { 676 | "cell_type": "code", 677 | "execution_count": 77, 678 | "metadata": { 679 | "id": "2XaPgSL5L6UK" 680 | }, 681 | "outputs": [ 682 | { 683 | "name": "stdout", 684 | "output_type": "stream", 685 | "text": [ 686 | "0.9666666666666667\n" 687 | ] 688 | } 689 | ], 690 | "source": [ 691 | "from sklearn.metrics import accuracy_score, confusion_matrix, ConfusionMatrixDisplay\n", 692 | "print(accuracy_score(y_test, y_pred))" 693 | ] 694 | }, 695 | { 696 | "cell_type": "code", 697 | "execution_count": 78, 698 | "metadata": { 699 | "id": "XmYcLV7uWwRj" 700 | }, 701 | "outputs": [ 702 | { 703 | "name": "stdout", 704 | "output_type": "stream", 705 | "text": [ 706 | "Confusion matrix:\n", 707 | "[[ 8 0 0]\n", 708 | " [ 0 11 1]\n", 709 | " [ 0 0 10]]\n" 710 | ] 711 | } 712 | ], 713 | "source": [ 714 | "cm = confusion_matrix(y_test, y_pred)\n", 715 | "disp = ConfusionMatrixDisplay(confusion_matrix = cm)\n", 716 | "print(\"Confusion matrix:\")\n", 717 | "print(cm)" 718 | ] 719 | }, 720 | { 721 | "cell_type": "code", 722 | "execution_count": 79, 723 | "metadata": { 724 | "colab": { 725 | "base_uri": "https://localhost:8080/", 726 | "height": 283 727 | }, 728 | "id": "pzs1eWydYEVU", 729 | "outputId": "1b189655-6f5b-453a-8f80-4e236e4258fa" 730 | }, 731 | "outputs": [ 732 | { 733 | "data": { 734 | "image/png": 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", 735 | "text/plain": [ 736 | "
" 737 | ] 738 | }, 739 | "metadata": { 740 | "needs_background": "light" 741 | }, 742 | "output_type": "display_data" 743 | } 744 | ], 745 | "source": [ 746 | "disp.plot()\n", 747 | "plt.show()" 748 | ] 749 | }, 750 | { 751 | "cell_type": "code", 752 | "execution_count": 80, 753 | "metadata": {}, 754 | "outputs": [ 755 | { 756 | "name": "stdout", 757 | "output_type": "stream", 758 | "text": [ 759 | "TP: 8\n", 760 | "FP: 0\n", 761 | "FN: 0\n", 762 | "TN: 11\n" 763 | ] 764 | } 765 | ], 766 | "source": [ 767 | "def get_confusion_matrix_values(y_true, y_pred):\n", 768 | " cm = confusion_matrix(y_true, y_pred)\n", 769 | " return(cm[0][0], cm[0][1], cm[1][0], cm[1][1])\n", 770 | "\n", 771 | "TP, FP, FN, TN = get_confusion_matrix_values(y_test, y_pred)\n", 772 | "print(\"TP: \", TP)\n", 773 | "print(\"FP: \", FP)\n", 774 | "print(\"FN: \", FN)\n", 775 | "print(\"TN: \", TN)" 776 | ] 777 | }, 778 | { 779 | "cell_type": "code", 780 | "execution_count": 81, 781 | "metadata": {}, 782 | "outputs": [ 783 | { 784 | "name": "stdout", 785 | "output_type": "stream", 786 | "text": [ 787 | "The Accuracy is 1.0\n", 788 | "The precision is 1.0\n", 789 | "The recall is 1.0\n" 790 | ] 791 | } 792 | ], 793 | "source": [ 794 | "print(\"The Accuracy is \", (TP+TN)/(TP+TN+FP+FN))\n", 795 | "print(\"The precision is \", TP/(TP+FP))\n", 796 | "print(\"The recall is \", TP/(TP+FN))" 797 | ] 798 | } 799 | ], 800 | "metadata": { 801 | "colab": { 802 | "collapsed_sections": [], 803 | "name": "TECOA136_NaiveBayesClassification.ipynb", 804 | "provenance": [] 805 | }, 806 | "interpreter": { 807 | "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186" 808 | }, 809 | "kernelspec": { 810 | "display_name": "Python 3.8.3 ('base')", 811 | "language": "python", 812 | "name": "python3" 813 | }, 814 | "language_info": { 815 | "codemirror_mode": { 816 | "name": "ipython", 817 | "version": 3 818 | }, 819 | "file_extension": ".py", 820 | "mimetype": "text/x-python", 821 | "name": "python", 822 | "nbconvert_exporter": "python", 823 | "pygments_lexer": "ipython3", 824 | "version": "3.8.3" 825 | } 826 | }, 827 | "nbformat": 4, 828 | "nbformat_minor": 0 829 | } 830 | -------------------------------------------------------------------------------- /GroupA-Assignment7/Code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "soAEGZmPZ0PQ" 7 | }, 8 | "source": [ 9 | "**NAME: Sumedha Zaware**\n", 10 | "\n", 11 | "**ROLL NO.: TECOC342**" 12 | ] 13 | }, 14 | { 15 | "cell_type": "markdown", 16 | "metadata": { 17 | "id": "pN4U1FFiPH9H" 18 | }, 19 | "source": [ 20 | "### Tokenization" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 1, 26 | "metadata": { 27 | "colab": { 28 | "base_uri": "https://localhost:8080/" 29 | }, 30 | "executionInfo": { 31 | "elapsed": 2509, 32 | "status": "ok", 33 | "timestamp": 1652528796765, 34 | "user": { 35 | "displayName": "TECOB206_ Rohit-Joshi", 36 | "userId": "15434995234180682292" 37 | }, 38 | "user_tz": -330 39 | }, 40 | "id": "zsw0Xt-rN97f", 41 | "outputId": "69caa78c-5634-414e-aee0-155e9c6b83d9" 42 | }, 43 | "outputs": [ 44 | { 45 | "name": "stderr", 46 | "output_type": "stream", 47 | "text": [ 48 | "[nltk_data] Downloading package punkt to\n", 49 | "[nltk_data] C:\\Users\\RBS\\AppData\\Roaming\\nltk_data...\n", 50 | "[nltk_data] Unzipping tokenizers\\punkt.zip.\n", 51 | "[nltk_data] Downloading package wordnet to\n", 52 | "[nltk_data] C:\\Users\\RBS\\AppData\\Roaming\\nltk_data...\n", 53 | "[nltk_data] Unzipping corpora\\wordnet.zip.\n", 54 | "[nltk_data] Downloading package averaged_perceptron_tagger to\n", 55 | "[nltk_data] C:\\Users\\RBS\\AppData\\Roaming\\nltk_data...\n", 56 | "[nltk_data] Unzipping taggers\\averaged_perceptron_tagger.zip.\n", 57 | "[nltk_data] Downloading package stopwords to\n", 58 | "[nltk_data] C:\\Users\\RBS\\AppData\\Roaming\\nltk_data...\n", 59 | "[nltk_data] Unzipping corpora\\stopwords.zip.\n" 60 | ] 61 | } 62 | ], 63 | "source": [ 64 | "import nltk\n", 65 | "nltk.download('punkt')\n", 66 | "nltk.download('wordnet')\n", 67 | "nltk.download('averaged_perceptron_tagger')\n", 68 | "nltk.download('stopwords')\n", 69 | "from nltk import sent_tokenize\n", 70 | "from nltk import word_tokenize\n", 71 | "from nltk.corpus import stopwords\n", 72 | "\n" 73 | ] 74 | }, 75 | { 76 | "cell_type": "code", 77 | "execution_count": 2, 78 | "metadata": { 79 | "executionInfo": { 80 | "elapsed": 573, 81 | "status": "ok", 82 | "timestamp": 1652528794264, 83 | "user": { 84 | "displayName": "TECOB206_ Rohit-Joshi", 85 | "userId": "15434995234180682292" 86 | }, 87 | "user_tz": -330 88 | }, 89 | "id": "l8wn2alROW8Y" 90 | }, 91 | "outputs": [], 92 | "source": [ 93 | "text='Real madrid is set to win the UCL for the season . Benzema might win Balon dor . Salah might be the runner up'" 94 | ] 95 | }, 96 | { 97 | "cell_type": "code", 98 | "execution_count": 3, 99 | "metadata": { 100 | "colab": { 101 | "base_uri": "https://localhost:8080/" 102 | }, 103 | "executionInfo": { 104 | "elapsed": 379, 105 | "status": "ok", 106 | "timestamp": 1652528800781, 107 | "user": { 108 | "displayName": "TECOB206_ Rohit-Joshi", 109 | "userId": "15434995234180682292" 110 | }, 111 | "user_tz": -330 112 | }, 113 | "id": "U60CN2r_O6bE", 114 | "outputId": "71db18ff-2f69-4a79-a28d-b28a88d66587" 115 | }, 116 | "outputs": [ 117 | { 118 | "name": "stdout", 119 | "output_type": "stream", 120 | "text": [ 121 | "['Real madrid is set to win the UCL for the season .', 'Benzema might win Balon dor .', 'Salah might be the runner up']\n" 122 | ] 123 | } 124 | ], 125 | "source": [ 126 | "tokens_sents = nltk.sent_tokenize(text)\n", 127 | "print(tokens_sents)" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": 4, 133 | "metadata": { 134 | "colab": { 135 | "base_uri": "https://localhost:8080/" 136 | }, 137 | "executionInfo": { 138 | "elapsed": 362, 139 | "status": "ok", 140 | "timestamp": 1652528804273, 141 | "user": { 142 | "displayName": "TECOB206_ Rohit-Joshi", 143 | "userId": "15434995234180682292" 144 | }, 145 | "user_tz": -330 146 | }, 147 | "id": "mFYUhpSoOfSW", 148 | "outputId": "c59a04eb-6f4c-4ab8-b3ec-6291e407fa9c" 149 | }, 150 | "outputs": [ 151 | { 152 | "name": "stdout", 153 | "output_type": "stream", 154 | "text": [ 155 | "['Real', 'madrid', 'is', 'set', 'to', 'win', 'the', 'UCL', 'for', 'the', 'season', '.', 'Benzema', 'might', 'win', 'Balon', 'dor', '.', 'Salah', 'might', 'be', 'the', 'runner', 'up']\n" 156 | ] 157 | } 158 | ], 159 | "source": [ 160 | "tokens_words = nltk.word_tokenize(text)\n", 161 | "print(tokens_words)" 162 | ] 163 | }, 164 | { 165 | "cell_type": "code", 166 | "execution_count": 5, 167 | "metadata": { 168 | "executionInfo": { 169 | "elapsed": 523, 170 | "status": "ok", 171 | "timestamp": 1652528807310, 172 | "user": { 173 | "displayName": "TECOB206_ Rohit-Joshi", 174 | "userId": "15434995234180682292" 175 | }, 176 | "user_tz": -330 177 | }, 178 | "id": "GEk-hq-tP4ze" 179 | }, 180 | "outputs": [], 181 | "source": [ 182 | "from nltk.stem import PorterStemmer\n", 183 | "from nltk.stem.snowball import SnowballStemmer\n", 184 | "from nltk.stem import LancasterStemmer" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": 9, 190 | "metadata": { 191 | "colab": { 192 | "base_uri": "https://localhost:8080/" 193 | }, 194 | "executionInfo": { 195 | "elapsed": 359, 196 | "status": "ok", 197 | "timestamp": 1652528810341, 198 | "user": { 199 | "displayName": "TECOB206_ Rohit-Joshi", 200 | "userId": "15434995234180682292" 201 | }, 202 | "user_tz": -330 203 | }, 204 | "id": "UsW8et7sP_m2", 205 | "outputId": "16fbff60-ddfd-4553-8240-00aaada81ffe" 206 | }, 207 | "outputs": [ 208 | { 209 | "name": "stdout", 210 | "output_type": "stream", 211 | "text": [ 212 | "['real', 'madrid', 'is', 'set', 'to', 'win', 'the', 'ucl', 'for', 'the', 'season', '.', 'benzema', 'might', 'win', 'balon', 'dor', '.', 'salah', 'might', 'be', 'the', 'runner', 'up']\n" 213 | ] 214 | } 215 | ], 216 | "source": [ 217 | "stem=[]\n", 218 | "for i in tokens_words:\n", 219 | " ps = PorterStemmer()\n", 220 | " stem_word= ps.stem(i)\n", 221 | " stem.append(stem_word)\n", 222 | "print(stem)\n", 223 | "\n" 224 | ] 225 | }, 226 | { 227 | "cell_type": "markdown", 228 | "metadata": { 229 | "id": "D5f0aXoYTahY" 230 | }, 231 | "source": [ 232 | "### Lemmatization\n" 233 | ] 234 | }, 235 | { 236 | "cell_type": "code", 237 | "execution_count": 10, 238 | "metadata": { 239 | "executionInfo": { 240 | "elapsed": 363, 241 | "status": "ok", 242 | "timestamp": 1652528813224, 243 | "user": { 244 | "displayName": "TECOB206_ Rohit-Joshi", 245 | "userId": "15434995234180682292" 246 | }, 247 | "user_tz": -330 248 | }, 249 | "id": "eQoEaQzVTdwI" 250 | }, 251 | "outputs": [], 252 | "source": [ 253 | "import nltk\n", 254 | "from nltk.stem import WordNetLemmatizer \n", 255 | "lemmatizer = WordNetLemmatizer()" 256 | ] 257 | }, 258 | { 259 | "cell_type": "code", 260 | "execution_count": 11, 261 | "metadata": { 262 | "colab": { 263 | "base_uri": "https://localhost:8080/" 264 | }, 265 | "executionInfo": { 266 | "elapsed": 1989, 267 | "status": "ok", 268 | "timestamp": 1652528818163, 269 | "user": { 270 | "displayName": "TECOB206_ Rohit-Joshi", 271 | "userId": "15434995234180682292" 272 | }, 273 | "user_tz": -330 274 | }, 275 | "id": "ufgukosbWDK_", 276 | "outputId": "95b51089-af8a-4805-875c-1e1610c6af70" 277 | }, 278 | "outputs": [ 279 | { 280 | "name": "stdout", 281 | "output_type": "stream", 282 | "text": [ 283 | "real madrid is set to win the ucl for the season . benzema might win balon dor . salah might be the runner up\n" 284 | ] 285 | } 286 | ], 287 | "source": [ 288 | "lemmatized_output = ' '.join([lemmatizer.lemmatize(w) for w in stem])\n", 289 | "print(lemmatized_output)" 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "execution_count": 12, 295 | "metadata": { 296 | "colab": { 297 | "base_uri": "https://localhost:8080/" 298 | }, 299 | "executionInfo": { 300 | "elapsed": 351, 301 | "status": "ok", 302 | "timestamp": 1652528821783, 303 | "user": { 304 | "displayName": "TECOB206_ Rohit-Joshi", 305 | "userId": "15434995234180682292" 306 | }, 307 | "user_tz": -330 308 | }, 309 | "id": "pF3rYxWHUwjQ", 310 | "outputId": "7155dd41-747b-4055-e33f-a08ffc21d5d5" 311 | }, 312 | "outputs": [ 313 | { 314 | "name": "stdout", 315 | "output_type": "stream", 316 | "text": [ 317 | "['real', 'madrid', 'is', 'set', 'to', 'win', 'the', 'ucl', 'for', 'the', 'season', '.', 'benzema', 'might', 'win', 'balon', 'dor', '.', 'salah', 'might', 'be', 'the', 'runner', 'up']\n" 318 | ] 319 | } 320 | ], 321 | "source": [ 322 | "leme=[]\n", 323 | "for i in stem:\n", 324 | " lemetized_word=lemmatizer.lemmatize(i)\n", 325 | " leme.append(lemetized_word)\n", 326 | "print(leme)" 327 | ] 328 | }, 329 | { 330 | "cell_type": "markdown", 331 | "metadata": { 332 | "id": "eNoaWrAmXPKI" 333 | }, 334 | "source": [ 335 | "### Part of Speech Tagging" 336 | ] 337 | }, 338 | { 339 | "cell_type": "code", 340 | "execution_count": 13, 341 | "metadata": { 342 | "colab": { 343 | "base_uri": "https://localhost:8080/" 344 | }, 345 | "executionInfo": { 346 | "elapsed": 344, 347 | "status": "ok", 348 | "timestamp": 1652528826976, 349 | "user": { 350 | "displayName": "TECOB206_ Rohit-Joshi", 351 | "userId": "15434995234180682292" 352 | }, 353 | "user_tz": -330 354 | }, 355 | "id": "TKAJLZkEXPXg", 356 | "outputId": "271effb6-1eb1-42e4-aa08-b95e887ce66a" 357 | }, 358 | "outputs": [ 359 | { 360 | "name": "stdout", 361 | "output_type": "stream", 362 | "text": [ 363 | "Parts of Speech: [('real', 'JJ'), ('madrid', 'NN'), ('is', 'VBZ'), ('set', 'VBN'), ('to', 'TO'), ('win', 'VB'), ('the', 'DT'), ('ucl', 'NN'), ('for', 'IN'), ('the', 'DT'), ('season', 'NN'), ('.', '.'), ('benzema', 'NN'), ('might', 'MD'), ('win', 'VB'), ('balon', 'NN'), ('dor', 'NN'), ('.', '.'), ('salah', 'NN'), ('might', 'MD'), ('be', 'VB'), ('the', 'DT'), ('runner', 'NN'), ('up', 'RP')]\n" 364 | ] 365 | } 366 | ], 367 | "source": [ 368 | "print(\"Parts of Speech: \",nltk.pos_tag(leme))\n" 369 | ] 370 | }, 371 | { 372 | "cell_type": "markdown", 373 | "metadata": { 374 | "id": "KWA2VzV2goj7" 375 | }, 376 | "source": [ 377 | "### Stop Word" 378 | ] 379 | }, 380 | { 381 | "cell_type": "code", 382 | "execution_count": 14, 383 | "metadata": { 384 | "colab": { 385 | "base_uri": "https://localhost:8080/" 386 | }, 387 | "executionInfo": { 388 | "elapsed": 340, 389 | "status": "ok", 390 | "timestamp": 1652528829409, 391 | "user": { 392 | "displayName": "TECOB206_ Rohit-Joshi", 393 | "userId": "15434995234180682292" 394 | }, 395 | "user_tz": -330 396 | }, 397 | "id": "smr18K1jY5bk", 398 | "outputId": "acf424e1-e4c3-45c8-86ce-b282d2f1f522" 399 | }, 400 | "outputs": [ 401 | { 402 | "name": "stdout", 403 | "output_type": "stream", 404 | "text": [ 405 | "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n" 406 | ] 407 | } 408 | ], 409 | "source": [ 410 | "sw_nltk = stopwords.words('english')\n", 411 | "print(sw_nltk)" 412 | ] 413 | }, 414 | { 415 | "cell_type": "code", 416 | "execution_count": 15, 417 | "metadata": { 418 | "colab": { 419 | "base_uri": "https://localhost:8080/" 420 | }, 421 | "executionInfo": { 422 | "elapsed": 7, 423 | "status": "ok", 424 | "timestamp": 1652528830878, 425 | "user": { 426 | "displayName": "TECOB206_ Rohit-Joshi", 427 | "userId": "15434995234180682292" 428 | }, 429 | "user_tz": -330 430 | }, 431 | "id": "PEdZYnCfYM-B", 432 | "outputId": "8b54050f-5c60-485b-d9f6-be641dc2cda0" 433 | }, 434 | "outputs": [ 435 | { 436 | "name": "stdout", 437 | "output_type": "stream", 438 | "text": [ 439 | "Real madrid set win UCL season . Benzema might win Balon dor . Salah might runner\n" 440 | ] 441 | } 442 | ], 443 | "source": [ 444 | "words = [word for word in text.split() if word.lower() not in sw_nltk]\n", 445 | "new_text = \" \".join(words)\n", 446 | "print(new_text)" 447 | ] 448 | } 449 | ], 450 | "metadata": { 451 | "colab": { 452 | "collapsed_sections": [], 453 | "name": "TECOB206_ASS7.ipynb", 454 | "provenance": [] 455 | }, 456 | "kernelspec": { 457 | "display_name": "Python 3", 458 | "language": "python", 459 | "name": "python3" 460 | }, 461 | "language_info": { 462 | "codemirror_mode": { 463 | "name": "ipython", 464 | "version": 3 465 | }, 466 | "file_extension": ".py", 467 | "mimetype": "text/x-python", 468 | "name": "python", 469 | "nbconvert_exporter": "python", 470 | "pygments_lexer": "ipython3", 471 | "version": "3.8.3" 472 | } 473 | }, 474 | "nbformat": 4, 475 | "nbformat_minor": 1 476 | } 477 | -------------------------------------------------------------------------------- /GroupA-Assignment9/Code.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "id": "OehSdf9lNX4I" 7 | }, 8 | "source": [ 9 | "NAME: Sumedha Zaware\n", 10 | "\n", 11 | "ROLL NO.: TECOC342\n", 12 | "\n", 13 | "**ASSIGNMENT-9**\n", 14 | "\n", 15 | "1. Use the inbuilt dataset 'titanic' as used in the above problem. Plot a box plot for distribution of age with respect to each gender along with the information about whether they survived or not. (Column names : 'sex' and 'age')\n", 16 | "2. Write observations on the inference from the above statistics." 17 | ] 18 | }, 19 | { 20 | "cell_type": "code", 21 | "execution_count": 16, 22 | "metadata": { 23 | "id": "W8NPhhK4NGdg" 24 | }, 25 | "outputs": [], 26 | "source": [ 27 | "#importing required library\n", 28 | "import pandas as pd\n", 29 | "import numpy as np\n", 30 | "import seaborn as sns\n", 31 | "import matplotlib.pyplot as plt\n", 32 | "\n", 33 | "#loading dataset\n", 34 | "data = pd.read_csv('https://raw.githubusercontent.com/dphi-official/Datasets/master/titanic_data.csv')" 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 8, 40 | "metadata": { 41 | "colab": { 42 | "base_uri": "https://localhost:8080/", 43 | "height": 595 44 | }, 45 | "id": "tVIl8_j3N-QE", 46 | "outputId": "bccecc52-cab8-4c0a-eaf2-9c29d0fd0c96" 47 | }, 48 | "outputs": [ 49 | { 50 | "data": { 51 | "text/html": [ 52 | "
\n", 53 | "\n", 66 | "\n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | " \n", 102 | " \n", 103 | " \n", 104 | " \n", 105 | " \n", 106 | " \n", 107 | " \n", 108 | " \n", 109 | " \n", 110 | " \n", 111 | " \n", 112 | " \n", 113 | " \n", 114 | " \n", 115 | " \n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n", 162 | "
" 163 | ], 164 | "text/plain": [ 165 | " PassengerId Survived Pclass \\\n", 166 | "0 1 0 3 \n", 167 | "1 2 1 1 \n", 168 | "2 3 1 3 \n", 169 | "3 4 1 1 \n", 170 | "4 5 0 3 \n", 171 | "\n", 172 | " Name Sex Age SibSp \\\n", 173 | "0 Braund, Mr. Owen Harris male 22.0 1 \n", 174 | "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", 175 | "2 Heikkinen, Miss. Laina female 26.0 0 \n", 176 | "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", 177 | "4 Allen, Mr. William Henry male 35.0 0 \n", 178 | "\n", 179 | " Parch Ticket Fare Cabin Embarked \n", 180 | "0 0 A/5 21171 7.2500 NaN S \n", 181 | "1 0 PC 17599 71.2833 C85 C \n", 182 | "2 0 STON/O2. 3101282 7.9250 NaN S \n", 183 | "3 0 113803 53.1000 C123 S \n", 184 | "4 0 373450 8.0500 NaN S " 185 | ] 186 | }, 187 | "execution_count": 8, 188 | "metadata": {}, 189 | "output_type": "execute_result" 190 | } 191 | ], 192 | "source": [ 193 | "data.head()" 194 | ] 195 | }, 196 | { 197 | "cell_type": "code", 198 | "execution_count": 12, 199 | "metadata": {}, 200 | "outputs": [ 201 | { 202 | "data": { 203 | "text/html": [ 204 | "
\n", 205 | "\n", 218 | "\n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | "
PassengerIdSurvivedPclassAgeSibSpParchFare
count891.000000891.000000891.000000714.000000891.000000891.000000891.000000
mean446.0000000.3838382.30864229.6991180.5230080.38159432.204208
std257.3538420.4865920.83607114.5264971.1027430.80605749.693429
min1.0000000.0000001.0000000.4200000.0000000.0000000.000000
25%223.5000000.0000002.00000020.1250000.0000000.0000007.910400
50%446.0000000.0000003.00000028.0000000.0000000.00000014.454200
75%668.5000001.0000003.00000038.0000001.0000000.00000031.000000
max891.0000001.0000003.00000080.0000008.0000006.000000512.329200
\n", 314 | "
" 315 | ], 316 | "text/plain": [ 317 | " PassengerId Survived Pclass Age SibSp \\\n", 318 | "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", 319 | "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", 320 | "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", 321 | "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", 322 | "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", 323 | "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", 324 | "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", 325 | "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", 326 | "\n", 327 | " Parch Fare \n", 328 | "count 891.000000 891.000000 \n", 329 | "mean 0.381594 32.204208 \n", 330 | "std 0.806057 49.693429 \n", 331 | "min 0.000000 0.000000 \n", 332 | "25% 0.000000 7.910400 \n", 333 | "50% 0.000000 14.454200 \n", 334 | "75% 0.000000 31.000000 \n", 335 | "max 6.000000 512.329200 " 336 | ] 337 | }, 338 | "execution_count": 12, 339 | "metadata": {}, 340 | "output_type": "execute_result" 341 | } 342 | ], 343 | "source": [ 344 | "data.describe()" 345 | ] 346 | }, 347 | { 348 | "cell_type": "code", 349 | "execution_count": 11, 350 | "metadata": {}, 351 | "outputs": [ 352 | { 353 | "name": "stdout", 354 | "output_type": "stream", 355 | "text": [ 356 | "\n", 357 | "RangeIndex: 891 entries, 0 to 890\n", 358 | "Data columns (total 12 columns):\n", 359 | " # Column Non-Null Count Dtype \n", 360 | "--- ------ -------------- ----- \n", 361 | " 0 PassengerId 891 non-null int64 \n", 362 | " 1 Survived 891 non-null int64 \n", 363 | " 2 Pclass 891 non-null int64 \n", 364 | " 3 Name 891 non-null object \n", 365 | " 4 Sex 891 non-null object \n", 366 | " 5 Age 714 non-null float64\n", 367 | " 6 SibSp 891 non-null int64 \n", 368 | " 7 Parch 891 non-null int64 \n", 369 | " 8 Ticket 891 non-null object \n", 370 | " 9 Fare 891 non-null float64\n", 371 | " 10 Cabin 204 non-null object \n", 372 | " 11 Embarked 889 non-null object \n", 373 | "dtypes: float64(2), int64(5), object(5)\n", 374 | "memory usage: 83.7+ KB\n" 375 | ] 376 | } 377 | ], 378 | "source": [ 379 | "data.info()" 380 | ] 381 | }, 382 | { 383 | "cell_type": "code", 384 | "execution_count": 9, 385 | "metadata": { 386 | "colab": { 387 | "base_uri": "https://localhost:8080/" 388 | }, 389 | "id": "DSGLwMcePIXp", 390 | "outputId": "bf231c46-e573-4086-8aa5-b5167a3c5dbf" 391 | }, 392 | "outputs": [ 393 | { 394 | "data": { 395 | "text/plain": [ 396 | "PassengerId 0\n", 397 | "Survived 0\n", 398 | "Pclass 0\n", 399 | "Name 0\n", 400 | "Sex 0\n", 401 | "Age 177\n", 402 | "SibSp 0\n", 403 | "Parch 0\n", 404 | "Ticket 0\n", 405 | "Fare 0\n", 406 | "Cabin 687\n", 407 | "Embarked 2\n", 408 | "dtype: int64" 409 | ] 410 | }, 411 | "execution_count": 9, 412 | "metadata": {}, 413 | "output_type": "execute_result" 414 | } 415 | ], 416 | "source": [ 417 | "data.isnull().sum()" 418 | ] 419 | }, 420 | { 421 | "cell_type": "markdown", 422 | "metadata": {}, 423 | "source": [ 424 | "Here, we can see there are Null values in the dataset. Hence, we need to replace these values by mean (in case of numerical variables) or mode (in case of categorical variables)" 425 | ] 426 | }, 427 | { 428 | "cell_type": "code", 429 | "execution_count": 19, 430 | "metadata": {}, 431 | "outputs": [], 432 | "source": [ 433 | "data['Age'] = data['Age'].fillna(np.mean(data['Age']))\n", 434 | "data['Cabin'] = data['Cabin'].fillna(data['Cabin'].mode()[0])\n", 435 | "data['Embarked'] = data['Embarked'].fillna(data['Embarked'].mode()[0])" 436 | ] 437 | }, 438 | { 439 | "cell_type": "code", 440 | "execution_count": 20, 441 | "metadata": {}, 442 | "outputs": [ 443 | { 444 | "data": { 445 | "text/plain": [ 446 | "PassengerId 0\n", 447 | "Survived 0\n", 448 | "Pclass 0\n", 449 | "Name 0\n", 450 | "Sex 0\n", 451 | "Age 0\n", 452 | "SibSp 0\n", 453 | "Parch 0\n", 454 | "Ticket 0\n", 455 | "Fare 0\n", 456 | "Cabin 0\n", 457 | "Embarked 0\n", 458 | "dtype: int64" 459 | ] 460 | }, 461 | "execution_count": 20, 462 | "metadata": {}, 463 | "output_type": "execute_result" 464 | } 465 | ], 466 | "source": [ 467 | "data.isnull().sum()" 468 | ] 469 | }, 470 | { 471 | "cell_type": "code", 472 | "execution_count": 22, 473 | "metadata": { 474 | "colab": { 475 | "base_uri": "https://localhost:8080/", 476 | "height": 334 477 | }, 478 | "id": "03XvwkW0OAcI", 479 | "outputId": "c382acb8-58ad-4eec-8aee-7e7b771248ee" 480 | }, 481 | "outputs": [ 482 | { 483 | "data": { 484 | "image/png": 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", 485 | "text/plain": [ 486 | "
" 487 | ] 488 | }, 489 | "metadata": { 490 | "needs_background": "light" 491 | }, 492 | "output_type": "display_data" 493 | } 494 | ], 495 | "source": [ 496 | "sns.boxplot(data['Sex'], data[\"Age\"], data[\"Survived\"], palette = 'Set2').set_title('Plot for distribution of age with respect to each gender along with the information about whether they survived or not')\n", 497 | "plt.show()" 498 | ] 499 | }, 500 | { 501 | "cell_type": "code", 502 | "execution_count": null, 503 | "metadata": {}, 504 | "outputs": [], 505 | "source": [] 506 | } 507 | ], 508 | "metadata": { 509 | "colab": { 510 | "name": "TECOC342_Assignment9.ipynb", 511 | "provenance": [] 512 | }, 513 | "interpreter": { 514 | "hash": "ad2bdc8ecc057115af97d19610ffacc2b4e99fae6737bb82f5d7fb13d2f2c186" 515 | }, 516 | "kernelspec": { 517 | "display_name": "Python 3.8.3 ('base')", 518 | "language": "python", 519 | "name": "python3" 520 | }, 521 | "language_info": { 522 | "codemirror_mode": { 523 | "name": "ipython", 524 | "version": 3 525 | }, 526 | "file_extension": ".py", 527 | "mimetype": "text/x-python", 528 | "name": "python", 529 | "nbconvert_exporter": "python", 530 | "pygments_lexer": "ipython3", 531 | "version": "3.8.3" 532 | } 533 | }, 534 | "nbformat": 4, 535 | "nbformat_minor": 0 536 | } 537 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data-Science-and-Big-Data-Analytics-SPPU-2019-Pattern 2 | This repository contains the assignments💻 of Data Science and Big Data Analytics(DSBDA) provided by Savitribai Phule Pune University(SPPU)🎓 3 | 4 | ## Group A- Data Science 5 | **Assignment 1** 6 | 7 |

Perform the following operations using Python on any open source dataset (e.g., data.csv)

8 | 9 |

1. Import all the required Python Libraries.

10 | 11 |

2. Locate an open source data from the web. Provide a clear description of the data and its source (i.e., URL of the web site).

12 | 13 |

3. Load the Dataset into pandas data frame.

14 | 15 |

4. Data Preprocessing: check for missing values in the data using pandas insult(), describe() function to get some initial statistics. Provide variable descriptions. Types of variables etc. Check the dimensions of the data frame.

16 | 17 |

5. Data Formatting and Data Normalization: Summarize the types of variables by checking the data types (i.e., character, numeric, integer, factor, and logical) of the variables in the data set. If variables are not in the correct data type, apply proper type conversions.

18 | 19 |

6. Turn categorical variables into quantitative variables in Python. In addition to the codes and outputs, explain every operation that you do in the above steps and explain everything that you do to import/read/scrape the data set.

20 | 21 | **Assignment 2** 22 | 23 |

Create an “Academic performance” dataset of students and perform the following operations using Python.

24 |

1. Scan all variables for missing values and inconsistencies. If there are missing values and/or inconsistencies, use any of the suitable techniques to deal with them.

25 |

2. Scan all numeric variables for outliers. If there are outliers, use any of the suitable techniques to deal with them.

26 |

3. Apply data transformations on at least one of the variables. The purpose of this transformation should be one of the following reasons: to change the scale for better understanding of the variable, to convert a non-linear relation into a linear one, or to decrease the skewness and convert the distribution into a normal distribution.

27 | 28 | **Assignment 3** 29 | 30 |

Perform the following operations on any open source dataset

31 |

1. Provide summary statistics (mean, median, minimum, maximum, standard deviation) for a dataset with numeric variables grouped by one of the qualitative variable. For example, if your categorical variable is age groups and quantitative variable is income, then provide summary statistics of income grouped by the age groups. Create a list that contains a numeric value for each response to the categorical variable.

32 |

2. Write a Python program to display some basic statistical details of the species of ‘Iris-setosa’, ‘Iris-versicolor’ and ‘Iris-versicolor’ of iris.csv dataset.

33 | 34 | **Assignment 4** 35 | 36 |

Create a Linear Regression Model using Python/R to predict home prices using Boston Housing Dataset. The Boston Housing dataset contains 37 | information about various houses in Boston through different parameters. There are 506 samples and 14 feature variables in this dataset. The objective is to predict the value of prices of the house using the given features.

38 | 39 | **Assignment 5** 40 | 41 |

1. Implement logistic regression using Python/R to perform classification on Social_Network_Ads.csv dataset.

42 |

2. Compute Confusion matrix to find TP, FP, TN, FN, Accuracy, Error rate, Precision, Recall on the given dataset.

43 | 44 | **Assignment 6** 45 | 46 |

1. Implement Simple Naïve Bayes classification algorithm using Python/R on iris.csv dataset.

47 |

2. Compute Confusion matrix to find TP, FP, TN, FN, Accuracy, Error rate, precision, recall on the given dataset.

48 | 49 | **Assignment 7** 50 | 51 |

1. Extract Sample document and apply following document preprocessing methods: Tokenization, POS Tagging, stop words removal, Stemming and Lemmatization.

52 |

2. Create representation of document by calculating Term Frequency and Inverse Document Frequency.

53 | 54 | **Assignment 8** 55 | 56 |

1. Use the inbuilt dataset 'titanic'. The dataset contains 891 rows and contains information about the passengers who boarded the unfortunate Titanic ship. Use the Seaborn library to see if we can find any patterns in the data.

57 |

2. Write a code to check how the price of the ticket (column name: 'fare') for each passenger is distributed by plotting a histogram.

58 | 59 | **Assignment 9** 60 | 61 |

1. Use the inbuilt dataset 'titanic' as used in the above problem. Plot a box plot for distribution of age with respect to each gender along with the information about whether they survived or not. (Column names : 'sex' and 'age')

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2. Write observations on the inference from the above statistics.

63 | 64 | **Assignment 10** 65 | 66 |

Download the Iris flower dataset or any other dataset into a DataFrame. Scan the dataset and give the inference as:

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1. List down the features and their types (e.g., numeric, nominal) available in the dataset.

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2. Create a histogram for each feature in the dataset to illustrate the feature distributions.

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3. Create a box plot for each feature in the dataset.

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4. Compare distributions and identify outliers.

71 | 72 | ## Group B- Big Data Analytics 73 | **Assignment 1** 74 | 75 | Write a simple program in SCALA using Apache Spark framework 76 | 77 | ## Group C- Mini Projects 78 | **Covid Vaccination Analysis** 79 | 80 |

Use the following covid_vaccine_statewise.csv dataset and perform following analytics on the given dataset https://www.kaggle.com/sudalairajkumar/covid19-in-india?select=covid_vaccine_statewise.csv

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a. Describe the dataset

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b. Number of persons state wise vaccinated for first dose in India

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c. Number of persons state wise vaccinated for second dose in India

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d. Number of Males vaccinated

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e. Number of females vaccinated

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