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+ + + + diff --git a/Classification/cell_samples.csv b/Classification/cell_samples.csv new file mode 100644 index 0000000..e6f7bb2 --- /dev/null +++ b/Classification/cell_samples.csv @@ -0,0 +1,700 @@ +ID,Clump,UnifSize,UnifShape,MargAdh,SingEpiSize,BareNuc,BlandChrom,NormNucl,Mit,Class +1000025,5,1,1,1,2,1,3,1,1,2 +1002945,5,4,4,5,7,10,3,2,1,2 +1015425,3,1,1,1,2,2,3,1,1,2 +1016277,6,8,8,1,3,4,3,7,1,2 +1017023,4,1,1,3,2,1,3,1,1,2 +1017122,8,10,10,8,7,10,9,7,1,4 +1018099,1,1,1,1,2,10,3,1,1,2 +1018561,2,1,2,1,2,1,3,1,1,2 +1033078,2,1,1,1,2,1,1,1,5,2 +1033078,4,2,1,1,2,1,2,1,1,2 +1035283,1,1,1,1,1,1,3,1,1,2 +1036172,2,1,1,1,2,1,2,1,1,2 +1041801,5,3,3,3,2,3,4,4,1,4 +1043999,1,1,1,1,2,3,3,1,1,2 +1044572,8,7,5,10,7,9,5,5,4,4 +1047630,7,4,6,4,6,1,4,3,1,4 +1048672,4,1,1,1,2,1,2,1,1,2 +1049815,4,1,1,1,2,1,3,1,1,2 +1050670,10,7,7,6,4,10,4,1,2,4 +1050718,6,1,1,1,2,1,3,1,1,2 +1054590,7,3,2,10,5,10,5,4,4,4 +1054593,10,5,5,3,6,7,7,10,1,4 +1056784,3,1,1,1,2,1,2,1,1,2 +1057013,8,4,5,1,2,?,7,3,1,4 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+ + + + diff --git a/Hierarchical/cars_clus.csv b/Hierarchical/cars_clus.csv new file mode 100644 index 0000000..9b9029e --- /dev/null +++ b/Hierarchical/cars_clus.csv @@ -0,0 +1,160 @@ +manufact,model,sales,resale,type,price,engine_s,horsepow,wheelbas,width,length,curb_wgt,fuel_cap,mpg,lnsales,partition +Acura,Integra,16.919,16.360,0.000,21.500,1.800,140.000,101.200,67.300,172.400,2.639,13.200,28.000,2.828,0.000 +Acura,TL,39.384,19.875,0.000,28.400,3.200,225.000,108.100,70.300,192.900,3.517,17.200,25.000,3.673,0.000 +Acura,CL,14.114,18.225,0.000,$null$,3.200,225.000,106.900,70.600,192.000,3.470,17.200,26.000,2.647,0.000 +Acura,RL,8.588,29.725,0.000,42.000,3.500,210.000,114.600,71.400,196.600,3.850,18.000,22.000,2.150,0.000 +Audi,A4,20.397,22.255,0.000,23.990,1.800,150.000,102.600,68.200,178.000,2.998,16.400,27.000,3.015,0.000 +Audi,A6,18.780,23.555,0.000,33.950,2.800,200.000,108.700,76.100,192.000,3.561,18.500,22.000,2.933,0.000 +Audi,A8,1.380,39.000,0.000,62.000,4.200,310.000,113.000,74.000,198.200,3.902,23.700,21.000,0.322,0.000 +BMW,323i,19.747,$null$,0.000,26.990,2.500,170.000,107.300,68.400,176.000,3.179,16.600,26.100,2.983,0.000 +BMW,328i,9.231,28.675,0.000,33.400,2.800,193.000,107.300,68.500,176.000,3.197,16.600,24.000,2.223,0.000 +BMW,528i,17.527,36.125,0.000,38.900,2.800,193.000,111.400,70.900,188.000,3.472,18.500,24.800,2.864,0.000 +Buick,Century,91.561,12.475,0.000,21.975,3.100,175.000,109.000,72.700,194.600,3.368,17.500,25.000,4.517,0.000 +Buick,Regal,39.350,13.740,0.000,25.300,3.800,240.000,109.000,72.700,196.200,3.543,17.500,23.000,3.672,0.000 +Buick,Park Avenue,27.851,20.190,0.000,31.965,3.800,205.000,113.800,74.700,206.800,3.778,18.500,24.000,3.327,0.000 +Buick,LeSabre,83.257,13.360,0.000,27.885,3.800,205.000,112.200,73.500,200.000,3.591,17.500,25.000,4.422,0.000 +Cadillac,DeVille,63.729,22.525,0.000,39.895,4.600,275.000,115.300,74.500,207.200,3.978,18.500,22.000,4.155,0.000 +Cadillac,Seville,15.943,27.100,0.000,44.475,4.600,275.000,112.200,75.000,201.000,$null$,18.500,22.000,2.769,0.000 +Cadillac,Eldorado,6.536,25.725,0.000,39.665,4.600,275.000,108.000,75.500,200.600,3.843,19.000,22.000,1.877,0.000 +Cadillac,Catera,11.185,18.225,0.000,31.010,3.000,200.000,107.400,70.300,194.800,3.770,18.000,22.000,2.415,0.000 +Cadillac,Escalade,14.785,$null$,1.000,46.225,5.700,255.000,117.500,77.000,201.200,5.572,30.000,15.000,2.694,0.000 +Chevrolet,Cavalier,145.519,9.250,0.000,13.260,2.200,115.000,104.100,67.900,180.900,2.676,14.300,27.000,4.980,0.000 +Chevrolet,Malibu,135.126,11.225,0.000,16.535,3.100,170.000,107.000,69.400,190.400,3.051,15.000,25.000,4.906,0.000 +Chevrolet,Lumina,24.629,10.310,0.000,18.890,3.100,175.000,107.500,72.500,200.900,3.330,16.600,25.000,3.204,0.000 +Chevrolet,Monte Carlo,42.593,11.525,0.000,19.390,3.400,180.000,110.500,72.700,197.900,3.340,17.000,27.000,3.752,0.000 +Chevrolet,Camaro,26.402,13.025,0.000,24.340,3.800,200.000,101.100,74.100,193.200,3.500,16.800,25.000,3.273,0.000 +Chevrolet,Corvette,17.947,36.225,0.000,45.705,5.700,345.000,104.500,73.600,179.700,3.210,19.100,22.000,2.887,0.000 +Chevrolet,Prizm,32.299,9.125,0.000,13.960,1.800,120.000,97.100,66.700,174.300,2.398,13.200,33.000,3.475,0.000 +Chevrolet,Metro,21.855,5.160,0.000,9.235,1.000,55.000,93.100,62.600,149.400,1.895,10.300,45.000,3.084,0.000 +Chevrolet,Impala,107.995,$null$,0.000,18.890,3.400,180.000,110.500,73.000,200.000,3.389,17.000,27.000,4.682,0.000 +Chrysler,Sebring Coupe,7.854,12.360,0.000,19.840,2.500,163.000,103.700,69.700,190.900,2.967,15.900,24.000,2.061,0.000 +Chrysler,Sebring Conv.,32.775,14.180,0.000,24.495,2.500,168.000,106.000,69.200,193.000,3.332,16.000,24.000,3.490,0.000 +Chrysler,Concorde,31.148,13.725,0.000,22.245,2.700,200.000,113.000,74.400,209.100,3.452,17.000,26.000,3.439,0.000 +Chrysler,Cirrus,32.306,12.640,0.000,16.480,2.000,132.000,108.000,71.000,186.000,2.911,16.000,27.000,3.475,0.000 +Chrysler,LHS,13.462,17.325,0.000,28.340,3.500,253.000,113.000,74.400,207.700,3.564,17.000,23.000,2.600,0.000 +Chrysler,Town & Country,53.480,19.540,1.000,$null$,$null$,$null$,$null$,$null$,$null$,$null$,$null$,$null$,3.979,0.000 +Chrysler,300M,30.696,$null$,0.000,29.185,3.500,253.000,113.000,74.400,197.800,3.567,17.000,23.000,3.424,0.000 +Dodge,Neon,76.034,7.750,0.000,12.640,2.000,132.000,105.000,74.400,174.400,2.567,12.500,29.000,4.331,0.000 +Dodge,Avenger,4.734,12.545,0.000,19.045,2.500,163.000,103.700,69.100,190.200,2.879,15.900,24.000,1.555,0.000 +Dodge,Stratus,71.186,10.185,0.000,20.230,2.500,168.000,108.000,71.000,186.000,3.058,16.000,24.000,4.265,0.000 +Dodge,Intrepid,88.028,12.275,0.000,22.505,2.700,202.000,113.000,74.700,203.700,3.489,17.000,$null$,4.478,0.000 +Dodge,Viper,0.916,58.470,0.000,69.725,8.000,450.000,96.200,75.700,176.700,3.375,19.000,16.000,-0.088,0.000 +Dodge,Ram Pickup,227.061,15.060,1.000,19.460,5.200,230.000,138.700,79.300,224.200,4.470,26.000,17.000,5.425,0.000 +Dodge,Ram Wagon,16.767,15.510,1.000,21.315,3.900,175.000,109.600,78.800,192.600,4.245,32.000,15.000,2.819,0.000 +Dodge,Ram Van,31.038,13.425,1.000,18.575,3.900,175.000,127.200,78.800,208.500,4.298,32.000,16.000,3.435,0.000 +Dodge,Dakota,111.313,11.260,1.000,16.980,2.500,120.000,131.000,71.500,215.000,3.557,22.000,19.000,4.712,0.000 +Dodge,Durango,101.323,$null$,1.000,26.310,5.200,230.000,115.700,71.700,193.500,4.394,25.000,17.000,4.618,0.000 +Dodge,Caravan,181.749,12.025,1.000,19.565,2.400,150.000,113.300,76.800,186.300,3.533,20.000,24.000,5.203,0.000 +Ford,Escort,70.227,7.425,0.000,12.070,2.000,110.000,98.400,67.000,174.700,2.468,12.700,30.000,4.252,0.000 +Ford,Mustang,113.369,12.760,0.000,21.560,3.800,190.000,101.300,73.100,183.200,3.203,15.700,24.000,4.731,0.000 +Ford,Contour,35.068,8.835,0.000,17.035,2.500,170.000,106.500,69.100,184.600,2.769,15.000,25.000,3.557,0.000 +Ford,Taurus,245.815,10.055,0.000,17.885,3.000,155.000,108.500,73.000,197.600,3.368,16.000,24.000,5.505,0.000 +Ford,Focus,175.670,$null$,0.000,12.315,2.000,107.000,103.000,66.900,174.800,2.564,13.200,30.000,5.169,0.000 +Ford,Crown Victoria,63.403,14.210,0.000,22.195,4.600,200.000,114.700,78.200,212.000,3.908,19.000,21.000,4.150,0.000 +Ford,Explorer,276.747,16.640,1.000,31.930,4.000,210.000,111.600,70.200,190.700,3.876,21.000,19.000,5.623,0.000 +Ford,Windstar,155.787,13.175,1.000,21.410,3.000,150.000,120.700,76.600,200.900,3.761,26.000,21.000,5.048,0.000 +Ford,Expedition,125.338,23.575,1.000,36.135,4.600,240.000,119.000,78.700,204.600,4.808,26.000,16.000,4.831,0.000 +Ford,Ranger,220.650,7.850,1.000,12.050,2.500,119.000,117.500,69.400,200.700,3.086,20.000,23.000,5.397,0.000 +Ford,F-Series,540.561,15.075,1.000,26.935,4.600,220.000,138.500,79.100,224.500,4.241,25.100,18.000,6.293,0.000 +Honda,Civic,199.685,9.850,0.000,12.885,1.600,106.000,103.200,67.100,175.100,2.339,11.900,32.000,5.297,0.000 +Honda,Accord,230.902,13.210,0.000,15.350,2.300,135.000,106.900,70.300,188.800,2.932,17.100,27.000,5.442,0.000 +Honda,CR-V,73.203,17.710,1.000,20.550,2.000,146.000,103.200,68.900,177.600,3.219,15.300,24.000,4.293,0.000 +Honda,Passport,12.855,17.525,1.000,26.600,3.200,205.000,106.400,70.400,178.200,3.857,21.100,19.000,2.554,0.000 +Honda,Odyssey,76.029,19.490,1.000,26.000,3.500,210.000,118.100,75.600,201.200,4.288,20.000,23.000,4.331,0.000 +Hyundai,Accent,41.184,5.860,0.000,9.699,1.500,92.000,96.100,65.700,166.700,2.240,11.900,31.000,3.718,0.000 +Hyundai,Elantra,66.692,7.825,0.000,11.799,2.000,140.000,100.400,66.900,174.000,2.626,14.500,27.000,4.200,0.000 +Hyundai,Sonata,29.450,8.910,0.000,14.999,2.400,148.000,106.300,71.600,185.400,3.072,17.200,25.000,3.383,0.000 +Infiniti,I30,23.713,19.690,0.000,29.465,3.000,227.000,108.300,70.200,193.700,3.342,18.500,25.000,3.166,0.000 +Jaguar,S-Type,15.467,$null$,0.000,42.800,3.000,240.000,114.500,71.600,191.300,3.650,18.400,21.000,2.739,0.000 +Jeep,Wrangler,55.557,13.475,1.000,14.460,2.500,120.000,93.400,66.700,152.000,3.045,19.000,17.000,4.017,0.000 +Jeep,Cherokee,80.556,13.775,1.000,21.620,4.000,190.000,101.400,69.400,167.500,3.194,20.000,20.000,4.389,0.000 +Jeep,Grand Cherokee,157.040,18.810,1.000,26.895,4.000,195.000,105.900,72.300,181.500,3.880,20.500,19.000,5.057,0.000 +Lexus,ES300,24.072,26.975,0.000,31.505,3.000,210.000,105.100,70.500,190.200,3.373,18.500,23.000,3.181,0.000 +Lexus,GS300,12.698,32.075,0.000,37.805,3.000,225.000,110.200,70.900,189.200,3.638,19.800,23.000,2.541,0.000 +Lexus,GS400,3.334,$null$,0.000,46.305,4.000,300.000,110.200,70.900,189.200,3.693,19.800,21.000,1.204,0.000 +Lexus,LS400,6.375,40.375,0.000,54.005,4.000,290.000,112.200,72.000,196.700,3.890,22.500,22.000,1.852,0.000 +Lexus,LX470,9.126,$null$,1.000,60.105,4.700,230.000,112.200,76.400,192.500,5.401,25.400,15.000,2.211,0.000 +Lexus,RX300,51.238,$null$,1.000,34.605,3.000,220.000,103.000,71.500,180.100,3.900,17.200,21.000,3.936,0.000 +Lincoln,Continental,13.798,20.525,0.000,39.080,4.600,275.000,109.000,73.600,208.500,3.868,20.000,22.000,2.625,0.000 +Lincoln,Town car,48.911,21.725,0.000,43.330,4.600,215.000,117.700,78.200,215.300,4.121,19.000,21.000,3.890,0.000 +Lincoln,Navigator,22.925,$null$,1.000,42.660,5.400,300.000,119.000,79.900,204.800,5.393,30.000,15.000,3.132,0.000 +Mitsubishi,Mirage,26.232,8.325,0.000,13.987,1.800,113.000,98.400,66.500,173.600,2.250,13.200,30.000,3.267,0.000 +Mitsubishi,Eclipse,42.541,10.395,0.000,19.047,2.400,154.000,100.800,68.900,175.400,2.910,15.900,24.000,3.750,0.000 +Mitsubishi,Galant,55.616,10.595,0.000,17.357,2.400,145.000,103.700,68.500,187.800,2.945,16.300,25.000,4.018,0.000 +Mitsubishi,Diamante,5.711,16.575,0.000,24.997,3.500,210.000,107.100,70.300,194.100,3.443,19.000,22.000,1.742,0.000 +Mitsubishi,3000GT,0.110,20.940,0.000,25.450,3.000,161.000,97.200,72.400,180.300,3.131,19.800,21.000,-2.207,0.000 +Mitsubishi,Montero,11.337,19.125,1.000,31.807,3.500,200.000,107.300,69.900,186.600,4.520,24.300,18.000,2.428,0.000 +Mitsubishi,Montero Sport,39.348,13.880,1.000,22.527,3.000,173.000,107.300,66.700,178.300,3.510,19.500,20.000,3.672,0.000 +Mercury,Mystique,14.351,8.800,0.000,16.240,2.000,125.000,106.500,69.100,184.800,2.769,15.000,28.000,2.664,0.000 +Mercury,Cougar,26.529,13.890,0.000,16.540,2.000,125.000,106.400,69.600,185.000,2.892,16.000,30.000,3.278,0.000 +Mercury,Sable,67.956,11.030,0.000,19.035,3.000,153.000,108.500,73.000,199.700,3.379,16.000,24.000,4.219,0.000 +Mercury,Grand Marquis,81.174,14.875,0.000,22.605,4.600,200.000,114.700,78.200,212.000,3.958,19.000,21.000,4.397,0.000 +Mercury,Mountaineer,27.609,20.430,1.000,27.560,4.000,210.000,111.600,70.200,190.100,3.876,21.000,18.000,3.318,0.000 +Mercury,Villager,20.380,14.795,1.000,22.510,3.300,170.000,112.200,74.900,194.700,3.944,20.000,21.000,3.015,0.000 +Mercedes-Benz,C-Class,18.392,26.050,0.000,31.750,2.300,185.000,105.900,67.700,177.400,3.250,16.400,26.000,2.912,0.000 +Mercedes-Benz,E-Class,27.602,41.450,0.000,49.900,3.200,221.000,111.500,70.800,189.400,3.823,21.100,25.000,3.318,0.000 +Mercedes-Benz,S-Class,16.774,50.375,0.000,69.700,4.300,275.000,121.500,73.100,203.100,4.133,23.200,21.000,2.820,0.000 +Mercedes-Benz,SL-Class,3.311,58.600,0.000,82.600,5.000,302.000,99.000,71.300,177.100,4.125,21.100,20.000,1.197,0.000 +Mercedes-Benz,SLK,7.998,$null$,0.000,38.900,2.300,190.000,94.500,67.500,157.900,3.055,15.900,26.000,2.079,0.000 +Mercedes-Benz,SLK230,1.526,$null$,0.000,41.000,2.300,185.000,94.500,67.500,157.300,2.975,14.000,27.000,0.423,0.000 +Mercedes-Benz,CLK Coupe,11.592,$null$,0.000,41.600,3.200,215.000,105.900,67.800,180.300,3.213,16.400,26.000,2.450,0.000 +Mercedes-Benz,CL500,0.954,$null$,0.000,85.500,5.000,302.000,113.600,73.100,196.600,4.115,23.200,20.000,-0.047,0.000 +Mercedes-Benz,M-Class,28.976,$null$,1.000,35.300,3.200,215.000,111.000,72.200,180.600,4.387,19.000,20.000,3.366,0.000 +Nissan,Sentra,42.643,8.450,0.000,13.499,1.800,126.000,99.800,67.300,177.500,2.593,13.200,30.000,3.753,0.000 +Nissan,Altima,88.094,11.295,0.000,20.390,2.400,155.000,103.100,69.100,183.500,3.012,15.900,25.000,4.478,0.000 +Nissan,Maxima,79.853,15.125,0.000,26.249,3.000,222.000,108.300,70.300,190.500,3.294,18.500,25.000,4.380,0.000 +Nissan,Quest,27.308,15.380,1.000,26.399,3.300,170.000,112.200,74.900,194.800,3.991,20.000,21.000,3.307,0.000 +Nissan,Pathfinder,42.574,17.810,1.000,29.299,3.300,170.000,106.300,71.700,182.600,3.947,21.000,19.000,3.751,0.000 +Nissan,Xterra,54.158,$null$,1.000,22.799,3.300,170.000,104.300,70.400,178.000,3.821,19.400,18.000,3.992,0.000 +Nissan,Frontier,65.005,$null$,1.000,17.890,3.300,170.000,116.100,66.500,196.100,3.217,19.400,18.000,4.174,0.000 +Oldsmobile,Cutlass,1.112,11.240,0.000,18.145,3.100,150.000,107.000,69.400,192.000,3.102,15.200,25.000,0.106,0.000 +Oldsmobile,Intrigue,38.554,$null$,0.000,24.150,3.500,215.000,109.000,73.600,195.900,3.455,18.000,$null$,3.652,0.000 +Oldsmobile,Alero,80.255,$null$,0.000,18.270,2.400,150.000,107.000,70.100,186.700,2.958,15.000,27.000,4.385,0.000 +Oldsmobile,Aurora,14.690,19.890,0.000,36.229,4.000,250.000,113.800,74.400,205.400,3.967,18.500,22.000,2.687,0.000 +Oldsmobile,Bravada,20.017,19.925,1.000,31.598,4.300,190.000,107.000,67.800,181.200,4.068,17.500,19.000,2.997,0.000 +Oldsmobile,Silhouette,24.361,15.240,1.000,25.345,3.400,185.000,120.000,72.200,201.400,3.948,25.000,22.000,3.193,0.000 +Plymouth,Neon,32.734,7.750,0.000,12.640,2.000,132.000,105.000,74.400,174.400,2.559,12.500,29.000,3.488,0.000 +Plymouth,Breeze,5.240,9.800,0.000,16.080,2.000,132.000,108.000,71.000,186.300,2.942,16.000,27.000,1.656,0.000 +Plymouth,Voyager,24.155,12.025,1.000,18.850,2.400,150.000,113.300,76.800,186.300,3.528,20.000,24.000,3.184,0.000 +Plymouth,Prowler,1.872,$null$,0.000,43.000,3.500,253.000,113.300,76.300,165.400,2.850,12.000,21.000,0.627,0.000 +Pontiac,Sunfire,51.645,13.790,0.000,21.610,2.400,150.000,104.100,68.400,181.900,2.906,15.000,27.000,3.944,0.000 +Pontiac,Grand Am,131.097,10.290,0.000,19.720,3.400,175.000,107.000,70.400,186.300,3.091,15.200,25.000,4.876,0.000 +Pontiac,Firebird,19.911,17.805,0.000,25.310,3.800,200.000,101.100,74.500,193.400,3.492,16.800,25.000,2.991,0.000 +Pontiac,Grand Prix,92.364,14.010,0.000,21.665,3.800,195.000,110.500,72.700,196.500,3.396,18.000,25.000,4.526,0.000 +Pontiac,Bonneville,35.945,13.225,0.000,23.755,3.800,205.000,112.200,72.600,202.500,3.590,17.500,24.000,3.582,0.000 +Pontiac,Montana,39.572,$null$,1.000,25.635,3.400,185.000,120.000,72.700,201.300,3.942,25.000,23.000,3.678,0.000 +Porsche,Boxter,8.982,41.250,0.000,41.430,2.700,217.000,95.200,70.100,171.000,2.778,17.000,22.000,2.195,0.000 +Porsche,Carrera Coupe,1.280,60.625,0.000,71.020,3.400,300.000,92.600,69.500,174.500,3.032,17.000,21.000,0.247,0.000 +Porsche,Carrera Cabriolet,1.866,67.550,0.000,74.970,3.400,300.000,92.600,69.500,174.500,3.075,17.000,23.000,0.624,0.000 +Saab,9-5,9.191,$null$,0.000,33.120,2.300,170.000,106.400,70.600,189.200,3.280,18.500,23.000,2.218,0.000 +Saab,9-3,12.115,$null$,0.000,26.100,2.000,185.000,102.600,67.400,182.200,2.990,16.900,23.000,2.494,0.000 +Saturn,SL,80.620,9.200,0.000,10.685,1.900,100.000,102.400,66.400,176.900,2.332,12.100,33.000,4.390,0.000 +Saturn,SC,24.546,10.590,0.000,12.535,1.900,100.000,102.400,66.400,180.000,2.367,12.100,33.000,3.201,0.000 +Saturn,SW,5.223,10.790,0.000,14.290,1.900,124.000,102.400,66.400,176.900,2.452,12.100,31.000,1.653,0.000 +Saturn,LW,8.472,$null$,0.000,18.835,2.200,137.000,106.500,69.000,190.400,3.075,13.100,27.000,2.137,0.000 +Saturn,LS,49.989,$null$,0.000,15.010,2.200,137.000,106.500,69.000,190.400,2.910,13.100,28.000,3.912,0.000 +Subaru,Outback,47.107,$null$,0.000,22.695,2.500,165.000,103.500,67.500,185.800,3.415,16.900,25.000,3.852,0.000 +Subaru,Forester,33.028,$null$,1.000,20.095,2.500,165.000,99.400,68.300,175.200,3.125,15.900,24.000,3.497,0.000 +Toyota,Corolla,142.535,10.025,0.000,13.108,1.800,120.000,97.000,66.700,174.000,2.420,13.200,33.000,4.960,0.000 +Toyota,Camry,247.994,13.245,0.000,17.518,2.200,133.000,105.200,70.100,188.500,2.998,18.500,27.000,5.513,0.000 +Toyota,Avalon,63.849,18.140,0.000,25.545,3.000,210.000,107.100,71.700,191.900,3.417,18.500,26.000,4.157,0.000 +Toyota,Celica,33.269,15.445,0.000,16.875,1.800,140.000,102.400,68.300,170.500,2.425,14.500,31.000,3.505,0.000 +Toyota,Tacoma,84.087,9.575,1.000,11.528,2.400,142.000,103.300,66.500,178.700,2.580,15.100,23.000,4.432,0.000 +Toyota,Sienna,65.119,$null$,1.000,22.368,3.000,194.000,114.200,73.400,193.500,3.759,20.900,22.000,4.176,0.000 +Toyota,RAV4,25.106,13.325,1.000,16.888,2.000,127.000,94.900,66.700,163.800,2.668,15.300,27.000,3.223,0.000 +Toyota,4Runner,68.411,19.425,1.000,22.288,2.700,150.000,105.300,66.500,183.300,3.440,18.500,23.000,4.226,0.000 +Toyota,Land Cruiser,9.835,34.080,1.000,51.728,4.700,230.000,112.200,76.400,192.500,5.115,25.400,15.000,2.286,0.000 +Volkswagen,Golf,9.761,11.425,0.000,14.900,2.000,115.000,98.900,68.300,163.300,2.767,14.500,26.000,2.278,0.000 +Volkswagen,Jetta,83.721,13.240,0.000,16.700,2.000,115.000,98.900,68.300,172.300,2.853,14.500,26.000,4.427,0.000 +Volkswagen,Passat,51.102,16.725,0.000,21.200,1.800,150.000,106.400,68.500,184.100,3.043,16.400,27.000,3.934,0.000 +Volkswagen,Cabrio,9.569,16.575,0.000,19.990,2.000,115.000,97.400,66.700,160.400,3.079,13.700,26.000,2.259,0.000 +Volkswagen,GTI,5.596,13.760,0.000,17.500,2.000,115.000,98.900,68.300,163.300,2.762,14.600,26.000,1.722,0.000 +Volkswagen,Beetle,49.463,$null$,0.000,15.900,2.000,115.000,98.900,67.900,161.100,2.769,14.500,26.000,3.901,0.000 +Volvo,S40,16.957,$null$,0.000,23.400,1.900,160.000,100.500,67.600,176.600,2.998,15.800,25.000,2.831,0.000 +Volvo,V40,3.545,$null$,0.000,24.400,1.900,160.000,100.500,67.600,176.600,3.042,15.800,25.000,1.266,0.000 +Volvo,S70,15.245,$null$,0.000,27.500,2.400,168.000,104.900,69.300,185.900,3.208,17.900,25.000,2.724,0.000 +Volvo,V70,17.531,$null$,0.000,28.800,2.400,168.000,104.900,69.300,186.200,3.259,17.900,25.000,2.864,0.000 +Volvo,C70,3.493,$null$,0.000,45.500,2.300,236.000,104.900,71.500,185.700,3.601,18.500,23.000,1.251,0.000 +Volvo,S80,18.969,$null$,0.000,36.000,2.900,201.000,109.900,72.100,189.800,3.600,21.100,24.000,2.943,0.000 +,newCar,$null$,$null$,$null$,21.500,1.500,76.000,106.300,67.900,175.000,2.932,11.900,46.000,$null$,1.000 +,newTruck,$null$,$null$,$null$,34.200,3.500,167.000,109.800,75.200,188.400,4.508,17.200,26.000,$null$,1.000 diff --git a/K_Means/Cust_Segmentation.csv b/K_Means/Cust_Segmentation.csv new file mode 100644 index 0000000..20d0287 --- /dev/null +++ b/K_Means/Cust_Segmentation.csv @@ -0,0 +1,851 @@ +Customer Id,Age,Edu,Years Employed,Income,Card Debt,Other Debt,Defaulted,Address,DebtIncomeRatio +1,41,2,6,19,0.124,1.073,0,NBA001,6.3 +2,47,1,26,100,4.582,8.218,0,NBA021,12.8 +3,33,2,10,57,6.111,5.802,1,NBA013,20.9 +4,29,2,4,19,0.681,0.516,0,NBA009,6.3 +5,47,1,31,253,9.308,8.908,0,NBA008,7.2 +6,40,1,23,81,0.998,7.831,,NBA016,10.9 +7,38,2,4,56,0.442,0.454,0,NBA013,1.6 +8,42,3,0,64,0.279,3.945,0,NBA009,6.6 +9,26,1,5,18,0.575,2.215,,NBA006,15.5 +10,47,3,23,115,0.653,3.947,0,NBA011,4 +11,44,3,8,88,0.285,5.083,1,NBA010,6.1 +12,34,2,9,40,0.374,0.266,,NBA003,1.6 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+ + + + diff --git a/happy_sad_prediction/h-or-s_Prediction.py b/happy_sad_prediction/h-or-s_Prediction.py new file mode 100644 index 0000000..2759eb4 --- /dev/null +++ b/happy_sad_prediction/h-or-s_Prediction.py @@ -0,0 +1,86 @@ +import tensorflow as tf +import os +import zipfile +from os import path, getcwd, chdir + +path = f"{getcwd()}/tmp2/happy-or-sad.zip" + +zip_ref = zipfile.ZipFile(path, 'r') +zip_ref.extractall("/tmp/h-or-s") +zip_ref.close() + + +# GRADED FUNCTION: train_happy_sad_model +def train_happy_sad_model(): + DESIRED_ACCURACY = 0.999 + + class myCallback(tf.keras.callbacks.Callback): + def on_epoch_end(self, epoch, logs={}): + if(logs.get('acc')>0.999): + print("\nReached 99.9% accuracy so cancelling training!") + self.model.stop_training = True + + + # This Code Block should Define and Compile the Model. Please assume the images are 150 X 150 in your implementation. + model = tf.keras.models.Sequential([ + # Your Code Here + # Note the input shape is the desired size of the image 300x300 with 3 bytes color + # This is the first convolution + tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(150, 150, 3)), + tf.keras.layers.MaxPooling2D(2, 2), + # The second convolution + tf.keras.layers.Conv2D(32, (3,3), activation='relu'), + tf.keras.layers.MaxPooling2D(2,2), + # The third convolution + tf.keras.layers.Conv2D(64, (3,3), activation='relu'), + tf.keras.layers.MaxPooling2D(2,2), + # The fourth convolution + tf.keras.layers.Conv2D(64, (3,3), activation='relu'), + tf.keras.layers.MaxPooling2D(2,2), + # The fifth convolution + tf.keras.layers.Conv2D(64, (3,3), activation='relu'), + tf.keras.layers.MaxPooling2D(2,2), + # Flatten the results to feed into a DNN + tf.keras.layers.Flatten(), + # 512 neuron hidden layer + tf.keras.layers.Dense(512, activation='relu'), + # Only 1 output neuron. It will contain a value from 0-1 where 0 for 1 class ('horses') and 1 for the other ('humans') + tf.keras.layers.Dense(1, activation='sigmoid') + ]) + callbacks = myCallback() + from tensorflow.keras.optimizers import RMSprop + + model.compile( + loss='binary_crossentropy', + optimizer=RMSprop(lr=0.001), + metrics=['acc']) + + + # This code block should create an instance of an ImageDataGenerator called train_datagen + # And a train_generator by calling train_datagen.flow_from_directory + + from tensorflow.keras.preprocessing.image import ImageDataGenerator + + train_datagen = ImageDataGenerator(rescale=1/255) # Your Code Here + + # Please use a target_size of 150 X 150. + train_generator = train_datagen.flow_from_directory( + '/tmp/h-or-s/', # This is the source directory for training images + target_size=(150, 150), # All images will be resized to 150x150 + batch_size=128, + # Since we use binary_crossentropy loss, we need binary labels + class_mode='binary') + # Expected output: 'Found 80 images belonging to 2 classes' + + # This code block should call model.fit_generator and train for + # a number of epochs. + # model fitting + history = model.fit_generator( + train_generator, + steps_per_epoch=8, + epochs=15, + verbose=1) + # model fitting + return history.history['acc'][-1] + +train_happy_sad_model() diff --git a/happy_sad_prediction/happy-or-sad.zip b/happy_sad_prediction/happy-or-sad.zip new file mode 100644 index 0000000..5452088 Binary files /dev/null and b/happy_sad_prediction/happy-or-sad.zip differ