├── CREDIT CARD FRAUD DETECTION TASK 5.ipynb ├── IRIS FLOWER CLASSIFICATION TASK 3.ipynb ├── IRIS FLOWER CLASSIFICATION.csv ├── MOVIE RATING PREDICTION WITH PYTHON TASK 2.ipynb ├── SALES PREDICTION USING PYTHON TASK 4.ipynb ├── SALES PREDICTION USING PYTHON.csv ├── TITANIC SURVIVAL PREDICTION.csv └── TITANIC_SURVIAL_PREDICTION_TASK1.ipynb /IRIS FLOWER CLASSIFICATION.csv: -------------------------------------------------------------------------------- 1 | Id,SepalLengthCm,SepalWidthCm,PetalLengthCm,PetalWidthCm,Species 2 | 1,5.1,3.5,1.4,0.2,Iris-setosa 3 | 2,4.9,3.0,1.4,0.2,Iris-setosa 4 | 3,4.7,3.2,1.3,0.2,Iris-setosa 5 | 4,4.6,3.1,1.5,0.2,Iris-setosa 6 | 5,5.0,3.6,1.4,0.2,Iris-setosa 7 | 6,5.4,3.9,1.7,0.4,Iris-setosa 8 | 7,4.6,3.4,1.4,0.3,Iris-setosa 9 | 8,5.0,3.4,1.5,0.2,Iris-setosa 10 | 9,4.4,2.9,1.4,0.2,Iris-setosa 11 | 10,4.9,3.1,1.5,0.1,Iris-setosa 12 | 11,5.4,3.7,1.5,0.2,Iris-setosa 13 | 12,4.8,3.4,1.6,0.2,Iris-setosa 14 | 13,4.8,3.0,1.4,0.1,Iris-setosa 15 | 14,4.3,3.0,1.1,0.1,Iris-setosa 16 | 15,5.8,4.0,1.2,0.2,Iris-setosa 17 | 16,5.7,4.4,1.5,0.4,Iris-setosa 18 | 17,5.4,3.9,1.3,0.4,Iris-setosa 19 | 18,5.1,3.5,1.4,0.3,Iris-setosa 20 | 19,5.7,3.8,1.7,0.3,Iris-setosa 21 | 20,5.1,3.8,1.5,0.3,Iris-setosa 22 | 21,5.4,3.4,1.7,0.2,Iris-setosa 23 | 22,5.1,3.7,1.5,0.4,Iris-setosa 24 | 23,4.6,3.6,1.0,0.2,Iris-setosa 25 | 24,5.1,3.3,1.7,0.5,Iris-setosa 26 | 25,4.8,3.4,1.9,0.2,Iris-setosa 27 | 26,5.0,3.0,1.6,0.2,Iris-setosa 28 | 27,5.0,3.4,1.6,0.4,Iris-setosa 29 | 28,5.2,3.5,1.5,0.2,Iris-setosa 30 | 29,5.2,3.4,1.4,0.2,Iris-setosa 31 | 30,4.7,3.2,1.6,0.2,Iris-setosa 32 | 31,4.8,3.1,1.6,0.2,Iris-setosa 33 | 32,5.4,3.4,1.5,0.4,Iris-setosa 34 | 33,5.2,4.1,1.5,0.1,Iris-setosa 35 | 34,5.5,4.2,1.4,0.2,Iris-setosa 36 | 35,4.9,3.1,1.5,0.1,Iris-setosa 37 | 36,5.0,3.2,1.2,0.2,Iris-setosa 38 | 37,5.5,3.5,1.3,0.2,Iris-setosa 39 | 38,4.9,3.1,1.5,0.1,Iris-setosa 40 | 39,4.4,3.0,1.3,0.2,Iris-setosa 41 | 40,5.1,3.4,1.5,0.2,Iris-setosa 42 | 41,5.0,3.5,1.3,0.3,Iris-setosa 43 | 42,4.5,2.3,1.3,0.3,Iris-setosa 44 | 43,4.4,3.2,1.3,0.2,Iris-setosa 45 | 44,5.0,3.5,1.6,0.6,Iris-setosa 46 | 45,5.1,3.8,1.9,0.4,Iris-setosa 47 | 46,4.8,3.0,1.4,0.3,Iris-setosa 48 | 47,5.1,3.8,1.6,0.2,Iris-setosa 49 | 48,4.6,3.2,1.4,0.2,Iris-setosa 50 | 49,5.3,3.7,1.5,0.2,Iris-setosa 51 | 50,5.0,3.3,1.4,0.2,Iris-setosa 52 | 51,7.0,3.2,4.7,1.4,Iris-versicolor 53 | 52,6.4,3.2,4.5,1.5,Iris-versicolor 54 | 53,6.9,3.1,4.9,1.5,Iris-versicolor 55 | 54,5.5,2.3,4.0,1.3,Iris-versicolor 56 | 55,6.5,2.8,4.6,1.5,Iris-versicolor 57 | 56,5.7,2.8,4.5,1.3,Iris-versicolor 58 | 57,6.3,3.3,4.7,1.6,Iris-versicolor 59 | 58,4.9,2.4,3.3,1.0,Iris-versicolor 60 | 59,6.6,2.9,4.6,1.3,Iris-versicolor 61 | 60,5.2,2.7,3.9,1.4,Iris-versicolor 62 | 61,5.0,2.0,3.5,1.0,Iris-versicolor 63 | 62,5.9,3.0,4.2,1.5,Iris-versicolor 64 | 63,6.0,2.2,4.0,1.0,Iris-versicolor 65 | 64,6.1,2.9,4.7,1.4,Iris-versicolor 66 | 65,5.6,2.9,3.6,1.3,Iris-versicolor 67 | 66,6.7,3.1,4.4,1.4,Iris-versicolor 68 | 67,5.6,3.0,4.5,1.5,Iris-versicolor 69 | 68,5.8,2.7,4.1,1.0,Iris-versicolor 70 | 69,6.2,2.2,4.5,1.5,Iris-versicolor 71 | 70,5.6,2.5,3.9,1.1,Iris-versicolor 72 | 71,5.9,3.2,4.8,1.8,Iris-versicolor 73 | 72,6.1,2.8,4.0,1.3,Iris-versicolor 74 | 73,6.3,2.5,4.9,1.5,Iris-versicolor 75 | 74,6.1,2.8,4.7,1.2,Iris-versicolor 76 | 75,6.4,2.9,4.3,1.3,Iris-versicolor 77 | 76,6.6,3.0,4.4,1.4,Iris-versicolor 78 | 77,6.8,2.8,4.8,1.4,Iris-versicolor 79 | 78,6.7,3.0,5.0,1.7,Iris-versicolor 80 | 79,6.0,2.9,4.5,1.5,Iris-versicolor 81 | 80,5.7,2.6,3.5,1.0,Iris-versicolor 82 | 81,5.5,2.4,3.8,1.1,Iris-versicolor 83 | 82,5.5,2.4,3.7,1.0,Iris-versicolor 84 | 83,5.8,2.7,3.9,1.2,Iris-versicolor 85 | 84,6.0,2.7,5.1,1.6,Iris-versicolor 86 | 85,5.4,3.0,4.5,1.5,Iris-versicolor 87 | 86,6.0,3.4,4.5,1.6,Iris-versicolor 88 | 87,6.7,3.1,4.7,1.5,Iris-versicolor 89 | 88,6.3,2.3,4.4,1.3,Iris-versicolor 90 | 89,5.6,3.0,4.1,1.3,Iris-versicolor 91 | 90,5.5,2.5,4.0,1.3,Iris-versicolor 92 | 91,5.5,2.6,4.4,1.2,Iris-versicolor 93 | 92,6.1,3.0,4.6,1.4,Iris-versicolor 94 | 93,5.8,2.6,4.0,1.2,Iris-versicolor 95 | 94,5.0,2.3,3.3,1.0,Iris-versicolor 96 | 95,5.6,2.7,4.2,1.3,Iris-versicolor 97 | 96,5.7,3.0,4.2,1.2,Iris-versicolor 98 | 97,5.7,2.9,4.2,1.3,Iris-versicolor 99 | 98,6.2,2.9,4.3,1.3,Iris-versicolor 100 | 99,5.1,2.5,3.0,1.1,Iris-versicolor 101 | 100,5.7,2.8,4.1,1.3,Iris-versicolor 102 | 101,6.3,3.3,6.0,2.5,Iris-virginica 103 | 102,5.8,2.7,5.1,1.9,Iris-virginica 104 | 103,7.1,3.0,5.9,2.1,Iris-virginica 105 | 104,6.3,2.9,5.6,1.8,Iris-virginica 106 | 105,6.5,3.0,5.8,2.2,Iris-virginica 107 | 106,7.6,3.0,6.6,2.1,Iris-virginica 108 | 107,4.9,2.5,4.5,1.7,Iris-virginica 109 | 108,7.3,2.9,6.3,1.8,Iris-virginica 110 | 109,6.7,2.5,5.8,1.8,Iris-virginica 111 | 110,7.2,3.6,6.1,2.5,Iris-virginica 112 | 111,6.5,3.2,5.1,2.0,Iris-virginica 113 | 112,6.4,2.7,5.3,1.9,Iris-virginica 114 | 113,6.8,3.0,5.5,2.1,Iris-virginica 115 | 114,5.7,2.5,5.0,2.0,Iris-virginica 116 | 115,5.8,2.8,5.1,2.4,Iris-virginica 117 | 116,6.4,3.2,5.3,2.3,Iris-virginica 118 | 117,6.5,3.0,5.5,1.8,Iris-virginica 119 | 118,7.7,3.8,6.7,2.2,Iris-virginica 120 | 119,7.7,2.6,6.9,2.3,Iris-virginica 121 | 120,6.0,2.2,5.0,1.5,Iris-virginica 122 | 121,6.9,3.2,5.7,2.3,Iris-virginica 123 | 122,5.6,2.8,4.9,2.0,Iris-virginica 124 | 123,7.7,2.8,6.7,2.0,Iris-virginica 125 | 124,6.3,2.7,4.9,1.8,Iris-virginica 126 | 125,6.7,3.3,5.7,2.1,Iris-virginica 127 | 126,7.2,3.2,6.0,1.8,Iris-virginica 128 | 127,6.2,2.8,4.8,1.8,Iris-virginica 129 | 128,6.1,3.0,4.9,1.8,Iris-virginica 130 | 129,6.4,2.8,5.6,2.1,Iris-virginica 131 | 130,7.2,3.0,5.8,1.6,Iris-virginica 132 | 131,7.4,2.8,6.1,1.9,Iris-virginica 133 | 132,7.9,3.8,6.4,2.0,Iris-virginica 134 | 133,6.4,2.8,5.6,2.2,Iris-virginica 135 | 134,6.3,2.8,5.1,1.5,Iris-virginica 136 | 135,6.1,2.6,5.6,1.4,Iris-virginica 137 | 136,7.7,3.0,6.1,2.3,Iris-virginica 138 | 137,6.3,3.4,5.6,2.4,Iris-virginica 139 | 138,6.4,3.1,5.5,1.8,Iris-virginica 140 | 139,6.0,3.0,4.8,1.8,Iris-virginica 141 | 140,6.9,3.1,5.4,2.1,Iris-virginica 142 | 141,6.7,3.1,5.6,2.4,Iris-virginica 143 | 142,6.9,3.1,5.1,2.3,Iris-virginica 144 | 143,5.8,2.7,5.1,1.9,Iris-virginica 145 | 144,6.8,3.2,5.9,2.3,Iris-virginica 146 | 145,6.7,3.3,5.7,2.5,Iris-virginica 147 | 146,6.7,3.0,5.2,2.3,Iris-virginica 148 | 147,6.3,2.5,5.0,1.9,Iris-virginica 149 | 148,6.5,3.0,5.2,2.0,Iris-virginica 150 | 149,6.2,3.4,5.4,2.3,Iris-virginica 151 | 150,5.9,3.0,5.1,1.8,Iris-virginica 152 | -------------------------------------------------------------------------------- /SALES PREDICTION USING PYTHON.csv: -------------------------------------------------------------------------------- 1 | TV,Radio,Newspaper,Sales 2 | 230.1,37.8,69.2,22.1 3 | 44.5,39.3,45.1,10.4 4 | 17.2,45.9,69.3,12 5 | 151.5,41.3,58.5,16.5 6 | 180.8,10.8,58.4,17.9 7 | 8.7,48.9,75,7.2 8 | 57.5,32.8,23.5,11.8 9 | 120.2,19.6,11.6,13.2 10 | 8.6,2.1,1,4.8 11 | 199.8,2.6,21.2,15.6 12 | 66.1,5.8,24.2,12.6 13 | 214.7,24,4,17.4 14 | 23.8,35.1,65.9,9.2 15 | 97.5,7.6,7.2,13.7 16 | 204.1,32.9,46,19 17 | 195.4,47.7,52.9,22.4 18 | 67.8,36.6,114,12.5 19 | 281.4,39.6,55.8,24.4 20 | 69.2,20.5,18.3,11.3 21 | 147.3,23.9,19.1,14.6 22 | 218.4,27.7,53.4,18 23 | 237.4,5.1,23.5,17.5 24 | 13.2,15.9,49.6,5.6 25 | 228.3,16.9,26.2,20.5 26 | 62.3,12.6,18.3,9.7 27 | 262.9,3.5,19.5,17 28 | 142.9,29.3,12.6,15 29 | 240.1,16.7,22.9,20.9 30 | 248.8,27.1,22.9,18.9 31 | 70.6,16,40.8,10.5 32 | 292.9,28.3,43.2,21.4 33 | 112.9,17.4,38.6,11.9 34 | 97.2,1.5,30,13.2 35 | 265.6,20,0.3,17.4 36 | 95.7,1.4,7.4,11.9 37 | 290.7,4.1,8.5,17.8 38 | 266.9,43.8,5,25.4 39 | 74.7,49.4,45.7,14.7 40 | 43.1,26.7,35.1,10.1 41 | 228,37.7,32,21.5 42 | 202.5,22.3,31.6,16.6 43 | 177,33.4,38.7,17.1 44 | 293.6,27.7,1.8,20.7 45 | 206.9,8.4,26.4,17.9 46 | 25.1,25.7,43.3,8.5 47 | 175.1,22.5,31.5,16.1 48 | 89.7,9.9,35.7,10.6 49 | 239.9,41.5,18.5,23.2 50 | 227.2,15.8,49.9,19.8 51 | 66.9,11.7,36.8,9.7 52 | 199.8,3.1,34.6,16.4 53 | 100.4,9.6,3.6,10.7 54 | 216.4,41.7,39.6,22.6 55 | 182.6,46.2,58.7,21.2 56 | 262.7,28.8,15.9,20.2 57 | 198.9,49.4,60,23.7 58 | 7.3,28.1,41.4,5.5 59 | 136.2,19.2,16.6,13.2 60 | 210.8,49.6,37.7,23.8 61 | 210.7,29.5,9.3,18.4 62 | 53.5,2,21.4,8.1 63 | 261.3,42.7,54.7,24.2 64 | 239.3,15.5,27.3,20.7 65 | 102.7,29.6,8.4,14 66 | 131.1,42.8,28.9,16 67 | 69,9.3,0.9,11.3 68 | 31.5,24.6,2.2,11 69 | 139.3,14.5,10.2,13.4 70 | 237.4,27.5,11,18.9 71 | 216.8,43.9,27.2,22.3 72 | 199.1,30.6,38.7,18.3 73 | 109.8,14.3,31.7,12.4 74 | 26.8,33,19.3,8.8 75 | 129.4,5.7,31.3,11 76 | 213.4,24.6,13.1,17 77 | 16.9,43.7,89.4,8.7 78 | 27.5,1.6,20.7,6.9 79 | 120.5,28.5,14.2,14.2 80 | 5.4,29.9,9.4,5.3 81 | 116,7.7,23.1,11 82 | 76.4,26.7,22.3,11.8 83 | 239.8,4.1,36.9,17.3 84 | 75.3,20.3,32.5,11.3 85 | 68.4,44.5,35.6,13.6 86 | 213.5,43,33.8,21.7 87 | 193.2,18.4,65.7,20.2 88 | 76.3,27.5,16,12 89 | 110.7,40.6,63.2,16 90 | 88.3,25.5,73.4,12.9 91 | 109.8,47.8,51.4,16.7 92 | 134.3,4.9,9.3,14 93 | 28.6,1.5,33,7.3 94 | 217.7,33.5,59,19.4 95 | 250.9,36.5,72.3,22.2 96 | 107.4,14,10.9,11.5 97 | 163.3,31.6,52.9,16.9 98 | 197.6,3.5,5.9,16.7 99 | 184.9,21,22,20.5 100 | 289.7,42.3,51.2,25.4 101 | 135.2,41.7,45.9,17.2 102 | 222.4,4.3,49.8,16.7 103 | 296.4,36.3,100.9,23.8 104 | 280.2,10.1,21.4,19.8 105 | 187.9,17.2,17.9,19.7 106 | 238.2,34.3,5.3,20.7 107 | 137.9,46.4,59,15 108 | 25,11,29.7,7.2 109 | 90.4,0.3,23.2,12 110 | 13.1,0.4,25.6,5.3 111 | 255.4,26.9,5.5,19.8 112 | 225.8,8.2,56.5,18.4 113 | 241.7,38,23.2,21.8 114 | 175.7,15.4,2.4,17.1 115 | 209.6,20.6,10.7,20.9 116 | 78.2,46.8,34.5,14.6 117 | 75.1,35,52.7,12.6 118 | 139.2,14.3,25.6,12.2 119 | 76.4,0.8,14.8,9.4 120 | 125.7,36.9,79.2,15.9 121 | 19.4,16,22.3,6.6 122 | 141.3,26.8,46.2,15.5 123 | 18.8,21.7,50.4,7 124 | 224,2.4,15.6,16.6 125 | 123.1,34.6,12.4,15.2 126 | 229.5,32.3,74.2,19.7 127 | 87.2,11.8,25.9,10.6 128 | 7.8,38.9,50.6,6.6 129 | 80.2,0,9.2,11.9 130 | 220.3,49,3.2,24.7 131 | 59.6,12,43.1,9.7 132 | 0.7,39.6,8.7,1.6 133 | 265.2,2.9,43,17.7 134 | 8.4,27.2,2.1,5.7 135 | 219.8,33.5,45.1,19.6 136 | 36.9,38.6,65.6,10.8 137 | 48.3,47,8.5,11.6 138 | 25.6,39,9.3,9.5 139 | 273.7,28.9,59.7,20.8 140 | 43,25.9,20.5,9.6 141 | 184.9,43.9,1.7,20.7 142 | 73.4,17,12.9,10.9 143 | 193.7,35.4,75.6,19.2 144 | 220.5,33.2,37.9,20.1 145 | 104.6,5.7,34.4,10.4 146 | 96.2,14.8,38.9,12.3 147 | 140.3,1.9,9,10.3 148 | 240.1,7.3,8.7,18.2 149 | 243.2,49,44.3,25.4 150 | 38,40.3,11.9,10.9 151 | 44.7,25.8,20.6,10.1 152 | 280.7,13.9,37,16.1 153 | 121,8.4,48.7,11.6 154 | 197.6,23.3,14.2,16.6 155 | 171.3,39.7,37.7,16 156 | 187.8,21.1,9.5,20.6 157 | 4.1,11.6,5.7,3.2 158 | 93.9,43.5,50.5,15.3 159 | 149.8,1.3,24.3,10.1 160 | 11.7,36.9,45.2,7.3 161 | 131.7,18.4,34.6,12.9 162 | 172.5,18.1,30.7,16.4 163 | 85.7,35.8,49.3,13.3 164 | 188.4,18.1,25.6,19.9 165 | 163.5,36.8,7.4,18 166 | 117.2,14.7,5.4,11.9 167 | 234.5,3.4,84.8,16.9 168 | 17.9,37.6,21.6,8 169 | 206.8,5.2,19.4,17.2 170 | 215.4,23.6,57.6,17.1 171 | 284.3,10.6,6.4,20 172 | 50,11.6,18.4,8.4 173 | 164.5,20.9,47.4,17.5 174 | 19.6,20.1,17,7.6 175 | 168.4,7.1,12.8,16.7 176 | 222.4,3.4,13.1,16.5 177 | 276.9,48.9,41.8,27 178 | 248.4,30.2,20.3,20.2 179 | 170.2,7.8,35.2,16.7 180 | 276.7,2.3,23.7,16.8 181 | 165.6,10,17.6,17.6 182 | 156.6,2.6,8.3,15.5 183 | 218.5,5.4,27.4,17.2 184 | 56.2,5.7,29.7,8.7 185 | 287.6,43,71.8,26.2 186 | 253.8,21.3,30,17.6 187 | 205,45.1,19.6,22.6 188 | 139.5,2.1,26.6,10.3 189 | 191.1,28.7,18.2,17.3 190 | 286,13.9,3.7,20.9 191 | 18.7,12.1,23.4,6.7 192 | 39.5,41.1,5.8,10.8 193 | 75.5,10.8,6,11.9 194 | 17.2,4.1,31.6,5.9 195 | 166.8,42,3.6,19.6 196 | 149.7,35.6,6,17.3 197 | 38.2,3.7,13.8,7.6 198 | 94.2,4.9,8.1,14 199 | 177,9.3,6.4,14.8 200 | 283.6,42,66.2,25.5 201 | 232.1,8.6,8.7,18.4 202 | -------------------------------------------------------------------------------- /TITANIC SURVIVAL PREDICTION.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 892,0,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 3 | 893,1,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 4 | 894,0,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 5 | 895,0,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 6 | 896,1,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 7 | 897,0,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 8 | 898,1,3,"Connolly, Miss. 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Michael J",male,,1,1,2668,22.3583,,C 420 | -------------------------------------------------------------------------------- /TITANIC_SURVIAL_PREDICTION_TASK1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "raw", 5 | "id": "0c7f4808-794c-415f-a4d8-8b9c8c7ca51a", 6 | "metadata": {}, 7 | "source": [ 8 | "\n", 9 | "\n", 10 | "▶TASK-1 : TITANIC SURVIVAL PREDICTION\n", 11 | "\n", 12 | " \n", 13 | "\n", 14 | "\n", 15 | "\n", 16 | "\n" 17 | ] 18 | }, 19 | { 20 | "cell_type": "raw", 21 | "id": "eaef0a02-1967-45f2-b118-1a600bb2a8dd", 22 | "metadata": {}, 23 | "source": [ 24 | "IMPORTING IMPORTANT LIBRARIES" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 1, 30 | "id": "b31ab819-4837-4ffd-8314-e3617560c596", 31 | "metadata": {}, 32 | "outputs": [], 33 | "source": [ 34 | "import numpy as np\n", 35 | "import pandas as pd\n", 36 | "import matplotlib.pyplot as plt\n", 37 | "import seaborn as sns" 38 | ] 39 | }, 40 | { 41 | "cell_type": "raw", 42 | "id": "b98f5bea-1231-4b3a-b0ea-a9710257ae58", 43 | "metadata": {}, 44 | "source": [ 45 | "IMPORTING DATASET" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 2, 51 | "id": "539291bd-5d0c-4f85-8376-2d3dc3169d96", 52 | "metadata": {}, 53 | "outputs": [], 54 | "source": [ 55 | "df = pd.read_csv(\"tested.csv\")" 56 | ] 57 | }, 58 | { 59 | "cell_type": "code", 60 | "execution_count": 3, 61 | "id": "bd926066-3a38-425f-a311-fc29ae8b52b7", 62 | "metadata": {}, 63 | "outputs": [ 64 | { 65 | "data": { 66 | "text/html": [ 67 | "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
089203Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
189313Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
289402Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
389503Wirz, Mr. Albertmale27.0003151548.6625NaNS
489613Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
\n", 177 | "
" 178 | ], 179 | "text/plain": [ 180 | " PassengerId Survived Pclass \\\n", 181 | "0 892 0 3 \n", 182 | "1 893 1 3 \n", 183 | "2 894 0 2 \n", 184 | "3 895 0 3 \n", 185 | "4 896 1 3 \n", 186 | "\n", 187 | " Name Sex Age SibSp Parch \\\n", 188 | "0 Kelly, Mr. James male 34.5 0 0 \n", 189 | "1 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 \n", 190 | "2 Myles, Mr. Thomas Francis male 62.0 0 0 \n", 191 | "3 Wirz, Mr. Albert male 27.0 0 0 \n", 192 | "4 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 \n", 193 | "\n", 194 | " Ticket Fare Cabin Embarked \n", 195 | "0 330911 7.8292 NaN Q \n", 196 | "1 363272 7.0000 NaN S \n", 197 | "2 240276 9.6875 NaN Q \n", 198 | "3 315154 8.6625 NaN S \n", 199 | "4 3101298 12.2875 NaN S " 200 | ] 201 | }, 202 | "execution_count": 3, 203 | "metadata": {}, 204 | "output_type": "execute_result" 205 | } 206 | ], 207 | "source": [ 208 | "df.head()" 209 | ] 210 | }, 211 | { 212 | "cell_type": "code", 213 | "execution_count": 4, 214 | "id": "67def7c7-ea86-48e7-91ed-3eeeefee4b67", 215 | "metadata": {}, 216 | "outputs": [ 217 | { 218 | "data": { 219 | "text/html": [ 220 | "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
089203Kelly, Mr. Jamesmale34.5003309117.8292NaNQ
189313Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000NaNS
289402Myles, Mr. Thomas Francismale62.0002402769.6875NaNQ
389503Wirz, Mr. Albertmale27.0003151548.6625NaNS
489613Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875NaNS
589703Svensson, Mr. Johan Cervinmale14.00075389.2250NaNS
689813Connolly, Miss. Katefemale30.0003309727.6292NaNQ
789902Caldwell, Mr. Albert Francismale26.01124873829.0000NaNS
890013Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.00026577.2292NaNC
990103Davies, Mr. John Samuelmale21.020A/4 4887124.1500NaNS
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" 406 | ], 407 | "text/plain": [ 408 | " PassengerId Survived Pclass \\\n", 409 | "0 892 0 3 \n", 410 | "1 893 1 3 \n", 411 | "2 894 0 2 \n", 412 | "3 895 0 3 \n", 413 | "4 896 1 3 \n", 414 | "5 897 0 3 \n", 415 | "6 898 1 3 \n", 416 | "7 899 0 2 \n", 417 | "8 900 1 3 \n", 418 | "9 901 0 3 \n", 419 | "\n", 420 | " Name Sex Age SibSp Parch \\\n", 421 | "0 Kelly, Mr. James male 34.5 0 0 \n", 422 | "1 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 \n", 423 | "2 Myles, Mr. Thomas Francis male 62.0 0 0 \n", 424 | "3 Wirz, Mr. Albert male 27.0 0 0 \n", 425 | "4 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 \n", 426 | "5 Svensson, Mr. Johan Cervin male 14.0 0 0 \n", 427 | "6 Connolly, Miss. Kate female 30.0 0 0 \n", 428 | "7 Caldwell, Mr. Albert Francis male 26.0 1 1 \n", 429 | "8 Abrahim, Mrs. Joseph (Sophie Halaut Easu) female 18.0 0 0 \n", 430 | "9 Davies, Mr. John Samuel male 21.0 2 0 \n", 431 | "\n", 432 | " Ticket Fare Cabin Embarked \n", 433 | "0 330911 7.8292 NaN Q \n", 434 | "1 363272 7.0000 NaN S \n", 435 | "2 240276 9.6875 NaN Q \n", 436 | "3 315154 8.6625 NaN S \n", 437 | "4 3101298 12.2875 NaN S \n", 438 | "5 7538 9.2250 NaN S \n", 439 | "6 330972 7.6292 NaN Q \n", 440 | "7 248738 29.0000 NaN S \n", 441 | "8 2657 7.2292 NaN C \n", 442 | "9 A/4 48871 24.1500 NaN S " 443 | ] 444 | }, 445 | "execution_count": 4, 446 | "metadata": {}, 447 | "output_type": "execute_result" 448 | } 449 | ], 450 | "source": [ 451 | "df.head(10)" 452 | ] 453 | }, 454 | { 455 | "cell_type": "code", 456 | "execution_count": 6, 457 | "id": "3d8dfe8b-0a32-4d1d-bd50-64e1f15c8526", 458 | "metadata": {}, 459 | "outputs": [ 460 | { 461 | "data": { 462 | "text/plain": [ 463 | "(418, 12)" 464 | ] 465 | }, 466 | "execution_count": 6, 467 | "metadata": {}, 468 | "output_type": "execute_result" 469 | } 470 | ], 471 | "source": [ 472 | "df.shape" 473 | ] 474 | }, 475 | { 476 | "cell_type": "code", 477 | "execution_count": 9, 478 | "id": "50efefdb-3702-4f9f-8987-b7ceb1de2ec6", 479 | "metadata": {}, 480 | "outputs": [ 481 | { 482 | "data": { 483 | "text/html": [ 484 | "
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PassengerIdSurvivedPclassAgeSibSpParchFare
count418.000000418.000000418.000000332.000000418.000000418.000000417.000000
mean1100.5000000.3636362.26555030.2725900.4473680.39234435.627188
std120.8104580.4816220.84183814.1812090.8967600.98142955.907576
min892.0000000.0000001.0000000.1700000.0000000.0000000.000000
25%996.2500000.0000001.00000021.0000000.0000000.0000007.895800
50%1100.5000000.0000003.00000027.0000000.0000000.00000014.454200
75%1204.7500001.0000003.00000039.0000001.0000000.00000031.500000
max1309.0000001.0000003.00000076.0000008.0000009.000000512.329200
\n", 594 | "
" 595 | ], 596 | "text/plain": [ 597 | " PassengerId Survived Pclass Age SibSp \\\n", 598 | "count 418.000000 418.000000 418.000000 332.000000 418.000000 \n", 599 | "mean 1100.500000 0.363636 2.265550 30.272590 0.447368 \n", 600 | "std 120.810458 0.481622 0.841838 14.181209 0.896760 \n", 601 | "min 892.000000 0.000000 1.000000 0.170000 0.000000 \n", 602 | "25% 996.250000 0.000000 1.000000 21.000000 0.000000 \n", 603 | "50% 1100.500000 0.000000 3.000000 27.000000 0.000000 \n", 604 | "75% 1204.750000 1.000000 3.000000 39.000000 1.000000 \n", 605 | "max 1309.000000 1.000000 3.000000 76.000000 8.000000 \n", 606 | "\n", 607 | " Parch Fare \n", 608 | "count 418.000000 417.000000 \n", 609 | "mean 0.392344 35.627188 \n", 610 | "std 0.981429 55.907576 \n", 611 | "min 0.000000 0.000000 \n", 612 | "25% 0.000000 7.895800 \n", 613 | "50% 0.000000 14.454200 \n", 614 | "75% 0.000000 31.500000 \n", 615 | "max 9.000000 512.329200 " 616 | ] 617 | }, 618 | "execution_count": 9, 619 | "metadata": {}, 620 | "output_type": "execute_result" 621 | } 622 | ], 623 | "source": [ 624 | "df.describe()" 625 | ] 626 | }, 627 | { 628 | "cell_type": "code", 629 | "execution_count": 10, 630 | "id": "87ac55b6-ee12-47a3-88a0-18ad7e5b0ee9", 631 | "metadata": {}, 632 | "outputs": [ 633 | { 634 | "data": { 635 | "text/plain": [ 636 | "0 266\n", 637 | "1 152\n", 638 | "Name: Survived, dtype: int64" 639 | ] 640 | }, 641 | "execution_count": 10, 642 | "metadata": {}, 643 | "output_type": "execute_result" 644 | } 645 | ], 646 | "source": [ 647 | "df['Survived'].value_counts()" 648 | ] 649 | }, 650 | { 651 | "cell_type": "code", 652 | "execution_count": 11, 653 | "id": "1c84d126-00b7-43c9-b3b0-c68d9eaa0ad3", 654 | "metadata": {}, 655 | "outputs": [ 656 | { 657 | "data": { 658 | "text/plain": [ 659 | "" 660 | ] 661 | }, 662 | "execution_count": 11, 663 | "metadata": {}, 664 | "output_type": "execute_result" 665 | }, 666 | { 667 | "data": { 668 | "image/png": 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", 669 | "text/plain": [ 670 | "
" 671 | ] 672 | }, 673 | "metadata": {}, 674 | "output_type": "display_data" 675 | } 676 | ], 677 | "source": [ 678 | "#let's visualize the count of survivals wrt pclass\n", 679 | "sns.countplot(x=df['Survived'], hue=df['Pclass'])" 680 | ] 681 | }, 682 | { 683 | "cell_type": "code", 684 | "execution_count": 12, 685 | "id": "3e494a15-617d-4943-8ac1-7c8d7ea78def", 686 | "metadata": {}, 687 | "outputs": [ 688 | { 689 | "data": { 690 | "text/plain": [ 691 | "0 male\n", 692 | "1 female\n", 693 | "2 male\n", 694 | "3 male\n", 695 | "4 female\n", 696 | " ... \n", 697 | "413 male\n", 698 | "414 female\n", 699 | "415 male\n", 700 | "416 male\n", 701 | "417 male\n", 702 | "Name: Sex, Length: 418, dtype: object" 703 | ] 704 | }, 705 | "execution_count": 12, 706 | "metadata": {}, 707 | "output_type": "execute_result" 708 | } 709 | ], 710 | "source": [ 711 | "df[\"Sex\"]" 712 | ] 713 | }, 714 | { 715 | "cell_type": "code", 716 | "execution_count": 13, 717 | "id": "6bb44ae9-d44c-4a63-913d-05acf9ef9e1b", 718 | "metadata": {}, 719 | "outputs": [ 720 | { 721 | "data": { 722 | "text/plain": [ 723 | "" 724 | ] 725 | }, 726 | "execution_count": 13, 727 | "metadata": {}, 728 | "output_type": "execute_result" 729 | }, 730 | { 731 | "data": { 732 | "image/png": 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", 733 | "text/plain": [ 734 | "
" 735 | ] 736 | }, 737 | "metadata": {}, 738 | "output_type": "display_data" 739 | } 740 | ], 741 | "source": [ 742 | "#let's visualize the count of survivals wrt Gender\n", 743 | "sns.countplot(x=df['Sex'], hue=df['Survived'])" 744 | ] 745 | }, 746 | { 747 | "cell_type": "code", 748 | "execution_count": 14, 749 | "id": "0fc48d62-2d08-45ad-b6a7-a14fa8fd08fe", 750 | "metadata": {}, 751 | "outputs": [ 752 | { 753 | "data": { 754 | "text/html": [ 755 | "
\n", 756 | "\n", 769 | "\n", 770 | " \n", 771 | " \n", 772 | " \n", 773 | " \n", 774 | " \n", 775 | " \n", 776 | " \n", 777 | " \n", 778 | " \n", 779 | " \n", 780 | " \n", 781 | " \n", 782 | " \n", 783 | " \n", 784 | " \n", 785 | " \n", 786 | " \n", 787 | " \n", 788 | " \n", 789 | " \n", 790 | "
Survived
Sex
female1.0
male0.0
\n", 791 | "
" 792 | ], 793 | "text/plain": [ 794 | " Survived\n", 795 | "Sex \n", 796 | "female 1.0\n", 797 | "male 0.0" 798 | ] 799 | }, 800 | "execution_count": 14, 801 | "metadata": {}, 802 | "output_type": "execute_result" 803 | } 804 | ], 805 | "source": [ 806 | "#Look at survival rate by sex\n", 807 | "df.groupby('Sex')[['Survived']].mean()" 808 | ] 809 | }, 810 | { 811 | "cell_type": "code", 812 | "execution_count": 15, 813 | "id": "e2bdc852-bedf-476a-9ba4-731c88fb7e2f", 814 | "metadata": {}, 815 | "outputs": [ 816 | { 817 | "data": { 818 | "text/plain": [ 819 | "array(['male', 'female'], dtype=object)" 820 | ] 821 | }, 822 | "execution_count": 15, 823 | "metadata": {}, 824 | "output_type": "execute_result" 825 | } 826 | ], 827 | "source": [ 828 | "df['Sex'].unique()" 829 | ] 830 | }, 831 | { 832 | "cell_type": "code", 833 | "execution_count": 16, 834 | "id": "228f8785-2654-481b-980e-8e5a8f910ff9", 835 | "metadata": {}, 836 | "outputs": [ 837 | { 838 | "data": { 839 | "text/html": [ 840 | "
\n", 841 | "\n", 854 | "\n", 855 | " \n", 856 | " \n", 857 | " \n", 858 | " \n", 859 | " \n", 860 | " \n", 861 | " \n", 862 | " \n", 863 | " \n", 864 | " \n", 865 | " \n", 866 | " \n", 867 | " \n", 868 | " \n", 869 | " \n", 870 | " \n", 871 | " \n", 872 | " \n", 873 | " \n", 874 | " \n", 875 | " \n", 876 | " \n", 877 | " \n", 878 | " \n", 879 | " \n", 880 | " \n", 881 | " \n", 882 | " \n", 883 | " \n", 884 | " \n", 885 | " \n", 886 | " \n", 887 | " \n", 888 | " \n", 889 | " \n", 890 | " \n", 891 | " \n", 892 | " \n", 893 | " \n", 894 | " \n", 895 | " \n", 896 | " \n", 897 | " \n", 898 | " \n", 899 | " \n", 900 | " \n", 901 | " \n", 902 | " \n", 903 | " \n", 904 | " \n", 905 | " \n", 906 | " \n", 907 | " \n", 908 | " \n", 909 | " \n", 910 | " \n", 911 | " \n", 912 | " \n", 913 | " \n", 914 | " \n", 915 | " \n", 916 | " \n", 917 | " \n", 918 | " \n", 919 | " \n", 920 | " \n", 921 | " \n", 922 | " \n", 923 | " \n", 924 | " \n", 925 | " \n", 926 | " \n", 927 | " \n", 928 | " \n", 929 | " \n", 930 | " \n", 931 | " \n", 932 | " \n", 933 | " \n", 934 | " \n", 935 | " \n", 936 | " \n", 937 | " \n", 938 | " \n", 939 | " \n", 940 | " \n", 941 | " \n", 942 | " \n", 943 | " \n", 944 | " \n", 945 | " \n", 946 | " \n", 947 | " \n", 948 | " \n", 949 | "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
089203Kelly, Mr. James134.5003309117.8292NaNQ
189313Wilkes, Mrs. James (Ellen Needs)047.0103632727.0000NaNS
289402Myles, Mr. Thomas Francis162.0002402769.6875NaNQ
389503Wirz, Mr. Albert127.0003151548.6625NaNS
489613Hirvonen, Mrs. Alexander (Helga E Lindqvist)022.011310129812.2875NaNS
\n", 950 | "
" 951 | ], 952 | "text/plain": [ 953 | " PassengerId Survived Pclass \\\n", 954 | "0 892 0 3 \n", 955 | "1 893 1 3 \n", 956 | "2 894 0 2 \n", 957 | "3 895 0 3 \n", 958 | "4 896 1 3 \n", 959 | "\n", 960 | " Name Sex Age SibSp Parch \\\n", 961 | "0 Kelly, Mr. James 1 34.5 0 0 \n", 962 | "1 Wilkes, Mrs. James (Ellen Needs) 0 47.0 1 0 \n", 963 | "2 Myles, Mr. Thomas Francis 1 62.0 0 0 \n", 964 | "3 Wirz, Mr. Albert 1 27.0 0 0 \n", 965 | "4 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 0 22.0 1 1 \n", 966 | "\n", 967 | " Ticket Fare Cabin Embarked \n", 968 | "0 330911 7.8292 NaN Q \n", 969 | "1 363272 7.0000 NaN S \n", 970 | "2 240276 9.6875 NaN Q \n", 971 | "3 315154 8.6625 NaN S \n", 972 | "4 3101298 12.2875 NaN S " 973 | ] 974 | }, 975 | "execution_count": 16, 976 | "metadata": {}, 977 | "output_type": "execute_result" 978 | } 979 | ], 980 | "source": [ 981 | "from sklearn.preprocessing import LabelEncoder\n", 982 | "labelencoder = LabelEncoder()\n", 983 | "\n", 984 | "df['Sex']= labelencoder.fit_transform(df['Sex'])\n", 985 | "\n", 986 | "df.head()" 987 | ] 988 | }, 989 | { 990 | "cell_type": "code", 991 | "execution_count": 17, 992 | "id": "a5c229ea-f2fe-4f78-9458-90317bd6ea7c", 993 | "metadata": {}, 994 | "outputs": [ 995 | { 996 | "data": { 997 | "text/plain": [ 998 | "(0 1\n", 999 | " 1 0\n", 1000 | " 2 1\n", 1001 | " 3 1\n", 1002 | " 4 0\n", 1003 | " ..\n", 1004 | " 413 1\n", 1005 | " 414 0\n", 1006 | " 415 1\n", 1007 | " 416 1\n", 1008 | " 417 1\n", 1009 | " Name: Sex, Length: 418, dtype: int64,\n", 1010 | " 0 0\n", 1011 | " 1 1\n", 1012 | " 2 0\n", 1013 | " 3 0\n", 1014 | " 4 1\n", 1015 | " ..\n", 1016 | " 413 0\n", 1017 | " 414 1\n", 1018 | " 415 0\n", 1019 | " 416 0\n", 1020 | " 417 0\n", 1021 | " Name: Survived, Length: 418, dtype: int64)" 1022 | ] 1023 | }, 1024 | "execution_count": 17, 1025 | "metadata": {}, 1026 | "output_type": "execute_result" 1027 | } 1028 | ], 1029 | "source": [ 1030 | "df['Sex'], df['Survived']" 1031 | ] 1032 | }, 1033 | { 1034 | "cell_type": "code", 1035 | "execution_count": 18, 1036 | "id": "5b8cf276-5bb3-4340-a56f-c4613f44ca26", 1037 | "metadata": {}, 1038 | "outputs": [ 1039 | { 1040 | "data": { 1041 | "text/plain": [ 1042 | "" 1043 | ] 1044 | }, 1045 | "execution_count": 18, 1046 | "metadata": {}, 1047 | "output_type": "execute_result" 1048 | }, 1049 | { 1050 | "data": { 1051 | "image/png": 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1052 | "text/plain": [ 1053 | "
" 1054 | ] 1055 | }, 1056 | "metadata": {}, 1057 | "output_type": "display_data" 1058 | } 1059 | ], 1060 | "source": [ 1061 | "sns.countplot(x=df['Sex'], hue=df[\"Survived\"])" 1062 | ] 1063 | }, 1064 | { 1065 | "cell_type": "code", 1066 | "execution_count": 19, 1067 | "id": "0ed899a2-1da6-49c7-bb93-92f6978000a3", 1068 | "metadata": {}, 1069 | "outputs": [ 1070 | { 1071 | "data": { 1072 | "text/plain": [ 1073 | "PassengerId 0\n", 1074 | "Survived 0\n", 1075 | "Pclass 0\n", 1076 | "Name 0\n", 1077 | "Sex 0\n", 1078 | "Age 86\n", 1079 | "SibSp 0\n", 1080 | "Parch 0\n", 1081 | "Ticket 0\n", 1082 | "Fare 1\n", 1083 | "Cabin 327\n", 1084 | "Embarked 0\n", 1085 | "dtype: int64" 1086 | ] 1087 | }, 1088 | "execution_count": 19, 1089 | "metadata": {}, 1090 | "output_type": "execute_result" 1091 | } 1092 | ], 1093 | "source": [ 1094 | "df.isna().sum()" 1095 | ] 1096 | }, 1097 | { 1098 | "cell_type": "code", 1099 | "execution_count": 20, 1100 | "id": "355059df-5520-4298-888e-02b37a28526b", 1101 | "metadata": {}, 1102 | "outputs": [], 1103 | "source": [ 1104 | "# After dropping non required column\n", 1105 | "df=df.drop(['Age'], axis=1)" 1106 | ] 1107 | }, 1108 | { 1109 | "cell_type": "code", 1110 | "execution_count": 21, 1111 | "id": "261a4319-8b26-411d-a7e2-46fc352d32c0", 1112 | "metadata": {}, 1113 | "outputs": [ 1114 | { 1115 | "data": { 1116 | "text/html": [ 1117 | "
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PassengerIdSurvivedPclassNameSexSibSpParchTicketFareCabinEmbarked
089203Kelly, Mr. James1003309117.8292NaNQ
189313Wilkes, Mrs. James (Ellen Needs)0103632727.0000NaNS
289402Myles, Mr. Thomas Francis1002402769.6875NaNQ
389503Wirz, Mr. Albert1003151548.6625NaNS
489613Hirvonen, Mrs. Alexander (Helga E Lindqvist)011310129812.2875NaNS
589703Svensson, Mr. Johan Cervin10075389.2250NaNS
689813Connolly, Miss. Kate0003309727.6292NaNQ
789902Caldwell, Mr. Albert Francis11124873829.0000NaNS
890013Abrahim, Mrs. Joseph (Sophie Halaut Easu)00026577.2292NaNC
990103Davies, Mr. John Samuel120A/4 4887124.1500NaNS
\n", 1291 | "
" 1292 | ], 1293 | "text/plain": [ 1294 | " PassengerId Survived Pclass \\\n", 1295 | "0 892 0 3 \n", 1296 | "1 893 1 3 \n", 1297 | "2 894 0 2 \n", 1298 | "3 895 0 3 \n", 1299 | "4 896 1 3 \n", 1300 | "5 897 0 3 \n", 1301 | "6 898 1 3 \n", 1302 | "7 899 0 2 \n", 1303 | "8 900 1 3 \n", 1304 | "9 901 0 3 \n", 1305 | "\n", 1306 | " Name Sex SibSp Parch Ticket \\\n", 1307 | "0 Kelly, Mr. James 1 0 0 330911 \n", 1308 | "1 Wilkes, Mrs. James (Ellen Needs) 0 1 0 363272 \n", 1309 | "2 Myles, Mr. Thomas Francis 1 0 0 240276 \n", 1310 | "3 Wirz, Mr. Albert 1 0 0 315154 \n", 1311 | "4 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 0 1 1 3101298 \n", 1312 | "5 Svensson, Mr. Johan Cervin 1 0 0 7538 \n", 1313 | "6 Connolly, Miss. Kate 0 0 0 330972 \n", 1314 | "7 Caldwell, Mr. Albert Francis 1 1 1 248738 \n", 1315 | "8 Abrahim, Mrs. Joseph (Sophie Halaut Easu) 0 0 0 2657 \n", 1316 | "9 Davies, Mr. John Samuel 1 2 0 A/4 48871 \n", 1317 | "\n", 1318 | " Fare Cabin Embarked \n", 1319 | "0 7.8292 NaN Q \n", 1320 | "1 7.0000 NaN S \n", 1321 | "2 9.6875 NaN Q \n", 1322 | "3 8.6625 NaN S \n", 1323 | "4 12.2875 NaN S \n", 1324 | "5 9.2250 NaN S \n", 1325 | "6 7.6292 NaN Q \n", 1326 | "7 29.0000 NaN S \n", 1327 | "8 7.2292 NaN C \n", 1328 | "9 24.1500 NaN S " 1329 | ] 1330 | }, 1331 | "execution_count": 21, 1332 | "metadata": {}, 1333 | "output_type": "execute_result" 1334 | } 1335 | ], 1336 | "source": [ 1337 | "df_final = df\n", 1338 | "df_final.head(10)" 1339 | ] 1340 | }, 1341 | { 1342 | "cell_type": "raw", 1343 | "id": "79990176-bd8d-4ab4-a073-b87051d76446", 1344 | "metadata": {}, 1345 | "source": [ 1346 | "MODEL TRAINING" 1347 | ] 1348 | }, 1349 | { 1350 | "cell_type": "code", 1351 | "execution_count": 22, 1352 | "id": "348a81c8-e345-42a3-848c-d5ae7dcc5719", 1353 | "metadata": {}, 1354 | "outputs": [], 1355 | "source": [ 1356 | "X= df[['Pclass', 'Sex']]\n", 1357 | "Y=df['Survived']" 1358 | ] 1359 | }, 1360 | { 1361 | "cell_type": "code", 1362 | "execution_count": 23, 1363 | "id": "c8fbb0db-5546-4cb4-b799-38451a5687d6", 1364 | "metadata": {}, 1365 | "outputs": [], 1366 | "source": [ 1367 | "from sklearn.model_selection import train_test_split\n", 1368 | "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)" 1369 | ] 1370 | }, 1371 | { 1372 | "cell_type": "code", 1373 | "execution_count": 25, 1374 | "id": "39a35b59-1450-4b07-b1dd-732f8f2fbac1", 1375 | "metadata": {}, 1376 | "outputs": [ 1377 | { 1378 | "data": { 1379 | "text/html": [ 1380 | "
LogisticRegression(random_state=0)
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" 1381 | ], 1382 | "text/plain": [ 1383 | "LogisticRegression(random_state=0)" 1384 | ] 1385 | }, 1386 | "execution_count": 25, 1387 | "metadata": {}, 1388 | "output_type": "execute_result" 1389 | } 1390 | ], 1391 | "source": [ 1392 | "from sklearn.linear_model import LogisticRegression\n", 1393 | "\n", 1394 | "log = LogisticRegression(random_state = 0)\n", 1395 | "log.fit(X_train, Y_train)" 1396 | ] 1397 | }, 1398 | { 1399 | "cell_type": "raw", 1400 | "id": "a4f09ae2-d9dd-41c1-955f-3b377fe43160", 1401 | "metadata": {}, 1402 | "source": [ 1403 | "MODEL PREDICTION" 1404 | ] 1405 | }, 1406 | { 1407 | "cell_type": "code", 1408 | "execution_count": 26, 1409 | "id": "58639165-2cac-49e1-b21d-ffdf70705689", 1410 | "metadata": {}, 1411 | "outputs": [ 1412 | { 1413 | "name": "stdout", 1414 | "output_type": "stream", 1415 | "text": [ 1416 | "[0 0 1 0 1 0 1 0 0 0 1 1 0 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 1 1 1 1 0 0\n", 1417 | " 1 1 1 1 0 1 1 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 1 1 0 0 1 1 1 1 0 0 1 1 1\n", 1418 | " 1 0 0 1 0 1 0 1 0 0]\n" 1419 | ] 1420 | } 1421 | ], 1422 | "source": [ 1423 | "pred = print(log.predict(X_test))" 1424 | ] 1425 | }, 1426 | { 1427 | "cell_type": "code", 1428 | "execution_count": 27, 1429 | "id": "ce375588-a4ea-4fef-b6f4-9a54c1ebfbcc", 1430 | "metadata": {}, 1431 | "outputs": [ 1432 | { 1433 | "name": "stdout", 1434 | "output_type": "stream", 1435 | "text": [ 1436 | "360 0\n", 1437 | "170 0\n", 1438 | "224 1\n", 1439 | "358 0\n", 1440 | "309 1\n", 1441 | " ..\n", 1442 | "100 1\n", 1443 | "7 0\n", 1444 | "22 1\n", 1445 | "68 0\n", 1446 | "328 0\n", 1447 | "Name: Survived, Length: 84, dtype: int64\n" 1448 | ] 1449 | } 1450 | ], 1451 | "source": [ 1452 | "print(Y_test)" 1453 | ] 1454 | }, 1455 | { 1456 | "cell_type": "code", 1457 | "execution_count": 28, 1458 | "id": "f82377d1-f4c0-4c94-8dcd-c658e147e0bd", 1459 | "metadata": {}, 1460 | "outputs": [ 1461 | { 1462 | "name": "stdout", 1463 | "output_type": "stream", 1464 | "text": [ 1465 | "So Sorry! Not Survived\n" 1466 | ] 1467 | } 1468 | ], 1469 | "source": [ 1470 | "import warnings\n", 1471 | "warnings.filterwarnings(\"ignore\")\n", 1472 | "\n", 1473 | "res= log.predict([[2,1]])\n", 1474 | "\n", 1475 | "if(res==0):\n", 1476 | " print(\"So Sorry! Not Survived\")\n", 1477 | "else:\n", 1478 | " print(\"Survived\")" 1479 | ] 1480 | }, 1481 | { 1482 | "cell_type": "code", 1483 | "execution_count": null, 1484 | "id": "5a18e326-1534-4b78-9c84-75570cce2efe", 1485 | "metadata": {}, 1486 | "outputs": [], 1487 | "source": [] 1488 | } 1489 | ], 1490 | "metadata": { 1491 | "kernelspec": { 1492 | "display_name": "Python 3 (ipykernel)", 1493 | "language": "python", 1494 | "name": "python3" 1495 | }, 1496 | "language_info": { 1497 | "codemirror_mode": { 1498 | "name": "ipython", 1499 | "version": 3 1500 | }, 1501 | "file_extension": ".py", 1502 | "mimetype": "text/x-python", 1503 | "name": "python", 1504 | "nbconvert_exporter": "python", 1505 | "pygments_lexer": "ipython3", 1506 | "version": "3.10.8" 1507 | } 1508 | }, 1509 | "nbformat": 4, 1510 | "nbformat_minor": 5 1511 | } 1512 | --------------------------------------------------------------------------------