├── Concrete_Prediction ├── Concrete.csv ├── multiple-regression-on-cement-data.ipynb └── readme.md ├── House-Price-Predictions ├── DATA BASE.docx ├── Design.jpeg ├── OIG.jpeg ├── OIG2.jpeg ├── Real_Estate.csv ├── Real_Estate_Prediction 2.0.ipynb ├── Real_Estate_Prediction 3.0.ipynb ├── database_setup1.mysql-notebook ├── database_setup2.mysql-notebook └── readme.md ├── Iris-Species-Prediction ├── Iris2023.csv ├── Iris_Classification.ipynb ├── database_setup.mysql-notebook ├── iris1.jpg ├── iris2.jpg └── readme.md ├── Outlier_Treatment ├── Outlier treat mine.ipynb └── readme.md └── README.md /Concrete_Prediction/Concrete.csv: -------------------------------------------------------------------------------- 1 | cement,slag,flyash,water,superplasticizer,coarseaggregate,fineaggregate,age,csMPa 2 | 540,0,0,162,2.5,1040,676,28,79.99 3 | 540,0,0,162,2.5,1055,676,28,61.89 4 | 332.5,142.5,0,228,0,932,594,270,40.27 5 | 332.5,142.5,0,228,0,932,594,365,41.05 6 | 198.6,132.4,0,192,0,978.4,825.5,360,44.3 7 | 266,114,0,228,0,932,670,90,47.03 8 | 380,95,0,228,0,932,594,365,43.7 9 | 380,95,0,228,0,932,594,28,36.45 10 | 266,114,0,228,0,932,670,28,45.85 11 | 475,0,0,228,0,932,594,28,39.29 12 | 198.6,132.4,0,192,0,978.4,825.5,90,38.07 13 | 198.6,132.4,0,192,0,978.4,825.5,28,28.02 14 | 427.5,47.5,0,228,0,932,594,270,43.01 15 | 190,190,0,228,0,932,670,90,42.33 16 | 304,76,0,228,0,932,670,28,47.81 17 | 380,0,0,228,0,932,670,90,52.91 18 | 139.6,209.4,0,192,0,1047,806.9,90,39.36 19 | 342,38,0,228,0,932,670,365,56.14 20 | 380,95,0,228,0,932,594,90,40.56 21 | 475,0,0,228,0,932,594,180,42.62 22 | 427.5,47.5,0,228,0,932,594,180,41.84 23 | 139.6,209.4,0,192,0,1047,806.9,28,28.24 24 | 139.6,209.4,0,192,0,1047,806.9,3,8.06 25 | 139.6,209.4,0,192,0,1047,806.9,180,44.21 26 | 380,0,0,228,0,932,670,365,52.52 27 | 380,0,0,228,0,932,670,270,53.3 28 | 380,95,0,228,0,932,594,270,41.15 29 | 342,38,0,228,0,932,670,180,52.12 30 | 427.5,47.5,0,228,0,932,594,28,37.43 31 | 475,0,0,228,0,932,594,7,38.6 32 | 304,76,0,228,0,932,670,365,55.26 33 | 266,114,0,228,0,932,670,365,52.91 34 | 198.6,132.4,0,192,0,978.4,825.5,180,41.72 35 | 475,0,0,228,0,932,594,270,42.13 36 | 190,190,0,228,0,932,670,365,53.69 37 | 237.5,237.5,0,228,0,932,594,270,38.41 38 | 237.5,237.5,0,228,0,932,594,28,30.08 39 | 332.5,142.5,0,228,0,932,594,90,37.72 40 | 475,0,0,228,0,932,594,90,42.23 41 | 237.5,237.5,0,228,0,932,594,180,36.25 42 | 342,38,0,228,0,932,670,90,50.46 43 | 427.5,47.5,0,228,0,932,594,365,43.7 44 | 237.5,237.5,0,228,0,932,594,365,39 45 | 380,0,0,228,0,932,670,180,53.1 46 | 427.5,47.5,0,228,0,932,594,90,41.54 47 | 427.5,47.5,0,228,0,932,594,7,35.08 48 | 349,0,0,192,0,1047,806.9,3,15.05 49 | 380,95,0,228,0,932,594,180,40.76 50 | 237.5,237.5,0,228,0,932,594,7,26.26 51 | 380,95,0,228,0,932,594,7,32.82 52 | 332.5,142.5,0,228,0,932,594,180,39.78 53 | 190,190,0,228,0,932,670,180,46.93 54 | 237.5,237.5,0,228,0,932,594,90,33.12 55 | 304,76,0,228,0,932,670,90,49.19 56 | 139.6,209.4,0,192,0,1047,806.9,7,14.59 57 | 198.6,132.4,0,192,0,978.4,825.5,7,14.64 58 | 475,0,0,228,0,932,594,365,41.93 59 | 198.6,132.4,0,192,0,978.4,825.5,3,9.13 60 | 304,76,0,228,0,932,670,180,50.95 61 | 332.5,142.5,0,228,0,932,594,28,33.02 62 | 304,76,0,228,0,932,670,270,54.38 63 | 266,114,0,228,0,932,670,270,51.73 64 | 310,0,0,192,0,971,850.6,3,9.87 65 | 190,190,0,228,0,932,670,270,50.66 66 | 266,114,0,228,0,932,670,180,48.7 67 | 342,38,0,228,0,932,670,270,55.06 68 | 139.6,209.4,0,192,0,1047,806.9,360,44.7 69 | 332.5,142.5,0,228,0,932,594,7,30.28 70 | 190,190,0,228,0,932,670,28,40.86 71 | 485,0,0,146,0,1120,800,28,71.99 72 | 374,189.2,0,170.1,10.1,926.1,756.7,3,34.4 73 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,3,28.8 74 | 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4 75 | 425,106.3,0,151.4,18.6,936,803.7,3,36.3 76 | 375,93.8,0,126.6,23.4,852.1,992.6,3,29 77 | 475,118.8,0,181.1,8.9,852.1,781.5,3,37.8 78 | 469,117.2,0,137.8,32.2,852.1,840.5,3,40.2 79 | 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4 80 | 388.6,97.1,0,157.9,12.1,852.1,925.7,3,28.1 81 | 531.3,0,0,141.8,28.2,852.1,893.7,3,41.3 82 | 425,106.3,0,153.5,16.5,852.1,887.1,3,33.4 83 | 318.8,212.5,0,155.7,14.3,852.1,880.4,3,25.2 84 | 401.8,94.7,0,147.4,11.4,946.8,852.1,3,41.1 85 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 86 | 323.7,282.8,0,183.8,10.3,942.7,659.9,3,28.3 87 | 379.5,151.2,0,153.9,15.9,1134.3,605,3,28.6 88 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 89 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,3,24.4 90 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 91 | 439,177,0,186,11.1,884.9,707.9,3,39.3 92 | 389.9,189,0,145.9,22,944.7,755.8,3,40.6 93 | 362.6,189,0,164.9,11.6,944.7,755.8,3,35.3 94 | 337.9,189,0,174.9,9.5,944.7,755.8,3,24.1 95 | 374,189.2,0,170.1,10.1,926.1,756.7,7,46.2 96 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,7,42.8 97 | 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2 98 | 425,106.3,0,151.4,18.6,936,803.7,7,46.8 99 | 375,93.8,0,126.6,23.4,852.1,992.6,7,45.7 100 | 475,118.8,0,181.1,8.9,852.1,781.5,7,55.6 101 | 469,117.2,0,137.8,32.2,852.1,840.5,7,54.9 102 | 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2 103 | 388.6,97.1,0,157.9,12.1,852.1,925.7,7,34.9 104 | 531.3,0,0,141.8,28.2,852.1,893.7,7,46.9 105 | 425,106.3,0,153.5,16.5,852.1,887.1,7,49.2 106 | 318.8,212.5,0,155.7,14.3,852.1,880.4,7,33.4 107 | 401.8,94.7,0,147.4,11.4,946.8,852.1,7,54.1 108 | 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9 109 | 323.7,282.8,0,183.8,10.3,942.7,659.9,7,49.8 110 | 379.5,151.2,0,153.9,15.9,1134.3,605,7,47.1 111 | 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9 112 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,7,38 113 | 362.6,189,0,164.9,11.6,944.7,755.8,7,55.9 114 | 439,177,0,186,11.1,884.9,707.9,7,56.1 115 | 389.9,189,0,145.9,22,944.7,755.8,7,59.09 116 | 362.6,189,0,164.9,11.6,944.7,755.8,7,22.9 117 | 337.9,189,0,174.9,9.5,944.7,755.8,7,35.1 118 | 374,189.2,0,170.1,10.1,926.1,756.7,28,61.09 119 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,28,59.8 120 | 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29 121 | 425,106.3,0,151.4,18.6,936,803.7,28,61.8 122 | 375,93.8,0,126.6,23.4,852.1,992.6,28,56.7 123 | 475,118.8,0,181.1,8.9,852.1,781.5,28,68.3 124 | 469,117.2,0,137.8,32.2,852.1,840.5,28,66.9 125 | 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29 126 | 388.6,97.1,0,157.9,12.1,852.1,925.7,28,50.7 127 | 531.3,0,0,141.8,28.2,852.1,893.7,28,56.4 128 | 425,106.3,0,153.5,16.5,852.1,887.1,28,60.29 129 | 318.8,212.5,0,155.7,14.3,852.1,880.4,28,55.5 130 | 401.8,94.7,0,147.4,11.4,946.8,852.1,28,68.5 131 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 132 | 323.7,282.8,0,183.8,10.3,942.7,659.9,28,74.7 133 | 379.5,151.2,0,153.9,15.9,1134.3,605,28,52.2 134 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 135 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,28,67.7 136 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 137 | 439,177,0,186,11.1,884.9,707.9,28,66 138 | 389.9,189,0,145.9,22,944.7,755.8,28,74.5 139 | 362.6,189,0,164.9,11.6,944.7,755.8,28,71.3 140 | 337.9,189,0,174.9,9.5,944.7,755.8,28,49.9 141 | 374,189.2,0,170.1,10.1,926.1,756.7,56,63.4 142 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,56,64.9 143 | 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3 144 | 425,106.3,0,151.4,18.6,936,803.7,56,64.9 145 | 375,93.8,0,126.6,23.4,852.1,992.6,56,60.2 146 | 475,118.8,0,181.1,8.9,852.1,781.5,56,72.3 147 | 469,117.2,0,137.8,32.2,852.1,840.5,56,69.3 148 | 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3 149 | 388.6,97.1,0,157.9,12.1,852.1,925.7,56,55.2 150 | 531.3,0,0,141.8,28.2,852.1,893.7,56,58.8 151 | 425,106.3,0,153.5,16.5,852.1,887.1,56,64.3 152 | 318.8,212.5,0,155.7,14.3,852.1,880.4,56,66.1 153 | 401.8,94.7,0,147.4,11.4,946.8,852.1,56,73.7 154 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 155 | 323.7,282.8,0,183.8,10.3,942.7,659.9,56,80.2 156 | 379.5,151.2,0,153.9,15.9,1134.3,605,56,54.9 157 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 158 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,56,72.99 159 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 160 | 439,177,0,186,11.1,884.9,707.9,56,71.7 161 | 389.9,189,0,145.9,22,944.7,755.8,56,79.4 162 | 362.6,189,0,164.9,11.6,944.7,755.8,56,77.3 163 | 337.9,189,0,174.9,9.5,944.7,755.8,56,59.89 164 | 374,189.2,0,170.1,10.1,926.1,756.7,91,64.9 165 | 313.3,262.2,0,175.5,8.6,1046.9,611.8,91,66.6 166 | 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2 167 | 425,106.3,0,151.4,18.6,936,803.7,91,66.7 168 | 375,93.8,0,126.6,23.4,852.1,992.6,91,62.5 169 | 475,118.8,0,181.1,8.9,852.1,781.5,91,74.19 170 | 469,117.2,0,137.8,32.2,852.1,840.5,91,70.7 171 | 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2 172 | 388.6,97.1,0,157.9,12.1,852.1,925.7,91,57.6 173 | 531.3,0,0,141.8,28.2,852.1,893.7,91,59.2 174 | 425,106.3,0,153.5,16.5,852.1,887.1,91,65.2 175 | 318.8,212.5,0,155.7,14.3,852.1,880.4,91,68.1 176 | 401.8,94.7,0,147.4,11.4,946.8,852.1,91,75.5 177 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 178 | 379.5,151.2,0,153.9,15.9,1134.3,605,91,56.5 179 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 180 | 286.3,200.9,0,144.7,11.2,1004.6,803.7,91,76.8 181 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 182 | 439,177,0,186,11.1,884.9,707.9,91,73.3 183 | 389.9,189,0,145.9,22,944.7,755.8,91,82.6 184 | 362.6,189,0,164.9,11.6,944.7,755.8,91,79.3 185 | 337.9,189,0,174.9,9.5,944.7,755.8,91,67.8 186 | 222.4,0,96.7,189.3,4.5,967.1,870.3,3,11.58 187 | 222.4,0,96.7,189.3,4.5,967.1,870.3,14,24.45 188 | 222.4,0,96.7,189.3,4.5,967.1,870.3,28,24.89 189 | 222.4,0,96.7,189.3,4.5,967.1,870.3,56,29.45 190 | 222.4,0,96.7,189.3,4.5,967.1,870.3,100,40.71 191 | 233.8,0,94.6,197.9,4.6,947,852.2,3,10.38 192 | 233.8,0,94.6,197.9,4.6,947,852.2,14,22.14 193 | 233.8,0,94.6,197.9,4.6,947,852.2,28,22.84 194 | 233.8,0,94.6,197.9,4.6,947,852.2,56,27.66 195 | 233.8,0,94.6,197.9,4.6,947,852.2,100,34.56 196 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,3,12.45 197 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,14,24.99 198 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,28,25.72 199 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,56,33.96 200 | 194.7,0,100.5,165.6,7.5,1006.4,905.9,100,37.34 201 | 190.7,0,125.4,162.1,7.8,1090,804,3,15.04 202 | 190.7,0,125.4,162.1,7.8,1090,804,14,21.06 203 | 190.7,0,125.4,162.1,7.8,1090,804,28,26.4 204 | 190.7,0,125.4,162.1,7.8,1090,804,56,35.34 205 | 190.7,0,125.4,162.1,7.8,1090,804,100,40.57 206 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,3,12.47 207 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,14,20.92 208 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,28,24.9 209 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,56,34.2 210 | 212.1,0,121.6,180.3,5.7,1057.6,779.3,100,39.61 211 | 230,0,118.3,195.5,4.6,1029.4,758.6,3,10.03 212 | 230,0,118.3,195.5,4.6,1029.4,758.6,14,20.08 213 | 230,0,118.3,195.5,4.6,1029.4,758.6,28,24.48 214 | 230,0,118.3,195.5,4.6,1029.4,758.6,56,31.54 215 | 230,0,118.3,195.5,4.6,1029.4,758.6,100,35.34 216 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,3,9.45 217 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,14,22.72 218 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,28,28.47 219 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,56,38.56 220 | 190.3,0,125.2,161.9,9.9,1088.1,802.6,100,40.39 221 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,3,10.76 222 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,14,25.48 223 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,28,21.54 224 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,56,28.63 225 | 166.1,0,163.3,176.5,4.5,1058.6,780.1,100,33.54 226 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,3,7.75 227 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,14,17.82 228 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,28,24.24 229 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,56,32.85 230 | 168,42.1,163.8,121.8,5.7,1058.7,780.1,100,39.23 231 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,3,18 232 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,14,30.39 233 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,28,45.71 234 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,56,50.77 235 | 213.7,98.1,24.5,181.7,6.9,1065.8,785.4,100,53.9 236 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,3,13.18 237 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,14,17.84 238 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,28,40.23 239 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,56,47.13 240 | 213.8,98.1,24.5,181.7,6.7,1066,785.5,100,49.97 241 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,3,13.36 242 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,14,22.32 243 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,28,24.54 244 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,56,31.35 245 | 229.7,0,118.2,195.2,6.1,1028.1,757.6,100,40.86 246 | 238.1,0,94.1,186.7,7,949.9,847,3,19.93 247 | 238.1,0,94.1,186.7,7,949.9,847,14,25.69 248 | 238.1,0,94.1,186.7,7,949.9,847,28,30.23 249 | 238.1,0,94.1,186.7,7,949.9,847,56,39.59 250 | 238.1,0,94.1,186.7,7,949.9,847,100,44.3 251 | 250,0,95.7,187.4,5.5,956.9,861.2,3,13.82 252 | 250,0,95.7,187.4,5.5,956.9,861.2,14,24.92 253 | 250,0,95.7,187.4,5.5,956.9,861.2,28,29.22 254 | 250,0,95.7,187.4,5.5,956.9,861.2,56,38.33 255 | 250,0,95.7,187.4,5.5,956.9,861.2,100,42.35 256 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,3,13.54 257 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,14,26.31 258 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,28,31.64 259 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,56,42.55 260 | 212.5,0,100.4,159.3,8.7,1007.8,903.6,100,42.92 261 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,3,13.33 262 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,14,25.37 263 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,28,37.4 264 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,56,44.4 265 | 212.6,0,100.4,159.4,10.4,1003.8,903.8,100,47.74 266 | 212,0,124.8,159,7.8,1085.4,799.5,3,19.52 267 | 212,0,124.8,159,7.8,1085.4,799.5,14,31.35 268 | 212,0,124.8,159,7.8,1085.4,799.5,28,38.5 269 | 212,0,124.8,159,7.8,1085.4,799.5,56,45.08 270 | 212,0,124.8,159,7.8,1085.4,799.5,100,47.82 271 | 231.8,0,121.6,174,6.7,1056.4,778.5,3,15.44 272 | 231.8,0,121.6,174,6.7,1056.4,778.5,14,26.77 273 | 231.8,0,121.6,174,6.7,1056.4,778.5,28,33.73 274 | 231.8,0,121.6,174,6.7,1056.4,778.5,56,42.7 275 | 231.8,0,121.6,174,6.7,1056.4,778.5,100,45.84 276 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,3,17.22 277 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,14,29.93 278 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,28,29.65 279 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,56,36.97 280 | 251.4,0,118.3,188.5,5.8,1028.4,757.7,100,43.58 281 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,3,13.12 282 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,14,24.43 283 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,28,32.66 284 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,56,36.64 285 | 251.4,0,118.3,188.5,6.4,1028.4,757.7,100,44.21 286 | 181.4,0,167,169.6,7.6,1055.6,777.8,3,13.62 287 | 181.4,0,167,169.6,7.6,1055.6,777.8,14,21.6 288 | 181.4,0,167,169.6,7.6,1055.6,777.8,28,27.77 289 | 181.4,0,167,169.6,7.6,1055.6,777.8,56,35.57 290 | 181.4,0,167,169.6,7.6,1055.6,777.8,100,45.37 291 | 182,45.2,122,170.2,8.2,1059.4,780.7,3,7.32 292 | 182,45.2,122,170.2,8.2,1059.4,780.7,14,21.5 293 | 182,45.2,122,170.2,8.2,1059.4,780.7,28,31.27 294 | 182,45.2,122,170.2,8.2,1059.4,780.7,56,43.5 295 | 182,45.2,122,170.2,8.2,1059.4,780.7,100,48.67 296 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,3,7.4 297 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,14,23.51 298 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,28,31.12 299 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,56,39.15 300 | 168.9,42.2,124.3,158.3,10.8,1080.8,796.2,100,48.15 301 | 290.4,0,96.2,168.1,9.4,961.2,865,3,22.5 302 | 290.4,0,96.2,168.1,9.4,961.2,865,14,34.67 303 | 290.4,0,96.2,168.1,9.4,961.2,865,28,34.74 304 | 290.4,0,96.2,168.1,9.4,961.2,865,56,45.08 305 | 290.4,0,96.2,168.1,9.4,961.2,865,100,48.97 306 | 277.1,0,97.4,160.6,11.8,973.9,875.6,3,23.14 307 | 277.1,0,97.4,160.6,11.8,973.9,875.6,14,41.89 308 | 277.1,0,97.4,160.6,11.8,973.9,875.6,28,48.28 309 | 277.1,0,97.4,160.6,11.8,973.9,875.6,56,51.04 310 | 277.1,0,97.4,160.6,11.8,973.9,875.6,100,55.64 311 | 295.7,0,95.6,171.5,8.9,955.1,859.2,3,22.95 312 | 295.7,0,95.6,171.5,8.9,955.1,859.2,14,35.23 313 | 295.7,0,95.6,171.5,8.9,955.1,859.2,28,39.94 314 | 295.7,0,95.6,171.5,8.9,955.1,859.2,56,48.72 315 | 295.7,0,95.6,171.5,8.9,955.1,859.2,100,52.04 316 | 251.8,0,99.9,146.1,12.4,1006,899.8,3,21.02 317 | 251.8,0,99.9,146.1,12.4,1006,899.8,14,33.36 318 | 251.8,0,99.9,146.1,12.4,1006,899.8,28,33.94 319 | 251.8,0,99.9,146.1,12.4,1006,899.8,56,44.14 320 | 251.8,0,99.9,146.1,12.4,1006,899.8,100,45.37 321 | 249.1,0,98.8,158.1,12.8,987.8,889,3,15.36 322 | 249.1,0,98.8,158.1,12.8,987.8,889,14,28.68 323 | 249.1,0,98.8,158.1,12.8,987.8,889,28,30.85 324 | 249.1,0,98.8,158.1,12.8,987.8,889,56,42.03 325 | 249.1,0,98.8,158.1,12.8,987.8,889,100,51.06 326 | 252.3,0,98.8,146.3,14.2,987.8,889,3,21.78 327 | 252.3,0,98.8,146.3,14.2,987.8,889,14,42.29 328 | 252.3,0,98.8,146.3,14.2,987.8,889,28,50.6 329 | 252.3,0,98.8,146.3,14.2,987.8,889,56,55.83 330 | 252.3,0,98.8,146.3,14.2,987.8,889,100,60.95 331 | 246.8,0,125.1,143.3,12,1086.8,800.9,3,23.52 332 | 246.8,0,125.1,143.3,12,1086.8,800.9,14,42.22 333 | 246.8,0,125.1,143.3,12,1086.8,800.9,28,52.5 334 | 246.8,0,125.1,143.3,12,1086.8,800.9,56,60.32 335 | 246.8,0,125.1,143.3,12,1086.8,800.9,100,66.42 336 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,3,23.8 337 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,14,38.77 338 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,28,51.33 339 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,56,56.85 340 | 275.1,0,121.4,159.5,9.9,1053.6,777.5,100,58.61 341 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,3,21.91 342 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,14,36.99 343 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,28,47.4 344 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,56,51.96 345 | 297.2,0,117.5,174.8,9.5,1022.8,753.5,100,56.74 346 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,3,17.57 347 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,14,33.73 348 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,28,40.15 349 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,56,46.64 350 | 213.7,0,174.7,154.8,10.2,1053.5,776.4,100,50.08 351 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,3,17.37 352 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,14,33.7 353 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,28,45.94 354 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,56,51.43 355 | 213.5,0,174.2,154.6,11.7,1052.3,775.5,100,59.3 356 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,3,30.45 357 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,14,47.71 358 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,28,63.14 359 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,56,66.82 360 | 277.2,97.8,24.5,160.7,11.2,1061.7,782.5,100,66.95 361 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,3,27.42 362 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,14,35.96 363 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,28,55.51 364 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,56,61.99 365 | 218.2,54.6,123.8,140.8,11.9,1075.7,792.7,100,63.53 366 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,3,18.02 367 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,14,38.6 368 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,28,52.2 369 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,56,53.96 370 | 214.9,53.8,121.9,155.6,9.6,1014.3,780.6,100,56.63 371 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,3,15.34 372 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,14,26.05 373 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,28,30.22 374 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,56,37.27 375 | 218.9,0,124.1,158.5,11.3,1078.7,794.9,100,46.23 376 | 376,0,0,214.6,0,1003.5,762.4,3,16.28 377 | 376,0,0,214.6,0,1003.5,762.4,14,25.62 378 | 376,0,0,214.6,0,1003.5,762.4,28,31.97 379 | 376,0,0,214.6,0,1003.5,762.4,56,36.3 380 | 376,0,0,214.6,0,1003.5,762.4,100,43.06 381 | 500,0,0,140,4,966,853,28,67.57 382 | 475,0,59,142,1.9,1098,641,28,57.23 383 | 315,137,0,145,5.9,1130,745,28,81.75 384 | 505,0,60,195,0,1030,630,28,64.02 385 | 451,0,0,165,11.3,1030,745,28,78.8 386 | 516,0,0,162,8.2,801,802,28,41.37 387 | 520,0,0,170,5.2,855,855,28,60.28 388 | 528,0,0,185,6.9,920,720,28,56.83 389 | 520,0,0,175,5.2,870,805,28,51.02 390 | 385,0,136,158,20,903,768,28,55.55 391 | 500.1,0,0,200,3,1124.4,613.2,28,44.13 392 | 450.1,50,0,200,3,1124.4,613.2,28,39.38 393 | 397,17.2,158,167,20.8,967,633,28,55.65 394 | 333,17.5,163,167,17.9,996,652,28,47.28 395 | 334,17.6,158,189,15.3,967,633,28,44.33 396 | 405,0,0,175,0,1120,695,28,52.3 397 | 200,200,0,190,0,1145,660,28,49.25 398 | 516,0,0,162,8.3,801,802,28,41.37 399 | 145,116,119,184,5.7,833,880,28,29.16 400 | 160,128,122,182,6.4,824,879,28,39.4 401 | 234,156,0,189,5.9,981,760,28,39.3 402 | 250,180,95,159,9.5,860,800,28,67.87 403 | 475,0,0,162,9.5,1044,662,28,58.52 404 | 285,190,0,163,7.6,1031,685,28,53.58 405 | 356,119,0,160,9,1061,657,28,59 406 | 275,180,120,162,10.4,830,765,28,76.24 407 | 500,0,0,151,9,1033,655,28,69.84 408 | 165,0,143.6,163.8,0,1005.6,900.9,3,14.4 409 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,3,19.42 410 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,3,20.73 411 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,3,14.94 412 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,3,21.29 413 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,3,23.08 414 | 167,75.4,167,164,7.9,1007.3,770.1,3,15.52 415 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,3,15.82 416 | 190.3,0,125.2,166.6,9.9,1079,798.9,3,12.55 417 | 250,0,95.7,191.8,5.3,948.9,857.2,3,8.49 418 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,3,15.61 419 | 194.7,0,100.5,170.2,7.5,998,901.8,3,12.18 420 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,3,11.98 421 | 165,0,143.6,163.8,0,1005.6,900.9,14,16.88 422 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,14,33.09 423 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,14,34.24 424 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,14,31.81 425 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,14,29.75 426 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,14,33.01 427 | 167,75.4,167,164,7.9,1007.3,770.1,14,32.9 428 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,14,29.55 429 | 190.3,0,125.2,166.6,9.9,1079,798.9,14,19.42 430 | 250,0,95.7,191.8,5.3,948.9,857.2,14,24.66 431 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,14,29.59 432 | 194.7,0,100.5,170.2,7.5,998,901.8,14,24.28 433 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,14,20.73 434 | 165,0,143.6,163.8,0,1005.6,900.9,28,26.2 435 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,28,46.39 436 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,28,39.16 437 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,28,41.2 438 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,28,33.69 439 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,28,38.2 440 | 167,75.4,167,164,7.9,1007.3,770.1,28,41.41 441 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,28,37.81 442 | 190.3,0,125.2,166.6,9.9,1079,798.9,28,24.85 443 | 250,0,95.7,191.8,5.3,948.9,857.2,28,27.22 444 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,28,44.64 445 | 194.7,0,100.5,170.2,7.5,998,901.8,28,37.27 446 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,28,33.27 447 | 165,0,143.6,163.8,0,1005.6,900.9,56,36.56 448 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,56,53.72 449 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,56,48.59 450 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,56,51.72 451 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,56,35.85 452 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,56,53.77 453 | 167,75.4,167,164,7.9,1007.3,770.1,56,53.46 454 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,56,48.99 455 | 190.3,0,125.2,166.6,9.9,1079,798.9,56,31.72 456 | 250,0,95.7,191.8,5.3,948.9,857.2,56,39.64 457 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,56,51.26 458 | 194.7,0,100.5,170.2,7.5,998,901.8,56,43.39 459 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,56,39.27 460 | 165,0,143.6,163.8,0,1005.6,900.9,100,37.96 461 | 165,128.5,132.1,175.1,8.1,1005.8,746.6,100,55.02 462 | 178,129.8,118.6,179.9,3.6,1007.3,746.8,100,49.99 463 | 167.4,129.9,128.6,175.5,7.8,1006.3,746.6,100,53.66 464 | 172.4,13.6,172.4,156.8,4.1,1006.3,856.4,100,37.68 465 | 173.5,50.1,173.5,164.8,6.5,1006.2,793.5,100,56.06 466 | 167,75.4,167,164,7.9,1007.3,770.1,100,56.81 467 | 173.8,93.4,159.9,172.3,9.7,1007.2,746.6,100,50.94 468 | 190.3,0,125.2,166.6,9.9,1079,798.9,100,33.56 469 | 250,0,95.7,191.8,5.3,948.9,857.2,100,41.16 470 | 213.5,0,174.2,159.2,11.7,1043.6,771.9,100,52.96 471 | 194.7,0,100.5,170.2,7.5,998,901.8,100,44.28 472 | 251.4,0,118.3,192.9,5.8,1043.6,754.3,100,40.15 473 | 446,24,79,162,11.6,967,712,28,57.03 474 | 446,24,79,162,11.6,967,712,28,44.42 475 | 446,24,79,162,11.6,967,712,28,51.02 476 | 446,24,79,162,10.3,967,712,28,53.39 477 | 446,24,79,162,11.6,967,712,3,35.36 478 | 446,24,79,162,11.6,967,712,3,25.02 479 | 446,24,79,162,11.6,967,712,3,23.35 480 | 446,24,79,162,11.6,967,712,7,52.01 481 | 446,24,79,162,11.6,967,712,7,38.02 482 | 446,24,79,162,11.6,967,712,7,39.3 483 | 446,24,79,162,11.6,967,712,56,61.07 484 | 446,24,79,162,11.6,967,712,56,56.14 485 | 446,24,79,162,11.6,967,712,56,55.25 486 | 446,24,79,162,10.3,967,712,56,54.77 487 | 387,20,94,157,14.3,938,845,28,50.24 488 | 387,20,94,157,13.9,938,845,28,46.68 489 | 387,20,94,157,11.6,938,845,28,46.68 490 | 387,20,94,157,14.3,938,845,3,22.75 491 | 387,20,94,157,13.9,938,845,3,25.51 492 | 387,20,94,157,11.6,938,845,3,34.77 493 | 387,20,94,157,14.3,938,845,7,36.84 494 | 387,20,94,157,13.9,938,845,7,45.9 495 | 387,20,94,157,11.6,938,845,7,41.67 496 | 387,20,94,157,14.3,938,845,56,56.34 497 | 387,20,94,157,13.9,938,845,56,47.97 498 | 387,20,94,157,11.6,938,845,56,61.46 499 | 355,19,97,145,13.1,967,871,28,44.03 500 | 355,19,97,145,12.3,967,871,28,55.45 501 | 491,26,123,210,3.9,882,699,28,55.55 502 | 491,26,123,201,3.9,822,699,28,57.92 503 | 491,26,123,210,3.9,882,699,3,25.61 504 | 491,26,123,210,3.9,882,699,7,33.49 505 | 491,26,123,210,3.9,882,699,56,59.59 506 | 491,26,123,201,3.9,822,699,3,29.55 507 | 491,26,123,201,3.9,822,699,7,37.92 508 | 491,26,123,201,3.9,822,699,56,61.86 509 | 424,22,132,178,8.5,822,750,28,62.05 510 | 424,22,132,178,8.5,882,750,3,32.01 511 | 424,22,132,168,8.9,822,750,28,72.1 512 | 424,22,132,178,8.5,822,750,7,39 513 | 424,22,132,178,8.5,822,750,56,65.7 514 | 424,22,132,168,8.9,822,750,3,32.11 515 | 424,22,132,168,8.9,822,750,7,40.29 516 | 424,22,132,168,8.9,822,750,56,74.36 517 | 202,11,141,206,1.7,942,801,28,21.97 518 | 202,11,141,206,1.7,942,801,3,9.85 519 | 202,11,141,206,1.7,942,801,7,15.07 520 | 202,11,141,206,1.7,942,801,56,23.25 521 | 284,15,141,179,5.5,842,801,28,43.73 522 | 284,15,141,179,5.5,842,801,3,13.4 523 | 284,15,141,179,5.5,842,801,7,24.13 524 | 284,15,141,179,5.5,842,801,56,44.52 525 | 359,19,141,154,10.9,942,801,28,62.94 526 | 359,19,141,154,10.9,942,801,28,59.49 527 | 359,19,141,154,10.9,942,801,3,25.12 528 | 359,19,141,154,10.9,942,801,3,23.64 529 | 359,19,141,154,10.9,942,801,7,35.75 530 | 359,19,141,154,10.9,942,801,7,38.61 531 | 359,19,141,154,10.9,942,801,56,68.75 532 | 359,19,141,154,10.9,942,801,56,66.78 533 | 436,0,0,218,0,838.4,719.7,28,23.85 534 | 289,0,0,192,0,913.2,895.3,90,32.07 535 | 289,0,0,192,0,913.2,895.3,3,11.65 536 | 393,0,0,192,0,940.6,785.6,3,19.2 537 | 393,0,0,192,0,940.6,785.6,90,48.85 538 | 393,0,0,192,0,940.6,785.6,28,39.6 539 | 480,0,0,192,0,936.2,712.2,28,43.94 540 | 480,0,0,192,0,936.2,712.2,7,34.57 541 | 480,0,0,192,0,936.2,712.2,90,54.32 542 | 480,0,0,192,0,936.2,712.2,3,24.4 543 | 333,0,0,192,0,931.2,842.6,3,15.62 544 | 255,0,0,192,0,889.8,945,90,21.86 545 | 255,0,0,192,0,889.8,945,7,10.22 546 | 289,0,0,192,0,913.2,895.3,7,14.6 547 | 255,0,0,192,0,889.8,945,28,18.75 548 | 333,0,0,192,0,931.2,842.6,28,31.97 549 | 333,0,0,192,0,931.2,842.6,7,23.4 550 | 289,0,0,192,0,913.2,895.3,28,25.57 551 | 333,0,0,192,0,931.2,842.6,90,41.68 552 | 393,0,0,192,0,940.6,785.6,7,27.74 553 | 255,0,0,192,0,889.8,945,3,8.2 554 | 158.8,238.2,0,185.7,0,1040.6,734.3,7,9.62 555 | 239.6,359.4,0,185.7,0,941.6,664.3,7,25.42 556 | 238.2,158.8,0,185.7,0,1040.6,734.3,7,15.69 557 | 181.9,272.8,0,185.7,0,1012.4,714.3,28,27.94 558 | 193.5,290.2,0,185.7,0,998.2,704.3,28,32.63 559 | 255.5,170.3,0,185.7,0,1026.6,724.3,7,17.24 560 | 272.8,181.9,0,185.7,0,1012.4,714.3,7,19.77 561 | 239.6,359.4,0,185.7,0,941.6,664.3,28,39.44 562 | 220.8,147.2,0,185.7,0,1055,744.3,28,25.75 563 | 397,0,0,185.7,0,1040.6,734.3,28,33.08 564 | 382.5,0,0,185.7,0,1047.8,739.3,7,24.07 565 | 210.7,316.1,0,185.7,0,977,689.3,7,21.82 566 | 158.8,238.2,0,185.7,0,1040.6,734.3,28,21.07 567 | 295.8,0,0,185.7,0,1091.4,769.3,7,14.84 568 | 255.5,170.3,0,185.7,0,1026.6,724.3,28,32.05 569 | 203.5,135.7,0,185.7,0,1076.2,759.3,7,11.96 570 | 397,0,0,185.7,0,1040.6,734.3,7,25.45 571 | 381.4,0,0,185.7,0,1104.6,784.3,28,22.49 572 | 295.8,0,0,185.7,0,1091.4,769.3,28,25.22 573 | 228,342.1,0,185.7,0,955.8,674.3,28,39.7 574 | 220.8,147.2,0,185.7,0,1055,744.3,7,13.09 575 | 316.1,210.7,0,185.7,0,977,689.3,28,38.7 576 | 135.7,203.5,0,185.7,0,1076.2,759.3,7,7.51 577 | 238.1,0,0,185.7,0,1118.8,789.3,28,17.58 578 | 339.2,0,0,185.7,0,1069.2,754.3,7,21.18 579 | 135.7,203.5,0,185.7,0,1076.2,759.3,28,18.2 580 | 193.5,290.2,0,185.7,0,998.2,704.3,7,17.2 581 | 203.5,135.7,0,185.7,0,1076.2,759.3,28,22.63 582 | 290.2,193.5,0,185.7,0,998.2,704.3,7,21.86 583 | 181.9,272.8,0,185.7,0,1012.4,714.3,7,12.37 584 | 170.3,155.5,0,185.7,0,1026.6,724.3,28,25.73 585 | 210.7,316.1,0,185.7,0,977,689.3,28,37.81 586 | 228,342.1,0,185.7,0,955.8,674.3,7,21.92 587 | 290.2,193.5,0,185.7,0,998.2,704.3,28,33.04 588 | 381.4,0,0,185.7,0,1104.6,784.3,7,14.54 589 | 238.2,158.8,0,185.7,0,1040.6,734.3,28,26.91 590 | 186.2,124.1,0,185.7,0,1083.4,764.3,7,8 591 | 339.2,0,0,185.7,0,1069.2,754.3,28,31.9 592 | 238.1,0,0,185.7,0,1118.8,789.3,7,10.34 593 | 252.5,0,0,185.7,0,1111.6,784.3,28,19.77 594 | 382.5,0,0,185.7,0,1047.8,739.3,28,37.44 595 | 252.5,0,0,185.7,0,1111.6,784.3,7,11.48 596 | 316.1,210.7,0,185.7,0,977,689.3,7,24.44 597 | 186.2,124.1,0,185.7,0,1083.4,764.3,28,17.6 598 | 170.3,155.5,0,185.7,0,1026.6,724.3,7,10.73 599 | 272.8,181.9,0,185.7,0,1012.4,714.3,28,31.38 600 | 339,0,0,197,0,968,781,3,13.22 601 | 339,0,0,197,0,968,781,7,20.97 602 | 339,0,0,197,0,968,781,14,27.04 603 | 339,0,0,197,0,968,781,28,32.04 604 | 339,0,0,197,0,968,781,90,35.17 605 | 339,0,0,197,0,968,781,180,36.45 606 | 339,0,0,197,0,968,781,365,38.89 607 | 236,0,0,194,0,968,885,3,6.47 608 | 236,0,0,194,0,968,885,14,12.84 609 | 236,0,0,194,0,968,885,28,18.42 610 | 236,0,0,194,0,968,885,90,21.95 611 | 236,0,0,193,0,968,885,180,24.1 612 | 236,0,0,193,0,968,885,365,25.08 613 | 277,0,0,191,0,968,856,14,21.26 614 | 277,0,0,191,0,968,856,28,25.97 615 | 277,0,0,191,0,968,856,3,11.36 616 | 277,0,0,191,0,968,856,90,31.25 617 | 277,0,0,191,0,968,856,180,32.33 618 | 277,0,0,191,0,968,856,360,33.7 619 | 254,0,0,198,0,968,863,3,9.31 620 | 254,0,0,198,0,968,863,90,26.94 621 | 254,0,0,198,0,968,863,180,27.63 622 | 254,0,0,198,0,968,863,365,29.79 623 | 307,0,0,193,0,968,812,180,34.49 624 | 307,0,0,193,0,968,812,365,36.15 625 | 307,0,0,193,0,968,812,3,12.54 626 | 307,0,0,193,0,968,812,28,27.53 627 | 307,0,0,193,0,968,812,90,32.92 628 | 236,0,0,193,0,968,885,7,9.99 629 | 200,0,0,180,0,1125,845,7,7.84 630 | 200,0,0,180,0,1125,845,28,12.25 631 | 225,0,0,181,0,1113,833,7,11.17 632 | 225,0,0,181,0,1113,833,28,17.34 633 | 325,0,0,184,0,1063,783,7,17.54 634 | 325,0,0,184,0,1063,783,28,30.57 635 | 275,0,0,183,0,1088,808,7,14.2 636 | 275,0,0,183,0,1088,808,28,24.5 637 | 300,0,0,184,0,1075,795,7,15.58 638 | 300,0,0,184,0,1075,795,28,26.85 639 | 375,0,0,186,0,1038,758,7,26.06 640 | 375,0,0,186,0,1038,758,28,38.21 641 | 400,0,0,187,0,1025,745,28,43.7 642 | 400,0,0,187,0,1025,745,7,30.14 643 | 250,0,0,182,0,1100,820,7,12.73 644 | 250,0,0,182,0,1100,820,28,20.87 645 | 350,0,0,186,0,1050,770,7,20.28 646 | 350,0,0,186,0,1050,770,28,34.29 647 | 203.5,305.3,0,203.5,0,963.4,630,7,19.54 648 | 250.2,166.8,0,203.5,0,977.6,694.1,90,47.71 649 | 157,236,0,192,0,935.4,781.2,90,43.38 650 | 141.3,212,0,203.5,0,971.8,748.5,28,29.89 651 | 166.8,250.2,0,203.5,0,975.6,692.6,3,6.9 652 | 122.6,183.9,0,203.5,0,958.2,800.1,90,33.19 653 | 183.9,122.6,0,203.5,0,959.2,800,3,4.9 654 | 102,153,0,192,0,887,942,3,4.57 655 | 102,153,0,192,0,887,942,90,25.46 656 | 122.6,183.9,0,203.5,0,958.2,800.1,28,24.29 657 | 166.8,250.2,0,203.5,0,975.6,692.6,28,33.95 658 | 200,133,0,192,0,965.4,806.2,3,11.41 659 | 108.3,162.4,0,203.5,0,938.2,849,28,20.59 660 | 305.3,203.5,0,203.5,0,965.4,631,7,25.89 661 | 108.3,162.4,0,203.5,0,938.2,849,90,29.23 662 | 116,173,0,192,0,909.8,891.9,90,31.02 663 | 141.3,212,0,203.5,0,971.8,748.5,7,10.39 664 | 157,236,0,192,0,935.4,781.2,28,33.66 665 | 133,200,0,192,0,927.4,839.2,28,27.87 666 | 250.2,166.8,0,203.5,0,977.6,694.1,7,19.35 667 | 173,116,0,192,0,946.8,856.8,7,11.39 668 | 192,288,0,192,0,929.8,716.1,3,12.79 669 | 192,288,0,192,0,929.8,716.1,28,39.32 670 | 153,102,0,192,0,888,943.1,3,4.78 671 | 288,192,0,192,0,932,717.8,3,16.11 672 | 305.3,203.5,0,203.5,0,965.4,631,28,43.38 673 | 236,157,0,192,0,972.6,749.1,7,20.42 674 | 173,116,0,192,0,946.8,856.8,3,6.94 675 | 212,141.3,0,203.5,0,973.4,750,7,15.03 676 | 236,157,0,192,0,972.6,749.1,3,13.57 677 | 183.9,122.6,0,203.5,0,959.2,800,90,32.53 678 | 166.8,250.2,0,203.5,0,975.6,692.6,7,15.75 679 | 102,153,0,192,0,887,942,7,7.68 680 | 288,192,0,192,0,932,717.8,28,38.8 681 | 212,141.3,0,203.5,0,973.4,750,28,33 682 | 102,153,0,192,0,887,942,28,17.28 683 | 173,116,0,192,0,946.8,856.8,28,24.28 684 | 183.9,122.6,0,203.5,0,959.2,800,28,24.05 685 | 133,200,0,192,0,927.4,839.2,90,36.59 686 | 192,288,0,192,0,929.8,716.1,90,50.73 687 | 133,200,0,192,0,927.4,839.2,7,13.66 688 | 305.3,203.5,0,203.5,0,965.4,631,3,14.14 689 | 236,157,0,192,0,972.6,749.1,90,47.78 690 | 108.3,162.4,0,203.5,0,938.2,849,3,2.33 691 | 157,236,0,192,0,935.4,781.2,7,16.89 692 | 288,192,0,192,0,932,717.8,7,23.52 693 | 212,141.3,0,203.5,0,973.4,750,3,6.81 694 | 212,141.3,0,203.5,0,973.4,750,90,39.7 695 | 153,102,0,192,0,888,943.1,28,17.96 696 | 236,157,0,192,0,972.6,749.1,28,32.88 697 | 116,173,0,192,0,909.8,891.9,28,22.35 698 | 183.9,122.6,0,203.5,0,959.2,800,7,10.79 699 | 108.3,162.4,0,203.5,0,938.2,849,7,7.72 700 | 203.5,305.3,0,203.5,0,963.4,630,28,41.68 701 | 203.5,305.3,0,203.5,0,963.4,630,3,9.56 702 | 133,200,0,192,0,927.4,839.2,3,6.88 703 | 288,192,0,192,0,932,717.8,90,50.53 704 | 200,133,0,192,0,965.4,806.2,7,17.17 705 | 200,133,0,192,0,965.4,806.2,28,30.44 706 | 250.2,166.8,0,203.5,0,977.6,694.1,3,9.73 707 | 122.6,183.9,0,203.5,0,958.2,800.1,3,3.32 708 | 153,102,0,192,0,888,943.1,90,26.32 709 | 200,133,0,192,0,965.4,806.2,90,43.25 710 | 116,173,0,192,0,909.8,891.9,3,6.28 711 | 173,116,0,192,0,946.8,856.8,90,32.1 712 | 250.2,166.8,0,203.5,0,977.6,694.1,28,36.96 713 | 305.3,203.5,0,203.5,0,965.4,631,90,54.6 714 | 192,288,0,192,0,929.8,716.1,7,21.48 715 | 157,236,0,192,0,935.4,781.2,3,9.69 716 | 153,102,0,192,0,888,943.1,7,8.37 717 | 141.3,212,0,203.5,0,971.8,748.5,90,39.66 718 | 116,173,0,192,0,909.8,891.9,7,10.09 719 | 141.3,212,0,203.5,0,971.8,748.5,3,4.83 720 | 122.6,183.9,0,203.5,0,958.2,800.1,7,10.35 721 | 166.8,250.2,0,203.5,0,975.6,692.6,90,43.57 722 | 203.5,305.3,0,203.5,0,963.4,630,90,51.86 723 | 310,0,0,192,0,1012,830,3,11.85 724 | 310,0,0,192,0,1012,830,7,17.24 725 | 310,0,0,192,0,1012,830,28,27.83 726 | 310,0,0,192,0,1012,830,90,35.76 727 | 310,0,0,192,0,1012,830,120,38.7 728 | 331,0,0,192,0,1025,821,3,14.31 729 | 331,0,0,192,0,1025,821,7,17.44 730 | 331,0,0,192,0,1025,821,28,31.74 731 | 331,0,0,192,0,1025,821,90,37.91 732 | 331,0,0,192,0,1025,821,120,39.38 733 | 349,0,0,192,0,1056,809,3,15.87 734 | 349,0,0,192,0,1056,809,7,9.01 735 | 349,0,0,192,0,1056,809,28,33.61 736 | 349,0,0,192,0,1056,809,90,40.66 737 | 349,0,0,192,0,1056,809,120,40.86 738 | 238,0,0,186,0,1119,789,7,12.05 739 | 238,0,0,186,0,1119,789,28,17.54 740 | 296,0,0,186,0,1090,769,7,18.91 741 | 296,0,0,186,0,1090,769,28,25.18 742 | 297,0,0,186,0,1040,734,7,30.96 743 | 480,0,0,192,0,936,721,28,43.89 744 | 480,0,0,192,0,936,721,90,54.28 745 | 397,0,0,186,0,1040,734,28,36.94 746 | 281,0,0,186,0,1104,774,7,14.5 747 | 281,0,0,185,0,1104,774,28,22.44 748 | 500,0,0,200,0,1125,613,1,12.64 749 | 500,0,0,200,0,1125,613,3,26.06 750 | 500,0,0,200,0,1125,613,7,33.21 751 | 500,0,0,200,0,1125,613,14,36.94 752 | 500,0,0,200,0,1125,613,28,44.09 753 | 540,0,0,173,0,1125,613,7,52.61 754 | 540,0,0,173,0,1125,613,14,59.76 755 | 540,0,0,173,0,1125,613,28,67.31 756 | 540,0,0,173,0,1125,613,90,69.66 757 | 540,0,0,173,0,1125,613,180,71.62 758 | 540,0,0,173,0,1125,613,270,74.17 759 | 350,0,0,203,0,974,775,7,18.13 760 | 350,0,0,203,0,974,775,14,22.53 761 | 350,0,0,203,0,974,775,28,27.34 762 | 350,0,0,203,0,974,775,56,29.98 763 | 350,0,0,203,0,974,775,90,31.35 764 | 350,0,0,203,0,974,775,180,32.72 765 | 385,0,0,186,0,966,763,1,6.27 766 | 385,0,0,186,0,966,763,3,14.7 767 | 385,0,0,186,0,966,763,7,23.22 768 | 385,0,0,186,0,966,763,14,27.92 769 | 385,0,0,186,0,966,763,28,31.35 770 | 331,0,0,192,0,978,825,180,39 771 | 331,0,0,192,0,978,825,360,41.24 772 | 349,0,0,192,0,1047,806,3,14.99 773 | 331,0,0,192,0,978,825,3,13.52 774 | 382,0,0,186,0,1047,739,7,24 775 | 382,0,0,186,0,1047,739,28,37.42 776 | 382,0,0,186,0,1111,784,7,11.47 777 | 281,0,0,186,0,1104,774,28,22.44 778 | 339,0,0,185,0,1069,754,7,21.16 779 | 339,0,0,185,0,1069,754,28,31.84 780 | 295,0,0,185,0,1069,769,7,14.8 781 | 295,0,0,185,0,1069,769,28,25.18 782 | 238,0,0,185,0,1118,789,28,17.54 783 | 296,0,0,192,0,1085,765,7,14.2 784 | 296,0,0,192,0,1085,765,28,21.65 785 | 296,0,0,192,0,1085,765,90,29.39 786 | 331,0,0,192,0,879,825,3,13.52 787 | 331,0,0,192,0,978,825,7,16.26 788 | 331,0,0,192,0,978,825,28,31.45 789 | 331,0,0,192,0,978,825,90,37.23 790 | 349,0,0,192,0,1047,806,7,18.13 791 | 349,0,0,192,0,1047,806,28,32.72 792 | 349,0,0,192,0,1047,806,90,39.49 793 | 349,0,0,192,0,1047,806,180,41.05 794 | 349,0,0,192,0,1047,806,360,42.13 795 | 302,0,0,203,0,974,817,14,18.13 796 | 302,0,0,203,0,974,817,180,26.74 797 | 525,0,0,189,0,1125,613,180,61.92 798 | 500,0,0,200,0,1125,613,90,47.22 799 | 500,0,0,200,0,1125,613,180,51.04 800 | 500,0,0,200,0,1125,613,270,55.16 801 | 540,0,0,173,0,1125,613,3,41.64 802 | 252,0,0,185,0,1111,784,7,13.71 803 | 252,0,0,185,0,1111,784,28,19.69 804 | 339,0,0,185,0,1060,754,28,31.65 805 | 393,0,0,192,0,940,758,3,19.11 806 | 393,0,0,192,0,940,758,28,39.58 807 | 393,0,0,192,0,940,758,90,48.79 808 | 382,0,0,185,0,1047,739,7,24 809 | 382,0,0,185,0,1047,739,28,37.42 810 | 252,0,0,186,0,1111,784,7,11.47 811 | 252,0,0,185,0,1111,784,28,19.69 812 | 310,0,0,192,0,970,850,7,14.99 813 | 310,0,0,192,0,970,850,28,27.92 814 | 310,0,0,192,0,970,850,90,34.68 815 | 310,0,0,192,0,970,850,180,37.33 816 | 310,0,0,192,0,970,850,360,38.11 817 | 525,0,0,189,0,1125,613,3,33.8 818 | 525,0,0,189,0,1125,613,7,42.42 819 | 525,0,0,189,0,1125,613,14,48.4 820 | 525,0,0,189,0,1125,613,28,55.94 821 | 525,0,0,189,0,1125,613,90,58.78 822 | 525,0,0,189,0,1125,613,270,67.11 823 | 322,0,0,203,0,974,800,14,20.77 824 | 322,0,0,203,0,974,800,28,25.18 825 | 322,0,0,203,0,974,800,180,29.59 826 | 302,0,0,203,0,974,817,28,21.75 827 | 397,0,0,185,0,1040,734,28,39.09 828 | 480,0,0,192,0,936,721,3,24.39 829 | 522,0,0,146,0,896,896,7,50.51 830 | 522,0,0,146,0,896,896,28,74.99 831 | 273,105,82,210,9,904,680,28,37.17 832 | 162,190,148,179,19,838,741,28,33.76 833 | 154,144,112,220,10,923,658,28,16.5 834 | 147,115,89,202,9,860,829,28,19.99 835 | 152,178,139,168,18,944,695,28,36.35 836 | 310,143,111,168,22,914,651,28,33.69 837 | 144,0,175,158,18,943,844,28,15.42 838 | 304,140,0,214,6,895,722,28,33.42 839 | 374,0,0,190,7,1013,730,28,39.05 840 | 159,149,116,175,15,953,720,28,27.68 841 | 153,239,0,200,6,1002,684,28,26.86 842 | 310,143,0,168,10,914,804,28,45.3 843 | 305,0,100,196,10,959,705,28,30.12 844 | 151,0,184,167,12,991,772,28,15.57 845 | 142,167,130,174,11,883,785,28,44.61 846 | 298,137,107,201,6,878,655,28,53.52 847 | 321,164,0,190,5,870,774,28,57.21 848 | 366,187,0,191,7,824,757,28,65.91 849 | 280,129,100,172,9,825,805,28,52.82 850 | 252,97,76,194,8,835,821,28,33.4 851 | 165,0,150,182,12,1023,729,28,18.03 852 | 156,243,0,180,11,1022,698,28,37.36 853 | 160,188,146,203,11,829,710,28,32.84 854 | 298,0,107,186,6,879,815,28,42.64 855 | 318,0,126,210,6,861,737,28,40.06 856 | 287,121,94,188,9,904,696,28,41.94 857 | 326,166,0,174,9,882,790,28,61.23 858 | 356,0,142,193,11,801,778,28,40.87 859 | 132,207,161,179,5,867,736,28,33.3 860 | 322,149,0,186,8,951,709,28,52.42 861 | 164,0,200,181,13,849,846,28,15.09 862 | 314,0,113,170,10,925,783,28,38.46 863 | 321,0,128,182,11,870,780,28,37.26 864 | 140,164,128,237,6,869,656,28,35.23 865 | 288,121,0,177,7,908,829,28,42.13 866 | 298,0,107,210,11,880,744,28,31.87 867 | 265,111,86,195,6,833,790,28,41.54 868 | 160,250,0,168,12,1049,688,28,39.45 869 | 166,260,0,183,13,859,827,28,37.91 870 | 276,116,90,180,9,870,768,28,44.28 871 | 322,0,116,196,10,818,813,28,31.18 872 | 149,139,109,193,6,892,780,28,23.69 873 | 159,187,0,176,11,990,789,28,32.76 874 | 261,100,78,201,9,864,761,28,32.4 875 | 237,92,71,247,6,853,695,28,28.63 876 | 313,0,113,178,8,1002,689,28,36.8 877 | 155,183,0,193,9,1047,697,28,18.28 878 | 146,230,0,202,3,827,872,28,33.06 879 | 296,0,107,221,11,819,778,28,31.42 880 | 133,210,0,196,3,949,795,28,31.03 881 | 313,145,0,178,8,867,824,28,44.39 882 | 152,0,112,184,8,992,816,28,12.18 883 | 153,145,113,178,8,1002,689,28,25.56 884 | 140,133,103,200,7,916,753,28,36.44 885 | 149,236,0,176,13,847,893,28,32.96 886 | 300,0,120,212,10,878,728,28,23.84 887 | 153,145,113,178,8,867,824,28,26.23 888 | 148,0,137,158,16,1002,830,28,17.95 889 | 326,0,138,199,11,801,792,28,40.68 890 | 153,145,0,178,8,1000,822,28,19.01 891 | 262,111,86,195,5,895,733,28,33.72 892 | 158,0,195,220,11,898,713,28,8.54 893 | 151,0,185,167,16,1074,678,28,13.46 894 | 273,0,90,199,11,931,762,28,32.24 895 | 149,118,92,183,7,953,780,28,23.52 896 | 143,169,143,191,8,967,643,28,29.72 897 | 260,101,78,171,10,936,763,28,49.77 898 | 313,161,0,178,10,917,759,28,52.44 899 | 284,120,0,168,7,970,794,28,40.93 900 | 336,0,0,182,3,986,817,28,44.86 901 | 145,0,134,181,11,979,812,28,13.2 902 | 150,237,0,174,12,1069,675,28,37.43 903 | 144,170,133,192,8,814,805,28,29.87 904 | 331,170,0,195,8,811,802,28,56.61 905 | 155,0,143,193,9,1047,697,28,12.46 906 | 155,183,0,193,9,877,868,28,23.79 907 | 135,0,166,180,10,961,805,28,13.29 908 | 266,112,87,178,10,910,745,28,39.42 909 | 314,145,113,179,8,869,690,28,46.23 910 | 313,145,0,127,8,1000,822,28,44.52 911 | 146,173,0,182,3,986,817,28,23.74 912 | 144,136,106,178,7,941,774,28,26.14 913 | 148,0,182,181,15,839,884,28,15.52 914 | 277,117,91,191,7,946,666,28,43.57 915 | 298,0,107,164,13,953,784,28,35.86 916 | 313,145,0,178,8,1002,689,28,41.05 917 | 155,184,143,194,9,880,699,28,28.99 918 | 289,134,0,195,6,924,760,28,46.24 919 | 148,175,0,171,2,1000,828,28,26.92 920 | 145,0,179,202,8,824,869,28,10.54 921 | 313,0,0,178,8,1000,822,28,25.1 922 | 136,162,126,172,10,923,764,28,29.07 923 | 155,0,143,193,9,877,868,28,9.74 924 | 255,99,77,189,6,919,749,28,33.8 925 | 162,207,172,216,10,822,638,28,39.84 926 | 136,196,98,199,6,847,783,28,26.97 927 | 164,163,128,197,8,961,641,28,27.23 928 | 162,214,164,202,10,820,680,28,30.65 929 | 157,214,152,200,9,819,704,28,33.05 930 | 149,153,194,192,8,935,623,28,24.58 931 | 135,105,193,196,6,965,643,28,21.91 932 | 159,209,161,201,7,848,669,28,30.88 933 | 144,15,195,176,6,1021,709,28,15.34 934 | 154,174,185,228,7,845,612,28,24.34 935 | 167,187,195,185,7,898,636,28,23.89 936 | 184,86,190,213,6,923,623,28,22.93 937 | 156,178,187,221,7,854,614,28,29.41 938 | 236.9,91.7,71.5,246.9,6,852.9,695.4,28,28.63 939 | 313.3,0,113,178.5,8,1001.9,688.7,28,36.8 940 | 154.8,183.4,0,193.3,9.1,1047.4,696.7,28,18.29 941 | 145.9,230.5,0,202.5,3.4,827,871.8,28,32.72 942 | 296,0,106.7,221.4,10.5,819.2,778.4,28,31.42 943 | 133.1,210.2,0,195.7,3.1,949.4,795.3,28,28.94 944 | 313.3,145,0,178.5,8,867.2,824,28,40.93 945 | 151.6,0,111.9,184.4,7.9,992,815.9,28,12.18 946 | 153.1,145,113,178.5,8,1001.9,688.7,28,25.56 947 | 139.9,132.6,103.3,200.3,7.4,916,753.4,28,36.44 948 | 149.5,236,0,175.8,12.6,846.8,892.7,28,32.96 949 | 299.8,0,119.8,211.5,9.9,878.2,727.6,28,23.84 950 | 153.1,145,113,178.5,8,867.2,824,28,26.23 951 | 148.1,0,136.6,158.1,16.1,1001.8,830.1,28,17.96 952 | 326.5,0,137.9,199,10.8,801.1,792.5,28,38.63 953 | 152.7,144.7,0,178.1,8,999.7,822.2,28,19.01 954 | 261.9,110.5,86.1,195.4,5,895.2,732.6,28,33.72 955 | 158.4,0,194.9,219.7,11,897.7,712.9,28,8.54 956 | 150.7,0,185.3,166.7,15.6,1074.5,678,28,13.46 957 | 272.6,0,89.6,198.7,10.6,931.3,762.2,28,32.25 958 | 149,117.6,91.7,182.9,7.1,953.4,780.3,28,23.52 959 | 143,169.4,142.7,190.7,8.4,967.4,643.5,28,29.73 960 | 259.9,100.6,78.4,170.6,10.4,935.7,762.9,28,49.77 961 | 312.9,160.5,0,177.6,9.6,916.6,759.5,28,52.45 962 | 284,119.7,0,168.3,7.2,970.4,794.2,28,40.93 963 | 336.5,0,0,181.9,3.4,985.8,816.8,28,44.87 964 | 144.8,0,133.6,180.8,11.1,979.5,811.5,28,13.2 965 | 150,236.8,0,173.8,11.9,1069.3,674.8,28,37.43 966 | 143.7,170.2,132.6,191.6,8.5,814.1,805.3,28,29.87 967 | 330.5,169.6,0,194.9,8.1,811,802.3,28,56.62 968 | 154.8,0,142.8,193.3,9.1,1047.4,696.7,28,12.46 969 | 154.8,183.4,0,193.3,9.1,877.2,867.7,28,23.79 970 | 134.7,0,165.7,180.2,10,961,804.9,28,13.29 971 | 266.2,112.3,87.5,177.9,10.4,909.7,744.5,28,39.42 972 | 314,145.3,113.2,178.9,8,869.1,690.2,28,46.23 973 | 312.7,144.7,0,127.3,8,999.7,822.2,28,44.52 974 | 145.7,172.6,0,181.9,3.4,985.8,816.8,28,23.74 975 | 143.8,136.3,106.2,178.1,7.5,941.5,774.3,28,26.15 976 | 148.1,0,182.1,181.4,15,838.9,884.3,28,15.53 977 | 277,116.8,91,190.6,7,946.5,665.6,28,43.58 978 | 298.1,0,107.5,163.6,12.8,953.2,784,28,35.87 979 | 313.3,145,0,178.5,8,1001.9,688.7,28,41.05 980 | 155.2,183.9,143.2,193.8,9.2,879.6,698.5,28,28.99 981 | 289,133.7,0,194.9,5.5,924.1,760.1,28,46.25 982 | 147.8,175.1,0,171.2,2.2,1000,828.5,28,26.92 983 | 145.4,0,178.9,201.7,7.8,824,868.7,28,10.54 984 | 312.7,0,0,178.1,8,999.7,822.2,28,25.1 985 | 136.4,161.6,125.8,171.6,10.4,922.6,764.4,28,29.07 986 | 154.8,0,142.8,193.3,9.1,877.2,867.7,28,9.74 987 | 255.3,98.8,77,188.6,6.5,919,749.3,28,33.8 988 | 272.8,105.1,81.8,209.7,9,904,679.7,28,37.17 989 | 162,190.1,148.1,178.8,18.8,838.1,741.4,28,33.76 990 | 153.6,144.2,112.3,220.1,10.1,923.2,657.9,28,16.5 991 | 146.5,114.6,89.3,201.9,8.8,860,829.5,28,19.99 992 | 151.8,178.1,138.7,167.5,18.3,944,694.6,28,36.35 993 | 309.9,142.8,111.2,167.8,22.1,913.9,651.2,28,38.22 994 | 143.6,0,174.9,158.4,17.9,942.7,844.5,28,15.42 995 | 303.6,139.9,0,213.5,6.2,895.5,722.5,28,33.42 996 | 374.3,0,0,190.2,6.7,1013.2,730.4,28,39.06 997 | 158.6,148.9,116,175.1,15,953.3,719.7,28,27.68 998 | 152.6,238.7,0,200,6.3,1001.8,683.9,28,26.86 999 | 310,142.8,0,167.9,10,914.3,804,28,45.3 1000 | 304.8,0,99.6,196,9.8,959.4,705.2,28,30.12 1001 | 150.9,0,183.9,166.6,11.6,991.2,772.2,28,15.57 1002 | 141.9,166.6,129.7,173.5,10.9,882.6,785.3,28,44.61 1003 | 297.8,137.2,106.9,201.3,6,878.4,655.3,28,53.52 1004 | 321.3,164.2,0,190.5,4.6,870,774,28,57.22 1005 | 366,187,0,191.3,6.6,824.3,756.9,28,65.91 1006 | 279.8,128.9,100.4,172.4,9.5,825.1,804.9,28,52.83 1007 | 252.1,97.1,75.6,193.8,8.3,835.5,821.4,28,33.4 1008 | 164.6,0,150.4,181.6,11.7,1023.3,728.9,28,18.03 1009 | 155.6,243.5,0,180.3,10.7,1022,697.7,28,37.36 1010 | 160.2,188,146.4,203.2,11.3,828.7,709.7,28,35.31 1011 | 298.1,0,107,186.4,6.1,879,815.2,28,42.64 1012 | 317.9,0,126.5,209.7,5.7,860.5,736.6,28,40.06 1013 | 287.3,120.5,93.9,187.6,9.2,904.4,695.9,28,43.8 1014 | 325.6,166.4,0,174,8.9,881.6,790,28,61.24 1015 | 355.9,0,141.6,193.3,11,801.4,778.4,28,40.87 1016 | 132,206.5,160.9,178.9,5.5,866.9,735.6,28,33.31 1017 | 322.5,148.6,0,185.8,8.5,951,709.5,28,52.43 1018 | 164.2,0,200.1,181.2,12.6,849.3,846,28,15.09 1019 | 313.8,0,112.6,169.9,10.1,925.3,782.9,28,38.46 1020 | 321.4,0,127.9,182.5,11.5,870.1,779.7,28,37.27 1021 | 139.7,163.9,127.7,236.7,5.8,868.6,655.6,28,35.23 1022 | 288.4,121,0,177.4,7,907.9,829.5,28,42.14 1023 | 298.2,0,107,209.7,11.1,879.6,744.2,28,31.88 1024 | 264.5,111,86.5,195.5,5.9,832.6,790.4,28,41.54 1025 | 159.8,250,0,168.4,12.2,1049.3,688.2,28,39.46 1026 | 166,259.7,0,183.2,12.7,858.8,826.8,28,37.92 1027 | 276.4,116,90.3,179.6,8.9,870.1,768.3,28,44.28 1028 | 322.2,0,115.6,196,10.4,817.9,813.4,28,31.18 1029 | 148.5,139.4,108.6,192.7,6.1,892.4,780,28,23.7 1030 | 159.1,186.7,0,175.6,11.3,989.6,788.9,28,32.77 1031 | 260.9,100.5,78.3,200.6,8.6,864.5,761.5,28,32.4 1032 | -------------------------------------------------------------------------------- /Concrete_Prediction/readme.md: -------------------------------------------------------------------------------- 1 | # Multiple Regression on Concrete Data 2 | 3 | ![Concrete](https://images.pexels.com/photos/545008/pexels-photo-545008.jpeg) 4 | 5 | ## Project Description 6 | 7 | This GitHub repository contains a data analysis project on multiple regression using the "Concrete Data" dataset. The dataset consists of various input features, such as cement, slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age, to predict the compressive strength (csMPa) of concrete. The project aims to explore and implement various regression models to predict concrete strength. 8 | 9 | ## Introduction 10 | This project explores the application of multiple regression techniques to predict the compressive strength of concrete based on various input features. The dataset used for this analysis includes the following columns: 11 | 12 | - `cement`: Amount of cement in the concrete mix (kg/m³) 13 | - `slag`: Amount of blast furnace slag in the concrete mix (kg/m³) 14 | - `flyash`: Amount of fly ash in the concrete mix (kg/m³) 15 | - `water`: Amount of water in the concrete mix (kg/m³) 16 | - `superplasticizer`: Amount of superplasticizer in the concrete mix (kg/m³) 17 | - `coarseaggregate`: Amount of coarse aggregate in the concrete mix (kg/m³) 18 | - `fineaggregate`: Amount of fine aggregate in the concrete mix (kg/m³) 19 | - `age`: Age of the concrete (days) 20 | 21 | ## Dataset 22 | 23 | The dataset used in this project can be accessed from the following link: 24 | [Concrete Data on Kaggle](https://www.kaggle.com/datasets/kushalvala/concrete/code) 25 | 26 | ## Models 27 | 28 | The project includes the following regression models for predicting concrete compressive strength (csMPa): 29 | 30 | 1. ADA Booster 31 | 2. GradientBoostingRegressor 32 | 3. DecisionTreeRegressor 33 | 4. ElasticNet 34 | 5. RandomForestRegressor 35 | 6. LinearRegression 36 | 7. Support Vector Regression (SVR) 37 | 8. KNeighborsRegressor 38 | 9. MLPRegressor 39 | 40 | ## Project Structure 41 | 42 | The project is structured as follows: 43 | 44 | - **data**: This directory contains the dataset file(s). 45 | 46 | - **notebooks**: Jupyter notebooks or other relevant documents for data exploration and modeling. 47 | 48 | - **models**: Saved model files, if applicable. 49 | 50 | - **results**: This directory may include visualizations, reports, or any other output from the analysis. 51 | 52 | - **README.md**: The main project documentation you are currently reading. 53 | 54 | ## Getting Started 55 | 56 | To get started with this project, follow these steps: 57 | 58 | 1. Clone the repository to your local machine: 59 | 60 | 61 | git clone https://github.com/hiranvjoseph/multiple-regression-concrete-data.git 62 | 63 | 64 | 2. Navigate to the project directory: 65 | 66 | 67 | cd multiple-regression-concrete-data 68 | 69 | 70 | 3. Install the required dependencies. You may want to set up a virtual environment before installing dependencies. 71 | 72 | 73 | pip install -r requirements.txt 74 | 75 | 76 | 4. Download the dataset from the provided [Kaggle link](https://www.kaggle.com/datasets/kushalvala/concrete/code) and place it in the `data` directory. 77 | 78 | 5. Run the Jupyter notebooks or Python scripts in the `notebooks` and `src` directories to explore the data, train the regression models, and evaluate their performance. 79 | 80 | ## Contact Information 81 | 82 | - **Username**: hiranvjoseph 83 | - **Name**: Hiran Joseph 84 | - **Email**: hiranvjoseph@gmail.com 85 | 86 | Feel free to reach out if you have any questions or suggestions regarding this project. Happy coding! 87 | -------------------------------------------------------------------------------- /House-Price-Predictions/DATA BASE.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/f3cacf9f9d92d883957375e2c04bb18b4e237491/House-Price-Predictions/DATA BASE.docx -------------------------------------------------------------------------------- /House-Price-Predictions/Design.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/f3cacf9f9d92d883957375e2c04bb18b4e237491/House-Price-Predictions/Design.jpeg -------------------------------------------------------------------------------- /House-Price-Predictions/OIG.jpeg: -------------------------------------------------------------------------------- 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10:52.3,1.1,1978.671,10,25.00384986,121.5283365,26.69426677 5 | 26:01.2,22.2,1055.067,5,24.962887,121.4821784,38.09163849 6 | 29:47.9,8.5,967.4,6,25.01103682,121.4799462,21.6547096 7 | 18:34.1,13.3,279.1726,2,24.99499354,121.5438234,36.97237579 8 | 07:23.4,38.5,377.7956,3,25.00989486,121.5589553,27.6373821 9 | 57:25.3,15.2,552.4371,5,24.99710934,121.5443775,44.11658501 10 | 06:48.4,24,617.4424,3,24.98762168,121.5278412,49.07124675 11 | 21:33.3,13,323.655,8,24.97866304,121.4834571,43.11435275 12 | 42:58.4,13.2,750.0704,5,24.93489597,121.5532889,32.74736266 13 | 36:29.3,6.5,289.3248,8,24.99309418,121.5014782,60.46035153 14 | 05:20.1,27.5,49.66105,9,24.96979399,121.5447769,58.16330444 15 | 29:47.8,12.9,373.8389,2,25.01166143,121.5106716,43.77117419 16 | 24:50.1,8,185.4296,5,24.93719703,121.5450593,40.25533345 17 | 06:31.0,4.9,639.6198,8,24.97546864,121.4765644,49.15705382 18 | 03:03.3,3.1,1360.139,5,24.95754044,121.5505921,13.67353159 19 | 54:06.6,10.3,4079.418,0,24.96029045,121.5482248,0 20 | 27:33.3,1.1,143.8383,4,24.97215779,121.4770057,56.25135518 21 | 14:19.0,13.7,193.5845,1,24.94355128,121.5154104,16.44401694 22 | 53:06.0,8.4,330.0854,6,24.96058183,121.4973468,47.35555099 23 | 48:38.9,31.3,1236.564,0,24.95706351,121.5447337,8.468854747 24 | 08:21.4,17.3,837.7233,6,24.95279903,121.4771533,52.76115275 25 | 28:39.9,14.1,193.5845,6,24.94856402,121.4880756,50.61249953 26 | 20:08.9,1.5,197.1338,0,24.98777578,121.4774391,12.5702765 27 | 50:20.3,16.1,3771.895,9,24.9707859,121.5452953,0 28 | 30:38.9,34.4,2408.993,3,24.99444419,121.5093054,22.91559838 29 | 31:31.6,18.2,519.4617,0,24.93804633,121.5522142,9.426271813 30 | 31:16.6,11.9,1978.671,7,24.99480823,121.5204076,25.88302273 31 | 04:04.8,5.4,1939.749,1,24.98419941,121.4755396,4.266377177 32 | 01:41.6,9.7,170.7311,3,24.99087701,121.5128241,27.20664522 33 | 39:42.5,38.5,1828.319,4,24.9970651,121.512329,15.5643239 34 | 27:20.2,32.8,258.186,3,24.99477474,121.510337,30.85539137 35 | 29:39.7,31,289.3248,2,24.9446364,121.476227,34.06650015 36 | 07:50.4,34.8,1156.777,0,24.9643233,121.5597265,18.41025026 37 | 46:26.9,9.9,4510.359,10,24.98221114,121.5490572,1.102852363 38 | 28:53.3,5.9,482.7581,5,24.94829725,121.5130819,31.43092068 39 | 09:24.9,37.1,461.1016,8,24.95407645,121.4900054,47.02921199 40 | 58:58.4,26.8,279.1726,4,24.98297993,121.4957039,29.48443087 41 | 53:12.9,30.4,1447.286,8,24.96259751,121.5358766,19.5472047 42 | 47:12.8,4,492.2313,9,24.95994863,121.5523771,26.33933576 43 | 08:58.7,5.4,90.45606,2,24.96891904,121.4767749,51.41138556 44 | 13:07.9,25.3,537.7971,4,25.01338189,121.4813469,48.54988028 45 | 11:55.7,16.4,1449.722,1,24.97434583,121.5469264,10.36893366 46 | 26:38.7,26.4,2615.465,7,24.94893492,121.4918407,20.58773654 47 | 22:04.7,8,405.2134,4,24.9936867,121.5299532,34.21865577 48 | 54:27.0,19.2,488.5727,1,24.94387683,121.5553412,39.28268043 49 | 59:42.9,30.3,1013.341,6,24.93467554,121.4944715,13.06073818 50 | 01:20.2,12.9,90.45606,9,24.93668959,121.4937013,36.4262787 51 | 36:22.5,13.5,2175.03,0,24.98582906,121.4953206,0 52 | 34:51.8,13.2,707.9067,6,24.99785939,121.4985328,29.76962852 53 | 13:42.0,8.1,373.8389,3,24.96410171,121.5646385,43.07206862 54 | 02:48.6,32.3,379.5575,4,24.98360353,121.5367396,36.17835788 55 | 12:05.1,38,4197.349,8,24.98514676,121.5593931,22.21030128 56 | 45:31.2,13,1159.454,6,24.94830167,121.5381399,38.90638549 57 | 17:15.2,34.4,414.9476,0,24.9567116,121.4886711,31.59628454 58 | 51:27.1,34.4,90.45606,1,24.94667072,121.555846,16.89875516 59 | 23:33.3,24.2,3078.176,5,24.93681248,121.4823517,0 60 | 34:01.2,13.2,204.1705,1,24.96757303,121.5505179,47.25603168 61 | 56:33.3,16.1,1867.233,4,24.99717954,121.5599689,26.53102136 62 | 58:18.6,5.6,2707.392,6,24.97085388,121.4966967,0.439332089 63 | 26:50.3,14.2,4082.015,6,24.93825252,121.4811714,10.60066495 64 | 46:04.5,3.8,393.2606,9,24.97452344,121.5288017,33.66850037 65 | 22:09.1,38.2,4082.015,1,24.99638681,121.4778317,0 66 | 23:38.1,25.3,1712.632,7,24.97384577,121.4982257,39.70830599 67 | 16:54.7,15.6,124.9912,4,24.99387525,121.4841221,54.33071172 68 | 53:26.5,26.8,918.6357,0,24.96011385,121.5206302,37.56244494 69 | 24:38.1,0,279.1726,5,24.98562809,121.5247019,23.10016713 70 | 01:12.6,18.9,6306.153,5,25.00618212,121.5605288,0 71 | 35:59.0,16.6,394.0173,5,24.9697705,121.5286527,34.91191452 72 | 38:47.3,7.6,3771.895,3,24.98937138,121.494687,8.784265127 73 | 50:51.4,12,2408.993,9,24.93284364,121.5510656,25.69646295 74 | 22:10.4,6.6,461.1016,4,24.93558667,121.4743979,45.2974607 75 | 04:51.2,37.1,1402.016,8,24.97320899,121.5210472,38.67655041 76 | 53:06.4,25.3,577.9615,5,24.98591548,121.5334037,20.16895031 77 | 02:42.2,11.6,186.9686,3,24.94076534,121.5012944,33.01791658 78 | 29:41.0,25.9,1931.207,9,25.01274015,121.5011039,38.01299433 79 | 54:43.5,29.4,201.8939,8,25.01368493,121.5336716,29.71119022 80 | 37:26.2,17.5,492.2313,5,25.00834258,121.5316479,21.01262993 81 | 20:02.3,32.6,289.3248,10,25.01282857,121.5458438,38.30368728 82 | 34:53.5,24.2,506.1144,3,24.96756562,121.549018,27.69035178 83 | 03:49.5,16.3,4510.359,7,24.93328972,121.494506,0 84 | 36:36.4,16.1,489.8821,5,24.94228562,121.4831658,28.19168613 85 | 28:17.9,0,1805.665,0,24.97965571,121.4783986,29.95679839 86 | 39:28.8,31.3,512.5487,7,24.93212707,121.5237032,47.74342784 87 | 27:18.4,39.2,371.2495,4,24.94440662,121.5453004,19.13224459 88 | 07:43.8,0,187.4823,0,24.9588108,121.4985449,18.39107727 89 | 15:43.6,18.4,390.5684,9,24.99901508,121.5246806,42.19357532 90 | 34:49.6,33,4449.27,9,25.00642117,121.5432131,3.957364719 91 | 21:39.7,13,421.479,0,24.93212916,121.4763012,32.12698068 92 | 41:47.0,17.7,1712.632,8,24.98334732,121.4739871,32.43492491 93 | 16:00.8,13.3,718.2937,2,24.98712844,121.5302675,11.24922184 94 | 56:01.7,11.6,170.1289,8,24.97090782,121.5614025,33.20955519 95 | 06:44.5,35.9,179.4538,2,24.9965586,121.5074983,38.40267729 96 | 52:29.8,5.6,383.2805,8,25.00884895,121.5092619,41.26128764 97 | 39:29.8,16.2,640.7391,4,24.93880207,121.5349301,30.47123455 98 | 48:54.4,11.6,2469.645,7,24.93573949,121.5156555,17.56245591 99 | 44:19.8,0,4066.587,6,24.96329585,121.5604822,0 100 | 13:57.5,35.4,318.5292,5,24.98383819,121.5210145,47.37204258 101 | 31:02.5,37.7,1935.009,7,25.00562019,121.528879,28.28137335 102 | 37:14.8,40.9,577.9615,3,24.9712575,121.5502236,41.58516426 103 | 02:52.4,17.3,4066.587,9,24.94827479,121.5331225,25.58971056 104 | 09:56.5,34.9,2185.128,6,25.00888597,121.4856729,30.85092896 105 | 40:15.8,32,186.9686,8,24.9386951,121.4981342,50.91810727 106 | 36:41.4,13.7,193.5845,9,25.00516591,121.5283525,57.80803669 107 | 11:16.5,12.7,512.5487,3,24.94504886,121.5105191,18.70607451 108 | 25:53.8,4.5,579.2083,7,25.01400301,121.5191006,42.82919278 109 | 04:53.3,33.6,1978.671,7,24.97461692,121.5592983,38.3613258 110 | 56:55.3,12.7,2147.376,5,24.99670579,121.4760711,27.74371289 111 | 10:44.5,12,167.5989,3,24.97682393,121.515875,23.69789473 112 | 31:00.8,39.8,1712.632,8,24.98165905,121.4973241,24.87151519 113 | 20:14.1,13.5,718.2937,3,24.94428211,121.5443766,9.21356696 114 | 16:45.5,6.8,323.655,1,24.99144059,121.4798598,34.79054681 115 | 58:06.1,18.3,90.45606,6,24.96978682,121.4786685,59.33493772 116 | 39:27.9,13.3,274.0144,0,24.99204975,121.5394968,38.83127557 117 | 34:50.0,38,161.942,0,25.00031825,121.4902145,46.87699098 118 | 17:24.2,17,482.7581,7,24.98236897,121.4959743,50.02525314 119 | 27:03.7,15.1,512.5487,5,24.95401458,121.5490746,21.71190524 120 | 01:30.9,34.8,279.1726,2,24.97166559,121.473888,34.94289478 121 | 05:32.3,13.3,390.9696,6,24.94244337,121.503169,39.0209329 122 | 53:11.8,34.5,1159.454,0,24.94609304,121.5166428,7.255057854 123 | 30:22.2,37.8,444.1334,0,24.93216211,121.5248138,42.75258978 124 | 46:06.8,32.6,438.8513,5,24.99510445,121.5369168,23.04599311 125 | 46:08.4,5.3,383.2805,0,24.93814132,121.5051919,35.49622988 126 | 26:00.8,4.3,512.5487,0,24.98995289,121.5491698,12.52701523 127 | 09:41.3,4,252.5822,8,24.98454181,121.5398238,24.99064473 128 | 31:52.0,14,187.4823,5,24.97134978,121.5459403,32.88421106 129 | 56:03.1,28.2,1497.713,3,25.00937842,121.49382,12.05637208 130 | 35:20.7,6.5,964.7496,4,25.01457794,121.513938,20.07341746 131 | 36:41.1,35.3,196.6172,5,24.95240312,121.523599,46.94012461 132 | 42:52.8,32.1,150.9347,2,24.93500621,121.5111629,13.35872546 133 | 08:54.7,15.7,964.7496,8,25.00816119,121.5066201,38.62841343 134 | 57:02.0,6.4,2077.39,6,24.96459616,121.4744727,4.466027389 135 | 26:32.6,32.7,90.45606,1,24.94245194,121.5194145,31.86071601 136 | 38:02.6,17.5,1935.009,8,24.97953047,121.5187721,40.76480979 137 | 33:58.5,14,250.631,6,24.9415406,121.4860343,20.82663171 138 | 40:57.2,20.6,2275.877,5,24.94012887,121.5105875,1.909460277 139 | 52:56.5,25.6,482.7581,0,24.97095398,121.5078486,30.38383626 140 | 54:35.8,13.2,90.45606,6,24.97010424,121.5381538,37.97072484 141 | 27:59.2,17.5,561.9845,4,24.97792463,121.5344733,28.08291593 142 | 10:46.8,12.6,208.3905,2,24.95219694,121.5361995,17.37338976 143 | 49:55.5,20.4,156.2442,7,24.9934262,121.5550663,45.66512995 144 | 36:37.2,0,377.7956,4,24.97182023,121.4830066,28.95693856 145 | 48:48.8,33.4,967.4,3,24.99421468,121.5078619,10.43617775 146 | 27:42.7,30,1157.988,3,24.95573062,121.50475,17.00908233 147 | 48:13.0,31,104.8101,6,24.97909272,121.4879391,36.32604829 148 | 40:38.5,17.7,4082.015,7,24.974418,121.5246831,7.468033918 149 | 49:08.0,10.4,287.6025,10,24.99574508,121.5452509,34.18072546 150 | 16:36.9,16.9,23.38284,1,24.96972976,121.5496603,47.25492942 151 | 14:21.5,16.2,1783.18,1,24.96476727,121.5151152,8.404123066 152 | 02:34.8,23,482.7581,6,24.95135562,121.5376014,39.83015889 153 | 34:49.2,1.1,577.9615,5,24.99621922,121.4871389,27.53145886 154 | 21:48.0,4.5,590.9292,1,24.94902779,121.4877087,44.71972693 155 | 23:36.3,12,292.9978,3,24.98857963,121.494622,32.15400137 156 | 17:42.9,32.5,2707.392,1,25.00807584,121.5110082,12.74237413 157 | 30:06.7,17.4,1402.016,5,24.99230981,121.5065692,44.80793563 158 | 37:47.2,16.1,1449.722,8,24.97525805,121.5112282,23.57740163 159 | 42:07.9,31,3171.329,9,25.01236987,121.5459015,3.398304762 160 | 27:23.1,0,639.6198,1,24.96396715,121.5133918,40.50698971 161 | 19:11.2,16.6,250.631,5,25.00647419,121.5284111,43.66163259 162 | 03:40.3,30.9,4197.349,4,24.99917721,121.4994669,7.608042222 163 | 30:52.9,35.8,451.2438,4,24.97189644,121.5523966,29.09677876 164 | 42:58.0,39.2,482.7581,1,24.9952665,121.5425095,45.34845422 165 | 53:44.3,30.6,90.45606,5,25.00355638,121.4826113,48.4698805 166 | 48:44.8,3.8,292.9978,0,24.95576492,121.532449,39.55300214 167 | 57:59.1,30.9,167.5989,7,24.9339128,121.563924,31.81233353 168 | 32:43.8,33.9,482.7581,3,24.94014838,121.5250364,39.84421932 169 | 18:34.3,15.9,557.478,5,24.9863846,121.4917531,50.76868745 170 | 01:12.9,24.2,201.8939,6,24.9957264,121.5073138,59.25370444 171 | 45:46.9,15,289.3248,2,24.95468383,121.4909726,32.96835265 172 | 41:25.6,17.8,383.8624,2,24.98435824,121.5432099,22.51603555 173 | 13:26.1,15.2,109.9455,8,24.94474363,121.4892008,35.46345475 174 | 41:17.9,0,292.9978,10,24.93280476,121.5103726,65.57171606 175 | 01:57.2,39.8,1236.564,8,24.96148342,121.5437778,22.5686886 176 | 14:01.5,18.2,732.8528,5,24.95250342,121.5069753,33.94583548 177 | 23:45.5,1.5,193.5845,7,24.93518629,121.5030631,32.29772613 178 | 23:38.0,15.5,167.5989,0,24.94188429,121.5527427,13.79480847 179 | 00:02.6,0,292.9978,7,24.95409837,121.4964938,35.25220099 180 | 25:21.5,18.1,535.527,1,24.98739389,121.5022454,26.86704988 181 | 39:50.9,11.5,289.3248,1,24.95570815,121.5254586,26.722181 182 | 25:43.2,13.9,189.5181,6,24.96098394,121.5401037,58.81516219 183 | 38:43.7,8.9,4082.015,6,24.98702875,121.553908,17.3475391 184 | 32:16.9,40.9,492.2313,2,25.01136611,121.5452423,15.46684727 185 | 41:26.0,30.6,461.1016,5,25.01113618,121.5029632,17.8779668 186 | 44:54.6,17.9,170.1289,5,24.9919231,121.5343821,33.81895543 187 | 22:11.6,27.5,451.6419,2,24.95193223,121.4843683,26.75930501 188 | 44:03.6,22.2,1758.406,8,24.94391398,121.5156229,12.23420462 189 | 11:23.3,13.9,329.9747,0,24.96134984,121.5432054,13.84893727 190 | 19:43.4,36.6,49.66105,3,24.98547843,121.5293248,38.98826969 191 | 43:32.1,26.9,90.45606,5,24.95241754,121.485552,28.72889336 192 | 13:08.6,3.8,372.1386,2,24.97748387,121.5036704,37.24170556 193 | 27:21.9,18,379.5575,5,25.00519246,121.5090848,49.48980857 194 | 11:30.3,13.3,2147.376,3,24.93373206,121.56445,0.365175618 195 | 03:53.6,30.4,143.8383,5,24.99132918,121.5488259,56.92159098 196 | 08:35.7,35.9,90.45606,5,24.95875412,121.5644754,26.78236924 197 | 15:01.3,17.4,289.3248,8,24.99569265,121.4910376,50.30015495 198 | 45:14.9,18.1,377.7956,6,24.97082041,121.5284403,56.50331702 199 | 05:57.3,8.1,1449.722,6,25.00001208,121.5065904,41.4221676 200 | 49:53.6,16.9,1455.798,6,24.97345066,121.5310783,12.44817753 201 | 52:37.9,31.9,2185.128,7,24.9897957,121.5603826,26.70263374 202 | 54:15.3,34,4082.015,2,24.99196167,121.5226182,0 203 | 43:24.2,17.2,1009.235,6,24.98506713,121.4937857,48.56706595 204 | 40:20.1,4.1,438.8513,0,24.93709483,121.5041317,14.62680374 205 | 27:08.0,37.7,4197.349,7,25.00441103,121.5612283,0 206 | 26:23.6,3.5,1805.665,8,24.95755375,121.5213053,23.37239442 207 | 40:24.7,18.2,1758.406,1,24.96544293,121.5522337,32.7927237 208 | 09:42.1,3.8,170.1289,0,24.94396765,121.4928792,45.48583821 209 | 26:23.8,10.3,1360.139,1,24.9944335,121.5364721,36.55305112 210 | 04:04.4,7.1,250.631,6,24.97390481,121.5435709,28.60194589 211 | 37:37.4,17.5,82.88643,5,24.97765738,121.5302841,37.16632244 212 | 22:36.6,12.8,211.4473,5,24.9838692,121.5154328,47.96156312 213 | 49:31.2,12.2,2147.376,5,24.95811916,121.4945025,26.98910401 214 | 27:09.9,6.5,515.1122,3,24.95988593,121.5641964,46.46906819 215 | 02:43.6,14.8,193.5845,0,24.97517763,121.5274137,19.23345158 216 | 08:10.5,34.8,451.2438,1,25.01222661,121.4953096,45.80993752 217 | 48:03.9,41.3,590.9292,5,24.97767434,121.4742645,50.6280124 218 | 18:51.9,32.5,617.4424,6,25.0021065,121.4761217,35.30334306 219 | 32:46.0,3.9,90.45606,5,24.94865244,121.4967426,49.5873159 220 | 51:26.9,1.1,377.7956,5,24.96204324,121.502779,22.44097284 221 | 55:23.9,8,104.8101,5,24.96671665,121.5161057,24.13315504 222 | 17:38.4,13.7,185.4296,4,24.98083914,121.5353246,27.640863 223 | 31:02.8,17.2,4066.587,1,25.003312,121.5528676,0 224 | 30:21.4,2.6,390.5684,7,24.99112314,121.5190613,45.10944319 225 | 08:59.4,23,480.6977,7,25.00618883,121.5242554,40.65124852 226 | 41:14.5,17.5,289.3248,5,25.0052952,121.5074865,42.79792519 227 | 48:02.6,18.9,4082.015,0,24.9957005,121.4826721,0 228 | 20:10.9,32.3,4573.779,3,24.96447994,121.4831001,0.566048677 229 | 57:03.9,40.9,90.45606,0,24.9976161,121.5167219,11.9655155 230 | 25:14.6,0,552.4371,0,24.98540508,121.5611869,5.962874681 231 | 00:01.6,4.7,4066.587,9,24.95243366,121.4953686,0 232 | 07:29.8,8.9,1487.868,9,24.98774483,121.5125722,43.57453104 233 | 28:31.4,10,2674.961,1,24.95029242,121.5522533,4.843244817 234 | 24:34.7,28,2408.993,3,24.95998498,121.5506031,20.05608383 235 | 16:12.4,4,373.3937,7,24.99591234,121.5520824,52.8640473 236 | 45:39.1,19.2,90.45606,5,24.98641801,121.4781166,26.48498095 237 | 23:51.7,34.6,170.7311,6,25.00357017,121.5085561,35.13161908 238 | 50:22.0,17.3,1264.73,5,24.97453685,121.4867785,26.20531812 239 | 24:24.6,24.2,1931.207,10,24.95709442,121.561199,43.92938556 240 | 05:55.1,11.5,2147.376,2,24.97333177,121.5281049,16.0679269 241 | 41:40.4,3.1,451.6419,1,24.97361463,121.4749118,12.30087069 242 | 13:01.3,12.5,90.45606,0,24.93480089,121.5088129,18.52797606 243 | 08:47.5,39.7,2408.993,6,24.94675276,121.4771512,5.978499096 244 | 50:58.2,26.9,482.7581,5,24.97566697,121.5208087,33.24944233 245 | 22:08.4,28.6,289.3248,2,25.01144114,121.5096643,45.33261286 246 | 11:37.1,35.5,383.2805,7,24.99393536,121.4852627,54.91633808 247 | 22:17.3,37.1,1559.827,4,24.95785841,121.564699,16.34467537 248 | 54:48.6,18.1,390.5684,7,24.99474312,121.5485203,38.56842027 249 | 40:14.3,20.6,567.0349,6,25.00860646,121.5458508,40.75218082 250 | 27:07.3,15.9,104.8101,8,24.97474286,121.5382245,46.54003257 251 | 10:39.3,14.4,1712.632,2,24.96231192,121.5517727,22.75869286 252 | 04:17.4,5.4,329.9747,1,24.96362085,121.5599593,24.74358956 253 | 33:33.1,36.1,189.5181,8,25.01430455,121.5266929,32.95758377 254 | 35:23.2,7.8,482.7581,1,24.93704887,121.4988836,29.86204123 255 | 17:50.0,16.9,1447.286,6,24.99571282,121.4837544,46.25856067 256 | 42:58.2,34.8,323.655,6,25.00609518,121.4849829,45.47558781 257 | 28:26.5,2,461.7848,0,24.932075,121.4788161,11.87222441 258 | 28:51.8,34.5,90.45606,7,24.93901638,121.5181664,47.58495975 259 | 28:59.2,16.2,519.4617,3,24.96803907,121.4931462,34.65070379 260 | 16:06.4,0,1867.233,3,24.97739306,121.5568249,0 261 | 17:49.6,8.3,289.3248,1,24.98547776,121.5404083,13.6939915 262 | 55:33.8,10,2180.245,5,24.9982804,121.5283552,4.226155753 263 | 51:41.2,4,336.0532,1,25.00882274,121.5332665,40.43652071 264 | 58:28.9,37.3,1449.722,1,24.9628587,121.5246111,6.332630455 265 | 45:43.7,40.9,533.4762,0,25.00864512,121.5212549,29.89509778 266 | 18:06.1,2,815.9314,3,24.9723232,121.5241743,19.59263558 267 | 58:15.4,0,2408.993,9,24.97171438,121.5515236,23.81788412 268 | 44:06.4,0,506.1144,0,24.97436054,121.5561409,36.90829304 269 | 46:32.4,12.7,383.2805,9,24.93924556,121.4847187,63.99400275 270 | 08:59.2,2.6,1360.139,6,24.95037137,121.4843937,39.74017781 271 | 44:55.2,6.8,196.6172,5,25.00670437,121.4977509,41.40176678 272 | 24:01.4,30.4,444.1334,0,24.95081496,121.5445012,11.2778173 273 | 04:44.2,7.1,184.3302,9,24.99973605,121.543042,59.0363953 274 | 40:39.7,30.8,185.4296,3,24.98121756,121.4771656,41.00843703 275 | 30:03.5,19.1,1156.412,0,24.93353627,121.5502071,9.617077735 276 | 33:56.5,19.2,390.5684,5,24.94051922,121.5237967,48.74103951 277 | 18:28.4,5.2,995.7554,0,24.98939896,121.5605427,3.113895827 278 | 24:49.1,21.7,4082.015,3,25.01054288,121.4775842,0 279 | 15:29.1,1.1,1144.436,8,24.94898754,121.5256559,40.48227489 280 | 33:56.1,34.9,490.3446,3,24.97908214,121.5648481,32.41133383 281 | 09:46.1,20.9,492.2313,5,24.96036782,121.4798635,31.27934661 282 | 19:39.6,30.9,750.0704,6,24.95107482,121.5584471,21.15534385 283 | 26:15.2,5.9,390.5684,5,24.93577761,121.5345645,41.78517079 284 | 13:32.0,10.4,837.7233,8,24.98188315,121.5468229,44.29544499 285 | 32:20.9,20.4,815.9314,0,24.98031448,121.4765661,15.59324741 286 | 49:42.0,6.8,443.802,5,24.99278327,121.5293412,47.93045608 287 | 28:34.7,33.2,1414.837,5,24.97531002,121.5247491,21.17947401 288 | 31:21.1,16.3,2469.645,0,25.00080449,121.5394202,0 289 | 57:18.6,16.2,3085.17,0,24.97971814,121.5415791,6.987268739 290 | 40:43.5,16.1,489.8821,0,24.97028089,121.5436483,33.3560109 291 | 26:09.3,5.6,1712.632,5,24.97590422,121.4747242,30.22499709 292 | 47:28.2,28,444.1334,1,24.99582724,121.533512,37.6754012 293 | 25:37.5,13.8,718.2937,1,24.97024233,121.5300246,36.94282621 294 | 54:15.4,14.1,1712.632,2,24.94481023,121.5354475,11.06078997 295 | 43:47.6,40.9,4079.418,3,24.95742894,121.5495543,0 296 | 29:29.6,40.9,292.9978,3,24.94176541,121.5390348,20.72704362 297 | 44:14.6,17,718.2937,6,25.01354781,121.5636251,36.50674061 298 | 12:00.2,29.3,1447.286,6,24.99748512,121.5155596,33.055904 299 | 49:27.6,31.5,292.9978,7,24.95693401,121.5103626,46.26609679 300 | 39:13.0,1.1,1828.319,0,25.00658382,121.4739703,25.96419362 301 | 47:57.5,0,1783.18,1,24.99578013,121.5196663,18.45922061 302 | 02:04.6,16.2,401.8807,5,24.94255723,121.5220431,44.4321919 303 | 15:13.9,16.5,250.631,0,24.95124076,121.4899762,29.38105374 304 | 06:23.3,15,1867.233,8,24.97415687,121.489834,46.62552973 305 | 42:50.1,13.5,815.9314,7,24.9325086,121.5079563,36.49521186 306 | 50:38.8,4,216.8329,2,25.01033552,121.5185035,33.16993857 307 | 07:00.4,35.8,279.1726,1,25.00215723,121.5631773,49.17659401 308 | 48:34.8,30.9,451.6419,5,24.97599751,121.5130862,28.39451475 309 | 45:25.1,12.6,312.8963,8,24.94194974,121.5629228,52.35345485 310 | 28:35.0,0,732.8528,0,24.96844874,121.4813394,32.41967627 311 | 12:19.0,8.5,431.1114,5,24.98528112,121.5508701,25.98841826 312 | 50:22.2,18.4,157.6052,0,24.9446743,121.4839531,36.37768525 313 | 28:51.9,3.6,104.8101,5,24.96712884,121.5640816,57.75577802 314 | 32:09.5,1.1,390.5684,0,24.9854184,121.5204792,40.7916612 315 | 25:26.1,13.6,170.1289,7,24.97678118,121.5319782,59.11426283 316 | 14:04.1,16.9,274.0144,2,24.94407135,121.5650999,45.88692038 317 | 53:21.3,9.9,104.8101,7,24.95503769,121.5633661,37.07228268 318 | 11:38.1,13.1,56.47425,8,24.9827127,121.49747,33.90075795 319 | 19:00.0,13.7,4082.015,0,25.0116121,121.4789519,0 320 | 25:58.5,37.8,323.6912,8,24.96546117,121.4878067,39.52934393 321 | 16:18.0,0,506.1144,5,24.99826885,121.5271412,37.95501636 322 | 00:44.2,16.9,2147.376,5,24.9421989,121.552601,30.40430372 323 | 10:59.6,17.3,56.47425,5,24.996894,121.5618092,22.79513539 324 | 59:58.7,6.3,1447.286,0,24.94043539,121.5584005,0 325 | 29:42.0,18,350.8515,3,24.99140032,121.5200639,39.52966962 326 | 57:45.6,17.1,2147.376,2,24.95291183,121.4946213,0 327 | 58:27.4,13.7,587.8877,3,25.00898521,121.5130203,47.16677813 328 | 17:47.2,16.4,4082.015,4,25.00848297,121.5019106,0 329 | 00:39.6,37.1,964.7496,1,24.95510725,121.5312667,12.76260554 330 | 06:45.3,4,639.6198,7,25.00306708,121.4792334,41.11505105 331 | 20:07.3,31.5,600.8604,1,24.95251576,121.4988142,43.61694052 332 | 50:54.2,42.7,1164.838,8,25.001585,121.5096325,20.30996605 333 | 40:38.5,1.1,461.1016,1,24.94547543,121.4820866,14.407178 334 | 17:33.4,18.2,186.5101,6,24.99629051,121.5526033,57.02314443 335 | 34:31.7,17.5,1626.083,5,24.93530662,121.5627837,21.22293983 336 | 29:46.3,40.9,1559.827,0,24.99288602,121.5053819,28.10885047 337 | 50:58.0,2.1,2185.128,5,24.96985056,121.538104,26.24541361 338 | 36:36.5,13.6,2216.612,0,24.94749606,121.5277872,0 339 | 56:58.4,18.5,918.6357,6,25.0126724,121.5517709,35.43147307 340 | 58:35.4,32,169.9803,4,24.95465387,121.4777661,28.72611053 341 | 57:50.1,0,617.7134,1,24.98470458,121.4960224,42.08379518 342 | 48:49.0,6.2,579.2083,0,24.94088366,121.5238677,13.39726056 343 | 53:12.1,21.2,157.6052,2,24.98521263,121.5230275,32.00781321 344 | 42:03.7,13,587.8877,6,24.96347673,121.5619635,35.99089994 345 | 43:27.2,20.3,84.87882,3,24.97772957,121.4875798,39.54575641 346 | 28:02.7,30.1,590.9292,3,24.99771841,121.5308501,24.03903076 347 | 23:08.8,3.6,6306.153,4,24.93319539,121.534005,0 348 | 59:41.5,16.6,4527.687,7,24.99016124,121.5298817,0 349 | 43:26.4,16.5,1406.43,3,24.95357374,121.5617352,41.43015538 350 | 38:19.0,20,552.4371,1,24.95806562,121.4773608,29.72640084 351 | 28:25.4,16.4,216.8329,0,24.97914134,121.4958455,34.33224998 352 | 58:44.1,18,1360.139,8,24.95380135,121.5653208,31.74366223 353 | 44:21.2,6.4,2175.03,0,24.9882756,121.5151222,0 354 | 01:22.6,2,757.3377,8,25.01327218,121.4772412,33.4417429 355 | 25:25.4,30.9,640.7391,0,25.00920814,121.5180726,19.59839536 356 | 07:13.1,32.6,492.2313,0,24.9447888,121.5022657,14.59856881 357 | 16:57.7,11.8,1236.564,8,24.93733687,121.5371775,21.18257465 358 | 38:08.2,31.9,4082.015,3,24.94495123,121.5594182,0 359 | 54:37.5,16,170.1289,5,24.96186851,121.502395,19.2173606 360 | 54:08.6,15.2,431.1114,7,24.98880987,121.5622015,35.76266672 361 | 50:06.3,13.6,461.1016,6,24.9730476,121.4973002,27.79083786 362 | 02:12.0,29.1,451.2438,7,24.9817234,121.5350223,21.83025828 363 | 12:30.5,6.2,390.9696,5,25.00775338,121.5550677,54.39069622 364 | 20:47.5,17.9,3529.564,6,25.00704258,121.4942776,0 365 | 08:18.0,30.9,1455.798,5,24.99079243,121.4744368,8.112291946 366 | 01:58.9,35.3,387.7721,3,24.95360038,121.5592157,48.13977011 367 | 06:20.2,0,482.7581,3,24.98323682,121.5138966,34.66588299 368 | 33:41.0,16.1,2185.128,2,24.93958926,121.502423,29.36461716 369 | 38:57.3,13,2103.555,0,24.93485683,121.5462163,22.49146982 370 | 51:20.5,13.7,3079.89,7,24.98888196,121.5117835,29.99029892 371 | 57:12.0,30.7,1414.837,2,25.00003116,121.5290064,39.41648222 372 | 57:14.1,39.8,515.1122,5,24.99605822,121.5322287,47.66577608 373 | 30:42.4,25.3,718.2937,1,24.95710832,121.5003461,38.85517961 374 | 37:54.8,40.1,424.7132,4,25.00301447,121.5437748,19.28513785 375 | 07:00.7,5.6,250.631,1,25.00803359,121.5565594,27.78689427 376 | 15:58.9,30.6,464.223,0,24.95393434,121.516533,44.48092654 377 | 37:38.7,6.3,319.0708,4,24.95842851,121.4826933,15.4729221 378 | 59:44.2,0,292.9978,2,24.95208368,121.5069047,44.12914007 379 | 03:19.9,10.5,472.1745,1,24.9728011,121.5480736,13.27087514 380 | 59:34.0,25.3,156.2442,6,24.95033413,121.514014,27.62109721 381 | 42:10.9,16.2,2261.432,5,24.943163,121.5098915,17.34588971 382 | 44:29.0,35.9,185.4296,8,25.01297342,121.5079639,31.02739054 383 | 36:43.1,32,289.3248,1,24.9495066,121.5204378,27.67835252 384 | 12:09.3,17.8,2185.128,8,24.9343045,121.5070436,9.78540164 385 | 05:47.0,4.6,451.2438,3,24.94436341,121.4967726,49.98740884 386 | 41:07.2,24.2,1264.73,8,25.01008597,121.4869215,52.54092548 387 | 42:20.1,0,3947.945,7,24.98759681,121.5455353,0 388 | 42:54.6,15.9,563.2854,0,24.99831158,121.5311967,23.04163671 389 | 05:40.3,7.1,482.7581,3,25.00216108,121.5498282,21.89934295 390 | 10:14.7,17.5,3771.895,0,24.96339966,121.5634999,0 391 | 46:23.5,40.9,837.7233,8,24.98966845,121.4971461,40.09707168 392 | 24:22.0,7.1,718.2937,3,24.98631048,121.5298222,16.6927426 393 | 54:47.2,0,451.6419,4,24.94989098,121.5503868,43.78332661 394 | 55:49.8,33.5,577.9615,8,24.98516495,121.5126919,34.47409439 395 | 16:21.7,13.6,942.4664,0,24.96765702,121.5541292,24.03759221 396 | 41:17.0,0,1360.139,8,24.95087389,121.5077602,28.88585628 397 | 16:21.2,28.2,187.4823,5,24.96893472,121.5367171,36.3156345 398 | 57:02.9,31.5,1717.193,5,25.0018065,121.5469372,27.03164301 399 | 25:34.6,34.4,123.7429,10,24.99739403,121.4962621,57.93798973 400 | 33:59.9,13.3,4510.359,2,24.99776558,121.5579614,0 401 | 48:39.7,35.9,208.3905,9,25.010412,121.5650267,54.83166378 402 | 42:46.0,31.5,5512.038,4,24.94456234,121.5085079,1.443337713 403 | 39:05.1,11.8,1712.632,0,25.00410918,121.5528041,31.35847071 404 | 40:29.4,21.7,600.8604,7,24.94808367,121.5465583,41.40918783 405 | 30:15.0,32.8,2185.128,1,24.93890894,121.5101367,0 406 | 46:53.9,13.8,259.6607,1,25.00205216,121.5280183,44.35062301 407 | 39:38.3,21.7,557.478,9,24.93832915,121.5222392,37.51299282 408 | 11:13.1,16.6,1712.632,4,24.93988442,121.5235117,31.69233586 409 | 36:26.1,20.6,312.8963,9,24.93516733,121.5163531,43.58892098 410 | 29:50.6,15.1,552.4371,7,24.93744358,121.5486092,29.72105262 411 | 30:36.6,18.3,170.1289,6,24.98118569,121.4867975,29.09630995 412 | 16:34.0,11.9,323.6912,2,24.95006992,121.4839182,33.87134651 413 | 47:23.3,0,451.6419,8,24.96390119,121.5433874,25.25510512 414 | 33:29.4,35.9,292.9978,5,24.99786261,121.5582859,25.2856203 415 | 49:41.5,12,90.45606,6,24.95290409,121.5263954,37.58055383 416 | -------------------------------------------------------------------------------- /House-Price-Predictions/Real_Estate_Prediction 2.0.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 11, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "\n", 12 | " \n", 20 | " " 21 | ], 22 | "text/plain": [ 23 | "" 24 | ] 25 | }, 26 | "metadata": {}, 27 | "output_type": "display_data" 28 | } 29 | ], 30 | "source": [ 31 | "import pandas as pd\n", 32 | "from sklearn.model_selection import train_test_split\n", 33 | "from sklearn.linear_model import LinearRegression\n", 34 | "import dash\n", 35 | "from dash import html, dcc, Input, Output, State\n", 36 | "import mysql.connector\n", 37 | "\n", 38 | "# Load real estate data\n", 39 | "real_estate_data = pd.read_csv(\"Real_Estate.csv\") # Replace the path with your path\n", 40 | "\n", 41 | "# Define features and target\n", 42 | "features = ['distance_to_mrt', 'stores', 'latitude', 'longitude']\n", 43 | "target = 'house_price_of_unit_area'\n", 44 | "X = real_estate_data[features]\n", 45 | "y = real_estate_data[target]\n", 46 | "\n", 47 | "# Split the data into training and testing sets\n", 48 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", 49 | "\n", 50 | "# Train the Linear Regression model\n", 51 | "model = LinearRegression()\n", 52 | "model.fit(X_train, y_train)\n", 53 | "\n", 54 | "# Database setup\n", 55 | "conn = mysql.connector.connect(\n", 56 | " host='localhost',\n", 57 | " user='root',\n", 58 | " password='root',\n", 59 | " database='house'\n", 60 | ")\n", 61 | "cursor = conn.cursor()\n", 62 | "\n", 63 | "cursor.execute('''\n", 64 | " CREATE TABLE IF NOT EXISTS real_estate_predictions (\n", 65 | " id INT AUTO_INCREMENT PRIMARY KEY,\n", 66 | " distance_to_mrt DOUBLE,\n", 67 | " stores DOUBLE,\n", 68 | " latitude DOUBLE,\n", 69 | " longitude DOUBLE,\n", 70 | " prediction DOUBLE,\n", 71 | " prediction_score DOUBLE\n", 72 | " )\n", 73 | "''')\n", 74 | "conn.commit()\n", 75 | "\n", 76 | "# Dash app setup\n", 77 | "app = dash.Dash(__name__)\n", 78 | "\n", 79 | "app.layout = html.Div([\n", 80 | "\n", 81 | " html.Div([\n", 82 | "\n", 83 | " html.H1(\"Real Estate Price Prediction\", style={'text-align': 'center'}),\n", 84 | "\n", 85 | " html.Div([\n", 86 | "\n", 87 | " dcc.Input(id='distance_to_mrt', type='number', placeholder='Distance to MRT Station (meters)',\n", 88 | " style={'margin': '10px', 'padding': '10px'}),\n", 89 | "\n", 90 | " dcc.Input(id='stores', type='number', placeholder='Number of Convenience Stores',\n", 91 | " style={'margin': '10px', 'padding': '10px'}),\n", 92 | "\n", 93 | " dcc.Input(id='latitude', type='number', placeholder='Latitude',\n", 94 | " style={'margin': '10px', 'padding': '10px'}),\n", 95 | "\n", 96 | " dcc.Input(id='longitude', type='number', placeholder='Longitude',\n", 97 | " style={'margin': '10px', 'padding': '10px'}),\n", 98 | "\n", 99 | " html.Button('Predict Price', id='predict_button', n_clicks=0,\n", 100 | " style={'margin': '10px', 'padding': '10px', 'background-color': '#007BFF', 'color': 'white'}),\n", 101 | "\n", 102 | " ], style={'text-align': 'center'}),\n", 103 | "\n", 104 | " html.Div(id='prediction_output', style={'text-align': 'center', 'font-size': '20px', 'margin-top': '20px'})\n", 105 | "\n", 106 | " ], style={'width': '50%', 'margin': '0 auto', 'border': '2px solid #007BFF', 'padding': '20px', 'border-radius': '10px'})\n", 107 | "\n", 108 | "])\n", 109 | "\n", 110 | "@app.callback(\n", 111 | " Output('prediction_output', 'children'),\n", 112 | " [Input('predict_button', 'n_clicks')],\n", 113 | " [\n", 114 | " State('distance_to_mrt', 'value'),\n", 115 | " State('stores', 'value'),\n", 116 | " State('latitude', 'value'),\n", 117 | " State('longitude', 'value')\n", 118 | " ]\n", 119 | ")\n", 120 | "def update_output(n_clicks, distance_to_mrt, stores, latitude, longitude):\n", 121 | " if n_clicks > 0 and all(v is not None for v in [distance_to_mrt, stores, latitude, longitude]):\n", 122 | "\n", 123 | " # Prepare the feature vector\n", 124 | " features = pd.DataFrame([[distance_to_mrt, stores, latitude, longitude]],\n", 125 | " columns=['distance_to_mrt', 'stores', 'latitude', 'longitude'])\n", 126 | "\n", 127 | " # Predict\n", 128 | " prediction = model.predict(features)[0]\n", 129 | "\n", 130 | " # Prediction score\n", 131 | " prediction_score = model.score(X_test, y_test)\n", 132 | "\n", 133 | " # Store input values and prediction score in the database\n", 134 | " cursor.execute('''\n", 135 | " INSERT INTO real_estate_predictions (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score)\n", 136 | " VALUES (%s, %s, %s, %s, %s, %s)\n", 137 | " ''', (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score))\n", 138 | " conn.commit()\n", 139 | "\n", 140 | " return f'Predicted House Price of Unit Area: {prediction:.2f}, Prediction Score: {prediction_score:.4f}'\n", 141 | "\n", 142 | " elif n_clicks > 0:\n", 143 | " return 'Please enter all values to get a prediction'\n", 144 | "\n", 145 | " return ''\n", 146 | "\n", 147 | "if __name__ == '__main__':\n", 148 | " app.run_server(port=8058)\n" 149 | ] 150 | }, 151 | { 152 | "cell_type": "markdown", 153 | "metadata": {}, 154 | "source": [ 155 | "# WITH BG IMAGE" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": 18, 161 | "metadata": {}, 162 | "outputs": [ 163 | { 164 | "data": { 165 | "text/html": [ 166 | "\n", 167 | " \n", 175 | " " 176 | ], 177 | "text/plain": [ 178 | "" 179 | ] 180 | }, 181 | "metadata": {}, 182 | "output_type": "display_data" 183 | } 184 | ], 185 | "source": [ 186 | "import base64\n", 187 | "import dash\n", 188 | "from dash import html, dcc, Input, Output, State\n", 189 | "import mysql.connector\n", 190 | "import pandas as pd\n", 191 | "from sklearn.model_selection import train_test_split\n", 192 | "from sklearn.linear_model import LinearRegression\n", 193 | "\n", 194 | "# Load real estate data\n", 195 | "real_estate_data = pd.read_csv(\"Real_Estate.csv\") # Replace the path with your path\n", 196 | "\n", 197 | "# Define features and target\n", 198 | "features = ['distance_to_mrt', 'stores', 'latitude', 'longitude']\n", 199 | "target = 'house_price_of_unit_area'\n", 200 | "X = real_estate_data[features]\n", 201 | "y = real_estate_data[target]\n", 202 | "\n", 203 | "# Split the data into training and testing sets\n", 204 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", 205 | "\n", 206 | "# Train the Linear Regression model\n", 207 | "model = LinearRegression()\n", 208 | "model.fit(X_train, y_train)\n", 209 | "\n", 210 | "# Database setup\n", 211 | "conn = mysql.connector.connect(\n", 212 | " host='localhost',\n", 213 | " user='root',\n", 214 | " password='root',\n", 215 | " database='house'\n", 216 | ")\n", 217 | "cursor = conn.cursor()\n", 218 | "\n", 219 | "cursor.execute('''\n", 220 | " CREATE TABLE IF NOT EXISTS real_estate_predictions (\n", 221 | " id INT AUTO_INCREMENT PRIMARY KEY,\n", 222 | " distance_to_mrt DOUBLE,\n", 223 | " stores DOUBLE,\n", 224 | " latitude DOUBLE,\n", 225 | " longitude DOUBLE,\n", 226 | " prediction DOUBLE,\n", 227 | " prediction_score DOUBLE\n", 228 | " )\n", 229 | "''')\n", 230 | "conn.commit()\n", 231 | "\n", 232 | "# Dash app setup\n", 233 | "app = dash.Dash(__name__)\n", 234 | "\n", 235 | "# Encode background image to base64\n", 236 | "image_filename = 'OIG2.jpeg' # Replace with the path to your image file\n", 237 | "encoded_image = base64.b64encode(open(image_filename, 'rb').read())\n", 238 | "\n", 239 | "app.layout = html.Div([\n", 240 | "\n", 241 | " html.Div([\n", 242 | "\n", 243 | " html.H1(\"Real Estate Price Prediction\", style={'text-align': 'center'}),\n", 244 | "\n", 245 | " html.Div([\n", 246 | "\n", 247 | " dcc.Input(id='distance_to_mrt', type='number', placeholder='Distance to MRT Station (meters)',\n", 248 | " style={'margin': '10px', 'padding': '10px'}),\n", 249 | "\n", 250 | " dcc.Input(id='stores', type='number', placeholder='Number of Convenience Stores',\n", 251 | " style={'margin': '10px', 'padding': '10px'}),\n", 252 | "\n", 253 | " dcc.Input(id='latitude', type='number', placeholder='Latitude',\n", 254 | " style={'margin': '10px', 'padding': '10px'}),\n", 255 | "\n", 256 | " dcc.Input(id='longitude', type='number', placeholder='Longitude',\n", 257 | " style={'margin': '10px', 'padding': '10px'}),\n", 258 | "\n", 259 | " html.Button('Predict Price', id='predict_button', n_clicks=0,\n", 260 | " style={'margin': '10px', 'padding': '10px', 'background-color': '#007BFF', 'color': 'white'}),\n", 261 | "\n", 262 | " ], style={'text-align': 'center'}),\n", 263 | "\n", 264 | " html.Div(id='prediction_output', style={'text-align': 'center', 'font-size': '20px', 'margin-top': '20px'})\n", 265 | "\n", 266 | " ], style={'width': '50%', 'margin': '0 auto', 'border': '2px solid #007BFF', 'padding': '20px', 'border-radius': '10px',\n", 267 | " 'background-image': f'url(\"data:image/png;base64,{encoded_image.decode()}\")'})\n", 268 | "\n", 269 | "])\n", 270 | "\n", 271 | "@app.callback(\n", 272 | " Output('prediction_output', 'children'),\n", 273 | " [Input('predict_button', 'n_clicks')],\n", 274 | " [\n", 275 | " State('distance_to_mrt', 'value'),\n", 276 | " State('stores', 'value'),\n", 277 | " State('latitude', 'value'),\n", 278 | " State('longitude', 'value')\n", 279 | " ]\n", 280 | ")\n", 281 | "def update_output(n_clicks, distance_to_mrt, stores, latitude, longitude):\n", 282 | " if n_clicks > 0 and all(v is not None for v in [distance_to_mrt, stores, latitude, longitude]):\n", 283 | "\n", 284 | " # Prepare the feature vector\n", 285 | " features = pd.DataFrame([[distance_to_mrt, stores, latitude, longitude]],\n", 286 | " columns=['distance_to_mrt', 'stores', 'latitude', 'longitude'])\n", 287 | "\n", 288 | " # Predict\n", 289 | " prediction = model.predict(features)[0]\n", 290 | "\n", 291 | " # Prediction score\n", 292 | " prediction_score = model.score(X_test, y_test)\n", 293 | "\n", 294 | " # Store input values and prediction score in the database\n", 295 | " cursor.execute('''\n", 296 | " INSERT INTO real_estate_predictions (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score)\n", 297 | " VALUES (%s, %s, %s, %s, %s, %s)\n", 298 | " ''', (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score))\n", 299 | " conn.commit()\n", 300 | "\n", 301 | " return f'Predicted House Price of Unit Area: {prediction:.2f}, Prediction Score: {prediction_score:.4f}'\n", 302 | "\n", 303 | " elif n_clicks > 0:\n", 304 | " return 'Please enter all values to get a prediction'\n", 305 | "\n", 306 | " return ''\n", 307 | "\n", 308 | "if __name__ == '__main__':\n", 309 | " app.run_server(port=8058)\n" 310 | ] 311 | }, 312 | { 313 | "cell_type": "code", 314 | "execution_count": null, 315 | "metadata": {}, 316 | "outputs": [], 317 | "source": [] 318 | } 319 | ], 320 | "metadata": { 321 | "kernelspec": { 322 | "display_name": "Python 3", 323 | "language": "python", 324 | "name": "python3" 325 | }, 326 | "language_info": { 327 | "codemirror_mode": { 328 | "name": "ipython", 329 | "version": 3 330 | }, 331 | "file_extension": ".py", 332 | "mimetype": "text/x-python", 333 | "name": "python", 334 | "nbconvert_exporter": "python", 335 | "pygments_lexer": "ipython3", 336 | "version": "3.12.0" 337 | } 338 | }, 339 | "nbformat": 4, 340 | "nbformat_minor": 2 341 | } 342 | -------------------------------------------------------------------------------- /House-Price-Predictions/Real_Estate_Prediction 3.0.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 7, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "data": { 10 | "text/html": [ 11 | "\n", 12 | " \n", 20 | " " 21 | ], 22 | "text/plain": [ 23 | "" 24 | ] 25 | }, 26 | "metadata": {}, 27 | "output_type": "display_data" 28 | } 29 | ], 30 | "source": [ 31 | "import atexit\n", 32 | "import base64\n", 33 | "import dash\n", 34 | "from dash import html, dcc, Input, Output, State\n", 35 | "import mysql.connector\n", 36 | "import pandas as pd\n", 37 | "from sklearn.model_selection import train_test_split\n", 38 | "from sklearn.linear_model import LinearRegression\n", 39 | "\n", 40 | "# Load real estate data\n", 41 | "real_estate_data = pd.read_csv(\"Real_Estate.csv\") # Replace the path with your path\n", 42 | "\n", 43 | "# Define features and target\n", 44 | "features = ['distance_to_mrt', 'stores', 'latitude', 'longitude']\n", 45 | "target = 'house_price_of_unit_area'\n", 46 | "X = real_estate_data[features]\n", 47 | "y = real_estate_data[target]\n", 48 | "\n", 49 | "# Split the data into training and testing sets\n", 50 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", 51 | "\n", 52 | "# Train the Linear Regression model\n", 53 | "model = LinearRegression()\n", 54 | "model.fit(X_train, y_train)\n", 55 | "\n", 56 | "# Database setup\n", 57 | "conn = mysql.connector.connect(\n", 58 | " host='localhost',\n", 59 | " user='root',\n", 60 | " password='root',\n", 61 | " database='house'\n", 62 | ")\n", 63 | "cursor = conn.cursor()\n", 64 | "\n", 65 | "cursor.execute('''\n", 66 | " CREATE TABLE IF NOT EXISTS real_estate_predictions (\n", 67 | " id INT AUTO_INCREMENT PRIMARY KEY,\n", 68 | " distance_to_mrt DOUBLE,\n", 69 | " stores DOUBLE,\n", 70 | " latitude DOUBLE,\n", 71 | " longitude DOUBLE,\n", 72 | " prediction DOUBLE,\n", 73 | " prediction_score DOUBLE\n", 74 | " )\n", 75 | "''')\n", 76 | "conn.commit()\n", 77 | "\n", 78 | "# Register function to close the database connection on exit\n", 79 | "atexit.register(lambda: (cursor.close() if cursor else None, conn.close() if conn else None))\n", 80 | "\n", 81 | "# Dash app setup\n", 82 | "app = dash.Dash(__name__)\n", 83 | "\n", 84 | "# Encode background image to base64\n", 85 | "image_filename = 'OIG.jpeg' # Replace with the path to your image file\n", 86 | "encoded_image = base64.b64encode(open(image_filename, 'rb').read())\n", 87 | "\n", 88 | "app.layout = html.Div([\n", 89 | " html.Div([\n", 90 | " html.H1(\"Real Estate Price Prediction\", style={'text-align': 'center'}),\n", 91 | " html.Div([\n", 92 | " dcc.Input(id='distance_to_mrt', type='number', placeholder='Distance to MRT Station (meters)',\n", 93 | " style={'margin': '10px', 'padding': '10px'}),\n", 94 | " dcc.Input(id='stores', type='number', placeholder='Number of Convenience Stores',\n", 95 | " style={'margin': '10px', 'padding': '10px'}),\n", 96 | " dcc.Input(id='latitude', type='number', placeholder='Latitude',\n", 97 | " style={'margin': '10px', 'padding': '10px'}),\n", 98 | " dcc.Input(id='longitude', type='number', placeholder='Longitude',\n", 99 | " style={'margin': '10px', 'padding': '10px'}),\n", 100 | " html.Button('Predict Price', id='predict_button', n_clicks=0,\n", 101 | " style={'margin': '10px', 'padding': '10px', 'background-color': '#007BFF', 'color': 'white'}),\n", 102 | " ], style={'text-align': 'center'}),\n", 103 | " html.Div(id='prediction_output', style={'text-align': 'center', 'font-size': '20px', 'margin-top': '20px'}),\n", 104 | " ], style={'width': '50%', 'margin': '0 auto', 'border': '2px solid #007BFF', 'padding': '20px',\n", 105 | " 'border-radius': '10px', 'background-image': f'url(\"data:image/png;base64,{encoded_image.decode()}\")'})\n", 106 | "])\n", 107 | "\n", 108 | "def predict_house_price(distance_to_mrt, stores, latitude, longitude):\n", 109 | " # Prepare the feature vector\n", 110 | " features = pd.DataFrame([[distance_to_mrt, stores, latitude, longitude]],\n", 111 | " columns=['distance_to_mrt', 'stores', 'latitude', 'longitude'])\n", 112 | "\n", 113 | " # Predict\n", 114 | " prediction = model.predict(features)[0]\n", 115 | "\n", 116 | " # Prediction score\n", 117 | " prediction_score = model.score(X_test, y_test)\n", 118 | "\n", 119 | " return prediction, prediction_score\n", 120 | "\n", 121 | "def save_prediction_to_database(distance_to_mrt, stores, latitude, longitude, prediction, prediction_score):\n", 122 | " cursor.execute('''\n", 123 | " INSERT INTO real_estate_predictions (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score)\n", 124 | " VALUES (%s, %s, %s, %s, %s, %s)\n", 125 | " ''', (distance_to_mrt, stores, latitude, longitude, prediction, prediction_score))\n", 126 | " conn.commit()\n", 127 | "\n", 128 | "@app.callback(\n", 129 | " Output('prediction_output', 'children'),\n", 130 | " [Input('predict_button', 'n_clicks')],\n", 131 | " [\n", 132 | " State('distance_to_mrt', 'value'),\n", 133 | " State('stores', 'value'),\n", 134 | " State('latitude', 'value'),\n", 135 | " State('longitude', 'value')\n", 136 | " ]\n", 137 | ")\n", 138 | "def update_output(n_clicks, distance_to_mrt, stores, latitude, longitude):\n", 139 | " if n_clicks > 0 and all(v is not None for v in [distance_to_mrt, stores, latitude, longitude]):\n", 140 | " prediction, prediction_score = predict_house_price(distance_to_mrt, stores, latitude, longitude)\n", 141 | "\n", 142 | " # Store input values and prediction score in the database\n", 143 | " save_prediction_to_database(distance_to_mrt, stores, latitude, longitude, prediction, prediction_score)\n", 144 | "\n", 145 | " return f'Predicted House Price of Unit Area: {prediction:.2f}, Prediction Score: {prediction_score:.4f}'\n", 146 | " elif n_clicks > 0:\n", 147 | " return 'Please enter all values to get a prediction'\n", 148 | " return ''\n", 149 | "\n", 150 | "if __name__ == '__main__':\n", 151 | " app.run_server(port=8058)\n" 152 | ] 153 | } 154 | ], 155 | "metadata": { 156 | "kernelspec": { 157 | "display_name": "Python 3", 158 | "language": "python", 159 | "name": "python3" 160 | }, 161 | "language_info": { 162 | "codemirror_mode": { 163 | "name": "ipython", 164 | "version": 3 165 | }, 166 | "file_extension": ".py", 167 | "mimetype": "text/x-python", 168 | "name": "python", 169 | "nbconvert_exporter": "python", 170 | "pygments_lexer": "ipython3", 171 | "version": "3.12.0" 172 | } 173 | }, 174 | "nbformat": 4, 175 | "nbformat_minor": 2 176 | } 177 | -------------------------------------------------------------------------------- /House-Price-Predictions/database_setup1.mysql-notebook: -------------------------------------------------------------------------------- 1 | { 2 | "type": "MySQLNotebook", 3 | "version": "1.0", 4 | "caption": "DB Notebook", 5 | "content": "\\about\nCREATE DATABASE house;\nuse house;\nSELECT * from 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190 | { 191 | "0": 10, 192 | "1": 2134346, 193 | "2": 1, 194 | "3": 11, 195 | "4": 1424, 196 | "5": -65754.27754997097, 197 | "6": 0.5496730534560775 198 | }, 199 | { 200 | "0": 11, 201 | "1": 23, 202 | "2": 2, 203 | "3": 323, 204 | "4": 323, 205 | "5": 11955.330849250791, 206 | "6": 0.5496730534560775 207 | }, 208 | { 209 | "0": 12, 210 | "1": 23, 211 | "2": 2, 212 | "3": 323, 213 | "4": 323, 214 | "5": 11955.330849250791, 215 | "6": 0.5496730534560775 216 | } 217 | ], 218 | "columns": [ 219 | { 220 | "title": "id", 221 | "field": "0", 222 | "dataType": { 223 | "type": 4 224 | } 225 | }, 226 | { 227 | "title": "distance_to_mrt", 228 | "field": "1", 229 | "dataType": { 230 | "type": 9 231 | } 232 | }, 233 | { 234 | "title": "stores", 235 | "field": "2", 236 | "dataType": { 237 | "type": 9 238 | } 239 | }, 240 | { 241 | "title": "latitude", 242 | "field": "3", 243 | "dataType": { 244 | "type": 9 245 | } 246 | }, 247 | { 248 | "title": "longitude", 249 | "field": "4", 250 | "dataType": { 251 | "type": 9 252 | } 253 | }, 254 | { 255 | "title": "prediction", 256 | "field": "5", 257 | "dataType": { 258 | "type": 9 259 | } 260 | }, 261 | { 262 | "title": "prediction_score", 263 | "field": "6", 264 | "dataType": { 265 | "type": 9 266 | } 267 | } 268 | ], 269 | "executionInfo": { 270 | "text": "OK, 12 records retrieved in 0s" 271 | }, 272 | "totalRowCount": 12, 273 | "hasMoreRows": false, 274 | "currentPage": 0, 275 | "index": 0, 276 | "sql": "\nSELECT * from real_estate_predictions;" 277 | } 278 | ] 279 | } 280 | ] 281 | } -------------------------------------------------------------------------------- /House-Price-Predictions/readme.md: -------------------------------------------------------------------------------- 1 | # Real Estate Price Prediction 2 | ![OIG](https://github.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/assets/32574833/389b8066-5b97-4b4c-bd23-9d68434795d6) 3 | 4 | Real Estate Price Prediction is a machine learning project that leverages Linear Regression to predict house prices based on various features. This project is valuable for investors, developers, and homeowners, providing insights for informed decision-making and investment planning. 5 | 6 | ## Dataset 7 | 8 | The dataset contains 414 entries with detailed information on real estate transactions. Each entry includes the following features: 9 | 10 | - **Transaction Date**: Date of the property transaction. 11 | - **House Age**: Age of the property in years. 12 | - **Distance to the Nearest MRT Station**: Proximity to the nearest Mass Rapid Transit station in meters, indicating convenience and accessibility. 13 | - **Number of Convenience Stores**: Count of convenience stores in the vicinity, reflecting the property’s accessibility to basic amenities. 14 | - **Latitude and Longitude**: Geographical coordinates of the property, defining its location. 15 | - **House Price of Unit Area**: The target variable representing the house price per unit area. 16 | 17 | ## Project Overview 18 | 19 | The project involves using a Linear Regression model to predict house prices based on the provided dataset. The model is trained on historical real estate transactions, learning the relationships between various features and the house price of unit area. This trained model can then be used to make predictions on new data. 20 | 21 | ## File Structure 22 | 23 | The repository is organized as follows: 24 | 25 | - **`Real_Estate.csv`**: The dataset file containing real estate transaction information. 26 | - **`Real_Estate_Prediction.ipynb`**: Jupyter Notebook containing the Python code for data exploration, model training, and predictions. 27 | - **`images/`**: Images used in the project, such as bg or results. 28 | - **`database_setup.sql`**: SQL file with the database setup for storing prediction results. 29 | - **`README.md`**: This file, providing an overview of the project, dataset, and file structure. 30 | 31 | ## Getting Started 32 | 33 | 1. **Clone the Repository:** 34 | ```bash 35 | git clone https://github.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/real-estate-price-prediction.git 36 | cd real-estate-price-prediction 37 | ``` 38 | 39 | 2. **Install Dependencies:** 40 | ```bash 41 | pip install -r requirements.txt 42 | ``` 43 | 44 | 3. **Run the Jupyter Notebook:** 45 | Open and run the `Real_Estate_Prediction.ipynb` notebook to explore the data, train the model, and make predictions. 46 | 47 | ## Usage 48 | 49 | The project demonstrates how to use Linear Regression for real estate price prediction. The Jupyter Notebook provides step-by-step guidance on data preprocessing, model training, and prediction. 50 | 51 | ## Database Setup 52 | 53 | The `database_setup.sql` file contains the SQL code for setting up a database to store prediction results. Make sure to run this script before making predictions if you want to store results in a database. 54 | 55 | ```bash 56 | mysql -u root -p house < database_setup.sql 57 | ``` 58 | 59 | ## Demo 60 | 61 | For a quick demonstration, please refer to the `demo/README.md` file. 62 | 63 | ## License 64 | 65 | This project is licensed under the [MIT License](LICENSE). 66 | 67 | 68 | 69 | ## Acknowledgments 70 | 71 | Special thanks to AMAN KHARWAL for his valuable contributions and insights throughout the development of this project. 72 | visit [thecleverprogrammer](https://thecleverprogrammer.com/) 73 | 74 | 75 | ## Contact 76 | 77 | For inquiries, suggestions, or collaboration opportunities, feel free to reach out: 78 | 79 | - **Hiran Joseph** 80 | - Email: [Hiran](hiranvjoseph@gmail.com) 81 | - LinkedIn: [Hiran Joseph](https://www.linkedin.com/in/hiranjoe/) 82 | - Kaggle: [@Hiran Joe ](https://www.kaggle.com/hiranjoseph) 83 | -------------------------------------------------------------------------------- /Iris-Species-Prediction/Iris2023.csv: -------------------------------------------------------------------------------- 1 | sepal_length,sepal_width,petal_length,petal_width,species 2 | 5.1,3.5,1.4,0.2,Iris-setosa 3 | 4.9,3,1.4,0.2,Iris-setosa 4 | 4.7,3.2,1.3,0.2,Iris-setosa 5 | 4.6,3.1,1.5,0.2,Iris-setosa 6 | 5,3.6,1.4,0.2,Iris-setosa 7 | 5.4,3.9,1.7,0.4,Iris-setosa 8 | 4.6,3.4,1.4,0.3,Iris-setosa 9 | 5,3.4,1.5,0.2,Iris-setosa 10 | 4.4,2.9,1.4,0.2,Iris-setosa 11 | 4.9,3.1,1.5,0.1,Iris-setosa 12 | 5.4,3.7,1.5,0.2,Iris-setosa 13 | 4.8,3.4,1.6,0.2,Iris-setosa 14 | 4.8,3,1.4,0.1,Iris-setosa 15 | 4.3,3,1.1,0.1,Iris-setosa 16 | 5.8,4,1.2,0.2,Iris-setosa 17 | 5.7,4.4,1.5,0.4,Iris-setosa 18 | 5.4,3.9,1.3,0.4,Iris-setosa 19 | 5.1,3.5,1.4,0.3,Iris-setosa 20 | 5.7,3.8,1.7,0.3,Iris-setosa 21 | 5.1,3.8,1.5,0.3,Iris-setosa 22 | 5.4,3.4,1.7,0.2,Iris-setosa 23 | 5.1,3.7,1.5,0.4,Iris-setosa 24 | 4.6,3.6,1,0.2,Iris-setosa 25 | 5.1,3.3,1.7,0.5,Iris-setosa 26 | 4.8,3.4,1.9,0.2,Iris-setosa 27 | 5,3,1.6,0.2,Iris-setosa 28 | 5,3.4,1.6,0.4,Iris-setosa 29 | 5.2,3.5,1.5,0.2,Iris-setosa 30 | 5.2,3.4,1.4,0.2,Iris-setosa 31 | 4.7,3.2,1.6,0.2,Iris-setosa 32 | 4.8,3.1,1.6,0.2,Iris-setosa 33 | 5.4,3.4,1.5,0.4,Iris-setosa 34 | 5.2,4.1,1.5,0.1,Iris-setosa 35 | 5.5,4.2,1.4,0.2,Iris-setosa 36 | 4.9,3.1,1.5,0.1,Iris-setosa 37 | 5,3.2,1.2,0.2,Iris-setosa 38 | 5.5,3.5,1.3,0.2,Iris-setosa 39 | 4.9,3.1,1.5,0.1,Iris-setosa 40 | 4.4,3,1.3,0.2,Iris-setosa 41 | 5.1,3.4,1.5,0.2,Iris-setosa 42 | 5,3.5,1.3,0.3,Iris-setosa 43 | 4.5,2.3,1.3,0.3,Iris-setosa 44 | 4.4,3.2,1.3,0.2,Iris-setosa 45 | 5,3.5,1.6,0.6,Iris-setosa 46 | 5.1,3.8,1.9,0.4,Iris-setosa 47 | 4.8,3,1.4,0.3,Iris-setosa 48 | 5.1,3.8,1.6,0.2,Iris-setosa 49 | 4.6,3.2,1.4,0.2,Iris-setosa 50 | 5.3,3.7,1.5,0.2,Iris-setosa 51 | 5,3.3,1.4,0.2,Iris-setosa 52 | 7,3.2,4.7,1.4,Iris-versicolor 53 | 6.4,3.2,4.5,1.5,Iris-versicolor 54 | 6.9,3.1,4.9,1.5,Iris-versicolor 55 | 5.5,2.3,4,1.3,Iris-versicolor 56 | 6.5,2.8,4.6,1.5,Iris-versicolor 57 | 5.7,2.8,4.5,1.3,Iris-versicolor 58 | 6.3,3.3,4.7,1.6,Iris-versicolor 59 | 4.9,2.4,3.3,1,Iris-versicolor 60 | 6.6,2.9,4.6,1.3,Iris-versicolor 61 | 5.2,2.7,3.9,1.4,Iris-versicolor 62 | 5,2,3.5,1,Iris-versicolor 63 | 5.9,3,4.2,1.5,Iris-versicolor 64 | 6,2.2,4,1,Iris-versicolor 65 | 6.1,2.9,4.7,1.4,Iris-versicolor 66 | 5.6,2.9,3.6,1.3,Iris-versicolor 67 | 6.7,3.1,4.4,1.4,Iris-versicolor 68 | 5.6,3,4.5,1.5,Iris-versicolor 69 | 5.8,2.7,4.1,1,Iris-versicolor 70 | 6.2,2.2,4.5,1.5,Iris-versicolor 71 | 5.6,2.5,3.9,1.1,Iris-versicolor 72 | 5.9,3.2,4.8,1.8,Iris-versicolor 73 | 6.1,2.8,4,1.3,Iris-versicolor 74 | 6.3,2.5,4.9,1.5,Iris-versicolor 75 | 6.1,2.8,4.7,1.2,Iris-versicolor 76 | 6.4,2.9,4.3,1.3,Iris-versicolor 77 | 6.6,3,4.4,1.4,Iris-versicolor 78 | 6.8,2.8,4.8,1.4,Iris-versicolor 79 | 6.7,3,5,1.7,Iris-versicolor 80 | 6,2.9,4.5,1.5,Iris-versicolor 81 | 5.7,2.6,3.5,1,Iris-versicolor 82 | 5.5,2.4,3.8,1.1,Iris-versicolor 83 | 5.5,2.4,3.7,1,Iris-versicolor 84 | 5.8,2.7,3.9,1.2,Iris-versicolor 85 | 6,2.7,5.1,1.6,Iris-versicolor 86 | 5.4,3,4.5,1.5,Iris-versicolor 87 | 6,3.4,4.5,1.6,Iris-versicolor 88 | 6.7,3.1,4.7,1.5,Iris-versicolor 89 | 6.3,2.3,4.4,1.3,Iris-versicolor 90 | 5.6,3,4.1,1.3,Iris-versicolor 91 | 5.5,2.5,4,1.3,Iris-versicolor 92 | 5.5,2.6,4.4,1.2,Iris-versicolor 93 | 6.1,3,4.6,1.4,Iris-versicolor 94 | 5.8,2.6,4,1.2,Iris-versicolor 95 | 5,2.3,3.3,1,Iris-versicolor 96 | 5.6,2.7,4.2,1.3,Iris-versicolor 97 | 5.7,3,4.2,1.2,Iris-versicolor 98 | 5.7,2.9,4.2,1.3,Iris-versicolor 99 | 6.2,2.9,4.3,1.3,Iris-versicolor 100 | 5.1,2.5,3,1.1,Iris-versicolor 101 | 5.7,2.8,4.1,1.3,Iris-versicolor 102 | 6.3,3.3,6,2.5,Iris-virginica 103 | 5.8,2.7,5.1,1.9,Iris-virginica 104 | 7.1,3,5.9,2.1,Iris-virginica 105 | 6.3,2.9,5.6,1.8,Iris-virginica 106 | 6.5,3,5.8,2.2,Iris-virginica 107 | 7.6,3,6.6,2.1,Iris-virginica 108 | 4.9,2.5,4.5,1.7,Iris-virginica 109 | 7.3,2.9,6.3,1.8,Iris-virginica 110 | 6.7,2.5,5.8,1.8,Iris-virginica 111 | 7.2,3.6,6.1,2.5,Iris-virginica 112 | 6.5,3.2,5.1,2,Iris-virginica 113 | 6.4,2.7,5.3,1.9,Iris-virginica 114 | 6.8,3,5.5,2.1,Iris-virginica 115 | 5.7,2.5,5,2,Iris-virginica 116 | 5.8,2.8,5.1,2.4,Iris-virginica 117 | 6.4,3.2,5.3,2.3,Iris-virginica 118 | 6.5,3,5.5,1.8,Iris-virginica 119 | 7.7,3.8,6.7,2.2,Iris-virginica 120 | 7.7,2.6,6.9,2.3,Iris-virginica 121 | 6,2.2,5,1.5,Iris-virginica 122 | 6.9,3.2,5.7,2.3,Iris-virginica 123 | 5.6,2.8,4.9,2,Iris-virginica 124 | 7.7,2.8,6.7,2,Iris-virginica 125 | 6.3,2.7,4.9,1.8,Iris-virginica 126 | 6.7,3.3,5.7,2.1,Iris-virginica 127 | 7.2,3.2,6,1.8,Iris-virginica 128 | 6.2,2.8,4.8,1.8,Iris-virginica 129 | 6.1,3,4.9,1.8,Iris-virginica 130 | 6.4,2.8,5.6,2.1,Iris-virginica 131 | 7.2,3,5.8,1.6,Iris-virginica 132 | 7.4,2.8,6.1,1.9,Iris-virginica 133 | 7.9,3.8,6.4,2,Iris-virginica 134 | 6.4,2.8,5.6,2.2,Iris-virginica 135 | 6.3,2.8,5.1,1.5,Iris-virginica 136 | 6.1,2.6,5.6,1.4,Iris-virginica 137 | 7.7,3,6.1,2.3,Iris-virginica 138 | 6.3,3.4,5.6,2.4,Iris-virginica 139 | 6.4,3.1,5.5,1.8,Iris-virginica 140 | 6,3,4.8,1.8,Iris-virginica 141 | 6.9,3.1,5.4,2.1,Iris-virginica 142 | 6.7,3.1,5.6,2.4,Iris-virginica 143 | 6.9,3.1,5.1,2.3,Iris-virginica 144 | 5.8,2.7,5.1,1.9,Iris-virginica 145 | 6.8,3.2,5.9,2.3,Iris-virginica 146 | 6.7,3.3,5.7,2.5,Iris-virginica 147 | 6.7,3,5.2,2.3,Iris-virginica 148 | 6.3,2.5,5,1.9,Iris-virginica 149 | 6.5,3,5.2,2,Iris-virginica 150 | 6.2,3.4,5.4,2.3,Iris-virginica 151 | 5.9,3,5.1,1.8,Iris-virginica 152 | -------------------------------------------------------------------------------- /Iris-Species-Prediction/Iris_Classification.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 1" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "pip install mysql-connector-python\n" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 1, 20 | "metadata": {}, 21 | "outputs": [ 22 | { 23 | "data": { 24 | "text/html": [ 25 | "\n", 26 | " \n", 34 | " " 35 | ], 36 | "text/plain": [ 37 | "" 38 | ] 39 | }, 40 | "metadata": {}, 41 | "output_type": "display_data" 42 | } 43 | ], 44 | "source": [ 45 | "import dash\n", 46 | "from dash import dcc, html\n", 47 | "from dash.dependencies import Input, Output, State\n", 48 | "import mysql.connector\n", 49 | "import numpy as np\n", 50 | "import pandas as pd\n", 51 | "from sklearn.neighbors import KNeighborsClassifier\n", 52 | "from sklearn.model_selection import train_test_split\n", 53 | "\n", 54 | "# Load Iris dataset\n", 55 | "iris = pd.read_csv(\"Iris2023.csv\")\n", 56 | "x = iris.drop(\"species\", axis=1)\n", 57 | "y = iris[\"species\"]\n", 58 | "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n", 59 | "\n", 60 | "app = dash.Dash(__name__)\n", 61 | "\n", 62 | "# Database setup\n", 63 | "conn = mysql.connector.connect(\n", 64 | " host='localhost',\n", 65 | " user='root',\n", 66 | " password='root',\n", 67 | " database='iris'\n", 68 | ")\n", 69 | "cursor = conn.cursor()\n", 70 | "\n", 71 | "# Create and fit the KNN model\n", 72 | "knn = KNeighborsClassifier(n_neighbors=1)\n", 73 | "knn.fit(x_train, y_train)\n", 74 | "\n", 75 | "# Create a table if it doesn't exist\n", 76 | "cursor.execute('''\n", 77 | " CREATE TABLE IF NOT EXISTS user_inputs (\n", 78 | " id INT AUTO_INCREMENT PRIMARY KEY,\n", 79 | " sepal_length DOUBLE,\n", 80 | " sepal_width DOUBLE,\n", 81 | " petal_length DOUBLE,\n", 82 | " petal_width DOUBLE\n", 83 | " )\n", 84 | "''')\n", 85 | "conn.commit()\n", 86 | "\n", 87 | "app.layout = html.Div([\n", 88 | " html.H1(\"Iris Flower Prediction\", style={'textAlign': 'center', 'marginBottom': 20, 'color': '#333'}),\n", 89 | "\n", 90 | " html.Label(\"Enter Sepal Length:\"),\n", 91 | " dcc.Input(id='sepal_length', type='number', value=5.1),\n", 92 | "\n", 93 | " html.Label(\"Enter Sepal Width:\"),\n", 94 | " dcc.Input(id='sepal_width', type='number', value=3.5),\n", 95 | "\n", 96 | " html.Label(\"Enter Petal Length:\"),\n", 97 | " dcc.Input(id='petal_length', type='number', value=1.4),\n", 98 | "\n", 99 | " html.Label(\"Enter Petal Width:\"),\n", 100 | " dcc.Input(id='petal_width', type='number', value=0.2),\n", 101 | "\n", 102 | " html.Button(id='store_button', n_clicks=0, children='Store Input'),\n", 103 | "\n", 104 | " html.Div(id='output_message', style={'marginTop': 20, 'fontSize': 18}),\n", 105 | "])\n", 106 | "\n", 107 | "@app.callback(\n", 108 | " Output('output_message', 'children'),\n", 109 | " [Input('store_button', 'n_clicks')],\n", 110 | " [\n", 111 | " State('sepal_length', 'value'),\n", 112 | " State('sepal_width', 'value'),\n", 113 | " State('petal_length', 'value'),\n", 114 | " State('petal_width', 'value')\n", 115 | " ]\n", 116 | ")\n", 117 | "def predict_and_store(n_clicks, sepal_length, sepal_width, petal_length, petal_width):\n", 118 | " if n_clicks > 0 and all(v is not None for v in [sepal_length, sepal_width, petal_length, petal_width]):\n", 119 | " # Store input values in the database\n", 120 | " cursor.execute('''\n", 121 | " INSERT INTO user_inputs (sepal_length, sepal_width, petal_length, petal_width)\n", 122 | " VALUES (%s, %s, %s, %s)\n", 123 | " ''', (sepal_length, sepal_width, petal_length, petal_width))\n", 124 | " conn.commit()\n", 125 | "\n", 126 | " # Predict\n", 127 | " x_new = np.array([[sepal_length, sepal_width, petal_length, petal_width]])\n", 128 | " prediction = knn.predict(x_new)[0]\n", 129 | "\n", 130 | " # Prediction probability scores\n", 131 | " prob_scores = knn.predict_proba(x_new)\n", 132 | "\n", 133 | " return f'Predicted Species: {prediction}, Prediction Score: {prob_scores.max():.2f}, Input values stored in the Iris database'\n", 134 | "\n", 135 | " elif n_clicks > 0:\n", 136 | " return 'Please enter all values to get a prediction'\n", 137 | "\n", 138 | " return ''\n", 139 | "\n", 140 | "if __name__ == '__main__':\n", 141 | " app.run_server(port=8056, debug=True)\n" 142 | ] 143 | }, 144 | { 145 | "cell_type": "markdown", 146 | "metadata": {}, 147 | "source": [ 148 | "# 4" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": 16, 154 | "metadata": {}, 155 | "outputs": [ 156 | { 157 | "data": { 158 | "text/html": [ 159 | "\n", 160 | " \n", 168 | " " 169 | ], 170 | "text/plain": [ 171 | "" 172 | ] 173 | }, 174 | "metadata": {}, 175 | "output_type": "display_data" 176 | } 177 | ], 178 | "source": [ 179 | "import dash\n", 180 | "from dash import dcc, html\n", 181 | "from dash.dependencies import Input, Output, State\n", 182 | "import mysql.connector\n", 183 | "import numpy as np\n", 184 | "import pandas as pd\n", 185 | "from sklearn.neighbors import KNeighborsClassifier\n", 186 | "from sklearn.model_selection import train_test_split\n", 187 | "import warnings\n", 188 | "\n", 189 | "warnings.filterwarnings(\"ignore\")\n", 190 | "\n", 191 | "# Load Iris dataset\n", 192 | "iris = pd.read_csv(\"Iris2023.csv\")\n", 193 | "x = iris.drop(\"species\", axis=1)\n", 194 | "y = iris[\"species\"]\n", 195 | "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)\n", 196 | "\n", 197 | "app = dash.Dash(__name__)\n", 198 | "\n", 199 | "# Database setup\n", 200 | "conn = mysql.connector.connect(\n", 201 | " host='localhost',\n", 202 | " user='root',\n", 203 | " password='root',\n", 204 | " database='iris'\n", 205 | ")\n", 206 | "cursor = conn.cursor()\n", 207 | "\n", 208 | "# Create and fit the KNN model\n", 209 | "knn = KNeighborsClassifier(n_neighbors=1)\n", 210 | "knn.fit(x_train, y_train)\n", 211 | "\n", 212 | "# Create a table if it doesn't exist\n", 213 | "cursor.execute('''\n", 214 | " CREATE TABLE IF NOT EXISTS iris_predictions (\n", 215 | " id INT AUTO_INCREMENT PRIMARY KEY,\n", 216 | " sepal_length DOUBLE,\n", 217 | " sepal_width DOUBLE,\n", 218 | " petal_length DOUBLE,\n", 219 | " petal_width DOUBLE,\n", 220 | " predicted_species VARCHAR(255),\n", 221 | " prediction_score DOUBLE\n", 222 | " )\n", 223 | "''')\n", 224 | "conn.commit()\n", 225 | "\n", 226 | "app.layout = html.Div([\n", 227 | " html.H1(\"Iris Flower Prediction\", style={'textAlign': 'center', 'color': 'darkviolet', 'fontFamily': 'Arial'}),\n", 228 | "\n", 229 | " html.Div([\n", 230 | " html.Label(\"Enter Sepal Length:\", style={'marginRight': '10px', 'textAlign': 'center'}),\n", 231 | " dcc.Input(id='sepal_length', type='number', value=5.1, style={'marginRight': '10px', 'textAlign': 'center'}),\n", 232 | " ], style={'marginBottom': '10px', 'display': 'flex', 'justifyContent': 'center'}),\n", 233 | "\n", 234 | " html.Div([\n", 235 | " html.Label(\"Enter Sepal Width:\", style={'marginRight': '10px', 'textAlign': 'center'}),\n", 236 | " dcc.Input(id='sepal_width', type='number', value=3.5, style={'marginRight': '10px', 'textAlign': 'center'}),\n", 237 | " ], style={'marginBottom': '10px', 'display': 'flex', 'justifyContent': 'center'}),\n", 238 | "\n", 239 | " html.Div([\n", 240 | " html.Label(\"Enter Petal Length:\", style={'marginRight': '10px', 'textAlign': 'center'}),\n", 241 | " dcc.Input(id='petal_length', type='number', value=1.4, style={'marginRight': '10px', 'textAlign': 'center'}),\n", 242 | " ], style={'marginBottom': '10px', 'display': 'flex', 'justifyContent': 'center'}),\n", 243 | "\n", 244 | " html.Div([\n", 245 | " html.Label(\"Enter Petal Width:\", style={'marginRight': '10px', 'textAlign': 'center'}),\n", 246 | " dcc.Input(id='petal_width', type='number', value=0.2, style={'marginRight': '10px', 'textAlign': 'center'}),\n", 247 | " ], style={'marginBottom': '20px', 'display': 'flex', 'justifyContent': 'center'}),\n", 248 | "\n", 249 | " html.Button(id='store_button', n_clicks=0, children='Species', style={'marginBottom': '20px','font-weight': 'bold', 'background-color': '#007BFF', 'color': 'white', 'align-self': 'center'}),\n", 250 | "\n", 251 | " html.Div(id='output_message', style={'marginTop': '20px', 'fontSize': '18px', 'color': 'red','font-weight': 'bold', 'fontFamily': 'Arial','textAlign': 'center'}),\n", 252 | "], style={'backgroundImage': 'url(\"https://wallpapercave.com/wp/qqGZC6u.jpg\")', 'backgroundSize': 'cover'})\n", 253 | "\n", 254 | "@app.callback(\n", 255 | " Output('output_message', 'children'),\n", 256 | " [Input('store_button', 'n_clicks')],\n", 257 | " [\n", 258 | " State('sepal_length', 'value'),\n", 259 | " State('sepal_width', 'value'),\n", 260 | " State('petal_length', 'value'),\n", 261 | " State('petal_width', 'value')\n", 262 | " ]\n", 263 | ")\n", 264 | "def predict_and_store(n_clicks, sepal_length, sepal_width, petal_length, petal_width):\n", 265 | " if n_clicks > 0 and all(v is not None for v in [sepal_length, sepal_width, petal_length, petal_width]):\n", 266 | " # Store input values in the database\n", 267 | " cursor.execute('''\n", 268 | " INSERT INTO iris_predictions (sepal_length, sepal_width, petal_length, petal_width)\n", 269 | " VALUES (%s, %s, %s, %s)\n", 270 | " ''', (sepal_length, sepal_width, petal_length, petal_width))\n", 271 | " conn.commit()\n", 272 | "\n", 273 | " # Predict\n", 274 | " x_new = np.array([[sepal_length, sepal_width, petal_length, petal_width]])\n", 275 | " predicted_species = knn.predict(x_new)[0]\n", 276 | "\n", 277 | " # Prediction probability scores\n", 278 | " prediction_score = knn.predict_proba(x_new).max()\n", 279 | "\n", 280 | " # Update the prediction in the database\n", 281 | " cursor.execute('''\n", 282 | " UPDATE iris_predictions\n", 283 | " SET predicted_species = %s, prediction_score = %s\n", 284 | " ORDER BY id DESC\n", 285 | " LIMIT 1\n", 286 | " ''', (predicted_species, prediction_score))\n", 287 | " conn.commit()\n", 288 | "\n", 289 | " return f'Predicted Species: {predicted_species}, Prediction Score: {prediction_score:.2f}, Input values stored in the database!'\n", 290 | "\n", 291 | " elif n_clicks > 0:\n", 292 | " return 'Please enter all values to get a prediction'\n", 293 | "\n", 294 | " return ''\n", 295 | "\n", 296 | "if __name__ == '__main__':\n", 297 | " app.run_server(port=8057, debug=True, external_stylesheets=external_stylesheets)\n" 298 | ] 299 | }, 300 | { 301 | "cell_type": "markdown", 302 | "metadata": {}, 303 | "source": [ 304 | "# 5" 305 | ] 306 | }, 307 | { 308 | "cell_type": "code", 309 | "execution_count": null, 310 | "metadata": {}, 311 | "outputs": [], 312 | "source": [] 313 | } 314 | ], 315 | "metadata": { 316 | "kernelspec": { 317 | "display_name": "Python 3", 318 | "language": "python", 319 | "name": "python3" 320 | }, 321 | "language_info": { 322 | "codemirror_mode": { 323 | "name": "ipython", 324 | "version": 3 325 | }, 326 | "file_extension": ".py", 327 | "mimetype": "text/x-python", 328 | "name": "python", 329 | "nbconvert_exporter": "python", 330 | "pygments_lexer": "ipython3", 331 | "version": "3.12.0" 332 | } 333 | }, 334 | "nbformat": 4, 335 | "nbformat_minor": 2 336 | } 337 | -------------------------------------------------------------------------------- /Iris-Species-Prediction/database_setup.mysql-notebook: -------------------------------------------------------------------------------- 1 | { 2 | "type": "MySQLNotebook", 3 | "version": "1.0", 4 | "caption": "DB Notebook", 5 | "content": "\\about\nUSE iris;\nSELECT * FROM iris_predictions;", 6 | "options": { 7 | "tabSize": 4, 8 | "indentSize": 4, 9 | "insertSpaces": true, 10 | "defaultEOL": "LF", 11 | "trimAutoWhitespace": true 12 | }, 13 | "viewState": { 14 | "cursorState": [ 15 | { 16 | "inSelectionMode": false, 17 | "selectionStart": { 18 | "lineNumber": 2, 19 | "column": 10 20 | }, 21 | "position": { 22 | "lineNumber": 2, 23 | "column": 10 24 | } 25 | } 26 | ], 27 | "viewState": { 28 | "scrollLeft": 0, 29 | "firstPosition": { 30 | "lineNumber": 1, 31 | "column": 1 32 | }, 33 | "firstPositionDeltaTop": 0 34 | }, 35 | "contributionsState": { 36 | "editor.contrib.folding": {}, 37 | "editor.contrib.wordHighlighter": false 38 | } 39 | }, 40 | "contexts": [ 41 | { 42 | "state": { 43 | "start": 1, 44 | "end": 1, 45 | "language": "mysql", 46 | "result": { 47 | "type": "text", 48 | "text": [ 49 | { 50 | "type": 2, 51 | "content": "Welcome to the MySQL Shell - DB Notebook.\n\nPress Ctrl+Enter to execute the code block.\n\nExecute \\sql to switch to SQL, \\js to JavaScript and \\ts to TypeScript mode.\nExecute \\help or \\? for help;", 52 | "language": "ansi" 53 | } 54 | ] 55 | }, 56 | "currentHeight": 120, 57 | "statements": [ 58 | { 59 | "delimiter": ";", 60 | "span": { 61 | "start": 0, 62 | "length": 6 63 | }, 64 | "contentStart": 0, 65 | "state": 0 66 | } 67 | ] 68 | }, 69 | "data": [] 70 | }, 71 | { 72 | "state": { 73 | "start": 2, 74 | "end": 3, 75 | "language": "mysql", 76 | "result": { 77 | "type": "resultIds", 78 | "list": [ 79 | "f9d60b57-9cb8-4943-916c-1596078e3021" 80 | ] 81 | }, 82 | "currentHeight": 352, 83 | "statements": [ 84 | { 85 | "delimiter": ";", 86 | "span": { 87 | "start": 0, 88 | "length": 9 89 | }, 90 | "contentStart": 0, 91 | "state": 0 92 | }, 93 | { 94 | "delimiter": ";", 95 | "span": { 96 | "start": 9, 97 | "length": 32 98 | }, 99 | "contentStart": 10, 100 | "state": 0 101 | } 102 | ] 103 | }, 104 | "data": [ 105 | { 106 | "tabId": "ff32b13a-a267-4838-f0c6-c9f1b99a4550", 107 | "resultId": "f9d60b57-9cb8-4943-916c-1596078e3021", 108 | "rows": [ 109 | { 110 | "0": 1, 111 | "1": 5.1, 112 | "2": 3.5, 113 | "3": 1.4, 114 | "4": 0.2, 115 | "5": "Iris-setosa", 116 | "6": 1 117 | }, 118 | { 119 | "0": 2, 120 | "1": 5.1, 121 | "2": 3.5, 122 | "3": 1.4, 123 | "4": 0.2, 124 | "5": "Iris-setosa", 125 | "6": 1 126 | }, 127 | { 128 | "0": 3, 129 | "1": 5.1, 130 | "2": 3.5, 131 | "3": 1.4, 132 | "4": 0.2, 133 | "5": "Iris-setosa", 134 | "6": 1 135 | }, 136 | { 137 | "0": 4, 138 | "1": 5.1, 139 | "2": 3.5, 140 | "3": 1.4, 141 | "4": 0.2, 142 | "5": "Iris-setosa", 143 | "6": 1 144 | }, 145 | { 146 | "0": 5, 147 | "1": 5.1, 148 | "2": 3.5, 149 | "3": 1.4, 150 | "4": 0.2, 151 | "5": "Iris-setosa", 152 | "6": 1 153 | }, 154 | { 155 | "0": 6, 156 | "1": 5.1, 157 | "2": 3.5, 158 | "3": 1.4, 159 | "4": 0.2, 160 | "5": "Iris-setosa", 161 | "6": 1 162 | }, 163 | { 164 | "0": 7, 165 | "1": 5.1, 166 | "2": 3.5, 167 | "3": 1.4, 168 | "4": 0.2, 169 | "5": "Iris-setosa", 170 | "6": 1 171 | }, 172 | { 173 | "0": 8, 174 | "1": 5.1, 175 | "2": 3.5, 176 | "3": 1.4, 177 | "4": 0.2, 178 | "5": "Iris-setosa", 179 | "6": 1 180 | }, 181 | { 182 | "0": 9, 183 | "1": 5.1, 184 | "2": 3.5, 185 | "3": 1.4, 186 | "4": 0.2, 187 | "5": "Iris-setosa", 188 | "6": 1 189 | }, 190 | { 191 | "0": 10, 192 | "1": 5.1, 193 | "2": 3.5, 194 | "3": 1.4, 195 | "4": 2.2, 196 | "5": "Iris-setosa", 197 | "6": 1 198 | }, 199 | { 200 | "0": 11, 201 | "1": 5.1, 202 | "2": 3.5, 203 | "3": 14.4, 204 | "4": 2.2, 205 | "5": "Iris-virginica", 206 | "6": 1 207 | }, 208 | { 209 | "0": 12, 210 | "1": 5.1, 211 | "2": 3.5, 212 | "3": 1.4, 213 | "4": 0.2, 214 | "5": "Iris-setosa", 215 | "6": 1 216 | }, 217 | { 218 | "0": 13, 219 | "1": 5.1, 220 | "2": 3.5, 221 | "3": 1.4, 222 | "4": 0.2, 223 | "5": "Iris-setosa", 224 | "6": 1 225 | }, 226 | { 227 | "0": 14, 228 | "1": 5.1, 229 | "2": 3.5, 230 | "3": 1.4, 231 | "4": 0.2, 232 | "5": "Iris-setosa", 233 | "6": 1 234 | }, 235 | { 236 | "0": 15, 237 | "1": 5.1, 238 | "2": 3.5, 239 | "3": 1.4, 240 | "4": 0.2, 241 | "5": "Iris-setosa", 242 | "6": 1 243 | }, 244 | { 245 | "0": 16, 246 | "1": 5.1, 247 | "2": 3.5, 248 | "3": 1.4, 249 | "4": 3.2, 250 | "5": "Iris-setosa", 251 | "6": 1 252 | }, 253 | { 254 | "0": 17, 255 | "1": 5.1, 256 | "2": 3.5, 257 | "3": 17.4, 258 | "4": 3.2, 259 | "5": "Iris-virginica", 260 | "6": 1 261 | }, 262 | { 263 | "0": 18, 264 | "1": 5.1, 265 | "2": 3.5, 266 | "3": 17.4, 267 | "4": 3.2, 268 | "5": "Iris-virginica", 269 | "6": 1 270 | }, 271 | { 272 | "0": 19, 273 | "1": 5.1, 274 | "2": 3.5, 275 | "3": 2.4, 276 | "4": 3.2, 277 | "5": "Iris-versicolor", 278 | "6": 1 279 | }, 280 | { 281 | "0": 20, 282 | "1": 5.1, 283 | "2": 3.5, 284 | "3": 1.4, 285 | "4": 0.2, 286 | "5": "Iris-setosa", 287 | "6": 1 288 | }, 289 | { 290 | "0": 21, 291 | "1": 5.1, 292 | "2": 3.5, 293 | "3": 1.4, 294 | "4": 0.2, 295 | "5": "Iris-setosa", 296 | "6": 1 297 | }, 298 | { 299 | "0": 22, 300 | "1": 5.1, 301 | "2": 3.5, 302 | "3": 1.4, 303 | "4": 0.2, 304 | "5": "Iris-setosa", 305 | "6": 1 306 | }, 307 | { 308 | "0": 23, 309 | "1": 5.1, 310 | "2": 3.5, 311 | "3": 1.4, 312 | "4": 0.2, 313 | "5": "Iris-setosa", 314 | "6": 1 315 | }, 316 | { 317 | "0": 24, 318 | "1": 5.1, 319 | "2": 3.5, 320 | "3": 1.4, 321 | "4": 0.2, 322 | "5": "Iris-setosa", 323 | "6": 1 324 | }, 325 | { 326 | "0": 25, 327 | "1": 5.1, 328 | "2": 3.5, 329 | "3": 1.4, 330 | "4": 0.2, 331 | "5": "Iris-setosa", 332 | "6": 1 333 | }, 334 | { 335 | "0": 26, 336 | "1": 5.1, 337 | "2": 3.5, 338 | "3": 1.4, 339 | "4": 0.2, 340 | "5": "Iris-setosa", 341 | "6": 1 342 | }, 343 | { 344 | "0": 27, 345 | "1": 5.1, 346 | "2": 3.5, 347 | "3": 1.4, 348 | "4": 0.2, 349 | "5": "Iris-setosa", 350 | "6": 1 351 | }, 352 | { 353 | "0": 28, 354 | "1": 5.1, 355 | "2": 3.5, 356 | "3": 1.4, 357 | "4": 0.2, 358 | "5": "Iris-setosa", 359 | "6": 1 360 | }, 361 | { 362 | "0": 29, 363 | "1": 5.1, 364 | "2": 3.5, 365 | "3": 1.4, 366 | "4": 0.2, 367 | "5": "Iris-setosa", 368 | "6": 1 369 | }, 370 | { 371 | "0": 30, 372 | "1": 5.1, 373 | "2": 3.5, 374 | "3": 7.4, 375 | "4": 0.2, 376 | "5": "Iris-virginica", 377 | "6": 1 378 | }, 379 | { 380 | "0": 31, 381 | "1": 5.1, 382 | "2": 3.5, 383 | "3": 1.4, 384 | "4": 0.2, 385 | "5": "Iris-setosa", 386 | "6": 1 387 | }, 388 | { 389 | "0": 32, 390 | "1": 5.1, 391 | "2": 3.5, 392 | "3": 1.4, 393 | "4": 5.2, 394 | "5": "Iris-versicolor", 395 | "6": 1 396 | } 397 | ], 398 | "columns": [ 399 | { 400 | "title": "id", 401 | "field": "0", 402 | "dataType": { 403 | "type": 4 404 | } 405 | }, 406 | { 407 | "title": "sepal_length", 408 | "field": "1", 409 | "dataType": { 410 | "type": 9 411 | } 412 | }, 413 | { 414 | "title": "sepal_width", 415 | "field": "2", 416 | "dataType": { 417 | "type": 9 418 | } 419 | }, 420 | { 421 | "title": "petal_length", 422 | "field": "3", 423 | "dataType": { 424 | "type": 9 425 | } 426 | }, 427 | { 428 | "title": "petal_width", 429 | "field": "4", 430 | "dataType": { 431 | "type": 9 432 | } 433 | }, 434 | { 435 | "title": "predicted_species", 436 | "field": "5", 437 | "dataType": { 438 | "type": 17 439 | } 440 | }, 441 | { 442 | "title": "prediction_score", 443 | "field": "6", 444 | "dataType": { 445 | "type": 9 446 | } 447 | } 448 | ], 449 | "executionInfo": { 450 | "text": "OK, 32 records retrieved in 0s" 451 | }, 452 | "totalRowCount": 32, 453 | "hasMoreRows": false, 454 | "currentPage": 0, 455 | "index": 0, 456 | "sql": "\nSELECT * FROM iris_predictions;" 457 | } 458 | ] 459 | } 460 | ] 461 | } -------------------------------------------------------------------------------- /Iris-Species-Prediction/iris1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/f3cacf9f9d92d883957375e2c04bb18b4e237491/Iris-Species-Prediction/iris1.jpg -------------------------------------------------------------------------------- /Iris-Species-Prediction/iris2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/f3cacf9f9d92d883957375e2c04bb18b4e237491/Iris-Species-Prediction/iris2.jpg -------------------------------------------------------------------------------- /Iris-Species-Prediction/readme.md: -------------------------------------------------------------------------------- 1 | # Iris Classification 2 | ![iris1](https://github.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/assets/32574833/2f1fcba9-0314-4a7d-9fb0-5362228af848) 3 | 4 | Iris Classification is a machine learning project that utilizes the K-Nearest Neighbors (KNN) model to predict the species of Iris flowers based on their sepal and petal characteristics. 5 | 6 | ## Dataset 7 | 8 | The dataset contains 151 entries, each with the following features: 9 | 10 | - **Sepal Length**: Length of the iris flower's sepal. 11 | - **Sepal Width**: Width of the iris flower's sepal. 12 | - **Petal Length**: Length of the iris flower's petal. 13 | - **Petal Width**: Width of the iris flower's petal. 14 | - **Species**: Target variable representing the species of the iris flower (Iris-setosa, Iris-versicolor, Iris-virginica). 15 | 16 | ## Project Overview 17 | 18 | The project involves using the K-Nearest Neighbors (KNN) algorithm to classify iris flowers into three species based on their sepal and petal characteristics. The model is trained on the provided dataset and can make predictions for new data. 19 | 20 | ## File Structure 21 | 22 | The repository is organized as follows: 23 | 24 | - **`Iris_Dataset.csv`**: The dataset file containing iris flower characteristics. 25 | - **`Iris_Classification.ipynb`**: Jupyter Notebook containing the Python code for data exploration, model training, and predictions. 26 | - **`images/`**: Folder containing images used in the project, such as visualizations or plots. 27 | - **`database_setup.sql`**: SQL file with the database setup for storing input values. 28 | - **`README.md`**: This file, providing an overview of the project, dataset, and file structure. 29 | 30 | ## Getting Started 31 | 32 | 1. **Clone the Repository:** 33 | ```bash 34 | git clone https://github.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/iris-classification.git 35 | cd iris-classification 36 | ``` 37 | 38 | 2. **Install Dependencies:** 39 | ```bash 40 | pip install -r requirements.txt 41 | ``` 42 | 43 | 3. **Run the Jupyter Notebook:** 44 | Open and run the `Iris_Classification.ipynb` notebook to explore the data, train the model, and make predictions. 45 | 46 | ## Usage 47 | 48 | The project demonstrates how to use the K-Nearest Neighbors (KNN) algorithm for iris flower species classification. The Jupyter Notebook provides step-by-step guidance on data preprocessing, model training, and prediction. 49 | 50 | ## Database Setup 51 | 52 | The `database_setup.sql` file contains the SQL code for setting up a database to store input values. Run this script if you want to store input values in a database. 53 | 54 | ```bash 55 | mysql -u root -p house < database_setup.sql 56 | ``` 57 | 58 | ## Demo 59 | 60 | For a quick demonstration, please refer to the `demo/README.md` file. 61 | 62 | ## Contact 63 | 64 | For inquiries, suggestions, or collaboration opportunities, feel free to reach out: 65 | 66 | **Hiran Joseph** 67 | - Email: [Hiran](hiranvjoseph@gmail.com) 68 | - LinkedIn: [Hiran Joseph](https://www.linkedin.com/in/hiranjoe/) 69 | - Kaggle: [@Hiran Joe ](https://www.kaggle.com/hiranjoseph) 70 | 71 | 72 | We appreciate your interest and look forward to hearing from you! 73 | 74 | -------------------------------------------------------------------------------- /Outlier_Treatment/readme.md: -------------------------------------------------------------------------------- 1 | # Outlier Treatment in Data Analysis on Car Crashes Data 2 | 3 | ![Car_Crashes](https://miro.medium.com/v2/resize:fit:1100/format:webp/1*0MPDTLn8KoLApoFvI0P2vQ.png) 4 | 5 | ## Introduction 6 | This project focuses on the detection and treatment of outliers in a dataset related to car crashes. The dataset contains various columns, including: 7 | 8 | - `total`: Total number of car crashes. 9 | - `speeding`: Number of car crashes attributed to speeding. 10 | - `alcohol`: Number of car crashes related to alcohol consumption. 11 | - `not_distracted`: Number of car crashes where distractions were not involved. 12 | - `no_previous`: Number of car crashes with no previous history of accidents. 13 | - `ins_premium`: Insurance premium total. 14 | - `ins_losses`: Insurance losses total. 15 | - `abbrev`: State abbreviations. 16 | 17 | The primary goal of this project is to identify and handle outliers in this dataset using the boxplot method, which is a powerful visualization technique for detecting data points that deviate significantly from the majority. 18 | 19 | ## Outlier Detection 20 | Outliers are extreme data points that deviate from the overall pattern of the dataset. In this project, we will use the boxplot method to identify potential outliers for each of the columns mentioned above. The boxplot visually represents the distribution of data and highlights data points that fall outside the "whiskers" of the plot. 21 | 22 | ## Outlier Treatment 23 | Once we have identified potential outliers, we will explore various methods to handle them. Outlier treatment methods may include: 24 | 25 | - **Removal**: Removing the identified outliers from the dataset. 26 | - **Transformation**: Applying mathematical transformations to the data to make the distribution more normal. 27 | - **Imputation**: Replacing outliers with more reasonable values. 28 | - **Winsorization**: Capping the extreme values to a predefined percentile. 29 | 30 | ## Getting Started 31 | Follow these steps to get started with this project: 32 | 33 | 1. Clone the repository to your local machine: 34 | 35 | ``` 36 | git clone https://github.com/hiranvjoseph/outlier-treatment-car-crashes.git 37 | ``` 38 | 39 | 2. Install the required dependencies. You can use a virtual environment for this: 40 | 41 | ``` 42 | pip install -r requirements.txt 43 | ``` 44 | 45 | 3. Run the Jupyter notebooks or Python scripts to detect and treat outliers in the car crashes dataset. 46 | 47 | 4. Analyze the results and evaluate the impact of outlier treatment on your data analysis. 48 | 49 | ## Data Source 50 | The dataset used in this project can be found at [insert_dataset_link_here]. 51 | 52 | ## License 53 | This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details. 54 | 55 | ## Contributing 56 | If you'd like to contribute to this project, please open an issue or submit a pull request. We welcome contributions and improvements. 57 | 58 | ## Contact 59 | For any questions or feedback, please contact [Hiran Joseph] at [hiranvjoseph@gmail.com]. 60 | 61 | Thank you for exploring the techniques and best practices for handling outliers in data analysis on car crashes data! 62 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine Learning Projects Repository 2 | ![_30d27dd9-f4d9-4d90-8675-b5d2dca5fe12](https://github.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/assets/32574833/d59040d4-0598-4785-b4f2-ae8a13e32d87) 3 | 4 | Welcome to the Machine Learning Projects Repository! This repository hosts a collection of predictive modeling projects, each focusing on different aspects of data analysis and machine learning. These projects cover a wide range of topics, including linear regression, multiple regression, outlier treatment, and more. 5 | 6 | ## Table of Contents 7 | - [Project Descriptions](#project-descriptions) 8 | - [Getting Started](#getting-started) 9 | - [Folder Structure](#folder-structure) 10 | - [Data Sources](#data-sources) 11 | - [Results and Insights](#results-and-insights) 12 | - [License](#license) 13 | - [Contributing](#contributing) 14 | - [Contact](#contact) 15 | 16 | ## Project Descriptions 17 | Explore our machine learning projects to gain insights and hands-on experience in different areas of predictive modeling: 18 | 19 | 1. **Linear Regression**: Learn about simple linear regression techniques where we model the relationship between a single input feature and the target variable. 20 | 21 | 2. **Multiple Regression**: Dive deeper into multiple regression, where we predict a target variable using multiple input features. 22 | 23 | 3. **Outlier Treatment in Data Analysis**: Discover the methods and techniques used to identify and manage outliers in your datasets, ensuring data quality and model accuracy. 24 | 25 | [Include brief descriptions and links to each project in your repository.] 26 | 27 | ## Getting Started 28 | To get started with any of the projects in this repository, follow these general steps: 29 | 30 | 1. Clone the repository to your local machine: 31 | 32 | 33 | git clone https://github.com/hiranvjoseph/Machine-Learning-Prediction-Projects-Repository/tree/main 34 | 35 | 2. Navigate to the specific project folder of your choice. 36 | 37 | 3. Refer to the project's README and Jupyter notebooks to understand the problem, dataset, and the steps taken in the analysis. 38 | 39 | 4. Run the Jupyter notebooks or Python scripts to explore, model, and evaluate the data. 40 | 41 | ## Folder Structure 42 | The repository is organized into project-specific folders, making it easy to find and explore each project. The folder structure for each project typically includes: 43 | 44 | - `data/`: Contains the dataset(s) used for the analysis. 45 | - `notebooks/`: Jupyter notebooks with code, explanations, and results. 46 | - `results/`: Results of the program. 47 | 48 | ## Data Sources 49 | Where available, we include links to the data sources used in the projects. Make sure to check the project-specific README for details. 50 | 51 | ## Results and Insights 52 | Each project provides insights, results, and the performance of the machine learning models used. We encourage you to analyze and interpret the findings to gain a deeper understanding of the topics covered. 53 | 54 | ## License 55 | This repository is open-source and available under the MIT License. See the [LICENSE](LICENSE) file for more details. 56 | 57 | ## Contributing 58 | We welcome contributions and improvements. If you have ideas for new projects, enhancements to existing projects, or general improvements to the repository, please feel free to open an issue or submit a pull request. 59 | 60 | ## Contact 61 | - **Hiran Joseph** 62 | - **Email**: [hiranvjoseph@gmail.com](mailto:hiranvjoseph@gmail.com) 63 | 64 | We hope you find these machine learning projects informative and instructive. Enjoy exploring the world of predictive modeling and data analysis! 65 | Explore, learn, and experiment with various machine learning prediction projects to gain valuable insights into data analysis and predictive modeling. Happy coding! 66 | --------------------------------------------------------------------------------