├── README.md ├── LICENSE ├── .gitignore ├── data └── diabetes.csv └── models ├── naive_bayes.ipynb ├── logistic_regression.ipynb └── gradient_boosting_machines.ipynb /README.md: -------------------------------------------------------------------------------- 1 | # diabetes-prediction-ml 2 | desc 3 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Cem Kahveci 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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!.vscode/settings.json 382 | !.vscode/tasks.json 383 | !.vscode/launch.json 384 | !.vscode/extensions.json 385 | *.code-workspace 386 | 387 | # Local History for Visual Studio Code 388 | .history/ 389 | 390 | # Windows Installer files from build outputs 391 | *.cab 392 | *.msi 393 | *.msix 394 | *.msm 395 | *.msp 396 | 397 | # JetBrains Rider 398 | *.sln.iml 399 | -------------------------------------------------------------------------------- /data/diabetes.csv: -------------------------------------------------------------------------------- 1 | Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome 2 | 6,148,72,35,0,33.6,0.627,50,1 3 | 1,85,66,29,0,26.6,0.351,31,0 4 | 8,183,64,0,0,23.3,0.672,32,1 5 | 1,89,66,23,94,28.1,0.167,21,0 6 | 0,137,40,35,168,43.1,2.288,33,1 7 | 5,116,74,0,0,25.6,0.201,30,0 8 | 3,78,50,32,88,31,0.248,26,1 9 | 10,115,0,0,0,35.3,0.134,29,0 10 | 2,197,70,45,543,30.5,0.158,53,1 11 | 8,125,96,0,0,0,0.232,54,1 12 | 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4,114,65,0,0,21.9,0.432,37,0 154 | 9,156,86,28,155,34.3,1.189,42,1 155 | 1,153,82,42,485,40.6,0.687,23,0 156 | 8,188,78,0,0,47.9,0.137,43,1 157 | 7,152,88,44,0,50,0.337,36,1 158 | 2,99,52,15,94,24.6,0.637,21,0 159 | 1,109,56,21,135,25.2,0.833,23,0 160 | 2,88,74,19,53,29,0.229,22,0 161 | 17,163,72,41,114,40.9,0.817,47,1 162 | 4,151,90,38,0,29.7,0.294,36,0 163 | 7,102,74,40,105,37.2,0.204,45,0 164 | 0,114,80,34,285,44.2,0.167,27,0 165 | 2,100,64,23,0,29.7,0.368,21,0 166 | 0,131,88,0,0,31.6,0.743,32,1 167 | 6,104,74,18,156,29.9,0.722,41,1 168 | 3,148,66,25,0,32.5,0.256,22,0 169 | 4,120,68,0,0,29.6,0.709,34,0 170 | 4,110,66,0,0,31.9,0.471,29,0 171 | 3,111,90,12,78,28.4,0.495,29,0 172 | 6,102,82,0,0,30.8,0.18,36,1 173 | 6,134,70,23,130,35.4,0.542,29,1 174 | 2,87,0,23,0,28.9,0.773,25,0 175 | 1,79,60,42,48,43.5,0.678,23,0 176 | 2,75,64,24,55,29.7,0.37,33,0 177 | 8,179,72,42,130,32.7,0.719,36,1 178 | 6,85,78,0,0,31.2,0.382,42,0 179 | 0,129,110,46,130,67.1,0.319,26,1 180 | 5,143,78,0,0,45,0.19,47,0 181 | 5,130,82,0,0,39.1,0.956,37,1 182 | 6,87,80,0,0,23.2,0.084,32,0 183 | 0,119,64,18,92,34.9,0.725,23,0 184 | 1,0,74,20,23,27.7,0.299,21,0 185 | 5,73,60,0,0,26.8,0.268,27,0 186 | 4,141,74,0,0,27.6,0.244,40,0 187 | 7,194,68,28,0,35.9,0.745,41,1 188 | 8,181,68,36,495,30.1,0.615,60,1 189 | 1,128,98,41,58,32,1.321,33,1 190 | 8,109,76,39,114,27.9,0.64,31,1 191 | 5,139,80,35,160,31.6,0.361,25,1 192 | 3,111,62,0,0,22.6,0.142,21,0 193 | 9,123,70,44,94,33.1,0.374,40,0 194 | 7,159,66,0,0,30.4,0.383,36,1 195 | 11,135,0,0,0,52.3,0.578,40,1 196 | 8,85,55,20,0,24.4,0.136,42,0 197 | 5,158,84,41,210,39.4,0.395,29,1 198 | 1,105,58,0,0,24.3,0.187,21,0 199 | 3,107,62,13,48,22.9,0.678,23,1 200 | 4,109,64,44,99,34.8,0.905,26,1 201 | 4,148,60,27,318,30.9,0.15,29,1 202 | 0,113,80,16,0,31,0.874,21,0 203 | 1,138,82,0,0,40.1,0.236,28,0 204 | 0,108,68,20,0,27.3,0.787,32,0 205 | 2,99,70,16,44,20.4,0.235,27,0 206 | 6,103,72,32,190,37.7,0.324,55,0 207 | 5,111,72,28,0,23.9,0.407,27,0 208 | 8,196,76,29,280,37.5,0.605,57,1 209 | 5,162,104,0,0,37.7,0.151,52,1 210 | 1,96,64,27,87,33.2,0.289,21,0 211 | 7,184,84,33,0,35.5,0.355,41,1 212 | 2,81,60,22,0,27.7,0.29,25,0 213 | 0,147,85,54,0,42.8,0.375,24,0 214 | 7,179,95,31,0,34.2,0.164,60,0 215 | 0,140,65,26,130,42.6,0.431,24,1 216 | 9,112,82,32,175,34.2,0.26,36,1 217 | 12,151,70,40,271,41.8,0.742,38,1 218 | 5,109,62,41,129,35.8,0.514,25,1 219 | 6,125,68,30,120,30,0.464,32,0 220 | 5,85,74,22,0,29,1.224,32,1 221 | 5,112,66,0,0,37.8,0.261,41,1 222 | 0,177,60,29,478,34.6,1.072,21,1 223 | 2,158,90,0,0,31.6,0.805,66,1 224 | 7,119,0,0,0,25.2,0.209,37,0 225 | 7,142,60,33,190,28.8,0.687,61,0 226 | 1,100,66,15,56,23.6,0.666,26,0 227 | 1,87,78,27,32,34.6,0.101,22,0 228 | 0,101,76,0,0,35.7,0.198,26,0 229 | 3,162,52,38,0,37.2,0.652,24,1 230 | 4,197,70,39,744,36.7,2.329,31,0 231 | 0,117,80,31,53,45.2,0.089,24,0 232 | 4,142,86,0,0,44,0.645,22,1 233 | 6,134,80,37,370,46.2,0.238,46,1 234 | 1,79,80,25,37,25.4,0.583,22,0 235 | 4,122,68,0,0,35,0.394,29,0 236 | 3,74,68,28,45,29.7,0.293,23,0 237 | 4,171,72,0,0,43.6,0.479,26,1 238 | 7,181,84,21,192,35.9,0.586,51,1 239 | 0,179,90,27,0,44.1,0.686,23,1 240 | 9,164,84,21,0,30.8,0.831,32,1 241 | 0,104,76,0,0,18.4,0.582,27,0 242 | 1,91,64,24,0,29.2,0.192,21,0 243 | 4,91,70,32,88,33.1,0.446,22,0 244 | 3,139,54,0,0,25.6,0.402,22,1 245 | 6,119,50,22,176,27.1,1.318,33,1 246 | 2,146,76,35,194,38.2,0.329,29,0 247 | 9,184,85,15,0,30,1.213,49,1 248 | 10,122,68,0,0,31.2,0.258,41,0 249 | 0,165,90,33,680,52.3,0.427,23,0 250 | 9,124,70,33,402,35.4,0.282,34,0 251 | 1,111,86,19,0,30.1,0.143,23,0 252 | 9,106,52,0,0,31.2,0.38,42,0 253 | 2,129,84,0,0,28,0.284,27,0 254 | 2,90,80,14,55,24.4,0.249,24,0 255 | 0,86,68,32,0,35.8,0.238,25,0 256 | 12,92,62,7,258,27.6,0.926,44,1 257 | 1,113,64,35,0,33.6,0.543,21,1 258 | 3,111,56,39,0,30.1,0.557,30,0 259 | 2,114,68,22,0,28.7,0.092,25,0 260 | 1,193,50,16,375,25.9,0.655,24,0 261 | 11,155,76,28,150,33.3,1.353,51,1 262 | 3,191,68,15,130,30.9,0.299,34,0 263 | 3,141,0,0,0,30,0.761,27,1 264 | 4,95,70,32,0,32.1,0.612,24,0 265 | 3,142,80,15,0,32.4,0.2,63,0 266 | 4,123,62,0,0,32,0.226,35,1 267 | 5,96,74,18,67,33.6,0.997,43,0 268 | 0,138,0,0,0,36.3,0.933,25,1 269 | 2,128,64,42,0,40,1.101,24,0 270 | 0,102,52,0,0,25.1,0.078,21,0 271 | 2,146,0,0,0,27.5,0.24,28,1 272 | 10,101,86,37,0,45.6,1.136,38,1 273 | 2,108,62,32,56,25.2,0.128,21,0 274 | 3,122,78,0,0,23,0.254,40,0 275 | 1,71,78,50,45,33.2,0.422,21,0 276 | 13,106,70,0,0,34.2,0.251,52,0 277 | 2,100,70,52,57,40.5,0.677,25,0 278 | 7,106,60,24,0,26.5,0.296,29,1 279 | 0,104,64,23,116,27.8,0.454,23,0 280 | 5,114,74,0,0,24.9,0.744,57,0 281 | 2,108,62,10,278,25.3,0.881,22,0 282 | 0,146,70,0,0,37.9,0.334,28,1 283 | 10,129,76,28,122,35.9,0.28,39,0 284 | 7,133,88,15,155,32.4,0.262,37,0 285 | 7,161,86,0,0,30.4,0.165,47,1 286 | 2,108,80,0,0,27,0.259,52,1 287 | 7,136,74,26,135,26,0.647,51,0 288 | 5,155,84,44,545,38.7,0.619,34,0 289 | 1,119,86,39,220,45.6,0.808,29,1 290 | 4,96,56,17,49,20.8,0.34,26,0 291 | 5,108,72,43,75,36.1,0.263,33,0 292 | 0,78,88,29,40,36.9,0.434,21,0 293 | 0,107,62,30,74,36.6,0.757,25,1 294 | 2,128,78,37,182,43.3,1.224,31,1 295 | 1,128,48,45,194,40.5,0.613,24,1 296 | 0,161,50,0,0,21.9,0.254,65,0 297 | 6,151,62,31,120,35.5,0.692,28,0 298 | 2,146,70,38,360,28,0.337,29,1 299 | 0,126,84,29,215,30.7,0.52,24,0 300 | 14,100,78,25,184,36.6,0.412,46,1 301 | 8,112,72,0,0,23.6,0.84,58,0 302 | 0,167,0,0,0,32.3,0.839,30,1 303 | 2,144,58,33,135,31.6,0.422,25,1 304 | 5,77,82,41,42,35.8,0.156,35,0 305 | 5,115,98,0,0,52.9,0.209,28,1 306 | 3,150,76,0,0,21,0.207,37,0 307 | 2,120,76,37,105,39.7,0.215,29,0 308 | 10,161,68,23,132,25.5,0.326,47,1 309 | 0,137,68,14,148,24.8,0.143,21,0 310 | 0,128,68,19,180,30.5,1.391,25,1 311 | 2,124,68,28,205,32.9,0.875,30,1 312 | 6,80,66,30,0,26.2,0.313,41,0 313 | 0,106,70,37,148,39.4,0.605,22,0 314 | 2,155,74,17,96,26.6,0.433,27,1 315 | 3,113,50,10,85,29.5,0.626,25,0 316 | 7,109,80,31,0,35.9,1.127,43,1 317 | 2,112,68,22,94,34.1,0.315,26,0 318 | 3,99,80,11,64,19.3,0.284,30,0 319 | 3,182,74,0,0,30.5,0.345,29,1 320 | 3,115,66,39,140,38.1,0.15,28,0 321 | 6,194,78,0,0,23.5,0.129,59,1 322 | 4,129,60,12,231,27.5,0.527,31,0 323 | 3,112,74,30,0,31.6,0.197,25,1 324 | 0,124,70,20,0,27.4,0.254,36,1 325 | 13,152,90,33,29,26.8,0.731,43,1 326 | 2,112,75,32,0,35.7,0.148,21,0 327 | 1,157,72,21,168,25.6,0.123,24,0 328 | 1,122,64,32,156,35.1,0.692,30,1 329 | 10,179,70,0,0,35.1,0.2,37,0 330 | 2,102,86,36,120,45.5,0.127,23,1 331 | 6,105,70,32,68,30.8,0.122,37,0 332 | 8,118,72,19,0,23.1,1.476,46,0 333 | 2,87,58,16,52,32.7,0.166,25,0 334 | 1,180,0,0,0,43.3,0.282,41,1 335 | 12,106,80,0,0,23.6,0.137,44,0 336 | 1,95,60,18,58,23.9,0.26,22,0 337 | 0,165,76,43,255,47.9,0.259,26,0 338 | 0,117,0,0,0,33.8,0.932,44,0 339 | 5,115,76,0,0,31.2,0.343,44,1 340 | 9,152,78,34,171,34.2,0.893,33,1 341 | 7,178,84,0,0,39.9,0.331,41,1 342 | 1,130,70,13,105,25.9,0.472,22,0 343 | 1,95,74,21,73,25.9,0.673,36,0 344 | 1,0,68,35,0,32,0.389,22,0 345 | 5,122,86,0,0,34.7,0.29,33,0 346 | 8,95,72,0,0,36.8,0.485,57,0 347 | 8,126,88,36,108,38.5,0.349,49,0 348 | 1,139,46,19,83,28.7,0.654,22,0 349 | 3,116,0,0,0,23.5,0.187,23,0 350 | 3,99,62,19,74,21.8,0.279,26,0 351 | 5,0,80,32,0,41,0.346,37,1 352 | 4,92,80,0,0,42.2,0.237,29,0 353 | 4,137,84,0,0,31.2,0.252,30,0 354 | 3,61,82,28,0,34.4,0.243,46,0 355 | 1,90,62,12,43,27.2,0.58,24,0 356 | 3,90,78,0,0,42.7,0.559,21,0 357 | 9,165,88,0,0,30.4,0.302,49,1 358 | 1,125,50,40,167,33.3,0.962,28,1 359 | 13,129,0,30,0,39.9,0.569,44,1 360 | 12,88,74,40,54,35.3,0.378,48,0 361 | 1,196,76,36,249,36.5,0.875,29,1 362 | 5,189,64,33,325,31.2,0.583,29,1 363 | 5,158,70,0,0,29.8,0.207,63,0 364 | 5,103,108,37,0,39.2,0.305,65,0 365 | 4,146,78,0,0,38.5,0.52,67,1 366 | 4,147,74,25,293,34.9,0.385,30,0 367 | 5,99,54,28,83,34,0.499,30,0 368 | 6,124,72,0,0,27.6,0.368,29,1 369 | 0,101,64,17,0,21,0.252,21,0 370 | 3,81,86,16,66,27.5,0.306,22,0 371 | 1,133,102,28,140,32.8,0.234,45,1 372 | 3,173,82,48,465,38.4,2.137,25,1 373 | 0,118,64,23,89,0,1.731,21,0 374 | 0,84,64,22,66,35.8,0.545,21,0 375 | 2,105,58,40,94,34.9,0.225,25,0 376 | 2,122,52,43,158,36.2,0.816,28,0 377 | 12,140,82,43,325,39.2,0.528,58,1 378 | 0,98,82,15,84,25.2,0.299,22,0 379 | 1,87,60,37,75,37.2,0.509,22,0 380 | 4,156,75,0,0,48.3,0.238,32,1 381 | 0,93,100,39,72,43.4,1.021,35,0 382 | 1,107,72,30,82,30.8,0.821,24,0 383 | 0,105,68,22,0,20,0.236,22,0 384 | 1,109,60,8,182,25.4,0.947,21,0 385 | 1,90,62,18,59,25.1,1.268,25,0 386 | 1,125,70,24,110,24.3,0.221,25,0 387 | 1,119,54,13,50,22.3,0.205,24,0 388 | 5,116,74,29,0,32.3,0.66,35,1 389 | 8,105,100,36,0,43.3,0.239,45,1 390 | 5,144,82,26,285,32,0.452,58,1 391 | 3,100,68,23,81,31.6,0.949,28,0 392 | 1,100,66,29,196,32,0.444,42,0 393 | 5,166,76,0,0,45.7,0.34,27,1 394 | 1,131,64,14,415,23.7,0.389,21,0 395 | 4,116,72,12,87,22.1,0.463,37,0 396 | 4,158,78,0,0,32.9,0.803,31,1 397 | 2,127,58,24,275,27.7,1.6,25,0 398 | 3,96,56,34,115,24.7,0.944,39,0 399 | 0,131,66,40,0,34.3,0.196,22,1 400 | 3,82,70,0,0,21.1,0.389,25,0 401 | 3,193,70,31,0,34.9,0.241,25,1 402 | 4,95,64,0,0,32,0.161,31,1 403 | 6,137,61,0,0,24.2,0.151,55,0 404 | 5,136,84,41,88,35,0.286,35,1 405 | 9,72,78,25,0,31.6,0.28,38,0 406 | 5,168,64,0,0,32.9,0.135,41,1 407 | 2,123,48,32,165,42.1,0.52,26,0 408 | 4,115,72,0,0,28.9,0.376,46,1 409 | 0,101,62,0,0,21.9,0.336,25,0 410 | 8,197,74,0,0,25.9,1.191,39,1 411 | 1,172,68,49,579,42.4,0.702,28,1 412 | 6,102,90,39,0,35.7,0.674,28,0 413 | 1,112,72,30,176,34.4,0.528,25,0 414 | 1,143,84,23,310,42.4,1.076,22,0 415 | 1,143,74,22,61,26.2,0.256,21,0 416 | 0,138,60,35,167,34.6,0.534,21,1 417 | 3,173,84,33,474,35.7,0.258,22,1 418 | 1,97,68,21,0,27.2,1.095,22,0 419 | 4,144,82,32,0,38.5,0.554,37,1 420 | 1,83,68,0,0,18.2,0.624,27,0 421 | 3,129,64,29,115,26.4,0.219,28,1 422 | 1,119,88,41,170,45.3,0.507,26,0 423 | 2,94,68,18,76,26,0.561,21,0 424 | 0,102,64,46,78,40.6,0.496,21,0 425 | 2,115,64,22,0,30.8,0.421,21,0 426 | 8,151,78,32,210,42.9,0.516,36,1 427 | 4,184,78,39,277,37,0.264,31,1 428 | 0,94,0,0,0,0,0.256,25,0 429 | 1,181,64,30,180,34.1,0.328,38,1 430 | 0,135,94,46,145,40.6,0.284,26,0 431 | 1,95,82,25,180,35,0.233,43,1 432 | 2,99,0,0,0,22.2,0.108,23,0 433 | 3,89,74,16,85,30.4,0.551,38,0 434 | 1,80,74,11,60,30,0.527,22,0 435 | 2,139,75,0,0,25.6,0.167,29,0 436 | 1,90,68,8,0,24.5,1.138,36,0 437 | 0,141,0,0,0,42.4,0.205,29,1 438 | 12,140,85,33,0,37.4,0.244,41,0 439 | 5,147,75,0,0,29.9,0.434,28,0 440 | 1,97,70,15,0,18.2,0.147,21,0 441 | 6,107,88,0,0,36.8,0.727,31,0 442 | 0,189,104,25,0,34.3,0.435,41,1 443 | 2,83,66,23,50,32.2,0.497,22,0 444 | 4,117,64,27,120,33.2,0.23,24,0 445 | 8,108,70,0,0,30.5,0.955,33,1 446 | 4,117,62,12,0,29.7,0.38,30,1 447 | 0,180,78,63,14,59.4,2.42,25,1 448 | 1,100,72,12,70,25.3,0.658,28,0 449 | 0,95,80,45,92,36.5,0.33,26,0 450 | 0,104,64,37,64,33.6,0.51,22,1 451 | 0,120,74,18,63,30.5,0.285,26,0 452 | 1,82,64,13,95,21.2,0.415,23,0 453 | 2,134,70,0,0,28.9,0.542,23,1 454 | 0,91,68,32,210,39.9,0.381,25,0 455 | 2,119,0,0,0,19.6,0.832,72,0 456 | 2,100,54,28,105,37.8,0.498,24,0 457 | 14,175,62,30,0,33.6,0.212,38,1 458 | 1,135,54,0,0,26.7,0.687,62,0 459 | 5,86,68,28,71,30.2,0.364,24,0 460 | 10,148,84,48,237,37.6,1.001,51,1 461 | 9,134,74,33,60,25.9,0.46,81,0 462 | 9,120,72,22,56,20.8,0.733,48,0 463 | 1,71,62,0,0,21.8,0.416,26,0 464 | 8,74,70,40,49,35.3,0.705,39,0 465 | 5,88,78,30,0,27.6,0.258,37,0 466 | 10,115,98,0,0,24,1.022,34,0 467 | 0,124,56,13,105,21.8,0.452,21,0 468 | 0,74,52,10,36,27.8,0.269,22,0 469 | 0,97,64,36,100,36.8,0.6,25,0 470 | 8,120,0,0,0,30,0.183,38,1 471 | 6,154,78,41,140,46.1,0.571,27,0 472 | 1,144,82,40,0,41.3,0.607,28,0 473 | 0,137,70,38,0,33.2,0.17,22,0 474 | 0,119,66,27,0,38.8,0.259,22,0 475 | 7,136,90,0,0,29.9,0.21,50,0 476 | 4,114,64,0,0,28.9,0.126,24,0 477 | 0,137,84,27,0,27.3,0.231,59,0 478 | 2,105,80,45,191,33.7,0.711,29,1 479 | 7,114,76,17,110,23.8,0.466,31,0 480 | 8,126,74,38,75,25.9,0.162,39,0 481 | 4,132,86,31,0,28,0.419,63,0 482 | 3,158,70,30,328,35.5,0.344,35,1 483 | 0,123,88,37,0,35.2,0.197,29,0 484 | 4,85,58,22,49,27.8,0.306,28,0 485 | 0,84,82,31,125,38.2,0.233,23,0 486 | 0,145,0,0,0,44.2,0.63,31,1 487 | 0,135,68,42,250,42.3,0.365,24,1 488 | 1,139,62,41,480,40.7,0.536,21,0 489 | 0,173,78,32,265,46.5,1.159,58,0 490 | 4,99,72,17,0,25.6,0.294,28,0 491 | 8,194,80,0,0,26.1,0.551,67,0 492 | 2,83,65,28,66,36.8,0.629,24,0 493 | 2,89,90,30,0,33.5,0.292,42,0 494 | 4,99,68,38,0,32.8,0.145,33,0 495 | 4,125,70,18,122,28.9,1.144,45,1 496 | 3,80,0,0,0,0,0.174,22,0 497 | 6,166,74,0,0,26.6,0.304,66,0 498 | 5,110,68,0,0,26,0.292,30,0 499 | 2,81,72,15,76,30.1,0.547,25,0 500 | 7,195,70,33,145,25.1,0.163,55,1 501 | 6,154,74,32,193,29.3,0.839,39,0 502 | 2,117,90,19,71,25.2,0.313,21,0 503 | 3,84,72,32,0,37.2,0.267,28,0 504 | 6,0,68,41,0,39,0.727,41,1 505 | 7,94,64,25,79,33.3,0.738,41,0 506 | 3,96,78,39,0,37.3,0.238,40,0 507 | 10,75,82,0,0,33.3,0.263,38,0 508 | 0,180,90,26,90,36.5,0.314,35,1 509 | 1,130,60,23,170,28.6,0.692,21,0 510 | 2,84,50,23,76,30.4,0.968,21,0 511 | 8,120,78,0,0,25,0.409,64,0 512 | 12,84,72,31,0,29.7,0.297,46,1 513 | 0,139,62,17,210,22.1,0.207,21,0 514 | 9,91,68,0,0,24.2,0.2,58,0 515 | 2,91,62,0,0,27.3,0.525,22,0 516 | 3,99,54,19,86,25.6,0.154,24,0 517 | 3,163,70,18,105,31.6,0.268,28,1 518 | 9,145,88,34,165,30.3,0.771,53,1 519 | 7,125,86,0,0,37.6,0.304,51,0 520 | 13,76,60,0,0,32.8,0.18,41,0 521 | 6,129,90,7,326,19.6,0.582,60,0 522 | 2,68,70,32,66,25,0.187,25,0 523 | 3,124,80,33,130,33.2,0.305,26,0 524 | 6,114,0,0,0,0,0.189,26,0 525 | 9,130,70,0,0,34.2,0.652,45,1 526 | 3,125,58,0,0,31.6,0.151,24,0 527 | 3,87,60,18,0,21.8,0.444,21,0 528 | 1,97,64,19,82,18.2,0.299,21,0 529 | 3,116,74,15,105,26.3,0.107,24,0 530 | 0,117,66,31,188,30.8,0.493,22,0 531 | 0,111,65,0,0,24.6,0.66,31,0 532 | 2,122,60,18,106,29.8,0.717,22,0 533 | 0,107,76,0,0,45.3,0.686,24,0 534 | 1,86,66,52,65,41.3,0.917,29,0 535 | 6,91,0,0,0,29.8,0.501,31,0 536 | 1,77,56,30,56,33.3,1.251,24,0 537 | 4,132,0,0,0,32.9,0.302,23,1 538 | 0,105,90,0,0,29.6,0.197,46,0 539 | 0,57,60,0,0,21.7,0.735,67,0 540 | 0,127,80,37,210,36.3,0.804,23,0 541 | 3,129,92,49,155,36.4,0.968,32,1 542 | 8,100,74,40,215,39.4,0.661,43,1 543 | 3,128,72,25,190,32.4,0.549,27,1 544 | 10,90,85,32,0,34.9,0.825,56,1 545 | 4,84,90,23,56,39.5,0.159,25,0 546 | 1,88,78,29,76,32,0.365,29,0 547 | 8,186,90,35,225,34.5,0.423,37,1 548 | 5,187,76,27,207,43.6,1.034,53,1 549 | 4,131,68,21,166,33.1,0.16,28,0 550 | 1,164,82,43,67,32.8,0.341,50,0 551 | 4,189,110,31,0,28.5,0.68,37,0 552 | 1,116,70,28,0,27.4,0.204,21,0 553 | 3,84,68,30,106,31.9,0.591,25,0 554 | 6,114,88,0,0,27.8,0.247,66,0 555 | 1,88,62,24,44,29.9,0.422,23,0 556 | 1,84,64,23,115,36.9,0.471,28,0 557 | 7,124,70,33,215,25.5,0.161,37,0 558 | 1,97,70,40,0,38.1,0.218,30,0 559 | 8,110,76,0,0,27.8,0.237,58,0 560 | 11,103,68,40,0,46.2,0.126,42,0 561 | 11,85,74,0,0,30.1,0.3,35,0 562 | 6,125,76,0,0,33.8,0.121,54,1 563 | 0,198,66,32,274,41.3,0.502,28,1 564 | 1,87,68,34,77,37.6,0.401,24,0 565 | 6,99,60,19,54,26.9,0.497,32,0 566 | 0,91,80,0,0,32.4,0.601,27,0 567 | 2,95,54,14,88,26.1,0.748,22,0 568 | 1,99,72,30,18,38.6,0.412,21,0 569 | 6,92,62,32,126,32,0.085,46,0 570 | 4,154,72,29,126,31.3,0.338,37,0 571 | 0,121,66,30,165,34.3,0.203,33,1 572 | 3,78,70,0,0,32.5,0.27,39,0 573 | 2,130,96,0,0,22.6,0.268,21,0 574 | 3,111,58,31,44,29.5,0.43,22,0 575 | 2,98,60,17,120,34.7,0.198,22,0 576 | 1,143,86,30,330,30.1,0.892,23,0 577 | 1,119,44,47,63,35.5,0.28,25,0 578 | 6,108,44,20,130,24,0.813,35,0 579 | 2,118,80,0,0,42.9,0.693,21,1 580 | 10,133,68,0,0,27,0.245,36,0 581 | 2,197,70,99,0,34.7,0.575,62,1 582 | 0,151,90,46,0,42.1,0.371,21,1 583 | 6,109,60,27,0,25,0.206,27,0 584 | 12,121,78,17,0,26.5,0.259,62,0 585 | 8,100,76,0,0,38.7,0.19,42,0 586 | 8,124,76,24,600,28.7,0.687,52,1 587 | 1,93,56,11,0,22.5,0.417,22,0 588 | 8,143,66,0,0,34.9,0.129,41,1 589 | 6,103,66,0,0,24.3,0.249,29,0 590 | 3,176,86,27,156,33.3,1.154,52,1 591 | 0,73,0,0,0,21.1,0.342,25,0 592 | 11,111,84,40,0,46.8,0.925,45,1 593 | 2,112,78,50,140,39.4,0.175,24,0 594 | 3,132,80,0,0,34.4,0.402,44,1 595 | 2,82,52,22,115,28.5,1.699,25,0 596 | 6,123,72,45,230,33.6,0.733,34,0 597 | 0,188,82,14,185,32,0.682,22,1 598 | 0,67,76,0,0,45.3,0.194,46,0 599 | 1,89,24,19,25,27.8,0.559,21,0 600 | 1,173,74,0,0,36.8,0.088,38,1 601 | 1,109,38,18,120,23.1,0.407,26,0 602 | 1,108,88,19,0,27.1,0.4,24,0 603 | 6,96,0,0,0,23.7,0.19,28,0 604 | 1,124,74,36,0,27.8,0.1,30,0 605 | 7,150,78,29,126,35.2,0.692,54,1 606 | 4,183,0,0,0,28.4,0.212,36,1 607 | 1,124,60,32,0,35.8,0.514,21,0 608 | 1,181,78,42,293,40,1.258,22,1 609 | 1,92,62,25,41,19.5,0.482,25,0 610 | 0,152,82,39,272,41.5,0.27,27,0 611 | 1,111,62,13,182,24,0.138,23,0 612 | 3,106,54,21,158,30.9,0.292,24,0 613 | 3,174,58,22,194,32.9,0.593,36,1 614 | 7,168,88,42,321,38.2,0.787,40,1 615 | 6,105,80,28,0,32.5,0.878,26,0 616 | 11,138,74,26,144,36.1,0.557,50,1 617 | 3,106,72,0,0,25.8,0.207,27,0 618 | 6,117,96,0,0,28.7,0.157,30,0 619 | 2,68,62,13,15,20.1,0.257,23,0 620 | 9,112,82,24,0,28.2,1.282,50,1 621 | 0,119,0,0,0,32.4,0.141,24,1 622 | 2,112,86,42,160,38.4,0.246,28,0 623 | 2,92,76,20,0,24.2,1.698,28,0 624 | 6,183,94,0,0,40.8,1.461,45,0 625 | 0,94,70,27,115,43.5,0.347,21,0 626 | 2,108,64,0,0,30.8,0.158,21,0 627 | 4,90,88,47,54,37.7,0.362,29,0 628 | 0,125,68,0,0,24.7,0.206,21,0 629 | 0,132,78,0,0,32.4,0.393,21,0 630 | 5,128,80,0,0,34.6,0.144,45,0 631 | 4,94,65,22,0,24.7,0.148,21,0 632 | 7,114,64,0,0,27.4,0.732,34,1 633 | 0,102,78,40,90,34.5,0.238,24,0 634 | 2,111,60,0,0,26.2,0.343,23,0 635 | 1,128,82,17,183,27.5,0.115,22,0 636 | 10,92,62,0,0,25.9,0.167,31,0 637 | 13,104,72,0,0,31.2,0.465,38,1 638 | 5,104,74,0,0,28.8,0.153,48,0 639 | 2,94,76,18,66,31.6,0.649,23,0 640 | 7,97,76,32,91,40.9,0.871,32,1 641 | 1,100,74,12,46,19.5,0.149,28,0 642 | 0,102,86,17,105,29.3,0.695,27,0 643 | 4,128,70,0,0,34.3,0.303,24,0 644 | 6,147,80,0,0,29.5,0.178,50,1 645 | 4,90,0,0,0,28,0.61,31,0 646 | 3,103,72,30,152,27.6,0.73,27,0 647 | 2,157,74,35,440,39.4,0.134,30,0 648 | 1,167,74,17,144,23.4,0.447,33,1 649 | 0,179,50,36,159,37.8,0.455,22,1 650 | 11,136,84,35,130,28.3,0.26,42,1 651 | 0,107,60,25,0,26.4,0.133,23,0 652 | 1,91,54,25,100,25.2,0.234,23,0 653 | 1,117,60,23,106,33.8,0.466,27,0 654 | 5,123,74,40,77,34.1,0.269,28,0 655 | 2,120,54,0,0,26.8,0.455,27,0 656 | 1,106,70,28,135,34.2,0.142,22,0 657 | 2,155,52,27,540,38.7,0.24,25,1 658 | 2,101,58,35,90,21.8,0.155,22,0 659 | 1,120,80,48,200,38.9,1.162,41,0 660 | 11,127,106,0,0,39,0.19,51,0 661 | 3,80,82,31,70,34.2,1.292,27,1 662 | 10,162,84,0,0,27.7,0.182,54,0 663 | 1,199,76,43,0,42.9,1.394,22,1 664 | 8,167,106,46,231,37.6,0.165,43,1 665 | 9,145,80,46,130,37.9,0.637,40,1 666 | 6,115,60,39,0,33.7,0.245,40,1 667 | 1,112,80,45,132,34.8,0.217,24,0 668 | 4,145,82,18,0,32.5,0.235,70,1 669 | 10,111,70,27,0,27.5,0.141,40,1 670 | 6,98,58,33,190,34,0.43,43,0 671 | 9,154,78,30,100,30.9,0.164,45,0 672 | 6,165,68,26,168,33.6,0.631,49,0 673 | 1,99,58,10,0,25.4,0.551,21,0 674 | 10,68,106,23,49,35.5,0.285,47,0 675 | 3,123,100,35,240,57.3,0.88,22,0 676 | 8,91,82,0,0,35.6,0.587,68,0 677 | 6,195,70,0,0,30.9,0.328,31,1 678 | 9,156,86,0,0,24.8,0.23,53,1 679 | 0,93,60,0,0,35.3,0.263,25,0 680 | 3,121,52,0,0,36,0.127,25,1 681 | 2,101,58,17,265,24.2,0.614,23,0 682 | 2,56,56,28,45,24.2,0.332,22,0 683 | 0,162,76,36,0,49.6,0.364,26,1 684 | 0,95,64,39,105,44.6,0.366,22,0 685 | 4,125,80,0,0,32.3,0.536,27,1 686 | 5,136,82,0,0,0,0.64,69,0 687 | 2,129,74,26,205,33.2,0.591,25,0 688 | 3,130,64,0,0,23.1,0.314,22,0 689 | 1,107,50,19,0,28.3,0.181,29,0 690 | 1,140,74,26,180,24.1,0.828,23,0 691 | 1,144,82,46,180,46.1,0.335,46,1 692 | 8,107,80,0,0,24.6,0.856,34,0 693 | 13,158,114,0,0,42.3,0.257,44,1 694 | 2,121,70,32,95,39.1,0.886,23,0 695 | 7,129,68,49,125,38.5,0.439,43,1 696 | 2,90,60,0,0,23.5,0.191,25,0 697 | 7,142,90,24,480,30.4,0.128,43,1 698 | 3,169,74,19,125,29.9,0.268,31,1 699 | 0,99,0,0,0,25,0.253,22,0 700 | 4,127,88,11,155,34.5,0.598,28,0 701 | 4,118,70,0,0,44.5,0.904,26,0 702 | 2,122,76,27,200,35.9,0.483,26,0 703 | 6,125,78,31,0,27.6,0.565,49,1 704 | 1,168,88,29,0,35,0.905,52,1 705 | 2,129,0,0,0,38.5,0.304,41,0 706 | 4,110,76,20,100,28.4,0.118,27,0 707 | 6,80,80,36,0,39.8,0.177,28,0 708 | 10,115,0,0,0,0,0.261,30,1 709 | 2,127,46,21,335,34.4,0.176,22,0 710 | 9,164,78,0,0,32.8,0.148,45,1 711 | 2,93,64,32,160,38,0.674,23,1 712 | 3,158,64,13,387,31.2,0.295,24,0 713 | 5,126,78,27,22,29.6,0.439,40,0 714 | 10,129,62,36,0,41.2,0.441,38,1 715 | 0,134,58,20,291,26.4,0.352,21,0 716 | 3,102,74,0,0,29.5,0.121,32,0 717 | 7,187,50,33,392,33.9,0.826,34,1 718 | 3,173,78,39,185,33.8,0.97,31,1 719 | 10,94,72,18,0,23.1,0.595,56,0 720 | 1,108,60,46,178,35.5,0.415,24,0 721 | 5,97,76,27,0,35.6,0.378,52,1 722 | 4,83,86,19,0,29.3,0.317,34,0 723 | 1,114,66,36,200,38.1,0.289,21,0 724 | 1,149,68,29,127,29.3,0.349,42,1 725 | 5,117,86,30,105,39.1,0.251,42,0 726 | 1,111,94,0,0,32.8,0.265,45,0 727 | 4,112,78,40,0,39.4,0.236,38,0 728 | 1,116,78,29,180,36.1,0.496,25,0 729 | 0,141,84,26,0,32.4,0.433,22,0 730 | 2,175,88,0,0,22.9,0.326,22,0 731 | 2,92,52,0,0,30.1,0.141,22,0 732 | 3,130,78,23,79,28.4,0.323,34,1 733 | 8,120,86,0,0,28.4,0.259,22,1 734 | 2,174,88,37,120,44.5,0.646,24,1 735 | 2,106,56,27,165,29,0.426,22,0 736 | 2,105,75,0,0,23.3,0.56,53,0 737 | 4,95,60,32,0,35.4,0.284,28,0 738 | 0,126,86,27,120,27.4,0.515,21,0 739 | 8,65,72,23,0,32,0.6,42,0 740 | 2,99,60,17,160,36.6,0.453,21,0 741 | 1,102,74,0,0,39.5,0.293,42,1 742 | 11,120,80,37,150,42.3,0.785,48,1 743 | 3,102,44,20,94,30.8,0.4,26,0 744 | 1,109,58,18,116,28.5,0.219,22,0 745 | 9,140,94,0,0,32.7,0.734,45,1 746 | 13,153,88,37,140,40.6,1.174,39,0 747 | 12,100,84,33,105,30,0.488,46,0 748 | 1,147,94,41,0,49.3,0.358,27,1 749 | 1,81,74,41,57,46.3,1.096,32,0 750 | 3,187,70,22,200,36.4,0.408,36,1 751 | 6,162,62,0,0,24.3,0.178,50,1 752 | 4,136,70,0,0,31.2,1.182,22,1 753 | 1,121,78,39,74,39,0.261,28,0 754 | 3,108,62,24,0,26,0.223,25,0 755 | 0,181,88,44,510,43.3,0.222,26,1 756 | 8,154,78,32,0,32.4,0.443,45,1 757 | 1,128,88,39,110,36.5,1.057,37,1 758 | 7,137,90,41,0,32,0.391,39,0 759 | 0,123,72,0,0,36.3,0.258,52,1 760 | 1,106,76,0,0,37.5,0.197,26,0 761 | 6,190,92,0,0,35.5,0.278,66,1 762 | 2,88,58,26,16,28.4,0.766,22,0 763 | 9,170,74,31,0,44,0.403,43,1 764 | 9,89,62,0,0,22.5,0.142,33,0 765 | 10,101,76,48,180,32.9,0.171,63,0 766 | 2,122,70,27,0,36.8,0.34,27,0 767 | 5,121,72,23,112,26.2,0.245,30,0 768 | 1,126,60,0,0,30.1,0.349,47,1 769 | 1,93,70,31,0,30.4,0.315,23,0 -------------------------------------------------------------------------------- /models/naive_bayes.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "NAIVE BAYES" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 2, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import pandas as pd\n", 17 | "from sklearn.model_selection import train_test_split\n", 18 | "from sklearn.naive_bayes import GaussianNB\n", 19 | "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n", 20 | "from sklearn.model_selection import cross_val_score\n", 21 | "import matplotlib.pyplot as plt" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "Veri" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 3, 34 | "metadata": {}, 35 | "outputs": [ 36 | { 37 | "name": "stdout", 38 | "output_type": "stream", 39 | "text": [ 40 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", 41 | "0 6 148 72 35 0 33.6 \n", 42 | "1 1 85 66 29 0 26.6 \n", 43 | "2 8 183 64 0 0 23.3 \n", 44 | "3 1 89 66 23 94 28.1 \n", 45 | "4 0 137 40 35 168 43.1 \n", 46 | "\n", 47 | " DiabetesPedigreeFunction Age Outcome \n", 48 | "0 0.627 50 1 \n", 49 | "1 0.351 31 0 \n", 50 | "2 0.672 32 1 \n", 51 | "3 0.167 21 0 \n", 52 | "4 2.288 33 1 \n" 53 | ] 54 | } 55 | ], 56 | "source": [ 57 | "# Veri setini yükle\n", 58 | "data = pd.read_csv('/data/diabetes.csv')\n", 59 | "\n", 60 | "# Veri setinin ilk 5 satirini incele\n", 61 | "print(data.head())" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 4, 67 | "metadata": {}, 68 | "outputs": [ 69 | { 70 | "data": { 71 | "text/html": [ 72 | "
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countmeanstdmin25%50%75%max
Pregnancies768.03.8450523.3695780.0001.000003.00006.0000017.00
Glucose768.0120.89453131.9726180.00099.00000117.0000140.25000199.00
BloodPressure768.069.10546919.3558070.00062.0000072.000080.00000122.00
SkinThickness768.020.53645815.9522180.0000.0000023.000032.0000099.00
Insulin768.079.799479115.2440020.0000.0000030.5000127.25000846.00
BMI768.031.9925787.8841600.00027.3000032.000036.6000067.10
DiabetesPedigreeFunction768.00.4718760.3313290.0780.243750.37250.626252.42
Age768.033.24088511.76023221.00024.0000029.000041.0000081.00
Outcome768.00.3489580.4769510.0000.000000.00001.000001.00
\n", 202 | "
" 203 | ], 204 | "text/plain": [ 205 | " count mean std min 25% \\\n", 206 | "Pregnancies 768.0 3.845052 3.369578 0.000 1.00000 \n", 207 | "Glucose 768.0 120.894531 31.972618 0.000 99.00000 \n", 208 | "BloodPressure 768.0 69.105469 19.355807 0.000 62.00000 \n", 209 | "SkinThickness 768.0 20.536458 15.952218 0.000 0.00000 \n", 210 | "Insulin 768.0 79.799479 115.244002 0.000 0.00000 \n", 211 | "BMI 768.0 31.992578 7.884160 0.000 27.30000 \n", 212 | "DiabetesPedigreeFunction 768.0 0.471876 0.331329 0.078 0.24375 \n", 213 | "Age 768.0 33.240885 11.760232 21.000 24.00000 \n", 214 | "Outcome 768.0 0.348958 0.476951 0.000 0.00000 \n", 215 | "\n", 216 | " 50% 75% max \n", 217 | "Pregnancies 3.0000 6.00000 17.00 \n", 218 | "Glucose 117.0000 140.25000 199.00 \n", 219 | "BloodPressure 72.0000 80.00000 122.00 \n", 220 | "SkinThickness 23.0000 32.00000 99.00 \n", 221 | "Insulin 30.5000 127.25000 846.00 \n", 222 | "BMI 32.0000 36.60000 67.10 \n", 223 | "DiabetesPedigreeFunction 0.3725 0.62625 2.42 \n", 224 | "Age 29.0000 41.00000 81.00 \n", 225 | "Outcome 0.0000 1.00000 1.00 " 226 | ] 227 | }, 228 | "execution_count": 4, 229 | "metadata": {}, 230 | "output_type": "execute_result" 231 | } 232 | ], 233 | "source": [ 234 | "# Veri setinin istatistikleri\n", 235 | "data.describe().T" 236 | ] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": 5, 241 | "metadata": {}, 242 | "outputs": [], 243 | "source": [ 244 | "# Giriş değişkenleri (bağımsız değişkenler)\n", 245 | "X = data.drop('Outcome', axis=1)\n", 246 | "\n", 247 | "# Çıkış değişkeni (bağımlı değişken)\n", 248 | "y = data['Outcome']\n", 249 | "\n", 250 | "# Veriyi eğitim ve test setlerine bölelim\n", 251 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n" 252 | ] 253 | }, 254 | { 255 | "cell_type": "markdown", 256 | "metadata": {}, 257 | "source": [ 258 | "Model" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "execution_count": 6, 264 | "metadata": {}, 265 | "outputs": [], 266 | "source": [ 267 | "# Naive Bayes modelini oluştur\n", 268 | "model = GaussianNB()\n", 269 | "model.fit(X_train, y_train)\n", 270 | "\n", 271 | "# Tahmin yap\n", 272 | "y_pred = model.predict(X_test)" 273 | ] 274 | }, 275 | { 276 | "cell_type": "markdown", 277 | "metadata": {}, 278 | "source": [ 279 | "Doğrulama" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": 8, 285 | "metadata": {}, 286 | "outputs": [ 287 | { 288 | "name": "stdout", 289 | "output_type": "stream", 290 | "text": [ 291 | "Accuracy: 0.7662337662337663\n", 292 | "Ortalama Cross-Validation Skoru: 0.7475674246430459\n", 293 | "Precision: 0.6610169491525424\n", 294 | "Recall: 0.7090909090909091\n", 295 | "F1 Score: 0.6842105263157895\n", 296 | "AUC Score: 0.8253443526170798\n" 297 | ] 298 | }, 299 | { 300 | "data": { 301 | "image/png": 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", 302 | "text/plain": [ 303 | "
" 304 | ] 305 | }, 306 | "metadata": {}, 307 | "output_type": "display_data" 308 | } 309 | ], 310 | "source": [ 311 | "\n", 312 | "# Doğruluk (Accuracy) ölçütü\n", 313 | "accuracy = accuracy_score(y_test, y_pred)\n", 314 | "print('Accuracy:', accuracy)\n", 315 | "\n", 316 | "# Cross-validation skorlarını al\n", 317 | "cv_scores = cross_val_score(model, X_train, y_train, cv=10, scoring='accuracy')\n", 318 | "print('Ortalama Cross-Validation Skoru:', cv_scores.mean())\n", 319 | "\n", 320 | "# Hassasiyet (Precision) ölçütü\n", 321 | "precision = precision_score(y_test, y_pred)\n", 322 | "print('Precision:', precision)\n", 323 | "\n", 324 | "# Duyarlılık (Recall) ölçütü\n", 325 | "recall = recall_score(y_test, y_pred)\n", 326 | "print('Recall:', recall)\n", 327 | "\n", 328 | "# F1 puanı ölçütü\n", 329 | "f1 = f1_score(y_test, y_pred)\n", 330 | "print('F1 Score:', f1)\n", 331 | "\n", 332 | "# ROC eğrisi ve AUC değeri\n", 333 | "y_probs = model.predict_proba(X_test)[:, 1]\n", 334 | "fpr, tpr, thresholds = roc_curve(y_test, y_probs)\n", 335 | "roc_auc = roc_auc_score(y_test, y_probs)\n", 336 | "\n", 337 | "print('AUC Score:', roc_auc)\n", 338 | "\n", 339 | "# ROC eğrisini çiz\n", 340 | "plt.figure(figsize=(10, 6))\n", 341 | "plt.plot(fpr, tpr, label='ROC Curve (AUC = {:.2f})'.format(roc_auc))\n", 342 | "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n", 343 | "plt.xlabel('False Positive Rate')\n", 344 | "plt.ylabel('True Positive Rate')\n", 345 | "plt.title('Naive Bayes ROC Curve')\n", 346 | "plt.legend()\n", 347 | "plt.show()" 348 | ] 349 | } 350 | ], 351 | "metadata": { 352 | "kernelspec": { 353 | "display_name": "Python 3", 354 | "language": "python", 355 | "name": "python3" 356 | }, 357 | "language_info": { 358 | "codemirror_mode": { 359 | "name": "ipython", 360 | "version": 3 361 | }, 362 | "file_extension": ".py", 363 | "mimetype": "text/x-python", 364 | "name": "python", 365 | "nbconvert_exporter": "python", 366 | "pygments_lexer": "ipython3", 367 | "version": "3.10.12" 368 | } 369 | }, 370 | "nbformat": 4, 371 | "nbformat_minor": 2 372 | } 373 | -------------------------------------------------------------------------------- /models/logistic_regression.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "LOJİSTİK REGRESYON" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 354, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import pandas as pd\n", 17 | "from sklearn.model_selection import train_test_split\n", 18 | "from sklearn.linear_model import LogisticRegression\n", 19 | "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n", 20 | "from sklearn.model_selection import cross_val_score\n", 21 | "import matplotlib.pyplot as plt\n" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "Veri" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 355, 34 | "metadata": {}, 35 | "outputs": [ 36 | { 37 | "name": "stdout", 38 | "output_type": "stream", 39 | "text": [ 40 | " Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n", 41 | "0 6 148 72 35 0 33.6 \n", 42 | "1 1 85 66 29 0 26.6 \n", 43 | "2 8 183 64 0 0 23.3 \n", 44 | "3 1 89 66 23 94 28.1 \n", 45 | "4 0 137 40 35 168 43.1 \n", 46 | "\n", 47 | " DiabetesPedigreeFunction Age Outcome \n", 48 | "0 0.627 50 1 \n", 49 | "1 0.351 31 0 \n", 50 | "2 0.672 32 1 \n", 51 | "3 0.167 21 0 \n", 52 | "4 2.288 33 1 \n" 53 | ] 54 | } 55 | ], 56 | "source": [ 57 | "# Veri setini yükle\n", 58 | "data = pd.read_csv('/data/diabetes.csv')\n", 59 | "\n", 60 | "# Veri setinin ilk 5 satirini incele\n", 61 | "print(data.head())" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 356, 67 | "metadata": {}, 68 | "outputs": [ 69 | { 70 | "data": { 71 | "text/html": [ 72 | "
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countmeanstdmin25%50%75%max
Pregnancies768.03.8450523.3695780.0001.000003.00006.0000017.00
Glucose768.0120.89453131.9726180.00099.00000117.0000140.25000199.00
BloodPressure768.069.10546919.3558070.00062.0000072.000080.00000122.00
SkinThickness768.020.53645815.9522180.0000.0000023.000032.0000099.00
Insulin768.079.799479115.2440020.0000.0000030.5000127.25000846.00
BMI768.031.9925787.8841600.00027.3000032.000036.6000067.10
DiabetesPedigreeFunction768.00.4718760.3313290.0780.243750.37250.626252.42
Age768.033.24088511.76023221.00024.0000029.000041.0000081.00
Outcome768.00.3489580.4769510.0000.000000.00001.000001.00
\n", 202 | "
" 203 | ], 204 | "text/plain": [ 205 | " count mean std min 25% \\\n", 206 | "Pregnancies 768.0 3.845052 3.369578 0.000 1.00000 \n", 207 | "Glucose 768.0 120.894531 31.972618 0.000 99.00000 \n", 208 | "BloodPressure 768.0 69.105469 19.355807 0.000 62.00000 \n", 209 | "SkinThickness 768.0 20.536458 15.952218 0.000 0.00000 \n", 210 | "Insulin 768.0 79.799479 115.244002 0.000 0.00000 \n", 211 | "BMI 768.0 31.992578 7.884160 0.000 27.30000 \n", 212 | "DiabetesPedigreeFunction 768.0 0.471876 0.331329 0.078 0.24375 \n", 213 | "Age 768.0 33.240885 11.760232 21.000 24.00000 \n", 214 | "Outcome 768.0 0.348958 0.476951 0.000 0.00000 \n", 215 | "\n", 216 | " 50% 75% max \n", 217 | "Pregnancies 3.0000 6.00000 17.00 \n", 218 | "Glucose 117.0000 140.25000 199.00 \n", 219 | "BloodPressure 72.0000 80.00000 122.00 \n", 220 | "SkinThickness 23.0000 32.00000 99.00 \n", 221 | "Insulin 30.5000 127.25000 846.00 \n", 222 | "BMI 32.0000 36.60000 67.10 \n", 223 | "DiabetesPedigreeFunction 0.3725 0.62625 2.42 \n", 224 | "Age 29.0000 41.00000 81.00 \n", 225 | "Outcome 0.0000 1.00000 1.00 " 226 | ] 227 | }, 228 | "execution_count": 356, 229 | "metadata": {}, 230 | "output_type": "execute_result" 231 | } 232 | ], 233 | "source": [ 234 | "# Veri setinin istatistikleri\n", 235 | "data.describe().T" 236 | ] 237 | }, 238 | { 239 | "cell_type": "code", 240 | "execution_count": 357, 241 | "metadata": {}, 242 | "outputs": [], 243 | "source": [ 244 | "# Giriş değişkenleri (bağımsız değişkenler)\n", 245 | "X = data.drop('Outcome', axis=1)\n", 246 | "\n", 247 | "# Çıkış değişkeni (bağımlı değişken)\n", 248 | "y = data['Outcome']\n", 249 | "\n", 250 | "# Veriyi eğitim ve test setlerine bölelim\n", 251 | "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n" 252 | ] 253 | }, 254 | { 255 | "cell_type": "markdown", 256 | "metadata": {}, 257 | "source": [ 258 | "Model" 259 | ] 260 | }, 261 | { 262 | "cell_type": "code", 263 | "execution_count": 358, 264 | "metadata": {}, 265 | "outputs": [], 266 | "source": [ 267 | "# Lojistik regresyon modelini oluştur\n", 268 | "model = LogisticRegression(max_iter=100000)\n", 269 | "model.fit(X_train, y_train)\n", 270 | "\n", 271 | "# Tahmin yap\n", 272 | "y_pred = model.predict(X_test)" 273 | ] 274 | }, 275 | { 276 | "cell_type": "markdown", 277 | "metadata": {}, 278 | "source": [ 279 | "Doğrulama" 280 | ] 281 | }, 282 | { 283 | "cell_type": "code", 284 | "execution_count": 372, 285 | "metadata": {}, 286 | "outputs": [ 287 | { 288 | "name": "stdout", 289 | "output_type": "stream", 290 | "text": [ 291 | "Accuracy: 0.7467532467532467\n", 292 | "Ortalama Cross-Validation Skoru: 0.7654151242728715\n", 293 | "Precision: 0.6379310344827587\n", 294 | "Recall: 0.6727272727272727\n", 295 | "F1 Score: 0.6548672566371682\n", 296 | "AUC Score: 0.8130394857667584\n" 297 | ] 298 | }, 299 | { 300 | "data": { 301 | "image/png": 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302 | "text/plain": [ 303 | "
" 304 | ] 305 | }, 306 | "metadata": {}, 307 | "output_type": "display_data" 308 | } 309 | ], 310 | "source": [ 311 | "\n", 312 | "# Doğruluk (Accuracy) ölçütü\n", 313 | "accuracy = accuracy_score(y_test, y_pred)\n", 314 | "print('Accuracy:', accuracy)\n", 315 | "\n", 316 | "# Cross-validation skorlarını al\n", 317 | "cv_scores = cross_val_score(model, X_train, y_train, cv=10, scoring='accuracy')\n", 318 | "print('Ortalama Cross-Validation Skoru:', cv_scores.mean())\n", 319 | "\n", 320 | "# Hassasiyet (Precision) ölçütü\n", 321 | "precision = precision_score(y_test, y_pred)\n", 322 | "print('Precision:', precision)\n", 323 | "\n", 324 | "# Duyarlılık (Recall) ölçütü\n", 325 | "recall = recall_score(y_test, y_pred)\n", 326 | "print('Recall:', recall)\n", 327 | "\n", 328 | "# F1 puanı ölçütü\n", 329 | "f1 = f1_score(y_test, y_pred)\n", 330 | "print('F1 Score:', f1)\n", 331 | "\n", 332 | "# ROC eğrisi ve AUC değeri\n", 333 | "y_probs = model.predict_proba(X_test)[:, 1]\n", 334 | "fpr, tpr, thresholds = roc_curve(y_test, y_probs)\n", 335 | "roc_auc = roc_auc_score(y_test, y_probs)\n", 336 | "\n", 337 | "print('AUC Score:', roc_auc)\n", 338 | "\n", 339 | "# ROC eğrisini çiz\n", 340 | "plt.figure(figsize=(10, 6))\n", 341 | "plt.plot(fpr, tpr, label='ROC Curve (AUC = {:.2f})'.format(roc_auc))\n", 342 | "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n", 343 | "plt.xlabel('False Positive Rate')\n", 344 | "plt.ylabel('True Positive Rate')\n", 345 | "plt.title('Logistic Regression ROC Curve')\n", 346 | "plt.legend()\n", 347 | "plt.show()" 348 | ] 349 | } 350 | ], 351 | "metadata": { 352 | "language_info": { 353 | "name": "python" 354 | } 355 | }, 356 | "nbformat": 4, 357 | "nbformat_minor": 2 358 | } 359 | -------------------------------------------------------------------------------- /models/gradient_boosting_machines.ipynb: -------------------------------------------------------------------------------- 1 | {"cells":[{"cell_type":"markdown","metadata":{"id":"aRWMNlK9yIHD"},"source":["GRADIENT BOOSTING MACHINES"]},{"cell_type":"code","execution_count":42,"metadata":{"executionInfo":{"elapsed":446,"status":"ok","timestamp":1703525036726,"user":{"displayName":"fg etry","userId":"14179005432404118369"},"user_tz":-180},"id":"7VBNFDZpyIHH"},"outputs":[],"source":["import pandas as pd\n","from sklearn.model_selection import train_test_split, cross_val_score\n","from sklearn.ensemble import GradientBoostingClassifier\n","from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, roc_curve\n","import matplotlib.pyplot as plt"]},{"cell_type":"markdown","metadata":{"id":"XJum_AqkyIHJ"},"source":["Veri"]},{"cell_type":"code","execution_count":43,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22,"status":"ok","timestamp":1703525037299,"user":{"displayName":"fg etry","userId":"14179005432404118369"},"user_tz":-180},"id":"Od2JgtRPyIHK","outputId":"a585d39f-6d75-4698-bb7d-e5e55e975ace"},"outputs":[{"name":"stdout","output_type":"stream","text":[" Pregnancies Glucose BloodPressure SkinThickness Insulin BMI \\\n","0 6 148 72 35 0 33.6 \n","1 1 85 66 29 0 26.6 \n","2 8 183 64 0 0 23.3 \n","3 1 89 66 23 94 28.1 \n","4 0 137 40 35 168 43.1 \n","\n"," DiabetesPedigreeFunction Age Outcome \n","0 0.627 50 1 \n","1 0.351 31 0 \n","2 0.672 32 1 \n","3 0.167 21 0 \n","4 2.288 33 1 \n"]}],"source":["# Veri setini yükle\n","data = pd.read_csv('/data/diabetes.csv')\n","\n","# Veri setinin ilk 5 satirini incele\n","print(data.head())"]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":332},"executionInfo":{"elapsed":18,"status":"ok","timestamp":1703525037299,"user":{"displayName":"fg etry","userId":"14179005432404118369"},"user_tz":-180},"id":"kN7zO3RdyIHN","outputId":"3793b4d8-4e92-4069-f2ae-26260bab067b"},"outputs":[{"data":{"text/html":["\n","
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countmeanstdmin25%50%75%max
Pregnancies768.03.8450523.3695780.0001.000003.00006.0000017.00
Glucose768.0120.89453131.9726180.00099.00000117.0000140.25000199.00
BloodPressure768.069.10546919.3558070.00062.0000072.000080.00000122.00
SkinThickness768.020.53645815.9522180.0000.0000023.000032.0000099.00
Insulin768.079.799479115.2440020.0000.0000030.5000127.25000846.00
BMI768.031.9925787.8841600.00027.3000032.000036.6000067.10
DiabetesPedigreeFunction768.00.4718760.3313290.0780.243750.37250.626252.42
Age768.033.24088511.76023221.00024.0000029.000041.0000081.00
Outcome768.00.3489580.4769510.0000.000000.00001.000001.00
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\n"],"text/plain":[" count mean std min 25% \\\n","Pregnancies 768.0 3.845052 3.369578 0.000 1.00000 \n","Glucose 768.0 120.894531 31.972618 0.000 99.00000 \n","BloodPressure 768.0 69.105469 19.355807 0.000 62.00000 \n","SkinThickness 768.0 20.536458 15.952218 0.000 0.00000 \n","Insulin 768.0 79.799479 115.244002 0.000 0.00000 \n","BMI 768.0 31.992578 7.884160 0.000 27.30000 \n","DiabetesPedigreeFunction 768.0 0.471876 0.331329 0.078 0.24375 \n","Age 768.0 33.240885 11.760232 21.000 24.00000 \n","Outcome 768.0 0.348958 0.476951 0.000 0.00000 \n","\n"," 50% 75% max \n","Pregnancies 3.0000 6.00000 17.00 \n","Glucose 117.0000 140.25000 199.00 \n","BloodPressure 72.0000 80.00000 122.00 \n","SkinThickness 23.0000 32.00000 99.00 \n","Insulin 30.5000 127.25000 846.00 \n","BMI 32.0000 36.60000 67.10 \n","DiabetesPedigreeFunction 0.3725 0.62625 2.42 \n","Age 29.0000 41.00000 81.00 \n","Outcome 0.0000 1.00000 1.00 "]},"execution_count":44,"metadata":{},"output_type":"execute_result"}],"source":["# Veri setinin istatistikleri\n","data.describe().T"]},{"cell_type":"code","execution_count":45,"metadata":{"executionInfo":{"elapsed":15,"status":"ok","timestamp":1703525037300,"user":{"displayName":"fg etry","userId":"14179005432404118369"},"user_tz":-180},"id":"rAmrG0XQyIHO"},"outputs":[],"source":["# Giriş değişkenleri (bağımsız değişkenler)\n","X = data.drop('Outcome', axis=1)\n","\n","# Çıkış değişkeni (bağımlı değişken)\n","y = data['Outcome']\n","\n","# Veriyi eğitim ve test setlerine bölelim\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"]},{"cell_type":"markdown","metadata":{"id":"2CaDxUoFyIHO"},"source":["Model"]},{"cell_type":"code","execution_count":46,"metadata":{"executionInfo":{"elapsed":14,"status":"ok","timestamp":1703525037300,"user":{"displayName":"fg etry","userId":"14179005432404118369"},"user_tz":-180},"id":"9Z64rcuPyIHP"},"outputs":[],"source":["# GBM modelini oluştur\n","model = GradientBoostingClassifier(n_estimators=100, random_state=42)\n","model.fit(X_train, y_train)\n","\n","# Tahmin yap\n","y_pred = model.predict(X_test)"]},{"cell_type":"markdown","metadata":{"id":"laotXKi9yIHQ"},"source":["Doğrulama"]},{"cell_type":"code","execution_count":47,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":668},"executionInfo":{"elapsed":2507,"status":"ok","timestamp":1703525039794,"user":{"displayName":"fg etry","userId":"14179005432404118369"},"user_tz":-180},"id":"wY_GmdbYyIHS","outputId":"89139026-dcef-4d70-cc23-6c02f7ae8bae"},"outputs":[{"name":"stdout","output_type":"stream","text":["Accuracy: 0.7467532467532467\n","Ortalama Cross-Validation Skoru: 0.7734796404019038\n","Precision: 0.6379310344827587\n","Recall: 0.6727272727272727\n","F1 Score: 0.6548672566371682\n","AUC Score: 0.8091827364554638\n"]},{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["\n","# Doğruluk (Accuracy) ölçütü\n","accuracy = accuracy_score(y_test, y_pred)\n","print('Accuracy:', accuracy)\n","\n","# Cross-validation skorlarını al\n","cv_scores = cross_val_score(model, X_train, y_train, cv=10, scoring='accuracy')\n","print('Ortalama Cross-Validation Skoru:', cv_scores.mean())\n","\n","# Hassasiyet (Precision) ölçütü\n","precision = precision_score(y_test, y_pred)\n","print('Precision:', precision)\n","\n","# Duyarlılık (Recall) ölçütü\n","recall = recall_score(y_test, y_pred)\n","print('Recall:', recall)\n","\n","# F1 puanı ölçütü\n","f1 = f1_score(y_test, y_pred)\n","print('F1 Score:', f1)\n","\n","# ROC eğrisi ve AUC değeri\n","y_probs = model.predict_proba(X_test)[:, 1]\n","fpr, tpr, thresholds = roc_curve(y_test, y_probs)\n","roc_auc = roc_auc_score(y_test, y_probs)\n","\n","print('AUC Score:', roc_auc)\n","\n","# ROC eğrisini çiz\n","plt.figure(figsize=(10, 6))\n","plt.plot(fpr, tpr, label='ROC Curve (AUC = {:.2f})'.format(roc_auc))\n","plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random')\n","plt.xlabel('False Positive Rate')\n","plt.ylabel('True Positive Rate')\n","plt.title('GBM ROC Curve')\n","plt.legend()\n","plt.show()"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.12"}},"nbformat":4,"nbformat_minor":0} 2 | --------------------------------------------------------------------------------