├── Credit_default_dataset.csv
├── License
├── Preprocessing_modeling_refinements_evaluations.html
├── Preprocessing_modeling_refinements_evaluations.ipynb
├── Project Report(By Nishant Sharma).pdf
├── Proposal.pdf
├── README.md
├── Xploratory_analysis.html
├── Xploratory_analysis.ipynb
├── deep_default.html
└── deep_default.ipynb


/License:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) [2017] [Nishant Sharma]
 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. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/Preprocessing_modeling_refinements_evaluations.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 85,
   6 |    "metadata": {
   7 |     "collapsed": false
   8 |    },
   9 |    "outputs": [
  10 |     {
  11 |      "data": {
  12 |       "text/html": [
  13 |        "<div>\n",
  14 |        "<style>\n",
  15 |        "    .dataframe thead tr:only-child th {\n",
  16 |        "        text-align: right;\n",
  17 |        "    }\n",
  18 |        "\n",
  19 |        "    .dataframe thead th {\n",
  20 |        "        text-align: left;\n",
  21 |        "    }\n",
  22 |        "\n",
  23 |        "    .dataframe tbody tr th {\n",
  24 |        "        vertical-align: top;\n",
  25 |        "    }\n",
  26 |        "</style>\n",
  27 |        "<table border=\"1\" class=\"dataframe\">\n",
  28 |        "  <thead>\n",
  29 |        "    <tr style=\"text-align: right;\">\n",
  30 |        "      <th></th>\n",
  31 |        "      <th>ID</th>\n",
  32 |        "      <th>LIMIT_BAL</th>\n",
  33 |        "      <th>SEX</th>\n",
  34 |        "      <th>EDUCATION</th>\n",
  35 |        "      <th>MARRIAGE</th>\n",
  36 |        "      <th>AGE</th>\n",
  37 |        "      <th>PAY_0</th>\n",
  38 |        "      <th>PAY_2</th>\n",
  39 |        "      <th>PAY_3</th>\n",
  40 |        "      <th>PAY_4</th>\n",
  41 |        "      <th>...</th>\n",
  42 |        "      <th>BILL_AMT4</th>\n",
  43 |        "      <th>BILL_AMT5</th>\n",
  44 |        "      <th>BILL_AMT6</th>\n",
  45 |        "      <th>PAY_AMT1</th>\n",
  46 |        "      <th>PAY_AMT2</th>\n",
  47 |        "      <th>PAY_AMT3</th>\n",
  48 |        "      <th>PAY_AMT4</th>\n",
  49 |        "      <th>PAY_AMT5</th>\n",
  50 |        "      <th>PAY_AMT6</th>\n",
  51 |        "      <th>default.payment.next.month</th>\n",
  52 |        "    </tr>\n",
  53 |        "  </thead>\n",
  54 |        "  <tbody>\n",
  55 |        "    <tr>\n",
  56 |        "      <th>0</th>\n",
  57 |        "      <td>1</td>\n",
  58 |        "      <td>20000.0</td>\n",
  59 |        "      <td>2</td>\n",
  60 |        "      <td>2</td>\n",
  61 |        "      <td>1</td>\n",
  62 |        "      <td>24</td>\n",
  63 |        "      <td>2</td>\n",
  64 |        "      <td>2</td>\n",
  65 |        "      <td>-1</td>\n",
  66 |        "      <td>-1</td>\n",
  67 |        "      <td>...</td>\n",
  68 |        "      <td>0.0</td>\n",
  69 |        "      <td>0.0</td>\n",
  70 |        "      <td>0.0</td>\n",
  71 |        "      <td>0.0</td>\n",
  72 |        "      <td>689.0</td>\n",
  73 |        "      <td>0.0</td>\n",
  74 |        "      <td>0.0</td>\n",
  75 |        "      <td>0.0</td>\n",
  76 |        "      <td>0.0</td>\n",
  77 |        "      <td>1</td>\n",
  78 |        "    </tr>\n",
  79 |        "    <tr>\n",
  80 |        "      <th>1</th>\n",
  81 |        "      <td>2</td>\n",
  82 |        "      <td>120000.0</td>\n",
  83 |        "      <td>2</td>\n",
  84 |        "      <td>2</td>\n",
  85 |        "      <td>2</td>\n",
  86 |        "      <td>26</td>\n",
  87 |        "      <td>-1</td>\n",
  88 |        "      <td>2</td>\n",
  89 |        "      <td>0</td>\n",
  90 |        "      <td>0</td>\n",
  91 |        "      <td>...</td>\n",
  92 |        "      <td>3272.0</td>\n",
  93 |        "      <td>3455.0</td>\n",
  94 |        "      <td>3261.0</td>\n",
  95 |        "      <td>0.0</td>\n",
  96 |        "      <td>1000.0</td>\n",
  97 |        "      <td>1000.0</td>\n",
  98 |        "      <td>1000.0</td>\n",
  99 |        "      <td>0.0</td>\n",
 100 |        "      <td>2000.0</td>\n",
 101 |        "      <td>1</td>\n",
 102 |        "    </tr>\n",
 103 |        "    <tr>\n",
 104 |        "      <th>2</th>\n",
 105 |        "      <td>3</td>\n",
 106 |        "      <td>90000.0</td>\n",
 107 |        "      <td>2</td>\n",
 108 |        "      <td>2</td>\n",
 109 |        "      <td>2</td>\n",
 110 |        "      <td>34</td>\n",
 111 |        "      <td>0</td>\n",
 112 |        "      <td>0</td>\n",
 113 |        "      <td>0</td>\n",
 114 |        "      <td>0</td>\n",
 115 |        "      <td>...</td>\n",
 116 |        "      <td>14331.0</td>\n",
 117 |        "      <td>14948.0</td>\n",
 118 |        "      <td>15549.0</td>\n",
 119 |        "      <td>1518.0</td>\n",
 120 |        "      <td>1500.0</td>\n",
 121 |        "      <td>1000.0</td>\n",
 122 |        "      <td>1000.0</td>\n",
 123 |        "      <td>1000.0</td>\n",
 124 |        "      <td>5000.0</td>\n",
 125 |        "      <td>0</td>\n",
 126 |        "    </tr>\n",
 127 |        "    <tr>\n",
 128 |        "      <th>3</th>\n",
 129 |        "      <td>4</td>\n",
 130 |        "      <td>50000.0</td>\n",
 131 |        "      <td>2</td>\n",
 132 |        "      <td>2</td>\n",
 133 |        "      <td>1</td>\n",
 134 |        "      <td>37</td>\n",
 135 |        "      <td>0</td>\n",
 136 |        "      <td>0</td>\n",
 137 |        "      <td>0</td>\n",
 138 |        "      <td>0</td>\n",
 139 |        "      <td>...</td>\n",
 140 |        "      <td>28314.0</td>\n",
 141 |        "      <td>28959.0</td>\n",
 142 |        "      <td>29547.0</td>\n",
 143 |        "      <td>2000.0</td>\n",
 144 |        "      <td>2019.0</td>\n",
 145 |        "      <td>1200.0</td>\n",
 146 |        "      <td>1100.0</td>\n",
 147 |        "      <td>1069.0</td>\n",
 148 |        "      <td>1000.0</td>\n",
 149 |        "      <td>0</td>\n",
 150 |        "    </tr>\n",
 151 |        "    <tr>\n",
 152 |        "      <th>4</th>\n",
 153 |        "      <td>5</td>\n",
 154 |        "      <td>50000.0</td>\n",
 155 |        "      <td>1</td>\n",
 156 |        "      <td>2</td>\n",
 157 |        "      <td>1</td>\n",
 158 |        "      <td>57</td>\n",
 159 |        "      <td>-1</td>\n",
 160 |        "      <td>0</td>\n",
 161 |        "      <td>-1</td>\n",
 162 |        "      <td>0</td>\n",
 163 |        "      <td>...</td>\n",
 164 |        "      <td>20940.0</td>\n",
 165 |        "      <td>19146.0</td>\n",
 166 |        "      <td>19131.0</td>\n",
 167 |        "      <td>2000.0</td>\n",
 168 |        "      <td>36681.0</td>\n",
 169 |        "      <td>10000.0</td>\n",
 170 |        "      <td>9000.0</td>\n",
 171 |        "      <td>689.0</td>\n",
 172 |        "      <td>679.0</td>\n",
 173 |        "      <td>0</td>\n",
 174 |        "    </tr>\n",
 175 |        "  </tbody>\n",
 176 |        "</table>\n",
 177 |        "<p>5 rows × 25 columns</p>\n",
 178 |        "</div>"
 179 |       ],
 180 |       "text/plain": [
 181 |        "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
 182 |        "0   1    20000.0    2          2         1   24      2      2     -1     -1   \n",
 183 |        "1   2   120000.0    2          2         2   26     -1      2      0      0   \n",
 184 |        "2   3    90000.0    2          2         2   34      0      0      0      0   \n",
 185 |        "3   4    50000.0    2          2         1   37      0      0      0      0   \n",
 186 |        "4   5    50000.0    1          2         1   57     -1      0     -1      0   \n",
 187 |        "\n",
 188 |        "              ...              BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
 189 |        "0             ...                    0.0        0.0        0.0       0.0   \n",
 190 |        "1             ...                 3272.0     3455.0     3261.0       0.0   \n",
 191 |        "2             ...                14331.0    14948.0    15549.0    1518.0   \n",
 192 |        "3             ...                28314.0    28959.0    29547.0    2000.0   \n",
 193 |        "4             ...                20940.0    19146.0    19131.0    2000.0   \n",
 194 |        "\n",
 195 |        "   PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
 196 |        "0     689.0       0.0       0.0       0.0       0.0   \n",
 197 |        "1    1000.0    1000.0    1000.0       0.0    2000.0   \n",
 198 |        "2    1500.0    1000.0    1000.0    1000.0    5000.0   \n",
 199 |        "3    2019.0    1200.0    1100.0    1069.0    1000.0   \n",
 200 |        "4   36681.0   10000.0    9000.0     689.0     679.0   \n",
 201 |        "\n",
 202 |        "   default.payment.next.month  \n",
 203 |        "0                           1  \n",
 204 |        "1                           1  \n",
 205 |        "2                           0  \n",
 206 |        "3                           0  \n",
 207 |        "4                           0  \n",
 208 |        "\n",
 209 |        "[5 rows x 25 columns]"
 210 |       ]
 211 |      },
 212 |      "execution_count": 85,
 213 |      "metadata": {},
 214 |      "output_type": "execute_result"
 215 |     }
 216 |    ],
 217 |    "source": [
 218 |     "import pandas as pd\n",
 219 |     "import sklearn\n",
 220 |     "import numpy as np\n",
 221 |     "\n",
 222 |     "credit=pd.read_csv(\"Credit.csv\")\n",
 223 |     "credit.head(5)"
 224 |    ]
 225 |   },
 226 |   {
 227 |    "cell_type": "code",
 228 |    "execution_count": 86,
 229 |    "metadata": {
 230 |     "collapsed": true
 231 |    },
 232 |    "outputs": [],
 233 |    "source": [
 234 |     "#We don't need the ID column,so lets drop it.\n",
 235 |     "credit = credit.drop([\"ID\"],axis=1)"
 236 |    ]
 237 |   },
 238 |   {
 239 |    "cell_type": "code",
 240 |    "execution_count": 87,
 241 |    "metadata": {
 242 |     "collapsed": true
 243 |    },
 244 |    "outputs": [],
 245 |    "source": [
 246 |     "#changing the name of  pay_0 column to pay_1 to make the numbering correct"
 247 |    ]
 248 |   },
 249 |   {
 250 |    "cell_type": "code",
 251 |    "execution_count": 88,
 252 |    "metadata": {
 253 |     "collapsed": false
 254 |    },
 255 |    "outputs": [],
 256 |    "source": [
 257 |     "credit.rename(columns={'PAY_0':'PAY_1'}, inplace=True)"
 258 |    ]
 259 |   },
 260 |   {
 261 |    "cell_type": "code",
 262 |    "execution_count": 89,
 263 |    "metadata": {
 264 |     "collapsed": false
 265 |    },
 266 |    "outputs": [
 267 |     {
 268 |      "data": {
 269 |       "text/html": [
 270 |        "<div>\n",
 271 |        "<style>\n",
 272 |        "    .dataframe thead tr:only-child th {\n",
 273 |        "        text-align: right;\n",
 274 |        "    }\n",
 275 |        "\n",
 276 |        "    .dataframe thead th {\n",
 277 |        "        text-align: left;\n",
 278 |        "    }\n",
 279 |        "\n",
 280 |        "    .dataframe tbody tr th {\n",
 281 |        "        vertical-align: top;\n",
 282 |        "    }\n",
 283 |        "</style>\n",
 284 |        "<table border=\"1\" class=\"dataframe\">\n",
 285 |        "  <thead>\n",
 286 |        "    <tr style=\"text-align: right;\">\n",
 287 |        "      <th></th>\n",
 288 |        "      <th>LIMIT_BAL</th>\n",
 289 |        "      <th>SEX</th>\n",
 290 |        "      <th>EDUCATION</th>\n",
 291 |        "      <th>MARRIAGE</th>\n",
 292 |        "      <th>AGE</th>\n",
 293 |        "      <th>PAY_1</th>\n",
 294 |        "      <th>PAY_2</th>\n",
 295 |        "      <th>PAY_3</th>\n",
 296 |        "      <th>PAY_4</th>\n",
 297 |        "      <th>PAY_5</th>\n",
 298 |        "      <th>...</th>\n",
 299 |        "      <th>BILL_AMT4</th>\n",
 300 |        "      <th>BILL_AMT5</th>\n",
 301 |        "      <th>BILL_AMT6</th>\n",
 302 |        "      <th>PAY_AMT1</th>\n",
 303 |        "      <th>PAY_AMT2</th>\n",
 304 |        "      <th>PAY_AMT3</th>\n",
 305 |        "      <th>PAY_AMT4</th>\n",
 306 |        "      <th>PAY_AMT5</th>\n",
 307 |        "      <th>PAY_AMT6</th>\n",
 308 |        "      <th>default.payment.next.month</th>\n",
 309 |        "    </tr>\n",
 310 |        "  </thead>\n",
 311 |        "  <tbody>\n",
 312 |        "    <tr>\n",
 313 |        "      <th>0</th>\n",
 314 |        "      <td>20000.0</td>\n",
 315 |        "      <td>2</td>\n",
 316 |        "      <td>2</td>\n",
 317 |        "      <td>1</td>\n",
 318 |        "      <td>24</td>\n",
 319 |        "      <td>2</td>\n",
 320 |        "      <td>2</td>\n",
 321 |        "      <td>-1</td>\n",
 322 |        "      <td>-1</td>\n",
 323 |        "      <td>-2</td>\n",
 324 |        "      <td>...</td>\n",
 325 |        "      <td>0.0</td>\n",
 326 |        "      <td>0.0</td>\n",
 327 |        "      <td>0.0</td>\n",
 328 |        "      <td>0.0</td>\n",
 329 |        "      <td>689.0</td>\n",
 330 |        "      <td>0.0</td>\n",
 331 |        "      <td>0.0</td>\n",
 332 |        "      <td>0.0</td>\n",
 333 |        "      <td>0.0</td>\n",
 334 |        "      <td>1</td>\n",
 335 |        "    </tr>\n",
 336 |        "  </tbody>\n",
 337 |        "</table>\n",
 338 |        "<p>1 rows × 24 columns</p>\n",
 339 |        "</div>"
 340 |       ],
 341 |       "text/plain": [
 342 |        "   LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_1  PAY_2  PAY_3  PAY_4  \\\n",
 343 |        "0    20000.0    2          2         1   24      2      2     -1     -1   \n",
 344 |        "\n",
 345 |        "   PAY_5             ...              BILL_AMT4  BILL_AMT5  BILL_AMT6  \\\n",
 346 |        "0     -2             ...                    0.0        0.0        0.0   \n",
 347 |        "\n",
 348 |        "   PAY_AMT1  PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
 349 |        "0       0.0     689.0       0.0       0.0       0.0       0.0   \n",
 350 |        "\n",
 351 |        "   default.payment.next.month  \n",
 352 |        "0                           1  \n",
 353 |        "\n",
 354 |        "[1 rows x 24 columns]"
 355 |       ]
 356 |      },
 357 |      "execution_count": 89,
 358 |      "metadata": {},
 359 |      "output_type": "execute_result"
 360 |     }
 361 |    ],
 362 |    "source": [
 363 |     "credit.head(1)"
 364 |    ]
 365 |   },
 366 |   {
 367 |    "cell_type": "code",
 368 |    "execution_count": 90,
 369 |    "metadata": {
 370 |     "collapsed": false
 371 |    },
 372 |    "outputs": [
 373 |     {
 374 |      "data": {
 375 |       "text/plain": [
 376 |        "2    14030\n",
 377 |        "1    10585\n",
 378 |        "3     4917\n",
 379 |        "5      280\n",
 380 |        "4      123\n",
 381 |        "6       51\n",
 382 |        "0       14\n",
 383 |        "Name: EDUCATION, dtype: int64"
 384 |       ]
 385 |      },
 386 |      "execution_count": 90,
 387 |      "metadata": {},
 388 |      "output_type": "execute_result"
 389 |     }
 390 |    ],
 391 |    "source": [
 392 |     "#Removing Unwanted categorical levels as mentioned in data exploration\n",
 393 |     "credit['EDUCATION'].value_counts()"
 394 |    ]
 395 |   },
 396 |   {
 397 |    "cell_type": "code",
 398 |    "execution_count": 91,
 399 |    "metadata": {
 400 |     "collapsed": false
 401 |    },
 402 |    "outputs": [],
 403 |    "source": [
 404 |     "#Changing Education levels 0,5,6 to others(4)\n",
 405 |     "credit[\"EDUCATION\"]=credit[\"EDUCATION\"].map({0:4,1:1,2:2,3:3,4:4,5:4,6:4})"
 406 |    ]
 407 |   },
 408 |   {
 409 |    "cell_type": "code",
 410 |    "execution_count": 92,
 411 |    "metadata": {
 412 |     "collapsed": false
 413 |    },
 414 |    "outputs": [
 415 |     {
 416 |      "data": {
 417 |       "text/plain": [
 418 |        "2    14030\n",
 419 |        "1    10585\n",
 420 |        "3     4917\n",
 421 |        "4      468\n",
 422 |        "Name: EDUCATION, dtype: int64"
 423 |       ]
 424 |      },
 425 |      "execution_count": 92,
 426 |      "metadata": {},
 427 |      "output_type": "execute_result"
 428 |     }
 429 |    ],
 430 |    "source": [
 431 |     "credit['EDUCATION'].value_counts()"
 432 |    ]
 433 |   },
 434 |   {
 435 |    "cell_type": "code",
 436 |    "execution_count": 93,
 437 |    "metadata": {
 438 |     "collapsed": false
 439 |    },
 440 |    "outputs": [
 441 |     {
 442 |      "data": {
 443 |       "text/plain": [
 444 |        "2    15964\n",
 445 |        "1    13659\n",
 446 |        "3      323\n",
 447 |        "0       54\n",
 448 |        "Name: MARRIAGE, dtype: int64"
 449 |       ]
 450 |      },
 451 |      "execution_count": 93,
 452 |      "metadata": {},
 453 |      "output_type": "execute_result"
 454 |     }
 455 |    ],
 456 |    "source": [
 457 |     "credit['MARRIAGE'].value_counts()"
 458 |    ]
 459 |   },
 460 |   {
 461 |    "cell_type": "code",
 462 |    "execution_count": 94,
 463 |    "metadata": {
 464 |     "collapsed": true
 465 |    },
 466 |    "outputs": [],
 467 |    "source": [
 468 |     "#similarly chaning marriage level 0 to level 3\n",
 469 |     "credit[\"MARRIAGE\"]=credit[\"MARRIAGE\"].map({0:3,1:1,2:2,3:3})"
 470 |    ]
 471 |   },
 472 |   {
 473 |    "cell_type": "code",
 474 |    "execution_count": 95,
 475 |    "metadata": {
 476 |     "collapsed": false
 477 |    },
 478 |    "outputs": [
 479 |     {
 480 |      "data": {
 481 |       "text/plain": [
 482 |        "2    15964\n",
 483 |        "1    13659\n",
 484 |        "3      377\n",
 485 |        "Name: MARRIAGE, dtype: int64"
 486 |       ]
 487 |      },
 488 |      "execution_count": 95,
 489 |      "metadata": {},
 490 |      "output_type": "execute_result"
 491 |     }
 492 |    ],
 493 |    "source": [
 494 |     "credit['MARRIAGE'].value_counts()"
 495 |    ]
 496 |   },
 497 |   {
 498 |    "cell_type": "code",
 499 |    "execution_count": 96,
 500 |    "metadata": {
 501 |     "collapsed": false
 502 |    },
 503 |    "outputs": [
 504 |     {
 505 |      "data": {
 506 |       "text/plain": [
 507 |        "array([ 0.06673129,  0.01107916,  0.03071698,  0.01828816,  0.06729232,\n",
 508 |        "        0.10502206,  0.04590552,  0.03639505,  0.02706571,  0.02889421,\n",
 509 |        "        0.02720425,  0.05080295,  0.04753515,  0.04445718,  0.04384626,\n",
 510 |        "        0.04408402,  0.04320006,  0.04511946,  0.042764  ,  0.04264839,\n",
 511 |        "        0.04227342,  0.04214917,  0.04652524])"
 512 |       ]
 513 |      },
 514 |      "execution_count": 96,
 515 |      "metadata": {},
 516 |      "output_type": "execute_result"
 517 |     }
 518 |    ],
 519 |    "source": [
 520 |     "#lets check which features are important\n",
 521 |     "from sklearn.ensemble import ExtraTreesClassifier\n",
 522 |     "from sklearn.datasets import load_iris\n",
 523 |     "from sklearn.feature_selection import SelectFromModel\n",
 524 |     "X = credit.drop('default.payment.next.month',axis=1)\n",
 525 |     "Y = credit['default.payment.next.month']\n",
 526 |     "clf = ExtraTreesClassifier()\n",
 527 |     "clf = clf.fit(X, Y)\n",
 528 |     "clf.feature_importances_"
 529 |    ]
 530 |   },
 531 |   {
 532 |    "cell_type": "code",
 533 |    "execution_count": 97,
 534 |    "metadata": {
 535 |     "collapsed": true
 536 |    },
 537 |    "outputs": [],
 538 |    "source": [
 539 |     "#Feature Scaling"
 540 |    ]
 541 |   },
 542 |   {
 543 |    "cell_type": "code",
 544 |    "execution_count": 98,
 545 |    "metadata": {
 546 |     "collapsed": false
 547 |    },
 548 |    "outputs": [
 549 |     {
 550 |      "data": {
 551 |       "text/plain": [
 552 |        "<matplotlib.axes._subplots.AxesSubplot at 0x21fe2edd5c0>"
 553 |       ]
 554 |      },
 555 |      "execution_count": 98,
 556 |      "metadata": {},
 557 |      "output_type": "execute_result"
 558 |     },
 559 |     {
 560 |      "data": {
 561 |       "image/png": 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WuiJpbbqb6sHCGDMzK4lWzji6gcF0Z9QsYCginpH034EhSZuB88ADABFxStIQcBq4BmxL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\n",
 562 |       "text/plain": [
 563 |        "<matplotlib.figure.Figure at 0x21fe2ed1518>"
 564 |       ]
 565 |      },
 566 |      "metadata": {},
 567 |      "output_type": "display_data"
 568 |     }
 569 |    ],
 570 |    "source": [
 571 |     "%matplotlib inline\n",
 572 |     "#feature scaling check\n",
 573 |     "credit['BILL_AMT1'].hist(bins=60, color='orange')"
 574 |    ]
 575 |   },
 576 |   {
 577 |    "cell_type": "code",
 578 |    "execution_count": 99,
 579 |    "metadata": {
 580 |     "collapsed": false
 581 |    },
 582 |    "outputs": [
 583 |     {
 584 |      "data": {
 585 |       "text/plain": [
 586 |        "array([[ 0.01010101,  1.        ,  0.33333333, ...,  0.        ,\n",
 587 |        "         0.        ,  1.        ],\n",
 588 |        "       [ 0.11111111,  1.        ,  0.33333333, ...,  0.        ,\n",
 589 |        "         0.00378311,  1.        ],\n",
 590 |        "       [ 0.08080808,  1.        ,  0.33333333, ...,  0.00234451,\n",
 591 |        "         0.00945777,  0.        ],\n",
 592 |        "       ..., \n",
 593 |        "       [ 0.02020202,  0.        ,  0.33333333, ...,  0.00468901,\n",
 594 |        "         0.00586382,  1.        ],\n",
 595 |        "       [ 0.07070707,  0.        ,  0.66666667, ...,  0.12417444,\n",
 596 |        "         0.00341236,  1.        ],\n",
 597 |        "       [ 0.04040404,  0.        ,  0.33333333, ...,  0.00234451,\n",
 598 |        "         0.00189155,  1.        ]])"
 599 |       ]
 600 |      },
 601 |      "execution_count": 99,
 602 |      "metadata": {},
 603 |      "output_type": "execute_result"
 604 |     }
 605 |    ],
 606 |    "source": [
 607 |     "from sklearn.preprocessing import MinMaxScaler\n",
 608 |     "\n",
 609 |     "scaler = MinMaxScaler()\n",
 610 |     "scaler.fit_transform(credit)"
 611 |    ]
 612 |   },
 613 |   {
 614 |    "cell_type": "code",
 615 |    "execution_count": 100,
 616 |    "metadata": {
 617 |     "collapsed": false
 618 |    },
 619 |    "outputs": [
 620 |     {
 621 |      "data": {
 622 |       "text/plain": [
 623 |        "LIMIT_BAL                     0\n",
 624 |        "SEX                           0\n",
 625 |        "EDUCATION                     0\n",
 626 |        "MARRIAGE                      0\n",
 627 |        "AGE                           0\n",
 628 |        "PAY_1                         0\n",
 629 |        "PAY_2                         0\n",
 630 |        "PAY_3                         0\n",
 631 |        "PAY_4                         0\n",
 632 |        "PAY_5                         0\n",
 633 |        "PAY_6                         0\n",
 634 |        "BILL_AMT1                     0\n",
 635 |        "BILL_AMT2                     0\n",
 636 |        "BILL_AMT3                     0\n",
 637 |        "BILL_AMT4                     0\n",
 638 |        "BILL_AMT5                     0\n",
 639 |        "BILL_AMT6                     0\n",
 640 |        "PAY_AMT1                      0\n",
 641 |        "PAY_AMT2                      0\n",
 642 |        "PAY_AMT3                      0\n",
 643 |        "PAY_AMT4                      0\n",
 644 |        "PAY_AMT5                      0\n",
 645 |        "PAY_AMT6                      0\n",
 646 |        "default.payment.next.month    0\n",
 647 |        "dtype: int64"
 648 |       ]
 649 |      },
 650 |      "execution_count": 100,
 651 |      "metadata": {},
 652 |      "output_type": "execute_result"
 653 |     }
 654 |    ],
 655 |    "source": [
 656 |     "#check for null values\n",
 657 |     "credit.apply(lambda x: sum(x.isnull()),axis=0) "
 658 |    ]
 659 |   },
 660 |   {
 661 |    "cell_type": "code",
 662 |    "execution_count": 101,
 663 |    "metadata": {
 664 |     "collapsed": true
 665 |    },
 666 |    "outputs": [],
 667 |    "source": [
 668 |     "#Initial Model training(except feed forward neural network)\n",
 669 |     "from sklearn.model_selection import train_test_split\n",
 670 |     "\n",
 671 |     "train, test = train_test_split(credit, test_size = 0.3)\n",
 672 |     "\n",
 673 |     "X_train = train.drop('default.payment.next.month',axis=1)\n",
 674 |     "\n",
 675 |     "y_train = train['default.payment.next.month']\n",
 676 |     "\n",
 677 |     "X_test =  test.drop('default.payment.next.month',axis=1)\n",
 678 |     "\n",
 679 |     "y_test = test['default.payment.next.month']"
 680 |    ]
 681 |   },
 682 |   {
 683 |    "cell_type": "code",
 684 |    "execution_count": 102,
 685 |    "metadata": {
 686 |     "collapsed": false
 687 |    },
 688 |    "outputs": [
 689 |     {
 690 |      "data": {
 691 |       "text/plain": [
 692 |        "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
 693 |        "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
 694 |        "          penalty='l2', random_state=10, solver='liblinear', tol=0.0001,\n",
 695 |        "          verbose=0, warm_start=False)"
 696 |       ]
 697 |      },
 698 |      "execution_count": 102,
 699 |      "metadata": {},
 700 |      "output_type": "execute_result"
 701 |     }
 702 |    ],
 703 |    "source": [
 704 |     "#Train benchmark model(Log Regression)\n",
 705 |     "from sklearn.linear_model import LogisticRegression\n",
 706 |     "clf0 = LogisticRegression(random_state=10)\n",
 707 |     "clf0.fit(X_train, y_train)"
 708 |    ]
 709 |   },
 710 |   {
 711 |    "cell_type": "code",
 712 |    "execution_count": 103,
 713 |    "metadata": {
 714 |     "collapsed": false
 715 |    },
 716 |    "outputs": [
 717 |     {
 718 |      "name": "stdout",
 719 |      "output_type": "stream",
 720 |      "text": [
 721 |       "accuracy: 0.777\n",
 722 |       "Precision: 0.603729\n",
 723 |       "Recall: 0.777\n",
 724 |       "f1_score: 0.679492402926\n"
 725 |      ]
 726 |     },
 727 |     {
 728 |      "name": "stderr",
 729 |      "output_type": "stream",
 730 |      "text": [
 731 |       "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
 732 |       "  'precision', 'predicted', average, warn_for)\n",
 733 |       "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
 734 |       "  'precision', 'predicted', average, warn_for)\n"
 735 |      ]
 736 |     }
 737 |    ],
 738 |    "source": [
 739 |     "from sklearn.metrics import accuracy_score\n",
 740 |     "from sklearn.metrics import recall_score\n",
 741 |     "from sklearn.metrics import precision_score\n",
 742 |     "from sklearn.metrics import f1_score\n",
 743 |     "y_pred = clf0.predict(X_test)\n",
 744 |     "accuracy_regr=accuracy_score(y_test, y_pred)\n",
 745 |     "precision_regr=precision_score(y_test, y_pred,average='weighted')\n",
 746 |     "recall_regr=recall_score(y_test, y_pred,average='weighted')\n",
 747 |     "f1_regr=f1_score(y_test,y_pred,average='weighted')\n",
 748 |     "print(\"accuracy:\", accuracy_regr)\n",
 749 |     "print(\"Precision:\", precision_regr)\n",
 750 |     "print(\"Recall:\", recall_regr)\n",
 751 |     "print('f1_score:',f1_regr)"
 752 |    ]
 753 |   },
 754 |   {
 755 |    "cell_type": "code",
 756 |    "execution_count": 104,
 757 |    "metadata": {
 758 |     "collapsed": false
 759 |    },
 760 |    "outputs": [
 761 |     {
 762 |      "data": {
 763 |       "text/plain": [
 764 |        "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
 765 |        "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
 766 |        "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
 767 |        "            min_samples_leaf=1, min_samples_split=2,\n",
 768 |        "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
 769 |        "            oob_score=False, random_state=10, verbose=0, warm_start=False)"
 770 |       ]
 771 |      },
 772 |      "execution_count": 104,
 773 |      "metadata": {},
 774 |      "output_type": "execute_result"
 775 |     }
 776 |    ],
 777 |    "source": [
 778 |     "#Train other classifiers\n",
 779 |     "#We start with randomforest\n",
 780 |     "from sklearn.ensemble import RandomForestClassifier\n",
 781 |     "clf1=RandomForestClassifier(random_state=10)\n",
 782 |     "clf1.fit(X_train,y_train)"
 783 |    ]
 784 |   },
 785 |   {
 786 |    "cell_type": "code",
 787 |    "execution_count": 105,
 788 |    "metadata": {
 789 |     "collapsed": false
 790 |    },
 791 |    "outputs": [
 792 |     {
 793 |      "name": "stdout",
 794 |      "output_type": "stream",
 795 |      "text": [
 796 |       "accuracy: 0.807222222222\n",
 797 |       "Precision: 0.785871777618\n",
 798 |       "Recall: 0.807222222222\n",
 799 |       "f1_score: 0.783502576417\n"
 800 |      ]
 801 |     }
 802 |    ],
 803 |    "source": [
 804 |     "y_pred1 = clf1.predict(X_test)\n",
 805 |     "accuracy_rf=accuracy_score(y_test, y_pred1)\n",
 806 |     "precision_rf=precision_score(y_test, y_pred1,average='weighted')\n",
 807 |     "recall_rf=recall_score(y_test, y_pred1,average='weighted')\n",
 808 |     "f1_rf=f1_score(y_test,y_pred1,average='weighted')\n",
 809 |     "print(\"accuracy:\", accuracy_rf)\n",
 810 |     "print(\"Precision:\", precision_rf)\n",
 811 |     "print(\"Recall:\", recall_rf)\n",
 812 |     "print('f1_score:',f1_rf)"
 813 |    ]
 814 |   },
 815 |   {
 816 |    "cell_type": "code",
 817 |    "execution_count": 106,
 818 |    "metadata": {
 819 |     "collapsed": false
 820 |    },
 821 |    "outputs": [
 822 |     {
 823 |      "data": {
 824 |       "text/plain": [
 825 |        "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
 826 |        "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
 827 |        "              max_features=None, max_leaf_nodes=None,\n",
 828 |        "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
 829 |        "              min_samples_leaf=1, min_samples_split=2,\n",
 830 |        "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
 831 |        "              presort='auto', random_state=10, subsample=1.0, verbose=0,\n",
 832 |        "              warm_start=False)"
 833 |       ]
 834 |      },
 835 |      "execution_count": 106,
 836 |      "metadata": {},
 837 |      "output_type": "execute_result"
 838 |     }
 839 |    ],
 840 |    "source": [
 841 |     "#gradeint boost\n",
 842 |     "from sklearn.ensemble import GradientBoostingClassifier\n",
 843 |     "clf2=GradientBoostingClassifier(random_state=10)\n",
 844 |     "clf2.fit(X_train,y_train)"
 845 |    ]
 846 |   },
 847 |   {
 848 |    "cell_type": "code",
 849 |    "execution_count": 107,
 850 |    "metadata": {
 851 |     "collapsed": false
 852 |    },
 853 |    "outputs": [
 854 |     {
 855 |      "name": "stdout",
 856 |      "output_type": "stream",
 857 |      "text": [
 858 |       "accuracy: 0.821444444444\n",
 859 |       "Precision: 0.805451059511\n",
 860 |       "Recall: 0.821444444444\n",
 861 |       "f1_score: 0.800503291948\n"
 862 |      ]
 863 |     }
 864 |    ],
 865 |    "source": [
 866 |     "y_pred2 = clf2.predict(X_test)\n",
 867 |     "accuracy_gb=accuracy_score(y_test, y_pred2)\n",
 868 |     "precision_gb=precision_score(y_test, y_pred2,average='weighted')\n",
 869 |     "recall_gb=recall_score(y_test, y_pred2,average='weighted')\n",
 870 |     "f1_gb=f1_score(y_test,y_pred2,average='weighted')\n",
 871 |     "print(\"accuracy:\", accuracy_gb)\n",
 872 |     "print(\"Precision:\", precision_gb)\n",
 873 |     "print(\"Recall:\", recall_gb)\n",
 874 |     "print('f1_score:',f1_gb)"
 875 |    ]
 876 |   },
 877 |   {
 878 |    "cell_type": "code",
 879 |    "execution_count": 108,
 880 |    "metadata": {
 881 |     "collapsed": false
 882 |    },
 883 |    "outputs": [
 884 |     {
 885 |      "data": {
 886 |       "text/plain": [
 887 |        "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
 888 |        "          learning_rate=1.0, n_estimators=50, random_state=10)"
 889 |       ]
 890 |      },
 891 |      "execution_count": 108,
 892 |      "metadata": {},
 893 |      "output_type": "execute_result"
 894 |     }
 895 |    ],
 896 |    "source": [
 897 |     "#adaboost\n",
 898 |     "from sklearn.ensemble import AdaBoostClassifier\n",
 899 |     "clf3 = AdaBoostClassifier(random_state=10)\n",
 900 |     "clf3.fit(X_train, y_train)"
 901 |    ]
 902 |   },
 903 |   {
 904 |    "cell_type": "code",
 905 |    "execution_count": 109,
 906 |    "metadata": {
 907 |     "collapsed": false
 908 |    },
 909 |    "outputs": [
 910 |     {
 911 |      "name": "stdout",
 912 |      "output_type": "stream",
 913 |      "text": [
 914 |       "accuracy: 0.818555555556\n",
 915 |       "Precision: 0.802242885074\n",
 916 |       "Recall: 0.818555555556\n",
 917 |       "f1_score: 0.793428250173\n"
 918 |      ]
 919 |     }
 920 |    ],
 921 |    "source": [
 922 |     "y_pred3 = clf3.predict(X_test)\n",
 923 |     "accuracy_ab=accuracy_score(y_test, y_pred3)\n",
 924 |     "precision_ab=precision_score(y_test, y_pred3,average='weighted')\n",
 925 |     "recall_ab=recall_score(y_test, y_pred3,average='weighted')\n",
 926 |     "f1_ab=f1_score(y_test,y_pred3,average='weighted')\n",
 927 |     "print(\"accuracy:\", accuracy_ab)\n",
 928 |     "print(\"Precision:\", precision_ab)\n",
 929 |     "print(\"Recall:\", recall_ab)\n",
 930 |     "print('f1_score:',f1_ab)"
 931 |    ]
 932 |   },
 933 |   {
 934 |    "cell_type": "code",
 935 |    "execution_count": 110,
 936 |    "metadata": {
 937 |     "collapsed": false
 938 |    },
 939 |    "outputs": [
 940 |     {
 941 |      "data": {
 942 |       "text/plain": [
 943 |        "VotingClassifier(estimators=[('rf', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
 944 |        "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
 945 |        "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
 946 |        "            min_samples_leaf=1, min_samples_split=2,\n",
 947 |        "            min_we...thm='SAMME.R', base_estimator=None,\n",
 948 |        "          learning_rate=1.0, n_estimators=50, random_state=10))],\n",
 949 |        "         flatten_transform=None, n_jobs=1, voting='hard', weights=None)"
 950 |       ]
 951 |      },
 952 |      "execution_count": 110,
 953 |      "metadata": {},
 954 |      "output_type": "execute_result"
 955 |     }
 956 |    ],
 957 |    "source": [
 958 |     "#voting classifier\n",
 959 |     "from sklearn.ensemble import VotingClassifier\n",
 960 |     "clf4 = VotingClassifier(estimators=[('rf',clf1),('gb', clf2),('ab',clf3)])\n",
 961 |     "clf4.fit(X_train,y_train)"
 962 |    ]
 963 |   },
 964 |   {
 965 |    "cell_type": "code",
 966 |    "execution_count": 111,
 967 |    "metadata": {
 968 |     "collapsed": false
 969 |    },
 970 |    "outputs": [
 971 |     {
 972 |      "name": "stdout",
 973 |      "output_type": "stream",
 974 |      "text": [
 975 |       "accuracy: 0.819777777778\n",
 976 |       "Precision: 0.803495820308\n",
 977 |       "Recall: 0.819777777778\n",
 978 |       "f1_score: 0.79663926916\n"
 979 |      ]
 980 |     }
 981 |    ],
 982 |    "source": [
 983 |     "y_pred4 = clf4.predict(X_test)\n",
 984 |     "accuracy_vc=accuracy_score(y_test, y_pred4)\n",
 985 |     "precision_vc=precision_score(y_test, y_pred4,average='weighted')\n",
 986 |     "recall_vc=recall_score(y_test, y_pred4,average='weighted')\n",
 987 |     "f1_vc=f1_score(y_test,y_pred4,average='weighted')\n",
 988 |     "print(\"accuracy:\", accuracy_vc)\n",
 989 |     "print(\"Precision:\", precision_vc)\n",
 990 |     "print(\"Recall:\", recall_vc)\n",
 991 |     "print('f1_score:',f1_vc)"
 992 |    ]
 993 |   },
 994 |   {
 995 |    "cell_type": "code",
 996 |    "execution_count": 112,
 997 |    "metadata": {
 998 |     "collapsed": false
 999 |    },
1000 |    "outputs": [],
1001 |    "source": [
1002 |     "#appending all socres\n",
1003 |     "arraycuracy=[]\n",
1004 |     "arraycuracy.append(accuracy_regr)\n",
1005 |     "arraycuracy.append(accuracy_rf)\n",
1006 |     "arraycuracy.append(accuracy_gb)\n",
1007 |     "arraycuracy.append(accuracy_ab)\n",
1008 |     "arraycuracy.append(accuracy_vc)"
1009 |    ]
1010 |   },
1011 |   {
1012 |    "cell_type": "code",
1013 |    "execution_count": 113,
1014 |    "metadata": {
1015 |     "collapsed": false
1016 |    },
1017 |    "outputs": [],
1018 |    "source": [
1019 |     "precisionray=[]\n",
1020 |     "precisionray.append(precision_regr)\n",
1021 |     "precisionray.append(precision_rf)\n",
1022 |     "precisionray.append(precision_gb)\n",
1023 |     "precisionray.append(precision_ab)\n",
1024 |     "precisionray.append(precision_vc)"
1025 |    ]
1026 |   },
1027 |   {
1028 |    "cell_type": "code",
1029 |    "execution_count": 114,
1030 |    "metadata": {
1031 |     "collapsed": true
1032 |    },
1033 |    "outputs": [],
1034 |    "source": [
1035 |     "recallray=[]\n",
1036 |     "recallray.append(recall_regr)\n",
1037 |     "recallray.append(recall_rf)\n",
1038 |     "recallray.append(recall_gb)\n",
1039 |     "recallray.append(recall_ab)\n",
1040 |     "recallray.append(recall_vc)"
1041 |    ]
1042 |   },
1043 |   {
1044 |    "cell_type": "code",
1045 |    "execution_count": 115,
1046 |    "metadata": {
1047 |     "collapsed": true
1048 |    },
1049 |    "outputs": [],
1050 |    "source": [
1051 |     "f1ray=[]\n",
1052 |     "f1ray.append(f1_regr)\n",
1053 |     "f1ray.append(f1_rf)\n",
1054 |     "f1ray.append(f1_gb)\n",
1055 |     "f1ray.append(f1_ab)\n",
1056 |     "f1ray.append(f1_vc)"
1057 |    ]
1058 |   },
1059 |   {
1060 |    "cell_type": "code",
1061 |    "execution_count": 116,
1062 |    "metadata": {
1063 |     "collapsed": false
1064 |    },
1065 |    "outputs": [
1066 |     {
1067 |      "data": {
1068 |       "image/png": 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1069 |       "text/plain": [
1070 |        "<matplotlib.figure.Figure at 0x21f90fdd8d0>"
1071 |       ]
1072 |      },
1073 |      "metadata": {},
1074 |      "output_type": "display_data"
1075 |     }
1076 |    ],
1077 |    "source": [
1078 |     "#Lets compare Accuracy,precison,racall\n",
1079 |     "import matplotlib.pyplot as plt\n",
1080 |     "classifier_names=('Log_reg','random_forst','grad_boost','adaboost','voting_class')\n",
1081 |     "y_axis=np.arange(len(classifier_names))\n",
1082 |     "plt.bar(y_axis, arraycuracy, alpha=0.4,color='navy',ecolor='navy')\n",
1083 |     "plt.xticks(y_axis, classifier_names)\n",
1084 |     "plt.ylabel('Accuracy Scores')\n",
1085 |     "plt.title('Classifier comparison wrt Accuracy')\n",
1086 |     "plt.show()"
1087 |    ]
1088 |   },
1089 |   {
1090 |    "cell_type": "code",
1091 |    "execution_count": 117,
1092 |    "metadata": {
1093 |     "collapsed": false,
1094 |     "scrolled": true
1095 |    },
1096 |    "outputs": [
1097 |     {
1098 |      "data": {
1099 |       "image/png": 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Sjqw9EjMz67gq5xSOBt4r6SHgWVITUkTEobVGZmZm/a5KUnh77VGYmdkWocptLh4CdgNO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\nbfW0AAAAAElFTkSuQmCC\n",
1100 |       "text/plain": [
1101 |        "<matplotlib.figure.Figure at 0x21f92a26400>"
1102 |       ]
1103 |      },
1104 |      "metadata": {},
1105 |      "output_type": "display_data"
1106 |     }
1107 |    ],
1108 |    "source": [
1109 |     "y_axis=np.arange(len(classifier_names))\n",
1110 |     "plt.bar(y_axis,precisionray, alpha=0.4,color='purple',ecolor='darkgreen')\n",
1111 |     "plt.xticks(y_axis, classifier_names)\n",
1112 |     "plt.ylabel('Precision Scores')\n",
1113 |     "plt.title('Classifier comparison wrt Precision')\n",
1114 |     "plt.show()"
1115 |    ]
1116 |   },
1117 |   {
1118 |    "cell_type": "code",
1119 |    "execution_count": 118,
1120 |    "metadata": {
1121 |     "collapsed": false
1122 |    },
1123 |    "outputs": [
1124 |     {
1125 |      "data": {
1126 |       "image/png": 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PJR2o7iMdGKeQvpyyY/n5bEgKB5MS48Ic13WlNj5fesx3SFcz/wqcUyr/b+Ad\nDcr/D+kEYSpwWKP3ZBPr9Xrg5tL8EuD91E9Gxf5XZ3+cCtwObEfa/x8nvS/r7k8N4vkNcEppHx/E\nxklhELB9nh4NzMvTr0iUpPfgL0pt79oX+0Lt3+bctbA5OAr4fp7+b+DoUvmVefr7tQ+q466IeDBP\nH096cedKmp/n9yN9q+zNEfFERLxUar8nHor0rbMA75Z0D+ls9CA27n74Sf5/N2lHhfQG/S5ARPwc\neDKXHw1cHRFrI+LZ/Nhj8rIHI2JhRKwnJYsbI+21C0vtduWA3M7v8vx/5Vg6lLuXFgDfk/ReUkJq\n5M3ATyPi+Yh4hpSQ67U5DLhe0kLSmeVBubyz7dGZFRFxW57+Lmm7dbZunZU/DTwP/Kekd5I+2Nln\nJB1Hupo7KiIOJe0bv23wkG+REsTBwN+RDm4daj/o1O0PPkXELaT1XgXMknRmd9sotXUv8GpJe0s6\nlPR6HQb8ICJejojHgJtJB/eu/DwiXoiINcAfSQml0f60EUk7AUMj4uoc2/MRUftaDgC+nfe7K9nw\n/pwLnC1pKnBwfq5lwH6SviVpPPCnJtah25wUNo21pWkB/xWpH/2wiDggIqZW8XySRpLOVI+PiEOA\nn7PxG/qF/P9levfp9hdK0+tL8+t72W5ZeRu+jfRbHYeTkmtPn6PcZqMDX3f0xUFyHekk4cfA24Hr\nehhLZ3YBnoyI5yQdCLyR1AV0rKQ9JA0A/ramfsd3kr2/pq0JkraXtAfpTHsu6Sr3NEltkoaQDvh3\ndVYuaV/gsYj4NqmLruPHtl7KsXTXlcCpwGlsnPi7q7xf9/Y90pmPAY8BhwJjSV13dRNlRDyZ691E\n6qG4rIJ4nBS6cDupDxvgPaSdGlJf5Lvy9MTaB3XhRuBUSa8GkLR7flPMJb0pd8sHuXc1aqRJO5MO\nfE/nwcETmnjMLeTBN0knkLpVIK37yZIGSXoVcAobtkdfWAqMkDQqz7+PdEa3kTxwvk9E/Aq4kHTA\n2hF4hnSJXes24KR84NqRdJCtp7MDX2fbozPDJR2Vp88g9Tt3tm51y3Ocu0TEHNJB49C8vLN17K7r\ngG0l3U/qg78TeITUZXIHaZvdX6o/FbhS0t288uuhFwC/ym18ISIeBq7O5feRxuQ+FRGPNig/DrhP\n0r2kA/m/5bZnAAt6MND8Q9L78lRSgugsSfVkeza7P5HP7ldKOhmKu+0G1VTbBXgkX2W/j9RVRL1E\nme962yaYARJRAAAB1klEQVQirgL+kQ3Js29V0Se1Jf6RzmpXlv4+DuxL/YHm0aS+wgXAV4BVDdo9\njlK/ZS47jdQvuYDUddPR/z+J1Ef5G1JXwpd6sB4jyP2VeX4WaQD4RlKXz1m5fDkbBpDHAjfl6fLA\n6rdpbqC59vlOrbesq21D44HmjhgGkA60C3McU3L5a/P2rDfQPDVvg1uBq4APkc62xpbqTCBdnt9N\nGpTscnt0su1/S+o2uj8/V7cHmkn93Xfl9VnIhnGtN5P6yO+llwPN/f0vb7df5WlRf6B5AOn9fR/1\nB5ovKLW3CBjR2f7UII7RbDiG3E3qKi7eF3l5R6K8mDyOQjoxWZRf61uBkaSTg3vyPj4fOKGKbefv\nPuqBnO3/HBEhaSJp0Ln2V+V60u6OEfFsvlK4mvQlglf3tt2tXWm7DiKd+U+KiHtaHZdtmfr7/rSl\n3J+9uXkDcIkkAU+R7kzqC1MlvYXUn30DcE0ftbu1m5E/9LU9aTyn37yBrSX69f7kK4U+Iulg0h1K\nZS9ExJGtiGdzIuls4CM1xbdFxLmtiKe38qDqjXUWHR8Rj2/qeKz1JE2j9IG/7N8i4vJWxNMbTgpm\nZlbw3UdmZlZwUjAzs4KTgpmZFZwUzMys8P8BJlX67L5/ar0AAAAASUVORK5CYII=\n",
1127 |       "text/plain": [
1128 |        "<matplotlib.figure.Figure at 0x21f92ab56d8>"
1129 |       ]
1130 |      },
1131 |      "metadata": {},
1132 |      "output_type": "display_data"
1133 |     }
1134 |    ],
1135 |    "source": [
1136 |     "y_axis=np.arange(len(classifier_names))\n",
1137 |     "plt.bar(y_axis,recallray, alpha=0.4,color='brown',ecolor='black')\n",
1138 |     "plt.xticks(y_axis, classifier_names)\n",
1139 |     "plt.ylabel('recall Scores')\n",
1140 |     "plt.title('Classifier comparison wrt Recall')\n",
1141 |     "plt.show()"
1142 |    ]
1143 |   },
1144 |   {
1145 |    "cell_type": "code",
1146 |    "execution_count": 119,
1147 |    "metadata": {
1148 |     "collapsed": false
1149 |    },
1150 |    "outputs": [
1151 |     {
1152 |      "data": {
1153 |       "image/png": 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xTZNC7jaaHxF7Aff1TEhmZtZbmnYfRcQrwAJJg3soHjMz60WtXH20IzBf0gzg2c7CiHhX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\nIXXqDGg0xgAAAABJRU5ErkJggg==\n",
1154 |       "text/plain": [
1155 |        "<matplotlib.figure.Figure at 0x21f92b3ba58>"
1156 |       ]
1157 |      },
1158 |      "metadata": {},
1159 |      "output_type": "display_data"
1160 |     }
1161 |    ],
1162 |    "source": [
1163 |     "y_axis=np.arange(len(classifier_names))\n",
1164 |     "plt.bar(y_axis,f1ray, alpha=0.6,color='orange',ecolor='black')\n",
1165 |     "plt.xticks(y_axis, classifier_names)\n",
1166 |     "plt.ylabel('f1 Scores')\n",
1167 |     "plt.title('Classifier comparison wrt f1')\n",
1168 |     "plt.show()"
1169 |    ]
1170 |   },
1171 |   {
1172 |    "cell_type": "code",
1173 |    "execution_count": 120,
1174 |    "metadata": {
1175 |     "collapsed": false
1176 |    },
1177 |    "outputs": [
1178 |     {
1179 |      "name": "stdout",
1180 |      "output_type": "stream",
1181 |      "text": [
1182 |       "Best parameters for Adaboost: {'n_estimators': 15}\n"
1183 |      ]
1184 |     }
1185 |    ],
1186 |    "source": [
1187 |     "#Some fine REFINEMENTS to GBboost,Rfforest,and Adaboost\n",
1188 |     "#PERFROM GRIDSEARCH CV on RF,GB and Adaboost to find best parameter values on 10 fold cross validation set\n",
1189 |     "from sklearn.metrics import classification_report\n",
1190 |     "from sklearn.grid_search import GridSearchCV\n",
1191 |     "from sklearn.metrics import make_scorer\n",
1192 |     "from sklearn.metrics import fbeta_score\n",
1193 |     "from sklearn.ensemble import RandomForestClassifier\n",
1194 |     "from sklearn.ensemble import AdaBoostClassifier\n",
1195 |     "\n",
1196 |     "\n",
1197 |     "parameters_ada ={'n_estimators' :[15,25,50],\n",
1198 |     "            }\n",
1199 |     "\n",
1200 |     "\n",
1201 |     "\n",
1202 |     "clf = GridSearchCV(AdaBoostClassifier(random_state=10), parameters_ada,cv=10,scoring='accuracy')\n",
1203 |     "clf.fit(X_train, y_train)\n",
1204 |     "print(\"Best parameters for Adaboost:\",clf.best_params_)"
1205 |    ]
1206 |   },
1207 |   {
1208 |    "cell_type": "code",
1209 |    "execution_count": 121,
1210 |    "metadata": {
1211 |     "collapsed": false
1212 |    },
1213 |    "outputs": [
1214 |     {
1215 |      "name": "stdout",
1216 |      "output_type": "stream",
1217 |      "text": [
1218 |       "Best parameters for GradeintBoosting: {'min_samples_split': 15, 'n_estimators': 50}\n",
1219 |       "Best parameters for RandomForest: {'min_samples_split': 15, 'n_estimators': 50}\n"
1220 |      ]
1221 |     }
1222 |    ],
1223 |    "source": [
1224 |     "parameters_boost ={'n_estimators' :[15,25,50],\n",
1225 |     "             'min_samples_split' :[3,10,15]\n",
1226 |     "}\n",
1227 |     "\n",
1228 |     "clf00 = GridSearchCV(GradientBoostingClassifier(random_state=10), cv=10, param_grid=parameters_boost, scoring='accuracy')\n",
1229 |     "clf00.fit(X_train,y_train)\n",
1230 |     "print(\"Best parameters for GradeintBoosting:\",clf00.best_params_)\n",
1231 |     "\n",
1232 |     "\n",
1233 |     "clf001 = GridSearchCV(RandomForestClassifier(random_state=10), cv=10, param_grid=parameters_boost,scoring='accuracy')\n",
1234 |     "clf001.fit(X_train,y_train)\n",
1235 |     "print(\"Best parameters for RandomForest:\",clf001.best_params_)"
1236 |    ]
1237 |   },
1238 |   {
1239 |    "cell_type": "code",
1240 |    "execution_count": 195,
1241 |    "metadata": {
1242 |     "collapsed": false
1243 |    },
1244 |    "outputs": [],
1245 |    "source": [
1246 |     "#Use the above parameter values for our Weighted voting CLassifier\n",
1247 |     "\n",
1248 |     "#First we have to calibrate our classsifers\n",
1249 |     "\n",
1250 |     "#Isotonic claibration\n",
1251 |     "from sklearn.calibration import CalibratedClassifierCV\n",
1252 |     "clfvc1 = RandomForestClassifier(n_estimators=50,min_samples_split=15,random_state=10)\n",
1253 |     "clfvc2=AdaBoostClassifier(n_estimators=15,random_state=10)\n",
1254 |     "clfvc3=GradientBoostingClassifier(n_estimators=50,min_samples_split=15,random_state=10)\n",
1255 |     "clf_isotonic1 = CalibratedClassifierCV(clfvc1, method='isotonic',cv=10)\n",
1256 |     "clf_isotonic2 = CalibratedClassifierCV(clfvc2, method='isotonic',cv=10)\n",
1257 |     "clf_isotonic3 = CalibratedClassifierCV(clfvc3, method='isotonic',cv=10)"
1258 |    ]
1259 |   },
1260 |   {
1261 |    "cell_type": "code",
1262 |    "execution_count": 185,
1263 |    "metadata": {
1264 |     "collapsed": false
1265 |    },
1266 |    "outputs": [
1267 |     {
1268 |      "data": {
1269 |       "text/plain": [
1270 |        "VotingClassifier(estimators=[('rf', CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
1271 |        "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
1272 |        "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
1273 |        "            min_samples_leaf=1, m..., subsample=1.0, verbose=0,\n",
1274 |        "              warm_start=False),\n",
1275 |        "            cv=10, method='isotonic'))],\n",
1276 |        "         flatten_transform=None, n_jobs=1, voting='soft',\n",
1277 |        "         weights=[1, 1, 1])"
1278 |       ]
1279 |      },
1280 |      "execution_count": 185,
1281 |      "metadata": {},
1282 |      "output_type": "execute_result"
1283 |     }
1284 |    ],
1285 |    "source": [
1286 |     "#Inputing the istonic calibration to voting classifier with weights.\n",
1287 |     "import time\n",
1288 |     "t0 = time.clock()\n",
1289 |     "clfvc = VotingClassifier(estimators=[('rf',clf_isotonic1),('ab', clf_isotonic2),('gb',clf_isotonic3)],voting='soft',weights=[1,1,2])\n",
1290 |     "clfvc.fit(X_train,y_train)"
1291 |    ]
1292 |   },
1293 |   {
1294 |    "cell_type": "code",
1295 |    "execution_count": 186,
1296 |    "metadata": {
1297 |     "collapsed": true
1298 |    },
1299 |    "outputs": [],
1300 |    "source": [
1301 |     "y_predvc = clfvc.predict(X_test)\n",
1302 |     "accuracy_vciso=accuracy_score(y_test, y_predvc)"
1303 |    ]
1304 |   },
1305 |   {
1306 |    "cell_type": "code",
1307 |    "execution_count": 187,
1308 |    "metadata": {
1309 |     "collapsed": false
1310 |    },
1311 |    "outputs": [],
1312 |    "source": [
1313 |     "precision_vciso=precision_score(y_test, y_predvc)"
1314 |    ]
1315 |   },
1316 |   {
1317 |    "cell_type": "code",
1318 |    "execution_count": 188,
1319 |    "metadata": {
1320 |     "collapsed": false
1321 |    },
1322 |    "outputs": [
1323 |     {
1324 |      "name": "stdout",
1325 |      "output_type": "stream",
1326 |      "text": [
1327 |       "accuracy: 0.821666666667\n",
1328 |       "Precision: 0.805790548828\n",
1329 |       "Recall: 0.821666666667\n",
1330 |       "f1_score: 0.800497169577\n",
1331 |       "Training_time: 59.46775733333334\n"
1332 |      ]
1333 |     }
1334 |    ],
1335 |    "source": [
1336 |     "#Results of weighted voting classifer\n",
1337 |     "y_predvc = clfvc.predict(X_test)\n",
1338 |     "accuracy_vciso=accuracy_score(y_test, y_predvc)\n",
1339 |     "precision_vciso=precision_score(y_test, y_predvc,average='weighted')\n",
1340 |     "recall_vciso=recall_score(y_test, y_predvc,average='weighted')\n",
1341 |     "f1_vciso=f1_score(y_test,y_predvc,average='weighted')\n",
1342 |     "print(\"accuracy:\", accuracy_vciso)\n",
1343 |     "print(\"Precision:\", precision_vciso)\n",
1344 |     "print(\"Recall:\", recall_vciso)\n",
1345 |     "print('f1_score:',f1_vciso)\n",
1346 |     "print('Training_time:',time.clock() - t0)"
1347 |    ]
1348 |   },
1349 |   {
1350 |    "cell_type": "code",
1351 |    "execution_count": 193,
1352 |    "metadata": {
1353 |     "collapsed": false
1354 |    },
1355 |    "outputs": [
1356 |     {
1357 |      "data": {
1358 |       "text/plain": [
1359 |        "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
1360 |        "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
1361 |        "              max_features=None, max_leaf_nodes=None,\n",
1362 |        "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
1363 |        "              min_samples_leaf=1, min_samples_split=15,\n",
1364 |        "              min_weight_fraction_leaf=0.0, n_estimators=50,\n",
1365 |        "              presort='auto', random_state=10, subsample=1.0, verbose=0,\n",
1366 |        "              warm_start=False)"
1367 |       ]
1368 |      },
1369 |      "execution_count": 193,
1370 |      "metadata": {},
1371 |      "output_type": "execute_result"
1372 |     }
1373 |    ],
1374 |    "source": [
1375 |     "#Training just the Gradient Boost(clfvc3) with tuned parameters from above\n",
1376 |     "t1 = time.clock()\n",
1377 |     "clfvc3.fit(X_train,y_train)"
1378 |    ]
1379 |   },
1380 |   {
1381 |    "cell_type": "code",
1382 |    "execution_count": 194,
1383 |    "metadata": {
1384 |     "collapsed": false
1385 |    },
1386 |    "outputs": [
1387 |     {
1388 |      "name": "stdout",
1389 |      "output_type": "stream",
1390 |      "text": [
1391 |       "accuracy: 0.822888888889\n",
1392 |       "Precision: 0.807469736307\n",
1393 |       "Recall: 0.822888888889\n",
1394 |       "f1_score: 0.801832784112\n",
1395 |       "Training_time: 1.9779253333335873\n"
1396 |      ]
1397 |     }
1398 |    ],
1399 |    "source": [
1400 |     "#results of Tuned Gradient Boosting classifer\n",
1401 |     "y_predgb2 = clfvc3.predict(X_test)\n",
1402 |     "accuracy_gb2=accuracy_score(y_test, y_predgb2)\n",
1403 |     "precision_gb2=precision_score(y_test, y_predgb2,average='weighted')\n",
1404 |     "recall_gb2=recall_score(y_test, y_predgb2,average='weighted')\n",
1405 |     "f1_gb2=f1_score(y_test,y_predgb2,average='weighted')\n",
1406 |     "print(\"accuracy:\", accuracy_gb2)\n",
1407 |     "print(\"Precision:\", precision_gb2)\n",
1408 |     "print(\"Recall:\", recall_gb2)\n",
1409 |     "print('f1_score:',f1_gb2)\n",
1410 |     "print('Training_time:',time.clock() - t1)"
1411 |    ]
1412 |   },
1413 |   {
1414 |    "cell_type": "code",
1415 |    "execution_count": 196,
1416 |    "metadata": {
1417 |     "collapsed": false
1418 |    },
1419 |    "outputs": [
1420 |     {
1421 |      "data": {
1422 |       "text/plain": [
1423 |        "0.82220287299291184"
1424 |       ]
1425 |      },
1426 |      "execution_count": 196,
1427 |      "metadata": {},
1428 |      "output_type": "execute_result"
1429 |     }
1430 |    ],
1431 |    "source": [
1432 |     "#FINAL EVALUATION USING Stratified 10-fold CV on VC\n",
1433 |     "from sklearn.model_selection import cross_val_score\n",
1434 |     "#accuracy\n",
1435 |     "scores = cross_val_score(clfvc3, credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10)\n",
1436 |     "scores.mean()"
1437 |    ]
1438 |   },
1439 |   {
1440 |    "cell_type": "code",
1441 |    "execution_count": 197,
1442 |    "metadata": {
1443 |     "collapsed": false
1444 |    },
1445 |    "outputs": [
1446 |     {
1447 |      "data": {
1448 |       "text/plain": [
1449 |        "0.80021882447113324"
1450 |       ]
1451 |      },
1452 |      "execution_count": 197,
1453 |      "metadata": {},
1454 |      "output_type": "execute_result"
1455 |     }
1456 |    ],
1457 |    "source": [
1458 |     "from sklearn.model_selection import cross_val_score\n",
1459 |     "#f1\n",
1460 |     "scores1 = cross_val_score(clfvc3, credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='f1_weighted')\n",
1461 |     "scores1.mean()"
1462 |    ]
1463 |   },
1464 |   {
1465 |    "cell_type": "code",
1466 |    "execution_count": 198,
1467 |    "metadata": {
1468 |     "collapsed": false
1469 |    },
1470 |    "outputs": [
1471 |     {
1472 |      "data": {
1473 |       "text/plain": [
1474 |        "0.80645997468495467"
1475 |       ]
1476 |      },
1477 |      "execution_count": 198,
1478 |      "metadata": {},
1479 |      "output_type": "execute_result"
1480 |     }
1481 |    ],
1482 |    "source": [
1483 |     "#Precision\n",
1484 |     "scores2 = cross_val_score(clfvc3, credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='precision_weighted')\n",
1485 |     "scores2.mean()"
1486 |    ]
1487 |   },
1488 |   {
1489 |    "cell_type": "code",
1490 |    "execution_count": 199,
1491 |    "metadata": {
1492 |     "collapsed": false
1493 |    },
1494 |    "outputs": [
1495 |     {
1496 |      "data": {
1497 |       "text/plain": [
1498 |        "0.82220287299291184"
1499 |       ]
1500 |      },
1501 |      "execution_count": 199,
1502 |      "metadata": {},
1503 |      "output_type": "execute_result"
1504 |     }
1505 |    ],
1506 |    "source": [
1507 |     "#Recall\n",
1508 |     "scores3 = cross_val_score(clfvc3, credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='recall_weighted')\n",
1509 |     "scores3.mean()"
1510 |    ]
1511 |   },
1512 |   {
1513 |    "cell_type": "code",
1514 |    "execution_count": 200,
1515 |    "metadata": {
1516 |     "collapsed": false
1517 |    },
1518 |    "outputs": [
1519 |     {
1520 |      "name": "stdout",
1521 |      "output_type": "stream",
1522 |      "text": [
1523 |       "Accuracy: 0.822202872993\n",
1524 |       "Precision: 0.806459974685\n",
1525 |       "Recall: 0.822202872993\n",
1526 |       "F1: 0.800218824471\n"
1527 |      ]
1528 |     }
1529 |    ],
1530 |    "source": [
1531 |     "#corss validated final results of tuned Gradient Boosting\n",
1532 |     "print(\"Accuracy:\",scores.mean())\n",
1533 |     "print(\"Precision:\",scores2.mean())\n",
1534 |     "print(\"Recall:\",scores3.mean())\n",
1535 |     "print(\"F1:\",scores1.mean())"
1536 |    ]
1537 |   },
1538 |   {
1539 |    "cell_type": "code",
1540 |    "execution_count": 260,
1541 |    "metadata": {
1542 |     "collapsed": false
1543 |    },
1544 |    "outputs": [
1545 |     {
1546 |      "data": {
1547 |       "text/plain": [
1548 |        "0.68193712829259523"
1549 |       ]
1550 |      },
1551 |      "execution_count": 260,
1552 |      "metadata": {},
1553 |      "output_type": "execute_result"
1554 |     }
1555 |    ],
1556 |    "source": [
1557 |     "#lets crossvalidate log regression \n",
1558 |     "#f1\n",
1559 |     "import warnings\n",
1560 |     "warnings.filterwarnings(\"ignore\")\n",
1561 |     "scores4 = cross_val_score(LogisticRegression(C=0.8,random_state=10), credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='f1_weighted')\n",
1562 |     "scores4.mean()"
1563 |    ]
1564 |   },
1565 |   {
1566 |    "cell_type": "code",
1567 |    "execution_count": 261,
1568 |    "metadata": {
1569 |     "collapsed": false
1570 |    },
1571 |    "outputs": [
1572 |     {
1573 |      "data": {
1574 |       "text/plain": [
1575 |        "0.77876670255926317"
1576 |       ]
1577 |      },
1578 |      "execution_count": 261,
1579 |      "metadata": {},
1580 |      "output_type": "execute_result"
1581 |     }
1582 |    ],
1583 |    "source": [
1584 |     "#accuracy\n",
1585 |     "scores5 = cross_val_score(LogisticRegression(C=0.8,random_state=10), credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10)\n",
1586 |     "scores5.mean()"
1587 |    ]
1588 |   },
1589 |   {
1590 |    "cell_type": "code",
1591 |    "execution_count": 262,
1592 |    "metadata": {
1593 |     "collapsed": false
1594 |    },
1595 |    "outputs": [
1596 |     {
1597 |      "data": {
1598 |       "text/plain": [
1599 |        "0.60652374650316576"
1600 |       ]
1601 |      },
1602 |      "execution_count": 262,
1603 |      "metadata": {},
1604 |      "output_type": "execute_result"
1605 |     }
1606 |    ],
1607 |    "source": [
1608 |     "#precision \n",
1609 |     "scores6 = cross_val_score(LogisticRegression(C=0.8,random_state=10), credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='precision_weighted')\n",
1610 |     "scores6.mean()"
1611 |    ]
1612 |   },
1613 |   {
1614 |    "cell_type": "code",
1615 |    "execution_count": 263,
1616 |    "metadata": {
1617 |     "collapsed": false
1618 |    },
1619 |    "outputs": [
1620 |     {
1621 |      "data": {
1622 |       "text/plain": [
1623 |        "0.77876670255926317"
1624 |       ]
1625 |      },
1626 |      "execution_count": 263,
1627 |      "metadata": {},
1628 |      "output_type": "execute_result"
1629 |     }
1630 |    ],
1631 |    "source": [
1632 |     "#Recall\n",
1633 |     "scores7 = cross_val_score(LogisticRegression(C=0.8,random_state=10), credit.drop('default.payment.next.month',axis=1), credit['default.payment.next.month'],cv=10,scoring='recall_weighted')\n",
1634 |     "scores7.mean()"
1635 |    ]
1636 |   },
1637 |   {
1638 |    "cell_type": "code",
1639 |    "execution_count": 264,
1640 |    "metadata": {
1641 |     "collapsed": false
1642 |    },
1643 |    "outputs": [
1644 |     {
1645 |      "name": "stdout",
1646 |      "output_type": "stream",
1647 |      "text": [
1648 |       "Accuracy: 0.778766702559\n",
1649 |       "Precision: 0.606523746503\n",
1650 |       "Recall: 0.778766702559\n",
1651 |       "F1: 0.681937128293\n"
1652 |      ]
1653 |     }
1654 |    ],
1655 |    "source": [
1656 |     "print(\"Accuracy:\",scores5.mean())\n",
1657 |     "print(\"Precision:\",scores6.mean())\n",
1658 |     "print(\"Recall:\",scores7.mean())\n",
1659 |     "print(\"F1:\",scores4.mean())"
1660 |    ]
1661 |   },
1662 |   {
1663 |    "cell_type": "code",
1664 |    "execution_count": 254,
1665 |    "metadata": {
1666 |     "collapsed": false
1667 |    },
1668 |    "outputs": [
1669 |     {
1670 |      "data": {
1671 |       "image/png": 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DnAEAMIzD6gGAUJbMedbqEQCgV7FyBgDAMCFXzn6/X8uXL9exY8cUHh6uVatWKSEhQZLU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\nAAAASUVORK5CYII=\n",
1672 |       "text/plain": [
1673 |        "<matplotlib.figure.Figure at 0x21f919a60b8>"
1674 |       ]
1675 |      },
1676 |      "metadata": {},
1677 |      "output_type": "display_data"
1678 |     }
1679 |    ],
1680 |    "source": [
1681 |     "#Free form visualization of all metrics of Gradient boosting(final model) and Log_regression(benchmark model)\n",
1682 |     "import seaborn as sns\n",
1683 |     "all_scores=[]\n",
1684 |     "all_scores.append(scores)\n",
1685 |     "all_scores.append(scores2)\n",
1686 |     "all_scores.append(scores3)\n",
1687 |     "all_scores.append(scores1)\n",
1688 |     "eval1=[]\n",
1689 |     "eval1.append('Accuracy')\n",
1690 |     "eval1.append('Precision')\n",
1691 |     "eval1.append('Recall')\n",
1692 |     "eval1.append('F1')\n",
1693 |     "ax=sns.boxplot(y=all_scores,x=eval1,palette=(\"RdBu_r\"),linewidth=2.0,width=0.6)\n",
1694 |     "#ax.set(ylim=(0.76, 0.82))"
1695 |    ]
1696 |   }
1697 |  ],
1698 |  "metadata": {
1699 |   "kernelspec": {
1700 |    "display_name": "Python 3",
1701 |    "language": "python",
1702 |    "name": "python3"
1703 |   },
1704 |   "language_info": {
1705 |    "codemirror_mode": {
1706 |     "name": "ipython",
1707 |     "version": 3
1708 |    },
1709 |    "file_extension": ".py",
1710 |    "mimetype": "text/x-python",
1711 |    "name": "python",
1712 |    "nbconvert_exporter": "python",
1713 |    "pygments_lexer": "ipython3",
1714 |    "version": "3.6.0"
1715 |   }
1716 |  },
1717 |  "nbformat": 4,
1718 |  "nbformat_minor": 2
1719 | }
1720 | 


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/Project Report(By Nishant Sharma).pdf:
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https://raw.githubusercontent.com/nishant1005/Credit-Risk-Modeling-using-Machine-Learning/b0f8a0d6de3eb4c9072f65cff859d86d5f69d0fa/Project Report(By Nishant Sharma).pdf


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/Proposal.pdf:
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https://raw.githubusercontent.com/nishant1005/Credit-Risk-Modeling-using-Machine-Learning/b0f8a0d6de3eb4c9072f65cff859d86d5f69d0fa/Proposal.pdf


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/README.md:
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 1 | # Credit-Risk-Modeling-using-Machine/Deep-Learning
 2 | Captstone Project by Nishant Sharma
 3 | 
 4 | This is a comprehensive comparison of risk prediction models. 
 5 | 
 6 | 
 7 | Here is what you need to see with respect to the steps followed->
 8 | 
 9 | 1.Data analysis and visualization can be found in Xploratory_analysis.ipynb
10 | 
11 | 
12 | 2.Preprocessing and Feed Forward Deep Neural network training and scores can be found in deep_default.ipynb
13 | 
14 | 3.Preprocessing,ensemble methods training and model evaluations can be found in Preprocessing_modeling_refinements_evaluations.ipynb
15 | 
16 | 
17 | Results-
18 | Finally,Gradient Boosting with tuned parameters was selected with optimal training/testing scores accross 10 folds of data.
19 | 
20 | 
21 | 
22 | References:
23 | 
24 | http://ufldl.stanford.edu/tutorial/supervised/MultiLayerNeuralNetworks/
25 | 
26 | https://medium.com/towards-data-science/decision-trees-and-random-forests-df0c3123f991
27 | 
28 | http://www.fon.hum.uva.nl/praat/manual/Feedforward_neural_networks_1_1__The_learning_phase.html
29 | 
30 | http://vinhkhuc.github.io/2015/03/01/how-many-folds-for-cross-validation.html
31 | 
32 | http://fastml.com/classifier-calibration-with-platts-scaling-and-isotonic-regression/
33 | 
34 | http://scikit-learn.org/0.15/auto_examples/grid_search_digits.html
35 | 
36 | https://svds.com/learning-imbalanced-classes/
37 | 
38 | https://stats.stackexchange.com/questions/117643/why-use-stratified-cross-validation-why-does-this-not-damage-variance-related-b
39 | 
40 | https://keras.io/optimizers/
41 | 
42 | https://keras.io/models/sequential/
43 | 
44 | http://seaborn.pydata.org/generated/seaborn.boxplot.html#seaborn.boxplot
45 | 
46 | https://jmetzen.github.io/2015-04-14/calibration.html
47 | 
48 | 
49 | 


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/deep_default.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import pandas as pd\n",
 12 |     "import numpy as np\n",
 13 |     "seed=10\n",
 14 |     "np.random.seed(seed)\n",
 15 |     "\n",
 16 |     "import tensorflow as tf\n",
 17 |     "tf.set_random_seed(seed)"
 18 |    ]
 19 |   },
 20 |   {
 21 |    "cell_type": "code",
 22 |    "execution_count": 2,
 23 |    "metadata": {},
 24 |    "outputs": [
 25 |     {
 26 |      "name": "stderr",
 27 |      "output_type": "stream",
 28 |      "text": [
 29 |       "Using TensorFlow backend.\n"
 30 |      ]
 31 |     }
 32 |    ],
 33 |    "source": [
 34 |     "from keras.models import Sequential\n",
 35 |     "from keras.layers import Dense\n",
 36 |     "from keras.layers import Activation\n",
 37 |     "from keras.layers.core import Dropout\n",
 38 |     "from keras import optimizers"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "code",
 43 |    "execution_count": 3,
 44 |    "metadata": {
 45 |     "collapsed": true
 46 |    },
 47 |    "outputs": [],
 48 |    "source": [
 49 |     "import keras"
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 4,
 55 |    "metadata": {
 56 |     "collapsed": true
 57 |    },
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "credit=pd.read_csv(\"Credit.csv\")"
 61 |    ]
 62 |   },
 63 |   {
 64 |    "cell_type": "code",
 65 |    "execution_count": 5,
 66 |    "metadata": {},
 67 |    "outputs": [
 68 |     {
 69 |      "data": {
 70 |       "text/html": [
 71 |        "<div>\n",
 72 |        "<style>\n",
 73 |        "    .dataframe thead tr:only-child th {\n",
 74 |        "        text-align: right;\n",
 75 |        "    }\n",
 76 |        "\n",
 77 |        "    .dataframe thead th {\n",
 78 |        "        text-align: left;\n",
 79 |        "    }\n",
 80 |        "\n",
 81 |        "    .dataframe tbody tr th {\n",
 82 |        "        vertical-align: top;\n",
 83 |        "    }\n",
 84 |        "</style>\n",
 85 |        "<table border=\"1\" class=\"dataframe\">\n",
 86 |        "  <thead>\n",
 87 |        "    <tr style=\"text-align: right;\">\n",
 88 |        "      <th></th>\n",
 89 |        "      <th>ID</th>\n",
 90 |        "      <th>LIMIT_BAL</th>\n",
 91 |        "      <th>SEX</th>\n",
 92 |        "      <th>EDUCATION</th>\n",
 93 |        "      <th>MARRIAGE</th>\n",
 94 |        "      <th>AGE</th>\n",
 95 |        "      <th>PAY_0</th>\n",
 96 |        "      <th>PAY_2</th>\n",
 97 |        "      <th>PAY_3</th>\n",
 98 |        "      <th>PAY_4</th>\n",
 99 |        "      <th>...</th>\n",
100 |        "      <th>BILL_AMT4</th>\n",
101 |        "      <th>BILL_AMT5</th>\n",
102 |        "      <th>BILL_AMT6</th>\n",
103 |        "      <th>PAY_AMT1</th>\n",
104 |        "      <th>PAY_AMT2</th>\n",
105 |        "      <th>PAY_AMT3</th>\n",
106 |        "      <th>PAY_AMT4</th>\n",
107 |        "      <th>PAY_AMT5</th>\n",
108 |        "      <th>PAY_AMT6</th>\n",
109 |        "      <th>default.payment.next.month</th>\n",
110 |        "    </tr>\n",
111 |        "  </thead>\n",
112 |        "  <tbody>\n",
113 |        "    <tr>\n",
114 |        "      <th>0</th>\n",
115 |        "      <td>1</td>\n",
116 |        "      <td>20000.0</td>\n",
117 |        "      <td>2</td>\n",
118 |        "      <td>2</td>\n",
119 |        "      <td>1</td>\n",
120 |        "      <td>24</td>\n",
121 |        "      <td>2</td>\n",
122 |        "      <td>2</td>\n",
123 |        "      <td>-1</td>\n",
124 |        "      <td>-1</td>\n",
125 |        "      <td>...</td>\n",
126 |        "      <td>0.0</td>\n",
127 |        "      <td>0.0</td>\n",
128 |        "      <td>0.0</td>\n",
129 |        "      <td>0.0</td>\n",
130 |        "      <td>689.0</td>\n",
131 |        "      <td>0.0</td>\n",
132 |        "      <td>0.0</td>\n",
133 |        "      <td>0.0</td>\n",
134 |        "      <td>0.0</td>\n",
135 |        "      <td>1</td>\n",
136 |        "    </tr>\n",
137 |        "    <tr>\n",
138 |        "      <th>1</th>\n",
139 |        "      <td>2</td>\n",
140 |        "      <td>120000.0</td>\n",
141 |        "      <td>2</td>\n",
142 |        "      <td>2</td>\n",
143 |        "      <td>2</td>\n",
144 |        "      <td>26</td>\n",
145 |        "      <td>-1</td>\n",
146 |        "      <td>2</td>\n",
147 |        "      <td>0</td>\n",
148 |        "      <td>0</td>\n",
149 |        "      <td>...</td>\n",
150 |        "      <td>3272.0</td>\n",
151 |        "      <td>3455.0</td>\n",
152 |        "      <td>3261.0</td>\n",
153 |        "      <td>0.0</td>\n",
154 |        "      <td>1000.0</td>\n",
155 |        "      <td>1000.0</td>\n",
156 |        "      <td>1000.0</td>\n",
157 |        "      <td>0.0</td>\n",
158 |        "      <td>2000.0</td>\n",
159 |        "      <td>1</td>\n",
160 |        "    </tr>\n",
161 |        "    <tr>\n",
162 |        "      <th>2</th>\n",
163 |        "      <td>3</td>\n",
164 |        "      <td>90000.0</td>\n",
165 |        "      <td>2</td>\n",
166 |        "      <td>2</td>\n",
167 |        "      <td>2</td>\n",
168 |        "      <td>34</td>\n",
169 |        "      <td>0</td>\n",
170 |        "      <td>0</td>\n",
171 |        "      <td>0</td>\n",
172 |        "      <td>0</td>\n",
173 |        "      <td>...</td>\n",
174 |        "      <td>14331.0</td>\n",
175 |        "      <td>14948.0</td>\n",
176 |        "      <td>15549.0</td>\n",
177 |        "      <td>1518.0</td>\n",
178 |        "      <td>1500.0</td>\n",
179 |        "      <td>1000.0</td>\n",
180 |        "      <td>1000.0</td>\n",
181 |        "      <td>1000.0</td>\n",
182 |        "      <td>5000.0</td>\n",
183 |        "      <td>0</td>\n",
184 |        "    </tr>\n",
185 |        "    <tr>\n",
186 |        "      <th>3</th>\n",
187 |        "      <td>4</td>\n",
188 |        "      <td>50000.0</td>\n",
189 |        "      <td>2</td>\n",
190 |        "      <td>2</td>\n",
191 |        "      <td>1</td>\n",
192 |        "      <td>37</td>\n",
193 |        "      <td>0</td>\n",
194 |        "      <td>0</td>\n",
195 |        "      <td>0</td>\n",
196 |        "      <td>0</td>\n",
197 |        "      <td>...</td>\n",
198 |        "      <td>28314.0</td>\n",
199 |        "      <td>28959.0</td>\n",
200 |        "      <td>29547.0</td>\n",
201 |        "      <td>2000.0</td>\n",
202 |        "      <td>2019.0</td>\n",
203 |        "      <td>1200.0</td>\n",
204 |        "      <td>1100.0</td>\n",
205 |        "      <td>1069.0</td>\n",
206 |        "      <td>1000.0</td>\n",
207 |        "      <td>0</td>\n",
208 |        "    </tr>\n",
209 |        "    <tr>\n",
210 |        "      <th>4</th>\n",
211 |        "      <td>5</td>\n",
212 |        "      <td>50000.0</td>\n",
213 |        "      <td>1</td>\n",
214 |        "      <td>2</td>\n",
215 |        "      <td>1</td>\n",
216 |        "      <td>57</td>\n",
217 |        "      <td>-1</td>\n",
218 |        "      <td>0</td>\n",
219 |        "      <td>-1</td>\n",
220 |        "      <td>0</td>\n",
221 |        "      <td>...</td>\n",
222 |        "      <td>20940.0</td>\n",
223 |        "      <td>19146.0</td>\n",
224 |        "      <td>19131.0</td>\n",
225 |        "      <td>2000.0</td>\n",
226 |        "      <td>36681.0</td>\n",
227 |        "      <td>10000.0</td>\n",
228 |        "      <td>9000.0</td>\n",
229 |        "      <td>689.0</td>\n",
230 |        "      <td>679.0</td>\n",
231 |        "      <td>0</td>\n",
232 |        "    </tr>\n",
233 |        "  </tbody>\n",
234 |        "</table>\n",
235 |        "<p>5 rows × 25 columns</p>\n",
236 |        "</div>"
237 |       ],
238 |       "text/plain": [
239 |        "   ID  LIMIT_BAL  SEX  EDUCATION  MARRIAGE  AGE  PAY_0  PAY_2  PAY_3  PAY_4  \\\n",
240 |        "0   1    20000.0    2          2         1   24      2      2     -1     -1   \n",
241 |        "1   2   120000.0    2          2         2   26     -1      2      0      0   \n",
242 |        "2   3    90000.0    2          2         2   34      0      0      0      0   \n",
243 |        "3   4    50000.0    2          2         1   37      0      0      0      0   \n",
244 |        "4   5    50000.0    1          2         1   57     -1      0     -1      0   \n",
245 |        "\n",
246 |        "              ...              BILL_AMT4  BILL_AMT5  BILL_AMT6  PAY_AMT1  \\\n",
247 |        "0             ...                    0.0        0.0        0.0       0.0   \n",
248 |        "1             ...                 3272.0     3455.0     3261.0       0.0   \n",
249 |        "2             ...                14331.0    14948.0    15549.0    1518.0   \n",
250 |        "3             ...                28314.0    28959.0    29547.0    2000.0   \n",
251 |        "4             ...                20940.0    19146.0    19131.0    2000.0   \n",
252 |        "\n",
253 |        "   PAY_AMT2  PAY_AMT3  PAY_AMT4  PAY_AMT5  PAY_AMT6  \\\n",
254 |        "0     689.0       0.0       0.0       0.0       0.0   \n",
255 |        "1    1000.0    1000.0    1000.0       0.0    2000.0   \n",
256 |        "2    1500.0    1000.0    1000.0    1000.0    5000.0   \n",
257 |        "3    2019.0    1200.0    1100.0    1069.0    1000.0   \n",
258 |        "4   36681.0   10000.0    9000.0     689.0     679.0   \n",
259 |        "\n",
260 |        "   default.payment.next.month  \n",
261 |        "0                           1  \n",
262 |        "1                           1  \n",
263 |        "2                           0  \n",
264 |        "3                           0  \n",
265 |        "4                           0  \n",
266 |        "\n",
267 |        "[5 rows x 25 columns]"
268 |       ]
269 |      },
270 |      "execution_count": 5,
271 |      "metadata": {},
272 |      "output_type": "execute_result"
273 |     }
274 |    ],
275 |    "source": [
276 |     "credit.head(5)"
277 |    ]
278 |   },
279 |   {
280 |    "cell_type": "code",
281 |    "execution_count": 6,
282 |    "metadata": {
283 |     "collapsed": true
284 |    },
285 |    "outputs": [],
286 |    "source": [
287 |     "#for convenience i have performed preprocessing here again,same is present for other algo's in preprocessing_and_modelling notebook\n",
288 |     "credit.rename(columns={'PAY_0':'PAY_1'}, inplace=True)\n",
289 |     "credit[\"EDUCATION\"]=credit[\"EDUCATION\"].map({0:4,1:1,2:2,3:3,4:4,5:4,6:4})\n",
290 |     "credit[\"MARRIAGE\"]=credit[\"MARRIAGE\"].map({0:3,1:1,2:2,3:3})\n",
291 |     "from sklearn.preprocessing import MinMaxScaler\n",
292 |     "\n",
293 |     "scaler = MinMaxScaler()\n",
294 |     "scaler.fit_transform(credit)\n",
295 |     "\n",
296 |     "#split\n",
297 |     "credit = credit.drop([\"ID\"],axis=1)\n",
298 |     "\n",
299 |     "\n",
300 |     "from sklearn.model_selection import train_test_split\n",
301 |     "\n",
302 |     "train, test = train_test_split(credit, test_size = 0.3)"
303 |    ]
304 |   },
305 |   {
306 |    "cell_type": "code",
307 |    "execution_count": 7,
308 |    "metadata": {
309 |     "collapsed": true
310 |    },
311 |    "outputs": [],
312 |    "source": [
313 |     "X_train = train.drop('default.payment.next.month',axis=1)\n",
314 |     "\n",
315 |     "y_train = train['default.payment.next.month']\n",
316 |     "\n",
317 |     "X_test =  test.drop('default.payment.next.month',axis=1)\n",
318 |     "\n",
319 |     "y_test = test['default.payment.next.month']"
320 |    ]
321 |   },
322 |   {
323 |    "cell_type": "code",
324 |    "execution_count": 40,
325 |    "metadata": {
326 |     "collapsed": true
327 |    },
328 |    "outputs": [],
329 |    "source": [
330 |     "model = Sequential()"
331 |    ]
332 |   },
333 |   {
334 |    "cell_type": "code",
335 |    "execution_count": 68,
336 |    "metadata": {
337 |     "collapsed": true
338 |    },
339 |    "outputs": [],
340 |    "source": [
341 |     "sgd = optimizers.SGD(lr=0.01, decay=1e-6)"
342 |    ]
343 |   },
344 |   {
345 |    "cell_type": "code",
346 |    "execution_count": 69,
347 |    "metadata": {
348 |     "collapsed": true
349 |    },
350 |    "outputs": [],
351 |    "source": [
352 |     "#Training Feed Forward Neural network.\n",
353 |     "model.add(Dense(128, input_dim=(23), activation='relu'))\n",
354 |     "model.add(Dense(64, activation='relu'))\n",
355 |     "model.add(Dense(32, activation='relu'))\n",
356 |     "model.add(Dense(32, activation='relu'))\n",
357 |     "model.add(Dense(16, activation='relu'))\n",
358 |     "model.add(Dense(8, activation='relu'))\n",
359 |     "model.add(Dense(4, activation='relu'))\n",
360 |     "\n",
361 |     "model.add(Dense(1,  activation='sigmoid'))"
362 |    ]
363 |   },
364 |   {
365 |    "cell_type": "code",
366 |    "execution_count": 70,
367 |    "metadata": {
368 |     "collapsed": true
369 |    },
370 |    "outputs": [],
371 |    "source": [
372 |     "model.compile(loss='binary_crossentropy', optimizer= sgd, metrics=['accuracy'])"
373 |    ]
374 |   },
375 |   {
376 |    "cell_type": "code",
377 |    "execution_count": 71,
378 |    "metadata": {},
379 |    "outputs": [
380 |     {
381 |      "name": "stdout",
382 |      "output_type": "stream",
383 |      "text": [
384 |       "Train on 21000 samples, validate on 9000 samples\n",
385 |       "Epoch 1/10\n",
386 |       "21000/21000 [==============================] - 21s - loss: 0.5717 - acc: 0.7801 - val_loss: 0.5340 - val_acc: 0.7758\n",
387 |       "Epoch 2/10\n",
388 |       "21000/21000 [==============================] - 13s - loss: 0.5274 - acc: 0.7801 - val_loss: 0.5323 - val_acc: 0.7758\n",
389 |       "Epoch 3/10\n",
390 |       "21000/21000 [==============================] - 13s - loss: 0.5269 - acc: 0.7801 - val_loss: 0.5323 - val_acc: 0.7758\n",
391 |       "Epoch 4/10\n",
392 |       "21000/21000 [==============================] - 13s - loss: 0.5268 - acc: 0.7801 - val_loss: 0.5322 - val_acc: 0.7758\n",
393 |       "Epoch 5/10\n",
394 |       "21000/21000 [==============================] - 12s - loss: 0.5268 - acc: 0.7801 - val_loss: 0.5323 - val_acc: 0.7758\n",
395 |       "Epoch 6/10\n",
396 |       "21000/21000 [==============================] - 12s - loss: 0.5268 - acc: 0.7801 - val_loss: 0.5322 - val_acc: 0.7758\n",
397 |       "Epoch 7/10\n",
398 |       "21000/21000 [==============================] - 12s - loss: 0.5268 - acc: 0.7801 - val_loss: 0.5325 - val_acc: 0.7758\n",
399 |       "Epoch 8/10\n",
400 |       "21000/21000 [==============================] - 12s - loss: 0.5268 - acc: 0.7801 - val_loss: 0.5323 - val_acc: 0.7758\n",
401 |       "Epoch 9/10\n",
402 |       "21000/21000 [==============================] - 12s - loss: 0.5269 - acc: 0.7801 - val_loss: 0.5322 - val_acc: 0.7758\n",
403 |       "Epoch 10/10\n",
404 |       "21000/21000 [==============================] - 12s - loss: 0.5268 - acc: 0.7801 - val_loss: 0.5323 - val_acc: 0.7758\n"
405 |      ]
406 |     },
407 |     {
408 |      "data": {
409 |       "text/plain": [
410 |        "<keras.callbacks.History at 0x1c9f2106c18>"
411 |       ]
412 |      },
413 |      "execution_count": 71,
414 |      "metadata": {},
415 |      "output_type": "execute_result"
416 |     }
417 |    ],
418 |    "source": [
419 |     "model.fit(np.array(X_train), np.array(y_train), batch_size=100,validation_data=(np.array(X_test),np.array(y_test)), epochs=10,verbose=1)"
420 |    ]
421 |   },
422 |   {
423 |    "cell_type": "code",
424 |    "execution_count": 53,
425 |    "metadata": {},
426 |    "outputs": [
427 |     {
428 |      "name": "stdout",
429 |      "output_type": "stream",
430 |      "text": [
431 |       "9000/9000 [==============================] - 3s     \n"
432 |      ]
433 |     },
434 |     {
435 |      "data": {
436 |       "text/plain": [
437 |        "[0.52837420458263817, 0.77577777777777779]"
438 |       ]
439 |      },
440 |      "execution_count": 53,
441 |      "metadata": {},
442 |      "output_type": "execute_result"
443 |     }
444 |    ],
445 |    "source": [
446 |     "#accuracy\n",
447 |     "model.evaluate(np.array(X_test),np.array(y_test))"
448 |    ]
449 |   },
450 |   {
451 |    "cell_type": "code",
452 |    "execution_count": 54,
453 |    "metadata": {
454 |     "collapsed": true
455 |    },
456 |    "outputs": [],
457 |    "source": [
458 |     "y_pred=model.predict(np.array(X_test))"
459 |    ]
460 |   },
461 |   {
462 |    "cell_type": "code",
463 |    "execution_count": 56,
464 |    "metadata": {},
465 |    "outputs": [
466 |     {
467 |      "name": "stderr",
468 |      "output_type": "stream",
469 |      "text": [
470 |       "C:\\ProgramData\\Anaconda3\\envs\\tensor\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
471 |       "  'precision', 'predicted', average, warn_for)\n"
472 |      ]
473 |     },
474 |     {
475 |      "data": {
476 |       "text/plain": [
477 |        "0.60183116049382712"
478 |       ]
479 |      },
480 |      "execution_count": 56,
481 |      "metadata": {},
482 |      "output_type": "execute_result"
483 |     }
484 |    ],
485 |    "source": [
486 |     "#precision\n",
487 |     "from sklearn.metrics import precision_score\n",
488 |     "precision_score(y_test, y_pred.round(), average='weighted')  "
489 |    ]
490 |   },
491 |   {
492 |    "cell_type": "code",
493 |    "execution_count": 58,
494 |    "metadata": {},
495 |    "outputs": [
496 |     {
497 |      "name": "stderr",
498 |      "output_type": "stream",
499 |      "text": [
500 |       "C:\\ProgramData\\Anaconda3\\envs\\tensor\\lib\\site-packages\\sklearn\\metrics\\classification.py:1113: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
501 |       "  'precision', 'predicted', average, warn_for)\n"
502 |      ]
503 |     }
504 |    ],
505 |    "source": [
506 |     "precision=precision_score(y_test, y_pred.round(), average='weighted') "
507 |    ]
508 |   },
509 |   {
510 |    "cell_type": "code",
511 |    "execution_count": 60,
512 |    "metadata": {
513 |     "collapsed": true
514 |    },
515 |    "outputs": [],
516 |    "source": [
517 |     "#recall\n",
518 |     "from sklearn.metrics import recall_score\n",
519 |     "\n",
520 |     "recall=recall_score(y_test, y_pred.round(), average='weighted')"
521 |    ]
522 |   },
523 |   {
524 |    "cell_type": "code",
525 |    "execution_count": 72,
526 |    "metadata": {},
527 |    "outputs": [
528 |     {
529 |      "data": {
530 |       "text/plain": [
531 |        "0.77577777777777779"
532 |       ]
533 |      },
534 |      "execution_count": 72,
535 |      "metadata": {},
536 |      "output_type": "execute_result"
537 |     }
538 |    ],
539 |    "source": [
540 |     "recall"
541 |    ]
542 |   },
543 |   {
544 |    "cell_type": "code",
545 |    "execution_count": 61,
546 |    "metadata": {},
547 |    "outputs": [
548 |     {
549 |      "data": {
550 |       "text/plain": [
551 |        "0.67782260598729127"
552 |       ]
553 |      },
554 |      "execution_count": 61,
555 |      "metadata": {},
556 |      "output_type": "execute_result"
557 |     }
558 |    ],
559 |    "source": [
560 |     "#F1 score\n",
561 |     "F1 = 2 * (precision * recall) / (precision + recall)\n",
562 |     "F1"
563 |    ]
564 |   }
565 |  ],
566 |  "metadata": {
567 |   "kernelspec": {
568 |    "display_name": "Python 3",
569 |    "language": "python",
570 |    "name": "python3"
571 |   },
572 |   "language_info": {
573 |    "codemirror_mode": {
574 |     "name": "ipython",
575 |     "version": 3
576 |    },
577 |    "file_extension": ".py",
578 |    "mimetype": "text/x-python",
579 |    "name": "python",
580 |    "nbconvert_exporter": "python",
581 |    "pygments_lexer": "ipython3",
582 |    "version": "3.5.3"
583 |   }
584 |  },
585 |  "nbformat": 4,
586 |  "nbformat_minor": 2
587 | }
588 | 


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