Question1-Group-92.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# *** Import Libraries ***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns #for confusion matrix\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression,LogisticRegressionCV\n",
    "from sklearn.metrics import roc_curve, auc, confusion_matrix, classification_report,accuracy_score\n",
    "from scipy.special import expit\n",
    "from sklearn.metrics import accuracy_score\n",
    "from mlxtend.plotting import plot_decision_regions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Data Set (1 Points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "kidneyDiseaseData=pd.read_csv('./data/kidney_disease.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>...</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>7800</td>\n",
       "      <td>5.2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>7.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>6000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>31</td>\n",
       "      <td>7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>abnormal</td>\n",
       "      <td>present</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>poor</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>51.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>normal</td>\n",
       "      <td>normal</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>notpresent</td>\n",
       "      <td>...</td>\n",
       "      <td>35</td>\n",
       "      <td>7300</td>\n",
       "      <td>4.6</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>good</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>ckd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   age    bp     sg   al   su     rbc        pc         pcc          ba  \\\n",
       "0   0  48.0  80.0  1.020  1.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "1   1   7.0  50.0  1.020  4.0  0.0     NaN    normal  notpresent  notpresent   \n",
       "2   2  62.0  80.0  1.010  2.0  3.0  normal    normal  notpresent  notpresent   \n",
       "3   3  48.0  70.0  1.005  4.0  0.0  normal  abnormal     present  notpresent   \n",
       "4   4  51.0  80.0  1.010  2.0  0.0  normal    normal  notpresent  notpresent   \n",
       "\n",
       "   ...  pcv    wc   rc  htn   dm  cad appet   pe  ane classification  \n",
       "0  ...   44  7800  5.2  yes  yes   no  good   no   no            ckd  \n",
       "1  ...   38  6000  NaN   no   no   no  good   no   no            ckd  \n",
       "2  ...   31  7500  NaN   no  yes   no  poor   no  yes            ckd  \n",
       "3  ...   32  6700  3.9  yes   no   no  poor  yes  yes            ckd  \n",
       "4  ...   35  7300  4.6   no   no   no  good   no   no            ckd  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kidneyDiseaseData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>bgr</th>\n",
       "      <th>bu</th>\n",
       "      <th>sc</th>\n",
       "      <th>sod</th>\n",
       "      <th>pot</th>\n",
       "      <th>hemo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>400.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>388.000000</td>\n",
       "      <td>353.000000</td>\n",
       "      <td>354.000000</td>\n",
       "      <td>351.000000</td>\n",
       "      <td>356.000000</td>\n",
       "      <td>381.000000</td>\n",
       "      <td>383.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>312.000000</td>\n",
       "      <td>348.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>199.500000</td>\n",
       "      <td>51.483376</td>\n",
       "      <td>76.469072</td>\n",
       "      <td>1.017408</td>\n",
       "      <td>1.016949</td>\n",
       "      <td>0.450142</td>\n",
       "      <td>148.036517</td>\n",
       "      <td>57.425722</td>\n",
       "      <td>3.072454</td>\n",
       "      <td>137.528754</td>\n",
       "      <td>4.627244</td>\n",
       "      <td>12.526437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>115.614301</td>\n",
       "      <td>17.169714</td>\n",
       "      <td>13.683637</td>\n",
       "      <td>0.005717</td>\n",
       "      <td>1.352679</td>\n",
       "      <td>1.099191</td>\n",
       "      <td>79.281714</td>\n",
       "      <td>50.503006</td>\n",
       "      <td>5.741126</td>\n",
       "      <td>10.408752</td>\n",
       "      <td>3.193904</td>\n",
       "      <td>2.912587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>3.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.750000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.010000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>10.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>199.500000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>138.000000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>12.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>299.250000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.020000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>142.000000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>399.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>1.025000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>490.000000</td>\n",
       "      <td>391.000000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>17.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id         age          bp          sg          al          su  \\\n",
       "count  400.000000  391.000000  388.000000  353.000000  354.000000  351.000000   \n",
       "mean   199.500000   51.483376   76.469072    1.017408    1.016949    0.450142   \n",
       "std    115.614301   17.169714   13.683637    0.005717    1.352679    1.099191   \n",
       "min      0.000000    2.000000   50.000000    1.005000    0.000000    0.000000   \n",
       "25%     99.750000   42.000000   70.000000    1.010000    0.000000    0.000000   \n",
       "50%    199.500000   55.000000   80.000000    1.020000    0.000000    0.000000   \n",
       "75%    299.250000   64.500000   80.000000    1.020000    2.000000    0.000000   \n",
       "max    399.000000   90.000000  180.000000    1.025000    5.000000    5.000000   \n",
       "\n",
       "              bgr          bu          sc         sod         pot        hemo  \n",
       "count  356.000000  381.000000  383.000000  313.000000  312.000000  348.000000  \n",
       "mean   148.036517   57.425722    3.072454  137.528754    4.627244   12.526437  \n",
       "std     79.281714   50.503006    5.741126   10.408752    3.193904    2.912587  \n",
       "min     22.000000    1.500000    0.400000    4.500000    2.500000    3.100000  \n",
       "25%     99.000000   27.000000    0.900000  135.000000    3.800000   10.300000  \n",
       "50%    121.000000   42.000000    1.300000  138.000000    4.400000   12.650000  \n",
       "75%    163.000000   66.000000    2.800000  142.000000    4.900000   15.000000  \n",
       "max    490.000000  391.000000   76.000000  163.000000   47.000000   17.800000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kidneyDiseaseData.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Check the unique coloumn value for each column and pre process the data accordingly "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id  :  [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305\n",
      " 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323\n",
      " 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n",
      " 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359\n",
      " 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377\n",
      " 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395\n",
      " 396 397 398 399]\n",
      "age  :  [48.  7. 62. 51. 60. 68. 24. 52. 53. 50. 63. 40. 47. 61. 21. 42. 75. 69.\n",
      " nan 73. 70. 65. 76. 72. 82. 46. 45. 35. 54. 11. 59. 67. 15. 55. 44. 26.\n",
      " 64. 56.  5. 74. 38. 58. 71. 34. 17. 12. 43. 41. 57.  8. 39. 66. 81. 14.\n",
      " 27. 83. 30.  4.  3.  6. 32. 80. 49. 90. 78. 19.  2. 33. 36. 37. 23. 25.\n",
      " 20. 29. 28. 22. 79.]\n",
      "bp  :  [ 80.  50.  70.  90.  nan 100.  60. 110. 140. 180. 120.]\n",
      "sg  :  [1.02  1.01  1.005 1.015   nan 1.025]\n",
      "al  :  [ 1.  4.  2.  3.  0. nan  5.]\n",
      "su  :  [ 0.  3.  4.  1. nan  2.  5.]\n",
      "rbc  :  [nan 'normal' 'abnormal']\n",
      "pc  :  ['normal' 'abnormal' nan]\n",
      "pcc  :  ['notpresent' 'present' nan]\n",
      "ba  :  ['notpresent' 'present' nan]\n",
      "bgr  :  [121.  nan 423. 117. 106.  74. 100. 410. 138.  70. 490. 380. 208.  98.\n",
      " 157.  76.  99. 114. 263. 173.  95. 108. 156. 264. 123.  93. 107. 159.\n",
      " 140. 171. 270.  92. 137. 204.  79. 207. 124. 144.  91. 162. 246. 253.\n",
      " 141. 182.  86. 150. 146. 425. 112. 250. 360. 163. 129. 133. 102. 158.\n",
      " 165. 132. 104. 127. 415. 169. 251. 109. 280. 210. 219. 295.  94. 172.\n",
      " 101. 298. 153.  88. 226. 143. 115.  89. 297. 233. 294. 323. 125.  90.\n",
      " 308. 118. 224. 128. 122. 214. 213. 268. 256.  84. 105. 288. 139.  78.\n",
      " 273. 242. 424. 303. 148. 160. 192. 307. 220. 447. 309.  22. 111. 261.\n",
      " 215. 234. 131. 352.  80. 239. 110. 130. 184. 252. 113. 230. 341. 255.\n",
      " 103. 238. 248. 120. 241. 269. 201. 203. 463. 176.  82. 119.  97.  96.\n",
      "  81. 116. 134.  85.  83.  87.  75.]\n",
      "bu  :  [ 36.   18.   53.   56.   26.   25.   54.   31.   60.  107.   55.   72.\n",
      "  86.   90.  162.   46.   87.   27.  148.  180.  163.    nan  50.   75.\n",
      "  45.   28.  155.   33.   39.  153.   29.   65.  103.   70.   80.   20.\n",
      " 202.   77.   89.   24.   17.   32.  114.   66.   38.  164.  142.   96.\n",
      " 391.   15.  111.   73.   19.   92.   35.   16.  139.   48.   85.   98.\n",
      " 186.   37.   47.   52.   82.   51.  106.   22.  217.   88.  118.   50.1\n",
      "  71.   34.   40.   21.  219.   30.  125.  166.   49.  208.  176.   68.\n",
      " 145.  165.  322.   23.  235.  132.   76.   42.   44.   41.  113.    1.5\n",
      " 146.   58.  133.  137.   67.  115.  223.   98.6 158.   94.   74.  150.\n",
      "  61.   57.   95.  191.   93.  241.   64.   79.  215.  309.   10. ]\n",
      "sc  :  [ 1.2   0.8   1.8   3.8   1.4   1.1  24.    1.9   7.2   4.    2.7   2.1\n",
      "  4.6   4.1   9.6   2.2   5.2   1.3   1.6   3.9  76.    7.7    nan  2.4\n",
      "  7.3   1.5   2.5   2.    3.4   0.7   1.   10.8   6.3   5.9   0.9   3.\n",
      "  3.25  9.7   6.4   3.2  32.    0.6   6.1   3.3   6.7   8.5   2.8  15.\n",
      "  2.9   1.7   3.6   5.6   6.5   4.4  10.2  11.5   0.5  12.2   5.3   9.2\n",
      " 13.8  16.9   6.    7.1  18.    2.3  13.   48.1  14.2  16.4   2.6   7.5\n",
      "  4.3  18.1  11.8   9.3   6.8  13.5  12.8  11.9  12.   13.4  15.2  13.3\n",
      "  0.4 ]\n",
      "sod  :  [  nan 111.  142.  104.  114.  131.  138.  135.  130.  141.  139.    4.5\n",
      " 136.  129.  140.  132.  133.  134.  125.  163.  137.  128.  143.  127.\n",
      " 146.  126.  122.  147.  124.  115.  145.  113.  120.  150.  144. ]\n",
      "pot  :  [ nan  2.5  3.2  4.   3.7  4.2  5.8  3.4  6.4  4.9  4.1  4.3  5.2  3.8\n",
      "  4.6  3.9  4.7  5.9  4.8  4.4  6.6 39.   5.5  5.   3.5  3.6  7.6  2.9\n",
      "  4.5  5.7  5.4  5.3 47.   6.3  5.1  5.6  3.   2.8  2.7  6.5  3.3]\n",
      "hemo  :  [15.4 11.3  9.6 11.2 11.6 12.2 12.4 10.8  9.5  9.4  9.7  9.8  5.6  7.6\n",
      " 12.6 12.1 12.7 10.3  7.7 10.9  nan 11.1  9.9 12.5 12.9 10.1 12.  13.\n",
      "  7.9  9.3 15.  10.   8.6 13.6 10.2 10.5  6.6 11.   7.5 15.6 15.2  4.8\n",
      "  9.1  8.1 11.9 13.5  8.3  7.1 16.1 10.4  9.2  6.2 13.9 14.1  6.  11.8\n",
      " 11.7 11.4 14.   8.2 13.2  6.1  8.  12.3  8.4 14.3  9.   8.7 10.6 13.1\n",
      " 10.7  5.5  5.8  6.8  8.8  8.5 13.8 11.5  7.3 13.7 12.8 13.4  6.3  3.1\n",
      " 17.  15.9 14.5 15.5 16.2 14.4 14.2 16.3 14.8 16.5 15.7 13.3 14.6 16.4\n",
      " 16.9 16.  14.7 16.6 14.9 16.7 16.8 15.8 15.1 17.1 17.2 15.3 17.3 17.4\n",
      " 17.7 17.8 17.5 17.6]\n",
      "pcv  :  ['44' '38' '31' '32' '35' '39' '36' '33' '29' '28' nan '16' '24' '37' '30'\n",
      " '34' '40' '45' '27' '48' '\\t?' '52' '14' '22' '18' '42' '17' '46' '23'\n",
      " '19' '25' '41' '26' '15' '21' '43' '20' '\\t43' '47' '9' '49' '50' '53'\n",
      " '51' '54']\n",
      "wc  :  ['7800' '6000' '7500' '6700' '7300' nan '6900' '9600' '12100' '4500'\n",
      " '12200' '11000' '3800' '11400' '5300' '9200' '6200' '8300' '8400' '10300'\n",
      " '9800' '9100' '7900' '6400' '8600' '18900' '21600' '4300' '8500' '11300'\n",
      " '7200' '7700' '14600' '6300' '\\t6200' '7100' '11800' '9400' '5500' '5800'\n",
      " '13200' '12500' '5600' '7000' '11900' '10400' '10700' '12700' '6800'\n",
      " '6500' '13600' '10200' '9000' '14900' '8200' '15200' '5000' '16300'\n",
      " '12400' '\\t8400' '10500' '4200' '4700' '10900' '8100' '9500' '2200'\n",
      " '12800' '11200' '19100' '\\t?' '12300' '16700' '2600' '26400' '8800'\n",
      " '7400' '4900' '8000' '12000' '15700' '4100' '5700' '11500' '5400' '10800'\n",
      " '9900' '5200' '5900' '9300' '9700' '5100' '6600']\n",
      "rc  :  ['5.2' nan '3.9' '4.6' '4.4' '5' '4.0' '3.7' '3.8' '3.4' '2.6' '2.8' '4.3'\n",
      " '3.2' '3.6' '4' '4.1' '4.9' '2.5' '4.2' '4.5' '3.1' '4.7' '3.5' '6.0'\n",
      " '5.0' '2.1' '5.6' '2.3' '2.9' '2.7' '8.0' '3.3' '3.0' '3' '2.4' '4.8'\n",
      " '\\t?' '5.4' '6.1' '6.2' '6.3' '5.1' '5.8' '5.5' '5.3' '6.4' '5.7' '5.9'\n",
      " '6.5']\n",
      "htn  :  ['yes' 'no' nan]\n",
      "dm  :  ['yes' 'no' ' yes' '\\tno' '\\tyes' nan]\n",
      "cad  :  ['no' 'yes' '\\tno' nan]\n",
      "appet  :  ['good' 'poor' nan]\n",
      "pe  :  ['no' 'yes' nan]\n",
      "ane  :  ['no' 'yes' nan]\n",
      "classification  :  ['ckd' 'ckd\\t' 'notckd']\n"
     ]
    }
   ],
   "source": [
    "for col in kidneyDiseaseData:\n",
    "    print(col,\" : \",kidneyDiseaseData[col].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Cleaning and Processing data to make it suitable for the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "kidneyDiseaseData.replace(regex=True,inplace=True,to_replace=r'\\t',value=r'')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "kidneyDiseaseData=kidneyDiseaseData.apply(lambda x: x.str.strip() if x.dtype == \"object\" else x )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Map text class to 1/0 and do some cleaning \n",
    "kidneyDiseaseData[['htn','dm','cad','pe','ane']] = kidneyDiseaseData[['htn','dm','cad','pe','ane']].replace(to_replace={'yes':1,'no':0})\n",
    "kidneyDiseaseData[['rbc','pc']] = kidneyDiseaseData[['rbc','pc']].replace(to_replace={'normal':1,'abnormal':0})\n",
    "kidneyDiseaseData[['pcc','ba']] = kidneyDiseaseData[['pcc','ba']].replace(to_replace={'present':1,'notpresent':0})\n",
    "kidneyDiseaseData[['appet']] = kidneyDiseaseData[['appet']].replace(to_replace={'good':1,'poor':0})\n",
    "kidneyDiseaseData['classification'] = kidneyDiseaseData['classification'].replace(to_replace={'ckd':1,'notckd':0})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Data Filtering\n",
    "According to the question, they didn't ask to work on missing data, so we will not be focusing on the missing data part\n",
    "will directly drop all the  values which has null value "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "filteredKidneyDiseaseData=kidneyDiseaseData.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "filteredKidneyDiseaseData=filteredKidneyDiseaseData.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  int64\n",
       "age               float64\n",
       "bp                float64\n",
       "sg                float64\n",
       "al                float64\n",
       "su                float64\n",
       "rbc               float64\n",
       "pc                float64\n",
       "pcc               float64\n",
       "ba                float64\n",
       "bgr               float64\n",
       "bu                float64\n",
       "sc                float64\n",
       "sod               float64\n",
       "pot               float64\n",
       "hemo              float64\n",
       "pcv                object\n",
       "wc                 object\n",
       "rc                 object\n",
       "htn               float64\n",
       "dm                float64\n",
       "cad               float64\n",
       "appet             float64\n",
       "pe                float64\n",
       "ane               float64\n",
       "classification      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filteredKidneyDiseaseData.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "filteredKidneyDiseaseData.pcv=filteredKidneyDiseaseData.pcv.astype(int)\n",
    "filteredKidneyDiseaseData.wc=filteredKidneyDiseaseData.wc.astype(int)\n",
    "filteredKidneyDiseaseData.rc=filteredKidneyDiseaseData.rc.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 158 entries, 0 to 157\n",
      "Data columns (total 26 columns):\n",
      "id                158 non-null int64\n",
      "age               158 non-null float64\n",
      "bp                158 non-null float64\n",
      "sg                158 non-null float64\n",
      "al                158 non-null float64\n",
      "su                158 non-null float64\n",
      "rbc               158 non-null float64\n",
      "pc                158 non-null float64\n",
      "pcc               158 non-null float64\n",
      "ba                158 non-null float64\n",
      "bgr               158 non-null float64\n",
      "bu                158 non-null float64\n",
      "sc                158 non-null float64\n",
      "sod               158 non-null float64\n",
      "pot               158 non-null float64\n",
      "hemo              158 non-null float64\n",
      "pcv               158 non-null int32\n",
      "wc                158 non-null int32\n",
      "rc                158 non-null float64\n",
      "htn               158 non-null float64\n",
      "dm                158 non-null float64\n",
      "cad               158 non-null float64\n",
      "appet             158 non-null float64\n",
      "pe                158 non-null float64\n",
      "ane               158 non-null float64\n",
      "classification    158 non-null int64\n",
      "dtypes: float64(22), int32(2), int64(2)\n",
      "memory usage: 31.0 KB\n"
     ]
    }
   ],
   "source": [
    "filteredKidneyDiseaseData.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Extract X as all Columns except the first column and Y as last column (1 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>age</th>\n",
       "      <th>bp</th>\n",
       "      <th>sg</th>\n",
       "      <th>al</th>\n",
       "      <th>su</th>\n",
       "      <th>rbc</th>\n",
       "      <th>pc</th>\n",
       "      <th>pcc</th>\n",
       "      <th>ba</th>\n",
       "      <th>...</th>\n",
       "      <th>pcv</th>\n",
       "      <th>wc</th>\n",
       "      <th>rc</th>\n",
       "      <th>htn</th>\n",
       "      <th>dm</th>\n",
       "      <th>cad</th>\n",
       "      <th>appet</th>\n",
       "      <th>pe</th>\n",
       "      <th>ane</th>\n",
       "      <th>classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>48.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.005</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>6700</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>53.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.020</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>12100</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>63.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32</td>\n",
       "      <td>4500</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.010</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>11000</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>61.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.015</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>9200</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   age    bp     sg   al   su  rbc   pc  pcc   ba  ...  pcv     wc   rc  \\\n",
       "0   3  48.0  70.0  1.005  4.0  0.0  1.0  0.0  1.0  0.0  ...   32   6700  3.9   \n",
       "1   9  53.0  90.0  1.020  2.0  0.0  0.0  0.0  1.0  0.0  ...   29  12100  3.7   \n",
       "2  11  63.0  70.0  1.010  3.0  0.0  0.0  0.0  1.0  0.0  ...   32   4500  3.8   \n",
       "3  14  68.0  80.0  1.010  3.0  2.0  1.0  0.0  1.0  1.0  ...   16  11000  2.6   \n",
       "4  20  61.0  80.0  1.015  2.0  0.0  0.0  0.0  0.0  0.0  ...   24   9200  3.2   \n",
       "\n",
       "   htn   dm  cad  appet   pe  ane  classification  \n",
       "0  1.0  0.0  0.0    0.0  1.0  1.0               1  \n",
       "1  1.0  1.0  0.0    0.0  0.0  1.0               1  \n",
       "2  1.0  1.0  0.0    0.0  1.0  0.0               1  \n",
       "3  1.0  1.0  1.0    0.0  1.0  0.0               1  \n",
       "4  1.0  1.0  1.0    0.0  1.0  1.0               1  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filteredKidneyDiseaseData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=filteredKidneyDiseaseData[filteredKidneyDiseaseData.columns[1:25]]\n",
    "Y=filteredKidneyDiseaseData[filteredKidneyDiseaseData.columns[25:26]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Visualize the Data (2 Points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Visualize the correlation of the parameters using Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_df = X.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "mask = np.zeros_like(corr_df, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(15, 15))\n",
    "\n",
    "# Generate a custom diverging colormap\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr_df, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "plt.title('Correlations between different features')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the above graph, that the correlation value is ranging from <b>0.0</b> to <b>0.9</b>. So for more feature visualization we can neglect the feature with correlation greater than or equal to <b>0.85</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = np.full((corr_df.shape[0],), True, dtype=bool)\n",
    "for i in range(corr_df.shape[0]):\n",
    "    for j in range(i+1, corr_df.shape[0]):\n",
    "        if corr_df.iloc[i,j] >= 0.85:\n",
    "            if columns[j]:\n",
    "                columns[j] = False\n",
    "selected_columns = X.columns[columns]\n",
    "X_without_corr = X[selected_columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Graph between 'age' and 'bp' w.r.t disease classification and 'pc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\envs\\python3.7\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a29f72ae48>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "g=sns.FacetGrid(filteredKidneyDiseaseData,row='classification',col='pc',margin_titles=True,size=4)\n",
    "g.map(plt.scatter,'age','bp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that when age is in between 25 to 80, bp is in between 60 to 110 and if pc is abnormal then there is not a single person who have survived CKD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Graph between 'age' and 'bp' w.r.t disease classification and 'pcc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\envs\\python3.7\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2a1d60940>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "g=sns.FacetGrid(filteredKidneyDiseaseData,row='classification',col='pcc',margin_titles=True,size=4)\n",
    "g.map(plt.scatter,'age','bp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that when age is in between 40 to 80, bp is in between 60 to 120 and if pcc is present then there is not a single person who have survived CKD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Graph between 'age' and 'bp' w.r.t disease classification and 'ba'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\envs\\python3.7\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2a21fbda0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "g=sns.FacetGrid(filteredKidneyDiseaseData,row='classification',col='ba',margin_titles=True,size=4)\n",
    "g.map(plt.scatter,'age','bp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that when age is in between 40 to 80, bp is in between 60 to 110 and if ba is present then there is not a single person who have survived CKD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 Graph between 'age' and 'bp' w.r.t disease classification and 'htn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\envs\\python3.7\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2a2606ef0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "g=sns.FacetGrid(filteredKidneyDiseaseData,row='classification',col='htn',margin_titles=True,size=4)\n",
    "g.map(plt.scatter,'age','bp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that when age is in between 35 to 80, bp is in between 60 to 120 and if htn is yes then there is not a single person who have survived CKD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.7 Graph between 'age' and 'bp' w.r.t disease classification and 'gm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda3\\envs\\python3.7\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1a2a220f668>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 600x400 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=100)\n",
    "g=sns.FacetGrid(filteredKidneyDiseaseData,row='classification',col='dm',margin_titles=True,size=4)\n",
    "g.map(plt.scatter,'age','bp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly see that when age is in between 20 to 80, bp is in between 60 to 120 and if dm is yes then there is not a single person who have survived CKD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Spilliting the data into training set and testing set (1 point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.30, random_state=323,stratify= Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(110, 24)\n",
      "(48, 24)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.0 Perform 10-fold cross validation (1 Point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegressionCV(Cs=1,max_iter=100,solver='liblinear',class_weight=None,scoring='f1',random_state=323,cv=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Train a logistic regression model for the dataset (2 Points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chib\\AppData\\Roaming\\Python\\Python36\\site-packages\\sklearn\\utils\\validation.py:744: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=1, class_weight=None, cv=10, dual=False,\n",
       "                     fit_intercept=True, intercept_scaling=1.0, l1_ratios=None,\n",
       "                     max_iter=100, multi_class='auto', n_jobs=None,\n",
       "                     penalty='l2', random_state=323, refit=True, scoring='f1',\n",
       "                     solver='liblinear', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper function to plot the ROC curve\n",
    "def auc_scorer(clf, X, y, model): \n",
    "    if model=='RF':\n",
    "        fpr, tpr, _ = roc_curve(y, clf.predict_proba(X)[:,1])\n",
    "    elif model=='SVM':\n",
    "        fpr, tpr, _ = roc_curve(y, clf.decision_function(X))\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    plt.figure()    # Plot the ROC curve\n",
    "    plt.plot(fpr, tpr, label='ROC curve from '+model+' model (area = %0.3f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('ROC Curve')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "    return fpr,tpr,roc_auc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Display the coefficients and form the logistic regression equation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.00138475 -0.00641296 -0.00017249  0.00234727  0.00042903 -0.00047131\n",
      "  -0.00064851  0.00019823  0.00023777  0.01324939  0.02054754  0.00239543\n",
      "  -0.0308621  -0.00016347 -0.00606702 -0.01858142  0.00021269 -0.00204699\n",
      "   0.00054081  0.00033757  0.00010546 -0.00043611  0.00028637  0.00024795]]\n"
     ]
    }
   ],
   "source": [
    "### The coefficients value of logistic model\n",
    "print(lr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test_predict=lr.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(48, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_predict.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'x': X_test_predict[:,0], 'y': y_test['classification'].tolist()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values(by='x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a2a3001c88>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sigmoid_function = expit(df['x'] * lr.coef_[0][20] + lr.intercept_[0]).ravel()\n",
    "plt.plot(df['x'], sigmoid_function)\n",
    "plt.scatter(df['x'], df['y'], c=df['y'], cmap='rainbow', edgecolors='b')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Compute the accuracy and confusion matrix (1 Point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ********************************** Train Results Start Here ********************************** \n",
      "\n",
      "Detailed classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      1.00      0.97        80\n",
      "           1       1.00      0.83      0.91        30\n",
      "\n",
      "    accuracy                           0.95       110\n",
      "   macro avg       0.97      0.92      0.94       110\n",
      "weighted avg       0.96      0.95      0.95       110\n",
      "\n",
      "Confusion Matrix:\n",
      "[[80  0]\n",
      " [ 5 25]]\n",
      "The Accuracy Score is 95.0  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ********************************** Test Results Start Here ********************************** \n",
      "\n",
      "Detailed classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      1.00      0.99        35\n",
      "           1       1.00      0.92      0.96        13\n",
      "\n",
      "    accuracy                           0.98        48\n",
      "   macro avg       0.99      0.96      0.97        48\n",
      "weighted avg       0.98      0.98      0.98        48\n",
      "\n",
      "Confusion Matrix:\n",
      "[[35  0]\n",
      " [ 1 12]]\n",
      "The Accuracy Score is 98.0  %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "print(\" ********************************** Train Results Start Here ********************************** \\n\")\n",
    "\n",
    "\n",
    "\n",
    "print(\"Detailed classification report:\")\n",
    "y_true, lr_pred = y_train, lr.predict(X_train)\n",
    "print(classification_report(y_true, lr_pred))\n",
    "\n",
    "confusion = confusion_matrix(y_train, lr_pred)\n",
    "print('Confusion Matrix:')\n",
    "print(confusion)\n",
    "\n",
    "accuracyScore=accuracy_score(y_true, lr_pred, normalize=True)\n",
    "print(\"The Accuracy Score is\",str(round(accuracyScore*100,0)),\" %\")\n",
    "\n",
    "# Determine the false positive and true positive rates\n",
    "fpr,tpr,roc_auc = auc_scorer(lr, X_train, y_train, 'RF')\n",
    "\n",
    "print(\"\\n ********************************** Test Results Start Here ********************************** \\n\")\n",
    "\n",
    "print(\"Detailed classification report:\")\n",
    "y_true, lr_pred = y_test, lr.predict(X_test)\n",
    "print(classification_report(y_true, lr_pred))\n",
    "\n",
    "confusion = confusion_matrix(y_test, lr_pred)\n",
    "print('Confusion Matrix:')\n",
    "print(confusion)\n",
    "\n",
    "accuracyScore=accuracy_score(y_true, lr_pred, normalize=True)\n",
    "print(\"The Accuracy Score is\",str(round(accuracyScore*100,0)),\" %\")\n",
    "\n",
    "# Determine the false positive and true positive rates\n",
    "fpr,tpr,roc_auc = auc_scorer(lr, X_test, y_test, 'RF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Plot the decision boundary\n",
    "for a decision boundaries we will train the model Random Forest Model to get the important features from the data and then take two top important features to create the decision boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "Features in sorted order\n",
      "[(0.1991, 'al'), (0.1504, 'hemo'), (0.1419, 'pcv'), (0.1106, 'sc'), (0.1073, 'sg'), (0.0978, 'rc'), (0.0692, 'bu'), (0.0315, 'htn'), (0.0181, 'dm'), (0.0128, 'appet'), (0.0119, 'bgr'), (0.0086, 'pc'), (0.0069, 'su'), (0.0067, 'sod'), (0.0064, 'ane'), (0.006, 'wc'), (0.0054, 'ba'), (0.0043, 'pcc'), (0.0026, 'rbc'), (0.0012, 'bp'), (0.0009, 'pot'), (0.0005, 'age'), (0.0, 'pe'), (0.0, 'cad')]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf=RandomForestClassifier(n_estimators=100)\n",
    "clf.fit(X_train,y_train['classification'].values)\n",
    "print(clf.score(X_train,y_train))\n",
    "names = X_train.columns\n",
    "print(\"Features in sorted order\")\n",
    "print(sorted(zip(map(lambda x: round(x, 4), clf.feature_importances_), names),reverse=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_sample=X_train[['al','sc']].values\n",
    "y_train_sample=y_train['classification'].values\n",
    "\n",
    "X_test_sample=X_test[['al','sc']].values\n",
    "y_test_sample=y_test['classification'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logModel=LogisticRegression()\n",
    "logModel.fit(X_train_sample,y_train_sample)\n",
    "plot_decision_regions(X_train_sample,y_train_sample, clf=logModel, legend=2)\n",
    "plt.xlabel('First Two Features')\n",
    "plt.title('Logistic Regression on Training Data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.21692518e-02 -7.05617965e-02 -9.37054246e-07  3.29503158e-03\n",
      "   2.97135213e-04 -1.46932738e-04 -2.45143675e-04  5.43316515e-05\n",
      "   5.80397472e-05  8.19941899e-01  1.04893188e+00  7.92795072e-03\n",
      "  -2.32003160e-01 -6.73608813e-04 -1.79905491e-02 -1.73760644e-01\n",
      "   6.47973246e-01 -2.07480872e-03  2.16324033e-04  1.24883689e-04\n",
      "   2.57431649e-05 -1.45345202e-04  9.24454431e-05  7.43851562e-05]]\n"
     ]
    }
   ],
   "source": [
    "print(np.std(X_train.values, 0)*lr.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Create an output .csv file consisting actual Test set values of Y (column name: Actual) and Predictions of Y(column name: Predicted). (1 points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "results=pd.DataFrame({'Actual':y_test['classification'].tolist(),'Predicted':lr_pred})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('result/output.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:python3.7] *",
   "language": "python",
   "name": "conda-env-python3.7-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}