Machine learning project real state.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n",
       "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n",
       "1  0.00000   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n",
       "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n",
       "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n",
       "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n",
       "\n",
       "        B  LSTAT  MEDV  \n",
       "0  396.90   4.98  24.0  \n",
       "1  396.90   9.14  21.6  \n",
       "2  392.83   4.03  34.7  \n",
       "3  394.63   2.94  33.4  \n",
       "4  396.90   5.33  36.2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house =pd.read_csv('data.csv')\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     506 non-null    float64\n",
      " 1   ZN       506 non-null    float64\n",
      " 2   INDUS    506 non-null    float64\n",
      " 3   CHAS     506 non-null    int64  \n",
      " 4   NOX      506 non-null    float64\n",
      " 5   RM       506 non-null    float64\n",
      " 6   AGE      506 non-null    float64\n",
      " 7   DIS      506 non-null    float64\n",
      " 8   RAD      506 non-null    int64  \n",
      " 9   TAX      506 non-null    int64  \n",
      " 10  PTRATIO  506 non-null    float64\n",
      " 11  B        506 non-null    float64\n",
      " 12  LSTAT    506 non-null    float64\n",
      " 13  MEDV     506 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "house.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    471\n",
       "1     35\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house[\"CHAS\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>506.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.613470</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>22.532806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.601568</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>9.197104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.385000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>17.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.256510</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.690000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538000</td>\n",
       "      <td>6.208500</td>\n",
       "      <td>77.500000</td>\n",
       "      <td>3.207450</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>330.000000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>391.440000</td>\n",
       "      <td>11.360000</td>\n",
       "      <td>21.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.677082</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.780000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>12.126500</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613470   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601568   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.000000    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677082   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "# %matplotlib inline\n",
    "# house.hist(bins=50,figsize=(20,15))\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## train test splitting\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def split_train_test(data,test_ratio):\n",
    "    np.random.seed(24)\n",
    "    shuffled=np.random.permutation(len(data))\n",
    "    print(shuffled)\n",
    "    test_set_size = int(len(data)*test_ratio)\n",
    "    test_indices=shuffled[:test_set_size]\n",
    "    train_indices=shuffled[test_set_size:]\n",
    "    return data.iloc[train_indices],data.iloc[test_indices]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[368 105 269 119 351 189 172 136  32   3 241  31 244  81 270   6 201 266\n",
      " 157   5 135 481 413 116 493 184   0 278 450  77  90 218 265 402 147 111\n",
      " 488 130 232 502 328  88 377  17 299 404  19 501  37  29  70 200 267  22\n",
      " 296 308 498 462 320 408 448 426 358  25 453 405 213 443 449 386 222 480\n",
      " 464  61 412 271 275  68 285 352 417 317 442 165 103 473 380 427 394 472\n",
      " 467 206 126  44 179 202 505 100  82 226 416  84 456 118 477  76 104 370\n",
      "  59  57 205 153 440 309 236 345 142 177 304 282 251 454 436 187 219 216\n",
      "  48 428  79 319 310 461 256 489  72 295 463  40 344  46 393 197 211  16\n",
      "  15 446 152 406  51 504 324 500  64 238  94 132 366 400  97 451 196 154\n",
      " 260 115  14 188  71 161  23 127 210 131 155 162 441  65 490 114 486 314\n",
      "  47 125 396 369 158 148 108 124 475  54 357 460 248 437 239 339 376 335\n",
      " 203 169 495 398 362  26 185 146 311 468 485  78 186 121 220 215 465 306\n",
      "  13  80 457 150 139 280 492 237 322 364 365 385  91 471 491  30 333 430\n",
      " 359 438 252 378 421 249 397 138 141 307 113 411 382 476 183 109  63 367\n",
      " 403  45 128 433 479 281 410  92 487 176 325 292 383 230 287 102 298 303\n",
      " 174  20 334 332  86 360 420 415  93 208   2 381 261 235 221  87  99 224\n",
      " 455  34  21 137  73  41 243  18 122 291 425 178  11 247 257 225 182 434\n",
      " 167 316  49 101 340 190 228  69 355  53  56 175 482 173 389 112 445 255\n",
      " 331 373   1 327 272   7 144 497  62  85 258 156 294 407 181 347 318 159\n",
      " 305 337 422  66 246  83 253 286  98  58 273 349 229 264  67 496  52 242\n",
      " 474 313 262 469 414 459 212 470   9  39 315 424 452 276 392 284 245 143\n",
      " 372 277 268 240 293 168  89 439 336 171 348  38 151 160 494  50  43 180\n",
      " 166 117  36  35 204 140  95 300 214  24 106 435 354 346 288 390 193 234\n",
      "  27 432 447 330  42 484 279 361 198 195   8 199 363 323 191 423 341 350\n",
      "  75 290 342  96 301 164 107 194 149 458 110 478 233 120 170 444 133 263\n",
      " 429 312 297  10 466 388 259  33 283 250 503 419  74 217 431 374 321 302\n",
      " 134 209 353 326  55  60 499 289  12 254 384  28 223 163 409 391 375 274\n",
      " 329 231 399 371 483 379 395 338 227   4 207 123 356 129 145 401 343 192\n",
      " 387 418]\n"
     ]
    }
   ],
   "source": [
    "train_set,test_set=split_train_test(house,0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in train set:405\n",
      "Rows in test set:101\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"Rows in train set:{len(train_set)}\\nRows in test set:{len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows in train set:404\n",
      "Rows in test set:102\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_set,test_set=train_test_split(house,test_size=0.2,random_state=24)\n",
    "print(f\"Rows in train set:{len(train_set)}\\nRows in test set:{len(test_set)}\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "split=StratifiedShuffleSplit(n_splits=1,test_size=0.2,random_state=24)\n",
    "for train_index,test_index in split.split(house,house['CHAS']):\n",
    "    strat_train_set=house.loc[train_index]\n",
    "    strat_test_set=house.loc[test_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    95\n",
       "1     7\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " strat_test_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    376\n",
       "1     28\n",
       "Name: CHAS, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " strat_train_set['CHAS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 95/7\n",
    "# 376/28"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "house= strat_train_set.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##looking for correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_matrix=house.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.697012\n",
       "ZN         0.346538\n",
       "B          0.320943\n",
       "DIS        0.193358\n",
       "CHAS       0.179838\n",
       "AGE       -0.341311\n",
       "RAD       -0.350928\n",
       "CRIM      -0.360527\n",
       "NOX       -0.396309\n",
       "TAX       -0.452105\n",
       "INDUS     -0.468746\n",
       "PTRATIO   -0.512794\n",
       "LSTAT     -0.729451\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix['MEDV'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000203E48DAEB0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E494A340>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4979790>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E49A2C10>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000203E49CF100>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4A08430>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4A08520>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4A349D0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4A9A220>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4AC5670>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4AF1AF0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4B1CF40>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4B553D0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4B81820>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4BAEC70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000203E4BE6130>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "attributes=['MEDV','RM','ZN','LSTAT']\n",
    "scatter_matrix(house[attributes],figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e4fa2cd0>"
      ]
     },
     "execution_count": 19,
     "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": [
    "house.plot(kind=\"scatter\",x='RM',y='MEDV',alpha=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## trying out attribute combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "house[\"tpm\"]=house[\"TAX\"]/house[\"RM\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "      <th>tpm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>0.10469</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.41</td>\n",
       "      <td>1</td>\n",
       "      <td>0.447</td>\n",
       "      <td>7.267</td>\n",
       "      <td>49.0</td>\n",
       "      <td>4.7872</td>\n",
       "      <td>4</td>\n",
       "      <td>254</td>\n",
       "      <td>17.6</td>\n",
       "      <td>389.25</td>\n",
       "      <td>6.05</td>\n",
       "      <td>33.2</td>\n",
       "      <td>34.952525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>0.07950</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.69</td>\n",
       "      <td>0</td>\n",
       "      <td>0.411</td>\n",
       "      <td>6.579</td>\n",
       "      <td>35.9</td>\n",
       "      <td>10.7103</td>\n",
       "      <td>4</td>\n",
       "      <td>411</td>\n",
       "      <td>18.3</td>\n",
       "      <td>370.78</td>\n",
       "      <td>5.49</td>\n",
       "      <td>24.1</td>\n",
       "      <td>62.471500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0.01432</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0.411</td>\n",
       "      <td>6.816</td>\n",
       "      <td>40.5</td>\n",
       "      <td>8.3248</td>\n",
       "      <td>5</td>\n",
       "      <td>256</td>\n",
       "      <td>15.1</td>\n",
       "      <td>392.90</td>\n",
       "      <td>3.95</td>\n",
       "      <td>31.6</td>\n",
       "      <td>37.558685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0.02187</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.401</td>\n",
       "      <td>6.800</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6.2196</td>\n",
       "      <td>1</td>\n",
       "      <td>265</td>\n",
       "      <td>15.6</td>\n",
       "      <td>393.37</td>\n",
       "      <td>5.03</td>\n",
       "      <td>31.1</td>\n",
       "      <td>38.970588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>0.10290</td>\n",
       "      <td>30.0</td>\n",
       "      <td>4.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.428</td>\n",
       "      <td>6.358</td>\n",
       "      <td>52.9</td>\n",
       "      <td>7.0355</td>\n",
       "      <td>6</td>\n",
       "      <td>300</td>\n",
       "      <td>16.6</td>\n",
       "      <td>372.75</td>\n",
       "      <td>11.22</td>\n",
       "      <td>22.2</td>\n",
       "      <td>47.184649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        CRIM     ZN  INDUS  CHAS    NOX     RM   AGE      DIS  RAD  TAX  \\\n",
       "276  0.10469   40.0   6.41     1  0.447  7.267  49.0   4.7872    4  254   \n",
       "351  0.07950   60.0   1.69     0  0.411  6.579  35.9  10.7103    4  411   \n",
       "57   0.01432  100.0   1.32     0  0.411  6.816  40.5   8.3248    5  256   \n",
       "193  0.02187   60.0   2.93     0  0.401  6.800   9.9   6.2196    1  265   \n",
       "242  0.10290   30.0   4.93     0  0.428  6.358  52.9   7.0355    6  300   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV        tpm  \n",
       "276     17.6  389.25   6.05  33.2  34.952525  \n",
       "351     18.3  370.78   5.49  24.1  62.471500  \n",
       "57      15.1  392.90   3.95  31.6  37.558685  \n",
       "193     15.6  393.37   5.03  31.1  38.970588  \n",
       "242     16.6  372.75  11.22  22.2  47.184649  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MEDV       1.000000\n",
       "RM         0.697012\n",
       "ZN         0.346538\n",
       "B          0.320943\n",
       "DIS        0.193358\n",
       "CHAS       0.179838\n",
       "AGE       -0.341311\n",
       "RAD       -0.350928\n",
       "CRIM      -0.360527\n",
       "NOX       -0.396309\n",
       "TAX       -0.452105\n",
       "INDUS     -0.468746\n",
       "PTRATIO   -0.512794\n",
       "tpm       -0.514295\n",
       "LSTAT     -0.729451\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix=house.corr()\n",
    "corr_matrix[\"MEDV\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x203e1c184f0>"
      ]
     },
     "execution_count": 23,
     "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": [
    "house.plot(kind=\"scatter\",x=\"tpm\",y=\"MEDV\",alpha=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "house=strat_train_set.drop('MEDV',axis=1)\n",
    "house_labels=strat_train_set[\"MEDV\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to take care of missing attributes we have three optins.\n",
    "#1. get rid of missing data points.\n",
    "#2. get rid of the whole attribute.\n",
    "#3. set the value to some values(0 ,mean or median).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# option 1\n",
    "a=house.dropna(subset=[\"RM\"])\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 12)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# option 2\n",
    "house.drop(\"RM\",axis=1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "276    7.267\n",
       "351    6.579\n",
       "57     6.816\n",
       "193    6.800\n",
       "242    6.358\n",
       "       ...  \n",
       "32     5.950\n",
       "45     5.682\n",
       "35     5.933\n",
       "477    5.304\n",
       "1      6.421\n",
       "Name: RM, Length: 404, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# option3\n",
    "median=house[\"RM\"].median() # computing median for option 3.\n",
    "house[\"RM\"].fillna(median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# house.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SimpleImputer(strategy='median')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.impute import SimpleImputer\n",
    "imputer=SimpleImputer(strategy=\"median\")\n",
    "imputer.fit(house)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.29015e-01, 0.00000e+00, 8.56000e+00, 0.00000e+00, 5.32000e-01,\n",
       "       6.20550e+00, 7.62500e+01, 3.29860e+00, 5.00000e+00, 3.29000e+02,\n",
       "       1.88500e+01, 3.92045e+02, 1.06100e+01])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imputer.statistics_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=imputer.transform(house)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_tr=pd.DataFrame(X,columns=house.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 404 entries, 0 to 403\n",
      "Data columns (total 13 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   CRIM     404 non-null    float64\n",
      " 1   ZN       404 non-null    float64\n",
      " 2   INDUS    404 non-null    float64\n",
      " 3   CHAS     404 non-null    float64\n",
      " 4   NOX      404 non-null    float64\n",
      " 5   RM       404 non-null    float64\n",
      " 6   AGE      404 non-null    float64\n",
      " 7   DIS      404 non-null    float64\n",
      " 8   RAD      404 non-null    float64\n",
      " 9   TAX      404 non-null    float64\n",
      " 10  PTRATIO  404 non-null    float64\n",
      " 11  B        404 non-null    float64\n",
      " 12  LSTAT    404 non-null    float64\n",
      "dtypes: float64(13)\n",
      "memory usage: 41.2 KB\n"
     ]
    }
   ],
   "source": [
    "house_tr.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "      <td>404.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.297293</td>\n",
       "      <td>11.162129</td>\n",
       "      <td>10.925446</td>\n",
       "      <td>0.069307</td>\n",
       "      <td>0.551039</td>\n",
       "      <td>6.284889</td>\n",
       "      <td>68.143564</td>\n",
       "      <td>3.805468</td>\n",
       "      <td>9.138614</td>\n",
       "      <td>400.757426</td>\n",
       "      <td>18.387624</td>\n",
       "      <td>359.627698</td>\n",
       "      <td>12.343317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.492157</td>\n",
       "      <td>22.374000</td>\n",
       "      <td>6.848338</td>\n",
       "      <td>0.254290</td>\n",
       "      <td>0.114741</td>\n",
       "      <td>0.694356</td>\n",
       "      <td>27.654221</td>\n",
       "      <td>2.047852</td>\n",
       "      <td>8.476831</td>\n",
       "      <td>166.396222</td>\n",
       "      <td>2.191840</td>\n",
       "      <td>87.749116</td>\n",
       "      <td>6.991196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.460000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.389000</td>\n",
       "      <td>3.561000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>1.129600</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>1.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.081437</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.130000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.448000</td>\n",
       "      <td>5.895750</td>\n",
       "      <td>45.475000</td>\n",
       "      <td>2.110500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>277.000000</td>\n",
       "      <td>17.200000</td>\n",
       "      <td>376.745000</td>\n",
       "      <td>7.092500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.229015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.560000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.532000</td>\n",
       "      <td>6.205500</td>\n",
       "      <td>76.250000</td>\n",
       "      <td>3.298600</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>329.000000</td>\n",
       "      <td>18.850000</td>\n",
       "      <td>392.045000</td>\n",
       "      <td>10.610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.745413</td>\n",
       "      <td>18.500000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>6.620500</td>\n",
       "      <td>93.100000</td>\n",
       "      <td>5.234500</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>396.232500</td>\n",
       "      <td>16.305000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>88.976200</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871000</td>\n",
       "      <td>8.704000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.710300</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>711.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>396.900000</td>\n",
       "      <td>37.970000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
       "mean     3.297293   11.162129   10.925446    0.069307    0.551039    6.284889   \n",
       "std      8.492157   22.374000    6.848338    0.254290    0.114741    0.694356   \n",
       "min      0.000000    0.000000    0.460000    0.000000    0.389000    3.561000   \n",
       "25%      0.081437    0.000000    5.130000    0.000000    0.448000    5.895750   \n",
       "50%      0.229015    0.000000    8.560000    0.000000    0.532000    6.205500   \n",
       "75%      2.745413   18.500000   18.100000    0.000000    0.624000    6.620500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.704000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  404.000000  404.000000  404.000000  404.000000  404.000000  404.000000   \n",
       "mean    68.143564    3.805468    9.138614  400.757426   18.387624  359.627698   \n",
       "std     27.654221    2.047852    8.476831  166.396222    2.191840   87.749116   \n",
       "min      2.900000    1.129600    1.000000  188.000000   12.600000    0.320000   \n",
       "25%     45.475000    2.110500    4.000000  277.000000   17.200000  376.745000   \n",
       "50%     76.250000    3.298600    5.000000  329.000000   18.850000  392.045000   \n",
       "75%     93.100000    5.234500    8.000000  666.000000   20.200000  396.232500   \n",
       "max    100.000000   10.710300   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT  \n",
       "count  404.000000  \n",
       "mean    12.343317  \n",
       "std      6.991196  \n",
       "min      1.730000  \n",
       "25%      7.092500  \n",
       "50%     10.610000  \n",
       "75%     16.305000  \n",
       "max     37.970000  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_tr.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scikit-Learn Design"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# primarily there are three types of objects\n",
    "#1. estimaters=it estimates some parameter based on a dataset,eg. imputer it has a fit method which fits the datasets.\n",
    "#2.Tranformers= It takes input and returns output based on the learning from fit().\n",
    "#It also has a convinience function which fits and then transforms.\n",
    "#3.Predictors=For example linear regression is an exmaple of predictors .\n",
    " it also give score function which will evaluate the predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## feature scalling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#primarily there are two types of feature scalling methods:\n",
    "# 1. Min-Max scalling (Normalization)\n",
    "# (value - min)/(max - min)\n",
    "# skalearn provides a class called minmaxscalar for this\n",
    "# Standardization \n",
    "# (value-mean)/std\n",
    "# Sklearn provides a class called standard scalar for this"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## creating a pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "my_pipeline=Pipeline([\n",
    "    (\"imputer\",SimpleImputer(strategy=\"median\")),\n",
    "    (\"std.scaler\",StandardScaler())\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_num_tr=my_pipeline.fit_transform(house_tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(404, 13)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house_num_tr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## selecting a desired model for the company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "# model=LinearRegression()\n",
    "# model= DecisionTreeRegressor()\n",
    "model= RandomForestRegressor()\n",
    "model.fit(house_num_tr,house_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_data = house.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "some_labels = house_labels.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "prepared_data=my_pipeline.transform(some_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([34.361, 25.39 , 30.911, 29.638, 22.143])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(prepared_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[33.2, 24.1, 31.6, 31.1, 22.2]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(some_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "house_prediction=model.predict(house_num_tr)\n",
    "lin_mse=mean_squared_error(house_labels,house_prediction)\n",
    "lin_rmse=np.sqrt(lin_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2308268180132373"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## using better evaluation technique \n",
    "##cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 2 3  4 5 56 7 8 9 10\n",
    "from sklearn.model_selection import cross_val_score\n",
    "scores=cross_val_score(model,house_num_tr,house_labels,scoring=\"neg_mean_squared_error\",cv=10)\n",
    "rmse_scores=np.sqrt(-scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.31582741, 2.63398163, 2.98708559, 2.97962186, 3.10544129,\n",
       "       3.27384057, 5.28893571, 2.98414167, 2.93453022, 2.89850125])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_scores(scores):\n",
    "    print(\"scores\",scores)\n",
    "    print(\"mean\",scores.mean())\n",
    "    print(\"standard deviation\",scores.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores [2.31582741 2.63398163 2.98708559 2.97962186 3.10544129 3.27384057\n",
      " 5.28893571 2.98414167 2.93453022 2.89850125]\n",
      "mean 3.1401907207367374\n",
      "standard deviation 0.758042362559953\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(print_scores(rmse_scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['real_estate.joblib']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from joblib import dump ,load\n",
    "dump(model,\"real_estate.joblib\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## testing the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=strat_test_set.drop(\"MEDV\",axis=1)\n",
    "y_test=strat_test_set[\"MEDV\"].copy()\n",
    "x_test_prepared=my_pipeline.transform(x_test)\n",
    "final_predictions=model.predict(x_test_prepared)\n",
    "final_mse=mean_squared_error(y_test,final_predictions)\n",
    "final_rmse=np.sqrt(final_mse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17.064 20.049 11.489 32.25  13.507 19.42  17.862 20.958 22.606 17.009\n",
      " 21.27  10.37  23.472 14.981 14.788 24.681 21.054 43.834 21.95  13.258\n",
      " 25.744 32.887 23.368 16.1   26.428 13.192 30.908 22.179 24.129  9.863\n",
      " 11.194 21.724 19.816 18.953 12.596 15.519 28.05  21.939 22.137 12.031\n",
      " 14.878 22.484 23.015 48.331 17.956 34.116 20.829 20.081 24.163 28.675\n",
      " 14.05  33.238 16.841 26.772 21.957 20.159  8.883 24.138 16.816 12.214\n",
      " 19.618 10.479 20.885 41.705 14.683 19.757 34.572 15.308 31.365 22.347\n",
      "  8.405 18.304 46.267 23.223 27.311 13.728 19.314 34.901 10.15  17.657\n",
      " 15.301  7.378  9.583 42.266 20.698 17.815 16.309 33.085 22.789 24.884\n",
      " 19.119 20.038 20.319  9.965 24.616 19.071 19.91  31.664 32.472 20.044\n",
      " 23.438 11.279] [17.3, 16.2, 12.8, 15.0, 14.9, 20.4, 27.5, 20.5, 21.4, 17.0, 19.3, 10.8, 25.0, 10.2, 14.1, 25.3, 21.7, 50.0, 22.0, 12.5, 22.0, 34.6, 22.9, 15.6, 24.8, 9.6, 28.5, 19.1, 23.7, 10.9, 8.4, 20.1, 18.4, 17.8, 13.4, 16.4, 23.9, 21.2, 22.6, 13.3, 13.1, 21.4, 24.4, 50.0, 16.5, 35.4, 16.1, 21.4, 24.1, 30.1, 19.1, 31.5, 15.6, 24.5, 25.0, 19.0, 10.2, 17.8, 15.6, 11.9, 22.6, 11.5, 23.3, 42.8, 14.5, 17.4, 33.4, 23.2, 35.1, 21.9, 7.2, 21.7, 42.3, 24.1, 25.1, 11.7, 17.5, 36.2, 13.9, 18.4, 14.8, 5.0, 13.1, 21.9, 21.7, 19.5, 14.3, 32.9, 22.6, 25.2, 15.0, 20.8, 21.7, 7.0, 22.4, 18.7, 21.5, 31.6, 34.9, 19.8, 24.6, 13.8]\n"
     ]
    }
   ],
   "source": [
    "final_rmse\n",
    "print(final_predictions,list(y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.37641342,  1.29049918, -0.6601666 ,  3.66450153, -0.90785283,\n",
       "        1.41617451, -0.69310581,  0.47999026, -0.60694677, -0.88306929,\n",
       "       -0.35978914,  0.33799806, -0.90129356])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prepared_data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## END OF THE"
   ]
  }
 ],
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