{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('/home/mahedi/projects/end_to_end_machine_learning_project/BostonHousing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>0.538</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
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       "      <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",
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       "    <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",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
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       "      <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",
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      "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  0.02731   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": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 14)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'medv': 'price'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
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       "      <td>21.6</td>\n",
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       "      <th>3</th>\n",
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       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.4</td>\n",
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       "      <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",
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      ],
      "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  0.02731   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  price  \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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "crim       0\n",
       "zn         0\n",
       "indus      0\n",
       "chas       0\n",
       "nox        0\n",
       "rm         0\n",
       "age        0\n",
       "dis        0\n",
       "rad        0\n",
       "tax        0\n",
       "ptratio    0\n",
       "b          0\n",
       "lstat      0\n",
       "price      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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  price    506 non-null    float64\n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 55.5 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>count</th>\n",
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       "      <th>crim</th>\n",
       "      <td>506.0</td>\n",
       "      <td>3.613524</td>\n",
       "      <td>8.601545</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>0.082045</td>\n",
       "      <td>0.25651</td>\n",
       "      <td>3.677083</td>\n",
       "      <td>88.9762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zn</th>\n",
       "      <td>506.0</td>\n",
       "      <td>11.363636</td>\n",
       "      <td>23.322453</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>12.500000</td>\n",
       "      <td>100.0000</td>\n",
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       "    <tr>\n",
       "      <th>indus</th>\n",
       "      <td>506.0</td>\n",
       "      <td>11.136779</td>\n",
       "      <td>6.860353</td>\n",
       "      <td>0.46000</td>\n",
       "      <td>5.190000</td>\n",
       "      <td>9.69000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>27.7400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chas</th>\n",
       "      <td>506.0</td>\n",
       "      <td>0.069170</td>\n",
       "      <td>0.253994</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nox</th>\n",
       "      <td>506.0</td>\n",
       "      <td>0.554695</td>\n",
       "      <td>0.115878</td>\n",
       "      <td>0.38500</td>\n",
       "      <td>0.449000</td>\n",
       "      <td>0.53800</td>\n",
       "      <td>0.624000</td>\n",
       "      <td>0.8710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rm</th>\n",
       "      <td>506.0</td>\n",
       "      <td>6.284634</td>\n",
       "      <td>0.702617</td>\n",
       "      <td>3.56100</td>\n",
       "      <td>5.885500</td>\n",
       "      <td>6.20850</td>\n",
       "      <td>6.623500</td>\n",
       "      <td>8.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>506.0</td>\n",
       "      <td>68.574901</td>\n",
       "      <td>28.148861</td>\n",
       "      <td>2.90000</td>\n",
       "      <td>45.025000</td>\n",
       "      <td>77.50000</td>\n",
       "      <td>94.075000</td>\n",
       "      <td>100.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>506.0</td>\n",
       "      <td>3.795043</td>\n",
       "      <td>2.105710</td>\n",
       "      <td>1.12960</td>\n",
       "      <td>2.100175</td>\n",
       "      <td>3.20745</td>\n",
       "      <td>5.188425</td>\n",
       "      <td>12.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rad</th>\n",
       "      <td>506.0</td>\n",
       "      <td>9.549407</td>\n",
       "      <td>8.707259</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>24.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax</th>\n",
       "      <td>506.0</td>\n",
       "      <td>408.237154</td>\n",
       "      <td>168.537116</td>\n",
       "      <td>187.00000</td>\n",
       "      <td>279.000000</td>\n",
       "      <td>330.00000</td>\n",
       "      <td>666.000000</td>\n",
       "      <td>711.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ptratio</th>\n",
       "      <td>506.0</td>\n",
       "      <td>18.455534</td>\n",
       "      <td>2.164946</td>\n",
       "      <td>12.60000</td>\n",
       "      <td>17.400000</td>\n",
       "      <td>19.05000</td>\n",
       "      <td>20.200000</td>\n",
       "      <td>22.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>506.0</td>\n",
       "      <td>356.674032</td>\n",
       "      <td>91.294864</td>\n",
       "      <td>0.32000</td>\n",
       "      <td>375.377500</td>\n",
       "      <td>391.44000</td>\n",
       "      <td>396.225000</td>\n",
       "      <td>396.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lstat</th>\n",
       "      <td>506.0</td>\n",
       "      <td>12.653063</td>\n",
       "      <td>7.141062</td>\n",
       "      <td>1.73000</td>\n",
       "      <td>6.950000</td>\n",
       "      <td>11.36000</td>\n",
       "      <td>16.955000</td>\n",
       "      <td>37.9700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>506.0</td>\n",
       "      <td>22.532806</td>\n",
       "      <td>9.197104</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>21.20000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>50.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count        mean         std        min         25%        50%  \\\n",
       "crim     506.0    3.613524    8.601545    0.00632    0.082045    0.25651   \n",
       "zn       506.0   11.363636   23.322453    0.00000    0.000000    0.00000   \n",
       "indus    506.0   11.136779    6.860353    0.46000    5.190000    9.69000   \n",
       "chas     506.0    0.069170    0.253994    0.00000    0.000000    0.00000   \n",
       "nox      506.0    0.554695    0.115878    0.38500    0.449000    0.53800   \n",
       "rm       506.0    6.284634    0.702617    3.56100    5.885500    6.20850   \n",
       "age      506.0   68.574901   28.148861    2.90000   45.025000   77.50000   \n",
       "dis      506.0    3.795043    2.105710    1.12960    2.100175    3.20745   \n",
       "rad      506.0    9.549407    8.707259    1.00000    4.000000    5.00000   \n",
       "tax      506.0  408.237154  168.537116  187.00000  279.000000  330.00000   \n",
       "ptratio  506.0   18.455534    2.164946   12.60000   17.400000   19.05000   \n",
       "b        506.0  356.674032   91.294864    0.32000  375.377500  391.44000   \n",
       "lstat    506.0   12.653063    7.141062    1.73000    6.950000   11.36000   \n",
       "price    506.0   22.532806    9.197104    5.00000   17.025000   21.20000   \n",
       "\n",
       "                75%       max  \n",
       "crim       3.677083   88.9762  \n",
       "zn        12.500000  100.0000  \n",
       "indus     18.100000   27.7400  \n",
       "chas       0.000000    1.0000  \n",
       "nox        0.624000    0.8710  \n",
       "rm         6.623500    8.7800  \n",
       "age       94.075000  100.0000  \n",
       "dis        5.188425   12.1265  \n",
       "rad       24.000000   24.0000  \n",
       "tax      666.000000  711.0000  \n",
       "ptratio   20.200000   22.0000  \n",
       "b        396.225000  396.9000  \n",
       "lstat     16.955000   37.9700  \n",
       "price     25.000000   50.0000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yAIyTnZ2thIQEpaSkaNSoUUpJSVFCQoKys7P9XRqARkaQAWCU7OxspaWlKTExUW63W6WlpXK73UpMTFRaWhphBrjE2CzLsvxdREMqKSmRw+FQcXEx91oCDFdRUaGEhAQlJiZq+fLlCgr6z+9iHo9HTqdTeXl5ys/P5zITYLiafn4zIwPAGC6XS3v37tXUqVN9QowkBQUFKT09XXv27JHL5fJThQAaG0EGgDEKCgokST179qy2v7K9chyAwEeQAWCMmJgYSVJeXl61/ZXtleMABD6CDABjJCcnKy4uThkZGfJ4PD59Ho9HmZmZio+PV3Jysp8qBNDYCDIAjGG32zVv3jzl5OTI6XT67FpyOp3KycnR3LlzWegLXEI4EA+AUVJTU5WVlaVJkyYpKSnJ2x4fH6+srCylpqb6sToAjY3t1wCMxMm+QGCr6ec3MzIAjGS32zVw4EB/lwHAz1gjAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGMuvQaaiokLTpk1TfHy8wsPD1blzZz3++OOyLMs7xrIsTZ8+XTExMQoPD9fgwYOVn5/vx6oBAEBT4dcgM3v2bC1YsEDPP/+8tm3bptmzZ2vOnDl67rnnvGPmzJmjP/zhD1q4cKE2btyoFi1aaOjQoSorK/Nj5QAAoCmwWedOfzSy4cOHKzo6WkuWLPG2jRgxQuHh4frzn/8sy7LUoUMHTZo0SZMnT5YkFRcXKzo6WkuXLtXIkSOrvGZ5ebnKy8u9j0tKShQbG6vi4mJFREQ0/B8KAABctJKSEjkcju/9/PbrjExSUpLWrFmjnTt3SpI+++wzvf/++7rlllskSXv27FFhYaEGDx7sfY7D4dANN9wgt9td7WtmZmbK4XB4v2JjYxv+DwIAAPwi2J9vPmXKFJWUlKhbt26y2+2qqKjQE088oTvuuEOSVFhYKEmKjo72eV50dLS377vS09M1ceJE7+PKGRkAABB4/Bpk3njjDb322mtatmyZevTooU8//VQTJkxQhw4dNGbMmDq9ZmhoqEJDQ+u5UgAA0BT5Ncg88sgjmjJlinetS2Jior788ktlZmZqzJgxat++vSSpqKhIMTEx3ucVFRXp2muv9UfJAACgCfHrGpmTJ08qKMi3BLvdLo/HI0mKj49X+/bttWbNGm9/SUmJNm7cqH79+jVqrQAAoOnx64zMj370Iz3xxBPq2LGjevTooU8++URPPfWUfvWrX0mSbDabJkyYoFmzZqlLly6Kj4/XtGnT1KFDBzmdTn+WDgAAmgC/BpnnnntO06ZN03333adDhw6pQ4cOuueeezR9+nTvmEcffVQnTpzQuHHjdOzYMQ0YMECrVq1SWFiYHysHAABNgV/PkWkMNd2HDgAAmg4jzpEBAAC4GAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABjLr3e/BoC6qqiokMvlUkFBgWJiYpScnCy73e7vsgA0MmZkABgnOztbCQkJSklJ0ahRo5SSkqKEhARlZ2f7uzQAjYwgA8Ao2dnZSktLU2Jiotxut0pLS+V2u5WYmKi0tDTCDHCJsVmWZfm7iIZUUlIih8Oh4uJiRURE+LscABehoqJCCQkJSkxM1PLlyxUU9J/fxTwej5xOp/Ly8pSfn89lJsBwNf38ZkYGgDFcLpf27t2rqVOn+oQYSQoKClJ6err27Nkjl8vlpwoBNDaCDABjFBQUSJJ69uxZbX9le+U4AIGPIAPAGDExMZKkvLy8avsr2yvHAQh8BBkAxkhOTlZcXJwyMjLk8Xh8+jwejzIzMxUfH6/k5GQ/VQigsRFkABjDbrdr3rx5ysnJkdPp9Nm15HQ6lZOTo7lz57LQF7iEcCAeAKOkpqYqKytLkyZNUlJSkrc9Pj5eWVlZSk1N9WN1ABob268BGImTfYHAVtPPb2ZkABjJbrdr4MCB/i4DgJ+xRgYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIuTfQEYiVsUAJCYkQFgoOzsbCUkJCglJUWjRo1SSkqKEhISlJ2d7e/SADQyggwAo2RnZystLU2JiYlyu90qLS2V2+1WYmKi0tLSCDPAJYa7XwMwRkVFhRISEpSYmKjly5crKOg/v4t5PB45nU7l5eUpPz+fy0yA4Wr6+c2MDABjuFwu7d27V1OnTvUJMZIUFBSk9PR07dmzRy6Xy08VAmhsBBkAxigoKJAk9ezZs9r+yvbKcQACH0EGgDFiYmIkSXl5edX2V7ZXjgMQ+AgyAIyRnJysuLg4ZWRkyOPx+PR5PB5lZmYqPj5eycnJfqoQQGMjyAAwht1u17x585STkyOn0+mza8npdConJ0dz585loS9wCeFAPABGSU1NVVZWliZNmqSkpCRve3x8vLKyspSamurH6gA0NrZfAzASJ/sCga2mn9/MyAAwkt1u18CBA/1dBgA/Y40MAAAwFkEGAAAYiyADAACMRZABAADGIsgAAABjEWQAAICxCDIAAMBYBBkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjOX3IHPgwAGNHj1al112mcLDw5WYmKh//etf3n7LsjR9+nTFxMQoPDxcgwcPVn5+vh8rBgAATYVfg8zRo0fVv39/NWvWTCtXrtTnn3+uefPmKTIy0jtmzpw5+sMf/qCFCxdq48aNatGihYYOHaqysjI/Vg4AAJoCm2VZlr/efMqUKfrggw/kcrmq7bcsSx06dNCkSZM0efJkSVJxcbGio6O1dOlSjRw58nvfo6SkRA6HQ8XFxYqIiKjX+gEAQMOo6ee3X2dk3nrrLV1//fW67bbb1K5dO/Xq1Usvvviit3/Pnj0qLCzU4MGDvW0Oh0M33HCD3G53ta9ZXl6ukpISny8AABCY/BpkvvjiCy1YsEBdunTR6tWrde+99+rBBx/UK6+8IkkqLCyUJEVHR/s8Lzo62tv3XZmZmXI4HN6v2NjYhv1DAAAAv/FrkPF4POrdu7cyMjLUq1cvjRs3TmPHjtXChQvr/Jrp6ekqLi72fu3fv78eKwYAAE2JX4NMTEyMrr76ap+27t27a9++fZKk9u3bS5KKiop8xhQVFXn7vis0NFQRERE+XwAAIDD5Ncj0799fO3bs8GnbuXOnrrzySklSfHy82rdvrzVr1nj7S0pKtHHjRvXr169RawUAAE1PsD/f/OGHH1ZSUpIyMjL0s5/9TB999JEWL16sxYsXS5JsNpsmTJigWbNmqUuXLoqPj9e0adPUoUMHOZ1Of5YOAACaAL8GmT59+ujvf/+70tPT9b//+7+Kj4/XM888ozvuuMM75tFHH9WJEyc0btw4HTt2TAMGDNCqVasUFhbmx8oBAEBT4NdzZBoD58gAAGAeI86RAQAAuBgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACMRZABAADGuqggU1ZWVl91AAAA1Fqtg4zH49Hjjz+uyy+/XC1bttQXX3whSZo2bZqWLFlS7wUCAACcT62DzKxZs7R06VLNmTNHISEh3vaePXvqpZdeqtfiAAAALqTWQeZPf/qTFi9erDvuuEN2u93bfs0112j79u31WhwAAMCF1DrIHDhwQAkJCVXaPR6Pzpw5Uy9FAQAA1EStg8zVV18tl8tVpT0rK0u9evWql6IAAABqIri2T5g+fbrGjBmjAwcOyOPxKDs7Wzt27NCf/vQn5eTkNESNAAAA1ar1jMxPfvITvf322/rnP/+pFi1aaPr06dq2bZvefvtt/fd//3dD1AgAVVRUVGjdunV6/fXXtW7dOlVUVPi7JAB+YLMsy/J3EQ2ppKREDodDxcXFioiI8Hc5AOpBdna2Jk2apL1793rb4uLiNG/ePKWmpvqvMAD1pqaf37WekenUqZO+/vrrKu3Hjh1Tp06davtyAFAr2dnZSktLU2Jiotxut0pLS+V2u5WYmKi0tDRlZ2f7u0QAjajWMzJBQUEqLCxUu3btfNqLiorUsWNHlZeX12uBF4sZGSBwVFRUKCEhQYmJiVq+fLmCgv7zu5jH45HT6VReXp7y8/N9jocAYJ6afn7XeLHvW2+95f1+9erVcjgc3scVFRVas2aN4uLi6lYtANSAy+XS3r179frrr/uEGOnbX7LS09OVlJQkl8ulgQMH+qdIAI2qxkHG6XRKkmw2m8aMGePT16xZM+/1aQBoKAUFBZK+PUm8OpXtleMABL4aBxmPxyNJio+P18cff6yoqKgGKwoAqhMTEyNJysvLU9++fav05+Xl+YwDEPjYtQTAGKyRAS4d9b5G5lwnTpzQ+vXrtW/fPp0+fdqn78EHH6zLSwLA97Lb7Zo3b57S0tLkdDqVnp6unj17Ki8vT5mZmcrJyVFWVhYhBriE1HpG5pNPPtGtt96qkydP6sSJE2rTpo2OHDmi5s2bq127dvriiy8aqtY6YUYGCDzVnSMTHx+vuXPnco4MECBq+vld6yAzcOBAXXXVVVq4cKEcDoc+++wzNWvWTKNHj9ZDDz3U5P4RIcgAgamiokIul0sFBQWKiYlRcnIyMzFAAGmwINO6dWtt3LhRXbt2VevWreV2u9W9e3dt3LhRY8aM0fbt2y+6+PpEkAEAwDwNdrJvs2bNvAvs2rVrp3379kmSHA6H9u/fX8dyAQAAaq/Wi3179eqljz/+WF26dNFNN92k6dOn68iRI3r11VfPe7YDAABAQ6j1jExGRob3jIYnnnhCkZGRuvfee3X48GEtWrSo3gsEAAA4H86RAQAATU6DrZE5n82bN2v48OH19XIAAADfq1ZBZvXq1Zo8ebKmTp3qPS9m+/btcjqd6tOnj/c2BgAAAI2hxot9lyxZorFjx6pNmzY6evSoXnrpJT311FN64IEH9POf/1x5eXnq3r17Q9YKAADgo8YzMs8++6xmz56tI0eO6I033tCRI0f0wgsvaMuWLVq4cCEhBgAANLoaL/Zt0aKFtm7dqri4OFmWpdDQUOXm5qp///4NXeNFYbEvAADmqfebRp46dUrNmzeXJNlsNoWGhnq3YQNAY+MWBQCkWh6I99JLL6lly5aSpLNnz2rp0qWKioryGcPdrwE0tOpuGhkXF6d58+Y1ufu9AWhYNb60FBcXJ5vNduEXs9m4+zWABpWdna20tDQNHz5cU6dOVc+ePZWXl6eMjAzl5OQoKyuLMAMEgAa7aaRpCDJA4KioqFBCQoISExO1fPly733fJMnj8cjpdCovL0/5+flcZgIM1+gH4gFAQ3O5XNq7d6+mTp3qE2IkKSgoSOnp6dqzZ49cLpefKgTQ2AgyAIxRUFAgSee9QW1le+U4AIGPIAPAGJU7JfPy8qrtr2xnRyVw6SDIADBGcnKy4uLilJGRUeWWKB6PR5mZmYqPj1dycrKfKgTQ2AgyAIxht9s1b9485eTkyOl0yu12q7S0VG63W06nUzk5OZo7dy4LfYFLSJ2CzO7du/W73/1Ot99+uw4dOiRJWrlypbZu3VqvxQHAd6WmpiorK0tbtmxRUlKSIiIilJSUpLy8PLZeA5egWm+/Xr9+vW655Rb1799f7733nrZt26ZOnTrpySef1L/+9S9lZWU1VK11wvZrIDBxsi8Q2Or9FgWVpkyZolmzZmnixIlq1aqVt33QoEF6/vnn61YtANSS3W7XwIED/V0GAD+r9aWlLVu26Kc//WmV9nbt2unIkSP1UhQAAEBN1DrItG7dutozGj755BNdfvnl9VIUAABATdQ6yIwcOVK//e1vVVhYKJvNJo/How8++ECTJ0/WL3/5y4aoEQAAoFq1DjIZGRnq1q2bYmNjdfz4cV199dW68cYblZSUpN/97ncNUSMAAEC16nzTyH379ikvL0/Hjx9Xr1691KVLl/qurV6wawkITOxaAgJbg+1aqtSxY0d17Nixrk8HgDrLzs7WpEmTtHfvXm9bXFyc5s2bxzkywCWm1kHGsixlZWUpNzdXhw4dqnJMeHZ2dr0VBwDflZ2drbS0NA0fPlyvv/66evbsqby8PGVkZCgtLY1D8YBLTK0vLT300ENatGiRUlJSFB0dLZvN5tP/8ssv12uBF4tLS0DgqKioUEJCghITE7V8+XIFBf1nmZ/H45HT6VReXp7y8/O5zAQYrsEuLb366qvKzs7WrbfeelEFAkBtuVwu7d27V6+//rpPiJGkoKAgpaenKykpSS6Xi8PygEtErXctORwOderUqSFqAYALqjzDqmfPntX2V7ZXd9YVgMBU6yDz2GOPaebMmTp16lRD1AMA5xUTEyNJysvLq7a/sr1yHIDAV+sg87Of/UxHjx5Vu3btlJiYqN69e/t81dWTTz4pm82mCRMmeNvKyso0fvx4XXbZZWrZsqVGjBihoqKiOr8HALMlJycrLi5OGRkZVTYaeDweZWZmKj4+XsnJyX6qEEBjq/UamTFjxmjTpk0aPXp0tYt96+Ljjz/WokWL9IMf/MCn/eGHH9aKFSv05ptvyuFw6P7771dqaqo++OCDi35PAOax2+2aN2+e0tLS5HQ6lZ6e7t21lJmZqZycHGVlZbHQF7iE1HrXUosWLbR69WoNGDCgXgo4fvy4evfurRdeeEGzZs3Stddeq2eeeUbFxcVq27atli1bprS0NEnS9u3b1b17d7ndbvXt27fa1ysvL1d5ebn3cUlJiWJjY9m1BASQ6s6RiY+P19y5c9l6DQSImu5aqvWlpdjY2HoNBOPHj9ewYcM0ePBgn/ZNmzbpzJkzPu3dunVTx44d5Xa7z/t6mZmZcjgc3q/Y2Nh6qxVA05Camqpdu3YpNzdXy5YtU25urvLz8wkxwCWo1peW5s2bp0cffVQLFy5UXFzcRb35X/7yF23evFkff/xxlb7CwkKFhISodevWPu3R0dEqLCw872ump6dr4sSJ3seVMzIAAovdbmeLNYDaB5nRo0fr5MmT6ty5s5o3b65mzZr59H/zzTc1ep39+/froYce0rvvvquwsLDalnFeoaGhCg0NrbfXA9A0ca8lAFIdgswzzzxTL2+8adMmHTp0yGenU0VFhd577z09//zzWr16tU6fPq1jx475zMoUFRWpffv29VIDADNxryUAleq0a6k+3HzzzdqyZYtP21133aVu3brpt7/9rWJjY9WsWTOtWbNGI0aMkCTt2LFD+/btU79+/eqlBgDmqbzX0rBhw/TII48oPDxcp06d0sqVK7nXEnAJqtGupZKSEu8C35KSkguOvZiFwAMHDvTuWpKke++9V++8846WLl2qiIgIPfDAA5KkDRs21Pg1udcSEDgq77UUFRWlI0eOVJmRiYqK0tdff829loAAUK+7liIjI3Xo0CFJUuvWrRUZGVnlq7K9Pj399NMaPny4RowYoRtvvFHt27fn7trAJazyXkubNm1SYmKi3G63SktL5Xa7lZiYqE2bNmnPnj1yuVz+LhVAI6nRpaW1a9eqTZs2kqTc3NwGK2bdunU+j8PCwjR//nzNnz+/wd4TgDkOHDggSfrhD3/oc/frvn37avny5Ro+fLhWrlzpHQcg8NUoyNx0002SpLNnz2r9+vX61a9+pSuuuKJBCwOA7zp8+LCkb8+Rqe7u106nUytXrvSOAxD4anUgXnBwsH7/+9/r7NmzDVUPAJxX27ZtJX274Le6ey0tX77cZxyAwFfrk30HDRqk9evXN0QtAHBBl19+uSRp5cqVcjqdPmtkKmdjzh0HIPDVevv1LbfcoilTpmjLli267rrr1KJFC5/+H//4x/VWHACcq/Lu11FRUfr3v/+tpKQkb19cXJyuv/56ff3119z9GriE1Pqmkd+9Lu3zYjabKioqLrqo+sT2ayCwnHuOzA9/+EPvOTKrVq3SihUrOEcGCBA1/fyu9YzMd69LA0BjSk1NVVZWliZNmqScnBxve3x8PCEGuATVekbGNMzIAIGJey0Bga1eD8STvj1L5uqrr672ZN/i4mL16NFD7733Xt2qBYBaqrz79e23366BAwcSYoBLVI2DzDPPPKOxY8dWm4ocDofuuecePf300/VaHAAAwIXUOMh89tln+uEPf3je/iFDhmjTpk31UhQAAEBN1DjIFBUVqVmzZuftDw4O5jRNAADQqGocZC6//HLl5eWdt//f//63YmJi6qUoAACAmqhxkLn11ls1bdo0lZWVVek7deqUZsyYoeHDh9drcQAAABdS4+3XRUVF6t27t+x2u+6//3517dpVkrR9+3bNnz9fFRUV2rx5s6Kjoxu04Npi+zUAAOap9wPxoqOjtWHDBt17771KT09XZf6x2WwaOnSo5s+f3+RCDAAACGy1Otn3yiuv1DvvvKOjR49q165dsixLXbp0UWRkZEPVBwAAcF61vkWBJEVGRqpPnz71XQsAAECt1HixLwAAQFNDkAEAAMYiyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWHW6+zUA+FtFRYVcLpcKCgoUExOj5ORk2e12f5cFoJExIwPAONnZ2UpISFBKSopGjRqllJQUJSQkKDs729+lAWhkBBkARsnOzlZaWpoSExPldrtVWloqt9utxMREpaWlEWaAS4zNsizL30U0pJKSEjkcDhUXFysiIsLf5QC4CBUVFUpISFBiYqKWL1+uoKD//C7m8XjkdDqVl5en/Px8LjMBhqvp5zczMgCM4XK5tHfvXk2dOtUnxEhSUFCQ0tPTtWfPHrlcLj9VCKCxEWQAGKOgoECS1LNnz2r7K9srxwEIfAQZAMaIiYmRJOXl5VXbX9leOQ5A4CPIADBGcnKy4uLilJGRIY/H49Pn8XiUmZmp+Ph4JScn+6lCAI2NIAPAGHa7XfPmzVNOTo6cTqfPriWn06mcnBzNnTuXhb7AJYQD8QAYJTU1VVlZWZo0aZKSkpK87fHx8crKylJqaqofqwPQ2Nh+DcBInOwLBDa2XwMAgIBHkAFgHG5RAKASQQaAUbhFAYBzsUYGgDHOvUXB3/72N33wwQfeNTL9+/fXiBEjuEUBECBYIwMg4FTeoiApKUldunTxubTUpUsX9evXj1sUAJcYtl8DMEblrQfS09MVHh7u03fo0CFNnTrVZxyAwMeMDABjtGvXrl7HATAfQQaAMSoqKrzfDxo0yGex76BBg6odByCwcWkJgDHWr1/v83jTpk36/PPPderUqSrjhgwZ0pilAfATggwAY+zfv1+SdPPNN2v16tVasWKFty84OFiDBg3S2rVrveMABD6CDABjxMbGSpLWrFmjYcOG6dZbb1V4eLhOnTqld955xxtsKscBCHyskQFgjJtuusnnca9evZSWlqZevXpdcByAwMWMDABjnHvI3dq1a30uLTVv3rzacQACGzMyAIxx6NAh7/dlZWU+fecu+D13HIDARpABYIyYmBhJks1mU0hIiE9faGiobDabzzgAgY8gA8AYSUlJCg4OVkREhNq3b+/TFx0drYiICAUHByspKclPFQJobAQZAMbYsGGDzp49q+LiYpWVlWnx4sU6ePCgFi9erLKyMhUXF+vs2bPasGGDv0sF0EhY7AvAGAcOHJD07W6lb775RuPGjfP2xcXFqVevXvrkk0+84wAEPmZkABjj8OHDkqT77rtPu3fvVm5urpYtW6bc3Fzt2rVLv/nNb3zGAQh8BBkAxmjbtq0kKTs7Wx6Px6fP4/Fo+fLlPuMABD4uLQEwxuWXXy5JWrlypRwOh8+W68oTfs8dByDwMSMDwBjJyclq166dJMmyrGrHtGvXTsnJyY1ZFgA/YkYGgFEqA8zNN9+shIQEnTp1SuHh4dq1a5fPSb8ALg1+nZHJzMxUnz591KpVK7Vr105Op1M7duzwGVNWVqbx48frsssuU8uWLTVixAgVFRX5qWIA/uRyuXT48GHdcccdWr16tZ599lktXrxYzz77rFavXq1Ro0bp0KFDcrlc/i4VQCPxa5BZv369xo8frw8//FDvvvuuzpw5oyFDhujEiRPeMQ8//LDefvttvfnmm1q/fr0OHjyo1NRUP1YNwF8KCgokSa+99pqCg30nlIODg7Vs2TKfcQACn1+DzKpVq3TnnXeqR48euuaaa7R06VLt27dPmzZtkiQVFxdryZIleuqppzRo0CBdd911evnll7VhwwZ9+OGH/iwdgB9Uro+RpMGDB8vtdqu0tFRut1uDBw+udhyAwNakFvsWFxdLktq0aSNJ2rRpk86cOePzD1S3bt3UsWNHud3ual+jvLxcJSUlPl8AAkNFRYWkb/+NyMrKUllZmd5++22VlZUpKytLkZGRPuMABL4ms9jX4/FowoQJ6t+/v3r27ClJKiwsVEhIiFq3bu0zNjo6WoWFhdW+TmZmpmbOnNnQ5QLwg8q1L998840iIyPPu/3a5XJpyJAhfqkRQONqMjMy48ePV15env7yl79c1Oukp6eruLjY+7V///56qhBAU1Z552sAl5YmMSNz//33KycnR++9956uuOIKb3v79u11+vRpHTt2zGdWpqioqMqdbyuFhoYqNDS0oUsG4AeV58O0bNlSrVu31ldffeXta9OmjYKCgnT8+HHOkQEuIX6dkbEsS/fff7/+/ve/a+3atYqPj/fpv+6669SsWTOtWbPG27Zjxw7t27dP/fr1a+xyAfiZ3W6XJB0/flwHDx706Tt48KCOHz/uMw5A4PPrjMz48eO1bNky/eMf/1CrVq28614cDofCw8PlcDj061//WhMnTlSbNm0UERGhBx54QP369VPfvn39WToAPzh3bVx191qqbhyAwObXILNgwQJJ0sCBA33aX375Zd15552SpKefflpBQUEaMWKEysvLNXToUL3wwguNXCmApuDcwzBDQ0NVXl7ufRwWFqaysrIq4wAENr8GmfPdK+VcYWFhmj9/vubPn98IFQFoyo4cOSJJioyMVEFBgdxutwoKChQTE6N+/fopJiZGR48e9Y4DEPiazK4lAPg+lYt7jx49qrS0NG3dulWnTp3S1q1blZaWpqNHj/qMAxD4msSuJQCoidjYWElSTEyMVq1apZycHG9fcHCwYmJiVFBQ4B0HIPAxIwPAGIMGDZL07b2UIiMjNWnSJM2fP1+TJk3yXm46dxyAwGezarJQxWAlJSVyOBwqLi5WRESEv8sBcBFOnz6t8PBweTwen5N8pf+c7BsUFKRTp04pJCTEj5UCuFg1/fxmRgaAMTZs2ODdZv3d38EqH3s8Hm3YsKHRawPgHwQZAMaovHQ0bNgw71brSmVlZRo2bJjPOACBjyADwBgxMTGSpBUrVigoyPefr6CgIK1YscJnHIDAR5ABYIwbbrjB+/2FTvY9dxyAwEaQAWCMytPA62scAPMRZAAYY926dd7vv3uX+3MfnzsOQGAjyAAwRuUdr4ODg33usyRJ5eXlCg4O9hkHIPARZAAYo3LW5ezZs9X2V7Z/d7YGQOAiyAAwxuWXX16v4wCYjyADwBj79u2r13EAzEeQAWCMvXv31us4AOYjyAAAAGMRZAAYIzo6ul7HATAfQQaAMZo3b+7z+KqrrlJqaqquuuqqC44DELiC/V0AANTUdwPKzp07tXPnzu8dByBwMSMDwBhcWgLwXQQZAMaIi4ur13EAzEeQAWCM1q1b1+s4AOYjyAAwxqefflqv4wCYjyADwBic7AvguwgyAIwRFhZWr+MAmI/t1wCMERUV5f2+WbNmSk5OVkxMjAoKCuRyuXTmzJkq4wAENoIMAGMcPnzY+/2ZM2e0du3a7x0HILBxaQmAMQ4dOlSv4wCYjyADwBitWrWq13EAzEeQAWCMrl271us4AOYjyAAwxvvvv1+v4wCYjyADwBgHDhyo13EAzEeQAWCMyu3VkhQU5PvPl91ur3YcgMDG9msAxggNDdXZs2clSUOGDFHXrl116tQphYeHa8eOHVq1apV3HIBLA0EGQKM6efKktm/fXqfntmrVSidOnJAkrVq1yhtcqhu3efPmOr1Ht27d1Lx58zo9F0DjI8gAaFTbt2/Xdddd16DvUVhYWOf32LRpk3r37l3PFQFoKAQZAI2qW7du2rRpU52ee/r0afXr1+97x7ndboWEhNTpPbp161an5wHwD4IMgEbVvHnzi5rxeOSRR/T73//+gv19+/at8+sDMAu7lgAYZc6cOXrkkUeq7XvkkUc0Z86cRq4IgD8RZAAYZ86cOSovL9fEiRMlSRMnTlR5eTkhBrgEEWQAGCkkJER33HGHJOmOO+6o85oYAGYjyAAAAGMRZAAAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwVrC/CwBgjn379unIkSP+LsNr27ZtPv9tKqKiotSxY0d/lwFcEggyAGpk37596t69u06ePOnvUqoYPXq0v0vw0bx5c23bto0wAzQCggyAGjly5IhOnjyph37/vK7olODvciRJp8vLdOjAfrW7PFYhoWH+LkeS9NUXu/TsI/fryJEjBBmgERBkANTKFZ0S1KnHD/xdhle33v/l7xIA+BGLfQEAgLEIMgAAwFgEGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxjIiyMyfP19xcXEKCwvTDTfcoI8++sjfJQEAgCagyQeZv/71r5o4caJmzJihzZs365prrtHQoUN16NAhf5cGAAD8rMmf7PvUU09p7NixuuuuuyRJCxcu1IoVK/THP/5RU6ZM8XN1wKVn83u5+uqL/Do//8zp0zp6qKgeK6pfke2i1SwkpM7PP/TVV/VYDYDv06SDzOnTp7Vp0yalp6d724KCgjR48GC53e5qn1NeXq7y8nLv45KSkgavE7gUREVFKTg4WK8/O9vfpTR5wcHBioqK8ncZwCWhSQeZI0eOqKKiQtHR0T7t0dHR2r59e7XPyczM1MyZMxujPOCS0rFjR23YsEE7d+68qNcpLy/XwYMH66mq+tehQweFhoZe1GtcddVV3DASaCRNOsjURXp6uiZOnOh9XFJSotjYWD9WBASOPn36qE+fPv4uAwC8mnSQiYqKkt1uV1GR7/X0oqIitW/fvtrnhIaGXvRvUwAAwAxNetdSSEiIrrvuOq1Zs8bb5vF4tGbNGvXr18+PlQEAgKagSc/ISNLEiRM1ZswYXX/99fqv//ovPfPMMzpx4oR3FxMAALh0Nfkg8/Of/1yHDx/W9OnTVVhYqGuvvVarVq2qsgAYAABcemyWZVn+LqIhlZSUyOFwqLi4WBEREf4uBwAA1EBNP7+b9BoZAACACyHIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAYiyADAACM1eRP9r1Ylef9lZSU+LkSAABQU5Wf2993bm/AB5nS0lJJUmxsrJ8rAQAAtVVaWiqHw3He/oC/RYHH49HBgwfVqlUr2Ww2f5cDoB6VlJQoNjZW+/fv5xYkQICxLEulpaXq0KGDgoLOvxIm4IMMgMDFvdQAsNgXAAAYiyADAACMRZABYKzQ0FDNmDFDoaGh/i4FgJ+wRgYAABiLGRkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMYiyAAwznvvvacf/ehH6tChg2w2m5YvX+7vkgD4CUEGgHFOnDiha665RvPnz/d3KQD8LODvfg0g8Nxyyy265ZZb/F0GgCaAGRkAAGAsggwAADAWQQYAABiLIAMAAIxFkAEAAMZi1xIA4xw/fly7du3yPt6zZ48+/fRTtWnTRh07dvRjZQAam82yLMvfRQBAbaxbt04pKSlV2seMGaOlS5c2fkEA/IYgAwAAjMUaGQAAYCyCDAAAMBZBBgAAGIsgAwAAjEWQAQAAxiLIAAAAYxFkAACAsQgyAADAWAQZAABgLIIMAAAwFkEGAAAY6/8BY7Av7joyB38AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set boxplot style and color\n",
    "boxplot = plt.boxplot(df['crim'], patch_artist=True, medianprops={'color':'black'})\n",
    "\n",
    "# Customize box colors\n",
    "colors = ['lightblue']\n",
    "for patch in boxplot['boxes']:\n",
    "    patch.set_facecolor(colors[0])\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Crime Rate Box Plot')\n",
    "plt.ylabel('Crime Rate')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>crim</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.200469</td>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.055892</td>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.625505</td>\n",
       "      <td>0.582764</td>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.388305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zn</th>\n",
       "      <td>-0.200469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.360445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>indus</th>\n",
       "      <td>0.406583</td>\n",
       "      <td>-0.533828</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.483725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chas</th>\n",
       "      <td>-0.055892</td>\n",
       "      <td>-0.042697</td>\n",
       "      <td>0.062938</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.175260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nox</th>\n",
       "      <td>0.420972</td>\n",
       "      <td>-0.516604</td>\n",
       "      <td>0.763651</td>\n",
       "      <td>0.091203</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.427321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rm</th>\n",
       "      <td>-0.219247</td>\n",
       "      <td>0.311991</td>\n",
       "      <td>-0.391676</td>\n",
       "      <td>0.091251</td>\n",
       "      <td>-0.302188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.695360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.352734</td>\n",
       "      <td>-0.569537</td>\n",
       "      <td>0.644779</td>\n",
       "      <td>0.086518</td>\n",
       "      <td>0.731470</td>\n",
       "      <td>-0.240265</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.376955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dis</th>\n",
       "      <td>-0.379670</td>\n",
       "      <td>0.664408</td>\n",
       "      <td>-0.708027</td>\n",
       "      <td>-0.099176</td>\n",
       "      <td>-0.769230</td>\n",
       "      <td>0.205246</td>\n",
       "      <td>-0.747881</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.249929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rad</th>\n",
       "      <td>0.625505</td>\n",
       "      <td>-0.311948</td>\n",
       "      <td>0.595129</td>\n",
       "      <td>-0.007368</td>\n",
       "      <td>0.611441</td>\n",
       "      <td>-0.209847</td>\n",
       "      <td>0.456022</td>\n",
       "      <td>-0.494588</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>-0.381626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tax</th>\n",
       "      <td>0.582764</td>\n",
       "      <td>-0.314563</td>\n",
       "      <td>0.720760</td>\n",
       "      <td>-0.035587</td>\n",
       "      <td>0.668023</td>\n",
       "      <td>-0.292048</td>\n",
       "      <td>0.506456</td>\n",
       "      <td>-0.534432</td>\n",
       "      <td>0.910228</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>-0.468536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ptratio</th>\n",
       "      <td>0.289946</td>\n",
       "      <td>-0.391679</td>\n",
       "      <td>0.383248</td>\n",
       "      <td>-0.121515</td>\n",
       "      <td>0.188933</td>\n",
       "      <td>-0.355501</td>\n",
       "      <td>0.261515</td>\n",
       "      <td>-0.232471</td>\n",
       "      <td>0.464741</td>\n",
       "      <td>0.460853</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.507787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.385064</td>\n",
       "      <td>0.175520</td>\n",
       "      <td>-0.356977</td>\n",
       "      <td>0.048788</td>\n",
       "      <td>-0.380051</td>\n",
       "      <td>0.128069</td>\n",
       "      <td>-0.273534</td>\n",
       "      <td>0.291512</td>\n",
       "      <td>-0.444413</td>\n",
       "      <td>-0.441808</td>\n",
       "      <td>-0.177383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>0.333461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lstat</th>\n",
       "      <td>0.455621</td>\n",
       "      <td>-0.412995</td>\n",
       "      <td>0.603800</td>\n",
       "      <td>-0.053929</td>\n",
       "      <td>0.590879</td>\n",
       "      <td>-0.613808</td>\n",
       "      <td>0.602339</td>\n",
       "      <td>-0.496996</td>\n",
       "      <td>0.488676</td>\n",
       "      <td>0.543993</td>\n",
       "      <td>0.374044</td>\n",
       "      <td>-0.366087</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.737663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.388305</td>\n",
       "      <td>0.360445</td>\n",
       "      <td>-0.483725</td>\n",
       "      <td>0.175260</td>\n",
       "      <td>-0.427321</td>\n",
       "      <td>0.695360</td>\n",
       "      <td>-0.376955</td>\n",
       "      <td>0.249929</td>\n",
       "      <td>-0.381626</td>\n",
       "      <td>-0.468536</td>\n",
       "      <td>-0.507787</td>\n",
       "      <td>0.333461</td>\n",
       "      <td>-0.737663</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             crim        zn     indus      chas       nox        rm       age  \\\n",
       "crim     1.000000 -0.200469  0.406583 -0.055892  0.420972 -0.219247  0.352734   \n",
       "zn      -0.200469  1.000000 -0.533828 -0.042697 -0.516604  0.311991 -0.569537   \n",
       "indus    0.406583 -0.533828  1.000000  0.062938  0.763651 -0.391676  0.644779   \n",
       "chas    -0.055892 -0.042697  0.062938  1.000000  0.091203  0.091251  0.086518   \n",
       "nox      0.420972 -0.516604  0.763651  0.091203  1.000000 -0.302188  0.731470   \n",
       "rm      -0.219247  0.311991 -0.391676  0.091251 -0.302188  1.000000 -0.240265   \n",
       "age      0.352734 -0.569537  0.644779  0.086518  0.731470 -0.240265  1.000000   \n",
       "dis     -0.379670  0.664408 -0.708027 -0.099176 -0.769230  0.205246 -0.747881   \n",
       "rad      0.625505 -0.311948  0.595129 -0.007368  0.611441 -0.209847  0.456022   \n",
       "tax      0.582764 -0.314563  0.720760 -0.035587  0.668023 -0.292048  0.506456   \n",
       "ptratio  0.289946 -0.391679  0.383248 -0.121515  0.188933 -0.355501  0.261515   \n",
       "b       -0.385064  0.175520 -0.356977  0.048788 -0.380051  0.128069 -0.273534   \n",
       "lstat    0.455621 -0.412995  0.603800 -0.053929  0.590879 -0.613808  0.602339   \n",
       "price   -0.388305  0.360445 -0.483725  0.175260 -0.427321  0.695360 -0.376955   \n",
       "\n",
       "              dis       rad       tax   ptratio         b     lstat     price  \n",
       "crim    -0.379670  0.625505  0.582764  0.289946 -0.385064  0.455621 -0.388305  \n",
       "zn       0.664408 -0.311948 -0.314563 -0.391679  0.175520 -0.412995  0.360445  \n",
       "indus   -0.708027  0.595129  0.720760  0.383248 -0.356977  0.603800 -0.483725  \n",
       "chas    -0.099176 -0.007368 -0.035587 -0.121515  0.048788 -0.053929  0.175260  \n",
       "nox     -0.769230  0.611441  0.668023  0.188933 -0.380051  0.590879 -0.427321  \n",
       "rm       0.205246 -0.209847 -0.292048 -0.355501  0.128069 -0.613808  0.695360  \n",
       "age     -0.747881  0.456022  0.506456  0.261515 -0.273534  0.602339 -0.376955  \n",
       "dis      1.000000 -0.494588 -0.534432 -0.232471  0.291512 -0.496996  0.249929  \n",
       "rad     -0.494588  1.000000  0.910228  0.464741 -0.444413  0.488676 -0.381626  \n",
       "tax     -0.534432  0.910228  1.000000  0.460853 -0.441808  0.543993 -0.468536  \n",
       "ptratio -0.232471  0.464741  0.460853  1.000000 -0.177383  0.374044 -0.507787  \n",
       "b        0.291512 -0.444413 -0.441808 -0.177383  1.000000 -0.366087  0.333461  \n",
       "lstat   -0.496996  0.488676  0.543993  0.374044 -0.366087  1.000000 -0.737663  \n",
       "price    0.249929 -0.381626 -0.468536 -0.507787  0.333461 -0.737663  1.000000  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate correlation matrix\n",
    "correlation_matrix = df.corr()\n",
    "\n",
    "# Create a heatmap with correlation values\n",
    "plt.figure(figsize=(18, 6))  # Adjust the figure size\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=.5)\n",
    "\n",
    "# Add labels and title\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='crim', ylabel='price'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x='crim', y = 'price', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#independant and dependent spliting \n",
    "\n",
    "\n",
    "\n",
    "X = df.iloc[:,:-1]\n",
    "\n",
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</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",
       "    </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",
       "    </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",
       "    </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",
       "    </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  0.02731   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  \n",
       "0  396.90   4.98  \n",
       "1  396.90   9.14  \n",
       "2  392.83   4.03  \n",
       "3  394.63   2.94  \n",
       "4  396.90   5.33  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test , y_train, y_test = train_test_split(X,y , random_state=42, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>173</th>\n",
       "      <td>0.09178</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0.510</td>\n",
       "      <td>6.416</td>\n",
       "      <td>84.1</td>\n",
       "      <td>2.6463</td>\n",
       "      <td>5</td>\n",
       "      <td>296</td>\n",
       "      <td>16.6</td>\n",
       "      <td>395.50</td>\n",
       "      <td>9.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>0.05644</td>\n",
       "      <td>40.0</td>\n",
       "      <td>6.41</td>\n",
       "      <td>1</td>\n",
       "      <td>0.447</td>\n",
       "      <td>6.758</td>\n",
       "      <td>32.9</td>\n",
       "      <td>4.0776</td>\n",
       "      <td>4</td>\n",
       "      <td>254</td>\n",
       "      <td>17.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>3.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>0.10574</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.74</td>\n",
       "      <td>0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>5.983</td>\n",
       "      <td>98.8</td>\n",
       "      <td>1.8681</td>\n",
       "      <td>4</td>\n",
       "      <td>711</td>\n",
       "      <td>20.1</td>\n",
       "      <td>390.11</td>\n",
       "      <td>18.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>0.09164</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.81</td>\n",
       "      <td>0</td>\n",
       "      <td>0.413</td>\n",
       "      <td>6.065</td>\n",
       "      <td>7.8</td>\n",
       "      <td>5.2873</td>\n",
       "      <td>4</td>\n",
       "      <td>305</td>\n",
       "      <td>19.2</td>\n",
       "      <td>390.91</td>\n",
       "      <td>5.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>5.09017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.713</td>\n",
       "      <td>6.297</td>\n",
       "      <td>91.8</td>\n",
       "      <td>2.3682</td>\n",
       "      <td>24</td>\n",
       "      <td>666</td>\n",
       "      <td>20.2</td>\n",
       "      <td>385.09</td>\n",
       "      <td>17.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>73.53410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.679</td>\n",
       "      <td>5.957</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.8026</td>\n",
       "      <td>24</td>\n",
       "      <td>666</td>\n",
       "      <td>20.2</td>\n",
       "      <td>16.45</td>\n",
       "      <td>20.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>0.15098</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.01</td>\n",
       "      <td>0</td>\n",
       "      <td>0.547</td>\n",
       "      <td>6.021</td>\n",
       "      <td>82.6</td>\n",
       "      <td>2.7474</td>\n",
       "      <td>6</td>\n",
       "      <td>432</td>\n",
       "      <td>17.8</td>\n",
       "      <td>394.51</td>\n",
       "      <td>10.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>0.14150</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0.448</td>\n",
       "      <td>6.169</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5.7209</td>\n",
       "      <td>3</td>\n",
       "      <td>233</td>\n",
       "      <td>17.9</td>\n",
       "      <td>383.37</td>\n",
       "      <td>5.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>0.35114</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0</td>\n",
       "      <td>0.493</td>\n",
       "      <td>6.041</td>\n",
       "      <td>49.9</td>\n",
       "      <td>4.7211</td>\n",
       "      <td>5</td>\n",
       "      <td>287</td>\n",
       "      <td>19.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>0.01870</td>\n",
       "      <td>85.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>0</td>\n",
       "      <td>0.429</td>\n",
       "      <td>6.516</td>\n",
       "      <td>27.7</td>\n",
       "      <td>8.5353</td>\n",
       "      <td>4</td>\n",
       "      <td>351</td>\n",
       "      <td>17.9</td>\n",
       "      <td>392.43</td>\n",
       "      <td>6.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>127 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         crim    zn  indus  chas    nox     rm    age     dis  rad  tax  \\\n",
       "173   0.09178   0.0   4.05     0  0.510  6.416   84.1  2.6463    5  296   \n",
       "274   0.05644  40.0   6.41     1  0.447  6.758   32.9  4.0776    4  254   \n",
       "491   0.10574   0.0  27.74     0  0.609  5.983   98.8  1.8681    4  711   \n",
       "72    0.09164   0.0  10.81     0  0.413  6.065    7.8  5.2873    4  305   \n",
       "452   5.09017   0.0  18.10     0  0.713  6.297   91.8  2.3682   24  666   \n",
       "..        ...   ...    ...   ...    ...    ...    ...     ...  ...  ...   \n",
       "418  73.53410   0.0  18.10     0  0.679  5.957  100.0  1.8026   24  666   \n",
       "117   0.15098   0.0  10.01     0  0.547  6.021   82.6  2.7474    6  432   \n",
       "42    0.14150   0.0   6.91     0  0.448  6.169    6.6  5.7209    3  233   \n",
       "322   0.35114   0.0   7.38     0  0.493  6.041   49.9  4.7211    5  287   \n",
       "347   0.01870  85.0   4.15     0  0.429  6.516   27.7  8.5353    4  351   \n",
       "\n",
       "     ptratio       b  lstat  \n",
       "173     16.6  395.50   9.04  \n",
       "274     17.6  396.90   3.53  \n",
       "491     20.1  390.11  18.07  \n",
       "72      19.2  390.91   5.52  \n",
       "452     20.2  385.09  17.27  \n",
       "..       ...     ...    ...  \n",
       "418     20.2   16.45  20.62  \n",
       "117     17.8  394.51  10.30  \n",
       "42      17.9  383.37   5.81  \n",
       "322     19.6  396.90   7.70  \n",
       "347     17.9  392.43   6.36  \n",
       "\n",
       "[127 rows x 13 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "sclar = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = sclar.fit_transform(x_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.40679535, -0.50281197, -1.24707626, ..., -0.20515671,\n",
       "         0.40451209, -1.07775629],\n",
       "       [ 0.00799171, -0.50281197,  1.21828293, ..., -1.57625236,\n",
       "        -3.09803734,  0.35116456],\n",
       "       [-0.41344957,  0.36438137, -1.12179223, ..., -1.48779457,\n",
       "         0.32659121, -1.22625199],\n",
       "       ...,\n",
       "       [-0.41595107,  2.96596138, -1.31187834, ..., -0.55898784,\n",
       "         0.36812613, -0.91385066],\n",
       "       [ 0.92638065, -0.50281197,  1.00515608, ...,  0.8563367 ,\n",
       "        -2.84814129,  1.50690937],\n",
       "       [-0.39020739, -0.50281197, -0.36864804, ...,  1.16593894,\n",
       "        -3.29495634, -0.26383177]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = sclar.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.40670502, -0.50281197, -1.0181089 , ..., -0.73590341,\n",
       "         0.42030222, -0.48657531],\n",
       "       [-0.41096131,  1.23157471, -0.67825799, ..., -0.29361449,\n",
       "         0.4363212 , -1.25847275],\n",
       "       [-0.40502371, -0.50281197,  2.39336067, ...,  0.81210781,\n",
       "         0.35862916,  0.77843992],\n",
       "       ...,\n",
       "       [-0.40071683, -0.50281197, -0.60625567, ..., -0.16092782,\n",
       "         0.28150923, -0.93906691],\n",
       "       [-0.37546815, -0.50281197, -0.5385735 , ...,  0.59096335,\n",
       "         0.4363212 , -0.67429629],\n",
       "       [-0.41550665,  3.18275972, -1.00370844, ..., -0.16092782,\n",
       "         0.38517489, -0.86201726]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.06546379  0.68154987  0.33928836  0.73726309 -1.93178062  3.14172655\n",
      " -0.25586276 -2.95561026  2.21416067 -1.65127265 -2.08714915  1.15235744\n",
      " -3.69504236]\n"
     ]
    }
   ],
   "source": [
    "## print the co-efficient and the intercept \n",
    "print(model.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.90791556728232\n"
     ]
    }
   ],
   "source": [
    "print(model.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'copy_X': True, 'fit_intercept': True, 'n_jobs': None, 'positive': False}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## on which param we use ??\n",
    "model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "## prediction with the test data \n",
    "\n",
    "prediction= model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f54cabd1f00>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##plot a scatter plot for thie prediction\n",
    "\n",
    "plt.scatter(y_test, prediction , )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "173   -5.238854\n",
       "274   -3.607833\n",
       "491   -1.483248\n",
       "72    -2.430909\n",
       "452   -2.778641\n",
       "         ...   \n",
       "418    5.515791\n",
       "117   -4.598796\n",
       "42    -0.433299\n",
       "322   -2.648156\n",
       "347   -1.630468\n",
       "Name: price, Length: 127, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## prediction of resuduals \n",
    "residuals = y_test - prediction\n",
    "residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f54cac35270>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(residuals, kind='kde')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f54c1c082e0>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## scatter plot with respect to prediction \n",
    "plt.scatter(prediction, residuals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.098694827097983\n",
      "--------------------------------------------------\n",
      "3.0609395954370324\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error , mean_absolute_error\n",
    "\n",
    "print(mean_squared_error(y_test, prediction))\n",
    "print(\"-\"*50)\n",
    "print(mean_absolute_error(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6844267283527132\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(r2_score(y_test, prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.41096131,  1.23157471, -0.67825799,  3.47404167, -0.93582581,\n",
       "        0.6042032 , -1.31014507,  0.14890207, -0.62415575, -0.91238672,\n",
       "       -0.29361449,  0.4363212 , -1.25847275])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.41096131,  1.23157471, -0.67825799,  3.47404167, -0.93582581,\n",
       "         0.6042032 , -1.31014507,  0.14890207, -0.62415575, -0.91238672,\n",
       "        -0.29361449,  0.4363212 , -1.25847275]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test[1].reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36.00783288])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(x_test[1].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "pickle.dump(model, open('regression_model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## we can also load this "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_model = pickle.load(open('regression_model.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36.00783288])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle_model.predict(x_test[1].reshape(1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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