Linear Regression Analysis of apy_1.csv.ipynb

{
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  {
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>State_Name</th>\n",
       "      <th>District_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>NALANDA</td>\n",
       "      <td>1997</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>81934.00</td>\n",
       "      <td>160425.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Assam</td>\n",
       "      <td>KARBI ANGLONG</td>\n",
       "      <td>2011</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Onion</td>\n",
       "      <td>257.00</td>\n",
       "      <td>514.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>ANAND</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Maize</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>UTTAR KANNAD</td>\n",
       "      <td>2005</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>Groundnut</td>\n",
       "      <td>2872.00</td>\n",
       "      <td>4572.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>JAUNPUR</td>\n",
       "      <td>2008</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>Onion</td>\n",
       "      <td>110.00</td>\n",
       "      <td>1290.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73822</th>\n",
       "      <td>73822</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>BUXAR</td>\n",
       "      <td>2004</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Onion</td>\n",
       "      <td>203.00</td>\n",
       "      <td>1918.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73823</th>\n",
       "      <td>73823</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>RAJGARH</td>\n",
       "      <td>2013</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Bajra</td>\n",
       "      <td>12.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73824</th>\n",
       "      <td>73824</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>RAJGARH</td>\n",
       "      <td>1998</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Groundnut</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>9100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73825</th>\n",
       "      <td>73825</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>TONK</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Bajra</td>\n",
       "      <td>34477.00</td>\n",
       "      <td>20137.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73826</th>\n",
       "      <td>73826</td>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>KODERMA</td>\n",
       "      <td>2002</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Rice</td>\n",
       "      <td>14685.71</td>\n",
       "      <td>18552.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73827 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0      State_Name  District_Name  Crop_Year       Season  \\\n",
       "0               0           Bihar        NALANDA       1997  Rabi          \n",
       "1               1           Assam  KARBI ANGLONG       2011  Whole Year    \n",
       "2               2         Gujarat          ANAND       2012  Summer        \n",
       "3               3       Karnataka   UTTAR KANNAD       2005  Rabi          \n",
       "4               4   Uttar Pradesh        JAUNPUR       2008  Rabi          \n",
       "...           ...             ...            ...        ...          ...   \n",
       "73822       73822           Bihar          BUXAR       2004  Whole Year    \n",
       "73823       73823  Madhya Pradesh        RAJGARH       2013  Kharif        \n",
       "73824       73824  Madhya Pradesh        RAJGARH       1998  Kharif        \n",
       "73825       73825       Rajasthan           TONK       2000  Kharif        \n",
       "73826       73826       Jharkhand        KODERMA       2002  Winter        \n",
       "\n",
       "            Crop      Area  Production  \n",
       "0          Wheat  81934.00   160425.00  \n",
       "1          Onion    257.00      514.00  \n",
       "2          Maize    100.00      100.00  \n",
       "3      Groundnut   2872.00     4572.00  \n",
       "4          Onion    110.00     1290.00  \n",
       "...          ...       ...         ...  \n",
       "73822      Onion    203.00     1918.00  \n",
       "73823      Bajra     12.00       11.00  \n",
       "73824  Groundnut   8700.00     9100.00  \n",
       "73825      Bajra  34477.00    20137.00  \n",
       "73826       Rice  14685.71    18552.95  \n",
       "\n",
       "[73827 rows x 8 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "df=pd.read_csv('apy_1.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7829265c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73827, 8)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ba1d8a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73827 entries, 0 to 73826\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Unnamed: 0     73827 non-null  int64  \n",
      " 1   State_Name     73827 non-null  object \n",
      " 2   District_Name  73827 non-null  object \n",
      " 3   Crop_Year      73827 non-null  int64  \n",
      " 4   Season         73827 non-null  object \n",
      " 5   Crop           73827 non-null  object \n",
      " 6   Area           73827 non-null  float64\n",
      " 7   Production     72731 non-null  float64\n",
      "dtypes: float64(2), int64(2), object(4)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24f20840",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>73827.000000</td>\n",
       "      <td>73827.000000</td>\n",
       "      <td>7.382700e+04</td>\n",
       "      <td>7.273100e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>36913.000000</td>\n",
       "      <td>2005.657551</td>\n",
       "      <td>1.218297e+04</td>\n",
       "      <td>6.210320e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>21312.163499</td>\n",
       "      <td>4.954910</td>\n",
       "      <td>5.348285e+04</td>\n",
       "      <td>1.749756e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>4.000000e-02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18456.500000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>8.100000e+01</td>\n",
       "      <td>8.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36913.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>5.900000e+02</td>\n",
       "      <td>7.290000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>55369.500000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>4.500000e+03</td>\n",
       "      <td>7.200000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>73826.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>8.580100e+06</td>\n",
       "      <td>1.125000e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0     Crop_Year          Area    Production\n",
       "count  73827.000000  73827.000000  7.382700e+04  7.273100e+04\n",
       "mean   36913.000000   2005.657551  1.218297e+04  6.210320e+05\n",
       "std    21312.163499      4.954910  5.348285e+04  1.749756e+07\n",
       "min        0.000000   1997.000000  4.000000e-02  0.000000e+00\n",
       "25%    18456.500000   2002.000000  8.100000e+01  8.900000e+01\n",
       "50%    36913.000000   2006.000000  5.900000e+02  7.290000e+02\n",
       "75%    55369.500000   2010.000000  4.500000e+03  7.200000e+03\n",
       "max    73826.000000   2015.000000  8.580100e+06  1.125000e+09"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e081a0aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          0\n",
       "State_Name          0\n",
       "District_Name       0\n",
       "Crop_Year           0\n",
       "Season              0\n",
       "Crop                0\n",
       "Area                0\n",
       "Production       1096\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff114ddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0f70f81d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>State_Name</th>\n",
       "      <th>District_Name</th>\n",
       "      <th>Crop_Year</th>\n",
       "      <th>Season</th>\n",
       "      <th>Crop</th>\n",
       "      <th>Area</th>\n",
       "      <th>Production</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>NALANDA</td>\n",
       "      <td>1997</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>Wheat</td>\n",
       "      <td>81934.00</td>\n",
       "      <td>160425.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Assam</td>\n",
       "      <td>KARBI ANGLONG</td>\n",
       "      <td>2011</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Onion</td>\n",
       "      <td>257.00</td>\n",
       "      <td>514.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>ANAND</td>\n",
       "      <td>2012</td>\n",
       "      <td>Summer</td>\n",
       "      <td>Maize</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>UTTAR KANNAD</td>\n",
       "      <td>2005</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>Groundnut</td>\n",
       "      <td>2872.00</td>\n",
       "      <td>4572.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>JAUNPUR</td>\n",
       "      <td>2008</td>\n",
       "      <td>Rabi</td>\n",
       "      <td>Onion</td>\n",
       "      <td>110.00</td>\n",
       "      <td>1290.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73822</th>\n",
       "      <td>73822</td>\n",
       "      <td>Bihar</td>\n",
       "      <td>BUXAR</td>\n",
       "      <td>2004</td>\n",
       "      <td>Whole Year</td>\n",
       "      <td>Onion</td>\n",
       "      <td>203.00</td>\n",
       "      <td>1918.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73823</th>\n",
       "      <td>73823</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>RAJGARH</td>\n",
       "      <td>2013</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Bajra</td>\n",
       "      <td>12.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73824</th>\n",
       "      <td>73824</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>RAJGARH</td>\n",
       "      <td>1998</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Groundnut</td>\n",
       "      <td>8700.00</td>\n",
       "      <td>9100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73825</th>\n",
       "      <td>73825</td>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>TONK</td>\n",
       "      <td>2000</td>\n",
       "      <td>Kharif</td>\n",
       "      <td>Bajra</td>\n",
       "      <td>34477.00</td>\n",
       "      <td>20137.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73826</th>\n",
       "      <td>73826</td>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>KODERMA</td>\n",
       "      <td>2002</td>\n",
       "      <td>Winter</td>\n",
       "      <td>Rice</td>\n",
       "      <td>14685.71</td>\n",
       "      <td>18552.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72731 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0      State_Name  District_Name  Crop_Year       Season  \\\n",
       "0               0           Bihar        NALANDA       1997  Rabi          \n",
       "1               1           Assam  KARBI ANGLONG       2011  Whole Year    \n",
       "2               2         Gujarat          ANAND       2012  Summer        \n",
       "3               3       Karnataka   UTTAR KANNAD       2005  Rabi          \n",
       "4               4   Uttar Pradesh        JAUNPUR       2008  Rabi          \n",
       "...           ...             ...            ...        ...          ...   \n",
       "73822       73822           Bihar          BUXAR       2004  Whole Year    \n",
       "73823       73823  Madhya Pradesh        RAJGARH       2013  Kharif        \n",
       "73824       73824  Madhya Pradesh        RAJGARH       1998  Kharif        \n",
       "73825       73825       Rajasthan           TONK       2000  Kharif        \n",
       "73826       73826       Jharkhand        KODERMA       2002  Winter        \n",
       "\n",
       "            Crop      Area  Production  \n",
       "0          Wheat  81934.00   160425.00  \n",
       "1          Onion    257.00      514.00  \n",
       "2          Maize    100.00      100.00  \n",
       "3      Groundnut   2872.00     4572.00  \n",
       "4          Onion    110.00     1290.00  \n",
       "...          ...       ...         ...  \n",
       "73822      Onion    203.00     1918.00  \n",
       "73823      Bajra     12.00       11.00  \n",
       "73824  Groundnut   8700.00     9100.00  \n",
       "73825      Bajra  34477.00    20137.00  \n",
       "73826       Rice  14685.71    18552.95  \n",
       "\n",
       "[72731 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77bc4cc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "048237ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2060f7c2eb0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df,x_vars='Area',y_vars='Production',size=4,aspect=1,kind='scatter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fdafee5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vd1a63bvTGTxoPIcOHaFJk3qMee4xwsPLFZp/6D8nsmzpOqpHVWX2pxNyyxo08AV27NgDwJHDx6hcpSIzZr5QJu0tDZPGPkTXDleyb/9hWnX8R7CrU+JUlYSERJYtW09ERHnGjHmcyy9vcFa6XbvSGDRorPMaqc/zzw8iPLwcs2cvJSnpEwAqVoxg+PBHuOyyupw4cZJevZ7i5MlTZGdn07nzdTz2WK+ybl6Rli/fxJhRb5Gdk8Mdd3agT58ePttVldGj3iIlZSMVIsqTMOqR3PdAQXkHDxzPjh9Pvwd+oXKVC5g+Y2xumXv2/I/bbh1I//538de/3VYm7SySywNIiY+wOR/sM4ClqlpfVZsA/wRivNKEnut+VFWBh4EXRSRCRCoCCUD/QMssiXr5IyVlAzt37mX+glcZMbIfI0Yk5pvuhXHv8Ze/3ML8BROpUqUS0z9JLjJ/z543kpj09FllvTh+MDNmvsCMmS/QsdM1dOx4dam0ray8N3UZ3e8fE+xqlJqUlPX8+OMeFi58nWee6c/w4a/lm27cuLfp3bs7CxcmUqVKJaZNWwRArVoxvP/+aD799BX69buHp59+FYDw8HK8804Cs2e/wsyZL7N8+QY2bdpaZu3yR3Z2DgnPvMmkxH8y+9PxzJ2zku3bd/ukWZ6ykZ0705g3/2WGj+jLyJFvFJn3hfEDmT5jLNNnjKVjp6u56Sbf98BzY97mhhuuLJtG+klDxe8lGEpjiiYeOKWqk06vUNVNQKiIfC4iHwKbnQ/9t0Rks4hsFJF4ABHpLSKzRGS+iHwrIsMK2pGqfg18CjwJDAPeBdJEZLKIrHXK7e6UW0dElovIBme51ll/o3e9SuF4nGVJ8lq6d2+HiNC8RSOOHD7GvowDedvGl6u/plPnPwDQo8eNJC9eU2T+Vq0vp2rVSgXuW1VZMP8Lut18fSm1rmysXLOVzINHg12NUpOcvJoePdojIrRocRmHDx8jIyPTJ42qsnp1Kp07XwdAz54dSE5eDUDLlo1zXwctWlxGWtr/ABARKlasAEBWVhZZWVmIy8bZN6duJ652LHFxMYSHh9Gt27V8vmStT5olS9ZxW/e2Z70H/MnreQ+s4uabr8tdl7x4DXFxMTRoUKtM2ug3Ef+XICiNANIUWF/AtjbAUKdX0h9AVZsB9wHviEiEV7peQAvgLhFpVcj+RgB/BLoCzwNDgSWq2hpPMBvr9E4ygI6q2hK4B3i5gHqVuoz0TGIvujD3cUxsFOnp+33SHDx4hMpVKhIWFnomjfMB4k/+gqxf9w1RUdWoU+fic22GKUXp6fuJjT3zHMfm8xwfOHCYKlUq5b5G8ksDMG3aQtq2vSr3cXZ2Nt27P8a11/6Za6+9kubNLy2lVgQmPSOTi2Kjch/HxESRnu4bPDPSM32Oz+n3hz9516/7P6KiqnJJnYsA+OWX47z5xiz6PXJXaTTn3ISI/0swqlfG+1ujqjucv68H3gNQ1a3ATqCRs22Rqu5X1V+B6U7afKnqMeAj4D1VPQF0Ap4SkU3AUiACqA2UA5JEZDMwFfAOFt718iEifUVknYisS0qcGkCT86kzmt9+fNNoPmkQv/MXZM6cFed97+P3IJ+n36/nOG+a1atTmTZtEUOG9M5dFxoayqxZL7Ns2Vukpm5j27ad51rdkpXfa9+f94eIX3nnzllJN6/ex8RXP+b+v9xMxYoRebMGnxRjKaookS7OqM52EXmqgDQ3isgmEdkiIsuKKrM0JtG3AHcWsO2Y19+FNTnvqyCft5OPHGc5Xe4dqvqtdwIRGQ6kA83xBM7jBdTLd8eqiUAiQLZ+XVQ9CvThB/OYOnUxAM2aNSBt7/9yt6Wn7Sc6urpP+sjIKhw5fIysrGzCwkKdNJGA51tVUfnzk5WVzeJFXzL1k7FFpjVl74MP5vDxxwsAaNasYe6wE0BaAa+Rw4eP5r5G8qbZunUH//rXKyQlDScysspZ+6tSpRJXX92M5cvX06jRJaXUquKLiYlib9qZnlR6+pnXfm6a2Cif45Oetp/oGpGcOplVaN6srGwWL17Dx9POzJ+lpm5n4YIveWHcBxw5cgwJEcLLh9OrV/DPxQkpoa/4zvzuRKAjsBtYKyKzVfUbrzTVgP8AXVT1JxGJLrJ+JVM9H0uA8iLSx6tirYF2edKl4BmmQkQa4eklnP7Q7ygi1UWkAtADWFmM/S8ABjiT+YjI6VmxqsBeVc0B/gyUyYT5aX/s1TV3ErtDhzbMmrUMVeWrTduoXPkCauR5g4gIba5uysIFqwCYOXMp7Tu0AaB9+9ZF5s/PqlWp1K1bk1ivLr5xj169bmbWrJeZNetlbrrpGmbOXIKqsmnTVipXvuCsACIiXH31FSxY4Hl7zJiRTPv2nonhPXsyGDBgNM8/P4i6dWvm5snMPMThw565o+PHT/DFF5uoV89d4/5Nm9Xnp5172b07g5Mns5g79wvi431HsePjWzF7Vkrue6CS8x4oKu+qVZupW/din/fAe++PZFHyRBYlT+TP93ejb9+erggeUKJTIG2A7ar6g6qeBKYA3fOk+SMwXVV/AlDVjKIKLfEeiKqqiPQEJjjdpON4TuOdmSfpf4BJzpBSFtBbVU84n/sr8AxvNQA+VNV1xajCM3hOIU51gsiPwC3O/j4RkbuAzymk11Ha2rZrSUrKBrp06k9ERHkSRp05ceyhvs/yzDOPEB1TncFD/sSQQeN56aX/0rhxXe64s0OR+YcMepE1a7dw8MAR4tv14dEB93DHnTcBMG/OCrrd8tsYvnrnlQHc8IfGXBhZme1fvsozL07jnY+WBrtaJaZdu1YsW7aOjh37UqFCeUaNejx3W58+w3n22QHExETxxBO9GTjweSZMeJ/Gjetx112dAJg4cQoHDx5mxAjP2VuhoaFMnz6ejIxMnnpqAtnZOajm0KXL9cTHtwlKGwsSFhbK0H/9jb4PJpCTk0PP2+Np0DCOj6YsBOCeezvRtt2VpKRsoGvnx4iICOfZUY8Umve0eXN9h6/cLqQYcxsi0hfo67Uq0RlBAagJ7PLathvIeypmI6CciCwFKgMvqeq7he4zv7HEYBKR3kArVX002HXJ61yGsH5rKl2SEOwquMavP40IdhVcIyvn12BXwTXCQpqf88x2g0kpfn/mbH+4bYH7c744d1bVB53HfwbaqOoArzSvAq2ADkAFYBVwc2G/37NfohtjjEuV4Nm5u4E4r8e1gD35pPmfc2LSMRFJwTNnfP4EEFV9G3jbe52IRAHJ+STvoKr+nb9qjDHnGSm5Weq1QEMRqQv8DNyLZ87D2yzgVREJA8LxDHGNL6xQ1wWQ/DhBokWw62GMMWWppHogqpolIo/iOckoFJisqltE5GFn+yRV/T8RmQ+k4jmr9Q3nx9oFOi8CiDHG/B6V5O1AVHUuMDfPukl5Ho8F/D7P3wKIMca4lMuuMnMWCyDGGONSbrtOWV4WQIwxxqVKcBK9VFgAMcYYl3J5B8QCiDHGuJUFEGOMMQEpybOwSoMFEGOMcSnrgRhjjAmIBRBjjDEBkSDdadBfFkCMMcalrAdijDEmIBZAjDHGBMTOwjLGGBMQ64EYY4wJiF3KxBhjTECsB2KMMSYgdjVeY4wxAQmxISxjjDGBcHkHxAKICcyvP40IdhVco0LtYcGugmtk/jAg2FVwjbAS6D24/IfoFkCMMcatLIAYY4wJSIhosKtQKAsgxhjjUtYDMcYYE5Aw64EYY4wJhPVAjDHGBMTlPwOxAGKMMW5lPRBjjDEBEZsDMcYYEwjrgRhjjAmInYVljDEmINYDMcYYExA7C8sYY0xArAdijDEmIHYtLGOMMQEJc3kPxO1DbMYY87sVIur3UhQR6SIi34rIdhF5qpB0rUUkW0TuLKpM64EYY4xLldQciIiEAhOBjsBuYK2IzFbVb/JJ9xywwK/6lUz1jDHGlLQQ8X8pQhtgu6r+oKongSlA93zSDQA+ATL8ql8x2mKMMaYMhRRjEZG+IrLOa+nrVVRNYJfX493OulwiUhPoCUzyt342hGWMMS5VnLOwVDURSCxgc359lLyFTwCeVNVsEf/GziyAGGOMS5XgWVi7gTivx7WAPXnStAKmOMHjQqCbiGSp6swC61di1TPGGFOiSvCHhGuBhiJSF/gZuBf4o3cCVa17+m8ReRv4rLDgAed5ABGRnsB0oLGqbg12fQqzfPlGRidMJjsnhzvv7ECfvrf7bFdVRiVMJiVlAxUiwhk1egBNLq9XaN6DB48weNCL/PxzBjVrRvPi+MFUrVoJgG+//ZHh/36do8d+IURC+Hjac5QvH86cz5aT+Pp0RCA6ujrPjX2cyMgqZXswCqCqJCQksmzZeiIiyjNmzONcfnmDs9Lt2pXGoEFjOXToCE2a1Of55wcRHl6O2bOXkpT0CQAVK0YwfPgjXHZZXU6cOEmvXk9x8uQpsrOz6dz5Oh57rFdZN6/UTBr7EF07XMm+/Ydp1fEfwa5OiVu5fDPPj/mQnOwcet7Rlr/1udlnu6ry/OgPWZGSSkSFcEYmPEDjJnVyt2dn5/DHu0cQHRPJK//5OwALF6xl0sSZ7PhhL+9PeZrLm9bFjUrqcu6qmiUij+I5uyoUmKyqW0TkYWe73/Me3s73SfT7gBV4oqkP53Q0V8jOzubZkUm8njSUTz+bwNw5K9i+fZdPmpSUDezcuZf5C15lxMh+jBiRWGTeN5JmcM01zZi/YCLXXNOMN5JmAJCVlc2TT7zEsBEP8elnL/HOuyMJCwslKyub0aMm8/a7I5g5ezyNLr2ED96fV7YHoxApKev58cc9LFz4Os8805/hw1/LN924cW/Tu3d3Fi5MpEqVSkybtgiAWrVieP/90Xz66Sv063cPTz/9KgDh4eV4550EZs9+hZkzX2b58g1s2uTq7xvF8t7UZXS/f0ywq1EqsrNzGJ3wHhMnDWT67ATmz/2S77f/7JNmxfJUftqZzux5Y3h6eG8SRr7ns/3D9xZRt95FPusaNKjJiy89SstWjUq9DeeiBM/CQlXnqmojVa2vqgnOukn5BQ9V7a2q04qsXyCNcgMRqQRcBzyAE0BE5EYR+VxEPgQ2i0ioiIwVkbUikioiD53OKyLJIrJBRDaLSH6ns5WYzanbqV07lri4WMLDy9G12/UsSV7rk2ZJ8lq6d2+HiNC8RSOOHD7GvowDheZdkryWHj3iAejRI57kxWsAWLlyE40urcNll9UBoFpkZUJDQ1FVVOGXX46jqhw9+ivR0ZGl2fRiSU5eTY8e7RERWrS4jMOHj5GRkemTRlVZvTqVzp2vA6Bnzw4kJ68GoGXLxrk9sBYtLiMt7X8AiAgVK1YAICsri6ysLPydJDwfrFyzlcyDR4NdjVLx9eYfiIuLplZcNOXCw+jcrQ1LP9/ok2bpko3cctu1iAhXNK/PkSO/sG/fQQDS0zJZnvIVt9/R1idPvfoXU6eub1Bxo+KchRUM5/MQVg9gvqpuE5FMEWnprG8DNFXVHc5pbIdUtbWIlAdWishCPKez9VTVwyJyIbDa+VFNqVx4Jj09k9iLLsx9HBtbndSvvvNJk5EnTUxsFOnp+wvNu3//QWo4AaBGdCSZmYcA2PnjXkSgzwMjyTxwmG7drueBB3tQrlwY/x7Wlx63DaLCBeW55JKLePrfD5ZGkwOSnr6f2FjvtnqOQXR09dx1Bw4cpkqVSoSFhfqkyWvatIW0bXtV7uPs7Gxuv30gP/20lz/+8WaaN7+0FFtiSkpG+gFiLzrz/MfEVGdz6ve+aTIOEhvrnSaSjPQD1KhRjbFj/svfB9/NsWPHy6zOJcnt18I6b3sgeIavpjh/T3EeA6xR1R3O352A+0VkE/AlEAU0xHNK2ygRSQUW4zkfOia/nXifW52UODWgiupZZ8sBeb4B55dGRPzKm1dWVjYb1m/l+XF/5/0PEli86EtWrUrl1KkspkxZwCczxrEs5Q0ubXQJSYkzitWW0pRf+Panp5A3zerVqUybtoghQ3rnrgsNDWXWrJdZtuwtUlO3sW3bznOtrikD+X185n2+8/veJyKkLN1EZPXKNLm8TulUrgyEhfi/BKV+wdntuRGRKKA90FQ8s0yheF5rc4Fj3kmBAaq6IE/+3kAN4CpVPSUiPwIR+e3L+9zqbP06oK8DsTFRpO39X+7jtLRMn2/VADF50qSneb55nzqVVWDeqKhq7Ms4QI3oSPZlHKB69aqe/cVG0bp1k9zJ8bbtWvLNNz9QqdIFANSuHQtAl67XkpQU3ADywQdz+Phjz9PTrFnD3GEngLS0/Wcdp8jIKhw+fJSsrGzCwkLPSrN16w7+9a9XSEoanu/JAVWqVOLqq5uxfPl6GjW6pJRaZUpKTEwkaXvPDGOmp2dSI7ra2WnSvNMcoEZ0NRYvXMuypZtYsTyVkydOcezYcf755OuMeu6hsqr+OXPNRG4BztceyJ3Au6p6iarWUdU4YAdwfZ50C4B+IlIOQEQaiUhFoCqQ4QSPeKBUP0maNmvAzp172b07nZMnTzFv7gri27fySdO+fWtmzVqGqvLVpm1UrnwBNaIjC80b374VM2d+DsDMmZ/TvkNrAK67vgXfbtvJr7+eICsrm7Vrt9Cgfhwx0dX5/vtduUNdX3yRSr16tUqz6UXq1etmZs16mVmzXuamm65h5swlqCqbNm2lcuULzgogIsLVV1/BggUrAZgxI5n27a8GYM+eDAYMGM3zzw+ibt0zP7LNzDzE4cOeOYLjx0/wxRebgt5u45/Lm9blp58y+Hn3Pk6dzGLB3DW0i7/SJ027+Cv5bPYXqCqpX31PpUoVqFGjGo8NvIuFS15k3qJxjBnXj9ZXNz6vggeU7MUUS8N52QPBM1yV97STT4B+gPcA6RtAHWCDePq9+/DMnXwAfCoi64BNQKmekhMWFsrQpx+kzwPPkJOTQ8872tOwYW2mTPF887733s60bdeSlJQNdOnUn4iI8iSM6l9oXoA+fW5n4MAX+OSTZC66qAbjJwwGoGrVSvyl963cfdc/EBHatm1Juxs98wGP9L+b+//0NGFhoVx8cQ1GjR5Qmk0vlnbtWrFs2To6duxLhQrlGTXq8dxtffoM59lnBxATE8UTT/Rm4MDnmTDhfRo3rsddd3UCYOLEKRw8eJgRIzxnb4WGhjJ9+ngyMjJ56qkJZGfnoJpDly7XEx/fJihtLA3vvDKAG/7QmAsjK7P9y1d55sVpvPPR0mBXq0SEhYXy1NBe9Ov7Ajk5OXTveQMNGtRk6keeL0533RPPDW2vYEVKKrd2fZKIiHBGPPtAkeUuWbyeMaM+4EDmEQY8MoFLL43jtaQhpd2cYnP7DaWklOaNf5MCHcL6LQqV8GBXwTUq1B4W7Cq4RuYP7vlCEmwVwq4954//51MX+f2Z848rOpZ5uDlfeyDGGPObV87lkwwWQIwxxqXcfhqvBRBjjHEpt8+BWAAxxhiXcvtpvBZAjDHGpawHYowxJiA2B2KMMSYgdhaWMcaYgNgQljHGmIBYADHGGBOQUJsDMcYYEwiXT4FYADHGGLcK1n0+/GUBxBhjXMqGsIwxxgTEJtGNMcYExAKIMcaYgFgAMcYYE5BQCyDGGGMCEWaT6MYYYwJhQ1jGGGMCYkNYxhhjAmKXczfGGBMQG8IyxhgTEAsgvyk5wa6Aa2Tl/BrsKrhG5g8Dgl0F16he75VgV8E1fv3p2nMuw+ZAjDHGBMTl8cMCiDHGuJW4PIK4/GLBxhjz+xVSjKUoItJFRL4Vke0i8lQ+23uJSKqzfCEizYsq03ogxhjjUlJCp/GKSCgwEegI7AbWishsVf3GK9kOoJ2qHhCRrkAicHVh5VoAMcYYlyrBs7DaANtV9QcAEZkCdAdyA4iqfuGVfjVQq8j6lVj1jDHGlCgpziLSV0TWeS19vYqqCezyerzbWVeQB4B5RdXPeiDGGONSxemBqGoinmGn/ORXUr7jYyISjyeAXF/UPi2AGGOMS5XgSVi7gTivx7WAPWftT+QK4A2gq6ruL6pQG8IyxhiXEvF/KcJaoKGI1BWRcOBeYLbvvqQ2MB34s6pu86d+1gMxxhiXKqkeiKpmicijwAIgFJisqltE5GFn+yTg30AU8B/xRKQsVW1VWLkWQIwxxqVK8lImqjoXmJtn3SSvvx8EHixOmRZAjDHGpVz+Q3QLIMYY41Yl9UPC0mIBxBhjXMp6IMYYYwLi9ospWgAxxhiXcvvvLCyAGGOMS9kdCY0xxgTEhrCMMcYExOXxwwKIMca4lQ1hGWOMCYjL48e5T/KLSLaIbBKRr0VkqohccA5lLRWRQq+9UkC+aiLyiNfji0VkWqD1KG3Ll2+kW5fH6NzpUZISZ5y1XVVJeHYynTs9So/bBvPNlh+KzDt//ipuvWUglze+m683f5+7PjX1O3r2GOJZug9h8aIvS7dxxbR8+SZu7vo4XToPIClp5lnbVZVRCZPp0nkAPbsPyXMs8s87eOB4bu/5BLf3fIKOHfpze88nfMrcs+d/tLrqz7w12edacq6ycvlmut/8/7i1y5NMTppz1nZV5blRH3Brlye5q+fT/N83P/psz87O4Z47hjHgkQm56xYuWMvttw3lyqZ/Y8vXO0q5BcExaexD7NwwiXWLng92VUpEiKjfS1DqVwJl/KqqLVS1KXASeNh7o3MrxdJWDcgNIKq6R1XvLIP9Flt2djbPjnyT15OG8uln45k7ZyXbt+/ySZOSspGdO/cyf8ErjBj5ECNGJBWZt2HDOF5+eQitWjX2Kathw9pMnfYcM2aOIzFpKMOHJZKVlV02jS1CdnYOCc+8yaTEfzL709Pt2e2TZnnKRnbuTGPe/JcZPqIvI0e+UWTeF8YPZPqMsUyfMZaOna7mppt878r53Ji3ueGGK8umkQHIzs5hdMJ7TJw0kOmzE5g/90u+3/6zT5oVy1P5aWc6s+eN4enhvUkY+Z7P9g/fW0Tdehf5rGvQoCYvvvQoLVs1KvU2BMt7U5fR/f4xwa5GiSnBq/GWipI+zXg50EBEbhSRz0XkQ2CziESIyFsisllENjo3LEFEKojIFOcm7h8BFU4XJCJHvf6+U0Tedv6OEZEZIvKVs1wLjAHqOz2hsSJSR0S+dtIXtO/eIjJdROaLyHciUiZfWTanbqd27Vji4mIIDy9H127XsSR5nU+aJclr6d69HSJC8xaNOHL4GPsyDhSat379WtStd/YNxipUKE9YmCeGnzh5EnHRaR2bU7cTl9ueMLp1u5bPl6z1SbNkyTpu694232NRVF5VZcH8Vdx883W565IXryEuLoYGDYq8W2fQfL35B+LioqkVF0258DA6d2vD0s83+qRZumQjt9x2LSLCFc3rc+TIL+zbdxCA9LRMlqd8xe13tPXJU6/+xdSp6xtUfmtWrtlK5sGjRSc8TxTnjoTBUGIBRETCgK7AZmdVG2CoqjYB+gOoajPgPuAdEYkA+gG/qOoVQAJwlR+7ehlYpqrNgZbAFuAp4HunJ/REnvQF7RugBXAP0Ay4R0TiKGXp6ZnEXhSV+zg2tjoZ6b73bcnIkyYmNor09Ey/8ubnq6++49ZbBtL9tsEMG94nN6AEW3pGJhfFerUzxtNObxnpmcTGXngmTWwU6RmZfuVdv+7/iIqqyiV1PB+av/xynDffmEW/R+4qjeaUmIz0A8ReVD33cUxMdTLSD/imyThIbKx3msjcNGPH/Je/D74bCXH7z9BMUUKKsQSrfueqgohsAtYBPwFvOuvXqOrpgdbrgfcAVHUrsBNoBLQF3nfWpwKpfuyvPfCakydbVQ8Vkb6gfQMkq+ohVT2O5+byl/ix/3NSwD0k86Q5O5WIf3nz07x5Qz79bDwfTx1DUuIMTpw46VddS53m1848x6KgNH7knTtnJd28eh8TX/2Y+/9yMxUrRuTN6ir5Pc/+HpeUpZuIrF6ZJpfXKZ3KmTLl9iGskjgL61dVbeG9wnmxH/NeVUj+gmZ/vNefyzu+sH2f8Po7m3yOh3Nj+r4Ar016mj59z21qJTamOml7z/Qa0tIyiY6u7pMmJibKJ0162n6io6tz6lRWkXkLU79+LSpUiOC7bbto2qz+ObSiZMTERLE3zaud6fuJjo70TRMbRVra/86kSdtPdI1ITp3MKjRvVlY2ixev4eNpZ8bDU1O3s3DBl7ww7gOOHDmGhAjh5cPp1atLaTQvYDExkaTtPdObSk/PpEZ0tbPTpHmnOUCN6GosXriWZUs3sWJ5KidPnOLYseP888nXGfXcQ2VVfVOi3DPknJ+y6vmkAL0ARKQRUBv4Ns/6psAVXnnSRaSxiIQAPb3WJ+MZ+kJEQkWkCnAEqFzMfftFVRNVtZWqtjrX4AHQtFkDdu7cy+7d6Zw8eYp5c1cS3973xLP27Vsxa9YyVJWvNm2jcuULqBEd6VfevHbvTs+dNP/5533s2LGHmrVqnHM7SkLTZvX5aededu/O4OTJLObO/YL4eN/2xMe3YvaslNxjUSn3WBSed9WqzdStezGxXsNc770/kkXJE1mUPJE/39+Nvn17ui54AFzetC4//ZTBz7v3cepkFgvmrqFdvO+kf7v4K/ls9heoKqlffU+lShWoUaMajw28i4VLXmTeonGMGdeP1lc3tuBxHguRUL+XYCir34H8B5gkIpuBLKC3qp4QkdeAt0QkFdgErPHK8xTwGbAL+Bqo5Kx/HEgUkQfw9Br6qeoqEVnpTJzPAyb6se/SamuhwsJCGfr0A/R5IIGcnBx63hFPw4ZxTJmyEIB77+1E23YtSUnZSJdOA4iICCdhVP9C8wIsXvQlCc9OJjPzMP0eHs1ll9Uh6c1/sWH9VpKSZhIWFkpISAhPD3uQyMgqQWl7XmFhoQz919/o+6DTntvjadAwjo+cY3HPvZ1o2+5KUlI20LXzY0REhPPsqEcKzXvavLm+w1fnk7CwUJ4a2ot+fV8gJyeH7j1voEGDmkz96HMA7ronnhvaXsGKlFRu7fokERHhjHj2gSLLXbJ4PWNGfcCBzCMMeGQCl14ax2tJQ0q7OWXqnVcGcMMfGnNhZGW2f/kqz7w4jXc+Whrsap0Dd/dAJL+xVJO/bE21g+Ww180Zp3KOFZ3od6J6vVeCXQXX+PWn/57zp/+hk/P9fqNVDe9S5tHGfolujDGu5e4eiAUQY4xxKc8UsHtZADHGGNeyHogxxpgAhLj8noQWQIwxxrUsgBhjjAmAm65dlx8LIMYY41oWQIwxxgRALIAYY4wJhOCOK2cXxAKIMca4lM2BGGOMCZAFEGOMMQEQO43XGGNMYKwHYowxJgA2B2KMMSYgbj8Ly90DbMYY87smxViKKEmki4h8KyLbReSpfLaLiLzsbE8VkZZFlWkBxBhjXEqK8a/QckRC8dyptSvQBLhPRJrkSdYVaOgsfYHXiqqfBRBjjHEpEfF7KUIbYLuq/qCqJ4EpQPc8aboD76rHaqCaiFxUWKEWQIwxxrVC/F5EpK+IrPNa+noVVBPY5fV4t7OOYqbxYZPoxhjjUsX5HYiqJgKJBRaVT5YA0viwAGKMMS5Vgqfx7gbivB7XAvYEkMaHDWEZY4xr+T+EVYS1QEMRqSsi4cC9wOw8aWYD9ztnY10DHFLVvYUVaj0QY4xxqZK6nLuqZonIo8ACIBSYrKpbRORhZ/skYC7QDdgO/AL8tcj6qRY6xGVcSET6OuOdv3t2LM6wY3GGHYuyYUNY56e+RSf53bBjcYYdizPsWJQBCyDGGGMCYgHEGGNMQCyAnJ9sbPcMOxZn2LE4w45FGbBJdGOMMQGxHogxxpiAWAAxxhgTEAsgARKROiLydZ51w0VkSLDq5A9/6ygi/8+5L8C3ItK5LOpm8iciPUVEReSyYNflXIlItohsEpGvRWSqiFxwDmUtFZFWAeSrJiKPeD2+WESmBVqP3zMLIOYszn0C7gUuB7oA/3HuJxBoebEiMkVEvheRb0Rkrog0Kqn6OvsQEVkhIl291t0tIvNLcj9Bch+wAs9z4uNcnpcg+VVVW6hqU+Ak8LD3xjJqTzUgN4Co6h5VvbMM9vubYwGklDjfjp4TkTUisk1EbnDW9xaR6SIyX0S+E5HnvfK85lyGeYuIjPBa/6OIjBKRVc72liKywPlAftgr3RMista5m5h3/qFOT2IxcKkf1e8OTFHVE6q6A8+lDdoEeBwEmAEsVdX6qtoE+CcQ45XmnD801HM2yMPAiyISISIVgQSgf6BluuHDWUQqAdcBD+AEEBG5UUQ+F5EPgc0iEioiY72e+4dO5xWRZBHZICKbRSTv/R+CbTnQIJ/2RIjIW06dN4pIPICIVHC+iKSKyEdAhdMFichRr7/vFJG3nb9jRGSGiHzlLNcCY4D6Tk9orPdoQiH7LvB9+3tm18IqXWGq2kZEugHDgJuc9S2AK4ETwLci8oqq7gKGqmqm88GVLCJXqGqqk2eXqv5BRMYDb+P5UIkAtgCTRKQTnjuJtcFzWebZItIWOIbng+dKPM/3BmA9gPheB8dbTWC11+Mi7wtQiHjglPc+VHXT6Q8NYC/QQjy3z3wNaAVkAYNU9XMR6Q30BMoDdYEPVXVE3p045X4tIp8CTwIVgXeBNBGZDDRz2j9cVWeJSB3gPScdwKOq+oWI3IjnudqL53nKe9e2stYDmK+q20QkU87cZrQN0FRVd4jnvg+HVLW1iJQHVorIQjz3duipqodF5EJgtYjMVheceikiYXjugHe6h+jdnsEAqtpMPMN2C50eaz/gF1W9QkSuwPNaLsrLwDJV7em8ryoBTzn7auHUpY5X+v4F7BsKft/+blkACVxBb0Lv9dOd/9cDdbzWJ6vqIQAR+Qa4BM+b/W7nwyAMuAjPh9fpAHL6ypmbgUqqegQ4IiLHRaQa0MlZNjrpKuEJKJWBGar6i7O/3Ctw5hM4Tiv2fQEK0RQnYOXDnw+N3HR4LvC2VkTmqOq6AsocgeeD5SSeYDQMWKKqf3OO0xqnJ5YBdFTV4yLSEPivk96nXgG2uSTdB0xw/p7iPJ4DrPGqXyfgChE5PQxTFc9zvxsY5XyRyMHzJSAGSCubquergohscv5eDrwJXItve64HXgFQ1a0ishNoBLTFExBQ1VQRSaVo7YH7nTzZwCERiSwkfUH7hoLft79bFkACtx/I+0KsDnh/6Jxw/s/G91if8Po7GwgTkbrAEKC1qh5wuuAR+eTJyZM/xylbgNGq+rp3hUTk7xT/w7/Y9wUIkD8fGgCLVHU/gIhMd9LmG0BU9ZgzvHFUVU84PbPb5MyJAxFAbac9r4pICzzPgfeczBo3BA8RicLzAdhURBTPVVQVz1VTj3knBQao6oI8+XsDNYCrVPWUiPyI72sqGH49/c3/NM8o51ntKYg/X9zOpY2F7fus9+057Oc3weZAAqSqR4G9ItIBQESq45lwXhFgkVXwvIkOiUgMnu59cSwA/uaMmSMiNUUkGkgBejrjx5WBW/0oazZwr4iUdwJbQ2BNMetz2hbgqgK2BfqhUVRAzHGW0+Xe4UzctlDV2qr6f8BAIB1ojqfnEV5AvYLpTjz3qL5EVeuoahyeLyjX50m3AOgnIuUARKSReOaAqgIZTvCIx/ON+XyQAvQCT1vwBPxv86xvClzhlSddRBqLSAieIc/TkvEMfSGeuaIqwBE8PfPi7NvkwwLIubkf+JfTJV8CjFDV7wMpSFW/wjP8tAWYDKwsZv6FwIfAKhHZDEwDKqvqBuAjYBPwCZ5hA8AzByJek/BeZW0BPga+wTNG3d/p/gdiCVBeRPp47bc10C5PusLeuB1FpLqIVMAzJ1CcY7MAGCDO11wRudJZXxXYq6o5wJ/xfLt3m/vwnIDg7RPgj3nWvYHnudrgTAa/jufb8QdAKxFZh+fYbi3d6paY/wChzuv4I6C3qp7AM0dWyRm6+ge+X2qeAj7D83rzvgnS40C8U9Z64HKnN7tSPKcSj/Vz3yYfdikTU+pE5GI84/hXAceBH4GZQHdVvcVJEwFMctLknUTvhmeyuwGFTKJ77W84niGscU7QmYBnnF2AH1X1Fmfe4xM88yqf4xkCquRMog85XS9jTMEsgBhXcwJIK1V9NNh1Mcb4siEsY4wxAbEeiDnvOGcnJeezqcPps7WMMaXPAogxxpiA2BCWMcaYgFgAMcYYExALIMYYYwJiAcQYY0xA/j8NlJ5F8kA9/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),cmap=\"YlGnBu\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3825f9b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df['Area']\n",
    "y=df['Production']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa366691",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.8,test_size=0.2,random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "656f9314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22943    90384.0\n",
       "30934      600.0\n",
       "40887    15107.0\n",
       "19628        2.0\n",
       "20896      100.0\n",
       "          ...   \n",
       "38407     2467.0\n",
       "29218    14864.0\n",
       "25625     3519.0\n",
       "4895     14595.0\n",
       "10349     2229.0\n",
       "Name: Area, Length: 58184, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a71e2a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22943    175616.0\n",
       "30934       300.0\n",
       "40887     51090.0\n",
       "19628         1.0\n",
       "20896       100.0\n",
       "           ...   \n",
       "38407      3894.0\n",
       "29218     11480.0\n",
       "25625      1569.0\n",
       "4895      16058.0\n",
       "10349       972.0\n",
       "Name: Production, Length: 58184, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c6a8a9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "X_train_sm=sm.add_constant(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1bd96dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    433264.117596\n",
       "Area         12.517891\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr=sm.OLS(y_train,X_train_sm).fit()\n",
    "lr.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2c0f79dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Production</td>    <th>  R-squared:         </th>  <td>   0.002</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.002</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   96.08</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 19 Sep 2022</td> <th>  Prob (F-statistic):</th>  <td>1.15e-22</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:06:27</td>     <th>  Log-Likelihood:    </th> <td>-1.0516e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td> 58184</td>      <th>  AIC:               </th>  <td>2.103e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td> 58182</td>      <th>  BIC:               </th>  <td>2.103e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td> 4.333e+05</td> <td> 7.26e+04</td> <td>    5.969</td> <td> 0.000</td> <td> 2.91e+05</td> <td> 5.76e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Area</th>  <td>   12.5179</td> <td>    1.277</td> <td>    9.802</td> <td> 0.000</td> <td>   10.015</td> <td>   15.021</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>168706.224</td> <th>  Durbin-Watson:     </th>    <td>   2.002</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>8103931935.368</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td>40.385</td>   <th>  Prob(JB):          </th>    <td>    0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>1829.533</td>  <th>  Cond. No.          </th>    <td>5.82e+04</td>   \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 5.82e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Production   R-squared:                       0.002\n",
       "Model:                            OLS   Adj. R-squared:                  0.002\n",
       "Method:                 Least Squares   F-statistic:                     96.08\n",
       "Date:                Mon, 19 Sep 2022   Prob (F-statistic):           1.15e-22\n",
       "Time:                        10:06:27   Log-Likelihood:            -1.0516e+06\n",
       "No. Observations:               58184   AIC:                         2.103e+06\n",
       "Df Residuals:                   58182   BIC:                         2.103e+06\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const       4.333e+05   7.26e+04      5.969      0.000    2.91e+05    5.76e+05\n",
       "Area          12.5179      1.277      9.802      0.000      10.015      15.021\n",
       "==============================================================================\n",
       "Omnibus:                   168706.224   Durbin-Watson:                   2.002\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       8103931935.368\n",
       "Skew:                          40.385   Prob(JB):                         0.00\n",
       "Kurtosis:                    1829.533   Cond. No.                     5.82e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 5.82e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ad8c7316",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train,y_train)\n",
    "plt.plot(X_train,4.333e+05+12.5179*X_train,'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f89bbd12",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred=lr.predict(X_train_sm)\n",
    "res=(y_train - y_train_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b6d8ff35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wqHpQrZZ57z4IM6uAvAlicvr1KODciNhUUDw9yxPlzKxq8i61caGkO4GngI9K6gOeLi6s3pPNCa5BmFkV5KpBRMQpwBuAgXRp7yeAuUUG1mvCE+XMrGJaeeToK0jmQ2TP+X6b4+lZXmrDzKom73Lf/wG8DLgZeC4tDpwgcvNEOTOrmrw1iAFgZvg325jVAiZNEFtqMWREk5lZt8o7iul2YJ9WLixpsaQNkpo+T1qJb0laI+lWSa/N7JsjaXW675RWPrdbRQQTJySL4bqJycyqIG+CmALcIekSScvqr1HOWQLMGWH/kcCM9LUA+A6ApInAWen+mcB8STNzxtm1ahFM2pogOhyMmVkOeZuYTmv1whFxtaRpIxwyF/h+2mx1vaTdJb0EmAasSZ9NjaTz0mPvaDWGblKrsbUG4bnUZlYFuRJERFwlaX9gRkRcLumFwMRxfvZU4N7M9mBa1qz8kHF+VsfVhjQxdTgYM7Mc8q7F9GHgx8DZadFU4IJxfraalMUI5cPFtkDSSkkrN27cOM6QihOB+yDMrFLy9kF8DDgMeBQgIn4L7DXOzx4E9sts7wusH6G8qYhYFBEDETHQ19c3zpCK4xqEmVVN3gTxTERsrm+kk+XG+2tuGfDBdDTTocAjEXEfsAKYIWm6pB2AeemxlRbApAnJt9ujhc2sCvJ2Ul8l6fPATpLeDHwUuHCkEySdC8wGpkgaBL5IuuhfRCwElpMs/rcGeBI4Id23RdLJwCUk/RyLI2JVi/fVdWoe5mpmFZM3QZwCnAjcBvwtyS/3c0Y6ISLmj7I/SJqumu1bnn5Gz6hPlAM8Uc7MKiHvKKaapAuACyKie3uCu5gnyplZ1YzYB5H2D5wm6UHgTmC1pI2STi0nvN6RbWJyfjCzKhitk/qTJKOXXh8Re0bEHiRzEg6T9Kmig+sltRpMmugahJlVx2gJ4oPA/Ii4u16QznD+QLrPckpqEOkopg7HYmaWx2gJYnJEPNhYmPZDTG5yvA0jAtIKhGsQZlYJoyWIzWPcZw08Uc7Mqma0UUyzJD3apFzACwqIp2cN7aR2hjCz7jdigoiI8S7IZ6mIbTOpa65CmFkF5F1qw8YpwE1MZlYpThAlGfrAIGcIM+t+ThAl8UQ5M6saJ4iSeKKcmVWNE0RJIjNRzn0QZlYFThAlGbKaq2sQZlYBThAlqUUwQc2epmpm1p2cIEpSC5iYfrc9D8LMqqDQBCFpjqTVktZIOqXJ/s9Kujl93S7pOUl7pPvWSbot3beyyDjL4D4IM6uavE+Ua5mkicBZwJuBQWCFpGURcUf9mIj4OvD19Pi3A5+KiE2ZyxzRbLHAKkqGuW57b2bW7YqsQRwMrImItRGxGTgPmDvC8fOBcwuMp6NqARMlJK/FZGbVUGSCmArcm9keTMu2I+mFwBzgJ5niAC6VdKOkBYVFWZKIQBITJDcxmVklFNbERLLia6PhfjW+HfhFQ/PSYRGxXtJewGWS7oyIq7f7kCR5LADo7+8fb8yFiYAJEhPkJiYzq4YiaxCDwH6Z7X2B9cMcO4+G5qWIWJ9+3QAsJWmy2k5ELIqIgYgY6OvrG3fQRUmGuYJcgzCziigyQawAZkiaLmkHkiSwrPEgSbsBhwM/zZTtLGnX+nvgLcDtBcZauFrAhAlJDcJ9EGZWBYU1MUXEFkknA5cAE4HFEbFK0knp/oXpoccAl0bEE5nT9waWKplYNgn4YURcXFSsZahFIJH2QThBmFn3K7IPgohYDixvKFvYsL0EWNJQthaYVWRsZdvWByGv5mpmleCZ1CWpRSCSnnv3QZhZFThBlKS+FpM8isnMKsIJoiS1gAlKOqrdSW1mVeAEUYJ6QvBEOTOrEieIEtQrDJ4oZ2ZV4gRRgnpC8EQ5M6sSJ4gS1BOCJ8qZWZU4QZSgtrUPwhPlzKw6nCBKMLQPwk1MZlYNThAlGNoH4U5qM6sGJ4gSbG1iIqlBDLvouZlZF3GCKEG9SUmuQZhZhThBlCC2NjG5D8LMqsMJogRbh7m6BmFmFeIEUYKtndQTvNy3mVWHE0QJYmsfhJfaMLPqKDRBSJojabWkNZJOabJ/tqRHJN2cvk7Ne26VRGaYqyfKmVlVFPZEOUkTgbOANwODwApJyyLijoZDr4mIo8d4biXUMhPlvBaTmVVFkTWIg4E1EbE2IjYD5wFzSzi369SG1CC8FpOZVUORCWIqcG9mezAta/QGSbdIukjSK1s8txJqfh6EmVVQYU1MJI9fbtT4q/EmYP+IeFzSUcAFwIyc5yYfIi0AFgD09/ePOdgiNT4PwjUIM6uCImsQg8B+me19gfXZAyLi0Yh4PH2/HJgsaUqeczPXWBQRAxEx0NfX187422bbUht+HoSZVUeRCWIFMEPSdEk7APOAZdkDJO0jSen7g9N4HspzbpVsex6EJ8qZWXUU1sQUEVsknQxcAkwEFkfEKkknpfsXAu8BPiJpC/AUMC+S9pem5xYVa9FqDUttOD+YWRUU2QdRbzZa3lC2MPP+TODMvOdWVQzppHYNwsyqwTOpSzB0LSZPlDOzanCCKEHjKCZ3UptZFThBlKDWsNSGh7maWRU4QZTAE+XMrIqcIEoQQ9Zicie1mVWDE0QJtm9i6nBAZmY5OEGUIPtMai+1YWZV4QRRgmwfhJfaMLOqcIIoQQyZSe0+CDOrBieIEmw/Ua6z8ZiZ5eEEUYJabWgNwn0QZlYFThAlGNpJ7aU2zKwanCBKEAxdzdVNTGZWBU4QJfBEOTOrIieIEniinJlVkRNECbb1QXiYq5lVR6EJQtIcSaslrZF0SpP9x0q6NX1dJ2lWZt86SbdJulnSyiLjLJprEGZWRYU9UU7SROAs4M3AILBC0rKIuCNz2N3A4RHxR0lHAouAQzL7j4iIB4uKsSyx3UxqZwgz635F1iAOBtZExNqI2AycB8zNHhAR10XEH9PN64F9C4ynY2q15GsyUQ7XIMysEopMEFOBezPbg2nZcE4ELspsB3CppBslLSggvtLUvNSGmVVQYU1MgJqUNf3NKOkIkgTxxkzxYRGxXtJewGWS7oyIq5ucuwBYANDf3z/+qAvgiXJmVkVF1iAGgf0y2/sC6xsPkvQa4BxgbkQ8VC+PiPXp1w3AUpImq+1ExKKIGIiIgb6+vjaG3z7Zxfq8FpOZVUWRCWIFMEPSdEk7APOAZdkDJPUD5wPHRcRdmfKdJe1afw+8Bbi9wFgLVctMlPNaTGZWFYU1MUXEFkknA5cAE4HFEbFK0knp/oXAqcCewLclAWyJiAFgb2BpWjYJ+GFEXFxUrEXbttQGXmrDzCqjyD4IImI5sLyhbGHm/YeADzU5by0wq7G8qjxRzsyqyDOpSxCZiXKSti7/bWbWzZwgSjB0mKuaD+UyM+syThAlqE+US4a5eqKcmVWDE0QJakOGuboPwsyqwQmiBOGJcmZWQU4QJah5opyZVZATRAk8Uc7MqsgJogSNz4NwDcLMqqDQiXLPdz+84fcA/OruTQAs/fUfWLX+UWq12LqvFe8/pDsXIzSz3uQaRAnqFQalo5gCNzOZWfdzgijB1pnUJCOZYJh1z83MuogTRAkisxaT0sdkuAJhZt3OCaIE25qYko5qM7MqcIIoQba/QU3KzMy6kRNECbIzqeudEE4PZtbtnCBKsLWJaWsPhPsgzKz7OUGUoN6cJGVGMTlDmFmXKzRBSJojabWkNZJOabJfkr6V7r9V0mvznlsl2U5quYnJzCqisAQhaSJwFnAkMBOYL2lmw2FHAjPS1wLgOy2cWxkPP/ksMLSJ6Tmvt2FmXa7IpTYOBtakz5dG0nnAXOCOzDFzge9H0t5yvaTdJb0EmJbj3LZZu/HxzPpIQ39xN7YENf5a337/toL7H3maezY9wYp1mxjY/8VMnCD22nVHAH74q9/zplfszaQJYtLE5Elzo7nrgcdy3E3v8IjgoXL8F3me8TekbtIEMW3Kzu2/btuvuM1U4N7M9iBwSI5jpuY8t23e9q1reerZ54q6PNP23Jl3HPRSAGbsvSvvG9iP828a5LvXrG3pOv/7it8WEZ6ZVdyUXXZk5T++qe3XLTJBNEvvjX+AD3dMnnOTC0gLSJqnAB6XtDp3hCW5B7hq2+YU4MFOxdImvofu0Qv34XsYp3sAfWHMp+8/3I4iE8QgsF9me19gfc5jdshxLgARsQhYNN5gyyJpZUQMdDqO8fA9dI9euA/fQ/cqchTTCmCGpOmSdgDmAcsajlkGfDAdzXQo8EhE3JfzXDMzK1BhNYiI2CLpZOASYCKwOCJWSTop3b8QWA4cBawBngROGOncomI1M7PtFfrAoIhYTpIEsmULM+8D+Fjec3tEZZrDRuB76B69cB++hy4lz+g1M7NmvNSGmZk15QRRkPEsM9ItctzDsWnst0q6TtKsTsQ5krxLtkh6vaTnJL2nzPjyyHMPkmZLulnSKklXNTum03L8f9pN0oWSbknv44ROxDkcSYslbZB0+zD7u/5numUR4VebXyQd678D/oRkyO4twMyGY44CLiKZ83EocEOn4x7DPfw58OL0/ZFVvIfMcf9N0uf1nk7HPYZ/h91JVhnoT7f36nTcY7yPzwP/kr7vAzYBO3Q69kx8fwG8Frh9mP1d/TM9lpdrEMXYusxIRGwG6kuFZG1dZiQirgfqy4x0i1HvISKui4g/ppvXk8xX6SZ5/h0APg78BNhQZnA55bmH9wPnR8TvASKiqvcRwK5KVrTchSRBbCk3zOFFxNUkMQ2n23+mW+YEUYzhlhBp9ZhOajW+E0n+euomo96DpKnAMcBCulOef4cDgBdLulLSjZI+WFp0+eW5jzOBV5BMir0N+ERE1MoJry26/We6ZYUOc30eG88yI92ileVOjiBJEG8sNKLW5bmHfwU+FxHPqTtXw8tzD5OA1wF/BewE/FLS9RFxV9HBtSDPfbwVuBn4S+BlwGWSromIRwuOrV26/We6ZU4QxRjPMiPdIld8kl4DnAMcGREPlRRbXnnuYQA4L00OU4CjJG2JiAtKiXB0ef8vPRgRTwBPSLoamAV0U4LIcx8nAF+NpEF/jaS7gQOBX5UT4rh1+890y9zEVIzxLDPSLUa9B0n9wPnAcV3212rdqPcQEdMjYlpETAN+DHy0i5ID5Pu/9FPgf0iaJOmFJCsf/6bkOEeT5z5+T1ILQtLewMuB1pY87qxu/5lumWsQBYhxLDPSLXLew6nAnsC307/At0QXLViW8x66Wp57iIjfSLoYuBWoAedERNOhmJ2S89/iy8ASSbeRNNd8LiK6ZpVXSecCs4EpkgaBLwKToRo/02PhmdRmZtaUm5jMzKwpJwgzM2vKCcLMzJpygjAzs6acIMzMKmq0BQQbjt1f0hXpQoJXShp1aRwnCCuNpAMknSZp9zZfd52kM9p5zaJJep+k49t8zdmSQtKr2nndTpH0Y0lXdjqOLrcEmJPz2DNI1op6DfAl4H+OdoIThJXpAJKx47u3+brHAN9q8zWL9j7g+DZf8ybgDSSrptrzQLMFBCW9TNLF6bpc10g6MN01E7giff9zmi9cOYQThHUlSS/Ie2xE/Lq+kmmvkTRZ0sQ8x0bEoxFxfUQ8VXRceUjaqdMxPE8tAj4eEa8DPgN8Oy2/BXh3+v4YkpVz9xzpQk4Qloukt0mqSZreUD49LX/HKOfPBi5MN+9Om0LWpfuOT7cPTttGnwI+m+77qqTbJD0uaVDSDyTt03DtIU1MkpZIWinpzWl76xOSrpX0yvF+H9Lr/0jSz5uUny7pAUmTRzl/CckP6uHpfYek09J9V6ZNKwsk/Q54GnippAMlnSfpXklPKnmgziclTchcd7smpnT7E5L+WdLGtL36LEk7tnC/6ySdIekLku5P/y1+IGm3Jp/9VknLJD1OsjorkvrT2DelsV8i6eUNn7GfpOWSnko/70N547NtJO1C8pyWH0m6GTgbqC85/hmS/3O/Bg4H/sAoy6l7qQ3L62KShcf+BjgtU348sJFkmYGR3ETyH/QM4F3AfcAzDcecC3wHOB14OC3bC/jn9LP7gE8D/y3p1RHx3Aif1w98Hfgn4Kn0c/9L0qti/MsHnANcJGl6RNwNydPEgA8C/xkRz45y/pfT+HYHPpqWDWb2H0aymunnSJZseISkeW418APgMeAgku/TTozelvxpkgcifQB4TXr8PcDXRjkvaz7JEhIfJvmF8zWS78N7G477HvBvJKvkPi1pD+Ba4CHgpPR+TgEul3RARDyVfu9+SrJY4okkSfF0YA/gty3EaMkf/Q9HxEGNOyJiPcnPXj2RvDsiHhnxap1+YpFf1XkBXwHuZtsSLQLWAWfkPP9okuWPpzWUH5+Wf2KU8yeSrK8fwF9kyofEQNJxtwWYkSl7Z3regW34Pkwg+QV7eqbsL9PrvyrnNX4MXNmk/EqShLbPCOeK5I+7zwNrM+WzG2NIt69uOP8C4PoW7ncdSTv3LpmyY0nWfXpFw2d/s+HcL5Mkhz0yZS8mSXofS7ePSs89JHPM/um/4XbfI7+2+/eZRuYpd8B1wHsz/1dmpe+nABPS9/8EfGm0a7uJyVqxmOQHd3a6fUS6/W9tuv7PGgskHankedePkPzCqP+lfcAo11oXEdm/Pu9Ivw47tE/SRCUrok6SNGztOpKH2CwhXbkzLT4eWBntWSTvxoi4vyG2F6RNWGtIal7PkvyQTx8p1tSlDdt30PrT/y6LiMcz2+eT/PJ5fcNxjf+GbwIuAx7NfF8fA24kWWodkqfNPRARN9RPioh70mNsBEoWEPwl8PK0CfZEkuR9oqRbgFVs64yeDayWdBewN8n/nxG5iclyi4i1SoYdnkAyCuIE4FcRsapNH/FAdkPS60mWUF4KfJXkkaBB8njT0TqxH27Y3px+Hem835EkvPrnT4+IdcMc+2/AF4AjJK0g6VP4zCgx5fVAk7J/AT5E0vRyE8n9zQX+keSeHm9yTt3DDdubGf3712jIY0wjaRp6nG3t23WNsU8heT7zXze5Zn1EzT6N18985q4txvm8EhHzh9m13dDXiPgxSc01NycIa9U5wHcl/QNJe+an23jtxr6BY0j6N/466vVlaf/tzmqftwPZztthH/YSEeskXU5Sc5hO0ux0bpviaNZH8l7g/0TE1n4DSW9r0+flsVd2Q8kIpV1I+pKyGmPfRJLkv9zkmo+lX+9vvH7mM7tiRNbzlROEtep84CySh85PSL/mleev+KydgGfrySF1bAuf15KIuK3FU75H0uz2SuCCiHi4hXNb/St+JzKd+kqGvs5r4fzxerOkXTLNTO8iSQYrRznvCpI5H6ti+OG3K4AvSjqk3syk5GFUrwV+Mf7QbazcB2EtiYinSUbSvBFY2uIvxdXp17+VdIikV49y/GVAv6R/lfRXkr5AMoqqW1xAMuLmtbTeD3Mn8GpJ75Q0IOmloxx/GfAxScelNYcLGVrbKdpTwM8kHS3pwyR/JCyNiDtGOe8bwA4kI8/eL+lwJbPIz5JUbx5ZTjJG/0eS5ks6hqQvo1mzk5XICcLG4oL06+JWTko7Hj9D8tfnL9g2L2K445eTDPV8N0kzxeEkI6G6QkQ8A1wE3Atc3uLp3ybpPF5M8hf0glGO/zhwDckv5sXA7eRYKqGNziPpd/oeyRDWi0iGpI4okifCHUqSEL9Jcs9fA3YjeQIeaQ3xHSSd54vT659J0vlqHeQnylnLJH2NpNNxejqi53kpHZFzD8njM7/Q6XiKomRC448jol2d8FYR7oOw3NLZrzOBj5DMAXheJgdJOwCzgPeTPJP77M5GZFYMJwhrxdnAISTNPUMWx0vnA4y0ZlCthxLKS4FfkbSR/21EZGdBky5/MWzzbUSMuLxBmdLObg2zO2Lk2erW49zEZG2hZK2l7dYnyjg9Ik4rJZgOS9daGqkzfaT5FaVKm4+GGzp8T0RMKy8a6zZOENYWknYFXj7CIesjWQum50maRjJBbDi3RsTmEfaXJh1JNtxoqGfGMPTXeogThJmZNeVhrmZm1pQThJmZNeUEYWZmTTlBmJlZU04QZmbW1P8HydlK3wqImPUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "sns.distplot(res,bins=15)\n",
    "plt.title('Error Terms',fontsize=15)\n",
    "plt.xlabel('y_train - y_train_pred',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "223fe54b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train,res)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6d477712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8555     463307.055130\n",
       "65858    433677.207987\n",
       "13878    611205.933035\n",
       "4248     435529.855801\n",
       "69272    437482.646741\n",
       "             ...      \n",
       "52318    435141.801192\n",
       "13737    451202.254882\n",
       "34477    923965.430661\n",
       "69865    455395.748246\n",
       "68253    440499.458385\n",
       "Length: 14547, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_sm = sm.add_constant(X_test)\n",
    "y_test_pred = lr.predict(X_test_sm)\n",
    "y_test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c181775c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0016957131314162543"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r_squared=r2_score(y_test,y_test_pred)\n",
    "r_squared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "60b33ab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,y_test)\n",
    "plt.plot(X_test,y_test_pred,'r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "88ff4a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train_lm, X_test_lm, y_train_lm, y_test_lm = train_test_split(X, y, train_size = 0.8, test_size = 0.2, random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a379c0cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(58184, 1)\n",
      "(14547, 1)\n"
     ]
    }
   ],
   "source": [
    "X_train_lm.shape\n",
    "X_train_lm=X_train_lm.values.reshape(-1,1)\n",
    "X_test_lm=X_test_lm.values.reshape(-1,1)\n",
    "print(X_train_lm.shape)\n",
    "print(X_test_lm.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "726a4014",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm=LinearRegression()\n",
    "lm.fit(X_train_lm, y_train_lm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "55c0f32d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept : 433264.11759569094\n",
      "Slope : [12.51789064]\n"
     ]
    }
   ],
   "source": [
    "print(\"Intercept :\",lm.intercept_)\n",
    "print(\"Slope :\",lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7dcdcdbd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0016486698798432453\n",
      "0.0016957131314162543\n"
     ]
    }
   ],
   "source": [
    "y_train_pred = lm.predict(X_train_lm)\n",
    "y_test_pred = lm.predict(X_test_lm)\n",
    "print(r2_score(y_train,y_train_pred))\n",
    "print(r2_score(y_test,y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "29c90f5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18962432.767530125\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "import math\n",
    "MSE = mean_squared_error(y_test,y_test_pred)\n",
    "RMSE=math.sqrt(MSE)\n",
    "print(RMSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04f72fa1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}