internship_house_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b8880271",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import seaborn as sn\n",
    "matplotlib.rcParams[\"figure.figsize\"] = [20,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0014751",
   "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>Transaction date</th>\n",
       "      <th>House Age</th>\n",
       "      <th>Distance from nearest Metro station (km)</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Number of bedrooms</th>\n",
       "      <th>House size (sqft)</th>\n",
       "      <th>House price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012.917</td>\n",
       "      <td>32.0</td>\n",
       "      <td>84.87882</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>1</td>\n",
       "      <td>575</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012.917</td>\n",
       "      <td>19.5</td>\n",
       "      <td>306.59470</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>2</td>\n",
       "      <td>1240</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013.583</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>3</td>\n",
       "      <td>1060</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013.500</td>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>2</td>\n",
       "      <td>875</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012.833</td>\n",
       "      <td>5.0</td>\n",
       "      <td>390.56840</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>1</td>\n",
       "      <td>491</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012.667</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2175.03000</td>\n",
       "      <td>3</td>\n",
       "      <td>24.96305</td>\n",
       "      <td>121.51254</td>\n",
       "      <td>3</td>\n",
       "      <td>828</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012.667</td>\n",
       "      <td>34.5</td>\n",
       "      <td>623.47310</td>\n",
       "      <td>7</td>\n",
       "      <td>24.97933</td>\n",
       "      <td>121.53642</td>\n",
       "      <td>3</td>\n",
       "      <td>1115</td>\n",
       "      <td>40.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013.417</td>\n",
       "      <td>20.3</td>\n",
       "      <td>287.60250</td>\n",
       "      <td>6</td>\n",
       "      <td>24.98042</td>\n",
       "      <td>121.54228</td>\n",
       "      <td>3</td>\n",
       "      <td>1316</td>\n",
       "      <td>46.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013.500</td>\n",
       "      <td>31.7</td>\n",
       "      <td>5512.03800</td>\n",
       "      <td>1</td>\n",
       "      <td>24.95095</td>\n",
       "      <td>121.48458</td>\n",
       "      <td>1</td>\n",
       "      <td>588</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013.417</td>\n",
       "      <td>17.9</td>\n",
       "      <td>1783.18000</td>\n",
       "      <td>3</td>\n",
       "      <td>24.96731</td>\n",
       "      <td>121.51486</td>\n",
       "      <td>2</td>\n",
       "      <td>1075</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013.083</td>\n",
       "      <td>34.8</td>\n",
       "      <td>405.21340</td>\n",
       "      <td>1</td>\n",
       "      <td>24.97349</td>\n",
       "      <td>121.53372</td>\n",
       "      <td>1</td>\n",
       "      <td>466</td>\n",
       "      <td>41.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013.333</td>\n",
       "      <td>6.3</td>\n",
       "      <td>90.45606</td>\n",
       "      <td>9</td>\n",
       "      <td>24.97433</td>\n",
       "      <td>121.54310</td>\n",
       "      <td>3</td>\n",
       "      <td>1423</td>\n",
       "      <td>58.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012.917</td>\n",
       "      <td>13.0</td>\n",
       "      <td>492.23130</td>\n",
       "      <td>5</td>\n",
       "      <td>24.96515</td>\n",
       "      <td>121.53737</td>\n",
       "      <td>3</td>\n",
       "      <td>1172</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012.667</td>\n",
       "      <td>20.4</td>\n",
       "      <td>2469.64500</td>\n",
       "      <td>4</td>\n",
       "      <td>24.96108</td>\n",
       "      <td>121.51046</td>\n",
       "      <td>3</td>\n",
       "      <td>928</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013.500</td>\n",
       "      <td>13.2</td>\n",
       "      <td>1164.83800</td>\n",
       "      <td>4</td>\n",
       "      <td>24.99156</td>\n",
       "      <td>121.53406</td>\n",
       "      <td>1</td>\n",
       "      <td>512</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Transaction date  House Age  Distance from nearest Metro station (km)  \\\n",
       "0           2012.917       32.0                                  84.87882   \n",
       "1           2012.917       19.5                                 306.59470   \n",
       "2           2013.583       13.3                                 561.98450   \n",
       "3           2013.500       13.3                                 561.98450   \n",
       "4           2012.833        5.0                                 390.56840   \n",
       "5           2012.667        7.1                                2175.03000   \n",
       "6           2012.667       34.5                                 623.47310   \n",
       "7           2013.417       20.3                                 287.60250   \n",
       "8           2013.500       31.7                                5512.03800   \n",
       "9           2013.417       17.9                                1783.18000   \n",
       "10          2013.083       34.8                                 405.21340   \n",
       "11          2013.333        6.3                                  90.45606   \n",
       "12          2012.917       13.0                                 492.23130   \n",
       "13          2012.667       20.4                                2469.64500   \n",
       "14          2013.500       13.2                                1164.83800   \n",
       "\n",
       "    Number of convenience stores  latitude  longitude  Number of bedrooms  \\\n",
       "0                             10  24.98298  121.54024                   1   \n",
       "1                              9  24.98034  121.53951                   2   \n",
       "2                              5  24.98746  121.54391                   3   \n",
       "3                              5  24.98746  121.54391                   2   \n",
       "4                              5  24.97937  121.54245                   1   \n",
       "5                              3  24.96305  121.51254                   3   \n",
       "6                              7  24.97933  121.53642                   3   \n",
       "7                              6  24.98042  121.54228                   3   \n",
       "8                              1  24.95095  121.48458                   1   \n",
       "9                              3  24.96731  121.51486                   2   \n",
       "10                             1  24.97349  121.53372                   1   \n",
       "11                             9  24.97433  121.54310                   3   \n",
       "12                             5  24.96515  121.53737                   3   \n",
       "13                             4  24.96108  121.51046                   3   \n",
       "14                             4  24.99156  121.53406                   1   \n",
       "\n",
       "    House size (sqft)  House price of unit area  \n",
       "0                 575                      37.9  \n",
       "1                1240                      42.2  \n",
       "2                1060                      47.3  \n",
       "3                 875                      54.8  \n",
       "4                 491                      43.1  \n",
       "5                 828                      32.1  \n",
       "6                1115                      40.3  \n",
       "7                1316                      46.7  \n",
       "8                 588                      18.8  \n",
       "9                1075                      22.1  \n",
       "10                466                      41.4  \n",
       "11               1423                      58.1  \n",
       "12               1172                      39.3  \n",
       "13                928                      23.8  \n",
       "14                512                      34.3  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"hp_int.csv\")\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "add86b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([32. , 19.5, 13.3,  5. ,  7.1, 34.5, 20.3, 31.7, 17.9, 34.8,  6.3,\n",
       "       13. , 20.4, 13.2, 35.7,  0. , 17.7, 16.9,  1.5,  4.5, 10.5, 14.7,\n",
       "       10.1, 39.6, 29.3,  3.1, 10.4, 19.2, 25.9, 29.6, 37.9, 16.5, 15.4,\n",
       "       13.9, 12. , 16.2, 13.6, 16.8, 36.1, 34.4,  2.7, 36.6, 21.7, 35.9,\n",
       "       24.2, 29.4, 31.3, 32.1, 16.1, 33.6,  3.5, 30.3, 11. ,  5.3, 17.2,\n",
       "        2.6, 17.5, 40.1,  1. ,  8.5, 30.4, 12.5,  6.6, 35.5, 32.5, 13.8,\n",
       "        6.8, 12.3, 20.5, 38.2, 18. , 11.8, 30.8, 25.3, 15.1,  1.8,  8.9,\n",
       "       23. ,  9.1, 20.6, 31.9, 40.9,  8. ,  6.4, 28.4, 16.4, 12.7,  1.1,\n",
       "       32.7, 12.2, 31.4,  4. ,  8.1, 33.3,  9.9, 14.8, 30.6, 30.9, 16.6,\n",
       "       31.5, 38.6,  3.8, 41.3, 38.5, 26.6, 33.4, 18.9, 11.4, 10. , 12.9,\n",
       "        5.1, 19.8, 11.9,  2.1,  3.2, 34.9, 35.8,  4.9,  6.5, 30.7, 11.6,\n",
       "       15.5, 16. , 13.7, 28.2, 27.6,  8.4, 24. ,  3.6,  4.3, 30.2, 33. ,\n",
       "       13.1, 14. , 26.9, 13.5, 17. , 14.1, 20.9, 16.3, 35.3, 43.8,  9.7,\n",
       "       15.2, 22.8, 34. , 18.2, 17.4, 38.3, 15.6, 12.8, 22.2, 11.5,  5.2,\n",
       "       17.6,  6.2, 18.1, 37.8, 28. , 37.2,  9. , 32.4, 31. , 27.1, 39.7,\n",
       "        2. , 32.8,  4.8,  7.5, 19. , 39.2,  5.9, 14.6, 17.3, 15.9,  3.9,\n",
       "       32.6, 15.7, 17.8, 34.7, 10.8, 27.5, 19.1, 21.2,  2.3,  4.7, 33.5,\n",
       "       15. , 30.1, 37.7,  3.4, 12.6, 26.4, 16.7, 33.2,  2.5, 38. , 20. ,\n",
       "       14.4, 10.3, 21.3, 35.4,  8.3,  3.7, 34.6, 28.6, 12.4,  4.1, 25.6,\n",
       "       39.8,  7.8, 30. , 27.3,  1.7,  5.7,  4.6, 18.4,  5.6, 32.9, 41.4,\n",
       "       17.1, 32.3, 14.2, 20.2, 33.9,  5.4, 37.3, 29.1, 18.3, 42.7, 37.1,\n",
       "       26.8,  7.6,  1.9, 18.5, 18.8])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['House Age'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3ea0300c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Transaction date                            0\n",
       "House Age                                   0\n",
       "Distance from nearest Metro station (km)    0\n",
       "Number of convenience stores                0\n",
       "latitude                                    0\n",
       "longitude                                   0\n",
       "Number of bedrooms                          0\n",
       "House size (sqft)                           0\n",
       "House price of unit area                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e061e644",
   "metadata": {},
   "outputs": [],
   "source": [
    "#transaction date  data is not required so we can drop. \n",
    "df1 = df.drop(['Transaction date'], axis = 'columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c868006",
   "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>House Age</th>\n",
       "      <th>Distance from nearest Metro station (km)</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Number of bedrooms</th>\n",
       "      <th>House size (sqft)</th>\n",
       "      <th>House price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>84.87882</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>1</td>\n",
       "      <td>575</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.5</td>\n",
       "      <td>306.59470</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>2</td>\n",
       "      <td>1240</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>3</td>\n",
       "      <td>1060</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.3</td>\n",
       "      <td>561.98450</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>2</td>\n",
       "      <td>875</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>390.56840</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>1</td>\n",
       "      <td>491</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   House Age  Distance from nearest Metro station (km)  \\\n",
       "0       32.0                                  84.87882   \n",
       "1       19.5                                 306.59470   \n",
       "2       13.3                                 561.98450   \n",
       "3       13.3                                 561.98450   \n",
       "4        5.0                                 390.56840   \n",
       "\n",
       "   Number of convenience stores  latitude  longitude  Number of bedrooms  \\\n",
       "0                            10  24.98298  121.54024                   1   \n",
       "1                             9  24.98034  121.53951                   2   \n",
       "2                             5  24.98746  121.54391                   3   \n",
       "3                             5  24.98746  121.54391                   2   \n",
       "4                             5  24.97937  121.54245                   1   \n",
       "\n",
       "   House size (sqft)  House price of unit area  \n",
       "0                575                      37.9  \n",
       "1               1240                      42.2  \n",
       "2               1060                      47.3  \n",
       "3                875                      54.8  \n",
       "4                491                      43.1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e94072d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(414, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "43840cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Number of bedrooms'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "90293bd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  9,  5,  3,  7,  6,  1,  4,  2,  8,  0], dtype=int64)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Number of convenience stores'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0d469cb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Number of convenience stores\n",
       "0     67\n",
       "1     46\n",
       "2     24\n",
       "3     46\n",
       "4     31\n",
       "5     67\n",
       "6     37\n",
       "7     31\n",
       "8     30\n",
       "9     25\n",
       "10    10\n",
       "Name: Number of convenience stores, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting info by number of convenience stores\n",
    "df1.groupby('Number of convenience stores')['Number of convenience stores'].agg('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e78cae22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  84.87882,  306.5947 ,  561.9845 ,  390.5684 , 2175.03   ,\n",
       "        623.4731 ,  287.6025 , 5512.038  , 1783.18   ,  405.2134 ,\n",
       "         90.45606,  492.2313 , 2469.645  , 1164.838  ,  579.2083 ,\n",
       "        292.9978 ,  350.8515 ,  368.1363 ,   23.38284, 2275.877  ,\n",
       "        279.1726 , 1360.139  ,  480.6977 , 1487.868  ,  383.8624 ,\n",
       "        276.449  ,  557.478  ,  451.2438 , 4519.69   ,  769.4034 ,\n",
       "        488.5727 ,  323.655  ,  205.367  , 4079.418  , 1935.009  ,\n",
       "        577.9615 ,  289.3248 , 4082.015  , 4066.587  ,  519.4617 ,\n",
       "        512.7871 ,  533.4762 ,  488.8193 ,  463.9623 ,  640.7391 ,\n",
       "       4605.749  , 4510.359  ,  512.5487 , 1758.406  , 1438.579  ,\n",
       "       1160.632  ,  371.2495 ,   56.47425,  336.0532 , 1931.207  ,\n",
       "        259.6607 , 2175.877  ,  995.7554 ,  123.7429 ,  193.5845 ,\n",
       "        104.8101 ,  464.223  ,  424.5442 ,  379.5575 ,  616.4004 ,\n",
       "       2185.128  ,  552.4371 , 1414.837  ,  377.7956 ,  150.9347 ,\n",
       "       2707.392  ,  383.2805 ,  338.9679 , 1455.798  , 1406.43   ,\n",
       "       3947.945  ,  274.0144 , 1402.016  , 1146.329  ,  167.5989 ,\n",
       "        617.4424 ,  964.7496 ,  170.1289 ,  208.3905 ,  392.4459 ,\n",
       "        189.5181 ,  592.5006 , 2147.376  ,  196.6172 , 2102.427  ,\n",
       "        393.2606 ,  143.8383 ,  737.9161 , 6396.283  , 4197.349  ,\n",
       "       1583.722  ,  414.9476 ,  185.4296 ,  804.6897 ,  124.9912 ,\n",
       "        216.8329 ,  535.527  ,  482.7581 ,  373.3937 ,  186.9686 ,\n",
       "       1009.235  ,  319.0708 ,  942.4664 , 1559.827  ,  640.6071 ,\n",
       "        489.8821 , 3780.59   ,  179.4538 ,  170.7311 ,  387.7721 ,\n",
       "        376.1709 , 1264.73   ,  815.9314 ,   49.66105, 1236.564  ,\n",
       "        330.0854 ,  515.1122 , 1962.628  , 4527.687  ,  401.8807 ,\n",
       "        432.0385 ,  472.1745 , 4573.779  ,  181.0766 , 1144.436  ,\n",
       "        438.8513 , 4449.27   ,  201.8939 , 2615.465  , 1447.286  ,\n",
       "       3078.176  ,  190.0392 ,  616.5735 ,  750.0704 ,   57.58945,\n",
       "        421.479  , 3771.895  ,  461.1016 ,  707.9067 ,  126.7286 ,\n",
       "        157.6052 ,  451.6419 ,  642.6985 , 1449.722  ,  665.0636 ,\n",
       "        175.6294 , 1805.665  ,  383.7129 ,  590.9292 ,  372.6242 ,\n",
       "        529.7771 ,  186.5101 ,  431.1114 ,  324.9419 ,  265.0609 ,\n",
       "       3171.329  , 1156.412  , 4074.736  , 4412.765  ,  333.3679 ,\n",
       "       2216.612  ,  250.631  ,  373.8389 ,  732.8528 ,  837.7233 ,\n",
       "       1712.632  , 2077.39   ,  204.1705 ,  639.6198 ,  389.8219 ,\n",
       "       1055.067  , 6306.153  ,  424.7132 , 1159.454  , 1735.595  ,\n",
       "        329.9747 ,  339.2289 ,  444.1334 , 1485.097  , 2288.011  ,\n",
       "        493.657  ,  252.5822 ,  394.0173 , 1554.25   ,  184.3302 ,\n",
       "       1978.671  ,  718.2937 ,  323.6912 ,  490.3446 ,  395.6747 ,\n",
       "        335.5273 , 2179.59   ,  567.0349 ,  121.7262 ,  156.2442 ,\n",
       "        461.7848 ,  439.7105 , 1626.083  ,  169.9803 , 3079.89   ,\n",
       "       1643.499  ,  537.7971 ,  318.5292 , 1756.411  ,  752.7669 ,\n",
       "        272.6783 ,  187.4823 ,  197.1338 ,  757.3377 , 1497.713  ,\n",
       "       1156.777  ,  617.7134 , 1013.341  ,  337.6016 , 1867.233  ,\n",
       "        600.8604 ,  258.186  ,  270.8895 ,  563.2854 , 3085.17   ,\n",
       "       6488.021  , 2180.245  , 2674.961  ,  211.4473 , 2408.993  ,\n",
       "         87.30222,  281.205  ,  967.4    ,  109.9455 ,  614.1394 ,\n",
       "       2261.432  , 1801.544  , 1828.319  ,  312.8963 , 1157.988  ,\n",
       "       1717.193  ,  587.8877 ,  132.5469 , 3529.564  ,  506.1144 ,\n",
       "         82.88643, 2103.555  , 2251.938  ,  122.3619 ,  377.8302 ,\n",
       "       1939.749  ,  443.802  , 4136.271  ,  918.6357 ,  161.942  ,\n",
       "        130.9945 ,  372.1386 , 2175.744  ,  390.9696 ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Distance from nearest Metro station (km)'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ad87c97f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "259"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['Distance from nearest Metro station (km)'].nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5078bdc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#I think that these distances are in meters but by mistake it is given in KM, so i will change it km\n",
    "df1['Distance from nearest Metro station (km)'] = df1['Distance from nearest Metro station (km)']/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14c249b4",
   "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>House Age</th>\n",
       "      <th>Distance from nearest Metro station (km)</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Number of bedrooms</th>\n",
       "      <th>House size (sqft)</th>\n",
       "      <th>House price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.084879</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>1</td>\n",
       "      <td>575</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.5</td>\n",
       "      <td>0.306595</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>2</td>\n",
       "      <td>1240</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.3</td>\n",
       "      <td>0.561984</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>3</td>\n",
       "      <td>1060</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.3</td>\n",
       "      <td>0.561984</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>2</td>\n",
       "      <td>875</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.390568</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>1</td>\n",
       "      <td>491</td>\n",
       "      <td>43.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.1</td>\n",
       "      <td>2.175030</td>\n",
       "      <td>3</td>\n",
       "      <td>24.96305</td>\n",
       "      <td>121.51254</td>\n",
       "      <td>3</td>\n",
       "      <td>828</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34.5</td>\n",
       "      <td>0.623473</td>\n",
       "      <td>7</td>\n",
       "      <td>24.97933</td>\n",
       "      <td>121.53642</td>\n",
       "      <td>3</td>\n",
       "      <td>1115</td>\n",
       "      <td>40.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>20.3</td>\n",
       "      <td>0.287603</td>\n",
       "      <td>6</td>\n",
       "      <td>24.98042</td>\n",
       "      <td>121.54228</td>\n",
       "      <td>3</td>\n",
       "      <td>1316</td>\n",
       "      <td>46.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31.7</td>\n",
       "      <td>5.512038</td>\n",
       "      <td>1</td>\n",
       "      <td>24.95095</td>\n",
       "      <td>121.48458</td>\n",
       "      <td>1</td>\n",
       "      <td>588</td>\n",
       "      <td>18.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17.9</td>\n",
       "      <td>1.783180</td>\n",
       "      <td>3</td>\n",
       "      <td>24.96731</td>\n",
       "      <td>121.51486</td>\n",
       "      <td>2</td>\n",
       "      <td>1075</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   House Age  Distance from nearest Metro station (km)  \\\n",
       "0       32.0                                  0.084879   \n",
       "1       19.5                                  0.306595   \n",
       "2       13.3                                  0.561984   \n",
       "3       13.3                                  0.561984   \n",
       "4        5.0                                  0.390568   \n",
       "5        7.1                                  2.175030   \n",
       "6       34.5                                  0.623473   \n",
       "7       20.3                                  0.287603   \n",
       "8       31.7                                  5.512038   \n",
       "9       17.9                                  1.783180   \n",
       "\n",
       "   Number of convenience stores  latitude  longitude  Number of bedrooms  \\\n",
       "0                            10  24.98298  121.54024                   1   \n",
       "1                             9  24.98034  121.53951                   2   \n",
       "2                             5  24.98746  121.54391                   3   \n",
       "3                             5  24.98746  121.54391                   2   \n",
       "4                             5  24.97937  121.54245                   1   \n",
       "5                             3  24.96305  121.51254                   3   \n",
       "6                             7  24.97933  121.53642                   3   \n",
       "7                             6  24.98042  121.54228                   3   \n",
       "8                             1  24.95095  121.48458                   1   \n",
       "9                             3  24.96731  121.51486                   2   \n",
       "\n",
       "   House size (sqft)  House price of unit area  \n",
       "0                575                      37.9  \n",
       "1               1240                      42.2  \n",
       "2               1060                      47.3  \n",
       "3                875                      54.8  \n",
       "4                491                      43.1  \n",
       "5                828                      32.1  \n",
       "6               1115                      40.3  \n",
       "7               1316                      46.7  \n",
       "8                588                      18.8  \n",
       "9               1075                      22.1  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d297e0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    414.000000\n",
       "mean      37.980193\n",
       "std       13.606488\n",
       "min        7.600000\n",
       "25%       27.700000\n",
       "50%       38.450000\n",
       "75%       46.600000\n",
       "max      117.500000\n",
       "Name: House price of unit area, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['House price of unit area'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2d580ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     414.000000\n",
       "mean      931.475845\n",
       "std       348.910269\n",
       "min       402.000000\n",
       "25%       548.000000\n",
       "50%       975.000000\n",
       "75%      1234.750000\n",
       "max      1500.000000\n",
       "Name: House size (sqft), dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['House size (sqft)'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2545528b",
   "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>House Age</th>\n",
       "      <th>Distance from nearest Metro station (km)</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Number of bedrooms</th>\n",
       "      <th>House size (sqft)</th>\n",
       "      <th>House price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>10.8</td>\n",
       "      <td>0.252582</td>\n",
       "      <td>1</td>\n",
       "      <td>24.9746</td>\n",
       "      <td>121.53046</td>\n",
       "      <td>2</td>\n",
       "      <td>1373</td>\n",
       "      <td>117.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     House Age  Distance from nearest Metro station (km)  \\\n",
       "270       10.8                                  0.252582   \n",
       "\n",
       "     Number of convenience stores  latitude  longitude  Number of bedrooms  \\\n",
       "270                             1   24.9746  121.53046                   2   \n",
       "\n",
       "     House size (sqft)  House price of unit area  \n",
       "270               1373                     117.5  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the outliers\n",
    "df2 = df1[df1['House price of unit area'] >= 100]\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b18c3a52",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ashwa\\ana\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='House price of unit area'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.boxplot(df1['House price of unit area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "12c9489b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ashwa\\ana\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='House size (sqft)'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAAJNCAYAAACLEojLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAaWUlEQVR4nO3df7Dld13f8debXYEEZZBGKW7AgIvNiLUFMq3WClStdQSDvzqlaotS69jpLKkdRmGwv6czTHFGQ5zaAYGgIpSiYGpRRHSKP/hhfhGigN7ir6w/EotaICka/PSPewK36y7Jbnb3vu7Zx2Nm557zPd9zzufuvHf3nOd+z/fOWisAAAAAdHnAfi8AAAAAgL9ItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQodPZ+dLLrlkXXbZZedoKQAAAAAXnhtuuOEP11qfduL204o2l112Wa6//vqztyoAAACAC9zM/NbJtvt4FAAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACh0eL8XAC2uueaa7Ozs7PcyACh2/PjxJMmRI0f2eSXAmTh69GiOHTu238sAuM9EG9jY2dnJzbe+Jx+9+OH7vRQASh2680+SJL//ES+h4KA5dOcH9nsJAKfNKw7Y46MXPzx3Xf4V+70MAEpd9N43Jol/K+AAuufPL8BB4pw2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFLrhoc8011+Saa67Z72UAAAAAZ+hCeW9/eL8XcL7t7Ozs9xIAAACA++FCeW9/wR1pAwAAAHAQiDYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCh/d7Aefb8ePHc9ddd+Wqq67a76VQZmdnJw/407XfywAA4Bx4wP/9P9nZ+aD3AbAldnZ2ctFFF+33Ms65ez3SZma+dWaun5nr77jjjvOxJgAAAIAL3r0eabPWekmSlyTJFVdcceAPQzhy5EiS5Oqrr97nldDmqquuyg3v/4P9XgYAAOfAnz/4oTn62Ed4HwBb4kI5as45bQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCh/d7Aefb0aNH93sJAAAAwP1woby3v+CizbFjx/Z7CQAAAMD9cKG8t/fxKAAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQof3ewHQ5NCdH8hF733jfi8DgFKH7vzfSeLfCjiADt35gSSP2O9lAJwW0QY2jh49ut9LAKDc8eN3J0mOHPHGDw6eR3i9Bxw4og1sHDt2bL+XAAAAAB/jnDYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBCog0AAABAIdEGAAAAoJBoAwAAAFBItAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsAAACAQqINAAAAQCHRBgAAAKCQaAMAAABQSLQBAAAAKCTaAAAAABQSbQAAAAAKiTYAAAAAhUQbAAAAgEKiDQAAAEAh0QYAAACgkGgDAAAAUGjWWvd955k7kvzWuVvOeXFJkj/c70XAOWK+2XZmnG1nxtl2ZpxtZ8Y5U5+51vq0EzeeVrTZBjNz/Vrriv1eB5wL5pttZ8bZdmacbWfG2XZmnLPNx6MAAAAACok2AAAAAIUuxGjzkv1eAJxD5pttZ8bZdmacbWfG2XZmnLPqgjunDQAAAMBBcCEeaQMAAABQb+uizcwcmpmbZuYnNtcfPjNvnplf33z91D37Pn9mdmbmfTPz9/Zv1XDfzczDZuZ1M/PemXnPzHyBOWdbzMy3z8yvzMytM/PqmXmw+eYgm5mXz8ztM3Prnm2nPdMz86SZeffmthfPzJzv7wVO5hQz/qLN65RbZub1M/OwPbeZcQ6Uk834ntueOzNrZi7Zs82Mc1ZtXbRJclWS9+y5/rwkb1lrPS7JWzbXMzOfk+SZSR6f5MuT/OeZOXSe1wpn4uokP7XWujzJX8vuvJtzDryZOZLkOUmuWGt9bpJD2Z1f881Bdm1253OvM5np70/yrUket/l14mPCfrk2f3Ee35zkc9dan5fk15I8PzHjHFjX5iTzODOPSvJ3k/z2nm1mnLNuq6LNzFya5GlJfmDP5mckeeXm8iuTfNWe7a9Za31krfUbSXaS/I3ztFQ4IzPz0CRPTvKyJFlr/ela649jztkeh5NcNDOHk1yc5HdjvjnA1lpvTfKBEzaf1kzPzCOTPHSt9ba1ezLCH9xzH9hXJ5vxtdZPr7Xu3lx9e5JLN5fNOAfOKf4eT5LvSfIdSfaeJNaMc9ZtVbRJ8r3Z/YPz53u2PWKt9XtJsvn66ZvtR5L8zp79bttsg2aPTXJHkldsPgb4AzPzkJhztsBa63iS787u/1j9XpI/WWv9dMw32+d0Z/rI5vKJ2+EgeHaSn9xcNuNshZm5Msnxtda7TrjJjHPWbU20mZmnJ7l9rXXDfb3LSbb5UVq0O5zkiUm+f631hCQfzuaw+lMw5xwYm/N6PCPJY5J8RpKHzMw3fqK7nGSb+eYgO9VMm3UOpJl5QZK7k7zqnk0n2c2Mc6DMzMVJXpDkX5/s5pNsM+PcL1sTbZJ8YZIrZ+Y3k7wmyRfPzA8n+YPN4WjZfL19s/9tSR615/6XZvcwfGh2W5Lb1lrv2Fx/XXYjjjlnG3xpkt9Ya92x1vqzJD+W5G/FfLN9Tnemb8vHP16ydzvUmplnJXl6km/YfBwkMeNsh8/K7n8wvWvz3vPSJDfOzF+OGecc2Jpos9Z6/lrr0rXWZdk9+dPPrrW+Mcl1SZ612e1ZSX58c/m6JM+cmQfNzGOyezKod57nZcNpWWv9fpLfmZm/stn0JUl+Neac7fDbST5/Zi7e/ESFL8nuibbNN9vmtGZ68xGqD87M52/+bPzjPfeBOjPz5Um+M8mVa60799xkxjnw1lrvXmt9+lrrss17z9uSPHHzOt2Mc9Yd3u8FnAcvTPLamfkn2X1D8PeTZK31KzPz2uy+4b07yT9fa310/5YJ99mxJK+amQcmeX+Sb85ugDXnHGhrrXfMzOuS3Jjdeb0pyUuSfHLMNwfUzLw6yVOTXDIztyX5Nzmz1yb/LLs/weSi7J4f5CcDBU4x489P8qAkb978VOO3r7W+zYxzEJ1sxtdaLzvZvmacc2E+frQiAAAAAC225uNRAAAAANtEtAEAAAAoJNoAAAAAFBJtAAAAAAqJNgAAAACFRBsA4LTNzIdOuP5NM/N9+7WevWbmypl53ll4nEfOzE+c4X0vn5mbZ+ammfmsmfn6Pbf91Zm59v6uDwDYfqINALBV1lrXrbVeeBYe6l8meekZ3verkvz4WusJSR6V5GPRZq317iSXzsyj7/cKAYCtJtoAAGfVzHzmzLxlZm7ZfH30Zvu1M/N1e/b70ObrI2fmrZsjU26dmS/abP+ymXnbzNw4M/9tZj75JM/1nJn51c1zvWaz7WNH/Wwe855fd83MU2bmITPz8pn55c2RMM84xbfytUl+avM4j5+Zd24e55aZedxm+wtm5n0z8zMz8+qZee7MfEWSf5HkW2bm55K8MMkXbe777ZvH/u9Jnnk/f6sBgC13eL8XAAAcSBfNzM17rj88yXWby9+X5AfXWq+cmWcneXF2jzw5la9P8qa11n+cmUNJLp6ZS5J8V5IvXWt9eGa+M7tHvvz7E+77vCSPWWt9ZGYeduIDr7X+epLMzFcm+Y4kv5Tk3yX52bXWszf3eefM/Mxa68P33G9mHpPkj9ZaH9ls+rYkV6+1XjUzD0xyaGaelN3w8oTsvqa6MckNa603zsx/SfKhtdZ3z8xTkzx3rfX0PUu7frP2//QJfl8AgAucaAMAnIm77gkiye7RLUmu2Fz9giRfs7n8Q7n3MPHLSV4+M5+U5A1rrZtn5ilJPifJL85MkjwwydtOct9bkrxqZt6Q5A0ne/DNUTEvSvLFa60/m5kvS3LlzDx3s8uDkzw6yXv23O2RSe7Yc/1tSV4wM5cm+bG11q9vjgh6/Vrrzs3zXJf77vYkn3Ea+wMAFyDRBgA419bm693ZfDR7dkvMA5NkrfXWmXlykqcl+aGZeVGSP0ry5rXWP7yXx35akicnuTLJv5qZx++9cWYekuS1Sf7pWut379mc5GvXWu/7BI97V3ZjTjZr/JGZecfm+d40M99ywvd2uh68eQ4AgFNyThsA4Gz7pXz8fC3fkOQXNpd/M8mTNpefkeSTkt1z4CS5fa310iQvS/LEJG9P8oUzc3Szz8Uz89l7n2RmHpDkUWutn8vuR58eluTE8968Iskr1lo/v2fbm5Ic24SjzMwTTvI9/FqSy/Y812OTvH+t9eLsfgzs85K8NclXz8xFM/MpSb7yFL8fH0zyKSds++wkt55ifwCAJKINAHD2PSfJN8/MLUn+UZKrNttfmuQpM/POJH8zyT3nkHlqkptn5qbsnvz36rXWHUm+KcmrN4/z9iSXn/A8h5L88My8O8lNSb5nrfXH99y4iUFfl+TZe05GfEWS/5DdYHTLzNy6uf7/2Zzf5n/dE42S/IMkt27O43N5ds/Zc2OS/5rk5iQ/muTnT3ycjVuS3D0z79pzIuK/k+R/nGJ/AIAkyax1pkf1AgBsr5n56iRPWmt9133c/99mc/Lhe9nvQUn+Z5K/vda6+34vFADYWs5pAwBwEmut18/MXzoHD/3oJM8TbACAe+NIGwAAAIBCzmkDAAAAUEi0AQAAACgk2gAAAAAUEm0AAAAACok2AAAAAIVEGwAAAIBC/w8b9hN2pxms/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#boxplot for checking outlier\n",
    "sn.boxplot(df1['House size (sqft)'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6daaa910",
   "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>House Age</th>\n",
       "      <th>Distance from nearest Metro station (km)</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Number of bedrooms</th>\n",
       "      <th>House size (sqft)</th>\n",
       "      <th>House price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>220</th>\n",
       "      <td>37.2</td>\n",
       "      <td>0.186510</td>\n",
       "      <td>9</td>\n",
       "      <td>24.97703</td>\n",
       "      <td>121.54265</td>\n",
       "      <td>2</td>\n",
       "      <td>1187</td>\n",
       "      <td>78.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>10.8</td>\n",
       "      <td>0.252582</td>\n",
       "      <td>1</td>\n",
       "      <td>24.97460</td>\n",
       "      <td>121.53046</td>\n",
       "      <td>2</td>\n",
       "      <td>1373</td>\n",
       "      <td>117.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>35.4</td>\n",
       "      <td>0.318529</td>\n",
       "      <td>9</td>\n",
       "      <td>24.97071</td>\n",
       "      <td>121.54069</td>\n",
       "      <td>1</td>\n",
       "      <td>433</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     House Age  Distance from nearest Metro station (km)  \\\n",
       "220       37.2                                  0.186510   \n",
       "270       10.8                                  0.252582   \n",
       "312       35.4                                  0.318529   \n",
       "\n",
       "     Number of convenience stores  latitude  longitude  Number of bedrooms  \\\n",
       "220                             9  24.97703  121.54265                   2   \n",
       "270                             1  24.97460  121.53046                   2   \n",
       "312                             9  24.97071  121.54069                   1   \n",
       "\n",
       "     House size (sqft)  House price of unit area  \n",
       "220               1187                      78.3  \n",
       "270               1373                     117.5  \n",
       "312                433                      78.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can see some outliers in house price of unit area, we have to remove that\n",
    "df1[df1['House price of unit area'] >= 75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1558ed25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we can drop these three rows\n",
    "df1.drop(220,inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8a64a385",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(270, inplace = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8458a76f",
   "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>House Age</th>\n",
       "      <th>Distance from nearest Metro station (km)</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Number of bedrooms</th>\n",
       "      <th>House size (sqft)</th>\n",
       "      <th>House price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>35.4</td>\n",
       "      <td>0.318529</td>\n",
       "      <td>9</td>\n",
       "      <td>24.97071</td>\n",
       "      <td>121.54069</td>\n",
       "      <td>1</td>\n",
       "      <td>433</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     House Age  Distance from nearest Metro station (km)  \\\n",
       "312       35.4                                  0.318529   \n",
       "\n",
       "     Number of convenience stores  latitude  longitude  Number of bedrooms  \\\n",
       "312                             9  24.97071  121.54069                   1   \n",
       "\n",
       "     House size (sqft)  House price of unit area  \n",
       "312                433                      78.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['House price of unit area'] >= 75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2b8631b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.drop(312, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bd4f4b23",
   "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>House Age</th>\n",
       "      <th>Distance from nearest Metro station (km)</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Number of bedrooms</th>\n",
       "      <th>House size (sqft)</th>\n",
       "      <th>House price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [House Age, Distance from nearest Metro station (km), Number of convenience stores, latitude, longitude, Number of bedrooms, House size (sqft), House price of unit area]\n",
       "Index: []"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[df1['House price of unit area'] >= 75]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a638ec7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ashwa\\ana\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='House price of unit area'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#no outliers left now. we can check it through boxplot once again\n",
    "sn.boxplot(df1['House price of unit area'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0b68ccad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'scatter plot between area and price')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df1['House size (sqft)'], df1['House price of unit area'], color = 'red')\n",
    "plt.xlabel(\"Area\")\n",
    "plt.ylabel(\"price of unit area\")\n",
    "plt.title(\"scatter plot between area and price\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8497b9e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABNkAAAMHCAYAAAAEu4E2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAADcAUlEQVR4nOzdd3xUVfrH8e8z6ZQACSUBG1VFhVAVCwoRQdeCvRfsDbEryro2FHV/uquyoq6r64rY1r4iIiIo0nuRJqBCEkpCqOlzfn/MJaQMGWBIM5/365UXmXufc+c5c8jk5sxzzzXnnAAAAAAAAADsP191JwAAAAAAAADUdkyyAQAAAAAAAGFikg0AAAAAAAAIE5NsAAAAAAAAQJiYZAMAAAAAAADCxCQbAAAAAAAAECYm2QAAAAAAAFCrmNm/zGyDmS3aw34zsxfNbKWZLTCzriX2DTCzZd6+Bw9UTkyyAQAAAAAAoLZ5S9KACvafLqm993WjpFckycwiJI309neUdKmZdTwQCTHJBgAAAAAAgFrFOTdZUlYFIedIetsFTJPU2MySJfWUtNI5t8o5ly/pPS82bEyyAQAAAAAA4I+mlaTfSzxe623b0/awRR6IgwDYewWbVrnqzgH7Z/JRQ6s7BYTBxI9ebRVpjF1tlm7R1Z0CwtDA76/uFLCftvuop6jNFsTwu6+2Gr7mXavuHCpTVf49G92s7U0KXOa5y2vOudf24RDBxsJVsD1sTLIBAAAAAACgRvEm1PZlUq2stZIOLvH4IElpkqL3sD1sTLIBAAAAAAAgNH9RdWewLz6XdLuZvSfpWElbnHPpZrZRUnszay1pnaRLJF12IJ6QSTYAAAAAAADUKmY2RtIpkpqa2VpJf5EUJUnOuVGSvpJ0hqSVknZKGuTtKzSz2yWNkxQh6V/OucUHIicm2QAAAAAAABCaqzlrdTrnLg2x30m6bQ/7vlJgEu6AYjVMAAAAAAAAIExUsgEAAAAAACA07jpdISrZAAAAAAAAgDBRyQYAAAAAAICQXA1ak60mopINAAAAAAAACBOVbAAAAAAAAAiNNdkqRCUbAAAAAAAAECYm2QAAAAAAAIAwcbkoAAAAAAAAQuPGBxWikg0AAAAAAAAIE5VsAAAAAAAACM1fVN0Z1GhUsgEAAAAAAABhopINAAAAAAAAobEmW4WoZAMAAAAAAADCRCUbAAAAAAAAQvNTyVYRKtkAAAAAAACAMFHJBgAAAAAAgJAca7JViEo2AAAAAAAAIExUsgEAAAAAACA01mSrEJVsAAAAAAAAQJioZAMAAAAAAEBorMlWISrZAAAAAAAAgDBRyQYAAAAAAIDQ/EXVnUGNRiUbAAAAAAAAECYq2VDtzGy7c65BicfXSOrunLu9GnKJlJQh6XXn3NCqfv7aathTz2vylBlKaNJYn74zqrrTQRkJfTqrw5PXyCJ8Shv9nX596bNyMR2GX6PE1C4qysnTz3e8om0LV8sXE6Wunz0qX3SULMKnDV9O1+rnPqyGHtQ9CX06q/2Tg2QRPqWPnhB0zNoPH6TE1C7y5+RpyR3/0PbiMXtMFh0pi4jQxi+nFY9Z63svVMsrUpWfuVWStOqpMcqcMLdK+1UXNOmTorZPBMYuY/QE/f7yp+Vi2j45SAmpXVWUk6flQ0Zq+8LVkqSeM0eqaHuuXJFfrqhIc/s/KElq/ciVSuzXTf6CQuWuWa9ld45U0dadVdmtOqXLE1cpObWzinLyNePOV7V54ZpyMfUPbqZeo25XdOMG2rxwjaYP/of8BUVq1utInfjW3drx20ZJ0tqvZmrJC58UtzOfqd/XTyonY7N+uOqvVdWlP7SOw69W89QUFeXka/4dr2hrkPGKO6SZurx6h6Ib19eWhWs077aRcgVFodv7TCd+85RyM7I064rnJEkdHrhQLQZ0l/P7lb9pq+bfMUp56zdXQU//+CrjZ69h22T1GjW4uH2DQ5tr0XMfafnrX1dVt+qkP/3lKh3eJ0UFOfn6772jlLZ4TbmYC/92m1od01r+wiKtnf+LPn3oDfkLd1dIterURjd/8rjeu/1FLR47owqzB8JDJRtQ2mmSlkm6yMysupOpLQae0U+jnn+yutNAMD7T4SOu1bzLnta0k+5Wi3NPUP0OrUqFJKamKK51kqYeN0RL731dhz97nSTJn1eguec9rhl979eM1AeU2Lez4ru1r45e1C0+0+EjrtP8y57S9JPuUvNzT1C9cmPWRfVaJ2nacXdo6b2v6fBnr5e0a8we08y+92tm6v1K6JtSasx+e/V/mpka2McEWyXw+dTu6eu06LLhmtX7LjU79wTV63BQqZAmqV0U1yZZM3sN1op7X1W7Z24otX/++Y9qzqn3FU+wSVL2pPmadcrdmtP3XuWsStMhd5xbJd2pi5L7dlbDNkn66vh7NOu+N9RtxKCgcZ2GXaJlr43VVyfco/wtO9T60lOK922avkzf9HtI3/R7qNQEmyS1v2GAtq5Iq8wu1CnNUlNUv3WSvj/uLi2893Ud7f3+KuuIYZdp9atf6fted6sge4cOvqzPXrVvfcPp2r5iXaltq0Z+qR/6PKAfU4dqw/g5an/PeZXTuTqmsn72tv2SXrxtfP+HVZiTp7VjZ1VFl+qsDqekqGnrJD1/yt369KF/6uzh1waNm//pFP0t9V692P8BRcZGq/slfYr3mc/U/8FLtWLygqpKG/vC+avuqxZikg01mpkdamYTzGyB9+8h3va3zOyCEnHbvX+TzWyymc0zs0VmdpK3/TQzm2pmc8zsQzNrEPwZdamkv0v6TdJxJY5/hpktNbMfzexFM/vS217fzP5lZjPNbK6ZnVM5r0TN1j3lGDWKb1jdaSCI+K7tlLN6vXJ/3SBXUKT1n/6kpgN6lIppNqCHMj6cLEnaOnuFIuPrK7p5Y0lS0c48SZJFRcgiIyXnqjT/uii+azvtXJ1RPGYbPv1JzcqMWdMB3fdqzHyREYxZFWrYpZ1yVmco97cNcgWF2vjpFCX2714qpmn/Hlr/wSRJ0rY5pcduTzZPWiAVBU40t85eoZjkxErJH1KrAd205sMfJEmZc1YqKr6eYoOMT4sTj9LaLwOVFWs+mKxWp3cvF1NWXHKCWqamaNW7Ew9oznVZiwHdtM4br+zZgfGKCTJeTU88ShlfTJckrf1gspK88aqofWxygpr366LfR5cer8LtOcXfR9SL5T32AKnMn71dmp90tHas2aCdazcdkJwR3JGnddPcjwNj+fvclYptWE8NmzUuF7f8+3nF36+d/4saJSUUP+51TX8tHjtDOzK3VHa6wAHHJBtqgjhvUmyemc2T9HiJfS9Lets510nSaEkvhjjWZZLGOedSJHWWNM/MmkoaJulU51xXSbMk3V22oZnFSUqV9KWkMQpMuMnMYiW9Kul059yJkpqVaPawpO+ccz0k9ZH0nJnV35fOA5UpNilBuWmZxY/z0jIVk9SkVExMchPlrisRk56pmGTvRMdn6jnhGZ20+HVlTVqgrXNWVknedVlMUoLyyo1ZQumY5ATlrtv9R0LZMesx4VmduPifypq0sNSYHXRtf/Wc+JyO+NstimzEW9WBFpNcZuzSsxRdZkIsulxMpqJ3jZ2TjnlvmLqMe0ZJV5wa9DmSLu2jrO+oQqwscUkJ2llifHLSsxSXXPo9MzqhgfK37JDzJj53pmepXon31cRu7dT/26fUe/T9ii9Rhdrl8Ss1/8kxcn4mZQ6U2OQE5ZT4/ZWbnqXY5NLvl1EJDVWwdfd45aZlFsdU1L7jE1fp58fflfOXr6Q4fOhF6jvnZbU6/wQtf5ZlFA6EyvzZ2+WQc47Tr5/+VEk9wC7xLZpoS1pW8eOtGVmKL3PuWZIvMkJdzj1RyyfNL27fsX8PzRj9baXniv3k91fdVy3EJBtqghznXMquL0mPlNjXS9K73vf/kXRiiGPNlDTIzB6VdIxzbpsCFWkdJU3xJvGulnRokLZnSpronNsp6b+SzjWzCElHSFrlnFvtxY0p0eY0SQ96x/1eUqykQ0J1GKgye3XVc5CYXZ/M+51mpD6gKSm3qFHXdqp/xMEHND0EEWTMnMr+UV7xmM1MvV8/pdys+K5ti8ds7b+/0dRjB2tG3/uVv36z2j121QFOHMGGpVyVS7CfSS9k3lnDNPe0B7To8uFqOai/Gh13ZKmwg4ecJ1fo14b//nBg8kV5ezGGwVaT2BWyeeEafdljiMad+pBWvDFOJ74Z+Ewv+dQuytu0RZsXrDnACddtFmTAXLnxKt9uV8ye2jfv10X5m7Zq64LV5fZL0rKnP9B3XW/Xuv9O0aHX9t+PzFFOJf3s7eKLilCr/t30u1fRiMpT0TgFc/YTg7R6xlL9OnOZJOmMR67SuBF8IIHaixsfoLbZ9W5bKG+S2Fs7LVqSnHOTzay3pD9J+o+ZPSdps6TxzrlLQxz7UkknmNka73GiAtVpmXtsETglON85t6yiA5vZjZJulKR//N+Tuv6qUKkAB0ZueqZiW+6upIlpmai8jNILNOelZym2VaJ2FeTHJJePKdy6U5unLFFin87asfT3yk67TstLz1RMmTHLLzdmmYpt1VRbFHjrqWjMEvqkaMfS31WwcfclF2nvTFCndx6oxF7UTXlpWaXHLjlB+RlZpWLy08qMb3JicUy+t3h6waatyhw7Qw27tNOWaT9LklpcdLIS+3XTggsfq+xu1DntrumnNpcH1gLKmr9K9UqMT1xygnIyskvF52VuU3Sj+rIIn1yRX/WSE5TjjV3JSwnTv5uvbiMiFJ3QQE17dlDL07opOTVFvpgoRTWM07Ev36Lpt79S+R38gzl0UD8dfEVfSdKWeasU1ypRu979YpMTyr0X5mduU1T87vGKLfF7MCc9M2j75LOOVfP+XdUnNUW+2ChFNYhTysjbNO+2kaWOnfbxFPUYfb9WPPdRZXb5D6sqfvbys7ZLkpL6pmjzwjXK27S1kntVNx17ZT/1uDQwlmvnr1KjlrsrSuOTErRtDzcH6TvkPNVPjNdnN71QvK1Vp9a6+KXAzSrqNWmoDqekyF/k18/fsJZejVFL10qrKlSyoab7SdIl3veXS/rR+36NpG7e9+dIipICa7hJ2uCce13SG5K6SpqmwORZOy+mnpl1KPkkZhavQJXcIc65w5xzh0m6TYGJt6WS2pjZYV74xSWajpM0eNdNEsysS7BOOOdec851d851Z4INVWnb3F9Ur02SYg9pJouKUIuBx2vTuNInKRvHzVLShb0lSfHd2qtw207lb8hWVGJDRcbXkyT5YqOU0Pto7VjJgt2VLTBmycVj1jzImG0qM2ZFexyzY7RzZWDR7pLrfjU7oyeTpZVg27yVimuTrNhDmsuiItVs4AnKLPNHQeY3s9TiopMlSQ277v5589WLUUT9WEmSr16MGp+8e0K7SZ8UHXT7QC2++hn5c/KrtlN1wMq3xhcvjL5u7CwdduFJkqTEru1UsC1HuRuyy7XZMGWJDjqzpyTpsIt6K+3r2ZKk2GaNimMSUtpIPlN+1nYtfOp9fdFtsL7seaem3vyyNvy4hAm2/fTrm+P1Y+pQ/Zg6VOvHzlIrb7wad2unwm07lRdkvDKnLFbSWcdKkg66qLfWe+O1YdycoO2XDX9P33W5XRN73KG5N72oTVMWF0+w1WudVHzcFv27aTs3sthvVfGzt8uhA3vpt0+4VLSyTP/PeL18xkN6+YyH9PM3s9TlvMBYHtylnfK25WjbxuxybbpffIra9e6k9we/VKoC9f9OulN/PXGI/nriEC0eO12f//lNJthQq1DJhpruDkn/MrP7JG2UtOtWQ69L+szMZkiaIGmHt/0USfeZWYGk7ZKucs5tNLNrJI0xsxgvbpik5SWe5zwF1lbLK7HtM0nPSrrV+/razDZJKnkP6Sck/U3SAm+ibY0Cl53WKff9ZYRmzl2g7OytSh14hW697kqdfxaXT9QErsivZUP/pS7vPSRF+JQ+5nvtWLZWra4KrPe07u1vlfntXDVN7aJe0/8uf06+lgwJ/OEX06KJOr54qxThk/l82vDZVGWOn1Od3akTXJFfy4f+SynvPSyL8CltzETtWLZWLa/qJ0lKe3u8Mr+dq8TUruo1/UUV5eTr5yH/kCRFt2iiji/eJovwST4rNWbtHrlCDY4+THJOOb9v1LJ7X6uuLv5xFfm18qE3dPSYwNhljJmoncvWKtkbu/S3xyvr2zlKSO2iHtNekj8nX8vuDPzhHt20kTq+eZ8kySIjtOHjH7V54jxJUrunrpMvOlLHvP9nSdLW2cu18oHXq75/dUD6hHlKTk3Rn6Y+r8KcfM2469XifSe9c59m3vO6ctdna/6TY9Rr1GAd88CFyl70q1aN+V6SdNCZPdXu6lPlCotUlFugqTe/XE09qRs2fDtXzVJTdMr0v6koJ08Lhuwerx6j79eCu19X3vrN+vnJMer66mAd/uBF2rpwjX73bj5RUfs9OWLYJWrQrqWc3yln7UYtvO+NSutfXVKZP3sRcdFq0ftozbqfsaoKyybOU4c+Kbp70gsqyMnTx/ftHsur3rxfnzzwmrZtyNbZw69T9rpNuvmTQIX24q9nauKLn+zpsKhJaulaaVXFyq5bAKA8M2vgnNvuTaSNlLTCOfdCqHbBFGxaxQ9dLTX5qKHVnQLCYOXWNUNtEWmMXW2WbtHVnQLC0IA/pmqt7T4uWqrNFsTwu6+2Gr7m3b1ZFLnWylswrsr+c8Z06l/rXksq2YC9c4OZXa3A2m9zFbjbKAAAAAAAdYZzRdWdQo3GJBuwF7yqtf2qXAMAAAAAAH98TLIBAAAAAAAgNO4uWiEu1AcAAAAAAADCRCUbAAAAAAAAQuOGOBWikg0AAAAAAAAIE5VsAAAAAAAACI012SpEJRsAAAAAAAAQJibZAAAAAAAAgDBxuSgAAAAAAABC8xdVdwY1GpVsAAAAAAAAQJioZAMAAAAAAEBo3PigQlSyAQAAAAAAAGGikg0AAAAAAACh+alkqwiVbAAAAAAAAECYqGQDAAAAAABAaKzJViEq2QAAAAAAAIAwUckGAAAAAACA0FiTrUJUsgEAAAAAAABhopINAAAAAAAAoVHJViEq2QAAAAAAAIAwUckGAAAAAACAkJwrqu4UajQq2QAAAAAAAIAwUckGAAAAAACA0FiTrUJUsgEAAAAAAABhYpINAAAAAAAACBOXiwIAAAAAACA0x+WiFaGSDQAAAAAAAAgTlWwAAAAAAAAIjRsfVIhKNgAAAAAAACBMVLIBVWzyUUOrOwXsp96Ln67uFBCGlKMure4UsJ8i+EywVluS+Vt1p4AwjEk8pbpTwH5q6C+q7hQQhn9unlfdKWA/Da/uBCoba7JViLNWAAAAAAAAIExUsgEAAAAAACA01mSrEJVsAAAAAAAAqHXMbICZLTOzlWb2YJD995nZPO9rkZkVmVmCt2+NmS309s06EPlQyQYAAAAAAIDQatCabGYWIWmkpH6S1kqaaWafO+eW7Ipxzj0n6Tkv/ixJdznnskocpo9zbtOByolKNgAAAAAAANQ2PSWtdM6tcs7lS3pP0jkVxF8qaUxlJsQkGwAAAAAAAELz+6vuK7RWkn4v8Xitt60cM6snaYCk/5bY7CR9Y2azzezG/XxFSuFyUQAAAAAAANQo3sRXycmv15xzr5UMCdLM7eFwZ0maUuZS0ROcc2lm1lzSeDNb6pybHE7OTLIBAAAAAAAgtCq8u6g3ofZaBSFrJR1c4vFBktL2EHuJylwq6pxL8/7dYGafKHD5aViTbFwuCgAAAAAAgNpmpqT2ZtbazKIVmEj7vGyQmTWSdLKkz0psq29mDXd9L+k0SYvCTYhKNgAAAAAAAIRWg+4u6pwrNLPbJY2TFCHpX865xWZ2s7d/lBd6rqRvnHM7SjRvIekTM5MCc2PvOue+DjcnJtkAAAAAAABQ6zjnvpL0VZlto8o8fkvSW2W2rZLU+UDnw+WiAAAAAAAAQJioZAMAAAAAAEBoVXjjg9qISjYAAAAAAAAgTFSyAQAAAAAAILQadOODmohKNgAAAAAAACBMVLIBAAAAAAAgNNZkqxCVbAAAAAAAAECYqGQDAAAAAABAaKzJViEq2QAAAAAAAIAwUckGAAAAAACA0FiTrUJUsgEAAAAAAABhopINAAAAAAAAoVHJViEq2QAAAAAAAIAwUckGAAAAAACA0Jyr7gxqNCrZAAAAAAAAgDBRyQYAAAAAAIDQWJOtQlSyAQAAAAAAAGFikg3AXkno01nHTXlBvab9XYcOPidoTIfh16jXtL+r58Rn1fCY1pIkX0yUun89XD2/e1bHTvqrWt93YVWmjb0w7Knn1ftPl2jgFTdXdyrYC0OH362x0z7SxxPf0ZHHHL7HuDuG3qz//fShPv/hPV1+/UVVmCHKeuDJu/Tl1A/10Xf/0ZHHdAga88Tfh2nsjP/qg2//rQ++/bcOP6q9JKlho4Z64V8j9NF3/9HosW+o3RFtqjL1Ou+F5x/X0iU/as7s8eqScnTQmL59TtSM6V9r1sxvNGniJ2rb9jBJ0uGHt9WPkz/Xjm2rdPddN1Vh1nVbyhNX6fSf/k/9JjytxsccFjSm3sHN1Pd/j2nAlP/TcaMGy6IiSu1v0rmNLlj7H7X6U8/ibe2u76/TJo7Qad8/o/Y3DKjMLtRJTft0Vu8pz+vkaX9Tm8FnB43pOPxqnTztbzpx4jOKLzG2e2rb/oGLdOLEZ3TihBHq8f5DimnRpLK7gSCGP/Owps0dp4lTPtMxnTsGjfls7Dua8MMnmvDDJ5q/dLLeGv1yFWcJHDghJ9nMrMjM5pnZYjObb2Z3m5nP29fdzF6soO1hZnbZgUx4X5jZHWb2s5mNrq4cqpKZPVTBvjVm9kOZbfPMbFGIY1bKGJpZYzO7dV/jzKylmX10APP4yMzaeN9vD+M4t5vZoAOVV43jMx0+4lrNu+xpTTvpbrU49wTV79CqVEhiaoriWidp6nFDtPTe13X4s9dJkvx5BZp73uOa0fd+zUh9QIl9Oyu+W/vq6AX2YOAZ/TTq+SerOw3shZNSj9ehrQ/W6cddoEfvHaFHnr0/aNzAS85UUssWOvOEi3T2SZdo7KfjqzhT7HJiai8d2uZgndnrQj1+7wgNeyb4mEnS84+/rItOvVoXnXq1li1eIUm6YcjVWrZ4uS7oe6UeHvy4HnjirqpKvc47fUBftW/XWkd0PFG33PKARr78dNC4l19+Wlddfbu69zhNY977VA8NHSJJysrK1p13/VnPv/BqVaZdpyX17awGbZI09vh7NPu+N9R1RPBTs07DLtGK18bq6xPuUf6WHWp96Sm7d/pMnYZdoozvFxRvij/8ILW5vI8mnPGIxqcOVfKpXdSgdYtK7k0d4jMdNeJazbxshCafdI9annuCGpQ5z2yWmqJ6rZM16bg7teje13X0s9eHbLt65Bf6sc8D+jH1QW0YP0ft7zmvqntW56X2663WbQ/VcV36694hj+jZ5/8SNO6c069Q6knnKvWkczVr5jz97wvOW2o0v7/qvmqhvalky3HOpTjnjpLUT9IZkv4iSc65Wc65Oypoe5ikaptkk3SrpDOcc5eX3GhmNX4tOjOLCB1Vzh4n2TwNzexg7/hH7uUxD9MexjDM17GxAuOzT3HOuTTn3AVhPG8xMztKUoRzbtUBONy/JFX0s1CrxXdtp5zV65X76wa5giKt//QnNR3Qo1RMswE9lPHhZEnS1tkrFBlfX9HNG0uSinbmSZIsKkIWGckdaWqY7inHqFF8w+pOA3uh74De+vzDsZKkBbMXqWF8QzVtnlgu7pJrztOo/3tDzvtZy9q0uUrzxG59+vfWFx94YzZnsRrGNwg6ZnvSpsNhmv7DLEnSmpW/quXBSUpoSjVGVTjrrP76z+jA53rTZ8xRo8aNlJTUvFycc07xDQPvoY0aNVR6+npJ0saNmZo1e74KCgqqLuk6ruWAbvr1w8BnyllzVio6vp5ivXORkpqfeJTWfjlDkrTmg8lqdXr34n3tr+uvtf+bqbxNW4u3xbdvqczZK1WUky9X5NfGaT+r1ek9yh0X+6dx13bauTpDOd55ZvqnP6nFgO6lYloM6K513nlm9uyVioyvp5jmjStsW7g9p7h9ZL0YTj+rwYA/perDMZ9JkmbPmq/4RvFq3qLZHuPrN6ivE3sfq7H/+7aqUgQOuH26XNQ5t0HSjZJut4BTzOxLSTKzk73KqHlmNtfMGkoaIekkb9tdXlXUD2Y2x/s63mt7ipl971UVLTWz0WZm3r4eZvaTV0U3w8wamlmEmT1nZjPNbIGZlavBN7NRktpI+tx77kfN7DUz+0bS22Z2qJlN8NpPMLNDvHZvmdkrZjbRzFZ5/fqXVxH3VrDXxasSe8zr00IzO8LbXt9rO9N7Tc7xtlf0Okw0s3clLdxTP80s2cwm76pEM7OTzGyEpDhv254q9z6QdLH3/aWSxpTow55e07JjeI2ZfWhmX0j6xswSzOxTr800M+sU5PU5yhu7eV5ce++4bb1tz5lZA28cdr2G55R4/pJxh5lXfWdmsWb2phc/18z6eNuvMbOPzexrM1thZs/u4fW4XNJnQfJtamZTzexP3phMMrMPzGy5mY0ws8u9/iw0s7aS5JzbKWmNmfUse7w/gtikBOWmZRY/zkvLVExS6T/yYpKbKHddiZj0TMUkJwQe+Ew9Jzyjkxa/rqxJC7R1zsoqyRv4o2me3EwZ69YXP16fvkEtksufrB586EEaMPBUvT/uLY169wUd0vrgqkwTJTRPbqaMtJJjtlHNg4yZJA1+8CZ99N1/dN9jQxQVHSVJWr54pVLPOEWSdHSXjko+KEktWpaf6MGB16plktb+nlb8eN3adLVqmVQu7qab7tUXn/9Ha1bN0uWXn69nnuUyp+oSl5SgnSXOV3amZykuufT5SnRCAxVs2SFXFKiQyEnPUpx3ThOb1EStTu+uX94u/Qf+lmVr1ey4IxTdpIEi4qKV3DdFcS0TKrk3dUfZ88yctCzFJJV+fWOTE0qdZ+amZyk2OSFk2w5DL1afOSPV8vwTteLZDyqxFwgmObmF1q1LL36cnpah5JZ7rgI948xT9cOkadq+bUdVpIf95fxV91UL7fOabF7Vj09S2TO8eyXd5pxLkXSSpBxJD0r6wauEe0HSBkn9nHNdFZjsKXmpaRdJd0rqqMDk2AlmFi3pfUlDnHOdJZ3qHfc6SVuccz0k9ZB0g5m1LpPnzZLSJPXxnluSukk6xzl3maSXJb3tnOskaXSZXJpI6ivpLklfSHpB0lGSjjGzlD28NJu8fr3ivRaS9LCk77w8+0h6zszqh3gdekp62DnXsYJ+XiZpnPdad5Y0zzn3oHZXHZaq3CvhI0m76qTP8vq2y56eq+wYSlIvSVc75/pKekzSXO91fEjS20Ge92ZJf/fy7S5prXfcX7zj3icpV9K53mvSR9L/eROtZeNKuk2SnHPHKDBp+G8zi/X2pXiv7TGSLjavgq+MEyTNLrnBzFpI+p+kR5xz//M2d5Y0xDvWlZI6OOd6SvqnpMElms9S4P/+H09gzjtUUPlNuz4y9DvNSH1AU1JuUaOu7VT/CP7gB/aHBfk5C/bJfHRMlPJy83Vx/2v00Tuf6cm/DauC7BBMsLdPF2TQ/j78FZ194iW6dMC1atQkXtfefqUk6Y2X3lZ844b64Nt/69JrL9DSRctVVFhU2WlDkgUZvGBjN2TIDTrr7Ct1WJvu+ve/39dfnwt+ORQq3179vAUNCvyT8viVWvDke5K/dJttK9K0dOQX6v3+gzrp3QeUveS34kk6HABBTzNDl50550K2Xf70+5rY9Tal/fdHHXpt//3NEPtrL38H7nLuBX/SJx/9b4/7gdpgfy/3C/Z2NkXS814V1cfOubVBTk6iJL3sTVQVSSq5+u8M59xaKbBWmAKXKW6RlO6cmylJzrmt3v7TJHUys12XDTaS1F7S6hB5f+6c21U33Eu7J5z+I6lktdMXzjlnZgslrXfOLfSed7GX17wgx/7Y+3d2ieOeJulsM9s16RYr6RAFJv8qeh1Wl2gfrJ8zJf3LzKIkfeqcC5ZPMFmSNpvZJZJ+lrSzxL49PVd+kOOMd85led+fKOl8SXLOfWdmiWbWyDm3pUT8VEkPm9lBCvzfWBHk/4ZJesrMekvyS2olKdRiFydKesl77qVm9qt2v5YTduVgZkskHSrp9zLtkyVtLPE4StIEBSaLJ5XYPtM5l+4d6xdJ33jbFyowIbjLBklHBEvUzG5UoApUdzbspjPj2oboWs2Sm56p2Ja7L2+KaZmovIzSl5/lpWcptlWidg18THL5mMKtO7V5yhIl9umsHUvLDgeAYC4ddIEuuCJQ3Lto3hIltdr91tgiubk2ZGws1yYjbYPG/2+iJOnbr77Xk3//c9UkC0nSxYPO1/mXBxbfXjzvZyW1LDlmzbQxY1O5Nps2BCoxCvIL9Ol7X+rqWwKfl+3YvlOP3Dm8OG7szI+17re0cu1xYNxy89W67rrAaz9r1jwddHDL4n2tDkpWWvr6UvFNmyao0zEdNWPmXEnSBx9+rv99WSeWAq4x2l7TT20uD5yOZc1fpXotE7WrrqlecoJyM7JLxednblNUo/qyCJ9ckV9xyQnKWR84X0no3FrHjbpdkhST0FBJqZ3lioqU9vVsrRkzSWvGBE4Pjx56kXLSsoQDIzc9q9R5ZlzLhHLnkLneeeYuscmBGF90ZMi2krTu4ynqMfoBrXjugC3tjD0YdP1luuLqwI3O5s1dqFatkov3JbdMUkb6hqDtmjRprC7dOmnQ5bdXSZ4IQy1dK62q7HMlmwUWiS9SYEKhmHNuhKTrJcVJmrbrksky7pK0XoHKoO6SokvsyyvxfZECE4Cm4B9jmKTBXnVTinOutXPumyBxZVVUd1ryeXbl4i+Tl197npjcFVdUIsYknV8iz0Occz+r4tehZI5B++mcmyypt6R1kv5jZldV0K+y3pc0UiUuFa3oufZwjLI5llVqzJxz70o6W4EqxHFm1jdIm8slNZPUzat4W6/ApGRFKiqvCvb/qaycMs9RqMAkadmPucr+Hyj5/6PkcWO9Y5bjnHvNOdfdOde9tk2wSdK2ub+oXpskxR7STBYVoRYDj9emcbNKxWwcN0tJF/aWJMV3a6/CbTuVvyFbUYkNFRlfT5Lki41SQu+jtWMlfyACe2vMmx/p/NQrdX7qlZowdrLOvvB0SVKnbkdr+7btxZMzJX339SQde2JgTZoex3fVr7/8VqU513Xvv/nf4hsYfPf1ZJ11kTdmXY/Stm07go5ZyXXa+g44WSuX/iJJahjfQJFRgV81519+tuZMm6cd23eWa48D45VR/1b3Hqepe4/T9Pnn43Tl5YHPHo/t2VVbt2xVRkbpPw43b96iRo3i1b594K6vp6b21tKlK6o877rsl7fGa3y/hzS+30NaN3aWDr0wcFFBQtd2KtiWo9wN2eXabJiyRAedGVjh47CLeivt68CFDV8de5e+6nmnvup5p9Z+OUNzHnyreF9MYrwkKa5Volqd0UO/ffpTFfSubtgy9xfVb5OkOO88M3ng8Vo/rtTFJlo/brZaeeeZjbu1U+G2ncrbkF1h23qtd1/e3aJ/N21fwflnVXjzn+8W38Rg7JcTdOGlgQ8Ku3XvrG1bt2nD+vIfDkrSWef21/ivv1deXrAaD6D22KdKNjNrJmmUpJe9Sq+S+9p6FV8LzayXAhU9v0squZp2I0lrnXN+M7taUqjF/ZdKamlmPZxzMy2wzluOpHGSbjGz75xzBWbWQdI659y+XLz9k6RLFKhiu1zSj/vQdm+NkzTYzAZ7r1cX59xc7f3rELSfkpoq0N/XvctPuypwmWaBmUU55ypaYfcTBSq4xklqWWL7np5rm0qPYVmTFXj9njCzUxS4bHZryQBvYnaVc+5F7/tOkuar/P+NDd5z91Gg8kwhnn/Xc3/n5XuIpGUKvB5742dJ7SSt8R47SddK+tDMHvQmjvdFBwUqOv9wXJFfy4b+S13ee0iK8Cl9zPfasWytWl11qiRp3dvfKvPbuWqa2kW9pv9d/px8LRnyiiQppkUTdXzxVinCJ/P5tOGzqcocP6c6u4My7vvLCM2cu0DZ2VuVOvAK3XrdlTr/LC6pqIkmfztFvVOP19jp/1VuTq6GDXmieN8ro1/QI3cP18b1m/TPF9/WM/94XFfddIl27sjRI3c/VY1Z120/fPuTTko9Xv+b9qFyc/L05zt338l35Oj/06N3P62N6zdpxD8eVZPEJjKTli5aoSfuDxTYt25/mIa/9Ij8RX79sny1/sJYVpmvxk7QgAF9teznKdqZk6Prr7+7eN8Xn72tG2++T+np63XTLffpg/dfk9/vlL05W9ffeI8kqUWLZpo+dazi4xvI7/frjsE36JjOp2jbtv2+mTlCyJgwT8mpKTp96vMqysnXzLt239n1xHfu06x7Xlfu+mwtfHKMjhs1WEc/cKE2L/pVq8d8H/LYvd4YopgmDeUvKNTcoW+pYAuT3QeKK/Jr8dA31dM7z1w7ZqK2L1urQ7zzzN/e/lYbv52r5qkpOnn63+XPydOCIaMqbCtJRwy7VPXbtZTz+5WzdpMW3ffPautjXfXtN5OUelpvTZ/3jXJ25mrIbbvv0zf6w1d19+A/a7334cXA8/6kl154rbpSxb7gLiIVsoquiZYkMytS4LK4KAUqff4j6XlvgugUSfc65840s5cUuHSuSNISSdcoUOnztQKTQm9J+lLSfxW4THGiApVTDUoex3vOlyXNcs69ZWY9FLgkME6BCbZTvfZPKrCumClwyd/AMpcoyszWSOrunNtkZo9K2u6c+6u37zAF7gjZ1Gs/yDn3mwVubvClc+4jL+ZL59zRXpvifRU8T3dJf3XOnWJmcZL+Jul4L8813mvVfi9fB1+wfnpf90kqkLRd0lXOudVm9owCFWNzyq7LVjLHEtuK+1fBc+0sM4abvePc7h0jQdKbklp7sTc653bf8zwQM1TSFV6+GZIuc85lWeAGD50kjZX0jAJrxEUpcDnuCZJOd86tKRM3skTOsQpM+nZT4P/m3c65iWZ2TZkcv/TG5PsyeV0p6XDn3DDv8XZvHKK9XD5T4P9yyTH53ns8K8h4zZF0WsnXOJgJLS7mXamW6r346epOAWFIOerS6k4B+ynC9rnwHjXIkiwqKWuzMYmnVHcK2E/1/azfWJsNyp1X3SlgP63fsnRvFrSutXL+/WCV/T0bd/WIWvdahpxkA/6ovEnQiZJOcM6FdRZiZl0UmOS7MlQsk2y1F5NstRuTbLUXk2y1G5NstRuTbLUXk2y1G5NstdcffpLtzfurbpJt0LO17rXkrBV1lncTjL8ocJOFcDWVxMriAAAAAADUUft7d1HgD8E5N+4AHWf8gTgOAAAAAAA1FncXrRCVbAAAAAAAAECYqGQDAAAAAABAaI5KtopQyQYAAAAAAACEiUo2AAAAAAAAhOT8VXZz0VqJSjYAAAAAAAAgTFSyAQAAAAAAIDTuLlohKtkAAAAAAACAMDHJBgAAAAAAAISJy0UBAAAAAAAQmuNy0YpQyQYAAAAAAACEiUo2AAAAAAAAhOZ31Z1BjUYlGwAAAAAAABAmKtkAAAAAAAAQmp812SpCJRsAAAAAAAAQJirZAAAAAAAAEBqVbBWikg0AAAAAAAAIE5VsAAAAAAAACM1xd9GKUMkGAAAAAAAAhIlKNgAAAAAAAITGmmwVopINAAAAAAAACBOVbAAAAAAAAAjNz5psFaGSDQAAAAAAAAgTlWwAAAAAAAAIzbEmW0WoZAMAAAAAAADCxCQbAAAAAAAAECYuFwUAAAAAAEBo3PigQlSyAQAAAAAAAGGikg2oYiZm/murlKMure4UEIZ5i8dUdwrYT7mP31HdKSAsidWdAMIw94P86k4B+6lQVt0pIAyrR/Sr7hSAoJyfGx9UhEo2AAAAAAAAIExUsgEAAAAAACA01mSrEJVsAAAAAAAAQJioZAMAAAAAAEBojjXZKkIlGwAAAAAAABAmKtkAAAAAAAAQGmuyVYhKNgAAAAAAACBMVLIBAAAAAAAgND9rslWESjYAAAAAAAAgTFSyAQAAAAAAIDTWZKsQlWwAAAAAAACodcxsgJktM7OVZvZgkP2nmNkWM5vnfT2yt233B5VsAAAAAAAACM3VnDXZzCxC0khJ/SStlTTTzD53zi0pE/qDc+7M/Wy7T6hkAwAAAAAAQG3TU9JK59wq51y+pPcknVMFbfeISTYAAAAAAADUKGZ2o5nNKvF1Y5mQVpJ+L/F4rbetrF5mNt/MxprZUfvYdp9wuSgAAAAAAABCq8IbHzjnXpP0WgUhFqxZmcdzJB3qnNtuZmdI+lRS+71su8+oZAMAAAAAAEBts1bSwSUeHyQprWSAc26rc2679/1XkqLMrOnetN0fVLIBAAAAAAAgJOevOTc+kDRTUnszay1pnaRLJF1WMsDMkiStd845M+upQLFZpqTsUG33B5NsAAAAAAAAqFWcc4VmdrukcZIiJP3LObfYzG729o+SdIGkW8ysUFKOpEucc05S0Lbh5sQkGwAAAAAAAEKrwjXZ9oZ3CehXZbaNKvH9y5Je3tu24WJNNgAAAAAAACBMVLIBAAAAAAAgtBpWyVbTUMkGAAAAAAAAhIlKNgAAAAAAAITmatTdRWscKtkAAAAAAACAMFHJBgAAAAAAgNBYk61CVLIBAAAAAAAAYaKSDQAAAAAAACE5KtkqRCUbAAAAAAAAEKY6W8lmZk7S8865e7zH90pq4Jx79AAc+y1JXzrnPgr3WCGe50JJj0vKcM71qcznCpeZfSXpMudcdg3IZaCk5c65JdWdS02X0Kez2j85SBbhU/roCfr1pc/KxbQfPkiJqV3kz8nTkjv+oe0LV8sXE6Wunz0mi46URURo45fTtPq5DyVJre+9UC2vSFV+5lZJ0qqnxihzwtwq7RekocPvVu/U45WTk6uH73hCPy9cFjTujqE3q/9ZqSoqKtL7//5Yo//5QRVnilCGPfW8Jk+ZoYQmjfXpO6OqOx2UEXFkV8Wed6Pk86lg6jfK/zb4qYHvkPaqd/dflfvWsyqcN0WSVP8vb8jl5Uh+v+Qv0s6/3lWVqdd5jF3t06RPito+EThvyRg9Qb+//Gm5mLZPDlJCalcV5eRp+ZCR2r5w9e6dPp+6jhuhvIwsLb5yhCSpfsdD1f7ZGxVRP1a5v2/Q0ltfVNH2nCrqUd20v+efxXymHt8ExnHBFc9UYeaQpClrNum5SUvl9zsNPPogXdujdbmYWb9n6blJy1To96txXLTeuLCHJOnRbxZp8uqNSqgXrY+uPKGqU8feopKtQnV2kk1SnqTzzOxp59ym6k5mFzOLcM4V7WX4dZJudc5NrMycDgTn3BnVnUMJAyV9KWmvJ9nMLNI5V1hpGdVEPtPhI67T3IueVF5aprqPe1obx83SzuXrikMSU7uoXuskTTvuDsV3a6/Dn71es09/WP68As097zEV7cyTRUao6xePK/O7edo6e4Uk6bdX/6ffX/miunpW552UerwObX2wTj/uAnXqdrQeefZ+XXr6deXiBl5yppJattCZJ1wk55wSmjaphmwRysAz+umy88/WQ0/8tbpTQVnmU+yFt2jnyGFy2Zmqd+8LKlw0Xf6M38vFxZx9jYp+Lv+BQ85LD8nt2FpFCaMYY1f7+Hxq9/R1WnjRE8pLz1KXr59W5jeztHP52uKQJqldFNcmWTN7DVbDru3V7pkbNO+Mh4r3t7rhDO1csU4RDeOKt3V4/mateuw/2jJ1iVpc2kcH3Xq2fn32/SrtWp0SxvnnLgffcIZ2rFinyBLjiKpR5HcaMfFnvXJeN7VoEKvLx0zTyW2aqW1ig+KYbbkFemrizxo5sKuS4+OUtTOveN9ZHVvq4pRD9OdxC6sjfeCAqMuXixZKek1SuY8WzewtM7ugxOPt3r+nmNkkM/vAzJab2Qgzu9zMZpjZQjNrW+Iwp5rZD17cmV77CDN7zsxmmtkCM7upxHEnmtm7ksq9o5jZpd7xF5nZM962RySdKGmUmT0XpM39Xpv5ZjbC25ZiZtO85/7EzJp42783s2e8fiw3s5O87dPN7KgSx/zezLqZWX0z+5fXj7lmdo63/xoz+9jMvjazFWb2bIm2a8ysqff9Fd5zzTOzV80sYtfrbGbDvZynmVkLb3sLL9/53tfxFR2nzOswwsyWeH3+q9f2bEnPee3ahnhdnjKzSZKGeH2fZGazzWycmSV7cXeUeI73yuZQW8V3baedqzOU++sGuYIibfj0JzUb0KNUTNMB3ZXx4WRJ0tbZKxQZX1/RzRtLkoq8X5gWFSFfZITk+MSjpug7oLc+/3CsJGnB7EVqGN9QTZsnlou75JrzNOr/3pDzxi5r0+YqzRN7p3vKMWoU37C600AQvkM7yL8xXS5zvVRUqMI5kxV5zHHl4qJOPlOF83+S255d9UkiKMau9mnYpZ1yVmco97cNcgWF2vjpFCX2714qpmn/Hlr/wSRJ0rY5pc9bopMTlHBqV2WMnlCqTVzbltoyNfC5bPakBWp6Zvn/Bzhwwj3/jElOUGK/rkovM46oGosytujgRvV0UKN6iorwqX+HJH3/y4ZSMWOXpSu1XXMlxwcmQRPqxRTv63ZQghrFRFVpzsCBVpcn2SRppKTLzazRPrTpLGmIpGMkXSmpg3Oup6R/ShpcIu4wSSdL+pMCE2GxClSebXHO9ZDUQ9INZrarfranpIedcx1LPpmZtZT0jKS+klIk9TCzgc65xyXNknS5c+6+Mm1OV6Ba61jnXGdJuya73pb0gHOukwKTeX8p0SzS68edJba/J+ki75jJklo652ZLeljSd14/+igwYVXfa5Mi6WLv9bnYzA4uk9uR3v4TnHMpkookXe7tri9pmpfzZEk3eNtflDTJ295V0uIQx9n1XAmSzpV0lNfnJ51zP0n6XNJ9zrkU59wvIV6Xxs65k70cXpJ0gXOum6R/SRruxTwoqYvX/mb9QcQkJSgvLbP4cV5apmKSEkrHJCcod93uQtC89EzFJHsxPlOPCc/qxMX/VNakhdo6Z2Vx3EHX9lfPic/piL/doshG9YWq1Ty5mTLWrS9+vD59g1okNysXd/ChB2nAwFP1/ri3NOrdF3RI64PLxQDYM1/jRPmzNxY/9mdvkjUqPaFtjRIV2amXCn4cG+QITnG3Pq569/1NUcf3r+RsURJjV/vEJJc5b0nPUnRy6TGLLheTqWjvvKXtE4O0+ol35Jy/VJsdS38vnqxrelYvxbQs/6EUDpxwzz/bP3GNfnn8HRZmryYbduSqRcPY4sctGsZq4468UjG/bt6prbmFuv7Dmbrs3an6YklaVaeJcPn9VfdVC9XpSTbn3FYFJlju2IdmM51z6c65PEm/SPrG275QgYm1XT5wzvmdcyskrZJ0hKTTJF1lZvMkTZeUKKm9Fz/DOVdiMYFiPSR975zb6F2uOFpS7xA5nirpTefcTq+fWd5EYmPn3CQv5t9ljvOx9+/sEv34QNKF3vcXSfrQ+/40SQ96/fheUqykQ7x9E5xzW5xzuQpcjnlomdxSJXWTNNNrnyqpjbcvX4HLOMvm0VfSK15fipxzW0IcZ5etknIl/dPMzpO0s8x+7cXrsut6gMMlHS1pvPd8wyQd5O1bIGm0mV2hQIXkH4NZuU1OZU9YyscUV6z5nWam3q+fUm5WfNe2qn9EYIJm7b+/0dRjB2tG3/uVv36z2j121QFOHKFYkHELVmgYHROlvNx8Xdz/Gn30zmd68m/DqiA74A+uzA9bzHk3KO/ztyRX/kRy5wv3a+dzdyrnlb8o6qQzFdH2qHIxqEKMXc0W5JSk3C+3IOc2clJCv64q2LRF2xesKrd7+V3/UMtBA9Rl3DOKaBArl//HOdWrkcI4/0zs11X5m7Zo24Jgf1KhSuzF3GaRc/p5w1a9NLCLRp7bTa/PWKVfN++o/NyAKlKX12Tb5W+S5kh6s8S2QnkTkGZmkqJL7Cs5Fe8v8div0q9n2bcYp8BvhMHOuXEld5jZKZL29M4S7JQhFAvy/KHs6keRvH4459aZWaaZdVKgauymEsc/3zlXaqV0MztWpV+f4mOVye3fzrmhQXIocK74bChY2709jrz8C82spwITcJdIul2BCbt9sWtcTNJi51yvIDF/UmBi7mxJfzazo8qu32ZmN0q6UZLuathNZ8aVnQ+sefLSM0t9WhvTMlH5GZvLxcS2aqotCvxXiElOVF6ZmMKtO7V5yhIl9EnRjqW/q2DjluJ9ae9MUKd3HqjEXmCXSwddoAuuOEeStGjeEiW1alG8r0Vyc23I2FiuTUbaBo3/X2DJx2+/+l5P/v3PVZMs8Afhz85UVOPdVaK+xk3ltmaViok4pJ3irr5fkmQN4hXRsbtUVKTChdOKY932LSpcMFW+Qzuo6JfFVdeBOoyxq33y0rJKn7ckJyg/o/SY5aeVObdJTlR+RpaannmcEk/rroTULvLFRCuiQZwOf3mwlt3+knJWpmnhJU9KkuLaJCvh1G5V06E6Kpzzz2ZnHaem/bsrMbWLfLHRimwQp44jB2vJbS9VaR/qsuYNYrV+W27x4/XbctWsfky5mMaxUYqLilRclNS1VRMt37hNhzbh6pZag0rRCtXpSjYpUOWlQMVWyVW/1yhQJSVJ50janwvDLzQzn7dOWxtJyySNk3SLmUVJkpl1KHGZ5Z5Ml3SymTX11hy7VNKkEG2+kXStmdXznifBq/7avGu9NQUudQ11HClwyej9kho553atFzdO0mBvAlJm1mUvjrPLBEkXmFnzXbmZWdlqt2BtbvHiI8wsfm+OY2YNvLy/UuAy2BRv1zZJDSVpH16XZZKamVkv79hRZnaUmfkkHezdfOJ+SY0lNSjb2Dn3mnOuu3Oue22YYJOkbXN/Ub02yYo9pJksKkLNBx6vTeNmlYrZNG6Wki4MFP7Fd2uvom07lb8hW1GJDRUZX0+S5IuNUkLvY7RzZWDB2l1rZkhSszN6asfSMotIo1KMefMjnZ96pc5PvVITxk7W2ReeLknq1O1obd+2XZs2ZJZr893Xk3TsiYFLZHoc31W//vJbleYM1Hb+35bL16ylLKGFFBGpyK69VbhweqmYHY9drx2PXacdj12nwnlTlPfhKypcOE2KjpFivEW7o2MUeUQX+dN/rYZe1E2MXe2zbd5KxbVJVuwhzWVRkWo28ARlflP6vCXzm1lqcdHJkqSGXdur0DtvWfPUu5re9WbN6HGbfr75BWVPWaRltwcmZqKaxgcam+mQu85X+tvfCJUnnPPPVcPH6Kcut2hqj9u1+Ka/afOURUywVbGjkuL1W/ZOrduyUwVFfo1bnqFT2jYvFXNK22aam5atQr9fOQVFWpSRrdYJTLDhj4NKtoD/U6DKaZfXJX1mZjMUmMzZn/rVZQpM1rSQdLNzLtfM/qnAJZBzvAmqjQqsnbZHzrl0MxsqaaIC1VRfOefK38e6dJuvzSxF0iwzy5f0laSHJF2twPpw9RS4hHXQXvTjI0l/l/REiW1PKFABuMDrxxpJZ+7FseScW2JmwyR9401QFUi6TVJFZ59DJL1mZtcpUOF2i3Nu6l4cp6EC4xirwGu36yYX70l63czukHSB9uJ1cc7lW+BmGC96l5hGeq/BcknveNtM0gvOuey9eS1qOlfk1/Kh/1LKew/LInxKGzNRO5atVcur+kmS0t4er8xv5yoxtat6TX9RRTn5+nnIPyRJ0S2aqOOLt8kifJLPtOGzqcocP0eS1O6RK9Tg6MMk55Tz+0Ytu/e16upinTX52ynqnXq8xk7/r3JzcjVsyO4f71dGv6BH7h6ujes36Z8vvq1n/vG4rrrpEu3ckaNH7n6qGrPGntz3lxGaOXeBsrO3KnXgFbr1uit1/lmsAVUj+P3K/WiU6t36uOTzqWDaePkzflPUCYFJ7oIpwdbyCrCGjRV3vXeJts+nwtmTVPTznKrIGhJjVxsV+bXyoTd09JjAeUvGmInauWytkr3zlvS3xyvr2zlKSO2iHtNekj8nX8vuHBnysM0GnqiWgwLvqZu+mqH1YyZWajfqunDOP1H9In0+PdDnCN36yRz5ndM5R7VS28QG+nBB4EP1CzsdrDYJDXT8oYm66J2p8pl07lEHqV3TwA2cHvxqgWavzVJ2boH6/3OSbj6urc49+qCKnhLVgUq2Cpnjjn9AlfquxUX80NVSgyucC0ZNN2/xmOpOAfsp9/F9WToVwIE094O46k4B+6nQ7c+qM6gpjnu0Regg1Ej1bnnpD/3Dt+3mAVX292zDUV/XuteSSjYAAAAAAACERKFWxer8mmwAAAAAAABAuKhkAwAAAAAAQGisyVYhKtkAAAAAAACAMFHJBgAAAAAAgNCoZKsQlWwAAAAAAABAmKhkAwAAAAAAQEiOSrYKUckGAAAAAAAAhIlKNgAAAAAAAIRGJVuFqGQDAAAAAAAAwsQkGwAAAAAAABAmLhcFAAAAAABAaP7qTqBmo5INAAAAAAAACBOVbAAAAAAAAAjJceODClHJBgAAAAAAAISJSjYAAAAAAACERiVbhahkAwAAAAAAAMJEJRsAAAAAAABC4+6iFaKSDQAAAAAAAAgTlWwAAAAAAAAIibuLVoxKNgAAAAAAACBMVLIBAAAAAAAgNNZkqxCVbAAAAAAAAECYqGQDAAAAAABASKzJVjEq2QAAAAAAAIAwUckGAAAAAACA0FiTrUJUsgEAAAAAAABhYpINAAAAAAAACBOXiwIAAAAAACAkx+WiFaKSDQAAAAAAAAgTlWxAFYs0bnlcW0XwuUStlvv4HdWdAvZT7CMvVncKCEPO0JurOwWEwe/iqjsF7Ccnq+4UEI6c3OrOAAiOSrYK8RcjAAAAAAAAECYq2QAAAAAAABASa7JVjEo2AAAAAAAAIExUsgEAAAAAACA0KtkqRCUbAAAAAAAAECYq2QAAAAAAABASa7JVjEo2AAAAAAAAIExUsgEAAAAAACAkKtkqRiUbAAAAAAAAECYq2QAAAAAAABASlWwVo5INAAAAAAAACBOVbAAAAAAAAAjNWXVnUKNRyQYAAAAAAACEiUk2AAAAAAAAIExcLgoAAAAAAICQuPFBxahkAwAAAAAAAMJEJRsAAAAAAABCcn5ufFARKtkAAAAAAABQ65jZADNbZmYrzezBIPsvN7MF3tdPZta5xL41ZrbQzOaZ2awDkQ+VbAAAAAAAAAipJq3JZmYRkkZK6idpraSZZva5c25JibDVkk52zm02s9MlvSbp2BL7+zjnNh2onKhkAwAAAAAAQG3TU9JK59wq51y+pPcknVMywDn3k3Nus/dwmqSDKjMhJtkAAAAAAAAQknNWZV9mdqOZzSrxdWOZdFpJ+r3E47Xetj25TtLYkt2R9I2ZzQ5y7P3C5aIAAAAAAACoUZxzrylweeeeBLsLgwsaaNZHgUm2E0tsPsE5l2ZmzSWNN7OlzrnJ+52wmGQDAAAAAADAXqhJa7IpULl2cInHB0lKKxtkZp0k/VPS6c65zF3bnXNp3r8bzOwTBS4/DWuSjctFAQAAAAAAUNvMlNTezFqbWbSkSyR9XjLAzA6R9LGkK51zy0tsr29mDXd9L+k0SYvCTYhKNgAAAAAAAITk/MGu0KwezrlCM7td0jhJEZL+5ZxbbGY3e/tHSXpEUqKkf5iZJBU657pLaiHpE29bpKR3nXNfh5sTk2wAAAAAAACodZxzX0n6qsy2USW+v17S9UHarZLU+UDnwyQbAAAAAAAAQnJBbyuAXViTDQAAAAAAAAgTlWyo1cxsu3OuQQX7G0u6zDn3D+9xS0kvOucuMLMUSS298tJ9ec5HJW13zv11vxOvJZr0SVHbJwbJInzKGD1Bv7/8abmYtk8OUkJqVxXl5Gn5kJHavnC1JKnnzJEq2p4rV+SXKyrS3P4PSpJaP3KlEvt1k7+gULlr1mvZnSNVtHVnVXarTnrgybt0Uurxys3J1Z+HPKGfFy4vF/PE34epe68u2rZ1uyTpz0Oe1LLFK9SwUUM9/sLDOviwVsrLy9df7hqulUtXVXUX6qyII7sq9rwbJZ9PBVO/Uf63HwWN8x3SXvXu/qty33pWhfOmSJLq/+UNubwcye+X/EXa+de7qjJ1hDDsqec1ecoMJTRprE/fGRW6AapUxFHdFXvJLTKfT/k/fK38r98PGuc7rIPqD/27cl59SoVzfpAkxV59tyI7HSe3LVs7Hr2xKtOu0xL6pKjdk4HzlvTRE/TbS5+Wi2k3fJASvfOWpXfsPm+RJPl86vbNCOVnZGnhFSNKtTv4lrPU9tGrNOXIa1WQta2Se1K3JfTprA5PXiOL8Clt9Hf69aXPysV0GH6NElO7qCgnTz/f8Yq2LVwtX0yUun72qHzRUbIInzZ8OV2rn/uwGnpQt/kOO0rRp1wi+XwqXPiDCmeWXuLKd1AHxZxzm9yWwA0eC1fOUeG0L2VNWijmTzcVx1mjpir46TMVzp1QpfkjtJq0JltNxCQb/ugaS7pV0j+k4lv0XuDtS5HUXWWu34bH51O7p6/TwoueUF56lrp8/bQyv5mlncvXFoc0Se2iuDbJmtlrsBp2ba92z9ygeWc8VLx//vmPqrDMiWj2pPlaPXy0VORX62GX65A7ztXqJ0dXWbfqohNTe+nQNgfrzF4XqlPXozTsmft1+RnlliWQJD3/+Msa/+XEUttuGHK1li1erruufVCHtTtUDz99r264cHBVpA7zKfbCW7Rz5DC57EzVu/cFFS6aLn/G7+XiYs6+RkU/zy13iJyXHpLbsbWKEsa+GHhGP112/tl66Ik//Gc2tY/5FHfZ7drxwoNymzep/sMvqXD+VPnTfysXF3v+9SpcPLvU5oKfxit/4ueKu/b+Kky6jvP51H7EdZp/0RPKS8tSt3FPa9O40uctCaldFNc6WdOPG6z4bu3V4dkbNOf03ectB91whnauWKfIhnGlDh3TMlFNTu6k3N83Vll36iyf6fAR12ruRcOVl5apHt447li+rjgkMTVFca2TNPW4IYrv1l6HP3udZp0+TP68As0973EV7cyTRUao2xePKfO7edo6e0U1dqiOMVN038uU998X5LZtVuzlD6vol/lyWemlwvzrVirv05dKbXOb1yv3nceLjxN343MqWln+vAao6bhcFH8IZtbAzCaY2RwzW2hm53i7Rkhqa2bzzOw5MzvMzBZ5t/d9XNLF3r6LzexRM7u3xDEXmdlh3vcPm9kyM/tW0uElYtqa2ddmNtvMfjCzI6qu15WrYZd2ylmdodzfNsgVFGrjp1OU2L97qZim/Xto/QeTJEnb5qxQZHx9RTdvXOFxN09aIBX5JUlbZ69QTHJipeSP3fr0760vPhgrSVowZ7EaxjdQ0+Z7/7q36XCYpv8wS5K0ZuWvanlwkhKaNqmUXFGa79AO8m9Ml8tcLxUVqnDOZEUec1y5uKiTz1Th/J/ktmdXfZLYb91TjlGj+IbVnQaCiGh9uPwb0+Q2ZUhFhSqYOUmRKceXi4vue44KZv8gty271PaiFQvldlDtVJXiu3rnLb8Gzls2fDpFTQeUOW8Z0EPrPwyct2ydXfq8JSY5QYn9uip9dPmqmXaPX6NfHn+HhYiqQGAc13vjWKT1n/6kpgN6lIppNqCHMj6cLKn8OBbtzJMkWVSELDKSMativqTWctkb5bZskvxFKlw6UxFtU/b9OIccKX/2RrltWQc+SaCSMcmGP4pcSec657pK6iPp/yxwL94HJf3inEtxzt23K9g5l6/ArXzf9/YFvwZEkpl1k3SJpC6SzpNU8jf9a5IGO+e6SbpXXsXcH0FMcoLy0jKLH+elZym6zIRYdLmYTEUnJwQeOOmY94apy7hnlHTFqUGfI+nSPsr6jk+oKlvz5GbKSFtf/Hh9+kY1T24WNHbwgzfpo+/+o/seG6Ko6ChJ0vLFK5V6ximSpKO7dFTyQUlq0bJ5pecNydc4Uf7s3ZUT/uxNskalfw6tUaIiO/VSwY9jgxzBKe7Wx1Xvvr8p6vj+lZwt8MdhjZvKn7X7Z89t3ihf4zI/e40TFdnlBBVM+l9Vp4cgYpLKnJOkZSkmqfSYxSQnKG9d6fOWGO+8pd0TgwITaX5/qTaJ/bsrLyNLO5b8WonZY5fYpATllhrHTMUklf5gLya5iXL3MI7ymXpOeEYnLX5dWZMWaOuclVWSNwKsQeNSE2Nu+2ZZw8bl4nzJbRR75SOKOfcOWWLLcvsjD++homUzKjNVhMH5rcq+aiMuF8UfhUl6ysx6S/JLaiWpxQE69kmSPnHO7ZQkM/vc+7eBpOMlfRiYz5MkxRyg56x+wd7Tyn4aaEGCvJB5Zw1T/vrNimoar2Pe/7NyVq7Tlmk/F4cdPOQ8uUK/Nvz3hwOXM4IKOkxBPtn9+/BXtGlDpqKio/SXvz6oa2+/Uq8+/y+98dLbeuDJu/TBt//Wip9/0dJFy1VUWFQFmSOoMmMXc94Nyvv8Lcn5y4XufOF+ua1ZsgaNFHfbk/KvX6uiXxZXUaJALRb0vL70z17sxbco7+N/Bv3ZQzUIekpS9ndd+SDnpMR+XZW/aYu2L1ilxsd3LN7ni4vWoXeep/kXPXmAk8UeBTtpKR9UftOu341+pxmpDygyvp46vXWv6h9xsHYs/b18PCrJnv822MW/4Tfl/PNBqSBPvtZHK+bsW5X75rDdAb4IRbTtrPwfP67cVIFKwiQb/igul9RMUjfnXIGZrZEUu4/HKFTp6s6S7YPVmvskZTvnUkId2MxulHSjJN3TsKvOrtdmH1OrenlpWYppufsT4JjkBOVnlC7Zzk/LLBOTWByTv36zJKlg01Zljp2hhl3aFU+ytbjoZCX266YFFz5W2d2osy4edL7Ov/xsSdLieT8rqeXuOecWyc20MWNTuTabNgQ+FS7IL9Cn732pq2+5XJK0Y/tOPXLn8OK4sTM/1rrf0iozfXj82ZmKary76tDXuKnc1tI/hxGHtFPc1YF1n6xBvCI6dpeKilS4cFpxrNu+RYULpsp3aAcm2YC94DZvki9h98+eNWkmf3aZn73DOijuhsB6XtagkSKP7qlcf5EK5/1UpbkiIC+9zHlLy/LnLXnpmYppVf68pdlZx6lp/+5KTO0iX2y0IhrE6ciRg/Xby58p9pDm6vHdc94xE9Vt/LOaM2Co8jdmV0m/6prc9EzFlhrHROVlbC4Vk5eepdhWidqyKya5fEzh1p3aPGWJEvt0ZpKtCgUq1xKKH1uDJuWXssjPLf7Wv3qR1DdCim0g5QZuvBXR+mj51/8m7eSS+5qKq7ArxuWi+KNoJGmDN8HWR9Kh3vZtkva04E3ZfWskdZUkM+sqqbW3fbKkc80szswaSjpLkpxzWyWtNrMLvTZmZp2DPZFz7jXnXHfnXPfaMMEmSdvmrVRcm2TFHtJcFhWpZgNPUOY3s0rFZH4zSy0uOlmS1LBrexVu26n8Ddny1YtRRP3AHKWvXowan7z7BKdJnxQddPtALb76Gflz8qu2U3XI+2/+VxederUuOvVqfff1ZJ110emSpE5dj9K2bTuKJ9RKKrlOW98BJ2vl0l8kSQ3jGygyKvCZzPmXn6050+Zpx3buCFsV/L8tl69ZS1lCCykiUpFde6tw4fRSMTseu147HrtOOx67ToXzpijvw1dUuHCaFB0jxXiLd0fHKPKILvKnc7kTsDeK1iyTr3krWdMkKSJSUT1OVuH8qaVitg+9qvirYM4Pyh39EhNs1Wjb3NLnLc0HnqBN40qft2waN0stLgyct8R3233esnr4u5ra5WZN63Gbltz0grKnLNLPt72kHT//pp+Oul7TetymaT1uU15apmb3u58Jtkq0be4vqtcmSbGHNJNFRajFwOPLjePGcbOUdGFvSaXHMSqxoSLj60mSfLFRSuh9tHas5EPBquTPWCNr3FwW31TyRSjyiB4qWjW/dFC9+OJvfUmHBaoXvQk2SYo4vKcKuVQUtRiVbPijGC3pCzObJWmepKWS5JzLNLMpZrZI0lhJI0u0mSjpQTObJ+lpSf+VdJX3eKak5d4x5pjZ+95xf5VU8vrGyyW9YmbDJEVJek9Smd8ktVSRXysfekNHj3lYFuFTxpiJ2rlsrZKv6idJSn97vLK+naOE1C7qMe0l+XPytezOwMsb3bSROr4ZWALPIiO04eMftXniPElSu6euky86Use8/2dJ0tbZy7Xygdervn91yA/f/qSTUo/X/6Z9qNycPP35zt2XvYwc/X969O6ntXH9Jo34x6NqkthEZtLSRSv0xP3PSpJatz9Mw196RP4iv35Zvlp/ufup6upK3eP3K/ejUap36+OSz6eCaePlz/hNUScEJk0LpgRbhy3AGjZW3PXe5Rc+nwpnT1LRz3OqImvspfv+MkIz5y5QdvZWpQ68Qrded6XOP4u182oEv1+5776senc+JTOf8qeMkz/tV0Wd/CdJCrkOW9wNQxXRoZOsQSM1eHa08j7/jwp+/LoqMq+zXJFfK4a+oU7vBc5b0r3zlpbeeUuad96SmNpFx05/SUU5+Vo2ZGSIo6KquSK/lg39l7q895AU4VP6mO+1Y9latboqsL7vure/Vea3c9U0tYt6Tf+7/Dn5WjLkFUlSTIsm6vjirVKET+bzacNnU5U5nt97Vcr5lT/xXcWcf6dkpsJFU+Qy0xTZKTC5XbhgkiI7dFNkp1MkVyRXWKD8/5X4OyAyWhGHdlT+t+9US/rYO7V1rbSqYsHW5QFQeSYnXcgPXS11u9ZWdwoIw5SLuJNtbRX7yIvVnQLCkDP05upOAWGY83mj6k4B+6mIi5ZqtV4PxIcOQo1U7+7X/9CzUKuOOa3K/p5ts/CbWvdaUskGAAAAAACAkJyrdfNeVYqPNwAAAAAAAIAwUckGAAAAAACAkJy/ujOo2ahkAwAAAAAAAMJEJRsAAAAAAABC8rMmW4WoZAMAAAAAAADCRCUbAAAAAAAAQuLuohWjkg0AAAAAAAAIE5VsAAAAAAAACMn5qWSrCJVsAAAAAAAAQJiYZAMAAAAAAADCxOWiAAAAAAAACMm56s6gZqOSDQAAAAAAAAgTlWwAAAAAAAAIiRsfVIxKNgAAAAAAACBMVLIBAAAAAAAgJL+jkq0iVLIBAAAAAAAAYaKSDQAAAAAAACE5KtkqRCUbAAAAAAAAECYq2QAAAAAAABCSc9WdQc1GJRsAAAAAAAAQJirZAAAAAAAAEBJ3F60YlWwAAAAAAABAmKhkAwAAAAAAQEjcXbRiVLIBAAAAAAAAYaKSDQAAAAAAACFxd9GKUckGAAAAAAAAhIlJNgAAAAAAACBMXC4KAAAAAACAkPzc+KBCVLIBAAAAAAAAYaKSDahi6RZd3SlgPy3J/K26U0BYEqs7AeynnKE3V3cKCEPc06OqOwWE4/MHqjsD7KcCUW1SqxUUVncGQFCOSrYKUckGAAAAAAAAhIlKNgAAAAAAAITEmmwVo5INAAAAAAAACBOVbAAAAAAAAAjJVXcCNRyVbAAAAAAAAECYqGQDAAAAAABASKzJVjEq2QAAAAAAAIAwUckGAAAAAACAkByVbBWikg0AAAAAAAAIE5VsAAAAAAAACMlf3QnUcFSyAQAAAAAAAGGikg0AAAAAAAAhObEmW0WoZAMAAAAAAADCRCUbAAAAAAAAQvK76s6gZqOSDQAAAAAAAAgTk2wAAAAAAABAmLhcFAAAAAAAACH5ufFBhahkAwAAAAAAAMJEJRsAAAAAAABCclSyVYhKNgAAAAAAACBMVLIBAAAAAAAgJH91J1DDUckGAAAAAAAAhIlKNgAAAAAAAITEmmwVo5INAAAAAAAACBOTbAAAAAAAAAjJX4Vfe8PMBpjZMjNbaWYPBtlvZvait3+BmXXd27b7g0k2AAAAAAAA1CpmFiFppKTTJXWUdKmZdSwTdrqk9t7XjZJe2Ye2+4w12QAAAAAAABBSDbu7aE9JK51zqyTJzN6TdI6kJSVizpH0tnPOSZpmZo3NLFnSYXvRdp9RyQYAAAAAAIDappWk30s8Xutt25uYvWm7z6hkQ41lZtudcw0O8DHPltTROTfCzAZKWu6c26eZajP7XtK9zrlZBzK3mqrLE1cpObWzinLyNePOV7V54ZpyMfUPbqZeo25XdOMG2rxwjaYP/of8BUVq1utInfjW3drx20ZJ0tqvZmrJC58UtzOfqd/XTyonY7N+uOqvVdWlOumF5x/X6QP6amdOjq677i7NnbeoXEzfPidqxIhh8vl82rF9h669/i798ssaHX54W73x+gvq0uVo/fmRZ/T8C69WQw/qrogjuyr2vBsln08FU79R/rcfBY3zHdJe9e7+q3LfelaF86ZIkur/5Q25vBzJ75f8Rdr517uqMnVIijiqu2IvuUXm8yn/h6+V//X7QeN8h3VQ/aF/V86rT6lwzg+SpNir71Zkp+PktmVrx6M3VmXa2AvDnnpek6fMUEKTxvr0nVHVnQ4kJfRJUbsnB8kifEofPUG/vfRpuZh2wwcpMbWrinLytPSOkdq+cPXunT6fun0zQvkZWVp4xQhJ0mEPXKymA3pIfqf8TVu09I6Ryl+/uYp6VHc07dNZRz55tRTh09rR32n1S5+Xizly+NVqmtpF/pw8LbzjFW31zkmP/ttNatavq/I3bdWUk+8rjm9x1rFqd+8FatChlaYOGKat81dVVXfqNF/roxWdepnk86lw/mQVTv+q9P6DD1fM+XfIZW+SJBUun63CnwLjHXvzc1J+ruT3y/mLlPf241WeP0KryruLmtmNClziuctrzrnXSoYEaebKHmYPMXvTdp8xyYY6xTn3uaRdv7UHSvpSYZaD/pEl9+2shm2S9NXx9yixazt1GzFI3/7pL+XiOg27RMteG6vfP5umbs9cq9aXnqJf3p4gSdo0fdkeJ9Da3zBAW1ekKaphXKX2o647fUBftW/XWkd0PFHH9uyqkS8/reNPPKtc3MsvP63zzh+kpUtX6uabrtZDQ4fouuvvUlZWtu68688655wB1ZB9HWc+xV54i3aOHCaXnal6976gwkXT5c/4vVxczNnXqOjnueUOkfPSQ3I7tlZRwijFfIq77HbteOFBuc2bVP/hl1Q4f6r86b+Vi4s9/3oVLp5danPBT+OVP/FzxV17fxUmjb018Ix+uuz8s/XQE3xIVCP4fGo/4jrNv+gJ5aVlqdu4p7Vp3CztXL62OCQhtYviWidr+nGDFd+tvTo8e4PmnP5Q8f6DbjhDO1esU2SJ85LfR36uNc8EJsdbXX+6DrvnAi2///Wq61dd4DN1HHGtZl40XLlpmeo17iltGDdbO5avKw5pmpqieq2T9cNxd6pRt3bq+Oz1mnb6MEnSuvcm6bc3xumYl28rddjtS3/XvGuf11HP3VCl3anTzBTd70rlvf9XuW1Zir36ERWtnCeXmVYqzP/7cuX99+9BD5E75hkpZ3tVZItawJtQe62CkLWSDi7x+CBJaXsZE70XbfcZl4uixvPuBvKcmS0ys4VmdrG3/RQz+97MPjKzpWY22szM23eGt+1H704iX3rbrzGzl83seElnS3rOzOaZWVvvWN29uKZmtsb7Ps7M3vPuRPK+pLgSuZ1mZlPNbI6ZfWhmB7Tyrrq1GtBNaz4MVFRkzlmpqPh6im3euFxcixOP0tovZ0iS1nwwWa1O7x7y2HHJCWqZmqJV7048oDmjvLPO6q//jA5UP02fMUeNGjdSUlLzcnHOOcU3bChJatSoodLT10uSNm7M1KzZ81VQUFB1SUOS5Du0g/wb0+Uy10tFhSqcM1mRxxxXLi7q5DNVOP8nue3ZVZ8k9iii9eHyb0yT25QhFRWqYOYkRaYcXy4uuu85Kpj9g9y27FLbi1YslNuxrYqyxb7qnnKMGsU3rO404Inv2k45qzOU++sGuYJCbfh0ipoOKH0+0nRAD63/cJIkaevsFYqMr69o77wmJjlBif26Kn30hFJtirbnFH8fUS9GLuwaB5TVuGs77VydoZxfN8gVFCnj05/UoszYtRjQXWkfTpYkbZkdOCeN8cZu87SlKsjeUe64O1akaccv6ZWeP3bzJbeRy94gt2Wj5C9S4c8zFNG+S3WnhQPMb1X3tRdmSmpvZq3NLFrSJdpdVLPL55Ku8uYVjpO0xTmXvpdt9xmVbKgNzpOUIqmzpKaSZprZZG9fF0lHKTDjPEXSCWY2S9Krkno751ab2ZiyB3TO/WRmn0v60jn3kSR583PB3CJpp3Ouk5l1kjTHi28qaZikU51zO8zsAUl3S/rD1DXHJSVoZ1pm8eOc9CzFJTdR7obs4m3RCQ2Uv2WHXFFgCcyd6Vmql9SkeH9it3bq/+1TylmfrXmPjdZW71PJLo9fqflPjlFkfarYKlurlkla+/vuD2XWrU1Xq5ZJysjYUCruppvu1Ref/0c5Obnaum2bTghS7Yaq5WucKH/2xuLH/uxNijj08FIx1ihRkZ16KeelhxVxWfsyR3CKuzXwllQwZawKfhpX2SmjBGvcVP6s3ePnNm9UROsjysQkKrLLCdr5f/crovXhZQ8BYC/FJCUor8Q5S15aluK7ln5PjElOUN66EjHpmYpJTlD+hmy1e2KQfnn8HUU2iC137NZDL1WLC3uraNtOzTvvscrrRB0Vk5SgnBJjl5uWpUZd25WOSU5QTomxy03PCoxniXNSVD9r2ERua1bxY7ctS77ktuXifK3aKXbQY3Lbs5U/8X25Td55qnOKveheSU4F875X0fxJVZQ5aivnXKGZ3S5pnKQISf9yzi02s5u9/aMkfSXpDEkrJe2UNKiituHmxCQbaoMTJY1xzhVJWm9mkyT1kLRV0gzn3FpJMrN5CtwhZLukVc65XYtsjFHp67j3VW9JL0qSc26BmS3wth+nwK1+p3gTdNGSpobxPDVP0KvUS3+EG2xyclfI5oVr9GWPISrcmafkvp114pt366sT7lHyqV2Ut2mLNi9Yo2a9jqyExFFS8DEq/1H8kCE36Kyzr9SMmXN1z90366/P/UU33XxfuThUszJjF3PeDcr7/C3Jlb/X084X7pfbmiVr0Ehxtz0p//q1Kvol7HMH7K2gn92UHr/Yi29R3sf/DDp+APZBkJ83txfL8jgnJfbrqvxNW7R9wSo1Pr5juZjVT4/R6qfH6JA7BqrVtQO05rkPDlDSkLRX75VBUVZYS5QeJ//6X5Xzyr1SQZ58bTop5tw7lPv6g5KkvNFPBary6zVU7MX3ymWmy792eTXkjNrEOfeVAhNpJbeNKvG9k3Rb2XZ7ahsuJtlQG1RUKJpX4vsiBf5P7+9KjIXafQl12Y8xg/0WN0njnXOXhjpwyQUbr4/vqVPrtQvRovq0u6af2lzeR5KUNX+V6rVMLN4Xl5ygnIzsUvF5mdsU3ai+LMInV+RXveQE5XgLAheWuMQi/bv56jYiQtEJDdS0Zwe1PK2bklNT5IuJUlTDOB378i2afvsrld/BOuKWm6/WddddLkmaNWueDjq4ZfG+VgclK827FHSXpk0T1OmYjpoxM7Cm1wcffq7/fTm66hJGUP7sTEU1blb82Ne4aalPiCUp4pB2irs6sGaXNYhXRMfuUlGRChdOK45127eocMFU+Q7twCRbFXKbN8mXsHv8rEkz+bPLjN9hHRR3Q2BNKGvQSJFH91Suv0iF836q0lyB2i4vPUsxJc5ZYlomKD8jq0xMpmJalYhJTlR+RpaanXWcmvbvrsTULvLFRiuiQZyOHDlYP9/2Uqn26z/+UZ1GD2WS7QDLS89SXImxi22ZoLyMzeVjWiUqe1dMcvkYVD+3bbMsPqH4sTVMKL+URX5u8bf+VQuk066U4hpIOdt3x+7cpqLlc+Rr2YZJthrIX4U3PqiNWJMNtcFkSRebWYSZNVOgsmxGBfFLJbUxs8O8xxfvIW6bpJKLqayR1M37/oIyz3+5JJnZ0ZI6edunKXB5ajtvXz0z6xDsiZxzrznnujvnutfkCTZJWvnWeH3T7yF90+8hrRs7S4ddeJIkKbFrOxVsyyl1qeguG6Ys0UFn9pQkHXZRb6V9HVi8O7ZZo+KYhJQ2ks+Un7VdC596X190G6wve96pqTe/rA0/LmGC7QB7ZdS/1b3Haere4zR9/vk4XXl54L/0sT27auuWreUuFd28eYsaNYpX+/ZtJEmnpvbW0qUrqjxvlOb/bbl8zVrKElpIEZGK7NpbhQunl4rZ8dj12vHYddrx2HUqnDdFeR++osKF06ToGCnGuxw7OkaRR3SRP/3XauhF3VW0Zpl8zVvJmiZJEZGK6nGyCueXLnjePvSq4q+COT8od/RLTLAB+2Hb3JWKa5Os2EOay6Ii1XzgCdo0rvSN4DeNm6UWF54sSYrv1l6F23Yqf0O2Vg9/V1O73KxpPW7TkpteUPaURcUTbHGtk4rbN+3fXTtXhL0mNsrYMvcX1WuTpLhDmsmiIpQ08HhtGFf6RjAbxs1Wywt7S5IadWungm07uVS0BvKnr5Y1aS5r1FTyRSjyyJ4qWlnmpkz144u/9SW3lswCNzqIipaivTqHqGj5Wh8t/8a1AmobKtlQG3wiqZek+QpUlN3vnMswsyOCBTvncszsVklfm9km7XlC7j1Jr5vZHQpMqv1V0gdmdqWk70rEvSLpTe8y0Xm7juec22hm10gaY2YxXuwwSX+Yj1vSJ8xTcmqK/jT1eRXm5GvGXa8W7zvpnfs0857Xlbs+W/OfHKNeowbrmAcuVPaiX7VqzPeSpIPO7Kl2V58qV1ikotwCTb355WrqSd321dgJGjCgr5b9PEU7c3J0/fV3F+/74rO3dePN9yk9fb1uuuU+ffD+a/L7nbI3Z+v6G++RJLVo0UzTp45VfHwD+f1+3TH4Bh3T+RRt28adnyqd36/cj0ap3q2PSz6fCqaNlz/jN0WdcLqkwDpre2INGyvu+sCd1+TzqXD2JBX9PKcqssYufr9y331Z9e58SmY+5U8ZJ3/ar4o6+U+SpIJJ/6uwedwNQxXRoZOsQSM1eHa08j7/jwp+/LoqMsdeuO8vIzRz7gJlZ29V6sArdOt1V+r8s/pXd1p1livya8XQN9TpvYdlET6lj5moncvWquVV/SRJaW+PV9a3c5SY2kXHTn9JRTn5WjZkZMjjthl2ueq1aynnd8pdu1HL7+POogeaK/JrydA31f29h2QRPq0dM1Hbl63VwVedKkn6/e1vtfHbuWqamqLe0/+uopw8LRxSfCWYOo8arCbHd1R0QkOdMnekVjz3kda9O1HNT++hjk9do+jEeHUbfb+2LfpVsy55urq6WTc4v/LHj1bMRfdI5lPhwh/kNqUpMuUUSVLhvO8VeXgPRXbpI/mL5AoLlP95YCytXiPFnHd74Di+CBUumSb/6kXV1BFUhAu1K2bB1uUBajsza+Cc2+7dbXSkpBXOuReqOy9Jej/5cn7oaqnLM7+v7hQQhs03c3er2srl5Fd3CghD3NOjQgehxppy1APVnQL2U64iqjsFhKH33dwcrLaq98Cbf+jrKT9NuqzK/p4dmPFurXstqWTDH9UNZna1AjcjmKvA3UYBAAAAAMB+4lZNFWOSDX9IXtVajahcAwAAAAAAf3xMsgEAAAAAACAkv9W6KzirFHcXBQAAAAAAAMJEJRsAAAAAAABC4i5+FaOSDQAAAAAAAAgTlWwAAAAAAAAIibuLVoxKNgAAAAAAACBMVLIBAAAAAAAgJD83F60QlWwAAAAAAABAmKhkAwAAAAAAQEh+UcpWESrZAAAAAAAAgDAxyQYAAAAAAACEictFAQAAAAAAEJKr7gRqOCrZAAAAAAAAgDBRyQYAAAAAAICQ/Nz3oEJUsgEAAAAAAABhopINAAAAAAAAIfmrO4Eajko2AAAAAAAAIExUsgEAAAAAACAk7i5aMSrZAAAAAAAAgDBRyQYAAAAAAICQuLtoxahkAwAAAAAAAMJEJRsAAAAAAABC4u6iFaOSDQAAAAAAAAgTlWwAAAAAAAAIiUq2ilHJBgAAAAAAAISJSjYAAAAAAACE5Li7aIWoZAMAAAAAAADCxCQbAAAAAAAAECYuFwWqWAM/S0XWVmMST6nuFBCGuR/kV3cK2E9+F1fdKSAcnz9Q3RkgDCcsfqa6U8B+Kpz9VXWngDDMufr76k4B++mEP/ivPf6arRiVbAAAAAAAAECYqGQDAAAAAABASFSyVYxKNgAAAAAAACBMVLIBAAAAAAAgJFfdCdRwVLIBAAAAAAAAYaKSDQAAAAAAACH5rbozqNmoZAMAAAAAAADCRCUbAAAAAAAAQuLuohWjkg0AAAAAAAAIE5VsAAAAAAAACIlKtopRyQYAAAAAAACEiUo2AAAAAAAAhOSqO4Eajko2AAAAAAAAIExUsgEAAAAAACAkv1V3BjUblWwAAAAAAABAmJhkAwAAAAAAAMLE5aIAAAAAAAAIyV/dCdRwVLIBAAAAAAAAYaKSDQAAAAAAACG56k6ghqOSDQAAAAAAAAgTlWwAAAAAAAAIyU8tW4WoZAMAAAAAAADCRCUbAAAAAAAAQuLuohWjkg0AAAAAAAAIE5VsAAAAAAAACIkV2SpGJRsAAAAAAAAQJirZAAAAAAAAEBJrslWMSjYAAAAAAAAgTFSy1UJm5iQ975y7x3t8r6QGzrlHD8Cx35L0pXPuo3CPFeJ5LpT0uKQM51yfEttPkXSvc+7M/TxuWO0hdRx+tZqnpqgoJ1/z73hFWxeuKRcTd0gzdXn1DkU3rq8tC9do3m0j5QqKQrf3mU785inlZmRp1hXPSZI6PHChWgzoLuf3K3/TVs2/Y5Ty1m+ugp7+8aU8cZWSUzurMCdfM+98VdlBxrLewc103KjbFd24gbIXrtH0wf8oHktJatK5jVL/95im3vSS1v1vhiSp3fX91ebyPpKZVo+eqBWvf11VXaozmvRJUdsnBskifMoYPUG/v/xpuZi2Tw5SQmpXFeXkafmQkdq+cPXunT6fuo4bobyMLC2+coQkqX7HQ9X+2RsVUT9Wub9v0NJbX1TR9pwq6lHdkdAnRe2eDIxd+ugJ+u2lT8vFtBs+SIne2C29o/zYdftmhPIzsrTwihGl2h18y1lq++hVmnLktSrI2lbJPambKmP8DnvgYjUd0EPyO+Vv2qKld4xUPr/nqtWwp57X5CkzlNCksT59Z1R1p4Mypiz5Vc9+PFl+v9O5vTrq2n7dS+1/a8IcfTVrmSSpyO/X6ozNmvjU9WpUP1anP/qW6sdEy+czRfp8eve+i6ujC3VO4z4pavPEICnCp/WjJ2hdkPOW1k9eqyapXeTPydeKIS9rh/feGRFfT+2ev0X1Dj9Eck4r7/qHts1erkPuv0QJA3rI+f0q2LRVK4e8zHtnDeG36s6gZqOSrXbKk3SemTWt7kRKMrOIfQi/TtKtJSfYKpOZMaG8F5qlpqh+6yR9f9xdWnjv6zr62euCxh0x7DKtfvUrfd/rbhVk79DBl/XZq/atbzhd21esK7Vt1cgv9UOfB/Rj6lBtGD9H7e85r3I6V8ck9e2sBm2SNPb4ezT7vjfUdcSgoHGdhl2iFa+N1dcn3KP8LTvU+tJTdu/0mToNu0QZ3y8o3hR/+EFqc3kfTTjjEY1PHarkU7uoQesWldybOsbnU7unr9Oiy4ZrVu+71OzcE1Svw0GlQpqkdlFcm2TN7DVYK+59Ve2euaHU/lY3nKGdZX7WOjx/s1YPH63Zfe7RprEzdNCtZ1d6V+ocn0/tR1ynBZcN14yT7lLzIGOXkNpFca2TNf24wVp+76vq8GzpsTsoyNhJUkzLRDU5uZNyf99YqV2o0ypp/H4f+blm9blXs1LvU+b42TrsngsqvSuo2MAz+mnU809WdxoIosjv19Mffq+RN5+tjx+6XF/PXq5f0rNKxVyT2lUfPHCpPnjgUt1x5vHq1q6VGtWPLd7/+uBz9cEDlzLBVlV8PrV5+notvmy45va+S83OPVFxezhvmdNrsFbeO0ptn7mxeF+bJ69V9nfzNPekIZqXeq92rlgrSVr3j880r+89mn/qfdo8frYOvvvCKu0WsL+YZKudCiW9JumusjvM7C0zu6DE4+3ev6eY2SQz+8DMlpvZCDO73MxmmNlCM2tb4jCnmtkPXtyZXvsIM3vOzGaa2QIzu6nEcSea2buSFgbJ51Lv+IvM7Blv2yOSTpQ0ysyeC9K/eDP7xMyWmNkoM/N57U4zs6lmNsfMPjSzBt72AWa21Mx+lHReied+1MxeM7NvJL1tZoea2QQv/wlmdogXt6ftb5nZK17/VpnZyWb2LzP72av42/W6vOX1b6GZlRuT2qTFgG5a9+EPkqTs2SsVFV9PMc0bl4treuJRyvhiuiRp7QeTlXR695DtY5MT1LxfF/0+emKpYxWWqKSJqBcrOe5XcyC0HNBNv3pjkTVnpaLj6yk2yFg2P/Eorf0yUKG25oPJanX67k+L21/XX2v/N1N5m7YWb4tv31KZs1eqKCdfrsivjdN+VqvTe1RuZ+qYhl3aKWd1hnJ/2yBXUKiNn05RYv/Sn+I37d9D6z+YJEnaNmeFIuPrK9ob3+jkBCWc2lUZoyeUahPXtqW2TF0iScqetEBNzzyu8jtTx8R39cbu18DYbfh0ipoOKDN2A3po/YeBsds6u/TYxSQnKLFfV6WXGTtJavf4Nfrl8Xd4j6xElTV+RaV+z8UwhDVA95Rj1Ci+YXWngSAW/bpeBzdrrIOaNlJUZIT6d+2g7xeu2mP82DnLNaBb+yrMEGU17NJOuaszlFfivCWhf+lzw4T+PbThg+8lSdvnrFBkfD1FNW+siAZxij/uSK1/N/C+6QoKVbR1p6TS752+ejFy3NOyxvDLVdlXbcQkW+01UtLlZtZoH9p0ljRE0jGSrpTUwTnXU9I/JQ0uEXeYpJMl/UmBibBYBSrPtjjnekjqIekGM2vtxfeU9LBzrmPJJzOzlpKekdRXUoqkHmY20Dn3uKRZki53zt0XJM+eku7x8myr3VV7wySd6pzr6rW/28vtdUlnSTpJUlKZY3WTdI5z7jJJL0t62znXSdJoSS96MXvaLklNvPzvkvSFpBckHSXpGDNL8frVyjl3tHPuGElvBulPrRGbnKCcdZnFj3PTsxSbnFAqJiqhoQq27pArCix5mZuWWRxTUfuOT1ylnx9/V85ffqnMw4depL5zXlar80/Q8mc/POD9qovikhK0M233WOxMz1JccpNSMdEJDVSwZfdY5qRnKS4pEBOb1EStTu+uX97+tlSbLcvWqtlxRyi6SQNFxEUruW+K4lqW/j+C8MQkJyivxNjlpWcpOjmxVEx0uZhMRXs/a22fGKTVT7wj50r/rO1Y+nvxZF3Ts3oppmXpYyJ8MUllxiUtSzFJpV/nmOQE5a0rPXYx3ti1e2JQYCKtzPtkYv/uysvI0o4lv1Zi9qis8ZOk1kMv1XFzXlGL80/Smmffr6QeALXfhuwdSmrcoPhxi8YNtGHL9qCxOfkF+unnX3Vq53bF20ymW/7xmS599j19NGVRpeeLwDlJftqm4sf5Jd4Xd8cklju3iUlOVOyhLVSQuVXt/n6bOo9/Tu3+72b56sUUxx3y4KXqPnuUmp1/kn7jvRP7yMwSzGy8ma3w/m0SJOZgr6jmZzNbbGZDSux71MzWmdk87+uMvXleJtlqKefcVklvS7pjH5rNdM6lO+fyJP0i6Rtv+0IFJtZ2+cA553fOrZC0StIRkk6TdJWZzZM0XVKipF0fG81wzpVYkKRYD0nfO+c2OucKFZjA6r0Xec5wzq1yzhVJGqNA1dtxkjpKmuLlcLWkQ73cVjvnVjjnnKR3yhzrc+fcro9Bekl61/v+P95xK9ouSV94x10oab1zbqEL/OW6WIHXbJWkNmb2kpkNkLRVtZip/AX2rsxH7hbkGvxdMXtq37xfF+Vv2qqtC4L9N5GWPf2Bvut6u9b9d4oOvbb/fmSOsioap4qDAv+kPH6lFjz5nuQv3WbbijQtHfmFer//oE569wFlL/mteJIOB0iwdS72cuwS+nVVwaYt2r6g/Kf+y+/6h1oOGqAu455RRINYufzCA5Mvdgs6LGU/hQ32Pikl9uuq/CBj54uL1qF3nqfVz/DHRaWrhPHbZfXTYzSt6y1a/98f1OraAQcgWeCPKVi1kgX7nSdp8qLVSmmdXOpS0bfuOl/v3X+JRt5ytj74YYFmryx/+T0OsCDjU/6cs3wz55wsMkINjmmjjLe+0fx+96loZ54Ouv3c4pjfRozRrG43a+N/f1Ay753Ydw9KmuCcay9pgve4rEJJ9zjnjlRgzuE2MytZPPSCcy7F+/pqb56Udapqt79JmqPS1VOF8iZPLfAbKbrEvrwS3/tLPPar9P+Fsr/dnAJvjYOdc+NK7vBuNLBjD/nt75KIe3r+8c65S8s8f0qQ+JL2lFuw5wm2veRrVPb1i3TObTazzpL6S7pN0kWSri17QDO7UdKNknR7w+4aENeubEi1OXRQPx18RV9J0pZ5qxTXKlG7lhSNTU5QXkbpBUbzM7cpKr6+LMInV+RXbMvE4pic9Myg7ZPPOlbN+3dVn9QU+WKjFNUgTikjb9O820aWOnbax1PUY/T9WvFcpd534w+r7TX9AjckkJQ1f5XqtUzUrs8M6yUnKDcju1R8fuY2RTXaPZZxyQnK8RaUTejcWseNul2SFJPQUEmpneWKipT29WytGTNJa8YELpc6euhFykkrvVYKwpOXllWqyiwmOUH5GaVf4/y0zDIxicrPyFLTM49T4mndlZDaRb6YaEU0iNPhLw/WsttfUs7KNC28JLAGUVybZCWc2q1qOlSH5KWXGbuW5ccuLz1TMa3Kj12zs45T0/7dlZjaRb7YwNgdOXKwfnv5M8Ue0lw9vnvOO2aiuo1/VnMGDFX+xuwq6VddURnj9/NtL5Vqv/7jH9Vp9FCtee6Dyu0MUEu1aNxAGdm7K9fWZ29Xs/j6QWO/nrNCA7p1KLWteaNAFVxCw3rq06mtFv26Xt3ataq8hKH8tExFt9y9VHh0cqLyy/794J237LplT/G5jQu8r26fu0KSlPnlNLUaPLDcc2z65Acd+c5D+p33zhqhFl3EeY6kU7zv/y3pe0kPlAxwzqVLSve+32ZmP0tqJWnJ/j4plWy1mHMuS9IHClzKucsaBS6RlAL/qaL249AXmpnPW6etjaRlksZJusXMoiTJzDqYWfDfeLtNl3SymTX1bopwqaRJe/H8Pc3+v737jpOrLPswft2bHkiAhBZ6B6lpIAhSjFQFAQWkNysooL4oTbGLoKiAigKiKKCiIkU6UpSeRui9CAkkBEIC6dn7/WPOJptksyWb7NnJXF8+89k5bfY3OeyU59zP88T6xVhshwD/BR4EdoyIjYrf3zsiNgGeBtZvNKbcoU0+YsX9wKeL+4cXj9vc+hYV3VjrMvPvwDeBwU3tl5m/zcyhmTm0MzWwAbxy+e38d9jp/HfY6bx583DWPOjDAKw4ZCNmT5nKjPGTFjpm4n1PsPq+HwRgrYN35s1bRgAw/taRTR7/zA/+zL8HfYm7tj2JUZ+/gLfue2JuA1vv9ef18F1tzyG899zYpfl0l2kv/P52bt/9DG7f/Qxev3k46xbnot/gjZg1ZRrTmziX4+97krU+vh0A6x28M2OLc3nTB7/CTdudwk3bncJrNz7MyNN+P3dbj/59Aei1Zn/W3GdbXv3n/R3w7GrHlNHP02uDAfRcZ1WiW1dW2X9HJt42fL59Jt42nNUO3gWAPoM3ZvaUqcwcP4mXf3gVDw3+Ag9veyJPfeFnTLrvcZ75UuVLfreVK+eNCNb5yicZd8VtaMmaMmr+c7fq/jvy1q3zn7u3bh3OagdVzl3fIfPO3Us/uIoHBn2BB7c9kSc/Xzl3T514Ie8/9Sr3b/EZHtz2RB7c9kRmjJ3IiN2/bgPbUrA0zh9Ar0bvcyvvOZSpvs9Ji7TFOqvx6oRJvD7xXWbNnsOtI59ll63WX2i/KdNmMOL519ltqw3mrps2YxbvT5859/4DT7/KRgMcGmFpa/jc0qPR55a3b3tkvn3evm04qx68KwDLF59bZo2fxKwJk5jx+kR6bbgGACt8eCumPVuZ+KBno9fOfntuyzSrEtV2qxWNaA2Naas2t3NErAcMotKO0eBLxdjtv2uqu2lTrGSrfj8FvtRo+RLguoh4mEpJZHOVXIvyDJXGsNWAL2Tm9Ii4lEr3yJFFhdwEYP/mHiQzx0XE6cBdVCrRbsrM61rx+x8AzqEyJtu9wLWZWR8RxwBXR0RDR/2zMvPZokrsXxHxFpUGsi0X8bgnAb+LiFOL/Me2sL411gQub5icATi9Dcd2OuPvGMUqwway60M/Z860GYw5+Tdzt2175dcZ89VLmPHmOzz1/asZ/Jsvs+lpBzP5sZf531V3tXj8omx21qdZfqM1yPpk2msTeOzUy5ba86slb9w5mgHDBrL3A+czZ9pMHvnKvHOx059OZfjXLmH6m5N47PtXs/3FX2bLbxzEO4+/wktX393iY+9w2cn0WKkP9bNmM+r03zPr3alL8ZnUoDn1PH/GZWx59ZlElzreuPoupj7zGgOO2h2AcVfcztt3jKTfsEFs++CF1E+byTOn/LKFB4VV9t+JNY6tdMd+66aHefPqu1o4Qm2Vc+p57vTL2PrPlXM3rjh3axTnbmxx7voPG8QHH7qQOdNm8szJLZ87dYyldf42OOtwehfvc9Nfm8Czp16ytJ+KWnDq2efwyKgxTJo0mWH7H8EJxx/JJ/d1uIrOoGuXOk771C588VfXU19fzye235yNBvTnmv9W5lc7aKetAPj3mBfZYbN16NVjXj3BxClT+eql/wJgdn2y95BN2HHzdTv+SdSaOfW8eMalbHH1WdCljvFX/5tpz7zG6kftAcAbV9zGO3eMZKVhgxn84EXUT5vB86f8au7hL515GZv86mSiW1emv/ImzxWfadY98wh6bbQG1CczXpvAC1//bSlPTwvryIFiGvcIK/w2M3/baPsdLDwuO8CZbfw9ywN/B04phuYC+DXwPSrFe9+j0vayUK+1hR5rof7Skpaqf612qH90VWpqncW/1Wy1nFl2BC2mel81pdLs+MSPy46gxTR7RKuGD1InNfLou8uOoMW04xt/W9xhk6rC6esd1mGfzH708lWL/W8ZEc8AuxbFPwOojBe/aRP7dQNuBG7NzPMX8VjrATdm5qIKeubyG6MkSZIkSZJaVE922K2drqcyYSLFz4V61RW99C4Dnlqwga1omGtwANCqKYttZJMkSZIkSdKy5Bxg94h4Dti9WCYi1oiIhlLfHYEjgY9ExOjitk+x7dyIeCwixgC7AV9pzS91TDZJkiRJkiS1qFpG8cjMicCwJtaPBfYp7v+XyvjxTR1/5OL8XivZJEmSJEmSpHaykk2SJEmSJEkt6sjZRauRlWySJEmSJElSO1nJJkmSJEmSpBYtgVk/l2lWskmSJEmSJEntZCWbJEmSJEmSWmQdW/OsZJMkSZIkSZLayUo2SZIkSZIktcjZRZtnJZskSZIkSZLUTjaySZIkSZIkSe1kd1FJkiRJkiS1KJ36oFlWskmSJEmSJEntZCWbJEmSJEmSWuTEB82zkk2SJEmSJElqJyvZJEmSJEmS1KJ6x2RrlpVskiRJkiRJUjtZySZJkiRJkqQWWcfWPCvZJEmSJEmSpHaykk2SJEmSJEktcky25lnJJkmSJEmSJLWTlWySJEmSJElqUX3ZATo5K9kkSZIkSZKkdrKSTZIkSZIkSS1Kx2RrlpVskiRJkiRJUjtZySZJkiRJkqQWOSZb86xkkyRJkiRJktrJRjZJkiRJkiSpnewuKnWw9+ps265WfernlB1B7TCbKDuCFlN67qraLM9fVZs94qayI2gxdR2yT9kR1A4z8t6yI0hNcuKD5vltX5IkSZIkSWonK9kkSZIkSZLUIic+aJ6VbJIkSZIkSVI7WckmSZIkSZKkFtWnY7I1x0o2SZIkSZIkqZ2sZJMkSZIkSVKLrGNrnpVskiRJkiRJUjtZySZJkiRJkqQW1VvL1iwr2SRJkiRJkqR2spJNkiRJkiRJLUor2ZplJZskSZIkSZLUTlaySZIkSZIkqUX1ZQfo5KxkkyRJkiRJktrJSjZJkiRJkiS1yNlFm2clmyRJkiRJktRONrJJkiRJkiRJ7WR3UUmSJEmSJLUo7S7aLCvZJEmSJEmSpHaykk2SJEmSJEktqi87QCdnJZskSZIkSZLUTlaySZIkSZIkqUWZjsnWHCvZJEmSJEmSpHaykk2SJEmSJEktqnd20WZZySZJkiRJkiS1k5VskiRJkiRJapGzizbPSjZJkiRJkiSpnaxkkyRJkiRJUovSMdmaZSWbJEmSJEmS1E5WsmmuiHgvM5dvtHwMMDQzv1ReqrlZ9gM2z8xz2vk4A4BLMvPji3HsZsCfgQQ+BXwwM68qtm0FfC0zj2lPvs5o0PeOYsCwbZgzbSYPn/Ib3nns5YX2WW7tVdjh4i/RfcXleeexl3noy7+iftYcVtnhA+z0+6/y/qsTAHjtpkd48mfX0mfDAexw8ZfnHr/8uqvy+Hl/49lLbumop7XMW3m3bdj8+0cTXer435X/5sULr19on81/cDSrDBvEnGkzGHPSr5lcnNtFHbvxNw5mtb2GQH0y463JjDnp18x4852OfFo1qd9u27Dx948lutQx7so7eeXC6xbaZ+MfHEv/YYOonzaDJ0/6Fe899tK8jXXBtredw4w33mbMET/uwOSCyvnb5PvHEF3qGHvlv5s8f5v84Bj6F3+LT530a6Y89hJ1Pbox+LpvU9e9G9GljvE3PsRL511TwjOoLSvvtg0f+P7R0KWO1678Ny818dr5gR8czcrF39tjjV47t/z551ll98HMfGsy9+1y6tz9V9v3g2z0f59i+U3W5IG9zmLyoy921NOpafc9+Qrn/uNe6uuTA3bYnON2Hzrf9t/fOZKbhj8DwJz6el564x3u+uFnWGG5nuz97d+zXI/u1NUFXevquOrUQ8p4ClqEs354Pvfe9zD9VlqRf/7p4rLjiMX/rFJ5r/sO0b0r0aULE258cL73urWO34s1j9uLnD2HiXeM5IXvXdmRT0uL4OyizbORTVUhM68HFv6k23ZfBS5ZzGP3B67LzLMjYlfgMOCqIt9jEbFWRKyTma8ugZydwoCPbEOfDVbnpg99jf6DN2LIOcdyx8fOXmi/rc/6NM/89mb+d92DDPnxcax/6K68cMWdALz10DP856ifzLf/lBfGcdvuZwAQdcG+oy7itZuHL/0nVCvqgi3OOY6HD/4B08dOZMdbf8j4W0fw3rOvz91llWED6b3+AO7Z/hRWHLIRW577Ge7f+6xmj33plzfw3I//CsC6n9mLjb92II9//bKynmVtqAs2Ped4Rh38fWaMncjQW3/EhFuHM7XRuew/bBC911+dB7c/ib5DNmbTcz/DiL3PnLt97c/uw/vPvU7XPr3KeAa1rS7Y9JzjGHXwD5gxdiLb3voj3rp1OO/Pd/4G0mv91Xlg+5OL83c8w/c+i/oZsxh14HeZM3UG0bULQ274DhP/PZrJI54r8Qkt4+qCzc85jkeK178dite/xudr5eK18z/bn8IKQzZi83M/w4N7nwXA63++h1cvu5WtLjpxvod97+n/Mfq489nivM926NOpZXPq6/nRNXdz8Yn7s9qKy3P4T/7CLltuwIYD+s3d55hhgzlm2GAA7nnsJf5092hWWK7n3O2XfPkAVlre183OaP99duewT+7HGd/7Scs7a+lrx2eVynvdd+a+1w2+4btz3+tW3HELVt5rKA/v9n/kzNl0W7lviU9Saj27i6pVImLdiLgzIsYUP9cp1v8+Ij7VaL/3ip8DIuLeiBgdEY9HxIeL9XtExAMRMTIiromI5Zv4XSdFxJPF7/pzse6YiLiouD+60W1aROwSEctFxO8i4pGIGBURn1jEU/kkcEvxOFtExMPF44yJiI2L9WdGxDMRcUdEXB0R/xcR+wCnAJ+JiLuAc4APF8d+pXjsG4BPt/OfulNZc68hvHzNfwCYOPJ5uvXtTc9VV1xov9V22oLXbnwYgJf/ei9r7j10oX0WZdUPb8n7L49n6mtvLZHMghUHb8TUl95g2ivjyVlzGPfP+1ltr/nPyWp7DeX1a+4FYNKI5+natzc9Vl2x2WNnvzdt7vFde/cgvYi11PUtzsf04nyM/+f9rLLXtvPts/JeQ3mjOJeTRzxH177L0b34O+0xoB/9dx/MuCvv7OjoonL+pr305tzz9+Y/72flBc7fKnttu8jzN2fqDACiWxeia1f8o1u6Fnz9e2MRr51ji/P17ojK+2KP4ny98+DTzJr0/kKP+/5zY3n/hXFLPb/mefyVN1l7lRVZa+UV6Na1C3sO3oS7H1t0BeHNI59lryEbd2BCtcfQgVuxQt8+ZcdQob2fVRq/19V17TL3vW7No/fglQuvI2fOBmDWW5M76BmpJZnZYbdqZCObGuvVuAEL+G6jbRcBV2Tm1sCVwAUtPNZhwK2ZORDYBhgdESsDZwEfzczBwHAqlWULOg0YVPyuLyy4MTMHFo/7zeIx7gfOBP6dmdsCuwHnRcRyjY+LiPWBdzJzRrHqC8AviscaCrwWEUOoNJQNAg4Eti1+503AxcDPMnO3IuN/iiw/Kx5vOPDhFv5dqkqv1fsxdezEucvTxr1NrwErzbdP937LM/Pd98k5lcmcp457m96rz9un/5CN2POOH7LzlV+n7yZrLvQ71vnE9rzyz/uX0jOoTT1X78f0xudt7Nv0WL3f/PsM6Mf01+ftM33c2/Qc0K/FYzc5/RB2G/lL1vjkTjx37l+X4rMQQI/V+zGj0fmYMXbiQueyx4B+TH99XiP1jHET6VFUa2z8vWN44bt/Iuur80NKtVvw76ly/uZ/De0xYKX5/hYbnz/qgu3u/DEffuIS3r5nDJNHPt8huWtVj9X7Ma3R+ZrexGtnjwH9mLbAa2ePAfPvo/KNn/Q+q6847zruaisuz/h332ty32kzZ3H/U6/w0W02mrsuCL74q+s49Nw/87f7Hl/qeaVq1t7PKtQF2955Ljs9cSlv3/PY3Pe63hsOYMUPbsaQm3/AoGu/TZ+BGy79JyMtATayqbFpDQ1YRcPTtxpt24GiayTwR2CnFh7rEeDYiPg2sFVmTgG2BzYH7isa8Y4G1m3i2DHAlRFxBDC7qQcvqs7OAw7JzFnAHsBpxePeDfQE1lngsAHAhEbLDwBnRMQ3gHUzcxqVRrJrM3NqZk6mbV1UxwNrtGH/zi+aWLfAFYWIhXdq2OWdx17mxm1P5taPnsFzl93KTpfP36Za160La+45hP/d8NCSSixo+ry1YuyEzGzx2Gd/9BfuGnwiY//+X9Y9bs/FTajWaurva6Fz2cRJy6T/7oOZ+da7TBnz0sLb1TGaOH9N7LTwqoYX0frk4WHf4L6BX2SFwRux3GZrL9F4WsBivnZaYdj5NDXzXVOfVwDuffwlBq4/YL6uor//yif589c/zS+/uB9//c8YRjz/epPHSqJdn1UAqE8eGfZ17h/4BfoO3nDue110raPrisszYu8zef67f2TLS76y8GNInZCNbFpcDa+csyn+P4rKp5fuAJl5L7Az8Drwx4g4isqr6+2NGvI2z8zjm3jsjwG/BIYAIyJivrEDiwq1vwKfzcyxDauBTzZ67HUy86kFHncalcY3ioxXAfsV62+NiI8s8NzaqmfxWAuJiM9FxPCIGH7H1M5dibDRMbuzx+0/ZI/bf8i0NyfRe43+c7f1GtCPaW9Mmm//GROn0H2F5YgulZeT3gP6Ma0YDH/2e9OYXZSAj/v3o9R160L3fvOuLK/+kYG889jLzLD8e4maPu5tejY+b2v0Y8Yb7yy8z5rz9uk5oLJPa44FeP0f97H6xz+4FNKrsRnjJtKj0fnosUZ/Zi5wPmaMm0jPNVeet8+A/sx44x1W2G5TVt5zKDs8chFb/OYUVtpxSzb/5ZdRx5k+buJ8f0891ui/0N/TjAX+FhvOX2OzJ0/lnfuepP9u2yzdwDVuxri36dXofPVs4vVvxri36dXEa6c6l9VWXJ43Js2rXHtz0nus0ne5Jve9ZeRz7DVkk/nWrbpC5bNKvz692W3rDXn8lTeXXlipyrXns0pjDe91/XYbWDlm7NtM+FflQvyUUS9AfT3d+ttNuDOo78BbNbKRTa11P/PGGzsc+G9x/2UqjWEAnwC6QWUMN2B8Zl4CXAYMBh4EdoyIjYp9ekfEfJ9qIqIOWDsz7wK+DqwILDhu2+XA5Zn5n0brbgW+XDT0ERGDmngOzwLrNfpdGwAvZuYFVCrWtgbuBQ6IiF4R0QfYdxH/HlOABV/lNwGa7FOQmb/NzKGZOfSjvTdqapdO4/nf385tu5/Bbbufwes3D2e9gyo9YPsP3ohZU6YxffykhY4Zf9+TrPXx7QBY7+CdGXvLCAB6rrLC3H36DdwA6oKZb8/70Lvu/jvw6rV2FV3S3h31AsttsDq91lmF6NaFAft/iDdvHTHfPm/eOoI1D9oZgBWHbMTsKVOZMX5Ss8f2Xn/1ucevtucQ3ntuLFq6pox6gd4bDKBncT5W3f9DvHXr/JOEvHXrcFYvzmXfIRszZ8pUZo6fxIs/uJr7B32RB7b9Ek98/ue8c9/jPHnihWU8jZpVOX+rzz1/qzVx/iYscP5mF+evW/8+dO3bG4C6nt3ot/OWvP+8f3NL07vF+Wp4/Vt9/w8xfoHXzvG3jmCN4nytMGQjZhWvnepctlhnNV6dMInXJ77LrNlzuHXks+yy1foL7Tdl2gxGPP86u221wdx102bM4v3pM+fef+DpV9loQP+FjpVU0Z7PKgu/123F1KJydMLNj7DSTlsC0GuDAUS3rsyaOKUDn5m0eJxdVK11EvC7iDiVSpfLY4v1lwDXRcTDwJ1Aw4i/uwKnRsQs4D3gqMycEBHHAFdHRI9iv7OoNH416AL8KSJWoFKd9rPMnNRQ4l803n0K2CQijiuO+QzwPeDnwJiioe1l4OONn0Bmvh8RL0TERpn5PHAIcESR8Q3gu5n5dkT8BRgNvAI0bshrbAwwOyIeBX5fjMu2G/Cv5v8Zq8u4O0czYNhAPvbA+cyeNpOHv/Kbuds+/KdTeeRrlzD9zUk8+v2r2eHiL7PVNw5i0uOv8OLVdwOw1se3Y6OjP0rOnsOc6bN44AsXzT2+S6/urLbzlgx3dsolLufU88Tpl7Pdn8+ALnW8dvVdvPfMa6xz1EcBePWKO5hwxyhWHTaQXR76BfXTZjDm5IubPRZgs7MOZbmN1iDr65n22ls8fuqlpT3HWpFz6nn29N8x8M9nEl3qGHv1Xbz/zGuscdTuAIy94nYm3jGK/sMGs8NDFzBn2kyeOvlXJadWg5xTzzOn/45Bxd/TuKvv5v1nXmPN4m/x9SvuYOIdo1h52CB2eOgX1E+byZMn/xqAHqutxOYXnABd6oi6OsZf9wATbx9Z5tNZ5uWcep48/XKG/vkMotHr39rF+fpf8dq58rCB7PzQL5gzbQaPFa+dANtc/GVW+tDmdO/Xh11H/ZLnzvsbr191F6vuvS2b//AYuvfvy5Arv86Ux19h+Kd/VNbTrAldu9Rx2qd24Yu/up76+no+sf3mbDSgP9f89zEADtppKwD+PeZFdthsHXr16Db32IlTpvLVSysf52bXJ3sP2YQdN29qdBOV5dSzz+GRUWOYNGkyw/Y/ghOOP5JP7usQFmVpz2eV7qutxOYXnFjpEVMX873Xjbv633zg5yew3T0/IWfO5qmTflnac9T8muqSr3miWmdskBZHRBwADMnMs1q5/7eB9zKz2TnCi0bDe4CdMrPJceQa/GXA4f7RVak+9XPKjqB26Fm1RefKpgfLUpWY5fmrartc0dIwvOqsug7Zp+wIaof/bHFa2RG0mD7y5l+X6Te+Pdbeq8O+z972v1uq7t/SSjbVlMy8NiKWRs3/OsBpLTWwSZIkSZJUreqtZGuWjWyqOZnZ6j5umfntVu73HPDc4maSJEmSJEnVzUY2SZIkSZIktahahhyLiH7AX6hMfvgycHBmLjQleES8TGViwznA7Mwc2pbjF+TsopIkSZIkSVqWnAbcmZkbU5mksbmBDnfLzIENDWyLcfxcNrJJkiRJkiSpRfVkh93a6RPAH4r7fwD274jjbWSTJEmSJElSpxIRn4uI4Y1un2vD4atl5jiA4ueqi9gvgdsiYsQCj9/a4+fjmGySJEmSJElqUXbg7KKZ+Vvgt4vaHhF3AKs3senMNvyaHTNzbESsCtweEU9n5r1tjDqXjWySJEmSJEmqKpn50UVti4g3I2JAZo6LiAHA+EU8xtji5/iIuBbYDrgXaNXxC7K7qCRJkiRJklpUn9lht3a6Hji6uH80cN2CO0TEchHRp+E+sAfweGuPb4qNbJIkSZIkSVqWnAPsHhHPAbsXy0TEGhFxU7HPasB/I+JR4GHgX5l5S3PHt8TuopIkSZIkSWpRx43I1j6ZOREY1sT6scA+xf0XgW3acnxLrGSTJEmSJEmS2slKNkmSJEmSJLWovmpq2cphJZskSZIkSZLUTjaySZIkSZIkSe1kd1FJkiRJkiS1yO6izbOSTZIkSZIkSWonK9kkSZIkSZLUokwr2ZpjJZskSZIkSZLUTlaySZIkSZIkqUWOydY8K9kkSZIkSZKkdrKSTZIkSZIkSS1KK9maZSWbJEmSJEmS1E5WskmSJEmSJKlFzi7aPCvZJEmSJEmSpHaykk2SJEmSJEktcnbR5lnJJkmSJEmSJLWTlWySJEmSJElqkWOyNc9KNkmSJEmSJKmdrGSTOtiYHrb8V6tL3xlddgS1w0vn7F52BC2uadPLTqD2mDW77ARqh5FH3112BC2mGXlv2RHUDh9+4pyyI0hNcky25lnJJkmSJEmSJLWTjWySJEmSJElSO9ldVJIkSZIkSS1Ku4s2y0o2SZIkSZIkqZ2sZJMkSZIkSVKL6tNKtuZYySZJkiRJkiS1k5VskiRJkiRJapFjsjXPSjZJkiRJkiSpnaxkkyRJkiRJUosck615VrJJkiRJkiRJ7WQlmyRJkiRJklrkmGzNs5JNkiRJkiRJaicr2SRJkiRJktQix2RrnpVskiRJkiRJUjtZySZJkiRJkqQWOSZb86xkkyRJkiRJktrJSjZJkiRJkiS1yDHZmmclmyRJkiRJktRONrJJkiRJkiRJ7WR3UUmSJEmSJLXIiQ+aZyWbJEmSJEmS1E5WskmSJEmSJKlFmfVlR+jUrGSTJEmSJEmS2slKNkmSJEmSJLWo3jHZmmUlmyRJkiRJktROVrJJkiRJkiSpRZlWsjXHSjZJkiRJkiSpnaxkkyRJkiRJUosck615VrJJkiRJkiRJ7WQlW4ki4r3MXL7R8jHA0Mz8UnmpFi0ihgJHZeZJS+nxNwP+DCTwqcx8oR2PtQZwQWZ+KiIGAmtk5k1LJmnt+tjZR7HpbgOZNW0mf/+/ixn7xMsL7XPQz09kza3Wp372HF579AX+ecZl1M+eM3f7mltvwBeu/S5//tIFPHHzwx2YXo394MdnMmyPnZk2dTonnXA6jz365EL7XHfzn1h++eUAWHmV/owaMYZjDu+UL0/LtPtefovz7nma+vpk/y3X4rht119on+H/e5vz7nmG2fX1rNirO5cdtC0A377tce59aQL9enfnb0fu2NHRBdSttwXdd/001NUx+7H/MPuRW+bfvtYm9PjEieS7EwGY/fxIZj94I7HSavT42Ofn7hcrrMys+69j9qg7OzR/Latbf0u6Dzuscu4evZfZD83/MaJu7U3p8cmTyElvATD72RHMvv96AHp+4TyYOR3q68n6Ocy44rsdnr8WrbjbQDb43rHQpY43r7yT1y/650L7rP/941hp2CDqp83kuZMv4v3HXgKgS9/ebHT+F+m96TqQyfNf+RVTRjzLOl//NP322pasr2fWW5N5/uSLmPnmOx38zJZ9/Xbbho2/fyzRpY5xV97JKxdet9A+G//gWPoPG0T9tBk8edKveO+xl6jr0Y3B132H6N6V6NKFCTc+yEvnXTP3mLWO34s1j9uLnD2HiXeM5IXvXdmRT0tNOOuH53PvfQ/Tb6UV+eefLi47jhaDY7I1z0Y2tUpEdM3M4cDwpfhr9geuy8yz2/tAmTkW+FSxOBAYCixWI1tEdMnMOS3vuWzbZNeBrLz+6py/61dZe9BG7PeD47h4/28ttN+j/7yPa075JQAHX/Alhn56Nx7+0x0ARF2w52mH8ty9Yzo0u+Y3bPedWX/Dddl+0J4MGboN555/NnsPO2Sh/T6x9xFz71/2xwu45V9+ue9oc+qTc+56il8fOITVlu/J4Vc/yC4brMKG/eden2HK9Fn88K6n+OX+gxnQtxdvT50xd9u+m6/BIQPX4Zu3PlZGfEXQ/SOHMePvPyOnvEPPw89kzguPkm+Pm2+3+tefZ8Y/L5xvXb7zJtP/9N25j9Prc+cx5/lRHZVcEXTf/Uhm/OUn5JS36Xn0t5jz/Ghy4tj5dqv/37PM+PsvmnyI6Vf/GKa91xFpBVBXxwY/+gxPHPxdZo57m21uOYe3bxvOtGdfm7vLSsMG0WuDAYzc4cssP3hjNvzx5xizz+kAbPD945j079E885mfEt26UterOwCv/+o6Xj33zwAMOH4f1v7qQbzwjd92/PNbltUFm55zPKMO/j4zxk5k6K0/YsKtw5n67Otzd+k/bBC911+dB7c/ib5DNmbTcz/DiL3PpH7GLEYd+B3mTJ1BdO3C4Bu+y8R/j2byiOdYccctWHmvoTy82/+RM2fTbeW+JT5JNdh/n9057JP7ccb3flJ2FGmpsLtoJxUR60bEnRExpvi5TrH+9xHxqUb7vVf8HBAR90bE6Ih4PCI+XKzfIyIeiIiREXFNRCzfxO+6OyJ+HhH3F8duV6z/dkT8NiJuA66IiF0j4sZi2/IRcXlEPFZk/GQbft/AiHiwOO7aiFgpIvYBTgE+ExF3NXHMe43ufyoift/o3+OCIvuLDf82EbFe8Vy6A98FDin+bQ5Z4HHXi4j/FHlHRsSHivW7RsRdEXEV8FhEdImI8yLikSL35xv9O9xZHPtYRHyidWe4+nxgjyGM+sd/APjfqOfp2ac3fVZZcaH9nr179Nz7rz36Aius3m/u8g7H7MkTNz/M+xPfXdpx1Yy9PjaMa66uXCEeMfxR+q7Ql1VXW2WR+y+3/HLstPMHuflfd3RURBUef+Nd1l6hN2ut0JtuXerYc5PVufuF8fPtc/Mz4xi20aoM6NsLgH69e8zdNmStfqzQo1uHZtY8dauvT06aQL77FtTPYfbTj9Blw4Ftf5x1PkD9pAnklLeXfEg1qW7ABuSk8eS7Eyrn7qmH6bLxoLJjqRl9Bm3E9JfeYMar48lZs5nwz/vot+e28+3Tb89tGf/XuwF4b+RzdO3bm26rrkiX5XvRd/sP8OZVlYtJOWs2cyZPBWDOe9PmHl/XuwfpWERLXN/BGzH1pTeY/sp4ctYcxv/zflbZa/5zt/JeQ3njmnsBmDziObr2XY7uq64IwJzi4lJ060Jd1y5QVNmsefQevHLhdeTM2QDMemtyBz0jNWfowK1YoW+fsmOoHeozO+xWjWxkK1evouFndESMptIY1OAi4IrM3Bq4Erighcc6DLg1MwcC2wCjI2Jl4Czgo5k5mEoV2lcXcfxymfkh4ATgd43WDwE+kZmHLbD/N4F3M3OrIuO/2/D7rgC+URz3GHB20ZXzYuBnmblbC891QQOAnYCPA+c03pCZM4FvAX/JzIGZ+ZcFjh0P7F7kPYT5/523A87MzM2B44vnuy2wLfDZiFgfmA4cUBy/G/DTiIg25q8KfVdbiXfHzvuCN/mNt+m7+kqL3L+uaxcGHbATz97z6NzjN99zWx6+0oaasg0YsBqvvz6vkmbc2DcYsMZqi9x/n49/lP/c8yDvTXm/I+KpkfHvT2e1Pj3nLq/WpycT3p8x3z6vvDOVydNn85lrHuGwqx7ghifHLvgwKkksv+J8DWP53jtEnxUX2q9uwAb0PPJb9DjgJKL/Ggtt77rptsx5xu71HSn6rERObnTuprxNLL/we17dmhvR89jv0OOgrxArNzp3mfQ8+P/oefTZdNlml46IXPO6D+jHzLFvzV2eOW4iPQb0W2Cf/swYO3Hu8oxxb9NjQH96rrsasyZOZqNfnMg2t5/HRj/9AnWNLlisc9qhDB1xMat88sO8eu6CHyXVXj1W7zf/eRk7kR6rz3/uegzox/TX553fGY3Pb12w7Z3nstMTl/L2PY8xeeTzAPTecAArfnAzhtz8AwZd+236DNxw6T8ZSTXPRrZyTSsafgYWjWON+97tAFxV3P8jlUak5jwCHBsR3wa2yswpwPbA5sB9RSPe0cC6izj+aoDMvBfoGxErFuuvz8xpTez/UeCXDQuZ+U5rfl9ErACsmJn3FKv+AOzcwnNryT8zsz4znwQW3VLQtG7AJRHxGHANlfwNHs7Ml4r7ewBHFc/rIaA/sDEQwA8jYgxwB7BmUxki4nMRMTwiho+a8nwbI3YOTbUdNndxYb/vHctLDz/NK488A8A+3zqKW8+5mqyvzisSy5QmmoGbG1vhgE99jGv/9q+lGEiL1Io/lzmZPDV+MhfuP4hfHjCESx5+kVfesUG0c2jqj23+xfrxrzLt0tOY/sfvMmv0v+mx3wnz71DXhS4bbsPsZ5fmaA1qnflPXv2brzDt1//H9MvPZtaIO+lxwLwha2dc+UOm/+HbTL/mfLoN/gh1a23S0WFrT5OfUxb4g1vE+1907cLyW23AG7+/jUd3P5U5U2ew1pcOmLvPq+dczfAhX2DC3//DgOP2WtLJ1dS5W+gNsMmTV/lZnzwy7OvcP/AL9B28IctttnbliK51dF1xeUbsfSbPf/ePbHnJV5ZwcKk2ZQf+V40ck616NPwfNpuicbSomOoOlcaxiNgZ+Bjwx4g4D3gHuD0zD23D4y+4vKhvatHEMdGG39dWjX9XzwW2NS7raGsV2VeAN6lU/9VRqUxr0Pi5B/DlzLy18cHFZBWrAEMyc1ZEvNxEPjLzt8BvAc5c77CqebX44JG7s+2hlcLC1x59kRXWmHdVse/q/ZiyiIF/P3LygSzXvy/Xff5nc9etufX6HHLhlwHovVIfNtl1IPVz6nnqNr84doRjP3MYRxx9EACjRz3GmmsOmLttwBqr88a48U0et9JKKzJoyNYc64QHpVh1+Z68OWXey9KbU6azynI9FtpnxZ7d6NWtK726weA1V+LZCVNYd6XlOjquFlCpXJv3uhnLr0S+N2n+nWbOO7/1Lz0OH+kCPZeH6ZVRErqsvyX1b74KU6d0RGQVcso7RN9G565Pv+bP3YtjYI8jodfyMO29eftOncKcZ0dSt8YG1L/27NIPXsNmjp1I9zVWnrvcfUB/Zr7xzkL79FijPw1/TT0G9GPmG29DViqj3hv1HAATb3yQNb+8/0K/461r/8MH/nQG/zvvr0vradSkGeMq56VBjzUWPnczxk2k55or8y6Vi7c9BvRnxgL7zJ48lXfue5J+uw3k/af/x4yxbzPhXw8BMGXUC1BfT7f+fZg10ddTSUuPlWyd1/3Ap4v7hwP/Le6/TKULJ8AnqFRiERHrAuMz8xLgMmAw8CCwY0RsVOzTOyIWdSn1kGKfnah0i2xp0KzbgLnfuiNipdb8vuJx32kYMw44EriHlr0ZER+IiDrggBb3nt8UYFEd/1cAxmVmfZGlyyL2uxX4YkQ0/HtvEhHLFcePLxrYdmPRlYJV6aE/3s5F+5zBRfucwVO3DWfQgZXTtvagjZgxZRpTJkxa6Jihh+zKRjtvzV++fOF8V5B/+uFT+MlOJ/OTnU7miZsf4vpvXm4DWwe6/NKrGPbhAxj24QO4+cY7OejQyvCBQ4Zuw5TJUxj/5oQmj9v3gD25/Za7mTFjZkfGVWGL1fvy6qSpvP7uVGbNqefWZ99g1w1XnW+fXTdchVFjJzG7vp5ps+bw+BuTWL+fDWydQf0bLxMrrkr0XRnqutB1s22Z8+Kj8+/Ue95A3HWrr1ep6Jg+b7D8Lptux2y7ina4+nEvESutSqxQnLsPbLfwxBPLNTp3A9avnLtp70G37tC9uN7WrTt1629J/YTX0NI1ZfTz9NpgAD3WWZXo1pVV9t+Rt297ZL593r5tOKsevCsAyw/emNlTpjJr/CRmTZjEjNcn0mvDSpffFT681dwJE3quv/rc4/vtuS3Tnn8dLVlTRr1A7w0G0HOdVYhuXVh1/w/x1q3zf0Z869bhrH5QpfNL3yEbM2fKVGaOn0S3/n3o2rc3AHU9u9Fv562YWpyjCTc/wko7bQlArw0GEN262sAmaamzkq3zOgn4XUScCkwAji3WXwJcFxEPA3cyr9pqV+DUiJgFvAcclZkTikqrqyOiofThLKCpS6nvRMT9QF/guFbk+z7wy4h4HJgDfCcz/9HK33c0cHFE9AZebPTcmnMacCPwP+BxYKEJFZpxF3Ba0dXzRwuMy/Yr4O8RcVCx36Iq9y4F1gNGFhWEE6jMhnolcENEDAdGA0+3IVdVeeau0Wyy20C+es/PmDVtBv849Tdztx11+de59hu/Zcr4Sez3g+OZ9PpbfOHa7wDwxC2PcNcF15YVW02447Z7GLbHzjw0+jamTZ3OySeeMXfbldf8hq9++Zu8+Ualsm3/Az/GhT9zFrWydK2r4xu7bcYJ146kPpNPbLEmG/ZfnmvG/A+Ag7Zemw36Lc+H1u3PwX96gLqAA7ZYi41WrlxXOO2mMYx47W0mTZ/Fnpfewxe235ADtlyrzKdUW7KemXddRY9PngIRzH78PnLiWLpuXRmja/aYe+i6yRC6br0r5Bxy9ixm/uuSecd37U6XdTdn5h1/KiV+Tct6Zt5+JT0O/hpEHbMf+w/51li6DtwVgNmj76brptvSddBuUF+cu+svBiB6r0CPA4vrkHVdmP3kg5UqRS1dc+p58YxL2eLqs6BLHeOv/jfTnnmN1Y/aA4A3rriNd+4YyUrDBjP4wYuonzaD50/51dzDXzrzMjb51clEt65Mf+VNnitmSl/3zCPotdEaUJ/MeG0CL3zd98QlLefU8+zpv2Pgn88kutQx9uq7eP+Z11jjqN0BGHvF7Uy8YxT9hw1mh4cuYM60mTx1cuXcdV9tJTa/4ESiSx3UBeOve4CJt48EYNzV/+YDPz+B7e75CTlzNk+d9MtFZlDHOfXsc3hk1BgmTZrMsP2P4ITjj+ST++5Zdiy1QXPDzAjCfyBFxN3A/2WmZUUdoJq6i2p+l74zsuwIaoeXztm97AhaXNOmt7yPOq9Zs8tOoHYY9TMrf6rVjLTTUjX78BPntLyTOqVuK2+wTE6E12C1FTbrsO+zb777dNX9W1rJJkmSJEmSpBbVV+mEBB3FRjaRmbuWnUGSJEmSJKma2cgmSZIkSZKkFjnkWPPsqC9JkiRJkiS1k5VskiRJkiRJalG9lWzNspJNkiRJkiRJaicr2SRJkiRJktQix2RrnpVskiRJkiRJUjtZySZJkiRJkqQW1WMlW3OsZJMkSZIkSdIyIyL6RcTtEfFc8XOlJvbZNCJGN7pNjohTim3fjojXG23bpzW/10Y2SZIkSZIktSgzO+zWTqcBd2bmxsCdxfKCz+WZzByYmQOBIcBU4NpGu/ysYXtm3tSaX2ojmyRJkiRJkpYlnwD+UNz/A7B/C/sPA17IzFfa80ttZJMkSZIkSVKL6jM77BYRn4uI4Y1un2tD1NUycxxA8XPVFvb/NHD1Auu+FBFjIuJ3TXU3bYoTH0iSJEmSJKlTyczfAr9d1PaIuANYvYlNZ7bl90REd2A/4PRGq38NfA/I4udPgeNaeiwb2SRJkiRJklRVMvOji9oWEW9GxIDMHBcRA4DxzTzU3sDIzHyz0WPPvR8RlwA3tiaT3UUlSZIkSZLUouzA/9rpeuDo4v7RwHXN7HsoC3QVLRrmGhwAPN6aX2ojmyRJkiRJkpYl5wC7R8RzwO7FMhGxRkTMnSk0InoX2/+xwPHnRsRjETEG2A34Smt+qd1FJUmSJEmS1KL6bHeFWYfIzIlUZgxdcP1YYJ9Gy1OB/k3sd+Ti/F4r2SRJkiRJkqR2spJNkiRJkiRJLcoqqWQri5VskiRJkiRJUjtZySZJkiRJkqQWLYFZP5dpVrJJkiRJkiRJ7WQlmyRJkiRJklrkmGzNs5JNkiRJkiRJaicr2SRJkiRJktQiK9maZyWbJEmSJEmS1E5WskmSJEmSJKlF1rE1z0o2SZIkSZIkqZ3C/rSSlpSI+Fxm/rbsHFo8nr/q5vmrXp676ub5q16eu+rm+atunj8tq6xkk7Qkfa7sAGoXz1918/xVL89ddfP8VS/PXXXz/FU3z5+WSTaySZIkSZIkSe1kI5skSZIkSZLUTjaySVqSHFehunn+qpvnr3p57qqb5696ee6qm+evunn+tExy4gNJkiRJkiSpnaxkkyRJkiRJktrJRjZJkiRJkiSpnWxkkySpykVEXUT0LTuHJEmSVMsck01Su0REL2CdzHym7Cxqm4gYCnwYWAOYBjwO3JGZb5caTK0SEVcBXwDmACOAFYDzM/O8UoNJUicVEScDlwNTgEuBQcBpmXlbqcHUoohYC/g0C39u+Rdwc2bWlxhPbRARqwI9G5Yz89US40hLnJVskhZbROwLjAZuKZYHRsT1pYZSiyLimIgYCZwO9AKeAcYDOwG3R8QfImKdMjOqVTbPzMnA/sBNwDrAkaUmUqtFxCYRcWdEPF4sbx0RZ5WdS60TEb0j4psRcUmxvHFEfLzsXGrRccXr5h7AKsCxwDnlRlJLIuJy4HfATODHwKHACcAdwF7AfyNi5/ISqjUiYr+IeA54CbgHeBm4udRQ0lLQtewAkqrat4HtgLsBMnN0RKxXYh61znLAjpk5ramNETEQ2BjwymLn1i0iulFpZLsoM2dFhOXp1eMS4FTgNwCZOaaoTvx+qanUWpdTqSDdoVh+DbgGuLG0RGqNKH7uA1yemY9GRDR3gDqFn2bm402sfxz4R0R0p3KhSZ3b94DtqfSaGBQRu1FpMJWWKVaySWqP2Zn5btkh1DaZ+ctFNbAV20dn5p0dmUmL5TdUrgIvB9wbEesCk0tNpLbonZkPL7BudilJtDg2zMxzgVkAxWuqjTWd34iIuI1KI9utEdEHsJthJ9fQwFZ0951PRJycmTMz8/mOT6Y2mpWZE4G6iKjLzLuAgSVnkpY4K9kktcfjEXEY0CUiNgZOAu4vOZNaKSLWB74MrEej94PM3K+sTGq9zLwAuKDRqleKq8KqDm9FxIZAAkTEp4Bx5UZSG8wsxiRtOH8bAjPKjaRWOJ7Kl/oXM3NqRPSn0mVU1eFo4BcLrDumiXXqnCZFxPLAf4ArI2I8XlzSMsiJDyQttojoDZxJZWyTAG4FvpeZ00sNplaJiEeBy4DHaHQlPzPvKS2UWi0iVgN+CKyRmXtHxObADpl5WcnR1AoRsQHwW+BDwDtUxqg5IjNfLjOXWicidgfOAjYHbgN2BI7JzLvLzKWWRcTWLHxx6R+lBVKLIuJQ4DAqkx7c22hTH2BOZn60lGBqk4hYjsqEFXXA4VQmbLqyqG6Tlhk2sklSjYqIhzLzg2Xn0OKJiJupjAt1ZmZuExFdgVGZuVXJ0dQGxZeOusycUnYWtU1RBbU9lYtMD2bmWyVHUgsi4nfA1sATzLu4lJl5XHmp1JKi8n5d4EfAaY02TQHGZKbVUFWiGNpi48y8o7hY38X3Py1rbGSTtNgi4gaKrjKNvAsMB35jRVvnVnT13ZhKFcbcbk6ZObK0UGq1iHgkM7eNiFGZOahYNzozB5YcTc2IiK82tz0zz++oLGq7iBjc3HZfPzu3iHgyMzcvO4faJiJGZOaQiLgzM4eVnUeLJyI+C3wO6JeZGxZDzVzsOdWyxjHZJLXHi8AqwNXF8iHAm8AmVGbOO7KkXGqdraico4/Q6Ip+sazO7/2ikqZhTKjtqTRyq3PrU/zcFNgWuL5Y3pf5u0Gpc/pp8bMnMBR4lEol29bAQ8BOJeVS6zwQEZtn5pNlB1Gb1EXE2cAmTV2o8OJE1TgR2I7KayWZ+VxErFpuJGnJs5FNUnsMysydGy3fEBH3ZubOEfFEaanUWgcAG2TmzLKDaLF8lUoDzYYRcR+VBu9PlRtJLcnM7wAUMxwObugmExHfBq4pMZpaITN3A4iIPwOfy8zHiuUtgf8rM5ta5Q9UGtreoFLBHVS6i25dbiy14NPA/lS+u/Zpfld1YjMyc2ZEZSLmYpgLu9VpmWMjm6T2WCUi1snMVwEiYh0qX/QBbLjp/B4FVgTGl5xDbRQRXYBditumVL4oPpOZs0oNprZYh/lfJ2dSGYxd1WGzhgY2gMx8PCIGlphHrfM7KhXc8034o84tM58BfhwRYzLz5rLzaLHdExFnAL2KyWNOAG4oOZO0xNnIJqk9vgb8NyJeoPIlf33gxGIg7z+UmkytsRrwdEQ8wvxjsu1XXiS1RmbOiYhPZObPqAzgrerzR+DhiLiWypX8A4Aryo2kNngqIi4F/kTl/B0BPFVuJLXCq5l5fcu7qZP6QER8YFEb7Tba6X0D+AyVRu7PAzcBl5aaSFoKnPhAUrtERA9gMyqNbE9TmUrdapoqEBG7NLU+M+/p6Cxqu4j4AbAC8Bfg/Yb1DrxePYpB9D9cLN6bmaPKzKPWi4iewBeBhiET7gV+7YQ/nVtE/IpKBfcNzH9x6R9lZVLrRcRVND2W5f9gXnd8dT4RUUdlJtgty84iLW02sklqt6gMrrAbcBiwb2auVnIktUJE7L1gt4uI+EJmXlxWJrVeRNzVxOrMTCeuqAJF9/qFNHS/l7TkRcTlTazOzDyuw8OozYqxLD/ZaCzLPsA1mblXucnUGhFxJXC673Na1tnIJmmxRcQHqTSsHQD0ozJr0PWZ+U6pwdQqEXE/cFZm/rtY/gawa2buXW4yadkXEY8xb8DnXlS62z+TmVuUl0qtFREv0cSA3Zm5QQlxpJoQEU8D22TmjGK5B/BoZm5WbjK1RkT8m0ol4sPMX4HvMCVapjgmm6Q2K7qpHQy8ClwNfBcYnpmOw1Zd9gNujIhTgb2odPv1g06ViIgVgLOZ113tHuC7mflueanUWpm5VePlouvo50uKo7Yb2uh+T+AgKheb1IlFxFrAhcCOVBpJ/wucnJmvlRpMrdXUWJZ+9qwedudVTbCSTVKbRcQE4Bng58CNmTk9Il70Cn71iYhVgTuAEcBx6ZtC1YiIvwOPM+8LxpFUrvAfWF4qtUdEjMzMwWXn0OKJiP9m5k5l59CiRcTtwFVUGmugMmHF4Zm5e3mp1BaOZSmps7ORTVKbRUQXYA/gUOAjwF3AR4G1M3N2mdnUsoiYwvzdnLoDs4t1mZl9SwmmNomI0Zk5sKV16pwi4quNFuuAwUD/zNyzpEhqg+KLfoM6KpVtX8zMbUqKpFbwdbO6RcSGwGuZOSMidgW2Bq7IzEll5lLrRMT2VCpJP0Dls2cX4H0/d2pZY3dRSW2WmXOAm4GbixnWPg70Bl6PiDsz87BSA6pZmdmn7AxaIqZFxE6Z+V+AiNgRmFZyJrVe47/D2cC/gL+XlEVt99NG92cDL1EZRkGd21sRcQSVoS6gcrFwYol51DZ/B4ZGxEbApVRmib0K2KfUVGqti4BPA9dQuTBxFLBxqYmkpcBGNkntkpnTgb8Bf4uIvlTGx1AnFhHrZebLzWwPYE3HqOn0vgBcUYzNBvAOcHSJedQ2T2bmNY1XRMRBVL58qPM7PjNfbLwiItYvK4xa7TgqX/R/VizfV6xTdajPzNkRcSDwi8y8MCLsLlpFMvP5iOhSXLC/vJiES1qm2MgmaYnJzMk4AG01OC8i6oDrqIzFNoHKwN0bAbsBw6gMqG8jW+c2OTO3KRq3yczJfsmvKqezcINaU+vUOf2NShffBdcNKSGLWikzX8UJfqrZrIg4lEoF1L7Fum4l5lHbTI2I7sDoiDgXGAcsV3ImaYmzkU2SakxmHhQRmwOHU7mCPwCYCjwF3AT8oKhQVOf2d2Bw0bjdwC/5nVxE7E2la9OaEXFBo019qXQ7VCcWEZsBWwArFNU0DfpSuVihTszZRavesVSquH+QmS8VF5b+VHImtd6RVMaw/BLwFWBt4JOlJpKWAic+kCSpijT6kn8ucGqjTX2BUzNzi1KCqVUiYhtgIPBd4FuNNk0B7srMd8rIpdaJiE8A+1Ophrq+0aYpwJ8z065PnZizi0qSljYb2SQttojoDXwNWCczPxsRGwObZuaNJUeTlll+yV82RERXZ2OuXhGxQ2Y+UHYOtY2zi1aniLgB+C1wS2bOWmDbBsAxwMuZ+bsS4knSfGxkk7TYIuIvVMb0Oiozt4yIXsADfliVlj6/5FeniPhrZh4cEY9R6a42n8zcuoRYaqWI+HpmnhsRF9L0+TuphFhqpYi4A/g9888uemxmDistlFoUEasDX6XStfBt5o0lux7wAnBRZl5XWkBJasQx2SS1x4aZeUgxCC2ZOa2YmVLS0ndARDwBTANuAbYBTslMx6fp3E4ufn681BRaXE8VP4eXmkKLq/Hsogncj7OLdnqZ+QbwdeDrEbEelbFkpwHPZubUMrOp9SLioKZm1V5wnVTtrGSTtNiKabeHAfdl5uCI2BC4OjO3KzmaWiki1gTWpdFFl8y8t7xEaq2GLk4RcQCV7qNfoTKm1zblJlNrRMSPM/MbLa2TtGRERBfgD5l5RNlZpFoUESMzc3BL66RqZyWbpPY4m0oFzdoRcSWV2bqOKTWRWi0ifgwcAjwJzClWJ2AjW3XoVvzch0rj9tsWklaV3YEFG9T2bmKdOqFijKgFr1S/S6XC7TfO0Nz5ZOaciFglIrpn5syy80i1wlm1VWtsZJO02DLz9ogYCWwPBHByZr5Vciy13v5UJqqYUXYQLZYbIuJpKl1mToiIVQC/2HdyEfFF4ARgg4gY02hTH+C+clJpMbwIrMK8sb0OAd4ENgEuAY4sKZea9zJwX0RcD7zfsDIzzy8tkbTsG0vlAsR+VMZybjCFShW+tEyxu6ikxRYROwKjM/P9iDgCGAz8IjNfKTmaWiEibgYOysz3ys6ixRMRKwGTiwqN5YA+xdg16qQiYgVgJeBHwGmNNk3JzLfLSaW2ioh7M3PnptZFxBOZuUVZ2bRoEXF2U+sz8zsdnUWLp5hka53MfKbsLGobZ9VWrbCRTdJiK6owtgG2Bq4AfgccmJm7lBpMrRIRf6dy/u4E5lazOTue1HEiYlUqs+QBkJmvlhhHrRQRTwF7NpyviFgHuCUzN4+IUZk5qNyE0rInIvYFfgJ0z8z1I2Ig8N3M3K/cZGqOs2qr1thdVFJ7zM7MjIhPABdk5mURcXTZodRq1xc3SR2s+LJ4PrAGMJ7KBCRPAVZAVYevAf+NiBeoDJewPpVu28sBfyg1mRayiDH05rKRpmp8G9gOuBsgM0cXs42qc3NWbdUUG9kktceUiDgdOALYuZi5q1sLx6iTyMw/RER3KmMIATyTmbPKzCTVkO9TGc/yjswcFBG7AYeWnEmtlJk3RcTGwGZUGtmebjTZwc9LC6ZF+Unx80BgdeBPxfKhVMZpU3WYnZnvOslPdcnMccVPh5NRTbCRTVJ7HAIcBhyfmW8U3WXOKzmTWikidqVScfEylS+Ja0fE0Znp7KJVICrfMg4HNsjM7xZ/f6tn5sMlR1PrzMrMiRFRFxF1mXlXMeOvqscQYD0qn6e3jggy84pyI6kpmXkPQER8b4Gx9G6ICN/zqsfjEXEY0KVo5D4JuL/kTGqliDgQ+DGwKpXPnQFkZvYtNZi0hDkmmyTVqIgYARzWMHhwRGwCXJ2ZQ8pNptaIiF8D9cBHMvMDxSQIt2XmtiVHUytExB1UZvj9EbAylS6j22bmh8rMpdaJiD8CGwKjgTnF6nRMy86tGEvvY5n5YrG8PnBTZn6g3GRqjYjoDZwJ7FGsuhX4fqMqUnViEfE8sG9mPlV2FmlpspFN0mKLiCnMG+OkO5Wuou9l5grlpVJrRcSYBQebbWqdOqeIGJmZgxsPsh4Rj2bmNmVnU8uKsbumU7mSfziwAnBlZk4sNZhapWis2Tz9IF1VImIv4LfAi8Wq9YDPZ+atpYVSq0XEh4H7M3NOo3WDM3NkibHUShFxX2buWHYOaWmzu6ikxZaZfRovR8T+VAakVXUYHhGXAX8slg8HRpSYR20zqxgHMQEiYhUqlW2qApn5fqNFB8qvPo9TGdtrXNlB1HqZeUujsfSgMpbejOaOUadyK/BIRBycmW8W6y4FBpeYSa03PCL+AvyT+We1/0dpiaSlwEY2SUtMZv4zIk4rO4da7YvAiVTGNAngXuBXpSZSW1wAXAusGhE/AD4FnFVuJLVkgQrg+Tbh2DTVZGXgyYh4mPm/LDpLZSdWdDf8KrBuZn42IjaOiE0z88ays6lVnqEy9u/dEXF8Zt5P5bVT1aEvMJV53X2h8n5oI5uWKXYXlbTYigFMG9QBQ4FdMnOHkiJJNSUiNgOGUfmScafjnEgdIyJ2aWp9wwD76pyKKpoRwFGZuWVE9AIeyMyB5SZTazQaJmFj4C/A74DjMtNKNkmdhpVsktpj30b3Z1OZpfIT5URRa0XEXzPz4Ih4jCYqahyTrTpExPbAE5n5y2K5T0R8MDMfKjmatMzLzHsiYjWgYaKRhzNzfJmZ1CobZuYhEXEoQGZOK2ZqVnUIgMx8rhif7XLAzyxVIiIup+nPnceVEEdaamxkk7TYMvPYsjNosZxc/Px4qSnUXr9m/nFo3m9inaSlICIOpui2RuWL/4URcWpm/q3UYGrJzKJ6rWEsyw1p1N1XnVvDJD/F/feBgyNinRIjqW0ad8vuCRwAjC0pi7TU2MgmabFFxFrAhcCOVD6w/hc4OTNfKzWYmpWZDQN1n5CZ32i8LSJ+DHxj4aPUCUXjmQ0zsz4ifF+XOsaZwLYN1WvFxCN3ADaydW5nA7cAa0fElVQ+vxxTaiK1KCK+npnnRsQFi9jlpA4NpMWSmX9vvBwRV1N53ZSWKXVlB5BU1S4HrgfWANYEbijWqTrs3sS6vTs8hRbXixFxUkR0K24nAy+WHUqqEXULdA+diJ+rO73MvB04kErD2tXA0My8u8xMapWG8UZHLOKm6rQxYCWiljlOfCBpsUXE6AUHC25qnTqXiPgicAKwAfBCo019gPsy84hSgqlNImJVKjOMfoRKJemdwCmOCyUtfRFxHpWxoK4uVh0CjFmwOlidTzFp004UFfiZeW3JkbQYIqIOWD4zJ5edRa3TxOzabwCnL1jhJlU7G9kkLbaIuAP4PfO+ZBwKHJuZw0oLpRZFxArASsCPgNMabZqSmW+Xk0qSqktEfJJKd8MA7rWxpvOLiF8BGzF/4+gLmXlieanUWhFxFfAFYA6VCrYVgPMz87xSg0lSIzaySVpsxWCzFwE7ULkydT+VMdleKTWY2qSoiOrZsJyZr5YYR61UjAH1WWA9Go2x6ixdktS0iHgC2LJhPMuiGuqxzNyi3GRqjYbeEhFxODCEyhiyI5wVXVJn4gDJkhZb0RizX9k5tHgiYl/gfCpj6o0H1qUy7olfNqrDdcB/qAwaPKfkLFJNaKK709xNQGZm3w6OpLZ5hsoYUA0XA9cGxpQXR23ULSK6AfsDF2XmrIiwYkRSp2Ijm6Q2i4gLafpLBgCZ6SxP1eH7wPbAHZk5KCJ2o9LlV9Wht+M/SR0rM/uUnUFtFxE3UPncsgLwVEQ8XCx/kEoVvqrDb4CXgUeBeyNiXcAx2Tq5iFg/M18qO4fUUewuKqnNIuLoRovfAc5uvD0z/9CxibQ4ImJ4Zg6NiEeBQZlZHxEPZ+Z2ZWdTyyLi+8D9mXlT2VkkqTOLiF2a256Z93RUFi05ERFAl8ycXXYWLVpEjMjMIRFxp+M2qxbYyCapXSJiVGYOKjuH2q6YuGJ/KhMgrEyly+i2mfmhMnOpdYpua8sBM4ub3dUkSVKnEhGjgH8CnwF+tuD2zDy/ozNJS1Nd2QEkVT1b6qvXJ4CpwFeAW4AXgI+Xmkitlpl9MrMuM3tmZt9i2QY2SZLUmXwamE5lqKo+TdykZYqVbJLaJSJGZubgsnOo7SLixwuO6dXUOnVORTeZw4H1M/N7EbE2MCAzHy45miRJ0nwiYu/MvLnsHNLSZiWbpDaLiCkRMTkiJgNbN9xvWF92PrXa7k2s27vDU2hx/QrYATisWH4P+GV5cSSpc4qIO4ufPy47ixZfRPSOiG9GxCXF8sYRYQV+9bg/Is6PiOHF7acRsULZoaQlzdlFJbWZs6tVt4j4InACsGFEjGm0qQ9wXzmptBg+mJmDi7FOyMx3IqJ72aEkqRMaUEx+sF9E/JnKGJZzZebIcmKpjS4HRlC5wATwGnANcGNpidQWvwMeBw4ulo+kck4PLC2RtBTYyCZJtecq4GYqEx6c1mj9lMx8u5xIWgyzIqILxbiIEbEKUF9uJEnqlL5F5f1uLWDBQdYT+EiHJ9Li2DAzD4mIQwEyc1oxdIKqw4aZ+clGy9+JiNFlhZGWFhvZJKnGZOa7wLsRcRbwRmbOiIhdqXT9vSIzJ5WZT612AXAtsGpE/AD4FHBWuZEkqfPJzL8Bf4uIb2bm98rOo8U2MyJ6Me/i0obAjHIjqQ2mRcROmflfgIjYEZhWciZpiXPiA0mqUcXVw6HAesCtwPXAppm5T4mx1AYRsRkwjErXpzsz86mSI0lSpxYR+wE7F4t3Z6ZdDatEROxO5WLS5sBtwI7AMZl5d5m51DoRsQ1wBdAwDts7wNGZOWbRR0nVx0Y2SapRDTPDRsTXgWmZeWFEjMrMQWVn06JFRN/MnBwR/ZrabpdfSWpaRPwI2A64slh1KDA8M08vL5XaIiL6A9tTubj0YGa+VXIktVFE9AXITCdL0zLJRjZJqlER8RDwc+BMYN/MfCkiHs/MLctNpuZExI2Z+fGIeIlKl5lo/DMzNyg1oCR1UsVkPwMzs75Y7gKMysyty02m1ii6F47OzPcj4ghgMPCLzHyl5GiSNFdd2QEkSaU5lsoMXT8oGtjWB/5Ucia1IDM/XvxcPzM3WPBn2fkkqZNbsdH9FRa1kzqlXwNTi26HpwKvUOl+KEmdhpVskiRVqYhYE1iXRhMZZea95SWSpM6rmJXyHOAuKtW/OwOnZ+afSw2mVmk0zMW3gNcz87KGdWVnk6QGNrJJklSFIuLHwCHAk8CcYnVm5n7lpZKkzi0iBgDbUmlkeygz3yg5klopIu4BbqFSib8zMIFK99GtSg2mVomI3sDXgHUy87MRsTGVCbecfETLFBvZJEmqQhHxDLB1Zs4oO4skSUtbRKwOHAY8kpn/iYh1gF0z0y6jVSAi/gKMAI7KzC0johfwQGYOLDeZtGTZyCZJNS4i+lCpgHqv7CxqvYi4GTjI8yZJkjq7iBiemUMbz2QfEY9m5jZlZ5OWpK4t7yJJWhZFxFZUBgzuV1mMCcDRmfl4ucnUSlOB0RFxJzC3mi0zTyovkiRJS0dETKEymzZAd6Ab8F5mOoFFdZhZVK8lQERsSKPPL9KywkY2SapdvwG+mpl3AUTErsBvgQ+VmEmtd31xkyS1ICLqgDGZuWXZWbR4MrNP4+WI2B/Yrpw0WgxnUxlTb+2IuBLYETim1ETSUmB3UUmqUU2V6Fu2X12KK8LrZOYzZWeRpM6u+GJ/ema+WnYWLRkR8WBmbl92DrVORPQHtqcy8ciDmflWyZGkJc5KNkmqXS9GxDeBPxbLRwAvlZhHbRAR+wI/odJlZv2IGAh819lFJWmRBgBPRMTDwPsNK33drA4RcWCjxTpgKPO6j6qTi4gDgH9n5r+K5RUjYv/M/Ge5yaQly0o2SapREbES8B1gp2LVvcB3MvOd8lKptSJiBPAR4O5GAwg/lplblZtMkjqniNilqfWZeU9HZ1HbRcTljRZnAy8Dl2Tm+HISqS0iYvSCM4k2ngRBWlZYySZJNSgiugDXZOZHy86ixTY7M9+NiMbrvHImSYuQmfdExLrAxpl5R0T0BrqUnUutk5nHlp1B7VLXxDrbI7TMaep/dEnSMi4z5wBTI8IZuarX4xFxGNAlIjaOiAuB+8sOJUmdVUR8FvgblYl/ANYE/llaILVJRKwVEddGxPiIeDMi/h4Ra5WdS602PCLOj4gNI2KDiPgZMKLsUNKSZiObJNWu6cBjEXFZRFzQcCs7lFrty8AWwAzgKuBd4JQyA0lSJ3cilRkNJwNk5nPAqqUmUltcTmVW7TWoNJDeUKxTdfgyMBP4C3ANlc+hJ5aaSFoKHJNNkmpURBzdxOrMzCs6PIzaLCIGZeaosnNIUrWIiIcy84MN40BFRFdgZGZuXXY2tWwRY3ottE6SymQfaEmqXStm5i8ar4iIk8sKozY7PyIGULka/OfMfKLsQJLUyd0TEWcAvSJid+AEKtVQqg5vRcQRwNXF8qHAxBLzqBUi4ueZeUpE3EATY8c6u6+WNVaySVKNioiRmTl4gXXO8lRFImJ14GDgEKAv8JfM/H65qSSpc4qIOuB4YA8ggFuBS9MvRFUhItYBLgJ2oNJYcz9wcma+UmowNSsihmTmCGf3Va2wkU2SakxEHAocBuwE/KfRpj7AHGccrT4RsRXwdeCQzOxedh5J6qwiojuwGZVGmmcyc2bJkaRlXjGr/R8y84iys0hLm91FJan23A+MA1YGftpo/RRgTCmJ1GYR8QEqFWyfotJd5s/A10oNJUmdWER8DLgYeIFKJdv6EfH5zLy53GRqTjF79iIrQzLzpA6Mo8WQmXMiYpWI6G7DtpZ1VrJJUg2LiHWBjTPzjojoBXTNzCll51LLIuJBKuPSXJOZY8vOI0mdXUQ8DXw8M58vljcE/pWZm5WbTM1ZYKKm7wBnN96emX/o2ERaHBHxG2AwlRli329Yn5nnlxZKWgqsZJOkGhURnwU+B/QDNgTWonKFf1iZudQ6mbl92RkkqcqMb2hgK7wIjC8rjFqncSNaRJxio1rVGlvc6qgMUSItk2xkk6TadSKwHfAQQGY+FxGrlhtJrRUROwLfBtal8n4eQGbmBmXmkqTOJiIOLO4+ERE3AX+l0v3wIOCR0oJpcdgNq0pl5ncAIqJvZdGeE1o22cgmSbVrRmbOjAgAIqIrfnitJpcBXwFGAHNKziJJndm+je6/CTTMcjgBWKnj40i1JyKGApdTVLFFxLvAcZk5otRg0hJmI5sk1a57IuIMoFdE7A6cANxQcia13rsO1i1JLcvMY8vOoMUXEVOYdxGwd0RMbthEpSKqbznJ1Ea/A07IzP8ARMROVBrdti41lbSEOfGBJNWoiKgDjgf2oPJB9Vbg0vSNoSpExDlAF+AfwIyG9Zk5srRQktSJRcT6wJeB9WhUbJCZ+5WVSaoVEXFfZu7Y0jqp2tnIJklSFYqIu5pYnZn5kQ4PI0lVICIepdLV/jGgvmF9Zt5TWiipRkTEz4DeVGZGT+AQ4B3g7+BFQi07bGSTpBoTEWOa256Zlu1LkpY5EfFQZn6w7BxSLVrExcEGXiTUMsNGNkmqMRExmsoVxKuojME2rfH2zHylhFhqo4hYATgb2LlYdQ/w3cx8t7xUktR5RcRhwMbAbdjNXpK0FNjIJkk1KCI2Aw6lMuPak1Qa3G7LzNmlBlOrRcTfgceBPxSrjgS2ycwDy0slSZ1XRPyIymvlC8zrLmoFjSRpibGRTZJqXEQcAvwS+HFmnld2HrVORIzOzIEtrZMkVUTE08DWmTmz7CySpGVT15Z3kSQtayJiTeDTwAFUBp39CnBtqaHUVtMiYqfM/C9AROzIAl1/JUnzeRRYERhfcg5J0jLKRjZJqjERcQ/QB/grcAzwdrGpe0T0y8y3F3WsOpUvAn8oxmaDSmPpMeXFkaRObzXg6Yh4hPnHZNuvvEhSbYiI3sDXgHUy87MRsTGwaWbeWHI0aYmyu6gk1ZiIeJnKxAc0+gkQVMam2aDDQ2mxRURfgMycXHYWSerMImKXptZn5j0dnUWqNRHxF2AEcFRmbhkRvYAHHOZCyxob2SRJqkIR8UPg3MycVCyvBHwtM88qNZgkSdICImJ4Zg6NiFGZOahY92hmblN2NmlJqis7gCRJWix7NzSwAWTmO8A+5cWRpM4tIqZExOTiNj0i5kSEVcBSx5hZVK8lQERsSKNu29KywjHZJEmqTl0iokdmzgAoPrj2KDmTJHVamdmn8XJE7A9sV04aqeacDdwCrB0RVwI74liyWgbZXVSSpCoUEV8H9gMup3JV+Djg+sw8t9RgklRFIuLBzNy+7BxSLYiI/sD2VMYBfjAz3yo5krTE2cgmSTUqIv6YmUe2tE6dV0TsBXyUyofV2zLz1pIjSVKnFREHNlqsA4YCu2TmDiVFkmpGROwIjM7M9yPiCGAw8IvMfKXkaNISZSObJNWoiBiZmYMbLXcBHsvMzUuMJUnSUhERlzdanA28DFySmePLSSTVjogYA2wDbA1cAfwOODAzm5z1V6pWjskmSTUmIk4HzgB6NRrwOYCZwG9LCyZJ0lKUmceWnUGqYbMzMyPiE8AFmXlZRBxddihpSbOSTZJqVET8KDNPLzuHJElLU0R8q5nNmZnf67AwUo2KiHuoTHxwLLAzMIFK99GtSg0mLWF1ZQeQJJXmxohYDiAijoiI8yNi3bJDqXkRcWfx88dlZ5GkKvF+EzeA44FvlBVKqjGHADOA4zPzDWBN4LxyI0lLnpVsklSjFhgb44/AZTg2RqcXEU8CXwQuBg6j0tV3rswcWUYuSaoGEdEHOJlKA9tfgZ86JpskaUlxTDZJql2Nx8b4hWNjVI1vAacBawHnL7AtgY90eCJJ6uQioh/wVeBw4A/A4Mx8p9xUUu2IiClUPqcAdAe6Ae9l5grlpZKWPBvZJKl2TSkmQTgS+HAxu2i3kjOpBZn5N+BvEfFNxxGSpJZFxHnAgVQm99kqM98rOZJUczKzT+PliNgf2K6cNNLSY3dRSapREbE6le6Gj2TmfyJiHWDXzLyi5GhqpYjYj8rgwQB3Z+aNZeaRpM4oIuqpjAU1m3mVNFDpbp+Z2beUYFKNi4gHM3P7snNIS5KVbJJUozLzjYj4O7Bxseot4NoSI6kNIuJHVK4AX1msOjkidnTGWEmaX2Y62ZtUsog4sNFiHTCU+Ru9pWWClWySVKMi4rPA54B+mblhRGwMXJyZw0qOplYoJq4YmJn1xXIXYFRmbl1uMkmSpPlFxOWNFmcDLwOXOPGIljVWsklS7TqRSiXUQwCZ+VxErFpuJLXRisDbxX0HDpYkSZ1SZh5bdgapI1g6LUm1a0ZmzmxYiIiuWLZfTX4EjIqI30fEH4ARwA9LziRJkrSQiFgrIq6NiPER8WZE/D0i1io7l7Sk2V1UkmpURJwLTAKOAr4MnAA8mZlnlplLrRcRA4BtqQze/VBmvlFyJEmSpIVExO3AVcAfi1VHAIdn5u7lpZKWPBvZJKlGRUQAnwH2oNJIcytwafrGIEmSpCUoIkZn5sCW1knVzjHZJKkGRUQdMCYztwQuKTuPJEmSlmlvRcQRwNXF8qHAxBLzSEuFY7JJUg0qZqR8NCLWKTuLJEmSlnnHAQcDbwDjgE8V66Rlit1FJalGRcS/qYzn9TDwfsP6zNyvtFBqlQUqESVJkiR1AnYXlaTa9Z2yA2jxZGZ9RDwaEetk5qtl55EkSWpKRFxIM7PXZ+ZJHRhHWupsZJOkGpWZ95SdQe0yAHgiIqxElCRJndXwRve/A5xdVhCpI9hdVJJqVERsD1wIfADoDnQB3s/MvqUGU6tExC5NrbfxVJIkdUYRMSozB5WdQ1qarGSTpNp1EfBp4BpgKHAUsHGpidRqmXlPRKwLbJyZd0REbyoNpZIkSZ2RFT5a5jm7qCTVsMx8HuiSmXMy83Jg15IjqZUi4rPA34DfFKvWBP5ZWiBJkiSpxlnJJkm1a2pEdAdGR8S5VKZTX67kTGq9E4HtgIcAMvO5iFi13EiSJEnzRMQU5lWw9Y6IyQ2bgHSYEi1rbGSTpNp1JJWK5i8BXwHWBj5ZaiK1xYzMnBkRAEREV+yGIUmSOpHM7FN2Bqkj2cgmSTUqM1+JiF7AgMz8Ttl51Gb3RMQZQK+I2B04Abih5EySJElSzXJMNkmqURGxLzAauKVYHhgR15caSm1xGjABeAz4PHATcFapiSRJkqQaFpn2LJGkWhQRI4CPAHc3TKceEWMyc+tyk6m1ijH1NqPSTfSZzJxZciRJkiSpZtldVJJq1+zMfLdhTC9Vl4j4GHAx8AKVwYPXj4jPZ+bN5SaTJEmSapONbJJUux6PiMOALhGxMXAScH/JmdR6PwV2y8znASJiQ+BfgI1skiRJUgkck02SateXgS2AGcDVwGTglDIDqU3GNzSwFV4ExpcVRpIkSap1jskmSVIViYgDi7u7A+sCf6UyJttBVMZl+1pZ2SRJkqRaZndRSapREbEJ8H/AejR6P8jMj5SVSa2yb6P7bwK7FPcnACt1fBxJkiRJYCWbJNWsiHiUysD5I4A5Deszc0RpoSRJkiSpStnIJkk1KiJGZOaQsnNo8UTE+lTG1VuP+SsR9ysrkyRJklTLbGSTpBoVEd+mMlD+tVQmPwAgM98uK5Nar6hEvAx4DKhvWJ+Z95QWSpIkSaphNrJJUo2KiJeaWJ2ZuUGHh1GbRcRDmfnBsnNIkiRJqrCRTZKkKhQRhwEbA7cxfyXiyNJCSZIkSTXM2UUlSapOWwFHAh9hXnfRLJYlSZIkdTAr2SRJqkIR8TSwdWbOLDuLJEmSJKgrO4AkSVosjwIrlh1CkiRJUoXdRSWphkXE1sB6NHo/yMx/lBZIbbEa8HREPML8Y7LtV14kSZIkqXbZyCZJNSoifgdsDTzB/GN62chWHc4uO4AkSZKkeRyTTZJqVEQ8mZmbl51DkiRJkpYFjskmSbXrgYiwka1KRcSUiJhc3KZHxJyImFx2LkmSJKlW2V1UkmrXH6g0tL1BZUyvADIzty43llojM/s0Xo6I/YHtykkjSZIkye6iklSjIuJ54KvAY8wbk43MfKW0UGqXiHgwM7cvO4ckSZJUi6xkk6Ta9WpmXl92CC2eiDiw0WIdMJTKxBWSJEmSSmAjmyTVrqcj4irgBirdRQHITGcXrQ77Nro/G3gZ+EQ5USRJkiTZXVSSalREXN7E6szM4zo8jCRJkiRVORvZJEmqIhHxrWY2Z2Z+r8PCSJIkSZqrruwAkqRyRMRaEXFtRIyPiDcj4u8RsVbZudSi95u4ARwPfKOsUJIkSVKts5JNkmpURNwOXAX8sVh1BHB4Zu5eXiq1RUT0AU6m0sD2V+CnmTm+3FSSJElSbbKSTZJq1yqZeXlmzi5uvwdWKTuUWhYR/SLi+8AYKpMYDc7Mb9jAJkmSJJXHRjZJql1vRcQREdGluB0BTCw7lJoXEecBjwBTgK0y89uZ+U7JsSRJkqSaZ3dRSapREbEOcBGwA5DA/cDJmflKqcHUrIioB2YAs6mct7mbqEx80LeUYJIkSVKNs5FNkmpQRHQB/pCZR5SdRZIkSZKWBXYXlaQalJlzgFUionvZWSRJkiRpWdC17ACSpNK8DNwXEdcD7zeszMzzS0skSZIkSVXKRjZJql1ji1sd0KfkLJIkSZJU1Wxkk6QaExF/zMwjgUmZ+Yuy80iSJEnSssCJDySpxkTEk8DewPXArlRmpZwrM98uIZYkSZIkVTUr2SSp9lwM3AJsAIxg/ka2LNZLkiRJktrASjZJqlER8evM/GLZOSRJkiRpWWAjmyRJkiRJktROdWUHkCRJkiRJkqqdjWySJEmSJElSO9nIJkk1LCLWjYiPFvd7RUSfsjNJkiRJUjWykU2SalREfBb4G/CbYtVawD9LCyRJkiRJVcxGNkmqXScCOwKTATLzOWDVUhNJkiRJUpWykU2SateMzJzZsBARXQGnnJYkSZKkxWAjmyTVrnsi4gygV0TsDlwD3FByJkmSJEmqSpFp0YIk1aKIqAOOB/YAArgVuDR9Y5AkSZKkNrORTZJqVEQsB0zPzDnFchegR2ZOLTeZJEmSJFUfu4tKUu26E+jVaLkXcEdJWSRJkiSpqtnIJkm1q2dmvtewUNzvXWIeSZIkSapaNrJJUu16PyIGNyxExBBgWol5JEmSJKlqdS07gCSpNKcA10TE2GJ5AHBIeXEkSZIkqXo58YEk1bCI6AZsSmV20aczc1bJkSRJkiSpKtnIJkk1LCI+BKxHo8rmzLyitECSJEmSVKXsLipJNSoi/ghsCIwG5hSrE7CRTZIkSZLayEo2SapREfEUsHn6RiBJkiRJ7ebsopJUux4HVi87hCRJkiQtC+wuKkm1a2XgyYh4GJjRsDIz9ysvkiRJkiRVJxvZJKl2fbvsAJIkSZK0rHBMNkmSJEmSJKmdHJNNkmpURGwfEY9ExHsRMTMi5kTE5LJzSZIkSVI1spFNkmrXRcChwHNAL+AzxTpJkiRJUhs5Jpsk1bDMfD4iumTmHODyiLi/7EySJEmSVI1sZJOk2jU1IroDoyPiXGAcsFzJmSRJkiSpKtldVJJq15FU3ge+BLwPrA0cWGoiSZIkSapSNrJJUu3aPzOnZ+bkzPxOZn4V+HjZoSRJkiSpGtnIJkm16+gm1h3T0SEkSZIkaVngmGySVGMi4lDgMGD9iLi+0aa+wMRyUkmSJElSdbORTZJqz/1UJjlYGfhpo/VTgDGlJJIkSZKkKheZWXYGSVIJImI5YFpm1kfEJsBmwM2ZOavkaJIkSZJUdWxkk6QaFREjgA8DKwEPAsOBqZl5eKnBJEmSJKkKOfGBJNWuyMypwIHAhZl5ALB5yZkkSZIkqSrZyCZJtSsiYgfgcOBfxTrH6pQkSZKkxWAjmyTVrlOA04FrM/OJiNgAuKvcSJIkSZJUnRyTTZIkSZIkSWonuwVJUo2JiJ9n5ikRcQOw0JWWzNyvhFiSJEmSVNVsZJOk2vPH4udPSk0hSZIkScsQu4tKUg2LiFUAMnNC2VkkSZIkqZo58YEk1Zio+HZEvAU8DTwbERMi4ltlZ5MkSZKkamUjmyTVnlOAHYFtM7N/Zq4EfBDYMSK+UmoySZIkSapSdheVpBoTEaOA3TPzrQXWrwLclpmDykkmSZIkSdXLSjZJqj3dFmxgg7njsnUrIY8kSZIkVT0b2SSp9sxczG2SJEmSpEWwu6gk1ZiImAO839QmoGdmWs0mSZIkSW1kI5skSZIkSZLUTnYXlSRJkiRJktrJRjZJkiRJkiSpnWxkkyRJkiRJktrJRjZJkiRJkiSpnWxkkyRJkiRJktrJRjZJkiRJkiSpnf4f2uCuTM40fGwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix to check the relation between columns\n",
    "corr_matrix = df1.corr()\n",
    "sn.heatmap(corr_matrix, annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a95392a0",
   "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>House Age</th>\n",
       "      <th>Distance from nearest Metro station (km)</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Number of bedrooms</th>\n",
       "      <th>House size (sqft)</th>\n",
       "      <th>House price of unit area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.084879</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>1</td>\n",
       "      <td>575</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.5</td>\n",
       "      <td>0.306595</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>2</td>\n",
       "      <td>1240</td>\n",
       "      <td>42.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.3</td>\n",
       "      <td>0.561984</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>3</td>\n",
       "      <td>1060</td>\n",
       "      <td>47.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.3</td>\n",
       "      <td>0.561984</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>2</td>\n",
       "      <td>875</td>\n",
       "      <td>54.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.390568</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>1</td>\n",
       "      <td>491</td>\n",
       "      <td>43.1</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>409</th>\n",
       "      <td>13.7</td>\n",
       "      <td>4.082015</td>\n",
       "      <td>0</td>\n",
       "      <td>24.94155</td>\n",
       "      <td>121.50381</td>\n",
       "      <td>3</td>\n",
       "      <td>803</td>\n",
       "      <td>15.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>5.6</td>\n",
       "      <td>0.090456</td>\n",
       "      <td>9</td>\n",
       "      <td>24.97433</td>\n",
       "      <td>121.54310</td>\n",
       "      <td>2</td>\n",
       "      <td>1278</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>18.8</td>\n",
       "      <td>0.390970</td>\n",
       "      <td>7</td>\n",
       "      <td>24.97923</td>\n",
       "      <td>121.53986</td>\n",
       "      <td>1</td>\n",
       "      <td>503</td>\n",
       "      <td>40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.104810</td>\n",
       "      <td>5</td>\n",
       "      <td>24.96674</td>\n",
       "      <td>121.54067</td>\n",
       "      <td>1</td>\n",
       "      <td>597</td>\n",
       "      <td>52.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.090456</td>\n",
       "      <td>9</td>\n",
       "      <td>24.97433</td>\n",
       "      <td>121.54310</td>\n",
       "      <td>2</td>\n",
       "      <td>1097</td>\n",
       "      <td>63.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>411 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     House Age  Distance from nearest Metro station (km)  \\\n",
       "0         32.0                                  0.084879   \n",
       "1         19.5                                  0.306595   \n",
       "2         13.3                                  0.561984   \n",
       "3         13.3                                  0.561984   \n",
       "4          5.0                                  0.390568   \n",
       "..         ...                                       ...   \n",
       "409       13.7                                  4.082015   \n",
       "410        5.6                                  0.090456   \n",
       "411       18.8                                  0.390970   \n",
       "412        8.1                                  0.104810   \n",
       "413        6.5                                  0.090456   \n",
       "\n",
       "     Number of convenience stores  latitude  longitude  Number of bedrooms  \\\n",
       "0                              10  24.98298  121.54024                   1   \n",
       "1                               9  24.98034  121.53951                   2   \n",
       "2                               5  24.98746  121.54391                   3   \n",
       "3                               5  24.98746  121.54391                   2   \n",
       "4                               5  24.97937  121.54245                   1   \n",
       "..                            ...       ...        ...                 ...   \n",
       "409                             0  24.94155  121.50381                   3   \n",
       "410                             9  24.97433  121.54310                   2   \n",
       "411                             7  24.97923  121.53986                   1   \n",
       "412                             5  24.96674  121.54067                   1   \n",
       "413                             9  24.97433  121.54310                   2   \n",
       "\n",
       "     House size (sqft)  House price of unit area  \n",
       "0                  575                      37.9  \n",
       "1                 1240                      42.2  \n",
       "2                 1060                      47.3  \n",
       "3                  875                      54.8  \n",
       "4                  491                      43.1  \n",
       "..                 ...                       ...  \n",
       "409                803                      15.4  \n",
       "410               1278                      50.0  \n",
       "411                503                      40.6  \n",
       "412                597                      52.5  \n",
       "413               1097                      63.9  \n",
       "\n",
       "[411 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fb74279f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.hist(df1['Number of convenience stores'],rwidth=0.8)\n",
    "plt.xlabel(\"Number of convenience stores\")\n",
    "plt.ylabel(\"Count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0f86035c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price of unit area')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.bar(df1['Number of convenience stores'],df1['House price of unit area'])\n",
    "plt.xlabel(\"Number of convenience stores\")\n",
    "plt.ylabel(\"price of unit area\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7b87308c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'price of unit area')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.rcParams[\"figure.figsize\"] = (20,10)\n",
    "plt.bar(df1['Number of bedrooms'],df1['House price of unit area'])\n",
    "plt.xlabel(\"Number of bedrooms\")\n",
    "plt.ylabel(\"price of unit area\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "63aee09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2e80db2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "std = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "83b3cd6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0dc90072",
   "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>House Age</th>\n",
       "      <th>Distance from nearest Metro station (km)</th>\n",
       "      <th>Number of convenience stores</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Number of bedrooms</th>\n",
       "      <th>House size (sqft)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.084879</td>\n",
       "      <td>10</td>\n",
       "      <td>24.98298</td>\n",
       "      <td>121.54024</td>\n",
       "      <td>1</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.5</td>\n",
       "      <td>0.306595</td>\n",
       "      <td>9</td>\n",
       "      <td>24.98034</td>\n",
       "      <td>121.53951</td>\n",
       "      <td>2</td>\n",
       "      <td>1240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.3</td>\n",
       "      <td>0.561984</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>3</td>\n",
       "      <td>1060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.3</td>\n",
       "      <td>0.561984</td>\n",
       "      <td>5</td>\n",
       "      <td>24.98746</td>\n",
       "      <td>121.54391</td>\n",
       "      <td>2</td>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.390568</td>\n",
       "      <td>5</td>\n",
       "      <td>24.97937</td>\n",
       "      <td>121.54245</td>\n",
       "      <td>1</td>\n",
       "      <td>491</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>13.7</td>\n",
       "      <td>4.082015</td>\n",
       "      <td>0</td>\n",
       "      <td>24.94155</td>\n",
       "      <td>121.50381</td>\n",
       "      <td>3</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>5.6</td>\n",
       "      <td>0.090456</td>\n",
       "      <td>9</td>\n",
       "      <td>24.97433</td>\n",
       "      <td>121.54310</td>\n",
       "      <td>2</td>\n",
       "      <td>1278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>18.8</td>\n",
       "      <td>0.390970</td>\n",
       "      <td>7</td>\n",
       "      <td>24.97923</td>\n",
       "      <td>121.53986</td>\n",
       "      <td>1</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.104810</td>\n",
       "      <td>5</td>\n",
       "      <td>24.96674</td>\n",
       "      <td>121.54067</td>\n",
       "      <td>1</td>\n",
       "      <td>597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>6.5</td>\n",
       "      <td>0.090456</td>\n",
       "      <td>9</td>\n",
       "      <td>24.97433</td>\n",
       "      <td>121.54310</td>\n",
       "      <td>2</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>411 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     House Age  Distance from nearest Metro station (km)  \\\n",
       "0         32.0                                  0.084879   \n",
       "1         19.5                                  0.306595   \n",
       "2         13.3                                  0.561984   \n",
       "3         13.3                                  0.561984   \n",
       "4          5.0                                  0.390568   \n",
       "..         ...                                       ...   \n",
       "409       13.7                                  4.082015   \n",
       "410        5.6                                  0.090456   \n",
       "411       18.8                                  0.390970   \n",
       "412        8.1                                  0.104810   \n",
       "413        6.5                                  0.090456   \n",
       "\n",
       "     Number of convenience stores  latitude  longitude  Number of bedrooms  \\\n",
       "0                              10  24.98298  121.54024                   1   \n",
       "1                               9  24.98034  121.53951                   2   \n",
       "2                               5  24.98746  121.54391                   3   \n",
       "3                               5  24.98746  121.54391                   2   \n",
       "4                               5  24.97937  121.54245                   1   \n",
       "..                            ...       ...        ...                 ...   \n",
       "409                             0  24.94155  121.50381                   3   \n",
       "410                             9  24.97433  121.54310                   2   \n",
       "411                             7  24.97923  121.53986                   1   \n",
       "412                             5  24.96674  121.54067                   1   \n",
       "413                             9  24.97433  121.54310                   2   \n",
       "\n",
       "     House size (sqft)  \n",
       "0                  575  \n",
       "1                 1240  \n",
       "2                 1060  \n",
       "3                  875  \n",
       "4                  491  \n",
       "..                 ...  \n",
       "409                803  \n",
       "410               1278  \n",
       "411                503  \n",
       "412                597  \n",
       "413               1097  \n",
       "\n",
       "[411 rows x 7 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df1.drop('House price of unit area', axis = 'columns')\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5a3c4beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df1['House price of unit area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e828ae92",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(x,y, test_size = 0.2,random_state = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f705939f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.59766491,  2.58283205, -1.33786074, ..., -2.30679947,\n",
       "         0.00369755,  1.52751911],\n",
       "       [ 0.39300856,  0.00372276, -1.33786074, ...,  1.06085348,\n",
       "         1.21649272,  0.99350452],\n",
       "       [ 0.06377878,  0.20031054, -1.00160149, ...,  0.98327797,\n",
       "         1.21649272,  0.48820039],\n",
       "       ...,\n",
       "       [ 1.3540036 , -0.55764243,  1.35221326, ...,  0.37393486,\n",
       "         0.00369755,  1.32654587],\n",
       "       [ 1.63874288, -0.43926521, -0.66534224, ...,  0.81561477,\n",
       "         0.00369755,  1.12557264],\n",
       "       [ 0.91799659, -0.48832325,  0.34343551, ...,  0.6285577 ,\n",
       "        -1.20909762, -1.00474361]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#scaling the Data for better accauracy\n",
    "x_train = std.fit_transform(x_train)\n",
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c2e47711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.84386399e-01,  1.19902273e-01, -1.73513367e-01,\n",
       "         1.68978240e+00,  7.22173807e-03,  4.52781834e-02,\n",
       "         8.65104438e-01],\n",
       "       [-9.40992162e-01, -7.08732112e-01,  1.95758670e-01,\n",
       "        -4.12409045e-01,  5.25967377e-01, -1.20741822e+00,\n",
       "        -9.75201689e-01],\n",
       "       [ 1.33628345e-02, -2.15765672e-01, -5.42785403e-01,\n",
       "         5.90866124e-01,  2.88736140e-01,  4.52781834e-02,\n",
       "         1.34104568e+00],\n",
       "       [ 1.34602071e+00, -3.44542877e-01,  1.95758670e-01,\n",
       "         3.13506926e-01,  3.64650136e-01,  1.29797459e+00,\n",
       "        -2.54078598e-01],\n",
       "       [-1.06995905e+00, -7.22563609e-01,  1.67284682e+00,\n",
       "         3.13506926e-01,  7.18124679e-01,  1.29797459e+00,\n",
       "        -2.68501060e-01],\n",
       "       [-1.26770829e+00, -3.93416088e-01,  9.34302743e-01,\n",
       "         9.03612530e-01,  2.97434618e-01,  1.29797459e+00,\n",
       "         8.76642408e-01],\n",
       "       [ 1.25134140e-01,  1.29585419e+00, -5.42785403e-01,\n",
       "        -7.49065727e-01, -1.71191396e+00,  4.52781834e-02,\n",
       "         1.07567238e+00],\n",
       "       [-1.47405531e+00, -4.51135400e-01,  9.34302743e-01,\n",
       "         1.79609382e-01,  4.93545774e-01,  4.52781834e-02,\n",
       "         9.60292686e-01],\n",
       "       [-1.06995905e+00, -7.22563609e-01,  1.67284682e+00,\n",
       "         3.13506926e-01,  7.18124679e-01,  1.29797459e+00,\n",
       "        -2.33192093e-02],\n",
       "       [ 1.35461851e+00, -6.40490907e-01,  1.30357478e+00,\n",
       "         2.31255577e-01,  6.87284618e-01,  1.29797459e+00,\n",
       "         1.19393657e+00],\n",
       "       [-1.25051270e+00,  1.38329958e+00, -5.42785403e-01,\n",
       "        -7.56717015e-01, -1.77992025e+00,  4.52781834e-02,\n",
       "         1.59199651e+00],\n",
       "       [-9.66785540e-01, -7.08732112e-01,  1.95758670e-01,\n",
       "        -4.12409045e-01,  5.25967377e-01,  4.52781834e-02,\n",
       "        -1.32929919e-01],\n",
       "       [ 1.33628345e-02, -3.65410997e-01,  1.95758670e-01,\n",
       "        -1.60696642e+00,  4.65078026e-01,  4.52781834e-02,\n",
       "         1.54007565e+00],\n",
       "       [-9.06600991e-01, -7.08732112e-01,  1.95758670e-01,\n",
       "        -4.12409045e-01,  5.25967377e-01,  4.52781834e-02,\n",
       "         1.41315799e+00],\n",
       "       [ 1.62115008e+00, -3.38939951e-01, -1.28132948e+00,\n",
       "         2.33168399e-01,  3.88525697e-02,  1.29797459e+00,\n",
       "         1.23789901e-01],\n",
       "       [-4.93906938e-01, -3.35414538e-01,  1.95758670e-01,\n",
       "        -5.64478398e-01,  2.65013016e-01, -1.20741822e+00,\n",
       "        -1.48287234e+00],\n",
       "       [-5.62689280e-01, -2.68200624e-01,  1.95758670e-01,\n",
       "         1.56927461e+00,  7.82177113e-01, -1.20741822e+00,\n",
       "        -1.02135357e+00],\n",
       "       [-2.44570948e-01,  1.21725076e+00, -5.42785403e-01,\n",
       "        -1.01686081e+00, -1.53399053e+00,  4.52781834e-02,\n",
       "         1.35546814e+00],\n",
       "       [-6.40069415e-01, -6.15182537e-01,  1.30357478e+00,\n",
       "         1.32347697e+00,  5.68669000e-01,  1.29797459e+00,\n",
       "         1.57757405e+00],\n",
       "       [ 1.50937877e+00, -3.38702328e-01,  1.30357478e+00,\n",
       "        -8.62728840e-02,  8.63626505e-01,  4.52781834e-02,\n",
       "         6.66074465e-01],\n",
       "       [ 9.50522245e-01, -1.17581681e-01, -5.42785403e-01,\n",
       "         3.86194164e-01,  1.91471332e-01,  1.29797459e+00,\n",
       "         1.54296014e+00],\n",
       "       [-2.44570948e-01,  3.11667317e+00, -1.65060151e+00,\n",
       "        -2.74509554e+00, -2.40779226e+00,  4.52781834e-02,\n",
       "         7.32417790e-01],\n",
       "       [ 4.76504177e-03, -3.65410997e-01,  1.95758670e-01,\n",
       "        -1.60696642e+00,  4.65078026e-01,  4.52781834e-02,\n",
       "         1.09586383e+00],\n",
       "       [ 1.91347503e+00, -4.22476028e-01, -1.73513367e-01,\n",
       "         1.16758197e+00,  8.36740298e-01,  1.29797459e+00,\n",
       "        -1.32929919e-01],\n",
       "       [ 1.05369576e+00, -2.30739956e-01,  1.95758670e-01,\n",
       "        -2.23996072e-01,  9.87777519e-01,  4.52781834e-02,\n",
       "         1.14778469e+00],\n",
       "       [ 1.28583616e+00, -6.57858957e-01,  9.34302743e-01,\n",
       "        -4.56403952e-01,  6.27976809e-01,  1.29797459e+00,\n",
       "         1.24874192e+00],\n",
       "       [ 3.40078959e-01,  2.99449785e+00, -1.65060151e+00,\n",
       "        -2.22098230e+00, -2.49794013e+00, -1.20741822e+00,\n",
       "        -1.33864773e+00],\n",
       "       [-1.32799642e-01, -4.28456105e-01,  1.95758670e-01,\n",
       "        -1.36882007e+00, -1.47674063e-03,  4.52781834e-02,\n",
       "        -1.70428320e-01],\n",
       "       [ 1.32882513e+00, -4.96613958e-01,  5.65030706e-01,\n",
       "         6.77899528e-01,  6.07416768e-01,  4.52781834e-02,\n",
       "        -1.58890350e-01],\n",
       "       [-2.10179777e-01, -5.30934459e-01,  1.95758670e-01,\n",
       "         1.04994342e+00,  7.48173969e-01, -1.20741822e+00,\n",
       "        -1.11654181e+00],\n",
       "       [ 9.59120038e-01, -3.54741193e-01, -5.42785403e-01,\n",
       "        -9.58369942e-02,  2.81619203e-01, -1.20741822e+00,\n",
       "        -1.24634397e+00],\n",
       "       [-1.36228401e+00, -3.37678218e-01,  1.30357478e+00,\n",
       "        -8.43600619e-02,  8.63626505e-01, -1.20741822e+00,\n",
       "        -1.20307659e+00],\n",
       "       [-5.45493695e-01, -6.45791168e-01, -1.28132948e+00,\n",
       "         2.54209442e-01, -3.30437390e-01, -1.20741822e+00,\n",
       "        -1.28672686e+00],\n",
       "       [-4.59515767e-01, -5.68219655e-01,  9.34302743e-01,\n",
       "        -4.77444995e-01,  7.07844659e-01, -1.20741822e+00,\n",
       "        -1.27807339e+00],\n",
       "       [-4.93906938e-01, -2.68200624e-01,  1.95758670e-01,\n",
       "         1.56927461e+00,  7.82177113e-01,  1.29797459e+00,\n",
       "         3.60318275e-01],\n",
       "       [ 8.81739903e-01,  6.23977145e-01, -9.12057439e-01,\n",
       "         4.17755728e-01, -1.32522704e+00,  1.29797459e+00,\n",
       "         2.18978149e-01],\n",
       "       [ 1.93916482e-01, -2.91507509e-01, -1.73513367e-01,\n",
       "         3.05855638e-01,  3.25902367e-01,  4.52781834e-02,\n",
       "        -1.70428320e-01],\n",
       "       [-1.29350167e+00,  1.25947651e+00, -5.42785403e-01,\n",
       "        -7.71063180e-01, -1.67474773e+00, -1.20741822e+00,\n",
       "        -1.06750544e+00],\n",
       "       [-2.96157705e-01, -8.43634390e-02, -9.12057439e-01,\n",
       "         6.59727718e-01,  3.88525697e-02, -1.20741822e+00,\n",
       "        -1.11654181e+00],\n",
       "       [-1.63741338e+00, -5.45687501e-01, -1.28132948e+00,\n",
       "         3.58458244e-01, -2.71129580e-01,  4.52781834e-02,\n",
       "         6.83381419e-01],\n",
       "       [ 1.76731256e+00, -3.46528270e-01, -1.73513367e-01,\n",
       "         2.36994044e-01,  3.82047093e-01, -1.20741822e+00,\n",
       "        -1.41941351e+00],\n",
       "       [-1.14733919e+00, -7.22563609e-01,  1.67284682e+00,\n",
       "         3.13506926e-01,  7.18124679e-01, -1.20741822e+00,\n",
       "        -1.27807339e+00],\n",
       "       [-9.06600991e-01, -7.08732112e-01,  1.95758670e-01,\n",
       "        -4.12409045e-01,  5.25967377e-01, -1.20741822e+00,\n",
       "        -1.06750544e+00],\n",
       "       [ 1.07938555e-01, -5.32594060e-01,  5.65030706e-01,\n",
       "         8.95961242e-01,  6.53281474e-01,  1.29797459e+00,\n",
       "         1.09874832e+00],\n",
       "       [-2.10283365e-02, -4.32989921e-01,  9.34302743e-01,\n",
       "         7.82148330e-01,  4.61914943e-01, -1.20741822e+00,\n",
       "        -1.24634397e+00],\n",
       "       [ 1.55236774e+00,  7.54670968e-02, -1.28132948e+00,\n",
       "         8.87503342e-02,  1.31357508e+00,  4.52781834e-02,\n",
       "        -8.10090566e-02],\n",
       "       [-5.19700317e-01, -3.35414538e-01,  1.95758670e-01,\n",
       "        -5.64478398e-01,  2.65013016e-01,  1.29797459e+00,\n",
       "        -1.30045427e-01],\n",
       "       [ 1.07089134e+00, -5.60939686e-01,  1.67284682e+00,\n",
       "        -2.27821716e-01,  7.34730866e-01, -1.20741822e+00,\n",
       "        -1.13961775e+00],\n",
       "       [ 1.33628345e-02, -2.72543070e-01, -1.73513367e-01,\n",
       "         3.00117171e-01,  3.12459263e-01,  1.29797459e+00,\n",
       "         5.62232740e-01],\n",
       "       [ 7.09784048e-01, -4.84415120e-01,  5.65030706e-01,\n",
       "        -6.44816925e-01,  5.22804294e-01,  4.52781834e-02,\n",
       "         1.15932266e+00],\n",
       "       [-1.19892594e+00,  6.93316560e-01, -5.42785403e-01,\n",
       "         1.03096500e-01, -1.40351335e+00,  4.52781834e-02,\n",
       "         6.11269110e-01],\n",
       "       [-1.63741338e+00, -6.31047472e-01, -1.65060151e+00,\n",
       "         4.58616373e-03, -1.83354023e-01,  4.52781834e-02,\n",
       "         1.21412801e+00],\n",
       "       [ 1.08808693e+00,  3.08653798e-01, -1.65060151e+00,\n",
       "        -2.04404626e+00, -3.10668120e-01,  1.29797459e+00,\n",
       "         2.91090458e-01],\n",
       "       [ 5.63621571e-01,  3.54542913e+00, -1.65060151e+00,\n",
       "        -2.17985662e+00, -3.01668577e+00, -1.20741822e+00,\n",
       "        -1.46844988e+00],\n",
       "       [-5.45493695e-01, -6.29069499e-01, -1.28132948e+00,\n",
       "         2.70468430e-01, -3.32809702e-01, -1.20741822e+00,\n",
       "        -1.14827123e+00],\n",
       "       [-3.99331218e-01, -6.45934358e-01, -1.28132948e+00,\n",
       "         2.52296620e-01, -3.34391244e-01,  1.29797459e+00,\n",
       "         1.06701890e+00],\n",
       "       [ 1.59525311e-01,  1.29585419e+00, -5.42785403e-01,\n",
       "        -7.49065727e-01, -1.71191396e+00, -1.20741822e+00,\n",
       "        -1.43960496e+00],\n",
       "       [-1.46545752e+00,  1.19203827e+00, -5.42785403e-01,\n",
       "        -7.15591341e-01, -1.63916305e+00,  1.29797459e+00,\n",
       "         7.32417790e-01],\n",
       "       [-2.96157705e-01,  8.82744144e-01, -9.12057439e-01,\n",
       "         1.16184351e+00, -1.25722076e+00,  4.52781834e-02,\n",
       "        -3.19726864e-02],\n",
       "       [-8.03427478e-01, -4.03591181e-01,  1.95758670e-01,\n",
       "         1.09106909e+00,  8.50183401e-01,  4.52781834e-02,\n",
       "         4.43968553e-01],\n",
       "       [-1.49125090e+00, -4.91764372e-01,  1.95758670e-01,\n",
       "         1.09872038e+00,  7.85340196e-01,  1.29797459e+00,\n",
       "         6.86265912e-01],\n",
       "       [ 1.35461851e+00, -6.26605680e-01,  1.30357478e+00,\n",
       "         5.75563548e-01,  7.19706221e-01, -1.20741822e+00,\n",
       "        -1.27807339e+00],\n",
       "       [-3.39146669e-01, -4.40399109e-01,  9.34302743e-01,\n",
       "        -3.54067972e-01,  8.39903381e-01,  4.52781834e-02,\n",
       "         6.31460557e-01],\n",
       "       [ 1.61255229e+00, -2.40309622e-01, -1.28132948e+00,\n",
       "         4.57118379e-02,  1.24255815e-01,  1.29797459e+00,\n",
       "         5.91077664e-01],\n",
       "       [-3.04755498e-01, -2.34983705e-02, -1.73513367e-01,\n",
       "         7.46761122e-01,  4.91325900e-02, -1.20741822e+00,\n",
       "        -1.16269369e+00],\n",
       "       [-6.05678244e-01,  5.00897973e-01, -1.28132948e+00,\n",
       "        -1.81833326e+00,  1.13881474e+00, -1.20741822e+00,\n",
       "        -1.22326803e+00],\n",
       "       [-1.42246856e+00, -6.59170411e-01, -1.73513367e-01,\n",
       "        -3.91368002e-01,  4.66659568e-01, -1.20741822e+00,\n",
       "        -1.13096428e+00],\n",
       "       [-2.10179777e-01, -4.97819128e-01,  5.65030706e-01,\n",
       "         7.03722625e-01,  6.94401555e-01, -1.20741822e+00,\n",
       "        -1.43095148e+00],\n",
       "       [-2.53168741e-01, -2.34983705e-02, -1.73513367e-01,\n",
       "         7.46761122e-01,  4.91325900e-02, -1.20741822e+00,\n",
       "        -1.32134077e+00],\n",
       "       [ 5.89414949e-01,  3.54542913e+00, -1.65060151e+00,\n",
       "        -2.17985662e+00, -3.01668577e+00,  4.52781834e-02,\n",
       "         2.56476550e-01],\n",
       "       [ 9.93511209e-01, -6.71124689e-01,  1.30357478e+00,\n",
       "         1.00403569e+00,  5.85275186e-01,  1.29797459e+00,\n",
       "         8.27606038e-01],\n",
       "       [ 9.76315624e-01, -3.62403228e-01,  5.65030706e-01,\n",
       "         8.21361182e-01,  3.18785430e-01,  1.29797459e+00,\n",
       "         1.06124992e+00],\n",
       "       [ 1.33628345e-02, -4.39982450e-01,  1.30357478e+00,\n",
       "         9.06631563e-02,  8.50183401e-01, -1.20741822e+00,\n",
       "        -1.46268090e+00],\n",
       "       [ 1.39760747e+00, -2.15598874e-01,  1.30357478e+00,\n",
       "         8.03189372e-01,  1.89889791e-01,  1.29797459e+00,\n",
       "        -3.60804816e-01],\n",
       "       [ 1.19126044e+00, -7.25602637e-01,  2.04211885e+00,\n",
       "         1.14271529e+00,  4.90382691e-01,  1.29797459e+00,\n",
       "         3.92047691e-01],\n",
       "       [-1.15604057e-01, -4.71647568e-01, -1.28132948e+00,\n",
       "         4.19668550e-01, -2.23683333e-01, -1.20741822e+00,\n",
       "        -1.05885197e+00],\n",
       "       [-1.54283766e+00, -6.23189442e-01,  5.65030706e-01,\n",
       "        -5.10919381e-01,  5.43364334e-01,  4.52781834e-02,\n",
       "         2.47823073e-01],\n",
       "       [ 1.44059643e+00, -6.45210890e-01,  9.34302743e-01,\n",
       "        -3.69370548e-01,  6.85703077e-01, -1.20741822e+00,\n",
       "        -1.13961775e+00],\n",
       "       [-1.32789284e+00, -4.49496998e-01,  2.04211885e+00,\n",
       "         1.16375633e+00,  2.87154598e-01,  1.29797459e+00,\n",
       "         5.24734340e-01],\n",
       "       [ 1.24284720e+00,  1.09691302e+00, -9.12057439e-01,\n",
       "         1.50041301e+00, -1.23191609e+00,  4.52781834e-02,\n",
       "        -1.09853980e-01],\n",
       "       [-1.24201850e-01,  9.30205108e-01, -9.12057439e-01,\n",
       "         1.49850019e+00, -1.03659571e+00,  4.52781834e-02,\n",
       "         1.27470235e+00],\n",
       "       [ 1.87908386e+00, -6.48229066e-01,  1.95758670e-01,\n",
       "        -4.54491130e-01,  4.93545774e-01,  4.52781834e-02,\n",
       "         1.41096855e-01],\n",
       "       [-1.92984192e-01,  3.10882082e+00, -1.65060151e+00,\n",
       "        -2.68579806e+00, -2.41965382e+00,  4.52781834e-02,\n",
       "         1.14778469e+00]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = std.fit_transform(x_test)\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "db91fa24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "598be18b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model1 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cbdde119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "f7655b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5976330275302708"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "68f0a4f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lasso model\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e251c23d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = linear_model.Lasso(alpha = 1, selection = 'cyclic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "67c474a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6139246873580149"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso.fit(x_train,y_train)\n",
    "lasso.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3735743a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "498baf09",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = cross_val_score(linear_model.Lasso(alpha = 1, selection = 'cyclic'),x_train, y_train, cv = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "a3d2ffca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48907861398359476"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3396dfad",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "04f29171",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf2 = cross_val_score(RandomForestRegressor(n_estimators = 10),x_train,y_train,cv = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "76abc4e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7461658500410574"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(clf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abf1f0c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "'''So random forest regressor is giviing the best accuracy.\n",
    "linear regression is also a good algorithm but in this case its accaracy is less.\n",
    "'''"
   ]
  }
 ],
 "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
}