Housing-Price-Prediction-and-Analysis.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["# Housing Price Prediction and Analysis\n"]},{"cell_type":"code","execution_count":1,"metadata":{},"outputs":[],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import seaborn as sns\n","from sklearn.pipeline import Pipeline\n","from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n","from sklearn.linear_model import LinearRegression\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{},"source":["## Importing Data Sets\n"]},{"cell_type":"code","execution_count":9,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[],"source":["file_name='USA-Housing-Dataset.csv'\n","df=pd.read_csv(file_name)"]},{"cell_type":"code","execution_count":3,"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>Unnamed: 0</th>\n","      <th>id</th>\n","      <th>date</th>\n","      <th>price</th>\n","      <th>bedrooms</th>\n","      <th>bathrooms</th>\n","      <th>sqft_living</th>\n","      <th>sqft_lot</th>\n","      <th>floors</th>\n","      <th>waterfront</th>\n","      <th>...</th>\n","      <th>grade</th>\n","      <th>sqft_above</th>\n","      <th>sqft_basement</th>\n","      <th>yr_built</th>\n","      <th>yr_renovated</th>\n","      <th>zipcode</th>\n","      <th>lat</th>\n","      <th>long</th>\n","      <th>sqft_living15</th>\n","      <th>sqft_lot15</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>7129300520</td>\n","      <td>20141013T000000</td>\n","      <td>221900.0</td>\n","      <td>3.0</td>\n","      <td>1.00</td>\n","      <td>1180</td>\n","      <td>5650</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>7</td>\n","      <td>1180</td>\n","      <td>0</td>\n","      <td>1955</td>\n","      <td>0</td>\n","      <td>98178</td>\n","      <td>47.5112</td>\n","      <td>-122.257</td>\n","      <td>1340</td>\n","      <td>5650</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>6414100192</td>\n","      <td>20141209T000000</td>\n","      <td>538000.0</td>\n","      <td>3.0</td>\n","      <td>2.25</td>\n","      <td>2570</td>\n","      <td>7242</td>\n","      <td>2.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>7</td>\n","      <td>2170</td>\n","      <td>400</td>\n","      <td>1951</td>\n","      <td>1991</td>\n","      <td>98125</td>\n","      <td>47.7210</td>\n","      <td>-122.319</td>\n","      <td>1690</td>\n","      <td>7639</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>5631500400</td>\n","      <td>20150225T000000</td>\n","      <td>180000.0</td>\n","      <td>2.0</td>\n","      <td>1.00</td>\n","      <td>770</td>\n","      <td>10000</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>6</td>\n","      <td>770</td>\n","      <td>0</td>\n","      <td>1933</td>\n","      <td>0</td>\n","      <td>98028</td>\n","      <td>47.7379</td>\n","      <td>-122.233</td>\n","      <td>2720</td>\n","      <td>8062</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>2487200875</td>\n","      <td>20141209T000000</td>\n","      <td>604000.0</td>\n","      <td>4.0</td>\n","      <td>3.00</td>\n","      <td>1960</td>\n","      <td>5000</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>7</td>\n","      <td>1050</td>\n","      <td>910</td>\n","      <td>1965</td>\n","      <td>0</td>\n","      <td>98136</td>\n","      <td>47.5208</td>\n","      <td>-122.393</td>\n","      <td>1360</td>\n","      <td>5000</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>1954400510</td>\n","      <td>20150218T000000</td>\n","      <td>510000.0</td>\n","      <td>3.0</td>\n","      <td>2.00</td>\n","      <td>1680</td>\n","      <td>8080</td>\n","      <td>1.0</td>\n","      <td>0</td>\n","      <td>...</td>\n","      <td>8</td>\n","      <td>1680</td>\n","      <td>0</td>\n","      <td>1987</td>\n","      <td>0</td>\n","      <td>98074</td>\n","      <td>47.6168</td>\n","      <td>-122.045</td>\n","      <td>1800</td>\n","      <td>7503</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 22 columns</p>\n","</div>"],"text/plain":["   Unnamed: 0          id             date     price  bedrooms  bathrooms  \\\n","0           0  7129300520  20141013T000000  221900.0       3.0       1.00   \n","1           1  6414100192  20141209T000000  538000.0       3.0       2.25   \n","2           2  5631500400  20150225T000000  180000.0       2.0       1.00   \n","3           3  2487200875  20141209T000000  604000.0       4.0       3.00   \n","4           4  1954400510  20150218T000000  510000.0       3.0       2.00   \n","\n","   sqft_living  sqft_lot  floors  waterfront  ...  grade  sqft_above  \\\n","0         1180      5650     1.0           0  ...      7        1180   \n","1         2570      7242     2.0           0  ...      7        2170   \n","2          770     10000     1.0           0  ...      6         770   \n","3         1960      5000     1.0           0  ...      7        1050   \n","4         1680      8080     1.0           0  ...      8        1680   \n","\n","   sqft_basement  yr_built  yr_renovated  zipcode      lat     long  \\\n","0              0      1955             0    98178  47.5112 -122.257   \n","1            400      1951          1991    98125  47.7210 -122.319   \n","2              0      1933             0    98028  47.7379 -122.233   \n","3            910      1965             0    98136  47.5208 -122.393   \n","4              0      1987             0    98074  47.6168 -122.045   \n","\n","   sqft_living15  sqft_lot15  \n","0           1340        5650  \n","1           1690        7639  \n","2           2720        8062  \n","3           1360        5000  \n","4           1800        7503  \n","\n","[5 rows x 22 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["df.head()"]},{"cell_type":"code","execution_count":4,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["Unnamed: 0         int64\n","id                 int64\n","date              object\n","price            float64\n","bedrooms         float64\n","bathrooms        float64\n","sqft_living        int64\n","sqft_lot           int64\n","floors           float64\n","waterfront         int64\n","view               int64\n","condition          int64\n","grade              int64\n","sqft_above         int64\n","sqft_basement      int64\n","yr_built           int64\n","yr_renovated       int64\n","zipcode            int64\n","lat              float64\n","long             float64\n","sqft_living15      int64\n","sqft_lot15         int64\n","dtype: object"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["df.dtypes"]},{"cell_type":"code","execution_count":8,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"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>price</th>\n","      <th>bedrooms</th>\n","      <th>bathrooms</th>\n","      <th>sqft_living</th>\n","      <th>sqft_lot</th>\n","      <th>floors</th>\n","      <th>waterfront</th>\n","      <th>view</th>\n","      <th>condition</th>\n","      <th>grade</th>\n","      <th>sqft_above</th>\n","      <th>sqft_basement</th>\n","      <th>yr_built</th>\n","      <th>yr_renovated</th>\n","      <th>zipcode</th>\n","      <th>lat</th>\n","      <th>long</th>\n","      <th>sqft_living15</th>\n","      <th>sqft_lot15</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>2.161300e+04</td>\n","      <td>21600.000000</td>\n","      <td>21603.000000</td>\n","      <td>21613.000000</td>\n","      <td>2.161300e+04</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>5.400881e+05</td>\n","      <td>3.372870</td>\n","      <td>2.115736</td>\n","      <td>2079.899736</td>\n","      <td>1.510697e+04</td>\n","      <td>1.494309</td>\n","      <td>0.007542</td>\n","      <td>0.234303</td>\n","      <td>3.409430</td>\n","      <td>7.656873</td>\n","      <td>1788.390691</td>\n","      <td>291.509045</td>\n","      <td>1971.005136</td>\n","      <td>84.402258</td>\n","      <td>98077.939805</td>\n","      <td>47.560053</td>\n","      <td>-122.213896</td>\n","      <td>1986.552492</td>\n","      <td>12768.455652</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>3.671272e+05</td>\n","      <td>0.926657</td>\n","      <td>0.768996</td>\n","      <td>918.440897</td>\n","      <td>4.142051e+04</td>\n","      <td>0.539989</td>\n","      <td>0.086517</td>\n","      <td>0.766318</td>\n","      <td>0.650743</td>\n","      <td>1.175459</td>\n","      <td>828.090978</td>\n","      <td>442.575043</td>\n","      <td>29.373411</td>\n","      <td>401.679240</td>\n","      <td>53.505026</td>\n","      <td>0.138564</td>\n","      <td>0.140828</td>\n","      <td>685.391304</td>\n","      <td>27304.179631</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>7.500000e+04</td>\n","      <td>1.000000</td>\n","      <td>0.500000</td>\n","      <td>290.000000</td>\n","      <td>5.200000e+02</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>290.000000</td>\n","      <td>0.000000</td>\n","      <td>1900.000000</td>\n","      <td>0.000000</td>\n","      <td>98001.000000</td>\n","      <td>47.155900</td>\n","      <td>-122.519000</td>\n","      <td>399.000000</td>\n","      <td>651.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>3.219500e+05</td>\n","      <td>3.000000</td>\n","      <td>1.750000</td>\n","      <td>1427.000000</td>\n","      <td>5.040000e+03</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>7.000000</td>\n","      <td>1190.000000</td>\n","      <td>0.000000</td>\n","      <td>1951.000000</td>\n","      <td>0.000000</td>\n","      <td>98033.000000</td>\n","      <td>47.471000</td>\n","      <td>-122.328000</td>\n","      <td>1490.000000</td>\n","      <td>5100.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>4.500000e+05</td>\n","      <td>3.000000</td>\n","      <td>2.250000</td>\n","      <td>1910.000000</td>\n","      <td>7.618000e+03</td>\n","      <td>1.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>7.000000</td>\n","      <td>1560.000000</td>\n","      <td>0.000000</td>\n","      <td>1975.000000</td>\n","      <td>0.000000</td>\n","      <td>98065.000000</td>\n","      <td>47.571800</td>\n","      <td>-122.230000</td>\n","      <td>1840.000000</td>\n","      <td>7620.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>6.450000e+05</td>\n","      <td>4.000000</td>\n","      <td>2.500000</td>\n","      <td>2550.000000</td>\n","      <td>1.068800e+04</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>4.000000</td>\n","      <td>8.000000</td>\n","      <td>2210.000000</td>\n","      <td>560.000000</td>\n","      <td>1997.000000</td>\n","      <td>0.000000</td>\n","      <td>98118.000000</td>\n","      <td>47.678000</td>\n","      <td>-122.125000</td>\n","      <td>2360.000000</td>\n","      <td>10083.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>7.700000e+06</td>\n","      <td>33.000000</td>\n","      <td>8.000000</td>\n","      <td>13540.000000</td>\n","      <td>1.651359e+06</td>\n","      <td>3.500000</td>\n","      <td>1.000000</td>\n","      <td>4.000000</td>\n","      <td>5.000000</td>\n","      <td>13.000000</td>\n","      <td>9410.000000</td>\n","      <td>4820.000000</td>\n","      <td>2015.000000</td>\n","      <td>2015.000000</td>\n","      <td>98199.000000</td>\n","      <td>47.777600</td>\n","      <td>-121.315000</td>\n","      <td>6210.000000</td>\n","      <td>871200.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              price      bedrooms     bathrooms   sqft_living      sqft_lot  \\\n","count  2.161300e+04  21600.000000  21603.000000  21613.000000  2.161300e+04   \n","mean   5.400881e+05      3.372870      2.115736   2079.899736  1.510697e+04   \n","std    3.671272e+05      0.926657      0.768996    918.440897  4.142051e+04   \n","min    7.500000e+04      1.000000      0.500000    290.000000  5.200000e+02   \n","25%    3.219500e+05      3.000000      1.750000   1427.000000  5.040000e+03   \n","50%    4.500000e+05      3.000000      2.250000   1910.000000  7.618000e+03   \n","75%    6.450000e+05      4.000000      2.500000   2550.000000  1.068800e+04   \n","max    7.700000e+06     33.000000      8.000000  13540.000000  1.651359e+06   \n","\n","             floors    waterfront          view     condition         grade  \\\n","count  21613.000000  21613.000000  21613.000000  21613.000000  21613.000000   \n","mean       1.494309      0.007542      0.234303      3.409430      7.656873   \n","std        0.539989      0.086517      0.766318      0.650743      1.175459   \n","min        1.000000      0.000000      0.000000      1.000000      1.000000   \n","25%        1.000000      0.000000      0.000000      3.000000      7.000000   \n","50%        1.500000      0.000000      0.000000      3.000000      7.000000   \n","75%        2.000000      0.000000      0.000000      4.000000      8.000000   \n","max        3.500000      1.000000      4.000000      5.000000     13.000000   \n","\n","         sqft_above  sqft_basement      yr_built  yr_renovated       zipcode  \\\n","count  21613.000000   21613.000000  21613.000000  21613.000000  21613.000000   \n","mean    1788.390691     291.509045   1971.005136     84.402258  98077.939805   \n","std      828.090978     442.575043     29.373411    401.679240     53.505026   \n","min      290.000000       0.000000   1900.000000      0.000000  98001.000000   \n","25%     1190.000000       0.000000   1951.000000      0.000000  98033.000000   \n","50%     1560.000000       0.000000   1975.000000      0.000000  98065.000000   \n","75%     2210.000000     560.000000   1997.000000      0.000000  98118.000000   \n","max     9410.000000    4820.000000   2015.000000   2015.000000  98199.000000   \n","\n","                lat          long  sqft_living15     sqft_lot15  \n","count  21613.000000  21613.000000   21613.000000   21613.000000  \n","mean      47.560053   -122.213896    1986.552492   12768.455652  \n","std        0.138564      0.140828     685.391304   27304.179631  \n","min       47.155900   -122.519000     399.000000     651.000000  \n","25%       47.471000   -122.328000    1490.000000    5100.000000  \n","50%       47.571800   -122.230000    1840.000000    7620.000000  \n","75%       47.678000   -122.125000    2360.000000   10083.000000  \n","max       47.777600   -121.315000    6210.000000  871200.000000  "]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["df.describe()"]},{"cell_type":"markdown","metadata":{},"source":["## Data Wrangling\n"]},{"cell_type":"markdown","metadata":{},"source":["Droping the columns <code>\"id\"</code>  and <code>\"Unnamed: 0\"</code>\n"]},{"cell_type":"code","execution_count":28,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"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>date</th>\n","      <th>price</th>\n","      <th>bedrooms</th>\n","      <th>bathrooms</th>\n","      <th>sqft_living</th>\n","      <th>sqft_lot</th>\n","      <th>floors</th>\n","      <th>waterfront</th>\n","      <th>view</th>\n","      <th>condition</th>\n","      <th>grade</th>\n","      <th>sqft_above</th>\n","      <th>sqft_basement</th>\n","      <th>yr_built</th>\n","      <th>yr_renovated</th>\n","      <th>zipcode</th>\n","      <th>lat</th>\n","      <th>long</th>\n","      <th>sqft_living15</th>\n","      <th>sqft_lot15</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>21613</td>\n","      <td>2.161300e+04</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>2.161300e+04</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","      <td>21613.000000</td>\n","    </tr>\n","    <tr>\n","      <th>unique</th>\n","      <td>372</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>top</th>\n","      <td>20140623T000000</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>freq</th>\n","      <td>142</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>NaN</td>\n","      <td>5.400881e+05</td>\n","      <td>3.372870</td>\n","      <td>2.115736</td>\n","      <td>2079.899736</td>\n","      <td>1.510697e+04</td>\n","      <td>1.494309</td>\n","      <td>0.007542</td>\n","      <td>0.234303</td>\n","      <td>3.409430</td>\n","      <td>7.656873</td>\n","      <td>1788.390691</td>\n","      <td>291.509045</td>\n","      <td>1971.005136</td>\n","      <td>84.402258</td>\n","      <td>98077.939805</td>\n","      <td>47.560053</td>\n","      <td>-122.213896</td>\n","      <td>1986.552492</td>\n","      <td>12768.455652</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>NaN</td>\n","      <td>3.671272e+05</td>\n","      <td>0.926378</td>\n","      <td>0.768818</td>\n","      <td>918.440897</td>\n","      <td>4.142051e+04</td>\n","      <td>0.539989</td>\n","      <td>0.086517</td>\n","      <td>0.766318</td>\n","      <td>0.650743</td>\n","      <td>1.175459</td>\n","      <td>828.090978</td>\n","      <td>442.575043</td>\n","      <td>29.373411</td>\n","      <td>401.679240</td>\n","      <td>53.505026</td>\n","      <td>0.138564</td>\n","      <td>0.140828</td>\n","      <td>685.391304</td>\n","      <td>27304.179631</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>NaN</td>\n","      <td>7.500000e+04</td>\n","      <td>1.000000</td>\n","      <td>0.500000</td>\n","      <td>290.000000</td>\n","      <td>5.200000e+02</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>1.000000</td>\n","      <td>1.000000</td>\n","      <td>290.000000</td>\n","      <td>0.000000</td>\n","      <td>1900.000000</td>\n","      <td>0.000000</td>\n","      <td>98001.000000</td>\n","      <td>47.155900</td>\n","      <td>-122.519000</td>\n","      <td>399.000000</td>\n","      <td>651.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>NaN</td>\n","      <td>3.219500e+05</td>\n","      <td>3.000000</td>\n","      <td>1.750000</td>\n","      <td>1427.000000</td>\n","      <td>5.040000e+03</td>\n","      <td>1.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>7.000000</td>\n","      <td>1190.000000</td>\n","      <td>0.000000</td>\n","      <td>1951.000000</td>\n","      <td>0.000000</td>\n","      <td>98033.000000</td>\n","      <td>47.471000</td>\n","      <td>-122.328000</td>\n","      <td>1490.000000</td>\n","      <td>5100.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>NaN</td>\n","      <td>4.500000e+05</td>\n","      <td>3.000000</td>\n","      <td>2.250000</td>\n","      <td>1910.000000</td>\n","      <td>7.618000e+03</td>\n","      <td>1.500000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>3.000000</td>\n","      <td>7.000000</td>\n","      <td>1560.000000</td>\n","      <td>0.000000</td>\n","      <td>1975.000000</td>\n","      <td>0.000000</td>\n","      <td>98065.000000</td>\n","      <td>47.571800</td>\n","      <td>-122.230000</td>\n","      <td>1840.000000</td>\n","      <td>7620.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>NaN</td>\n","      <td>6.450000e+05</td>\n","      <td>4.000000</td>\n","      <td>2.500000</td>\n","      <td>2550.000000</td>\n","      <td>1.068800e+04</td>\n","      <td>2.000000</td>\n","      <td>0.000000</td>\n","      <td>0.000000</td>\n","      <td>4.000000</td>\n","      <td>8.000000</td>\n","      <td>2210.000000</td>\n","      <td>560.000000</td>\n","      <td>1997.000000</td>\n","      <td>0.000000</td>\n","      <td>98118.000000</td>\n","      <td>47.678000</td>\n","      <td>-122.125000</td>\n","      <td>2360.000000</td>\n","      <td>10083.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>NaN</td>\n","      <td>7.700000e+06</td>\n","      <td>33.000000</td>\n","      <td>8.000000</td>\n","      <td>13540.000000</td>\n","      <td>1.651359e+06</td>\n","      <td>3.500000</td>\n","      <td>1.000000</td>\n","      <td>4.000000</td>\n","      <td>5.000000</td>\n","      <td>13.000000</td>\n","      <td>9410.000000</td>\n","      <td>4820.000000</td>\n","      <td>2015.000000</td>\n","      <td>2015.000000</td>\n","      <td>98199.000000</td>\n","      <td>47.777600</td>\n","      <td>-121.315000</td>\n","      <td>6210.000000</td>\n","      <td>871200.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["                   date         price      bedrooms     bathrooms  \\\n","count             21613  2.161300e+04  21613.000000  21613.000000   \n","unique              372           NaN           NaN           NaN   \n","top     20140623T000000           NaN           NaN           NaN   \n","freq                142           NaN           NaN           NaN   \n","mean                NaN  5.400881e+05      3.372870      2.115736   \n","std                 NaN  3.671272e+05      0.926378      0.768818   \n","min                 NaN  7.500000e+04      1.000000      0.500000   \n","25%                 NaN  3.219500e+05      3.000000      1.750000   \n","50%                 NaN  4.500000e+05      3.000000      2.250000   \n","75%                 NaN  6.450000e+05      4.000000      2.500000   \n","max                 NaN  7.700000e+06     33.000000      8.000000   \n","\n","         sqft_living      sqft_lot        floors    waterfront          view  \\\n","count   21613.000000  2.161300e+04  21613.000000  21613.000000  21613.000000   \n","unique           NaN           NaN           NaN           NaN           NaN   \n","top              NaN           NaN           NaN           NaN           NaN   \n","freq             NaN           NaN           NaN           NaN           NaN   \n","mean     2079.899736  1.510697e+04      1.494309      0.007542      0.234303   \n","std       918.440897  4.142051e+04      0.539989      0.086517      0.766318   \n","min       290.000000  5.200000e+02      1.000000      0.000000      0.000000   \n","25%      1427.000000  5.040000e+03      1.000000      0.000000      0.000000   \n","50%      1910.000000  7.618000e+03      1.500000      0.000000      0.000000   \n","75%      2550.000000  1.068800e+04      2.000000      0.000000      0.000000   \n","max     13540.000000  1.651359e+06      3.500000      1.000000      4.000000   \n","\n","           condition         grade    sqft_above  sqft_basement      yr_built  \\\n","count   21613.000000  21613.000000  21613.000000   21613.000000  21613.000000   \n","unique           NaN           NaN           NaN            NaN           NaN   \n","top              NaN           NaN           NaN            NaN           NaN   \n","freq             NaN           NaN           NaN            NaN           NaN   \n","mean        3.409430      7.656873   1788.390691     291.509045   1971.005136   \n","std         0.650743      1.175459    828.090978     442.575043     29.373411   \n","min         1.000000      1.000000    290.000000       0.000000   1900.000000   \n","25%         3.000000      7.000000   1190.000000       0.000000   1951.000000   \n","50%         3.000000      7.000000   1560.000000       0.000000   1975.000000   \n","75%         4.000000      8.000000   2210.000000     560.000000   1997.000000   \n","max         5.000000     13.000000   9410.000000    4820.000000   2015.000000   \n","\n","        yr_renovated       zipcode           lat          long  sqft_living15  \\\n","count   21613.000000  21613.000000  21613.000000  21613.000000   21613.000000   \n","unique           NaN           NaN           NaN           NaN            NaN   \n","top              NaN           NaN           NaN           NaN            NaN   \n","freq             NaN           NaN           NaN           NaN            NaN   \n","mean       84.402258  98077.939805     47.560053   -122.213896    1986.552492   \n","std       401.679240     53.505026      0.138564      0.140828     685.391304   \n","min         0.000000  98001.000000     47.155900   -122.519000     399.000000   \n","25%         0.000000  98033.000000     47.471000   -122.328000    1490.000000   \n","50%         0.000000  98065.000000     47.571800   -122.230000    1840.000000   \n","75%         0.000000  98118.000000     47.678000   -122.125000    2360.000000   \n","max      2015.000000  98199.000000     47.777600   -121.315000    6210.000000   \n","\n","           sqft_lot15  \n","count    21613.000000  \n","unique            NaN  \n","top               NaN  \n","freq              NaN  \n","mean     12768.455652  \n","std      27304.179631  \n","min        651.000000  \n","25%       5100.000000  \n","50%       7620.000000  \n","75%      10083.000000  \n","max     871200.000000  "]},"execution_count":28,"metadata":{},"output_type":"execute_result"}],"source":["df.drop(['Unnamed: 0','id'],axis=1,inplace=True)\n","df.describe(include='all')"]},{"cell_type":"markdown","metadata":{},"source":["Missing values for the columns <code> bedrooms</code>  and <code> bathrooms </code>\n"]},{"cell_type":"code","execution_count":11,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["number of NaN values for the column bedrooms : 13\n","number of NaN values for the column bathrooms : 10\n"]}],"source":["print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n","print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())\n"]},{"cell_type":"markdown","metadata":{},"source":["Replacing the missing values\n"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[],"source":["mean=df['bedrooms'].mean()\n","df['bedrooms'].replace(np.nan,mean, inplace=True)"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["mean=df['bathrooms'].mean()\n","df['bathrooms'].replace(np.nan,mean, inplace=True)"]},{"cell_type":"code","execution_count":14,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["number of NaN values for the column bedrooms : 0\n","number of NaN values for the column bathrooms : 0\n"]}],"source":["print(\"number of NaN values for the column bedrooms :\", df['bedrooms'].isnull().sum())\n","print(\"number of NaN values for the column bathrooms :\", df['bathrooms'].isnull().sum())"]},{"cell_type":"markdown","metadata":{},"source":["## Exploratory Data Analysis\n"]},{"cell_type":"markdown","metadata":{},"source":["### Question 3\n","\n","Use the method <code>value_counts</code> to count the number of houses with unique floor values, use the method <code>.to_frame()</code> to convert it to a dataframe.\n"]},{"cell_type":"code","execution_count":16,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"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>count</th>\n","    </tr>\n","    <tr>\n","      <th>floors</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>1.0</th>\n","      <td>10680</td>\n","    </tr>\n","    <tr>\n","      <th>2.0</th>\n","      <td>8241</td>\n","    </tr>\n","    <tr>\n","      <th>1.5</th>\n","      <td>1910</td>\n","    </tr>\n","    <tr>\n","      <th>3.0</th>\n","      <td>613</td>\n","    </tr>\n","    <tr>\n","      <th>2.5</th>\n","      <td>161</td>\n","    </tr>\n","    <tr>\n","      <th>3.5</th>\n","      <td>8</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        count\n","floors       \n","1.0     10680\n","2.0      8241\n","1.5      1910\n","3.0       613\n","2.5       161\n","3.5         8"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["floors_counts=df['floors'].value_counts().to_frame()\n","floors_counts.rename(columns={\"floors\":\"value_counts\"},inplace=True)\n","floors_counts.index.name=\"floors\"\n","floors_counts.head(10)"]},{"cell_type":"markdown","metadata":{},"source":["### Question 4\n","\n","Use the function <code>boxplot</code> in the seaborn library  to  determine whether houses with a waterfront view or without a waterfront view have more price outliers.\n"]},{"cell_type":"code","execution_count":18,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["<Axes: xlabel='waterfront', ylabel='price'>"]},"execution_count":18,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.boxplot(x=\"waterfront\",y=\"price\",data=df)"]},{"cell_type":"markdown","metadata":{},"source":["### Question 5\n","\n","Use the function <code>regplot</code>  in the seaborn library  to  determine if the feature <code>sqft_above</code> is negatively or positively correlated with price.\n"]},{"cell_type":"code","execution_count":25,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["<Axes: xlabel='sqft_above', ylabel='price'>"]},"execution_count":25,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAioAAAHACAYAAACMB0PKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACgVElEQVR4nOy9eXhcV53n/T3nbrWoqrRYsixbdrzHW1ZnIYSEdNNAhpetZ+gXOjQBmhkmHZaBgXk7NNCkGzA0kKGnGzzpwBMS1gaahG5mIEB3xyaYLHbiWN7XxJYsWZIl1V53O+f941ZdVZVKqpIsqUry7/M8ehKVqu49VZLv+d7f8v0xKaUEQRAEQRBEA8LrvQCCIAiCIIjJIKFCEARBEETDQkKFIAiCIIiGhYQKQRAEQRANCwkVgiAIgiAaFhIqBEEQBEE0LCRUCIIgCIJoWEioEARBEATRsJBQIQiCIAiiYSGhQhAEQRBEw7JohMru3bvxxje+EV1dXWCM4fHHH5/2MaSU+PKXv4wNGzbAMAwsX74cn/vc52Z/sQRBEARB1IRa7wXMFul0GldffTXe+9734g//8A9ndIwPf/jD+OUvf4kvf/nL2LZtG0ZGRjAyMjLLKyUIgiAIolbYYhxKyBjDY489hre85S3+Y6Zp4i/+4i/w/e9/H2NjY9i6dSu++MUv4tWvfjUA4MiRI7jqqqtw8OBBbNy4sT4LJwiCIAiihEWT+qnGBz7wAfzud7/DD37wAxw4cABve9vb8PrXvx4nTpwAAPzLv/wL1qxZg5/97GdYvXo1rrjiCrzvfe+jiApBEARB1JHLQqicPXsWDz/8MH70ox/hVa96FdauXYuPfexjuPXWW/Hwww8DAE6fPo2XX34ZP/rRj/Doo4/iW9/6Fvbt24f/9J/+U51XTxAEQRCXL4umRmUqenp64LouNmzYUPK4aZpoa2sDAAghYJomHn30Uf953/zmN3H99dfj2LFjlA4iCIIgiDpwWQiVVCoFRVGwb98+KIpS8rOmpiYAwLJly6CqaomY2bRpEwAvIkNChSAIgiDmn8tCqFx77bVwXReDg4N41ateVfE5r3zlK+E4Dk6dOoW1a9cCAI4fPw4AWLVq1bytlSAIgiCIcRZN108qlcLJkycBeMLkgQcewB133IHW1lasXLkS73znO/Hb3/4WX/nKV3DttddiaGgI//qv/4qrrroKb3jDGyCEwA033ICmpiZ89atfhRAC9957L6LRKH75y1/W+d0RBEEQxOXJohEqTz75JO64444Jj99999341re+Bdu28dnPfhaPPvoo+vr6sGTJEtx88824//77sW3bNgDA+fPn8cEPfhC//OUvEQ6Hceedd+IrX/kKWltb5/vtEARBEASBRSRUCIIgCIJYfFwW7ckEQRAEQSxMSKgQBEEQBNGw1LXrx3VdfOYzn8F3vvMdDAwMoKurC+9+97vxyU9+Eoyxqq8XQuD8+fOIRCI1PZ8gCIIgiPojpUQymURXVxc4nzpmUleh8sUvfhE7d+7EI488gi1btmDv3r14z3veg1gshg996ENVX3/+/Hl0d3fPw0oJgiAIgphtzp07hxUrVkz5nLoKlT179uDNb34z3vCGNwAArrjiCnz/+9/Hs88+W9PrI5EIAO+NRqPROVsnQRAEQRCzRyKRQHd3t7+PT0Vdhcott9yCf/iHf8Dx48exYcMGvPjii3jqqafwwAMP1PT6QronGo2SUCEIgiCIBUYtZRt1FSp//ud/jkQigSuvvBKKosB1XXzuc5/DXXfdVfH5pmnCNE3/+0QiMV9LJQiCIAiiDtS16+eHP/whvvvd7+J73/senn/+eTzyyCP48pe/jEceeaTi83fs2IFYLOZ/UX0KQRAEQSxu6mr41t3djT//8z/Hvffe6z/22c9+Ft/5zndw9OjRCc+vFFHp7u5GPB6n1A9BEARBLBASiQRisVhN+3ddUz+ZTGZCW5KiKBBCVHy+YRgwDGM+lkYQBEEQRANQV6Hyxje+EZ/73OewcuVKbNmyBS+88AIeeOABvPe9763nsgiCIAiCaBDqmvpJJpP41Kc+hcceewyDg4Po6urCO97xDnz605+GrutVXz+d0BFBEARBEI3BdPbvBT2UkIQKQRAEQSw8prN/06wfgiAIgiAaFhIqBEEQBEE0LHUtpiUIgiCIyw0hJA6dT2AkY6E1pGNLVxSc02DdySChQhAEQRDzxJ6Tw9i56xRODaZguxKawrC2own33L4Wt6xbUu/lNSSU+iEIgiCIeWDPyWF84rEeHOlPIGyo6IgYCBsqjvQn8YnHerDn5HC9l9iQkFAhCIIgiDlGCImdu04hZTrojAYQ0BRwzhDQFHRGDaRMFzt3nYIQC7YRd84goUIQBEEQc8yh8wmcGkyhJaRPmBjMGENzSMOpwRQOnadhu+WQUCEIgiCIOWYkY8F2JXSl8rZrKBy2kBjJWPO8ssaHhApBEARBzDGtIR2awmC5lWfZma6AxhlaQ9Vd2S83SKgQBEEQxByzpSuKtR1NGM3YKDeEl1JiLGNjbUcTtnSRy3o5JFQIgiAIYo7hnOGe29eiyVAwkDCRtV0IIZG1XQwkTDQZCu65fS35qVSAhApBEARBzAO3rFuCz791GzYtiyBjOhhMmciYDjYti+Dzb91GPiqTQIZvBEEQBDFP3LJuCW5e00bOtNOAhApBEARBzCOcM2xbEav3MhYMlPohCIIgCKJhIaFCEARBEETDQkKFIAiCIIiGhYQKQRAEQRANCwkVgiAIgiAaFhIqBEEQBEE0LCRUCIIgCIJoWEioEARBEATRsJBQIQiCIAiiYSGhQhAEQRBEw0JChSAIgiCIhoWECkEQBEEQDQsJFYIgCIIgGhYSKgRBEARBNCwkVAiCIAiCaFhIqBAEQRAE0bCo9V4AQRAEQVwKQkgcOp/ASMZCa0jHlq4oOGf1XhYxS5BQIQiCIBYse04OY+euUzg1mILtSmgKw9qOJtxz+1rcsm5JvZdHzAKU+iEIgiAWJHtODuMTj/XgSH8CYUNFR8RA2FBxpD+JTzzWgz0nh+u9RGIWqKtQueKKK8AYm/B177331nNZBEEQRIMjhMTOXaeQMh10RgMIaAo4ZwhoCjqjBlKmi527TkEIWe+lEpdIXYXKc889h/7+fv/rV7/6FQDgbW97Wz2XRRAEQTQ4h84ncGowhZaQDsZK61EYY2gOaTg1mMKh84k6rZCYLepao9Le3l7y/Re+8AWsXbsWt99+e51WRBAEQSwERjIWbFdCVyrfbxsKR1xIjGSseV4ZMds0TI2KZVn4zne+g/e+970T1DFBEARBFNMa0qEpDJYrKv7cdAU0ztAa0ud5ZcRs0zBdP48//jjGxsbw7ne/e9LnmKYJ0zT97xMJCukRBEFcjmzpimJtRxOO9CfRGeUlN7hSSoxlbGxaFsGWrmgdV0nMBg0TUfnmN7+JO++8E11dXZM+Z8eOHYjFYv5Xd3f3PK6QIAiCaBQ4Z7jn9rVoMhQMJExkbRdCSGRtFwMJE02GgntuX7uo/FSEkOjpjWPX8SH09MYvm0JhJqWs+zt9+eWXsWbNGvzkJz/Bm9/85kmfVymi0t3djXg8jmiUVDNBEMTlRomPipDQ+OL0UVlsfjGJRAKxWKym/bshUj8PP/wwOjo68IY3vGHK5xmGAcMw5mlVBEEQRKNzy7oluHlN26J2pi34xaRMBy0hHbrCYbnC94v5/Fu3LUixUit1FypCCDz88MO4++67oap1Xw5BEASxwOCcYduKWL2XMSeU+8UUanECXEFnlGMgYWLnrlO4eU3bohJnxdS9RuXXv/41zp49i/e+9731XgpBEARBNBTkF9MAEZXXvva1aIAyGYIgCIJoOMgvpgEiKgRBEARBVIb8YkioEARBEETDUvCLGc3YE7IPBb+YtR1Ni9ovhoQKQRAEQTQol6NfTDkkVAiCIAiigbll3RJ8/q3bsGlZBBnTwWDKRMZ0sGlZZNG3JgMNUExLEARBEMTUXA5+MZNBQoUgCIIgFgCL2S9mKij1QxAEQRBEw0JChSAIgiCIhoWECkEQBEEQDQsJFYIgCIIgGhYSKgRBEARBNCwkVAiCIAiCaFhIqBAEQRAE0bCQUCEIgiAIomEhoUIQBEEQRMNCQoUgCIIgiIaFhApBEARBEA0LCRWCIAiCIBoWEioEQRAEQTQsJFQIgiAIgmhYSKgQBEEQBNGwkFAhCIIgCKJhIaFCEARBEETDQkKFIAiCIIiGhYQKQRAEQRANCwkVgiAIgiAaFhIqBEEQBEE0LCRUCIIgCIJoWEioEARBEATRsJBQIQiCIAiiYSGhQhAEQRBEw0JChSAIgiCIhoWECkEQBEEQDUvdhUpfXx/e+c53oq2tDcFgENu2bcPevXvrvSyCIAiCIBoAtZ4nHx0dxStf+Urccccd+PnPf4729nacOHECLS0t9VwWQRAEQRANQl2Fyhe/+EV0d3fj4Ycf9h9bvXp1HVdEEARBEEQjUdfUzz//8z9j+/bteNvb3oaOjg5ce+21eOihh+q5JIIgCIIgGoi6CpXTp09j586dWL9+PZ544gncc889+NCHPoRHHnmk4vNN00QikSj5IgiCIAhi8cKklLJeJ9d1Hdu3b8eePXv8xz70oQ/hueeew+9+97sJz//MZz6D+++/f8Lj8Xgc0Wh0TtdKEARBEMTskEgkEIvFatq/6xpRWbZsGTZv3lzy2KZNm3D27NmKz7/vvvsQj8f9r3Pnzs3HMgmCIAiCqBN1LaZ95StfiWPHjpU8dvz4caxatari8w3DgGEY87E0giAIgiAagLpGVD7ykY/g6aefxuc//3mcPHkS3/ve9/AP//APuPfee+u5LIIgCIIgGoS6CpUbbrgBjz32GL7//e9j69at+Ou//mt89atfxV133VXPZREEQRAE0SDUtZj2UplOMQ5BEARBEI3BgimmJQiCIAiCmAoSKgRBEARBNCwkVAiCIAiCaFhIqBAEQRAE0bCQUCEIgiAIomEhoUIQBEEQRMNCQoUgCIIgiIaFhApBEARBEA1LXWf9EARBEFMjhMSh8wmMZCy0hnRs6YqCc1bvZRHEvEFChSAIokHZc3IYO3edwqnBFGxXQlMY1nY04Z7b1+KWdUvqvTyCmBco9UMQBNGA7Dk5jE881oMj/QmEDRUdEQNhQ8WR/iQ+8VgP9pwcrvcSCWJeIKFCEATRYAghsXPXKaRMB53RAAKaAs4ZApqCzqiBlOli565TEGLBjmojiJohoUIQBNFgHDqfwKnBFFpCOhgrrUdhjKE5pOHUYAqHzifqtEKCmD9IqBAEQTQYIxkLtiuhK5Uv0YbCYQuJkYw1zysjiPmHhApBEESD0RrSoSkMlisq/tx0BTTO0BrS53llBDH/kFAhCIJoMLZ0RbG2owmjGRtSltahSCkxlrGxtqMJW7qidVohQcwfJFQIgiAaDM4Z7rl9LZoMBQMJE1nbhRASWdvFQMJEk6HgntvXkp8KcVlAQoUgCKIBuWXdEnz+rduwaVkEGdPBYMpExnSwaVkEn3/rtln3URFCoqc3jl3Hh9DTG6eOIqJhIMM3giCIBuWWdUtw85q2OXemJWM5opFhsjwBuoBIJBKIxWKIx+OIRilXSxAEMV0KxnIp00FLSIeucFiuwGjGRpOhzEn0hiCms39T6ocgiAUHpSlmBzKWIxYClPohCGJBQWmK2WM6xnLbVsTqtEricociKgRBLBho/s3sQsZyxEKAhApBEAsCSlPMPmQsRywESKgQBLEgoPk3sw8ZyxELARIqBEEsCChNMfuQsRyxECChQhDEgoDSFHPDfBvLEcR0oa4fgiAWBIU0xZH+JDqjvCT9U0hTbFoWoTTFDJgvYzmCmAkkVAiCWBAU0hSfeKwHAwkTzSENhsJhugJjeXOyhZSmEEI2lDDgnFELMtGQkFAhCGLBUEhTFHxU4kJC4wyblkUWlI8KecEQRO2QhT5BEAuORotGTAeyrCeI6e3fFFEhCGLBsVDTFOVeMIU6mwBX0BnlGEiY2LnrFG5e07ZghBdBzDXU9UMQBDFPkBcMQUyfugqVz3zmM2CMlXxdeeWV9VwSQRDEnEFeMAQxfeqe+tmyZQt+/etf+9+rat2XRBAEMScUe8EEuDLh5+QFQxATqbsqUFUVnZ2d9V4GQRDEnENeMAQxfepeo3LixAl0dXVhzZo1uOuuu3D27Nl6L4kgCGJOIMt6gpg+dW1P/vnPf45UKoWNGzeiv78f999/P/r6+nDw4EFEIpEJzzdNE6Zp+t8nEgl0d3dTezJBEAuKEh+VvBcM+agQlxPTaU9uKB+VsbExrFq1Cg888AD+9E//dMLPP/OZz+D++++f8DgJFYIgFhoL2QuGIC6VBStUAOCGG27Aa17zGuzYsWPCzyiiQhAEQRALn+kIlbrXqBSTSqVw6tQpLFu2rOLPDcNANBot+SIIgiAIYvFSV6HysY99DLt27cJLL72EPXv24K1vfSsURcE73vGOei6LIAiCIIgGoa7tyb29vXjHO96Bixcvor29HbfeeiuefvpptLe313NZBEEQBEE0CHUVKj/4wQ/qeXqCIAiCIBqchqpRIQiCIAiCKIaECkEQBEEQDQsJFYIgCIIgGpa6z/ohCIIohozQCIIohoQKQRANQ4m1vCuhKWQtTxCXO5T6IQiiIdhzchifeKwHR/oTCBsqOiIGwoaKI/1JfOKxHuw5OVzvJRIEUQdIqBAEUXeEkNi56xRSpoPOaAABTQHnDAFNQWfUQMp0sXPXKQjRUBM/CIKYB0ioEARRdw6dT+DUYAotIR2MldajMMbQHNJwajCFQ+cTdVohQRD1goQKQRB1ZyRjwXYldKXyJclQOGwhMZKx5nllBEHUGxIqBEHUndaQDk1hsFxR8eemK6BxhtaQPs8rIwii3pBQIQii7mzpimJtRxNGMzakLK1DkVJiLGNjbUcTtnTRxHSCuNwgoUIQRN3hnOGe29eiyVAwkDCRtV0IIZG1XQwkTDQZCu65fS35qZQhhERPbxy7jg+hpzcOxxEl31PxMbEYIB8VgiAaglvWLcHn37rN91GJCwmNM2xaFiEflQqUe84IKeBKQGEMnDHyoCEWDUyWx1kXEIlEArFYDPF4HNEohYQJYjFAzrTVKXjOpEwHLSEdliNwPp6F40oonGF5SxC6wjGasdFkKPj8W7eRWCEaiuns3xRRIQiioeCcYduKWL2X0bCUe84AQH88CyEBXWVwBXAxZeGKJSF0Rg0MJEzs3HUKN69pI8FHLEioRoUgCGIBUe45k7MFTEdA5QyccSicwXRc5CxBHjTEooCECkEQxAKi3HPGEQJSAoVYCWOAlN7jAHnQEAsfEioEQRALiHLPGZVzT5zkfy6lJ1ZU7l3eyYOGWOiQUCEIglhAlHvOBDQOQ+VwRL7zR0gYqoKAzsmDhlgUkFAhCIJYQJR7zuQcgbawAc4Ay/HiKm1NOnK2IA8aYlFAQoUgCGKBUfCc2bQsgozpIGO7iAZUxEIaYkENGctFxnSwaVmEWpOJBQ+1JxMEQSxAblm3BDevaSvxnNnUGcGRgSR50BCLChIqBEEQC5RKnjPkQUMsNij1QxAEQRBEw0IRFYIgiCkgS3+CqC8kVAiCICahfPAfDfojiPmHUj8EQRAVKAz+O9KfQNhQ0RExEDZUHOlP4hOP9WDPyeF6L5EgLgtIqBAEQZRRPvgvoCngnCGgKeiMGkiZLnbuOgUhFuzweYJYMJBQIQiCKKN88F8xNOiPIOaXGQuVb3/723jlK1+Jrq4uvPzyywCAr371q/jpT386a4sjCIKoB+WD/8qhQX8EMX/MSKjs3LkTH/3oR/Ef/sN/wNjYGFzXBQA0Nzfjq1/96myujyAIYt4pH/xXDg36I4j5Y0ZC5e/+7u/w0EMP4S/+4i+gKIr/+Pbt29HT0zNriyMIgqgH5YP/iqFBfwQxv8xIqJw5cwbXXnvthMcNw0A6nb7kRREEQdST8sF/WduFEBJZ26VBfwQxz8xIqKxevRr79++f8PgvfvELbNq06VLXRBAEUXfKB/8NpswpB/0JIdHTG8eu40Po6Y1TRxBBzBIzMnz76Ec/invvvRe5XA5SSjz77LP4/ve/jx07duAb3/jGjBbyhS98Affddx8+/OEPU50LQRANQaXBf5WcackYjiDmjhkJlfe9730IBoP45Cc/iUwmgz/+4z9GV1cX/vZv/xZvf/vbp3285557Dg8++CCuuuqqmSyHIAhizqg0+K+YgjFcynTQEtKhKxyWK3xjuErRF4IgamfG7cl33XUXTpw4gVQqhYGBAfT29uJP//RPp32cVCqFu+66Cw899BBaWlpmuhyCIIh5h4zhCGLumXEx7YkTJwAAoVAIHR0dAIATJ07gpZdemtax7r33XrzhDW/Aa17zmqrPNU0TiUSi5IsgCKJekDEcQcw9MxIq7373u7Fnz54Jjz/zzDN497vfXfNxfvCDH+D555/Hjh07anr+jh07EIvF/K/u7u6az0UQBDHbkDEcQcw9MxIqL7zwAl75yldOePzmm2+u2A1UiXPnzuHDH/4wvvvd7yIQCNT0mvvuuw/xeNz/Onfu3HSWTRAEMauUG8NJSGQtF8mcjazlwnRdMoYjiEtkRsW0jDEkk8kJj8fjcd+lthr79u3D4OAgrrvuOv8x13Wxe/du/P3f/z1M0ywxkwM8nxbDMGayZIIgiFmnYAx3pD+JJkNgOGXBdFx4HnESjDFc2RkhYziCuARmFFG57bbbsGPHjhJR4rouduzYgVtvvbWmY/z+7/8+enp6sH//fv9r+/btuOuuu7B///4JIoUgCKLRKBjDKRw4O5JB1nLAAHAOSAm4QmIwaeLp0xfrvVSCWLDMKKLyxS9+Ebfddhs2btyIV73qVQCA3/zmN0gkEvi3f/u3mo4RiUSwdevWksfC4TDa2tomPE4QRGMghKzqKbIYzjkdbl7Tho6IgZG0V4ciJMAABHUFS5rGO39uXtPWUOsmiIXCjITK5s2bceDAAfz93/89XnzxRQSDQbzrXe/CBz7wAbS2ts72GgmCaADqYWq2EIzUDp1P4GLKwqrWEAAGRwionCOgcTDGoCrc7/yZyo+FIIjKMFk+cWsBkUgkEIvFEI/HEY1SDpgg5orJTM1GMzaaDGVOTM3qcc6ZsOv4ED72wxfRETEqRkyEkBhMmfjy267G7Rva67BCgmg8prN/1xxROXDgALZu3QrOOQ4cODDlc8lhliAWD+WmZgW/kABX0BnlGEiYs57aqMc5Z0px50+AT6ytM11BnT8EcQnULFSuueYaDAwMoKOjA9dccw0YYxPGnwNeR1CtnT8EcbnR6PUWlZiOqdlspTbqcc6ZUtz50xnlJeuVUmIsY2PTMur8IYiZUrNQOXPmDNrb2/3/JwhieiyEeotK1GJqFp9lU7N6nHOmFDp/PvFYDwYSJppDGgyFw3QFxvJpqntuX9vwgpQgGpWahcqqVasAALZt4/7778enPvUprF69es4WRhCLiXoNriuO4DQHNQDAWNaeVjSnHqmNhZZOuWXdEnz+rdt8IRoXEhpn2LQs0vBClCAanWl3/Wiahn/6p3/Cpz71qblYD0EsOupVb1EcwUmbLrK2C8aAgKYgrCs1R3PmO7UhhISQEi1hHX1jWSxvDoCz8cjKfKRTZpKiu2XdEty8pm3BpfYIotGZUXvyW97yFjz++OP4yEc+MtvrIYhFx2T1FhISOVtAVxiO9ifR0xfH1d3Ns3LO4giOoXJkbQdufoJvRkg0GWrN0Zz5TG2Ui6uU5eD4hRQ6IgE0B7V5SadcSoqOc1b3mhmCWGzMSKisX78ef/VXf4Xf/va3uP766xEOh0t+/qEPfWhWFkcQi4FK9RYp08FQ0oTpuBBCQgL4i8d68In/sOmS0wTFEZylEQMvj2TgSkBTOMAAx5WIZ22sagviQsKqKZozH6mN8vRYS0jHWNbCYNLEQCKHlOkgrCtzmk6pV4qOIIjJmZGPylS1KYwxnD59+pIWVSvko0IsBHp643j/t/cibKgIaApSpoO+0SyElFA4g4SEEECToaI5pOGzb9mKWFCfcfqg+HxSAi+PpMEZA89Hc4T0UiurWsMAAzKmgwf/ZHtNkYC56loSQuLuh5/Fkf5ESXrM+5lA31gOK1qC+Nxbt2Hb8ticRFKmWoOUEgMJE5uWRfDIe26kdA5BXCJz4qNSTHHXT0HnlLcQEgThUVzjsTTKMJQ0IaSEqjBAAo4AghrH8uYAzo3m8IHvv4CQpsy4M6g4gpO2HEgJFP/zZAyQAnCEQFhXp9U9M1epjanakTnnWJK3qOeMzZlIWEgt0QRxOTGjoYQA8M1vfhNbt25FIBBAIBDA1q1b8Y1vfGM210YQi4JCjUeToaBvLIec7YAzb2idLSQUxtAeCSBjC2RtB4msDYUzdEQMhItqSfacHK7pfMUdMyrnnjAp+nlBuKicN0z3TC3tyPYctyM3whoIgpjIjITKpz/9aXz4wx/GG9/4RvzoRz/Cj370I7zxjW/ERz7yEXz605+e7TUSxIKnUOOxvDkIIZH/kl4kpSWIsK5gKJmDkBIMgMK9yEFAU9AZHR9sJ0T1TG0hgjOasWGoDIbK4QgJKSUkJFwhYagKDI1hLGNjbUdT3c3IisVVJeZDUDXCGgiCmMiMhMrOnTvx0EMPYceOHXjTm96EN73pTdixYwf+4R/+AV//+tdne40EsSgoiJXWsI6OiIFVrWFc0RZGk6EiZwuYjvBTGyof/6dZnnaoRnEE50LSQjSoQWGA7QrYjgADEAtquJCwGsaMrFhclZfNFdqR51pQNcIaCIKYyIyEim3b2L59+4THr7/+ejiOc8mLIojFyrblMWxaFoXlSn+6LuDVi0jpFXQaqoKAXvpPc7pph4Io2rQsAkggqKtQOIeqcIQMFVJKbFoWaZgulmJxNZAwkbW9bqis7WIgYc6LoGqENRAEMZEZdf188IMfhKZpeOCBB0oe/9jHPoZsNouvfe1rs7bAqaCuH2IhMt4C6/qeJPGcjfNjWXDG0N0aQpNRWueetd1pdecUmA1n2vmkxMMk3wI9n2MGhJD43rNn8f1nz2IwYQLAghl1QBALiens3zMWKo8++ii6u7tx8803AwCeeeYZnD17Fu9617ugaZr/3HIxM5uQUCHmmtlox610jKdPXyzZkFUGZB0BKSW6W0I1tcYuxAGHtVCv91UskixHAAzojAbw9htX4o9vXLkoPluCaBTmXKjccccdNT2PMYZ/+7d/m+7ha4aECjGXzMYQwamOUW63Hs9a+OTjB0siLcVOrMVpmoU64LBRmczobbTCZ08QxKUz50KlUSChQswVs7FxzeQYtaQ+aFOdXcjojSDmnzk3fCOIxUy1IYL98Rz+5olj+G+OQFuTUTE1MdNBhNUG29VrwOFihozeCKKxIaFCEGVMtXGlLRcZy0VP7xg+8sP9CGqVpxBfyuY3lfsrbaqzTy1Gb9Nx7yUIYnaZsTMtQTQCQkj09Max6/gQenrjNRmiVWOyjaswo8dyXQBAJKBO6hw7Vy6njeqeOhe/h/miUYzeFvJnuBigz79xoYgKsWCZq4LS4o0rwBUAXq3CUDIHV3qW95IBuqLknWMnplwqHaOYmW5+c3Xc6VDelRPPWnhw9+kFW9hbPIupM8on1KiMZWxsWhaZU6M3Ko6uL/T5NzYUUSEWJIWC0iP9CYQNdcZzcSpRyaG04ByrcM/+vtiUrZJz7Fy5nNbbPXXPyWHc/fCzeP+39+JjP3wR7374Gbzv0b040Ds267+H+aLeRm9z+bdMVIc+/8aHhAqx4CgvKA1oyozn4lSi0sZluwJCAq4rwRlDe8QAw/jGVZ5ymavNr56bavkFvb1JR9YSMG2BjOnCEXJWfw/zSbGTb8Z0MJgykTGdOXfvneu/ZWJq6PNfGFDqh1hwzFZB6VTGYoWNqxAOztguICV0VUFnLDDBObZSyqX8GPF8u/GmZZGKIeVajc6me9zZoFK3UdZyYQsBTWUQAhhKmggbChjYtAt7C+99OG1iLG2jJaRN2lE1k7XX+rlO1XE1F1BxdH2hz39hQEKFqAuX4j46G10ateSkizeu4bSJv/31CfSOZhDWS2tDpqpjqHXzm26OfL431UoX9MJ8IoUxMA6YjoucJRDMfz61dssU3vvh83Ekcg5EPjITDWjY3BW9JPE13c91qo6ruYA6juoLff4LAxIqxLxzqYVrl1pQOplhWiEnXRzqL964DIXjE4/1YCBhVnSOnSzlUm3zm856pnPc2aTSBV3lHIwBEvD+KzzxAni/k1oKewvvfSRteWks6YkUISTiWRsHesem/AymYqaf63zSCMXRlzP0+S8MqEaFmFdmo3DtUgpKLyUnPRd1DAslR16phTegcRgqhyMkhJBgzBMvQG2FvYX3nszZcIWEkIDGOVTOoanecRxXImU60/4MFsrnWu/i6Msd+vwXBiRUiHljtjaPSykonU5OuhK3rFuCR95zIx78k+348tuuxoN/sh2PvOfGGd+ZX+p6qjFb3hCVLuiMMbRHAlAYYLsSKufQFYaxjIVzIxnoCsP7b1szaTqq8N5DugrLFVA58z8DBgaFe8IoqClVP4Py99nTF5/yc40FVRzpT+Dbv3u5rp4Z9e44utyhz39hQKkfYt6YzcK1mRaUzkZOejZTLnOZI59Nb4jCBb089aVwhoCmgnMXmsJwYijl15ioCsODu0+DM1bxfIX3rikcUnrpo2IK6STGMKWJXaX32RLWkTZdtFQI2adMB4OJHLK2iwd+fRxhvbK78HxRj+JoYhz6/BsfEirEvHEpm3Kl4tta5uKU/6zRctLl65GQyFkCjhBeGoXJGa1nLuozJrugX90dw6vWteHhPS8DAGJBDRFDhS3klOcrvHch5XitS9HPC+JFSkz6GUz2PvvGskhZDsayFlrDhv/8gruwKwQ4A9rCOhTO6l63Uo+OI2Ic+vwbGxIqxLwxU5FQLTJQKbox2Wvef9uauruQFlPsitpkCAynLJiOCy+7IsEYw5Wd01vPXA4urHRB39QZwXseeQ62K7CyNeSfT1Ew5fkK7/3w+QR0hSPnCGjci65JSLhCwlA5sraLTcuiEz6Dqd7n8uYAjl9IYTBpojmogXNe5C7s1dkENBWhfDt1Iwx0nO+OI6IU+vwbF6pRIeaNmRSuzaT4dqrXfPLxg7ht/ZKGyUkXUioKB86OZJC1HDAAnHuRBFdIDCZNPH36Ys3HnOu6l8IF/fYN7di2IoYjA8kZna/w3iMBFQrn4AywhRdNsh1PTKgKQ5OhVvydTPU+OePoiAQgJNA35qV5MpaLnC0ACSicl5j2zcbnQhDE3EBChZg3plu4NpPi21pes/vEMD77lq3z7kJaCSEkQpqC4j1YSE+kBHUFK1tDcAWm1aEylMwhaToYy1gYTVsQsnTYXrXBhdMtwL2UQYmFdNLV3TFEAyo4Y36NSyyo4aoVzSW/k+K17Ts7CssRk563OaihyVCxoiWIjOngYtqCkBIBTcHyluAE0756DXQkCGJq6pr62blzJ3bu3ImXXnoJALBlyxZ8+tOfxp133lnPZRFzyHQK12ZSfFvra2JBHY+858a65qT3nBzGjp8fwdGBJGx3XAxoHGiPGGjNvwdV4TUXGT+0+xT+17+eRNJ0kMw/1h8H2iMBtEe8Wo2p6nBmUoB7qXU/5cZ6kznTlq8NABI5B4ZWWodSfN6wruBzb90Gzhj2nR3F1/7tJGIhFUFt4qVvoXhmXIpZIkEsROoqVFasWIEvfOELWL9+PaSUeOSRR/DmN78ZL7zwArZs2VLPpRFzSK2FazMpvp3Oa2aSk651k6j2vD0nh/GRH+7HUNJEWRYMtisxmDBhqAqaDLXmzp+Hdp/CF39xDK6QYPCKUwHAlcBAIgcAWNKkT1qHU2sBbvl729QZqVr3c2VnBEJK7Do+VPHzqPS7KD7PuZEMHvrNaaSL1ma6LsayFvrjOWgKRySgTTjvpmURbFseA+cMW7qi+NcjF3CkP4lAVKl7fdJMoCm/xOVIXYXKG9/4xpLvP/e5z2Hnzp14+umnSagscmoRCTO5U5/Lrp5aN4lqzxNC4utPnsJI2gIDoKkcVr4moyAuHCExmMgh3B6uac2OI/C1J0/BFRK6yiDB/GMWuJDIwXZdRALahJqPWgtwhZR4cPfpCe/ttvVLcG4kU9G1V+VAPGvhnu/sq3lzLf4MLUcgkXMgIbG8OYiA5v1eg1zF8uYgzo5k0DeWxcpWhoCqTOoWPFmLdS3uwo3AQnDaJYi5oGFqVFzXxQ9+8AOk02m84hWvqPgc0zSRSCRKvojFy0yKb+fKabJSgW7IUNDTF8dHfrgf33n6ZQghayr+PXQ+gWMDSUgpoSqeDX2l9lzTcZE13apOuz29cXzpiWNIZG2oCgNnHJClxwK84wd1peKGVkvK7PD5BD7+4wMV39t3nzmLu25aOaHuZ1nMgATQH89N+nmU18Q8dWKo5DOMBjUI6bnfnh/LIWU6/toiAQ3LYkFwxhDP2iX1Rp99y1ZEAlpJrU29piRfKgvFaZcg5oK6tyf39PTgFa94BXK5HJqamvDYY49h8+bNFZ+7Y8cO3H///fO8QqJezOQOeC7umitFG1Kmg6GkiZztIC6Av/qXw/h5z3kkck7VqMR7b10NyxW+TwiDV4diOwKy5LzAcMpES1ivuObiqMNY1vaKcF0JKQVcIVFpy0qZbsX3WEiZuUIimbOhco6Azv2uGF1hSORsBDXFb0GWkJAOENY5xrI2dh0fxrfefQOODCQxkrHQHNTwpSeOoj+em/Tz2PHzI4gFNZweSufdbYGsIyClRHeLd55kzgbgdQC5QvpTmgEgZwkoHAjpCu69Yx3WtDehNaQjnrUqRn4KUZxG9syolDakKb/E5UzdhcrGjRuxf/9+xONx/PjHP8bdd9+NXbt2VRQr9913Hz760Y/63ycSCXR3d8/ncol5ZiaukZfiNFnLJlEwDRNSem213Lvbf7E3gaztlmzKBYo3k7G0DT0fSSmIFcUblAPHFSi+KV7d3oT/8bqNE9ZcngZgDMhYLiS8tNH4efP/I72IiuvKil4h50YySORsjGZMAAyMAYaqoD1ioMlQkcxPNY4FtRKhVuz58txLI/jB3nN4582rAAA9vXGcHkpPurkaKsfh/iQihoL2SAC6wpHI2UgkTXDGkLZcNBmqP/wQYFDyU5pH0hYSWQem4/pRhJ/uP4//8bqNSOZsfPLxg1VTJI24oU+WNrxl7RKa8ktcttRdqOi6jnXr1gEArr/+ejz33HP427/9Wzz44IMTnmsYBgxjYnU/sbiZyR3wTF4z+SbR5m8SEt4dvZASqsLAwCAlIOC1Gactry24Jaz50YgChc2kJaRhY2cEz5yx4LgCmupFLhTGwBQGx/XUy9auKH78/ldAVUs3p0oRHl1lGIjnUNQ8NEGkKJxhadSYcOe95+QwHtrt1Z9ICagKADDkbBd9o1l0NQeQyNrgnCFiqGVCjYFxL/pjuQJ/968nsGZJGLesWzJlYbOExFjGaxeOBXW/7kThDJyxvDmbFzkpDD/M2gJq/lyDCRMAvLZuBhiKgnMjGdz3kwOIBrU5Mbuba6aqQTk5mIKQomEclQliPqm7UClHCAHTNOu9DKLBmEmHznReU32TkF66xvHu6BXOfCEi4YkCXeVQGIPpuMhZAkG9dEMpbCZtTQb+7NVrcWIwiaGkCdsRUDgDmBfxkADam3T8f6+/coJIASrXk3DG0R4J+N09/sLG/4P2JgOGqiCec/w774LoSVsuljcHcX4sB0d4KRiFA07ejn5JkwFVYbCEmCDUkH//KgdMR/hCYKrC5pwlvM+RMWhFQqYQPWFln2N7JIC+0SwcV8CVAJeAqjK4AlAYx9JYAGFdQe9oFgMJE92twQWVIqlezOyJ0JG0hWWx0ojdQulYIoiZUtdi2vvuuw+7d+/GSy+9hJ6eHtx333148skncdddd9VzWcRlRrVCRcsRcKXEaMaC7bolA/SklHDyVu+xoApD5XAFYLultSDlhby3rFuC//lH12BLVxSqwuEICcf1UklbuqL4n390jd8hVG6+Nlmkoj1ioC08fkctMR5J6Yx6Pirld97FoicS0LC8JYigxiGkhCu8zZ2B4c9evRabu2IYTlnI2U6pUCv6DNqadF8ITFXYbLsuXAEYKkdAG38fhehJoXjWEV7nUpOhoqt5fINm3ItkBTXum7cxxhDUFdiugChtePJpVFO36jUoOhTGoKu8IRyVCWI+qWtEZXBwEO9617vQ39+PWCyGq666Ck888QT+4A/+oJ7LIi4zqm0SLWEdo2kTmsIRzzkAJER+qq8jJBTG0B4JgHOOWEhDzhGI5xxoqjJlIe8t65bgp/feip6+OPafHYNkwLXdzb7vx2SpqNdt6Zw0UrEsFkDGcpCzBZpDGsKGiuZ8XUmlO+9y0dNkqAjrYeRsz8qeM6+IdmVbGPfcvhYf+eF+xAXAuZcmKtTEFD6DgKogkY/YTFXYHM854NyLcBR/5ix/nN7RDFzpFfcKIWG6AinTRWtIQ8YWaAvr0BRP5BS/PphPIeVsF2Fj4Zi61eL/wznDu16xCntOXaQpv8RlRV2Fyje/+c16np4gAJRuElJKf5NWubcRepsEx7tecQV+e3IYz700Asv16iWCmpdyaTJUSClhORKbl0UQC+o4PVR9M+Gc4eruZlzd3Vzy+FSpqLMjGbQ16eiPmxMM1gBAUxQ/khLQFEgJ5By3oliqlJ4pRCYABVnbha5wtIZ0bFsRwwd/bz3+6l8OQwgJN59U0hXvjl9KiXjOhsrgC4HJCpu3dsUQz1roj5uQUpa8h7CuIKip4Dw/6yhl+p/h67Z04uv/fhK6yv26lmK8FBRHxnbRWnbcRk6R1Or/c+u6drz/trUN27FEEHNBw9WoEMR8U9gkxrIW4lkbpjPeOmyoHNGglt8kluD9t63B9549i7/71xMwHYG2Jh0B1dvQC0Lgvjs3XVL7ay3ma9GAirDOK7Zgt4Y13HXTWuw+MTzhzvv9t63xvUVqdZUt3tj/+MaV+MGzL+PoQNKLqEggKyRy8Rx43gsmGtQQz46nViYrbH769MVJ28hbwxo++5atiAX1ktcAwBOHBiZdbzzrYGNnExI5Z05M3ebKvr54ina13wNN+SUuN0ioEJc9W7qiaGvSceh8wr8jLxiwFabubumK+pvEO29ehTVLwn6UIJFzoDJgRUsQr9uyFJGAdknGW7V4ZlxMWfizO9bhFwf7/VlBmsKwcWkT7tzWhe7WEG5c3QYAGMvaU3qLTOUqW76xP336IoZSltdCLccLdSU8q36FeRvrJx8/WGKgVmlznWkbeTWfnPvu3AQAM2pPn4q5tK9f6K65jQ7NR1rYMFle5baASCQSiMViiMfjiEYbK5RLLByEkHjz157yhArgu8VK6XW9SHhi5qf33jrBdv7Q+QSeOjmMJw4NYDCRg+1KrxBVSijM68aZ7oa26/gQPvbDF9ERMSadIzSYMvHeV67Gb08O49hAEpbrVY8yeN065ecFUDGVNJrfBO+6aaUfgbHzG3u57X9PXxx/8VgPekcziAZU9I2Nt0MXVhnQONa0h3EhYWHTsggeec+NVTeEmWwiJaKhwnpnetypzjfV5zdbrra1vC9ietB8pMZkOvs3CRXisqenN473f3svWN6GvWBiVjA9iwU1SCnx4J9snxAVKN/ALFegbzQLV3jtu12xIHSV17ShFTbWalN+s7aL0bQJXfU6XKqdN6x76atyh1jAi34MJExsWhbBw3ePu8oWb+yFC/3R/iQupj0zNk3hsFwXPN8VVDikkBKrWsMAAzKmU/Ezmy3m6y5ZCIm7H34WR/oTU35+tYiyWs9Hd/+zw3wJTGL6TGf/ptQPcdlTKKbtiOhoCWvIWUXFtDqHFMBgypzQ0lpeSwLmzbQBAF1lcARwMW3iirYwOqPGlGZjlYbwjWUtLG8OTpgKPJq24ErAdkVN5y14i7RHDKRMp8Qev9hb5MhAckohpiscDADPu8MK6f2/ohT8ZCSkABwhENbVSZ1SZ2sjnq9ajfm2r6calNmh1kGbjWj+R5RCQoW47CnvuCh0vBTIuW7FltbyDSxrub4ZHGcMKpcwHYGc7ZmWTbahld/1tYQ4DM1CfzyHsyMZLIsF0RzU/HoFPT9tudbz8rwRW/9Y1vNFKbPHn8x+vfxCn7MFeN4/ReWA5Xr+KQqX+fbn8a6bydqAF2IYvpbWYbKvbzxoPtLioWGmJxMEgIoGZ3PNTCcul29gjhAlZnAM+TqXvPtYsdlY4X0+eXQQf/PEMSRzdonZXGvYwMrWEBTOMJwyMZgcn/L7rlesAmespvOmTAeJrDfUjzHkpyuP2+OnTGdSUVF+oQ/oHIaqwBUyL1i8IlohJSQ8zxNDVWBorOJnVstk6UakWMhWolG9WS53ahGYjWj+R0yEIipEwzCXd9tTpRsmdlyoEMIzDcvYLpqDWsWOi/JIjN8tlBcNBWt9lXsXysKGdm4kg7sffhanBlPI2i4SWRuGqvhD+ApEAhpWtnp1Mx+4Yx2uWdkMAHjh3Fj+eC6CXJ30vApjuJDM+S6tMt+pwxkDUwDHlRhM5BDUFWxaFp1SiElI5CyBJkOB6biwXelZ7AvvOExIcMYQC2q4kLD8LhXAqwEaTpv421+fWJBh+Om0DhONQ63eNCQwKyOEQC6Xg5QS4XC4rmshoUI0BFMZnBVPvJ3psSsJoPfftqbEp+Ozb9mKL/7iKI5fSMHO3z2rnGNFcxCnh9OwhSwROeUbWCHikLNdgEs4wjOEC2jc39CWxQw89JvTSOffp6owJLI2TNdF32gGS5oM6Cr3zeYCqoIEc5CxXXz5l8cm1LAsadJhqJ5I8kzoxs8LeCkfxoCAyiGEhJ2f48PgiZms7SJWRYiNZe2SIuNCzEnkhRFnrMhcTvptwAB8QZaxXCRzNvQKguxSwvDzUXhKrcMLExKY00MIgWw2i2w2i0wmA9P0zBhjsVjdhQp1/RB1Zy67KiYTQIPJHExHIKwrfitvW5OOC4kcLEcgpKsIaApytuP7hnj28kpJlGf8+C6aQxrsCt03msoxVqH7BgwYy9heIayUcKUnIDjzNseC2ZxpuyUdPrrCcSGRxXDaS+lwAGCecAAALX9eyxUYSOSgMoYVrSEAwFD+fcv8ucCAj/7BRtzz6rUVfy/lbdsy/ztx84td2RrC//x/rwFnzPdrKTZz87uhHIHesSwgvXlGhfk8xecaTJn48tuuxu0b2mv+3c5FBG4y8UOtwwuP8n+f5QLzcu76mUyYFLAsCwcPHsTBgwcxOjqKr33ta7N6fur6IerCTO9u56robbKqf8eWyJguHCHBwXBFWwCWK3B0IAlXSKxsDSES0JAyHQynbIh8GMFxBUKGXhLluXlNG973qjX4/rNnMZjwpn7Hgprvo5KxXWiuwKZlEbx281L8r389CV3hGMlYXiTFFnCL6nAK1veMMd9sLqgrYIz57yFlOkjkXPC8OJHIp3MgweBFUzK2Cym9tM2SJsMXBcVzfBwh4boCt07iO7KpM1KyLssRKLmryUdUrl7RPMFfpvxzl9ITYIwzCCExlDQRNhR/sOF0w/BzFYGrJn4uxXGYmH9maiq4GKkmTNLpNF588UU899xz2Lt3Lw4cOIBcbnwa+1//9V+jtbW1HksnoULMDpdyd1up6K1QE1EYjGe70y96qySAJLxNUgLQVAZbCFiuRMGyjAEYTlkIGwqGkjm4UkLjXiTBcgUgGTqjBvrjOXzq8R7I/PMhJXSVozMWxNtvXIm3b+8u8SSJZy184edHcTFtAhIQ+XOpynhRagGRd3gtPGjZAsv8DV/669KV/JRhKdEeMRDSFIxlLXS3hvHh16xHa0jHl544lre7l/mOH2+Oj5Tcj1Rt6YpW/P11RAM4P5ZDa0jHcLryZ//yxQy+9+xZvPPmVRU/dzAga7mwXTefnnKhMAbTcZGzRH4t0wvDz1Xbaa3iZy47RMhDZfa5XAVmQZhkMhlks9kS0QEAo6Oj2LdvH/bu3Yu9e/fi8OHDcMumvhfz1FNP4U1vetNcL7siJFSIS+ZS727Li95SpoOhpOnXRABeuuDcSGZa66okgHKW8Ft5GQBHSr8rR0ovmpGzXVxImMjZAgpHfiMc9whJWxKJrI2LRZs3A6A7EpabxTd+cxprloT9i+NTJ4fw6O9eRsZ0wBmDm7+LkQBstyRGAcCbRswYENRVGCrDaMb20zo5W8B0BNR81IUzQLjeTKKQoYJxhsFEDkvCBratiOHPXl29tqI8TVP4/Z0eSiNl2lCmuJ4LCTy0+xS2LY/5qZ+LKRO2K2G5Av3xXFFtizd1WjLvzSRyFkzHm5PUZKg113nUEoE7eSGJn+4/j9YmvaaNqRE8NxZi6/ZC4XLwpqkmTPr7+7F3714899xz2LdvH06ePFn1mLqu47rrrsPv//7vY8OGDXO19KqQUCEuidm4wBcXvTUZAufHchBSQuEMYBKO67XAPrT7FNYsCdd80a5U9e+38vLxLplCV05BOEgAIynLi3oIAKr0i08tR2AwacIpa5uW8NIXrvREz46fH/EnKA+lTLhCIqBycMbg5EMl5ZGUwmOMAUujATSHNGRMF6MZGznbRdhQJ7Qil7+Hck+PaqHvm9e04e6Hn634+2tv0pHIjYuk8nUWHn55JIs//dZzYIz5kRjTcTGaX4PCWf7zZrCE8I83lLTAGRAJarjrppU1/16rtZ3ajsBw2sJn/8/hvItu9Q2/3p4bc1lMTixOXNf1UznlwkRKidOnT2Pfvn2+MOnr66t6zKamJlx33XXYvn07tm/fjm3btqG9vR1Lly6dy7dSFRIqxCVRuMAHNWVK19OpLvCFror7HutB31gWIl+ICnjtrwrn6GoOIGW607qrrVT1X2jlFULm0ycKbNeF5QoIIf3NlysMIi9abEeAMyCgKRjNWCUipbCKwiOOALK2g0PnE4gGNEQCKmRedBWKWFH2muJjqYpXz6HmjdU48zb6eNZBUFOg5A3bXCnB8gW4QU1BQC9tgS6u9Zgq9N3TG590g3ZlBYUyydodIRAL6hBS4uXhFFKmAyk9p1yeP67ntzL+XrtbgpAAMpaL7z5zFlu6YjVtxlO1naZMB+fjXjFzSFcQDWg1bfj1NHVrhGjO5cJCTq0VC5NCjUkBx3Fw9OhRP42zb98+jIyMVD3mkiVLcP3112P79u244YYbsGHDBijKxFbuekNChbgknjo5hKFUoSirdtfTcm5ZtwT/+VVr8Ff/ctjbiAXAmERQ42iPBNBkqFAVPq272kptpbrqiZWs7YIBsOB6w/WKRAoAMEhweLUknqkZvHoWZ2IkpZxCOiegeQ6yQuTt5auu1yuKFZBQOUcyZ6NvLAtIIGs5OHPRE4KOW3osVwikcg4UxjCUsrCmPVxSCFv4LCp9ZpNt0BLSq72pkUTOQcp089Ed5rUuw0tjqVwCUpakuThn0FXPBTgWlFNuxpWKfCu1nUrp+cI4ridSYiENDKymDb+enhv1juZcLiy01NpUwsQ0TRw4cMAXJi+88ALS6XTVY3Z3d2P79u24/vrrccMNN2DVqlUV/+YCgQCCwSCCwSACgcCsv7fpQkKFmDF7Tg7j0d+9DCdv/lX4e89aDvpGBZa3BKFwVvMFvrs1hGhARTSoQUjpe4kU/iHN5K62YupDYcja490yQKngKBicgY2bhniRlel18g+nLK/YtcbnuwKQwiswzdqOP79nWSwAVeEYiGdhOqVHUzlg2gIvXcx4niYAzo1k8J5Hnqt4AS7f9JuDWsUNulDLo+ZN3arBOaAy5qXAil6gKwocMR6tYgC0fBGwVxukTLkZT7a53LZ+Cc6NZEpqb+I5G1nbqz/qiAb8jiKg+oZfT88NsuifexZCaq0gTAo1JsXCJJlM4vnnn/dTOT09PbBtu+oxN2zY4EdLrr/++oopHFVVS4SJYRgTxEu9IaFCzIhCuDpt2mCssJmN13I4QkzpelqJ1pAOXeVQOEO4wtTgmd7VFqc+LqZMfPVfT+D0UBKuQD7l4z2v4GGiKUBnNARXSpi28NxdZ+A2xDngOtN7jYDnVzKcsqBw5g8llJC4mOKwHTd/bG/eji3GhRADsKI1CF1RKl6AK236a9qb0Nakoz9ulmzQ5eJiqrfPAKjMey2DF1Gx8lGfaEBBkxHIt3pbUJWi1/DxjbnSZjzV5nJuJIO7blqJ3SeGfQHqugIKZ+hqLvVomeocBepp6kYOqnNLo6bWphImw8PDfrRk7969OHbsGISY+o5BVVVs3brVT+Vcd911aG5unvA8wzD8SEkwGISmaRMP1mCQUCFmxKHzCRw+H0fWLv3H4xmCef+fsSZ3Pa3EXN7VFlIfPb1xDCZyWBoNwtA4cpZAxnIwmDS94l14w/YYY4gYKlTugiW9duH2iIHRtA1zkpkvJecDUGg8rrbRF2AAogEVb7+hG//0fC+agxqCuuq/94zl+qJECK+DBsx7ncK982lcQUCbeAGerLPn6EASCvdeX7xBF1JhBSt+ewqlpnIvkiKEhEDeDC7PcMpC2nIRDWrgHP7zAkV1NcDEzbiWzWX3iWE8fPcNfhv4SMrCl544OmlkotqGXy/PDXJQnVsaJbXmuq4vSjKZDCzLE8xSSvT29pYIk5deeqnq8YLBIK655ho/lXP11VcjFAqVPIdzPiGNw3nlfx+NDAkVYkYMp00kcg5Ewc8DniFascU6ALzz5lU1XeALKYlb1rbhxAXvjjlkqAhqChgD4llnVu5qRzIWLEcgoEmkcl7NR0tYQyLnIGe7UHjxIEHPDVYACGkKlkQMSAAXEmaVs+Rn7sjxzZ7JycWKwgrP9wRVJKCCgcFQC63aOU+klB2AAShoAs4mT6e82DuGv3niGEbSFtqbDBga92s3lkYYzo5kEdIVtIQ8k7vCBh3UFaRNF+4UMstbg4TjVK7BkdLzUTFtF4xz2PnW6vaI4admKm3GtW4uRwaS/uYihMRPXui9pA2/Hp4bZNE/t9QrtTaZMBFC4MSJE37R6969e3HhwoWqx2tubi7pyNm8efOEaIiqqr4oKaRxFgMkVIgZMZa2IQpTdBmDAoCr3BMqEhDSa0NdFgtWPVZxSiJtuchYDhxXIpFz/NbbjZ1NuO/OTZd8V3tuJJOfkzM+UdhQOZoMDZYj4Lgi70/iOcPGszY4Y2gO6WBgiBgahrmZL5AtRcmLDTBv8/EDC1OIFMATGzz/PM4YljeH8jN2LAwlLbhSVEw9FVsz2cITAOXplCHLxX//4Yt4+aJXaJe13Xyxs45E1sFoxhsPkDS9qFI4oOL/2daJd9y4Cu//9l6kzckNoAqRosmagxgAQ/O6qhwBKJAwNA4jn94TQk66Gc9kc5mtDb8enhvkoDp3zFdqzXGcklROQZjYto3Dhw/7jq/PP/884vF41eMtXbrUry3Zvn071q1bVxINYYzBMIySiImqzu6W3ihdUiRUiBnREtLA85uN5NJrR853/Ugm4TpeW21LaOr8Z3EdgqEqyJhelIYz7x9iLKjClV5XyaWy5+QwHvrNaa8DRxZaoBmytoDlWGgJaxhJW5BCYjDpFbJ2xYJY3mzgQtKGlBIBnSOoqchaDiBLxULBzl7jDCuagxhKWUibTk1pn4IQaQ5peMPWTnxrzxkcPB/3beqr4bU085J0ymjWQsp04HgtVFDzQ4GyloMzwxM/T8aAdM7Bj/b24qXhNAamiBwVp7MmS21xBqxeEoTlABnLge1KfOj31+GXhy9U3Yxnurks5A1/NqI5jbKxNBJzlVqbTJhks1ns37/fT+O8+OKLyGazVY+3evVqP1py/fXXY8WKFSVr5ZyXpHDmOo3TSF1SJFSIGdHWZCAa0BDP2iVdP1ICrvBqPKIBDaMZG7uOD1W8aBbXISyNGnj5YjZvbe8VM9hCwnQEVrWGcCFpYeeuU7jxitYSa/paL8Tjxb8OljcHcT7fkqxwr3PGdgQupkwYKofD4EcSTg2lENQVaAr379LbmnT0jjgojzUUNmvbleiPZ7GsOYhoUMVgwpzQ/jwZKdPBex99Di8Np+GXwtRYyBsLan46RQiBoaQJzjzxM5Aw/WnHk5WbFKz7XQk8fWa05GfFn7BEqUgp1PaUm+CBMVgOENQVGCrHYMrEyrYwHnnPjVU300vZXBayZfqlRHMaaWNpJGYr0jaZMBkbG8Pzzz/vG6sdOnQIjjP1jRXnHJs2bfKjJddffz2WLCn9HWmaViJM5jON02hdUjQ9mZgRhYnHB3rH4OTt0gsuqYVwvaZyBFUOR6DkolnYRPadHcXX/u0kYiEVkAwvj6TBWalBmJASq1rDAANG0ya6W8MYTOQqXoinupvs6Y3j/d/ei7DhTUUu1H7kbJEvAp2Ikvc1cVwvwtPdGkLa9MzhUjkbbpV/OSpnWN4SRCrnlNjtVyOsc8SCOgYStXcbMQArWoKIBjSYrsBw0kQiZ0NTGFwha2oxruUchfBJYVktIQ05W8ByhG8QVzCpkxJY3hKExrkfUfnG3dtxdXdzTecrn3yrc4aE6SCRdRA2FHzpP12FW9fXNml5sTPZxjJKU4J9pjv92nEcX5QUC5MLFy6UWNEfP3686rk1TcNVV13lR0yuu+46NDU1+T8vpHGK60vqZbw2l9Psi6HpycScU3yXkjIdtGq6H1FJ5GykTc/Poimglajxj/xwPzoiBi6mLKRNF0nTRsZyvBZc6VnbF2AYL2yVEhjN2LDdFJZGAxMUfnGraiURU7nmgU3pc+IK5M3XGBwh0TuaRTSgwBWyokgptrUHvCLT82NZ38bdUDlSU9R8FPBae2VNIqUQAQnp3roGUyY07hWbJnJetEtVOBiTFecKTYdCq3Ix6bwDLTCeAirMIXKkxGDChO26cIXnn/KlJ47iz169rmRjmExgFqdxDp+Pe8Xb+booTWF4cPdpcMYu+w24UdtvG41qkbZiYZLJZGDbXrr3pZdeKunI6e3trXqucDiMa6+9FjfccINvRV8cEVEUpaRFOBAITCgarxeN0iVVDAkVYsaU1wMU7lIYYwjpCla2hkoumk2GwNmRDEbSFla1hhDQFKQtr9vGcoVfmOlv+Bhvjz2f8GpG2psMBDTFP2ZnlOPsSBZf+dVxhHUFIV2FpnC4UuBA7xg+8sP9+ODvrce25TG/5sGxJfpGs3CFmLQQtIAE/NZcR0hoigJNkRMER/G/58KGHQuoEPkC2ZCu+HUv1bBdieFk9c4iYLzr523bV+A/XteNkYyF5qCGv/zpIZxFBorC/CiVlGJiemaalH9eliuhKywfuUF+GKEEy8dcTOG51WoKQ3tEx9GBVEnouFq64pZ1SyCkxMd/fABBTSIW9MYS2K70Repn37IVsaC+4NI8s0UjbiyNSnFqzXEcpFLJEmHiui6OHTtWYkU/PDxc9bitra1+tGT79u3YuHFjSWGrruslaRxdb1xPnEY0ICShQlwS5XcpIykLf/OLI2gKaKUhw7wle5FXKEK65zybtV2IfAGFJwYASE8YBDUOKSVMx+tWCRoTw6G2K2DaAgzI33WPz5WJZx3c/y+HsL6jCUFdwUjam4ospISiMIhpus2arkCmQlSkILCKN3KFj88Limdt1GC/Mv6ehJdCKW/3roTCgX8/NoTXbu7E7Rva0dMbx4VEFgFVgekKcO7VDCn5yNBsY7kSKvLOtNz7vRXOouYjSYUxCIXQ8c5dpyCkxCcfPzhlHvzmNW14cPdp2K4oEb4Khy9SP/D9FxDSFD9i1BE18I4bV+KPb1xZc/3SQqxnKdCIG0sjUoiYFKImtm3DsiwcOHDAd3x94YUXkEqlqh5r+fLlJY6vq1ev9v82K1nQN+L8nMloRANCEirEJVN8l7Lr+BAcgQkXzYIlu5KfA+MIAcYUtEcCXnRDCsh8t0/Bql7hDNGghuG0DQZvA2JlyQevPsL1rdu9tuDSzdh2JQ73J8GZF91w80MPZYUW42qksvakrymPNoymbYB5s48sx4WqsGmlX4T00iiTFeLyfOt2+cDGkYwFR3gGdf3xHGwh8x0/tZ9byZ+3VhxXQjBv+KKhejOO2pp0NAf1kjEIxXf4X/7l8arpirChThotSFsusrYDNz8WIWO5MB0XwykTf/nPh/CPz52t2tK+GApQG3FjaQRs2y4pfrVtG6lUCi+88IIfMTlw4IBfezIV69ev9+fjbN++HZ2dnf7PFoIF/XRoRANCEirErDLZRbNQZ8J4qX16k6FieUsQg4mc7/HhCAkpvW4RSGBNexjnRjIV7xht1/XTH2pRxKC8ZVZlheGC0ndHnQkCXhpDuLJq2ig/VQBZO297L2t3qS3g5tuoKwkcQ1XQEvbav4Pa+MDGwu9AVzmWtwQxlMz5k5t5UddPYejiZBQKcaf6qIrfj5BAZ8SAwoC+sRzCuur9DsvXrXBctF2cvZhGW9PEi3qxmNl/dqzy0EQpMZTMQeRDTqNpzxdH4RwKl3BciaMDSdz3kwPY8YdXVRQdjdbZMFMacWOpB5WEycWLF31Ttb179+LIkSNVregVRcGWLVtKCl9bWlr8ny9EC/rp0IgGhCRUiFllsoumJ0wkXNcTIAFtfONpMlTwWADxrI0P3rEe16xsBgCMZW1/Wu57Hnmu4oW4+K6fMQY5iRhgnEHJR1M8DxWOpREdQykbluNW7eABvI1dU3i+OFbCqqGVRsuLJ6/WxZv/I6eRApJyfBqzF0FhfhcV58BQ0swLJi+989TJIbz/trVFvwMD4bYwcraAIzxDu96xXD66BHB43TkKBwKagqzl5oWiV7Raomww0eCt3EtlNG2jrUkHgycKK2G6wo9sVUtXyHx9y4ShibbwImiMwYEnGjWV5SNuDKridYzFs07FQtLFVIDaiBvLfGDb9oTi176+vhLH19OnT1c9TiAQwNVXX+2ncoqt6BeLBf10aTQ/IhIqxKwy2UUTzNtIhZRYUnYXLaVEPGtjRUsIK5eEwBmbUCcw2YU4nvX8CjiKLOsrrEsI6XcUaQqH7QpoioLOmIK+0UzJfJpKsPx78zZk6Q/fm6zmg8Frz1YYA+fSnygsxeQGaYV1T/YzIb0UC8+nZTwfGAbGvQ4lV0g8+ruXcfWK5omfl8oB1/NpCWgcusLR3mR4EZv8lGoAODuShZQSacuBWVS/U6ZXJlD4kem4SOUcRIJaftaTrHiH390awoV4tmq64tru5orCtxChK0TGVIWVpAULHWNBXalYSLrYClAbbWOZC8qFiWVZOHXqVEnha39/f9XjRKNRXH/99X4qZ/PmzX5xq6ZpE9I4lyuN5EdEQoWYdSa7aF7ZGcFg0kTKdKEq3BcbhdTEuZE0/sePDlSsE5jsmGs7mnB6KIWMNR4VqbSfuhJwXU9gREMq4lmBoZSFjqiBZbEgBhI5X0x4E5Q5dIVBgiGoeXb4YxkbTj4VUrxpa4rntVLY7xTuFa4WNs5C5xLgtUK3hXWonGEwYZakXsrXXRAHKvciRW6+sFbJC5PC5iylt6agpsByBHbuOoVH3nPjJBtXFLetX4LvPnMW8Zzji76c4919t4Y1vOOGFfjKr0/AcrzalkJdkagh7CQlYGgc77t1Nb77zNlJ7/A/9toNeHD36arpim3LYxVFqvd78D7zgiNyyTryn3tQU5A0nQmFpIuxALWRNpbZwLIs38OkIFCOHDlSIkzGxsaqHqejo6PE8XXDhg3gnJd4lxTEyWxb0C906jFOohL0WyEAzH7nw2QXzadPXyzZPEXefVZXOFrDxoQ6gULr6cWUidGMjXe/8gokMg5aQhramgw/LXSgdwxZy53gFltOoQMnbKhY0543jxMSrSENYUOFIyRSphel0RXuCyYhJf7rd/aVtCUXjM0Kxa5qPmKksNK7e2/YoOfmezFlQQgJNz8eIJ5zpnSKDeTrTNz8ukbSlicgFK/oRkDCEd452yMGHCFxqC+On+4/jzdetQxhQ8XzZ0cxEM9hWTSAa1e1YNvyGLZ0xSrefb//tjV4cPdpBFQFkK4vjsoLciaL/qic4YO/tx7vvHlVyTku2i44Y+huDeFjr92AW9e3gzNWU7ri5jVteN+r1uAHz57FQL5NXcsXWjuuQM52IeW4WJFS+h1jjKFiIeliLUBtlI1lJhSESUGUJJNJvPjii74o2b9/PzKZTNXjXHHFFb7j6w033OBb0Zdb0AeDwQVd9Ho5UVehsmPHDvzkJz/B0aNHEQwGccstt+CLX/wiNm7cWM9lXXbMVedDpYtmsYC5mDLx1X89gXMjaSyLBSfUCRRaTxXGkMiND0GMBlRs7vLutFWV47b1S/DM6YuwarjjV/JRCs4Zfvifb8axwVSJkAJQUbAJIXFFWxiH+xPecTgDz1u1ivxoZG90gFcAqyBfMwMvRRPQFIR1BU5Qw3+8bgWytosXz43h0Pl4xanIfoEqvKnUBVO3lOlNIIYEnHxEIah5QxWHUyZMW8CREp/+54O4/2eHIIRA1hZwhQRnDGFDxbblMfzZq9dWtLIvpESWRgNwhDcKwLTFBEEyWYqtuzWIbctjEEL6Hihf/uVxnL2YhiskLsSzJUZt1dIV5X+bDMxvP76iLYS/ePwgzo9l4bjCn91UEG5LmgzEs07FQlIqQK0/5cLk4sWLeP75531hcvDgQdi2PeUxGGO48sor/WjJ9u3b0d7uuRXX04KemF3qaqH/+te/Hm9/+9txww03wHEcfOITn8DBgwdx+PBhhMPhqq8nC/1Lp7jzoTmkQQggZ7vI2C5iQQ07Ztj5UEuEptjW3lC5X/Cpcg5HCPSNZf05QjIvLryBhQxBTUVrWMNdN63Ed55+GedGslN2sBSjcKA5qOFb77nJF1KF9RYiN81hDUvChr/uwloZ8yIyhS4alo+q2K4o8Ukp1LBIeC3RhYGHnDEEVAUpy/ELTjlDzRb3DF4kpzWsI2yo/md1fiwHN38sT5RMfkyFA0uaDHzlbVdPMEr7zclhfOyHL6IjYiBju+gdycDNFyjXUnAc1DhaQjrWdjT5KaZqtu6T/a3UYgsvpMRf/vMhnBlO54uCvfboWEiD5cgp7ePLLfrLIzoLpetnoVAuTPr6+ko6co4fP45q25Gmadi2bZufyrn22msRjUYbyoKeqI3p7N8NNetnaGgIHR0d2LVrF2677baqzyehcmkUz3RoMlQMpyyYjlvSRXJlZwQ/vffWaaWBqkVoChvTruOD+MZvziAaUDGSsUo2f5FPp4h8S62ucr8ewxYSAZXDUDkEANtxkbZqb6UJqBxNARVf+aNrcPuGdn+9h88nkMjZfvQhqHGs7YjgY6/dAFfC38AZgy+qklXm+ARUjiZD9WsdOqNed1POdv1py2pegNXaMV0wguuMBaArDINJC5br+h1BAID88Yq7coDS78OGgmhAK/kdvW5LJ77+7ycR0hX0x7PI2gIKY5BMwqnu/g8OwNAU6CrzU3rFRm3e0qrPC6ll3siymIFY0Ct6TeZc5BzXr0tpMtSaooLTnf9SL+bKlG4uze4syyopfi0UvhbEydmzZ6seg2sBtK3aiFe/8ia86TWvwlVXXeUbqDWqBT1RGwt21k88Hgfg2RFXwjRNmOa4tXgikZiXdS1WCmF+Q1VwfszzpCh0kUjJ4LgCh/uT+N6zZ/HOm1fVdMzyu2CNMyRNBy+ei+O//+hFvOeWVfjNyYs4NZhC1nYRz9oYy9rgAFQ130IsS2fplBamMqgcsFyBsKHWbDUPwG/JtV0JSInWkO6vdyRtIm2OF+QKKZE0vfTM+x7di3fcuHK8pkFTENQVJHOi6rBBRwiMZbxIyoqWAFSuYChlQs0XcZrOzGztJYD+eKklf6HNuJDaqtTJVEgrSQAp00VIV9HepMMWEkf6Ezg5mIKhcvSOZZGzhf9ZTGddlitguxKu8NqfJ6yhhq6aal05hspxuD+JiOGZBraEdCRyNkYzNhQGvOmaLty0pg2RgOanDCuxEApQ5yo1O9vHLRYmyWQSR48exXPPPeeLk6GhoarHUIMRBLvWoXXVJrSu2gSjvRtJU+Js0EDzuqtxxRUrGt6Cnph9GkaoCCHw3/7bf8MrX/lKbN26teJzduzYgfvvv3+eV7Z4KXQ+ZCwbIm8sVtypoioMlivxg2fP1mRHXu5NkbZcr8YhHylJ5mx84RfH0GSoiAQ0L3pTeC3gd5mUU/5QofVUTjL1eNL1+d06EllHYDRj4qHfnMFI2kIq51Y8loQXPfnuMy9jfXsTLiQtdEa9FZ2PZ6ue08273yrwogAhXRm32y+r/Mg3BlU1hJtM10gpYaiqV1RcJi4mO+bFlImM5aDJ0JC1XIzY1pTnmIrCX4fKvS6ogmjJ2WKC8Vu1rpqpunIkJMYyFoSUiAX18Voax5uEnbaAB3edxvefOYewoVTdfBu5AHWuTOlm47imafpdOWNjY37h6969e/H8888jmUxWXUdXV5dfW/LkxQguIIaOSACKbkDRDHDNQKuq4ULKwT8eSuI/3BhpKBFJzA8NI1TuvfdeHDx4EE899dSkz7nvvvvw0Y9+1P8+kUigu7t7Ppa3KCl0NHjW9nyCPT3AoDBgIJGryU+i+C44bbl5a3xv+jCYhCM8gZG2HGQst2I+ulJ0QaBUrBRaT53866s5rFaCAfj//qkHadNBynSqvt5yJE4PpxEJqBhImAioDFYNc4IKz3Al4NrCj1KgqGW5+MmTHbEWR1svSuJUXVPJcRmQtVykTBecoeZalErrK6zBE2Isn36SsF2BIEqFSrWumqm6cvxxDIzBFgLDccuro2EoaVFPWw6aAgoOn0/gv//oRbzrFatw67r2houYTMZcmdLN9LgFYZLJZDA8POzPx9m3bx9efPHFkmj3ZKxdu7ZkeF9XVxdUVcVLozZ+9tMjWBoOIVShG6c5xBaUrw0xuzSEUPnABz6An/3sZ9i9ezdWrFgx6fMMw6DK7VlkS1cUHVEDwykTCi/t4yi0eAbyIY6p/CSKa04ylotYQEV/0ivuLExTFhJg+RiCm/eWV2q8tnrGZl7Hh5ACjiuhKRym7SLfeANWQySimLawjpG0jZTp1Pw6LzIALG828PLF6m2SU+G4EpLXFvWo9rNLQeHcd9i9pHmFRUrKLppKLSRwIZED5wxNhne5qaWrZktXFGvam3DwfByxgApNURDQPTFtuy5cAYR0jnjGyovhcQffwlKElLiYtsABjGYEHvjVcXz7dy83ZA1KJebKlK7W475wZghr23RkMhn09fXhueee81M5R44cgetOXbSkKAo2bdrkz8e57rrr0NraWtGC/tzxIUi9CcFA5Tk5C9HXhpg96ipUpJT44Ac/iMceewxPPvkkVq9eXc/lXHZwzvCOG1fiL//5EBxXQlXG7ecLLZ6xkAZITHrnW5znztoukjmvUNQbhMf8i06lKcBT3bmrvKhrRQK2I7woSv4x1xFeBIDVNmG4nItpCyFNwTRKXLw7dlcgFtTxR9tb8Y2nXprmWUuZzjTlRqfEVr/sl5FzBHpHM+iKBaGpvCZb96dPX0Q8ayGZcxDP2FC4N9uoOaQjbTngnCGkKxjL2l7EDvDHJwDj3VE5W/h+NzJfg7VQZvlMx5RuOkWxheNqCsuPTPA67TTmQNoWpJnDxbMv4Rv/8BRGX/YM1k6dOlV1vYZh+Fb027dvxzXXXINIJFKTBf1i9bUhZoe6CpV7770X3/ve9/DTn/4UkUgEAwMDAIBYLIZgMFjPpV02/PGNK/GPz53F0YGkN7BPFhw9OZY0GUjmHHS3hnAxZaKnN15yASzPczeHNORsMT6Ej3mtuQAmOIdWg2H8zjioK8jZri9SOLyiUU9Qzex9ZyzXH4xY63oK05xPD6Ww/YqWmZ24hvPMZxteoZZkrvAmPHnRjvPxLJaE9aq27sV/V51RA2MZrx08Y7vIJXLY1BkBY8DLF7PjXWJy3NvFawkfF4IK85yCHdf7HXZGjQUxy6fWzfvli2n8z195XjWeQzHHuqWTf8atIR1CSpwaGINtZuFaJpyLL8MeOAExeArJ3mOwEhdxvMr6IpEIrrvuOl+YbN26FaFQqKRFuNYIOPnaEFNRV6Gyc+dOAMCrX/3qkscffvhhvPvd757/BV2GcM5w352bcN9PDiCedRDUFQQ1BYwBQ/m213MjGXz8x6XW9jevaauY5+6MBXDuYhqO9DYnhUk/QjMdiko5oCgMAXj28J4/iPR/PlOEBJLZqc2kiim0EUcCKoZS1rSF13TOM5+UF93ONsW/JiGBt1y3Ah9/7cZJxUF5/QQABFQFGdsFpETG8jxP/uvta/HxHx9AyrThFmpiMC5WPG+bfA0TZ76gUfPW6ZXSJnPZqjsTatm8IwEFn/+/R2A5wnNfBWA5HC+ei0+IGpmmiUwmg+MnX8LAgd8g3XsU1sBJmAMnIHKpqutpb2/3C1+3b9+OjRs3IhQKlURMZmpBf7kOViRqo6F8VKYL+ajMHuV+EkIIpC0XhsrREQlMMNt636vW4Ov/fhJhQ0VAK73bS+ZsnB3J+B4oCmdQOYfpuNOqgyh0ALU1GRhMmCV3zLPFdApxVc6wNGogZbpoMryW7gX7j2ceKf6ddTTp+Orbr500mlJsAugI6c+BKhYaIZ3jW++5CfGshQ98/wUksrY/tBHw5jQV/HY4vKnKrvCmQ1+xJOTVOgmJwZSJL7/t6hIvndluAb5UpjKl48wTK7YroanMm4SNQtoW0JnA5g4Dn7vzCjz77LN+fcnTz+2Da+WqnnvlypUljq+rV6/2IyaBQGBOJgkvFF8b4tJZsIZv04WEyuxSuKMcTpv421+fQO9oZlKzrc6ogQsJEx0RI3/HKkucZS3HQe9YDgFNQVtYB2NA32jW7wqp5Y+uYPTWEtIneIbMFh1RAyMpq2rEp9ww7XKH59XHdANbQU3B9aua8eh7b6p4d7zr+BA+9sMX84ZzOb9rbLx2yhMtH3/dlbjn1Wvx1IkhfPzHB5AxHegax1jaa7X3imm9FCEDA2cMy1uCfkFv1naRMR08+CfbkczZVR1w6y1WyjfvNe1h9MdzODOchsoZFM4hHAvSNmEnR5A6dxjOwAlYF07CGnoJThUreoBBa12OYNd6RFZswGf/81tw+7UbS9I48+Vd0miRLWJuWLCGb0R9KfhJ9PTGMZjITdkVMJDIgcHLnzt2pTtfhkBeZMSz3tRhifyMHABWDbURQgKWI3AhMTcipUlXEAtoCGkKzsdzfvdLJYrXygDoijdReSZmbQsZr0DaE5uiFtOXMkzHxbNnRvDlJ47hprVtEzai1pAOlQNDSbOkawzIp3QYgyMlnjg0gPfftga3rm/HV952tb+Rhww1P6RQws3XXAU0jo5ooGLXUWGo5Wy3AM8mlUzphJR490O/hcilYCYHkTt3GLn+48j1n4A13ItqvxjGFUS61iDWvRHhrg0Idq1HKNoKIxDAqMWwctM1WL26c37eYBmN7GtD1AcSKsQEauk2ALxoxLmRDDKW67vaKtwrYszmi0gyY6WmaCJfN1B4XjVUhU8pIGaKwhlWtYVwbjQHVwgIIaaVVqpFaM0XhYLVuV5P4a+h4IDLJPN/n7UipPfZ7dx1Ct995myJIdvNa9ogpERQ97xqVAVgbPxvUMIbC2CoCi7Es/jp/vNobdLRGtLx8N034MhAEiMZC81BDQCw59RFPPq7l2C7Akp+sGR5zcORgWRNrbo9fXFwxmbtLn+6UQPOGdYvMZDJuDhy5CAeffwXOPN/n0S67zic+GDV84VCIVxzzTW4YuM2PJNsxpIrNqIp2gxFKxiree8/a7sIKA7aI9TMQDQOJFSICRTuahM5268vCWjcnwacyNmQEljTFsKR/oRfGyDzm1YtW1etrblzlZlUGMP5eA5p04aQnguvwmVNXUSNIlAKzFeXcyigwHa84YuOKye0l09H6El40ZX2iI4j/Ul85If70RExcDFlIZ61851CAKSAonjFsIUZTJGgiuG0hc/+n8PgjJXUk9y+od0/x9Xdzbh6RWzK6cy7jg9VFeVDlou/eKwHI2lrVupXaqmHkVLCNE2kUim88MIL+O1vf+vXmAwOVhcmPBCGvnQdVqzfis//l7fgmmuuRjQaha4buPeHB3F8KIdA1KDuGmJBQDUqxASeOjHkFyly5oX6DZWjydCQzNnI2t7wNynmb5OcCxjGW7EtV8IVtQ8GvJzR8ha29kwsbMtY0RyAwhnOjWahcIal0QAcV2AwYfp/W4WCbEP1hg1eTJtwhcTy5iCiAa1qPclU0Yvi4t3yonAAGElbGEjk/JlCl1q/Mpl1/UjaQlgR+PgdKyCHTuGpp57yregLM9CmQgm3ILBsPQJd66F3rgdv7UYgFMLOd92M39vaXTJJmKZGE40A1agQM2bPyWF88vGDkNK7e5XSm6KcydusA143jso5TDEuU7S8zexsbF7zRaGwNxbUAQZcSJgT3cqICdhCQp2lco2+sZw/Ddp2JQbyRdOF3wKHV1C9PBZEQOd4aTgDx5UI6QpiIQ0MrGo9yVQ1D1O1AAspMJj01re8Oeh3uMy0fqW49XppxIB0beTicYye7sHI6QM4duYw3vKXpyGc6u6ra9aswRUbt+EMWwrWsQ4s1gWXqYCqA4oGQ1Pw3/9gA/7g6ismvPaWdUvw+bdumzLSRBCNBAmVyxQhJHr64th/dgySAdd2N2PLsqh/Ie1uCSFtuRhKmiVtxZwB0YCG0YxdEua3XYmFWpjvjRDgJSZzxNQUjzlS2MzmAwHeZ1362vwEb+b5oAh4rsQSQDzrIGu7UDhDRzRQMpuquJ6kuHalltqPyfw7hlMmhAQ6o8aENtzpWthLKbHv1AUcPHwMZu9B9Jw9jLGzR5EaeAlSTG1FzznHpk2bfP+Sm2++Gd3d3QgGg9jfl8ZDe876ztCcMXS3hvCx127Areu9NFiliNJCmBpNEAVIqFyG7Dk5jB0/P4LjF1Kw88UiKudY2RrEaMZGa9grrGsyVIQNBWMZG/3xXH5WD8NY1q64mS/UtInlSqDK3BKiMsX+JbOBI7wZPSrn0POurK70xKTXhsvQ1TzeZlyM7QoMpUz89c8OQeEcmuK18b5+6zJ0t4YqbsZCSEQCGv7fG1biiUMDGEzk/AjD8uYgekeyaA5Wbsudav6MlBKZTBa/2LMfu3fvwomefTiw/3n0vXym+oegqAh3rkGwawNaV12Jv/zTt+K12zf4bcLFoum2aBS3buycVHBUq4eh7hpiIUBC5TJjz8lhfOSH+zGUNMGA/GRjb4bNyaF0vrUYkFL3h8AVfCw4y2/qBFHEbP9FCOmJDk3xxIYrJN5+40ps6oziS08crVj4mjId9I1mIaRE2FARDWgYy1p45swIfnd6xBPdulKySZdv4ioHlsaCeN2Wpbh1XTuElLjnO/tqmj8jpUQ2m0VPTw927dqFn/3qSezbuxeZsaGq75dpAeid67yvZRthdG2AEghjaSwCoWj4eZ+Ct7928vTSZKmtyephFsqsI4IoQELlMkIIia8/eQojaQsMgKZyP3wumfB9Jy4kLYxkbBiqgvaIAZVzSEhYFHQgypgr2SoBOK4A8oW0r1jbhtvXt+MnL/ROqCeRkBhM5OCKfO1KUMunLS2IvLug4wqEDN3fpO+6aSW++8zZCZt472gW//jcOVy9ohk3r2mbvH5FCFwcjWNJ9hz+6Ru/wcd+9zvs27cPFy9erPrelGAELas2AUvWgHduhNaxFlwPgKk6mDJ+SR7OSXQ18xlNSC4fRdCI/jAEUSskVC4DCjnqfWdHcfB8HFJIqMq4SHGFnGBcJoRExnJwbsRFW1iDWMjtPURNNFp9jgCgMiAaULEkbEyoJ4kFVUjptdFnLBcKBzry84GGkrm8YRyHBGC5ApDeQML+eA5fe/IUVM68eUIMyFmeq3IsoCKes/1N/J7b1+K+nxzAuZEMdDcLs+8w4md6MHLmIDL9JyGsHH5d5X0EmtsRW3klmruvhN2+EaxlOfRAAGlXKfGJKUfCM76LBfWK6aWpOHQ+UZM/zHQFEEHUAxIqi5zi8HbadJEwPQ8Unh+cI6X0bMnLXlfI8AgpcSE5vYsksTAomKApHDA0BWFNhaECfXGvHqTeaT7PAVjB5q6Y7+tR6Fj5/P89jKMDKbhi3HivMA07ZwuYjsi76HrySwrPfp8xBUFdQf9YFl3NwZKC8eJ5Qof64njyxRP47W924+zP/i/6T/TAHHwJEE7VdQfaViC2ciOWrNmCljVXIbxkObiqQ9F0ZG0Xg4ksMpYAY9U/X89YUKI1ND37+lpMGyerryGIRoOEyiKmPEcd0BSkTAcupFdEy7xOl1o7cltCGsYylQtpiYUFgxdJA7wCVsd0kTZdcACGxtHdGsLJwVTdC6RjIQ333L4WgOd5MpKx8OSxQZwYTMOVEox5LcxuftxC32gWLWHNFx0ASgQI4Jn9CQCW4+JCwoSQXseam7yAzEs9yJw9iFzfUbzmM32QcupQoqqq2LJlC7Zv345XvOIVMFZsxgNPDWNpSxNUTZvwfJ0zZG0BXWVwrdrcjaMBbdoGbK0hHVq+GLlafQ1BNDokVBYplXLUUkoEdI606fo1AApnEy6WDN5mVXADdYR3IbddgfYmDYOpagPOiEZEUxjawjoG8pOoKyHgjT8YiOcQ0lWkzOoRhLnkPbesAgDc/fCzflQwnvP+/lTuFXoLSLiO18rsCIFE1gHyHWqAZ+QX0BQEdE+ouFICQuDC2VNIv3QAZu9h5M4fhT12oep6uGYgunwdtM712HjV9fjOJ96Fjo52BALev7Ge3jgCz++FA17x4powHQgh0RoNYFCYNY2HuHX9kmnXkUzlD0MOtMRCg4TKIqVSjpoxho5IAOfsDJy8C2ulWS2cAUIU7ki9nzMAGctFhipqFyyukJ6pXQ0kcw6uXNqEE8NpOHVKAQU1jn850I/vPnsOadNBc1DDaFGqwhUSnDMojENThOe7Ir1IiaZwv/Ve4RxtIRXxl49i+MQL6D++H+lzR+GkR6uuQQmE0dy9Ec2rNqNt7VWIrdoELRCGKTkypoOEEsOq4PhcnGoCIZF1wDlDNKBCCInzVaaCq5zhP163Yrof3ZT+MMWzjqiQllgIkFBZpEyWo24yVHS3htA3mpm0BsGzFRETHiMWPtOZxXMxY9fFqZcB4BxoCes4OpCErnJ0RgL5othx4VyICnKV5zvTBNy8WIFwkD13CObZHrj9x3Cm7xicbKrquZVwM4xl69G8agvcziuxZsNmtMaiYGXpE0PIijUe1QRC2FCgKZ6ZXWuTjuG0NWlUhQHYtCyCbctnVuxKDrTEYoGEyiJlqhx12FC8GT6QXkcPgLG0vaDn9hDVmW69ScZyah4eOZtIePOELqZM2K6E67o462S8NGWRcCrUV0kJCDuD3MsHkXrpAOzzR2EPnoJtTh2tAAA1thSBZethLN+EwMptCHV0ozkSBsAwnDShGMEJIgWYusZjKoHw/tvW4MHdp/MRFwPLm4M4N5KZ0HWncqCtycB9d266pKgHOdASiwESKouUqULQWdOF5QoENQWdMS9snbFc5GySKrXCsbAHMtZC1nLrVjidc6RvkK8oDJwx2I7wxZaTTSD30gHkentg9x2FOfQSUMWKnjGOZavWINC1EXLpRrhLNwFN7TB0DZrK4bgSQkiMpG2vwJYzxLM2dIXDzbvlBnQOSFSt8ZhKIHDGSiIuy1uCGEzkkLMFGPOinletiOHPXr1uVqIeU806IoiFAAmVRUq1GSYA0B7xxrxnLReukFA5m3BnR1RmsYsUoP7pvsLpGQOcxBDSZ/Yj9XIPzL6jcEb6UC2RpWka1m7cDNG+HnLpRqhdW5DTI+CcoyNiwHYlhpImLFfCKhqhwOC1bqucIZlzkMw54Mxbh6Yo0FWGlpBetcZjMoFQHnGxhURLSEdHNIA/2LwUXbEgWkIaIgENIl+HQxCXMyRUFjGThaBXtzfh3EgauurVr9iuyBu6kUghZgdD9Wb1pE132qJOSgl7pA+5l1+E1eu1CruJWqzoDazbdBXe+vo7sHLz9ci1rcePe0ZguwLNIQ39Yzkw2/W8gaYqKs6bzCVzDiS84nKvJoZ5fivguOumlZcU7agUcYlnLTy4+/Skc3kI4nKFSVmHarlZIpFIIBaLIR6PIxqlNrvJKJ+euqkzgnd/61kc7EtAVznGMlbdzb2IxYXKGT7+uo3YfXwQvzs9MmV9jBQurMEzyL38IszeQzD7jkJkE1XPwQMRBJdvQNPKLVi28Tp84I9eh40r2vDg7tM4eSGJ4bQFV0gENQWxkIahpAnOGCAlzPzfu6YAdlHGSFMY8q77AACFezUwSyMBqAqDwryhnJu7onjkPTdOGe2oNLV4sudPNpdnNN+hQ3N5iMXGdPZviqgsIia7MJaHoPecHEY8ayORs+tu6EUsTiQkfnNiGI+85yb8YO85fPmJoxjLep4s0rFh9h9D7uUDnjDpPw5pZaseU4m0wejaiMDyzdiy/SZs23Y1Dp5PIZ6zoXKGrz91DmnrDHSFIxbUAOmlcExHYDBhQkpAUeH7q3iLKR0cwMAAJuHkIxocDI6UUBWGSMAzcGOcVbWfrza1uBiay0MQU0NCZZFQ64WxcOeWzNmgSx4xV0gJHBtI4shAEm/e0orndvfhkZ//H2TPHYZ14STgVjeSU1u6YHRdiUD3FgRXXgW9rStv+c9wUeX4+aFBT3xwwFAVOELAcryC2ICmQEiv6FnhDI7rFeJKyUo6rvOTJPz/siLd4vXFlbraAtXt56c7tZjm8hDE1JBQWQTUemEsvnPTFF73YkliceJm4kif7cFY30G89btncOb4EYhqUy0Zh9a+CsEVmxBcuRXq8m1Qm1pKniLzDslSSqTzxoMK9wzdco7IF4R7RnDDKRNCegXBTEgUVLnjSihF3cacAYKhJN1T+K+QAlIyBDWOgDYuVKZqTZ5JdITm8hDE1JBQWSAU0jrDaRMjKQuJnA3GGK5eEcPXnzw5wSpfSiCsKxjL2Pj6kyf9wr1Tgyk0hzScHcnU+y0RiwQnPojc2R7kzh6A2XcEzuh5/2eTeb8yRYPRuQ768k0wurfC6N4KxQh5P8s/p1xH6xrPO8+Oix5XAEJ4oyAK35e/TpYdzHHGvxFSlqQ/zfyxWf5YmgK0R8YFRzX7+ZlER2guD0FMDQmVBUAhrXP4fBxjWccfJuc5eHqx6qV5kZIyHQwlczAd4Ye4nz0ziu89exbdrSHPZlyUXqwJolaklLAvnkPubA/Mcz0we4/ATV2s+jpuhKAv24BQ9xaErrgKgeVXwoFS0fi28BBnpSZ1DBJWhb9bifEBi8U/VTkrma4MAIx5gwxlXtAUNA8vi6qAeaZzAU31p0zXYj8/k+gIzeUhiKkhodLgFNI6I2kL6bzfSYHiC/T5sRyyljcB15Ui7zzr1QrarsDf/dsJfPD31kNTGOJZ67LwASEuHSlcWBdOIXf2oCdM+o5A5Kpb0fNQMwLLN+L1r7kD1970KnzrmIAjxjdguwadXF7onZ3iRZUGayp5czVHFBnFCa+zJ2woyNrevyeFjxfRagpHNKDBdARWtATQHDJweqh2+/mZREcu57k80+mMIi5fSKg0MIV8dzJnwxWyRKRUYjQzXiArUHonmTYd/OLgAFrDGg73V7cWJy5PhG3C6j+G3NmDyJ3rgdV/HNKuPshQiS31bOi7t8BYeTW0lmXgjOFMWMO5XgYGG3Pp01Pau1NAguUFe8ELRUigvclAQFNwdiQNhQEcDK1NOiKGhoDmRTSytouRtI3PvfUqcMZq3khnGh25HOfyTKcziri8IaHSwBTy3SFdRTxbm7jw3TzLHs9aLo70J9Ac1KglmfARuRRyfUdgnu1B7txBWBdOVbWiBxi0Jd0wlm9GYOU2GCu3QW1qrfAsz2qeM4bWsIaLabuq2J4p5Ufl3KsxYczrAHKlhO0IKBwI6SocIQAwqIoXZUmbDtqbDF9YFFI0Y1kbt29or3kdlxIduZzm8ky3M4q4vCGh0sAU8t2awjFdX77yZ7sSuJi2MEqdA5c1TmoE5rlDyJ07CPPcQdjDZ1E10sEV6EvXwlixGYGVVyGwYjN4oKnquQpmx0JKDKe8v7vKkY/ZReFAR1MAhlaYqiz94vGApiKgcYxlvTlGIt/ebDoCOVsgqHvpmkspYL2U6MjlMJeHfGOI6UJCpYEp5LuFlLN2cadoyuWDlBLOWH+JMHHiF6q+jqkG9K4NCKzYisCqq6Av2wCuGZe0lvn4u9MVhrawjgtJExeSOXTGAgjrKkYyFhwhoTKGJkPFSxczMG0XQki4yBel52tZAGVWClgvp+jIdCHfGGK6kFBpYAr57kN98XovhVgASClgD72E3LlDMM8dRK73MER6sgbhcXigCcbyTTC6tyGwciv0pWvBKhSCNjIKA5Y0GWgN6zA0BX1jWQwnLb+VWVc4mgwVI2kLQnqmcRpjsF3hFaVLiZwjoNrurBWwXg7RkZlAvjHEdCGh0mCUV8G//7Y1+PiPD0Bm7HovjWgwpGvD7D8JszcfMek7Ammmq75OaWqDsWILjJVbEejeCq2te8KdbaOicM8nqDxCIyQwlDKRyNlojwSwqi2EeMbBvb+3Dtd2N+NvfnEUz740AiEkNJXnu3w8O30773w4nMyBI7CoC1gbAfKNIaZLXYXK7t278aUvfQn79u1Df38/HnvsMbzlLW+p55LqymRV8LdvWIJ/er4Prph4gSYuH4SVhdl31Bcm1vljkG51Aau2LkdgxRYYK7chsGIL1FjHPKx2buAs7zaL8VoXlSNfiwJkbYG+0SyWxQIAA1YvCePq7mbcuW0Znj4zAgB57xbp16joCkM0qEEIiY+/7kq8+ZouStHMIeQbQ0yXugqVdDqNq6++Gu9973vxh3/4h/VcSt2Zqgr+5GAK0YCGkK5A4QxDSdO3ECcWL24mDrP3MHK9h2CeO+R15MgarOg7ViOwYgsCK7fCWLEFSmhxpB8Y4M/6kRJe3QkHtLwnPgOgccAWEoNJE81B1b8r724NoSnf7WO5nhkiY0BQ42iPBBDSFAymTLQ26SRS5pjL2TeGmBl1FSp33nkn7rzzznouoSGYqgp+aYShdzQLJ++jonCQSFmkOIkhL4WTFyb2xXPVX6RoMJZtgNG91fMw6boSPG9Fv5hgAJpDKt5+4yp0RAx87d9PIpVz4AgJCemlcuAVYypcwnRcdESbIKTEruNDGElZCOkcYUMH4BXOqpyX+KZMN91QPNZiLG2jJaShrcmgotkauBx9Y4iZs6BqVEzThGmOm08lEok6rmb2qFQFL6XESNryCs8cCVdKACRQFgtSSjgjvV7haz6V4yaGqr6O6cG8f4kXLTE614Op2jysuD6o3EvxBFQFnHEc7IvjvbeuBgPD0lgA/WM5b9Ag9yIkUgKu63nhx7M27vnOPtiuhKowpEwX8ayDZbEAmgx10vk9tbilFo+1SOQcCCHBOUM0oGFzV5Q22xqgziiiVhaUUNmxYwfuv//+ei9j1imvgk+ZDvrjWeRsMrpfLEjhwho8k+/GOQTz3GGIbPVuLh5q9iIl3VsRWLEFWvuqBdeRU42pvFWk9KYjd0QDUDjDqcEUxtI2NIVBVziWtwQxlDRhOi6k8MSKwjkc4aUR2iMGLFdgMGEiZ3veKaeH0whoCjoiBlSF4WLKgqFyvG5LJ/acGsaDu09P6ZZaPNYia7sQ0hMpQkjEszYO9I6RaVmNUGcUUQsLSqjcd999+OhHP+p/n0gk0N3dXccVzQ7FVfCOLdE7koFNVbMLGulYMPuPj3uY9B2BtKu7C6uxpeNpnBVboLZ0LZiOnJky1V+6pnIsiwXRZKieEBASLSGtqBjTQHhJCDlLwBECHMC5sSwUzrC8JYCMJdA/loOQnuhw8v+ucraLc6MZcMa8mhcA//NXx5C2XBgqR0ckUNEt9eY1bSVjLYQENO6ljySXcFzvK2U6ZFpGELPEghIqhmHAMC7NeKreVAorF6rgD5+PI2O5/sWUWDgIMw2z94hf+GoOHAdcp8qrGLQlK0uFSaRtXtbb6HiDAoHlsSBChgoJiUTOhhASoxkb779tDT75+MHxYkyVAy4wnDQhJPxar6GkCSG91A8DA2MSQkrEgioupmyAAStaAjAUJW8EJ+C6Ek5YIqCxCW6pYUP1x1okcjmonPlCkoFB4YDlCrRqOpmWEcQssaCEykKlIE6eOjmEJw5dwMBYBjlHgjNgZVsYH3vtBty2fgmeOXOx4hh7ovFw06NF9SWHYA+9VL0jhyvQO9d5rcLdW2Gs2AylBiv6xYCa9/ZyKnxECvOcYVvCGpI5F5brAhII6CqChoKU6WAwkUPWdqFwhr/5xRGsWxrBXTetxO4TwyXFmCtagjg3mkVzUEPOEjAd7zV+sS28dFLKdL1BhQxg4LBcCVsIaCqDEMBQ0kTYUPLiZtwt9YVzY0VjLbzXF8MY/BSUTaZlBDEr1FWopFIpnDx50v/+zJkz2L9/P1pbW7Fy5co6rmz2GC+6S2AsY0HkL26Fu7ADvWN477eeQ9hQ8xdTEiqNhpQSTvyCFynJF746o+ervo6pBvTlVyKQry/RuzaAa4F5WHEDIr16BIVJuGV/4pwxtEcNtAR1qIqFgbhXNB4LakjmHPSNZuEKLyrSFQtCVzmO9CdxbiSDz75lK2JBHRdTJkYzNkYyJv7u3076M62kBBgvWQYAwHEFFM4gJPLW+fl6GMbAOGA6LnLW+Oyfglsqk/DHWjDmHa9YqxTEi5Qg0zKCmCXqKlT27t2LO+64w/++UH9y991341vf+ladVjVzytM68ayFTz5+ECnTQcZyIb1mBM9wSnoeEIwDlithkfNswyClgD18dry+pPcQ3NRI1dfxQMQb3JdP4+hL14IpFLQEAMfzqfe/58wTIm+9djmOX0ji9FAagykTGme+0dfFlIXz+fqSkK6gI+p16wDw0zEP7j6N99+2Bt/87RkcPh9HPGvDEcAYbHDkhQQD1LyjrSO8WhXbkUA+vaRyT8n4wiMfFSnM/gHG3VKvWdmcT9MmoCscOUdA496Nh4RnIWCoHFnbxaZlUTItI4hZoK5X0Ve/+tXTngrcqFRylc3kB5+1NxkYy1gTYiWVwuDE/CNdB9bASa++pPcQzN7DELlU1dcpTW0l9SXakm4wVnl+CeHBUBgCCOgqx2s2LcUn37B5Qt0WAPx0/3n89c8OIWyoiAW1kqLiQjrm8Pk4Pv7jA0ibDrL5rh6FA67IT28GYLsSUnozfRTmRTkGkzm4rkRQVxDQvN+ZJzAEFFYQN97jxe3L25bHfLMy25XgTMAWApx5XT+MMaiKN/yQTMsIYnag271ZoJKrbCJnI5G1oXCGlOX40RSi/ggrB/P80bwoOQSz7xikY1Z9ndq6AoEVm31xokQ7Fn1HzmzD8vUohsqRtYTfGVOp4LS1SYfCOaIBreLnrHOGRM5BQBUQEiUdOA4T/gwfwHOxDWpeN4+mMrCUl75Z0mT4x26PBNA3moHtSgQ0BbrqGcGVu6UWm5WRjwpBzD0kVC6RyVxlvQI+QEiJRNYhkVJH3GwSZu9hv/DVunASEFXM8xiH3rEaxootCBQKX8PN87LexUyhSybnCHAmcPh8fNLOmGrD6xKmJxBCuoqRjFXSgaNyDgYBISXCuoq05SKsq8jYLjSX4crOCAaTJlKmC1XhMBQOhTMENBWcuwjpCoZS1qRuqcVmZeRMSxBzCwmVS6SSqyzgXSgLFytHCGgKh0m5nnnBSQx7oqRgRT/8cvUX+Vb0W7yunOWbFqUVfb3xDNxYfiaPQCLnYDhdOZpVbXhdIuuAcwZDq9yBwzmDcIFYSIOmcnzg99Zj9ZKwn156+vTFCRbuV3fH8P7b1iAW1Ku6pZJZGUHMDyRULpFyV9kCAZ3DUBVkLcfPpw8mTYqszDKeFX3feH3JuUNw4heqvs63ou/eAmPFZhjLNoCp1KFRKworTWXWbP2T3+8ZY35dx1i6ciF5teF1YUOBprDxThtM3oGjKxzXr2wpERZk4U4QCwMSKpfIZOFpBob2iIFzI57FtqZytEcNDCaq10IQk+Nb0edFSa73MERmrOrreKg5X1+S78jpWL3orOjnCwb4EUKvxXf68ltC+nUdLaHJZxVNNbzu/betwYO7T19SBw5FRQii8SGhcolMFZ4O64pnGsUYXFcgnavmVEqU41vR9x72DNb6jkBamaqvU2JL/W6cwIotUFuXU+HrLKBwoCWkYSxjgzGgLaxjOG3le+6rw5hXt+XmO2SiAa+uYyqminxwxma1A6eWgYQEQcwvJFQukWrh6ZaQjs++ZSuCuoJ3fuOZei+34RFmBmZfkRV9/3HAre4x41vRr8hb0Uep42IuCGoKAAaFcyxtMhDUFIxkrJrmejN4k40591qBVYVhc1dtXiOTRT5mswOnksVA+UBCgiDmHyYXsJFJIpFALBZDPB5HNFpfY6WSi1w+PF24yAkp8ZF/3I+hFNlpl+Omx/LREs9YzRo8U5sV/dK1RVb0m6AEyVhrLtE4Q1Dn+C+3rUXE0PD3/34SHREDjAOnBlPIVpn0zRnQGtahcgZXSNiuRHNIm3TC8HQjG4Xnz7QDp5LFgOUKjOZbk2kSMkHMLtPZvymiMktMFp7+xm9O4Qu/OFZ7seEiRkoJNzHopXDOHUSu9zCckd6qr2OqAb1rY16YbIHRdSW4fpla0dcJV0roqoLbN3QAQMm0b7vcE7+MzqiBoK7g/FgOtusJGpVzdLcGKz5/JpGNS6k1mcxioHwgIU1CJoj6QEJlFim/WD646xR2/PxYHVdUXzwr+nO+f4nZewhucrjq67gR9jpxCjNyOteCKZMXXBJzT2EicSFN49VlJZC1vKSPrnA4QpQIcg5g9ZIw/vJNm/Gpxw8iqHG0hXUENAWcAf1xE594rKckWjFZZONIf3LCc2eLySwGgHEHXJqETBD1g4TKDJkqNC2ExHefeRl/84ujdV7l/CJdB9aFU0XC5DBELln1dUpT67ixWvcWaEtWkhV9A3LD6lb/b/ye29fiv//oRYzYFhTOwBmgKgyu6xWxdkQMhAwF6ZyDB351AmnLxYqWUIkQCGhKSbQCQF0iG5NZDBQoDCSkScgEUR9IqMyAqULTjhD4658dwamh1KJP9wg7B+v88Xzh60GY549C2jVY0bd0lQgTNbaUOnIWAMcHkn6h6i3rluBdr7gCD/zqGCABJz9NOKgraI94wwOFkBh2LJwbyaA1PHW04qf7zyOes3G0P4nm0ETL/LmMbFRzwC0MJKRJyARRH0ioTJOpQtP/5dt7kTJr6X9YmLi5VJEV/UFYA6cAUa3lmkHrWF3SKqw0tczLeonZgzPg+IVSkXDruiV4dM8ZqHn7eZVzBLTxFn3TFeDw2pEni1bYrsBQysRf/+wQXAEkTRsZyymZlFxgriIb1RxwCwMJaRIyQdQHEirTYLKiO4NzCCEWnUhxkhf9wX25c4dgD72MqqMVFRVG5wYY3ZvHregDTfOyXmLuEBLIWE6JSNjSFcW6pZH8Bm9U3OBXtoUxmMhVjFakTAd9o1lvHo+hQlM40paDnO2ibzSL5S3BErEyV5GNahYDxQMJCYKYf0ioTINKRXcp08GFRBYZa2HP8ZFSwhk9nxclh2H2HoQzNlD1dUwPwui60p+Roy/bAK5NbeBFNA4MU0vPwtYsAWRtF+dGxs32atngP/baDXhw9+kJ0QoJicFEDq6QCOkKYkGvWDqgeU6yrhAYSpqeYSLYnEc2pnLAJR8VgqgvJFSmQXnRXcp0cG4kA2cBFqNI4cIeenncWK33ENz0aNXX8WB0fHDfii3Ql64hK/qFDAOYnFqsFH6mMIZfHOzHH9+40o8u1LLBF9xji8VMImcja7tQFYaOouhkeySAvtEsXCmQsx1kTBecs3mJbNDsH4JoTEioTIPiojuDcwzEswtGpEjHhjlwwq8vMfuOQprpqq9Toh2lVvRtK6jwdbExhVgpPKZyhvaogdND6QnFrNU2+EpiRggJhTN0xUrTO02GiuUtQQwmcsjaLi6mLYR1Zd4iGzT7hyAaDxIq06C46C4aUJCr4sZZT4SZgXn+aH5w3yFY/cchnepFiFrbShjdm/NdOVugRjvmYbXEfKJzwBaeCClMGJ4MBsDQOJbFgghpCgZTZsVi1mobfLmYGUlZ+JtfHIGuTiyybTJU8FgA8ayND96xHtetaqHIBkFcxpBQmSav29KJ4xeS6I+b1cpK5xU3Ey8xVrMunK5uRc849M61frTEWLEZSojuJhczDEBQ1xBVGC6mLV+slBMNqAgbKkKagqDuDdbM2u4lFbMWixkhJH7yQu+knTbxrINNy6L4k1esIoFCEJc5JFRqpNg7JW06MJ36dvg4vhW9J0zsi+eqvoap+rgV/YotMJZfCa5XtjEnFh43r27B02cmrzNiAHSVY/WSMHrHsljZFsRgwoKVt7XnABzhDQ3sbgmC8/Fox2wXs1KnDUEQtUJCpQZ2Hx/ER3/4IpJZG66UcOY54yOlhH3Rs6IvpHLcxFDV1/lW9Pk0jt65jqzoFxE837LDOXBlZwTf+dOb8Qdf3YUzw5mS5xW2esaADUub8LHXbcAnHz+IlOmiqzkAKb2OnqzlwlAZwBguJK05Fw/UaUMQRC3Q9OQqPLjrFL70xLF5LZqVwvWs6POixOw9DJFNVH1dwYreWLEZge6t0NpXkRX9IoQBMFSO1rCOjO2iOTg+hXjPyWF85If7cTFlQeT/aXMAjDO0hXU88EfX+M+bbNo3gEl/NhfiYbqTkgmCWPhMZ/8moTIFD+0+hR0/PzrnVvjCNmH1H/Pn45h9RyDtXNXXqc3L/GiJ0b0FavMy6shZ5HAGcMYgpERb2MCVFaIPe04O4+tPnsKxgSQsV0BXODZ2RvBnry59XrV5VSQeCIKYK6azf1PqZxIcR+Bv//XEnIgUkUsh13fEry8x+0/UaEV/xXh9SfcWqE2ts784oqFZGjEQ1FVcTFu49/fW4V03Tyw2rdUPZKpOHWrTJQiiUSChMgk/ffH8rFniu6lRf3BfrvcQ7MGXUNWKnqswOtfByA/uC5AV/WWPrnC0RQzkbIGwruD6lS2TRjlIaBAEsVggoTIJ+8+Ozeh1Uko4YwPjxmq9h+CM9ld9HdMCMJZvyteXbIG+bCNZ0S9iVM6gKgyWIyCrOMMWWN4SACRoSB5BEJcVJFQqIIREyqqWivGQUnhW9OcO+pOF3dRI1dfxYNQTJfk0jr50LVnRXyYYKkd7k46sLbA0ouD3Ni3Fj/aemzKCtzRiQOEcAwmTWncJgrisIKFSRqEb4kh/5S4b6dqwBk76xmpm72GIWqzoI+1+0auxYgu0thXUkXOZwAAovBA1YQhoCoQENndF/ULYP9i0FJ//v0dw7EISjpvv1mGe70lIVyEAZEyHWncJgrjsIKFSxJ6Tw/jEYz1ImQ6aQxpG0xZsMwvz/LF8q/BBWOeP1WRFr7auGJ+R070FamzpPLwDYr5QGSAZ4ApPiPw/V3UiFtIR1lXcuaUTCdPGA786gXMjGQgpEdQUrGlvwuu3dqK7NVRxHs4/f+BW9PTFsf/sGCQDru1uxpZlURwZSFL3DUEQly0kVPIIIbFz1ykkUhnw8y/izOkeDJ3Yj2Tv8dqs6JeuGW8VXrGFrOgXKArz6kds4U3JjgRUBHUFUgIjaQtZ2wUkIOAN8msOabj31Wvxn29bO+FYt65rn1aLL+cMV3c34+ru5pLHqSiWIIjLGRIqeQ6dT+DUYAoxg+PJb35qanGiaDC6NuZn5GyGsXwTuBGav8USJXB4qRU1LwJMV3rzaxgQMVREAho2LYtgbXsYe04O48RgCqYjkX8KmGfGioCqIBpQsW5pBLetX4LdJ4ZxajCFrC2gcYbrVrXgv9y6BsNpC31jGSxvDuGNVy2DWmGwHkCdNwRBELMBCZU8IxkLtivREmlCrHsD4meP+j9TAmHoXVf6/iVG5wYwlazo64nCgfYmAw/80TUASp1UmxmwNBbE67Ysxa3r2isamQ2nTYylbbSENLSEvSF7Y1m7JPLxp7euIdMzgiCIOkNCJU9rSIemMFiuQOe2WxFq7UTr2qvRuvZqRLpW4/RwFll7nof8XIZwBoR0BWvbm7BpWRRHBxI4M5xB2nTgSgnOgCZdw7YVsRKn1VoMzoDpRTkoIkIQBFF/GkKofO1rX8OXvvQlDAwM4Oqrr8bf/d3f4cYbb5zXNWzpimJtRxOO9Cex7rXvKrGiz5gOTEfAUDnM+Z5IuEhZ2mTgFWtbsW1FDM0hAy0hDfGsg5aQhrYmwxcahQjIxZSJ0YyN5rCGJWFjghAhUUEQBLE4qbtQ+cd//Ed89KMfxf/+3/8bN910E7761a/ida97HY4dO4aOjo55W8dUY+eHUyYAoDNi4EIyh5yzYMcjzToKA/5gUzvWd8YwkrYwmjZx/EISg0kLQngpmmXNBpY3h9Ec1LCqLYz/etsa6HptnjEkQAiCIC5v6j6U8KabbsINN9yAv//7vwcACCHQ3d2ND37wg/jzP//zKV87F0MJK02V7YgGcG4kjdawAUdIvHwxPeeDCusFg5d+aQ2p2NAZxcalTXj2pTEcv5CE7Y4XoOoqx9tv6MZfvnHLhBQLDbQjCIIgpmLBDCW0LAv79u3Dfffd5z/GOcdrXvP/t3fvQVGd5x/Av7sLuywuyyKEXUAu3kHZGpBowVSncUdExyJ2EiXEoOk0kwRbLEZr2iadNFUZM+ZSxyRtZqKdqcbUjprGSUjpekl0kFsEgyJYiYVfChiLK6BGYff5/eGP8/MUorEKu4vfz8zOeM77cM57zuMsD+ec9z0OlJWV9Yu/evUqrl69qix3dg48KdudGOiFbkm2ECz/YyXqW7tgMxsQHz4CLR2X4A93gTS4PhOqIVAHvU6LAJ0GAVoNNBqBVqtBXFgwZk24D6nxI6HVaPo9UNqnt9eDD463crQLERENKa8WKufPn4fb7YbVqp4MzWq14tSpU/3iN2zYgBdffHHQ+zXQL9r/vC00ITIE7V1f48LlniG7uhKo1UCnEfTdeQoxBCAmNAgXr7oRFhyIJdNikWg14/j/XERr59ewhQYhNS7srkwaFhCgRU5qzCAcFRER0Tfz+jMqt+O5555DUVGRstzZ2YnY2Ngh2XfGuAisz7Ert4UuegSGAB0yxpoxNzkKMWFGnO+6/nzGlR4PTAYdjrdcQOO5S7h8tRfX3NcvvwTqtLjWK9dHsADQ6TQwBuoQbtJjTLgRl656cLnHjZEj9EiODoHrihsjDNdnO02OCf1WBUdqwsh+63iFg4iI/JFXC5WIiAjodDq0t7er1re3t8Nms/WLNxgMMBi890bhgW4L3ezqxI3PaliM1+ddcV3pUf37dq9wsOAgIqJ7iVcLFb1ej6lTp8LpdGLhwoUArj9M63Q6sWLFCm927RtxHg4iIqKh4/VbP0VFRcjPz0daWhqmTZuG1157DZcuXcLy5cu93TUiIiLyMq8XKosXL8ZXX32FF154AW1tbbj//vtRUlLS7wFbIiIiuvd4fR6VOzEY86gQERHR4Lqd398DT4RBRERE5ANYqBAREZHPYqFCREREPouFChEREfksFipERETks1ioEBERkc/y+jwqd6JvZPVgvEWZiIiIBkff7+1vM0OKXxcqXV1dADBkLyYkIiKiu6erqwuhoTd/1YxfT/jm8Xjwr3/9CyEhIdBo+r/Ur+/tyi0tLZwQzsuYC9/BXPgO5sJ3MBdDS0TQ1dWF6OhoaLU3fwrFr6+oaLVajBo16pZxZrOZ//F8BHPhO5gL38Fc+A7mYujc6kpKHz5MS0RERD6LhQoRERH5rGFdqBgMBvz617+GwWDwdlfuecyF72AufAdz4TuYC9/l1w/TEhER0fA2rK+oEBERkX9joUJEREQ+i4UKERER+axhXahs2bIFCQkJCAoKwvTp01FRUeHtLvm1DRs24IEHHkBISAgiIyOxcOFCNDQ0qGK+/vprFBQUIDw8HCaTCT/84Q/R3t6uimlubsb8+fMRHByMyMhIrF69Gr29vaqYgwcPIjU1FQaDAePGjcO2bdsG+/D8VnFxMTQaDVauXKmsYx6GzpdffonHHnsM4eHhMBqNsNvtqKqqUtpFBC+88AKioqJgNBrhcDhw+vRp1TY6OjqQl5cHs9kMi8WCH/3oR+ju7lbFHD9+HN/73vcQFBSE2NhYbNy4cUiOz1+43W48//zzGD16NIxGI8aOHYuXXnpJNUU7c+GnZJjauXOn6PV6eeedd+TEiRPy4x//WCwWi7S3t3u7a34rMzNTtm7dKnV1dVJTUyPz5s2TuLg46e7uVmKeeuopiY2NFafTKVVVVfLd735XMjIylPbe3l5JTk4Wh8Mhx44dkw8//FAiIiLkueeeU2KampokODhYioqK5OTJk7J582bR6XRSUlIypMfrDyoqKiQhIUG+853vSGFhobKeeRgaHR0dEh8fL8uWLZPy8nJpamqSjz/+WP7xj38oMcXFxRIaGip79+6V2tpa+cEPfiCjR4+WK1euKDFz586VKVOmyNGjR+XTTz+VcePGSW5urtJ+8eJFsVqtkpeXJ3V1dfLuu++K0WiU3//+90N6vL5s3bp1Eh4eLvv27ZMvvvhCdu3aJSaTSV5//XUlhrnwT8O2UJk2bZoUFBQoy263W6Kjo2XDhg1e7NXwcu7cOQEghw4dEhERl8slgYGBsmvXLiWmvr5eAEhZWZmIiHz44Yei1Wqlra1NiXnzzTfFbDbL1atXRURkzZo1MnnyZNW+Fi9eLJmZmYN9SH6lq6tLxo8fL6WlpTJr1iylUGEehs7Pf/5zefDBB7+x3ePxiM1mk5dffllZ53K5xGAwyLvvvisiIidPnhQAUllZqcR89NFHotFo5MsvvxQRkTfeeEPCwsKU3PTte+LEiXf7kPzW/Pnz5YknnlCtW7RokeTl5YkIc+HPhuWtn2vXrqG6uhoOh0NZp9Vq4XA4UFZW5sWeDS8XL14EAIwcORIAUF1djZ6eHtV5T0xMRFxcnHLey8rKYLfbYbValZjMzEx0dnbixIkTSsyN2+iLYe7UCgoKMH/+/H7ninkYOn/961+RlpaGhx9+GJGRkUhJScHbb7+ttH/xxRdoa2tTncfQ0FBMnz5dlQuLxYK0tDQlxuFwQKvVory8XImZOXMm9Hq9EpOZmYmGhgZcuHBhsA/TL2RkZMDpdKKxsREAUFtbi8OHDyMrKwsAc+HP/PpdP9/k/PnzcLvdqi9hALBarTh16pSXejW8eDwerFy5EjNmzEBycjIAoK2tDXq9HhaLRRVrtVrR1tamxAyUl762m8V0dnbiypUrMBqNg3FIfmXnzp347LPPUFlZ2a+NeRg6TU1NePPNN1FUVIRf/OIXqKysxE9/+lPo9Xrk5+cr53Kg83jjeY6MjFS1BwQEYOTIkaqY0aNH99tGX1tYWNigHJ8/Wbt2LTo7O5GYmAidTge3241169YhLy8PAJgLPzYsCxUafAUFBairq8Phw4e93ZV7TktLCwoLC1FaWoqgoCBvd+ee5vF4kJaWhvXr1wMAUlJSUFdXh7feegv5+fle7t295c9//jO2b9+OHTt2YPLkyaipqcHKlSsRHR3NXPi5YXnrJyIiAjqdrt8oh/b2dthsNi/1avhYsWIF9u3bhwMHDqjeXm2z2XDt2jW4XC5V/I3n3WazDZiXvrabxZjNZv4Vj+u3ds6dO4fU1FQEBAQgICAAhw4dwu9+9zsEBATAarUyD0MkKioKkyZNUq1LSkpCc3MzgP8/lzf7LrLZbDh37pyqvbe3Fx0dHbeVr3vd6tWrsXbtWixZsgR2ux1Lly7Fz372M2zYsAEAc+HPhmWhotfrMXXqVDidTmWdx+OB0+lEenq6F3vm30QEK1aswJ49e7B///5+lz+nTp2KwMBA1XlvaGhAc3Ozct7T09Px+eefq74MSktLYTablS/89PR01Tb6Ypi762bPno3PP/8cNTU1yictLQ15eXnKv5mHoTFjxox+Q/QbGxsRHx8PABg9ejRsNpvqPHZ2dqK8vFyVC5fLherqaiVm//798Hg8mD59uhLzySefoKenR4kpLS3FxIkTeavh/1y+fBlarfpXmk6ng8fjAcBc+DVvP807WHbu3CkGg0G2bdsmJ0+elCeffFIsFotqlAPdnqefflpCQ0Pl4MGD0traqnwuX76sxDz11FMSFxcn+/fvl6qqKklPT5f09HSlvW9Y7Jw5c6SmpkZKSkrkvvvuG3BY7OrVq6W+vl62bNnCYbG3cOOoHxHmYahUVFRIQECArFu3Tk6fPi3bt2+X4OBg+dOf/qTEFBcXi8Vikffff1+OHz8u2dnZAw6JTUlJkfLycjl8+LCMHz9eNSTW5XKJ1WqVpUuXSl1dnezcuVOCg4M5JPYG+fn5EhMTowxP3r17t0RERMiaNWuUGObCPw3bQkVEZPPmzRIXFyd6vV6mTZsmR48e9XaX/BqAAT9bt25VYq5cuSLPPPOMhIWFSXBwsOTk5Ehra6tqO2fPnpWsrCwxGo0SEREhq1atkp6eHlXMgQMH5P777xe9Xi9jxoxR7YP6+89ChXkYOh988IEkJyeLwWCQxMRE+cMf/qBq93g88vzzz4vVahWDwSCzZ8+WhoYGVcy///1vyc3NFZPJJGazWZYvXy5dXV2qmNraWnnwwQfFYDBITEyMFBcXD/qx+ZPOzk4pLCyUuLg4CQoKkjFjxsgvf/lL1TBi5sI/8e3JRERE5LOG5TMqRERENDywUCEiIiKfxUKFiIiIfBYLFSIiIvJZLFSIiIjIZ7FQISIiIp/FQoWIiIh8FgsVIiIi8lksVIjorhARPPnkkxg5ciQ0Gg1qamoGZT8JCQl47bXXBmXbROR7WKgQ0V1RUlKCbdu2Yd++fWhtbUVycjI0Gg327t3r7a4RkR8L8HYHiGh4OHPmDKKiopCRkeHtrhDRMMIrKkSk+Mtf/gK73Q6j0Yjw8HA4HA5cunQJbrcbRUVFsFgsCA8Px5o1a5Cfn4+FCxcCAJYtW4af/OQnaG5uhkajQUJCAhISEgAAOTk5yrpbOXPmDLKzs2G1WmEymfDAAw/g73//e7+4rq4u5ObmYsSIEYiJicGWLVtU7c3NzcjOzobJZILZbMYjjzyC9vZ2AEBjYyM0Gg1OnTql+plXX30VY8eOVZbr6uqQlZUFk8kEq9WKpUuX4vz587dxNonobmChQkQAgNbWVuTm5uKJJ55AfX09Dh48iEWLFkFEsGnTJmzbtg3vvPMODh8+jI6ODuzZs0f52ddffx2/+c1vMGrUKLS2tqKyshKVlZUAgK1btyrrbqW7uxvz5s2D0+nEsWPHMHfuXCxYsADNzc2quJdffhlTpkzBsWPHsHbtWhQWFqK0tBQA4PF4kJ2djY6ODhw6dAilpaVoamrC4sWLAQATJkxAWloatm/frtrm9u3b8eijjwIAXC4XHnroIaSkpKCqqgolJSVob2/HI4888t+fYCL673j35c1E5Cuqq6sFgJw9e7ZfW1RUlGzcuFFZ7unpkVGjRkl2dray7tVXX5X4+HjVzwGQPXv23FG/Jk+eLJs3b1aW4+PjZe7cuaqYxYsXS1ZWloiI/O1vfxOdTifNzc1K+4kTJwSAVFRUKH0dO3as0t7Q0CAApL6+XkREXnrpJZkzZ45qHy0tLQJAGhoa7uh4iOj28IoKEQEApkyZgtmzZ8Nut+Phhx/G22+/jQsXLuDixYtobW3F9OnTldiAgACkpaXd9T50d3fj2WefRVJSEiwWC0wmE+rr6/tdUUlPT++3XF9fDwCor69HbGwsYmNjlfZJkybBYrEoMUuWLMHZs2dx9OhRANevpqSmpiIxMREAUFtbiwMHDsBkMimfvrYzZ87c9eMmom/GQoWIAAA6nQ6lpaX46KOPMGnSJGzevBkTJ07E2bNnh6wPzz77LPbs2YP169fj008/RU1NDex2O65du3ZX92Oz2fDQQw9hx44dAIAdO3YgLy9Pae/u7saCBQtQU1Oj+pw+fRozZ868q30hoptjoUJECo1GgxkzZuDFF1/EsWPHoNfr4XQ6ERUVhfLyciWut7cX1dXVt9xeYGAg3G73t97/kSNHsGzZMuTk5MBut8Nmsw1YKPVdCblxOSkpCQCQlJSElpYWtLS0KO0nT56Ey+XCpEmTlHV5eXl47733UFZWhqamJixZskRpS01NxYkTJ5CQkIBx48apPiNGjPjWx0NEd46FChEBAMrLy7F+/XpUVVWhubkZu3fvxldffYWkpCQUFhaiuLgYe/fuxalTp/DMM8/A5XLdcpsJCQlwOp1oa2vDhQsXbhk/fvx47N69GzU1NaitrcWjjz4Kj8fTL+7IkSPYuHEjGhsbsWXLFuzatQuFhYUAAIfDAbvdjry8PHz22WeoqKjA448/jlmzZqluVy1atAhdXV14+umn8f3vfx/R0dFKW0FBATo6OpCbm4vKykqcOXMGH3/8MZYvX35bhRcR3TkWKkQEADCbzfjkk08wb948TJgwAb/61a+wadMmZGVlYdWqVVi6dCny8/ORnp6OkJAQ5OTk3HKbmzZtQmlpKWJjY5GSknLL+FdeeQVhYWHIyMjAggULkJmZidTU1H5xq1atQlVVFVJSUvDb3/4Wr7zyCjIzMwFcvyr0/vvvIywsDDNnzoTD4cCYMWPw3nvvqbYREhKCBQsWoLa2VnXbBwCio6Nx5MgRuN1uzJkzB3a7HStXroTFYoFWy69NoqGkERHxdieIyP8sW7YMLpeLM88S0aDinwZERETks1ioENGQmTx5smrI742f/5yAjYgI4K0fIhpC//znP9HT0zNgm9VqRUhIyBD3iIh8HQsVIiIi8lm89UNEREQ+i4UKERER+SwWKkREROSzWKgQERGRz2KhQkRERD6LhQoRERH5LBYqRERE5LNYqBAREZHP+l95c9v5D5m/+wAAAABJRU5ErkJggg==","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["sns.regplot(x=\"sqft_above\",y=\"price\",data=df,line_kws={\"color\":\"black\"})"]},{"cell_type":"markdown","metadata":{},"source":["We can use the Pandas method <code>corr()</code>  to find the feature other than price that is most correlated with price.\n"]},{"cell_type":"markdown","metadata":{},"source":["# Module 4: Model Development\n"]},{"cell_type":"markdown","metadata":{},"source":["We can Fit a linear regression model using the  longitude feature <code>'long'</code> and  caculate the R^2.\n"]},{"cell_type":"code","execution_count":29,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"data":{"text/plain":["0.00046769430149007363"]},"execution_count":29,"metadata":{},"output_type":"execute_result"}],"source":["X = df[['long']]\n","Y = df['price']\n","lm = LinearRegression()\n","lm.fit(X,Y)\n","lm.score(X, Y)"]},{"cell_type":"markdown","metadata":{},"source":["### Question  6\n","\n","Fit a linear regression model to predict the <code>'price'</code> using the feature <code>'sqft_living'</code> then calculate the R^2. Take a screenshot of your code and the value of the R^2.\n"]},{"cell_type":"code","execution_count":36,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["R^2 is :  0.4928532179037931\n"]}],"source":["X1=df[[\"sqft_living\"]]\n","Y1=df['price']\n","lr=LinearRegression()\n","lr.fit(X1,Y1)\n","yhat=lr.predict(X1)\n","print(\"R^2 is : \",lr.score(X1,Y1))\n"]},{"cell_type":"markdown","metadata":{},"source":["### Question 7\n","\n","Fit a linear regression model to predict the <code>'price'</code> using the list of features:\n"]},{"cell_type":"code","execution_count":43,"metadata":{},"outputs":[],"source":["features = [\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]   "]},{"cell_type":"markdown","metadata":{},"source":["Then calculate the R^2. Take a screenshot of your code.\n"]},{"cell_type":"code","execution_count":45,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["R^2 is :  0.6576379123728886\n"]}],"source":["Z = df[[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]]   \n","lr.fit(Z,Y)\n","lr.predict(Z)\n","print(\"R^2 is : \",lr.score(Z,Y))"]},{"cell_type":"markdown","metadata":{},"source":["### This will help with Question 8\n","\n","Create a list of tuples, the first element in the tuple contains the name of the estimator:\n","\n","<code>'scale'</code>\n","\n","<code>'polynomial'</code>\n","\n","<code>'model'</code>\n","\n","The second element in the tuple  contains the model constructor\n","\n","<code>StandardScaler()</code>\n","\n","<code>PolynomialFeatures(include_bias=False)</code>\n","\n","<code>LinearRegression()</code>\n"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]"]},{"cell_type":"markdown","metadata":{},"source":["### Question 8\n","\n","Use the list to create a pipeline object to predict the 'price', fit the object using the features in the list <code>features</code>, and calculate the R^2.\n"]},{"cell_type":"code","execution_count":46,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["R^2 with pipeline is :  0.7464776733724359\n"]}],"source":["Input=[('scale',StandardScaler()),('polynomial', PolynomialFeatures(include_bias=False)),('model',LinearRegression())]\n","pipe=Pipeline(Input)\n","Z = df[[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]]  \n","pipe.fit(Z,Y)\n","yhat=pipe.predict(Z)\n","print(\"R^2 with pipeline is : \",pipe.score(Z,Y))"]},{"cell_type":"markdown","metadata":{},"source":["# Module 5: Model Evaluation and Refinement\n"]},{"cell_type":"markdown","metadata":{},"source":["Import the necessary modules:\n"]},{"cell_type":"code","execution_count":47,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["done\n"]}],"source":["from sklearn.model_selection import cross_val_score\n","from sklearn.model_selection import train_test_split\n","print(\"done\")"]},{"cell_type":"markdown","metadata":{},"source":["We will split the data into training and testing sets:\n"]},{"cell_type":"code","execution_count":48,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["number of test samples: 3242\n","number of training samples: 18371\n"]}],"source":["features =[\"floors\", \"waterfront\",\"lat\" ,\"bedrooms\" ,\"sqft_basement\" ,\"view\" ,\"bathrooms\",\"sqft_living15\",\"sqft_above\",\"grade\",\"sqft_living\"]    \n","X = df[features]\n","Y = df['price']\n","\n","x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n","\n","\n","print(\"number of test samples:\", x_test.shape[0])\n","print(\"number of training samples:\",x_train.shape[0])"]},{"cell_type":"markdown","metadata":{},"source":["### Question 9\n","\n","Create and fit a Ridge regression object using the training data, set the regularization parameter to 0.1, and calculate the R^2 using the test data.\n"]},{"cell_type":"code","execution_count":49,"metadata":{},"outputs":[],"source":["from sklearn.linear_model import Ridge"]},{"cell_type":"code","execution_count":50,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["R^2 of Ridge using the test data :  0.6478759163939111\n"]}],"source":["RidgeModel=Ridge(alpha=0.1)\n","RidgeModel.fit(x_train,y_train)\n","Rscore_test=RidgeModel.score(x_test,y_test)\n","print(\"R^2 of Ridge using the test data : \",Rscore_test)"]},{"cell_type":"markdown","metadata":{},"source":["### Question 10\n","\n","Perform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, set the regularisation parameter to 0.1, and calculate the R^2 utilising the test data provided. Take a screenshot of your code and the R^2.\n"]},{"cell_type":"code","execution_count":52,"metadata":{"collapsed":false,"jupyter":{"outputs_hidden":false}},"outputs":[{"name":"stdout","output_type":"stream","text":["R^2 of Ridge with Polynomial using the test data :  0.7002744271804466\n"]}],"source":["pr=PolynomialFeatures(degree=2)\n","x_train_poly=pr.fit_transform(x_train)\n","x_test_poly=pr.fit_transform(x_test)\n","RidgeModel=Ridge(alpha=0.1)\n","RidgeModel.fit(x_train_poly,y_train)\n","Rscore_test_poly=RidgeModel.score(x_test_poly,y_test)\n","print(\"R^2 of Ridge with Polynomial using the test data : \",Rscore_test_poly)"]}],"metadata":{"kernelspec":{"display_name":"Python 3","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":"3.12.2"}},"nbformat":4,"nbformat_minor":4}