House_Price_Prediction_California.ipynb

{
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   "cell_type": "markdown",
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    "_uuid": "a40153021c15eb45ff2026d456945763c79c11ef"
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   "source": [
    "# California house price prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8731c498e69012e3b8e407399ab813f0f15fc732"
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   "source": [
    "The problem that we are going to solve here is that given a set of features that describe a house in California, our machine learning model must predict the house price. To train our machine learning model with California housing data, we will be using scikit-learn’s fetch_california_housing dataset.\n",
    "\n",
    "In this dataset, each row describes a california town or suburb. There are 20640 rows and 8 attributes (features) with a target column (price).\n",
    "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_uuid": "c72424fcefad6b27f904bdc3301ff272baf3b212"
   },
   "outputs": [],
   "source": [
    "# Importing the libraries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_uuid": "7ad691aeebc4b3845eefa1ac8ca32fc4b631209d",
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   "outputs": [],
   "source": [
    "# Importing the California Housing dataset\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "california = fetch_california_housing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "5853d3c3d9e4b52f37ea42665f573bfd92941ddf"
   },
   "outputs": [],
   "source": [
    "# Initializing the dataframe\n",
    "data = pd.DataFrame(california.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "7b6f7755accf4b1c5f4e90a3d4aea7f41f89a482"
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       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
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       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
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       "      <td>37.85</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
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       "        0     1         2         3       4         5      6       7\n",
       "0  8.3252  41.0  6.984127  1.023810   322.0  2.555556  37.88 -122.23\n",
       "1  8.3014  21.0  6.238137  0.971880  2401.0  2.109842  37.86 -122.22\n",
       "2  7.2574  52.0  8.288136  1.073446   496.0  2.802260  37.85 -122.24\n",
       "3  5.6431  52.0  5.817352  1.073059   558.0  2.547945  37.85 -122.25\n",
       "4  3.8462  52.0  6.281853  1.081081   565.0  2.181467  37.85 -122.25"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# See head of the dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "6ecbc294bdbabbafee3893b5c66bd89709d6a069",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "      <td>8.3252</td>\n",
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       "      <td>322.0</td>\n",
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       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
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       "      <td>52.0</td>\n",
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       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
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       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
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      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Adding the feature names to the dataframe\n",
    "data.columns = california.feature_names\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "45b04cfb9bfc68cfab0ebf412ad402bac6fc5343"
   },
   "source": [
    "MedInc median income in block group <br>\n",
    "HouseAge median house age in block group <br>\n",
    "AveRooms average number of rooms per household <br>\n",
    "AveBedrms average number of bedrooms per household <br>\n",
    "Population block group population <br>\n",
    "AveOccup average number of household members <br>\n",
    "Latitude block group latitude <br>\n",
    "Longitude block group longitude <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "d16fdd57c4ee58a812935d5a3e1f4bfe5923e23e"
   },
   "source": [
    "Each record in the database describes a Caliornia suburb or town."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "293641aa8ae59c57884bd19c0ee6fc43143b19b8"
   },
   "outputs": [],
   "source": [
    "#Adding target variable to dataframe\n",
    "data['PRICE'] = california.target \n",
    "# Median value of owner-occupied homes in $1000s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "b5edd7daea054f44df78e956846596fd9e82274f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check the shape of dataframe\n",
    "data.shape"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "264fd03458b3b349d2d52ac02b83e2e85b6190b2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup',\n",
       "       'Latitude', 'Longitude', 'PRICE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "data.columns"
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   "execution_count": 9,
   "metadata": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc        float64\n",
       "HouseAge      float64\n",
       "AveRooms      float64\n",
       "AveBedrms     float64\n",
       "Population    float64\n",
       "AveOccup      float64\n",
       "Latitude      float64\n",
       "Longitude     float64\n",
       "PRICE         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "data.dtypes"
   ]
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  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "e86b006860c2efe29294aca3ba4419d9c758ced2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc        12928\n",
       "HouseAge         52\n",
       "AveRooms      19392\n",
       "AveBedrms     14233\n",
       "Population     3888\n",
       "AveOccup      18841\n",
       "Latitude        862\n",
       "Longitude       844\n",
       "PRICE          3842\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying the unique number of values in the dataset\n",
    "data.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "cef1bbf6ab92b5cd42a9a0771a24f48a09acf218",
    "scrolled": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "MedInc        0\n",
       "HouseAge      0\n",
       "AveRooms      0\n",
       "AveBedrms     0\n",
       "Population    0\n",
       "AveOccup      0\n",
       "Latitude      0\n",
       "Longitude     0\n",
       "PRICE         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for missing values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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    {
     "data": {
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       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
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       "      <th>AveOccup</th>\n",
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       "Empty DataFrame\n",
       "Columns: [MedInc, HouseAge, AveRooms, AveBedrms, Population, AveOccup, Latitude, Longitude, PRICE]\n",
       "Index: []"
      ]
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    "# See rows with missing values\n",
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    {
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       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
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       "      <th>count</th>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.870671</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>5.429000</td>\n",
       "      <td>1.096675</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>3.070655</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>2.068558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.899822</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2.474173</td>\n",
       "      <td>0.473911</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>10.386050</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>1.153956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.499900</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.846154</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>-124.350000</td>\n",
       "      <td>0.149990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.563400</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>4.440716</td>\n",
       "      <td>1.006079</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>2.429741</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>-121.800000</td>\n",
       "      <td>1.196000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.534800</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.229129</td>\n",
       "      <td>1.048780</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>2.818116</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>-118.490000</td>\n",
       "      <td>1.797000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.743250</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.052381</td>\n",
       "      <td>1.099526</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>3.282261</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>-118.010000</td>\n",
       "      <td>2.647250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000100</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>141.909091</td>\n",
       "      <td>34.066667</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>1243.333333</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>-114.310000</td>\n",
       "      <td>5.000010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             MedInc      HouseAge      AveRooms     AveBedrms    Population  \\\n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  20640.000000   \n",
       "mean       3.870671     28.639486      5.429000      1.096675   1425.476744   \n",
       "std        1.899822     12.585558      2.474173      0.473911   1132.462122   \n",
       "min        0.499900      1.000000      0.846154      0.333333      3.000000   \n",
       "25%        2.563400     18.000000      4.440716      1.006079    787.000000   \n",
       "50%        3.534800     29.000000      5.229129      1.048780   1166.000000   \n",
       "75%        4.743250     37.000000      6.052381      1.099526   1725.000000   \n",
       "max       15.000100     52.000000    141.909091     34.066667  35682.000000   \n",
       "\n",
       "           AveOccup      Latitude     Longitude         PRICE  \n",
       "count  20640.000000  20640.000000  20640.000000  20640.000000  \n",
       "mean       3.070655     35.631861   -119.569704      2.068558  \n",
       "std       10.386050      2.135952      2.003532      1.153956  \n",
       "min        0.692308     32.540000   -124.350000      0.149990  \n",
       "25%        2.429741     33.930000   -121.800000      1.196000  \n",
       "50%        2.818116     34.260000   -118.490000      1.797000  \n",
       "75%        3.282261     37.710000   -118.010000      2.647250  \n",
       "max     1243.333333     41.950000   -114.310000      5.000010  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the data statistics\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "f35fcf15ca8787f7c2b27c9567e02bec12dd8232"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9, 9)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding out the correlation between the features\n",
    "corr = data.corr()\n",
    "corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "5a2f669e42f1c4da0678d3012fe90996ce8dc476"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the heatmap of correlation between features\n",
    "plt.figure(figsize=(20,20))\n",
    "sns.heatmap(corr, cbar=True, square= True, fmt='.1f', annot=True, annot_kws={'size':15}, cmap='Greens')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "e8b29232f337d8d269fdeab876ecf1b6cf692556",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Spliting target variable and independent variables\n",
    "X = data.drop(['PRICE'], axis = 1)\n",
    "y = data['PRICE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "8c44aa7df1ef21f85c1baba09dbb5807c223c51a"
   },
   "outputs": [],
   "source": [
    "# Splitting to training and testing data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "af76da42b96f93ae8bd6234053182ecee90f255e"
   },
   "source": [
    "# Linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f585ceafa562d74250058bf0ff05cdf8ccebb577"
   },
   "source": [
    "#### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "74c1df4cd4ba074712c8fe65802acc568e0b6ba9",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import library for Linear Regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Create a Linear regressor\n",
    "lm = LinearRegression()\n",
    "\n",
    "# Train the model using the training sets \n",
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "998fb3c39c9a86eb1bcd4d921dd183a6491335d7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-36.9258842988459"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value of y intercept\n",
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "014c51bc46bb492a44f253c3c04e113ddbc39af6",
    "scrolled": 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>Attribute</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MedInc</td>\n",
       "      <td>0.438284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>HouseAge</td>\n",
       "      <td>0.009021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AveRooms</td>\n",
       "      <td>-0.113436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AveBedrms</td>\n",
       "      <td>0.648835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Population</td>\n",
       "      <td>-0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AveOccup</td>\n",
       "      <td>-0.003978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Latitude</td>\n",
       "      <td>-0.421168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Longitude</td>\n",
       "      <td>-0.43468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Attribute Coefficients\n",
       "0      MedInc     0.438284\n",
       "1    HouseAge     0.009021\n",
       "2    AveRooms    -0.113436\n",
       "3   AveBedrms     0.648835\n",
       "4  Population    -0.000008\n",
       "5    AveOccup    -0.003978\n",
       "6    Latitude    -0.421168\n",
       "7   Longitude     -0.43468"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting the coefficient values to a dataframe\n",
    "coeffcients = pd.DataFrame([X_train.columns,lm.coef_]).T\n",
    "coeffcients = coeffcients.rename(columns={0: 'Attribute', 1: 'Coefficients'})\n",
    "coeffcients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0a51c9fddb34d1d64b08a1ae5b52a0fa5de220c2"
   },
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "fbf88954f6ea78c339dfd4d1bbb4731fe1b3c489"
   },
   "outputs": [],
   "source": [
    "# Model prediction on train data\n",
    "y_pred = lm.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "40c2d862d0798c825949e665b23a0d93e7acbf22",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.6081901093734017\n",
      "Adjusted R^2: 0.6079730251483852\n",
      "MAE: 0.5319076620403791\n",
      "MSE: 0.5258454910434389\n",
      "RMSE: 0.7251520468449627\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "print('R^2:',metrics.r2_score(y_train, y_pred))\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e7eceb24e150551eff5f3c946772437ffcfdce8d"
   },
   "source": [
    "𝑅^2 : It is a measure of the linear relationship between X and Y. It is interpreted as the proportion of the variance in the dependent variable that is predictable from the independent variable.\n",
    "\n",
    "Adjusted 𝑅^2 :The adjusted R-squared compares the explanatory power of regression models that contain different numbers of predictors. It identifies the percentage of variance in the target field that is explained by the input or inputs.\n",
    "\n",
    "MAE : It is the mean of the absolute value of the errors. It measures the difference between two continuous variables, here actual and predicted values of y. \n",
    "\n",
    "MSE: The mean square error (MSE) is just like the MAE, but squares the difference before summing them all instead of using the absolute value. \n",
    "\n",
    "RMSE: The mean square error (MSE) is just like the MAE, but squares the difference before summing them all instead of using the absolute value. \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "c00f457edc850676c7ac951f2a77994bc798f709"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the differences between actual prices and predicted values\n",
    "plt.scatter(y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "65b07c5f88bbc8863b56057568bb09443484d806"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking residuals\n",
    "plt.scatter(y_pred,y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c399b5c2f66818caf7600c0f10338c0ce5afebc8"
   },
   "source": [
    "There is no pattern visible in this plot and values are distributed equally around zero. So Linearity assumption is satisfied"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "9814045d0aaa99146a32653f4e5449a11b05ff8f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sahaj\\anaconda3\\envs\\New\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking Normality of errors\n",
    "sns.distplot(y_train-y_pred)\n",
    "plt.title(\"Histogram of Residuals\")\n",
    "plt.xlabel(\"Residuals\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1d767c0ab0732c66838be5aecf56f23f859c1021"
   },
   "source": [
    "Here the residuals are normally distributed. So normality assumption is satisfied"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a91b3c5ee714c81642b90d02760c7e10f827fd48"
   },
   "source": [
    "#### For test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "d1c22569bf33b54bc8e67dd9768beeb48520c5f0"
   },
   "outputs": [],
   "source": [
    "# Predicting Test data with the model\n",
    "y_test_pred_LR = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "5bd7bf701f57b013f7b4bb3ee30debdf216647f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.6010291338754374\n",
      "Adjusted R^2: 0.6005129173253814\n",
      "MAE: 0.5303872778945684\n",
      "MSE: 0.5213468821374595\n",
      "RMSE: 0.7220435458734186\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "acc_linreg = metrics.r2_score(y_test, y_test_pred_LR)\n",
    "print('R^2:', acc_linreg)\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred_LR))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_test, y_test_pred_LR))\n",
    "print('MSE:',metrics.mean_squared_error(y_test, y_test_pred_LR))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_test_pred_LR)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e7a9ed698fd4f011bae24ce4b966317de91f9d91"
   },
   "source": [
    "Here the model evaluations scores are almost matching with that of train data. So the model is not overfitting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6d4c438bc27d2e3093f625e04ee9aeb5282a4710"
   },
   "source": [
    "# Random Forest Regressor "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7f50fab16d7fc4871f56d60d84ff245e16a70172"
   },
   "source": [
    "#### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "34e2ffdaf66c5bb6d30daa5acd5276a6da21e809"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Random Forest Regressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Create a Random Forest Regressor\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "# Train the model using the training sets \n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3b1ab519a44ec90e993e152eb908f867db07f381"
   },
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "96b76c70a914d3b9d20a4aefe0742e625890f73e"
   },
   "outputs": [],
   "source": [
    "# Model prediction on train data\n",
    "y_pred = rf.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "1a0a1be1b53f2e4e5ee260d9bc27099403c703fa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.973242056598494\n",
      "Adjusted R^2: 0.9732272312264314\n",
      "MAE: 0.12309003039175012\n",
      "MSE: 0.035911660792368565\n",
      "RMSE: 0.18950372237074545\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "print('R^2:',metrics.r2_score(y_train, y_pred))\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "b02cec6f66440794e5108df5f5474e920d1fe98a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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u3YQXX3xRuHbtmum8P//8U7j77ruFsLAwITw8XLj77ruFn3/+2e6qH6NPP/1U6NGjhxAUFCSEhYUJXbp0MbtfYWGhcM899wiRkZGCSqUyu0ZVVZXw1ltvCZ06dTLdv3379sLjjz8uHD9+3HReRUWF8OyzzwpNmjQRgoKChF69egnp6elCQkKC6FU/ixYtEubOnSs0b95cCAwMFLp06SJ8//33Zucan+Ply5frXcfR5y8IgrBjxw5h+PDhQnR0tKBWq4VmzZoJw4cPF/773/8KgiAI5eXlwhNPPCGkpKQIERERQnBwsHDDDTcIs2fPFkpLS20+PyJnqQRBEDyXJhERNWynT59GYmIi3nzzTTz33HOeDodIcVijQkRERIrFRIWIiIgUi1M/REREpFgcUSEiIiLFYqJCREREisVEhYiIiBTLqxu+GQwGXLhwAeHh4aJbnBMREZFnCYKAq1evIi4uDn5+tsdMvDpRuXDhgkN7eBAREZHnnTt3zu7GoV6dqBg3BTt37pykVtVERETkOSUlJYiPjzd9jtvi1YmKcbonIiKCiQoREZGXEVO2wWJaIiIiUiwmKkRERKRYTFSIiIhIsZioEBERkWIxUSEiIiLFYqJCREREisVEhYiIiBSLiQoREREpFhMVIiIiUiyv7kxLRERErtHh+W9xvdbvwQCOvT7c7XF4dERlzpw5UKlUZj86nc6TIRERETV4LeskKQBw/a/j7ubxqZ8bb7wRubm5pp8jR454OiQiIqIGy14y4u5kxeOJSkBAAHQ6nemncePGng6JiIioQeogMgkRe54cPJ6oHD9+HHFxcUhMTMTo0aNx6tQpq+dWVFSgpKTE7IeIiIjkUXe6x9nz5ODRROWmm27Cp59+iu+//x4fffQR8vLycPPNN6OgoMDi+QsXLoRWqzX9xMfHuzliIiIi36E3CEg/WYCNmeeRftLyZ6+nqQRBEDwdhFFpaSlat26NGTNmYPr06fVur6ioQEVFhen3kpISxMfHo7i4GBEREe4MlYiIyKulZeVi7tfZyC0ud+j+p51YAVRSUgKtVivq81tRy5NDQ0PRsWNHHD9+3OLtGo0GGo3GzVERERH5lrSsXExalQHFjFTY4PEaldoqKipw7NgxxMbGejoUIiIin6Q3CJj7dbZXJCmAh0dUnnvuOYwYMQItWrTApUuXMH/+fJSUlGDcuHGeDIuIiMjn6A0C9uUUYs+JfIenezzBo4nKn3/+iTFjxiA/Px+NGzdGr169sHfvXiQkJHgyLCIiIq+gNwjYe7IA6afyAaiQ2joGvVrFwN9PZXaes/UonuTRRGXNmjWefHgiIiKvlZaVi+fXHUFRWZXp2NKfTiAyRI3XR3XE4CQd9uUUYmt2Hj7ec9pzgTpJUcW0REREZJveIGDptuNYvNXywpOisio8sSoDkSFqsyTGWzFRISIi8hJpWbmYsykbeSX2p3B8IUkBmKgQERF5BW9aUiwnRS1PJiIiovq8bUmxnJioEBERKdy+nEKvXLEjB079EBERKZSx98l3WbmeDsVjmKgQEREpkDf3PpETExUiIiKFaaiFs5awRoWIiEhBGnLhrCVMVIiIiBSkIRfOWsJEhYiISCH0BgG7j1/2dBiKwhoVIiIiBdh8OBcvbcxCYWmlp0NRFCYqREREHmBcenzpajm2Zufh68N5ng5JkZioEBERuYkxOdmSnYcNmRc4eiICExUiIiI3YF8UxzBRISIicjH2RXEcExUiIiIn1K41aRIehJ6J0fD3U5ndzr4ojmOiQkRE5CBL0zmx2iDMHpGEIcmxANgXxVnso0JERGSD3iAg/WQBNmaeR/rJAugNNWMjxumcuklIXnE5Jq3KQNpfGwleuqrcJKVpeCCiQtSeDsMmjqgQERFZoDcIWLrtBFbsyUHR9SrT8VhtEF4enoR531qezhEAqADM/Tobg5N0aBIe5K6QJRvWMQ6RIYFYsvUPxU5NMVEhIiKqIy0rF8+vO4Kisqp6t+UVl+PJ1Rk27y8AyC0ux96TBQCAyGC1WbKjFCt+Pg0AiAxRo6ragNJKvWcDsoCJChERUS1pWbl4YpX1RETKyMNjnx1Q5Id/XZYSMqVgjQoREdFfjCt05OINSYrScUSFiIgaPOMS493HL3OFjsIwUSEiogaNHWOVjYkKERH5lLoN2LolROHgmSsWG7KxY6zyMVEhIiKfYWl0xE8FGGplIsaGbIOTdOwY6wWYqBARkU+wNjpiqHMgr7gcT6zKwD1dm3G6xwtw1Q8REXk9KfvpGM/5X8Z5V4ZEMmGiQkREXo/76fguJipEROT1lLyfDjmHiQoREXk9Je+nQ85hokJERF6vZ2I0YrVBUHk6EJIdExUiIvJ6/n4qzB6RBABMVnwMExUiIvIJQ5JjsfyBrtBpOQ3kS9hHhYiIfMaQ5FgMTtKZOtPmX63AvG+PeToscgITFSIi8in+fiqkto4BAGw+fKFeZ1ryLkxUiIjIpxj3+tmanYeP95z2dDjkJCYqRETkM7gTsnu4s8CViQoRESle3R2Ra++AbOTITsgqFSBwWkgyjRuzByYqRESkSMbkZEt2HjZkXkBhaaXpNuMOyEOSY03nOrITcr82MdhxvEDGqD0vwA+oNrj2MQTBfYvAuTyZiIgUJy0rF30WbcOYj/bikz2nzZIUoGYH5EmrMpCWlQvA8b1+fC1JidUG4Z3RXVz+OILklNBxTFSIiEhRjFM4thIP4a+fuV9nQ28QsCU7z23xKZUKwOwRSRiWEodlY7tA5cJBD4OLR2xqY6JCRESKIXUKJ7e4HEu3ncAnDXx1T0igP94Z08U0FRYVqnFp7U21G+t6WKNCREQuVbcQtltCFA6euWKxMNaRKZz3tp9wRdhepaxSj6lrDiHrfBFmDUty+W7S7qw/ZqJCREQuY2m5cN0GbLULYx35gK10deWolzAIwAc7cwAAt97Q1KWP5c7pGE79EBGRS1irNanbJbZ2YWyTcO7T46yPduWgc3ykS3eTjgnxd9GV62OiQkREspNSa2I8Z+7X2ci/VuHKsBoEgwCs/uWMS3eTDvBnokJERF5Maq2JgJrC2NmbjrouqAbkTGEZBifpMG1QO2iD1bJfv9yN022sUSEiItk5WsxZt18KOaasohp9Fm0zSxYjg9Xo2zYGXx92fil3sNp94xwcUSEiItmx1sRzVADWZpyvN6JVfL0K3xzOw8S+ifBzcj7I2ftLeiz3PRQRETUU3RKiEB0a6OkwGqSQQH+LtUHGY+syztcraJaqtIqdaYmISKH0BgHpJwuwMfM80k8WQF/nUy8tKxe3vPkTp3HczE8FjEjRobRSb/UcAUCBHH8vbPhGRERKZKkvSu0+KGJ2MFbBvQ3DfFWsNggP926Jc1euIyE6BA+mtsR3Wbmy1KDYE6bhpoRERKQw1vqiGPugbD58QdSS5KYRGtyREuu6QBuIO1JikdwsEt0SopAUp4W/n0p0bVB0qNqpZcuBAe5bnswRFSIiMlO35X3PxGgAsJqECKgZJXlpYxYKS6vsXj+vpALfHM6VNeaG6OPdOfhoV47pd11EEF65owN0EUHIK7G+6ipWG4SXh3fA5NWHHB7d8vdz3zgHExUiIjKxNrUzukcLu7sZi0lSSD71OvyWlOPJ1YcQGGA7iUiKDUdUqAbvje2Ced8eM/t7DQpQoVzEjoMdYrUOxewIJipERAQAVutL8orLsXjrHx6JiaSzt/fRj79dxo+/Xf5rZCUJUaGBptGz8opqTPj0gN3HGNW5mVzh2sUaFSIistnyXsrUQHRooMv2lyF55RWXY/LqDBRfr8TIzs2Q2joGfmIbpLCPChERuZPUlveWxGqDMH9kMlf0eInaeywZl5ivO3Re1H3FnicHJipERORwy/va7uwUi9uTdYgMkX9vGXIN4x5L+3IKAQBnC6+Jup/Y8+TARIWIiGRpeb/p11zsPVWAojIW1XobY6J6oUhcwir2PDkoJlFZuHAhVCoVpk2b5ulQiIganG4JUQjVONcbI7e4HCv35Ng/kRzySO+W+PzRm6ANln8djDFRDRBZoyL2PDkoIlHZv38/PvzwQ6SkpHg6FCKiBictKxf93vgJpRXWW6+LteXYJRkiotpiQgOxbGxXvDziRvRu0wiL7pbvs1KFmtoiY6+cJuEaUfcTe54cPJ6oXLt2Dffffz8++ugjREVFeTocIqIGwbhfz6tfH8UTqzJsNggj25qEmW++qAlwbrQhVOOPqJC/R00KSisx79tspGXVNMkbkhyLZWO7yLKDsQBg9ogk+P91sW4to0XdT+x5cvB4ojJ58mQMHz4cgwYNsntuRUUFSkpKzH6IiEiatKxc9Fm0DWM+2otP9pz2dDher3tL852iK0Q0TLOltEKPK2XVZseM2xQYk5WoUI3TOyADQFSIGoOTdKbff8u7Kup+Ys+Tg0cbvq1ZswYZGRnYv3+/qPMXLlyIuXPnujgqIiLfJWbTQJJmc9ZF2a4VEuiHssr6DduM2xTM/Tobg5N0sqzSAoArZVVYuScHjcI1aBIehCA7XW2NgtQNYK+fc+fOYerUqfjhhx8QFCSu2nzWrFmYPn266feSkhLEx8e7KkQiIp9SWW3ArHWHmaQomKUkxaj2UmI5VmkZzfv2mOnPYuupG9WZ7nIljyUqBw8exKVLl9CtWzfTMb1ej507d2Lp0qWoqKiAv7/5K6bRaKDRuK+Ah4jIW1jaSNC/VhFDWlYunvvvYVyrqLZxFfIGl66W446UOMRqg5BXXC5r4im2nlqOwmuxPJaoDBw4EEeOHDE7NmHCBLRv3x4zZ86sl6QQEZFl1jYSnD0iCUOSY5GWlYsnVmV4MEKSU5PwIPj7qTB7RBImrcqotwOyozsiS5FbfN3Fj/A3jxXThoeHIzk52ewnNDQUMTExSE5O9lRYRERexVhzUrf9vbH4cvPhXMzZdNRD0ZGc6i4lHpIci+UPdIVOaz4NpNMG4ZlB7Vwai9rJlU1ScPdkIiIvZW8jQRWAlzdmoaC00s2RkdyMaUHtpcRATbIyOElXb9oPANbsPyv71JBRo5AGUKNiyfbt2z0dAhGR17C3kaAAMEnxEbpaU3l1+fupkNo6pt5xa1NDcvDzc9+EjKISFSIiEk+uJaqkLCrUJCZv3dMJ+aUVFoujxTBODdWtX9JFaFBebUBxWZXDCYxKxakfIiKyQ+wS1RC1H8qqrC97JeWoPcXTu20jm+faW+kFWJ8a2pKd59RoS7OoYAfu5RgmKkREXqpnYrTdJaohaj9cZ5LiNWxN8dRmb6VXbZamhqyNtkSFqHFFxO7XN7e2nUTJSSUIgtf2/ikpKYFWq0VxcTEiIiI8HQ4RkdsZV/0Arl+SSq4z4IZGmNivjagpHmvdhY33Wv5AV7uJjlHdUZkbmoaj6/wtdu/36yu3QRuiFvUYlkj5/Pb4Xj9EROQ4a0tUyXsMTmqCTybchNTWMXaTFHsrvYCaNvt6kRsBGUdbRnZuhtTWMZixNlPU/Z797yFR58mBiQoRkZcbkhyL3TMH4PNHb0JksOPfcsm9IoIC8O6YLvjooR6i7yNmpZexzb4jfs+7Jut5cmCNChGRD/D3U8FPpULRdfv1BeR5MaGBSJ81EIEiNwE0ErvSy9EVYdrgAJy7Iu48d+GIChGRj+ByZe9RUFqJg2dEZAR1iF3p5eimhcM6iqttEXueHDiiQkTkxWoXQ+ZfrfB0OCSBI4mlvZVexh4sxu60UsVGhsh6nhyYqBAReZiYfhiWfJN5HrM2HMHV8r93snXHhnQkD0dGPextRgjUb7MvhS5CXExiz5MDExUialAcTQpcRUo/jNoe/c8+bD12ud5xJinewU8FdEuIcui+VjvOiuzBYotxxMZWwW6sEyM2jmAfFSJqMBxNClwZjyP9MB79z35sPXbJ5fGRa30xsZfFPXrEclXSba03jyN9WqyR8vnNERUiahCsJQV5xeWYtCpDlv/5AuI/PMTsfDz362wMTtIBAPaeKkD6yQIcv3iVSYqPcLb42dpmhM5y5YiNI5ioEJHPk5IUOPONVMqIjdh+GEu3ncCKn3NQJKKtOXkXR1fmuMOQ5Fjc0q4JXtucjdMFZWgZE4IXhiUhONDf7bEwUSEinyelSZaj31CljtiI/Ta9eOsfDsVDyuXsyhx3WLg5Gx/tyoGxwe2u48Dnv5zFxL6JmDUsya2xsI8KEfk8VzfJcqStuZK/TZNrCXBuZY6rLdycjQ92/p2kGBkE4IOdOVi4Odut8TBRISKf5+omWY60NTeurlDmRxVJFR3qG1sXVFYb8NGuHJvnfLQrB5XV7tuRm4kKEfk8e0mBCs4tuXRkxMbYD8P4+HXjIe+hAnB312ai/96MNVFiNw6Um94gIP1kATZmnkf6yQKzOD5LP11vJKUug1BznrswUSEinycmKXBmKN7RERtrOx/rtEF4ZlBbh2Ih9xMA/N+u03isXyJiRexi7ezGgc5Iy8pFn0XbMOajvZi6JhNjPtqLPou2IS0rFwBwprBM1HXEnicHFtMSUYMgx5JLa0uP7bU1BwBdhMbiiM2Q5FgMTtLVuy4AfLHvHPJKuH+Pt9j0ay52/Ks/3vnxOJb+dMLu+e7em0lMwXdCtLjW+GLPkwMTFSJqMKwlBWJGUuwtPbbW1tyovNqALdl5FhMia/0w5tyZhCf+arxF7tM4NABVgkrSknDjKMnBM1fQu00jUYmKOwuqxS7R3/bsrViw+ZjN6R8/FfBgakvXBGrp8dz2SERECmBMCkZ2bobU1jGik5RJqzLqFcwav4mmZeWaRmy0IZaLKovLqkznimXwUA1DQ1dQVo0eLaMcqhXacyIfecXXEaaxPQ4QFaJ26/JksQXfmeeKMLFvos1rTeybiMAA96UPTFSIiGyQsvR4cJIOQQGWG2IJf/2ILaLUGwTMWHvY0bDJCQYB2JJ9CYOSmoiqOalt6U8n8MxXv+JaRbXN8+RIQW0VxdYlpeB71rAkPN4vEXVzeD8V8Hg/9/dR4dQPEbmF0jYDFEvq0mN7NSW5xeWY8d9M9LuhiVk9irFFPiAgtVUj7D9diGsVepvXItf68dglHJ07BJnnipBXUo5Xv87ClTLbCYhYRWVVTjcYlLJvldSC71nDkvDsbe3xWfppnCksQ0J0CB5MbenWkRQjJipE5HJK2wxQClc0i1t76ALWHroAAIgMUaOq2oDSyr+TkqU/nZQWJLmEQQBW/3IGj/RthbSsXFRWyzsV52gxrSP7Vtkr+LbULTcwwA+P9G3lUIxy4tQPEbmUmPoOJZPyTdSR4siisiqzJIWU5UxhGdKycvHEqgzZ/56iQwIl38eRLsiA65fouxITFSJyGUf/p6okUprFdUuIQnSo9A8fUq74qBDM2eSalvG/5V2VfB9HuiAb2erbI9fu4a7AqR8ichl3bAYoN0u1NNaWHtf+JrolOw9zv85GYWmlB6ImV/BTAe114S7rZXPWgaZpzk5FOrNE31OYqBCRy7h6M0C52aqlsdUsDoDFmgHybhP7JqKwzJWJp/R3jBz7Vlnr26NUTFSIyGVcvRmgnMQUKO6eOcBiB9k+i7YxSfEhfipgWMdYJMVpkX+1wmWP07l5pOT79EyMRmSI2mYzOnf3aHE1JipE5DKOrDRwN71BwN5TBXh+7RGbtTQvrD+C61UG6CKCcEdKnGmoPP1kgc3pLXKPWG0QXh6ehOOXruGDnSdRJqLwVQVg/M0tMaB9E/yWdxXnrpShrKIau45fxjeHc/HN4ZpCb5UKEERkolEhAZKWL8dFuaYNva8lzUxUiMhljCsN7NV3eGp+3NJUjzWFpVV45stMAIAuIghjerZAy0YhOH5RekEkye/l4R0wLCUWeoOAL/adFZWoCABuu1GH1NYx6NuusdVRNTFJSmSIGr+8MAgHz1xBXvF1zPv2mM16JUd3696XU2i3tb+zPVqUhokKEbmUHJsBuoK1DyUx8krKsXjrH7LHRI57eeNRDErS1SQKEopfjfVRtlaoifH6qI4IDPAzJQfBgf5W318qOJ6ge1vdlxyYqBCRyyltpYGzH0qkPAWllei1cCv+0bmZpPsZ66PsrVAz0gSoUFGr8ZsuQoM5d95YL+G2lqA72+jQm+q+5CI5UfnPf/6DRo0aYfjw4QCAGTNm4MMPP0RSUhK++OILJCQkyB4kEXk/Ja00EPuhRN6lsLQKH+85Lfr82tMvYkcggtUB+GRcV+SXVthNuF2RoHtD3ZfcJDd8e+211xAcHAwASE9Px9KlS/HGG2+gUaNGeOaZZ2QPkIhIbnnF1z0dArmQn6p+91VL7uwUa0oaxI5AFF2vgp+fSvTu247s1m3vet7aYdZRkhOVc+fOoU2bNgCADRs24J577sFjjz2GhQsXYteuXbIHSEQkp7SsXMz79pinwyAXMgjiVr5s+jXX1BW5Z2I0IoPVoq7v6foPb+0w6yjJUz9hYWEoKChAixYt8MMPP5hGUYKCgnD9Or+lEDV0St4l2ZkCWvIuQ25sirSjF22eU7srsr+fChN6J4oqklZC/YfS6r5cSXKiMnjwYDz66KPo0qUL/vjjD1OtytGjR9GyZUu54yMiL+LpXZJtJUksoG1YgtX+os6rPToyZUAbrPg5x+ryX6XVfyip7suVJE/9vPfee0hNTcXly5exdu1axMTUvEgHDx7EmDFjZA+QiLyDp3dJTsvKRZ9F2zDmo72YuiYTYz7aiz6LtpkelwW0DUtcVLCo82qPjvj7qfD6qI4Wz/PV+g9voBIEMa1slKmkpARarRbFxcWIiIjwdDhEDZbeIKDPom1WEwHjN9HdMwe45H/y1qZ0jI+0/IGuqKg2YOqaTNkfm5Tp80duwnP/+9Xu6hhL70lPjww2BFI+vx3qo7Jr1y588MEHOHXqFP773/+iWbNm+Oyzz5CYmIg+ffo4FDQReS9P7pJsa0pHQM0H0tyvs/HWPzvJ+rikXJEhavRqHeNwV+SGVP/hDSRP/axduxa33347goODkZGRgYqKmg2brl69itdee032AIlI+TzZLVNskgSh5lsxP2p8X1FZFbZk5zm1OkbuZcXkOMkjKvPnz8f777+Phx56CGvWrDEdv/nmm/Hqq6/KGhwReQdnu2U6s1JIbPKTX1ph9Rs2+RbjKNrgJB1HR3yA5ETl999/R79+/eodj4iIQFFRkRwxEZGXcaZbprP1AGKTpOMXr6F3m0Z4b2wXzPv2GAtrfVjdqcaGsjrGV0me+omNjcWJEyfqHd+9ezdatWolS1BE5F0c7ZZpbaVQbnE5nhC5UsiYJNmz9KcTGPPRXry0MQsvDu2Azx+9SXSDL/JOdUfb9AYB6ScLsDHzPNJPFpiavZGySU5UHn/8cUydOhW//PILVCoVLly4gM8//xzPPfccnnzySVfESEReQGo9gJi+Js+vO2L3w8TfT4WXhyeJjrOwtApT1hzCzj8u4b4ezUXfjzzDmRma2qNt9pavk3JJnvqZMWMGiouL0b9/f5SXl6Nfv37QaDR47rnnMGXKFFfESEReQko9gJi+JkVlVVi67QSmDmpr87yo0EDJsX6wM0fyfch54UH+uFquF32+QQBeHt4B54uu45M9p0XVF9WdarS2fN3Y48cX2877EoeWJy9YsAAvvvgisrOzYTAYkJSUhLCwMLljIyIvJLYeQGwR7IqfczBlQBubxY+e3nuFxJOSpBg1Ctfgkb6t0DMxul49U111pxrFLl8fnKRjga1CSU5UiouLodfrER0dje7du5uOFxYWIiAggI3XiEgU0bvVllXZ7b+ihL1XyHWMf791R+xO55fhi31nkVfyd+Kiq1OI7ckePyQPyYnK6NGjMWLEiHr1KF999RU2bdqEzZs3yxYcEfku4261Rdct76tSm70Rk56J0YgOVaOw1P61yLvEhAaarRarO2I3ZUAbm1ONnuzxQ/KQXEz7yy+/oH///vWO33rrrfjll19kCYqIfF/NbrUtRZ1rb8TE30+FV0ckyxAVKc28kcmmKRxLK3bsNWZztscPeZ7kEZWKigpUV1fXO15VVYXr16/LEhQRNQxTBrTFip9PW92tFqhZ9XGltKLe8cpqAz5LP40zhWVIiA5Bu6bhrgyVPODxfokYlhLrVK8dZ3r8kDJI3pTw1ltvRceOHfHuu++aHZ88eTIOHz6MXbt2yRqgLdyUkMg72Oo8a21FRm0qwGxlxsLN2fhoVw5qr1xmt1nfER7kj0WjUjAsJU7UhpP2khXjNQDLe/5w1Y/7Sfn8lpyo7NmzB4MGDUKPHj0wcOBAAMCPP/6I/fv344cffkDfvn0dj1wiJipEymVMTrZm52F95nmz+pG634Y3H87FlC8yYK1lSu2dbt9IO8alxV4oKkSNKzZGzoxiQgORPmsgAgP8ZN2VmzsiK4tLd0/u3bs30tPT8eabb+Krr75CcHAwUlJS8PHHH6NtW9u9DojIu4ndk8fSh0JtdftXRIUGWk1SgL9XZry84Qi+2HdOpmdD7rRwVEcANU38LE31Gd9FC/6RjMCAmvJJOVfscM8f7+VQH5XOnTvj888/lzsWIlIwsd9IxUzl1O1fIXbFxWomKV5p2sC2pvfI4CQdlm47jhV7Tput+Kq7rBiQf8UO9/zxTqISlZKSEtPQTElJic1zOQVD5HvEdvYU0xbfqPa3Ya648G2JjUNNf/b3U2HqoHaYMqCt3dENrtghQGSiEhUVhdzcXDRp0gSRkZFQqeoPlQmCAJVKBb1eetdBIlIuKZ09xbTFr+vS1XKo/fzgp4LN6R/yXpYSCTGjG1yxQ4DIRGXbtm2Ijq55I/z0008uDYiIlEVKnYAjTbNO55dhydY/uGLHBzmbSBh35Z60KqPeqi5bu3KTbxGVqNxyyy0AgOrqamzfvh0PP/ww4uPjXRoYESmDlDoBKUPwKgBNIzRY/csZJik+SK5Ewrgrd936KEs1LeSbJHWmDQgIwFtvvSXb9M7y5cuRkpKCiIgIREREIDU1Fd99950s1yYieUipEzAO1Yv9WOreMhoXr9Zv5kbeLzo0ULb+JEOSY7F75gB8MbEX3h7dGV9M7IXdMwcwSWkgJLfQHzhwILZv3y7Lgzdv3hyvv/46Dhw4gAMHDmDAgAEYOXIkjh49Ksv1ich53RKiYO8LsZ+q5jzjUD0Am8lKrDYIj/VLxDeHc+ULlBTlpeEdZE0k7LXKJ98leXny0KFDMWvWLGRlZaFbt24IDQ01u/3OO+8Ufa0RI0aY/b5gwQIsX74ce/fuxY033ig1NCJygYNnrtgtcjUINeelto6xOlQfExqIkZ3jMDhJh24JUbjlTda7+TKdNtjTIZCPkJyoTJo0CQDw73//u95tzqz60ev1+O9//4vS0lKkpqY6dA0ikp8jvSzsNddKP1kgeXUQeY/IYDUMggC9QeDIBzlNcqJiMBhkDeDIkSNITU1FeXk5wsLCsH79eiQlJVk8t6KiAhUVf89n2+vpQkTO0RsE5IusIalby2Jr+akjq4PIexRdr8L9//cLW9STLCTXqMjthhtuQGZmJvbu3YtJkyZh3LhxyM7OtnjuwoULodVqTT9ceUTkOmlZueizaBvmfXvM5nkq1NScSFmCygZdvsHeYImxIWBaFmuRyHGSNyUEajYhXLx4MY4dOwaVSoX27dtj2rRpGDRokNMBDRo0CK1bt8YHH3xQ7zZLIyrx8fHclJBIZmLa4APidp+1tD8QAPRZtM1qIy9SvvtvisdLw29ExpkrmLw6w6wdfm1SNg6khsOlmxIuXboUzzzzDO655x5MnToVALB3714MGzYM//73vzFlyhTHov6LIAhmyUhtGo0GGo3GqesTkW16g4Dn1x0RlUBEhaoxf2Sy1STF1v5A1hp5kXf4/Jdz2PbbZYzu0cJqkgJI2ziQyBLJIyrNmjXDrFmz6iUk7733HhYsWIALFy6IvtYLL7yAoUOHIj4+HlevXsWaNWvw+uuvIy0tDYMHD7Z7fykZGRGJs3jL73j7xxOizzcmHnWLZ6+UVmLy6vqjMrVHYQDghfVZKCytlCd4cispSebboztjZOdmrgyHvIhLR1RKSkowZMiQesdvu+02zJw5U9K1Ll68iAcffBC5ubnQarVISUkRnaQQkTiWpl6sDcFvPpyLdyQkKUBNHcITqzIQGaJGUdnf36z9VJY/xGrvD7R75gBcr9Tjma9+lfSYpAxSvuWyLokcJTlRufPOO7F+/Xr861//Mju+cePGen1R7Pn444+lPjwRSWBr6qXudE1aVi6eXJ0h+TGMH1a1kxTA9gaDxumAlXtycODMFcmPSd5FF6HhxoHkMMmJSocOHbBgwQJs377d1O9k79692LNnD5599lm88847pnOffvpp+SIlIkmsFcQaV2LULoA17pDsbvZWFJFvGNOzBQtpyWGSa1QSExPFXVilwqlTpxwKSizWqBBZpjcI6LNom9WmanVXYqSfLMCYj/a6N0hSlFC1H978Zye8tPGo7DVDrE+hulxao5KTk+NwYETkHvtyCm12fq27EoMN2OjOznEYlhIHQOXQFKAtrE8hZ3i84RsRyU9q23t+kNCfRTXvhWEpsXi8n8iRcxG3S20GSFQXExUiHyQ28TCe1zMxGrHaIJsfPKo6/yXfkhAdYvrzrGFJWDa2C6JD1Wbn1C0z0WmD8Hi/RKhQ/31h/H32iCTWp5BTJE/9EJGy6Q0CDAYBkcFqu91Cjd90/f1UdhuwvTe2K/z8gOfXHam3woe838Abmpj9PiwlDrcnx5otbe+WEIWDZ67UW+repUVUvdVlOu7zQzJhokLkQywtR67L2jfdIcmxWP5AV5vLmdOycpmk+Kj9ZwvRP6mp2TFLG0ta6i5rb7dsImcwUSHyEWL357H1TbfuB06jUA2gAvKvVWDPiXzM2XTUNcGTx10ocq6g2tZu2UTOEJWoHD58WPQFU1JSHA6GyJdI6Qgrx2PN/TrbZpISGazGe/d3Ra9WNR8m6ScLLMZm/MBJy8rFc//71eboDPmOpuHcR42USVSi0rlzZ6hUKgiCAJXK9v9o9Xq9LIEReTMpHWHlYG85MgAUXa+Cn0qFLdl5dmMTOzpDvuNkfqmnQyCySNSqn5ycHJw6dQo5OTlYu3YtEhMTsWzZMhw6dAiHDh3CsmXL0Lp1a6xdu9bV8RIpnvFDvm7iYOwIm5aVK/tjil2OvDU7z2JsubViEzM6Q8oRHRqIkEB/p69TVlktQzRE8hM1opKQkGD68z//+U+88847GDZsmOlYSkoK4uPj8fLLL+Ouu+6SPUgib2HrQ772ZnyDk3SyTgOJXY68PvO81QRE+Cu28CA1p3u8iFxdZEMDWbJIyiS5j8qRI0csttFPTExEdrb79wohUhIpHWHlZK8PigpATGggCkttr9jJLS7H/w6ckzU28g6336jzdAhEFklOVDp06ID58+ejvPzv/xlXVFRg/vz56NChg6zBEXkbqR1h5WLsgwJYb7w1opO42pj1mRfkC4y8RrOoEPsnEXmA5LG+999/HyNGjEB8fDw6deoEAPj111+hUqnwzTffyB4gkTeR2hFWTtb6oBiXI5+/cl32xyTvoFIBtrafjQpRs809KZbkRKVnz57IycnBqlWr8Ntvv0EQBNx3330YO3YsQkNDXREjkdcwTsHkFZdbrAWp2xFWCjHLnW013lp/6LxjT4q8lvHdEaz2R1ml9RWZLJwmJXOoeiokJASPPfaY3LEQeT1breid2fvE0nLnyGA1JvROxJQBbcyuZ63xli6CGw82NDptEEb3aIHFW/+weV5RWZVpJ20ipXFoU8LPPvsMffr0QVxcHM6cOQMAWLx4MTZu3ChrcETO0BsEpJ8swMbM80g/WQC9wT3fG41TMDqteWKg0wZh+QNdJfdRsbbcueh6FRZv/QMd53yPt7f+Yff5GUd7SJkm9k1Ev7aNZL3mW/d0QstG4mpP5K6bIpKL5BGV5cuX45VXXsG0adMwf/58U4O3qKgoLFmyBCNHjpQ9SCKp3N1wrS5n9j6pPcXTKEyDOZuO2hyaL6vUY/HW41jx82m8PqojhiTHWp0mmj0iCU+sypDviZLTokPVmD8yGcNS4rD+0HnsPJ4v27XzSytqtkEQQex5RO4mOVF599138dFHH+Guu+7C66+/bjrevXt3PPfcc7IGR+QIa11VjQ3XHBnVkMrR9vliNhW0pqisCpNWZeCxfonY9GuuxSSNlCEowA9jb2qBwUk6s/eG3NNzTcKDUK03iDrXYKvalsiDJCcqOTk56NKlS73jGo0GpaVswUye5amGa7U5OpojR9t6AcAHO3PqHTcmadpgtRNXJ7lUVBuwYs/peglsz8RoRIcGimriFhWixhUrO1nXLtpevMV2fYrRLzmF6NuusahzidxJco1KYmIiMjMz6x3/7rvvkJTEb2zkWZ5quGbkaPt8V7etF/76Kbpuu+EbuYfx73nu19lmtUX+firc1TlO1DX+0aUZVLDeN8dYtC2IHCkRex6Ru0lOVP71r39h8uTJ+PLLLyEIAvbt24cFCxbghRdewL/+9S9XxEgkmqcargH2R3OA+h9MRmI2FSTfYi1pHpwkrkPs4CSdqKLtyBBxo2hizyNyN8lTPxMmTEB1dTVmzJiBsrIyjB07Fs2aNcPbb7+N0aNHuyJGItE82XBNymhO3WWgXHHRcNX9uzeuzrL2Xqo9rePvp7JbtN1I5Htd7HlE7uZQH5WJEydi4sSJyM/Ph8FgQJMmTeSOi8ghrmy4Zo8zozmn81nf1VDVTZpr9+IB7PfisdY3x0hsgS777JBSSZ76GTBgAIqKigAAjRo1MiUpJSUlGDBggKzBEUklZs8bRxquieHoaI7eIOCLfWdlj4eUTYWaImtLSbOcvXi6JUTB3tvdT1VzHpESSR5R2b59Oyor61ekl5eXY9euXbIEReQMe3veuGppsqOjOftyCpFXUiHqMYzXuLd7PP7z82kWx3o5W0mzM714ajt45grs9To0CDXnsTMtKZHoROXw4cOmP2dnZyMvL8/0u16vR1paGpo1ayZvdORxjvYD8TS5/icvhaPt86XWpxiTrRuahuPJ1Wze5o3ENh+0N60jhicLzInkIDpR6dy5M1QqFVQqlcUpnuDgYLz77ruyBkee5enurs6S43/yUjkymiN2yig6VI3X/vF359l532bLFje5T3SoGi8P7+C2f0OeLDAnkoPoRCUnJweCIKBVq1bYt28fGjf+uzFQYGAgmjRpAn9/f5cESe6nhO6u3qruaE6jMA0g1LQzTz9ZYLHJl60pIwCICQ1E+qyBCAyoKSvjcmbvdaW0CpNXH8JyP5Vb/g15ssCcSA6iE5WEhAQAgMEgrh0zeS8ldHf1dsbRnLSsXDz3319tjkqJmTJa8I9k+PupkH6yAJeuluP4xWtuey4kL3f/G3LVjt5E7iJ51c/ChQvxySef1Dv+ySefYNGiRbIERZ7l6e6ucvPULspSutRaW+WhDVFj2qB2MBgE9Fm0DWM+2oupazKx9KcTbnkO5Bru/jck947eRO4kedXPBx98gNWrV9c7fuONN2L06NGYOXOmLIGR5/hS8Z2n6mwcGZUyThm9++NxfLT7FEor9Cgqq8LireL2aiFliAkNRHIzLXb8cdnuue78N+SJAnMiOUhOVPLy8hAbW/9/8I0bN0ZuruV9TMi7+ErxnSfrbBztUvtG2jGLmwqSsk0b2AaJjcNMH/77cgpFJSru/jfkiQJzImdJnvqJj4/Hnj176h3fs2cP4uLEbaZFymYsvrP2PctWoyqlcGbfHUcfr/b0Ul7xdVH3q/2NevPhC0xSvJAKwJcH/sQdKXFIbR0Dfz+VT/wbIlIKySMqjz76KKZNm4aqqirTMuUff/wRM2bMwLPPPit7gOR+vlB858y+O1JZml4K04hbAWf8Rq03CPjX2sN2ziYlsvResvVvyHifl4cr+98QkVJITlRmzJiBwsJCPPnkk6YOtUFBQZg5cyZmzZole4DkGZ7q7ioXV9TZWGp+tyU7z+L00rUKvc1r1V0SuvdUAUrt3IeUre57ydq/IaN532bDzw/1/i15a5NFIleRnKioVCosWrQIL7/8Mo4dO4bg4GC0bdsWGo3GFfGRB3lz8Z3cdTaWRk10ERqUVxus9j6xx7gnUfrJAryR9puDVyGlsPReGpIcC4NBwJOrD9W7zVKtlLc3WSRyBYd2TwaAsLAw9OjRQ85YSIG8tfhOziZXVotyRe7PU5efCpjYNxEGA9BjwVYUltbfO4u8h633Uk0H4WMW71d39Ze10Tk2WaSGTlSiMmrUKKxcuRIREREYNWqUzXPXrVsnS2BEYlkbKpejzsZWUa6jDAL+Kppl4ay3s/deElsrtfdUAZssElkhKlHRarVQqVSmPxMphb2hcmfrbNiqvmGJDlWjsFT8jtT23ktia6DSTxa4rfibyNuISlRWrFhh8c9EniS2T4ozdTbe0NSOnGecvtnxr/44eOYKtmTnYUPmBbNpuVhtEF4enoSo0EDR7yXxfVLEjdnx/UgNkcM1KkSeJLXzq6PfQpXe1I6cV3v6JjDAD6mtY5DaOgYvDk9yupBcbK1UaqtGWPrTSbvX4/uRGiJRiUqXLl1MUz/2ZGRkOBUQkRju6pMiZmdj8i51a5asTd/IUUgutlaqV+sY7nBMZIWoROWuu+4y/bm8vBzLli1DUlISUlNTAQB79+7F0aNH8eSTT7okSKK63LUfUe0PGvIN747ujJjwILctuRdbK+XtTRaJXEVUojJ79mzTnx999FE8/fTTmDdvXr1zzp07J290RFa4cz8i4wfNC+uPiC60HHJjU+w6fhmllQanH5/kFRMe5PaCVDG1Ut7eZJHIVVSCIEga0dZqtThw4ADatm1rdvz48ePo3r07iouLZQ3QlpKSEmi1WhQXFyMiIsJtj0uepzcI6LNom92h8t0zB4j+FmqvI2hltQG9Fv4ouu9JZIgaRWXiV5CQe7w9ujNGdm7m6TCsYmdaagikfH5LLqYNDg7G7t276yUqu3fvRlAQC73IPeTej0hMR9DAAD+89o9kPCFyGsiYpISq/VBaxZEVpVB6Qaq3NlkkchXJicq0adMwadIkHDx4EL169QJQU6PyySef4JVXXpE9QCJr5BoqF7vM2fiYD/duiU/2nBYdZxmTFEVgQSqRd5KcqDz//PNo1aoV3n77baxevRoA0KFDB6xcuRL33nuv7AES2eJMnxS9QcDekwV4fu0Rq8ucAeDF9VkY0L4p/P1U2JdTiJBAcTsj170OeZYAFqQSeSPJNSpKwhoVcpSlqR5bQjX+UPv7sebEy73P/XKIFEHK57efIw9QVFSE//u//8MLL7yAwsJCADX9U86fP+/I5YjcyjjVI6U1fmmFnkmKwqgAdG4u/guKsQmg3uC1382IGiTJUz+HDx/GoEGDoNVqcfr0aTz66KOIjo7G+vXrcebMGXz66aeuiJNIMkurJwDIvskgeYYAIPPPEknnc78cIu8jOVGZPn06xo8fjzfeeAPh4eGm40OHDsXYsWNlDY7IUdZW8YzuEc9NBn2Mn6pmR2qxuF8OkXeRnKjs378fH3zwQb3jzZo1Q15enixBETnD1iqexVuPeyQmch2pMzlKX55MROYk16gEBQWhpKT+cOvvv/+Oxo0byxIUkaPsbVZIvunh3i2hi9DYPEeFmlE1Lk8m8i6SE5WRI0fi1VdfRVVVTWGhSqXC2bNn8fzzz+Puu++WPUAisfQGASv35HBqpwEanKTDnucH4plB7Szezv1yiLyX5ETlrbfewuXLl9GkSRNcv34dt9xyC9q0aYPw8HAsWLDAFTES2ZWWlYs+i7Zh3rfHRN+HH1fKFxmsRlSI2urttUdJ/P1UmDqoLd5/oGu90ZWmERqzxn1E5D0k16hERERg9+7d2LZtGzIyMmAwGNC1a1cMGjTIFfER2WWtJsWWqQPb4qsD50SPvtRt00/uUXTd+pJw26Mk9n4nIm8hqeFbdXU1goKCkJmZieTkZFfGJQobvpFxc0Kp0z2fP3oTerWKwdJtJ7B46x+i7tOqUQhO5Zc5Eia5QN29mADrSasxTeGoCpEyuGxTwoCAACQkJECv1zsVIJFc9uUUOlSTkn+tAgCwZv9Z0fdhkuJ6wWo/XLeyN5IKQHRoIF4a3gE6bXC9rRLsFVIbG74NTtKxToXIi0iuUXnppZcwa9YsU0daInfRGwSknyzAxszzSD9ZAL1BcLgnRpPwIIeTHHKNbgmRVpMUoCbZKCithE4bjNTWMfWSDXt/n7UbvhGR95Bco/LOO+/gxIkTiIuLQ0JCAkJDQ81uz8jIEH2thQsXYt26dfjtt98QHByMm2++GYsWLcINN9wgNSwSyVK3Vm/4dmm9gVsLh66Xf60CBu/d5sonHTxTJOo8a8mp2KSVDd+IvIvkRGXkyJFQqeT5YNuxYwcmT56MHj16oLq6Gi+++CJuu+02ZGdn10uAyHnWPuzrzvMrja0Gbku2/oHIEDWKy6okFbs+9cUhq0tZSdmsNWwT28iNDd+IvIuidk82LnvesWMH+vXrZ/d8FtOK561FhmKKZaNC1LhSViV5ZU6sNggGg4CLVyucjpPcI1YbhN0zB1gcBTS+V/KKyy2+D1QAdDbuT0Tu45Ldk8vKyjB58mQ0a9YMTZo0wdixY5Gfn+90sLUVFxcDAKKj2TlSTmK6tSp1V1kxdSRXyqrwzKC20GmlfVPOLS5H37bspuxNbDVs8/dTYfaIJADWFyez4RuR9xGdqMyePRsrV67E8OHDMXr0aGzZsgWTJk2SLRBBEDB9+nT06dPH6tLniooKlJSUmP2Qfd5cZJhXIq6eoEVMKHbPHIAXh3WQdP3L11iv4C2eGdTW7qjfkORYLH+ga72kVacNUuyoIRHZJrpGZd26dfj4448xevRoAMADDzyA3r17Q6/Xw9/f3+lApkyZgsOHD2P37t1Wz1m4cCHmzp3r9GM1NEotMhRT2Ft4Tdy0TOG1Cvj7qdDEzn4vdWWcLZJ0PnlGrDYIUwa0FXXukORYDE7SeWXROBHVJzpROXfuHPr27Wv6vWfPnggICMCFCxcQHx/vVBBPPfUUNm3ahJ07d6J58+ZWz5s1axamT59u+r2kpMTpx24IlFhkKLawNzo0UNT1jOdJeQ4xoYEoKK0UfT7Jw09lvuNxrDYId3aKxYc7cwCY1xk5OmXj76dCausY54MlIo8Tnajo9XoEBpp/aAQEBKC6utrhBxcEAU899RTWr1+P7du3IzEx0eb5Go0GGo20b8wE9EyMRqw2yG6Robt2lbW1imfSqgyzIXqdNljUNY3n2XuuRioAd3aOw4o9pyXHT44xphlLx3RBVKim3mhHlxZR9ZJXnResSiMi1xKdqAiCgPHjx5slCuXl5XjiiSfMlhKvW7dO9INPnjwZq1evxsaNGxEeHo68vDwAgFarRXCwuA8oss9YZDhpVUa9lTHuLjKU2j3UmHjYK6j95VSB6YPv5eFJmLy6/nOtTRuixtXrjifZZJk2OACDOzRFeFAANv56AYWlf+/VYy/p4JQNEVkiennyhAkTRF1wxYoV4h/cSj+WFStWYPz48Xbvz+XJ0iihj0r6yQKM+Wiv3fO+mNjLNHS/+fAFPLn6kOjHME4lbPo112qCw00GnRcZrMZr/+iIqNBAi4mFtzYXJCLXc8leP1ISELEU1MKlQVDCN1ZHCnujQqVN9+UVl+PDnTl4d3RnvPJ1Ngot1KHwnee8Sbe2xrAU6wku60SISA6SO9OSd/PUh4fx2/Xxi9dEnV+7KFbqaiRjEjJrwxFcLecGmq5SdJ2FyETkekxUyOUsTTlZY6mw19HVSExSXCu3iD1oiMj1JO+eTCSFcYWP2CQFqF/YayyoZXWDssSJXJFFROQMJirkMrZW+FhirXuordbo5DlRInvcEBE5g1M/5DJi9ukBgCn9W6N3m8Y2C3uNrdHFTiGR6zUKY6JCRK7HERVyGbFFsG2bhiO1dYzd1UdDkmOxe+YAvDxc2n4+5Bru7GRMRA0XR1TIZaS07rfVc6PubWLb6pOLcR6OiNyAiQq5TM/EaESGqFFUVmX1nKgQNa6UVqDPom0WG9EBqDfdw0RFGfJFbhhJROQMJipewlu6fNaOs1Goxm5ntYpqAyavPmRx358nVmVYvM8VbiSoCJz6ISJ3YKLiBZTQ+l4MKf1SjMoqLfc6sZXfsKus58W6cRNLImrYWEyrcNb6kBh3Gk7LyvVQZOak9EuRU1SI2q2PRzXu7BSryBE9IvI9TFQUzN5Ow0BN/Ybe4NkxBqn9UuQ0tmcLDzwqbfo11+PvOyJqGJioKJi9PiQCgNzicuzLKXRLPHqDgPSTBdiYeR7pJwtMH1Ri+6W4gsCJIFk83i8RkcHiZ4Ld+b4jooaNNSoK5shOw65iq06motog+Xoq1CRaQWo/lFdJvz8ARAYH4LO9Zx26L/0tVOOP9rFavHd/Exj0Aj775Qx+yL5o937ueN8RETFRUTApfUhcyVh/YmllzqRVGZg2qJ3ka0aGqCEIQNF160uX7Sm6Xu3wfelvpRV6PPNlJoCa5HN0jxaiEhWu+iEid2CiomDGzfjyisstTnBY2mnYHqnLnO3VyagArNl/FroIDS6WVFidiNFFaPDmPZ3wS04hTl6+iu+y7H8QkvvlFZdjydY/EBmiRnFZlWzvOyIiRzFRUTDjZnyTVmWYpkqMrO00bIsjy5zF1sk8M6gtlmw9bvW8js21mLH2MPfpcbO7u8ZhbcYF0ecbk09VrT87+74jInIGi2kVzrgZn05rPsxubadhaxxd5pxXfF3U9Vs2CsVj/RKt3r4l+xKTFDeLClFj4ahOiNUGSep2LwC4UlaFZwa1dfp9R0TkLI6oeIEhybEYnKRzuDOtmOmbuV9nY3CSzuyaaVm5mPftMVGP0ShUg02/KqOnC9VYOKojAgP8rI7K2dOyUSh2zxzgFR2Rich3MVHxEv5+KqS2jnHovlKWORsfw1oBrSWx2iBABY6YuFlEUADu6xGPr3/NRV6J9ek846ic1K7BTcKDnHrfERHJgYlKAyB1mbPUBm53dorlBnVuFh7kjwMvDUZggB+eH9rB7qhH7VG5vOLrmPftMVwprWSxLBEpHhOVBkDqMmepDdw2/ZqLfu2aOBQbOWbRqBQEBtSUmIkd9ah9XnCgv2xF2kRErsRi2gbAuMzZ2seOCuabzElt5JVbXA6DQUB0aKBzgZIoj/dLxLCUOKeuIVeRNhGRq3FEpQGQuszZkUZeT31xyKnmbSTOtIFtMW2w9AZ7ljhbpE1E5A5MVBoIawWVOgt9VLolRMFPBUjZc45JinskNg6V9XosliUipWOi0oCI/QZ98MwVSUkKuU+T8CDJ3YWJiLwZE5UGRsw3aLFN3sSKDg1EYWmlrNdsaIwrca6UVqDPom2SugsTEXkzFtNSPXInFS8ObY+7uzhX/NmQGcdK7uwUi8mrD0nuLkxE5M2YqFA90WEaWa83++tsrD0kfr+Zhk4XUX8lzntju2LTr7lWuwsDNd2F9ZyzIyIfw6kfqqfuB6WzrlVUy3o9X/f//tkJfn4qsxoUR7oLExH5AiYqXkJqAaUzBZfGvitsie8Z+aUVGNm5mdkxqd2FiYh8BRMVL5CWlVtvWbGtAkqp59fl76fCnZ1i8cHOHIdjjgoJwJUyjqQ4wlIfG6ndhYmIfAVrVBTOuDmg2AJKqedbojcITu2EfHfXZnhlRLLD92/IYq3ssSO1uzARka9goqJgtjYHtFRAKfV8a6Tu9VPXd1m5WPPLGYfv35C9PLyDxSk6Y3dhAPWSFe7PQ0S+jImKgkkpoJRy/so9OdiYeR7pJwssJi3O1jmUVRrwy+krTl2joYoKtb7iivvzEFFDxBoVBZNaQCn2/HnfHjP92VLtSiOZlyeTePb+Drk/DxE1NExUFExqAaUjhZTG2hWzb+RsxeExYv4OuT8PETUknPpRMKkFlPbOt8RS7Up+aYWjIZODWAxLRGQZExUFk1pAaet8W+rWunCJq3zCg+wPWrIYlojIOk79KJyxgLJuXxSdlb4o1s4Xw1gfYRyZySsu5yyQk+aMuBFxkcGmepIrpZWY9624v0siImKi4naOdIyVWkBZ9/z8qxVmBbTWGEdSjCMzT6zKkP4EyUxcZHC9epLbk1kMS0QkFhMVN3KmY6ylAkqxSU/72AjoIjS4WFJhcYREhZpv9cb6CL1BwO951yQ/PzJnreaExbBEROIxUXETY8fYuomCxVU3Iq9nLekBUO+2yBA1BNQkJbVjqFsfkZaVizmbjiKvhAW1zlCBNSdERHJQCYLgtWUIJSUl0Gq1KC4uRkREhKfDsUpvENBn0TarNSPGEY3dMweI+mCzlvTUTUIsiQxRo6isyvR77REda9claaTsq0RE1BBJ+fzmiIobSOkwa29KQEybfFuC1f5475GuuHS1HIWllYgO00AbHIjKagOeX3eESYqTnhnUFlMGtOVIChGRTJiouIHYjrF5xdeRfrLAZs2Js/vw5BaX48CZK1iz/6zZdcI0AbhWwd2OHdU0PBBzRyZzFIWISGZMVNxAbF+Sed8eQ2Fppel3S1MIzu7DAwCLt/5R7xiTFMc9M6gdpgxow1EUIiIXYMM3NxDbMbZ2kgL8XWiblpVrOsZmbMrhpwKWje2KqYM41UNE5CpMVFxAbxCQfrLAtEMxAIc7xgLm7e0daZNPrrF0TBcMS+FUDxGRK3HqR2a2lg1b6hgbHapGYWmVpUsBqF9oa2zGNmlVhqhVPiQ/ruohInIfLk+Wka1lwwCw/IGu9TrM5pWU45kvM+1ee/G9naDT1m7FXtNttnbSo/FXoULvtX+dihemCcAHD3RDr78SRiIicgyXJ3uAvWXDKtRM4QxO0pktQTZODdljqdD25eFJiAoNxKWr5TidX4rFW4879yTIpjfuTkHvto08HQYRUYPCGhWZSOmVUpszhbaTV2eg+Hol7kiJw5r95xyMnMR4vF8i61GIiDyAiYpMxC4brnueseYEcLzQ9ufj+U71VqEa3RIiERmsNjsWHarGsrFdMGtYkoeiIiJq2Dj1IxOxy4YtnTckOdapQttJqw9Kjpf+Fh2qxvyRyRiWEufQ7tZEROQ6TFRkYpzCySsuF7VDcV1DkmMdLrS9VqF3KvaGKDY8EM8PT6qXjHBnYyIiZWGiIhNby4br7lBs6xqOFNqSdDe1boSRnZt5OgwiIrKDNSoyMk7hNI3QmB1vGqHB8ge6Su67weZurtMsKtjTIRARkQhMVFyibmrhWKrhaKEt2XdzKy4zJiLyBkxUbKjbCt/Yxt4aY8O3vBLzFTgXS+rv2SOWcZRGpzUvwg3T+Eu+FtWIDFGjF+tQiIi8AmtUrLDVCt/SFI6Uhm9SV5HULbRlczfnvD6qI1fyEBF5CY6oWGAcGanbm8TSbsZGjjZ8s8c4qvPN4QsAgKHJsWzuJkKw2h9NwwPNjukiNHjfgVohIiLyHI+OqOzcuRNvvvkmDh48iNzcXKxfvx533XWXJ0NyeGTE0YZvtlga1YkODazXpZbqG90zHi8NT2JPFCIiL+fRRKW0tBSdOnXChAkTcPfdd3syFBMpIyO1lxJLbfhmr7GYtQ0OmaSIc9tfiSR7ohAReTePJipDhw7F0KFDPRlCPY6OjEhp+Gav/sXWqA7ZF2ujsR4REXkXr6pRqaioQElJidmP3E7nl4k6r+4Iiq2lxLUbvm3JzrNb/2JvVIcsU/31Y6+xHhEReQ+vSlQWLlwIrVZr+omPj5f1+mlZuViy9Q+b56hg/Ru7taXEOm0Qlj/QFYOTdDbrX4Ca+pe84uuOPYEGwpiCRIaYbyBofJ1ZLEtE5Du8annyrFmzMH36dNPvJSUlsiUrYqdbBNj+xm5pzx5j/Un6yQJR9S+sQ7FN99c0mbXXmYiIfIdXJSoajQYajcb+iQ4QO93yzKC2dr+xWyviFFv/Eh2mQaw2iNM/Frw8vAPG9040JSQsliUi8m1eNfXjSmKTiJaNQh1+DLErg3QRQaZ6F/pbrDbILEkhIiLf59ERlWvXruHEiROm33NycpCZmYno6Gi0aNHCrbFIXV7sCHsrgwAgTBOAbglRCAzwwy3tGmHHH/kOP54vYZEsEVHD5NERlQMHDqBLly7o0qULAGD69Ono0qULXnnlFbfHYm+nYltFtGIY+6YMTdbZrIO5VlGNnq9tRVpWLvq1bezQY/maWBbJEhE1WB4dUbn11lshCMroFmJcXjxpVQZUgFkyUXt5sSPf6C31TbGlqKwKT6zKwNLRnaFSAQp5idxqSv82aNs0jEWyREQNHGtUarG3vNiRb/TW9g0S4+VNWVD7N8y/ot5tGmFk52ZIbR3DJIWIqAHzqlU/7mBrebFUznaYvVJW7eA9vVftDr5ERERMVCyQa48YdpiVxtkpNiIi8j0Nc17BTaTslNzQqABoAszffuwsS0REdXFExYWcWcrsi+K0Ggzs0BQtY0LxYGpL+Pup2FmWiIhsYqLiQmL6pjQkj/RphUf6tjI7xs6yRERkC6d+ZKA3CEg/WYCNmeeRfrIAekNNWiJmR+URKTpEBqvh6/xUwIOpLT0dBhEReRmOqDjJUo+U2L82zRuSHGta8lz3HF2tc4zN4PKKr2Pet8dwpbTS50ZgJvZNRGAA82IiIpJGJSil45oDSkpKoNVqUVxcjIiICLc/vrFHSt0X0DhaUrsw1JiM2KvHSMvKxROrMlwbuBv5qWqSlFnDuHcRERHVkPL5zREVB9nqkSKgJlmZ+3U2Bifp4O+nkrTkOTJEjaKyKjnDdYvIEDXm35mMi1fLcaawDAnRIXgwtSVHUoiIyGFMVBxkr0eKACC3uBz7cgpFJyjWRmiUrmu8Fs/e1h692EWWiIhkxkTFQWJ7pIg9r7LagBfWZ3lVkqL2V+Ht+7pgWAr7nhARkWswUXGQ2B4pYs5Ly8rFC+uPoLDUu6Z7Vo7vid5tG3k6DCIi8mFMVCQyrdApKUd0qNpqciF2zxpvne7RRWjQiz1QiIjIxZioSGBpKbIlYvescXbTQk+ac+eNrEchIiKXY6IikpSRj9o9Umzxxk0LI4MD8PrdKdyPh4iI3IKJigj2Rj5UAKJDA/HS8A7QaYPRLSEKB89cwcbM8zZ7pmzNznNp3K7w3v3d0LsN61KIiMg9mKiIIGYpckFpJXTaYBRfr8Qtb/5ktVOtkd4gYM3+s64M2yXyr1V4OgQiImpA2IlLBLFLjLdm52HSqox6SU1ecTkmrcpAWlau6djPx/NRWmmQNU534I7QRETkThxREUHsh/P6zPNWO9UCwAvrj6C0Qo/0k/nYkHletvjkoIvQAFDhYonlnZ7FrmIiIiKSExMVEbolRMFPBRhsVNL6qWC3D0phaRWe/e+vMkfnPBVqVvEAwKRVGVABZsmK2FVMREREcuPUjwgHz1yxmaQAtpMYJQtS+5k2TzTu9KzTmo8g6bRBZhssEhERuQtHVEQQW6PibYIC/HB49u1mmwYOSY7F4CSdqJ2eiYiIXI2Jighia1SiQ9W4UlrlNQ3clozubHFnYyk7PRMREbkSp35E6JkYjcgQtc1zIkPUmD8yGcDfNR1KFasNwvucyiEiIi/AERWZVOsFDErSYfkDXTH9q0yUKWjpsR+AJ/u3QtumEZzKISIir8JERYR9OYUoKrO9oudaRTW6zd+Cf3ZtrpgkpXuCFlMH3ICb2zZiYkJERF6JiYoIYotpr5ZX45OfT7s2GJEm9k3Ei8OTPB0GERGRU1ijIoK3dWN9pE8CkxQiIvIJTFREEFNMqxSP9m6Jl+9I9nQYREREsuDUjw/hdA8REfkaJioW6A2CWcMzgyDYLab1pBC1H976ZycMS4nzdChERESyYqJSR1pWLuZsykZeyd8FtNpg5U37aPyAB29OQP8bdOjVOoareoiIyCcxUaklLSsXT6zKqHe8+LpyRlNUAN4b24WjJ0RE1CAwUfmL3iDg+XVHPB2GTbff2ATL7u/O0RMiImowmKj8Ze+pAsXWoQSogHfGcBSFiIgaHiYqf0k/WSDqvDCNP65V6E2/x2qD0C0hCt8czpU9JhWApwa0wdRB7TiKQkREDRITFRNxex4/lJqAvm2bmFYEGffNuSMlFy+sz0JhaaUs0QzvqMM7Y7oyQSEiogaNicpfUls1wtKfTto9r3frxkhtHVPv+JDkWFyv1OOZr351OAY/FTCqSzO8NioFgQHsxUdERMRE5S+9WscgMkRts04lMkSNXhaSFCOdNljSY6r9VIgKVaNtk3A81q8V+rRtzBEUIiKiWpio/MXfT4XXR3W0uDzZ6PVRHW0mEj0ToxGrDUJecbnViaToUDVevuNG6CL+njYiIiIiyzi/UMuQ5Fi8/0BX6CI0Zsd1ERq8/0BXDEmOtXl/fz8VZo+oaWFfN/1Q/fXz2j864h9dmiGVTdqIiIjsUgmCIK6KVIFKSkqg1WpRXFyMiIgI2a5bt4W+1JGPtKxczP06G7nFf3e3jdUGYfaIJLvJDhERka+T8vnNRMVFnE12iIiIfJWUz2/WqLiIv5/K4uogIiIiEo81KkRERKRYTFSIiIhIsZioEBERkWIxUSEiIiLFYqJCREREisVEhYiIiBSLiQoREREpFhMVIiIiUiwmKkRERKRYXt2Z1tj9v6SkxMOREBERkVjGz20xu/h4daJy9epVAEB8fLyHIyEiIiKprl69Cq1Wa/Mcr96U0GAw4MKFCwgPD4dKZXnDv5KSEsTHx+PcuXOK27jQV/E1dz++5u7H19z9+Jq7n6tec0EQcPXqVcTFxcHPz3YVilePqPj5+aF58+aizo2IiOAb2834mrsfX3P342vufnzN3c8Vr7m9kRQjFtMSERGRYjFRISIiIsXy+URFo9Fg9uzZ0Gg0ng6lweBr7n58zd2Pr7n78TV3PyW85l5dTEtERES+zedHVIiIiMh7MVEhIiIixWKiQkRERIrFRIWIiIgUy6cTlWXLliExMRFBQUHo1q0bdu3a5emQfNrOnTsxYsQIxMXFQaVSYcOGDZ4OyactXLgQPXr0QHh4OJo0aYK77roLv//+u6fD8mnLly9HSkqKqflVamoqvvvuO0+H1aAsXLgQKpUK06ZN83QoPmvOnDlQqVRmPzqdzmPx+Gyi8uWXX2LatGl48cUXcejQIfTt2xdDhw7F2bNnPR2azyotLUWnTp2wdOlST4fSIOzYsQOTJ0/G3r17sWXLFlRXV+O2225DaWmpp0PzWc2bN8frr7+OAwcO4MCBAxgwYABGjhyJo0ePejq0BmH//v348MMPkZKS4ulQfN6NN96I3Nxc08+RI0c8FovPLk++6aab0LVrVyxfvtx0rEOHDrjrrruwcOFCD0bWMKhUKqxfvx533XWXp0NpMC5fvowmTZpgx44d6Nevn6fDaTCio6Px5ptv4pFHHvF0KD7t2rVr6Nq1K5YtW4b58+ejc+fOWLJkiafD8klz5szBhg0bkJmZ6elQAPjoiEplZSUOHjyI2267zez4bbfdhp9//tlDURG5VnFxMYCaD05yPb1ejzVr1qC0tBSpqameDsfnTZ48GcOHD8egQYM8HUqDcPz4ccTFxSExMRGjR4/GqVOnPBaLV29KaE1+fj70ej2aNm1qdrxp06bIy8vzUFREriMIAqZPn44+ffogOTnZ0+H4tCNHjiA1NRXl5eUICwvD+vXrkZSU5OmwfNqaNWuQkZGB/fv3ezqUBuGmm27Cp59+inbt2uHixYuYP38+br75Zhw9ehQxMTFuj8cnExUjlUpl9rsgCPWOEfmCKVOm4PDhw9i9e7enQ/F5N9xwAzIzM1FUVIS1a9di3Lhx2LFjB5MVFzl37hymTp2KH374AUFBQZ4Op0EYOnSo6c8dO3ZEamoqWrdujf/85z+YPn262+PxyUSlUaNG8Pf3rzd6cunSpXqjLETe7qmnnsKmTZuwc+dONG/e3NPh+LzAwEC0adMGANC9e3fs378fb7/9Nj744AMPR+abDh48iEuXLqFbt26mY3q9Hjt37sTSpUtRUVEBf39/D0bo+0JDQ9GxY0ccP37cI4/vkzUqgYGB6NatG7Zs2WJ2fMuWLbj55ps9FBWRvARBwJQpU7Bu3Tps27YNiYmJng6pQRIEARUVFZ4Ow2cNHDgQR44cQWZmpumne/fuuP/++5GZmckkxQ0qKipw7NgxxMbGeuTxfXJEBQCmT5+OBx98EN27d0dqaio+/PBDnD17Fk888YSnQ/NZ165dw4kTJ0y/5+TkIDMzE9HR0WjRooUHI/NNkydPxurVq7Fx40aEh4ebRhC1Wi2Cg4M9HJ1veuGFFzB06FDEx8fj6tWrWLNmDbZv3460tDRPh+azwsPD69VdhYaGIiYmhvVYLvLcc89hxIgRaNGiBS5duoT58+ejpKQE48aN80g8Ppuo3HfffSgoKMCrr76K3NxcJCcnY/PmzUhISPB0aD7rwIED6N+/v+l341zmuHHjsHLlSg9F5buMS+9vvfVWs+MrVqzA+PHj3R9QA3Dx4kU8+OCDyM3NhVarRUpKCtLS0jB48GBPh0Ykmz///BNjxoxBfn4+GjdujF69emHv3r0e+/z02T4qRERE5P18skaFiIiIfAMTFSIiIlIsJipERESkWExUiIiISLGYqBAREZFiMVEhIiIixWKiQkRERIrFRIWIPO7WW2/FtGnTPB0GESkQExUiktX48eOhUqmgUqmgVqvRqlUrPPfccygtLbV6n3Xr1mHevHlujJKIvIXPttAnIs8ZMmQIVqxYgaqqKuzatQuPPvooSktLTW3/jaqqqqBWqxEdHe2hSIlI6TiiQkSy02g00Ol0iI+Px9ixY3H//fdjw4YNmDNnDjp37oxPPvkErVq1gkajgSAI9aZ+KioqMGPGDMTHx0Oj0aBt27b4+OOPTbdnZ2dj2LBhCAsLQ9OmTfHggw8iPz/fdPv//vc/dOzYEcHBwYiJicGgQYNsjugQkXIxUSEilwsODkZVVRUA4MSJE/jqq6+wdu1aZGZmWjz/oYcewpo1a/DOO+/g2LFjeP/99xEWFgYAyM3NxS233ILOnTvjwIEDSEtLw8WLF3Hvvfeabh8zZgwefvhhHDt2DNu3b8eoUaPAbc2IvBOnfojIpfbt24fVq1dj4MCBAIDKykp89tlnaNy4scXz//jjD3z11VfYsmULBg0aBABo1aqV6fbly5eja9eueO2110zHPvnkE8THx+OPP/7AtWvXUF1djVGjRpl2e+3YsaOrnh4RuRhHVIhIdt988w3CwsIQFBSE1NRU9OvXD++++y4AICEhwWqSAgCZmZnw9/fHLbfcYvH2gwcP4qeffkJYWJjpp3379gCAkydPolOnThg4cCA6duyIf/7zn/joo49w5coV+Z8kEbkFR1SISHb9+/fH8uXLoVarERcXB7VabbotNDTU5n2Dg4Nt3m4wGDBixAgsWrSo3m2xsbHw9/fHli1b8PPPP+OHH37Au+++ixdffBG//PILEhMTHXtCROQxHFEhItmFhoaiTZs2SEhIMEtSxOjYsSMMBgN27Nhh8fauXbvi6NGjaNmyJdq0aWP2Y0yCVCoVevfujblz5+LQoUMIDAzE+vXrnX5eROR+TFSISFFatmyJcePG4eGHH8aGDRuQk5OD7du346uvvgIATJ48GYWFhRgzZgz27duHU6dO4YcffsDDDz8MvV6PX375Ba+99hoOHDiAs2fPYt26dbh8+TI6dOjg4WdGRI5gokJEirN8+XLcc889ePLJJ9G+fXtMnDjRtLw4Li4Oe/bsgV6vx+23347k5GRMnToVWq0Wfn5+iIiIwM6dOzFs2DC0a9cOL730Ev7f//t/GDp0qIefFRE5QiVwzR4REREpFEdUiIiISLGYqBAREZFiMVEhIiIixWKiQkRERIrFRIWIiIgUi4kKERERKRYTFSIiIlIsJipERESkWExUiIiISLGYqBAREZFiMVEhIiIixWKiQkRERIr1/wHMDfFiZ3d84gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the differences between actual prices and predicted values\n",
    "plt.scatter(y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "daf4eff8a6666267fda29751966ec7d6fe7e6d98"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking residuals\n",
    "plt.scatter(y_pred,y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "5beaa188e7383427c260763f5ce481bdbf11bb8b"
   },
   "source": [
    "#### For test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "78c445bf1a9e2a03850b6042742b53e0f1620d7c"
   },
   "outputs": [],
   "source": [
    "# Predicting Test data with the model\n",
    "y_test_pred_RF = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "bb95f57e021dcda311e9da08163f9ca0479dc600"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.8007605105132707\n",
      "Adjusted R^2: 0.800502720457328\n",
      "MAE: 0.33386207556524566\n",
      "MSE: 0.2603520594161262\n",
      "RMSE: 0.510247057234166\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "acc_rf = metrics.r2_score(y_test, y_test_pred_RF)\n",
    "print('R^2:', acc_rf)\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred_RF))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_test, y_test_pred_RF))\n",
    "print('MSE:',metrics.mean_squared_error(y_test, y_test_pred_RF))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_test_pred_RF)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "67ad608854dda892dfbb07892b9386fd46b19b3b"
   },
   "source": [
    "# XGBoost Regressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4cc3f92ac593e0fc054716eefabd5e01f4bec9b4"
   },
   "source": [
    "#### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "c6d191c4c3ded9af5367084d4261e22e499ffa6d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "             gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.300000012,\n",
       "             max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=100, n_jobs=8,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=0,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
       "             validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import XGBoost Regressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "#Create a XGBoost Regressor\n",
    "xg = XGBRegressor()\n",
    "\n",
    "# Train the model using the training sets \n",
    "xg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "7625a387d88cfe7a97bff1955ecb4f336dd20743"
   },
   "source": [
    "max_depth (int) – Maximum tree depth for base learners.\n",
    "\n",
    "learning_rate (float) – Boosting learning rate (xgb’s “eta”)\n",
    "\n",
    "n_estimators (int) – Number of boosted trees to fit.\n",
    "\n",
    "gamma (float) – Minimum loss reduction required to make a further partition on a leaf node of the tree.\n",
    "\n",
    "min_child_weight (int) – Minimum sum of instance weight(hessian) needed in a child.\n",
    "\n",
    "subsample (float) – Subsample ratio of the training instance.\n",
    "\n",
    "colsample_bytree (float) – Subsample ratio of columns when constructing each tree.\n",
    "\n",
    "objective (string or callable) – Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).\n",
    "\n",
    "nthread (int) – Number of parallel threads used to run xgboost. (Deprecated, please use n_jobs)\n",
    "\n",
    "scale_pos_weight (float) – Balancing of positive and negative weights.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0615c8ed00975272b8caa2d6aad13adcf677f44c"
   },
   "source": [
    "### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "242ef4a922e91e75c0d61be702a3c6ef57ac955b"
   },
   "outputs": [],
   "source": [
    "# Model prediction on train data\n",
    "y_pred = xg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "41e25ef2b58e5b0a5c1300425a4282dd8e0a0955"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9467753349431811\n",
      "Adjusted R^2: 0.9467458455519175\n",
      "MAE: 0.18818589321214796\n",
      "MSE: 0.0714324747843043\n",
      "RMSE: 0.26726854432256764\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "print('R^2:',metrics.r2_score(y_train, y_pred))\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "85df05cbc687636df454c4e5e0059492bcd77ce6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the differences between actual prices and predicted values\n",
    "plt.scatter(y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "04df87e9670c3cc1b07d4525dd67605d8d4eb542"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking residuals\n",
    "plt.scatter(y_pred,y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e48d52c87b4fe3b93a6068349c4d7401a10307f4"
   },
   "source": [
    "#### For test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "_uuid": "df2ead1c6dcee03e3143e1e139a42b050e1de3ca"
   },
   "outputs": [],
   "source": [
    "#Predicting Test data with the model\n",
    "y_test_pred_XG = xg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "_uuid": "4cfe00224641dca28997a6f4b05bdc2dece952ca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.8331080832284813\n",
      "Adjusted R^2: 0.8328921467358124\n",
      "MAE: 0.30944652988864313\n",
      "MSE: 0.21808254148464745\n",
      "RMSE: 0.46699308505014014\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "acc_xgb = metrics.r2_score(y_test, y_test_pred_XG)\n",
    "print('R^2:', acc_xgb)\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred_XG))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_test, y_test_pred_XG))\n",
    "print('MSE:',metrics.mean_squared_error(y_test, y_test_pred_XG))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_test_pred_XG)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "561bc791f61ca4cac779395f30cfaec14bb59e1e"
   },
   "source": [
    "# SVM Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "_uuid": "e7d40a6ff89aa230a6e3bea996aa93316300ed9a"
   },
   "outputs": [],
   "source": [
    "# Creating scaled set to be used in model to improve our results\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fe78f0e481006b004881a6c13750e80ff9de4f00"
   },
   "source": [
    "#### Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "_uuid": "519df75a3e65e691e203efa21693814f25cc305d",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Import SVM Regressor\n",
    "from sklearn import svm\n",
    "\n",
    "# Create a SVM Regressor\n",
    "svm = svm.SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "_uuid": "35d7e0b6ac09dfdca6d9fc5328cf42bfb7b287ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model using the training sets \n",
    "svm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4f3a0067425edc3f1dfa6feb4526d120ed23c0e7"
   },
   "source": [
    "C : float, optional (default=1.0): The penalty parameter of the error term. It controls the trade off between smooth decision boundary and classifying the training points correctly.\n",
    "\n",
    "kernel : string, optional (default='rbf’): kernel parameters selects the type of hyperplane used to separate the data. It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed’ or a callable.\n",
    "\n",
    "degree : int, optional (default=3): Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.\n",
    "\n",
    "gamma : float, optional (default='auto’): It is for non linear hyperplanes. The higher the gamma value it tries to exactly fit the training data set. Current default is 'auto' which uses 1 / n_features.\n",
    "\n",
    "coef0 : float, optional (default=0.0): Independent term in kernel function. It is only significant in 'poly' and 'sigmoid'.\n",
    "\n",
    "shrinking : boolean, optional (default=True): Whether to use the shrinking heuristic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "eb1fe4ad5131395f7a6936a8747e5677fdd83d40"
   },
   "source": [
    "#### Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "_uuid": "829074eccb90898b90ce05559579d433ebd7d6d2"
   },
   "outputs": [],
   "source": [
    "# Model prediction on train data\n",
    "y_pred = svm.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "_uuid": "e8a70552dd201f2ee41d9e66fc976807cf6aeff4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.7478602270878776\n",
      "Adjusted R^2: 0.7477205277885288\n",
      "MAE: 0.3847450385143313\n",
      "MSE: 0.33839513976157776\n",
      "RMSE: 0.5817174054139843\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "print('R^2:',metrics.r2_score(y_train, y_pred))\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "_uuid": "378d3d7cef08763ac0ba33f678fe312b489abb10"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ScHCwFBcXJ3Xv3l166aWXpMuXL5uO++OPP6S//e1vUkREhBQZGSn97W9/k3788Ue7u59kn332mdSjRw9Jp9NJERERUteuXc3uV1BQIN15551STEyMpNFozM5RWVkpvf3221Lnzp1N92/Xrp302GOPSb/99pvpuPLycunZZ5+VGjVqJOl0OqlXr15SRkaGlJSUJLz76Y033pBmzZolNWvWTAoJCZG6du0q/fe//zU7Vn6OFy5cqHMeR5+/JEnStm3bpKFDh0pxcXFScHCw1LRpU2no0KHSv//9b0mSJKmsrEx6/PHHpU6dOklRUVFSaGiodM0110gzZsyQSktLbT4/ImdpJEmSPBdSERGRqBMnTiA5ORlvvfUWnnvuOU8Ph8jrMKeGiIiI/AKDGiIiIvILXH4iIiIiv8CZGiIiIvILPhXUnDlzBmPHjkWDBg0QFhaGLl26YN++fZ4eFhEREXkBn6lTc+nSJfTu3Rt9+/bFd999h0aNGuH48eOIiYnx9NCIiIjIC/hMTs3UqVOxa9cu7Nixw+FzGI1GnD17FpGRkcKl7ImIiMizJElCSUkJmjRpgoAA64tMPhPUpKSk4NZbb8Uff/yBbdu2oWnTpnjiiScwfvx44XM42teFiIiIPO/06dM2G6/6TFCj0+kAAM888wzuuusu7NmzB5MnT8aHH36I+++/3+J9ysvLUV5ebvq+qKgIzZs3x+nTpxWVJCciIiLPKS4uRmJiIgoLCxEdHW31OJ8JakJCQnDttdfixx9/NN321FNPYe/evcjIyLB4n5kzZ1rshVJUVMSghoiIyEcUFxcjOjra7vXbZ3Y/JSQkICUlxey29u3b49SpU1bvM23aNBQVFZm+Tp8+7ephEhERkYf4zO6n3r1749dffzW77ejRo0hKSrJ6H61WC61W6+qhERERkRfwmZmaKVOmYPfu3Xjttddw7NgxrFixAkuWLMHEiRM9PTQiIiLyAj4T1PTo0QNr1qzBypUrkZqaitmzZ2PBggW49957PT00IiIi8gI+kyisBtFEIyIiIvIefpcoTERERGQLgxoiIiLyCwxqiIiIyC8wqCEiIiK/4DN1aoiIiMg7GYwS9uQU4HxJGRpF6tAzOQ6BAe5vHM2ghoiIiByWnpWLWeuzkVtUZrotIVqHGcNSMCg1wa1j4fITEREROSQ9KxcTlmeaBTQAkFdUhgnLM5GelevW8TCoISIiIsUMRgmz1mfDUrE7+bZZ67NhMLqvHB6DGiIiIlJsT05BnRmamiQAuUVl2JNT4LYxMaghIiIixc6XWA9oHDlODQxqiIiISLFGkTpVj1MDgxoiIiJSrGdyHBKidbC2cVuD6l1QPZPj3DYmBjVERESkWGCABjOGpVhMFAaqc2pmDEtxa70aBjVERETkkP2nLjn1c7UxqCEiIiLFKqqM+GhHjs1jPtqRg4oqo5tGxKCGiIiIHPB5xgnYK0FjlKqPcxcGNURERKTYifxSVY9TA4MaIiIi8gsMaoiIiEixLomxqh6nBgY1REREpFiTmFBVj1MDgxoiIiJSrHtSLOyVoAnQVB/nLgxqiIiISLF9Jy8J7X7ad9J9tWoY1BAREZFieUVXVT1ODQxqiIiISLGLlytUPU4NDGqIiIhIsUulYsGK6HFqYFBDREREip0uuKLqcWoIctsjERERkVsZjBL25BTgfEkZGkXq0DM5TrWu2ccvFKt6nBoY1BAREfmh9KxczFqfjdyiMtNtCdE6zBiWgkGpCU6f/0Jxmf2DFBynBi4/ERER+Zn0rFxMWJ5pFtAAQF5RGSYsz0R6Vq7Tj1FUblD1ODUwqCEiIvIjBqOEWeuzYamEjHzbrPXZMNgrMmOHJHh30ePUwKCGiIjIj+zJKagzQ1OTBCC3qAx7cgqceyDRYIVBDRERETnifIlYDovocdZwpoaIiIhcqlGkTtXjrKlS+Tg1MKghIiLyIz2T45AQrYO1jdsaVO+C6pkc585huQWDGiIiIj8SGKDBjGEpAFAnsJG/nzEsRbV6Nd7EZ4KamTNnQqPRmH3p9XpPD4uIiMjrDEpNwKKx3aCPNl9i0kfr8P6YbogODcHXB84g43i+07ugvIlPFd/r0KEDNm3aZPo+MDDQg6MhIiLyXoNSEzAwRW9WUfhSaTlmb7BfkM+VlYhdyaeCmqCgIM7OEBERWWEpGElr1QBAdUG+iSv219lhLRfkWzS2GwalJri8ErEr+VRQ89tvv6FJkybQarW47rrr8Nprr6Fly5aeHhYREZHqlM6W2ApGBqbobRbk06C6IN+R3GK888OxOsfIgc/7Y7ohNjwEeW5sfaCERpLcuYPccd999x2uXLmCtm3b4ty5c5gzZw7+97//4fDhw2jQoIHF+5SXl6O8vNz0fXFxMRITE1FUVISoqCh3DZ2IiAiAeKCidLZEbotQ+4Iun3nygDaYv+k3p8cfoAEcScE58fpQpx63uLgY0dHRdq/fPhPU1FZaWopWrVrh+eefxzPPPGPxmJkzZ2LWrFl1bmdQQ0TkX3whB0Q0ULEXoMjLRDKDUcINb2y2WUU4LDgQVyrd14OpNncFNT61/FRTeHg4OnbsiN9+sx55Tps2zSzgkWdqiIjIf/hCDoi1QKV2PotI36aX1mShX7vGCAmq3sBsry0CAI8GNO7kM1u6aysvL8eRI0eQkGD9F1ar1SIqKsrsi4iI/Ic7ulGLMBglZBzPt7hNWkmDSZEAJb+0Ar3mbTI9N2fbHfgTn5mpee655zBs2DA0b94c58+fx5w5c1BcXIxx48Z5emhEROQB9oIFOfl1YIpe0VKUGgm6ceHBuKNLUwxI0cNolIQbTIoGKAWllaYZHmfbHfgTnwlq/vjjD4wePRoXL15Ew4YN0atXL+zevRtJSUmeHhoREXmAkm7U8rZme9RK0C0orcQnu07gk10nEBYiVlNNDqKUmLU+G9v+3hcJ0TrkFZW5syG2V/KZoGbVqlWeHgIREXkRtbtRWwtQcovK8PjyTHwwpiuGdGpiut3WTFFNVyrE8llOXLyC2zo1EQ5Q5KBt38lLmDEsBROWZ0IDuCSwcdV51eazOTVERFS/ic5qxIdr7R4jEqBMWrkf3x78K0dHJP9FiQWbjmJjdh5mDEtRFEDkFV212hZBDVMGtMX7Y7qqfl5XYFBDREQ+yV43atmz//7FYsJwzeTeZbty7AYoRgl4YkV18rHBKGHXsYtOjN6yWeuzYVRYCGb2hiNIz8rFoNQEbPt7X8SGqbMIE6ABPhjTFU8PaIMhnZrggzHd4GW75Ovw2To1jhDd505ERJ5TM1E3PkILSMDF0nI0itShe1Is9p28ZNbLaOKK/QCsL49Yqu9iKXdGVExYMHRBgS6rqhsXHoyC0krh4+Xn9/6YbsgtuorZG46oNpaV43uZ5SN9e/Asnvjz9VaCdWqIXMAXCnQR1Wf2go3aFW0TonV49MZkfH3gLPKKyy3ep/ZOqI3ZeRZzZ0QVXqkEIB50KKUkoAH+CuYmrcx0qNqvLbXzkYZ0aoLFARqHA0JXY1BD9YYvFOgiqs+sJerWVPuinVdUhiXbc/B0/zZY8IP1YqxyUu3u3/OFknt9kdoBDWA5b0nu/t3qxW/Vf0AnMaeG6gVvKdBFRJaJ7iSqTT5+WcYJoeMzjud75QwDUD2b1CA8xNPDAFA9loTo6tlsS7x1hptBDfk9JdU8icgznNlJJEFeEhI92vvIIcLsEalCyc/uGMuMYSleG7xYw6CG/J6SAl1E5BlqlPqPCQ22GgzIMw9pLeOdfhxHxYUHY/49XfB0/zaIDQs2+5k+WodFY7thSKcEzBiW4pLHFw1P5LH44rI8gxrye2oX6CIi9alR6v/B3i0A1L1415x56NWqARIEarmM75Nc57joUMfTUDUAXrujI0KDA/Dlz6dxqcbMUlx4CKYP/Su3T645ExcebOVsyoWFBArNUU0f2h47X+hXJ6Cx1dvKmzBRmPye6Jsl+6cQeY5cc8aRUv8aVM8uTOrXBtfoI+tsCIgODcaDvVuYekBNH9re7rbk1Zln8OLgdii8Wom4CC0aRWjx7L9/QdHVKuVPDsDkAW0BwGIi9KXSCjyxIhMPnaweY8/kOAxKTcDVSiOm/OuAQ49Xm2hV47gIbZ0lJ2ubLLwRZ2rI79kr0GUvIY6IXC8wQGNadlGSxVE7/2NQagJ2vtAPUwa0RUxo9UxH4dVKzN/0G254YzPSs3IRK1BhOL+0As/+5yBmbziCN9P/h59PFjhVl6Z5XKjd3L5Pd53A6I92m8apj3J/4LDrtwtm39vaZOGNGNSQ37P1ZunLCXFE3kLp0oS140VK/df+M7WU/7ExOw8LNh1F4VXz5GF5t+PG7DxFzy+vqAzzN1nfLi6ioLRCOBFaHuel0nK3Jw1/eygXa/ZX/7tUVBntBmLehhWFqd5gnRoi9TnS1dre8UoqCtcuoGkwSrjhjc1WAwgNgHBtEC6XO7aMpJS8NPb8oHaKlpLk+00fmoKJKzIBuD+QUFrZ2BZ3VRRmUEP1CisKE6nHWrE8S20JbB0vW6zCjpuM4/kY/dFup86htikD2qDSYMTCLccV33fl+F4oulrhtRV8RbFNApELBAZozPqYEJFj7NV/qtmWIDBAI1Rcb+rqQ6bjlYyj5geVvKKrCp+Ja4VrA51aujpfUobbOjVBpDYYGb9fBKCBJEl4f6vyAKk+YFBDRERWWZvdVFL/Ka1VA6HieoVXKrFw8zE8PaCN0NgsLWXFeUlFXllpudiuI2tOXLxSZznN256jN2FQQ0REdRiMEhZu/g1Ld50wS7iV81/Kq4xC55HrP4nWgVqy4ziaNwiDPsr28rC1payC0gqhx/EFEdogLNh01OIWcLKMQQ0REZlJz8rF1NWHLLYekHfmPNW/tdC5GkXqYDBKuFhiuYN2baXlBlNCrT5Kh5nD6yYcO9onytsMTtXjuyzrO7GsJTP7+vN2JW7pJiKv5CsVTL2Ns6+bPANirZeS9OfXuz8cs3uumLBg/PR7Pnq/vhmzNxxRNA4AyCsuw+MWGs460yfKmwxK1WPx2G5OFbJztupwtC4IsWHW20v4Gs7UEJHX4fZ7xzj7uimZARE5pvBKJRb84Fx9FwB49stfzBKI3dXS5ImbWyG38CrWHDjrkvNfLCnHA72T0a9dY/Sat8mh7dPTb+sAfZQO50vKcOLiFSzYdBSA2L+PBsAbd3YCUF3pWCN4P2/GmRoi8iq2KphOsPCpnaqp8bp56wxIaYUB79UIjtzV0mTFT6dcFtAAwOwNR3DDG5uxaOtxh+vB6KN0SGvVACO6NMXTA9rYLV5Y06M3JmNQaoJQ0UNfwaCGiLyGvW3CQPU2YXcuRXl6GUzk8dV63by5qevHO3NM4++eFOuWHUC1KxK7QnW14qMO3bdBeAi6J8Wa3Sa3iXi6v/0dZOt+yTWr5rzzhX5YOb4X3hnVBV88ch30UVqfW5bi8hMReQ2l24RdTelyjtrFHUUfX63XzZubul4ur8KenAJTITp/2eXkTIicX1qBm97aYvH38fPdJ+zev/bvRO06XjOHd8CE5ZlOjND9OFNDRF5DdKbAHTMK6Vm5eFzBck56Vi5ueGMzRn+0G0+vOmDWmNDRxxddThJ9Pb7LyrU529QzOc7tNVCGdRbPkVq87ZjFf5P6zNLvw56cAuHlLFu/O4NSE/D+mK51+m15MwY1ROQ1RGcKXD2jYDBKmLr6kMWfWVrOUTsPSOlykujr8VnGSZvBVmCABrd3aaJorM7a83sBtEFil6JtRy+6eDS+R96NNvWrQ9h17CIMRklR0G/vdyc2XAtnV1yD3BgUcfmJiLxGz+Q4JETrkFdUZvGCLjf565kc59JxLNx8zOqWZsB8OadncpyidgEilC4nya+bki7Qjy/PxJQBbdA8LgwFpRWIi9BCH6VD/3aN8emuE0LnUcP5knKf33HjDQqvVuLej39CpC4QvVvFC90nQhsEo1HC1wfOmJZLAZi3nSh2flZMMGZVBYMaIvIagQEazBiWYnF7qRwOzBiW4tImpAajhKW7coSOPV9S5pI8IKXLcIEBGkwfmoInVojlP8ivq6WeROHaQIQEalBhcE+oIT+KLigAZYJVism6kjID0g+fEzr2cnkV7v3kJ9P3MWHVNW9qBvTO1sEBAHe2zebyExF5FWvbS/XRujpdn11hT06B8K6XRpE6l+QBObIMF6tSLkxpucFtAU1NDGg8r/BKZZ0ZSke3mtekDXZfqMGZGiLyOoNSEzAwRa/qTiJRosFHTGgweibHYU9OgdDxSvKAHFmG8+bt2OQfHC3O585ddZypISKvJG8vHdGlKdJaNXBLQAOIvwE/2LsFAgM0pgDE1uhiQoNhlCThGjfyMhyAOue1tgznzduxyT84OhsYH6lVeSTWMaghIqqhe1Ks3S2sGg0w4ebqho62AhCZnMSpZIu30mW47kmxPlcojZSJ0AahcaR7t9zXNELB9vuaiq6INTNVA5efiIhq2Hfykt0trJJUfZyc+CsHILUL5dUmb/G2lxskF/ErrzLi7bs6AxJwsbTc5jLcvpOXuIvIz10ur0JgQDD+1rUp0g/nobTC4NbH//oXx2ouXbzs+srMMgY1ROST1K7eK3M08VfOA9p9PB8TV2RaTDa2tcVbfj4bs/Ow9sBZs4q5chVhW7unmFNTPxRdrcRX+88g1I3JtxoAseHBDldxvlxRpe6AbGBQQ0Q+x5VdvJ0pABgYoEFAgMbm7qnaNW6sBTI1iczwMKemfrla6d7dYnd0aYpPHKxf5M7ddMypISKf4orqvTUbRnZPirWZ+KtBdQBlrQCg6IzJpuw8U1uFT3edsPkpWKQppUjCMvmfmNAgl7e2eKRPMgak6B2+v8GN8RdnaojIZ9hrH6C0eq+1GZ/hnROwZHuOQwUARWdMlH7qtVfEz1bhQhHBgRpUeqA+DTmn8GoVvnj4OgQEaJBXdNVUHfr4+RIs3HJclcf4eGcOOjeNtllmwFv47EzNvHnzoNFoMHnyZE8Phchn1J6VEN1i7C2UVO+1x9aMz5LtORiQ0giaWnGLRgM8emOyzSUuecbEVew1ILS0Y0rEsgd6YnL/Ns4MjTzkfEkZ0lo1wB3dmuHhPi1xR9em6N26oWrnlyRg0qoDGP7n7idvng30yZmavXv3YsmSJejUqZOnh0LkM1yZh+IualXvFWkYuTH7fJ2fGSVgyfYcdG0ea/U1q25Z0B5PrNgvNFal5Jkga4nSg1IT0K9dY3yecQIn8q9gzf4/cLnc9i6Z2LBg9EiOQ+828dBoNJi/6ahLxk6use3oBeijQ82S5XsmxyEmLNhmDzOl1v2Si3dHd8WMdYcdThp2NZ8Lai5fvox7770XH330EebMmePp4RD5BHlWovZFXHSLsbdQq4u3vRkfe+wtccWGq19srGYVYVsBqjw+Jc/v0pVK3PTWFswYloIW8WFqD71e0mjc1/No7YGzWHvgrMs/pOQWlXl1QAP44PLTxIkTMXToUAwYMMDuseXl5SguLjb7IqpvRGYlbCWgehN7ybD2knhlzmx/FlniUnt7dc1cno3ZeRaXzXL/7Lz9uIWfiZAD3BMXr6gwYooODcbnD/VEtM59cwc1k+X35BSoOksj8+aABvCxoGbVqlXIzMzEvHnzhI6fN28eoqOjTV+JiYkuHiGR91EzD8XTHGkfYIka259tBS5qb6+WqwgPTNFbDVCdJZ9z1d5TiA1zvjNzfVd4pRJBgQF4sHcLtz1mzQ8pecX1s26RzwQ1p0+fxtNPP43ly5dDpxN7w5g2bRqKiopMX6dPn3bxKIm8jyu6SHuSGl281dj+bC1wMRglGCUJMaHOBwaDU/VYOb4Xdr7QD4NSE5xeNrNHDnDvT0ty2WPUJ+dLyty+o0z+Nyy47L7WBN7EZ3Jq9u3bh/Pnz6N79+6m2wwGA7Zv346FCxeivLwcgYGBZvfRarXQat3XSIvIG6mVh+ItDEYJkbpgjOzaFGcKr6JJTCh6t45Hr5biTS/lGZ/Hl2cqfnxLHbJllnJdLN0fAEJDAnHFRpl7fZQWC8d0M3tO7go8s3OL0Ss5FrtzLrnl8fzViYtX8PHOHKfO4Whn7ILSCsSGBeOSC5aglNK6cfrEZ4Ka/v3749ChQ2a3Pfjgg2jXrh1eeOGFOgENEVWTZyWs1ZewdZH2NulZuZi6+lCdXIEVe07h9ZEdFSVIDkzRO7w7xNISl7Vk7Nr0NRJ6LR0vn/WV21Lq7G5yV+BpaecXKRMWEqjKLjJH53ne33ocYSHecV10Z/FjnwlqIiMjkZqaanZbeHg4GjRoUOd2IvqLraJsSvJQPC09K9fqzErhlUo8vjwTixXs4nIkkTJAAywcXfcxbCVjy2LCgvH+6G7o1eqvGaX3x3TFy19noaD0r3Ho/yz+N3vDkTq7m6YPTUFMaLDNNgzkHWzNwtWnMbibz+TUEJHj1MhD8SSDUcLMddl2j5u57rDwLq6N2XmKx2GUgEhtED7Z8Tte+ToLn+z4HRVVRqFcl8IrlQgI0JgCmvSsXMzecMQsoAnXBqJ781gs2Z5jsSjgxBWZ6NPGelNLIjWo/fHGnVlFPjNTY8nWrVs9PQQinyF3kXZFZ2tX25NTILSbI6+43GobgZoMRglrD5x1aCz3L91j9iY999sj6NAkSui+3/3Zl+pSaTkmrthf582+tNyAbw5Z7l0lt4HYe+ISdMEBKHNzQ0OqH6YMaINVe0+rmpDOoIaIXCIwQGP1gm+tQq03UJIgK3LsnpwCh+tt1H6DNkrAoTNiNbA+yziJzzJOIkDj2Bu9hOrAbVCHRkg/zLwXUpc+SotrW8QhMTYMM9YfRklZldVjlSyDuvNdhEEN+QylF11vvkh7G29voaAkQVbkWE9vX3e2zmHrRlEAg5p6JSY0CLrgIJwrdl1DybIqI+79+CehY5XkdQW68W2XQQ35BKUXXW+/SHsTX2ih0DM5Dvoond0lKH2UVmgXl69sX7cmKEADfZQWecX1sxZJfaTRaPDKbe0xccV+h7d52+OKCsTuxkRh8nq2uinLJcGdOb4+85UWCoEBGozoYj+wmjm8g93Zu4zj+cgruoq48BDhafGwYO/YGiv7Z8YJ3NODFdLrk0tXKhEbrnW4C7sttbvRq40zNUR/snfR1cC8uaDS4+s7JS0U7CXfKqVkeTA9KxdLtlsvYhYWEoh/3N3Z5oySSGE84K/1/6f7t0GV0QhAg8IrFVj+0yl7T8ltLl2pRJUP9OoideUVl0EfpcPzg9qh4HI5YkKDMfe7/+FSaYVTMzeubrxZwTo1RNWUXnQ9eZH2RZ5qoaBkeVCkBkx0aDAGpuhtPp5IYTygept796RYLPvxhFfXgzl76aqnh0AWuLI796vrD5tVCE6I1uGu7k3xoY2Av75hUENeTelF19/6HLmau1oo1JyVOXGxFPM3/VbnGGs5PCI1YHKLyrBsVw7iI7V1Zn1EgqK48GBMv60DTuWX4tOdOfjmoHuWKJ3JjWgSE8pCfF5INKAJDtCgUuFsW+2WB3lFZViyPQdhdlpu2OKOVgrc/UT0J6UXXX/rc+Rq7mihILrsY215UDQAnb3hiOn/9VFazBzeQbgJZEFpJU7lWw62XEkC0DUxBvtPFyq+7/Wt4hEcGIAFP7h3zKQOpQGNJfIZnKkcPPf21DpVrdWmC3ZfWMNEYfJq9ropa1A9BStfdJUeX1/JCbPfHDyLUT2aA6j7aUqNFgrWkratqbk8KHMkAM0rLsfjfyaFiwZFC7ccU/w4ztIAOFVwRfn9NMDWo+exaq/35PmQb4kJC8bisd0wpFMT3NGlqaeHoxrO1JBXU9q3yF/6HLmSpZmTmLBgAOZbOvVOboEXWfaxpmYgYm82yZapqw/h/THdhI6tNLg/8VYCkO9AN2VJAj7awTwKEqcL0qBvu8Zo1TAcaS3jzXqQDUjR45NdJ1z34K7ORK6BQQ15PblvUe0LsbWLrtLj6xNrCbNFVyohobpEeov4cFWKFYos+1iTc6EUGcfzTTujpg9NwcQVdQNVewqvVMJokBwOityl2EblViI1lFVJGNGlicX3v57JcQ53rBfhxpiGQQ35BqV9i3y5z5GriGx3X7X3NHa+0E+V18mZZOx3N/8GY41ckYRoHR69MRnrfslVHCj9dCLf6uydt/B0HSDyf/bKWVRUuW7fdbkbm4UzqCGfYatvkRrH+zt3b3d3Jhm79jVe3uXx/piuiA3X4nxJGb7YfRJ7TlwSOJsGA1P0mDygLZbuyuFuIaqXbP19v/fDUaeSje1xZ+tVBjVEPsbRnlau3u5ee1zdk2JVW/aRZ5JmbziCnS/0w8bsPMGABjh2/jJ6zN3o0t0dRL6i9t93elYuFvzg/iR5V2FQQ+RDnOlpJTpzEh+uVW1cwzsn2KwErIT8SXP37/mYtT5bfGyH81R5fCJ/UPN9wGCUMHPdYQ+ORn3c0k3kI5ztaWVvu7vs2X//oqg/lq1xLdmeg0dvTEZceIjw+ez5548nHE5AJqrP4sKD0T0p1vT9ws3H3NIUNcSNqYwMaoh8gBqNJ+Xt7oDtCp/nisUbf4qMa90vudj+976I1KnTFPL77HOqnIfIUT1bxNo/yAsVlFbipre2ID0rF+lZuZi/6ahbHlcb7L5Qg0ENkQ9QkuQrkwvsfX3gDDKO58NglEzb3RtHWV9iUtKdW3RcN761BSVlbtwCQeRCcj6XL26mzCsqw+PLMzF19SG3PWaAGyMN5tQQ+QClSb72cm8idcG49+OfrJ5HdCeU6LgKSiuEjiPyJe6sv6IWeciuqklj8TEltkkgohqU9LQSyb25eFlsHd1e0OJsDy1f/KRLJPPBmMYpk/q2whePXIdBHRorup9G475XikENkQtYWvpxhmhPq+5JsTZzXCRULyuJ7nD67VyJzfGLJh9bw5pzRN5Pfn9JSYjCc//+BemHleW1hQW7b1GIy09EKnNm27U1oj2t9p28ZHdnUG5RGVbv/wP6KB3OFduuIbNwy3Es3HLc6vhrjouI/I/8/jK8cwImrtjv0OyUwY3rdJypIVKRs9uubZGTfPXR5ks++mgdFo3thkGpCcI5Ll9lnkFxWaWpqJ09tsYvjysmNFjosYnI88JCAqGB/b9/fbQO74/phnW/5Dq83FbmwhYMtXGmhkglIr2VbPVeEWGvp5WSHBe5LHq0QCM7+Tm9uOYQ+rVrjJAg889DIsnHROQ9HruxJa7RR1qcVR7VozlaxIeZ3l+caU4LAEEa9yXPMaghUokreyuJtkaQc1yUvAHpggIwuX8bLKjRQNKagtJK9Jr3A167I7XOUlSvlg2gj9K6pZgXETkuNiwYk/q1QWCARqjxrzPNaQEAEmdqiHyOq3orWcrR0UfpMLpnczSPC0VBaQXiIrTQR1W/Ic0YloLHFeS45BWX47PdJ4WPLyitwITlmaYlL1lggAajezbH/E32gyMi8px5IzuaAheRxr/O7nIsKmNQQ17C0eaJ9ZGSbdei5Byd2ktaecVlFquBygm9d3Zriv9knhF+HEfqyNReSjMYJVQaXJsQGKENwuXyKpc+BpEviQ4NQtFVsb+JAA2wcHRXxRsW5BlgR5vTurPsJoMassoVu3j8mb0/fA2qk+56JscJnc9Wjo41uX8m9D7Qu4WCeyknL6Ut25WDB3onY2N2Xp3fFVdgQENkTjSgAYCFo7thSCfl792+tMuRu5/IIlfu4vFXgQEaDO+cYDMImTEsRXimy9HkPAnAv38+LXx8XLjju5ZmbziCjjP/i8ct/K4QkfeICQuu065AST0teZejI81p3Tm3z6CG6lCjeaK/EfnjT8/KxZLtOVbP8eiNyYpmuJxJzrtcLj7hO2dEqlMF9ORdVETkvYquVJp9IE3PysUNb2zG6I924+lVBzD6o9244Y3NNj+wDkpNwO5p/RV/EIoLU6eZrQjFQc0///lPbNiwwfT9888/j5iYGFx//fU4eVI82ZC8lyPNE/2ZyB+/yFLRul9yFQWCzibn2RMTFozFY7thSKcmmDEspd6VfCeqT2p+IP324FmHZ+JDggLw2h0dFX0I0gZ5cVDz2muvITQ0FACQkZGBhQsX4s0330R8fDymTJmi+gDJ/ZzZxaN2ewBPs7YMl/tnp9vZ6w8j43g+dh/PF6rkqyQQ7Jkch5gw9QvaxYQFY8qAttj38kDTzNHAFL1LHouIvIf8gfTlr7NstlJ5cc0hVNQomFf7fX1gih6LxnYTflxtsPsWhRQnCp8+fRqtW7cGAKxduxZ33nknHn30UfTu3Rs333yz2uMjD3B0F49aicXesuNKZPblk10n8MmuE8LVdO0FjDWfu2h/JlFP3NwKfdo0NHs95cfbdeyCW7v2EpHnFJTa/lsvKK1E19nfY3CHBETqAvH1L2fN7iO/rzcIC0a+wPtGcZn7lqgVBzURERHIz89H8+bN8f3335tmZ3Q6Ha5evar6AMn9HNnFY3Xr8Z/TmbVrmljjTTuulCTqFl4VCwh+PlGAwakJdSryApafu5pW/HQKnZpFmwKa9KxczFyXjbxiJvgSkbnScgP+k/mHxZ/J7+tawQmYqkr3fWBSPCc0cOBAPPLII3jkkUdw9OhRDB06FABw+PBhtGjRQu3xkQfI2/eAulnrNZsn1vy0r0ZisbftuHK6iqYFn+8+hXbTv8O8b7PNbrf23NVUePWvRMH0rFw8vjyTAQ0RKSa/k4vW1CtSXgbLYYqDmvfffx9paWm4cOECvvrqKzRoUF2JcN++fRg9erTqA5QtWrQInTp1QlRUFKKiopCWlobvvvvOZY9X34k0T5SpkVjsjTuuXJWoa5SAD7fn4MkV+2AwSg7Vo3HGzHWHMXX1ITc9GhH5I2/NllS8/BQTE4OFCxfWuX3WrFmqDMiaZs2a4fXXXzfl8/zzn//EiBEjsH//fnTo0MGlj11f2WueKFOjPYAr+yY5ytEqmjGhwULLUesP5mHviR/Qu3W822q8SAB7MxF5kQBN9QcdUodDKck7duzA2LFjcf311+PMmepS7J9//jl27typ6uBqGjZsGIYMGYK2bduibdu2mDt3LiIiIrB7926XPSb91RdkRJemSGvVwGLCrhrtAVzVN0mUpV1btpbhbHn/3m64r1dzoWPzisvxlYJ2BjJ3p01rLeQAEZHjJvVthZXje+GdUV0dKmjnS4Lc+Ial+J3qq6++wq233orQ0FBkZmaivLz6U19JSQlee+011QdoicFgwKpVq1BaWoq0tDSrx5WXl6O4uNjsi9Qnz2hY+73VoDrZ11Z7AFf0TRJlqQ5Nj7kbMXv9YUSHhuD9MV3rLMNZIj/PXi0bQKNx7V+xu98EOzWLduvjEfmzCG0Q0lrG41JpBZ5aud+h3muepuQdTnLjTJTi5ac5c+Zg8eLFuP/++7Fq1SrT7ddffz1effVVVQdX26FDh5CWloaysjJERERgzZo1SElJsXr8vHnzXL4sVh9Z2nIt9wXRwHyt1VJisSVq900SZW3XVkFppWm7dkK0DtOHpiA2PAQbs/Pw6a4TFs8l4a/nmRQXptoYNQAaR2nxf3d3wcXL5WgUqUNe0VVM+fIX1R7Dnr0nLrntsYj83eXyKtz7yU8I0HhvboqIIAAinaeC3Vd7T/lMza+//oobb7yxzu1RUVEoLCxUY0xWXXPNNThw4AB2796NCRMmYNy4ccjOzrZ6/LRp01BUVGT6On1avB+OKH8rNmePteq6AIQTiy1RuuNKDaIJunlFZZi4IhNFVyvsBlXGP3cD3JfWAmoNVQIwc3gH9GrZAI0idThfUubWT3bsyU7kGr56uYgJC8aisd0QrhOLVrRujGoUz9QkJCTg2LFjdbZv79y5Ey1btlRrXBaFhISYEoWvvfZa7N27F++88w4+/PBDi8drtVpoteoWMKvJm2qquINILZqdL/RzuHCevOOq9muqd9FrKlqHRkL1hX3W+mxIduZRJ63MxEJ0xZBOTTC+TzI+tNELStTT/at/5294Y7PZeN2VYOij77tEBKBBeAheHtoepwquYuWeU6qUcSj6s+Ce6HuDO99DFAc1jz32GJ5++ml8+umn0Gg0OHv2LDIyMvDcc8/hlVdeccUYrZIkyZTT425qFZvzFfa2XMsX/YEpeqd2J9nacaV2pWElScfy7it7jBLwxIr9+ADAzdc0xrHzl/HD/y44PEYAOHquBO/+cKzOa++rn/KIyH1mj0jFkE7V16JJ/Vr/WUH8IhZuOebUeWetz4Yk+CYkCdazUYPioOb5559HUVER+vbti7KyMtx4443QarV47rnnMGnSJFeMEQDw4osvYvDgwUhMTERJSQlWrVqFrVu3Ij093WWPaY2SC7wnyvu7gju3XMs7rmqyVP1WH6XDzOGOz+Co3Yagpkkr95sFHdogDcqrHItCdvx2kbMlROSQ2BqbCuT31rwi56r/y+/3YYIRRIDGfVGN4qAGAObOnYuXXnoJ2dnZMBqNSElJQUREhNpjM3Pu3Dncd999yM3NRXR0NDp16oT09HQMHDjQpY9riTfWVHE1T265lqvf1pZXXN1UcrEDs2JykOQqtT/AOBrQAMDlcvf1TSEi/1I7gEnPysXsDUdUOXelYKxSZXTfh3vFQU1RUREMBgPi4uJw7bXXmm4vKChAUFAQoqKiVB2g7JNPPnHJeR3h6ZoqnuCpLdcGo2S3+u201YcUzYpZWzq0Rd59JUkSi9cRkc+YveEIQkMCMSg1waH3PltEg5pyg/tmahTvfho1apTZVm7Zl19+iVGjRqkyKG/nyZoqnqJGLRpH7P4932736EtXKrH793yh8znSkkB+zqN6NMeQjv6TJ0VE/u9SaQUmLM/Etwdz3dqOpSZ31u5U/FA//fQT+vbtW+f2m2++GT/99JMqg/J2nrrAe5IntlwDQMZxsWBF9DglnbdlMWHBiA4LxvxNR63WqCEi8kZyEDP96yy3tWOpzasrCpeXl6Oqqm65ncrKSly96lzyka/w1AXe05Q0uVSPupsGlSwJPty7BSb3b41LVyrtzhYREXkrCUC+B6sWl4lU6FOJ4pyaHj16YMmSJXjvvffMbl+8eDG6d++u2sC8nbtrqngL0SaXaklrGY+FW44LHSdCyZLg6v1n7AYzbEZH5FsidUG4XFbFHYVu5M73SMVBzdy5czFgwAD88ssv6N+/PwDghx9+wN69e/H999+rPkBv5u4LvLewtOXaVXq1aoCYsGCbwUVMWDB6CY5HXjoUmYa9JDA7Y5SA+3o1x+e7Twk9PhF5VnKDMBw6U1ynpUt9EBcejEullW5/3l7dJqF3797IyMhAYmIivvzyS6xfvx6tW7fGwYMH0adPH1eM0auJdLEmxwUGaPD6yI42j3l9ZEfh173m0qFaujWPtZljRUTe4+CZYkT/mSdXX8h5nnNGpHooUdh9744O5SR36dIFX3zxBQ4fPoyff/4Zn376Kdq0aaP22IgAVM+IPXZjssX8pcduTFa81DcoNQFTBqj3+6qPDsWMYSn17lMfka8qqkd5cjXzPG9NTUCMR4I59707Ci0/FRcXm+rPFBcX2zzWVXVqqP5Kz8rFku05df4sJABLtuega/NYocCmZpuFa5Pi0DhSi3MlztWckXe5bczOc+o8ROQ+9ekDSM08z4zj9ktkuEJIsPsCKaGgJjY2Frm5uWjUqBFiYmKg0dSdSpIkCRqNBgYDq5+SekTqyoi0pLDUfDRc6/xC79VKA/6blYtXv3FddWIi8i6dmkahe4tYLN110tNDsejObk3Rp23DOnmenioIq48KsX+QSoSCms2bNyMurrrmypYtW1w6IPJPjjajVKMlhbU2C6UqtB8oulKJJ1bsd/o8ROQ7Dp4pxqEzxWgUGYLzJZ7bKm1N79bxGNGlaZ3bPVUQtsKJNjFKCQU1N910EwCgqqoKW7duxUMPPYTExESXDoz8h6VZkgTBre/OtqSoqDLiuX//Ij5YherTNDYR/UUCzAKakMAAtNNH4OAZ2yka7lBgoSaNwSjBKEmICQ1G4VXnlqBeGtIen+zMMWswbMsFN84QKUoUDgoKwttvv80lJhIm9xqpPduSV1SGCcszkZ6Va/P+zrSkSM/KRbfZG9kQkohcrsJg9IqABgDiIrRm36dn5eKGNzbj3o9/shrQyPPmMWHBdqvlP3RDMmYOF99FelW0SZQKFO9+6t+/P7Zu3eqCoZCaDEYJGcfz8fWBM8g4ng+DByrE2cqHkW+btT7b5tjkujK2WGpJIS85XS53YylLIiIvoI/66z3T2gfLOveJ1mHx2G6mEhr2quUPSk0QDyC8ufje4MGDMW3aNGRlZaF79+4IDw83+/nw4cNVGxw5xpnlHjU5kg9jKfdmeOcEfLg9x+p5hndOMMvPkYMpIiIACAsJwJWKv2YLInWBKCnzzxncmh/yRDZaxIQF4/3R3dCrRp010Wr5ovMvld4c1EyYMAEA8I9//KPOz7j7yfOstZaXl3tc16OpLqX5MJaCMX2UFmVVtv901v2Si+cHtTf9QTrStJKI/Jcc0ISFBCJAo0GJO5sRWRGhDVR9aVwD876DIu+FhVcqERCgMftg6MvV8hUHNUaj+9bGSNmuIXvLPRqIbX9Wi5J8GKvBWLH9OjK1Z3s2sWYMEVlwpcLzH7obhIdg7h2p6NeuMXrN+8FiUq8z5635oVX0g+XHO47X2T0q0g4nUAMYBGZhAt0YCykOash9lC4jqbH9WU1yPkxeUZnFQEuD6inN7kmxuOmtLU4tu8p/vAajhDUHzjhxJiIi1xnWOcH0/v3aHamY8Ge5CWfe/3TBAciY1h8hQeZZLqIfLDf/7wIqqox17m9PfEQQzpXYn/WKj3BfqOFQm4QffvgBt912G1q1aoXWrVvjtttuw6ZNm9QeW73myK4hZ7c/q61mnyVLgboEYPrQ9th38pLTy0XyH++enAIUlNaP8udE5Hv+tfc01mT+gYzj+RiYoseisd3QOEpr/442aIMCLM6+90yOQ6TOfkAhAfg840Sd22tuONn120XsOnbRbPNJg3CxcYsepwbF4dPChQsxZcoU3HnnnXj66acBALt378aQIUPwj3/8A5MmTVJ9kPWNo8tIzmx/dpVBqQkWk85kszccweBUvcPnl2d75MQ4T1XMJCIScbXSiClfVtfOkmfe/+/uLrj3458cPmfR1SqLM/CBARp0ax6LbUcv2D3HyYIrZt9bWimoKSFah9BgsXmRUG9rk1DTvHnzMH/+fLPg5amnnkLv3r0xd+5cBjUqcHQZSXS5p/b2Z1scrQRc06DUBBiNwBMr6lb1zSsqw6e7Tig6n0yD6tdicGp1QlvP5DiPVcwkIvWEBAAV9SB9U555f6h3C6fPZe0D3Y1t4oWCmqS4MNP/W8txrMnadcaSJrGhgkc6T/HyU3FxMQYNGlTn9ltuucVus0sS4+gykq3lnto1BkTIBZtGf7QbT686gNEf7cYNb2y2WzCvNoNRwuwNlrdYyzNPtoakQfW2w5q1FwBAbkH26a4TprFdKq1AQrTOavEoAIgL90SXWiIS1aFpNDSwvGztT+SgYNXeU06fy9oHuvvSWth8fwWq33/vS2sBQGwbOKAsByjEjdm7ioOa4cOHY82aNXVu//rrrzFs2DBVBlXfObOMJC/36GsVrNNH6xRt53a2EnBNIjNPcv09a8HY6yM7YvvzfTF9aHvc1KZ6dqp2zb7cojI8sSITt3XSWz2XBsCcEal2Ax8i8pz9p4vwSJ8WTuea+AIJQKkT01JylV9rM/AhQQEY3yfZ5jnG90k2JQm7oiRGxrECVc9ni+L4qX379pg7dy62bt2KtLQ0ANU5Nbt27cKzzz6Ld99913TsU089pd5I6xFnl5GcrTGg9tZw0Zmnh3u3wLdZeRYLPgHATW9tEfpj+3jnCTzcuwU2WDnXoNQEBARoTLsOiMj7rNxzGhFazqraI2+4sPVePG1I9XvoRztyzD4MBmiqAxr554Br8hLzr7iv6afioOaTTz5BbGwssrOzkZ3915JCTEwMPvnkE9P3Go2GQY2D5GWkCcszTXkjMkeWkZRSe2u46MxTv/aN8eLQlDrB2MbsPLvru2bjk4CPd53A+D7J6NeuMc6XlCE+XAtogIuXy5FxPB/92jXGk/1aY9G246gUKbRARG51udzAvm2CZm84goA/WxdYM21ICp69pR0+zziBkwVXkBQXhvvSWji8DVyJYDcW7VMc1OTkWC9XT46xlIxrbdeQpVLVtTnbJkHtreH2Zp5kz355ADOHdzAbo+j6riUf7chBWEggLpdVYc2BM9zqTUR+SbRifEhQAB7u09LmuUTfr5VoGuPFW7pJXfYCEKXLSGq0SXAmp8fabilrM081nSsuNxujwSjh0505Tq3vvvPDMYfvS0QkCw7QoFKgMXB7fSSO5JW4YUR/UbNivOj7ta2f1T1noMPjUYpBjQeJBiCi1X/VyoVxNKfHXoC2aGw3zFyXjbxiy0FKzTEajcCLaw+h8ApnV4jIvmuTYvDzyUKXnV8koAHg9oBGpmbFeHv1xYDqa0AgJPxRZL+VjTsxqFGRp/s0qZUL40hOj2iAFqkNxr2fWC8yJY/RUk0bIvJOkbogjzWJlD9kTRl4jVMF7NzplpTG+D77nEvOrVai78AUPSJ1wcg4ng9AwnUtGiAgUIOLl8tN17e7Fv8oFNTo3Linm0GNSjzRp6l2EGVtBqQ2kV96JTk9SgK0i6XeFdUTkXM0ADKm9seNb212e95azQ9ZvVo2UD0XxFVc2VBYjURfS9ezr6LPYMawFIzo0tR0Wzt9JDJPFdo9Xzt9pNNjEsWgRgWO5LE4m4xr6ZdOtKic6C99zZyevKKrKCitQFyEFtGhITAYJeH29jUDNFb8JfIvEoBf/ijEuLRkzN901K2PHVerM/X0oSk+Mcu7+/d86KPEP4jK7OW4KK0Yb4mS61lijFilYNHj1CAU1Bw8eFD4hJ06dXJ4ML7IE32arP3S2fuU5MgvfWCABkVXK/Dmf3+1OgulJEC7rVMTn/k0RURiJn6RicKr7s9/e3loe9MFNj0r12rlcm9z6UolpgxIxoJNR4XfBwM0wPy7u2Dyvw4AcE2pD6XXs59PiRXVEz1ODUJBTZcuXaDRaCBJEjQa2y+YwVC/6gq4u0+T6BZnterbiETtSgI00cx6IvIdnghogL8+9In0KvI2RVcr7Cbj1mSUgEZROodLfYhQej07fUnsA63ocWoQCmpq1qbZv38/nnvuOfz97383VRTOyMjA//3f/+HNN990zSi9mLN9mpQW2BMuYV3rpJo/K0da+qW3luBcUWXEi2uy7Ebt2/7e126A1jhKC6Mk4esDZ9AoUof3x3TF7A1HzJ5LTGiwx94cicgHafDn+9Qh1QKaAA3qVN0V3PikyKe7TqBnchx2vtAP8zcexcIt9stPnC8pw4guTZ2qGG/v/CJ2HbuI8yVlkCSxFyZC62VbupOSkkz/f9ddd+Hdd9/FkCFDTLd16tQJiYmJmD59Om6//XbVB+nN1OjTpCTqFv2lq/27ZpSAJdtz0LV5rNl5rSU4D++cgC9//gOXbGyplqP2fScvYcawFDxupe2ABKCsymi2MyEhWofpQ1MQGx5i+sM0SpLP7F4gIs/74cg5PLlyv6oJyg/f0AL92ulN70uXSssxccV+l8wCTV19CANT9OjdOl4oqJGvI4EBGqe3bVv6MCt6PRMZa009kpzL81FCcaLwoUOHkJxctzlWcnKyWduE+sLdfZqcTbStuR5qbco2t6gMH24Xrxy987cL6NAk2uYxtevN5BWVYeKK6uWrEV2awmCUsPv3fM7WEJGwT3edUP2cH+84ge5JcWa7fBYFaPDimkOq7+4qvFKJ3cfz0atVA8SEBdusyxUbFux0ErDM2ofZ6UPbuyTn0U7WiqoUd+lu37495syZg7Kyv16M8vJyzJkzB+3bt1d1cDXNmzcPPXr0QGRkJBo1aoTbb78dv/76q8seT5S8jARY7zBtL49FjrpHdGmKtFYNhArjOfI7UnM91Jn2A7W9v/U4Jq5UtuNAftxZ67Px7cFc3PDGZtz78U8MaIhIiCvbCc1anw1DjTWnQakJmH5bB5c8VsbvF4WOUyvIkD/M1k5jqP6guR/DO1fP5Kv58h47f1nFs9mmOKhZvHgxNm3ahMTERAwYMAADBgxAs2bNsHHjRixevNgVYwQAbNu2DRMnTsTu3buxceNGVFVV4ZZbbkFpaanLHlOUvIykjzafRdFH64TaEihhK4gSdb6kTPX28oJLq+b3wV+F9tRudU9E/s0VeS7AX+9Lu4/nI+N4Pr4+cAYZx/PRKEKsf1GUTukCiAZ7cgrsVk8vvFKJPTnO7SKyt7sJANb9kov3x9S9njnjaqX7NhApXn7q2bMncnJysHz5cvzvf/+DJEm45557MGbMGISHh7tijACA9PR0s++XLl2KRo0aYd++fbjxxhtd9riiHOnT5MxjWcrFiQsPFpoebRSpc0l7eSIipe7s1hT/yTyj6D6DOuiRfjjPRSOqNnGF+TZ1fZQOMWHBKLpSaXXWJC48GC8PScEz//5F+HHSWjVQvYmwNaK7m2LDQ7DzhX7Yk1OATdl5WLX3NEorHA9Mrla4r9q0Q8X3wsLC8Oijj6o9FkWKiooAAHFx1tcYy8vLUV7+VwXb4uJil45JjeQtUZaCqO5JsbjprS1C+T27f893yziJiGzp3ToeiXHhWLorR3j5+acc179/1R7LueK/3let7Vh97Y6OiA4NEX6M2LBg9GrZQHgGxtmcSiVV5+UaZZ/uOuH00pc7t9orXn4CgM8//xw33HADmjRpgpMnTwIA5s+fj6+//lrVwVkjSRKeeeYZ3HDDDUhNTbV63Lx58xAdHW36SkxMdMv43KV2Lk5IUIB4fo8vFXQgIr81e8MRzN90VFE+na1dmTK1k1PlMhaxYcFoHGW+FFUz1UBJ3uO8kR0RGKBBz+Q4xITZrgjvbKJwelYuZn9zWOjYRpE6VfMuAwMcCjUcoviRFi1ahGeeeQaDBw/GpUuXTMX2YmNjsWDBArXHZ9GkSZNw8OBBrFy50uZx06ZNQ1FRkenr9OnTbhmfJ4nm97AHExF5g4LSCqfub+kDnAbAo32STf9v7XilcY+E6oDq/+7ugpXje+GdUdX/3flCP9N7q0jeY0xYMBYrzLd0JriQk4NFqs4n/Dmbr2be5S0pelXOI0Lx8tN7772Hjz76CLfffjtef/110+3XXnstnnvuOVUHZ8mTTz6JdevWYfv27WjWrJnNY7VaLbRaseQuf2Irv0euTfDbOfdloxMRuUpseIhZYFSzzlfX5rFW64ABEK7mW9vFy+VmW75rs5b3GBMWjAevT8akfq3N8i2VJAorTXFQUoUe+Gs2X628Sw2AB3rXLQPjKoqDmpycHHTt2rXO7Vqt1qU7kSRJwpNPPok1a9Zg69atFmvl1AfWqv/WZim/x1JtAiIidxPd1CBiVI9E9GnT0OJ7or0NHANT9Fi2KwezNxxR9JgiuS0im0fk9/PvsnKFHteRQEN0xqV2c1C1mg8/emMyQoLct/ykOKhJTk7GgQMHzKoMA8B3332HlJQU1QZW28SJE7FixQp8/fXXiIyMRF5edeZ7dHQ0QkPd1wHUk6wVTLJUfbhm8BMfrsXeE/lY8IOyKpBERGq6Py0Jg1MTkFdchil/NmZ0VoAGNmcvbG3gCAzQID5SfDZfaVNgW4/tyIdMRwIN0UCoZnNQwH5hWVFdm8c6cW/lFAc1f//73zFx4kSUlVX3fdizZw9WrlyJefPm4eOPP3bFGAFU5/IAwM0332x2+9KlS/HAAw+47HG9hZJ28JyRIfI9QQEaVLmq+IqXGJyagLRWDbDrN7GCcyLSWsY7dX+lgYKznbAB5Q04lQZTNYk+v1MFV82+V6P5cO2u3u6geE7owQcfxIwZM/D888/jypUrGDNmDBYvXox33nkHo0aNcsUYAVQvP1n6qg8BjUjBJLkCprVqkUTknUKDAjCsk15RQDP2Ot/byZlQ86Ks0vUtQhuI85fLkXE836wCsBKiu5X0UVpViqk6sqtIguPBlMjOKgBYsOko0mstg1nbeCKqZhV7d3GoTs348eMxfvx4XLx4EUajEY0aNVJ7XFSDaMGk3cfzMXW1et1qicj1QkOCsOOYeN0VfZQW0WHitVC8Rc2L8sXL6uy+vFxuMC1jWVuKt0dkRuLObk3x2shOquSGqF3NXS0SLM+q1MwN+i4rF59lnFR8bncWe1X8L9SvXz8UFhYCAOLj400BTXFxMfr166fq4Kia6C/E29//z24GPRF5l4IrFYr+bsuqjPj9gm/tXlw4qqtZsKF0yUdkgkJeiq892yDC3ozEfzLP4Ka3tjh07tocucDLyziOzEaJ7KySWZtVkXODBjs4S6VW0rEIxUHN1q1bUVFRt65AWVkZduzYocqgyJzoL8T+00UuHgkReVrRlUp8l3XO08NQpEGtZFyljXmNEjB9aHvMv7sz4sItz1LVXopXalBqAna+0A9TBrSx+HNngqaaHLnAO7OMozSIsnW8Iw2VAzRA9yT3JQsLBzUHDx7EwYMHAQDZ2dmm7w8ePIj9+/fjk08+QdOm1vftkziDUTJrpNY9KRYJKjYXIyLf5YvLy7UvlDUL1ImKj9RCHx1qs1ifGjkcq/ZaLtLqbNAkcyQwkDkyy6M0iLJ1vCMNlY0SsO/kJUVjcIZwTk2XLl2g0Wig0WgsLjOFhobivffeU3Vw9ZG1bdvDOyfgw+05HhwZEZFjLF0o5SWfF9dkCVUVVtKI19EcDtH8RUeK4Mmc2VXkyCyPkq3ZCQI7rKwVFrRFtOeUGoRnanJycnD8+HHTNu6cnBzT15kzZ1BcXIyHHnrIlWP1e9Z2LuUVlWHJ9hx0S4z20MiIiBwToAHOXrpicYfSoNQE7J7WH3Hh1nfn1CzdL3pRdzSHw13dspXuKqr5GiglOiumgfgOK3mpbux1zYXGcLHEfW15hGdq5GJ7RqPRZYOpz+xt29YAOHret5IDiYiMEvDsf6pTFyztUAoJCsBrd3TEhOWZACx3v5YvtvZmHeR6Lt2TYpFxPN9u5fXaXB001VS74vCJi1ewYNNRALZfA0cfy9bsiiM7xwIDNIjQBQodm1/qvpkaxVu6582bh8aNG9eZlfn0009x4cIFvPDCC6oNrj4Rmfa8XG5w+nEitAG4L60Fjp+/jO+zzzt9PiLybpG6IJSUVXl6GAAsFwsFrF909bUutraWbuTL/fDOCbjprS1ClddrEw2anOmWXVPtisPX6CPsvgaOqhlE5RWXoeByOeLCQ6CPDhUO+mrLKxKbgRE9Tg2Kg5oPP/wQK1asqHN7hw4dMGrUKAY1DhKdzgwPCURphePBzeVyI+LDtbghrSGDGqJ6wFsCGuCvWWe5HgoAs95I2/7eF/tOXrI5w2IrABreOQFLtucIVV63RCRokpdyHJkJsmdQagL6tWuMzzNO4GTBFSTFheG+tBaq9U6y1bbBEY2jxVpMiB6nBsVBTV5eHhIS6v5SNGzYELm5zu/hr69EpzMfvbEV5v85Remo2RuOQB+lQ0xYMIquVPrkbgoiXxcWEogHe7fA+1uOe3oobiUn2777w1F8vvukWWNLeUbFVgdswHKzyO5JsbjprS02l/BFSvbbmzUCgBve2OzQTJA9ljaKfLwzR5Vzu8Llq2IBs+hxalAc/iUmJmLXrl11bt+1axeaNGmiyqDqo57JcdBHWY9m5USxSf1aY/HYbtBHmQdBCdE6TO5vub6CJeeKy1D4Z0Djno4cRFTTlQoD4nywMrA1St9H3vnhWJ1O3bmCtWBqNuyVZ0r2nbwkvHPJHjkRduX4XnhnVBesHN8LO1+o3vVrbTOHszVsbG0UUaM+jiucF0wAFj1ODYpnah555BFMnjwZlZWVpq3dP/zwA55//nk8++yzqg+wvtiYnYeyKstJ2LUTxSx9SrlUWo5Xv8kWfjw5mIkJC0ZIoAbnSv7aUhmjC0RhmfP5O0T1UXhIIO7pkYhPd52we2xceIgqnZDVFKwBKgUHI783PXpjMtb9kqtK+X9r5fpl1speDE7VC51fdKm/9lKNyGYOR5s3ivb3c2djSBHhWrEQQvQ4NSh+pOeffx4FBQV44oknTJWFdTodXnjhBUybNk31AdYH9jq2RocF4/WRHc2mH2v+waVn5WLiiv2K3xQlAJeuVCI8xDyDXRMQAIBBDZEjltx/LQI0GqGgRh8dihnDUvD4nzt/RERoAwFocLlc3Sl9+VLZL6Ux/ntYrGJxzSTW5we1NyWhzv7mcJ1ZGCWs1YKx9l6ZV1Qm9HoDju9ccmUNG5F+UM7Wx3GFv3VthrUHzgod5y6KgxqNRoM33ngD06dPx5EjRxAaGoo2bdpAq3VfIpA/EenYGhocaEqqc+T+9tROPL7E/lFEisk7Y3q1bACDUUKApno7sy0XL5djWOcmmDKgDeZv+k3ocWbf3hGQJEz58henxqsLDkBZ5V+zw3KAknWmWCiouaNLE7x9dxfTzEHND1qhwQEWt2grUXtGRWSmRGPjNXd255Ira9iIFqdzZxE7Ede3iUdYSCCu2Ni8EhYSiOvbxLttTA6nVEdERKBHjx5ITU1lQOMEJRG6o/cnIufFhgXjsRuTqy+etX5We4l438lLdgMaAJi57jAMRgkt4sOFx6GP0kEfHSp8vDVllUbEhYfgod4tTDkjg1IT0EOwT8/tXZpaXQqxVlwuWif+Obr2jIrITIn8mtv797Gndqsag1FyaQ2bAsGu5aLHWWPpeTkjMECDf9zd2eYx/7i7s1uXzIR+w0aOHIlly5YhKioKI0eOtHns6tWrVRlYfeFM9G8wSth17ILaQyIiC14Z1gF3dG2Krs1j7dYSEf27zi+twJ6cAuELYVx4sGmmQY1cnEulFVi664TZluT/nSsRuu//zpXgpnaNrP7cUu5f1pkizP32iN1zR+oC68yoiL6mD/dugW+z8hyu9WItZ2f60BSX1bCx1qTT0eMssfa8vHVnlaOEgpro6GhoNBrT/5N6HI3+Lf2CEtVHD17fAjFhwcLLN46SdxxauljXrlOi5NP6+ZIy3NapCRKidXb/nueMSDU9jlxPxRmWElxFmw+KHFc72VZ0+eTObs3qfLoXfU0HpOjx4tAUm/8+1tjK2Zm4IhOP3piMJdtzbNawcWRWQnTmzdEZOlvPS6R+jzUGo4Spqw/ZPGba6kNuTXAWCmqWLl1q8f/JeY5UsLSXWExUn6z75Sz2vDQAgMbpGk7W1O67Y6+IWc/kOMSFBwsly564WIrAAI3dprUDUxphSKe/ymbISzwz12U7lWtRO8E1LESs9L3ocTU1ihRLVbilQ90LrJL3SkeKzInk7Kz7JRfvj+mK2RuOqFr1V35utoJaR3s/uXLX1u7f81FoJwfz0pVK7P49H71buyevRp0yhWSRyPqlrVbulqJ/NRKDifyJvITTIj7MZY+h9BN4YIAGc0akCh27cs8pVFQZse4X23VIss4UW2wIuWtqP0wZIF6jyhp5eadd40ih40WPk6Vn5eLZLw/YPc7axVvpe6VSorubYsO1FmvYOLOEIz83ayNX0myyNiW7tpTa8ZtY+oPocWoQmqnp2rWrafnJnsxM56ZD/UV6Vm6dT1D6KB1G92yOFvFhZlOiIn1P5GJTu45d5JITUS2bsvMwwMoOQWdN7t/GoQvWkE5NcFtWHr45aDtYySsux+cZJxze0hsYoMHTA9riGn2kU0vS8vJO0VWx3Y+ixwFis8sigYlojyhHKMlvVLvdAGD9uTmb9+LKXVuH/ihS9Tg1CAU1t99+u+n/y8rK8MEHHyAlJQVpaWkAgN27d+Pw4cN44oknXDJIX5OelWux7kRecZnZ9HjNX1Zb6/TfHszFy19noaC0os45iQj4ct9pPHdrO7vJsyLbrGtLbii2M6l2ldvuSbFo0UDsvicLrggdZ+vCY6nr88o9p+wuTdVe4lZ7e7Ho7LJGA4zvk2z34i2S0+QId3botsYVz82Vz8uVS5WOEgpqZsyYYfr/Rx55BE899RRmz55d55jTp0+rOzofJJI4JaudpGUp+p/3bbbNdXYiAkrKDOj9xmbc1b2pzUTOhaO7IjosBBnH83G6oBRf21nyAYD4cPt5IJYS95UEUElxYktnli48lloGyBfBSf1aY09OATZl5+ETC8XpLM2ONIkVS0YVPU607IRRApZsz0HX5rF2AxtXzJS4u0O3NWo/N1c+r57JDbDxiP3GyD2T3VcwUHFOzb///W/cf//9dW4fO3YsvvrqK1UG5ctEEqdkNctfW8q3+fbgWQY0RIIKSiuwZHsOHr0xuU59FH20DovGdkNAgAbP/fsXLNxyTCigAWC3qZG1nj0iAY3c0+2+tBZIiNbZzKmwlGuSnpWLG97YjNEf7cbTqw5g9Ee70X32Rryz6SgMRsl0gZw+rAMWj+2GBCuvS80g4vqWYgmdoscpXdaw9n7oaq7O2fEUVz6vcde3gL3MFI2m+jh3UVxRODQ0FDt37kSbNuaJaTt37oRO57ppOV+RcTxf0fHWSmsbjBJe/jpL5dER+b91v+Ri29/7Yt/JS2azFxuz8xzaNXjRRsEzZxL3a15QQoICTFu0RbcLW8tTKbxaifmbfsPSH0+YtVcRXdro9ecOKFtVYsNDAtFLcDZBybKGM60G1ODKnB1RtmbeHOWq5xUSFIBH+yTb/PD9aJ9khAS5b0+S4qBm8uTJmDBhAvbt24devXoBqM6p+fTTT/HKK6+oPkDf49gnjNqfZvbkFCjunRKpC0QJG1FSPSZfFPedvCTcjNAeWxdlZyp6176gKLnwiDyfwiuVdWqQiC5thAQF2AxqggUuUvLFOa+4THh7u8yRpFW1uCpnR0T1BpPDyCv+K5DWR2kxc3gHpwMqVz2vaUOqZ4E+2pFjNjsZ8GeOlPxzd1Ec1EydOhUtW7bEO++8gxUrVgAA2rdvj2XLluHuu+9WfYC+Jq1lPBZuOa74frXfOJX8UU/q2wq9WzdE96RY3PTWFu6OonrP0ocEpX8XIrkGjl58J/VthSkDr6lzQRG98Ig+H3sdry3Zk1Ngdwm98EqlzdkUZ4uDujIZV4Qrcnbssb7BpByPL8/EYgcL5NXkquc1bUgKnr2lHT7POIGTBVeQFBeG+9JauHWGRuZQP/C7776bAYwVPZLj6kwf22LtjVO8bHqI2Zvj9KEpeGIFt9VT/Vb772dTdp6i+4vkGhiMEi6WONaLp3frhlbPK3LhURJMKV3OcXYLsDPFQd2VjOttRDaYTHVzZV6lQoIC8HCflp4ehmPF9woLC/Hxxx/jxRdfREFBdcGezMxMnDlzRtXB+aJ9Jy8pCmgAy2+ccsa6PUajERtrvGHHOtEbhMjXWUqoNRglrDmg7L3JUgJtTXKC7uwN9vsY2RtfzXGKNhtUOpOhJAhyZguwvWUxDYAIreXP0r6cjOus3cftbzApvFKJ3QpzNusjxTM1Bw8exIABAxAdHY0TJ07gkUceQVxcHNasWYOTJ0/is88+c8U4fYaSNw9bSVpyxrq9TzxFV6vM1s2VfiIl8rSY0CBAAgrLqpw6j7WLomh+WpQuCLNGpEIfZTvXwNGZCFsXbaXNBkXK6tekJAhyZguwSPXay+VVmDKgDVbtPe2xZFxvk/H7ReHjerdxT7sBX6V4puaZZ57BAw88gN9++81st9PgwYOxfft2VQfni0TfPKYPbW+3tLacOCg30rOk5rbwbw/mWqxFQeTNCq9W4cEbWkIDu7unbbI2uyL6QePO7s1wR9emSGvVwOaSk2jCce1TWBuftS3hch2r9Ky6W89rbtO1xdbMkDXyua09RwnWZ1NEX+sW8eGqtxrwbaK/+d47g6VkptGVFM/U7N27Fx9++GGd25s2bYq8PM4S2PuUAwAxocEwShLW/XLW7qfCQakJiNQG495PfrL6mPKOj5fWihX9I/I2LeLDLO78Ec1Pu69Xc8wc/lcH65rbYkXzXprGhOLrA2ds7goRTdCdPrQ97ktrUWdbee1zOtNscFBqAhaP7Yapqw9ZXLrwxHKOkqUrTyTjequ0Vg2wcMsxoeO8kdKZRldSHNTodDoUFxfXuf3XX39Fw4YNVRmUL6u5bGTtDbnwaiXmfvs/0/f2/vEvloq9KV8SLPpH5G0aReqQ1qpBnZ0/Zy5dwXP/OWj3/t2S/goYHKnuG6CBWX6Mtb9J0ZmI+EgtQoIC7F6ElDQbtHQuebfUws3HsHRXDgpr9GNydDlHDrSssRVoeUtVXl/Tq2UDxIQF28yriQ0LRq+W3hfUWFuOrV0x310ULz+NGDECr776Kiorq198jUaDU6dOYerUqfjb3/6m+gB9kWnZSCDRF6h+05qwPBPfHsy1OH3n6e2N5L80AOLCgz36+DWXR+RP7yO6VC8DnSkUCyLkJVpHq/vW/rm1pR+1++io0WywuqFlG+ybPlCV5Rxnujr7a1VeVwsM0OD1kR1tHjNvZEeve93szTTKJQXcuRSlOKh5++23ceHCBTRq1AhXr17FTTfdhNatWyMyMhJz5851xRh90qDUBOx8oR++eOQ6xITav2hIACatzDQrd37DG5uRnpWLnslxiAnz3IWH/JP89jhnRKrNEv2ufnxrF7n0rFwsqNEA1ho5KBLJd6n9MNauEdZamMgzEUrbGVijZpBUOyB09ALobKBl7UOdvR1l9Z28nFg7hzIhWqdKjRpXEFmOtRYAu4ri5aeoqCjs3LkTmzdvRmZmJoxGI7p164YBAwa4Ynw+LTBAgwCNxmxK2BZrnxbfH9PVBaOj+q7m8kRAgMbmkqmS2kuWhGsDEakNMq+UamN5RElCrhwUZRzPt/sGa5Sq813iI7W4WFJuc0u2paUfW8vLjsxEeONyjUgDT3vHebIqry/ztddN7Y7ualAU1FRVVUGn0+HAgQPo168f+vXr56pxWbR9+3a89dZb2LdvH3Jzc7FmzRrcfvvtbh2DUs6U+5YTBV/+Oku4SSaRNfJF+KHeLTAwRW/2ZmmtRH9MWDAevD4ZE25uhZ6vbXL497C03IAl912LAI1G6M3akQrASvJdRnRpiq8Fa9fUPq+afXTUDpJUodJGHCYCO8aXXrcCG33RHDlODYqCmqCgICQlJcFg8Ex/odLSUnTu3BkPPvigz+TvOJsPIwGKe0ARWaLRVDeXs9aLxdKnxO5Jsdh38hJe/+6I04H1xcvlGNGlqdCxSj4MyEmrSpdynFn6UfMTtTc0UazJVgNPR44j/xUnWOxV9Dg1KF5+evnllzFt2jQsX74ccXHuzWAfPHgwBg8e7NbHdJbSIllErmKUgCXbc9C1eazVC2XNT4npWbmq9hJTEuArOVZeIlK6lOPs0o+an6i9adlB7WRo8l/66FBVj1OD4kThd999Fzt27ECTJk1wzTXXoFu3bmZf3qS8vBzFxcVmX+4mTy9754oo1UciuxGs7SJyhCMF4ETbhMjOl5Qp3nkjUsDOnUs/aiX6Oqt7UqzVBGpZgKb6OKrfRP5Olf7tO0vxTM2IESOg0fjGJXrevHmYNWuWp4dhdXqZyN3k5Nf5G4+id+t4xQXh7FErL0QOOCx1LbZEnjVQupQzKDUBj96YjI925Jgl6gdogPF9kr1yx4mr7Tt5SWgL/L6Tl3wm94Ncw147Hw3cnxOmkSTJM7WMnaTRaOwmCpeXl6O8/K913+LiYiQmJqKoqAhRUVFuGKU5ucppXtFVfJX5B3YeY3My8ix9lBYzh3cwW/qwtyvImikD2mLlnlNmOx2crSr67cFcTFqZafUiKy8R7Xyhn9kbZ82KwraWcqwVDpOPrI9bkL8+cAZPrzpg97h3RnURzpEi/+aOisLFxcWIjo62e/0Wnqm5cuUK/v73v2Pt2rWorKzEgAED8O677yI+3nuba2m1Wmi1YtsT3aHmGvzxC5cZ1JDH5RWX4/HlmXarmdoiBxZtGoWj9sZvZz8zDemUgIXoiidW7Lf4uIDlT4Ii+S6iLQr6tWtst92BP2FODSnlTTlhwkHNjBkzsGzZMtx7773Q6XRYuXIlJkyYgH//+9+uHJ/fSmsZj4Vbjnt6GOTDokODUWShBpK9lgCWOBPQAMDwzgmYuGJ/nQDhXHG506XSh3RqgsUBGtV3B4lWzu017wcUlFaYbvdUTxt38cbaOeT9vGUrunBQs3r1anzyyScYNWoUAGDs2LHo3bs3DAYDAgMDXTbAmi5fvoxjx/5q+pWTk4MDBw4gLi4OzZs3d8sY1NKrlf1eH0S2fDCmGwICNMgruoqC0grERWihj9LhUmk5Jv45s+HqtWV9tA7Th6Zg9gbHmjKKcsUnQdFt4zUDGsBzPW3cxStr5xAJEt79dPr0afTp08f0fc+ePREUFISzZ8+6ZGCW/Pzzz+jatSu6dq2usPvMM8+ga9eueOWVV9w2BrUEBmhwz7XNPD0M8lFx4cHokRyHtFYNMLxLU6Q0iTbtWLn1z2TZaBe21ni4dwtTf6HY8BCHewUpofbuIEeXT6y1UPAnbHVAvkp4psZgMCAkxLyATlBQEKqqqlQflDU333yz02v03sJglLDul1z7BxJZUFBaiV7zNqFrYgz2ny6qszwyvHMCilwwC2hp6UWNpoyeYG+ZxRZ73bP9gTflSRCJEg5qJEnCAw88YJZ4W1ZWhscffxzh4eGm21avXq3uCP2UI2XgiWoqKK3ED/+7UOf2vKIyfLg9R7XHkXslWbuo+Wpiqa1lFlHeFqipzVvyJIhECQc148aNq3Pb2LFjVR1MfeLvb4ZkX4Q2EBHaIJwrLreZkFlWacAlBbMuas1lyo//QO9km5/OfS2xtPZ27/fHdMPsDeZJyHHhwULtSbwtUCOq74SDmqVLl7pyHPUO3wypTaMIPHZTK5sJmaN6NMf8TUfdPjYlCaG+lFhqrZ7G9KHtERuuNet5ddNbW3wmUCOiaorbJJA65E+3nn+bJ085WXAVA1P0NhMyW8SHuWUsteMNpQmhvpBYaq31Q15RGSau2I+iqxWmJOSQoABFLReIyDv4bEVhR4hWJHQXa9VMqf5YOb4X0lo1sFoBN+N4PkZ/tNstY7GXOyNCtJKvuxmMEm54Y7PVPDZrlYndUSmViOxTvaIwqU/uO6NmUif5Fjm3ylpCpjM7dJSKj9Q6XfbeWxNLRQvt1d7NxB1ARL6Fy08exG3dZC+3SqSTdG2aP78euzFZUadrf87zcmbbubd0zyYi+xjUeBC3dfseDYBYFYraaVC9jCGSaCrnq4gGKHIey7QhKdj5Qj988ch1iAm1PmYlY/FVvrrtnIiU4fKTB3Fbt2+RP5/PG9kRADB19SGH2lw4kmhacxlkY3Ye1h44a1ZwLy48GHd0aYoBKXqz5ZHAAA16t47H63/riAnLMwF49+4kV/G1bedE5BgmCjvJmcRIdyaBkvNqJ4gajBIWbj6GD7cfx5UKg8PncYQjv3f1PelVTswHLAd23rJLi4jqEr1+M6hxgjMXCYNRwu7f8zHxi0wUWui0TN5DFxyAT+7vgV5W8ikMRgmTV2Vi/cE8u+eaMqANJvVr47FZEW/dneQu9T2wI/JVDGosUDOosbYdW+RTn6U3VvJeN7WNxz8fus7ucd8cOIsn/7Uf1v6irG0bJveq74EdkS/ilm4XMhglzFqfbXFtXkL1xWvW+mwMTNHXebNkbRrfc2ObhkLHNYjUWg1oAPWaIPKi7Bxv3XZORM5jUOMAR2te2AqGyDsFaID70loIHeuObtXuXj5RGkAx4CIiT2JQ4wBHL17cwu17xvdJRkiQWOUDV28btjbLl1dUhgnLM1VPdFUaQKVn5WLmumzkFf91vD5Kh5nDma9CRO7BOjUOcPTixS3c3qlb8xiLtw9MaYRpQ8QK3xmMEoxGCdEuqgdjb8kTqF7yNBjVmQe01SdpwvJMpGfl1jn+8eWZZgENAOQVl+FxC8cTEbkCgxoH9EyOQ4ydAmwxYcF1Ll4s7OU6ceHBeLh3C0wZ0Bb6KGWvc+apQou3b8o+L3QxTs/KxQ1vbMa9n/yEIis72ZytB6NkydNZSgMog1HC1NWHbJ5z2upDqgVcRETWcPnJRSxdttzZx8ffTezbCte3jMfF0vI6uRuT+rXGnpwCfJeVi88yTjr1ONYSvmWiid8xYcGYN7Kjw8sw7sjXkSnNGdv9e77dIoSXrlRi9+/56N063unxERFZw5kaB+zJKRB7Ez+ej4zj+fj6wBlkHM8HAFMfH6ZOOueG1g3Ru028xX488u6WwU7mcdib/VCS+K0NCsDAFL3DY3FnmX+lAZT8u22P6HFERI7iTI0DRN/0J64wL6wnJ1kuGtsNL67JMitzT2KUlLNXa2bM2r+3ksTvvOJyp7Zyu7PMv/IASvTV5fwkEbkWZ2ocIPqmX7tSsJxkCQDTh7ZXfVze7P605k6fQ2leSs0O17WPVjJTZu3fW+lSjzNLQyLPRa3+TXIAZe1MtROe01qKLSmJHkdE5CgGNQ6w96ZvTc0ky/qWNDw4tQk+GNMNzlxz5e7TSvJS5A7X+lodrvXROnwwppuii3dtSv8Nfzt3GRnH8x1OmLX1XNTczq00gOrVqoFQ4nwvFrwjIhdjmwQHWWuOJ+qLR67Dc//+pV4kDSfUaA3w7cGzeGLFfsXnmNS3FaYMvMbhmQhrReGcaXJoMEq44Y3NimsPOVssz10F7pTUqZG3dFuzmM0iicgJ7P1kgTsaWsaEBgs1qHxnVBdogwKsXlD96R+l9gXNkd5XK8f3cllpe2eq9M77Nhsfbs9R9Hi+1BVaSQBVXXzvMPKKy0236aO0mDm8g9c/TyLybgxqLFA7qAHqvukbJQn3fvyT3fvJF2lrF9TpQ9tj9oYjPl+BeMqANnh6QNs6t8uvW15xGWZ/cxgFpdbru7ijCaQjsx+OztQA/tvckm0SiMgV2NDSTWo3xzMYJUW7VAalJmBgit7ihSAgQON1zS8fvqEFvj2UJ3QhT4jWYVK/NhZ/VvN1Cw22PmMFqJcAa4sjTQ6daXuhVnNLb8NmkUTkSUwUVpkju1TkC0HtmityYmisnSRMd/p05wkM75xgN0laA/FgxF0JsGpTo9AdW2cQEamHMzUuIF+kay8r6R1IEB2UmoCb2jZC+1fSXTFUAIAuOABllUbh49f9kov3x3TD7A2W82IcSYS1NWPlrdTYwVbfdsEREbkSgxoXUfMivWT77y4Y4V8e7p2M97ceFzpWXjaJDQ/Bzhf6VefFFF1FQWkF4iK00Ec5/jx9benCmeJ+ahbLIyKiagxqXEiNi7TBKGHpLmW7a0TJF9ZeyQ2EgxrZ+ZIyrw9CXJ20Ki81TlieWWfHWs3vLf0McE+uEBFRfcKgxsvtySkQ2iKulHwpHd45AU/+S3ndGG9fNnFmm7YS9pYaAaiyDElERPYxqPFyaiWShocEorTCYPpeH63D8M4JDtVY8fZlE2uds+U2FWonH9tbavS1XCEiIl/FoMbLnbhY6vQ5IrRByJw+EPtOXjJdWLsnxeLGN7coOo8vLJvY6pwtofo5zFqfjYEpetWXoqwtxXn7Mh0Rkb9gUOPFDEYJK/eccvo8d1/bDCFBAWYX1ozj+cgrVjYL5AvLJvZqx/hrfRgiImJQ49WqK+6W2z/QjoEp+jq3KVnWuj8tCYNTE3xi2UT0ebE+DBGR//G54nsffPABkpOTodPp0L17d+zYscPTQ3IZNS681rpMK0n0HZyaYFYU0JuJPi9vT3QmIiLlfCqo+de//oXJkyfjpZdewv79+9GnTx8MHjwYp045v0TjjZRceC1VL7ZV1bdnchz0UfbPr4/SenVScG2XSsthK/bSwHqgR0REvs2ngpp//OMfePjhh/HII4+gffv2WLBgARITE7Fo0SJPD80l5OJu1q7R8gX6gzHKWwwEBmgwc3iK3THMHN7BJ2ZogOpdTxNX7IfRTiU8b050JiIix/lMTk1FRQX27duHqVOnmt1+yy234Mcff7R4n/LycpSX/5WTUlxc7NIxqs1ecTcApsTdW1OVbxselJqAxWO7YerqQyi8Yl4LJyYsGK+P7OjVScE12dr1JAvQAAtHe28vKSIico7PBDUXL16EwWBA48aNzW5v3Lgx8vLyLN5n3rx5mDVrljuG5zKifaQc3TYs11jZ/Xs+Mo7nA5CQ1jIevXwkh0Ym0jHbKAGx4SFuGhEREbmbzwQ1Mo3G/EIrSVKd22TTpk3DM888Y/q+uLgYiYmJLh2fK7i62WNggAa9W8ejd+t4Vc7nCdz1REREPhPUxMfHIzAwsM6szPnz5+vM3si0Wi20Wq07hudyLOBmG3c9ERGRzyQKh4SEoHv37ti4caPZ7Rs3bsT111/voVGRtxBNquauJyIi/+UzQQ0APPPMM/j444/x6aef4siRI5gyZQpOnTqFxx9/3NNDIw+Tk6oBy9vbAe56IiLydz6z/AQA99xzD/Lz8/Hqq68iNzcXqamp+Pbbb5GUlOTpoZEbGIySzbwi0aRqch17/0ZERK6kkSTJTlUP/1FcXIzo6GgUFRUhKirK08MhBdKzcusEKwlWghVeWD1Dyb8REZESotdvBjXk9dKzcjFheWadGjRymGKryCC5B/+NiMiVRK/fPpVTQ/WPraJ68m2z1mfDYK+MMLkM/42IyFswqCGvZq+ongQgt6gMe3IK3DcoMsN/IyLyFgxqyKuxqJ73478REXkLBjXk1VhUz/vx34iIvAWDGvJqLKrn/fhvRETegkENeTUW1fN+/DciIm/BoIa8nlxUTx9tvnyhj9Zxq7CX4L8REXkD1qkhn8Giet6P/0ZE5Aqi12+fapNA9Rs7lXs//hsRkSdx+YmIiIj8AmdqyG9xKYSIqH5hUEN+ic0ViYjqHy4/kd+RmyvWLt2fV1SGCcszkZ6V66GRERGRKzGoIb/C5opERPUXgxryK2yuSERUfzGoIb/C5opERPUXgxryK2yuSERUfzGoIb/C5opERPUXgxryK2yuSERUfzGoIb/D5opERPUTi++RXxqUmoCBKXpWFCYiqkcY1JDfYnNFIqL6hctPRERE5BcY1BAREZFfYFBDREREfoFBDREREfkFBjVERETkFxjUEBERkV9gUENERER+gUENERER+QUGNUREROQXGNQQERGRX2BQQ0RERH6BQQ0RERH5BZ8JaubOnYvrr78eYWFhiImJ8fRwiIiIyMv4TFBTUVGBu+66CxMmTPD0UIiIiMgLBXl6AKJmzZoFAFi2bJlnB0JEREReyWeCGkeUl5ejvLzc9H1xcbEHR0NERESu5DPLT46YN28eoqOjTV+JiYmeHhIRERG5iEeDmpkzZ0Kj0dj8+vnnnx0+/7Rp01BUVGT6On36tIqjJyIiIm/i0eWnSZMmYdSoUTaPadGihcPn12q10Gq1Dt+fiIiIfIdHg5r4+HjEx8d7cghERETkJ3wmUfjUqVMoKCjAqVOnYDAYcODAAQBA69atERER4dnBERERkcf5TFDzyiuv4J///Kfp+65duwIAtmzZgptvvtlDoyIiIiJvoZEkSfL0INyluLgY0dHRKCoqQlRUlKeHQ0RERAJEr99+vaWbiIiI6g8GNUREROQXGNQQERGRX2BQQ0RERH6BQQ0RERH5BQY1RERE5BcY1BAREZFfYFBDREREfoFBDREREfkFBjVERETkFxjUEBERkV9gUENERER+gUENERER+QUGNUREROQXGNQQERGRX2BQQ0RERH6BQQ0RERH5hSBPD4DI0wxGCXtyCnC+pAyNInXomRyHwACNp4dFREQKMaihei09Kxez1mcjt6jMdFtCtA4zhqVgUGqCB0dGRERKcfmJ6q30rFxMWJ5pFtAAQF5RGSYsz0R6Vq6HRkZERI5gUEP1ksEoYdb6bEgWfibfNmt9NgxGS0cQEZE3YlBD9dKenII6MzQ1SQByi8qwJ6fAfYMiIiKnMKiheul8ifWAxpHjiIjI8xjUUL3UKFKn6nFEROR5DGqoXuqZHIeEaB2sbdzWoHoXVM/kOHcOi4iInMCghuqlwAANZgxLAYA6gY38/YxhKaxXQ0TkQxjUUL01KDUBi8Z2gz7afIlJH63DorHdWKeGiMjHsPge1WuDUhMwMEXPisJERH6AQQ3Ve4EBGqS1auDpYRARkZO4/ERERER+gUENERER+QUGNUREROQXGNQQERGRX2BQQ0RERH6BQQ0RERH5BQY1RERE5BcY1BAREZFfYFBDREREfqFeVRSWJAkAUFxc7OGREBERkSj5ui1fx62pV0FNSUkJACAxMdHDIyEiIiKlSkpKEB0dbfXnGsle2ONHjEYjzp49i8jISGg0dRsWFhcXIzExEadPn0ZUVJQHRlj/8DV3P77m7sfX3P34mrufK19zSZJQUlKCJk2aICDAeuZMvZqpCQgIQLNmzeweFxUVxT8CN+Nr7n58zd2Pr7n78TV3P1e95rZmaGRMFCYiIiK/wKCGiIiI/AKDmhq0Wi1mzJgBrVbr6aHUG3zN3Y+vufvxNXc/vubu5w2veb1KFCYiIiL/xZkaIiIi8gsMaoiIiMgvMKghIiIiv8CghoiIiPwCg5oaPvjgAyQnJ0On06F79+7YsWOHp4fkt7Zv345hw4ahSZMm0Gg0WLt2raeH5PfmzZuHHj16IDIyEo0aNcLtt9+OX3/91dPD8muLFi1Cp06dTMXI0tLS8N1333l6WPXKvHnzoNFoMHnyZE8PxW/NnDkTGo3G7Euv13tkLAxq/vSvf/0LkydPxksvvYT9+/ejT58+GDx4ME6dOuXpofml0tJSdO7cGQsXLvT0UOqNbdu2YeLEidi9ezc2btyIqqoq3HLLLSgtLfX00PxWs2bN8Prrr+Pnn3/Gzz//jH79+mHEiBE4fPiwp4dWL+zduxdLlixBp06dPD0Uv9ehQwfk5uaavg4dOuSRcXBL95+uu+46dOvWDYsWLTLd1r59e9x+++2YN2+eB0fm/zQaDdasWYPbb7/d00OpVy5cuIBGjRph27ZtuPHGGz09nHojLi4Ob731Fh5++GFPD8WvXb58Gd26dcMHH3yAOXPmoEuXLliwYIGnh+WXZs6cibVr1+LAgQOeHgpnagCgoqIC+/btwy233GJ2+y233IIff/zRQ6Micq2ioiIA1RdZcj2DwYBVq1ahtLQUaWlpnh6O35s4cSKGDh2KAQMGeHoo9cJvv/2GJk2aIDk5GaNGjcLvv//ukXHUq4aW1ly8eBEGgwGNGzc2u71x48bIy8vz0KiIXEeSJDzzzDO44YYbkJqa6unh+LVDhw4hLS0NZWVliIiIwJo1a5CSkuLpYfm1VatWITMzE3v37vX0UOqF6667Dp999hnatm2Lc+fOYc6cObj++utx+PBhNGjQwK1jYVBTg0ajMftekqQ6txH5g0mTJuHgwYPYuXOnp4fi96655hocOHAAhYWF+OqrrzBu3Dhs27aNgY2LnD59Gk8//TS+//576HQ6Tw+nXhg8eLDp/zt27Ii0tDS0atUK//znP/HMM8+4dSwMagDEx8cjMDCwzqzM+fPn68zeEPm6J598EuvWrcP27dvRrFkzTw/H74WEhKB169YAgGuvvRZ79+7FO++8gw8//NDDI/NP+/btw/nz59G9e3fTbQaDAdu3b8fChQtRXl6OwMBAD47Q/4WHh6Njx4747bff3P7YzKlB9ZtO9+7dsXHjRrPbN27ciOuvv95DoyJSlyRJmDRpElavXo3NmzcjOTnZ00OqlyRJQnl5uaeH4bf69++PQ4cO4cCBA6ava6+9Fvfeey8OHDjAgMYNysvLceTIESQkJLj9sTlT86dnnnkG9913H6699lqkpaVhyZIlOHXqFB5//HFPD80vXb58GceOHTN9n5OTgwMHDiAuLg7Nmzf34Mj818SJE7FixQp8/fXXiIyMNM1MRkdHIzQ01MOj808vvvgiBg8ejMTERJSUlGDVqlXYunUr0tPTPT00vxUZGVknTyw8PBwNGjRg/piLPPfccxg2bBiaN2+O8+fPY86cOSguLsa4cePcPhYGNX+65557kJ+fj1dffRW5ublITU3Ft99+i6SkJE8PzS/9/PPP6Nu3r+l7ed113LhxWLZsmYdG5d/kcgU333yz2e1Lly7FAw884P4B1QPnzp3Dfffdh9zcXERHR6NTp05IT0/HwIEDPT00ItX88ccfGD16NC5evIiGDRuiV69e2L17t0eun6xTQ0RERH6BOTVERETkFxjUEBERkV9gUENERER+gUENERER+QUGNUREROQXGNQQERGRX2BQQ0RERH6BQQ0R+Zybb74ZkydP9vQwiMjLMKghIo964IEHoNFooNFoEBwcjJYtW+K5555DaWmp1fusXr0as2fPduMoicgXsE0CEXncoEGDsHTpUlRWVmLHjh145JFHUFpaamrtIKusrERwcDDi4uI8NFIi8macqSEij9NqtdDr9UhMTMSYMWNw7733Yu3atZg5cya6dOmCTz/9FC1btoRWq4UkSXWWn8rLy/H8888jMTERWq0Wbdq0wSeffGL6eXZ2NoYMGYKIiAg0btwY9913Hy5evGj6+X/+8x907NgRoaGhaNCgAQYMGGBzpoiIvBODGiLyOqGhoaisrAQAHDt2DF9++SW++uorHDhwwOLx999/P1atWoV3330XR44cweLFixEREQEAyM3NxU033YQuXbrg559/Rnp6Os6dO4e7777b9PPRo0fjoYcewpEjR7B161aMHDkSbItH5Hu4/EREXmXPnj1YsWIF+vfvDwCoqKjA559/joYNG1o8/ujRo/jyyy+xceNGDBgwAADQsmVL088XLVqEbt264bXXXjPd9umnnyIxMRFHjx7F5cuXUVVVhZEjR5q6Cnfs2NFVT4+IXIgzNUTkcd988w0iIiKg0+mQlpaGG2+8Ee+99x4AICkpyWpAAwAHDhxAYGAgbrrpJos/37dvH7Zs2YKIiAjTV7t27QAAx48fR+fOndG/f3907NgRd911Fz766CNcunRJ/SdJRC7HmRoi8ri+ffti0aJFCA4ORpMmTRAcHGz6WXh4uM37hoaG2vy50WjEsGHD8MYbb9T5WUJCAgIDA7Fx40b8+OOP+P777/Hee+/hpZdewk8//YTk5GTHnhAReQRnaojI48LDw9G6dWskJSWZBTQiOnbsCKPRiG3btln8ebdu3XD48GG0aNECrVu3NvuSAyaNRoPevXtj1qxZ2L9/P0JCQrBmzRqnnxcRuReDGiLyaS1atMC4cePw0EMPYe3atcjJycHWrVvx5ZdfAgAmTpyIgoICjB49Gnv27MHvv/+O77//Hg899BAMBgN++uknvPbaa/j5559x6tQprF69GhcuXED79u09/MyISCkGNUTk8xYtWoQ777wTTzzxBNq1a4fx48ebtmQ3adIEu3btgsFgwK233orU1FQ8/fTTiI6ORkBAAKKiorB9+3YMGTIEbdu2xcsvv4z/+7//w+DBgz38rIhIKY3EfYtERETkBzhTQ0RERH6BQQ0RERH5BQY1RERE5BcY1BAREZFfYFBDREREfoFBDREREfkFBjVERETkFxjUEBERkV9gUENERER+gUENERER+QUGNUREROQXGNQQERGRX/h/1TTLQe2jWzoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the differences between actual prices and predicted values\n",
    "plt.scatter(y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "_uuid": "61aa86222c5a5f704cb9de6d3f4a76302ea2bb2e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking residuals\n",
    "plt.scatter(y_pred,y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "87809e684366967bdd103085e5f041692911fae8"
   },
   "source": [
    "#### For test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "_uuid": "0c4a9a7cd7ec0910b662763d1a93ada5ec2aab61"
   },
   "outputs": [],
   "source": [
    "# Predicting Test data with the model\n",
    "y_test_pred_SV = svm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "_uuid": "9184c04875d7b80ee068c5fa158fbc96ba8491e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.7263792917547527\n",
      "Adjusted R^2: 0.7260252620497613\n",
      "MAE: 0.398389131637503\n",
      "MSE: 0.357548170164801\n",
      "RMSE: 0.5979533177136832\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "acc_svm = metrics.r2_score(y_test, y_test_pred_SV)\n",
    "print('R^2:', acc_svm)\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred_SV))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_test, y_test_pred_SV))\n",
    "print('MSE:',metrics.mean_squared_error(y_test, y_test_pred_SV))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_test_pred_SV)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "011417c6027493065c9ab2e7244ea968d32cc93b"
   },
   "source": [
    "# Evaluation and comparision of all the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "_uuid": "57ebbecee4e34cd60bd0f03cc2f291ff52b263cc"
   },
   "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>Model</th>\n",
       "      <th>R-squared Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>83.310808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>80.076051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>72.637929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>60.102913</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  R-squared Score\n",
       "2                  XGBoost        83.310808\n",
       "1            Random Forest        80.076051\n",
       "3  Support Vector Machines        72.637929\n",
       "0        Linear Regression        60.102913"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Linear Regression', 'Random Forest', 'XGBoost', 'Support Vector Machines'],\n",
    "    'R-squared Score': [acc_linreg*100, acc_rf*100, acc_xgb*100, acc_svm*100]})\n",
    "models.sort_values(by='R-squared Score', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "73de738a3bf2d3f9eca0e2906034403dd8c89450"
   },
   "source": [
    "## Hence XGBoost Regression works the best for this dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregating the Results of the Four Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  PRICE  \n",
       "0    -122.23  4.526  \n",
       "1    -122.22  3.585  \n",
       "2    -122.24  3.521  \n",
       "3    -122.25  3.413  \n",
       "4    -122.25  3.422  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_data = {\n",
    "    'Linear Regression': [],\n",
    "    'Random Forest': [],\n",
    "    'XGBoost': [],\n",
    "    'SVM': [],\n",
    "    'y_true': []\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop('PRICE',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sahaj\\anaconda3\\envs\\New\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n",
      "C:\\Users\\sahaj\\anaconda3\\envs\\New\\lib\\site-packages\\sklearn\\base.py:451: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
      "  \"X does not have valid feature names, but\"\n"
     ]
    }
   ],
   "source": [
    "lin_pred = lm.predict(np.array(x))\n",
    "sv_pred = svm.predict(np.array(x))\n",
    "xg_pred = xg.predict(np.array(x))\n",
    "rf_pred = rf.predict(np.array(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.12923097, 3.9785459 , 3.65936151, ..., 0.17683048, 0.32509552,\n",
       "       0.5211656 ])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_data['Linear Regression'] = lin_pred\n",
    "n_data['Random Forest'] = rf_pred\n",
    "n_data['SVM'] = sv_pred\n",
    "n_data['XGBoost'] = xg_pred\n",
    "n_data['y_true'] = np.array(data['PRICE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Linear Regression': array([4.12923097, 3.9785459 , 3.65936151, ..., 0.17683048, 0.32509552,\n",
       "        0.5211656 ]),\n",
       " 'Random Forest': array([4.1991226, 3.9087007, 3.8947013, ..., 0.90783  , 1.01957  ,\n",
       "        0.92737  ]),\n",
       " 'XGBoost': array([4.1796966 , 3.796542  , 4.096528  , ..., 0.92656034, 1.1689541 ,\n",
       "        0.94739187], dtype=float32),\n",
       " 'SVM': array([2.34566992, 2.34566992, 2.34566992, ..., 2.34566992, 2.34566992,\n",
       "        2.34566992]),\n",
       " 'y_true': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894])}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Linear Regression  Random Forest   XGBoost      SVM  y_true\n",
      "0           4.129231       4.199123  4.179697  2.34567   4.526\n",
      "1           3.978546       3.908701  3.796542  2.34567   3.585\n",
      "2           3.659362       3.894701  4.096528  2.34567   3.521\n",
      "3           3.236729       3.579700  3.397922  2.34567   3.413\n",
      "4           2.403091       2.962980  3.161297  2.34567   3.422\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame(n_data)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(tf.keras.Input(shape=(4)))\n",
    "model.add(Dense(4, activation=\"relu\"))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer=\"adam\", loss=\"mse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 25\n",
      "Trainable params: 25\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X_train, X_test, y_train, y_test = train_test_split(n_data.drop('PRICE',axis=1), n_data['PRICE'], test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.fit(X_train, y_train, batch_size= 50, epochs=100,validation_data=(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Linear Regression</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>XGBoost</th>\n",
       "      <th>SVM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.129231</td>\n",
       "      <td>4.199123</td>\n",
       "      <td>4.179697</td>\n",
       "      <td>2.34567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.978546</td>\n",
       "      <td>3.908701</td>\n",
       "      <td>3.796542</td>\n",
       "      <td>2.34567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.659362</td>\n",
       "      <td>3.894701</td>\n",
       "      <td>4.096528</td>\n",
       "      <td>2.34567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.236729</td>\n",
       "      <td>3.579700</td>\n",
       "      <td>3.397922</td>\n",
       "      <td>2.34567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.403091</td>\n",
       "      <td>2.962980</td>\n",
       "      <td>3.161297</td>\n",
       "      <td>2.34567</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Linear Regression  Random Forest   XGBoost      SVM\n",
       "0           4.129231       4.199123  4.179697  2.34567\n",
       "1           3.978546       3.908701  3.796542  2.34567\n",
       "2           3.659362       3.894701  4.096528  2.34567\n",
       "3           3.236729       3.579700  3.397922  2.34567\n",
       "4           2.403091       2.962980  3.161297  2.34567"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.iloc[:, [0, 1, 2, 3]]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df1, df['y_true'], test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 14448 samples, validate on 6192 samples\n",
      "Epoch 1/100\n",
      "14448/14448 [==============================] - 2s 158us/sample - loss: 0.7024 - val_loss: 0.1796\n",
      "Epoch 2/100\n",
      "14448/14448 [==============================] - 1s 74us/sample - loss: 0.1244 - val_loss: 0.1093\n",
      "Epoch 3/100\n",
      "14448/14448 [==============================] - 1s 79us/sample - loss: 0.1023 - val_loss: 0.1029\n",
      "Epoch 4/100\n",
      "14448/14448 [==============================] - 1s 81us/sample - loss: 0.0991 - val_loss: 0.1008\n",
      "Epoch 5/100\n",
      "14448/14448 [==============================] - 1s 81us/sample - loss: 0.0975 - val_loss: 0.0994\n",
      "Epoch 6/100\n",
      "14448/14448 [==============================] - 1s 79us/sample - loss: 0.0964 - val_loss: 0.0985\n",
      "Epoch 7/100\n",
      "14448/14448 [==============================] - 1s 75us/sample - loss: 0.0957 - val_loss: 0.0978\n",
      "Epoch 8/100\n",
      "14448/14448 [==============================] - 1s 80us/sample - loss: 0.0952 - val_loss: 0.0972\n",
      "Epoch 9/100\n",
      "14448/14448 [==============================] - 1s 83us/sample - loss: 0.0944 - val_loss: 0.0968\n",
      "Epoch 10/100\n",
      "14448/14448 [==============================] - 1s 77us/sample - loss: 0.0941 - val_loss: 0.0964\n",
      "Epoch 11/100\n",
      "14448/14448 [==============================] - 1s 75us/sample - loss: 0.0937 - val_loss: 0.0961\n",
      "Epoch 12/100\n",
      "14448/14448 [==============================] - 1s 86us/sample - loss: 0.0932 - val_loss: 0.0959\n",
      "Epoch 13/100\n",
      "14448/14448 [==============================] - 1s 76us/sample - loss: 0.0932 - val_loss: 0.0954\n",
      "Epoch 14/100\n",
      "14448/14448 [==============================] - 1s 82us/sample - loss: 0.0925 - val_loss: 0.0955\n",
      "Epoch 15/100\n",
      "14448/14448 [==============================] - 1s 71us/sample - loss: 0.0922 - val_loss: 0.0967\n",
      "Epoch 16/100\n",
      "14448/14448 [==============================] - 1s 77us/sample - loss: 0.0922 - val_loss: 0.0954\n",
      "Epoch 17/100\n",
      "14448/14448 [==============================] - 1s 80us/sample - loss: 0.0918 - val_loss: 0.0948\n",
      "Epoch 18/100\n",
      "14448/14448 [==============================] - 1s 77us/sample - loss: 0.0917 - val_loss: 0.0952\n",
      "Epoch 19/100\n",
      "14448/14448 [==============================] - 1s 81us/sample - loss: 0.0916 - val_loss: 0.0946\n",
      "Epoch 20/100\n",
      "14448/14448 [==============================] - 1s 72us/sample - loss: 0.0915 - val_loss: 0.0942\n",
      "Epoch 21/100\n",
      "14448/14448 [==============================] - 1s 80us/sample - loss: 0.0914 - val_loss: 0.0942\n",
      "Epoch 22/100\n",
      "14448/14448 [==============================] - 1s 78us/sample - loss: 0.0912 - val_loss: 0.0953\n",
      "Epoch 23/100\n",
      "14448/14448 [==============================] - 1s 85us/sample - loss: 0.0911 - val_loss: 0.0951\n",
      "Epoch 24/100\n",
      "14448/14448 [==============================] - 1s 92us/sample - loss: 0.0913 - val_loss: 0.0942\n",
      "Epoch 25/100\n",
      "14448/14448 [==============================] - 1s 82us/sample - loss: 0.0912 - val_loss: 0.0941\n",
      "Epoch 26/100\n",
      "14448/14448 [==============================] - 1s 76us/sample - loss: 0.0914 - val_loss: 0.0940\n",
      "Epoch 27/100\n",
      "14448/14448 [==============================] - 1s 76us/sample - loss: 0.0910 - val_loss: 0.0953\n",
      "Epoch 28/100\n",
      "14448/14448 [==============================] - 1s 69us/sample - loss: 0.0911 - val_loss: 0.0941\n",
      "Epoch 29/100\n",
      "14448/14448 [==============================] - 1s 72us/sample - loss: 0.0909 - val_loss: 0.0943\n",
      "Epoch 30/100\n",
      "14448/14448 [==============================] - 1s 75us/sample - loss: 0.0911 - val_loss: 0.0947\n",
      "Epoch 31/100\n",
      "14448/14448 [==============================] - 1s 72us/sample - loss: 0.0909 - val_loss: 0.0943\n",
      "Epoch 32/100\n",
      "14448/14448 [==============================] - 1s 73us/sample - loss: 0.0909 - val_loss: 0.0944\n",
      "Epoch 33/100\n",
      "14448/14448 [==============================] - 1s 81us/sample - loss: 0.0909 - val_loss: 0.0939\n",
      "Epoch 34/100\n",
      "14448/14448 [==============================] - 1s 72us/sample - loss: 0.0910 - val_loss: 0.0953\n",
      "Epoch 35/100\n",
      "14448/14448 [==============================] - 1s 66us/sample - loss: 0.0911 - val_loss: 0.0941\n",
      "Epoch 36/100\n",
      "14448/14448 [==============================] - 1s 74us/sample - loss: 0.0908 - val_loss: 0.0940\n",
      "Epoch 37/100\n",
      "14448/14448 [==============================] - 1s 75us/sample - loss: 0.0908 - val_loss: 0.0954\n",
      "Epoch 38/100\n",
      "14448/14448 [==============================] - 1s 74us/sample - loss: 0.0911 - val_loss: 0.0939\n",
      "Epoch 39/100\n",
      "14448/14448 [==============================] - 1s 74us/sample - loss: 0.0908 - val_loss: 0.0941\n",
      "Epoch 40/100\n",
      "14448/14448 [==============================] - 1s 76us/sample - loss: 0.0911 - val_loss: 0.0947\n",
      "Epoch 41/100\n",
      "14448/14448 [==============================] - 1s 75us/sample - loss: 0.0912 - val_loss: 0.0942\n",
      "Epoch 42/100\n",
      "14448/14448 [==============================] - 1s 61us/sample - loss: 0.0909 - val_loss: 0.0940\n",
      "Epoch 43/100\n",
      "14448/14448 [==============================] - 1s 73us/sample - loss: 0.0910 - val_loss: 0.0946\n",
      "Epoch 44/100\n",
      "14448/14448 [==============================] - 1s 91us/sample - loss: 0.0907 - val_loss: 0.0947\n",
      "Epoch 45/100\n",
      "14448/14448 [==============================] - 1s 73us/sample - loss: 0.0909 - val_loss: 0.0940\n",
      "Epoch 46/100\n",
      "14448/14448 [==============================] - 1s 66us/sample - loss: 0.0910 - val_loss: 0.0942\n",
      "Epoch 47/100\n",
      "14448/14448 [==============================] - 1s 65us/sample - loss: 0.0908 - val_loss: 0.0945\n",
      "Epoch 48/100\n",
      "14448/14448 [==============================] - 1s 73us/sample - loss: 0.0910 - val_loss: 0.0945\n",
      "Epoch 49/100\n",
      "14448/14448 [==============================] - 1s 80us/sample - loss: 0.0912 - val_loss: 0.0940\n",
      "Epoch 50/100\n",
      "14448/14448 [==============================] - 1s 80us/sample - loss: 0.0910 - val_loss: 0.0942\n",
      "Epoch 51/100\n",
      "14448/14448 [==============================] - 1s 63us/sample - loss: 0.0908 - val_loss: 0.0957\n",
      "Epoch 52/100\n",
      "14448/14448 [==============================] - 1s 78us/sample - loss: 0.0909 - val_loss: 0.0961\n",
      "Epoch 53/100\n",
      "14448/14448 [==============================] - 1s 78us/sample - loss: 0.0910 - val_loss: 0.0946\n",
      "Epoch 54/100\n",
      "14448/14448 [==============================] - 1s 80us/sample - loss: 0.0909 - val_loss: 0.0942\n",
      "Epoch 55/100\n",
      "14448/14448 [==============================] - 1s 72us/sample - loss: 0.0909 - val_loss: 0.0946\n",
      "Epoch 56/100\n",
      "14448/14448 [==============================] - 1s 71us/sample - loss: 0.0912 - val_loss: 0.0942\n",
      "Epoch 57/100\n",
      "14448/14448 [==============================] - 1s 66us/sample - loss: 0.0911 - val_loss: 0.0942\n",
      "Epoch 58/100\n",
      "14448/14448 [==============================] - 1s 45us/sample - loss: 0.0910 - val_loss: 0.0943\n",
      "Epoch 59/100\n",
      "14448/14448 [==============================] - 1s 57us/sample - loss: 0.0907 - val_loss: 0.0964\n",
      "Epoch 60/100\n",
      "14448/14448 [==============================] - 1s 67us/sample - loss: 0.0909 - val_loss: 0.0943\n",
      "Epoch 61/100\n",
      "14448/14448 [==============================] - 1s 63us/sample - loss: 0.0911 - val_loss: 0.0941\n",
      "Epoch 62/100\n",
      "14448/14448 [==============================] - 1s 73us/sample - loss: 0.0908 - val_loss: 0.0940\n",
      "Epoch 63/100\n",
      "14448/14448 [==============================] - 1s 75us/sample - loss: 0.0908 - val_loss: 0.0959\n",
      "Epoch 64/100\n",
      "14448/14448 [==============================] - 1s 79us/sample - loss: 0.0907 - val_loss: 0.0940\n",
      "Epoch 65/100\n",
      "14448/14448 [==============================] - 1s 69us/sample - loss: 0.0909 - val_loss: 0.0941\n",
      "Epoch 66/100\n",
      "14448/14448 [==============================] - 1s 66us/sample - loss: 0.0910 - val_loss: 0.0940\n",
      "Epoch 67/100\n",
      "14448/14448 [==============================] - 1s 61us/sample - loss: 0.0909 - val_loss: 0.0944\n",
      "Epoch 68/100\n",
      "14448/14448 [==============================] - 1s 56us/sample - loss: 0.0909 - val_loss: 0.0942\n",
      "Epoch 69/100\n",
      "14448/14448 [==============================] - 1s 64us/sample - loss: 0.0909 - val_loss: 0.0940\n",
      "Epoch 70/100\n",
      "14448/14448 [==============================] - 1s 62us/sample - loss: 0.0911 - val_loss: 0.0940\n",
      "Epoch 71/100\n",
      "14448/14448 [==============================] - 1s 71us/sample - loss: 0.0912 - val_loss: 0.0947\n",
      "Epoch 72/100\n",
      "14448/14448 [==============================] - 1s 78us/sample - loss: 0.0908 - val_loss: 0.0943\n",
      "Epoch 73/100\n",
      "14448/14448 [==============================] - 1s 64us/sample - loss: 0.0910 - val_loss: 0.0949\n",
      "Epoch 74/100\n",
      "14448/14448 [==============================] - 1s 65us/sample - loss: 0.0909 - val_loss: 0.0943\n",
      "Epoch 75/100\n",
      "14448/14448 [==============================] - 1s 71us/sample - loss: 0.0907 - val_loss: 0.0939\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 76/100\n",
      "14448/14448 [==============================] - 1s 71us/sample - loss: 0.0910 - val_loss: 0.0940\n",
      "Epoch 77/100\n",
      "14448/14448 [==============================] - 1s 68us/sample - loss: 0.0908 - val_loss: 0.0941\n",
      "Epoch 78/100\n",
      "14448/14448 [==============================] - 1s 69us/sample - loss: 0.0907 - val_loss: 0.0941\n",
      "Epoch 79/100\n",
      "14448/14448 [==============================] - 1s 76us/sample - loss: 0.0909 - val_loss: 0.0942\n",
      "Epoch 80/100\n",
      "14448/14448 [==============================] - 1s 73us/sample - loss: 0.0906 - val_loss: 0.0946\n",
      "Epoch 81/100\n",
      "14448/14448 [==============================] - 1s 61us/sample - loss: 0.0909 - val_loss: 0.0948\n",
      "Epoch 82/100\n",
      "14448/14448 [==============================] - 1s 64us/sample - loss: 0.0913 - val_loss: 0.0958\n",
      "Epoch 83/100\n",
      "14448/14448 [==============================] - 1s 73us/sample - loss: 0.0910 - val_loss: 0.0940\n",
      "Epoch 84/100\n",
      "14448/14448 [==============================] - 1s 75us/sample - loss: 0.0909 - val_loss: 0.0939\n",
      "Epoch 85/100\n",
      "14448/14448 [==============================] - 1s 74us/sample - loss: 0.0910 - val_loss: 0.0943\n",
      "Epoch 86/100\n",
      "14448/14448 [==============================] - 1s 71us/sample - loss: 0.0908 - val_loss: 0.0942\n",
      "Epoch 87/100\n",
      "14448/14448 [==============================] - 1s 81us/sample - loss: 0.0909 - val_loss: 0.0940\n",
      "Epoch 88/100\n",
      "14448/14448 [==============================] - 1s 70us/sample - loss: 0.0908 - val_loss: 0.0940\n",
      "Epoch 89/100\n",
      "14448/14448 [==============================] - 1s 75us/sample - loss: 0.0908 - val_loss: 0.0943\n",
      "Epoch 90/100\n",
      "14448/14448 [==============================] - 1s 73us/sample - loss: 0.0907 - val_loss: 0.0940\n",
      "Epoch 91/100\n",
      "14448/14448 [==============================] - 1s 74us/sample - loss: 0.0907 - val_loss: 0.0941\n",
      "Epoch 92/100\n",
      "14448/14448 [==============================] - 1s 79us/sample - loss: 0.0909 - val_loss: 0.0940\n",
      "Epoch 93/100\n",
      "14448/14448 [==============================] - 1s 71us/sample - loss: 0.0907 - val_loss: 0.0940\n",
      "Epoch 94/100\n",
      "14448/14448 [==============================] - 1s 82us/sample - loss: 0.0908 - val_loss: 0.0939\n",
      "Epoch 95/100\n",
      "14448/14448 [==============================] - 1s 75us/sample - loss: 0.0911 - val_loss: 0.0942\n",
      "Epoch 96/100\n",
      "14448/14448 [==============================] - 1s 76us/sample - loss: 0.0911 - val_loss: 0.0956\n",
      "Epoch 97/100\n",
      "14448/14448 [==============================] - 1s 74us/sample - loss: 0.0908 - val_loss: 0.0961\n",
      "Epoch 98/100\n",
      "14448/14448 [==============================] - 1s 78us/sample - loss: 0.0907 - val_loss: 0.0940\n",
      "Epoch 99/100\n",
      "14448/14448 [==============================] - 1s 70us/sample - loss: 0.0909 - val_loss: 0.0940\n",
      "Epoch 100/100\n",
      "14448/14448 [==============================] - 1s 69us/sample - loss: 0.0909 - val_loss: 0.0942\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, y_train, batch_size= 50, epochs=100,validation_data=(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'val_loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Viewing results\n",
    "print(history.history.keys())  # Display available metrics\n",
    "\n",
    "# Plotting training and validation loss over epochs\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9295921288459672\n",
      "Adjusted R^2: 0.9295466089680593\n",
      "MAE: 0.17969797324938677\n",
      "MSE: 0.09415201084308802\n",
      "RMSE: 0.30684199654396727\n"
     ]
    }
   ],
   "source": [
    "y_pred_agg = model.predict(X_test);\n",
    "\n",
    "acc_agg = metrics.r2_score(y_test, y_pred_agg)\n",
    "print('R^2:', acc_agg)\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_pred_agg))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_test, y_pred_agg))\n",
    "print('MSE:',metrics.mean_squared_error(y_test, y_pred_agg))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_pred_agg)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregating the results of the four models produced a better R^2 value."
   ]
  }
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