Ibrahim_Ahmed House Pricing Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5d7fc589",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "The proposed project, titled \"Lagos state house pricing model\", seeks to address a pressing issue within the Lagos real estate market. This project is pivotal as it aims to provide an AI-driven solution for precisely predicting building prices. By doing so, it empowers buyers, sellers, and real estate professionals with data-driven insights to make informed decisions.\n",
    "\n",
    "The primary challenge at hand is the unpredictable nature of building prices in Lagos, Nigeria. The real estate market is subject to various factors, making it arduous for stakeholders to accurately estimate property values. As Ayodele and Olaleye (2022) emphasized, this complexity makes precise predictions a formidable task.\n",
    "\n",
    "Source\n",
    "https://www.kaggle.com/datasets/abdullahiyunus/nigeria-houses-and-prices-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "707974d3",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "48154912",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.optimizers import Adam\n",
    "from keras.losses import MeanAbsoluteError\n",
    "from keras.callbacks import EarlyStopping\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "2b869239",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('nigeria_houses_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "551b4f39",
   "metadata": {},
   "source": [
    "# Data Cleaning and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "310a4c0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 24326 entries, 0 to 24325\n",
      "Data columns (total 8 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   bedrooms       24326 non-null  float64\n",
      " 1   bathrooms      24326 non-null  float64\n",
      " 2   toilets        24326 non-null  float64\n",
      " 3   parking_space  24326 non-null  float64\n",
      " 4   title          24326 non-null  object \n",
      " 5   town           24326 non-null  object \n",
      " 6   state          24326 non-null  object \n",
      " 7   price          24326 non-null  float64\n",
      "dtypes: float64(5), object(3)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "#Displaying summary statistics of the dataset\n",
    "data.describe()\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2fa01f7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bedrooms         0\n",
       "bathrooms        0\n",
       "toilets          0\n",
       "parking_space    0\n",
       "title            0\n",
       "town             0\n",
       "state            0\n",
       "price            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for missing values in the dataset using isnull() and sum()\n",
    "\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "aab425dd",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>toilets</th>\n",
       "      <th>parking_space</th>\n",
       "      <th>title</th>\n",
       "      <th>town</th>\n",
       "      <th>state</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Detached Duplex</td>\n",
       "      <td>Mabushi</td>\n",
       "      <td>Abuja</td>\n",
       "      <td>450000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Terraced Duplexes</td>\n",
       "      <td>Katampe</td>\n",
       "      <td>Abuja</td>\n",
       "      <td>800000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Detached Duplex</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>120000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Detached Duplex</td>\n",
       "      <td>Ajah</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>40000000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Semi Detached Duplex</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>75000000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  toilets  parking_space                 title     town  \\\n",
       "0       6.0        5.0      5.0            4.0       Detached Duplex  Mabushi   \n",
       "1       4.0        5.0      5.0            4.0     Terraced Duplexes  Katampe   \n",
       "2       4.0        5.0      5.0            4.0       Detached Duplex    Lekki   \n",
       "3       4.0        4.0      5.0            6.0       Detached Duplex     Ajah   \n",
       "4       4.0        4.0      5.0            2.0  Semi Detached Duplex    Lekki   \n",
       "\n",
       "   state        price  \n",
       "0  Abuja  450000000.0  \n",
       "1  Abuja  800000000.0  \n",
       "2  Lagos  120000000.0  \n",
       "3  Lagos   40000000.0  \n",
       "4  Lagos   75000000.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6ec4ba69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting specific columns to the 'int64' data type\n",
    "\n",
    "data[['bedrooms', 'bathrooms', 'toilets', 'parking_space', 'price']] = data[['bedrooms', 'bathrooms', 'toilets', \n",
    "                                                                             'parking_space', 'price']].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d457e9b3",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>toilets</th>\n",
       "      <th>parking_space</th>\n",
       "      <th>title</th>\n",
       "      <th>town</th>\n",
       "      <th>state</th>\n",
       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Detached Duplex</td>\n",
       "      <td>Mabushi</td>\n",
       "      <td>Abuja</td>\n",
       "      <td>450000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Terraced Duplexes</td>\n",
       "      <td>Katampe</td>\n",
       "      <td>Abuja</td>\n",
       "      <td>800000000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>Detached Duplex</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>120000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>Detached Duplex</td>\n",
       "      <td>Ajah</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>40000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Semi Detached Duplex</td>\n",
       "      <td>Lekki</td>\n",
       "      <td>Lagos</td>\n",
       "      <td>75000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bedrooms  bathrooms  toilets  parking_space                 title     town  \\\n",
       "0         6          5        5              4       Detached Duplex  Mabushi   \n",
       "1         4          5        5              4     Terraced Duplexes  Katampe   \n",
       "2         4          5        5              4       Detached Duplex    Lekki   \n",
       "3         4          4        5              6       Detached Duplex     Ajah   \n",
       "4         4          4        5              2  Semi Detached Duplex    Lekki   \n",
       "\n",
       "   state      price  \n",
       "0  Abuja  450000000  \n",
       "1  Abuja  800000000  \n",
       "2  Lagos  120000000  \n",
       "3  Lagos   40000000  \n",
       "4  Lagos   75000000  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "8231838b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Detached Duplex', 'Terraced Duplexes', 'Semi Detached Duplex',\n",
       "       'Detached Bungalow', 'Block of Flats', 'Semi Detached Bungalow',\n",
       "       'Terraced Bungalow'], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve the unique values in the 'title' column of the DataFrame\n",
    "\n",
    "data.title.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "5f55e141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "189"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve the unique values in the 'title' column of the DataFrame\n",
    "\n",
    "data.town.unique().size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3f0aaddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24326, 8)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f4f764c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extracting Lagos dataset\n",
    "df = data[(data['state'] =='Lagos')]\n",
    "\n",
    "#creating a list of the categorical features\n",
    "cat_cols = [col for col in df.columns if df[col].dtype == 'object']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c5c6b02d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['title', 'town', 'state']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3f4181d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#use onehotencoding to preprocess and encode the cat features\n",
    "title_dm= pd.get_dummies(df['title'])\n",
    "town_dm =pd.get_dummies(df['town'])\n",
    "features_dm = df.drop(['title', 'town', 'state'], axis=1)\n",
    "\n",
    "df_1 = pd.concat([features_dm, title_dm, town_dm], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "548f3018",
   "metadata": {},
   "outputs": [
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>65000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    bedrooms  bathrooms  toilets  parking_space      price  Block of Flats  \\\n",
       "ID                                                                           \n",
       "1          4          5        5              4  120000000               0   \n",
       "2          4          4        5              6   40000000               0   \n",
       "3          4          4        5              2   75000000               0   \n",
       "4          5          5        6              1  450000000               0   \n",
       "5          4          5        5              4   65000000               0   \n",
       "\n",
       "    Detached Bungalow  Detached Duplex  Semi Detached Bungalow  \\\n",
       "ID                                                               \n",
       "1                   0                1                       0   \n",
       "2                   0                1                       0   \n",
       "3                   0                0                       0   \n",
       "4                   0                1                       0   \n",
       "5                   0                1                       0   \n",
       "\n",
       "    Semi Detached Duplex  ...  Ojo  Ojodu  Ojota  Oke-Odo  Orile  Oshodi  \\\n",
       "ID                        ...                                              \n",
       "1                      0  ...    0      0      0        0      0       0   \n",
       "2                      0  ...    0      0      0        0      0       0   \n",
       "3                      1  ...    0      0      0        0      0       0   \n",
       "4                      0  ...    0      0      0        0      0       0   \n",
       "5                      0  ...    0      0      0        0      0       0   \n",
       "\n",
       "    Shomolu  Surulere  Victoria Island (VI)  Yaba  \n",
       "ID                                                 \n",
       "1         0         0                     0     0  \n",
       "2         0         0                     0     0  \n",
       "3         0         0                     0     0  \n",
       "4         0         0                     0     0  \n",
       "5         0         0                     0     0  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1['ID'] = range(1, len(df_1) + 1)\n",
    "\n",
    "# Set the 'ID' column as the index\n",
    "df_1.set_index('ID', inplace=True)\n",
    "\n",
    "df_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5ffc369a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18445, 60)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a7489bd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18445, 59)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Defining the features and target variables\n",
    "\n",
    "target = df_1.price\n",
    "features = df_1.drop('price', axis=1)\n",
    "features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f3290e07",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splittng the dataset into training and testing sets\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c1acb3d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the validation sets\n",
    "\n",
    "X_val = X_train.sample(frac=0.2, random_state=0)\n",
    "y_val = y_train.sample(frac=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "47fb09d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Create a StandardScaler for the input features (X)\n",
    "scaler_X = StandardScaler()\n",
    "\n",
    "# Fit and transform the training set\n",
    "X_train_scaled = scaler_X.fit_transform(X_train)\n",
    "\n",
    "# Transform the testing and validation sets using the same scaler\n",
    "X_val_scaled = scaler_X.transform(X_val)\n",
    "\n",
    "# Creating a StandardScaler for the target variable\n",
    "scaler_y = StandardScaler()\n",
    "# Fit and transform the training set target variable\n",
    "y_train_scaled = scaler_y.fit_transform(y_train.values.reshape(-1, 1)).ravel()\n",
    "\n",
    "# Transform the testing and validation sets target variable using the same scaler\n",
    "y_val_scaled = scaler_y.transform(y_val.values.reshape(-1, 1)).ravel()\n",
    "y_test_scaled = scaler_y.transform(y_test.values.reshape(-1, 1)).ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f090db13",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "99ca3a9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sorting the data by 'price' in descending order and select the top 10\n",
    "data_sorted = data.sort_values(by='price', ascending=False).head(10)\n",
    "\n",
    "# Extracting 'town' and 'price' columns\n",
    "town= data_sorted['town']\n",
    "price = data_sorted['price']\n",
    "\n",
    "# Creating a horizontal bar chart showing the most expensive houses in lagos and their locations\n",
    "plt.barh(town, price)\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Town')\n",
    "plt.title('Most Expensive Houses in Lagos')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f778c8ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Setting Seaborn style to whitegrid\n",
    "sns.set(style='whitegrid')\n",
    "\n",
    "# Creating a scatter plot using Seaborn\n",
    "plt.figure(figsize=(18, 8))\n",
    "sns.scatterplot(x= 'title', y ='price', hue='bedrooms', data =data, palette='inferno', s=100)\n",
    "plt.title('The relationship between type of house, Nr. of bedrooms and the price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f071a964",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_8324\\2290337363.py:4: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  sns.scatterplot(x= 'bedrooms', y ='price', data =data, palette='inferno', s=100)\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_8324\\2290337363.py:8: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  sns.scatterplot(x= 'bathrooms', y ='price', data =data, palette='inferno', s=100)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='whitegrid')\n",
    "plt.figure(figsize=(18, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.scatterplot(x= 'bedrooms', y ='price', data =data, palette='inferno', s=100)\n",
    "plt.title('The relationship between Nr. of bedrooms and the price')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.scatterplot(x= 'bathrooms', y ='price', data =data, palette='inferno', s=100)\n",
    "plt.title('The relationship between Nr. of bathrooms and the price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "25baf836",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_8324\\667205350.py:4: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  sns.scatterplot(x= 'toilets', y ='price', data =data, palette='inferno', s=100)\n",
      "C:\\Users\\USER\\AppData\\Local\\Temp\\ipykernel_8324\\667205350.py:8: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  sns.scatterplot(x= 'parking_space', y ='price', data =data, palette='inferno', s=100)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style='whitegrid')\n",
    "plt.figure(figsize=(18, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.scatterplot(x= 'toilets', y ='price', data =data, palette='inferno', s=100)\n",
    "plt.title('The relationship between Nr. of toilets and the price')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.scatterplot(x= 'parking_space', y ='price', data =data, palette='inferno', s=100)\n",
    "plt.title('The relationship between Nr. of parking spaces and the price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cea783f4",
   "metadata": {},
   "source": [
    "# Summary of the Visualization\n",
    "\n",
    "Looking at the plots, it appears that in Lagos state, the location of a house is the primary variable that influences its price. Number of bedrooms, bathrooms, toilets and parking spaces are only intervening variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c7cfe11",
   "metadata": {},
   "source": [
    "# Training Model 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "35271fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining the model\n",
    "model = Sequential([\n",
    "    Dense(units= 128, input_shape= (X_train_scaled.shape[1],), activation='relu'),\n",
    "    Dense(units=64, activation='relu'),\n",
    "    Dense(units=16, activation='relu'),\n",
    "    Dense(1, activation='linear')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "21f6536c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Compiling the model\n",
    "optimizer= Adam(learning_rate= 0.001)\n",
    "model.compile(optimizer=optimizer, loss=MeanAbsoluteError())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "7ebca23e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fitting/training the model\n",
    "model_history = model.fit(X_train_scaled, y_train_scaled, batch_size=56, epochs=500, \n",
    "                          verbose=0, validation_data=(X_val_scaled, y_val_scaled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "86febd3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8v32vvktERERERERE9nnBYBDTNElOTo73VGQvaWyMHgwG9/hcCqBEREREREREZLdpy92BqyW/tgqgRERERERERERkr4p7DygRERERERERkXi68cYb+d///rfDMb/sUb0rzj33XNq2bcsDDzywu1M7YCiAEhEREREREZGEdsstt3DttdfGHh922GHcfPPNjBs3bo/O+/jjj+9zzcXjRQGUiIiIiIiIiCS01NRUUlNTmx3Lzc3do/NmZGTs0esPJOoBJSIiIiIiIiKyA2+99RbHHHMM9957L0OHDuXyyy8H4PPPP+fUU09l0KBB9O/fn/Hjx/Ptt9/GXnfuuedy4403NjlH45/9+vVjwoQJ/PTTT/G4pFanCigRERERERERaVGmaeIPhOPy3k6Hda/cmW/dunWUlpby9ttv4/P5WLBgAX/4wx/44x//yFFHHUV9fT2PPPIIN9xwA9988w0Oh6PZOYqKinj99dd56KGHSE5O5o477uD222/nk08+afH57msUQImIiIiIiIhIizFNkz8+MY3Fayrj8v69O2Xx4JWH7ZUQ6vLLL6d9+/YALF68mNtuu42zzjor9vzEiRO5+OKLqaiooLCwsNnrg8Egd911F7179wZg0qRJ/OEPf6C8vJyCgoIWn+++RAGUiIiIiIiIiMgu6NSpU+zvvXv3Jj09nWeffZZVq1axdu1alixZAkA4vP3qr65du8b+3th3KhAI7J0J70MUQCWQek8Q0zTjPQ0RERERERE5gBmGwYNXHnbAbcEDcLlcsb/PmDGDCy+8kDFjxjB06FBOOukkvF4vV1xxxQ7Psa2teYlAAVSCWLiqgpuenMaovqn06RPv2YiIiIiIiMiBzDAMXM4DO3J4/vnnGTFiBI8//njs2EsvvQSg4o9tOLC/GyRmQ2kdpgkl1cF4T0VERERERERkv1dYWMjnn3/OrFmzKCgoYPr06Tz22GNAYmyp+7UUQCWIxvJDhbAiIiIiIiIie27y5MmUl5fz+9//HoBu3brxpz/9ieuvv5758+c36fUkCqAShmXz9lcFUCIiIiIiIiI7tnTp0iaPx48fz/jx45scy8zMbLL9rtHYsWNjf2/ckre9cwwfPpw5c+Y06S11oLLEewLSOlQBJSIiIiIiIiLxogAqQcQCKJRAiYiIiIiIiEjrUgCVILQFT0RERERERETiRQFUgtAWPBERERERERGJFwVQCcIS24InIiIiIiIiItK6FEAlCGPzV9pUCZSIiIiIiIiItDIFUAlCW/BEREREREREJF4UQCWIWBPy+E5DRERERERERBKQAqgEEauAisR5IiIiIiIiIiKScBRAJQiLpbEJuWqgRERERERERKR1KYBKEBb1gBIRERERERFp5txzz2X8+PHbff7WW2/l2GOP3eE5Hn/8cY488sjY4549e/LWW29td/yNN97Iueeeu8tzDAaDvPDCC9t9v/2BAqgEYTT2gFIAJSIiIiIiIhIzYcIEFi5cyMqVK5s95/f7+fjjj5kwYcKvOue0adMYN25cS02R999/n/vvvz/2+IILLuDNN99ssfO3BgVQCSLWAyrO8xARERERERHZlxx77LGkpqby3nvvNXvu888/x+v1csopp/yqc+bm5uJyuVpohmD+opokOTmZrKysFjt/a1AAlSAsqoASERERERGRVmKaJpGALy7//DKs2RmXy8UJJ5zA+++/3+y5//3vfxx++OFUVVVx6aWXMmzYMPr168dRRx3F888/v91zbr0FzzRNnnzySUaPHs2gQYO46aab8Pv9TcbPmjWLiRMnMmTIEPr168fxxx/PO++8A8Bbb73FTTfdFDvv9OnTm23BKyoq4rrrruPQQw9l0KBBXHjhhSxZsiT2/I033siNN97Igw8+yMiRIxk4cCCXXnopJSUlv+qz2hO2VnsniatYBZQSKBEREREREdmLTNNk04u34N+wNC7v72zXizYT7439Hrwrfve73/H6668zd+5cBg8eDEBZWRnff/89jz/+OBdccAGHHnoor7/+OlarlTfeeCMW5vTu3XuH53722Wf5xz/+wd13302fPn3497//zVtvvcXw4cMBKC0t5ZJLLuGcc87hnnvuIRgM8ve//51bbrmFQw89lHHjxlFXV8ef/vQnpk2bRnp6OjNmzIidv76+njPPPJP27dvz1FNP4XA4ePzxxznnnHN45513aNu2LRDdxnfSSSfx8ssvU1FRwTXXXMNf//rXJlv79iZVQCUIi7bgiYiIiIiISKvZ9fBnXzBgwAB69OjRZBveu+++S3Z2NgMHDmTixIncfvvtdO3alU6dOjF58mQAli7dcchmmiYvvfQSEydO5MQTT6RLly7cdNNNTUIrv9/PFVdcwXXXXUfHjh3p1q0bl1xyCcFgkDVr1uByuUhNTQWiW/scDkeT93j33XepqqriscceY8CAAfTq1YtHHnkEl8vFK6+8EhuXmprK3XffTdeuXRk+fDjjxo1jzpw5e/zZ7SpVQCUINSEXERERERGR1mAYBm0m3osZ9O988N54f7vzV1U/Nfrd737HM888w80334zNZuPtt9/mt7/9LTk5OZx11lm8//77LFq0iHXr1sW2t0UikR2es6qqirKyMvr379/k+KBBg2JNz9u3b8/48eN58cUXWbZsWZPzh8Phnc572bJldOrUqUlPKJfLxYABA1i2bFnsWIcOHbDb7bHHqampBIPBnZ6/pSiAShBbtuDFeSIiIiIiIiJywDMMA8PRck24W8PJJ5/Mww8/zHfffUdubi7Lly/niSeeoKysjNNPP52srCyOPPJIDjvsMPr378/hhx++03Nurx2OzbYljlm1ahUXXHAB/fr145BDDmHs2LFkZmZy6qmn7tK8t9dqJxKJNHmfX1ZOtTYFUAnCogBKREREREREZLsaA6YPP/yQnJwchg0bRseOHfnnP/9JdXU1n3zySayCqHHr3c76LGdmZlJYWMjs2bM5+uijY8cXLFgQO9ebb75JTk4O//znP2PPf/nll03Ov6OKrp49e/L2229TUVFBdnY2EN3Wt2DBgl999769ST2gEoSx+SttqguUiIiIiIiIyDZNmDCBr776ik8++YQJEyYAUFBQgNfr5eOPP2bTpk1MmzaNa665BoBAILDTc1588cW88sorvPHGG6xevZq//vWvzJs3L/Z8fn4+RUVFfPPNN2zcuJFPP/2UO++8s8n53W43EA2ufD5fk/OfdNJJZGRkcNVVVzFv3jyWLFnCddddh8fj4fTTT9/jz6SlqAIqQagCSkRERERERGTHDjvsMNxuN9XV1Rx77LEAHHfccSxcuJAHHniA+vp62rZty6mnnsoXX3zB/PnzOfPMM3d4zrPPPptIJMJTTz1FeXk5o0aNYsKECaxevRqAM888k/Xr13PDDTcQCATo1KkT11xzDVOmTGH+/PmMHj2agw8+mIEDB3LGGWfw0EMPNTl/amoqL7/8Mg888ACTJk0CYOjQobz22mu0b9++5T+k3WSYO6sXE+bPnw/QrGnY/mT5+iqu+etU0t1Wnr3pyFh6KiJRHo+HxYsX07t3b60PkW3QGhHZPq0PkR3TGjlw+Xw+Vq9eTefOnXG59q9+T/uKcDiMz+fD5XJhtVrjPZ1mdvY1/jV5ibbgJYhY47M4z0NEREREREREEo8CqATRuAUvooI3EREREREREWllCqAShMWiHlAiIiIiIiIiEh8KoBJE4x0bFUCJiIiIiIiISGtTAJUgLOoBJSIiIiIiIiJxogAqQWypgFIEJSIiIiIiIi1Hv2ceuFrya6sAKkHEKqD0c0FERERERERagN1uxzAMGhoa4j0V2Us8Hg8Q/VrvKdsen0H2C4YCKBEREREREWlBVquV9PR0ysrK8Pv9pKWlYbPZYr9/ys6Fw2H8fj8Q/Tz3FaZp4vF4KC0tJSMjo0XmpgAqQcS24MV3GiIiIiIiInIAKSgoICkpidLSUmpra+M9nf1OJBIhFAphs9mwWPa9TWoZGRkUFBS0yLkUQCUIbcETERERERGRlmYYBhkZGaSnpxMOhwmFQvGe0n7F6/WyatUqOnToQFJSUryn04Tdbm/RqiwFUAliyxY8JVAiIiIiIiLSsgzDwGazYbMpZvg1IpEIAE6nE5fLFefZ7F37Xn2X7BWNlXyKn0RERERERESktSmAShBqQi4iIiIiIiIi8aIAKkHEmpArgBIRERERERGRVqYAKkFYtroNpvpAiYiIiIiIiEhrUgCVIIwmAVQcJyIiIiIiIiIiCUcBVIKwbMmfiCiBEhEREREREZFWpAAqQagCSkRERERERETiRQFUgrBY1ANKREREREREROJDAVSCMJpswYvfPEREREREREQk8SiAShC6C56IiIiIiIiIxIsCqARhKIASERERERERkThRAJUgLNqCJyIiIiIiIiJxogAqQagCSkRERERERETiRQFUgti6CbnyJxERERERERFpTQqgEoRhGLEQKqIESkRERERERERakQKoBNK4DU/5k4iIiIiIiIi0JgVQCaSxAko9oERERERERESkNSmASiCWzQmU7oInIiIiIiIiIq1JAVQCUQWUiIiIiIiIiMSDAqgEoh5QIiIiIiIiIhIPCqASiKXxLnjagyciIiIiIiIirUgBVAIxYj2gFECJiIiIiIiISOtRAJVALNqCJyIiIiIiIiJxoAAqgagJuYiIiIiIiIjEgwKoBGKJbcGL80REREREREREJKEogEogqoASERERERERkXhQAJVADPWAEhEREREREZE4UACVQCybK6B0FzwRERERERERaU0KoBLIlgooBVAiIiIiIiIi0noUQCWQLT2g4jsPEREREREREUksCqASyJa74CmBEhEREREREZHWowAqgagCSkRERERERETiQQFUAlEPKBERERERERGJBwVQCcSIbcGL80REREREREREJKEogEogltgWPCVQIiIiIiIiItJ6FEAlkFgFlEqgRERERERERKQVKYBKIGpCLiIiIiIiIiLxoAAqgVgsjT2glECJiIiIiIiISOuJewAViUSYMmUKo0aNYtCgQVx88cWsX79+u+Orqqq49tprGTZsGMOHD+euu+7C6/U2GTN27Fh69uzZ5J8bb7xxb1/KPs+iCigRERERERERiQNbvCfw5JNP8uqrr/LAAw9QUFDAQw89xEUXXcR7772Hw+FoNn7y5Ml4vV5eeOEFamtrueWWW/B4PDz44IMAeDwe1q9fzzPPPEPfvn1jr3O5XK12Tfuqxh5QakIuIiIiIiIiIq0prhVQgUCA559/nsmTJzNmzBh69erFo48+SnFxMZ9++mmz8XPnzmXGjBk8+OCD9O3bl5EjR3L33XfzzjvvUFJSAsCKFSuIRCIMHjyY3Nzc2D+pqamtfXn7nC0BVJwnIiIiIiIiIiIJJa4B1JIlS2hoaGDkyJGxY2lpafTp04eZM2c2Gz9r1ixyc3Pp2rVr7Njw4cMxDIPZs2cDsHTpUnJyckhPT9/7F7CfadyCpx5QIiIiIiIiItKa4roFr7i4GIDCwsImx/Py8mLPba2kpKTZWIfDQUZGBkVFRUA0gHK73UyePJk5c+aQmZnJ7373OyZOnIjFsvt5m2maeDye3X79vqBx653f79/vr0WkpTX2kvtlTzkRidIaEdk+rQ+RHdMaEdm+/X19mKYZ2221M3ENoBo/4F/2enI6ndTU1Gxz/Lb6QjmdTvx+PwDLly+ntraWY489liuuuILZs2fz0EMPUVNTw//93//t9lyDwSCLFy/e7dfvC/x+HwAlJaUsXlwf59mI7JvWrFkT7ymI7NO0RkS2T+tDZMe0RkS2b39eH9vKabYlrgFUY2PwQCDQpEm43+8nKSlpm+MDgUCz436/H7fbDcDf//53/H5/rOdTz549qa+v56mnnuIPf/jDbldB2e12unXrtluv3VckTasFAuTk5tG7d/t4T0dkn+L1elmzZg2dOnXa5s8fkUSnNSKyfVofIjumNSKyffv7+lixYsUuj41rANW4na60tJQOHTrEjpeWltKzZ89m4wsKCvj888+bHAsEAlRXV5OXlwdEk7dfpm89evTA4/FQU1NDZmbmbs3VMIxYyLW/slmtQDRM29+vRWRvSUpK0voQ2QGtEZHt0/oQ2TGtEZHt21/Xx65uv4M4NyHv1asXKSkpTJ8+PXastraWRYsWMWzYsGbjhw0bRnFxMWvXro0dmzFjBgBDhw7FNE2OPvponnjiiSavmz9/Prm5ubsdPh0ottwFT03IRURERERERKT1xLUCyuFwcM455/Dwww+TlZVF27ZteeihhygoKGDs2LGEw2EqKytJTU3F5XIxcOBAhgwZwtVXX82dd96Jx+Ph9ttv55RTTiE/Px+AY445hueee44uXbrQr18/fvjhB/7xj39wyy23xPNS9wlb7oIX33mIiIiIiIiISGKJawAFMHnyZEKhELfeeis+n49hw4bx3HPPYbfb2bBhA0cddRT3338/48ePxzAMnnjiCe666y7OO+88nE4nxx13HDfddFPsfNdeey0pKSn85S9/obi4mHbt2nHLLbdw2mmnxfEq9w2qgBIRERERERGReIh7AGW1Wrn++uu5/vrrmz3Xrl07li5d2uRYdnY2U6ZM2e75bDYbV1xxBVdccUWLz3V/17g1U/mTiIiIiIiIiLSmuPaAktZl2ZxARbQHT0RERERERERakQKoBGLEekApgBIRERERERGR1qMAKoFs6QEV54mIiIiIiIiISEJRAJVALBY1IRcRERERERGR1qcAKoFs2YIX33mIiIiIiIiISGJRAJVALIYqoERERERERESk9SmASiCNFVDKn0RERERERESkNSmASiCNTch1FzwRERERERERaU0KoBKIRXfBExEREREREZE4UACVQLZswVMCJSIiIiIiIiKtRwFUAtmyBS/OExERERERERGRhKIAKoFYVAElIiIiIiIiInGgACqBGOoBJSIiIiIiIiJxoAAqgTT2gNJd8ERERERERESkNSmASiBb7oKnAEpEREREREREWo8CqASy5S548Z2HiIiIiIiIiCQWBVAJJHYXPN0GT0RERERERERakQKoBGJRBZSIiIiIiIiIxIECqARi2ZxAqQm5iIiIiIiIiLQmBVAJxIg1IY/zREREREREREQkoSiASiBbmpArgRIRERERERGR1qMAKoFYDG3BExEREREREZHWpwAqgRhqQi4iIiIiIiIicaAAKoFs6QGlBEpEREREREREWo8CqASy+SZ4RJQ/iYiIiIiIiEgrUgCVQFQBJSIiIiIiIiLxoAAqgagHlIiIiIiIiIjEgwKoBGLoLngiIiIiIiIiEgcKoBKIJbYFL84TEREREREREZGEogAqgWzZgqcESkRERERERERajwKoBLJlC16cJyIiIiIiIiIiCUUBVAKxbK6AiiiBEhEREREREZFWpAAqgRixHlAKoERERERERESk9SiASiAWi5qQi4iIiIiIiEjrUwCVQBqbkEeUQImIiIiIiIhIK1IAlUAshiqgRERERERERKT1KYBKII0VUOoBJSIiIiIiIiKtSQFUAjGIBk+6CZ6IiIiIiIiItCYFUAnCX7KG3tPvZbRzsSqgRERERERERKRVKYBKEIHiVdhCXnrbNymAEhEREREREZFWpQAqQRgOFwAOI6gm5CIiIiIiIiLSqhRAJQiLIwkAlxEkogRKRERERERERFqRAqgE0RhAOY2QKqBEREREREREpFUpgEoQjVvwnEZQPaBEREREREREpFUpgEoQllgAFSKi/ElEREREREREWpECqASx9RY8IuE4z0ZEREREREREEokCqATRuAUPwGIG4jgTEREREREREUk0CqAShGFzYBrRL7c9EozzbEREREREREQkkSiAShCGYWBaHQBYVQElIiIiIiIiIq1IAVQCidicANgjCqBEREREREREpPUogEogpi3aB8qmCigRERERERERaUUKoBJI4xY8m6keUCIiIiIiIiLSehRAJZDGCii7KqBEREREREREpBUpgEogZqwHlCqgRERERERERKT1KIBKJJsDKPWAEhEREREREZHWpAAqgZj26BY8hwIoEREREREREWlFCqASSeMWPLQFT0RERERERERajwKoBGLaNwdQugueiIiIiIiIiLQiBVAJxGi8Cx7agiciIiIiIiIirUcBVAJprIByaAueiIiIiIiIiLQiBVCJJNaEXAGUiIiIiIiIiLQeBVAJxLA3bsFTACUiIiIiIiIirUcBVCKxNW7BUw8oEREREREREWk9CqASSeMWPFVAiYiIiIiIiEgrUgCVQCwKoEREREREREQkDhRAJRDD0RhAhTBNM86zEREREREREZFEoQAqgTQ2IbcYJmZIfaBEREREREREpHUogEoght0R+3vE743jTEREREREREQkkSiASiAWixWfaQPADCiAEhEREREREZHWoQAqgRgW8Jt2ACIBX5xnIyIiIiIiIiKJQgFUAjEMA//mCqiIKqBEREREREREpJUogEogFsOIVUCZqoASERERERERkVaiACqBGMbWW/BUASUiIiIiIiIirUMBVALRFjwRERERERERiQcFUAnEYoBPW/BEREREREREpJUpgEogTSugFECJiIiIiIiISOtQAJVADAP8qAeUiIiIiIiIiLQuBVAJZOu74EX8CqBEREREREREpHUogEog0bvgqQm5iIiIiIiIiLQuBVAJxGIY+EwHABFfQ5xnIyIiIiIiIiKJQgFUAjEMg8pIMgCh6tI4z0ZEREREREREEoUCqARiMaAsnAZAsKoY04zEeUYiIiIiIiIikggUQCWQxgqokGmBcJBQbXm8pyQiIiIiIiIiCUABVAIxDDCxUB5JBSBYURTnGYmIiIiIiIhIIlAAlUAshgFAWXhzAFWpAEpERERERERE9j4FUAlkc/5EaWMfqMpNcZyNiIiIiIiIiCSKuAdQkUiEKVOmMGrUKAYNGsTFF1/M+vXrtzu+qqqKa6+9lmHDhjF8+HDuuusuvF7vNscGAgFOOukkbrzxxr01/f2K0VgBFVEAJSIiIiIiIiKtJ+4B1JNPPsmrr77KPffcw+uvv04kEuGiiy4iEAhsc/zkyZNZu3YtL7zwAo899hjffPMNd9555zbH/vnPf2bZsmV7cfb7l+YVUNqCJyIiIiIiIiJ7X1wDqEAgwPPPP8/kyZMZM2YMvXr14tFHH6W4uJhPP/202fi5c+cyY8YMHnzwQfr27cvIkSO5++67eeeddygpKWky9ttvv+Wjjz6ie/furXU5+7xYBdTmACpUXYoZDsZzSiIiIiIiIiKSAOIaQC1ZsoSGhgZGjhwZO5aWlkafPn2YOXNms/GzZs0iNzeXrl27xo4NHz4cwzCYPXt27FhlZSU33XQT99xzD5mZmXv3IvYzhgG1ZhLYXWBGCFaXxntKIiIiIiIiInKAi2sAVVxcDEBhYWGT43l5ebHntlZSUtJsrMPhICMjg6KiLdvJbrnlFo444giOPPLIvTDr/Vu0CMrAkpEPQLBCfaBEREREREREZO+yxfPNG5uHOxyOJsedTic1NTXbHP/LsY3j/X4/AK+//jorV67kkUceadG5mqaJx+Np0XO2Nq/Xy+Y2UJgpuVC2Fk/JWox2feM6L5F9QePPo+3d1EAk0WmNiGyf1ofIjmmNiGzf/r4+TNOMtfvZmbgGUC6XC4j2gmr8O4Df7ycpKWmb47fVnNzv9+N2u1m1ahUPPfQQzz33HG63u0XnGgwGWbx4cYueMx6i3xgmlSE7mUDF4lmsc3UES9z70YvsE9asWRPvKYjs07RGRLZP60Nkx7RGRLZvf14f2yoU2pa4BlCN2+lKS0vp0KFD7HhpaSk9e/ZsNr6goIDPP/+8ybFAIEB1dTV5eXl8+OGHNDQ0cP7558ee9/l8zJkzh08++YS5c+fu9lztdjvdunXb7dfvC7xeL4axEYDM3iNg/Q84ypbj/unfpB97Mfac9nGeoUj8eL1e1qxZQ6dOnbYZgIskOq0Rke3T+hDZMa0Rke3b39fHihUrdnlsXAOoXr16kZKSwvTp02MBVG1tLYsWLeKcc85pNn7YsGE8/PDDrF27lo4dOwIwY8YMAIYOHcohhxzCSSed1OQ11113HQUFBVx33XV7NFfDMFq8qioeGgvjXJ0Hk3ryZCo+fZ5Q6WoqXrmdzFGnkjHyFAxrXL8tROIqKSnpgFjrInuL1ojI9ml9iOyY1ojI9u2v62NXt99BnAMoh8PBOeecw8MPP0xWVhZt27bloYceoqCggLFjxxIOh6msrCQ1NRWXy8XAgQMZMmQIV199NXfeeScej4fbb7+dU045hfz8aFPtjIyMJu/hcrlITk6OBVaJzrL5e8M0IbX/4SR1GkD5R8/gWT6Tqm9eI1xXSc7xl8TGhxtqKP/4WVIHHIm7+9A4zVpERERERERE9mdxb/wzefJkJkyYwK233sqZZ56J1Wrlueeew263U1RUxGGHHcaHH34IRJO1J554gnbt2nHeeedx1VVXMXr0aO688874XsR+pDGdjERMAGypmeSf+kdyTrgMgLqfvyTsrYuNr5n5IQ1LfqT8s+cxTbP1JywiIiIiIiIi+72477WyWq1cf/31XH/99c2ea9euHUuXLm1yLDs7mylTpuzy+V966aU9nuOBxIhVQJlbHTNIHXgUtbM+JlCymrp5X5Mx4iRM06Rh8XcAhKqKCZSswVnQOR7Tjqv6hd9iz2qDs7BrvKciIiIiIiIisl+KewWUtC5jqy14TY8bpA0ZC0DdnE8xTZNAyRqClUWxMQ2LvycS9FP9/Vv4S9a00ozjy7dxGaVv/5WS//0l3lMRERERERER2W8pgEowje3BItvYTpfSdxSGw0WwchO+tQti1U8WVwoADUt+oPzjv1P51SuU/PchzEh4l97TNE08K+ZQ/O8/UTv74xa5jtbi37gMiFaAhb31cZ6NiIiIiIiIyP5JAVSC2V4FFIDFmURKv9EAlH/0DHXzvwEg66hzMax2gpVF1M/7CogGMvULv93p+/k2LKXo5Tso/vd9eFbMpvzjv1Px+b+IBP2EfQ3N+kqZoSB1P3/ZpPIKos3Qi167m5pZrRtg+YtXx/4eKFvbqu8tIiIiIiIicqCIew8oaV2xJuTbaSieMfIUPMtmxQIgw+Ygpc9heJbPxrNsBgD27DYEKzZRPe2/pPQdRcTvJdxQTbi+Cs+KOXhWzcWw2jFsDvwblkTPY7WT1HUQnmUzqZn+LjXT3wUgddDR5G5ugB721lPy5p/xrVuINTWL9r+fgsWRBESboXtX/YxvwzJSBx6Bxe5skc/DNE3q5n6GI78zrrbdmz3vL14V+3ugZC1JHfq2yPuKiIiIiIiIJBIFUAmmcQve9u5oZ8/Ip90lf6H8k3/QsHAaqQOOwOJwkTpgDJ5lM3C26U7BGbey/qkrCVZuYv1TVxKqLt3BG1pIHTCGzFGnYUvPpW7+15R/9Cxm0A9A3U9fkD78RKzuNDa9fDvB8g0AhOsqqf7uLbKOOBszHKTup8+j8w548ayYQ0rvkdt8O9M08a1dgD2nPbaUjJ1+Hg1Lf6T8o2ewpmTRYfIzGMaWosBI0B+bD0CgbN1OzyciIiIiIiIizSmASjA72oLXyJqUSv4pVxM6ciLWzSFOcs8RtJl4H478jlgcSWQcfDKVX70SC58srmQsrmRcbXvi7jkCw2YnXF+Nq30vHDntYudO7T+G5F4jMcMhyt7/G56l06ma9gYRv4dg+QasqVmkH3Q8lV+9QvWP75Iy4AgCxSsJN1THzlG/8NsmAVRjmBbx1lH23hN4VszGlpFPu0se3WmlVN3czwAI11fi37AUV/vesecCpWvBjDR9LCIiIiIiIiK/mgKoBNMYQG1vC97WbGnZTR672veK/T19xMkYdhdWdxpJnfpjTU7f5TlY7E6wO8k87FQ8S6fTsCja7NywOSg84zbsue3xrl2Ed9Vcil+/F8Ma/TZN7nUwDUt+xLNiNmFfAxanm7qfv6Dyq1eI+BqiFxcOARCqLqH6h7fJGn06EV8DhsOFYbE2mUewuhTvqnmxx/WLf8Ce1YbiN/+Mq30v7Om50c8hPY9QTSmB0nWYZqRJlZSIiIiIiIiI7JwCqARjbN6Et1Vhz+6dx2ojfdi4PTqHs6Az7u4H4Vk+C4DsoyfhyOsAQM6xF7LpxVsJVZdEB1usZI+9iEDFJoJl66j47HlCNWX41i5sck57dltS+o6iaurr1Hz/P8K15dTN/wZbei45Yy/E3W1IbGzdT18AJhZXChFfPQ1LfsQM+PBvWIJ/wxIcBV0BSO5zCLUzPsAM+ghVl2LPLNij6w576zDsTiw2xx6dR0RERERERGR/oQAqwfyaCqjWkHn4mXjXLiC55whSh4yNHbdnFdL+sieomfEetbM/IWXAGGypmaT2G0XlV69QP+9rIFo1lXn4GaT0OQwzFMCWkQeGBd/6RXhXz6Pu5y+B6F37iv99H468Trja9cSanBHrK5V9zPmUf/J3wnUV1P38RWwOgeKVADjbdMOe255A8SoCJWv3KIBqWD6L0v8+jC0jlzaTHsDqSt7tc4mIiIiIiIjsLxRAJZh9LYBy5nei07UvgmGJ3aGvkcWZROao08gcdVrsWOqgo/GsnIthseLq2I+UvodtMxDKPvZiil65E1t6DpmHnYp39TxqZn5AoHQNgdI1W97DnUZK30PxrJwT2wqY1GUgvnWLMUOB6BwLuuDI6xANoErXktxrxDavxTTN2FY9a3I6zsKueFbOpfr7/2FYrbja9ab6h7chEiJYsYmy954gf8INza5bRERERERE5ECjACrBbGlCvm8EUECz3kw7YnWn0ebce3Y6zpHdho6Tn409dncdTPqIk/FtWIx/03IiAR+GYSG59yEYVjvJvUfSsOg7DJuD3BMup3b2J1R//xYWVzK29DwceR0B8K1fRKBsPd4182lY8gMWRxLubkMIVhZRv3Bak2bphiMJM+CNPW7cLpjUeSDedQvxLJtB5Vcvk3HwKVjdqTu8nmB1KZVfv4I9PY/MMWcCBg2LvsOanE5Sp/679Nk1LJuJxZlEUsd+uzReREREREREpKUogEowjbU2e9oDan9kS80kpfchpPQ+pNlzyT2GkznqdByFXbCl5ZBxyG8JVpeQ1KEPhmHgzOsEgHfNfDY8e1WT13pWzN7ywGLDkdOWYE0Zpt8DFhvpw0/A6k6jYel0HPmdyDn2ImrnfErFJ/+g5oe3qZn+Pu6ug0jpN5qI34Nn+WwsziSSex+C1Z2Gb/1iqqa9gRnwAWCGQ5iRMLUzPwAg6+hJZIw4KTaFiN8DFmuTOwB61y6k5I0HwGKj/e8fa1I1FqqvwrBYsbrT9vQjFhEREREREdkmBVAJpnG7176yBW9fYVisZI7estXP4nST/9trYo9dHfuSOvAofJuWEaouw55VSOqAMUSCAbyrf8LqTiel/+G4uwzEsNoxw0H8RauwpeXE7iaYMfKU2PnShh6HYbFSO+dTAiWr8SyfFWvG3qh+wdQmj+25HQiWraNm+rtNjld+/gLBsvWkDT0Oz8o5VH/3X6ypWbQ9/wGsSamYkTAVnz4fHRwJUTX13+T95v+i77HkB0rf/isWh4u2kx7AnlW4R59jxO+l+M0HIRwi96Qr97hhu4iIiIiIiBwYFEAlmC1b8OI7j/2NYbGSe+Ll23wu89Dxzcdb7bja9dz++QyDtCFjSRsylkDZeuoXTKVh6Y8Y9iSSe40g4qmN3pXPjODI7YC7+zDShhxDzfT3qPzyJcAg98TLCXtqqfzyJep+/qJJA/VQVTFl7/2N/FP/SN3PXxIoXYPhcGEGfNQv+Ja0Icfi27iMyi9eBEwi3npK/vtn2px3PxaHC9M0CVYWYbE7YwHatgSrS6LhlmGQM/ZCKj5/Ad+a+QBsfP4Gck+4HHePYc22WZpmhIpP/4kZCZFz3MUYhmUHn/6+zzRNyj94Cn/JagrPvE3VZCIiIiIiIr+gACrB7GtNyAUcue3JOuJsso44u8nx7GPObzY2/eDfYM8sxJqchqt97+jrCzpTO/sTPMtnY01KIX34iVROfR3P8pkUvXQb/uJVAGQdfia+jctoWPQdm168JXbOlAFj8K78iUDpOjb96xbsWYX4S1YTqioGDNzdD8LddTCmaWLPzCep8wDMSJi6n76k8quXYlsD162cC+FQdBtibnsCJasp+e9DWFOzSB14FBkjT8HicAFQ/f3b1M76EABXm+6kDjxyp59TY9+ynTVtN8NBTNPEYnMAEKwqJlRXiat9r70WdHlX/RQLAKumvUnO2Au2OzbiayDsq8eekb9X5iIiIiIiIrIvUgCVYGI9oBRA7ZcMw2h2Fz5354G4Ow8kEgpgWG0YhgXD7qTi0+fwrV8MgLNdT9KGHoe721A8y2ZihgLYMgtIHzaOtIPG4d+whE0v39H0LoEWG0RCeJbPxLN8Zuz9rKlZmOEQEU8tAK72vTHDIfyblgOQc+yFpA44gspvXqPup88J11VSPe0N6ud/TcahE7A4nFR981rsfJVfv0pSp/5Ufv0qZiRM7olXYLE78RetJOL34OrYj3BtOcVvPEjE10DOCb/H3XngNj+fsKeOTS/dSsRbT7uL/4JhtbHxnzcS8dZhz2pDxiG/3W7YFQn6MAKe2GMzEoZIBMMW3VJZ/d3/CNVVkH3M+bEgrXFcxRf/ij2unf0J6cPGbXP7YSToZ+MLNxGsLKLgzFu3ex0iIiIiIiIHGgVQCaaxekT504GnseIHIO2g48GMEPF5SOoyEGebbhgWK/asQtpe9AhmKIAjr2Ps+8HVvjftLvkr/g1LiAR8WFMzcXceRKiugto5nxKqKQUMfOsXE66rBMCWlkP6wSeTNvQ4ME1q53yKYbWRNmQsANlHTSTr8DNpWDqdyi9fIlRTRvmHT8XmmNznUPyblhOqLmX905MxQwEADMOCu9cISv/3KETCuDr2JVhRRLg++r7Fr96Nu9tQMCyYoQCRoB97ei7pI0+h4rN/EizfAED1D29jSUoh4q0DIFi5ibL3/4ZpmqQOPJL6Bd8Qqq0kfcSJhOsqKX/pdtLrq2gInYHRsTflHzxJqLaClH6jCZSujQVsobpKCk79I4Y1+uOz7ucvCZatx+JKwZHbHt/6xVR89k/SBo/FmpKJs7BL7Jorv3qZYMVGAMremULbix7GlpLZ5OsY9tYTqi3Hmd9pm19nz4o5BCo2kj5s3K+6g+SuMk0TM+DD4kxq8XPLFpGgH8+yGbh7jmiydkVEREREDlQKoBKMtuAlBsMwSB9+4jafc2S32e7xXz7ncLZrsp3MDAXxrPoJw2IlqcvAJgFI+rBxzedhs5PS9zDc3Q+i5sd38a6dT7BiE7bMfHLHXYZn1U+UvvUwZiiANS2HcF0l9Qu/pX7ht7Fz+NYuBKJN2F1te1D30+dN7zwI+Dcs2fIaqw3CIWpnf4yx+U6AOcddQrCqiJrp71Hx8d/xrpxDw5IfAWhY9B0RfwORugoMoG7qq9Rtde66nz4HwOJKxgwF8a6cQ9n7fyPriHPwrl0Qa/CeOepUXB36sPG565s0lU/qMpD0YScSbqimdmZ026E1NZtwXQVl7zxGwRm3xsKssLeeDf+4lnBtOTknXE7aoKO2fPaRMJVfvhxrQr/11zjsqaX6+7cI1ZSRc/zvsbpTt/k13hnTjFDy5kN4V/1E3m/+j+ReB+/WefZngYpN1M39jOQ+h+Jq022vvU/5x89SP+9r0g8+meyjzttr7yMiIiIisq9QAJVgtjQhVwAlv55hs5PcY9ivfp3F4SJz9GlkclqT48m9Dib94JOJeBvIOvJc6hdNo+KTfwCQ0m80maNPp2raGxAOk33cxVhdyaQOGIO/aCWGzYFhd2LY7NQvnIZn6XTAIH/8dVT/8Db+DUtiWw1TBx8NhkGwsgjP8lnR8MmwYHG6Y1sOrRkF1OX3xb1iKoSDpAw4kpR+h1E/72sifi/ZYy8gULqWkjf/TP2CqdQv+BaIrqOkrkNIG3oshtVO5pizaVj8PRgWAqVr8a76Ge+qn2PXnDroaNJHnMTG52/Au2Y+xf/+E/njr8XiSqb842cJ15YDUP7Rsziy2+Bs0x3P8llUTXuTQMnq2Hmqpr1BSv8xNCz6joqvXsb0R7cPGo4k8k66MjauZsb7eFb9TPYx528zfAxWl+IvWkFyj2HUzvkUz7IZAJS+8xiFqVm42vYg4vcQKF0HmDjb9WrSh6v2p8+pXzAVV4e+JHc/CEdBl5326doVZihIqLZ8j+/M+Kve0zQpe3cK/k3LqZn+Lu6eI8gacxaOnHYt+j7ByiLq50fvcln305dkjj4Dy+awdH8Vqq2gatqbpPQ5hKRO/eM9HRERERHZBymASjCNvxZGlD/JPsAwjCbVH2lDjwMMzICH9IN/g2GxknfSH5q8xtW+d6wBe6OU3ofgL16NGQnjatMNi8NF0St3ApBxyG9jlVp5J09m00u3EqopJ+83V+Eo6EL5x88Qbqgh7fjLKd9QSsfDTsRhhmJb57bu02TPyCP/1D9S88Pbsf5aGaNOI/OwCbH3yDx0fOzOiMGqYqqmvYF/4zIMqw1bRgHZR5+Hxekmf/x1lLz1CN7VP7PhH9fhyO+IZ9lMMCy42vfCt24Rm166HczIls/L4SJ33GVUffcmwbL1bPrXTQQrNkXnltOOYPkG6ud9ReqAI0jq2JeaWR9T8dk/Adi0aRnZx0RDNDPgI/3gkzHDITa9dBsRTy32rDaENodftow8QtWlFL18B1htsXALoqFh7olXYnEm4Vkxm/IPngZMfGsXUv3tf3DkdSB18FjSBh8Tq+wC8K1fjG/DUtKGHLvT7X3+4lWU/u9RgpWbyD7mfNKHn0iotgLv2vm4uw/D6kre7mvNcBDfxuV4lk4nWFlE9tgLttmPC6Lb4GpmfIBv7QKyjppIxO+JbrW0WME08SydjmfZTFL6jyH76IlYk1IJVGykbs6nmOEQlqQU0oYchy01k0jAi3ftQlxte+z0LohV370V+7pGfPU0LP6e1AFH7PA1LS1YXUL192+TOuionVZ6mWZkhw38TTNC6Tt/xbduEXU/f0HuSVeS2m90S085riJ+DzWzPsLVridJHfvFezoHhEDpWgy7c7vrc2tmOEjFly9jsTnIHH0ahtW+jfOtIxL0R7ect0AILiIiIi1PAVSCUQWU7MsMwyD9oON267XOgs6xvyd16k/6iJMJ1VeS2v/w2HGLK5m2FzwEpolhi/4CU3DqjQB4PB6gFGtaDk63e7vvk9z9IJK7H0SwuhQz6MeR2367Y+2ZBc0CtEbubkNoc+49FP/nT4RqSjf32Ypu5UsfcTKbXrqdQPHKzfNOIW3ocaQPG4c1OR2L003xv++LhU9ZR5xN+shTKP/o79TN/ZSy958gqdMA6n7+EgBrShbh+krK3p0Se/+6Bd9gsbtizeSDldFzJXUZSP746yl65Q78RSthc28ua2o24YYaGpb8SKB0Le5uQ6n9+UvAxN1jGBgWvCvnEihdR8Un/6B+3lfkjLss2h9s9kexuTQsm0HBaTdRv3Aa3jXzsaXn4shqgyUpBTMUwLt2AfULp0XvqAhUfB5t8F79/VuEG2owHEmkDT2W9GEnYkuN9s+K+D3Uzv6E+oXTCJRvgEgodp1mOEjBmbcTrNxE/byvsWXkYU1Kw7dpGfULpsZ6mpX8535smdE7E6YOPJL0YeOo/Po1PMtmUD/vS/xFK8g+8lxK350S6ysGUDfva7KPmUTVV69GP0PDQlLngWSPPR9HdtvYuGBlUbRqzjCon/919Hug5wg8S6dTO/uT7QZQnuWzqfjyRVL7H076wb+JhmQbluHq1G+3q6bMSJjSt/6Cv2gF9Yum0ebsu5r0KgPwrV9C9Y/vEChZQ6i2nMzRp+Mc0nybLUDd3M/xrVsUfRAJU/bOY4RqyqLh71bBlWlGCNWUE6ouwZHXabe3iu5t0Rs62GMhRrCqmOL/3B/rL5dxyG/JPPxMDIsV3/ollH/6HMk9R5B52AQgWg1mTU7bZkjSKOxroGHJD1iT0nB3HUygbD31C77B1ak/yd0P2jKuoYbS957AWdCZzMPPwAz6qZr2JtbkDFIHjMGa1PqfoXfNfCxJqc361O0sqNyaf9MKNv7rZix2J+0ueRRbWs52x5pmhNJ3H6dh0XebX7uc/N9dj2WrINq7blH0fzpEwju94URLMk2Tik/+QdhbR/KRk/b6+4mIiOzvDFNJxE7Nnz8fgP799+9tBR6Phz8+MZU1JX5uOOcgRg1uu/MXiSQIj8fD4sWL6d27N+4dBFAtLexrwLt6HsGy9Rg2W6zyq3ELmsWVgiUp+Re/yJuUvv0o3pVzyTnhMlJ6HxI9l7eeDc9MJtxQExubOuhoso85n7L3/4Z39c8kdR1MuLYiVsFlz21Pwak3Ujv3M4Jl68kZ93tsqVmYkTCBkjUYDhfW5AysrmR8G5ZS8t+HCNdXxc7vat+bwrPvwLDaCXvrqV/wDVVT/0PEV9/sWg27EzPox7A5Yk3nt8fdYxiGzRH7pXPr1wMYVjvuHsMwg358G5YQ8TXExlmSUnB3GUzDkh8xw0Gyjp5EzQ9vE26obvY+tvRcwIgFgGDQ7vdTYtsVo9f8cKwJPoCjoCvubkNoWPRdLLj75fysyRkUnn0H9pz21C/4hvKP/o4Z9MXGJnUZRO5Jf2Dd45dCJET+aTfh7ja0SeVGqL6aDc/+HxFv9LO0Z7clVFMWvYlAQVcKTrsRW2oWABG/l0D5Bhx5HbYZTJmmScRTi8WdSs2M96n8fMudGy3uNNqcczeO3PaEfQ1Ufv5CLDDcWvrxl7HGzKR37964bBYCpWvwF62i8pvXMP0eso6eRKimjNqZH0S/ht0PIqXvYUQCvuhW1NU/E2msprPYcHcfSmr/w3F3G0KwuhTf2oXYUrNxFHbBDPgI1ZQR8XuIBP1YUzKwZ+QTbqgmVF9FUoe+WN1phBtqqJ7+Ls423UjueXCzypdQfRW1Mz/A1b4P7m5Dml3T1iIBH1XT3qBm+vskdepP/vhr8RetoOSth4l46zGc7lg1oD2rMNrbbtZHsbA0/9QbCddXUf7Rs9izCyk48zYMw0L1j+/gyGlP6sAjifgaqJnxPjWzP96ybXbr9WCx0WbiPbja9gCiW2HrF0S3a6b0P5xA2XoCxatir0sdeCQZh4zHlpa9ja/5rgdC0fHmlhuVhIIEa0oJVRYTrCnF1aY7zjbdqF/4LaVv/xUMSzT4Pvg3GIZBqK6Kopdvw+JOo2DCH7Empzc5d6B0HWUfPIk1OYPcEy6j6LV7YluKk7oMivbCMwyClZuo+OIlrO40co69CKxWKj55jtrZH4PFhmG1YQZ92HM7UHjGLdjScgjWlLLx+T9uDtMNGrdGF557N0kd+u7y9UeCfjAjWBw7rtCMBP0QiWBxJlG/+AdK33oYAGfnQRR3P5beffs1+3eIGQlT/J8H8G9chi09l6QuA8kac1aTPoqmGcEMBff77bgi2xOv/84S2R/s7+vj1+QlCqB2wYEUQN34xFRWl/i57uyhHD6kZfuaiOzP9rcf/KZpghlpdic8f8kaPEtnYEbC2DJySR1wRLMxZiRMzYwP8BevJPuoSbFKol0R9tTRsGw6geLVRII+so+c2OyXzVBtBWUf/A3vqp+xuNNw5ncic9TpGDYHRa/eRcRXj8WdRvpBxxP2NRCqKibi94IZwdmuJ+7OA3F16o8ZCrDpX7cQKFmNu9tQ8k65Cu+6RVR//xb+DUubvKc9uy3pB/+GpM79saXlYhgGlV+/SvV3/90yJqsNtow8wvXVOAu74OrYj+TeIwlVFrPxhRsxg37cPUdQMOGGJucOVpdQ9MpdhKpLcLbrReEZt2Bxugl76yh540F86xeT1GUgeb+5irC3ntL//YVAyWosTjdYbbEqM2e7ntgzCzGDfjLHnIUjuw0l//tLLGSzpuVEty2aEVL6HEagfD2eZTOxZeQT9tRiBrzRCVmsEAljTc3CWdiNcEMN/qIVEAljOJJwdxlEsKqYQOla7NltcOS0w7dxOeG6CqzJ6UT8XsxQgKyjzotWjRWvxOJKJuuo86j54X8EK4uAaCVYyoAxeJbNpGb6e2C1E8xoiyviJ1xd0mR7qLNNd9qcdx8YFup++oKKT/6BGQ42/way2LCmZMR6nQG7FEj+kjU5g+yxF1D1zeuxENCR3xmrO41gVRGO/M4kdexH9XdvxgLZ5D6HkjXmrNiWr3BDDf6SNQRKVuMvXoVv3aIm4ao9q5BgVfQ6nW26kz/hBnwbllD+4TNNAlZbWg6h2nIMRxJmwEdjAGJNziAS8MWCR1tGHuGGmlhIac9uSyTgjVbhWWzYM/IIVm7CmppFuwsfxl+6huJX7waMaPny5s/b4k7DlpJJoHRt9POz2kk/+GQyR50W2/basGwmZR88iS0li8wxZ5LUqT8Rbz1WdxqGLRoW1875FMwIrnY98a5bRN2cTwl767E4nLH1uOWLZCFj5CnUzPywSZCa3OdQ8k6eTOk7j0V73xENtdMGHR0LzhwFXalf8E3sui1ONxG/J3pjh2AAMxwkbfNdPWtnfxL7XkjqMgjD7oz198s75SrsWW0o/vd9hBuqsaZkkTroKOoXTI1W1eV3pvDM26j4/AXqF0zFnt2Wdhc9QsTvwTQjze442sg0TeoXTKXi0+cwrHYKz72nWb+8xnCufvEPlH8Y3XacP/46yj58ilB1aWycv7AvHSZcg5Mwpe9OwQz4KDj9ZhqWzWxyF1iA9BEnkX30pOjXa8mPlH/yHJgR2l7w4A4rwkL11dEQbhe2LjaKhAJUffMavnWLsSZn4MjrQPqwE5r97G4N4YYawg3VOPI6tvp7S3ztb/+dJdKa9vf1oQCqhR1IAdRNf5vKqmI/1541hDFDt791SCTR7O8/+PdFZigY2+rYKFi5Cc+qeaT0G7XDXk6NIn4v/k3LcXXs2yRI865bhG/NgmhlTHYbXO17NwvaIgEf65/+A+G6SmwZebSZ+Kfthm2eFXOomfk+2WMv2maz9rCnFu/qebi7H4TF4dpyjZEwgbL1OPI6xKpNwt46il69e0ulitVOxqG/I+PQ8c3mGPbUUvH5C9Fqrc2/oDdhsdL2/AewuFKoX/gtSZ36Y3WnNdmC2SgagHh38Glu4erYl8Kz7yLiraf4jfubBHq2tBzyTrkaV/tesWssefMhPMtnNjmHNSUTZ0EXHIVdST9oXJMtdf6iVVR99yYRvwfDYsFZ2B1394NwFnTGsNoIlK6lbv430W2Q9VVgseFq15NwQzXBio0YNge29FwsSalYbHZCdRWEqsuwbP6FeesAy5oS7cEVDX+as6XnRfubbQ5UosGPj3BdxTbG5pI+4mSqpr0RCw5T+o0mZ9zvY5UpkYCXunnRuSd1HkDGoePZ9MLNsYqelAFH4t+0LLZlz1nYlWBN2ZYgsrArGYf+Lrp1FQiUrMWakonF7mTjP/8YvX67C8NiIeL3kHbQ8bja9aL03SnYM/IoOONWbBn5+NYuoGrqv2PVjM52PUkbdDS+Tcupm/PpNj8LiysZd/eD8KycG5vP9hgOF/aMAgyHs8n3h6tjX5J7HRLtLxcJ4SzsFg1ADcvmqrTqbZ7P1ak/wYqNsW2vuSdeQdhbR+UXLzYd1743/uJVW9aD1UbuCZeR2n8MAMGaUopfvy/2+UI07Gt7/gPY0nObVII68joQKNsQC7cduR0JN1RjhoMYNgdEwoRqK5rc4MGWkU/bSfdjSYquuepp/yVUW441JZNQdUmz67KmZJF9zKRoZZgZwZrVBoK+2HW62vcmULGRiKeWjMMmYHWnU/HpcwCkDTuBQMkafOsWxs7n7j6M/FP/SMPi7/GtW7S5CjUdR057vGsXUDvjA8xImJxxl5LcYwQVn/+TYFUJqf1GY8sqwLNsFuGGauw57bBn5mPYnFR//1bs59HWX9/0g8aRMuAILE439QumRm/KEYlgTc0ifcRJ2FIyCXvqCJSti1YlNlQTrNhA2FOHxe7E6k7Hkd8JZ9vuzQK+UE0Zwcoi7NltsSanE26ooe7nL6n+4X+YwQAFp0erPiMBL97V86IhYXJG7EYX9Qu/xbtuERkjTsKete279zYyI2FqZ3+Mb/1iUgcfQ1KnAU2qIc1IuNnPXml9+u8ske3b39eHAqgWdiAFUDc/OZWVRX6uPnMwRx7UId5TEtln7O8/+GXbvOsWUTf3MzJHn/6rKgb2VMTvxbNyDvaMfOx5HbDYHDseH/Di37QCw2onVF9F9Xf/JVCymszDz4z1FtpaYw8hwmEsTnf0F8CMPHzrl+BbuwB7VhscBV0IVmwkWL4BR34nXG174C9dQ6BkLSl9D4s1S4+EApS99wQNi77D1aEP+eOva1YZEQn6qZzzBUWlZbTr1pvUdt1/VeXc9jQGePb03FhPn1/2YGr+Wfkoe/9vNCz+HntOOwrPvB3DaqN+4bcYDhe29Fy8K+bQsGwm7m5DyTryHILlG6j86hW8a+Y3qeyxZRbgLOiCs6AzjvzOuDr0wWJ3bt4K9iJJnfqTdtC4nTa1DlRsovTtv5LUqS9ZR55LxNdA1bQ3ceZ1JGXAGMyAj7p5X2PPbkNS54HbPV+gfANFr90TC9isKVm0v/SvWFzJhBtqsLiSmzT3N02ThiU/UP7BU1u2N26WdtA4DLuD2pkfbq4q2rI9DaI3LnDktse3YRm29BzSh52Aq11PIgEfFlcy1uQMDMPANE1qpr9H5RcvYk3JoO2FD2FLycSzYg7Fbz4Y24KYPuJk0oYcQ/F/HgCLlbTBx2BJSsG/fgn2nHakDT2WcH015R89gy0th+zjLgYzQuUXLxKsLsGanEFSh74k9z0M/4alFP/nTxhWO/kTrsfVrleTawv7Gij/6BnMgA93j2Ek9zy4SQBat2AqZe88ttUrml57MxYrGSN/S/3CqYSqSzEcLohEtlGZZ5BxyG/xl6zBu3IOQLTp/oAjqFr4AxUfPYXFH90ObM9uQ6iuKhYK27Pb0u7iv2BYbVR+9QrV37/V5P3TBh9D7dzPIBImqVP/6PfqTlhcKdvc7rzNse40ssacjRkObe5rt3Kry7I0rXgjGlC52vXEu2YBRMI7Prlhwd11MO5uQwj7GvCtXYh39Tx29Jnbs9rQZuK9bPzXzYSqimPHk7oMxJaeR93czzZP3EZKn0MIe+swbA6yj5rY5Gd5oGwdZe8/Gb2BxGbOtj3IPGwCtswCyj96Bv+mFWSOPp304SfiXfkTnjXziDTUgNVO5mG/a9V/NyQy/XeWyPbt7+tDAVQLO5ACqFue/JYVRT7+7/TBHD1cAZRIo/39B78cWEwzQriuapu9ffbO+5mEqoqxZeRtt1JgX1ojpmkSKF6FPafdr+qZE/bW4Vu3KNZE2+Lc99a6GQkTLN+Iv2Q1rrY9sGcV7vQ1wapiKr96mYivAWtqDim9DsbdfSiwuWdROIThcOFbt4iG5bOwZ+STNuSYHTZK39Z7WFzJTRqfe1bMoeS/D2FLz6XtBX9uUh24pyJ+D1htOw1vt8U0Taq/+y8Rbx2pg4/B4kiiYel0wt46bMnpGDYHkWAAw2LB4k7Fmd8Ze2YBgfINsTuDQjTgyRh5Cu6eIwjXlmNNy8GR3QYzHKRq6n8wIyGyjjwXw7Dg8XhY8vMcCst/xkqE7GMuwLd2ISVvPghAwRm34u46ODq/SJjyj/9BsGID7m5DSe49EntG/i+2DRukDj4Gw+4gXFtBoHw9FkcSGYf+Dv/GZbEAy57TjpR+o2lYNI2wpw5318Gxu6KGaiuIBLzYUrPJPmZSbGufaZo0LP2RurmfR4OizdtMo7337DQs+r5JoGPLyMfiSsGalBKtaErJxAz5o9VjxSsJlK7b5tfBlpEfrT6MhMGwYM9uQ8bIU6j88iXCDTVYkzMIN1RjcafhyG6Lf9OKJlt3nYVdmwZlRKvdCk67CVt6LrVzPqVq2psQCWE43SR3PyhaTbqdLb1b93GLHXMkkTP2ApxtumFNzowFmaG6KgKla0jqMrBZL7VoGOz+VetH9q1/h4jsa/b39aEAqoUdSAHUrU99y/JNPiafNohjRmj/vUij/f0Hv8jepjUi2xL2NWBYbQdM8+xI0E+wYhMWZxK21Oxm24i3Z3vro2HJj0T8nl26K19kc9+7UHUJeSf/XyxE3Jb6xd8Tqi4l7aDj9+izD3vqMIO+zTdkiDLNCA1LphOqKsLdfdgO7/YK0QrAup8+J1C2HmtyOvbMAlL6HoY9swAzHCTi82Bxp8aCnNq5n23upRXtAdfmvD/hLOhMsHIT5Z88R6BkDTnjfk9yj2F4Vs7Ft34xtrQcamd/Et0m+Avu7geRc9wl2NKyCdVXUTP9XWpnf4oZ9JHUZTDuroOiN0vYXN2X0ucwbJkFNCydjn/Dkq3OZJAy4AhcbbpFA12/h9TBY8k5/hIMwyAS9FP61iN4VswGDKypWThyO+Bs04304Sds866UZjhI2Fu/3R5kiUT/DhHZvv19ffyavMS20xG7aMGCBWzatImDDz6YtLS0ljqttDDL5qr/iGJHERER2UO70sttf2KxO3EWdG6x8yX3OnjX39vmoO2k+wGabLXclsa7n+6paMVP0+DEMCyk9B65y+dwZLch+6iJ23zOsNqbbelNHXgkdXM/w1+0ktwTLo993vasNhSeeVuTOzK6uw6OVY6l9DmUkv89gnfVz0C0X13WkeeQ3Oew2HhbSibZR51HxsjxBKuKcbbphmEYJPc6GN/G5bi7Do5V6qUPP4Hqaf+lbsE3RHwNRLx11M/7kvp5W+4CWjf3UyzOJJJ7DKfyq5djPdfAJFxXgbeuAu+qudT9/AW5J12Ju/NAAHzrl1A19XV8G5ZihgLknHAZaYOOJuytx79hKa7O/ZtU95mRMP5NKwhvrr5zdxuy3WpUMxxU9ZWI7Ld2K4AqLS3l2muvZeTIkVx++eW8/PLL3HfffZimSUZGBi+99BLdu3dv6blKC2hsO6HCNxEREZF9y86CpwOBYbFSeNYdhOqrcOQ0vyPz9nqkWVzJFJ55OxFfA4bDtcPG4lZ3apO+YLa0HFJ+cXdBw2Ilc/RpZI4+DQDfxmVUfvEi/qKVZBw2AYszmYpP/k7Nj+9Q8+M70Tk43RScfsvmu2QWEShZQ82MDwhWbqL41bvJHHMW7i6DKHrtniZ3jCz/+O8YVhtVU/9NqLoUW1oOmaNPJ6XvKCJ+D8VvPIh/45Zm/2kHHU/OsRc1u66qb9+gatob5J54Ban9D9/u9YuI7Kt2699yDz30EKtXr+aSSy4hEonw9NNPc8ghh3D99ddz77338sgjj/D000+39FylBTT+S10BlIiIiIjEg8WVjGM3q+cse6nqztW2B20m3osZDm0VBJrUzvwA0zSj1VXHXICzsAsA1uR0XO16kdJ/DBWf/4u6uZ9S9fWrVH/7BmY4iKtTf3LGXkDlN6/jWTqdsncfj71XqLacsvf/RsWXL2FY7YTrKjAcrmgvrKKV1M76CGdBlyZbN/3Fq6j69j9gRij/8GkceR1x5neKzjIcIlC+ockdWUVE9kW7FUBNmzaNm2++mVGjRjFr1izKy8u577776NWrFxdddBHXXXddS89TWkjj/1PSFjwRERERkaa2rkJLP+h40g86fofjLQ4XueMuxZHXgYpPn8cMB3HkdaJgwg1YnG7yTrqSjeUbCFZsxNm2J/njr6F+0ffUzHiPcF0lEL0jZ8HpN+PIbkvl1H9T/e1/KP/oWSIBH6mDjsKw2ij74GkwIxg2B2YoQMl/H6LwrDswbA5K3vwz/o1LcbXvTfbmyqmwpwZX+9671chfRGRv2a0AyuPxUFAQvWXp1KlTcTgcHHxwdI+7w+FQdc0+TFvwRERERERaVvpBx+PIbkvD8plkjBwfu8umxemmzbn34F27AHePYVhsDjIOPpn04SfgXfUT/uLVpA0Zi9Ud7aGbOepUAiWr8SybScWnz1H1zWtgtRHx1GJxJdPmvD9R/Pq9hKqKWf/kFVhcyUS8dQD41i9m4z+ujc3JmppN5mEToiHWDrYsioi0lt2q0ezUqROzZs0iGAzyySefMHz4cJzO6B043n33XTp16tSSc5QWZMSakCuAEhERERFpKUmdB5Az9kJsqU3vemdNTielz6FNqpEMixV3t6FkHjYhFj5BtAl8/vhryTnuYmxpOUT8HiKbm5NnH3M+jpx2FJxxK0mdB4AZIeKtw55VSOFZd5DUdQgQ3aZocacRrqug/KNnKP/oWf3PZxHZJ+xWBdTFF1/MH//4R5577jk8Hg+33347ABMmTGDRokU8/PDDLTpJaTmNW/D07yARERERkX2PYbWTNvQ4UgcdRaB0PVgsWJNSsaVlA+DIaUfhWXfgL16Nb/1iUvqNxpqUQlLnAUQCXgy7CzMcpHb2x1R+8RJ1P32ONTkdw+7Cv3EZmaNP3+HdHsMNNdTN+4q0wcfstZ5bIpKYdiuAOvHEEyksLGT27NkMHz6cQYMGATBs2DAmT57M6NGjW3KO0oLUhFxEREREZN9nWO2xpufb4izo3CxIsjiSoq+1OcgYcTKG1U7FJ/+g+rv/xsb4Ni6l7Xn3Yc9q0+ycpmlS8r9H8K1dSKimjJzjLm6hqxER2c0ACmDo0KEMHTo09jgUCnHppZeSkZHREvOSvSS2BS8S33mIiIiIiMjelX7Q8YTrKqj+/n+42vcm4vcQKF1L0St34erYj4ivgYi/ATDIOOS3RHz1+NYuBKB+4TSyjj5PjcxFpMXsVgAVCoV4+umn6dixIyeddBLTp09n8uTJ1NbWMnz4cKZMmUJ6enpLz1VagJqQi4iIiIgkjqwjziHjsFOx2J2EG2rY+K+bCVUVUz//6ybjitctwnAmxR5HfPV4ls8ipfchAJiRML4NS3AWdsNid7biFYjIgWK3mpBPmTKFp556itraaEO8e++9l4yMDG666SbWrVvHI4880qKTlJbT2ANKTchFRERERBJDY2BkTU6nzcR7yTryXLKOPJeccb8nb/y1pA46GjAx/R5smQWkjzgJgLqfv4qdo/LrVyl66XY2Pnc9/qJV8bgMEdnP7VYF1AcffMA111zD2WefzcqVK1m+fDkPPPAAp5xyChkZGfz5z3/m7rvvbum5SgvY0gMqzhMREREREZFWZ0vJJGPkKU2OpfQ+BGeb7tTN+4rsoyZiSUqlZvp7eFf9RKiuCoszibo5nwIQrNjIxhduJPuoiaQNOyH2+4WIyM7sVgVUaWkpAwcOBODrr7/GYrHEGo8XFBRQV1fXcjOUFhXrAaUESkRERERENksbfDRtz7sPV7ueOLLb4GzXE8wINTPeo37RNCJ+D7aMPJJ7HQyRMBWf/ZOydx4jEvQ3OY9pmoRqK5q/QSSMb9VPzcaLSOLYrQAqLy+PDRs2APDll1/Su3dvsrKyAJg7dy4FBQUtN0NpUY3/f8KMKIASEREREZFtyzj4NwDU/PguVVP/DUDakGPJG38d2WMvAMNC/cJvKXnjASKhQOx1tbM+Yt3jl1A17c3YMdM0Sf75barfeYSqb//TuhciIvuM3QqgTjzxRO6//34uvPBCZs+eze9+9zsA7rvvPh5//HFOOumkFp2ktJwtFVDxnYeIiIiIiOy7knuOiPWGCtdVgtVG6oAjMAyD9GEnUHjW7RgOF97V8yj978OY4SDm5oopgKppbxCs3ASAZ87HOEqWAtCw+AfdEEkkQe1WAHXVVVdxwQUXYBgG1157LWeddRYA8+fP54ILLuDyyy9v0UlKy9Fd8EREREREZFdkj70Ae047INonypq85U7nSZ36U3DazRg2B54Vs6n44iW8q+cTqi6NDgiHKP/kOWpmfkDdt6/HXheqLiFYvqFVr0NE9g271YTcMAwuvfRSLr300ibHX3/99e28QvYVjU0C1QNKRERERER2xGJ3UnD6LdTO+YT0YSc2ez6pY1/yfnsNJW88QO3MD/GtWxQ93nUI3tXz8K76Ce+qnwDwF/Yl1WklsGYenuWzcOS2b81LEZF9wG4FUACVlZU8//zzzJgxg9raWjIzMznooIOYNGkS2dnZLTlHaUGxHlDKn0REREREZCfsGXlkH3nudp9P7jGMlH6jqV8wlUDJagCyjjibhjbdqP72P9jS83APPZ61tgJyQ8UE1syjYfksMg75bWtdgojsI3ZrC15xcTG//e1v+de//oXT6aRPnz7YbDb++c9/csopp1BSUtLS85QWoi14IiIiIiLSkrKPnoQlKRUAZ2E3nPmdyBx1Gu0ufYz2lz2Oe+DRYLHi7DIYAP/GZYQ9tc3OE/E16PcUkQPYbgVQDz30EDabjQ8//JCXXnqJv/zlL7z00kt89NFHuFwuHn300Zaep7QQNSEXEREREZGWZE1OJ/fEK7Cl55J5+BlAtPWHI6cdhnXLphtrajaO/M5gRvCsmN3kHLU/fc6aRyZSO/vjJsfDDTWUf/RsrKG5iOy/diuAmjZtGpMnT6Z9+6b7dtu3b88VV1zB1KlTW2Ry0vIae0Dp/yyIiIiIiEhLSe4xjA5XPo276+AdjnP3GAZA3byvYsfC3joqv3gpevynL5qMr5nxHrVzPqHym6b9hs1IGM+K2USC/paYvoi0gt0KoMLhMJmZmdt8Lisri/r6+j2alOw9jT2g1IRcRERERERaW9qgo8Cw4Fu7EH9xtGdU9bQ3ifiiv0MGSlYTqq2IjW8c49+4rMl5amd9RPG//0Tl5/9qpZmLyJ7arQCqZ8+evPfee9t87p133qFHjx57NCnZe7b0gIrvPEREREREJPHY0nJI7j0SgJqZ7xMoW0/NrOi2O4s7DaDJ9rxAyRoAQjVlhOqqYscblk4HoH7xd5jhUGtMXUT20G7dBe/yyy/nwgsvpKamhnHjxpGbm0tZWRkffPAB06ZNY8qUKS09T2khsQBKTaBERERERCQO0oefSMOi76hfMI2GJT9CJERSl8G4OvSm6utX8SyfRdqQsYQbagg3VMde59+0DFvPEUT8HnwblgIQ8dbjW7eIpM4D4nQ1IrKrdiuAOvTQQ3nggQd4+OGHm/R7ysnJ4f777+eYY45psQlKy9IWPBERERERiSdX2x442/bEv3EpZiCEq31vck+8grCnhqqvX8W7Zj6RoB//5uqnRr6Ny0juOQLvmvkQCceONyz5UQGUyH5gt7bgAZxyyil8++23fPDBB7z66qt88MEHfPvtt+Tn53Pbbbe15BylBW1pQh7niYiIiIiISMLKPvo8HAVdyDriHArPuQtbaiaOvI5Y03IwQwF8axYQKF0THbz5Tnr+jcsB8Kz6CQB7Tjsguh0v4vdQv/gHwr6G1r4UEdlFux1AQTTM6Nq1K0OGDKFr164YhsGyZct48803W2p+0sIat+CpAkpEREREROLF1a4n7S58iIxDfothsQLR3y+Tuw0FoH7x97H+T8k9RwDgL1qBGQnjXfkTAFmHn4XF6SbcUM26v11G6VsPU/XVK61+LSKya/YogJL9TyyAUg8oERERERHZx6T0PxyA+kXT8K1bFD3W5zAMpxsz6KdhyY+EakrBaiOpywDcPYYB0V5QAN51C+MzcRHZKQVQCaaxB5QKoEREREREZF/jbNsDZ2E3CIcI1ZZHjxV0xtWmGwCl70ZveJXUvjcWRxIZI3+Lq31v0g/+DQDB8g3ahieyj1IAlWAae0BpC56IiIiIiOxrDMMgbfgJsccWVzLWtBxc7XtHD4RDWJJSSR9xMgCO3Pa0mXgv2UdNxJaRD4B/0/JWn7eI7Nxu3QVP9l+NW/BMBVAiIiIiIrIPSuk9ksovXiRcX4UjrxOGYZA+4iQMmwNHbnuSOg/EsDb/VdbVrif11SX4Ny7D3WVQ609cRHZolwOoiRMn7tK44uLi3Z6M7H2WWAAV33mIiIiIiIhsi2G1kz7iJCq/eJGkTv0ANm+3O2WHr3O26U79gqn4NixrhVmKyK+1ywHUrlbM5Ofnk5+fv9sTkr2rsQeUtuCJiIiIiMi+Kn3EySR17Icjr8Muv8bVtgcQ3YJnmhFCVcXYMvJjd9kTkfja5QDqpZde2pvzkFbS2ANK+ZOIiIiIiOyrDMPAWdj1V73Gkd8Jw+Yg4qun5I0/41k+k4xDxpN1xNl7aZYi8muoCXmCaewBpQooERERERE5kBhWWyy08iyfCUDDshnxnJKIbEUBVIJRE3IRERERETlQOdt2j/7FiP6qGyzfQLihJo4zEpFGCqASTGMPKOVPIiIiIiJyoEkbPJakroPJ/911OPI6AeBdt3CnrwuUrmP9U3+gfuG3e3mGIolLAVSCsW6+DZ4/GI7zTERERERERFqWPauQwjNuJbnnCFwd+wLgW7vzAKp27qcEKzdR9/NXe3uKIglLAVSCcTujX/K6hkCcZyIiIiIiIrL3JG0OoLxrF+x0rHfNfACCVUV7dU4iiUwBVIJpDKBqFUCJiIiIiMgBzNWhD2DstA9UqK6SYPmG6N9ryjHDwT1+74jfQ8UX/8JftHKPzyVyoFAAlWAUQImIiIiISCKwJqXiyOsI7LgPlHfNvC0PzAjB6rI9fu+GpTOo+fFdqr59Y4/PJXKgUACVYNwuKwBef4hgSH2gRERERETkwOXq2AfYcR+oxu13jUJVxXv8vmFPbZM/RUQBVMJx2Q0smxuRqwpKREREREQOZEkd+gHb7gMV9tRhmhG8q6MBlCUpFYBgCwRQEV9D9E9/wx6fS+RAYYv3BKR1GYZBqttOTX2A2oYA2elJ8Z6SiIiIiIjIXhHtA0WsD5Q1OR2A6h/fofKLl7Cl5xCuqwCrjZQ+h1I7++MWaUQe8Xua/CkiqoBKSKluOwC19aqAEhERERGRA5fV3bwPVM3MD6n84kXAJFQT7ffkatszNi5Y2QIVUP7GCigFUCKNVAGVgNLcDqBBW/BEREREROSA5+rYl0DpWnxrF2LYHFR8+hwAGYf8FltGPt5VP5E+/KTY3e9aogdUxBcNnsyADzMSxrBY9/icIvs7BVAJKFYB1eCP80xERERERET2rqQO/aid+SHeNfNivaDShhxL5pizMQyDtMHHABCsKY3+WV26x6HR1r2fIn4P1s39pUQSmbbgJaDU5MYAShVQIiIiIiJyYIv1garYRLB8AxZXCplHRMOnrdlSs8Fqg0iIUG3FHr1nYwUUaBueSCMFUAko1e0AFECJiIiIiMiBL9oHqkPscfrBv8HqSm42zrBYsWfkA+xxI/ImFVA+BVAioAAqIaUlK4ASEREREZHE4erQDwBrcjrpw47f7jh7ZgEAgeLVeFbM2e3qpYhv6y14DTsYKZI41AMqATX2gKpRDygREREREUkAaUPG4lu/mMzDJmBxJG13nC2rEIDKL18CwJHXgcKz7sSanL7L72WaESJ+b+yxKqBEohRAJaAtTchVASUiIiIiIgc+R2572l308E7HOfM7b3lgWAiUrqPo1btwdxtKsLqE9GEn4GrXMzbEjIRpWDodV/s+2FIyosf8XsCMjVEPKJEoBVAJKE09oERERERERJpJ6TcKw2bHnpGP4XBR9PIdBErXEihdC0C4vpo2594dG9+w+HtK3/4rKf1Gk/eb/wOaB04KoESiFEAloK3vgmeaZrO7P4iIiIiIiCQiw2Ilpc+hsceF59xF5ZcvY9gdNCz6Dv/GZZihIIYt+juVv3g1AIGy9bHXhH1Nez5FfOoBJQIKoBJS413wgqEIvkCYJKe+DURERERERH7JkdOOgtNuxDRN1q1dSLihGt+m5SR16ANAsHITAKGasthrVAElsm26C14CctotOGzRL7224YmIiIiIiOyYYRi4OvQGwLduUex4sLIIgIivPlbp9MuKJwVQIlEKoBKQYRikpTgBqNWd8ERERERERHbK1T5a9dQYQJmRMMHK4tjzwc1VUBH/LwMobcETAQVQCSstWY3IRUREREREdpVr87Y734almOFQdNtdJBR7vnEbXsSnLXgi26IAKkEpgBIREREREdl1jrwOWFwpmEEf/uLVBCs2NXk+FkBtDpysyenRxz4FUCKgACphNQZQNfUKoERERERERHbGMCy42jf2gVoYa0DeKPSLLXi2tNwmj0USXdwDqEgkwpQpUxg1ahSDBg3i4osvZv369dsdX1VVxbXXXsuwYcMYPnw4d911F16vN/Z8OBxmypQpHHHEEQwYMIDx48fz9ddft8KV7F+2VECpB5SIiIiIiMiucHWMbsPzrPopVgFlcboBCNWUAlsqnmzpuU0eiyS6uAdQTz75JK+++ir33HMPr7/+OpFIhIsuuohAYNuVOZMnT2bt2rW88MILPPbYY3zzzTfceeedsecfe+wxXnvtNe644w4++OADjjnmGC6//HIWLFjQSle0f0hLbmxCrgooERERERGRXZHccwQAvjUL8K5bCICrU38AgtW/qIBKz9n8WAGUCMQ5gAoEAjz//PNMnjyZMWPG0KtXLx599FGKi4v59NNPm42fO3cuM2bM4MEHH6Rv376MHDmSu+++m3feeYeSkhIAgsEgt9xyC2PGjKF9+/ZcdtllJCcn8+OPP7b25e3T0lOiFVCllfphKCIiIiIisivsGfmbt+GZBMs3AODuMgiAUG3TJuSNFVBmKIAZDrb6XEX2NXENoJYsWUJDQwMjR46MHUtLS6NPnz7MnDmz2fhZs2aRm5tL165dY8eGDx+OYRjMnj0bgD/+8Y+ceOKJAPh8Pl566SW8Xi8jRozYy1ezf+nfNZrG/7y8jPJq705Gi4iIiIiICEBK/zFNHid1HgBAxFNLJOAj4mvsAZUTG6NteCJgi+ebFxcXA1BYWNjkeF5eXuy5rZWUlDQb63A4yMjIoKioqMnxd999lxtuuAHTNPnDH/5A//7992iupmni8ezfPzQae2V5vV6yU5Po0ymTRWuqeP/bFZx2VNedvFrkwLb1+hCR5rRGRLZP60Nkxw60NWLpNAisdggHMVwpBJ1pGE43pt9Dfcl6Qr56AIKGHcPuwgz6aKipxGbY4ztx2Sft7+vDNE0Mw9ilsXENoBo/YIfD0eS40+mkpqZmm+N/ObZxvN/ftJn2sGHDePvtt/nuu+/4y1/+QlZWFmedddZuzzUYDLJ48eLdfv2+ZM2aNQD0aWuwaA18Mn0NfQr8WC279k0jciBrXB8ism1aIyLbp/UhsmMH0hpJzu2Go3gxQVc6ixcvJtWRis3vYfXCOSQ31GIB1mwqIcVix4KPVUsWEk6vjPe0ZR+2P6+PbeU02xLXAMrlcgHRXlCNfwfw+/0kJSVtc/y2mpP7/X7cbneTY4WFhRQWFtKrVy/Wrl3Lc889t0cBlN1up1u3brv9+n2B1+tlzZo1dOrUiaSkJLp3j/DZT99S0xCggWxG9M6P9xRF4uaX60NEmtIaEdk+rQ+RHTsQ10gg00HVW38mc+DhtOvdm6rlbfHXldA2PYnazf2euvbqS9WSNEL+Ojq2ycfZoXecZy37ov19faxYsWKXx8Y1gGrcTldaWkqHDh1ix0tLS+nZs2ez8QUFBXz++edNjgUCAaqrq8nLyyMUCvH111/Tp08f2rRpExvTs2dP3nrrrT2aq2EYzUKu/VVSUlLsWsYe3JE3vljOm1+v5pBBHXDarXGenUh8bb0+RKQ5rRGR7dP6ENmxA2mNuLv0J/3aF2NbjzxZhfhXAnVlsDmASs7IpjYphRDgIHzAXLvsHfvr+tjV7XcQ5ybkvXr1IiUlhenTp8eO1dbWsmjRIoYNG9Zs/LBhwyguLmbt2rWxYzNmzABg6NChWK1WbrvtNl577bUmr/v555/3++qlveXkUV3JSHWyrriO595dEO/piIiIiIiI7Be2/sXbnpEHgH/j8sZnMZxJWJzRQCHi37/7CYu0hLgGUA6Hg3POOYeHH36YL774giVLlnD11VdTUFDA2LFjCYfDlJWV4fP5ABg4cCBDhgzh6quvZt68efz444/cfvvtnHLKKeTn52MYBhdccAEvvvgi7733HmvWrOHZZ5/l/fff5w9/+EM8L3WflZHq5OozhwDw0fdr+PanjXGekYiIiIiIyP7F1aEPAP5N0QDK4kzCMCwKoES2EtcteACTJ08mFApx66234vP5GDZsGM899xx2u50NGzZw1FFHcf/99zN+/HgMw+CJJ57grrvu4rzzzsPpdHLcccdx0003xc534YUXYrfbefzxxykqKqJLly5MmTKFo446Ko5XuW8b0jOP3x3Rjf9+tYJHXpmNxTA4dGCbnb9QREREREREcBZ0wdWhD751iwCwuJKjfzqjf0Z8CqBE4h5AWa1Wrr/+eq6//vpmz7Vr146lS5c2OZadnc2UKVO2ez6LxcKkSZOYNGlSS0/1gHbu8b0pq/Yyde5G/vzSTE4v7skJh3YmPcUZ76mJiIiIiIjs89KHn7QlgNpc+WRxRf8M+xviNi+RfUVct+DJvsNqtXDNWUM5ZngHIia89ulSzr/nU17/bCmRiBnv6YmIiIiIiOzT3N2HYsssAFQBJbItca+Akn2H1WJw5amDGNA9l3emrmTF+mpe+XgJa4pqGdwjj01l9fTvlsPQXnm/qtO9iIiIiIjIgc6wWMkYcTLlHz+LLWNzEBXrAaUKKBEFUNKExWIwZkg7Dh/cls9mrOOp//7Mdz9v4rufNwHw1tcrGNg9hwtO6keXtulU1vr4+Ic19O2SzcDuuXGevYiIiIiISPykDhmLLTMfZ0EXAOxZ0SDKv2EJZjiEYdWv4JK49N0v22QYBmNHdKRtbgovfbQYu9VCZpqTb3/axM/Ly7nq0a8Z3qeA+SvL8fhCAIwe1Banw8ry9dUM6pHLxHG9sduscb4SERERERGR1mEYBu4ug2KPkzr2w5qcTrihBu+qn3F3Hxq/yYnEmQIo2aG+XbJ54IrDYo/PPq43L36wiKk/bWT6wmIA2uQkU1zRwNSfNsbGrSmqZcHKck47uif5WW5qG/wUV0T3PbucNpIcVpJcNtrnpZKZ5mrdixIREREREWkFhtVGct9R1M54n7r5XyuAkoSmAEp+lfwsN9efexAnj+7C+9NW069rNkcP78iqjdW8O3UVackO2uVFq6ZWbKjhTy/M2Ok52+Qkc3C/Qo49uCMF2ckEgmGcDqv6TImIiIiIyH4vtf/h1M54H8+ymYR9DVg3NygXSTQKoGS39OyYRc+OWbHH3dtncu3ZW9L8g3oX8Moni1lbVEtplZdUt4PCnGSsFgNfIITXH6LeE6SoooFN5Q289fUK3vp6Rez1bpeNjgVp5Ge5yUh1UtsQoLTKQ5/O2fzuiG64XXZM06Sy1semsgYKspPJzUxq1c9ARERERERkZxz5nbHndiBYto6Gxd+TNviYeE9JJC4UQMlekZuZxFVnDNnpuHpPgHkryvlsxjrmLCkhYkaPe3whFq+pZPGayibjF6ys4PMZa8nPSmZdcS0Nm/tPAfTulEV+lhtfIITFYuC0W+nVKYsxQ9phs1pYV1JHYXYyyUn2Fr1WERERERGR7TEMg9T+h1P55Us0LJ1B2uBjME2TQPEq7NltsTjUkkQSgwIoiasUt4NDBrThkAFtqPcGCYcj2G0WSqu8rCuupbzaS2Wtn9RkO2luB//7eiVFFQ1U1voBsBiQk5FEWbV3m4HVV7M38Px7CwmHTULhCKluO5ec0p+RA9pQXecnLdlBklPLQERERERE9h5n2+4ABCuifXO9q+dR/NrdpAwYQ95Jf4jn1ERajX7zln1GylaVSZ0K7XQqTGs25ujhHfhuXhEG0KEglXZ5KdhtVsqrvcxYVBztH2W3EjGhzhPgmzkb2FBaD4DDZqHOE+SRV+fAq3MAMAxok5NCfpabFLedNjkp9OiQQWaaC6vFwOMLUecJUNcQoM4TxGIxSHbZ6NI2nS5t03erT5VpmupvJSIiIiKSQOyZbQAI1ZRhhoP4NiwBIFCyNp7TEmlVCqBkv2K3WRkzpF2z4zkZSYw7pHOz46cf3YNVG2tIctrIy3Lz36+W8/qnywiFI9isBqGwycayejaW1f/qubTNTWZYnwJ6dcoiGAyzpqgWp91K+4JU+nTOJmsbd/fbVF7P3f/4kbzMaDP3VLfjV7+viIiIiIjsX6wpGRgOF2bAR7CqhGD5BgDC9VVxnplI61EAJQc0wzDo2i4j9vj0o3ty8qiuhCMmyS4b1fV+Vm+sparOR21DgDVFtazYUE2DN0gkYuJy2EhNtpPqdpDqdhAxTWobAixaVcHGsgY2frMSvlnZ7H0tBgzumYfFYrBkTRUdClI5c2xP/vbmzxSVN7CxrIGb/jaNq88cQorbQVaaC7vN0oqfjIiIiIiItBbDMLBnFhIoWU2wsohgxeYAqqEGMxzCsOpXcznw6btcEs7WPZ8yU11k9vr1Tf88viAzF5WwcFUFS9dV4bRb6dwmjWAowqpNNazcUMPsJaWx8QtXVXDr098DkJeZRCgcYW1xHVc9+g0AyS4bI/oV0rtTFqnJDjCh3hukvNpLcUUDLqeNbu0yKKvyMHtpKWnJDn4zqiuDe+ZucztfnSfAj/OLSHHbGd63EKtFW/5EREREROLJnt0mGkBVbCRYUbT5qEm4oQZbWnZc5ybSGhRAiewGt8vO4UPacfg2tgMCbCqr59ufN2K3WuneIYP3p63i+3lFpCTZufPikdhtFv7y6hzWFdfiD0Zo8IX4ctZ6vpy1fpfnMGdJKTkZSfTplEVuZhIADb4Q5dVefl5eRjAUAaBjQSqjBrXFBJJddtrkJpOTkURKkp0Gb5BN5Q047Fa6t8/QlkARERERkb3EnlkIgHf1z5jhYOx4qK5SAZQkBAVQIntBm9wUTj+6Z+xx/645LFtXRWaqKxYW/fkPowCIREwWr6nk+/mbKKnwUFPvx2IxcLvsZKe7KMxOprYhwMqN1aS4HQzrnc+qTTV8+uNayqu9TP1p4zbn0LEglfIaH2uL61j78ZJdmndmqpPMNBeZqU6y0lzRuwS6bORmJNG5TTr+YJhFqyqx2QwO6pVPm9yU2GsjERMAi6qtRERERESasWdvDqDWLmxyPFxfua3hIgccBVAiraRHh8xtHrdYDPp2yaZvl13/vx5HAecc15tla6tYsraSOs//t3ff4XHU59rHv7N9pVXvxXLvNu4GE0xLIISEhJAGCSWHEt6cBHJCySEnhEBID6QQkkM4gRDSSKGGXkIHG/deZUuWrN7L9p15/1hppZVkY4Nl2dL9uS4udmdmZ2dlDbJunuf5RbCwSHE7yc7wMKU0gymlmXQHIjz11l7qm/3YbAbtXSFqmrpp6wzR5Q/jdjkozkvFH4xS29RNa2eI1s7QIV3D/7GZ7HQ3Rbk+wpEYlXWd2G0wdVwWDoeNipoOfClOzlpaxrypeYQiMVI9TkryfAcNqSzLwrRQ26CIiIiIjCrO7PhKeJixpO3RTg0il7FBAZTIccrrdjBvWh7zpuUd8BhfiiupEqs/y4pXLPXOkOr0h6lv8dPWGaKlI0hrR5CuQCTRpre3ph27zcasidkEw1G27GmmpSNES0dyYLVxd1PicUtHkPueSP4/PCkeB6X5PtJT3QC0dgZx2G1MKEqnOxBh/c5GguEohTmplOT5KM7zMakkg8Uz8mnvDvPEa+XUNHWT6nVSVpDGR5ZNIBCO8oentuIPRPnCR2YwY3z2IX8dYzFTVVsiIiIiMuyc2UVDblcFlIwVCqBExqiBw8t7V/o7VP5ghOqGLmqaunHYDSYWZxCNmuzY10rMtJhQmE5FXQfPr6igoTWAx+2gvSuEPxhl5762QefbUZn8f36qG7qobuhKPLfbDEzLoic3A+BN4B8v7QIgGovPvFq/q5E5k3NI9TgJhWPUtXRjYDCpNIO8TC/+YJTuYAR/IEJTe5Dapi4yfW6u/excIB6abdvXiWWBz+tkcmlmYoXCxtYAq7fX09wWIDvDQ0muj1mTcnA6bIQiMTq6wnjddrweZ1IFVyRq0tIRJGaaFGSnJu0LRWJYpoXHrf8ci4iIiIxmdm8aNq8PMxD/O64rfwLhhgpVQMmYod94ROQ9SfE4mVaWNai1cHxReuLxzInZfGTZhMTzWMxkX30n9S1+2rvCGEZ87lQwHKOitgOHzWD+tHxyMjzUNHWxv7Gb6oZONu1uorKuE4Clswo5aU4h/lCUN9bvZ3tPcLVwej7Z6R5eWr2PzeXNg663trn7gJ+lqT3I7b9fw9RiNzuq9xMz+1Iuj8tOYU4q7V1Dtyd63Q7ys7xUNXQl5mDZbQYF2SmkeJ00twVo6wolgjOX005ephfLsvCHorR1hnDYDU6cXcTZJ41n/tQ8bDYjEfBFoiZul50JRelYFqzb2cD+hi6y0tykeJ0EQ1FSPE4WTM9X26KIiIjIMc6ZXUxo/04AvBPnEm6oUAWUjBkKoETkqLHbbUwszmBiccagfcvnlyQ9z89OYf60vud1zd3YDIP87JTEto8vn8SOfa2EIzHmTs7FMAzOP30yu/a1EYmZuBw2CrJTiMZMdlW10dEdJtXrJMXjINXjJDPNTVFOKvc9sYV3ttaxdV8AgHEFPtwuBw0tfjq6w1TUdgBgGDBjfDbji9Jp7QiyY18rbZ2hRDhmtxnETIuYaVHTlBx4OR02DCAcibG/sStpXzRm8ebGGt7cWEN+lpdxBWls2NWUqOqCeHDldNjoDkQYysTidD5x6mTCkRjVjV3srGwlGI5x6oIS5k3No7y6ja5AhKWzCpNCwu5AhIZWP9GYSTRqEY2ZdAXC1DR209QWIBCO4nLYOW1hKbMmZtPSEaSjO0xZQRp2u41I1KS9K4TP68TtsmMYBpZlxavjGrtI8TrJ9Lkpzk3Fbrcd6FsjwbLiXz/HAY4NhqKU729n+visAx7TKxKN4XTY3/U9RURERI4WZ3ZRPIAybHjKZtO+8l8KoGTMUAAlIseFwpzUQdsMwxg072l8YTrjC9MHHTt/Wv4Bz/0/X1zCA09uYldFPRd+eA7zZ8QHRJqmRWVdBy0dQTJ8bgqyU5LaFE3TYldVK+1dYSaVZJCT4SEas2jtDFLb1E0gFCU3w0tuppcMnwvTigdpLR1BbIaBx2WnIDuFxrYAz6+o5OW11TS0BmhojQdh2eluPC4HHd1hugIRwpEYWWluZk3KobM7jD8Ywet2smd/G3trOvjFQ+sGfbZ4eLYt8fzBp7fFq7M8DgKhKHXN/oN/4Xs883YFXnf8NRCv/CrKSWVffWciKHM5bKSluojGTNq7wkmvdzpsjC9KZ+7kXKaWZhKKROkKROjyR2jvDtPQ6qehxU9jW4BI1GTu5BwWTs/H5bTjcTlYNreIYDjKd+59m8q6TnIzvZyxqJRQJEYgGGVySQazJuVQVphOe1eIX/19PWt3NPDx5ZO45CMzsdttdHSHyOiZPbZ1bzO7qtooyk1lcklmYnXKXg2tflZtrQcg1eNg8axCfF5nYn97V4h1OxvxuOxkp3sozffhcTmorOugur6L6ROyyM9K4UAi0RjtXWFyMjyD2mFFRERk9OodRO7MKsCZGf/76YFa8CzLxDDe/X/giRwvFECJyJhnt9u46KypbNsWZVpZZmK7zWYcsGKrd//0AQGY02GQn5UyZPhgN6Akz0dJni9puy/FxdUXnMAXz5vN2xtraG4PsnhmAWWFaRiGgWlaVDd0EghFmTIua1CrXUd3mH+8tJPtFS1k+NzkZXmZOi6LWMzkuRWVVDd2MbU0E6fTxrodDdS3JIdO6aku3C47DrsNh92G122nKMdHfrYXr9tBbVM3r63fTyAUxWaA2xUPovbUtMe/DgaYFoSjJs3tQSAeRo0rTCMYitLcHiQYjrG7qo3dVW2H9GeyYVcTG3b1DbT/3eObcLsctHTEz9/UFkjM/wJ4oeffKR4HBtAdjAdlj71azmvr9uMPRgiGY3hcdjxuB20D2iknFKWzZFYBmT43NU3dPLeiMqkCze2yc/rCUqaUZtLeFeLhl3cnwrheXredQKhvVZup4zI5+YRi5k/Lo6U9yN7adiprO6mobWd/YzemaVGUk8op84spyE7B6bDT6Q/THYhQkJ1Cab6PzDQPLoeN6oYuapu78XmdZKd7yE73kJbqor0rRHtXCJfTjttlxx+IEgjHA7kUj5ODiURNItFYz9ct+dhgKMrmPc34vE6KclPJ8LkPeq5AKMqa7fVEoyYup53cTC8F2Sl43Q4cdtt7GvQf7Km+e7fXWpaVFOK1dATxeZ24nKp+ExGRY4+7ZGr836XTsfvif480A51Y0QiGo+/ncbi5hpo/fJOMJR8la/lniXW3U//onaTN+yBpc08bkWsXeb8UQImIHCPcTjunLxo3aLvNZlA2RFVXr/RUF1d8fM6Q+846cXzS805/vKUwEjVx2A0mFGWQnvruw+ev+Pgc6pq7Kcn34XTY2VvTTmOrnwlFGRTmpBAMx+joDtPZHSZmmkwszkgEAKZpUd/iZ8e+VjbuaqSqvpMUjxNfihOf10laqqsntPOSn52CZcHKzXXsrIr/38DK2g6qG7roDkYpzk3l21ecyLa9LWze00yGz43LYWPHvlZ2VLbg7wmeJpdmcM5JE/jzs9sToRVAMBwjGI6R4nEwd3IuDa1+Kus6qajtSLRa9po5IZusdDdV9V1U1Xfy3IpKnqMysb8034fX7aC5PUBLR4hAKB5wFef62Fvbzq6qNnZVtfGHp4b+mhpGfDZZ/yDtSHHYbcyelE16qpuYGQ8GO7rCjCtIY3xRGlv2NLO9ooXecWeTSjI4+YQifB4n9a0BXnynkk5/X7tnUW4qM8Zn0NHezrMbNlLXEqCh1U9ZQRrji9J5c0MNXQdoDwXI9LnJz/YyqSSTicXplFe3s6OyhbLCdBbNyKehNcDOffE/b7vNYF99J7VN3bgcNgpzU1kys4Bzlk2godXP+p2NhCIxYrF4BeKe/e1MKc3kg0vKeGPDfjbsaiIzzc35p05mSmkmFvHFC2KmRV1zNzVN3XjdDnIzvcybmktxbjwQbm4PEDMtUtwOmtqD1DV3U5STmhQE72/sYm9NOxmpbqaMyyQSNWls8ydCwYNVswXDUTq6w0SjJgXZKUktqW2dId7aVENTW4BQOMbMidmcPLf4XcO33dVt/PHpeIXjgul5nDSnaMhq0aGYpoXV8/UeysBgT0REjgzvhBMoufJOnNlFGA4X2B0QixLtak1URAEEKzdjBrro3rmarOWfxV++lmDlFqxYVAGUHLcMy+q/ppQMZdOmTQDMnTt3hK/k/fH7/Wzbto2ZM2eSknLg1hCRsUj3x7HLNC1WbqljV1UrH18+mcy0oatxYjGTitoOOrrDzJ2Si8Nuo9MfZtveFgpzUijKTaWu2U9bV4jpZVmJgKzTH2bV1jq27GkhEIpiGHD20vHMm5YHxH8R37i7ibc31VLf4icYjnLW0vGcvrA0ERB0+sM0twcpyfPhdNho7QiyYnMtb22sZWdVK/lZKUwoTmdiUTrji9KZUJROisfJyi11rNvRQHcgQigcIy3VhdftoK65m/2NXXR0h4lETfKzUyjN8+EPRmjpDNHSHiQaM3E6bGT43ESjJsFwlFSvE4P4YP33KzfDA4ZBU1vgkI4vykmlIDuFQDhKY6uflo7BQ/uPRXMn59LSERw0m61XToYHl8NOS2eQUDg25DEAmWluppRmMrkkg0jUpKktQHGej0Uz8nljQw1Pv7WXSDReVVeS5+P6LyzENC0eeWU3KzfXJS1+ADBlXCazJmTT3B5MLMpQlJPCaQtLyc9KobKug0df2U001vc6w4AF0/NZPKOA/CwvU8ZlkpPR114ai5lUN3bx7FsVvLBqHw67jfnT8jhraRmLZhQkjtu5r5UfPbiK3Awv131+YVKoFYuZvL25lpdWVeHzOvn4qZOYOi55MYqx6Hj/GbJnfzt1zd2cfELxSF+KjFLH+z0ynPbd/WWi7Q0UX/Z9PKUzEttbX/s7ra//Dbsvi/Ff+x2tbz5C6yt/xplXxrgv/XwEr1iOtOP9/jicvEQB1CFQACUy+un+kGNVLGYOGuBuWVaipXBglUrvEPjNe5qJRk0MA7LTPfhSnJRXt1NZ18GU0kyWzCokK82NPxhlxeZa1u1sAOLzvU6cXcjS2UXYbQbdgQhb9jSzubyB1pZmykoLGV+cRV6ml5372thb086cyTksm1ucVE0TicYIR0zC0Rgt7UHqmv1sr2yhoraDcQVpzJ6Yw66qVjaVN1GQncrsidm4XXZCEZOi3FQmFWfgD0XYs7+dZ96qYOPuJtJSXCyZVUB2ugfLsigrTGdicTpvbaxl5ZZaJpdk8pkPTWXrnhaeX1mJPxjBMAwMIz4zLi/TS3Gej1A4SnVDF5vKmxIrVNpsBjYjvihAqsdBQXYq1Q2dhKPJrZgTitJp7QzR0OLHMCDD56ajK4R5CH+b6h2cH42ZidbVXtPKMplWloVpWry8piqpnfNgls0tYtbEbFZtrWfj7qakfYYRr+RL9TqpaeymvqU7KbDq76ylZZy3fBKVtR3c/c8NibAtxePgso/O4gMnFLN2RwN/enY7DQPaeNNSXBgGTB+fxeXnzaY0P41AKMo7W+p4ff1+6pq76Q5GKchOYfn8EpbPLxlUefluFVd7e1p+D9QSPdKO558hMdPi8tufo6UjxB3XLh/UWi5yJBzP98hw2/+H/yFUvYP8C27AN3NZYnvj0/fQue4FMGxMvOkhmp+/n441z+JIz6Xsmt+O4BXLkXa83x+Hk5eoBU9EROQYNtTqgYZh4HUP/SPcMAzGFaQxriBt0L4TpuQN2uZy2jln2QTOWTZhyPOlep0snV3InInpbNsWYebM8Ym/HB0sDHA67DgddlJxkpXmYXJpJh+Yl1xdMfD5QJlpbopzfZwyr4Qufxiv2zHk12NicQZfOKfv/xoX5/r40NKyg54b4osCrNhcR06Gh4XT80nxOIjGTBx2G4ZhEIrE2FnZis1mkJXupiCrr3Wuyx/G7Yp/xmA4SkVNB7ur4wsCuHuG02+vaGH9rkbGF6Zx8TkzmT8tj65AhF//YwNvbqzBbjM4fVEp5582hQn9Vqe88OzpPPNWBeFIjOx0D4W5qRTlpLK9ooU3N9YQiZqkpbg4cU4hpy8sja8AetoUapu6eWn1PvbVdVLX3M3emg627k1eWcnpsDF3Si6fPG0yHpeDV9ZW8/Rbe3nhnX288M6+xHELpuURDMfYVtHC/z68kf99eGPfn4vPzYdPGk9Dq5/X1u2n0x9fdGDV1nrW7WigIDuV2qauQaFcU1uALXua+eMz27jy47OZOTGHNzbsZ+OuJnbsa6U038f1n1/EuII09ta0k+JxUpCdwu6qNm646zVipsWJswu57KOzhvz+hniQBah98DBs29ucqFhcva1hTAVQa7bX8+raar50/lx8Ke/eji4yHBy+bEIwaCW8WFfPYHLLJNbdTrTnuRk+tMpkkWORAigRERE55g3HL4eFOamcf9rkpG1OR9/wcrfTztwpue96PR6XgxkTspkx4d1/cU9LcfHfly5mW0ULeZkpg1ZgBMhK8/D5D88YtH1cQdqguW79FeWmcvE5MxPPG1sDrNpWB0BxbirFeT5yM7xJs6VmTMjmA/OKuffRTbR2BklxxwPHL35sFgD/en0P/15dRUVtB163g898cCofP3Uy7p4W1is+Poe2rhDBUJSHXtjJ6m31iXbGopxUTl1YwuyJOaR4HGyraOWFdyrZV9fJL/+2ftD1l1e3c/0vX6Mk38fuqjY8Ljv/88Wl3PfE5kSL4sotdWzc3cQvrjstMb8L4N+rq3h5TRU7KlsoyvHxo6+ecsCQ9kh6eU0V7V0hzlxQOOzvNVze3lSbeLx+Z0NSmDuaWZbFbx/ZRG1zNyV5Pj531vSRviQZo+xp8TbmaOcBAqiefbGe/WYooDl9ctxSACUiIiJyFBmGwayJOcP+PnlZXs49eeK7Hjd3ci6/uuGMIfd98vQpfPL0KTS3B/C6HYNWTMzwuROrJN5yxYnsqGylKxBhYnH6oMHs08dnc94pE3ns1XL+8tx2YqbFvGl5nDi7kMklGTzw1FY2lzcnVssMhmPccu/bPe/j4puXLeX+f21m5742fvLH1fz0muU4HXbe3FDDz/+6NvE+e2rauffRTXztwgWH9fU6XHtr2vnZX+Lv+9bG/Xxs4eAw8VhnWRZv9Qugdla10R2IkOo9+Cqao8G+uk5qm7sBeHXdfj77oWn6hb5HVX0nD7+8i4vOnkFB9vHXDnS8cfSshNc/cAKIdvY9j3W29AVUlokVCWG4PEftGkWOFAVQIiIiInJQ/YeZH4hhGO9aBWa32/jUmVP56AcmEjUtfP2CjtuvPpnHXy0H4NQFpfzm4Q2s3lYPwJcvmMfsSTl887KlXHvnK5RXt/Orv6/n/NOmcPc/1gPxOVYnTMnl539dy4ur9rFwej7LF5S8x08c1+UPY1oMuVro317cmXi8raKNmsZOikq7mTr+2P2F3bIsojELpyPeSrq7uo2mtgAel50Mn5v6Fj8bdzexbG7RsF5HTWMX7V1hZk4cuXa/tzf3BW9V9fHVUI/VGWNH2/3/2sLqbfWEwjH++9IlI305o549LX4fRDv65vhZZoxYd1viebSzOSmgMkMBbAqg5DikAEpEREREjirPEO1xjp5wqtf/fHEJf31+B2kprsS8sNzM+Mp8t/1uBS+vqeblNdVAfNXAL39qHk6HjeqGLv724k7u+MsaVm6pY/n8YlxOO4U5qRTlptLaEeTvL+3EYbdx6bkzcTrsxGImbV0hQuEYNptBWoqLZ96u4KEXdmAzDG67ahn52V5+/OBqAqEoF5wxhbc21gDwjUsW8+BTW6hrCXDL/63iO1edxIyjPEeporaD9Tsb2d/Yhdtp57zlkwZVrrR3hbjtdyuoa+7mf764lDmTcxPtd4tmFpDpc/PUm3tZt7PhkAOo1o74iptZ6Yf2i/Cuqlb++PQ21u1sBOAbFy8eMiT0ByM0tAYSs9FCkRgdXeEhW1YPVSxm8uq6/eyr6+DTZ05NfHav204gFOO1dfsPK4AaaoGI0aC1M8jaHfFFKVZsrqWtM3TA1WflyHBmxVt4Iy11iW0xfydYfYtghOsrkp6bYT+gFUjl+KMASkRERESOOU6HnUvPnTVo++KZBXzrP5bytxd3sruqDa/bzo1fWJSo6rnw7OlUN3bx5oYaXl1XzavrqhOvnVicTl2zn0AoCsCuqjZOPqGIf760i9bO0AGv5Tv/9zapXidNbfHhv72tdyfNKWT5/BKmlqRy6/+9xf7mCN/6zZv8vwtOYMq4TH776Ca6/GHOP20KU8syWbOtnvL97dQ3++n0h7GAjFQXJ0zNY0JhOuFojI7uMA2tfroD8VUcbT0rOaanulgwLR+n08aKzXW0d4UYV5DG7qo23uwJw3o99eYeTplfQllBGoXZqWRneLjnkY1U1Hb0fJ4VnL20jBdWxQfPnzy3CJfTzlNv7mXV1np+Y22gsS1Aab6P7HQPoUgMp93GlHGZuJ12Vm+rZ/X2esqr46sTfuaDU/nUGVN5dV01FTUd5GZ6MQzYua+VaMziK5+eR8y0+PZv36Y7EElc572PbWLB9DyqG7sor27njEWltHeF+fZv36K+xc/C6fnMn5bHI6/spq0zxMUfmcGnzpjKk2/sYde+Nj531jTGFaTx0qp9bNnTwvIFJcyfmofNZiRm5MSDp2oeemEntU3xlrv1uxrZs78dmwGXnTuLex7dxGvr93PpufEZagdrxYvFTO76+3peW1fNB5eUceFZ08nNHL72y/6zfmoau/j3mirOXDSO4jzfu7zyvXlt3X7Mnplr0ZjFS6v2JQXDcuQ5s+MBe6yzGTMSwuZ0D2rHC9WWJz03QxpELscnw+pdLkQO6HCWFTyWHe/LO4oMJ90fIgene0SONZZlsbemgxSPg8Kc1EH7d1e38fir5VQ3dhGJxKhq6Er8Yj25NIPapm78wWjieJsBbld8JcRI1CTD5+LSc2fx6tpqNu6Ot8aU5KVSVpieqJ75+ddPY0ppJn6/nw2btvDshjBrd8SPNQw4Wn/LNgxYNKOASSUZ7KxsZf2uxiGPy053M64gjQ27+lp95kzO4darlhGNmnz+lmcSX6PDZbcZiWHxAxXlpJKa4mR3VRvTyjL5+kUL+cED71BV38W4Ah/VDV1YPa2ONsOgrevAYWCGz0V7V3zlRafDxvii9MTcMIiv3BmJxDAtKCtMIxCMJmY9paW4iJlm4s999qQcbr3qJC699VkCoRgQ/z5I8TjJzfTywSVlnLW0jGjMpDsQwWYzeODJrUmBn8NuY8msAmZPymHXvjbqWrqZWprJnMm5FOelkp7qor7FT2tHCLfLTorHQarHibf3325HYmGAqvpO1u1s4PSF4/B5nTzw1FZeWrWPq86fy+KZBXztZ6/Q0OLvqRacwnmnTErMYDtS/uvn8RbX6WVZ7NjXSlFuKvf89weTFi94L/Qz5OAq7rwMM9hFyZV34i6YgH/3Gur+9oO+A2x2MGOJp0Wf/w7eiSeMwJXKcDje74/DyUtUASUiIiIixx3DMJhUcuCWqSmlmVz/hUWJ5+1dIVZtrSfV6+DE2UVU1Xfyvd+vJBiOceGHpnH2SRNwOmxYloU/GMXjsmO32zh1fgm//ucGguEoX/3MfNJTXazaVo/NMJhSmpk4v8th48bPz+fpFfv587PbMC1YNreIaWVZPP5aOV3+CCdMyWXulFyKc1PJ8LkxDKhp7Gbdzgaa24O4XXZ8Hid5WV7SU11YFliAaVrsb+xi/c4GQuEYS2YVUpLvo6quE5fTzvmnTWZ8T7sawKbyJjbsaqS+xU99s5/6Fj/ZGR6u//xCCrJTuPsfG9hb085nzpzGKfOLMQwDt9POucsmsGJLHYtm5DOxOCMxq8njttMdiLBjXyuBYJR50/JYPKOARTPz2bKnmbv+tp5AKEpRTirL5hbR1hUiEjWZXJLBM29XxAOg5ng49N+XLCE/O4WvfHo+N/36Darq46smZqe7aemIB08TitL5z0/N47HXdrNnfzvnnzYFLIt7H99Me1eYtBQXE4rS2VTexO6qNlxOOyfNKWT1tvqkCqs9++MVWmkpLi44Ywof/cBEdle1ccu9bxONmSybW4TH5eCsE8fzxGt7ADAt6ApE6ApEuO+Jzdz3xOZB31sOu8FlH53Fis11bNnTzNubapNWE9xR2cqTb+49pO9jpyMeYOVkeHn6zb3ETIt/vrSLaWVZrNwSb8n6+V/XMqk4nYYWPy6HjXDU5G8v7OSfL+1KfD+lp7px2A0soDsQIRSJUZrvozA7lX31nVTVd2KaFg67jblTclg8s3DQbLO9Ne2UV7fjsBt849LFfPWnL1Pb1M3jr5WzbG4Rj71azsotdVx67kzOWDSOTeVN/Ov1PXz6zKlMKzv0drBozGTnvlamlGbictrf/QVjgDO7iFDNLiIttbgLJhDtqYAy7E6sWCQpfAJVQMnxSxVQh0AVUCKjn+4PkYPTPSKjUSxmxtvcjnB1x5797fiDEeZMzgXiAZJpxX/5H42a2gLsb+xizuRc7AO+lg2tfr71v2/S0BrgpkuXJM2X+vuLO3lnSx2f//AMTpiay0urqqhu6ORzZ01PGlDfa8ueZjaXN/GRkyeSluLk5TVVbKto5VNnTKEwJ5VgKEpNUzcpHgeWFQ9UguEoJ80pSlrBcf3OBlZuqeOyc2cl5pG1dYawsDDNeAC5ZU8zj71azv7GeECW4nEQjVmkpTj56mfms3hmARB/j1fXVlNR28GUcZmU5PnYXtHCzqo26pu76QpEyMv0kpPhJRyN4Q9E8YcidAeiRGPmoM+YluKk0x8P0Ww2g1kTs9lc3gzEg6+fXLOchpYA//j3zkQL5Hthtxmct3wSn/3QNN7cUMPzKyspr27DtODE2YXcfPmJ3PvYJv71+p5Br7UZ8Kkzp/L4q+WEe6oFf/a108gfYsW83sH3HZ1d7CnfSUZuGb95dCvl1e2MK/DxXxcuPKzwarRqePyXdG1+jazTv0DWBy6g9Y1/0vrqX3EXTSFUu3vQ8XnnfZW0E4ZevVSOP8f737EOJy9RAHUIFECJjH66P0QOTveIyIHp/ji4cCRGW2doyIDiWGaaFh3dYXwpzvccHsZMa1Ao1ysSjbGvrpNX1lZT09jN2SeWMX96Pn96ZhvvbKnjP86bzZKZBfz8r+t4fX01/++CE/jIyRMTr69u6GTDriZaO4J0+MPEYvFf61K9Thx2g8raThpa/ZTm+5hQnI7baaejO8yqrfWJeWA2I1711asoN5UbvrCIaWVZRKIxHnu1nOdWVFLf4md6WRbZGZ6kai+nw0YkajK+MI3CnFTKq9tYOKOA0xaW8Mqaal5dt59wJF69YxjxysX+bZ42m8HF58zg02dOPejsrdGu9fV/0PraQ/hOOIP8875K0zP30rH2OdIWnk3n2ucHHZ9z9hVkLDl3BK5UhsPx/jNELXgiIiIiInJMcDntx134BPFw5P2uAHeg8Anig/Ynl2YyuV8rJ8AVH5/DFR+fk3h+w8WL+H+fOmFQVVhpfhql+WmHfU2XnjuLNdvr+d+HN8bbM9M9XHDGFE6eW5y00qDTYeczH5zGp86YSkd3mAyfC9O0+PEfV/P2plpOnF3IFR+fwzd+9TqVdZ1U1nUC8PzKSp5fWTnofS0rXhG1cHo+/3HebP7+4k5eX7+fB5/exs59rfznp+eR6XPz9qZaXnhnH2ctLePkE+IDumuaunjk5d28tbGGvMwUpo/PYvr4LEryfKzaVs+GXY0U5aQyZ3Iuy+YWDWovPJiaxi5+9Y/1fOLUyZw059BWgDzSnNnx9420xOeL9bbgufLKwO6AWM+8OpsDzChmyD8i1ynyfimAEhEREREROYYN1ZL4fiyaUcDdN57Bzn2tzBiffdBZTP2DOLvd4KZLl1BZ18H4wnRsNoNvX3Eif31+B9PGZTKpJIPnVlayYWcjJ0zN49NnTmV8UTrhUJCNm7cxcdJkyoqyMQyDb1yymHlTc7nnkU2s2FzHqq31jCtIS1Rnrd5Wz5mLx9HWGWLdzobEUP9Ofzt7atp55u2KpOvcUdnKK2uruffRjZy2sJQLz55Ofta7B59/eW4Hm8ubKa9u5+4bzxjyNZGomVhpczj0roQXaYlXl/WugufwZePwZRNtb+g5rpBIUzVmWDOg5PikAEpERERERGSM8bgcnDAl77BfZ7MZTCzuWwBgWlkW37nypMTzE4eoIvJbEdK8dvIyvUmtdh8+aQITizO497FN7KhspaK2A4fdxqIZ+azcUse/V1cljl08s4BPnDqJ7mCUHZWtbK9oobqhk+njszl5bhH1rX5WbalnT007L7yzj5Vb6rjp0iXMnpRDKBLD2zPvKxyJsWJzLfOm5mGaFm9u3A9AIBTlV39fz3e/tCzpGtfvbODW/1vBqQtKuPZzC4ZllltvBZTp7yAW7E5UQNnTsrCnZfUFUDkl8QBKFVBynFIAJSIiIiIiIiNiWlkWd1x7Kntr2tlW0cL8qXkU5/lYs72ex14pZ/qELD64uIyi3NTEaz7Q05o30Bc+PINtFS3c+9gmyqvbufmeN7HZbMRMk7NPHM//u+AEfvzgat7ZWkdpvo/FMwuIxixK8300tPhZv7OR3zy8kU+fOZWC7BQsy+IPT28jZlq8vKYafzDKf1+6GKfj0FfvC4SiPPjUVk5bWMqMCdlDHmNze7GnZhLrbiPSXEOsqw0Ahy8Lhy+bUM9xrpwS/IClVfDkOKUASkREREREREbUxOKMpMqqRTMKWDSj4LDOYRgGsybm8OOvLufuv6/nlbXVmD2rDT63opKNu5uobeoGoLqhi+qG+CqHnztrOh1dIf7v8c08+3YFz6+o4MKzpjNtfBa7q9pwOWxYwMotdXzv/nf45heX4HEd2q/Sz7xVwZNv7mXD7iZ+feMZBxy27swuItbdRqhmJ5jxmU92Xyb2tJ5VAm0OHJnxr4cqoOR4pQBKRERERERERg230851n1/IRR+ejsNmY0dlK3f8ZU0ifPr82dN5+JXdhMIxMn1uPnBCEU6HneI8H4+/Ws76XY385fkdidlbHzl5IktmFnD771eydkcDt/1uBd++/ERSPO8+m2vdjnj7XFV9J+X725kyYOh8L2d2McGqbQQqtwBg86Zh2J04fPGqKUdaFjZPfD6VZkDJ8Wr4JqmJiIiIiIiIjADDMCjO9ZGfncLyBSXcdOliinNTufqTc7nowzO48QuLSE91ceHZ0xMtdYtnFnD7/zs5sQphVyCC02Hjk6dPZt60PL77pWWkeBxsLm/mu/etJBiOVypV1HbwwspKHnhyC+XVbYlrCIajbNnbnHj+8pq+mVYD9c6BCuxeC8QDJwB7ek78eXouNld8lUJTLXhynFIFlIiIiIiIiIxqy+YWs2xu3+yoE+cU8echBqYDnH/aZAwD7n9iMx9fPomcjHjwM2tiDt/7fyfz7XveYsueZn7w+3cwDIO1PVVOAE+/VcGtV53ErIk5bNnTTCRqYrMZmKbFa2v3c/nHZmMfYpC5p2wmAFYsAoArbzwAqVOXEJx3JqmzTsHm8gBqwZPjlwIoERERERERkX4+cepkzlpallg9r9fUcVl8+4qTuOXet1m3sxEAhz0+e8ofjLC7up3v3Ps237nypEQwdfrCUlZvq6etK8S6nY0snjl4tpWndAbj/vPXRNsaMKNhvGWzgPiA8ryPfQWAcOM+4N1b8KIdzdh9mRi2Qx+WLnI0KIASERERERERGeBAM55mT8rhm5ct4Vd/X8/Midlcdu4sinJTCYajfP/377B+ZyO3378yEV4tnVVIisfBk2/s5c/PbWdaWRbpqa5B53VmFeLMKsQ0LXpnlQdDUR58ZhvL5hYxM69nBtRBKqBCdXvZf9+N+OaeSv7Hr32fXwGRI0szoEREREREREQOw+KZBfzhOx/mpkuXUJSbCoDH5eDbl5/InMk5+INRmtuD2Aw4YWou550yiVSvk91VbXzjV69R3xIPkUzT4rV11TS2xqua9ta085lvPsnvn9wKwDNvV/Cv1/dw76ObEjOgiEWxopEhrytQsRGwCDdWD+8XQOQ9UAAlIiIiIiIicgS4nHZu/o8TmVicDsRb9tJSXBTn+fjJV08hL8vL/sZufvSHdzBNi0df2c1P/7SGn/11DQDPragkHDV56s29+IMR3txQA0BlXQdd0b6WugNVQYXrKwCwoqFh/JQi740CKBEREREREZEjJNXr5LarlvGxUyZy5SfmJLaXFabzk68ux+t2sLu6nSdeL+fvL+0EYHN5M3XN3azYXAtAOBLj8VfL2bGvFQDLgu2VbRg9g8g72zuGfO9Q/d748REFUHLsUQAlIiIiIiIicgRlpXu4+pMnMGNCdtL23Ewvn/3QNADue2IL/mA0se93j2+muT2YeP7QizuTXrt5TzNRmxuAvzy5ftB7mpEQkab9AFjR8BH5HCJHkgIoERERERERkaPk48snkZ+dknh++qJSAFZuqQNg1sRsDCM+HwpgxvgsALbsaaIzHG/Dq9xXT6xnf69IYxVYJhAPow5XQ4ufQCj67geKvEcKoERERERERESOEpfTzpUfnw3AyScUcfUnT8Bh7/vV/Lzlk5g7ORcAwyDRxre7qo22cPw4IxqkuqGThhY/V//wRf7x0k5CPfOfAKxIGMtKDqgOpqq+ky/98EV+9pc17/fjiRyQY6QvQERERERERGQsWTa3mP/7nw+Rk+HF6bCxdHYBb22sxemwsWhGAdGYxcbdTcydnMv08dnkZ6fQ0OInZDkB8BgRdla20tQepKapm0dfKef0k/utfGeZYEbB7jyk69m6t5mYabFmewORaAynw/7uLxI5TAqgRERERERERI6ywpzUxONzTprAWxtr+cC8YrxuB6ctKCHF42BqaSYAcybl8O8WP8F+AdSOfa1U1MaHkXf6w3RU7cbod34rEsY4xACqtqkbgEjUZM/+dqaPz36XV4gcPgVQIiIiIiIiIiNowfR8fvvND5KT4QXAMAyWzipM7J87OZd/r64CV3y/x4iwbmcjTa3++PFYmM376F+3FAmHcHtSORQ1PQEUwI7KVgVQMiw0A0pERERERERkhBXn+nA7h259O31RKZ8/ezpzZ8QHlnuMCA0tfnrnkOfaOrGbYUzDQdiKn+O2e15le0XLIb13TWNX4vGOytb38SlEDkwBlIiIiIiIiMgxzGG3cdGHZ5CdE18RL9NtJvYtm1tEkT0eGtXEMhNzopqa2rn9/pV0dIcPem7TtKht9ieeb688tNBK5HApgBIRERERERE5Dtjc8Ra8vNS+aU/nnTKJMl88ZGqI+DBt8Uk7pTluOrrDPPDkloOes6UjSDgSw2YzMAxoaA3Q0hEcpk8gY5kCKBEREREREZHjgM2dAkCWJ14BleJxMHNiNlOyYgC0mKmkpMbnPn3mtPEAvPDOPu55ZCM/enAV72ypG3TO3gHkBdkpjC9MB9SGJ8NDAZSIiIiIiIjIcaC3Airba+F1O/jwSRNw2G0Ue3oqllJzSE2LB1Dj87x8+KR4CPXUm3t5c0MNv/7nBszewVE9apri85+KclOZPj7e4rdDbXgyDLQKnoiIiIiIiMhxwJYSr1Cyhzv5+w8+imXFw6Q0q4MwcN45SzA3NgBgRUL8x8cWYZoWNpvBa+uqaekIsru6jWllWYlz1jTGK6CKc1OZVJzBcysq2a4KKBkGCqBEREREREREjgOOtBwAYp3xCiXDiM+CirY3AZBTUkrzNjcAZiREmtfJtZ9bAEBXIMKbG2pYsbk2KYCqbe4NoHzMmJANwK6qNmIxE7tdTVNy5Oi7SUREREREROQ44EiLB0RmyI8ZDvY9Dsbb6BwZeRgOFwBWNHn1u5PmFAGwYnPyHKiaxr4WvJI8H6keB+FIjL21HcP3QWRMUgAlIiIiIiIichwwXF4MpweAWFe8Cira3giAzevD5vZiOHsCqEgo6bWLZxZgtxlU1Xfy1sYabvvdCv724g5qm/0AFOelYrMZieooDSKXI00BlIiIiIiIiMhxwDCMRBVUtKcNL9ITQDnS8+LHOOIteAMroHxeJ3On5ALwwz+sYvW2ev70zHbCkRg2m0F+VnyFvd42PA0ilyNNAZSIiIiIiIjIccI+IIDqrYByZMQDKFtPBZQ5oAIK+trwAGZOyMbltANQkJ2Co2feU+9KeBpEPrx27mtld1XbSF/GUaUh5CIiIiIiIiLHid4KqNgBAqhEC96ACiiAMxaVsrm8ienjs/n48knsq+/kvic2c8q84sQx03ta8GqbumnvCpHhcw/fhxmjOrrDfPPXb+B02rn/f04f6cs5ahRAiYiIiIiIiBwn3q0CKtGCFxkcQKV4nPz3pUsSzycUpXP71ScnHeNLcVGa76O6oYud+1pZMqvwyH+I48DD/95Fhs/Nh5aWHfFzbypvIhw1KcxNPeLnPpapBU9ERERERETkOHGgCijnoBa8wQHUoTpQG15Hd5iv/+JV/vzs9vd87mORZVk88vIuXllTBUBVfScPPLWVX/1jPf5g5Ii/38Zd8T+zE3pmco0VCqBEREREREREjhPvWgHl7B1CPngG1KGaPj7+Hk+9sYef/ml1YiD5is217K5q49FXdxOJmod8vprGLh55eRfbK47NweY797Xy+ye38su/rSMcibFnfzsApmmxde+Rv+aNu5sAOGFK3hE/97FMAZSIiIiIiIjIccKRlgNArKsFMxom1t0W334ILXiHavGMAtJTXXQHo7y2bj93/nktlmWxZU8zAKFw7JBWyQuGo9x+30qu/tFL/P7Jrfz4wVVYlvWur7Msi4de2MFbG2ve82c4HCu31AEQjVlU1Hawt6Y9sW9zedMRfa/m9gDVDV0YBsydnHNEz32sUwAlIiIiIiIicpxw+OLtcdHO1kT1k+H0YPOm9TzuHUL+3iug8rK8PHDL2Xz/yyfjsNuobe5mf2NXIoACWN/TRnYwL6+p5p2tddgMMAxoag9S3+J/19dtKm/iz89u52d/XUswHH3Pn+NQvdMTQAHsrm5jb21H4vnmfp/5SNhUHj/fpJIMfCmuI3ruY50CKBEREREREZHjhN2XBRhgRgnVlgPgyMjFMAwAbD0VUO9nBhSA02HnhCl5zOmp0nluRWVSeLRh57sHUC+srATgso/OZtq4eHC2racNb3tFCw095wuGonzv/pX87cUdAGza3VdptXZ7AwDl1W00tQUO+3PETOugVVd1zd1U1nUmnu+uaqOiXwXU7qo2gqHDD8GiMZNYbHCbYt/8p7HVfgcKoERERERERESOG4bdgT01A4CujS8D4Mof37e/twIq8t4roPpbPLMAgKfe3AtAToYHgJ1VbTS3B/j9v7bw2rrqQa+rqO1gV1UbdpvBmYvHMXNifK7Utr0t7NzXyjfufp2bf/sWpmnxxoYaVm6p46/P7aArEGHznr62t7c21rK9soXrfvEqN9z1GoFQlGjM5A9PbeXFd/Yd9Nq37m3m8tuf5zv3vn3AY3qrn1xOOwDrdjTQ0hHCMCAzzU3MtBKh2cFU1Hawv7ELiLce/udP/s11v3iNmBkPv55bUcEvHlrLis3x9xtrA8gBHCN9ASIiIiIiIiJy6Oy+LGLdbQT2bgQgdeayxL4j0YLX3+KZBfzu8c2JoeMfmFfMqq311DZ18993v5GoimpuD/LJ06ckXtdb/bR0diGZaW5mTsjmsVfL42GOAZYFtU3dbCpv4vUN+4F4tdLbG2vY0W/1vXe21tHY5se04u/x6Cu7sdkM/vnvXRgGFOSkMHtiDq+srSYn3cO8afHKolfWVPHLv60nGjNp6Qiyr66DssJ0qhs6icYsxhemYRhGYv7TuSdP4LFXy2lqDwJQmJPK9PFZvLKmmudWVvK3F3eSl+Xl6xcuxGYzaGjx43DYyE730NDi57pfvIrHZef+m89mw65Gapu6Adi1r5X0VBd3/2ND4jO5nHZm9QRyY4kCKBEREREREZHjiCMtm3B9vCLJcHpImbwwse9IDCHvrzg3laKcVGqb44HKnEk5RCImtU3d1Lf4sdkMTNPi/n9t4Y2eIMnjcrC7ug2As0+MV2f1VkBV1nXQ2NrXyvf4a+VJ7Xx/fWEHkahJZpobu82guT2YtBLdwy/vTrS2WRb84q9rmT4+m9fXx9/7nGUT6A5EEs9dDhvhqMmbG2qw2QyuueMVojGT/CwvLqed6oZ41dK5J0/klTXVtHXFg7uJxenMmZTLK2uqeXND3zD0JTMLGF+YznW/fI0Ut4Pf/PeZPPnmXiJRk0jUZNXW+qT5WKu21ZPidiTOecaiccyckE2Kx/m+/lyORwqgRERERERERI4j9rS+1dNSpi7C5nQnntt6KqDM6KEHULFAF7V/+S6+2R8g86RPJO0zDINFM/N58o144DVrYg6mBc+8XYFhwE2XLqayrpM/P7udnfvakl6bm+llwfR8ALLSPIkgqzsYTQRDq7bWA5CW4qTTH6GxNT7nac6kHDLT3In3PW/5JHbta2V7T3XU0lmFVNR10NDip6F1P3abQcy0ePbtivjXwYBPf3Aahdkp3PX39byxsYbGtgDRnvCqobVvntRJcwopyk1lyrhMVm+LX8+EogzmTun7OhflplLb1M0fnt5GWoqTcCRGOBLjgSe38mZP8Abw6rpqduzrq+BavbUepyM+/egjyybwkZMnHvKfy2ijAEpERERERETkOOJI62vf6t9+B2A4emdAHXoAFareQbiunM5YZFAABXDSnCKefGMvk4ozyPC5OXF2IR8/dRIzxmezbG4xy+bCwun5NLUFcNht+IMROvxh5k3Nw24zEueZOTE7UUl17gcm8s6WOmp6WtU+efoUnn5zb6IFbs6kHCYUZ/DkG3vxuOx89oPTqGvu5r/vfp10n5trPzefqvpOvnXPW3hddr75xaXETItf/X09WWlu/vPT85hSmkmXP8xvHt7AvrrORLXT7VcvIxqLz2aaXJpBVlp8rtWU0r4AamJxOsW5Pq7/wiJcDhsLp+dz9Y9eigdeLeB22QmFYzzf02rYG6D1tvR5XHZCkRh7egaaGwacOKfokP9MRiMFUCIiIiIiIiLHEXtafEW5ge138W29LXghLMtKrI53MGbPvCjrAFVT86bm8e3LT6Q03weAw27jqk/MTTpmWlkW08qyDvo+Mydk8+/VVQCcuXgcPq+TPz27HYDl80toaQ/yZM+w8zmTcxlflM7XL1pIYU4KmWluMtPc/Oy/TiM91U2GL/7P/37jTFK9TjJ88c99/81nJX1mX4qLeVPzWLO9AdO0mD0ph/nT8oe8vqnjMhOPJxbHB72fvrA0se3ic2Zw19/XA/DVz8zntXXViQquC8+ezjNvVSRCrkUzC2hqCyTmWc0Yn012uuegX5/RTgGUiIiIiIiIyHHEO+EEbN400hecldR+B2DrqYACC2JRcLz7rKHeFfMOtnLe0tmF7/l6ey2ckY/HZWfquCwmFmeQnuriX2/sYcb4bApzUjn5hGKefHMvmT434wrSgHhQ1d/k0syk58V5vqTnQwVup8wrZs32BgA+dcaUQft7TR+fhdftIMPnIj/LO2j/mUvK2Fvbgc/r5LQFJUwpzWD9zkY8LgcfWlJGlz/CX5/fAcRbBBtb/YkA6qQxXv0ECqBEREREREREjivOzHwmXPfAkPuMfoGUGQlhP5wA6jDmRr0X+Vkp3Hfz2bic8ZlIORle/vCdc+iNjOZMzuFrn5tPcZ4Pm+3dK7cO1bK5xfz9xV3kZ3tZPLPggMdl+Nz84uun4XLahwyy7DaDL53fV/lVmp/GXdefjtNhJ8XjZPn8Ev76/A7sNoPFMwtobPUnKryWzVUApQBKREREREREZJQw7A6w2cGMHXKgZPbMizIPUgF1pKSnupKe958RZRgGH1o6/oi/Z6rXyb3/86FDakkcWFH1bkrz0xKPxxWkcePFi/C4HKSnukhLcfKZD07F7bJTlJv6nq59NFEAJSIiIiIiIjKKGA4XVjhw0Ja6/hLHxaJYZgzDZh/Gqxs5hzIP6/06dUHfzCjDMLj03FnD/p7HC9tIX4CIiIiIiIiIHDm9c6EOtQLKiob6PR7eNjwZuxRAiYiIiIiIiIwiRs8g8kNtqet/nBVRACXDQwGUiIiIiIiIyChiOOMB1CFXQPULnczo8M+BkrFJAZSIiIiIiIjIKGI4elrwDncGFKqAkuEz4gGUaZrcddddLF++nPnz53PVVVdRVVV1wONbW1u5/vrrWbJkCUuXLuW2224jEAgkne93v/sdH/7wh5k/fz4f/ehH+cc//nE0PoqIiIiIiIjIiLM5e1vwDnUVPAVQMvxGPID6zW9+w1/+8hduv/12HnroIUzT5MorryQcHvqb/tprr6WyspIHHniAX/7yl7z66qvceuutif2//e1v+e1vf8vXvvY1nnjiCS699FJuvfVWHnvssaPzgURERERERERGUF8L3iFWQCUNIVcLngyPEQ2gwuEw999/P9deey2nn346M2bM4Oc//zl1dXU8//zzg45ft24d77zzDj/+8Y+ZPXs2y5Yt47vf/S6PP/449fX1APz1r3/l8ssv59xzz6WsrIzPfe5zfOITn1AVlIiIiIiIiIwJfS1472EG1CG27YkcrhENoLZv3053dzfLli1LbEtPT2fWrFmsWrVq0PGrV68mLy+PyZMnJ7YtXboUwzBYs2YNpmny4x//mE9+8pNJr7PZbHR0dAzfBxERERERERE5RhzuEHK14MnR4BjJN6+rqwOgqKgoaXt+fn5iX3/19fWDjnW5XGRmZlJbW4vNZksKswBqamp46qmnuPDCC9/XtVqWhd/vf1/nGGm9s7L6z8wSkTjdHyIHp3tE5MB0f4gcnO6Ro8807ACE/F34/X4syySw8WWcJdNw5o4bdHwsHEw8DnZ3Yhznv/seT473+8OyLAzDOKRjRzSA6v0Cu1yupO1ut5v29vYhjx94bO/xodDgMsGmpiauuuoqcnJy+PKXv/y+rjUSibBt27b3dY5jRUVFxUhfgsgxS/eHyMHpHhE5MN0fIgene+To8XZ04QEa62qo2rYNZ8MufGv/QSRrHF0nXjLo+HR/F/aexzVVlYStzKN5ucLxfX8MldMMZUQDKI/HA8RnQfU+BgiFQni93iGPH2o4eSgUIiUlJWnbnj17+NKXvkQsFuPBBx8kPT39fV2r0+lkypQp7+scIy0QCFBRUcGECROG/PqKjGW6P0QOTveIyIHp/hA5ON0jR19n0wa690FORjrpM2fS2byRbsBjRRg3c+ag4+tftbB6Hhfm5ZA6xDEyPI73+2P37t2HfOyIBlC97XQNDQ2UlZUltjc0NDB9+vRBxxcWFvLiiy8mbQuHw7S1tZGfn5/YtmbNGr785S9TUFDA7373OwoKCt73tRqGMSjkOl55vd5R81lEjjTdHyIHp3tE5MB0f4gcnO6Royfk9dENODBJSUmhrbEyviMSGPrPoN+sKKdh6c9pBByv98ehtt/BCA8hnzFjBj6fj5UrVya2dXR0sHXrVpYsWTLo+CVLllBXV0dlZWVi2zvvvAPAokWLANi4cSNXXnklU6dO5c9//vMRCZ9EREREREREjhe9Q8jNaAjLjBGqiVepmMHBs50sy8LSEHI5Cka0AsrlcnHxxRdzxx13kJ2dTUlJCT/96U8pLCzk7LPPJhaL0dLSQlpaGh6Ph3nz5rFw4UK+/vWvc+utt+L3+7nllls4//zzKSgoIBqNcsMNN5CTk8OPfvQjQqEQjY2NANjtdrKzs0fy44qIiIiIiIgMO8PRswpeJEykuQYrHJ+/bMUimNEwNkffzJ6BK+WZ0cHzlUWOhBENoACuvfZaotEoN998M8FgkCVLlnDffffhdDqprq7mgx/8ID/84Q+54IILMAyDu+++m9tuu43LLrsMt9vNOeecwze/+U0gXv3UWx31oQ99KOl9SkpK+Pe//33UP5+IiIiIiIjI0dRbAWVFQoRqdiXtM4N+bL5+AdSAiidVQMlwGfEAym63c+ONN3LjjTcO2ldaWsqOHTuStuXk5HDXXXcNea6FCxcOOl5ERERERERkLLE53UC8uik4MIAK+cGXmXhuDah46t+OJ3IkjXgAJSIiIiIiIiJHjuGKr6YWbqwi2tGUtM8MJc+BMgcETgNb8kSOlBEdQi4iIiIiIiIiR5Z3/Byc2cXEutuItNQCYPP4ADCD3UnHDqx4GhhIiRwpCqBERERERERERhGby0Phhd/ClpIOgD01E1feOGBwBdSgGVCqgJJhogBKREREREREZJRxZhVS+Nn/wZGRR9qCs7C5UwAwQ8kVUINa8FQBJcNEAZSIiIiIiIjIKOQpmUrZV+8h+7QLsXlSgfgqeP0NDJxUASXDRQGUiIiIiIiIyCh3oAqoxCp4Rjwe0AwoGS4KoERERERERERGub4AauhV8Gze+JDygTOhRI4UBVAiIiIiIiIio1yiBe8AQ8jtPQPLx1oLXrBqO+HGqsTzcGMV0Y6mEbyi0UsBlIiIiIiIiMgol6iACg5oweupgLJ70+L7x1ALXqy7nZo/3ULdQ98D4l+b/ffdSM2D3x7hKxudFECJiIiIiIiIjHLv3oIXD6DGUgVUtLMFzBjRzhYsyyLa3YYVixBtb8AcQ1+Ho0UBlIiIiIiIiMgo11cBNaAFL5pcAYUZw4pFj+q1jZREGGeZYEaTVgQ0/Z0jdFWjlwIoERERERERkVGubwbUwBa8eKWPLSWtb9sYqf7pXw1mRsJJA9hj/o6RuKRRTQGUiIiIiIiIyCj3bi14do8PMJK2jXb9wzgrEkr63LGAAqgjTQGUiIiIiIiIyCjXvwXPsqzE9t62M8PpxnC64tvGSgVUv3ZEKxIa0IKnAOpIUwAlIiIiIiIiMsr1tuBhmUlBS3IA5U7aNtolt+CFkoK3mGZAHXEKoERERERERERGOcPpBiMeAZjBvtaz3tXebE43hiNeAWVGxkgFVP8WvGg4uQVPFVBHnAIoERERERERkVHOMAxsnsFzoPpXQNkSLXhjpAJKLXhHlQIoERERERERkTHA5u5dCW+oAMqF4ehtwRsrFVAHacHTEPIjTgGUiIiIiIiIyBjQN4i8XwteTwBl6z+EfAwGUINWwdMMqCNOAZSIiIiIiIjIGDB0C148bDIcbmy9M6DGTAtevxlQA1rwNAPqyFMAJSIiIiIiIjIGJCqg+gdQ0aFWwRuDFVDRsGZADTMFUCIiIiIiIiJjgM0zeAZUcgteTwA1Viqgkr4O4aTV/2KBTizLGonLGrUUQImIiIiIiIiMAQNnQFlmDGJRoHcI+dieAZUUvJmxpP3y/imAEhERERERERkDBrbg9Q+aDKcbW08FVP9h3KOVFYsmtdxZ0eQZUKA2vCNNAZSIiIiIiIjIGDCwBa8vaDIw7M6+Cqgx0IJnhgLJzyODA6hYQCvhHUkKoERERERERETGgEEteP0HkBsGhnPstOCZoe6k59aAGVAAse72o3lJo54CKBEREREREZExwOaOV0BFO5qwLDNR8dMbPBmO3ha88KgfwG0Gk+c7Wf0roGz2+DGqgDqiFECJiIiIiIiIjAHuwolg2Ag3VNL0zL2Y4b4V8OL/jgdRkcZK9v3qappf/MOIXetwG1gBZUZCWNF4BZQjPQeAmGZAHVEKoERERERERETGAGdWIfmf+Bpg0LnuBeof/gkQb8Hr/+9QbTmxzmb8u1aP1KUOu4Er3FnRUGImliMjD1AAdaQpgBIREREREREZI3yzTyHvY/8Jho1YZwsAjvR44NI7hLzXwJDmcFmxCE3P3UfX1jff13mGw6AAKhJOzMTq/XrE/GrBO5IcI30BIiIiIiIiInL0pM07E8+EOUSa9mOG/HjKZgF9rXi93m8A1bX1LTpWP41/9xp8sz7wvs51pPV+NsPlxQoH4nOvIskteGbg8CugLDMWP2/PHCnpowBKREREREREZIxxZuTjzMhP2ma4PD0PbGCZWNEwViyKYX9v0UF3T+WTGex6X9c6HHqHkDt8WURaAliRQL8ZULnA4bfgWbEo1fd+HZsnleIv/hDDMI7sRR/n1IInIiIiIiIiInjKZpE6Yxm551yV2GaGA4d1js5NrxCq20ss0Il/z/r4OUKBY25Vvd4KKLsvC4BYoC8ke68zoKKdzURaagjV7EqEWYF9Wwnu33UkLvm4pwooEREREREREcHmcFHwqRsAaH7h91jRMGbIj92bdkivD9XspvGJX2G4PKQvPBt62tGwTKxIEMPlHa5LP2xmML4Knt2XGX/eP4DqrYDqbsOKRTDszkM7Z7+ZUWagC0yTur98FxxOJlz3wJhvy1MFlIiIiIiIiIgksblTgHj10qEKN+8HwAoHaV/xRNK+3pa3Y0VvBZSjpwIKywTig9id2YXYUzPjn2P1M4d8zligXwAV7E4EWFbIf1hfx9FKAZSIiIiIiIiIJOkLoA49OIp2NA1xonjVz+G28g23RAteWnbSdsPpwrA7yTr98wC0vf4PYt3th3TO/gFULNiV1NZnhrrf7yUf9xRAiYiIiIiIiEgSmzveLndYAVR7IwDeSfMwnG68k+bjSMs57PMcDQNnQPUyHPGVANNOOB1XwUTMkJ/W1/9+aOccUAHVf/h6b8vfWKYASkRERERERESSGD0VUNZhtI5F2+MVUKkzT6bsmnsp/Ow3+4KsYyyA6a1IcqRmJm23OeMBlGGzk/OhywDoXP8SViz6rueM9Z8BFexKmit1rH3+kaAASkRERERERESSvLcWvHgFlCMjD7vXh2F39J3nKLfg+fesJ1RfccD9vTOpbCnpiTZBAKMngALwjJ+N4XRjxSJE2urf9T0HVkDFkiqgjq0KsJGgAEpEREREREREkhxuC55lWYkKKEd6Xr/z9ARQRzGAiXY0UffX71H/jx8ltgX2bSHcVJ143vu5bO6UpNDJcLr6Hhs2nDklAET6vfZAkmdAdSdXQPVUXJnhIGYkdLgfaVRQACUiIiIiIiIiSQ63AsoMdGFFggA40nP6zuNJPazzHAmRllrAItreiGXGiHa2UvunW6l76HsA8ZXpouH49blTsDn6QidbvzAKwJVbCkC4af+7vm9yBVRXUgVULNiNFYtS9duvsf93N2D1rLo3liiAEhEREREREZEkNtfhtc71tt/ZUzOSQhyb6+CVVN07VxFurDrgec1QAMuMHdI19Ip1tfU99ncQbasHy4wHUtFIUjWWze1NroByJAdQiQqo5kOogPIPGEI+YAZUtKuFWEcTkZYazMOYrTVaKIASERERERERkSSH24I3VPsdgM1z4EqqcFM19f/4EfWP/mzIc8b8HVT+6kuJyqVDFe1q6TtHdzux7vZ+52wn1jMQ3HB5MWz2A7bgQV8FVKSpGsuM0fjkr2l+8Q9Dvm9SBVSgK/l5qBuzu2PIY8cKBVAiIiIiIiIikuRwW/D6BpDnHvA8lmXRseZZgtXbAYi01sVf29Yw5DlD9XuxQn4ClVsPq2Ut1tkvgPK3E+tu63ve1UasqzV+rb7M+DUeQgVUuHk/wertdG74N+0rnyC4f9fg9w0kt9wNHEIeGxBQjTUKoEREREREREQkSV9wdIgteL0VUBnJFVCGqy+ACtfvpenZ/6PxX78G+lrlrMjQg7l7z4kZJdbZesjXHu3qO9b0dyRXQHW3JwIouy8rfo3O/jOgkiugnNmFYNiwwkE6172Y2N6x+umk48xoODEDC+IzoAa24MX8/a4jOPYCKMdIX4CIiIiIiIiIHFsO1oJnhgLU//PHxPyd2FMzSJv/QaLtPRVQ6QMqoHpa8KyQn2hb/JhIWwOWZSZVJpmBrkEDwGMdzYnH0fbGpOHmBxPrF0DFW/D63ifa3Zb4TIkAytG/BS/5Ggy7E2d2IZHmGrq2vpnY3rX1LbLPvBRHWvwcpj+5pc4M+sGK9XveRczfrwWvpw1wLFEFlIiIiIiIiIgksbkPvHqdv3wtgYpNhBsqCOzdQOOTvyZUvxcYXAHVvwUvUZlkRjH9nclBUb9wpldvWx9ApH3oNr2hRDsHzIDyH7wCqn/V08AACsCZU9pz3TEwbLjyx4MZpWPd833nDQwMoLqIBfpCJjPUjenXDCgRERERERERkYTeCihriFXwQjW7AUiZugRXwUSsSIhozzynQUPI+1VS9Q+cop0tSa1yscBQAVS/CqgDzIkayLKsQcFWcgte/xlQvS14/VbtGyKA6h1EDuApnU7mBz4FQOfa5xOzqXoDpd5Qy4qGwYz2vW/QT6zfEPKYZkCJiIiIiIiIyFiXqFwKDq6ACtXGA6jU6UvJPu2ipH2Dh5D3VlIFBgwDb0luwfMPrgiKdjT1PW5vHLQ/8dpQgP2/v4nGp+/BCgew+s2TGtiC1z+AsvcMIU9qwXMkz4ACcOaWJB6nTFlE6vQTMVxeYt1thOsq4uftCZQcmfmAMfgag90DWvAUQImIiIiIiIjIGGf0BFBWLIIVjSS2W2aMUO0eANxFU/BOWYi7aHL8NU43Nm9a0nn6t+ANrIDqHUIOg1vwLMsaEEAlV0AF9+8i0lYPQPeuVYRqdtG5/iUiLXVJx8VXwUtuwYu+1xY84gGUYXfgLZsFQKBiY/zz9VZApaRj86T2vdgeH71thQcEcKqAEhEREREREZGxzubyJB6b/drwIs37sSJBDKcHZ24JhmGQtfxzALjyx2MYydU/iVa+aJhIvyqmaGdyBdTAAMoMdiVVMkX6teB1rHmWmgduoubBb2PFIvh3rIzvsEwCe9cnnSfa0ZQ0xyq5BS8bePcWPHf+eFwFE/FOmoczbxwA3oknAH0BVO/1271pSQGUs99MrEhrbdLnG2u0Cp6IiIiIiIiIJDFsdgyXByscxAz5saekA33zn9yFEzFsdgBSpi6i6OLv4sjMG3Se3googGhLXwATD7L6AqaBQ7mj7U29FwKWSbS9Ccsy6dr4Ck3P/h8Asc5muja/jr98XeJ1/t1rAbCn5RDrbCbWbyA5xAMpKxyMHzPEDKihKqAMh5PSK+9I2uadEA+ggvu2YUbDieu3edOweXxAvDrLnppJtLMVKxLE7Ff1ZKoCSkREREREREQEbK6+OVAda58nVFtOqLYcAHfxlKRjveNn48zIH3QOw2bHcMarqaxYXytfuG5P0nEDK6B62+9ceWXxEMqMEqzaRuNT/wuAI6sQgKYXfp8UZAWrd8Svr2BC8nX0zHbqDZ8MuzNRqWRLCqAGz4AaijNvHPbUTKxomFD1jkRLnd2bht3bVwFl86Ri86QMen1sDFZAKYASERERERERkUF62+e6d62i6ZnfUvPHb+PftRqIz3863PP0F+lXDQUQG1gB1RNAOTLzcaTHB5u3v/04WCaestkUX/I9sDmwetrr7D3H0LMqnTO3BGx9TV/OnJJ4kNXD7stKtAv2Hzw+VAveUAzD6NeGt6mvAioluQXP5vUlz4TqoQooERERERERERH62ucCe9YDYEVCiWBoYAXUoZxnaPEQyPR3YJkxGv51N+2rnuoLoDJyE619/t1rAPDNWY4jLYvUmSclzpL1gU8lndXuy8aemt7veWaijTD+PKvvCvpXQDkOLYAC8E6YC0Bg78ZEgGZPtOD1vI/Hl1gJsD8z0IVlWYf8XqOBAigRERERERERGaQ3OOqd+9RbQWTz+nBkFhz2eQDsadlJ+xxZ8fPE/J0E922la+PLNL/4h8R7OtJzcfRv7TNspE5bCkDG4nPj50zNJG3eGRiuvkorR1o29pSMvvdNzcSemtn33Nf3+L204EHfIPJQzS7CDfvi5/IOrIBKw97vuT0tB+hdXTB8yO81GiiAEhEREREREZFBEq1zPW1tuedejWfcTDJPOn/QancHPU+/GUiuvDKwO/o9j68qFwt0EGneH99oxghWbgbiAVT/2VKespnYU+PBkqd0OoUX3kzhRd/GsDuT5j7ZfVnJFVCpGdh9fYGUo38F1Htoweu9Nt8JZwBgRXoGmw8InGye5BY8Z1YB9AxvH2tteAqgRERERERERGSQga1zqdNPpPjS75F58icP7zyufhVQviwcvr4qKFfe+PiDWJRgTfmg1zrSc5NW10udflLS/pTJCxLBk6tfADW4AipjQAXU+2/BA8g58xJs3n4tdynpyS14Xl9yBVhKOvae480xNohcAZSIiIiIiIiIDGL0C04cmfnYvWnv6Tz9AxiHLwt7Wl/448wqSARAoeptg147sAUvdfqJB3wfV8HExON4BdTAFryMpP29jPfYghc/bwbZZ1zS82LboKHj8VXw+rfkpSeex8ZYBZTj3Q8RERERERERkbGmf3DkLpz03s/jGVABlZZNqPd5aiZ2bxrRSCixMp4rfwLhhgqw2bH7MrF5fbhLp+MumIgjPeeA7+Mumhx/v5R0bE73QSug+rfg2fqFTv3b8Q5V2vwzibY3Yk/NwLDZkyqgBs6EildIxYM8M9DFoTcyHv8UQImIiIiIiIjIIIkZUIDr/QRQA1rw7P1a8Oy+TGwp6dCz6h1AzllfpPav38NdMB7DZsew2Sm57Afv+j7uggnknH0Fzp4B6bZ+q945UjOJHaACytZT2WVLST+s2Va9DMNG9ukX9Z27f+DkTV4Fz57a14IXC3aOqVBmLH1WERERERERETlE/YOjI1UB5fBl4ui3Ep49NQt7Sl9rn82bhnfCXEqv/GkiGDocGUvO7Xfug62C1xdAOXxZ5J//X0kVU+9H/5lQA4eQ21PSE/vH2hByBVAiIiIiIiIiMkjS6nX95isd9nncAyqgEgGU0VMR1Fep5Mwpib9fXtl7fr/Ee/UGSj2zmRIBlGFLWiEPwDd7+ft+v8T7pmZiOFwYTjc2T0rS19HWb0i5Gew+Yu95PFAAJSIiIiIiIiKD9LaO2dOycfgy3/N5jH6tfL0zoCDejmbY7Nj6VUC5egKoI8GZXYTh9ODMKcaw2XHlluAumYYzpxTDGL412WwuD8WXfh/D4cAwbNj7t+B5+7XgqQJKRERERERERMY6z7gZpM4+hZTJC9/XeXoroAx3CjanG3fJNLyTF+AdPwcguQIq98gFUHavj7Kv/Caxyp1hd1LyxR8esfMfjLuor2Vx8BDy3gqozqNyLccKBVAiIiIiIiIiMojN6abg/K+/7/M4s4oAI9FWZ3O4KLrw5sR+e8rgFrwjpf8cqJFiS0kHwxZv/dMMKBERERERERGRI8+ZmU/pVT9LGvzdX//V6o5kC96xwu5JJe9j/xmfC+VwYveoBU9ERERERERE5Ihz5R94qHhiFTy7A0dm/lG6oqMr7YQzEo8TFVBBBVAiIiIiIiIiIkeFK68MmzsFz7iZGDb7SF/OsEsMIdcqeCIiIiIiIiIiR4c9JZ2yr/0Ow+Ec6Us5KmzeeMWXFQ5iWeYIX83RowBKREREREREREaUrWelurHA7k0j69TPgc2BYdhG+nKOGgVQIiIiIiIiIiJHUdbyzwLg9/tH+EqOnrETtYmIiIiIiIiIyIhQACUiIiIiIiIiIsNKAZSIiIiIiIiIiAwrBVAiIiIiIiIiIjKsFECJiIiIiIiIiMiwUgAlIiIiIiIiIiLDSgGUiIiIiIiIiIgMKwVQIiIiIiIiIiIyrBRAiYiIiIiIiIjIsFIAJSIiIiIiIiIiw0oBlIiIiIiIiIiIDCsFUCIiIiIiIiIiMqwUQImIiIiIiIiIyLBSACUiIiIiIiIiIsNqxAMo0zS56667WL58OfPnz+eqq66iqqrqgMe3trZy/fXXs2TJEpYuXcptt91GIBAY8tg1a9Ywc+bM4bp0ERERERERERE5BCMeQP3mN7/hL3/5C7fffjsPPfQQpmly5ZVXEg6Hhzz+2muvpbKykgceeIBf/vKXvPrqq9x6662DjluzZg3/+Z//iWmaw/wJRERERERERETkYEY0gAqHw9x///1ce+21nH766cyYMYOf//zn1NXV8fzzzw86ft26dbzzzjv8+Mc/Zvbs2Sxbtozvfve7PP7449TX1wMQjUb54Q9/yGWXXUZJScnR/kgiIiIiIiIiIjLAiAZQ27dvp7u7m2XLliW2paenM2vWLFatWjXo+NWrV5OXl8fkyZMT25YuXYphGKxZswYAv9/PqlWr+N3vfsfFF188/B9CREREREREREQOakQDqLq6OgCKioqStufn5yf29VdfXz/oWJfLRWZmJrW1tUA8wHrkkUc46aSThumqRURERERERETkcDhG8s17h4e7XK6k7W63m/b29iGPH3hs7/GhUGh4LhKIRCJYlsXGjRuH7T2OBsuyANi1axeGYYzw1YgcW3R/iByc7hGRA9P9IXJwukdEDux4vz8ikcghX/eIBlAejweIz4LqfQwQCoXwer1DHj/UcPJQKERKSsqwXWfvF/N4/GbozzCMIQM8EdH9IfJudI+IHJjuD5GD0z0icmDH+/1hGMbxEUD1ttM1NDRQVlaW2N7Q0MD06dMHHV9YWMiLL76YtC0cDtPW1kZ+fv6wXeeCBQuG7dwiIiIiIiIiIqPdiM6AmjFjBj6fj5UrVya2dXR0sHXrVpYsWTLo+CVLllBXV0dlZWVi2zvvvAPAokWLhv+CRURERERERETksI1oBZTL5eLiiy/mjjvuIDs7m5KSEn76059SWFjI2WefTSwWo6WlhbS0NDweD/PmzWPhwoV8/etf59Zbb8Xv93PLLbdw/vnnU1BQMJIfRUREREREREREDmBEK6AArr32Wj796U9z8803c9FFF2G327nvvvtwOp3U1tZyyimn8PTTTwPx3sK7776b0tJSLrvsMv7rv/6LU089lVtvvXVkP4SIiIiIiIiIiByQYfWOXBcRERERERERERkGI14BJSIiIiIiIiIio5sCKBERERERERERGVYKoEREREREREREZFgpgBIRERERERERkWGlAEpERERERERERIaVAigRERERERERERlWCqBERERERERERGRYKYAaA0zT5K677mL58uXMnz+fq666iqqqqpG+LJGj7re//S2XXHJJ0rZt27Zx8cUXM3/+fM4880wefPDBpP26f2Q0a2tr45ZbbuHUU09l4cKFXHTRRaxevTqx/+233+aCCy5g3rx5nHPOOTz11FNJrw+FQtx2220sW7aMBQsWcP3119PS0nK0P4bIsGlububGG2/kpJNOYsGCBXzpS1+ivLw8sV8/Q0Ti9u7dy4IFC3jkkUcS23R/yFhXX1/P9OnTB/3Te5+MxXtEAdQY8Jvf/Ia//OUv3H777Tz00EOYpsmVV15JOBwe6UsTOWr+/Oc/84tf/CJpW2trK//xH/9BWVkZDz/8MF/5yle44447ePjhhxPH6P6R0ey6665j3bp1/OxnP+Phhx9m5syZXHHFFezZs4fy8nKuvvpqli9fziOPPMJnPvMZvvGNb/D2228nXn/rrbfyxhtv8Ktf/Yo//OEP7Nmzh2uvvXYEP5HIkfWVr3yFyspK7r33Xv75z3/i8Xj44he/SCAQ0M8QkR6RSIQbbrgBv9+f2Kb7QwS2b9+O2+3m9ddf54033kj8c+65547de8SSUS0UClkLFiyw/vznPye2tbe3WyeccIL1r3/9awSvTOToqKurs66++mpr/vz51jnnnGNdfPHFiX333HOPdcopp1iRSCSx7c4777TOPvtsy7J0/8joVlFRYU2bNs1avXp1YptpmtaHPvQh6xe/+IX17W9/2/r0pz+d9JrrrrvOuvzyyy3Lit9bM2bMsF555ZXE/j179ljTpk2z1q5de3Q+hMgwamtrs6677jprx44diW3btm2zpk2bZm3YsEE/Q0R63Hnnndall15qTZs2zXr44Ycty9LfsUQsy7Luvfde67zzzhty31i9R1QBNcpt376d7u5uli1bltiWnp7OrFmzWLVq1QhemcjRsWXLFpxOJ0888QTz5s1L2rd69WqWLl2Kw+FIbDvppJOoqKigqalJ94+MallZWdx7773MnTs3sc0wDAzDoKOjg9WrVyd970P8/lizZg2WZbFmzZrEtl4TJ06koKBA94eMChkZGdx5551MmzYNgJaWFh544AEKCwuZMmWKfoaIAKtWreJvf/sbP/rRj5K26/4QgR07djB58uQh943Ve0QB1ChXV1cHQFFRUdL2/Pz8xD6R0ezMM8/kV7/6FePGjRu0r66ujsLCwqRt+fn5ANTW1ur+kVEtPT2d0047DZfLldj23HPPUVlZyfLlyw94f/S2HtXX15OVlYXb7R50jO4PGW2+/e1vs2zZMp566im+//3vk5KSop8hMuZ1dHTwjW98g5tvvnnQ97nuDxHYuXMnLS0tfOELX+Dkk0/moosu4rXXXgPG7j2iAGqUCwQCAEm/YAC43W5CodBIXJLIMSMYDA55b0B8uLLuHxlL1q5dyze/+U3OPvtsTj/99CHvj97n4XCYQCAwaD/o/pDR6bLLLuPhhx/mYx/7GF/5ylfYsmWLfobImHfrrbeyYMECzjvvvEH7dH/IWBeNRtmzZw/t7e1cc8013HvvvcyfP58vfelLvP3222P2HnG8+yFyPPN4PED8l4XexxD/pvZ6vSN1WSLHBI/HM2iIX+9/0FNSUnT/yJjx4osvcsMNN7Bw4ULuuOMOIP4XnIH3R+9zr9c75P0Duj9kdJoyZQoA3//+99mwYQN/+tOf9DNExrTHHnuM1atX869//WvI/bo/ZKxzOBysXLkSu92e+B6fM2cOu3bt4r777huz94gqoEa53pK9hoaGpO0NDQ0UFBSMxCWJHDMKCwuHvDcACgoKdP/ImPCnP/2Ja665hjPOOIN77rkn8X/fioqKhvzeT0lJIS0tjcLCQtra2gb95Un3h4wWLS0tPPXUU0Sj0cQ2m83GlClTaGho0M8QGdMefvhhmpubOf3001mwYAELFiwA4Dvf+Q5XXnml7g8RIDU1NSk8Apg6dSr19fVj9h5RADXKzZgxA5/Px8qVKxPbOjo62Lp1K0uWLBnBKxMZeUuWLGHNmjXEYrHEthUrVjBx4kRycnJ0/8io17u07xe+8AV+9rOfJZV5L168mHfeeSfp+BUrVrBw4UJsNhuLFi3CNM3EMHKAvXv3Ul9fr/tDRoWmpiauu+463n777cS2SCTC1q1bmTx5sn6GyJh2xx138PTTT/PYY48l/gG49tpr+f73v6/7Q8a8Xbt2sXDhwqTvcYDNmzczZcqUMXuPKIAa5VwuFxdffDF33HEHL730Etu3b+frX/86hYWFnH322SN9eSIj6lOf+hRdXV1861vfYvfu3TzyyCM88MADXH311YDuHxnd9u7dyw9+8APOOussrr76apqammhsbKSxsZHOzk4uueQSNm7cyB133EF5eTn3338/zz77LFdeeSUQ/79zH/3oR7n55ptZuXIlGzdu5LrrrmPp0qXMnz9/ZD+cyBEwbdo0Tj31VL73ve+xatUqdu7cyU033URHRwdf/OIX9TNExrSCggLGjx+f9A9ATk4OBQUFuj9kzJs8eTKTJk3iu9/9LqtXr6a8vJwf/vCHrF+/ni9/+ctj9h4xLMuyRvoiZHjFYjF+9rOf8cgjjxAMBlmyZAm33HILpaWlI31pIkfVTTfdxP79+/njH/+Y2LZx40a+//3vs3XrVvLy8rj88su5+OKLE/t1/8hodc899/Dzn/98yH2f/OQn+dGPfsRrr73GT3/6UyoqKigtLeWaa67h3HPPTRzn9/v5wQ9+wHPPPQfAqaeeys0330xWVtZR+Qwiw62zs5M777yTF198kc7OThYvXsxNN93E1KlTAf0MEelv+vTp/PCHP+SCCy4AdH+INDU1ceedd/L666/T0dHBrFmzuOGGG1i8eDEwNu8RBVAiIiIiIiIiIjKs1IInIiIiIiIiIiLDSgGUiIiIiIiIiIgMKwVQIiIiIiIiIiIyrBRAiYiIiIiIiIjIsFIAJSIiIiIiIiIiw0oBlIiIiIiIiIiIDCsFUCIiIiIiIiIiMqwUQImIiIiIiIiIyLByjPQFiIiIiIxWN910E48++ugB9+fm5vLmm28exSuC6dOn89WvfpVrrrnmqL6viIiIjG0KoERERESGUV5eHnffffeQ+5xO51G+GhEREZGRoQBKREREZBi5XC7mz58/0pchIiIiMqIUQImIiIiMsEsuuYSSkhImTJjAgw8+SCgU4sQTT+Rb3/oWJSUlieM2bdrEL37xCzZv3kwkEmHp0qVcf/31TJ06NXFMQ0MDd955J6+99hrBYJDZs2dz/fXXs2DBgsQxXV1dfOtb3+KFF14gEomwfPlybrnlFnJzc4/q5xYREZGxQ0PIRURERIZZNBod8h/LshLHvPTSSzzyyCPcfPPN3HbbbWzbto1LLrmEQCAAwIoVK7jooosA+MEPfsD3vvc9amtrufDCCykvLwegu7ubiy66iJUrV3LjjTdy991343a7ufzyy6moqEi814MPPkgkEuGXv/wl119/Pf/+97/57ne/e/S+ICIiIjLmqAJKREREZBjt37+f2bNnD7nvG9/4BldccQUAgUCARx55hHHjxgEwadIkPvnJT/LYY49x0UUXceeddzJ+/Hjuvfde7HY7AKeccgpnnXUWd911F7/85S959NFH2b9/P48++igzZ84EYOHChZx//vmsWrWKCRMmADB37lx+8pOfALBs2TI2bNjAq6++OpxfBhERERnjFECJiIiIDKO8vDz+93//d8h9RUVFiccLFy5MhE8As2bNYty4caxatYpPfOITbNq0ia9+9auJ8AkgPT2dM844IxEerVmzhtLS0kT4BOD1ennuueeS3nfRokVJz0tLS+no6HjvH1JERETkXSiAEhERERlGLpeLuXPnvutxBQUFg7bl5OTQ3t5OZ2cnlmUNOaMpNzeXzs5OANra2sjJyXnX90pJSUl6brPZktoBRURERI40zYASEREROQa0trYO2tbU1ER2djZpaWkYhkFTU9OgYxobG8nMzAQgLS2NlpaWQcesXbs2MSdKREREZCQogBIRERE5BqxZsyYphNq8eTPV1dUsW7aMlJQU5syZwzPPPEMsFksc09nZySuvvJJoqVu8eDFVVVXs2rUrcUwoFOKaa67hn//859H7MCIiIiIDqAVPREREZBiFw2HWr19/wP3Tp08H4kPIr7zySr785S/T3d3Nz3/+c6ZNm8bHPvYxAK6//nquuOIKvvSlL/H5z3+eSCTCvffeSzgc5itf+QoAF1xwAX/84x/58pe/zLXXXktWVlZixbvPf/7zw/5ZRURERA5EAZSIiIjIMGpsbORzn/vcAfc/9thjQLx66aSTTuJb3/oWAGeeeSbf+MY3cLlcQHy1ut///vfcddddXHfddbhcLhYvXsyPf/xjpk6dCoDP5+NPf/oTP/nJT7j99tsxTZP58+fz4IMPJg04FxERETnaDEsTJ0VERERG1CWXXALAH//4xxG+EhEREZHhoRlQIiIiIiIiIiIyrBRAiYiIiIiIiIjIsFILnoiIiIiIiIiIDCtVQImIiIiIiIiIyLBSACUiIiIiIiIiIsNKAZSIiIiIiIiIiAwrBVAiIiIiIiIiIjKsFECJiIiIiIiIiMiwUgAlIiIiIiIiIiLDSgGUiIiIiIiIiIgMKwVQIiIiIiIiIiIyrBRAiYiIiIiIiIjIsPr/3U4QISosoiYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the training and validation losses\n",
    "\n",
    "model_history_df = pd.DataFrame(model_history.history)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Plot training & validation loss values\n",
    "\n",
    "plt.plot(model_history_df['loss'], label='Train')\n",
    "plt.plot(model_history_df['val_loss'], label='Validation')\n",
    "plt.title('Model loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6bc5a0d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#making predictions with the model\n",
    "def make_pred(input):\n",
    "    input_scaled = scaler_X.transform(input)\n",
    "    y_pred = model.predict(input_scaled)\n",
    "    y_pred_unscaled = scaler_y.inverse_transform(y_pred).flatten()\n",
    "    return y_pred_unscaled.astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "33480035",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116/116 [==============================] - 0s 1ms/step\n"
     ]
    }
   ],
   "source": [
    "y_preds = make_pred(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6e105ed8",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_array = np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ac0fdc",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "edae6e62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999977650797093"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating the coefficient of determination using r2_score\n",
    "eval_r2 = r2_score(y_test_array, y_preds)\n",
    "eval_r2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4559407",
   "metadata": {},
   "source": [
    "# Training Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6840a936",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the features and target variables\n",
    "X = df.drop(['price', 'state'], axis = 1)\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "452eb4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the training and testing sets\n",
    "\n",
    "X_train_2, X_test_all, y_train_2, y_test_all = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "#Creating the validation sets\n",
    "\n",
    "X_test_2, X_val_2, y_test_2, y_val_2 = train_test_split(X_test_all, y_test_all, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "df60bba0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bedrooms          int64\n",
       "bathrooms         int64\n",
       "toilets           int64\n",
       "parking_space     int64\n",
       "title            object\n",
       "town             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4621516e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating the nunerical and categorical features\n",
    "\n",
    "num_features = X.select_dtypes(include=['int64']).columns\n",
    "cat_features = X.select_dtypes(include=['object']).columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b90cf6d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining the preprocessing steps\n",
    "\n",
    "numerical_transformer = StandardScaler(with_mean=False)\n",
    "\n",
    "cat_transformer = Pipeline(steps=[\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore')),\n",
    "    ('scaler', StandardScaler(with_mean=False))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e32c20d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating the preprocessor, combining the transformation steps for numerical and cat features\n",
    "\n",
    "preprocessor = ColumnTransformer(transformers=[\n",
    "    ('num', numerical_transformer, num_features),\n",
    "    ('cat', cat_transformer, cat_features)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "381b0f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#defining the model using a pipepline and randomforest regressor\n",
    "\n",
    "model_2 = Pipeline(steps=[\n",
    "    ('preprocess', preprocessor),\n",
    "    ('model', RandomForestRegressor(\n",
    "        n_estimators=2000,\n",
    "        max_depth=None,\n",
    "        min_samples_split=2,\n",
    "        min_samples_leaf=1,\n",
    "        max_features='auto',\n",
    "        random_state=42))\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5fdeadb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\USER\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:413: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features=1.0` or remove this parameter as it is also the default value for RandomForestRegressors and ExtraTreesRegressors.\n",
      "  warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  StandardScaler(with_mean=False),\n",
       "                                                  Index([&#x27;bedrooms&#x27;, &#x27;bathrooms&#x27;, &#x27;toilets&#x27;, &#x27;parking_space&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler(with_mean=False))]),\n",
       "                                                  Index([&#x27;title&#x27;, &#x27;town&#x27;], dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;model&#x27;,\n",
       "                 RandomForestRegressor(max_features=&#x27;auto&#x27;, n_estimators=2000,\n",
       "                                       random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  StandardScaler(with_mean=False),\n",
       "                                                  Index([&#x27;bedrooms&#x27;, &#x27;bathrooms&#x27;, &#x27;toilets&#x27;, &#x27;parking_space&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),\n",
       "                                                                  (&#x27;scaler&#x27;,\n",
       "                                                                   StandardScaler(with_mean=False))]),\n",
       "                                                  Index([&#x27;title&#x27;, &#x27;town&#x27;], dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;model&#x27;,\n",
       "                 RandomForestRegressor(max_features=&#x27;auto&#x27;, n_estimators=2000,\n",
       "                                       random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(with_mean=False),\n",
       "                                 Index([&#x27;bedrooms&#x27;, &#x27;bathrooms&#x27;, &#x27;toilets&#x27;, &#x27;parking_space&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;onehot&#x27;,\n",
       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)),\n",
       "                                                 (&#x27;scaler&#x27;,\n",
       "                                                  StandardScaler(with_mean=False))]),\n",
       "                                 Index([&#x27;title&#x27;, &#x27;town&#x27;], dtype=&#x27;object&#x27;))])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;bedrooms&#x27;, &#x27;bathrooms&#x27;, &#x27;toilets&#x27;, &#x27;parking_space&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler(with_mean=False)</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>Index([&#x27;title&#x27;, &#x27;town&#x27;], dtype=&#x27;object&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler(with_mean=False)</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_features=&#x27;auto&#x27;, n_estimators=2000, random_state=42)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocess',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  StandardScaler(with_mean=False),\n",
       "                                                  Index(['bedrooms', 'bathrooms', 'toilets', 'parking_space'], dtype='object')),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('onehot',\n",
       "                                                                   OneHotEncoder(handle_unknown='ignore')),\n",
       "                                                                  ('scaler',\n",
       "                                                                   StandardScaler(with_mean=False))]),\n",
       "                                                  Index(['title', 'town'], dtype='object'))])),\n",
       "                ('model',\n",
       "                 RandomForestRegressor(max_features='auto', n_estimators=2000,\n",
       "                                       random_state=42))])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting the model\n",
    "\n",
    "model_2.fit(X_train_2, y_train_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "cc1563a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling the target feature\n",
    "scale_y_val = StandardScaler(with_mean=False)\n",
    "y_val_scl = scale_y_val.fit_transform(y_val_2.values.reshape(-1, 1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "9da9a251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.62920486e+16],\n",
       "       [9.25024408e+16],\n",
       "       [1.24207477e+17],\n",
       "       [9.52498708e+16],\n",
       "       [1.86493128e+16]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def predict_prices(inputs):\n",
    "    result = model_2.predict(inputs)\n",
    "    return scale_y_val.inverse_transform(result.reshape(-1, 1))\n",
    "\n",
    "#making predictions\n",
    "model_2_preds = predict_prices(X_val_2)\n",
    "model_2_preds[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7ec245f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13835     68000000\n",
       "19855    200000000\n",
       "14738    100000000\n",
       "17413    130000000\n",
       "204       33000000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val_2[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea13efee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "39183bee",
   "metadata": {},
   "source": [
    "# Evaluating Model 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "8caf4001",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculating the mean squared error\n",
    "y_val_2_array = np.array(y_val_2)\n",
    "\n",
    "mse_val =mean_squared_error(y_val_2_array, model_2_preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "bf9c57da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.158736026645274e+37"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6ccede65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.079400587262781e+20"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_2 = r2_score(y_val_2_array, model_2_preds)\n",
    "eval_2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5287fd6f",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "The r2 of bothe models (model_1 = 0.99, model_2=1.079e+20) shows that model_1 performed better on the dataset and it's the model we'll deploy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aabbc159",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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