House_Price_Prediction_Prodigy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e761fb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b95dae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aa452e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8622c27b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     588 non-null    object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "80d8b08a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 0\n",
       "MSSubClass         0\n",
       "MSZoning           0\n",
       "LotFrontage      259\n",
       "LotArea            0\n",
       "                ... \n",
       "MoSold             0\n",
       "YrSold             0\n",
       "SaleType           0\n",
       "SaleCondition      0\n",
       "SalePrice          0\n",
       "Length: 81, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9a91aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling missing value\n",
    "df['LotFrontage']=df['LotFrontage'].fillna(df['LotFrontage'].mean())\n",
    "df['BsmtCond']=df['BsmtCond'].fillna(df['BsmtCond'].mode()[0])\n",
    "df['BsmtQual']=df['BsmtQual'].fillna(df['BsmtQual'].mode()[0])\n",
    "df['FireplaceQu']=df['FireplaceQu'].fillna(df['FireplaceQu'].mode()[0])\n",
    "df['GarageType']=df['GarageType'].fillna(df['GarageType'].mode()[0])\n",
    "df['GarageFinish']=df['GarageFinish'].fillna(df['GarageFinish'].mode()[0])\n",
    "df['GarageQual']=df['GarageQual'].fillna(df['GarageQual'].mode()[0])\n",
    "df['GarageCond']=df['GarageCond'].fillna(df['GarageCond'].mode()[0])\n",
    "df['MasVnrType']=df['MasVnrType'].fillna(df['MasVnrType'].mode()[0])\n",
    "df['MasVnrArea']=df['MasVnrArea'].fillna(df['MasVnrArea'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aba61058",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Alley','GarageYrBlt','PoolQC','Fence','MiscFeature'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d2b8063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 76)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "21289b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "MSSubClass       0\n",
       "MSZoning         0\n",
       "LotFrontage      0\n",
       "LotArea          0\n",
       "                ..\n",
       "MoSold           0\n",
       "YrSold           0\n",
       "SaleType         0\n",
       "SaleCondition    0\n",
       "SalePrice        0\n",
       "Length: 76, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bc862de3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),cbar= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8d1a9f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['BsmtFinType2']=df['BsmtFinType2'].fillna(df['BsmtFinType2'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bef13c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m5pg9ezb69esHAHjw4AHq1auH3bt3o2XLlhgxYgRGjBhR4PUbNmxAjx49kJqaCi0t5XMwTtsmIiIqfUpcccrs7Gxs2LABqampaNCgAQAgJycHQUFBCAkJkeuxeZfk5GSYmJiolMwQERHR56NIMoTLly+jQYMGeP36NYyMjLBlyxZ4eHgAAGbOnAktLS0MGzZMqVjPnj3D5MmTMWjQoHeex2rbREREn68iSWhcXV0RGRmJ58+fY9OmTejVqxcOHTqEV69eYcGCBTh//rxSiUZKSgpatmwJDw8PTJgw4Z3nTp8+XWG1bRmrbRMREX3yPsoYGh8fH1SsWBHu7u4YOXKkXKXt7OxsaGhooEKFCrhz5460/8WLF/Dz84OBgQF27NgBPT29dz6Hoh6aMhZu7KEhIiIqZUrcGJpcQgikp6cjKCgIPj4+csf8/PwQFBSEPn36SPtSUlLg5+cHXV1dhIeHvzeZAVhtm4iI6HOm9oTmhx9+QEBAACpUqIAXL15g3bp1OHjwIHbt2gULCwtYWFjIna+trQ0bGxu4uroCeNMz4+vri7S0NKxduxYpKSlISUkBAFhZWUFTU1PdTSYiIqJSTu0JzaNHjxAUFISHDx/C1NQU1apVw65du/DVV18pdf25c+dw6tQpAJDWsskVGxsLR0dHdTeZiIiISrmPMoamuHAdGiIiotKnMGNoWMuJiIiISj0mNERERFTqMaEhIiKiUu+jV9t+9OgRevfuDTs7OxgYGMDf3x/R0dFyMWJiYtC2bVtYWVnBxMQEnTp1wqNHj9TdVCIiIvpEqD2hya22ffbsWZw9exbNmzdHYGAgrl69CiEE2rRpg9u3b2Pbtm24cOECHBwc4OPjg9TUVABAamoqfH19IZPJEBERgWPHjiEjIwOtWrVCTk6OuptLREREn4CPWm37iy++gKurK65cuSIVpszOzoa1tTVmzpyJ/v37Y8+ePQgICEBSUhJMTN6ULUhKSoK5uTn27t2bb2G+d+EsJyIiotKnxM1yys7Oxrp166Rq27mlCfKu/KupqQkdHR0cPXoUwJsSBjKZTG7VXz09PWhoaEjnEBEREeVVJAnN5cuXYWRkBF1dXQwePFiqtu3m5gYHBweEhoYiKSkJGRkZmDFjBhISEvDw4UMAQP369WFoaIgxY8YgLS0NqampCAkJQU5OjnSOIunp6dKqwrnbJ7zEDhEREeVRJAlNbrXtkydP4ptvvkGvXr1w7do1aGtrY9OmTbh58ybMzc1hYGCAgwcPIiAgQCppYGVlhQ0bNmD79u0wMjKCqakpkpOTUbNmzXeWPZg+fTpMTU3lNpHzoiheHhEREZUwH7Xa9tKlS6V9ycnJyMjIgJWVFerVq4fatWvjt99+k7vu6dOn0NLSgpmZGWxsbDBq1CiEhIQofA5W2yYiIvo0lPhq23mZmpoCAKKjo3H27FlMnjw533WWlpYAgIiICDx+/BitW7cu8DlYbZuIiOjz9VGrbQPAhg0bYGVlBXt7e1y+fBnDhw9HmzZt4OvrK8VYuXIl3N3dYWVlhRMnTmD48OEIDg6WKnITERER5fXRq20/fPgQI0eOxKNHj2Bra4uePXti/PjxcjGioqIQGhqKxMREODo6Yty4cQgODlZ3U4mIiOgTwWrbREREVKKUuHVoiIiIiD4GJjRERERU6jGhISIiolKvyBOa6dOnQyaTYcSIEdK+3r17QyaTyW3169fPd+2JEyfQvHlzGBoawszMDM2aNcOrV6+KuslERERUyhTpOjRnzpzBsmXLUK1atXzH/P39sXLlSumxjo6O3PETJ07A398foaGh+PXXX6Gjo4OLFy9CQ4OdSkRERCSvyBKaly9fonv37vjjjz8wZcqUfMd1dXVhY2NT4PXBwcEYNmwYxo4dK+2rVKlSkbSViIiISrci6+747rvv0LJlS/j4+Cg8fvDgQVhbW6Ny5coYMGAAHj9+LB17/PgxTp06BWtrazRs2BBly5ZF06ZNWW2biIiIFCqShGbdunU4f/48pk+frvB4QEAA/vrrL0RERGDu3Lk4c+YMmjdvLpVHuH37NgBg4sSJGDBgAHbt2oWaNWviyy+/RHR0dFE0mYiIiEoxtd9yunfvHoYPH449e/ZAT09P4TmdO3eW/l21alXUrl0bDg4O2LlzJ9q1a4ecnBwAwKBBg9CnTx8AgJeXF/bv348VK1YoTJQUFacUQrCeExER0WdA7T00586dw+PHj1GrVi1oaWlBS0sLhw4dwsKFC6GlpYXs7Ox819ja2sLBwUHqfbG1tQUAeHh4yJ3n7u6OuLg4hc87ffp0mJqaym0i54WaXx0RERGVRGpPaL788ktcvnwZkZGR0la7dm10794dkZGR0NTUzHfNs2fPcO/ePSmRcXR0hJ2dHaKiouTOu3nzJhwcHBQ+b2hoKJKTk+U2mYaxul8eERERlUBqv+VkbGyMqlWryu0zNDSEhYUFqlatipcvX2LixIlo3749bG1tcefOHfzwww+wtLRE27ZtAQAymQwhISGYMGECqlevjho1amDVqlW4ceMGNm7cqPB5dXV1oaurK7ePt5uIiIg+D0W6Do0impqauHz5MlavXo3nz5/D1tYW3t7eWL9+PYyN/69HZcSIEXj9+jWCg4ORmJiI6tWrY+/evahYseLHbjIRERGVcKy2TURERCUKq20TERHRZ4kJDREREZV6TGiIiIio1GNCQ0RERKWe2hOaiRMnQiaTyW15i1Bu3rwZfn5+sLS0hEwmQ2RkZL4YgwYNQsWKFaGvrw8rKysEBgbixo0b6m4qERERfSKKpIemSpUqePjwobRdvnxZOpaamopGjRphxowZBV5fq1YtrFy5EtevX8fu3bshhICvr6/CVYaJiIiIimQdGi0tLblembyCgoIAAHfu3Cnw+oEDB0r/dnR0xJQpU1C9enXcuXOH69AQERFRPkXSQxMdHQ07Ozs4OTmhS5cuUvXswkhNTcXKlSvh5OSEChUqqLGVRERE9KlQe0JTr149rF69Grt378Yff/yBhIQENGzYEM+ePVMpzu+//w4jIyMYGRlh165d2Lt3L3R0dAo8Pz09HSkpKXLbJ7xmIBEREeWh9oQmICAA7du3h6enJ3x8fLBz504AwKpVq1SK0717d1y4cAGHDh1CpUqV0KlTJ7x+/brA81ltm4iI6PNV5NO2DQ0N4enpiejoaJWuMzU1RaVKldCkSRNs3LgRN27cwJYtWwo8n9W2iYiIPl9FXpwyPT0d169fxxdffPFBcYQQSE9PL/A4q20TERF9vtSe0Hz//fdo1aoV7O3t8fjxY0yZMgUpKSno1asXACAxMRFxcXGIj48HAERFRQEAbGxsYGNjg9u3b2P9+vXw9fWFlZUVHjx4gJkzZ0JfXx8tWrRQd3OJiIjoE6D2W073799H165d4erqinbt2kFHRwcnT56Eg4MDACA8PBxeXl5o2bIlAKBLly7w8vLCkiVLAAB6eno4cuQIWrRoARcXF3Tq1AmGhoY4fvw4rK2t1d1cIiIi+gTIxCc8FUhLp1xxN4GIiIhUlJXxQOVrWMuJiIiISj0mNERERFTqMaEhIiKiUu+jV9t++1juNnv2bOmc9PR0DB06FJaWljA0NETr1q1x//59dTeViIiIPhFFsg5NlSpVsG/fPumxpqam9O+HDx/Knfvff/+hX79+aN++vbRvxIgR2L59O9atWwcLCwuMGjUKX3/9Nc6dOycXi4jU41X8EZWv0bf7sLWliIjU6aNX2357/7Zt2+Dt7Q1nZ2cAQHJyMpYvX441a9bAx8cHALB27VpUqFAB+/btg5+fX1E0meizxuSEiEq7Yq22/ejRI+zcuRP9+vWT9p07dw6ZmZnw9fWV9tnZ2aFq1ao4fvx4UTSXiIiISrlirba9atUqGBsbo127dtK+hIQE6OjooEyZMnLnli1bFgkJCepuLhEREX0C1H7LKSAgQPq3p6cnGjRogIoVK2LVqlUYOXKk3LkrVqxA9+7doaen9964Qoh31mZKT0/PV+vpfdcQERHRp6HYqm0fOXIEUVFR6N+/v9x+GxsbZGRkICkpSW7/48ePUbZs2QKfZ/r06TA1NZXbRM4L9b0QIiIiKrGKPKHJrbZta2srt3/58uWoVasWqlevLre/Vq1a0NbWxt69e6V9Dx8+xJUrV9CwYcMCnyc0NBTJyclym0zDWL0vhoiIiEqkj15tGwBSUlKwYcMGzJ07N9/1pqam6NevH0aNGgULCwuYm5vj+++/h6enpzTrSRFdXV3o6urK7ePtJiIios+D2hOa3GrbT58+hZWVFerXry9XbRsA1q1bByEEunbtqjDGL7/8Ai0tLXTq1AmvXr3Cl19+ibCwMK5BQ0RERAqx2jYRERGVKKy2TURERJ8lJjRERERU6jGhISIiolKPCQ0RERGVekWS0Dx48AA9evSAhYUFDAwMUKNGDZw7dw4AkJmZiTFjxsDT0xOGhoaws7NDz549ER8fL11/584dyGQyhduGDRuKoslERERUiql9llNSUhK8vLzg7e2Nb775BtbW1oiJiYGjoyMqVqyI5ORkdOjQAQMGDED16tWRlJSEESNGICsrC2fPngUAZGdn48mTJ3Jxly1bhlmzZiEhIQFGRkZKtYWznIiIiEqfwsxyUntCM3bsWBw7dgxHjhxR+pozZ86gbt26uHv3Luzt7RWe4+XlhZo1a2L58uVKx2VCQ0REVPqUiGnb4eHhqF27Njp27Ahra2t4eXnhjz/+eOc1ycnJkMlkMDMzU3j83LlziIyMRL9+/dTdXCIiIvoEqL2HJrdy9siRI9GxY0ecPn0aI0aMwNKlS9GzZ898579+/RqNGzeGm5sb1q5dqzDmt99+i4MHD+LatWsFPq+iattlLNxY/oCIiKiUKRG3nHR0dFC7dm0cP35c2jds2DCcOXMGJ06ckDs3MzMTHTt2RFxcHA4ePAgTE5N88V69egVbW1uMHz8eo0aNKvB5J06ciEmTJsntk2kYQUMzf0wiIiIquUrELSdbW1t4eHjI7XN3d0dcXJzcvszMTHTq1AmxsbHYu3evwmQGADZu3Ii0tDSFvTt5sdo2ERHR50vtxSkbNWqEqKgouX03b96UK06Zm8xER0fjwIEDsLCwKDDe8uXL0bp1a1hZWb3zeVltm4iI6POl9oQmODgYDRs2xLRp09CpUyecPn0ay5Ytw7JlywAAWVlZ6NChA86fP48dO3YgOzsbCQkJAABzc3Po6OhIsW7duoXDhw/j33//VXcziYiI6BNSJNW2d+zYgdDQUERHR8PJyQkjR47EgAEDALxZNM/JyUnhdQcOHECzZs2kxz/88APWrFmDu3fvQkND9btjnLZNRERU+pSIQcElCRMaIiKi0qdEDAomIiIi+tiY0BAREVGpx4SGiIiISr2PXm0bAF6+fIkhQ4agfPny0NfXh7u7OxYvXqwwlhACAQEBkMlk2Lp1a1E0l4iIiEo5tU/bTkpKQqNGjeDt7Y3//vtPqradt05TcHAwDhw4gLVr18LR0RF79uzBt99+Czs7OwQGBsrFmz9/PteTISIiondSe0Izc+ZMVKhQAStXrpT2OTo6yp1z4sQJ9OrVS5qiPXDgQCxduhRnz56VS2guXryIefPm4cyZM7C1tVV3U4mIiOgTUSzVths3bozw8HA8ePAAQggcOHAAN2/ehJ+fn3ROWloaunbtikWLFsHGxkbdzSQiIqJPiNoTmtu3b2Px4sWoVKkSdu/ejcGDB2PYsGFYvXq1dM7ChQvh4eGB8uXLQ0dHB/7+/vj999/RuHFj6ZzcFYffvgVFRERE9Da133LKyclB7dq1MW3aNACAl5cXrl69isWLF0sFJhcuXIiTJ08iPDwcDg4OOHz4ML799lvY2trCx8cH4eHhiIiIwIULF5R+3vT0dKSnp8vtE0Jw/A0REdFn4KNX23716hV++OEHzJs3D61atUK1atUwZMgQdO7cGXPmzAEARERESAOJtbS0oKX1Ju9q3769XGmEvKZPnw5TU1O5TeS8UPfLIyIiohJI7QnN+6ptZ2ZmIjMzM19tJk1NTeTk5AAAxo4di0uXLiEyMlLaAOCXX36RG2ycV2hoKJKTk+U2mYaxml8dERERlUQfvdq2iYkJmjZtipCQEOjr68PBwQGHDh3C6tWrMW/ePACAjY2NwoHA9vb2BRa21NXVha6urtw+3m4iIiL6PHz0atsAkJCQgNDQUOzZsweJiYlwcHDAwIEDERwcXGASIpPJsGXLFrRp00bpdrA4JRERUenDattvYUJDRERU+rDaNhEREX2WmNAQERFRqceEhoiIiEo9JjRERERU6qk9oXF0dIRMJsu3fffdd/nOHTRoEGQyGebPny+3f9myZWjWrBlMTEwgk8nw/PlzdTeTiIiIPiFqT2jOnDmDhw8fStvevXsBAB07dpQ7b+vWrTh16hTs7OzyxUhLS4O/vz9++OEHdTePiIiIPkFqX1jPyspK7vGMGTNQsWJFNG3aVNr34MEDDBkyBLt370bLli3zxRgxYgQA4ODBg+puHhEREX2CinQMTUZGBtauXYu+fftKC+bl5OQgKCgIISEhqFKlSlE+PREREX0m1N5Dk9fWrVvx/Plz9O7dW9o3c+ZMaGlpYdiwYWp9LlbbJiIi+nwVaQ/N8uXLERAQII2TOXfuHBYsWICwsDC1Jxqstk1ERPT5KrKE5u7du9i3bx/69+8v7Tty5AgeP34Me3t7aGlpQUtLC3fv3sWoUaPg6Oj4Qc/HattERESfryK75bRy5UpYW1vLDfoNCgqCj4+P3Hl+fn4ICgpCnz59Puj5WG2biIjo81UkCU1OTg5WrlyJXr16QUvr/57CwsICFhYWcudqa2vDxsYGrq6u0r6EhAQkJCTg1q1bAIDLly/D2NgY9vb2MDc3L4omExERUSlWJLec9u3bh7i4OPTt27dQ1y9ZsgReXl4YMGAAAKBJkybw8vJCeHi4OptJREREnwiZEEIUdyOKipZOueJuAhEREakoK+OBytewlhMRERGVekxoiIiIqNRjQkNERESl3kevtv3o0SP07t0bdnZ2MDAwgL+/P6Kjo+ViJCQkICgoCDY2NjA0NETNmjWxceNGdTeViP6/V/FHVN6IiEoStU/bPnPmDLKzs6XHV65cwVdffYWOHTtCCIE2bdpAW1sb27Ztg4mJCebNmwcfHx9cu3YNhoaGAN6sV5OcnIzw8HBYWlrif//7Hzp37oyzZ8/Cy8tL3U0m+uzp231R3E0gIvogRT7LacSIEdixYweio6MRHR0NV1dXXLlyRSpMmZ2dDWtra8ycOVNaVdjIyAiLFy9GUFCQFMfCwgKzZs1Cv379lH5uznIiIiIqfUrcLKe3q23nFo/U09OTztHU1ISOjg6OHj0q7WvcuDHWr1+PxMRE5OTkYN26dUhPT0ezZs2KsrlERERUShVpQvN2tW03Nzc4ODggNDQUSUlJyMjIwIwZM5CQkICHDx9K161fvx5ZWVmwsLCArq4uBg0ahC1btqBixYoFPld6ejpSUlLktk94iR0iIiLK46NW29bW1samTZtw8+ZNmJubw8DAAAcPHkRAQAA0NTWl63788UckJSVh3759OHv2LEaOHImOHTvi8uXLBT4Xq20TERF9vopsDM3du3fh7OyMzZs3IzAwMN/x5ORkZGRkwMrKCvXq1UPt2rXx22+/ISYmBi4uLnLjbADAx8cHLi4uWLJkicLnS09Pl25p5Spj4cYClURERKVMYcbQfNRq23mZmpoCAKKjo3H27FlMnjwZAJCWlgYA0NCQ7zzS1NRETk5Ogc/HattERESfr49abRsANmzYACsrK9jb2+Py5csYPnw42rRpA19fXwBvxtm4uLhg0KBBmDNnDiwsLLB161bs3bsXO3bsKIrmEhERUSlXJAnNu6ptP3z4ECNHjsSjR49ga2uLnj17Yvz48dJxbW1t/Pvvvxg7dixatWqFly9fwsXFBatWrUKLFi2KorlERERUyrHaNhEREZUoJW4dGiIiIqKPgQkNERERlXpMaIiIiKjUU3tCk5WVhR9//BFOTk7Q19eHs7Mzfv75Z7kp1717985Xjbt+/fpycZo1a5bvnC5duqi7uURERPQJUPssp5kzZ2LJkiVYtWoVqlSpgrNnz6JPnz4wNTXF8OHDpfP8/f2xcuVK6bGOjk6+WAMGDMDPP/8sPdbX11d3c4mIiOgToPaE5sSJEwgMDJQW1HN0dMTff/+Ns2fPyp2nq6sLGxubd8YyMDB47zlEREREar/l1LhxY+zfvx83b94EAFy8eBFHjx7Nt4bMwYMHYW1tjcqVK2PAgAF4/Phxvlh//fUXLC0tUaVKFXz//fd48YK1mYiIiCg/tffQjBkzBsnJyXBzc4Ompiays7MxdepUdO3aVTonICAAHTt2hIODA2JjYzF+/Hg0b94c586dk8oXdO/eHU5OTrCxscGVK1cQGhqKixcvYu/evepuMhEREZVyal9Yb926dQgJCcHs2bNRpUoVREZGYsSIEZg3bx569eql8JqHDx/CwcEB69atQ7t27RSec+7cOdSuXRvnzp1DzZo18x1ncUoiIqJPQ4lYWC8kJARjx45Fly5d4OnpiaCgIAQHB2P69OkFXmNrawsHBwdER0cXeE7NmjWhra1d4DnTp0+Hqamp3CZyeIuKiIjoc6D2hCYtLU3lStnPnj3DvXv3YGtrW+A5V69eRWZmZoHnhIaGIjk5WW6TaRgX7kUQERFRqaL2MTStWrXC1KlTYW9vjypVquDChQuYN2+eVKjy5cuXmDhxItq3bw9bW1vcuXMHP/zwAywtLdG2bVsAQExMDP766y+0aNEClpaWuHbtGkaNGgUvLy80atRI4fPq6upK429y8XYTERHR50HtY2hevHiB8ePHY8uWLXj8+DHs7OzQtWtX/PTTT9DR0cGrV6/Qpk0bXLhwAc+fP4etrS28vb0xefJkVKhQAQBw79499OjRA1euXMHLly9RoUIFtGzZEhMmTIC5ubnSbWFxSiIiotKnMGNoWG2biIiISpQSMSiYiIiI6GNjQkNERESlHhMaIiIiKvWY0BAREVGpVyQJzYsXLzBixAg4ODhAX18fDRs2xJkzZ6Tjmzdvhp+fHywtLSGTyRAZGSl3fWJiIoYOHQpXV1cYGBjA3t4ew4YNQ3JyclE0l4iIiEo5ta9DAwD9+/fHlStXsGbNGtjZ2WHt2rXw8fHBtWvXUK5cOaSmpqJRo0bo2LEjBgwYkO/6+Ph4xMfHY86cOfDw8MDdu3cxePBgxMfHY+PGjUXRZKLP2qv4Iypfo2/3RRG0hIiocNQ+bfvVq1cwNjbGtm3b0LJlS2l/jRo18PXXX2PKlCnSvjt37sDJyQkXLlxAjRo13hl3w4YN6NGjB1JTU6GlpVwexmnbREREpU+JmLadlZWF7Oxs6Onpye3X19fH0aNHCx03OTkZJiYmSiczRERE9PlQe3ZgbGyMBg0aYPLkyXB3d0fZsmXx999/49SpU6hUqVKhYj579gyTJ0/GoEGDCjxHUbVtIQTLHxAREX0GimRQ8Jo1ayCEQLly5aCrq4uFCxeiW7du0NTUVDlWSkoKWrZsCQ8PD0yYMKHA81htm4iI6PNVJAlNxYoVcejQIbx8+RL37t3D6dOnkZmZCScnJ5XivHjxAv7+/jAyMsKWLVugra1d4Lmstk1ERPT5KtIBKYaGhjA0NERSUhJ2796NWbNmKX1tSkoK/Pz8oKuri/Dw8Hxjct7GattERESfryJJaHbv3g0hBFxdXXHr1i2EhITA1dUVffr0AfBmnZm4uDjEx8cDAKKiogAANjY2sLGxwYsXL+Dr64u0tDSsXbsWKSkpSElJAQBYWVkV6tYVERERfbqKJKFJTk5GaGgo7t+/D3Nzc7Rv3x5Tp06VbhmFh4dLyQ0AdOnSBQAwYcIETJw4EefOncOpU6cAAC4uLnKxY2Nj4ejoWBTNJiIiolJK7evQlCRch4aIiKj0KRHr0BARERF9bExoiIiIqNRjQkNERESlXrFU2waA69evo3Xr1jA1NYWxsTHq16+PuLg46XhMTAzatm0LKysrmJiYoFOnTnj06FFRNJfos/cq/ojCjYiotCiShKZ///7Yu3cv1qxZg8uXL8PX1xc+Pj548ODNIJ+YmBg0btwYbm5uOHjwIC5evIjx48dLa82kpqbC19cXMpkMEREROHbsGDIyMtCqVSvk5OQURZOJPmv6dl8o3IiISotiqbbdpUsXaGtrY82aNQpj7NmzBwEBAUhKSoKJiQkAICkpCebm5ti7dy98fHyUagtnOREREZU+JWKW0/uqbefk5GDnzp2oXLky/Pz8YG1tjXr16mHr1q3Suenp6ZDJZHIr/+rp6UFDQ+ODKnYTERHRp0ntCU3eatvx8fHIzs7G2rVrcerUKTx8+BCPHz/Gy5cvMWPGDPj7+2PPnj1o27Yt2rVrh0OHDgEA6tevD0NDQ4wZMwZpaWlITU1FSEgIcnJy8PDhQ3U3mYiIiEq5j15tO3cMTGBgIIKDg1GjRg2MHTsWX3/9NZYsWQLgTXmDDRs2YPv27TAyMoKpqSmSk5NRs2bNAssepKenSyUScrdPeM1AIiIiyuOjV9u2tLSElpYWPDw85K5xd3eXm+Xk6+uLmJgYPH78GE+fPsWaNWvw4MGDAit2T58+HaampnKbyHlRFC+PiIiISpgiXYfG0NAQtra2UrXtwMBA6OjooE6dOlJBylw3b96Eg4NDvhiWlpYwMzNDREQEHj9+jNatWyt8rtDQUCQnJ8ttMg3jInldREREVLIUS7XtkJAQdO7cGU2aNIG3tzd27dqF7du34+DBg1KMlStXwt3dHVZWVjhx4gSGDx+O4OBguLq6KnxOXV1duUHEACCTyYri5REREVEJUyzVttu2bYslS5Zg+vTpGDZsGFxdXbFp0yY0btxYihEVFYXQ0FAkJibC0dER48aNQ3BwcFE0l+izV5hF9LhODRGVJKy2TURERCVKiViHhoiIiOhjY0JDREREpV6RjKEhotKFY2iIqLTjGBoiIiIqUT7KGJrDhw+jVatWsLOzg0wmk6vBBABCCEycOBF2dnbQ19dHs2bNcPXqVel4YmIihg4dCldXVxgYGMDe3h7Dhg1DcnJyvufauXMn6tWrB319fVhaWqJdu3Yqv0AiIiL69Kmc0KSmpqJ69epYtGiRwuOzZs3CvHnzsGjRIpw5cwY2Njb46quv8OLFm1V74+PjER8fjzlz5uDy5csICwvDrl270K9fP7k4mzZtQlBQEPr06YOLFy/i2LFj6NatWyFeIhEREX3qPuiWk0wmw5YtW9CmTRsAb3pn7OzsMGLECIwZMwbAmxpLZcuWxcyZMzFo0CCFcTZs2IAePXogNTUVWlpayMrKgqOjIyZNmpQv0VEFbzkRERGVPsU+bTs2NhYJCQnw9fWV9unq6qJp06Y4fvx4gdclJyfDxMQEWlpvxiifP38eDx48gIaGBry8vGBra4uAgAC5W1dEREREudSa0CQkJAAAypYtK7e/bNmy0rG3PXv2DJMnT5brvbl9+zYAYOLEifjxxx+xY8cOlClTBk2bNkViYqLCOKy2TURE9PkqknVo3q6hJIRQWFcpJSUFLVu2hIeHByZMmCDtz8nJAQCMGzcO7du3R61atbBy5UrIZDJs2LBB4XOy2jYREdHnS60JjY2NDQDk6415/Phxvl6bFy9ewN/fH0ZGRtiyZYtU5wkAbG1tAQAeHh7SPl1dXTg7OyMuLk7hc7PaNhER0edLrQvrOTk5wcbGBnv37oWXlxcAICMjA4cOHcLMmTOl81JSUuDn5wddXV2Eh4dDT09PLk6tWrWgq6uLqKgoqWBlZmYm7ty5AwcHB4XPzWrbRIXHhfWIqLRTOaF5+fIlbt26JT2OjY1FZGQkzM3NYW9vjxEjRmDatGmoVKkSKlWqhGnTpsHAwECacv3ixQv4+voiLS0Na9eulca7AICVlRU0NTVhYmKCwYMHY8KECahQoQIcHBwwe/ZsAEDHjh3V8bqJKA8mJ0RU2qmc0Jw9exbe3t7S45EjRwIAevXqhbCwMIwePRqvXr3Ct99+i6SkJNSrVw979uyBsfGb2z/nzp3DqVOnAAAuLi5ysWNjY+Ho6AgAmD17NrS0tBAUFIRXr16hXr16iIiIQJkyZQr1QomIiOjTxdIHREREVKIU+zo0RERERMWBCQ0RERGVekxoiIiIqNT76NW2gTfr1AQFBcHGxgaGhoaoWbMmNm7cKHdO69atYW9vDz09Pdja2iIoKAjx8fGqv0IiIiL65H30atsAEBQUhKioKISHh+Py5cto164dOnfujAsXLkjneHt7459//kFUVBQ2bdqEmJgYdOjQoRAvkYiIiD51xVJt28jICIsXL0ZQUJAUy8LCArNmzSqwunZ4eDjatGmD9PR0uVWF34WznIiIiEqfYp/lpGy17caNG2P9+vVITExETk4O1q1bh/T0dDRr1kxh3MTERPz1119o2LCh0skMERERfT6Kpdr2+vXrkZWVBQsLC+jq6mLQoEHYsmULKlasKHfdmDFjYGhoCAsLC8TFxWHbtm3qbC4RERF9Ioql2vaPP/6IpKQk7Nu3D2fPnsXIkSPRsWNHXL58We66kJAQXLhwAXv27IGmpiZ69uyJgu6QpaenS2UUcrdPeM1AIiIiykOtxSnzVtvOrZgNyFfbjomJwaJFi3DlyhVUqVIFAFC9enUcOXIEv/32G5YsWSJdZ2lpCUtLS1SuXBnu7u6oUKECTp48iQYNGuR77unTp2PSpEly+2QaRpBpmqjzJRIREVEJpNYemrzVtnPlVttu2LAhACAtLe3NE2vIP7WmpiZycnIKjJ3b25Kenq7weGhoKJKTk+U2mYbxB70eIiIiKh0+erVtNzc3uLi4YNCgQZgzZw4sLCywdetW7N27Fzt27AAAnD59GqdPn0bjxo1RpkwZ3L59Gz/99BMqVqyosHcGeDP4WFdXV27f27e+iIiI6NP00atta2tr499//8XYsWPRqlUrvHz5Ei4uLli1ahVatGgBANDX18fmzZsxYcIEpKamwtbWFv7+/li3bl2+pIWIiIiI1baJiIioRCn2dWiIiIiIigMTGiIiIir1mNAQERFRqceEhoiIiEo9lROaw4cPo1WrVrCzs4NMJsPWrVvljm/evBl+fn6wtLSETCZDZGRkgbGEEAgICFAYJykpCUFBQTA1NYWpqSmCgoLw/PlzVZtLREREnwGVE5rU1FRUr14dixYtKvB4o0aNMGPGjPfGmj9/foFrxXTr1g2RkZHYtWsXdu3ahcjISLnq3ERERES5VF6HJiAgAAEBAQUez0067ty58844Fy9exLx583DmzBm5MgkAcP36dezatQsnT55EvXr1AAB//PEHGjRogKioKLi6uqrabCIiIvqEFcsYmrS0NHTt2hWLFi2S6j/ldeLECZiamkrJDADUr18fpqamOH78+MdsKhEREZUCai1Oqazg4GA0bNgQgYGBCo8nJCTA2to6335ra2skJCQovCY9PT1fnae3q3wTERHRp+mj99CEh4cjIiIC8+fPf+d5ihKRdyUo06dPlwYQ524i54U6mkxEREQl3EdPaCIiIhATEwMzMzNoaWlBS+tNJ1H79u3RrFkzAICNjQ0ePXqU79onT56gbNmyCuOy2jYREdHn66Pfcho7diz69+8vt8/T0xO//PILWrVqBQBo0KABkpOTcfr0adStWxcAcOrUKSQnJ6Nhw4YK47LaNhER0edL5YTm5cuXuHXrlvQ4NjYWkZGRMDc3h729PRITExEXF4f4+HgAQFRUFIA3vS55t7fZ29vDyckJAODu7g5/f38MGDAAS5cuBQAMHDgQX3/9NWc4ERERUT4q33I6e/YsvLy84OXlBQAYOXIkvLy88NNPPwF4M0bGy8sLLVu2BAB06dIFXl5eWLJkiUrP89dff8HT0xO+vr7w9fVFtWrVsGbNGlWbS0RERJ8BmRBCFHcjioqWTrnibgIRERGpKCvjgcrXsJYTERERlXpMaIiIiKjUY0JDREREpV6xVNuOiYlB27ZtYWVlBRMTE3Tq1Elu3ZmDBw9CJpMp3M6cOaPyiyQiIqJP20evtp2amgpfX1/IZDJERETg2LFjyMjIQKtWrZCTkwMAaNiwIR4+fCi39e/fH46Ojqhdu7aqTSYiIqJP3Eevtn3s2DHcuXMHFy5cgImJCQBg5cqVMDc3R0REBHx8fKCjoyO3Vk1mZibCw8MxZMgQLpZHRERE+Xz0MTTp6emQyWRyq/rq6elBQ0MDR48eVXhNeHg4nj59it69e3+kVhIREVFp8tETmvr168PQ0BBjxoxBWloaUlNTERISgpycHDx8+FDhNcuXL4efnx8qVKhQYNz09HSkpKTIbZ/wEjtERESUx0dPaKysrLBhwwZs374dRkZGMDU1RXJyMmrWrAlNTc1859+/fx+7d+9Gv3793hmX1baJiIg+Xx+9OCUA+Pr6IiYmBk+fPoWWlhbMzMxgY2Mj1XLKa+XKlbCwsEDr1q3fGTM0NBQjR46U21fGwk2t7SYiIqKSqVgSmlyWlpYAgIiICDx+/Dhf0iKEwMqVK9GzZ09oa2u/MxarbRMREX2+Pnq1beBNr4u7uzusrKxw4sQJDB8+HMHBwfkqaUdERCA2Nva9t5uIiIjo86ZyccqDBw/C29s73/5evXohLCwMYWFh6NOnT77jEyZMwMSJEwEAY8eORVhYGBITE+Ho6IjBgwcjODg4X49Kt27dcPfuXRw7dkyVJkpYnJKIiKj0KUxxSlbbJiIiohKF1baJiIjos8SEhoiIiEo9JjRERERU6qm12nZmZibGjBkDT09PGBoaws7ODj179pRmPOVq1qxZviraXbp0Ufh86enpqFGjRoGVu4mIiIjUWm07LS0N58+fx/jx43H+/Hls3rwZN2/eVLgo3oABA+SqaS9dulTh840ePRp2dnaqNpOIiIg+I2qttm1qaoq9e/fK7fv1119Rt25dxMXFwd7eXtpvYGAgV1Fbkf/++w979uzBpk2b8N9//6naVCIiIvpMFPkYmuTkZMhkMpiZmcnt/+uvv2BpaYkqVarg+++/x4sX8nWXHj16hAEDBmDNmjUwMDAo6mYSERFRKVakpQ9ev36NsWPHolu3bjAxMZH2d+/eHU5OTrCxscGVK1cQGhqKixcvSr07Qgj07t0bgwcPRu3atXHnzp2ibCYRERGVckWW0GRmZqJLly7IycnB77//LndswIAB0r+rVq2KSpUqoXbt2jh//jxq1qyJX3/9FSkpKQgNDVX6+dLT05Geni63TwjBek5ERESfgSK55ZSZmYlOnTohNjYWe/fuleudUaRmzZrQ1tZGdHQ0gDc1nE6ePAldXV1oaWnBxcUFAFC7dm306tVLYYzp06fD1NRUbhM5LxSeS0RERJ+WDyp9IJPJsGXLFrRp00bal5vMREdH48CBA7CysnpvnCtXrsDT0xOHDh1CkyZNEBcXh5SUFOl4fHw8/Pz8sHHjRtSrVw/ly5fPF0NRD00ZCzf20BAREZUyhSl9oNZq23Z2dujQoQPOnz+PHTt2IDs7GwkJCQAAc3Nz6OjoICYmBn/99RdatGgBS0tLXLt2DaNGjYKXlxcaNWoEAHKzoQDAyMgIAFCxYkWFyQwA6OrqQldXV24fkxkiIqLPg1qrbU+cOBFOTk4Krztw4ACaNWuGe/fuoUePHrhy5QpevnyJChUqoGXLlpgwYQLMzc0VXnvnzh04OTnhwoULqFGjhtJtZXFKIiKi0ofVtt/ChIaIiKj0YbVtIiIi+iwxoSEiIqJSjwkNERERlXpMaIiIiKjUUzmhOXz4MFq1agU7OzvIZDJs3bpV7vjEiRPh5uYGQ0NDlClTBj4+Pjh16pTcOcuWLUOzZs1gYmICmUyG58+f53ueqVOnomHDhjAwMMhXB4qIiIgoL5UTmtTUVFSvXh2LFi1SeLxy5cpYtGgRLl++jKNHj8LR0RG+vr548uSJdE5aWhr8/f3xww8/FPg8GRkZ6NixI7755htVm0hERESfGbWvFPy2lJQUmJqaYt++ffjyyy/ljuWuaZOUlFRgL0xYWBhGjBihsBfnfThtm4iIqPQpcdO2MzIysGzZMpiamqJ69epF+VRERET0GSuSats7duxAly5dkJaWBltbW+zduxeWlpZF8VQSVtsmIiL6fBVJD423tzciIyNx/Phx+Pv7o1OnTnj8+HFRPJWE1baJiIg+X0WS0BgaGsLFxQX169fH8uXLoaWlheXLlxfFU0lCQ0ORnJwst8k0jIv0OYmIiKhkKJJbTm8TQuS7HaRurLZNRET0+VI5oXn58iVu3bolPY6NjUVkZCTMzc1hYWGBqVOnonXr1rC1tcWzZ8/w+++/4/79++jYsaN0TUJCAhISEqQ4ly9fhrGxMezt7aWK23FxcUhMTERcXByys7MRGRkJAHBxcYGRkdGHvGYiIiL6xKg8bTt3qvXbevXqhSVLlqBbt244deoUnj59CgsLC9SpUwc//vgj6tSpI507ceJETJo0KV+MlStXonfv3gCA3r17Y9WqVfnOOXDgAJo1a6ZUWzltm4iIqPQpzLTtD1qHpqRjQkNERFT6lLh1aIiIiIg+BiY0REREVOoxoSEiIqJSr1iqbQPAiRMn0Lx5cxgaGsLMzAzNmjXDq1evpOM3b95EYGAgLC0tYWJigkaNGuHAgQOqv0IiIiL65BVLte0TJ07A398fvr6+OH36NM6cOYMhQ4ZAQ+P/mtOyZUtkZWUhIiIC586dQ40aNfD1118jISGhEC+TiIiIPmXFUm27fv36+OqrrzB58mSF1zx9+hRWVlY4fPgwvvjiCwDAixcvYGJiorBqd0E4y4mIiKj0KXGznBRV2378+DFOnToFa2trNGzYEGXLlkXTpk1x9OhR6ToLCwu4u7tj9erVSE1NRVZWFpYuXYqyZcuiVq1aRdlkIiIiKoWKJKHZsWMHjIyMoKenh19++UWu2vbt27cBvBlrM2DAAOzatQs1a9bEl19+iejoaABven727t2LCxcuwNjYWIqza9cumJmZFUWTiYiIqBT76NW2c3JyAACDBg1Cnz594OXlhV9++QWurq5YsWIFgDe1n7799ltYW1vjyJEjOH36NAIDA/H111/j4cOHCp8zPT0dKSkpctsnvGYgERER5fHRq23b2toCADw8POSucXd3R1xcHAAgIiICO3bswLp169CoUSPUrFkTv//+O/T19RWWQwCA6dOnw9TUVG4TOS+K4uURERFRCfNR1qHJW23b0dERdnZ2iIqKkjvn5s2bcHBwAACkpaW9aZyGfPM0NDSkHp63hYaGIjk5WW6TaRir+6UQERFRCfTRq23LZDKEhIRgwoQJqF69OmrUqIFVq1bhxo0b2LhxIwCgQYMGKFOmDHr16oWffvoJ+vr6+OOPPxAbG4uWLVsqbJeuri50dXXl9slkMlVfHhF9pl7FH1G4X9/ui4/cEiIqDJUTmrNnz8pV2x45ciSA/6u2fePGDaxatUqu2vaRI0dQpUoV6ZoRI0bg9evXCA4ORmJiIqpXr469e/eiYsWKAABLS0vs2rUL48aNQ/PmzZGZmYkqVapg27Zt0mwpIiJ1YuJCVLqx2jYRERGVKCVuHRoiIiKij4EJDREREZV6TGiIiIio1GNCQ0RERKWeygnN4cOH0apVK9jZ2UEmk2Hr1q0Fnjto0CDIZDLMnz9f2peYmIihQ4fC1dUVBgYGsLe3x7Bhw5CcnCx3raOjI2Qymdw2duxYVZtLREREnwGVp22npqaievXq6NOnD9q3b1/geVu3bsWpU6dgZ2cntz8+Ph7x8fGYM2cOPDw8cPfuXQwePBjx8fHSOjS5fv75ZwwYMEB6bGRkpGpziYiI6DOgckITEBCAgICAd57z4MEDDBkyBLt37863EF7VqlWxadMm6XHFihUxdepU9OjRA1lZWdDS+r8mGRsbw8bGRtUmEhER0WdG7WNocnJyEBQUhJCQELnF9N4lOTkZJiYmcskMAMycORMWFhaoUaMGpk6dioyMDHU3l4iIiD4BKvfQvM/MmTOhpaWFYcOGKXX+s2fPMHnyZAwaNEhu//Dhw1GzZk2UKVMGp0+fRmhoKGJjY/Hnn38qjJOeni7Vi8olhGD5AyIios+AWhOac+fOYcGCBTh//rxSiURKSgpatmwJDw8PTJgwQe5YcHCw9O9q1aqhTJky6NChg9Rr87bp06dj0qRJcvtkGkaQaZoU8tUQERFRaaHWW05HjhzB48ePYW9vDy0tLWhpaeHu3bsYNWoUHB0d5c598eIF/P39YWRkhC1btkBbW/udsevXrw8AcoUx82K1bSIios+XWntogoKC4OPjI7fPz88PQUFB6NOnj7QvJSUFfn5+0NXVRXh4OPT09N4b+8KFCwAAW1tbhcdZbZuIiOjzpXJC8/LlS7lektjYWERGRsLc3Bz29vb5bgdpa2vDxsYGrq6uAN70zPj6+iItLQ1r165FSkoKUlJSAABWVlbQ1NTEiRMncPLkSXh7e8PU1BRnzpxBcHAwWrduDXt7+w95vURERPQJUjmhOXv2LLy9vaXHI0eOBAD06tULYWFh773+3LlzOHXqFADAxcVF7lhsbCwcHR2hq6uL9evXY9KkSUhPT4eDgwMGDBiA0aNHq9pcIiIi+gzIhBCiuBtRVLR0yhV3E4iIiEhFWRkPVL6GtZyIiIio1GNCQ0RERKUeExoiIiIq/cRn4PXr12LChAni9evXn3w8tq1kxGPbij9WSY/HthV/rJIej21TzWeR0CQnJwsAIjk5+ZOPx7aVjHhsW/HHKunx2Lbij1XS47FtquEtJyIiIir1mNAQERFRqceEhoiIiEq9zyKh0dXVxYQJE/LVevoU47FtJSMe21b8sUp6PLat+GOV9Hhsm2o+6ZWCiYiI6PPwWfTQEBER0aeNCQ0RERGVekxoiIiIqNRjQkNERESlHhMaKjX69u2LFy9e5NufmpqKvn37FkOLiIiopOAsJyo1NDU18fDhQ1hbW8vtf/r0KWxsbJCVlVVMLSMq/bKzs3H06FFUq1YNZcqUKe7m5PP8+XNs3LgRMTExCAkJgbm5Oc6fP4+yZcuiXLlyxd28T5KXlxdkMplS554/f/6Dnuv169fQ09P7oBjsoSmE169fqy3WrVu3sHv3brx69QoAUJLzy4sXL0JTU7NQ12ZlZWHfvn1YunSp1MsSHx+Ply9fvvfalJQUJCcnQwiBFy9eICUlRdqSkpLw77//5ktyilNGRgaioqKYYH1kr169Qnh4uMJevJSUFISHhyM9PV3peDk5Oepsnpy0tDTcuHEDly5dktsKKyMjA/fv30dcXJzcpgpNTU34+fnh+fPnhW5HUbl06RIqV66MmTNnYs6cOVIbt2zZgtDQUJXjZWdnY86cOahbty5sbGxgbm4utxXG8+fP8eeffyI0NBSJiYkA3nzIP3jwoFDxSoI2bdogMDAQgYGB8PPzQ0xMDHR1ddGsWTM0a9YMenp6iImJgZ+fX6Hi5+TkYPLkyShXrhyMjIxw+/ZtAMD48eOxfPlyleNpFaoVJdjIkSOVPnfevHlKn5uTk4OpU6diyZIlePToEW7evAlnZ2eMHz8ejo6O6Nevn0rtfPbsGTp37oyIiAjIZDJER0fD2dkZ/fv3h5mZGebOnatSvDJlyijMpGUyGfT09ODi4oLevXujT58+KsV9W2ESrrt378Lf3x9xcXFIT0/HV199BWNjY8yaNQuvX7/GkiVL3nm9mZkZZDIZZDIZKleunO+4TCbDpEmTlG5PUX3rSEtLw9ChQ7Fq1SoAkH5Hhg0bBjs7O4wdO/a9McLDw5V+vtatW3/0eO8TExODAQMGICIiQqnze/bsid9++w3GxsYA3iTNHh4e0NbWVvm5ly1bhvDwcIWvw8TEBAsXLsS9e/fw3XffKRVPW1tbrkcwJCQEoaGhhf7AA4AnT56gT58++O+//xQez87OViledHQ0+vbti+PHj8vtF0JAJpOpHM/T0xO3b9+Gk5OTStflUiUpq1atmtLnjhw5Er1798asWbOk3xUACAgIQLdu3VRqIwBMmjQJf/75J0aOHInx48dj3LhxuHPnDrZu3YqffvpJ5XiXLl2Cj48PTE1NcefOHQwYMADm5ubYsmUL7t69i9WrV6scMycnB7du3cLjx4/zJddNmjRRKdaZM2ewYcMGxMXFISMjQ+7Y5s2bC7xuwoQJ0r/79++PYcOGYfLkyfnOuXfvnkrtyTVlyhSsWrUKs2bNwoABA6T9np6e+OWXX1T+XP3kqm03a9ZMbjM2NhYGBgbCy8tLeHl5CUNDQ2FiYiK8vb1Vijtp0iTh7Ows1q5dK/T19UVMTIwQQoj169eL+vXrq9zOoKAg4efnJ+7duyeMjIykeLt37xYeHh4qx5s3b56wsLAQPXr0EAsXLhQLFiwQPXr0EJaWlmLq1Kmif//+QldXVyxbtqzAGG3btn3n1rx5c6GhoaFy2wIDA0WPHj1Eenq63Gs9ePCgcHFxee/1Bw8eFAcOHBAymUxs3rxZHDx4UNqOHz8uHjx4oFJ7Jk6cqPSmimHDholatWqJI0eOCENDQ+l1btu2TdSoUUOpGDKZTG7T0NDI9zh3K4547xMZGalSLA0NDfHo0SPpsbGxsfT/pqo6deqI8PDwAo9v375d1KlTR+l4MplMbW3L1a1bN9GwYUNx+vRpYWhoKPbs2SPWrFkjXF1dxY4dO1SO17BhQ9GkSRPx77//igsXLojIyEi5TVW7d+8WNWrUENu3bxfx8fEiOTlZbnsfRb9jin7/VP19MzExEbdu3RJCCLn3kDt37ghdXV2VX6ezs7P0/21kZCTFXrBggejatavK8b788ksREhKSr33Hjh0TDg4OKsc7ceKEcHJyUvh/qer/3d9//y20tbVFy5YthY6Ojvj666+Fq6urMDU1Fb1791Y6jomJibh582a+/Tdv3hQmJiYqtSlXxYoVxb59+4QQ8v9v169fF2ZmZirH++R6aA4cOCD9e968eTA2NsaqVauke8JJSUno06cPvvjiC5Xirl69GsuWLcOXX36JwYMHS/urVauGGzduqNzOPXv2YPfu3Shfvrzc/kqVKuHu3bsqxzt69CimTJki1zYAWLp0Kfbs2YNNmzahWrVqWLhwoVwmnNf27dvx1VdfoWzZsgqPq/ptL2/bjh07Bh0dHbn9Dg4OSnXHNm3aFAAQGxsLe3t7pXtXCpL3W4c6bd26FevXr0f9+vXl2ujh4YGYmBilYuT9JrZv3z6MGTMG06ZNQ4MGDSCTyXD8+HH8+OOPmDZtWrHEW7hw4TuPq9q9Lt7q8Xv7sSqio6NRvXr1Ao9Xq1YN0dHRhY7/IW3LFRERgW3btqFOnTrQ0NCAg4MDvvrqK5iYmGD69Olo2bKlSvEiIyNx7tw5uLm5fXDbAMDf3x/Am966vL/DQsken9jYWLW04216enpISUnJtz8qKgpWVlYqx0tISICnpycAwMjICMnJyQCAr7/+GuPHj1c53pkzZ7B06dJ8+8uVK4eEhASV4w0ePBi1a9fGzp07YWtr+0HvedOmTcMvv/yC7777DsbGxliwYAGcnJwwaNAg2NraKh1HX18fR48eRaVKleT2Hz16tNBjXx48eAAXF5d8+3NycpCZmalyvE8uoclr7ty52LNnj9wAtzJlymDKlCnw9fXFqFGjlI6l7v/41NRUGBgY5Nv/9OnTQtWy2L17N2bOnJlv/5dffim9zhYtWrzztoe7uzvat29fYDdfZGQkduzYoXLbcnJyFL4R3r9/X677WJG3u7AvX75c4LmqdGHncnZ2xpkzZ2BhYSG3//nz56hZs6Z0T1cZT548UTiWJzU1tVBvSCNGjMCSJUvQuHFjaZ+fnx8MDAwwcOBAXL9+/aPHGzFiBGxtbfMlp7ne7s7+mLKysvDkyRPY29srPP7kyZNiH9eUmpoq/Y6Ym5vjyZMnqFy5Mjw9PQs1qNLDwwNPnz5VW/vyfiEsDAcHBzW1RF5gYCB+/vln/PPPPwDe3GaOi4vD2LFj0b59e5XjlS9fHg8fPoS9vT1cXFywZ88e1KxZE2fOnCnU+6+6E67o6Ghs3LhR4WeOqmJiYqREWVdXV3o/Cg4ORvPmzZW+XT9ixAh88803OHfuHOrXrw8AOHnyJFasWFGo23QAUKVKFRw5ciTf782GDRvg5eWlcrxPOqFJSUnBo0ePUKVKFbn9jx8/Vjhw8F3U/R/fpEkTrF69WrofKZPJkJOTg9mzZ8Pb21vleObm5ti+fTuCg4Pl9m/fvl2655+amvrOBKJWrVo4f/58gQmNrq5ugR8W7/LVV19h/vz5WLZsGYA3r/Xly5eYMGECWrRo8c5ra9SoAZlM9t5vx4UZLwAAd+7cUXhdeno67t+/r1KsOnXqYOfOnRg6dKjUJgD4448/0KBBA5XbFhMTA1NT03z7c+/TF0c8BwcHzJw5E506dVJ4PDIyErVq1VKpXdeuXZO+xQohcOPGjXyDxZVJVqtUqYJ9+/YV+Px79+7N917wPj/99JP0xSMjIwNTp07N93+oylg8V1dXREVFwdHRETVq1MDSpUvh6OiIJUuWKP1tOe8H58yZMzF69GhMmzYNnp6e+cYemZiYKN024P96Q9Xp2rVrCsduqDJma86cOWjRogWsra3x6tUrNG3aFAkJCWjQoAGmTp2qcpvatm2L/fv3o169ehg+fDi6du2K5cuXIy4uLt97qDLUnXDVq1cPt27dUktCY25uLn3elStXDleuXIGnpyeeP3+OtLQ0peOMHTsWzs7OWLBgAf73v/8BePMlOCwsrMD3g/eZMGECgoKC8ODBA+Tk5GDz5s2IiorC6tWrC/Xl+ZMbQ5NXUFCQsLe3Fxs2bBD37t0T9+7dExs2bBCOjo6iZ8+eKsUKDw8XpqamYsaMGcLAwEDMnj1b9O/fX+jo6Ig9e/ao3LarV68KKysr4e/vL3R0dESHDh2Eu7u7KFu2rHQ/VxXLli0TmpqaolWrVmLy5MliypQponXr1kJLS0v8+eefQggh5syZIzp16lRgjNevX4vU1FSVn/t9Hjx4ICpXrizc3d2FlpaWqF+/vrCwsBCurq5yYxQUuXPnjtKbKrZt2ya2bdsmZDKZWL16tfR427ZtYvPmzeK7774TlStXVinmsWPHhLGxsRg8eLDQ09MTw4cPFz4+PsLQ0FCcPXtWpVhCCPHFF1+I5s2bi/j4eGnfw4cPhY+Pj2jSpEmxxGvfvr0YPXp0gccjIyOFTCZTuk3vGnOh6niLpUuXCkNDQ7F9+/Z8x8LDw4WhoaFYunSp0m1r2rRpvjF5b2+qjsVbu3atWLlypRBCiPPnzwsrKyuhoaEh9PT0xLp165SK8fbYp7cfF3acSt4xMjt37pT7myjM+J6YmBhRrVq1fD/jDxmztX//fjF79mwxc+ZMsXfv3kLFUOTEiRNi7ty5Ytu2bYW6Pjk5WTRq1EiYmZkJTU1NUaFCBaGtrS2aNGkiXr58qVSMixcvStvmzZuFh4eHWLlypTh79qzcsYsXL6rUtq5du4q5c+cKIYSYMmWKsLKyEv379xcODg6ibdu2Kr9Wddu1a5do0qSJMDQ0FPr6+qJRo0Zi9+7dhYr1Sa9Dk5aWhu+//x4rVqxAZmYmhBDQ1tZGv379MHv2bBgaGqoUb/fu3Zg2bRrOnTuHnJwc1KxZEz/99BN8fX0L1b6EhAQsXrxYLt53332n0n3NvI4dO4ZFixYhKioKQgi4ublh6NChaNiwYaHiqdOrV6/w999/4/z589Jr7d69O/T19YulPRoab1YsUNT7o62tDUdHR8ydOxdff/21SnEvX76MOXPmyP1Mx4wZI92vV8WtW7fQtm1bREVFST1jcXFxqFy5MrZu3arytzd1xLt27RrS0tJQu3ZthcczMzMRHx+v9K0HZceLKRuvR48e+N///gc3Nze4urpCJpPh+vXruHnzJjp16oS///5bqTgfS+70bXt7e1haWip1zaFDh5SOr2yPy44dOzB+/HhcuHABAGBsbIzU1FTpuEwmw/r169GhQweln7tVq1bQ1NTEH3/8AWdnZ5w+fRrPnj3DqFGjMGfOHJXHMZYGERERcu9xPj4+Sl+roaHxzt7o3GOq9kYnJibi9evXsLOzQ05ODubMmYOjR4/CxcUF48ePL5FrDhXWJ53Q5EpNTUVMTAyEEHBxcVE5kfkcqXPK4IcIDw9HQEAAtLW13zsFuTDTjp2cnHDmzBmlP0w+NiEE9u7dixs3bkAIAQ8PD/j4+BR6kKC645VE//zzD/73v/8hOjoaQghUrlwZ3bp1K3S3+NuysrLw+vVrGBkZFTpGRkYGYmNjUbFiRWhpFf7Of1xcHCpUqJDv5yeEwL1795S+Rdy6dWsEBgZKt5uNjY1x8eJFODs7AwBmzZqFgwcP4t9//1W6bZaWloiIiEC1atVgamqK06dPw9XVFRERERg1apSUPClr//79+OWXX3D9+nXIZDK4ublhxIgRKiUNea1ZswZLlixBbGwsTpw4AQcHB8yfPx9OTk4IDAxUKdadO3fg6OhYqHbkUmUySFGNVXpbQcuBKJK79o4qzpw5g5ycHNSrV09u/6lTp6CpqVngF6eCfJIJTbt27ZQ6713z7z+GpKQkLF++XPoDdXd3R58+fZRe5yIlJUW6R65oQFpeqtxLP3nyJLp164a7d+/m+7ag7LcDda1/oqGhgYSEBFhbW0u9KooUdgyNumVnZ2PLli1yP9PAwMAP+tAqSSIiItCkSRO1vZ7ExESkpaXJzfa7evUq5syZg9TUVLRp06ZQ64yow7///otnz54hKChI2jd16lRMnjwZWVlZaN68OdavX6/SN1x1rFWUV0GrZz979gzW1tZK/004Ojpi48aN0gfI2wnN5cuX8eWXX+Lx48dKt61MmTI4d+4cnJ2dUbFiRfz555/w9vZGTEwMPD09VRq/sWjRIgQHB6NDhw7SeLSTJ09i48aNmDdvHoYMGaJ0LABYvHgxfvrpJ4wYMQJTp07FlStX4OzsjLCwMKxatUrlwdEaGhpo2LAhgoKC0LFjxw9aq6goxMTEYOXKlYiJicGCBQtgbW2NXbt2oUKFCu8cV5b7e6qMXr16qdyuunXrYvTo0fl6/jZv3oyZM2fi1KlTKsX7JBMaZRePW7lypdIx1b1w3aFDhxAYGAgTExPpTeTcuXN4/vw5wsPDleoqzvtmlttd+bbCdFHWqFEDlStXxqRJkxROGVQ0sPRt70o+8ioJiUhqaioOHTqkcODisGHDlI5z5coVBAYGIiEhAa6urgDefGBZWVkhPDy8ULed9u/fj/379yvsKVuxYsVHj/f2B2j9+vWxadOmQi8937VrV9ja2koDax8/fgw3NzfY2dmhYsWK+O+//7B8+XK5pOJ9Hjx4gE2bNuHmzZvQ0dGBq6srOnXqpHLXevPmzdG+fXtpIb7jx4/jiy++wM8//wx3d3eMGzcOAQEBKg0KHj58OI4dO4b58+fD398fly5dgrOzM8LDwzFhwgSVey00NDTw6NGjfDNp7t69Cw8PD7nbRu+ip6eH69evSwvqnT17FtWrV5cGGcfGxsLNzU2llZa/+OILjBo1SkpKk5KS8OOPP2LZsmU4d+4crly5onSscuXKITQ0NF/i8ttvv2Hq1KmIj49XOhbwZnbYtGnT0KZNG7nk7cqVK2jWrJnKM8fOnz+Pv//+G+vWrcOTJ0/g5+eHHj16oHXr1oWaNTV9+nSULVs2X426FStW4MmTJxgzZozSsQ4dOoSAgAA0atQIhw8fxvXr1+Hs7IxZs2bh9OnT2Lhxo8rtUxcjIyPpbyCv2NhYVKtWTeXJO5/0oGB1UsfCdXlVqVJFDBgwQGRlZUn7srKyxMCBA0WVKlWUinHw4EGRmZkp/ftdmyoMDAxEdHS0SteUVufPnxc2NjbCxMREaGpqCisrKyGTyYShoaFwcnJSKVa9evVEq1atRGJiorQvMTFRtG7dulCLL06cOFFoaGiIunXrisDAQNGmTRu5rTjivb3YXN7FsArD0dFRHDhwQHo8e/ZsUbFiRen3evbs2aJevXpKx/vtt9+Erq6ukMlkwszMTJiamgqZTCYMDAzE//73PyGEEDk5OeL8+fPvjWVlZSV3XnBwsPDz85Me79y5U6mFIfOyt7cXJ06cEELI/99FR0cLY2NjpeMEBweL4OBgoaGhIQYNGiQ9Dg4OFsOGDRP16tUTDRs2VDqera3tOwfZ7t69W9jY2CgdT4g3gz03bdokhHgzQNjd3V3IZDJhaWkp9u/fr1IsIyMjhe9JN2/eFIaGhirFEkIIPT09aSJB3p/DzZs3hZ6ensrxcuXk5IiIiAjRv39/UaZMGWFiYiL69OmjchwHBwdx7NixfPtPnjwpHB0dVYpVv359aVBw3td6+vRpYWdnp1KsrKwssXHjRmniyebNm+U+w1Rlbm4ujh8/nm//sWPHCrWwHhMaJbVr104sXrw43/4lS5aIdu3aCSGEWLhwoahatapS8fT09MSNGzfy7b9x44bSf1Bt27aVZiasWrVKvH79Wqnr3sfb21v8999/aoklhBC3b99WS5xJkya9cyuMpk2bSoll7h97XFycaNKkifRmrCw9PT1x5cqVfPsvX75cqDdJGxsbsXr1apWvK8p46k5o8n6wCCFEQECA+P7776XHUVFRwtzcXKlYO3bsEJqammLUqFFyM7ni4+NFcHCw0NbWFkeOHBFdu3ZV6vdFT09P3L17V3pcp04dMXPmTOnxnTt3hIGBgVJty5V3lfG8/3eRkZEqrbaaO8tKJpOJhg0bys288vX1FQMHDlS4qmtBOnfuLFq1alXg8ZYtW75zhqSynj17JnJyclS+rlu3bmLWrFn59s+ePVt06dJF5Xju7u5i69atQgj5n8OCBQtEzZo1VY6nyLlz50SNGjUKNaNLV1dX4ftmTEyMyisjGxoaSrHyvtbY2FiVYkVHR4tKlSpJK+/XqFFDGBgYCFdX10LNzBXize9d06ZNxfPnz6V9SUlJomnTpqJjx44qx2NCoyRDQ0OF3xCio6Olbwi3bt1S+g2uYcOGYsuWLfn2b9myRelv89ra2tIb99tLyH8IdU4ZzG1bs2bNxJo1a8SrV68K3a4aNWrIbVWqVBEGBgbCxMREeHl5FSqmqamplFiampqKa9euCSHefBNydXVVKVb16tUVfvPcv3+/0oluXubm5oV+oyiqeBoaGuLx48fSY2Nj4w9KWK2treWW6LewsBAbN26UHqvyDbxJkyZi3LhxBR4fN26c0NPTE46OjkpN83d2dha7du0SQgjx4sULoaOjI44ePSodP3funLC0tFSqbXnbuHDhQiHEmw+X3P+77777Tq73R1m9e/dWqiTB+5w/f17o6uqKDh06iNOnT4vnz5+L58+fi1OnTol27doJXV1dce7cuQ9+nsKaPHmyMDU1FS1atBCTJ08WkydPFi1bthRmZmZi8uTJYsGCBdKmjBUrVohy5cqJdevWCUNDQ/H333+LKVOmSP8urLi4ODFz5kxRvXp1oaGhIRo1aiR+//13leO4uLiINWvW5Nu/evVqlXuOy5UrJ/X25E1oNm/eLJydnZWOExAQIPz9/cWzZ8+kfU+fPhX+/v6iRYsWKrUp1/3794Wzs7MwNTWVEnIzMzPh6uoq4uLiVI7HhEZJFSpUEPPmzcu3f968eaJChQpCiDfrCJQtW1apeOvWrRP29vZi9uzZ4siRI+LIkSNi9uzZwtHRUaxbt06pBMLT01P06tVLhIWFCZlMJn799VexatUqhZsq1FmDRYg3PRTBwcHC2tpamJqaioEDB4pTp06pHEeR5ORk0bZt20L3PFhaWoqoqCghhBCVK1eWPsCuX78u9PX1VYq1c+dOUaVKlXzrHnl6eoqdO3eqVBNHCCFGjx4tfv75Z9VeUBHHk8lkwtPTU6qNpqmpKapUqSI9zt2U9fXXX4u+ffuK7OxssWHDBqGjoyN3y27Hjh3Czc1NqVjGxsYKez1z3bhxQ8hkMrlel3cZPXq0cHNzE6tXrxZdunQR9vb2ct3rS5cuFY0aNVIqVi51r1WkTlu3bhWWlpb51rSxsLBQ+OXrfV6+fCl+/PFH0aBBA1GxYkXh5OQkt6nC0dFRqU2VuMuWLRP29vbSe1z58uWlNbtUtXTpUtGkSROhqakpPDw8xNSpU0VsbGyhYgkhxIwZM4SFhYVYsWKFtM7W8uXLhYWFhZg2bZpKsUJCQkTjxo3Fw4cPhbGxsYiOjhZHjx4Vzs7OKtWrMzAwEJcuXcq3PzIyslC3/XK9fPlSLF26VHz77bdi1KhRYtWqVSIjI6NQsT7JQcFF4Y8//sA333yDFi1aoG7dupDJZDh9+jT+/fdfLFmyBP369cPcuXNx+vRprF+//r3x3jdoVpk1B44fP46RI0ciJiYGiYmJMDY2LnDgsipT6t43fbCwUwazsrKwfft2hIWF4b///kOlSpXQr18/BAUFFWp58FxXrlzB119/XajVc319fdG7d29069YNgwcPxoULFzBs2DCsWbMGSUlJKo2yz/szzf055P555X2s7EDo4cOHY/Xq1ahWrRqqVauWbxVYVQajqiuessukK1svKzIyEj4+Pnjx4gWysrLwww8/yFXzDQoKgqGh4XsrsgMFDzDMdfv2bXh6eio9UDYtLQ2DBg3Cjh07YGNjg2XLlsmtneLt7Q1/f3+VBmgCb35fZ8+erZa1ioDCV1JWJC0tDbt375ZqXlWqVAm+vr6FWuqia9euOHToEIKCghROLhg+fLjKMdUhKysLf/31F/z8/GBjY4OnT58iJydHYdkSZVWoUAFdunRB9+7dUaNGjQ9uoxACY8eOxcKFC6WfqZ6eHsaMGaNymYHMzEz07t0b69atgxACWlpayM7ORrdu3RAWFgZNTU2l4pibm2PHjh351jU7duwYWrVqVahp2+rGhEYF6ly4Tt1rDuSd3lzSpaen4/fff0doaCgyMjKgra2Nzp07Y+bMmYVaVPDo0aNo1aoVkpKSVL727NmzePHiBby9vfHkyRP06tVLWnRq5cqV7yx2+LaDBw8qvWaDMrPY3lUCQyaTISIiQum2FUU8dXny5AmOHz8OGxubfOtR7Ny5Ex4eHtLsm3epV68eunTpUuDS9fPmzcO6detw+vRppdsmhMDdu3dhbW2tsPaaKjIzMzFw4ECMHz++wKRLVevWrUPPnj3h6+uLvXv3wtfXF9HR0UhISEDbtm1VmskJqGc9lVxmZmbYuXMnGjVq9MGxDh48iGbNmn14o/4/AwMDXL9+XW3rueR+UVGH7OxsHD16FJ6entDR0cH169ehr6+PSpUqqTxjSgiBuLg4WFlZISEhQVr0z8vLK1+Ryffp2bMnzp8/j+XLl6Nu3boA3qwXM2DAANSqVQthYWFKxSnStcUK3U9EJcqdO3cKNdiuILdu3RJDhgwRX375pfDx8RFDhw794PEXZ86cEd98840oU6aMKF++vBg3bpy4ffu2OHr0qGjevLmoU6fOO6/Pe598wYIFYv78+WLMmDHCzs6uUAMDSX0OHjwodu7cKXe76H3KlCkjnjx5IoQQok+fPiIlJeWD2hAWFib09fXFb7/9Js2SEkKIzMxMsWjRIqGvry+VHVBWdna20NbWVmmA7buYmpp+0CDqt3l6eopFixYJIf5vfEROTo4YMGCA+Omnn1SOJ5PJRKNGjcSSJUvkxkoUhqOjozQm7UPp6uoKZ2dnMXny5EKNrXhbs2bNCnUbLa+LFy+K7Oxs6d/v2lRV0KBgVanz9zcpKUm0bt1ayGQyoaOjI3R0dISGhoZo06aN3KDe98k7sUDR8Ia8wxxUxYSmENLS0uTGQ3zIoLyrV6+K//77T65uSmHriSQlJYndu3eLNWvWfNAYml27dgkdHR1Rt25dERwcLEaMGCHq1q0rdHV1C1W3au7cuaJq1apCW1tbBAYGiu3bt0tvBLmio6OFpqbmO+O8fb/c2dlZ1KtXT4SGhn7wh6E6ODo6ikmTJik9RkMV9+7dE/fv3y/2eLNmzZL7oMzJyRF+fn7Sm1DZsmUVzvRSxNDQUPpwf3uwcWGNGjVKyGQyaaC4l5eXMDExERoaGmLEiBGFiunh4SFNtf5QvXv3lqbQqoOBgYE0VsPCwkIa43Dt2jWVp1kL8Wag86hRo0T58uWFrq6uaN26tfjnn38KNYNyzZo1okOHDmqpD/fs2TOxYMECadyWr6+vWL9+vUhPTy9UvH/++Uc4OzuLX3/9VRw/frxQCcjbH8xv1yT7kHGHtWvXFvv27VP5OkXU+fsrxJv36vDwcLFt27YSt7wHExolpaamiu+++04qJvf2pip1F24LDw8XxsbGQkNDQ5iamgozMzNpK1OmjEqxatSoIcaMGZNv/5gxYwo1m8jFxUVMmzZNPHz4sMBz0tPTRVhYmMqxC8vb21upTRULFy4UNWvWFJqamsLHx0f8/fffHzSVPjs7W0yaNEn6QM792f7888/5EsKPFc/Ly0uuiOI///wj9PX1xdGjR8WzZ89Ey5YtlZ5u6ePjIzw9PUXv3r2FTCYTXbp0EX369FG4qeLEiRNi2LBhIiAgQAQEBIhhw4Z90Bv6jh07ROPGjcXly5cLHSPXlClThJmZmWjfvr2YNm1avl5HVZUvX15KYqpVqyattXP8+HGVpoG/TR3rqdSoUUMYGxsLIyMjUbVq1UIPHH/bhQsXxNChQ4WlpaUwNzcXQ4cOlZsppwx1THzI2yuuzsK5QrxZ96dGjRpi+/btIj4+/oO+QKvz9zevzMxM8eLFi0Jfn5GRIZo1ayZNylAHjqFR0nfffYcDBw7g559/Rs+ePfHbb7/hwYMHWLp0KWbMmIHu3burFE/dhdsqV66MFi1aYNq0aR98r19PTw+XL1/Od4/15s2bqFatGl6/fq1SvDt37sDe3j7fQGihYr0ZddLQ0ICDgwNatmyZb3BsXr/88ovKsS9evIgVK1bg77//RlZWFrp164a+ffuiZs2aKsUJDQ3F8uXLMWnSJDRq1AhCCBw7dgwTJ07EgAEDMHXq1I8er0yZMjh+/Djc3d0BvFmVOysrC2vWrAHwZjn6jh074t69e++N9ejRI/zyyy+IiYnB5s2b4efnV+AYgS1btqjwStWrTJkySEtLQ1ZWFnR0dPIVVFVlMOS7xgLJZDLcvn1bpbZ169YNtWvXxsiRIzF16lQsWLAAgYGB2Lt3L2rWrKmW8i7nz59Hv379cOnSJZVW9X7fAHJlB44rEh8fj2XLlmHGjBnQ0tLC69ev0aBBAyxZsuSdS/nnUufEh6IYG6VoggFQuJXfP/T3tyhKgOSysrLC8ePHVR7PUyC1pUafuAoVKkgrmuZOfRPizboAAQEBKsezsLCQujZNTEyk6ab79+8XNWrUUDmegYGB2u7Nly9fXvzzzz/59q9fv16aoq6KgtbIefr0qdLfhiIiIsScOXOkdUCWLFkiKlSoICwtLUX//v1FWlqaSm2aOXOmcHd3F9bW1iI4OFjt316EePMNZP78+UJXV1doaGiIatWqieXLlys91snW1lbh7cetW7eqvMKnuuLlvU0khBCurq5y62zcvXu3UIsIOjo6iqdPn6p8nSIFjWO4dOmSuHnzpsq9ZmFhYe/citOzZ8/EgwcPhBBveuBmzpwpWrVqJYKDg1Uaz/Q2da2nok4ZGRliw4YNIiAgQGhpaYn69euLP/74Q7x8+VLExcWJrl27Cnd39w96jqysrEKNrVH32Ch1rvz+ob+/3t7e0jgtId4sPaChoSGmTJkiNm3aJNzc3ERwcLDKr1EIIUaOHKnwbkBhMaFRkqGhodR1WK5cOWkdldu3bxdqDr6ZmZn0B+Ds7CwiIiKEEG8G46q6/okQb1YNXr9+vcrXKTJp0iRhZmYmZsyYIQ4fPiyOHDkipk+fLi1ipaq3V5fNpexKq8uWLROampqiYsWKQldXV0ybNk0YGhqKwYMHi2+//VaYmJgU+o/i+PHjon///sLExETUqVNHLF68+IMXKsvIyBDr168X/v7+QlNTUzRq1EisWLFCTJkyRdjY2IiuXbsqFUdXV1dhd6wqq0mrO1716tWlgbV3794VMplMXL16VTp+7NgxUa5cOZXbpk55b92+fStXQ0ND6Orqip49e37QIo/qdunSJTF8+PDiboba11MRQoizZ8+KNWvWiLVr1ypVciIvb29vkZSUJIYMGSIsLCyEhYWFGD58uMIvILm/j4Vx/fp1ERISIqytrYW2trbK16t7bNTHosy4taIoAZJryJAhwsTERNSsWVMMHDhQroRHYZKkT6ME8Efg7OyMO3fuwMHBAR4eHvjnn39Qt25dbN++HWZmZirHq1q1qrRmRr169TBr1izo6Ohg2bJlheq2bNmyJUJCQnDt2jV4enrmu42iyvS38ePHw9jYGHPnzkVoaCgAwM7ODhMnTlSpWOPIkSMBvOky/emnn+RuhWVnZ+PUqVNKrdmwYMEC/PLLLxg6dCh27dqFVq1a4c8//5SquzZr1gyhoaGYMWOG0m3L1aBBAzRo0AALFizAhg0b8Ntvv+H7779HfHy80hXKV69ejc6dO+Pq1atYuXIl/v77b2hqaiIoKAi//PIL3NzcpHN9fX3RpEkTpeJWr14dixYtwsKFC+X2L1q0SKXp5OqM980332DIkCE4cuQITp48iQYNGsDDw0M6HhERAS8vL5XbBqivEOeWLVswZswYhISEoG7duhBC4MyZM5g7dy4mTJiArKwsjB07Fj/++CPmzJmjMMb7qtfnpUol+7ef4++//8by5ctx9uxZVKtWrVBxcgkhcODAAbx69QoNGzYs1C2AyZMno0uXLliwYMEHr6fy+PFjdOnSBQcPHoSZmRmEEEhOToa3tzfWrVun1NpThw4dQkZGBq5du4Zff/0V7du3h46OjsJz7ezsVKqSnZqaivXr12P58uU4efIkvL29MXXqVLRp00bpGLlcXFwwefJkHD9+HLVq1cq3bo8q75u5nj9/juXLl+P69euQyWTw8PBA3759lSoO/C5CCPz333/4888/sXPnzvcWHH3x4gUsLCykx0ePHpWrjl2lShWVi4PmunLlinQr/ubNm3LHCjUNvlBp1Wdo3rx50qC9iIgIoa+vL01bmz9/vsrx1Fm4TQj1T3/LlZKSUugZROqqN6Ovry83sE5bW1tuOujdu3eFjo5OodqY68iRI6JPnz7CyMhI1KtXT6VbWLm31DQ0NISfn5/4559/Clzp8uXLl6J3795KxT148KAwNDQU7u7uom/fvqJfv37C3d1dGBkZicOHDyvdPnXH+/PPP0WbNm3E4MGD8w30/uabb1SugSWEegtx1qlTR1rxOa9du3ZJSwNs2bLlncu+v92ro2gr7N/WwYMHRVBQkDAwMBAaGhpizJgxKs8WSUpKEj179hRVq1YV/fv3F8nJyaJRo0bS37y1tXWhpgurc+mHTp06iVq1asn9rV69elXUrl1b6WUWCurd/RDHjx8Xffv2FUZGRsLLy0vMmTNHaGpqyvU0qkpdqxfnOnPmjDA3NxflypUTbdu2FW3atBHly5cXFhYWhS5BERMTI8aNGyfKly8vzMzMRPfu3cXmzZvfe11RlAApKhwUXEhxcXE4e/YsKlasWKhvy4okJiaiTJkyalugqaTo06cPFixYUOhvsm8vGmhsbIyLFy9KPVmPHj2CnZ2dSgPlgDcDC8PCwhAWFoaUlBT06NEDffv2letxUKV9r169UttCXXnb+Ntvv+HGjRsQQsDDwwPffvst7OzsSkQ8dbG1tcWsWbPkBh4Wlr6+Pi5cuCDXMwYAN27cgJeXF169eoU7d+7Aw8MDaWlpCmMcOnRI6edTZpHEhw8fYuXKlVixYgVSU1PRtWtXdOvWDQ0aNMDFixdV/p3r378/Dh8+jJ49e2LHjh3Q0NCAEALz58+HhoYGRo8eDSMjI2zfvl2luACQlJQk1zPg5uaGvn37wtzcXKU4pqam2LdvH+rUqSO3//Tp0/D19cXz58/fG0NDQwPR0dHv7c1R9r0l92ferVs39OjRQ/p/19bWLtTPoah88cUXcHFxwR9//AEtrTc3UrKystC/f3/cvn0bhw8fVirO69evsXHjRvz55584efIkvvrqK/z333+IjIxE1apVlYoxZswYhIeH44cffsC///6L48eP4/bt29IKw8uWLcPq1atx9OhRlV7jhg0bsHXrVmRmZsLHxwcDBw5U6XqFijWdKkUKqmadnp6u8jovJZGXl5c0iLBGjRr5pliqY7plrjt37oirV68qPVVYQ0ND3Lp1SyQnJ4vnz58LY2NjcfHiRWkK482bN1X+phwQECD09PRE69atxdatW+UWYlOVTCZTyxoqpU12draIiooSR44cEYcOHZLbVKXOQpw1atQQvXr1klujJCMjQ/Tq1UsacH/06FHh6OioludThq6urujRo4fYtWuX3O+9lpZWoXoG7OzspMGh9+/fFzKZTJq0IIQQp06dUrquXF4HDx4UJiYmokKFCqJt27aibdu2wt7eXpiYmKg8GNXIyEhcuHAh3/7z588LY2NjpWK8r6dM1V4ybW1tERQUJPbs2SPXG1XYn0NR0dPTE9evX8+3/+rVq0qPscxdxLR+/fpi0aJF0qB7VV9ramqq6NGjhzAzMxNubm75enObNWsmZsyYoXQ8Id6M1ZLJZKJy5cqiWrVqQkNDQ4wdO1alGIqwh0ZJmpqaePjwYb7SAs+ePYO1tbXKvQOpqamYMWNGgWMGVJ2+Cbz5Vjlnzhzpm5W7uztCQkKUmgI+adIkhISEwMDAABMnTnxnL5Gy0y1XrVqFpKQkjBgxQto3cOBALF++HADg6uqK3bt3o0KFCu+Mo6GhoXDq4tuPVfkZaGhowNbWFtbW1u98refPn1cqVkBAwHuXJVd2Cm10dDR++uknLF26NN83z+TkZHzzzTeYMmWK0mOt1B0PeDM9u1u3brh79y7efgtR9WcBvPkWaGRkhPHjx6t0nSLHjx9H69atoaGhgWrVqkEmk0lTjnfs2IH69etjzZo1SEhIQEhISIFxcpdnMDY2BgDpG/y7pvkXxNXVFRkZGejWrRuCgoKk3qPC9gxoaWnh3r17UqkQAwMDXL58GRUrVgQAJCQkoFy5cir/HKpWrYqGDRti8eLF0jfw7OxsfPvttzh27BiuXLmidKzAwEA8f/4cf//9t9QD+ODBA3Tv3h1lypRRaiq+hoYGNm3a9N7eIWV6yXKfPywsDCtXrsSrV6/QtWtXdO/eHfXq1UNkZGShemhSU1Mxc+ZMbN68GXfu3IFMJoOTkxM6dOiA77//vlDLaJQtWxZr1qyBr6+v3P7du3ejZ8+eePTo0XtjaGlpYcyYMRg7dqz0OwwU7ndOqLEECAB4enqiTZs2Ut22sLAwDB06FC9evPiguExolKShoYFHjx7l6/q8ePEivL29VS7Mpe7CbWvXrkWfPn3Qrl07aY2R48ePY8uWLQgLC0O3bt1UiqcODRo0wMCBA9GnTx8AkAb0hoWFwd3dHUOGDIGHhwf+/PPPd8ZRtvtf2Tc1QL1FFjU0NNCpU6d86zu8Tdm6OgMHDoSZmRlmzZql8PiYMWOQkpKCxYsXF0s8AKhRowYqV66MSZMmKfz9VXXgoroLcb58+RJr167FzZs3pbpr3bp1k3tjf5+3v8SYmJggMjKy0GuNHDt2DMuXL8eGDRtQuXJl9OjRA6NHj8alS5ekdX2UVVS3YfX19REZGQlXV1e5/VFRUahRowZevXqldKx79+4hMDAQV65cQYUKFSCTyRAXFwdPT09s27YN5cuXf2+MoqxRFxERgRUrVmDz5s14/fo1vv/+e/Tv3x+VK1dWOkZGRgYaNmyIK1euICAgAG5ubhBC4Pr169i1axdq1qyJw4cPq5wEDxs2DFu2bMGcOXPQsGFDyGQyHD16FCEhIWjfvj3mz5//3hj/+9//sHLlSpw4cQItW7ZEUFAQ/P39oa+vr3JCk5OTAz09PVy9elUta8YYGhri8uXL0u9rdnY29PX1ERcXBxsbm8IH/uA+nk9c7u0XDQ0N4enpKXfrpVq1asLY2FjplVHzMjU1lRtY9aHc3NzEvHnz8u2fO3eucHNzUymWk5OTwjVBkpKSVBrgZm5uLldufvDgwaJdu3bS4wMHDijd7Z+ZmSnCwsLeudqwsrZt21bo8vSKqHvgoqurqzh9+nSBx8+ePSsqV65cbPGEeLPukTqXPc87YPztTdUVm9Xl7Z9rbq2kD/XixQuxbNkyUb9+fSGTyUSzZs3EsmXLVLptKZPJxNSpU6UVhvX09MT48eOlx1OmTCnUgOWGDRsqXIdly5Yton79+irHE0KIPXv2iIULF4oFCxaIvXv3qnRtUQwKftvz58/Fb7/9JmrVqiVkMpnw9PRU+tr58+eLsmXLSuuI5XX9+nVRtmxZsXDhQpXblJ6eLoYNGyZNPMldbmDEiBEqr6MUGxsrfvrpJ2Fvby8sLS2FhoaG2LBhg8ptUmcJBUU/V3X8fbGH5j1yv8lPmjQJo0aNgpGRkXRMR0cHjo6O75xKWBAnJyf8+++/Kn8zK4iuri6uXr0KFxcXuf23bt1C1apVVVrdt6BvRY8ePUKFChWkcvbv83ZF2+rVq6Nv375S71NcXBxcXV2V/tanrgq5mpqaSEhIgJWVVYG3ElWN96Ex8tLX18eNGzcKfJ13796Fu7t7gQNaizoeADRv3hyjR4+Gv7+/0td8TDdv3sTBgwcV3s796aeflIrxvl4Qdbh+/TqWL1+ONWvWIDExEZmZmUpd5+joqNTkgdjY2Peec+nSJbn2jB49GkOHDkX9+vUBvLm9+Ntvv2HGjBno3LmzUu1TFycnJ5w9e1Zu2nBRioyMxIoVK/ItbVCQpk2bolOnTvjuu+8UHv/111+xceNGlQaZ55WWloaYmBgIIeDi4vJBt3uEENi9ezdWrFiB8PBwWFpaol27dkq/1p07d2LGjBlYvHix0gOKC6KhoYEpU6bIfZ7mLrVgaWkp7VN1ujvXoXmP3FsOjo6O6Ny5M/T09NQSd/Lkyfjpp5+watUqtdyTrFChAvbv358vodm/f/97x6jkylvKfffu3XK3DbKzs7F///53Lt3+NgcHB5w7dw4ODg54+vQprl69isaNG0vHExISVLo1Ua9ePVy4cOGDExorKyucPHkSrVq1yjcepzDU/Z3A1NQUMTExBb7OW7duqTRjTN3xAGDo0KEYNWoUEhISFK579KFrqnyIP/74A9988w0sLS1hY2Mj9/PNXRNJWdeuXUNCQgKANz/nGzdu4OXLl3LnfMhrdXd3x5w5czBjxgy5v7/3uXPnTqGf8201atSATCaT+z0ePXp0vvO6dev23oRm4cKFGDhwIPT09N77QanMh1XehCwrKwsHDx5ETEyMdPswd72ovB+MylIUz9raGtOmTVM6xrVr19CsWbMCj3t7e+Pnn39WOl5aWhpCQkLkZv8sXLhQ7kO+sGQyGfz9/eHv74/ExESsXr1a6dvgANCjRw+kpaWhevXqH1wCxN7eHn/88YfcPhsbG6mESm57VU1o2EOjooyMDIXf+pSpR+Tl5SX35nrr1i0IIeDo6JjvA0GZwah5LV68GCNGjEDfvn3l7rmGhYVhwYIFGDRo0Htj5NYPefvNDXgzkMzR0RFz587F119/rVSbpk+fjoULF+Lbb79FREQEnjx5IjeocP78+dixYwf27dunVLwNGzZg7NixCA4OVrh4lbIfLBMnTsTPP/+sVCKjzBiEQ4cOoWHDhvD19cXSpUtVugevSKdOnZCZmVngoMnAwEDo6Ohgw4YNxRIPQL66XMD//d6oMijY29tb4c/B1NQUrq6u+O6775ROyHM5ODjg22+/xZgxY1S67m25g9EVvUUW5rUCb15vjx490KFDhw9eIE1d3lfXKK/3fZnI26OizrpVd+/ehb+/P+Li4pCeno6bN2/C2dkZI0aMwOvXr7FkyRKlY70vXnp6utLjybS1tXHv3r0Cx308fPgQDg4OSvdqh4SE4Pfff0f37t2hp6eHv//+G82aNVPpb1Pd7t+/j/LlyyMsLOyd75m5C50WJ/bQKCk6Ohp9+/bF8ePH5far8qZWmBUolfXNN9/AxsYGc+fOxT///APgzbe/9evXIzAwUKkYuUmak5MTzpw588HfCsaMGYO0tDRs3rwZNjY2+f4ojx07hq5duyodL/fbYd6svTAfLBMnTkSXLl1w69YttG7dGitXrizUas+5cgcjX7lyRS1rCIWGhqJBgwbo0KEDRo8eLQ3QvHHjBmbNmoXdu3fn+z38mPEA5W5lKKOg1WifP3+Of//9F4sWLcLRo0dVWrU2KSkJHTt2/OC2qes15uXp6Ykff/wRQ4YMQYsWLRAUFIQWLVqodMta2VsEgHK9IMr0eGZnZ2P79u3vPTfv/5k6//+GDx+O2rVr4+LFi3K3n9q2bYv+/fsXW7ycnBxpNpgiGhoaKiW8mzdvxvLly9GlSxcAb3pFGjVqhOzs7Hc+z9tyV2l/H5lMhrlz577znKpVq+LXX39F7969lX7+wnj9+vUH3wFhD42SGjVqBC0tLYwdO1bhrA51La5HBVNnhdxceaerf6hRo0ZBW1u7UCUY3rZjxw707dsXz549k9tvYWGBP//8U6VSFkUR72P57rvvEBsbi3///Vfpa/r164c6depg8ODBamlDXFycNEtH0TFVq8Xn5ORg3759+N///octW7ZAU1MTHTp0QPfu3ZWaqafsbd/CVO9+240bN7BixQppCQZlexoA4Oeff1Y4bfnVq1eYPXu2Srf+LC0tcezYMbi6usqNZXrfAolFHU9DQwNVq1aVFr97W1ZWFq5evap0UqOjo4PY2FiUK1dO2qevr4+bN2+q1FPp7e2t1HkymQwRERHvPOf333/H2LFj8dVXX2HZsmVqHc+UnZ2NadOmYcmSJXj06JHUUzZ+/Hg4OjqiX79+KsVjQqMkQ0NDnDt3Lt/qo+rw+vVrrF+/Hqmpqfjqq69UmhaXlJSEtWvXolevXgrXGFm9erXCY29T973vtz1//hwbN25ETEwMQkJCYG5ujvPnz6Ns2bJyf7zF5cmTJ4iKioJMJkPlypWVqjPztqFDh2L16tVwcXFB7dq1890SU3Xq8atXr7B7925ER0dDCIHKlSvD19e30MnXh8YLDw9HQEAAtLW13zveQ10J0qVLl+Dn54eHDx8qfc306dMxb948tGzZUuH4HlV/f9W9BlVer1+/xvbt2zF16lRcvnz5g2Kpi6IaR126dEGbNm1U6rVV5/+bubk5jh49Cg8PD7kE5OjRo2jfvr1S67IURTx1Lv8AyE9YyGVsbIxLly6pNH5R3WJjY9GvXz9cu3YNy5YtU9vf988//4xVq1bh559/xoABA3DlyhU4Ozvjn3/+wS+//IITJ06oFI8JjZLq1KmDX375RW5Qa2GEhIQgIyMDCxYsAPBmTE7dunVx7do1GBgYICsrC3v27EHDhg2Vijd58mRcunSpwHusnTp1QvXq1TFu3Lh3ximqe9/Amw+lL7/8EmZmZrhz5w6ioqKkLPzu3btYvXq1SvGuXbuGuLi4fN8WC/NHlpaWhiFDhmDNmjXSG6ympiZ69uyJX3/9VaXk4V3fipT5JpRXZmam2sbk5Motovn2AoAZGRlYt24devbs+c7r8876UTSGJldhFtYrSHR0NOrWrYukpCSlr1H3729Ba1DdvXsXHh4eSE1NVSleroSEBKxbtw5r167F+fPnUadOHZw6dapQsdThxIkT+PPPP/HPP/+gUqVK6N69O8aMGYNLly4VasG5gv7fIiIi0LlzZzx58kTpWJ07d4apqSmWLVsmfcBbWVkhMDAQ9vb2Kg1uLYp46qJokc7t27ejefPmcl+QlF2k823379+HTCYr9JfIRYsWITg4GO7u7vl6pVQd9wm8Keq5dOlSfPnll3KJ5Y0bN9CgQQOV/u4BJjRKi4iIwI8//ohp06Yp/Nan7CyRqlWrYtq0adKH78qVKzFq1ChcuHAB9vb26Nu3Lx4/foydO3cqFa9GjRqYO3cuvvzyS4XH9+/fj++//x4XLlxQKl5R8PHxQc2aNTFr1iy5X9rjx4+jW7duSs/YuH37Ntq2bYvLly/LDdbMvRVQmA/RQYMGYd++fVi0aBEaNWoE4E012WHDhuGrr75SabE5dbOyssLx48fVspAVULQ9DUXlt99+w+rVq4vlgz53HMKCBQswYMAAhdXiNTU1cezYMaVjpqSkYNOmTfjf//6HgwcPwtnZGd26dUP37t3zzVB8X7uUoWyvoDprHOXWo0tOToaJiYncrbrs7Gy8fPkSgwcPxm+//aZ0zPj4eHh7e0NTUxPR0dGoXbs2oqOjYWlpicOHD6u8ZIK646lL7iKk76NKwpWTk4MpU6Zg7ty50gw9Y2NjjBo1CuPGjXvnl5O87t69i969e+PatWsYOHBgvoRG2V6ovPIuKZH3s+HatWuoW7duvhmF78NBwUry8fEBgHyJg6oDUuPi4uTeHPbs2YMOHTpI4z+GDx+OFi1aKN2umJiYd37gVapUCTExMUrHKwpnzpzB0qVL8+0vV66cNCVWGcOHD4eTkxP27dsHZ2dnnD59Gs+ePcOoUaMwZ86cQrVt06ZN2Lhxo9zUyxYtWkBfXx+dOnUq1oSmZ8+eWL58uVrG5AD5S0bkun//vtIzbuzt7XHhwgXpPvqiRYvQs2fPQhceLejWVXJyMs6cOYPly5cjLCysULE/VO6XACEELl++LDdwV0dHB9WrV8f333+vUsyyZcuiTJky6NSpE6ZNm5avcKMq7XofVQao37p1C126dIG3t/cHr401f/58CCHQt29fTJo0Se53K3ftrgYNGqgU087ODpGRkfj7779x/vx55OTkoF+/fujevft7V+guqnhvz1p9F2V7L4qiZ2jcuHHS+0juKvLHjh3DxIkT8fr1a0ydOvW9Mf744w+MGjUKPj4+uHLlSqFuyStSpUoVHDlyJN/4xw0bNsDLy0vleExolHTgwAG1xMmtipvr5MmTcvVrzMzMVOpm09TURHx8fIEDE+Pj45XKwIviW18uPT09pKSk5NsfFRWl0h/GiRMnEBERASsrK2hoaEBDQwONGzfG9OnTMWzYsEL1QqWlpaFs2bL59ltbW6s80FDd9bkyMjLw559/Yu/evR80Jif3jVcmk+HLL7+U+2aVnZ2N2NhYpRfIu3//vlzy/sMPP6BFixaFTmgKmvlnbGwMNzc3hIWFKTVjaeTIkZg8eTIMDQ3f+7us7P9b7t/8h1aLz2vbtm3w8fFR+lvxu9qlTrGxsQgLC8M333wjV+OoMLP2cqfvOjk5SZMp1EFfXx99+/ZF3759S0S8vL+7r1+/xu+//w4PDw8pWTt58iSuXr2Kb7/9Vh3NLbRVq1blG/hfvXp1lCtXDt9+++17Exp/f3+cPn1a+vKiThMmTEBQUBAePHiAnJwcbN68GVFRUVi9ejV27NihcjwmNEpSpU7Qu7i5uWH79u0YOXIkrl69iri4OLmxF3fv3lX4AVsQLy8vbN26VVrV821btmxRKtN9Oxk4d+4csrOzpSm+N2/ehKamJmrVqqV023IFBgbi559/lqaT59Z0GTt2LNq3b690nOzsbGkBLUtLS8THx8PV1RUODg6IiopSuV3Am3pTEyZMwOrVq6Upg69evcKkSZNU/hbZv3//d9bnUtWVK1dQs2ZNAG/+//NSJXbuG29kZCT8/PwKXO26MD70jvXbSV9hXbhwQVplV923V9/+1pySkoKIiAi4ubmpPEng7WKDJUW5cuUwbtw4jBs3Tqpx1KhRI2RlZSEsLEzlGkfAmwR///798PPzk9u/e/du5OTkICAgQOlYBfXkyWQy6OnpwcXFRaVBs+qIl/cWS//+/TFs2DCp2GLec+7du6dUm9q1a6fUeYBqY2gSExMV/p66ubkptRhednY2Ll26pFTtLVW1atUK69evx7Rp06RFL2vWrInt27fjq6++Uj3gBxVO+MwkJSWJOXPmiH79+on+/fuLefPmiefPn6sUY+PGjUJbW1s0b95clC1bVnz99ddyx0ePHq1SbaiNGzcKLS0t8euvv4qsrCxpf1ZWlli4cKHQ1tZWuW7H3LlzRatWrURiYqK0LzExUQQGBoo5c+aoFEsIIZKTk0WjRo2EmZmZ0NTUFBUqVBDa2tqiSZMm4uXLl0rHady4sVRnpmvXrsLf318cPXpU9OzZU1SpUkXldgkhxOXLl0W5cuWEhYWFaN68ufjyyy+FhYWFKFeunLhy5YpKsdRdn0vdwsLCxKtXrz4oRlHVN1q1apXCGjXp6eli1apVHxz/Q3Ts2FH8+uuvQggh0tLSRKVKlYS2trbQ0tISGzdufO/1Xl5e0t9Sbm24gjZV5da6Kmj7EB9S40gIITw9PcXOnTvz7f/vv/9EtWrVVIolk8mEhoaGkMlkclvuPg0NDdGkSRO596yPGc/ExETcvHkz3/6bN28KExMTpWL07t1b2nr16iVMTExEhQoVRNu2bUXbtm2Fvb29MDExEb1791YqXq66deuKoUOH5ts/ZMgQUa9ePZVilXQcFKyks2fPws/PD/r6+qhbty6EEDh79ixevXqFPXv2SN+klbFv3z7s3LkTNjY2GDp0qNxgw0mTJqFp06bvXE77bePGjcP06dNhbGwMZ2dnyGQyxMTE4OXLlwgJCVF5DEa5cuWwZ88eVKlSRW7/lStX4Ovri/j4eJXi5YqIiJDuV9esWVMal6Ss3bt3IzU1Fe3atcPt27fx9ddf48aNG7CwsMD69evRvHnzQrXr1atXWLt2LW7cuAEhBDw8PAp1b17d9bly3bp1CzExMWjSpAn09fXVUq7h5cuX+XpHlLml8nYNFkX1V4DinRrdt2/f/9femYfVmP5//H1OibRqISkVqSmijLFkN2hjBj9jzUgZQ9ZmZE/WGdugGUy2pEKyDD8NYlC2BjEVKlqoLGPJ2A6R+vz+6Or5dTqHnuf05IT7dV3PdX3Pc5z39z7TOc+5n/t+f94fhISEKHTWlslkmDhxIsLCwgSNzczMDHFxcWjVqhW2b9+O4OBgpKSkYOvWrdiwYUOlK0Lls44qK/MVaqwMCAiQe1xUVITk5GRcuXIFI0eO5Kopq4rQHkdA6ZZOeno6rK2t5c7fvHkTzZs3F1QdduzYMcyePRuLFy9G27ZtAQDnz5/HnDlzEBQUBAMDA3z//fdo164dNm/e/N71zMzM8PPPPyuYerds2YIZM2YILiufPn06Hj16hNDQUC5Qr7i4GP7+/tDX18fy5ct5ayUkJMDLywuNGzdGhw4dIJFIcPbsWeTn5+PgwYPo3LmzoLHVaNQ6nfqA6NSpE/n4+FBRURF3rqioiEaOHEmdO3cWrJebm0vFxcUK50tKSig3N1ew3rlz52jSpEnk6elJHh4eNHnyZDp37pxgHaLSu+5jx44pnD927Bjp6uqqpFmR//77TxSdgoICKikpEUWrqkRGRtLAgQNJJpOJovfw4UPq0aMHd8dYthLi6+tLP/zwg2C9nJwc8vT0pLp163IdfMvfkfLBysqKrK2t33kI6chehkQiUdptOjk5merVqydISyqVKu3Q/ODBA9LQ0BA8tjp16lBeXh4REY0YMYKmT59ORKXfYR0dHcF6ynj9+rVK3/u3ERwcTD/++KNoeqrQoEEDpdeRo0ePkqmpqSCt5s2b05kzZxTOnz59mhwdHTldS0tLtej9/PPPVLt2bRo/fjxFRkZSZGQkjR8/nrS1tennn3/mpVEeExMTpR28MzIyyMjISLDe7du3adasWTRgwADq378/zZ49m27fvi1YRwwMDQ2pXr16vA6hMA8NT5KSkrBx40Y5g5umpiamTZuGNm3aCNazsbFRekf66NEj2NjYCC6hNTMzw6pVq5SaDYWmmfbv3x+jRo3CL7/8ItdxNzAwUNA+bxlLly7lmnsCpdk4e/bsgZmZGQ4ePCg4Zbn8ioWRkVGVfRzZ2dlYvXo10tPTIZFI4ODggMmTJ6Np06aCdH755RdkZ2ejQYMGovTnCggIQK1atZCXlye36jN48GAEBARUGllekeHDhwMAwsLC0KBBA5VWecRsigiIa1h++vQpiAhEhGfPnsnFqBcXF+PgwYMqleNaWloiMTERRkZGOHz4MKKjowGUhlqK1aw2LS0NrVu3Fq103tvbG23bthVc/Xfv3j1MnTqVM7ZX/G4JGd9XX32FKVOm4I8//uC+S1lZWfjxxx8FZ0ZlZ2crXUHU19fnzPbNmjXDw4cP1aI3Y8YMNGnSBCEhIdi+fTuA0tYz4eHhGDRoEC+N8rx58wbp6emch7GM9PR0lXxn5ubmvKqZ3gerV6+uNm02oeGJvr4+8vLyFMxV+fn5CkvbfHjbj/Dz589Vuki+bYJUUFAgeIIUGhqKqVOnwtvbmzNaampqws/PT9BSZxnr169HVFQUAODo0aM4evQoDh06hJiYGAQGBuLIkSO8dAoKCjBo0CCcOHECEokEmZmZaNKkCUaPHg1DQ0PBP/BA6TbWV199BWdnZ66k8ezZs2jevLlgY5rYvbqOHDmCuLg4BTNes2bNBDUULCM1NRUXL15UuEiqQlVD+soQ07BsaGjITY6UGVglEgnvZNfyTJkyBcOHD4euri6srKy47eCTJ0/CyclJsN77IDExUaXriI+PD/Ly8hAUFFRlY/vy5cvh7u6Ozz77jPsM37p1C507dxY80fr8888RGBiIiIgIrjLywYMHmDZtGlf6npmZydu4KrYeUHqjpsrkRRmjRo2Cr68vsrKy5G4qlyxZwjurpjyPHz/G5s2buZs2R0dH+Pr6qqVBarU2sRR1LekjZuLEiWRhYUHR0dGUl5dH+fn5tGPHDrKwsKDJkyfz1gkICKCAgACSSqX0/fffc48DAgJo0qRJ1K5dO3J1dRU8vopmzTJu3rxJdevWFaxHRPT8+XNKSUmh5ORkQebdipRfsp80aRKNGTOGiIiuXbtGhoaGvHVGjBhBbm5ulJ+fL2dGjYuL45aJheLs7MxtIZRn+vTpvE2a5bchxURXV5czGpZ/v+fPn1dp2blbt2509OhRUcb2tm2dhw8f8t6+Ko8YhuX4+Hg6ceIESSQS2rt3L8XHx3PH2bNnq7TEnpSURHv37qVnz55x52JjY5VuW6hCcnKySv/dygyjZUe/fv2oXbt2pKGhQfPmzROsp6urS//884/g172NkpISiouLo2XLltFvv/1GCQkJKulkZGSQvb09aWlpUdOmTcnW1pa0tLTos88+o2vXrhER0R9//EERERFq0SMq3UbfuHEjzZw5kwoKCoiI6OLFi3Tr1i2B75aouLiYli5dSubm5pxh2dzcnJYuXSpX/MGHCxcukJGRETVq1Ij7jFhYWJCxsTFdvHhR8NiqixcvXtCTJ0/kDqEwUzBPXr9+jcDAQISGhuLNmzcASlM0x40bhyVLlijcqb6NshLthIQEdOjQQSGsy9raGlOnTuWdDlsdaaZiY25ujt27d8PV1RX29vZYtGgRvvnmG1y7dg1ffPGF0owaZZQ3Z5ZPlbxx4wacnJwEp0oCpRk5ly9fVvjvff36dbRs2RKFhYWVapiammLkyJHw8/MT1RDs5eWF1q1bY+HChVw8u5WVFYYMGYKSkhLs3r1bkF52djbGjh0Lb29vtGjRQmFLrGXLlry13hZrn5KSgu7du/MqB30bqhqWy8jNzYWlpWWVsl74kJ+fj+DgYMEmY2WkpKQI2nLKyclR2rxPKpXC1NQUPXr0UKlE3NHREdu2bVMp1Ky6ISLExcXh+vXrICJ89tln6NWrl8p/ZzH1UlNT0bNnTxgYGIjS3qU8ZddHVXOQOnfuDFtbWznLxJs3bzB69Gjk5OTg5MmTKo+tqshkMkyfPh0xMTEKjXMBFdLfRZ1ifaS8efOG4uPjqaCggGQyGaWmplJKSkqVzJ8+Pj4qzUAr0q1bN+rWrRtJJBJydXXlHnfr1o169+5NY8aMUVpO+C6eP39Oc+bMoQ4dOlDTpk3JxsZG7hDK+PHjycrKinr27EnGxsbcXW50dLSgUlWxVyyIiCwsLCgmJkbh/M6dO3kbAn/66Seys7MjqVRK7du3p02bNsndyavK1atXydTUlNzd3UlLS4sGDhxIDg4O1KBBA8rKyhKsl5iYSDY2Nm8tU+VDWdmxVColJycnuZLjli1bkp6enqDYgTLEMCxXRCaTUXp6OqWkpMgdYiFkVaXiGCoeO3fuFPQ+K66QDRo0iP7991/B76EicXFx1Lt3b7px40aVtYiI/vrrL5o5cyb5+fnRqFGj5I6PiS+//JICAwOJSP7adObMGbKyslJJs6ioiI4ePUqhoaH09OlTIio19wq9ttSpU4fS09MVzl+9epW0tbVVGptY+Pv7k4ODA+3atYu0tbUpLCyMFi5cSBYWFhQVFSVYj3loeKChoQE3Nzekp6fDyMhIlH3z8mFdVWkYVh1ppmIHxK1atQrW1tbIz8/HsmXLOJ/E3bt3eaVo3rp1CxYWFujcuTMiIiK48CqJRIKSkhIsX778nY0h38V3332HMWPGICcnB66urpBIJDh9+jSWLFnCO9Z+5syZmDlzJk6dOoWwsDBMmTIFU6ZMwcCBAzF69GiuR5RQHB0dkZqait9//x0aGhpcyfr48ePRsGFDwXq+vr5wcXHBjh07VDYFV1dInxiG5TIePHiAUaNG4dChQ0qf53vXV1lHcSHJz87OznL9x8pTdl7Ie66oc+jQIZWbZJZn8ODBePHiBZo2bYq6desqrOIJWXmbP38+FixYgDZt2ohyHZHJZEhISFDamFZoTIDYemK1dykjNzcX7u7uyMvLw6tXr9CrVy/o6elh2bJlKCwsRGhoKG8tsf2fYnLgwAFERESgW7du8PX15VaTrKyssG3bNu66wBuxZ1wfK23atKG//vpLNL3i4mKaP38+6evrc3ejBgYGtGDBAqXl3ELIz89Xad+2jJoWEGdgYEARERGUlpYm6ooFUeke/8qVK6lRo0bcqkWjRo0oJCSE8/0I5fnz57Rp0ybq1KkTSSQSsrOzo6VLlwrWyc3NfWtJuiolvnXr1qXMzEzBr1OGGJ6X8ujo6CgtU1WFYcOGkaurK50/f550dHToyJEjFBkZSfb29hQbG8tb523haxVXuPhw8+ZNXoeQsVVHwGF4ePg7DyGYmZkJ8qC8i0uXLpGZmRnp6+uThoYGmZqakkQiIR0dHZVWjcXWq1+/Pl26dImISMHfZ2FhIVjv66+/Jm9vb3r16pWcXnx8PNna2grSEsv/WR3o6Ohwn/tGjRpxUSM5OTkqRSKwCQ1P4uLiyNnZmQ4cOEB37typsnlpxowZZGpqSuvWreOMt2vXriVTU1OaNWuWYD0xJ0jW1taUlpYmeAxvY+vWre88KmPt2rWkp6dHAwYMoLy8PJo7dy55eXmRh4cHzZ49m+7cuSPKOJ8+fUpPnz6lO3fu0IQJE6hOnTpV1oyNjSUjIyOVtk3ENt726dOHV7KtUJ49e1bl74OYhmUzMzPuwqinp8eZPPfv308dO3bkrWNubs4lUyvjn3/+EfR3KCoqonnz5qk8US6PVCqVy+3R1dWlnJycKuuKiZGRkco3GhXp2rUrfffdd/TmzRvuBz4vL4+6dOlCe/bsUbved999R/369aPXr19zf4vc3FxycXFRadJgbGzMTfDLT2hu3LgheJvo1atXNHnyZNLS0uJ+G2rXrk1TpkxRms79PnFycqL4+HgiIurVqxeXnRQSEkKNGjUSrMcmNDypeFdW1T3+hg0b0v79+xXO79u3j8zNzQXriTlBEjsgztDQUO7Q0dEhiURCtWvX5h2elJOTQ927d6cGDRrQvn37qjym//77j4YNG0YmJibUsGFDCgkJoeLiYpo7dy7VrVuX2rRpQ9u3b1dJWyaTUVhYGHXu3JmkUik1a9ZMpXCtt4XNqVq5tn79erK0tKTg4GDavXs37d+/X+4Qgtiel6ysLOrZsyeFh4dTUlJSlXwvenp6nAfEysqKW23MyckR9GPQt29fCgoKeuvzycnJJJFIBI1NR0dHFH+KRCIhT09PrrpJU1OTevfurVD1pApZWVk0e/ZsGjJkCDehPnTokOBWINOmTaMFCxaoNIaKGBgYcD/wBgYG3A3X33//Tfb29mrXE6u9Sxn16tWjq1evEpH8hObUqVNUv359XhoymYz8/f3J3NycTE1NadCgQXTy5Mkq+z/FZOXKlRQSEkJERMePHydtbW1u4rV69WrBesxDwxOxO9xWtWFYRaraUbU8YgfEKesenpmZiXHjxiEwMJCXho2NDY4fP441a9Zg4MCBcHBwUOjiK2Rcs2bNwsmTJzFy5EgcPnwYAQEBOHz4MAoLC3Hw4EGVmpGeOnUKW7Zswe7du1FcXIyBAwdi0aJF6NKliyCdsso1iUSCoKAgpZVrzs7Ogsc3duxYAMCCBQsUnpNIJIIqCsT0vAClvpfs7Gy5jI3y3hIhY7O3t8e1a9dgbW0NZ2dnrF+/HtbW1ggNDRXkPQoMDHynL8XW1lbwdaFnz56Ij4+Hj4+PoNdVpGKWh7e3d5X0ykhISICHhwc6duyIkydPYvHixahfvz5SU1OxadMmQZV1hYWF2LBhA/766y+0bNlS4TrCt+s5UFpRWvYZa9CgARc2aWBggLy8PN461aWnr6+P06dPV7m9Sxm9evXC6tWrsWHDBgCl34Xnz58jODgYnp6evDSCg4MRHh7OtXHZvn07SkpKsGvXLpXGVB2Ub93RvXt3ZGRkICkpCU2bNhUcuAqwYL1K+fbbb7F27VruBy4lJQWOjo4KX06htGrVCmvWrFHojbJmzRpB5bNliDlBEjsgThnNmjXDkiVL4O3tjYyMDF6vyc3NxZ49e2BkZISvv/5aYUIjhD///BNbtmxBz5494e/vD1tbW9jZ2amUYvnTTz8hPDwc2dnZaNOmDZYvX46hQ4eqbNAu6w1ERLh8+bJCaX+rVq14G5bLI1Zna0DckD5AHMNyGVOmTMHdu3cBlF7U3dzcsG3bNmhpaSE8PJy3TmU9bnR0dARPfD08PDBz5kxcuXIFn3/+OXR0dOSe55ugW7EDuFjMmDEDixYtwg8//CBnGO3evbvgvlCpqancxPvKlStyzwn9+7q4uCApKQl2dnbo3r075s6di4cPHyIyMlKlIg2x9W7evAlra2v06NFD5Z5y5Vm1ahW6d+8OR0dHFBYWYtiwYcjMzISJiQl27NjBS2Pv3r3YvHkzhgwZAqD0JqRjx44oLi7m+kOpi3PnzuHRo0dyHdcjIiIQHBwMmUyGfv364bfffuMdh8Ih7gLSx0dFH4Oenp4o5rv4+HjS0dEhBwcH8vX1JT8/P3JwcCBdXV06efKkYL13dVRt27ZtlcdbHVy6dIn09PR4/dsNGzaQnp4e9e/fX+k2jFA0NTXlgta0tbXp8uXLKmmZmJjQlClTVH792xCrtP9dqNpTS0zPC5G4huWKyGQyunjxIj148KBa9IUghsG4OtHR0eG8OBW9G7Vr11bbuC5cuEDHjx8nIqL79++Th4cH6enpkYuLCyUnJ6tdTyKRUMeOHSk0NJQL1asqL168oLCwMBo/fjyNGzeONm7cSC9evOD9+lq1aikUh5QPOVUn7u7utGTJEu5xamoqaWpq0ujRo2nlypVkZmZGwcHBgnXZhKYSqquagEh5w7Dc3FyVMhrEniARlaajRkZGUlRUFOfgV4WKXo19+/bR77//Ts2bNyd3d/dKX+/m5kb16tXjZSDmi5imytevX7/1uao04XxXrogqeSpLliyh6Oho7vHAgQO5BFKhF3ExPS9E1WNYfvXqFWVkZFRbkvPHSKNGjbj04/LXur1791KTJk3UMqaSkhK6efOmoB/z96lHVJoIPHXqVLKwsKDatWvTV199RTExMWo13Va8xhHVHPO4mZkZXbhwgXs8a9YsOcN+TEwMOTg4CNZlScGVIJVK8e+//3I9kson1FYHQhNDy3Pnzh2sXbsWGRkZICI4OjpizJgxmDdvnqA00/v372PIkCGIj4+HoaEhiAhPnjxB9+7dER0drZAOWxkVkzclEgmXZvrLL79U6mvo1asXtmzZIqivCp8xeXh4cEuaBw4cQI8ePRS2APbu3ctbU+wmnPXr11fwRQHAihUrEBQUhJcvXwrSa9KkCaKiouDq6oqjR49i0KBB2LlzJ2JiYpCXl8e7pxZQ2ldm2LBhcs0qVfW8AMCGDRuwaNEi+Pr6wsnJSWFLV0gzwxcvXmDixInYunUrgNLU5yZNmmDSpEkwNzfHjBkzBI1NDF6+fIljx46hT58+AEqzi169esU9r6mpiQULFojW7FJVpk2bhsTEROzatQt2dna4dOkS7t27h2+//RbffvstgoODeWt17979nVtLx48f56VTUlKCOnXq4OrVq7wT1N+nXnmICPHx8di+fTv27NmD4uJi/M///I/gNOmtW7fCxMQEXl5eAEr/Lhs2bICjoyN27NgBKyurSjUqXuMA5dc5Idc4sahTpw4yMzNhaWkJAOjUqRPc3d0xZ84cAKVbeE5OTnj27JkgXTahqQSpVIrjx4/DyMgIAODq6oqYmBiFH1dVfC/KqMqERiy9wYMHIzs7G5GRkVyUf1paGkaOHAlbW1vee7g1Gb4N3oR4FcScMACl5uw5c+Zg5MiRWLVqFR49eoQRI0bg6tWr2Lhxo+COxdra2rh+/TosLS0xefJkFBYWYv369bh+/TratWun1Lz9NhwdHeHg4IBp06Yp9bzwueCW511x80InSJMnT8aZM2ewevVquLu7IzU1FU2aNMH//u//Ijg4mPMovU/Wr1+P2NhYHDhwAEDpjVHz5s2hra0NAMjIyEBgYCBnCFcXRUVF8PHxQXR0NIgImpqaKC4uxrBhwxAeHi7Ie1He8FmmnZycjCtXrmDkyJGCPDnNmzfH5s2buUaNVUVsPWVcunQJfn5+SE1NFXw9t7e3x++//44ePXogMTERX375JVavXo3Y2FhoamrymoRUxzVOLKysrBAZGYkuXbrg9evXMDQ0xIEDB/Dll18CAC5fvoyuXbsKL5CpyrLRp8C7wrWqGs2uDFWb1Impp6+vT+fPn1c4f+7cOTIwMKjSeEpKSt4aFvehI1YTzvIkJydTixYtyNbWloyMjMjT01PliPuGDRty2wl2dnZcy4eMjAzeXqYyqtPzUlUaN25MiYmJRCS/bZKZmSn4fYpF586dae/evdzjilvXkZGR1L59e3UMTSlZWVm0a9cu2rlzp+DWKZURHBzM5Y3wJTY2ljp16iSaT01svTLy8vJo6dKl1KpVK5JKpdSxY0dat26dYB1tbW0uPHPatGk0YsQIIiK6cuUKmZiYiDpmdTBmzBjq0KEDnTx5kn744QcyNjamV69ecc9HRUVRmzZtBOuyKqdKuHHjhrqH8N4pKSlRWsVVq1YtlStlIiIisHz5cmRmZgIA7OzsEBgYiBEjRlRprDWJevXqIT8/H5aWljh8+DAWLVoEoHQZWtUVtyZNmqB58+bYs2cPgNJtrAYNGqikNWDAAAwbNgzNmjVDQUEBV2GQnJwMW1tbQVo9evRASkqK4NcJ4fHjxzA0NBT8ugcPHnBbxOWRyWRVLi9XlevXr8POzo57XKdOHblVqbZt22L8+PHqGJpSmjZtiqZNm1aLtre3N9q2bYsVK1YIes2LFy/QqlUraGlpcStbZQi9kxdbb8OGDdi2bRvOnDkDe3t7DB8+HPv27YO1tbUgnTJ0dXVRUFCAxo0b48iRI9xqV506dQRvNddEFi1ahAEDBqBr167Q1dXF1q1b5ao5w8LCVGquyiY0lVC2dJ6XlwdLS0ulF0QhuQUDBgx45/OPHz8WNL7qoEePHpg8eTJ27NgBc3NzAMDt27cREBDALQkKYeXKlQgKCsKECRPQsWNHEBHOnDmDsWPH4uHDhwpL0x8qYk4YAODMmTPw9vaGsbExUlNTcebMGUycOBF//vkn1q9fj3r16gnSq2pPrfL07dsXAQEBuHz5cpU9L4Ci/+ibb77Bnj170LBhQ8H+oy+++AJ//vknJk6cCOD/S4Q3btyIDh06CBqXWDx58kQuZuDBgwdyz5eUlMh5atRFcXExwsPDcezYMdy/f1/hBoav7+VdJCYmCvYKqRKn8D71Fi5ciCFDhiAkJESljKiK9OrVC6NHj4aLiwuuX7/OeWmuXr2q8iSpJmFqaopTp07hyZMn0NXVVdjK3LVrl1yfOL4wDw1PNDQ0cPfuXYU7v4KCAtSvX5/3HbjY+5p8JkgJCQmCVgjy8/Px9ddf48qVK9wkLi8vD05OTti/f79gc66NjQ3mz5+Pb7/9Vu781q1bMW/evI9mFayoqAghISHIz8+Hj48PXFxcAJRePHV1dTF69GhBerVr10ZAQAAWLlzITRiys7MxYsQI5OXl4datW4L0CgoKYGxsDKD0b7xx40a8fPkSffv2FRz+J6bnBRDXf3T27Fm4u7tj+PDhCA8Px/fff4+rV68iMTERCQkJ+PzzzwWNTQzKcpfe1rgzJiYGs2bNQlZW1nsemTwTJkxAeHg4vLy8lDaUXLVqFW+titcmIsLdu3eRlJSEoKAgQQbjmg4JbC5aGY8fP8acOXOQn5+PcePGwd3dHUBprpKWlhZmz54t2v/XxwSb0PBEKpXi3r17ChU+ubm5cHR0FKXTrSpUp/Hr6NGjchVTqqZe1qlTB1euXFFYpcjMzISTkxMKCwtV0v3YSUhIUBrcVlJSgsWLFyMoKIiXzuXLl9G3b1/k5+ejWbNmiI6Ohru7O2QyGaRSKWQyGXbv3v1eAhXfhpiGZaD0Pa9YsQIXL17kUlunT5+uUmiaGEyePBl//fUXLl68qLA68fLlS7Rp0wY9e/YUHF4nNiYmJoiIiOCdRquMnJwcWFtbw8/PT+68VCrlqhtV2U4o4+XLlygqKpI7p2qIpdh6L168UNq9W6yiEca7YROaSiirOggJCcF3332nNIZeQ0MDZ86cUdcQReP48eOYMGEC/v77b4Uv9JMnT+Dq6orQ0NBKE1Qr0qJFCwwbNgyzZs2SO79o0SLs3LkTly9frvLYaxJpaWlKL2p8t2E8PT2xY8cOGBgYAAAWL16M8ePHc36SgoICdO7cGWlpabz0PDw8oKmpienTpyMqKgqxsbHo3bs3Nm3aBACYOHEiLl68iL///pvnO1SOqp4XADA3N8fu3bvh6uoKe3t7LFq0CN988w2uXbuGL774Ak+fPq1Ug8+/Aar246cq9+7dg7OzM7S0tDBhwgTY2dlBIpEgIyMDa9aswZs3b/DPP/+o7I8SC3Nzc8THx8v5fYRScTV78ODB+PXXX6v03mQyGaZPn46YmBgUFBQoPC90RVBsvQcPHsDHxweHDx9W+rwqHrrHjx9j8+bNSE9Ph0QigYODA/z8/LjrAkMJVbYrf+R069aNunXrRhKJhFxdXbnH3bp1o969e9OYMWNErwJQF3379qWVK1e+9fmQkBDq16+fYN3du3eThoYGubm50YIFC2jhwoXk5uZGmpqacpUfHzrZ2dnUsmVLhcq4ssaNfKksnfrff/8VpGdsbMyF3T179owkEolcqFV6errg6jUxQ/qIiMaPH09WVlbUs2dPMjY2pmfPnhERUXR0NLm4uPDSqNg4tuKh7jTenJwccnNzU/hsuLm5iRbWWVVWrFhB/v7+VapErBhGKka6ur+/Pzk4ONCuXbtIW1ubwsLCaOHChWRhYUFRUVFq1xs2bBi5urrS+fPnSUdHh44cOUKRkZFkb29PsbGxgvUuXLhARkZG1KhRI+rfvz/169ePLCwsyNjYmC5evChY71OBTWh48j5i6NVN48aNua6zykhPTydLS0uVtJOSkmj48OHUunVrcnFxoeHDh1cpfbgm0qdPH/r666/p/v37pKurS2lpaXTq1Clq27atoLTmytKphU5oxNYjIrKxseFKwI8cOUKGhoYUFxdHfn5+1KtXL0FaRKVpy8uXL6dJkybJfS5WrVpFGzdu5KURHx/PHSdOnCBtbW3atm2b3Pn4+HjBYxObgoICOnfuHJ07d060mPyqULFDt4GBAdnY2FCfPn1U6t5dHenqlpaWdOLECSIqnSCVRQZERESQh4eH2vXMzMzo3LlznN61a9eIqDQlvXwCLl86depEPj4+cinXRUVFNHLkSOrcubNgvU8FVuXEk/IelFu3bkEikaBRo0ZqHJH43Lt3751NNzU1NRWqM95F+S2AZs2aYd26dUr/jTq2AKqDxMREHD9+HKamppBKpZBKpejUqRN+/vlnTJo0SS2BbmVUNCxW1cB49+5dLuUzNjYWgwYNQu/evWFtbY127doJ1nv69CnXcDM/Px9z584VbFiu6DfS0NBA+/btqy3VW1WMjIzQtm1bdQ+Do+IWRv/+/aukJ5FIRP+8PXr0CDY2NgBKtwzLyqo7deqEcePGqV1PJpNxW2xGRkZ48OAB7Ozs4OTkhEuXLgnWS0pKwsaNG+Uq4zQ1NTFt2jS0adNGsN6nApvQ8KSkpASLFi3CL7/8gufPnwMoTfv88ccfMXv27HdWfXwoNGrUCJcvX35riXFqamqlbQrKY2hoyOtCJlYqsropLi7mSg1NTExw584d2Nvbw8rKCteuXeOtUx0/CD4+PlwEemFhIcaOHcvFn6tSLixW5k5lhuVVq1ap3bD8sSN2UiwRvfPzVoaQyP0mTZrg5s2bsLKygqOjI2JiYtC2bVscOHBAJd+W2Hr29va4du0arK2t4ezsjPXr18Pa2hqhoaGCrpll6OvrIy8vD5999pnc+fz8fLku6Ax52ISGJ7Nnz8bmzZuxZMkSuSyVefPmobCwEIsXL1b3EKuMp6cn5s6dCw8PD6WVGMHBwVwvGj6cOHGC+99EBE9PT2zatOmjW9kqo0WLFlzMfrt27bBs2TJoaWlhw4YNglYJKvtBEDoBGTlypNxjb29vhX9TsaS+MsTK3Jk2bRqcnJwQFRWFqKgo9OnTh/ucAKWG5SVLlrAJzXvixo0bePPmjUKPo8zMTNSqVYtXBgqfz5tQRo0ahZSUFHTt2hUzZ86El5cXfvvtN7x58wYrV65Uu96UKVNw9+5dAKWl1W5ubti2bRu0tLQQHh4uWG/w4MHw8/PDihUr4OrqColEgtOnTyMwMBBDhw4VrPepwKqceGJubo7Q0FCFSpX9+/fD398ft2/fVtPIxOPevXto3bo1NDQ0MGHCBNjb20MikSA9PR1r165FcXExLl26pHK1QnU39lQ3cXFxkMlkGDBgAHJyctCnTx9kZGTA2NgY0dHRvEMJa3IPljLEytwxMTHB8ePH0bJlSzx//hz6+vo4f/48t6yekZGB9u3bqxQ4qaenh9TUVG5rgVE5Xbt2ha+vr8KkJCoqCps2bUJ8fLx6BlaBvLw8JCUloWnTpoKbvoqp9+LFCwQGBmLfvn0oKipCz5498euvv6Ju3brIyMhA48aNYWJiIng8r1+/RmBgIEJDQ/HmzRsApUnt48aNw5IlS+QaTjL+Hzah4UmdOnWQmpqqUM547do1ODs7fxRx1EBprs64ceMQFxeHso+GRCKBm5sb1q1bV6WUyo99QqOMR48eoV69emqL3K8uxArpq6yb/b1792Bubs5rG6tikJsYHdQ/NfT19XHp0iWFVbasrCy0adPmvSeZi92lXGy9wMBArFu3DsOHD4e2tja2b9+Obt26YdeuXXzf4jt58eIFsrOzQUSwtbWViw1hKMK2nHjSqlUrrFmzBr/++qvc+TVr1nxUoUlWVlY4ePAg/vvvP2RlZYGI0KxZM8Ex+58Svr6+vP5dWFhYNY+k+qkOz4tYfqGK5lYxtjo+NSQSCZ49e6Zw/smTJ2rxukVERCA2NpabgKxZs0ahS3nDhg15t08RW2/v3r3YvHkzhgwZAgAYPnw4OnbsiOLiYkGdyd9G3bp11RYG+SHCVmh4kpCQAC8vLzRu3BgdOnSARCLB2bNnkZ+fj4MHDwoOm/sU+Vi3AKRSKaysrODi4oJ3fZ3++OOP9ziq6kHskD6pVAoPDw9uCb3iqsqrV69w+PDhj8Y4XtPp06cP6tatix07dnA/yMXFxRg8eDBkMhkOHTr0XsfTpUsXBAQEcJVXFVfwoqKisHbtWiQmJqpFT0tLCzdu3JDzBZZPvRZCZW1sysNWGZXDVmh40rVrV1y/fh1r167l2gEMGDAAY8aMwbx589iERgkVv6BiVDvURMaOHYvo6Gjk5OTA19cX3t7eMDIyUvewqoULFy5wnhdnZ2ds2LAB/v7+XJXfxIkT0b59e9561WFYZqjO0qVL0bVrV9jb23PXtFOnTuHp06eiNKYUithdysXWKy4ulusSDZRuW5X5XoTAEoCrDluhqSIpKSlo3bo1u4NUwodgbhWLV69eYe/evQgLC8PZs2fh5eUFPz8/9O7d+6Pyz4jpeWHUTO7cuYM1a9YgJSUF2traaNmyJSZMmKCWSbq2tjaSk5Nhb2+v9PmMjAw4Ozvz7gcntl7FFUZAuXfrQ79p+1BgKzSMauNjmKjwpXbt2hg6dCiGDh2K3NxchIeHw9/fH0VFRUhLS+PyaT4GxM7IYdQc8vLyYGlpiZ9++knpc40bN36v47GwsMCVK1feOgFJTU2FhYWF2vQqrjAC4ni3xCif/xRhExoGQ2TKgvGICCUlJeoejuiIHdLHqDnY2NjINZYso6CgADY2Nu995a0sG8vLy0tpNtb8+fPh5eWlNr3qumnz8fGBr6+vwoTm3LlzNap8vqbBtpyqCNtyYgDyW06nT59Gnz59MGrUKLi7u38UKdJlfErbiJ8iUqkU9+7dg6mpqdz53NxcODo6QiaTvdfxiN2l/EPpel7Tyuc/FNgKTSVU5jxnHyyGv78/oqOj0bhxY4waNQrR0dFcRsvHBpuofJz88MMPAEpXF4OCguTyToqLi3Hu3Dk4Ozu/93E1aNAAZ8+exbhx4zBjxgy5bKxevXph3bp1giYfYutVFzWtfP5Dga3QVAK7I2VUhlQqRePGjeHi4vJOPwkzBjJqKt27dwdQGk/RoUMHucodLS0tWFtbY+rUqQpbIO+TR48eISsrCwBga2tbZZOy2HpiUtPK5z8U2ISGwagiPj4+vIyxbNLLqOmMGjUKISEh0NfXV/dQPmnS0tLQpUsXGBoaKi2fb9GihZpHWDNhExoGg8FgMGoYNal8/kOBTWgYDAbjE2bAgAEIDw+Hvr5+pZ5Btm3KqMkwUzCDwWB8whgYGHBbpu9Kq33y5Mn7GhIDpQUn58+fx/379xXiH1h6tnLYCg2DwWB84qxYsQJTp0596/NPnz5F7969effoYlSNAwcOYPjw4ZDJZNDT05Pz6EkkEjx69EiNo6u5sAkNg8FgfOJoa2tj3bp1Sqs6nz9/jt69e+Px48dIS0tTw+g+Pezs7ODp6YmffvpJroSe8W4+nsQvBoPBYKhEZGQk/P39sW/fPrnzZZOZgoICtTSn/FS5ffs2Jk2axCYzAmETGgaDwfjEGThwIH777TcMGzYMJ06cAFA6mXF3d8fDhw8RHx8PMzMzNY/y08HNzQ1JSUnqHsYHBzMFMxgMBgOjR4/Go0eP0K9fP+zfvx9BQUH4999/kZCQgIYNG6p7eJ8UXl5eCAwMRFpaGpycnFCrVi2557/66is1jaxmwzw0DAaDweCYOXMmli1bBmtrayQkJAjqPs0Qh3f1f5NIJKz9wVtgKzQMBoPxiVMxf6ZWrVowMTHBpEmT5M6zHJr3Q8UybQY/mIeGwWAwPnEMDAzkjqFDh8LR0VHhPKN68fT0lMv7Wbx4sVwD5IKCAjg6OqphZB8GbMuJwWAwGIwagIaGBu7evYv69esDAPT19ZGcnIwmTZoAAO7duwdzc3O25fQW2AoNg8FgMBg1gIrrC2y9QRhsQsNgMBgMBuODh01oGAwGg8GoAUgkErk2B2XnGPxgVU4MBoPBYNQAiAg+Pj6oXbs2AKCwsBBjx46Fjo4OAODVq1fqHF6Nh5mCGQwGg8GoASjrpaWMLVu2VPNIPkzYhIbBYDAYDMYHD/PQMBgMBoPB+OBhExoGg8FgMBgfPGxCw2AwGAwG44OHTWgYDAaDwWB88LAJDYPBYDAYjA8eNqFhMBgMBoPxwcMmNAwGg8FgMD54/g9QG0rM2Pyz+wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.isnull(),cbar= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f3284175",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef9200a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1421, 76)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6022417b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>...</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</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>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</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>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</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>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>...</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</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>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "0   1          60       RL         65.0     8450   Pave      Reg         Lvl   \n",
       "1   2          20       RL         80.0     9600   Pave      Reg         Lvl   \n",
       "2   3          60       RL         68.0    11250   Pave      IR1         Lvl   \n",
       "3   4          70       RL         60.0     9550   Pave      IR1         Lvl   \n",
       "4   5          60       RL         84.0    14260   Pave      IR1         Lvl   \n",
       "\n",
       "  Utilities LotConfig  ... EnclosedPorch 3SsnPorch ScreenPorch PoolArea  \\\n",
       "0    AllPub    Inside  ...             0         0           0        0   \n",
       "1    AllPub       FR2  ...             0         0           0        0   \n",
       "2    AllPub    Inside  ...             0         0           0        0   \n",
       "3    AllPub    Corner  ...           272         0           0        0   \n",
       "4    AllPub       FR2  ...             0         0           0        0   \n",
       "\n",
       "  MiscVal MoSold  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0       0      2    2008        WD         Normal     208500  \n",
       "1       0      5    2007        WD         Normal     181500  \n",
       "2       0      9    2008        WD         Normal     223500  \n",
       "3       0      2    2006        WD        Abnorml     140000  \n",
       "4       0     12    2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 76 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "808e2fc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns=['MSZoning','Street', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition2','BldgType', 'Condition1', 'HouseStyle', 'SaleType', 'SaleCondition','ExterCond', 'ExterQual', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual','Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "622bd619",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cb06d89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# handling categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f69d1a78",
   "metadata": {},
   "outputs": [],
   "source": [
    "def category_onehot_multcols(multcolumns):\n",
    "    df_final = final_df\n",
    "    i=0\n",
    "    \n",
    "    for fields in multcolumns:\n",
    "        print(fields)\n",
    "        df_1=pd.get_dummies(final_df[fields],drop_first=True)\n",
    "        final_df.drop([fields],axis=1,inplace=True)\n",
    "        \n",
    "        if i==0:\n",
    "            df_final=df_1.copy()\n",
    "        else: \n",
    "            df_final=pd.concat([df_final,df_1],axis=1)\n",
    "            \n",
    "        i=i+1\n",
    "    \n",
    "    df_final=pd.concat([final_df,df_final],axis=1)\n",
    "    \n",
    "    return df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "161c7375",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_df=df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b02d2f20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 74)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# combine test data\n",
    "\n",
    "test_df=pd.read_csv('test.csv')\n",
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "93de7e18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "0          20       RH         80.0    11622   Pave      Reg         Lvl   \n",
       "1          20       RL         81.0    14267   Pave      IR1         Lvl   \n",
       "2          60       RL         74.0    13830   Pave      IR1         Lvl   \n",
       "3          60       RL         78.0     9978   Pave      IR1         Lvl   \n",
       "4         120       RL         43.0     5005   Pave      IR1         HLS   \n",
       "\n",
       "  Utilities LotConfig LandSlope  ... OpenPorchSF EnclosedPorch 3SsnPorch  \\\n",
       "0    AllPub    Inside       Gtl  ...           0             0         0   \n",
       "1    AllPub    Corner       Gtl  ...          36             0         0   \n",
       "2    AllPub    Inside       Gtl  ...          34             0         0   \n",
       "3    AllPub    Inside       Gtl  ...          36             0         0   \n",
       "4    AllPub    Inside       Gtl  ...          82             0         0   \n",
       "\n",
       "  ScreenPorch PoolArea  MiscVal  MoSold  YrSold  SaleType SaleCondition  \n",
       "0         120        0        0       6    2010        WD        Normal  \n",
       "1           0        0    12500       6    2010        WD        Normal  \n",
       "2           0        0        0       3    2010        WD        Normal  \n",
       "3           0        0        0       6    2010        WD        Normal  \n",
       "4         144        0        0       1    2010        WD        Normal  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "524c0933",
   "metadata": {},
   "outputs": [],
   "source": [
    "# handling missing data in test.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "33ac0a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filling missing value\n",
    "\n",
    "test_df['BsmtCond']=test_df['BsmtCond'].fillna(test_df['BsmtCond'].mode()[0])\n",
    "test_df['BsmtQual']=test_df['BsmtQual'].fillna(test_df['BsmtQual'].mode()[0])\n",
    "test_df['FireplaceQu']=test_df['FireplaceQu'].fillna(test_df['FireplaceQu'].mode()[0])\n",
    "test_df['GarageType']=test_df['GarageType'].fillna(test_df['GarageType'].mode()[0])\n",
    "test_df['GarageFinish']=test_df['GarageFinish'].fillna(test_df['GarageFinish'].mode()[0])\n",
    "test_df['GarageQual']=test_df['GarageQual'].fillna(test_df['GarageQual'].mode()[0])\n",
    "test_df['GarageCond']=test_df['GarageCond'].fillna(test_df['GarageCond'].mode()[0])\n",
    "test_df['MasVnrType']=test_df['MasVnrType'].fillna(test_df['MasVnrType'].mode()[0])\n",
    "test_df['MasVnrArea']=test_df['MasVnrArea'].fillna(test_df['MasVnrArea'].mode()[0])\n",
    "test_df['BsmtFinType2']=test_df['BsmtFinType2'].fillna(test_df['BsmtFinType2'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "58c6573b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>BsmtExposure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MSZoning  LotFrontage BsmtExposure\n",
       "0       RH         80.0           No\n",
       "1       RL         81.0           No\n",
       "2       RL         74.0           No\n",
       "3       RL         78.0           No\n",
       "4       RL         43.0           No"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.loc[:,test_df.isnull().any()].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "969dd893",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df['Utilities']=test_df['Utilities'].fillna(test_df['Utilities'].mode()[0])\n",
    "test_df['Exterior1st']=test_df['Exterior1st'].fillna(test_df['Exterior1st'].mode()[0])\n",
    "test_df['Exterior2nd']=test_df['Exterior2nd'].fillna(test_df['Exterior2nd'].mode()[0])\n",
    "test_df['BsmtFinType1']=test_df['BsmtFinType1'].fillna(test_df['BsmtFinType1'].mode()[0])\n",
    "test_df['BsmtFinSF1']=test_df['BsmtFinSF1'].fillna(test_df['BsmtFinSF1'].mean())\n",
    "test_df['BsmtFinSF2']=test_df['BsmtFinSF2'].fillna(test_df['BsmtFinSF2'].mean())\n",
    "test_df['BsmtUnfSF']=test_df['BsmtUnfSF'].fillna(test_df['BsmtUnfSF'].mean())\n",
    "test_df['TotalBsmtSF']=test_df['TotalBsmtSF'].fillna(test_df['TotalBsmtSF'].mean())\n",
    "test_df['BsmtFullBath']=test_df['BsmtFullBath'].fillna(test_df['BsmtFullBath'].mode()[0])\n",
    "test_df['BsmtHalfBath']=test_df['BsmtHalfBath'].fillna(test_df['BsmtHalfBath'].mode()[0])\n",
    "test_df['KitchenQual']=test_df['KitchenQual'].fillna(test_df['KitchenQual'].mode()[0])\n",
    "test_df['Functional']=test_df['Functional'].fillna(test_df['Functional'].mode()[0])\n",
    "test_df['GarageCars']=test_df['GarageCars'].fillna(test_df['GarageCars'].mean())\n",
    "test_df['GarageArea']=test_df['GarageArea'].fillna(test_df['GarageArea'].mean())\n",
    "test_df['SaleType']=test_df['SaleType'].fillna(test_df['SaleType'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e844f8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 74)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4792ea6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df.to_csv('test.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dd3c1c0b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>...</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>74.0</td>\n",
       "      <td>13830</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>78.0</td>\n",
       "      <td>9978</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5005</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>HLS</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>...</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Street LotShape LandContour  \\\n",
       "0          20       RH         80.0    11622   Pave      Reg         Lvl   \n",
       "1          20       RL         81.0    14267   Pave      IR1         Lvl   \n",
       "2          60       RL         74.0    13830   Pave      IR1         Lvl   \n",
       "3          60       RL         78.0     9978   Pave      IR1         Lvl   \n",
       "4         120       RL         43.0     5005   Pave      IR1         HLS   \n",
       "\n",
       "  Utilities LotConfig LandSlope  ... OpenPorchSF EnclosedPorch 3SsnPorch  \\\n",
       "0    AllPub    Inside       Gtl  ...           0             0         0   \n",
       "1    AllPub    Corner       Gtl  ...          36             0         0   \n",
       "2    AllPub    Inside       Gtl  ...          34             0         0   \n",
       "3    AllPub    Inside       Gtl  ...          36             0         0   \n",
       "4    AllPub    Inside       Gtl  ...          82             0         0   \n",
       "\n",
       "  ScreenPorch PoolArea  MiscVal  MoSold  YrSold  SaleType SaleCondition  \n",
       "0         120        0        0       6    2010        WD        Normal  \n",
       "1           0        0    12500       6    2010        WD        Normal  \n",
       "2           0        0        0       3    2010        WD        Normal  \n",
       "3           0        0        0       6    2010        WD        Normal  \n",
       "4         144        0        0       1    2010        WD        Normal  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7150aa8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          1\n",
      "1          2\n",
      "2          3\n",
      "3          4\n",
      "4          5\n",
      "        ... \n",
      "1455    1456\n",
      "1456    1457\n",
      "1457    1458\n",
      "1458    1459\n",
      "1459    1460\n",
      "Name: Id, Length: 1421, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "extracted_col = df[\"Id\"]\n",
    "print(extracted_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8440d7e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df=pd.concat([df,test_df],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bf710233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2880, 76)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "809b9c7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSZoning\n",
      "Street\n",
      "LotShape\n",
      "LandContour\n",
      "Utilities\n",
      "LotConfig\n",
      "LandSlope\n",
      "Neighborhood\n",
      "Condition2\n",
      "BldgType\n",
      "Condition1\n",
      "HouseStyle\n",
      "SaleType\n",
      "SaleCondition\n",
      "ExterCond\n",
      "ExterQual\n",
      "Foundation\n",
      "BsmtQual\n",
      "BsmtCond\n",
      "BsmtExposure\n",
      "BsmtFinType1\n",
      "BsmtFinType2\n",
      "RoofStyle\n",
      "RoofMatl\n",
      "Exterior1st\n",
      "Exterior2nd\n",
      "MasVnrType\n",
      "Heating\n",
      "HeatingQC\n",
      "CentralAir\n",
      "Electrical\n",
      "KitchenQual\n",
      "Functional\n",
      "FireplaceQu\n",
      "GarageType\n",
      "GarageFinish\n",
      "GarageQual\n",
      "GarageCond\n",
      "PavedDrive\n"
     ]
    }
   ],
   "source": [
    "final_df=category_onehot_multcols(columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a72d5720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2880, 235)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b47455b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df=final_df.loc[:,~final_df.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6e967c80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2880, 175)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "da03a8d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>Min1</th>\n",
       "      <th>Min2</th>\n",
       "      <th>Typ</th>\n",
       "      <th>Attchd</th>\n",
       "      <th>Basment</th>\n",
       "      <th>BuiltIn</th>\n",
       "      <th>CarPort</th>\n",
       "      <th>Detchd</th>\n",
       "      <th>RFn</th>\n",
       "      <th>P</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 175 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0  1.0          60         65.0     8450            7            5       2003   \n",
       "1  2.0          20         80.0     9600            6            8       1976   \n",
       "2  3.0          60         68.0    11250            7            5       2001   \n",
       "3  4.0          70         60.0     9550            7            5       1915   \n",
       "4  5.0          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  ...   Min1   Min2   Typ  Attchd  \\\n",
       "0          2003       196.0       706.0  ...  False  False  True    True   \n",
       "1          1976         0.0       978.0  ...  False  False  True    True   \n",
       "2          2002       162.0       486.0  ...  False  False  True    True   \n",
       "3          1970         0.0       216.0  ...  False  False  True   False   \n",
       "4          2000       350.0       655.0  ...  False  False  True    True   \n",
       "\n",
       "   Basment  BuiltIn  CarPort  Detchd    RFn      P  \n",
       "0    False    False    False   False   True  False  \n",
       "1    False    False    False   False   True  False  \n",
       "2    False    False    False   False   True  False  \n",
       "3    False    False    False    True  False  False  \n",
       "4    False    False    False   False   True  False  \n",
       "\n",
       "[5 rows x 175 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "e79a270a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature selection (to see the significance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e5bd6b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correlation between output columns and all other columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d4bbb64b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass    -0.084478\n",
       "LotFrontage    0.335424\n",
       "LotArea        0.263798\n",
       "OverallQual    0.788045\n",
       "OverallCond   -0.095440\n",
       "                 ...   \n",
       "BuiltIn        0.235030\n",
       "CarPort       -0.061047\n",
       "Detchd        -0.352216\n",
       "RFn            0.159679\n",
       "P             -0.094179\n",
       "Name: SalePrice, Length: 174, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df.columns[1:]].corr()['SalePrice'][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "662a90e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f2d634e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The important features are :\n",
      "\n"
     ]
    },
    {
     "data": {
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       "      <th>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>7</td>\n",
       "      <td>1710</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>856</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>8450</td>\n",
       "      <td>208500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1262</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1976</td>\n",
       "      <td>9600</td>\n",
       "      <td>181500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1786</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>920</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2001</td>\n",
       "      <td>11250</td>\n",
       "      <td>223500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1717</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>961</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1915</td>\n",
       "      <td>9550</td>\n",
       "      <td>140000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2198</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>1145</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2000</td>\n",
       "      <td>14260</td>\n",
       "      <td>250000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1647</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>953.0</td>\n",
       "      <td>953</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1999</td>\n",
       "      <td>7917</td>\n",
       "      <td>175000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2073</td>\n",
       "      <td>2.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1542.0</td>\n",
       "      <td>2073</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1978</td>\n",
       "      <td>13175</td>\n",
       "      <td>210000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2340</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1188</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1941</td>\n",
       "      <td>9042</td>\n",
       "      <td>266500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1078.0</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1950</td>\n",
       "      <td>9717</td>\n",
       "      <td>142125.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1256.0</td>\n",
       "      <td>1256</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1965</td>\n",
       "      <td>9937</td>\n",
       "      <td>147500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1421 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id  OverallQual  GrLivArea  GarageCars  GarageArea  TotalBsmtSF  \\\n",
       "0        1.0            7       1710         2.0       548.0        856.0   \n",
       "1        2.0            6       1262         2.0       460.0       1262.0   \n",
       "2        3.0            7       1786         2.0       608.0        920.0   \n",
       "3        4.0            7       1717         3.0       642.0        756.0   \n",
       "4        5.0            8       2198         3.0       836.0       1145.0   \n",
       "...      ...          ...        ...         ...         ...          ...   \n",
       "1455  1456.0            6       1647         2.0       460.0        953.0   \n",
       "1456  1457.0            6       2073         2.0       500.0       1542.0   \n",
       "1457  1458.0            7       2340         1.0       252.0       1152.0   \n",
       "1458  1459.0            5       1078         1.0       240.0       1078.0   \n",
       "1459  1460.0            5       1256         1.0       276.0       1256.0   \n",
       "\n",
       "      1stFlrSF  FullBath  TotRmsAbvGrd  YearBuilt  LotArea  SalePrice  \n",
       "0          856         2             8       2003     8450   208500.0  \n",
       "1         1262         2             6       1976     9600   181500.0  \n",
       "2          920         2             6       2001    11250   223500.0  \n",
       "3          961         1             7       1915     9550   140000.0  \n",
       "4         1145         2             9       2000    14260   250000.0  \n",
       "...        ...       ...           ...        ...      ...        ...  \n",
       "1455       953         2             7       1999     7917   175000.0  \n",
       "1456      2073         2             7       1978    13175   210000.0  \n",
       "1457      1188         2             9       1941     9042   266500.0  \n",
       "1458      1078         1             5       1950     9717   142125.0  \n",
       "1459      1256         1             6       1965     9937   147500.0  \n",
       "\n",
       "[1421 rows x 12 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The important features are :\\n\" )\n",
    "dfc = final_df[['Id','OverallQual','GrLivArea','GarageCars','GarageArea','TotalBsmtSF','1stFlrSF','FullBath','TotRmsAbvGrd'\n",
    ",'YearBuilt','LotArea','SalePrice']]  \n",
    "dfc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "053264fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (16,5))\n",
    "sns.heatmap(dfc.corr(),annot=True,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "83ea671d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Right Skewed Data: More houses with price between 1 million and 3 million \n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(final_df['SalePrice'],bins=100)\n",
    "print(\"Right Skewed Data: More houses with price between 1 million and 3 million \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fd8d2778",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for outliers\n",
    "# using box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ab77a48a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='OverallQual', ylabel='SalePrice'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,5))\n",
    "sns.boxplot(x='OverallQual',y='SalePrice',data=dfc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4fc1f5cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Id              0\n",
      "OverallQual     0\n",
      "GrLivArea       0\n",
      "GarageCars      0\n",
      "GarageArea      0\n",
      "TotalBsmtSF     0\n",
      "1stFlrSF        0\n",
      "FullBath        0\n",
      "TotRmsAbvGrd    0\n",
      "YearBuilt       0\n",
      "LotArea         0\n",
      "SalePrice       0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(dfc.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "d0c816e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No missing values\n"
     ]
    }
   ],
   "source": [
    "print(\"No missing values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f262685d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "49b8728c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dividing the dataset into independent and dependent variable\n",
    "\n",
    "X=dfc.iloc[:,:-1] \n",
    "y=dfc.iloc[:,-1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0283068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>LotArea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1710</td>\n",
       "      <td>2.0</td>\n",
       "      <td>548.0</td>\n",
       "      <td>856.0</td>\n",
       "      <td>856</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2003</td>\n",
       "      <td>8450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1262</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>1262.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1976</td>\n",
       "      <td>9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1786</td>\n",
       "      <td>2.0</td>\n",
       "      <td>608.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>920</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2001</td>\n",
       "      <td>11250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>7</td>\n",
       "      <td>1717</td>\n",
       "      <td>3.0</td>\n",
       "      <td>642.0</td>\n",
       "      <td>756.0</td>\n",
       "      <td>961</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1915</td>\n",
       "      <td>9550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>2198</td>\n",
       "      <td>3.0</td>\n",
       "      <td>836.0</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>1145</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2000</td>\n",
       "      <td>14260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456.0</td>\n",
       "      <td>6</td>\n",
       "      <td>1647</td>\n",
       "      <td>2.0</td>\n",
       "      <td>460.0</td>\n",
       "      <td>953.0</td>\n",
       "      <td>953</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1999</td>\n",
       "      <td>7917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457.0</td>\n",
       "      <td>6</td>\n",
       "      <td>2073</td>\n",
       "      <td>2.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1542.0</td>\n",
       "      <td>2073</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1978</td>\n",
       "      <td>13175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458.0</td>\n",
       "      <td>7</td>\n",
       "      <td>2340</td>\n",
       "      <td>1.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>1152.0</td>\n",
       "      <td>1188</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1941</td>\n",
       "      <td>9042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1078</td>\n",
       "      <td>1.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>1078.0</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1950</td>\n",
       "      <td>9717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1256</td>\n",
       "      <td>1.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>1256.0</td>\n",
       "      <td>1256</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1965</td>\n",
       "      <td>9937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1421 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Id  OverallQual  GrLivArea  GarageCars  GarageArea  TotalBsmtSF  \\\n",
       "0        1.0            7       1710         2.0       548.0        856.0   \n",
       "1        2.0            6       1262         2.0       460.0       1262.0   \n",
       "2        3.0            7       1786         2.0       608.0        920.0   \n",
       "3        4.0            7       1717         3.0       642.0        756.0   \n",
       "4        5.0            8       2198         3.0       836.0       1145.0   \n",
       "...      ...          ...        ...         ...         ...          ...   \n",
       "1455  1456.0            6       1647         2.0       460.0        953.0   \n",
       "1456  1457.0            6       2073         2.0       500.0       1542.0   \n",
       "1457  1458.0            7       2340         1.0       252.0       1152.0   \n",
       "1458  1459.0            5       1078         1.0       240.0       1078.0   \n",
       "1459  1460.0            5       1256         1.0       276.0       1256.0   \n",
       "\n",
       "      1stFlrSF  FullBath  TotRmsAbvGrd  YearBuilt  LotArea  \n",
       "0          856         2             8       2003     8450  \n",
       "1         1262         2             6       1976     9600  \n",
       "2          920         2             6       2001    11250  \n",
       "3          961         1             7       1915     9550  \n",
       "4         1145         2             9       2000    14260  \n",
       "...        ...       ...           ...        ...      ...  \n",
       "1455       953         2             7       1999     7917  \n",
       "1456      2073         2             7       1978    13175  \n",
       "1457      1188         2             9       1941     9042  \n",
       "1458      1078         1             5       1950     9717  \n",
       "1459      1256         1             6       1965     9937  \n",
       "\n",
       "[1421 rows x 11 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "f5f71069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       208500.0\n",
       "1       181500.0\n",
       "2       223500.0\n",
       "3       140000.0\n",
       "4       250000.0\n",
       "          ...   \n",
       "1455    175000.0\n",
       "1456    210000.0\n",
       "1457    266500.0\n",
       "1458    142125.0\n",
       "1459    147500.0\n",
       "Name: SalePrice, Length: 1421, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9dfe5c74",
   "metadata": {},
   "outputs": [],
   "source": [
    "# splitting the dataset\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lin_reg=LinearRegression()\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "726abd7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#feature scaling\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)\n",
    "lin_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8f49e3a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# predicting the test set results\n",
    "\n",
    "y_pred = lin_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "54ddf646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting Scatter graph to show the prediction\n",
    "\n",
    "plt.scatter(y_test, y_pred)\n",
    "plt.xlabel(\"Price: in $1000's\")\n",
    "plt.ylabel(\"Predicted value\")\n",
    "plt.title(\"Linear Regression = True value vs predicted value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3e3bb808",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New RMSE:  55261.42148701343\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "print(\"New RMSE: \", math.sqrt(mean_squared_error(y_pred, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "64c0cac0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "285"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "f6c6accf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "residuals = y_test - y_pred\n",
    "plt.scatter(y_test, residuals)\n",
    "plt.axhline(y=0, color='red', linestyle='--')\n",
    "plt.xlabel(\"Actual Prices\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.title(\"Residual plot\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}