Big Mart Sales Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Big Mart Sales Predcition\n",
    "\n",
    "#### Big Mart, a fictional retailer of supermarkets and grocery stores, has collected sales data for 1559 different products across 10 outlets in the year 2013. Using the data, Big Mart will try to understand the key aspects of its products and then try to identify key factors that determine sales of products.\n",
    "\n",
    "#### The aim is to build a predictive model to estimate the sales of products. \n",
    "\n",
    "#### The Ordinary Least Squares algorithm will be used here to build the predictive model. Using backward elimination method, the model's fit will be improved. \n",
    "\n",
    "#### Data source: https://www.kaggle.com/mrmorj/big-mart-sales\n",
    "\n",
    "#### Methodology: After loading the data and looking at the data types & columns, the data was cleaned (handling of missing values & invalid values). Then, the data was pre-processed to split into 80:20 for model training & predictions. Fianlly, an initial OLS model was built and fine-tuned using the backward elimination method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>OUT018</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>OUT049</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>OUT010</td>\n",
       "      <td>1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>OUT013</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0           FDA15         9.30          Low Fat         0.016047   \n",
       "1           DRC01         5.92          Regular         0.019278   \n",
       "2           FDN15        17.50          Low Fat         0.016760   \n",
       "3           FDX07        19.20          Regular         0.000000   \n",
       "4           NCD19         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092            OUT049   \n",
       "1            Soft Drinks   48.2692            OUT018   \n",
       "2                   Meat  141.6180            OUT049   \n",
       "3  Fruits and Vegetables  182.0950            OUT010   \n",
       "4              Household   53.8614            OUT013   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998         NaN               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Item_Outlet_Sales is our outcome variable. \n",
    "\n",
    "#### Every row in the dataset refers to a product in one particular outlet. There are 1559 distinct products and 10 different outlets. \n",
    "#### The dataset has 8532 rows and 12 columns. \n",
    "#### Column 1 - Item_Identifier -- Unique identifer for every product.\n",
    "#### Column 2 - Item_Weight -- Weight of the product.\n",
    "#### Column 3 - Item_Fat_Content -- If the product is Low Fat or Regular\n",
    "#### Column 4 - Item_Visibility -- The % of total display area of all products in a store allocated to the particular product\n",
    "#### Column 5 - Item_Type -- The category to which the product belongs\n",
    "#### Column 6 - Item MRP -- The Maximum Retail Price of the item\n",
    "#### Column 7 - Outlet_Identifier -- Unique identifier for every Outlet\n",
    "#### Column 8 - Outlet_Establishment_Year -- The year the outlet opened for business\n",
    "#### Column 9 - Outlet_Size -- Outlet size (Small/Medium/High)\n",
    "#### Column 10 - Outlet_Location_Type -- The type of nighborhood the outlet is located in (Tier1/ier2/ier3)\n",
    "#### Column 11 - Outlet Type -- The type of outlet, if it is a grocery store or a type of supermarket.\n",
    "#### Column 12 - Item_Outlet_Sales -- The sales of the product in an outlet. This is the outcome variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8523, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   object \n",
      " 1   Item_Weight                7060 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   object \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   object \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   object \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                6113 non-null   object \n",
      " 9   Outlet_Location_Type       8523 non-null   object \n",
      " 10  Outlet_Type                8523 non-null   object \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 799.2+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Item_Identifier              False\n",
       "Item_Weight                   True\n",
       "Item_Fat_Content             False\n",
       "Item_Visibility              False\n",
       "Item_Type                    False\n",
       "Item_MRP                     False\n",
       "Outlet_Identifier            False\n",
       "Outlet_Establishment_Year    False\n",
       "Outlet_Size                   True\n",
       "Outlet_Location_Type         False\n",
       "Outlet_Type                  False\n",
       "Item_Outlet_Sales            False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isna().any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2 columns, Item_Weight and Outlet_Size have missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7060.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "      <td>8523.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12.857645</td>\n",
       "      <td>0.066132</td>\n",
       "      <td>140.992782</td>\n",
       "      <td>1997.831867</td>\n",
       "      <td>2181.288914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.643456</td>\n",
       "      <td>0.051598</td>\n",
       "      <td>62.275067</td>\n",
       "      <td>8.371760</td>\n",
       "      <td>1706.499616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.555000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.290000</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>33.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.773750</td>\n",
       "      <td>0.026989</td>\n",
       "      <td>93.826500</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>834.247400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.600000</td>\n",
       "      <td>0.053931</td>\n",
       "      <td>143.012800</td>\n",
       "      <td>1999.000000</td>\n",
       "      <td>1794.331000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>16.850000</td>\n",
       "      <td>0.094585</td>\n",
       "      <td>185.643700</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>3101.296400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>21.350000</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>266.888400</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>13086.964800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
       "count  7060.000000      8523.000000  8523.000000                8523.000000   \n",
       "mean     12.857645         0.066132   140.992782                1997.831867   \n",
       "std       4.643456         0.051598    62.275067                   8.371760   \n",
       "min       4.555000         0.000000    31.290000                1985.000000   \n",
       "25%       8.773750         0.026989    93.826500                1987.000000   \n",
       "50%      12.600000         0.053931   143.012800                1999.000000   \n",
       "75%      16.850000         0.094585   185.643700                2004.000000   \n",
       "max      21.350000         0.328391   266.888400                2009.000000   \n",
       "\n",
       "       Item_Outlet_Sales  \n",
       "count        8523.000000  \n",
       "mean         2181.288914  \n",
       "std          1706.499616  \n",
       "min            33.290000  \n",
       "25%           834.247400  \n",
       "50%          1794.331000  \n",
       "75%          3101.296400  \n",
       "max         13086.964800  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the distribution of the variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:839: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "/opt/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py:840: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "image/png": 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d7XYf8N7W/k7gnW05DL5E5H7gHmDzMqj7WQzC+nlDbcM1v6s9n7sYvAD4yhnV+QkG0wE/ZnDWsh04C7gFONDuz2xjNwMfHdr2HcDBdnv7jGs+yGCu9M52+9s29oXATSc6lmZc9z+2Y/ZuBuFzzvy62/rFDK76uH8p6x5Vc2v/h6PH8tDYZbGvgV9jMJVy99DxcPFyP65P5ubHD0hSh7qYlpEkHctwl6QOGe6S1CHDXZI6ZLhLUocMd0nqkOEuSR36XwmVr5xVJhlxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Weight\n",
    "plt.hist(train['Item_Weight'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Fat_Content\n",
    "train['Item_Fat_Content'].value_counts().plot(kind='barh')\n",
    "plt.show()\n",
    "\n",
    "# The values in this column have to be cleaned. \"reg\" refers to Regular, \"low fat\" & \"LF\" refer to Low Fat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Visibility\n",
    "plt.hist(train['Item_Visibility'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Type\n",
    "train['Item_Type'].value_counts().plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_MRP\n",
    "plt.hist(train['Item_MRP'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlet_Identifier\n",
    "train['Outlet_Identifier'].value_counts().plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9076317d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlet_Establishment_Year\n",
    "train['Outlet_Establishment_Year'].value_counts().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb90773eed0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlet_Size\n",
    "train['Outlet_Size'].value_counts().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb90784fd10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlet_Location_Type\n",
    "train['Outlet_Location_Type'].value_counts().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9072c16d0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Outlet_Type\n",
    "train['Outlet_Type'].value_counts().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Item_Outlet_Sales\n",
    "plt.hist(train['Item_Outlet_Sales'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values and Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There are 3 columns to be cleaned: Item_Weight, Item_Fat_Content, and Outlet_Size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Item_Fat_Content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Regular', 'low fat', 'LF', 'reg'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Item_Fat_Content\n",
    "\n",
    "train['Item_Fat_Content'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       Low Fat\n",
      "1       Regular\n",
      "2       Low Fat\n",
      "3       Regular\n",
      "4       Low Fat\n",
      "         ...   \n",
      "8518    Low Fat\n",
      "8519    Regular\n",
      "8520    Low Fat\n",
      "8521    Regular\n",
      "8522    Low Fat\n",
      "Name: Item_Fat_Content, Length: 8523, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Creating a Pandas Series object with NA values to fill.\n",
    "Fat_Content = train['Item_Fat_Content']\n",
    "print(Fat_Content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n",
      "/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "# Filling up the NA values in the Pandas Series object and assigning it back to the Data Frame.\n",
    "for i in range(0,len(Fat_Content)):\n",
    "    if Fat_Content[i] == 'LF':\n",
    "        Fat_Content[i] = 'Low Fat'\n",
    "    elif Fat_Content[i] == 'low fat':\n",
    "        Fat_Content[i] = 'Low Fat'\n",
    "    elif Fat_Content[i] == 'reg':\n",
    "        Fat_Content[i] = 'Regular'\n",
    "    else:\n",
    "        Fat_Content[i] = Fat_Content[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Low Fat', 'Regular'], dtype=object)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Fat_Content.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Low Fat' 'Regular']\n"
     ]
    }
   ],
   "source": [
    "train['Item_Fat_Content'] = Fat_Content\n",
    "print(train['Item_Fat_Content'].unique())\n",
    "\n",
    "# The Item_Fat_Content column in train DF only contains valid values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Item_Weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDA15</td>\n",
       "      <td>9.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DRC01</td>\n",
       "      <td>5.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDN15</td>\n",
       "      <td>17.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDX07</td>\n",
       "      <td>19.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCD19</td>\n",
       "      <td>8.930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8518</th>\n",
       "      <td>FDF22</td>\n",
       "      <td>6.865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8519</th>\n",
       "      <td>FDS36</td>\n",
       "      <td>8.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8520</th>\n",
       "      <td>NCJ29</td>\n",
       "      <td>10.600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8521</th>\n",
       "      <td>FDN46</td>\n",
       "      <td>7.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8522</th>\n",
       "      <td>DRG01</td>\n",
       "      <td>14.800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7060 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight\n",
       "0              FDA15        9.300\n",
       "1              DRC01        5.920\n",
       "2              FDN15       17.500\n",
       "3              FDX07       19.200\n",
       "4              NCD19        8.930\n",
       "...              ...          ...\n",
       "8518           FDF22        6.865\n",
       "8519           FDS36        8.380\n",
       "8520           NCJ29       10.600\n",
       "8521           FDN46        7.210\n",
       "8522           DRG01       14.800\n",
       "\n",
       "[7060 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Locating product weights using Product Identifiers in non-missing rows and mapping them to the missing values.\n",
    "# In other words, using non-NA rows of the same product to fill in the missing poduct weights.\n",
    "\n",
    "train[train.Item_Weight.isnull() == False].loc[:,['Item_Identifier', 'Item_Weight']]\n",
    "# Removing NA rows and selecting the 2 columns. Making sure only NA rows in Item_Weight column are dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = dict(train[train.Item_Weight.isnull() == False].loc[:,['Item_Identifier', 'Item_Weight']].values)\n",
    "# Creating a dictionary for mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Item_Weight'] = train['Item_Weight'].fillna(train['Item_Identifier'].map(mapping))\n",
    "# Replacing NA values with values from the mapping dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>927</th>\n",
       "      <td>FDN52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.130933</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>86.9198</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>1569.9564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1922</th>\n",
       "      <td>FDK57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.079904</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>120.0440</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>4434.2280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4187</th>\n",
       "      <td>FDE52</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.029742</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>88.9514</td>\n",
       "      <td>OUT027</td>\n",
       "      <td>1985</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>3453.5046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022</th>\n",
       "      <td>FDQ60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.191501</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>121.2098</td>\n",
       "      <td>OUT019</td>\n",
       "      <td>1985</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>120.5098</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "927            FDN52          NaN          Regular         0.130933   \n",
       "1922           FDK57          NaN          Low Fat         0.079904   \n",
       "4187           FDE52          NaN          Regular         0.029742   \n",
       "5022           FDQ60          NaN          Regular         0.191501   \n",
       "\n",
       "         Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "927   Frozen Foods   86.9198            OUT027                       1985   \n",
       "1922   Snack Foods  120.0440            OUT027                       1985   \n",
       "4187         Dairy   88.9514            OUT027                       1985   \n",
       "5022  Baking Goods  121.2098            OUT019                       1985   \n",
       "\n",
       "     Outlet_Size Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "927       Medium               Tier 3  Supermarket Type3          1569.9564  \n",
       "1922      Medium               Tier 3  Supermarket Type3          4434.2280  \n",
       "4187      Medium               Tier 3  Supermarket Type3          3453.5046  \n",
       "5022       Small               Tier 1      Grocery Store           120.5098  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.Item_Weight.isna() == True]\n",
    "# Rows which do not have any other non-NA rows with the same product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filling the missing values with median Item_Weight.\n",
    "med_wt = np.nanmedian(train['Item_Weight'])\n",
    "\n",
    "train['Item_Weight'] = train['Item_Weight'].fillna(med_wt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Item_Weight.isna().sum()\n",
    "# No more missing values in Item_Weight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlet_Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2410"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Outlet_Size.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['OUT010', 'OUT045', 'OUT017'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identifying unique Outlets with missing Outlet Sizes\n",
    "train[train.Outlet_Size.isnull() == True].Outlet_Identifier.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Going through the data, OUT017 and OUT045 have Outlet Locations as Tier 2 and Outlet Types as Supermarket Type 1.\n",
    "# Most other outlets with similar Location and Supermarket Types have Outlet Size as Small.\n",
    "# Using that, Outlet Size for OUT017 and OUT045 will be filled as 'Small'.\n",
    "\n",
    "# OUT010 has Outlet Type as Grocery. Grocery Stores type have Outlet Size Small in the data.\n",
    "# Using that, Outlet Size for OUT101 will be filled as Small.\n",
    "\n",
    "# All 3 unique outlets with missing Outlet Sizes will be filled as Small."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['Outlet_Size'] = train['Outlet_Size'].fillna(\"Small\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.Outlet_Size.isna().sum()\n",
    "# No more missing values in Outlet_Size."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Pre-Processing\n",
    "\n",
    "#### To effectively build the regression model, the categorical predictor variables have to be converted to numeric and the train data must be split into 2 parts of 80% and 20% for validation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical predictors\n",
    "\n",
    "#### Categorical columns: Item_Identifier, Item_Fat_Content, Item_Type, Outlet_Identifier, Outlet_Size, Outlet_Location_Type, Outlet_Type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Item_Identifier and Outlet_Identifier\n",
    "\n",
    "# Item_Indicator and Outlet_Indicator are unique ID numbers of Products and Outlets. To convert it to numeric, the\n",
    "# respective frequencies of every product/outlet are added to the train data. For ex, if OUT010 has 600 counts in the\n",
    "# entire data frame, 600 will be input to every cell that has the value OUT010 in the Outlet_Indicator column.\n",
    "\n",
    "col = ['Item_Identifier', 'Outlet_Identifier']\n",
    "\n",
    "for i in col:\n",
    "    x = dict(train[i].value_counts())\n",
    "    train[i] = train[i].map(x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9.30</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>930</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>5.92</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>928</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>17.50</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>Meat</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>930</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>19.20</td>\n",
       "      <td>Regular</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>555</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>8.93</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>Household</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>932</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
       "0                8         9.30          Low Fat         0.016047   \n",
       "1                6         5.92          Regular         0.019278   \n",
       "2                7        17.50          Low Fat         0.016760   \n",
       "3                6        19.20          Regular         0.000000   \n",
       "4                6         8.93          Low Fat         0.000000   \n",
       "\n",
       "               Item_Type  Item_MRP  Outlet_Identifier  \\\n",
       "0                  Dairy  249.8092                930   \n",
       "1            Soft Drinks   48.2692                928   \n",
       "2                   Meat  141.6180                930   \n",
       "3  Fruits and Vegetables  182.0950                555   \n",
       "4              Household   53.8614                932   \n",
       "\n",
       "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
       "0                       1999      Medium               Tier 1   \n",
       "1                       2009      Medium               Tier 3   \n",
       "2                       1999      Medium               Tier 1   \n",
       "3                       1998       Small               Tier 3   \n",
       "4                       1987        High               Tier 3   \n",
       "\n",
       "         Outlet_Type  Item_Outlet_Sales  \n",
       "0  Supermarket Type1          3735.1380  \n",
       "1  Supermarket Type2           443.4228  \n",
       "2  Supermarket Type1          2097.2700  \n",
       "3      Grocery Store           732.3800  \n",
       "4  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
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       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>930</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>5.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>928</td>\n",
       "      <td>2009</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>930</td>\n",
       "      <td>1999</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Tier 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>555</td>\n",
       "      <td>1998</td>\n",
       "      <td>Small</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>932</td>\n",
       "      <td>1987</td>\n",
       "      <td>High</td>\n",
       "      <td>Tier 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0                8         9.30                 0         0.016047          4   \n",
       "1                6         5.92                 1         0.019278         14   \n",
       "2                7        17.50                 0         0.016760         10   \n",
       "3                6        19.20                 1         0.000000          6   \n",
       "4                6         8.93                 0         0.000000          9   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year Outlet_Size  \\\n",
       "0  249.8092                930                       1999      Medium   \n",
       "1   48.2692                928                       2009      Medium   \n",
       "2  141.6180                930                       1999      Medium   \n",
       "3  182.0950                555                       1998       Small   \n",
       "4   53.8614                932                       1987        High   \n",
       "\n",
       "  Outlet_Location_Type        Outlet_Type  Item_Outlet_Sales  \n",
       "0               Tier 1  Supermarket Type1          3735.1380  \n",
       "1               Tier 3  Supermarket Type2           443.4228  \n",
       "2               Tier 1  Supermarket Type1          2097.2700  \n",
       "3               Tier 3      Grocery Store           732.3800  \n",
       "4               Tier 3  Supermarket Type1           994.7052  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Item_Fat_Content and Item_Type\n",
    "\n",
    "# These 2 categorical variables are nominal.\n",
    "# Using label encoding, the variables will be converted into numeric.\n",
    "\n",
    "from sklearn import preprocessing\n",
    "le1 = preprocessing.LabelEncoder()\n",
    "train['Item_Fat_Content'] = le1.fit_transform(train['Item_Fat_Content'])\n",
    "\n",
    "le2 = preprocessing.LabelEncoder()\n",
    "train['Item_Type'] = le2.fit_transform(train['Item_Type'])\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Size</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Outlet_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>930</td>\n",
       "      <td>1999</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>5.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>928</td>\n",
       "      <td>2009</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>930</td>\n",
       "      <td>1999</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>555</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>932</td>\n",
       "      <td>1987</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0                8         9.30                 0         0.016047          4   \n",
       "1                6         5.92                 1         0.019278         14   \n",
       "2                7        17.50                 0         0.016760         10   \n",
       "3                6        19.20                 1         0.000000          6   \n",
       "4                6         8.93                 0         0.000000          9   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  Outlet_Size  \\\n",
       "0  249.8092                930                       1999            2   \n",
       "1   48.2692                928                       2009            2   \n",
       "2  141.6180                930                       1999            2   \n",
       "3  182.0950                555                       1998            1   \n",
       "4   53.8614                932                       1987            3   \n",
       "\n",
       "   Outlet_Location_Type  Outlet_Type  Item_Outlet_Sales  \n",
       "0                     3            4          3735.1380  \n",
       "1                     1            3           443.4228  \n",
       "2                     3            4          2097.2700  \n",
       "3                     1            1           732.3800  \n",
       "4                     1            4           994.7052  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Outlet_Size, Outlet_Location_Type and Outlet_Type\n",
    "\n",
    "# These 3 are ordinal categorical variables as each value has a specific order (Small < Medium < High, etc.).\n",
    "# The respective values in each column will be replaced by an integer.\n",
    "\n",
    "# Outlet_Size -- Small = 1, Medium = 2, High = 3.\n",
    "\n",
    "train['Outlet_Size'] = train['Outlet_Size'].replace(\"Small\", 1)\n",
    "train['Outlet_Size'] = train['Outlet_Size'].replace(\"Medium\", 2)\n",
    "train['Outlet_Size'] = train['Outlet_Size'].replace(\"High\", 3)\n",
    "\n",
    "#Outlet_Location_Type -- Tier1 = 3, Tier2 = 2, Tier3 = 1 (Tier1 has more points than Tier3)\n",
    "train['Outlet_Location_Type'] = train['Outlet_Location_Type'].replace(\"Tier 1\", 3)\n",
    "train['Outlet_Location_Type'] = train['Outlet_Location_Type'].replace(\"Tier 2\", 2)\n",
    "train['Outlet_Location_Type'] = train['Outlet_Location_Type'].replace(\"Tier 3\", 1)\n",
    "\n",
    "# Outlet_Type -- Grocery Store = 1, Supermarket Type3 = 2, Supermarket Type2 = 3, Supermarket Type1 = 4.\n",
    "train['Outlet_Type'] = train['Outlet_Type'].replace(\"Grocery Store\", 1)\n",
    "train['Outlet_Type'] = train['Outlet_Type'].replace(\"Supermarket Type3\", 2)\n",
    "train['Outlet_Type'] = train['Outlet_Type'].replace(\"Supermarket Type2\", 3)\n",
    "train['Outlet_Type'] = train['Outlet_Type'].replace(\"Supermarket Type1\", 4)\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8523 entries, 0 to 8522\n",
      "Data columns (total 12 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   Item_Identifier            8523 non-null   int64  \n",
      " 1   Item_Weight                8523 non-null   float64\n",
      " 2   Item_Fat_Content           8523 non-null   int64  \n",
      " 3   Item_Visibility            8523 non-null   float64\n",
      " 4   Item_Type                  8523 non-null   int64  \n",
      " 5   Item_MRP                   8523 non-null   float64\n",
      " 6   Outlet_Identifier          8523 non-null   int64  \n",
      " 7   Outlet_Establishment_Year  8523 non-null   int64  \n",
      " 8   Outlet_Size                8523 non-null   int64  \n",
      " 9   Outlet_Location_Type       8523 non-null   int64  \n",
      " 10  Outlet_Type                8523 non-null   int64  \n",
      " 11  Item_Outlet_Sales          8523 non-null   float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 799.2 KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All variables are now numeric."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-Collinearity Check amongst Predictor Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb907268e10>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.heatmap(train.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assuming a threshold of +/- 0.3:\n",
    "\n",
    "#### Outlet_Size is highly correlated with Outlet_Establishment_Year, Outlet_Location_Type, and Outlet_Identifier.\n",
    "\n",
    "#### Outlet_Type is highly correlated with Outlet_Establishment_Year, Outlet_Identifier, and Outlet_Location_Type.\n",
    "\n",
    "#### Hence, Outlet_Size and Outlet_Type are removed.\n",
    "\n",
    "#### For Item_MRP and Outlet_Identifier being highly correlated with Item_Outlet_Sales, the correlation is not taken into consideration since Item_Outlet_Sales is the outcome variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Item_Identifier</th>\n",
       "      <th>Item_Weight</th>\n",
       "      <th>Item_Fat_Content</th>\n",
       "      <th>Item_Visibility</th>\n",
       "      <th>Item_Type</th>\n",
       "      <th>Item_MRP</th>\n",
       "      <th>Outlet_Identifier</th>\n",
       "      <th>Outlet_Establishment_Year</th>\n",
       "      <th>Outlet_Location_Type</th>\n",
       "      <th>Item_Outlet_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016047</td>\n",
       "      <td>4</td>\n",
       "      <td>249.8092</td>\n",
       "      <td>930</td>\n",
       "      <td>1999</td>\n",
       "      <td>3</td>\n",
       "      <td>3735.1380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>5.92</td>\n",
       "      <td>1</td>\n",
       "      <td>0.019278</td>\n",
       "      <td>14</td>\n",
       "      <td>48.2692</td>\n",
       "      <td>928</td>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>443.4228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>17.50</td>\n",
       "      <td>0</td>\n",
       "      <td>0.016760</td>\n",
       "      <td>10</td>\n",
       "      <td>141.6180</td>\n",
       "      <td>930</td>\n",
       "      <td>1999</td>\n",
       "      <td>3</td>\n",
       "      <td>2097.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>19.20</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>182.0950</td>\n",
       "      <td>555</td>\n",
       "      <td>1998</td>\n",
       "      <td>1</td>\n",
       "      <td>732.3800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>8.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>53.8614</td>\n",
       "      <td>932</td>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>994.7052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  Item_Type  \\\n",
       "0                8         9.30                 0         0.016047          4   \n",
       "1                6         5.92                 1         0.019278         14   \n",
       "2                7        17.50                 0         0.016760         10   \n",
       "3                6        19.20                 1         0.000000          6   \n",
       "4                6         8.93                 0         0.000000          9   \n",
       "\n",
       "   Item_MRP  Outlet_Identifier  Outlet_Establishment_Year  \\\n",
       "0  249.8092                930                       1999   \n",
       "1   48.2692                928                       2009   \n",
       "2  141.6180                930                       1999   \n",
       "3  182.0950                555                       1998   \n",
       "4   53.8614                932                       1987   \n",
       "\n",
       "   Outlet_Location_Type  Item_Outlet_Sales  \n",
       "0                     3          3735.1380  \n",
       "1                     1           443.4228  \n",
       "2                     3          2097.2700  \n",
       "3                     1           732.3800  \n",
       "4                     1           994.7052  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_clean = train.drop(['Outlet_Size','Outlet_Type'], axis=1)\n",
    "train_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9078695d0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ztxlHEjSNSXFJWdrxvXHLqcmpBAUsMJYJ2rnc2Hbv+eBG/HX2b9hLbbfa/LM/+zjYB+kvbB9n5vLpKG6n3ubpOs88so8fC2rW/H+a9kADIUR3bbkCUBu4AxyQUkYDCCHOAPcCxaNA64ftWAhRAagopdymrfo/oNMj1LtfShml7eMQhuEEO/N4fO2A5zNlF2yFEPf65dblFIT+Fzh/+DRO1VxwrOpE4tUEmnRuyYoR87PYHNoURotunpwNP0Vjn2ac3G0Y3jurx2SjTedRb5KWcjtLgOTRpRX71+f168rOhlXBbFhlmHnfqE1jOvZ9hV3rdlC7YR1u3UzJFtwlxSaSmpJK7YZ1iIw4hWe31vz1zf8ACAvZj1e3Nvz+5Vq8urXhwCZDoDms1SBj+WFfjODglrB8BaFg8HHlTD726NyS5SPmZbE5vCmMFt28NB8351+jjycZbbqM6sFtEx8XBEXpV51OR7kKNtja23Ij4Qb1WzTI04/imcOROFd3odJTTiTEJNC8cysWjZibxeZgyAFe6taayPCTNPVpwfHdR82upyBY9+161n27HoCmbTx4rV8Xtv4RSt2Gz5Fy81a2gCkhNoHUlFvUbfgc/0T8i3e3dvy+8g/AMH70nn2rji04f/I8ALGXr+HW0o2QXzdTukwp6jZ8jrUrfsuz5r9XBfO31kYat3GnU99X2LluO3UaPsutm7dINL2xik3kdkoqdRo+y6mIk3h1a03wN38+sA5be1uSk5LR6/VUfqoyLtVduWpG4JxZYyNN465126mtacy9HT9LpInG7O3YMNmyrG057qSmkX43nXa92vPP/uOkJqdSqkwphE7H7RTD/y++7MbP8817SsHj8LHTU5WJu3INfYaeSlUqUaVGFWKjrpqls9ApxhlOc1GBqPkIDBnLDVlWCuEFpGVapc+0rOfRfC0wDKHJT70Zj1hXbuiA5qYBpxaYpuRYopAJmDKTAxFHSEq6Qduu7+A74F26dc7f2CJz0Wfo+WHyV4xaNRFhoWPXmq1ciYyii39PLhw9w+GQMHau2cKAQD9mhC4kJSmZZX5zH7pf69LWPN+qAd99uKxQdIdvOUjD1u4s3L6EO6lpLB670LhtdvBcAnwMj49aPnGJ9pghaw6FhhOx9SAAvwWtZXRQAG16tiPuyjUCh84qFJ1wz8crGLXqI3QWOnat2cKVyChe8+/Jec3HO9Zs5v3AEXyq+XjpI/h44IJRPNusHjZ25Zm1Zynr5q5m55ot+dL6uP2q1+v5vxkrmfzDdISAs0fPsPnHjQ8sk+N+MvR8M3k541dNQWehI3TNZi5HXqL76N6cPXKa8JADhK4OwXfuKAK3BZGSlMzC4XOM5efvXEqZ8mWwtLKkcXsPZr77MZcjo+g9oQ8tXnsJ6zKlWLh3OaE/hbB2XsE9Bmnflv14tGnCqp0rSUtNY/aY+5qW/B3EkI6+Bn0fLiQgcCylSluzf2uYcXb8wA8HUKteTaSUxERdZd54Q7buj2/XETBnDCtCliEEbFizkXP/nisQzQe3hNGodWOCti/VHi10P0M4J3geY3wMHWRLJ36pPVrImvDQcMK1NtK0QzPe/3gQtvYVmLhyMudOnGV6n6k837QevUa/jT49A71ez9IPg0jO42S8cE3jIk1jUCaNs4PnEaBpXD7xS+3xTdZEmLTjMUEf0LanN3FXrjFn6OcAVK1VFb9Af/QZeqJOXzJmRys4VuSDZR8ChuEmO/7YxqFt4XnSDoXn47rudXnddxIZd9ORUrLsoyXcTHz8vQIPpBiP+TQX8ShjJf6LCCGSpZQ2QojGQKCU0lNbPwjwAd6UUt4VQtQBLgNNyNqFH6oth2nBonFbDnVNBZKllF8IIY4AvlrX+OfAK1LK+mbUuwgIk1J+I4Q4CnSRUp7Ttp0H3LXu+Mz/99P+Hy6E+AGIkFLO1sq4SSkPZdb4MN+VtK55X/dxRS3BLJKk+bOli5oKopiNr3oErsu7RS3BLKxK4JD/WH3BzFB/nNjqShW1BLMoea3CkLkpafx6YV1OMwkKjbRjm8z+nS1V3/uxanxUVEb04RwB0oUQh4FvMIyhrAaEC0Oa8BrQtQDr6w98LYS4hWF85j1W5KHeZcBfQohoKeVDhwZojAAWawGxJbAdGGKGfoVCoVAoFIWJyogqFLmjMqKFi8qIPh5URrTwURnRwqfktQqVEX0Ubh8ONvt3tvSLPiojqlAoFAqFQqHIJ2qykiIvCCEmAm+arP5ZSjmjKPQoFAqFQqEogTxBXfMqEH2MaAGnCjoVCoVCoVDkHZURVSgUCoVCoVAUCU/QA+1L4jhmhUKhUCgUCsUTgMqIKhQKhUKhUJQkVNe8QpE7Je1xSEFhnxe1BLN4o9GIopZgNjLXF4YVX2yFdVFLMIuMEujjiiXsUUhQ8h4tJCiWT+x5IKVVZ+3DUZOVFAqFQqFQKBRFgsqIKhQKhUKhUCiKBJURVSgUCoVCoVAUCSoQVSgUCoVCoVAUBVI+OY9vUoGoQqFQKBQKRUlCZUQVCoVCoVAoFEWCmqykUCgUCoVCoSgSVEZUoVAoFAqFQlEkqIyoQqFQKBQKhaJIUBlRhSJ/1PN0o9fk/ugsdOxYvZm/v/w9y3ZLa0veC/Tjmfo1SE66ybLhc4mPumbcbu/qyMeb5rJ+3ho2Ll9P5RquDF7kb9zu+JQTf8xdzeavgx/bMd3jo08D2b5rP/Z2Ffn9uyWPvX5TBn08iMat3UlLTWP+mHmcOXYmm03NF2oyao4/1qWtObg1jGVTlgHQ/8P+eLTz4O7ddGIuxDB/7DxSbqTg2dWLNwa/YSxfrW41RvmM5NyJc3nS+N7UgTRs7c6d1DQWjZ3HuWNns9nUqF+TYXNGYl26FBFbw/h66nIAbCrY4L/4A5yqOhEbFUug7+ek3EihnG05fGePwPkZF+6k3SEoYAGXTl3EwcURv7mjqFjJDqmXbPphA8Er1+dJN0B9TzfemtwfobXl4Bza8vtaW05JSubL4YHER12j+ou16PvZYACEEPwxbw3hG/YDUMa2LP1nDqXKs08jpWTlB0GcCT+VZ42ZecHTjbcnv4fOQse21Zv535e/ZdM7KHAE1bRzL2h4IHFR16jXqgE9xr2DhZUlGXfT+enTVfyz5xgAFlaW9Pn4fZ5rVg+9lKyd/QNhf+/Nt9bCaBcA9ZrVp9/k97G0suRGwg2m9PwQAJ/+nWnXuz1CCEJ+3Mj/vl6XL/0Dpg6kkXbuLRo7j7O56PfT9IdvDeMrTX9zn5b09O9N1VpVGddlLGeOngag1ou1GfrZMMDQblbP+5F9G/Lu6/5TB9KodWPSUtNYPHb+A3w8QtN4kJVZfBxApapOXIuKJdB3Fik3UmjV1ZOuQwzXh9u3brN84pdc+Oc8AD79X6VtJh8Hf533c6+BZ0P6TBmAzkLH1p9CWP/lr1m2W1pbMjRwJNVfqEly4k0WDP+CuKhr2FQsz8glAdRsUIvtv2zlm8nLjWXGfTuJik52WFha8O/+f1g5aRmyOAZ9T1BGVL1HS/HYETodb00bwPx+M5js7Y9Hl5a41KqaxaZVjzbcup7MRC8/Qr76k27j38myvcekvhwLjTAuXz17hWk+AUzzCWD6q+O4c/sOEdqP+uOmq483SwI/KZK6TWnc2h3Xaq4MfnkQi8cvYugM3xztfGcMY9H4RQx+eRCu1Vxp7NUYgEM7DjHMexgjOvhx+dxlug97E4Btv4cystMIRnYaQeCoOcRGxeY5CG3YujEu1V3x8xzMkgmLGfTJ0BztBs4YytIJi/HzHIxLdVcaejUCoKtvd47uOoyf1xCO7jrM677dAXhj+JucP3GOMR1HsHD0XN6bOhCAjIwMvv3ka0a1HcaErgF07OND1dpP5Um70Ol4Z9r7zO03g4+8/WnapRWuJm35pR5tSbmewgQvPzZ+9Sdvam358smLTOs8jqk+AQT2+YQ+MwajszBckt+a8h5Htx1iYtuRTOk0liuno/KkLye9faYNZE6/GUzwHkWzHPS+3KMtKdeT+cBrOBu++pMe498F4GbiTeYO+IyPOo5m2ZiFDJ57/1WzXYZ340b8dca18ePDdiP5d9/xfGstrHZR1rYc738yhM/f/wR/7+HM8TW84vepOk/Trnd7xncZw5iOI2jc1h3nai551t9I0z/sIfoHzxjKlxMWM8xE/8VTF5g1+DNOmPjy4skLBHQezRifUUzvO5Uhn/oa2425GHzsgp/nEJZOWMzAXH08hKUTgvDzHIJLdRfcjD7uxtFdRxjhNZSju47Q1bcbALGXrjKlx4eM7TiSXxasZrAWOD9V52na9m7PhC5jGdtxJI3bNsmzj4VOR//pg5jVdzoB7UbQoksrqtTO2pa9erYj5XoKoz19+eur9fQe3weAu2l3+OWLH/l+xrfZ9rtg2BdM6DSaD7xHYutgS7NXWuRJX6Gj15v/KaaoQDSfCCGStb/VhBBvFWI9FYUQ8UIIoS03F0JIIURVbbmCECJBCJHrdyqEGCKE6POQevoJIRblsu3D/BzDPaq71eLahRjiLsWScTedA+t34dbePYuNW/sm7F67DYCDwXt5rkX9LNviLsZyJfJSjvuv27I+1y7EkHA5riDkmo272wtUsC1fJHWb0qx9U7as3QLAyYiTlLMth52TXRYbOyc7ytqU4WT4vwBsWbuFZh2aARCxIwJ9huECdjL8JI7OjtnqePk1T7b/sS3PGpt4NyV07VYAIiNOUta2HBVNNFZ0sqOsTVlOhZ8EIHTtVpq0b6aV9yBUO8bQtVto0r4pAFVrP8XRXYcBuHLmMpWqOlHBsSJJsYnGrM/tlFQun47CvrJDnrTXcKtF7IUYrmlted/6Xbi1b5LFpmH7JuxeGwpAWPAe6rZ4AYA7t+8YfWtVyhopDe+KL21ThjoeddmxejMAGXfTSb1xK0/6ctJ79UIM1y5d1fTupJGJ3kbtPdip6T0QvIfnNb0Xj58jKTYRgMunLmFVyhpLa0On2ktvtmF9kCEbJaUkOfFmvrUWVrt46bWX2ff3HuKuGK4PN+KvA1C11lOcijhp/F5O7DtO0w7N86zfI5P+Uw8498qY6G+q6b98OoorZy9n22/2dpNniTTx9mCb0cenKJeLjzNr3LZ2Kx6aLw3f0X0fe2jaTx3815h9jgw/iYOL4fyqUqsqkRGnMvn4GB7atcZcarnV5ur5aGK1trxn/U4ae3tksXH39mCHdnz7gndTv2UDANJS0zgZ9g930+5k229qcioAFpYWWFpZGs/LYkchBaJCiI5CiJNCiNNCiPE5bH9aCLFVCBEhhDgihPDJ76GoQLTgqAYUWiAqpUwCYoC62qoWQIT2F6AZsE/K3PP1UsolUspV+ZBRIIFoxcr2JFyJNy4nRidQ0SQQqFjZnkTth0KfoSf15i1s7MpjXaYUHYd0Zf38n3Pdf5POLdm/bldBSC3xODg7EBd9PyCPj4nHwdkhu03M/e8jLgcbAO+e3hwMDcu2/qXOL7Htj+350hh/5f6wi4SYeBxM2oNDZQfiY+4fR0J0nFFjRS24BEiKTaSCY0UALpw4T9NOhkCi1ou1qVTFKdtxVarqRLV6NYg8dDJP2g1t+b6uxOh47Crb52qTuS0D1HCrzfSNc5m2YQ7/99Ey9Bl6Kj1dmZvxN3jvi2FM+d9s+s0cgnWZUnnSZ4qdid6E6ATsTHxt9wC993Dv1IwLx8+RfiedsrZlAeg2pjcf/zmbYYvHYOtYId9aC6tduFSvgk0FGz7+aQaf/xmI5xutAUMG8nmPethULI91aWsatm6Mg2v2G69Hxd7ZgbhM+uNj4rPd8Nib6I+PjsM+h3PPlNpudZi3aRFzNyxg6cQgY2CaF43xVzJfH+Jy0Xj/+hAfHW/UWMGxQhYf5/S9t+nlTURoOACXTl2krsfzRh83at0Yxzz62M7ZnvjozN99fDbf2WU6Pn2Gnls3b1He7uFJgvGrJrMk/BtSU1LZF7wnT/oKHak3//MQhBAWwGKgE/A80FsI8byJ2UfAGillQ6AXEJTfQ1GBaMExE3hJCHFICOEvhLAQQswWQhzQ7hoGAwghvIQQ24QQa4QQp4QQM4UQbwsh9gshjgohaj6gjl3cDzxbAHNNlndrddQUQvwthBWTVSgAACAASURBVDgohNghhHhOWz9VCDFW+7+JpmuPpvNYpnpctfKRQohZmv1MoIx2fN+bChNCDBJChAkhwv69mX2MUVbbHFaa3HWKHIyklHTx70HIV3+Sdut2jvu2sLLkxXbuhBXXi8djJ2c/ZrXIySbrco/hPchIzyD0t9As6+u41SEtNY2Lpy4UpMTsWYhHsTHhty9/oZytDbOD59Gp36ucO36WjIz7byMpXbY0Y5eM55tpK4xZEHPJrZ0+qs3ZQ5FMau/P9C7j8Rn6OpalrLCwsOCZ+jUI/W4jH78SQFpqGq8MfT1P+gpaL0CV2k/Rc/y7fPOhYfyzzsICB1dHIsP+ZcqrAZwOP0WvD/sWgNjsqwqiXVhYWlCjfi0+7T+NT96dQvcRPXGp7srl01H8vuRXJn8/jY9WfcyFE+fQp+f97TU5Xeey+9p8/QCRh04xyns4H3QZwxu+3bEqZZVHjTkKKBCNAPWav0Cbnu347jNDF/jl01H8seRXJn3/MRNXTeX8ifNkpOctiM75uvUo2h++75l9puHb5D2srK2op/UIFDsKJyPqAZyWUp6VUt4BfgJeM7GRgK32fwXgSn4PRU1WKjjGA2OllK+CITADrkspmwghSgG7hBAbNdsXMWQ2E4CzwAoppYcQYiTgB4zKpY7dwMvACqAG8DMwWNvWAvhM+38ZMERKGSmEaIrhjqWNyb5WAoOklLu1IDMzbkBDIA04KYRYKKUcL4QYLqV0y0mYlHKZVi8Dq735wFM9MSYBe9f7d652LvYkxSaY2MRj5+pIYkwCOgsdZcqXJSUpmRputWns04xuE96hrG05pF5yN+0uW1f9DUB9LzcuHjvHzbjrD5LwROPT5xU69O4AQOSRSBxd7mccHJwdSLia1ddxMXE4ZsokODo7kHD1fgakTfc2NGnrwUe9J2ar6+UuL+epW75jHx/a9moPwJkjkTi4VgL+AQxZmgST9mDI5N4/DnsXR+NxJMUlUdHJjqTYRCo62XE9LgkwdLEFBSwwlgnauZzYS1cBQzAydsl4dvy+jX1/5/2mJTEmHvtMGR07FwdjhsjUxrQtZyb6zGXSUtOoWudpEmLiSYyJ5+yhSADCgvfiM7RrnjVmJsFEr30O517CA/TaOdszYukHLBu9gNiLBl8mJ94k7dZtDm7YB8CB4N149mybJ32Po13ER8dxM+EGaalppKWmcWL/carVrU70uStsWb2JLas3AfBWwLtZspWPqt9b03/6SCSOmfQ7ODuQ+BD9Di6OJJqcnw/i8ukobqfe5uk6zxgnMz2MDn18aNfLW9N4OkvW18HZMRcf378+OLg4GDVej7uexcc3Ml13n37uGYZ8PoxP+04jOen+UI0tq0PYsjoEgN4B72TJtppDQkw8Di6Zv3uHbL5LiI7HwdWRhJh4dBY6ypYvm0XLg7ibdpeDmw7g3t6DYzsP50ljCaQKkHnMWxTQ1MRmKrBRCOEHlAPa5bdSlREtPNoDfYQQh4B9gANQW9t2QEoZLaVMA84A9wLUoxi6+HNjF9BCCFEdOC+lvA0IIYQN0BjYr/3fAvhZq3spkGU0uBCiIlBeSrlbW/WDST2bpZTXtf2fAJ4x89gfyPnDp3Gq5oJjVScsrCxp0rklhzdl7fI9tCmMFt08AWjs04yTuw0J21k9JjOh1TAmtBpGyNf/I3jxr8YgFMCjSyv2r99ZkHJLHMGr/mecSLR3wx7adDPcgzzb8Flu3bxFommgFJtIakoqzzZ8FoA23dqwd6MhqGjk2YhuQ7szfcA00m6nZSknhKDlK63Yvt78bvm/VwUT4DOKAJ9R7N+4D69uhu7R2ppG02AuSdNYW9Po1a01BzYZNIaF7MdLO0avbm04sMkwSa2sbTksrQz32u16teef/ceNmU/fWX5EnY7izxV/mK09M+cOn6ZyprbctHNLDm06kMXG0Ja9AHD3ac6/Wlt2rOpknGTiUMURlxquxEXFcuNaEglX4nGu4QrA8y1f4EpkwUxWyq63FREm517EpgO00vQ28WnOP5resrZlGb1yIj/P+p7Ig1mHMkRsDuO5ZvU0vQ24nMv47YfxONrFgU37qOvxPDoLHdalrantVoeo0wa9tg6GrmVHV0eadmzOTjOHnPy9KpgxPqMYY6K/zgPOvdspqdTJpH+/pj83nJ6qbGw3lapUokqNKsRGXX1kjRtWBRPg40+Ajz8HNu7F0+jjOty6mfIAH9cBwLNba6Mvs/vYoN3R1ZGApRNY6D+P6HNZE2amPt6Vx2E9Zw5H4lzdhUpPGdpy886tOGhy7h0MOcBL2vE19WnB8d1HH7jPUmVLG8fI6ix0uLVuxJUzBXPuFTh56JrP3HOpfQaZ7DXH/kqT5d7AN1LKqoAP8H8PmpvyKKiMaOEhAD8p5YYsK4XwwpBpvIc+07KeB3wnWobTDugM3EvjHAT6A+eklMlCCFsgKbfMZSZtDyKzvowHacoL+gw9P0z+ilGrJiIsdOxas5UrkVF08e/JhaNnOBwSxs41WxgQ6MeM0IWkJCWzzG/uQ/drXdqa51s14LsPlxWkXLMJmDKTAxFHSEq6Qduu7+A74F26de5QJFrCtoTh3tqdZTuWGx7fNHaecdv8vxYwspNh5nPQxKBMj286yMGthuBk8PQhWFlbMf17w1MATkacJOjDxQDUa1qfuOg4rl589B/BnAjfEkaj1o1ZtH0paalpBI29n8WcHTyPAB9DB8HyiV9qj+mxJiI0nIitBwH4LWgtY4I+oG1Pb+KuXGPOUMMs6Kq1quIX6I8+Q0/U6UvG7Ohz7nXx7NaGC/+cZ3awwR8/zP4/4/7MQZ+h57vJKxi96iN0Fjp2rtnClcgouvr35PzRMxwKCWP7ms0MDBzBZ1pbXqq15dpNnsNn6OtkpKcj9ZL/m7TcOMnn+6lfMWjeSCysLLl26Spfj12cR+9m1/t/k1cQsGoSOgsd29ds4XLkJV7378X5o6eJ0PQOChzBrNBFpCQlE6TpbdenE5WfcabLiO50GWGYgT773WncjL/BmpnfMThwBG9Pfo8bCddZEZB/vYXVLi6fjiJiWzhzNixA6iWbf9rEpVMXAQhYMh4bu/Jk3M1gxeQlxgk3eeGgpj9I078ok/45wfMYo+lfOvFL7fFN1oSHhhOu6W/aoRnvfzwIW/sKTFw5mXMnzjK9z1Tqutfldd9JZNxNR0rJso+WcDOPk8PCtxykYWt3Fm5fwp3UNBaPXWjcNjt4LgE+hkfiLZ+4RHt8kzWHTHw8OiiANj3bEXflGoFDZwHQfWQvbOzKM3C6ocMuI0PP+M5jABi7ZBzl7WxJv5vOislL8+xjfYaebyYvZ/yqKegsdISu2czlyEt0H92bs0dOEx5ygNDVIfjOHUXgtiBSkpJZOHyOsfz8nUspU74MllaWNG7vwcx3P+Zm4k3GrJiAlbUVOgsdx3cfJeS7DQ9QUYTkYRZ85p7LXIgCMj9CpCrZu94HAB21/e0RQpQGHIFYswVpiGI7I6yEIIRIllLaCCEaA4FSSk9t/SAMdwtvSinvCiHqAJeBJmTtwg/VlsO0INW4LZf6fgdeAPpJKXcIIXoDnwDBUko/zWY3MFdK+bM2y76BlPKwEGIqkCyl/EIbE/q+lHKvEOJToIuUsr4Qoh/gLqUcru3rT+ALKWWoECIRcJJS3n2QTx7WNV/cCAr7vKglmMUbjUY83KiYUSp/N8xFgq2wLmoJZpGRLXFR/El58KWkWFJ8H4KTMxYPzTsUP6xKYGftDxd+e6yOTv31U7NP+DJvfPhAjUIIS+AU0BZDvHIAeEtKeTyTzV/AainlN0KIusBmoIrMRzBZ8r7t4ssRIF0IcVgI4Y9hHOcJIFwL+pZSMJnFXRjuWO71p+3BMF50dyabt4EBQojDwHGyDzYGw13NMiHEHgwZ0kcZVLkMOJLTZCWFQqFQKBSPiUKYrCSlTAeGAxswDG5eI6U8LoSYJoToopmNAQZq8cWPGJJi+boLVhnR/yhCCBsp5b1noI4HXKSUIwti3yojWriojOjjQWVECx+VES18VEb08fDYM6JrppmfEe0xuVg2BjVG9L/LK0KICRjawAWgX9HKUSgUCoVC8Ug8QUlEFYgWQ4QQE4E3TVb/LKWcUVB1SClXA6sLan8KhUKhUCgeE8X4lZ3mogLRYogWcBZY0KlQKBQKheIJQgWiCoVCoVAoFIoi4RFe2VlSUIGoQqFQKBQKRUlCZUQVCoVCoVAoFEWCmqykUCgUCoVCoSgSVEZUocidJHmnqCWYRUl7Luev4QseblTM8HUfV9QSzGZPat7el15UvFDauaglmI1FCXxepEVRC/gPoCuBzz597KhAVKFQKBQKhUJRJKjJSgqFQqFQKBSKokDq1RhRhUKhUCgUCkVR8AR1zZe8AToKhUKhUCgUiicClRFVKBQKhUKhKEmoMaIKhUKhUCgUiiJBjRFVKBQKhUKhUBQJT9AYURWIKhQKhUKhUJQkVCCqUOSf/lMH0qh1Y9JS01g8dj7njp3NZlOjfk2GzRmBdelShG89yMqpywGwqWCD/+IAKlV14lpULIG+s0i5kWIsV7NBLT79fRZzh3/B3uDdBaZ50MeDaNzanbTUNOaPmceZY2ey2dR8oSaj5vhjXdqag1vDWDZlmeF4P+yPRzsP7t5NJ+ZCDPPHziPlRgqeXb14Y/AbxvLV6lZjlM9Izp04V2C6H8ZHnwayfdd+7O0q8vt3Sx5bvabU83Sj1+T+6Cx07Fi9mb+//D3LdktrS94L9OOZ+jVITrrJsuFziY+6Ztxu7+rIx5vmsn7eGjYuX0/lGq4MXuRv3O74lBN/zF3N5q+DC+0YJswYzUttm3M7NY2JI6bzz9GT2WxGTBhClzc7YVuxPB412mTb7v1qa+Z+9Rk92/fj+OF/863pRc+G9JnyPjoLHVt/2sS6L3/Nst3S2hLfwFFUf6EmyYk3mT/8C+KiYgF4zbcbXj3boc/Q8+3U5RzZfgiAsrblGPT5MKrWeRqQLA1YRGT4SZr6tKC7fy9ca1VlUpcAzh7Nfo7kRX8/Tf+WnzbxRw76hwWOosYLNbmp6b+m6e/q243Wmv5vpi7n8PZDWJWyYuqaGVhZW6GztGBf8G5+nvtTidLpN9+fGi/UIiM9ndOHI1k+4Usy0jOKVC/AkNnDadTGnRvx1xnbfmSW/XXs9wod+viQkZFBxJaDfP/Zt2bpNaWBZ0PenfIeOgsdoT+FsP7L37LpHxI4kuov1OBm4k0WDZ9DXNQ1bCraMGJJADUa1GL7L1tZNXmFsUzTV1vy2vBu6Cx0HNpykJ8++798aSw0nqBXfKpZ84oioWHrxrhUd8HPcwhLJyxm4CdDc7QbOGMISycE4ec5BJfqLrh5NQIMF8Gju44wwmsoR3cdoatvN2MZnU7HOxP6cmh7RIFqbtzaHddqrgx+eRCLxy9i6AzfHO18Zwxj0fhFDH55EK7VXGns1RiAQzsOMcx7GCM6+HH53GW6D3sTgG2/hzKy0whGdhpB4Kg5xEbFPtYgFKCrjzdLAj95rHWaInQ63po2gPn9ZjDZ2x+PLi1xqVU1i02rHm24dT2ZiV5+hHz1J93Gv5Nle49JfTkWev97v3r2CtN8ApjmE8D0V8dx5/YdIjbsL7RjeKltc56u/hQ+zd5k6tjPmDTrgxztQjfuoFfH93LcVrZcWd5+vweHDx4rEE1Cp6P/9MF83ncaY9v50aLLS1SpndWvrXt6k3I9GX/PoQR/tY63xvcBoErtqjTv3IoAbz9m9v2Y9z4ZgtAZfjb6ThnA4W3hjG07nHEd/bl8OgqAS6cuEjh4Jv/uO1Fg+t+bPpjP+k5jdDs/Wuagv42mf2QO+lt0bsUYbz8+zaT/btpdpvWezAed/BnXyZ8XPRtRu2GdEqVzx+/b8W8zjLHtR2Jdypo2vbyLXC/Atp+38Fnfadnqq9e8Pu7eHgR0HMlY7xGsX/Z7Nhtz9fedPpBZfT/hg3YjadblJVxN9Hv1bEfK9WTGeA7j76/W00vTfzftLr988SM/zMgaCNtUtKH3h3347K2pjPceRQXHitRr+UK+dBYaer35n2LKEx2ICiGStb/VhBBvFXJdU4UQl4UQh7TPzIfY9xNCuD7ExkoIMVMIESmEOCaE2C+E6JRHfV2FEM/npaxWvkB92MTbg21rtwIQGXGKcrblqOhkl8WmopMdZWzKcirckFHatnYrHu2bauWbErp2CwCha7fg0b6ZsVzHfq+w96893Ii7XlByAWjWvilbtDpPRpyknG057Ew02znZUdamDCfDDVmsLWu30KyDQVvEjgj0GYaLwcnwkzg6O2ar4+XXPNn+x7YC1f0ouLu9QAXb8o+93sxUd6vFtQsxxF2KJeNuOgfW78KtvXsWG7f2Tdi91uCfg8F7ea5F/Szb4i7GciUy51dz1m1Zn2sXYki4HFdox9C648us+9mQbT1y8DjlbW1wdHLIZnfk4HHiYuNz3Iff+EGsXPwdd24XzKtya7nVJuZ8NLGXrpJxN50963fi7t00i01jbw+2a+fjvuDd1G/ZAAB376bsWb+T9DvpXLsUS8z5aGq51aaMTRmea1qPrT+FAJBxN51bWo/EldNRRJ+9UiDa7+m/mkn/7vU7aWKi3z3T9WRvJv1NvJuyO5P+q5p+gLRbtwGwsLTA0soCmc8M0+PWeWjrQeN+Tx+OxN4lezsrCr3/7D9BclJytvq83+nEH0FrSb+TDsCN+Pxdn2u61eLq+Wiuafr3rt9JY2+PLDaNvJuwQ9O/P3iPMahMS03jVNi/3E27m8Xe6WlnYs5d4WbCDQCO7TxCk07N86Wz0NBL8z/FlCc6EM1ENaBQA1GNuVJKN+0z/iG2/YAHBqLAdMAFqC+lrA90BvIaLXQF8hyIUsA+tHd2IP7K/YAgPiYO+8pZL6T2lR2Ij7n/Yx0fHY+9s8GmgmMFkmITAUiKTcTWsYJWxp6mHZqx6bu/C0qqEQdnB+KiM2uOx8HZIbtNJs1xOdgAePf05mBoWLb1L3V+iW1/bC9A1SWHipXtSbhy33eJ0QlUNGkTFSvbk6i1G32GntSbt7CxK491mVJ0HNKV9fN/znX/TTq3ZP+6XYUjXqOySyViLscal69Gx1LZpdIjl3+ufh2cXSuzbVPB6bRztic+c7uNjsfO2T6Ljb2zvfF81GfouXXzFuXtymcrmxBjKOv0tDM34q8z5IsRfBYcyMDPh1GqTKkC05xNWz70m56z9lpZodPxefBclod/y5Edhzl9KLJE6rSwtODlN7w4HGpeD1Bh6c0Nl+quPOfxPJ/8Pospqz+hZoNaZuk1xc7ZgYTo+9eLhBz02zk7GK8p9/Tb2OX+ExpzPhrXmlVxrFoJnYWOxh08zA7wHxtSb/6nmPJfCURnAi9pmUp/IYSFEGK2EOKAEOKIEGIwgBDCSwixTQixRghxSstGvq1lIo8KIWqaW7EQYrJWzzEhxDJhoDvgDnyvaSqTQ7mywEDAT0qZBiClvCqlXKNt761pOiaE+DxTuWQhxAwhxGEhxF4hRGUhRAugCzBbq6+m9vlbCHFQCLFDCPGcVv4bIcQCIcRuIcRZTWs2H5rrhxyOL/tKk4xEziYPvqvrN+V9vpv5LfpC6YbILshUj8jRJutyj+E9yEjPIPS30Czr67jVIS01jYunLuRbaUkkp+87e5vI+Tvo4t+DkK/+NGaPTLGwsuTFdu6EBe8pCKm5kvP3/2iZCCEE46aNYvbUBYWuCRNJOfs1t/MULCx0VK9fk03f/cUEn9Gk3bpNl0zDYwqSR9GfU+PJTf+9r0Pq9Yzz8Wdos/ep5Vabp+o8XSJ1DvhkMP/sO8G/B8wbClFYenPDwlJHuQo2fNT1A7779FtGBQWYoTY7OV0usrfrnGxyF3rrRgorJy5l+KIxTPplBnFR19CnF9MA7gnKiP5XJiuNB8ZKKV8FEEIMAq5LKZsIIUoBu4QQGzXbF4G6QAJwFlghpfQQQowE/IBRD6jHXwhxb9DaOCnlBmCRlHKaVu//Aa9KKX8RQgzXNGVPixmoBVyUUt4w3aB16X8ONAYSgY1CiK5Syt+BcsBeKeVEIcQsYKCU8hMhxDrgTynlL9o+NgNDpJSRQoimQBBwb9aEC9AKeA5YB/xi6sMcNA0CBgE0sm9ADZtq2Ww69PGhnTaO6fSR0zi43u+adnB2JCE2IYu9acbRwcWBxKsGm+tx16noZEdSbCIVneyM3fA1G9Ri1MKxANja29KwdWMy0jM4sHFfTrIfik+fV+jQuwMAkUcicXTJrNmBhKtZNcfFxOGYSbOjswMJV+/ftbfp3oYmbT34qPfEbHW93OXlIumWLy4kxiRg73rfd3Yu9iSZtInEmHjsXB1JjElAZ6GjTPmypCQlU8OtNo19mtFtwjuUtS2H1Evupt1l6ypDZry+lxsXj53jZgEP1wDo1b8b3d95DYBjh/7BuYqTcVtlFydiYx5tKEA5m7LUeq4GK38NAsDRyZ6Fq2bj1ycgXxOWEmLiccjcbjOdR/eIj47HwdWRhJh4dBY6ypYvS3LSTRKis5a1dzaUjY+JJyE6njNadm5f8B5e832DwiD+EfQnPEC/6TlrWvbWjRRO7DnGi14NuXTqYonS2X1kT2ztKzBnwgNHghWJ3mz1Rcez/++9AJw5HIleLylvb2vsBjeXhJj4LNlK+1z027s6mOjPPmwgMxGbw4jYbPhZbt3bG32GeRPAHheyGI/5NJf/SkbUlPZAHyHEIWAf4ADU1rYdkFJGa1nIM8C9APUohu7pB5G5a36Dtq61EGKfEOIohkCvXgHobwKESimvSSnTge+Bl7Vtd4A/tf8P5qRZCGEDtAB+1nywFEPweY/fpZR6KeUJoPKjCJJSLpNSuksp3XMKQgE2rAomwMefAB9/Dmzci2e31gDUbliHWzdTjF3t90iKTSQ1JdU4ON+zW2sObDJMNAkL2Y9XN0Pc7NWtDQc2GQLNYa0GGT97g3ezYtLSPAehAMGr/mecSLR3wx7aaHU+2/BZbt28RaKJ5kRN87MNnwWgTbc27NXqb+TZiG5DuzN9wDTSbqdlKSeEoOUrrdi+/r/ZLQ9w/vBpnKq54FjVCQsrS5p0bsnhTVnv0w5tCqNFN08AGvs04+Ruw4SeWT0mM6HVMCa0GkbI1/8jePGvxiAUwKNLK/av31koun9auZbubfvQvW0ftvy1jS5v+gDQoHE9km8m5zoW1JTkmym89HxHOjR5nQ5NXufIweP5DkLB8KPvXN2FSk8Z/Nq8cysObso6YetgyH5e1s7Hpj4tOL77qGH9pv0079wKS2tLKj3lhHN1F04fiuT6tSTio+NwqWEYXVS/ZQOichmbm19M9bfo3IowE/1hIfuN15NmmfSHbdpPixz0l7e3paxtOQCsSllTv9WLXDl9uUTpbNOrHQ08GzLfb06exrcWht4HcWDjPuq1MIzRdKnuiqWVZZ6DUICzh09n0d+scyvCNx3IYhMecoCXNP0ePs05oel/ELYOhmFeZW3L0e7djoRq46CLHSojWuIRGLq8N2RZKYQXkDlC0Gda1mOmv4QQpTFkGt2llJeEEFOB0o9Y/DTwtBCivJTyZg76c+OuvH9VyshFsw5IklK65bKPzD54UF15JnzLQRq2dmfh9iXcSU1j8diFxm2zg+cS4GPo/V8+cYn2+CZrDoWGE6EN0P8taC2jgwJo07MdcVeuETh0VmHIzELYljDcW7uzbMdyw+Obxs4zbpv/1wJGdhoBQNDEoEyPbzrIwa2GYGrw9CFYWVsx/XvD7PSTEScJ+nAxAPWa1icuOo6rF68W+nHkRMCUmRyIOEJS0g3adn0H3wHv0q1zh8eqQZ+h54fJXzFq1USEhY5da7ZyJTKKLv49uXD0DIdDwti5ZgsDAv2YEbqQlKRklvnNfeh+rUtb83yrBnz34bJCP4btIbt5qW0L/tr3C6mpt5k08v6TCH7ZvIrubQ2zdkdPGo7PG+0pXaY0IRHr+PX7dQR9sSK33eYLfYaebyYvZ8KqKegsLAhdE0JU5CW6j+7NuSOnORhygNDVIfjOHcXcbV+SnHSThcPnABAVeYm9/9vFFyGLyEjPYOWkZcZMzDdTljN8/mgsrSy5evEqS8cahhS4d2hKv48HYmtfgQ9WTuL8iXPM7PNxvvR/PXk5H5rof3N0b85q+reuDmH43FHM1/TPz6R/z/92MSdkEfr0DL7W9Ns52eEbOBKdTodOJ9jz5y7Ct+TWOVU8db4/YyjXLl/jk98MI7P2/72HtQvWFKlegBELRvN88/qUt7MlaO8Kfp77E1tXh7B1zWaGzh7OFxvnk343naAx8/Pt728nr+CDVZPRWejYtmYzlyMv0W10L84dOUN4yAG2rd7MkLkjmbNtMclJySwaHmgsP3fnEsqUL4OllSXu7Zsy892PuRIZxbtT3uPp56sB8Nv8NcSci86XzkKjGI/5NBeR35mCxRkhRLKU0kYI0RgIlFJ6ausHAT7Am1LKu0KIOsBlDJnGzF34odpymBakPqhreiqQLKX8ItO6isBJDFlJC2Av8IuUcqoQYr2maesD9M8CKgGDpZR3hBAuQFtgs7ave13zG4CFUso/7h2zVr47hqEA/YQQC4FwKeVKbdtuDBncn4VhwE8DKeVhIcQ3ZO3Cz9GHD+LNZ14rUY3qtiyeXS+58Wt4wY4hfBz4uo8raglmsye1cDJ8hcULpZ2LWoLZPDk/pYqCxKoEdtZ+d+HXQkna5EbKtLfN/p0tN/n7x6rxUSl533beOAKkaxN4/IEVwAkgXAhxDEPXdIFnh6WUScByDN36vwOZ+w2+AZbkNllJ4yPgGnBC0/k7cE1KGQ1MALYChzEEmH88RM5PQIAQIkKbdPU2MEAIcRg4Drz2kPKmPlQoFAqFQlEUPEHPEX2iM6KKokFlRAsXlRF9PKiMaOFTfH8aFUWJyog+nJTJvczPiE77qVhmByLVdQAAIABJREFURP+rY0QVCoVCoVAoSiZP0BhRFYiaiRBiIvCmyeqfpZQz8rHP34DqJqvHmU6mUigUCoVCoXiSUIGomWgBZ56Dzlz2+XpB7k+hUCgUCsUTTDF+HJO5qEBUoVAoFAqFogTxJD3QXgWiCoVCoVAoFCUJlRFVKBQKhUKhUBQJKhBVKBQKhUKhUBQJata8QpE7FYRVUUswC0nJurMsic/kDAr7vKglmM3rjfyKWoJZpJewdgzwW3T+XqtZFLRyqlvUEsziGcsKRS3BbA7dLqav1SxOFFJGVAjREZiP4W2QK6SUM3Ow6QFMBSRwWEr5Vn7qVIGoQqFQKBQKRQlCFkIgKoSwABYD3kAUcEAIsU5KeSKTTW0Mb3ZsKaVMFEI45bfekvf6AoVCoVAoFIr/Mnpp/ufheACnpZRnpZR3MLwa3PT13wOBxVLKRAApZWx+D0UFogqFQqFQKBQliTy8a14IMUgIEZbpM8hkr1WAzO82jtLWZaYOUEcIsUsIsVfrys8XqmteoVAoFAqFoiSRh655KeX/s3fecVEc7x9/zx3FgiigCGqMPSZRg4rYSMCuJBoTkphm7wUUlcTeTYwF7D3RGFM08ZtiQuxiLyD2JLZoYgGRpoKIwM3vjzvPAw6FAwTym/frdS9ud5/d+ezcs8Ozz+zMrgRWPsbE3LvoMxdkBdQGvIEqwD4hRD0pZUKuBZkcUKFQKBQKhUJRXCiYwUrXgGdMlqsAN8zYHJZSpgKXhRDn0AemYZYWqrrmFQqFQqFQKIoRUspcf3JAGFBbCFFdCGEDvAv8ksnmJ6AVgBCiPPqu+r/zci4qI6pQKBQKhUJRnCiAjKiUMk0IMQzYin76pi+klGeFENOAcCnlL4Zt7YUQfwDpQKCUMjYv5apAVKFQKBQKhaI4UUDziEopQ4CQTOsmmXyXwEjDJ19QgaiiUHjRy433JvVGo9Wwb8NOfl/2U4btVjZW9A3y49l6NUhMSGTFsCBir90ybnesVJ5p24P5Zf73bFul7znoNXsIDVo35m7sbSZ3yLdrhD5T+tOwlTsPklNYPHo+l89k7YWoUa8mQ+cNx6aELcd3h/PFlFUA2JW1I2DJRzhXcSb6WjRBQz4j6U4Spe1LM2SOPy7PuvIg5QFLAxdy9fy/OLmWxy94BOUqOCB1ku3fbCVkzWaLtb/o5ca7JvW8xUw99zHW811WDgvOUs9Ttwezef5Gtq3aTMUalRi4OMC4vfwzzvwcvIGdX2Rot54KEz4JYu+Bozg6lOOn9cufevmmDJg6EPdW7qQkpzB/VDCXzlzKYlOzfi0C5gVgU8KG8N3hrJy8AoAPR31I0/bNkDpJQmwC80cFE3czjvrN6jNh9URuXr0JwMEtB/luwbcWa+w9pT+NWjUmJTmFJaMXPMaP/bEpYUvE7mOsyeDHgVSo4syta9EEDZlN0p0kKtWszNC5/lR/sSbfzl3P5pV6/7K2tWbaxk+wsrFGa6XlcMhBNgZbrj0zwUHT6NSxNfeSk+nbN4DjJ85k2F6yZAk2fLuSGjWfJT09nd9+28648Z8CULVqZVavDKJ8BUfi4xLo0cuf69cLZgJ1v2lDadbag/vJKcwKmM2FMxez2NSpX5sxwR9hW8KGw7uOsmjSEgAmLZ1A1ZpVALCztyPxTiL9OgzCpUpFvgz9gquX9IOb/4j4k6CxC/Kstb6XGx9M6oNGq2HPhp38tuzHDNutbKwYEORPNUNbsXRYEDHXbvGiZwPe+fhDtNZWpKem8d0n6/jzkP73aNbFk9eGvAkS4qPjWDFiAYnxd/OsNTs+nhGAZ5vm3E++z8ThM/jr9PksNsPGDKTz2x2xL1eG5jXbGte/3aMr3Xr7kp6eTnJSMtMCP+Pv81cKTGteKYh5RAsL9Yyo4qkjNBo+mNaP+b1mMrFdAB5dPHGtVSWDjec7bUi6ncQ4bz+2f/4rb435MMP2bhN7cSb0RIZ1B37YzfyeM/JVa8NWjXGtXgk/r4EsH7uEATMGm7XrP3MwK8Yuwc9rIK7VK9HQuxEAXYe8xekDJ/HzHsTpAyd5Y8hbALw57G2u/HGZUR39WTQymD5T+gOQnp7OlzO+YESboYztGkjHHj5Uqf2M2TKfhNBoeH9aXxb0msmkdgF4dGlppp5bc+92IuO9/djx+a/4Zqrndyb25EzocePyzb9vMM0nkGk+gUx/7WMe3H/A8a1HLdKXV7r6tGN5UP7+3pbg3sqdStUqMeCV/iwes4ghM4eatRs6cwiLxyxiwCv9qVStEo29GwOwacUm/DoMw7+TH2E7j/Le8PeM+5wNO4t/Jz/8O/nlKQjV+7Erfl6DWDF2Cf2z9eNBrBi7FD+vQbhWd8XN6Me+nD5wCn/vwZw+cIquQ3wBSExI5IvJq9i8KuMNTmpKKlPfm0hgpxEEdhqBm1cjajesY7F+Uzp1bE3tWtWp+4Ingwd/zJLFn5q1CwpeTr36Xrg36UCL5k3o2KEVALM/m8RXX/9Ao8btmDFzPjNnjM0XXZlp2tqDKtUr84FnT+Z9HEzAp8PN2gV8Opy5HwXxgWdPqlSvjEerJgBMGzKDfh0G0a/DIPaE7GPv7/uN+9y4csO4LT+CUKHR0GNaf+b1msnYdiNo1sWTSpnailfeaUPS7UQ+8h7G1s9/5Z0x3QG4G3+X4L6fMqHjSFaOWsTAYH8ANFoNH0zqw6z3JjOh00iu/vkPbXt2yrPW7PBs05yqNarQufk7TBv9GRM+CzRrt2fbfj7o1C/L+pD/beOtVt3p1rYXa5Z8zegp/gWmNV8omHlECwUViOYAIUSi4W81IUSeXmX1hHK8hRCHMq2zEkLcFEK4CiGmCSHaPmb/QUKIHobvoUIIdzM2XYQQYwzfpwghRhu+G48thBghhCiVn+dmSnW3WkT/E0XM1WjSU9M4uvkAbu2bZLBxa9+Eg5tCATgWcoi6Lepn2Hbr35vcuHA1wz4Xjv5J0u3EfNXapF1TQjft1h//+DlK2ZemnLNDBptyzg6UsivF+YhzAIRu2k2T9s0M+3sQummXYf0umrRvCkCV2s9w+sBJAG5cuk6FKs6ULV+OhOh4Y6bqflIy1y9ew7Gik0Xaq7vV4pZJPYdtPoBb+4wuoa/nPQAcCzlM3Rb1MmyL+Tc6Sz0/5PmW9bj1TxRx12Ms0pdX3N3qU9a+TKGUbUrT9s3YZfiNzx0/R2n70jhk8hEHZwdK2pXir4i/ANi1aRfNOjQHIDkx2WhXolSJnA4qyBVN2nmwx+jH5ymdjR+XNPHjPZt242HwV/118MiPPQz+fSf2NpdOXSQtNS1Lmffv3QdAa6VFa60lv06rc+cOfPX1DwAcORpB2XJlcXHJ+HKX5OT7hO45CEBqaioRx09TubIrAM8/X5tdu/RB3e7QA3Tp3D5/hGWiZfsWbP1hO6DPWtrZ2+Ho7JjBxtHZkdJ2pfgj4k8Atv6wHc8OLbMcq1VnL3b+vLtAdALUcKvFzX+iuHX1JumpaRzZvJ9GmdrkRu092G9ok8NCDvGCoU3+9+xlEqLjAbh+/irWtjZY2VghhAABtqVKAFCyTEkSbsYX2Dm06vAymzduAeB0xFnK2NtR3jlr23k64iwx0VkfaUxKvGf8XrJUyWL36ufijApEc0c1oMACUWAvUEUIUc1kXVvgjJQyUko5SUq5I7udpZTLpZTrHleAlPIXc++OzXTsEUCBBaIOFR2Jv/EoeImPjMWhomO2Nrp0Hcl372HnUAabkrZ0GtSVzQu+Lyh5GXBycSL2xqOu6rioWJwyBYZOFZ2IjXp0PnGRMTi56G3KGYJLgIToeMqWLwfAP39coWknfSBS66XaVKjsbNznIRWqOFPtxRpcOHHOIu3lKjoSd+NRgxsfGUe5TNrLPaaeOz6hnpt0bsnRXw5YpO2/hJOLEzGRj3wkNiomy2/p5OJEbFRstjbdA3uw5vBavLt6s37eeuP6uo3qsmjLIqZ8OZWqdaparNHRxYlYk2suNiomyw2OY8VMGiNjcTRoLFu+bAY/ti//5PeXazQa5oQE83nEOk7tO8HFE1m7SS2hciUXrl19NKPM9WuRVK7kkq192bL2vPZqO3bt1gefp079wZtv+ADQtWsn7O3L4OjokO3+llLBpTy3TNqOW5G3qOBSPqtNZMxjbRo0rU/8rXiuX75uXOdS1YVVW5Yz/4d51PeoR15xqOhI3A3TNiwOh0z+YWpj2laY4t6pGf+cvUzagzTS09L5csJKZm4JYsHR1VSu9Qx7NuzMs9bscHatwM0bN43LNyNv4exaIVfH6Nb7TX49/D0BE4fw2fjg/JaYv+gs+BRRVCCaO2YBLwshTgghAoQQWiHEHCFEmBDilBBiIBgzm3uEEBuFEOeFELOEEB8IIY4KIU4LIWqaO7iUUgd8D3QzWf0u8K3huGuFEG8Zvs8SQvxhKHeuYZ0xw2ngQyHEQSHEGSGEh8GmlxBiceayHx5bCOEPVAJ2CyF2CyH6CiGCTez6CyGCLK9CQGSdMzdLFigbm9cDurH9819JMWRbChwz0/tm1ZoDm0z8uOwHStvbMSdkPp16vcbls3+Tnp5u3F6iVAlGLx/D2mmrM2TMcoOZKiRzWkpkU89dAt5hx2PqWWttxUtt3QkPOWR2+/8nhBkHyOoiZp3E+PWrOevo3awXoT+F8lqvzgBcPHORPs1749fRj1/XbmbCqgmWazTnDFl8wZyJ5VkhnU5HoE8AA5v1pZZbHZ7JQyBtSnY+aw6tVsvXXy1h8ZIvuHz5XwA++ng6r7zSjLCjW3nl5WZcuxZJWlrWjG4+CH2yzhzYtHm9dYZsaGx0HN08PqB/x0EsnbqciYvHUcoub3mDnNTpk2wq136GbmO6s3ac/nltrZWW1h92YNKroxnu0Y+rf/1D5yFv5EnnY8mFX2THhjX/47VmbzN/xlL6B/TKJ2EFg9TJXH+KKmqwUu4YA4yWUr4GYHg91m0pZRMhhC1wQAixzWD7EvA8EId+jq3VUkoPIcRwwA991tEc36J/88FnhmP6AAGmBkIIR+ANoK6UUgohymVzrNJSyhZCiFeAL4An3jpLKRcKIUYCraSUMUKI0sApIcRHhglsewMDM+9nqIsBAC0dG1K3TI1sy4iPisWh0qO7fgdXJ2O2JbNNfFQcGq2GkmVKkZSQSHW32jT2acZbY7tTyr40UqcjNeUBu9dtedKp5ZiOPXxo866+u+7SqQs4VaoA6LvOHF2ciIuOy2AfGxWLk0kWw9G1PHE39TYJMQmUc3YgITqecs4O3I7Rv3wiOTGZpYELjfss3b+KaMOgFK2VltHLx7Dvpz0c2WJ5oBcfFYdjpUdZDQdXRxIyac+unmsY6tl37IeGepakpqQa67metxv/nrnM3ZjbFusrzrza41U6vKd/s92FU+cpb5J5cXIpT9zNjF1/MZkyoE4u5Ym9mfG3AAj9KZQpa6fwTdDXGW5AwneHM3jGEOwd7LkTfydHGjv08KHtu+0AuHjqIk4m15yTS/ls/NhEo6sT8QaNt2NuZ/DjO7n43e/dSeLsodO4eTfi6vl/c7yfKYMH9aRv3w8ACA8/QZVnKhm3Va7iyo3Im2b3W75sNhcuXmbhotXGdZGRN3n7Hf0z2aVLl+LNN17lzp38GUDTtWcXXntfn2396+R5KlR65BcVXCsQk8kvbkXeooJr+WxttFoNL3fyZKDPo2d6Ux+kkvogFYDzpy9w459InqlRhXOnLM84x0XF4ljJtA3L2lY8tMncVgA4uDjiv+IjVo5cSPS/+t+i6gvVAIzLR387yKuD8zcQ7db7Td78oAsAZ0/8RcVKFY3bKrpW4FaUZY8NbflpB+Ozeca0yFCEA8vcojKieaM90EMIcQI4Ajihf8MAQJihOz0FuAQ8DFBPo+/iN4uUMgywE0I8B3RC/waDzA/W3AHuA6uFEG8C9zDPt4Zj7gXsHxOwZouUMgnYBbwmhKgLWEspT5uxWymldJdSuj8uCAW4cvIiFau5Ur6KM1prKzw6t+Tk9owvZTi5PZwWvt4ANPZpzl8H9aMwZ78zkTGeQxjjOYQdX/zGb0t+zNcgFGDLuhACfUYQ6DOCo9uO4O2rH+RQu+Fz3Lt7L0vQnBAdT3JSMrUbPgeAt28rwrYfASB8x1G8fVsb1rcmbLt+YE8p+9JYWevvA9u+254/j541Bh5DZvtx7eI1fl39c57O48rJizib1HOTzi05uT08g82J7eG08PUCoLFPM84Z63kSYz2HMtZzKDu++I2QJf/LUM8eXTw5unk//1/5bd1vxkFEh7YeprXhN36u4XPcu5tEfOYbK4OPPGfwkda+rTmy7TAAlao9CqqatmvGtUvXAChX4VF3cZ2X6iA0IsdBKMDWdSEE+gQQ6BNA2LbDeBn9uA737iY9xo/1g4q8fFsZ/TWrHx95bNn2jvaUsi8NgI2tDQ08X+L6xWs51p6ZZcu/xL1Je9ybtOeXX7bS/QP9oL+mHo24c/sOUVHRWfaZNvUjypYtw8hRkzOsd3JyMGb3xnzsx9ovv7NYV2Z++vIX4yCi/VsO0OEt/Y3AC42eJ+luUpbgPy46jnuJybzQ6HkAOrzVjgPbDhq3N365Mf9e+jdD931Zx7JoNPp/3a5VXalcvTI3/s3bqP/Lmdrkpp09OZ6prTi+PQxPQ5vcxKc5fxrailL2pRi5Zjzfz/6aC8cePUYUHxVH5drPUMbRHoAXPRtwIw8+YI4Na/5Ht7a96Na2F7u37KXzO/qbw/qNXiTxbpLZZ0Gzo2r1R4OzXmnbgn8vm382vsjwH+qaVxnRvCEAPynl1gwrhfAGUkxW6UyWdTy53r9D3yX/PIZg0hTDpLMeQBuD3TCgtZnjZL5lsvQWajUwDvgLWGPhMYzo0nV8M2k1I9ZNQKPVcGDjLm5cuMbrAd24cvoSJ3eEs2/jTvoF+fNJ6CKSEhJZ4ffk53X6LxzBc81exM6hDLMPreCX4A3s37grT1ojdoXTqFVjFu9dQUpyCktHP8pizgmZT6CPPrG9avwyw/RNNhwPjeD47mMA/Lh0E6OWfkSbbu2IuXGLeYM/A6BKrSr4BQWgS9dx7eJVY3a0rvvzePm25p8/rzAnZD4A38z5yni83KCv588ZsW48QqvhwMbd3LhwjS4B3fjHUM/7N+6ib5AfMw31vDIH9WxTwoYXPBuwftzjXllc8AROnkXY8VMkJNyhTdcPGdK3O76dOzx1HeG7wnBv5c6qfav10zeNflSHC39fhH8nPwCWjl9imL7JlmO7wwnfrf9H33NML6rUrIxOJ7l1PZolY/XT93j6tKRTdx90aemk3H/A7GGzLdYYsesYDVu5s2jvch4kp7Bk9CLjtjkhwQT66DtdVo1fbpi+yYYTmfx45NJAWndrS8yNWwQN1mspV6EcszbPo6RdKaROx6t9OhPQdhjlnB0YFjQCjUaD0AgO/XqAiF3hWYVZQMjvO+nYsTXn/jzAveRk+vV7NFVbeNg23Ju0p3JlV8aNHc6ff10g7Ki+eV66dA1frPkWL68WzJw+Folk377D+PmPzxddmTm86whNW3vw9f51pNxP4bORc4zbVm9dTr8OgwAIHreAMUGB2JSw5WjoUY7sejQLResu3uz6KeMgpZeaNaD3qJ6kp6ejS9cRNGY+dxPyltHVpev4atJqAtdNRKPVsHfjLq5fuMobAe9y5fRFju8IZ+/GnQwI8md26GKSEhJZamgr2vboRMVnXeji/xZd/PU3CHO6TyMhOp6fFmxk3MbppKemE3P9FqtM/C6/2bfjIJ5tmvPr4e+5n3yfSSNmGrdt2LGWbm17ATBi4hB83mhPiZIl2BbxE//7ZjPL537Ou33eotkr7qSmpnH39l0m+hf+jByPoyh3tecWURAjNP9rCCESpZR2QojGQJCU0suwfgD6rvO3pZSpQog6wHWgCRm78EMNy+GGINW4LZvyXgB+BsoC1Q1ZSYQQa4FfgS1AKSlltKGb/qKU0lEIMQVIlFLONZT5l5RykBDCE1gmpawvhOgFuEsph2WyXwv8KqX8QQhxGugipbxsoikCqAA0MJOhzUC/am8VK6dKkA8KW0KucBC2hS0h1ywN/6ywJeSaNxr5FbaEXFFSFL+8wo+R+ROcPk08nZ8vbAm54lmrJw8sK2qcuF8w87oWJCejDpp7Kr/AiPf1zvX/WYdNoU9VY04pfi1X4XIKSBNCnATWAgvQd7NHCH1fzy2ga14LkVL+IYS4Bxx7GIRmogzwsxCiBPqsbIAZG4B4IcRBwB7okwsJK4HfhRCRUspWhnUbAbcnBaEKhUKhUCgKlv9SRlQFojlASmln+JuKvjvclHGGjymhhs/D/b1NvmfY9pgyXzKzrpfJooeZ7VPMlZnJZi36IDqzfS+T74uAzH0onkARn89CoVAoFIr/BxThZz5zixqspHgsQohyQojzQLKUsuAmgVMoFAqFQpEjpC73n6KKyogWEkKI8cDbmVZ/L6Wcac6+sJBSJgD5824+hUKhUCgUeacIB5a5RQWihYQh4CxSQadCoVAoFIqiT1HOcOYWFYgqFAqFQqFQFCdUIKpQKBQKhUKhKAxURlShUCgUCoVCUSioQFShUCgUCoVCUSioQFSheAy3ZWphS8gV9sKmsCXkikPJRfwdyGYobm8pAvgxouBeR1gQDHAPLGwJucbXtUlhS8g1xW3Ow3syrbAl5Jr6ti6FLaHoI4vkS5IsQgWiCoVCoVAoFMWI/1JGtLjd3CkUCoVCoVAo/iOojKhCoVAoFApFMULqVNe8QqFQKBQKhaIQ+C91zatAVKFQKBQKhaIYIdVgJYVCoVAoFApFYaAyogqFQqFQKBSKQkE9I6pQKBQKhUKhKBSkLGwF+YcKRBUKhUKhUCiKESojqlBYSO8p/WnUqjEpySksGb2Ay2f+zmJTo15Nhs7zx6aELRG7j7FmyioA7MraEbAkkApVnLl1LZqgIbNJupNk3K9mg1p88tNsgofN5XDIQQDKVyrPoM+G4VSpPEj4pNc0bl2Ltkh7PS833p/UG6HVsG/DTkKW/ZRhu5WNFf2C/Hi2Xg2SEhJZNiyI2Gu3qP5SLXp+OhAAIQQ/z99IxNajAJS0L0XvWYOp/FxVpJSs+WgplyLOW6QvJ4ydOZKX2zTnfnIK4/2n8+fpc1ls/McOosvbnbAvVwaPGq2zbG/3WiuCP/+Ubu17cfbkX/muccDUgbi3ciclOYX5o4K5dOZSFpua9WsRMC8AmxI2hO8OZ+XkFQB8OOpDmrZvhtRJEmITmD8qmLibcdRvVp8Jqydy8+pNAA5uOch3C77Nd+2PY8InQew9cBRHh3L8tH75Uy07M3pf7oNGq2Hvhp2ELPsxw3YrGyv6B/nzbL0aJCbczeDLvT4dpDcSgp/nbyBi61GsbK0Zu2E6VrbWaLVawn8/xE/BG/Kk8SWvhvSa3A+NVsOu77bz87L/ZdE4NGgENerX5G78XRYMm2u8trsO8aVVt7bo0nWsnbKKk3tPYG1rzZSNM7G2sUZjpeVIyEG+D/4OgMFz/Xmh2Yvcu3MPgKWjF/LPH5fzrL+HQf/u77bzixn9Q4JGUL1+TRIN+mMM+l8f4ou3Qf+XU1Zxau8JABbuX0lyUjK6dB269HTGdx6dJ41QMG2yezsP3h31AVKnIz1dx9qpq/kr/E8Axn85mdoN6/BX+J/M6jMjT9obeDWk+2S9H4d+t4PNZvx4UNBwqtevwd34uyweNo+Ya7ewK2eH//JAajSoxd4fdrNu0moASpQuwcTvZxr3d3R14sCPe1k/7Ys86SwIVCCqsAghRKKU0k4IUQ1oIaX8poDKGQ+8bVisD5w2fP9CSrmwIMrMCQ1bNca1uit+XoOo3bAO/WcMZlzXrK8l7D9zECvGLuV8xDnGfTkJN+9GnAiNoOsQX04fOMVPyzbRdbAvXYf48vWsdQBoNBo+HNuTE3uPZzjWsKAR/G/x95zaf5ISpUqg01n2hLfQaPhwWj/mfTiNuKg4Jv0yixPbw7lx8ZrR5uV32pB0O4mx3n54dG7J22M+ZPmwYK6f+5dpnT9Gl66jbIVyTP19Hid2hKNL1/H+5D6c3nOCpUPmobW2wqZkwb1u9OU2zala/Rl8mr1Ng8YvMnH2R7zfqW8Wu9Bt+/jm8+8JOfx9lm2lSpfig37vcPLYmQLR6N7KnUrVKjHglf481/A5hswcyqjXR2axGzpzCIvHLOKviL+Y8uVUGns35ljoMTat2MT6eesB6Ny7M+8Nf48l45YAcDbsLNN6Ty0Q3Tmhq0873vftwrjpcwtNA+h9ufu0/sz9cBpxUbFM+uUzTmwPM+PLiYzxHoZH55a8M6Y7y4YFcf3cv0zt/JHRl6f9HsSJHeGkpaQy+/0ppNy7j9ZKy9gfZnAqNIK/j1+wWGOf6QOZ+cFkYqNi+fSXOYTvOMr1C480tu7WjqTbiQz3GkyLzp68P6YHC4bNpXLtKrTo7Mmodn44VHRkwtfTGOE9hNSUVKa9N8moceoPn3IiNIILx/U3fus/WcuRkEN5q1wT/b2nD+QTg/6Zv8zhWCb9rQz6A7wG09ygf6FBf/POngQa9I//ehoB3kOQhrZrxrsTuBt/N190FlSbfObAKUZv199sV637LCOXfMSINkMB+Hnlj9iWsKXdBx3ypF1oNPSc3p9ZH0wlLiqWab/M5tiOMG6Y1LF3t7Yk3U5klNdQmnVuybtjerB42DxSU1L5Ye63VHmuKlWeq2q0v590n/E+o4zL03+dQ9iWw3nSWVD8l7rm1ZuVCodqwPsFdXAp5UwppZuU0g1Ifvi9MINQgCbtPNizaTcAF46fp7R9aco5O2SwKefsQEm7UpyP0Gfq9mzajUf7pob9mxK6aRcAoZt24dG+mXG/jr1e5fDvh7gTc9u4rkrtZ9BaaTm1/yQA9+/d58H9BxZpr+FWi+h/orh1NZr01DSObD6AW/uM78lu2L4JBzeFAhAecojnW9Q3IJriAAAgAElEQVQH4MH9B+jS9f9ErG1tkIYWpIRdSep4PM++DTsBSE9NI9mQkSkIWnV8hV++DwHg1LGzlLG3o7yzUxa7U8fOEhMda/YYfmMGsGbJeovr8Uk0bd+MXYbf+Nzxc5S2L41DJh9xMPjIXxH6bOyuTbto1qE5AMmJyUa7EqVKGOu6KODuVp+y9mUKW4aJL98kPTWNo5v30zCTLzdq78GBXPgyQMq9+wBorbRYWVlBHqq+llttbl6JJNqg8eDm/TRp1zSDjbtJe3I45CD1WjYA9O3Ewc37SXuQxq2r0dy8Ekktt9pZNVprC8w/arnVJspE/6HN+3HPpL9xOw/2GvQfMdHv3q4ph0z0R5noz28Kqk2+b6hnMFyHJs5w5sApkpMeXaeWUtOtFjevRBr9+PDm/TRu55HBplG7JuwznN/RkEO82FLvxynJKZwP/4vUlNRsj1+xmiv2TmU5d/SPPGstCKRO5PpTVFGBaOEwC3hZCHFCCBEghNAKIeYIIcKEEKeEEAMBhBDeQog9QoiNQojzQohZQogPhBBHhRCnhRA1c1OoEKKcEOJvIYSVyfJlQ/n7hRDzhRCHDMd2N9jYCSHWGso8LoTobOlJO7o4EXsjxrgcGxWDY8WMgZBjRSdiox4FQbGRsTi66G3Kli9LQnQ8AAnR8diXL2vYx5GmHZqxff2WDMdyrV6JpDtJjF4xhtkhwXQf1wuNxjKXL1fRkTgT7fGRsThUdMzWRpeuI/nuPewc9IFHDbfaTN8WzLSt8/hqwkp06ToqVK3I3dg79Jk7lMm/zaHXrEHYlLS1SF9OqOhagajrjx5LuBkZTUXXCjnev269OrhUqsie7QcKQh4ATi5OxETeMi7HRsXg5OKUxSaDj2Sy6R7YgzWH1+Ld1duYHQWo26gui7YsYsqXU6lapyr/X3HI5MtxkXE4ZLoOn+TLM7bNZ/rWINZNWGEMTIVGw9SQuSw49gVn95/k7xOWZUMBHF0ciY00aSsiY3FwccxqY6Lx3t17lHEog4OLIzGm+0bF4mjYV2g0fBYSzKqILzm17yQXTTS+O/pDZm+ZT4+JfbCyyVtnoUMe9ZvuGxf1aF+JZOz6Kcz8dR6t32ufJ416DQXTJgN4dGjG/J1LGLtmIssCF+VZa2YcXJyIi3ykK85MHTu4OBF3Q2/zsI4f+vGTaN7Fk8O/Flxbl1ekFLn+FFVUIFo4jAH2GbKUwUBf4LaUsgnQBOgvhKhusH0JGI6+i707UEdK6QGsBvxyU6iUMgE4AHQ0rHof2CilTDcs20opmxvKW21YNwnYYiizNTBPCFEi87GFEAOEEOFCiPC/E6+YLV8IMxdCpoyEeZPHZy16Te7H+llfZul211ppeb7JC6ybsYYxnUfhXLUi3m9nfeYxJ5jTnlnX42z+PnGBie0DmN5lDD6D3zA+S/dsvRqErt/G1FcDSUlO4dXBb1ikLycInnwO2e4rBB9PG8GcKQWbVDev8ck2pkZfzVlH72a9CP0plNd66e+bLp65SJ/mvfHr6MevazczYdWEfNVdrMgHX57QfgTTunzMq4PfxMrWWr9dp2Oyz2hGNh9A9ZdqU7nOM5ZLNPsbZzYy7yvmtWPU+LFPAIOb9aOWW22eMdyQfDv7KwJaD2Vcl9HYlbPj9UFvWqw9p/qz02m+ndT/mfLmGMa9OorPek6jfY9O1PV4IW86C6hNBji69TAj2gxldv9P6DbqA0slZovZsCpLHZuzyVmb17yLJ4d+3pdbWU8Nqcv9p6iiAtGiQXughxDiBHAEcAIe9sWESSkjpZQpwCVgm2H9afRd/LllNdDb8L03sMZk27cAUspdgLMQws6gbbxB226gBJAlnSSlXCmldJdSuteweySrQw8f5oQEMydEP2jEqVJ54zYnl/LERcdlOE5sVGyG7JaTqxPxN/U2t2NuG7uNyjk7GLvhazaoxYhFo1myfyXNfFrQb/pAmrRvSmxkDJfP/k301Zvo0nWEbT1C9Xq5SiIbiY+KxdFEu4OrkzETYM5Go9VQskwpkhISM9hEXrpOSnIKVepUJS4qlvioWGPmKDzkMFXrVSc/ebe3Lz/sXMcPO9cRfTMGl8rOxm0VXZ2Jjop5zN6PKG1Xilp1a7Dmf0vZGvYjDRq/yKJ1c3jxpbp51vhqj1dZ+PsiFv6+iLjoWMqbZGmdXMoTdzPjYwIxmTKgTi7lib2Z0Y8AQn8KpWWnFoC+y/5hd2H47nC0VlbYO9jnWXtxJLMvO7o6kpDpOsytL5uSfOce5w6fob5XQ4s1xkbF4uRq0laYtAMPiYuMNbYnGq2GUmVKkZhwl7jIWMqb7uuSdd97d5L449AZXvLWa3x4Lac9SCP0+13UzGNXeFwO9Mc+Rr/pvo4m+uMNOu/E3iZs6xGLdD6NNtmUP4/+gcuzLpTJYSYyp8RFxeLo+kiXYzY+4lhJb/OojjP6sTmqPl8NjVbLFTMDt4oKOily/ckJQoiOQohzQoiLQogxj7F7SwghH/ae5gUViBYNBOBn8ixndSnlw4AzxcROZ7Ksw4LBZlLKPUAdIUQrIFVKaTrsOfOtojRo62qiraqUMsfDureuCyHQJ4BAnwDCth3Gy7cVALUb1uHe3aQswVxCdDzJScnUblgHAC/fVoQZHnoP33EUb199RtPbtzVh248AMNRzgPFzOOQgqyeuIGzbES6dvEjpsnbYO+oDjnotGnDtwtWcSs/A5ZMXqVjNlfJVnNFaW9G0c0tObA/LYHNiezgtfL0BcPdpzl8H9QN6yldxRqPVX2pOlcvjWqMSMdeiuXMrgbgbsbjUqATACy3rZ3jQPj/4bs0m3mrTg7fa9GDX73vo8rYPAA0av0ji3cRsnwXNTOLdJF5+oSMdmrxBhyZvcOrYWfx6BObLqPnf1v2Gfyc//Dv5cWjrYVobfuPnGj7HvbtJxn++D4k3+MhzDZ8DoLVva45s0w8oqFStktGuabtmXLukr89yFR4991bnpToIjeBO/J08ay+OXD55EWcTX/bo7Mnx7eEZbI5vD6OliS//adaXK+Bi8OUyjvaUtC8F6J8dfaFlAyIvXbdY46WTF3Cp7kqFZ/QaW3T2JNzQDjwkfMdRY3vSzKcFZw/qx2SGbz9Ki86eWNlYUeEZZ1yqu3LxxAXKONpTyr60UWM9z5e4cVGv0fS5yCbtm3L13L8Wazenv3lnT45l0n9sx1FeMehvaqL/2PajNDej37akLSVK6zujbEva0uAVN65ZoPNptMkuz7oY969erwZW1lb5NsDqIX+fvJihjpt19iQiU5scsSOMlw3n5+HTnD8OnjZ3qCw07+LJoV+KbjYUCqZrXgihBZYAnYAXgPeEEFnS7kKIMoA/+sRZnlGj5guHu4Dp7eFWYLAQYpeUMlUIUQewvBV/MuuBr4HJmdZ3A/YJIbyBm1LKJCHEVvQONxxACNFQSnkcC4jYdYyGrdxZtHc5D5JTWDL60XNDc0KCCfQJAGDV+OWGqUJsOBEawfHdxwD4cekmRi4NpHW3tsTcuEXQ4NmPLU+n0/HVzDVM+mY6QsDfpy+x89ttj90n22Ol61g/aTUj101Ao9Wwf+Mubly4RteAblw5fYkTO8LZu3En/YP8+TR0EUkJiazwCwagdpO6+Ax+g/S0NKRO8tXEVSQaGuWvp3zOgPnD0VpbcevqTb4YvcQifTlh746DvNymBb8f+YHk5PtMHP5o6pQfdq7jrTY9ABg5cRg+b7anRMkS7Dj+C//7+heWzl2d3WHzlfBdYbi3cmfVvtX66ZtGBxu3Lfx9Ef6d9E+jLB2/xDB9ky3HdocTvlsfSPUc04sqNSuj00luXY9myVh9fXr6tKRTdx90aemk3H/A7GGP952CIHDyLMKOnyIh4Q5tun7IkL7d8e2ct5HDlqBL1/H1pNWMWjcRjVbDvo27uHHhKl0D3uXK6YtGXx4Q5M+s0MUkJSSy3OjLz/OqGV+uUvdZ+s0bhkajRWgEYb8d5OSuY3nS+MWkVYxbNxmNVkvoxh1cu3CVt0e+x9+nLnJsRxi7N+xgWPAIFuxZRmLCXRYMmwfAtQtXOfTbAebtWIwuLZ0vJq5E6nQ4ODswJGg4Go0GjUZw6NcDROzS+43fggDsHcsiBFz54zKrxuVtei1duo61k1YxNpP+t0a+x2WD/tANOxgSPIJgg/5FJvoP/3aAuTsWk56WzhqD/rLlyzFypT5BpbXScuDnvZzcY1FTbKSg2uSmnVrg5duK9NQ0HqQ8IHjoHONxp33/CZVrVqFE6RIsP/w5yz5azMm9uT8PXbqOLyet5qN1k9BoNezZuJPrF67iO/JdLp+6RMSOMPZs2Mmg4OHM27OExIREFg8LMu4fvH85JcuUxMraCvf2TZnVfaoxEdD0tRbM6TUzu6KLBAU0+MgDuCil/BtACPEd8DqQecTWdGA2kPf5wwBRlEaV/tcxmb7JGtgClAfWAguAGUBn9BnIW0BXoCEwWkr5mmH/UMNyuCFYNG57UpmZ1lUBzgGuUso7hnX7gb2AN/ogubehnNLAfKAZ+gz6RSnl648r8+1nXy9WTlVGWBe2hFxxNDl/s6ZPg2q2WUfnF3V+jMj/ARYFyQD3rNPuFHXuGR9PLz4Ut27EtLxMX1BI2KItbAm5Zv0//3uqo4H+rO2T6x/2hYu/DwQGmKxaKaVc+XBBCPEW0FFK2c+w3B1oKqUcZmLTEJggpfQ1jUksPA1AZUSfKg8DQillKtAm0+Zxho8poYbPw/29Tb5n2PakMjPhiX6QUua+yY1SygwapJRJQP8nlaNQKBQKhaLoYgg6Vz7GxOzwLuNGITRAMNArP3WpQPT/GUKIZUBbHo2cVygUCoVCUYwooK75a4DpdBdVgBsmy2WAekCoYcYFF+AXIUSXvGRFVSBazMn0FqWHfC+lNPuAi5RycDbrPfNbm0KhUCgUivwnp6Pgc0kYUNswfeR14F1MXr4jpbyN/pFCIOPjgnkpVAWixRxDwFm0n6pWKBQKhUKRbxTEBPVSyjQhxDD0A6i16F8LflYIMQ0Il1L+ku+FogJRhUKhUCgUimJFQY0zl1KGACGZ1k3KxtY7P8pUgahCoVAoFApFMaKAuuYLBRWIKhQKhUKhUBQjivK743OLCkQVCoVCoVAoihH/pSngVSCqyHesi9mUz+nFbMLn+iVcnmxUxCiOk2oXtwniV4bPebJREaNH45GFLeE/j8bs1JBFmyWt/3++fjc3qK55hUKhUCgUCkWhoLrmFQqFQqFQKBSFgsqIKhQKhUKhUCgKheL3sFP2qEBUoVAoFAqFohihMqIKhUKhUCgUikJBPSOqUCgUCoVCoSgUdIUtIB9RgahCoVAoFApFMUIWw2m5skMFogqFQqFQKBTFCN1/aLRS8Zp5XKFQKBQKhULxn0FlRBWFQgOvhvSY3BeNVsPu73awedn/Mmy3srFicNBwqtevSWL8XRYOm0vMtVvYlSvD8OWB1GxQi70/7GbtpFXGfd4J/ICX3/SmdNnS9Hnh/XzXXN/LjQ8m9UGj1bBnw05+W/ZjFs0DgvypVq8GiQl3WTosiJhrt3jRswHvfPwhWmsr0lPT+O6Tdfx56AwAWmsrekztR91mL6KTkk1zviF8y2GLNb7k1ZAek/sZ6nU7v5ip1yFBI4z1umDYXGKuRQPw+hBfvLu1RZeu48spqzi19wQApexLM+CzoVSpUxWQrAhczIWIczT1acFbAe9SqVYVJnYJ5O/TlyzW3XtKfxq1akxKcgpLRi/g8pm/s9jUqFeTofP8sSlhS8TuY6yZov/t7craEbAkkApVnLl1LZqgIbNJupNEpZqVGTrXn+ov1uTbuevZvPInAKxtrZm28ROsbKzRWmk5HHKQjcHfWqy9npcb7xv8Yu+GnYSY8Yv+Qf48a/CLZcOCiL12i+ov1aLXp4P0RkLw8/wNRGw9ipWtNWM3TMfK1hqtVkv474f4KXiDxfrywoRPgth74CiODuX4af3yp1p2QfgygNBo+OTXucRFxTKnz8wMx+w1tT9eb7em9wvvFUn9C/evJDkpGV26Dl16OuM7j7ZIW68p/WhouN6WjV5o9nqrXq8mQ+b5Y1PChuO7j7F2ymoASpe1Y8SS0cbrbf6QOSTdScr2uM++UJ1+MwdS0q4UunQdPy7+nkO/HgBg4Oxh1KxfE4Qg8vINlo5aSMq9+xadk1W9JpR4fwhoNKTu/Z2UkO8ybLdu2Z4S3QYg42MASNn5M6l7f0db9yVKvjfYaKdxrcq9ZTNIO37QIh1PC91/qGteZUQVTx2h0dB7+gBm95xOYFt/WnTxpHLtKhlsvLu1Jel2EiO9hvD755t5b0wPAFJTHvDD3G/5euaXWY4bsSOMia9/VGCae0zrz7xeMxnbbgTNunhSqVZGza+804ak24l85D2MrZ//yjtjugNwN/4uwX0/ZULHkawctYiBwf7GfboM8+VO7G0+bu3HuLbD+evI2Txp7D19IJ/1nMbotn606PJylnpt1a0dSbcTCfAaTMjnv/C+oV4r165C886eBLbzY1bPqfSZMQih0TcPPSf35eSeCEa3GcbHHQO4fvEaAFfP/0vQwFn8deQPizUDNGzVGNfqrvh5DWLF2CX0nzHYrF3/mYNYMXYpfl6DcK3uipt3IwC6DvHl9IFT+HsP5vSBU3Qd4gtAYkIiX0xexeZVP2U4TmpKKlPfm0hgpxEEdhqBm1cjajesY5F2odHQfVp/gnvNZHy7ETQ14xcvG/xijPcwtpn4xfVz/zK180dM9hlNUI/p9Jw5CI1WQ1pKKrPfn8LkTqOY7DOKel5u1GhY2yJ9eaWrTzuWB8146uUWlC8DdOrzmtGHTalRvyal7EsXef0z3p3AWJ8Ai4NQt1aNcanuynCvwawau5S+MwaZtes3cyArxy5luNdgXDJdb2cOnGKE9xDOHDjF64brLbvjPkhOYUnAAka38+fTHlPpObmvsZ7XTfucjzoF8FHHEcTcuEXHnj4WnRNCQ4nufiQFjyNxfF+sm7ZCU6lqFrPUo6EkTh5E4uRBpO79HYD0v04a1yXNDkSm3Cft7DHLdDxFJCLXn6KKCkQLCSFEouFvNSFE/qfvMpY1RQghhRC1TNYFGNa5G5avCCFOCyFOCSH2CCGeNbFNF0KcEEKcEUJ8L4QolRc9tdxqc/NKJNFXb5Kemsahzftp3M4jg417Ow/2bdoNwJGQg9Rr2QCAlOQUzoX/SWrKgyzHvXj8PAnR8XmRli013Gpx858obhk0H9m8n0btm2SwadTeg/2bQgEICznECy3qA/Dv2ctGXdfPX8Xa1gYrG31nxMtvt2bzUn2mREpJYvxdizXWcqtNVKZ6dW/XNINN43Ye7DVTr+7tmnJo837SHqRx62o0UVciqeVWm5J2Janb9EV2f7cDgPTUNO4Zsh83Ll4j8u8bFut9SJN2HuwxaLpw/Dyl7UtTztkhg005ZwdK2pXifMQ5APZs2o1H+6aG/ZsSumkXAKGbduHRvhkAd2Jvc+nURdJS07KUed+QddFaadFaa5EWPm9Vw60W0SZ+cXTzfhqa8YsDBr8IDznE8wa/eHD/Abp0/dhXa1sbpImIFBN9VlZWhTZ7tbtbfcral3nq5RaELwM4ujjRsLU7u7/bnuFYQqPh/fG9+ObTrDe4RUl/ftCknQd7Df74pOvtguF627splCaG683d5Hrds2m3cX12x428fIOoK5EAxEfHcyfmNvaO9gAkJyYby7TJdA3kBm2N59BF30DeioT0NFKPhmLdsGWuj2Pl/gppp8PgQYpFOp4mOgs+RRUViBY+1YACDUQNnAbeNVl+C8icymolpWwAhAITTNYnSyndpJT1gAeA+VvoHOLg4khsZIxxOS4yFkcXp0w2TsTe0Nvo0nXcu3uPMg5P/x+iUU9FR+JumGqOw6GiU7Y2unQdyXfvYZdJs3unZvxz9jJpD9IoZa+P531HvcfUX+cwdMko7MuXtVxjpnqNjYzFwcUxg42ji6PZes3ym0Tp93Wu6sKd2NsMmuvPpyFB9P9sKLYlbS3WaA5Hk98aIDYqBsdMdetY0YnYqNgM5/bQZ8qWL2sM9BOi43NUhxqNhjkhwXwesY5T+05w8cR5i7TnxC/KPcYvarjVZsa2+UzfGsS6CSuMganQaJgaMpcFx77g7P6T/H3igkX6iisF4csAPSb35ZtPvkSXaaRHh54+HNt+NN9uZAtKv0Qydv0UZv46j9bvtbdcW4brLRbHipm0VXQkzuR6izPRX7Z8ObPXW06OW/Ol2ljZWHHznyjjusFz/FgRvpbKtaqwZe1vFp2TcCiPjIs2LuvibiEcnLLYWTd+GbtpKyk1ZBLCsUKW7TZNvUk9sssiDU8blRFV5CezgJcNGccAIYRWCDFHCBFmyE4OBBBCeBsylRuFEOeFELOEEB8IIY4aMpk1n1DOT8DrhmPVAG4Dt7KxPQRUzmbbPqBW5pVCiAFCiHAhRPjFxCuPFSLMXBCZ74SFmWvG0qxVfiDMCMqq+fE2lWs/Q7cx3Vk7Tv+snUarxalSeS6E/8Xk1wK5GHGed8f1tFyjuYYmU52Z12h+PRK0Wg3V69Vk+/rfGeszkpR79+li6IrLL8yXnRN/sNwhdDodgT4BDGzWl1pudXimTtZuvByRR7/4+8QFJrQfwbQuH/Pq4DexsrXWb9fpmOwzmpHNB1D9pdpUrvOMZfqKKQXhyw1bu3Mn9jaXz2R8ltnB2YGmr7Zgq4VBkDkKQj/AlDfHMO7VUXzWcxrte3SirscLudeWTblPsnlSVv5Jxy3n7MCw4BEsG70owzWyLHARgzz6cP3iNVp09nx8IdmX/kS9aScOczfwQxInDSDtjwhK9cv4GJco64imSnXSzoRbqOHpojKiivxkDLDPkHEMBvoCt6WUTYAmQH8hRHWD7UvAcKA+0B2oI6X0AFYDfk8o5w5wVQhRD3gPeNzoh47oA9cMCCGsgE7os6sZkFKulFK6Synda9lVe6yQuKhYnFzLG5cdXZ2IvxmX0SYyFqdKehuNVkOpMqVITLC82zqvxEXF4ljJVLMjCdFx2dpotBpKlilFUkIioM8W+K/4iJUjFxL9700AEuPvknLvPse2HgEgLOQg1erVyJNG03p1MlOvsdnUa1xkpt/ERb9vbFQscZGxXDJk5I6EHKJ6HjQ+pEMPH+aEBDMnJJi4m3FGTQBOLuWJy1S3sVGxOJlkzU3P7XbMbWPXYjlnB+7E3M6xjnt3kjh76LTx+bfcEp8Dv4h/jF88JPLSdVKSUwwDwh6RfOce5w6fob5XQ4v0FVcKwpefc69Lo7ZNWLh/Jf6LRvFiiwYMnT+CavVq4PKsK/P3LGfh/pXYlLQleM+yIqcf9F3boH/sJGzrEWrmsMu+fY9OfBYSzGchwcRnud6ciDdzvZn2UjlmuN4SzF5vpm125uOWtCvJmDUT2DD3ay4cz9r7IHU6Dm7ej0en5jk6nyz7x99CODoblzWOFZAJsRltku5AWioAD/aEoH0243Ph1h5epB07AOnpFml42qhAVFGQtAd6CCFOAEcAJ+BhaxMmpYyUUqYAl4BthvWn0XfxP4nv0HfPdwV+NLN9txAiGmgLfGOyvqRBTzjwL/B5rs4oE5dOXsCluisVnnFGa21F886eHNselsHm2I4wXvZtBUBTnxacPZgl9n2qXD55kYrVXClfRa+5aWdPjm/PeOd8fHsYnr7eADTxac6fB/Uj40vZl2LkmvF8P/trLhw7l3GfneHUbfYiAC+0bMD1C1ct1mi+Xo9msDm24yivmKnXY9uP0ryzJ1Y2VlR4xhmX6q5cPHGB27cSiI2MwbVGJQDqtWzAtTxofMjWdSEE+gQQ6BNA2LbDeBk01W5Yh3t3k7J0kSZEx5OclGwcVOTl24oww7mF7ziKt29rALx9WxO2/chjy7Z3tDcOlrCxtaGB50tmB6/khMsnL+Js4hce2fhFS4NfuJv4Rfkqzmi0+ibYqXIFXGpUIuZaNGUc7SlpeGzD2taGF1o2IPLSdYv0FVcKwpe/m72eYc364e85gIV+8zh78BRLRszn+K5jDG7SG3/PAfh7DuBBcgoBXuYHzBWmftuStpQoXQIA25K2NHjFjWvn/s2Rnm3rfudjnwA+9gkgbNsRXjH44+Out/sm19srvt4ZrreH16uXbyvCTdabO67W2opRK8eyd1Moh0MyjkSv+KyL8Xvjtk24YaGfp18+h9a5MqK8C2itsPbwJjXTqHdR9tFjAlYNm5MembHurJu25kEx6ZaH/1bXvJq+qeghAD8p5dYMK4XwBkyfoNaZLOvI2W+5GZgDhEsp75jpSmkFJAFrgWnASMP6ZCmlW85P4fHo0nWsnbSKMesmo9FqCN24k+sXrvLWyPf4+9RFInaEEbphB0OCRxC0ZylJCYksGjbPuP+C/SsoWaYkVtZWNG7vwazuU7l+4Rrvje1Bi9dfxqakLYsOryL0ux1smp8/097o0nV8NWk1gesm6qfp2biL6xeu8kbAu1w5fZHjO8LZu3EnA4L8mR26mKSERJb6BQPQtkcnKj7rQhf/t+ji/xYAc7pP427sHTbOWs/AIH8+mNSHO3G3WR24JE8a105axdh1k9FotYRu3ME1Q71ePnWRYyb1GrxnGYkJd431eu3CVQ7/doC5OxaTnpbOmokrkTr9PfTayasYtmAkVtZW3Pz3JitGLwTAvUNTek3tj71jWT5aM5Erf1xmVo+pudYdsesYDVu5s2jvcv0I29GLjNvmhAQT6BMAwKrxyw3TN9lwIjSC47v1I1t/XLqJkUsDad2tLTE3bhE0eDYA5SqUY9bmeZS0K4XU6Xi1T2cC2g7TdxEGjUCj0SA0gkO/HiBil2Xdcbp0HV9PWs0og1/s27iLGxeu0tXgFydM/GKWwS+WG/yidpPneXXwG6SnpSF1kq8mriIx/i5V6j5Lv3nD0Gi0CI0g7LeDnNxVOKN4AyfPIlcu290AACAASURBVOz4KRIS7tCm64cM6dsd384dCrzcgvLlp0VB6C9bvhwjV44B9IPYDvy8l5N7juda2/Fdx2jYqjELDNfbMsP1DPBZSDAfG6631eOXM2SeP9YlbDkReowThuvt56X/Y8TSQFp1a0vMjRiCDddbdsdt/lpLnvd4gTLlyuD1lv6Gcenohfz75xWGBg2npF0phIB//rzC6vEWThGm05H89SJKj5qln75p3xZ0N/7BtmtP0q+cJ+3EIWzavYG1W3NIT0cm3SV59Wzj7sKpIhrHCqSfO2VZ+YWArujGlblG5OU5K4XlCCESpZR2QojGQJCU0suwfgDgA7wtpUwVQtQBrqPvph8tpXzNYBdqWA43BKnGbWbKmgIkSinnCiHeBc5LKSMyHeMK4C6ljBFCuKLPstaRUsY91JrTc3v/2TeKlVNZi+LVMfBAFo+uI1PSCmvYdx6wE8XrPn1l+JzClpBrejQe+WQjRZ4oyl2y2bGydeKTjYoYZdfseKqh4c8u7+e6UX096psiGb4Wr5b2v8kpIE0IcRJ9JnIB+m72CKFPWd5C35WeL0gpv8uBTaQQ4ltgKDA9v8pWKBQKhUKRd4rfrX32qEC0kHiYYZRSpgJtMm0eZ/iYEmr4PNzf2+R7hm1mypqSzXrTY1TLtM3P5HuOs6EKhUKhUCgKluKY6c4OFYgqFAqFQqFQFCN05qbYKqaoQPQ/hBBiPPB2ptXfSylnmrNXKBQKhUJR/FBd84oiiSHgVEGnQqFQKBT/YVTXvEKhUCgUCoWiUPgvTd+kAlGFQqFQKBSKYoSuCE9Qn1uK1wSKCoVCoVAoFIr/DCojqsh3onX3CltCriinsS1sCblCWwzvH3+MtOzNRYWJr2uTwpaQK4rj5PDrjgUVtoRcs6Xe+MKWkCvmW8U+2aiIcfNw8QtNyj7l8tRgJYVCoVAoFApFoaCeEVUoFAqFQqFQFAr/pVHzxa+PT6FQKBQKheL/MdKCT04QQnQUQpwTQlwUQowxs32kEOIPIcQpIf6PvfOOr/n6//jz3EwEWSKRGEEUpRIkRrVIJUhLtfr9Gm2tqhEz9mitGqXEqK0DbX+tlg5atZrgayX2HqG2RDYig9x7fn/cj7iZEkKkPc8+7qPu+bzPOa/P+37yue/7PuMj/hJCVH7Sc1GBqEKhUCgUCkUxwiAK/noUQggzYBHQFqgNdBFC1M5idhhoKKV8CVgLzHrSc1GBqEKhUCgUCkUxwvAYr3zgA5yXUv4tpbwH/AC8aWogpQyVUj5YkbwPcHvCU1GBqEKhUCgUCkVx4ikFoq7AVZP317Sy3PgA+LNAwnNALVZSKBQKhUKhKEbIx1g1L4ToA/QxKVoupVxuapJTV7m09R7QEGhecCWZUYGoQqFQKBQKRTHicVbNa0Hn8jxMrgEVTd67ATeyGgkhWgHjgeZSyrTHkJIJNTSvUCgUCoVCUYx4SkPz+wEPIYS7EMIS6AysNzUQQngBy4D2UsroQjgVlRFVFB0DJvfHx9eHtJRUZg2bw/kT57PZeNStzqjgEVhaWxEeEs6iiUsA6Bb0HgFd25IYdwuAr2Z+TXjofszMzRg+KwiPutXRmZmxbd02vl+05ol09pr0IV4tG3IvJY2FI+Zx8cTf2Wyq1qnGgDlDsLS24nDoAb6atAIAm7I2BC0ahZObE9HXogkOnMnd23cBeLFxHXpM6I25hTm3428zsdM4AAJ6tqNVF3+EEGz7fgt/fLU+W3/5pV5zL3pM7I3OTEfID1v5bcnPmY6bW5ozIHgoVetW407CHeYPnE3MNeO9pUNgR1p2aoVBb2DlpBUc3XkECysLJv04DQtLC3TmZoRt3MNPc394bH2PYm7wFNq28SU5JYUPPgji8JETmY6XKGHNmu+XU7VaZfR6PX/8sZVx42cAUKmSK18sD8axnD0J8Yl06zGY69cjC0XXs/Rr/9mDqd34RZJvG9cHLB6xgMunLhZYbzdNb+gPW1mfg97A4KG4161GkqY3VtP7ZmBHWmh6V01awbGdRzLqCZ2O6b/PJj4qjs96TcvUZo/JH9L8P770rN2lQFqflI+mB7Nzdzj2drb8+u3SZ9p3bpRrWY86n3RDmOm48l0o5xdm/puu2jeASu+2RKYbSIu7zdGgZaRciwWg1sddKd/KC4QgZudxTn606qnpHDAlkEa+3qSlpDEraDYROd6TPRg1dwRW1paEhexn0YTFGcc69HyTDj3ao0/XExYSzvJpX/CC5wsMmzkUACFgVfC37N60u9C1l2zWAKdx/UGn49baTSR88WOOdjb+zagw/yMuvzOItJMRlGzqheOwXggLc+T9dGI++4KUsKOFrq+weRpPVpJSpgshBgKbATPgKynlSSHEFOCAlHI98BlgA/wkhAC4IqVs/yT9qoyookjwaemNq7sr3V/pydzR8xkyfVCOdkOmDyZ49Hy6v9ITV3dXvFs0zDi27otf6NcmkH5tAgkP3Q9A8zdexcLKgg/9+hEYMJDX3w2gvFv5x9bp1bIBLu4VGNS8L0vHLqLP1P452n04rT/Lxi5iUPO+uLhXwKtFfQA6BL7D8d1HGdSiH8d3H+WtwHcAKFmmFL2n9mNm76kE+Q1kTuBMACrWqESrLv6MaT+c4W0G0+C1hjhXcXks7UKno9cnfZnRfQrDWg3i5fav4OqReYGjbyc/7t5KYkjz/mz8cj1dx3QDwNXDjabtmjHcbxDTu0+m19R+CJ2O+2n3mdJlAqPaBjG6bRD1mtfHw6vGY+l7FG3b+OJR3Z2atZvRv/9oFi2ckaNd8Nyl1KnbnIberWnaxJs2rVsCMGvmBL75bi31G/gxddo8pk0dWyi6isKv305fyeiAIEYHBBU4CBU6HT0/6cvM7lMY0WoQTXPQ21LTG5SD3ibtmjHSbxCfmuh9QNteb3D9/LVsfVatW42SZUoVSGdh0SHAj6XBU4uk7xzRCerO6ElY15mEvjqCCm81xaZG5vUft05c4n+tx7PDdzSRv4dR6+OuANg19MDeuwbbW45ie4uR2HpWxaFpraci08fXGzd3V7o160nw6HkMmTE4R7uhMwYxd9Q8ujXriZu7Kz4tjY/C9Wxaj6b+TfjQrx8fvNaHH5euBeDSmUv0DxhA39b9GfPeeII+HYLOrJBDD50Op48HcL3PR1xq14cyr7fAslqlbGaiZAls33+TlKOnM8r0Cbe53n8il9/sT9TY2bjMHFm42p4ST2P7JgAp5UYpZQ0pZTUp5TStbIIWhCKlbCWlLC+l9NReTxSEwr84EBVCuAkhfhNCRAghLggh5mup6LzqjMvyPukR9rZCiMBH2FQRQpzI5dh2IUTDnI49CiFECyFEU5P3/YQQ3bR/1xRCHBFCHBZCVBNC7HmcPp6Epv5N2LpuGwCnD5/Bpkwp7J3sM9nYO9lT0qYkpw8Zbxpb123j5dZNs7VlipQS6xLW6Mx0WFlbkn4/neSk5Dzr5IW3XyO2rwsFIOLwWUqWKYWtk10mG1snO0ralOTcobMAbF8Xird/Y62+D9vXhWjlIXj7NwLglTdfJWzTXmJvGLMet7XMrlv1ipw7fJZ7qfcw6A2cCjtJo9ZNHkt7dU8Pbl6KJPrqTfT309mzYRfefo0y2TT082GHdn77Nu6hzssvZZz3ng27SL+XTszVaG5eiqS6pwcAacmpAJiZm2FuYYaUT+epx+3ateab74xfZmHhhyhrWxZnZ6dMNikpqWzfYbx879+/z6HDx3F1NQbutWp5EBKyC4DQ7btp386/UHQVN79W9/QgykTv3g27aJhFbwM/H3ZqesNM9Db0a8ReE71RJnrtnR3w8m1I6A9bM7UldDq6ju/B/814epm7vGjoWZeyZUoXSd85YedVnbsXo0i+Eo28r+fGr3txbp35th63+xT6lHsAJBw8TwkX7V4oQWdlgc7SHDMrC3QW5qTF3HoqOl/2b8qWtcbP8vShvO7JpTil3ZO3rN2acU9u9/4b/LBoDffv3QcgMS4RgLTUNAx648CwpZUlPIX7hfVLL3D/SiT3r0XB/XRub9xBKd/s903HId1I+PInZNr9jLK00xfQx8QDcC/iMsLKEmFhUegaC5unNDRfJPwrA1FhzCf/DPwqpfQAamBMNU/LsyKMe8TxrNgCeQaiT5EWQEbUJqVcKqVcrb3tAPwmpfSSUl6QUuYd3ZkgjDzxdePo7EjMjZiM9zGRsTg6O2SxcSA2MjaLjWPG+ze7t2P5liWMmD0Mm7I2AOz843+kpqTy48Hv+S7sW35atpY7iXceW6eDswNxJjrjo+JwKJ9Zp0N5B+KiHuqMj4zFQTsXW0dbEqMTAEiMTqCsoy0ALu6u2JS1YfIP05j5ezDN3zZm8a6cu0xtnxexsS2NpbUlXi0b4FDBkcfB3tmeOBP/xUXGYedsn91GC4YNegPJd5IpbVcaO2f7TL6Pi4rDXqsrdDpmbpzLikOrOPa/o5w/EvFY+h6FawVnrl19OE/++rVIXCs452pftmwZ3njdj5BQY/B57Ngp3n4rAIAOHdpSpkxp7O3tcq2fX4rCr51HvMesTfPo9nEvzC0LNqPK7gn1mtaNj3pYt9vED/i/6aswGDIHFq27B3Bwa3jGdf9vx9rFjpQbcRnvUyPjsHbJ/Tqs1LUF0SHGoeGEgxHE7TmF/9El+B1dQkzoUZIisq0dKRQcnR3ydU+OiXxoE2ti41bVjbqN6rBwwwKC187mhXoPM/o1vWry5V/L+WLbMuaOXZARmBYW5k4OpEc91JV+MxaLLPdpq1rVMHcux93t4bm2Y+PfjNTTF5D37+dq87ygAtHijy+QKqX8GkBKqQeCgF5CiEAhxMIHhkKI37Xs4qdACS2T+F3WBoUQI4UQ+7XHXk3Wij8Fqml1PnuUKCFECSHED1oba4ASJsf8hRB7hRCHhBA/CSFstPJLQojJWvlxLdtZBegHBGl9vyKEmCSEGCGECACGAr2FEKFaG0km/WQ7Dy1re1oIsRg4ROZVdQ/q9RFCHBBCHLielH2oLrt99rKsGSCRg9EDm/Xf/E63Zj3p2zqQuOh4+n1s3JGipucLGPQGOjXsyvtNu/FOn464VMo9eHm00EfrzJdNFszMzahapzrTe05h6vsTeWdwJ1zcK3D9/DV+XfozE76bwkerJ3P51EUM6frHlJ6TsKxGOfk4N99r/zcYGB0QRP/Gvanu6UHFGtmHwAqDvD7/rJiZmfHdN4tYuOgrLl68AsCo0Z/w6quN2R++mVdfacy1a5Gkp6c/ua5n7NfvZ31DkO8AxrUfgY2tDW/2e7vQ9eamK6dyJHj5NuR23C0unriQ6ZCdkx2NXm/K5pV/FEjjP5pcfJgTrh2bYVuvKhcWbwCgZJXy2Hi4stVrAFs9A3Fo9iL2jWs+M53ZbnV52JiZmWFTtjQD2w1m2dQVfLzkowybM4fP8MFrfQh8fSBdB3bCwqqQM46PulcIQbkxfYmZuSLXJiyrV8ZxeC+iJy4oXG1Piaf1iM+i4N+6WOlF4KBpgZTythDiCrn4REo5RggxUErpmfWYEMIf8MD4VAIBrBdCvAqMAerkVCcX+gPJUsqXhBAvYQz6EEI4Ah8BraSUd4UQo4FhwBStXqyUsr42DWCElLK3EGIpkCSlnK218Zp2HhuzHsvHeVwBXgB6SilzzPCabgvRqmLrHK/59t3bEdClLQDnjp6jXIVyGcfKuTgSdzM+k31MZCyOLo5ZbIyZhcTYxIzyjf/3J1NXGl3h26El+7cfQJ+uJzHuFicPnKLGSzWIvBKVk6QcadMtgNc6G4dxLxyLwKFCOcA4FGXv7EB8dGadcVFxOJhkau1dHInXziUxNhFbJzsSoxOwdbLjlqY7LjKWO/G3SUtJIy0ljVPhJ6lSy53IizcIWbOVkDXGIbKuI9/PlG0tCHFRcTiY+M/BxYGELD6Oj4zDoYIj8VFx6Mx0lCxdkqTEO8RHxmXyvYNz9rrJt+9yau8J6rXw4uq5K4+lMSv9+3Xngw/eBeDAgSO4VayQcczVzYUbkTdzrLd0ySwizl9kwedfZJRFRt7kP//9EIBSpUry9luvc/v242fHH/Cs/fogs5h+L53tP4XwRp9MDzp5JPH50BuXh17Tuvaa3gatvKnfyhvPFg2wsLKgROmSDJg3lD3r/4dzZRfm7TAuErIsYcXcHUsIap7z3Op/A6k34ilR4WF2ztrFgdSo7Nlix1fq4DGkA3venoLhnvEHk0uANwkHI9AnG3fIiQ45il0DD+L3nSkUbW92b0dAV+OowdmjZ3O4J8dlso+JjKWcy0MbRxObmKgYdv1pHI04e+Qs0mCgrH1ZbsU/nEpw5fxVUpNTcX+hCueOFd5ISvrNWMydH+oyL+9Iusl9WleqBFYelam42vg0SjNHO1wXT+J64CTSTkZgXt6RCp9/TNSY2dy/WjgLGp82+Z3zWRz4t2ZEBTn/QMit/FH4a6/DGIPHmhgDuoLyKvAtgJTyGHBMK2+M8bmvu4UQR4DuQGWTeg+WwB4EqjxGvw/I6zwuSyn3PUHbrF+1IWNx0e7Ne/Dr2AqAWl41uXsnOVuAFx8dT8rdZGp5GTMAfh1bsWfLXoBMc5eatWnKpbOXAIi+HoPny8a437qEFbW8anLl/FUKwqbVGxkZMJSRAUMJ3xJGi47GYXMPrxdIvpOcbcgxMTqBlLspeHi9AECLji3ZvzUMgAPbwmnR0Vcr92X/VuOw0P6tYdTyqY3OTIeltSUenjW4puks41AWAMcKjjRq04Rdv+0skP4HXDgagbO7C+UqOmFmYU7Tds04sDXzsNSBbeE0186vcUBTTu45bizfGk7Tds0wtzSnXEUnnN1dOH8kgtL2ZTIWoVhYWVKnWT1unL/+WPpyYsnSVTT09qehtz/r12/m/XeNi7sa+dTn9q3bREVl3y1kyuRRlC1bmmHDJ2Yqd3Cwy8jgjBk9iJWrCmd1/7P2q+mcZG//Rlw9W7CgP6veJu2acTCL3oPbwnlV09vIRO/BreE0yUHvD7O+ZWDj3gxu1ocFg+Zwcs8xFg2dx+GQg/T37sngZn0Y3KwP91LS/tVBKEDikQuUqupMiUrlEBZmVOjQhKgtmfIglKlThZc+683+7rO5F3s7ozzleiwOTWohzHQIczMcmtQi6Vzh/b39tmoDfVv3p2/r/uzetAf/d/wAqFW/Jnfv3M3xnpyclEyt+sZ7sv87fuzeYpyjvXvTHry0e6+buyvmlhbcir+Fc0XnjMVJTq5OuFWtSNTVnH9QPi6px89iUbkC5q7lwcKcMgHNuRv68OvKkJTMhaaduNiqOxdbdSf16JmMIFRXuhSuS6cQG/w1qYdPFaouRf74t2ZETwIdTQuEEGUwDjnfInOAbp2P9gQwQ0q5LEubVR5DW24B8lYpZW77oDzYUFbPk32meZ3H3SdoNxthIeH4+HqzetfXpKWk8dnwORnHlm5aTL82xsTr/HGfMzLYuFVIeOiBjNXxH477gOovVkNKSdS1m8wbYxxO+W3VekbOGc4X25YjBGz+cQsXzxRslbEph0IOUL9lAxbuXEZaShqLRzwctvls4zxGBhi3JVkxfom2fZMlh7cf4nCo8Yvml8XrGL54FK918iP2Rgxz+htXx18/f43DOw4xZ/MCpEHy1w9bM7KKI5eOwcauNPr7er6YsDRju6eCYtAb+GrCCsatnojOzIztP27jWsRV/jOsC38fO8/BbfsJXbONgXOHMn/HEpIS7zB/oPFzuBZxlb1/7GbOtoUY0vV89fFypMGAnZMdgcFD0Ol06HSCvb/v5lDIgcf2b15s/PMv2rTx5ezp3SSnpNC797CMYwf2b6Ghtz+uri6MGzuE02ci2B++GYDFi7/mq6+/p3nzpkz7ZCwSyf/+t49Bg8cXiq5n7ddB84MoY18WIeDSqYusGFewLYkMegMrJ6xgbBa97wzrwkVN7/Y12wicO5S5mt7PTfTu+2M3s7ctRJ+u52tN7/PMyImfsv/wMRITb/Nah/cI/OB9OrZrXWR6pN7AiXErafz9WISZjqvfbyfp7DVeGPUOiUcucnPLQWpP6Ip5KWsarBgCQMr1OPZ3n82NDWE4vvwizUNnAZLokKPc3HroqegMCwmnka8P3+xaSWpqGp8NezhgtmzzEvq2Nv6gmD9uAaOCRxrvydv3Ex5ivCdvWrM5496bfv8+M4caZ6PV8XmRLoFTSE/XIw0GFoz/nNsJt7MLeBL0BmKmLsbti2mg03H75y3cO38Zh0Hvk3oiIlNQmhXbd9tjUakC9v27Yt/fuFvB9d7j0Mc/nUVhhcXz/VdYMMTTWvH6PKMtVtoPLJBSrhZCmAFLgdvAL8AsoBnGZ6yexLhx63YhRALgJKW8r7WTJKW00Ya0PwFek1ImCSFcgfsYA8NDUsrKWTWYaKkC/C6lrCOEGAbU1obW6wBHMGZDL2PMdvpKKc8LIUoCblLKc0KIS0BDKWWstsJ+tpSyhRBiOFBGSjlR62cS2nC86b/zeR4lH2jMj39zG5p/XrHVWRW1hAJhVgwHMtZF7i9qCQWmo4t3UUsoEMXvqoDVB4OLWkKB2VSncH7QPCvmmcc92ug5Y0np4nc11zi96ZkOls+o/F6Bv2fHXv72uRzQL36fdiEgjdH3W8B/hBARwDkgFeOq+N3AReA4MBttnqbGcuBY1sVKUsotwP8Be4UQx4G1QGkpZRzG4fQT+VmsBCwBbIQQx4BRQLjWfgzQA/heO7YP47B5XmwA3nqwWCkffed6Hvmpq1AoFAqF4tlgQBb49bzybx2aR0p5FWiXy+F3c6kzGhht8t7G5N/zgfk51On6CB2XgDrav1MwPlIrJ7sQIFuKRkpZxeTfBzBu24SU8hzwkonp/0zsJmVp45Hn8UCjQqFQKBSKouWfNDT/rw1EFQqFQqFQKIojz29+s+CoQPQZIYSoC3yTpThNStkoJ3uFQqFQKBSKnFAZUUWBkVIeB/K7n6hCoVAoFApFjvyT9hFVgahCoVAoFApFMeJ5XnxUUFQgqlAoFAqFQlGM+OeEoSoQVSgUCoVCoShWqDmiCkUelClmG8QXtz9os6IW8Bg0c6pV1BIKzL9yk+VnTHHbHB6gzYlpRS2hQIQ2HFfUEgrMoKTn+6lGObH5GfenhuYVCoVCoVAoFEXCPycMVYGoQqFQKBQKRbGiuI3k5YUKRBUKhUKhUCiKEWpoXqFQKBQKhUJRJPxzwlAViCoUCoVCoVAUK9TQvEKhUCgUCoWiSJD/oJyo2qFEoVAoFAqFQlEkqIyoQqFQKBQKRTFCDc0rFAqFQqFQKIoEtWpeoSgEPpj0IfVbNiQtJY2FI+bx94m/s9lUrVONQXOGYGltxaHQA3w5aQUATQJeplNQF9yquzG6/QguHD8PQDk3Jxb8tYgbF64DcO7wWZaNX/Jca65ez4P+MwYAIIRgzbzvCdu877H01WvuRY+JvdGZ6Qj5YSu/Lfk503FzS3MGBA+lat1q3Em4w/yBs4m5Fg1Ah8COtOzUCoPewMpJKzi68wgWVhZM+nEaFpYW6MzNCNu4h5/m/gDAoPlBVK1bHX16OuePRrBi7BL06frH0m3KoCkDaOzrQ2pKGp8GzSLixPlsNjXqejBm7iisrC3ZFxLO5xMWATBh8UdUquYGgE0ZG5JuJ9G7dT+c3cqzavtXXL1wFYBTh04TPHb+E2sFo8+7aT4P/WEr63PweWDwUNzrViNJ83ms5vM3AzvSQvP5qkkrOLbzCAALdi0n5W4KBr0Bg17P+HYjCkVrcdb8gHIt61Hnk24IMx1Xvgvl/ML1mY5X7RtApXdbItMNpMXd5mjQMlKuxQJQ6+OulG/lBUIQs/M4Jz9a9VQ0FoSPpgezc3c49na2/Prt0iLTUbN5PTpM6I7OTMe+NSGELMnsVzNLc7oGD6BiHXfuJiaxeuB8Eq7FoDM3o9PMPri96I7O3IwDP+/kr8W/AfBKz7Y07uyLELDvhxB2fvVnoevuP7kfPr7epKakMWfYHM6fuJDNpnrd6owIHoaVtRXhIftZMjGzn9/p25EPP+rNf17qxO2E29iUtWHY7CBcKrtwP+0ec0bM5fLZy4Wu/Un454Shao6oooio37IBLu4VGNC8L0vHLqLP1P452vWd1p8lYxcxoHlfXNwr4NWiPgBXzl1mVt8ZnAo7ma3OzctRDA8YyvCAoYUahD4tzVfOXmZku2EMDxjKJ90n0W96IDqzgv9pCp2OXp/0ZUb3KQxrNYiX27+Cq4dbJhvfTn7cvZXEkOb92fjlerqO6QaAq4cbTds1Y7jfIKZ3n0yvqf0QOh330+4zpcsERrUNYnTbIOo1r4+HVw0A/vfrToJ8BzDCfwiWVpb4dvYrsOasNPL1wc3dlXebdWfO6LkEzRiSo13QjCHMHhXMu8264+buik9LbwCmBE6ld+t+9G7djx0b/8fOP3dl1Llx6UbGscIKQoVOR89P+jKz+xRGtBpE0xx83lLzeVAOPm/Srhkj/QbxqYnPHzC180eMDQgq9ICuOGrOQCeoO6MnYV1nEvrqCCq81RSbGq6ZTG6duMT/Wo9nh+9oIn8Po9bHXQGwa+iBvXcNtrccxfYWI7H1rIpD06J/9GyHAD+WBk8tUg1CJ3h7Si+W9/iUmX7Dqd/+ZcpXz+zXRv9tScqtJKa3GMqOL//gjTFGv3oGNMbc0oLP2owi+I2xNOnaCju3cjjXcKNxZ1/mvTme2W1HU9u3Po5VnAtVt3dLb1zdK9DzlQ+YP3oBg6YPzNFu8PSBzB+9gJ6vfICrewUatmiYcayciyNer3hx89rNjLLOAztx4eQF+vsH8tnQ2fSf1K9QdRcGBmSBX88r+fq2E0K4CSF+E0JECCEuCCHmCyEsH1FnXJb3SY+wtxVCBD7CpooQIkUIccTk1S0P+x5CiAp5tanZbRdCNMyhvIcQYqH2736P6GuSEOIp3X1z7TPXhwgLI7uEEG1Nyv4rhNj0bNTl82pbpwAAIABJREFUjY9fI7avCwWMWctSZUph52SXycbOyY4SNiU5d+gsANvXhdLIvzEA189f48bf1/8Rmu+l3sOgN874sbCyRD7m/aK6pwc3L0USffUm+vvp7NmwC2+/RplsGvr5sEM7h30b91Dn5ZcA8PZrxJ4Nu0i/l07M1WhuXoqkuqcHAGnJqQCYmZthbmGG1AQeCT2Y0e75oxHYuzg8nnATXvZvyua1WwFj1tKmjA32TvaZbOyd7CllU5JTh04DsHntVpq1fjlbWy3bNeev30KfWFNeVPf0IMrE53s37KJhFp838PNhp+bzMBOfN/RrxF4Tn0eZ+Fxpzhk7r+rcvRhF8pVo5H09N37di3PrzLfuuN2n0KfcAyDh4HlKuGjXjwSdlQU6S3PMrCzQWZiTFlP0zzRv6FmXsmVKF6mGSp7Vib0cRfzVaPT39RzesIc6/pn9Wse/IfvX7QTg2MYwPJq+CBhXb1uWsEJnpsPC2pL0e+mk3UmmfHVXLh+O4L52f7sQdpq6rb0LVXcT/8ZsW/cXAGcOn6FUGRvss9yT7Z3sKGlTktOHzgCwbd1fNG3dJON434l9+XLal5nuu5U8KnFk91EArl64RvmK5bF1tC1U7U+K4TFezyuPDESFEAL4GfhVSukB1ABsgGmPqJprkJQLtkCegajGBSmlp8lrdR62PYBHBqL5QUq59BF9FQW5+lgao4V+QLAQwloIUQrjZzbgSTsVQjzxlA57Zwdib8RkvI+LisO+fOZAxr68A3FRsQ9tImOxd350sONUsTyzN87jkzXTqeVd+0mlPhPNHp41mLd1IXM3L2DZ+MUZgWnB9NkTF2nadxx2zvbZbW4YbQx6A8l3kiltVxo7Z3tiTetGxWGv1RU6HTM3zmXFoVUc+99Rzh+JyNSmmbkZr77dgqPbDxdYc1bKOTsSY+LjmMgYyjk7Zrcx0ZqTzUuN6pIQk8D1iw8Df+dKzqzYtJR5a+dQ16fOE2sFsHtCn5vWjY96WFciGfvtJKb9PgffLv6ForU4a36AtYsdKTfiMt6nRsZh7WKXq32lri2IDjEGFAkHI4jbcwr/o0vwO7qEmNCjJEXceCo6ixtly9uTaOLXxMh4ypa3z9XGoDeQeieFUnalOboxjHspaUwKX8rHexayfcXvJN+6S+TZq1T1qUVJWxssrC2p1dIT20L4sWqKo7MDMTceXo+xkbE4ZLkXODg7Zrq3xUbG4qjdkxv7NSI2Kpa/T1/MVOfi6b95uW1TAF7wrEF5VyccXTK3W9TIx/jveSU/AYUvkCql/BpASqkXQgQBF4UQF4HaUsqBAEKI34HZQBughBDiCHBSSvmuaYNCiJHAfwEr4Bcp5UTgU6CaVmerlHJkfk9CCGEGfAk0xDh14ivgqvb+OyFECtAEGAm0A0oAe4C+Umb8DnpPCLEAKAP0klKGZ+ljEpAkpZwthBiMMchLB05JKTtrZrWFENuBSsA8KeUCIUQVYBOwC2gMHAW+BiYDTsC7UspwLVD8HKiL8XOZJKX8TQjRA2gPlASqaf4aJYT4NC8fA0gpTwghNgCjgVLAainlBe18umMMSi01XwyUUhqEEMuB+pqP1kgpp2j214BlGD/becBPWfzTB+gD4Gn/Eu42lXP8rB7aZy+TWVKB+bHJSkJ0PH2afEBS4h2q1qnGmBXjGOI3kJSklDzr5YenpRkg4sg5hvoNxLW6G4PnDOXQ9oPcT7tfMH3k1HlWo+w2UhrnpuZUDiANBkYHBFGyTClGLB9DxRqVuHruSobdB1P7cjrsFGf2nyqQ3hzJUYcssM1rb/pmyobGRcfTyeddbifepkZdD6Z+OZkevr1JTkp+Mrn58Hluvs2p/EHdSW+PISE6gTIOZRn37SRuXLjGmfBC8C/FU7OJsFz7z4prx2bY1qvKnremAFCySnlsPFzZ6mX8Ld74x3HYN65J/L4zhauxGPIk97ZK9aph0BuY1Kg/JcuWYuCPkzi36zjRF24QunQ9/b4dT9rdVG6cvvxYP7ALKjy77pyvZStrK7oM6szYd8dnO75m0U/0n9yXxZsWcvHMJc6fvIChEOa/FybPc4azoOQnEH0ROGhaIKW8LYS4klt9KeUYIcRAKaVn1mNCCH/AA/ABBLBeCPEqMAaok1OdLDwIVh8wCEgGXKWUdbQ+bKWUiUKIgcAIKeUBrXyhSWD1DfAGsEFrp5SUsqmm5Ssgr5TJGMBdSpkmhDDN19cEWgKlgbNCiAcTFKsD/8EYqO0HugLNMAaY44AOwHggRErZS2szXAixTavvCXgBaVq7n+fl4yxMBg4B9zAG5ggh6gBvAU2llOla8NkZ+D9gjJQyXst6hgoh1kopH3yb3JVSZh8DBaSUy4HlAG9Xbp/jV0ObbgH4dTZmSs4fi8CxQjnAOLzq4OxAQnR8Jvu4qLhMv24dXBxJuJnZJivp99JJuncHgL9PXCDqchQV3F0zFgYVlGeh2ZTr56+RmpJKpRqVC6w5LioOBxfTvh2y9R0fGYdDBUfio+LQmekoWbokSYl3iI+My/SL38E5e93k23c5tfcE9Vp4ZQSi7wzpRBn7sswZ+2mBtJrSoXt73ugaAMCZo+coV6FcxrFyLuWIvRmXyT4mMoZyJlqz2piZ6XilbTP6Bjycw3v/3n3u3zMG9ueOR3DjciQVq7px9ti5x9YNxozgo3wel4fPTevam/g8IToBgNtxt9i/OYxqnh6FFtQVR80PSL0RT4kKD7Nq1i4OpEYlZLNzfKUOHkM6sOftKRjupQPgEuBNwsEI9MlpAESHHMWugYcKRIHEqHhsTfxq62LP7eiEHG1uRcWjM9NhXboEyYlJ1H/zZc7sOIohXU9S3G0uHjxLxZeqEn81mrAfQwn70fiDMGBkZxIjM/8tPw7tur9B2y5tADh39BzlKjy8Hh1dHInPcr+IjYzJdG9zdHEk7mYcLlVccK7ozJLNiwHjXNFFf37O4HZDSYhJYM7wuRl1Vu1ZSdTVmzxPPM8ZzoKSnzmigpx/c+ZW/ij8tddhjAFSTYyBaX7JOjT/P+BvoKoQ4nMhRBvgdi51WwohwoQQxzFmel80OfY9gJRyJ1AmS4CZlWMYM63vYcyKPuAPKWWalDIWiAbKa+UXpZTHpZQG4CTwl5aJPQ5U0Wz8gTFakL0dsMaYWUWzvyWlTAVOAXmnG02QUt4F1gDfSCnTtOJWgDdwQOuvOcZsK0AXIcQhjJ9NLcB0bHtNfvvNiU2rN2YsIgrfEkaLji0BqOH1Asl3kjO+yB6QEJ1A6t0Uani9AECLji0J3xqWZx9l7Mug0xZPlK9YHhf3Cty8EvVca3aqWD5jcVI513K4VnUl+lrBb3oXjkbg7O5CuYpOmFmY07RdMw5szZTY58C2cJpr59A4oCkn9xw3lm8Np2m7ZphbmlOuohPO7i6cPxJBafsylCxTCjDOX63TrB43zhuHu307t+Kl5l7MHzQnX1nf3Ph11fqMRUS7Nu2m9TvGRU+169fi7p27xGcJ9uOj40lOSqF2feNCk9bv+LF7y56M4w1eacCVC1cyDd+XtS+bcV24VHLB1d2VG1ciH1vzA7L6vEm7ZhzM4vOD28J5VfN5IxOfH9waTpMcfG5VwgrrUtYAWJWw4qVXPbl29gqFRXHU/IDEIxcoVdWZEpXKISzMqNChCVFbMuVJKFOnCi991pv93WdzL/bhV0HK9VgcmtRCmOkQ5mY4NKlF0rlnO8/8eeXq0QuUq+KMvVs5zCzM8GrXlBNbM/v15NaDeHd8FYCXAhpxfo9x0WXijbiM+aKWJayo7OVB9AXjlAcbhzIA2FZwoG4bbw6v38OTsmHV7wS2GUhgm4Hs2byXVh1fA6CmV02S79wlPss9OT46geS7KdT0qglAq46vsXfLPi6duUQnry50b9qD7k17EBMZy4C2g0iISaBUmVKYWxjzbG27tOFE2PEnHj0pbP5Jc0TzkxE9CXQ0LRBClAEqArfIHMxa56M9AcyQUi7L0maVfNTNESllghCiHtAa43Dzf4FeWdq3BhYDDaWUV7WhdlO9Wb9J8/pmfR14FWNG82MhxIOANs3ERs9D/5qWG0zeG0xsBNBRSnk2i+5GebSbX7JehwL4Skr5cZa+PIAhgI+WUf6WzD66W8B+c+VgyAHqt2zA4p3LtK2QFmQcm7NxHsMDhgKwbPwSbSskSw5tP8QhbYFMo9aN6T25D2XsyzL+6wlcPPU3n3SbRO1GL9J52LsY0vUYDAaWjVtM0q0818kVueZaDWvxVuDH6O+nI6Vk+UdLuZNwp8D6DHoDX01YwbjVE9GZmbH9x21ci7jKf4Z14e9j5zm4bT+ha7YxcO5Q5u9YQlLiHeYPnAPAtYir7P1jN3O2LcSQruerj5cjDQbsnOwIDB6CTqdDpxPs/X03h0IOANB7Wn9irscw9ZeZAIRv2su6BT8+kY/3hYTRyNeH73atJi01jZnDPss49sXmpfRubVy9OnfcfMYEj8TS2orw7eGEhTwMpHzbtyDk18yLlOo1fomew7uj1+sx6A0Ej5nHncSC+zgrBr2BlRNWMDaLz98Z1oWLms+3r9lG4NyhzNV8/rmJz/f9sZvZ2xaiT9fztebzso62DFs+BjDOv939206O7njy+bfFWfMDpN7AiXErafz9WISZjqvfbyfp7DVeGPUOiUcucnPLQWpP6Ip5KWsarDDuuJByPY793WdzY0MYji+/SPPQWYAkOuQoN7ceKnSNBWXkxE/Zf/gYiYm3ea3DewR+8D4d27V+phoMegM/T/iaPqvHoTPTEf5jKDcjrtEm6D9cPf43J7cdJOzHULoGD2Dc9nkkJyaxepDx/rdr9WY6f9afUVs+AyHY/9N2Is8Yf4T0WDKMknY2GNL1/Pzx16TcLrSvEADCQ/bj7evN17u+Ii0lNVMWc/GmhQS2Ma6i/3zcQkYED8PS2ooDofvZH7o/z3YrVa/IyHkjMOgNXI64wtyR8wpVd2FgeIIf/88b4lGZDG2x0n5ggZRytTYfcynGrOMvwCyMw8yuGIPW9lLK7UKIBMBJSnlfaydJSmmjDc1/ArwmpUwSQrgC9zEGWIeklLlm+7Rg9fcHQ/Am5Y7APW3KgCewUkrpqc2PDJZShmoZzrMYM5BmwD5grZRykjav84yUsp8QohmwREpZV5uf2VBKOfDBHFEgGKgkpbwkhLAArgEvAEPR5pBqmk5gHPrHVLMQYqX2fq3p+QghpmOcnzpISimFEF5SysOmGrT6vwOzc/JxHn6blEVbXWAt8LKUMlYI4YBxDmk5jMPr3hizuceAICnlt9oc0TpSysS8+oLch+YVhYNFMdx17aa+cH4MPEtczGyKWsI/nq5pJYpaQoFpc+JR63SfL0Y3LOi64aLnpL7odzMoKJuv/pnDLNqnx3uV3y7w9+y3l39+pEZtVHk+xjjpCynlp1mOWwGrgQZAHNBJSnmpoFpMeWRmTQuK3gIWCyE+xpgB3YhxbuM94CLGIeYTGIdzH7AcOCaEOGS6kEZKuUUIUQvYq00iTgLek1JeEELs1gK4P/NYrJR1juhXwA7gayHEg2/osdr/VwJLTRYrrdC0XsIYXJuSIITYg7ZYKQ+XmAHfCiHKYswsztWyh3lUyRefYFwEdEwL/i/xMJDNjRx9/CiklMeFEJOBbZrP7mNcfHUA49D/CYzTHXYX+CwUCoVCoVA8VZ7GvqBaonER4IcxybZfCLHeZJ0IwAdAgpSyuhCiMzAT6PRE/T7J3C6FIidURvTpojKizwaVEX36qIzo00dlRJ8Nzzoj2qVyhwJ/z35/+dc8NQohmmDcsae19n4sgJRyhonNZs1mr7aoOQooJ58gmCx+32gKhUKhUCgU/2Ke0mIlV4xbXz7gmlaWo42UMh3jWqEn2iD2uXzWvDaH8ZssxWlSykY52f/b0eZ4/pXDodeklE++X4ZCoVAoFIrnhscZmjfd71tjubb1YoZJDtWy7UadD5sC8VwGolLK4xj3zlTkAy3YVP5SKBQKheJfwOPsI2q633cuXMO4I9ID3ICsjx97YHNNG5ovC+R/s+wcUEPzCoVCoVAoFIr9gIcQwl0IYYnxQTfrs9isB7pr/34H44N4/nkZUYVCoVAoFApFzjyNDeq1Jy0OBDZj3CHoKynlSSHEFOCAlHI9xsepfyOEOI8xE9o59xbzhwpEFQqFQqFQKIoRT2vHIynlRoxbdJqWTTD5dyrGR5YXGioQVSgUCoVCoShGPI19RIsKFYgqCp3iNvFY5LgIUFGYVDYvW9QSCkyyTC9qCQVCVwyv43nmxW9Tj9Biti/nzAPTi1pCgWlRr3dRS3jueZ6fHV9QVCCqUCgUCoVCUYx4nFXzzysqEFUoFAqFQqEoRqiheYVCoVAoFApFkfBPejy7CkQVCoVCoVAoihFqjqhCoVAoFAqFokhQc0QVCoVCoVAoFEWCmiOqUCgUCoVCoSgS1BxRhUKhUCgUCkWRoDKiCsVj0mvSh3i1bMi9lDQWjpjHxRN/Z7OpWqcaA+YMwdLaisOhB/hq0goAbMraELRoFE5uTkRfiyY4cCZ3b9+lVJlSBH42GOfKLtxLu8fikQu4eu4KAIt3rSDlbgoGvQGDXs/odsMLrLnnpA+p37IBaSlpLBoxPw/Ng7G0tuJQ6EG+zqR5JOXcnIi5Fk1w4Czu3r5Lsw7N6dDvbQBSk1NZMX4Jl09fAiCg5xu81sUfIQTbvt/Cxq825FtrveZe9JjYG52ZjpAftvLbkp8zHTe3NGdA8FCq1q3GnYQ7zB84m5hr0QB0COxIy06tMOgNrJy0gqM7jwDQ77OB1PdtyO24W4zwH5KpvTY9Xqd1twD0ej2HQw7y3YxV+daaE3Wbe/LuhF7ozHTsWPMXfyz5JZv+PsGDqVKnKkmJd1g8MJjYazG82Owl/jv6PcwszNHfT+eH6as5vfcEAI3bN+ONwLdBQkJ0PMuGzicp4c4T6YSnc1009POh8/B3kQYDer2BlZO/4MyB0wCMXzURD68anDlwmk97Tc2Xxh6TeuOlaVwyYkGOGt3rVCNwzmAsrS05HHqQlZO+AKBUWRuGLhqRoXFe4GfcvX0313Yr13an97S+lLApiUFv4JeFP7H3990A9J01kGp1q4EQRF68weLhC0hLTi2QvwdMCaSRrzdpKWnMCppNxInz2Ww86nowau4IrKwtCQvZz6IJizOOdej5Jh16tEefricsJJzl077gBc8XGDZzKABCwKrgb9m9aXeBdOVGzeb16DChOzozHfvWhBCyZH2m42aW5nQNHkDFOu7cTUxi9cD5JFyLQWduRqeZfXB70R2duRkHft7JX4t/A+CVnm1p3NkXIWDfDyHs/OrPQtFaUD6aHszO3eHY29ny67dLi0TDA4ZOGUgT30akpqQyLWgW505EZLN5oa4H4+eOxsrair0hYcybsBAAjxerMfLTICytLNGn65k9bj6nj5yhdFkbxs4ZhWtlF+6l3Wf68FlcPHvpGZ9Z3vyT5ogWt4fgKIoxXi0b4OJegUHN+7J07CL6TO2fo92H0/qzbOwiBjXvi4t7Bbxa1AegQ+A7HN99lEEt+nF891HeCnwHgLcH/odLpy4yvM1gPh82l16TPszU3qTO4xkZMPSxglCjZhcGNe/HsrGL+DBXzf1YNnYxg5r3w8XdBc8MzR05vvsYg1v05/juY3QI7AhA9NWbTPzvOEa0GcLaBWvoO2MAABVrVOK1Lv6MbT+CEW2G0OA1b5yruORLq9Dp6PVJX2Z0n8KwVoN4uf0ruHq4ZbLx7eTH3VtJDGnen41frqfrmG4AuHq40bRdM4b7DWJ698n0mtoPoTPeHnb8FMKM7lOy9fdikzo09PNhZJshjPAbzIblv+ZLZ176u035kDk9pjHWbyiN2zejQvXM+l/972vcvZXEqBYD2fzl7/x3zPsA3Em4w9wPZvBRm2EsH/45fecOBkBnpuPdCb34tMtEPmo7jKunL9Oqe9sn0glP77o4sfsYI9oMYWRAEItHLqDfzIEZbf22/Bc+D5qXb42eLRvg7O7CkOb9WTF2MR9M7ZejXe9pfVk+djFDmvfHOYvGE7uPMbRFICd2H+NNTWNu7d5LSWNR0HxG+A1mRrfJdJ/4ASXLlAJg9ZQvGdU2iFFthhJ7I4Y23QPyfR4APr7euLm70q1ZT4JHz2PIjME52g2dMYi5o+bRrVlP3Nxd8WnpbdTctB5N/ZvwoV8/PnitDz8uXQvApTOX6B8wgL6t+zPmvfEEfToEndmTfy0KneDtKb1Y3uNTZvoNp377lylf3TWTTaP/tiTlVhLTWwxlx5d/8MaYrkatAY0xt7TgszajCH5jLE26tsLOrRzONdxo3NmXeW+OZ3bb0dT2rY9jFecn1vo4dAjwY2lw/n4MPU2a+DbCzd2VTs3eZ9boYEbMGJqj3YgZQcwcHUynZu/j5u5K45Y+AASO78tXwavp4d+HL2avJHB8HwC6DXqXiJPn6e73IZ8MmcHQKQNzbLcoMUhZ4NfzynMXiAoh3IQQvwkhIoQQF4QQ84UQlo+oMy7L+6RH2NsKIQIfYVNFCHEi/8rzT9b+hRAVhBBrC7H9MCHEESHEFSFEjPbvI0KIKoXVx+Pg7deI7etCAYg4fJaSZUph62SXycbWyY6SNiU5d+gsANvXheLt31ir78P2dSFaeQje/o0AcPOoyPHdRwG4ceE65dycKOtoW0iafdiRofkcpXLRXMJE8451ofho2ozn/FCzj3Yu5w6eycguRRw6i4OLAwCu1d2IOHyOe6n3MOgNnAo7gU/rxvnSWt3Tg5uXIom+ehP9/XT2bNiFt1+jTDYNTc5n38Y91Hn5pQydezbsIv1eOjFXo7l5KZLqnh4AnA4/RVJi9j8pv/fa8tvidaTfMz4K83bcrXzpzI2qntW5eTmKGE1/2IZd1Pf3zmRT39+HXeu2A7B/415qN60LwJWTF0mMTgDg+rmrWFhZYm5pjhACBFiVtAagROkSJN5MeCKd8PSui1STLKF1SetMWY8Tu4+RcjelQBp3ar56lMYITePOddsz/q5Mr5Ud60IzynNrN/LiDaIuRQKQEJ3A7dhblLEvA0BK0kPdllaWBZ7f9rJ/U7as3QrA6UNnsClTCnsn+0w29k72lLQpxalDxgzylrVbebl1UwDavf8GPyxaw/179wFIjEsEIC01DYPekKGLQvqyruRZndjLUcRfjUZ/X8/hDXuo498wk00d/4bsX7cTgGMbw/Bo+iJgzHRZlrBCZ6bDwtqS9HvppN1Jpnx1Vy4fjuC+dm+4EHaauq29s/X9LGjoWZeyZUoXSd+mNGvdlE3adXHy0GlKl7XBIct14eBkT6nSJTl58BQAm9Zu5ZU2LwPGeZalSpcEoFTpUsTeND5ytkqNyhzcdQiAKxeu4uLmjJ1j5r+dokY+xut55bkKRIUQAvgZ+FVK6QHUAGyAaY+oWtCH/9oCeQaiT5lM/Uspb0gp3ymsxqWUjaSUnsAEYI2U0lN7XSqsPh4HB2cH4m7EZLyPj4rDobxDZpvyDsRFxT60iYzFwdloY+tomxFsJEYnZASbl09dolHbJgBUr+dBOVenjDoS+PjbKcz8PZhWXVoXWLO9swNxNx7qiYuKxT6LZvvyDsRFPXxmdlxkHPZa/2Udy2bSXMYx+zPXfTv7cXi78aZ39dwVavnUxsa2NJbWltRv2QDHCo751GpPXKSJ1sg47Jzts9to52PQG0i+k0xpu9LYOdsTa1o3Kg77LHWz4uJegZo+tZn66ywmrplKtZeq50tnbtiVtyf+hulnH49dFl+b2hj0BlLuJGNjl/kLsWHbxlw+eZH0e+no0/Ws+mg50zYFMz/8C1yrV2THmr+eSCc83evCp3Vj5v21iLFff8ySkZ8/tkY7k8/aqDEO+/JZrofy9sSbaIw3uWbKZvl7e6AxP+1Wq+eBuaU5Ny9HZZT1/2wQyw6sxLW6G5tW/lGgc3F0diDG5N4RExmLo7NDdpvIhzaxJjZuVd2o26gOCzcsIHjtbF6oVyPDrqZXTb78azlfbFvG3LELMgLTJ6FseXsSbzz0a2JkPGWz+MjUxqA3kHonhVJ2pTm6MYx7KWlMCl/Kx3sWsn3F7yTfukvk2atU9alFSVsbLKwtqdXSE1uXzD74t1HO2ZHoG9EZ76MjYyjn7JjdJtL02nloM3/iIgI/6svP+39g4Mf9WDrDOC3l/KkLNA94BYBanjUp71YeJ5f83YefFQZkgV/PK89VIAr4AqlSyq8BpJR6IAjoJYQIFEIsfGAohPhdCNFCCPEpUELL+H2XtUEhxEghxH4hxDEhxGSt+FOgmlbns4IIFEJ4CiH2ae39IoSw08qrCyG2CSGOCiEOCSGqCSFshBB/ae+PCyHezKl/0+yrEMJaCPG1Zn9YCNFSK+8hhPhZCLFJyxbPKohurY2+pucrhOgvhJilaT8phPhG6/dHIUQJzcZbCLFDCHFQCPGnEKJ8Qft9KCB7UbbMSH5ssvDLkrWUKmPDZxvn0bbHG1w8+Td6vR6Aj94ezajXg5jWfTJtugVQy+fFgkkWOQrKYlNwzQ94sUldfDu14lttbuX189f4benPfPzdZMavnsSlU5fQp+fvi1Hk6LysRtltpMz5PB91CmbmOkqVteGjDqP4dvoqhi4emS+duZGzhqy+ztvG1aMinca8z8pxSzWNZvi+15oJr49giE9vrp65TLvAt55IZ246Cuu6CN+8j6GvDWDWh9PpNPzdx5WYr8805/N4snZtnewYOHcoS0Z8nul8l4z8nH4+vbh+/hpN2zXLu5PsnebZ56N0mZmZYVO2NAPbDWbZ1BV8vOSjDJszh8/wwWt9CHx9IF0HdsLCyqJg2vInN4drOWebSvWqYdAbmNSoP9NeGUyL3q9jX9GJ6As3CF26nn7fjqfPqrHcOH25UILm4kx+7hl5XTtvdWvP55MW87Z3ZxZMXsTYOSMA+GaRkoGdAAAgAElEQVTh95QuW5qVW5bzTq+3iDgRkfGdoih8nrfFSi8CB00LpJS3hRBXyEWrlHKMEGKglgHMhBDCH/AAfDCGOOuFEK8CY4A6OdXJB6uBQVLKHUKIKcBEYCjwHfCplPIXIYQ1xiD/HvCWdg6OwD4hxPqs/WcZMh+gnVddIURNYIsQ4sHPd0/AC0gDzgohPpdSXi2A9v8Djgghxkop04GeQA/tWG3gAynlPiHEaqCvEGIJMB9oL6WMFUK8C3wC9MnasBCiz4NyL/uXqGpTGYA23QJ4rbM/ABeOReBQoRxgHDqzd3YgPjo+UztxUXE4mPyitXdxJP6m0SYxNhFbJzsSoxOwdbLjVqxxeC0lKYXFIxdk1Fm8awXRV28CxsUpYBw2Dt+8Dw9PD06Hn8zTSa27BdCqsx8A54+dx8EkI+ng7JiL5oeZCQcXBxI0zbdib2XSfDv24fB1pZqV6TdzANO7TyEp8eHimZA12whZsw2ALiPfy5RVy4u4qDgcTH61m+p4QHxkHA4VHImPikNnpqNk6ZIkJd4hPjIOR9O6ztnrZusvMo7wTfsAuHA0AoNBUtq+DHfib+dLb1bio+Kwr2D62duTmMXXD2wSouLRmekoUbokd7VpA3bO9gxeNorlwxYQfcX4+VeqXQUg4334H3t4vf/jBaLP6rp4wOnwUzhXdqa0XWnu5HNxlX+3tln+3rJ8pjlotDfRaJ9JY2KOGh9cQzm1W8KmBGO+/og1s78j4vC5bPqkwcCeDbto17cD238KyfNc3uzejoCuxrmkZ4+epVyFchnHyrk4Encz899FTGQs5Vwe2jia2MRExbDrz13Gto6cRRoMlLUvy634h36/cv4qqcmpuL9QhXPHsi94KQiJUfHYVvh/9s47TIoye9v3M0POkoOKqJgwERRBDCigouAq+MOwCqsrCIIBRVFWMa2YQDEiZnRXxbBr+FwDWUFyFAFBwQSSkZyG8/1R1TM9Mz0JZqbehvfm6mum367ufqaorj51YsZ+rVKnKhtXrU+4zZ/hsVymYlm2bthMk4tPZ+H4OezZncbmtRtZOmMRh5x4OOt+XcWUkWOZMjJIl2jf73I2rMjfuWF/4tKuF9PxqgsBWDB7ETXr1kx/rGadGunh9RirV6ymZp34Y6cGa1YGHv0LLmuXXrg05pPx9H88MES3bt7Kw30zfD3vT/43y3/5A5dw2cNZUFzziIrE1+M5redFu/A2C5gJHENgmO6dOKkyUMXMxodLbwBnSqoI1DOz/wCY2XYz2xrqfljSXGAUUA/Iy6PYCngzfJ2FwM8EKQoAo83sTzPbDnwP1C+IfjPbBEwALpDUCEgzs+/Dh5ea2eTw97dCHccSXByMkjSbwIA+JIfXHm5mzcysWcwIBfh8xGf0a38L/drfwtQvp3B2p9YANGx8NFs3bU0P/cXYsGo927Zso2HjowE4u1Nrpn01BYDpo6ZydqdzwvVzmPbVVADKVSpPiZLBdUqby9uxYOp8tm3eRumypSlTviwApcuW5qQzT+aXRb/kuZ++GPEZ/drfSr/2tzLty8mcla75KLZu2pKL5uC/6axOrdO1Zdcc/C3V61an34t38cytT7Fi6fJMr1epWuX0bZqf34KJH03IUzMExmDtBnWocUhNUkuWoGWHVkwPdcSYPmpq+t9zWvuWzJ80L1j/aiotO7SiRKkS1DikJrUb1GHJ7Ny/jKd9OYVGYY5mnQZ1KVGyxF4boQBL5yyh1mF1qH5woL95h1bM+mp6pm1mfTWNVp3OBuCU9i1YMClI4y5XqRx9XxvAe4/9i8UzFqVvv/6PddRreAgVw1zFRq1OZPmS3/ZKX3EcF7XrZxSfNDj+8GCfFqDC/8sR/+PO9rdyZ/tbmfblFM4M91VuGrfHaTyz09mZNMb+xrM6tU4/lqaPmprwdVNLluC24Xcx4YNxTP5sUqb3qRX3dzVtcwrLf/w9z7/lozc+ocd5PelxXk8mfj6Jdp2Di4BjmxzDlk1bshn+61atY+vmrRzb5BgA2nVuy8QvAx0TP59E49MDv8PBDepRolRJ/lz3J7UPqZ1enFSzXk0OPvwQ/ggvYveFX+f8SI3DalP14BqklkylcYeWfPdVJh8L87+awSmdzgTgxPbNWTIpuEDesHxter5oqbKlqd+4Iat+DM4RFaoFx3GVutU44fxTmPVx5v18IPDhGx/RrV13urXrzoQvvuH88Lho1ORYNm/cwtqsF1vhcdGoybEAnN+5Ld98Eey3NSvX0rjFSQA0bdWYX5cGx2WFuO+UDldeyOwpc9m6eWux/H35xcwKfHMV1zyi84FO8QuSKhEYP3+S2XAuk4/XEzDIzF7M8pqH7ZPKxO+TiKuAGkBTM9slaRl5687ptSDwhMZIY+/+/14G+gLLgNfi1rMepRZqmWtmZ+zF+2Rj5pjpNGndlGcnvMiObTt4/vYML+bjnz1Fv/ZBxeNLA14I2zeVYta4mcwaG5zA//P8B9z2/B2c26Uta5avZnDPRwE4+MiD6TPkVvak7eG3Jb+me0crV6/CHcOD9OHUEql8/dF4Zo+fWUDNM2jcuhnPTBgWVAXfnpGz9/hnT9Kv/a2h5mFhm55SzM6iue/z/TinSxvWLF/NkJ7BVXbnmy+nwkEVuf7BHgCkpe2hf1jVf/uwO6l4UCV279rNy/e+mF7UlBd70vbw6r0vcfeIgaSkpjJu5Ch+W/wrl/W9gp/mLmHGqGmMfXcUvZ+8haHjX2Dzhk0M7T0YgN8W/8q3/28ig0c9y57dabx6z3BsTxD2u+npvhzX4ngqHlSJ5ye/zHtPvsPYd0cxduRoej7emye+HMruXbt5/rahBdq3ifS/ee/L9BtxDympKUwYOYbfF//KJbdezrJ5S5g1ajoTRo6m+5CbeGzcs2zZsJnn+zwJQJtrLqBW/dp0vKkzHW8K0q0fv/oBNqxaz3+HjuTukQ+StiuNNb+v5qXb9z7vMkZRHRfNL2jJWZ1ak7ZrNzt37OTJGzMyhx5472HqHXEwZcqXYdjkV3jhjmeZM2FWjhpnjZlB49ZNGRpqfCHu8/boZ09yZ6jx5QHD6DX4JkqWKc3scTOYHWr86PkPueX5frTu0oY1y9fwZKgxp9dtcdHpHHvqcVSsUpGzOgdG9vO3P80vC5Zx45CbKVuhHBL8vGAZLw8oWMufKWOm0vycU3nzm9fZvn0Hj/d9Iv2xF794gR7nBV0Lht79NHcM6UfpMqWYOm4aU8dMA+Dzd7+g3+DbeHnUcHbv2sWjtwT79fhTG3FFrwfYvTsN27OHpwc8w8b1e38xFWNP2h4+vPc1uo+4m5TUFKaOHMvKxb9x/q2X8eu8n5g/agZTRo7lyiE3cve4p9i6YTMj+gT78ZsRX3D54z2548vHQWLae+NYsTC4gO72Ql/KHVSBPbvT+PCe19iWz3NDYdNv4CNMmzWXDRs2cu5f/kqv666mU4eC5+DvK9+OnkKLc5ozcuJbbN+2PZMX8/Uvh9OtXRC8e+Kup9LbN00eO5VvxwQXfo/2G8zND/QmtUQqO7fv5LE7gvNh/Yb1uWdof/ak7WHZDz8z6PYCZfAVC/uTR1QuWclhsdI04GkzGyEpFRgGbAT+AzxG4KmrR2C0djSzcZLWAzXNbFf4OpvNrEIYmn8QONfMNkuqB+wiMOJmmlmOHsXQWP3UzI7Psj4H6G1mX0u6D6hsZrdKmkwQmv+vpNJAKnA9cKSZ9QlzPccADYBN8e8f/16S+gKNzOy6MCT/FYFH9AqgmZn1Dp/zKfCEmY3L5W/oFv+cuPXZQFXgBDP7U9KRwA9AczObJuk1YHa47xcAl5vZVAXdCxqaWa6x7c71O7pzUOWDhLmVDpOSZHoByig1agkFZqvtjlpCgUjG42JNmltepvxwUonci/hc49HpD0ctocCcfdLfo5ZQYCb+PqZYP4Cn1D2zwN+z05ZPcPIk4VRo3gKr+BLgMkmLCYyj7QRV8ROBpcA84AmCUHuM4cDcrMVKZvYlQV7kt5LmAe8DFc1sLTBR0nd5FCsdLem3uNtlQFfg8TDcfjIQa7B4NXBTuD4JqE2QN9pM0nQC7+jCUFdu7/88kBrqfRfoZmY7KFzeByaYWXxi2nzg+lB/eWB4+L6dgSGhAT4LaJ7t1Twej8fj8RQb+1No3imPqKd4kPQ5QcrC+PD+kcD7e1m8lQ3vES1aktHz5T2iRU8yHhfeI1r0eI9o8VDcHtEmdVoV+Ht25opvnDxJOOUR9RQtkqpJ+gFYH1dw5fF4PB6PJ4nYnzyirhUrFTuSTiCsUo9jh5klRQha0hSgdJblq81sXtZtw5SAoxKsLyFIM/B4PB6Px+M4+1Ox0gFviIYGW9IaYcliMHs8Ho/H4ykczBuiHo/H4/F4PJ4o2ONwqL2geEPU4/F4PB6PJ4nwHlGPx+PxeDweTyR4j6jH4/F4PB6PJxK8R9TjyYU9UQsoIGWSrItZMvaLnL19RdQSCswJpWvnvZFDPHfOvo+mLG5WTk6+r6A+m//MeyOHSMaenOPmvBy1BOfxHlGPx+PxeDweTyR4j6jH4/F4PB6PJxL2J49ocsUkPR6Px+PxeDz7Dd4j6vF4PB6Px5NE+NC8x+PxeDwejycSzJKtLDhnfGje4/F4PB6PJ4nYgxX4tq9IqirpK0mLw58HJdjmZEnfSpovaa6kLnm9rjdEPR6Px+PxeJIIMyvwrRDoD4w2s4bA6PB+VrYC15hZI+B84ClJVXJ7UW+Iejwej8fj8SQRUXhEgYuBN8Lf3wD+knUDM/vBzBaHvy8HVgE1cntRnyPqiYzr7rueJq2bsWPbDp69/Sl++u6nbNscfvwR9Bl8M6XKlGbm2Om8ct9LALRofzpdbr2Cg488mDs73s6P85YAcORJDek56EYAJPHuU28z5YvJhaL3xLMac83A60hJTWHsO6P45IUPMz1eolQJeg65mQYnHMHm9Zt4uvcTrPltNRWqVOTmYf044sQjmfD+WF6/96X059z5xj1UqXkQqSVSWTh1Aa/dMxzbU3i5Pyee1ZirB15LSmoK494ZxScv/Ceb5huG3EyDEw5n0/pNPNt7cKi5AjcN68fhoeYR92Y0mG5+0elc3LsTKakpzB4zg3cGvVloerNy50O30urcFmzftp17bn6IhfN+yLZN7/496HDZ+VSqUpEWR7RJX7/smr/Q5W+dSEtLY9uWbTzQ71F++mFZoWss7H1cpnwZ7nnvn+nPr1qnGhP/M4G3Hni10LUDlDj+FMpc2QtSUtg14X/s+OydTI+XPL0dZbp0x9avAWDH6I/YNeF/pB5zEmWv6Jm+XUqdQ9n6wkPsnjWpSHTGKNeqKTXv7gkpKfz5/uesf3lkwu0qtGtF3aH/4OfOfdgxfzHlWjamet9rUckS2K7drH78ZbZNmVOkWnvefwOnnnMK27ftYHDfwSz57sds2xx5wpHcPqQvpcuUZuqYabwwcFimxzv36MT1//g7l53YhY3rN1KhcgX6PnErderXYdeOnQy+/Ul+XvRzoei95YHetDinOdu3beeftz7GD98tzrbN0Sc0ZMCTd1K6TGm+HTOFp+59FoCGjY6g3yO3Uqp0KdJ2p/HE3UNZMHshFStX4K7Bd1Cvfh127tjFw7c9xtJFywpFb0H4x8NDmDBxKlUPqsJ/3xqW9xMcZ288nJK6A93jloab2fACvEQtM1sRvv8KSTXzeL9TgVJA9gM/Du8R9URCk9ZNqdOgLjee1YNhdz1H94d6Jtyuxz978sJdz3HjWT2o06Aujc9uAsAvP/zMYz0G8f2U+Zm2/2XRz/Tr0Jfb2t/Cg13v44aHe5GSuu+HuVJS+NuD3Xms64P0a3MTLTu2ol7DgzNtc3aXNmz5cwt9z+rF/175hCv6XwPArh07ef+Jt/nXP9/I9rpP3/gEd13Qlzva3kylapU47cKW+6w1XnPXB6/nsa4PcUebmzmt4xnUTah5M7eddSOfv/IJl6dr3sX7T7zNv7NorlClAlfcfQ2DrryP/m1voXL1KjQ6/YRC0xxPq3NbcOjhB9Ohxf/xwO2P8o9H+yXcbvyX33DVBdmnx3z24Zd0bn01Xdp047Xn/sXt991U6BqLYh9v37KdAe1vS7+t+X010z4vnIupBH8AZa7uw5Yn72bzgOso2bw1KXUPzbbZrqnj2DzwBjYPvIFdE/4HQNrCOelrWx7rh+3Yzu75M4pGZ4yUFGrecyO/d/8Hyzp0p9KFZ1PqiOx6Va4sVa6+mG1zFqSvpa3fyO89B/LzxT35464nqJPD8VRYnNL6FOo1qMvfzriOoXc+TZ+Heyfc7qaHezP0zqf52xnXUa9BXZqd3Sz9sRp1qtP4jMas/G1l+trlvbvw4/wf6dmuF4/f8gQ977uhUPS2OKc5BzeoR5dWV/PYnUO4fdAtCbe7fdCtPHrnELq0upqDG9TjtNanAtBrQA9eHTKCbu268/ITr9NrQGDvXNPnKhbPX0LXttfz4M2DuOWBxPuhqPlL+7YMG/JQJO9dFOwxK/DNzIabWbO4WzYjVNIoSd8luF1cEH2S6gBvAn+zPCqr9ktDVNLm8Odhkq4shvfrLmlheJsqqVU+nnO2pJZx9++TdHsez/mLpOPy2OY0SVMkzZa0QNJ9+dDxaV56C5tT2zZn3AdjAfhh1iLKVyrPQTUz5z0fVPMgylYoxw8zFwEw7oOxNG93GgC/L/mN5T/9nu11d27fyZ604JgvWboUhdXz98iTG7Jy2QpW/bqStF27+faTb2ja9tRM2zRreypfh3/TlM8mcfzpJwKwY9sOFk1fwK4dO7O97rbN2wBILZFKiZIlCiuPB4AjTj6SlctWsDrUPDmB5iZtT0nXPPWzb9ONyh3bdvDD9IXs2rEr0/Y1D63NH0uXs2ldME7yu2/mcsoFLQpNczytzzuDT0Z+DsC8mfOpWKkC1WtWy7bdvJnzWbNqbbb1LZu3pv9etlzZIml3UhT7OJ5ah9WhUrXKLJr6faFrB0g9/Gj2rFqOrV4BabvZNXUcJRufXuDXKdHsTHbPmwY7dxSBygzKnHg0u35Zwa7f/oBdu9n42XjKn5P9+Kt+8zWsf+U9LG7f7ljwI2mr1wGwc/HPqHQpVLJkkWlt0e40Rn0wGoCFsxZSvlIFqmY5x1WteRDlKpRjwcyFAIz6YDQtz8v4e3oM7MEr/3wl03ns0IaHMnti4Mn99cffqHVILapUzzUFL1+0Oq8ln7//FQDzZy6gYuUKVKtZNdM21WpWpXzFcsyfERyPn7//FWecHxwvZkb5iuUAKF+xPGtWBp/Jw46qz4xvZgLwy4+/Uufg2hxUPVuNS5HT7OQTqFypYrG/b1Fhe/EvX69r1sbMjk9w+whYGRqYMUNzVaLXkFQJ+H/AP8wsz6vo/dIQjeMwoEgNUUkXAT2AVmZ2DHAD8G9JeQ2qPhsoqPvrL0CuhihB3kZ3MzsZOB5IHLeKmKq1q7Fm+er0+2v/WEvVWpmNjKq1qrH2jzUZ26xYQ9Xa2Q2RrDQ8+Sie+upZnvziaV4c8Hy6YbovHFS7KmtXZGhZt2JtNi0H1a7G2uXBNnvS9rB101YqHpT3ia//iHsZNvN1tm3ZxpTPvt1nrfF61q3IMNDWrVjLQbWrZt9m+dpMmivkovmPZSuoe8TBVD+4BimpKTQ971Sq1sn7/2RvqFmnBiuXZ3iCVq5YTc06uaYaZaPL3y7l08nvces9vXh0wJOFLbFI9nE8LTq2YvKnEwtPcBZ0UHVsXcZ3yZ51q9FB2f8/SzY9gwoPDKdcr3tR1ez/B6Wan82uKWOKTGeMEjWrsfuPjPPG7pVrKJnlvFH62CMoUbsGW8ZNzfF1KrRrxfYFP2K7cr4I2Feq167G6uUZ54w1K9ZQrXb1TNtUq12dNSsyb1M9PK+c1rY5a/5Yw08LlmZ6ztIFP3H6BcFXx9EnH0WtejWpXifz6+4NNWpXZ9XyjGNh1YrV1Miit0bt6qxakbH/V8dtM3Tgc/T6Rw8+nPYOve+5gWGDglSTJd//yFntzwDg2JOPodbBtahZCHoPdCIqVvoY6Br+3hX4KOsGkkoB/wFGmNl7+XnR/d0QfQQ4I/QO3iopVdLjkqaFbQV6QLpXcLykkZJ+kPSIpKtC7+Y8SUfk8h53Av3MbA2Amc0kMAZvDF97maTq4e/NJI2TdBiBwXprqO2M+BeUdISkzyXNkPS1pGNC72lH4PHwOTlpqgnEcjjSzOz78DVPlTRJ0qzw59FZnyipvKRXw/0zK+aKl9Qo3Bezw/3WMB/7Plek7GtZPyj52SYRi2f/wC1te3NHx9u4tFdnSpbed6+HyC4mf3rzfu1HrnmAXqdcS8lSJWnUsvDC3AnkkPWiOJHm3ERv3biF1wa8SO9nb+Oe9//Jmt9Ws2d3EfWzSyCuoCfTd1/7kItOu4ynHnqe62/tVkjCMiiKfRxPi46t+PajrwsqqwAkOmgz3909ezKb+v2Vzfd2Z/f3Myn39zsyv0LlqqQc3IDd300vQp2xN8vjmJCo0b8Hqx99Kdt2MUodWZ/qt13LqoFPF4XCTFqykv2ckWgbKF2mNFf0uZwRg7PnX7/73HtUrFyB5z9/lo7dOrJk/o/s2Z1WCHLz8XnLQS/AJdd05Jn7nufSUy7n6fuf467BQYDvzWffpmLlirz+5XA6X3sJi79bTFravus90ImoWOkRoK2kxUDb8H7MtokVEvwfcCbQLbQZZks6ObcX3d+LlfoDt5vZRZCeqPunmZ0iqTQwUdKX4bYnAccC64CfgJfN7FRJNwN9gMQJM9AIyJoYNZ2Mq4ZsmNkyScOAzWb2RKjt3LhNhgM3mNliSc2B583sHEkfA5+a2fu5/M1PAoskjQM+B94ws+3AQuBMM9stqQ3wMNApy3MHAGPM7Nqw3cJUSaMIjOahZvav8GonNeubxidBn1z1RBpUqJ9N2PnXtKft5e0AWDJ3MdXr1gCCHK5qtauxftW6TNuv/WNtJg9CtTrVWb8y8za58fuS39i+bTuHHlU/vZhpb1n3x1qqxV3FV61TLZuWdSvWUq1uddb9sZaU1BTKVSzH5g2b8vX6u3bsYsZX02jW7lS++6ZwCijW/bE2k7cyJ81V61bLonlzrq87a/R0Zo0OjI7WV7RlTyF+qXT526VcelVHAObPXkiturXSH6tVpwar4zzkBeHz/45iQBHkBBbVPgY49NjDSElNZVmCIr7CwtavRlUz6g1SqtbANmROc7AtG9N/3zn+M8pcdn2mx0ueeha7Z0yEYjAudq9cQ4naGR7ZErWqszvuvJFSviylG9bnkBGPAZBa/SDqPX8fv/e6jx3zF1OiVnXqPnMPf/R/gl2/rih0fR26XsQFV5wPwA9zfqBG3YxzRvU61Vm3MvO+XbNidSZvZvU61Vm7ci11DqtD7UNq88IXzwNBruhz/3uGmzrcwvrV6xl8W4Z3/41Jr/PHryvZGy7tejEdr7oQgAWzF1GzbsaxULNOjfTweozVWaISNerUYM3K4DN5wWXt0guXxnwynv6PB4bo1s1bebjvY+nPeX/yv1n+yx97pdeTQWGmcRXgPdcC5yZYnw78Pfz9LeCtgrzu/u4RzUo74BpJs4EpQDUg5t2bZmYrzGwHQYVXzECdRxDiLwgim18hn0+UKhCE7N8Ldb4I1Mnv883sAaAZgf4rCYxRgMrha35HYKw2SvD0dkD/8H3HAWWAQ4Fvgbsl3QnUN7NtCd43PQk6kREK8PmIz7it/S3c1v4Wpn45hbM7tQbgqMZHs3XTVtavWp9p+/Wr1rN9yzaOahw4b8/u1JqpX03J9e+veUit9OKkGvVqUO/weqz6be9O0vH8OGcxtRvUocYhNUktWYIWHVox46tpmbaZMWoaZ4R/U/P2LZk/aV6ur1m6XBmqhDljKakpnNy6Cct//G2ftcb4ac6STJpP69CKmVk0z4zTfGr7Fnyfh2aAStUqA1CuUnnaXH0+494ZVWia333tQ7q06UaXNt0Y+/kEOvxf8KV+QpNGbN60JWEuaE4c2iCjaOjMNi35ZemvhaYzRlHtYwi9oR8XpTcU0pYuIrVmPVS9NqSWoOSpZ7MrS9W7KmekGpRo3IK0Fb9kerxk83PYWQxheYDt8xZRsn5dStSrBSVLUKn9WWwZm5GCtmfzVn5s2YWlbbqytE1Xts9ZmG6EplQsT71hD7BmyGtsn1U0ObefvPEpvc7vTa/zezPpi29p0yn4zj6m8TFs3bSFdVnOcetWrWfrlm0c0/gYANp0Opdvv5zMsoXL6NL4Crq27EbXlt1YvWINN17Qh/Wr11O+UnlKlAx8SBdccT7fTZnH1rh86ILw4Rsf0a1dd7q1686EL77h/M5tAWjU5Fg2b9zC2qzOgVXr2Lp5K42aHAvA+Z3b8s0XwfGyZuVaGrc4CYCmrRrz69Igh79CnN4OV17I7Clz91qvJ4O9KVZylf3dI5oVAX3M7ItMi9LZQHyW/Z64+3vIfT99DzQF4s/ETcJ1gN1kGPxl8qExBdgQ5njuFWb2I/CCpJeA1ZKqAQ8CY83skjA1YFyCpwroZGaLsqwvkDQFuBD4QtLfzWyfvnlmjJlOk9ZNeX7Ci2H7poww2eDPnuK29oED+sUBL4Ttm0oxc9xMZo4NnM/NzzuNv9/fnUpVKzPgtXtZ+v1PPHjNfRzb7Fgu6XUPabt2Y2YM/8cwNq3Pn1cyN/ak7eH1e1+i/4iBQZuekaP5ffGvdO57BT/NXcLMUdMY9+4oej15C0PGP8+WDZt5pvfg9OcP/eZFylYsS4mSJWja7lQeufp+Nq3fxG0v30XJUiVJSU1h/qR5jHrri1xUFFzzG/e+zB0j7iUlNYXxoeZOfS9n6dwfmTlqGuPfHc0NT97M4PHPsXnDZp7tPST9+U9+Myxdc7N2zXnk6vtZvvg3rh54LYcedxgA/xk6kj+WFr5nCeDrUZNodW4LPp38Htu3befeWzJaGr076n1TkVgAACAASURBVHW6tOkGwC339KL9Je0oU7YMX878Lx/++xOGPfEKl1/bmdPObMauXbvZ9Ocm7rmp8Ctmi2ofAzS/qCWPd/tnTm9dSH/AHrb96xnK3/ZI0L7p68/Zs/xnSv+lK2nLfmD37G8p1fYSSp7cAtLSsC2b2PZyhndL1WqRUrUGaYvmFq3OGGl7WP3Q8xz88j8hJYWNH37JziU/U63P1Wz/bnEmozQrVa7qSMlD61K155VU7RmUDvz+97tJW/dnkUidOmYap5xzCq998yo7tm3P5MV8/vNn6XV+UD3+zN3PcvuQvpQqU5rpY6cxbey0nF4SgEOPPIR+T93OnrQ9/Lz4F57s91Sh6P129BRanNOckRPfYvu27Zm8mK9/OZxu7YIq+Cfueiq9fdPksVP5dkzgHHi032BufqA3qSVS2bl9J4/dEZz/6jeszz1D+7MnbQ/LfviZQbc/Xih6C0q/gY8wbdZcNmzYyLl/+Su9rruaTh3Oi0RLYRCFR7So0P70x8SQtNnMKkhqCgwxs7PC9e5Ae+AyM9sl6Sjgd+AUMofwx4X3p4dGavpjCd6rI3APcL6ZrQ1zIT4Gmod9tkYBg83sf5KeBBqb2dmSbgMqmdnA8HXuIwzVS5oEPGlm7ylI3DnRzOZIegaYaWav5fK3Xwh8ZmYm6Vjga6AW8D7wlpl9EL5XNzM7LP7vk/QwUInAWDdJjc1slqTDgaXh2lPAMjPL8ex3af2OSXVQlcmeaeA0KYkzE51m3o7kC8WdUDqvekO3eO6cjXlv5BgrJyefL6TP5uT6/G3eU7SdDIqCcXNeznsjxyhZ/fBiPTAqVziiwN+zf27+0cmDd38Pzc8FdkuaI+lW4GUCT+XMMET9IvvoFTazj4FXgUmSFgIvAX+NNX0F7geGSvoaiE+i+gS4JFGxEnAVcJ2kOcB8gmkGAO8A/cJCopyKla4myBGdTdDD6yozSwMeAwZJmkiCHM+QB4GSwNxw/zwYrncBvgtf8xhgRG77xOPxeDweT9ERUdV8kbBfekQ90eI9okWL94gWD94jWvR4j2jR4z2ixUNxe0QrlGtQ4O/ZzVuXOnnw7u8eUY/H4/F4PB6PoyTf5WhESBoAXJZl+T0zK+Jqghz1PAdkHYEyNLf8UY/H4/F4PMlPUUyKiwpviOaT0OCMxOhMhJndGLUGj8fj8Xg8xY/L7ZgKijdEPR6Px+PxeJKI/am+xxuiHo/H4/F4PEmED817PB6Px+PxeCLBe0Q9Ho/H4/F4PJGwPxmivo+oJ2mQ1N3MhketoyAkm+Zk0wvJpznZ9ILXXBwkm17wmj2Fg+8j6kkmukctYC9INs3JpheST3Oy6QWvuThINr3gNXsKAW+Iejwej8fj8XgiwRuiHo/H4/F4PJ5I8IaoJ5lIxryeZNOcbHoh+TQnm17wmouDZNMLXrOnEPDFSh6Px+PxeDyeSPAeUY/H4/F4PB5PJHhD1OPxeDwej8cTCd4Q9Xg8Ho/H4/FEgjdEPR6Px5P0KOCQqHXkF0mpkjpFrcPjiRpviHqcJTxRvxW1joIi6c38rLmEpJvzs+YKkp6Q1ChqHQVFUilJR0atIy8klZF0i6RnJfWQ5Pw4aAsqb/8btY78YmZpwC1R6ygokmpIelHSp+H94yR1i1hWrkg6QlLp8PezJd0kqUrUujwB3hD1OEt4oq4hqVTUWgpIJgNJUirQNCIt+aVrgrVuxS2iACwEhkuaIukGSZWjFpQXki4E5gFfhfdPlvSfaFXlyBtAMwK9FwCDo5WTbyZLOiVqEQXgi9DgryOpUuwWtag8eB0YD8S8z4uB2yJTkz8+ANLCi8BXgAbAv6OV5Inh2zd5nEbSi0AT4GNgS2zdzIZEJioHJN0F3A2UBbbGloGdwHAzuysqbTkh6QrgSqAV8HXcQxWBNDNrE4mwfCLpaOBvwBXAROAlMxsbrarESJoBnAuMNbPG4do8MzshWmXZidcVekOnmlmTiGXliaTvgaOBZQTnCxE4S0+MUldOSPo1wbKZ2aHFLiafSJpmZqdImhV3HM82s5Oj1pYTkmaaWRNJ/YDtZvZMvH5PtDgfbvEc8CwPbykExpGzmNkgYJCkQS4anTkwCVgBVCez12sTMDcSRfkk9DQfE97WAHOAvpJ6mNnlkYpLzC4z2yApfs1VT8Cu2C9mtjuLZpe5IGoBBcHMkianNY4tkqoSHruhB3pTtJLyZFd40d0V6BCulYxQjycO7xH1JAWSypvZlry3dANJ9YD6xF3smdmE6BTtX0gaQvCFMgZ4xcymxj22yMyOjkxcDkh6DfgfMAD4C3AzUM7MukcqLAGS0siIQIgML3/Mw+hs+FhSK6Chmb0mqQZQwcyWRq0rJyQdAxwHlImtmZmzYWNJzYChBClIc4B6QGczmx2psFyQdBxwA/Ctmb0tqQHQxcweiViaB2+IehxHUguCnJ4KZnaopJOAHmbWK2JpOSLpEeBy4HsgLVw2M+sYnarckXQp8ChQk8DYcNrgkHQt8I6ZbU3wWGUz+zMCWbkiqTxwL9COYP9+Adyf6G/w7B2SBhLkth5tZkdJqgu8Z2anRywtIZL+QXA8HENwPJwHfGNml0YqLA/CvP1jCY7j781sZ8SS8kRSWeBQM1sUtRZPZrwh6nEaSVOAzsDHcflI35nZ8dEqyxlJi4ATzWxH1Fryi6QlQAczWxC1lvwgabSZnZvXmotIKkdg5G+LWktBCSuNbzSzf0atJRGSZgONgZlx54u5DueIzgNOJtB7kqQ6wIuOX7SWBnoQ5JUbQW75Sy6f7yR1AJ4ASplZA0knAw+4vJ8PJHzVvMd5zCxrQn9awg3d4SeSL/9oZTIYoWFboapAdUkHSaoa3g4D6karLnckNZE0C/gBWCxphiQnC4AkHSJpuKRPJf1dUjlJgwkqpGtGrS8XdoZtnGL5i+Uj1pMX28LuILslVQT+AA6PWFNevEHQBeQl4GWCYtI3IlWUN/cBpwIbAMI0ggZRCvJk4IuVPK7zq6SWgIXhoJsAJw0mSc8QfAFuBWZLGg2kewnM7KaotOWD6ZLeJejDGK/5w+gkJaQHQe/FusDMuPWNwHORKMo/rwG3xKr6JZ0drp0UpagcGEHQoucD4HxgMjAfOMHM/ohSWB6MDDttVJF0PXAtgcHkKrNCL/OrwHSC43hm7k+JnOOyeJi/kjQnMjX5Y7eZ/ZkkhYIHHD4073EaSdUJEuPbEOQjfQncbGZrIxWWAEmJenGmY2bOeg3CQpqsmJldW+xi8oGkPmb2TNQ6CoKkSWbWMsvaRBfzFyXNMbOT4u6vJMivczb8GkNSW+LycM3sq4gl5Yuwx2UlM3PaEJU0AnjGzKaF95sC15vZDdEqyxlJrwCjgf5AJwKHRkmXNR9IeEPU4/EkDZLOMbMxYXFVNhz04KYj6UmgFPA2gTemC4EHbCSAmTnTLiv0cJ1NYMwBjI2/b2brIhG2HyLpcuAIM/unghGlNc1sRtS6ckLSdwRV/j+FSw0IvOVpBBevzqWbhHnZA8hcKPigmW2PVJgH8Iaox1Ek3WFmj8WFuzPhcpg7LEDIqvlPgtDbQ456c48CXgBqmdnxkk4EOprZQxFLy4Sk+81sYLJ5cAEkfZ3Lw2ZmZxabmDyQtAzYQ4YhGo+ZmVN5jJK+MbNWkjaR+bPneveHZwnyyc80s2PD/OcvzMzZ6VCSjsjtcTP7sbi0ePYPvCHqcRJJF5nZpzmFux0Pcz9G4B2I9QK8nOAL8U+glZl1yOm5USFpPNCPoGI3KboTJBuSZP6EWyRIOtzMfsp7S7eIm/gTP6UoU1qEa4Tt6V5LhjZIkj4hl1xQXzXvBr5YyeMqXYBPgSpmNjRqMQXk9Cx5f/NiuYCS/hqZqtwpZ2ZTsyTz745KTE5I6pvb4+bg6Nc4fgwLwl41s8VRi8mNvKr5HcxjfA9omiwtvOLYJSmFjCr/agSeaJdZBoyQtJug2O5dM3N1stITUQvw5I03RD2u0lRSfeDaMDk+c7mj2zlqFSQ1N7MpAJJOBSqEjzln3IWsCUNusS/EzgSjP13D6TGvedAYuBL4l6SdBJXSI81sc7SyEjKdIO9vdXg//vNnwDnFrih3UsJm9kclulhx+ALlOYLOBDUk3Q/8H3B/tJJyx8yGAcPCaUXXElxoTyDoJZpb+kmxY2bjo9bgyRsfmvc4iaSbgJ4EPfV+J8sXoWs5avEomL38KoHxKYKClL8TfLFfaGYjI5SXEEmHA8OBlsB6YClwlZn9HKmw/ZSwddO/gEoExUoPmUNjKCXdSlBd/CfwDvAfRw1mACQdTTA29RZgWNbHzcxZ405SIzK6gowys+8ilpQnoRf3AuBvwBHA+wQN7teamXNRH0kNgUFkH6Xq7PfIgYQ3RD1OI+kFM+sZtY69QVJlgs/Yhqi15IWkBma2NGwAnmJmm2JrUWuLJ8mL2FIIenL+DTiKwBD9F3AGwajPoyOUlxAFM7mvAC4GfgYeNrdnil9gZv+LWkdeSPoM6GVmy6LWUlDCHPhLCfrMvmJmk+IeW+TocfwNMBB4EuhA8BmUmQ2MVJgH8KF5j6NIqmRmG4EBYSVpJlwMzUv6q5m9lTU0GMu7dDg8CEF4sImZbYlbe59ggopLxIYZTI9Uxd6xGPiGoAfjhLj1dyQ5UzEfT3hx8hFQFriawIB2zhCNffaA4yQdm/VxBz97rwNfSnoDeMzMdkWsJ08kHWpmvwBLgJNz8JC3KGZZ+aWsmY0OCwZ/Bu4Lu1h4Q9QBvCHqcZV/AxcBMwg8X1lz1FwMqcTGCSZNHqOkY4BGQOUsvTkrERfCcgUz+yT8+QYEFyzBXWeLJZDU28yeJTD0/0y0jZn1KmZZuRKmalxO4An9lSA8/0+H+y7GPnsVEjzmXNjPzEZK+n/AvQRTzd4krkjJQcMZgqlrTcxseE4buOggCNkeRiQWS+pNkO7l8qjaAwofmvd4DmAkXUyQW9cR+DjuoU3AO/FhN5eQ1IygYrciwUXKBuBaFxuBx1r0RK2jIEjaA8wFPiLIcc70ReGooYSk081sYl5rLqBgZHF/ggK2d8lsiDqX0xrfYirZCPP2FwBVgAeBygSe6MmRCvMA3hD1OE6idiyut2hJlubw8UhqYWbfRq0jv0iaC9wYq9KV1Ap43jLPwHaCJDVE7yP3/ovOGUqQeF+7uP8lnQ8MIbj4e8DMtkYsKU8krSLwjCfE5fzseCQdBGzwPX3dwYfmPU4iqQxQDqgenjhioflKQN3IhOWPlwibw0MwulHSvwFnDVFgiaS7gcOIOy84PKloU3yrGDP7Jpyq4yInStqYYN3ZqT9mdl9+tpN0l5kNKmI5+dHRgqDjQ40sOdqVgNRoVOXKAOAyM5uf20aSDjKz9cWkKS+2EaRKJQ2S7iVokbZQUmngc+AkYLekK81sVLQKPeANUY+79CBoxVKX4OQXM0Q3EvTec5mkaA6fhY+Ar4FRBFOhnCSu0fpUSS+SeW77uKh05cG8ZA1p5oPLCNriRE0pgvzQEmTO0d4IdI5EUS6Y2Rn53HQ04Io3d63LE+1yoAtBKB4gNqWvBkHR3RsE5ztPxHhD1OMk4TSloZL6mNkzUespIMnSHD6ecmZ2Z9Qi8sHgLPfjq159qK34STSLvtgJG5ePl/T6ftb71on9G7IzPxtJapSXp7cY2RkXgj+PIO89DVggyds/juD/IzxOY2bPSGpJ9pDxiMhE5c2NBM3hj5H0O2Fz+Ggl5cmnktqb2WdRC8kNM2sdtYa94L38bORKmLuAuGb8l5Y0nOznC9cmQeUXZ/avmZ2Wz03fxB0v7g5JxwMrgdbA7XGPlYtGkicrvljJ4zRhW5MjCHoXxkLGlgyJ8fHN4aPWkhdhfmV5Aq9HrKehc/mLOfVqjeFqNXd+cLGoJi9cq6SWNIdgstIM4lJMXOymkB/8MbFvSGpOEIKvATxlZg+G6+2Bq83siij1eQK8R9TjOs2A45KhwlHSWmAyMAmYCExNBiMUwMySpfdp0vVqLQAuhWHzS768vcXIbjN7IWoRhUgyHhPOnKvNbApwTIL1z4D06I+krkmY/7rf4D2iHqeR9B5wk5m5nmMZa65+GkH1bkuCqUQ/ERqm5uCM+XgkdQRiE37GmdmnUeo50HDR+xWO+OxD9lB3x6g05UbYdmoV8B9gR2zd1Ubrkt40s6tzWpNU1VXtOeHicZwXyah5f8Iboh6nkTQWOBmYSuYvFie/COMJQ/N/I6j+b2BmLraRAUDSI8ApBLPPIZgvPsPM+kenKmfCedcPEbSUibVkuSUc85iUuBTSjBGGul8B5pG54fr4yETlgqSlCZbNzFycxJbNAJKUStBl4bgIZe0TkiYXIJ/UCVz87B1I+NC8x3Xui1pAfpFUlwxv6Cnh8gzgH4DrzeLbE8yP3gMQzsCeRTD5xUXamdkdki4BfiNoIzQWSFpDFPfC3ADbzezpqEXkFzNrELWG/CDpLuBuoGxcj1kR5GjnOELTFSTVA+qT2Us+IfyZVEZoiPfIRYg3RD1OY2bjJdUHGprZKEnlcLNBNQQG0UzgSaC/meWr3YlDVAFiYcDKUQrJByXDn+2Bt81sXZa+rc6RV5jbzB6ORlmuDJU0EPiSzBGJmdFJypnw/NAXONTMuktqCBztWppJ2B1hkKRBZnZX1HoKgqRHCfpzfk9cASkwITJR+47bJ4/9HG+IepxG0vVAd6AqQfV8PYKqWBdHfJ4OtAAuAfpKWkbgCf0WmG5mO3J5btQMAmaFqRAiyBV1+QvyE0kLCULzvSTVALZHrCkv/ksQ5v6EuDC345wAXA2cQ4ZmC++7yGsEUYiW4f3fCDzNThmiMczsrty8i47yFwLj3uXzWSYkNTCzpbmsTYxAlifE54h6nEbSbOBUYEosh0fSPDM7IVpleSPpMKADcDNwsJmViVRQHkiqQ5BSIIL9/UfEknIlHP260czSQk9YJZc1S5piZs2j1lEQQmP/xGTx7kuabmbN4nP+JM0xs5Oi1paIMDf7crJ4F13OgZf0P4LxpJuj1pJfEhUjSZphZk2j0uTJwHtEPa6zw8x2xsKu4TQMZ6+eJB1DRp7o6cBBBB7RYVHqyglJ5wEVzez9sDPBx+H6VZJWmdlX0SrMjKRzzGyMpEvj1uI3+bD4VeWbpApzh8whSNlYFbWQfLJTUlkyppodQdy+dpBLSDLvIrAVmC1pNJmPY+d6O4fn40ZA5fhzBlAJcNoxcCDhDVGP64yXFEvqbwv0IghtOoekNQSjPCcRzG1/xMyWRKsqT+4n8NpmZTRBCxynDFHgLGAMiTUbbhuiyRbmBqgFLJQ0jeToWjGQoIvCIZL+RXAx2C1SRbnzE0G+czIZoh+Ht2TgaOAigoup+HPGJuD6SBR5suFD8x6nkZQCXAe0IwgZfwG87GKDe0mVzezPfGznzChHSXPN7MSCPhYVkmqZ2cqodewNyRbmBpB0VqJ1V9s3AUiqRtDPV8BkM1sTsaQckfQBQesx572L8UgqBRwV3l1kZrty2z5qJLUwM9c7lxyweEPU4ylmXGqeLOkHgslVu7OslwS+N7OG0ShLjKQ/CHpavg18kB/D3xUkvQv0MbNkCXMDkKhrhWsTwyTl+nlyNf1BUtdE6y5P+ZF0NsHYzGUExv4hQFeXC6wkHQW8ANQys+MlnQh0NLOHIpbmwRuiHkeRNI9cckFd89QVBJeaJ4fFErWA3ma2JVwrDzwNrDGzO6PUl5Ww4XcbggKP9gT5t28DH5vZtii15YWkccCJQLKEuTN1rTCzI8J2SMPMzKmuFWG3Bwjy/poR5LaKYH9PMbNWUWnLizCn9VAzWxS1lvwgaQZwZUxvaOS97XLhj6TxQD/gxbgitu/M7PholXnA54h63OWi8OeN4c83w59XESTLJzMuXf39g2BC0c+Sfg7XDiVoM3RPZKpywMzSCNIzvgjDgxcQGKVDJY02s6siFZg7A6MWsBfcSNi1AsDMFkuqGa2k7JhZawBJ7wDdzWxeeP944PYoteWGpA7AE0ApoIGkk4EHXL44AUrGG81m9kMYQXGZcmY2NUth4+6cNvYUL94Q9TiJmf0MIOl0Mzs97qH+kiYCD0SjrFBwpnlyGJLvL+l+4MhweUlW76Kktq5V0IfdFL4HFgBNAafHIibZcIYYSdW1AjgmZoQCmNl3oXHnKvcRGPrjAMxsdjj4wGWmS3qFzM6BGRHqyQ9rwg4KsW4KnQkKSz0O4A1Rj+uUl9TKzL4BkNQSKB+xpn3FuVGOoeE5L5dNHsWRCnpJhxJMdrmC4Fh4B7jYzBZEKiwPkmw4Q4yk6VoRskDSywSjXg34K8GFiqvsNrM/s3jqXDb0AXoSeMpvIriongA8H6mivLmRYHTqMZJ+B5YSHBseB/A5oh6nkdQUeJWMkZMbgGtdLT6AvEc5JiOu5LVKmkRgwL0HvGNm0yOWlG+ScThDoq4VZvZStKpyRlIZAkPpzHBpAvCCmTk5dSv0LI4G+gOdCIy7kmZ2Q6TC9lPC/PcU14rtDnS8IepJCiRVIjhena+SljSHIMdyHnGjHF1ueZMXrlT6h+2EJuTVvsulFlkxYpOVYkZ9GOae6XLhnaSbzWxoXmuevSNMzxhA5vZ0D7poOEsaaWb/l1MhqYvHsaS+uT1uZkOKS4snZ7wh6nGSZD6BKAlHOeaFK4ZofnFRr6THCDz61xB4zHsRtMgaEKmwXMhhNKIT3vF49ucuG64gqY6ZrQjznLMRy+t3iXCSWU6YmSVzrcF+g88R9bhKxagF7ANJN8pRUumsYwazrC0rflX7hDMFYXH0JwhzzwN6AJ+5GuaWdAVwJUEld/wUnYrA2mhU5cpFeW/iHpKaAXeTPY3HOcM5HAEM0CtrWzdJjwJOtXoDMLP7Ib3odWL8Y5JOT/wsT3HjPaKepMbREOwgglGOPxI3ytHMnB3lmIPnyzmvYn5xUXsyhblDr1cDYBCBAR1jEzA36wCEZEHSt2bWImodMSQtIuhvmTWNxznvYowczhXOTWGLZ387v+1veI+oJ9m5jODL0iUuAQ5PhlGOkmoTFP+UldSYDE9iJaBcZML2HRc9ol2BrEZntwRrkRMaQj8DzhhthUSZqAVkYbWZJcXcdkk9CdJJDpc0N+6hisDExM+KFkktgJZAjSzpXpVwv3XaAYM3RD3JjosGxxygCpAMoxzPIzCGDgbi8243EYQMkxVnWmQlYZgbSZtInHMpAu9+pWKWVFi4FgIcGLabyjpr/sPoJOXIv4H/kcBLbmbropGUJ6WACgS2Tny610agcySKPNnwoXlPUuNieCVJRzl2MrMPotaRX5KpRdb+GuZORlw7X0h6CzgGmE/mNJ5ro1OVP8IJW+keZjP7JUI5uSKpvsvpDgc63hD1JDWOVvCelWjd9fZNki4EGpH5y8XJqtL9sUWWp+hx7Xzheh/ZRIRjSYcAdQmiPvWBBWbWKFJhuSBpLIlbTjmbt38g4UPznmTHmRBsjGQc5ShpGEFOaGvgZYKw1dRIReXOdjN7OmoR+WE/DnM7h6RHE1V0x61dHYGs3Jgs6Tgz+z5qIQXgIeA0YFTYD7c1wZQzl7k97vcyBMMDfCTCEbxH1OM0yRSCjRE/ytHMjpDUEBhmZs6OcoxVvcb9rAB8aGbtotaWCElXAg1JohZZnqIn2Sq6JS0gGPe6lOA4jl2cOKkXQNJ0M2sWRiUam9keSVPN7NSotRUESePNLGH0ylO8eI+ox3X+SxCC/YS4EKzj3Eg4yhHAzBaH+VQusy38uVVSXYIimgYR6smLEwi8W+cQl1sX3vccYCRjRXfI+VEL2As2hBeqE4B/SVqF495FSVXj7qYATYHaEcnxZMEboh7XSZoQbBw7zGynFBT0h6McXQ89fCqpCvA4MJNAr5PN1kOSpkWWp1hIxopuzOxnSalALZLn+/higgvXW4GrgMqAk7nkccyI+303gQf6uoi0eLLgQ/Mep0nGEGwyjnKMR1JpoIyZ/Rm1lpyQ9C7Qx8ySoUWWpxiR1IogP/s1SdWBima2NGpdiZDUBxgIrCRz1bzLofkGwAoz2x7eLwvUMrNlkQrzJC3eEPU4TZJOKUohuNpuR5Dz9YXDoxwfNrO7w9/bmtlXUWvKD8nYIstT9ISjdZsBR5vZUWGayXtm5uQ4R0lLgOZm5mQ/2URImg60jEUjJJUCJprZKdEqS0yYFnUjQUcQA74HnvMXse6QLKEAz4FLMoZg+4RjG9ONT1dHORLkqMUa1z8KJIUhSuBF8niycgnQmCC9BDNbLqli7k+JlF8BZyMPOVAi/nwcpiGVilJQToTz5P8NvA6MIHAMNAGmSroq6/x5TzR4Q9TjOsk0pShG0oxyTFaSsUWWp1jYaWYmyQAklY9aUCLixk3+BIyT9P/I7NkfkvCJbrBaUsfYaFJJFwNrItaUE4OBv5jZrLi1jyT9B3gRaB6NLE883hD1uE4tYKEk50OwyTjKEagZfikq7vd0XP1CjG+RRdD+ph4wDHC2RZanWBgp6UWgSniMXIubRXcxL+0v4a1UeEsGbiColn8uvP8r7vVnjVEpixEKgJnNdtxTfkDhc0Q9TpNMU4qScZRjmFOXI2Z2f3FpKQiSZhO2yIpNyknGKTWewkdSWzLnZydFukmYW17BzDZGrSU/hC2cZGabotaSE2Gf1pZmtj7LelVgkpkdE40yTzzeEPU4T6IQrMsnv/0RSXeZ2aCodcSQNMXMmsdGNoYtsma6XG3s8WRF0r8JPIxpBC2GKgNDzOzxSIXlgqTKBDnaZ4ZL44EHXOyyIak7cD3BZKVYp5WmBPnwr5rZi1Fp82TgDVGP0yTTlKL9eZRjook1UZLsLbI8hUvcZ09k/gw6/dmTNNvMTpZ0FYGBdCcww+ULKkkfAN8Bb4RLVwMnmdml0anKGUkXAXeQuWr+cTP7JFJhnnS8IepxGh+CdYOY5zFqHTGSqUWWx5MTkuYDJxNUdj8bFuHNKThxMQAAC4pJREFUMbOTIpaWIzHjOa+1ZMK1iM+BRkrUAjyePNgR3yokSaYU7Y+4ts/7mNlLZnaZmXU2s5ck3Ry1KE+0SHozP2sO8SKwDCgPTAjTkFzPEd0WDg0A0lskbctl+2TgsqgFHMh4Q9TjOuMl3Q2UDYsQ3iOYO+8pXhS1gCx0TbDWrbhFeJyjUfyd8MK1aURa8sTMnjazembW3gJ+BlpHrSsPbgCek7RM0jLgWaBHtJL2GdfObwcUvn2Tx3X6E4Rg5xGc7D7zIdhIeC9qAZC0LbI8RYykuwgGM5SVFPMoCtgJDI9MWA5I+quZvZW1XVocTrZNAzCzOcBJkiqF9zdK6gTMjVbZPuFaxOeAwhuiHtdJpilFSUs4P7oPcBhx54VYv1YzezgaZdmYBKwAqhM0q46xieT+IvTsA2F+3yBJg8zsrqj15INYo/2k7WWZpc3Uk8AHUWkpBLxHNEJ8sZLHaRJVa7tWOLM/IGkO8AqB53lPbN3Ffq0eT05IOjPRuplNKG4tBxKSfjWzQ6LWsbdIutuhi+0DDm+IepwkLgTbCvg67qGKQJqZtYlE2H5KrC9n1DryYn9ukeXZdyTF54+XIei4McPMzolIUkIkPZ3b42Z2U3FpKQwk/WJmh0atIyfyivh4osWH5j2u4kOwxcvQcMrSl2QepToz56cUP2aWtKFMT9FjZh3i70s6BHgsIjm5MSNqAQVF0jxyvgisVcxyCsp/CSI+nxAX8fG4gfeIejweJA0iaEz9IxknanPNk+TxFARJIhiv63Tf4bDwx1yeGBe2lsqRsOLfSZIl4nOg4g1Rj5P4EGzxImkhcGJ8z1aPJ9mQ9AwZ540UoDGw1Mz+Gp2qnJHUDHiNIOVIBNPCrjWzpPOYxpD0rZm1iFpHPJKuBBrieMTnQMWH5j1O4kOwxc4coAqwKmohHs8+8D2QSmCM/gm8bWYTo5WUK68Cvczsa4CwUfxrgLMjPvNBmagFJOAEgojPOcRFfML7nojxhqjH44Egx2uhpGlk9hj4ZH6P84SN6x8GrgV+IfAuHgK8Kmmqme2KUl8ubIoZoQBm9k0YDUpmXAyzXgIc7iM+buINUY/HAzAwagEezz7wOEF4u0EszzLMu3wivDk1/lVSrCXdVEkvAm8TGHBdgHFR6dqP8REfh/E5oh6PB0gvRmhoZqMklQNSXS6e8HhiSFoMHGVZvtAkpQILzaxhNMoSI2lsLg8ndZGgi32eJY0jSHfwER8H8R5Rj8eDpOuB7kBV4AigHjAMODdKXR5PPrGsRmi4mCbJOW+Lmbk+Tz5HJD1qZnfmsnZ1BLLywkd8HMZ7RD0eD5JmEzT/nhLzZkia53rbG48HQNJ/gQ/NbESW9b8C/+ey50vShUAj4op8zOyB6BTlTg7T7uaamdMFVj7i4y7eI+rxeAB2mNnOoO1ievGHv0r1JAs3Ah9KupagWbwBpwBlCQpVnETSMKAc0Bp4GegMTI1UVA5I6gn0Ag6XFD9UpCLgcmcCH/FxHO8R9Xg8SHqMoIfhNQSj8HoB35vZgEiFeTwFQNI5BN5FAfPNbHTEknIl5kmM+1mBwLPbLmptWZFUGTgIGAT0j3tok5mti0ZV/vARH7fxHlGPxwPBF8t1wDygB/CZmb0UrSSPp2CY2RhgTNQ6CsC28OdWSXWBtUCDCPXkiJn9SdCb9Yqw32lDM3tNUnVJDcxsacQSc8NHfBzGG6Iejwegj5kNBdKNT0k3h2sej6do+FRSFYL2UzMJjKOXo5WUO5IGAs2Aowma75cC3gJOj1JXHoyXdDdQVlJbgojPJxFr8oT40LzH48mpAMG5Niwez/6EpNJmtiP2O0HB0vbYmouEYe7GwMy4MLfTxUqSUggiPu0I0ja+8BEfd/AeUY/nAEbSFcCVQANJH8c9VJEgTOjxeIqOb4EmAKHxuUPSzNiao+w0M4u1xZJUPmpB+cBHfBzGG6Iez4HNJGAFUB0YHLe+CZib8Bkej2efkFSboHK7rKTGBF46gEoEVfQuMzKcBlUlrEa/ljgDz1G6AlmNzm7/v737CZXzqsM4/n2SLhLKjbF1Zc0ikUQkWk3/gVi6CP4BoUJwGytWXOheaBbSXYNNd11poRW6EBQD4qqt3VSCUMgihYZA1dJiBReK7cUW2qY/F+9cMhnnktjF+c3c+/3AhXnPbJ7dfeac856zZEwNXJqXJGmgJN9jKkL3MN32s1VEN4FfVtX5pmg3ZbbPcn6Z+4XmSEvNrfjcD/xx7qsN4GpVfa0lmK5jEZV2sSSbLH97NEy31RwYHEnaNZJ8p6p+251jp5odYn+YJUdOAa9U1YctwXQdl+alXayqNrozSLvYZ5IcYCpGTzHtDX2kqp7vjfW/5n60hut/vK7sj9aqegN4A/hKdxZtzxlRSZIaJLlUVV9K8k2m26F+CjyzeIKFPh5XfNbDnu4AkiTtUlt7Q7/FVEAvzY2tpCTP3szYKqiqjao6sORvwxK6OiyikiT1uJjkeaYi+lySDeCj5kw3cnz+YXZL0d1NWbQDuEdUkqQePwC+DPy1qt5Ncjvw/eZMSyU5A2zdTvTO1jDwPvCLtmBae86ISpI0UJLTAFX1EbC/qv49e/4n8EBntu1U1dnZy43nFpa4b6+qM935tL58WUmSpIHmr9RdvF532XW7qyTJ0qJcVS+NzqKdwaV5SZLGyjaflz2vmp/Mfd4H3AdcBE72xNG6s4hKkjRWbfN52fNKqaoH55+THAIeb4qjHcCleUmSBkryLvBnptnPz84+M3s+UlW3dmX7fyUJ0y1FX+zOovXkjKgkSWN9vjvAx5XkSa7N2u4BTgCX+hJp3TkjKknSCkryp6paqespk/wI2MtURt8GXq+qC72ptM6cEZUkaTXt6w6wZXZw/WPAw8CbTNsIDgFPJ3m5qj7ozKf15TmikiStplVasjwH3AYcrqq7quoEcAQ4CDzRmkxrzaV5SZJW0CqdKZrkNeBYLZSGJHuBK1V1tCeZ1p0zopIkraZVOlO0FkvobPAqqzVzqzVjEZUkqUGSn91g7LsD49zI5SQPLQ7Oriu90pBHO4RL85IkNVi29J7klaq6syvTdpLcAZwH3mO6SamAe4H9wKmqeqsxntaYRVSSpIFmRyD9mOlln7/MfbUBXKiq0y3BbkKSk8Bxpm0Dr1bVi82RtOYsopIkDZTkE8AngbPAI3NfbVbVv3pSST0sopIkNUlyP3C0qp5J8ilgo6pe784ljWIRlSSpQZJHgXuAz1XVsSSfBn5TVV9tjiYN41vzkiT1OAV8G/gPQFX9nWmfqLRrWEQlSerx/uxszgJIcmtzHmk4i6gkST1+neTnwMEkPwT+ADzVnEkayj2ikiQ1SfJ14BtMxyE9V1UvNEeShrKISpIkqcUt3QEkSdpNkmwy7QsN19/THqY73Q+0BJMaOCMqSZKkFr6sJElSgyTP3syYtJNZRCVJ6nF8/iHJLcDdTVmkFhZRSZIGSnJmtk/0ziTvzP42gX8Av2uOJw3lHlFJkhokOVtVZ7pzSJ0sopIkNUjywLLxqnppdBapi0VUkqQGSX4/97gPuA+4WFUnmyJJw3mOqCRJDarqwfnnJIeAx5viSC18WUmSpNXwN+AL3SGkkZwRlSSpQZInuXaz0h7gBHCpL5E0nkVUkqQel4G9TGX0beBXVXWhN5I0lkVUkqSBZgfXPwY8DLzJdMf8IeDpJC9X1Qed+aSR3CMqSdJY54DbgMNVdVdVnQCOAAeBJ1qTSYN5fJMkSQMleQ04Vgv/gJPsBa5U1dGeZNJ4zohKkjRWLZbQ2eBVrr28JO0KFlFJksa6nOShxcEkp4ErDXmkNi7NS5I0UJI7gPPAe8BFplnQe4H9wKmqeqsxnjSURVSSpAZJTgLHmd6af7WqXmyOJA1nEZUkSVIL94hKkiSphUVUkiRJLSyikiRJamERlSRJUguLqCRJklr8F7z0lssJ06c6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,5))\n",
    "sns.heatmap(train_clean.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8523, 10)\n"
     ]
    }
   ],
   "source": [
    "print(train_clean.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Splitting Train data into 80% training data and 20% validation data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_clean[train_clean.columns[0:9]], train_clean[train_clean.columns[9]], test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6818, 9)\n",
      "(6818,)\n",
      "(1705, 9)\n",
      "(1705,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1346    1454.1072\n",
       "2274     703.0848\n",
       "1701     127.8336\n",
       "770     2287.0230\n",
       "5250    3599.3148\n",
       "Name: Item_Outlet_Sales, dtype: float64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "y_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building the Linear Regression Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fitting a regular multivariate linear regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "lm = LinearRegression() \n",
    "lm.fit(X_train, y_train) \n",
    "pred = lm.predict(X_test) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5377246799195246"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R-squared value of the model\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test, pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using the statsmodels package, the different coefficients of the predictors can be seen, along with p-values.\n",
    "#### This would help select the best and most significant predictors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.regression.linear_model as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Item_Outlet_Sales</td> <th>  R-squared (uncentered):</th>      <td>   0.810</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared (uncentered):</th> <td>   0.809</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th>          <td>   3217.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 09 Nov 2020</td>  <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:01:44</td>      <th>  Log-Likelihood:    </th>          <td> -58054.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6818</td>       <th>  AIC:               </th>          <td>1.161e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6809</td>       <th>  BIC:               </th>          <td>1.162e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     9</td>       <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Identifier</th>           <td>   10.0076</td> <td>   10.137</td> <td>    0.987</td> <td> 0.324</td> <td>   -9.864</td> <td>   29.879</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Weight</th>               <td>    1.5902</td> <td>    3.154</td> <td>    0.504</td> <td> 0.614</td> <td>   -4.593</td> <td>    7.773</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Fat_Content</th>          <td>   64.2000</td> <td>   31.045</td> <td>    2.068</td> <td> 0.039</td> <td>    3.343</td> <td>  125.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Visibility</th>           <td> -209.8152</td> <td>  296.108</td> <td>   -0.709</td> <td> 0.479</td> <td> -790.279</td> <td>  370.649</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Type</th>                 <td>    1.4002</td> <td>    3.505</td> <td>    0.400</td> <td> 0.690</td> <td>   -5.470</td> <td>    8.271</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_MRP</th>                  <td>   15.5605</td> <td>    0.236</td> <td>   66.021</td> <td> 0.000</td> <td>   15.098</td> <td>   16.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Identifier</th>         <td>    5.4471</td> <td>    0.119</td> <td>   45.640</td> <td> 0.000</td> <td>    5.213</td> <td>    5.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Establishment_Year</th> <td>   -2.3393</td> <td>    0.074</td> <td>  -31.625</td> <td> 0.000</td> <td>   -2.484</td> <td>   -2.194</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Location_Type</th>      <td> -127.8934</td> <td>   18.084</td> <td>   -7.072</td> <td> 0.000</td> <td> -163.343</td> <td>  -92.444</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1173.395</td> <th>  Durbin-Watson:     </th> <td>   2.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3376.762</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.909</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.929</td>  <th>  Cond. No.          </th> <td>4.43e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.43e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:      Item_Outlet_Sales   R-squared (uncentered):                   0.810\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.809\n",
       "Method:                 Least Squares   F-statistic:                              3217.\n",
       "Date:                Mon, 09 Nov 2020   Prob (F-statistic):                        0.00\n",
       "Time:                        22:01:44   Log-Likelihood:                         -58054.\n",
       "No. Observations:                6818   AIC:                                  1.161e+05\n",
       "Df Residuals:                    6809   BIC:                                  1.162e+05\n",
       "Df Model:                           9                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Item_Identifier              10.0076     10.137      0.987      0.324      -9.864      29.879\n",
       "Item_Weight                   1.5902      3.154      0.504      0.614      -4.593       7.773\n",
       "Item_Fat_Content             64.2000     31.045      2.068      0.039       3.343     125.057\n",
       "Item_Visibility            -209.8152    296.108     -0.709      0.479    -790.279     370.649\n",
       "Item_Type                     1.4002      3.505      0.400      0.690      -5.470       8.271\n",
       "Item_MRP                     15.5605      0.236     66.021      0.000      15.098      16.023\n",
       "Outlet_Identifier             5.4471      0.119     45.640      0.000       5.213       5.681\n",
       "Outlet_Establishment_Year    -2.3393      0.074    -31.625      0.000      -2.484      -2.194\n",
       "Outlet_Location_Type       -127.8934     18.084     -7.072      0.000    -163.343     -92.444\n",
       "==============================================================================\n",
       "Omnibus:                     1173.395   Durbin-Watson:                   2.006\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3376.762\n",
       "Skew:                           0.909   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.929   Cond. No.                     4.43e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.43e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_opt = X_train\n",
    "ols = sm.OLS(endog = y_train, exog = X_opt).fit()\n",
    "ols.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Item_Outlet_Sales</td> <th>  R-squared (uncentered):</th>      <td>   0.810</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared (uncentered):</th> <td>   0.809</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th>          <td>   3620.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 09 Nov 2020</td>  <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:01:44</td>      <th>  Log-Likelihood:    </th>          <td> -58054.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6818</td>       <th>  AIC:               </th>          <td>1.161e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6810</td>       <th>  BIC:               </th>          <td>1.162e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>       <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Identifier</th>           <td>   10.0023</td> <td>   10.136</td> <td>    0.987</td> <td> 0.324</td> <td>   -9.868</td> <td>   29.873</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Weight</th>               <td>    1.6302</td> <td>    3.152</td> <td>    0.517</td> <td> 0.605</td> <td>   -4.549</td> <td>    7.810</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Fat_Content</th>          <td>   62.5106</td> <td>   30.754</td> <td>    2.033</td> <td> 0.042</td> <td>    2.224</td> <td>  122.797</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Visibility</th>           <td> -213.7592</td> <td>  295.925</td> <td>   -0.722</td> <td> 0.470</td> <td> -793.865</td> <td>  366.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_MRP</th>                  <td>   15.5633</td> <td>    0.236</td> <td>   66.065</td> <td> 0.000</td> <td>   15.101</td> <td>   16.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Identifier</th>         <td>    5.4466</td> <td>    0.119</td> <td>   45.641</td> <td> 0.000</td> <td>    5.213</td> <td>    5.681</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Establishment_Year</th> <td>   -2.3341</td> <td>    0.073</td> <td>  -32.060</td> <td> 0.000</td> <td>   -2.477</td> <td>   -2.191</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Location_Type</th>      <td> -127.8430</td> <td>   18.082</td> <td>   -7.070</td> <td> 0.000</td> <td> -163.290</td> <td>  -92.396</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1173.946</td> <th>  Durbin-Watson:     </th> <td>   2.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3379.234</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.910</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.930</td>  <th>  Cond. No.          </th> <td>4.43e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.43e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:      Item_Outlet_Sales   R-squared (uncentered):                   0.810\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.809\n",
       "Method:                 Least Squares   F-statistic:                              3620.\n",
       "Date:                Mon, 09 Nov 2020   Prob (F-statistic):                        0.00\n",
       "Time:                        22:01:44   Log-Likelihood:                         -58054.\n",
       "No. Observations:                6818   AIC:                                  1.161e+05\n",
       "Df Residuals:                    6810   BIC:                                  1.162e+05\n",
       "Df Model:                           8                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Item_Identifier              10.0023     10.136      0.987      0.324      -9.868      29.873\n",
       "Item_Weight                   1.6302      3.152      0.517      0.605      -4.549       7.810\n",
       "Item_Fat_Content             62.5106     30.754      2.033      0.042       2.224     122.797\n",
       "Item_Visibility            -213.7592    295.925     -0.722      0.470    -793.865     366.346\n",
       "Item_MRP                     15.5633      0.236     66.065      0.000      15.101      16.025\n",
       "Outlet_Identifier             5.4466      0.119     45.641      0.000       5.213       5.681\n",
       "Outlet_Establishment_Year    -2.3341      0.073    -32.060      0.000      -2.477      -2.191\n",
       "Outlet_Location_Type       -127.8430     18.082     -7.070      0.000    -163.290     -92.396\n",
       "==============================================================================\n",
       "Omnibus:                     1173.946   Durbin-Watson:                   2.006\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3379.234\n",
       "Skew:                           0.910   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.930   Cond. No.                     4.43e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.43e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing Item_Type with highest p-value:\n",
    "X_opt = X_train.drop(['Item_Type'],axis=1)\n",
    "ols2 = sm.OLS(endog = y_train, exog = X_opt).fit()\n",
    "ols2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Item_Outlet_Sales</td> <th>  R-squared (uncentered):</th>      <td>   0.810</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared (uncentered):</th> <td>   0.809</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th>          <td>   4137.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 09 Nov 2020</td>  <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:01:44</td>      <th>  Log-Likelihood:    </th>          <td> -58055.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6818</td>       <th>  AIC:               </th>          <td>1.161e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6811</td>       <th>  BIC:               </th>          <td>1.162e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>       <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Weight</th>               <td>    1.7891</td> <td>    3.148</td> <td>    0.568</td> <td> 0.570</td> <td>   -4.382</td> <td>    7.960</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Fat_Content</th>          <td>   62.2989</td> <td>   30.753</td> <td>    2.026</td> <td> 0.043</td> <td>    2.014</td> <td>  122.584</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Visibility</th>           <td> -206.5372</td> <td>  295.834</td> <td>   -0.698</td> <td> 0.485</td> <td> -786.464</td> <td>  373.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_MRP</th>                  <td>   15.5666</td> <td>    0.236</td> <td>   66.086</td> <td> 0.000</td> <td>   15.105</td> <td>   16.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Identifier</th>         <td>    5.4411</td> <td>    0.119</td> <td>   45.645</td> <td> 0.000</td> <td>    5.207</td> <td>    5.675</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Establishment_Year</th> <td>   -2.3033</td> <td>    0.066</td> <td>  -35.015</td> <td> 0.000</td> <td>   -2.432</td> <td>   -2.174</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Location_Type</th>      <td> -128.1765</td> <td>   18.079</td> <td>   -7.090</td> <td> 0.000</td> <td> -163.617</td> <td>  -92.736</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1173.809</td> <th>  Durbin-Watson:     </th> <td>   2.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3378.671</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.909</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.930</td>  <th>  Cond. No.          </th> <td>4.43e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.43e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:      Item_Outlet_Sales   R-squared (uncentered):                   0.810\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.809\n",
       "Method:                 Least Squares   F-statistic:                              4137.\n",
       "Date:                Mon, 09 Nov 2020   Prob (F-statistic):                        0.00\n",
       "Time:                        22:01:44   Log-Likelihood:                         -58055.\n",
       "No. Observations:                6818   AIC:                                  1.161e+05\n",
       "Df Residuals:                    6811   BIC:                                  1.162e+05\n",
       "Df Model:                           7                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Item_Weight                   1.7891      3.148      0.568      0.570      -4.382       7.960\n",
       "Item_Fat_Content             62.2989     30.753      2.026      0.043       2.014     122.584\n",
       "Item_Visibility            -206.5372    295.834     -0.698      0.485    -786.464     373.390\n",
       "Item_MRP                     15.5666      0.236     66.086      0.000      15.105      16.028\n",
       "Outlet_Identifier             5.4411      0.119     45.645      0.000       5.207       5.675\n",
       "Outlet_Establishment_Year    -2.3033      0.066    -35.015      0.000      -2.432      -2.174\n",
       "Outlet_Location_Type       -128.1765     18.079     -7.090      0.000    -163.617     -92.736\n",
       "==============================================================================\n",
       "Omnibus:                     1173.809   Durbin-Watson:                   2.006\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3378.671\n",
       "Skew:                           0.909   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.930   Cond. No.                     4.43e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.43e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing Item_Type & Item_Identifier with highest p-value:\n",
    "X_opt = X_train.drop(['Item_Type', 'Item_Identifier'],axis=1)\n",
    "ols3 = sm.OLS(endog = y_train, exog = X_opt).fit()\n",
    "ols3.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Item_Outlet_Sales</td> <th>  R-squared (uncentered):</th>      <td>   0.810</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared (uncentered):</th> <td>   0.809</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th>          <td>   4827.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 09 Nov 2020</td>  <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:01:44</td>      <th>  Log-Likelihood:    </th>          <td> -58055.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6818</td>       <th>  AIC:               </th>          <td>1.161e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6812</td>       <th>  BIC:               </th>          <td>1.162e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     6</td>       <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Fat_Content</th>          <td>   61.6868</td> <td>   30.732</td> <td>    2.007</td> <td> 0.045</td> <td>    1.442</td> <td>  121.932</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Visibility</th>           <td> -208.6047</td> <td>  295.797</td> <td>   -0.705</td> <td> 0.481</td> <td> -788.459</td> <td>  371.250</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_MRP</th>                  <td>   15.5705</td> <td>    0.235</td> <td>   66.135</td> <td> 0.000</td> <td>   15.109</td> <td>   16.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Identifier</th>         <td>    5.4401</td> <td>    0.119</td> <td>   45.643</td> <td> 0.000</td> <td>    5.206</td> <td>    5.674</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Establishment_Year</th> <td>   -2.2914</td> <td>    0.062</td> <td>  -36.748</td> <td> 0.000</td> <td>   -2.414</td> <td>   -2.169</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Location_Type</th>      <td> -128.2540</td> <td>   18.078</td> <td>   -7.095</td> <td> 0.000</td> <td> -163.692</td> <td>  -92.816</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1174.517</td> <th>  Durbin-Watson:     </th> <td>   2.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3385.708</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.910</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.934</td>  <th>  Cond. No.          </th> <td>4.43e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.43e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:      Item_Outlet_Sales   R-squared (uncentered):                   0.810\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.809\n",
       "Method:                 Least Squares   F-statistic:                              4827.\n",
       "Date:                Mon, 09 Nov 2020   Prob (F-statistic):                        0.00\n",
       "Time:                        22:01:44   Log-Likelihood:                         -58055.\n",
       "No. Observations:                6818   AIC:                                  1.161e+05\n",
       "Df Residuals:                    6812   BIC:                                  1.162e+05\n",
       "Df Model:                           6                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Item_Fat_Content             61.6868     30.732      2.007      0.045       1.442     121.932\n",
       "Item_Visibility            -208.6047    295.797     -0.705      0.481    -788.459     371.250\n",
       "Item_MRP                     15.5705      0.235     66.135      0.000      15.109      16.032\n",
       "Outlet_Identifier             5.4401      0.119     45.643      0.000       5.206       5.674\n",
       "Outlet_Establishment_Year    -2.2914      0.062    -36.748      0.000      -2.414      -2.169\n",
       "Outlet_Location_Type       -128.2540     18.078     -7.095      0.000    -163.692     -92.816\n",
       "==============================================================================\n",
       "Omnibus:                     1174.517   Durbin-Watson:                   2.006\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3385.708\n",
       "Skew:                           0.910   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.934   Cond. No.                     4.43e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.43e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing Item_Type, Item_Identifier, and Item_Weight with highest p-value:\n",
    "X_opt = X_train.drop(['Item_Type', 'Item_Identifier', 'Item_Weight'],axis=1)\n",
    "ols4 = sm.OLS(endog = y_train, exog = X_opt).fit()\n",
    "ols4.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Item_Outlet_Sales</td> <th>  R-squared (uncentered):</th>      <td>   0.810</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared (uncentered):</th> <td>   0.809</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th>          <td>   5792.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 09 Nov 2020</td>  <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:01:44</td>      <th>  Log-Likelihood:    </th>          <td> -58055.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6818</td>       <th>  AIC:               </th>          <td>1.161e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6813</td>       <th>  BIC:               </th>          <td>1.162e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</td>       <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_Fat_Content</th>          <td>   60.6218</td> <td>   30.694</td> <td>    1.975</td> <td> 0.048</td> <td>    0.452</td> <td>  120.792</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_MRP</th>                  <td>   15.5699</td> <td>    0.235</td> <td>   66.135</td> <td> 0.000</td> <td>   15.108</td> <td>   16.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Identifier</th>         <td>    5.4635</td> <td>    0.115</td> <td>   47.714</td> <td> 0.000</td> <td>    5.239</td> <td>    5.688</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Establishment_Year</th> <td>   -2.3085</td> <td>    0.057</td> <td>  -40.175</td> <td> 0.000</td> <td>   -2.421</td> <td>   -2.196</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Location_Type</th>      <td> -128.1397</td> <td>   18.076</td> <td>   -7.089</td> <td> 0.000</td> <td> -163.575</td> <td>  -92.705</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1176.151</td> <th>  Durbin-Watson:     </th> <td>   2.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3395.359</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.910</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.939</td>  <th>  Cond. No.          </th> <td>4.59e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.59e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:      Item_Outlet_Sales   R-squared (uncentered):                   0.810\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.809\n",
       "Method:                 Least Squares   F-statistic:                              5792.\n",
       "Date:                Mon, 09 Nov 2020   Prob (F-statistic):                        0.00\n",
       "Time:                        22:01:44   Log-Likelihood:                         -58055.\n",
       "No. Observations:                6818   AIC:                                  1.161e+05\n",
       "Df Residuals:                    6813   BIC:                                  1.162e+05\n",
       "Df Model:                           5                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Item_Fat_Content             60.6218     30.694      1.975      0.048       0.452     120.792\n",
       "Item_MRP                     15.5699      0.235     66.135      0.000      15.108      16.031\n",
       "Outlet_Identifier             5.4635      0.115     47.714      0.000       5.239       5.688\n",
       "Outlet_Establishment_Year    -2.3085      0.057    -40.175      0.000      -2.421      -2.196\n",
       "Outlet_Location_Type       -128.1397     18.076     -7.089      0.000    -163.575     -92.705\n",
       "==============================================================================\n",
       "Omnibus:                     1176.151   Durbin-Watson:                   2.006\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3395.359\n",
       "Skew:                           0.910   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.939   Cond. No.                     4.59e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.59e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing Item_Type, Item_Identifier, Item_Weight, Item_Visibility with highest p-value:\n",
    "X_opt = X_train.drop(['Item_Type', 'Item_Identifier', 'Item_Weight', 'Item_Visibility'],axis=1)\n",
    "ols5 = sm.OLS(endog = y_train, exog = X_opt).fit()\n",
    "ols5.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>Item_Outlet_Sales</td> <th>  R-squared (uncentered):</th>      <td>   0.809</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>        <th>  Adj. R-squared (uncentered):</th> <td>   0.809</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>   <th>  F-statistic:       </th>          <td>   7236.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 09 Nov 2020</td>  <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>22:01:45</td>      <th>  Log-Likelihood:    </th>          <td> -58057.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  6818</td>       <th>  AIC:               </th>          <td>1.161e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  6814</td>       <th>  BIC:               </th>          <td>1.161e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>       <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>     <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "              <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Item_MRP</th>                  <td>   15.5714</td> <td>    0.235</td> <td>   66.128</td> <td> 0.000</td> <td>   15.110</td> <td>   16.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Identifier</th>         <td>    5.4628</td> <td>    0.115</td> <td>   47.698</td> <td> 0.000</td> <td>    5.238</td> <td>    5.687</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Establishment_Year</th> <td>   -2.2979</td> <td>    0.057</td> <td>  -40.157</td> <td> 0.000</td> <td>   -2.410</td> <td>   -2.186</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Outlet_Location_Type</th>      <td> -127.9822</td> <td>   18.080</td> <td>   -7.079</td> <td> 0.000</td> <td> -163.424</td> <td>  -92.540</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>1176.679</td> <th>  Durbin-Watson:     </th> <td>   2.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3396.570</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 0.911</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td> 5.939</td>  <th>  Cond. No.          </th> <td>2.71e+03</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.71e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:      Item_Outlet_Sales   R-squared (uncentered):                   0.809\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.809\n",
       "Method:                 Least Squares   F-statistic:                              7236.\n",
       "Date:                Mon, 09 Nov 2020   Prob (F-statistic):                        0.00\n",
       "Time:                        22:01:45   Log-Likelihood:                         -58057.\n",
       "No. Observations:                6818   AIC:                                  1.161e+05\n",
       "Df Residuals:                    6814   BIC:                                  1.161e+05\n",
       "Df Model:                           4                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "=============================================================================================\n",
       "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------------------\n",
       "Item_MRP                     15.5714      0.235     66.128      0.000      15.110      16.033\n",
       "Outlet_Identifier             5.4628      0.115     47.698      0.000       5.238       5.687\n",
       "Outlet_Establishment_Year    -2.2979      0.057    -40.157      0.000      -2.410      -2.186\n",
       "Outlet_Location_Type       -127.9822     18.080     -7.079      0.000    -163.424     -92.540\n",
       "==============================================================================\n",
       "Omnibus:                     1176.679   Durbin-Watson:                   2.006\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3396.570\n",
       "Skew:                           0.911   Prob(JB):                         0.00\n",
       "Kurtosis:                       5.939   Cond. No.                     2.71e+03\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.71e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing Item_Type, Item_Identifier, Item_Weight, Item_Visibility, Item_Fat_Content with highest p-value:\n",
    "X_opt = X_train.drop(['Item_Type', 'Item_Identifier', 'Item_Weight', 'Item_Visibility', 'Item_Fat_Content'],axis=1)\n",
    "ols6 = sm.OLS(endog = y_train, exog = X_opt).fit()\n",
    "ols6.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Assuming a significane level of <0.05, all the remaining predictor variables are statistically significant.\n",
    "\n",
    "#### Using the validation set, we can predict the results with the model before calculating errors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_opt = X_test.drop(['Item_Type', 'Item_Identifier', 'Item_Weight', 'Item_Visibility', 'Item_Fat_Content'],axis=1)\n",
    "y_pred = ols6.predict(X_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating RMSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1203.8642367327916"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2181.2889135750324\n"
     ]
    }
   ],
   "source": [
    "import statistics\n",
    "print(statistics.mean(train.Item_Outlet_Sales))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The RMSE of the best fit model is calculated to be about 1192.64, while the mean value of Item_Outlet_Sales was about 2181.29."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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