From 69f88ff460eaa4860288fcbd9ac712a277d72fde Mon Sep 17 00:00:00 2001
From: Roshan Rai <67222316+roshanrai1304@users.noreply.github.com>
Date: Wed, 7 Sep 2022 14:20:26 +0530
Subject: [PATCH 1/2] Add files via upload

---
 Lasso Regressor.csv         | 1706 ++++++++++++++++++++++++++++
 LinearRegression.csv        | 1706 ++++++++++++++++++++++++++++
 Random Forest Regressor.csv | 1706 ++++++++++++++++++++++++++++
 main.ipynb                  | 2083 +++++++++++++++++++++++++++++++++++
 xgb Regressor.csv           | 1706 ++++++++++++++++++++++++++++
 5 files changed, 8907 insertions(+)
 create mode 100644 Lasso Regressor.csv
 create mode 100644 LinearRegression.csv
 create mode 100644 Random Forest Regressor.csv
 create mode 100644 main.ipynb
 create mode 100644 xgb Regressor.csv

diff --git a/Lasso Regressor.csv b/Lasso Regressor.csv
new file mode 100644
index 0000000..339752d
--- /dev/null
+++ b/Lasso Regressor.csv	
@@ -0,0 +1,1706 @@
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diff --git a/LinearRegression.csv b/LinearRegression.csv
new file mode 100644
index 0000000..3498535
--- /dev/null
+++ b/LinearRegression.csv
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diff --git a/Random Forest Regressor.csv b/Random Forest Regressor.csv
new file mode 100644
index 0000000..adc6e6e
--- /dev/null
+++ b/Random Forest Regressor.csv	
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diff --git a/main.ipynb b/main.ipynb
new file mode 100644
index 0000000..569bc5a
--- /dev/null
+++ b/main.ipynb
@@ -0,0 +1,2083 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Problem Statement : The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim of this data science project is to build a predictive model and find out the sales of each product at a particular store."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Hypothesis Generation\n",
+    "Features can impact the sales of a store Item_Weight, Item_Visibility, Item_Type, Item_MRP, Outlet_Location and Outlet_Type."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Loading packages and data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import mean_absolute_error as MAE\n",
+    "from sklearn.metrics import mean_squared_error as MSE\n",
+    "from sklearn.metrics import r2_score as R2\n",
+    "from sklearn.model_selection  import cross_val_score as CVS\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.linear_model import Lasso\n",
+    "import xgboost as xg\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_train = pd.read_csv(\"D:\\Machine Learning\\Datasets for Machine learning\\BigMart\\Train.csv\")\n",
+    "data_test = pd.read_csv(\"D:\\Machine Learning\\Datasets for Machine learning\\BigMart\\Test.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data Exploration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8523, 12)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_train.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Exploratory Data Analysis"
+   ]
+  },
+  {
+   "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": [
+    "data_train.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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_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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_train.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Item_Identifier                 0\n",
+       "Item_Weight                  1463\n",
+       "Item_Fat_Content                0\n",
+       "Item_Visibility                 0\n",
+       "Item_Type                       0\n",
+       "Item_MRP                        0\n",
+       "Outlet_Identifier               0\n",
+       "Outlet_Establishment_Year       0\n",
+       "Outlet_Size                  2410\n",
+       "Outlet_Location_Type            0\n",
+       "Outlet_Type                     0\n",
+       "Item_Outlet_Sales               0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Total missing values\n",
+    "data_train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Handling Missing Values\n",
+    "median1 = data_train[\"Item_Weight\"].median()\n",
+    "mode1 = data_train[\"Outlet_Size\"].mode().values[0]\n",
+    "\n",
+    "data_train[\"Item_Weight\"] = data_train[\"Item_Weight\"].replace(np.nan, median1)\n",
+    "data_train[\"Outlet_Size\"] = data_train[\"Outlet_Size\"].replace(np.nan, mode1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Item_Identifier              0\n",
+       "Item_Weight                  0\n",
+       "Item_Fat_Content             0\n",
+       "Item_Visibility              0\n",
+       "Item_Type                    0\n",
+       "Item_MRP                     0\n",
+       "Outlet_Identifier            0\n",
+       "Outlet_Establishment_Year    0\n",
+       "Outlet_Size                  0\n",
+       "Outlet_Location_Type         0\n",
+       "Outlet_Type                  0\n",
+       "Item_Outlet_Sales            0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_train.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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>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>Medium</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      Medium               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": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "###\n",
+    "data_train.drop_duplicates(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_train['Outlet_Age'] = data_train['Outlet_Establishment_Year'].apply(lambda year : 2022 - year)\n",
+    "data_test['Outlet_Age'] = data_test['Outlet_Establishment_Year'].apply(lambda year : 2022 - year)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#### Finding Outliers\n",
+    "\n",
+    "### No outliers in Item_weight\n",
+    "data_train.boxplot(column=[\"Item_Weight\"])\n",
+    "plt.show()\n",
+    "\n",
+    "#@# There are outliers in Item_Visibility\n",
+    "data_train.boxplot(column=[\"Item_Visibility\"])\n",
+    "plt.show()\n",
+    "\n",
+    "#### Removing the outliers in Item_Visibility\n",
+    "def remove_outlier(col) :\n",
+    "    sorted(col)\n",
+    "    Q1, Q3 = col.quantile([0.25, 0.75])\n",
+    "    # print(f'{Q1}, {Q3}')\n",
+    "    IQR = Q3-Q1\n",
+    "    lower_range = Q1 - 1.5 * IQR\n",
+    "    upper_range = Q3 + 1.5 * IQR\n",
+    "    return lower_range, upper_range\n",
+    "\n",
+    "lower_value, upper_value = remove_outlier(data_train[\"Item_Visibility\"])\n",
+    "data_train[\"Item_Visibility\"] = np.where(data_train[\"Item_Visibility\"]>upper_value, upper_value, data_train[\"Item_Visibility\"])\n",
+    "data_train[\"Item_Visibility\"] = np.where(data_train[\"Item_Visibility\"]<lower_value, lower_value, data_train[\"Item_Visibility\"])\n",
+    "# print(f'{lower_value}, {upper_value}')\n",
+    "# data_train.boxplot(column=[\"Item_Visibility\"])\n",
+    "# plt.show()\n",
+    "\n",
+    "### No Outliers in Item_MRP\n",
+    "data_train.boxplot(column=[\"Item_MRP\"])\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Item_Fat_Content', ylabel='count'>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "sns.countplot(x='Item_Fat_Content',  data=data_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Item_Type column\n",
+    "plt.figure(figsize=(25,7))\n",
+    "sns.countplot(x='Item_Type', data=data_train)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Bivariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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_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",
+       "      <th>Outlet_Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Item_Weight</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.013744</td>\n",
+       "      <td>0.024951</td>\n",
+       "      <td>0.007739</td>\n",
+       "      <td>0.009693</td>\n",
+       "      <td>-0.007739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Item_Visibility</th>\n",
+       "      <td>-0.013744</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.001163</td>\n",
+       "      <td>-0.064105</td>\n",
+       "      <td>-0.117918</td>\n",
+       "      <td>0.064105</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Item_MRP</th>\n",
+       "      <td>0.024951</td>\n",
+       "      <td>-0.001163</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.005020</td>\n",
+       "      <td>0.567574</td>\n",
+       "      <td>-0.005020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Outlet_Establishment_Year</th>\n",
+       "      <td>0.007739</td>\n",
+       "      <td>-0.064105</td>\n",
+       "      <td>0.005020</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.049135</td>\n",
+       "      <td>-1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Item_Outlet_Sales</th>\n",
+       "      <td>0.009693</td>\n",
+       "      <td>-0.117918</td>\n",
+       "      <td>0.567574</td>\n",
+       "      <td>-0.049135</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.049135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Outlet_Age</th>\n",
+       "      <td>-0.007739</td>\n",
+       "      <td>0.064105</td>\n",
+       "      <td>-0.005020</td>\n",
+       "      <td>-1.000000</td>\n",
+       "      <td>0.049135</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           Item_Weight  Item_Visibility  Item_MRP  \\\n",
+       "Item_Weight                   1.000000        -0.013744  0.024951   \n",
+       "Item_Visibility              -0.013744         1.000000 -0.001163   \n",
+       "Item_MRP                      0.024951        -0.001163  1.000000   \n",
+       "Outlet_Establishment_Year     0.007739        -0.064105  0.005020   \n",
+       "Item_Outlet_Sales             0.009693        -0.117918  0.567574   \n",
+       "Outlet_Age                   -0.007739         0.064105 -0.005020   \n",
+       "\n",
+       "                           Outlet_Establishment_Year  Item_Outlet_Sales  \\\n",
+       "Item_Weight                                 0.007739           0.009693   \n",
+       "Item_Visibility                            -0.064105          -0.117918   \n",
+       "Item_MRP                                    0.005020           0.567574   \n",
+       "Outlet_Establishment_Year                   1.000000          -0.049135   \n",
+       "Item_Outlet_Sales                          -0.049135           1.000000   \n",
+       "Outlet_Age                                 -1.000000           0.049135   \n",
+       "\n",
+       "                           Outlet_Age  \n",
+       "Item_Weight                 -0.007739  \n",
+       "Item_Visibility              0.064105  \n",
+       "Item_MRP                    -0.005020  \n",
+       "Outlet_Establishment_Year   -1.000000  \n",
+       "Item_Outlet_Sales            0.049135  \n",
+       "Outlet_Age                   1.000000  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_train.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(data_train.corr())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Item_MRP and Item_Outlet_Sales has very high correlation.\n",
+    "- Item_Weight and Item_Outlet_Sales have a good correlation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SW5aDQSyMXwCQh6jwfj6An0RE7/GcAHMUL2hNPfWZYa4kfUodI+Vulxseqk0vHINaLz6Efgr31Iq9TNbDs+W2Jix+wKmWR9/HHQCBdOFphaGhMRgC6a4eFgahN7+LmcvMfAzALwH4Ydmz4jmDQBLrpnEyMQNGh9n9qcU6jpyp9H2cVjn55GwFR89Uo9/lvzm7z9s3oOD94JcPJqwpAHjyTBmPnkqP0aTtfiUxm4CjLCkNDY3+wUOuw+gpcYjoN4noYvk5Q0S3AngCgnH2+4c4tjVF2kWP8w7GZ+nPzFW7Whg/8Yk78RtfeGjwMUhroCexYA8rlDt87obW2X3TW8aMf5kWgiouHGbwTkPj2Yh+2RyWi36mqD8K4DH5WbmgpgG8HMDvDGNQ6wWtgjpgDsMIcbNvvtpEEGBFBX1pODxTxtGz1d4rSiyjAV/f+yLqZEmsPlT8Yq0rzjU0Nho4wJpzSTU5muq9FsBnmNkHcJCIBgmabyikCkfpwheFZQgDzA3PF8pk9chaAQA114chacyJYvUWKYPrpCyoy29dx7DCh24l2RqBCBZpl5SGxoBoTaFfbfRjYTSI6HIimoaov/hq7Lf8cIa1PuF38A96nhByg1B19IMowJ32Y/8COVGvscyHiajz8Vb7AQ2YYYC0S0pDY0CsOfkgRHbU5yDcUB9h5icBgIheD+DeoY1sHaKTD99jQTfeKeg9CDrJSM9fWdZQ/CHqZzepD12qZbP6D6ewqEi7pDQ0+kSp7qLhBcv2KPSLnhYGM9/BzBcz8xQzfzC2/GZmfrv6TkTPqhTbNIEpPSVQtdUKnh+ELqknZsuYq/Sm9uh7HLLggsGYLTXk0ZM4PFPGB770CCrN9LatnR4gZsbTfcZICNSFsrh9TCtTbiIepCnONTT6Q8MLUGv4K6Lk6QeD1GH0wi+s4r7WBboFeeO/eT7gS6FZbfhYqqX3o26tYwgCRsNL6R0e27maZAcBSxbXdiH6f77yGB45uYQ7n+zdhDD+MLk+Y6HWn3JbTtB7uTojCETGmW6ipKHRHwJmeBysH/LBPvCs4hXtVVsQ/1m5pJT/sNMNO3gyWcdwcqmOx0+Ve48FQnH86W1P4Ef//I7eg0/bR0crY7Dlqeumuq/6374VAYR7T7ukNDT6g+pjo1LSh4XVVBgdh0lEJhHdS0Rflt/3EdEdRHSYiP6eiBy5PCO/H5a/743t49fk8seI6LWrOO5UHF+odfTPx3OdlbBsLsN94veISzCSF/XmB0+J5S2ziH5NUE8Wkqj144HsTuf60PFFPHhsUfhGB9ACK3loOYBWGBoaA0C4xdX7vDFcUt0sjF8AcDD2/fcgAugXAJgH8E65/J0A5uXyj8j1QESXAngbgMsAXAfgT4jIXMWxJ/Dvh2bxC5+5D1+470THdeIC0SAKO8R1KzhLj4ukLEs5Vldh3eUnoRTECicX6u2FPfLLyQ6dABPEhR3i4GmHX8kjG0DEMLTC0NDoD37Agh0hWCcWBhHt67HsWx222wnBavsX8jsBeCVE5hUAfBLAm+Xn6+V3yN9fJde/HqL+oyGztA4DuKbfsQ+KwzPCTfTwicXkD5z+OWuZ2FTMDHyj0txXah911w8X9EqH7RVHUEI9zZoJpIJrtnCah5ZHzBo5V9AxDA2NwaA4pFopfVYbg1gY/5iyTAl9MPN7Omz3/wPwqxATRwCYArDAzCql5xgA1eJtB4Bn5P48AIty/XB5yjarjrCau8PUWc34Kb4QkQLodMvS7mUnl9RC1ZXKojOUNVNrCuXSSagv1Vzc9/SC2FfLDnsqoz6sm/TLtJIsKU5YbRqriyDgaEKi8ayAFwTgAG2koauNnnUYkkfqMgBjRPRDsZ9GAWR7bPtGADPM/F0iesUKxtkXiOhGADcCwO7du5e9H0NxfHS49n7AqMkXTgnb5UzA0yjKO60XR8BB6tg67et3/uVRHJmt4O9/9oVtCqKjS2mFM5WVbB+wuAe+bogxFJSbHs6UGjhv+tnbneC5BmFhDJ8doZ/CvYsAvBHAOIA3xZaXAPxsj21fAuAHZJFfFkLJ/AGAcSKypBWxE6K3BuS/uwAck7QjYwDOxpYrxLcJwcwfBfBRADhw4MCyL52i/Gg171j+9gdfO4TDs2U89P6U2HuXLIVWa6LXzLwt3tCyXb8neHy+BgDwWAXSGUdmy8jYphxy98C7uBrU2WpYZTP49GIdR2bLuHrPxKruV0OAebAmWRrrHyp+kZZ2v5rop3Dvi8z80wDeyMw/Hfv7eWb+do9tf42ZdzLzXoig9b8x848DuBXAj8jVbgDwRfn5JkQEhz8i12e5/G0yi2ofgP0A7hzsVPtHaC20um+kYDw8W058j1bvLjgHcd20t1WNKZIOh0lzSbVmUyklVGn4WKy6XdOA2/aVqrxUsF/8e2y+imfmquG6C9UmZpbqYOa+zeWvPnIKH/7aIS3UIAT70QGo8fsCD96GV2N9ww9kD5kh39dBYhhniejrRPQQABDR9xDRbyzzuP8DwC8R0WGIGMXH5fKPA5iSy38JwHsBgJkfBvBZAI8A+FcA75YEiEOBkrutorRVYKZnM6X9In8PWveX7PlQd/1EZlC8V3f82ErwxskQuyHcNkUBdXKLhYukxdQt6K3WPXhyCfMVF/PVqBjQCxhNP8BMqYGHTyz1GKlA3Q3gB4xyU/vZvYDD5lmrBd2c6tkHT/aPGfZdHYRt9mMAfgXAnwMAMz9ARH8H4Lf72ZiZvwHgG/LzEaRkOTFzHcBbOmz/IQAfGmC8y4cUjmkT4rjcjL9zy8kiai2yefx0KfZbtPz+ZxZgma0h9uUFllutFpU91ehjytnraK4fhIy64RhZKLZGEPQ9XkUJUm14gqNrtXnjNxACZrirHPwXRV7axHg2IWAGB+K+DvNtGURh5Jn5zpae0as79VknUCSCys0SzujbTIp0K6PWDHC23MBUMdPyW+vahLjLsc2Ckd8/+M8H25b3Er6PnSqhkDExlrPDdYOY/ROm27IoUmxNq42dIgidadLjFoqyRBLLYiPtd1KrBGS16cNnwVz7XMWwepuvthLSWFsEAUKXFA0xB34Ql9QZIjofUkYS0Y8AODmUUa0xjBYLY7bcwJlKQ4i+2L3gmACOI2BGpdHuTmlTF5RsxNRvZlGoANr6xEYf664fptuGP8vmKsfnazh6tiKPKYt+WoJlSUukP7THS8QSNZvtdz+hhdH0n/Ouk4B51etRGOu/KHJmqY6z5cZaD2PDwA+EG3fYduMgFsa7ITKQLiai4wCeBPATQxnVGqM1hqHcIq2vWOtLF3JJdfD5p72iSiCeKTdiGUk9hKv80Q0YpYbXd1qsyvpqekFU46AsjU6H6hG/WKy5+Ni/H8Hzdo2FKyf2xcB81UWm3/7jiBRGvekPtWq1VHdRzFhDnZGtFMLNsNouqfXfzbDm+rCN1SSieHbDZ1EXJtosDO84fd8RZj7CzN8P0RfjYmZ+KTMfHdrI1hAUuqTE94DTS+47mfVBByHb+pISRQqj1vTbFVAHMR6EvwOVRrpXMG0MaTQfkVJM3U1srJQqvD/670fw74fO4O/veiYRKI/OQV2/lgF0gVJmlaY3VAvj+HwNdXd9+/JVq9rV7DvCaE8ZX29oegFcHWfpG36g6jB4qBOgfgr3fqnDcgAAM394lce05ghn+bF3Sgmu+Ivr+QHufXoef3bbEfzBW69KrJt209o8SDIgrPaZTJ3t8kK3BgVS1mVpr8R7YwfcHrRndMia4eR+OkG5xQypUOLZV4YhA/tdc8faoSyMStMfaiFSQ/YxWc8IWNybXpbeIGCO8vbXa0JB0wvOKR3NWqLpBTANgrmCexFwAATGukirHeny96wsFaXWoDdH1ME33R8RErpBgL+5/WksVF2cXKzJbcRvafe+NTMlYBb8L4Hy9UcBabWvVMJCVU8hv59aksSB8fiK/PFz3z0WCt3WtN7wfFOaI9VcP8rikuZumsRXApeIUhUDc6Qo+6tqZ7hBZ6urX/RDfeF6G0FhyF4oq7xfcV/W77k3/KAnNUyp7mKhunrNytYKpxZrKzoPlZ4fsMhEHOYcoKeFwczvBwAiegkzJwgGieglwxrYWqI16A0IIfjQ8UV8/t6owNz1GHlHVEvXXD98qYU/Mc3CaI95BEHkPmrNmGqd2xMiF49a598encHppfbgIEN0APzyA1FeQgAGcTJKEhorLYJ5plRHzo4IgeOWQvKcxL9GhwZLanasxtQJrh/ANg0RhA+zpLxlu2KePFPG+dMjcKz0ORHLdNX15so/Nl/FWM7GSNYGEFl/4jqsjiRgMHysv3OPo+mJ56EbKg0PTS/AeN45R6MaDupeAMdavmkQn2CKCdb6yJL6oz6XbXgoUzhexRwEwGfvPpZ4ybwgQN4ROrcay0gKgk4WRvK72pVyYQUqAq1+T36NjSsa36OxpkzxQ6YJAyV8b7r/BP7kG08kfgtahHnr9p3cAxyzMOLjFuMRlkvHtOQYHj9VguuLUSjfdXUFLinX616cJqzGYFVjA6uBuusnUpwF3UP/7ry+IF2hDc9P7/i4DuD6Qc8WvU0vWFYfmvUGLwhWFEsLpAdATSaH6crrJ4bxIgAvBjDdEs8YBTC0nhRribD4LPaW+hy0uTlOL9Vx3zMLAIBq7DdmTlXyrQIsCDg5A+f+XA8caYzEw8FyH3GXWqsSYTA+9u9H2nbWKjeDgEEt04nWsfkBJyyMTmPt9SowCzJHXx7Ti9VhLNdt0itg6gccVseeC5wtN5BzzHCC0Qmun6SoVk2vVtN9pATLmVIDjmVgx0Q+fb0hB1A7IQgYvs/wehSTNvtwW20E+EF/LtROiFd4+wEPtWqpHwvDgYhVWEjGL5YQ8UE9q0AtLikVV/BlnwaFX/rs/eFn4T6R2yEq/oujtW4iAIdCnJCeuRJf0hpbYbQoDAYeOr6EmaXezZDiaHpBm+BUs5Za08cff+MJcX4QfuOZUh2LNRePnFgKBZmRiGEoq0OOtYM7S8GTwlu54byEhTG4QGBOKuI0qPqGc+XHP1tuttXFpMELGG5MUPp9WGeDIpCKsuEFHSv8a00fR2Z7tw8eBrxAxPbcHtrc83uvsxHgB53vQz+It1rw1zpLiplvA3AbEf0VMz81tJGsI7QJZinI/IBhm1Hr0PmqG25TafiJyvC0W9aWJaWqM1UMo8UF1SokEtlbygZNjFsIwqqsX2glrmx1OwFRimWaa8YgwpceOIFbHjmNnRM5vOqSLZgpNVBt+Ng+nhVjD+LKoX3cDDWjTbkgEn7AssUkg5liFoYXxjMGgR+wTCDovG3YoewcKYxq08ME9/a1twoPL1j9wLxKpug2q/WCtXP3BKx6unc/fsML1n16cD/wfcALlm9hRC5gAAx86f4TKDc8vOnK7asyvjgGKdz7KyJquzvM/MpVHM+6QCSYI4URBAwvCGAZBtKcLOVYPUQnU771xVfB8UD+qwLL4jPao95qPxC+QLVuHGp2HR0z+u2h44v46DefbNtfWiGXehHDF1KaWfHe3nF3U+v5qnTcpLst/eV2pWshkOvELYzlVDmHmUXdlBRHVk3f+w0Yc9UmNrVQvvSDfq2lIEjW9wRB+0RipQggzrvpBx1no4ze9CF+wFisuZgsrG7QWd1zdR87pf6qZ2ajQ0wSVuKSUhpDyJAvP3gShYy15grjl2OfswB+GM9SLqm04HIAIWDiJIBxlOsumr6Ic6hYQitaF4lQhyANqzQ9qNq2eNA9rhC80D0hhTFas6gokW4bAIlZ4t1H51PHlJa6Go+TACJAGl9LHUedp0GR6y4aT5Ql1U3oxeMJzC1cUstSGCIO0E2YcCDdUgPMopt+gOPztYEVRhCwrK7vfayAOXHPvCCQ13plgpGZ8eipJVyybUzeC2GFdQosc5ffFJpegNOL9VVXGL7PUNOyblxi7hCsr7WAmrx4fgCrR2ZYGlQMgyCSc5pegGJmENHeP/reKzN/t2XRt4hoaD0p1hJpQW9F0dCJrqDSENktJxbqmB5Jf4Fam5sEUjs0fB/Vhp9wIS3WXPRCawxD7TOMpXAyC6hTYVBqb/EACeWTUBYcfYiC3slK8Mgw4Sg1tMN5KAp0Nda4S8r1B595KYuwmyjxWTacGUAh+QGj7vkDF7y5QSBiE32417yAYcRcUuW6j3uensNL92/q+3ipY/CVq5LD+hqhnNLHxOg93mHwXAHSDQdItxTD7pBa01ynGV6DQgSqSU5IB9+eo5cNNVfM4YvZ4SiMvtUZEU3G/jYR0WshOuI96xAFveWMHmLG6nexMEoNNxKOHfabVumtWGDTqq1bZ+wKfsxNkWa1qAfI9ZIul1aFEQlztJEP+iniNk6qGCklZWG0X5d+XSmuF8APgtDCEK4/QsBIJXFcqndXpr60MLq6pGTZ+yACz2cRkB6UssL1hZvN62O7gDkhCL95aBYf/eaTOCULQ/0BlZyCJ5WWUBQR5YjrpacWBwy5fudjCYWx+nEO5QZtda8mjh2o5zaFhHODwQ9EKmwvi64TAlZZihS2ji6stYUBIG5heBDkg+9c3eGsD4T05vK7Enx+wLA6zCyTMYz0/ba+6Kr+IhK+Md9/DyfEkdkyHjmxlJqmqV4y5atWSLUwWPjJ22pEWpVX7MVkACcXawmXVDzordYJz02eV6fr0vACMFMogAIGJgs2zpSbWEixtI7MlHHp9rG2orzFmgvHNGLcVenHA2RgFTSQ8A2kJeT5jEHeRxHQb7/GnY7hxS6Ueq5Unc+pxRpMg7B1LNf/ACAmD67PLRao+JA2ERIKRYy7wxwJAS9fyHUfawCDkrQ2rfDDd4c3NAW+soQJWHaKcNRXh0OFUcwMp+JhEJfUvqGMYB3CaLEw1GcvYDgdfIzlunixw2B1Cu57eiHxXQjgaBYVCtW2dKgkmBm/c/OjAIAfu2Z3YoxAUjHFJ/5pVoDarlWYBwHHHkTI/UbjUtaAmmAaROF5Jzix0DtLquEFMA0xjoYsYBrLCYWx1KIwgoAFbUQQwGkxkBerTeQzFmzDCFtWdkLTEwkMKluqHxdTwEDT44Ff7KYfSIHQW7j6UoCoMalUXFXMV2n6HSct3eAGATxPxEMCRMFuAlJdIWqCEDDD7CCMmQe/Fv2g4QXS8u7cctSXjnsVJ9uoCJhx8OQSJvIOLt2+vPOIYhiC4RkYnoXRl0uKiDYT0fuJ6HPy7/1EtHkoI1oPUEFnjv4NVAyjg8KIu046+ev/5o6nE99d6btUrqe+C/d6LI9M+uTvbRaOFE5ppn/azE6ZzoB0VyByWYigd7RN3fVRrnuJa9jpva67PixD0II0pFBVdA+tCsNnEUB2vfadRS4XDmtcOsHzRU1Nwwvw6Kn+Wsf6gXAXDeqSarhCOfVyf8Xvv1pXzRhVFk254aG+DN+96wVh3YlyhSrE7/3x+SpKdTcMinezwALunJK9EjS9AP/v1sP4i/94sqMyEKnYPlw/GDrh3jARMPDx/3gSN91/YtmZUvHrX2uKizGsoHdPhSH5ou6SXz8l/wDgzmcrl1Rrqupjp0p48ky5rXAvDuXfBvpPgWQW+eaBfOIDTiqpjoohPuuPfVEPjprI9lIC6muav7/WkhXlB5G/mmPbqH1QrA8GM/CLn7kPP/7xO8SY1Hl1OCHXFzELzw9C//1YTnAptcYrfOkWShPayqpQ8aZugqzh+firbz+Jx04tJWhdukG5aBoDVuXWXR+2ST1n46oeJz5rjiwM8b3a8JZFIyHIJCPFHU9SiGffHZ4p44FjC6E7qNuzrGj/VzuE0PQDHJuv4cRCPXy+W/ua+8z4y28/hT/6t8NDrcVYqTKsNLyulmXAwo20WHOXXe2trj8jFvReQwvj9wG8mZnfx8w3yb/3AXgzgK7U5kSUJaI7ieh+InqYiBSR4T4iuoOIDhPR3xORI5dn5PfD8ve9sX39mlz+mAy4Dw1h0Fveifd8+l788uce6JpWC0C8yMw4Nl/tq1sYQ8zMFUttPHjHAA7PlPHkmUr7dnFXWcrb2om7KS2GooT5Nx+fTfxWawlu/8N3j+E9n7432lY+26oYMOaRAiMqagxUNKbLe7dU8/Dn3zyCZ+aqoetlIi8URqmeFBQBM1yPU4W2clOE/Us6HxKz5Tq+9cRZ3H9sse+UV9cXLsnagC92tenDsYyeAWLlAmSkWRiCW6npt587M2Ou0p3ttO75ocvz4RNL+JeHTkmlRGFxpHKHuT4nlG8n9JO+vBw0vQBLdTfs6Q4Ah0+XEzPwIGCcXKzh9FJjaM2gluou7ntmoe+gusiSrCWWnViodc14ZBbblepe6I4dFAkLY8hB734Uxigz39u6kJnvg6AI6YYGgFcy85UAngfgOiK6FsDvAfgIM18AYB5R8PydAObl8o/I9UBElwJ4G4DLAFwH4E+IaGg8VsrX33r7urmkgIgP5oP/fBBv/fPbex4nCDN1gvB4Z8tNnFisAgDe/6VH8D+/8GDbdvFUyLibgxn4p3uO4/5jC+nHa3mxw5kJMz591zOJ32pukJqBpf5VKcJqGcl0r7ZXSy54Zr6KO4+ebR9TwDh6tow7j87h3mcWwipnZWGUGknGWl+6VdKsApVBpCyfbnJsoSoUUUMS2PUzS3X9ALZlDPxiN1wfttnbJRWwYKst1V34vrCQlJBseiJt2yAkUpAB8f2ps5Wus2FllTAYv/j39+FLD5yAaYjkCjUu1bVNnWvQQxmInhqrH0NoSHdmpenLos4AVddPuJ68gFFp+qi5/lCypMoND/c/vYD5itt3xXvN9TFXSU4U614QxjfT4PkBXJ+xVHOXTUCoCn+BKDliLS0MIqKJlIWTvbZnAUVIY8s/BvBKAJ+Tyz8JYa0AwPXyO+TvryIRnbsewGeYucHMTwI4DOCaPsa+LBgtFoZCtywpQDwcaot+6ij+5BtP4Kf+8q6wG51JwLv/7h685+/u6yrt/vXBiLI8YW0w8Hd3Po33f+mR1O1aBZaqvk47UugC4NZt5AxeLlbXaGapkXBJxddnAL/6jw/gI187lDqmBWmNqFoWIIph1FoYa/1A8P3XU7h31GxXNd/pZmOo2EhDxkP6maWqpIdBX+y6JxRGL6qLgBkfvuUQbrrvhDwXhMqpKdlbVRp2vEbCDxj1ZtBVIdVdH6ZMVVYYzdoi/18KxPg1UIWU3YSx60dFmauJhVoTDPEMNj0hUBuen7DQgoBRaXioNL2huKQOny7BMg2YBvqeILh+O+usHwSpmX4KVelCWqq74ec0nC03OsoUFVskCKVFQNh2YbXRj8L4CICvEtHLiWhE/r0CwL/I37qCiEwiug/ADIBbADwBYIGZ1dU5BmCH/LwDwDMAIH9fBDAVX56yTfxYNxLR3UR09+zsbOvPA6P1MYyoQdJRc33MlvpvXH/Hk3MAIuHcKYupFV97dCb8HCSEc4rLKf49zSXVsg+FuusDDHwh1jAqvl28hiNclrKfHtmt8AMOhbco1BMvXN4x4ZhGW7W3UNoGaimtaZUiU4VQ3eRIScZGGq4fFtb1gusFA7ukXJlSq4R1r9lwpeFhvuqGHFLK4mp4QeiKEplNkWBSArVTiquqNDcNSjwjSomoY6jaFJLjjseo0qCsvdWU14JuRNzbgIUgbfoBmm5SqTc9X8YHGLXm6hNO1L0AWcsAA30Hoxuu30Yi6PviHDpZfxVpEQQMLFSbHZ+PSsPreJ5xhtp600fOMYdGQNhTYTDzRwG8H8AHARyFqL/4AIDfZuY/72N7n5mfB2AnhFVw8QrG23OszHyAmQ9MT0+vwv6S34Uw6jzbqLs+Ppwyi+4FT+Vix3tK9LltXCm0+k9b99E6EwsCxlLdxdceOd2235rr4+RSrW05IIPqHFkb6mBpM/rW1NxWeEGA+VBhRC+cbRgoZMw2DiafReJBWqaQco94gRCO3YTzUl25pMT++yE5dIMAlknwgqBvv7nnRy9zrxTQgBmuL/zZqhtgqDDcQKbUilc2niXm+QHqnp+aOQZElqU4frS85vowiUJFE69d8XxO9JxP369M011FjeEFSRfOfLWJpie4luLXfL7qhucy3yN+sxw0PaFMbcPoWSiqUG36bbEwLwjk+DuzAiss1b2OExdPKv1OvynZUXMDZDuVxq8C+nJ0MfOXAXy52zpE9GvM/L+77GOBiG4F8CIA40RkSStiJwDVxu44gF0AjhGRBVFJfja2XCG+zdDQXrzGXa2A5VIUq1z35cwJEhZGy2+t7/HBWLMlALj1sVn8xX88mbrfmttZAIX8UIhbKZyq5b5w7/HUwL2C50cWRq0ZuaRqro9CxkK16SUVhs8wZVGX6tIXH5diqjUonS5eQRXE1V3hvurHwlCpuGoGbhq9X8y4/1tcry7rSpr5csNDU1bpK6ui6flCYZgEN0ju1/UD1Ls0E1KuLDUI2yS4PuPpuSo2FRyM5oQYiFstXqAy+LopOKQyFKwEfsAoxazHhaqHuuuHmW8Kcabobi6f5Y5BZKwRHMtIFOV2g5rcxOt6POkuqrt+qiCPK4xSzU2tL1Jj6jRJEey+gA9CreklOmWuNgZnuuqMt7QuIKJpIhqXn3MAXg3gIIBbEfXSuAHAF+Xnm+R3yN//jYXUvgnA22QW1T4A+wEMnceqdcbM3M7KGkc9JRDbT6pc2nPQ7zsYV2rxbf7TJ+/Gh/75YNdtH+gQHAfQNbc9dEnFXBaMdKvoE986ilsf6+wejMcwqrFucwEHKDhWm0uq6QcwpNBuTVNVLhLPD3DodDn1fqj1lBuw6QewDaPPLKlA9v0Q2Vv9zDw9P4prKWbiTlDKoVT3QmK9uEuq0vBgmwYswwhdaoBQehZRx2ctfp0CjhI3PnzL4/iT254Ia198mbTAkLEidE8F9iSVbvz5Pb1Yx3ylf7ds+z45cW6LVVdkmZlGYkIWtyoWqqurMLwgUrC2aaQGrReqzbbrXXP9sDZFwQ/EM1NNobgBkp06l+peR/4uv4uFoZQbiFFzA+SGFL8ABqMG6YU0SboNwCdlRpMB4LPM/GUiegTAZ4jotwHcC+Djcv2PA/hrIjoMYA4iMwrM/DARfRbAIxC0JO9m5qEzj7UKzYAZ3cgk0wKxj50qpayZBAfLDxx+81AkjOMKrtzw8ODxxa7bdlN+nVJAq00P85VmSG4W9QABwMLFc3wh3ZWVBtcLwmBePeaScizhkoq7HgCEmUI+hLDPxZo+Ksvn5GIdH/7a48g5BgIAe6byyFjJ9dQL3JRxiX6sQ9cPkHfEg356qY6MZWBU9t7utk1kO3afjdfkGMoNkZPPHFmtdTdAwxVKNGNxYlZdd31kbLNjbMUNIqXFgFQYPkp1D2fKzVAQKUoORbdhGtQ1FdgPEFJqK8xVGzhTauIF+yaX5RoJAk4I6FLDRaXpIWOZaMaIKOdrkcJoLe5cKeITFNMguEHSmmVmPDFbwXmbCuE5qow2M0aYyFKZZiwTi3UX29FO5xKvL1mquR0nLgFzqNhbESo4FkprWBlSwOoqjLY3gZkfAHBVyvIjSMlyYuY6UiwV+duHAHxo5cPsDW75NxoDulJIxGcctqzX6MfXHa9liMbQnwaJV5gPqnS6xcU6uSJ+7tP3YqbUwMd/6oBYL7QwxH+//5XHcefRua77jV/DxbobKoya64dCIWOJdqbH52uJsbhBNMtvfbn8QAYPpTBZqnt46mwFc5UmvmfnWMi7JSwMcRyDSAqjHhlM0k0hOI6EQvaC3gZ6temHxZ6MpOur7vp47FQJF20dQdY2Q4tIBEBdaWH44bVRcCwDSzURSCUSQeusZXTs6Od6AQyQrH5PZvotVl00ZHZO05ONi6T1aBjdW936QdDGIdb0hFV0aKaEy7ePDRx89VpcUos1D7Wmj4xlJFykizGrYqlL2moaGp6fmECkjSEOglDcSmFUmj7mK03wpkK4TlMmCcTjPmo3GcvomOEUv6+lutcxccFndLYwAvHML1Rd1Fwf08XVpZuPYzVdUhuT/asL2mIYQGpabZShEN1QxTnVSmmeBj8IZHHbyi7hoEZKt3iMSJds3+OMzAJT6ZQhD5YMYXRTFkDyZZxZquPEQjWyMGJxk6xjImcb0syPKQxPxRGo7QUKOEDAUdBU1T9wABw8uRRbL3JJuX4gBKOKnTR9LFTbg6hxN4NpEKpNr6+Wq3VPcD/9z88/iJvuP5EYc8MLcGqpFlqh8QnH2UoDrh9RjIsKXvFZKa3I+hACMC58PJmGK7aNKS1Govi06YtgehAwXJksoNazDSPxTLddEx9hPEnB9QNM5B2cLTfDDKBB4PkBlmpeOEsu191wdh9X6nEBvNjiGuTY/U3DoydLXe+d77dP1+LFkmdLjbbnUrnulDsPiN4N2zRQd9sD4oAoWlUoN7yQ2qNtTEHEgtD+G+Nv73ga/+/Ww6jJLKlhYTUVxj+s4r7WFOo5SJtkp1GDWCbBMgh1L7r5ikm1n5oflV4bx3JYQActYOo2+VN1AB3BsT8oC6M31MtUa/o4eHIJJkUFbXXXD4VC3jaRsU1UZcBTwfUCfOOxWSEUWvLWRdpqLKAtBWoxa6FUiwS8HzCqsQpq06DQ3C/VXZwppyiMWOqiZZD0KXPPa15riFjEQ8cX8fTZasLd4voBMpaJuUoTpbqbcGnOVZoJoddwWxvsRi1WG56PjG2IJlfyWp1equNbh8/gqTMVlBteqCSY24tPF2tumIWzVGuCGZgt1XHHk2dxttLszBgrXSEJl6EfKZ3lUF00Zaxm62gWBKDc9ECQriFPTU44ET8qxZRHEDAOzZRw39PpFdquH2Ch2uyqUOKTmvlqU8Qgmor5gHFisYacbSYsK5VYEL8efiDu++mlukyUaB/PXFVMwGyTUKp7CRkSh+93Dnz7zJivCKLOmuuvj6C3pPP4MBH9ExHdpP7U78z8O8MZ4tohTQSmzcoNImTsZEFX5O/sfZyZpQaYgQ/888Phst//6uPLGPFg6JaX1WvcDVdUF8ctjHuemu95TCX8K00PjEi4E4SAVy6YvGMia5kizz7mdnvs9BL++BuH8cCxhUQgMRApOwiAhPUQprSSmLWLdZFggTUoslaqTT+1aVP8ehQyFqYKouteL1dW3RO1Oepc4zPjhuvDIIJtGjixUEsI2KWam1AuTT+AGXv2CBTyFClXWbygz5O1KEfPVnBqqQ7LIBw+XU4EvaNjefADxiMnl/Cuv7kH337iDG57fBZ//I0nUHf9RBA6Dl9mDXJipi1iTJaRHizuhaYfoNzwMJa3ZZacch0idFf6AaPcEPxclkEJF9Zjp0s4Pl8X+0lRCnXXR1VyN3WCcuHd/8wC3vGJO/H02SpKDbF+qeGh4Yn06tZqezV/ilPz/Mk3nsA/3C1KyFrde8yMRck4sKmYwVK9c7W3HwTwg/TYYhAwyg1RGd/01k/Q+wsQQekvoZ0141mFeM/qVqRZGAYRspaZCJyGq3URvNvHsjixGBGsnVioh7/dldJOtee4VzOGwd3J+z7+rSO45+kF7JnMh+v/8a1P9Dxma72D4kDaNJKRxWmqcM9CxhaCbbHWBFCQ60cZVTPlBmaW6tg8mhUvKkQPBxXXcWMZSoWMhZMLNeycyMNnDt0lysJoym0qDS/14VYZRIC434YpaDWafue89yAQ9N8nF8V9LdW9hPCtNn3YhoF8xsSJxToq8WBvSxZWw02mEDuWgcW6i6liJlT8Kt1X8VZZBmE0J/zZ9z2zgN+++SD2bsq3uVUXZTrniXmRrHDnk/PYPp4V5x0InipVeR+HIIuMalKU8iIiZCxDxpIKbdulXaeTizXsmMij6Qco1V3smcqjmLFEJ0pICyOWzVVpeBjJ2vBlxTcgJgGnF+vYVMxgodrEYtVtS0qou0LxzldcoEOpVsMTmXhfflAUrT41V8GFW4oARMW1KV2C8Se5Lmtk/CDi3/L8ALWmj5NLdRlz47bjKFfXdDGDM+VGR2JLmV/QkUW6VPfC39aFhQGgzsx/yMy3MvNt6m9oI1tDhLVoaQojRcgSQVoYsQB0+G9noat+6ZT+OSgG7fvcra1C6wvRikOnBePLoG6H1hmSyqffMpJBw/VDt0wha4X0BmHlbxC5IsoNHxM5B4+cXMJizYWilPeCqIlM/AXNWCYqDVEdHI9hND0/jAko4ZPmDoy7pOJwOwQiG56PY/NVAIxjMmtsqS5cP8qKUgFxgwgGgDMxwspyw0skNDQ8PxF7EIFvL6SZD6+vPOcgSFrDR2UtzFLda5sILNZc+LFxLdXc0AJjbq9zOHS6hErDw3eOzOEXP3sfynL2HeeiciwDpZRjpaFU9/DEbEW4JL0AS3UPo1kbhYyJStODQQSS98iTdOaVhohzFDLivgYB40y5EU6CsraJ00v1lGO5yDsWyg2vo6ut7gkL5E7pKj65UEfNFYrx9GIDhYwFQnLyU5V9ShiRm7LiCitauaRaYxi1WFbgVNHBYt1Fw0/vgOjL4ry0eqGAOVErsl4Uxh8Q0fuI6EVEdLX6G9rI1gHSHqi0rA+CEEhx4SkyK7oL09B3v0q9iQcNenfLYFE8U72gLlG/4ZPWYrp5GWAey9mCCNCLOoapB18pCZ85ZK9dqrmwTQMZy8SxuaoIugeCRVcJu1bBTySya4KA2xoTqfWrrp9a9R3ImWOchdgAdeQZqjR8HJmtgCF6TACiyRYHHLodam6kBEazdkIwCwsjiqU0vaQrSQVS65I7CBD3X7nIPJnBpPCMHEPd9duEjophqGyjxVoUTwmYUYkJ/iBgHF+o4cFjizi1WEfdDcRsHcn3xSDBgttPuvLpUl244BoeyjXh8hnL2ShmhGBX563iAF4gAvU520TGEmwAnhxXMSMsiqxtotzw2t7BUt1DxjLA4NQ05KYXYKZUx38cmkXAwGTBkWniHNZe2KYBtBSGVmVigYHIEqrUxf4FXRChlTCzLCcnpkGYyDuoNPywb0krRD1Hu4UOJBUPgHUT9L4CwM8C+F0IyvPfB/B/hzGotYa6X2kzzVQhSyJ1LlmHQWHmUCeoGUetGQxsHaRhUJdUdwujc7czoN2C6reJTasSnqs0kbUNjOVtND1RsUwEFBw7fPBDhRFzPyjXTsYyQqsBEIJSCQLX50ScxjQM6SN34fqiOlbN6AjC7RPv+5EYNzO++9Q83vmpu3Fcum4sk1DpEDytNb2QCE7VpTCAUtNDw/XDvH3lHrJMIyHcyg0vQe2uigZbUa57CVeZmrT4QdLleEyOueG105os1Vw0vCCcpS7GLIympCipx0gQCaI+Q8UOVKxAUdwoqArnbmBmzCzVMZ53cLbcCONMlinTnb0ABcdErSmuWTNmYeQzJvKOiWrTw0KtiZqkkW+9PnE0pGAntPfYAIQ14AeMWw6exlW7xvE9O8ZCC3FmKbJgVAtZhVrTxyMnl/D7tzweCm91PUWadHuh34JU1BnLwKhiZ655oZVYd33MlOpgZjx1toqZpXqqMmmNx6wXC+MtAM5j5pcz8/fJv1cOa2DrAa10BEC6kCUICoG0POluMjzMGHI9PHy8v65v3TCoyulezd0fR5BapZ9KaUC8EDMxV8FCtYnxnIO8Y4rAcM2DZYjrWZAKoxSS0UWzYCVMLYME9TWLkGOAiMrb9ZOzbIMkd5V0g43lbDBHgq7qCvdH2kQhCBizJdF74RuPz+DQ6RIePLbYsfnSoZkyHj4hWm8en69hRBY61ho+lupCYVUbPh46Ed33rKwNyNkmlupR0Dvuu77lkVP4t0cj7q9S3Q37WduGEVMYSRqbY9LCaLRYGGM5O1QQShmr2ToQMTDH+3IAjLGcE7leGpHL8Jm5aliFrQLz3SAsBkYxY2G21AitrELGQs4xUWmI4sIf//jtePhECa6kolcuqbxUJicX6m3tkx3TSLj5xPlIjijTaKsQ9xRNfACcKTfxwn2T2DmRw2ypgbrrY6HqhrU86hqrf10/wB1PzuH+YwshxXn83M+Wm22ehMWqC89nOJYRPh9LDTcMjteafpgQ8/H/eBKfvvOZtveMmdsmLevFwngIwPiQxrGuEM2a2/O502Z5rV3S1DKxs85CV7249z2ziN/qQEk+CAbtDtZNIfzLQ6fw8InOleLhNZL76LdtadX1E0JWBFTt8EVcrLuwDAMGARMFEWhVM1k/iHy1pVCYipqMhhvg7+58Bl99+FQoNJsts3KDBNWFEhRqVqd6SFebviy8ar+Wrh8F07/5+Cx+6R/ux//+10c7Moh+/p7j+ONbD2O23ECl6ePSbaMAhDJbkgVaf3P7U/ifn38w9LWrrnoTBQfluoeaVHxFx5IFcoy/vv0pfEb2LrEMI3SFAKo+JFIY6tQXa26oaJt+chK0cyInFIabtGiUoFWZXEr4NWMU/upYKlvNCxgf+doh/MV/HAEQBea7QVlVoqo8cjmOZi0UHOFWevx0Ca7POLVYlwpP3IuCI4o7q00f89WIfUAh71iYLTXDe6k4ogwiZG0Tc+VmYnJwclHM4NUzNlFwsHNCJHWcKTVRarphgoMRK9BT1fzPzAmlrCYk8Syt2XIjkQGl6NqbfoCMaWBUKYx6ZGG4QYCFWlO4BRviHFtdfAG3NxlbLxbGOIBHiegraWm1zypw9E+p1ofCgKgAf+psNbEstqtUrHansNVUGADwR/92uONvalarzqFTFWorFG23wtlKE2M5O+wQtlR3YZkiEKxSV9VMOwiiz0keJ0bd8/HAsQU8cnIpEfSOW4SKZFBtOyEziJQQrDa8tvNScP1IkJxYjCykpbrXlu/PzJgpi8yYR6QFoRRG1fVQqYs+D7NSKD8qC/dU/GGyYEuFIbunZS24vpj1z1ddnFysY6HaRMYyUGtEcRDLpHCbuIWhrAtAuDnUuRkEbBvLYqkuCP7KDS+8XuoW1b1AZjwpgkgvTO9VM9tqeL0DLFZdPHR8CczC1bJY7Uztra59HEr4FTMW8hkTrs/h9am5HqpNX1w/PwgtjHjVfhymbPtbicW04hxRTT/APU/No9wQdDeHTpcwnnPCcx3L2dg5Ieg8zlabGM1EGVeCFwzhfWMIMkcgShKIWxizpUai8K7eDMI4p2MZmJTPepwepOGK/hpNyUa8VPPa0riZuc2KWy8WxvsA/CCA30EUw/j9YQxqraEebwJwpoVILTVOTCll7hRVQ3fCcorzumJA/dPr8N26C4ZtbEMLo7+Dq/4QgBBkJxfrOH+6ELqfynXhkjKIMCktjHLoKgnCfPi4wmCImXvTE3+h/90PEjEng4TvXdFKjMs2sE0vkDxYQSh8W09HWBgepkcyibTUM+VG20vc8ILQinmoRWGU6l5ILaJSPh+RVehuqDAclBpeOHMfyVhw/QAPxdyWj58uwbEMVF0PRILa3jIITVm8p4rqxHWOuL2asaBqIWNhIu9gsSZcZOW6h21jSb4jVUWu+JrKzSgIrQSVEtjzVRc+M+aqTVH7YQq6km7tiqtNPxT0BccK/fHFrIWCtDofkpxo1aYfWhMAkJduq6YfJGbV9z2zgP/71cdQd30YBmFRrq9a0CpMyFThu4/O4cHjCxjN2TANCscwkXewbSwHgri+8banh2dKOC0nDq5sKau2U89X6FIEMFOqh1leQMTvVXdFGvT0iLSmpfIGhBvQk02ZGl6AxbrbFqgXz1Jy2bqwMGQK7VEAtvx8F4B7hjSuNUMQcBjUBNqZMNMtDEqtz+jcz04ea3UNDPzFt54caP3vHGlvmRrHTB/NoJSsTEsvHc22l/l4XpQ2+B+Hz8I0CC86fyp8GUVVsgEiIdDjgWWfI2K6SiOaKZtEqDYE06frBR0tDJG5E3EPTRSEwP71LzyIph/ILK0A85Vmm/XlBiIovHU0i+fvmQiXn15qtCn+hhfFSR4+sQjHMnDetMjjF0JFZNyo2fQj0vUX9TN3xEy6KYWnUhgnFjGWs2GQsEoMIuRtC3cfnce7/ua7uO1xkdkj4gJRgsax+Vr4fDa8ILSIto/lMJ53wiZW5YaHrWPZ5LlIl5fri6LKasOPKQyVHizOY7YUWV4PS0U5mrNx8GQpFKbVpofDM6VQ4SsGXkBkNjELy6fgRGnVR86IFG7VkvWsrMQvZiwU5Trq2t3x5Fm8/0sP47bHZ/Hg8UXBNqsmHPL5+MCXH8aTcp95x8JY1kbOtkJ+qcVYjMuxDGwZzSaUbsPz8ds3H8Rn7npaPBt+gJOxGqpQucr7u3Usi1Mt1d6qOLAhac/H8w5yton5SpShpiY+qvOgHzDmW1gIghYLw5AJOMPCIJXePwvRNlU1TdoBUcz3rIKi7gaS/R4U0oPe7YqEsHwG2uWi0oFCeZhQqYX/8N1jbb+lnX7Di7KZvvPEWRzYM4GRrI1xOdsu1z3Y0sJwLBN520RV5tl7MpNHCRIlCGxTzLRdX5jvUZZUnClWWEVuEIRUEsrlNVNq4KmzFTQ8H399+1P4/a8+3u6S8oR/fTRr4caXnYdffe1Fctt6m1ul4fmh0Dk2X8OO8Rwcy8BE3sapxTqIhOtICdGjZ6tYqDbDFMsxeS1mS01YhmARcH3Gg8cXceXOMZy3qRhyY00VM6Er5C+/dVRcp0Bwaqln9dh8Fbuka8X1A3jMeN3lW/Hbb74cE9LKmqs0UW54mCokC/SU8CIIN0q1GdGMqOuvLKG5GOW4csVlLBNZ28Q9T83jgWMLuPPIHJ6crYaJD+WGlwhWL9VdFDMWHNsIJxHqVlRkmqyyMEazVliYWGn4OL1Ux//+l0exb1MBlkF48PgiLINCl5QXBDi+UMNdR+dx2+NnwmNappEovlyoieued0ws1lzsnMgl3HoPHFsUdPOhQPdxKuamVPdVuaZ2TeRxWrYwDgPa0vqZKTUwWXCQtU1MFR3MVRuhomi4gtY9zm02W07WljCL6+KYBnK2OdRue8BgLql3A3gJgCUAYOZDADYPY1BrCRLNoAHI/hdpv6egzfJQaXc9lEZrZsdGgTqtbnGYtF4Kav07jsxhsebilReLR2hUCi7RJEgEvU2DkJO03QEz5qtNBAzsGBfCb6nmyj7KPhqyp7Xrc1gI6QWMeEfdgyeX8N2n5sMg+lSM1fPEQh0NL8CRM5WwELD1XEp1FyNZG5tHsnjpBZuQd0zMLDXaFMZcuZlwHajx7p0q4MmzFaHgpGvlsu3CVXXvMwtoegFsk8Lg7WypAccyYJsGZkp1zFWauHzHGC7dPorHT5dDy+aUFL5z1aZwkXlRxTUglNauSVHhrTrXKbJC5V47vVSXjKyUSE1N0JXUmzHG3ighRJ2rqscYy9mJhImsbWI8Z6Pa8DFZcDBVdHCm3AxJAu1YQeJSTVzjnGUlqrRtk1BueGBGyPU1no+aP1WaHh45uQQ/YPz8K/fjoq0joYURr8tRLZQfPdU5K3Gh6mI874iq84aL7eNZnFioh8+EKuiLE1YqfilxndzwXIAosUAQEMqUWU+4m85Wmtg5nodjGpgqCNJGlXZd93w4lokzpUhhnK20WxjlhoeiLHSNZ3ENA4NIqwYzh6OVHfHO8Rz63CDkRwK30T6klmFQu+VBLIK0vS7QSIrbZkNAnlg3hZGWOaVqHG555DQKjokX7J0EIISMgi2D3qZByMm0SZ85FBQ75Gx5qe7i03c+jV/9xwdildtBOCteqDZx22OzoXD7/77yGD5z59MoN1yYBmE8dszjCzVwwDg2X0Xd89tSjhu+CAqr+0VE2DqWlVlQyaCjmvErqPHu3VTA02ersAzCXKWBhhfg+XsmMJazcc9T82j6ojeHEoJnyg1kLAOOaYSujMu3j+Gy7aNo+gEOzwi3yomFWvj8na0kM2nqrph57xzPwTIpdG1YhhD66ljH54XSGcnaYTxJbC/25ZgmZkvCVfe57x7DqcV6OPMPFYacCV+zbxInFuvhd0DM4gsZCyS5s+quj5JUAPFJ2GLNxUjWQtY2QusHAC7ZOhq68NTMfaJgh4kLlYaHwzNlZCwDuybzuGLHGI7MlgUDsh/ISvbIVXhoptwxFXyx5goF1/QxnnWwdTSLplQ2zIy7JCOzIqysuT6Oz9ewezKPvGOiLC3iUkMkCGwdFZbsbKkRHrPuBmF23K6pHDKWgcmCg7MVkX7rBaqXhpEo6FyoJntm+FJhjGYt5DMW8kOMXwCDKYzbiOh/AsgR0ash2Gm/NJxhrS049qFVYaT1w6C05dRe1ZyGDasw+kCahaGEzNlyE1vGsrJK3khcB8swYBgEk4RboOYKAa6KunbKGXup7uHRUyXMlBqohN3qopfrTLmJP7r1MP76O0/hn+45Lmd5IiUz75iJe3Z8oQbTMEJK8UYLAWGpJho5jcaUzLbRLGZLjTZXYGtv9Z0xC8MLWMzmXXEhJgsOrto1jnueng/5opRLar7aRMYyQ5/0uMzaUQH0h0+IbKTjCzV8z85xsU3FTVDNfPPQLBjA9+wchy0bRfkBw1QV5vJ8VN1FPMUZiNJeM5Jq/pm5Gj75naP4s9si3rC6GwW9AeDafWIS8MiJzrN4QFhirU/IUt3DSEbwiKm06smCg+3jOZQbIsBflvd4UyETJi5Umj4OzZRx3nQRpkG4YscYAhYJBar6uuEFYbpw0wtw9GwVaViouRjL2wiYUcia2Dwq4jrH5mt4YrYSzvIVe2616UkrLodixkKl4aHu+bKxlYFNI0JhnC1HxXsNqcgBYPdkHpYpFIZ6RlUFv4jBxHp/yGI/BV9m7xUzlswsWz8WxnsBzAJ4EMC7ANzMzL8+lFGtMZScF2mGSUWQfsGo3SXFqoVl92PlhmxCrjcoM77h+ciYgto855iJB135yA1DKOya7JWsiAeL2SgFV3EkqUBoWg+Gz91zDP94z7HwuNWmj7xtYrsU5MWMhWfmqiF9BiBJCAMRnPaDqMNdPJC/bSyHmVIjweNTd/0wXVa5dlQu/z7ZcCeefh0EjKv3TGCp7uHgqSXY0jUBCOWasY1wP5ftEA2JxvMOdozn8PCJRSzWXFQaPq5UCqMqYhEihsb45wdOYu9UHpdtH4VjRoV9lqx2ztkmbJNwcjGuMKJJ0tEzFTwxWxZpqJ6POXmd731mIVxHVWErV8yVu8aRd0x89+n5tnuhYBsGzlYE2d6f3/ZEGIReqrsoZEQAeqooBO15mwoYyVoo1QWtS6ku0n/H83aYuLBYc3Fktoz9m0VywUVbR8I4BkNYF3XXx5lyM7RmH+vgllqouhjL2SAQNo9ksVlaCMfmq7jr6BwIws3k+sJ6Wap5WKi52D2Zx0hWKAxF15G1DExLhTFfaYpCSFmxfmKxHu7LMii87/OVyKUZr61R5xm36lXdyEjWxk9euwc/ee2ejtd8NTCIwvg5Zv4YM7+FmX+EmT9GRL8wtJGtIVQWz1Lda/Nlm6k+qRRSQpIuqR5WxjAzGtYjot4XAWzLgOcz8o6VaCupsmZMEi6pqqxOVumZypV09EwljEe0VvSqrCACcOXOMQDAtedNoiHTbvMZC9vHc/jLnzqAt71gF5bqXpi+CQDH5mq4/cmzIuZRd0OlEPerbx3Lwg8E06q6z3PlZhj0VIFmxfy6cyIH0xCU4yGHVt7G8+T4Ti7WYZuETaNRplLWEn1BAOAKGe8AgEu3j+LgqaUwe2ffpgJGsxbmY300Dp4q4ciZCl5/xbaYKyjyuzOEO2gi74RxkPGck8jjP3iqhA/dLHrDu75ImQWSLti6tFrKdQ+FjOB3unr3BO46Otex1icXFuWV8eUHT+LnP3MfZiSnVDErLIycbWL3ZB5X7Z5AMWOJDDBPuHoKjoWsbYap1/90zzE0vABX7R4HIILtKo7BEPGLphdgtlTHxVtHMJl3wvqOOFQDrbGshYmCoKcZzVgYyVg4eHIJNz90EpduH8WmYgZNX1R4K4ty16Rg2FXZXHWZATWSsWAZhHlZUe8FoqPh8YUapkcyyDsWTJMwKWNqZyvN8P4AyVqVxVqSHNOTBaWjWQtX7BjDFTvGUq/3amEQaXVDyrKf6rYBEe0ioluJ6BEielgpGCKaJKJbiOiQ/HdCLici+kMiOkxED8TJDYnoBrn+ISJKG8uqIf6It1ZRUocrlmZhNL0g0Xu4r+02COI9CAaBHyoMUbDkBiKHPmuZYQBWBUFNmalSd33MLNUxp5htR3Ph7DEcT8t9KmSE0MvaJt73psvw5z/xfOydKsjsFpFpZRsGDCLskhTt33oiSjN+Zr4ajklQbov9j0iFUWl4of9/ZqkuW3QynpmvotYUlCSXbhuVfm1LnpeBXRM5PHmmEmYUbRnJopi1cYFMu7VNA8WMFQZQM5aBrJxUXB4TBpdtG0Wl4Yep0TvGc5geyWCu2kRTugL/+YGTKDgmXnHh5vC6KgvDMChUImM5O1QkE3m7zQ8+W2rgTLmBaUkbnrNNvPqSLWI/JNwrqrBRKdQX7pvEQtUNWY1bYZuim9+pGE3MLY+cRsDCTWsbBjKWgQ9efzl+4Mrtocuy7vlSMVmwLQMjWZFmPFNq4CXnT+HAnslwf5erOEbTR8P1UWt6mC01sGU0i4u2jqQqjHuenocXMC7aNoLNIxlkLANEhB0TOXzribMo1z2862XnwTaFm8v1IhqV3ZN5FLO2sDAaPhpuIJQ9EaaKjqjUloFvAnB8voZtY1nZPySyMM5WGqg2vTDDT90z2yQs1dyEAlEFl8UeveVXCz0VBhG9nYi+BGBfvMKbiG4F0N4qLgkPwH9n5ksBXAvg3UR0KYR76+vMvB/A1+V3AHgdgP3y70YAfyrHMAlROPhCiF7g71NKZhiI104cPVtJ/Ga05U3J5S0xjED2Svilzz7Q/VjnOvd2jeEHKugnXFIBMzK2qLtQ1lbITkqEvGOh1vRxfKEGV/auGM2ZGMlaCR/0XEv2iLJYMrbIMto8mg3jUYITyIRtiXu2WyqM2VIjnDlnLVO0d4WYWatcd6Ukam6UgnpKZkqVGqISeanmYixr460HduH3fvgKsX5TuC/2biqEFoZlCEHi+gGulrUdjgxyKwHp2AZeesEmfP8lm0PFBgCXbRfK49bHZmAZhOmRDDYVMzhbbsLzA8xXm/jWE2fwqku2hBaDHSPItAxRWZqxjETCwWQhk1op/NipEizTwEypgc0jGfz4tXvw1gO7cOGWETS8IKxTUSyxz98zAYO6t+zdVMzg1GIdk3kHuyfz+LdHZ8J7Z5oEW7LKAggFYsMVBZSFjAnLEPc271gYyVr4zy8/P7F/Fcd4QnYdXKp7qHsBposZXLx1BKeW6pgpJdNUbz9yFqNZCxduHkExa0uFEWW6ve2a3di3qQjbNOB6Io37xILIkJouZlCU/Feq0VLWFhl/U4WMdEn5cCXbwbGFGraN5WCbhkjCyDsgCPdqTSrTrz5yKlTmW0ezWKq7SRdVXbio0mqehoF+LIxvQ1R0P4pkhfd/B/Dabhsy80lmvkd+LgE4CFG/cT2AT8rVPgngzfLz9QA+xQK3Axgnom3yOLcw8xwzzwO4BcB1/Z7kwIjJ8CfPJBVGvxaG6H7Wu5K7n8D4swkRpXsQ+uaztgnToND1Eq8wH8lYYfOZckMFRCMXlhKsrUWGaqarCP3EZ7FfRSJnGUIhTBWc0G+/Z0rEGeLppK6XtDDqrnBpTRYc2CZhttRAteHh9GIdtmlgrtrEWN5G1hFCDRBpn6W6i31TBZwpN/H0XBVjORs5WzCyXi1dKY5pIGtHCiNrGfieXeP4yWv3Jp6xLaMZURFe97BtXLi6VBMeBnDb47PwA8YbrtgWbmMbRtigx5DZ44WMGQa+HcvASM5KxDAU1Gx8ptTA5tEMJvIOfvLaPShkLDQ8Qcut7k/D8zGStXHptlHc+WTn4lDHMvDMfBW7JnN4wd7J8B6OZm3YhhHGWYAoi64kKcGLGQumITLp3nJgJ/7n6y4JmzyJuIKLi7aIOMZjp0oo1b0wDjVZcHDNvkk4loEPfOmR0IXo+gHuOjqHa/ZNwjAIjmmEk5bv3T+N6y7bih+5emd4n1xftMk9sVDHrsmcYCcmUfdRd300PEHB7lhmmAEVsHi25ipNNL0AW8eysAyCLZXfeN7GfNVF0w/w9YOn8Uf/djhMz50qZmTMKklqCKCNR2tY6KkwmPkpZv4GM78o3jiJme9h5r79EkS0F8BVAO4AsIWZT8qfTgHYIj/vAPBMbLNjclmn5a3HuJGI7iaiu2dnZ/sdWhviYr5VYaRZGCJLqmUfzH0pjIw13DS49YZWl5RJhKJjiTa3UqDH4zrqRTANEkVdWQuOSaHCuGjLCBzLaFMYocC1o30pheSzCLQ7MUtmlwxMX7hlBABC4j9VjVuRPEsFRzT12T2Rh2WIKuAz5QZmyg2cXKyjmLEwXxWEihnLALM4Z9MgMESmFAA8eHwx5NDymXHRlhHkHROOZcCSbilAJEWI0qDkxIKIwhqOHTJGsmkkEwqre59ewGXbR8PAPiBdUsrCMMXYRrJ2ODsdkcHm1lz+QsbEY6elwliqY8tIPMZioOGKGpiKrAdQb8g1+yZx9GwymYCZ8We3PYF/fvCkcOHNiRqRa/ZFrqRi1oJlUnjNAISdHY/IuFXeMWWLVgOvuHA6dNct1EQMx2fBAnvR1hEcPLmEuhsVUzqWge3jOfzmGy/FycU6fuMLD2Kp5uKh44uoNH286LwpcY2kuVnMWLho6wje/X0XhLExW7pTq00Pxxdq2DWRR831MZqzBA1/U7TjHc3aYYrw2XITYFEAqbowbh3Nirojg2CQsLrmZDKAeqbvlq2Pp0cyWGrp2qiSEEbWkUuqRERLKX8lIuqLk5uIigD+EcAvMnNiG2bJS70KYOaPMvMBZj4wPd2h/2IfUOmgOdsMs3AU0tJqgfZg+OmlRl/d6LLPsaC3av1adwUz7FTRESm0BoXCPV44NhqLGSzVRPqgbRqhItm3qYDxnJ2gpYhvF0+LjiuivGPCMqMZrHJLqVaccV9/zfND/7wq7BzNifTTLaNZzCw1MCfpREyDMF9pYiInrA9VA7J5NAOTCLun8nL/AcbzEemiZRr4Ly8/H2+6chssg0KFl7ONjr3XL9umFIbY5yaZVTRXaaLS8MPvCrYZWRi2pF/J2mYobIoZCxnLaJutfu8F03hipoyFahOVph9mDanrW5eUKiq2oMpdX7Z/GgXHxEdueTycPH3j8Vn884Mn8U/3HMNsuYGa62P3ZB4XbRkJz3kkK4LEthlNz+arTWwfy+KJmTLKdS9hYSjhwcwImHHNvilMFBzUXR97pwo4sViDJzPeAMCxRCznyp3j+PU3XILjCzX8/GfuxZ9/8wiytoHLd4wJPjP5ro9krTbqGxXDOLVUDzOkAIT3c6bUwHzVxY6JHHK2FRYClhseyg03TKndOpYNLceMZQq3oqylUe6yI2cqIADTRUHjcrYSMfCqGOm6cUkx8wgzj6b8jTDzaK/ticiGUBZ/y8z/JBeflq4myH9n5PLjAHbFNt8pl3VaPhQohbF3Kt9WhJVKDULpiuST336q57HsFSiMjRguV1xHAQsXyXRstqqsrUxCYciYQdNHKbQwDBTlLHjHuMh9r7d0vlOxhnglfTwdUQkcJYz3bynCsQycL1NfazHXjajyFkV7ni+yu/KOiZGcheliBqdLdTTdAAVHzC4Xay6mio5webEgQZwuZjBecJCzo5hBnKUXAK49bwpX7BxPWhi21bHR1RUylXbPlFIYMmhabqLqegnXUkMWg6miPscywr4jY/JaFbNCYVy7bxI/98oL8BMv3I0Lpou4ctc4mrLfAwBsjlsYtomGJ7OCPOEqAolJwVQxg5975X4cminjC/cdx3y1iY998wgy0iL81mFBz7FrIg/TIByQcZypggOiSBnUmoK/at90AYdmSqjILDfLMBIcbq7PKDgWHMvAVN5BwwuwqZhBpSGsLuWS2jySDScEV++ewPvfdBku2FxEwwvw/Zdsgd1CFZK1TQQtc1rXD+D5HE4ot40Lwa+e1ydkUeWuiTxysSLE+WoT1aaP00sN5B0T4zkrfEYztizek0wBM0uR1ZyxjTA7cKHqhvdRKcF4luEwMdSjkCjh/DiAg8z84dhPN0FkXf2u/PeLseXvIaLPQAS4F5n5JBF9BcDvxALdrwHwa8MatwrM7pkq4GBLJkWnrKa05Qe70A8orMTCED0Slr15R/yvN16CD3754OrvGMDT81V87aBoAFTIWomCPfWSxi2MkZD6wcdS3cMFmy1kHRMFud3WsWzbjFjEAcSyuCJ3WhRR3NXzmku34pq9k6GiqEt6hn9+8CQO7J0QFoas/p0ezYCkK21T0UHdDULOpDnpp940koFlGGGMqpi1MN60MV9pYu9UHvcfW8R4Xrgr8rYZzsBNImlhCOGQd0zR0zplerB7Mo8Pv+XKkNhQsa/OV5uoN4OQ7RWADEgbkcIwhevLMQ2My2rpYkYI24xt4sCeCTCAH33B7pBO45uHhJt380hkYTjSJaW4oUYly6zrMxyL8JILNuFF503hM3c+g/ufWUDN9fG+N12G//XFh3DT/ScAIAzm/+iB3di7qYAxeR6WQSASlPDFrIW9UwV867CIiYxIhQ9EvSmafhAK5mLOQsAc1kCcrTSxUHWRtYUFFZ88XLFzPFS+gFBQmRRXZhy2acD1gzDZopixRF2IHLuqwt+zKYesbYaThLlKE14gaG6mi+I5UskXBUfExRrSWvMCxp7JPJ6aExl7I7mo5qTpiyLPkmwDO7peXFIrxEsA/CSAVxLRffLv9RCK4tVEdAjA98vvAHAzgCMADgP4GID/CgDMPAfggxAMuXcB+IBcNhSowOzeqXzbb2mstETUFsMAIlK2bkh7GPvFsFJyL5guYlsLa+lq4WuPnMZHvnYIgJhhx2dyUSpptGw8Rv1QksR0GcvAxVtHcMWOMUyPZBIUH4DI8c/IlzBORR63XMbyNgyK3BmmQZgqZsKXt+76OHiqhL+942nc9tgsytLCcIMgdPXkHDMUSCdk4ZsSINOFDCyZxjqSsZGxTBQyQoipOMZ4Xlgh4wUHDVdkzij3nFKCkcKIBNxcpRn6sfdvGYFpCJ4lnwU773ylCZ8ZeZlaXHdV/+vo/B0rCrQqa0wVzAFJH/H0SAZTBQcPHhNpzL5sL1trCj4kL+DQ3z6RF72445Tv73rZeTANwv3HFvH2a3bjebvGcd6mQlhEp9Kod0zkcN1lW5GJxZaUlZZ3zPC6qfsXnYsput55QTgBUanBm1SqaqmBpZqLibwjMp9AHTMUfZk9ptBaK+X6okdIwBF/Vs42ZeW5ON6TZ4UbaddEAZYMZgPCOnB9QX8/WXDAiKzggmNFNUYyA/A1l20VY7CjfSzVXdn7OwirwM9V0HuoR2Hm/0Bnz8mrUtZnCJLDtH19AsAnVm90naEUxp7YA6pgdkiTSivoq7q9cwJaexAPAqEwhpNlNSxlFFeiW1p87ErAxV9Q9QKdWhLcRcJtYuKKHWN4+YWbcbbSSNB1ACLQrQSfUvCuHyTcP2NZOyWzjWEZhmxEFODmB0VeRqXpo9wUMQxGMmV3qyyyO7VYx2Xbx0K/8/RoBrZJqDY9XLRVBNJztgmw4JRS5ybYaS2RmmmYMA2h5JRro5C1QAYSfnoGh7UWCg3PRyFjYiLv4Kk5obyUhVFueLh8x2jCPWfL81SKkiDceLYpkg+qTUFMyMwgIly0dQTffuIssraB6aKDsuRJUsc4tSSOOVHIYCRrCXeKvL3CNXUB7jo6hx+6SuSqHNg7iSNnKtg1kcNsuYHtY0J5eT4nZ/eWcA8VM1YYJwAQCmZAvENBwAjAIXOCZYo+2ep5a8i0Z2HVmSFNSiv1D4CQnDG8VqYYm0peqLt+mBigCkZHshaKOSsktDw2V8OmkUwYKxMTFGEdNFxhmShCSEvRuztmWLmuikgP7JnAP97jiMp3qfyWaqJnRs4xsVB1MZq1uvauWU08tyKufeDrB0/jL791FICYAU+20D2nWRIGpWvFerN3ltRKFMawav4Y6bEahW6/9UKcxXX7RLJZT95OiWFIwXDb48IdcsFmEWtQ4pKA0AevoOgugMjCqLt+whIZzztt59HwAmRMUUR4arEW+tirDQ/luicq0mUmEyBmhlNFsZ+Ti3UwM75w3wmM521cunVEpGZaZjgbzlgiG+bq3eN4wd4JXCKznHIxGm+TjJD+AxBZWYIdViiFpbqHyYITMsYCCMkEM6agyH56TvjV844ZBuInC5mEULEtI7xGEwUHv/Lai/DaS7eCZPtS0eI16ip3sVR6m0cyKGTskCo+64h9ziwJy0qkKFttPv/v3T+NX3r1RaFwfIGMV+wYz2EkY4WMsj5zogFQzjGxbTSLjCUqv5WCnoxZGBnJSBsESctgU0FUrRsEnKk0RTvgnKQSLzhtzYgUgoAT2XWAyBRTbsN4w6YzlaYoNDRNFBwLmyRlPkNwjYUps4Zw/anivflqE+M5O0GJ41hGSIfy0IlFGCSsux+5egdedN4kNhVFIeFcRSQfCNeW2yajhgmtMFoQf+AYHKbzKcQVxn9/9YXh57QM2tZObKnHW8HMYFhWgEgT7rzvPSmuun4RJ05rnd2plye+fESmaT49V8XW0SwulRxBanSM9pRC4X5J7rvpB4nmQBMFW/YDj4onG16AiaKIK3znyFl4AaPgmJivCsK3QiaZcqoYWLeP5/DEbBl3PzWPgyeX8OYrd6CYtWGSyHZSAXrDEDPyQsbCr7724tC1pRQlc9Tw6bJtY/jBq7bjhfsmYZuE8bwNQ2ZP7ZkqYFPRCQu4aq6PqaKY2U8WnJDVt5CxUHcFpXi8zkVdY6X48raJy7ePhe41JQxFbZ+4NhdtFcptUzGD0ZyN7WM5eH7E5qziHFNFRxRiojv2bxnBy/ZP45p9kyhkrJDZOGCGE7t328dymBpxYFsGAjDOl1xRcSGpLIbLd4wlrMjRnFBskwUHs6UG5qvCBeZI66MTbUkADhWbQjFjRRXWHKVtny03MJ6zMZ63ZPFdFFvZOpaV1olwKE4VHSxUXVRdHwELt1q8ql4VbFoGoVT3hIvUNPD6K7bj+qt2IO9Y2DGew/GFmnBB+ozFWjNs77pQbSYYgocBrTBakCDBM6jNLWXFXFJbZHohES27AG9lLqllb9rHvrvtfHUO3NpKUtFqx6+JbUQZK9+7fxOyjnohFVcUhYFGhaxthtaXz5A0CwhZQ4Gon3c8LdMPAunjNhEw8Lxd49g5kQ/dDgXHCmfUCsWsjf2bi3jsVAmf+s5RbBvL4qUXTsGS1btbRjIJ5Tuas2S/Cg4nC5YpAt9NLwgnJLmMiesu24bRrBjPVbsncPXuCVy5axxjORvTsUyfhidSaHOOmbgWiul3WrpJHCsaR1ZyNan1GrFCShU3MYhQaniYrzZxwXRR8FwVhZtl61gWkwUnVIaKcFHQaZg9HaWmQfiV116E/ZtHMJ6zwyeKkIw7TRTE+asCyIu2FEEEbInxbe3dVMA1+yZDVtno/EXR53Qxg2fmqqi7AcZyoj4m74iJSBo9PyGip1EYzdqirap00SnFdLbcxEjWDi0D2zJDWpqtY1mYJoX9XaZk8Z46l9FskuhR9T5REwllTQXMMpHDxA7ZzKna8EILIyKr5Lb+8qsNrTBaEO8FYBrUFvhOzLxjH5d7o1akMIaoMdJ2rWZOq0Vn0kpBkU9RGIYRrffi86eQs00ZrBZjYHCbSZ51zPDW5GxDzsSFYjFJZN6ouIclfdNiX7IWQbojXn/5VuQcMwxk5xwTeTvp/hrJWDhvuohSw8PRs1X8xAv3iDiIQRjL2dgnM5gUxnI2mrI/Q/w8xwsi20rFyDKWAS+Ieoy3YjTmhiM57mLGSvSQUPEFRasRn7mP5x3skMWKWcdEw/dDq0xUqFOCDt2xDHzozVfgDd+zDTlH1G5cvmMsDLaeKTdgm4QRGZdRLMO9EIAxLi0g1XM77ZxNaQ2+4Yrt+I3XX5KoMTFjNRNxOJaBgiQoVAW4Y3lLZIdZBnZP5cMq7zgY7f3sMzKwXmn6GMlFXF8LNRdjOSu0ci2DQiW6dTSqschaJiYKDs5WGpiTNP1jOauNrTpvW2F6tFIYfsCwTMGrtn0si/mqi4Wai0pD9BGPP/+mSQkrfrWhFUYL4i4Hg9otjPhzZMqZAgFtPtt+sZJK72GpCxHDaN+7mvn1Y031ky7camGoly5+TUwZAL5oywimRwQflBVTXAZFaZ5KoeXtaIabtU1csLmIiYIQZI5tyCphMb5JWeDV9ILQT67SZa/ZN4WsZYQvYCFjtin4QsbCeTKIfd6mAl66fxMI4sVVtQSJ6yKVnx8km3ONZS00fD/GZWXIjoHpdzljmRiVRHeWpBNxLCPRYlVcKyNUxIksqZhQVsFw9a9tiH3ZFoVxgYbn45Jto2Gfa7V/ld48V26iKGsjABHzCMBhD5M4GrLbXHycW0azqEha9rQArindOo5l4PzNxdRsxTRMFh2M553wHo7nnPD52T1ZQNY2EorN84PENVMYyVq4cMsIPD/AVN5JpmjnoqZTlmmEXoqtY9lwvVzGwkTeQaUhqEQAkQbd/jyZYR+QLWORhSGSFIywGdeJ+RqOLdTAHLnnGCLm0U/B8HKhFUYL4g+KbRnYtymPn3rx3vCBiD+oiYu3DH3xP667MPHipqGbBTJMCyPtdNSLltJIrw39pPm1xzCUYItdYyK862Xn4Vevu0gK2YhGwQtY9I8oOiCKrMOsbYTBWssg7JzI4eKtozAl/UjescJrN1VwUGl6WKq72L+5CMMg/MS1u/Fbb7pMFPbFLvFo1mortMzYBraPZ/HGK7bh3d93QahoU2nwEXFbBS2pmzlZwa6eL8cSboxursEtY4KddnokEzLPxmebImjqhB3t4vEy20hmAVmSHRZAWJioLLmRrB0KVUKyGHJETrAWaiLlWVkH43kHL9g7GXJlxbFU97AYo7fIyJ4RbiCmXWnKIB63Em6r/kTXeN4J01EBETdQzaNMg3Dx1lGUm1Ebg2rTxxZZZxOHYQhW4xedP4Wdk/nEvZvMO2HMwzIEbY2iA1HrxQs2D82UZLKG3daiuSAVCwBskROhIFDuKgpJEI8t1HBcUttPFkQFuG0a2DyShWMOj25IK4wWxINmeduCFzBecdF0WDSTeFBjz9Ry7IuMZbUF11qxb6rQsSZiiCGMVLeTcg31Y2H0k7nR7pKS1dktFsa28Rw2j2TB4HD2acnCqYxthj0HBG0IYTRnh+mN28ZzIBIVzYahFEZkpRSz4gW9es8ENskK5h0TeeycyMPzg4TLoJgRtNtxOKagSH/Xy88PeaiAdssiXF/6qT2ZohleC9tExjYiwSML6zopHkAIQyemJBzLwIQMgJL831QxWWSnYMWUsm0aoi4jxuU1mrNlWq1oVORLShdQC9dXLrpXRdn3QcE0CFtGs2F/DkCk+I7L5kQqu8s2FU05hVTfrYjXzDAQCv1eaLW6JuR5KYzlbeyayIUV024QJK5ZK1RsKj7ZifeGV93+XnHhZthmlN5dcKyocdPpUpjE0DohzDlRj4+pooOZUh011w9Te6cKIoZ0fL4WJjdMFhyZNi5YnHMZrTDOGZKzPiOkCVEPWVoIQ2TaDH4sk5CY/aShUyaHGMtwVIbIjmlf/qYrt4sx9eEjjb+kr7t8K37tuovb1ml1Sakiq3j6a1xgEhAKVFu2U81KOvCxnAgg/uYbL8UrL57G6y7bine+dC9+9MCuxDGytom8Y4bCOmOZYV/t8JiGSFmtu0FYQUsQRICtwVBFfx2/TwxOFXoKozkbTS9ITD5s08BIxgotGpWK2YkdGRAW1fRIJqwLsU0DEwUhEHOOKYgdYxOguJCLn4dtErJWdE1s08B508VQSCvl0fAC5CwzMfsezUb3uZC12hTlRN5JTDBqrofzNxdFy1dJ8QGIa76p6MBp2b+CFaNxIVDX6xuHE+tgaJviurS6vPZuKgjqd+nK6adtctwyiMdTLIPw/Zduxs+8ZF8ie8+xosK700sNTBSchEWpkLEMXL17HD989Q5sHcti50ROuEKlwgAJupETi7UwM22y4KDpicp+2zQSKcerDa0wWhB/WIuOFfo+1QMaD8gl6Ro6C9Frz5tM/4FSWru2wA847AndimEpDDNGaRFHMeZ/74WpQvQSve7ybakz7laX1JW7xvEbb7gY3yM70AEIK5+VQFaCzrYITZnZk7FNXL5jDBduFeysxYyYvV21e6JtBvcjz9+J11+RHE+rgLJldXHD91HMijEq66XVIiQiFBw7wUxMlO5WURiTtCSts+TxvB0+U6ZJsE3qamEQES7fMZa4jkVH1A7lHRNZx0z8Nj0SK3aLuS1U1k/rmE1DPJ+2Sdg8msVizU1Y4AASijYewwiXZUWAuNb0UW16GM87GMvZ2DKawUKtiZHY/jaPZhN0JnEYBqGQNVFtej0VchyWEXWyU+nFrQrDNg1cum0UC5JluJ8iuGzMOo5TpRAJWvSa6ycUj7D+oms1kXdS+444sj/JT714H0wSLWJfsHdSZFwZQlHumsxJC6MBgpg4NP0gjHmcN10cGreUVhhdkHVMeEGQSPUzYpcsfJepXV387Ev3hZ9fesGm1P1TB/M7Di9g/JeXX5D6W7yX9GpCZay0QglLP2D8+At3d91H3HKyTUrttZ0moC7cMhomE4Tby/EQUWjiCwsjCHtp/OgLduGtB3aKAKHsY9CaogkAL9w7iSu2j3W97qYpLAZCxM80krXaaL8VRnJRjn7TC4Rl0EXQq8Y8rWPYPp4PhYxy1fQK7rb+XsiI4r2sbOgTxyXbIq7QVktpqui0UWAoAWWQSKete36bwsjHMtJGOiidi7eNotx0UWl6YYLAZD4DBicE20TewZ5NnWt8zt9URKXp9VTIcVjS+nRMA+N5p+M9HM872DddxNaR/ihx4rHHLS3pvI5phj1BFGzTQNYyw/Mdz9mpQt2RLMLCJSwsIiOWBZa1TeyayGOp7uGpsxWM5WyUGh4u3TaaYGju9/oMinNDQLJBkTGjl0EJy0Q8KR7DkPL1P710H67ZN4GHj0fEg52CUITeD74XMPLZ9O07Vaq24h0v2oMrdozhVz73QF/rWwaFrrg41MwrYO5ZZR6f2cfz/7tB0VW37ts2RRc3NUMEon4EqqcGQSptppC6W1knyWMYsMygq0C3DAM134NpGJiUCqOYtVJpJAAh6E4s1BAwY6HexJU9+ipnbQM5x2pTGHEBYhqClG5QIzLvmLj+yh2oNr0E3xKQtPpaFcbOiXZBbRkEMsRYVAFivsU/roLldS9I0HXEMZazcf6mIs5UGqFFUsiYsldEa0ZcZ3fKeN7GeN5BQzYq6hd5x8L28Sw2FzNtbtA4Lthc7PhbK9S4BbVK8rwztrDQ49XiwoUl3G7lhhc2z2qFatjU8AIY1M5jlc+YYUzz8EwFW0Yz2DySCa2LYUMrjC6wY4IudEnFs6Tkxzg1nGUSHNNMBEtbXzKFfmZKfj8pST3w0gs2JczhXjBiLqA4lK/WD7inOyx+Wo5lop/X25BpqK37tkxCpemF9BQAQp4j2zDClEsi8WdbYh+i5qJ9X07Q3bC2DHG8HRM5nFyUtQwy8yUNStDPVZo4b1MhDJ53QtYyU2fjrWOwje5B7zTkHAsXbxtJTU+NH8/pI53bNAgmxHU0DOGWyrRMfgxDVJALhdH5Gds9lQ8TEAAxAds5kU91y3QCEeH8TUUcW6j2XjmGrG3iV157kciS63APB4Xaz1jKRCIjExvi6eEiwC1qgY6erWIsb3ccSz5jYmaxgXHJRhDHZN4J+5HUXB/jeTvhFhw2tMLoAscUnEUi2KqC3gZ+602XikwGbk+TUtkpcZ92J/oP4s7ZNOFueeWcUdSBHrsTDEoPtiurQbGi9jqmggoM9z6ucoEkl9sWwSIjQTIo3DVCWZiS88g2CIYhqmIJkQKKwzIIQY8aEcsk1D0fU0UnYj91rI7+9awdsb7umWwnrGw7T4Nw6fbRrrNkIsJ22Xp1ECSq5FusCCLC6y/fipsfOtV1pq0gOsFF6dv7txTbrCLLiDoldsuME5lqyW0VCeMgGMvbKGRGeq8YQ9YWPE6C6mV1RF5OFnCO5e22uJaq5+mWATWRb0+pVSg6Fo40KqnXJ58R2VbFjCUtFaet+G+Y0AqjCyxT+JldP0jkWU8VMtgpA08AEn0VlBCIuxc61VIwAedP9zaDV+qN7JXP3368iHQujlBhBIyd4935pOLCMN45TSEthqBcUq1jtQ3R5Cc+k1MNgES8QGRu2bYJ3+cw1dU02ivWTYPg9AjdWTJDSVVPA0AxpWgvfq6XbBtFMWP1XRvTj0ulleqiHyhlyWivVTAIeMuBnfjBq3b0NdMmSrpTUwvqYimmU32kUq8GeqWityJvW/CDOhjcta5pEChhP9aSpgsIiyatA17BsUK33UQ+0zG4nnMsgNKztfK2iGnsnszjkZNLohXwKllN/UAHvVtgkKAUVjxReZkppR6K0P0BhBk0Lz5/KrE9kGxoYhqU+jK1cuIAUde01vX++6svxNW7x5d1TkS9eaemE5ke6ZlQasbqB4x9mwthO9M0xI8n4hLtrqG0bRSjaxyOabRdJ5W9E8Y9DLFPw0AihtF6XDMl970VpkEoZEzkYu1LR7LtM8k4JgvtVbtrAcXkS9RuYRjSRWdbndu+tq5v94g/GRRdz271C2sJxzZCJoaVdLiMQynJsZzTZgWqVqutED098hjL2dhUdDoqDMcSk5U0d51lGig6dljA181SGQbW/glfZyAi/ML378dH3vo8ALLym6NUPIvC8CrGcg5+74euwHtfd3HokjJkALYQK2iyTOrAj5MUrGlCnSBm/FfsHMN1spnKoDCo94z2D+T5ApC01mkuKVWlLK5Dq3mfnBFFx9s6lm2jhU97WYgIl25vDxhvHs1i63hSYYigsKAxt5Sv3SDYpglLKgzTMFItjF5pk7YpUjGJCNMjGWQtUc19Ll/M5UKdn2O2Z2oRRXelH0OIEFWmd4JlUEgD05qVtV6g7rcBWrV76FiEgmNi62imzZIby9vYlpIKn7FNXLFzDH/9M9cgnzHbFHq4niVYaztd+6miEwa+VUOocwXtkkqBGfP5K6oQ9dDFC6kIwNSIMC2VeDUM8VZmJMFZ0wvCRjWtUIyg7/m+C/D/bj2cKsjifR8GyQyJg9A728aIP7wdWC8zsXUytgk3JZMq3F9sd7ZhtLmZBnlx02ZaphRUURBVKYzIrWUiJYDeh8LIZ6zIR52z8Ydvv0rWYKzUOXhuUMiYqS5FIgrb+vbzLMWth47rGBTSmfcqQl0r2CaFfG+r1WjINAx88M2XYySlWLHzOIyQITfToUAREArjvE3Fju7N0ZyNi7aOYDxv48ItI8uWC8vB+p8yrQGUiwMQ/kQyIp+7SbHmPRQFaINY0Fv1YFZc9xalV6aSzD554b5J7BjP4caX7WtzFYRBb+rMT9QLYmY5wLYUnc8vvybq+WG3BFTdln4fcaMkPlSR3po8xEqFr2lQkvdLxpsylqhdIACG2Z6FNpK1O+biK4xm7TAlVQnNfiyT9YKCk57RZRCkO7W/p8GxjLZeI2nI2yLddiXthocJ2xTcYlZKmvVyQRBuSPXc9QNlCXgBd006IKKQgDANecfEzokc/vTHn499y0gcWAmG+gYQ0SeIaIaIHootmySiW4jokPx3Qi4nIvpDIjpMRA8Q0dWxbW6Q6x8iohuGOWYAoUsDgCzJj3h2ElRSJMxcirmplEuKKLJOxOw35QU2ouP97g9fgReet6k9wk3qBaeuNBG90Gv2FxfwzBFfVDx2EE8TJBC8lpRfSnyOvqW5xFYqfLOWkQgK25YhaavNyMKgdstqouC01Sd0AxHg+RxaHBsB+RQadkDyMbFI0Ohn7lHIWImmU53wyos3441XbOtbcJ5rWHLCspr3UM3+LSOdWj0NyhPh+TxQOnErsrZwu9bd9kLKYWPYU6a/AnBdy7L3Avg6M+8H8HX5HQBeB2C//LsRwJ8CQsEAeB+AFwK4BsD7lJIZFiwjomRwLAOOYYakc6pIbDxvC2Eqn5XQJSW1BRGFD4VppM9ClLvEMIRATa1WYCVwl08F0s92rWsol1SmxaqIo9UlFf8Wj4EYRFB5Uuq6rlRhWLFGM4AiAQS2jWeRlw2UlMtvJTClizG7ghf8XGOykEk0i1IwRDrfitybabh6zwRefenWoVUXrxREgisrl1ldcWfQYK5VZaU2PL+vtOZumCo4govrHD+XQ1UYzPxNAHMti68H8En5+ZMA3hxb/ikWuB3AOBFtA/BaALcw8xwzzwO4Be1KaFURF+7CuqCQCsCQlcgGiepXFUhU8pEoWk+5PuINXn75tReG5HrqBTOJYACpFgRD+KQIvYv8OqHfjJg4/FBhJNt6hvsk0degbawS8RciXg+hupF1CvgtFyLN1sBo1g6zo9JSdAeFsFQEPfVGQc4xU2knpL5YdVp82xLZaevVwgCEwk+zulYCgww4Az4XOdndsBNrQL+Y6MB2O2ysxVuwhZlPys+nAGyRn3cAeCa23jG5rNPyoUFRGKvPGcsMXVLCPy5+o1hQVQlYOSGFCeFjF9k6FHLobBnNhmJVzU4MihRK6ysnGaXFsZf5Pg7qyhJuC4F4jnfSJQVUGn7bdgqtXfPU+Skl2m8/g35hG+28S7sm8yue9Sp32kpnhOsBIuDdu0p/UDiShn3Q+ohzibxjrvps3DTaqTt6jsM24cuWqytBIWtjNGutqAHbcrCmd5gFw9aq9RMkohuJ6G4iunt2dnbZ+5keySR8t/umC2HqoBnLTTSMaGbOYQxDOF/IELNslXH1My/Zi994wyXYM5lPHEfsR1kt6UV+RCLY3mlm+NMv2bOs83zDFdtSl8d7YRRitCats+yKJD+8Zu8k3vGiPYl4R6LRlJylA9ELtlr58AqO1Z5Y0E9Pjl5Q7KPrNaA7CCxZ5LjahoAjMwLXM6ZHsn0F8AdBP1lkrShkLMFosEILO2eLWo/ngoVxWrqaIP+dkcuPA4g3L9gpl3Va3gZm/igzH2DmA9PT08se4GTBwbaxKI+6mLHCG2MYkYNH+chjMe/wZTQNYM9UATsmcjBINIe/ZNtojIlSzIABlVmlitHa+X92TeRhW50zPK7ZO5W6XKHTTf7RF0SXNTHpJOBVF28GkCSsK8ZeOKKIP+tXXnsRfuyFu/G+N10a/v6W5++Mjk9RAZ26jr06DQ6KyUKmI/ndSpGR1B8bHaYhamdWOw3TsYy+uKnWEmM5e0WB5jSYBg08w8/apmhWtcLnyTQIF24djCJlNbAWb8FNAFSm0w0Avhhb/g6ZLXUtgEXpuvoKgNcQ0YQMdr9GLjunCBUGRcJVkLKJz1EMI8pqetUl0/itN10axiDUaxrVVlBiG6A9iLapmAmbrXR6zTspknBpBwGR6AmRoN4F3vXy8/Cxdzw/sU5yfeA933c+zp8uhLOliZjAjisXg6Jt1XVcbZdUzjFXXSAoZK3ORVYbDWN5e9np2Z1gGsazwmU3KPphDWiFYxrIWe2NuDYKhpqTRUSfBvAKAJuI6BhEttPvAvgsEb0TwFMA3ipXvxnA6wEcBlAF8NMAwMxzRPRBAHfJ9T7AzK2B9KEjpDePzZYNmU2lUhUB6aKSz4JtGgiCIOxgpxSJgnpvVWEdod1U/eMfuwqAmOV2mhl2CjaqIq1Oj3RcBbW6uyzDCOsPOu37lRdvwfN2TaQqstYGRUoPXr59FA+fWMK7Xn5eh1GtP+yczD9rBOJYzka50R8tfr+wWmpinitQvdAH2sYiFHOdi/bWO4aqMJj57R1+elXKugzg3R328wkAn1jFoQ2Mi7aMYO9UHqM5OyEgDUmjwZHZENJsO6aJphdgIu9gXvYMBpCgEQGAS7eP4oFjCwlWXIVpGRfoFqjsNFshqTE6bRoPhrfOOpWLrJvCMEjuo2XbK2Md8xRGs4Kk7eKtozj6u2/oeC7rEeeSPnrYyNmdadqXi7Gcfc7rAdYDnA4MDt2Qs02ct6n/vhvrDc+9u7xMPH/PBP7PW66U6XDJmXkQyypSgp1AgnrbIOyazGOh6ralQIXZT7IClblzfUI3mvO07JSCTN8DBJVAWrFa/Fk/f3OyYpRkfKY1PfP9P3ApzpsuQlYUih7PiKykT/3MC1Kb8YwXHPzh256H8wdoUqOx+ihkTGxdBgtuNxgGwVnHKbXDwkShe0OmNKh2uBsVGz+Sd46gWn7GPE4AxMzcNiM6cPXeEKn8dLFg82gmFPhpaWGXbBuV/SC63JIO+WRpgbcP/dDl4fF2TxZSO5nFrZYEXQZFtSat+eIXbhkN6QgMandlFTJWqtKzDNHP4tkQPN7IsExj3bLKbjRMj5z7LKW1xnPrbFcAoSyirCjlg7xi55gUtjKGYUS/q74MgPDzWvK3qHdGtH/bNHD5jtH2pjexz00/vfvepduT2RKvuHAaO8fz4bHTmGdbjx+HLWtN4mnD8W2YhYspjL3EVulUJGi3BL01NDQ2HjaubXSOoRryAOlCkWNxCZUR5cSIySxJD+LHhHfrfkhaK2lgMJpeusLIdqhgVYK8k8IwifDB6y9ro1G+aOsIji/U0PD8tjM1DbG/PVMFnCk3cGS2DNsUWTJNL4BB6ccyZHrZRqAI19DQSIdWGP2CkDqjVgjTahHxPtlmVGxXyJgYzdlYqLpdqTrsFpdU/FiNDgojdawE/NBVO/F3dz7dMVeciHDFjrHQTfQnP341io7sJwHZmKgt7kJhVbuKq6he24vH3Y7ZH7Yp6cafg75uDY1nC/R0r0/EU2DTcM2+SQDAHunfP2+6AMs0QleMZRrYM1VIuKTSdvX2a3Z3HMOFW0f6cuns31yEQYS3v3AXPnPjteE2/+sNl+A33nBJuF5rfGI8Z4cMsIWMKbmykoM0jShew2CpRCPupk5uLtMwBN34Bk0n1NDQ0AqjbxAUqyxSBf1rLt2Cv33nC7F7Mg+CyIQwjfa6ivi3NNl5xc4xfPpnr00dw1jWxt/9pxd2HadBwOsv3ypSdFuslWv2TYWKDRCup8TYYhH9qWIGF2xuryRVacQA2oLwF24pwpK9sFuRsQTfkLlBC5Y0NDS0wugbIQ0I0rl4zttcRD6TnLGbRHDMdncQdzYwOkK5fwwivPSCTR3X2795BGSIIj87ZaBtFlJMSaiAdhpU3/KpohNWdKve0QqWaeDS7aOpzV+2j+dEIaC2MDQ0Nix0DKNPhJQflO6Usoz25cWsBduKVTyrHYX77FwU1wrbNOAHDIMY/+O6i/ELnoe3/Nnt4e//9RXn47tPzePGl50XxlFa6zP2b0mvgUgqiXaN8bF3PB/7pbXRWmMxSCe/1ex4pqGhce6hLYw+IajMERappa/U7u9vbQdKAF5xkSBGHIRPZt+mAi7eOgLVl5lAGM1F+371pVvw3tddHObYK+r1uHzO2mZb3KKXiwwQHFFpsZNuxYSp+5GcWBoaGhsT2sLoE0SIgro9hGQnIarm7r/xhkvww1fvHIjpUszMCaM5G34QoO76+MzPXouz5YY4pjq2PP6W0QyaftCz90GyhiLdJRUy8radDw/kVoszAGtoaGw86Olen8jaJvZuKoQFeSq1VCHe6Kibm0a5ipZbbbtvUyEU4I5lIqcsmFi+r3Kd5WxzoBm92Lx97J28SNwaxNDQ0HhWQ1sYA0AJ36t2T6TKyTD1tqOA5YjSvMexfvONl2C6i1IxiBKNjkJlRcCeTXmMZERKbFrTmJ975QUhvcf0SCbJf5WmXzqcEPdxHhoaGs8eaIWxDCy374JlGtgylgkLANPgM8M0RHqtnZJhpUCQ/ZljOzKIwGBkrIgKPS328KqLN2NEMrDG3USd2Gk7WRgmrbxntoaGxsaBdkmtEpK9vjtj80gWjmV0dFuNZm3snSq2dM5oB8l6iDjZYbf9tmzc8RzSRr91LJva7nTzSKZj5pWGhsazD1phrBIcy8AVqg9Ej1n3WM7G5TtGU3/L2mbY67tjHwsZwxBZSpE7KWMZGM/bPfmaVEOnVuyZymPHeHtgevNINjVAbxjtbLYaGhrPXmiX1BDQj5OmV8ctUSSYTne+ayIH189ittSAbYruaeN5W/S96KPZz3jOQSHTLuiH1RNbQ0Pj2QGtMNYpiICRrJUag7BMA5YJ7J6Kiuh2TbY3LeqE+HYaGhoa/UK7pIaA1YgDE1GnfkkaGhoaa4INZWEQ0XUA/gCACeAvmPl313hIbSg4JuZXQWOIIrqNrTJ+9bUXYdvY6rYD1dDQWDtsGIVBRCaAPwbwagDHANxFRDcx8yNrO7IkxvPOqsQCerZr3QD43v3TyNob+xw0NDQibBiFAeAaAIeZ+QgAENFnAFwPYF0pjNXCxdtGNjyz6/mbC5psUEPjWYSNNP3bAeCZ2PdjclkIIrqRiO4mortnZ2fP6eBWG/FufRsVeccaiC9LQ0NjfWMjKYyeYOaPMvMBZj4wPT291sPR0NDQeFZhIymM4wB2xb7vlMs0NDQ0NM4BNpLCuAvAfiLaR0QOgLcBuGmNx6ShoaHxnMGGCXozs0dE7wHwFYi02k8w88NrPCwNDQ2N5ww2jMIAAGa+GcDNaz0ODQ0NjeciNpJLSkNDQ0NjDaEVhoaGhoZGX9AKQ0NDQ0OjL9BG5yvqBCKaBfDUEA+xCcCZIe5/GNBjPjfYiGMGNua49ZhXH3uYObWQ7VmrMIYNIrqbmQ+s9TgGgR7zucFGHDOwMcetx3xuoV1SGhoaGhp9QSsMDQ0NDY2+oBXG8vHRtR7AMqDHfG6wEccMbMxx6zGfQ+gYhoaGhoZGX9AWhoaGhoZGX9AKQ0NDQ0OjL2iF0QVEdJSIHiSi+4jo7pTfiYj+kIgOE9EDRHT1WoyzZUwXyfGqvyUi+sWWdV5BRIuxdX5zDcb5CSKaIaKHYssmiegWIjok/53osO0Ncp1DRHTDGo/5/xDRo/L+f56Ixjts2/VZGiY6jPu3iOh47Bl4fYdtryOix+Qz/t41HvPfx8Z7lIju67DtmlxrItpFRLcS0SNE9DAR/YJcvq6f64HAzPqvwx+AowA2dfn99QD+BQABuBbAHWs95pbxmQBOQRTixJe/AsCX13hsLwNwNYCHYsv+PwDvlZ/fC+D3UrabBHBE/jshP0+s4ZhfA8CSn38vbcz9PEtrMO7fAvDLfTw/TwA4D4AD4H4Al67VmFt+/30Av7merjWAbQCulp9HADwO4NL1/lwP8qctjJXhegCfYoHbAYwT0ba1HlQMrwLwBDMPs+J9WWDmbwKYa1l8PYBPys+fBPDmlE1fC+AWZp5j5nkAtwC4bljjjCNtzMz8VWb25NfbIRp7rSt0uNb94BoAh5n5CDM3AXwG4h4NHd3GTEQE4K0APn0uxtIvmPkkM98jP5cAHIRoI72un+tBoBVGdzCArxLRd4noxpTfe/YZX2O8DZ1fqhcR0f1E9C9EdNm5HFQXbGHmk/LzKQBbUtZZz9f8ZyAszjT0epbWAu+RrrRPdHCTrNdr/b0ATjPzoQ6/r/m1JqK9AK4CcAc2/nMdQiuM7ngpM18N4HUA3k1EL1vrAfUL2ZXwBwD8Q8rP90C4qa4E8EcAvnAOh9YXWNjpGybnm4h+HYAH4G87rLLenqU/BXA+gOcBOAnh4tkoeDu6Wxdreq2JqAjgHwH8IjMvxX/baM91K7TC6AJmPi7/nQHweQgTPY713Gf8dQDuYebTrT8w8xIzl+XnmwHYRLTpXA8wBaeVS0/+O5Oyzrq75kT0UwDeCODHpUBoQx/P0jkFM59mZp+ZAwAf6zCe9XitLQA/BODvO62zlteaiGwIZfG3zPxPcvGGfK7ToBVGBxBRgYhG1GeI4OZDLavdBOAdMlvqWgCLMdNzrdFxFkZEW6UfGER0DcRzcPYcjq0TbgKgskNuAPDFlHW+AuA1RDQh3SivkcvWBER0HYBfBfADzFztsE4/z9I5RUus7QeRPp67AOwnon3SYn0bxD1aS3w/gEeZ+Vjaj2t5reU79XEAB5n5w7GfNtxz3RFrHXVfr38QmSH3y7+HAfy6XP6fAfxn+ZkA/DFEJsmDAA6s9bjluAoQCmAstiw+7vfIc7ofIlD74jUY46chXCEuhL/2nQCmAHwdwCEAXwMwKdc9AOAvYtv+DIDD8u+n13jMhyF8z/fJvz+T624HcHO3Z2mNx/3X8pl9AEKgbWsdt/z+eohsnyfO5bjTxiyX/5V6jmPrrotrDeClEO6mB2LPw+vX+3M9yJ+mBtHQ0NDQ6AvaJaWhoaGh0Re0wtDQ0NDQ6AtaYWhoaGho9AWtMDQ0NDQ0+oJWGBoaGhoafUErDA0NDQ2NvqAVhsZzFkRUlv/uJaIfG+JxxonobKxY8kVExES0U34fI6I5Ikp9H4loOxF9ro/jlDssfzMRXbqSc9DQALTC0NAAgL0AhqYwmHkBogjtErnoxQDulf8Cghr/ThY0HWnbn2DmH1nBEN4MQbOtobEiaIWhoQH8LoDvlQ13/hsRmSQaI90l2VzfBYSNp24joi8S0REi+l0i+nEiulM27Dm/yzG+jUhBvBjAR1q+f6vLcfeSbCRERHki+qxs0vN5IrqDiA6ogxDRhyQL8e1EtIWIXgxBQvl/5Pl1G6OGRldohaGhIZra/DszP4+ZPwJBnbHIzC8A8AIAP0tE++S6V0LQrFwC4CcBXMjM1wD4CwA/1+UY30KkIM6DYBFWgv7FEAql23EV/iuAeWa+FMD/AvD82G8FALezYCH+JoCfZeZvQ1B//Io8vyf6vioaGi3QCkNDox2vgSCVvA+in8EUgP3yt7tYNMppQPArfVUufxDCtdUJ3wbwYqkAjjJzHYKvrggh9O/ocVyFl0I0MgIzPwTBW6TQBPBl+fm7PcajoTEwrLUegIbGOgQB+DlmTrCFEtErADRii4LY9wBd3idmPkSi3/ebAHxHLv4ugJ+GUCBlGRRPO+7ePsftckQO53cbj4bGcqAtDA0NoATRg1nhKwD+i+xtACK6UFJlrxS3A/gFRArjOwB+EcJd1e9xvwXRnhQy8+mKPo7ben4aGsuCVhgaGsKt48tg8X+DiEc8AuAeGWz+c6zObP1bEE1y7pbfvwMRz/i2/N7Pcf8EwDQRPQLgtyEovBd7HPczAH6FiO7VQW+NlUDTm2tobCAQkQnAZua6FP5fA3ARMzfXeGgazwFoH6eGxsZCHsCt0m1FAP6rVhYa5wrawtDQWEUQ0a8DeEvL4n9g5g+txXg0NFYTWmFoaGhoaPQFHfTW0NDQ0OgLWmFoaGhoaPQFrTA0NDQ0NPqCVhgaGhoaGn3h/w9q+8SkMl9s1AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### Numerical vs Numerical\n",
+    "\n",
+    "sns.lineplot(x=\"Item_Weight\", y=\"Item_Outlet_Sales\", data= data_train)\n",
+    "plt.show()\n",
+    "\n",
+    "sns.lineplot(x=\"Item_Visibility\", y=\"Item_Outlet_Sales\", data= data_train)\n",
+    "plt.show()\n",
+    "\n",
+    "sns.lineplot(x=\"Item_MRP\", y=\"Item_Outlet_Sales\", data= data_train)\n",
+    "plt.show()\n",
+    "\n",
+    "sns.lineplot(x=\"Outlet_Age\", y=\"Item_Outlet_Sales\", data= data_train)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OUT027    935\n",
+       "OUT013    932\n",
+       "OUT049    930\n",
+       "OUT046    930\n",
+       "OUT035    930\n",
+       "OUT045    929\n",
+       "OUT018    928\n",
+       "OUT017    926\n",
+       "OUT010    555\n",
+       "OUT019    528\n",
+       "Name: Outlet_Identifier, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_train.Outlet_Identifier.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "FDW13    10\n",
+       "FDG33    10\n",
+       "NCY18     9\n",
+       "FDD38     9\n",
+       "DRE49     9\n",
+       "         ..\n",
+       "FDY43     1\n",
+       "FDQ60     1\n",
+       "FDO33     1\n",
+       "DRF48     1\n",
+       "FDC23     1\n",
+       "Name: Item_Identifier, Length: 1559, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_train.Item_Identifier.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### Categorical vs Numerical\n",
+    "\n",
+    "### Sales in types of Outlet\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=\"Outlet_Type\", y=\"Item_Outlet_Sales\", data=data_train)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ATJHTjQEAAABg4ISEAAAAADBwQkIAAAAAGDjXJGRDO2S/G+3wCAAAAMD6ExKyoT3jHreddxUAAAAA9nhONwYAAACAgdOTEACm7EYHVJLWPwIAAGw8QkIAmLLbPsTmFgAA2NicbgwAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBw+8y7AgAAMA91wP47PAIADJmQEACAQbrRQ+4/7yoAAGwYTjcGAAAAgIETEgIAAADAwAkJAQAAAGDghIQAAAAAMHBCQgAAAAAYuDWHhFV1m6q6Uf/8mKp6SlUdPLWaAQAAAAAzMUlPwrcmubaqfjTJSUluneT1U6kVAAAAADAzk4SE32+tXZPkEUle3Fr7v0kOnU61AAAAAIBZmSQk/F5VPTrJcUne3ZfdYP2rBAAAAADM0j4TDPu4JL+Z5LmttS9U1eFJXjOdagEAAAAwb4fsd7MdHtlzrTkkbK1dVFW/l+SH+tdfSPL8aVUMAAAAgPl65l2fPO8qMCOT3N34IUnOTfKP/esjq+rUKdULAAAAAJiRSa5JeGKSuya5Iklaa+cm+ZF1rxEAAAAAMFMT3biktXblkrLvr2dlAAAAAIDZm+TGJRdW1a8k2buqjkjylCT/Np1qAQAAAACzMklPwicnuUOSq5O8Icl/J3naFOoEAAAAAMzQJHc3/naSZ/V/AAAAAMAeYtWQsKrelaSNe7+19tB1rREAAAAAMFNr6Un4l1OvBQAAAAAwN6uGhK21D82iIgAAAADAfKz5moT9HY3/PMntk9x4sby19iNTqBcAAAAAMCOT3N3475O8LMk1Se6d5NVJXjuNSgEAAAAAszNJSLhva+2MJNVa+1Jr7cQkD5pOtQAAAACAWVnz6cZJrq6qvZJ8rqqelGRbkv2nUy0AAAAAYFYm6Un41CT7JXlKkjsneUyS46ZRKQAAAABgdtbck7C1dlb/9Kqq+p0kV7TW2nSqBQAAAADMyqo9Cavqj6rqdv3zG1XVB5L8R5JLq+rnp11BAAAAAGC61nK68S8n+Wz/fPH04k1J7pXkz6ZRKQAAAABgdtYSEn535LTi+yV5Y2vt2tbapzPZjU8AAAAAgA1oLSHh1VX1E1W1Kcm9k7x/5L39plMtAAAAAGBW1tIT8KlJ3pLuFOO/aq19IUmq6oFJPjnFugEAAAAAM7BqSNha+1iS2y1TflqS0xZfV9VxrbVXrW/1AAAAAIBpW8vpxmv11HUcFwAAAAAwI+sZEtY6jgsAAAAAmJH1DAnb6oMAAAAAABuNnoQAAAAAMHBrDgmr6vBVys5clxoBAAAAADM1SU/Cty5T9pbFJ621J+16dQAAAACAWdtntQGq6nZJ7pDkoKr6hZG3Dkxy42lVDAAAAACYjVVDwiQ/luTBSQ5O8pCR8m8m+Y0p1AkAAAAAmKFVQ8LW2juTvLOq7tZa+8gM6gQAAAAAzNAk1yT8WlWdUVUXJElV3bGq/mClD1TVyVV12eJn+rITq2pbVZ3b/z1w5L1nVtXFVfXZqrrfSPn9+7KLq+oZE9QZAAAAAFjFJCHhK5I8M8n3kqS1dl6SR63ymVOS3H+Z8r9qrR3Z/52WJFV1+358d+g/89Kq2ruq9k7yN0kekOT2SR7dDwsAAAAArINJQsL9WmsfX1J2zUofaK19OMnX1zj+hyV5Y2vt6tbaF5JcnOSu/d/FrbXPt9a+m+SN/bAAAAAAwDqYJCT8alXdJklLkqo6NsklOzndJ1XVef3pyDfty26Z5L9GhvlyXzau/Hqq6oSqOruqzr788st3smoAAAAAMCyThIRPTPK3SW5XVduSPC3Jb+3ENF+W5DZJjkwXMr5gJ8axrNbaSa21o1prR23atGm9RgsAAAAAe7RV7268qLX2+SQ/X1U3SbJXa+2bOzPB1tqli8+r6hVJ3t2/3Jbk1iOD3qovywrlAAAAAMAuWjUkrKrfGVOeJGmtvXCSCVbVoa21xdOUH5Fk8c7HpyZ5fVW9MMktkhyR5ONJKskRVXV4unDwUUl+ZZJpAgAAAADjraUn4QErvNdW+mBVvSHJMUkOqaovJ3l2kmOq6sj+s19M8oQkaa1dWFVvSnJRuhuiPLG1dm0/nicleV+SvZOc3Fq7cA31BgAAAADWYNWQsLX2x0lSVUe31s4cfa+qjl7ls49epvjvVhj+uUmeu0z5aUlOW62uAAAAAMDkJrlxyYvXWAYAAAAA7EbWck3CuyX52SSbllyf8MB0p/8CAAAAALuxtVyT8IZJ9u+HHb0+4X8nOXYalQIAAAAAZmct1yT8UJIPVdUprbUvzaBOAAAAAMAMraUn4aJTqup6dzNurf3cOtYHAAAAAJixSULCp488v3GSX0xyzfpWBwAAAACYtTWHhK21TywpOrOqPr7O9QEAAAAAZmzNIWFV3Wzk5V5J7pzkoHWvEQAAAAAwU5Ocbjzak/CaJF9I8vj1rQ4AAAAAMGuTnG58+DQrAgAAAADMx5pCwqr6wSRPTHKHvujCJH/TWrtsWhUDAAAAAGZjr9UGqKqjk5zVv3x1/5ckH+/fAwAAAAB2Y2vpSfiCJA9vrX1ypOzUqnp7kr9N8tNTqRkAAAAAMBOr9iRMcuCSgDBJ0lo7N8kB614jAAAAAGCm1hISVlXddJnCm63x8wAAAADABraWkO+vkry/qu5VVQf0f8ckeW//HgAAAACwG1v1moSttZOq6itJ/iTd3Y1bkouS/Glr7V1Trh8AAAAAMGVruXFJWmvvTvLulYapqme21v58XWoFAAAAAMzMel5T8JHrOC4AAAAAYEbWMySsdRwXAAAAADAj6xkStnUcFwAAAAAwI3oSAgAAAMDArWdI+OZ1HBcAAAAAMCNrurtxklTV4UmenOSw0c+11h7aP/7ZelcOAAAAAJi+NYeESd6R5O+SvCvJ96dSGwAAAABg5iYJCb/TWnvR1GoCAAAAAMzFJCHhX1fVs5O8P8nVi4WttXPWvVYAAAAAwMxMEhL+rySPSfJz2X66cetfAwAAAAC7qUlCwkcm+ZHW2nenVRkAAAAAYPb2mmDYC5IcPKV6AAAAAABzMklPwoOTfKaqzsqO1yR86HpXCgAAAACYnUlCwmdPrRYAAAAAwNysOSRsrX2oqn44yRGttX+qqv2S7D29qgEAAAAAs7DmaxJW1W8keUuSv+2LbpnkHVOoEwAAAAAwQ5PcuOSJSY5O8t9J0lr7XJIfnEalAAAAAIDZmSQkvLq19t3FF1W1T5K2/lUCAAAAAGZpkpDwQ1X1+0n2rar/neTNSd41nWoBAAAAALMySUj4jCSXJzk/yROSnNZae9ZUagUAAAAAzMya726c5Mmttb9O8orFgqp6al8GAAAAAOymJulJeNwyZcevUz0AAAAAgDlZtSdhVT06ya8kObyqTh1564AkX59WxQAAAACA2VjL6cb/luSSJIckecFI+TeTnDeNSgEAAAAAs7NqSNha+1KSLyW52/SrAwAAAADM2lpON/5mkrbcW0laa+3Ada8VAAAAADAza+lJeMAsKgIAAAAAzMckdzcGAAAAAPZAQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBm2pIWFUnV9VlVXXBSNnNqur0qvpc/3jTvryq6kVVdXFVnVdVdxr5zHH98J+rquOmWWcAAAAAGJpp9yQ8Jcn9l5Q9I8kZrbUjkpzRv06SByQ5ov87IcnLki5UTPLsJD+d5K5Jnr0YLAIAAAAAu26qIWFr7cNJvr6k+GFJXtU/f1WSh4+Uv7p1Pprk4Ko6NMn9kpzeWvt6a+0bSU7P9YNHAAAAAGAnzeOahDdvrV3SP19IcvP++S2T/NfIcF/uy8aVX09VnVBVZ1fV2Zdffvn61hoAAAAA9lBzvXFJa60laes4vpNaa0e11o7atGnTeo0WAAAAAPZo8wgJL+1PI07/eFlfvi3JrUeGu1VfNq4cAAAAAFgH8wgJT02yeIfi45K8c6T8sf1djn8myZX9acnvS3Lfqrppf8OS+/ZlAAAAAMA62GeaI6+qNyQ5JskhVfXldHcpfl6SN1XV45N8Kckv9YOfluSBSS5O8u0kj0uS1trXq+pPkpzVD/ec1trSm6EAAAAAADtpqiFha+3RY966zzLDtiRPHDOek5OcvI5VAwAAAAB6c71xCQAAAAAwf0JCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABg4ISEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgRMSAgAAAMDACQkBAAAAYOCEhAAAAAAwcEJCAAAAABi4uYWEVfXFqjq/qs6tqrP7sptV1elV9bn+8aZ9eVXVi6rq4qo6r6ruNK96AwAAAMCeZt49Ce/dWjuytXZU//oZSc5orR2R5Iz+dZI8IMkR/d8JSV4285oCAAAAwB5q3iHhUg9L8qr++auSPHyk/NWt89EkB1fVoXOoHwAAAADsceYZErYk76+qT1TVCX3ZzVtrl/TPF5LcvH9+yyT/NfLZL/dlO6iqE6rq7Ko6+/LLL59WvQEAAABgj7LPHKd999batqr6wSSnV9VnRt9srbWqapOMsLV2UpKTkuSoo46a6LMAAAAAMFRz60nYWtvWP16W5O1J7prk0sXTiPvHy/rBtyW59cjHb9WXAQAAAAC7aC4hYVXdpKoOWHye5L5JLkhyapLj+sGOS/LO/vmpSR7b3+X4Z5JcOXJaMgAAAACwC+Z1uvHNk7y9qhbr8PrW2j9W1VlJ3lRVj0/ypSS/1A9/WpIHJrk4ybeTPG72VQYAAACAPdNcQsLW2ueT/OQy5V9Lcp9lyluSJ86gagAAAAAwOPO8uzEAAAAAsAEICQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAAAAAAyckBAAAAICBExICAAAAwMAJCQEAAABg4ISEAAAAADBwu1VIWFX3r6rPVtXFVfWMedcHAAAAAPYEu01IWFV7J/mbJA9Icvskj66q28+3VgAAAACw+9ttQsIkd01ycWvt86217yZ5Y5KHzblOAAAAALDbq9bavOuwJlV1bJL7t9Z+vX/9mCQ/3Vp70sgwJyQ5oX/5Y0k+O/OKrs0hSb4670rsRsyvyZlnkzPPJmeeTcb8mpx5NjnzbHLm2WTMr8mZZ5MzzyZjfk3OPJuceTa5jTrPfri1tmm5N/aZdU2mqbV2UpKT5l2P1VTV2a21o+Zdj92F+TU582xy5tnkzLPJmF+TM88mZ55NzjybjPk1OfNscubZZMyvyZlnkzPPJrc7zrPd6XTjbUluPfL6Vn0ZAAAAALALdqeQ8KwkR1TV4VV1wySPSnLqnOsEAAAAALu93eZ049baNVX1pCTvS7J3kpNbaxfOuVo7a8OfEr3BmF+TM88mZ55NzjybjPk1OfNscubZ5MyzyZhfkzPPJmeeTcb8mpx5NjnzbHK73TzbbW5cAgAAAABMx+50ujEAAAAAMAVCQgAAAAAYOCHhLqqqa6vq3Kq6sKo+VVW/W1UrztequkVVvWVWdZyWqnpW/73P6+fBT68y/D364c+tqrtV1QPHDHdMVV1ZVZ+sqs9W1Yer6sErjPehVfWMVaZ9TFW9e23fbGOqqlZVrx15vU9VXb6z36uqDquqX9nJzy4u94t/h03w2d+sqsf2z4+vqlvsTB12Rj+9lywpO6yqvrx0vV3LMr0T0171u1bVB6vqqDGff8lyn1lm2Kt29rO7qqq+WFWHTDD82Lot/R67WK/FZfZTVXVOVf3sKsMfVlUXLFN+VFW9aB3r9Wt9G7q4DXllVR28DuNd1zZv0vZ+J8a/6nLTD3P+SLuz4v9wjdNdt2VsZJw73T5OoS7H99uJxbq8eh3GeWJVPX096jfhdCdahycY77Lrev/eG/pl/rcnHOeR4/Zx+vfXrY0eXdf75z878t4pVXXszox3wjosux6t8/dct7Z3GtvIcW3YSLt1flVdVFV/WlU3XmE8/7az09ooqmpzVb2xqv6jqj5RVadV1W3nUI+ZLP/rZVpt3DLTmUsbvqQOy+5TVNXTqmq/dZrGLn3P9d6Wr7StmbWqulVVvbOqPtevp39dVTdcuu3aCMvKvKzHfm9Vbaqqj1WXadxjF+szs2O5ZDe6cckG9j+ttSOTpKp+MMnrkxyY5NnjPtBa+0qS6220qmqf1to1U6rnuqqquyV5cJI7tdau7ndWbrjKx341yZ+31l5bVccnOSrJaWOG/ZfW2oP7aR2Z5B1V9T+ttTOW1GOf1tqpGcadrr+V5Ceqat/W2v8k+d9Jtu3C+A5L8ivpltlJXbfcL1VVle56p99f7v3W2stHXh6f5IIkX9mJOqyL1toXq+o/k9wjyYeSpKpul+SA1trH1nFSx2fO33XgRtvq+yX58yT3mnQkrbWzk5y9HhWqqvsn+e0kD2itbauqvZMcl+TmSa5Yj2msh51s76fl3q21r85p2mu10+3jlPxDa+1JM5zetKy6Dq/nflRVbU5yl9baj+7Ex4/Myvs403JMkquSrBo07W7Ws+3dVTuxnN27tfbVqto/3QXs/zZdW3+9cbbWphIMzUrfxr09yataa4/qy34y3Xbt3+dZt93ATNu4eVlln+JpSV6b5NsTjG/v1tq1617RFbblu7N+HX1bkpe11h7W73uelOS5SS7MOm67pvi/map13O+9T5LzW2u/vq4VnAE9CddRa+2yJCckeVJ1Dquqf+l/DbruF6HRXxL6VPjUqvrnJGdU1aur6uGL46yq11XVw+bxfVZxaJKvttauTpLW2lf78DNVdZ8+MT+/qk6uqhtV1a8n+aUkf1JVb0jynCS/3Cfzv7zShFpr5/bDP6kf/ylV9fKq+liSraPJev/ei6rq36rq88v9glhVd+nrd5uqutfIL0SfrKoD1m0OTcdpSR7UP390kjcsvlFVN+nn98f77/KwvnzZ5TDJ85Lco//uE/WSWKqfxmer66VyQZJb18gv9VV1bFWd0j8/saqe3v9vjkryur4O+1bV86r7pf28qvrLZaZz16r6SP/9/q2qfqwvP76q3lZV/1jdr2JbRz7zuKr696r6eJKjx3yFNyR51MjrRyV5Y1XtXVV/UVVn9XV6Qj/OvarqpVX1mao6vbpfyY/t37tzVX2oul/P31dVh475rn/Uj/eCqjqp32gvekw/3AVVdddl5sOmqnpr//mzqurovvxeVXVukn3HLc/9/+qf++9zRlX9UF++wy/ui/+/vv4fHqnPPfry+/b/i3Oq6s3VHfwsenJffn51gWuq6mZV9Y5+uh+tqjsuU7fD+3GeX1V/OuZ/tR4OTPKNfpr79/Nhsb7Xa2+r6kf6+XmX2rHXzon9OvfB6tqbp4x85g/7deJfq+uNtNwvsc9K8vTW2rYkaa1d21o7ubX22X4c12tLVym/f79MnpPkF0bqsqvt3Ert/Rer6o+X+X+PW1f3rqq/7Jel86rqyUvm9b5V9d6q+o21VGyF5Xlc+bLL2LjlfD3U8u3jX/TTOb/6bWBVPWfk/7Stqv6+L/+16tr1c6vqb6vboU9VXVVVz62u18lHq+rmE9Tpd/rpX1BVT1tD+bOqa0f/NcmPjZQ/pba32W/cxVk1idF1+JjqtnGnJrmoxrfbE63rSd6f5Jb9fL9HVf1GP85PVdf+7td/5pH9/PpUvwzdMBPs4yxTh3Ht+7Lr1MjnDkvym0l+e7HO/Vv3rBX2ifrPvqO6bdaFVXXCSPmyy9i49WiC73hAVX2hqm7Qvz5w8XV17exiz42/qO37y6Nt77j9nZX2Ba7bZiW5ce24zRqt20Nqe8+Pfxr5zidW1Wuq6swkr6mqH6iq9/fz7JVJarnxjWqtXZXuf/Tw6raJOyy7i/N85Pt+sKreUl27/rqqHfYTdmgv+3nynv5/dcGky906uneS743+INxa+1SSTy63/lXXPn66ql7Rz8v3V9W+/XsfrKrn9//nf6/t+x/j1vGqqpdU197+U5IfnPm3Xz/r2sbVxmrDl92nqG4f6hZJPlBVH+jr97KqOrtfNv54pN5f7JeNc5I8srr9n3P65X+0Q8nta8k+WnXb2qeNjOu5VfXUtVS8up52H+3n19ur6qarlN+5r9OnkjxxZDx3qO3b9fOq6oidmpM75+eSfKe19vdJt++Z7gfrX0+yNdffdl1vHvbfYaV9kxf03/lutcqx3QY1bhm93jFektQy+wfVdXLamuRhtf3Y79H9unlBVT1/cWIrlD+uVj+GnY7Wmr9d+Ety1TJlV6T7xWy/JDfuy45Icnb//LAkF/TPj0/y5SQ361/fK8k7+ucHJflCkn3m/T2X+Y77Jzk33a+CL01yr778xkn+K8lt+9evTvK0/vkpSY4d+d4vGTPuY5K8e0nZkUk+PTKedyfZe+m4+vfenC4Av32Si0fHmeRnk3wiyQ/15e9KcvTId9pw83p0WUtyxyRv6efzuaPzKsmfJfm1/vnB/f/mJissh9ebzxPU5dp++uem+8X4sCTfT/Izy60b6XrOntI/PzFdMJIkH0xyVP/8B5J8NrnurusHLzPdAxf/R0l+PslbR5aBz6dbZ26c5EtJbp2ukf/PJJvS/QJ05nLLXbr19ZKRcX86yU+kC/3/oC+7UbpeDIf33+e0fjnbnG5H7tgkN0jXg2NT/5lfTnLy0u/av77ZyPPXJHnIyHCv6J/fMzu2FYvL+euT3L1//kPZvm68K91G5Nok5438j/5z5LPvSnJc//z/ZHt7c0r69XP0/5fkd5M8q3++d5IDkhyS5MNJbtKX/16SP+qffzHJk/vn/1+SV/bPX5zk2f3zn0ty7jLf69Qkj+2fPzHLtK+7sP4sLrOfSXJlkjv35fskObB/fkiSi9Md7B2WLtD5sSSfTPKTS9ebdMvyv/XLxiFJvtYvA3fpp3Xjfn59Lv0yv6ROX09y0Jj6LtuWrqH8iL7+bxqp5y61cxnT3q/y/x63rv5WujZs8b2bjYznsCT/tLgMLFOPLyY5v6/Lx1ZZnseVL7uMZZnlfB2WtXOzTPuY5BeTnN5P5+bp1s9DRz5/cP8975zkx/vvcoP+vZeO1L9le7uxNX1btaQuxye5fKQ+j+vHe3667cP+6XoO/NQayvfr/68XZ3sb/pUkN1qs93qtrxOuw8ek62l/eP96XLs96bp+WPr2t3/9AyPP/zTbl/vzk9xydB5khX2cZZaRc7NjGz2ufR+3Th2THdukp49M55Qss0+0TH0W18N9+3nxAystY1ljW73K9/z7JA8f+Z+9oH9+QZK79c+fl+3bwNHvOW5/5/gsvy+wdJv1/XTb/OXqddNs3w/59ZF6nZhu/3Hf/vWLsn2796B+Xh0ypt06ZEnZuUl+OkuW3f69q0a+75VJbtX//z6S7cvFF7OkvUzXrrxiZDwHTXN9XGHZfkqSv1qmfKX175okR/bvvWnkf/vBkfn/wCT/tMo6/gvZ3rbeIt3x2LHT+J5TmnfTauM2RBs+8j1X26c4ZOT1Ytu0d7883HFkuC39803p9n8OX/KZE7P8PtphSc7ph9kryX9kpH1f5v9xbpK392XnZfsx73OS/L81lN+zf/4X2d6evTjJr/bPb5i+XZnR/B+3jn6yf+8lI2Xj5uFq+ya/1D9f9dhuI/4tt4xm5WO8cfsHx2f7tuUW2X5Muk+Sf07y8BXK13QMO60/pxtP1w2SvKRPkq9NMu56HKe31r6eJK21D1XXQ2lTug3+W9sG7FbeWruqqu6c7hTNeyf5h+quC/jJJF9orS2eUvCqdDuQ/28XJ7n0F9o3t/Hdl9/RulO5Lqode1b8eLru1PdtfS+YdCvcC6vqdUne1lr78i7Wc6paa+dV11vg0bl+V/D7Jnlobe+xdON0BxhfydqWw0ns0AW/r9OXWmsf3YVxXpnkO0n+rrreAstdT+2gJK/qf3Fr6daxRWe01q7s63NRkh9Ot0H7YGvt8r78H7LM92+tXVpdb4X7VNWlSa5prV1QVScmuWNt731xULoQ5u7plsHvJ1lY/MUz3UHmTyQ5vbof/PdOdyCynHtX1ZZ0O203S3dA/q7+vTf09fpwdT0sDl7y2Z9P98ve4usDq+sVcWaSF6bb4X7g4vJc20/vT5K7ZXsvs9ekO/BbyVlJTq6ux8c7WmvnVtW90h1wntnX4YbpDmAWva1//MTItO6erk1La+2fq+uFceCSaR29OExft+dn/YyexnO3JK+uqp9I17b8WVXdM92B4y3TBTdJt2F+Z5JfaK1dNGa872ndL41XV9Vl/WePTvLO1tp3knynqt415rPXqar/le47H5Dk99MdJCzXln5gTPkH+/LP9eN7bboDiWQX27lx7X1r7ZR+kOX+3+PW1Z9P8vLF7dritq/3ziRbW2uvW6E6S083Hrc8jysft4xdbzlfoQ6rWa19vHuSN/TbsEur6kPpguVTq1uhXpvkha21T1TVk9Id4J3Vr2v7JrmsH893s72d/ES6S1AsZ4fTjfseE29vrX2rf/22dP/bGlO+V1/+7b589PIe56XrIf2OJO9Y6wzaSePW4ST5eGvtC/3z+2b5dvvL2bV1/Seq6zV3cLoDiPf15WcmOaWq3pTt68Kav0v/fY7P9jZ6XPu+0vZvJeP2iUY9paoe0T+/dbr59bWMX8bW2lav9D1fmWRLuuXmcUl+o9/WHdBaW9yevD7dKV9LjdvfSZbfFzg4O26zkuS01trjl6nXrdK1cYem27YtLldJcmrrLveSdD/i/UKStNbeU1XfGDMPljO6Tzu67C718ZHt+Lnpwo1/7d9b2l6en+QFfS+Ud7fW/mWC+szCStvaL4y0uZ9I9z0XvW2Z8nHr+D2zvW39SnVnae1OptXG3SMbow1PsqZ9ilG/VF3v5n3ShSa37+ucJP/QP/5Mkg8vzp8l+xXX20dr3WWGvlZVP5Vu/nyytfa1Zaa9tP06KF3I9aG+6FVJ3rxC+cF9+Yf78tckeUD//CNJnlVVt0q3X/a58XNs7pbbz71Pxu+bXJvkrf3ztRzbbTjLLaPpwr9xx3jj9g9G3SU7HpO+Ll2b1caUJ2s4hp0WIeE6q6ofSbdyXJbuuoSXJvnJdDvZ3xnzsW8tef3qJL+W7pTHx02npruu3wh/MMkHq+r8dNdX+eSUJvdT6Xp3LVo6z0ZdPfJ8dEfsknQ7kj+V/rpwrbXnVdV70v1CeWZV3a+19pn1qfLUnJrkL9P9svgDI+WV5Bdbf6ridYVd0LWW5XBXLf2ftJHnYy/Sfd3ArV1T3am190nXK+9J6XqcjfqTJB9orT2iP/D+4Mh7o//3azN5+7Z4yvGl2X4ad6X7NWiHxr7GX5C+klzYWrvbShOq7qLlL03Xs/C/+v/R6DxqSz6y9PVe6XolLf1fLi7PZ2fy5fmafryp7iYuN0yuCyrvma6nxClV9cJ0PSdPb609esy4Fv8XO/N/WPpd111r7SPVXV9kU7p1f1O6X+y/V1VfzPb/xZXpfsW7e/pTwZaxK8vdhUnulG6ZPj/JkdVdOmHfCcaxqvVo58a096f0by/3/15pXR3nzCT3r6rXt9amuRxcb9zLLeettV2+yceIlbZZo05M8uXWnwaUrk15VWvtmcsM+72R+bQz69p6eFC6ndmHpDvg+V+z+GFzyTqc7Dh/x7Xbx2fX1vVT0vV8+1Q/rmP6uvxmdRc0f1CST/QHFrti2fa9bxsmXaeS8ftEi+M9Jl0webfW2rer6oPZPl9WWsZ2aR1trZ1Z3ammx6Q7M+SCZX4QG2fc/s5PZ/k2uTKyzaqqqxYDwmW8OF1If2pftxNH3lvrejy+4t3lHg5L10PlJ1cZ50rblx3ay9bav1fVndK1839aVWe01p6zq/XdCRdmmeuup7su+bj1b+n3HN0GLrd9mXTfbLczhTZunHm14SvtUyTpLmuQ5Onprg37jeouWzT6fdayPo5bh16ZrpfX5iQnT/wFdlFr7fXVXTbrQUlOq6ontNZmFWpflCXraHU/2v9QumOBpca1qeP2Tb7T/3/Xemy3IS2zjD4x44/xTsky+we7M9ckXEfV9f57ebquoC3dLzuX9L/gPiZd4rwWp6Q7fSwr/KI9V1X1Y7Xj9ROOTHdax2eTHFZVixf6fkz6m0Es8c10PWbWMq07JvnDJH+z0xXuXJGuMf7zfscvVXWb1tr5rbXnp+tJcrtdnMYsnJzkj/tQYdT70l0LrpKk/4UsGb8crvl/sJMuraof7wOnR4wZ5ro6LPaUaK2dlu7aGD+5zPAHZfvNWo5fQx0+luRe1fVau0GSR64w7NvS7Vz/cpLF67K8L8lv1fbrJt22qm6Sbuf8F6u7NuHNs31j8Nkkm/pfgFPd9ZXusPS7ZvtOzuKFzJfuUC9eo+zuSa5c7BUx4v1JrruWW3W9RK9bnpN8L+OX53/L9usv/mqSxd4GX0z3q2CSPDR9L5Wq+uEkl7bWXpFup+pOST6a5OjF9by6ayGt9uvWv/TTWzwo/Wpr7b+XDHPmkrpNRXXXzds7XU+Zg5Jc1u9Q3ztdr5NF30237D62JrsT+JlJHlJVi9e9Gnd39j9P8pf9L8mLFg+OxrWl48o/05ffpi+/LsDd1XZuhfZ+JePW1dOTPKGq9unHfbOR9/4oXQA9SVs/bnkeV77sMjZmOZ+Wf0l3zZ+9+/2Geyb5eFU9JF1Y85SRYc9Icmx1N0ZLddcx++HrjXHy6T+8umvm3CTdMv4vK5R/uC/ftw84HtLXZa8kt26tfSDdJQcOSvcL+tQtWYeXGtdu7+q6fkCSS/rxji47t2mtfay19kfpTu2+dXZt+7ps+561bf92ZroHJflGHxDeLl2vnNWsV1v96nS9Bf8+SVprVyT5Zm2/i+Sjxnxu3P7OODtss/rPjNtmjc7n41YY54fT3fwtVfWAdKcpr6jfHrw0Xe/OSXoeLmeH9rKqbpHk262116Y7rXGabdhK/jnJjWrHa1veMd36Nm79m9S4dfzD2d62HpquB9BuaZ3buA3Vhq+yTzHahh2YLgi8st/XfkCW99F01149vB//zcYMN+rtSe6frnfXcr2+rqffF/9Gbb/e62OSfGiF8iuSXNHvyyc7bjd+JMnnW2svStcr+HrX6Z6iM5LsV1WP7euyd5IXpMsfLs3atiFr2jdZ47HdhjNmGf10xh/jLbt/sMTH0x2THtLP80en238fVz7JMey605Nw1+1b3SkAN0iXvr8m3el+Sbcj8NZ+JfzHrPEXyNad+vjpzKjb907aP8mLq/vV95p017c4obX2nap6XLpu1vukOyB9+TKf/0CSZ/Tz7s9ba/+w5P17VNUn052KeVmSp7QldzbeGf28fXCS91bV/0nya/2G9Pvpfv18765OY9pad+rJi5Z560/SndZ9Xr/h/0K6cGLccnhekmuru7DsKa21v1rnqj4jXbfyy9P1bFtux+OUJC+vqv9Jt/F/Z3W97CrJ7ywz/NZ0p1v9QZL3rFaB1tol1fXS+0i6kPjcFYa9oqo+kmRza+3zffEr01+7pD8YuTzddSLemu5XsYvSXQflnHRh3nerO/3jRdWdfrBPuv/JhUu+692SvCLdtZcW0q0no77TL/83SHc9taWekuRvquq8fhofTncx9Kf1y/O+6YLC5ZbnJyf5+6r6v/33Weyt/Ip08/9T2XE5OSbJ/62q76W7LuZjW2uXV/dL2Ruqv2lGkj/IyncuPDHd6Zznpbtr3XIHX09N8vqq+r10O03rabGtTrrl67jW2rXVdet/V3W/Ep6dLmy7TmvtW32bcXp1F5RfGmxeT2vtrOpO5zkv3Q7X+el6Ki0d7rQ+JHpvv2NwRbpl4n3j2tLW3WVtXPkJSd5TVd9OF+4s7ugtLhc7284t296v8plx6+or050ucV6/TL0iyUtG3n9quuVka2ttyxrqNm55Hlc+bhk7JkuW8zVMe2e9PV0b8Kl0vbG2tNYWqup30p0e9vE++zi1tfZH/Tx8f9+ufy/dr9mrhbRjtdbOqa5Hxsf7ole21j6ZJCuU/0Nf38uyvb3aO8lr+7aukryoPyialnHr8NLhxrXbk67r52VHf5hup/3y/nFx/fqL/mCi0h04fSpdr8SV9nFWMq59X8v2711J3lLdDQuePGaYpf4xyW/2+52fTXfAvZr1aqtfl+4UrjeMlD0+ySuq6vvpDpSu13Zm/P7OspbZZu2b7seS5bZZJ6ZrX7+RLvA6fMxo/7gf34XpfpT4z3HTT3cjhkp/6n5f//VwXXuZbtn7i36+fS/d9V9nrrXWqjt1/f/1y8d30v0IeWK6faNl178JjVvH356ul9JF6f4fHxnz+Y1qKm1c3+ZvhDZ80Ur7FCcl+ceq+kpr7d79vvBn0u1rn7ncyPr1+4Qkb+vbg8sy/vIbi5/5bnWXCrqiTXb33ePS7cvvl+76p49bpfxx6dbRlu4HoEW/lO4mhd9LdxzwZxPUYZeMrKMvrao/TNcunZbuUjc3yci2a4VxXLTGfZMDsvqx3UY0bhk9Kcsf443bP7hOf0z6jHQZSKU7jfudSbJC+YlZwzHsNCxeRJINpG9gzk932+3ldo6AOauq/Vt3zYofSHdQfXRrbWHe9WJjGFk+9kt3kH9Ca+2cedcLYKPof1R7WGvtMSNl+7fuLsCLB06HttaeOq86AnuePtg6J8kj28a+HiDMhZ6EG0xV/XySv0t31yEBIWxc7+5/Ybphkj8RELLESVV1+3Snlr9KQAiwXVW9ON0ZBEuvI/egqnpmumOUL2VtlxYBWJN+3+zd6W7mIiCEZehJCAAAAAAD58YlAAAAADBwQkIAAAAAGDghIQAAAAAMnJAQAAAAAAZOSAgAMHBVdVX/eFhV/coUp/Osqjq3/7t25PlTpjVNAADWxt2NAQAGrqquaq3tX1XHJHl6a+3Bs5rmtKcDAMDa6EkIAMCi5yW5R9+777erau+q+ouqOquqzquqJyRJVR1TVR+qqndW1eer6nlV9atV9fGqOr+qbrPWCVbVc6rqaSOvn1tVT+2n8eGqek9VfbaqXl5Ve/XD3LeqPlJV51TVm6tK2AgAsIuEhAAALHpGkn9prR3ZWvurJI9PcmVr7S5J7pLkN6rq8H7Yn0zym0l+PMljkty2tXbXJK9M8uQJpnlykscmSR8CPirJa/v37tqP6/ZJbpPkF6rqkCR/kOTnW2t3SnJ2kt/Zye8LAEBvn3lXAACADeu+Se5YVcf2rw9KckSS7yY5q7V2SZJU1X8keX8/zPlJ7r3WCbTWvlhVX6uqn0py8ySfbK19raqS5OOttc/303hDkrsn+U660PDMfpgbJvnILn1LAACEhAAAjFVJntxae98Ohd21C68eKfr+yOvvZ/J9zFcmOT7J5nQ9CxctvXh26+t0emvt0RNOAwCAFTjdGACARd9McsDI6/cl+a2qukGSVNVtq+omU5ju25PcP90pzaOB5F2r6vD+NORfTvKvST6a5Oiq+tG+TjepqttOoU4AAIOiJyEAAIvOS3JtVX0qySlJ/jrJYUnOqe7c3suTPHy9J9pa+25VfSDJFa21a0feOivJS5L8aJIPJHl7a+37VXV8kjdU1Y364f4gyb+vd70AAIakWlt6FgcAAMxO31PwnCSPbK19ri87JsnTW2sPnmPVAAAGw+nGAADMTVXdPsnFSc5YDAgBAJg9PQkBAFh3VfWsJI9cUvzm1tpz51EfAABWJiQEAAAAgIFzujEAAAAADJyQEAAAAAAGTkgIAAAAAAMnJAQAAACAgfv/AVGuI3gdazzaAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1584x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Sales of particular food category\n",
+    "\n",
+    "plt.figure(figsize=(22,10))\n",
+    "sns.barplot(x=\"Item_Type\", y=\"Item_Outlet_Sales\", data=data_train)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Sales in cities\n",
+    "\n",
+    "plt.figure(figsize=(10,7))\n",
+    "sns.barplot(x=\"Outlet_Location_Type\", y=\"Item_Outlet_Sales\", data=data_train)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 2160x1080 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 492.5x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "### Categorical vs Categorical\n",
+    "plt.figure(figsize=(30,15))\n",
+    "g = sns.catplot(\n",
+    "    data=data_train, kind=\"bar\",\n",
+    "    x=\"Outlet_Location_Type\", y=\"Item_Outlet_Sales\", hue=\"Outlet_Type\",\n",
+    "    ci=\"sd\"\n",
+    ")\n",
+    "\n",
+    "g.despine(left=True)\n",
+    "g.set_axis_labels(\"\", \"Item_Outlet_Sales\")\n",
+    "g.legend.set_title(\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 7200x2160 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 503.875x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(100,30))\n",
+    "\n",
+    "h = sns.catplot(\n",
+    "    data=data_train, kind=\"bar\",\n",
+    "    x=\"Outlet_Identifier\", y=\"Item_Outlet_Sales\", hue=\"Item_Type\",\n",
+    "    ci=\"sd\"\n",
+    ")\n",
+    "\n",
+    "h.despine(left=True)\n",
+    "h.set_axis_labels(\"\", \"Item_Outlet_Sales\")\n",
+    "h.legend.set_title(\"\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Encoding Categorical Variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rectifying the labels in columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Low Fat    5089\n",
+       "Regular    2889\n",
+       "LF          316\n",
+       "reg         117\n",
+       "low fat     112\n",
+       "Name: Item_Fat_Content, dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_train['Item_Fat_Content'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Low Fat    3396\n",
+       "Regular    1935\n",
+       "LF          206\n",
+       "reg          78\n",
+       "low fat      66\n",
+       "Name: Item_Fat_Content, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_test['Item_Fat_Content'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_train.replace({'Item_Fat_Content' : {'low fat' : 'Low Fat', 'LF': 'Low Fat', 'reg':'Regular'}}, inplace=True)\n",
+    "data_test.replace({'Item_Fat_Content' : {'low fat' : 'Low Fat', 'LF': 'Low Fat', 'reg':'Regular'}}, inplace=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Low Fat    5517\n",
+       "Regular    3006\n",
+       "Name: Item_Fat_Content, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_test['Item_Fat_Content'].value_counts()\n",
+    "data_train['Item_Fat_Content'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "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>Outlet_Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>FDW58</td>\n",
+       "      <td>20.750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.007565</td>\n",
+       "      <td>Snack Foods</td>\n",
+       "      <td>107.8622</td>\n",
+       "      <td>OUT049</td>\n",
+       "      <td>1999</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Supermarket Type1</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>FDW14</td>\n",
+       "      <td>8.300</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.038428</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>87.3198</td>\n",
+       "      <td>OUT017</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Supermarket Type1</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NCN55</td>\n",
+       "      <td>14.600</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.099575</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>241.7538</td>\n",
+       "      <td>OUT010</td>\n",
+       "      <td>1998</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Grocery Store</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>FDQ58</td>\n",
+       "      <td>7.315</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.015388</td>\n",
+       "      <td>Snack Foods</td>\n",
+       "      <td>155.0340</td>\n",
+       "      <td>OUT017</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Supermarket Type1</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>FDY38</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.118599</td>\n",
+       "      <td>Dairy</td>\n",
+       "      <td>234.2300</td>\n",
+       "      <td>OUT027</td>\n",
+       "      <td>1985</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Supermarket Type3</td>\n",
+       "      <td>37</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Item_Identifier  Item_Weight  Item_Fat_Content  Item_Visibility  \\\n",
+       "0           FDW58       20.750                 0         0.007565   \n",
+       "1           FDW14        8.300                 1         0.038428   \n",
+       "2           NCN55       14.600                 0         0.099575   \n",
+       "3           FDQ58        7.315                 0         0.015388   \n",
+       "4           FDY38          NaN                 1         0.118599   \n",
+       "\n",
+       "     Item_Type  Item_MRP Outlet_Identifier  Outlet_Establishment_Year  \\\n",
+       "0  Snack Foods  107.8622            OUT049                       1999   \n",
+       "1        Dairy   87.3198            OUT017                       2007   \n",
+       "2       Others  241.7538            OUT010                       1998   \n",
+       "3  Snack Foods  155.0340            OUT017                       2007   \n",
+       "4        Dairy  234.2300            OUT027                       1985   \n",
+       "\n",
+       "   Outlet_Size  Outlet_Location_Type        Outlet_Type  Outlet_Age  \n",
+       "0            1                     0  Supermarket Type1          23  \n",
+       "1            3                     1  Supermarket Type1          15  \n",
+       "2            3                     2      Grocery Store          24  \n",
+       "3            3                     1  Supermarket Type1          15  \n",
+       "4            1                     2  Supermarket Type3          37  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Label Encoder\n",
+    "encoder = LabelEncoder()\n",
+    "\n",
+    "Label = ['Item_Fat_Content', 'Outlet_Size', 'Outlet_Location_Type']\n",
+    "\n",
+    "for i in Label :\n",
+    "    data_train[i] = encoder.fit_transform(data_train[i])\n",
+    "    data_test[i] = encoder.fit_transform(data_test[i])\n",
+    "\n",
+    "data_train.head()\n",
+    "data_test.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
+      "  warnings.warn(msg, category=FutureWarning)\n",
+      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
+      "  warnings.warn(msg, category=FutureWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "### One Hot Encoder\n",
+    "\n",
+    "cols = ['Item_Type', 'Outlet_Type']\n",
+    "\n",
+    "### Applying One hot encoder\n",
+    "OH_enocder = OneHotEncoder(handle_unknown='ignore', sparse=False)\n",
+    "train_oh = pd.DataFrame(OH_enocder.fit_transform(data_train[cols])).astype('int64')\n",
+    "test_oh = pd.DataFrame(OH_enocder.fit_transform(data_test[cols])).astype('int64')\n",
+    "\n",
+    "### feature names\n",
+    "train_oh.columns = OH_enocder.get_feature_names(cols)\n",
+    "test_oh.columns = OH_enocder.get_feature_names(cols)\n",
+    "\n",
+    "### One-hot encoding removed index; put it back\n",
+    "train_oh.index = data_train.index\n",
+    "test_oh.index = data_test.index\n",
+    "\n",
+    "### Add one-hot enocded columns to our main dataframe new name: train_fe, test_fe(means feature engineering)\n",
+    "train_fe = pd.concat([data_train, train_oh], axis=1)\n",
+    "test_fe = pd.concat([data_test, test_oh], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "### Dropping columns that are not relevant\n",
+    "\n",
+    "\n",
+    "train_fe = train_fe.drop(['Item_Identifier', 'Outlet_Identifier', 'Outlet_Establishment_Year', 'Outlet_Type', 'Item_Type'], axis=1)\n",
+    "test_fe = test_fe.drop(['Item_Identifier', 'Outlet_Identifier', 'Outlet_Establishment_Year', 'Outlet_Type', 'Item_Type'], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "28\n",
+      "27\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train_fe.head()\n",
+    "# test_fe.head()\n",
+    "\n",
+    "train_columns = train_fe.columns\n",
+    "test_columns = test_fe.columns\n",
+    "print(len(train_columns))\n",
+    "print(len(test_columns))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Data Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y = train_fe['Item_Outlet_Sales']\n",
+    "X = train_fe.drop('Item_Outlet_Sales', axis=1)\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6818"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.head()\n",
+    "len(X_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Modelling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cross_val(model_name, model, X, y, cv) :\n",
+    "\n",
+    "    scores = CVS(model, X, y, cv=cv)\n",
+    "    print(f'{model_name} Scores:')\n",
+    "    for i in scores :\n",
+    "        print(round(i, 2))\n",
+    "    print(f'Average {model_name} score : {round(scores.mean(), 4)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
+      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
+      "\n",
+      "from sklearn.pipeline import make_pipeline\n",
+      "\n",
+      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
+      "\n",
+      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
+      "\n",
+      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
+      "model.fit(X, y, **kwargs)\n",
+      "\n",
+      "\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
+      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
+      "\n",
+      "from sklearn.pipeline import make_pipeline\n",
+      "\n",
+      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
+      "\n",
+      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
+      "\n",
+      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
+      "model.fit(X, y, **kwargs)\n",
+      "\n",
+      "\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
+      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
+      "\n",
+      "from sklearn.pipeline import make_pipeline\n",
+      "\n",
+      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
+      "\n",
+      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
+      "\n",
+      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
+      "model.fit(X, y, **kwargs)\n",
+      "\n",
+      "\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
+      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
+      "\n",
+      "from sklearn.pipeline import make_pipeline\n",
+      "\n",
+      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
+      "\n",
+      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
+      "\n",
+      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
+      "model.fit(X, y, **kwargs)\n",
+      "\n",
+      "\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
+      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
+      "\n",
+      "from sklearn.pipeline import make_pipeline\n",
+      "\n",
+      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
+      "\n",
+      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
+      "\n",
+      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
+      "model.fit(X, y, **kwargs)\n",
+      "\n",
+      "\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\HP\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\sklearn\\linear_model\\_base.py:141: FutureWarning: 'normalize' was deprecated in version 1.0 and will be removed in 1.2.\n",
+      "If you wish to scale the data, use Pipeline with a StandardScaler in a preprocessing stage. To reproduce the previous behavior:\n",
+      "\n",
+      "from sklearn.pipeline import make_pipeline\n",
+      "\n",
+      "model = make_pipeline(StandardScaler(with_mean=False), LinearRegression())\n",
+      "\n",
+      "If you wish to pass a sample_weight parameter, you need to pass it as a fit parameter to each step of the pipeline as follows:\n",
+      "\n",
+      "kwargs = {s[0] + '__sample_weight': sample_weight for s in model.steps}\n",
+      "model.fit(X, y, **kwargs)\n",
+      "\n",
+      "\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Mean Absolute Error: 852.26\n",
+      "\n",
+      " Mean Squared Error: 1282399.56\n",
+      "\n",
+      " R^2 Score: 0.5619\n",
+      "\n",
+      "LinearRegression(normalize=True) Scores:\n",
+      "0.57\n",
+      "0.55\n",
+      "0.55\n",
+      "0.56\n",
+      "0.56\n",
+      "Average LinearRegression(normalize=True) score : 0.558\n"
+     ]
+    }
+   ],
+   "source": [
+    "lr_model = LinearRegression(normalize=True)\n",
+    "\n",
+    "lr_model.fit(X_train, y_train)\n",
+    "\n",
+    "y_predict = lr_model.predict(X_test)\n",
+    "\n",
+    "### Scores\n",
+    "\n",
+    "lr_MAE = round(MAE(y_test, y_predict),2)\n",
+    "lr_MSE = round(MSE(y_test, y_predict),2)\n",
+    "lr_R_2 = round(R2(y_test, y_predict),4)\n",
+    "lr_CS  = round(CVS(lr_model, X, y, cv=5).mean(),4)\n",
+    "\n",
+    "print(f\" Mean Absolute Error: {lr_MAE}\\n\")\n",
+    "print(f\" Mean Squared Error: {lr_MSE}\\n\")\n",
+    "print(f\" R^2 Score: {lr_R_2}\\n\")\n",
+    "cross_val(lr_model,LinearRegression(),X,y,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Linear_Regression = pd.DataFrame({'y_test':y_test, 'prediction':y_predict})\n",
+    "Linear_Regression.to_csv('LinearRegression.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Regularized Linear Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Mean Absolute Error: 852.45\n",
+      "\n",
+      " Mean Squared Error: 1283347.33\n",
+      "\n",
+      " R^2 Score: 0.5615\n",
+      "\n",
+      "Lasso(alpha=0.05) Scores:\n",
+      "0.57\n",
+      "0.55\n",
+      "0.55\n",
+      "0.56\n",
+      "0.56\n",
+      "Average Lasso(alpha=0.05) score : 0.5581\n"
+     ]
+    }
+   ],
+   "source": [
+    "#model\n",
+    "LS = Lasso(alpha = 0.05)\n",
+    "#fit\n",
+    "LS.fit(X_train,y_train)\n",
+    "\n",
+    "#predict\n",
+    "y_predict = LS.predict(X_test)\n",
+    "\n",
+    "#score variables\n",
+    "LS_MAE = round(MAE(y_test, y_predict),2)\n",
+    "LS_MSE = round(MSE(y_test, y_predict),2)\n",
+    "LS_R_2 = round(R2(y_test, y_predict),4)\n",
+    "LS_CS  = round(CVS(LS, X, y, cv=5).mean(),4)\n",
+    "\n",
+    "print(f\" Mean Absolute Error: {LS_MAE}\\n\")\n",
+    "print(f\" Mean Squared Error: {LS_MSE}\\n\")\n",
+    "print(f\" R^2 Score: {LS_R_2}\\n\")\n",
+    "cross_val(LS,Lasso(alpha = 0.05),X,y,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Lasso_Regressor=pd.DataFrame({'y_test':y_test,'prediction':y_predict})\n",
+    "Lasso_Regressor.to_csv(\"Lasso Regressor.csv\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Random Forest Regressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Mean Absolute Error: 778.69\n",
+      "\n",
+      " Mean Squared Error: 1202754.94\n",
+      "\n",
+      " R^2 Score: 0.5891\n",
+      "\n",
+      "RandomForestRegressor(max_depth=5, min_samples_leaf=100, n_estimators=200,\n",
+      "                      n_jobs=4, random_state=101) Scores:\n",
+      "0.57\n",
+      "0.53\n",
+      "0.52\n",
+      "0.55\n",
+      "0.57\n",
+      "Average RandomForestRegressor(max_depth=5, min_samples_leaf=100, n_estimators=200,\n",
+      "                      n_jobs=4, random_state=101) score : 0.5499\n"
+     ]
+    }
+   ],
+   "source": [
+    "#model\n",
+    "RFR= RandomForestRegressor(n_estimators=200,max_depth=5, min_samples_leaf=100,n_jobs=4,random_state=101)\n",
+    "#fit\n",
+    "RFR.fit(X_train, y_train)\n",
+    "#predict\n",
+    "y_predict = RFR.predict(X_test)\n",
+    "\n",
+    "#score variables\n",
+    "RFR_MAE = round(MAE(y_test, y_predict),2)\n",
+    "RFR_MSE = round(MSE(y_test, y_predict),2)\n",
+    "RFR_R_2 = round(R2(y_test, y_predict),4)\n",
+    "RFR_CS  = round(CVS(RFR, X, y, cv=5).mean(),4)\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(f\" Mean Absolute Error: {RFR_MAE}\\n\")\n",
+    "print(f\" Mean Squared Error: {RFR_MSE}\\n\")\n",
+    "print(f\" R^2 Score: {RFR_R_2}\\n\")\n",
+    "cross_val(RFR,RandomForestRegressor(),X,y,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Random_Forest_Regressor=pd.DataFrame({'y_test':y_test,'prediction':y_predict})\n",
+    "Random_Forest_Regressor.to_csv(\"Random Forest Regressor.csv\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "XGBoost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[23:40:51] WARNING: C:/Users/administrator/workspace/xgboost-win64_release_1.6.0/src/objective/regression_obj.cu:203: reg:linear is now deprecated in favor of reg:squarederror.\n",
+      " Mean Absolute Error: 776.28\n",
+      "\n",
+      " Mean Squared Error: 1217556.36\n",
+      "\n",
+      " R^2 Score: 0.584\n",
+      "\n",
+      "XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
+      "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
+      "             early_stopping_rounds=None, enable_categorical=False,\n",
+      "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
+      "             importance_type=None, interaction_constraints='',\n",
+      "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
+      "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
+      "             missing=nan, monotone_constraints='()', n_estimators=10, n_jobs=0,\n",
+      "             num_parallel_tree=1, objective='reg:linear', predictor='auto',\n",
+      "             random_state=123, reg_alpha=0, ...) Scores:\n",
+      "0.54\n",
+      "0.51\n",
+      "0.48\n",
+      "0.53\n",
+      "0.55\n",
+      "Average XGBRegressor(base_score=0.5, booster='gbtree', callbacks=None,\n",
+      "             colsample_bylevel=1, colsample_bynode=1, colsample_bytree=1,\n",
+      "             early_stopping_rounds=None, enable_categorical=False,\n",
+      "             eval_metric=None, gamma=0, gpu_id=-1, grow_policy='depthwise',\n",
+      "             importance_type=None, interaction_constraints='',\n",
+      "             learning_rate=0.300000012, max_bin=256, max_cat_to_onehot=4,\n",
+      "             max_delta_step=0, max_depth=6, max_leaves=0, min_child_weight=1,\n",
+      "             missing=nan, monotone_constraints='()', n_estimators=10, n_jobs=0,\n",
+      "             num_parallel_tree=1, objective='reg:linear', predictor='auto',\n",
+      "             random_state=123, reg_alpha=0, ...) score : 0.5227\n"
+     ]
+    }
+   ],
+   "source": [
+    "#model\n",
+    "xgb_r = xg.XGBRegressor(objective ='reg:linear', n_estimators = 10, seed = 123)\n",
+    "#fit\n",
+    "xgb_r.fit(X_train, y_train)\n",
+    "#predict\n",
+    "y_predict = xgb_r.predict(X_test)\n",
+    "\n",
+    "#score variables\n",
+    "xgb_MAE = round(MAE(y_test, y_predict),2)\n",
+    "xgb_MSE = round(MSE(y_test, y_predict),2)\n",
+    "xgb_R_2 = round(R2(y_test, y_predict),4)\n",
+    "xgb_CS  = round(CVS(RFR, X, y, cv=5).mean(),4)\n",
+    "\n",
+    "\n",
+    "\n",
+    "print(f\" Mean Absolute Error: {xgb_MAE}\\n\")\n",
+    "print(f\" Mean Squared Error: {xgb_MSE}\\n\")\n",
+    "print(f\" R^2 Score: {xgb_R_2}\\n\")\n",
+    "cross_val(xgb_r,xg.XGBRegressor(),X,y,5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xgb_Regressor=pd.DataFrame({'y_test':y_test,'prediction':y_predict})\n",
+    "xgb_Regressor.to_csv(\"xgb Regressor.csv\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this project I have built an model for predicting the items sales in each store by comparing different algorithms such as Linear regression, Regularized linear regression, Random Forest Regressor and XGBoost out of which XGBoost gave us the best results of all. I have also made the csv files of the results obtained from different models"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.5 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "a9cff5a362bc38ef45d817ae74b1af54d6a076e3d773891282bce078b815ba34"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/xgb Regressor.csv b/xgb Regressor.csv
new file mode 100644
index 0000000..99bc7cd
--- /dev/null
+++ b/xgb Regressor.csv	
@@ -0,0 +1,1706 @@
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From 5a8f419154aa8e64079a839abe53fc5af7d89f03 Mon Sep 17 00:00:00 2001
From: Roshan Rai <67222316+roshanrai1304@users.noreply.github.com>
Date: Thu, 6 Oct 2022 10:30:04 +0530
Subject: [PATCH 2/2] Update README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 29360b9..85fa0f0 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,2 @@
 # Product-Sales-Outlet-at-BigMart-Datawarehouse-
-he data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim of this data science project is to build a predictive model and find out the sales of each product at a particular store.
+The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. Also, certain attributes of each product and store have been defined. The aim of this data science project is to build a predictive model and find out the sales of each product at a particular store.