Supermarket sale prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><center>Product Supermarket Sales Prediction</center></h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip freeze > requirements.txt"
   ]
  },
  {
   "attachments": {
    "forecast2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![forecast2.png](attachment:forecast2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Abstract\n",
    "\n",
    "One stage of business life cycle that requires strategic and careful measures is the growth stage. Growth stage comes after the Business has been launched and part of a business growth  can be expansion into more locations. \n",
    "\n",
    "It however requires proper and critical analysis to know what location and product type is best for a specific location. For example, a tin of milk which sells for N100 in one supermarket branch may also be sold at N110 at another supermarket within the same chain of supermarkets. Hence, there's a need to understand what type of product, market clusters and supermarket type (location, age, size) will give more margin as business is expanded to more locations.\n",
    "\n",
    "In this analysis, a predictive mode is developed using machine learning algorithms to improve and accurately forecasts product sales. The proposed model is especially targeted to identify key characteristics of products and supermarkets driving sales so as to be better informed on an optimal template for expansion of Chuwkwudi Supermarket to other states in Nigeria. The model is not intended to change current subjective forecasting methods. A model based on real supermarket store's data is developed in order to validate the use of the various machine learning algorithms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "This is an in house kaggle competition organized by **AI+ OAU** where the task is to\n",
    "predict product supermarket sales to help  identify key characteristics of products and supermarkets driving sales so as to be better informed on an optimal template for expansion of Chukwudi Supermarket to other states in Nigeria.\n",
    "\n",
    "For this particular problem, I have analyzed the data as a supervised learning problem. In order to\n",
    "forecasts the sales, I have compared different regression models like Linear Regression, Decision Tree,\n",
    "ExtraTreeRegressor, Gradient Boosting, Random Forest, XgBoost and Neural Network.\n",
    "\n",
    "The data comes in the shape of multiple files BUT to demonstrate my SQL proficiency had to load the data into a MySQL database. First, the train table essentially contains the sales by supermarket, product and so on. The test table contains the same features without the product supermarket sales information, which I am tasked to predict. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Data Sources\n",
    "The data has earlier said comes in the shape of multiple files BUT to demonstrate my SQL proficiency had to load the data into a MySQL database. \n",
    "The file containing the mysql scripts that created the database and the data is saved as `chukwudi_supermarket.sql` in the `db and data scripts` folder.\n",
    "The database contain the following tables:\n",
    "- `train` contains the sales by supermarket, product and so on.\n",
    "- `test` contains the same features as train but without the product supermarket sales information\n",
    "- `sample_submission` contains Supermarket id and dummy product supermarket sales values. This serve as a submission template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all the necessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "import seaborn as sns\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import mean_squared_error\n",
    "import statsmodels.formula.api as sm\n",
    "import statsmodels.api as sm\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.linear_model import LinearRegression, Ridge, LassoCV, ElasticNetCV\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from sklearn.metrics import mean_squared_error, make_scorer\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [],
   "source": [
    "host=\"localhost\"\n",
    "user=\"root\"\n",
    "password=\"delelinus\"\n",
    "database=\"chukwudi_supermarket\"\n",
    "port = 3306\n",
    "\n",
    "# create engine\n",
    "engine = create_engine(f'mysql+mysqlconnector://{user}:{password}@{host}:{port}/{database}', echo=False)\n",
    "\n",
    "# read in the data\n",
    "df = pd.read_sql_table(table_name= \"train\", con= engine)\n",
    "df2 = pd.read_sql_table(table_name= \"test\", con= engine)\n",
    "sample_submission = pd.read_sql_table(table_name= \"sample_submission\", con= engine)\n",
    "\n",
    "\n",
    "# close engine\n",
    "engine.dispose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_report(df):\n",
    "    \"\"\"This gives quick report about the missing values, unique numbers and datatypes.\n",
    "    The argument is the DataFram\n",
    "    \"\"\"\n",
    "    n_missing = (df.isnull().sum()/df.isnull().count() * 100).sort_values(ascending=False)\n",
    "    n_unique = df.nunique()\n",
    "    D_types = df.dtypes\n",
    "    report = pd.concat([n_missing,n_unique,D_types],axis=1 ,keys= [\"%_missing\", \"no_unique\", \"D_types\"])\n",
    "    return display(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DRB24</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRB24_CHUKWUDI017</td>\n",
       "      <td>8.785</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.020694</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>382.91</td>\n",
       "      <td>2014</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4633.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDR31</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>FDR31_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.086078</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>359.53</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>729.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDP16</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>FDP16_CHUKWUDI017</td>\n",
       "      <td>18.600</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.039517</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>609.20</td>\n",
       "      <td>2014</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>14740.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDY16</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>FDY16_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.091780</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>456.32</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>16137.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY48</td>\n",
       "      <td>CHUKWUDI046</td>\n",
       "      <td>FDY48_CHUKWUDI046</td>\n",
       "      <td>14.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.023735</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>258.08</td>\n",
       "      <td>2004</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4613.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "0              DRB24            CHUKWUDI017              DRB24_CHUKWUDI017   \n",
       "1              FDR31            CHUKWUDI019              FDR31_CHUKWUDI019   \n",
       "2              FDP16            CHUKWUDI017              FDP16_CHUKWUDI017   \n",
       "3              FDY16            CHUKWUDI027              FDY16_CHUKWUDI027   \n",
       "4              FDY48            CHUKWUDI046              FDY48_CHUKWUDI046   \n",
       "\n",
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility  \\\n",
       "0           8.785             Low Fat                  0.020694   \n",
       "1             NaN          Normal Fat                  0.086078   \n",
       "2          18.600             Low Fat                  0.039517   \n",
       "3             NaN          Normal Fat                  0.091780   \n",
       "4          14.000             Low Fat                  0.023735   \n",
       "\n",
       "            Product_Type  Product_Price  Supermarket_Opening_Year  \\\n",
       "0            Soft Drinks         382.91                      2014   \n",
       "1  Fruits and Vegetables         359.53                      1992   \n",
       "2           Frozen Foods         609.20                      2014   \n",
       "3           Frozen Foods         456.32                      1992   \n",
       "4           Baking Goods         258.08                      2004   \n",
       "\n",
       "  Supermarket _Size Supermarket_Location_Type   Supermarket_Type  \\\n",
       "0              None                 Cluster 2  Supermarket Type1   \n",
       "1             Small                 Cluster 1      Grocery Store   \n",
       "2              None                 Cluster 2  Supermarket Type1   \n",
       "3            Medium                 Cluster 3  Supermarket Type3   \n",
       "4             Small                 Cluster 1  Supermarket Type1   \n",
       "\n",
       "   Product_Supermarket_Sales  \n",
       "0                    4633.97  \n",
       "1                     729.05  \n",
       "2                   14740.81  \n",
       "3                   16137.33  \n",
       "4                    4613.99  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show training data\n",
    "df.head()"
   ]
  },
  {
   "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>%_missing</th>\n",
       "      <th>no_unique</th>\n",
       "      <th>D_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <td>30.090861</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Weight</th>\n",
       "      <td>16.060930</td>\n",
       "      <td>389</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Identifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1367</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3742</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3481</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Type</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Price</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2866</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2293</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                %_missing  no_unique  D_types\n",
       "Supermarket _Size               30.090861          3   object\n",
       "Product_Weight                  16.060930        389  float64\n",
       "Product_Identifier               0.000000       1367   object\n",
       "Supermarket_Identifier           0.000000         10   object\n",
       "Product_Supermarket_Identifier   0.000000       3742   object\n",
       "Product_Fat_Content              0.000000          3   object\n",
       "Product_Shelf_Visibility         0.000000       3481  float64\n",
       "Product_Type                     0.000000         16   object\n",
       "Product_Price                    0.000000       2866  float64\n",
       "Supermarket_Opening_Year         0.000000          9    int64\n",
       "Supermarket_Location_Type        0.000000          3   object\n",
       "Supermarket_Type                 0.000000          4   object\n",
       "Product_Supermarket_Sales        0.000000       2293  float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_report(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>%_missing</th>\n",
       "      <th>no_unique</th>\n",
       "      <th>D_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <td>25.961538</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Weight</th>\n",
       "      <td>16.105769</td>\n",
       "      <td>289</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Identifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>815</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1248</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1176</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Type</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>16</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Price</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1118</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                %_missing  no_unique  D_types\n",
       "Supermarket _Size               25.961538          3   object\n",
       "Product_Weight                  16.105769        289  float64\n",
       "Product_Identifier               0.000000        815   object\n",
       "Supermarket_Identifier           0.000000         10   object\n",
       "Product_Supermarket_Identifier   0.000000       1248   object\n",
       "Product_Fat_Content              0.000000          3   object\n",
       "Product_Shelf_Visibility         0.000000       1176  float64\n",
       "Product_Type                     0.000000         16   object\n",
       "Product_Price                    0.000000       1118  float64\n",
       "Supermarket_Opening_Year         0.000000          9    int64\n",
       "Supermarket_Location_Type        0.000000          3   object\n",
       "Supermarket_Type                 0.000000          4   object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_report(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3742 entries, 0 to 3741\n",
      "Data columns (total 13 columns):\n",
      " #   Column                          Non-Null Count  Dtype  \n",
      "---  ------                          --------------  -----  \n",
      " 0   Product_Identifier              3742 non-null   object \n",
      " 1   Supermarket_Identifier          3742 non-null   object \n",
      " 2   Product_Supermarket_Identifier  3742 non-null   object \n",
      " 3   Product_Weight                  3141 non-null   float64\n",
      " 4   Product_Fat_Content             3742 non-null   object \n",
      " 5   Product_Shelf_Visibility        3742 non-null   float64\n",
      " 6   Product_Type                    3742 non-null   object \n",
      " 7   Product_Price                   3742 non-null   float64\n",
      " 8   Supermarket_Opening_Year        3742 non-null   int64  \n",
      " 9   Supermarket _Size               2616 non-null   object \n",
      " 10  Supermarket_Location_Type       3742 non-null   object \n",
      " 11  Supermarket_Type                3742 non-null   object \n",
      " 12  Product_Supermarket_Sales       3742 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 380.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3742</td>\n",
       "      <td>3742</td>\n",
       "      <td>3742</td>\n",
       "      <td>3141.000000</td>\n",
       "      <td>3742</td>\n",
       "      <td>3742.000000</td>\n",
       "      <td>3742</td>\n",
       "      <td>3742.000000</td>\n",
       "      <td>3742.000000</td>\n",
       "      <td>2616</td>\n",
       "      <td>3742</td>\n",
       "      <td>3742</td>\n",
       "      <td>3742.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1367</td>\n",
       "      <td>10</td>\n",
       "      <td>3742</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NCL31</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRB24_CHUKWUDI017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>9</td>\n",
       "      <td>461</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2272</td>\n",
       "      <td>NaN</td>\n",
       "      <td>563</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1155</td>\n",
       "      <td>1438</td>\n",
       "      <td>2495</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.870640</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066613</td>\n",
       "      <td>NaN</td>\n",
       "      <td>391.815315</td>\n",
       "      <td>2004.778461</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6103.735353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.730432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.052951</td>\n",
       "      <td>NaN</td>\n",
       "      <td>120.204222</td>\n",
       "      <td>8.276712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4456.169125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.555000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.730000</td>\n",
       "      <td>1992.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.630000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026971</td>\n",
       "      <td>NaN</td>\n",
       "      <td>308.392500</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2728.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.053517</td>\n",
       "      <td>NaN</td>\n",
       "      <td>393.910000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5374.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095094</td>\n",
       "      <td>NaN</td>\n",
       "      <td>466.480000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8524.737500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.350000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.328391</td>\n",
       "      <td>NaN</td>\n",
       "      <td>667.220000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28612.760000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Product_Identifier Supermarket_Identifier  \\\n",
       "count                3742                   3742   \n",
       "unique               1367                     10   \n",
       "top                 NCL31            CHUKWUDI017   \n",
       "freq                    9                    461   \n",
       "mean                  NaN                    NaN   \n",
       "std                   NaN                    NaN   \n",
       "min                   NaN                    NaN   \n",
       "25%                   NaN                    NaN   \n",
       "50%                   NaN                    NaN   \n",
       "75%                   NaN                    NaN   \n",
       "max                   NaN                    NaN   \n",
       "\n",
       "       Product_Supermarket_Identifier  Product_Weight Product_Fat_Content  \\\n",
       "count                            3742     3141.000000                3742   \n",
       "unique                           3742             NaN                   3   \n",
       "top                 DRB24_CHUKWUDI017             NaN             Low Fat   \n",
       "freq                                1             NaN                2272   \n",
       "mean                              NaN       12.870640                 NaN   \n",
       "std                               NaN        4.730432                 NaN   \n",
       "min                               NaN        4.555000                 NaN   \n",
       "25%                               NaN        8.630000                 NaN   \n",
       "50%                               NaN       12.600000                 NaN   \n",
       "75%                               NaN       17.100000                 NaN   \n",
       "max                               NaN       21.350000                 NaN   \n",
       "\n",
       "        Product_Shelf_Visibility Product_Type  Product_Price  \\\n",
       "count                3742.000000         3742    3742.000000   \n",
       "unique                       NaN           16            NaN   \n",
       "top                          NaN  Snack Foods            NaN   \n",
       "freq                         NaN          563            NaN   \n",
       "mean                    0.066613          NaN     391.815315   \n",
       "std                     0.052951          NaN     120.204222   \n",
       "min                     0.000000          NaN      78.730000   \n",
       "25%                     0.026971          NaN     308.392500   \n",
       "50%                     0.053517          NaN     393.910000   \n",
       "75%                     0.095094          NaN     466.480000   \n",
       "max                     0.328391          NaN     667.220000   \n",
       "\n",
       "        Supermarket_Opening_Year Supermarket _Size Supermarket_Location_Type  \\\n",
       "count                3742.000000              2616                      3742   \n",
       "unique                       NaN                 3                         3   \n",
       "top                          NaN            Medium                 Cluster 3   \n",
       "freq                         NaN              1155                      1438   \n",
       "mean                 2004.778461               NaN                       NaN   \n",
       "std                     8.276712               NaN                       NaN   \n",
       "min                  1992.000000               NaN                       NaN   \n",
       "25%                  1994.000000               NaN                       NaN   \n",
       "50%                  2006.000000               NaN                       NaN   \n",
       "75%                  2011.000000               NaN                       NaN   \n",
       "max                  2016.000000               NaN                       NaN   \n",
       "\n",
       "         Supermarket_Type  Product_Supermarket_Sales  \n",
       "count                3742                3742.000000  \n",
       "unique                  4                        NaN  \n",
       "top     Supermarket Type1                        NaN  \n",
       "freq                 2495                        NaN  \n",
       "mean                  NaN                6103.735353  \n",
       "std                   NaN                4456.169125  \n",
       "min                   NaN                  83.230000  \n",
       "25%                   NaN                2728.120000  \n",
       "50%                   NaN                5374.675000  \n",
       "75%                   NaN                8524.737500  \n",
       "max                   NaN               28612.760000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* missing values are represented with `None`\n",
    "\n",
    "To check the mode of the supermarket size values `df['Supermarket _Size'].value_counts().idxmax()`\n",
    "\n",
    "However, the basuc statistical description showed the most frequent supermarket size to be Medium sized supermarkets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling missing values\n",
    "\n",
    "Since values are missing `Supermarket _Size` and `Product_Weight`:\n",
    "* check to see if there's by chance a supermarket size value for a `Supermarket_Identifier`corresponding to missing values\n",
    "* check to see if there's by chance a product weight value for a `Product_Identifier`corresponding to missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CHUKWUDI017', 'CHUKWUDI045', 'CHUKWUDI010'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get supermarket identifiers for missing Supermarket_Sizes\n",
    "df[df[[\"Supermarket _Size\"]].isnull().any(axis=1)][\"Supermarket_Identifier\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only three supermarkets have Supermarket size missing. Let's see if there's a case where supermarket size is available for any of these IDs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[None]\n",
      "[None]\n",
      "[None]\n"
     ]
    }
   ],
   "source": [
    "for supermarket_id in ['CHUKWUDI017', 'CHUKWUDI045', 'CHUKWUDI010']:\n",
    "#     print(df.loc[df[[\"Supermarket _Size\"]].notnull().any(axis=1) & df[\"Supermarket_Identifier\"]==supermarket_id])\n",
    "    print(df.loc[df[\"Supermarket_Identifier\"]==supermarket_id][\"Supermarket _Size\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Either for the training set or test set, there are no Supermarket Size values for 'CHUKWUDI017', 'CHUKWUDI045' and 'CHUKWUDI010'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Supermarket_Identifier</th>\n",
       "      <th>Product_Identifier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>DRA24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>DRA24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>DRA59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>DRA59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>DRB01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3238</th>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>NCX42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>NCY54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2154</th>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>NCZ53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1743</th>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>NCZ54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3262</th>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>NCZ54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>601 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Supermarket_Identifier Product_Identifier\n",
       "2977            CHUKWUDI019              DRA24\n",
       "1502            CHUKWUDI027              DRA24\n",
       "190             CHUKWUDI019              DRA59\n",
       "345             CHUKWUDI027              DRA59\n",
       "862             CHUKWUDI027              DRB01\n",
       "...                     ...                ...\n",
       "3238            CHUKWUDI027              NCX42\n",
       "3392            CHUKWUDI027              NCY54\n",
       "2154            CHUKWUDI027              NCZ53\n",
       "1743            CHUKWUDI019              NCZ54\n",
       "3262            CHUKWUDI027              NCZ54\n",
       "\n",
       "[601 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get product and supermarket identifiers for missing Product_Weight\n",
    "df[df[[\"Product_Weight\"]].isnull().any(axis=1)][[\"Supermarket_Identifier\",\"Product_Identifier\"]].sort_values(\"Product_Identifier\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>DRA24</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRA24_CHUKWUDI017</td>\n",
       "      <td>19.35</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.040154</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>411.72</td>\n",
       "      <td>2014</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>2866.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>DRA24</td>\n",
       "      <td>CHUKWUDI010</td>\n",
       "      <td>DRA24_CHUKWUDI010</td>\n",
       "      <td>19.35</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.066832</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>409.72</td>\n",
       "      <td>2005</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>818.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1502</th>\n",
       "      <td>DRA24</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>DRA24_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.039735</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>414.47</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>12284.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2977</th>\n",
       "      <td>DRA24</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>DRA24_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.069909</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>408.22</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>1228.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3374</th>\n",
       "      <td>DRA24</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>DRA24_CHUKWUDI035</td>\n",
       "      <td>19.35</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.039921</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>408.47</td>\n",
       "      <td>2011</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>8598.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3610</th>\n",
       "      <td>DRA24</td>\n",
       "      <td>CHUKWUDI013</td>\n",
       "      <td>DRA24_CHUKWUDI013</td>\n",
       "      <td>19.35</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.039895</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>406.22</td>\n",
       "      <td>1994</td>\n",
       "      <td>High</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>11055.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "56                DRA24            CHUKWUDI017              DRA24_CHUKWUDI017   \n",
       "518               DRA24            CHUKWUDI010              DRA24_CHUKWUDI010   \n",
       "1502              DRA24            CHUKWUDI027              DRA24_CHUKWUDI027   \n",
       "2977              DRA24            CHUKWUDI019              DRA24_CHUKWUDI019   \n",
       "3374              DRA24            CHUKWUDI035              DRA24_CHUKWUDI035   \n",
       "3610              DRA24            CHUKWUDI013              DRA24_CHUKWUDI013   \n",
       "\n",
       "      Product_Weight Product_Fat_Content  Product_Shelf_Visibility  \\\n",
       "56             19.35          Normal Fat                  0.040154   \n",
       "518            19.35          Normal Fat                  0.066832   \n",
       "1502             NaN          Normal Fat                  0.039735   \n",
       "2977             NaN          Normal Fat                  0.069909   \n",
       "3374           19.35          Normal Fat                  0.039921   \n",
       "3610           19.35          Normal Fat                  0.039895   \n",
       "\n",
       "     Product_Type  Product_Price  Supermarket_Opening_Year Supermarket _Size  \\\n",
       "56    Soft Drinks         411.72                      2014              None   \n",
       "518   Soft Drinks         409.72                      2005              None   \n",
       "1502  Soft Drinks         414.47                      1992            Medium   \n",
       "2977  Soft Drinks         408.22                      1992             Small   \n",
       "3374  Soft Drinks         408.47                      2011             Small   \n",
       "3610  Soft Drinks         406.22                      1994              High   \n",
       "\n",
       "     Supermarket_Location_Type   Supermarket_Type  Product_Supermarket_Sales  \n",
       "56                   Cluster 2  Supermarket Type1                    2866.27  \n",
       "518                  Cluster 3      Grocery Store                     818.93  \n",
       "1502                 Cluster 3  Supermarket Type3                   12284.01  \n",
       "2977                 Cluster 1      Grocery Store                    1228.40  \n",
       "3374                 Cluster 2  Supermarket Type1                    8598.81  \n",
       "3610                 Cluster 3  Supermarket Type1                   11055.61  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Product_Identifier\"]=='DRA24']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Good. We will have to fill missing Product weight values with values corresponding to the particular product ID with the missing proudct weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ nan 6.46]\n",
      "[  nan 18.35]\n",
      "[ nan 20.5]\n",
      "[ nan 7.93]\n",
      "[ nan 14.5]\n",
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      "[ nan 12.6]\n",
      "[  nan 18.25]\n",
      "[  nan 13.65]\n",
      "[ nan 8.42]\n",
      "[nan 17.]\n",
      "[7.865   nan]\n",
      "[  nan 15.35]\n",
      "[  nan 19.35]\n",
      "[ nan 12.6]\n",
      "[ nan 16.7]\n",
      "[  nan 9.695]\n",
      "[  nan 20.25]\n",
      "[nan  9.]\n",
      "[  nan 13.15]\n",
      "[ nan 8.63]\n",
      "[  nan 16.25]\n",
      "[ nan 8.27]\n",
      "[ nan 18.2]\n",
      "[  nan 18.35]\n",
      "[nan 6.3]\n",
      "[ nan 15.2]\n",
      "[ nan 15.5]\n",
      "[9.06  nan]\n",
      "[ nan 13.5]\n",
      "[ nan 16.1]\n",
      "[7.315   nan]\n",
      "[ nan 19.6]\n",
      "[5.655   nan]\n",
      "[ nan 13.5]\n",
      "[ nan 17.6]\n",
      "[ nan 7.47]\n",
      "[nan 15.]\n",
      "[nan 18.]\n",
      "[nan]\n",
      "[9.695   nan]\n",
      "[7.39  nan]\n",
      "[ nan 14.8]\n",
      "[  nan 16.35]\n",
      "[nan 5.4]\n",
      "[ nan 20.2]\n",
      "[18.35   nan]\n",
      "[15.85   nan]\n",
      "[ nan 13.1]\n",
      "[ nan 19.7]\n",
      "[ nan 10.5]\n",
      "[13.1  nan]\n",
      "[  nan 12.65]\n",
      "[  nan 18.75]\n",
      "[  nan 15.85]\n",
      "[18.85   nan]\n",
      "[ nan 12.8]\n",
      "[ nan 20.2]\n",
      "[  nan 11.35]\n",
      "[  nan 20.25]\n",
      "[ nan 14.5]\n",
      "[21.2  nan]\n",
      "[11.15   nan]\n",
      "[12.6  nan]\n",
      "[ nan 20.1]\n",
      "[10.195    nan]\n",
      "[nan 9.3]\n",
      "[4.61  nan]\n",
      "[ nan 20.2]\n",
      "[  nan 20.35]\n",
      "[ nan 17.6]\n",
      "[ nan 11.6]\n",
      "[nan 13.]\n",
      "[19.5  nan]\n",
      "[ nan 18.5]\n",
      "[6.65  nan]\n",
      "[15. nan]\n",
      "[ nan 17.5]\n",
      "[ nan 8.06]\n",
      "[ nan 13.5]\n",
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      "[ nan 6.78]\n",
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      "[nan 9.6]\n",
      "[  nan 5.985]\n",
      "[ nan 19.6]\n",
      "[7.68  nan]\n",
      "[ nan 18.7]\n",
      "[  nan 7.475]\n",
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      "[ nan 16.7]\n",
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      "[20.25   nan]\n",
      "[nan]\n",
      "[11. nan]\n",
      "[14.15   nan]\n",
      "[ nan 8.92]\n",
      "[ nan 8.93]\n",
      "[5.035   nan]\n",
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      "[10.395    nan]\n",
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      "[ nan 4.92]\n",
      "[ nan 11.1]\n",
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      "[ nan 11.8]\n",
      "[ nan 10.8]\n",
      "[8.355   nan]\n",
      "[  nan 6.635]\n",
      "[ nan 19.1]\n",
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      "[ 9. nan]\n",
      "[ nan 17.7]\n",
      "[nan 9.6]\n",
      "[14.1  nan]\n",
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      "[  nan 7.435]\n",
      "[11.35   nan]\n",
      "[ nan 16.7]\n",
      "[nan]\n",
      "[  nan 12.15]\n",
      "[  nan 5.785]\n",
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      "[ nan 19.2]\n",
      "[ nan 14.6]\n",
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      "[15.1  nan]\n",
      "[ nan 11.1]\n",
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      "[ nan 17.1]\n",
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      "[ nan 11.3]\n",
      "[13.35   nan]\n",
      "[nan]\n",
      "[  nan 8.785]\n",
      "[13.15   nan]\n",
      "[nan]\n",
      "[19.2  nan]\n",
      "[6.575   nan]\n",
      "[11.65   nan]\n",
      "[nan 9.8]\n",
      "[17.7  nan]\n",
      "[ nan 12.1]\n",
      "[5.82  nan]\n",
      "[ nan 11.3]\n",
      "[18.25   nan]\n",
      "[17.25   nan]\n",
      "[20.75   nan]\n",
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      "[  nan 7.435]\n",
      "[nan]\n",
      "[17.7  nan]\n",
      "[nan 14.]\n",
      "[ nan 12.8]\n",
      "[7.725   nan]\n",
      "[ nan 11.8]\n",
      "[   nan 10.395]\n",
      "[7.27  nan]\n",
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      "[nan 17.]\n",
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      "[  nan 20.75]\n",
      "[ nan 14.5]\n",
      "[14. nan]\n",
      "[ nan 14.5]\n",
      "[16.7  nan]\n",
      "[ nan 12.6]\n",
      "[16.85   nan]\n",
      "[5.825   nan]\n",
      "[  nan 7.275]\n",
      "[ nan 15.1]\n",
      "[14. nan]\n",
      "[15.6  nan]\n",
      "[13.35   nan]\n",
      "[ nan 15.7]\n",
      "[5.78  nan]\n",
      "[20.25   nan]\n",
      "[20.25   nan]\n",
      "[12.15   nan]\n",
      "[  nan 14.15]\n",
      "[6.865   nan]\n",
      "[  nan 16.25]\n",
      "[nan]\n",
      "[20.25   nan]\n",
      "[6.055   nan]\n",
      "[ nan 6.63]\n",
      "[15.15   nan]\n",
      "[nan 16.]\n",
      "[21.25   nan]\n",
      "[  nan 4.615]\n",
      "[20.6  nan]\n",
      "[6.035   nan]\n",
      "[17.75   nan]\n",
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      "[10.195    nan]\n",
      "[6.785   nan]\n",
      "[  nan 17.75]\n",
      "[15.1  nan]\n",
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      "[nan]\n",
      "[12.15   nan]\n",
      "[ 9. nan]\n",
      "[7.825   nan]\n",
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      "[17.35   nan]\n",
      "[17. nan]\n",
      "[19.35   nan]\n",
      "[nan]\n",
      "[15.2  nan]\n",
      "[20.25   nan]\n",
      "[6.71  nan]\n",
      "[ nan 7.56]\n",
      "[15.1  nan]\n",
      "[5.88  nan]\n",
      "[nan 11.]\n",
      "[12.15   nan]\n",
      "[nan]\n",
      "[  nan 13.15]\n",
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      "[ nan 14.8]\n",
      "[ nan 5.44]\n",
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      "[nan]\n",
      "[9.5 nan]\n",
      "[7.3 nan]\n",
      "[7.855   nan]\n",
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      "[12.35   nan]\n",
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      "[14.65   nan]\n",
      "[15.25   nan]\n",
      "[nan 9.6]\n",
      "[ nan 16.5]\n",
      "[12. nan]\n",
      "[11.1  nan]\n",
      "[8.315   nan]\n",
      "[4.805   nan]\n",
      "[12.85   nan]\n",
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      "[nan]\n",
      "[16. nan]\n",
      "[ nan 16.7]\n",
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      "[ nan 6.86]\n",
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      "[nan 9.8]\n",
      "[5.305   nan]\n",
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      "[12. nan]\n",
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      "[nan]\n",
      "[6.425   nan]\n",
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      "[12.6  nan]\n",
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      "[13. nan]\n",
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      "[11. nan]\n",
      "[  nan 19.35]\n",
      "[6.215   nan]\n",
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      "[ nan 16.1]\n",
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      "[21.35   nan]\n",
      "[nan 18.]\n",
      "[  nan 17.75]\n",
      "[ nan 19.2]\n",
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      "[5.26  nan]\n",
      "[19.2  nan]\n",
      "[ nan 15.7]\n",
      "[14.35   nan]\n",
      "[16.6  nan]\n",
      "[8.155   nan]\n",
      "[11. nan]\n",
      "[20.35   nan]\n",
      "[nan]\n",
      "[11.395    nan]\n",
      "[18.25   nan]\n",
      "[ nan 15.7]\n",
      "[ nan 8.96]\n",
      "[9.13  nan]\n",
      "[7.52  nan]\n",
      "[18.35   nan]\n",
      "[9.695   nan]\n",
      "[5.365   nan]\n",
      "[17.7  nan]\n",
      "[9.17  nan]\n",
      "[18.25   nan]\n",
      "[7.02  nan]\n",
      "[11.1  nan]\n",
      "[6.635   nan]\n",
      "[15.6  nan]\n",
      "[17.5  nan]\n",
      "[15.1  nan]\n",
      "[19.7  nan]\n",
      "[7.07  nan]\n",
      "[10.8  nan]\n",
      "[13.65   nan]\n",
      "[12.15   nan]\n",
      "[8.42  nan]\n",
      "[16.75   nan]\n",
      "[12.3  nan]\n",
      "[ nan 12.5]\n",
      "[nan]\n",
      "[12.35   nan]\n",
      "[7.81  nan]\n",
      "[17.5  nan]\n",
      "[12.1  nan]\n",
      "[ nan 18.2]\n",
      "[4.905   nan]\n",
      "[8.71  nan]\n",
      "[nan]\n",
      "[14.15   nan]\n",
      "[12.85   nan]\n",
      "[nan]\n",
      "[8.26  nan]\n",
      "[17.6  nan]\n",
      "[16.1  nan]\n",
      "[  nan 17.75]\n",
      "[10.6  nan]\n",
      "[21.1  nan]\n",
      "[nan]\n",
      "[13.8  nan]\n",
      "[12.6  nan]\n",
      "[19.2  nan]\n",
      "[14.5  nan]\n",
      "[15.25   nan]\n",
      "[17.25   nan]\n",
      "[17.35   nan]\n",
      "[13.6  nan]\n",
      "[11.395    nan]\n",
      "[6.985   nan]\n",
      "[ nan 15.7]\n",
      "[nan]\n",
      "[17.7  nan]\n",
      "[6.615   nan]\n",
      "[6.03  nan]\n",
      "[16.6  nan]\n",
      "[  nan 6.235]\n",
      "[16.7  nan]\n",
      "[9.695   nan]\n",
      "[7.42  nan]\n",
      "[11.8  nan]\n",
      "[16.75   nan]\n",
      "[12.6  nan]\n",
      "[6.965   nan]\n",
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      "[17.85   nan]\n",
      "[16.35   nan]\n",
      "[7.97  nan]\n",
      "[13.35   nan]\n",
      "[14.6  nan]\n",
      "[8.88  nan]\n",
      "[18.7  nan]\n",
      "[18.85   nan]\n",
      "[14.85   nan]\n",
      "[15.1  nan]\n",
      "[12. nan]\n",
      "[16.75   nan]\n",
      "[17.75   nan]\n",
      "[18.75   nan]\n",
      "[9.395   nan]\n",
      "[8.75  nan]\n",
      "[nan 10.]\n",
      "[11.15   nan]\n",
      "[nan]\n",
      "[12.65   nan]\n",
      "[12.85   nan]\n",
      "[nan]\n",
      "[17.2  nan]\n",
      "[16. nan]\n",
      "[nan]\n",
      "[17.2  nan]\n",
      "[5.88  nan]\n",
      "[6.78  nan]\n",
      "[13.85   nan]\n",
      "[12.15   nan]\n",
      "[13.6  nan]\n",
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      "[5.94  nan]\n",
      "[16.1  nan]\n",
      "[11.6  nan]\n",
      "[5.78  nan]\n",
      "[11.3  nan]\n",
      "[8.945   nan]\n",
      "[16.35   nan]\n",
      "[17.5  nan]\n",
      "[16.2  nan]\n",
      "[17.7  nan]\n",
      "[6.36  nan]\n",
      "[6.425   nan]\n",
      "[nan]\n",
      "[15.5  nan]\n",
      "[11.65   nan]\n",
      "[7.52  nan]\n",
      "[12.1  nan]\n",
      "[19.35   nan]\n",
      "[18.6  nan]\n",
      "[9.3 nan]\n",
      "[7.365   nan]\n",
      "[17.6  nan]\n",
      "[20.2  nan]\n",
      "[6.825   nan]\n",
      "[ 5. nan]\n",
      "[8.43  nan]\n",
      "[10.1  nan]\n",
      "[17.75   nan]\n",
      "[16.25   nan]\n",
      "[9.3 nan]\n",
      "[19.85   nan]\n",
      "[18.6  nan]\n",
      "[8.27  nan]\n",
      "[11.6  nan]\n",
      "[19.2  nan]\n",
      "[15.2  nan]\n",
      "[  nan 9.035]\n",
      "[12.85   nan]\n",
      "[7.075   nan]\n",
      "[20.7  nan]\n",
      "[19. nan]\n",
      "[19.75   nan]\n",
      "[10.1  nan]\n",
      "[10.8  nan]\n",
      "[18.35   nan]\n",
      "[12.5  nan]\n",
      "[nan]\n",
      "[12.35   nan]\n",
      "[9.8 nan]\n",
      "[17.6  nan]\n",
      "[13. nan]\n",
      "[9.695   nan]\n",
      "[16.5  nan]\n",
      "[16.2  nan]\n",
      "[14.15   nan]\n",
      "[9.5 nan]\n",
      "[7.475   nan]\n",
      "[19. nan]\n",
      "[8.77  nan]\n",
      "[18. nan]\n"
     ]
    }
   ],
   "source": [
    "for i in df[df[[\"Product_Weight\"]].isnull().any(axis=1)]['Product_Identifier'].unique():\n",
    "#     print(df.loc[df[[\"Product_Weight\"]].notnull().all(axis=1) & df[\"Product_Identifier\"]==i])\n",
    "    print(df.loc[df[\"Product_Identifier\"]==i][\"Product_Weight\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### treat the missing values\n",
    "`Product_Weight`: \n",
    "* sort data frame by `Product_Identifier`,`Product_Weight` (makes non-NaN entries appear first), then forward fill the Product Weight to have the values copied to the NaN entries\n",
    "\n",
    "`Supermarket _Size`:\n",
    "* try replacing with mode or\n",
    "* try replacing with a unique value (identify it as missing value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DRB24</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRB24_CHUKWUDI017</td>\n",
       "      <td>8.785</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.020694</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>382.91</td>\n",
       "      <td>2014</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4633.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDR31</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>FDR31_CHUKWUDI019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.086078</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>359.53</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>729.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDP16</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>FDP16_CHUKWUDI017</td>\n",
       "      <td>18.600</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.039517</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>609.20</td>\n",
       "      <td>2014</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>14740.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDY16</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>FDY16_CHUKWUDI027</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.091780</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>456.32</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>16137.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY48</td>\n",
       "      <td>CHUKWUDI046</td>\n",
       "      <td>FDY48_CHUKWUDI046</td>\n",
       "      <td>14.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.023735</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>258.08</td>\n",
       "      <td>2004</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4613.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FDK45</td>\n",
       "      <td>CHUKWUDI046</td>\n",
       "      <td>FDK45_CHUKWUDI046</td>\n",
       "      <td>11.650</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.033858</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>283.47</td>\n",
       "      <td>2004</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6508.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "0              DRB24            CHUKWUDI017              DRB24_CHUKWUDI017   \n",
       "1              FDR31            CHUKWUDI019              FDR31_CHUKWUDI019   \n",
       "2              FDP16            CHUKWUDI017              FDP16_CHUKWUDI017   \n",
       "3              FDY16            CHUKWUDI027              FDY16_CHUKWUDI027   \n",
       "4              FDY48            CHUKWUDI046              FDY48_CHUKWUDI046   \n",
       "5              FDK45            CHUKWUDI046              FDK45_CHUKWUDI046   \n",
       "\n",
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility  \\\n",
       "0           8.785             Low Fat                  0.020694   \n",
       "1             NaN          Normal Fat                  0.086078   \n",
       "2          18.600             Low Fat                  0.039517   \n",
       "3             NaN          Normal Fat                  0.091780   \n",
       "4          14.000             Low Fat                  0.023735   \n",
       "5          11.650       Ultra Low fat                  0.033858   \n",
       "\n",
       "            Product_Type  Product_Price  Supermarket_Opening_Year  \\\n",
       "0            Soft Drinks         382.91                      2014   \n",
       "1  Fruits and Vegetables         359.53                      1992   \n",
       "2           Frozen Foods         609.20                      2014   \n",
       "3           Frozen Foods         456.32                      1992   \n",
       "4           Baking Goods         258.08                      2004   \n",
       "5                Seafood         283.47                      2004   \n",
       "\n",
       "  Supermarket _Size Supermarket_Location_Type   Supermarket_Type  \\\n",
       "0              None                 Cluster 2  Supermarket Type1   \n",
       "1             Small                 Cluster 1      Grocery Store   \n",
       "2              None                 Cluster 2  Supermarket Type1   \n",
       "3            Medium                 Cluster 3  Supermarket Type3   \n",
       "4             Small                 Cluster 1  Supermarket Type1   \n",
       "5             Small                 Cluster 1  Supermarket Type1   \n",
       "\n",
       "   Product_Supermarket_Sales  \n",
       "0                    4633.97  \n",
       "1                     729.05  \n",
       "2                   14740.81  \n",
       "3                   16137.33  \n",
       "4                    4613.99  \n",
       "5                    6508.20  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Product_Weight = df.sort_values(['Product_Identifier','Product_Weight']).Product_Weight.ffill()\n",
    "df2.Product_Weight = df.sort_values(['Product_Identifier','Product_Weight']).Product_Weight.ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Product_Identifier</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DRB24</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>DRB24_CHUKWUDI017</td>\n",
       "      <td>8.785</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.020694</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>382.91</td>\n",
       "      <td>2014</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4633.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FDR31</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>FDR31_CHUKWUDI019</td>\n",
       "      <td>6.460</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.086078</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>359.53</td>\n",
       "      <td>1992</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>729.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FDP16</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>FDP16_CHUKWUDI017</td>\n",
       "      <td>18.600</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.039517</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>609.20</td>\n",
       "      <td>2014</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>14740.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FDY16</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>FDY16_CHUKWUDI027</td>\n",
       "      <td>18.350</td>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>0.091780</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>456.32</td>\n",
       "      <td>1992</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>16137.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FDY48</td>\n",
       "      <td>CHUKWUDI046</td>\n",
       "      <td>FDY48_CHUKWUDI046</td>\n",
       "      <td>14.000</td>\n",
       "      <td>Low Fat</td>\n",
       "      <td>0.023735</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>258.08</td>\n",
       "      <td>2004</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>4613.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FDK45</td>\n",
       "      <td>CHUKWUDI046</td>\n",
       "      <td>FDK45_CHUKWUDI046</td>\n",
       "      <td>11.650</td>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>0.033858</td>\n",
       "      <td>Seafood</td>\n",
       "      <td>283.47</td>\n",
       "      <td>2004</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6508.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Identifier Supermarket_Identifier Product_Supermarket_Identifier  \\\n",
       "0              DRB24            CHUKWUDI017              DRB24_CHUKWUDI017   \n",
       "1              FDR31            CHUKWUDI019              FDR31_CHUKWUDI019   \n",
       "2              FDP16            CHUKWUDI017              FDP16_CHUKWUDI017   \n",
       "3              FDY16            CHUKWUDI027              FDY16_CHUKWUDI027   \n",
       "4              FDY48            CHUKWUDI046              FDY48_CHUKWUDI046   \n",
       "5              FDK45            CHUKWUDI046              FDK45_CHUKWUDI046   \n",
       "\n",
       "   Product_Weight Product_Fat_Content  Product_Shelf_Visibility  \\\n",
       "0           8.785             Low Fat                  0.020694   \n",
       "1           6.460          Normal Fat                  0.086078   \n",
       "2          18.600             Low Fat                  0.039517   \n",
       "3          18.350          Normal Fat                  0.091780   \n",
       "4          14.000             Low Fat                  0.023735   \n",
       "5          11.650       Ultra Low fat                  0.033858   \n",
       "\n",
       "            Product_Type  Product_Price  Supermarket_Opening_Year  \\\n",
       "0            Soft Drinks         382.91                      2014   \n",
       "1  Fruits and Vegetables         359.53                      1992   \n",
       "2           Frozen Foods         609.20                      2014   \n",
       "3           Frozen Foods         456.32                      1992   \n",
       "4           Baking Goods         258.08                      2004   \n",
       "5                Seafood         283.47                      2004   \n",
       "\n",
       "  Supermarket _Size Supermarket_Location_Type   Supermarket_Type  \\\n",
       "0              None                 Cluster 2  Supermarket Type1   \n",
       "1             Small                 Cluster 1      Grocery Store   \n",
       "2              None                 Cluster 2  Supermarket Type1   \n",
       "3            Medium                 Cluster 3  Supermarket Type3   \n",
       "4             Small                 Cluster 1  Supermarket Type1   \n",
       "5             Small                 Cluster 1  Supermarket Type1   \n",
       "\n",
       "   Product_Supermarket_Sales  \n",
       "0                    4633.97  \n",
       "1                     729.05  \n",
       "2                   14740.81  \n",
       "3                   16137.33  \n",
       "4                    4613.99  \n",
       "5                    6508.20  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace the missing 'Supermarket_Size' values by the most frequent \n",
    "df[\"Supermarket _Size\"].replace(np.nan, \"Medium\", inplace=True)\n",
    "df2[\"Supermarket _Size\"].replace(np.nan, \"Medium\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>%_missing</th>\n",
       "      <th>no_unique</th>\n",
       "      <th>D_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Product_Identifier</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1367</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3742</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Weight</th>\n",
       "      <td>0.0</td>\n",
       "      <td>389</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3481</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Type</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Price</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2866</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2293</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                %_missing  no_unique  D_types\n",
       "Product_Identifier                    0.0       1367   object\n",
       "Supermarket_Identifier                0.0         10   object\n",
       "Product_Supermarket_Identifier        0.0       3742   object\n",
       "Product_Weight                        0.0        389  float64\n",
       "Product_Fat_Content                   0.0          3   object\n",
       "Product_Shelf_Visibility              0.0       3481  float64\n",
       "Product_Type                          0.0         16   object\n",
       "Product_Price                         0.0       2866  float64\n",
       "Supermarket_Opening_Year              0.0          9    int64\n",
       "Supermarket _Size                     0.0          3   object\n",
       "Supermarket_Location_Type             0.0          3   object\n",
       "Supermarket_Type                      0.0          4   object\n",
       "Product_Supermarket_Sales             0.0       2293  float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_report(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>%_missing</th>\n",
       "      <th>no_unique</th>\n",
       "      <th>D_types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Product_Identifier</th>\n",
       "      <td>0.0</td>\n",
       "      <td>815</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Supermarket_Identifier</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1248</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Weight</th>\n",
       "      <td>0.0</td>\n",
       "      <td>301</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1176</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Type</th>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Price</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1118</td>\n",
       "      <td>float64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "      <td>int64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Type</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>object</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                %_missing  no_unique  D_types\n",
       "Product_Identifier                    0.0        815   object\n",
       "Supermarket_Identifier                0.0         10   object\n",
       "Product_Supermarket_Identifier        0.0       1248   object\n",
       "Product_Weight                        0.0        301  float64\n",
       "Product_Fat_Content                   0.0          3   object\n",
       "Product_Shelf_Visibility              0.0       1176  float64\n",
       "Product_Type                          0.0         16   object\n",
       "Product_Price                         0.0       1118  float64\n",
       "Supermarket_Opening_Year              0.0          9    int64\n",
       "Supermarket _Size                     0.0          3   object\n",
       "Supermarket_Location_Type             0.0          3   object\n",
       "Supermarket_Type                      0.0          4   object"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_report(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "redundant_col = ['Product_Identifier','Supermarket_Identifier','Product_Supermarket_Identifier']\n",
    "\n",
    "def object_type_distribution_plot(df):\n",
    "    \"\"\" This gives the distribution of the variables with the object data type\n",
    "    \"\"\"\n",
    "    for var in df.drop(redundant_col, axis=1).columns:\n",
    "        if df[var].dtype == \"object\":\n",
    "            #print(df[var].value_counts().name)\n",
    "            #print(df[var].value_counts())\n",
    "            print(df[var].describe(include='object'))\n",
    "            #without the plt.figure line, it won't bring out all the plots and only show for the last plot even wrongly\n",
    "            plt.figure(figsize=(10,5))\n",
    "            ax = sns.countplot(data=df, x=var)\n",
    "            #rotate label if too long\n",
    "            plt.xticks(rotation=90)\n",
    "            plt.show()\n",
    "\n",
    "            #plt.tight_layout()\n",
    "\n",
    "\n",
    "def numeric_type_distribution_plot(df):\n",
    "    for var in df.drop(redundant_col, axis=1).columns:\n",
    "        if df[var].dtype != \"object\":\n",
    "            print(df[var].describe())\n",
    "            #without the plt.figure line, it won't bring out all the plots and only show for the last plot even wrongly\n",
    "            plt.figure(figsize=(10,5))\n",
    "            ax = sns.distplot(df[var])\n",
    "            #rotate label if too long\n",
    "            plt.xticks(rotation=90)\n",
    "            plt.show()\n",
    "\n",
    "#             plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count        3742\n",
      "unique          3\n",
      "top       Low Fat\n",
      "freq         2272\n",
      "Name: Product_Fat_Content, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count            3742\n",
      "unique             16\n",
      "top       Snack Foods\n",
      "freq              563\n",
      "Name: Product_Type, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count       3742\n",
      "unique         3\n",
      "top       Medium\n",
      "freq        2281\n",
      "Name: Supermarket _Size, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count          3742\n",
      "unique            3\n",
      "top       Cluster 3\n",
      "freq           1438\n",
      "Name: Supermarket_Location_Type, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count                  3742\n",
      "unique                    4\n",
      "top       Supermarket Type1\n",
      "freq                   2495\n",
      "Name: Supermarket_Type, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "object_type_distribution_plot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    3742.000000\n",
      "mean       12.900290\n",
      "std         4.716667\n",
      "min         4.555000\n",
      "25%         8.710000\n",
      "50%        12.600000\n",
      "75%        17.200000\n",
      "max        21.350000\n",
      "Name: Product_Weight, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    3742.000000\n",
      "mean        0.066613\n",
      "std         0.052951\n",
      "min         0.000000\n",
      "25%         0.026971\n",
      "50%         0.053517\n",
      "75%         0.095094\n",
      "max         0.328391\n",
      "Name: Product_Shelf_Visibility, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    3742.000000\n",
      "mean      391.815315\n",
      "std       120.204222\n",
      "min        78.730000\n",
      "25%       308.392500\n",
      "50%       393.910000\n",
      "75%       466.480000\n",
      "max       667.220000\n",
      "Name: Product_Price, dtype: float64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    3742.000000\n",
      "mean     2004.778461\n",
      "std         8.276712\n",
      "min      1992.000000\n",
      "25%      1994.000000\n",
      "50%      2006.000000\n",
      "75%      2011.000000\n",
      "max      2016.000000\n",
      "Name: Supermarket_Opening_Year, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     3742.000000\n",
      "mean      6103.735353\n",
      "std       4456.169125\n",
      "min         83.230000\n",
      "25%       2728.120000\n",
      "50%       5374.675000\n",
      "75%       8524.737500\n",
      "max      28612.760000\n",
      "Name: Product_Supermarket_Sales, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_type_distribution_plot(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Product shelf visibility isn't normally distributed, let's see how it looks when log-transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py:397: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n",
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(np.log(df[\"Product_Shelf_Visibility\"]).replace([np.inf,-np.inf],0))\n",
    "#rotate label if too long\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* log transformation resultrd in many infinity values and hence would not be appropriate for treating the skewness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\pandas\\core\\arraylike.py:397: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "231"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(df[\"Product_Shelf_Visibility\"]).replace([np.inf,-np.inf],np.nan).isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multivariate"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**product that gives a better margin at specific stores**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "grpd_1 = df[['Product_Type','Supermarket_Identifier','Product_Supermarket_Sales', \"Product_Price\"]].copy()\n",
    "grpd_1[\"Profits\"] = grpd_1['Product_Supermarket_Sales'] - grpd_1['Product_Price']\n",
    "\n",
    "# grpd_1.drop(columns=['Product_Supermarket_Sales', \"Product_Price\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Product_Type</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Profits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>Starchy Foods</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>10260.930000</td>\n",
       "      <td>491.717143</td>\n",
       "      <td>9769.212857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>Starchy Foods</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>10196.014286</td>\n",
       "      <td>530.237143</td>\n",
       "      <td>9665.777143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>Starchy Foods</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>9732.888889</td>\n",
       "      <td>487.145556</td>\n",
       "      <td>9245.743333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Starchy Foods</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>8741.955000</td>\n",
       "      <td>508.576250</td>\n",
       "      <td>8233.378750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>Starchy Foods</td>\n",
       "      <td>CHUKWUDI046</td>\n",
       "      <td>7449.885000</td>\n",
       "      <td>396.960000</td>\n",
       "      <td>7052.925000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Product_Type Supermarket_Identifier  Product_Supermarket_Sales  \\\n",
       "155  Starchy Foods            CHUKWUDI027               10260.930000   \n",
       "157  Starchy Foods            CHUKWUDI045               10196.014286   \n",
       "156  Starchy Foods            CHUKWUDI035                9732.888889   \n",
       "152  Starchy Foods            CHUKWUDI017                8741.955000   \n",
       "158  Starchy Foods            CHUKWUDI046                7449.885000   \n",
       "\n",
       "     Product_Price      Profits  \n",
       "155     491.717143  9769.212857  \n",
       "157     530.237143  9665.777143  \n",
       "156     487.145556  9245.743333  \n",
       "152     508.576250  8233.378750  \n",
       "158     396.960000  7052.925000  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpd_1=grpd_1.groupby(['Product_Type','Supermarket_Identifier'],as_index=False).mean()\n",
    "grpd_1 = grpd_1.sort_values(['Product_Type','Profits'], ascending=False)\n",
    "\n",
    "grpd_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Supermarket_Identifier</th>\n",
       "      <th>CHUKWUDI010</th>\n",
       "      <th>CHUKWUDI013</th>\n",
       "      <th>CHUKWUDI017</th>\n",
       "      <th>CHUKWUDI018</th>\n",
       "      <th>CHUKWUDI019</th>\n",
       "      <th>CHUKWUDI027</th>\n",
       "      <th>CHUKWUDI035</th>\n",
       "      <th>CHUKWUDI045</th>\n",
       "      <th>CHUKWUDI046</th>\n",
       "      <th>CHUKWUDI049</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baking Goods</th>\n",
       "      <td>342.953913</td>\n",
       "      <td>3361.910513</td>\n",
       "      <td>6142.820968</td>\n",
       "      <td>5569.298800</td>\n",
       "      <td>606.238333</td>\n",
       "      <td>10876.145238</td>\n",
       "      <td>7467.126500</td>\n",
       "      <td>7503.396087</td>\n",
       "      <td>5092.351667</td>\n",
       "      <td>6269.787586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breads</th>\n",
       "      <td>462.541000</td>\n",
       "      <td>3473.561429</td>\n",
       "      <td>7151.914167</td>\n",
       "      <td>5288.007692</td>\n",
       "      <td>524.851250</td>\n",
       "      <td>11210.451818</td>\n",
       "      <td>7247.884167</td>\n",
       "      <td>6182.535833</td>\n",
       "      <td>5738.472222</td>\n",
       "      <td>5791.264286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Breakfast</th>\n",
       "      <td>383.732500</td>\n",
       "      <td>2680.308000</td>\n",
       "      <td>5212.357500</td>\n",
       "      <td>4999.657500</td>\n",
       "      <td>889.078000</td>\n",
       "      <td>14364.006000</td>\n",
       "      <td>8987.905000</td>\n",
       "      <td>6237.188333</td>\n",
       "      <td>4426.235000</td>\n",
       "      <td>6097.457143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Canned</th>\n",
       "      <td>258.170000</td>\n",
       "      <td>3374.157879</td>\n",
       "      <td>7909.467500</td>\n",
       "      <td>4621.122759</td>\n",
       "      <td>934.246667</td>\n",
       "      <td>12457.702143</td>\n",
       "      <td>7641.667714</td>\n",
       "      <td>7281.329375</td>\n",
       "      <td>6223.914062</td>\n",
       "      <td>5552.057429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dairy</th>\n",
       "      <td>206.923333</td>\n",
       "      <td>3016.171935</td>\n",
       "      <td>7502.210303</td>\n",
       "      <td>4791.127500</td>\n",
       "      <td>554.982083</td>\n",
       "      <td>13083.603571</td>\n",
       "      <td>8826.983600</td>\n",
       "      <td>6630.504839</td>\n",
       "      <td>4620.380435</td>\n",
       "      <td>5082.457742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Frozen Foods</th>\n",
       "      <td>309.128333</td>\n",
       "      <td>3542.243137</td>\n",
       "      <td>7392.632955</td>\n",
       "      <td>5067.853871</td>\n",
       "      <td>771.871000</td>\n",
       "      <td>12420.451667</td>\n",
       "      <td>7576.304667</td>\n",
       "      <td>7221.765484</td>\n",
       "      <td>5559.290000</td>\n",
       "      <td>5045.170345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fruits and Vegetables</th>\n",
       "      <td>321.620250</td>\n",
       "      <td>3864.213636</td>\n",
       "      <td>7447.375077</td>\n",
       "      <td>5412.672759</td>\n",
       "      <td>525.432059</td>\n",
       "      <td>13650.385882</td>\n",
       "      <td>6888.145185</td>\n",
       "      <td>7232.594444</td>\n",
       "      <td>5266.057255</td>\n",
       "      <td>6452.157297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hard Drinks</th>\n",
       "      <td>128.511429</td>\n",
       "      <td>3741.352222</td>\n",
       "      <td>7246.401667</td>\n",
       "      <td>3865.574000</td>\n",
       "      <td>446.806667</td>\n",
       "      <td>10435.394000</td>\n",
       "      <td>6842.853333</td>\n",
       "      <td>6349.703846</td>\n",
       "      <td>5956.172500</td>\n",
       "      <td>6052.948750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Health and Hygiene</th>\n",
       "      <td>439.697895</td>\n",
       "      <td>3668.942187</td>\n",
       "      <td>5762.615517</td>\n",
       "      <td>4103.040625</td>\n",
       "      <td>930.330000</td>\n",
       "      <td>10724.130000</td>\n",
       "      <td>7280.343000</td>\n",
       "      <td>6476.720500</td>\n",
       "      <td>5639.113333</td>\n",
       "      <td>5630.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Household</th>\n",
       "      <td>367.451176</td>\n",
       "      <td>3945.223778</td>\n",
       "      <td>6418.773571</td>\n",
       "      <td>5419.296389</td>\n",
       "      <td>432.920345</td>\n",
       "      <td>12170.184186</td>\n",
       "      <td>7000.217500</td>\n",
       "      <td>7013.035946</td>\n",
       "      <td>5494.103659</td>\n",
       "      <td>6297.330222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Meat</th>\n",
       "      <td>296.060588</td>\n",
       "      <td>3116.377391</td>\n",
       "      <td>6828.157391</td>\n",
       "      <td>5600.507619</td>\n",
       "      <td>862.313125</td>\n",
       "      <td>11995.804500</td>\n",
       "      <td>7439.976500</td>\n",
       "      <td>6304.774737</td>\n",
       "      <td>4621.504444</td>\n",
       "      <td>6137.908400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Others</th>\n",
       "      <td>243.298000</td>\n",
       "      <td>4407.817273</td>\n",
       "      <td>8151.265000</td>\n",
       "      <td>4996.017778</td>\n",
       "      <td>458.967778</td>\n",
       "      <td>9829.955000</td>\n",
       "      <td>6459.054286</td>\n",
       "      <td>5468.665000</td>\n",
       "      <td>5016.070833</td>\n",
       "      <td>7068.807778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seafood</th>\n",
       "      <td>404.850000</td>\n",
       "      <td>2251.410000</td>\n",
       "      <td>5555.912500</td>\n",
       "      <td>9004.446667</td>\n",
       "      <td>241.957500</td>\n",
       "      <td>8632.895000</td>\n",
       "      <td>10721.266667</td>\n",
       "      <td>8141.010000</td>\n",
       "      <td>8848.692500</td>\n",
       "      <td>5047.790000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snack Foods</th>\n",
       "      <td>425.381714</td>\n",
       "      <td>4514.483393</td>\n",
       "      <td>7154.761389</td>\n",
       "      <td>5439.520645</td>\n",
       "      <td>571.947297</td>\n",
       "      <td>11490.118333</td>\n",
       "      <td>7575.291207</td>\n",
       "      <td>6582.592963</td>\n",
       "      <td>5286.447119</td>\n",
       "      <td>5844.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Soft Drinks</th>\n",
       "      <td>336.488667</td>\n",
       "      <td>4604.622000</td>\n",
       "      <td>7128.698333</td>\n",
       "      <td>4908.922105</td>\n",
       "      <td>584.509231</td>\n",
       "      <td>11871.105385</td>\n",
       "      <td>6571.998889</td>\n",
       "      <td>7031.035556</td>\n",
       "      <td>5140.891923</td>\n",
       "      <td>6919.515000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Starchy Foods</th>\n",
       "      <td>369.171667</td>\n",
       "      <td>3534.448000</td>\n",
       "      <td>8233.378750</td>\n",
       "      <td>5674.088333</td>\n",
       "      <td>218.220000</td>\n",
       "      <td>9769.212857</td>\n",
       "      <td>9245.743333</td>\n",
       "      <td>9665.777143</td>\n",
       "      <td>7052.925000</td>\n",
       "      <td>6236.786250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Supermarket_Identifier  CHUKWUDI010  CHUKWUDI013  CHUKWUDI017  CHUKWUDI018  \\\n",
       "Product_Type                                                                 \n",
       "Baking Goods             342.953913  3361.910513  6142.820968  5569.298800   \n",
       "Breads                   462.541000  3473.561429  7151.914167  5288.007692   \n",
       "Breakfast                383.732500  2680.308000  5212.357500  4999.657500   \n",
       "Canned                   258.170000  3374.157879  7909.467500  4621.122759   \n",
       "Dairy                    206.923333  3016.171935  7502.210303  4791.127500   \n",
       "Frozen Foods             309.128333  3542.243137  7392.632955  5067.853871   \n",
       "Fruits and Vegetables    321.620250  3864.213636  7447.375077  5412.672759   \n",
       "Hard Drinks              128.511429  3741.352222  7246.401667  3865.574000   \n",
       "Health and Hygiene       439.697895  3668.942187  5762.615517  4103.040625   \n",
       "Household                367.451176  3945.223778  6418.773571  5419.296389   \n",
       "Meat                     296.060588  3116.377391  6828.157391  5600.507619   \n",
       "Others                   243.298000  4407.817273  8151.265000  4996.017778   \n",
       "Seafood                  404.850000  2251.410000  5555.912500  9004.446667   \n",
       "Snack Foods              425.381714  4514.483393  7154.761389  5439.520645   \n",
       "Soft Drinks              336.488667  4604.622000  7128.698333  4908.922105   \n",
       "Starchy Foods            369.171667  3534.448000  8233.378750  5674.088333   \n",
       "\n",
       "Supermarket_Identifier  CHUKWUDI019   CHUKWUDI027   CHUKWUDI035  CHUKWUDI045  \\\n",
       "Product_Type                                                                   \n",
       "Baking Goods             606.238333  10876.145238   7467.126500  7503.396087   \n",
       "Breads                   524.851250  11210.451818   7247.884167  6182.535833   \n",
       "Breakfast                889.078000  14364.006000   8987.905000  6237.188333   \n",
       "Canned                   934.246667  12457.702143   7641.667714  7281.329375   \n",
       "Dairy                    554.982083  13083.603571   8826.983600  6630.504839   \n",
       "Frozen Foods             771.871000  12420.451667   7576.304667  7221.765484   \n",
       "Fruits and Vegetables    525.432059  13650.385882   6888.145185  7232.594444   \n",
       "Hard Drinks              446.806667  10435.394000   6842.853333  6349.703846   \n",
       "Health and Hygiene       930.330000  10724.130000   7280.343000  6476.720500   \n",
       "Household                432.920345  12170.184186   7000.217500  7013.035946   \n",
       "Meat                     862.313125  11995.804500   7439.976500  6304.774737   \n",
       "Others                   458.967778   9829.955000   6459.054286  5468.665000   \n",
       "Seafood                  241.957500   8632.895000  10721.266667  8141.010000   \n",
       "Snack Foods              571.947297  11490.118333   7575.291207  6582.592963   \n",
       "Soft Drinks              584.509231  11871.105385   6571.998889  7031.035556   \n",
       "Starchy Foods            218.220000   9769.212857   9245.743333  9665.777143   \n",
       "\n",
       "Supermarket_Identifier  CHUKWUDI046  CHUKWUDI049  \n",
       "Product_Type                                      \n",
       "Baking Goods            5092.351667  6269.787586  \n",
       "Breads                  5738.472222  5791.264286  \n",
       "Breakfast               4426.235000  6097.457143  \n",
       "Canned                  6223.914062  5552.057429  \n",
       "Dairy                   4620.380435  5082.457742  \n",
       "Frozen Foods            5559.290000  5045.170345  \n",
       "Fruits and Vegetables   5266.057255  6452.157297  \n",
       "Hard Drinks             5956.172500  6052.948750  \n",
       "Health and Hygiene      5639.113333  5630.083333  \n",
       "Household               5494.103659  6297.330222  \n",
       "Meat                    4621.504444  6137.908400  \n",
       "Others                  5016.070833  7068.807778  \n",
       "Seafood                 8848.692500  5047.790000  \n",
       "Snack Foods             5286.447119  5844.842105  \n",
       "Soft Drinks             5140.891923  6919.515000  \n",
       "Starchy Foods           7052.925000  6236.786250  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_pivot = grpd_1.pivot(index='Product_Type',columns='Supermarket_Identifier',values=\"Profits\" )\n",
    "grouped_pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MFsNmUKvPeHo0q51hr71D7uzEBofSfMUsel/YQ9Nfp2NqaWHUtc4FvCjSuhGrarRjecWmSFodxds3NepaWw83It+kGFeR0Vi+N648yxZHbWZK2LNXaVXxj+Buk4WyOeyZfeIxU48+RCfJxr4x2FuYEhqbBIBOgrgkLdZmagTQpoQn2275frZ+Hj58mHr1G6SZd+f2bTq0bcuAvn15+lReXXj+7BlHjxxm9R9r2Lh5C2qVioMHjAt3CgwMxM3dHQATExOsra2JCA8HwN/fn/Zt29CoQX26duuWYa/9+xw9fJi69dLRdec2Hdu1ZWC/1LpWrl7Dhk1bUKlVHDponK6goPR1Bfj707FtG5o0rM93Xbtl2msPgt4L19Pu59/Zduaa0Vc98wvi0NV7rB3Rna3je6NSqdh/ybhV24DwKNz0zwETtQprC3PCY+IA8A+NoNWU5dQdtZDv632bYa/9az9/HOzsGDv7F1r1GsyEuUvSfLYf+/sijbv2pc+YaUwd3g+Apy9fc/DkWdYvnsGO3xeiUqnYd9y4FfbA4FDcXdN7tgfRoudAarXvSY/2Lf/fe+3/qQ21kiStkCSpbIqfFR/piheQG7ilD7fJBlwXQrgje+SzpyibDfDVp2dLI52U1wghTAA7IPRjf4+vOiwHsBBC3ATMgazA2zXHeKCFJEmR+l3KF4UQe95eJIQoAGwCukuSdFMf7pKSgkANwAZ4KIRYBpQAWgGlkP9u14G0nipF9HmpEEKYI2++qCVJ0iN9XFUfIcTyD6T/rtf1BNicorphQF9Jks4JIaz1mjOFpNMxt2wjzO1s+H77b7gXyY//vUfJ+c3nT2Df6JlI73k48tf8lmylizLkohxSZmphTnSQbNh237Ycp1zZUZuZ4pDDg2FX5bj5M4v/4PLabWl6rlOGkNm6u9BpzXw2fj/UID1jwiR8F4xDZW6Ja7eBmLpnI8n/3cqXU7POhO3fxPtLFeb5CmPmmQuPgZMBEKZmyZ59164DMXF0QZiYYGLvhMfgaQBEnj1M9JUPG8vaiFB85o9FbWuPW7dBxNy+jO69FYMPXq/Vcv/xM8b0+54ShfIzfelqVm7ayYDuHT567cUbd7j3+Blt+44CICEhEUd7WwD6T5zNG/9AkpI0+AUG06K3PGft0qIhLevXTPPvL/TOhKyuzuz6fT6BwaH0nzibulUr4exgb7Smt6yfOgRXRztCIqLoOW0JeTzcKFs4r6H2569ZNb4fCYlJdBw3nxL5cnPx7kPuP39Fu9FzZF2JSTjZyl+sA+b8zpvAEJI0WvyCQ2k5fKasq2F1WtSomLYu/ecyq7MDO+eOJjA0gv5zVlC3Ykmc9X8vY1CZqMlasggHhkzB58ot6s8dR+VhvTk5ZeFHr81T4xs8Sheh11l5j4iJhTkx+nHVbvOvOOTMhtrMDLvsWfnxovxYu7h0LTf/3J7mClNKndbuLrRYNZddP4zI/LjKBIXcbMjpYMmY2vL+EFO1ICpBXrHo800unK3k+GlHS1PG15HLHH8cxPkXoem6rarndeauXyRhcUmfpY9JSUmcOX2afv1TL+IWLFiIvQcOYmlpydm//2bY4MHs3LOXy5cv433fm+86y+FR8QkJODjKRtCwIYPx9fEhKUmDv78fHdu1BaB9x440bdY81XMHSL5/7u7ubNqylaDAQIYOGUyt2nVwcsrcCmZSUhJnzpzmpzR0FShYiD37ZV3nzv7NiCGD2b57L1cuX+aBtzddu8i6EhIScHCQdQ0fKuvS6HV1aq/X1aEjTZo1T/tzpdfl5u7Oxi1bCQoKZPiQwdTMpK51I7rjam9DSGQMvRetJ5e7M2Xz5/zodZcePMf7lR8dp68EID5Jg6ONHNozaNlmfILDSdJq8QuNoM3U3wDoVLMCzb8tmaav9O1n093Rju0TfiQwPIpByzZTp0whnNIJMUwLrVaH9+OnjB3wA8UL5WfGkpWs/Gs7A743DLurXaUitatU5Oqteyz+YyOr5k7h4vXb3H/8lHZ95Od2QkIiTvbyJGTA+Bm88Q8gSaPBLyCYlj8MAqBLyya0aFArnWe7TFZXF3auXCQ/28fPoG7Vb3B2tDda0/8qkiTdAZJnrXoDv6wkScF6O3SjEGI+4IG8cfayJElaIUSUEKIicAn4DngbK7cH6ApcAFoDJz4Wbw9fv3EfJ0lSSQAhRCVgnRCiKPLnc7oQoiqgQ45ZehsW44K8waGVJEn30ql3v37DQ4IQIlB/bWVgtyRJcfr29n6sc0KIYsCfyJOEMcAD4LkkSW8t57VAX+BkOumn9OmP9fWtR95NDXAOmC+E2ADskCQpZbzW2/Z7vS3/22+/fay7xEdE8fT0RQrWq2Zg3GcvU4zvNsifMytnBwo1qI5Wo0EIwZU/t7N/7JxUdf3R+kcg/Zj7cB9/7LNnJcLHH5VajbmdDbGh4YAc0vDDntUcmDAvVex/ZtDFxxL/9AEWBYobGPdm2XPj0rkvAGorGywLlSBYq0MgiL56lrCDW1LVFbh2EZB+zL0mIhQTeye0EWGgUqGysEQXG21QRhsZTmKAD+Z5CmRoA6+bixNuLk6UKJQfgLpVK7Lyr11GXStJEs3qVGdIz06p8hZPljfdpRdz7+7shH9QMO4uTmi0WqJiYrF774vL1dkRr1zZuXbHm3pVKxmtKfl6vUfMyc6G2uVKcOfJSwPj3s3JHgcbayzNs2BpnoWyhbx4+NIHJGhWrQKDO6b2bP8yXN5kl17MvZuTPf4h4bg7Oci6YuOwey+G19XRjrzZs3LtwVPqVSxltJ5IH38iffzxuXILgPs7D1F5qJEbFQXcXL+T4xPmpsra3O4nIP2Y+0gff2yzZSXy7biytSYuxbjqtGMlJyYv4M17q3P/BhdehLLzjl+q9GXnXwDpx9yHxSXhaGlKeFwSKgEWpmpiErXkcbIkn7M11fI6Y26iQq0SxGt0abZhDOfOnqVgwYJpGpvWKUIkKlepwqwZ0wkPC0OSJBo3aUK/AakN57nzFwDpx9y7urkR4O+Pm5sbGo2G6Oho7OwMV4hcXF3x8vLixvXr1K6T8b0ZAOfPGafr28pVmP1WFxKNmjShbxoTgjnz3ulKK+be1dUIXS6u5Mnjxc0b16lVO+O63nrGnWytqFmyAHdf+Bhl3EsSNK1UgoEtaqXKW9inHZB+zL2bgw0BoRG4O9ii0eqIjovHzspwNc7V3gavrC5ce/yKumWM3+z69tlePPnZXomVf6V9AARA2RJFeD3Ln7CISJAkmtWtyeAfuqQq98vU0bKmdGLu3Vyc8A8Mxt3FOcWz3XDVwdXZkby5snPtzn3qVfv0jcn/FF/qnHshxF9AdcBZCPEGmChJUpqnI0iSdE8IsQW4j7wfs68kSW9jqPogO3ktkDfSvj21ZBXwp37zbSjyaTsf5T8TlqPfZeyMbLx30v9bRm/8ByB79wEikDcnfPuB6lLuZNEiT4KMDZK+hxxnjyRJd/TtH0S+YenV8aG605yhSZI0E+ipr/eiEKJgGmWSl5N69Uo7TMzK2RFzffyyqXkW8teqTOBDw/CVafmqMjVvFabmrcKt7QfZ3m8Cd/cc5dGJc5Ro2SA5Jt7SwQ6HHJ6p2kiLu3uPUb5LKwBKtGrAk5MXAFCbmvL99uVcWb+DW9szfprHW1RWNqjMZUNNmJhika8ISYGGy/dvpg9J/om5fYXgHWuIvXeNuCf3sCpeDpW17KlVWVhh4mCcdyn23g2sy8onq1gVL0/ck/uyLjsHhImpvj5LzHPlIykwY0aIi6MD7i5OPH8tnwhy8fodvHJm+8hVMhVLF+PI3xcICYsAIDwyCp8A404yqfFNWXYdOQXAkTMXqFCyKEII/INCkjd9RURFc+PuA3JnMz6m9y2x8QnE6JegY+MTOH/7AXlzZDUoU7Nsca49eIpGqyUuIZHbT16Sx9ONCsXyc+TizeQNtuHRMfgGfXTFUtZVphi7T8nhPUcu3qRCkfyyrpAw4hPljcoR0bHcePiM3B4Z2zITHRBMxBs/nPLlBiBP9W8IevDkI1fJPD95gcIt6mPlIntKLRzssMth3N/14f7jlOzUAoDCLevz/LS8d0ttakq7zb9ya+NO7n/iSVeZ4UFgFKWz2WOTRfYnWZqpcbQ0NeraW76RVMol/y3KZLPnQaB8r1ddesWo/fcZs/8+W2/5cjGdyYOxHD50iHr10z4RJDg4ONnLeffuHXSShJ29PeXLl+f4saOEhsqfuYiICPx8jQsTqlqtGvv2yj6i48eOUa5cOYQQBAQEEB8vj4fIyEhu3bxJrly5Mq3ryKFD1K33cV33UugqV748Jz5B1/59sq4Tx49RNj1dt26SM2fGdcUmJBITn5D8/wv3n5HXw7jwngoFc3P0ujchkXL4XURMHL4h4UZdW714AfZcvA3A0ev3KV8wt/y8CIskPlFePYqMiePm09fkcs/YaoSLowPurs48f/X22X4br5zZDcq89PFLvlf3Hz0lKUmDva0NFUqX4MiZ84SEyTrCI6Pw9Q80qt0a35Rn95GTABw5fZ4KpYrpn+3Bhs/2ew/InT3jz/b/BSRJ6iBJUlZJkkwlScr2vmEvSVIuSZKCU/z+syRJXpIkFZAk6WCK9KuSJBXV5/V7652XJClekqQ2kiTllSSpvCRJz4zp19fuuU9Gb9yqgRDkmKRASZKShBA1gJRT+kTkI4YOCyGiJUnaaGQTZ4HfhBAzkP9ujZBDZt5nBjBXCNEshTf97fT+AZBLCJFXkqQnQBfg9EfScwshvCRJegoku7/1aXeAO/pVi4L68hnCNqsrHVfPRaVWI1SCm9v2c3//Cb7p1RGA8yvS//MEeD/hwIR5/HhwHUKlQpuUxPYBEwh79fHj6C6t3kyntQsY8+AksWER/NmxPwAl2zTCq0p5rBwdKP9dawA29hiG7y3vDOlS29rj0r4XQqhApSLm1iXivG9iU0mO3PrQMZRJAb6EHdqG+w8jEEIg6bSE7FiLJiwk3WveEn35NC4dfiTbqLnoYqMJXC+fZmDq6olTkw7JO2ciTh00WEUwlrH9ejBixiKSkjRky+rGz8P7smnvYQDaN6lHUGgYbX8aSXRsHCoh+HPHfvauWkjenNkZ2K0DPUdNRdLpMDExYXz/nni6uXy0zVYNajFy5i/U+64f9jbWzB07GIBnr94we/la+W8kSXRv05T8eT7uPXufkIgoBsyVh5JWq6NR5bJUKVmYzUfkjcnt6lbGK5s7lUsWosWwmahUglY1K5FPb/AOaN+IH6YtRZIkTNRqxvVog4fLx+NDW9WsxKgl66jffzJ21pbMHdRd1uUTwJx1O+UbJUG3JrXIb6RxnZKDQ6bQ6o/5cnz7i9fs6jWSsj3lIXx15V9YuznT69wu+XhKnY6K/bqztFR9gh484cTk+XTZu0Y/rjQcGDyJiFcfN65urNlCi9XzGHD3OHFh4WzrMgiAIq0akrNyOSwd7SnZWd66s6vXSPxvZ2xc9ayYkwIu1lhnMWFW48LsueePWiX7Js48DcHW3ISxtfNjbqpGkqB2PhcmHnqAX2QCu+/6MaiqF0KAViex8fqb5Fj6D3H2WQg9KuRkWoNCxCRqDE7K+VzEx8Vx+dJFxo4bl5y2betWAFq3acPxY8fYvnULarUJWcyzMH3GTIQQ5PHyok/ffvTr8yM6ScLExISRo0YbtXG1WfMWTBg3luZNm2Bra8v0mfLx1c+fP2Ph/PkIBBISnb/7jrz58mVa16VLFxk99p2u7dtkXa1at+HEsWNs3ybrMs+ShZ/f6srjxY8/9aP/Tz8i6WRdw43U1bR5CyaOH0vLpk2wtbPl5xmyrhfPn7Fo/nw5REeS6Nwlc7pCI2MYtPztKUA6GpQvSuWiedly+ioAbauVJTgimvbTfycmPgGVEKw/foldk37Cy8OFfk1r8OOi9fL9UqsZ06EBHvq9Nx+iReVSjFm9k0bjFmNnZcHsnrJz6rlfEHO3HU1+DnatU4n8nhk/P2NM/x8YOX0+SRr52T5txAA27zkEQLum9Tl65gJ7jpzExESNeZYszJ0wDCEEeXNlZ8D3nfhhxKR3z8CBvfFw//iEp1XD2oyavpD6nX/EzsaGueOHAvDs5RvmLP+Dtw/Bbm2bkT9Prgxr+jcx9tCB/xXEvxl3+blJcRQmyJ/CMZIk7dfH2e8FTIGbyF76t7uJ9kmSVFQIYQ8cBaYhe/OHSZLUWAgxCYiWJGmuvo27QGNJkl7o8zoAL4Eg4JQkSakMfCFEV+SYeDUQDtxFXqrxE0LUAuYiTxCuAH0kSUr4QHp9YCHysUdngaL6fi5G3hegRV7i6aYPJUoPabBJbiP+ql8PCzTPeT4s9VLk10zuuX8CoH2dsaM5/7+jzl4MAM2tI1+4J58XkxJ1mWSR9+MFvyImxckrDL223PyyHfnMrGhbkqjYuC/djc+KjX6TdkTMf0uXnZUFCac2fLzgV0SW6nIIncYnYxPq/++YeBYC4yMb/jHsa4/7R4zZ8GPTvri2zPBVe+4lSUozyEq/BJJe4G9RfZlwoFyK9FP69Env1ZXyTPy5kiRN0h91dAaYl077a5Hj5tPKO468KdfY9EPIXvn30/unVb+CgoKCgoKCwv8SXyrm/v8rX7Vx/wVYIeTXB5sDayVJSvNUHAUFBQUFBQUFhX8Hxbg3RDHuM4AkSR2/dB8UFBQUFBQUFBQU0kMx7hUUFBQUFBQUFL5aFM+9Icr2YgUFBQUFBQUFBYX/CIrnXkFBQUFBQUFB4atFqBXPfUoU415BQUFBQUFBQeGrRQnLMUQJy1FQUFBQUFBQUFD4j/BVv8RKIUMoN1pBQUFBQUHhc/PFX/Tk2mL+P2LjBO4c8sW1ZQbFc6+goKCgoKCgoKDwH0GJuf8fYkwWry/dhc/K9ISnvBrd/Ut347OSY8YfAMTHxnzhnnxezC2tAEi8sP0L9+TzYlapFePN/1vjamr8UwBarb70hXvyedn+fQXi4uO/dDc+Kxbm5gBoX9/5wj35vKizF0P74O8v3Y3PirpgFQB0j8594Z58XlT5v/3SXQBApcTcG6AY9woKCgoKCgoKCl8tyoZaQ5SwHAUFBQUFBQUFBYX/CIrnXkFBQUFBQUFB4atF8dwbonjuFRQUFBQUFBQUFP4jKJ57BQUFBQUFBQWFrxbFc2+I4rlXUFBQUFBQUFBQ+I+geO4VFBQUFBQUFBS+WhTPvSGKca8AgFCp6HthF5G+Aaxr8UOq/NxVK9Bo7jjUpibEBofxe52On9Se2syMNqvn4lm6KLEhYfzVeQDhL33IWrwQzRZPIYutNTqtjlMzf+XOtv0Zb8DEBLdeoxEmJqBSE3f3KhHHdhkUyZK7AC7fDUATGgxA7L1rRJ7Y80m6UJvg1PYHzDxzoouNJnjjMrThIajtnXDp3A+ECtRqos8fI/ryqQxV/eLFC0aMHJX8+xsfH37q8yOdO3VKTtt/4AB/rFkDgKWFJWPHjKFAgfyfJCkxMZGx48fj7e2NnZ09s2fNxNPDA19fX4YMG4ZOqyNJo6FD+/a0bdM6U23UGzobS4ssqIUKtVrF5kl9DfJPXL/Pkh3HUAmBWq1iZMdGlM6f69N0JWkY8/tW7r/wwd7akjl9OuDp4oBvcBiDFm9Ap5PQaLV0rF2JtjUrZLj+IQ9PkxgVg06rRafRsvzb5gb5xds3pcrQ3nJfomPZO2A8/ncefJImtZkZrVbNxUM/rrZ0kceVe/FCNPllCub6cXV61q/czcS4+qlybspmdyAiPonBO1Ofre5pZ07fKnnI42TFxmuv2XPX/5P0AJioBAOqepHH2YqoBA3zTz4mKDoxOd/CVM2ilsW5/DKUlRdfZrj+Fy9eMGLEiOTffd68oc9PP9G5c+fktP3797PmD/kdFBaWlowdO5YCBQp8gip5XI0bO1Y/ruyYNXs2np6e+Pr6MnTIELQ6HZqkJDp06ECbtm0z1UZkdAwT5i3j8YtXCCGYNuwnShZ+1++9x8+watMuACwtzJkwsBcFvXJ9oq4kRs1azL3Hz7C3tWb+uCF4urviExDEwElzZF0aDZ2aN6B9k3qZaqP2DyOxsjBHpVJholKxdf54g/xVOw6x74z8ngatVsuzN36cXbcAexvrzOtKSmLUglXce/oSextr5g/vjaebMz6BIQycuRStTkKj0dKpUU3aN6ie4fpr9RiOlYU5apX8DNy2YKJBflRMLCPm/Y5fUAgarY7vW9ajZe0qmdbzVtPI+Su5//Ql9jZWzB/RR68pmAHTl6LT6UjSaOncpBbtG9T4pLb+aRTj3hDFuP8MCCG0wB3kVzBrgX6SJJ3/B9qZBERLkjT3c9f9Tf9uBD14Shbb1A8/czsbmv0ymT+adCfitR9WLk5G12uf05PWv89mZd1OBullu7chLjyCeYVrUrxNY+r/PJJNnQeQGBfH1h7DCXnyApusrvS9sJvHR88QHxGVMUEaDYErZyMlJoBKjduPo4l7eJvE188MiiW8eETQ2kUZqxtQ2zvh1KYngb/PMki3LlcFXVwMfnNHYVm8PPYN2hLy1zK0UeH4L/sZtBqEWRayDppGnPdNtFHhRreZK1cutmzeBMhfWHXq1admDcMHrqeHJ6tXrsTW1pazZ88xZdo0Nvy5zqj6fXx9mTBhIqtW/m6QvnPXLmxtbNm3Zw8HDx1m4aJFzJk1CxcXF9atWYOZmRmxsbG0at2G6tWq4erqYrSmlKwe2RMHG6s08yoW9qJGqUIIIXj42o9hS/9i78whxukKCmPcym38Mdpw0rrjzFVsLS04MHsYBy/eYsHWQ8z9qQMu9jasH/cjZqYmxMYn0GLsIqqXKoSrg23GNdXrRGxIWJp5YS/esKpOB+LDI8lXtxpNl/7MiqqtjKrXPqcnLX+fzer3xlWZbvK4WlikJsXaNKbutJFs6TKApNg4tvcYTuhTeVz1Ob+bJ5kYV6ceB3PQO4ABVdN+cVdUgoZVF19SIadDhuoFcLE2o18VLyYe9DZIr5XfhehEDf223eLb3I50KZuD+aeeJOd3KJ2N+/6RGW7vLbly5WLLli2APK7q1qlDzZo1Dcp4enqyavVq/bg6y9QpU1i/YYNR9fv4+DBhwgRWrVplkL5z505sbW3Zu28fhw4eZNHChcyeMwcXFxfWrlv3bly1akW16tVxdXXNsLYZS1dTuVxJFk4cRmJSEvEJiQb52dxdWTt/CnY21py5fJ2JC5azeclM43T5BzJm9hLWzp9ikL794HFsbaw4vG4JB06eZd7v65k/fggujvZsXPQzZmamxMTF0aznEGpWKoers2OGdQGsmTYMB1ubNPN6tKxPj5b1ATh5+Sbr9hwz2rD3CQhmzC+rWfvzCIP07UfPYmttxeHfZnDgzGXmrd3G/BE/4uJgx8ZZozEzNSUmLp5mAyZSs3xJXJ3sM6xp7c8jcLBLW9PG/SfwyuHBsgkDCY2IpOGPY2lcrRJmph8343wCghm9cBXrZow0SN925G/srK04vGIm+89cYu6arSwY2QcXB3v+mjMmWVPTfuP1mjI+rhW+DErM/echTpKkkpIklQBGAzPeLyCE+H87rbT1dKdggxpc+WNLmvkl2jfl3q4jRLz2AyAmKCQ5r2SHZvQ5u4N+l/fSfOk0hMq4j1ShJrW5/ucOAO7uOIhXjUoAhDx+QciTFwBE+QUSExSSoclESqTEBACEWo1QZWwea1myEm4/jce9/2QcmncFIYy6zqJQaWKuy28gjL17FXOvQnKGVgtajdwfExOj60uPS5cvkz1bNjw8PAzSS5Ysga2tbIQWL16MgICA5Lx9+/fTsXMX2rZrz5Rp09BqtUa1dfLUKZo2aQxAndq1uHz5CpIkYWpqipmZGSB7IXWS9EmaPoSleRaE/m8Wl5CU/H+Avedv0GHyr7Qev5jJa3ai1emMqvPkDW+aVi4NQJ1yRbl0/6msy8Qk+QszUaP9x3S9vnid+HDZKH19+QZ2nu7JeSU6NKP33zv46dJemi4xflwVbFKbm+vlcXVvx0HyvB1XT14Q+vQF8G5cWTpnfFzdD4giOkGTbn5kvIanwTFodKn/ZlW9nJjZpAhzmxWl9ze5UBk5BMrncODUY3l17cKLUIp5vJtk5XGyxM7ClFs+ERkTkg6XLl0iW/bsaYyrkinGVXGDcbV/3z46dexI27ZtmTplitHj6tTJkzRp2hSA2nXqcPny5TTHlWTk5/l9omNiuXrHm1YNagFgZmqKrbXh5LlUkYLY6Y3eEoXyExAUmpy359gZ2vUdRYvew5i44DejdZ04f4XmdasDULdqJS7euIMkSZiZmmJmZgpAUqIGXRqfkX+CA39fpmHV8sm/7zl1gXbDptFi0GQm/roOrda4v++JSzdpXvMbAOp+W4aLtx/odZlgZqrXlfTP6RJCEBMbjyRJxMYlYGdjhYlafi7sOXmBtkOm0mLARCYuWZsBTTdoVkvWVO/bsly85Z1KU2KSBulfulefglCr/5GfrxXFuP/82AJhAEKI6kKIk0KIjcAdIYRaCDFHCHFFCHFbCNFbX85aCHFcCHFdCHFHCNHsbWVCiLFCiIdCiGNAgRTpA4QQ9/X1bPqUDjeeO46Do2el+yXinC83Fg629Dyygb4XdlOqUwsAXAp6UaxNI36r3pYl5Zug02op2aFZmnW8j52HOxFv5MmCTqslPjIKy/e8AtnKFkdtZkro04wvtQMgBO79J+M5dhHxT+6l8toDmOXIi/uAybh0G4ypq/yFbuKSFavi5QlYPh3/xRNB0mFVspJRTapt7dGG678gdTp08XGoLOUvT7WdI+4DpuAxch6Rpw9kyGv/PocOH6Z+/Q8vae/ctYvK38qvBn/27BmHjxxh7R+r2bJ5E2qVmgMHDhrVVmBgEO7usuFpYmKCtbU14eFy3/39/Wndti31GjSke7eumfbaCyHoPfcP2k5cwtZTl9Msc/zaPZqMmk/fBWuZ0kP2cD/zDeTwpTusG9ubbVP7o1ap2H/hpnG6wiJwd7STdanVWFuYEx4dK+sKCafluF+oM2QW3zesmimvPZJE131r+PH8bsr2aP/BomW6teXRkdMAuBTwomjrRvxeoy2/VpDHVQkjx5Xte+MqIY1x5akfV2HPMjmuMoGnnTnf5nZi7L77DNt9F50EVbycjbrW0cqM4BjZ46yTIDZRi00WEwTQtXxO1l159dn6efjQIRrUr//BMjt37qRy5cqAflwdPsyatWvZsmULKrWaAwcOGNVWYGDgB8dVm9atqV+vHt26d8+U1/61XwCOdraMnbOUlr2HMX7eMmLj4tMtv/3gcaqULwXA05dvOHTqHOsXTWPnb3NRq1TsO/63Ue0GhITi7iLfWxO1GhsrS8Ij5RUiv8Bgmv8whJode9OzfbNMe+0Fgp4TF9B6yBS2HD6dbrm4hAT+vn6XOpXkSfzT174cOnuF9TNHsXPhRFnX6YvG6QoNw93ZIYUuC8KjomVdQaE0HzCRmj1G0LNl/Ux57QWCHhPm0WrQZLYcOpUqv1Ojmjx740fVrkNo1n8Co3/ogEql4ulrXw7+fZkNs0ez85fJqFQq9p6+YJymkHCy6u9BsqbId5qa9Z9Aze7D6NG6geK1/8pQwnI+DxZCiJuAOZAVSLmmWx4oKknScyFELyBCkqRyQogswDkhxBHgNdBCkqRIIYQzcFEIsQcoDbQHSiHfq+vANX29o4DckiQlCCHsM9vxAg1rEB0Ugu+Nu+SumnZcscpEjUepoqyq3wVTC3N+PLONV5dv4FXjGzxLFeWn8zsBMLUwJyZQ9up32rIMh1zZMDEzxS67B/0u7wXg/JI1XF+3XQ5geg8phYfUxt2FNn/MY1uP4QbpGUKS8F88EWFugUvn/pi6eZIU4JOcnej7Et9Zw5ASEzAvUBznLgPwmzcK87yFMfXMiXvfCQAIU1N00bKH1blzP0wcXBBqNWp7J9z7TwYg6vxRYq6dTccjL/dfGxGK/y8TUNvY49ylP7F3rybXmxGSkpI4ffoMA/v3T7fM5StX2LlrF2tWrwZkT7/3fW86de4CQHxCAo6O8sN60JCh+Pr4kJSUhJ+/P23byYZox44daN6sWZp//7eec3d3d7Zt2UJgYBCDhgyhTu3aODll3CO8bmxvXB1sCYmMptec1eTO6kLZArkNytQqU4RaZYpw9eFzluw4ysoRPbh4/yn3X/rQYfKvACQkJeGoD+0Z+Mt6fILCSNJq8AuJoPX4xQB0qvsNLaqUIa2PVbIuJ3t2TBtAYFgkA39ZT51yRXFOZ7k8PX6v0ZYov0CsXJzotn8tQQ+f8vLslVTlclerSJlubfi9ZjsA8tT4Bo9SRfnxXIpxpV8t67BZHldq/bj66ZI8ri4sXcONddvT/PilvH/W7i60Xj2P7T0/YVxlguIeduRxtmJW0yIAmJmoiIhPAmBErXy4WmfBRKXC2dqMuc2KArD/vj8nHwen9ahAAuoXcuP6m3BCYhLTKJFx5HF1mgEDB6Zb5srly+zauTN5X8vlS5fw9vamk37fS0J8PI6OsrE0eNAgfHx90SQl4efnR1t93HzHjh1p3rz5R8fV1m3bCAwMZPCgQdSpUyfD40qr1XL/8TPG9PueEoXyM33palZu2smA7h1Slb108y47Dp1g/YJpAFy8cYd7j5/Rtq+8xychIRFHe3mC23/ibN74B5KUpMEvMJgWvYcB0KVFQ1rWr5m2Lv1dzOrqzK7f5xMYHEr/ibOpW7USzg72GdIFsGHmKFyd7AkJj6TnxPnkyZaVskVS7y06dfkWpQvlTQ7JuXj7AfeevKTtsJ/f6dKP6/7Tl/ImMFjWFRxKi0Hys71L41q0rF057efFW10ujuz6ZTKBIeH0n7GEut+WwdneLkOaNs4ejauTAyHhkfQYP5fc2bJSrui7/RFnb9yjYO7srPl5OK/8Aukxfh5li+Tn4i1v7j19QdshUwGIT0zEyV7W1O/nxfgEBJOk0eAXFEqLAXIcf5emtWlZu0razwDxTtPuxVMIDAmj389LqPdNWZwdMqbp30SJuTdEMe4/D3GSJJUEEEJUAtYJIYrq8y5LkvRc//+6QHEhxNtdh3ZAPuANMF0IURXQAZ6AG1AF2ClJUqy+7pS7PW8DG4QQu4BdaXVKP5noBfDbb7+l2fGclcpQqFEtCtSrjol5FrLYWtPmj3ls7T40uUzEG39ig8NIio0jKTaOF39fJmsxOf75+vodHBmfegvAhrZ9gPRj7iN8/LHLlpVIH39UajXmtjbEhYYDkMXGmu92reToxPm8vnwzzX5nBCk+jvjnDzHPX8zAuJcS3nmx4h/eRjTrkuxlj7l+nojD21LVFbx+CZB+zL02Igy1vSPayDBQqVCZW6CLjTEsExVOUoAPWXLlJ+7u1QzrOXv2HAULFkz3y/7Ro0dMnjKVpUsWY29vL2uVoEmTJgwckHpCsHD+PCD9mHs3N1f8/f1xc3NDo9EQHR2NnZ3hQ97V1QUvrzxcv36DOnVqZ1jTW8+4k601tUoX5u6zN6mM+7eULZCbN4GhhEXFIEkSTb8tzaA2qVcxFg2QN0SmF3Pv5miHf6jsvddotUTHxWNnZZGqX16erlx/9IK65YplSFOUXyAgh7Hd33OEbGVLpDLu3YoWoPmy6axr+n3y5x8huLlhB0fTGFd/tXs3rtKKuX9/XGV5b1x12bmSY5Pm8+YzjKuMcupxMBuuvU6VPvv4YyD9mPuQmEScrcwIjU1EJcDSTE10gob8rtYUcrOhfkE3zE3ljZXxGh3rr6ZuwxjOnj378XE1eTJLly5NMa4kmjRpkuaEYMHChUD6Mfdubm5GjCtXvLy8uH79OnXq1MmQHjcXJ9xcnChRSDZ661atyMq/dqUq9/DZCybMW8ZvM8Zirzd0JUmiWZ3qDOnZKVX5xZPlWPT0Yu7dnZ3wDwrG3cUJjVZLVEwsdu/t5XJ1dsQrV3au3fGmXlXjVkUNrtd7xp3sbalVsRS3Hz1P07g/8PcVGlZ557SSJIlmNb9hyHep97YsHiNv4k8v5t7dyQH/4DDcnR31uuKwe2+PkKuTPV7ZPbh27zH1vi2bQU0OyZpqVyrNnUfPDYz7HcfO8kPrhgghyOnhRjZ3Z5698UOSJJrX/JYhXVMfZrBkbP9kTWnF3Ls7O+AXHGqgyT6VJgfy5vDg2v2Ma/o3UYx7Q5SwnM+MJEkXAGfgbXxCSstOAP318fklJUnKLUnSEaCTvnwZ/SQhAHkVAN66fVPTCFgKlAGuCSFSTdQkSVohSVJZSZLK9urVK81KjoyfyyyvyswpUI1NXQby7NQFA8MewHvfMXJVLodKrcbUwpzs5UsS9OApT0+cp2jLBskx8RYOdtjn8EirmVQ82Hec0l1aAlC0ZQOenZKXEdWmpnTeuowbG3Zyd4dxYSNpobKyQZjLhpowMcXcqzBJQX6GZazfhVqYZcsNQqCLjSb+qTeWRcuispK/6FQWVqjtjfOaxXnfwKq0HApjWbQs8U9lQ0Vt64AwkWMYhbklWXLlQxOUuZNEDh46RIN0QnL8/PwYMmwYP0+dSq6cOZPTK5Qvz7FjxwgJlUOGIiIi8PX1Naq96tWqsWfvPgCOHjtO+XLlEEIQEBBAfLw8QYqMjOTmzVvkypXzQ1WlSWxCIjFxCcn/P3/vCXk93QzKvAoISfYy3X/hQ5JGi721JRULe3H06l1C9EvJEdGx+AanvYE1la6SBdlz9rqs68pdyhfKgxAC/9AI4hNlr3JETBw3H78kl3vGwo1MLS0w08c3m1pakLdWFQLuPTIoY5c9Kx02L2Pb98OS95kAPDt5niItDMeVXQbGVcnO8rgq0rIBz1OMqw5blnFzw07ufcK4yix3/CKplMsRW3P5MWVtpsbFysyoa6+8Dqd6PjnMo1IuR+76yatdi04/5cctN+mz9SbrLr/i9JOgTBv2AIcOHqR+gwZp5vn5+TF0yBCm/fwzOXPlSk4vX6ECR48dIzREXlnJyLiqVr06e/fI/ppjR49Srnz5dMbVTXKlaNNYXBwdcHdx4vlr2aFx8fodvHJmMyjjGxDEgElzmTmqP7myvfuMVSxdjCN/XyAkTN7LEB4ZhU9AkFHt1vimLLuOnALgyJkLVChZVB5XQSHEJ8jjPCIqmht3H5A7m3Gf65TExicQExuf/P/zN+6TL6dnqnJRMbFcufeQmhVKvtNVvBBHzl8jRL/XJTwqGp/AkFTXpqmrfAl2nZDPyThy7hoViheUdQWHJm9UjoiO4caDp+ROsX/GeE1xyf8/d+NeKk1ZXRy5eOs+AMFhETx/4092NxcqlijE4XNX39MUbJymCiXZfVzWdPjcVSqmo+m695MMa1L4siie+8+MEKIgoAbSemIcBvoIIU5IkpQkhMgP+CB78AP1aTWAtxbSGWCNEGIm8r1qAvwmhFAB2SVJOimEOAt0BKyB8M+lo/wP8tLt5d//IujBUx4dOcOAa/uRdBJX/thMwH3ZUDk6cT7d969BqFTokjTsGTiR8Fcf/3K7+scW2vwxj6H3TxAbGs6mLrLnq1jrhuSqXA4LR3tKd5G9K9t7jsDvtveHqkuF2sYOpzY95aMnhSD2zhXiH9zCunx1AKIvn8KyWDmsK9QAnRYpKYngv5YDoAn0JeLIDly/HyYvUeq0hO7+E234x78Eoq+ewbltL7IOm4kuNia5TlPXrNg3bI88VxNEnjlEUsCbDGkCiIuL4+KlS4wfNzY5bctWeYWhbZvW/Lbid8LDI5g+Q97TrVar+WvjBry88tC370/06fMTOkmHiYkJY0aNSrVxMC1aNG/O2HHjady0Kba2dsyeKdf97Plz5s2fj0AgIdH1uy7ky5cvw5pCIqIZtHg9AFqtjoYVS1C5eH62nJCPsmtbswJHr95l77kbmKjVZDEzYc5P7RFC4OXpRv+Wdeg95w90koSJWsXYLk3xcP54fGjLqmUZvWIrDUfMxc7Kktl95HCkZ76BzN10ECHkFY+uDaqQP3vGvtis3ZzpuHkZIIe13d68lydHz1Cupzyurqz8i+pj+mPpaE+TRfLy/9vjMoMePOHYpPl03SePK22Shn2DJhJhxLi6vmYLrVbPY9C9E8SFhrPlO3lcFdWPK0tHe0rpx9WOH0bgn8FxNbi6F0XcbbExN2FFu1Jsvv4GtX5n7JGHgdhbmDK7aVEsTNVIkkTjIlkZuOM2b8Lj2Hj9NRPqFUQlBBqdxO8XXhBkREjN8UeBDKjqxZLWJYhO0LAgxUk5n4u4uDguXrzIuPHvjlTcqj9Bp03btqz47TfCw8OZPn06IMcnb/zrL7y8vOjXty8/9umDpJPH1egxY4wbVy1aMHbsWJo0boytrS2zZs8G5Dj++fPmIYRAkiS+69o1U+MKYGy/HoyYsYikJA3Zsrrx8/C+bNp7GID2TeqxbP02IiKjmPLLSr0uFVt/nU3enNkZ2K0DPUdNTdY1vn9PPN0+Pslt1aAWI2f+Qr3v+mFvY83csYNlXa/eMHv52mRd3ds0JX+ejDsDQsIjGTBjKQAarY5GVctTpXRRNh08JevSH0N57OINvi1ZBEvzLMnX5s3hwcBOzek5aYFel5rxvTvh6fpx502rOlUYuWAl9XqPxt7GirnD5GNsn73xY/bqLe90Na9L/lzZPlLb+5oi6P/zkmRNjatVoEqZYmw6eFKvqQY/tWvC6IWradpvPJIEQ7u1wcHOBgc7GwZ2aUnPCfP0z0A143/sjKfrx/e0tK5TlZHzf6der1HYWVsxb4Ss6elrP2av3oxA/sb6vkW9DGv6t1E894aIfzPu8r9KiqMwQfbOj5Ekab8QojowTJKkxvpyKmAaspEugCCgOWAK7NX/exP4FmggSdILIcRY4DvgJXL4zn1gEXASeVIggPWSJH3s/DJpTJa0j6/7Wpme8JRXo7t/6W58VnLMkM/Sjn8vlOdrx9xS9mInXtj+hXvyeTGr1Irx5v+tcTU1/ikArVZf+sI9+bxs/74CcfHpbyj9GrEwlxd4ta9Tv3Pga0advRjaB8Zt4P1aUBeUz6TXPTr3hXvyeVHl/xbS3EX375K717Z/xJh9vqL1F9eWGRTP/WdAkqQ0p4ySJJ0CTqX4XQeM0f+8T5qBh5Ik/Qz8nEZW5Yz2U0FBQUFBQUHhv4bK2LN1/0dQjHsFBQUFBQUFBYWvFqEY9wYoG2oVFBQUFBQUFBQU/iMonnsFBQUFBQUFBYWvFvGJb33/r6F47hUUFBQUFBQUFBT+IyieewUFBQUFBQUFha8WZUOtIYrnXkFBQUFBQUFBQeE/guK5V1BQUFBQUFBQ+GpRTssxRDHuFRQUFBQUFBQUvloU494Q5Q21/zsoN1pBQUFBQUHhc/PFLetCg/f8IzaO94KmX1xbZlA89/9DDDPN86W78FmZm/SMG83rfulufFZK7ToCQOK5LV+4J58Xs2/bApBw7I8v3JPPS5ba3Zlvk/9Ld+OzMiTqEQBmpb7/wj35vCTeWM3fz0K+dDc+K1XyOAGgeXPvC/fk82KSrQgxf0370t34rFh1GAeA9uWtL9yTz4s6Z4kv3QUAVMpRmAYoG2oVFBQUFBQUFBQU/iMonnsFBQUFBQUFBYWvFiXm3hDFuFdQUFBQUFBQUPhqUYx7Q5SwHAUFBQUFBQUFBYX/CIrnXkFBQUFBQUFB4atFeUOtIYrnXkFBQUFBQUFBQeE/guK5V1BQUFBQUFBQ+GoRiqvaAMW4/x/HJIsZP53cjEkWM1RqNbd3HOLIlIUGZcxtbei4dj72OTxQqdWcXrCSK2u3fVK7ajMzOvwxl2ylixIbGs6fHfsT9tIHjxKFaLlkKuY21uh0Oo7PWMqtrfszXL+psws5Bw7H1N4RSdIRcuQAQft2GZRxbd4Gh2o1ARAqNebZsnOna1u00VGZ1iVMTMk5aDiWXvnQREXxYu7PJAYGYOriSp5RE0GlQqjVBO3fTcjhjOuqN3weluZmqFUq1CoVmyf2Mcjfd+EWqw/+DYBlFjPGd2lCgRxZM60HIDFJw5iV27n/0hd7K0vm9GmLp7MDvsHhDFr6FzqdDo1WS8daFWlbo/wntZWS+uN/xdI8C2ohUKtVbBrZ7ZPq233xDr8fOg/AD/W/oVnFYgBMXH+Ae6/8kCTI6erItC6NsDQ3+9TuU7pvN4p2bQOSRPC9RxzuMwptQmJyfhZ7W+r9OgO73NnRxidy+KfRhHg//qQ21Wam1F8xB7eSRYgLDWd/t0FEvvLBpVghai2chJmNNZJWy6U5y3m040CG618xsTsNq5YgKDSSUm0mpFuuTOFcnF03jk6jlrHj2LVPkYSZqQl/TO1JqUI5CY2IodPIZbz0e3devY2VObd3/MzuE9cZNGvDJ7WVkmO7NnPm0B6QoEr9ptRp0e6T6jt39AD7N60BoFH7bnxbpyEAaxZM58XjB0iShLtndroPHYe5heWndh+AyOgYJsxdypMXrxECpg7rR8kiBZLz9x07zapNuwCwtDBn/KBeFPTK/UltJiYmMXrWIu49eoa9rQ3zxg/F090V34BABk6cjVanQ6PR0qlFQ9o1qZepNrQ6HZ1XHMDFxpJfOtVMs8w9n2C6rjzEzNZVqF0k56dIIlGjZfzOc3j7hmJvacbM1lXxcLDGNzyaYZtPo9NJaHQ62pcvSOtymXvvRWR0DBPmL+fxi9cIIZg2tA8lC7+r69krH8bO+5X7T54zsFt7vm/T9JM0gXyvRs1Zwr3Hz7C3sWH+2EF4urviExDEwMlz5Xul1dKpWX3aN/7//U4ZoZxzb4Ay13kPIYRWCHFTCHFLCHFdCPHNZ6o3lxDibjp5fwkhbgshBmewzpJCiIaf0i9NQiLL63RifplGzC/bmIL1qpKjQkmDMt/06UKA9xPml2nEstodaTJ7DGpTU6Pqd8jpSZ9jG1OlV/i+LXHhkcwsVJMzi1bTaPpIABJj49nUfRhzS9ZnZaNuNJs3HnM7mwzrkrRafP5YgXf/njwaMRDnBk0xz5bDoEzgrq08HNyHh4P74Lt+NdH37hht2Ju5upF32pxU6U516qONjuZ+n+4E7tmBx3c9ANCEhfJo5CAeDu7DoxEDcGvVDhMHxwzrAlg94nu2Te6byrAHyObiwB8je7BjSj96N6nO5LV7jK7XJziM7rNWpUrf8fc1bK0sODBzMF3qVmLBVvlFWy721qwf8wPbJvdl47jerDrwN4FhkZnSlB6rBnZg65jvM2TYf79wAz4h4QZpETFxLD9wlg3Dv2PjiK4sP3CWyNh4AIa3qsW2MT3YPrYHWR1t+evMpxmjANZZ3Sj1Yxc2Vm3JugqNEWoVBVo3MihTYdiPBN725s9KTTnYewQ1Zo8zun7bHJ60OfBnqvSi37UhPjyC1SXrcH3pGqpMGQ5AUlwch3qNYF35Ruxo0ZPqs8aQJRPjat3eczTuO/+DZVQqwfSBbThyIc3HXbrkzOrE0d9HpErv3rwKYVExFG42ml82HGH6wDYG+ZN+asHf1x5mqK2P4fPiKWcO7WHswlVM/HUtty+fI8DntVHXzh7Rl+AAP4O06KhI9m5czZiFKxm7cCV7N64mJkoeK+16DWTSr+uYvOxPHF3dOLH30xwnKZmxZBWVy5Vi35rFbF8xnzw5sxnke2Z1Y82CqexcuYAfO7dh0vzlRtft4x9ItyHjU6VvP3gMW2trDv35K9+1asL839cB4OzowIZfZrBjxXz+WjqTlX/tIDA4NFO6/rr4gNzOdunma3U6Fh29TiWvjDk2fMOi+eGPI6nSd11/gq25GXsGNqdTxUIsOnYdABdrC9b0qM+mPo1Z17MBf5y9S1BkbMbE6Jnx6x9ULleS/asXsmP5HPLk8DTIt7OxZsxP3eneukmG6/bxD6TrsEmp0rcfOoGttRWH1yyma8tGzFslT45dHB3YuHAaO5fPYdMv01m5eTeBIZm7VwpfBsW4T02cJEklJUkqAYwGZrxfQAih/lyNCSHcgW8kSSouSdKCDF5eEvgk4x4gMUZ+GKlNTVCZmoD03lucJYksNlYAZLG2JDY0HJ1GA0Dpjs0YcH4ng6/uo9Wv0xAq4z5SRZrU5uqf2wG4vf0g+WrKc6jgx88JfvICgEi/QKKDQrB2ccqwJk1YKHHPngCgi48j/s0rTJ2c0y3vUKU6YX+ffPd7tVrkn/0LBRYsI3ufgWCkLrvylQg5eRSA8PNnsCleCgBJo0HSJAEgTE0R/9AaYsm8ObCzsgCguFd2AsIikvP2XrhJh6nLaT1xKZPX7kar0xlV58kbD2j6TUkA6pQtwiXvZ0iShKmJCWam8uJfokaL7v3PzT/A66AwflyymXYz/6Dr/PU89zfujaPnvJ9TqWBu7KwssLU0p1LB3Jy9/wwAa4ssAEiSRHyiBvGZ3qSuMjHBxMIcoVZjamlBjF+gQb5jwby8On0BgLBHz7DN4Yml/rNeqF1TOp7cRudzu6m9aIrR48qrUS3ub9wJwKNdh8hRvRIA4U9eEP70JQAx/oHEBYVi4ZzxyeXZ648Ii4j5YJm+7Wuz8/g1gkINJ3odG1bk3J/juLJpEkvHfmf0Brgm1Uvx5155xWX7savUKF8oOa9UoZy4Odly9MLnfUOr3+uX5ClYlCzm5qjVJuQvVorr508T6PuGBeMGM6V/d2YN64Pf6xdG1Xfv2kUKlyqHtY0tVja2FC5VjrvXLgJgYSU/WyVJIjEh8bN9/qJjYrl25z6tGtYGwMzUFFtrK4MypYoUxM7GGoDihfMTEPRuPO09epp2P42gZa8hTJq/DK1Wa1S7J85foVndGgDUrVaJi9fvIEkSZqammJnJTqGkRE2mnxcBETH8/diH5qXzpltm06WH1CqcE0crc4P0/bee0WXFAdov28e0vReNfgaeeviaxiW9AKhVOCdXnvnrn4FqzExkcyBRq0PKpKbomFiu3vGmVX15FcLM1CTVvXJysKNYgbyYqFObH3uOnaFd/9G0+HE4ExeuQKs1TteJC1dpXqc6AHWrVuTijbv6e2Xy7l4lJaEz8u/0JVGpxD/y87WiGPcfxhYIAxBCVBdCnBRCbATuCCHUQog5Qogreq97b305ayHEcb3X/44Qotn7lQoh8gghbgghygFHAFf9akEVIcQP+jpvCSG2CyEs9de0EULc1aefEUKYAVOAdvprM71mLFQqBl/dxyTfKzw+do5Xlw1fj33u13W4FvRiwquLDL1xkN1DpiJJEq4FvSjZpjFLqrZhQdnGSFodpTumkpsmdh5uhL+WvVs6rZa4iCgsnRwMymQvVxy1qSkheqMks5i5umGZJy8xjx6kmS/MsmBbqizhF84CkCVbdhwqV+PR6ME8HNwHSafDsWraS7/vY+roTFJwkPyLToc2Nga1ja2c5+xCwYXLKbpyAwE7NqMJy7gnRAjoPW8tbScvY+upKx8su/Pva1QuJi/rPvMN5PDlu6wbLXva1SoV+y8Y9xr0wPBI3B1lL5mJWo21RRbCo+UJoX9oBC0nLKHOsLl836AKrg62GdaULkLQW2/Ibzt7E4DJGw8xum0dNo/qztAWNZm2+bCRGqJwd3jnqXZzsCEw/N0qzfg/91Nj9GJeBITQoXqZT+56tF8AV39ZRc/7p+j95BwJEVG8PHHOoEzQnQfkayovdbuXKY5tDg+sPd1xLOBF/lYN2VSnPeu/bYZOq6VgO+OW4K093Ih6I48rSaslISIK8/fGlXuZ4qjMTAl/9uqTdb6Ph4s9zWqWZsW2kwbpBXNnpU3d8lTrPoNy7Seh1eno2LCSUXV6utrzxl8eK1qtjojoOJzsrRFCMHtIO0Yt2PL5deTMw+O7N4mOjCAhPp47V84TFhTIul9m0bHPECYs/oM2PfuxYelco+oLDw7G0cU1+XcHZ1fCg4OTf189fxpDOjbG/81LajZtk1YVGea1XwAOdraMnb2EVr2HMmHuUmLj4tMtv+PgMaqUl50RT1++4eCpc6z/ZTo7VsxHpVax7/gZo9oNDA7B3VWepJqo1dhYWRIeKY81v8BgWvQcTK0OP9CjXQtcMzHBnHvoKgPrlEaVThhGYGQsJx+8onXZfAbpz4IiOHLvBav1nna1EBy8/dyoNoMiY3G3tdRrUmFtbkp4bAIA/hExtP11Lw3nb6dr5aK42GY8pOq1fyCO9raMnfsrLfuMYPz85R+8Vyl5+uoNh06fZ/2CqexcPge1SsW+E38bdW1AcCjuLunfq+a9h1GzUx96tmuGq1PmVpoVvgxKzH1qLIQQNwFzICuQ0qorDxSVJOm5EKIXECFJUjkhRBbgnBDiCPAaaCFJUqQQwhm4KIRIjo0QQhQANgHdJUm6KYRoCuyTJKmkPv++JEm/6/8/DegBLAYmAPUkSfIRQthLkpQohJgAlJUkqd+nCJZ0OhaUbYy5nQ3dti3HvUh+/O89Ss4vULcqvre8WV6nE05eOel9cB3Pzl4hX81v8CxdlIEXdwFgam5OdKDs+em6dRmOubNjYmqKfQ4PBl/dB8DZxWvkeP20HswpvB427i50+GM+m3oMy7Q3BEBlbk7ukRN4s2oZuri0l0vtylUk5sH95JAcm+KlsPTKR4G5S+Q6zMzQRIQDkHvURMzc3BEmJpg5u1JgwTIAgvbuJPTEEdJ2usn9TwoO4sGgHzFxcCTP6EmEn/87uV5jWTf6B1wdbAmJjKbX3DXkzupC2QK5UpW77P2MHX9fY93oHwC46P2M+y986TBVXnZPSEzCUb8aM3DxRnyCw0jSaPELjaD1xKUAdKpdiRZVSqf5938b3+juaMeOKf0IDItk4JKN1ClbBGc76wxpSlfrkM642tsQEhVD78WbyOXmyK3nPgxbuSu5TKJG9ibuunCbDSevAvAqKIy+v27FVK3G09mOhb1apVqMAsNbNbVLI7Q6HTO2HOXwNW+aVyr+SX3PYm+LV6NarCpWk4TwKBr/+QuF2jXFe/O7MKkr83+j+uxxdD63m+B7jwi85Y1OoyVHtUq4lSxCx9PyypaJRRbigmTjtunGpdjmzIbazBSbbFnpfG43ADeWreXe+h0fHVdWbi7U/302h3uPTL1C9xmYN7wDYxZtRaczrLtG+UKUKpyLC+vlMA6LLGYEhcrjbeu8fuTydMbM1ITs7o5c2TQJgMUbj7Fuz9k0Y2klSeLHtjU4dPY2bwLCPrsOjxy5qN+mM/PHDCSLhQXZ8+RDpVbz1PsOy6e/C59KSpL3UJw9so/ju7cCEOj7hkXjh2JiaoqzW1b6TpiJxIc/gN8PGYdOq2XjsvlcOXOMynUbf7IGrVaL9+NnjO3fk+KF8jNjySpWbtrBgO4dU5W9dOMOOw4e58+F0wG4eOM29x8/pd1PcphUQkIiTvbyBH/AhJm88Q8kKUmDX2AwLXsNAaBLy0a0qF8rnbEmi83q6szOlQsIDA6l/4SZ1K1aCWdHe6M1nXn4Bkcrcwp7OHH1uX+aZeYeusKA2qVRv7fadfmZH96+oXRZIe81SdBocdB79oduOoVPWDRJWh3+ETG0XyZ/X3WoWJBmpfKmdffePQPtrNjyUxOCImMZsukUtQvnwMnawmhNIN+r+4+fM+an7ylRKB/Tf/2DlZt3MaBb+49ee/HGXe49fk7bfqNlXYmJONrLTpb+k+bI90oj36sWP8phel1aNKRlvRppfi7f6srq6syu3+YSGBJK/0lzqFulIs4O9hnS9W+ivMTKEMW4T01cCkO7ErBOCFFUn3dZkqS3U/26QHEhRGv973ZAPuANMF0IURXQAZ6Am76MC7AbaCVJUnrryEX1Rr09YA28dU2eA9YIIbYAO4wRop+A9AL47bffPlo+PiKKp6cvUaBuVQPjvlzX1pyYLRuFIU9fEvriNa4F84AQXP1zBwfHpY49X9tGjgd3yOlJ+1VzWFbb8Aslwscf++xZifDxR6VWY2FnQ2xoOABZbKzpsWcVhybO49Wlm8ZITRu1mtwjJxB6+gQRF8+lW+z9kByEIOTEUfzWr05V9vnMyYC8GpBjwDCejBtukJ8UEoypswtJIcGgUqG2tEIbZRjHrwkLJf71S6wLFyP8gnEelre89Yw72VpTq3Rh7j5/k8q4f/jan4lrdrFs8HfYW8teJEmCpt+WZFDr1JuiFvWX741PcBjjVu3gj5E9DPLdHOzwD43A3dEOjVZLdFxCcuhPyn55ebhy/fEL6pYtyufA1V72tDvZWFGzRH6uPn6FjUUWto75PlXZ5pWKJxvk3y/cwNQujfB0sk+hwYarj955qgPCoiib33APhlqlon6ZQqw5dumTjfsc1b8h8uUb4oJlw/PxniNkrVDKwLhPjIrhSJ/Ryb/3uHuCyJev4dty3N+4i7OT5qWqd0/HvoAcc19v+Uy2NuxikB/t449NtqxE+wYg1Gqy2NkQrx9XZjZWNN+2gnNTFuJ3xbhVm4xSunAu1s/8EQBne2vqVy6ORqNDCMH6vecYt3h7qmvaDJUn0TmzOrFySg/q/DDbIP9NQBjZ3B3xCQxDrVZhZ21BaEQMFYt78W2p/PRuWxNriyyYmZoQE5fA2F8+T8x6lXpNqFJPjm/esWY5tg6OWFrZMHHp2lRlK9dtnGyQzx7Rl++HjsPZ7V28t4OzCw9v30j+PSw4kAL6kL23qNRqylWtzeHtGz6Lce/m4oSbixPFC8mrd3WrVmLlptRfHQ+fvmDivF9ZPmM89m/3YUgSzerWYHDPzqnK/zJlFCDHcY+dvZg186ematc/MAR3F2c0Wi1RMbHY2RpO+F2dHcmbKzvX7tynXjXjt7Xdeh3I6YdvOPvYh0SNlpiEJMZuP8vPrSonl7nvG8LobfJzNTw2gbOPfVDrDb8mJfPQv3bpVPXOa18dkGPuJ+46z+/dDZ+TrraW+EfG4mZnhUarIzo+CTsLw033LraW5HG158bLwAxv4HVzlu9ViULyakPdKhVZuXmXUddKkkSzOtUY0iP1pG3xJPn7ycc/kDFzf2Xt3EkG+e7OTvgHheDu4vTuXtm8d6+cHPHKmZ1rdx5Qr2rFDOn6N/lSxr0QYjXQGAiUJKmoPm0O0ARIBJ4iO3PD9XmjkZ22WmCAJEmH9ellgDWABXAAGChJkqR3Hq8DygAhQDtJkl58rF9KWM4HkCTpAuCMbJQDpAw2FUB/fXx+SUmSckuSdATopC9fRj9JCEBeBQCIQPbsf/uBZtcA/SRJKgZMfnutJEk/AuOA7MBNIcRHA9ElSVohSVJZSZLK9urVK80yVs6OyRtWTcyzkK/WtwQ+fGZQJuy1b3JMvLWrMy758xDy7DVPTpyneMsGyTHxFg52OOTw+Fi3ALi37zhlu7QCoHirBjw5Kcceq01N6bZtOdfW7+T29oNG1ZUeOfsNIf7NK4L2pDYo3qKytMS6SDEiLl1ITou+dQP7b6pgYmcv98naBtMUS+ofIuLyBZxq1AHA/puqRN25CYCpkzPCTP4yUFtZY1WwCPG+xm3Qe0tsQiIxcQnJ/z9/7wl5Pd0MyviFhDN46V/M+KE1udzf7TGoWCgPR6/eIyQyWu5ndCy+weFGtVu9ZEH2nJd1HL16j/IFcyOEwD80gvhEeR9BREwcN5+8MmjzU4hNSCQm/p3WC94vKJrTA08ne45cl8OrJEni4ZsAo+r7tlBuzj94TmRsPJGx8Zx/8JxvC+VGkiReBYYl13fqzhNyuWV8j8f7RL3xxb1cSUws5KGfo3olQt8bV1nsbFDpN6YX69YWn3NXSYyK4dWp8+RrVi85Jt7cwQ6b7MaNq6cHTlC4YwsA8jevnxzTrzI1penGX7n/1y4e7zr0yfrSo0DjkeRvNIL8jUaw49hVBsz4kz2nbnDysjctapfFRR8a5WBrRY6sxv2d952+SZcm8vOnVe2ynLoi3/+uY38nb8Ph5G80gpELtrB+3/nPZtgDRIbLqyUhgf5cP3eKSjXr4+yelat/nwDkz8vrZ8adblSkTEXuX79MTFQkMVGR3L9+mSJlKiJJEgG+b5Lru3XpLO7ZPu1kl7e4ODrg7uLM89c+gOyN98qZ3aCMb0AQAyfNZsbogeRK8RmrUKo4R85cICQsHIDwyCh8Awz3jKRHjUrl2H1EdpYcOX2BCqWKyc+LoGDiE+QxHREVzY27D8id3fNDVaWif+3SHBraiv2DWzKjdRXK5nY3MOwB9g1qyf7B8k/twjkY3agCNQrloHxud47df0VodJzch9gEfMOjjWq3WoHs7Lv5FIDj919SLrc7QggCImKIT5L3n0XGJXDrVSA5nTMemujiaI+7ixPPX/sCcPHGHbxyZPvIVTIVSxXjyN8XCdHvrwqPjMYnIMioa2tUKsOuo6cAOHLmIhVKFtHfqxDi9Sd7RURFc+PeQ3Ib+Qz6H2QNUP+9tKPIUR7FgUfI+zcRQhQG2gNF9Nf8mmIP5zJkZ2w+/c/bOnsAYZIk5QUWALOM6ZTiuf8AQoiCgBp5tvQ+h4E+QogTkiQlCSHyAz7IHvxAfVoNIOWTOhFoDhwWQkRLkpT6GBmwAfyEEKbIEwUffV+8JEm6BFwSQjRBNvKj9OUzjW1WV9qvnoNQq1EJwa1tB/A+cIJKvWQvwIUVGzn282LarZrD0BsHEcD+MbOIDQkjNiSMQxPn8cPBtQiVCl1SEjsGTCTsle9H2728ejMd1sxnlPcJYsMiWN9pAAAl2jQkT5VyWDrZU/Y72fjf3GM4vre8M6TLqlARHGvUIe7Fs+TQGb/1qzF1lo30t8dQ2lf8lqib19ElvItvjH/zCr8Na/CaNAMhBJJWy+vfFpMU9PEvt5Bjh8g5aCSFl/0hH4U5T17mNs+WA8/uvWQXuhAE7t5G/MsXGdIUEhHNoCXyR0ar09GwQnEqF8vHlpOXAWhbozzL95wiPDqWaX/uBUg+LtPL05X+LWvTe95adJKEiVrN2M6N8XC2/2i7LauWZvTv22k4agF2VhbM7t0WgGd+QczdfAiBHHjUtd635M/mniFN6REaFcugFfKkTKuVaFCuMJWL5CGXmyPTNh1mxaFzaLQ66pcpRIFsbh+pDeysLOhd/xs6zFoDwI8NvsXOygKdTmLcn/uIjk9EkiQKeLoyrn3mjudLif/V2zzedZjOZ3eh02gIvOXNnT82Ufx7eZn99upNOBbwov5vs5F0OkIePOFI3zGy9odPOTd1Ia12/4FQCXRJGk4MnUzU64+Pq7vrttLg9zl8f/Mo8WER7O8uH8BVoGUDPL8ti7mjPUU6tQTg8I+jCLqTsXH154zeVC1TAGd7a54dmsuU5bsx1W8o/H3bqXSv837my6SlOziwbCgqIUjSaBkwcz2v/D6+IfqPXWdYM+0H7u+eQVhkDJ1HfXwV8nOwbNpYoiMjUJuY0OmnYVjZ2NJzxCTWL5nDvr/WoNVoKF+tNtnz5PtoXdY2tjTu0J1pA+VVscYdu2NtY4tOp2P1vKnEx8YgSRLZc+ejc7/hH6nNeMb078nI6QtJStKQLasb00b0Y/NeeTG4XZN6LP9zCxGRUUxdtAKQ4663LJtD3lzZGdC9Az+MnIKkkzAxUTNuwA94uH3cydGqYS1GzVhE/S4/YWdjzdxxctjOs5dvmLN8LW8fGN3aNiN/ns8zkdl2RV5p/tAxlHlc7fmpZkl++vO4/hmoYlTD8njYfzyMsHmpvIzfeZami3ZhZ2HGjNZVAHgeHMH8w9fk7wlJoss3hcnn5vCR2tJmbN/vGTHzF5I0GrK5u/LzsJ/YtE8+uad947oEhYbTtt8oomPjUAnBnzsPsPf3+eTNmY2B3drTc/Q0JP2zfXz/Hni6uXykRWhVvyYjZy2hXrf+2NtYM3fMIEA+dnP2inXJurq3bkL+3Dk+XNkXJr09GP80kiSdEULkei8t5ZFLF4G3ER7NgE2SJCUAz4UQT4DyQogXgK3eoYwQYh2yrXhQf80k/fXbgCVCCCF9JF5ZfEo8838RIYQWuPP2V2CMJEn7hRDVgWGSJDXWl1MB05CXXgQQhHwzTIG9+n9vInvpG+jr2ydJUlEhhD3yzG4acOttur7ePsAI4KW+HzaSJHUTQuxAns0J4DgwCHBAnmSYAjMkSdr8AWnSMNM8mfyr/P9kbtIzbjT//332bkYptUt+JiSe+/ybBL8kZt/KE4KEY3984Z58XrLU7s58m8yda/3/lSFRsqFkVip1+NPXTOKN1fz9zLjTlb4WquSRVz80bz7vaUFfGpNsRYj5a9qX7sZnxaqDvFdD+/KfCYn7UqhzloB0dpv9m1SYcvQfMWYvTajzUW164z7Zjnsvby+wWZKk9UKIJcBFSZLW6/NWIRvwL4CZkiTV1qdXAUZKktRYf4R6fUmS3ujzngIVJEkKfr+tlCie+/eQJCnNYy4lSToFnErxuw4Yo/95n/SOgSiqvzYcKPd+uj5vGfLyzPvtt0yjvtD36lFQUFBQUFBQ+J/in4q5T7l3Uc8KSZJWGHntWEADvH27XlqdlD6Q/qFrPohi3CsoKCgoKCgoKCi8h96QN8qYT4kQoivyRttaKUJo3iCHVL8lG+CrT8+WRnrKa94IIUyQQ78/eo62sqFWQUFBQUFBQUHhq0WoxD/yk6m+CFEfGAk0lSQp5Rnce4D2QogsQojcyKHWlyVJ8gOihBAVhXwW6XfIJyu+vaar/v+tgRMfi7cHxXOvoKCgoKCgoKDwFfOl3iYrhPgLqA44CyHeABORT8fJAhzVvzfgoiRJP0qSdE9/nPl95HCdvpIkvX31cx/eHYV5UP8DsAr4U7/5NhT5tJ2Pohj3CgoKCgoKCgoKChlEkqQOaSSv+kD5n4Gf00i/Sor9lynS44EMv7ZaMe4VFBQUFBQUFBS+WtJ6k/X/MkrMvYKCgoKCgoKCgsJ/BMVzr6CgoKCgoKCg8NUiFFe1AYpxr6CgoKCgoKCg8NXypTbU/n9FeUPt/w7KjVZQUFBQUFD43Hxxy7ravFP/iI1zemj1L64tMyie+/8hBqhzf+kufFZ+0T7n+bAuX7obn5Xcc/8EwC885gv35POS1d4KgNjtc79wTz4vlq2Gsci2wJfuxmdlYORDAIqN2P+Fe/J5uTO7ESFRsR8v+BXhZGMJQFx8/BfuyefFwtwczZt7X7obnxWTbEUA0Nw68oV78nkxKVH3S3cB+OfeUPu1okQpKSgoKCgoKCgoKPxHUDz3CgoKCgoKCgoKXy3KUZiGKMa9goKCgoKCgoLCV4uyodYQJSxHQUFBQUFBQUFB4T+C4rlXUFBQUFBQUFD4alE21BqieO4VFBQUFBQUFBQU/iMonnsFBQUFBQUFBYWvFrXiuTdAMe7/x7HPlpUua+Zh4+6CpNNx/ve/OL14TapyeatVoOX8CahNTYgJDuOXmu0/qV0TMzM6r51H9tJFiQkJZ02HfoS+9MGzRCHaLp2Gua01Oq2OIzOWcGNLxs/bFiamZP1pLJiYIlQqYm5fIfzIjjTLmmXPjUf/SQSuX0Ls7SufpAu1CS4depMlW260sdEE/bkETVgwJg5OuHYdCEKFUKuJPHeUqAsnMlT1q5cvmDx2VPLvfj4+dO/1I206dEpOi4qMZNa0yfj6vMbMLAsjxk0kj1feT5KUmJjIjMnjefjAGzs7eyZMm0lWDw/8/XyZMHIYWp0OrUZDi7btadaydaba0Op0dFq6C1dbS37pWj9V/tVnvszZfwGNVoe9pTmrejX5NE0aLeO3nsLbJxg7yyzM6lALDwcbfMOiGLbhGFpJh0aro32lIrSpUDhTbZTs8x1FurZBCMHdtVu5+etag/w8DWtRadxAJJ0OnUbLmVHT8b147ZN0qc1MqfvbbFxLFSE+NJwD3QYT9coH52IFqblgEmY21khaHZfnLuPxjoMZrn9Km+JULeRKaHQiLeefSZXfqJQH31f3AiA2QcvUnXd45Bf1SZpM1Sqmty9BYU87wmMTGb7hBr5hccn5VllM2D2sGifu+jN9d8bPRn/54gUTxoxM/t3Hx4cfevehXcdOBuWuX73Kovlz0Gg02Nnb8+uKVZkXhTyupk4czwNvb+zs7Jg6YxZZPTzw8/NlzPBh6HRaNBoNrdu2p0XrNhmu/8WLF4wYMeKdrjdv6PPTT3Tu3Dk57eTJk/y6dClCpcJErWb48OGUKl36k3WNGzsWb72uWbNn4+npia+vL0OHDEGr06FJSqJDhw60ads2U21ERscwYe5Snrx4jRAwdVg/ShZ5976JfcdOs2rTLgAsLcwZP6gXBb0+7T0viYlJjJ61iHuPnmFva8O88UPxdHfFNyCQgRNny7o0Wjq1aEi7JvUyXH+dvhOxMs+CSqXCRK1iy8wRqcpcvveYmWu2o9FqcbCxZu3kgZ+mKSmJ0Uv+5N6z19jbWDFvUHc8XZ3wDQpl4NyVsiatlk71q9GubuVPakvh30Ux7jOJEMIdWAiUAxKAF8AgSZIe/cv9WAPskyRpW2au12k07Bz+M29u3COLtRXDr+zl4bGz+Hs/SS5jYWdD2yVTWdawG2GvfbF2cTK6fsecnnRaPZfFtToYpFf8vi2xYRFMLVCD0u0a03TmKNZ06E9ibDzruw0l6MkLbLO6MvzKXh4cPkNcRMYMBEmThN/yGUiJCaBSk7XfeOIe3CLh1VPDgkLg2Kg9cQ/vZKh+EwdnnNv3wn/ZdIN0mwrV0MXF8GbmMKxKVsShUTuC1i9FExmO7+IpoNUgzLLgOWwGsfeuo40MN7rNHDlzsWr9JgC0Wi2tG9enSvUaBmXWr1lF3vz5mTZ7Hi9fPGfRnJnMX/qbUfX7+foyc+pEFi373SD9wJ5dWNvYsnH7Ho4fOcyKpYuY+PMsnJxdWLJyDWZmZsTGxtK9Yxu+rVINZxcXozW9ZeP5u+R2sScmITFVXlRcAtN3n2Np9wZktbcmNDoujRrSxjcsignbTrPyh8YG6buuPsTGwow9w9px6NZTFh26zKwOtXCxsWTNj00xM1ETm5BE60XbqFYoJ662VhnS41QoH0W6tmFzjTZoE5NovmMlLw6fIvzpy+Qyr09f4NmB4wA4FylAg7UL+bNsA6Pqt8nhSd1lM9je6DuD9CLftSEhPJK1JeuSv1VDKk8exsHug9HExXOk90jCn77Eyt2VDme28/L4WRIzOK52X33DX+df8HO7kmnmvwmNo/vyC0TGaahcwIWJrYrRacl5o+r2cLBgWtsSfP/bRYP0luWzExmXRKPZp6hfIiuDGxZk+IYbyfn96uXn2rOQDOlISc5cuVi7cTMgj6tmDetRtYbhuIqKimLurOnMX7wUd/eshIaGGl2/n68v0yZNYOmKlQbpe3fvwsbGhq279nD08CF+XbyIqTNm4ezswm+r342rzu1aU7laNVxcXDOkK1euXGzZsiVZV906dahZs6ZBmQoVKlC9enWEEDx69IgRw4eza/duo+r38fFhwoQJrFplOMnZuXMntra27N23j0MHD7Jo4UJmz5mDi4sLa9etS9bVqlUrqlWvjqtrxnQBzFiyisrlSrFw0ggSk5KIf++54ZnVjTULpmJnY83fl64zaf5yNi2dZZwu/0DGzl7MmvlTDdK3HzyGrbU1h/78lQMnzjL/93XMGz8MZ0cHNvwyAzMzU2Li4mjeYxA1KpXD1dkxw7r+mDgAB1vrNPMiY2KZunILv43tg4ezIyEZGLs+gSGM/XU9ayYZTga2n7iArZUlhxZP5MC5a8zfsJt5g7/H2cGWDdMGY2ZqSkx8As2HTqdG2WK4OtplWNO/heK5N0SJuc8EQj5QdSdwSpIkL0mSCgNjALcv27OME+kfxJsbsrcrITqGgAdPsPN0NyhTpkMzbu08TNhrXwCig959kZbt1JyhF3Yx4tp+2i37GaEy7iNVrFkdLq/bDsDNbQfJX/MbAIIePyfoyQu5b36BRAeGZGgykRIpMQEAoVYjVOo0y9hWrkvM7StooyMN0q1Kf4PHgEl4DJ6GU6vuYOQZupZFShN99SwAMbcvY5FPfishWi1oNXJ/TEw/+Uze61cu45ktG+5ZPQzSXz5/Tumy5QHImSs3/n5+hIbI9+vIwf382L0LPTq3Z96MaWi1WqPaOnfmFPUbycZxtZq1uHblCpIkYWpqipmZGQBJSYlIusy9/TsgIpqzD17Tolzab3o9eOsptYrkIqu9/KXnaG2RnLf/xmM6/7qLdou3M23n32h1OqPaPOX9gial8wNQu2huLj/1kTWZqDEzkT8riVotkpQ5TQ4FvPC/cgtNXDySVovPuSt4Na5jUCYp5t3bUk2sLCBFWwXaNaXdya10PLuLmgsnGz2u8jSqyf2/dgLweNdhslevBED4kxfJE4sY/0Big0KxzITxce15KBGxSenm33oZRmSc/Dm//SoMN7t396pxKU829vuWrYMqM6FlUYz9Lq5R2I09V98AcPSOPxXyOifnFfa0xck6C+cfBWdYS1pcvXIZT89sZH1vXB05dJBqNWrh7p4VAEfHd3+7Qwf20+O7znTt2I5ZPxs/rv4+fYoGjeUVqBq1anP18uXU4yox8+MqJZcuXSJb9ux4eBjqsrS0TH4WxcXFGTyX9u/bR6eOHWnbti1Tp0wxWtepkydp0rQpALXr1OFyGroSExORjByr7xMdE8u1O/dp1bA2AGampthaG06+SxUpiJ2N/LwoXjg/ASm+s/YePU27n0bQstcQJs1fZrSuE+ev0KyuPOmrW60SF6/fQZIkzExNMTMzBSApUYMuk8+Mj7H/7FVqVyiBh37cOtnZJOftPXOFdqPn0HL4TCat2GT0c/DE1Ts0q14BgLoVS3Lx7iNZk4kJZqZ6TUkadJ/hM/hPo1aJf+Tna0Ux7jNHDSBJkqTlbxMkSboJ3BBCHBdCXBdC3BFCNAMQQuQSQngLIX4XQtwTQhwRQljo804JIWYJIS4LIR4JIaro09VCiDlCiCtCiNtCiN76dCGEWCKEuC+E2A9k3O2RDo45PfEsWZiXl24apLvmz42lgx39j//F8Mt7KNelJQBuBb0o3bYxC6q0ZnaZRui0Wsp2am5UW3YeboS/9gNAp9USHxGFlZODQZkc5UqgNjMlOIW3M0MIgcfgaeSYtJS4x3dTee3Vtg5YFi1L1IXjBummrh5Yl6yI75Kp+C4YB5IO69LfGNWkiZ0jmnD9F4lOhy4uFpWl/CWjtnPEc8jPZB+3kPCT+zPktX+fE0cPU7Nu6qVfr3z5+PuUHO7jfe8u/v5+BAUG8PL5M04eO8KS31ezav0mVGo1xw4bF5YRFBSEi6s84TMxMcHa2pqICLnvgQH+fN+pLW2bNKRDl66Z8trP2XeRgQ3Ko0pnwvMyOILIuER6/r6Pjkt2sve6vDj2LDCMI3ee8Ufvpmzu3wqVSnDg5pM063ifwIhY3O1kg8BErcLa3IzwWHky6B8eTdtfttNg1ka6VS2RYa89QMj9R3h+WxZzR3tMLMzJVbcq1tncU5XzalybLlcP0mzrbxztOwYAh/x5yN+yAVvrdGBj5eZIOh0F2hkXhmSV1Y3oN/K4krRaEiKjMHc0HFduZYqhNjMl/NmrDOvKCC3K5eDsw0AAcrtaU69EVr779TxtFp5Fq4NGpTyNqsfVzhz/iHgAtDqJ6Pgk7C1NEQKGNS7MvP3en63Pxw4fpk691GFhr1+9JCoqkr69etK9c0cO7tsLwIvnzzh+9Ai/rf6DtRs3o1KrOHLwgFFtBQUG4ub2blxZpRhXAf7+dGnfluaNGtC5a7cMe+3f5/ChQzSon1oXwInjx2nerBn9+/Vj0uTJADx79ozDhw+zZu1atmzZgkqt5sAB43QFBgbi7m74vAgPl3X5+/vTpnVr6terR7fu3TPltX/tF4CDnS1jZy+hVe+hTJi7lNi4+HTL7zh4jCrlSwHw9OUbDp46x/pfprNjxXxUahX7jqcOL0tTV3AI7q6yo8lErcbGypLwSNl77hcYTIueg6nV4Qd6tGuRKa+9AH74eSltRs5my7FzqfJf+AURGR1Lt0mLaDNyNrtPX5I1vfHn4PnrrJ86hB1zRqFSCfb9bVx4aWBoBO5O9u80WVoQHhUjawoOo8WwGdTqM54ezWr/v/baK6RGCcvJHEWBtIJj44EWkiRFCiGcgYtCiD36vHxAB0mSfhBCbAFaAev1eSaSJJUXQjQEJgK1gR5AhCRJ5YQQWYBzQogjQCmgAFAMeaXgPrD6UwWZWVnSY+sydgyZSnxUtEGeysSE7KWLsqROJ0wtzBl8bjsvLt4gf81vyV66KMMuycu4phbmRAfKhm2P7ctxypUdEzNTHHJ4MOKaHDd/evEfXFqzLU3PdUovqa27C13WzmdD96GZ9p4iSfguGIfK3BLXbgMxdc9Gkv+b5GynZp0J27/JwGMKYJ6vMGaeufAYKH/RCVOzZM++a9eBmDi6IExMMLF3wmPwNAAizx4m+srfH+yONiIUn/ljUdva49ZtEDG3L6N7b8XAGJKSkjj39xl++Kl/qryO33Vn8fw59OjcnjxeecmXvwBqtQnXrl7m0QNvenfrAkBiQgL2DrLRN27EUPx8fdAkJREQ4E+PzvJ+itbtOtCgSbNUfx949zZAVzd3Vm/YQnBQEONGDKFazdo4Ohm/0nLmwUscrc0p7OnC1We+aZbR6nR4+wbzW4+GxCdp6bp8N8VzuHL5qS/3fYLp/KvsqU5I0uJoJXuKh6w/gk9YFEkaHf4R0bRbLK8SdfymKM3KFEAiLU3yv+721mwZ0IrAyBiGrD9K7aK5cbKxNFoTQNijZ1xbsJIWu1aTFBNL8J2HSJrUHsKn+47xdN8xPL4pS6WxA9nZrDvZq1fCtWRR2p+SI+1MLMyJ1XseG21Ygl3ObKjMTLHJlpWOZ3cBcHPZOu5v2JHOitA7rZZuLtRbMYcjP45M875+Lsp5OdGyXHa+WyaH5FTM60ThbHb8NeBbALKYqgmNkSdTC78rg6ejBaZqFVntLdg6SI7t3XD2BbuuviE9Re0r5eTvB4EERKRv2GWEpKQkzp45TZ9+qceVVqPlobc3vyz7jYSEeHp170qRYsW5evkyD73v0+M7OY49IT4BBwfZsBs1bAh+vj4kJSUR4O9P147tAGjTviONmzaDtD6DerVu7u78uWkLQUGBjBo6hBq1Mjau3td1+vRpBgxMOz67Zq1a1KxVi2vXrvHr0qX8tmIFly9dwtvbm06dOul1xSevVgweNAgfX180SUn4+fnRVh8337FjR5o3b57m8/rt59Ld3Z2t27YRGBjI4EGDqFOnDk4Z1KXVavF+/Iyx/XtSvFB+ZixZxcpNOxjQvWOqspdu3GHHweP8uVAOn7x44zb3Hz+l3U9yPHtCQiJO9rLROmDCTN74B5KUpMEvMJiWvYYA0KVlI1rUr5XmcHl7v7K6OrNz5QICg0PpP2EmdatWwtnRPkO61k8dgqujHSERUfSctoQ8Hm6ULfxuv5RWq+X+89esGt+PhMQkOo6bT4l8ubl49yH3n7+i3eg5sqbEJJxsZa/+gDm/8yYwhCSNFr/gUFoOnylralidFjUqfvBeZXV2YOfc0QSGRtB/zgrqViyJs71thjT9m3zNXvZ/AsW4/7wIYLoQoiqgAzx5F6rzXO/dB3likCvFdTvSSK8LFBdCvN2haIc8QagK/CVJkhbwFUKkuytTCNEL6AXw22/px12rTEzosW0ZVzfu5vbOw6nyw9/4ERMcSmJsHImxcTz9+zKeJQohhODyuu3sHTsn1TWrWv0IpB9zH+7jj332rIT7+KNSqzG3syE2NBwAcxtreu9dzf4J83jx3ipCZtDFxxL/9AEWBYobGPdm2XPj0rkvAGorGywLlSBYq0MgiL56lrCDW1LVFbh2EZB+zL0mIhQTeye0EWGgUqGysEQXazhZ0kaGkxjgg3meApnawHvp/DnyFyiY5pe9lbU1oybIkxJJkmjfojFZPTy4ffM69Ro2oVff1IbLtNnzgPRj7l1cXQkK9MfVzQ2NRkN0dDS2toZeHGcXF3LlzsPtmzeoXqu20VpuvgzgtPcrzj78i0SNlpiERMZuOcnPbd/FPLvaWWFvaY6FmSkWZqaUzuXOI79QJEmiSel8DKhXPlW98zvXBdKPuXezs8I/IgY3O2s0Wh3R8YnYWWQxKONqa4WXqwPXX/hTp1geozW95d6f27j3p2ygfzNhMNG+AemW9T1/FbvcOTB3dEAIgffGnZyfPD9Vuf2d+gHpx9xH+/pjnS0r0b4BCLWaLLY2xOvHlZmNFc22/sb5qQvxv3Irw3qMJb+7DZNbF6PPqivJITwCwZ6rb1h06GGq8oPWyX6S9GLuAyLicbczJyAiHrVKYG1uSkRsEiVyOlA6lyPtKuXEMosJpmpBbKKGhQdTt2EMF86dJX/BtMeVi5srdvb2WFhYYGFhQclSpXnyWA5haNC4CX36DUh1zcy58v1LL+bexdWNgIB34yomOhpbO8Nx5eLiSm4vL27euE7N2oZhXcZy9uxZChYs+FEjukyZMrx+/ZqwsDB5bDVpkuaEYMHChUD6Mfdubm74+/vjluJ5YfeeLldXV7y8vLh+/Tp16mRMl5uLE24uThQvJIfV1a1aiZWbUh+W8PDpCybO+5XlM8Zj/zaERZJoVrcGg3t2TlX+lynyYQXpxdy7uTjhHxiCu4szGq2WqJhY7N6Lj3d1diRvruxcu3OfetWMW+1NvlbvGXeys6F2uRLcefLSwLh3c7LHwcYaS/MsWJpnoWwhLx6+9AEJmlWrwOCOTVNrGv6DrCmdmHs3J3v8Q8Jxd3KQNcXGYWdt6MhwdbQjb/asXHvwlHoVS2VIk8KXQwnLyRz3gDJppHcCXIAykiSVBAIAc31eQopyWgwnVglppAugvyRJJfU/uSVJOqLPM8rlJknSCkmSykqSVLZXr17pluu4chYB3k84uTDt0x/u7DlKnsrlUKnVmFqYk7N8SQK8n/DoxDlKtGqQHBNv6WCHQw7jltrv7jlG+e9aAVCydQMen7wAgNrUlB7bl3Plzx3c3GbcMnBaqKxsUJnLDylhYopFviIkBRp6ht9MH5L8E3P7CsE71hB77xpxT+5hVbwcKmvZS6GysMLEwTjvUuy9G1iXlT2PVsXLE/fkvqzLzgFhYqqvzxLzXPlICvTLlLbjRw5RK42QHJA3/iUlyQbV/t07KVGyNFbW1pQuW57TJ44Rpt8IGBkRgb9f2p7y9/mmSjUO7d8HwOkTxyldthxCCAIDAkiIl72mUZGR3L19ixw5c2ZIy4B65Tk8qiMHRnRgZvualMvjYWDYA1QvlJMbL/zRaHXEJWq4+zqI3C72lPfy5Njd58kbbCNi4/ENM26TWbWCOZPDe47dfU65PB4IIQiIiCY+SY4Zj4xL4OZLf3K52GdI01ss9EvzNtmy4tW0Lg+37TPIt8uTI/n/LiUKozYzJT40jNenLpCveb3k67M42GGT3TBWOj2eHThB4Q4tAMjXvB6vT8uGssrUlMYbluK9aTdPdh3KlB5jcLc3Z8F3ZRi96RYvg2OS0y8+CaZO8aw4Wskx17YWpmS1t0ivGgNO3Q+gadlsANQp5s7lJ3J8/ai/blJ3xgnqzzzJvH3e7L3mk2nDHuDo4UNphuQAVK1WnVs3b6DRaIiPj+Pe3bvkzJWbsuXLc/L4seQNtpEREfgZOa6qVK2WHN5z8vgxypRLPa4iIyO5c+smOXPlyrSuQwcPUr9B2hu1X716ley99fb2JikpCXt7e8pXqMDRY8eS9+tERETg62ucrmrVq7N3j7xgfezoUcqVLy+PrYAA4lPounnzJrkyocvF0QF3F2eev/YBZG+8V87sBmV8A4IYOGk2M0YPJFeKsVOhVHGOnLlASFg4AOGRUfgGBBrVbo1K5dh95CQAR05foEKpYggh8A8KJj5B/gqPiIrmxt0H5M5u3PfgW2LjE4jRhxbFxidw/vYD8ubIalCmZtniXHvwFI1WS1xCIrefvCSPpxsViuXnyMWbyRtsw6Nj8A0ybsN3jTLF2H1KDu85cvEmFYrklzWFhBGfKG9SjoiO5cbDZ+T2+P+9pVCJuTdE8dxnjhPIHvofJEn6HUAIUQ7ICQRKkpQkhKih/z2zHAb6CCFO6OvLD/gAZ4DeQoh1yPH2NYCNmW0kz7dlKd+lJT63HySHzuwbNweHHPID8dxvGwl48BTvw2cYdfMgOp2Oi6s243dPNoz2T5jHT4fWIVQqdElJbO0/gbBXPh9t98LqzXRZt4DxD08SGxrBmo6yR7lU20bkrVoeKycHyneVFy02fD8Mn1sZi6tV29rj0r4XQqhApSLm1iXivG9iU0k+LeJDx1AmBfgSdmgb7j+MQAiBpNMSsmMtmrCPn8gRffk0Lh1+JNuouehiowlcvxQAU1dPnJp0QEKetUWcOmiwimAs8fFxXLt8iaGjxyan7d4he4ebtWzNqxfPmD5pAiq1mly5czNi7EQAcuXJQ48ff2LYgJ+QJB0mahMGDh+VakNuWjRs2pzpk8bTsVVTbG3tmDBtBgCvXjzn11/mIxBISLTr1IU8efNlWFNabL0kT4raVChMHlcHvsmfjba/bEclBC3KFSCvu2z49q1Tlj5/HECSwESlYlTTb/BwsPlQ1QA0L1uAcVtP0XTuZmwtszCzvfy5eB4YzvyDl5LLfVelOPncMx4/C9Bo/WLMHe3RJWk4NXQyCeH/x95Zh0d1fA34nd1NiG5cSJBAcKdYKbS4FXcIFIdCcQot7u7Q0kKLF2hxdylQoLhD8ASJE+K+u/f74y4hIQlsAv0o/d33efJk78zcmTl775w9c+6ZudGU7iGHPN1Y+QeFmjWgeMfmGFJ06BIT2d9tKAAv7j7kzJSFtNyxEqFSoU9J4fjwycQ8fbtxdWvtFhr8MoeuVw+RGBHF/u5ynYVbNcLDuAaghI9s/B/qN5LnN+5kS6ZZPuWoVNAJe2tzjoyuzZLD99Go5R/AzWef0LduYeytzBnbUl5IrjdIdFh8mkehsfxw8C7LestrK3R6iWk7bhIU+fadj7ZdeMqMDuXY+11NouJT+G7D5Wz12RQSExO4cP4c348Zm5q2fctmAFq2aYtXgYJ8WvUzunRshxAqmrVoiXch2avap19/hg7oh8EgodFo+Pb7kRkW5GZGk+YtmDx+LG1bNEOr1TJ5uhwy4e/nxw8L5yOEHDnVsXMXvHM4rhISEjh79ixjx41LTdts3EGnbbt2HD1yhN27d6MxM8MiVy5mz56NEAJvb28G9O9P3379kAwGNBoNo0aPzrAgNzNatmzJmDFjaNqkCVqtllmzZwNyHP/8efNknSpJdOnalcKFcybX6IG9+H76QlJSdOTJ7cbU7wawcbf81Ll90wYs/W0TUdExTFn0CyDHk2/6eQ6FvPIyqHtHen8/GckgodGoGTuoNx5ub4/9b/1lHUbOWETDr77BztaGuWPlsJ1Hj58xZ+kaWbFL0K1dc4oUzN5Pf3hUDIPmyk9M9XoDjatX5PNyJdh4SN6coX396njncad6ueK0HD4TlUrQunZVCht/pwd1aEzvqUuQJAmNWs3Ynm3xcHm73mpduyojf1xLw4GTsLOxYu6Q7rJMASHMWbv9lUxN61Akn2kOhg+F5iM2xP8JRI7jmf/HEUJ4IG+FWQE51t4fmAgsBsyAq0A14KXLZI8kSaWM5w4HbCRJmiiEOA4MlyTpojFO/6IkSV5CCBUwFWiKPMTCgBZANPADUBt4ue3mOhO2wpQGqd9tn99/G4v1fvgN/+pDd+O9UmDubwAERca9peTHRW57eVFq/Na5H7gn7xer1sNZpM18l5+PlcHRsve79HfZf7/Ev5kbsxsTHhP/9oIfES/XgiQkvp91B/8WLC0s0D3L/jsL/s1o8sgTXt21Q28p+XGhKVsfyHRpzP8r7Vef/0eM2Y3dKn9w2XKC4rnPIZIkBQKZvYGjahanlEpz7tw0n2um+fwcY8y9JEkG5O01R2dS14Bsd1hBQUFBQUFB4T/IxxxC80+gxNwrKCgoKCgoKCgo/EdQPPcKCgoKCgoKCgofLYrnPj2Kca+goKCgoKCgoPDRojbxLd7/KyjfhoKCgoKCgoKCgsJ/BMVzr6CgoKCgoKCg8NGihOWkR/HcKygoKCgoKCgoKPxHUDz3CgoKCgoKCgoKHy2K5z49iudeQUFBQUFBQUFB4T+C8oba/x2UC62goKCgoKDwvvngbvN+W679IzbOz23KfnDZcoISlqOgoKCgoKCgoPDRohYfpQ3+j6EY9/9D9Fd5feguvFeWGPzx7d7sQ3fjvVJ81S4AouISPnBP3i921pYAJOxZ8oF78n6xbNKfcRbeH7ob75UpiQ8ByN9jwwfuyfvl8UofLjyJ+NDdeK9UyucAQErYkw/ck/eLmUs+4rfO/dDdeK9YtR4OQNLx9R+4J++XXDU7feguKGSCYtwrKCgoKCgoKCh8tCgLatOjLKhVUFBQUFBQUFBQ+I+geO4VFBQUFBQUFBQ+WhTPfXoU415BQUFBQUFBQeGjRaMY9+lQwnIUFBQUFBQUFBQU/iMonnsFBQUFBQUFBYWPFiUsJz2K515BQUFBQUFBQUHhP4Liuf8fxz5PbrqumY/W3QXJYODUr79zfPGqdGXcinrTeeUc8n5Skt1j53J03q/v3K7G3Jwua+aTr0Ip4sIjWdFhAC8ePyNP2RK0/2kqllobDHo9B6Yv4fKmPe/cntCYkX/UDITGDKFWE33xNM93/P5OddpVq41z03YAPN+9iajTxwDI3X0gFl6FQAiSgwMIXLEIKSnxnWV47O/P6JHfpR4HBgTQp28/OnbqnJp26eIFhg8bioeHBwC1atehV5+v36nd5ORkJo4byx1fX+zs7Zg2cxYeHp4EBQby/fBv0Rv06HQ62nXoSOs2bXPUht5gwGfBH7ja2fBDr/TvLohJSGLMhoMER8SiMxjoUvMTWlQu8W4y6XSM3XAY32eh2FlbMOurRng6agl8Ec23a/aiN0jo9AY6Vi9L289K56iNYXdPkBwTh0Gvx6DTs7Rai3T5Xl9UodPmZUT4PwXg9s6DHJ/+4zvJpTY3p/WKuXh8Uor48Ag2fTWIyMcBuJcpTtPFk7HQ2mDQGzgx6ydubtmb7frndK9C7bKehEcnUn/8vgz59cp58m3LMhgk+ZpO+v0yF++HvZNM5hoV83tVpXR+RyLikhjw82mehcel5ttYaDg6rQkHLz9j/PqLOWpj/9bfOb5/F0II8nh502fEWMzNc6Xmx8fF8vPMCYSHhqDX6/myTSdqNGzyTnKlJCezdPYk/O7fxVarZcCYqbi4e/A8JIiFk0Zi0BvQ63XUb96WOk1b5aiN6JhYJsyaz4NH/iBgyqjhlCuVfuycv3yNWYt/QqfT42CvZfWP899JruTkZEZNnc3tu/ex12qZO3kMnrndCQwOYcjoSUZ9ocenTXPat2iaozb0BgOdluzAVWvF4q4NM+RffBTInL1/o9MbsLeyYEWfnLWTKpNOz7jNx/ENeI6dVS5mdayDh4MtgRExDF9/BL1kQKc30KFqSdpWyZluajh6EVa5cqFWCdQqFX+M6Z0u3y/4OeNW78T3aTADm9eiW/3P3kkmgOQUHWNW7eD2kyDsrC2Z07sNns72BIZHMnTpZgwGWa6OtSrRrkbFd27vn0Tx3KdHMe7/HxBC6IEbgBmgA9YACyVJMrzhHA9gsSRJbf7Jvhl0OrYNn8rTK7fIZWPN9xd3c+fwXwT7PkgtE/ciks2DJ1K2Rf1s1++YPw9frZrLotod0qVX7dmO+MgoJhapSYX2TWkxcyQrOw4gOT6BtV2HEfbAH7vcrnx/cQ++B0+SEBX9TnJKuhQezx4rG9lqNV6jZhJ7/TKJj+6+9dx8308jaPkiUsJDU9NU1jY4N+uA3+RhIEkUmLCAmCvnMMTHEfL7cgyJ8kuoXDv0wLFOY8L3bX2n/gPk9/Ji/R+bANDr9TRuWJ+atWpnKFeuXHkWLP4h2/UHBgYwecJ4lv66Il36rh3bsdVq2bZrN4cOHuDHRYuYPms2zi4uLF+9BnNzc+Lj4+nYtjVf1KiBi4trttve8NdVCrg5EpeYnCFv4+nrFHRzYnHPZryIjafFzN9o/ElRzDTqt9Yb8CKa8X8cZsU3rdOlbz93G61VLnaP7sqBK/dYtOc0s7s0wkVrzZqBbTHXaIhPSqb1nPXUKFkAVzubbMsEsLJBJ+LDs35x0uPTF1jXqneW+Vlhn9+TVr/OZmX99C+QqdCtLQmRUSwsWZvSbZtQf+r3bPpqECnxCWztOYIXD/2xze1KvzM7eXD4JIlRMdlqd/PpR6w5eo/5vapmmn/aN4TDV/cDUCyPPUv6VaPOGNMmEXmcrJnb81M6zD6aLr39595ExSVTY9RumlbOz8i25Riw9HRq/rcty3Lubujr1ZnMi+ehHNqxiVnLf8c8lwWLp4zh7J+H+aLBK+P98M4teOYrwLdT5hEdGcGIHu2pVqcBGjOzt9YfFhzIsjlTGDvv53Tpxw/swtpGy/w1W/j7z8P8sXwJA8dOw97RmQkLf8XM3JzEhHhG9vbhk6qf4+Dskm3ZZi76iWpVKrJg6nhSUlJISExKlx8dE8vU+YtZNncGud1dCY8w/SVfAUHBjJk2h9U/zkuXvm3PAbS2NuzfuIZ9R/5k/s/LmTd5LC5OjqxbutCoLxJo0aU3tapXxdXZOdtybThzkwIu9sQlZdQXMQlJTN95miXdG5Hb3oYXsaa/EDAwIobxW06wvHf6iduOi3extTRn1/D2HLj2kEUHzjOrYx1cbK1Y3bcZ5ho18UkptFm0hRrF8+Oqtc62TAArvu2Cg41VpnlaK0tGdmjIsatv/816nYDnkYxbs5OV33ZNl77t9BW01pbsnTqQ/RdusnDbEeb0aYOLnS2/fdcdczMN8YnJtJr8MzXLFsXV3jZHcv1/oBj36VHCcv5/SJAkqZwkSSWBesCXwIQ3nSBJUmBmhr0Q4r1OyKKDw3h65RYASbFxhPg+xN7TPV2Z2LBwnly8jj5Fl+H8Sp1aMOLsDkZd3kfHpdMRKtNuqTLN6nNujWzwXtmyj6J1ZC9E6H0/wh74AxAVFEpMaDg2Lo45FS8dL73nQq1GaDSAhJmLO3mHTcRrwnzyj5qBubunSXXZlPqEuNtXMcTFYoiPI+72VWxKVwBINewBVGa5AOm99D8tF86fI0+ePOQ2euhNYf/evXT7qhOdOrRjxtQp6PV6k847cfw4jZvInq/adepy4cJ5JEnCzMwMc3NzQPbWGaScyRkSGcNft/1pVaVkpvlCQFxSMpIkkZCUgp2VBWrjfbb30h06LdxIu3kbmLL5GHpDlvPldBy/+YimFYsDULdMIc7ffyrLpFFjrpGHWLJOj5RDmd6Vsh2b8/Vf2/jm3G6a/TjV5HFVrGldrq7bBsCtbfspWEs2wsMf+PPioT8AMUGhxIWFY+XslO1+nb8XRmRcRoPqJfFJr3SEVS5Nulu/5ade7BzbgH0TGzG9SyVUJr4uvl75PGw94wfAvotPqFbcLTWvVH4HnLUWnLwVlE1J0qPX60lOSkKv15GclIiDU3pDWghBQkI8kiSRmJCAta0WlVqeXJ46sp/xA3ow+uuvWLFwJgYTx9XlM3/xef0vAaj8RS1uXbmIJElozMwwM46rlJQUJEPO7sHYuDguXbtB6yaNADAzM0Nrm36Suu/wMep+UZ3c7vKE3MnBITVv98EjdOg9gNbdvmbS7IUm64tjp87QvJHsCKpf8wvOXbqSUV+kpGAwcay+TkhULKfuPKVlpaKZ5u+/9pA6Jb3IbS/L6mhjmZq398p9Ov+0g/Y/bGXq9r9M1xe+/jT9pAgAdUsV4PzDgDT6Qr4PkvX/rL5w0lpTyssTjTqjLthz9jo+M5bTdsoyJq/bY7pc1+7S7NMyANT7pATn7vi9ksvspR7UYcjhPajw4VCM+/9nJEkKBfoAA4SMlxDiLyHEZePfZwDG9JvGz92EEJuFELuBQ0KI34QQzV/WKYRYL4RolmmD2cAxfx7ylC+B/7mrJpV3K+ZNhXZNmFe9DTM++RKDXk+lTi1MOtfe042Ip4EAGPR6EqJisHZySFcmf6WyaMzNeP7wcXbEyBqhosCkhRRZ9Buxt66S+Ogeubv1J3j9MvwnDSNk4yrcu/QzqSqNgyMpL56nHutehKNxeDUJyd1jEIUXrsU8tycvjrx7WNHrHD54kPoNGmWad+PGdXzat2PwgP48fCg/gfF79IjDhw6yfOVq1v+xCZVaxYH9GcMqMiMsLBQ3d3nCp9FosLGxISoyEoCQ4GB82rWl6ZcN6dK1W4689nN2nmRIk+qILIy9DtXK4hfygnqTVtBm7gZGtPgClUrwKOQFB6/eY/XANmz61geVSrDvsmlerdDoWNyNP/4atQobS3Mi4+TJX3BEDG3nrqfhlFV0q1Uhx157JImue1bT98xOKvbskGmRvFXK0//8Hr7auRLX4oUBcCnqTak2jfm1Vjt+qtIUg15P2Y7NMz3/dbQe7kQ9kw1dg15PUnQMVq+NK8+KZVCbmxHx6D2Nq9do8Ekejk5rzKrBNRix6hwAhXJraVI5P61nHOLLifsxGCRaVPUyqT53e0sCX8hhOHqDRExCCg42uRACxrb/hOmbrrxTfx2dXfmyTScGd2rBgPZNsLK2pnTFKunK1GvehsAn/gzo0IRRfTrx1TdDUalUBDz249yJI4xf+AvTl/2GSqXi9LGDJrUbER6Go4s8UVGrNVhZ2xAbHQVAeGgIo/p0YrBPM5q0/ypHXvtngUE42Nsxdvoc2nTvy/iZ84hPSO/F9n/6jOiYGLoN+JZ2Pb5h5/7DADz0f8yBoyf47eeFbF29DJVKxZ5Dx0xqNzQsHHdXub8ajRoba2sijU9eg0JCadm1D3Vb+dCzU/scee3n7DnL4EaVs5wcPn4eRXRCMr1+3YPPj9vZffkeAI9CIzh04xGrvm7GxoGtZX1x9UGmdWSQKSoedzvZG69Rq7CxMCcyXn4KEhwZS7vFW2k0awPdviibY689CL5euI72035ly8lLJp/1KCiMAxdvsea77mwe9zUqlYq9526YdG5IZAxujnbASz1oQWScfI8Ev4ii9eSl1B+5kB4Nqv2rvfaAMZzp/f99rChhOR8ASZIeCSFUgCsQCtSTJClRCFEY+B3ILLitKlBGkqQXQogawFBgpxDCDvgM6Pr6CUKIPsgTCZYtW/bGPuWytqL3lp/ZMnQyiTGxJslRrE418lYozffndwFgZpmLmNBwAHpvXYZzgbyozc1wzOfBqMuyIfnn4lWcXb05c0MujddD6+5C17XzWdtt+PvzhkgG/CYMQWVpTZ6Bo8jlmQ/LQsXI8833qUWERn7Mble9Do71ZG+1uWtu8g4dj6TXkRIWwrMfZwCZ9f/Vx6CVi0GocO/cB23lz4k6dTRj+RySkpLCyZMn+GbgoAx5RYsVZ9fe/VhZWXH61F98N2woW3fu5sL589zx9aXrV3IYR1JSEg7GyciIb4cSGBCALkVHcHAQnTrI6wg6dPShafMWmX//xuvn5u7Ohk2bCQsLZcSwodSuWw8nJ9M9widv++FgY0WJvK5cePAs0zJn7j6mqKcLv/ZrxdPwKPou28EnBT04f/8pvs/C6LRwoyxTii7VSzd01R4CXkSj0+sJioil3bwNAPh8Xo4WlUvwBpFwd7Bl8/BOhEbFMnTVXuqVLYyTbeaPyt/Er7XaERMUirWLE932riHs7kMen7qQmh905RbzinxBclw8hRvUxGfzUhaWqkPBWp/hUb4UfU9vB8DM0oK4MHlcddz4Mw5eeVCbm2GX14Nvzu0G4O8lq7mydiuZD6tXwtq4u9Bm5Ty29hrxj3kZD15+xsHLz6hcxIVvW5ah09xjVCvuTmkvB3aNk+OjLczVPI+RjaNlAz4nr7MN5hoVHo5W7JsoT1pXHbnL5lOPspSpS60i/Hk9kKCI+Hfqb1xMNJf/PsmC37ZhZWPLD1NGc+rIfqrXfTV5vnHxHPm9izB6zhJCAp8xa+QgipYqx60rF/G7d5fx/bsDkJychNZenkwtmPg9YUGB6HQphIeGMPrrrwBo0LI9NRo2eeO4cnJ1Y8Yv64l4HsaCid9T+Yta2Dlk70mLTq/H9959Rg/pT5mSxZmxcAkr1m1kYO9uqWX0ej23795n+aLZJCUl06nvIMqWLM65S1e4ffceHXr1ByApKRlHB3sABo2aSEBQECk6HUEhobTuJq/p6dy2JS0bN8xUrpf6PrebK9vX/ELo8+cMGjWRerW+wNnRIUP5rDh55zGONhaU8HTh4qPATMvoDQZ8A5+zrOeXJKbo6bp0J2XyuXL+YSC3A57T+Sd5XCWl6HG0lvXFsHWHCIiIIUVnIDgqlvY/yE+VfT4rRfMKRZEyefqaqi/sbdg0qDWh0XEMW3eYuqUK5EhfrP2uO672toRHx/H1onV4uTtTsUj+t5537o4fvk+C8Jm+HIDEFB2OxvaH/LyRgOeRpOj1BL2Iou0U2Q7oVLsKLaqVy/Sh8svh5u5ox9bxfQmNjGHIzxupV6E4TtocOjoU/t9RjPsPx8sxZAb8KIQoB+iBIlmUPyxJ0gsASZJOCCGWCCFcgVbAVkmSMsTMSJL0C/DLy8P+fadnWrFKo6HXlqVc2LCDa9tN8zrJEgjOrd3KrtGzM2T92lpW+FnF3Ec8C8YhrweRAcGo1Gos7WyJexEJgIWtDf32rGL3uHn4n3s3r1xmGBLiiL97E9sKVTHEx+E3YUiGMlGnjqYa5JnF3OsiwrEqVir1WOPoRPydm+krkQxEn/8Lx4at3qtxf+b0KYoVK5apEW1j80r5Vqv+ObNnTCcyIgIJicZNm9I/kwnBnHkLgKxj7l1d3QgJDsbNzQ2dTkdsbCx2dnbpyri4uFKwoDdXr1ymTt16Jsty1S+QE7ceccrXn2SdnrjEZEavP8j0Tg1Sy+y84EuP2hUQQpDP2R5PRy1+oRFIEjStWIxBjatlqHdBdzlmNquYezc7G4IjY3Gzt0WnNxCbkIydlUV6ue1s8HZ35PKjAOqVLWyyTC+JCZLvl7iwcG7vOkSeimXTGfdJaSbR9w8eR7V4kuxlF4Kr67dxeNzcDHX+3l5+spRVzH1UQDB2eXITbRxXubS2JBjHVS5bG77avpwjE+fz7PzVbMuTXc7fCyO/i02ql33LaT9mb72WodzXP/4FZB1zHxSRgIejNcERCahVAltLMyLjkvnE25lKRVz4qnZhrHNpMNOoiUtKYdaWjG28iZuXL+Di7pFqlFesXpP7t2+kM+5PHNxD0w5dEELg7pkXF3cPgp76AxKf1/+S9j2/yVDv0ImzgKxj7h2dXXkRFoKTiyt6vY74uFhsbLXpyjg4u+CZvwB3b1yj8hcZ19e8CXcXF9xcXChTUg4/q1/rC5av+yNdGTcXF+zt7LCytMTK0pIKZctw98FDJAmaNarP0L49M9S7eMZEIOuYezdXZ4JDw3B3dUGn0xMbF4edNr3X19XZmUIF8nP52g3q1/rCZJmuPg7hhO8TTt39XdYXScmM2fQn09rVelW3nTX2VhZYmpthaW7GJ17u3At6gSRJNP2kMIMaVM5Q7/zOchhRVjH3bnbWBEfF4WZnI+uLxGTsLHOlK+Oqtcbb1YHL/sHUK13QZJlSzzd6xp201tQuV5Sb/gEmGfeSBM2qlmVwyzoZ8hb2aw9kHXPv5mBLyIso3B20Rj2YiJ21Zboyrva2eOd24dL9J9Sv8G4bGfyTfMxe9n8CJSznAyCEKIhsyIcie+BDgLLIHnvzLE6Le+34N6AT0B1YlbG46XRePovgOw84tmDF2wun4e7R05Rv3QgbF9nItHKwwzGfaTHrN3YfpkpX2eAq3+ZL7h07A4DazIw+25Zx/rdtXNliWtiIKahttags5celwswc6xJlSXz8kOTnIdhWfGUc5srrZVJ9sTcvY12yPCora1RW1liXLE/szcsAmLnmTi1nU64yycGZe6RzyqEDB6jfIOMOEQDPnz9P9ZzdunkDgyRhZ29PpcqVOXbkMC9evAAgKiqKoMDMPV+v80WNGuzdI3uIjx09QsVKlRBCEBISQmKiHMoSHR3NtWtXyZ/fK1uyDGpcjUPje7J/bHdmdm5IpUJ50hn2ALntbTl3X95RJjwmHv/QCPI42lG5cB4OX3/AixjZcxsVn0jgC9MWXtcoWYDdF30BOHL9AZUK55Fliowh0bi2JDo+kat+QXi5mu5ZfImZlSXmNtapnwvV+ZyQW/fSlbFxexWS4FmxDEKlIj48gkd/nqFky0ZYG8eVpYMddvlMW1txZ89RynWWd1Yp2aoRfsf/BuRx1XHTz1xdv51b2/ZnWx5Tye/6anJZKp8DZhoVEbFJnPYN5suK+XCylQ0iO2tzPJ1M824eufqM1p8VAODLivk4cycEgMG/nuGzETup/t0upm26wrYzftk27EH2kj/wvUlSYiKSJHHrykU883mlK+Ps6satK/LELCoinKCnT3DN7UnJ8pU4f/IYURHyuIqNjuJ5iGnx/59U/Zy/Dsk67vzJPylRriJCCMLDQkk2rg+Ki4nm/q3r5M6bL9tyOTs54u7qgt8TeeycvXgFb6/0xmKtz6ty+foNdDo9CYmJ3Lh9h4Je+fi0QnkOHz+ZusA2KjqawOAQk9qtVa0qO/cfAuDQ8ZNU+aQcQgiCQ8NITEoy1hfDleu38MqXN1syDWpQmYMjfdj3XUdmdqhNpYIe6Qx7gJrF83PFPxid3kBCso6bT8Mo4GJPZW9Pjtz0S11gGxWfSGCEaQvKaxTLnxrec+SmH5UKesj6Iir2lb5ISOLq42C8XOyzJRNAfFIyccbFzvFJyfx9+xGFPEwLcaxSrACHL/sSHi2bCFFxCQSGR5p0bs0yRdl19joAhy/fpnKxAvK1iogmMTkFgOi4BK4+fIqXe/bX6Ch8OBTP/f8zQggXYCnwoyRJkjGs5pkkSQYhRFfg7VuAyKwGzgPBkiTdyml/vKtVpEqX1gRc900Nndk1ZjYORiP91LL1aN1c+O7CLiy0NkgGiVqDezC1ZD2CfR+we9w8Bh78DaES6FN0bBwwnhdPAt7a7pkVm+i6dj4T7x0n7kUkKzsOBOCTdo0p9EVlrJ0c+LSrvJ74t+7DeXbtdk5FBEBj54hHryGgUoEQxFw4Rey1iyQFPMW9Sz+cm7aTt8g8/xdJT/3fWp8hLpbnuzdSYLy8bdzzXX9giIsFIfDoNQSVpSUgSHrqR/Dan99cWTZITEjg3LmzjBozNjVt65bNALRu05ZjR46wdcsm1GoNFrlyMW3GTIQQFCzoTd9vBjDwm75IBgmNRsOIkaNMWpDbrEVLJowbQ6tmTdHaaZk2Q/ZI+vs9YtH8+fLzaUmi81ddKFQ4+x7uzNh8Ro4ZbftZaXrXq8T4Pw7TZs56JCSGNKmGg40lDjaWDGhYlb6/7JAXIqpVjGpVEw9H7Vtqh5ZVSjJmwyGaTl+D1sqCWV/Jk6VHIRHM370LgUBCokvNTyicO/txwTZuzvhslK+7SqPm+sbdPDh8kkq9OgJwYfnvlGzZiMp9fDDo9KQkJLLpq8EAhN15wJGJ8+m6ZzVCpUKfomPPkAlEPXn7ZOzy6k20XjmPIbeOkfAikk1d5DpLtfkSr+qVsHK0p/xX8qR6W+/vCL7umy25Fn/9GVWLuuFgk4uzc1uwYOf11AV+648/oFGFvLT+rAApeomkZD39jbva3A+MZu62a/z2bW1UAnR6iXHrLhAQ/vaQmo0nH7Kg92ecmNGUyLhkBiw7la0+v41CxUtR+fPajP2mK2q1mvzeRaj1ZQuO7pYXJtdp2ooWnXqwbM4URvbuBEi07/UNtnb22NrZ07b718waORhJMqDWaOg2YATObrnf3ChQo1FTls6cxLCubbCx1TJgzBQAAp/4sWHZYoQQSJLEl207kbdAoRzJNnpof76fNIMUnY68HrmZMmo4G3fIE/X2LZri7ZWfalUq0apbH1RCReumjShcUJ5IDezdnT5DR2KQJMzUGsYMG4CHu9ubmgOgVZNGjJoyk0btu2KntWXOxDEAPHr8hDk/LksdW906tqWId4EcyfU6m8/Jvw9tq5SgoKsDnxXJQ7vFW1EJQctKRSnkLocg9q9XkX6r9iFJoFGpGNnsMzwc3h5L3qJiUcZuPk6zuRvRWuViZgf5KYpfaCTz959LLdfl8zIUds/+BhAvouMYsvTlTmgGGlUuRfVShdh0Qt7atV2NijyPiqXD9F+JS0xCJQTrjp5jx8Rv8PZwYUCzWvRdtA6DJKFRqxndsREeTvZvbbdl9fKMXrmdxmN/wM7aktm9ZN3gFxTG3C2HU+/BrvWqUsTz7df+Q6J47tMjPtRuEP9LZLIV5m/AfKNBXxjYCsQDfwIDJUmyEUJ4AXskSSolhOgGVJQkacBr9R4AdkiStNSEbkj9VV7vS6R/BUsM/vh2f+d1xP8qiq+S1y9ExZm+fdvHwMtHvQl7lnzgnrxfLJv0Z5yF94fuxntlSuJDAPL32PCBe/J+ebzShwtPTN/q8WOgUj75qVJK2JMP3JP3i5lLPuK3ZgxL+5ixaj0cgKTj6z9wT94vuWp2gkwXof3/MufEg3/EmB1Ro9AHly0nKJ77/wckScrSGy9J0n2gTJqkUcZ0f6CU8fNqZE99KkIIK+DlAlwFBQUFBQUFBQUFxbj/GBFC1AVWInv/oz50fxQUFBQUFBQUPhRKWE56FOP+I0SSpCNA9ldYKSgoKCgoKCgo/KdRdstRUFBQUFBQUFD4aPlQL7ESQqwUQoS+fOmoMc1RCHFYCHHf+N8hTd4oIcQDIcRdIUSDNOkVhBA3jHmLhfHlEEKIXEKIjcb0c8b1mG9FMe4VFBQUFBQUFBQ+Wj7gG2pXA6/vTT0SOCpJUmHgqPEYIUQJoANQ0njOT0KIl2syf0Z+6Whh49/LOnsCEZIkFQIWALNM6ZRi3CsoKCgoKCgoKChkE0mSTgIvXktuDqwxfl4DtEiT/ockSUmSJPkBD4DKQojcgFaSpL8leQvLta+d87KuLUCdl179N6HE3CsoKCgoKCgoKHy0qN9u7+YIIUQfZI/6S36RJOmXt5zmJklSEIAkSUFCiJdvJPMEzqYp98yYlmL8/Hr6y3OeGuvSCSGiACfg+Zs6oBj3CgoKCgoKCgoKCq9hNOTfZsybSmYzEOkN6W86540oxv3/EEsM/h+6C++dly99+q/x8qVP/zUsm/T/0F1477x86dN/jccrfT50F947L1/69F/DzOW/t3nay5c+/dcwvvRJ4T2j+oc89zkkRAiR2+i1zw2EGtOfAXnTlMsDBBrT82SSnvacZ0IIDWBHxjCgDCgx9woKCgoKCgoKCh8tavHP/OWQXUBX4+euwM406R2MO+AUQF44e94YwhMjhPjUGE/f5bVzXtbVBjhmjMt/I4rn/n+IoZoCH7oL75UFOj+eju35obvxXsk7dQUAsfEJH7gn7xcbK/lJRNKxtR+4J++XXLW7MNO68IfuxntlZNx9AEp/t/cD9+T9cmN2YyJj4z90N94r9jZWACQm/Lf0hYWlJbprhz50N94rmrL1gf+mDvxfRgjxO1ATcBZCPAMmADOBTUKInsAToC2AJEm3hBCbgNuADugvSZLeWFU/5J13LIH9xj+AFcBvQogHyB77Dqb0SzHuFRQUFBQUFBQUPlpUH+gNtZIkdcwiq04W5acB0zJJvwiUyiQ9EePkIDsoYTkKCgoKCgoKCgoK/xEUz72CgoKCgoKCgsJHyz+1FebHiuK5V1BQUFBQUFBQUPiPoHjuFRQUFBQUFBQUPlr+ZVthfnAU415BQUFBQUFBQeGj5R22rfxPooTlKCgoKCgoKCgoKPxHUDz3/+Nocpkz4PgmNObmqDVqrm3bz4FJC9OVcS1akI4r5pCnfEn2jpvH8fm/vnO7anNzOq2eR55PShH/IpI1HQcQ8TgAj7LFabtkKha2Nhj0Bg7P+JGrm3Ow37ZGg2uv7xFqM4RKRfytS0Qf25muSK4CRXHuNABdxHMAEm5fJvrP3e8omAanNj0x88iPIT6O8I1L0UeGo7Z3wrnjN6BSIVRqYs4eJe7CiWxV7e/vz6jvv0s9DggIoG+/fvh06pyh7K1bN+nWpQszZs6ibr167yRScnIy48eNxdfXFzs7O2bOmoWHhydBgYEMH/4tBr0enU5H+w4dadM22zt2AdBwzI9YWZijVgnUKhV/jEr//gK/4OeMW7sH36fBDGxWk271Pn0nmQCSU3SMWbOL20+CsbO2ZE6vlng62RMYHsXQZVswSAZ0egMda1ak3RcVslW3Y+ECNF+7KPXY3isvf01dxMUlq1PTKg/pRcn2zQBQadQ4FfVmcf4qJEZE5Vgmtbk5TX6djXv5UiS8iGRnl8FEPQnAtUxxGiychLmtDZJBz5nZP3Nn675s1z+5bRm+KO7Ki9hkWs0/mSG/cXkPetT0BiA+Sc+U7Te4FxSTY3kAzNQqpncoSwlPOyLjkxmx/gqBEa/2dbfOpWHn8BocuxnM9J23sl3/Y39/xoz6PvU4ICCAPn370dEn/ZtEL128yIJ5c9DpdNjb27P01xU5Fwp5XE0aP447xnE1deYsPDw8CAoK5PvhwzEY5HHVrn0HWrXJ/rjy9/fnu+9e6YtnAQF8068fnTu/0hd79+5l1erVAFhZWjJmzBiKFi36znKNGftKX8yeNQtPT08CAwMZ9q2sL1J0Ojp27Ei7HOqLev0nYG2RC5VKhUatYtPM7zKUOX/rPjNXb0Wn1+Nga8OaSYPfTa6UFEb9+Bu3Hj3F3taaeUO64+nqRGDYCwbPXY7eYECn19OpYQ3a16/+Tm295G16Mbvs/Ps6v+4/BUDvRtVpXrUMABN+28Otx0FIQH5XR6Z2aYqVhfm7dv//lQ+1Fea/lf+UcS+E0AM30iS1kCTJ/wP1pRswBwgwJl2XJOmd3vYghJgIxEqSNPfdevcKXVIyP9X1ITkuHpVGw6CTm/E9cJzH566mlol/EcW2IZMo3bx+tut3yO+Jz8q5LKmTfivYT3u0IyEiiunFalG+XROazhjJWp+BpMQnsr7btzx/4I82tyvfnt/NnUMnSYzKpoGg0xG2ci5SchKo1Lj2HknivRskP3uUrliS/32er1ucbbnU9k44tu5B2Io56dJtKnyOISGe4AWjsSxdGfsGbQjfuAx9TCQhv8wAvQ5hngv3gZNJuHMNQ0ykyW16eXnx+8ZNAOj1eho1qE+tWrUzlNPr9SxetIiqVatmS6bAwAAmjh/PL8vTGy07dmxHa6tl567dHDxwgMWLFjFz1mycXVxYtXoN5ubmxMfH065Na2rUqIGLq2u22n3JiqGdcTC+lOd1tFaWjGxXn2PX7ma73oDwSMat2c3KYV+lS9925ipaKwv2Tv6G/RdusXD7Meb0aoWLnQ2/jeiKuZmG+MRkWk35hZpliuBqb2tymy/u+7Gqqmy4C5WK/g9OcW9X+pfynF+4nPMLlwNQqFFtKg3sZrJhb5fPk8bLZrGhUfqJXZmubUiMjGZZmboUb9OYmlNGsLPrEFLiE9jTewQRDx9j4+5Kt9Pb8TvyF0nZHFc7Lz7j9zP+TGtfLtP8Zy8S6L70b6ITdFQv6sKE1qXp9OMZk+r2cLBkaruy9Fh2Nl16q8p5iU5IofHs4zQsm5uhXxZjxPorqfkDGhTh0qPwbMmRlvxeXqz7fSMgj50mjRpQs1atdGViYmKYPXM6i35Ygnvu3Lx48da3v6cSGBjIlInj+fmX5enSd+3Yga3Wlq07d3Ho4AGWLF7EtJmzcHZ2Yfmq1anjyqddGz6vUQMXl+yNKy8vLzZteqUv6tWvT+3a6fWFp6cnK1esQKvVcurUKSZPmcL6detMqj8gIIDx48ezYkV6fbF9+3a0Wi17du9m/4EDLFy0iDmzZ+Pi4sLaNa/0RevWralZowauOdQXqyYMwkFrk2ledFw8U5ZvYtmYfng4OxKejfs8IDScMT+tY/XE9JOBrcf+RmttxYEfJrDv9CXmr9/JvKE9cHbQsn7qUMzNzIhLTKLFt9OpVbE0ro52OZLrdd6kF7Oix/zfmNK1KZ5O9qlpUXEJLN37F3+M6oEA2s9YSa0yhdFaWzKiTT1sLHMBMGfLYX4/cZGeDT57L/1X+DD818JyEiRJKpfmz/9lhpD5/5Z3Y5q+/Gtf45YcJ7+1UW2mQa3R8PqLjWPDwnl68Tr6lJQM51bwacGQv3cw/OJe2v40DaEy7Ssu1awe53/bCsC1rfspXFtWJGH3/Xj+wB+A6KBQYkLDsXFxypFcUnISAEKtRqjVwFvf2JyKVdlPce07Brf+E3Bo/hWYuFjHong54q7IxkzCrYvkKlhcztDrQa8z9kdjcn1Zcf78OfLkyUNuD48MeRv/+J06derg4OiYLn3f3r106dyJju3bMW3qFPR6fYZzM+PE8eM0adoUgDp163L+/HkkScLMzAxzc9m7k5ycjOHtb8TOMU5aa0p5eaBRqzPk7Tl3A5+ZK2k77Vcmr9+H3mAwqc7j1+7T7FPZc1Xvk+Kcu+Mvy6VRY24m+z2Sdbp3lit/rc+IfPSE6KeBWZYp3q4JtzftST0u2aEZXU5sofvfu2iweIrJ46pwk7rcWL8NgDvbD5C/pjzBi3jgT8TDxwDEBocSHxaOlbNjlvVkxSW/F0TFZ9QDL7n2OILoBPk+v/4kAjc7y9S8JuU92TCgGpuHVGd8q1KY6mirVcKNXRefAXD4RjBVCjmn5pXw1OJkk4sz955nW5bMuHD+vDyucqcfVwf376dW7Tq4584NgGOasbV/3166d+lM547tmTFtqsnj6uSJ4zRuIo+r2nXqciGTcZWSnIzB8O7j6ty5c+TNkweP1/RFuXLl0Gq1AJQpU4aQkJDUvD179+LTqRPt2rVj8hTT9cWfx4/TzKgv6n0gfbH31EXqVimLh/Eed7J7NTHfffIC7UfNodWImUz85Q+T9cWxizdoXrMKAPU/LcfZm/eQJAlzjQZzMzMAUlJ07+V6vYmnYRH0/eF32k9fQde5a/ELNu3eP337EVWLF8DO2hKttSVVixfg1G3Z2fXSsJckicRkHR+jD1wlxD/y97HyXzPu0yGE8BJC+AohfgIuA3mFEHOEEDeFEDeEEO2N5SYLIa4a/wKEEKuM6Z2FEOeN6cuEEGpjeqwQYpoQ4poQ4qwQwi0bfRpmbP+mEGKICeljhBB3hRBHgKJp0gcJIW4LIa4LIf54p+9JpWL4xb1MCbrI3aOneHL+qknnuRbzpny7Jiz+vA1zKzbGoNdTwaeFSefaebgR+TQIAINeT2JUDNZODunK5KtUFo25GeFGoyTbCIFb/wl4jFxA4oPbJD/zy1DEPJ83bv0n4txlCBpX+YdP45Ibq9KVCP1lJiFLJoFBwqqsaWEgGq0D+iijV89gQEpKQGUle5fUdg64DZhI7hFziPlrf7a89q9z6OBBGjRslCE9NDSEP4/9SevXHuP7PXrEoUMHWbFqNb9v3IRKpWL/PtPCMsJCQ3Fzd5fl02iwsbEhMlLue3BwMO3bteXLRg3p1q1bjr32CPh68QbaT1/Blr8um3zao6DnHLh0mzUjurJ5TG9UKsHe8zdNOjckMgY3B9mw0ahV2FjmIjJODvcIfhFN66m/Un/0D/SoXzVbXvvXKdGmMbc378kyX2NpQcG6n3N350EAnIp6U7x1Y9bV6cCqqs2Q9HpKdmhmUlu2Hm7EPAsGQNLrSYqOxfK1cZW7QhlUZuZEPHqSQ4lMo2WlfJy6GwpAAVcbGpTNTZefztB24Sn0Bmhc3tOkelztLAiOSgRAb5CITUzB3soMIWB4kxLM2+v73vp8+NBB6jdomCH9yZPHREdH069PL7p08mHfHjl8z8/vEUcOHeLXFatY9/tG1CoVB/ebOK7CQnF1Sz+uoozjKiQ4mE7t29H0y0Z81a1btr32r3Pg4EEaNsqoL9Kyfft2qleXw0kePXrEwYMHWbN6NZs2bUKtUrHPRH0RGhqK+xv0RZu2bWnQsCHdu3XLsddeAL2nLaHt97PZdOR0hnz/oDCiY+PpNnERbb+fzc4T5wB4+CyY/Wcus27KMLbNGYlKJdjz1wXT5HoRhbvRE65Rq7G1siQyJg6AoOcRtBw+gzr9xtGzed335rXPTC9OWr+PUe0bsHF0T75tXYepvx8wrf+RMbgb9R2Am4MtoZGvnmiMW7ubWt8vwj8knI61Kr2f/v8/ohb/zN/Hyn8qLAewFEJcNX72A4YiG8TdJUn6RgjRGigHlAWcgQtCiJOSJI0Hxgsh7IC/gB+FEMWB9kA1SZJSjBOETsBawBo4K0nSGCHEbKA3MDWT/rQXQrwMvlsEXAe6A1WQ9dM5IcQJ5ElWVukdgPLI1+oycMlY30iggCRJSUII+3f50iSDgbkVG2NhZ0uPrctwL1mE4Fv33npekdrVyPNJKYadlWPZzSwtiA2TH49337IUJ6+8qM3NcMjnwfCLctz8yR9WcX7NFkQmM2IpjSdH6+5Cp9Xz2dDj23Tp2RNMImTJJISFJc4+AzBz9SQlNCA1OznwMUFzv0NKTsKiSGmcfQYQvHA0FgWLY+7hhVu/sQAIjTn6uGgAnHz6o3FwRqg1qO0cces/AYDYv48Qdznjj8zLfgDooyII+XEiKlt7nDv1J/7mJQzGerNDSkoKJ06cYMDAQRny5s6Zw6DBg1G/5uE+f/48vrd96dJZjiNOSkpK9T5+O2wogQEBpKToCA4OomP7dgB09PGhWfMWmX7/L6+fu7s7GzdtJiw0lG+HDaVO3Xo4OWX/Scva4V1xtbclPDqOrxdvwMvdmYqF8731vHN3/fB9EozPzJUAJCbrcLS1BmDI0s0EhEeSojMQFBFF22nyWpFOtSrT4rOyZPYk5+Vd6e6oZevY3oRGxjBk6WbqfVIMpyxCAN6EysyMQl/W5viErCPpCn1Zm4Czl1NDcvLXrIpb+ZJ0/Uv2wGsschFvHFetfl+CnVde1GZmaPPmpvvfuwC4+NMabvy2NY0Er0h7/azdXWiyfA57+3xPhkd075FK3k60qpSXLj/LT7E+LeREiTx2/D6oGgC5zNS8iJOfrC3sUgFPR0vM1Cpy21uyeYisMtef8mfHxWeZehEloEPV/Px1J5QQo+H/rqSkpPDXiRN8M2Bghjy9Xs8dX1+WLF1GUmIiPbt3pVTpMlw8f547vrfp1kUOj0pKSkp9Yvbdt8MIDAwgJSWFkOBgOndsD0D7jj40bdb8jePKzd2d9Rs3ERYWynffDqN2nbo5Glcv5Tpx4gSDB2XUFy85f+EC23fsYPWqVQCcO38eX19fOnWS9UViGn0xZKhRX+h0BAUF0a6drC98fHxo0eLt+mLL5s2EhoYyZOhQ6tXLmb5YN2UYro52hEfF0GvqjxT0cKNiiUKp+Xq9ntt+T1kxbgBJySn4jJ1P2cIFOHvzLrf9ntB+lBxOmZScgpNWnrgPmvMrz0LDSdHpCXr+glYjZgLw1Zc1aVnr0zfKldvZge1zRxH6IoqBc36h/qflcLbXZiifXTLTi9cePWP4r1tTyyTr5CcqO85cY/2f5wF4EhZB/x83YqZR4elkz8K+bbPo/6vPU7o0RW8wMGPjQQ5evG3UkQofK/814z5BkqRyLw+EEF7AY0mSXgZxVgd+lyRJD4QYDehKwC4hj9L1wAJJki4JIQYAFZAnAACWQKixnmTgpSvuEpDVisWNkiQNSNOfwcB2SZLijMfbgM+Rf5EzS1cZ0+ON6bvS1H0dWC+E2AHsyKxxIUQfoA/AsmXLsujiKxKjYnh44izFGtQwybgXQnDht63sHTMnQ96qNn2BrGPuIwOCsc+bm6iAYFRqNRZ2tsS/iAQgl60NvXetZN/4eeli/3OKlJhAkt9dLAqXSmfcS0mvjILEezcQTTvLXnYBcVdOE3V4W4a6wjcsAbKOuddFR6C2c0QfHSEvns1liSEhLl0ZQ0wkutBAcnkVJuHWJbLL6VOnKFasWKY/ir63bzNqpLw4MDIyktOnTqHWqJEkiSZNmzIwkx/4efMXAFnH3Lu6uRESHIybmxs6nY7Y2Fjs7NJ7plxcXSno7c2Vy5dztID3pWfcSWtN7XJFuekfaJJxL0nQ7NMyDG5RK0Pewr7y04usYu7d7LWERETj7qBFpzcQm5CEnbVlujKu9rZ4e7hw6cFT6n9SPNtyedf/gpBrt4kPzTom/HXPvhCCm+u3c2LCvAxlt3XsD2Qdcx8TGIxtHndiAoMRajW5tDYkGseVua0Nbbf+ysnJCwi8cDXbsphKEXdbJrUpTb8VF1JDeASCXRefsehAxjUTQ9bKYyCrmPuQqETc7SwIiUpErRLYWJgRFZ9C2fwOfOLlSPuq+bHKpcFMLYhP1rFwf/bXZQCcOX2KolmMK1dXV+zt7bG0tMTS0pLyn3zC/XtyWMaXTZrSP5OJ9ux584GsY+5dXd0IDUk/rrSvjysXVwoU9ObqlcvUqZuzhfGn3qAvAO7du8ekSZNYsmQJ9vb2gDwhbNq0aaYTgoULZH2RVcy9m5sbwW/RF66urnh7e3P58mXq5URfGD3jTna21K1UlhsPHqcz7t2c7HGwtcHKIhdWFrmoWNybu48DQILmNaow1Cfjk7DFI3rLcmURc+/mZE9weCTuTg7o9Hpi4hOwey0W3tXRjkJ5c3PpzkMafFo+23JlkPM1vXjx3mNsLXOxeUzvDGVbfFY21SDPLObezUHLxXuvnoKHRMRQsUj+dHWoVSoaVijB6sNnPzrj/mMOofkn+E+H5RhJa1m96epPBJ5JkrQqTdk1aWLmi0qSNNGYlyK9mgbrMX2SlFX7b+pXVu61xsAS5AnIJSFEhj5IkvSLJEkVJUmq2KdPn0wrsXZ2xMIYj2hmkYsidaoTevfhG7rzinvHTlO2VaPUmHgrBzsc8pn2qP3m7iNU/qo1AGVbN+LBn38DoDYzo8fWpVxYt41rOdjN4yUqKxuEhWyoCY0ZFt7FSXkelL6MzSvPirlnARACQ3wsiQ99sSxZEZW1/L2oLK1R25vmXUq8cxXr8vL6AcuSFUl6dEeWS+uA0MhxmcLCCvN8hdA9D86RbAcPHKBhw4yhAwC79+5jz7797Nm3nzp16zJy1Ghq1apN5cqVOXrkcOpCwKioKIICs44BT0uNGjXYs1sOQzh65AiVKlVCCEFISAiJifIEKTo6mmtXr5Lfyyvb8sQnJROXmJT6+W/fRxTycDHp3CrFvDh82ZfwaHmYR8UlEBhu2qLUmmUKs+vsdQAOX/alclEvhBAER0STmCwbpdFxCVx9+Awvt5x5TYu3bfLGkJxcWhvyVq/M/T1HUtP8j/9N0RYNsXKRPaUWDnZo82ZcW5EZD/YepXSnVgAUa9mQxydkQ1llZkarP5Zwc8MO7m437TF+TnC3t2BBlwqM+uMaj5+/Ur1nHzynXpncOFrLMddaSzNy21tmVU06jt8OoVnFPADUK+3O+QdyjPHI369Sf8YxGs78k3l7fNl9KSDHhj3AoYMHqJ/FuPqiZk2uXrmCTqcjMSGBWzdv4lWgABUrV+bY0SPpx1WQaePq8xo12GsM7zl29AgVsxhX169dJX9+rxzLtf/AARplIVdQUBDDvv2WaVOn4pX/laFXpXJljhw+THgauQJN1Bc1a9Rgl1FfHD5yhMpZyHX16lW8cqIvEpOIS0hM/Xzm+h0K5cudrkztimW4dOchOr2ehKRkrj94TEFPN6qULsKhs1dTF9hGxsYRGGba4uhaFUqz87gc3nPo7FWqlCwi64vwCBKTkwGIio3nyt1HFPAwOVI3azkz0YulvDzwdLbn0CU5FE2SJO4+C3lTNalUK1GQM76PiI5LIDougTO+j6hWoiCSJPEk9EVqfcdv3MfLPWf6TuHfw3/Nc/82TgJfCyHWAI7AF8AIIUQTZO97zTRljwI7hRALJEkKFUI4AraSJOUwADy1/dVCiJnIBn1L4Cvj57ela4CmwDLjwuC8kiT9KYQ4BfgANkBkdjukze2Kz8q5qNRqhEpwdctebu89xmd9fAA488sGbN2cGXZuFxZaGySDRI1B3ZlZuj4hvg/YN34effevRahU6FNS2DpoPBFPAt7SKpxbuZFOaxYw+s6fxEdE8ZuP/Ci8XNvGeH9eGWtHByp3aQPAhp7DCbyWvbhata09jq17gkoghIr4mxdIvHsd60o1AIi7cAKrkhWxqVwTyWBA0iUTvlF+uqELCyLqyHZcug2Tn1vq9UTsXo8+8u07csRe+gunNr1xHzodQ0Jcap0al9zYN2onT9UExJw6SErI27+n10lISODcubOMHjs2NW3L5s0Ab9yGsqC3N9/0H0D/fn0xSBIajYaRI0dluiD3dZq3aMm4sWNo3qwpdlot02fOAuR44wXz5yMQSEh81aULhQsXzrZML6LjGLJsCwB6g4FGlUpSvaQ3m07KHt12X1TgeVQsHWauJC4xCZUQrDt2nh3jv8Y7twsDmtWk7w8bMEigUakY3aEhHk5vj3ltWa0co1fvpPH4n7CzsmB2z5ayXMHPmbv1KAL5cnWtW4UintmPDdZYWlCgdjUODhqXmlaup/wE6+qK3wEo0qw+fkdPkRL/amvH8DsPODl5Ae13rUaoBIYUHYeGTnrjgtyXXFuzmabL5/L19SMkRESys+tQAIq3bkTeapWwdHSgdGfZ+N/79feEXs/euJrlU45KBZ2wtzbnyOjaLDl8H40xMHXz2Sf0rVsYeytzxrYsCcgx8h0Wn+ZRaCw/HLzLst6VUQmBTi8xbcdNgiIT3tQcANsuPGVGh3Ls/a4mUfEpfLfB9DUZppKYkMD5c+cYNfrVuNq2RR5Xrdq0pUCBgnz62Wd06tAOlUpFsxYt8S4ke4r7ftOfQf37IRkk1BoNI0aOzLAgNzOaNW/BxHFjad28GVo7LVOny2Eg/n5+LF4wn5c3YKevulAoB+MKZH1x9uxZxqXRF5uM+qJd27Ys++UXIiMjmT59OgBqjYbfN2zA29ub/gMG0K/vK30xetSoDAtyM6Nly5aMGTOGJk2botVqmT1L1hePHj1i3vz5CCGQJImuOdQX4VExDJorh9jp9QYaV6/I5+VKsPGQvMVj+/rV8c7jTvVyxWk5fCYqlaB17aoUzif3fVCHxvSeugRJktCo1Yzt2RYPl7cvLm9duyojf1xLw4GTsLOxYu6Q7rJcASHMWbs99Xp1a1qHIvlMm4y/iaz0opebE1N/388v+0+h0xtoWLEERfO8fTJhZ23J119Wp+Ms2X/Z98vPsbO2xGCQGLtmN7GJSUgSFM3jytiOb16f8W9ErWyFmQ6R43jmfyFCiFhJkmzSHHsBeyRJKmU8FsBsoBHy7/ZUSZI2CiH+BArwyjjeJUnSeOOC21HITzhSgP6SJJ1N244Qog3QRJKkbq/1pRtQMW1YjjF9GNDDeLhckqSFb0kfA3QBHgPPgNvI8ft/AnbIKmWdJEkz3/L1SEM1Bd5S5ONigc6Pp2Pfbd/ffxt5p8qPuGPj3270fEzYWMle2qRjaz9wT94vuWp3YaZ1zgyvfysj4+4DUPq7HLxf4l/MjdmNiYyN/9DdeK/YG8NCEhP+W/rCwtIS3bVDby/4EaEpK28l/V/Ugbw5+uD/hT2+If+IMdukuNsHly0n/Kc892kNe+OxP1AqzbEEjDD+pS2XMWBXTt8IbHxTO5IkbQG2ZFJmNbA6k/T5wPxspE8DpmXSvffzlgwFBQUFBQUFBYX/DP8p415BQUFBQUFBQeF/i49528p/gv+FBbUKCgoKCgoKCgoK/xMonnsFBQUFBQUFBYWPFmUrzPQonnsFBQUFBQUFBQWF/wiK515BQUFBQUFBQeGjRdkKMz2Kca+goKCgoKCgoPDRotj26VHCchQUFBQUFBQUFBT+I/ynXmKl8EaUC62goKCgoKDwvvngfvMTD5//IzZODW/nDy5bTlA89woKCgoKCgoKCgr/EZSY+/8hhmoKfOguvFcW6PwInTv4Q3fjveI6fBEAMfH/rdfJ21pZAqC7tPcD9+T9oqnQmHEW3h+6G++VKYkPAXD6MrMXY3+8hO8bQ3LU8w/djfeKuZ0zAIkJ/y19YWFpSfLfWz90N94r5lVbA5AS/PAD9+T9Yub+79B/ylaY6VGMewUFBQUFBQUFhY8WtRKHkg7l61BQUFBQUFBQUFD4j6B47hUUFBQUFBQUFD5alLCc9CieewUFBQUFBQUFBYX/CIrnXkFBQUFBQUFB4aNFrXju06EY9woKCgoKCgoKCh8tSlhOepSwHAUFBQUFBQUFBYX/CIrn/n8cTS5zBhzfhMbcHLVGzbVt+zkwaWG6Mp90bE6dEX0BSIqLY0v/cQRe932ndtXm5nRaPY88n5Qi/kUkazoOIOJxAB5li9N2yVQsbG0w6A0cnvEjVzfnYG90tQaHDoNArUGoVCTdu0bcmf3piohclmgb+qC2d0bSpRB98Hf0z4PeSS7UarSNOqNxy4uUGEfU7jUYol+g0jpg16wnqARCpSb+yl8kXjudrar9/f0Z/f13qccBAQF83a8fPp06Zyh769ZNunfpwvSZs6hbr947iZScnMyEcWPx9fXFzs6OGbNm4eHhSVBgICOGf4tBr0en09GuQ0fatG2bozbqDZqCtWUuVCoVGpWKTdOGZVruxsMn+IxfxNxBXWhQpey7iEVyio5RP2/glt9T7G2smTeoC54ujgSGvWDwgtXoJQM6nZ5ODT6nfd3Psl3/sLsnSI6Jw6DXY9DpWVqtRbr8Yk3qUmfCUCSDAYNOz74RU3hy5tI7yaQ2N6f1irl4fFKK+PAINn01iMjHAbiXKU7TxZOx0Mrj6sSsn7i5JfvjavGQJtSvXIjnkXFU/+bXLMuVL5ybg/O70XPmdnafvvMuImGuUfPT8GaULeROREwCPWds52loVGq+raU5fy/ry96/7/L9zwdz1EZ0TAwTp83k/sNHCCGYPHY05cqUSs2Pio5m/JQZPA0IIJe5OZPHjaawd8F3kis5OZnRE6dw+85d7O3smDNtMp4euQkMCmbI96NTx5VPuza0a90y2/X7+/vz3Xev9MWzgAC+6dePzp1f6Yu9e/eyavVqAKwsLRkzZgxFixZ9Z7nGjH2lL2bPmoWnpyeBgYEM+1bWFyk6HR07dqRdDvVFg29nY2WZC7VQoVar2Dixf7r8Y5dv8+O2I6iEQK1W8b1PYz4p4vVucqXoGP3rZm77B2BvY8Wcfh3xdHEg8HkEQ35Yj8EgodPr8alblXa1q2S7/uiYWCbMWcQDv8eAYMr3QyhXqnhq/vkr1xk0ZjKeud0BqPv5Z/Tr5vNuMiWnMGr6XG7fe4C91pa5E0bhmduNwOAQhoybht5gkO/BVk1p37zxO7X1T6NshZmetxr3Qgg9cCNNUgtJkvxNqVwI0ReIlyRprRCiG3BIkqTAnHQ0uxjbqyhJ0oA0aV7AKSCfJEmGNOlXgT6SJJ1/j22/VVYhxHFguCRJF9/W938KXVIyP9X1ITkuHpVGw6CTm/E9cJzH566mlnnh/5Qfa7cnITKaYg1r0G7pdBZ+ZtqPjUN+T3xWzmVJnY7p0j/t0Y6EiCimF6tF+XZNaDpjJGt9BpISn8j6bt/y/IE/2tyufHt+N3cOnSQxKiZ7gul1RG76ESklGVQqHDoOJsnvNrqgx6lFrD6tR0poAFE7V6B2dMW2TlsiNy8xqXqV1hFtIx8iN/6YLt2ydFWkxARerJhKrqLlsfmiKdF71mCIjSbi9wWg1yPMzHHsNpLkBzcwxEWbLJKXlxcbNm6SxdPr+bJBfWrVqp1RdL2eHxYt4tOqVU2uGyAwMICJ48fzy/IV6dJ37tiOra2WHbt2c/DAAX5YtIgZs2bj7OLCytVrMDc3Jz4+nvZtWlOjRg1cXF2z1e5LVo35BgetTZb5eoOB+b/voVqZ7BkfAWEvGLP0d1aPS28AbD1+Dq21JQcWjGHfmSvM/30P8wZ1wdlBy/pJgzA30xCXmESL72ZTq0JJXB3ssi3TygadiA+PyDTv0Z9nuLPnCABupYrSfv0PLC5b36R67fN70urX2ays3yldeoVubUmIjGJhydqUbtuE+lO/Z9NXg0iJT2BrzxG8eOiPbW5X+p3ZyYPD2R9Xvx+5xvLdF/np26ZZllGpBBN61ObY5UfZqjuvqx0/DmtK85Hr0qV3blCOyNhEKvX6mZZflGBCj9r0mrk9NX9Ulxqcvvn49eqyxax5C6n2aRXmz5xGSkoKCYmJ6fKXr15LsSKFWTRnBo/8HzN99jyW/7TYpLoDAoMYO3kaq5am1xXbdu1Ba2vLvm2b2H/oCAt+/Im506fg4uzEuuVLU8dVy45fUfOL6ri6uGRLJi8vLzZteqUv6tWvT+3a6fWFp6cnK1esQKvVcurUKSZPmcL6desyqy6jXAEBjB8/nhUr0uuL7du3o9Vq2bN7N/sPHGDhokXMmT0bFxcX1q55pS9at25NzRo1cM2hvlj5fS8cbK0zzfu0hDe1yhdHCMHdp0EMX/I7u2dm7jDIIFdYBGOXb2HVqN7p0redvIjWypJ9s4ez/+w1Fmw+wNxvOuJib8u6sX0xN9MQn5hEyzGLqFm+OK4O2mzJM/OHZVSrXIEFk8cY78GkDGU+KVOSn2ZOyla9AAFBIYyZOZ/Vi2all2nvQbS2NuzfsIJ9R08wf9lK5k0chYuTI+uWzMPc3Iz4+ARadO9HrWqf4urslO22FT4Mpsx1EiRJKpfmz/9lhpDJsg5JkpZKkrTWeNgN8Hin3r4jxr4/BT5/mSaEKAbYvi/D3kg3PrCs2SE5Lh4AtZkGtUaDJKXP9//7MgmRshH6+OwV7DzdU/Mq+LRgyN87GH5xL21/moZQmTZ9LtWsHud/k99AeG3rfgrXlj2jYff9eP7AH4DooFBiQsOxccmZQpFSkuUPKrX895pcGid3Up7cA0D/IhS1nSPCyhaAXMUr4tBpGA5dRmBbrx2YGM9n7l2KhFvyrZR07xrm+YrIGQY96PXyZ7UGsh42JnHh/Dk88+Qht0fG22zjH79Tu04dHB0d06Xv27uXLp074dO+HdOmTkH/sj9v4cTx4zRpKhtzderW5fz580iShJmZGebm5oDsrTO8fuO8Z9Yf/It6lcvgaGebLn33qYu0H7uAVqPmMnH5JvQGQxY1pOfYxZs0/7wSAPWrlOHszftIkoS5RoO5mez3SEnR/WNyvRx3AObWVqQdeGU7Nufrv7bxzbndNPtxqsnjqljTulxdtw2AW9v2U7CWPMELf+DPi4f+AMQEhRIXFo5VDn6o/775lIiYN78NtXfTiuw+fYfnkXHp0tvWKsXhBd05/kMv5g1ohEpl2phq9Glh/jhyHYBdp3z5oqxXal7ZQu642Ftz/LJf9gRJQ2xsHJeuXKNVc/keNzMzQ2ub/h576OdPlUoVACjolZ+AoCCeh78AYPf+g3Ts1os2nboyacZsk8fVnyf+olnjLwGoV7sm5y5cymRcpWAwvPv9d+7cOfLmyYPHa/qiXLlyaLWyEVqmTBlCQkJS8/bs3YtPp060a9eOyVNM1xd/Hj9OM6O+qPeB9IWVRS6EUWcnJKWkfgbYfeYKHSf9RJtxPzBp9XaT9cWfV3xpVv0TAOpVKsW52w9ludLoi2SdPkdyxcbFc+naTVo3bgC8vAezdnS8zu5Dx+jw9RBa9xzApLk/mHytjp0+S/MGdQGoX6M65y5fS3OtzABITnk/9+A/jUqIf+TvYyXbFoYQwksI4SuE+Am4DOQVQsSmyW8jhFht/DxRCDFcCNEGqAisF0JcFUJYCiFmCiFuCyGuCyHmZtJOZSHEGSHEFeP/osb0bkKIbUKIA0KI+0KI2WnO6S6EuCeEOAFUy0KE34EOaY47AL8LIdRCiDlCiAvGPn1trFMlhPhJCHFLCLFHCLHPKA9CiApCiBNCiEtCiINCiNxZyDreWO9NIcQvQqS7Yzob5bsphKicyffgIoTYajz/ghCimjG9hrH+q8bvyPb1c01FqFQMv7iXKUEXuXv0FE/OX82ybJUe7blz4AQArsW8Kd+uCYs/b8Pcio0x6PVU8GlhUpt2Hm5EPpVDYAx6PYlRMVg7OaQrk69SWTTmZoQ/zKFXTggcuozA+ZtpJD++iy44fT260EByFS4DgMY9HyqtA2pbO9SOblgUK0/E7wuJWDsHSZKwKF7RpCbVtvYYYoyeWsmAlJyIsJS9Sypbexy7fo/z15OIP38kW1771zl48CANGjbKkB4aGsLxY3/Suk36x91+jx5x+NBBVq5azYaNm1CrVOzft8+ktkJDQ3Fzlyd0Go0GGxsboiIjAQgODqZDu7Y0btSQrt265dhrL4Sg98xltB09n01H/86QH/IikqMXbmQIj3kYEML+v6+ybuIgts0YjkqlYs8p00JbQiOicHeyl+VSq7G1siAyRjZIg8IjaPn9HOoMnEzPprVz5LVHkui6ZzV9z+ykYs8OmRYp3qw+g64dovP25Wz/eiQALkW9KdWmMb/WasdPVZpi0Osp27G5SU1qPdyJevZqXCVFx2D12rjyrFgGtbkZEY/ezdudGbmdbGn8WVFW7bucLr1IXidafFGCRsPXUHPgcgwGibY1S2VRS8Y6A8PksaI3SETHJ+GotUQImNyrLhNWHH2nPj8LDMDBwZ6xk6fRtnM3JkydQXxC+glM0cKFOPKnrPdu3LpNUHAIIaGhPPLz5+Dho6xdvpQt69egVqnYe+CQSe2GhoXh7iaPF3lcWRMZJYcbBYeE0MqnC/WatqRHl07Z9tq/zoGDB2nYKKO+SMv27dupXr06AI8ePeLgwYOsWb2aTZtkfbEvG/rC/TV9EZlGX7Rp25YGDRvSvVu3HHvthRB8PXcV7Sb8yObjmfvljl66RdOR8+m/YA2Te7aW5QoM5eC5G6wd8zVbpgyUr9ffV02TKyIKd0dZD2jUamwsLYiMlSfoweGRtBq7mHrDZtHjyy+y7bV/FhiEg70dY2cuoE3PAYyfvZD4hMQM5a7dukOrHv3pO2KcMXwHHvo/4cCxk/y2ZC5bV/wo68DDx02T6Xk47q7yvaXRqLGxtiIySh5rQaFhtOz+DXXbdqWnTxvFa/+RYUrMvaUxbAXADxgKFAW6S5L0DYB4y+xGkqQtQogBGENQhBCOQEugmCRJkhDCPpPT7gBfSJKkE0LUBaYDrY155YDyQBJwVwjxA6ADJgEVgCjgT+BKJvVuAq4IIQZKkqQD2gNtgZ5AlCRJlYQQuYDTQohDxvq8gNKAK+ALrBRCmAE/AM0lSQoTQrQHpkmS1COtrMbv50dJkiYbP/8GNAF2G/tjLUnSZ0KIL4CVwOu/eIuABZIknRJC5AMOAsWB4UB/SZJOCyFsgIyawEQkg4G5FRtjYWdLj63LcC9ZhOBb9zKUK1TzUz7t3o7FNWTDsUjtauT5pBTDzu4EwMzSgtiwcAC6b1mKk1de1OZmOOTzYPhFOb735A+rOL9mS6b3jJTG46F1d6HT6vls6PFtuvTsCSYRsXYOIpclds17onbOnS6mPv78YWxqt8ahywj0YUHoQgPAYMA8fxE0bnlx6PwtAEJjhhQvhy/YNe+Jys4RodagsnXAocsIABIunyTx5rks+wFgiInkxZpZqKy12LXoReK9a6n1ZoeUlBROnjjBgIGDMuTNmzOHgYMHo1ar06WfP38e39u+dOksh3EkJiXhYPTsDx82lMCAAFJSdAQHB+HTvh0AHXx8aNa8BRke5UDqkwx3d3f+2LSZsNBQvh02lDp16+HklP0fgXUTB+LqYEd4VAy9ZiyloIcrFYt7p+bPXLuTYR2boH7Ng3325j1u+z2j/bgFACQlp+BkDO0ZNH8lz8JekKLTE/Q8glajZB/CVw2+oGXNypneVy/vy9xODmyfNYLQiCgGzltJ/SplcbbL3vz511rtiAkKxdrFiW571xB29yGPT11IV8Z31yF8dx0if/VK1JkwlNVfdqFgrc/wKF+Kvqfl0BMzSwvijOOq48afcfDKg9rcDLu8HnxzTlYjfy9ZzZW1WzN9wJRWTht3F9qsnMfWXiNyPq7ewLQ+9Zi88lgGT98XZQtQrpA7Rxb2AMAyl4awKHkitXZsG/K52WNupsLTxY7jP/QC4Jdd59lw+HoWugJ6Nq7IkYsPCHye/TGUFr1Oj+/de4waPpQypUoyc95CVqz5jYF9+6SW6dnlK2bOX0ibTl0pXMibYkUKo1GrOXvhIrfv3KFj154AJCUl4eggT6YGjxhFQGAgKTodQcEhtOnUFYBOHdrRsmnjzO8/jOPKzY1tG9YSGhbG4BGjqFe7Fs5OjhnKm0JKSgonTpxg8KCM+uIl5y9cYPuOHaxetQqAc+fP4+vrS6dOr/TFyyeBQ4Ya9YVOR1BQEO3ayfrCx8eHFi1avHFcubu7s2XzZkJDQxkydCj16uVMX6wd8zWuDlrCo2PpM2clBXK7ULFogXRl6lQoSZ0KJbl4148ftx1m+Xc9OXv7IbcfB9Bx0k8AJKWk4GgM7Rm8eB0BYRGk6HUEhUfRZtwPAHSq/xktP6+QhRo0yuVkz7apgwiNiGbw4nXUq1QqW/pCp9fje/8Bowf3pUyJYsxYvJQVGzYxsGeX1DIlihTi8MbVWFlZcvLsBQaNmcK+Dcs5d/kat+89oMPXQ2SZkpJwNDojBo2ZQkBwCCkpKQSFhtG6pxzp27l1M1p+Wf/NOtDVhe2rfiL0eTiDxkyhXo3qODs6ZCj/b+EjdrL/I5hi3CdIklTu5YExbv2xJEln36HdaGRjdLkQYi+wJ5MydsAaIURh5IAKszR5RyVJijL25zaQH3AGjkuSFGZM3wgUeb1SSZKChRC3gDpCiBAgRZKkm0KIiUCZl155Y/uFgerAZmOMfrAQ4k9jflFkQ/ywcTCogaxWY9YSQnwHWAGOwC1eGfe/G/t1UgihzWSiUxcokeYHTmv00p8G5gsh1gPbJEl69nqjQog+QB+AZcuWZdG1VyRGxfDwxFmKNaiRwbjPXboY7ZfN5Jcm3Yl/Efmyfi78tpW9Y+ZkqGtVG3kBblYx95EBwdjnzU1UQDAqtRoLO9vUenPZ2tB710r2jZ+XLvY/p0hJCSQ/fYC5VzES0hj3UnISMQc2pB479R6PPiocszzeJN46T9xfGW/LqJ1yfGlWMff6mEhUtg4YYqNAqBDmFkiJ8enKGOKi0YUHY56nIEn3rmVbntOnTlGsWLFMfxR9b99m9MjvAYiMjOT0qVNoNGokSaJJ06YMyOQHfu582TDOKube1c2NkOBg3Nzc0Ol0xMbGYmeX3pPt4uqKt7c3Vy5fztEC3peecSc7W+pWLM2Nh0/SGfe3/J4y/IffAIiIieOvq75ojIZ+8y8qMrRDkwx1Lh4mG5JZxdy7OdoTHB6Ju5M9Or2emPhE7GysMvSrUB53Lt15lO0FvDFBoQDEhYVze9ch8lQsm8G4f8njUxdwLJhP9rILwdX12zg8LsMDTX5v3w/IOuY+KiAYuzy5iTaOq1xaWxLSjKuvti/nyMT5PHvD07l3oVzh3Pw6Ul6P46i1om6lQugNBoSAP45eZ8rq4xnO6TJ1C5B1zH3g82g8XLQEhsegVgm0VrmIiEmgYnFPqpbMS4/GFbC2MMfcTE1cQjKTV/+ZoY034ebqipurC2VKlQTkEJkVa9P3wcbGmqnjxwDyZKlhizZ4enhw8cpVmjVuxJD+/TLUu2jODCDrmHs3V1eCQ0Jxd3M1jqs47OzSe3xdXVzwLliAy1evUb9OrWzJ9ZJTb9AXAPfu3WPSpEksWbIEe3v7VBmbNm2a6YRg4QJZX2QVc+/m5kbwW/SFq1FfXL58mXo50hfy9+SktaHOJyW4+ehZBuP+JRWLFuBZ6AsiYuKQJIlm1T5hSNsGGcotGiQvNM4q5t7N0Y7gF7L3XqfXE5uQiJ21ZYZ+eXu6cvmeP/UrlTZZHncXZ9xcnClTohggh8gs37A5XRkb61e66YtPKzF1wRIiIqNkmRrWYWif7hnqXTxtnCxTFjH3bi7OBIeG4e7qjE6nJzYuHjtt+kmJq7MThbzycfn6LerXrG6yTP/fqFCs+7TkNPA37rXjtNM/i7edbPSYVwa2Ai2AA5kUmwL8KUlSKaDpa/WmXWmi59UkxVRX1MvQnA7GzwACGJhmbUEBSZIOGdMzQwC30pQvLUlShtVwQggL4CegjSRJpYFfX5Pl9T6/fqwCqqZpx1OSpBhJkmYCvQBL4Kxx7UD6iiTpF0mSKkqSVLFPnz6vZwNg7eyIhdHDYGaRiyJ1qhN692G6MvZ5Pei++WfWdxtG2P1Xsa33jp2mbKtGqTHxVg52OOTzzLSd17m5+wiVv5IfxJRt3YgHf8qhGGozM3psXcqFddu4ttW0x8CZISytEbmMildjhnn+IuhfhKYvk8tSjsUHLEpXJfnZQ6TkJJKf3CNXkXIIK9kDLCysUGlN81gkP7yJZUk5uipXkbIkP70PgMrGDjRmqe2aeRRA91p/TOXggQM0aNgw07xde/exe99+du/bT526dfl+1Ghq1qpN5cqVOXrkMC9eyHHCUVFRBAWatrb9ixo12LNbnosePXKESpUqIYQgJCSEROPCw+joaK5dvYqXl1e25YlPTCLO+Ag6PjGJMzfuUSive7oyhxaN5fDicRxePI76Vcoytntr6lQqTZWShTl07jrhxoWhkbFxBIa9MKndWhVKsvMv2dg+dO46VUoWQghBcHgkicnyeo2o2Hiu3POnQO7shUWYWVlibmOd+rlQnc8JeW3C7Fgwf+rn3OVKojYzIz48gkd/nqFky0ZYG8eVpYMddvlMW8JzZ89RynVuBUDJVo3wO/5qXHXc9DNX12/n1rb9b6rinfikxxLKd5f/dp/yZcSSA+z7+x4nr/rTtFpxnO1kA8XexoI8rqaFLhw4d58OdeXwuWbVi/PXdX8A+s7ZSdluP1K++xImrDjKxqM3sm3YAzg7O+Hu6orfYznM4dyFS3gX8EpXJjomhpSUFAC27txNhXLlsLGx5tNKFTl87DjhL+RQvKioaAKDgk1qt+YX1dm1V9Zxh48dp3LFCvL9FxJKonExZVR0NFev3cArf75sy/WS/QcO0CgLfREUFMSwb79l2tSpeOV/dT9WqVyZI4cPE55GXwSaqC9q1qjBLqO+OHzkCJWz0BdXc6ovkpKJS0hK/Xzm1gMKebqlK/MkJDzVK33bP4AUnR57Gys+LeHN4Ys3CY+WI4mjYuMJfJ75gvcMcpUrxq5TcrjZ4Qs3qVy8oHy9XkSRmCzfG1FxCVy9/xgv9+zpC2cnR9xdXPB7Ivvozl6+irdX+mv+PPxFqkw3fO9iMEjY22n5tEI5Dh8/TXhEpNyH6BgCg0MwhVrVqrDzoLyo/9CJU1QpX0aWKfQ5iUnGezAmhis3b+OV17TfdoV/B+9rK8wQIURx4C5yuE1mz0ljAFsAYxiJlSRJ+4QQZ4EHmZS3AwKMn7uZ0IdzwCIhhBPyk4G2QFZu0a3IYT7xwMvtAw4C/YQQxyRJShFCFDG2fwroKoRYA7gANYENRlldhBBVJUn62ximU0SSpFtpZeWVIf/cKHcbYEuavrQH/hRCVEcOC4p67TH0IWAAMAdACFFOkqSrQghvSZJuADeEEFWBYsihTNlCm9sVn5VzUanVCJXg6pa93N57jM/6yFtsnfllAw3GDsLayYE2P0wBwKDTMf/T5oT4PmDf+Hn03b8WoVKhT0lh66DxRDwJeFOTAJxbuZFOaxYw+s6fxEdE8ZvPQADKtW2M9+eVsXZ0oHIX+SHKhp7DCbyWva03VdZ2aBt1khciCkHi3SskP7qFRVl5KUbitdOoHd3QftkZDAZ04cHEHJTnefrwEOJO7cW+TT+EUCHp9cQc3Ywh+u0/Agk3zqL9sjOOPcciJcYTtWcNAGond+xryo+shRDEXzyWo203ExMSOH/uLGPGjk1N27JZ9vC8aRvKgt7e9Os/gAH9+mKQJDQaDd+PHJXpgtzXad6iJePHjqFFs6ZotVqmz5S9P35+j1g4fz4CgYRE5y5dKFS4cLZlCo+KZdCClQDo9QYaV/uEz8sWZ+ORMwBv3IayUB53BrVrRO+Zy5AMEhq1mrHdW+Hh8vYQhtY1qzDypw00HDoNO2sr5g6UH4E/Cgxhzrpd8vRdgm6Na1LEROP6JTZuzvhs/BkAlUbN9Y27eXD4JJV6yU+wLiz/nZItG1CuU0v0KTpSEhLZ+JXsJQ2784AjE+fTdc9q47jSsWfIBKKevN24urx6E61XzmPIrWMkvIhkU5fBAJRq8yVe1Sth5WhPeeOkelvv7wjO5pa2v3zXgmpl8uOkteTG2oHMXHcSM408QV79Wpx9Wu4+fc70346zZaoPKhWk6Ax8/9MBnoW+fd3JuoNX+Xl4cy4s70dkTCK9Zm1/6znZZdSIoYwcN4kUnY48Hh5MGT+aTVvldtq1bskjv8eMmTQFlUqFdwEvJo0dBYB3wQIM7NubrwcOSR1XY0YMwyO3+5uaA6BVsyaMmjCFL1u1w06rZfY0eReUR/7+zF30Y+q46tq5I0UKeb+ltsxJSEjg7NmzjEujLzYZ9UW7tm1Z9ssvREZGMn36dADUGg2/b9iAt7c3/QcMoF/fV/pi9KhRGRbkZkbLli0ZM2YMTZrK+mL2LFlfPHr0iHnz5yOEQJIkunbpQuEc6oshP8hPVvR6A19+WpbqZYqw6ZgcFtmudhUOX7zJ7tNX0KjV5DLXMOebDggh8PZ0Y2Crenw9Z5Usl1rFmK+a4eH8dudNqy8qMuqXzXz53VzsrK2Y3U9eR/MoMJS5f+xHCDlcrGujzymS9+3X/3VGD+7L91Nnk5KiI6+HO1NGDmXjTjmctX3zxhw6cZqNO/eiVquxyGXOnAnfyzJ55WNgr6/oM3wsBoMBM42GMUO+wcPd7S0tQqsvGzBq2lwa+fTEztaWORPkp76PHj9hzk/LU69Vt/atKeKd+ZORfwtKWE56xNviLoUQsZIk2aQ59gL2GD3qL9PaALOQd6K5CdhIktTNGOoSK0nSXCFEa2SDOgFoBOxENnwFMFeSpDWvtVsVWAOEAceAryRJ8hKvbRMphNhjPP+4EKI7MAo5POYqoM5qO0khxE7ATZKkT43HKmAq8lMCYWy3BbKh/hPwBXAPyAXMlyTpsBCiHLAYeSKiARZKkvTra7JWBcYgPyXwN35HjyVJmijkrTD/BmoAWqCHJEnn08oohHAGliDH2WuAk5Ik9TWuM6iF/OTiNtBNkqSMe2e9Qhqq+XcPzuyyQOdH6NzBH7ob7xXX4YsAiIl/844kHxu2VvJTFN2lHLyz4F+MpkJjxlnkzPD6tzIlUX5y5/TltA/ck/dL+L4xJEc9/9DdeK+Y2zkD8qT/v4SFpSXJf2/90N14r5hXlSfVKcEP31Ly48LM3RuyjnD4f+N2cPQ/sqVPCXftB5ctJ7zVuFeQnzRIkhRrfCpwHqgmSZJpz17/PSjG/UeAYtx/XCjG/ceDYtx/PCjG/cfDv8W4vxPyzxj3xdw+TuNeeUOtaewxLnQ1B6Z8hIa9goKCgoKCgsJ/EiUsJz2KcW8CkiTV/NB9UFBQUFBQUFBQUHgbinGvoKCgoKCgoKDw0aJshZmenG6FqaCgoKCgoKCgoKDwL0Px3CsoKCgoKCgoKHy0KDH36VGMewUFBQUFBQUFhY8WlWLcp0MJy1FQUFBQUFBQUFD4j6B47hUUFBQUFBQUFD5aFMd9epSXWP3voFxoBQUFBQUFhffNB7et/Z7H/CM2TgFn2w8uW05QPPf/Q/QVXh+6C++VpZI/gVP7fehuvFc8xv4MQEJi4gfuyfvF0sIC4D/5RuHvzQt+6G68V2YlPwJgwsE7H7gn75dJDYqRHBn6obvxXjG3dwVAF3j3A/fk/aLxKIrh0cUP3Y33iqpgRQD0/lc/bEfeM2qvch+6CwCoPuCKWiHEUKAXshP1BtAdsAI2Al6AP9BOkqQIY/lRQE9ADwySJOmgMb0CsBqwBPYBg6UceuCVmHsFBQUFBQUFBQWFbCKE8AQGARUlSSoFqIEOwEjgqCRJhYGjxmOEECWM+SWBhsBPQgi1sbqfgT5AYeNfw5z2SzHuFRQUFBQUFBQUPlqE+Gf+TEQDWAohNMge+0CgObDGmL8GaGH83Bz4Q5KkJEmS/IAHQGUhRG5AK0nS30Zv/do052QbxbhXUFBQUFBQUFD4aFH9Q39CiD5CiItp/vqkbVeSpABgLvAECAKiJEk6BLhJkhRkLBMEuBpP8QSepqnimTHN0/j59fQcocTcKygoKCgoKCgoKLyGJEm/AL9klS+EcED2xhcAIoHNQojOb6gys+cB0hvSc4Ri3CsoKCgoKCgoKHy0iA+3oLYu4CdJUpixH9uAz4AQIURuSZKCjCE3L1fzPwPypjk/D3IYzzPj59fTc4QSlqOgoKCgoKCgoKCQfZ4AnwohrIQ8w6gD+AK7gK7GMl2BncbPu4AOQohcQogCyAtnzxtDd2KEEJ8a6+mS5pxso3juFRQUFBQUFBQUPlpUH8hxL0nSOSHEFuAyoAOuIIfx2ACbhBA9kScAbY3lbwkhNgG3jeX7S5KkN1bXj1dbYe43/uUIxbj/H+erFbMp3aQ2MaHhTCndIEN+2Wb1aDplGJJBwqDTsWnIZB6efrf9hzXm5nRbO598FUoRFx7J8vYDCH/8jDxlS+Dz81QstDYY9Hr2T1vCpU17ctaIWoNzl28RGg2oVCT6XiHmZPq6rD+th1WpSvKBSo3G2Z3g+SOQEuNzLpxag0OzrpjlzochIY6IbcvRR71AbeeIQ5uv5UeHajVxF44Tf/mvbFXt7+/Pd999l3oc8OwZ/b75hs6dX4X37d27l9WrVgFgaWXFmDFjKFq0aM7lAZKTkxk7Zgy+vr7Y2dkxa/ZsPD09CQwM5Nthw9AbDOhSUujYsSNt27XLfgNqDQ4dBoFag1CpSLp3jbgz6XWayGWJtqEPantnJF0K0Qd/R/886J3kQq1G26gzGre8SIlxRO1egyH6BSqtA3bNeoJKIFRq4q/8ReK10zlqQqhUDDy7k+iAEFa37JUur+AXVei69Rde+Mtrq27uOMjRaT+8m0jm5rRfNRfP8qWIfxHJhk4DiXgcQO6yxWn5wxTj2DJwbOYSrm/e+05tveTc+sUE3rqIha0djUa9W/8B/M4d49ahTQCUrN+OAlVqp8u/tOUX/M4epc3cje/c1kuiY2KYOG0W9x/5IYRg8tiRlCtdKjU/JjaWUROmEBQcgl6vp2unDrRs2vid2kxOTmb0pGncvnMXezstc6ZOwtMjN4FBwQz5fgwGgwGdTodPu9a0a9UiZ3LFxjJ+zo888HuMEIIp3w2iXMliqfnHTp3lh1XrEUKFRq3m+wG9qFC6xDvKlcKoGQu4de8B9lot8yaMwNPdjcDgUAaPnyHrC52OTq2a0L5Zo3dq6yXRsXGMW/gr9x8/QwjB1KF9KF+8cI7r23H4JD//sQOAfh1a0KLeFwCMWfALt+77IUkSXp7uTP+2L9aWFu9DBKJj4xi/YBn3/Z8iBEwd1o9yJYqk5j96EsCY+T9z+4Efg7t2oEfbpu/cZnJyCiPnLOHW/UfYa22ZP3ownu6uBISEMXjyPOO10tOpeUM6NKn3zu39k3zAbe6RJGkCMOG15CRkL35m5acB0zJJvwiUynhG9vmfMO6FELGSJNmkOe6GvCfpgHeo099Yx/NM0mOMh2pgGzBFkqSkLOo5I0nSZzlp633w9+otHP9xDd3Wzs80/87R01zbdRgAz9LF6L1pCROLZ3q/ZsApfx66rp7L/Fod0qVX69mO+IgoxheuScX2TWk5ayTLOwwgOT6B1V2GEfrAH7vcroy+tIfbB0+SEBWdfcH0OsLXLURKSQKVCueuw0l8eIuUAL/UInFnDxN3VpYtV+HS2FSpY7Jhr7ZzxL5ZV8J/W5Au3arcZxgS4wn9aQIWJSqird2SiO0r0MdE8Xz1HNDrEGa5cPl6HIn3rmOIjTJZJC8vLzZtko0evV5P/Xr1qF07veHj6enJipUr0Wq1nDp1iimTJ7Nu/XqT6g8ICGD8+PGsWLEiXfr27dvRarXs3rOHA/v3s2jhQmbPmYOLiwtr1q7F3Nyc+Ph4WrduTY2aNXF1dc2ihSzQ64jc9CNSSjKoVDh0HEyS3210QY9Ti1h9Wo+U0ACidq5A7eiKbZ22RG5eYlL1Kq0j2kY+RG78MV26ZemqSIkJvFgxlVxFy2PzRVOi96zBEBtNxO8LQK9HmJnj2G0kyQ9uYIjL/n1YfWB3Qu88xMLWJtN8v1MXMhj9puCQ35O2y+fwSz2fdOmVurcjISKaOSVqU7ZdExpN/54NnQaREp/Ixh7DCX/gj21uVwad3cW9QydJjIrJogXTKVClDoW/aMy5dQuzdd7RxWOo0mkQNk5uqWlJcTHcPPAH9YfPQwjBwTnD8CxdGXMr+ft78eQ+yfFx79zn15k1fzHVqlZh/syppKSkZHiJ3B9btlGwgBc/zpvFi4gImrbrRJOG9TEzM3tr3QGBQYydMp1VP6ef+GzbtRetrS37tv7B/kNHWLBkKXOnTcLF2Yl1y39OHVctfbpS8/PquLo4Z1uuGT/8SvXKn7Bw0kiSU1JITEr/M1SlQllqVauCEIK7D/34dtJs9qz92aS6A4JDGDNzEasXTk+XvnXfYbS2NhxY/wv7jp1k/rI1zJvwHc5ODqz/cTbm5mbEJSTQovtAan1WGVdnp2zL9TrTl/5G9YplWTR2CMkpugxyZkWX76Yy49uv8XRzSU2LjIllyYZtbF48FYGgzaAx1Pq0Ana21ozq0xkbaysAZv6yjg27D9G7XbN37j/AjJ9XU71iWRaOG5apDHZaG0b368bRM9l3rgUEhzJ63s+smZPe/tx68BhaG2sOrl7MvuOnmbdiA/PHDMHF0YENC6YYr1Uizb8eTu2qFXB1cnwnGRX+/1Bi7k3AuHdpdqglSVJpoDJQkExWWr98acHbDPt/mgd/nSf+RdYGZlLcK2PX3NqKtC9Lq9ypBSPP7WDMlX34LJ2OUJl2O5VpXp+/12wF4PKWfRSrI38Foff9CH3gD0BUUCgxoeHYuuRcmUgpsnIUKjWo1PCGF71ZlqxEwq0Lr45LVca5+/e49BqN3Zc+JrsFLIqUJf76WQASfS9jXsDoJTPoQa+TP2s07+xmOHfuHHny5sXDwyNderly5dBqtQCUKVOGkJCQ1Ly9e/bQyceHdu3aMWXyZPR6PaZw/M8/adpM/gGrW68e58+fR5IkzMzMMDc3B2QvpGQw5FgeKSVZ/pB6rdLna5zcSXlyDwD9i1DUdo4IK1sAchWviEOnYTh0GYFtvXYmf7fm3qVIuHUegKR71zDPZ/SSGfTw8rtRa0DkTE3aebpTrFEtLqzMvoe5vE9zBpzezuALe2i1ZKrJY6tk07pc+k0eWze27qdQLXlsPb/vR7hxbMUEhRIbFo61y7sbVQCuhUqmGt8viQkL4vhPEzk4exhHFo4iOuRZFmenJ/jOFdyLliOXtS3mVja4Fy1HkO9lAAwGPVd3rKZc865vqSV7xMbGcenKNVo1awKAmZkZWlvbdGUEgvj4eCRJIj4hATutFrVafu/M7v0H6di9D206d2fSjDkmj6s/T/5Fs8byO2rq1a7JuQuXMo6rlBQMORxXsXHxXLp+i9Zfyh5XczMztDbpr5O1pWXqQsSExKR0ixJ3H/6T9v2+pVWvwUyct8RkuY6dPkfzBrLToX6Napy9fA1JkjA3M8PcXJ4MpSSnYJByri/SEhsXz8Wbd2jToCYA5mYatDbWPAkMoffYWbQeOIbOwyfz6KlpaxNPX7rOZ+VLY29rg52tNZ+VL82pS9cAUg17SZJITEom8w1OcijDDV9aN6ydToa0ONnbUbpoITQadYbzdx39i/YDR9Oy33dMWPQLer1p3+2xvy/Sol4NAOp//ilnr940XivNq2v1Dvfg/yf/1FaYHysfc9/fC0KIpkKIc0KIK0KII0IIN2P6RCHEL0KIQ8BaIYSTEOKQsdwyTBjVkiTFAn2BFkIIRyFETSHEn0KIDcivKEYIEWv8X1MIcVwIsUUIcUcIsV68tvxbCGEphDgghOgthLAWQuwVQlwTQtwUQrR/z19NKuVaNGCi71EG7F3J2h5yWIh7MW8qtm/C7GptmFb+SyS9nsqdWphUn72nGxFGRWvQ60mIisHaySFdGa9KZVGbmxH28HFmVZiGELj0Go3bsNkk+fmSEuifeTGNGRbeJUjwvQLIhqRliQo8XzOHsOXTwWDAslRlk5pU29qjj46QDyQDUlICKktZSau0Drj0HoPboOnEnjmULa/96xw8cIBGDd/88rrt27dTvXp1AB49esTBgwdZvWYNmzZtQqVWs2/fPpPaCg0Nxd3dHQCNRoONjQ2RkZEABAcH07ZNGxo2aEC37t2z77V/iRA4dBmB8zfTSH58F11w+uuuCw0kV+Eych/c86HSOqC2tUPt6IZFsfJE/L6QiLVzkCQJi+IVTWpSbWuPISbNtUpORLy8Vrb2OHb9HuevJxF//kiOvPZN541j36iZb5z05Pu0PIMv7qXHrpW4lZDDCFyLeVOmbRN+qtGWRZWaYNAbKO/T3KQ2tZ5uRD2Tw5UMej2JUTFYvTa28lQsg8bcjBfvMrbewoWNP1GhTR8afDef8i26cXHTUpPOS4gMx8rhlYfa0t6JhMhwAO6f3Idn6cpY2r1f7+GzwEAcHOwZO2U6bb/qwYRpM4lPSEhXpmPb1jzye0ztxi1o5dONkUMHoVKpeOTnz8Ejx1j7609sWbcKtVrF3oOHTWo3NOw57sbxIo8rayKjZJ0QHBJCq05dqde0NT2+6pQjr/3ToGAc7O0YM2sRrXsPZvycH4hPSMxQ7shff9OkSz/6jZrMlO8GAfDw8VP2/3mKdT/MYtvyRahUKvYcOWGaXM/DcXeV+6tRq7G1sSYyWn5CFBQaRsueA6nTvgc9O7R+L177p8GhONrZMnr+Mlr1H83Yhb8Sn5jIhMUrGNOvK1t/mMaIXj5MXrLKpPpCnkfgnsap5ObsSMjziNTj0fOX8bnPN/g9C6Rzs/rv3P9XMmgZM+9nWn3zPeMWLCU+MeO1yoyHT55x4MQZ1i2YzPafZ6NWqdhzzLSQz5DnL3A3TvI1ajW21lZprtVzWvQdQe3O39CrXXPFa/+R8T8RloP85rCraY4dkVcsA5wCPpUkSRJC9AK+A7415lUAqkuSlCCEWAyckiRpshCiMfIrgt+KJEnRQgg/5BXRIHvzSxnfTPY65ZFfSRwInAaqGfsH8uKMP4C1kiStFUK0BgIlSWoMIISwM6U/OeHqjoNc3XGQQp9XptmUYSyq15lidaqRr0JpRl2Qv0Yzy1zEhMo/wn23LcOpQF405mY45PNgzBXZiDy2aBV/r96c+ZZVabzqWncXuv02nzVdh6d7UpBtJImw5dMRuSxxbPs1GhcPdGEZvTe5ipQh+enD1JAc8wJFMcudD5ceIwEQZuYY4mWF59DmazT2TqDWoLZzwKXXaABiL/xJwrW/M53yvZTAEB1B2K/TUNnY4di2L4l3LmOIy35YREpKCidOnGDQ4MFZlrlw/jw7tm9n1erVAJw/dw5fX186deoEQFJiIo6OsrIeOmQIAYGB6FJSCAoKop0xbt7Hx4cWLVpkeg1eXkN3d3c2b9lCaGgoQ4cMoV69ejg55eAHW5KIWDsHkcsSu+Y9UTvnThdTH3/+MDa1W+PQZQT6sCB0oQFgMGCevwgat7w4dJaHrNCYIRmvlV3znqjsHBFqDSpbBxy6jAAg4fJJEm+ey7IfAIaYSF6smYXKWotdi14k3ruWWq8pFPuyNrGh4QRcuUnBL6pkWibgyi1mFvqc5Lh4ijasSZfNy5hTsjbetT4jT/lSDPx7BwBmlhbEhslj66vNP+PolRe1uRn2eT0YfEFeR3L6h9VcXLvlrWPL1t2FDqvns6nHO46tN5CSlEC43x1Or5qdmmbQpQDw6OwR7p2Q+xwbFsTJpZNRacywdnLl816jkTLb1lkIEqLCeXr1NLUHZghTfWf0ej2+d+8x6tvBlClVkpnzFrFizXoG9n0VLnX67DmKFinEip8W8fRZAH0GDuOTcmU5e/ESt+/cpWO33gAkJSXh6CBPpgZ/N5qAwCBSUlIICgmlTefuAHRq34aWTRu/eVy5ubFt/RpCw54z+LvR1KtdE+dsGld6vR7few8ZM7APZUoUZcYPv7L89y0M6pF+C+66n1el7udVuXjtJj+sXM+KeVM4e/kat+89pH1feVwlJSfj5CD/xAwaN51nQSGk6HQEhYTRqpesh75q3ZSWjepmLpfxf25XF7av+IHQ5+EMHDed+jU+w9nRIUP57Mlp4PYDf8b060rZYoWYvnQti9Zs5orvPYZOX5RaLjlFfnK67dAJftt5AIAngSF8PW42ZmYaPN1c+XH80EzvwbTDavqwr9HrDUz9eQ37T56lVf0a79R/WQY9tx/4Mbp/d8oWK8z0n1ezfONOBnV9u8/u7JWb3LrvR7uB8m9RUnIyjvbytRo4aS7PgkPlaxX6nJb9ZOfcVy0a0apBrUwfZr+8B3O7OrNj6RxCw18wcOJc6n9eBWcH+3eW9Z/iA26F+a/kf8W4T5AkqdzLg5cx98bDPMBG4z6k5kBao3uXJEkvXThfAK0AJEnaK4SIwHTS3nXnszDsX+Y9M/bxKuDFK+N+JzBbkqSXAdQ3gLlCiFnAHkmSMkzVjW9S6wOwbNmybHQ3cx78dR4X7/yyl10Izq7Zyo7RszOUW9rqayDrmPuIZ8E45PUgMiAYlVqNpZ0tcS8iAbCwtWHA3lXsGjsPv3NX3rnPAFJSAkmP75PLu0Smxr1liYok3HoVxygQxF8/S8yfGXehitgif49ZxdzroyNRax0wxESCUCFyWSIlpI8RNsRGoXseiHneQiTeyb6Mp06dolixYlka0ffu3WPSpEksWbIEe3t7QH6M3LRp00wnBAsWLgSyjrl3c3MjODgYNzc3dDodsbGx2Nmln0u6urri7e3N5cuXqVcv5wuvpKQEkp8+wNyrGAlpjHspOYmYAxtSj516j0cfFY5ZHm8Sb50n7q+MC6+jdspyZBVzr4+JRGXrID9BESqEuUWGNReGuGh04cGY5ylI0r1rJsvh9VkFSjSpQ9GGNTGzyEUurQ3tV89nY7dhqWWSYmJTP989cBzV4slYOTkghODSum0cGDsnQ72/te0HZB1zH/UsGLs8uYkyji0LO1vijWMrl60N3Xeu4OCEeTw5f9VkWbKNQcLM0pqG3y/MkFXw07oU/LQukHnMvZW9M6H3b6YeJ0SG41q4FBHP/IgNC2LPlL4A6FKS2DP5a5qMf3e95ubqgpurC2VKlQTkEJkVa9elK7Njzz56dumMEIJ8efPg6ZEbv8ePkSSJZl82ZEj/vhnqXTRbjkXPKubezdWF4NBQ3N1cjeMqDjtjSN1LXF2c8S7gxeWr16hfp1b25HJxxs3FmTIl5AX19Wt8xvINW7MsX7FsKZ4GLiQiKhokaN6gFkN7ZwyBWjxFNiKzirl3c3EmOPQ57i7O6PR6YmLjsNOmD3NydXaikFc+Lt24TYMa1bIlVwY5nR1xc3akbLFCspzVK/PDb1uwtbZm+5IZGcq3ql8j1SDPLObe3dmR89d9U49Dnr+gcpni6epQq1U0+uJTVm7d816MezdnJ9xcnChbrLBRhios32TaLogSEs3rfcGwHj4Z8n6YMBzIOube3cWR4LBw3F2c5GsVF4/da+uDXJ0c8c6fh0s379Dg809zIt7/Cx9qt5x/K//zYTnAD8CPxhj5r4G0S99fX7mVbVeXEMIW2Ui/l0WdaUm7gkZP+snXaaDRy1AdSZLuIT9ZuAHMEEKMf70ySZJ+kSSpoiRJFfv0MelBQwZcvPOnfs5bviQaczPiwiO4e/Q0n7RphK3xkZ6Vgx2O+Ux7U/L1XYep2rU1AJ+0+ZK7x84AoDYzo+/2ZZxdu43LW0wLGckKlZUNIpelfKAxI1eBYuieB2coJ3JZkCt/YRLTGG5J/newLP4JKmNMt7CwQm1iKEDivetYlZEVoEXx/2PvrMOjOto+fM/uxt1DgkNxd0pxd3eXQmmRYsUdilMo9aItpQWKFXdKcXd3iBP3rJzvj7MEloSShLR89J37unJld2Z2Zn57dmaf88wzs+VIeXBT7Y+TK+isUuuzzlkAQ3jIq6r5W3bt3EmjxumfMhEUFMSI4cOZMXMmefLmTU2vVLkye/ftIyJc9QBHR0cTGJixGNSatWqx9Q91hWbf3r1UrFQJIQQhISEkmZeOY2JiuHDhAnlfaDOjCDsHi2tlnacQxohQyzI2dmosPmBbsiopT+6ipCST8ugWNoXKIMwx38LWHo1zxjyBKXevYFdcDbeyKVSalMe3AdA4ujy/VjZ2WPnlw/BSf17Hrgnz+Dx/NeYUqsGabkO4e/C4hWEP4OjzPNQiZ4VSaDQaEsIjuXPwGCVbN06Nibdzc8E1t+Xeildxbdt+yndXx1bJto25e+g4oI6tHuu/49zqTVzekOXT1TKElZ09Du7ePDqvnjCkKAqRAa/yZ1jiW6QswTfOk5IQR0pCnBqDX6QsfsUr0GrmKlpM+ZEWU35EZ2WTLYY9gKeHB77e3tx/+AiAk2fOUiBfXosyOXx9OHnmLABPwyN48OgROf39qFKhPHsP/El4hOrriY6OITAo7TyTHrWqf8Af21UP8t4Dh6hUoRxCCIJDQklKUr8KomNiuXDpMnnz5M60Li93N3y9Pbn/SN3vcOLcRQrkzWVR5mFAYKqn/dqtu+gNBlydnahcrhR7/jxGeGQUAFExsQQGZ2wM1H6/Elt2HwBgz59HqVy2lKor7GnqJtHo2DjOX7lOvlwZ+874NVSkdwABAABJREFUe52u5PDy4P4TdT47ceEqJd7LT05fL3b9pa7QKYrCjXsZC0OrVr4UR89dJjo2nujYeI6eu0y18qVQFIWHgcGp9R06eY78OTM2LjOiwdfTg/uPn2m4QoHcOV/zKpUqZUqy56+ThEepIV1RMXEEhIRl6LW1q1Rg81413GrPXyeoXLq4+VqFm/cUmK/VtVvkyyatkn+H/xXP/d/hAgSYH//dTq3DQFdghhCiMfBaC0II4Qh8A2xWFCXyDZeNJgETzfUNFEL4ARGKoqw2x+33ykqlfdd8SaFaVXD0dGPW4+NsnfwFWvMJEH99/wtl2zamSo82GPUG9IlJ/NhRPWAo6PodtkxYwJA9PyM0AqPewG+fTCLiUcDfNQfA0WXr6P3zQqbdPkRCRBRLOw0GoHyHprxXoxIOHm5U7dUOgFW9RvLk4rVM69I4uuDWoqe6nio0JF4/S/KdK9iXqw6QegylbeEyJN+7/nxDJ2B4GkzMoT/w6DIYhEAxmYje9RvG6IjXtptw4ShuLXvh/fFUTIkJRG5SvcdWnr4412ubWi7uxL50VxFeR2JiIidOnGDCxImpaevNJ+i079CBH77/nqioKD7/XPWm6bRa1vz6KwUKFGDQJ5/w0cCBKCYTOp2OsePGpdmQmx6tW7dm/PjxNG/WDGdnZ+bMVVdr7t27x8IF6qkmiqLQo2dP3nsv88fPaRxccG7cVd00KgRJN8+Tcu8qtqVVj17SxaNo3X1wbtINTCYM4cHE7v4VAGN4CPFHtuPabiBCaFCMRmL3r8cU8/qFtcTLJ3Bu0g33vhNQkhKI3rYKAK2HL6611HAkIQQJZw68+bGbZip/qHrXTv64hpJtGlN1QFeMBiOGxCTWdFPjnUOv32H3lAX027EKodFg1OvZMmQyUY9e/3k5vWItHVcuZNS1AyRGRqfWWap9E/JVr4i9hyvle6ifw3X9RhF08frfVZchjq2cT+idKyTHxbBlYh9KNOlM1Z4jOLP2W67tXofJaCB3ueq4+ed7bV02Dk4Ub9iRPfPVcJDijTpi4+D0mle9OWNHfsqYSdPQG/Tk9PNj+sRxrNu4GYAObVoxoE8vJkz7nNZdeoKi8OknH+Hm6oqbqyuDP+rHgCHDMSkmdFod40cNxy+H72vbbNOiKWOnzKBJ2064ODszd8YUAO49eMj8L79CIFBQ6Nm1M4UKFsiSrnFD+jN65kJVVw5fZoweyto/1Ju7ji0as/fwcf7YfQCdToetjTXzJ32GEIKCeXMzpE83Phw1GcWsa8KnA/Dzff2emrZN6zPm84U06tofF2cn5k9Uw+HuPXzMvG+Xoy5kK/Tq0IpC+fNmSdfLjB/Yg1Fzv0GvN5Arhzczhw0gNj6eqV+t4LtfN2MwGGhcsypF8ud5bV2uTo4M7NyKDkPVOfbjLq1xdXLEZDIxdsF3xCUkoihQJF9uJg/qnS39Bxj/SW8+m7MEvcFATl9vZo4YyG/b1P0bnZrVJywiig6DxxKXkIhGCH7evIOtPyygYJ6cDO3ZkX5jZ6IoCjqtlomD+lisRryKto1qM3ruVzTsNQRXJ0fmj1NXdu89CmDujz+brxT0bteMQvkyf4P5byId95aIfyru8v8Tf3cUphCiJfAFqoF/AqioKEotIcQUIE5RlPnm13gAvwKewJ+oITrl/+YoTIG6MrIJ9SjMJCFELWCkoijNXu7by3lCiK+AM4qirHx2FCYQDiwHwoD9wDzABOiBgeYzUl+F8pHIm/E37R3gO+UBgTMGvu1uZCt+E9Rj6F4+iu9dx85WXRALnf/qfQLvIt4jFzPaOv/b7ka2MiflHgCTd994yz3JXqY2LEJKVOZWYP6/Y+2qGtuGwJtvuSfZi86vMKZ7b/Z7Kv/f0ORXI4GNDy683Y5kM9q8ZeD/gW0dEZvwjxiz7k72b11bVvif8Ny/aNibn69E/RUwFEXZQjo/8asoypSXnocDL26NH/aKtvL+TT8OAYfS69vLeS+ewf9SnS+6Cna/qi2JRCKRSCSS/wVkzL0l/xPGvUQikUgkEonkv4k8LccSuaFWIpFIJBKJRCL5jyA99xKJRCKRSCSSdxYZlmOJ9NxLJBKJRCKRSCT/EaTnXiKRSCQSiUTyziId95ZIz71EIpFIJBKJRPIfQXruJRKJRCKRSCTvLBp5Wo4F0riXSCQSiUQikbyzSNvekv+JX6iVAOqvSEskEolEIpFkJ2/dtE5MSvpHbBw7W9u3ri0rSM/9/xCjrfO/7S5kK3NS7hEwdcDb7ka24j/5ewBMt46+5Z5kL5pC1QCI/HbMW+5J9uI2cDaLnQu/7W5kK0Njbqr/N11+yz3JXha3LkliUtLb7ka2YmdrC4A+5P5b7kn2YuWTD+O1Q2+7G9mKtlgtAIxX9r/djmQz2hJ133YXABDSUW2B3FArkUgkEolEIpH8R5Cee4lEIpFIJBLJu4tiets9+H+FNO4lEolEIpFIJO8sQhr3FsiwHIlEIpFIJBKJ5D+C9NxLJBKJRCKRSN5dpOfeAum5l0gkEolEIpFI/iNIz71EIpFIJBKJ5N1FHoVpgTTuJQAIjYbBJ7YQExDCytb90i2Ts3wpPjmygTVdh3B54843ak9rbU3HFfPxL1uChIgo1nQdTOTDAHKULkrrJdOxdXbEZDRxYPbXXFq/PQsN6PDqPRKh1YFGS+L1c8Qe2mpRxPH9BtiVrASo+nWeOQiaNwIlKeENhOlwa9Uba7/cmBLiifj9R4zR4Whd3HHv8BFoNAiNlrhTB0k4ezjT1dftOwoHO1u0Gg1arYbfv5hskR8bn8BnC34kKCwcg9FEnzYNaVOvetb1ACl6PaMXLuXa3Ye4Ojmw8LOB+Pt4EhD6lCGff43JZEJvMNKteV06Na6dpTaMJoXevx7Ey9GWBS3ft8h7EBHLjD1nuRkWzUfvF6Nr+ffeSA9AisHI1N1nuRkahbOtNTOaVMTPxYGgmATGbDuJyaRgMJloX6YAbUrly1IbZQb2oHjP9gghuLJqPRe+WWWRn79JXapOGIpiMmEyGDk85nMCT5x9I11aaysafD8X77LFSYqIYkevYcQ+CsCzZBHqfDEFaydHFKOJU/O/5XYWxnDncv4U93UmLtnA7P230+R7O9rQpXxOcrnYsu1aCAfvPH0jPQBajaBb+ZzkcrUjPsXIqtOPiEjQp+bb6DSMq1eIS4ExbLgUmOn6Hzx4wGeffZb6PODJEwZ+/DHdunVLTTt48CDffP21Ok9otYwaNYqy5cq9ka6UlBQmjB/P9evXcXFxYc7cufj7+xMYGMiI4cMxmkwY9Ho6d+5M+w4dstRGTGwck+cu4s79B4Bg+phhlClRLDX/1PmLDBk3Ff8cvgDUq1GNgb26vrGusTPnc+3WbVydnZk/ZSz+OXwJDA7h0wnTVV0GA13atqRjy6ZZaqNe/3E42Nmg0WjQaTWsnz8+TZlTV24ya9k6DEYjbk6O/DRz5Jvp0usZs3gFV+8+UufBkR/i7+1JQGg4Q+d8p+oyGunapDadGtXMvKaPJuBgZ/tc09y0vwly6sotZq34HYPBiJuzAz9NH/7mmr5cxdV7j1VNw/vi7+2hapr3A0aTgsFgpGuTmnRqWOON2vrHkWE5FmSLcS+EiFMUxfGF572ACoqiDMpCXbWAkYqiNDM/TlEU5Zg5byWwTVGU39+813/bBws9r0p/Q50VgB6Kogx5o85mEx8M7k3ojbvYOqWRDajGb+PPP+PWnr8yVa9bHn/aL53HD/W7WKRX7N2BxMgY5hWrQ+kOzWj8+WjWdB2CPiGJtX1GEn7nAU45vBly4g9u7TlMUnRs5gQZDTxd9QWKPhk0Grx6f0bS7SvoA57/2EvcsT3EHdsDgG2hUjhWqZthw17r4oFbq548XbXQIt2hbDWUpHhClkzErngFnOu1IXLDjxhjowlbPheMBoSVDd4fTyLp5kVMcdGZ0wWsmvkZbi5O6eat2X6AArn9+HbSUCKiY2jy0Xia1ayKtdXrh3pAyFPGLlrGT7NGW6T/vucvXBwd2P3DbLYfPsn8lev5YvRAvNxc+XXeOKytrIhPTKLFoInUqVQGbw+3TGtae+EOed2diE/Rp8lztrVmeK3S/Hk384ZbYHQ80/ec49v2ljc4f1x9iLOtFb/3bsDem0/4+shVZjathKeDLT92qIG1TktCioEuP++nen5fvBztMtWuR9H3KN6zPWtrt8eYoqfVxqU82H2IqLsPU8s8/vM493aoP2jjWbwwjVct4ucKjTNUv1Nufxp8O4sNTXtYpBfv0Z7kqBhWlWlAobZN+GDqSHb2HoYhMYk9A0YTdfchDr7edD68gYf7j5CSyXF16mEkf90Np1uFXOnmJ6QY2HgxkJJ+zpmqF8Dd3oou5XLy1RHLH2SqmseNRL2RGXtvUdbfhebFfVl1+nFqftOiPtx9Gp/p9p6RN29e1q1bB4DRaKRB/frUqVPHokzlypWpVasWQghu3brFZ6NGsXnLlgzVHxAQwKRJk1i2bJlF+qZNm3B2dmbrtm3s2rmTxYsWMXfePLy8vFj1009YW1uTkJBA27ZtqVmrFt7e3pnWNvvL76hWuTxfTJ+AXq8nMSk5TZlypUrwzZxpma47ICiY8bMWsPLLeRbpG7fvxtnJkZ2/rmDH/kMs/G45C6aOw8vDndXfLDTrSqRVrwHUrlYFb0+PTLcNsHL6CNyc0/++iolPYNr3v/LDpCH4ebkTHhWTcV2hTxn35SpWzRhhkb5h31GcHRzY/e0Mdvx1mgU/bWThyP54ubmwZvZnqfNgy6HTqFOpNN7urpnXNPXTv9f042/8MGGQqikTYzcgNJxxX/3EqmnDLDXtP4azoz27v57KjiNnWPDzJhaO6Kdq+nzkc03DZlCnYqksaZK8Hf6/x9zXAt5/XaF3EUVRzvx/Mexd/H0p0rg2p5evfWWZap/05Mqm3cSFWXriynZpyaCjmxh6ehttvp6B0GTsI1W8eT3O/rwBgMsbdlKwtnqZn96+T/idBwDEBoUSFxaOg1fWJn9Fr36RCY0WtFrg1ct2diUqknDl9PPnJSvj1W8MXgMm4NqsK4iM/QK1beHSJFw8AUDitXPY5C+iZpiMYDSo/dHpQPwzQ08IQXxCEoqikJCYjIuTAzqt2tYfB4/TYfh0Wg+ZzOSvVmE0ZszTceDkeVrWVa9Pw2oVOHHxOoqiYG2lw9rKCoAUvQHFlLVl0dDYRI7dD6FFibzp5rvb21DM1w1dOp+tndcf0efXQ3RffYDZ+85jzGAf/robRJOiuQGo/Z4fZx6HoSgKVloN1jotAHqjEeVvPjN/h1vhAgSfvoghMQnFaCTg6GkKNKtvUUYf//xGUudgZ7GsXLhjCzoeXE+XI5ups2hqhsdV/qZ1uPbrJgBub95NrlpVAYi68yD1xiI+OJSEsAjsPd0zretueAIJeuMr8+NSjDyKSkz3OlTI5crwmgUYVbsgHcr4Zfj36kvkcObUoygALgZGU8jrufGT09UWJ1sdN0IzefP/Ck6ePEnOXLnw8/OzSLe3t0eY54DExMTUxwDbt22ja5cudOjQgenTpmE0vvr9eZFDBw/SvEULAOrVr8+pU6fUz6CVFdbW1oDqBVdMWfNIxsXHc/biZdo2bQSAlZUVzq9w3qTH1j376dR/CG37fMzUeYszrOvAkeO0bFQPgAY1q3Py3IW0uvR6TFmcLzLC9sOnqF+lDH5e6mfcw/X5zeYfh07QcdQsWg+bzuRvV2d8Hjx1kVa1qwDQ4P1ynLh0I808qNcbMP1DHuTtf52mfuUXNL3g4Pnjz5N0HD2H1iM+Z/J3azKh6RKtapk1VS3Lics302oyGDC9AyEvQjH9I3/vKv+4cS+E8BJCbBBCnDb/VTOnVxJCHBNCnDf/L/zS6/ICHwHDhBAXhBDPXG81zOXvCSHavaLNzUKIs0KIq0KI/i+kxwkhZgohLgohTgghfMzp+YQQx839m54FjU5CiPtCCCvzc2chxAMhhJUQoqIQ4pK5/nlCiCvmMrWEENvMjx2EEMvN7Z8XQrQ0p/cSQmwUQuwSQtwWQsx9oc0G5jrPCSHWCyEyPmu/RPMFE9kxdvYrv0Sc/Xwo3rIBJ374xSLdu0gBSrVvxjc127O4YjNMRhNlu7TMUJvO/j5EPwkCwGQ0khQdi/1LHt+cFUqhs7Yi4gVvZ6YQAq8BE/AdNZ/ke9fRBzxIv5jOCtuCxUm8dg4AnacvdsUrELZ8LmHfz0AxmbArWTlDTWqdXTFER6hPFBNKUiIaOwdznhveH03EZ9hs4o7uzpLXXiDoO2kBbT+dyrpdh9Lkd21ah3tPgqjRczgtB09i7Ied0Wg03H0cyM6/TvHL3LFs+nIqGo2GrX8ez1CbIeFR5DAbgjqtFicHO6Ji4gAICoug5eBJ1Ok9kr7tGmfJa//Fn5cY9EHxDBt7z7gfEcO+WwH80KEGP3erg0Yj2H3j8etfCITFJ+LjZA+ATqPB0caK6KQUAEJiE+i6ej8tlu2me4VCmfbaA4Rfu4V/tQrYuruis7Mlb4MaOOb0TVOuQLN6dD+zk5brv2fvJ+MAcCuUn0JtGrO+fmfWfNAKxWSicMfmGWrXIYcPceZxpRiNJMfEYutueU18ypdEa21F1L1HmdaVVXycbCjr78Kiw3eZd/AOiqIa+xnB1c6KyAT12pgUSNIbcbDWIoBWJXKw5UpwtvVz965dNG7UKN28A/v306plSwYPGsSUqVMBuHfvHrt372blqlWsW7cOjVbLjh07MtRWaGgovr7qZ0Kn0+Ho6EhUVBQAwcHBtG/XjkYNG9Krd+8see2fBAbj5urChFkLaNf3EybN+YKExKQ05S5evU6b3gP5aNQEc/gO3H3wiF0HDvPzNwvZsPwbNFot2/YezJiup+H4enuZdWlxdHAgKlr1nAeFhNG610fUa9edvl3aZ9lrLwT0m7qIdiNmsm5P2vDGB4EhxMQl0HPCAtqNmMmWg+pcd/dxELuOnmH1rM/Y9MVEtBoN2w6fzFCbIeFR+L44D9rbERWrrhgFPY2g1afTqPPhGPq1bpglD7cQgn7TltBu1CzW7TmSjqZQYuIT6DnpC9qNmsWWQ6oT6e6TIHYdPcvqmSPZtGAcWo1g21+nMqYpIgpfT7dXaxo2gzr9x9OvVQPptX/HyK6YezshxIUXnrsDf5gfLwa+UBTliBAiN7AbKArcAGooimIQQtQDPgfaPqtAUZQHQojvgDhFUeYDCCH6AjmAD4Ai5jbSC9HpoyhKhBDCDjgthNigKEo44ACcUBRlvNlQ/hCYYe7jt4qi/CSE+CSzOhVFiRVCHAKaApuBTsAGRVH0QogVQH9FUY4JIWa/ot7xwAFFUfoIIVyBU0KIfea8MkBZIBm4KYRYAiQCE4B6iqLECyFGA8OBTK+tFmlSh7jQcALOXyF/jfQN2OYLJrJz3Jw0xn+B2u+Ts2wJBh/fDICVnS1xYeEAdF//Le55c6G1tsI1lx9DT28D4OiSlZz56XcLz1cqL3gHnHy96LRyIev6jETJqtdAUQj7fgbCxg6PjgPReflhCEsb2mFbuDTJj+6mhuTY5CuCtV9uvD5UjS2hs8IUr3oG3Tt8hNbNE6HVonVxx2vABADiTx4g4cIx+BsT1RgTSeh309E4uuDRaSCJ186m1ptR1swdi7eHG+FRMfSdOJ98OXNQscTz++Ij569SJF8uVs4cxaOgUPpOXECF4oU4cfE6V+8+oMNw9d41KSUFD1fV8zNo5hICQp6iNxgICoug9RA1jr97i3q0qVc9/ffffP1yeLmzZck0QsMjGTTzKxq+XwFPN5cM6zlyLwg3exuK+Lhx9nFYpt6LM4/CuBkaRe9fDwGQbDTiZmcDwOitJwiMTkBvMhESm0D31QcA6Fi2AM2K50l379WzK+fjZM8v3eoSFpfI6K0nqV3QDw8H20z1LfLWPc5+sZTWm5ejj0/g6eWbKIa0ns+72/Zxd9s+/N6vQNXxQ9nUsje5alXFu0wJOh1SpzadnS0J5nHV9JevcMmTE421FU45c9DlyGYALnz7E9d+2Zj+uHph9cHex4uGP8xjz0ej/9UNaIW8HMnlaseIWgUBsNJqiE1WV7L6Vs6Nu701Oo3Azd6KUbXVMofvhnPyUWS69SnAB/k9uBYSS1Ri2lCurKDX6/nzzz8ZMnRouvl16talTt26nD17lm++/prvf/iBUydPcv36dbp2VePUk5OScHdXDcBhn35KQGAgBr2eoKAgOpjj5rt06UKrVq3SHVfPrp+vry/rf/+d0NBQhn36KfXr18fDI3OGsMFo5PrtO4z79GNKFSvCrMXfsuyXtQzu1zO1TLFCBdm77ifs7e04fPwUQ8ZNY8evyzl59gLXbt6mU391cTk5ORl3V3VcDxk/jYCgYPR6A0GhobTt8zEA3dq1onWTBn+rK4ePF5tWfkfo03CGjJtK/VrV8XTPvEPgl1mf4e3uSnhUDP2mLia/vy8VihdKzTcaTVy994jlU4eRnKKn85g5lC6UnxOXb3D17iM6jPpc1ZWix93sAR88+1uehDxFbzAS9DSC1sPUubJ7szq0qVst3VW8Z6Mth6c7mxdNIjQiisGzvqXB++XxdM1caNovM0eomqJj6Tf1S/L7+1Ch+PP9RUajiat3H7F8ylBV09h5lC6UjxOXbnL13mM6jJ5j1pTyXNOc73kSGq7O7U8jaT1C1d29aW3a1Kma/jwoXtD0xQRV05zvaVC1bKY1/au8w172f4LsMu4TFUUp8+zJs1h089N6QLEXvnSchRBOgAuwSgjxHupcbZXBtjYrimICrj3zvKfDECFEa/PjXMB7QDiQAmwzp58Fnq2TV+P5jcXPwJxX1Pt3OpcCn6Ea972BD82GutOzPQPAGqBZOvU2AFoIIZ7t+LEFcpsf71cUJdrc3jUgD+AKFAOOmt9XayCNG9a8atEf4Pvvv09XUN73y1OsWV0KN6qFla0NNs6OdFy5kLW9nm/UyVmuJJ1XfwmAg6cbRRrVwmgwIITg7OqN7JowL029P7cfCLw65j76STAuOXMQHRCMRqvF1sWJhIgoAGycHOm9ZRm7Jy/g0akL6fY7MyjJiSQ/vIVtweLEpWPc2xWvQOKVFzwdQpBw8Tgx+zenKRux7jvg1TH3xphIdC7upMRGgdAgbO0wJVrGA5viotGHBmGd+z2Srp/LlJZnnnEPV2fqVS3H5Vv3LYz7jfuO8GG7JgghyOPnQ05fT+49CUJRFFrVqcbwnmkXu74aPxh4dcy9r6cbQU8j8PV0x2A0EhufiKuTQ5p+Fcztx9lrt2lYrQIZ5VJgBH/dC+LY/RBSjEbiUwxM3nWGqY1eX4cCNCmam48/KJ4mb05zdan5VTH33o52hMQm4O1kh8FkIi5Zj7OttUUZL0c78nk4cTEwnDrv+WdY0zOu/vw7V39WDfT3Jw0jLjDklWUDj53BJV9ubN3dEEJwfc0mjk1dmKbc9q7q9p5XxdzHBQbjmDMHcYEhCK0WG2cnkszjytrJgZbrv+fY9EUEn76YaT1vyqlHkWy7lvY9WHZSXUF4Vcx9VKIeN3tropMMaATYWmlJSDGS192eAh72fJDPAxudBp1GkGI0svXqq9/nv+PIkSMUKVLktUZ0+fLlefz4MZGRkSiKQvPmzdO9Ifhi0SLg1TH3Pj4+BAcH4+Pjg8FgIC4uDhcXyxtjb29vChQowLlz56hf3zKs63X4enni4+VJqWJqaGCDWtVZ+otl6KWjw/NxXKNqJWZ88RWRUdEoKLRoVI9hA/qkqffLmZNUXa+Iuffx8iQ4NAxfby8MBiNx8fG4OFvuEfL29KBgvjycu3SFBrUyv+H/mRfZw9WZupXLcOn2Awvj3sfDDVdnR+xtbbC3taFCsfe48eAJiqLQsnZVhndvnabOJWPU76xXxdz7ergR/DQCX083dR5MSMTl5XnQ3ZUCuXOo8+D75bOmycWJupVLc+nOAwvj3sfDFVdnhxc0FeTGgwAUFFrWqszwbq3Saho9wKwp/Zh7Xw9Xgp9G4uvxgibHdDTlysHZ63doWPXNNpFL/j3+jZh7DVBVUZQy5j9/RVFigenAQUVRSgDNUQ3ajPDijqA0birzJtx65jZLA+dfqFuvPHcrGLG8uXkjN5aiKEeBvEKImoBWUZQr6fXvFQig7QvvUW5FUa6b817U+6zPAtj7QvliiqL0TadPPyiKUkFRlAr9+/d/ORuAXRPm8Xn+aswpVIM13YZw9+BxC8MeYE7hmswpVIM5hWpweeNONg+ZzLU/9nLn4DFKtm6cGhNv5+aCa26/9JpJw7Vt+ynfXb2fKtm2MXcPqfcmWisreqz/jnOrN3F5Q9ZP5NHYOyJszOEUOits8hXB8DTt8r2wscUmbyGSbj43dpLv3cCuaDk09uoXkrC1R+uSsfjkpFuXsC+tGpZ2xcqRfP+G2h8nV9BZpdZnk7sAhvDMhRMkJCUTn5CY+vjo+au8l8fS6Mzh5c6Ji9cAeBoZzf0nweTy8aJK6aLsPnomdWNZVGwcAaEZO8mkduUybNmv3p/uPnqGKqWKIIQg+GkESclquER0XDznrt8hn3/a0JO/4+MPirO1X2M2923I9MYVqZDLM0OGPUDFXF4cuBNARII6RKKTUgiKydiG6OoFcrDjumpUHrwdSIVcXgghCI1NJMnsYY9JSuFSYDi53bIW8WZnXsJ3ypmDAi0acPP3bRb5Lvlzpz72Kl0MrbUVSRGRPD50nPdaNUx9vY2bC065Mjau7u04QLHOquHyXquGPP5TXbrXWFnR7Jevuf7bFu5s3pUlPW/CrbA4yvi74Git7mewt9LiZpcxf86VoBgq5XYFoLSfC7fD1JCwn888Zsrum0zbc5MtV4I49Sgqy4Y9wK6dO2nUOP0NzY8ePUr1SF+/fh29Xo+rqyuVKldm7759RISrKyvR0dEEBmZs43fNWrXY+oe6wL1v714qVqqEEIKQkBCSktTwmZiYGC5cuEDevHkzrcfTwx1fby/uP1JD1U6cPU+BvLktyjwNj0jVdfnaTUwmBVcXZ6qUL8PeQ0cIj4xSdcXEEhicsfe2drUqbNmlLjzv+fMvKpcrrc4XoWEkJZvHamws5y9fI2+unJnWlZCUTLw5vCghKZljF67x3kvfO3UqlebstdsYjEYSk1O4dOs+BXL6UqVUEfYcP/fCPBhPQGh4xnRVLMXmg+p42nPsHJVLPpsHIy3mwfPX72Z6Hkyj6eL1dDSV4uz1u8813X6gaipZhD3Hz6dusM20JnN4z57j56lcorCqKfxFTQmcv3GPfH6v8qX+P0Ex/TN/7yj/xlGYe4BBwDwAIUQZRVEuoHruA8xler3itbFAZteBXIBIRVEShBBFgCoZeM1R1FCa1cCbnAP2E/Ar6o0LiqJECiFihRBVFEU5YW4jPXYDg4UQgxVFUYQQZRVFOf837ZwAvhZCFFQU5Y4Qwh7IqSjKrTfouwWVP1Q97Sd/XPPKMqHX77B7ygL67ViF0Ggw6vVsGTKZqEev/3I7vWItHVcuZNS1AyRGRrOmm7r8W6p9E/JVr4i9hyvle6jG/7p+owi6eP3vqkuDxtEFt1a91I2IQpB49SxJty9jX149zuvZMZR2RcqSdPcaij4l9bWGp0HEHPwDj+5DEUKgGI1E7fgV47NY+r8h/twR3Fv3wWfwdEyJ8UT8vhQAK68cuDRop4ZCCEHssb0YQjN3+kt4VDSDZ36l9tFoolnNylQvX5LfdqqxsJ0a1+bjjs0Zu2g5LQZNRFFgRK/2uLk44ebixNDubeg3aQEmRUGn1TLxo274e3u+tt129WsweuGPNOw/BhdHBxZ8pnqD7j4OYu7ytQjUO+M+rRtSKG/mv6zTY+Ml1XvbplQ+wuOT6PXrQeJTDGgQ/Hb+Dr91r0c+D2cGVC3G0I1HMaGg02gYVbs0OZztX1t/8+J5mLr7DO1W7MHZ1prpTSoCcD8ili//upyqqWv59yjomfEwoxdpunoJtu6umPQGDo2YSnJUDCX7qFPA5eW/UbBFQ4p2bolJb8CQlMTOXqpXLeLmXY5NX0TrzctTx9WhkdOIffz6z8vVn36n4Q/z6HlhD0mR0ezsrdb5XpvG+Jn3ABTrohr/ewaO4enlG5nS1KNCLgp6OeBorWNqoyLsvB6C1rwqe/RBBE42OkbWLoitToNJgVoFPfl83y1CYpPZfi2EgdXyoRFgNMH6iwFEZiCk5sTDSLpVyMWE+oVIMB+Fmd0kJiZy4sQJJkycmJq23nyCTvsOHdi/bx9bt25FZ2WFrY0Nc+fORQhBgQIFGPTJJ3w0cCCKyYROp2PsuHFpNuSmR+vWrRk/fjzNmzXD2dmZOXPVLVX37t1j4YIF6tyjKPTo2ZP33sva8a/jhn7M6Olz0ev15PLLwfSxw1m7RT1auGPLpuw5dIS1W7ah1WqxtbFh3uSxqq68eRjcryf9R4zDZDJhpdMxftgn+Pm+3sBr07QRY2fOpXHn3rg4OTFvylhV18PHzPv6h1RdvTq1pVCBzB8zGx4Vw5A56gqqwWikafVKVC9Xgt92/QlAp0Y1KZArBx+ULU6rT6ejEYJ29aulOkKGdmlBv6mLUZ7Ng/074+/9+pCntvU+YPSi5TQcOAFXRwfmj1CPjL73JIi5K39P1dW7VX0K5cncSl94VCxD5n5v1mSiafUKVC9bnN92q99TnRrWoEDOHHxQphiths9UNdWrlnoDMLRLc/pNW2L+DGqZ+GGnjGmq+z6jv1xJw08m4+poz/xhfc2agpm7csNzTS3qZVrTv847bIj/E4gsxzO/WMnfHBEphPAEvkaNs9cBhxVF+UgIURVYBYQBB4DuiqLkFZZHYRZCjak3AYOBvrxwFObL7ZrTbFBDY/yBm4AXMEVRlEMvljdvxm2mKEovIUQ+1JAZHbABmJCVozCFEL7AfSCHoihR5rTKwI9APHAIdZ9BtZd02gGLUE8GEsADc/rL9W8D5pu11EENH7Ixd2eCoijP9jmkhzLaOv/fZL97zEm5R8DUAW+7G9mK/2R1gjfdOvqWe5K9aApVAyDy27RnN7/LuA2czWLnwq8v+A4xNOam+n/T5bfck+xlceuSJCal3VD6LmNnqy5K60Puv6bku4WVTz6M1w697W5kK9pitQAwXtn/djuSzWhL1IWMRyn8Y6REBP4jm4is3f3euraskC2e+5cNYUVRVgIrzY+fAh3Tec1xoNALSRPN6YdQjWDMnuhSL5SxOGQ9PQNcUZRkIN211RfLm28Qfjc/vg9UfaFouhtf/06nmQ+A358Z9mauKopSCkAIMQY4Y37tIZ7rTATSWKkv168oSrMXHh8AKqbXT4lEIpFIJJL/GbJ4ZOx/FfkLtdmE+RSbxkCTl7KaCiHGor7XD3l1CJJEIpFIJBKJRPJGSOM+m1AUZfAr0tcCr/51KIlEIpFIJBJJlnmXf3Dqn0Aa9xKJRCKRSCSSdxdp3FvwbxyFKZFIJBKJRCKRSP4FpOdeIpFIJBKJRPLu8i/+4va7gPTcSyQSiUQikUgk/xGk514ikUgkEolE8u4iY+4tkMa9RCKRSCQSieSdRZ6WY0m2/EKt5J1AXmiJRCKRSCTZzVv/FVdD0O1/xMbR5XjvrWvLCtJz/z/ESKv8b7sL2cp8/T3Cvhj2truRrXgN+wIAffDdt9yT7MXKtwAAhrPb33JPshdd+aaMtv5vjas5KfcAqDB591vuSfZyZmpDkmOj3nY3shUbJ1eA/6Quw/ldb7sb2YqubCMADAHX33JPshedf9G33QUV6bm3QG6olUgkEolEIpFI/iNIz71EIpFIJBKJ5N1Feu4tkJ57iUQikUgkEonkP4L03EskEolEIpFI3l2k594CadxLJBKJRCKRSN5Z5FGYlsiwHIlEIpFIJBKJ5D+CNO4lEolEIpFIJO8uJtM/85cBhBCuQojfhRA3hBDXhRBVhRDuQoi9Qojb5v9uL5QfK4S4I4S4KYRo+EJ6eSHEZXPel0KILJ+xL8Ny/sfR2Vjz8cG16Gys0Wi1XNq4iz3TFlmUqTX8Q8p2aQmAVqvFu2hBJueoQGJkdJbb1Vpb03nFfHKWK0FCRBQ/dxlM5MMA/EoXpc1X07F1csRkMrF/1tdcXJ+Fs9G1Olw7DEJodaDRknz7IgnHLc9NFjZ2ODXohNbFE8WoJ3bPbxjDg7OsSW1Xi1PDrlj55MSUmEDMjlWYYiLROLnh3Lw3QmhAqyXxwl8kXTqW6epjYuOYPG8xd+4/BATTR39KmRJpzxm+fP0WXT8ezvzJY2hQ64M3kpSSomfs5/O5dusOrs5OzJ88Fv8cPgQGh/DpxJkYTSYMBgNd2jSnY8umWWqj/pDpONjZoNFo0Gk0rJs5PN1yl+8+osukxcwf0oOGlUu/iSxS9AbGfruGq/cf4+rowIIhPfD3cicwLIKhX6zEqJgwGIx0bVidjvXez1IbQqNh8IktxASEsLJ1P4u8Ys3r0WDKcBSTCZPByNYR03lw7MwbadJaW9NxxXz8y6rjak1XdVzlKF2U1kumY+vsiMlo4sDsr7mUhXE1qWVxPijkRWR8Ch2/Sfv5bVQyBz0/yAdAQoqB2duuczsk9o00WWkFU9uUpGgOF6ITUxi7/iJBUUmp+Q42WtYP+oBD10OZuyN7zhC//+Ahn40bn/r8SUAAHw/oT/cunbNc55Zt2/lx2XIAPuzbh5bN1LEyedoMrl6/jqJAnty5mDFlEvb29m8m4BX8V3TVHzT1+Xyh1bDu85EW+aeu3mbw/KX4e3sAUK9SKT5u2+iN2kzRGxj79ern88XQnvh7e6jzxcJlGE0KBqN5vqif+Tk3Ji6OSfO/5s79RwghmD5qEGWKF0nNP3D0JEtWrEEIgU6rZfQnfSlfstibaUrRM3b2Iq7euoursxMLJo3E39eHwOBQhk6ebZ7bjXRt3ZSOLd7s/fuPsxjYpShKOyGENWAPjAP2K4oyWwgxBhgDjBZCFAM6AcUBP2CfEKKQoihG4FugP3AC2AE0AnZmpUP/08a9ECJOURTHF573AiooijLoX2j7gbmtpxks34tX9O1lHZnBkJzCd/W7khKfgEanY9Cf67ix+xCPTl5ILXNo4Y8cWvgjAMWa1qHG0D4ZNuzd8vjTadk8vq3XxSK9cp8OJEbFMLtoHcp0aEbTz0ezuusQUhKS+K33SJ7eeYBzDm8+PfkHN/ccJik6kwaC0UDU79+APgU0Glw7DCHl/nUMwQ9Ti9hXqochLJCYrSvQunnjWKct0Ru+zVD1Gmc3nBp0Ifr3ry3SbYtXQUlOJGLF59gUKovDB82J3fETpvgYotYuBqMRrKxx7z6alLtXMMXHZErW7CXfU61Seb6YNh69Xk9iUnJa6UYjX3y/nGoVy2Wq7oCgEMbPXsjKxXMs0jdu342zkyM71yxjx/4/Wfj9chZMGYuXhzurv16AtbUVCQmJtOo9kNrVquDt6ZGpdp+xYvzHuDm/+mNsNJlY+Os2qpUqnDldYRGM/+5XVk78xCJ9w6GTODvYseuL8ew4dp6Fv25jwZAeeLo588vUIVhb6YhPSqbVZ3OpXb443m4umdb0weDehN64i61TWl13Dhzj2tZ9APiWLELXNUtYULJ+hup1y+NP+6Xz+KG+5biq2LsDiZExzCtWh9IdmtH489Gs6ToEfUISa/uMJPzOA5xyeDPkxB/cysK42nohkLWnHjGtdcl08wOjEum/4hSxSQbeL+jJ+BbF6PXjyQzVncPVlimtSjJg5WmL9JblchKbaKD1l3/RoIQvg+sXYtz6S6n5H9V5j3MPIjOl43Xky5uH9WtWA+p4qtekGXVr18rQa/v0H8j0KRPx9/NLTYuOjua7H5fy208rEULQsXtPateojrOzM6OGf4qjo/r5mLdwEb+uW0/fXj2zVc8z/ku6Vkwc9LfzRfki+flm9IBM1xsQGs74b9ewcvJgi/QNB4/j7GjHrsUT2XHsHAvXbGXBp73U+WLasOfzxcjZ1C5fEm/3zM0Xs75axgcVy7FoymhS9HqSki3n9srlSlH7/UoIIbh59wEjps1j26qvX1HbS5qCQxg/50tWfjHTUtPOvTg7ObJr9XfsOPAXC3/4iQWTRuHp4cYvS+ZgbW1FfGIirfoMofb7lfD2dM+Upn8V5R/5gdrXIoRwBmoAvdRuKClAihCiJVDLXGwVcAgYDbQEflMUJRm4L4S4A1Qy24TOiqIcN9f7E9CKLBr3MixHQkp8AgBaKx0aK93fDpIyHVtwfu3W1OflurRkyLFNDDuzjbbfzEBoMvaRKt68Hmd+3gDApQ07ea+O6hl9evs+T+88ACAmKJS4sHAcvbJmLKJPUf9rtOoflrq07r6kPLoFgDEyFK2zO8Je/bKwKVIe186f4tZ1JI5120MGV8dsCpQg6dopAJJvX8Q693tqhsmoGvagriZkYbUtLj6Bsxev0LapuopnZWWFczpG45qNW6lfsxrubq4W6Vv3HKDTgE9p23cQU+cvwWjuz+s4cPQELRvWA6BBzQ84ee4iiqJgZWWFtbUVACl6PSbTPzu5/rL7L+pXKoW7i5NF+tYjZ+g44QvajJ3PlKXrMGZwKfXAmSu0rF4RgAaVS3Hiym0URcFap8PaSvV76PUGTFn80nDx96VI49qcXr423fxn4w7A2t7OYtyV7dKSQUc3MfT0Ntp8nblxddY8ri5v2EnB2s/HVbh5XMWax5VDFsbV+YeRxCTqX5l/6XEUsUkGtf0nUXg726bmNS6Vg1UfVuGXj6oyrnkxNBkcAjWLeLPtQgAA+6+FUCnf834XyeGMh4M1J+5myEeSJU6ePk0u/5z45cjB4ydP+GjwUDp260HPfv25/+BBhuo4evwEVStVwsXFBWdnZ6pWqsSRY8cBUg1gRVFISk5G8O/82v1/Vdfr2PrXaTqOX0Cb0XOZ8uPazM0XNSoB0KByaU5cvfWK+SLzGzvj4hM4e+kqbZuo86y1lRXOjpZzu4OdHc+iNBKTkngxYmPr3kN0HDiKNh9+ypSF32Ribj9Fywa1VU013+fEuUuqphfmdn2KPstz4L+KYvpn/l5PfiAMWCGEOC+EWCqEcAB8FEUJAjD/9zaX9wcev/D6J+Y0f/Pjl9OzhDTuX4EQIo8QYr8Q4pL5f25z+kohRLsXysWZ/+cQQhwWQlwQQlwRQlQ3pzcQQhwXQpwTQqwXQrw4Ygeb0y8LIYqYy7sLITab2z0hhCiVTt/ymes8LYSY/sZaNRqGndnGlMDT3N53lEenLqZbzsrOliINa3Bpoxre4l2kAGXaN+OrGu35okIzFKOJcubwndfh4udD1OMgAExGI4nRsdh7uFmUyVWxFForK8LvPkyvigwIE7h1HYnngOnoH93EEPzIItv4NACbgurbq/PJjcbZDa2jK1p3b2wKlyVq7ZdE/jIfFBM2RcpnqEmNowumZz8Fr5hQkpMQtg7mPFfcuo3Co99kEs7sz7TX/klgEG6uLkyY/QXt+g5i0txFJCQmWZQJCXvK/r+O0aFFE4v0uw8esevAYX7+ej4bln2FRqNh295DGWo39Gk4vt5eAOh0Whwd7ImKVvseFBpG694fU699T/p2aZdlr70Qgg9nf0/7cQtZt/94mvyQiCj2n76cJjzmbkAIO49fYPWUIWycNVLVdeRsxnRFRuPr4arq0mpxsrclKjZe1RUeSevR86g7eBp9m9fJkte++YKJ7Bg7G+VvjIfiLRsw4vJeem9ZxvoPRwPquCrVvhnf1GzP4orNMBlNqWFxr8PZ34foJ8/HVVI64ypnhVLorK2IyOq4yiAty+Xk2G3V6M7r6UD9Er70WXaSrt8dx2hSaFzK7zU1qHg72RASo37OjSaFuGQDLvZWCAHDGhZm8Z5b/5gGgF2799K4YQMAps6cxdhRI1i7+idGfDqEGbPnZqiO0LAwfH18Up/7+HgTGhaW+nzi1GnUbtiYBw8e0LlTh+wV8AreZV1CwIeff0v7sfNYty/98MYLtx/Q+rM5DJj1HXfM3zV3A4LZefw8q6d+ysY5n6HRCLYdyVgoXGhEFL7msaTTanGye2G+eBpJ689mU/eTyfRtUS/TXvvHQcG4ubgwfu6XtO0/jEnzv0oztwPs++sEzXp+wsBxM5g+Sl3Ev/vwMTsPHmH1klls/HGROgfuP5wxTU8j8PX2fK7JwZ6oGHU1Lyg0jNb9hlK3Uz/6dmrz/9tr/w8ihOgvhDjzwl//l4rogHLAt4qilAXiUUNwXlllOmnK36Rnif/psBzATghx4YXn7sAf5sdfAT8pirJKCNEH+BJ1ieRVdAF2K4oyUwihBeyFEJ7ABKCeoijxQojRwHBgmvk1TxVFKSeE+BgYCfQDpgLnFUVpJYSoA/wElHmprcWoH6SfhBCf8IYoJhNfVGiGrYsTvX7/Dt/ihQi+mvYLs1izujw4djY1JOe9Ou/jX64EQ09sBsDK1pa40HAAeq7/Fvd8udBZWeGa249hZ7YBcGTJSk6v+j19z/UL3gEnXy86r1jIb31HomTVa6AoRP4yH2Fji3PzPmg9fC1i6hNO78exVmvcuo7EEB6EITQAxWTCKlchdN45cetsjvvWWWFKiAPAuXlvtM4eoNWidXLDrasa65lw/jDJ1069wiOv9t8UF0Xk6nloHJxxbtGH5NsXUcz1ZgSD0cj123cYN/QjShUrwqwvv2PZmnUM7tsjtcycJT8wbEAftFqtxWtPnrvItVt36DTgUwCSk5NxNxusQ8ZPJyA4BL1eT1BoGG37ql8a3dq2oHWTBum+/8+8Rjm8vdi04htCn4YzZPx06tf8AE93tzTlX8fqKYPxdnMhPDqWfrO+I7+fNxWKFkjNn/3TFoZ3bob2JQ/2iSu3uHb/CR0nfqHqStHjYV6qH7JwOU/CItAbjAQ9jaTN2PkAdG9Yg9a1Kv29Lg83Ns0ZRWhkNIMXLKdB5dJ4vrRi8HcUaVKHuNBwAs5fIX+Nyq8sd3XLHq5u2UO+DyrSYMpwljbuToHa75OzbAkGH98MqDfVcWHquOq+/lvc8+ZCa22Fay4/hp5Wx9XRJSs589PvFt68VF4aV51WLmRdnzcYVxmgfF53Wpbzp98ydRWrUn4PiuZw5qf+VQCwtdISGa+urM3rVAY/VzustBp8XWz55aOqAPx24iFbLwS+Yq6A9hVzc/R2WKrh/0+g1+s5dPgvhg76mISEBC5euszIMeNS81NS1FWMzX9s5Zff1BWaR4+f8MnQYVhZWeHv58ei+XP/9rMGMH3yJIxGI7PmLWD3nr20atH8H9P0X9C1euqneLub54uZ35Df35sKRQum5hfLl4u9X03BwdaGw+evMnjBUnYumsiJy7e4dv8xHccvAJ7NF+q4HrJgKU9CI9AbDOp8MVq9weneuAata1VJ18pKnS883dg0dwyhEdEMXrBUnS9cnTOsx2g0cf32XcYP+ZBSRQsx66ulLP11A0P6dLUoV696FepVr8KZi1dZsmINy+ZP48S5S1y7fZeOA9XvouTkFDxczXP7xFk8CQ5RNYU8pc2Hn6qa2jSndeO66V8/8/8c3l5sWrqY0KcRDJ44iwY13sfT3TXDmv5t/qmjMBVF+QH44W+KPAGeKIryLP7wd1TjPkQIkUNRlCAhRA4g9IXyuV54fU4g0JyeM530LPG/btwnKopS5tmTZ3Ht5qdVgTbmxz8Dr3NlnAaWCyGsgM2KolwQQtQEigFHzZOANfCiW3Kj+f/ZF9r6AGgLoCjKASGEhxDiZTdAtWdlzH2bQzqY7zD7A3z//fev6T4kRcdy98+TFG5QI13jvkyHZhYhOQjBmZ83snPCvDRlV7UfCLw65j46IBjXXDmIDghGo9Vi5+JEQkQUADZOjvT9Yxm7Ji+wiP3PKkpyEvond7HOW4TEF4x7JSWZ2D2/pT537zMRU0w45CxA8rXTxB9Nu+EwZusK4NUx96bYKDROrpjiokFoEDa2KEkJlmXiYzCGB2PlX4CU2+mvkqSHr5cnPl6elCqmbrJqUPMDlq5Zb1Hm6s3bjJo2G4DI6Bj+OnEarVaDoii0aFSXYf17p6n3y5kTgVfH3Pt4eRIcGoavtycGg5G4+ARcnC0NXW9PDwrmzc25S1eztIH3mWfcw8WJehVKcvnuIwvj/ur9x4xc8rOqKzaevy5cR2c29FvWqMCwTs3S6hreR9X1iph7H3dXgsOj8PVwxWA0EpuQhIuj5YY/bzcXCub05eyNe5nawJv3/fIUa1aXwo1qYWVrg42zIx1XLmRtr/Q3Ct8/chqP/Lmx93BDCMHZ1RvZlc64+vmFcZVezH30k2Bccj4fV7YvjaveW5axe/ICHp26kGEtmaWgjyMTWxZnyOqzRJtDeISAbRcD+Xrf7TTlR/2m9uVVMfehMUn4ONsSGpOMViNwtNERnainZC4XyuZ2o13F3Nhba9FpNSSkGPgqnTayypGjxyhapDAeHh7ExcXh5OiYGrP+Iq1aNE81XNOLTffx9ubM2XOpz0NCQqlQ3nJPjFarpVH9eqz8efU/bty/67qeecY9XJyoV7EUl+88sjDuHe2fh4PVKFuc6ct+JzJGdaS0rFGJYZ3T9uPLEeqG91fF3KvzReTz+SIxnfnC/YX5okqZDOvx8fLAx8uDUkULAdCgRlWW/rrxleUrlC7O4znBREbHgKLQskEdhn3YPa2m6WNVTa+Iuffx8iA49Cm+Xp6qpnTndncK5s3F2cvXaFgzawcL/JdRFCVYCPFYCFFYUZSbQF3gmvmvJzDb/H+L+SV/AGuEEAtRN9S+B5xSFMUohIgVQlQBTgI9gCVZ7ZcMy8k4z25xDZjfN/MxRdYAiqIcRt1UEQD8LITogXoTvFdRlDLmv2KKovR9oc5nO2aMPL/RyujSzGvdboqi/KAoSgVFUSr07//ySpKKg6c7tmaPpM7WhvfqViP05r005WydnShQozJX/9ibmnbnwDFKtWmcGhNv5+aCW+6MLbVf3bafCt3V+5NSbRtz56B6z6O1sqLX799xdvUmLm3I0j4SAISdA8LGPMFrrbDOXQhjRKhlGRtbcyw+2Jaogj7gLkpKMvpHt7B+rzTCztFczh6NU8a80cn3rmBbTI3LtHmvNCmP7wBquA5aK3N9duj88qXpz+vw9HDH18uL+4/UsLwT5y5QIG9uizK7165gz9qV7Fm7kgY1P2DCsE+oW/19qpQvw95DRwmPjAIgOiaWwOCQDLVbu1pltuxWN37u+fMIlcuWQghBcOjT1E1f0bGxnL9yjby5Mh8imJCUTLx5CTohKZljl29RMJevRZk9iyew98uJ7P1yIg0ql2ZC77bUrViSysXfY8/JS4SbN4ZGxcUTGBaRMV3li7PlL9WQ3HPyEpWLF1R1hUeRlKJ6laPjEjh/6wH5cnhlStOuCfP4PH815hSqwZpuQ7h78Hgaw96jQJ7Ux35liqO1tiIhPJI7B49RsnXj1Jh4OzcXXDM4rq5t209587gq2bYxdw89H1c91n/HudWbuPwG4+p1+LjYMq9jWSZtvMyj8Oc3tafuhVO3mA9uDtYAONtZ4eti+6pqLDh8M5RmZdTPVd1iPpy+r17fiRsu0+yLw7RYdJhFe26y42Jgthr2ADt370kNXXF0dMTf3489+/YDaiz5zVsZCwmqVrUKx06eJCYmhpiYGI6dPEm1qlVQFIVHjx+n1nfor7/ImzfPa2p7c95lXWnmi0s3KJgrh0WZsKiYVK/0pTsPMSkmXJ0cqFyiEHtOXsjifFGCLYfVlag9Jy9Sufh76c8XN++Tz8/776pKg5e7G77entx/pO4tOXHuEgXy5LIo8zAgKFXTtVt30esNuDo7UblcafYcPpY6t0fFxBIYnLHvltrvV2LLnoOqpj+PUblsSVVT2Itzexznr94gX66MzUFvjbcXcw8wGPhFCHEJNdLic1Sjvr4Q4jZQ3/wcRVGuAutQjf9dwCfmk3IABgJLgTvAXbK4mRak5/7vOIZ6XNHPQFfgiDn9AVAe9eK0BKxAjdEHAhRF+dG8maIcMBP4WghRUFGUO0IIeyCnoih/N3MeNrc3XQhRCzV0J+al5faj5r6tNpfNMs45vOm0fB5Cq0UjBBd/38H1HQeo2l/1CB7/YQ0AJVo14Obev0hJSEx9bcj1O+yavIAPd65CaDSY9Ho2DplM5KPXrySdWr6WzisXMub6ARIio1nddQgApds3IX/1ith7uFKhh2qkrO07isCLmTviTuPgjFPDLurRk0KQfOsCKfevYVtK9TwkXTqG1t0Hp4ZdQTFhDA8hdq/qxTdGhJBwbAeubT4CIVBMRuIObMAU+/oTOZKunMS5UVfce4/DlJRAzA7V26x198GlRkuehdYlnj2EMTwoU5oAxg39iNEz5qLXG8jl58v0McNYu0VdYfi7YygL5M3N4H7d6T9yAiaTCSudjvGffoyfr88rX/OMNk0aMnbmfBp36YuLkxPzJqux4fcePmLeN0sRQqAoCr06tqVQgXyZ1hQeHceQL9Sj9IxGE02rlaN66aKsNcfS/t0xlAVz+jKkQ2M+nP09iklBp9UyoXcb/LxeHx/atlZlxnyzhkbDZuLiYM/8wWp4073AEOat/kO9zVagV9NaFMqgcf06Kn+ojquTP66hROtGlO/WGqPegD4xiTXmMRB6/Q67pyyg3w51XBn1erYMmUxUBsbV6RVr6bhyIaOuHSAxMpo13dQ6S7VvQj7zuCpvHlfr+o0iKJPjama7UpTP646rvRXbh9fkh0N30Jl3xm4484QPaxbAxd6K0U3V41mNJoUeP5zgflg83+6/w1fdy6MRAoPJxJzt1wmOfn1IzZZzAUxrU5JNQ6oTk6hn3O8ZX+16ExKTkjh+6hQTx49NTZs1fRozZs/hh2XLMRgMNGpQn8KFCr22LhcXFwb07UPnHurK2Uf9+uLi4oLJZGLC5GnExcejKAqFC73HhDGf/WOa4N3XFR4dy5AFywD1BK2m1cpTvUxR1u5Vv6I71v+APScusHbfUbQaDbbWVswf0gshhHm+aMqHn3+LopjU+aJP+4zNF7WrMObr1TQaOh0XR3vmD1FP/rkXEMy81Zt5NmH0alYnS/PFuMEfMvrzhegNBnLm8GHGZ0NY+4e6v61ji0bsPXycP/YcRKfTYmtjw/xJI1VNeXMxpE9XPvxsCopingOHDsDP9/U3GG2b1GPM54to1O0jXJycmD9xhKrp4RPmfbfiuaYOLSmUP2+mNf2rvMVfqFUU5QLPoz5epO4rys9EtQ9fTj8DlMiOPol/Mu7y/zt/dxSmECIvsBzwRN0J3VtRlEdCCB/U5RUNsB8YrCiKoxCiJzAK0ANxQA9FUe6b4+bnADbmZiYoivLHi0dhCiEqAPMVRaklhHAHVgD5gASgv6Iol17qWz5gDerN2QZzna87ClMZaZX/Td6u/3fM198j7Ithb7sb2YrXMDV2XB989y33JHux8lVDbAxns/CbBf+P0ZVvymjr/9a4mpOirtxVmLz7LfckezkztSHJzza7/0ewcXIF+E/qMpzf9fqC7xC6suo58YaA7Pkthv8v6PyLQvoRB/8qpjsn/hFjVlOwylvXlhX+pz33LxvEiqKsBFaaHz8A6qTzmhCgygtJY83pq1DPMn25/AGgYjrpeV94fAbzeaiKokSgrgi8XP7Fvt1H3RPwjNkvl5dIJBKJRCL5n8CUseM//1eQMfcSiUQikUgkEsl/hP9pz71EIpFIJBKJ5N3m735P5H8R6bmXSCQSiUQikUj+I0jPvUQikUgkEonk3UXG3FsgjXuJRCKRSCQSybuLNO4tkGE5EolEIpFIJBLJfwTpuZdIJBKJRCKRvLMoRum5fxHpuZdIJBKJRCKRSP4j/E//Qu3/GPJCSyQSiUQiyW7e+q+4Gs7v+kdsHF3ZRm9dW1aQYTkSiUQikUgkkncXuaHWAmnc/w8xTJfvbXchW/nCcJ+b/du87W5kK4V/2AhASlToW+5J9mLt6g2A8cr+t9yT7EVboi4LnQq97W5kK8NjbwFQZca+t9yT7OXEhHrEJiS+7W5kK072dgAYAm++5Z5kLzq/wiRsmP+2u5Gt2LcdCYDx8eW33JPsRZur5NvugiQdpHEvkUgkEolEInlnUaTn3gK5oVYikUgkEolEIvmPID33EolEIpFIJJJ3F5Ppbffg/xXSuJdIJBKJRCKRvLPIsBxLZFiORCKRSCQSiUTyH0F67iUSiUQikUgk7y7Sc2+B9NxLJBKJRCKRSCT/EaTnXpIGnY01gw6tQ2dtjVan5eLGneyauuiN6qzYvQ31xw0CYO/nX3H6Z/U8924/fUGu8qUw6vU8On2RdQPHYzIY3lQCQmdFrlEzEDorhFZD7NnjhG9da1HG2tcf356DsMmdn6eb1xC5d0s2tKvDt/dQbPPkxxgfS+APCzCEh6Fz98J/4Geg0SC0WiIP7CD68J5M1x8TG8uUmXO4fe8+QgimTRhDmZIlUvNj4+IYO3k6QcEhGI1GenbtROvmTd9IU0pKCuOmzuTajZu4ujgzb8ZU/P1yEBgUzKejx2MymTAYDHTp0JYObVplqY16H03Awc4WjUaDTqth/dwxacqcunKLWSt+x2Aw4ubswE/Th7+ZLr2eMV+u4uq9x7g6ObBweF/8vT0ICA1n6LwfMJoUDAYjXZvUpFPDGpmuv9wnvSjRsz0oCk+v3mL3wDEYk1MsyuT8oBK15oxHY6UjKTySdY27vZEmrbUVjX6Yh0+Z4iRGRLG916fEPArAq2RR6i6agrWTI4rRyMl533Fr445M1z++WTGqvedJZHwKXX84kSa/YQlfulfNA0BCipG5O29wJzTujTRZaQWTWxSncA5nYhL1TNh4maDopNR8e2stv31UlT9vhrFgd+bPe3/w4AHjRn+W+jwgIIABAwfSpWvaa3H16hV69+jB57PnUK9+/awJMpOSksLkiRO4fv06Li4uzJozBz8/f4ICAxk1cgQmoxGDwUCHTp1p1759ltqIiYtj0ryvuHP/IUIIpn82hDLFi6TmHzhygiUrfkEIDTqtltGD+lG+ZLE31KVn7KwvuHrrDq7OziyYPAp/Xx8Cg0MZOmkWRvN80bVNMzq2aJylNowmE12/3oy3sz1f9myUJv/MvUDmbT+OwWjC1d6WZf2bv5kmg5GJ6w9xPeApLvY2zOlcFz83JwIjYxn5yz6MigmD0USnqsVpXzlr719MXDyTFnzL7QePEEIwY+THlClWODX/3qMAxs/7mmt37jG0d2f6dGj5RppAvVZj5izh6u17uDo7snDCcPx9vQkICWPolHnPr1WrxnRq3vCN2/tHkRtqLZDG/b+IEEIBViuK0t38XAcEAScVRWmWhfryAu8rirImO/tpSE7hm3pdSIlPQKPTMeTweq7vOsTDkxde+9pP9v/Kmj4jiXwYkJpm7+ZCw4lDWVi5BYqiMOLUVq5s3UdiVAxnf93C6h7DAOi+ejFV+nbk2Pe/vLEGxaDn8cLJKMlJoNWS+7OZxF85T9L9W6lljPFxhP62DMeylTJdv87Dixy9BvN4wSSLdJdq9TAlxHF/wic4VayGV5seBP24AEN0JI/mjEUxGBA2tuSbvIi4i6cxRkdmqt05C7+kWtXKLJw9A71eT2JSkkX+b79vJH++vHy1YA4RkZE079CVZo0aYGVl9dq6AwKDmDD9c1Z8u8QifeMf23F2cmLHht/YuWcfX3z9HfNnTsXL04PVS7/F2tqahIQEWnfpSa3qH+Dt5ZkpTc9YOfVT3Jwd082LiU9g2o+/8cOEQfh5uRMeHZvhegNCwxn31U+smjbMIn3D/mM4O9qz++up7DhyhgU/b2LhiH54ubmw5vORWFtZEZ+YRMthM6hTsRTe7q4ZbtMxhw9lP+rOqopNMCQl03TVIgq3a8q1XzallrFxcaLuF1PY2LovsU+CsPN0z3D9zrn9afjdbNY36W6RXqJHe5Kiollepj6F2zal+rRRbO/1KfrERHb1/4youw9x8PWm618bebj/L5Iz8T4CbL8UyO9nHjOpRfF08wOjEhn481likwxULeDB2KZF6bvidIbqzuFiy8QWxfn457MW6S3K+BOTZKD9N8eoV8yHT+oUZMKmK6n5A2oV4PyjqEzpeJG8efOyZu06AIxGI00aNqB27TppyhmNRpYsXkyVqlUzVX9gYABTJk3ih6XLLNK3bN6Ek5Mzm//Yyu5du1iyeDGz5szF08uL5StXpY6rju3aUrNmTby8vTOtbdaSH/mgUjkWTR1Dil5PUnKyRX7l8qWpXa0yQghu3r3PiKlz2fbTtxmqOyA4hPGzF7Ny0ecW6Rt27MXZyZFdv/zAjgOHWfj9KhZM/gxPDzd++Wou1tZWxCcm0qr3YGq/XwlvT49M61pz7Ar5vFyJf+lmGSA2MZnPtxzl696NyeHqSERcxn+4LDAylkm//8nSDy2/jjefuYmTnTV/jOzIrot3WbzrFHM618XLyZ6VH7XAWqclIVlPu8W/U7NoHrydHTKtadbXy/mgYhkWTR5pvlaW2lycHBn3SR/2HzuV6boDgkMZN/crVi2cZpG+Yed+nJ0c2P3TV+w4eIQFP65m4cTheLm7smbxzNRr1bLfcOpUrYh3JuYoydtFhuX8u8QDJYQQdubn9YGAvyn/OvICXd60U+mREp8AgNZKh1anQ1HAI39u+m9fyfCTfzD40Dq8C+fPUF2FG9Tg5r4jJERGkxgVw819RyjSsCYA13ceSi336PRFXHPmyDYNSrJq+AqtFqHVAYpFvjE2mqSHd1CMaWP1nCvXIPfYOeSZuACfbh+ByNhQcSxTkejjBwGIPXsc+6LmX+8zGlDMKxJCpwONyLSeuLh4zp6/SJsW6hePlZUVzk5OFmUEgoSEBBRFISExERdnZ7RaLQBbd+6mc+/+tOvWm6mz5mFMR3d6HDz8Fy2aqt6x+nVqcfL0WRRFwcrKCmtra0D1gpv+Qc/J9r9OU79yGfy81C8XD5fnuv/48yQdR8+h9YjPmfzdGozGjPXjwKlLtKpVBYAGVcty4vJNFEXB2kqHtflmSG8wYFKUv6vmlWh0OnR2tgitFit7O+KDLH91uEj75tz+Yw+xT4IASHwakZpXtGMLuhz8nW5Ht1Bv8TSEJmOfvwJN63JtjXoDcWvzLnLXUg3RqDsPiLr7EID44FASwyIydTPxjAuPoohJ1L8y//KTaGKT1M/5lYBovJxsUvMalfBlWe+K/NSvMqObFMnwEKheyIsdl9T36OD1UCrke97vwr5OuDtYc+peeKa1pMfpUyfxz5mTHH5+afLW/vYrderWxd3d8n3bsX07Pbp1pUvHDsycMT3D4+rPQ4do1lz1KNetV49Tp06lHVcpKVn+/MXFJ3D20lXaNlFXGKytrHB2tLx5drCzQwj1QiQmJac+Bti69yAdB46gTb+hTFnwdYZ1HTh6kpYN1ZujBjWrceLcRfO4ssLa2jyuUvSYlKzNFyHRcRy58ZjWFQunm7/z4l3qFs9LDldVq7ujXWre9vO36fbNZjou2cCMTX9hzOCcdej6A5qXU3+Jul6JfJy6G6BeK50Wa506v6YYjShvcK3OXL5O28Z1gWfXyvIGwcPNhZJFCqIzz+cv8se+w3T8ZAytB4xk8hffZ/xaHTtNqwa1AGhQoyonzl9O51oZMJmypuvfRDEa/5G/dxVp3P/77ASexUl0Bn59liGEcBBCLBdCnBZCnBdCtDSn5xVC/CWEOGf+e9/8ktlAdSHEBSGEpVvyDREaDSPPbGd60Blu7j/Co1MX6PDd52wcOoWFlVuw5bPPaffV9AzV5eLvS5TZgAGICgjGxd/XooxGp6NC19bc2P1ndoogz8QFFJy/gvhrF0m6fztDL7P29cepQjUezR3Hw+kjUEwmnCtnLCxD5+qBIcJsaJhMmBIT0DqqhqjOzYO8kxZSYM6PROzalGmv/ZPAQNzcXJkw/XPad+/D5JmzSUi09Ep1bt+We/cfUqdpK9p06cWYYUPQaDTcu/+A3fsO8NOP3/D76hVotRq2796boXZDw57ia/Ya6nQ6HB0diIqOBiA4JIQ2XXtSv3lb+nTvmmWvvRCCftOW0G7ULNbtOZIm/0FgKDHxCfSc9AXtRs1iyyE1JOTukyB2HT3L6pkj2bRgHFqNYNtfGfNshURE4evppurSanGytyMqNh6AoKcRtBo2gzr9x9OvVYNMee0B4oJCOPPlMvpdO8SAO0dJjo7l4YGjFmXcCubF1tWF9jt+puvhjRTt3AoA98IFKNS2Cb/V78Tqai0xGY0U6dgiQ+06+vmk3iwoRiPJ0bHYerhZlPEtXwqNtRVR9x5lSlNmaV7GjxN31bGQ18OeesV86L/qDD2WnsRkUmhYImM38l5ONoTEqDfqRkUhLtmAi50VAhhavxBL9mdsXGeE3bt307BR2lCR0NAQDh04SNt2luEx9+/dY++e3SxfsZI1a9eh1WjYuSNj4U6hoaH4+KrzoDquHImOigIgODiYTh3a07RxI3r26pUlr/3joGDcXF0YP2cxbT8cyqR5S0hITEpTbt9fx2nWYyADx05j+mdDALj78DE7Dx5h9ZI5bFy6GI1Gw7Z9GZubQ5+G4+utzgM6rRYnRweiYtQVoqDQMFr3HUzdjn3o26ltlrz287adYGjjSmhE+neHD59GE5OYQr8ft9Hlq01sPaeu1t4LjWTP5XusGNCCtYPbotEIdly4kzFN0Qn4ujiYNWlwtLUmKkFdBQmOiqPDlxtoPGcNvWqUzpLX/nFQCO4uzoyf9zVtBoxk4oJv071W6XH34RN2HTrK6sUz2PT9fLQaDdv2/5Wh14aER+Dr9cK1crB/4Vo9pdWHw6nTZQD9OrX8/++1Nxn/mb93FBmW8+/zGzBJCLENKAUsB6qb88YDBxRF6SOEcAVOCSH2AaFAfUVRkoQQ76HeEFQAxgAjXxXSI4ToD/QH+P777zPVScVkYn6Fpti6ONFnw/f4Fi9E3qrl6fXb16lldDaqd6lSz3bUGNwbAM+Ceei/dQXGFD3hDx6zot1HFt6g5w1YegLafTWdu3+d4t6RjC3hZ1AED6ePQGNnj//Ho7H2y01K4OsNGvuipbDNU4A84+YCoLGyxhirGrN+A0dj5emN0Oqwcvckz8QFAETu307MsQOQrlRVqyEynAfThqN1ccP/4zHEnj2eWm9GMBqNXL95i7EjhlKqRHFmL1jMslW/MPijfqlljp44SeFCBVn2zWIePwmg/+DhlCtTmhNnznLtxk069/oQgOTkZNzdVKNv6GfjCAgMQq/XExQSSrtu6rXs2rEdrZs3Tdcb9eya+vr4sPGXVYSGPWXoZ+OoX6cWnh6Z/xL4ZeYIvN1dCY+Opd/UL8nv70OF4u+9oN3E1buPWD5lKMkpejqPnUfpQvk4cekmV+89psPoOaqulBTczV79wXO+50loOHqDgaCnkbQeoYYPdG9amzZ1qr78ETTrUv/n8HRn8xcTCI2IYvCc72lQtSyers4Z1mPj6kyBpnVZVrIOyVGxNPv5S4p2bMH1tX+kltHodPiULc76Zj3R2dnSed9agk5fIHfNqviUKU6XPzcAoLOzITFM9eq3WPM1znlyorW2wilnDrodVfeJnP92FVdXb3wu4EVeEOrg40WjH+eye8DoNGMwOymXx40WZfzpv+oMABXyuVM4hzMr+qghcDZWGiIT1BWA2e1K4edqh5VW4ONiy0/9KgOw9vQjtl8MSl8S0LZCTo7deUpoTHLaAllAr9dz+M8/GTR4SJq8BfPmMXjo0NRVsGecOnWK69eu06NbVwCSkpNxM3v2Rw4fRmBAAHq9geDgILp07ABApy5daNGyVfrv/7Nx5evLb+vWExYayojhw6hbrz4eHpkzhI1GI9dv3WX84P6UKlaYWUt+ZOmvvzOkj+VegnrVq1KvelXOXLzCkuW/sGzBdE6cu8i1W3fp+NEIQB1XHm4uAAyZ+DlPgkLUcRUSRpt+QwHo3rY5rRvXS3++MP/P4e3FpmVLCH0azuCJn9Og5vt4urulKf8qDt94iLujLcX8vThzLzB93SYT1wOf8n3fJiTpjfT8bgulcntz6m4g1wKe0u0bdWUrWW/E3UH16g9fvYeAyFj0BhPB0XF0XKKOvS7vl6Bl+cIopDcHqv99XR1ZN6QtoTHxDF+9l3ol8uHhZJ9hTaBeq2u37zFuUB9KFy3E518vZ+lvmxjSu/NrX3vi/GWu3r5Hh0/UfUrJySm4m+eqwZPn8iQ4FL3eQFDoU1oPGAlA99ZNaNOoziuulSosh7cnm39cSOjTCAZPnkuDGlXxdHPNlC7J20Ma9/8yiqJcMsfKdwZedvE0AFoIIUaan9sCuYFA4CshRBnACBTKYFs/AD88ezrs41mZ7m9SdCx3/zxBqdYNSYqKYX6FtJszT636nVOrfgfSj7mPehJEwZpVUp+7+vty58/nm/EaThyCo5c7KwaOy3T/MoIpMYGEm1dxKF42Q8Y9CKKPH+TpprSx/4Hfqkbkq2LuDZHh6Nw9MESFg0aDxs4eU7zlhkJjdCQpgY+xe68YceeOZ1iHj7cXPt5elCqhxjvXr1OLZT+ttiizedsO+vbohhCC3Lly4u+Xg/sPH6IoCi2aNOLTTz5KU+/iuarR+6qYex9vL4JDQ/H18cZgMBAXF4+Ls6Wh6+3lSYF8eTl34SIN6tbOsKbU15s94x4uTtStXJpLdx5YGPc+Hq64Ojtgb2uDva0NFYoV5MaDABQUWtaqzPBurdLUuWT0AFXXK2LufT1cCX4aia+HGwajkdiERFxeWgr3dnelQK4cnL1+h4ZVy2VYT+5a7xPz8AmJT9XVmdt/7CFH5bIWxn1sYDCJ4ZEYEhIxJCQScOw0XiWKgBBcW7OZI1MWpKn3jy6fAK+OuY8LCMYpZw7iAkMQWi02Lk4kRUQBYO3kQKvff+DotEUEnb6YYS2ZpaC3I+OaFWXYrxdSQ3gEgh2XAvn24N005cf8fgl4dcx9aEwyPs62hMUmoxUCRxsdMYl6SuZ0oXQuN9qWz4mdtRYrrYbEFCPfHMyYN/Zljh45QpEiRdI1oq9fu8a4MaMBiIqK4uiRI+h0WhRFoVnz5gwakvaGYP7CL4BXx9x7+/gQEhyMj4+PeVzF4eLiYlHGy9ubAgUKcP7cuUxv4PXx8sTHy5NS5k2ZDWq+z9I1G15ZvkLpEjwOXERkdAwo0LJhbYZ92DNNuS+nq/P0q2Lufbw8CQ59iq+Xpzqu4uJxcbYMH/T29KBg3tycvXyNhjWrZVjThYch/Hn9EUdu/kqKwUh8cgrj1x1kZofnc463iwOu9rbYWVthZ21Fuby+3AqKQFEUmpd7jyEN0+6xWtitAfDqmHsfFweCo+PxcXHEYDQRl5SCi52NRRlvZwcKeLtx7kEw9UtmLGQ1tX4vD3y8PChdVP1qb1CjCkt/3Zyh1yqKQsv6tRjer2uavCVT1Y3ir4q59/X0IDjsKb5eHuq1ik/A5aV9T96e7hTIm4uzl6/TsEbm9pv8q7zDXvZ/AhmW83b4A5jPCyE5ZgTQVlGUMua/3IqiXAeGASFAaVSPvfU/2TkHT3dszd5PK1sbCtX9gCfnrxL+4DGl2zZJLedXqmiG6ru55zCF61fHztUZO1dnCtevzs09hwGo3KcjhRvU4OeuQ7Icr5geWkdnNHaq90RYWWNftBQpwU8y9NqEG5dwKlcVrZP6Rauxd0Tn7pWh18ZdPI1LVfWLxql8VRJuXAbUcB1hZW2uzwG7gkVICcncdgtPDw98vb25/1C9QTl55iwF8uW1KJPD14eTZ1Tj6Gl4BA8ePSKnvx9VKpRn74E/CY9Qjc3o6BgCg4Iz1G6t6h/wx/ZdAOw9cIhKFcohhCA4JJSkJNVrGh0Ty4VLl8mbJ3emNAEkJCUTb16CTkhK5tjF67yX2zLmuU6lUpy9fheD0UhicgqXbj+gQE5fqpQswp7j51M32EbFxhMQmrH469oVS7HZHN6z5/h5KpcorOoKj0zdzBYdl8D5G/fI5+eTKU2xTwLxrVgGnZ0tALlrVSXi5j2LMne378f//QoIrRadnS2+FUoTcfMujw4d472WDVNj4m3dXHDKlTYGPD3u7jhAsS6tASjUqhGP/lRvHjVWVrRY8w3Xft3M7c27MqUlM/g42zCrXSmmbrnK44iE1PTTDyKoU9QHN3s1jtfZVoevi22G6vzrVhhNSqkhPLWLenPmgfoZnrz5Kq2WHKH1V0dZsu82Oy4FZdmwB9i9axcNG6U9eQXgj+072LpjJ1t37KRuvXqMHjuOWrXrUKlSJfbv20tEhLqyEh0dTVBg+h7ll6lRsybbtm4FYP++fVSsWBEhBCEhISSZN8rHxMRw8cIF8ubNm2k9Xu5u+Hp7cv+ROu+dOHeRAnlzWZR5GBCYOu9eu3UXvcGAq7MTlcuVYs+fxwiPjAIgKiaWwGDLPSOvovb7ldiy+wAAe/48SuWypdRxFfY0dUNvdGwc569cJ18u/0xpGtKwErvHdGHHZ52Z3akOFfP7WRj2ALWK5uH8g2AMRhOJKQauPA4jn5crlQr4s+/K/dQNttEJSQRGZmxDec0ieVLDe/ZduU/F/H7qtYqOI0mv7jGJSUzmwsNg8nq5ZkoTmK+Vlwf3H6vfCSfOXaZAnpwZem2VciXZ89dxwiPVVeComFgCQsIy9Nra71dg855DAOw5fJzKZUqYr1X4S9fqBvlyZmwOkvz/QHru3w7LgWhFUS4LIWq9kL4bGCyEGKwoiiKEKKsoynnABXiiKIpJCNETeLY2HAtYukSyAecc3nRZPh+NVovQCC78vp1r2w8QfPUW7b6eQf1xg9Ba6Ti/biuBl66/tr6EyGj2zFzCsBNqCMGeGV+SYJ6I2n8zg8iHAQw9oh6NeWnzLvbMWPLKujKKzsUN396D1Y2IQkPsmaPEXz6LSw3VQxN9eA9aZ1fyjJ+HxtYOFAW3es14MHkIKUFPeLrlV3J+OgkhBIrRSMiaHzFEvH7CjD6ynxx9h5JvxtcY4+MI+nEhANY5cuLdvieKoi7nRuzZQkpA5mOex478lDGTpqE36Mnp58f0ieNYt3EzAB3atGJAn15MmPY5rbv0BEXh008+ws3VFTdXVwZ/1I8BQ4ZjUkzotDrGjxqOXw7fv28QaNOiKWOnzKBJ2064ODszd8YUAO49eMj8L79CIFBQ6Nm1M4UKFsi0pvCoWIbMVcPGDEYTTatXoHrZ4vy2W70B7NSwBgVy5uCDMsVoNXwmGiFoV69a6g3A0C7N6TdtCYrJhE6nZeKHnfD3fn0IQ9u67zP6y5U0/GQyro72zB/WV9X1JJi5Kzeo115R6N2iHoXyZM4ICT5zidubd9PtyGZMBgOhF69zecVvlOrTCYBLy38j4uZdHuw7TI8TW1FMJi6vWk/4dTV+/Oj0RbTdsgKhEZj0Bg6MmErs49cbjVd+Wk/jH+fR58JekiKj2d5bXa0o3KYx/tUqYOvuSvGubQDY/dEYwi6/fvy+yLTWJSiX2w1Xeyv+GPIBPx6+h868M3bTuQD6Vs+Pi50Voxqpxy0aTQq9l5/iwdN4vj90l8VdyqERYDApzNt1g+Do18cVb70QyOSWxVn/8fvEJOqZ+MJJOdlFUmIip06eYPyECalpv69fD/C3x1DmL1CAgZ8MYtDAjzApCjqdjtFjxqa7IfdlWrZqzaQJ42nVojnOzs58PltdFbx//x6LFi5MHVfdevSg4Hvvvaa29Bk3pD+jZy5U54scvswYPZS1f+wEoGOLxuw9fJw/dh9Ap9Nha2PN/EmfIYSgYN7cDOnTjQ9HTUYxzxcTPh2An+/rY//bNq3PmM8X0qhrf1ycnZg/cRQA9x4+Zt63y1F9WAq9OrSiUP68WdL1MutPXgOgfeVi5Pd24/1COenw5QY0QtC6YmEK+qo3yp/Ur8DAFTtQFNBpNIxp8T5+bq//Cm1VoTAT1h+ixfy1ONvbMLuTumH4fmgUC3eeTC3Xo3op3vPNWmz6+EF9+WzWYvR6Azlz+DBz1Cf8tnU3AJ2aNyQsIpIOH48mLiERjRD8vHE7W5ctomCeXAzt1Zl+Y6ab50AdEwf3w9/n9Q6pto3rMnr2lzTsMQhXJ0fmj1fni3uPnjD3u1XP58D2LSiUP0+WdP1bKPIoTAtEdnpLJX+PECJOURTHl9JqYY6bN5+iswh4H3UGfGBOfw/YACQAB4HBiqI4CiGsgF2AJ7BSUZQv/qZ5ZZguX3ZLeqt8YbjPzf5t3nY3spXCP6g3OSlRGfOSvStYu6pGgfHK/rfck+xFW6IuC50yFCX3zjA8VvVQVpmx7y33JHs5MaEesQkZPxbxXcDJXo0ZNwRm/nz//8/o/AqTsGH+2+5GtmLfVo22NT6+/JZ7kr1oc5WEdHeb/bsk7frhHzFmbRv1f+vasoL03P+LvGzYm9MOAYfMjxOBAemUuY26+fYZY83peqDuP9BViUQikUgkEsk7iDTuJRKJRCKRSCTvLnJDrQVyQ61EIpFIJBKJRPIfQXruJRKJRCKRSCTvLHJDrSXSuJdIJBKJRCKRvLvIsBwLZFiORCKRSCQSiUTyH0F67iUSiUQikUgk7y7Sc2+B9NxLJBKJRCKRSCT/EeSPWP3vIC+0RCKRSCSS7Oat/9BTwob5/4iNY9925FvXlhWk514ikUgkEolEIvmPIGPu/4cYos33truQrXxpvE/cL9PedjeyFceukwC4/zT2Lfcke8nn6QRA8r4Vb7kn2YtNvd6Mts7/truRrcxJuQfA8jOP3nJPspc+FXKjD777truRrVj5FgDAcH7XW+5J9qIr2+g/OVcAJMdFv+WeZC82ji5vuwsq8ihMC6RxL5FIJBKJRCJ5d5Ebai2QYTkSiUQikUgkEsl/BOm5l0gkEolEIpG8syjSc2+B9NxLJBKJRCKRSCT/EaTnXiKRSCQSiUTyzqLIDbUWSONeIpFIJBKJRPLOohilcf8iMixHIpFIJBKJRCL5jyA99//juObMQfeVC3Dy9UIxmTj246/8uWRlumVzVyjF8GMbWdl5MBc27HyjdnXW1nRbtYBc5UoQHx7Fys6DiHgYgH/ponT4ega2zo6YjCb2zPqK8+u2Z6kNo8lE96W78HKyY3Hn2umWuRoQTq/lu5nV9gPqFcv9JpJIMRiZtPkY14MicLGzYXa7D/BzdSQoKo6R6//CZFIwmEx0rFiIdhUKZamNjb/9wq6tWxAC8hYoyIhxk7G2sUnNP7B7J+t+WQWAnZ09g0eOIf97WWsrVVdKCvOnT+b2zes4u7gwdtosfHP4ERIcxPRxozAZTRgMBlq260DT1u3eqK0XaTTxG+xtbdAKgVar4bfRvd6ovi0nLvPjrmMAfNjofVpWKQnA5NU7uPooCEWBPN7uzOjeFHtb6zftPgBCo2HwiS3EBISwsnW/NPn5a1Sm+YKJaK10xD+N5Pt6nd+oPa21NR1XzMe/bAkSIqJY03UwkQ8DyFG6KK2XTE8dVwdmf82l9VkbV2d2beTiwZ0oikLp2k2o2LiNRX5yQjxbv5lNTHgoJqORSk3bUapmozfSZdCnsP3buQQ/uI2dozMtB4/HxcuX6LAQNi2aimIyYjQaKd+gJWXrNc90/TGxcUyet5g79x8CgumjP6VMiaKp+afOX2LI+Gn45/AFoF719xnYq8sbaUpJ0TP28/lcu3UHV2cn5k8ei38OHwKDQ/h04kyMJnVcdWnTnI4tm2apjfqDpuJgZ4NGo0Gn1bDu85EW+aeu3mbw/KX4e3uouiqV4uO2b3atUvQGxn69mqv3H+Pq6MCCoT3x9/YgMCyCoQuXYTQpGIxGujasTsf6H7xRW8941+eK+w8e8tnYcanPnwQE8vFH/ene5fl8cP/+AyZOncb1GzcZ/PFAevXo9sbtpqSkMH7SFK5dv4GLiwvzZs/E38+PwKAgho0cjclkxGAw0LljBzq0a/vG7f2TSM+9JdK4z0aEEDmBr4FiqKsi24BR5ud+iqLsMJebAsQpijL/LXU1FZPBwKZRM3ly/io2jg6MOr2Vm/uOEHz9jkU5odHQYtZoru85nKn63fP403X5fJbUtTRaqvTpQEJkNNML16Zcx2a0mD2GlZ0Hk5KQxOpeIwi78wDnHN6MOr2VG7sPkxid+R91+vXkTfJ6OhOfrE8332gy8eX+81QtkCNT9QZGxTFly3F+6FnfIn3z+bs421mzZXBLdl95wJf7zjO7XXU8nexY0bsB1jotCSl6Ony7nZqFc+LlZJ+pdp+GhbLl97X88Ms6bGxsmTlxDIf27aFB0+fGjK+fH/O++gEnZ2dOHz/K4rkzWfzjqgzVHxwUyIKZU5j31Q8W6bu3bcHRyYkV6zZzaN9uln+zhHHTZ+Hu4cnC75ZjbW1NYkICA7p3pMoHNfHw8sqUrr9j2dDOuDlm7n3qs+gXpndvir+Ha2padHwi3+04wm+jeyGEoOPsFdQu9R7O9raMalsXRzv1Bmnehv38evgsfRtUzZb+fzC4N6E37mLr5Jgmz9bFiVZLprG8WW+iHgfi4OWR4Xrd8vjTfuk8fqhvaWBW7N2BxMgY5hWrQ+kOzWj8+WjWdB2CPiGJtX1GEn7nAU45vBly4g9u7TlMUibHVdjj+1w8uJMe05ag1Vmxbs5YCpSthLtvztQy5/ZuwdM/N+1GTichJoofR/aheLW6aHVWr60/OiyY7d/Po8uEBRbplw7twtbBkQELV3Ht+EEO/bqUlkMm4OjmTrcpi9BZWZOSlMiy0R9SsHxVnNw8M6Vr9pLvqVapPF9MG49erycxKTlNmXKlivPN7KmZqhcgICiE8bMXsnLxHIv0jdt34+zkyM41y9ix/08Wfr+cBVPG4uXhzuqvF2BtbUVCQiKteg+kdrUqeHtm/PPxIismDsLNOe3n7xnli+Tnm9EDMl1vQGg4479dw8rJgy3SNxw8jrOjHbsWT2THsXMsXLOVBZ/2wtPNmV+mDcPaSkd8UjKtRs6mdvmSeLtnz48gvctzRb68eVj/6y8AGI1G6jVuSt3atSzKOLs4M2bUSA4cOpTp+gMCA5k4ZRrLf/jOIn3j5j9wdnZi+5aN7Ny9h0VffsW82Z/j5enJzyuWYm1tTUJCAm06dKZWzRp4Z+PcLvlnkWE52YQQQgAbgc2KorwHFAIcgZlAGaBJNralza66YoLDeHL+KgDJcfGE3LiDi79vmnI1B/Xk4sZdxIWGW6RX6NqKEcc389nZ7XT8diZCk7GPVMmW9Tn10wYALvy+k0J13gcg7PZ9wu48UPsWFEpcaDiOmTB6nhESk8CR2wG0KlvwlWXWnrpF3aK5cHOwtUjfcek+PZbuovP3O5i57STGDG7U+fPmE5qVUn+ttG6x3Jy6H4KiKFhptVjr1EuWYjBhUpRM63mG0WgkJTkZo8FAclISHp6Wk22xkqVxcnYGoEjxkjwNDU3N2797B0P69eDjnl1YPHcmRmPGjg47/tef1GvSDIDqtepy4ewpVZeVFdbWqtdKr09BUf55z8njsEg++motHWevoOfC1dwPDn/9i4Cj1+9TtUg+XBzscLa3pWqRfBy5pv4S67Mva0VRSEoxIBDZ0lcXf1+KNK7N6eVr080v06klVzbvJupxIADxYc+1lO3SkkFHNzH09DbafD0jw+OqePN6nP1ZHVeXN+ykYG11XD29fZ9w87iKDQolLiw8UzcTzwgPfIRfwSJY2dii0WrJVbQUt08ffamUICUpEUVRSElKxNbRCY1G/fxfPbKPnyYOYsXYAexatghTBo+vu332GCVqNACgSKUaPLx6HkVR0Oqs0Fmpn0GjXp+lz2BcfAJnL16hbdOGAFhZWeGczs3Yq9i65wCdBnxK276DmDp/SYbH1YGjJ2jZsB4ADWp+wMlzF18YV+qNUIpej8mU9fniTdj612k6jl9Am9FzmfLj2gzPgwfOXKFljUoANKhcmhNXb6EoCtY6HdZWqj9Rrzdg+ofni3dprniRk6dOkytnTvxyWDqdPNzdKVG8GDpdWp/sth076dKjF+07d2XazFkZ/gwe+vNPWjRTV4Xq163DyVOn08ztKSkpmN6BzaqKyfSP/L2rSOM++6gDJCmKsgJAURQjMAzoB8wFOgohLgghOprLFxNCHBJC3BNCDHlWiRCimxDilLns988MeSFEnBBimhDiJFBVCDFbCHFNCHFJCJEtKwDuefzxL1OMhycvWKS7+PlQqlVDjnz/i0W6T5EClOvQjC+qt2Nu+aaYjEYqdG2VobZc/HyIehwEgMloJCk6FgcPN4syuSuWRmttxdO7DzOtZcHuMwytVxaNSH/yDY1J4OCNx7Qt/55F+v2waPZcfciy3g34dUATNBrBzssPMtRmWGwCPi4OAOg0GhxtrYhKVD2AwdHxdPxuO00WbaJXtWKZ9toDeHp5065zN7q3aUaXlo1wcHCkfOUqryy/e9sWKlRRjbtHD+5zeP9eFn63nG9WrUGr0XJwT8ZCq8LDQvHy9gFAq9Ph4OBITLT6E+phIcF81KMT3Vs3pX3XntnqtUcIBpi/nH8/cgGAqWt2MbZDfdaO6c2I1nWYsXZ3hqoKjYrF180p9bmPmxOhUc+91hN/3k7tsUt4EBJO51rls6X7zRdMZMfY2a/8gvB6Lx92ri7037uGwSe2UK5bawC8ixSgVPtmfFOzPYsrNsNkNFG2S8sMtens70P0E8txZf/SuMpZoRQ6aysisjCuPHPm5fGNyyTGxqBPTuLehVPERIRZlCnXoCXhAY/4elAnlo/pT73uHyM0Gp4GPOT6iT/pOnkRvWd9j0aj4drRAxlqNy4yHCd39bOl0WqxsXcgMS4GgJjwUJaP6c83Q7pQpVnHTHvtnwQG4ebqwoTZX9Cu7yAmzV1EQmJSmnIXr96gTZ9P+GjURHP4Dtx98IhdBw7z89fz2bDsKzQaDdv2HspQu6FPw/H1VjXpdFocHeyJilY1BYWG0br3x9Rr35O+Xdpl2WsvBHz4+be0HzuPdfuOpVvmwu0HtP5sDgNmfccd85x8NyCYncfPs3rqp2yc8xkajWDbkTMZ0xURha/5M6fTanGysyUqNl7V9TSS1p/Npu4nk+nbol62ee3f9bniRXbt2Uvjhg0yXP7e/fvs2rOXVcuWsv7XX9BoNGzfuStDrw0JC8PHR53bdTodjo6OREWpc3twcAhtO3ahQZPm9OnV4/+9114xmv6Rv3cVGZaTfRQHzr6YoChKjBDiAbACKKQoyiBIDcspAtQGnICbQohvgYJAR6Caoih6IcQ3QFfgJ8ABuKIoyiQhhDuwDCiiKIoihHB9085bO9jTd/23bBw+naTYOIu8Nl9M4o90jJRCdaqRq1wJRp7cAoCVnW2qZ7/vhu/wyJsLnbUVbrn9+OysGt/755IVnFz5OyIdo1t5waPt7OtF91UL+aX3CIv0jHD41hPcHGwp6ufBmQch6ZaZv/ssQ+qVRfuSR/TU/WCuB0XQY6k6OSYbDLjbq579EWv/JDAqHr3RSHB0Ap2/3wFA58qFaVGmAOn18plnx9fFgbUfNSUsNoERaw9Tt2huPBztMqUrNiaG43/9ycr1f+Do5MTMCaPZv3sHdRumXRS6ePYMu7dtYcG3SwG4cOYUt29cZ0jfHqqu5CRc3NQv4GljRxIcGIjBoCc0JJiPe6qhHq06dKJB0xak9/Y/u3xePr5899NvhIeFMXXsCKrXroube9YMkZf5aXg3vF2dCI+NZ8CS38jr487F+wGMXLo5tUyKQfVQbT5+iV8OqsbHo7BIPvlmPVZaLf6eLizq3zZ9DS88nt69KUaTiVnr9rL77HVaVS31Rn0v0qQOcaHhBJy/Qv4aldMto9FpyVmuBD807IaVnS2fHN7Ao5MXKFD7fXKWLcHg46pOKztb4sxe/e7rv8U9by601la45vJj6OltABxdspIzP6U/rl4U7+TrRaeVC1nXZ2SmxxWAp38eKjfvyNrZo7GyscM7d/5Ur/wz7l86g3eeAnQaP4+okEDWzh5DzsIleHj1PCH3b/HTxE8ANY7e3tkVgI1fTCE6NAijwUBMeCgrxqphIuUbtaZUzUbp9vXZ2HL28KbP7B+IjXzKpoVTKFy5Bg4ubmnKvwqD0cj123cYN/QjShUrwqwvv2PZmnUMNo8VgGKFCrJ37Urs7e04fOI0Q8ZPZ8eapZw8d5Frt+7QacCnACQnJ+PuphqsQ8ZPJyA4BL1eT1BoGG37DgKgW9sWtG7SIH1N5uuXw9uLTSu+IfRpOEPGT6d+zQ/wdM+4pmesnvop3u4uhEfH0m/mN+T396ZC0eermcXy5WLvV1NwsLXh8PmrDF6wlJ2LJnLi8i2u3X9Mx/FqeFRyih4PZ9XgHbJgKU9CI9AbDAQ9jaTN6LkAdG9cg9a1qqQ/Dz7T5enGprljCI2IZvCCpTSoXBpPV+dM63qZd3mueBG9Xs+hPw8zdNDHGX7NyVOnuX79Bl169AQgKTkZd/Nn5dMRowgIDESvNxAUHEz7zl0B6Nq5E61aNCc9sc+mEF9fHzasXUNoWBifjhhF/bp18PDInrld8s8jjfvsQ8Ar7Lv007cripIMJAshQgEfoC5QHjhtngztgGdxFUZgg/lxDJAELBVCbEeN7U/bsBD9gf4A33///Ss7rtHp6Pv7t5xZs4VLm9J6N3KXL0nPNUsAcPR0o1jjWhgNBoQQnPppA1vHz0vzmmVtPwJeHXMfFRCMa64cRAUEo9FqsXVxIiEiCgBbJ0cGbF3O9kkLePDSKkJGuPg4jMM3n3D0diApBiNxyXombDrKjNbVUstcDwpn7IYjal8Skjl6OwCtRqAAzUrnY3DdsmnqXdCxJvDqmHtvJ3tCouPxcbbHYDIRl6THxc5ys5WXkz35vVw4/ygs0xt4z585hY+fH65mo7xazdpcv3wpjXF/785tFs2ezvQFX+Ls4gqoN071Gjejz8BBaeqdNEtd+HlVzL2ntzdhoSF4eftgNBiIj4/DydnS4+bh5UWefAW4cvE81WvXy5SuV+HtqhoTHk4O1CldiDO3H+FkZ8P6cX3SlG1VtVTql2x6cbQ+bk6cufUo9XlIZCwVClm+/1qNhkbli7Jy38k3/sLO+355ijWrS+FGtbCytcHG2ZGOKxeyttfw1DLRAcHEh0eiT0hEn5DI/SOnyFGqCEIIzq7eyK4JacfVz+0HAq+OuY9+EoxLzhxEpzOubJwc6b1lGbsnL+DRqQtZ1la6VmNK12oMwJ9rl6V61J9x+fBuqjTvhBACN19/XLx8CQ96DAqUqN6Amp36pqmzzbApav9fEXPv5O5JbEQYzh5emIxGkhPisXV0sizj5olHzjw8vnGZIpVrZFiPr5cnPl6elCpWBFBDZJauWW9RxtHh+UpbjSoVmfHF10RGRaMoCi0a1WVY/95p6v1y5kTg1TH3Pl6eBIeG4evticFgJC4+ARdnS03enh4UzJubc5eu0qBW5jefPvOMe7g4Ua9iKS7feWRh3DvaPw9JrFG2ONOX/U5kjOrcaVmjEsM6p92c/OUIdWP4q2LufdxdCQ6PxNfDFYPRSGxiEi4vxcJ7u7tQMKcvZ2/co2GVMpnWlUbnOzxXvMiRo8coWqRIpoxoRVFo0awpQwd/kiZv0QJ1DnlVzL2PtzchISH4+vhgMBiIi4vDxcVybvf28qJA/vycPX+BBvXqZkHVv8Pb9LKbIyzOAAGKojQzO2DXAnmBB0AHRVEizWXHAn1RbbohiqLsNqeXB1ai2n07gKFKVjwwZmRYTvZxFajwYoIQwhnIhXoRX+bFHVtG1BstAaxSFKWM+a+woihTzGWSzKE+KIpiACqhGvutgHTX4BRF+UFRlAqKolTo37//KzveZekcQq7f4eCiZenmTy1Yg6kFqjO1QHUubNjJ+kGTuLxlL7cOHKV028apMfH2bi645fZ/ZTsvcuWPfVTqoe6+L9OuMbcPHgdAa2VF3w3fcfrnjVz4fUeG6nqZwXXLsnNYG7YNbcXnbT+gYj4fC8MeYOuQVmwbqv7VLZabMU0qUbtILirl82X/9cdExKvL8tGJyQRFxaXXTBpqFvZn2yU1NnP/tUdUzOeDEIKQmASS9AYAYhKTufg4jDweTn9XVbp4+/hy48oVkpKSUBSFC2dOkytPXosyocHBTB83ilGTppEzd57U9DIVKnHk0H6iIiMAiI2JJiQ4KEPtVvmgBvt2qPePfx3aT+nyFRFCEBYaQnJykrm+GK5dvkjO3Hn/pqaMk5CcQrx5U2NCcgrHrz+gRB4//D1c2XPuBqB+qd18kv7KzMtUK5qPYzfuE5OQRExCEsdu3Kda0XwoisKj0MjU+g5dvkNenzf3Tu2aMI/P81djTqEarOk2hLsHj1sY9gDXtu4lX7WKaLRarOxsyVWpNKE37nLn4DFKtm6cGhNv5+aCa26/DLV7bdt+yndXx1XJto25e+j5uOqx/jvOrd7E5Tc86So+Wn2/Yp6Gcuv0UYq9b3kSlbOHNw+vnk8tGxH0GFfvHOQpXpabpw6nvj4xLobosIxdv/fKVeXK4T0A3Dh1mNzFyyCEICY8DH2K+jlJio8l4NZVPHLkypQeTw93fL28uP/oCQAnzl2gQF5LY+5peESqp/3y9ZuYTAquLs5UKV+GvYeOEh4ZBUB0TCyBwRnTVLtaZbbs3gfAnj+PULlsKYQQBIc+JSlZ1RQdG8v5K9fImytj8+qLJCQlE28OL0pISubYpRsUzGUZxx0WFZOq69Kdh5gUE65O/8feWYdHdXwN+J3dJMTdCO7uVooUd3d3KA6ltLi7FwrFtUCLFiju7u4eNB7ivrvz/XGXwJIASaAfP9r7Pk+e7M7MnZmzc2fuuWfOzNhQpmBu9p+7SrBxwXVoZBQ+ga9SJleJgmw/fl6R69w1yhTIpcgVHEpsfLwiV2Q0V+55k83LPdVyJZHzKx8r3mbPvv3UrpVylxyAMqVLceDQYYJfKe0TFhaGj2/KxvZK31Vkx05lVv3AocOULlVSaSt/f2JjlXsnPDycq9eukTVLlg9l9V9nAHDnre9DgUPG9ZeHjN8RQuQHWqF4etQCfntrDeVCFGNsLuPfJ21bpVruPx+HgKlCiA5SyjXGBpuF8ibmDyQ/N580j+1CiDlSygDj25+dlNLEOVYIYQtYSyl3CyHOAg+TyywlZC9XktLtm/Dy+t1E15mdI2fgZFQmTi1e/95r/e48ZNfoWfTeuwah0WBISGBTv9GEPHv50XLPrNhA+zVzGHXvCNGvwljVRrH+FGtRl5wVS2Pj4kTpjsq2iuu6DObltTsfyi5FbL54H+CD21Bmd3Ogd+XC9Fl7GIOUmGk1DK1divSOH19g17BYTkb9dZqGv27HwSodk5sqLxTegWHMOXAZIZRZ0PZl85HLI/VT7HkLFKRC5ar07dwWrVZLjtx5qN2wCbv+2gxA3cbNWLdyKRHhYcyfqVgJtVotv674nSzZstOxey+GD+yLQRowMzOjz6AheHh+fKegWvUaMn3CaDq3aISdvT3Dxk0G4PkTb5bM/wUhBFJKmrZuR7Yc71/AnBpeRUQzcIkyUaXXS2qXyk/5AtnJ6uHMxD/3sWTvKXR6A7VK5CNPRo+P5udgY8X3tb6l9bRVAPSsXQ4HGysMBsnI33cSGRuPlJI8GdwZ2armZ5EhOcp0Vyzt55auJ+DuI+7tP8bAy7uRBgMXVmzE/5Zyj+4bO4tuu1cjNBr0CQls7z+G0Gc+H83/wsoNtFw1m59uHyYmJIz17ZTlPIWb1yFbhVJYuzhSwvhSvbHbT/imoV9tmzuemIhwNGZmVO/UF0sbO64c/BuAYtXq823jtuxeNIPlQ7oDUKlVN6ztHLC2c6BC885snDoUKSUarXK9g9vH269wpdrsXDiVxYM6YmVjR4N+IwBlge+RdYt53blK122OW+ZsqZZp+ICeDJk4nYQEHZm8PJkw9Ac2bFfGw5YN67L/2Ck2bN+FVqvFMp0FM8YMQQhBjqyZ6detPT0Gj8RgMGBuZsaIgb3x8vy4TE3q1GTYpJnUbtMVBzs7ZowZAsDjp8+Y8duyxH7VqWVTcudIvUzBYRH0n6UYbPQGA3XLlaBC0XxsOKDMWLasXp79Z6+y4eAptBoNlhbmzOyv7BCTM6Mn/VvUpfvkhUhpwEyrZWSX5ni5OX+03KaVv2HogrXUGjABB1trZvZX3EUev/RjxtptvJ7M7lSvCrlT+NL6If4tY0VMTCxnzp1j1PBhiWEbNytytWjWlKCgIFq170RUVBQaIVj7x59s2/QnObJnp2/vnvTs0w+DQWJmZsbwoT8lWZCbHI0bNmD4qDHUbdgEBwd7pk+eBCjbbs6cMzfxmdWxfTty5/o8Y/s/hSGFi4g/N8ZdEuuibJ7y2oLTEKhk/LwaOAoMMYb/afTa8BZCPARKG9237aWUZ4x5rkEx3KbZEiM+weqv8g5CiEzAbyj+9BqUqZXBKP7y+wBzYAqQj7e2whRC3ATqSSmfGBfcDjNenwD0kVKeFUJESiltjenTA9sBS5SRcqaU8mP7Hcr+2tQ/IP6Xmaf3JnLd+C9djc+KbdvRAHgHpX7rz/9lsrkqMxVxB1d+4Zp8XtJV68wQi+xfuhqflWnxyuzTiovPPpLy66JLycwk+D360tX4rJh75gBAdyVlCyi/FsyK1fpXjhUAcZFhX7gmn5d0tg7AP7BtUCoJnv/TP6LMuvSd8UHZhBCbUfQ6O2Cw0S0nVErp+FaaECmlkxBiPnBWSrnWGL4cRYF/AkyVUlYzhlcAhkgp66W13qrl/jMipXwOJHeCShxQ6gPXFXzr8wYUX61309i+9dkXxS1HRUVFRUVFReU/zT+1beXbaxeNLJFSLjHG1QMCpJSXhBCVUpJdMmHyA+FpRlXuVVRUVFRUVFRUvlr+qQW1RkV+yXuiywENhBB1UDwp7IUQawF/IUR6KaWv0dPi9cYoL1DWYb4mI+BjDM+YTHiaURfUqqioqKioqKioqKQCKeUwKWVGKWVWlIWyh6WU7YAdQEdjso4obtQYw1sJIdIJIbKhLJw9b/TGiBBCfGM8ELXDW9ekCdVyr6KioqKioqKi8tXyP3bg1FRgoxCiK/AMaA4gpbwlhNgI3AZ0KGsqX68E7sWbrTD38AmLaUFV7lVUVFRUVFRUVFTSjJTyKMquOEgpg1HOLUou3SSUnXXeDb8IFEx6RdpQlXsVFRUVFRUVFZWvln9qQe3Xiqrcq6ioqKioqKiofLUY/rfccr446oJaFRUVFRUVFRUVlX8J6iFW/x3UhlZRUVFRUVH53HzxQ6x8Jvb6R3Qcr5ELv7hsaUG13KuoqKioqKioqKj8S1B97v9DDDbP/qWr8FmZmfCYU+UrfOlqfFbKnTwBgO7a/i9ck8+LWZEaAJx4HPyFa/J5qZDdhf7abF+6Gp+VeXpvAG74hn3hmnxeCqV3YMBfN750NT4rcxsXAsA7KOIL1+Tzks3VjgvPQr50NT4rpTI7ARB3eM0XrsnnJV2VDl+6CsD/3FaYXxxVuVdRUVFRUVFRUflqUXfLMUV1y1FRUVFRUVFRUVH5l6Ba7lVUVFRUVFRUVL5aVLccU1TLvYqKioqKioqKisq/BNVyr6KioqKioqKi8tWiWu5NUZV7FRUVFRUVFRWVrxaDuqDWBNUtR0VFRUVFRUVFReVfgmq5V0kWodEw8Nx2wl76s6JRt0/Kq2T7JlQd1heAQ1Pmc/H3rQC0WTOHjMULYUhI4NnF62zuNQKDTvfJdQewcHcn98gRmDs7g5T47diB76bNJmmcy5cnc7duSGkAvZ7H8+YRcf3T9sEW5ubkHjkCmzx50IWHc2/0GOL8/Ejn4UHeyZNAo0FjZobv5i34bd+e6vyr9xmDjWU6NBoNZloNG6f+nCTN+VsPmLpqCzq9Hic7W1aPG/BJMsUnJDBs/u/cevwcRzsbZg3sTAZ3F3wCXzFg5jL0BgM6vZ62tb6jZY3yn1TWaw5u28DxvTtAQoVaDajeuOUn5XfqwG52/bkKgLqtOlGueh0AVs2ZzJMHd5FS4pkhE51/HImllfWnVh/HjOlpv2oWdp5uSIOB00v/4Nivq5JNm7lkYQad3sqq1v24umXPJ5VrZmFBu9WzyFS8IFHBoaxq3ZdXT1+SoUg+WiyYiKW9LQa9gf1T5nNl4640lfH3pvUc2rUdgSBz9pz0GTIKi3TpTNLcvHKJVfNno9PrsHdwZPzcxZ8kV0J8PL9OGcvje3exdXBg0OhJuKf3ItDPlxmjh2DQ69HpddRu3IKaDZumOv/WxTNQwNOeyDgdUw89SBLvbpuONiUyksnBkp23/TnyMOiT5AHQagTtSmQkk6MVUfF6Vl94xqvohMT4dGYahlfLzXWfcLZc90lTGVv/XMfev7cjBGTNkZMfh48xaavnT58wa9I4Ht2/S8cevWnWpv0nyxUfH8/MCWN4cO8O9g4ODBs/Bc/0Xvj7+TJh+E8Y9AZ0Oh0Nm7WgbuNmqc5/z5Y/OLpnB0IIMmbNQY+fRmJh8Uam6KhIFk4dQ3CAP3q9njrN2vJdrXqfJFNCfDyLpo/D+8E97Ozt6TtiIm6eXgT5+/LLuKEY9Ab0eh01Gjanav0mn1TW29QaMR9rSwu0GoFWo+HPYV0/Kb/tZ66zdM9JALrXLk/DsoUBGPP7Tm499UUCWdydmdihPtaWFp9a/f9XVLccU1TLfSoRQowQQtwSQlwXQlwVQpRJQx5uQohzQogrQohPOoVJCNFJCDH/U/JIjgr9O+N/51Gqrul1cD1OWTKYhFk5OVB9ZH/mlWvMvG8bUX1kf6wc7QG4vH470wtWY2ax2phbWlKm66cpcG8j9Xq85y/gSrv2XO/xPembNMEqa1aTNKGXLnG1Uyeude7CgylTyTlkSIrzT+fpScFf5yUJ96hXF11EBJdbtcZnw0ay9uoJQHxwMNd79uJa5y5c6/E9Gdq1xcLFJU2yrRzTn60zhiar2IdHRTNh2UbmD+nBjtkjmD2oS4rzfRkQTKexc5OEbzl8Bnsba/b+OoYOdSsze53yUuLqZM+6iT+wdcZQ/pg8mGXbDxDw6tMPPnr55BHH9+5gxC/LGfPbaq6fP4X/y+cpunb6z30I8vc1CYuMCOfv9SsY/ssyRvyyjL/XryAqIhyAlj0GMPa3NYxb+DvO7h4c/ntzctmmGoNOx18/TWJywerM/rYJFXp3wDNfziTphEZDgylDuLP/eKryd86SgX6H/kgS/k2XFkSHhDEhT2WOzl1Og6lDAYiPjmVtpx+ZUrgmC+t0pMns0Vg52KVaruDAAPZs2cC0xauZs+pPDAY9pw4fMEkTFRHBsl+mM2TyLH5ZtYEfx05Jcf4Bvj6MHtAzSfih3TuwsbVj/vqt1GvWmrVLlCHP0cWVSfOXMXP5Oqb8tpJt69fwKigw1XKdfxrColPe742Pjtex9ZoPh9Og1Dtbm9O3fNKDzspmcSImQc/EA/c5+jCI+gU8TeLr5vPgUVBUqst7TVBgANs3b+DXFWtYvHYjBoOBowdND8ezs7en1w+Dadq6Xarz9/P14ae+PZKE79u5HVs7O1Zu3Ebjlm1Y8duvADi7uDJ70Qp+W72euUtXsWHtaoIDU9dWr4IC2L9tIxMWrGTq0vUYDAbOHjG9/w5s30yGzNmYvHgtI2b+xvol89AlJLwnR1MC/XyY+GOvJOFH9+7Axtae2as3U6tJa/5ctgAAR2dXxvyylMmLf2fcr8v5e8MaQtJw/32I5T+0Y9OI7qlS7LvM/p2XwaEmYWFRMSzadYJ1QzqzfkhnFu06QXhUDAA/NavO5pHd2TKyO+md7fnj2MXPKYLKF0BV7lOBEKIsUA8oLqUsDFQDUqZ1mFIVuCulLCalPPE56/g5cMjgSb7alTm/YkNimEv2zHTbuZKB57bT+8gG3PKk7LTbPDUqcv/QSWJCwogJDef+oZPkqfkdAHf3Hk1M9+ziNRwyeL4nl9STEBxM1P37AOhjYoh+8gQLV1eTNIaYmMTPWktLkDLxu1uNGhRespgiK1eQ46fBoElZV3EuX4GAPXsBCDp6FIcSJQCQOh3S+IDRmJsjUphfatl18iLVyhTBy9UZAJe3FLi/j1+g5bAZNPlpKmOX/Ik+hT6Khy/eoGEl5R22xjdFOXvzPlJKLMzMsDA3ByAhQYfBID+UTYrxff6U7HkLks7SEq3WjNyFinH59DECfF4wZ+QPjO/XmWmDe+H7/EmK8rt16Sz5i5XC1s4eGzt78hcrxc1LZwGwsrEBQEpJfFw8AvFZZAj3C+TFlVsAxEVG4X/3YbL393d9O3Jt614iA0xP7i3ZthE/ntnGz5d20XLhpBTfL4UaVuf8mi0AXN28h9xVvgUg8IE3gQ+fKHXzDSAyIBhbt7S9XOr1euLj4tDrdMTFxuL0Tr86cWgfZSpUws1DkdfByTkx7vj+PQzt2YnBXduyeNYU9Hp9isq8cOoYlWrVBaDsd1W4cekCUkrMzc0xt1AsjLqEeGUWLg08Co4mOuH9dYmM1/MsNAZ9Mvd4yUyODPouBz9VzkmLol4pvoMKprfn/LNQAK75hJHbzTYxLqOjJXaWZtwN+LSTZ99tKxdXN5N4Rydn8uQrgNYs6ST+oX276d+tA707tmHu9EkpbqszJ45RrY5iKa9QqSpXL51PbCsLY1slfEJbJcqk1xEfF4uTi6lMQghiYqKRUhIbE4ONnT0arRaAkwf3MLpvF4Z/357lv0zFkEKZLp8+QYUaymxf6YqVuXXlIlJKzN66/xISEpCfaQz8EM8DQ+j56x+0nLycjjPX4O2XshfOU7cfUzZfNhxsrLC3saJsvmycvP0YAFsrZeZDSklsvO4zjYL/v0i9/h/5+1pRlfvUkR4IklLGAUgpg6SUPkKIEkKIY0KIS0KIfUKI9ABCiO5CiAtCiGtCiC1CCGshRFFgOlDHaPm3EkK0FkLcEELcFEJMe13YB8I7CyHuCyGOAeU+t5ANZ41i57CpJie+NVs4mW0Dx/FLmYbsHDKZpr+OT1FeDl4ehD5/Y0kNe+GHg5eHSRqNmRkl2jbi3r7UWS9TSjpPT2xz5yby9u0kcc4VK1Bs3VryzZjOwylTAbDKkgXXqlW40as31zp3QRoMuNWonqKyLNxciQsIUL7o9eiiojBzcFDi3N0pumoVJbdu4cW6dcQHB38gp+QRQPdJC2g+ZDobD55KEv/EN5DwyGg6jZ1L8yHT2X7sHACPXvix5/Rl1k4YxNYZQ9FoBDtPXEhRmQGvwvB0cQTATKvFztqK0AjFougbFELjwVOo2msUXRtWw93ZIdUyvYtXluw8uHmVyPAw4mJjuXHhNCGBAayZN402vQYx+teVNO/Wl3ULZqYov9CgIJzd3BO/O7m6Exr05oG4YvZEBrWph9+Lp1Rp0PyT6/8uzlkykKFofp6eu2oS7uDlQeFGNTm5eJ1JuEfeHBRvUY85FZoxvURdDHo9Jds2SlFZb/c3g15PbFgENi5OJmkylyqC1sKcoEdPUy2Li5s7DVq2o1eLBnRvWgdrW1uKlvrGJI3v82dERkYwekBPfu7RgaP7FPefF0+9OXXkABONlnaNRsOJg3tTVO6rwEBc3ZRxQ2tmhrWtLRFhyixRUIA/g7q04fsW9WnYugPO7yiw/yQedukolsGBX44/YsaRh0ipKPspwdHKnJDoeAAMEmIT9NhYaBFAo4Lp2X7T75Pq5urmTrPW7WjfpB5tGtbCxsaWEmW++fiFwLMn3hw/dCDR0q7VaDmyP2UuY8GBAbi5v2krGxtbwo1tFejvR88OrWjfuC7N23bExS11beXs6k6dZm0Z0LYRfVvWw9rGhkIlTSfPqzdshs+zJ/RtVY9hPdrSvvcPaDQaXj715tyxg4z+ZQmTF/+ORqPh1OF9KSo3JDgQ59f3n9YMaxtbIsMVmYID/BnWoy0D2jSgXsv2OH3O+0/A9/PW03LycjafuAzAuHW7GdayJhuGd+XHplWZ+EfK+lBAaASeTvaJ3z2c7AgIffPyOGrN31QeMpcn/sG0rlzq88mg8kVQfe5Tx35gtBDiPnAQ2ACcBn4FGkopA4UQLYFJQBdgq5RyKYAQYiLQVUr5qxBiNFBSStlXCOEFTANKACHAfiFEI+D8e8LPAeOM4WHAEeDK5xIwX50qRAYG8/LyTXJUVAZNCxtrspYtTvs/33j/mBmtFaU6NqN8v04AuObIQrcdK9AlJPDK+zmrm/dCiGRsANLUutFk/ngen7iA96mUKZupQWNlRd5JE3k8dx766Ogk8a+On+DV8RPYFylC5u7duDXwBxxKlMA2Tx4KL1sKgDZdOhJCQgDIO3kS6dKnR2NmTjoPd4qsXAGA76bNBOze/UF54wMCuNqpExYuLuSdMpngI0cT800paycMwt3ZgeCwCLpNnE92Lw9K5n/j7qHX67nt/Zzlo/oSF59Am5GzKZIrG2dv3uO29zNaDpsBQFx8Ai72ilW//4ylvAgIJkGnxzfoFU1+Ul5y2tepROPK3yBlUmvUaznTuzrx18xhBLwKo9+MJdT4piiujvZJ0qcGr8xZqdW8HbOHDyCdlRWZsudCo9Xy6M4NFk0emZguIUFRjE7u38mh7ZsACPB5wdxRP2Jmbo6rR3r6jJ6KJBlr2lvN1GXQSAx6PesXzubC8YOUr/Fp/rlvY2FjTddNC9k6aAKxEZEmcU3mjGbHOy/RALmrlCNT8YIMPqe4P5lbWSZa9rtuWYRL1kyYWZjjlNmLny8pivOxX1dybtXmZO+/t9vP3tON9qtns67zj8m268eIjAjnwqljLPhzGza2dswaM5Tj+/dQsUbtxDR6vZ7H9+4yZvYC4uPiGN6nK7nzF+TGpQs8vn+Xod93BCA+Pg57R+XFY/rInwjw9UGn0xHk78fgrm0BqNOsFVVq10+2DV+L6uruwewV63kVFMj0kT9R9rsqODqnbVYiteR2syWToxU/VlL6oLlWQ0Scsm6oa5nMOFtbYKYROFmb81NlJc3xR8Gce5Z8v5dA+ewu3PaPIDQmZa4k7yMiPJwzJ46xatMObO3smDRyCIf27aZqzTofvfbqxfM8uHuH/l07ABAXF4uDk9JW44cNxs/HB50ugQB/P3p3bANAoxatqFG3wbvDO/Cmrdw8PFm05k+CAwMZN+xHKlSuilMq2ioqIpzLZ44z5/etWNva8euE4Zw8uIfy1d7cfzcuniNLjtwMn7EAf58XTBvanzwFi3LrykW8799jdJ/OgOn9N2fsEAJ9FZmCA/wZ/r2y9qBm45Z8V6te8n3FKJSLuwdTlqwjJCiQOWOHULpiZRycPs/9t2ZwR9wd7QgOj+L7eevJ6unKtccvGLx0S2KaeJ1iXd52+hrrjpwH4FlgCH3mb8DcTEMGF0d+6dn8PeP4m88TOtRHbzAwZcM+9l28TaNvi3wWGf6/eHcc/a+jKvepQEoZKYQoAVQAKqMo9xOBgsAB44NVC7w2VRc0KvWOgC2QnJmgFHBUShkIIIRYB1REGeeTC+ed8A1A7uTqK4ToAfQAWLw4ZQvasn5bgvz1qpK3ViXMLNNhaW9L65WziAkNZ07JpErPhdWbubBa8VPudXA9f3b9iZCnLxPjQ1/6keO7N5YVh4yePDJakwGqj+yPraszq3sl9XP8VIRWS96JEwncf4BXxz88KxB+7RqWXl6YOTgghCBgz16eJvOb3R0+AlBmA3KNGM7Nfv1N4uMCAknn7k58YCBotZjZ2KALDzdJEx8cTLT3E+yLFCH46NFUyfTaMu7iYEe1UkW48fCpiXLv4eKIk50t1pbpsLZMR8l8Obj39CVIaPhdGX5o0yBJnvN+6g4oPvcjflvLqrGmC3A9XBzxCw7F08UJnV5PRHQMDrami07dnR3ImSk9l+4+ouY3xVIlU3JUqFmfCjXrA7B11SLsnZyxtrFjzILVSdKWr1EvUSGf/nMfuvw4EleP9InxTq5u3Lv+5v03JCiAPIVN66jRailVsRr7tqz7bMq9xsyMrpsXcnH9dq7/lbTrZy5RiI7rFX9kW1cn8teuhF6nQwjB+TVb+HvEjCTXLG+q+KM7Z8lA2xUz+bVqa5P40Jd+OGZKT+hLPzRaLZYOdkS/CgXA0s6W7/9ewa7Rs3jyzixCSrl+6Tzu6b1wMCpFZSpW5t6t6ybKvYubO3YODlhaWWFpZUX+IkV58ugBEkmlmnVp26NPknx/nqjIGuDrw/yp4xk/d5FJvIubO0GB/ri4e6DX6YiOjMTW3nSWyNnVjUxZs3Pn+lXKVqqaJvnSwvlnIey87Z8kfPm5Z0q9rM1pUzwj80+a+vSHxiTgZG1BWKwOjQBLcy3R8XqyOluTw8Wa8tlcSGemwUwjiNfr+ftW0jI+xJWL5/Hw8sLRqJSX+64yd25cT5FyL6WkWu16dOnVN0nc6CnKjJmfrw+zJo1lxvwlJvGu7u4EBvjjZmyrqKhI7N5pKxc3N7Jky8HNa1eoULlaimW6efkCbp5eiUp5yfKVeHD7holyf2zfTuq36oAQAs8MmXDz9DK68Ekq1KhDy669k+T7w1hlYjzQz4fFMyYwctZCk3hnV3deBfrj4uaOXq8jOioSWztTI4aTqxsZsmTj3o1rlK5YJcUyfQh3R8UA42JvQ5Wiebh4/yl2VunYNKJ7krSNvi2SqJB3mf07EzrWJ4NxxhXAw8mei/ffzNb5h0RQMncWkzy0Gg21SuRn1YGzX59yry6oNUF1y0klUkq9lPKolHIM0BdoCtySUhY1/hWSUtYwJl8F9JVSFkKxtlsmk+X73Ns+5PaWIpOblHKJlLKklLJkjx5JFz4lx56RM5iYrRyTc1VkXdv+PDxyhtUtevHqyXMKN30zgKYvnDdF+d3bf5w81Spg5WiPlaM9eapV4J5x8WDpLi3IU6MCa9sNSJMV8WPkHDaUmKdP8NmwIdl4ywxvFv/a5M6NMDdHFxZG6KVLuFT6DnNHRwDM7OxI5+GRbB7v8urUSdxr1wLAtVIlwi4rU6kWbm5ojLMdWjtb7AsXIubZs1TJEx0bR1RMbOLn09fvkjNzepM0VUoW5tLdR+j0emLi4rn+8CnZM3hQplBu9p+9SnCYMg0bGhmFT+CrFJVbuUQhth9VXsj2n71KmQK5EULgFxxCbLxiPQ+LjObKvcdk80rZ7/QxwkOVugUH+HH51FHKVqmFq2d6Lp44DCjKx/PHSXc1SY4CJb7h9uXzREWEExURzu3L5ylQQpmR8Pd5kZjftXMn8cyY5SO5pZw2y6bhf+chR35Znmz8uJwVGZejAuNyVODqlj1s6juaG9sPcP/wKYo0rZ3oE2/t5IBT5gzJ5vEuN3ccpHQHZbeYos1q8+DIGQC05uZ03bKIC79v5erm3WmWydXdk/u3bxIXG4uUkhuXL5AhS1aTNKXKV+TOjauJft4Pbt8iY+ZsFCpeijPHDhMWorRtRHgYgX6+yZSSlJLfVuToXmWW4syxwxQsXhIhBMEB/sTFKX0iMiKcuzev4ZX587Xhx7gfGEnRDA7YWig+3dbmWpyszFN07U3fcEpndgSgiJcDDwKVmZ3fLz5n7L57jN9/j+03fTn/LDTVij2Au4cnd2/eJNbYVlcvXiDTO231PoqWLM3Jo4cIfaut/FPYVt+Ur8jB3TsBOHH0EEVKlEIIQeBbbRURHs7tG9fImDll9XmNi7sHD++8uf9uXblIhnfycHX34NYVZRY4LCQY3+fPcE+fgQLFSnH++Jv7LzI8LMni+/dRvGwFTuxX+s3540fIX9R4/wUGEG+UKSoinAe3rpM+U+ZUyfQ+ouPiiYqNS/x85s5jCmb1IoOrI/sv3QGUcevei5TdG+XyZ+f0nceER8UQHhXD6TuPKZc/O1JKngW8Sszv6I0HZPX8/5n5UvnnUC33qUAIkQcwSClfaxVFgTtADSFEWSnlGSGEOZBbSnkLsAN8jWFtgZfJZHsOmCuEcEVxv2mN4uZz/iPhLkA40By49o8I/BbrO/xAk/kTqDa8L1ozM65u3Inv9bsfvS4mJIwDk+cz4Mw2AA5M+pWYEMVXsemCiYQ8fUm/k8oU482/9nFg0q+fpb52hQvhXqsWUQ8fJbrOPFu8JFFJ99u+HZdK3+FeqxYGnQ5DXBz3xoxR6vzkCc+WLiP/nNkIoUHqdTyaPZs4/48Pov47d5F71EiK//mHshXm2LGA4sefrW9flPcywcs//iD68eNUyRQcFkH/mYqrkF5voG75klQomp8N+5WtzVrWKE+OjJ6UL5qPxoOnotEImlYpS67MXgD0b1WX7hMXKAvBtFpGdm2Ol5vze8t7TdMqZRk6fw21+o3DwdaamQOVae3HL/2ZseYv5TVUQqf6VcltLOtTWThxBJHhYWjNzGjbezA2dvZ0+3ksa+fPYOcfq9DrdJT+rhqZsuf6aF62dvbUa92ZiQOU3SbqtemMrZ09BoOBFbMmEBsdhZSSTNly0a7vT5+l/tnLlaR0+ya8vH430XVm58gZOBl/n1OL17/3Wr87D9k1eha9965BaDQYEhLY1G80Ic+SGz5MObNiA+3XzGHUvSNEvwpjVZt+ABRrUZecFUtj4+JE6Y7K9oPrugzm5bU7qZIrd/6ClP2uKj91b49WqyVbrjxUr9eYfduVPlyzYVMyZslGsdJl+bFrW4QQVK3bkMzZcwDQumtPJgzuh0FKzMzM6DbgJ9w803+oSACq1mnAvMlj6NumCbb29vwwehIAL549YfVvcxFC8X5r0LIdWbIn3ZXoY3QomYmcbjbYWpgxrlZe9tzxR2v0WTj15BV26cwYXDknlmYaDBIq5XRl8sH7+EfEseu2P73KZUMjQG+ATddeEpICl5qzT0NoVzITI6vnJtq4FebnJG+BglSoXJW+ndui1WrJkTsPtRs2Yddfykxr3cbNeBUcRP+uHYiOikJoBNs2/sHidRvJki07Hbv3YvjAvhikATMzM/oMGoJHCtqqVr2GTJ8wms4tGmFnb8+wcZMBeP7EmyXzf0EIgZSSpq3bkS1H6toqZ76ClK5QhZG9O6LVasmSIzeV6zTi0N/K9spV6zehUdsuLJ4xgaHd2wKSlt16Y+fgiJ2DI807f8+0oQOQ0oDWzIxOfX8ymeV7H9/Vrs+iqeMY1LEZtnb29B0xAQCfZ96sXzwvUaY6zduSKVvq77/keBUexcDFSlvpDQZqlypA+QI5yOrhwsQ/9rBkz0l0egO1SuYnT8aPG1UcbKz4vk55Wk9bCUDPOhVwsLHCYJCMXP03kbFxSAl5MrozsnXtj+T2v4dquTdF/BMW038rRpecX1HcbHTAQxS3l4zAPMAB5YXpFynlUiFEL+Bn4ClwA7CTUnYSQnTC6HNvzLcNMAxFTdotpfz5I+GdjeG+wFVA+zqvDyAHm6dsh5uvhZkJjzlV/pN2Ev2fo9xJZfMk3bX9H0n5dWFWRJnMOvE49YuI/5epkN2F/tqk2xx+zczTK+4jN3w/fVvT/yUKpXdgwF+fdo7F/xpzGxcCwDvo03bV+V8jm6sdF96zLuFrpVRmxZUo7vCaL1yTz0u6Kh3gw54G/y886NXsH1Fmcy3c/MVlSwuq5T4VSCkvAd8mExXEG3/4t9MvBBYmE74KxWXn9ff1QBJT3gfCVwIrU15zFRUVFRUVFZV/JwbVcm+CqtyrqKioqKioqKh8tai75ZiiLqhVUVFRUVFRUVFR+ZegWu5VVFRUVFRUVFS+WtQFtaaolnsVFRUVFRUVFRWVfwmq5V5FRUVFRUVFReWrRerVnR/fRlXuVVRUVFRUVFRUvlrU3XJMUd1yVFRUVFRUVFRUVP4lqJZ7FRUVFRUVFRWVrxZpUN1y3kY9ofa/g9rQKioqKioqKp+bL36K643Wdf4RHafQH7u/uGxpQbXc/4cYYpH9S1fhszIt/jHRG6Z86Wp8VqxbDgMgNjrqC9fk82JpbQOA7tKuL1yTz4tZibqMsszxpavxWZkQ+wiAavNPfuGafF4O9i1PaGT0l67GZ8XR1hqAhMBnX7gmnxdzt8zEn9nypavxWbEo2xQAw8OzX7gmnxdNzm++dBUAMKgLak1Qfe5VVFRUVFRUVFRU/iWolnsVFRUVFRUVFZWvFvUQK1NU5V5FRUVFRUVFReWrRd3n3hTVLUdFRUVFRUVFRUXlX4JquVdRUVFRUVFRUflqURfUmqJa7lVUVFRUVFRUVFT+JaiWexUVFRUVFRUVla8WdUGtKapyrwKA0Gjod3Y74S/9WdW4m0lc/vrVqDF2ENJgwKDT8/ePE3hy+uInlae1sKDlyplkKFaQ6FehrG/bj5CnL0lfJB+Nf52Apb0tBr2Bw1MXcH1T2vZG1xsMtF20E3d7a+a1q2YSd9Hblx/WH8bLyRaAKvmy8H3lop8kU7xOz6itJ7jjE4yDVTqmtfgOLyc7fEIjGfzHEfTSgE4vafVNXpqXypvq/J88ecLPQ4Ymfn/x8iW9e/WkXdu2iWG7du9m5apVAFhbWTNi+HDy5Mn9aXLFxzNi1Cju3LmDg4Mj06dNJYOXFz4+PgwaPBiD3kCCTkfrVq1o0bxZmsqo3n8CNlbp0Gg0mGk0bJw0KNl0Nx49o83ouczs34GaZYp8iljEJ+gYtnA9t7yf42hrw6z+Hcjg5oxP4CsGzFmltJdOT9uaFWhZ7dtU5z/o3jHiI6Iw6PUYdHoWlWtkEl/uh+4UadUAAI2ZGW55czA1YyliQsLSLJPWwoKmy2fiVbwg0cEhbGzfn9CnL/EsnI/688Yn9qtj037j5ubU96vBVXJRJqsToTEJdP/jSpL4TI5W/FQtFzndbFl59imbrrxMsyyvMdcIhlTPTS43W8JjdUzcdxf/iLjEeGtzLSvaFufk42DmH3+c6vyfPnnCiGFDEr+/fPmSHj170bpNW5N0ly5eZM6sGeh0OhwdHVm0dHnahULpV+NGj+LunTs4ODgwceo0vLy88PX1YcjgwRgMenQ6HS1atqJJs+ZpKiM8IpIx02bz8PETEDBh2GCKFsxvkub85WtMm/cbOp0eJ0d7Vs2f/clyDZs4ndv3HuBob8/M8SPIkN4THz9/Bg4fh96gR6fT06ZZQ1o2qp+mMmr+OB1rq3RohQatVsOGsX1M4g9fvs38rQfRCIFWq2FIm7oUz5310+RK0DF86SZuP3mJo601M3q1JoObEz5BIQz8dR0Gg0Sn19OmWllaVCmT6vyrdv4RGytLtBpFps1zx5nER0RF8/PMxfgGBqPT6+nSpDZNqlf8RJkSGDJrCbcfPsHRzpbZQ3uTwcONlwFB9J84D4NBkqDX0a5+dVrVqfJJZf3TGNQTak341yr3QogRQBtADxiA76WU5z5j/k+AklLKoI+kiTDWAaC3lPL0J5YbKaW0/ZQ8kqN8v84E3H2EpV3SrB8ePs3tvw8C4FkoL23X/8qsQtVTlK9Tlgw0XzaDJdXbmISX6tyCmJBwZuSvQpEW9ag9eQjr2/YnITqWDV0GE/zwCXbp3el/dgf39x8nNiwi1TKtP3OHbG4ORMUlJBtfLItHEqU/JfiERDD6r5Ms61LbJHzb5QfYWVqwY2BT9t54zNwDl5jWohJutlas6l4HCzMt0XEJNFuwje/yZMbd3jpV5WbNmpWNG/4EQK/XU71mLapUrmySJoNXBlYsW4a9vT0nT55i/MSJrPt9TYryf+njw+jRY1i+bKlJ+F/btmFvZ8/OHTvYs3cfv8ydy4xp03Bzc2PNqlVYWFgQHR1N02bNqfTdd7i7u6VKrtesHNEbJ/v339p6g4HZf+ykXOE8qcr3ZeArRiz6g1WjTBWALUfPYW9jxd45I9h9+gqz/9jJrP4dcHWyZ924/liYmxEVG0ejn6dTuUQB3J0cUi3TipptiQ4OSTbu1JylnJqj/NZ56lTh2/5dUqzYO2bJQJOl01lRw1QBLdGpOTGhYfxSoAqFmtejxsQhbGzfn4ToGLZ0/YlXj5R+1ev0dh4eSH2/2nfXn203fBhSLfkXxog4HQuOP+bb7C6pyhfAwy4dP1fLzY9/3TAJr53fg4g4HR3XXqJSLle6f5uVifvuJcZ3+iYL133S/kKUJWtW1v6xAVD6Vb3aNan0Tr+KiIhg+tTJzP11AZ7p0/Pq1asU5+/j48OEsaNZuGSZSfiObduws7djy/Yd7N+3lwXz5jJp6jRcXd1YtvJNv2rTohkVvvsONzf3VMs2de5vlCtTkjkTR5OQkEBMbJxJfHhEJBNnz2PxzCmk93QnOCT5ezU5Xvr6MWLSDFbNn2USvnXnXuztbNmzYTW7Dx5h9sJlzBo/EjcXZ9Yu+sUoVwyNOnSncvmyuLu6plougBVDuuFkZ5Ns3Df5c1C5WD6EENx77svgBX/w99TkDQZJ5AoMYeSyzawc1t1UruMXsbe2Yvf0wew5e405m/Yys3dr3BztWDuyJxbmZkTHxtF4xFwqFcuHu5N9qmVaPWUoTg52ycat33mIHJm8WDjmB16FhVOnx1DqVfoWC/OPq3Ev/QMZNmcZa6YOMwnfvO84DrY27Fs2g13HzjJz5UbmDO2Dm5Mjf8wahYW5OVExsTToPYIqZYrh7uKUaplUvgz/Sp97IURZoB5QXEpZGKgGPP9C1akspSxq/Pskxf6fwiGDJ3lrV+bCig3JxsdHvTnV0cLaCuSbN+RibRrS99RfDLiwkyYLJiI0KbulCtSvxqXflRMIb2zZQ87KimU06IE3wQ+fABDhG0BkYDA2bqlXFPzDojh5/wWNS6Tear3r2iPaLd5Jy9+2M3HHafSGlE33Hb3zjPpFcwJQLX9Wzj/2RUqJuZkWCzMtAPF6/ds/X5o5d/48mTJmxMvLyyS8aNEi2NsrD5XChQvh7++fGLdz1y7atGtPi5atGD9xInq9npRw5OhRGtSvB0D1alU5f/6CIpe5ORYWFopc8fEYPodgH2DdvhNUL10Y53cefn+fvEjLkXNoMmwmY5dtTHF7Hb54k4YVSgFQo0xhzt58gJQSCzOzxAdmQoLuH5cLoHDL+lzf+Hfi9yKtG/L9ia30Pvc3DeanvF/lrV+Nq2u3AnBr6x6yVy4LQPDDJ7x69ARQ+lVUYDDWrqnvVzd8womI1b03PjQmgXsBkeiTsaJVze3G/OZFWNSyKAMr5UCTwkPdv83uwv67AQAcfxhEsYyOiXG53GxwsjLn4rPQ1IjxXi6cP0/GjBlJn960X+3bs4fKVarimT49AM7Ozolxe3bvonOHdrRr3ZIpk1Ler44fO0rdeorlukrValw4fz5Jv0qIj0+zRTIyKopL127QtJ5ihDA3N8f+HePN7gOHqVaxPOk9lRcHF6c3ytvf+w7Sqntfmnb6nnHTf0mxXIdPnqZh7RoA1KhUkXOXriQdLxISMKSwn6YFa8t0CKHcYDFxCYmfAf4+fYXW436j2ahfGbfqrxSPF0eu3KFB+eIAVC9VkHO3HxnH9zfjRbxO/4+NF0JAVEwsUkqiY+JwsLPBTKuMCzsOn6LFD2Np3HcUY35diT6FLiqHz12mYdXyANQsX4qz124rY6C5GRbm5opMCTqk/N93eZF6+Y/8fa38K5V7ID0QJKWMA5BSBkkpfUCxpgshxgkhLgshbggh8hrDSwshTgshrhj/5zGGa4UQM41prwsh+r1dkBDCSgixVwjRnRQghMgihDhkzOuQECLzR8KzCSHOCCEuCCEmvJVPeiHEcSHEVSHETSFEhbT+WPVnjWL3sKnIDwxyBRrW4McbB+i8fTmbuitT2O55c1C4eT1++645c0vVw6A3UKxNwxSVaZ/Bg7AXvgAY9HpiwyKwfscqkLFkYcwszHn16GmqZZqx5zwDapb4oAJx/XkgLRZsp8+aAzwKUCxWjwND2X/Dm5Xd6rChd0M0QrD7esqm+gMiovF0UCxJZloNtuksCI1WLGV+YVG0WLCd2rM20al8wVRb7d9l77591KpV84Np/tq2jfLlygHw+PFj9u3fz+qVK9i44U+0Gi27d+9JUVkBAYF4enoCYGZmhq2tLaGhoQD4+fnRrEULatauQ+dOHdNstRdC0H3qYpoPn83GQ2eSxPu/CuXQhRtJ3GMevfRnz5mrrB3bn61TBqPRaNh58lLK5AoJw9PFUZFLq8XO2pLQiCgAfINDaDxkBlX7jadr/SppstojJR13rqLn6e2U7NrqvcnMrSzJWb0it//aC4BbnhwUbFaXpZVb8FuZ+hj0eoq0TmG/8vI06Vdx4Un7VYaShdFamBPyOPX9Kq1kdrKiUi43Bmy5Ts8NVzFIqJo7ZZZoFxsLAo1uOAYJUfE67C3NEEDP8tlZctr7s9XzwP591KhZK0n4s2dPCQ8Pp1ePbnRo24bdO5UXMW/vxxzcv5+ly1ey9o8NaDUa9u3ZnaKyAgMDcPcw7Vdhxn7l7+dH25YtqF+nNu07dUqT1f6Fjy9Ojg6MnDyDZp17MnrqLKJjYkzSPHn+gvCICDr1/ZEWXXqzfc8BAB49ecreQ8f4feEvbFm1WOlX+w+nqNyAwGA8jeOAmZkWWxsbQsPCAfD1D6Bxxx5Ua9KGrm1bptlqL4Tg+5kraTFmPpuOnk82zaFLt6g/dDZ95qxmfNemADz2CWDfuRusGfE9myf0Q6vRsOvM1ZTJFRKGp7MyDphptdhaWRIaqRi+/IJDaTJyHtUHTaNLnYppstoLAV1HzaBp/9Fs3HMkSXzbetV4/NyHiu0H0LDPCIb1aItGo+HRMx/2nDjPuhkj+Wv+BDQaDX8fTZkd0T84hPRuzoky2VlbERoeCYBvYDAN+4ygSqcf6Nqsrmq1/8r4t7rl7AdGCyHuAweBDVLKY2/FB0kpiwshegODgW7AXaCilFInhKgGTAaaAj2AbEAxY5zzW/nYAn8Ca6SU7/N9OCKE0ANxUsoywHxj+tVCiC7APKDRB8LnAgullGuEEG/7FbQB9kkpJwkhtECatMW8daoQGRDMyys3yV7x/X6Ct7bv59b2/WQrX4oaYwexrHZ7clT+lozFCtLvzDZAUVIiA4MBaL9pIc5ZM6G1MMcxkxcDLuwE4NSvq7i4ZrOJJSWRtywedp5utFo1m41dBiNTaQk5fu85zjaW5Pdy5aK3b/Jyp3dh96BmWKcz58T9F/yw/jA7Bjbl/GNfbvsG026x8vCOS9DjbGMJwKA/DvMyJIIEvQG/sCha/rYdgDbf5Kdh8VzJWuRfi+npYMPGPg0JCI9m0B+HqVYgKy62VqmS6zUJCQkcO3acAf36vTfN+QsX+GvbNlatWAEolv47t+/Qtl17AGLj4nB2VgbrgYN+xOflSxISEvD186NFS0URbdOmNY0aNkz293/dfp6enmzeuJGAgEAGDhpE9WrVcHFJvUV47dh+uDs5EBwWQbcpi8ju5U7JfDkS46eu2c6g1vXQvmPBPnvzPre9X9By1BwA4uITcDG69vSfvYIXga9I0OnxDQqhybCZALSvWZHGlUp/UK70Lk78Ne0nAkLC6DdrBTXKFMH1PdPl72Np5RZE+AZg4+ZCp12rCbz3iKcnLyRJl6duVZ6duZTokpO98rd4FStIz1N/AUq/ijL2q9YbFuKUNSNaC3McMnnR+5xyn55ZsIora7aQfLd6I6etpxvNVsxiS7efUt2vPoViGR3J5W7DgubKOol0ZhpCYxR3ubG18+Fpnw5zrQZ323QsalkUgL+u+7DvTgDvez9vUCg95568IjAy/rPUMSEhgRPHjtG7b9J+pdfruXvnDgsWLSYuNpaunTtSsFBhLp4/z907t+nUoR0AcXFxOBmt+j//OAgfH6Vf+fv50a51SwBatm5D/QYf7lcenp6s27CRwMAAfv5xEFWqpr5f6fR67tx/wPCBfShcIB9TflnA8rUb6Ne9k4lct+89YNnc6cTFxdO2Z3+KFMjHuUtXuH3vPq269THKFY+zkyMA/YeN5aWvLwk6Hb7+ATTt9D0A7Zo3pnHdWh/uVx7u/LV6CQFBQfQfNpbqlSvi6px6pXHNiO9xd7InODySHjNWkC29GyXzZDNJU7VEAaqWKMDFe97M33qAZT935eztR9x++pLW435T5EpIwNno2jNg3lpeBoaQoNfhGxxGs1G/AtC2xrc0rlDiPeO7cRx0cWTrxP4EhIQzYN5aqpcqmOrxYv2Mkbi7OBEcGk7XkdPJlik9pQq+WZt18vJN8mbPzKopQ3nmG0DXkdMpWTAPZ6/d4tbDJ7QYqPjox8bH4+KovFz0nTiXl35BSlsFBtO47ygA2jesTpPqFZMfA163lZsL2xdMIiA4hL4T51KzXClc02Lk+H/CoC6oNeFfqdxLKSOFECWACkBlYIMQYqiUcpUxyVbj/0tAE+NnB2C1ECIXIAFzY3g1YJGUUmfM+21ny+3AdCnlug9Up/I7fvll3yrzd2D6R8LLobxkvA6fZvx8AVghhDAHtkkpr75bsBCiB8rLCYsXL062clm/LUH+elXJU6sS5pbpSGdvS8tVs9nQKXn/RO+TF3DJnhlrFyeEEFxau5W9I2ckSfd7817A+33uw1744ZAxPWEv/dBotVg62BH9KhSAdHa2dN6+nH1jZvHsfBKxPsrVZwEcu/eckw9eEK/TExWXwIjNx5nU7M3iI1tLi8TPFXJnZMrOM4RExSIl1C+ak/7VSyTJd3ZrZUHR+3zuPeyt8QuLwsPBBp3eQGRcPA5W6UzSuNtbk8PdkctP/aleIGuqZQM4efIUefPmfe/D/v79+4wbP4EF83/F0dERUN6b6tevz4D+SRWXX2YrPrPv87n38HDHz88PDw8PdDodkZGRODiYDvLu7m7kyJGdy5evUL166tcxvLaMuzjYUa1kIW48emai3N/yfs7gX38HICQiihNX72BmVPQbVizJD63qJclz3qAuilzv8bn3cHbELzgUTxdHdHo9EdGxONiaviO7OzmQM6Mnl+4+TvUC3ghfxZUkKjCY2zv2k7FkkWSV+0LN63HjLZcchODquq0cGDUzSdo/Wir96n0+92EvlX4VbuxX6eztiHmrX7X/axkHx87mRRr61acgBBy4G8DyM0lnC8buuQO83+c+KCoeN7t0BEXFoxFgY2FGeKyO/J52FPKyp0Gh9FiZazHTCmIT9CxLpoyUcPrUSfK8p1+5u7vj6OiIlZUVVlZWFCtenAf37yOlpE69+vTp1z/JNdNnKQtT3+dz7+7uQYC/ab+yf6dfubm5ky17Dq5euUzVailb5/QaTzc3PNzcKFwgHwA1Kldk2do/TdJ4uLnh6OCAtZUV1lZWlChSmHsPHyElNKhdgx96dk2S77wpY4H3+9x7uLviFxCIp7sbOp2eyKgoHOxNFV13V1dyZsvC5Ws3qFE59YtCX1vGXextqVo8Pzcfv0ii3L+mZJ5svAh4RUhEFFJKGpQrzsDmSWc95/ZXXtDe53Pv4eyA3yvFeq/T64mMicXBxtRA4+5kT44M7ly+/4QapQqlTiajZdzF0Z5qZUtw495jE+V+64ETdG9eFyEEWbw8yOjhxuPnPkgJjaqWY1CnFknynD9ygCLTe3zuPV2d8Q18haers3EMjMHxnXUM7i5O5MycgUu37lOzfKlUyfT/ydfsQvNP8G91y0FKqZdSHpVSjgH68kZBBni9qkjPmxecCcARKWVBoD5gaQwXKMp+cpwCaotkzdApr2oKwpOkkVIeByoCL4HfhRAdkkmzREpZUkpZskePHskWsnfkDCZnL8e03BVZ364/j46cSaLYu+TIkvjZq2gBtBbmRAeH8PDIaQo1rp3oE2/l5IBjZlNf1fdxe+chSrRXmqRQ09o8Oqq4YmjNzemwaRGX1/7FjS0pcxt5l/7VS7BvcAt2D2rO1ObfUSpbehPFHiAoIjrRanHzRSBSgqN1OkpnT8/BW094FalMX4dFx+ETGpmicr/Lm4m/rz4E4ODtJ5TKlh4hBP5hUcQmKH7K4TFxXH0WQFbXtFtA9uzdS+33uOT4+voyaPBgJk2YQNYsb9qtTOnSHDx4kGDjQsCwsDB8fHxSVF6l775jx9/KzMuBg4coXaqUIpe/P7GxsYpc4eFcvXqNrFmzfCirZImOjSMqJjbx8+kb98mZydMkzf65IzkwbxQH5o2iRpkijOzclKqlClGmQC72n7tOsHFhaGhkFD6BKVvsWLlEAbafUJTt/eeuU6ZAToQQ+AWHEhuvWIPDIqO5cv8J2dKnzt3I3NoKC1ubxM85q1bA/9b9JOnS2duStUJp7hgXrAM8PnKaAu/0K4cU9qu7Ow9RtJ1iIyjQpDbeb/Wr1hsXcnXdX9zamrZ+9Slcfh5KhRyuOFopNhO7dGa426X7yFUKp71fUSOv4pZSMacrV1+EAjDlwH3arL5IuzUXWXzKmwN3A9Ks2APs37eXGrWSuuQAVKxUiatXrqDT6YiNieHWzZtkzZaNkqVLc/jQwcQFtmFhYfj6pqxfVfjuO3YZ3XsOHzpIyff0q+vXrpIlS9ZUy+Pq4oynuxvez5TlZmcvXiHHO/2zcoWyXL5+A51OT0xsLDdu3yV71sx8U6IYB44eT1xgGxYejo+ff5IykqNyubJs37MfgP1Hj1OmeFGlXwUEEhsXZ8wvgivXb5E1c6ZUyxUdF09UTFzi59O3HpIzg4dJmmf+wYnj++0nL0nQ6XG0teab/Dk4cPEmwUbXk7DIaHyCUraIuFLRvOw4eRmAAxduUjpfdkWuV2HExiuzUGFRMVx98JSsnqkbL6Jj44iKjkn8fOryTXJlyWiSJr27M2ev3QYgKCQM75e+ZPJ055ui+dl36iLBoYrrU2hEJC8D3rvPhwmVyxRj+6GTAOw7eYFvCiuLkP2CXhEbZxwDI6K4fPsB2TJ6figrlf8x/pWWe6O/vEFK+cAYVBT42KjvgKIoA3R6K3w/0FMIcfS1W85b1vvRwCjgN6BXCqt3GmiFYoVvC5z8SPgpY/haY/hrGbMAL6WUS4UQNkBxIGXboqSAMt0VS/u5pesp2LgWJdo1Rp+gIyEmlvVtFStVwJ2H7Bs7i267VyM0GvQJCWzvP4bQZx9/uF1YuYGWq2bz0+3DxISEsb6dkmfh5nXIVqEU1i6OlOigKP8bu/2E77U7nyzTpgt3AWheKi8Hbz9l0/l7aDUCS3MtU5p/hxCCHO6O9KlanF5r9iMlmGk0DK1XBi/Hj29Q1Kh4LkZuPUGDX7Zgb5WOqc2/A8A7MIzZ+95YbDuUK0Auj7T5L8bExHD23DlGjRyRGLZx02YAWjRvxuIlSwkNDWPylCkAaLVa/li/jhw5stOnT2969eqNQRowMzNj+NChSRbkJkfjRo0YMXIU9Ro0wN7egelTlbwfe3sza/ZsBAKJpGOH9uTKlSvVMgWHRdJ/juI+pNcbqFuuOBWK5GPDQcVv9EPbUObM6En/FrXpPnUx0iAx02oZ2bkJXm7O773mNU0rlWHob+up9cMkHGysmdlPeT9+7OPPjLU7El/rO9WtRO4UKtevsfVwpc2GhQBozLRc3/A3Dw8cp1S31gBcWPYHAPkb1uTRwZMkRL/xhQ68+5CDY2fTcecqY7/SsXPgGMJS0K8ur9pI0xWzGHjrMDGvQtnYQbHcFWxWh6zlS2Ht7Egx40v11u4/43c9df1qeI08FMnggIOlGX90KsXqc88wMy5s2XnLDydrc35rURRrCy1SQpMiXnRdd5lnITGsOvuUqQ0KoBECnUHy67FHBETEfaRE2HPbj6HV87C6XQki4nRM2nc3VXVOCbExMZw/d45hw0cmhm3dvAmAJs2aky1bdr759lvatmqBRqOhQaPG5MipLJ7v2bsP/fv0QhokWjMzfho6NMmC3ORo0LARY0eNpGnDBtg72DNx8lQAnnh7M2/O7MT7r237DuRMQ78CGP5DH4aMm0KCTkcmr/RMGDaYDduUF4qWjeqTI2sWypUpRZNOPdAIDU3r1yZXdsUC3q97Z3r8MBSDlJhrzRgxqC9enh4fKk75verVZtiEqdRu2REHeztmjFXGqsdPnzFj/uLE8aJT6+bkzpG8tf1DBIdFMvDXtYAyXtT5pgjlC+dm42FlM7wWVcpw4OJN/j51BTOtlnQWZszo3UoZ3zN40K9Jdb6fsRKDlJhpNYxo3wAv14+Px00qlmTYkk3U+XkmDjbWTO+luC8+9glg5p97EEKZIe1YuwK5M6VOEQ4OCaPfpHmA4k5V77uyVChZmD93K+scWtWpQu9WDRk2ZykNeo9AIvmxUwucHOxwcrBjQPumdBs5QxnbtVpG9e5ABvePr2doVqMiQ2YuoWa3n3Cws2HWz70BePTch+nL/kAIgZSSLk1qkztr6l/E/j9RLfemiP9Pv8v/L4wuOb8CjoAOeAj0kFIGvb2FpRCiJDBTSlnJuMPOaiAQOAy0l1JmFUKYobjI1AISgKVSyvmv8wGCgRVAoJTy53fqkVjWW2FZjeldjWV1llI++0B4NmA9yovYFmCklNJWCNER+MlYp0igg5TyQyvL5BCL7Kn8Jf+3mRb/mOgNU750NT4r1i2VadPY6KgvXJPPi6W1YsXWXUrbmQX/q5iVqMsoyxwfT/gVMSH2EQDV5p/8SMqvi4N9yycugPy34Gh0I0sIfPaFa/J5MXfLTPyZLV+6Gp8Vi7LKS7Xh4dkvXJPPiybnN8B7l8b8v3GqfIV/RJktd/LEF5ctLfwrLfdSyktAsuY+KWXWtz5fBCoZP58B3t43cZQxXAcMMv4lmw/Q+WNlvRX2BEhyGsQHwr1R/PFfM9UYvhrlZURFRUVFRUVF5T+LuqDWlH+tz72KioqKioqKiorKf41/peVeRUVFRUVFRUXlv4FM42Fv/1ZUy72KioqKioqKispXi0Ev/5G/jyGEyCSEOCKEuCOEuCWEGGAMdxZCHBBCPDD+d3rrmmFCiIdCiHtCiJpvhZcwHpj6UAgx71N2YlSVexUVFRUVFRUVFZXUowN+lFLmA74B+ggh8gNDgUNSylzAIeN3jHGtgAIoG7X8ZjyIFGAhytlEuYx/ye/NmwJU5V5FRUVFRUVFReWrReoN/8jfR8uV0ldKedn4OQK4A2QAGvJm05PVQCPj54bAn1LKOOOGKQ+B0kKI9IC9lPKMVLaxXPPWNalGVe5VVFRUVFRUVFRUPgHjlubFgHOAh5TSF5QXAMDdmCwD8Pyty14YwzIYP78bnibUBbUqKioqKioqKipfLf/UIVZCiB4orjKvWSKlXJJMOluUs4gGSinDP+Aun1yE/EB4mlCVexUVFRUVFRUVla+WlCx+TQtGRT6JMv82QghzFMV+nZRyqzHYXwiRXkrpa3S5CTCGvwDePu43I+BjDM+YTHia+FeeUKuSLGpDq6ioqKioqHxuvvgprgcLlPpHdJxqty58UDbjjjargVdSyoFvhc8AgqWUU4UQQwFnKeXPQogCwHqgNOCFstg2l5RSL4S4APRDcevZDfwqpdydlnqrlvv/ED+YZfvSVfiszNF5E71l5peuxmfFuulgAOKiIr5wTT4v6WzsAIjd+0EDyFeHZa0ejLXK+aWr8VkZG/MQgHJTD3/hmnxeTg2tQkLAky9djc+KuXtWAOIiw75sRT4z6WwdiD/x55euxmfFokIrAOLCX33hmnxe0tk7f+kqACANX+yE2nJAe+CGEOKqMWw4MBXYKIToCjwDmgNIKW8JITYCt1F22ukjpdQbr+sFrAKsgD3GvzShKvcqKioqKioqKioqqURKeZL3z1xUfc81k4BJyYRfBAp+jnqpyr2KioqKioqKispXyz/lc/+1oir3KioqKioqKioqXy3/1G45XyvqPvcqKioqKioqKioq/xJUy72KioqKioqKispXS0pOk/0voVruVVRUVFRUVFRUVP4lqJZ7FRUVFRUVFRWVrxZ1Qa0pqnL/H8csnQV9j27EzMICrZmWa1v3sHfcL8mmzVSyMANPbWVN635c25rm7VcB0FpY0HbVLDIWL0j0q1BWt+5LyNOXeBXJR/MFE7G0s8WgN3BgynyubtqVpjL0BgNtF2zD3d6aeR1rJYm/+NiHGbvOoNMbcLS2ZHmP+p8kU7xOz6hNR7nzMggH63RMa10VLyc7fEIiGLzuIHppQKc30KpsAZqXyZ/q/L2fPOHnocMTv794+ZLePb+nfds2b9J4P2HU2HHcuXuXfn1606lD+0+SCSA+Pp4Ro8Zw+84dHBwdmDF1Chm8vPDx8eWHwT9hMBjQ6XS0btWCFs2apakMvcFA65lrcXewY/73jU3iwqNjGb1+Hy+CQrEwN2Nc65rk8nL9NJl0Okas3cOd5wE42FgyvWM9Mrg44PMqnEHLt2OQkgS9gdYVitGifJE0lTHw7lHiIqKQej0GnZ4l5U3lSmdvS5MVs3HIlB6NmRmnf1nG1d+3fJJcWgsLGi+fgVexgkS/CmFzuwGEPnuJZ+F81J03nnR2tki9nuPTf+PW5tSfjTKsTl7K5XAlJDqe9svPJ4mvkd+Dtt9kASAmXs/M/fd4GBD5STKZawWj6uUnj6cdYTEJjN5+C7+w2MR4awst67t/w/H7gcw+cD9NZYRHRDJm2hweej8BIZgwdBBFC5r20fNXrjFt3iJ0Oh1ODg6smv9pZ2zEx8czbNIMbt97gKO9PTPHDSdDek98/PwZOGI8emO/atO0IS0b1fuksl7j/eQpPw97ewzxoXfPHrRv0zrNeW7/eydLl68EoHvXzjSsr9R1zPgJ3Lp9BykhS5bMTBw7Gmtr608TwEjNIXOwtrRAq9Gg1WjYMOp7k/jHvoGMWrmNO8986d+4Kp1qlvvkMuMTdAxfvpXbT31xtLVixvfNyeDqhE9wKAMX/IlBSnR6PW2qlKFFpVKpzj88IoKxE6fw8NEjhBCMHzWCIoULvYkPD2f0hEk8f/GSdBYWjBs1glw5c3yaTPHxjBgzntt37+Lg4MCMyRPJ4JUeH19ffvh5GAa9cWxv2YwWTZt8Ulkq/7/8J5V7IcQIoA2gBwzA91LKcx9IXwFYBCSgHDLglNypYUKISsB24DFgDfgD06WUO9+TbwMgv5Ry6gfKrgQMllJ+ntH9HXRx8fxWrQ3xUdFozMzof3wTd/Ye5em5q6b10GioP2UId/cfT1X+Tlky0GbFTBZUNX14fNOlBTEhYUzOW5liLepRf8pQ1rTpR0J0LOs6/UjQwyfYp3fnx/N/c3f/cWLDUn+o0/rTN8nm5khUXHySuIiYOCZvP8WCzrVJ72jLq8iYFOfrExLB6M3HWNbdtEm2XbyHnZUFOwa3ZO+1R8zde55praviZmfNqp4NsDDTEh2XQLO5m/kuXxbc7W1SJU+2rFnZ9Od6APR6PdVq1aFq5comaewd7Bn682AOHzmaqrwBXvr4MGrMWFYsNT1oauu27djb27Frxzb27NvHL3N/Zca0Kbi5ufL7qhVYWFgQHR1Nk+YtqfTdd7i7uaW67HXHLpPdw4XI2KRttezAOfJmcOOXbg3x9g9m8qbDLO3bPGUyBYcxev1elvdraRL+15mb2FtZsnNUV/Zcvssvfx9nRqf6uNnbsOaH1liYmREdF0/TqaupVCgH7g62qZYJYHWtdkQHhyQbV/r79gTefcAfzXpg7epMv2v7ufHnDvQJCR/N1zFzBhotnc6qmm1Nwot3ak5sSBjzClalYPO6VJv0M5vbDyAhOoa/ug7m1aOn2KV3p8epbTw6cCLV/Wr3DT+2XHrBqHrJv5z6hMXQd91lIuJ0fJPdmZ9r5aHHmkspytvTwZIRdfPRb/0Vk/B6hb2IiNXRcvFZquZzp3elHIzefisxvnvF7Fx5lvxvnFKmzltIuTIlmTNxFAkJCcTExpnEh0dEMnHWfBbPmkR6D3eCQ0JTnPdLXz9GTJ7Fql9nmIRv3bUPeztb9vy5it0HjzJ70XJmjRuBm4szaxfOMfarGBp1/J7K5cvi7urySTICZMuahU1/rAOMY0jtulStXClF13bp0ZMJY0eTwcsrMSwsLIxFS5fx5++rEULQsl0HKn9XEXt7e34a9AO2tkq/mTF7Dn9s2ETXzh0/WYbXrBjcCSe75MdQBxsrhrWuw+Erd1Kd78ugEEau2MbKnzubhG89eRl7Gyt2TxnAnvM3mLP5ADN7tsDNwZa1w7phYW5GdGwcjcf8RqWieXB3tE9VudNmzaFc2W+YPW2y8R6MNYlfunI1eXLn5pcZ0/B+8oRJ02aybOH8lMnk48uocRNYsfg3U5m2/62M7X9tZs/+A/zy6wJmTJmIm6srvy9f8mZsb9WWShUrpGls//9C3S3HlP+cz70QoixQDygupSwMVAOef+SytsBMKWVRIA9Q5wNpT0gpi0kp8wD9gflCiCQHGQghzKSUOz6k2P9/ER8VDYDW3AytmRkymT5SoW9Hrm3dS2RAsEl4iTaNGHhmG4Mv7qL5b5MQmpTdUgUbVOe80Up5bcseclX5FoDAB94EPXwCQLhvABEBwdi6pf6h5h8Wycm7z2lcKk+y8XuuPaJqgaykd1QePs62Volxu648oN1v22j56xYm/nUCfQpPvjt65wn1i+cGoFrBbJx/9BIpJeZmWizMtADE6/XI5H7gVHLu/AUyZcyAl1d6k3AXZ2cKFiiAmVnS9/adu3bTpn0Hmrdqw/iJk9Dr9UnSJMfRo8doUE95kaletSrnLpxX5DI3x8LCAlAsQAaZtgVN/qERnLjlTeOyhZKNf+wXTOncmQHI5uGCz6swgsOjFJku3KbNrHW0mL6G8RsOpLitjtx8SIPSBRSZiuTm/P1nb7WV8tvF6/QYDP/cA0NKSTqj8mNhY01MSBgGnQ6Awq0a0v3EFnqe3UG9XyekuF/lqVeNq+v+AuD21r1kr1QWgOCHT3j16CkAEb4BRAUGY+2a+pMlrz0PJTxW9974my/DiYhT4m+9DMfdzjIxrkYBD5Z2KMmqzqX4qWYeNCk8sL5CLld23/AF4OjdQEpkcUqMy+Nhh7O1BReepP3Uz8ioKC5du0HTesrsnrm5OfZ2pi9zuw8eodp35Ujv4Q6Ai5NjYtzf+w7Rqkc/mnbuxbgZc1Pcrw6fOEPDWtUBqFGpAucuXU3arxISMPxDJ28qY0hGvNKn5/nzF/Ts25+WbTvQsWt3vL2fpCiPU2fOUrZMGRwcHLC3t6dsmTKcPH0GIFGxl1ISGxuHSGF7fw5c7G0pmC0DZlptkri/z1yj9cQlNBu3kHFrdqR8zLh6lwbfFgWgeon8nLvrbRwzzLAwf2vMSMP4HhkZxaUrV2nSUJk9Vu5BO5M0j72fUKZUSUAx9Pj4+hEcrNz3O3fvpU3HLjRv04Hxk6emfGw/foIGdRV1pnqVypy7cDGZsT3hHx0HPxcGKf+Rv6+V/5xyD6QHgqSUcQBSyiAppQ+AEKKqEOKKEOKGEGKFECKdEKIb0AIYLYT4AxgPtBRCXBVCtHxvKUreV43p+xrzXyWEmC2EOAJME0J0EkLMfytunhDitBDisRAiiX+DEKKUsX7ZhRDfGetw1Rhm9276lCI0GgZf3MUE34vcO3SSZ+evmsQ7eHlQqFFNTi9eZxLunjcHxVrUY16FZswsWReDXk+JNo1SVKaDlwehz5UHtkGvJzYsAhsXJ5M0mUsVwczCnGCjUpIaZuw8y4DapdG854nyNCiM8Jh4ui3dSZv5f/H3ZWU6/3FACPtvPGbl9w3Y0K8pGo1g99WHKSozICwaTwfFkmSm1WBraUFotGIB9AuNpMW8LdSetp5OFYuk2mr/Lnv37aN2zZopTv/4sTd79x9g9YoVbPpzPRqtll17UuZa5R8YgIenBwBmZmbY2toSGqocd+/n50fTFq2oUacuXTp2TJNlZ/rWI/zQsOJ72yq3lxuHrittcOOpL74h4fiHRfLYL5h9V+6xemArNv7cAa0Q7L6YMktdQGgknk5Kl1HaKh2hUcrsjV9IOM2mrqbmmCV0rlYqzVZ7KSXt/15Fj1PbKNEl6VBxftHvuObNwY+PT9P74i72DJ6AlBLXPDko0Kwuyyu3ZNE3DZB6A4VbNUhRmfZeHoS/eKtfhUdi/U6/ylCyMFoLc0IeP0uTXCmlXpH0nH2sGAOyuFhTNZ8HPddeotPKCxikpEYBzxTl42aXjoAIpR/ppSQqTo+DlTkC6Fs1JwuOpKx/vo8XPn44OTowcvIsmnXpzeipc4iOMbWaPnn+gvCISDr1+4kWXfuwfe8BAB49ecbew8f4/bc5bFm5EI1Gw84Dh1NUbkBQEJ7uSn8xM9Nia2NDaFg4AL7+ATTu2JNqTdvRtW2Lz2K1f5e9+w9Qu2YNAMZNmsywnwezYd0afhw4gIlTp6VMhoBAPI0vPAAe7u4EBAQmfh81djyVa9TmyZOntG75wcdlqhACvp/zOy3GL2LTsYspvu6xTyD7LtxkzdCubB7TC61Gw66z11N0bUBIBJ5OijXeTKvF1iodoZGKYczvVRhNxvxG9Z9n06VWuVRb7V+8fImzoyOjxk2kRdsOjJk4megY09nk3Llycsg4I3vj1i18/fzwDwjgsfcT9h44yOrlS9i0fg0ajZZde/elqFz/gEA8PN4Z28Nej+3+NG3djhr1GtKlQ7v/aau9SlL+i245+1EU9fvAQWCDlPKYEMISWAVUlVLeF0KsAXpJKX8RQpQHdkopNwshOgElpZR9U1jeZeCnt77nBqpJKfXGvN4mPVAeyAvsADa/jhBCfAv8CjSUUj4TQswF+kgpTwkhbIFY0og0GJhZsi6WDnZ02bIYzwK58bv1xne10ezR7Bw2FfmOhSN3lXJkLF6QQWe3A2BuZUlkoPIw77x5ES5ZM6G1MMcpsxeDLyp+88d/Xcn51ZsRyShyb1u07T3daLtqNuu7/JhqS/fxu09xtrUkfwY3Lj72STaN3mDgjk8Qi7vWITZBT8dF2ymc2Z3zj3y4/TKIdr8p1s+4BD3ONopVf9Da/bwMiSBBZ8AvLJKWvyozD22+LUjDEnmQJK3nazE9HW3Z2L8pAeFRDFp7gGoFs+Filzb/04SEBI4eP86Afim9BeHc+fPcuXOHNu07ABAbF4uzk6L0DfxxMC9f+pCQkICvnx/NWyk+/G1bt6JRwwYkI9YbuTw92bLxTwICAxk46EeqV6uKi0vKFZFjNx/hbGtN/kweXHiQ/ARal+qlmbblCC2mryFnelfyZnBHqxGcu/+MO8/9aTtLeemMTdDhbPxNBy7bjs+rMBJ0enxDImgxfQ0AbSoWp9E3BZMTKfGe9HSyZ/PQjgSERTJw2XaqF8mNSxpexlZUaUmEbwA2bs6037maoHuPeXrqQmJ8zuoV8Lt+h9W12uGcPQvtd61i0amLZK/8LV7FC9Dj5FYAzKwsiTL2q5YbfsMpS0a0FhY4ZEpPz7M7ADi7YLXir/+RfmXr6Ubj5TPZ1v3nzzKD9D6KZ3akXmEveq1VXHJKZnEir4cdyzsqlsd0ZhpCohX3o8lNCuHlYImZVoOHfTpWdVb8lTdefMHuG77JnusupaRJ8QyceRScqPinFZ1ez537Dxk+oA+FC+RlytyFLF+3gX7d3riQ6PV6bt97wLJfphEXF0fbXgMpkj8f5y5d4fa9B7Tq3g+AuLh4nI1W/f7Dx/HS14+EBB2+AQE07dwLgHbNGtG4bs1kf//X92B6D3f+Wr2IgKBg+g8fS/VKFXB1dkqSPq0kJCRw9NhxBvTtTXR0NNeu32DwkGGJ8fHxStts2/E36/74E4Bnz1/Qp/8PmJubkcHLi19mzXjPmPemxSaMHY1er2fK9JnsO3CARg0+bV3Ta9YM7Yq7oz3B4ZH0mL2GbOldKZk760evO3v3Mbef+tJ6kuJ6GBefgLPRtWfAgj94GRSqjBmvwmg2biEAbat+Q+PyxT4oq6ezA1vH9SYgNJwB8/+keokCuKbCKKDX67lz7z5Df/qRwgULMHXmHFasWkPfXm/WEnTt2IFps+bQvE0HcuXMQd7cudFqtZy7cIE7d+/RpkMXAGLj4nA23isDfxrCy5e+JOgS8PXzp3kbZfxv26oFjRrUI7lpemHscZ6eHmz5Y60ytg8eQvWqVXBxSf1s3/8X+q/Yyv5P8J9T7qWUkUKIEkAFoDKwQQgxFLgCeEspX2u1q4E+wC+fWOS7z6ZNUsr3zZltk1IagNtCCI+3wvMBS4Aar2cZgFPAbCHEOmCrlPJFkoKF6AH0AFi8ePFHKxobFsGjY2fJW/M7E+U+U4lCdFj3KwA2rk7kq10JvU6HEIILv29h14gZSfJa2awn8H6f+9CXfjhmSk/YSz80Wi2WDnZEvwoFIJ2dLd13rGD36FlJfP9TwtWn/hy784yT9/4gXqcnKi6eERuPMKnFG/90dwcbHK0tsbIwx8rCnOJZPbnv+wopJfWL56J/zdJJ8p3dTrFyvc/n3sPBBr+wKDwcbNHpDUTGxuNglc4kjbu9DTncnbj8xI/qhbKnWjaAk6dOkS9v3lQp0RJJg/r1kn0h+GWWsjDwfT73Hu7u+Pv54+nhgU6nIzIyEgcHB5M07m5u5Mieg0tXrlCjWrUU1+uqtw9Hbz7i5B1v4hJ0RMXGM2zNbqZ0eOP5ZmuZjgltFZcJKSV1xi8jg4sDlx69oH7pAgyoXyGpTN0aKjK9x+few9EWv5AIPBztjG0Vh4O1pUkadwdbcni6cPnxS6oXzZ1imV4T4RsAQFTgK+7uOECGUoVNlPui7ZtycpbSL189fkrokxe45skOAq6u/YtDo5Mu2NzQsjfwfp/78Jd+2GdMT/jrfmVvS8xb/art1mUcHjeHF+/Mzn1OcrjZMLR2Pn7ceDXRhUcIwZ6bviw69jhJ+uFbbwDv97kPiIjD3S4dgRFxaIXAJp2W8FgdBTM4UDijI02KZ8DKXIu5VkN0vJ5Fxx6lqr6ebq54uLlRuEBeAGpUKs+ytRtN0ni4ueHo4IC1lSXWVpaUKFKIe48eIyU0qFWdH3p2SZLvvMljgPf73Hu4ueEXEIinuxs6nZ7IqCgc7E0nYN1dXciZNQuXr92kRuWk93laOXnqdOIYEhkZiZ2tbaIv/ts0alA/USFPzufew92di5cuJ373DwigZIniJnlotVpq1ajOqjW/fzbl/rVl3MXelqrF8nHT+2WKlHspJQ2+LcLAptWTxM3tozyj3udz7+Fkj19IOJ7ODuj0eiJj4nCwsTJJ4+5oTw4vNy4/eEqNkgVSLI+Huzse7m4ULmh0FaxamRWrfzdJY2trw4QxIxPlqN2wCRm8vLh05SoN6tZmQN/eSfL9ZYYyA/M+n3sPD3f8/f3x9HB/a2w3nXVQxvbsXLp6lRpVq6RYJpUvy3/RLQcppV5KeVRKOQbFZaYpSZXwz0Ux4G1fgagPpH3bBPV2fXxRLPPFXgcYffW7AVbAWSFE3nczk1IukVKWlFKW7NGjR7IF2rg6Y+mgPFDMLdORu2p5Au6ZPhwn5qrIhJwVmJCzAte27GFL39Hc3HGA+4dPUaRJ7USfeGsnB5wyZ/iAeG+4+fdBSrdvCkCRprV5eETx09Sam9NlyyIurN3KtS2p380DoH/N0uwb2obdP7dmaqsqlMruZaLYA1TKl4UrT/zQ6Q3ExOu4+TyQbG6OlM6RgYM3vRMX2IZFx+ITkrJFh9/lzZLo3nPwpjelsnshhMA/LJLYBEXJCY+J4+pTP7K6OaZJNoA9e1PnkgNQpnRpDhw8RPArxUczLCwMHx/fFF1b6buK7NiprAk/cOgQpUuVQgiBn78/scZFX+Hh4Vy9do2sWbKmql4D6lfgwPjv2TOmO9M61qNUrswmij0ou+Uk6JT34a1nblA8R0ZsLdNRJncWDl69T3CEMjUeFhWDz6vwlMlUMAc7ziuLMg9cu0/pXJmVtgqNINZotQyPjuWqtw9Z3VNvMTW3tsLC1ibxc45q5Qm49cAkTdhzH7JXUtaa2Li74JI7GyHez/E+cob8jWth46ZYyaycHHDI7EVKuLfrEEXbKrvy5G9SC+9jZwGlX7Xc8BvX1v/F7U/c6epDeNinY3KTQozfeYvnIW/cCi4+eUWlPO44WpsDYGdphoe95fuyMeHkwyDqFFLWllTK68alp8ri2XF/36bpwtM0W3iGBUcesvemX6oVewBXF2c83V3xfqbMHJ29dJUcWTObpKlcviyXr91Ep9MTExvLjdt3yZ4lM9+UKMqBYycSF9iGhYfj4+efonIrl/8m0b1n/9ETlCleROlXAYHEximPgrCICK7cuE3WzBlTLdeH2LNvP7VrKcYKW1tbMmTwYv+Bg4CiON67n7Jdh8qV/YbTZ88SHh5OeHg4p8+epVzZb5BS8uz588T8jh4/QdasWT9L3aPj4okyLniOjovn9O1H5Mzg/pGrFL7Jl50Dl24THK7s4BQWGY1PcGiKrq1UJA87Tl8F4MCl25TOm01pr1dhiWNGWFQMVx89J6tn6nbzcnV1wcPDA+8nigvquQsXyZ4tq0ma8IgIEoyL7bds20HxYkWxtbWhTKmSHDh8xHRs903h2F6hPDt2Kc/ZA4ePULpUCePYHmA6tl+/TtYsmT+U1RdHL/+Zv6+V/5zlXgiRBzBIKV8/aYsCT4G7QFYhRE4p5UOgPXAsmSwigBT5twshCgOjUJTwTyEU6ArsF0JESSmPCiFySClvADeMi4TzGmVIFfbp3WmzYiYarRahEVzdvIvbuw7zbQ/FNeP0kvXvvdb/zkN2j55Fzz1rEBoN+oQEtvQfTcizlx8t99yKDbRdPYfhd48QHRLG722Uae2izeuSo0JpbJydKN1BWXawvutgfK6lfteDd9l07jYAzcvkJ7u7E9/mzkiLeVvQCEHjUnnI6akoU32ql6TXyt1ICWYaDUMbfIuX08ebvFHJPIzcdJQGMzdgb52Oqa0UK4d3QCiz97zZjKlDhcLk8kzb9GZMTCxnzp1n1IgRiWEbNyveWy2aNSMoKIhW7ToQFRWFRgjWrv+DbZs3kiN7dvr27kXP3n0xGAyYmZkxfOiQJAtyk6Nxo4YMHzWaug0a4eBgz/QpkxW5vL2ZOfsXhBBIKenYvh25c+VMk1zvsvHkNUWm8kXw9n/FyLV70GgE2T1dGNdaebHJ4elCn7rl6LVwMwaDxEyrYXjzqng5f9zftfE3hRixdg/1JizH3tqS6R3rAsri3Vnbjr2RqUpJcnml3tfU1t2VlhsUK5nGzIwbG3bw8MBxSnZTrIMXl/3B8akLaLRkOr0u7EIIwcERM4gODiE6OITD42bT/u9Vxn6lY/cPYwl7lryL2dtcWbWRxitm0f/mIWJCQtncfiAABZrWIUv5Ulg7O1K0nbKl3bYeQ/C7nrp+NbZBAYpldsTRypy/en/L8pPemBlXxm676kPnctmwtzJncA1lIbveIOm6+iJPgqNZevwxv7QsihACncHA7P338Q//uDfhzmu+jKqfnw3ff0N4jI4x22+mqs4pYfjAPgwZP42EBB2ZvDyZMPxHNmxTXmhbNqpHjqyZKVemJE069USjETStV4tc2bMC0K9bR3oMGobBoCzIHjGoL16eHh8oTaFJ3VoMmzid2q064WBvx4yxyhaVj58+Y8b8pQiheE10at2M3DmyfTZZlTHkHKOGv3HDmTJxPBOnTGPJ8hXodHpq1ahOntwfn61ycHDg+25dad2+EwA9u3fDwcEBg8HAyDHjiIyMQiLJkysXI4cN+Sz1Dw6PZOACxVVIbzBQp3QhyhfMxcajyqxYi0qlCAqLoOXEJUTFxKERgt8PnmX7+D7k8HKnX6OqfD/n98QxY0Tbuni5OH603CYVijNs2VbqDJuLg40V079Xnk+PfYOYuXFfYnt1rPEtuTN+vP3fZdjgQQwbPZaEhAQyZsjAhNEj2LhFcc1r0bQJ3t5PGDF2PBqNhhzZsjFulHK/5Miejb49v6dn34EYpHFs/3kwXulTMLY3rM/wMeOo27gZDvb2TJ80AVC2XZ75y7w342DbNuTO+XnG9n8K1S3HFPFP+l3+L2J0yfkVcAR0wEOgh5QyyLirzUyUl54LKD73cUKIVbzxuXcG9gHmwBQp5Ya38q6E6VaYAShbYf5tjE/Mx/i9E0b//WTiIqWUtm9vhSmEyAzsAboA7VDcivTAbaDT60XC70H+YPb5HhD/C8zReRO95dP2mv5fw7rpYADiolK/9ef/MulslJej2L1LPpLy68KyVg/GWv1vP/RSy9gYZYFquakpWxj6tXBqaBUSAp586Wp8VszdswIQFxn2ZSvymUln60D8iT+/dDU+KxYVWgEQF572nZ3+F0ln7wz/nOdDitngnv8fUWZbBtz+4rKlhf+c5V5KeQn49j1xh3jL9eWt8E5vfX4FJHtChZTyKOCQXNy7+Ri/r0JZxJtcnO1beR41fn4GvHbke+++/CoqKioqKioq/xW+Zheaf4L/pM+9ioqKioqKioqKyr+R/5zlXkVFRUVFRUVF5d+D6nNviqrcq6ioqKioqKiofLWobjmmqG45KioqKioqKioqKv8SVMu9ioqKioqKiorKV4vqlmOKarlXUVFRUVFRUVFR+ZegWu5VVFRUVFRUVFS+WlSfe1NUy72KioqKioqKiorKv4T/3Am1/2HUhlZRUVFRUVH53HzxU1wXOub9R3ScXqF3v7hsaUF1y1FRUVFRUVFRUflqURfUmqIq9/8hBptn/9JV+KzMTHjM85Fdv3Q1PiuZJi4H4HFQxBeuyeclu6sdAPrbR79sRT4z2vyV6KPJ+qWr8VlZYHgCwOpLz79sRT4zHUtkYt+9gC9djc9KzTzuAKy/8uIL1+Tz0qZYRuIiw750NT4r6WwdANC9uPWFa/J5MctY4EtXQSUZVOVeRUVFRUVFRUXlq0VdUGuKuqBWRUVFRUVFRUVF5V+CarlXUVFRUVFRUVH5alF97k1RlXsVFRUVFRUVFZWvFtUtxxTVLUdFRUVFRUVFRUXlX4JquVdRUVFRUVFRUflqUd1yTFGV+/84Zuks6H1kA2bpLNBotVzfupf9438xSVNpUHeKtWkIgFarxT1fTsakL0lMSNq3KtNaWNB65UwyFi9I9KtQfm/Tj5CnL/Eqko8m8ydgaWeLwWDg0JQFXNu0Kw2CmeHebQhCa47QaIi+dYnww9tNkqTLlgfXtn3RhQQBEHP7MuFH/k6zTIpgZrg064q5VxYM0VEEb1iEPjQYraMLrq17g0aD0GiJOHuIqAvHUp39X3+uY+/f2xECsubIyaDhY7BIly4x/vC+PWxatxoAKytr+g4eSvZcuT9JpPj4eGZNGMODe3ewd3Bg2PgpeKT3wt/Pl4nDf8KgN6DT6WjQrAV1GzdLUxnVegzHxiodGo0GM62GTTNHmMQv/2sfO4+fB0CvN/D4pS8nV83C0c4m7XIlJDB07kpuPXqGo50Nswd3J4O7Ky8DghkwbRF6gwGdXk/bOpVpVeu7VOXtmDE9HVfPxt7TDWkwcHLpHxydt9IkTak2Dan+c08A4iKj+bP3SF5ev5NmeQDMLCzosHo2mUsUJCo4lOWt+vLq6QsyFslPy98mYmVvi0GvZ+/kBVzeuDNNZZzfs5WrR3aDlBStUofStZuaxMdGR7JjwVTCgwMw6PWUqducIpVqfZJcuoR4/l44DT/vB1jZ2tOo/0gc3TwJC/Rny5yxGKQBg05HyZqNKF6tfqrzP7pjE2f2/42UkrI16lO5YQuT+AtH93NoyzoALKysadnrRzJky/lJMiUkxLN2ziSeP7yHjb09nX4ah4tHel4F+LFsygikwYBep6NivaaUr90oTWWc3b2Fy4d3A5LiVeryTR3Ttjr19wZunDwEgEGvJ+jlM35augUrW/s0y6VLiGfbgmn4eN/H2taeZgNG4ejuSWigPxtnj8FgMGDQ6yhdszElq6e+rbyfPOXnYcMTv7946UPvnj1o36b1mzTeTxg1bjx37t6jX+9edOrQLs3yvCY+Pp4Ro8dy+85dHBwcmDF1Ehm8vPDx9eWHwUMwGPTodDpat2xBi2ZNP57hO4RHRjF65gIePnmOEDBhcF+KFsiTGL/z4DGW/7kNAGsrS0YN7EHeHNk+UaYEhk2by637j3G0t2PWqB/J4OmOj38AA8ZMV8ZAnZ62jevQsn7NTypL5f+Xr1a5F0KMANoAesAAfC+lPCeEGAgskVJGf4YyxgKRUsqZabxeD9x4K6iRlPLJJ9QnK7BTSlkwrXm8iy4unkXV2xIfFY3GzIy+xzZyd99Rnp27mpjm6OylHJ29FID8datQcUCXFCv2Tlky0Gr5DBZWa2MSXqZLC2JCw5marwpFW9Sj7uQhrG3bn/joWP7sPJigh0+wT+/OwHM7uLf/OLFhqdz3XacjcMVMZHwcaLS4dx9K7P0bxL94bJIs7skDgtbOS13egNbRBeemXQhcPsMk3LZEBQwx0fjNGY5VodI41mxG8IbF6CNC8V8yBfQ6hEU6PPuNJ+buNQwRoSkuMygwgO2bN7B43UbSpbNk8qihHDu4n+p13zwgPb28mD5/CXb29lw4c4p50yfxy9LVKcrf39eHWZPGMn3+EpPw/Tu3Y2tnx4qN2zh6cB8rfvuVYROm4OziyqxFK7CwsCAmOpqe7VvyTfnvcHFzS7FMb7Nqwo842dsmG9e1cU26NlYeLkcuXGPNjkMpVuxfBgQxfN5qVk/80SR8y8FT2NvYsG/hRHafuMCsNVuZPbgHbk4OrJ/6Mxbm5kTFxNJwwHiqlC6Cu7NjimUx6HRsHTyR51dukc7WhiEX/+bugRP43XmYmCbI+zlzKrUkJjSc/LUq0WbxFGaUbZSi/J2zZKT9ypnMrdLKJLxs1xZEh4YxNnclSrSsT6OpQ1nRui/x0TGs6TiIwIdPcEjvzpCLO7mz7zgxYeEplgkg4Lk3V4/spvOE+WjNzPlz6lByFi2Dc/qMiWku7d+Ba8YstPhpIlHhoSz+sTMFy1dFa2b+0fxDA/3YuWg67UbNNgm/dnQPljZ29Jqzhlunj3Dkj6U07j8KWydnOoybi5m5BfGxMSz9uRu5SpTFzsk1xTL5PH3Mmf1/8+OsJWjNzFg4djAFSpXF3StTYhoXj/T0nzIfa1s7bl86y58LpvPjzCUfyPUNwf6+rJs7mf6TfzUJP3tgF9a2doxe8ieXjh9kx+pFdP55HPZOLvwwfSHm5hbExUQzpV9HCpUuj4NLymUCpa0uH95N90kL0JqZs3bKUHIVK4PLW21Vrn5LytVvCcC9S6c5uzvlin1ogB/bFk6n0xjTtrpyZA+Wtrb0n/s7N08f5uD6pTQbOAo7J2e6jJ+X2Fa/De5KnhJlsXNOnVzZsmZh0x/Ki5Zer6da7bpUrVzJJI29gz1DfxrM4aNHU5U3wEsfH0aNHc+KJYtMwrdu24G9vR27tm9lz779/DJvPjOmTsbN1ZXfVy7DwsKC6OhomrRoTaXvKuKeynFwyvzllC9VjF/G/kx8QgKxcfEm8RnSe7BqzgQc7Gw5ce4yY2cv4s8F01Imk18AI6b/yqrZE0zCt+w5iL2tLXt//43dh08ye+kaZo0ajKuzE+vmTcHCwpyomBgadR1I5bKlcHd1TpVM/5+oPvemfJU+90KIskA9oLiUsjBQDXh94spAwDqV+Wk/awXfECOlLPrW35N/qJxPIj5KeQ/SmpuhMTeDD0xvFW3ZgCsb3li3i7dpSP/Tf/HDxZ00/W0iQpOyW6pA/Wpc/H0LANe37CFXlW8BCHrgTdDDJwCE+wYQGRiMrZtLWsRSFHtAaLUIrRZIee+3LvIN7j1H4NFnDE4N24NI2QnUlvmKEnXlNAAxty6SLns+JUKvB73OWB+zFOf3Lnq9nvi4OPQ6HXGxsTi7mj5A8hcqgp298nDOW6AQQQFvDu05vG83A7p1oE/HNsybPgm9Xp+iMs+cOEa1OvUAqFCpKlcvnUdKibm5ORYWFoBihZTSkCaZUsvuExeoU6FU4vcdR8/S8qcpNP5hAmMWrkWvT1k9Dp+/RqPK3wBQ49vinL1+FyklFuZmWJgrimhCgg5DGuQK9wvk+RXlsJq4yCj87zzCMYOnSRrvM5eJCVWUa++zl3HM+Ca+VNtG/HR2G8Mu76b1oskp7leFG9Tg3GqlX13ZvJs8VZV+FfDAm0BjvwrzDSAiIBhbt9Q/qINfPiNDznyYp7NEo9WSOV8R7l08ZZJGCIiPiUZKSUJsDFa2dmg0yhB78+RBVo7sw7Jh37N72RwMhpTdg/cvnqZQhRoA5CtTkSc3ryClRGtmjpm5cg/q0ngP+j9/SpY8+bFIZ4lWa0bOAkW5fua4SZrs+QphbascxJY1TwFCgwIT4y4c2cfMH3swbUBn/lwwA0MK+9WNcycoXUWZ0SharhL3r11CSomZuTnmiTIlIA1p61eBL5+RMdebtsqSrzB3L5x8b/qbp45Q8Nsqid+vnzjA0hG9WTSkB38vnZ3itrp38TRFKiptlb/Mdzy+dfk9bfXp2ti58xfIlDEjXunTm4S7ODtTsEB+zMyS2i937t5Dmw6daN66LeMnTUnxOHj02DEa1KsLQPWqVTh3/kKScTA+Ph5DGtorMiqaSzdu07RONQAszM2xtzU1XhQrkBcHO8X4UTh/bvwDgxPj/j5wjJa9f6ZJj0GMnb0wxTIdPn2BhjUqA1Dju7KcvXzDOAaaY2FhHAPjdRi+ApcXvZT/yN/Xylep3APpgSApZRyAlDJISukjhOgPeAFHhBBHAIQQC4UQF4UQt4QQ415nIIR4IoQYLYQ4CTQXQtQSQlwWQlwTQhx6q6z8QoijQojHxvwRQkwQQgx4K69Jr+M+hhCiqBDirBDiuhDiLyGE00fCSxjrdAbo81Y+BYQQ54UQV43X5ErbTwlCo+GHizsZ63OBBwdP8ez8tWTTmVtZkrdmRa5v3QuAe94cFG1ej/kVmzOnZD2k3kBxo/vOx3Dw8iD0uS+gTAfHhEVg7eJkkiZTqcJozc0JfvQ0jYIJPPqMwWvoHGIf3ib+hXeSJBaZc+DRZyyuHQZi5u4FgJlbeqwLlSJgyVT8F4wDg8S6yDcpKtLM3gl92Cvli8GAjItBY60MyFoHJzz6jiX9TzOIOLEnVVZ7AFc3d5q2bkeHJvVo07AW1ja2lCjz/nrt27mdkt8oyt2zJ94cO3SAWYtWsGD1ejQaLUf270lRucGBAbi6eygymJlhbWNLeJgycxPo70evDq3o0Lguzdt2TLPVXgjoNu4Xmv04iY37j783XUxcPCeu3KJ62eIAPHruy95TF1k75Wf+mjMKrUbDzuPnUlSmf3AonkZLlJlWi521FaERUQD4Br2i0cDxVOk+lG6Na6bKav8uzlkykrFYfp68NRv2Lt92bcmtvUcB8MibgxIt6jGrfDOmFK+DQa+nVNtGKSrLMYMHIc99gDf9yuadfpWlVBHMLMwJSkO/csuUled3rxMdEUZCXCyPrp4jPNj01NcSNRoR5POMeX1asnRId6p36I3QaAh6+ZTbZ47SYexcuk1ZjEaj4dbJQ+8pyZSIkGDsXZR7S6PVks7ahpgI5cUoPDiApUO6M79fG76p3ypVVnuA9Fmy8ejWNaLCw4iPi+X2pbOEBr3/JNszB3aSr0QZAPyeP+HyycP8MO03hsxdiUaj4eKxAykqNyw4CEdX5YRZrdYMSxsboiKUfhUS6M/Ufh0Z3aUpVZu2TbXVHsA9U1ae3nnTVg+vniMsODDZtAlxsTy8doH8ZSoAEPjyKbfOHKXLuHn0nLYEjUab6L7zMcJfBeHgosil0WqxtHrTVmFBASz8uRtz+rSmXIOWqbbav8ve/QeoXbNGitM/9vZm7/4DrF6+jE1/rEOj0bBrz94UXesfGIiHhzIOmpmZYWtrS2io0l5+fv40bdmGGnXq06VTh1Rb7Z/7+uPkYM+I6fNp+v2PjJ65gOiY2Pem37rnIBVKFwPg0dMX7Dl6irXzJrN1yWw0Wg07D71/DH2bgKBgPN0V45mZVoudjTWh4cosuW9AEI27/UDV1t3p2rLx/7TVXiUpX6tbzn5gtBDiPnAQ2CClPCalnCeEGARUllIGGdOOkFK+MlrnDwkhCksprxvjYqWU5YUQbsBloKKU0lsI8fZdnBeoDNgB94QQC4HlwFZgrhBCA7QCSidTTyshxFXjZ28pZWNgDdBPSnlMCDEeGIMy2/C+8JVvhb/tA9ITmCulXCeEsADSPPsgDQbmlKyHpYMdnTYvwrNAbvxu3U+SLn+9qjw5fSnRJSdXlW/JULwgA85uA8Dc0pLIAMWa0HHTQpyzZcLM3BzHzF78cFHx7z356yourN6cvOX6rbdkO083Wq+czZ9dB6fdwiMl/gvGISytcG3TF3P3DCQEvEyMjvd5iu/Mn5HxcVjmLoRrm774/TIcy+z5sPDKikevkQAIMwv0UcrDyaVNH8ycXBFaM7QOznj0GQNA5JmDRF0+lbQOb8mlDwvBf/5YNHaOuLbtQ/TNSxiiUu4WEREeztkTx1i5aQe2dnZMHjmEw/t2U6VmnSRpr126yP6d25m5cBkAVy+e5+HdOwzo2gGAuLhYHJ0UpW/8sMH4+/iQoEsg0N+PPh0VF6qGLVpRo26DZCdyXjefm4cnC9f8SXBgIOOH/Uj5ylVxck79TMu6KT/j7uxIcGg43cbNJXsGT0oWSLpW4OiFaxTPmyPRJefsjbvcevSMFj9NVuSKT8DZQbGw9pu6kBf+QSTo9PgGvaLxD8qUdPt6VWhStRwymZmc13dleldntv0ymoBXofSbspAa35bA1TH1fsjpbKzpvnkhm38YT2xEZLJpclUqy7ddWjK7grJeIW/VcmQqUYgh53cAYG6Vjghjv+q+ZTGu2TKhtTDHObMXwy7vBuDIvJWcXbUJ8ZF+Ze/pRsc1s1nTKW39yjVDFr6p34o/pgzBwtIK9yw50GhNh57H1y/ikSUHbUfMJMTfhz+mDCFTnkI8uXkFP+8HrByl2Ch08XFY2zsCsHn2GEID/dDrEggPCmDZsO8BKFWzseKvn+xNaJTJxZ3u05YSERLE5lljyFumIrYOTknTvwfPTFmp1qQtC0b/QDpLazJky5lEptfcv36Zswd2MXDqAuX7tUs8f3SPmT92ByAhPi6x7GWThxPs74tOl0BIYADTBnQG4Lv6zfimWt1kf39hFMrJzYOhv64mLDiIpZOHU/TbStg7pU65csuQhXINWvH7pJ+xsLTCI0uOxBmUd7l36QyZ8xRIdMnxvnEFH+8HLB3RG1DaysbYVhtmjSYkQGmrsKAAFg3pAUCZ2k0oVqkWyc6QGtvKwdWdXtOXEfEqiD9njSZ/mYrYOqZNaUxISODoseMM6Ns7xdecO3+BO3fu0qZDRwBi4+Jwdlbaa+CPP/HSx4eEBB2+fn40b90WgLatW9GoQf1k78HX3c3T04MtG9YTEBjIwB9/onrVKri4pHwc1Ov13HnwmBH9ulE4X26mzF/Osj+30r9zmyRpz125wdY9h/j9F2XMO3vlOrcfPKJl758BiIuLx8XRAYD+o6fywi9AkSkgiCY9BgHQvkldGteq+p5upQiV3t2Vv5bNISDoFf1GT6VGxbK4foKR45/m/2fe+Ovhq1TupZSRQogSQAUUxXuDEGKolHJVMslbCCF6oMiaHsgPvFbuNxj/fwMcl1J6G/N/9db1u4wzBHFCiADAQ0r5RAgRLIQoBngAV6SUwSQlRkpZ9PUXIYQD4CilfL2ScjWwKRXhvwO1jZ/PACOEEBmBrVLKB+8WbpS7B8DixYuTqZ4psWERPDp2jjw1Kiar3BdtUc/EJQchuPj7VvaMnJEk7ermvYD3+9yHvfTDMVN6wl76odFqsXKwI/pVKADp7GzpumM5e8fMMvH9TysyNoY473tY5ipootzLuDeWkdj7NxD12ylWdgFRV04RdmBrkryC1ysP9ff53OvCQ9A6OKMPD1EWz6azwhATZZLGEBGKLsCHdFlzEXPrUorluHrxPB5eXolK+bffVeb2jetJlHvvhw/4ZeoEJsyah72DoyKrlFSrXY/Ovf6vvTsPr6I+Fzj+fUmwbAkECIuyhMVdkU0QFxTXghatqJVWBapQa0Wv3nqlFJdar7RudaXUrW5VtCpWrDuIiiAism8WEQGRJUgCCYskee8fcxJCEiA5c2Cc976f58nzJHOy/L6cw+R35vxmcnWl73vz6OCUkt2tuW/arBm569aS3aw5xUVFbCksICOz4S6f0yQ7m7btOjB/zixO6nN6tZtKlR4Zb9Iok9N6dmbuf5ZXObl/Y8pn9Dtp5/NoVeXcPr24/tKfVvrcB0cEj8Hdrblv0SSLNbnf0aJpFkXFxWzespWGFdbxN2vciA5tWjJz4X846/huNWqqlZ7OFS+NZcZzrzJn/NtVfs6BRx/GLx79E2P6DaYw8fhHhOlPv8xrI++s9PmPDggmvbtbc79x1RqyWh9IXrn/V6Xft05GA379+t+ZcNM9LJ8+q0Yt5XXu05fOfYLd0ORxj5NR4ajy3A/eolf/gYgIjVscRKPsFmxYvRJFObr3GfS5+IpK3/OC64MXVXe35j6jcVM2bVhPZpNsSoqL2b6lsNLa8IyspmS3ymHl4nkc3rN3jZp6nXkOvc4Mlp5NePpvZUfUy/vmq6U8/9Cf+fUtd1E/8fhXlB59fkz/QVdW+vwrRgaTr92tuW/UNJu83HVkNW1GcXER2woLqZexa1PDJk1p2SaHLxfOocsJfWrUBND11H50PTXYP0x8/rGyVz8qWjBt1yU5inJM7zM5fWDl++pn/30bsPs195mNs8nfsK7svtq2tYr7qnFTmrXKYcXieRxxXM1OVi815eOpHH7YYTWaRKsq/c85m2uH/6bSbffdE+zLd7fmvnmzZqxdu5YWzZtTVFREQUEBDRvuuh9slp1Nh/btmTlrNmeeflq1x9U8uwnNs5vQ6fBgn3dm7148Nq7y758lXy7nlnvGMHb0TTRKHMRAlXPP7MN1V1Q+afiB20YETbtZc988uwlr1m2gRXbTYB9YuIWGFc57ata0MR1zWjNz3kLOOvn4aje5aMV1WQ6qWqyqk1X1FuBqoNLp6SLSDvgtcFpibf6/gTrlPqV01iXsfkH29nLvF7PzCdFjwGBgCPBEkhl7s9txqepzQH9gK/C2iJxaxec8oqrdVbX7sGHDqvwB9Zs2pk5iJ5Fe50ccfNoJrFuyrNLn1cnMoEPvnix4bedLzksnTaXT+X3L1sTXzWpIVpsDqxW24PWJdL80uMs6DejL0venAZBWuzaDXxrLzGfHM/fl6i0bqUqteg2QOnUBkPTa1OlwODtyv931c8r9wjngoHYgQsmWArZ9uYi6R3anVv3g36VW3fqkNareL5Bti2dTv0uwA6x7ZHe2L1scdGVmIYkTCqVOPQ5o05Gi3DU1aspu3oLF8+ezbds2VJXZn82gdducXT5n3Zo1/HHkDdxw8220atO2bHvn7j2YMnkieRuD562bN+Wzds2u/x67c9yJvXnvjeCVl48mT+SYbsciIqxft5btiSdImzdtYuG8ObRqk7OH71S1Ldu2U5h4CXrLtu1Mnb2Qg6t4HG0u3MqMBV9wao9jdo6t02G8M+1zNiTWrudtLuSbdVU9z66sz7GdePX9TwB4Z+rn9Dz6MESENbkby05myy8oZNaiL2lXYb18dVzy2J9Zs3gpk/7yeJW3Z7U+kGEvj+Wpy65j3X92LhlbMvFjugzY+f+qXlZDGrc5qFo/c96Ed+k5KPh/1eWCfnwxKTj/I612bYa98jc+feYVZr30Ro1byivM3whAfu5aFs+YwhG9dt31ZDZpxvL5nwNQkL+RDd+upFGzluQc2ZXF0z8q+/qtBZvIX7+2Wj/z4G7HM++jdwBYNP1D2oOs7ZcAABdZSURBVB7ZGRFh04b17EicW7O1YDOrvpi/ywmj1bU5LxjTd+vXMmfah3TrvesT1O/Wr+Xx0aO49LpRNDuoTdn2Qzp1Y87UD8q+vnDzJr5bV73/10f1OJFPJwVLQmZ/PJmDO3VFRNiYu47vtwdNWwo2s2zRPJqX+5k1Uf6+WjRjyi4T+FLbthSwfOFcDu2+c+LW/qguLJr+4S73VV4176tDuvVizofBfbVw+ge0O7JLlffViiXzaVLupOWaevPtd+j74+ovyQHo2eNY3p04iQ3fBfvB/Px8Vn9bvf3gKSf35rXXgyu3vTtxEj2O7R7sL9auZdu2YP+1adMmZs+ZQ07btnv6VpVkN86iRXZTvloZHHz6ZNZcOrTd9d9m9dr1XHvrnYz+3bXktN65f+zZpRPvfDiNDRvzAMjbtJnVa3e/rKy8Pr2O5V/vvA/AOx9Mo2eXo4Om9blsSzwG8zcXMGv+Ytq1rt4+KCq+5n5XsTxyLyKHAiXljlZ3BkoXkG4mWEKTC2QSTODzRaQ5wVHvyVV8y2nAwyLSrnRZToWj91UZD9wG1Ca4as9eqWq+iGwUkZNU9SPgUuCDPWzPE5F8ETlRVacAvyj3b9AeWJZYitQe6ARMqs44ysts2YyLn7gLSUujlghzXnqDRW9MotewIGnaI88BcNR5Z7Lk3Y/4fsvWsq9du2gpb91yD0PffAqpVYuSHTt45Zpb2Lhi9V5/7qdPvMDAJ+9lxKJJbNmYz7O/CE5ZOObCfrQ/6VjqNWlE98uCScoLl9/A6jk1u0RgWkYjGg+4HGoJIrXYMn8G25bMpf6xwVGiwhkfUO/I7jTocQpaUoIWfc+GF4JXN4rWf0v+e+PJHnx98LprcTEbJ/yD4ry9TxoLZn5EkwuG0uK6OyjZWlj2PdOzW9Ko70XBUzWBzVPeZsfab/b8zSo47MijOLHPaQwf8gvS0tLocMih9D33fP49/iUAzv7pBTz390fZvCmfh+8OrqKQlpbGA088Q9t27bls6K/5/X9dTYmWkJ6ezlXX30jzFi339CMBOOucc7nrjzfzy4vOIyMzkxF/CI5Irlz+FY8+dB8igqpy/sBLaNeh5pcH3JC3iWv+HBwlKyou5uyTenBS16MY91bwglXpZSjfmz6LEzofQb06Oy/92bH1gVz78/5c8Yf7g5MR09K4adhADmq29ydjA04/kRvve4Kzfj2KRg3qc/d/B0cpl636ljuffKmsa8h5Z3BI25r9YutwQnd6XjaAb+YuKls689rv7yQrMUmf8rd/0Pfma6jfJIuLH74dgOKiIu7s0Z81i5Yy4aZ7GP72M0gtoXhHES9cfTPfrdj742Xq4y8y6Ol7ufWLyRR+l8cTA4cD0PWis+nYuwf1m2Rx3KBg+c8zQ37LqjkLa9QF8PJ9f2BrwSbS0tI5a8hw6jbI4PP3glf0up7+E048/xJeH3sXj954Bapw6sCh1MtsSL3Mhpx80WCe/9MItKSk7OsbZjff68/sfEpfXhvzJ/563WXUqZ/BecODS6VuWL2C954dW3Zf9Tz7Qpq1aV/jpsf/NIrCzfmkpaVz4ZXXUa9BBlPefBWAE/uex1vj/k7h5nz+OTY4Sl0rLY0b7n2Mlm3acfYlVzDmluvRkhJqpadz4a+up3GzvT8Z7HXG2Txz7+3cNuxi6mVkMviGW4HgBN9Xn3go2Peocup5Azkwp0ONmwBevPdWtiTuq35DrqFugww+eze4r0ovQ7n40yl06NSNAxIHQwCyW+XQ56IhPHPHjagG91W/X15Do2rcV1379GP8w6N54NpLqdsggwuuCZY3rv/ma955diyCoCjHn3MRzZO4rwC2bt3GtOnTuWnk73a2vhScSH7RBQPIzc3l4ksHU1hYSC0Rnn1+HK/+cxwd2rfn6quu5MrfDKekRElPT2fkiBsqnZBblZ+e25+RN93C2eeeT8OGmdx5x/8CwWU37/7L/aV3F4MuvYRDDq75fnDk8Cu48Y772LGjiFYtm3P7/1zNCxOCV/x+9pOzGPvMi+Rv2swf7w9eWU1PS+PFv95Fx5zWXDNkIENvvA0tUdLT0xh1zVAObF751aeKBvQ7jRGj7+fHl15Fw4wG3D0qWLaz7OtV3DX2qbLDi4MvOpdD2tfsCYuLlqTijPX9LbEk50GgEVAELAWGqWquiAwnOPH0W1XtIyJPAj2BZQRH4V9T1SdFZDnQvXRtvoj0Be4geDVjnaqeUfFSmCIyHzin9Ko3IjIWyFPVEbsZZ4GqNqiwrTMwluCKPsuAIaq6cQ/buxG8MrAFeBu4QFWPEpHfAZcAO4A1wM/38oREf1s7uR3pD9XdO5axctTlUQ8jpVrfHhzlXZZbw0t//sC1bxq8ClK8cHK0A0mxtCNO4Te1cqIeRko9XLIcgKdmrtzzJ8bMoG6teXtJ9Y5oxsVZhwYTuOdmrYp4JKn18y6t2F6Q/N9R+SH6UYNgCU/RqgURjyS10lsdCTtPV4rMyB912CeT2Tu2fxl5WzJieeReVWcCVS7+UtUHCSb+pR8P3s3n5VT4+E3gzQrbbq3wcdn15RMn0h4HXLiHcVa6aLeqzk58XXW3zwSOKbfp1sT20cDo3f1s55xzzrn/D+K8hGZfiO2a+yiJyBEErxZMrOpEVuecc84556IQyyP3UVPVhYCtNS7OOeecczHkf6F2V37k3jnnnHPOOSP8yL1zzjnnnIstX3O/K5/cO+ecc8652PJlObvyZTnOOeecc84Z4UfunXPOOedcbPmynF35kXvnnHPOOeeMiOVfqHVJ8TvaOeecc6kW+V9xvVJy9skcZ6wuj7wtGT65dyknIsNU9ZGox5FKFpvAu+LEYhPY7LLYBDa7LDaB3S5XPb4sx+0Lw6IewD5gsQm8K04sNoHNLotNYLPLYhPY7XLV4JN755xzzjnnjPDJvXPOOeecc0b45N7tCxbX+VlsAu+KE4tNYLPLYhPY7LLYBHa7XDX4CbXOOeecc84Z4UfunXPOOeecM8In984555xzzhnhk3vnnHPOOeeM8Mm9SwkRaSwiWVGPwzkRaRr1GFJJRLJEJCPqcThnjYhkikg3K7+7fF/hSvnk3iVNRNqIyDgRWQ9MB2aIyLrEtpyIh5c0ETlMRN4UkX+LSAcReVJE8kTkUxE5POrxJUNEvhORx0TkNBGJ5Z/TroqI9BWRr0Rkioh0EZEFwHQRWSUip0U9vmSJyIEi8rSI5AO5wAIRWSEit4pI7ajHlwzDj8Fflnu/lYhMTOwvporIIVGOLVkWmwBE5NnSJ/8ichawAPgzMFtELox0cEmyuK9w4fnk3oXxAjAeaKGqB6tqR6Al8CowLsqBhfQIMAZ4FpgEvAVkAX8EHopwXGGsB2YDtwGrROR+ETku2iGlxGigH3AD8B5wuap2AM4A7opyYCE9Czyhqg2BC4GXgcOBdODhKAcWgtXH4NXl3r8XeBFoTPD4+2skIwrPYhPAMaqam3j/FuAkVT0d6AaMim5YoVjcV7iQfHLvwmiqqi+oanHpBlUtVtVxQJMIxxVWhqpOUNXngR2qOk4DEwgm+XFUqKoPqeoJQC/gG2CMiCwTkTsiHlsYJaq6SFWnAVtU9RMAVV1EvPdvTVR1MoCqvgL0VtVCVR0F9I50ZMmz+hgs7xBV/ZuqlqjqeIIJcdxZaqolIpmJ90uAFQCJCX96ZKMKx+K+woUU1wez+2GYKSJjgKeAlYltrYFBwKzIRhVeWrn3761w2wH7cyApVLYMQlVXAHcCd4rIocDFkY0qvDwR+RWQCWwUkesIjjKeDhREOrJw1ovIJQSvHA0AlgMklrPE9UmL1cdgKxF5gKAvW0Rqq+qOxG1xXRZhsQngD8D7IvIw8DHwTxH5F3AqwSu0cWRxX+FC8sm9C+My4HKCHeZBBL8IVgITgMcjHFdYD4tIA1UtUNUxpRtFpCPB0o84er+qjaq6hOD+i6tBBC+nlwBnAgOBt4GvgaERjiusXwJ3AyMIlrKULpNoDPwuojGFZfUxeEO59z8DGhA80WwBvBbNkEKz2ISqviginxPsGw4hmAP1Ap5X1bcjHVzyLO4rXEj+F2qdc84555wzwl+ycfuEiNwc9Rj2BYtdFpvAu36IROQsEbm84tW0yl+dJW4kcJGIXJh4/zQReUBErhIRM79jRWRS1GPYFyx2WWxyNeNH7t0+ISIrVLVN1ONINYtdFpvAu35oRGQ0cALwOfAT4D5VfTBx2+eq2jXK8SUrcd5RM4LzcTYBPyJYmtgPWKuq10Y4vKSIyNyKmwiWsSwBUNVO+31QKWCxy2KTC8/X3Lukicim3d0E1N2fY0kli10Wm8C7YuYcoIuqFonIrcBzItJeVa+j3Mm2MXSSqh6duKb4GqClqn4vIs8R3wsLLCd4onI7sJXg/vmI4ElZnC3HXtdy7DW5kMy8ZOgikQccrKqZFd4ygG8jHlsYedjrysNeE3hXnKSrahGAquYRTD4yReSfxPcqVAClTTuAGar6feLjIqB4T1/4Q6Wq/Qmul/4IwbXhlxNcFvhrVf060sGFYLHLYpMLzyf3Loyngba7ue25/TmQFLPYZbEJvCtOvhSRk0s/SPxNjMsJlg/E8i8/J6wRkQYAqvrj0o2JK8t8H9moQkpc074vcIqIvEa8n4CVsdhlscmF42vunXPO7XMiUhdAVbdWcdtBqvrN/h/VviMi9YH6qrou6rGEJSLHAL1UdWzUY0kli10Wm1zN+Zp7F0riD2X0ILjOvQKrgU815s8aLXZZbALvigtV3Zq4mkxPKjfFemK/h/uqMNKBhVBF0ywRkbg+/kpZ7LLY5MLxI/cuaSJyJjAG+A/Bn5IHaAV0BK5S1XeiGlsYFrssNoF3xYnFJrDZZbEJbHZZbHLh+eTeJU1EFgF9EyfwlN/eDnhDVWO5jtZil8Um8K44sdgENrssNoHNLotNLjw/odaFkQ6sqmL7N0Dt/TyWVLLYZbEJvCtOLDaBzS6LTWCzy2KTC8nX3LswngBmiMg4YGViW2vgYuDxyEYVnsUui03gXXFisQlsdllsAptdFptcSL4sx4UiIkcA/QlO5BGCIwivqerCSAcWksUui03gXXFisQlsdllsAptdFptcOD65d84555xzzghfluOSJiLzCC67VekmQFW1034eUkpY7LLYBN4VJxabwGaXxSaw2WWxyYXnR+5d0kRkd39BE4C4/ulri10Wm8C74sRiE9jsstgENrssNrnwfHLvQhORRsDBiQ+/UNX8CIeTMha7LDaBd8WJxSaw2WWxCWx2WWxyyfPJvUuaiBwAPAKcB3xF8DJgW2A8cKWqfh/d6JJnsctiE3hXnFhsAptdFpvAZpfFJheeX+fehTGK4Dq6rVW1i6p2BtoQnMtxU5QDC8lil8Um8K44sdgENrssNoHNLotNLiQ/cu+SJiLzgR6quqXC9gbAJ6p6VDQjC8dil8Um8K44sdgENrssNoHNLotNLjw/cu/CKKm4QwFQ1QKqPns/Lix2WWwC74oTi01gs8tiE9jsstjkQvJLYbowVESyCNb4VVSyvweTQha7LDaBd8WJxSaw2WWxCWx2WWxyIfnk3oXREJhJ1TuVOB8xsNhlsQm8K04sNoHNLotNYLPLYpMLydfcO+ecc845Z4QfuXdJE5GuFTYpkKuqK6MYT6pY7LLYBN4VJxabwGaXxSaw2WWxyYXnR+5d0kTk/So2NwYOAAaq6uz9O6LUsNhlsQm8K04sNoHNLotNYLPLYpMLzyf3LuVEpDtwr6r2jnosqWSxy2ITeFecWGwCm10Wm8Bml8UmV31+KUyXcqr6GdAg6nGkmsUui03gXXFisQlsdllsAptdFptc9fnk3qWciDTH4Fn6FrssNoF3xYnFJrDZZbEJbHZZbHLV5yfUuqSJyINU3nk0Bo4Hrt3/I0oNi10Wm8C74sRiE9jsstgENrssNrnwfM29S5qIDKqwSYENwAxVXRfBkFLCYpfFJvCuOLHYBDa7LDaBzS6LTS48P3LvwsgHphrcgVjsstgE3hUnFpvAZpfFJrDZZbHJheRH7l3SROQloBewBfgYmAp8rKoLIh1YSBa7LDaBd8WJxSaw2WWxCWx2WWxy4fnk3oUmIjkE6/uOJ9jJtCF4SbBflOMKy2KXxSbwrjix2AQ2uyw2gc0ui00ueb4sx4WmqstFpA5QN/FW+n6sWeyy2ATeFScWm8Bml8UmsNllscklz4/cu6SJyEiCIwTZwBLgk8TbXFUtjnJsYVjsstgE3hUnFpvAZpfFJrDZZbHJheeTe5c0EVkMFACvE6zzm66q+dGOKjyLXRabwLvixGIT2Oyy2AQ2uyw2ufB8cu9CEZHS6+keDxxH8Bfx5hCcvf/3KMcWhsUui03gXXFisQlsdllsAptdFptcOD65dykhIulAN6A38CugnaqmRTuq8Cx2WWwC74oTi01gs8tiE9jsstjkkuOTe5c0EelPcKTgBOBIYAHBy4JTCY4YrI9weEmz2GWxCbwrTiw2gc0ui01gs8tikwvPJ/cuaSLyColr6gIzVfX7cre1U9WvIhtcCBa7LDaBd8WJxSaw2WWxCWx2WWxy4fmlMF0Y9YAHVXV7+Y0icgzwLyAnikGlgMUui03gXXFisQlsdllsAptdFptcSLWiHoCLtc+AN0Sk7Fq6InIKwVn7QyMaUypY7LLYBN4VJxabwGaXxSaw2WWxyYWlqv7mb0m/Ab8HPiI4O38AsALoHvW4vOv/R5N3xevNYpPVLotNVrssNvlbuDdfluNCUdX/FZGtwExAgFNVdWnEwwrNYpfFJvCuOLHYBDa7LDaBzS6LTS4cP6HWJU1EJgBKsDM5AVgKrCm9XVX7RzS0UCx2WWwC74oTi01gs8tiE9jsstjkwvPJvUuaiJy8p9tV9YP9NZZUsthlsQm8K04sNoHNLotNYLPLYpMLzyf3zjnnnHPOGeFr7l3SRGQewcuBpRTIBd4H7lbVbZEMLCSLXRabwLvixGIT2Oyy2AQ2uyw2ufD8yL1Lmoi0rWJzY2AQUF9VY3kZLotdFpvAu+LEYhPY7LLYBDa7LDa58Hxy7/YJEZmlql2iHkeqWeyy2ATeFScWm8Bml8UmsNllsclVj/8RK7evWH1sWeyy2ATeFScWm8Bml8UmsNllsclVg6+5d0kTka5VbM4CLgE+3M/DSRmLXRabwLvixGIT2Oyy2AQ2uyw2ufB8WY5Lmoi8X2GTAhuAycAjqrpjvw8qBSx2WWwC74oTi01gs8tiE9jsstjkwvPJvXPOOeecc0b4shyXNBG5fk+3q+q9+2ssqWSxy2ITeFecWGwCm10Wm8Bml8UmF56fbOHCyCj39tsKH2dEOK6wLHZZbALvihOLTWCzy2IT2Oyy2ORC8mU5LiWsXnLLYpfFJvCuOLHYBDa7LDaBzS6LTS45fuTepYrVZ4kWuyw2gXfFicUmsNllsQlsdllscknwyb1zzjnnnHNG+LIclzQRmcfOIwUdgaXlb1fVTvt9UClgsctiE3hXnFhsAptdFpvAZpfFJheeT+5d0kTkYKA5sLLCTW2B1aq6tPJX/fBZ7LLYBN4VJxabwGaXxSaw2WWxyYXny3JcGH8BNqnq1+XfgC2J2+LKYpfFJvCuOLHYBDa7LDaBzS6LTS4kn9y7MHJUdW7Fjar6GZCz/4eTMha7LDaBd8WJxSaw2WWxCWx2WWxyIfnk3oVRZw+31d1vo0g9i10Wm8C74sRiE9jsstgENrssNrmQfHLvwpghIkMrbhSRy4GZEYwnVSx2WWwC74oTi01gs8tiE9jsstjkQvITal3SRKQ5MB74np07ke7AAcBPVXVNVGMLw2KXxSbwrjix2AQ2uyw2gc0ui00uPJ/cu9BEpA9wVOLDBao6KcrxpIrFLotN4F1xYrEJbHZZbAKbXRabXPJ8cu+cc84555wRvubeOeecc845I3xy75xzzjnnnBE+uXfOOeecc84In9w755xzzjlnhE/unXPOOeecM+L/ALNS16og0zjlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "sns.heatmap(grouped_pivot, annot= True, ax=ax, linewidths=0.6, cmap='RdBu')\n",
    "\n",
    "#move ticks and labels to the center\n",
    "ax.set_xticks(np.arange(grouped_pivot.shape[1]) + 0.5, minor=False)\n",
    "ax.set_yticks(np.arange(grouped_pivot.shape[0]) + 0.5, minor=False)\n",
    "\n",
    "#insert labels\n",
    "# ax.set_xticklabels(row_labels, minor=False)\n",
    "# ax.set_yticklabels(col_labels, minor=False)\n",
    "\n",
    "#rotate label if too long\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# don't show labels\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Product that gives a better margin at specific stores are as shown in the heatmap above:\n",
    "* Seafood product gave the best margin at Chukwudi018, Chukwudi035 and Chukwudi046"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Supermarket Type analysis**"
   ]
  },
  {
   "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>Supermarket_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Grocery Store</td>\n",
       "      <td>821.426611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Supermarket Type1</td>\n",
       "      <td>6434.596537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Supermarket Type2</td>\n",
       "      <td>5588.015335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Supermarket Type3</td>\n",
       "      <td>12541.373382</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Supermarket_Type  Product_Supermarket_Sales\n",
       "0      Grocery Store                 821.426611\n",
       "1  Supermarket Type1                6434.596537\n",
       "2  Supermarket Type2                5588.015335\n",
       "3  Supermarket Type3               12541.373382"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_2=df[['Supermarket_Type', 'Product_Supermarket_Sales']]\n",
    "grpd_2=grouped_2.groupby(['Supermarket_Type'],as_index=False).mean()\n",
    "grpd_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 2, 3]),\n",
       " [Text(0, 0, 'Supermarket Type1'),\n",
       "  Text(1, 0, 'Grocery Store'),\n",
       "  Text(2, 0, 'Supermarket Type3'),\n",
       "  Text(3, 0, 'Supermarket Type2')])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Supermarket_Type\", y=\"Product_Supermarket_Sales\", data=df)\n",
    "#rotate label if too long\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 837.407760674732 , P = 0.0\n"
     ]
    }
   ],
   "source": [
    "grpd_2=grouped_2.groupby(['Supermarket_Type'],as_index=False)\n",
    "f_val, p_val = stats.f_oneway(grpd_2.get_group('Supermarket Type1')['Product_Supermarket_Sales'], grpd_2.get_group('Supermarket Type2')['Product_Supermarket_Sales'], grpd_2.get_group('Supermarket Type3')['Product_Supermarket_Sales'], grpd_2.get_group('Grocery Store')['Product_Supermarket_Sales'])  \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We see that the distributions of sales between the different supermarket-type categories are distinct, and enough to take Supermarket Type as a potential good predictor of sales price\n",
    "\n",
    "Let's examine Supermarket_Location and Supermarket sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Supermarket_Location_Type analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>Supermarket_Location_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>4907.270494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>7581.598620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>5770.437719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Supermarket_Location_Type  Product_Supermarket_Sales\n",
       "0                 Cluster 1                4907.270494\n",
       "1                 Cluster 2                7581.598620\n",
       "2                 Cluster 3                5770.437719"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_3=df[['Supermarket_Location_Type', 'Product_Supermarket_Sales']]\n",
    "grpd_3=grouped_3.groupby(['Supermarket_Location_Type'],as_index=False).mean()\n",
    "grpd_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cluster 3    1438\n",
       "Cluster 2    1210\n",
       "Cluster 1    1094\n",
       "Name: Supermarket_Location_Type, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Supermarket_Location_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Supermarket_Location_Type', ylabel='Product_Supermarket_Sales'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=\"Supermarket_Location_Type\", y=\"Product_Supermarket_Sales\", data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We see that the distributions of sales between the different Supermarket-Location categories have a significant overlap, and so Supermarket-Location would not be a good predictor of sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 116.808702799811 , P = 6.199756735425813e-50\n"
     ]
    }
   ],
   "source": [
    "grpd_3=grouped_3.groupby(['Supermarket_Location_Type'],as_index=False)\n",
    "f_val, p_val = stats.f_oneway(grpd_3.get_group('Cluster 1')['Product_Supermarket_Sales'], grpd_3.get_group('Cluster 2')['Product_Supermarket_Sales'], grpd_3.get_group('Cluster 3')['Product_Supermarket_Sales'])  \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The Anova result says otherwise from what the Boxplot suggests. I might have to consider Supermarket location as a potential predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Product_Fat_Content analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Low Fat          2272\n",
       "Normal Fat       1341\n",
       "Ultra Low fat     129\n",
       "Name: Product_Fat_Content, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Fat_Content'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Fat_Content</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Low Fat</td>\n",
       "      <td>6038.504696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Normal Fat</td>\n",
       "      <td>6287.411447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ultra Low fat</td>\n",
       "      <td>5343.226899</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Fat_Content  Product_Supermarket_Sales\n",
       "0             Low Fat                6038.504696\n",
       "1          Normal Fat                6287.411447\n",
       "2       Ultra Low fat                5343.226899"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_4=df[['Product_Fat_Content', 'Product_Supermarket_Sales']]\n",
    "grpd_4=grouped_4.groupby(['Product_Fat_Content'],as_index=False).mean()\n",
    "grpd_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Product_Fat_Content', ylabel='Product_Supermarket_Sales'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Product_Fat_Content', y='Product_Supermarket_Sales', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 3.265164001492282 , P = 0.03829959562702081\n"
     ]
    }
   ],
   "source": [
    "grpd_4=grouped_4.groupby(['Product_Fat_Content'],as_index=False)\n",
    "f_val, p_val = stats.f_oneway(grpd_4.get_group('Low Fat')['Product_Supermarket_Sales'], grpd_4.get_group('Normal Fat')['Product_Supermarket_Sales'], grpd_4.get_group('Ultra Low fat')['Product_Supermarket_Sales'])  \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In the boxplot, we see that the distribution of sales between the different product-fat-content categories have a significant overlap and as such Product-fat-content could not be a predictor of sales.\n",
    "\n",
    "* With low F test score showing a weak correlation and a P value of almost 0 implying almost certain statistical significance, it further confirm that Product-fat-content cannot be a predictor of supermarket sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**supermarket_size analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Medium    2281\n",
       "Small     1015\n",
       "High       446\n",
       "Name: Supermarket _Size, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Supermarket _Size'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>Supermarket _Size</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>High</td>\n",
       "      <td>3998.670359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Medium</td>\n",
       "      <td>6844.184498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Small</td>\n",
       "      <td>5364.715143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Supermarket _Size  Product_Supermarket_Sales\n",
       "0              High                3998.670359\n",
       "1            Medium                6844.184498\n",
       "2             Small                5364.715143"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_5=df[['Supermarket _Size', 'Product_Supermarket_Sales']]\n",
    "grpd_5=grouped_5.groupby(['Supermarket _Size'],as_index=False).mean()\n",
    "grpd_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Supermarket _Size', ylabel='Product_Supermarket_Sales'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Supermarket _Size', y='Product_Supermarket_Sales', data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA results: F= 100.26367160481566 , P = 3.8331989326796805e-43\n"
     ]
    }
   ],
   "source": [
    "grpd_5=grouped_5.groupby(['Supermarket _Size'],as_index=False)\n",
    "f_val, p_val = stats.f_oneway(grpd_5.get_group('Medium')['Product_Supermarket_Sales'], grpd_5.get_group('Small')['Product_Supermarket_Sales'],grpd_5.get_group('High')['Product_Supermarket_Sales'])  \n",
    "print( \"ANOVA results: F=\", f_val, \", P =\", p_val) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that the distribution of sales between the different supermarket size categories differs; as such supermarket-size could potentially be a predictor of supermarket sales."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Product_Type analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Snack Foods              563\n",
       "Fruits and Vegetables    556\n",
       "Household                414\n",
       "Frozen Foods             334\n",
       "Canned                   294\n",
       "Dairy                    263\n",
       "Baking Goods             259\n",
       "Health and Hygiene       222\n",
       "Meat                     202\n",
       "Soft Drinks              193\n",
       "Breads                   115\n",
       "Hard Drinks               97\n",
       "Others                    82\n",
       "Starchy Foods             62\n",
       "Breakfast                 54\n",
       "Seafood                   32\n",
       "Name: Product_Type, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Product_Type</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>5635.721351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Breads</td>\n",
       "      <td>5875.077043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Breakfast</td>\n",
       "      <td>5867.887222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Canned</td>\n",
       "      <td>6392.173197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dairy</td>\n",
       "      <td>6081.969734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>5980.055359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>6429.236259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Hard Drinks</td>\n",
       "      <td>6097.784845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Health and Hygiene</td>\n",
       "      <td>5476.370586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Household</td>\n",
       "      <td>6158.381329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Meat</td>\n",
       "      <td>5881.733663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Others</td>\n",
       "      <td>5460.596341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Seafood</td>\n",
       "      <td>6651.707500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Snack Foods</td>\n",
       "      <td>6280.732575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>5965.085389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Starchy Foods</td>\n",
       "      <td>7166.425000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Product_Type  Product_Supermarket_Sales\n",
       "0            Baking Goods                5635.721351\n",
       "1                  Breads                5875.077043\n",
       "2               Breakfast                5867.887222\n",
       "3                  Canned                6392.173197\n",
       "4                   Dairy                6081.969734\n",
       "5            Frozen Foods                5980.055359\n",
       "6   Fruits and Vegetables                6429.236259\n",
       "7             Hard Drinks                6097.784845\n",
       "8      Health and Hygiene                5476.370586\n",
       "9               Household                6158.381329\n",
       "10                   Meat                5881.733663\n",
       "11                 Others                5460.596341\n",
       "12                Seafood                6651.707500\n",
       "13            Snack Foods                6280.732575\n",
       "14            Soft Drinks                5965.085389\n",
       "15          Starchy Foods                7166.425000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_6=df[['Product_Type','Product_Supermarket_Sales']]\n",
    "grpd_6=grouped_6.groupby(['Product_Type'],as_index=False).mean()\n",
    "grpd_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15]),\n",
       " [Text(0, 0, 'Starchy Foods'),\n",
       "  Text(1, 0, 'Soft Drinks'),\n",
       "  Text(2, 0, 'Snack Foods'),\n",
       "  Text(3, 0, 'Seafood'),\n",
       "  Text(4, 0, 'Others'),\n",
       "  Text(5, 0, 'Meat'),\n",
       "  Text(6, 0, 'Household'),\n",
       "  Text(7, 0, 'Health and Hygiene'),\n",
       "  Text(8, 0, 'Hard Drinks'),\n",
       "  Text(9, 0, 'Fruits and Vegetables'),\n",
       "  Text(10, 0, 'Frozen Foods'),\n",
       "  Text(11, 0, 'Dairy'),\n",
       "  Text(12, 0, 'Canned'),\n",
       "  Text(13, 0, 'Breakfast'),\n",
       "  Text(14, 0, 'Breads'),\n",
       "  Text(15, 0, 'Baking Goods')])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='Product_Type', y='Product_Supermarket_Sales', data=grpd_1)\n",
    "#rotate label if too long\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We see that the distributions of supermarket-sales between the different Product types categories have a significant overlap, and so product-types would not be a good predictor of sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>Supermarket_Opening_Year</th>\n",
       "      <th>Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1992</td>\n",
       "      <td>CHUKWUDI027</td>\n",
       "      <td>12541.373382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2011</td>\n",
       "      <td>CHUKWUDI035</td>\n",
       "      <td>7907.270484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014</td>\n",
       "      <td>CHUKWUDI017</td>\n",
       "      <td>7468.619197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2009</td>\n",
       "      <td>CHUKWUDI045</td>\n",
       "      <td>7398.398568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2006</td>\n",
       "      <td>CHUKWUDI049</td>\n",
       "      <td>6352.267561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004</td>\n",
       "      <td>CHUKWUDI046</td>\n",
       "      <td>5753.361804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016</td>\n",
       "      <td>CHUKWUDI018</td>\n",
       "      <td>5588.015335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1994</td>\n",
       "      <td>CHUKWUDI013</td>\n",
       "      <td>3998.670359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1992</td>\n",
       "      <td>CHUKWUDI019</td>\n",
       "      <td>1064.223020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005</td>\n",
       "      <td>CHUKWUDI010</td>\n",
       "      <td>606.450625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Supermarket_Opening_Year Supermarket_Identifier  Product_Supermarket_Sales\n",
       "1                      1992            CHUKWUDI027               12541.373382\n",
       "7                      2011            CHUKWUDI035                7907.270484\n",
       "8                      2014            CHUKWUDI017                7468.619197\n",
       "6                      2009            CHUKWUDI045                7398.398568\n",
       "5                      2006            CHUKWUDI049                6352.267561\n",
       "3                      2004            CHUKWUDI046                5753.361804\n",
       "9                      2016            CHUKWUDI018                5588.015335\n",
       "2                      1994            CHUKWUDI013                3998.670359\n",
       "0                      1992            CHUKWUDI019                1064.223020\n",
       "4                      2005            CHUKWUDI010                 606.450625"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_7=df[['Supermarket_Opening_Year','Supermarket_Identifier','Product_Supermarket_Sales']]\n",
    "grpd_7=grouped_7.groupby(['Supermarket_Opening_Year','Supermarket_Identifier'],as_index=False).mean()\n",
    "grpd_7.sort_values(['Product_Supermarket_Sales'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 13146.66742793651)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Engine size as potential predictor variable of price\n",
    "sns.regplot(x=\"Supermarket_Opening_Year\", y=\"Product_Supermarket_Sales\", data=grpd_7)\n",
    "plt.ylim(0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>Supermarket_Opening_Year</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.095753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "      <td>0.095753</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Supermarket_Opening_Year  Product_Supermarket_Sales\n",
       "Supermarket_Opening_Year                   1.000000                   0.095753\n",
       "Product_Supermarket_Sales                  0.095753                   1.000000"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grpd_7.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Product_Weight</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Product_Weight</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015260</td>\n",
       "      <td>0.037564</td>\n",
       "      <td>0.000649</td>\n",
       "      <td>0.015314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <td>-0.015260</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.028941</td>\n",
       "      <td>-0.084218</td>\n",
       "      <td>-0.161008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Price</th>\n",
       "      <td>0.037564</td>\n",
       "      <td>-0.028941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.161113</td>\n",
       "      <td>0.524002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Supermarket_Opening_Year</th>\n",
       "      <td>0.000649</td>\n",
       "      <td>-0.084218</td>\n",
       "      <td>0.161113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "      <td>0.015314</td>\n",
       "      <td>-0.161008</td>\n",
       "      <td>0.524002</td>\n",
       "      <td>0.057508</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Product_Weight  Product_Shelf_Visibility  \\\n",
       "Product_Weight                   1.000000                 -0.015260   \n",
       "Product_Shelf_Visibility        -0.015260                  1.000000   \n",
       "Product_Price                    0.037564                 -0.028941   \n",
       "Supermarket_Opening_Year         0.000649                 -0.084218   \n",
       "Product_Supermarket_Sales        0.015314                 -0.161008   \n",
       "\n",
       "                           Product_Price  Supermarket_Opening_Year  \\\n",
       "Product_Weight                  0.037564                  0.000649   \n",
       "Product_Shelf_Visibility       -0.028941                 -0.084218   \n",
       "Product_Price                   1.000000                  0.161113   \n",
       "Supermarket_Opening_Year        0.161113                  1.000000   \n",
       "Product_Supermarket_Sales       0.524002                  0.057508   \n",
       "\n",
       "                           Product_Supermarket_Sales  \n",
       "Product_Weight                              0.015314  \n",
       "Product_Shelf_Visibility                   -0.161008  \n",
       "Product_Price                               0.524002  \n",
       "Supermarket_Opening_Year                    0.057508  \n",
       "Product_Supermarket_Sales                   1.000000  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Product_Identifier', 'Supermarket_Identifier',\n",
       "       'Product_Supermarket_Identifier', 'Product_Weight',\n",
       "       'Product_Fat_Content', 'Product_Shelf_Visibility', 'Product_Type',\n",
       "       'Product_Price', 'Supermarket_Opening_Year', 'Supermarket _Size',\n",
       "       'Supermarket_Location_Type', 'Supermarket_Type',\n",
       "       'Product_Supermarket_Sales'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Opening Year would as well not be a good predictor of sales"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "<p>We now have a better idea of what our data looks like and which variables are important to take into account when predicting the supermarket sales price. We have narrowed it down to the following variables:</p>\n",
    "\n",
    "Continuous numerical variables:\n",
    "<ul>\n",
    "    <li>Product_Weight</li>\n",
    "    <li>Product_Price</li>\n",
    "    <li>Product_Shelf_Visibility</li>\n",
    "</ul>\n",
    "    \n",
    "Categorical variables:\n",
    "<ul>\n",
    "    <li>Supermarket _Size</li>\n",
    "    <li>Supermarket_Location_Type</li>\n",
    "    <li>Supermarket_Type</li>\n",
    "    <li>Product_Type</li>\n",
    "</ul>\n",
    "\n",
    "<p>As we now move into building machine learning models to automate our analysis, feeding the model with variables that meaningfully affect our target variable will improve our model's prediction performance.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transformation\n",
    "* Assign dependent and independent variables (drop redundant columns)\n",
    "* One-hot encode categorical variables\n",
    "* Do label encoding for hierachical categories\n",
    "* Normalize the eatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label encoding\n",
    "# X2 = X.copy()\n",
    "\n",
    "# sm_size_map = {'Small':1, 'Medium':2, 'High':3}\n",
    "# X2['Supermarket _Size'] = X2['Supermarket _Size'].map(sm_size_map).astype(\"float\")\n",
    "\n",
    "# sm_loc_map = {'Cluster 1':1, 'Cluster 3':3, 'Cluster 2':2}\n",
    "# X2['Supermarket_Location_Type'] = X2['Supermarket_Location_Type'].map(sm_loc_map).astype(\"float\")\n",
    "\n",
    "# sm_type_map = {'Grocery Store':1, 'Supermarket Type2':2, 'Supermarket Type1':3, 'Supermarket Type3':4}\n",
    "# X2['Supermarket_Type'] = df['Supermarket_Type'].map(sm_type_map).astype(\"float\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# prod_fat_map = {'Ultra Low fat':1, 'Low Fat':2, 'Normal Fat':3}\n",
    "# X2['Product_Fat_Content'] = X2['Product_Fat_Content'].map(prod_fat_map)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* model performances were poor with label encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {},
   "outputs": [],
   "source": [
    "# independent features\n",
    "feature_set = ['Product_Weight', 'Product_Shelf_Visibility', 'Product_Type', 'Product_Price',\n",
    "               'Supermarket _Size', 'Supermarket_Location_Type', 'Supermarket_Type'] \n",
    "\n",
    "X = df.drop(['Product_Identifier','Supermarket_Identifier','Product_Supermarket_Identifier',\n",
    "           'Product_Fat_Content','Supermarket_Opening_Year','Product_Supermarket_Sales'], axis=1)\n",
    "# dependent variable\n",
    "y = df['Product_Supermarket_Sales'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.785</td>\n",
       "      <td>0.020694</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>382.91</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.086078</td>\n",
       "      <td>Fruits and Vegetables</td>\n",
       "      <td>359.53</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Grocery Store</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.600</td>\n",
       "      <td>0.039517</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>609.20</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.091780</td>\n",
       "      <td>Frozen Foods</td>\n",
       "      <td>456.32</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.000</td>\n",
       "      <td>0.023735</td>\n",
       "      <td>Baking Goods</td>\n",
       "      <td>258.08</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight  Product_Shelf_Visibility           Product_Type  \\\n",
       "0           8.785                  0.020694            Soft Drinks   \n",
       "1             NaN                  0.086078  Fruits and Vegetables   \n",
       "2          18.600                  0.039517           Frozen Foods   \n",
       "3             NaN                  0.091780           Frozen Foods   \n",
       "4          14.000                  0.023735           Baking Goods   \n",
       "\n",
       "   Product_Price Supermarket _Size Supermarket_Location_Type  \\\n",
       "0         382.91              None                 Cluster 2   \n",
       "1         359.53             Small                 Cluster 1   \n",
       "2         609.20              None                 Cluster 2   \n",
       "3         456.32            Medium                 Cluster 3   \n",
       "4         258.08             Small                 Cluster 1   \n",
       "\n",
       "    Supermarket_Type  \n",
       "0  Supermarket Type1  \n",
       "1      Grocery Store  \n",
       "2  Supermarket Type1  \n",
       "3  Supermarket Type3  \n",
       "4  Supermarket Type1  "
      ]
     },
     "execution_count": 323,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "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>Product_Weight</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Supermarket _Size</th>\n",
       "      <th>Supermarket_Location_Type</th>\n",
       "      <th>Supermarket_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.100</td>\n",
       "      <td>0.178246</td>\n",
       "      <td>Dairy</td>\n",
       "      <td>395.32</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 1</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.250</td>\n",
       "      <td>0.061434</td>\n",
       "      <td>Household</td>\n",
       "      <td>327.49</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.785</td>\n",
       "      <td>0.020573</td>\n",
       "      <td>Soft Drinks</td>\n",
       "      <td>391.16</td>\n",
       "      <td>Small</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.100</td>\n",
       "      <td>0.080131</td>\n",
       "      <td>Household</td>\n",
       "      <td>427.28</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Cluster 3</td>\n",
       "      <td>Supermarket Type2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.965</td>\n",
       "      <td>0.028710</td>\n",
       "      <td>Household</td>\n",
       "      <td>395.40</td>\n",
       "      <td>None</td>\n",
       "      <td>Cluster 2</td>\n",
       "      <td>Supermarket Type1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight  Product_Shelf_Visibility Product_Type  Product_Price  \\\n",
       "0          18.100                  0.178246        Dairy         395.32   \n",
       "1          15.250                  0.061434    Household         327.49   \n",
       "2           8.785                  0.020573  Soft Drinks         391.16   \n",
       "3          12.100                  0.080131    Household         427.28   \n",
       "4           6.965                  0.028710    Household         395.40   \n",
       "\n",
       "  Supermarket _Size Supermarket_Location_Type   Supermarket_Type  \n",
       "0            Medium                 Cluster 1  Supermarket Type1  \n",
       "1            Medium                 Cluster 3  Supermarket Type2  \n",
       "2             Small                 Cluster 2  Supermarket Type1  \n",
       "3            Medium                 Cluster 3  Supermarket Type2  \n",
       "4              None                 Cluster 2  Supermarket Type1  "
      ]
     },
     "execution_count": 324,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.drop(['Product_Identifier','Supermarket_Identifier','Product_Supermarket_Identifier',\n",
    "                'Product_Fat_Content','Supermarket_Opening_Year'], axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Product_Weight</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Product_Type_Baking Goods</th>\n",
       "      <th>Product_Type_Breads</th>\n",
       "      <th>Product_Type_Breakfast</th>\n",
       "      <th>Product_Type_Canned</th>\n",
       "      <th>Product_Type_Dairy</th>\n",
       "      <th>Product_Type_Frozen Foods</th>\n",
       "      <th>Product_Type_Fruits and Vegetables</th>\n",
       "      <th>...</th>\n",
       "      <th>Supermarket _Size_High</th>\n",
       "      <th>Supermarket _Size_Medium</th>\n",
       "      <th>Supermarket _Size_Small</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 1</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 2</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 3</th>\n",
       "      <th>Supermarket_Type_Grocery Store</th>\n",
       "      <th>Supermarket_Type_Supermarket Type1</th>\n",
       "      <th>Supermarket_Type_Supermarket Type2</th>\n",
       "      <th>Supermarket_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.100</td>\n",
       "      <td>0.178246</td>\n",
       "      <td>395.32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.250</td>\n",
       "      <td>0.061434</td>\n",
       "      <td>327.49</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.785</td>\n",
       "      <td>0.020573</td>\n",
       "      <td>391.16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12.100</td>\n",
       "      <td>0.080131</td>\n",
       "      <td>427.28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.965</td>\n",
       "      <td>0.028710</td>\n",
       "      <td>395.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight  Product_Shelf_Visibility  Product_Price  \\\n",
       "0          18.100                  0.178246         395.32   \n",
       "1          15.250                  0.061434         327.49   \n",
       "2           8.785                  0.020573         391.16   \n",
       "3          12.100                  0.080131         427.28   \n",
       "4           6.965                  0.028710         395.40   \n",
       "\n",
       "   Product_Type_Baking Goods  Product_Type_Breads  Product_Type_Breakfast  \\\n",
       "0                          0                    0                       0   \n",
       "1                          0                    0                       0   \n",
       "2                          0                    0                       0   \n",
       "3                          0                    0                       0   \n",
       "4                          0                    0                       0   \n",
       "\n",
       "   Product_Type_Canned  Product_Type_Dairy  Product_Type_Frozen Foods  \\\n",
       "0                    0                   1                          0   \n",
       "1                    0                   0                          0   \n",
       "2                    0                   0                          0   \n",
       "3                    0                   0                          0   \n",
       "4                    0                   0                          0   \n",
       "\n",
       "   Product_Type_Fruits and Vegetables  ...  Supermarket _Size_High  \\\n",
       "0                                   0  ...                       0   \n",
       "1                                   0  ...                       0   \n",
       "2                                   0  ...                       0   \n",
       "3                                   0  ...                       0   \n",
       "4                                   0  ...                       0   \n",
       "\n",
       "   Supermarket _Size_Medium  Supermarket _Size_Small  \\\n",
       "0                         1                        0   \n",
       "1                         1                        0   \n",
       "2                         0                        1   \n",
       "3                         1                        0   \n",
       "4                         0                        0   \n",
       "\n",
       "   Supermarket_Location_Type_Cluster 1  Supermarket_Location_Type_Cluster 2  \\\n",
       "0                                    1                                    0   \n",
       "1                                    0                                    0   \n",
       "2                                    0                                    1   \n",
       "3                                    0                                    0   \n",
       "4                                    0                                    1   \n",
       "\n",
       "   Supermarket_Location_Type_Cluster 3  Supermarket_Type_Grocery Store  \\\n",
       "0                                    0                               0   \n",
       "1                                    1                               0   \n",
       "2                                    0                               0   \n",
       "3                                    1                               0   \n",
       "4                                    0                               0   \n",
       "\n",
       "   Supermarket_Type_Supermarket Type1  Supermarket_Type_Supermarket Type2  \\\n",
       "0                                   1                                   0   \n",
       "1                                   0                                   1   \n",
       "2                                   1                                   0   \n",
       "3                                   0                                   1   \n",
       "4                                   1                                   0   \n",
       "\n",
       "   Supermarket_Type_Supermarket Type3  \n",
       "0                                   0  \n",
       "1                                   0  \n",
       "2                                   0  \n",
       "3                                   0  \n",
       "4                                   0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = pd.get_dummies(X)\n",
    "df2 = pd.get_dummies(df2)\n",
    "# show\n",
    "X.head()\n",
    "df2 .head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Product_Type_Baking Goods</th>\n",
       "      <th>Product_Type_Breads</th>\n",
       "      <th>Product_Type_Breakfast</th>\n",
       "      <th>Product_Type_Canned</th>\n",
       "      <th>Product_Type_Dairy</th>\n",
       "      <th>Product_Type_Frozen Foods</th>\n",
       "      <th>Product_Type_Fruits and Vegetables</th>\n",
       "      <th>...</th>\n",
       "      <th>Supermarket _Size_High</th>\n",
       "      <th>Supermarket _Size_Medium</th>\n",
       "      <th>Supermarket _Size_Small</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 1</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 2</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 3</th>\n",
       "      <th>Supermarket_Type_Grocery Store</th>\n",
       "      <th>Supermarket_Type_Supermarket Type1</th>\n",
       "      <th>Supermarket_Type_Supermarket Type2</th>\n",
       "      <th>Supermarket_Type_Supermarket Type3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.251861</td>\n",
       "      <td>0.063015</td>\n",
       "      <td>0.516882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.262120</td>\n",
       "      <td>0.477153</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.836261</td>\n",
       "      <td>0.120336</td>\n",
       "      <td>0.901409</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.279484</td>\n",
       "      <td>0.641625</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.562370</td>\n",
       "      <td>0.072276</td>\n",
       "      <td>0.304763</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product_Weight  Product_Shelf_Visibility  Product_Price  \\\n",
       "0        0.251861                  0.063015       0.516882   \n",
       "1             NaN                  0.262120       0.477153   \n",
       "2        0.836261                  0.120336       0.901409   \n",
       "3             NaN                  0.279484       0.641625   \n",
       "4        0.562370                  0.072276       0.304763   \n",
       "\n",
       "   Product_Type_Baking Goods  Product_Type_Breads  Product_Type_Breakfast  \\\n",
       "0                        0.0                  0.0                     0.0   \n",
       "1                        0.0                  0.0                     0.0   \n",
       "2                        0.0                  0.0                     0.0   \n",
       "3                        0.0                  0.0                     0.0   \n",
       "4                        1.0                  0.0                     0.0   \n",
       "\n",
       "   Product_Type_Canned  Product_Type_Dairy  Product_Type_Frozen Foods  \\\n",
       "0                  0.0                 0.0                        0.0   \n",
       "1                  0.0                 0.0                        0.0   \n",
       "2                  0.0                 0.0                        1.0   \n",
       "3                  0.0                 0.0                        1.0   \n",
       "4                  0.0                 0.0                        0.0   \n",
       "\n",
       "   Product_Type_Fruits and Vegetables  ...  Supermarket _Size_High  \\\n",
       "0                                 0.0  ...                     0.0   \n",
       "1                                 1.0  ...                     0.0   \n",
       "2                                 0.0  ...                     0.0   \n",
       "3                                 0.0  ...                     0.0   \n",
       "4                                 0.0  ...                     0.0   \n",
       "\n",
       "   Supermarket _Size_Medium  Supermarket _Size_Small  \\\n",
       "0                       0.0                      0.0   \n",
       "1                       0.0                      1.0   \n",
       "2                       0.0                      0.0   \n",
       "3                       1.0                      0.0   \n",
       "4                       0.0                      1.0   \n",
       "\n",
       "   Supermarket_Location_Type_Cluster 1  Supermarket_Location_Type_Cluster 2  \\\n",
       "0                                  0.0                                  1.0   \n",
       "1                                  1.0                                  0.0   \n",
       "2                                  0.0                                  1.0   \n",
       "3                                  0.0                                  0.0   \n",
       "4                                  1.0                                  0.0   \n",
       "\n",
       "   Supermarket_Location_Type_Cluster 3  Supermarket_Type_Grocery Store  \\\n",
       "0                                  0.0                             0.0   \n",
       "1                                  0.0                             1.0   \n",
       "2                                  0.0                             0.0   \n",
       "3                                  1.0                             0.0   \n",
       "4                                  0.0                             0.0   \n",
       "\n",
       "   Supermarket_Type_Supermarket Type1  Supermarket_Type_Supermarket Type2  \\\n",
       "0                                 1.0                                 0.0   \n",
       "1                                 0.0                                 0.0   \n",
       "2                                 1.0                                 0.0   \n",
       "3                                 0.0                                 0.0   \n",
       "4                                 1.0                                 0.0   \n",
       "\n",
       "   Supermarket_Type_Supermarket Type3  \n",
       "0                                 0.0  \n",
       "1                                 0.0  \n",
       "2                                 0.0  \n",
       "3                                 1.0  \n",
       "4                                 0.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = MinMaxScaler()\n",
    "scaler.fit(X)\n",
    "X_scaled = X.copy()\n",
    "X_scaled[X_scaled.columns] = scaler.transform(X_scaled)\n",
    "\n",
    "df2[df2.columns]  = scaler.transform(df2)\n",
    "\n",
    "# show\n",
    "X_scaled.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Weight</th>\n",
       "      <th>Product_Shelf_Visibility</th>\n",
       "      <th>Product_Price</th>\n",
       "      <th>Product_Type_Baking Goods</th>\n",
       "      <th>Product_Type_Breads</th>\n",
       "      <th>Product_Type_Breakfast</th>\n",
       "      <th>Product_Type_Canned</th>\n",
       "      <th>Product_Type_Dairy</th>\n",
       "      <th>Product_Type_Frozen Foods</th>\n",
       "      <th>Product_Type_Fruits and Vegetables</th>\n",
       "      <th>...</th>\n",
       "      <th>Supermarket _Size_High</th>\n",
       "      <th>Supermarket _Size_Medium</th>\n",
       "      <th>Supermarket _Size_Small</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 1</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 2</th>\n",
       "      <th>Supermarket_Location_Type_Cluster 3</th>\n",
       "      <th>Supermarket_Type_Grocery Store</th>\n",
       "      <th>Supermarket_Type_Supermarket Type1</th>\n",
       "      <th>Supermarket_Type_Supermarket Type2</th>\n",
       "      <th>Supermarket_Type_Supermarket Type3</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.636797</td>\n",
       "      <td>0.187076</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.251861</td>\n",
       "      <td>0.062649</td>\n",
       "      <td>0.530901</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.449241</td>\n",
       "      <td>0.244012</td>\n",
       "      <td>0.592279</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.143495</td>\n",
       "      <td>0.087426</td>\n",
       "      <td>0.538106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 29 columns</p>\n",
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      "text/plain": [
       "   Product_Weight  Product_Shelf_Visibility  Product_Price  \\\n",
       "0        0.806490                  0.542785       0.537970   \n",
       "1        0.636797                  0.187076       0.422709   \n",
       "2        0.251861                  0.062649       0.530901   \n",
       "3        0.449241                  0.244012       0.592279   \n",
       "4        0.143495                  0.087426       0.538106   \n",
       "\n",
       "   Product_Type_Baking Goods  Product_Type_Breads  Product_Type_Breakfast  \\\n",
       "0                        0.0                  0.0                     0.0   \n",
       "1                        0.0                  0.0                     0.0   \n",
       "2                        0.0                  0.0                     0.0   \n",
       "3                        0.0                  0.0                     0.0   \n",
       "4                        0.0                  0.0                     0.0   \n",
       "\n",
       "   Product_Type_Canned  Product_Type_Dairy  Product_Type_Frozen Foods  \\\n",
       "0                  0.0                 1.0                        0.0   \n",
       "1                  0.0                 0.0                        0.0   \n",
       "2                  0.0                 0.0                        0.0   \n",
       "3                  0.0                 0.0                        0.0   \n",
       "4                  0.0                 0.0                        0.0   \n",
       "\n",
       "   Product_Type_Fruits and Vegetables  ...  Supermarket _Size_High  \\\n",
       "0                                 0.0  ...                     0.0   \n",
       "1                                 0.0  ...                     0.0   \n",
       "2                                 0.0  ...                     0.0   \n",
       "3                                 0.0  ...                     0.0   \n",
       "4                                 0.0  ...                     0.0   \n",
       "\n",
       "   Supermarket _Size_Medium  Supermarket _Size_Small  \\\n",
       "0                       1.0                      0.0   \n",
       "1                       1.0                      0.0   \n",
       "2                       0.0                      1.0   \n",
       "3                       1.0                      0.0   \n",
       "4                       0.0                      0.0   \n",
       "\n",
       "   Supermarket_Location_Type_Cluster 1  Supermarket_Location_Type_Cluster 2  \\\n",
       "0                                  1.0                                  0.0   \n",
       "1                                  0.0                                  0.0   \n",
       "2                                  0.0                                  1.0   \n",
       "3                                  0.0                                  0.0   \n",
       "4                                  0.0                                  1.0   \n",
       "\n",
       "   Supermarket_Location_Type_Cluster 3  Supermarket_Type_Grocery Store  \\\n",
       "0                                  0.0                             0.0   \n",
       "1                                  1.0                             0.0   \n",
       "2                                  0.0                             0.0   \n",
       "3                                  1.0                             0.0   \n",
       "4                                  0.0                             0.0   \n",
       "\n",
       "   Supermarket_Type_Supermarket Type1  Supermarket_Type_Supermarket Type2  \\\n",
       "0                                 1.0                                 0.0   \n",
       "1                                 0.0                                 1.0   \n",
       "2                                 1.0                                 0.0   \n",
       "3                                 0.0                                 1.0   \n",
       "4                                 1.0                                 0.0   \n",
       "\n",
       "   Supermarket_Type_Supermarket Type3  \n",
       "0                                 0.0  \n",
       "1                                 0.0  \n",
       "2                                 0.0  \n",
       "3                                 0.0  \n",
       "4                                 0.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2[X.columns]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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iVHglmV3RhVNHp/j8rr6mleZx27nlPPraTrYeavY6joiIpDAVXklmT21kY+yi7JDXUeLKl66aREbQz9f+vMHrKCIiksJUeCWZ3bWtlBdmYCmyMfZAFWWn8fkrJvDSpioWbjzodRwREUlRKrySSHtXD1VNHZSn8MKpx3Pn+WM4Y3g2//LH9bR2dnsdR0REUpAKrySyt64NB5QXqPDqTyjg479vmk5lfRvfeX6z13FERCQFqfBKInvrIhPrVXgd27ljCrl9Tjk/eXUn6yobvI4jIiIpRoVXEtld20pRdhoZIb/XUeLal989hYLMEF95Yi3dPWGv44iISApR4ZUknHPsqWujojDD6yhxLy8zyL9fP5W1lQ384OVtXscREZEUosIrSdS1dtHS0U2ZhhkH5NoZo3jvWSV8d+EW1u/TkKOIiMSGCq8ksSc6v6tCdzQO2H/cMJX8zBBf/PUabaItIiIxocIrSeypbSXoN0bkpnsdJWHkZ4b45s0z2HSwiW89u8nrOCIikgJUeCWJPbWtlOZn4Pdp4dSTcdnk4Xz4vNH8aPEOXnhLC6uKiMjQUuGVBLp7wuxraNfCqafoH6+dwpRRuXzpN2vY39DmdRwREUligYG8ycx+B/wE+ItzTvffx5n9De30hJ3W7zqG+Ut3n/A975k6kgdf3Mq9C1Yz/+7zCPr1N4mIiAy+gf52eQi4A9hiZl83s8lDmElOUu/EevV4nbqinDRunFnK8p11/NfTG72OIyIiSWpAPV7Oub8CfzWzPOB24Hkz2wP8CPilc65rCDPKCeypbSU3PUBeRtDrKAnt7PJ89ta18tNXd9Lc3s3MioLjvv+OuRUxSiYiIsliwOMpZjYM+CjwcWA18F1gFvD8yZ7UzPLN7HEze8vMNprZ+Sf7GfK2PXVt6u0aJO+ZNopxRVn8fnUllXWa7yUiIoNrQIWXmT0BLAYygfc65653zv3aOfc5IPsUzvtd4Bnn3GTgLEBjO6eouaOb2pZOze8aJH6fcducCrLTA/x8yU7qWzu9jiQiIklkoD1ejzjnznTO/bdzbj+AmaUBOOdmn8wJzSwXuBj4cfTnO51z9SfzGfK2vbWa3zXYstMCfOT8MXT1hPnZaztp69TiqiIiMjgGWnh9rZ9jr5/iOccBVcBPzWy1mT1iZllHv8nM7jGzFWa2oqqq6hRPlfz21LXiMyjN1x6Ng2lEbjofnDuamuZOfrV0lzbTFhGRQXHcwsvMRprZOUCGmc00s1nRr0uJDDueigCRuWEPOedmAi3Al49+k3PuYefcbOfc7OLi4lM8VfLbU9vGyNx0QgEtfzDYxhdnc/M5pWyvbuGJ1ZU457yOJCIiCe5EdzVeTWRCfRlwf5/jTcBXT/Gce4G9zrml0eeP00/hJScWdo49da2cXZ7vdZSkdXZ5AfWtXTy34SD5mUGuOnOk15FERCSBHbfwcs49CjxqZjc75343GCd0zh0wsz1mNsk5twm4HNgwGJ+daqqaOujoDmti/RC7ZGIxda2dvLSpivyMEHPGFnodSUREEtRxCy8z+5Bz7pfAGDP74tGvO+fu7+fHBuJzwK/MLARsB+46xc9JaXu1cGpMmBnXn1VKY1s3f3yjksyQn2mleV7HEhGRBHSiocbeSe+nsmTEMTnn3gBO6m5Ieac9tW2kB30Myw55HSXp+X3G7XMq+MmrO/j1ij1khPxeRxIRkQR0oqHGH0a//3ts4sjJqKxvoyw/E5+Z11FSQijg487zR/Ojxdv5xZJd3HB2CTPK8r2OJSIiCWSgC6h+08xyzSxoZgvNrNrMPjTU4eTYunvCHGhop7RAy0jEUmYowF0XjCUr5OejP13OtqpmryOJiEgCGegaBFc55xqB64jclTgR+LshSyUndKCxnR7ntH6XB3Izgtw1byw+gw8/spR99dpaSEREBmaghVfv7svXAAucc7VDlEcGqDL6y149Xt4oyk7jZ3fNoam9mzt/soy6Fm0tJCIiJzbQwuspM3uLyIT4hWZWDLQPXSw5kcq6NjJDfvIzgid+swyJaaV5/Ogjs9ld28pHf7aclo5uryOJiEicG1Dh5Zz7MnA+MNs510VktfkbhjKYHF9lfRtlBRmYJtZ76rxxw/j+HbNYV9nAJ3+5ko5u7esoIiLHdjL7zEwBbjWzO4FbgKuGJpKcSFdPmION7ZrfFSeuPHME37h5Bou3VPPFX6+hJ6ythUREpH8nWscLADP7BTAeeAPo/ZPeAT8fmlhyPPsb2gk7KM3Xwqnx4pZzyqhv7eRrf95IXmaQ/7xxmnojRUTkHQZUeBGZ23Wm0y7BcaEyumK9JtbHl49fNI6alk4eemkbw7JCfOmqSV5HEhGRODPQwmsdMBLYP4RZZID21rWRkxYgN32g//kkVv7+6knUtXTyvRe2Ul6QyQfOLfc6koiIxJGB/uYuAjaY2TKgo/egc+76IUklx1VZ30apJtbHJTPjP26cRmV9G1/9/VpKCzKYd0aR17FERCRODLTw+rehDCED19HdQ1VTB9O1SXPcCvp9fP+Ds7jlodf45C9X8vtPX8AZw3O8jiUiInFgoMtJvAzsBILRx8uBVUOYS45hX307Ds3vine56UF+/JFzSQv4uOtny6lp7jjxD4mISNIb6F6NdwOPAz+MHioF/jBEmeQ4Dq9Yr6Uk4l55YSY/unM2hxo7uOcXK2nv0hpfIiKpbqBDjZ8B5gBLAZxzW8xs+JClkmOqrGslLyNITrpWrE8EMysK+M6tZ/PpX63iAz98nVtnlw9obt4dcytikE5ERGJtoAuodjjnDm9GZ2YBIut4SYxV1reptyvBXDN9FH939STe3NvAK1urvY4jIiIeGmjh9bKZfRXIMLMrgd8CTw1dLOlPe1cP1c2dmt+VgD596XimleTyzLoDbKtq9jqOiIh4ZKBDjV8GPgasBT4BPA08MlShpH+a3xVf5i/dfVLvv3lWGQebtrFg2W4+e9kZ5GeGhiiZiIjEq4He1RgmMpn+0865W5xzP9Iq9rFXWafCK5GlBf18aO5oesKO+ct209UT9jqSiIjE2HELL4v4NzOrBt4CNplZlZn9S2ziSV+V9W0UZAbJStOK9YmqOCeN959Txt66Np5as8/rOCIiEmMn6vG6D5gHnOucG+acKwTmAvPM7AtDHU6OpIn1yeHMkjwunVjMil11LN9R63UcERGJoRMVXncCtzvndvQecM5tBz4UfU1ipLWzm9qWTkoLMr2OIoPgijNHMGF4Nk++ue/w3D0REUl+Jyq8gs65d9z/7pyrArSQVAxpfldy8Zlx6+xystMCPLZstxZXFRFJEScqvDpP8TUZZLqjMflkpgW4dXY5da2d/OGNSnS/iohI8jvRLO2zzKyxn+MGpA9BHjmGyvo2CrNCZIT8XkeRQTSmKIsrpozguQ0HGV+czbljCr2OJCIiQ+i4hZdzTr/l48S++jbKNL8rKV08sZjtVS08tWYf5YWZjMzV3zQiIslqoCvXi4fqWzupa+2iRMOMSclnxvtnl5Ee9PPYst10dmt9LxGRZKXCKwGs3xcZ7S3JV09IsspJD/KB2eVUNXXw1Jta30tEJFmp8EoA6/c1AFCSpx6vZHbG8GwunVTMyl11/GF1pddxRERkCKjwSgDrKhvJy9CK9angXZNHMHpYJv/4+7XsrG7xOo6IiAwyFV4JYN2+Bs3vShF+X2R9r4Dfx2cXrKKjW+t7iYgkExVeca6lo5sd1S2a35VC8jNDfPOWGayrbOSbz2zyOo6IiAwiFV5xbuP+RpzT/K5Uc/XUkXzk/NH8+JUdLNx40Os4IiIySFR4xbl1ldGJ9RpqTDlfuWYKU0bl8re/XcOBhnav44iIyCBQ4RXn1u1rpCg7RG66JtanmvSgnwfvmElHd5jPP7aanrC2FBIRSXQqvOLc+n2NTC3Jw8y8jiIeGF+czf+7YRpLd9Ty4AtbvY4jIiKnSYVXHGvv6mHLwSamluR6HUU8dPOsUm48u4TvLtzM0u01XscREZHToMIrjm0+2ER32DGtNM/rKOIhM+Nr75tORWEm9/36DepaOr2OJCIip0iFVxzr3SpoWokKr1SXnRbge7fPorq5g797fA1hzfcSEUlIKrzi2LrKBnLSA5QX6o5GgelleXzlPVP468ZDPPTyNq/jiIjIKfCs8DIzv5mtNrM/eZUh3q3b18jUklxNrJfD7po3hutmjOLbz23i1a3VXscREZGT5GWP1+eBjR6eP65194R5a3+jhhnlCGbGN26ewfjibD63YDX76tu8jiQiIifBk8WhzKwMuBb4T+CLXmSId9uqWujoDmtifYqav3T3cV+/dsYoHnppG7f84DUWfvFSMkL+GCUTEZHT4VWP1/8Cfw+Ej/UGM7vHzFaY2YqqqqqYBYsXvSvWaykJ6c/wnHRuPbec/fXt/O3ja3BOk+1FRBJBzAsvM7sOOOScW3m89znnHnbOzXbOzS4uLo5Ruvixbl8D6UEf44qzvY4icWryyFyunjqSP7+5nwcWanFVEZFE4MVQ4zzgejO7BkgHcs3sl865D3mQJW6tr2zkzFG5+H2aWC/HdtGEIjLT/Hznr5spL8zgplllXkcSEZHjiHmPl3PuK865MufcGOA24AUVXUcKhx0b9jdqfpeckJnx9ZtmMO+MYfz942/y8ubUG5YXEUkkWscrDu2qbaW5o1vzu2RAQgEfP/jQOUwYkcOnfrmSNXvqvY4kIiLH4Gnh5Zx7yTl3nZcZ4tHbE+vV4yUDk5Me5NG7zqUwK8SdP1l2+BoSEZH4oh6vOLR+XyNBvzFxRI7XUSSBDM9NZ8Hd55GdFuBDP17Kxv2NXkcSEZGjqPCKQ+v3NTBxRA6hgP7zyMkpL8xk/t1zyQj6+eAjS3lzb73XkUREpA/9Zo8zzjnWVjYwXRPr5RSNHpbFgrvPIzPk5/aHl2hrIRGROKLCK87srWujvrWLqSq85DSMKcrid5+6gLKCTO766XKeXLPP60giIoIKr7izfl9kUrR6vOR0jchN5zefOJ+zyvO4d8Fq7n9uE+GwVrgXEfGSJ3s1yrGtrWzA7zMmj9TEejl9eZlBfvnxufzT79fxwAtbWfjWIW6eVUZ6cGB7O94xt2KIE4qIpBb1eMWZtZWNTBiePeBfjCInkhbw881bZvBP105hw75GHnxxK3tqW72OJSKSklR4xRHnHOs1sV6GgJnx8YvGcc/F4wiHHT9ctI1Fm6sIa3NtEZGY0lBjHNnf0E5NSyfTy1R4ydAYPSyLz71rAk+s3ssz6w+wtaqZ959TRk56sN/3z1+6e8CfrWFJEZETU49XHFkbXW1cezTKUMoI+bljTgU3nl3KzuoWHli4RYutiojEiAqvOLKusgGfwZSR2qNRhpaZMWdsIZ+57Axy0oP8Yskunli1l46uHq+jiYgkNRVecWRdZQMThueQEdLEeomNEbnpfPrS8Vw8oZiVu+p44IUt7Kxu8TqWiEjS0hyvOBFZsb6RSyYWex1FEszJzMPqT8Dv493TRjJ5ZA6/XbmHHy3ezkUTirliynACfv1tJiIymPSvapw42NhBdXMH00o1zCjeGFOUxb3vmsA5owtYtKWK/3tpGwca2r2OJSKSVFR4xYl1lVqxXryXFvRz06wyPnzeaJo6uvn+S1tZvEXLToiIDBYVXnFibWUDZnBmiXq8xHtTRuXy+csnMGlEDn9Zd4BHFu+grqXT61giIglPhVecWFfZwPjibDJDmnYn8SE7LcAH51Zwy6wy9je08cALW3hjT73XsUREEpoKrzixbp9WrJf4Y2bMGl3Ave+awMi8dH6zYg+/X11JV0/Y62giIglJhVccONTUzsHGDi2cKnGrICvExy8cxyUTi1m+s5YfvLyNmuYOr2OJiCQcFV5xQBPrJRH4fcbVU0dy5/mjqW/t4sEXtx7ebUFERAZGhVccWFfZqIn1kjAmj8zlc+86g+E5aSxYtpun1uyjW0OPIiIDosIrDqytbGBsURbZaZpYL4khPzPE3RePY974Yby+vYaHF29nT22r17FEROKeCq84sK5SE+sl8QR8Pq6dUcIH51ZQ3dzBtQ8s5rn1B7yOJSIS11R4eay6uYP9De1MK1HhJYlpakken71sAqOHZXHPL1bytT9t0F2PIiLHoMLLY70T63VHoySywqwQj3/qfO48fzSPvLKDW3/4Ovvq27yOJSISd1R4eay38JqqPRolwaUF/Py/G6bxvdtnsvlgM9c+sJgXNx3yOpaISFzRbG6Pra1sYMywTHLTg15HERkU7z2rhKkluXz6V6u466fL+dSl4/nSlRMJ+N/+O2/+0t0D/rw75lYMRUwREU+ox8tj6yobNcwoSWdccTZ/+Mw8bp9TzkMvbeOOR5ZysLHd61giIp5T4eWhQ03tVNa3cXZ5vtdRRAZdetDPf980g+/cehZr9zbw7v9dxPMbDnodS0TEUyq8PPTG7noAFV6S1N43s4ynPnchJfkZ3P3zFfzj79fS2a27HkUkNanw8tAbe+oJ+ExDjZL0zhiezROfvoB7Lh7Hr5bu5vsvbdVdjyKSklR4eeiNPfVMHpVDetDvdRSRIZcW8PPVa6bwy4/Npb2rh4de3sYrW6oIO+d1NBGRmFHh5ZGesOPNvQ0aZpSUc+GEIu591wQmjsjh6XUH+NlrO2ls7/I6lohITKjw8si2qmaaO7o5u7zA6ygiMZeVFuBDcyu48exSdtW08MDCLYfXtBMRSWYqvDyiifWS6syMOWML+cxlZ1CQGWL+st0sWLablo5ur6OJiAwZLaDqkdV76slJDzCuKMvrKCKD4mQWRe1reE46n7xkPC9vruLFtw6xvaqZG84uPeWbTrQ4q4jEM/V4eWTNnnrOKsvH5zOvo4h4zu8z3jV5OJ++bDx5mcHDvV/N6v0SkSSjwssDbZ09bDrYpGFGkaOMysvgU5ecwRVTRrBhXyP3P7+JX7y+k56w7nwUkeSgwssDaysb6Ak7FV4i/ejt/frsu86gJD+Df/7jem74/ius2l3ndTQRkdMW88LLzMrN7EUz22hm683s87HO4LXeXyBnV+R7G0Qkjo3ITedj88byvdtnUtXUwU3/9xp///gaqpo6vI4mInLKvJhc3w18yTm3ysxygJVm9rxzboMHWTyxYmct44qyKMpO8zqKSFwzM957VgmXTR7O9xZu4cev7OCpNfu584LRfOLi8RRmhbyOKCJyUmLe4+Wc2++cWxV93ARsBEpjncMr4bBjxa46Zo/R+l0iA5WdFuAr10zhuS9czNVTR/Dwou1c9I0X+J9n36K+tdPreCIiA+bpHC8zGwPMBJb289o9ZrbCzFZUVVXFPNtQ2VrVTH1rF+eOKfQ6ikjCGVeczf/eNpPn7ruYyyYP5/svbuOib7zIN595i/0N2vtRROKfZ4WXmWUDvwPuc841Hv26c+5h59xs59zs4uLi2AccIst31gKo8BI5DRNG5PDgHbN45r6LuHBCEQ+9vI0Lv/Ein52/it01LTjt/ygiccqTBVTNLEik6PqVc+4JLzJ4ZfmOWoqy0xg9LNPrKCIJb/LIXB760DnsqW3l0dd28usVe2hq76asIIPzxw1jemkeAb9u3haR+OHFXY0G/BjY6Jy7P9bn99rynXXMGVtApBlEZDCUF2byT9edyZKvXM71Z5XQ3hXmtyv38o1nN/H8hgM0tGkTbhGJD170eM0DPgysNbM3ose+6px72oMsMbWvvo3K+jY+duFYr6OIJKWstADnjRvGnLGFbDvUzOvba3hpUxUvb67izJI85o0fxuhh2qZLRLwT88LLOfcKkJLdPb3zu+aM1fwukaHkM2PCiBwmjMihtqWTJdtrWLGrlnWVDYwuzOSSicVMGpnjdUwRSUHaJDuGVuysIyvkZ7L+wReJmcKsENdMH8UVU0awclcti7dW8/MluyjJT6e0IINLJhZr6F9EYkazTmNo+c5aZo0u0GRfEQ+EAj7OH1/El66cxC2zymjr7OGjP13OrQ8vYV1lg9fxRCRFqMcrRmqaO3jrQBPXTh/ldRSRlOb3GbNGFzCjPA+A//3rFq5/8BVun1PB3109ifxMrYYvIkNHXS8x8uq2GgAunFDkcRIRAQj4fNx5/hhe/NKl3Hn+GB5bvocr7l/Es+sPeB1NRJKYCq8YeXVLNbnpAWaU5XsdRUT6yMsM8m/XT+XJz85jeE4an/jFSu5dsFpLUIjIkFDhFQPOOV7ZWs0F44vw+zSJVyQeTS3J44+fnccXr5zI02v3c813F7Nqd53XsUQkyajwioEd1S1U1rdpmFEkzgX9Pu69fAK//eT5mMH7f/A6P1q0XVsQicig0eT6GHhlazUAF6nwEokr85fuPuZrfzNvLL9btZf/fHojf1m3nwX3nEdawB/DdCKSjNTjFQOLt1RTVpBBRaH2ZxRJFOlBP7fPqeDyycNZtbue2x9ewqGmdq9jiUiCU+E1xLp7wizZVsNFE4q0SKNIgvGZcfmUEdw+p4IN+xu58cFXteaXiJwWDTUOsTV762nq6ObCM4q9jiKSkI43HBgr00vz+ODcCu75+Qre/4PXuf8DZ/EercknIqdAPV5D7OXN1ZjBBeOHeR1FRE7DtNI8/vjZC5kyKodPz1/Fj1/Z4XUkEUlAKryG2HPrDzB7dAEFWVoNWyTRFeekMf/u87j6zJH8x5828B9/2kA4rDseRWTgNNQ4hHbVtPDWgSb+6dopXkcRkdPUd8jzwglFNLR38eNXdrBsRy23nFNGsM8erHfMrfAioogkABVeQ6h365Grp470OImIDCafGddNH0VBRpCn1x2gqb2LD503msyQ/kkVkePTUOMQenb9QaaW5FKuZSREko6ZceGEYm47t5w9dW38cNF26lo7vY4lInFOhdcQOdTYzspddertEklyM8ry+Zt5Y2lq7+IHL21jX32b15FEJI6p8Boiz204CMC7p6nwEkl2Y4uy+MTF4/H7jIcXb2fR5iqvI4lInFLhNUSeXX+AsUVZTBie7XUUEYmBEbnpfPKS8QzLCvE3P1vOb1fs8TqSiMQhFV5DoLalk9e31XDV1BFarV4kheRmBLn7onGcP34Yf/f4mzywcIs22BaRI6jwGgJPrNpLd9hx08wyr6OISIylB/385KPncvOsMu5/fjNfeWIt3T1hr2OJSJzQvc+DzDnHr5fvYWZFPpNG5ngdR0Q8EPT7+Nb7Z1Can84DL2zlYGM7D94xi6w0/ZMrkurU4zXIVu2uZ8uhZm47t9zrKCLiITPji1dN4r/eN52XN1dx28NLqGrq8DqWiHhMhdcg+/Xy3WSF/Fw3o8TrKCISB+6YW8GP7pzN1kPN3PTQq2yravY6koh4SP3eg6ipvYun1uznhrNLNKQgksL6bi/U6655Y3j0tZ1c98ArvP+cMiaPyj38mrYYEkkd6vEaRE+t2U9bVw+3zdE/oiJypLKCTD516RnkZwb5+ZJdPLPuAD3aYFsk5ajwGiTdPWEeWbydM0flclZZntdxRCQOFWaF+OQl45kzppBFW6p4eNE2qjXvSySlqPAaJE+9uY/t1S3ce/kErd0lIscU9Pu4cWYpt51bTnVzJ997cQuPvraTsHq/RFKCCq9B0N0T5nsLtzJlVC5XnTnC6zgikgBmlOXz+csnMLYoi399cj03PfQa6yobvI4lIkNMhdcg6O3t+vzlE/D51NslIgOTmxHkI+eP4f4PnMWe2lauf/AV/vkP6zjU1O51NBEZIiq8TpN6u0TkdJgZN80q44UvXcoH545m/rLdXPo/L/Ht5zZR19LpdTwRGWQqvE7TI6/sYHt1C1+4Qr1dInLq8jKD/MeN0/jrFy/hXZOH870XtnL+1xfyL39cx3at/SWSNLTY1GnYeqiJ+5/fzNVTR3ClertEZBCMLcriwTtmce/lTTyyeDuPLdvDz1/fxbljCrjlnDKuOnMkBVkhr2OKyCky5+L/TprZs2e7FStWeB3jCD1hx80PvcbOmhae+8LFDM9JH9Lz9bcgo4gkh+MtoHqosZ3frarktyv3sL2qBb/PmD26gHdNHs7cccOYVpJLwK/BC5F4YmYrnXOz+3tNPV6n6IeLtvHGnnq+e9vZQ150iUhyO9EfVnkZQT42byyV9W34fcbzGw7y3395C4DMkJ8po3KZOCKHySNzDn8vyAqd1B9sWj1fJDZUeJ2CJ9fs43+e3cS100dx/Vnak1FEhp6ZUVaQyR1zK/jSVZM41NTOsh21LN9Ry8YDTTy9dj8Llr1daOWmB8gMBcjPDJKXESQ/M0RueoDstADZ0e9ZaQF8WndQJKZUeJ2kRZur+NJv3uDcMYV8+wNnabFUEfHE8Jx0rptRwnUzIn/8Oec41NTBWwea2Hygib11rSzdUUtDWxe7alpp6+p5x2cYkJkWICctwF/W7acoO42i7FD0expFOWkUZoYixVtmkJy0gP7NEzlNKrxOwvMbDvL5x1ZzxvAcHvnIbNKDfq8jiYgAkR6xEbnpjMhN55KJxcCRQ5gdXT00tXfT1NFNc+9XezfNHV00t3fT1N7NjuoWqpo66OgO93sOv8/Iz4gUYfkZQQoyQ9HHIXIzIr1oOekBstOCh3vVcvr0smWFAvh197ekOBVeA9DVE+Zbz27ih4u2M600l5989Fxy04NexxIRGbC0oJ+0oJ+inLTjvs85R0d3+HBh1tbVQ2tnN62dPbR19tDa2UNrVw8ZIT8HGtt560ATda2dtHa+s0etP6GAj/SAj7SAn7Sgj/To997ns0cXHDEceriQO6qICyboDQW97dvS0U1LRw8tnd20dkYe9/3e3c8WUgGfkRkKkBnykxHykxVtk2FZaRRmhVTUJggVXsfhnOOlTVV8+/lNrKts5MPnjeYfr52ini4R8cxQ3+FsZqQH/aQH/RRlH7tIO3oyfndPmJbOniN60praIz1rCzceoqOrh/buMB1dPXR0h4943NjeRUd3mI7uHpZsq2Eg99oHfEZ+ZvCIIi07LXhEcda3WMtKC5AW8BHy+wgGfAR8RtDvIxTwEfRHnveEHWHnCDui3x3hcORxR3eY9q5I8dnWFflqj361dYYPP2/t7Kals4fWjuj3zm5aewusjkj7DMVaAkbkRovs9AA5aUHyMyPz+gr6fM9JDx4uznQzhXc8KbzM7N3AdwE/8Ihz7ute5DiWupZOnt9wkPnLdvPGnnrKCjL4/h2zuHbGKK+jiYjEhZMpAM8dUzjg9zrn6OwJ09EVpr27h46u8OGip7c4a++KfO/7ngPt7XR0tx5+X3tXD7HcdzzgMwJ+I+T3EQr4I0VetNDLTg9QmBV6+1jATyjgI83f+9x3xPtDAV+/vVc9YUdnd5jOnjBd3WE6esK0d0V6J1v6DB83tXex6UA7TR3dR/y8zyLbVBVkhlixq5ay/AxG5mUwMi+NEbnpjMrLoCAzqHl8QyzmhZeZ+YHvA1cCe4HlZvakc25DrLP0OtDQzrKdtazdW8+aPQ2s3F1HT9hRUZjJf980nVvOKUvYbm0RkURiZpFhx4CfXE59Sodzju5wpKeqt7etJ+z6fEWfO+gJhwmHwSxyfjO4eEIxPgOfz/CZEQr4yAj6I18h3+FewYygn9+vrozLu0O7esI0tHZR19ZJfWsXda1vf1+yrYYDje3vKE5DAR8jctMYmZtOUXYaeRlBcjMid8bmpgfIjT7PTQ+SEfT3WzSmBXxaW+44vOjxmgNsdc5tBzCzx4AbAM8Krz+9uY+v/XkjoYCPM0flcs/F47h2+iimluSq8hcRSUBmRtAfGU7MTjv5X3UNbV0Dfm88Fl0AQb+Popy0fuf13TG3gu6eMFXNHRxoaI98Nb79fX9DO1sONdPY1kVtS2e/c86Ox2ccLsaCfh8+nxGIFrH+3sc+wx997o8+D0SP+XwQ8B39c5FjhwtkIsUyEH0cOdZ7wLCjXo88/uDc0ZxVnn/yDTpIvCi8SoE9fZ7vBeYe/SYzuwe4J/q0w8zWxSAbW4A/Al+OxclOThFQ7XUIj6kN1AagNuildlAbwCm2wQeHIIiHTqoN/mcIg/Qx+lgveFF49fenwTtKaefcw8DDAGa24lhL76cKtYHaANQGoDbopXZQG4DaABKvDbwYhN0LlPd5Xgbs8yCHiIiISEx5UXgtByaY2VgzCwG3AU96kENEREQkpmI+1Oic6zazzwLPEllO4ifOufUn+LGHhz5Z3FMbqA1AbQBqg15qB7UBqA0gwdrAnIvhQiciIiIiKUwLbYiIiIjEiAovERERkRhR4SUiIiISIyq8RERERGLEk02y5cTMbDKRrZRKiSwwuw940jm30dNgHjGzC4lsN7XOOfec13liRdfBkXQd6DoAXQfoOgAS9zpQj1ccMrN/AB4jssr/MiJrnxmwwMzicDejwWdmy/o8vht4EMgB/jWF2kDXga4DXQfoOgBdB5A810HcLSdhZu92zj0TfZwH3A+cC6wDvuCcO+hlvlgws83AVOdc11HHQ8B659wEb5LFjpmtds7NjD5eDlzjnKsysyxgiXNuurcJh56uA10HoOsAdB2ArgNInusgHnu8/qvP428D+4H3Eqnuf+hJotgLAyX9HB8VfS0V+MyswMyGEfkDoQrAOdcCdHsbLWZ0Heg6AF0HoOsAdB1AklwH8T7Ha7Zz7uzo4++Y2Ue8DBND9wELzWwLsCd6rAI4A/isV6FiLA9YSaQr3ZnZSOfcATPLpv+N1pPRfeg60HWg6wB0HYCuA0iS6yAehxr3EhleNOAzwHgXDWlmbzrnZniZL1bMzEdk0mApkbbYCyx3zvV4GsxjZpYJjHDO7fA6SyzoOuifrgNdB6DrAF0HQOJdB/FYeP3rUYf+LzqGOxL4pnPuTi9yecHMRtDn7pVUmN92NLVB/8ws2znX7HUOL6kNJNWZWaFzrtbrHF5KxDaIu8JLwMzOBn5ApFt1L5G/bMqAeuDTzrlVnoWLETObCTxEpA0qo4dTqg2Ox8x2O+cqvM7hpVRpAzObQWQT4FLgL8A/OOfqoq8tc87N8TJfLKgNwMzmAY8Qmc/1N8DXgPFAEPiAc+51D+PFRLK0QVzO8dJaJfwM+IRzbmnfg2Z2HvBT4CwvQsXYT0nxNjCzLx7rJSA7llm8ojYA4P+AfwOWAB8HXjGz651z24j8wkkFagP4DvABItf9n4EbnXOvmNks4HvAPC/DxUhStEHc3dWotUoAyDq64ABwzi0BsjzI4wW1QeQO3wIi69T0/comDv+/O0TUBpDtnHvGOVfvnPsWkYnUz0T/CEmVIQu1AQSdc2ujvTpVzrlXAKK9/xneRouZpGiDeOzx+hj9r1VyP7Ae+LonqWLrL2b2Z+DnvH33SjlwJ/CMZ6liS20Aq4A/OOdWHv2CmX3cgzxeUBuAmVmec64BwDn3opndDPwOKPQ2WsyoDY78Q+MrR70WimUQDyVFG8TdHC8zewu42jm366jjo4HnnHOTvEkWW2b2Ht4ebu29e+VJ59zTngaLoVRvAzObBNQ456r7eW1EKtxooDYAM7sD2B7t7e17vAL4Z+fc3d4kix21AZjZ9cBfnXOtRx0fD9zsnPumN8liJ1naIB4Lr3cT2Qag37VKele1FxEREUk0cTdHIlpYTQT+HXgWeI7IpMpJKrrAzO7xOoPX1AZqA1AbgNoA1AagNoDEaoN4nOOFcy5M5O4VeaeEWZ13CKkN1AagNgC1AagNQG0ACdQGcTfUeDxm9ifn3HVe54gFLamhNgC1AagNQG0AagNQG0BytEHcDTWeQNJPoAQtqQFqA1AbgNoA1AagNgC1ASRPGyRUj1eqMLPN9L+kRghY75yb4E2y2FEbqA1AbQBqA1AbgNoAkqcN4q7Hy8zyzOzrZvaWmdVEvzZGj+V7nS9GwkBJP8dHRV9LBWoDtQGoDUBtAGoDUBtAkrRBPE6u/w3wAnCpc+4AQHSD7I8AvwWu9DBbrNwHLDSzfpfU8CpUjN2H2uA+1Ab3oTa4D7XBfagN7kNtcB9J0AZxN9RoZpuOtUjq8V5LNmbmA+Zw5OKhy51zPZ4GiyG1gdoA1AagNgC1AagNIDnaIB4Lr+eAvwKP9q5KbWYjgI8CVzrnrvAwnoiIiMgpi7s5XsCtwDDgZTOrNbNa4CUi+3F9wMtgIiIiIqcj7nq8jmZm1zvnnvQ6h4iIiMjpSoTC603n3Ayvc4iIiIicrngcajxawmwDICIiInI8iVB4xXeXnIiIiMgAJULhJSIiIpIUVHiJiIiIxEgiFF4HvQ4gIiIiMhji/q5GERERkWSRCD1eIiIiIklBhZeIiIhIjKjwEhEREYkRFV4i0i8z6zGzN8xsnZn91swyT+OzXjKz2afwc/lm9ukTvMdnZg9Ec641s+VmNjb6WvNJnu+jZvZg9HGxmS01s9VmdlE/7/2ZmX3iqGM3mtnTZjbbzB44znlKzOzxo8/Zz/tei34fY2broo8Pf7aZXWpmF5zM/0YR8ZYKLxE5ljbn3NnOuWlAJ/DJvi+amT8GGfKB4xZewK1ACTDDOTcdeB9QPwjnvhx4yzk30zm3uJ/XFwC3HXXsNmCBc26Fc+7eY32wc26fc+6WEwVwzr2jqDrqsy8FVHiJJBAVXiIyEIuBM6I9LC+a2XxgrZmlm9lPoz1Nq83sMgAzyzCzx8zsTTP7NZDR+0F9e6HM7BYz+1n08Qgz+72ZrYl+XQB8HRgf7Xn7n2NkGwXsd86FAZxze51zdX3O8Z/Rz1tiZiOix4rN7HfR3rHlZjav7wea2dnAN4FroufO4J3+Ckw2s1HRn8kErgD+EG2nP0WPXxL9jDeibZTTtwcrqtzMnjGzTWb2r/21VZ9jl5rZn8xsDJFi+AvRz77IzHaYWTD6vlwz29n7XETiQ8DrACIS38wsALwHeCZ6aA4wzTm3w8y+BOCcm25mk4HnzGwi8Cmg1Tk3w8xmAKsGcKoHgJedc++L9qZlA1+Onuvs4/zcb4BXosOBC4FfOudWR1/LApY45/7RzL4J3A18Dfgu8B3n3CtmVgE8C0zp/UDn3Btm9i/AbOfcZ/s7qXOux8yeAD4Q/bzrgRedc01mR2wx+7fAZ5xzr5pZNtDez8fNAaYBrcByM/uzc27Fcf4345zbaWY/AJqdc9+CyJAucC3wByK9b79zznUd73NEJLbU4yUix5JhZm8AK4DdwI+jx5c553ZEH18I/ALAOfcWsAuYCFwM/DJ6/E3gzQGc713AQ9Gf6XHONQwkpHNuLzAJ+AoQBhaa2eXRlzuBP0UfrwTGRB9fATwY/d/3JJBrZjkDOd9R+g433hZ9frRXgfvN7F4g3znX3c97nnfO1Tjn2oAniLTrqXgEuCv6+C7gp6f4OSIyRNTjJSLH0nZ0T1O0J6el76Hj/PyxVmfuezz9lJId/YHOdQB/Af5iZgeBG4n0fnW5t1eJ7uHtf/N8wPnRQuewo3qqBuJVYJSZnUVkrtXRc75wzn3dzP4MXAMsMbMreGev19FtdUorW0d71caY2SWA3zm37oQ/JCIxpR4vETkdi4APAkSHGCuATUcdnwbM6PMzB81sipn5iEyE77WQyBAlZuY3s1ygCThuT5SZzTKzkuhjX/Rcu06Q+zng8BBidE7XSYsWdb8BHgWeds69YxjRzMY759Y6575BpPdwcj8fdaWZFUbnkt1IpKAbiP7a5+dEet7U2yUSh1R4icjp+D/Ab2ZrgV8DH432Pj0EZJvZm8DfA8v6/MyXiQz/vQDs73P888Bl0c9aCUx1ztUAr1pkqYhjTa4fDjwVnaz+JtAN9Ls8Qx/3ArOjk/83cNQdmydpAXAW8NgxXr8vmn8N0EakZ+5orxAZsn2DyLys487v6uMp4H29k+ujx34FFND/sKeIeEx7NYqIJBEzuwW4wTn3Ya+ziMg7aY6XiEiSMLPvEbkD9Rqvs4hI/9TjJSIJwcymE72Dso8O59zcGJx7KZB21OEPO+fWDvW5RSS5qPASERERiRFNrhcRERGJERVeIiIiIjGiwktEREQkRlR4iYiIiMTI/wenLD0K8MCYFAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(X[\"Product_Shelf_Visibility\"])\n",
    "#rotate label if too long\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model\n",
    "\n",
    "I am proposing 3 algorithms:\n",
    "* Linear Resressor\n",
    "* DecisionTree Regressor and\n",
    "* Random Forest Regressor \n",
    "* ExtraTree Regressor\n",
    "* Random Forest Regressor\n",
    "* Gradient Boosting Regressor\n",
    "* Xtreme Gradient Boosting\t\n",
    "* Neural Network\t<br>\n",
    "\n",
    "from which the best model will be selected\n",
    "\n",
    "For better evaluation, we'll be using Cross-Validation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linear Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DistributionPlot(RedFunction, BlueFunction, RedName, BlueName, Title):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "\n",
    "    ax1 = sns.distplot(RedFunction, hist=False, color=\"r\", label=RedName)\n",
    "    ax2 = sns.distplot(BlueFunction, hist=False, color=\"b\", label=BlueName, ax=ax1)\n",
    "\n",
    "    plt.title(Title)\n",
    "    plt.xlabel('Price (in naira)')\n",
    "    plt.ylabel('Proportion of Products')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "def PollyPlot(xtrain, xtest, y_train, y_test, lr,poly_transform):\n",
    "    width = 12\n",
    "    height = 10\n",
    "    plt.figure(figsize=(width, height))\n",
    "    \n",
    "    \n",
    "    #training data \n",
    "    #testing data \n",
    "    # lr:  linear regression object \n",
    "    #poly_transform:  polynomial transformation object \n",
    " \n",
    "    xmax=max([xtrain.values.max(), xtest.values.max()])\n",
    "\n",
    "    xmin=min([xtrain.values.min(), xtest.values.min()])\n",
    "\n",
    "    x=np.arange(xmin, xmax, 0.1)\n",
    "\n",
    "\n",
    "    plt.plot(xtrain, y_train, 'ro', label='Training Data')\n",
    "    plt.plot(xtest, y_test, 'go', label='Test Data')\n",
    "    plt.plot(x, lr.predict(poly_transform.fit_transform(x.reshape(-1, 1))), label='Predicted Function')\n",
    "    plt.ylim([-10000, 60000])\n",
    "    plt.ylabel('Price')\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression()\n"
     ]
    }
   ],
   "source": [
    "# Fit the linear model\n",
    "lre=LinearRegression()\n",
    "results = lre.fit(X_train,y_train)\n",
    "print(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9132569287176418.0 [-2.71254583e+01 -7.13649310e+02  8.82484317e+03 -3.79502987e+15\n",
      " -3.79502987e+15 -3.79502987e+15 -3.79502987e+15 -3.79502987e+15\n",
      " -3.79502987e+15 -3.79502987e+15 -3.79502987e+15 -3.79502987e+15\n",
      " -3.79502987e+15 -3.79502987e+15 -3.79502987e+15 -3.79502987e+15\n",
      " -3.79502987e+15 -3.79502987e+15 -3.79502987e+15 -2.05098209e+15\n",
      " -2.05098209e+15 -2.05098209e+15  2.06246194e+15  2.06246194e+15\n",
      "  2.06246194e+15 -5.34901927e+15 -5.34901927e+15 -5.34901927e+15\n",
      " -5.34901927e+15]\n"
     ]
    }
   ],
   "source": [
    "# Print the coefficients\n",
    "print (results.intercept_, results.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train score: 0.5567631629208285\n",
      "test score: 0.5594024666325306\n"
     ]
    }
   ],
   "source": [
    "print(\"train score:\", lre.score(X_train,y_train))\n",
    "\n",
    "print(\"test score:\", lre.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.557</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.553</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   149.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 19 Aug 2022</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:23:45</td>     <th>  Log-Likelihood:    </th> <td> -28140.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  2993</td>      <th>  AIC:               </th> <td>5.633e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  2967</td>      <th>  BIC:               </th> <td>5.649e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    25</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                   <td></td>                      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Weight</th>                      <td>  -26.4701</td> <td>  193.494</td> <td>   -0.137</td> <td> 0.891</td> <td> -405.867</td> <td>  352.927</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Shelf_Visibility</th>            <td> -716.6102</td> <td>  352.334</td> <td>   -2.034</td> <td> 0.042</td> <td>-1407.453</td> <td>  -25.767</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Price</th>                       <td> 8821.8359</td> <td>  346.777</td> <td>   25.439</td> <td> 0.000</td> <td> 8141.888</td> <td> 9501.784</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Baking Goods</th>           <td>    0.2637</td> <td>  208.560</td> <td>    0.001</td> <td> 0.999</td> <td> -408.673</td> <td>  409.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Breads</th>                 <td> -248.6158</td> <td>  295.909</td> <td>   -0.840</td> <td> 0.401</td> <td> -828.823</td> <td>  331.592</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Breakfast</th>              <td>  -63.9440</td> <td>  424.434</td> <td>   -0.151</td> <td> 0.880</td> <td> -896.160</td> <td>  768.272</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Canned</th>                 <td>   91.2765</td> <td>  195.753</td> <td>    0.466</td> <td> 0.641</td> <td> -292.550</td> <td>  475.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Dairy</th>                  <td> -256.5026</td> <td>  206.297</td> <td>   -1.243</td> <td> 0.214</td> <td> -661.002</td> <td>  147.997</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Frozen Foods</th>           <td>  109.7384</td> <td>  184.585</td> <td>    0.595</td> <td> 0.552</td> <td> -252.189</td> <td>  471.666</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Fruits and Vegetables</th>  <td>  262.0497</td> <td>  151.236</td> <td>    1.733</td> <td> 0.083</td> <td>  -34.488</td> <td>  558.587</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Hard Drinks</th>            <td>  -20.5937</td> <td>  323.638</td> <td>   -0.064</td> <td> 0.949</td> <td> -655.171</td> <td>  613.984</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Health and Hygiene</th>     <td> -152.0829</td> <td>  224.123</td> <td>   -0.679</td> <td> 0.497</td> <td> -591.536</td> <td>  287.370</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Household</th>              <td> -197.1269</td> <td>  166.218</td> <td>   -1.186</td> <td> 0.236</td> <td> -523.041</td> <td>  128.787</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Meat</th>                   <td>   18.1185</td> <td>  227.273</td> <td>    0.080</td> <td> 0.936</td> <td> -427.511</td> <td>  463.748</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Others</th>                 <td> -310.7060</td> <td>  377.694</td> <td>   -0.823</td> <td> 0.411</td> <td>-1051.275</td> <td>  429.863</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Seafood</th>                <td>  703.9602</td> <td>  569.992</td> <td>    1.235</td> <td> 0.217</td> <td> -413.660</td> <td> 1821.580</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Snack Foods</th>            <td>   42.0077</td> <td>  150.146</td> <td>    0.280</td> <td> 0.780</td> <td> -252.394</td> <td>  336.409</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Soft Drinks</th>            <td>  -85.3506</td> <td>  237.121</td> <td>   -0.360</td> <td> 0.719</td> <td> -550.289</td> <td>  379.588</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product_Type_Starchy Foods</th>          <td>  323.5387</td> <td>  400.604</td> <td>    0.808</td> <td> 0.419</td> <td> -461.951</td> <td> 1109.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket _Size_High</th>              <td>-1633.6966</td> <td>  341.707</td> <td>   -4.781</td> <td> 0.000</td> <td>-2303.704</td> <td> -963.689</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket _Size_Medium</th>            <td>  831.2637</td> <td>  109.932</td> <td>    7.562</td> <td> 0.000</td> <td>  615.713</td> <td> 1046.814</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket _Size_Small</th>             <td> 1018.4640</td> <td>  150.780</td> <td>    6.755</td> <td> 0.000</td> <td>  722.820</td> <td> 1314.108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket_Location_Type_Cluster 1</th> <td> -728.0603</td> <td>  210.311</td> <td>   -3.462</td> <td> 0.001</td> <td>-1140.431</td> <td> -315.690</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket_Location_Type_Cluster 2</th> <td> -534.9412</td> <td>  261.339</td> <td>   -2.047</td> <td> 0.041</td> <td>-1047.366</td> <td>  -22.517</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket_Location_Type_Cluster 3</th> <td> 1479.0326</td> <td>  161.509</td> <td>    9.158</td> <td> 0.000</td> <td> 1162.352</td> <td> 1795.713</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket_Type_Grocery Store</th>      <td>-4357.8670</td> <td>  155.870</td> <td>  -27.958</td> <td> 0.000</td> <td>-4663.492</td> <td>-4052.242</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket_Type_Supermarket Type1</th>  <td> 1632.3347</td> <td>  173.862</td> <td>    9.389</td> <td> 0.000</td> <td> 1291.432</td> <td> 1973.238</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket_Type_Supermarket Type2</th>  <td>-1298.6317</td> <td>  238.239</td> <td>   -5.451</td> <td> 0.000</td> <td>-1765.762</td> <td> -831.501</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Supermarket_Type_Supermarket Type3</th>  <td> 4240.1951</td> <td>  266.004</td> <td>   15.940</td> <td> 0.000</td> <td> 3718.623</td> <td> 4761.767</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>198.436</td> <th>  Durbin-Watson:     </th> <td>   2.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 362.904</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.482</td>  <th>  Prob(JB):          </th> <td>1.57e-79</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.408</td>  <th>  Cond. No.          </th> <td>1.25e+16</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 3.85e-29. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.557\n",
       "Model:                            OLS   Adj. R-squared:                  0.553\n",
       "Method:                 Least Squares   F-statistic:                     149.1\n",
       "Date:                Fri, 19 Aug 2022   Prob (F-statistic):               0.00\n",
       "Time:                        20:23:45   Log-Likelihood:                -28140.\n",
       "No. Observations:                2993   AIC:                         5.633e+04\n",
       "Df Residuals:                    2967   BIC:                         5.649e+04\n",
       "Df Model:                          25                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=======================================================================================================\n",
       "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------------------------\n",
       "Product_Weight                        -26.4701    193.494     -0.137      0.891    -405.867     352.927\n",
       "Product_Shelf_Visibility             -716.6102    352.334     -2.034      0.042   -1407.453     -25.767\n",
       "Product_Price                        8821.8359    346.777     25.439      0.000    8141.888    9501.784\n",
       "Product_Type_Baking Goods               0.2637    208.560      0.001      0.999    -408.673     409.200\n",
       "Product_Type_Breads                  -248.6158    295.909     -0.840      0.401    -828.823     331.592\n",
       "Product_Type_Breakfast                -63.9440    424.434     -0.151      0.880    -896.160     768.272\n",
       "Product_Type_Canned                    91.2765    195.753      0.466      0.641    -292.550     475.103\n",
       "Product_Type_Dairy                   -256.5026    206.297     -1.243      0.214    -661.002     147.997\n",
       "Product_Type_Frozen Foods             109.7384    184.585      0.595      0.552    -252.189     471.666\n",
       "Product_Type_Fruits and Vegetables    262.0497    151.236      1.733      0.083     -34.488     558.587\n",
       "Product_Type_Hard Drinks              -20.5937    323.638     -0.064      0.949    -655.171     613.984\n",
       "Product_Type_Health and Hygiene      -152.0829    224.123     -0.679      0.497    -591.536     287.370\n",
       "Product_Type_Household               -197.1269    166.218     -1.186      0.236    -523.041     128.787\n",
       "Product_Type_Meat                      18.1185    227.273      0.080      0.936    -427.511     463.748\n",
       "Product_Type_Others                  -310.7060    377.694     -0.823      0.411   -1051.275     429.863\n",
       "Product_Type_Seafood                  703.9602    569.992      1.235      0.217    -413.660    1821.580\n",
       "Product_Type_Snack Foods               42.0077    150.146      0.280      0.780    -252.394     336.409\n",
       "Product_Type_Soft Drinks              -85.3506    237.121     -0.360      0.719    -550.289     379.588\n",
       "Product_Type_Starchy Foods            323.5387    400.604      0.808      0.419    -461.951    1109.028\n",
       "Supermarket _Size_High              -1633.6966    341.707     -4.781      0.000   -2303.704    -963.689\n",
       "Supermarket _Size_Medium              831.2637    109.932      7.562      0.000     615.713    1046.814\n",
       "Supermarket _Size_Small              1018.4640    150.780      6.755      0.000     722.820    1314.108\n",
       "Supermarket_Location_Type_Cluster 1  -728.0603    210.311     -3.462      0.001   -1140.431    -315.690\n",
       "Supermarket_Location_Type_Cluster 2  -534.9412    261.339     -2.047      0.041   -1047.366     -22.517\n",
       "Supermarket_Location_Type_Cluster 3  1479.0326    161.509      9.158      0.000    1162.352    1795.713\n",
       "Supermarket_Type_Grocery Store      -4357.8670    155.870    -27.958      0.000   -4663.492   -4052.242\n",
       "Supermarket_Type_Supermarket Type1   1632.3347    173.862      9.389      0.000    1291.432    1973.238\n",
       "Supermarket_Type_Supermarket Type2  -1298.6317    238.239     -5.451      0.000   -1765.762    -831.501\n",
       "Supermarket_Type_Supermarket Type3   4240.1951    266.004     15.940      0.000    3718.623    4761.767\n",
       "==============================================================================\n",
       "Omnibus:                      198.436   Durbin-Watson:                   2.011\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              362.904\n",
       "Skew:                           0.482   Prob(JB):                     1.57e-79\n",
       "Kurtosis:                       4.408   Cond. No.                     1.25e+16\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 3.85e-29. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lre2 = sm.OLS(y_train, X_train)\n",
    "results2 = lre2.fit()\n",
    "# Statsmodels gives R-like statistical output\n",
    "results2.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Here the Ordinary Least Squares (OLS) method has given the r^2 value of 0.557 which is above average but still poor for model to be predicted on these variables. The higher (closer to one) the better."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean square error of price and predicted value using multifit is:  8589898.856294755\n",
      "The mean square error of price and predicted value using multifit is:  9507257.271002539\n"
     ]
    }
   ],
   "source": [
    "print('The mean square error of price and predicted value using multifit is: ', \\\n",
    "      mean_squared_error(y_train, lre.predict(X_train)))\n",
    "\n",
    "print('The mean square error of price and predicted value using multifit is: ', \\\n",
    "      mean_squared_error(y_test, lre.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Title = 'Distribution  Plot of  Predicted Value Using Training Data vs Training Data Distribution'\n",
    "DistributionPlot(y_train, lre.predict(X_train), \"Actual Values (Train)\", \"Predicted Values (Train)\", Title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([14926.,   124.,  2726.,  -998.,  6758.])"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_test=lre.predict(X_test)\n",
    "yhat_test[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Title = 'Distribution  Plot of  Predicted Value Using Training Data vs Training Data Distribution'\n",
    "DistributionPlot(y_test, lre.predict(X_test), \"Actual Values (test)\", \"Predicted Values (test)\", Title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Using Cross validation**\n",
    "* R^2 value is basically dependant on the way the data is split. Hence, there may be times when the R^2 value may not be able to represent the model's ability to generalize. For this we perform cross validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of the folds are 0.5519293377802535 and the standard deviation is 0.025799316279967534\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([-9212802.17754359, -8915912.61166138, -9494408.32067818,\n",
       "       -7863440.49679572])"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lre=LinearRegression()\n",
    "Rcross = cross_val_score(lre, X_scaled, y, cv=5)\n",
    "Rcross\n",
    "\n",
    "print(\"The mean of the folds are\", Rcross.mean(), \"and the standard deviation is\" , Rcross.std())\n",
    "\n",
    "rc=cross_val_score(lre, X_scaled, y, cv=4,scoring='neg_mean_squared_error')\n",
    "rc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Regularization**\n",
    "* Rregularization helps to alter the loss function to penalize it for having higher coefficients for each feature variable (large coefficients leads to overfitting)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5593934460747929"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge = Ridge(alpha=0.001, normalize = True)\n",
    "ridge.fit(X_train,y_train)\n",
    "ridge_pred=ridge.predict(X_test)\n",
    "ridge.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DecisionTree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtree = DecisionTreeRegressor(max_depth=10,min_samples_leaf=5,max_leaf_nodes=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.5269352991786305 / 1.0\n",
      "MSE score =  10207837.030234888 / 0.0\n"
     ]
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)\n",
    "y_pred=dtree.predict(X_test)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 54 candidates, totalling 162 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ExtraTreesRegressor(max_depth=5, min_samples_leaf=5, min_samples_split=10,\n",
       "                    n_estimators=10)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etr = ExtraTreesRegressor()\n",
    "\n",
    "# Choose some parameter combinations to try\n",
    "\n",
    "parameters = {'n_estimators': [5,10,100],\n",
    "              'criterion': ['mse'],\n",
    "              'max_depth': [5,10,15], \n",
    "              'min_samples_split': [2,5,10],\n",
    "              'min_samples_leaf': [1,5]\n",
    "             }\n",
    "grid_obj = GridSearchCV(etr, parameters,\n",
    "                        cv=3, \n",
    "                        n_jobs=-1, #Number of jobs to run in parallel\n",
    "                        verbose=1)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "etr = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "etr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.5753803801079813 / 1.0\n",
      "MSE score =  9162484.26942916 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = etr.predict(X_test)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(min_samples_leaf=5)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFR = RandomForestRegressor()\n",
    "\n",
    "parameters = {'n_estimators': [5, 10, 100],\n",
    "              'min_samples_leaf': [1,2,5]\n",
    "             }\n",
    "\n",
    "grid_obj = GridSearchCV(RFR, parameters,\n",
    "                        cv=5, \n",
    "                        n_jobs=-1, \n",
    "                        verbose=1)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "RFR = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "RFR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.5636086340545008 / 1.0\n",
      "MSE score =  9416496.173227103 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = RFR.predict(X_test)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 54 candidates, totalling 270 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(max_depth=5, min_samples_split=10)"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFR = RandomForestRegressor()\n",
    "\n",
    "# Choose some parameter combinations to try\n",
    "parameters = {'n_estimators': [5,10,100],\n",
    "              'criterion': ['mse'],\n",
    "              'max_depth': [5,10,15], \n",
    "              'min_samples_split': [2,5,10],\n",
    "              'min_samples_leaf': [1,5]\n",
    "             }\n",
    "\n",
    "\n",
    "#We have to use RandomForestRegressor's own scorer (which is R^2 score)\n",
    "\n",
    "grid_obj = GridSearchCV(RFR, parameters,\n",
    "                        cv=5, \n",
    "                        n_jobs=-1, \n",
    "                        verbose=1)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "# Set the clf to the best combination of parameters\n",
    "RFR = grid_obj.best_estimator_\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "RFR.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.5822145143353916 / 1.0\n",
      "MSE score =  9015016.643298889 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = RFR.predict(X_test)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GradientBoostingRegressor(criterion='mse', max_depth=10, min_samples_leaf=5,\n",
       "                          n_estimators=15)"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbr=GradientBoostingRegressor()\n",
    "\n",
    "parameters = {'n_estimators': [5,15,150],\n",
    "              'loss':['ls','huber'],\n",
    "              'criterion': ['mse'],\n",
    "              'max_depth': [10,15], \n",
    "              'min_samples_split': [2,5],\n",
    "              'min_samples_leaf': [1,5]\n",
    "             }\n",
    "grid_obj = GridSearchCV(gbr, parameters,\n",
    "                        cv=5, \n",
    "                        n_jobs=-1, \n",
    "                        verbose=1)\n",
    "grid_obj = grid_obj.fit(X_train, y_train)\n",
    "\n",
    "gbr = grid_obj.best_estimator_\n",
    "\n",
    "\n",
    "# Fit the best algorithm to the data. \n",
    "gbr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score using Gradient Boosting=  0.5274261908532631 / 1.0\n",
      "MSE score using Gradient Boosting=  10197244.520995773 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = gbr.predict(X_test)\n",
    "\n",
    "print('R2 score using Gradient Boosting= ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score using Gradient Boosting= ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Xtreme Gradient Boosting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DeleLinus\\anaconda3\\lib\\site-packages\\xgboost\\data.py:262: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  elif isinstance(data.columns, (pd.Int64Index, pd.RangeIndex)):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "             colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "             gamma=0, gpu_id=-1, importance_type=None,\n",
       "             interaction_constraints='', learning_rate=0.08, max_delta_step=0,\n",
       "             max_depth=4, min_child_weight=2, missing=nan,\n",
       "             monotone_constraints='()', n_estimators=64, n_jobs=12,\n",
       "             num_parallel_tree=1, predictor='auto', random_state=0, reg_alpha=1,\n",
       "             reg_lambda=1, scale_pos_weight=1, subsample=0.7,\n",
       "             tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 296,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_reg = XGBRegressor(n_estimators=64,learning_rate=0.08,max_depth=4,\n",
    "                     subsample=0.7, min_child_weight=2,reg_alpha=1)#**parame)\n",
    "\n",
    "xgb_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score using XG Boost=  0.5722156240299222 / 1.0\n",
      "MSE score using XG Boost=  9230773.689944329 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred=xgb_reg.predict(X_test)\n",
    "print('R2 score using XG Boost= ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score using XG Boost= ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([13207.81, 13851.97,  1433.13, ...,  7939.67,  9447.7 ,  1867.57])"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.17594522 0.05281717 0.84669238 0.         0.         0.\n",
      "  0.         0.         0.         1.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         1.         0.         0.         0.\n",
      "  1.         0.         0.         0.         1.        ]\n",
      " [0.72313188 0.11722664 0.49287159 0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         1.         0.\n",
      "  0.         1.         0.         0.         0.         0.\n",
      "  1.         0.         1.         0.         0.        ]\n",
      " [0.38969932 0.59006672 0.67499873 0.         0.         0.\n",
      "  0.         0.         0.         1.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         1.         1.         0.\n",
      "  0.         1.         0.         0.         0.        ]\n",
      " [0.38969932 0.13649413 0.60626349 0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         1.         0.\n",
      "  0.         0.         0.         1.         1.         0.\n",
      "  0.         0.         1.         0.         0.        ]\n",
      " [0.74099434 0.16914534 0.80298731 0.         0.         0.\n",
      "  0.         1.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         1.         0.         0.         1.\n",
      "  0.         0.         1.         0.         0.        ]\n",
      " [0.44924085 0.06258229 0.48930313 0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         1.         0.\n",
      "  0.         1.         0.         0.         0.         0.\n",
      "  1.         0.         1.         0.         0.        ]\n",
      " [0.55343852 0.07885871 0.56724838 0.         0.         0.\n",
      "  0.         0.         1.         0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         1.         1.         0.\n",
      "  0.         0.         1.         0.         0.        ]\n",
      " [0.87496279 0.08263176 0.70018182 0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         1.         0.         0.         0.         0.\n",
      "  0.         0.         1.         0.         0.         1.\n",
      "  0.         0.         1.         0.         0.        ]\n",
      " [0.97916046 0.15075281 0.69608659 0.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         0.         1.         0.\n",
      "  0.         0.         1.         0.         0.         0.\n",
      "  1.         0.         0.         1.         0.        ]\n",
      " [0.6576362  0.52558356 0.35412666 0.         0.         0.\n",
      "  0.         0.         0.         1.         0.         0.\n",
      "  0.         0.         0.         0.         0.         0.\n",
      "  0.         0.         0.         1.         1.         0.\n",
      "  0.         0.         1.         0.         0.        ]]\n",
      "[[13207.81]\n",
      " [13851.97]\n",
      " [ 1433.13]\n",
      " [ 4815.4 ]\n",
      " [13302.68]\n",
      " [ 5567.75]\n",
      " [10195.06]\n",
      " [ 7856.44]\n",
      " [ 2906.22]\n",
      " [ 5407.96]]\n"
     ]
    }
   ],
   "source": [
    "# Convert data as np.array\n",
    "features = np.array(X_train)\n",
    "#targets = np.array(y_train.reshape(y_train.shape[0],1))\n",
    "targets = np.array(y_train.reshape(y_train.shape[0],1))\n",
    "features_validation= np.array(X_test)\n",
    "#targets_validation = np.array(y_test.reshape(y_test.shape[0],1))\n",
    "targets_validation = np.array(y_test.reshape(y_test.shape[0],1))\n",
    "\n",
    "print(features[:10])\n",
    "print(targets[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_3 (Dense)             (None, 32)                960       \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 16)                528       \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 16)                0         \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 17        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,505\n",
      "Trainable params: 1,505\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Building the model\n",
    "model = Sequential()\n",
    "model.add(Dense(32, activation='relu', input_shape=(X_train.shape[1],)))\n",
    "model.add(Dropout(.2))\n",
    "model.add(Dense(16, activation='relu'))\n",
    "model.add(Dropout(.1))\n",
    "model.add(Dense(1))\n",
    "\n",
    "# Compiling the model\n",
    "model.compile(loss = 'mse', optimizer='adam', metrics=['mse']) #mse: mean_square_error\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step :  0 / 1000\n",
      "94/94 [==============================] - 0s 739us/step - loss: 8373279.0000 - mse: 8373279.0000\n",
      "Training MSE: 8373279.0\n",
      "24/24 [==============================] - 0s 783us/step - loss: 9382947.0000 - mse: 9382947.0000\n",
      "Validation MSE: 9382947.0 \n",
      "\n",
      "Step :  250 / 1000\n",
      "94/94 [==============================] - 0s 747us/step - loss: 8304181.0000 - mse: 8304181.0000\n",
      "Training MSE: 8304181.0\n",
      "24/24 [==============================] - 0s 769us/step - loss: 9297875.0000 - mse: 9297875.0000\n",
      "Validation MSE: 9297875.0 \n",
      "\n",
      "Step :  500 / 1000\n",
      "94/94 [==============================] - 0s 746us/step - loss: 8295906.0000 - mse: 8295906.0000\n",
      "Training MSE: 8295906.0\n",
      "24/24 [==============================] - 0s 1ms/step - loss: 9287012.0000 - mse: 9287012.0000\n",
      "Validation MSE: 9287012.0 \n",
      "\n",
      "Step :  750 / 1000\n",
      "94/94 [==============================] - 0s 730us/step - loss: 8282380.0000 - mse: 8282380.0000\n",
      "Training MSE: 8282380.0\n",
      "24/24 [==============================] - 0s 745us/step - loss: 9250526.0000 - mse: 9250526.0000\n",
      "Validation MSE: 9250526.0 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training the model\n",
    "epochs_tot = 1000\n",
    "epochs_step = 250\n",
    "epochs_ratio = int(epochs_tot / epochs_step)\n",
    "# hist =np.array([])\n",
    "\n",
    "for i in range(epochs_ratio):\n",
    "    history = model.fit(features, targets, epochs=epochs_step, batch_size=100, verbose=0)\n",
    "    \n",
    "    # Evaluating the model on the training and testing set\n",
    "    print(\"Step : \" , i * epochs_step, \"/\", epochs_tot)\n",
    "    score = model.evaluate(features, targets)\n",
    "    print(\"Training MSE:\", score[1])\n",
    "    score = model.evaluate(features_validation, targets_validation)\n",
    "    print(\"Validation MSE:\", score[1], \"\\n\")\n",
    "#     hist = np.concatenate((hist, np.array(history.history['mean_squared_error'])), axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 score =  0.5713002006085784 / 1.0\n",
      "MSE score =  9250526.787316661 / 0.0\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(features_validation, verbose=0)\n",
    "\n",
    "print('R2 score = ',r2_score(y_test, y_pred), '/ 1.0')\n",
    "print('MSE score = ',mean_squared_error(y_test, y_pred), '/ 0.0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclussion:\n",
    "Using **R^2** as the evaluation metric, the following scores are gotten for the algorithms used:\n",
    "\n",
    "| Model      | score |\n",
    "| ----------- | ----------- |\n",
    "| Linear Resressor | 0.559       |\n",
    "| ExtraTree Regressor | 0.575    |\n",
    "| Random Forest Regressor| 0.582    |\n",
    "| Gradient Boosting Regressor| 0.527    |\n",
    "| Xtreme Gradient Boosting| 0.572    |\n",
    "| Neural Network| 582    |0.571|\n",
    "\n",
    "* Neural network appears to perform best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Further Improvement\n",
    "* Feature interaction or engineering can be done to improve the models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39/39 [==============================] - 0s 651us/step\n"
     ]
    },
    {
     "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>Product_Supermarket_Identifier</th>\n",
       "      <th>Product_Supermarket_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FDC15_CHUKWUDI049</td>\n",
       "      <td>5608.434082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NCY06_CHUKWUDI018</td>\n",
       "      <td>4197.079102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DRB24_CHUKWUDI035</td>\n",
       "      <td>6403.812012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NCG54_CHUKWUDI018</td>\n",
       "      <td>5901.750488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NCA42_CHUKWUDI017</td>\n",
       "      <td>6227.226074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Supermarket_Identifier  Product_Supermarket_Sales\n",
       "0              FDC15_CHUKWUDI049                5608.434082\n",
       "1              NCY06_CHUKWUDI018                4197.079102\n",
       "2              DRB24_CHUKWUDI035                6403.812012\n",
       "3              NCG54_CHUKWUDI018                5901.750488\n",
       "4              NCA42_CHUKWUDI017                6227.226074"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub = sample_submission.copy()\n",
    "sub['Product_Supermarket_Sales'] = model.predict(df2)\n",
    "sub.to_csv('NN_submission.csv', index = False)\n",
    "sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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