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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# E-Commerce Product Delivery Prediction  "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The aim of this project to predict whether the product from an e-commerce company will reach on time or not. This project also analyzes various factors that affect the delivery of the product as well as studies the customer behavior.\n",
+    "\n",
+    "### Context\n",
+    "An international e-commerce company based wants to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers. The company sells electronic products.\n",
+    "\n",
+    "### Data Dictionary\n",
+    "The dataset used for model building contained 10999 observations of 12 variables. The data contains the following information:\n",
+    "\n",
+    "\n",
+    "| Variable | Description |\n",
+    "| --- | --- |\n",
+    "|ID|ID Number of Customers|\n",
+    "|Warehouse_block|The Company have big Warehouse which is divided into block such as A,B,C,D,E|\n",
+    "|Mode_of_Shipment|The Company Ships the products in multiple way such as Ship, Flight and Road|\n",
+    "|Customer_care_calls|The number of calls made from enquiry for enquiry of the shipment|\n",
+    "|Customer_rating|The company has rated from every customer. 1 is the lowest (Worst), 5 is the highest (Best)|\n",
+    "|Cost_of_the_Product|Cost of the Product in US Dollars|\n",
+    "|Prior_purchases|The Number of Prior Purchase|\n",
+    "|Product_importance|The company has categorized the product in the various parameter such as low, medium, high|\n",
+    "|Gender|Male and Female|\n",
+    "|Discount_offered|Discount offered on that specific product|\n",
+    "|Weight_in_gms|It is the weight in grams|\n",
+    "|Reached.on.Time_Y.N|It is the target variable, where 1 Indicates that the product has NOT reached on time and 0 indicates it has reached on time|\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Importing the libraries\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ID</th>\n",
+       "      <th>Warehouse_block</th>\n",
+       "      <th>Mode_of_Shipment</th>\n",
+       "      <th>Customer_care_calls</th>\n",
+       "      <th>Customer_rating</th>\n",
+       "      <th>Cost_of_the_Product</th>\n",
+       "      <th>Prior_purchases</th>\n",
+       "      <th>Product_importance</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Discount_offered</th>\n",
+       "      <th>Weight_in_gms</th>\n",
+       "      <th>Reached.on.Time_Y.N</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>D</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>177</td>\n",
+       "      <td>3</td>\n",
+       "      <td>low</td>\n",
+       "      <td>F</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1233</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>F</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>216</td>\n",
+       "      <td>2</td>\n",
+       "      <td>low</td>\n",
+       "      <td>M</td>\n",
+       "      <td>59</td>\n",
+       "      <td>3088</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>A</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>183</td>\n",
+       "      <td>4</td>\n",
+       "      <td>low</td>\n",
+       "      <td>M</td>\n",
+       "      <td>48</td>\n",
+       "      <td>3374</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>B</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>176</td>\n",
+       "      <td>4</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>M</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1177</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>C</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>184</td>\n",
+       "      <td>3</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>F</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2484</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID Warehouse_block Mode_of_Shipment  Customer_care_calls  Customer_rating   \n",
+       "0   1               D           Flight                    4                2  \\\n",
+       "1   2               F           Flight                    4                5   \n",
+       "2   3               A           Flight                    2                2   \n",
+       "3   4               B           Flight                    3                3   \n",
+       "4   5               C           Flight                    2                2   \n",
+       "\n",
+       "   Cost_of_the_Product  Prior_purchases Product_importance Gender   \n",
+       "0                  177                3                low      F  \\\n",
+       "1                  216                2                low      M   \n",
+       "2                  183                4                low      M   \n",
+       "3                  176                4             medium      M   \n",
+       "4                  184                3             medium      F   \n",
+       "\n",
+       "   Discount_offered  Weight_in_gms  Reached.on.Time_Y.N  \n",
+       "0                44           1233                    1  \n",
+       "1                59           3088                    1  \n",
+       "2                48           3374                    1  \n",
+       "3                10           1177                    1  \n",
+       "4                46           2484                    1  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Loading the dataset\n",
+    "df = pd.read_csv('E_Commerce.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10999, 12)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking the shape of the dataset\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID                      int64\n",
+       "Warehouse_block        object\n",
+       "Mode_of_Shipment       object\n",
+       "Customer_care_calls     int64\n",
+       "Customer_rating         int64\n",
+       "Cost_of_the_Product     int64\n",
+       "Prior_purchases         int64\n",
+       "Product_importance     object\n",
+       "Gender                 object\n",
+       "Discount_offered        int64\n",
+       "Weight_in_gms           int64\n",
+       "Reached.on.Time_Y.N     int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking data types of the columns\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Dropping column ID because it is an index column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Drop column\n",
+    "df.drop(['ID'], axis=1, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Warehouse_block        0\n",
+       "Mode_of_Shipment       0\n",
+       "Customer_care_calls    0\n",
+       "Customer_rating        0\n",
+       "Cost_of_the_Product    0\n",
+       "Prior_purchases        0\n",
+       "Product_importance     0\n",
+       "Gender                 0\n",
+       "Discount_offered       0\n",
+       "Weight_in_gms          0\n",
+       "Reached.on.Time_Y.N    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking for null/missing values\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking for duplicate values\n",
+    "df.duplicated().sum()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Descriptive Statistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Customer_care_calls</th>\n",
+       "      <th>Customer_rating</th>\n",
+       "      <th>Cost_of_the_Product</th>\n",
+       "      <th>Prior_purchases</th>\n",
+       "      <th>Discount_offered</th>\n",
+       "      <th>Weight_in_gms</th>\n",
+       "      <th>Reached.on.Time_Y.N</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10999.000000</td>\n",
+       "      <td>10999.000000</td>\n",
+       "      <td>10999.000000</td>\n",
+       "      <td>10999.000000</td>\n",
+       "      <td>10999.000000</td>\n",
+       "      <td>10999.000000</td>\n",
+       "      <td>10999.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>4.054459</td>\n",
+       "      <td>2.990545</td>\n",
+       "      <td>210.196836</td>\n",
+       "      <td>3.567597</td>\n",
+       "      <td>13.373216</td>\n",
+       "      <td>3634.016729</td>\n",
+       "      <td>0.596691</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.141490</td>\n",
+       "      <td>1.413603</td>\n",
+       "      <td>48.063272</td>\n",
+       "      <td>1.522860</td>\n",
+       "      <td>16.205527</td>\n",
+       "      <td>1635.377251</td>\n",
+       "      <td>0.490584</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>96.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1001.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>169.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1839.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>214.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>4149.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>251.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>5050.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>310.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>65.000000</td>\n",
+       "      <td>7846.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Customer_care_calls  Customer_rating  Cost_of_the_Product   \n",
+       "count         10999.000000     10999.000000         10999.000000  \\\n",
+       "mean              4.054459         2.990545           210.196836   \n",
+       "std               1.141490         1.413603            48.063272   \n",
+       "min               2.000000         1.000000            96.000000   \n",
+       "25%               3.000000         2.000000           169.000000   \n",
+       "50%               4.000000         3.000000           214.000000   \n",
+       "75%               5.000000         4.000000           251.000000   \n",
+       "max               7.000000         5.000000           310.000000   \n",
+       "\n",
+       "       Prior_purchases  Discount_offered  Weight_in_gms  Reached.on.Time_Y.N  \n",
+       "count     10999.000000      10999.000000   10999.000000         10999.000000  \n",
+       "mean          3.567597         13.373216    3634.016729             0.596691  \n",
+       "std           1.522860         16.205527    1635.377251             0.490584  \n",
+       "min           2.000000          1.000000    1001.000000             0.000000  \n",
+       "25%           3.000000          4.000000    1839.500000             0.000000  \n",
+       "50%           3.000000          7.000000    4149.000000             1.000000  \n",
+       "75%           4.000000         10.000000    5050.000000             1.000000  \n",
+       "max          10.000000         65.000000    7846.000000             1.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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",
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+       "\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>Warehouse_block</th>\n",
+       "      <th>Mode_of_Shipment</th>\n",
+       "      <th>Customer_care_calls</th>\n",
+       "      <th>Customer_rating</th>\n",
+       "      <th>Cost_of_the_Product</th>\n",
+       "      <th>Prior_purchases</th>\n",
+       "      <th>Product_importance</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Discount_offered</th>\n",
+       "      <th>Weight_in_gms</th>\n",
+       "      <th>Reached.on.Time_Y.N</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>D</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>177</td>\n",
+       "      <td>3</td>\n",
+       "      <td>low</td>\n",
+       "      <td>F</td>\n",
+       "      <td>44</td>\n",
+       "      <td>1233</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>F</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>216</td>\n",
+       "      <td>2</td>\n",
+       "      <td>low</td>\n",
+       "      <td>M</td>\n",
+       "      <td>59</td>\n",
+       "      <td>3088</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>183</td>\n",
+       "      <td>4</td>\n",
+       "      <td>low</td>\n",
+       "      <td>M</td>\n",
+       "      <td>48</td>\n",
+       "      <td>3374</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>176</td>\n",
+       "      <td>4</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>M</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1177</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C</td>\n",
+       "      <td>Flight</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>184</td>\n",
+       "      <td>3</td>\n",
+       "      <td>medium</td>\n",
+       "      <td>F</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2484</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Warehouse_block Mode_of_Shipment  Customer_care_calls  Customer_rating   \n",
+       "0               D           Flight                    4                2  \\\n",
+       "1               F           Flight                    4                5   \n",
+       "2               A           Flight                    2                2   \n",
+       "3               B           Flight                    3                3   \n",
+       "4               C           Flight                    2                2   \n",
+       "\n",
+       "   Cost_of_the_Product  Prior_purchases Product_importance Gender   \n",
+       "0                  177                3                low      F  \\\n",
+       "1                  216                2                low      M   \n",
+       "2                  183                4                low      M   \n",
+       "3                  176                4             medium      M   \n",
+       "4                  184                3             medium      F   \n",
+       "\n",
+       "   Discount_offered  Weight_in_gms  Reached.on.Time_Y.N  \n",
+       "0                44           1233                    1  \n",
+       "1                59           3088                    1  \n",
+       "2                48           3374                    1  \n",
+       "3                10           1177                    1  \n",
+       "4                46           2484                    1  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Exploratory Data Analysis\n",
+    "\n",
+    "In the exploratory data analysis, I will be looking at the relationship between the target variable and the other variables. I will also be looking at the distribution of the variables across the dataset, in order to understand the data in a better way."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Customer Gender Distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Gender Distribution')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.pie(df['Gender'].value_counts(),labels = ['F','M'], autopct='%1.1f%%', startangle=90)\n",
+    "plt.title('Gender Distribution')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The dataset has the equal number of both males and female customers, with percentage of 49.6% and 50.4% respectively."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Product Properties"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Cost of the Product')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,3,figsize=(15,5))\n",
+    "sns.histplot(df['Weight_in_gms'], ax=ax[0], kde=True).set_title('Weight Distribution')\n",
+    "sns.countplot(x = 'Product_importance', data = df, ax=ax[1]).set_title('Product Importance')\n",
+    "sns.histplot(df['Cost_of_the_Product'], ax=ax[2], kde=True).set_title('Cost of the Product')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These three graphs explain the distribution of product properties - Weight, Cost and Importance in the dataset. Firstly, looking at the weight distribution, we can see that the products weighing between 1000-2000 grams and 4000-6000 grams are more in number. This means that the company is selling more of the products in these weight ranges. The second graph is about the product importance, where majority of the products have low or medium importance. The third graph is about the cost of the product. Third graph is about the cost distribution of the products, where there is increased distribution between 150-200 and 225-275 dollars.\n",
+    "\n",
+    "From this, I conclude that majority of the products are lighter than 6000 grams, have low or medium importance and costs between 150-275 dollars."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Reached on Time')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,3,figsize=(15,5))\n",
+    "sns.countplot(x = 'Warehouse_block', data = df, ax=ax[0]).set_title('Warehouse Block')\n",
+    "sns.countplot(x = 'Mode_of_Shipment', data = df, ax=ax[1]).set_title('Mode of Shipment')\n",
+    "sns.countplot(x = 'Reached.on.Time_Y.N', data = df, ax=ax[2]).set_title('Reached on Time')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above graphs visualizes the logistics and delivery of the product. In the first graph, we can see that the number of products from warehouse F is most i.e. 3500, whereas rest of the warehouses have nearly equal number of products. The second graph is about the shipment of the product, where majority of the products are shipped via Ship whereas nearly 2000 products are shipped by flight and road. Third graph is about the timely delivery of the product where we can see that the number of products delivered on time is more than the number of products not delivered on time.\n",
+    "\n",
+    "From all the above graph, I assume that warehouse F is close to seaport, because warehouse F has the most number of products and most of the products are shipped via ship. "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Customer Experience"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Discount Offered')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2,2,figsize=(15,10))\n",
+    "sns.countplot(x = 'Customer_care_calls', data = df, ax=ax[0,0]).set_title('Customer Care Calls')\n",
+    "sns.countplot(x = 'Customer_rating', data = df, ax=ax[0,1]).set_title('Customer Rating')\n",
+    "sns.countplot(x = 'Prior_purchases', data = df, ax=ax[1,0]).set_title('Prior Purchases')\n",
+    "sns.histplot(x = 'Discount_offered', data = df, ax=ax[1,1], kde = True).set_title('Discount Offered')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above graphs visualizes the customer experience based on their customer care calls, rating, prior purchases and discount offered. The first graph shows the number of customer care calls done by the customers, where we can see that majority of the customers have done 3-4 calls, which could be a potential indicator, which shows that customers could be facing with the product delivery. In the second graph, we can see that the count of customer ratings across all ratings is same, but there are little more count in rating 1, which means customers are not satisfied with the service. \n",
+    "\n",
+    "The third graph is about the prior purchases done by the customers, where we can see that majority of the customers have done 2-3 prior purchases, which means that customers who are having prior purchases, they are satisfied with the service, and they are buying more products. The fourth graph is about the discount offered on the products, where we can see that majority of the products have 0-10% discount, which means that the company is not offering much discount on the products."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Customer Gender and Product Delivery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Gender vs Reached on Time')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x = 'Gender', data = df, hue = 'Reached.on.Time_Y.N').set_title('Gender vs Reached on Time')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The number of products timely delivered for both the genders is same, which means there is no relation of customer gender and product delivery."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Product Properties and Product Delivery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Cost of the Product')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,3,figsize=(15,5))\n",
+    "sns.violinplot(y = df['Weight_in_gms'], ax=ax[0], kde=True, x = df['Reached.on.Time_Y.N']).set_title('Weight Distribution')\n",
+    "sns.countplot(x = 'Product_importance', data = df, ax=ax[1], hue = 'Reached.on.Time_Y.N').set_title('Product Importance')\n",
+    "sns.violinplot(y = df['Cost_of_the_Product'], ax=ax[2], kde=True, x = df['Reached.on.Time_Y.N']).set_title('Cost of the Product')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above plots visualizes the relationship between product properties and product delivery. From the first graph, it is quite clear that product weight has an impact of timely delivery of the product. Products that weight more than 4500 grams are not delivered on time, in addition to that more products that weight between 2500 - 3500 grams are delivered timely. The second graph is about the product importance and product delivery, where we can see that there is no major difference between the product delivery based on the product importance. The third graph shows the relationship between the cost of the product and product delivery, where we can see that products that cost more than 250 have higher count of not delivered on time.\n",
+    "\n",
+    "From this I conclude that product weight and cost has an impact on the product delivery."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistics and Product Delivery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Mode of Shipment')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2,figsize=(15,5))\n",
+    "sns.countplot(x = 'Warehouse_block', data = df, ax=ax[0], hue = 'Reached.on.Time_Y.N').set_title('Warehouse Block')\n",
+    "sns.countplot(x = 'Mode_of_Shipment', data = df, ax=ax[1], hue = 'Reached.on.Time_Y.N').set_title('Mode of Shipment')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These graphs explain the relationship between the Logistic and timely delivery of the product. Since most of the products are shipped from warehouse F, I assumed that warehouse F is close to seaport, and most of the products are shipped via ship. In both the graphs, the difference between the number of products delivered on time and not delivered on time is constant across all the warehouse blocks and mode of shipment. This means that the logistic and mode of shipment has no impact on the product delivery."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Customer Experience and Product Delivery"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Discount Offered')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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++OOPYvt8fX3l6+tbxhFJZ8+evewbRN3d3XXDDTeUcUQAAABVR3nMDwGUDxTeAAAAAAAAADvgUVMAAAAAAADADii8AQAAAAAAAHZA4Q1AuTJ06FA1aNDA0WE4RIMGDXTXXXc5OgwAAIASmzRpkiwWi6PDqJDS0tJ03333qXbt2rJYLJo9e7Yk6dixY+rVq5e8vb1lsVi0cuVKh8ZZiM8aKB0KbwBsZvHixbJYLObi5uamm266STExMUpLS3N0eEV069bNKl5fX1917NhRCxcuVEFBgaPDAwAAKFPF5XJBQUGKiIjQ66+/rt9//93RIZaZyZMnX3PB67ffftOYMWPUpEkTubm5ydfXVxEREVq9enWx40ePHq2vv/5acXFx+ve//63evXtLkqKionTgwAG9+uqr+ve//60OHTpc7+kAcCBnRwcAoPKJj49XaGioLl68qK1bt2r+/Pn68ssvdfDgQdWoUeOK277zzjtlWvSqV6+eEhISJElnzpzRBx98oGHDhun777/Xa6+9VmZxAAAAlBeFuVxeXp5SU1O1adMmjRo1SjNnztSqVasUFhZmjh03bpyef/55B0ZrH5MnT9Z9992n/v37l2j80aNH1aNHD505c0aPPvqoOnTooIyMDC1ZskT9+vXTs88+q2nTpllts2HDBt1999169tlnzbY//vhDO3bs0IsvvqiYmBhbnhIAB6HwBsDmIiMjzW/mHn/8cdWuXVszZ87UZ599pkGDBhW7TXZ2tmrWrKnq1avbLI6CggLl5ubKzc3tsmO8vb318MMPm+tPPPGEmjRpojfeeEMvv/zydcdTeF4AAAAVxV9zOUmKi4vThg0bdNddd+kf//iHjhw5Ind3d0mSs7OznJ2r9p+VeXl5uu+++3Tu3Dlt2bJFnTp1MvtGjx6twYMHa/r06erQoYMeeOABsy89PV0+Pj5W+zpz5owkFWm/HhcvXpSLi4ucnHjgDXAE/ssDYHfdu3eXJJ08eVLSn/O4eXh46MSJE+rTp488PT01ePBgs+/vc7xlZ2frmWeeUXBwsFxdXdWkSRNNnz5dhmFYjbNYLIqJidGSJUvUokULubq6as2aNdcUa40aNXTLLbcoOztbZ86c0Y8//iiLxaLFixcXGWuxWDRp0iRzvXDei8OHD+uhhx5SrVq11KVLF7P/ww8/1M0336waNWqoVq1a6tq1q9auXVtkv1u3btXNN98sNzc3NWzYUB988IFV/9mzZ/Xss8+qVatW8vDwkJeXlyIjI/Xdd98V2dfcuXPVokUL85gdOnTQ0qVLrcb88ssveuyxx+Tv7y9XV1e1aNFCCxcuLNW+AABA5dS9e3eNHz9eP/30kz788EOzvbh5vxITE9WlSxf5+PjIw8NDTZo00QsvvGA15uLFi5o0aZJuuukmubm5KTAwUPfee69OnDhhjilJDliaXO348eMaOnSofHx85O3trUcffVQXLlyw2i47O1vvv/+++djt0KFDL3tt/vvf/+rgwYN6/vnnrYpuklStWjW9/fbb8vHxMWMpfKTXMAzNmzfPPMakSZMUEhIiSRozZowsFotVXlySnG3Tpk2yWCz66KOPNG7cON1www2qUaOGMjMzJUm7du1S79695e3trRo1auj222/Xtm3bipzT1q1b1bFjx/+PvTsPi6ru3zh+zwzMsIO4gKYiLrnlUlpGmuWSZGqatltaWZahppiVLZrak+1W5lK5ZU/92rOy0kxNnwrNJXPN3ApNwRUQkG3m/P7AGUFAAQeH5f26rnPBnOV7PqOUh3u+i3x8fNSoUSO9/fbbRb5/AGdXtT+aAHBBOB+gqlev7tqXk5Oj6OhoderUSa+88kqRQ1ANw9CNN96oFStWaMiQIWrbtq2WLFmisWPH6t9//9XUqVPznb98+XJ98sknGj58uGrUqFGqhRr27Nkji8WikJAQHTp0qMTX33LLLWrSpImef/5514PhxIkT9eyzz+qqq67SpEmTZLVatWbNGi1fvlw9evRwXbtr1y7dfPPNGjJkiAYPHqy5c+fqnnvuUbt27dSyZUtXfQsXLtQtt9yiyMhIJSYm6u2339Y111yjbdu2qU6dOpJyh+2OHDlSN998sx555BFlZGRo06ZNWrNmje68805JuZP6Xnnlla7QsmbNmvr+++81ZMgQpaSkaNSoUcVuCwAAVG533323nnzySf3www964IEHCj1n69at6t27t1q3bq1JkybJZrNp165d+cIdu92u3r17a9myZbr99tv1yCOP6MSJE1q6dKm2bNmiRo0alfgZsCRuvfVWRUZGasqUKdqwYYNmz56tWrVq6cUXX5Qkvf/++7r//vt1xRVXaOjQoZKkRo0aFdneN998I0kaNGhQoceDg4PVt29fvffee9q1a5c6d+6s999/X3fffbeuu+4613WtW7dWSEiIRo8erTvuuEM33HCDAgICJBX/mc1p8uTJslqtevTRR5WZmSmr1arly5erZ8+eateunSZMmCCz2ax58+apa9eu+t///qcrrrhCkrR582b16NFDNWvW1LPPPqucnBxNmDBBYWFhpf4zB6o0AwDcZN68eYYk48cffzQOHz5s7Nu3z/joo4+M6tWrG76+vsb+/fsNwzCMwYMHG5KMJ554okAbgwcPNiIiIlyvFy5caEgynnvuuXzn3XzzzYbJZDJ27drl2ifJMJvNxtatW4tV7zXXXGM0a9bMOHz4sHH48GFj+/btxsiRIw1JRp8+fQzDMIy9e/cakox58+YVuF6SMWHCBNfrCRMmGJKMO+64I995O3fuNMxms3HTTTcZdrs93zGHw+H6PiIiwpBkrFq1yrXv0KFDhs1mM8aMGePal5GRUaCdvXv3GjabzZg0aZJrX9++fY2WLVue9c9gyJAhRu3atY0jR47k23/77bcbwcHBRnp6erHbAgAAFZvzWW7t2rVFnhMcHGxceumlrtfO5x+nqVOnGpKMw4cPF9nG3LlzDUnGa6+9VuCY89mouM+ApXlWu++++/Kdd9NNNxnVq1fPt8/f398YPHhwke8hr7Zt2xrBwcFnPee1114zJBlff/11vvpiYmLyned8Py+//HK+/cV9ZluxYoUhyWjYsKFrn2Hk/rk2adLEiI6Ozvf8mZ6ebkRGRhrXXXeda1+/fv0MHx8f459//nHt27Ztm2GxWAwiBKDkGGoKwO26d++umjVrql69err99tsVEBCgL7/8UhdddFG+84YNG3bOtr777jtZLBaNHDky3/4xY8bIMAx9//33+fZfc801atGiRbFr/fPPP1WzZk3VrFlTzZs317Rp09SrV69Ch1oW10MPPZTv9cKFC+VwODR+/PgCc2ucOTSjRYsWuvrqq12va9asqaZNm2rPnj2ufTabzdWO3W7X0aNHXcM4NmzY4DovJCRE+/fv19q1awut0zAMff755+rTp48Mw9CRI0dcW3R0tJKTk13tnastAABQNQQEBJx1dVPn3GRfffVVkQtmff7556pRo4ZGjBhR4Jjz2aikz4Alceaz2tVXX62jR4+6hmOW1IkTJxQYGHjWc5zHS3OPkjyzOQ0ePNg1D58kbdy4UTt37tSdd96po0ePuq5PS0tTt27dtGrVKjkcDtntdi1ZskT9+vVT/fr1Xdc3b95c0dHRJa4dAENNAZSB6dOn6+KLL5aXl5fCwsLUtGnTAoGTl5eX6tate862/vnnH9WpU6fAw0zz5s1dx/OKjIwsUa0NGjTQu+++K5PJJB8fHzVp0kS1atUqURtnOrOG3bt3y2w2FysQzPuA41StWjUdP37c9drhcOiNN97QjBkztHfvXtntdtexvMN5H3/8cf3444+64oor1LhxY/Xo0UN33nmnOnbsKCl38t6kpCS98847eueddwqtxznU9lxtAQCAqiE1NfWsz0q33XabZs+erfvvv19PPPGEunXrpv79++vmm292PQ/u3r1bTZs2PeuiDCV9BiyJM5+3qlWrJkk6fvy4goKCStxeYGCgjhw5ctZznGHluQK6wpTkmc3pzOfRnTt3SsoN5IqSnJyszMxMnTx5Uk2aNClwvGnTpvruu+9KWj5Q5RG8AXC7K664It9KWIXJ22vLnfJ+slcc/v7+6t69e5HHz+yR5pQ37DrfGvKyWCyF7jfyTCL8/PPP65lnntF9992nyZMnKzQ0VGazWaNGjcr3yXLz5s21Y8cOLVq0SIsXL9bnn3+uGTNmaPz48Zo4caLr3LvuuqvIh7DWrVsXqy0AAFD57d+/X8nJyWrcuHGR5/j6+mrVqlVasWKFvv32Wy1evFgff/yxunbtqh9++KHIZ53SKs2zWnGet0qiefPm2rhxo+Lj4wv9EFWSNm3aJEklGpnhVJJnNqczn0edbbz88stq27ZtoW0EBAQoMzOzxPUBODuCNwDlWkREhH788ccCXfj//PNP1/Gy5PwENCkpKd/+knzK2qhRIzkcDm3btq3IB52S+Oyzz9SlSxfNmTMn3/6kpCTVqFEj3z5/f3/ddtttuu2225SVlaX+/fvrP//5j8aNG6eaNWsqMDBQdrv9rOFjcdry8fE57/cFAADKt/fff1+Szjnk0Gw2q1u3burWrZtee+01Pf/883rqqae0YsUKde/eXY0aNdKaNWuUnZ0tb2/vQtso7jOgO57VClNUoFeY3r176//+7/+0YMECPf300wWOp6Sk6KuvvlKzZs3OGloWpaTPbIVxLg4RFBR01jZq1qwpX19fVw+5vHbs2FGqewNVHXO8ASjXbrjhBtntdr311lv59k+dOlUmk0k9e/Ys0/sHBQWpRo0aWrVqVb79M2bMKHYb/fr1k9ls1qRJkwrMdVKaT1YtFkuB6z799FP9+++/+fYdPXo032ur1aoWLVrIMAxlZ2fLYrFowIAB+vzzz7Vly5YC9zl8+HCx2wIAAJXb8uXLNXnyZEVGRmrgwIFFnnfs2LEC+5wfPDp7Uw0YMEBHjhwp8HwnnX42Ku4zoDue1Qrj7+9fIMwrys0336wWLVrohRde0Lp16/IdczgcGjZsmI4fP64JEyaUqpaSPLMVpV27dmrUqJFeeeUVpaamFtmGxWJRdHS0Fi5cqPj4eNfx7du3a8mSJaWqH6jq6PEGoFzr06ePunTpoqeeekp///232rRpox9++EFfffWVRo0addal3d3l/vvv1wsvvKD7779f7du316pVq/TXX38V+/rGjRvrqaee0uTJk3X11Verf//+stlsWrt2rerUqaMpU6aUqJ7evXtr0qRJuvfee3XVVVdp8+bN+uCDD9SwYcN85/Xo0UPh4eHq2LGjwsLCtH37dr311lvq1auX65PjF154QStWrFCHDh30wAMPqEWLFjp27Jg2bNigH3/80fXwXJy2AABA5fD999/rzz//VE5OjhITE7V8+XItXbpUERER+vrrr8/a033SpElatWqVevXqpYiICB06dEgzZsxQ3bp11alTJ0nSoEGDtGDBAsXGxuq3337T1VdfrbS0NP344496+OGH1bdv3xI9A57vs1ph2rVrpx9//FGvvfaa6tSpo8jISHXo0KHQc61Wqz777DN169ZNnTp10r333qv27dsrKSlJH374oTZs2KAxY8bo9ttvL3U9xX1mK4rZbNbs2bPVs2dPtWzZUvfee68uuugi/fvvv1qxYoWCgoL0zTffSJImTpyoxYsX6+qrr9bDDz+snJwcTZs2TS1btnQNmQVQAh5ZSxVApVScJegNwzAGDx5s+Pv7F3ksIiIi374TJ04Yo0ePNurUqWN4e3sbTZo0MV5++eV8S6EbRuFLsp/NNddcY7Rs2fKc56WnpxtDhgwxgoODjcDAQOPWW281Dh06VOQS9YcPHy60nblz5xqXXnqpYbPZjGrVqhnXXHONsXTpUtfxiIgIo1evXoXWec0117heZ2RkGGPGjDFq165t+Pr6Gh07djTi4uIKnPf2228bnTt3NqpXr27YbDajUaNGxtixY43k5OR87ScmJhoxMTFGvXr1DG9vbyM8PNzo1q2b8c4775S4LQAAUHE5n+Wcm9VqNcLDw43rrrvOeOONN4yUlJQC1ziff5yWLVtm9O3b16hTp45htVqNOnXqGHfccYfx119/5bsuPT3deOqpp4zIyEjX88fNN99s7N6923VOcZ8Bz/dZzfm+9+7d69r3559/Gp07dzZ8fX0NScbgwYPP+ed36NAhIzY21mjcuLFhs9mMkJAQo3v37sbXX39d6PmFPbvu3bvXkGS8/PLLBc4vzjPbihUrDEnGp59+Wug9f//9d6N///6uZ7qIiAjj1ltvNZYtW5bvvJUrVxrt2rUzrFar0bBhQ2PWrFkF/q4BFI/JMEo5gyQAAAAAAACAIjHHGwAAAAAAAFAGCN4AAAAAAACAMkDwBgAAAAAAAJQBgjcAAAAAAACgDBC8AQAAAAAAAGWA4A0AAAAAAAAoA16eLqAicDgcOnDggAIDA2UymTxdDgAAqCAMw9CJEydUp04dmc183lke8ZwHAABKo7jPeQRvxXDgwAHVq1fP02UAAIAKat++fapbt66ny0AheM4DAADn41zPeQRvxRAYGCgp9w8zKCjIw9UAAICKIiUlRfXq1XM9S6D84TkPAACURnGf8wjeisE57CAoKIgHMgAAUGIMYSy/eM4DAADn41zPeUw2AgAAAAAAAJQBgjcAAAAAAACgDBC8AQAAAAAAAGWAOd4AAJWO3W5Xdna2p8tAFWCxWOTl5cUcbgAAACgUwRsAoFJJTU3V/v37ZRiGp0tBFeHn56fatWvLarV6uhQAAACUMwRvAIBKw263a//+/fLz81PNmjXphYQyZRiGsrKydPjwYe3du1dNmjSR2cwsHgAAADiN4A0AUGlkZ2fLMAzVrFlTvr6+ni4HVYCvr6+8vb31zz//KCsrSz4+Pp4uCQAAAOUIH8sCACoderrhQqKXGwAAAIrCkyIAAAAAAABQBgjeAAAAAAAAgDJA8AYAwAViMpm0cOFCt7d77bXXatSoUW5v93yV17oAAACAC4XgDQBQJdxzzz0ymUwymUzy9vZWZGSkHnvsMWVkZHi6tArn77//dv1ZFrXNnz9fX3zxhSZPnuyRGjMzM9WyZUsNHTq0wLHHHntMkZGROnHihGvftddeK5PJpI8++ijfua+//roaNGhQ1uUCAIAKwLmiOVASBG8AgCrj+uuv18GDB7Vnzx5NnTpVb7/9tiZMmODpsiqcevXq6eDBg65tzJgxatmyZb59t912m0JDQxUYGOiRGm02mxYsWKD58+dryZIlrv2rV6/W1KlTNX/+/AK1+fj46Omnn1Z2dvaFLhcAAFQAzzzzjHr16qX//e9/ni4FFQjBGyqc+EmtLtgGoHKx2WwKDw9XvXr11K9fP3Xv3l1Lly6VJDkcDk2ZMkWRkZHy9fVVmzZt9Nlnn7mutdvtGjJkiOt406ZN9cYbbxS4x9y5c9WyZUvZbDbVrl1bw4cPz3f8yJEjuummm+Tn56cmTZro66+/znd8y5Yt6tmzpwICAhQWFqa7775bR44ccR1PS0vToEGDFBAQoNq1a+vVV18t1nufOXOmGjVqJKvVqqZNm+r999/Pd9xkMmn27Nlnrc3JYrEoPDzctQUEBMjLyyvfPl9f3wJDTRs0aKDnnnvOVX9ERIS+/vprHT58WH379lVAQIBat26tdevW5bvfzz//rKuvvlq+vr6qV6+eRo4cqbS0tHO+53bt2umpp57SkCFDlJSUpIyMDN17770aMWKErrnmmgLn33HHHUpKStK7775bjD9RAABQ1fz888/Kzs52PT8CxeHR4G3KlCm6/PLLFRgYqFq1aqlfv37asWNHvnOcQz/ybg899FC+c+Lj49WrVy/5+fmpVq1aGjt2rHJycvKd89NPP+myyy6TzWZT48aNNX/+/LJ+ewCAcmzLli369ddfZbVaJeX+m7RgwQLNmjVLW7du1ejRo3XXXXdp5cqVknKDubp16+rTTz/Vtm3bNH78eD355JP65JNPXG3OnDlTMTExGjp0qDZv3qyvv/5ajRs3znffiRMn6tZbb9WmTZt0ww03aODAgTp27JgkKSkpSV27dtWll16qdevWafHixUpMTNStt97qun7s2LFauXKlvvrqK/3www/66aeftGHDhrO+1y+//FKPPPKIxowZoy1btujBBx/UvffeqxUrVhS7NneZOnWqOnbsqN9//129evXS3XffrUGDBumuu+7Shg0b1KhRIw0aNEiGYUiSdu/ereuvv14DBgzQpk2b9PHHH+vnn38uEGgW5amnnlJ4eLhGjhypp59+WiaTSc8//3yh5wYFBempp57SpEmTihXsAQCAqsNut7u+dzgcHqwEFY1Hg7eVK1cqJiZGq1ev1tKlS5Wdna0ePXoUeNh94IEH8g1feemll1zH7Ha7evXqpaysLP3666967733NH/+fI0fP951zt69e9WrVy916dJFGzdu1KhRo3T//ffnG3oCAKj8Fi1apICAAPn4+KhVq1Y6dOiQxo4dq8zMTD3//POaO3euoqOj1bBhQ91zzz2666679Pbbb0uSvL29NXHiRLVv316RkZEaOHCg7r333nzB23PPPacxY8bokUce0cUXX6zLL7+8wOIC99xzj+644w41btxYzz//vFJTU/Xbb79Jkt566y1deumlev7559WsWTNdeumlmjt3rlasWKG//vpLqampmjNnjl555RV169ZNrVq10nvvvVfgw6YzvfLKK7rnnnv08MMP6+KLL1ZsbKz69++vV155pdi1ucsNN9ygBx98UE2aNNH48eOVkpKiyy+/XLfccosuvvhiPf7449q+fbsSExMl5QaiAwcO1KhRo9SkSRNdddVVevPNN7VgwYJizc/n5eWlBQsW6NNPP9W0adO0YMEC+fj4FHn+ww8/LB8fH7322mtue88ovQYNGhQ6h2BMTIwkKSMjQzExMapevboCAgI0YMAA188OAADuxLzAKC0vT9588eLF+V7Pnz9ftWrV0vr169W5c2fXfj8/P4WHhxfaxg8//KBt27bpxx9/VFhYmNq2bavJkyfr8ccf17PPPiur1apZs2YpMjLSNRynefPm+vnnnzV16lRFR0eX3RsEAJQrXbp00cyZM5WWlqapU6fKy8tLAwYM0NatW5Wenq7rrrsu3/lZWVm69NJLXa+nT5+uuXPnKj4+XidPnlRWVpbatm0rSTp06JAOHDigbt26nbWG1q1bu7739/dXUFCQDh06JEn6448/tGLFCgUEBBS4bvfu3a57dujQwbU/NDRUTZs2Pes9t2/fXmCRgY4dOxYYKnu22twl7z3CwsIkSa1atSqw79ChQwoPD9cff/yhTZs26YMPPnCdYxiGHA6H9u7dq+bNm5/zni1atNCAAQOUlJSk9u3bn/Vcm82mSZMmacSIERo2bFiJ3hvcb+3atfl6GGzZskXXXXedbrnlFknS6NGj9e233+rTTz9VcHCwhg8frv79++uXX37xVMkAgErq5MmTru/P9aEnkJdHg7czJScnS8r9JSKvDz74QP/9738VHh6uPn366JlnnpGfn58kKS4uTq1atXI9qEtSdHS0hg0bpq1bt+rSSy9VXFycunfvnq/N6OjoAr0QnDIzM5WZmel6nZKS4o63BwDwMH9/f9fQz7lz56pNmzaaM2eOLrnkEknSt99+q4suuijfNTabTZL00Ucf6dFHH9Wrr76qqKgoBQYG6uWXX9aaNWskSb6+vsWqwdvbO99rk8nkGq6QmpqqPn366MUXXyxwXe3atbVr164SvNuSO1ttZXEPk8lU5L68fyYPPvigRo4cWaCt+vXrF/u+Xl5e8vIq3mPPXXfdpVdeeUXPPfccK5p6WM2aNfO9fuGFF9SoUSNdc801Sk5O1pw5c/Thhx+qa9eukqR58+apefPmWr16ta688kpPlAwAqKTyroaemprqwUpQ0ZSb4M3hcGjUqFHq2LGj6xcgSbrzzjsVERGhOnXqaNOmTXr88ce1Y8cOffHFF5KkhISEfKGbdPrT8oSEhLOek5KSopMnTxb4ZWnKlCmaOHGi298jAKD8MJvNevLJJxUbG6u//vpLNptN8fHxhU66L0m//PKLrrrqKj388MOufbt373Z9HxgYqAYNGmjZsmXq0qVLqWq67LLL9Pnnn6tBgwaFhkSNGjWSt7e31qxZ4wqdjh8/rr/++qvIuqXcnt6//PKLBg8enO/9tGjRolR1XkiXXXaZtm3bVmCuvLJkNps1ZcoU9e/fn15v5UhWVpb++9//KjY2ViaTSevXr1d2dna+D1ebNWum+vXrKy4ursjgjQ9YAQClkTdsI3hDSZSbVU1jYmK0ZcsWffTRR/n2Dx06VNHR0WrVqpUGDhyoBQsW6Msvv8z3y467jRs3TsnJya5t3759ZXYvAIDn3HLLLbJYLHr77bf16KOPavTo0Xrvvfe0e/dubdiwQdOmTdN7770nSWrSpInWrVunJUuW6K+//tIzzzyjtWvX5mvv2Wef1auvvqo333xTO3fudLVRXDExMTp27JjuuOMOrV27Vrt379aSJUt07733ym63KyAgQEOGDNHYsWO1fPlybdmyRffcc4/M5vz/nI8bN06DBg1yvR47dqzmz5+vmTNnaufOnXrttdf0xRdf6NFHHy3Rn1ezZs305Zdfluia8/X444/r119/1fDhw7Vx40bt3LlTX331VbEXVyjKud5Lr1691KFDB9ccf/C8hQsXKikpSffcc4+k3A9WrVarQkJC8p0XFhbm+vC1MFOmTFFwcLBrq1evXhlWDQCoLPL2eMv7PXAu5aLH2/Dhw7Vo0SKtWrVKdevWPeu5znltdu3apUaNGik8PLzAxM/OSXWd88KFh4cXmGg3MTFRQUFBhQ4NstlsrqFFAIDKy8vLS8OHD9dLL72kvXv3qmbNmpoyZYr27NmjkJAQXXbZZXryySclSQ8++KB+//133XbbbTKZTLrjjjv08MMP6/vvv3e1N3jwYGVkZGjq1Kl69NFHVaNGDd18883FrqdOnTr65Zdf9Pjjj6tHjx7KzMxURESErr/+ele49vLLL7uGpAYGBmrMmDGuqRqcDh48qPj4eNfrfv366Y033tArr7yiRx55RJGRkZo3b56uvfbaEv157dixo8C9ylrr1q21cuVKPfXUU7r66qtlGIYaNWqk22677bzaLc57efHFF3XVVVed133gPnPmzFHPnj1Vp06d82pn3Lhxio2Ndb1OSUkhfAMAnFPeld6PHz8uh8NR4MNPoDAmwzAMT93cMAyNGDFCX375pX766Sc1adLknNf88ssv6tSpk/744w+1bt1a33//vXr37q2DBw+qVq1akqR33nlHY8eO1aFDh2Sz2fT444/ru+++0+bNm13t3HnnnTp27FiBBR4Kk5KSouDgYCUnJysoKKj0bxhuET+p1blPcpP64zef+yQA5UZGRob27t2ryMjIs65cCbjT2X7ueIZwj3/++UcNGzbUF198ob59+0qSli9frm7duun48eP5er1FRERo1KhRGj16dLHa5u8IAFAc//3vfzV79mzX66+++krBwcEerAieVtxnCI/GszExMfrvf/+rDz/8UIGBgUpISFBCQoJrtZDdu3dr8uTJWr9+vf7++299/fXXGjRokDp37uxaFa1Hjx5q0aKF7r77bv3xxx9asmSJnn76acXExLh6rT300EPas2ePHnvsMf3555+aMWOGPvnkk2I/kAEAAMBz5s2bp1q1aqlXr16ufe3atZO3t7eWLVvm2rdjxw7Fx8crKirKE2UCACqxvD3eCnsNFMWjQ01nzpwpSQWGusybN0/33HOPrFarfvzxR73++utKS0tTvXr1NGDAAD399NOucy0WixYtWqRhw4YpKipK/v7+Gjx4sCZNmuQ6JzIyUt9++61Gjx6tN954Q3Xr1tXs2bMVHR19Qd4nAABwv//973/q2bNnkceZ+LhycDgcmjdvngYPHpxv0ZHg4GANGTJEsbGxCg0NVVBQkEaMGKGoqChWNAUAuN2hQ4cKvI6MjPRQNahIPBq8nWuUa7169bRy5cpzthMREaHvvvvurOdce+21+v3330tUHwAAKL/at2+vjRs3eroMlLEff/xR8fHxuu+++wocmzp1qsxmswYMGKDMzExFR0drxowZHqgSAFDZOeeNN8mQIVOBeeSBopSLxRUAAABKytfXV40bN/Z0GShjPXr0KPLDWh8fH02fPl3Tp0+/wFUBAKoaZ9B2cUiOdiR5E7yh2FiCAwAAAAAAoAgpKSlKSUmRJF1SLVuStH//fk+WhAqE4A0AAAAAAKAI+/btkySF2uxqEJiTbx9wLgRvAAAAAAAARYiPj5ckhfvZFe5nl5Tb483hcHiyLFQQBG8AAAAAAABF+PvvvyVJdfwcquHjkLfZUFZWlg4cOODZwlAhELwBAAAAAAAUYc+ePZKkegE5spili/zt+fYDZ8OqpgAAVBDtxi64oPdb//KgC3o/AACA8mjXrl2SpPoBuYFbvYAc/X3CS7t371bnzp09WRoqAHq8AQAAt5o+fboaNGggHx8fdejQQb/99punSwIAACiVo0eP6vjx4zLJUN2A3IUVnAGcM5ADzobgDQAAuM3HH3+s2NhYTZgwQRs2bFCbNm0UHR2tQ4cOebo0AACAEvvzzz8lSXX87bJZcvdFnlrZ1HkMOBuCNwAA4DavvfaaHnjgAd17771q0aKFZs2aJT8/P82dO9fTpQEAAJTYjh07JEkNT4VtkhQRmCOTDB09elSHDx/2VGmoIAjeAACAW2RlZWn9+vXq3r27a5/ZbFb37t0VFxfnwcoAAABKZ/v27ZKkyKDTwZvNItU9tcACvd5wLgRvAADALY4cOSK73a6wsLB8+8PCwpSQkOChqgAAAErH4XC4grdGeYI3SWoUnPt627ZtF7wuVCwEbwAAAAAAAGfYt2+fUlNTZTUbqndqQQWnxqeCuK1bt3qiNFQgBG8AAMAtatSoIYvFosTExHz7ExMTFR4e7qGqAAAASsfZmy0yMEcWk5Rpz90M43SPtx07dignJ+dszaCKI3gDAABuYbVa1a5dOy1btsy1z+FwaNmyZYqKivJgZQAAACW3ZcsWSVLj4BxlOaQHVlbXAyurK8sh1fazy8/LoczMTO3atcvDlaI8I3gDAABuExsbq3fffVfvvfeetm/frmHDhiktLU333nuvp0sDAAAoEecw0sbB2QWOmU25gZzEPG84Oy9PFwAAAIpn/cuDPF3COd122206fPiwxo8fr4SEBLVt21aLFy8usOACAABAeXbixAn9/fffkqQmwYUPJW0clKNNR63asmWL+vfvfwGrQ0VC8AYAANxq+PDhGj58uKfLAAAAKLU///xTklTLx64gq6FMe8Fz6PGG4mCoKQAAAAAAQB7bt2+XdHoRhcI0DMqRSYYSEhJ07NixC1UaKhiCNwAAAAAAgDycvdgaBRUdvPl5Garjn9sVzhnUAWcieAMAAAAAADjFMAzXUNOzBW+S1DAw9/iOHTvKvC5UTARvAAAAAAAApxw5ckRJSUkymwzVCzh78NYgKLfH219//XUhSkMFRPAGAAAAAABwijNEu8jPLqvl7Oc2ONXjjeANRSF4AwAAAAAAOGXXrl2SpIjAs/d2k6T6AbkLLBw7dowFFlAogjcAAAAAAIBT/v77b0lS3QD7Oc+1WaRavo581wF5EbwBAAAAAACcsnfvXklSXf9zB2+SdNGp85zXAXkRvAEAAAAAAEjKycnRvn37JEl1ih285Q5JpccbCuPl6QIAAEDxxE9qdUHvV3/85gt6PwAAAE9LTEyU3W6Xt9lQqM1RrGvC/XLP+/fff8uyNFRQ9HgDAABusWrVKvXp00d16tSRyWTSwoULPV0SAABAiRw4cECSVMvXLrOpeNfU8rXnuxbIi+ANAAC4RVpamtq0aaPp06d7uhQAAIBSOR28Fa+3W+65ucHboUOHlJNz7pVQUbUw1BQAALhFz5491bNnT0+XAQAAUGqHDx+WJFUv5jBTSQq2GrKYDNkdDh09elRhYWFlVR4qIHq8AQAAAAAA6HTwVs1WvIUVJMlskkKsuUHdkSNHyqQuVFwEbwAAACi3/v33X911112qXr26fH191apVK61bt8513DAMjR8/XrVr15avr6+6d++unTt3erBiAEBFdvToUUlStRL0eMt7vjO4A5wI3gAAAFAuHT9+XB07dpS3t7e+//57bdu2Ta+++qqqVavmOuell17Sm2++qVmzZmnNmjXy9/dXdHS0MjIyPFg5AKCiOn78uCQpyGqU6LrgU+cnJye7vSZUbMzxBgAAgHLpxRdfVL169TRv3jzXvsjISNf3hmHo9ddf19NPP62+fftKkhYsWKCwsDAtXLhQt99+e4E2MzMzlZmZ6XqdkpJShu8AAFDROP9dCPAuWY835/kEbzgTPd4AAABQLn399ddq3769brnlFtWqVUuXXnqp3n33XdfxvXv3KiEhQd27d3ftCw4OVocOHRQXF1dom1OmTFFwcLBrq1evXpm/DwBAxeEM3gK9S9bjLeDU+XyggzMRvAEAALdITU3Vxo0btXHjRkm5ocjGjRsVHx/v2cJQYe3Zs0czZ85UkyZNtGTJEg0bNkwjR47Ue++9J0lKSEiQpAKrx4WFhbmOnWncuHFKTk52bfv27SvbNwEAqDCys7NdvaL9vEoWvPmf6vF24sQJt9eFio2hpgAAVBD1x2/2dAlntW7dOnXp0sX1OjY2VpI0ePBgzZ8/30NVoSJzOBxq3769nn/+eUnSpZdeqi1btmjWrFkaPHhwqdq02Wyy2WzuLBMAUEmkp6e7vvexlCx4c55/8uRJt9aEio8ebwAAwC2uvfZaGYZRYCN0Q2nVrl1bLVq0yLevefPmrl6U4eHhkqTExMR85yQmJrqOAQBQXM7QzNtsyFLCtITgDUUheAMAAEC51LFjR+3YsSPfvr/++ksRERGSchdaCA8P17Jly1zHU1JStGbNGkVFRV3QWgEAFZ9zRWxbCXu75V6T+5XgDWdiqCkAAADKpdGjR+uqq67S888/r1tvvVW//fab3nnnHb3zzjuSJJPJpFGjRum5555TkyZNFBkZqWeeeUZ16tRRv379PFs8AKDCycrKkpTb462knNdkZ2e7tSZUfARvAAAAKJcuv/xyffnllxo3bpwmTZqkyMhIvf766xo4cKDrnMcee0xpaWkaOnSokpKS1KlTJy1evFg+Pj4erBwAUBGdDt5Kfq0zeHO2ATgRvAEAAKDc6t27t3r37l3kcZPJpEmTJmnSpEkXsCoAQGXk7K1Wmh5vXub8bQBOzPEGAKh0DKPkD0tAafHzBgBA5WC32yVJFlPJr3Ve42wDcCJ4AwBUGhZL7qy2dPHHhZSeni5J8vb29nAlAADgfDhDM3MpgjezycjXBuDEUFMAQKXh5eUlPz8/HT58WN7e3jKb+XwJZccwDKWnp+vQoUMKCQlxBb8AAKBicgVvpbjWTI83FIHgDQBQaZhMJtWuXVt79+7VP//84+lyUEWEhIQoPDzc02UAAIDz5HA4JEkmU8mnkXCGdc42ACeCNwBApWK1WtWkSROGm+KC8Pb2pqcbAACVhHPe1lKMNJWpNBehSiB4AwBUOmazWT4+Pp4uAwAAABWIK3grRYjmvIQebzgTk98AAAAAAIAqzx0rlbPaOc5E8AYAAAAAAHAKQ03hTgRvAAAAAAAAQBkgeAMAAAAAAADKAMEbAAAAAAAAUAYI3gAAAAAAAIAy4NHgbcqUKbr88ssVGBioWrVqqV+/ftqxY0e+czIyMhQTE6Pq1asrICBAAwYMUGJiYr5z4uPj1atXL/n5+alWrVoaO3ascnJy8p3z008/6bLLLpPNZlPjxo01f/78sn57AAAAAAAAqMI8GrytXLlSMTExWr16tZYuXars7Gz16NFDaWlprnNGjx6tb775Rp9++qlWrlypAwcOqH///q7jdrtdvXr1UlZWln799Ve99957mj9/vsaPH+86Z+/everVq5e6dOmijRs3atSoUbr//vu1ZMmSC/p+AQAAAABA+cYCpXAnL0/efPHixflez58/X7Vq1dL69evVuXNnJScna86cOfrwww/VtWtXSdK8efPUvHlzrV69WldeeaV++OEHbdu2TT/++KPCwsLUtm1bTZ48WY8//rieffZZWa1WzZo1S5GRkXr11VclSc2bN9fPP/+sqVOnKjo6ukBdmZmZyszMdL1OSUkpwz8FAAAAAAAAVEblao635ORkSVJoaKgkaf369crOzlb37t1d5zRr1kz169dXXFycJCkuLk6tWrVSWFiY65zo6GilpKRo69atrnPytuE8x9nGmaZMmaLg4GDXVq9ePfe9SQAAAAAAUO4YhuHpElAJlZvgzeFwaNSoUerYsaMuueQSSVJCQoKsVqtCQkLynRsWFqaEhATXOXlDN+dx57GznZOSkqKTJ08WqGXcuHFKTk52bfv27XPLewQAAAAAAOUcY03hRh4dappXTEyMtmzZop9//tnTpchms8lms3m6DAAAAAAAUAGYlNtbjl5zOFO56PE2fPhwLVq0SCtWrFDdunVd+8PDw5WVlaWkpKR85ycmJio8PNx1zpmrnDpfn+ucoKAg+fr6uvvtAAAAAACACsYZmpWmw5vzGoI3nMmjwZthGBo+fLi+/PJLLV++XJGRkfmOt2vXTt7e3lq2bJlr344dOxQfH6+oqChJUlRUlDZv3qxDhw65zlm6dKmCgoLUokUL1zl523Ce42wDAAAAAABUbecTvDkvInjDmTw61DQmJkYffvihvvrqKwUGBrrmZAsODpavr6+Cg4M1ZMgQxcbGKjQ0VEFBQRoxYoSioqJ05ZVXSpJ69OihFi1a6O6779ZLL72khIQEPf3004qJiXENF33ooYf01ltv6bHHHtN9992n5cuX65NPPtG3337rsfcOAAAAAADKD1fwVorkrVwMJ0S55NGfjZkzZyo5OVnXXnutateu7do+/vhj1zlTp05V7969NWDAAHXu3Fnh4eH64osvXMctFosWLVoki8WiqKgo3XXXXRo0aJAmTZrkOicyMlLffvutli5dqjZt2ujVV1/V7NmzFR0dfUHfLwAAAAAAKJ9O91Yrfa81h8PhnmJQaXi0x1txumD6+Pho+vTpmj59epHnRERE6LvvvjtrO9dee61+//33EtcIAAAAAAAqP2doVpoeSmaGmqII9IYEAAAAAABVnjM0M5diqKlzeCo93nAmgjcAAAAAAFDlOUOz85njjeANZyJ4AwAAAAAAVZ7dbpdUulVNzabc3nIEbzgTwRsAAAAAAKjynKGZpTQ93vIMNWWeN+RF8AYAAAAAAKo81+IKppIHZ3nnhaPXG/IieAMAAAAAAFWec6hpaRZXyHuNsx1AIngDAAAAAABwBWalGWpqydNLjuANeRG8AQAAoFx69tlnZTKZ8m3NmjVzHc/IyFBMTIyqV6+ugIAADRgwQImJiR6sGABQkeXk5EjKH6IVl4UebygCwRsAAADKrZYtW+rgwYOu7eeff3YdGz16tL755ht9+umnWrlypQ4cOKD+/ft7sFoAQEXmDN7Od6ipsx1Akrw8XQAAAABQFC8vL4WHhxfYn5ycrDlz5ujDDz9U165dJUnz5s1T8+bNtXr1al155ZWFtpeZmanMzEzX65SUlLIpHABQ4biGmpaii5LZJJlkyJCJHm/Ihx5vAAAAKLd27typOnXqqGHDhho4cKDi4+MlSevXr1d2dra6d+/uOrdZs2aqX7++4uLiimxvypQpCg4Odm316tUr8/cAAKgYnIGZVymGmkqS16mEhR5vyIvgDQAAAOVShw4dNH/+fC1evFgzZ87U3r17dfXVV+vEiRNKSEiQ1WpVSEhIvmvCwsKUkJBQZJvjxo1TcnKya9u3b18ZvwsAQEVxeo630l3vvI7gDXkx1BQAAADlUs+ePV3ft27dWh06dFBERIQ++eQT+fr6lqpNm80mm83mrhIBAJXI+czxJjkXZTARvCEferwBAACgQggJCdHFF1+sXbt2KTw8XFlZWUpKSsp3TmJiYqFzwgEAcC6uHm/m0g01dQZ2zPGGvAjeAAAAUCGkpqZq9+7dql27ttq1aydvb28tW7bMdXzHjh2Kj49XVFSUB6sEAFRUp+d4K931Xgw1RSEYagoAAIBy6dFHH1WfPn0UERGhAwcOaMKECbJYLLrjjjsUHBysIUOGKDY2VqGhoQoKCtKIESMUFRVV5IqmAACcjWtV01IurmA+dR3BG/IieAMAAEC5tH//ft1xxx06evSoatasqU6dOmn16tWqWbOmJGnq1Kkym80aMGCAMjMzFR0drRkzZni4agBAReUM3ko9x9upMYUOh8NNFaEyIHgDAABAufTRRx+d9biPj4+mT5+u6dOnX6CKAACV2ekeb6W73sIcbygEc7wBAAAAAIAq73x7vDmHmhK8IS+CNwAAAAAAUOU5h4iaSzvH2xntABLBGwAAAAAAgCswK2WHN5lM+dsBJII3AAAAAACA08FbqYea5m8HkAjeAAAAAAAAXErb4w0oDMEbAAAAAAAAUAYI3gAAAAAAANzEMEq3OAMqJ4I3AAAAAAAANzGbiVpwmpenCwAAAEDl8Oabbxb73JEjR5ZhJQAAlJwzMCttfzXHqQtNpV2dAZUSwRsAAADcYurUqfleHz58WOnp6QoJCZEkJSUlyc/PT7Vq1SJ4AwCUO87AzFHK5M15GT3ekBc/DQAAAHCLvXv3urb//Oc/atu2rbZv365jx47p2LFj2r59uy677DJNnjzZ06UCAFCAxWKRJDmM0vVYcwZ2BG/Ii58GAAAAuN0zzzyjadOmqWnTpq59TZs21dSpU/X00097sDIAAArnDN7spezxZj8V2Hl5MbgQpxG8AQAAwO0OHjyonJycAvvtdrsSExM9UBEAAGfnDMxKO9TU4cj96gzwAIngDQAAAGWgW7duevDBB7VhwwbXvvXr12vYsGHq3r27BysDAKBwzuAtx1G6oaY5pwI7gjfkRfAGAAAAt5s7d67Cw8PVvn172Ww22Ww2XXHFFQoLC9Ps2bM9XR4AAAW4grdS9nhzBnZWq9VdJaESYOAxAAAA3K5mzZr67rvv9Ndff+nPP/+UJDVr1kwXX3yxhysDAKBwzsDsfHu8eXt7u6skVAIEbwAAACgzDRo0kGEYatSoEZNNAwDKNee/U9mO0l3vDOz49w55MdQUAAAAbpeenq4hQ4bIz89PLVu2VHx8vCRpxIgReuGFFzxcHQAABbl6vBkl7/FmGKcDO4aaIi+CNwAAALjduHHj9Mcff+inn36Sj4+Pa3/37t318ccfe7AyAAAK5wzMsu0lv9ZuSIaY4w0F0f8RAAAAbrdw4UJ9/PHHuvLKK2Uyne450LJlS+3evduDlQEAUDhX8FaKOd7yDk8leENe9HgDAACA2x0+fFi1atUqsD8tLS1fEAcAQHnhXBQhuxSrmuYN61hcAXkRvAEAAMDt2rdvr2+//db12hm2zZ49W1FRUZ4qCwCAIp1fj7fca7y9vWU2E7XgNIaaAgAAwO2ef/559ezZU9u2bVNOTo7eeOMNbdu2Tb/++qtWrlzp6fIAACjg9BxvpR9qSm83nIkYFgAAAG7XqVMn/fHHH8rJyVGrVq30ww8/qFatWoqLi1O7du08XR4AAAWcXtW05NfmOFhYAYWjx1sFEj+p1QW7V/3xmy/YvQAAQOWSnZ2tBx98UM8884zeffddT5cDAECxuOZ4K8VQ0yx6vKEI9HgDAACAW3l7e+vzzz/3dBkAAJSIMzTLcZzjxELk5JnjDciL4A0AAABu169fPy1cuNDTZQAAUGzn0+PNOTyV4A1nYqgpAAAA3K5JkyaaNGmSfvnlF7Vr107+/v75jo8cOdJDlQEAUDgvr9yIxH4ec7w52wCc+IkAAACA282ZM0chISFav3691q9fn++YyWQieAMAlDvO3mr2UvR4s9PjDUUgeAMAAIDb7d2719MlAABQIhaLRVLpVjW1G6Z8bQBOzPEGAACAMpOVlaUdO3YoJyfnvNt64YUXZDKZNGrUKNe+jIwMxcTEqHr16goICNCAAQOUmJh43vcCAFQ9ztDMUYrgzXkNQ01xJoI3AAAAuF16erqGDBkiPz8/tWzZUvHx8ZKkESNG6IUXXihxe2vXrtXbb7+t1q1b59s/evRoffPNN/r000+1cuVKHThwQP3793fLewAAVC3O4K00c7zZHfnbAJwI3gAAAOB248aN0x9//KGffvpJPj4+rv3du3fXxx9/XKK2UlNTNXDgQL377ruqVq2aa39ycrLmzJmj1157TV27dlW7du00b948/frrr1q9erXb3gsAoGo43eOt5HO8OXu8mc3ELMiPnwgAAAC43cKFC/XWW2+pU6dOMplO/wLTsmVL7d69u0RtxcTEqFevXurevXu+/evXr1d2dna+/c2aNVP9+vUVFxdXaFuZmZlKSUnJtwEAIJ0OzUrR4U0OMccbCsfgYwAAALjd4cOHVatWrQL709LS8gVx5/LRRx9pw4YNWrt2bYFjCQkJslqtCgkJybc/LCxMCQkJhbY3ZcoUTZw4sdj3BwBUHc7g7XzmeCvJv3GoGujxBgAAALdr3769vv32W9dr5y8is2fPVlRUVLHa2Ldvnx555BF98MEH+Yarno9x48YpOTnZte3bt88t7QIAKr7zCd6clzDUFGeixxsAAADc7vnnn1fPnj21bds25eTk6I033tC2bdv066+/auXKlcVqY/369Tp06JAuu+wy1z673a5Vq1bprbfe0pIlS5SVlaWkpKR8vd4SExMVHh5eaJs2m002m+283hsAoHIzVPJeawY93lAEj0axq1atUp8+fVSnTh2ZTCYtXLgw3/F77rlHJpMp33b99dfnO+fYsWMaOHCggoKCFBISoiFDhig1NTXfOZs2bdLVV18tHx8f1atXTy+99FJZvzUAAIAqrVOnTtq4caNycnLUqlUr/fDDD6pVq5bi4uLUrl27YrXRrVs3bd68WRs3bnRt7du318CBA13fe3t7a9myZa5rduzYofj4+GL3qgMAwOl8QjNnjzeCN5zJoz3e0tLS1KZNG913331FLvt+/fXXa968ea7XZ35COXDgQB08eFBLly5Vdna27r33Xg0dOlQffvihJCklJUU9evRQ9+7dNWvWLG3evFn33XefQkJCNHTo0LJ7cwAAAFVMbGysJk+eLH9/f61atUpXXXWV3n333VK3FxgYqEsuuSTfPn9/f1WvXt21f8iQIYqNjVVoaKiCgoI0YsQIRUVF6corrzyv9wIAqHryDhN1GJK5FBkawRvO5NHgrWfPnurZs+dZz7HZbEUOFdi+fbsWL16stWvXqn379pKkadOm6YYbbtArr7yiOnXq6IMPPlBWVpbmzp0rq9Wqli1bauPGjXrttdeKDN4yMzOVmZnpes1qVwAAAOc2bdo0Pf744/L391eXLl108ODBQhdYcKepU6fKbDZrwIAByszMVHR0tGbMmFGm9wQA4EwMNUVRyv0cbz/99JNq1aqlatWqqWvXrnruuedUvXp1SVJcXJxCQkJcoZskde/eXWazWWvWrNFNN92kuLg4de7cWVar1XVOdHS0XnzxRR0/flzVqlUrcE9WuwIAACi5Bg0a6M0331SPHj1kGIbi4uIKfdaSpM6dO5fqHj/99FO+1z4+Ppo+fbqmT59eqvYAAHCHUqzHgCqiXAdv119/vfr376/IyEjt3r1bTz75pHr27Km4uDhZLBYlJCQU+BTVy8tLoaGhriXkExISFBkZme+csLAw17HCHgbHjRun2NhY1+uUlBTVq1fP3W8PAACgUnn55Zf10EMPacqUKTKZTLrpppsKPc9kMslut1/g6gAAOLu8vdUMQyrFGgv0eEMB5Tp4u/32213ft2rVSq1bt1ajRo30008/qVu3bmV2X1a7AgAAKLl+/fqpX79+Sk1NVVBQkHbs2FHmQ00BAHCXvHO8lbQHW2lWQkXV4NFVTUuqYcOGqlGjhnbt2iVJCg8P16FDh/Kdk5OTo2PHjrnmhQsPD1diYmK+c5yvi5o7DgAAACUXGxurtLQ0BQQEaMWKFYqMjFRwcHChGwAAlYlzjre84R0gVbDgbf/+/Tp69Khq164tSYqKilJSUpLWr1/vOmf58uVyOBzq0KGD65xVq1YpOzvbdc7SpUvVtGnTIuccAQAAQMlNmzZNqampkqSuXbvq2LFjHq4IAIDiyzfUtITXMscbiuLRoaapqamu3muStHfvXm3cuFGhoaEKDQ3VxIkTNWDAAIWHh2v37t167LHH1LhxY0VHR0uSmjdvruuvv14PPPCAZs2apezsbA0fPly333676tSpI0m68847NXHiRA0ZMkSPP/64tmzZojfeeENTp071yHsGAACorC7E4goAAJSVfENNS5ikOU9njjecqVTBW9euXfXFF18oJCQk3/6UlBT169dPy5cvL1Y769atU5cuXVyvnQsaDB48WDNnztSmTZv03nvvKSkpSXXq1FGPHj00efLkfPOvffDBBxo+fLi6devmWkr+zTffdB0PDg7WDz/8oJiYGLVr1041atTQ+PHjNXTo0NK8dQAAABSBxRUAABXZefV4Y6gpilCq4O2nn35SVlZWgf0ZGRn63//+V+x2rr32WhlniZGXLFlyzjZCQ0P14YcfnvWc1q1bl6guAAAAlByLKwAAKrLz6fFWWBuAVMLgbdOmTa7vt23bpoSEBNdru92uxYsX66KLLnJfdQAAAKhw8i6u4OXl0ZlNAAAotrw93hwyqST93hxGwTYAqYTBW9u2bWUymWQymdS1a9cCx319fTVt2jS3FQcAAICK6ZprrpHdbtfnn3+u7du3S5JatGihvn37ymKxeLg6AAAKyttbzVHCHm/O8/k3DmcqUfC2d+9eGYahhg0b6rffflPNmjVdx6xWq2rVqsUPGQAAALRr1y716tVL+/fvV9OmTSVJU6ZMUb169fTtt9+qUaNGHq4QAID88uYZJQ/eTAXaAKQSBm8RERGSJIfDUSbFoGJrN3bBBbnPl4EX5DYAAOA8jBw5Ug0bNlRcXJxCQ0MlSUePHtVdd92lkSNH6ttvv/VwhQAA5GcymWQ2m+VwOEocvNlZXAFFKPWkGzt37tSKFSt06NChAkHc+PHjz7swAAAAVFwrV67U6tWrXaGbJFWvXl0vvPCCOnbs6MHKAAAomsVikcPhkN0o3Rxv9HjDmUoVvL377rsaNmyYatSoofDw8HyTB5pMJoI3AACAKs5ms+nEiRMF9qempspqtXqgIgAAzs1isSg7O9vVg6247KeGmrKoEM5Uqp+I5557Tv/5z3/0+OOPu7seAAAAVAK9e/fW0KFDNWfOHF1xxRWSpDVr1uihhx7SjTfe6OHqAAAonDM4K2nwlkOPNxShVIOPjx8/rltuucXdtQAAAKCSePPNN9WoUSNFRUXJx8dHPj4+6tixoxo3bqw33njD0+UBAFAoV/DmMJ3jzPzsBG8oQql6vN1yyy364Ycf9NBDD7m7HgAAAFQCISEh+uqrr7Rr1y5t375dktS8eXM1btzYw5UBAFA0Z/CWU9KhpqeCOqZTwJlKFbw1btxYzzzzjFavXq1WrVrJ29s73/GRI0e6pTgAAABUbI0bNz5r2BYUFKSNGzeqYcOGF7AqAAAK58w3ckrY4y3Hkf96wKlUwds777yjgIAArVy5UitXrsx3zGQyEbwBAACgWAyjhF0KAAAoQ64eb46SXZfN4gooQql+Ivbu3evuOgAAAAAAADzK2WMtmx5vcJNSLa4AAAAAAABQ2biGmpZ0VdNTQR3BG85Uqh5v991331mPz507t1TFAAAAAAAAeApzvMHdShW8HT9+PN/r7OxsbdmyRUlJSeratatbCgMAAEDlZzKV7BcbAADKknNV0uxSzvHGqqY4U6mCty+//LLAPofDoWHDhqlRo0bnXRQAAACqBhZXAACUJ2fO8WZ3SMcyzcrKE8QdyTDLapZCbQ5ZTk3glU2PNxTBbXO8mc1mxcbGaurUqe5qEgAAABXUpEmTlJ6eXmD/yZMnNWnSJNfr77//XhdddNGFLA0AgCI5e6w5h44eyzRrTFw1jVtTzXXOuDXVNCaumo5lno5UmOMNRXHr4gq7d+9WTk6OO5sEAABABTRx4kSlpqYW2J+enq6JEye6Xnfq1Ek2m+1ClgYAQJFcPd6Mkk2FQI83FKVUQ01jY2PzvTYMQwcPHtS3336rwYMHu6UwAAAAVFyGYRQ6f9sff/yh0NBQD1QEAMC5nV5coWTXOXu8MccbzlSq4O3333/P99psNqtmzZp69dVXz7niKQAAACqvatWqyWQyyWQy6eKLL84XvtntdqWmpuqhhx7yYIUAABTt9FDTEq5qemrKUnq84UylCt5WrFjh7joAAABQCbz++usyDEP33XefJk6cqODgYNcxq9WqBg0aKCoqyoMVAgBQtPPt8UbwhjOVKnhzOnz4sHbs2CFJatq0qWrWrOmWogAAAFAxOacdiYyM1FVXXcUvIACACsUVvJVwjrcc5nhDEUoVvKWlpWnEiBFasGCBHI7cny6LxaJBgwZp2rRp8vPzc2uRAAAAqFiuueYaORwO/fXXXzp06JDrmdGpc+fOHqoMAICilbrHm0GPNxSuVKuaxsbGauXKlfrmm2+UlJSkpKQkffXVV1q5cqXGjBnj7hoBAABQwaxevVqNGzdW8+bN1blzZ1177bWurUuXLsVqY+bMmWrdurWCgoIUFBSkqKgoff/9967jGRkZiomJUfXq1RUQEKABAwYoMTGxrN4SAKAK8PLK7Z9U2h5vzusBp1IFb59//rnmzJmjnj17uh6EbrjhBr377rv67LPP3F0jAAAAKpiHHnpI7du315YtW3Ts2DEdP37ctR07dqxYbdStW1cvvPCC1q9fr3Xr1qlr167q27evtm7dKkkaPXq0vvnmG3366adauXKlDhw4oP79+5fl2wIAVHLO4Mxegh5vDkMyZMp3PeBUqp+I9PR0hYWFFdhfq1Ytpaenn3dRAAAAqNh27typzz77TI0bNy51G3369Mn3+j//+Y9mzpyp1atXq27dupozZ44+/PBDde3aVZI0b948NW/eXKtXr9aVV155XvUDAKqm0vR4yzsslaGmOFOperxFRUVpwoQJysjIcO07efKkJk6cyCpVAAAAUIcOHbRr1y63tWe32/XRRx8pLS1NUVFRWr9+vbKzs9W9e3fXOc2aNVP9+vUVFxdXZDuZmZlKSUnJtwEA4FSaHm924/T3FovFzRWhoitVj7fXX39d119/verWras2bdpIkv744w/ZbDb98MMPbi0QAAAAFc+IESM0ZswYJSQkqFWrVgV6ALRu3bpY7WzevFlRUVHKyMhQQECAvvzyS7Vo0UIbN26U1WpVSEhIvvPDwsKUkJBQZHtTpkzRxIkTS/x+AABVgzM4K8naCo48veMI3nCmUgVvrVq10s6dO/XBBx/ozz//lCTdcccdGjhwoHx9fd1aIAAAACqeAQMGSJLuu+8+1z6TySTDMGQymWS324vVTtOmTbVx40YlJyfrs88+0+DBg7Vy5cpS1zVu3DjFxsa6XqekpKhevXqlbg8AULk4g7O8vdjOhR5vOJtSBW9TpkxRWFiYHnjggXz7586dq8OHD+vxxx93S3EAAAComPbu3euWdqxWq2ueuHbt2mnt2rV64403dNtttykrK0tJSUn5er0lJiYqPDy8yPZsNptsNptbagMAVD6u4M1R/DneHKeCN7PZLJOpZKuhovIr1Rxvb7/9tpo1a1Zgf8uWLTVr1qzzLgoAAAAVW0RExFm30nI4HMrMzFS7du3k7e2tZcuWuY7t2LFD8fHxzDkMACg1szk3JilBhzfXUFN6u6EwperxlpCQoNq1axfYX7NmTR08ePC8iwIAAEDFtmDBgrMeHzRo0DnbGDdunHr27Kn69evrxIkT+vDDD/XTTz9pyZIlCg4O1pAhQxQbG6vQ0FAFBQVpxIgRioqKYkVTAECpOYM3RwmSN+e59HZDYUoVvNWrV0+//PKLIiMj8+3/5ZdfVKdOHbcUBgAAgIrrkUceyfc6Oztb6enpslqt8vPzK1bwdujQIQ0aNEgHDx5UcHCwWrdurSVLlui6666TJE2dOlVms1kDBgxQZmamoqOjNWPGjDJ5PwCAqqE0wZvzVOe1QF6lCt4eeOABjRo1StnZ2erataskadmyZXrsscc0ZswYtxYIAACAiuf48eMF9u3cuVPDhg3T2LFji9XGnDlzznrcx8dH06dP1/Tp00tVIwAAZypNrzVn8EaPNxSmVMHb2LFjdfToUT388MPKysqSlPvg8/jjj2vcuHFuLRAAAACVQ5MmTfTCCy/orrvu0p9//unpcgAAKFJJ5ngzSnIyqpxSBW8mk0kvvviinnnmGW3fvl2+vr5q0qQJK0QBAADgrLy8vHTgwAFPlwEAQKFO91oree81eryhMKUK3pwCAgJ0+eWXu6sWACUUP6nVBblP/fGbL8h9AACVx9dff53vtWEYOnjwoN566y117NjRQ1UBAHB2hqv7Wsm7sRl0fUMhzit4AwAAAArTr1+/fK9NJpNq1qyprl276tVXX/VMUQAAnIMzPCvJMglmU/5rgbwI3gAAAOB2DofD0yUAAFBirvCsFKNGCd5QGNa6BQAAQJkyDINfRgAAFYLzg6OShCXOqd340AmFIXgDAABAmViwYIFatWolX19f+fr6qnXr1nr//fc9XRYAAEWy2+2STg8fLQ6Lych3LZAXQ00BAADgdq+99pqeeeYZDR8+3LWYws8//6yHHnpIR44c0ejRoz1cIQAABTl7rVlKFLydvtYwDFY3RT4EbwAAAHC7adOmaebMmRo0aJBr34033qiWLVvq2WefJXgDAJRLOTk5kiSzqfhTJOTtHWe32+XlRdSC0xhqCgAAALc7ePCgrrrqqgL7r7rqKh08eNADFQEAcG7O4M2rBGmJV56QjuGmOBPBGwAAANyucePG+uSTTwrs//jjj9WkSRMPVAQAwLm5grcS9HjLG9JlZ2e7uyRUcPR/BAAAgNtNnDhRt912m1atWuWa4+2XX37RsmXLCg3kAAAoD5zBmaUE3ZTyzgdH8IYz0eMNAAAAbjdgwACtWbNGNWrU0MKFC7Vw4ULVqFFDv/32m2666SZPlwcAQKGysrIkSd7m4vd4M5lO95AjeMOZ6PEGAACAMtGuXTv997//9XQZAAAU2+ngrWTXeZsN5dhNrusBJ3q8AQAAwO2+++47LVmypMD+JUuW6Pvvv/dARQAAnFtperxJktWS+zUzM9PdJaGCI3gDAACA2z3xxBOFruxmGIaeeOIJD1QEAMC5OYM3awmDN2dQR483nIngDQAAAG63c+dOtWjRosD+Zs2aadeuXR6oCACAc8vIyJB0ugdbcTmDOuf1gBPBGwAAANwuODhYe/bsKbB/165d8vf390BFAACcm3OoaEl7vDHUFEUheAMAAIDb9e3bV6NGjdLu3btd+3bt2qUxY8boxhtv9GBlAAAUzRW8WUoWvNno8YYiELwBAADA7V566SX5+/urWbNmioyMVGRkpJo3b67q1avrlVde8XR5AAAUyhmc2Uo61NTCHG8onEeDt1WrVqlPnz6qU6eOTCaTFi5cmO+4YRgaP368ateuLV9fX3Xv3l07d+7Md86xY8c0cOBABQUFKSQkREOGDFFqamq+czZt2qSrr75aPj4+qlevnl566aWyfmsAAABVWnBwsH799Vd9++23evjhhzVmzBgtW7ZMy5cvV0hIiKfLAwCgUKUfakqPNxTOo8FbWlqa2rRpo+nTpxd6/KWXXtKbb76pWbNmac2aNfL391d0dHS+H+SBAwdq69atWrp0qRYtWqRVq1Zp6NChruMpKSnq0aOHIiIitH79er388st69tln9c4775T5+wMAAKjKTCaTevToobFjx2r48OFq3bq1p0sCAOCsXIsrlDB4s5nzXw84eTR469mzp5577jnddNNNBY4ZhqHXX39dTz/9tPr27avWrVtrwYIFOnDggKtn3Pbt27V48WLNnj1bHTp0UKdOnTRt2jR99NFHOnDggCTpgw8+UFZWlubOnauWLVvq9ttv18iRI/Xaa69dyLcKAABQpbz44ov6+OOPXa9vvfVWVa9eXRdddJH++OMPD1YGAEDRSjvHm/epoI7FFXCmcjvH2969e5WQkKDu3bu79gUHB6tDhw6Ki4uTJMXFxSkkJETt27d3ndO9e3eZzWatWbPGdU7nzp1ltVpd50RHR2vHjh06fvx4offOzMxUSkpKvg0AAADFN2vWLNWrV0+StHTpUi1dulTff/+9evbsqbFjx3q4OgAAClfaOd5sFoI3FM7L0wUUJSEhQZIUFhaWb39YWJjrWEJCgmrVqpXvuJeXl0JDQ/OdExkZWaAN57Fq1aoVuPeUKVM0ceJE97wRAACAKighIcEVvC1atEi33nqrevTooQYNGqhDhw4erg4AgMKVfo633K8nT550d0mo4MptjzdPGjdunJKTk13bvn37PF0SAABAhVKtWjXXM9TixYtdoxgMw5DdbvdkaQAAFMowjFIPNXUGdaxqijOV2x5v4eHhkqTExETVrl3btT8xMVFt27Z1nXPo0KF81+Xk5OjYsWOu68PDw5WYmJjvHOdr5zlnstlsstlsbnkfqHrajV1wwe71ZeAFuxUAACXSv39/3XnnnWrSpImOHj2qnj17SpJ+//13NW7c2MPVAQBQUN7QzFrCbkrM8YailNvgLTIyUuHh4Vq2bJkraEtJSdGaNWs0bNgwSVJUVJSSkpK0fv16tWvXTpK0fPlyORwO1xCGqKgoPfXUU8rOzpa3t7ek3HlGmjZtWugw09K4UEELIQsAAKgopk6dqgYNGmjfvn166aWXFBAQIEk6ePCgHn74YQ9XBwBAQXmDN+9SDjUleMOZPBq8paamateuXa7Xe/fu1caNGxUaGqr69etr1KhReu6559SkSRNFRkbqmWeeUZ06ddSvXz9JUvPmzXX99dfrgQce0KxZs5Sdna3hw4fr9ttvV506dSRJd955pyZOnKghQ4bo8ccf15YtW/TGG29o6tSpnnjLAAAAVYK3t7ceffTRAvtHjx7tgWoAADg3Z/BmkiGvUvZ4y87OdndZqOA8GrytW7dOXbp0cb2OjY2VJA0ePFjz58/XY489prS0NA0dOlRJSUnq1KmTFi9eLB8fH9c1H3zwgYYPH65u3brJbDZrwIABevPNN13Hg4OD9cMPPygmJkbt2rVTjRo1NH78eA0dOvTCvVEAAIAq4Ouvv1bPnj3l7e2tr7/++qzn3njjjReoKgAAiscZvHmXYjZ8b+Z4QxE8Grxde+21Moyiu2+aTCZNmjRJkyZNKvKc0NBQffjhh2e9T+vWrfW///2v1HUCAADg3Pr16+dadd45QqEwJpOpWAssTJkyRV988YX+/PNP+fr66qqrrtKLL76opk2bus7JyMjQmDFj9NFHHykzM1PR0dGaMWOGaxV7AACK63TwVrJhprnX5G8DcGJVUwAAALiFw+FQrVq1XN8XtRV3VdOVK1cqJiZGq1ev1tKlS5Wdna0ePXooLS3Ndc7o0aP1zTff6NNPP9XKlSt14MAB9e/fv0zeHwCgcnNHjzeGmuJM5XZxBQAAAFRMDodD8+fP1xdffKG///5bJpNJDRs21IABA3T33XfLZDIVq53Fixfnez1//nzVqlVL69evV+fOnZWcnKw5c+boww8/VNeuXSVJ8+bNU/PmzbV69WpdeeWVbn9vAIDKyxm8eZWix5uXKX8bgBM93gAAAOA2hmHoxhtv1P33369///1XrVq1UsuWLfX333/rnnvu0U033VTqtpOTkyXlTjUiSevXr1d2dra6d+/uOqdZs2aqX7++4uLiCm0jMzNTKSkp+TYAAKTTvdWY4w3uRI83AAAAuM38+fO1atUqLVu2LN8iWpK0fPly9evXTwsWLNCgQYNK1K7D4dCoUaPUsWNHXXLJJZKkhIQEWa1WhYSE5Ds3LCxMCQkJhbYzZcoUTZw4sUT3BgBUDaeDN+Z4g/vQ4w0AAABu83//93968sknC4RuktS1a1c98cQT+uCDD0rcbkxMjLZs2aKPPvrovOobN26ckpOTXdu+ffvOqz0AQOWRmZkpqZTBm4UebygcwRsAAADcZtOmTbr++uuLPN6zZ0/98ccfJWpz+PDhWrRokVasWKG6deu69oeHhysrK0tJSUn5zk9MTFR4eHihbdlsNgUFBeXbAACQTodmVoaawo0I3gAAAOA2x44dU1hYWJHHw8LCdPz48WK1ZRiGhg8fri+//FLLly9XZGRkvuPt2rWTt7e3li1b5tq3Y8cOxcfHKyoqqnRvAABQZTl7vFktJe/x5gzrnG0ATszxBgAAALex2+3y8ir6EdNisSgnJ6dYbcXExOjDDz/UV199pcDAQNe8bcHBwfL19VVwcLCGDBmi2NhYhYaGKigoSCNGjFBUVBQrmgIASiw9PV2S5FOK4M3XK/eanJwcZWVlyWq1urU2VFwEbwAAAHAbwzB0zz33yGazFXq8JD0BZs6cKUm69tpr8+2fN2+e7rnnHknS1KlTZTabNWDAAGVmZio6OlozZswoVe0AgKotLS1NUumCt7zXpKenE7zBheANAAAAbjN48OBznlPcFU0N49y/+Pj4+Gj69OmaPn16sdoEAKAozuDN2XutJMwmyWYxlGk3KTU1tcCK26i6CN4AAADgNvPmzfN0CQAAlIpzsZ4gq6NU1wd5O3TYblFSUlK+xYBQtbG4AgAAAAAAqPKOHj0qSQq2lrzHW+51uYHdsWPH3FYTKj6CNwAAAAAAUOWdDt5K1+Mt2JZ73ZEjR9xWEyo+gjcAAAAAAFCl2e12/fvvv5KkMF97qdqo5ZsbvDnbASSCNwAAAAAAUMUlJCQoKytL3mZDNX1L1+Otjl9uYPf333+7sTJUdARvAAAAAACgSvvnn38kSeF+dplNpWvjIn+CNxRE8AYAAAAAAKq0TZs2SZIaBOSUuo16ATmymAwdPXpUBw4ccFdpqOAI3gAAAAAAQJW2fv16SVLL0OxSt2GzSI2DcvK1BxC8AQAAAACAKispKUk7d+6UdH7BW97r165de951oXIgeAMAAAAAAFXWjz/+KEmKCMhRsNU4r7ZaV8+SJK1evVopKSnnXRsqPoI3AAAAAABQJRmGoa+//lqSdE2djPNuLzLQrnoBOcrKytKSJUvOuz1UfARvAAAAAACgStq4caPi4+NlsxjqGJ513u2ZTFK3i3IDvK+//loOh+O820TFRvAGAAAAAACqHMMwNHfuXEnSVWGZ8vU6v2GmTlFhmfKxOLRv3z7XMFZUXQRvAAAAAACgyvnxxx+1efNmWc2G+jY46bZ2fb2kPhG57c2aNUtpaWluaxsVD8EbAAAAAACoUtLT0zVr1ixJUt8G6Qr1ce+Q0OvrZyjM165jx47pvffec2vbqFgI3gAAAAAAQJVhGIZee+01HT16VGG+dl1f//wXVTiTt1m66+Lcnm6fffaZNmzY4PZ7oGIgeAMAAAAAAFXGZ599ph9//FFmk6EhzVLlXUbJSJvq2eoYniGHw6GJEycqISGhbG6Eco3gDQAAAAAAVAkbNmzQzJkzJUl3Nk5Xs2o5ZXq/e5umqUFgjpKTk/XMM88oI8P9vetQvhG8AQAAAACASm/Hjh2aMGGCHA6HOoZn6Lq6ZR+CWS3SyFYnFOjt0M6dOzVx4kRlZWWV+X1RfhC8AQAAAACASm379u2KjY3ViRMn1CQ4W/c2TZPJdGHuXcPHoRGXnJC32VBcXJyeeeYZZWZmXpibw+MI3gAAAAAAQKW1detWPfroGKWlpeni4Gw92iZFVsuFraFZtRyNaZ0iq9nQmjVr9PTTTxO+VREEbwAAAAAAoFKKi4vTo48+qrS0dDUPyQ3dfL08U0uL0Bw92iZFNouhtWvXauzYsUpKSvJMMbhgCN4AAAAAAEClYhiGPvzwQz355JM6efKkWlbLUmybFPl4KHRzalYtR2PbpMjXYmjTpk168MEHtWvXLs8WhTJF8AYAAAAAACqNjIwMPffcc3rnnXdkGIa6XpShMW1OyHaBh5cW5eKQHI1vn6wwX7sSExM1fHiMVq5c6emyUEYI3gAAAAAAQKXw77//auTIkVq2bJksJkP3NE3VPU3T5FXO0o+L/O16tn2yLgnNUkZGpiZMmKB3331XOTk5ni4NblbOfvQAAAAAAABKxjAMLVmyRPfff7/++usvBXo79HjbFHW9qPwuYODvbWhM6xO6vt5JSdIHH3ygESNG6MCBAx6uDO5E8AYAAAAAACqs1NRUPffcc5oyZYpOnjyppiHZmnR5sppVK/+9xyxm6c4m6Rp+yQn5eTm0fft23X///frhhx88XRrcxMPTCgIAAAAAAJTOpk2b9PzzzyshIUFmk6H+kSfVO+KkzCZPV1YyV9TKUsOgHL29NUA7ktP1/PPP67ffftMjjzyiwMBAT5eH80CPNwAAAAAAUKFkZGRo+vTpeuSRR5SQkKCaPnY9fVmKbmxQ8UI3pxo+Do27LEX9I9NlNhn68ccfde+992r16tWeLg3ngR5vAAAAACoFwzCUkZGhEydO6NixYzp+/LhrS05Olt1ud+v9TCaTAgMDVa1aNVWrVk2hoaGqVq2agoOD5efnJ5Opgv72D5RzW7Zs0QsvvKD9+/dLkq6unaG7mqTL18vwcGXnz2yS+kWeVMvQbL2zLUCJR47oiSeeUM+ePRUTE6OAgABPl4gSIngDUCbajV1wwe71ZeDLF+Q+9cdvviD3AQCgKjIMQ1lZWTp58qRrS01NVXp6utLS0pSWlqb09PR8+06cOKGUlBSlpqYqJSVFJ06cUHZ2tqffiiTJbDYrKChIgYGBCgwMVFBQkAICAhQQECB/f3/5+fnJ398/3+bn5yc/Pz/5+vrKz89PNpuN8A7IIzMzU3PnztUnn3wiwzBUzerQfc1S1aZG+fjv3p2aBOfouSuS9NkeP/2wz0fff/+91q1bp7Fjx+qKK67wdHkoAYI3AAAAlFurVq3Syy+/rPXr1+vgwYP68ssv1a9fP9dxwzA0YcIEvfvuu0pKSlLHjh01c+ZMNWnSxHNFV3J5A7KMjAzX5gzL8r4+c/+Z+9LT0/MFbe7qkWaYzDK8fGR4+57evHxkuDvEMgyZcjJlzjkpU/ZJmbIzcr8adjkcDiUlJSkpKanUzZtMJlcI5+vrW2Dz8fEp9Kvz+8KO+/j4yGazyWxm1iFULFu2bNGLL76offv2SZI6hWdoYJN0+XtX/F5uRbFZpIFN0tW+ZpZmbw9Q4uHDeuyxx3TDDTfo4YcfpvdbBUHwBgAAgHIrLS1Nbdq00X333af+/fsXOP7SSy/pzTff1HvvvafIyEg988wzio6O1rZt2+Tj4+OBisuXnJwcV8Dl3PIGXWdu5wrKMjIylJmZKYfDUaZ1G2YvGWYvyWKVYfGWcepr3teyeMvwssmw2GR4WfN8b5PMXlJpQjbDkBynVkEsbRuS5MiRKSczd7NnyZSTKeVkymTPlMmenbvPni2d+mqyZ0l59ptO1WAYhuvvzd2cIVxhoV1hIV7e75098/L2zvPz85O3t7fb6wQyMjI0d+5cffrppzIMQyFWh+5tlqpLK2Evt6I0Dcnt/fbpbj8t3e+j7777Tr/99pseffRRXXnllZ4uD+dA8AYAAIByq2fPnurZs2ehxwzD0Ouvv66nn35affv2lSQtWLBAYWFhWrhwoW6//fYC12RmZiozM9P1OiUlpWwKd6Ps7GxXz6nk5GQdP35cJ06cUGpqapGbM2zL+17LgmGySGYvGZZTQZnZW4bl1NdT+53fy+Ilw+x96qtXbpBm9s4N0szeuUGaxetU4OWh3liOHAVueF+SdOKyuyVLKYMks5cMq5cMq3/prj8VAJoc2acCuTzfO3JfK+9XR4506py838uRc+oc57HTPQqdPRPdydvbW76+vq6hs86htWdugYGBCgkJUXBwsEJCQhQSEkJQjkJt2rRJL7300um53MIzdGcl7+VWFJtFuuvidF1e61Tvt1Nzv11//fWKiYlh5dNyjOANAAAAFdLevXuVkJCg7t27u/YFBwerQ4cOiouLKzR4mzJliiZOnHghyzwnwzB09OhR7d69W3v27FFCQoISExOVkJCgw4cPKy0t7fzvYTLnC7pOB15nBmLehYZl+c5zBWoeDMgqO5PpVBDpLXlLbosYXIFezqkQL8f12mR3BnXZBc45vS87N9Q7FQDmhoLZMhm5gV52drays7NLFWj7+PioRo0aCgsLU1hYmMLDw9WgQQM1atRItWvXZmhsFZOVlaU5c+acnsvNZtd9TdMq5VxuJeXs/eac+23x4sVat26dnnjiCbVv397T5aEQBG8AAACokBISEiRJYWFh+faHhYW5jp1p3Lhxio2Ndb1OSUlRvXr1yq7IIjgcDm3YsEE//fSTfvvtNx06dOis5xsynZqzzCf3q1fu8EpZbLnDML2sp4ZjWvMMxzwdsslsuUDvDOVavkDP142BnuN0COcK5LJkysk6NXw2S3J9f2oIbk5G7px4ORkyGQ5lZGRo//79rp5NeQUGBqp9+/bq3LmzOnbsKKvV6q7KUQ7t2bNHzz33nPbs2SMpd8XSOxtXzV5uRXHO/XZFrSy9uz1ACUeO6NFHH9WAAQM0dOhQ2Ww2T5eIPAjeAAAAUGXYbDaP/kKSnZ2txYsX66OPPtK///7r2m/IJIdPsBx+1eSwBcmwBchh9ZdhDZDD21eyWEs/3xhQ1kxmySt3fr0SRyOGkTtcNvukzFnpMmWlypyZKnNWqszpx2Q+maQTJ05oxYoVWrFihUJCQnTTTTdpwIABTCxfyTgcDn366aeaPftdZWfnKNDboSHNUnVZTXq5FaVJcI4mX56kj3b5a9m/Pvr888+1bt06PfXUU7r44os9XR5OIXgDAABAhRQeHi5JSkxMVO3atV37ExMT1bZtWw9VVbSjR4/qscce0+7duyVJhsWq7NCGyqlWX/aAcMnCozmqIJPpVC9Nq+w+wQWPOxwypx+RV1K8vI/sVlJSkubNm6evvvpKU6ZMUdOmTS98zXC748ePa/LkydqwYYMkqW31LA1pnqpgK73czsVmkQY3TVPbGrlzv/3zzz8aNmyYHnroId18880y8aGNxzFQHgAAABVSZGSkwsPDtWzZMte+lJQUrVmzRlFRUR6srHCfffaZK3TLrN1aqW1uU2aDq2QPrkvoBhTFbJYjoJay6rZXWptblNGgkyTp2LFjmj17toeLgzts375dQ4cO1YYNG2Q1G7q3aapGtz5RLkO33r17a8GCBerdu7dMJpOSMstPqNWmeraevyJJ7Wtmym63a/r06Zo8ebJOnjzp6dKqPII3AAAAlFupqanauHGjNm7cKCl3QYWNGzcqPj5eJpNJo0aN0nPPPaevv/5amzdv1qBBg1SnTh3169fPo3UXpkmTJq7vrQc3y3f3Cnkd2SXZczxYFSRJhkOmzBMyZaa6dpkyU2XKPJE7fxk8y3DIkrRPPnv+J1v8atfuxo0be7AouMOiRYs0cuQIHT58WOF+dk28PFldLsostyPrb731VtWvX1+33nqrDMPQkYzyNX9moNXQiEtSdVeTNFlMhpYvX66HH3640LkTceHw0RoAAADKrXXr1qlLly6u186FEQYPHqz58+frscceU1pamoYOHaqkpCR16tRJixcvlo+Pj6dKLlLXrl3l4+Oj999/X9u3b5dX8n55Je+XEb9aWbVbKyv8ElYJ9RBTVpoCNn2ab1/A1i8lSamtb5FhC/REWZDkdXSPbPvXyZx1OhS96KKLdOutt6p3794erAznIycnR6+//roWLVokSWpXM1NDm6fJ16v89XLL65NPPtGtt96qTz75RCaTSTV87J4uqQCTSepRL0MRgTl6a0ug9u7dqwcffFATJkzQFVdc4enyqiSCNwAAAJRb1157rQyj6F/ETCaTJk2apEmTJl3Aqkrvqquu0lVXXaX4+HgtW7ZMS5YsUUJCQm6wkJGsjMirPV0iUG54J26VT/waSVJQUJCuu+46de3aVS1atGDeqgosOztbkydP1qpVq2SSoZsbpqt3REa57eWW17fffqtFixbJZDLJMAyF2MpvUNg0JHfhhWlbArUzOU1PPvmkJkyYoKuv5t+ZC42P1AAAAIALrH79+rr33nv1wQcf6NFHH5XZbJb3kZ3yTtji6dKAcsGSvF+2fWslSbfffrs+/fRTjRgxQi1btiR0q8AyMzM1fvx4rVq1Sl4mQyNbnVCfBhUjdJPk+iDobB8IlSchNkPjLk3RFbUylZOTowkTJmj58uWeLqvKIXgDAAAAPMRisah3794aMmSIJMln32+yxa+RKsgvdUBZ8D78l3z/WiqT4VCXLl304IMPymazebosnKfMzEw99dRTiouLk7fZ0KjWJ9SuZrany6r0vMzSsBap6hieKYfDoeeee06LFy/2dFlVCsEbAAAA4GF33nmnHnjgAUmSNXGrLCcSPFwR4Bmm7AzZ/vlFJhnq0aOHxo0bRw+3SuKNN97QunXrZLMYGtMmRa2rE7pdKBaz9EDzVHWpkyGHw6GXXnpJW7bQw/pCKdfB27PPPiuTyZRva9asmet4RkaGYmJiVL16dQUEBGjAgAFKTEzM10Z8fLx69eolPz8/1apVS2PHjlVODitHAQAAoPw4efKkjh49enoHK2miynJIpzp8pqWlKSkpyaPVwD2WLl2q7777TiYZeqTVCbWoxu/kF5rZJN3TNE1X1srt+TZp0iQlJyd7uqwqoVwHb5LUsmVLHTx40LX9/PPPrmOjR4/WN998o08//VQrV67UgQMH1L9/f9dxu92uXr16KSsrS7/++qvee+89zZ8/X+PHj/fEWwEAAAAKSEpK0rBhw/TFF19IkrJqtZA9qI6HqwI8w/D2U2ZElAyTWb/88ovuv/9+7dy509Nl4TzEx8fr1VdflST1bXBSl4TS081TTCbp3mapCvO169ChQ3rhhRcqzHx1FVm5D968vLwUHh7u2mrUqCFJSk5O1pw5c/Taa6+pa9euateunebNm6dff/1Vq1evliT98MMP2rZtm/773/+qbdu26tmzpyZPnqzp06crKyvLk28LAAAAkCS9//77+ueff+Tw9lP6xdHKjLhSFWamcaAMZNdqpvQWfWX3q66UlBS99dZbni4J5+HVV19VRkaGmodkq1/kSU+XU+X5eknDLzkhb7OhuLg4Flu4AMp98LZz507VqVNHDRs21MCBAxUfHy9JWr9+vbKzs9W9e3fXuc2aNVP9+vUVFxcnSYqLi1OrVq0UFhbmOic6OlopKSnaunVrkffMzMxUSkpKvg0AAABwt99//11LliyRJGVGRMkefJGHKwLKB4dfNZ1s3FWS9Mcff+j777+Xw8EQ7Ipm9+7d+uOPP2Q2GXqgRarMfKZQLkQE2tU7IjcEdfa2Rtkp18Fbhw4dNH/+fC1evFgzZ87U3r17dfXVV+vEiRNKSEiQ1WpVSEhIvmvCwsKUkJA7GW1CQkK+0M153HmsKFOmTFFwcLBrq1evnnvfGAAAAKq0PXv26LHHHtPo0aOVmpoqu1915QTX9XRZQLli2AKVXb2xJOnFF1/UAw88oHXr1nm4KpTEwoULJUntamSphg/BaXnSpU6GLCZDW7duZTh3GSvXwVvPnj11yy23qHXr1oqOjtZ3332npKQkffLJJ2V633Hjxik5Odm17du3r0zvBwAAgKpj165devDBB/Xbb7/JkElZNZspvVlPyWzxdGmQ1Lt3by1YsEC9e/fOXeAtK93TJVVpGZGdlHlROxlmL+3evVuPPvqoli1b5umyUAwZGRlaunSpJKl73QwPV4MzhdgMXV4zdwquRYsWebiayq1cB29nCgkJ0cUXX6xdu3YpPDxcWVlZBVa5SUxMVHh4uCQpPDy8wCqnztfOcwpjs9kUFBSUbwMAAADcYe3atcrOzp1cPOuiS3PndLNYPVwVnG699VbVr19ft956qwzDkDkr1dMlVW0ms7LqtFFGZGfXrl9++cWDBaG4Dh8+rIyMDPlYDDULYRXT8qhtjdzgzTmlF8pGhQreUlNTtXv3btWuXVvt2rWTt7d3vk87duzYofj4eEVFRUmSoqKitHnzZh06dMh1ztKlSxUUFKQWLVpc8PoBAACAHj16qE6d3FVLbf9ukN/Wr2ROP+bhquD0ySefKD4+Xp988olMJpMc1gBPl1SlmbLT5fvXEvnuzp0A3t/fX7fccouHq0JxpKWlSZL8vBysF1NO+Xvnrmjq/LtC2fDydAFn8+ijj6pPnz6KiIjQgQMHNGHCBFksFt1xxx0KDg7WkCFDFBsbq9DQUAUFBWnEiBGKiorSlVdeKSn3oaZFixa6++679dJLLykhIUFPP/20YmJiZLPZPPzuAAAAUBVVr15ds2fP1ueff66PP/5YqanH5btjsdJa3yJZvD1dXpX37bffatGiRTKZTDIMQ4bVz9MlVWk+u5bLK/WQvLy81Lt3b919992qXr26p8tCMaSm5vYW9fMyPFwJiuL8u3H+XaFslOvgbf/+/brjjjt09OhR1axZU506ddLq1atVs2ZNSdLUqVNlNps1YMAAZWZmKjo6WjNmzHBdb7FYtGjRIg0bNkxRUVHy9/fX4MGDNWnSJE+9JQAAAEB+fn66++671adPHw0aNEgpKSmypB+VPbDo6VBwYRiGke8rPMieLa/U3NFLM2fOVJMmTTxcEErCx8dHkpSUZZbDECualkPHM3MHQVqtTHdQlsp18PbRRx+d9biPj4+mT5+u6dOnF3lORESEvvvuO3eXBgAAAJy3kJAQNWzYUBs3bpTX0T0Eb0Ae3kd3S5ICAgLUuHFjD1eDkmrWrJkCAwN14sQJ7Uz2UlPmeSt3NhzODdw6dOjg4Uoqtwo1xxsAAABQ2dxxxx2SJOvhP2VJOeDhaoDywZSVLlv8Gkm5/42YmCSswvHy8nJNA+UMeFB+5DikjUdzpzfo2LGjh6up3AjeAAAAAA8KCQmR2Zz7WG7OSPZwNUD5YMpKlcmwS5Lq1avn4WpQWldffbUkacUBHx3JIH4oTxbv81F6jlnVqlVTy5YtPV1OpcZPPgAAAOAhcXFxiomJkcPhkMMWqOzQSE+XBJQLDv8aygmsLUkaP368/u///s/DFaE0OnbsqEsuuUQZdpPmbPdXRZw6MdTm0KtRxzWlw3HXvikdjuvVqOMKtTk8WFnp7U+16Is9uQvHDB06VBaLxcMVVW4EbwAAAICH/N///Z9ycnJk9wlWWos+kpePp0sCygeTWScv7qHsag0kSQsWLPBsPSgVi8Wixx9/XDabTVuPW7X8X5unSyoxi1mq6etQDZ/TIVsNH4dq+jpkqYCJSo5Demd7gHIMk6688kpdf/31ni6p0quAPyYAAABA5eAcQmfOSpM5M9XD1QDli8meJUv6EUkMN63I6tWrpwceeECS9N+d/lp/2NvDFVVdOQ5p5tYA/X3CS4GBgXr00UeZP/ECIHgDAAAAPCQmJkYXX3yxTI4cWQ9u8nQ5QLniffgvmTNTFRwcrEmTJnm6HJyH/v37q0uXLrIbJr21JVBrD7HYwoWW45Cmbw3Q2sM2eXl56cknn1SNGjU8XVaVQPAGAAAAeIjFYtHJkyclSYY3w0yBvJz/TWRlZXm4Epwvs9msp556St27d5fdMGn61gCtSSR8u1CyHdK0LYFaf9gmb29vPffcc4qKivJ0WVUGwRsAAADgIZs3b9a+ffskSVl1LvVwNUD5kl29sRzWAJ08eVIrVqzwdDk4T15eXho3bpyio6PlMEyasTVAC/f6ylEBF1yoSI5mmPXC70H6/YhVVqtVzz//vK688kpPl1WlELwBAAAAHnLRRRfJbM59JPc+stPD1QDli+XEQZmzcuc+jIiI8HA1cAeLxaLHHntMffv2lSGTvtjrp1c2Bioli3nGysIfR7z1zNpg7Uz2lr+/v6ZMmaLLL7/c02VVOQRvAAAAgIfUrl1bd955pyTJO3Grh6sByhdr4jZJ0pVXXsmwuErEYrFo9OjReuKJJ2Sz2bTluFXP/BaiP497ebq0SsPukD7Z7adXNwUpNdusiy++WO+++67atWvn6dKqJII3AAAAwEOys7MVFxcnSXLYgjxcTdVlWP2V2voWpba8ybUvteVNSm19iwyrvwcrq9qc/01s375dhw8f9nA1cLfrr79es2bNUkREhI5nmfXCxiB9uttXWXZPV1ax7U+16LkNQVr0j68k6aabbtJbb72lOnXqeLiyqovgDQAAAPCQbdu2affu3ZKkrIuY481jTGYZtkAZtgDXLsMWIMMWKJn4lclTssIvkSQlJydr1apVHq4GZSEyMlIzZ85Ujx495DBM+uYfPz1F77dSyXZIX+zx1TNrg7U7xVt+fn569tln9cgjj8hqZSELT+KnGQAAAPCQevXqKSAgQKmpqfL9a4lyQiKUXauZ7IG1JRNzHqFqMqcflffhHfI+siv3tdmsZs2aebgqlBU/Pz89+eSTuvrqq/X6668r8ehRPf97sLrUydCtjdLl783qC+eyM9lLc//0179puRFPVFSURo8erVq1anm4MkgEbwAAAIDHhIaGavr06Zo2bZrWrVsn7+N/y/v433JY/ZUTGqmc4Lqy+9eULN6eLhUoOw67zOnH5JXyr7yO/S3LyWOuQ40aNdLDDz+sSy65xIMF4kK4+uqr1bZtW73zzjv65ptvtOKAj34/YtWdTdLUoVYWn0UUIjXbpM/3+Gn5vzYZMqlatWoaOXKkrr32Wpn4Ays3CN4AAAAAD4qIiNArr7yiXbt26euvv9ayZcuUlpYma8IWWRO2yJBk+ATJ7ltNhjVQDluAHLZAGd5+Mrx9ZHj5SmaGQ6IcMxwy5WTKlH1SpuyTMmelypR5QubMVJkzkmQ+mSST4XCd7uXlpU6dOqlPnz667LLLCBCqkMDAQI0ZM0bdu3fXK6+8on379mnG1kAt25+tuy5OU0QgE8BJksOQVvxr02d7/JSWk/v//549e2rYsGEKCmK+0PKG4A0AAAAoBxo3bqzY2FjFxMQoLi5OcXFxWr9+vY4cOSJTRorMGSlFXmtYbHJ4+8jw8pHhZZMsVhleNhn5vlpz91u8ZZi9Xd/LbLmA7xIVluGQ7Nky2bNlsmfJZM+WTn012TNzg7WcLMme+9W1L/tk7ledfbhgYGCg2rZtqw4dOqhz586EB1VcmzZtNHv2bH388cf64IMPtCNZGr82WF0uytSAhukKrMLDT/887qX/7vRXfGpunBMZGamRI0fq0kuZJ7S8IngDAABAhTd9+nS9/PLLSkhIUJs2bTRt2jRdccUVni6rVGw2m6699lpde+21kqTjx49r586d2rdvnxISElzb0aNHlZSUJIfDIZM9UxZ7pqTkEt/PMFlyA7hTgVzu916nwjnnPq/8x81e+b7mnnvqGrMX89N5mmFIDrtMjuzcsMyR4/qauy/3a254lvf4qX2OnFOBWrZ0ap/JkXNeJZlMJgUFBalatWoKDw93bRdddJGaNGmisLAwerYhH5vNpkGDBun666/XzJkztWLFCi3/10drEq3q3/CkutbJkKUKdfY9kmHWx7v8tOaQTVJuWH3fffepT58+8vIi2inP+NsBgEoiflKrC3Kf+uM3X5D7AEBxffzxx4qNjdWsWbPUoUMHvf7664qOjtaOHTsqxcTS1apV0xVXXFFokOhwOJSSkqLjx4/r+PHjSklJ0YkTJ1xbamqq6/u0tDSlpaUpPT1daWlpysjIkCSZDLtMOXYpJ8NtNRtmr3xhXN7gLverM7jLDfIKfJ8vBPS+cCuLmr104rK7Xd9fMIZxOuByhWHZ+XuV5QnMcnuenfq+QEiWk/u1jEq1Wq3y9/eXn5+f62tgYGCBLSAgwBW0VatWTcHBwYQDKJVatWppwoQJ6tu3r6ZNm6bdu3fr/b/8tfxfm+5onK7W1bMvSB1Ws/TuNUdd318omXZp0T+++i7eV9kOk0wmk/r06aP77rtPISEhF64QlBr/5wMAAECF9tprr+mBBx7QvffeK0maNWuWvv32W82dO1dPPPGEh6srW2azWSEhIQoJCVFkZGSJrs3JydHJkyddQVxaWppOnjzp2uf8Pu/rM/dnZGTke20YucO/cntW5Ujn10nKJTeos8qwWCUvq+t71+ZlO7X5SHm+N7xsJet9ZzKVfiELw8gNyXIyZcrJOPU18/Rre1bulpN1Kkw7vcledkGZj4+PfH19C2w+Pj7y8/NzvXZ+X9hXZ8Dm5+cnb28W+oBntG3bVm+//bYWLVqkuXPn6t+UFL3yR5DaVM/SHY3TVMffce5GzoPJJNku4Mh8hyHFJVj1yW5/Hc/KTfratGmj4cOHq0mTJheuEJw3gjcAAABUWFlZWVq/fr3GjRvn2mc2m9W9e3fFxcUVOD8zM1OZmZmu1ykpRc+bVtl5eXm5eie5g2EYyszMdAV0Z4ZyZ4Z2ZwZ6hW3Z2bk9WVxBXnZ6yWoymWR4+Z5aiMI3dzEKt0Vchkw5WTJnp7sWDTAZ5zfxu8VicQVcRW2FhWhFbT4+PjKz8AYqES8vL/Xr10/dunXTggUL9MUXX+iPo1ZtOeatbhdl6KbIk/KvBPO/7Uz20gc7/bQnJTforl27th566CF17tyZIdkVEMEbAAAAKqwjR47IbrcrLCws3/6wsDD9+eefBc6fMmWKJk6ceKHKq1JMJpN8fHzk4+OjatWquaXN7OxsV2+8tLQ0paamur7m3ZKTk3XixAmlpKQoOTlZKSkpSk1NlckwZMpOL3Fgdz58fX0VFBSk4OBgBQUFuTbn8Et/f38FBAS4Nn9/f9dmtVr5pRoohsDAQMXExOjGG2/UjBkzFBcXpx/2+you0aZbGqWrc+1MmSvgf0pJmSZ9vNtPvyT4SMr9/8ndd9+tAQMGyGazebg6lBbBGwAAAKqMcePGKTY21vU6JSVF9erV82BFOBtvb2/XUNqSysnJ0fHjx3X06FEdO3ZMR48edXsPRz8/P1WvXl3Vq1dXaGioQkND+eUYuIDq1aunKVOmaN26dZo2bZr++ecfzf0zQD/966O7m6apUZCbxruXsRyH9ON+H32x108Z9tzE8IYbbtCQIUNUvXp1D1eH80XwBgAAgAqrRo0aslgsSkxMzLc/MTFR4eHhBc632WwEI1WEl5eXatasqZo1a3q6FABlrH379pozZ46+/PJLzZs3T3tOpGviumBdUztDtzRKV5C1/A4/3XbMS+/v9Ne/abnxTLNmzfTII4+oefPmHq4M7sKAfwAAAFRYVqtV7dq107Jly1z7HA6Hli1bpqioKA9WBgC4kLy8vHTLLbfo/fffV3R0tCRp5UEfPb46RD8ftMooZ9lbarZJb2/z1wsbg/VvmpeCg4M1duxYzZgxg9CtkqHHGwAAACq02NhYDR48WO3bt9cVV1yh119/XWlpaa5VTgEAVUf16tU1btw49enTR6+//rp27dqld7YHavWhLN3TNE01fMp29dPiWHvIqgV/+Ss5yyyTyaS+fftqyJAhblvsBuULwRsAAAAqtNtuu02HDx/W+PHjlZCQoLZt22rx4sUFFlwAAFQdl1xyiWbNmqWPP/5Y8+fP16aj0pNrvHVbozR1ucgziy8kZZr0/l/+Wns4d8qDiIgIPfbYY2rZsuWFLwYXDMEbAJShdmMXXLB7fckHZACqsOHDh2v48OGeLgMAUI54eXlp4MCB6tSpk15++WVt2bJF7/0VoLWHbXqoxQmF2C7c+NMNh7317vYApeWYZTabdeedd2rQoEGyWq0XrAZ4BnO8AQAAAACASisiIkJvvPGGRowYIR8fH2077q1n1oZo+/Gy74uU45D+b5efXt8cpLQcs5o0aaK3335b999/P6FbFUHwBgAAAAAAKjWLxaIBAwbo7bffVmRkpJKzzHrh9yB987ePHGXU8e1YZu49vo/3lSTdfPPNmjFjhpo0aVI2N0S5RPAGAAAAAACqhIiICM2cOVPR0dEyZNKne/w1dVOgMnLce58dSV565rdg/ZXsLX9/P02cOFHDhw+Xt7e3e2+Eco/gDQAAAAAAVBk+Pj564oknNHbsWFmtVv1x1KoXNwYpNds9Ky78ccRbL20M0olssxo1aqS3335H11xzjVvaRsVD8AYAAAAAAKoUk8mkXr166Y033lBQUKB2p3hryoYgJWWeX/i2JtGq1zcHKtthUlRUlGbMmKG6deu6qWpURARvAAAAAACgSmrevLlef/0NhYaGal+al/6zIVhHMkoXlaw8YNOMrQGyGyZ169ZNkydPls1mc3PFqGgI3gAAAAAAQJXVsGFDTZs2TeHh4Uo8adFrfwTqZAnnfPvjqLfm/ukvQyb16dNHTz75pLy8yn7VVJR/BG8AAAAAAKBKu+iii/Tmm28qNDRU+9O89M72gGKvdnowzayZWwNkyKQbbrhBsbGxslgsZVswKgyCNwAAAAAAUOXVqlVLkydPlre3l9YftmnhXt9zXpOeY9Lrm4OUnmPWJZdcotGjR8tkcs8iDagcCN4AAAAAAAAktWzZUrGxYyRJC//2046ksw8Xff8vfx1Mt6hmzZqaNGmSvL29L0SZqEAI3gAAAAAAAE7p2bOnbrjhBkm5wVpRQ07/SvLSLwk2mUwmPfvsswoNDb2AVaKiIHgDAAAAAADIY+jQoQoICFB8qpdW/FtwZVKHkRvKSblBXcuWLS90iaggCN4AAAAAAADyCAkJ0X333SdJ+myPX4FVTn9JsOmfVC/5+/vrgQce8ECFqCgI3gAAAAAAAM5w4403qm7dukrLMWt1Yv5eb0v3+0iSBg4cqGrVqnmiPFQQBG8AAAAAAABn8PLyUp8+fSRJKw74uPbvSbHo7xNe8vb2ds0FBxTl7MtzAAAAAAAAVFHR0dGaPXu2/j4hxf4aIkk6mWOSJF1zzTUKCQnxXHGoEOjxBgAAAAAAUIiQkBD16NFDknQkw6IjGRal5ZhlNpvVv39/D1eHioAebwAAAAAAAEUYPXq0brzxRtntdte+atWqqXbt2h6sChUFwRsAAKi04ie1uiD3qT9+8wW5DwAAuPC8vLzUtGlTT5eBCorgDQBQqRC0AAAAACgvmOMNAAAAAAAAKAP0eAMAoBKi5x8AAADgeQRvAADggmo3dsEFu9eXgRfsVgAAAEABDDUFAAAAAAAAygDBGwAAAAAAAFAGCN4AAAAAAACAMsAcbwCAMsecXrn4cwAAAACqFnq8AQAAAAAAAGWgSgVv06dPV4MGDeTj46MOHTrot99+83RJAAAAAAAAqKSqTPD28ccfKzY2VhMmTNCGDRvUpk0bRUdH69ChQ54u7f/bu/O4mvP9D+CvUzqno/XaqkOdsoxsIUbSw2RpxLg9dKdL14PpiIs7ikiWrNnXlMENg2KMi4sw7rWkIXuyhGiSZdRvJNvYoqLz+f3Rw7nOKGXpfKPX8/E4j4fz+W6v85Fvb5/z/X6+RERERERERET0CaoyA2+LFi3C4MGDERgYiKZNm2L58uWoXr061qxZI3U0IiIiIiIiIiL6BFWJhysUFhbi9OnTCA8P17UZGRnBy8sLx48ff239goICFBQU6N4/fPgQAPDo0aMS919U8OwDJy7ZY5MigxwHKP2zvgn7oZih+gEwXF+wH4qxH4qxH4qxH4qxH4qV1g8v24UQBslBb+/l3827/CwTERFR1VXeOk8mqkAlePPmTdStWxfHjh2Du7u7rn3s2LFISkpCcnKy3voRERGYNm2aoWMSERHRJyo7Oxv16tWTOgaV4P/+7/9gb28vdQwiIiL6SJVV51WJK97eVnh4OEJDQ3XvtVot7t+/j5o1a0Imk0mS6dGjR7C3t0d2djYsLS0lyVAZsB/+h31RjP1QjP1QjP1QjP1QrDL0gxACjx8/hkqlkuT4VDaVSoXs7GxYWFhIVudR5VIZzh1EVDnx/ECvKm+dVyUG3mrVqgVjY2Pk5ubqtefm5sLW1va19RUKBRQKhV6btbV1RUYsN0tLS/4DB/vhVeyLYuyHYuyHYuyHYuyHYlL3g5WVlWTHprIZGRnxakQqkdTnDiKqvHh+oJfKU+dViYcryOVytGnTBomJibo2rVaLxMREvVtPiYiIiIiIiIiIPpQqccUbAISGhkKj0aBt27Zo164doqOjkZeXh8DAQKmjERERERERERHRJ6jKDLz5+/vjzp07mDJlCm7duoVWrVphz549sLGxkTpauSgUCkydOvW1W2CrGvbD/7AvirEfirEfirEfirEfirEfiOhd8NxBRKXh+YHeRZV4qikREREREREREZGhVYk53oiIiIiIiIiIiAyNA29EREREREREREQVgANvREREREREREREFYADb0RERERERERERBWAA2+V3Jw5c/D555/DwsICderUga+vLzIyMqSOZXAxMTFwcXGBpaUlLC0t4e7ujt27d0sdS3Jz586FTCbDyJEjpY5iUBEREZDJZHovZ2dnqWNJ4rfffkP//v1Rs2ZNKJVKtGjRAqdOnZI6lkE5Ojq+9vMgk8kQFBQkdTSDKioqwuTJk+Hk5ASlUokGDRpgxowZqArPUDp06BB8fHygUqkgk8mwfft2veVCCEyZMgV2dnZQKpXw8vJCZmamNGGJqFJbtmwZHB0dYWpqCjc3N5w8eVLqSERUCZRVaxC9CQfeKrmkpCQEBQXhxIkTSEhIwPPnz9GtWzfk5eVJHc2g6tWrh7lz5+L06dM4deoUunTpgl69euHixYtSR5NMSkoKVqxYARcXF6mjSKJZs2bIycnRvY4cOSJ1JIP7/fff4eHhARMTE+zevRuXLl1CZGQk/vSnP0kdzaBSUlL0fhYSEhIAAL1795Y4mWHNmzcPMTExWLp0KdLT0zFv3jzMnz8fS5YskTpahcvLy0PLli2xbNmyEpfPnz8f3333HZYvX47k5GSYmZnB29sb+fn5Bk5KRJXZpk2bEBoaiqlTp+LMmTNo2bIlvL29cfv2bamjEZHEyqo1iN5EJqrCV+GfkDt37qBOnTpISkrCF198IXUcSdWoUQMLFizAoEGDpI5icE+ePIGrqyv++c9/YubMmWjVqhWio6OljmUwERER2L59O1JTU6WOIqnx48fj6NGjOHz4sNRRKpWRI0di165dyMzMhEwmkzqOwfz5z3+GjY0NVq9erWvz8/ODUqnE+vXrJUxmWDKZDPHx8fD19QVQfLWbSqXC6NGjERYWBgB4+PAhbGxsEBcXh7/97W8SpiWiysTNzQ2ff/45li5dCgDQarWwt7fH8OHDMX78eInTEVFl8cdag6gsvOLtI/Pw4UMAxYNOVVVRURE2btyIvLw8uLu7Sx1HEkFBQejZsye8vLykjiKZzMxMqFQq1K9fH/369UNWVpbUkQxu586daNu2LXr37o06deqgdevW+P7776WOJanCwkKsX78eAwcOrFKDbgDQoUMHJCYm4vLlywCAc+fO4ciRI+jRo4fEyaR1/fp13Lp1S+98aWVlBTc3Nxw/flzCZERUEkBDdQAAFB5JREFUmRQWFuL06dN65wojIyN4eXnxXEFERO+lmtQBqPy0Wi1GjhwJDw8PNG/eXOo4BnfhwgW4u7sjPz8f5ubmiI+PR9OmTaWOZXAbN27EmTNnkJKSInUUybi5uSEuLg6NGzdGTk4Opk2bho4dOyItLQ0WFhZSxzOYa9euISYmBqGhoZgwYQJSUlIwYsQIyOVyaDQaqeNJYvv27Xjw4AEGDBggdRSDGz9+PB49egRnZ2cYGxujqKgIs2bNQr9+/aSOJqlbt24BAGxsbPTabWxsdMuIiO7evYuioqISzxW//PKLRKmIiOhTwIG3j0hQUBDS0tKq5FxWANC4cWOkpqbi4cOH2LJlCzQaDZKSkqrU4Ft2djZCQkKQkJAAU1NTqeNI5tUreFxcXODm5ga1Wo3NmzdXqVuPtVot2rZti9mzZwMAWrdujbS0NCxfvrzKDrytXr0aPXr0gEqlkjqKwW3evBk//vgjNmzYgGbNmiE1NRUjR46ESqWqsj8PRERERERS48DbRyI4OBi7du3CoUOHUK9ePanjSEIul6Nhw4YAgDZt2iAlJQWLFy/GihUrJE5mOKdPn8bt27fh6uqqaysqKsKhQ4ewdOlSFBQUwNjYWMKE0rC2tsZnn32GK1euSB3FoOzs7F4beG7SpAm2bt0qUSJp3bhxA/v378e2bdukjiKJMWPGYPz48bo5y1q0aIEbN25gzpw5VXrgzdbWFgCQm5sLOzs7XXtubi5atWolUSoiqmxq1aoFY2Nj5Obm6rXn5ubqziNERETvgnO8VXJCCAQHByM+Ph4///wznJycpI5UaWi1WhQUFEgdw6C6du2KCxcuIDU1Vfdq27Yt+vXrh9TU1Co56AYUP2zi6tWrev+prgo8PDyQkZGh13b58mWo1WqJEkkrNjYWderUQc+ePaWOIomnT5/CyEj/17qxsTG0Wq1EiSoHJycn2NraIjExUdf26NEjJCcnV9l5QonodXK5HG3atNE7V2i1WiQmJvJcQURE74VXvFVyQUFB2LBhA3bs2AELCwvdfDRWVlZQKpUSpzOc8PBw9OjRAw4ODnj8+DE2bNiAgwcPYu/evVJHMygLC4vX5vczMzNDzZo1q9S8f2FhYfDx8YFarcbNmzcxdepUGBsbo2/fvlJHM6hRo0ahQ4cOmD17Nvr06YOTJ09i5cqVWLlypdTRDE6r1SI2NhYajQbVqlXNX20+Pj6YNWsWHBwc0KxZM5w9exaLFi3CwIEDpY5W4Z48eaJ3xev169eRmpqKGjVqwMHBASNHjsTMmTPRqFEjODk5YfLkyVCpVHwaGRHpCQ0NhUajQdu2bdGuXTtER0cjLy8PgYGBUkcjIomVVWsQvZGgSg1Aia/Y2FipoxnUwIEDhVqtFnK5XNSuXVt07dpV7Nu3T+pYlYKnp6cICQmROoZB+fv7Czs7OyGXy0XdunWFv7+/uHLlitSxJPHTTz+J5s2bC4VCIZydncXKlSuljiSJvXv3CgAiIyND6iiSefTokQgJCREODg7C1NRU1K9fX0ycOFEUFBRIHa3CHThwoMTflRqNRgghhFarFZMnTxY2NjZCoVCIrl27VumfFSIq3ZIlS4SDg4OQy+WiXbt24sSJE1JHIqJKoKxag+hNZEIIYfDRPiIiIiIiIiIiok8c53gjIiIiIiIiIiKqABx4IyIiIiIiIiIiqgAceCMiIiIiIiIiIqoAHHgjIiIiIiIiIiKqABx4IyIiIiIiIiIiqgAceCMiIiIiIiIiIqoAHHgjIiIiIiIiIiKqABx4IyIiIiIiIiIiqgAceCOiSs/R0RHR0dFSx6gwMpkM27dvlzoGERERkSQqqhbq1KkTRo4c+cH3+74qay4iqhgceCMigxowYABkMhlkMhnkcjkaNmyI6dOn48WLF6Vuk5KSgiFDhhgwJREREVHV8GptZmJiAicnJ4wdOxb5+flSR/vo/Prrr7q+LO0VFxeHbdu2YcaMGZJkLCgoQLNmzUqsrceOHQsnJyc8fvxY19apUyfIZDJs3LhRb93o6Gg4OjpWdFyiT0I1qQMQUdXTvXt3xMbGoqCgAP/9738RFBQEExMThIeH661XWFgIuVyO2rVrv9fxXu5HCs+fP4eJiYkkxyYiIiIqj5e12fPnz3H69GloNBrIZDLMmzdP6mgfFXt7e+Tk5OjeL1y4EHv27MH+/ft1bVZWVlAqlVLEAwAoFAqsW7cO7u7u8PPzg7e3NwDgxIkTiIqKwv79+2FhYaG3jampKSZNmgQ/Pz/WtUTvgFe8EZHBKRQK2NraQq1W49tvv4WXlxd27tyJAQMGwNfXF7NmzYJKpULjxo0BvH6raVZWFnr16gVzc3NYWlqiT58+yM3N1S2PiIhAq1atsGrVKjg5OcHU1LTMTJ06dUJwcDCCg4NhZWWFWrVqYfLkyRBC6NYp6TYIa2trxMXFAfjft5ybNm2Cp6cnTE1N8eOPPwIA1qxZg2bNmkGhUMDOzg7BwcF6+7l79y7+8pe/oHr16mjUqBF27typW1ZUVIRBgwbByckJSqUSjRs3xuLFi/W2P3jwINq1awczMzNYW1vDw8MDN27c0C3fsWMHXF1dYWpqivr162PatGm6qwyFEIiIiICDgwMUCgVUKhVGjBhRZp8RERHRp+FlbWZvbw9fX194eXkhISEBAKDVajFnzhxdHdKyZUts2bJFt2156hTg/WohAEhLS0OPHj1gbm4OGxsbfPPNN7h7965ueV5eHgICAmBubg47OztERkaW67PHxMSgQYMGkMvlaNy4MX744Qe95TKZDKtWrXpjtpeMjY1ha2ure5mbm6NatWp6bUql8rVbTR0dHTFz5kxdfrVajZ07d+LOnTu6mtfFxQWnTp3SO96RI0fQsWNHKJVK2NvbY8SIEcjLyyvzM7dp0wYTJ07EoEGD8ODBA+Tn5yMwMBDDhw+Hp6fna+v37dsXDx48wPfff1+OHiWiP+LAGxFJTqlUorCwEACQmJiIjIwMJCQkYNeuXa+tq9Vq0atXL9y/fx9JSUlISEjAtWvX4O/vr7felStXsHXrVmzbtg2pqanlyrF27VpUq1YNJ0+exOLFi7Fo0SKsWrXqrT/P+PHjERISgvT0dHh7eyMmJgZBQUEYMmQILly4gJ07d6Jhw4Z620ybNg19+vTB+fPn8dVXX6Ffv364f/++7jPXq1cP//73v3Hp0iVMmTIFEyZMwObNmwEAL168gK+vLzw9PXH+/HkcP34cQ4YMgUwmAwAcPnwYAQEBCAkJwaVLl7BixQrExcVh1qxZAICtW7ciKioKK1asQGZmJrZv344WLVq89ecmIiKij19aWhqOHTumu1tgzpw5WLduHZYvX46LFy9i1KhR6N+/P5KSkgCUXacAeO9a6MGDB+jSpQtat26NU6dOYc+ePcjNzUWfPn10248ZMwZJSUnYsWMH9u3bh4MHD+LMmTNv/Kzx8fEICQnB6NGjkZaWhqFDhyIwMBAHDhwod7YPJSoqCh4eHjh79ix69uyJb775BgEBAejfvz/OnDmDBg0aICAgQPel8NWrV9G9e3f4+fnh/Pnz2LRpE44cOfLagGZpJk6cCFtbW4wYMQKTJk2CTCbD7NmzS1zX0tISEydOxPTp08s1sEdEfyCIiAxIo9GIXr16CSGE0Gq1IiEhQSgUChEWFiY0Go2wsbERBQUFetuo1WoRFRUlhBBi3759wtjYWGRlZemWX7x4UQAQJ0+eFEIIMXXqVGFiYiJu375d7lyenp6iSZMmQqvV6trGjRsnmjRponsPQMTHx+ttZ2VlJWJjY4UQQly/fl0AENHR0XrrqFQqMXHixFKPDUBMmjRJ9/7JkycCgNi9e3ep2wQFBQk/Pz8hhBD37t0TAMTBgwdLXLdr165i9uzZem0//PCDsLOzE0IIERkZKT777DNRWFhY6vGIiIjo06TRaISxsbEwMzMTCoVCABBGRkZiy5YtIj8/X1SvXl0cO3ZMb5tBgwaJvn37lrrPV+sUId6/FpoxY4bo1q2b3jbZ2dkCgMjIyBCPHz8WcrlcbN68Wbf83r17QqlUipCQkFKP26FDBzF48GC9tt69e4uvvvqq3NneZOrUqaJly5avtXt6eurlUqvVon///rr3OTk5AoCYPHmyru348eMCgMjJyRFCFP8dDBkyRG+/hw8fFkZGRuLZs2dlZhOiuIY2NTUVcrlcpKSklLjOy6z5+flCrVaL6dOnCyGEiIqKEmq1ulzHIarqeMUbERncrl27YG5uDlNTU/To0QP+/v6IiIgAALRo0eKN87Glp6fD3t4e9vb2uramTZvC2toa6enpuja1Wv3Wc8O1b99ed5UYALi7uyMzMxNFRUVvtZ+2bdvq/nz79m3cvHkTXbt2feM2Li4uuj+bmZnB0tISt2/f1rUtW7YMbdq0Qe3atWFubo6VK1ciKysLAFCjRg0MGDAA3t7e8PHxweLFi/XmFzl37hymT58Oc3Nz3Wvw4MHIycnB06dP0bt3bzx79gz169fH4MGDER8f/8aHXRAREdGnpXPnzkhNTUVycjI0Gg0CAwPh5+eHK1eu4OnTp/jyyy/16oh169bh6tWruu3fVKd8iFro3LlzOHDggF4GZ2dnAMVXfl29ehWFhYVwc3PT7aNGjRq6aUtKk56eDg8PD702Dw8PvZqyrGwfyqvHsLGxAQC9OxBetr3aJ3FxcXp94u3tDa1Wi+vXr5frmE2bNoWfnx++/PJLvfq1JAqFAtOnT8fChQv1bvElorLx4QpEZHCdO3dGTEwM5HI5VCoVqlX736nIzMzsgxzjQ+3nVTKZTG/ON6D44QlvOnZ5J8/940S1MpkMWq0WALBx40aEhYUhMjIS7u7usLCwwIIFC5CcnKxbPzY2FiNGjMCePXuwadMmTJo0CQkJCWjfvj2ePHmCadOm4euvv37tuKamprC3t0dGRgb279+PhIQEDBs2DAsWLEBSUhIn0CUiIqoCzMzMdLd+rlmzBi1btsTq1avRvHlzAMB//vMf1K1bV28bhUIBoOw65UPUQk+ePIGPj0+JD3uws7PDlStX3uLTvr03ZauIY7z8Iriktlf7ZOjQoSXOy+vg4FDu41arVk2vFn+T/v37Y+HChZg5cyafaEr0FjjwRkQG92px97aaNGmC7OxsZGdn6656u3TpEh48eICmTZu+V65XB7KA4qc7NWrUCMbGxgCA2rVr611JlpmZiadPn75xnxYWFnB0dERiYiI6d+78TrmOHj2KDh06YNiwYbq2V79lfql169Zo3bo1wsPD4e7ujg0bNqB9+/ZwdXVFRkbGG/tcqVTCx8cHPj4+CAoKgrOzMy5cuABXV9d3ykxEREQfJyMjI0yYMAGhoaG4fPkyFAoFsrKySpx0Hyi7TvkQtZCrqyu2bt0KR0fHEgeJGjRoABMTEyQnJ+sGnX7//Xdcvny51NxAcV159OhRaDQavc/zvjWlIbi6uuLSpUvvXFO/CyMjI8yZMwdff/01vv32W4Mdl+hjx1tNieij4uXlhRYtWqBfv344c+YMTp48iYCAAHh6epZ5iXxZsrKyEBoaioyMDPzrX//CkiVLEBISolvepUsXLF26FGfPnsWpU6fwj3/8o1xXhEVERCAyMhLfffcdMjMzcebMGSxZsqTcuRo1aoRTp05h7969uHz5MiZPnoyUlBTd8uvXryM8PBzHjx/HjRs3sG/fPmRmZqJJkyYAgClTpmDdunWYNm0aLl68iPT0dGzcuBGTJk0CAMTFxWH16tVIS0vDtWvXsH79eiiVSqjV6nJnJCIiok9H7969YWxsjBUrViAsLAyjRo3C2rVrcfXqVV0ds3btWgBl1ynA+9dCQUFBuH//Pvr27YuUlBRcvXoVe/fuRWBgIIqKimBubo5BgwZhzJgx+Pnnn5GWloYBAwbAyEj/v7vh4eEICAjQvR8zZgzi4uIQExODzMxMLFq0CNu2bUNYWNhb9ZezszPi4+Pfapv3NW7cOBw7dgzBwcFITU1FZmYmduzYUe6HK5SmrM/Ss2dPuLm5YcWKFe91HKKqhFe8EdFHRSaTYceOHRg+fDi++OILGBkZoXv37m9VvJUmICAAz549Q7t27WBsbIyQkBAMGTJEtzwyMhKBgYHo2LEjVCoVFi9ejNOnT5e5X41Gg/z8fERFRSEsLAy1atXCX//613LnGjp0KM6ePQt/f3/IZDL07dsXw4YNw+7duwEA1atXxy+//IK1a9fi3r17sLOzQ1BQEIYOHQoA8Pb2xq5duzB9+nTMmzcPJiYmcHZ2xt///ncAgLW1NebOnYvQ0FAUFRWhRYsW+Omnn1CzZs236T4iIiL6RFSrVg3BwcGYP38+rl+/jtq1a2POnDm4du0arK2t4erqigkTJgAou04B3r8WUqlUOHr0KMaNG4du3bqhoKAAarUa3bt31w2uLViwQHdLqoWFBUaPHo2HDx/q7ScnJ0c39xwA+Pr6YvHixVi4cCFCQkLg5OSE2NhYdOrU6a36KyMj47VjVTQXFxckJSVh4sSJ6NixI4QQaNCgAfz9/d9rv+X5LPPmzUOHDh3e6zhEVYlM/HHCIiKiKqhTp05o1aoVoqOjpY5CREREREREnwjeakpERERERERERFQBeKspEX3ysrKy3jhJ7qVLlwyYhoiIiIio4hw+fBg9evQodfmTJ08MmIaIeKspEX3yXrx4gV9//bXU5aU9IYuIiIiI6GPz7Nkz/Pbbb6UuN+STUImIA29EREREREREREQVgnO8ERERERERERERVQAOvBEREREREREREVUADrwRERERERERERFVAA68ERERERERERERVQAOvBEREREREREREVUADrwRERERERERERFVAA68ERERERERERERVYD/B4J1FBVA42vFAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1500x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2,2,figsize=(15,10))\n",
+    "sns.countplot(x = 'Customer_care_calls', data = df, ax=ax[0,0],hue = 'Reached.on.Time_Y.N').set_title('Customer Care Calls')\n",
+    "sns.countplot(x = 'Customer_rating', data = df, ax=ax[0,1],hue = 'Reached.on.Time_Y.N').set_title('Customer Rating')\n",
+    "sns.countplot(x = 'Prior_purchases', data = df, ax=ax[1,0],hue = 'Reached.on.Time_Y.N').set_title('Prior Purchases')\n",
+    "sns.violinplot(x = 'Reached.on.Time_Y.N', y = 'Discount_offered' ,data = df, ax=ax[1,1]).set_title('Discount Offered')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is important to understand the customer experience and respond to services provided by the E-Commerce company. The above graphs explain the relationship between customer experience and product delivery. The first graph is about the customer care calls and product delivery, where we that the difference in timely and late delivery of the product decreases with increase in the number of calls by the customer, which means that with the delay in product delivery the customer gets anxious about the product and calls the customer care. The second graph is about the customer rating and product delivery, where we can see that customers who  rating have higher count of products delivered on time.\n",
+    "\n",
+    "The third graph is about the customer's prior purchase, which also shows that customers who have done more prior purchases have higher count of products delivered on time and this is the reason that they are purchasing again from the company. The fourth graph is about the discount offered on the product and product delivery, where we can see that products that have 0-10% discount have higher count of products delivered late, whereas products that have discount more than 10% have higher count of products delivered on time."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Data Preprocessing 2"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Label Encoding the Categorical Variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Warehouse_block [3 4 0 1 2]\n",
+      "Mode_of_Shipment [0 2 1]\n",
+      "Product_importance [1 2 0]\n",
+      "Gender [0 1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "#Label encoding object\n",
+    "le = LabelEncoder()\n",
+    "\n",
+    "#columns for label encoding\n",
+    "cols = ['Warehouse_block','Mode_of_Shipment','Product_importance', 'Gender']\n",
+    "\n",
+    "#label encoding\n",
+    "for i in cols:\n",
+    "    le.fit(df[i])\n",
+    "    df[i] = le.transform(df[i])\n",
+    "    print(i, df[i].unique())"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Correlation Matrix Heatmap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "sns.heatmap(df.corr(), annot=True, cmap='coolwarm')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the correlation matrix heatmap, we can see that there is positive correlation between cost of product and number of customer care calls."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Customer_care_calls', ylabel='Cost_of_the_Product'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.violinplot(x = 'Customer_care_calls', y = 'Cost_of_the_Product', data = df)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is clear that customer are more concern regarding the delivery of the product when the cost of the product is high. This is the reason that they call the customer care to know the status of the product. So, it is important to make sure the delivery of the product is on time when the cost of the product is high."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(df.drop('Reached.on.Time_Y.N', axis=1), df['Reached.on.Time_Y.N'], test_size=0.2, random_state=0)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Building\n",
+    "\n",
+    "I will be using the following models to predict the product delivery:\n",
+    "\n",
+    "- Random Forest Classifier\n",
+    "- Decision Tree Classifier\n",
+    "- Logistic Regression\n",
+    "- K Nearest Neighbors"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Random Forest Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "\n",
+    "#Random Forest Classifier Object\n",
+    "rfc = RandomForestClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 256 candidates, totalling 1280 fits\n",
+      "Best parameters:  {'criterion': 'gini', 'max_depth': 8, 'min_samples_leaf': 8, 'min_samples_split': 2, 'random_state': 42}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Using GridSearchCV for hyperparameter tuning\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "#Parameter grid\n",
+    "param_grid = {\n",
+    "    'max_depth': [4,8,12,16],\n",
+    "    'min_samples_leaf': [2,4,6,8],\n",
+    "    'min_samples_split': [2,4,6,8],\n",
+    "    'criterion': ['gini', 'entropy'],\n",
+    "    'random_state': [0,42]\n",
+    "}\n",
+    "\n",
+    "#GridSearchCV object\n",
+    "grid = GridSearchCV(estimator=rfc, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2, scoring='accuracy')\n",
+    "\n",
+    "#Fitting the model\n",
+    "grid.fit(X_train, y_train)\n",
+    "\n",
+    "#Best parameters\n",
+    "print('Best parameters: ', grid.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=8, min_samples_leaf=8, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=8, min_samples_leaf=8, random_state=42)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(max_depth=8, min_samples_leaf=8, random_state=42)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Random Forest Classifier Object\n",
+    "rfc = RandomForestClassifier(criterion='gini', max_depth=8, min_samples_leaf=8, min_samples_split=2, random_state=42)\n",
+    "\n",
+    "#Fitting the model\n",
+    "rfc.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training accuracy:  0.7253096942834413\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Training accuracy\n",
+    "print('Training accuracy: ', rfc.score(X_train, y_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#predicting the test set results\n",
+    "rfc_pred = rfc.predict(X_test)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Decision Tree Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "\n",
+    "#Decision Tree Classifier Object\n",
+    "dtc = DecisionTreeClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 256 candidates, totalling 1280 fits\n",
+      "Best parameters:  {'criterion': 'gini', 'max_depth': 6, 'min_samples_leaf': 6, 'min_samples_split': 2, 'random_state': 0}\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Using GridSearchCV for hyperparameter tuning\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "#Parameter grid\n",
+    "param_grid = {\n",
+    "    'max_depth': [2,4,6,8],\n",
+    "    'min_samples_leaf': [2,4,6,8],\n",
+    "    'min_samples_split': [2,4,6,8],\n",
+    "    'criterion': ['gini', 'entropy'],\n",
+    "    'random_state': [0,42]}\n",
+    "\n",
+    "#GridSearchCV object\n",
+    "grid = GridSearchCV(estimator=dtc, param_grid=param_grid, cv=5, n_jobs=-1, verbose=2, scoring='accuracy')\n",
+    "\n",
+    "#Fitting the model\n",
+    "grid.fit(X_train, y_train)\n",
+    "\n",
+    "#Best parameters\n",
+    "print('Best parameters: ', grid.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, max_depth=6, min_samples_leaf=6,\n",
+       "                       random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(class_weight=&#x27;balanced&#x27;, max_depth=6, min_samples_leaf=6,\n",
+       "                       random_state=0)</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "DecisionTreeClassifier(class_weight='balanced', max_depth=6, min_samples_leaf=6,\n",
+       "                       random_state=0)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Decision Tree Classifier Object\n",
+    "dtc = DecisionTreeClassifier(criterion='gini', max_depth=6, min_samples_leaf=6, min_samples_split=2, random_state=0, class_weight='balanced')\n",
+    "\n",
+    "#Fitting the model\n",
+    "dtc.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training accuracy:  0.6913285600636436\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Training accuracy\n",
+    "print('Training accuracy: ', dtc.score(X_train, y_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#predicting the test set results\n",
+    "dtc_pred = dtc.predict(X_test)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "#Logistic Regression Object\n",
+    "lr = LogisticRegression()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fitting the model\n",
+    "lr.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6356404136833731"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Training accuracy\n",
+    "lr.score(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#predicting the test set results\n",
+    "lr_pred = lr.predict(X_test)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### K Nearest Neighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "#KNN Classifier Object\n",
+    "knn = KNeighborsClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#fitting the model\n",
+    "knn.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7782702579838618"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#training accuracy\n",
+    "knn.score(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#predicting the test set results\n",
+    "knn_pred = knn.predict(X_test)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, mean_absolute_error, r2_score, mean_squared_error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'KNN Classifier')"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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Pm8+ff/65LBaL+vbtq2PHjlm3sLAwNWzY0FrSXPxdrVy5UqdPn76kcV9MVlaWzfdyOc5d3+HkyZPKzMzUrbfeavN3o3he/hdffGEzfe1cQUFB+vPPP+1S3l6SRYsW6dZbb1XVqlVtvueYmBgVFRVpw4YNNv379OmjGjVqXJGxAABQmfn7+1vfVnfixAmtW7dOffv21alTp6w/P48fP67Y2Fjt27fP+objzz77TM2aNbNWPJ2reBrV3xWvw/jNN9+UONWsNF999ZXCwsJ03333Wds8PT31xBNPKDs7W+vXr7fpf7H4tLwee+wxm8+LFi1SYGCgOnfubBN3REVFyd/f3xrfrV69WhkZGbrvvvts+rm7u6tVq1YlTm07V3ljuUuN84OCgpSTk2MzPe7vgoKCtGvXLu3bt++yx1MawzD02WefqUePHjIMw+a7io2NVWZm5nm/lwwcOPCKrEMGuAqm1KHcZs6cqeuuu06ZmZn697//rQ0bNpS42PKCBQv02muv6eeff1ZBQYG1/e9vbpOkOnXq2Hwu/uFeHEQcPHhQktSwYcPzjr3++uttflgcPHhQ4eHh5/0gLS61LT5XadcuTtJcc801JbZfamBzyy23lLhoePH1r7/++vP2NW7cWCtXrjxv4ci/f2f79u2TYRglfh+SrOXWERERGjVqlF5//XV9+OGHuvXWW3XnnXfqgQcesEnclUVAQEC5A61ly5Zp0qRJ2rFjh/Ly8qzt5waW9957r9599109/PDDeu6559SpUyf17t1bd999t3XxxmeffVZr1qzRLbfcogYNGqhLly66//771bZt23KNr9i+ffv0448/lppESk9Pt/lc0t9tAABcQXZ2tkJCQiRJv/76qwzD0NixYzV27NgS+6enp6tWrVr67bff1KdPnzJdy2w26+WXX9ZTTz2l0NBQtW7dWt27d9eDDz6osLCwUo87ePCgGjZseN4i0JcaI/49Pi0PDw+P85aF2LdvnzIzM63f498Vxx3FyZmOHTuW2C8gIOCC1w4ICLAmBy/XpcT5jz/+uD755BN17dpVtWrVUpcuXdS3b1/dcccd1j4TJkxQz549dd111+mGG27QHXfcoQEDBtjljYdHjx5VRkaG3n77bb399tsl9iGWA+yLhBPK7dxESq9evdSuXTvdf//92rt3r/z9/SWdXcx60KBB6tWrl0aPHq2QkBC5u7srMTHRZnHxYn9/i1gx42+LfF8JpV3bkWP6u78/abFYLDKZTFq+fHmJ4yz+c5Ck1157TYMGDdIXX3yhVatW6YknnlBiYqI2bdp0XqBzKRo1aqTt27frjz/+OC8pdyn+97//6c4771T79u01a9Ys1axZU56enpo3b57NYt8+Pj7asGGDvv76a/33v//VihUr9J///EcdO3bUqlWr5O7ursaNG2vv3r1atmyZVqxYoc8++0yzZs3SuHHj9MILL5R5bH9nsVjUuXNnPfPMMyXuv+6662w+80QMAOCK/vzzT2VmZqpBgwaSZK1MfvrppxUbG1viMcV9L9eIESPUo0cPLVmyRCtXrtTYsWOVmJiodevWqUWLFuU6d7ErGQuazebzEl8Wi0UhISH68MMPSzym+AFY8ff7/vvvl5hg+/sb3f6uUaNG2rFjh/Lz8y9rjaJLjfNDQkK0Y8cOrVy5UsuXL9fy5cs1b948Pfjgg9aF2tu3b6/ffvvNGqe+++67mjp1qubMmaOHH364zGM7V/H39MADD2jgwIEl9vl7YotYDigfEk6wq+IfLrfffrvefPNNPffcc5KkTz/9VNdee60+//xzm6qV559//rKuU7duXUkqsdx279695/Vds2aNTp06ZVPl9PPPP9ucy1GKr//3cUtnx1i9evWLvha3fv36MgxDERER5yU9StK0aVM1bdpUY8aM0caNG9W2bVvNmTNHkyZNklR6yXpJevTooY8++kgffPCBEhISLvm4Yp999pm8vb21cuVKm8q4efPmndfXzc1NnTp1UqdOnfT666/rxRdf1L/+9S99/fXXiomJkST5+fnp3nvv1b333qv8/Hz17t1bkydPVkJCQrlfjVu/fn1lZ2dbrwUAAM73/vvvS5I1uXTttddKOltxfbGfofXr19dPP/10WdetX7++nnrqKT311FPat2+fmjdvrtdee+28t+UVq1u3rn788UdZLBabZE9liRHr16+vNWvWqG3bthdMfBS/3CUkJOSyYpQePXooKSlJn332mc30wktVljjfy8tLPXr0UI8ePWSxWPT444/rrbfe0tixY61Jx+DgYD300EN66KGHlJ2drfbt22v8+PHlTjjVqFFDVapUUVFREbEcUEFYwwl2d9ttt+mWW27RtGnTlJubK+mvJ0LnPgHavHmzkpKSLusaNWvWVPPmzbVgwQJlZmZa21evXn3eq+e7deumoqIivfnmmzbtU6dOlclkUteuXS9rDPZy7r1kZGRY23/66SetWrVK3bp1u+g5evfuLXd3d73wwgvnPWUzDEPHjx+XdHaOfmFhoc3+pk2bys3NzWYqm5+fn81YLuTuu+9W06ZNNXny5BL/PE+dOqV//etfpR7v7u4uk8mkoqIia9uBAwe0ZMkSm34nTpw479jit54Uj734Pot5eXkpMjJShmHYlHdfrr59+yopKUkrV648b19GRsZ53y0AAK5m3bp1mjhxoiIiItS/f39JZxMht912m9566y0dOXLkvGOOHj1q/e8+ffrohx9+0OLFi8/rV1ol0enTp60xZ7H69eurSpUqNvHN33Xr1k2pqan6z3/+Y20rLCzUjBkz5O/vrw4dOlz4Zq+wvn37qqioSBMnTjxvX2FhoTVWi42NVUBAgF588cUS451zv9+S/OMf/1DNmjX11FNP6Zdffjlvf3p6uvWhZEkuNc7/e5zm5uZmrSgqLZbz9/dXgwYNLvjneKnc3d3Vp08fffbZZyUmNS/2PQEoOyqccEWMHj1a99xzj+bPn69//OMf6t69uz7//HPdddddiouL0/79+zVnzhxFRkYqOzv7sq6RmJiouLg4tWvXToMHD9aJEyc0Y8YMNWnSxOacPXr00O23365//etfOnDggJo1a6ZVq1bpiy++0IgRI6xPhRzplVdeUdeuXRUdHa0hQ4bozJkzmjFjhgIDAzV+/PiLHl+/fn1NmjRJCQkJOnDggHr16qUqVapo//79Wrx4sYYOHaqnn35a69at07Bhw3TPPffouuuuU2Fhod5//33rD+BiUVFRWrNmjV5//XWFh4crIiLCukj633l6eurzzz9XTEyM2rdvr759+6pt27by9PTUrl27tHDhQlWtWlWTJ08u8fi4uDi9/vrruuOOO3T//fcrPT1dM2fOVIMGDfTjjz9a+02YMEEbNmxQXFyc6tatq/T0dM2aNUu1a9dWu3btJEldunRRWFiY2rZtq9DQUO3Zs0dvvvmm4uLiyr2wuXT27/XSpUvVvXt3DRo0SFFRUcrJydHOnTv16aef6sCBA6W+rhkAAGezfPly/fzzzyosLFRaWprWrVun1atXq27dulq6dKlNZfHMmTPVrl07NW3aVI888oiuvfZapaWlKSkpSX/++ad++OEHSWd/1n766ae65557NHjwYEVFRenEiRNaunSp5syZo2bNmp03jl9++UWdOnVS3759FRkZKQ8PDy1evFhpaWnq169fqeMfOnSo3nrrLQ0aNEjJycmqV6+ePv30U3333XeaNm2aXWKH8ujQoYMeffRRJSYmaseOHerSpYs8PT21b98+LVq0SG+88YbuvvtuBQQEaPbs2RowYIBuuukm9evXTzVq1FBKSor++9//qm3btuc9eD1X1apVtXjxYnXr1k3NmzfXAw88oKioKEnS999/r48++kjR0dGlHn+pcf7DDz+sEydOqGPHjqpdu7YOHjyoGTNmqHnz5tZ1syIjI3XbbbcpKipKwcHB2rZtmz799FMNGzbMLt/pSy+9pK+//lqtWrXSI488osjISJ04cULff/+91qxZU+IDTgDl4IA348FJXOiVuEVFRUb9+vWN+vXrG4WFhYbFYjFefPFFo27duobZbDZatGhhLFu2zBg4cKBRt25d63HFr50t6bW2+ttrTQ3DMD777DOjcePGhtlsNiIjI43PP//8vHMaxtnXxY4cOdIIDw83PD09jYYNGxqvvPKK9fW6514jPj7epq20MRW/LnXRokWX/T2da82aNUbbtm0NHx8fIyAgwOjRo4exe/dumz7Fr4E9evRoief47LPPjHbt2hl+fn6Gn5+f0ahRIyM+Pt7Yu3evYRiG8fvvvxuDBw826tevb3h7exvBwcHG7bffbqxZs8bmPD///LPRvn17w8fHx5BkDBw48IJjN4yzr78dN26c0bRpU8PX19fw9vY2brjhBiMhIcE4cuSItV9Jfz5z5841GjZsaJjNZqNRo0bGvHnzznvl7dq1a42ePXsa4eHhhpeXlxEeHm7cd999xi+//GLt89Zbbxnt27c3qlWrZpjNZqN+/frG6NGjjczMTGuf4j+Pv7+O2M/P77x7qlu37nn3furUKSMhIcFo0KCB4eXlZVSvXt1o06aN8eqrrxr5+fmGYVz47zEAAFe74p+lxZuXl5cRFhZmdO7c2XjjjTeMrKysEo/77bffjAcffNAICwszPD09jVq1ahndu3c3Pv30U5t+x48fN4YNG2bUqlXL8PLyMmrXrm0MHDjQ+ir74p+z8+bNMwzDMI4dO2bEx8cbjRo1Mvz8/IzAwECjVatWxieffGJz3g4dOhgdOnSwaUtLSzMeeugho3r16oaXl5fRtGlT63mLlTU+vZBXXnnlkuOQYm+//bYRFRVl+Pj4GFWqVDGaNm1qPPPMM8bhw4dt+n399ddGbGysERgYaHh7exv169c3Bg0aZGzbtu2Sxnb48GFj5MiRxnXXXWd4e3sbvr6+RlRUlDF58mSbWOrv3+Olxvmffvqp0aVLFyMkJMTw8vIy6tSpYzz66KM2ceKkSZOMW265xQgKCjJ8fHyMRo0aGZMnT7bGWIZhnBcjFo+pSZMm591TSX8+aWlpRnx8vHHNNdcYnp6eRlhYmNGpUyfj7bfftvkuLyXOB3BhJsNwwIrHAAAAAAAAcFqs4QQAAAAAAAC7IuEEAAAAAAAAuyLhBAAAAAAAALsi4QQAAAAAAAC7IuEEAAAAAAAAuyLhBAAAAAAAALsi4QQAgIspKirS2LFjFRERIR8fH9WvX18TJ06UYRjWPoZhaNy4capZs6Z8fHwUExOjffv22ZznxIkT6t+/vwICAhQUFKQhQ4YoOzu7om8HAAAAlZCHowdQ7L+e1zt6CIBTSLzjbUcPAXAK337ZocKuZY+fgXEFey+578svv6zZs2drwYIFatKkibZt26aHHnpIgYGBeuKJJyRJU6ZM0fTp07VgwQJFRERo7Nixio2N1e7du+Xt7S1J6t+/v44cOaLVq1eroKBADz30kIYOHaqFCxeW+37gXIjzAPsgzgPsp6JivYqO8yqTSpNwAgAAFWPjxo3q2bOn4uLiJEn16tXTRx99pC1btkg6W900bdo0jRkzRj179pQkvffeewoNDdWSJUvUr18/7dmzRytWrNDWrVvVsmVLSdKMGTPUrVs3vfrqqwoPD3fMzQEAAKBSYEodAAAOZvI0lXvLy8tTVlaWzZaXl1fi9dq0aaO1a9fql19+kST98MMP+vbbb9W1a1dJ0v79+5WamqqYmBjrMYGBgWrVqpWSkpIkSUlJSQoKCrImmyQpJiZGbm5u2rx585X6qgAAAK4q9ojzrlYknAAAcDA3D1O5t8TERAUGBtpsiYmJJV7vueeeU79+/dSoUSN5enqqRYsWGjFihPr37y9JSk1NlSSFhobaHBcaGmrdl5qaqpCQEJv9Hh4eCg4OtvYBAABwdfaI865WTKkDAMDBTJ7lf/6TkJCgUaNG2bSZzeYS+37yySf68MMPtXDhQjVp0kQ7duzQiBEjFB4eroEDB5Z7LAAAADjLHnHe1YqEEwAATsBsNpeaYPq70aNHW6ucJKlp06Y6ePCgEhMTNXDgQIWFhUmS0tLSVLNmTetxaWlpat68uSQpLCxM6enpNuctLCzUiRMnrMcDAADAdbluqg0AgEqiokutT58+LTc32xDA3d1dFotFkhQREaGwsDCtXbvWuj8rK0ubN29WdHS0JCk6OloZGRlKTk629lm3bp0sFotatWp1uV8FAACAU2FKHQAAcJiKXgyyR48emjx5surUqaMmTZpo+/btev311zV48OCz4zGZNGLECE2aNEkNGzZURESExo4dq/DwcPXq1UuS1LhxY91xxx165JFHNGfOHBUUFGjYsGHq168fb6gDAAD4f1fzot/lRcIJAAAHq+gnVzNmzNDYsWP1+OOPKz09XeHh4Xr00Uc1btw4a59nnnlGOTk5Gjp0qDIyMtSuXTutWLFC3t7e1j4ffvihhg0bpk6dOsnNzU19+vTR9OnTK/ReAAAAKrOruUKpvEg4AQDgYqpUqaJp06Zp2rRppfYxmUyaMGGCJkyYUGqf4OBgLVy48AqMEAAAAFc7Ek4AADiYK5daAwAAODNXjvNIOAEA4GCuXGoNAADgzFw5zuMtdQAAAAAAALArKpwAAHAwk7vrPvkCAABwZq4c55FwAgDAwdxcOBABAABwZq4c55FwAgDAwUxurhuIAAAAODNXjvNYwwkAAAAAAAB2RYUTAAAOZnLn+Q8AAIAzcuU4j4QTAAAO5spz+wEAAJyZK8d5JJwAAHAwV57bDwAA4MxcOc5z3douAAAAAAAAXBFUOAEA4GCuXGoNAADgzFw5ziPhBACAg5lcOBABAABwZq4c5zGlDgAAAAAAAHZFhRMAAA5mcuP5DwAAgDNy5TiPhBMAAA7mym8vAQAAcGauHOeRcAIAwMFceTFJAAAAZ+bKcZ7r1nYBAAAAAADgiiDhBACAg5ncTOXeAAAAUPlUdJxXVFSksWPHKiIiQj4+Pqpfv74mTpwowzCsfQzD0Lhx41SzZk35+PgoJiZG+/btsznPiRMn1L9/fwUEBCgoKEhDhgxRdnZ2mcZCwgkAAAczubmVewMAAEDlU9Fx3ssvv6zZs2frzTff1J49e/Tyyy9rypQpmjFjhrXPlClTNH36dM2ZM0ebN2+Wn5+fYmNjlZuba+3Tv39/7dq1S6tXr9ayZcu0YcMGDR06tExjYQ0nAAAcjAolAAAA51TRcd7GjRvVs2dPxcXFSZLq1aunjz76SFu2bJF0trpp2rRpGjNmjHr27ClJeu+99xQaGqolS5aoX79+2rNnj1asWKGtW7eqZcuWkqQZM2aoW7duevXVVxUeHn5JY+GRKAAAAAAAQCWVl5enrKwsmy0vL6/Evm3atNHatWv1yy+/SJJ++OEHffvtt+rataskaf/+/UpNTVVMTIz1mMDAQLVq1UpJSUmSpKSkJAUFBVmTTZIUExMjNzc3bd68+ZLHTcIJAAAHc3M3lXsDAABA5WOPOC8xMVGBgYE2W2JiYonXe+6559SvXz81atRInp6eatGihUaMGKH+/ftLklJTUyVJoaGhNseFhoZa96WmpiokJMRmv4eHh4KDg619LgVT6gAAcDCm1AEAADgne8R5CQkJGjVqlE2b2Wwuse8nn3yiDz/8UAsXLlSTJk20Y8cOjRgxQuHh4Ro4cGC5x1IWJJwAAHAwFv0GAABwTvaI88xmc6kJpr8bPXq0tcpJkpo2baqDBw8qMTFRAwcOVFhYmCQpLS1NNWvWtB6Xlpam5s2bS5LCwsKUnp5uc97CwkKdOHHCevylIMIFAAAAAABwAqdPn5bb35Jc7u7uslgskqSIiAiFhYVp7dq11v1ZWVnavHmzoqOjJUnR0dHKyMhQcnKytc+6detksVjUqlWrSx4LFU4AADgYU+oAAACcU0XHeT169NDkyZNVp04dNWnSRNu3b9frr7+uwYMHnx2PyaQRI0Zo0qRJatiwoSIiIjR27FiFh4erV69ekqTGjRvrjjvu0COPPKI5c+aooKBAw4YNU79+/S75DXUSCScAAByOhBMAAIBzqug4b8aMGRo7dqwef/xxpaenKzw8XI8++qjGjRtn7fPMM88oJydHQ4cOVUZGhtq1a6cVK1bI29vb2ufDDz/UsGHD1KlTJ7m5ualPnz6aPn16mcZCwgkAAAAAAMAJVKlSRdOmTdO0adNK7WMymTRhwgRNmDCh1D7BwcFauHBhucZCwgkAAAejwgkAAMA5uXKcR8IJAAAH4y11AAAAzsmV4zwSTgAAOJibu+s++QIAAHBmrhznuW6qDQAAAAAAAFcEFU4AADiYK8/tBwAAcGauHOeRcAIAwMFceW4/AACAM3PlOI+EEwAADubKT74AAACcmSvHea6bagMAAAAAAMAVQYUTAAAO5spPvgAAAJyZK8d5JJwAAHAwV57bDwAA4MxcOc5z3TsHAKCSMLmZyr2VRb169WQymc7b4uPjJUm5ubmKj49XtWrV5O/vrz59+igtLc3mHCkpKYqLi5Ovr69CQkI0evRoFRYW2u07AQAAcAYVHedVJiScAABwMVu3btWRI0es2+rVqyVJ99xzjyRp5MiR+vLLL7Vo0SKtX79ehw8fVu/eva3HFxUVKS4uTvn5+dq4caMWLFig+fPna9y4cQ65HwAAAFQ+TKkDAMDBKrrUukaNGjafX3rpJdWvX18dOnRQZmam5s6dq4ULF6pjx46SpHnz5qlx48batGmTWrdurVWrVmn37t1as2aNQkND1bx5c02cOFHPPvusxo8fLy8vrwq9HwAAgMqKKXUAAMBxTKZyb3l5ecrKyrLZ8vLyLnrp/Px8ffDBBxo8eLBMJpOSk5NVUFCgmJgYa59GjRqpTp06SkpKkiQlJSWpadOmCg0NtfaJjY1VVlaWdu3aZf/vBwAA4GplhzjvakXCCQAAJ5CYmKjAwECbLTEx8aLHLVmyRBkZGRo0aJAkKTU1VV5eXgoKCrLpFxoaqtTUVGufc5NNxfuL9wEAAABMqQMAwMHssRhkQkKCRo0aZdNmNpsvetzcuXPVtWtXhYeHl3sMAAAAsHU1L/pdXiScAABwMHvM7TebzZeUYDrXwYMHtWbNGn3++efWtrCwMOXn5ysjI8OmyiktLU1hYWHWPlu2bLE5V/Fb7Ir7AAAAgDWcAACAAznqdbnz5s1TSEiI4uLirG1RUVHy9PTU2rVrrW179+5VSkqKoqOjJUnR0dHauXOn0tPTrX1Wr16tgIAARUZGXua3AAAA4HwcFedVBlQ4AQDggiwWi+bNm6eBAwfKw+OvcCAwMFBDhgzRqFGjFBwcrICAAA0fPlzR0dFq3bq1JKlLly6KjIzUgAEDNGXKFKWmpmrMmDGKj48vc5UVAAAAnBMJJwAAHMwRpdZr1qxRSkqKBg8efN6+qVOnys3NTX369FFeXp5iY2M1a9Ys6353d3ctW7ZMjz32mKKjo+Xn56eBAwdqwoQJFXkLAAAAlZ4rT6kj4QQAgIM5olS6S5cuMgyjxH3e3t6aOXOmZs6cWerxdevW1VdffXWlhgcAAOAUruYpceVFwgkAAAdz5UAEAADAmblynOe6tV0AAAAAAAC4IqhwAgDA0Vx4bj8AAIBTc+E4j4QTAAAOZjK5bqk1AACAM3PlOM91U20AAAAAAAC4IqhwAgDAwVz5dbkAAADOzJXjPBJOAAA4mCu/vQQAAMCZuXKcR8IJAABHc+EnXwAAAE7NheM8171zAAAAAAAAXBEknAAAcDCTm6ncGwAAACqfio7z6tWrJ5PJdN4WHx8vScrNzVV8fLyqVasmf39/9enTR2lpaTbnSElJUVxcnHx9fRUSEqLRo0ersLCwzPfOlDoAABzMZOL5DwAAgDOq6Dhv69atKioqsn7+6aef1LlzZ91zzz2SpJEjR+q///2vFi1apMDAQA0bNky9e/fWd999J0kqKipSXFycwsLCtHHjRh05ckQPPvigPD099eKLL5ZpLCScAABwNCqUAAAAnFMFx3k1atSw+fzSSy+pfv366tChgzIzMzV37lwtXLhQHTt2lCTNmzdPjRs31qZNm9S6dWutWrVKu3fv1po1axQaGqrmzZtr4sSJevbZZzV+/Hh5eXld8lh4pAoAAAAAAOBk8vPz9cEHH2jw4MEymUxKTk5WQUGBYmJirH0aNWqkOnXqKCkpSZKUlJSkpk2bKjQ01NonNjZWWVlZ2rVrV5muT4UTAAAOZnLht5cAAAA4M3vEeXl5ecrLy7NpM5vNMpvNFzxuyZIlysjI0KBBgyRJqamp8vLyUlBQkE2/0NBQpaamWvucm2wq3l+8ryyIcAEAcDAWDQcAAHBO9ojzEhMTFRgYaLMlJiZe9Npz585V165dFR4eXgF3ej4qnAAAcDQWDQcAAHBOdojzEhISNGrUKJu2i1U3HTx4UGvWrNHnn39ubQsLC1N+fr4yMjJsqpzS0tIUFhZm7bNlyxabcxW/xa64z6UiwgUAAAAAAKikzGazAgICbLaLJZzmzZunkJAQxcXFWduioqLk6emptWvXWtv27t2rlJQURUdHS5Kio6O1c+dOpaenW/usXr1aAQEBioyMLNO4qXACAMDBmBIHAADgnBwR51ksFs2bN08DBw6Uh8dfaZ/AwEANGTJEo0aNUnBwsAICAjR8+HBFR0erdevWkqQuXbooMjJSAwYM0JQpU5SamqoxY8YoPj7+okmuvyPhBACAo7FoOAAAgHNyQJy3Zs0apaSkaPDgweftmzp1qtzc3NSnTx/l5eUpNjZWs2bNsu53d3fXsmXL9Nhjjyk6Olp+fn4aOHCgJkyYUOZxkHACAAAAAABwEl26dJFhGCXu8/b21syZMzVz5sxSj69bt66++uqrco+DhBMAAA5mMjGlDgAAwBm5cpxHwgkAAEdjSh0AAIBzcuE4j4TT1czNTdeNG65a998pc1h15R5O15/vLdavL84q9RBzWA01nvKsAqNukF+Dujrw5vva/dSLV3yowe1vUeSrz8k/sqFy/ziiXxNn68/3Flv3139mqMLu6iL/669V0ZlcnUzarp//+apyftl/xccGXEizJoG6v/c1ur6+v6pXMyth8k/636bjV/SavbuF677e1yi4qpd+25+tqW/9qj37TkmSqvh7aMj99XRLi6oKrWFWRlaBNmw6pnc/OKCc00VXdFy4clg0HMB5LiPOk6Tw+3qo/tMPy69BXRVkntLRlf/TnmenqOBExhUbKnEeXE2vrjXVq2u4aoZ6S5L2p5zW/I8PalPyCUnSnbE11blDiK6r7y8/Xw/d0e9bZecQp7kqV47zXDfV5gTqj35EdR+9T7uenKD1Tbvp53++qvpPP6x6wwaUeoyb2Uv5x07q18TZyvrxZ7uMw6duLcUV7C19f73aunnpWzr+zWZ927Kn9s9YoKZvTVL1zu2sfYLb36KDsz/Ud+36anPXh+Tm6aFbvpord18fu4wRuFw+3u76dX+2Xp+zzy7n69opVDNebFbq/o7tamjYw/U176MDGjIi+ey1JzRVUKCnJKl6sJeqV/PSzH//rgHDtmnytL1qfVOwnnvieruMDwBQOVxOnFe1zU1qPu9l/THvU61v1l3f3zdCQS2b6sY5Ey97HMR5wPmOHsvXnAX7NWTE93p45Pf6/seTSvxXE0XU8ZUkmc1u2vz9Cb2/KMXBIwUciwqnq1jV6BZK+3Kt0pevlySdOXhI4ffGKejmG0s95szBQ9o9arIk6ZpBfUrtd83gu3XtiMHyiaitMwcO6cDM93VwzsLLGmfdof10Zv+f2vPMy5Kk7J9/V3CbKEU8OUjHVn8rSdra/WGbY34Y8pw6H9mkwJua6MS32y7ruoA9bEo+YX1aVRJPD5OGPhihmPYh8vfz0P6DOZo9/3dt/ynzsq7Xr1dtfbnyiL5amyZJemXWPkXfXE3dO4fpg0//0P6U0xqTuNva/3Bqrt5+f7/GPtVY7m5SkeWyLgtHM/H8B4Cty4nzqrZqrtMHDunAm++fPebAn0p59z+69ulHbPoR5wHl891W22r3t98/oF5dwxV5fYD2p5zWoqWHJEktbgh0xPBQ2bhwnFfmhNOxY8f073//W0lJSUpNTZUkhYWFqU2bNho0aJBq1Khh90GiZCeTtqvOw33l17CecvYdUJUbr1dw2yjtHv1Suc4bfl8PXff8k9r15ARl7tijwOaN1XTORBXmnNah95eU+XxBrZvr2Lokm7ajq79V5Gv/LPUYj8AqkqT8k5f3SztQUUb+o6HqXeOr56fs0bETeeoQXV2vvnCjBg7bpj+PnCnTuTw8TLquQRW9/+lfT8MMQ9q246SaXB9Q6nF+fh7KOV1Isulq5sKl1qhciPMqj8uJ805u3qHrJ41UjTva6+iKDfIKqaaw3rE6umK9tQ9xHmBfbm7S7W1ryNvbXbt+znL0cFAZuXCcV6aE09atWxUbGytfX1/FxMTouuuukySlpaVp+vTpeumll7Ry5Uq1bNnyigwWtn6b8rY8AvzV4aflMoqKZHJ3196xU3X4oy/Ldd7rxg3XnmdeUuqS1ZLOPh3zb9xAdR+597ICEXNodeWlHbNpy0s7Js/AKnLzNsuSm2d7gMmkyNf+qRPfJSt7l32mMQFXQmgNs7rFhKnP4E06fiJfkvTR4j/V6qZgdYsJ09vvl21tisAAT3m4m3TiZIFN+4mMAtWt7VvKMR4adG9dfbnyyOXdBCoFkws/+ULlQZxXuVxOnHdy4/fa/uBo3bRwmty8veTm6am0L9fpp+ETrH2I8wD7uLaun+a80kJeXm46c6ZI/5y8Swf+OO3oYaEScuU4r0wJp+HDh+uee+7RnDlzznu1n2EY+sc//qHhw4crKSmplDOclZeXp7w82x8+BYZFni78B3E5at7TVbXu66HtA55S9u5fFdCssSJfS1DukfTLChgkyd3XR34N6urGtyer6Tnz/U0eHirMPGX93H7HMvnUDf//nWf/LsSe/N66/8S3ydraw7Z8+1LdMON5VWnSUEm33X9ZxwMV5dq6fvJwN+mjObfYtHt5mpR56mzSKLSGWe/PvNm6z93dJA93k1Z98tfaFu8vSrmsOf6+Pu56ZVxTHfjjtOYuPHiZdwEAZxHnVS6XE+f5N66vJq//S/smzdTR1d+efVnMy8+o6awX9OPQfxHnAXaUcui0Hnpym/x9PXRb2xr618jrNTzhB5JOwDnKlHD64YcfNH/+/POCEEkymUwaOXKkWrRocdHzJCYm6oUXXrBpu88UrP7u1csyHJfX+KVn9Nsrb+vIJ19Jkk799It86oSrwTOPXn7Cyf9sFcWP/xirjC0/2Owzzpmvs/XOoTJ5nv3r4x0equh1H+h/LXtZ9xedybX+d17aMZlDbf9szaHVVZB56rynXk3eGKuQbrcpqeMDyj2Udln3AFQUH293FRYZGjIyWRaLYbPvzJmzbyI5djxPDz351/oUHaJr6LY21fXCa3usbVmnCiVJmVkFKiwyFFzV0+ZcwUGeOn4y3/baPu567YWmOn2mSP+c/JOKimyvj6uMC5dao/IgzqtcLifOq//sozq58Xv9/vrcs8fs3KufTp9Rm28Wau+4aTIsZ2M54jyg/AoLDR06cvbfwt7fstW4YRXdc2ctvTKTyj38jQvHeWVKOIWFhWnLli1q1KhRifu3bNmi0NDQi54nISFBo0aNsmlbFxxVlqFAkruvt4y//ZJrFBWV6y90fvpx5R5Kk2/ENRcs2T6Tcvivaxae/cX69G8lV2hkbNqhGl3b27RV79RGJzftsGlr8sZYhfXsrKSYATpz4M/LvAOg4uz7PVse7iZVDfTSj7tLXoeiyCJrMCJJJzPzlZdvsWkrVlho6JdfTynqxqr636azi1GaTFJUs6r6/L+HrP18fdz1+oQbVVBg0bOTflJ+Acmmq53JjcoPOB5xXuVyOXGeu4+3NS6zOUaSTCbiPOAKMpkkT09+nuN8rhznlSnh9PTTT2vo0KFKTk5Wp06drEFHWlqa1q5dq3feeUevvvrqRc9jNptlNptt2iizLru0/36tBs/9Q7kph3Vq968KaN5YESMe0p/zP7P2uX7SKHnXCtUPDz1rbQtodjaQdPf3k1eNYAU0ayRLfoGy9/wmSfplwnQ1mTpGhVmndHTl/+Rm9lJg1A3yrBqg/dPml3mcB9/+WHUf769GiaP1x/zPVP321qp5T1dtvfNRa58bZjyv8H7dta334yo6lWN9UlbS0zGgIvl4u6lWzb9e21wz1FsNIvx0KrtQfxw+o5Vfp2nMqOv15tzfte/3bAUFeCqqWZB+O5CjpG2lv92uNB8v+VP/GtlIP/96Snt+OaW+PWvJx9tN/11zdvFeXx93TZ1wo8xmN014bY/8fNzl5+MuScrIKpCFhcMBXCbivMrlcuK89P9+raZzJqrOo/fp6Kr/ybtmiCJf+6dObvlBeUfSJRHnAfbw6IMR2pR8QmlHc+Xr46HOHULUommQRj2/U9LZ6vTgql6qFX42hry2rr9OnylU2tE8ncoudOTQgQpVpoRTfHy8qlevrqlTp2rWrFkq+v8nJu7u7oqKitL8+fPVt2/fKzJQnG/Xk5N0/QtPqsmM52UOqabcw+lKeec/2jdpprWPuWYN+VxT0+a4W7d9Yf3voKgbVOu+Hjp94E993bCTJOmPf3+qotO5uvapIWr00jMqyjmtUz/9ov3TF1zWOM8c+FNb73xUka8lqN7wB5X7Z6p2PjrG+qpcSar7j7Pz+KPXfWBz7A9DntOf7y2+rOsC9tCoQRXNSGxu/fzEww0kSV+tTdWL0/bqxTf2auC9dTRsyLWqEWxWZlaBdu3N0satZU82SdK6b48qKNBTD/evp+CqXvr192w99fxOncw4uybU9fX91aTR2TfWffJOK5tj7x6ySanpBO5XpRKmMAEVjTivcrmcOO/P9xbLo4qf6j3WX5FTnlVBxikd/2aT9iS8Yu1DnAeUX9VAT40Z2UjVgr2Uk1Oo3w7kaNTzO7Vtx0lJUq+u4Rp8fz1r/1kvN5ckTZ72s5avZTqpy3HhOM9kGMZlzcUoKCjQsWNn30hRvXp1eXp6XuSIC/uv5/XlOh7AWYl3vO3oIQBO4dsvO1TYtU7Pf+HinS7Cd9DzdhgJcBZxHlA5EecB9lNRsZ4rx3llqnA6l6enp2rWrHnxjgAA4MJc+MkXKifiPAAA7MSF4zwm1AMAAAAAAMCuLrvCCQAA2Icrv70EAADAmblynEfCCQAAR+MNXgAAAM7JheM8Ek4AADiam+vO7QcAAHBqLhznuW6qDQAAF3bo0CE98MADqlatmnx8fNS0aVNt27bNut8wDI0bN041a9aUj4+PYmJitG/fPptznDhxQv3791dAQICCgoI0ZMgQZWdnV/StAAAAoBIi4QQAgIOZTG7l3sri5MmTatu2rTw9PbV8+XLt3r1br732mqpWrWrtM2XKFE2fPl1z5szR5s2b5efnp9jYWOXm5lr79O/fX7t27dLq1au1bNkybdiwQUOHDrXb9wIAAHC1q+g4rzJhSh0AAI5WwaXWL7/8sq655hrNmzfP2hYREWH9b8MwNG3aNI0ZM0Y9e/aUJL333nsKDQ3VkiVL1K9fP+3Zs0crVqzQ1q1b1bJlS0nSjBkz1K1bN7366qsKDw+v0HsCAAColJhSBwAAHMbkVv6tDJYuXaqWLVvqnnvuUUhIiFq0aKF33nnHun///v1KTU1VTEyMtS0wMFCtWrVSUlKSJCkpKUlBQUHWZJMkxcTEyM3NTZs3by7nFwIAAOAkKjjOq0yu3pEDAACrvLw8ZWVl2Wx5eXkl9v399981e/ZsNWzYUCtXrtRjjz2mJ554QgsWLJAkpaamSpJCQ0NtjgsNDbXuS01NVUhIiM1+Dw8PBQcHW/sAAADAdZFwAgDA0Uymcm+JiYkKDAy02RITE0u8nMVi0U033aQXX3xRLVq00NChQ/XII49ozpw5FXzjAAAATs4Ocd7VijWcAABwNLfyP/9JSEjQqFGjbNrMZnOJfWvWrKnIyEibtsaNG+uzzz6TJIWFhUmS0tLSVLNmTWuftLQ0NW/e3NonPT3d5hyFhYU6ceKE9XgAAACXZ4c472rluncOAIATMZvNCggIsNlKSzi1bdtWe/futWn75ZdfVLduXUlnFxAPCwvT2rVrrfuzsrK0efNmRUdHS5Kio6OVkZGh5ORka59169bJYrGoVatW9r49AAAAXGWocAIAwNEqeDHIkSNHqk2bNnrxxRfVt29fbdmyRW+//bbefvvts8MxmTRixAhNmjRJDRs2VEREhMaOHavw8HD16tVL0tmKqDvuuMM6Fa+goEDDhg1Tv379eEMdAABAsat40e/yIuEEAICjVfDrcm+++WYtXrxYCQkJmjBhgiIiIjRt2jT179/f2ueZZ55RTk6Ohg4dqoyMDLVr104rVqyQt7e3tc+HH36oYcOGqVOnTnJzc1OfPn00ffr0Cr0XAACASq2C47zKhIQTAACO5oAnX927d1f37t1L3W8ymTRhwgRNmDCh1D7BwcFauHDhlRgeAACAc3DhCifXvXMAAAAAAABcESScAABwNBd+XS4AAIBTc0Ccd+jQIT3wwAOqVq2afHx81LRpU23bts263zAMjRs3TjVr1pSPj49iYmK0b98+m3OcOHFC/fv3V0BAgIKCgjRkyBBlZ2eXaRwknAAAcDQ3t/JvAAAAqHwqOM47efKk2rZtK09PTy1fvly7d+/Wa6+9pqpVq1r7TJkyRdOnT9ecOXO0efNm+fn5KTY2Vrm5udY+/fv3165du7R69WotW7ZMGzZs0NChQ8s0FtZwAgDA0ahQAgAAcE4VHOe9/PLLuuaaazRv3jxrW0REhPW/DcPQtGnTNGbMGPXs2VOS9N577yk0NFRLlixRv379tGfPHq1YsUJbt25Vy5YtJUkzZsxQt27d9Oqrr17yG4l5JAoAAAAAAOAEli5dqpYtW+qee+5RSEiIWrRooXfeece6f//+/UpNTVVMTIy1LTAwUK1atVJSUpIkKSkpSUFBQdZkkyTFxMTIzc1NmzdvvuSxkHACAMDRTG7l3wAAAFD52CHOy8vLU1ZWls2Wl5dX4uV+//13zZ49Ww0bNtTKlSv12GOP6YknntCCBQskSampqZKk0NBQm+NCQ0Ot+1JTUxUSEmKz38PDQ8HBwdY+l4IIFQAAR2MNJwAAAOdkhzgvMTFRgYGBNltiYmKJl7NYLLrpppv04osvqkWLFho6dKgeeeQRzZkzp4JvnIQTAACOx1vqAAAAnJMd4ryEhARlZmbabAkJCSVermbNmoqMjLRpa9y4sVJSUiRJYWFhkqS0tDSbPmlpadZ9YWFhSk9Pt9lfWFioEydOWPtcChJOAAAAAAAAlZTZbFZAQIDNZjabS+zbtm1b7d2716btl19+Ud26dSWdXUA8LCxMa9eute7PysrS5s2bFR0dLUmKjo5WRkaGkpOTrX3WrVsni8WiVq1aXfK4eUsdAACOxhpMAAAAzqmC47yRI0eqTZs2evHFF9W3b19t2bJFb7/9tt5+++2zwzGZNGLECE2aNEkNGzZURESExo4dq/DwcPXq1UvS2YqoO+64wzoVr6CgQMOGDVO/fv0u+Q11EgknAAAcjylxAAAAzqmC47ybb75ZixcvVkJCgiZMmKCIiAhNmzZN/fv3t/Z55plnlJOTo6FDhyojI0Pt2rXTihUr5O3tbe3z4YcfatiwYerUqZPc3NzUp08fTZ8+vUxjIeEEAAAAAADgJLp3767u3buXut9kMmnChAmaMGFCqX2Cg4O1cOHCco2DhBMAAI7GW+YAAACckwvHeSScAABwMIMpdQAAAE7JleM8Ek4AADgai4YDAAA4JxeO81z3zgEAAAAAAHBFUOEEAICjufCTLwAAAKfmwnEeCScAABzMlef2AwAAODNXjvNIOAEA4Ggu/OQLAADAqblwnOe6dw4AAAAAAIArggonAAAczYVLrQEAAJyaC8d5JJwAAHA0NwqOAQAAnJILx3mue+cAAAAAAAC4IqhwAgDAwVz57SUAAADOzJXjPBJOAAA4mgu/vQQAAMCpuXCcR8IJAAAHM1w4EAEAAHBmrhznue6dAwAAAAAA4IqgwgkAAEdz4bn9AAAATs2F4zwSTgAAOJgrl1oDAAA4M1eO80g4AQDgaC785AsAAMCpuXCc57qpNgAAAAAAAFwRVDgBAOBoLlxqDQAA4NRcOM4j4QQAgIMZLlxqDQAA4MxcOc4j4QQAgKO58JMvAAAAp+bCcZ7r3jkAAAAAAACuCCqcAABwMEOuW2oNAADgzFw5ziPhBACAgxkuXGoNAADgzFw5znPdOwcAAAAAAMAVQYUTAACO5sJPvgAAAJyaC8d5rnvnAABUEobJVO6tLMaPHy+TyWSzNWrUyLo/NzdX8fHxqlatmvz9/dWnTx+lpaXZnCMlJUVxcXHy9fVVSEiIRo8ercLCQrt8HwAAAM6iouO8yoQKJwAAHMwRc/ubNGmiNWvWWD97ePwVEowcOVL//e9/tWjRIgUGBmrYsGHq3bu3vvvuO0lSUVGR4uLiFBYWpo0bN+rIkSN68MEH5enpqRdffLHC7wUAAKCycuU1nEg4AQDggjw8PBQWFnZee2ZmpubOnauFCxeqY8eOkqR58+apcePG2rRpk1q3bq1Vq1Zp9+7dWrNmjUJDQ9W8eXNNnDhRzz77rMaPHy8vL6+Kvh0AAABUMq6bagMAoLIwmcq95eXlKSsry2bLy8sr9ZL79u1TeHi4rr32WvXv318pKSmSpOTkZBUUFCgmJsbat1GjRqpTp46SkpIkSUlJSWratKlCQ0OtfWJjY5WVlaVdu3ZdoS8JAADgKmSHOO9qRcIJAAAHM0xu5d4SExMVGBhosyUmJpZ4vVatWmn+/PlasWKFZs+erf379+vWW2/VqVOnlJqaKi8vLwUFBdkcExoaqtTUVElSamqqTbKpeH/xPgAAAJxljzivLCrTWp1MqQMAwMEMlf/JVUJCgkaNGmXTZjabS+zbtWtX63/feOONatWqlerWratPPvlEPj4+5R4LAAAAzrJHnFdWlWWtThJOAAA4AbPZXGqC6WKCgoJ03XXX6ddff1Xnzp2Vn5+vjIwMmyqntLQ065pPYWFh2rJli805ip+MlbQuFAAAACpOZVmrkyl1AAA4WEWXWv9ddna2fvvtN9WsWVNRUVHy9PTU2rVrrfv37t2rlJQURUdHS5Kio6O1c+dOpaenW/usXr1aAQEBioyMLNdYAAAAnIk94ryrda1OEk4AADhaBS8m+fTTT2v9+vU6cOCANm7cqLvuukvu7u667777FBgYqCFDhmjUqFH6+uuvlZycrIceekjR0dFq3bq1JKlLly6KjIzUgAED9MMPP2jlypUaM2aM4uPjL7vKCgAAwCnZIc67WtfqZEodAAAu5s8//9R9992n48ePq0aNGmrXrp02bdqkGjVqSJKmTp0qNzc39enTR3l5eYqNjdWsWbOsx7u7u2vZsmV67LHHFB0dLT8/Pw0cOFATJkxw1C0BAAA4rat1rU4STgAAOJhRwQXHH3/88QX3e3t7a+bMmZo5c2apferWrauvvvrK3kMDAABwKvaI867WtTqZUgcAgIMZJlO5NwAAAFQ+jo7zHLlWJxVOAAA4WHkX/QYAAEDlVNFx3tNPP60ePXqobt26Onz4sJ5//vkS1+oMDg5WQECAhg8fXupanVOmTFFqauplr9VJwgkAAAAAAMAJVKa1Okk4AQDgYIaYEgcAAOCMKjrOq0xrdZJwAgDAwZhSBwAA4JxcOc4j4QQAgIOx6DcAAIBzcuU4z3VTbQAAAAAAALgiqHACAMDBWMMJAADAOblynEfCCQAAB3Pluf0AAADOzJXjPBJOAAA4mCs/+QIAAHBmrhznuW6qDQAAAAAAAFcEFU4AADiYK5daAwAAODNXjvNIOAEA4GCuXGoNAADgzFw5znPdVBsAAAAAAACuCCqcAABwMFcutQYAAHBmrhznkXACAMDBXLnUGgAAwJm5cpxXaRJOnT58xNFDAJzCTZvfcvQQACfRocKuZJhcNxCBa7h9zr2OHgLgFDpV2eroIQBOpGJiPVeO81y3tgsAAAAAAABXRKWpcAIAwFUZhus++QIAAHBmrhznkXACAMDBDAqOAQAAnJIrx3kknAAAcDBXXkwSAADAmblynOe6qTYAAAAAAABcEVQ4AQDgYK785AsAAMCZuXKcR8IJAAAHc+VABAAAwJm5cpxHwgkAAAdz5UAEAADAmblynMcaTgAAAAAAALArKpwAAHAww3DdJ18AAADOzJXjPBJOAAA4mCuXWgMAADgzV47zmFIHAAAAAAAAu6LCCQAAB3PlJ18AAADOzJXjPBJOAAA4mCsHIgAAAM7MleM8Ek4AADiYKy8mCQAA4MxcOc5jDScAAAAAAADYFRVOAAA4mMWFS60BAACcmSvHeVQ4AQDgYIZM5d4AAABQ+Tg6znvppZdkMpk0YsQIa1tubq7i4+NVrVo1+fv7q0+fPkpLS7M5LiUlRXFxcfL19VVISIhGjx6twsLCMl2bhBMAAA5mGKZybwAAAKh8HBnnbd26VW+99ZZuvPFGm/aRI0fqyy+/1KJFi7R+/XodPnxYvXv3tu4vKipSXFyc8vPztXHjRi1YsEDz58/XuHHjynR9Ek4AAAAAAABOJDs7W/3799c777yjqlWrWtszMzM1d+5cvf766+rYsaOioqI0b948bdy4UZs2bZIkrVq1Srt379YHH3yg5s2bq2vXrpo4caJmzpyp/Pz8Sx4DCScAABzM0aXWAAAAuDIcFefFx8crLi5OMTExNu3JyckqKCiwaW/UqJHq1KmjpKQkSVJSUpKaNm2q0NBQa5/Y2FhlZWVp165dlzwGFg0HAMDBmBIHAADgnOwR5+Xl5SkvL8+mzWw2y2w2l9j/448/1vfff6+tW7eety81NVVeXl4KCgqyaQ8NDVVqaqq1z7nJpuL9xfsuFRVOAAC4OEcuJgkAAIALS0xMVGBgoM2WmJhYYt8//vhDTz75pD788EN5e3tX8EhtkXACAMDBHDmlztGLSQIAADgze8R5CQkJyszMtNkSEhJKvF5ycrLS09N10003ycPDQx4eHlq/fr2mT58uDw8PhYaGKj8/XxkZGTbHpaWlKSwsTJIUFhZ23oPG4s/FfS4FCScAABzMUW8vqQyLSQIAADgze8R5ZrNZAQEBNltp0+k6deqknTt3aseOHdatZcuW6t+/v/W/PT09tXbtWusxe/fuVUpKiqKjoyVJ0dHR2rlzp9LT0619Vq9erYCAAEVGRl7yvZNwAgDAwSx22PLy8pSVlWWz/X2u/99VhsUkAQAAnJk94ryyqFKlim644Qabzc/PT9WqVdMNN9ygwMBADRkyRKNGjdLXX3+t5ORkPfTQQ4qOjlbr1q0lSV26dFFkZKQGDBigH374QStXrtSYMWMUHx9faqKrJCScAABwAmWZ2y/9tZhkSX0qcjFJAAAAVKypU6eqe/fu6tOnj9q3b6+wsDB9/vnn1v3u7u5atmyZ3N3dFR0drQceeEAPPvigJkyYUKbr8JY6AAAczB5vL0lISNCoUaNs2kp7AlW8mOTq1asdvpgkAACAM6sMbyP+5ptvbD57e3tr5syZmjlzZqnH1K1bV1999VW5rkuFEwAADmaPxSTLMre/Mi0mCQAA4Mwc+XIYRyPhBACAg1X0ouGVaTFJAAAAZ+aol8NUBkypAwDAxRQvJnmucxeTlGRdTDI4OFgBAQEaPnx4qYtJTpkyRampqZe1mCQAAACcEwknAAAcrDKWSk+dOlVubm7q06eP8vLyFBsbq1mzZln3Fy8m+dhjjyk6Olp+fn4aOHBgmReTBAAAcGaVMc6rKCScAABwMIvh6BE4bjFJAAAAZ1YZ4jxHIeEEAICDufKTLwAAAGfmynEei4YDAAAAAADArqhwAgDAwa7mt48AAACgdK4c55FwAgDAwQwXntsPAADgzFw5zmNKHQAAAAAAAOyKCicAABzM4sKLSQIAADgzV47zSDgBAOBgrjy3HwAAwJm5cpxHwgkAAAdz5bn9AAAAzsyV4zzWcAIAAAAAAIBdUeEEAICDGS48tx8AAMCZuXKcR8IJAAAHs7hwqTUAAIAzc+U4j4QTAAAO5sqLSQIAADgzV47zWMMJAAAAAAAAdkWFEwAADubKby8BAABwZq4c55FwAgDAwSwuvJgkAACAM3PlOI+EEwAADubKT74AAACcmSvHeazhBAAAAAAAALuiwgkAAAdz5beXAAAAODNXjvNIOAEA4GAWFy61BgAAcGauHOcxpQ4AAAAAAAB2RYUTAAAO5sqLSQIAADgzV47zSDgBAOBghgu/LhcAAMCZuXKcR8IJAAAHc+W5/QAAAM7MleM81nACAAAAAACAXVHhBACAg7ny3H4AAABn5spxHgknAAAczJUDEQAAAGfmynEeU+oAAHAwi2Eq9wYAAIDKp6LjvNmzZ+vGG29UQECAAgICFB0dreXLl1v35+bmKj4+XtWqVZO/v7/69OmjtLQ0m3OkpKQoLi5Ovr6+CgkJ0ejRo1VYWFjmeyfhBAAAAAAA4ARq166tl156ScnJydq2bZs6duyonj17ateuXZKkkSNH6ssvv9SiRYu0fv16HT58WL1797YeX1RUpLi4OOXn52vjxo1asGCB5s+fr3HjxpV5LEypAwDAwVy51BoAAMCZVXSc16NHD5vPkydP1uzZs7Vp0ybVrl1bc+fO1cKFC9WxY0dJ0rx589S4cWNt2rRJrVu31qpVq7R7926tWbNGoaGhat68uSZOnKhnn31W48ePl5eX1yWPhQonAAAczDDKvwEAAKDysUecl5eXp6ysLJstLy/votcuKirSxx9/rJycHEVHRys5OVkFBQWKiYmx9mnUqJHq1KmjpKQkSVJSUpKaNm2q0NBQa5/Y2FhlZWVZq6QuFQknAAAAAACASioxMVGBgYE2W2JiYqn9d+7cKX9/f5nNZv3jH//Q4sWLFRkZqdTUVHl5eSkoKMimf2hoqFJTUyVJqampNsmm4v3F+8qCKXUAADiYhQolAAAAp2SPOC8hIUGjRo2yaTObzaX2v/7667Vjxw5lZmbq008/1cCBA7V+/fryD6SMSDgBAOBgBm+ZAwAAcEr2iPPMZvMFE0x/5+XlpQYNGkiSoqKitHXrVr3xxhu69957lZ+fr4yMDJsqp7S0NIWFhUmSwsLCtGXLFpvzFb/FrrjPpWJKHQAADsYaTgAAAM6pMsR5FotFeXl5ioqKkqenp9auXWvdt3fvXqWkpCg6OlqSFB0drZ07dyo9Pd3aZ/Xq1QoICFBkZGSZrkuFEwAAAAAAgBNISEhQ165dVadOHZ06dUoLFy7UN998o5UrVyowMFBDhgzRqFGjFBwcrICAAA0fPlzR0dFq3bq1JKlLly6KjIzUgAEDNGXKFKWmpmrMmDGKj48vU5WVRMIJAACHYw0nAAAA51TRcV56eroefPBBHTlyRIGBgbrxxhu1cuVKde7cWZI0depUubm5qU+fPsrLy1NsbKxmzZplPd7d3V3Lli3TY489pujoaPn5+WngwIGaMGFCmcdCwgkAAAdjShwAAIBzqug4b+7cuRfc7+3trZkzZ2rmzJml9qlbt66++uqrco+FhBMAAA5GwgkAAMA5uXKcx6LhAAC4mNmzZ+vGG29UQECAAgICFB0dreXLl1v35+bmKj4+XtWqVZO/v7/69OljfTtJsZSUFMXFxcnX11chISEaPXq0CgsLK/pWAAAAUEmRcAIAwMEsRvm3sqhdu7ZeeuklJScna9u2berYsaN69uypXbt2SZJGjhypL7/8UosWLdL69et1+PBh9e7d23p8UVGR4uLilJ+fr40bN2rBggWaP3++xo0bZ8+vBQAA4KpX0XFeZcKUOgAAHKyiS6179Ohh83ny5MmaPXu2Nm3apNq1a2vu3LlauHChOnbsKEmaN2+eGjdurE2bNql169ZatWqVdu/erTVr1ig0NFTNmzfXxIkT9eyzz2r8+PHy8vKq2BsCAACopJhSBwAAHMZiKf92uYqKivTxxx8rJydH0dHRSk5OVkFBgWJiYqx9GjVqpDp16igpKUmSlJSUpKZNmyo0NNTaJzY2VllZWdYqKQAAADg2znM0KpwAAHACeXl5ysvLs2kzm80ym80l9t+5c6eio6OVm5srf39/LV68WJGRkdqxY4e8vLwUFBRk0z80NFSpqamSpNTUVJtkU/H+4n0AAAAAFU4AADiYYZR/S0xMVGBgoM2WmJhY6jWvv/567dixQ5s3b9Zjjz2mgQMHavfu3RV41wAAAM7PHnHe1YoKJwAAHMwegURCQoJGjRpl01ZadZMkeXl5qUGDBpKkqKgobd26VW+88Ybuvfde5efnKyMjw6bKKS0tTWFhYZKksLAwbdmyxeZ8xW+xK+4DAACAqzthVF5UOAEA4ATMZrMCAgJstgslnP7OYrEoLy9PUVFR8vT01Nq1a6379u7dq5SUFEVHR0uSoqOjtXPnTqWnp1v7rF69WgEBAYqMjLTfTQEAAOCqRYUTAAAOVtGvu01ISFDXrl1Vp04dnTp1SgsXLtQ333yjlStXKjAwUEOGDNGoUaMUHBysgIAADR8+XNHR0WrdurUkqUuXLoqMjNSAAQM0ZcoUpaamasyYMYqPjy9TkgsAAMDZVXScV5mQcAIAwMEMu9Ramy65Z3p6uh588EEdOXJEgYGBuvHGG7Vy5Up17txZkjR16lS5ubmpT58+ysvLU2xsrGbNmmU93t3dXcuWLdNjjz2m6Oho+fn5aeDAgZowYYId7gMAAMB5VHScV5mQcHJSaVk5mrZyi77b94dyCwp1TXCAJvTuoCa1alyxa368eZcWfPujjmWf0XVhwXouro2a1g6RJGWeztWsdclK+vWQUjOzVdXPW7c3rqf4Ti1Vxdvrio0JuFRuAVUV0P0+mRs1k8nLrMJjqcr8+C0V/Lm/1GN823aWX9sucg+uoaKTx5S95gudSf7fFR2nV/3GCrjzAXmE1VZRxnFlr1miM1s3WPf7dbxT3k1vlkdIuIyCfBUc3KesZR+p6OiRKzoulE9Fz+2fO3fuBfd7e3tr5syZmjlzZql96tatq6+++sreQwNwjvRTp/XG+h/13e9HlFtYpGuC/DW+6y1qUjO41GPyC4v09sZd+u/ugzqek6vqft4a2qaJet147RUb57aUdL22brt+O56lsCq+ejg6Unc2jbDun7tpt9b98qcOHD8ls6e7moVX15MdblS9agFXbEzAucr6u9H3B1P1xqot2n80Q7kFhaoZ5K+7b26sAW2aXtFxrvrpd81cu02HM7JVJzhAI2Jv0a3X1ZEkFRRZ9Oaarfr2lz/058lTquLtpVbXhuvJLrcoJMDvio4L5ePKaziRcHJCWWfyNOidpWoZEa6ZD96hqn7eSjmepQCfy5/m8MX3v2jp9l80d0j3Evev2PmbXl2+SWPubKemtUP0YdJPemzBcn3xZF9V8/dR+qnTOnrqtEbd0Ur1Q6rqcMYpTVr6rY5mndZr98Vc9rgAezD5+Kna8PHK/3W3TrwzRZacLLlXD5PlTE6px/hGx6hKt3uVuehdFaT8Ls869RV4z8OynMlR3u7vL2sc7lWrK2TMdB156v6S9wfXUNUho3U6aa0yPpwpr4Y3KPCeR1SUlaH8vT9KOpuQOr1xtQpSfpPc3FWl270KHvqcjr3yjIz8vMsaFwCg4mXl5mvQh2t1c50QvXlPe1X1MSvlZLYCLvKg7pmlG3UiJ1fP33Gz6lStoqPZZ8r1dP1wZo7i3lqm7c/cW+L+QxnZGv7ZBt3drL4m94jWloNpmrBiq6r7e6tNRE1J0vd/HNW9LRqqSc1gFVosenPDTj22aL0+H9xVPl78OoIr63J+N/Lx9FC/VpFqGBosHy9PbT+YqolLv5WPp4fuvrnxZY1j6/7DGvf5ei1/6r4S9+9ISdNzi9bpic43q/11dfTVj79pxMLV+vixu9QwNFi5BYX6+chxDb2tha4Pq6as3Dy9/FWSnvxwlT567K7LGhNwpfF/eCf07//9oNBAP03s3cHaVruq7ROk/MIizVizVct//E2ncvPVILSqRnS5RTdHhF/WNd/fuFO9WzZSr5uulySN6dFOG/amaMn3ezWkfXM1DA3W6/d1tva/JjhAw2Nu1j8//VqFRRZ5uLN+PRzHv2MPWTKOK/M/b1nbik4cveAxPi3b6XTSOuXu2PT//dPlec218u/Ywybh5NPqNvl3iLNWQeX8b4VOb1xzWeP0je6kohNHderLDyVJhemH5RVxvfzad7UmnE6+87LNMZkfz1HohLfkWTtC+b//fFnXxZVnsTh6BAAqm3mb9ygswFcvdGtlbasV5H/BY777/YiS/ziqZUPjFPj/v0yHB55f+fD5D7/pg617dSgzR+GBfrovqqH6tmh4WeP8dMdvqhXop6c6tpAkXVstQNv/PKoPt/1iTTjNvKeDzTEvdLtFnd78QrvTTijqmpDLui5wqS7ld6O/axxeXY3Dq1s/16paRWt3H9D3B1OtCSeLxdC8//2gT7ft0fHsM6pbLVBDb2uhzjdcXjXhh0k/qU2D2hrUrpkkaVhMS2367ZA+3rxLY++8VVW8vfTWoG42xyTEtVX/t5boSEa2al7k/w9wHFeO80g4OaH1Px9Umwa19fTHa7TtwBGFVPHTva0i1adlI2ufxGXf6fejGZrSt6NqBPhp3e4Devy9Ffp0WB/VrRZYpusVFBZpz+FjGnJrc2ubm5tJrevX0o9/pJd6XHZuvvzNXiSb4HDmyJuUt/dHBT34pLyubSRL1knlfLdaZzZ/XeoxJndPGYUFNm1GQb48r6kvublLliJ539RWVWLvUdbi+So4dECeteop8J6HZeTn6cy2sk+986zbUPn7frJpy9v7owJ6Dih9nN6+kiTL6ewyXw8Vx5VLrQGUbP2vh9WmXphGf/Gdkv84qhB/H/Vt0UC9m9W/wDGHFBkWrPlbftZ/dx2Uj6e7OjSopcfb3SBvz7Nh/1e7Dmj2tz/puc5RahQSpJ/TMzRxxVZ5e3rozhsiSj13aX44fFyt6obatLWJCNOra3eUekx23tmfn4Esq4AKcCm/G13MnsPH9MMfaRrWqaW1be6GHfrvD79qzJ3tVLdaoJIPpOqfn32jqn4+avn/yday+PGPtPOm7LVpUFtf7zlQ6jHZefkymcQSJZWcK8d5dv9N/48//tDgwYPtfVqUwZ8nT+mTrXtUp1qgZj/YVX1vaayX/7tRS7f/Ikk6kpGtL7b/olfu7aSb6tXUNcEBGtjuRrWoE6ovvv+lzNc7eTpXRRZD1fx9bNqr+fvoWPbpko/JydXb32wv0//ogSvFo1qI/NrEqOhoqk6885JyNq5R4F0D5dPy1lKPydv7o3xb3SaP2meDc8/aEfJtdbtMHh5y86siSaoS20dZX36g3J1bVXTiqHJ3blXOhuXyje50WeN0DwhS0alMmzbLqUy5+fhKHp7nH2AyKaDXAOXv36vC1D8v65qoGBaj/BtQEYjzKs6hjGwt2vGr6lStoln3dNA9zRtoytrtWvpT6WsLHsrM0Y4/j+q3o5l6/a62erpjC63Z+4cSVydb+8z5bpdG3d5cna6rrVpB/up0XW31b3mdPtvx22WN83jOGQX7edu0Bft6Kzu/QLkFhef1txiGXl27Xc1rVVeDGkGXdU2gLC72u9GFdH5loVqOn6v75yzRvbdEqvf//+6SX1ikdzfs0At3tVfbhteodnCAet50neKaNdCnW/dc1jiPZZ8p5fepMyX2zyso1LRVW9S1aX35k3Cq1Fw5zrN7hdOJEye0YMEC/fvf/y61T15envLybNcSMQoKZfak4MoeLIahJuHV9UTnmyWdLQn9Nf2kFm3doztbXKd9aSdUZDF05xuf2BxXUFikQN+zAcORjGzdNWORdV+RxVBhkUWtJ86ztj3cvrke7tCizOPLzs3XsA9W6NqQIP2jY9Tl3CJgXyY3Ffz5u04t/48kqfDQQXmG1ZZvdEyplUinVn8ut4BAVX/iBUkmWbIzdWbbBvl3vFMyLDJ5meVRPUxBfYfKuOeRcy7lJkvuX4FD9dFT5F61us25Q1/86/+f+b//rJPvTrms2wro/ZA8wq7R8TdfuKzjAeDvLjfOKyLOKzOLIUWGVdXw9jdKkhqFVtWvxzL16Y7fSq1EshiGTCaTJvdorSrms7+A5hdZNHrJd0roHCXDkP7IyNaEFVs1ceU263FFFov8zX89uOgzd7mOZJ19aGjo7G86baZ+Zt3fonb186bJXarE1cn69Vim5vW/vIcvQFld7HejC5n3cHedySvUj3+m641VW1SnWoC63thAKcczlVtQqEcX2L48o6DIokY1q1k/n/u7k8ViKL+oyKYtrlkDjb2z9AecpSkosmj0f9bKMAz9q0e7Mh8PVJQy/+RfunTpBff//vvvFz1HYmKiXnjB9hegf93dWWPu6VLW4aAENfx9dW1IVZu2a2sEac2us0/ETucXyN3NpI//cZfc3Gxfr+jrdTbYqFHFV5883tvavnb3Aa3ZvV+Jd99ubSteG6Cqr7fc3Uw6/rfs+/HsM6ru72vTlpOXr8ffWy4/L09Nva+zPJlOh0qgKOukCtMO2bQVph2W9423lH5QYYEy//O2MhfNlVuVQFmyTsq3dSdZck/LknPKWuWUsehdFRz81fZY46+J3CffnXJ2Cp4k98CqqhY/TsdeS/ira0H+OePMkHsV2ymvblUCZTlzWvrb9L6AuwbJO7KFjs+cIEvmiYt/CXAoVy61RuVypeK8f/Zor3/1vLwEhauq7u+ta//2FreIagFa+0vpFavV/XwU4u9jTTYVH2NISjt1xppUGhvbUjeEV7M51t30V0w44+72Kvz/RUfST53RIx9/rY8H/RWne3u4W/+7mp+PTuTk2pzrxOlc+Xt5WqfxFXtpdbL+99thzb2vo0Kr2MaIwJVysd+NLqR4raeGYcE6nn1as9d9r643NtDp/LPVe28+cIdCAmz/Lnud8+/j3N+ndv55VNNWbdHcwXHWNr9z/q1W9/cp5fcp26qns8mmNTqSka13BsdR3XQVcOU4r8wJp169eslkMl3wbRcmk6nUfZKUkJCgUaNG2bQZX84q61BQiuZ1QnXgWIZN28FjmQr//4XkGtWspiKLoRM5Z3RTvZLnF3u4u6nOOWs5Bfv5yNvDw6atmKeHuxqHV9fm3w+pY2Q9SWcz+Jt/P6x+rSKt/bJz8/XYe8vl5e6uN/rH8qQTlUbBgV/kUcP234J7jTAVnTx28YMtRdaEjneLaOXt3i4ZhizZWSrKPCGP4BDlfv9dqYfbXMNSdLbteFrJ4zy4T+bGzW3azNfdoPyD+2zaAu4aJO+mLXV81qSLLn6OysGwS630hX/2ApfiSsV5RQtfssv4XEnzWtV18OQpm7aUE6dUM6D0RE3zWtW1Zu8fOp1fYH2IePDEKbmZTAqt4iNvTw/V8PfRn5k56takXqnnOXehcQ+3sw8H61StUmLfZuHV9O3vR2zaNh1I0421/kpoGYahl9d8r3X7DumdfrdfdPFzwJ4u9rvRpbIYUkHR2VitfkiQvDzcdSQz+4LrNZ37u1NaVo483Ewl/j4lSTdeE6rNvx/WA+es47Tptz91Y52/FtYvTjalHM/Su4PjFOTrXdKpUMm4cpxX5vKSmjVr6vPPP5fFYilx+/77i78O3Gw2KyAgwGYj+WA/D7Rpqp1/pOvd9duVcjxTX/3wqz7d9rPubdVEklSvepC63dhA//rsG63ZtV9/nszSzj/TNXf9Dm3Ym3JZ1xzQpqk+T96rpdt/0e/pJzXpy291Jr9AvW46W6aanZuvfyxYrjP5hRp/V3vl5OXr2KnTOnbqtIpcedl+VAo5G5bLs24D+XXqKfdqofJu0Ua+rTsq57vV1j5Vut2rwPses352rx4mn5vayr16mDyvqa+gB4bLM6y2Tn31H2ufUys/lX+nO+XbLlbu1cPkEXaNfG7uIL/2tm8YuVSnk9bKPThEVbrfJ/eQcPm2iZF3s9bK2bDc2ieg90PyiWqrjA/elJF3Rm5VAuVWJbDkNZ4A4G+I8yqPB1pep52Hj2tu0m6lnDyl5bsP6rMff9O9LRpY+0xf/6PG/HeT9XPXyDoK9PHS88u36LdjmUr+I13Tvtmhnk0jrNVG/2jbRPM27dHC5F908MQp7TuaoS92/q73t+69rHHe3by+/szM1rRvftD+41n6ZPs+rf75D/Vv+ddUpcTVyfrv7oN6sXtr+Xl56Fj2GR3LPlPiGk+AvV3sdyNJemPVFv3r079eFvPx5l365ueDOng8UwePZ+rz5J/13nc/Kq7Z2X9/fmYvDWzbVK8uT9LS7b/ojxNZ2nP4mBZu+umS1oYqSf/oG7Rx3x9a8N2P2n80Q7PXJWvX4WPq9//jLCiy6OmP12j3oWNKvPt2WSyG9fepgsKicnxDwJVT5p/+UVFRSk5OVs+ePUvcf7GnYrjybqhdQ6/f31nTV23VW99sV62gKnqmW7T1f5CSNKF3B73zzXa9tmKT0k+dVlVfbzWtHaL219e5rGve0bS+TubkatbaZB3LPq3ra1bTrAe7qtr/T6nbc+SYdv559o113af+x+bYr0b1U61SnpoBFaHgj991ct5UVYm7V1U636WiE0eV9cX7NpVJbgFBcg/662mtyc1NfrfFKbBGTRlFRcr/bbeOzRhvU7F0ZvM3MvLz5X97dwX0uF9Gfp4KjvxhkyAqi6ITR3Vy7isK6DlAfrfeoaKME8pc9I7y9/5o7ePXtrMkqVr8OJtjMz6eozNbN1zWdXHlXc2LQcK5EOdVHk1qVtNrvdppxoYf9fbGXaoV6KfRHVvYVCYdyzmj1Ky/XtDi6+Wp2X1v08trvtcD761WoI+XOl9/jeJv/ationez+vLx9NCCLT9r2jc/yMfTQw2qB9okiMqiVpC/ZvRpr1fXbdfC5F8UWsVH4+64WW3OqfpY9P8Lkj/yse3bX1/oeovubFr2N+MBZXEpvxsdyz6t1Mwc62eLYWj66q06dPKUPNxMqh0coBFdbtHdLRtb+8R3aqmqfj6au2GH/jx5SlW8vdS4ZnU93KH5ZY2zeZ1QJd7TUW+u2aYZq7eqTrVATbu/sxqGBkuS0rNy9M3PByVJfWd9bnPsu4PjdHNE+GVdF1eeK8d5JqOMUcP//vc/5eTk6I477ihxf05OjrZt26YOHco2Tz/3k1fL1B9AyU5uvvjTZwAXV/O1hRV2rZc/LX+l57N3syYeyu9KxXmn5467eCcAF+VWJeDinQBcEu++T1fIdVw5zitzhdOtt154FX0/P78yByEAALgyiys/+kKlQpwHAIB9uXKcd3WmyQAAAAAAAFBpsYIjAAAOxpI4AAAAzsmV4zwSTgAAOJgrByIAAADOzJXjPBJOAAA4mMWVIxEAAAAn5spxHms4AQAAAAAAwK6ocAIAwMGM8r8tFwAAAJWQK8d5JJwAAHAww4VLrQEAAJyZK8d5TKkDAMDBLJbybwAAAKh8KjrOS0xM1M0336wqVaooJCREvXr10t69e2365ObmKj4+XtWqVZO/v7/69OmjtLQ0mz4pKSmKi4uTr6+vQkJCNHr0aBUWFpZpLCScAAAAAAAAnMD69esVHx+vTZs2afXq1SooKFCXLl2Uk5Nj7TNy5Eh9+eWXWrRokdavX6/Dhw+rd+/e1v1FRUWKi4tTfn6+Nm7cqAULFmj+/PkaN25cmcbClDoAABzMlUutAQAAnFlFx3krVqyw+Tx//nyFhIQoOTlZ7du3V2ZmpubOnauFCxeqY8eOkqR58+apcePG2rRpk1q3bq1Vq1Zp9+7dWrNmjUJDQ9W8eXNNnDhRzz77rMaPHy8vL69LGgsVTgAAOJjFKP8GAACAyscecV5eXp6ysrJstry8vEu6fmZmpiQpODhYkpScnKyCggLFxMRY+zRq1Eh16tRRUlKSJCkpKUlNmzZVaGiotU9sbKyysrK0a9euS753Ek4AAAAAAACVVGJiogIDA222xMTEix5nsVg0YsQItW3bVjfccIMkKTU1VV5eXgoKCrLpGxoaqtTUVGufc5NNxfuL910qptQBAOBgBiVKAAAATskecV5CQoJGjRpl02Y2my96XHx8vH766Sd9++235R7D5SDhBACAg7GEEwAAgHOyR5xnNpsvKcF0rmHDhmnZsmXasGGDateubW0PCwtTfn6+MjIybKqc0tLSFBYWZu2zZcsWm/MVv8WuuM+lYEodAAAOZrEY5d4AAABQ+VR0nGcYhoYNG6bFixdr3bp1ioiIsNkfFRUlT09PrV271tq2d+9epaSkKDo6WpIUHR2tnTt3Kj093dpn9erVCggIUGRk5CWPhQonAAAAAAAAJxAfH6+FCxfqiy++UJUqVaxrLgUGBsrHx0eBgYEaMmSIRo0apeDgYAUEBGj48OGKjo5W69atJUldunRRZGSkBgwYoClTpig1NVVjxoxRfHx8mSqtSDgBAOBgFf26XAAAAFSMio7zZs+eLUm67bbbbNrnzZunQYMGSZKmTp0qNzc39enTR3l5eYqNjdWsWbOsfd3d3bVs2TI99thjio6Olp+fnwYOHKgJEyaUaSwknAAAcDDD4ugRAAAA4Eqo6DjvUhJc3t7emjlzpmbOnFlqn7p16+qrr74q11hIOAEA4GAWKpwAAACckivHeSwaDgAAAAAAALuiwgkAAAdjDScAAADn5MpxHgknAAAcrKyvuwUAAMDVwZXjPBJOAAA4mAs/+AIAAHBqrhznsYYTAAAAAAAA7IoKJwAAHMxw4VJrAAAAZ+bKcR4JJwAAHMyVX5cLAADgzFw5zmNKHQAAAAAAAOyKCicAABzMlUutAQAAnJkrx3lUOAEA4GCGxSj3VhaJiYm6+eabVaVKFYWEhKhXr17au3evTZ/c3FzFx8erWrVq8vf3V58+fZSWlmbTJyUlRXFxcfL19VVISIhGjx6twsLCcn8fAAAAzqKi47zKhIQTAAAOZjHKv5XF+vXrFR8fr02bNmn16tUqKChQly5dlJOTY+0zcuRIffnll1q0aJHWr1+vw4cPq3fv3tb9RUVFiouLU35+vjZu3KgFCxZo/vz5GjdunL2+FgAAgKteRcd5lQlT6gAAcDErVqyw+Tx//nyFhIQoOTlZ7du3V2ZmpubOnauFCxeqY8eOkqR58+apcePG2rRpk1q3bq1Vq1Zp9+7dWrNmjUJDQ9W8eXNNnDhRzz77rMaPHy8vLy9H3BoAAAAqCSqcAABwMEeXWmdmZkqSgoODJUnJyckqKChQTEyMtU+jRo1Up04dJSUlSZKSkpLUtGlThYaGWvvExsYqKytLu3btKtd4AAAAnIWj4zxHosIJAAAHM+zwuty8vDzl5eXZtJnNZpnN5gseZ7FYNGLECLVt21Y33HCDJCk1NVVeXl4KCgqy6RsaGqrU1FRrn3OTTcX7i/cBAADAPnHe1YoKJwAAHMxiMcq9JSYmKjAw0GZLTEy86LXj4+P1008/6eOPP66AOwUAAHAt9ojzrlZUOAEA4AQSEhI0atQom7aLVTcNGzZMy5Yt04YNG1S7dm1re1hYmPLz85WRkWFT5ZSWlqawsDBrny1btticr/gtdsV9AAAA4LqocAIAwMEMwyj3ZjabFRAQYLOVlnAyDEPDhg3T4sWLtW7dOkVERNjsj4qKkqenp9auXWtt27t3r1JSUhQdHS1Jio6O1s6dO5Wenm7ts3r1agUEBCgyMvIKfEsAAABXH3vEeVcrKpwAAHCwil4MMj4+XgsXLtQXX3yhKlWqWNdcCgwMlI+PjwIDAzVkyBCNGjVKwcHBCggI0PDhwxUdHa3WrVtLkrp06aLIyEgNGDBAU6ZMUWpqqsaMGaP4+PiLVlYBAAC4iqt50e/yIuEEAICLmT17tiTptttus2mfN2+eBg0aJEmaOnWq3Nzc1KdPH+Xl5Sk2NlazZs2y9nV3d9eyZcv02GOPKTo6Wn5+fho4cKAmTJhQUbcBAACASoyEEwAADlbRT74upTTb29tbM2fO1MyZM0vtU7duXX311Vf2HBoAAIBTocIJAAA4jOUqnpsPAACA0rlynEfCCQAAB3PlJ18AAADOzJXjPN5SBwAAAAAAALuiwgkAAAe7ml93CwAAgNK5cpxHwgkAAAezuHCpNQAAgDNz5TiPhBMAAA7mynP7AQAAnJkrx3ms4QQAAAAAAAC7osIJAAAHc+W5/QAAAM7MleM8Ek4AADiYYbE4eggAAAC4Alw5zmNKHQAADmaxGOXeAAAAUPk4Is7bsGGDevToofDwcJlMJi1ZssRmv2EYGjdunGrWrCkfHx/FxMRo3759Nn1OnDih/v37KyAgQEFBQRoyZIiys7PLNA4STgAAAAAAAE4iJydHzZo108yZM0vcP2XKFE2fPl1z5szR5s2b5efnp9jYWOXm5lr79O/fX7t27dLq1au1bNkybdiwQUOHDi3TOJhSBwCAg7ny3H4AAABn5og4r2vXruratWuJ+wzD0LRp0zRmzBj17NlTkvTee+8pNDRUS5YsUb9+/bRnzx6tWLFCW7duVcuWLSVJM2bMULdu3fTqq68qPDz8ksZBhRMAAA5mWIxybwAAAKh8Kluct3//fqWmpiomJsbaFhgYqFatWikpKUmSlJSUpKCgIGuySZJiYmLk5uamzZs3X/K1qHACAAAAAACopPLy8pSXl2fTZjabZTaby3yu1NRUSVJoaKhNe2hoqHVfamqqQkJCbPZ7eHgoODjY2udSUOEEAICDVbYnXwAAALAPe8R5iYmJCgwMtNkSExMdfWsXRYUTAAAOZjFc93W5AAAAzswecV5CQoJGjRpl03Y51U2SFBYWJklKS0tTzZo1re1paWlq3ry5tU96errNcYWFhTpx4oT1+EtBhRMAAA5GhRMAAIBzskecZzabFRAQYLNdbsIpIiJCYWFhWrt2rbUtKytLmzdvVnR0tCQpOjpaGRkZSk5OtvZZt26dLBa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+      "text/plain": [
+       "<Figure size 1500x1000 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2,2,figsize=(15,10))\n",
+    "sns.heatmap(confusion_matrix(y_test, rfc_pred), annot=True, cmap='coolwarm', ax=ax[0,0]).set_title('Random Forest Classifier')\n",
+    "sns.heatmap(confusion_matrix(y_test, dtc_pred), annot=True, cmap='coolwarm', ax=ax[0,1]).set_title('Decision Tree Classifier')\n",
+    "sns.heatmap(confusion_matrix(y_test, lr_pred), annot=True, cmap='coolwarm', ax=ax[1,0]).set_title('Logistic Regression')\n",
+    "sns.heatmap(confusion_matrix(y_test, knn_pred), annot=True, cmap='coolwarm', ax=ax[1,1]).set_title('KNN Classifier')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest Classifier: \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.57      0.89      0.70       908\n",
+      "           1       0.87      0.54      0.66      1292\n",
+      "\n",
+      "    accuracy                           0.68      2200\n",
+      "   macro avg       0.72      0.71      0.68      2200\n",
+      "weighted avg       0.75      0.68      0.68      2200\n",
+      "\n",
+      "Decision Tree Classifier: \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.57      0.97      0.72       908\n",
+      "           1       0.95      0.49      0.65      1292\n",
+      "\n",
+      "    accuracy                           0.69      2200\n",
+      "   macro avg       0.76      0.73      0.68      2200\n",
+      "weighted avg       0.80      0.69      0.68      2200\n",
+      "\n",
+      "Logistic Regression: \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.55      0.58      0.57       908\n",
+      "           1       0.69      0.67      0.68      1292\n",
+      "\n",
+      "    accuracy                           0.63      2200\n",
+      "   macro avg       0.62      0.62      0.62      2200\n",
+      "weighted avg       0.64      0.63      0.63      2200\n",
+      "\n",
+      "KNN Classifier: \n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.58      0.61      0.59       908\n",
+      "           1       0.71      0.68      0.70      1292\n",
+      "\n",
+      "    accuracy                           0.65      2200\n",
+      "   macro avg       0.65      0.65      0.65      2200\n",
+      "weighted avg       0.66      0.65      0.66      2200\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "#classification report\n",
+    "print('Random Forest Classifier: \\n', classification_report(y_test, rfc_pred))\n",
+    "print('Decision Tree Classifier: \\n', classification_report(y_test, dtc_pred))\n",
+    "print('Logistic Regression: \\n', classification_report(y_test, lr_pred))\n",
+    "print('KNN Classifier: \\n', classification_report(y_test, knn_pred))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Model Comparison"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Accuracy')"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "models = ['Random Forest Classifier', 'Decision Tree Classifier', 'Logistic Regression', 'KNN Classifier']\n",
+    "accuracy = [accuracy_score(y_test, rfc_pred), accuracy_score(y_test, dtc_pred), accuracy_score(y_test, lr_pred), accuracy_score(y_test, knn_pred)]\n",
+    "sns.barplot(x=models, y=accuracy, palette='magma').set_title('Model Comparison')\n",
+    "plt.xticks(rotation=90)\n",
+    "plt.ylabel('Accuracy')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Conclusion\n",
+    "\n",
+    "The aim of the project was to predict whether the product from an e-commerce company will reach on time or not. This project also analyzes various factors that affect the delivery of the product as well as studies the customer behavior. From the exploratory data analysis, I found that the product weight and cost has an impact on the product delivery. Where product that weighs between 2500 - 3500 grams and having cost less than 250 dollars had higher rate of being delivered on time. Most of the products were shipped from warehouse F though ship, so it is quite possible that warehouse F is close to a seaport. \n",
+    "\n",
+    "The customer's behaviour also help in predicting the timely delivery of the product. The more the customer calls, higher the chances the product delivery is delayed. Interestingly, the customers who have done more prior purchases have higher count of products delivered on time and this is the reason that they are purchasing again from the company. The products that have 0-10% discount have higher count of products delivered late, whereas products that have discount more than 10% have higher count of products delivered on time.\n",
+    " \n",
+    "Coming to the machine learning models, the decision tree classifier as the highest accuracy among the other models, with accuracy of 69%. The random forest classifier and logistic regression had accuracy of 68% and 67% respectively. The K Nearest Neighbors had the lowest accuracy of 65%."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/E-Commerce Product Delivery Prediction.pdf b/E-Commerce Product Delivery Prediction.pdf
new file mode 100644
index 0000000..ad02f9d
Binary files /dev/null and b/E-Commerce Product Delivery Prediction.pdf differ
diff --git a/E_Commerce.csv b/E_Commerce.csv
new file mode 100644
index 0000000..ef42930
--- /dev/null
+++ b/E_Commerce.csv
@@ -0,0 +1,11000 @@
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diff --git a/description.md b/description.md
new file mode 100644
index 0000000..6e9f86a
--- /dev/null
+++ b/description.md
@@ -0,0 +1,30 @@
+# E-Commerce Product Delivery Prediction
+![](https://globalskylogistics.com/wp-content/uploads/2018/04/THE-CHANGING-NATURE-OF-E-COMMERCE-DELIVERY.jpg)
+## Project Overview:
+The aim of this project is to predict whether products from an international e-commerce company will reach customers on time or not. Additionally, the project analyzes various factors influencing product delivery and studies customer behavior. The company primarily sells electronic products.
+
+## Data Dictionary:
+The dataset used for model building contains 10,999 observations of 12 variables, including:
+
+| Variable             | Description                                             |
+|----------------------|---------------------------------------------------------|
+| ID                   | ID Number of Customers                                 |
+| Warehouse_block      | The Company's Warehouse block (A, B, C, D, E)          |
+| Mode_of_Shipment     | The mode of shipment (Ship, Flight, Road)              |
+| Customer_care_calls  | Number of calls made for shipment inquiries            |
+| Customer_rating      | Customer rating (1 - Lowest, 5 - Highest)              |
+| Cost_of_the_Product  | Cost of the product in US Dollars                     |
+| Prior_purchases      | Number of prior purchases                              |
+| Product_importance   | Product importance categorization (low, medium, high)  |
+| Gender               | Gender of customers (Male, Female)                    |
+| Discount_offered     | Discount offered on specific products                  |
+| Weight_in_gms        | Weight of the product in grams                         |
+| Reached.on.Time_Y.N  | Target variable (1 - Product did not reach on time, 0 - Product reached on time) |
+
+## Conclusion:
+The project aimed to predict product delivery timeliness while examining factors affecting delivery and customer behavior. Exploratory data analysis revealed that product weight and cost significantly impact delivery. Products weighing between 2500 - 3500 grams and costing less than $250 had a higher likelihood of timely delivery. Most products were shipped from Warehouse F via ship, suggesting its proximity to a seaport.
+
+Customer behavior also plays a crucial role in predicting timely delivery. Higher customer call volumes correlated with delayed delivery. Interestingly, customers with more prior purchases had a higher rate of on-time deliveries, indicating their loyalty to the company. Products with a discount of 0-10% were more likely to be delivered late, whereas those with discounts exceeding 10% had a higher on-time delivery rate.
+
+In terms of machine learning models, the decision tree classifier achieved the highest accuracy at 69%, outperforming other models. The random forest classifier and logistic regression achieved accuracies of 68% and 67%, respectively, while the K Nearest Neighbors model had the lowest accuracy at 65%.
+