From eb13df2513e5fae728c750eaf2e8eff426ab2b5e Mon Sep 17 00:00:00 2001
From: andrei <asmirnov@anna.money>
Date: Thu, 6 Feb 2020 14:28:16 +0300
Subject: [PATCH] notebook with outputs

---
 churn-eda.ipynb | 1060 +++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1060 insertions(+)
 create mode 100644 churn-eda.ipynb

diff --git a/churn-eda.ipynb b/churn-eda.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "d690122aa31f6af2e76d958c9b72209c12a46907"
+   },
+   "source": [
+    "<img src=\"https://s16353.pcdn.co/wp-content/uploads/2018/06/Churn.png\" style=\"float: left;\" width=\"400\" height=\"100\" />\n",
+    "# PREVENTING BANK CUSTOMER CHURN!\n",
+    "# \" Exploratory Data Analysis  & Prediction!\"\n",
+    "\n",
+    "# 2018\n",
+    "\n",
+    "[** by_Keldine Malit** ]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "33e888aab1d3b8c07f3645d2ad9bbd1963c64c70"
+   },
+   "source": [
+    " ## 1. Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "e0d1ffc59c43795371a0910eb93a9758a91210a8"
+   },
+   "source": [
+    "We aim to accomplist the following for this study:\n",
+    "\n",
+    "1. Identify and visualize which factors contribute to customer churn:\n",
+    "    \n",
+    "2. Build a prediction model that will perform the following:\n",
+    "    * Classify if a customer is going to churn or not\n",
+    "    * Preferably and based on model performance, choose a model that will attach a probability to the churn to make it easier for customer service to target low hanging fruits in their efforts to prevent churn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "3c8a99d0766612f59d4b3e983e19933e9118c873"
+   },
+   "source": [
+    "## 2. Data set review & preparation\n",
+    "\n",
+    "In this section we will seek to explore the structure of our data: \n",
+    "1. To understand the input space the data set\n",
+    "2. And to prepare the sets for exploratory and prediction tasks as described in section 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
+    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
+    "trusted": true
+   },
+   "outputs": [],
+   "source": [
+    "## REQUIRED LIBRARIES\n",
+    "# For data wrangling \n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# For visualization\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns\n",
+    "pd.options.display.max_rows = None\n",
+    "pd.options.display.max_columns = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
+    "trusted": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10000, 14)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read the data frame\n",
+    "df = pd.read_csv('Churn_Modelling.csv', delimiter=',')\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "e46d6a276236ba04ec8cf1c3897c14a30d1ff754"
+   },
+   "source": [
+    "The Df has 1000 rows with 14 attributes. We review this further to identify what attributes will be necessary and what data manipulation needs to be carried out before Exploratory analysis and prediction modelling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "2a62bb5da5b4b6d4407bd0637c80a22c49bb1786"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RowNumber          0\nCustomerId         0\nSurname            0\nCreditScore        0\nGeography          0\nGender             0\nAge                0\nTenure             0\nBalance            0\nNumOfProducts      0\nHasCrCard          0\nIsActiveMember     0\nEstimatedSalary    0\nExited             0\ndtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check columns list and missing values\n",
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "88de32a4de96b3d27f12ba0d4c5904976e53cdf6"
+   },
+   "source": [
+    "Well isn't that a rare find; no missing values! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "9e98bb09ddde1439ea2dcdc0428d204a779fca9d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RowNumber          10000\nCustomerId         10000\nSurname             2932\nCreditScore          460\nGeography              3\nGender                 2\nAge                   70\nTenure                11\nBalance             6382\nNumOfProducts          4\nHasCrCard              2\nIsActiveMember         2\nEstimatedSalary     9999\nExited                 2\ndtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get unique count for each variable\n",
+    "df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "2b87b84346aca580e9b89ebdea888a4dfe217300"
+   },
+   "source": [
+    "From the above, we will not require the first 2 attributes as the are specific to a customer. It is borderline with the surname as this would result to profiling so we exclude this as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "6cd50b315694153db48b27de69c168a88ac1d356"
+   },
+   "outputs": [],
+   "source": [
+    "# Drop the columns as explained above\n",
+    "df = df.drop([\"RowNumber\", \"CustomerId\", \"Surname\"], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "88bde8bd630802a3daf982c6626dafaa0afd923a"
+   },
+   "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>CreditScore</th>\n",
+       "      <th>Geography</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Tenure</th>\n",
+       "      <th>Balance</th>\n",
+       "      <th>NumOfProducts</th>\n",
+       "      <th>HasCrCard</th>\n",
+       "      <th>IsActiveMember</th>\n",
+       "      <th>EstimatedSalary</th>\n",
+       "      <th>Exited</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>619</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>42</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>101348.88</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>608</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>83807.86</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>112542.58</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>502</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>42</td>\n",
+       "      <td>8</td>\n",
+       "      <td>159660.80</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113931.57</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>699</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>93826.63</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>850</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>43</td>\n",
+       "      <td>2</td>\n",
+       "      <td>125510.82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>79084.10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<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>CreditScore</th>\n",
+       "      <th>Geography</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Tenure</th>\n",
+       "      <th>Balance</th>\n",
+       "      <th>NumOfProducts</th>\n",
+       "      <th>HasCrCard</th>\n",
+       "      <th>IsActiveMember</th>\n",
+       "      <th>EstimatedSalary</th>\n",
+       "      <th>Exited</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>619</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>42</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>101348.88</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>608</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>41</td>\n",
+       "      <td>1</td>\n",
+       "      <td>83807.86</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>112542.58</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>502</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>42</td>\n",
+       "      <td>8</td>\n",
+       "      <td>159660.80</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113931.57</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>699</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>39</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>93826.63</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>850</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>43</td>\n",
+       "      <td>2</td>\n",
+       "      <td>125510.82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>79084.10</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Review the top rows of what is left of the data frame\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "e81bc964f8dfaf541e671da7d1fccdcd6ca25abb"
+   },
+   "source": [
+    "From the above, a couple of question linger:\n",
+    "1. The data appears to be a snapshot as some point in time e.g. the balance is for a given date which leaves a lot of questions:\n",
+    "    * What date is it and of what relevance is this date\n",
+    "    * Would it be possible to obtain balances over a period of time as opposed to a single date.\n",
+    "2. There are customers who have exited but still have a balance in their account! What would this mean? Could they have exited from a product and not the bank?\n",
+    "3. What does being an active member mean and are there difference degrees to it? Could it be better to provide transaction count both in terms of credits and debits to the account instead?\n",
+    "4. A break down to the products bought into by a customer could provide more information topping listing of product count\n",
+    "\n",
+    "For this exercise, we proceed to model without context even though typically having context and better understanding of the data extraction process would give better insight and possibly lead to better and contextual results of the modelling process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "45aef4b958a6648bbd4f7891e7acb0bc4a72e517"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CreditScore          int64\nGeography           object\nGender              object\nAge                  int64\nTenure               int64\nBalance            float64\nNumOfProducts        int64\nHasCrCard            int64\nIsActiveMember       int64\nEstimatedSalary    float64\nExited               int64\ndtype: object"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Check variable data types\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "4d7b2fcd64b9a0a6420134147be484156a5c4be3"
+   },
+   "source": [
+    "So we moslty have categorical variables and 5 continuous variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "trusted": true,
+    "_uuid": "975e978c67d06e124c5d3aab2c73a666fce6267e"
+   },
+   "source": [
+    "## 3. Exploratory Data Analysis\n",
+    "Here our main interest is to get an understanding as to how the given attributes relate too the 'Exit' status."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "9dbc0f4cc6e42fb0278f9aa113262c0fb4148c80"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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8ge0YfAX2Wi1vHeS+e4wx38J+7qwP39tl2JbCidhz8aJ+NjFY/4r9rHgbsC48jnXAO7EDHVaOwj4ACIKg0xhzJbavTYsx5gHsIJA27Ot5OrZVZjp9PzRyvueCINhhjPklcBXwuDHmNuyP09djW+QfZ4xaA12eG0XF9TAxFxNkvw5PjnUbGMRQSeyX/k+wiUtn+PenQFOWdf1wmxdiO7r1XrNkN/YLdPpo7qOfmBdiT/y92Df5sWGK5BhOOZpxDPa1zXhMBfZaHk+Fr9khbIvSu3Osf/dgj3XG46qwHYufxH4YHsImM98ApmSsuxQ7yuMQNoG5G/sL6n3pr2m47iXhMX4GW2dvxbaafYvw2k8Z235P2vlxwnmL7dfyXewQ4A7sdTp+C5yeZVsnxJO2rL/jnfM4YS9A+Tns6K7DYZwvAC3An5M2jLy//Y/mMSHLMPJBPs+V2KSxHfue+CW2deeE7Q3m3O3v3BvgWDQA38YO4W7HXsPqWex77vIc7+P/wSabrdgvuUuH+npjfwB/KnxNj2D78/xkuK9BruePvdDm17EdftvD5/ZpbPIxnGHpJxzLjPWmANdhPzPawvN0U/iavYe0a3oN9J7D/sD6En3X99mGvQBqfbbnmyvGQjs3imUy4Ysi4ygcVvhF4KIgCO52G42IiEjxUx8eERERKXpKeERERKToKeERERGRoqc+PCIiIlL01MIjIiIiRU8Jj4iIiBQ9JTwiIiJS9JTwiIiISNFTwiMiIiJFTwmPiIiIFD0lPCIiIlL0lPCIiIhI0VPCIyIiIkVPCY+IiIgUPSU8IiIiUvSU8IiIiEjRU8IjIiIiRU8Jj4iIiBQ9JTwiIiJS9JTwiIiISNFTwiMiIiJFTwmPiIiIFD0lPCIiIlL0lPCIiIhI0Yu5DkBE8l/DqpYyYDIwCUgA0XCKZfx9dut1l25yFaeISC5KeERKUMOqllr6EpjJadOkHP+uGeSmPwtcN9rxioiMlBIekSLVsKolBiwEFofTkvBvE1DlMDQRkXGnhEekwDWsaqnAJjHpSc0SbLJT5jA0EZG8oYRHpIA0rGqpBs4BzgOWYRObeWgAgohIv5TwiOSxhlUtdcC5wAXhtBy9b0VEhkwfnCJ5pGFVSz1wPn0JzjLUeiMiMmJKeEQcaljVMg2b2PQmOUsA4zQoEZEipIRHZJw1rGppBN4BvB041XE4IiIlQQmPyDhoWNXShE1y3oEtU4mIyDhSwiMyRhpWtSyiL8k52XE4IiIlTQmPyChqWNWyhL4kZ6njcEREJKSER2SEwpacq7BJzhLH4YiISBZKeESGoWFVSxS4DLgWuMhxOCIiMgAlPCJD0LCqZRLwYeAaYI7jcEREZJCU8IgMQsOqltOAjwHvAsodh1McfC8KeEA30HXcXz8VuAxNRIqPEh6RHBpWtZRhE5xrgTMch1OMTgYey7rE9wLgCLAX2DPoyU91jHnUIlKQlPCIZGhY1TIL+CjwIWCK43BKlQES4TR70I/yvYPY5OclYCOwAXg2/LtNLUcipUsJTwkwxnQD69Nm/TIIguv6Wf8m4Orwv1cHQfDvQ9yfDxwOguBrQ43VpYZVLWcDn8J2RtZ7ozDVhtN84MKMZa343nP0JUG9idAm/NTR8QxSRMafPtRLw5EgCAZ9C4MgCN4MYIxpAP4CGFLCU2gaVrW8BvgS8AbXsRSzlU3xedjh+91Az3lzolM/c864doeqwt5tfnnG/G58bys2AXoGeAi4Fz+1ezyDE5GxpYSnRBljPGAtsDIIgo3GmF8AdwZB8ANjzFbgNOA6YIEx5nHg9iAIPmOM+QzwTmzH3RuCIPhiuL3PA38G7AK2AY+O+5MaovAigf8IXOk6lhIxB3tbjR2AaesMJjuOp1cUWBBOlx6b63vPAvcem/zUVhfBicjoUMJTGirDpKXXl4Mg+JUx5lrgemPMN4EJQRD8IONxq4CTeluHjDGXAElsB14DrDbGnA+0Yn+5n4o9p9aRxwlPw6qWBYAfBMHVxpiI63hKzFEgBRCPmgrHsQxkUTh9GADf245Nfu4J/z6jPkEihUMJT2nIWtIKguB2Y8w7gO8ApwxiO5eEU+/ImmpsAlSDbe1pAzDGrB6VqEdZ2Bn574Ig+IAxJmaMcR2SFJZZwLvDCWAvvnc/fa1Aj+KnulwFJyL9U8JTwsLWjcVAGzAB2D7QQ7CtQ9/L2M4nxybC0dGwqmUK8NkgCK4xxlQo0ZFRUg+sDCeA/fjeTcDvgFvwU4ecRSYiJ1Bzfmn7K+wolauBHxtj4hnLD2Fbb3rdCnzAGFMNYIyZaYyZgm3iv9wYU2mMqQHeOvahD6xhVUtdw6qWLwVBsAX4pDF5X0KRwjYB+BPgv4Hd+N5N+N5H8L3pjuMSEdTCUyoy+/DcAvwYe52ZM4IgOGSMuQf4W+CLvSsFQbDXGHO/MeYp4Oaw0/Ji4MGwleQw8J4gCNYZY34FPIHttPzw+Dyt7BpWtVRgk7m/BurUoiMOlANvCqfv4ntrsInQr/FTA7WkisgYUMJTAoIgiOZYtDhtnU+l/bsh7d9Xpz8gCIJvAt/Mso8vYYd2O9WwquVi4D+Aha5jEQkZ4Mxw+ld87wH6kp9XnUYmUkKU8EhRaFjVMhn4OvAe17GI9MMA54TTv+F79wE/BX6On2p1GplIkVMfHil4DataPhAEwUaU7EhhiQDnA98HXsH3/i++t3iAx4jIMKmFRwpWw6qWRUEQfM8Yc7766UiBq8XepPZafO+P2Kub34Cf6nQblkjxUMIjBadhVUs58LkgCD6bZWSZSKG7IJx24Hv/CXwfP7XNcUwiBU8lLSkoDataLgqCnqeALyjZkSI3DTty8gV877f43hvwPTVligyTWnikIDSsaqkPguBfjTF/prtBSImJApeF02Z873vAj/BT+9yGJVJY9M0hea9hVct7g6BnozHmz1zHIuLYQuCrwMv43rfxvZmuAxIpFGrhkbzVsKplQhD0/KcxkSvVqiNynArgL4EP4nvfB76Mn9rhOCaRvKZvEclLDatazgt6up82JnKl61hE8lgF8HFgC773dXxviuuARPKVEh7JKw2rWqJz/s8NXw6C4G4TieoeRCKDU4m9ncoL+N5X8L1JrgMSyTdKeCRvNKxqmdPTefThSKxslVENS2Q4EsBnsInPl/G9ia4DEskX+lKRvDDn0795W9DT9UwkXr7cdSwiRaAaWAVsxff+Cd+b4DogEdfUaVmcaljVEus52vatSHnio65jESlCNcDnsVdw/gbwdfzUQccxiTihFh5xZu5nfjujp+PIo0p2RMacB3wReA7f0z3npCQp4REnZn/8528mCDZEyiqXuY5FpIRMBX6C792lG5VKqVFJS8ZVw6oW092W+kqksubTxkR0mXwRNy4EnsD3vg78A36qzXE8ImNOLTwybmZ/7Gfl3W2p26MJ7/8o2RFxLg78DbAB37vcdTAiY00Jj4yL6e/9+jRM5MlowrvYdSwicpw5wA343u/xvXmugxEZK0p4ZMxNeYd/WmzizPXRRG2j61hEJKe3AE/je3+L75W5DkZktCnhkTE15e1ffFfF7JPviVZU68qvIvmvEvhHYD2+9zrXwYiMJiU8MiYSyWYz9aov/U3lvBU/jZRVVLqOR0SGpBG4Hd/7Jb432XUwIqNBCY+MukSyOVLb/PZ/r5i77MsmGtNIQJHC9S5sa8+bXQciMlJKeGRUJZLNZXXnvuf3FbOWXKORWCJFYSrQgu/9O76n1lopWEp4ZNTUnfeeugkXffDBsqnz9WtQpPh8FFiH773GdSAiw6GER0ZF/Rs/Nr962esfi0+cucJ1LCIyZhYBD+J7n8P39P0hBUUnrIzY5JV/3ZxoOnttrGZSg+tYRGTMxYEvAX/A96a7DkZksJTwyIjUv/Ha11UuOO32aGVtvetYRGRcXQQ8ju9d4joQkcFQwiPDkkg2m7rz33t5YtG5/xspr6pxHY+IODEFuAXfuw7f04hMyWtKeGTIEsnmSLx+9ntrTn3T9dGKGiU7IqXNYO/J9Ud8b47rYERyUcIjQ5JINkejtVM+Wnv6Ff8WTdR6ruMRkbxxNvCYrtAs+UoJjwxaItkciVbXf7jurHf+Q7SqboLreEQk70wEbsb3/tx1ICKZlPDIoCSSzZFIou593tlX/WO0euJE1/GISN6KAd/D9/5VQ9cln+hklAElks0mUln7J3XnvPvLsZp63QRURAbjU8AN+F6V60BEQAmPDCCRbDaRipp31p3z7q/EaidPcR2PiBSUlcC9+N5M14GIKOGRnBLJZmPKE5d7Z7/r6zFv6jTX8YhIQVoOrMX3dBV2cUoJj2SVSDYbE6+4tO6sq74VnzBjhut4RKSgzQDuwfcudx2IlC4lPJJdNP467+yrvhOvnzXLdSgiUhSqgN/ge59xHYiUJiU8coJEsvmC2tMu+1bZpDm6iJiIjKYI8BV87wf4Xtx1MFJalPDIcRLJ5nOqFl/w5YpZSxa5jkVEitaHsLekqHMdiJQOJTxyTCLZ3Fw+c8nfJRad1+w6FhEpeq8F7sL3dONhGRdKeASARLL55Jg39TO1p60830QiOi9EZDycCtyhpEfGg77YhESyeZYpq/ykd867zzexskrX8YhISTkFuBPf00VNZUwp4SlxiWRzLfDJunOuvjBaWTvZdTwiUpKWYZMefQbJmFHCU8ISyeY4cE3t6Ze/Nj5x5nzX8YhISTsZJT0yhpTwlKhEstkAVyUaz7q0Ys6y5a7jEREBTsJ2ZNZtbGTUKeEpXReWTVt4VdXS157tOhARkTRLsUnPVNeBSHFRwlOCEsnmxdHq+o/WnnHl+SYSjbmOR0QkwxJs0qN7+MmoUcJTYhLJ5mkmVvZXdedefV4kXlHtOh4RkRwWA3fje9NdByLFQQlPCUkkm6uAT3hnX3VBtGqCfjmJSL5rwiY9uoGxjJgSnhKRSDbHgA8nms45s2xyQ6PreEREBqkRW97SdXpkRJTwlIBwRNaV0drJ51YtPv8s1/GIiAxRI/BbfK/cdSBSuJTwlIazMeYt3pnvPM1E4/rAEJFCdA5wPb5nXAcihUkJT5FLJJtnAx+oWf6WWbGa+rmu4xERGYGrgH9yHYQUJiU8RSyRbC4HPlI2dUGiYu4p57uOR0RkFHwO33u/6yCk8CjhKW4rTaxsTs1pl11sIpGo62BEREbJ9/C9i10HIYVFCU+RSiSbFwFvqW1+W2O0olr3phGRYhIHfoPvLXEdiBQOJTxFKJFsrgY+UtGwvKps6sJm1/GIiIwBD2jRLShksJTwFJlwCPrVkYqa+uplr7/EGA1oEJGi1QCsxvcqXQci+U8JT/E5HTjXO+tdKyLxihrXwYiIjLEzgJ/ie/o+k37pBCkiiWTzJOADVUsunBifOGOp63hERMbJlcBXXAch+U0JT5FIJJujwAdi3tTqRONZr3Mdj4jIOPs0vvcR10FI/lLCUzwuBpbWnn756bqasoiUqG/he69xHYTkJyU8RSC8mvJVlQvOKI95U3VjUBEpVWXAf+N7nutAJP8o4Slw4dWUryEab69afJ5KWSJS6uYDP3IdhOQfJTyF763AjJrlb05GyqvqXQcjIpIHrsT3Pu46CMkvSngKWCLZPBO4NFo7OVUxa6nulSUi0uer+N4ZroOQ/KGEp0CFFxh8N9Beu+ItF5lorMx1TCIieaQM+BW+V+c6EMkPMdcByLCdApxcMWdZV2zirGWugylkXQd3s6fl6/S0HgAM1ae+gdrTLqP7yCH2/O5f6Dq4k1jtVCZdvopoRXXWbfQcbeOV//woicYzmfj6jxJ0dbLrf/+R7kN7qFl+KTUrLgVg7y3/l+pT30T5tIXj+AxFSlYD8F3sj0MpcWrhKUBhR+U/xZi9VUtf+ybdPmKEIlEmXPRBZnzou0z7069xaF0LHXte4uBDv6ai4RRm/vkPqGg4hYMP/TrnJg7c+xPKZ5907P9HXlhH+awlTP/Atzn89J0AdOzaQtDTo2RHZHxdhe+913UQ4p4SnsL0OmBi9bJLktFE7XTXwRS6WPXEY0lIpDxBvH423Yf20rZ5DVUnXQxA1UkX07bpoayPP7pjM92tB6ict/zYPBOJEnQehe5uCOy8A/f+lLrz3jO2T9w4pzUAACAASURBVEZEsvk2vjffdRDilhKeAhPePuKKSGXtvsqG5a91HU+x6UrtpGPnFspnNNHdeoBY9UQAolUT6G49cML6QdDD/jv/kwkXffC4+RXzltOV2sWrP/k0tae9lbZNayibuoBYjQbSiThQA/wM31M3jhKmg19Awo7K7wB6al7z1nNNrCzhOqZi0tNxhN03/DMTL/4wkfLjX1pjDNkKh4fWtVC54DRitZOOXz8SZfLKzwAQdHex87+/wJQr/5Z9d/yA7oO7qTrpYhLJ5rF6KiJyojOBvwO+6DoQcUMtPIWlCTizbNrCrrIp8093HUwxCbq72H3DP1O15EISTWcDEK2qo+vwPgC6Du8jUnXiYI+jrzzLoUdb2P7dD7D/rh9x+Kk72X/39cetc+ixFqpPei1HX9lIpLyKSZf9DQcfvmHMn5OInODz+N7ZroMQN9TCUyASyeYY8KfAgeplb1hp1FN51ARBwN6bv0m8fja1Z1xxbH5iYTOtT92Bd+Y7aH3qDhILT2yRmfzWzxz79+H1f6BjxyYmXPi+Y/O62w9zZPPDTHnXP3Bk81owBowh6Do6ps9JRLKKAt/D91bgpzpdByPjSy08heM8YGblwub6WE19g+tgisnRl5+h9em7aH/pSV758cd45ccf48jzD1N75ttp3/oYL3//w7RvfZzaM99h1391E3tv/tagtp26/xd4Z78TYyJUzlvB0e3P8OoPr6VqqbpfiThyEvAp10HI+DNBELiOQQaQSDZ7wHVAqv4NH7s6Wj1htuuYRHL47NbrLr0u24KVTfELgPcDWwHOnBWd9rnzyj8yjrGJ9GoDluKntroORMaPWngKw2VAvGLeimlKdkRERiwBfMd1EDK+lPDkuUSyeS5wEfBKInmm7pclIjI63ozvvc11EDJ+lPDkv5VAe/nsk2fEaibNcx2MiEgR+Sa+V+M6CBkfSnjyWCLZPBtYAeyqajpHrTsiIqNrJvCProOQ8aGEJ7+9Cegon7FoasybknQdjIhIEboW31vhOggZe0p48lQi2TwdOAvYmVh0nlp3RETGRhT4D3xP34dFTgc4f70R6CybuqA+VjdtsetgRESK2OnAX7gOQsaWEp48lEg2T8FeaHBn1ZILztNFlUVExtyX8L3proOQsaOEJz9dAnTHJ831YhNmnuQ6GBGRElALfMN1EDJ2lPDkmUSyuR573Z0dVUsvPFf3zBIRGTfvxPcucB2EjA0lPPnndQCxCTOq4/WzT3EdjIhIifkn1wHI2FDCk0cSyeY64PXAq9VLLzrbmIiOj4jI+DoX33uj6yBk9OkLNb+8FjCmrNLEJ81V646IiBu6GGERUsKTJxLJ5hrsUPQdVU3nLDXRWLnrmEREStRp+N4VroOQ0aWEJ39cAMSAzvIZi3XVTxERt/5BFyMsLjqYeSCRbC4HLgV2xqfMnxStnjDbdUwiIiXuJOBdroOQ0aOEJz+cDFQARxMLz1juOhgREQHAx/eiroOQ0aGEJz+8HjhENBaJT25QZ2URkfzQCLzXdRAyOpTwOJZINk8DmoD9ieRZTZFYWZXrmERE5Jgv4Htx10HIyCnhce8MoBsIKmafpM7KIiL5pQH4kOsgZOSU8DiUSDbHsOWs3bG66bXRmkkLXMckIiIn+Dy+V+E6CBkZJTxuLQaqgfZE49nLdd8sEZG8NBP4qOsgZGSU8Lj1WqANY0zZ1PkanSUikr9W4Xu6IGwBU8LjSCLZPBE4BdhbOf/0+ZGySs91TCIiktMUdF2egqaEx53XhH97KuYuU2dlEZH8d63rAGT4lPA4kEg2R4A3AHujVRMqY960JtcxiYjIgE7H985wHYQMjxIeNxYC9UBr5YLTF5tIRFfyFBEpDGrlKVBKeNw4H+gAKJsyf5HjWEREZPDeie9Ndh2EDJ0SnnGWSDZXA2cCu0x5VVm0ZtJ81zGJiMiglaMLERYkJTzj7xQgCnRXzn/NQpWzREQKzjW6qWjhUcIz/s4FDgKUT0sudhyLiIgM3Rzgra6DkKFRwjOOEsnmGuyNQg8QjUVi3pSk65hERGRY1Hm5wCjhGV+9w8+DyoYV80w0rqt2iogUpovxPQ06KSBKeMbXmcARgI5dW3Yc2fLIzV0Hdz8f9PR0O45LRESGTq08BSTmOoBSkUg2V2A7LO8A6D60p/XQYzetBdaa8qqyynkrFpRNXdAYq5uWjMTKqpwGKyIig/FefO+z+KlDrgORgSnhGT9JwtFZmQuCo60dbc/eu6Ht2Xs3YIwpn3XSzPKZixrjE2c1Ritrpo5/qCIiMgg1wHuB77gORAamhGf8nAJUAnGgM+daQRAc3bZ++9Ft67cDd8YmzPAq5p7SWDa5oTFaXT9Pw9hFRPLKB1HCUxCU8Iyfe7EJz2uAMqAH2Ae09fegrv2vpA7vf+Vh4GFTlohXzluxoGzagsaYN60xEi9X6UtExK3l+F4Tfmqj60Ckf0p4xknbpjUvAj9IJJt/DDQAy7CdmOcCAXAIOBD+O6ugo62zbeN9z7ZtvO9ZgPJZS2eWz1rSGJ84sylaWavSl4iIG+8GfNdBSP+U8Iyztk1ruoDNwOZEsvkGYAqwGJv8NAIGOArspb/SF3B0+9MvH93+9MvAXbG66bUVDac2lk2a2xStqW8wkaiOrYjI+LgKJTx5T1+KDrVtWhMAO8Pp7vA+W0ls2es0+kpf+4HW/rbVdeDVg4cff/UR4BFb+lo+v2zqwsZY3bTGSLy8ekyfiIhIaWvC95bjpx5zHYjkpoQnj7RtWnMYeAx4LJFsvh5b+joJOAtb+gJ7W4oUNhHKypa+7t/YtvH+jQDls5bOKJ+5uDFeP6spWlk7bQyfgohIqboK+/kteUoJT57KKH39Dlv6asKWvnqv7tnB4Epfrxzd/vQrwN0xb1pNRcOpjWWTG5qiNfXzVPoSERkV78L3VuGncvbDFLf0ZVcAMkpf9ySSzVXY0tcKbOmrgsGWvlI7Dh1+4pZHgUdNWWW8smH5vLJpC5tiddNV+hIRGb65wBnAGteBSHZKeApQ26Y1rcDjwOOJZPN/Yd9oJwFnY+/iC3AYO+qrn9LXkc625x54ru25B54DKJ+5eHr5rKVN8YmzGiOVNdONMWP6PEREiswVKOHJW0p4ClxY+noeeD6RbF4NTMaWvs4K/xps6Wtf+Denoy9vePXoyxteBe6OeVN7S1+N0ZpJ81X6EhEZ0BXAKtdBSHb6EisiYelrVzjdm0g2J+grfZ0O9N6dfT+2BSinrtTOQ4efuNWWvuIVsWOjviZMb4zEK2rG7lmIiBSsRnxvCX7qGdeByImU8BSxtk1r2oAngCfSSl9L6St9GfoueJi79NXZ3tX23IPPtT33YF/pa+aSxnj9rMZIZe0Mlb5ERI65AlDCk4eU8BQC31sAXAb8Hj+1aTibaNu0phvYAmxJJJtvBCbRV/pahE1+OrGjvgZb+vpjtHZKdWXDqY3xyfMaY7WT5ptIND6c+EREisSVwJdcByEnUsJTGK4Avgr8K773HPD7cLofP9U11I2Fpa/d4XRfWPpaSN8FDyvCVQcsfXUf3HX48JO3rQPWmXh5rKJh+bzyacne0lftUGMTESlwK/C9ufipF10HIsdTwlMY3pT270bg0+G0H9+7BZv83IyfOjCcjYelryeBJ8PS1xz6Lng4+FFfnUe7jmx6aNORTQ9tAlrKZjRNq5i1tDFeP7tJpS8RKSFvBr7rOgg5nhKePLSyKR7HXmjw1dXvTiSAc3OsOgF707p3A1343v3Y5OfG4d65Nyx9vQC8EJa+6rElr2bsPb8iDLL01fHKxh0dr2zcAdwTrZ1cXdmwPBmf3NBkS18xlb5EpFhdgBKevKOEJz+9HngPsOd3z3Z2XrYoXjaIx8Swb7ILgK/he5voTX7g3hGUvvYA92FLX5XYUV/LsaO+Eti7uw+i9LX78OEnb3sMeMzEy2MVc09tKJ+ebIzVzWiKlKn0JSJF5QLXAciJlPDkpzOBV4HOGTWRlcPcRhL4VDgdwPdupa/0tW84G2zbtOYIfaWvn2LLXUs5/l5fh7EJUP+lr81rNh/ZvGYzcFPZ9KapFbOW2NJXwpup0peIFLhp+F7TcFvaZWwo4ckzK5viHjALeAlg8eTIjFHYbB3wrnDqDktfN2JHfT07nA2ml76AGxPJ5knY/kVnAksYSunr1Y07O17duBO4N1ozqaqyYXkyPqWhKVYzeb6JxgbTuiUikm8uAJTw5BElPPkn2fuPU6dFJlWXGW+Utx8Fzg+nr+B7m+lNfmzpq98bkebStmnNHmz564Gw9LUQW/o6g77S1wHsdX9y6j60p/Xw+tsfBx43sbJoRcOpDeXTGptiE6Y3RsoqR/u1EBEZKxcA33cdhPRRwpN/lgNHAVZMj84eh/0tBD4ZTqm00tdNIyx9rQfWJ5LNPwNm03fBw7nY5KeVgUpfXR3dRzavff7I5rXPAzeVTUtOqZh9UlO8fnZjWPpS7UtE8pX68eQZJTx5ZGVTPIq9DcQ+gAUTIrPGOQQPeGc4deN7D9J7zR8/tWE4GwxLX1vDqSWRbK7Hlr7Ooq/01YUtfR3tb1sdOzbt6tixaRdwb7Sm3pa+Js9rjNVOXqDSl4jkmZn43gL81POuAxFLCU9+mQOUYfu+MLPWzHQYSxQ7HP5c4F/wveexpa8bgT+OoPS1F3gQeDCRbK7g+NLXlHC1QZS+9rYeXv8HW/qKxqMVDcsbyqYnG+MTZjRGyirrhhObiMgouxB7c2fJA0p48stC7C0eqC0nXldhpgyw/nhaAHwinA6Gpa8bsaWvPcPZYNumNe3AU8BTaaWvJfSVvqCv9NWdaztBd2f3kefXPn/k+bXPAzeXTVs4pWLWSY3xSbMbI4m6WSp9iYgjFwA/dB2EWEp48stJhNezOXNWdEYkf7+oa4F3hFNPWPrqHfX19HA22LZpTQ/wYjjdnEg2T6Rv1NdSbIvTIEtfm3d17Ni8C7gvWj0xUdGwPFk2ZV5jrHbKQpW+RGQcqR9PHlHCkydWNsUj2C/4fQBLJkfHu//OcEWAc8Lpy/jeC/SN+vojfqrfIem5tG1asw94CHgoLH0toK/0NTVcbT8Dlb4O72trfeqOJ1rhCRONRyvmnjK3bEZTY3zCjCaVvkRkjM3B9xrwU1tdByJKePLJJNL67zTUjXuH5dEyD/hYOB3C926jb9TX7uFsMCx9PQ08nUg2/xx7naLeCx7OwZYBB1f62vLIliNbHtkC3FI2dcHk8tknNZbVz2mKVKn0JSJj4gLsoA1xTAlP/phF2H8HYFq10w7Lo6UGeFs49eB7a+gb9fXUcDYYlr5eCqf00lczcDK2xakbe02g/ktfO5/f3bHz+d3A/dGqCZUV81Yky6bMa4rVTllgorHy4cQnIpLhAuD/uQ5ClPDkkwWErRMLJ0ZqE3FT4zie0RbBtsicBfwzvreVvlFfd41y6esUbN+f3tLXAeBgf9vpbt1/pPWpO55shSeJxiKVc0+dWza90Za+yhMThhObiAj2Iq+SB5Tw5I+lhF/Kr5lesOWsoWgArg2nw2Hp60agBT+1azgbzCh9/RLbaraY4+/1NWDpi+6uniNbHnnhyJZHXgBuLZsyf1L5nJMb4/Wzm6JVE2ar9CUiQzAf36vCT7W6DqTUKeHJAyub4mXYIdnbARrro8VQzhqKauDKcOrB99bSN+rryeFsMKP0dWsi2TwBW/o6A9sC1Fv62gu097etjl1b9nTs2rIHeCCSqKusnLdiYdmUeU0xb8pCE42r9CUi/THYH16PuA6k1CnhyQ8zsLdbCABm1ZpSaOHJJYItR50J/BO+9yLQgu37cxd+qt9+Obm0bVqzH1gDrEkkm8vpK301Yy94GAAp7KivINd2etoOHGl9+s71rU+znkg0UjH31DnlMxqb4hNmNkbKExOHE5uIFL2lKOFxTglPfpiF/aInHiEyucpMdxxPPpkL/EU4Hcb3/oBNflrwUzuHs8G2TWuOAs8AzySSzb8CZmJ/gZ1N36ivNuwlAnKXvnq6e9pfeHRr+wuPbgVujU+eV18x5+Sm+KQ5jdGqCXNU+hKR0BLXAYgSnnyxmLCsctqM6JRYxMQdx5OvqoHLwynA9x6mb9TXE8PZYFj62hZOtyWSzXVkL33tY4DSV+fuF/Z27n7hAeCBSMKrqJy3Ilk2ZX5jWPqqGE58IlIUlPDkASU8jq1sihtgEWGH5cb6yNT+HyEhg01KzgD+Ed/bRt+orzvxU/0mJ7m0bVpzAFgLrA1LX/PpG/XVe6uPFPZ49VP6SrW3Pn3X+tan7wpLX6fMKZ/e1BifOKMxUl5VP5zYRKRgKeHJA0p43KsFJmA71zKt2mgI9PDMBj4aTq1h6csmQH5qx3A2GJa+NgAbspS+ekd9HWFQpa91W9tfWLcVuC0+uaG+Ys7JjfH6OY3R6glzjIlEhhOfiBSMBnwvgZ9qcx1IKVPC495M0loKJiWMOr6OXBVwWTgF+N4j9I36emw4G2zbtCbAjqLbDtwelr6S2E7Py7D3+uphEKO+Ondv3du5e+uDwIORytqKynkrFpZNnd8Yq52aNDGVvkSKUATbkr/OdSClTAmPew3p/6mrUAvPKDPA6eH09/jedvpGfd0xwtLXw8DDiWRzGcMtfR052N76zN1PtT5z91OYiKmYe8qc8hlNjfEJM5siFSp9iRSRJSjhcUoJj3sN2LIIALXlSnjG2CzgI+HUhu/dgU1+bsRPvTqcDbZtWtMBPAs8m0g2/zf2MgO9FzycF652BHvBw66cGwp6gvatj73YvvWxF4Hb45PmTqyYs6wxPmlOk0pfIgVP/XgcU8Lj3nTChMcrp6wybqocx1NKEsBbwynA99bRN+prWL/EwtLXy+H0h0Sy2cOO+jodOBX7nusd9XUk13YAOve8uK9zz4sPAQ9FKmvK7QUPFzTFvKkLTSxeOZz4RMSZpa4DKHVKeBwKR2hNBXYCNE2KqnXHHQO8Jpx8fO8V+kZ9/QE/1W9ykkvbpjUpTix9LcOWviaHqw2i9HXoaOszf3y69Zk/Po2JmIo5y2aXz1zUFJ8wozFSUT1pOLGJyLhSC49jSnjcqqHvFz9zPZWz8sgM4M/D6UhY+uod9fXycDaYUfr6dbiPRdjS14Jwtd5RX/2Xvl58/KX2Fx9/Cbg9Xj9nQsXc3tLXxLkqfYnkpXn4XsVw+w3KyCnhcWsCdmQPADNqIhqhlZ8qgbeEE2Hpy476gkfxUzlbZnLJKH3dkUg212JLX6cBy4E4gy197X1pf+fel9YAayIVNeUV85YvKJ+6oCnmTU2aWJlKXyL5IQo0AcO6SKqMnBIetyZiSykATEqohadArAinLwCv4nu9o77+MNzrbLRtWnMQe6+dR8LS1zzgZGzrz5xwtYPY8lfu0lf7oaNtG+55pm3DPc9gjKmYs2xW+YxFTbGJMxujFdWTcz1ORMbFQpTwOKOEx6160hKeiZVKeArQdOBD4dSO791J36iv7cPZYFj62ghsTCSbfxPuI7P01c6Apa8gaH/xiW3tLz6xDfhDvH72hIo5yxrjk+c2RqsmzjWRSHQ48YnIsOlK+g4p4XFrJnDs7t+ersFT6CqAN4fTd/G9x+kd9QWPjKD09Uo43RmWvpLYUV/Lse/hAJv89Nu61Ll32/7OvdvC0ld1WcW8FQvLpi5ojNvSV2KosYnIkE0ZeBUZK0p43JpJ2D8jFsFUxalzHI+MrlPD6e+AHWHp60bgdvxU63A2GJa+HgUeTSSb49jS1zKGXPo63HFc6Wv2ybPKZy5ujE2c0RitqNGHssjY0HvLISU8bk0DDgMsnBjxohGj0TXFaxrwwXBqx/fuou92F9uGs8G2TWs6geeA58LS1zT6LnjYW/o6ir3dRf+lr5ee3Nb+0pPbgDtiE2fVVc5d1hifNLcxWl3foNKXyKhRwuOQEh5HVjbFy4FqbCmChrqIylmlowJ4Uzh9B997gr5RX2tHUPp6NZzuTCSbazhx1NegSl9d+7YfOLRv+1pgrSmvKquct2JB2dQFjfG6aY0qfYmMiBIeh5TwuHPckPQJFUZfJKXrlHD6PLAT37sJm/zcNoLS1yFOLH1ljvo6BBygn9JXcLS1o+3Zeze0PXvvBowx5bNOmlkxc3FjbOLMpmilSl8iQ6T3jENKeNw57po7VWWUuwpE8spU4P3hdBTfu5u+2128NJwNZpS+/hdb+lqEvdpzMlztKLb1pzPnhoIgOLpt/faj29ZvB+6MTZjplc9cfEZi4elHTTR+NugcFhmAEh6HlPC4MwF7ISoAEnGjLwvJVA68IZy+je+tp2/U11r8VE9/D84mo/R1V1j6StJ3W43Bl772v5zq2v/yhtan/vC5KW/7QhvweuwNUkUkuzp8L46fyv3DQsaMEh53ppLWkbQyRoXDWKQwnBxOnwN2haWvG4Fb8VOHh7PBsPS1DliXSDb/mL7S15kMofS19bpLDwM3DCcGkRJisPfQe8V1IKVICY87NaSVDyrVwiNDMwV4Xzh1hKWv3lFfW4ezwbZNa7qATcCmRLL5BmxSnl76itB3wUP9QhUZniko4XFCCY871YQ3DQWoiKn/gwxbGXBJOH0L33uKvlFfD42g9LUjnO5OJJurOb70VY7tdB8flWcgUjrUj8cRJTzuVJFW0iqPqoVHRs1J4bQK2I3v3YxNfm7FTx0azgbbNq05DDwGPJZINl8PNIT7mA8MaySZSIlSwuOIEh53qkhr4SlXHx4ZG5OB94ZTB753D32jvl4YzgbD0tfmcBKRoVHC44gSHncSHNfCo5KWjLky4HXh9E187xn6Rn09OJzSl4gMmRIeR5TwuJMgbQhvXCUtGX9LwulvgL1po75uwU8ddBqZSPGqcR1AqVLC48DKprjB3l7gWEmrTC084lY98Kfh1BmWvnpHfT3vNDKR4lLmOoBSpYTHjTh2iO+x65oo4ZE8EgcuDqd/w/c20Dfq6wH8VHd/DxaRfmlkoyNKeNwoJy3ZiUeIxCJGbwLJV4vD6TPY0tct2OTnFvxUymlkIoVHn/WOKOFxo4K0G4dOrFT/HSkY9cCfhFMnvncffaO+NGpLZGBKeBxRwuPGcQmOV2FU05VCFAcuCqev43sb6Rv1db9KXyJZKeFxRAmPG2rRkWLUFE7/B9gfXvDwRuBm/NQBp5GJ5A8lPI5EXAdQoo5LeHqC3DdlFClQE4CrgZ9jr/Z813WvK39nY32k1nFcIq4p4XFELTxuVGDvmgtAd0+ghEeKWQy4cMnk6IVfuyRKqj04f9O+nud2tfbsH/CRIsVHn/eOKOFx47gMXy08Ukq8ClN/2ozoWRB1HYqIC7qiuSMqablxXIKjhEdEpGTo894RJTxuBKSXtJTwiIiUCrXwOKKEx43jEpzuHiU8IiIlQp/3jijhcSMg7aRv7wp0vRIRkdKgFh5HlPC4cVyG39pJp6tARERkXKmFxxElPG4cd8J39RB09wTK+kVEit9h1wGUKiU8bpyQ3HQHauURESkB+1wHUKqU8LhxQp+d7h66XAQiIiLjSgmPI0p43OjixLKWWnhERIqfEh5HlPC4cUJy0x2ohUdEpAQo4XFECY8bJyQ3nd1Bh4tARERkXCnhcUQJjxsntPC0dqrnvohICdjrOoBSpYTHjRNaeA4dDQ65CERERMaVWngcUcLjRidp99ICSCnhEREpBUp4HFHC48ZRMhKefUeU8IiIFLke4IDrIEqVEh43jmBP/GOv/962QH14RESKWwo/pavqO6KEx4HVGzsDYD9Q1jtvZ6taeEREipzKWQ4p4XFnH2kJz/aDPUp4RESKm0ZoOaSEx529QHnvf7YfDFp7gkB30RURKV5q4XFICY87u0lr4ekJCNq7dC0eEZEiphYeh5TwuLMXiKXPaO1Qx2URkSL2gusASpkSHncOYUdqHdPaifrxiIgUr42uAyhlSnjcOUTGHdMP6uKDIiLF7DnXAZQyJTzunJDcKOERESlqauFxSAmPO4fIeP11tWURkaK1Cz+Vch1EKVPC484JV1t+5VCgS46LiBQnte44poTHkWxXW16/s3unu4hERGQMqf+OY0p43NpH2sUHtx0MWo90Bq0O4xERkbGhFh7HlPC4tYe0Fh6AvUcCtfKIiBQftfA4poTHrW1AZfqMVw8p4RERKUJKeBxTwuPWK2Rci+elVI8SHhGR4tINPO86iFKnhMetE5KbjXuV8IiIFJmt+KkO10GUOiU8bu3BtvAcOw6P7+je3RMEPbkfIiIiBUYdlvOAEh6HVm/s7Aa2A1W989q76E616466IiJFRP138oASHveeJy3hAdjdprKWiEgRedp1AKKEJx9sIWNo+ssHNVJLRKSIPOA6AFHCkw92kjFSa8t+tfCIiBSJ/cAG10GIEp58sBMw6TOe2qVbTIiIFIkH8FPBwKvJWFPC497hcDpW1np+f3DwaFdwxF1IIiIySu53HYBYSngcC28i+gJQnT5/T5v68YiIFAElPHlCCU9+2Awk0mdsPdDzkqNYRERkdHQAa10HIZYSnvywnYx+PI/t6N7iKBYRERkd6/BT7a6DEEsJT37YQcZIrXte7N7e1RN0OopHRERGTuWsPKKEJz/sDv8eOx7tXXS/eihQWUtEpHAp4ckjSnjywOqNnV3YS4976fM37et5wU1EIiIyCpTw5BElPPljHVCTPuPhl9WPR0SkQG3GT+1yHYT0UcKTPzaT0Y/nwe3dO3Q9HhGRgnSf6wDkeEp48sd2oAuI9c7oCQi2Hwy2OotIRESGS+WsPKOEJ0+E/XieAurS5z+7R2UtEZECpIQnzyjhyS/ryLgA4YPbu9VxWUSksLyEn9INQ/OMEp788gIZ/Xie3Nmzt7UjOOgoHhERGbobXQcgJ1LCk192AG2k3UgU4MWUhqeLiBSQ37sOQE6khCePrN7Y2QM8AUxIn//M7h714xERKQyHgbtcByEnUsKTf54AVGdJ3AAAIABJREFUKtJn3PNil1p4REQKw234qaOug5ATKeHJPyf049l6IDi0t61np6N4RERk8FTOylNKePLPXmAfUJk+86ldPU+7CUdERAapB2hxHYRkp4Qnz6ze2Blgh6cf14/n9i1dz7iJSEREBul+/NTugVcTF5Tw5KengWj6jCd39uxVWUtEJK/9j+sAJDclPPlpc/j3uOOjspaISN4KgN+4DkJyU8KTh1Zv7GwFngQmps9XWUtEJG89iJ962XUQkpsSnvx1Pxm3mVBZS0Qkb6mcleeU8OSvDdgm0uOO0ZM7e55yE46IiOQQoIQn7ynhyVO5ylo3Pte1PgiC7A8SEREX1uKntrkOQvqnhCe/nVDW2rSvJ/Xq4WCrm3BERCSL/3IdgAxMCU9+y1rWevjl7ifdhCMiIhlagZ+6DkIGpoQnj4VlrSeA+vT5v32265munqDLTVQiIpLml/ipg66DkIEp4cl/95Bxm4m9R4KjL+zv2egoHhER6fMfrgOQwVHCk/82AB1APH3m/dtU1hIRcWwdfuoR10HI4CjhyXOrN3YexXZenpw+//cbuza3dgSH3EQlIiLA91wHIIOnhKcwPATE0md09tDz8CvdDzuKR0Sk1B0Cfu46CBk8JTyF4XkgRUZfnp+v73xEnZdFRJz4GX7qsOsgZPCU8BSA1Rs7e4A7gUnp83ccDo5s2N2jvjwiIuNP5awCo4SncKzFHi+TPvM3GzofchOOiEjJWoOfetx1EDI0SngKxOqNnTuBdcCU9PnrXu3ZvS3Vs8VNVCIiJUmtOwVICU9huZWMfjwAtz3fpVYeEZHxkQJ+5ToIGTolPIVlE7Ad8NJnrt7YtelAe7DHTUgiIiXlJ/ipNtdByNAp4Skgqzd2BsBqoC59fgDc/1LXGidBiYiUFl1ZuUAp4Sk8j2NvVleRPvPn6zufONoVtLsJSUSkJNyIn3radRAyPEp4CszqjZ0dwM1kXHn5UAedT+zsftRNVCIiJcF3HYAMnxKewnR/+DeaPvOXT3Wt7QmCHgfxiIgUuxb8lH5UFjAlPAVo9cbOA8B9wNT0+Zv39Rx8fl/PBjdRiYgUNd91ADIySngK1x1AWebM1Rs1RF1EZJTdpLuiFz4lPIVrG7ARqE+f+ccXu7e/lOrZ7CYkEZGi5LsOQEZOCU+BCoeotwDVmct++mTnHUEQjH9QIiLF52b81MOug5CRU8JT2J4G9gFV6TMf2t6947m9PevdhCQiUlT+3nUAMjqU8BSw1Rs7u4HfkzFEHeBHj3Xe1d2jEVsiIiNwC35KF3UtEjHXAciIPQhcgW3lae2duWFPz/4nd/Y8snx69AxnkYmUoI17unnX/xw59v8t+3v4h4vKuaghxjUt7RzuCGioi/CzKyupLTcnPP5Ae8CHVh/hqV09GAM/WlnBWbNj/M3t7dy8uYtTp0X5ryvsLfV++mQHe9oCPnlm+bg9vxKj1p0iohaeArd6Y+dR7I3sTmjl+cG6jns6u4OO8Y9KpHQ1TYry+DXVPH5NNY/+eRWJuOGKRXE+9PsjXHdxOes/Ws0Vi2J89f6jWR//iVvaeePCGM9eW80T11SxeHKUVHvAuh3dPPnRasqisH5nN0c6A378eCd/efoJgzVldNyKn9Ko1yKihKc4rAV2knFT0e0Hg9a1L3frDSviyB0vdLNgYoS5dRGe29vD+XPttUJfPz/GbzZ0nbB+qj3gnhe7+ODyOABlUUNdhSFioLMbgiCgrTMgHoWvPdDBx84oIx49sZVIRoVad4qMEp4isHpjZxfwC2BC5rLvP9pxf3tXoDv7ijjwy6c6efdJNnlZOjnK7zbaJOfXz3Sy7eCJXexeONDD5ITh/b9rZ/n3DvOh1Udo7QioKTe8ORlj+fdamV4dwSs3rHm5m8sXxcf1+ZSQ2/BTD7oOQkaXEp7i8STwPBnX5dnfTsc9L3b///buPT7uqs7/+OvMZHJrGtq09F4plzJcClSoXCqoWND1Ni6uoqjsquuu19/ubx+rqw9/ulsVUNcVEV2BxQUEFRTlMiAXsVDaQltaWtqml2lL7zRN0jSXTi4z3/l+z++PM7VpmkBvmW8y834+HvNIk+83zWegmbxzzuecszCckkRKV9a3JFM5PnKOa5W864OV/HxZlov+J83+jBu96SsXwIqGgC/MirHyczWMiBm+v8hNff3bWyt45fM1/OjdlXzruQzfubKCX6zIcu2DXdywoP/pMTl61h3P8/Ww65ATT4GnSCRTXgD8DqgFDnkl/d8V2WXprG0PpTCREvXkphwXTowwvsa9zJ41Nsqfrh/By/9Yw3XnlXH66MMDz5Raw5RawyVTXEj68DllrNhz6EjQygYfayE+JsKD6zx+95FqXm0N2NTiD/6TKgHGmNuZ274y7DrkxFPgKS4bgVX0aWDuzuE/vTn3XDgliZSm+3tNZwE0dbrgEljLDQuyfH7W4c3GE2oiTD0pQmqvCy/ztuY4Z+yhL9Pfei7Dd99ZgReAn89CEaDLG5znUUqstc3AN8OuQwaHAk8Rye++/Hugij7/b+9b7a3e122bQilMpMR0Zi3PbPH50NkHA8/9azzO/Gmas37WyaSRhk/PdNd27w94768Pttn99D2VfOKhbs6/Lc0rewK+ccXBJeePbPCYNSnCpJERRlUaZk6Ict5taXp8ywUTooV7gkXKGPN15ra3hl2HDA6jIwiKTyIe+0dgFrC798evOavszE+/ufy6cKoSERm6rLVLjDGzmduuH4pFSiM8xelR3KaSh/zK9/CG3MZtbcHGcEoSERmarLWBMeZLCjvFTYGnCCVTXiPwDDCx77WfLMn8MavNCEVE/sIYcwdz21eEXYcMLgWe4vUkEACHdEa+2mo75m3JzQunJBGRocVa2wL8v7DrkMGnwFOkkimvDXew6IS+1+542Vu2Jx3sLHxVIiJDizHma2pULg0KPMXtGWAffY6cCCz29uXZx/zAauMOESlZ1tqlwF1h1yGFocBTxJIprwf3zVxHn//XKxqC5iW7/EWhFCYiEjI1KpceBZ7itx5YAEzqe+EnS7ML23rs3sKXJCISLmPM/zC3/eWw65DCUeApcvnNCB8EMsCI3td6cvh3r8wmtReTiJSSfKPyN8KuQwpLgacEJFNeB3APMJ4+52w9t83fuaoxWBZGXSIiYTDGfE6NyqVHgad0LAdW0M+qrVuWZOd1Zm1H4UsSESmswNpfMbf9D2HXIYWnwFMi8lNbv8KN8FT0vrav22YeXOf9MZTCREQKxA/s7ogxXw67DgmHAk8JSaa8vcAD9NPA/ND63MbN+/x1ha9KRGTwWWttNGKuZ257e9i1SDgUeErP88BmYFzfCz9enH2i27OdhS9JRGRweQH/zdz2Z8OuQ8KjwFNikinPB+4GqnAHjP7Fzg7b+es13iNatSUixSTr283lUfPVsOuQcCnwlKBkytsFPAJMPvxabvPy3cGLha9KROTEC6z1yqPmw8xt7wm7FgmXAk/pegpoAkb3vfCDFzLzmjqD1wpfkojIieX5/Btz21eFXYeET4GnRCVTXga4E3fOVqz3taxP8F8vZn+f9W0mlOJERE6ALs/+qeKGjlvCrkOGBgWeEpZMeZuB3wNT+l7bsDdo+8O63GOFr0pE5Phlcra5OmauC7sOGToUeORJYC0wse+F++u9tasb/eWFL0lE5NgF1gaB5Vrmtu8LuxYZOhR4Slx+1dYvgBxQ0/f6jQsyTzV3BrsLXpiIyDHqzPLDqhs75oddhwwtCjxCMuXtA24DTgaiva915/C/vyj7u56c7Q6lOBGRo9CRsfNHVhgdDCqHUeARAJIprx54HJja99qmfUH7L1/xfh9ogx4RGcI6MnZHbYX5AHPbg7BrkaFHgUd6ewS3C/P4vhf+uCm3ZdEOf37BKxIROQLdnu1s7bZzmNueDrsWGZoUeOQvkinPw01tBcDIvtd/9GJ2wba2YGPBCxMReR25wPobW4LrTrll/+awa5GhS4FHDpFMeS3Az4Ax9NmfxwLfeT7zcGu3bQ6jNhGR/tQ3Bd++4Pa0ttGQ16XAI4dJprz1wIO4fh7T+9reLttzw4LMr7s8uz+U4kREetnY4j848/b0d8OuQ4Y+BR4ZyJPAMvo5b2vTvqD9x4uzv9ZOzCISpp3twcqH1+c+FnYdMjwo8Ei/kikvwJ2qvg83vXWIpa/5jXet9H7nB1arIUSk4Fq6goZUSzDna3/u0WuQHBEFHhlQMuWlgZ8C5fSzKeETm3JbHt6Qe1Sr1UWkkDqztrO+Kbj6qns7W8OuRYYPBR55XcmUtxO4BRgLVPa9fu8qb/X8bf68ghcmIiUpF1h/TZP/8bff07k27FpkeFHgkTeUb2K+HZhEn5VbAD9ekl30yh5/WcELE5GSU98UfPPSX3Qmw65Dhh8FHjkiyZS3BHgAt3LrsH83c+dnntzSGmwoeGEiUjLqm/x7Zt6e/n7YdcjwpMAjR+NJ4ClgGn2WqwcW+81ne/7QmA52hVGYiBS3FQ3+Y9+Yl/n7sOuQ4UuBR45YMuVZ4LfAS/Rz5lY6S+5bz2V+095jWwpenIgUraW7cvPnzs98JL96VOSYKPDIUUmmPB/4BfAqrqfnEHvStvvGhZlfdXu2s+DFiUjRWbwzt+zGhdlrkilP+37JcVHgkaOWTHk9uOXq+4Bxfa9v2Bu0/XhJ9lc9Odtd8OJEpGgs3plb/b1F2b9Jpry2sGuR4U+BR45JMuV1ADcDPjC67/Ulu/w9P3whc49GekTkWLz0Wm7t9xZlP5jfGkPkuCnwyDFLprwm4EdANf1sTLhsd9B008LM3Z1ZnbslIkfu5d3++h8syiaSKW9b2LVI8VDgkeOSf0EacGPCVY1By3eez9ydztr2QtcmIsPPygZ/440LM4k/rPe2hF2LFBcFHjluyZS3FrgDmEg/oWf93qD1P57L3N2RsfsKXpyIDBurG/3N31uUSTy03tscdi1SfIzOQZITJRGPXQx8CWgEuvpenzbKjPzOlZV/O6rSjC14cSIypNU3+VtuWpj5wG/WeOvCrkWKkwKPnFCJeOxC4P8Ae4HDGpYnjTTVN82p+Nu6qsj4ghcnIkPS+mZ/+00LMx+4b7W3JuxapHgp8MgJl4jHzgP+L9AKpPteHz/CVN00p+KTJ4+IHLaPj4iUlmWv+ambF2c+en+9tyrsWqS4KfDIoEjEY2cD/wp05B+HqKsyFd+/quITE2oih+3YLCLFz1rLU5tzL9+23Pv7ZEphRwafAo8MmkQ8Nh34Cq6f57BVWrUVxH5wVeXHJ9dGphW6NhEJjx9Y/95V3gsPb8j9SzLlrQi7HikNCjwyqBLx2GnAV4EsborrEDXllN00p/LaaaMi0wtenIgUXCZne25dmp2/cIf/78mUtyzseqR0KPDIoEvEY6fgQo8FDjtYtCyC+cYVFVfPmhS9rODFiUjBdGRsx00LM8+saw5uSKa8V8KuR0qLAo8URCIemwx8Dbf3097+7vn0zNjMRLzs/dGIiRa0OBEZdI3poHnu/Mzjr+23NyRT2lRQCk+BRwomEY9NwIWeCqCpv3uunBad+vlZ5R+tipkRBS1ORAbNphZ/x9z5md/vz/KfyZTXGHY9UpoUeKSgEvHYOFwj8yhgd3/3nFEXqf3GFeXXja2OTChocSJywr30Wm7D9xdl78sF/Cx/6LBIKBR4pOAS8Vgt8AXgbGAHEPS9Z2Q5sW9fWfHXZ9RFzyl0fSJy/Ky1PLEpt+KOl707gbuTKS8Tdk1S2hR4JBSJeCwGfAy4GtiFW8V1CAN8ZXb52y9/U/QdxpgCVygixyoX2Ny9q7zFj2zI/RR4KJny/LBrElHgkdAk4jEDvAP4O9zqrf393ffRc8vOvvbc2DWxqIkVsDwROQZtPbb1v17MLFzdGNwCzE+mPP2QkSFBgUdCl9+V+Z9wU1vN/d1zyeTo+H++tPy6mnJzUkGLE5Ejltrrb75hQebF9gw3a/dkGWoUeGRISMRj44F/BsYDO/u7Z0qtGfHvb6/4qI6jEBla/MAGT2zKLb5zhbcSuDmZ8raGXZNIXwo8MmQk4rERwGeBi4DtwGHz/uVRIl97a8WcWZMis9XXIxK+zqzt+MnS7IIlu/x64MfJlNfvlhMiYVPgkSElEY9FgWuABG7Zek9/971vetlp118Qu6Y6ZmoKWZ+IHLS1Ndh8w4LMiuYuOx/4ZTLldYVdk8hAFHhkyMk3M18K/APupPW2/u6bNNJUf/3yig9OGxU5s5D1iZQ6P7D+E5tyi+5c4W0F7sM1Jx+2vYTIUKLAI0NWIh47HdfXUwE0DHTfF2bFLr769LKryyKmrGDFiZSoth679+bFmQWv7Al2AD9LprxXw65J5Ego8MiQlojH6nB9Pefi9uvx+rvvLZMi4774lvIPjamOjC9kfSKlZHWjv/KmhZl1XR4rgF9o52QZThR4ZMjL9/W8G/gIboqrtb/7KsuI/sul5W+/ZEr08og6mkVOmG7Pdj5Q781/eENuL/Ag8JQ2E5ThRoFHho1EPHYa8EVgNG60p99/vG87JTr5sxeWXzOq0owpZH0ixcZay9rmYOWPXsyubem2TcBtyZS3Oey6RI6FAo8MK/ml69cBb8P19XT3d19NOWVfmV1x1ZsnRC7RYI/I0WvrsS2/fCX79Lytvg+8CNyXTHmdYdclcqwUeGTYya/iuhj4TP5DAzY0v/v0smnXXxD7QG2FqStIcSLDnB9Yf/Euf9GtS7Pre3KUAfcAL+qICBnuFHhk2ErEY2Nx53BdwOvs2VNVRvQLbym/7K1To2/TeVwiA2vYH2z/72XZp1c3BhXANuCOZMob8BcKkeFEgUeGtUQ8FgGuAD4J5IDGge49dZQZ+aWLy9915pjojELVJzIcZHK258nNuWfuXuk1WIgAv8XtrZMLuzaRE0WBR4pC/iyuzwBx3GhPdqB7rz4tesonzo+9p65KS9hFNrb49Tcvzi7Yvd+OBF4GfpNMef0e4isynCnwSNHIL1+fA1yLO4drDwOs5CqLYD57YWzWnFPL3llRZioLWKbIkLA/Y9vur/f++PjGXAboxPXqrCxEr44xxgfWAGXAVuB6a22/O6rn7x8FfNxa+/Mj+LtftNbOPgE1TgMet9ZqRLhIKPBI0UnEYyfjQs/FuD17BnwhnVBjqr58cfmcGeMiF2rvHikFnm+95bv9JbcuzdZ3elQDfwYeSaa8dKFqMMakrbU1+T//Ethorb3xde6fRoHDhwJP8VHgkaKUX8l1NnA9MBG3kisz0P2zp0Ynfmpm7D0TaiJTC1SiSEH5gfVXNwYv37kiu3hXhx2Fm/q9K4x9dfoEns8D51trv5h//6u4X1gqgIettf9hjHkA+CCQAp4Bvg08ituTKwZ801r7aO+/2xjzDmAusBeYgZuu+6S11hpjLgJuBmry1z9lrW3If/yufJl/At6jwFM8FHikqCXisRhuz55rcc2YDUC/hxwa4PoLYue/d3rZ1TqFXYpFYK3dsDdYdddKb/7GlqAKN430EPDnZMobsNdtMPUKJVHgAeB/rbVPGWPeBXwY+BzuWzIJ/Cewg16jLcaYMqDaWtthjBkLLAGm58NM78DzKO5Ymt3AC8BXgaXA88AHrbXNxpiPAu+21n7GGLMa+LK1doEx5oco8BQVHbYoRS2Z8jxgXiIeexn4EC787Ada+t5rgXtXeasfS3kbPv3m8osvnRK9rLLMVBe2YpETw1rLlla7/t5V2WdX7gm6gPFAPW4DwT0hl1dljHkFmAysx43aALwr/1iZf78GmI4LPL0Z4CZjzNtwv8BMxj2/vs/rJWvtLoD815uGm+KeATyTn8WOAg35PqFR1toF+c+9D3jPcT9TGTIUeKQkJFNeG3BXIh57HjfNdSruxfGwnZpbe8jevDi7qLaCpZ+aWT5r9tTobI34yHCysz149TdrvHkv7PT3ARNwef424KVkyut3hLPAuq21M40x1cDTwJeAW3FB5nvW2jt635zvp+ntE8DJwEXWWs8Ysw3ob/FB72lsH/czzwBrrbWX9fkao4752ciwEAm7AJFCSqa8V4HvAnfifnucivsN7zAdGbxbl2YXf+bR7p88tTn3VGfW7i9gqSJHbU862PmzlzL3fOmJnt++sNOPAaOA+4GvJlPekiESdv7CWtsF/BPwr/lpqqeBzxhjDvT3TDbGjMONyo7s9aknAU35sHMlcMpRfNkUcLIx5rL814gZY87NrxJrM8Zcnr/vE8f15GTI0QiPlJz8Kc+LEvHYKuD9uJPYe3CbFh7W1Nblkfv5suzSu1ey/PoLYm9+x7Syy2vKzUmFrVpkYPu6g8ZHN+SefXhDbguuSX8crn9lXiFXXx0La+3KfO/Mddba+4wxZwOL89NNaVyj8avGmBeMMfXAk8APgMeMMWuA5cCGo/h6WWPMh4FbjTEn4X4O3gKsBT4N3GWMsbimZSkialqWkpeIx6biVoBchNuwsJEBGpsByqNErj8/dsGVp5ZdUVthRheoTJHDNKaDXc9u9Zc8UO+tty7oRHD9ME/lp3FFJE+BRyQvH3zeB1zCEQSfsgjm4+fFzrvqtLK3jao0YwpUppQ4P7D+pn3B2sdSuaULd/gNuB6dctzKoz9ql2SR/inwiPSRiMcmAe8FZnNwx2Z/oPsjBvOxGbFz5pwanX3yiMikApUpJabLs+kVDf7y+9d4y3d22C7ctFUVbpn1o8mUtzvcCkWGNgUekQEk4rEJuP6et+NGehp4neADcOmU6IT3TS+76OyTI+eVR01FAcqUItfUGbw2f5u/9HdrvbVZHwuMBUYAq4GHkilvW6gFigwTCjwibyB/VMW7gSvzH2rAncw+oJHlxD58Tuzcy6ZGL9TuzXK0/MD6m/cF6x7bmFu6YLv/Gq6xdnz+7XrgEWBTIc69EikWCjwiRygRj40Brgauwu3lsQfw3ujzLpoYOfn9Z8YunDEuckFFmaka5DJlGMtPW738QL23fEe7TeNGcsbgRhbnA/OTKe+1MGsUGa4UeESOUiIeG407lf1duHN82oD2N/q8qjKif3NO7JzL3xS9cNLIyLTBrVKGCz+w/vZ2u2nxztyaR1O5VE+OAKjD7RO1D3gCWDrUl5eLDHUKPCLHKBGPjQBm4qa7puCmuZo5glGfGeMidX99VtmF542LzqyKmRGDW6kMNdZaGtJ22/Ld/ppHNuTW7e2yPRw+bfUksC6/b5SIHCcFHpHjlD+Z/U3AW3ENzuVAB9D6Rp9bHiXyV2eUnXbplOjZZ9RFztLZXcWtuTNoWN0Y1D++0at/tdV25D9cjWtEDjg4bbUrrBpFipUCj8gJlIjHqoDzcaM+p3Jw1OcNT6Uui2DmnFp2yuyp0bPPHBM5a0S5qR3camWwWWtp6rS71jQF657enFufagkObAYYwfXmVOOmRB/HnXOl40tEBokCj8ggyI/6TMbt5XMl7mDD/bhRnzf8pjPAFadEJ1/+puhZZ46JTK+riowfzHrlxAmstXvSdseqPf66pzbn1m9t+8sZbAZ3tlUtbjRnFW5EZ10y5b3uqj8ROX4KPCKDLBGPVQAzcE3OZ+ICzz7cOUFH5Iy6SO07T41OnzEueuaUWnNqWcTEBqdaOVrWWlp7bOO2NrttTaO/9fnt/vZ8T84BI4HRuMCzCXgOqNdojkhhKfCIFFAiHpsIXAhcgWtQtbgpjY7X+7zeqmOUXXVa2bSZE6KnTa01U8dWm4nRiOn3xHcZHK3dtnl7e7BtbZO/dcF2f3tD2nb1uaUWN5pjgN3As8CqZMrbW+haRcRR4BEJQX7KawJwLi78vAkXfvbjAtARf2NWlRG9ZEp04nnjolNPHR2ZMrHGTB1RbkYORt2lqr3HtmxvD7ataw62Ltye27azw3b2ucXgQs5J+T/vwp1ttRbYow0CRcKnwCMyBOR3cz4H1/NzBu6HZhY39fWGDc99Ta+LnPSWydEpZ46JTJ1Sa6aMqTIToxETOaFFF6lcYL3Wbtvc2Gn3rG8Oti3akdvWqw+ntwrcKE5l/v2twELcdJUO8BQZYhR4RIaYRDxWgws9bwZmcfAHajtuBOiov2lryim7ZHJ00oxx0SnTRkWmjKk2J48sZ3QpT4X5gQ06MrTs7QqaGtK2aVtb0LSuOWjcsDdoC2y//43LORhwLNCJO89qNfBqMuW1FK56ETlaCjwiQ1giHoviprvOBi4GDpzLZYAuXO/PUY8AgTvl/Yy6SO3poyN1U08yY8aPiNSNrTZ1oypNXW0FdcUShqy17M/S2tJlm/akg6Yd7bZpY4vftKYpaOnJve5hsDFcwKnGBZxuXLhZBWwDGjVVJTJ8KPCIDCP5FV+TcSFoBnAW7gcyuPOW2nEjD8f1jR0xmNNHR2rPqIvUTT3J1I0fERkzttrU1VYwqqLMVJVHqSyPmvLj+RonQiZne7pzpLs8m05nbXp/hnRHxna29dh0S7dNN6bt/vV7/X0dmTfe/Rq3w/GBgAOQAeqBlbiAsyeZ8oJBeSIiMugUeESGsXzz81jc0RbTcSFoCi7wRHBL3zs4guMujlZZBDO22lTWVZnK0ZWm8qRKUzWynMqRFaZyRMxUVseorHJvqyqipjIWpfdS+kNeeKwF35LzA/xcYHO5AN+35HIBfi4g1+3ZTFuPTe/rtunmLptu2G/TuzqCdPfrj9AMJIILNSNwfTiWgz1T64BXgC1Ag451ECkeCjwiRSYRj1XiQk/vUaBK3GZ3Jn9bD26KphuOKTQMBwaowgWbKg6GLItbKr4l/2jKP1o1RSVSvBR4RIpcfhSoFncCdx1wMnAKbmpsPK5XpfcLwXAJQxFcI3Hvx4HncmDUpgk3HfUq0JB/v0UjNyKlR4FHpIQdRRjq/UIRwYWJAHdWWA43ZZbr9fA58j6iSK+Hyb+NcmiQoU8dkfzXaMMd19EKtAB7839uApqTKe+YGrpFpPgo8IhIv/JhqAY3HVTZ6+1nwUbIAAAA7klEQVSBx0hcWBqZv+/Ao5qDK5sMhwafA1NqB3qMDoQmr9dbDzfKtC//aMb1IXXiepIOPHo0BSUiR0qBR0ROuEQ8FoGBm5TzfCBQaBGRQlDgERERkaKnreZFRESk6CnwiIiISNFT4BEREZGip8AjIiIiRU+BR0RERIqeAo+IiIgUPQUeERERKXoKPCIiIlL0FHhERESk6CnwiIiISNFT4BEREZGip8AjIiIiRU+BR0RERIqeAo+IiIgUPQUeERERKXoKPCIiIlL0FHhERESk6CnwiIiISNFT4BEREZGip8AjIiIiRU+BR0RERIre/wc2MLF3Ae+1+wAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels = 'Exited', 'Retained'\n",
+    "sizes = [df.Exited[df['Exited']==1].count(), df.Exited[df['Exited']==0].count()]\n",
+    "explode = (0, 0.1)\n",
+    "fig1, ax1 = plt.subplots(figsize=(10, 8))\n",
+    "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
+    "        shadow=True, startangle=90)\n",
+    "ax1.axis('equal')\n",
+    "plt.title(\"Proportion of customer churned and retained\", size = 20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "cc79dde5b0abdc85e25b0b4919aa264de464474c"
+   },
+   "source": [
+    "So about 20% of the customers have churned. So the baseline model could be to predict that 20% of the customers will churn.\n",
+    "Given 20% is a small number,  we need to ensure that the chosen model does predict with great accuracy this 20% as it is of interest to the bank to identify and keep this bunch as opposed to accurately predicting the customers that are retained."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "1ac3573f4043cc77ed39cf23e03f87ff04042579"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12adeef50>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x864 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "    # We first review the 'Status' relation with categorical variables\n",
+    "    fig, axarr = plt.subplots(2, 2, figsize=(20, 12))\n",
+    "    sns.countplot(x='Geography', hue = 'Exited',data = df, ax=axarr[0][0])\n",
+    "    sns.countplot(x='Gender', hue = 'Exited',data = df, ax=axarr[0][1])\n",
+    "    sns.countplot(x='HasCrCard', hue = 'Exited',data = df, ax=axarr[1][0])\n",
+    "    sns.countplot(x='IsActiveMember', hue = 'Exited',data = df, ax=axarr[1][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "9bb1084317d055a6c9f896abe428f606a3e9c81e"
+   },
+   "source": [
+    "We note the following:\n",
+    "* Majority of the data is from persons from France. However, the proportion of churned customers is with inversely related to the population of customers alluding to the bank possibly having a problem (maybe not enough customer service resources allocated) in the areas where it has fewer clients.\n",
+    "* The proportion of female customers churning is also greater than that of male customers\n",
+    "* Interestingly, majority of the customers that churned are those with credit cards. Given that majority of the customers have credit cards could prove this to be just a coincidence. \n",
+    "* Unsurprisingly the inactive members have a greater churn. Worryingly is that the overall proportion of inactive mebers is quite high suggesting that the bank may need a program implemented to turn this group to active customers as this will definately have a positive impact on the customer churn.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "b905d0409004fb4d6dae46d95dbe17d957afa87b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x12eb29790>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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rO4YkDRh77703hx12WNkxJEmS+t2zzz67y3YtNeoczEJSLzo7O1ny8FI2Dz2w7Ch7ZK9XK0vpFj+xsuQke2bQ+ufKjiCpJI1Q2LeoL0mS1L/e/va3b7Mi6e1vf3t5YRqEhaQqbB56IBvePb7sGAKGPDqv7AiSStLZ2cmyR37FmGGby47ypu2zsbI14StPLio5yZ556sVBZUeQJEmqysqVK3fZ1u6zkCRJGjDGDNvM547/Xdkxmt7nH9y/7AiSJElVaWlp2WZV+6GHHlpimsZQs6e2RcQREbGkx8/vIuJTEXFgRCyMiGXF77cV4yMivhQRnRHxHxFxfK2ySZIkNZvdnZtJktQItt8a4T//8z9LStI4alZIyszHMvPYzDwWOAFYD3wfmA7ck5mHA/cUbYAzgcOLn6nArbXKJkmS1GzexNxMkiTpDfrr1rZTgccz88mImAicUvTPBu4DrgAmAndkZgK/iIgDIuKQzHymnzJKkiQ1i2rmZpIk1VwZD1Sp1YNPmuVhJDVbkbSdScC3itejehSHngVGFa9bgKd7HLOi6JMkSVLfqmZuto2ImBoRiyJi0erVq/sjoyRJe+yggw7apj1y5MiSkjSOmq9Iioh9gAnAldu/l5kZEbmb55tK5dY3xowZ0ycZJUmSmsWbnZtl5ixgFkB7e/tuzd8kSdqZ/ljBc8opp2x9/Z3vfKfm12t0/bEi6Uzgwczc8oy9lRFxCEDxe1XR3wX03D59dNG3jcyclZntmdluJVGSJGm3VTs3kySpIWxZlTR58uSSkzSG/igkncPrS6cB7gamFK+nAHf16J9cPL3tJOAF90eSJEnqc9XOzSRJaggtLS0cc8wxnH/++WVHaQg1vbUtIvYDTgP+pkf3DcCdEXEB8CRwVtE/DxgPdFJ5ish5tcwmSZLUbHZzbiZJkvQGNS0kZeZLwIjt+rqpPClk+7EJXFLLPJIkSc1sd+ZmkiRJO9JfT22TJEmSJEnSAGchSZIkSZIkSVWxkCRJkiRJkqSqVFVIioihEfFfI+IrRfvwiPhIbaNJkiRJkiSpnlS7IunrwCvAB4p2F3BdTRJJkiRJkiSpLlVbSHpnZv5/wEaAzFwPRM1SSZIkSZIkqe5UW0h6NSKGAAkQEe+kskJJkiRJkiRJTWJwleOuBuYDh0bEN4GTgXNrFUqSJEmSJEn1p9dCUkQE8Cjw58BJVG5p68jMNTXOJkmSJEmSpDrSayEpMzMi5mXme4Ef90MmSZIkSZIk1aFqb217MCLGZuYva5qmDnV1dTFo/QsMeXRe2VEEDFrfTVfXprJjSJIkSZLUlKotJL0f+KuIeBJ4icrtbZmZ76tZMkmSJEmSJNWVagtJp9c0RR1raWnh2VcGs+Hd48uOImDIo/NoaRlVdgxJJejq6uKldYP4/IP7lx2l6T25bhD7dXWVHUOSJEkl2KuaQZn5JHAA8GfFzwFFnyRJkiRJkppEVSuSIqIDuAj4XtH1jYiYlZkza5ZMkqQeWlpaeGXTM3zu+N+VHaXpff7B/XlLS0vZMfQmRMQBwFeBo4EEzgceA74NtALLgbMyc21JESVJUp2rakUScAHw/sz8u8z8O+AkKoUlSZIkDRwzgPmZ+W7gGGApMB24JzMPB+4p2pIkSTtUbSEpgM092puLPkmSJA0AEfFWYBxwG0BmvpqZzwMTgdnFsNnAR8tJKEmSBoJqN9v+OnB/RHy/aH+UYhIiSZKkAeEwYDXw9Yg4BlgMdACjMvOZYsyzgE+1kCRJO1XtZts3AecBzxU/52XmF2sZTJIkSX1qMHA8cGtmHge8xHa3sWVmUtk76Q0iYmpELIqIRatXr655WEmSVJ+qKiRFxEnAssz8UmZ+CXg8It5f22iSJEnqQyuAFZl5f9H+LpXC0sqIOASg+L1qRwdn5qzMbM/M9pEjR/ZLYEmSVH+q3SPpVuDFHu0Xiz5JkiQNAJn5LPB0RBxRdJ0K/Aa4G5hS9E0B7iohniRJGiCq3SMpiqXOAGTmaxFR7bGSJEmqD9OAb0bEPsATVLYu2Au4MyIuAJ4EzioxnyRJqnPVFoOeiIjLeH0V0sVUJh+SJEkaIDJzCdC+g7dO7e8skiRpYKr21rb/C/gDoKv4eT8wtVahJEmStGMRMSoibouInxTto4rVRJIkSTVX7VPbVmXmpMw8uPj5RGbucCNGSZIk1dTtwL8Av1e0/xP4VGlpJElSU9llISkiLoqIw4vXERFfi4gXIuI/IuL4/okoSZKkHg7KzDuB1wAycxOwudxIkiSpWfS2R1IHlW+9AM4BjgHeARwHzAD+cFcHR8QBwFeBo4EEzgceA74NtALLgbMyc21ERHHO8cB64NzMfHB3P5AkSVKDeykiRlCZWxERJwEvlBtpYJo5cyadnZ39es2Ojo6anLetrY1p06bV5NySJPXU261tmzJzY/H6I8AdmdmdmT8F9qvi/DOA+Zn5bipFqKXAdOCezDwcuKdoA5wJHF78TOX1jb0lSZL0usuBu4F3RsT/Au6g8jQ2SZKkmuttRdJrEXEIsJbK0zyu7/HekF0dGBFvBcYB5wJk5qvAqxExETilGDYbuA+4AphIpVCVwC8i4oCIOCQzn9mdDyRJktTIMvPBiPgj4AgggMd6fPGn3dAfK3hOOeWUra/vu+++ml9PkqRa662Q9HfAImAQcHdmPgJQTF6e6OXYw4DVwNcj4hhgMZVb5Ub1KA49C4wqXrcAT/c4fkXRt00hKSKmUjwxbsyYMb1EkCRJaiwR8efbdb0rIl4AHvJhKJIkqdZ2WUjKzB9FxO8DB2fm/+7x1iLgrCrOfTwwLTPvj4gZvH4b25bzZ0Tk7gTOzFnALID29vbdOlaSJKkBXAB8ALi3aJ9C5Qu7wyLiv2Xm/ygrmN7omGOOAWDGjBklJ5EkqW/0tkfSlieB/Gi7vpeA/9nLoSuAFZl5f9H+LpXC0sridjmK31u+OesCDu1x/OiiT5IkSa8bDByZmX+RmX8BHEVl4+33U9kuQJIkqWZ2WUiKiLdHxAnAkIg4LiKOL35OAYbu6tjMfBZ4OiKOKLpOBX5DZXPIKUXfFOCu4vXdwOSoOAl4wf2RJEmS3uDQzFzZo72q6HsOcK8kSZJUU73tkXQ6lc2yRwM39ehfB3yuivNPA74ZEftQ2VPpPCrFqzsj4gLgSV6/RW4eMB7oBNYXYyVJkrSt+yLiR8B3ivZfFH37Ac+XF0uSJDWD3vZImg3Mjoi/yMx/3t2TZ+YSoH0Hb526g7EJXLK71+gPg9Y/x5BH55UdY4/s9fLvAHht3/1LTrJnBq1/jtf3Z5ckqSldAvw58MGivYjKw0xeAv64tFSSJKkp7LKQFBF/nZnfAFoj4vLt38/Mm3ZwWENpa2srO0Kf6OxcB0DbOwZ6EWZUw/w3kSTpzSgeVvIEcBLwl8Bvgd3+wk+SJOnN6O3Wtv2K38NqHaReTZs2rewIfaKjowPwiSGSBranXhzE5x8cuCsrV66vbE04auhrJSfZM0+9OIjDyw7RhCLiXcA5xc8a4NtAZKarkCRJUr/p7da2/7/4fW3/xJEkaccaYTXiq52dALzl9wf2ZzmcxvjvMQA9SuWpuR/JzE6AiPh0uZEkSVKz6e3Wti/t6v3MvKxv40iStGONsELU1aHaQ38OTALujYj5wFwgducEEbGcykNTNgObMrM9Ig6ksrqpFVgOnJWZa/sutiRpoJo5cyadxRdhA9mWz7BlLjaQtbW1lT4v7u3WtsXF75OBo6hMMqByP/5vahVKkiRJ28rMHwA/KJ7ONhH4FHBwRNwKfD8zF1R5qj/OzDU92tOBezLzhoiYXrSv6MvskqSBqbOzk2WP/IoxwzaXHWWP7LOxsr3AK08uKjnJnnnqxUFlRwCqe2obEfF/Ax/MzE1F+5+oLK2WJElSPyqezjYHmBMRb6PyBd8VQLWFpO1NBE4pXs8G7sNCkiSpMGbYZj53/O/KjiGom71C96py3NuAnomHFX2SJEkqSWauzcxZmXlqtYcACyJicURMLfpGZeYzxetngR0+4jUipkbEoohYtHr16j1MLkmSBqrebm3b4gbgVxFxL5V78ccB19QqlCRJkmrig5nZFREHAwsj4tGeb2ZmRkTu6MDMnAXMAmhvb9/hGEmS1PiqKiRl5tcj4ifA+4uuKzLz2drFkiRJUl/LzK7i96qI+D5wIrAyIg7JzGci4hBgVakhJUlSXavq1raICODDwDGZeRewT0ScWNNkkiRJ6jMRsV9EDN/yGvgT4GHgbmBKMWwKcFc5CSVJ0kBQ7a1tXwZeAz4E/Dcqj439Z2BsjXJJkiSpb40Cvl/5fpDBwJzMnB8RvwTujIgLgCeBs0rMKEmS6ly1haT3Z+bxEfErqGzsGBH71DCXJEmS+lBmPgEcs4P+bqDazbolSVKTq/apbRsjYhCVJ30QESOprFCSJEmSJElSk6i2kPQl4PvAwRFxPfBvwOdrlkqSJEmSJEl1p9qntn0zIhZTWfYcwEczc2lNk0mSJEmSJKmu9FpIKm5peyQz3w08WvtIkiRJkiRJqke93tqWmZuBxyJiTD/kkSRJkiRJUp2q9qltbwMeiYgHgJe2dGbmhJqkkiRJkiRJperq6uKldYP4/IP7lx1FwJPrBrFfV1fZMXZdSIqINmAU8F+3e+sPgWdqFUqSJEmSJEn1p7cVSV8ErszMh3p2RsRzVJ7adlutgkmSJEmSpPK0tLTwyqZn+Nzxvys7ioDPP7g/b2lpKTtGr3skjdq+iARQ9LXWJJEkSZIkSZLqUm+FpAN28d6QvgwiSZIkSZKk+tZbIWlRRFy0fWdEXAgsrk0kSZIkSZIk1aPe9kj6FPD9iPgrXi8ctQP7AB+rZTBJkiRJkiTVl10WkjJzJfAHEfHHwNFF948z82c1TyZJkiRJkqS60tuKJAAy817g3hpnkSRJkiRJUh2rqpD0ZkXEcmAdsBnYlJntEXEg8G0qT31bDpyVmWsjIoAZwHhgPXBuZj5Yy3ySJEnNJiIGAYuArsz8SEQcBswFRlDZyuCTmflqmRkBZs6cSWdnZ9kx9tiWz9DR0VFykj3X1tbGtGnTyo4hSSpZTQtJhT/OzDU92tOBezLzhoiYXrSvAM4EDi9+3g/cWvyWJElS3+kAlgL7F+0vADdn5tyI+CfgAirzsFJ1dnay5OGlbB56YNlR9sheryYAi59YWXKSPTNo/XNlR5Ak1Yn+KCRtbyJwSvF6NnAflULSROCOzEzgFxFxQEQckpnPlJBRkiSp4UTEaOBPgeuBy4sV4R8CPlEMmQ1cQx0UkgA2Dz2QDe8eX3YMAUMenVd2BElSndirxudPYEFELI6IqUXfqB7FoWeBUcXrFuDpHseuKPq2ERFTI2JRRCxavXp1rXJLkiQ1oi8C/wV4rWiPAJ7PzE1Fe4fzL0mSpC1qXUj6YGYeT+W2tUsiYlzPN4vVR7k7J8zMWZnZnpntI0eO7MOokiRJjSsiPgKsyszFb/J4v8yTJEm1LSRlZlfxexXwfeBEYGVEHAJQ/F5VDO8CDu1x+OiiT5IkSXvuZGBC8TCUuVRuaZsBHBARW7Y72On8yy/zJEkS1LCQFBH7RcTwLa+BPwEeBu4GphTDpgB3Fa/vBiZHxUnAC+6PJEmS1Dcy88rMHJ2ZrcAk4GeZ+VfAvcDHi2E952aSJElvUMvNtkcB36/s4chgYE5mzo+IXwJ3RsQFwJPAWcX4ecB4oBNYD5xXw2ySJEmquAKYGxHXAb8Cbis5jySpjjz14iA+/+D+vQ+sYyvXV9bQjBr6Wi8j69tTLw7i8LJDUMNCUmY+ARyzg/5u4NQd9CdwSa3ySJIkqSIz76Py5Nwtc7YTy8wjSapPbW1tZUfoE692dgLwlt8f2J/ncOrjv0ktVyRJkiRJkqQBatq0aWVH6BMdHR0AzJgxo+QkjaHWT22TJEmSJElSg7CQJEmSJEmSpKp4a5skSZLqTldXF4PWv8CQR+eVHUXAoPXddHVtKjuGJKkOuCJJkiRJkiRJVXFFkiRJkupOS0sLz74ymA3vHl92FAFDHp1HS8uosmNIkuqAK5IkSZIkSZJUFQtJkiRJkiRJqoqFJEmSJEmSJFXFQpIkSZIkSZKqYiFJkiRJkiRJVbGQJDMVZ/YAACAASURBVEmSJEmSpKpYSJIkSZIkSVJVLCRJkiRJkiSpKhaSJEmSJEmSVJXBZQeQJElS7UXEvsDPgbdQmQN+NzOvjojDgLnACGAx8MnMfLW8pK8btP45hjw6r+wYe2Svl38HwGv77l9ykj0zaP1zwKiyY0iS6oCFJEmSpObwCvChzHwxIvYG/i0ifgJcDtycmXMj4p+AC4BbywwK0NbWVnaEPtHZuQ6AtncM9CLMqIb5byJJ2jMWkiRJkppAZibwYtHcu/hJ4EPAJ4r+2cA11EEhadq0aWVH6BMdHR0AzJgxo+QkkiT1DfdIkiRJahIRMSgilgCrgIXA48DzmbmpGLICaNnJsVMjYlFELFq9enX/BJYkSXXHQpIkSVKTyMzNmXksMBo4EXj3bhw7KzPbM7N95MiRNcsoSZLqm4UkSZKkJpOZzwP3Ah8ADoiILdsdjAa6SgsmSZLqnoUkSZKkJhARIyPigOL1EOA0YCmVgtLHi2FTgLvKSShJkgYCN9uWJElqDocAsyNiEJUvE+/MzB9FxG+AuRFxHfAr4LYyQ0qSpPpmIUmSJKkJZOZ/AMftoP8JKvslSZIk9cpb2yRJkiRJklQVC0mSJEmSJEmqSs1vbSvuw18EdGXmRyLiMGAuMAJYDHwyM1+NiLcAdwAnAN3A2Zm5vNb56sHMmTPp7Oys6TW2nL+jo6Om12lra2PatGk1vYYkSZIkSSpHf6xI6qDyRJAtvgDcnJltwFrggqL/AmBt0X9zMU59ZMiQIQwZMqTsGJIkSZIkaQCr6YqkiBgN/ClwPXB5RATwIeATxZDZwDXArcDE4jXAd4FbIiIyM2uZsR64gkeSJEmSJA0EtV6R9EXgvwCvFe0RwPOZualorwBaitctwNMAxfsvFOO3ERFTI2JRRCxavXp1LbNLkiRJkiSph5oVkiLiI8CqzFzcl+fNzFmZ2Z6Z7SNHjuzLU0uSJEmSJGkXanlr28nAhIgYD+wL7A/MAA6IiMHFqqPRQFcxvgs4FFgREYOBt1LZdFuSJEmSJEl1oGYrkjLzyswcnZmtwCTgZ5n5V8C9wMeLYVOAu4rXdxdtivd/1gz7I0mSJEmSJA0U/fHUtu1dQWXj7U4qeyDdVvTfBowo+i8HppeQTZIkSZIkSTtR06e2bZGZ9wH3Fa+fAE7cwZiXgb/sjzySJEmSJEnafWWsSJIkSZIkSdIAZCFJkiRJkiRJVbGQJElSP1m/fj0PPfQQnZ2dZUeRJEmS3pR+2SNJkqR6N3PmzJoXeJYtWwbAxRdfzJFHHlmz67S1tTFt2rSanV+SJEnNyxVJkiT1g/Xr1299/eqrr7Jhw4YS06gZRcShEXFvRPwmIh6JiI6i/8CIWBgRy4rfbys7qyRJql+uSJIkCWq+gueTn/zkNu0NGzYwa9asml5T2s4m4G8z88GIGA4sjoiFwLnAPZl5Q0RMB6YDV5SYU5Ik1TFXJEmS1A+efvrpXbalWsvMZzLzweL1OmAp0AJMBGYXw2YDHy0noSRJGggsJEmSJDWZiGgFjgPuB0Zl5jPFW88Co3ZyzNSIWBQRi1avXt0vOSVJUv3x1jZJkqQmEhHDgH8GPpWZv4uIre9lZkZE7ui4zJwFzAJob2/f4RhJknZXfzzw5LHHHuPll19m6tSpDBkypGbXaZYHnrgiSZKkfrDXXnvtsi31h4jYm0oR6ZuZ+b2ie2VEHFK8fwiwqqx8kiTVwquvvgrAk08+WXKSxuCKJEmS+sH+++/P888/v01b6k9RWXp0G7A0M2/q8dbdwBTghuL3XSXEkyQ1qVqv4Ons7OTCCy8E4JVXXmHatGm0tbXV9JqNzkKSJEn9oGcRaUdtqR+cDHwSeCgilhR9n6NSQLozIi4AngTOKilfv+uP2ym2nL+jo6Om12mW2ykkaXddd911b2jffvvt5YRpEBaSJEmSmkBm/hsQO3n71P7M0kxquReHJKl3y5cv32Vbu89CkiRJ/SAiyMxt2pLK5QoeSWp8ra2t2xSPWltbS8vSKNzpU5KkfvCpT31qm/bll19eUhJJkqTmcdVVV+2yrd1nIUmSpH4wceLErauQIoI/+7M/KzmRJElS42tra9u6Cqm1tdWNtvuAhSRJkvrJllVJrkaSJEnqP1dddRX77befq5H6iHskSZLUTyZOnMjEiRPLjiFJktRU2tra+PGPf1x2jIbhiiRJkiRJkiRVxUKSJEmSJEmSqhI9H0U80ETEauDJsnOoFAcBa8oOIalf+e++Of1+Zo4sO4S25Rysqfm3WGou/ptvXjudgw3oQpKaV0Qsysz2snNI6j/+u5ek8vm3WGou/pvXjnhrmyRJkiRJkqpiIUmSJEmSJElVsZCkgWpW2QEk9Tv/3UtS+fxbLDUX/83rDdwjSZIkSZIkSVVxRZIkSZIkSZKqYiFJkiRJkiRJVbGQpAElIs6IiMciojMippedR1JtRcTXImJVRDxcdhZJambOwaTm4hxMu2IhSQNGRAwC/hE4EzgKOCcijio3laQaux04o+wQktTMnINJTel2nINpJywkaSA5EejMzCcy81VgLjCx5EySaigzfw48V3YOSWpyzsGkJuMcTLtiIUkDSQvwdI/2iqJPkiRJteMcTJK0lYUkSZIkSZIkVcVCkgaSLuDQHu3RRZ8kSZJqxzmYJGkrC0kaSH4JHB4Rh0XEPsAk4O6SM0mSJDU652CSpK0sJGnAyMxNwKXAvwBLgTsz85FyU0mqpYj4FvDvwBERsSIiLig7kyQ1G+dgUvNxDqZdicwsO4MkSZIkSZIGAFckSZIkSZIkqSoWkiRJkiRJklQVC0mSJEmSJEmqioUkSZIkSZIkVcVCkiRJkiRJkqpiIUlSv4uIzRGxpMfP9F7Gz4uIA4qfi9/E9a6JiM+8+cSSJEkDn3MwSX1hcNkBJDWlDZl5bLWDM3M8QES0AhcDX65NLEmSpIbmHEzSHnNFkqS6EBFvjYjHIuKIov2tiLioeL08Ig4CbgDeWXyDdmPx3mcj4pcR8R8RcW2P8/0/EfGfEfFvwBElfCRJkqS65xxM0u5yRZKkMgyJiCU92v9vZn47Ii4Fbo+IGcDbMvMr2x03HTh6yzdpEfEnwOHAiUAAd0fEOOAlYBJwLJW/cw8Ci2v6iSRJkuqfczBJe8xCkqQy7HBZdWYujIi/BP4ROKaK8/xJ8fOroj2MyqRmOPD9zFwPEBF390lqSZKkgc05mKQ95q1tkupGROwFHAmsB95WzSFUvkk7tvhpy8zbahpSkiSpwTgHk7Q7LCRJqiefBpYCnwC+HhF7b/f+OirfdG3xL8D5ETEMICJaIuJg4OfARyNiSEQMB/6s9tElSZIGLOdgkqrmrW2SyrD9/fnzga8DFwInZua6iPg5cBVw9ZZBmdkdEf8rIh4GfpKZn42II4F/jwiAF4G/zswHI+LbwK+BVcAv++djSZIk1TXnYJL2WGRm2RkkSZIkSZI0AHhrmyRJkiRJkqpiIUmSJEmSJElVsZAkSZIkSZKkqlhIkiRJkiRJUlUsJEmSJEmSJKkqFpIkSZIkSZJUFQtJkiRJkiRJqoqFJEmSJEmSJFVlcNkB9sRBBx2Ura2tZceQJEk1snjx4jWZObLsHNqWczBJkhrbruZgA7qQ1NrayqJFi8qOIUmSaiQiniw7g97IOZgkSY1tV3Mwb22TJEmSJElSVSwkSZIkSZIkqSoWkiRJkiRJklSVUvZIioivAR8BVmXm0UXfgcC3gVZgOXBWZq4tI58kSY1k48aNrFixgpdffrnsKDu17777Mnr0aPbee++yo0iSJPWJRp2DlbXZ9u3ALcAdPfqmA/dk5g0RMb1oX1FCNkmSGsqKFSsYPnw4ra2tRETZcd4gM+nu7mbFihUcdthhZccpRUQcSmVeNApIYFZmzoiIa4CLgNXF0M9l5rzimCuBC4DNwGWZ+S9F/xnADGAQ8NXMvKHoPwyYC4wAFgOfzMxXI+ItxbVPALqBszNzec0/tCRJDa5R52ClFJIy8+cR0bpd90TglOL1bOA+mqSQNHPmTDo7O2t6ja6uLgBaWlpqep22tjamTZtW02tIUi008t/il19+uW4nMAARwYgRI1i9enXvgxvXJuBvM/PBiBgOLI6IhcV7N2fm3/ccHBFHAZOA9wC/B/w0It5VvP2PwGnACuCXEXF3Zv4G+EJxrrkR8U9UilC3Fr/XZmZbREwqxp1d008rSVLBOVh53uwcrJ72SBqVmc8Ur5+l8o3cG0TE1IhYFBGLmnzCuVs2bNjAhg0byo4hSU2tzL/F9TqB2aLe89VaZj6TmQ8Wr9cBS4FdzXYnAnMz85XM/C3QCZxY/HRm5hOZ+SqVFUgTo/J/4A8B3y2Onw18tMe5ZhevvwucGs3+H0SS1FCcg+3cm8lX1q1tu5SZGRG5k/dmAbMA2tvbdzhmoOmPFTwdHR0AzJgxo+bXkqSBqBn/Fg8aNIj3vve9W9uTJk1i+vTpOx0/fvx45syZA8CcOXO4+OKLd+t611xzDcOGDeMzn/nMmwvcJIpV28cB9wMnA5dGxGRgEZVVS2upFJl+0eOwFbxeeHp6u/73U7md7fnM3LSD8S1bjsnMTRHxQjF+zXa5pgJTAcaMGbOnH1OSJMA5GAy8OVg9FZJWRsQhmflMRBwCrCo7kCRJjWzIkCEsWbKk6vHz5s0DYPny5Xz5y1/e7UmMehcRw4B/Bj6Vmb+LiFuB/05l36T/DvwDcH4Z2RrxyzxJksow0Odg9XRr293AlOL1FOCuErNIktSUXnjhBY444ggee+wxAM455xy+8pWvANDa2sqaNWuYPn06jz/+OMceeyyf/exnAbjxxhsZO3Ys73vf+7j66qu3nu/666/nXe96Fx/84Ae3nlM7FhF7UykifTMzvweQmSszc3NmvgZ8hcqtawBdwKE9Dh9d9O2svxs4ICIGb9e/zbmK999ajJckSf1kIM3BSlmRFBHforKx9kERsQK4GrgBuDMiLgCeBM4qI5skSc1iw4YNHHvssVvbV155JWeffTa33HIL5557Lh0dHaxdu5aLLrpom+NuuOEGHn744a3fpC1YsIBly5bxwAMPkJlMmDCBn//85+y3337MnTuXJUuWsGnTJo4//nhOOOGEfv2MA0WxJ9FtwNLMvKlH/yE99pD8GPBw8fpuYE5E3ERls+3DgQeAAA4vntDWRWVD7k8U2wbcC3ycyr5JPb+02/Jl3r8X7/8sM11xJElSjQz0OVhZT207ZydvndqvQSRJamI7W1Z92mmn8Z3vfIdLLrmEX//6172eZ8GCBSxYsIDjjjsOgBdffJFly5axbt06PvaxjzF06FAAJkyY0LcfoLGcDHwSeCgitvxH+RxwTkQcS+XWtuXA3wBk5iMRcSfwGypPfLskMzcDRMSlwL8Ag4CvZeYjxfmuAOZGxHXAr6gUrih+/4+I6ASeo1J8kiRJNTLQ52D1tEeSJEmqA6+99hpLly5l6NChrF27ltGjR+9yfGZy5ZVX8jd/8zfb9H/xi1+sZcyGkpn/RmU10fbm7eKY64Hrd9A/b0fHZeYTvH5rXM/+l4G/3J28kiSp7w2UOVg97ZEkSZLqwM0338yRRx7JnDlzOO+889i4ceM27w8fPpx169ZtbZ9++ul87Wtf48UXXwSgq6uLVatWMW7cOH7wgx+wYcMG1q1bxw9/+MN+/RySJEkDyUCZg7kiSZKkJrX9/flnnHEG5513Hl/96ld54IEHGD58OOPGjeO6667j2muv3TpuxIgRnHzyyRx99NGceeaZ3HjjjSxdupQPfOADAAwbNoxvfOMbHH/88Zx99tkcc8wxHHzwwYwdO7bfP6MkSVK9GehzsBjIeym2t7fnokWLyo4xIHR0dAAwY8aMkpNIUvMq62/x0qVLOfLII/v1mm/GjnJGxOLMbC8pknbCOZgkaSBxDrZruzsH89Y2SZIkSZIkVcVCkiRJkiRJkqpiIUmSJEmSJElVsZAkSZIkSZKkqlhIkiRJkiRJUlUsJEmSJEmSJKkqFpIkSVK/mD9/PkcccQRtbW3ccMMNZceRJElqeLWYfw3uk7NIkqQB49LLP8uqNc/12fkOPuhAbrnpxl2O2bx5M5dccgkLFy5k9OjRjB07lgkTJnDUUUf1WQ5JkqR61t9zsFrNvywkSZLUZFateY7HR/1R351w5b/2OuSBBx6gra2Nd7zjHQBMmjSJu+66y0KSJElqGv09B6vV/Mtb2yRJUs11dXVx6KGHbm2PHj2arq6uEhNJkiQ1tlrNv1yRJEmSpKY0c+ZMOjs7a3qNLRP2lpaWml6nra2NadOm1fQakiSBhSRJktQPWlpaePrpp7e2V6xYUfP/x1qqBxs2bCg7giSpSdVq/mUhSZIk1dzYsWNZtmwZv/3tb2lpaWHu3LnMmTOn7Fhqcv2xgqejowOAGTNm1PxakiT1VKv5l4UkSZJUc4MHD+aWW27h9NNPZ/PmzZx//vm85z3vKTuWJElSw6rV/MtCkiRJTebggw6s6klru3W+KowfP57x48f32XUlSZIGkjLmYLWYf1lIkiSpydxy041lR5AkSWo6jTIH26vsAJIkSZIkSRoYLCRJkiRJkiSpKhaSJEmSJEmSVBULSZIkSZIkSaqKhSRJkiRJkiRVxUKSJEmqufPPP5+DDz6Yo48+uuwokiRJTaMWc7DBfXamPhARnwYuBBJ4CDgvM18uN5UkSY3lc397KS+sWdln53vrQaP4/D/csssx5557LpdeeimTJ0/us+tKkiQNJI0yB6ubQlJEtACXAUdl5oaIuBOYBNxeajBJkhrMC2tWcsU7H+2z833h8d7HjBs3juXLl/fZNRtNRBwK3AGMovKF2qzMnBERBwLfBlqB5cBZmbk2IgKYAYwH1gPnZuaDxbmmAFcVp74uM2cX/SdQmVcNAeYBHZmZO7tGjT+yJElNp1HmYHVTSCoMBoZExEZgKPC/S84jSaoTM2fOpLOzs+wYe2RL/o6Ojn697llnncVTTz21tb1586Z+vb6qsgn428x8MCKGA4sjYiFwLnBPZt4QEdOB6cAVwJnA4cXP+4FbgfcXRaGrgXYqBanFEXF3URi6FbgIuJ9KIekM4CfFOXd0DUmSpDeom0JSZnZFxN8DTwEbgAWZuWD7cRExFZgKMGbMmP4NKUkqTWdnJ8se+RVjhm0uO8qbts/GytaErzy5qF+vm5s+Sr66vkdH9uv11bvMfAZ4pni9LiKWAi3AROCUYths4D4qRZ6JwB2ZmcAvIuKAiDikGLswM58DKIpRZ0TEfcD+mfmLov8O4KNUCkk7u4YkSdIb1E0hKSLeRmUicxjwPPCdiPjrzPxGz3GZOQuYBdDe3u5MWJKayJhhm/nc8b8rO8aA88I+r/H2oa8X4AaF//NZzyKiFTiOysqhUUWRCeBZKre+QaXI9HSPw1YUfbvqX7GDfnZxje1z+WWeJEmqq6e2fRj4bWauzsyNwPeAPyg5kyRJUr+JiGHAPwOfysxtqqbF6qOaVgF3dY3MnJWZ7ZnZPnLkyFrGkCRJdayeCklPASdFxNBiA8lTgaUlZ5IkSX3gnHPO4QMf+ACPPfZ/2Lv3MLvK8vD73zuTQIIhHENeOkMMNhFFqhwC0mr5oVQMqSban3LopRyF+gLptB5qsH3loFJ8rdo0WPpDQaAKkaIWasMhRSn1feWQQEQw2AwIMvMDEhIggQRCwv37Y6+BTZiZ7MzsPWvvme/nuva117rXs551b+bK5rnuvdbz/JqOjg4uu+yyslNqOhExjkoR6XuZ+cMi/GTxyBrF+6oi3gPsU3V6RxEbKN7RR3yga0iSpBbXiDFY0zzalpl3RsR1wD1UJpy8l+IRNkmSVD+Tdt2dr/z34M/fkgERtLVVhhG77Nnnk1Cvcc011wz+gqNA8SPaZcCKzPx61aEbgJOAi4r366viZ0fEIiqTbT+bmY9HxM3AhcWUAQBHA+dk5tqIWBcRh1N5ZO5EYOE2riFJkupolz2n1LTS2vb0ty2NGIM1TSEJIDPPpbLSiCRJapDzPv+ZIZ3/xIY2YoednCenvt4FfBz4ZUQsL2Kfp1LcuTYiTgMeBY4tji0GZgNdwAbgFICiYPRF4O6i3QW9E28DZwJXABOoTLJ9YxHv7xqSJKmOLvzaxWWnUBdNVUiSJEkajTLzZ0D0c/ioPtoncFY/fV0OXN5HfClwQB/xNX1dQ5IkqS/NNEeSJEmSJEmSmpiFJEmSRrykcgNL82r2/CRJkgaj2cc4g8nPQpIkSSNc27rHeOb5TU07kMlM1qxZw/jx48tORZIkqW7Gjx/PmjVrRtwYzDmSJEka4Xa691us5XRWT9qH/qfhqd2zm8YQY3fg+eefH3pyhfHjx9PR0bHthpIkSS2io6OD7u5uVq9eXXYq/RrMGMxCkiRJI9yYTeuZeOfXt92wRt+8ZxI7vnEmCxYsqFufkiRJI824cePYd999y06j7ny0TZIkSZIkSTWxkCRJkiRJkqSaWEiSJEmSJElSTSwkSZIkSZIkqSYWkiRJkiRJklQTV23bhoULF9LV1VV2GkPW+xk6OztLzmTopk+fzrx588pOQ5IkSZKkUcdC0jZ0dXWx/P4VbNlp97JTGZIxmxKAZQ8/WXImQ9O2YW3ZKUiSJEmSNGpZSKrBlp12Z+NbZpedhoAJDy4uOwVJkiRJkkYt50iSJEmSJElSTSwkSZIkSZIkqSYWkiRJkiRJklQTC0mSJEmSJEmqiYUkSZIkSZIk1cRCkiRJkiRJkmpiIUmSJEmSJEk1sZAkSZIkSZKkmlhIkiRJkiRJUk0sJEmSJEmSJKkmFpIkSZIkSZJUEwtJkiRJkiRJqklTFZIiYteIuC4iHoyIFRHx+2XnJEmS1GgRcXlErIqI+6ti50VET0QsL16zq46dExFdEfHriHh/VXxWEeuKiPlV8X0j4s4i/v2I2KGI71jsdxXHpw3PJ5YkSa2qqQpJwALgpsx8C/AOYEXJ+UiSJA2HK4BZfcS/kZkHFq/FABGxP3A88LbinH+MiLaIaAO+CRwD7A+cULQF+ErR13TgaeC0In4a8HQR/0bRTpIkqV9NU0iKiF2AI4DLADJzU2Y+U25WkiRJjZeZtwNra2w+F1iUmS9m5m+ALuCw4tWVmQ9n5iZgETA3IgJ4L3Bdcf6VwIeq+rqy2L4OOKpoL0mS1KemKSQB+wKrge9ExL0R8e2IeMPWjSLijIhYGhFLV69ePfxZSpIkbUNEvDsiTim2J0fEvoPs6uyIuK949G23ItYOPFbVpruI9RffA3gmMzdvFX9NX8XxZ4v2kiRJfWqmQtJY4GDgksw8CHgemL91o8y8NDNnZubMyZMnD3eOkiRJA4qIc4HPAecUoXHAdwfR1SXA7wIHAo8DX6tLgoPkj3mSJAmGWEiKiOMj4q+L7X0i4pAhdNcNdGfmncX+dVQKS5IkSa3kw8AcKj+KkZn/G9h5ezvJzCczc0tmvgx8i8qjawA9wD5VTTuKWH/xNcCuETF2q/hr+iqO71K07ysff8yTJEmDLyRFxMXAe4CPFaHngX8abH+Z+QTwWETsV4SOAn412P4kSZJKsikzE0iAvh7Vr0VE7F21+2Ggd0W3G4DjixXX9gVmAHcBdwMzihXadqAyIfcNRS4/BT5SnH8ScH1VXycV2x8BflK0lyRJ6tPYbTfp1x9k5sERcS9AZq7tXUp2COYB3yv6eRg4ZYj9SZIkDbdrI+J/UbkL6HTgVCp3FPUrIq4BjgT2jIhu4FzgyIg4kEpB6hHgzwAy84GIuJbKD26bgbMyc0vRz9nAzUAbcHlmPlBc4nPAooj4EnAvxeImxfs/R0QXlcm+jx/6x5ckSSPZUApJL0XEGF79tW0P4OWhJJOZy4GZQ+lDkiSpTJn5dxHxPmAdsB/whcxcso1zTugjfFkfsd72Xwa+3Ed8MbC4j/jDvPpoXHX8BeCjA+UmSZJUbSiFpG8CPwAmR8T5wLHA+XXJSpIkqUUVj5v9V2/xKCImRMS0zHyk3MwkSZKGbtCFpMy8KiKWAX8EBPDRzLx/G6dJkiSNdP8C/EHV/pYidmg56UiSJNXPoApJEdEG3JeZbwMe2FZ7SZKkUWRsZm7q3cnMTXWYR1KSJKkpDGrVtmJCx4cjor3O+UiSJLW61RExp3cnIuYCT5WYjyRJUt0MZY6kicCKiPg58HxvMDP/ZMhZSZIkta5PUlmF9mIqj/8/BpxYbkqSJEn1MZRC0pfqloUkSdIIkZkPAYdHxMRi/7mSU5IkSaqboUy2fWs9E5EkSRoJImJH4H8C04CxEQFAZl5QYlqSJG23hQsX0tXVVXYaQ9b7GTo7O0vOZOimT5/OvHnzSs1h0IWkiFgPZFU/bcCLmTmpHolJkiS1qOuBZ4FlwIsl5yJJ0qB1dXWx8oF7mTpxS9mpDMkOL1Wmh37x0aUlZzI0v32urewUgKHdkbRz73ZEjAH+BDiwHklJkiS1sI7MnFV2EpIk1cPUiVv4/MHryk5DwIX3NMd9O4NatW1rmflyZl4H/HE9+pMkSWph/39E/F7ZSUiSJDXCUB5tm1O1OwaYCWwackaSJEmt7d3AyRHxGyqPtgWQmfn2ctNqLc7L0XyaYV4OSVL5hrJq20ertjcDjwBzh5RNE+rp6aFtw7NMeHBx2akIaNuwhp6ezWWnIakEPT09PL++rWlu6R3NHl3fxht6espOo5kdU3YCI0FXVxfL71/Blp12LzuVIRmzqTKl6LKHnyw5k6Fp27C27BQkSU1iKHMkfbyeiUiSJI0EmfkoQETsBYwvOZ2WtmWn3dn4ltllpyHwR1VJ0iuG8mjbnsCpFEvb9sYz84yhp9U82tvbeeLFsQ5imsSEBxfT3j6l7DQklaC9vZ0XNz/uZI9N4MJ7JrFje3vZaTSt4vH/rwG/A6wC3gisAN5WZl6SJEn1MJRH264H7gB+BrT2WoCSJEn180XgcOA/1klqfAAAIABJREFUMvOgiHgP8LGSc5IkSaqLoRSS3pCZn65bJpIkSSPDS5m5JiLGRMSYzPxpRPx92UlJkiTVw1AKSTdGxNGZeUvdspEkSWp9z0TEROB24HsRsQp4vuScJEmS6mLMEM79JHBTRDwXEWsj4umIcDkHSZI02s0FNgJ/CdwEPAR8sNSMJEmS6mQodyTtWbcsJEmSRojMrL776MrSEpEkSWqAQReSMnNLRBwPvCkzL4yIDmAKsKxu2UmSJLWIiFgPZF+HgMzMScOckiRJUt0NupAUERcD44AjgAuBDcA/AYfWJzVJkqTWkZk7l52DJElSow3l0bY/yMyDI+JegMxcGxE71CkvSZKklhYRewHje/cz87clpiNJklQXQykkvRQRYyhu4Y6IPYCX65KVJElSi4qIOcDXgN8BVgFvBFYAbyszL0mStldPTw/Pr2/jwnt8OrsZPLq+jTf09JSdxvav2hYRvcWnbwI/ACZHxPnAz4Cv1DE3SZKkVvRF4HDgvzNzX+Ao4I5yU5IkSaqPwdyRdBdwcGZeFRHLgD+iMonkRzPz/rpmJ0mS1Hpeysw1ETEmIsZk5k8j4u/LTkqSpO3V3t7Oi5sf5/MHrys7FQEX3jOJHdvby05jUIWk6N3IzAeAB+qXjiRJUst7JiImArcD34uIVcDzJeckSZJUF9v9aBuVR9k+1d9rqAlFRFtE3BsRPx5qX5IkSSWYS2U1278EbgIeAj440AkRcXlErIqI+6tiu0fEkohYWbzvVsQjIv4hIroi4r6IOLjqnJOK9isj4qSq+CER8cvinH+IiBjoGpIkSf0ZTCGpDZgI7NzPa6g6qUxIKUmS1HIy8/nMfDkzNwP/DizMzDXbOO0KYNZWsfnArZk5A7i12Ac4BphRvM4ALoFKUQg4F3gncBhwblVh6BLg9KrzZm3jGpIkSX0azKNtj2fmBXXPBIiIDuCPgS8DQ767SZIkabhExOHARcBaKhNu/zOwJzAmIk7MzJv6Ozczb4+IaVuF5wJHFttXArcBnyviV2VmAndExK4RsXfRdklmri3yWQLMiojbgEmZeUcRvwr4EHDjANeQJEnq02DuSIptNxm0vwf+Cni534tHnBERSyNi6erVqxuYiiRJ0na5GLgQuAb4CfCJzPy/gCOAvx1Ef1My8/Fi+wlgSrHdDjxW1a67iA0U7+4jPtA1XscxmCRJgsEVko6qexZARHwAWJWZywZql5mXZubMzJw5efLkRqQiSZI0GGMz85bM/Bfgid47gDLzwaF2XNx9lEPtZyjXcAwmSZJgEIWk3tulG+BdwJyIeARYBLw3Ir7boGtJkiTVW/Ud1Ru3OjaYItCTxSNrFO+ringPsE9Vu44iNlC8o4/4QNeQJEnq02DuSGqIzDwnMzsycxpwPPCTzPxYyWlJkiTV6h0RsS4i1gNvL7Z7939vEP3dAPSuvHYScH1V/MRi9bbDgWeLx9NuBo6OiN2KSbaPBm4ujq2LiMOL1dpO3Kqvvq4hSZLUp8FMti1JkqStZGbbYM+NiGuoTHq9Z0R0U1l97SLg2og4DXgUOLZovhiYDXQBG4BTiuuvjYgvAncX7S6oupP8TCorw02gMsn2jUW8v2tIkiT1qSkLSZl5G5VVQyRJkka8zDyhn0Ovm5uymMvorH76uRy4vI/4UuCAPuJr+rpGM+jp6aFtw7NMeHBx2akIaNuwhp6ezWWnIUlqAk3zaJskSZIkSZKaW1PekSRJkqTRrb29nSdeHMvGt8wuOxUBEx5cTHv7lLLTkCQ1Ae9IkiRJkiRJUk0sJEmSJEmSJKkmFpIkSZIkSZJUEwtJkiRJkiRJqomFJEmSJEmSJNXEQpIkSZIkSZJqYiFJkiRJkiRJNbGQJEmSJEmSpJpYSJIkSZIkSVJNxpadQCto27CWCQ8uLjuNIRnzwjoAXh4/qeRMhqZtw1pgStlpSJIkSZI0KllI2obp06eXnUJddHWtB2D6m1q9CDNlxPxNJEmSJElqNRaStmHevHllp1AXnZ2dACxYsKDkTCRJkiRJUqtyjiRJkiRJkiTVxEKSJEmSJEmSamIhSZIkSZIkSTWxkCRJkiRJkqSaWEiSJEmSJElSTSwkSZIkSZIkqSYWkiRJkiRJklQTC0mSJEmSJEmqiYUkSZIkSZIk1cRCkiRJkiRJkmpiIUmSJEmSJEk1sZAkSZIkSZKkmjRNISki9omIn0bEryLigYjoLDsnSZKkskXEIxHxy4hYHhFLi9juEbEkIlYW77sV8YiIf4iIroi4LyIOrurnpKL9yog4qSp+SNF/V3FuDP+nlCRJraJpCknAZuDTmbk/cDhwVkTsX3JOkiRJzeA9mXlgZs4s9ucDt2bmDODWYh/gGGBG8ToDuAQqhSfgXOCdwGHAub3Fp6LN6VXnzWr8x5EkSa1qbNkJ9MrMx4HHi+31EbECaAd+VWpikqSm8dvn2rjwnkllpzFoT26o/H4zZaeXS85kaH77XBszyk5Cc4Eji+0rgduAzxXxqzIzgTsiYteI2LtouyQz1wJExBJgVkTcBkzKzDuK+FXAh4Abh+2TSJKkltI0haRqETENOAi4s49jZ1D5hY2pU6cOa16SpPJMnz697BSGbFNXFwA7vrG1P8sMRsbfo4UkcEtEJPC/MvNSYErxIxzAE8CUYrsdeKzq3O4iNlC8u4/46zgGkyRJ0ISFpIiYCPwA+IvMXLf18WLwdCnAzJkzc5jTkySVZN68eWWnMGSdnZXp/xYsWFByJmox787MnojYC1gSEQ9WH8zMLIpMDeUYTJIkQXPNkUREjKNSRPpeZv6w7HwkSZLKlpk9xfsq4EdU5jh6snhkjeJ9VdG8B9in6vSOIjZQvKOPuCRJUp+appBUrBByGbAiM79edj6SJElli4g3RMTOvdvA0cD9wA1A78prJwHXF9s3ACcWq7cdDjxbPAJ3M3B0ROxWTLJ9NHBzcWxdRBxejMVOrOpLkiTpdZrp0bZ3AR8HfhkRy4vY5zNzcYk5SZIklWkK8KNKjYexwNWZeVNE3A1cGxGnAY8CxxbtFwOzgS5gA3AKQGaujYgvAncX7S7onXgbOBO4AphAZZJtJ9qWJEn9appCUmb+DIiy85AkSWoWmfkw8I4+4muAo/qIJ3BWP31dDlzeR3wpcMCQk5UkSaNC0zzaJkmSJEmSpObWNHckSZIkSZKk5vLb59q48J5JZacxJE9uqNxDM2Wnl0vOZGh++1wbM8pOAgtJkiRJkiSpD9OnTy87hbrY1NUFwI5vbO3PM4Pm+JtYSJIkSZIkSa8zb968slOoi87OTgAWLFhQciYjg3MkSZIkSZIkqSYWkiRJkiRJklQTC0mSJEmSJEmqiYUkSZIkSZIk1cRCkiRJkiRJkmpiIUmSJEmSJEk1sZAkSZIkSZKkmlhIkiRJkiRJUk0sJEmSJEmSJKkmFpIkSZIkSZJUEwtJkiRJkiRJqomFJEmSJEmSJNXEQpIkSZIkSZJqYiFJkiRJkiRJNbGQJEmSJEmSpJpYSJIkSZIkSVJNLCRJkiRJkiSpJhaSJEmSJEmSVBMLSZIkSZIkSaqJhSRJkiRJkiTVxEKSJEmSJEmSamIhSZIkSZIkSTVpqkJSRMyKiF9HRFdEzC87H0mSpNHAMZgkSapV0xSSIqIN+CZwDLA/cEJE7F9uVpIkSSObYzBJkrQ9xpadQJXDgK7MfBggIhYBc4FflZrVMFi4cCFdXV0NvUZv/52dnQ29zvTp05k3b15DryFJjeB3sUaxph2DtW1Yy4QHFzes/zEvrCNefqlh/Q+nHDOOl8dPalj/bRvWAlMa1r+k0csxWOtppkJSO/BY1X438M6tG0XEGcAZAFOnTh2ezEaACRMmlJ2CJI16fherSTXlGGz69OkNv0ZPz2Y2btzY8OsMhwkTJtDe3shCz5Rh+ZtIUiM4BquvyMyycwAgIj4CzMrMTxT7HwfemZln93fOzJkzc+nSpcOVoiRJGmYRsSwzZ5adx0jmGEySJG1toDFY08yRBPQA+1TtdxQxSZIkNY5jMEmSVLNmKiTdDcyIiH0jYgfgeOCGknOSJEka6RyDSZKkmjXNHEmZuTkizgZuBtqAyzPzgZLTkiRJGtEcg0mSpO3RNIUkgMxcDDRuaQ5JkiS9jmMwSZJUq2Z6tE2SJEmSJElNzEKSJEmSJEmSamIhSZIkSZIkSTWJzCw7h0GLiNXAo2XnoVLsCTxVdhKShpX/7kenN2bm5LKT0Gs5BhvV/C6WRhf/zY9e/Y7BWrqQpNErIpZm5syy85A0fPx3L0nl87tYGl38N6+++GibJEmSJEmSamIhSZIkSZIkSTWxkKRWdWnZCUgadv67l6Ty+V0sjS7+m9frOEeSJEmSJEmSauIdSZIkSZIkSaqJhSS1lIiYFRG/joiuiJhfdj6SGisiLo+IVRFxf9m5SNJo5hhMGl0cg2kgFpLUMiKiDfgmcAywP3BCROxfblaSGuwKYFbZSUjSaOYYTBqVrsAxmPphIUmt5DCgKzMfzsxNwCJgbsk5SWqgzLwdWFt2HpI0yjkGk0YZx2AaiIUktZJ24LGq/e4iJkmSpMZxDCZJeoWFJEmSJEmSJNXEQpJaSQ+wT9V+RxGTJElS4zgGkyS9wkKSWsndwIyI2DcidgCOB24oOSdJkqSRzjGYJOkVFpLUMjJzM3A2cDOwArg2Mx8oNytJjRQR1wA/B/aLiO6IOK3snCRptHEMJo0+jsE0kMjMsnOQJEmSJElSC/COJEmSJEmSJNXEQpIkSZIkSZJqYiFJkiRJkiRJNbGQJEmSJEmSpJpYSJIkSZIkSVJNLCRJGnYRsSUille95m+j/eKI2LV4nTmI650XEZ8ZfMaSJEmtzzGYpHoYW3YCkkaljZl5YK2NM3M2QERMA84E/rExaUmSJI1ojsEkDZl3JElqChGxS0T8OiL2K/aviYjTi+1HImJP4CLgd4tf0L5aHPtsRNwdEfdFxPlV/f11RPx3RPwM2K+EjyRJktT0HINJ2l7ekSSpDBMiYnnV/t9m5vcj4mzgiohYAOyWmd/a6rz5wAG9v6RFxNHADOAwIIAbIuII4HngeOBAKt9z9wDLGvqJJEmSmp9jMElDZiFJUhn6vK06M5dExEeBbwLvqKGfo4vXvcX+RCqDmp2BH2XmBoCIuKEuWUuSJLU2x2CShsxH2yQ1jYgYA7wV2ADsVsspVH5JO7B4Tc/MyxqapCRJ0gjjGEzS9rCQJKmZ/CWwAvhT4DsRMW6r4+up/NLV62bg1IiYCBAR7RGxF3A78KGImBAROwMfbHzqkiRJLcsxmKSa+WibpDJs/Xz+TcB3gE8Ah2Xm+oi4Hfgb4NzeRpm5JiL+v4i4H7gxMz8bEW8Ffh4RAM8BH8vMeyLi+8AvgFXA3cPzsSRJkpqaYzBJQxaZWXYOkiRJkiRJagE+2iZJkiRJkqSaWEiSJEmSJElSTSwkSZIkSZIkqSYWkiRJkiRJklQTC0mSJEmSJEmqiYUkSZIkSZIk1cRCkiRJkiRJkmpiIUmSJEmSJEk1GVt2AkOx55575rRp08pOQ5IkNciyZcueyszJZeeh13IMJknSyDbQGKylC0nTpk1j6dKlZachSZIaJCIeLTsHvZ5jMEmSRraBxmA+2iZJkiRJkqSaWEiSJEmSJElSTSwkSZIkSZIkqSbDOkdSRLQBS4GezPzAVsd2BK4CDgHWAMdl5iPDmZ8kSY105JFHvrJ92223Ddt1X3rpJbq7u3nhhReG7Zrba/z48XR0dDBu3LiyUylFROxDZRw0BUjg0sxcEBG7A98HpgGPAMdm5tMREcACYDawATg5M+8p+joJ+Jui6y9l5pVF/BDgCmACsBjozMzs7xoN/siSJI14I3UMNtyTbXcCK4BJfRw7DXg6M6dHxPHAV4DjhjM5SZJGou7ubnbeeWemTZtGpf7QXDKTNWvW0N3dzb777lt2OmXZDHw6M++JiJ2BZRGxBDgZuDUzL4qI+cB84HPAMcCM4vVO4BLgnUVR6FxgJpWC1LKIuKEoDF0CnA7cSaWQNAu4seizr2tIkqQhGKljsGF7tC0iOoA/Br7dT5O5wJXF9nXAUdGM/6UlSRqE6ruR+tpvpBdeeIE99tijKQcwABHBHnvs0dS/1jVaZj7ee0dRZq6n8sNbO68dH10JfKjYngtclRV3ALtGxN7A+4Elmbm2KB4tAWYVxyZl5h2ZmVTufqruq69rSJKkIRipY7DhvCPp74G/Anbu53g78BhAZm6OiGeBPYCnhic9SZJGrmYdwPRq9vyGU0RMAw6icufQlMx8vDj0BJVH36Bq3FToLmIDxbv7iDPANUa8hQsX0tXV1dBr9PT0ANDe3r6NlkMzffp05s2b19BrSFIjjPTv4mYf4wwmv2G5IykiPgCsysxldejrjIhYGhFLV69eXYfsJEmSmkNETAR+APxFZq6rPlbcSZSNvP5A13AMNjgbN25k48aNZachSaOa38X1NVx3JL0LmBMRs4HxwKSI+G5mfqyqTQ+wD9AdEWOBXahMuv0amXkpcCnAzJkzGzqYkiRpJGtra+P3fu/3Xtk//vjjmT9/fr/tZ8+ezdVXXw3A1VdfzZlnnrld1zvvvPOYOHEin/nMZwaX8AgXEeOoFJG+l5k/LMJPRsTemfl48XjaqiLeO27q1VHEeoAjt4rfVsQ7+mg/0DVeYySOwYbjDp7Ozk4AFixY0PBrSVIrGo3fxa0+BhuWQlJmngOcAxARRwKf2aqIBHADcBLwc+AjwE+KX8UkSVIDTJgwgeXLl9fcfvHixQA88sgj/OM//uN2D2LUv2JeyMuAFZn59apDveOji4r366viZ0fEIiqTbT9bFIJuBi6MiN2KdkcD52Tm2ohYFxGHU3lk7kRg4TauIUmSGqDVx2DDNtl2XyLigoiYU+xeBuwREV3Ap6isGCJJ0ohw2223DbjfLJ599ln2228/fv3rXwNwwgkn8K1vfQuAadOm8dRTTzF//nweeughDjzwQD772c8C8NWvfpVDDz2Ut7/97Zx77rmv9PflL3+ZN7/5zbz73e9+pU/16V3Ax4H3RsTy4jWbSnHnfRGxEvijYh8qq649DHQB3wLOBMjMtcAXgbuL1wVFjKLNt4tzHqKyYhsDXEOSJA2TVhqDDedk2wBk5m1UbrEmM79QFX8B+Ohw5yNJ0mi1ceNGDjzwwFf2zznnHI477jguvvhiTj75ZDo7O3n66ac5/fTTX3PeRRddxP333//KL2m33HILK1eu5K677iIzmTNnDrfffjtveMMbWLRoEcuXL2fz5s0cfPDBHHLIIcP6GVtFZv4M6G+2y6P6aJ/AWf30dTlweR/xpcABfcTX9HUNSZLUGK0+Bhv2QpIkSaNVs92F1N9t1e973/v4l3/5F8466yx+8YtfbLOfW265hVtuuYWDDjoIgOeee46VK1eyfv16PvzhD7PTTjsBMGfOnIG6kSRJGhVafQxW6qNtkiSp+bz88susWLGCnXbaiaeffnqb7TOTc845h+XLl7N8+XK6uro47bTThiFTSZKkkaNVxmAWkiRJ0mt84xvf4K1vfStXX301p5xyCi+99NJrju+8886sX7/+lf33v//9XH755Tz33HMA9PT0sGrVKo444gj+9V//lY0bN7J+/Xr+7d/+bVg/hyRJUitplTGYj7ZJkjRKbf18/qxZszjllFP49re/zV133cXOO+/MEUccwZe+9CXOP//8V9rtsccevOtd7+KAAw7gmGOO4atf/SorVqzg93//9wGYOHEi3/3udzn44IM57rjjeMc73sFee+3FoYceOuyfUZIkqdm0+hgsKnM1tqaZM2fm0qVLy05DkqSmtmLFCt761reWncY29ZVnRCzLzJklpaR+OAarXWdnJwALFiwoORNJGr3K+i4eqWMwH22TJEmSJElSTSwkSZIkSZIkqSYWkiRJkiRJklQTC0mSJEmSJEmqiYUkSZIkSZIk1cRCkiRJkiRJkmoytuwEJEnS8Dr7U59l1VNr69bfXnvuzsVf/+o229100010dnayZcsWPvGJTzB//vy65SBJktTsyhiDNWL8ZSFJkqRRZtVTa3loyv+oX4dP/uc2m2zZsoWzzjqLJUuW0NHRwaGHHsqcOXPYf//965eHJElSExvuMVijxl8+2iZJkhrurrvuYvr06bzpTW9ihx124Pjjj+f6668vO62mERGXR8SqiLi/Kvb9iFhevB6JiOVFfFpEbKw69k9V5xwSEb+MiK6I+IeIiCK+e0QsiYiVxftuRTyKdl0RcV9EHDzcn12SJDVGo8ZfFpIkSVLD9fT0sM8++7yy39HRQU9PT4kZNZ0rgFnVgcw8LjMPzMwDgR8AP6w6/FDvscz8ZFX8EuB0YEbx6u1zPnBrZs4Abi32AY6pantGcb4kSRoBGjX+spAkSZJUssy8Hehz0oTirqJjgWsG6iMi9gYmZeYdmZnAVcCHisNzgSuL7Su3il+VFXcAuxb9SJIk9clCkiRJarj29nYee+yxV/a7u7tpb28vMaOW8ofAk5m5siq2b0TcGxH/GRF/WMTage6qNt1FDGBKZj5ebD8BTKk657F+znmNiDgjIpZGxNLVq1cP4eNIkqTh0Kjxl4UkSZLUcIceeigrV67kN7/5DZs2bWLRokXMmTOn7LRaxQm89m6kx4GpmXkQ8Cng6oiYVGtnxd1Kub1JZOalmTkzM2dOnjx5e0+XJEnDrFHjL1dtkyRplNlrz91rWmltu/rbhrFjx3LxxRfz/ve/ny1btnDqqafytre9rW45jFQRMRb4E+CQ3lhmvgi8WGwvi4iHgDcDPUBH1ekdRQzgyYjYOzMfLx5dW1XEe4B9+jlHkiTV0XCPwRo1/rKQJEnSKHPx179aynVnz57N7NmzS7l2C/sj4MHMfOWRtYiYDKzNzC0R8SYqE2U/nJlrI2JdRBwO3AmcCCwsTrsBOAm4qHi/vip+dkQsAt4JPFv1CJwkSaqjMsZgjRh/DcujbRExPiLuiohfRMQDEXF+H21OjojVVUvZfmI4cpMkSSpbRFwD/BzYLyK6I+K04tDxvH6S7SOA+yJiOXAd8MnM7J2o+0zg20AX8BBwYxG/CHhfRKykUpy6qIgvBh4u2n+rOF+SJKlfw3VH0ovAezPzuYgYB/wsIm4sVgep9v3MPHuYcpIkSWoKmXlCP/GT+4j9APhBP+2XAgf0EV8DHNVHPIGztjNdSZI0ig1LIakYpDxX7I4rXts9yaMkSZIkSZLKM2yrtkVEW3EL9ipgSWbe2Uez/xkR90XEdRGxTx/HJUmSJEmSVJJhKyRl5pbMPJDKaiCHRcTWt13/GzAtM98OLAGu7KufiDgjIpZGxNLVq1c3NmlJkiRJkiS9YtgKSb0y8xngp8CsreJriuVsoTJJ5CFbn1u0uzQzZ2bmzMmTJzc2WUmSJEmSJL1iWOZIKpapfSkzn4mICcD7gK9s1WbvquVm5wArhiM3SZJGm89/+myeferJuvW3y55TuPBrFw/Y5tRTT+XHP/4xe+21F/fff3/dri1JktQqRsoYbLhWbdsbuDIi2qjcBXVtZv44Ii4AlmbmDcCfR8QcYDOwFjh5mHKTJGlUefapJ/nc7z5Yt/6+8tC225x88smcffbZnHjiiXW7riRJUisZKWOw4Vq17T7goD7iX6jaPgc4ZzjykSRJw+uII47gkUceKTsNSZKkUaURY7BhnyNJkiRJkiRJrclCkiRJkiRJkmpiIUmSJEmSJEk1sZAkSZIkSZKkmgzXqm2SJKlJ7LLnlJpW+die/rblhBNO4LbbbuOpp56io6OD888/n9NOO61+SUiSJDW5kTIGs5AkSdIoc+HXLh72a15zzTXDfk1JkqRmMlLGYBaSJEmSShYRlwMfAFZl5gFF7DzgdGB10ezzmbm4OHYOcBqwBfjzzLy5iM8CFgBtwLcz86Iivi+wCNgDWAZ8PDM3RcSOwFXAIcAa4LjMfKThH1iS1BIWLlxIV1dX2WkMWe9n6OzsHNbrHnvssfz2t7+ta5877rgjU6Zs+06kRrKQJEmSVL4rgIupFHWqfSMz/646EBH7A8cDbwN+B/iPiHhzcfibwPuAbuDuiLghM38FfKXoa1FE/BOVItQlxfvTmTk9Io4v2h3XiA8oSWo9XV1drHzgXqZO3FJ2KkOyw0uV6aFffHTpsF43N3+I3LShbv1tapI/g4UkSZJGgcwkIspOo1+ZWXYKpcrM2yNiWo3N5wKLMvNF4DcR0QUcVhzrysyHASJiETA3IlYA7wX+tGhzJXAelULS3GIb4Drg4oiIHO1/EEnSK6ZO3MLnD15Xdhot6dkdtjBlwua6jcGe2NBWl36qDeZ/+RaSJEka4caPH8+aNWvYY489mrKYlJmsWbOG8ePHl51KMzo7Ik4ElgKfzsyngXbgjqo23UUM4LGt4u+k8jjbM5m5uY/27b3nZObmiHi2aP/U1olExBnAGQBTp04d+ifbBh+naD7Tp09n3rx5ZachSS2jbd1jPPP87uz6hh1G1BjMQpIkSSNcR0cH3d3drF69etuNSzJ+/Hg6OjrKTqPZXAJ8Ecji/WvAqWUlk5mXApcCzJw5s+F3LHV1dbH8/hVs2Wn3Rl+qocZsqvynWvbwkyVnMjRtG9aWnYIktZyd7v0Wazmd1ZP2AYZeSHp20xhi7A48//zzQ0+uMJgxmIUkSZJGuHHjxrHvvvuWnYa2U2a+UnmIiG8BPy52e4B9qpp2FDH6ia8Bdo2IscVdSdXte/vqjoixwC5F+6awZafd2fiW2WWnIWDCg4vLTkGSWs6YTeuZeOfX69bfN++ZxI5vnMmCBQvq1udgjCn16pIkSepTROxdtfth4P5i+wbg+IjYsViNbQZwF3A3MCMi9o2IHahMyH1DMd/RT4GPFOefBFxf1ddJxfZHgJ84P5IkSRqIdyRJkiSVLCKuAY4E9oyIbuBc4Mi2snQ1AAAgAElEQVSIOJDKo22PAH8GkJkPRMS1wK+AzcBZmbml6Ods4GagDbg8Mx8oLvE5YFFEfAm4F7isiF8G/HMxYfdaKsUnSZKkfllIkiRJqqOI+CDw75n5cq3nZOYJfYQv6yPW2/7LwJf7iC8GXvcMUrGS22F9xF8APlprnpIkST7aJkmSVF/HASsj4v+NiLeUnYwkSVI9WUiSJEmqo8z8GHAQ8BBwRUT8PCLOiIidS05NkiRpyLa7kBQRnRExKSoui4h7IuLoRiQnSZLUijJzHXAdsAjYm8pk2fdExLxSE5MkSRqiwdyRdGoxODoa2A34OHBRXbOSJElqURExNyJ+BNwGjAMOy8xjgHcAny4zN0mSpKEazGTbUbzPBv65WDkkBjpBkiRpFPkw8I3MvL06mJkbIuK0knKSJEmqi8HckbQsIm6hUki6uXjev+ZVSSRJkkaqiGgD3rh1EalXZt46zClJkiTV1WAKSacB84FDM3MDsANwykAnRMT4iLgrIn4REQ9ExPl9tNkxIr4fEV0RcWdETBtEbpIkSaXJzC3AyxGxS9m5SJIkNcJgHm1bkplH9e5k5pqIuBY4aoBzXgTem5nPRcQ44GcRcWNm3lHV5jTg6cycHhHHA1+hsnyuJElSK3kO+GVELAGe7w1m5p+Xl5IkSVJ91FxIiojxwE7AnhGxG6/OlTQJaB/o3MxMKoMqqEw6OQ7IrZrNBc4rtq8DLo6IKM4d0RYuXEhXV1dDr9HT0wNAe/uAf6ohmz59OvPmuSCNpNbjd7Hq6IfFS5IkacTZnjuS/gz4C+B3gGW8WkhaB1y8rZOLOQOWAdOBb2bmnVs1aQceA8jMzRHxLLAH8NRW/ZwBnAEwderU7Uh/dNu4cWPZKUjSqOd38eiQmVeWnYMkSVKj1FxIyswFwIKImJeZC7f3QsWcAQdGxK7AjyLigMy8fxD9XApcCjBz5swRcbfScPxq3NnZCcCCBQsafi1JakV+F6teImIG8LfA/sD43nhmvqm0pCRJkupkMJNtv1wUgwCIiN0i4sxaT87MZ4CfArO2OtQD7FP0ORbYBVgziPwkSZLK9B3gEmAz8B7gKuC7pWYkSZJUJ4MpJJ1eFIMAyMyngdMHOiEiJvcWnyJiAvA+4MGtmt0AnFRsfwT4yWiYH0mSJI04EzLzViAy89HMPA/445JzkiRJqovBFJLaIqJ3fqTeuY922MY5ewM/jYj7gLuprPz244i4ICLmFG0uA/aIiC7gU8D8QeQmSZJUthcjYgywMiLOjogPAxMHOiEiLo+IVRFxf1XsqxHxYETcFxE/qvpRblpEbIyI5cXrn6rOOSQifhkRXRHxD71jtojYPSKWRMTK4n23Ih5Fu67iOgc34j+IJEkaOQZTSLoJ+H5EHBURRwHXFLF+ZeZ9mXlQZr49Mw/IzAuK+Bcy84Zi+4XM/GhmTs/MwzLz4UHkJkmSVLZOKivd/jlwCPBxXr3ruj9X8PrH/pcAB2Tm24H/Bs6pOvZQZh5YvD5ZFb+Eyp3iM4pXb5/zgVszcwZwK6/+YHdMVdszivMlSZL6tT2rtvX6HJUV3P7vYn8J8O26ZSRJktTCMvPuYvM54JQaz7k9IqZtFbulavcOKo/+9ysi9gYmZeYdxf5VwIeAG4G5wJFF0yuB26iM6eYCVxXTCdwREbtGxN6Z+XgteUuSpNFnuwtJmfkylV+r/MVKkiSpEBH/BvQ7v2NmzunvWA1OBb5ftb9vRNwLrAP+JjP/C2gHuqvadBcxgClVxaEngCnFdjvwWB/nvK6QFBFnULlrialTpw7ho0iSpFa23YWkiPgNfQySXNJWkiSNcn/XiE4j4q+prAD3vSL0ODA1M9dExCHAv0bE22rtLzMzIrZ7QZPMvBS4FGDmzJkuiCJJ0ig1mEfbZlZtjwc+Cuxen3QkSZJaU2b+Z737jIiTgQ8AR/WuZpuZLwIvFtvLIuIh4M1AD9BRdXpHEQN4sveRteIRuFVFvAfYp59zJEmSXme7J9vOzDVVr57M/Htc0laSJAmAiJgREddFxK8i4uHe1yD6mQX8FTAnMzdUxScXq+YSEW+iMlH2w8Wja+si4vBitbYTgeuL027g1Qm/T9oqfmKxetvhwLPOjyRJkgYymEfbqpeFHUPlDqXB3NkkSZI0En0HOBf4BvAeKhNuD/jjXURcQ2Uy7D0jors4/xxgR2BJpS7EHcUKbUcAF0TES8DLwCczc23R1ZlUVoCbQGWS7RuL+EXAtRFxGvAocGwRXwzMBrqADdQ4ObgkSRq9BlMA+lrV9mbgEV4djEiSJI12EzLz1oiIzHwUOC8ilgFf6O+EzDyhj/Bl/bT9AfCDfo4tBQ7oI74GOKqPeAJn9ZeXJEnS1gazatt7GpGIJEnSCPFiRIwBVkbE2VTmHJpYck6SJEl1UXMhKSI+NdDxzPz60NORJElqeZ3ATsCfA18E3sur8xNJkiS1tO25I2nn4n0/4FAqkzMCfBC4q55JSZIktarMvLvYfK74Ie6Z3hXXJEmSWl3NhaTMPB8gIm4HDs7M9cX+ecC/NyQ7SZKkFhERXwCuzcwHI2JH4CbgHcDmiPjTzPyPcjOUJEkaugFXEOnHFGBT1f6mIiZJkjSaHQf8utjufZRtMvA/gAtLyUiSJKnOBrNq21XAXRHxIyCAuVSWmZUkSRrNNlU9wvZ+YFFmbgFWRMRgxlySJElNZzCrtn05Im4E/hBI4JTMvLfumUmSJLWWFyPiAOBJ4D3AZ6qO7VROSpIkSfU12F/HtgAvUykkvVy/dCRJklrWXwDXUXmc7RuZ+RuAiJgN+KObJEkaEba7kBQRncDpwA+oPNr23Yi4NDMX1js5SZKkVpGZdwBv6SO+GFg8/BlJkiTV32DuSDoNeGdmPg8QEV8Bfg5YSJIkSaNWRHxqoOOZ+fXhykWSJKlRBlNICiqPtvXaUsQkSZJGs52L9/2AQ4Ebiv0PAneVkpEkSVKdDaaQ9B3gzmLVNoAPAZfVLyVJkqTWk5nnA0TE7cDBmbm+2D8P+PcSU5MkSaqbwaza9vWIuA14dxFy1TZJkqRXTQE2Ve1vKmKSJEktb7sKSRHRBjyQmW8B7tmO8/YBrqIyiErg0sxcsFWbI4Hrgd8UoR9m5gXbk58kSVITuAq4a6u7t68c6ISIuBz4ALAqMw8oYrsD3wemAY8Ax2bm0xERwAJgNrABODkz7ynOOQn4m6LbL2XmlUX8EOAKYAKVib87MzP7u8bQPr4kaaTo6enh+fVtXHjPpLJTEfDo+jbe0NNTdhqM2Z7GmbkF+HVETN3O62wGPp2Z+wOHA2dFxP59tPuvzDyweFlEkiRJLSczvwycAjxdvE7JzAu3cdoVwKytYvOBWzNzBnBrsQ9wDDCjeJ0BXAKvFJ7OBd4JHAacGxG7FedcQmXV3d7zZm3jGpIkSX0azBxJuwEPRMRdwPO9wcyc098Jmfk48HixvT4iVgDtwK8GcX1JkqRmtxOwLjO/ExGTI2LfzPxNf40z8/aImLZVeC5wZLF9JXAb8LkiflVmJnBHROwaEXsXbZdk5lqAiFgCzCqmJJiUmXcU8auo3CV14wDXKF1PTw9tG55lwoOLy05FQNuGNfT0bC47DUnDrL29nRc3P87nD15XdioCLrxnEju2t5edxqAKSf/PUC5YDJIOAu7s4/DvR8QvgP8NfCYzH+jj/DOo/PrG1Knbe2OUJElSY0XEucBMKqu3fQcYB3wXeNd2djWl+DEO4AlenWepHXisql13ERso3t1HfKBrvI5jMEmSBNs/R9KHgOnALzPz5u29WERMhP/T3r1H21nXdx5/f3JBMwINCFIMBNGkWLSV0izEOss6uqQQq1GX7QJngHFQvABFx7YEp0usl1anXhaiRSOmyIwSrdfUFaQ4tWXsCCZGRK71iBcS0SAUAiSCge/88TzRTTgn7HPZt5z3a61n7f38nt/ze757nXVOfvnu34XPAm+oql1TmhuBw6rq3iTLgS/QDL1+mKpaBawCWLZsWU02BkmSpB57Kc2XZhsBqurHSfaZToPtekY97fc82jP63QdbtGgRP7l/HtufurzXj1IXFty0jkWLXDNekjSJNZKS/C3wRuDxwNuTTGpkUpL5NEmkT1TV53a9XlVbq+re9v06YH6SAybzDEmSpCHwQDvtrACSPG6K7fy0nbJG+7qlLd8MHNpR75C2bHflh4xTvrtnSJIkjWsyi20/B3heVZ1LM5f+Jd3e2O4u8jHgxqp63wR1fr2tR5Jj2tjumER8kiRJw+DTST4CLEzyauArwEVTaGctcGr7/lSa3W13lp+SxrHA3e30tMuB45Ls1y6yfRxweXtta5Jj277WKbu0Nd4zJEmSxjWZqW0PtLu2UVXbdiZ9uvRs4GTgO0muacveDCxu2/sw8HLgdUl2ANuBE9tv8yRJkkZGVb0nyQuArTTrJL2lqq7Y3T1JLqX5ou6AJJtodl97F01S6jTgh8Aft9XXAcuBMWAbzQ5xVNWdSd4OrG/rvW3nwtvA62l2hltAs8j2ZW35RM+QJEka12QSSU9Ncm37PsBT2vPQTKv/7YlurKqvtfUmVFUfBD44iXgkSZKGTpJ3V9U5wBXjlI2rqk6a4NLzx6lbwBkTtLMaWD1O+Qbg6eOU3zHeMyRJkiYymUTS0TQjhSRJkjSxFwC7Jo1OGKdMkiRp5EwmkfTJqjo6yf+qqpN7FpEkSdIISvI6milkT+4YxQ2wD/Cvg4lKkiRpZk0mkbRXklcAv5fkZbteHG8nNkmSpFnkkzRrD/01sLKj/J6OtYokSZJG2mQSSa8F/jOwEHjRLtcKMJEkSZJmraq6G7gbOAkgyROAxwJ7J9m7qn40yPgkSZJmQteJpHbB7K8l2VBVH+thTJIkSSMryYuA9wFPBLYAhwE3Ak8bZFySJEkzYc5kKrffrB2W5DPt8ZdtmSRJkhrvAI4F/q2qDqfZFe2qwYYkSZI0M7pOJCV5NrCeZhrbJe0B8I32miRJkuAXVXUHMCfJnKr6KrBs0EFJkiTNhMmskfRe4CVV9a2OsrVJPg98BHjmjEYmSZI0mu5KsjdwJfCJJFuA+wYckyRJ0oyYzNS2fXdJIgFQVdfQbGsrSZIkWAFsB94IfBn4Ho/cqESSJGkkTWZEUpLsV1X/vkvh/kxyrSVJkqQ9VVXdB5BkX+AfBhyOJEnSjJpMAuj9wD8m+f0k+7THc4HL2muSJEmzXpLXJPkJcC2wAfhm+ypJkjTyuh6RVFWrkvwYeDu/2r72euAdVeW3bZIkSY0/BZ5eVT8bdCCSJEkzbVJT0qrqS1X1nKp6fHs8xySSJEnSw3wP2DYTDSU5Isk1HcfWJG9I8tYkmzvKl3fcc26SsSQ3J/mDjvLj27KxJCs7yg9PcnVb/qkke81E7JIkac80mTWSgKazAZwFPKnz/qp68cyFJUmSNLLOBf5fkquB+3cWVtWfTLahqroZOAogyVxgM/B54JXA+6vqPZ31kxwJnEgzevyJwFeS/EZ7+UPAC4BNwPoka6vqBuDdbVtrknwYOA24cLKxSpKk2WHSiSTgC8DHaBaPfGhmw5EkSRp5HwH+CfgOM9tXej7wvar6YZKJ6qwA1lTV/cD3k4wBx7TXxqrqFoAka4AVSW4Enge8oq3zceCtmEiSJEkTmEoi6edV9YEZj0SSJGnPML+q/nsP2j0RuLTj/Mwkp9As5P2mdmfdRcBVHXU2tWUAt+5S/kzg8cBdVbVjnPqSJEmPMKk1klrnJzkvybOSHL3zmPHIJEmSRtNlSU5PcnCS/Xce02mwXbfoxcDft0UXAk+hmfZ2G/DeaUXcXQynJ9mQZMPtt9/e68dJkqQhNZURSb8FnEwzDHrncO1qzyVJkma7k9rXczvKCnjyNNo8AdhYVT8F2PkKkOSjwJfa083AoR33HdKWMUH5HcDCJPPaUUmd9R+mqlYBqwCWLVtW0/gskiRphE0lkfRHwJOr6oGZDkaSJGnUVdXhPWj2JDqmtSU5uKpua09fClzXvl8LfDLJ+2gW214KfAMIsLTdNGUzzTS5V1RVJfkq8HJgDXAq8MUexC9JkvYQU0kkXQcsBLbMcCySJEkjK8nzquqfkrxsvOtV9bkptvs4mt3WXtNR/D+THEUz0ukHO69V1fVJPg3cAOwAzqiqB9t2zgQuB+YCq6vq+ratc4A1Sd4BfItmUxVJkqRxTSWRtBC4Kcl6Hr6l7YsnuiHJocAlwEE0HZ5VVXX+LnUCnA8sB7YB/7WqNk4hPkmSpEH4fZrd2l40zrUCppRIqqr7aBbF7iw7eTf13wm8c5zydcC6ccpv4Vc7u0mSJO3WVBJJ503hnh00u4lsTLIP8M0kV1TVDR11TqAZfr2UZheRC9tXSZKkoVdVO/tIb6uq73dea6eUSZIkjbxJJ5Kq6l+mcM9tNDuKUFX3JLmRZmvZzkTSCuCSqirgqiQLd5n/PxCvetWruO22gYYwI7Zv3w7AC1/4wgFHMn0HH3wwF1100aDDkNRnF1xwAWNjY4MOY1p2xn/22WcPOJLpW7JkCWedddagwxhWnwV23dH2M8DvDiAWSZKkGTXpRFKSe2iGZwPsBcwH7quqfbu8/0nA7wBX73JpEXBrx/mmtuxhWZwkpwOnAyxevHhywU/BXXfdxb33bYO5Uxm8NUTan9i9Px/xNdIf3MFdd9016CgkDcDY2Bjfvf5bLN77wUGHMmV7/WIOAPf/cMOAI5meH907d9AhDKUkTwWeBvzaLusk7Qs8djBRSZIkzaypjEjaZ+f7dl2jFcCx3dybZG+ab+neUFVbJ/vs9vl93Xp20aJF/OT+eWx/6vJeP0pdWHDTOhYtOmjQYUgakMV7P8ibj57SPx+aQX+1savvjmajI4A/pFlPsnOdpHuAVw8kIkmSpBk2rWE27TS0LyQ5D1i5u7pJ5tMkkT4xwa4lm4FDO84PacskSZKGXlV9EfhikmdV1dcHHY8kSVIvTGVqW+dQ7TnAMuDnj3JPaLaSvbGq3jdBtbXAmUnW0Cyyffeg10eSJEmagpcmuR7YDnwZ+G3gjVX1vwcbliRJ0vRNZURS51DtHcAPaKa37c6zgZOB7yS5pi17M7AYoKo+TLMd7XJgDNgGvHIKsUmSJA3acVX150leStNPehlwJWAiSZIkjbyprJE06QRPVX0NyKPUKeCMybYtSZI0ZOa3ry8E/r6q7m4GZ0uSJI2+rhNJSd6ym8tVVW+fgXgkSZJG3T8kuYlmatvrkhzIoywDIEmSNCrmTKLufeMcAKcB58xwXJIkSSOpqlYCvwcsq6pf0EzZf7RlACRJkkZC14mkqnrvzgNYBSygWcdoDfDkHsUnSZI0EpL8ecfp86vqQYCqug/4k8FEJUmSNLMmMyKJJPsneQdwLc20uKOr6pyq2tKT6CRJkkbHiR3vz93l2vH9DESSJKlXJrNG0t/Q7DqyCvitqrq3Z1FJkiSNnkzwfrxzSZKkkTSZEUlvAp4I/AXw4yRb2+OeJFt7E54kSdLIqAnej3fetSQ/SPKdJNck2dCW7Z/kiiTfbV/3a8uT5ANJxpJcm+TojnZObet/N8mpHeW/27Y/1t5r0kuSJE2o6xFJVTWpaXCSJEmzzDPaL9cCLOj4oi3AY6fZ9n+qqp91nK8E/k9VvSvJyvb8HOAEYGl7PBO4EHhmkv2B84BlNEmtbyZZW1X/3tZ5NXA1sI5mGt5l04xXkrSH+NG9c/mrjfsOOoxp+em2Jp1x0H94aMCRTM+P7p3L0kEHwSQSSZIkSZpYVc3t4+NWAM9t338c+GeaRNIK4JKqKuCqJAuTHNzWvaKq7gRIcgVwfJJ/Bvatqqva8kuAlzAkiaS52+5kwU3rBh3GtMz5eZNPfOixo/2fsLnb7gQOGnQYkvpsyZIlgw5hRjwwNgbAYw4b7c+zlOH4mZhIkiRJGm4F/GOSAj5SVauAg6rqtvb6T/jV//AXAbd23LupLdtd+aZxyh8hyenA6QCLFy+ezufpyjB0lGfC2Ng9ACx58qgnYQ7aY34mkrp31llnDTqEGXH22WcDcP755w84kj2DiSRJkqTh9h+ranOSJwBXJLmp82JVVZtk6qk2gbUKYNmyZT1/nv95kSRpOLnukSRJ0hCrqs3t6xbg88AxwE/bKWu0r1va6puBQztuP6Qt2135IeOUS5IkjctEkiRJ0pBK8rgk++x8DxwHXAesBXbuvHYq8MX2/VrglHb3tmOBu9spcJcDxyXZr93h7Tjg8vba1iTHtru1ndLRliRJ0iM4tU2SJGl4HQR8vsnxMA/4ZFV9Ocl64NNJTgN+CPxxW38dsBwYA7YBrwSoqjuTvB1Y39Z7286Ft4HXAxcDC2gW2R6KhbYlSdJwMpEkSZI0pKrqFuAZ45TfATx/nPICzpigrdXA6nHKNwBPn3awkiRpVnBqmyRJkiRJkrpiIkmSJEmSJEldMZEkSZIkSZKkrphIkiRJkiRJUldMJEmSJEmSJKkrJpIkSZIkSZLUlb4kkpKsTrIlyXUTXH9ukruTXNMeb+lHXJIkSZIkSerevD4952Lgg8Alu6nzf6vqD/sTjiRJkiRJkiarLyOSqupK4M5+PEuSJEmSJEm9MUxrJD0rybeTXJbkaRNVSnJ6kg1JNtx+++39jE+SJEmSJGlWG5ZE0kbgsKp6BnAB8IWJKlbVqqpaVlXLDjzwwL4FKEmSJEmSNNsNRSKpqrZW1b3t+3XA/CQHDDgsSZIkSZIkdRiKRFKSX0+S9v0xNHHdMdioJEmSJEmS1KkviaQklwJfB45IsinJaUlem+S1bZWXA9cl+TbwAeDEqqp+xCZJkjSskhya5KtJbkhyfZKz2/K3Jtmc5Jr2WN5xz7lJxpLcnOQPOsqPb8vGkqzsKD88ydVt+aeS7NXfTylJkkbJvH48pKpOepTrHwQ+2I9YJEmSRsgO4E1VtTHJPsA3k1zRXnt/Vb2ns3KSI4ETgacBTwS+kuQ32ssfAl4AbALWJ1lbVTcA727bWpPkw8BpwIU9/2SSJGkkDcXUNkmSJD1SVd1WVRvb9/cANwKLdnPLCmBNVd1fVd8HxoBj2mOsqm6pqgeANcCKdmmB5wGfae//OPCS3nwaSZK0JzCRJEmSNAKSPAn4HeDqtujMJNcmWZ1kv7ZsEXBrx22b2rKJyh8P3FVVO3YpH+/5pyfZkGTD7bffPgOfSJIkjSITSZIkSUMuyd7AZ4E3VNVWmqlnTwGOAm4D3tvrGKpqVVUtq6plBx54YK8fJ0mShlRf1kiSJEnS1CSZT5NE+kRVfQ6gqn7acf2jwJfa083AoR23H9KWMUH5HcDCJPPaUUmd9SVJkh7BEUmSJElDql3D6GPAjVX1vo7ygzuqvRS4rn2/FjgxyWOSHA4sBb4BrAeWtju07UWzIPfadpfcr9LsoAtwKvDFXn4mSZI02hyRJEmSNLyeDZwMfCfJNW3Zm4GTkhwFFPAD4DUAVXV9kk8DN9Ds+HZGVT0IkORM4HJgLrC6qq5v2zsHWJPkHcC3aBJXkiRJ4zKRJEmSNKSq6mtAxrm0bjf3vBN45zjl68a7r6puodnVTZIk6VE5tU2SJEmSJEldMZEkSZIkSZKkrphIkiRJkiRJUldMJEmSJEmSJKkrJpIkSZIkSZLUFRNJkiRJkiRJ6oqJJEmSJEmSJHXFRJIkSZIkSZK6YiJJkiRJkiRJXTGRJEmSJEmSpK6YSJIkSZIkSVJXTCRJkiRJkiSpKyaSJEmSJEmS1JW+JJKSrE6yJcl1E1xPkg8kGUtybZKj+xGXJEmSIMnxSW5u+2IrBx2PJEkaXv0akXQxcPxurp8ALG2P04EL+xCTJEnSrJdkLvAhmv7YkcBJSY4cbFSSJGlYzevHQ6rqyiRP2k2VFcAlVVXAVUkWJjm4qm7rR3yPZu62O1lw07qetT/n51vJQ7/oWfv9VHPm89Bj9+1Z+3O33Qkc1LP2JQ2vzZs3c+dd83jNv+zXk/Z/8VB4qHrSdN/NCcyf07sPc/+DYf95m3vWvvruGGCsqm4BSLKGpm92w0Cj6oMLLriAsbGxnj5jZ/tnn312T5+zZMkSzjrrrJ4+Q5J6wb/Fo6cviaQuLAJu7Tjf1JY9IpGU5HSaUUssXry454EtWbKk58/YvHkH27dv7/lz+mHBggUsWtTLRM9BffmZSBo+Cxcu7O3fyvvvh4ce6l37/TRnDnMe85ieNb+A5uehPcZ4/bBn7lqp332wPcWCBQsGHYIkzXr+LZ5Zw5JI6lpVrQJWASxbtqzn3x3PhmyiJI2Ciy66aNAhSLNav/tg/WA/T5IGz7/Fo2dYdm3bDBzacX5IWyZJkqTesh8mSZK6NiyJpLXAKe3ubccCdw/L+kiSJEl7uPXA0iSHJ9kLOJGmbyZJkvQIfZnaluRS4LnAAUk2AecB8wGq6sPAOmA5MAZsA17Zj7gkSZJmu6rakeRM4HJgLrC6qq4fcFiSJGlI9WvXtpMe5XoBZ/QjFkmSJD1cVa2j+WJPkiRpt4ZlapskSZIkSZKGnIkkSZIkSZIkdcVEkiRJkiRJkrqSZnmi0ZTkduCHg45DA3EA8LNBByGpr/y9n50Oq6oDBx2EHs4+2Kzm32JpdvF3fvaasA820okkzV5JNlTVskHHIal//L2XpMHzb7E0u/g7r/E4tU2SJEmSJEldMZEkSZIkSZKkrphI0qhaNegAJPWdv/eSNHj+LZZmF3/n9QiukSRJkiRJkqSuOCJJkiRJkiRJXTGRJEmSJEmSpK6YSNJISXJ8kpuTjCVZOeh4JPVWktVJtiS5btCxSNJsZh9Mml3sg2l3TCRpZCSZC3wIOAE4EjgpyZGDjUpSj10MHD/oICRpNrMPJs1KF2MfTBMwkaRRcgwwVlW3VNUDwBpgxYBjktRDVXUlcOeg45CkWc4+mDTL2AfT7phI0ihZBNzacb6pLZMkSVLv2AeTJP2SiSRJkiRJkiR1xUSSRht7z8gAAAKrSURBVMlm4NCO80PaMkmSJPWOfTBJ0i+ZSNIoWQ8sTXJ4kr2AE4G1A45JkiRpT2cfTJL0SyaSNDKqagdwJnA5cCPw6aq6frBRSeqlJJcCXweOSLIpyWmDjkmSZhv7YNLsYx9Mu5OqGnQMkiRJkiRJGgGOSJIkSZIkSVJXTCRJkiRJkiSpKyaSJEmSJEmS1BUTSZIkSZIkSeqKiSRJkiRJkiR1xUSSpL5L8mCSazqOlY9Sf12She3x+ik8761J/nTqEUuSJI0++2CSZsK8QQcgaVbaXlVHdVu5qpYDJHkS8Hrgb3sTliRJ0h7NPpikaXNEkqShkOTXktyc5Ij2/NIkr27f/yDJAcC7gKe036D9TXvtz5KsT3Jtkr/saO9/JPm3JF8DjhjAR5IkSRp69sEkTZYjkiQNwoIk13Sc/3VVfSrJmcDFSc4H9quqj+5y30rg6Tu/SUtyHLAUOAYIsDbJc4D7gBOBo2j+zm0EvtnTTyRJkjT87INJmjYTSZIGYdxh1VV1RZI/Aj4EPKOLdo5rj2+153vTdGr2AT5fVdsAkqydkaglSZJGm30wSdPm1DZJQyPJHOA3gW3Aft3cQvNN2lHtsaSqPtbTICVJkvYw9sEkTYaJJEnD5I3AjcArgL9LMn+X6/fQfNO10+XAf0uyN0CSRUmeAFwJvCTJgiT7AC/qfeiSJEkjyz6YpK45tU3SIOw6P//LwN8BrwKOqap7klwJ/AVw3s5KVXVHkn9Nch1wWVX9WZLfBL6eBOBe4L9U1cYknwK+DWwB1vfnY0mSJA01+2CSpi1VNegYJEmSJEmSNAKc2iZJkiRJkqSumEiSJEmSJElSV0wkSZIkSZIkqSsmkiRJkiRJktQVE0mSJEmSJEnqiokkSZIkSZIkdcVEkiRJkiRJkrry/wHOPmy4ED/TxAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x864 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "    # Relations based on the continuous data attributes\n",
+    "    fig, axarr = plt.subplots(3, 2, figsize=(20, 12))\n",
+    "    sns.boxplot(y='CreditScore',x = 'Exited', hue = 'Exited',data = df, ax=axarr[0][0])\n",
+    "    sns.boxplot(y='Age',x = 'Exited', hue = 'Exited',data = df , ax=axarr[0][1])\n",
+    "    sns.boxplot(y='Tenure',x = 'Exited', hue = 'Exited',data = df, ax=axarr[1][0])\n",
+    "    sns.boxplot(y='Balance',x = 'Exited', hue = 'Exited',data = df, ax=axarr[1][1])\n",
+    "    sns.boxplot(y='NumOfProducts',x = 'Exited', hue = 'Exited',data = df, ax=axarr[2][0])\n",
+    "    sns.boxplot(y='EstimatedSalary',x = 'Exited', hue = 'Exited',data = df, ax=axarr[2][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "969a3b302aacffa0a28bcc0d9a157101a3214929"
+   },
+   "source": [
+    "We note the following:\n",
+    "* There is no significant difference in the credit score distribution between retained and churned customers. \n",
+    "* The older customers are churning at more than the younger ones alluding to a difference in service preference in the age categories. The bank may need to review their target market or review the strategy for retention between the different age groups\n",
+    "* With regard to the tenure, the clients on either extreme end (spent little time with the bank or a lot of time with the bank) are more likely to churn compared to those that are of average tenure.\n",
+    "*  Worryingly, the bank is losing customers with significant bank balances which is likely to hit their available capital for lending.\n",
+    "* Neither the product nor the salary has a significant effect on the likelihood to churn."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "170ef4a92a699333453cf6e18c42822303137db2"
+   },
+   "source": [
+    "## 4. Feature engineering\n",
+    "We seek to add features that are likely to have an impact on the probability of churning. We first split the train and test sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "13a8d524dc0ec3ca08f4ea47d2192bf8a0797871"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8000\n2000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Split Train, test data\n",
+    "df_train = df.sample(frac=0.8,random_state=200)\n",
+    "df_test = df.drop(df_train.index)\n",
+    "print(len(df_train))\n",
+    "print(len(df_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "296fb7912c926c64449c83d394ceaf1f5da3bde4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-1, 5)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_train['BalanceSalaryRatio'] = df_train.Balance/df_train.EstimatedSalary\n",
+    "sns.boxplot(y='BalanceSalaryRatio',x = 'Exited', hue = 'Exited',data = df_train)\n",
+    "plt.ylim(-1, 5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_uuid": "05cc52ec8ac78b936b10291cd46a320259b49425"
+   },
+   "source": [
+    "we have seen that the salary has little effect on the chance of a customer churning. However as seen above, the ratio of the bank balance and the estimated salary indicates that customers with a higher balance salary ratio churn more which would be worrying to the bank as this impacts their source of loan capital."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "4e13d0df114dbf1d6d9965e776403f4861380295"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEKCAYAAAA8QgPpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAdj0lEQVR4nO3df5RVdb3/8edLEAHF4pdkMygYpFjpqIN9y5ZloSJ9v2h9M6F1r6gY9U26rlu6RO17EcMuZWXd1G+XjMRrieVdJXUJRdNc914NhxoVJOKolDPXBAb0goA6w/v7x9mDh2HmzJk958yeYV6Ptc6a89n7s/d+4xJes/f+7M9WRGBmZtZVh2RdgJmZ9U0OEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUMg0QSUskbZa0toP1kvRPknKSnpZ0asG6WZI2Jp9ZPVe1mZlB9mcgdwJTi6w/D5iYfOYA/w9A0ghgPvB+4HRgvqThFa3UzMz2k2mARMRjwLYiXc4H7oq8J4C3SzoaOBdYFRHbImI7sIriQWRmZmU2MOsCOlEFvFjQbkiWdbT8AJLmkD974fDDDz/thBNOqEylZmYHqTVr1myNiNFtl/f2AOm2iFgMLAaora2Nurq6jCsyM+tbJP25veVZ3wPpTCMwtqBdnSzraLmZmfWQ3h4gy4GLk9FY/wN4NSJeAh4AzpE0PLl5fk6yzMzMekiml7Ak3QN8BBglqYH8yKpDASLi+8AKYBqQA3YBlybrtkn6KvBksqsbI6LYzXgzMyuzTAMkImZ2sj6AKzpYtwRYUom6zMzK6c0336ShoYE9e/ZkXUpRgwcPprq6mkMPPbSk/gf9TXQzs6w1NDQwbNgwxo0bh6Ssy2lXRNDU1ERDQwPjx48vaZvefg/EzKzP27NnDyNHjuy14QEgiZEjR3bpLMkBYmbWA3pzeLTqao0OEDMzS8UBYmaWoQEDBlBTU7Pvs2jRoqL9p02bxiuvvMIrr7zC7bff3uXj3XDDDXzzm99MW+5+fBPdzCxDQ4YMob6+vuT+K1asAGDTpk3cfvvtfOELX6hUaZ3yGYiZWS/z6quvcvzxx7NhwwYAZs6cyQ9+8AMAxo0bx9atW5k3bx7PPfccNTU1XH311QDcfPPNTJ48mZNOOon58+fv299NN93Eu9/9bj70oQ/t22c5+AzEzCxDu3fvpqamZl/72muv5aKLLuLWW2/lkksu4corr2T79u189rOf3W+7RYsWsXbt2n1nLw8++CAbN25k9erVRATTp0/nscce4/DDD2fZsmXU19fT3NzMqaeeymmnnVaW2h0gZmYZ6ugS1tlnn83PfvYzrrjiCp566qlO9/Pggw/y4IMPcsoppwCwc+dONm7cyI4dO/jEJz7B0KFDAZg+fXrZavclLDOzXmjv3r2sX7+eoUOHsn379k77RwTXXnst9fX11NfXk8vlmD17dkVrdICYmfVCt9xyC5MmTeInP/kJl156KW+++eZ+64cNG8aOHTv2tc8991yWLFnCzp07AWhsbGTz5s2ceeaZ/OIXv2D37t3s2LGDX/7yl2Wr0ZewzMwy1PYeyNSpU7n00ku54447WL16NcOGDePMM89k4cKFLFiwYF+/kSNHcsYZZ/De976X8847j5tvvpn169fzgQ98AIAjjjiCu+++m1NPPZWLLrqIk08+maOOOorJkyeXrXbl5yvsH/xCKTPLwvr165k0aVLWZZSkvVolrYmI2rZ9fQnLzMxScYCYmVkqDhAzM0vFAWJmZqk4QMzMLJVMA0TSVEkbJOUkzWtn/S2S6pPPnyS9UrCupWDd8p6t3MzMMnsORNIA4DbgbKABeFLS8oh4trVPRPx9Qf8vAqcU7GJ3RNRgZtbHzP3S1Wzeuq1s+ztq1Ahu/fbNnfZbuXIlV155JS0tLVx++eXMm3fA7+1dkuWDhKcDuYh4HkDSMuB84NkO+s8E5newzsysz9i8dRvPjflw+Xb48m877dLS0sIVV1zBqlWrqK6uZvLkyUyfPp0TTzwx9WGzvIRVBbxY0G5Ilh1A0rHAeOA3BYsHS6qT9ISkCypXpplZ37d69WomTJjAcccdx6BBg5gxYwb3339/t/bZV26izwDui4iWgmXHJk9Gfgb4jqR3tbehpDlJ0NRt2bKlJ2o1M+t1GhsbGTt27L52dXU1jY2N3dpnlgHSCIwtaFcny9ozA7incEFENCY/nwceZf/7I4X9FkdEbUTUjh49urs1m5lZIssAeRKYKGm8pEHkQ+KA0VSSTgCGA48XLBsu6bDk+yjgDDq+d2Jm1u9VVVXx4otv3TVoaGigqqrduwYlyyxAIqIZmAs8AKwHfhoR6yTdKKnwjSczgGWx/6yPk4A6SU8BjwCLCkdvmZnZ/iZPnszGjRt54YUXeOONN1i2bFm3Xy6V6XTuEbECWNFm2T+0ad/Qznb/CbyvosWZmVXIUaNGlDRyqkv768TAgQO59dZbOffcc2lpaeGyyy7jPe95T7eO6/eBmJn1sFKe2aiEadOmMW3atLLtr6+MwjIzs17GAWJmZqk4QMzMLBUHiJmZpeIAMTOzVBwgZmaWiofxmpn1sOu+PJdXt75ctv29bdQYvvatW4v2ueyyy/jVr37FUUcdxdq1a8tyXAeImVkPe3Xry1zzrj+WbX9ff67zPpdccglz587l4osvLttxfQnLzKwfOPPMMxkxovMn1rvCAWJmZqk4QMzMLBUHSB+Ty+X4+Mc/Ti6Xy7oUM+vnHCB9zMKFC3nttddYuHBh1qWYWT/nUVh9SC6XY9OmTQBs2rSJXC7HhAkTsi3KzLrsbaPGlDRyqiv768zMmTN59NFH2bp1K9XV1SxYsIDZs2d367gOkD6k7VnHwoULufPOO7MpxsxS6+yZjUq45557Ou/URb6E1Ye0nn101DYz60kOkD5k3LhxRdtmZj3JAdKHfOUrXynaNrPeKyKyLqFTXa0x0wCRNFXSBkk5SfPaWX+JpC2S6pPP5QXrZknamHxm9Wzl2ZgwYQIDB+ZvWw0cONA30M36iMGDB9PU1NSrQyQiaGpqYvDgwSVvk9lNdEkDgNuAs4EG4ElJyyPi2TZd742IuW22HQHMB2qBANYk227vgdIzk8vlaG5uBqC5udmjsMz6iOrqahoaGtiyZUvWpRQ1ePBgqqurS+6f5Sis04FcRDwPIGkZcD7QNkDacy6wKiK2JduuAqYC5R9m0It4FJZZ39DU1MSCBQuYP38+I0eO5NBDD2X8+PFZl1V2WV7CqgJeLGg3JMva+t+SnpZ0n6SxXdwWSXMk1Umq6+3p3xmPwjLrG5YuXcozzzzDXXfdlXUpFdXbb6L/EhgXEScBq4ClXd1BRCyOiNqIqB09enTZC+xJhxxySNG2mWWvqamJlStXEhGsXLmSpqamrEuqmCz/BWoExha0q5Nl+0REU0S8njTvAE4rdduD0d69e4u2zSx7S5cu3fd3s6Wl5aA+C8kyQJ4EJkoaL2kQMANYXthB0tEFzenA+uT7A8A5koZLGg6ckywzM8vUQw89tN9gl1WrVmVcUeVkFiAR0QzMJf8P/3rgpxGxTtKNkqYn3f5O0jpJTwF/B1ySbLsN+Cr5EHoSuLH1hrqZWZamTJmy33D7s88+O+OKKke9eVxyudXW1kZdXV3WZaT2kY985IBljz76aI/XYWYda2pq4qKLLqK5uZlDDz2UZcuWMXLkyKzL6hZJayKitu1y34U1Myuj1mG7kD8D6evhUYwDxMysjHK5HLt37wZg9+7dB/XL3zydew/73ve+V9b/oa688soubzNhwgS++MUvlq0GM3vLddddt1/7+uuv5957782omsryGYiZWRlt3rx5v/bLL7+cUSWV5zOQHtad3/zr6uq46qqr9rW/9a1vcdpppxXZwsyscnwG0ofU1r41CGLAgAEODzPLlAOkj2mdkO0b3/hGxpWYWX/nAOljjjzySE4++WSffZj1Um3fpzFkyJCMKqk8B4iZWRnt2bNnv3brkN6DkQPEzMxS8SgsM7M2uvO8lqT9Xl0rKdXzWtD7n9nyGYiZWRmNGzduv/bB+CbCVj4DMTNro7u/9Z911llEBEOHDmXJkiVlqqr38RmImVmZtZ6FfPWrX822kApzgJiZlVl/GW7vADEzs1QcIGZmlkqnASJpiKRrJX0/aU+QdF7lSzMzs96slDOQJYCADyXt/wK+Vo6DS5oqaYOknKR57az/kqRnJT0t6WFJxxasa5FUn3yWl6MeMzMrXSkBMjEivga8CRARu8gHSrdIGgDcBpwHnAjMlHRim25/AGoj4iTgPqBwBsHdEVGTfKZ3tx4zM+uaUgLkDUmDgQCQNB54owzHPh3IRcTzEfEGsAw4v7BDRDySBBbAE0B1GY5rZmZlUEqA3AisBKolLQUeAa4tw7GrgBcL2g3Jso7MBn5d0B4sqU7SE5Iu6GgjSXOSfnVbtmzpXsVmZrZPp0+iR8RKSWuAD5K/dHV1RGzuZLOykvQ3QC3w4YLFx0ZEo6TjgN9IeiYinmu7bUQsBhYD1NbWRtv1ZmaWTqcBIumk5OsLyc93SBoCvBgRe7tx7EZgbEG7OlnW9vhTgOuBD0fE663LI6Ix+fm8pEeBU4ADAsTMzCqjlLmwfgjUAOvIn4FMAp4FhkmaExEPpzz2k8DE5J5KIzAD+ExhB0mnAP8MTC0865E0HNgVEa9LGgWcwf432CuiOzN0lkvr8dPO7lkuvX2WUDOrvFICZBMwOyKeBpD0PuD/AteRHxlVk+bAEdEsaS7wADAAWBIR6yTdCNRFxHLgZuAI4GeSAP6SjLiaBPyzpL3k7+Msiohn09TRFblcjvq162kZOqLSh+rQIW/kr8Ktef7lzGoYsGtbZsc2s96jlACZ1BoeABHxjKQTIyKX/KOeWkSsAFa0WfYPBd+ndLDdfwLv69bBU2oZOoLdJ0zL4tC9xpA/rui8k5kd9EoJkD9K+h75YbYAFwEbJB0GNFesMjMz69VKCZCLgS8CrU+K/0fyvRn4WIXqMjNLLev7lf3lXmUpw3h3AV9PPkh6J/C5iLgFeLVilZmZpZTL5di47g8cc0RLJscf9Gb+EbvX/1yXyfEB/rJzQMWPUdIbCSWNAD4FzATGA7+oZFFmZt11zBEtXHfqf2ddRma+9vsjK36MDgNE0uHABeSH1r4HuB94d0QUe1rczMz6iWJnIJuBOuAG4LcRsVdSv560sLGxkQG7Xu33o5AG7GqisdHjJ8z6u2JzYc0HDge+DVyVTKXuqUDMzAwocgYSEd8EvilpIvl7H78G3inpy8DPI+L5Hqqx16iqquKvrw/0cyB/XEFV1ZisyzCzjJUyCmsj+Rl5b5RUQz5MHiZ/M93MrNdpbGzktR0DeuRGcm/15x0DOLzxgOkFy6qUV9r+H0lHAkREfURcExEODzOzfq6UYbzHAn+Q9Dvy81U9VOGazMy6paqqitebX+r3w3gPq6rsoNlOz0AiYh4wEfgx8HlJGyXdKGlcRSszM7NerZQ3EpK892NT8tkLHA3cL+kfK1aZmZn1aqW8UOoKYBbw3+TfDXJ98h6OQ4Ac5Xm9bZ8xYNe2TJ8DOWRP/pR87+Dsbg7mp3P3KCyz/q6UeyDvBGa2fV1sf3ywcMKECVmXQC63A4AJx2X5D/iYXvHfwqyYv+zMbhTWy7vyF3fGDO3OS1u75y87BzCxwscoZRjv9QCSBgMnkH+p07Zk3drKlte79IY38LXO7vnd734340rMeq+sf8F5I5mN97Bjs6tjIpX/71BsLqz/CXwH2AZ8Bfh+8n2spC9HxN0VrczMLKWsf9nrL7/oFTsDuQmYDrwNWAWcEhEbJb0jaTtAzMz6sWKjsPZGxLMR8TiwKXkinYj4K2V6E6GkqZI2SMpJmtfO+sMk3Zus/13h0GFJ1ybLN0g6txz1mJlZ6YoFyCGShkl6G9CSfD8yeSq9pOG/xUgaANwGnAecCMyUdGKbbrOB7RExAbiFt15qdSIwg/w081OB25P9mZlZDyl2CWsksA5Q0n6W/Gy8ojyz8p4O5FonZZS0DDg/OU6r88lPJw9wH3CrJCXLl0XE68ALknLJ/h4vQ10V1d1XbZbjVZmVfs2lWV/XG/6eQu//u1psNt7qCh+7CnixoN0AvL+jPhHRLOlV8sFWBTzRZtt2n9mXNAeYA3DMMceUpfAsDRkyJOsSzKwT/eXvaamvtJ0BHBcRX5NUDYyJiDWVLa08ImIxsBigtrY28/eZ9ObfJswsz39PS1PKbLy3AmcBf5ss2kV+SG93NQJjC9rVybJ2+0gaSH5EWFOJ25qZWQWVcjP8gxHxOWAPQPIQ4aAyHPtJYKKk8ZIGkb8pvrxNn+Xkp1EB+BTwm4iIZPmMZJTWePLPzKwuQ01mZlaiUi5hvZnMexUAkkaSn1CxW5J7GnOBB4AB5KeKXyfpRqAuIpaTn3vrX5Kb5NvIhwxJv5+Sv+HeDFwRES3drcnMzEqn/C/0RTpIFwOfAGqBJcCngQURsazy5ZVXbW1t1NXVZV2GmVmfImlNRNS2XV7KXFh3SVoDTCE/hPfC/jYHlpmZHahogCQP5z0dEe8h/0yImZkZ0MlN9OS+wvOSKvteRDMz63NKuYl+BLBe0uPAa60LI+KTFavKzMx6vVICZGHFqzAzsz6nlJvoD/dEIWZm1reU8k70Hbw1eeJA8s9svB4R2b2U28zMMlfKGciw1u/JA4WfBGoqWZSZmfV+XXqvR0TsjYj7gI9XqB4zM+sjSrmENb2geQj5J9LfqFhFZmbWJ5QyCuvCgu/NwCbyL3QyM7N+rJR7IH/bWR8zM+t/SrmENQq4DBhX2D8i5lSuLDMz6+1KuYR1P/nXx/474CnTzcwMKC1ADo+IL1e8EjMz61NKGcb7a0nnVLwSMzPrU0oJkM8DKyXtlLRN0nZJ2ypdmJmZ9W6lXMIaVfEqzMysz+n0DCR5J8iFwDXJ96Pp5lQmkkZIWiVpY/JzeDt9aiQ9LmmdpKclXVSw7k5JL0iqTz6eWsXMrId1GiCSbgXOAlqfB9kFfL+bx50HPBwRE4GHk3Zbu4CLk7chTgW+I+ntBeuvjoia5FPfzXrMzKyLSrkH8sGI+BywByAitgGDunnc84GlyfelwAVtO0TEnyJiY/L9v4DNwOhuHtfMzMqklAB5M5mFNwAkjQT2dvO4YyLipeT7X4ExxTpLOp18aD1XsPim5NLWLZIOK7LtHEl1kuq2bNnSzbLNzKxVhwEiqfUG+23AvwKjJS0g/0Dh1zvbsaSHJK1t57PfPFoREbz1vpH29nM08C/ApRHRGlzXAicAk4ERwDUdbR8RiyOiNiJqR4/2CYyZWbkUG4W1Gjg1Iu6StAaYAgi4MCLWdrbjiJjS0TpJL0s6OiJeSgJicwf9jgT+Dbg+Ip4o2Hfr2cvrkn4EXNVZPWZmVl7FAkStXyJiHbCujMddDswCFiU/7z/g4NIg4OfAXck7SArXtYaPyN8/6TTQzMysvIoFyGhJX+poZUR8uxvHXQT8VNJs4M/ApwEk1QKfj4jLk2VnAiMlXZJsd0ky4urHkkaTD7l68g87mplZDyoWIAOAIyg4EymXiGgCPtbO8jrg8uT73cDdHWz/0XLXZGZmXVMsQF6KiBt7rBIzM+tTig3jLfuZh5mZHTyKBcgBl5jMzMxadRggyRPnZmZm7SrlSXQzM7MDOEDMzCwVB4iZmaXiADEzs1QcIGZmlooDxMzMUnGAmJlZKg4QMzNLxQFiZmapOEDMzCwVB4iZmaXiADEzs1QcIGZmlooDxMzMUskkQCSNkLRK0sbk5/AO+rVIqk8+ywuWj5f0O0k5SfdKGtRz1ZuZGWR3BjIPeDgiJgIPJ+327I6ImuQzvWD514FbImICsB2YXdlyzcysrawC5HxgafJ9KXBBqRtKEvBR4L4025uZWXlkFSBjIuKl5PtfgTEd9BssqU7SE5JaQ2Ik8EpENCftBqCqowNJmpPso27Lli1lKd7MzGBgpXYs6SHgHe2sur6wEREhKTrYzbER0SjpOOA3kp4BXu1KHRGxGFgMUFtb29FxzMysiyoWIBExpaN1kl6WdHREvCTpaGBzB/toTH4+L+lR4BTgX4G3SxqYnIVUA41l/wOYmVlRWV3CWg7MSr7PAu5v20HScEmHJd9HAWcAz0ZEAI8Anyq2vZmZVVZWAbIIOFvSRmBK0kZSraQ7kj6TgDpJT5EPjEUR8Wyy7hrgS5Jy5O+J/LBHqzczM5T/hb5/qK2tjbq6uqzLMDPrUyStiYjatsv9JLqZmaXiADEzs1QcIGZmlooDxMzMUnGAmJlZKg4QMzNLxQFiZmapOEDMzCwVB4iZmaXiADEzs1QcIGZmlooDxMzMUnGAmJlZKg4QMzNLxQFiZmapOEDMzCwVB4iZmaXiADEzs1QyCRBJIyStkrQx+Tm8nT5nSaov+OyRdEGy7k5JLxSsq+n5P4WZWf+W1RnIPODhiJgIPJy09xMRj0RETUTUAB8FdgEPFnS5unV9RNT3SNVmZrZPVgFyPrA0+b4UuKCT/p8Cfh0RuypalZmZlSyrABkTES8l3/8KjOmk/wzgnjbLbpL0tKRbJB1W9grNzKyogZXasaSHgHe0s+r6wkZEhKQosp+jgfcBDxQsvpZ88AwCFgPXADd2sP0cYA7AMccc04U/gZmZFVOxAImIKR2tk/SypKMj4qUkIDYX2dWngZ9HxJsF+249e3ld0o+Aq4rUsZh8yFBbW9thUJmZWddkdQlrOTAr+T4LuL9I35m0uXyVhA6SRP7+ydoK1GhmZkVkFSCLgLMlbQSmJG0k1Uq6o7WTpHHAWOC3bbb/saRngGeAUcDCHqjZzMwKVOwSVjER0QR8rJ3ldcDlBe1NQFU7/T5ayfrMzKxzfhLdzMxScYCYmVkqDhAzM0vFAWJmZqk4QMzMLBUHiJmZpeIAMTOzVBwgZmaWigPEzMxScYCYmVkqDhAzM0vFAWJmZqk4QMzMLBUHiJmZpeIAMTOzVBwgZmaWigPEzMxScYCYmVkqDhAzM0slkwCRdKGkdZL2Sqot0m+qpA2ScpLmFSwfL+l3yfJ7JQ3qmcrNzKxVVmcga4FPAo911EHSAOA24DzgRGCmpBOT1V8HbomICcB2YHZlyzUzs7YyCZCIWB8RGzrpdjqQi4jnI+INYBlwviQBHwXuS/otBS6oXLVmZtaegVkXUEQV8GJBuwF4PzASeCUimguWV3W0E0lzgDlJc6ekzoLLSjcK2Jp1EWbt8P+b5XVsewsrFiCSHgLe0c6q6yPi/kodt62IWAws7qnj9SeS6iKiw3tYZlnx/5s9o2IBEhFTurmLRmBsQbs6WdYEvF3SwOQspHW5mZn1oN48jPdJYGIy4moQMANYHhEBPAJ8Kuk3C+ixMxozM8vLahjvJyQ1AB8A/k3SA8nyd0paAZCcXcwFHgDWAz+NiHXJLq4BviQpR/6eyA97+s9ggC8NWu/l/zd7gPK/0JuZmXVNb76EZWZmvZgDxMzMUnGAWJd1NMWMWdYkLZG0WdLarGvpDxwg1iWdTDFjlrU7galZF9FfOECsq9qdYibjmswAiIjHgG1Z19FfOECsq9qbYqbDqWTM7ODlADEzs1QcINZVHU0xY2b9jAPEuqrdKWYyrsnMMuAAsS7pZIoZs0xJugd4HDheUoMkv2yugjyViZmZpeIzEDMzS8UBYmZmqThAzMwsFQeImZml4gAxM7NUHCBmZSSpRVJ9wafobMWSVkh6e/L5Qorj3SDpqvQVm6U3MOsCzA4yuyOiptTOETENQNI44AvA7ZUpy6z8fAZiVmGS3pa8P+X4pH2PpM8m3zdJGgUsAt6VnLXcnKy7WtKTkp6WtKBgf9dL+pOkfweOz+CPZAb4DMSs3IZIqi9o/2NE3CtpLnCnpO8CwyPiB222mwe8t/XsRdI5wETy0+cLWC7pTOA18tPH1JD/+/t7YE1F/0RmHXCAmJVXu5ewImKVpAvJv4zr5BL2c07y+UPSPoJ8oAwDfh4RuwAkeR4yy4wvYZn1AEmHAJOAXcDwUjYhf/ZSk3wmRMQPK1qkWRc5QMx6xt+Tn3zyM8CPJB3aZv0O8mcXrR4ALpN0BICkKklHAY8BF0gaImkY8L8qX7pZ+3wJy6y82t4DWQn8CLgcOD0idkh6DPgKML+1U0Q0SfoPSWuBX0fE1ZImAY9LAtgJ/E1E/F7SvcBTwGby0+ubZcKz8ZqZWSq+hGVmZqk4QMzMLBUHiJmZpeIAMTOzVBwgZmaWigPEzMxScYCYmVkq/x96iQ4mXPsEVwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Given that tenure is a 'function' of age, we introduce a variable aiming to standardize tenure over age:\n",
+    "df_train['TenureByAge'] = df_train.Tenure/(df_train.Age)\n",
+    "sns.boxplot(y='TenureByAge',x = 'Exited', hue = 'Exited',data = df_train)\n",
+    "plt.ylim(-1, 1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "e2444ab18fd23f562b2653e4a731a510b750cdfb"
+   },
+   "outputs": [],
+   "source": [
+    "'''Lastly we introduce a variable to capture credit score given age to take into account credit behaviour visavis adult life\n",
+    ":-)'''\n",
+    "df_train['CreditScoreGivenAge'] = df_train.CreditScore/(df_train.Age)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "trusted": true,
+    "_uuid": "54f01a2ae85f53e265700b712352a0b0fd62c689"
+   },
+   "outputs": [
+    {
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CreditScore</th>\n",
+       "      <th>Geography</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Tenure</th>\n",
+       "      <th>Balance</th>\n",
+       "      <th>NumOfProducts</th>\n",
+       "      <th>HasCrCard</th>\n",
+       "      <th>IsActiveMember</th>\n",
+       "      <th>EstimatedSalary</th>\n",
+       "      <th>Exited</th>\n",
+       "      <th>BalanceSalaryRatio</th>\n",
+       "      <th>TenureByAge</th>\n",
+       "      <th>CreditScoreGivenAge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8159</th>\n",
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+       "      <td>0.00</td>\n",
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+       "      <th>6332</th>\n",
+       "      <td>619</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>35</td>\n",
+       "      <td>4</td>\n",
+       "      <td>90413.12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
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+       "    <tr>\n",
+       "      <th>8895</th>\n",
+       "      <td>699</td>\n",
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+       "      <td>8</td>\n",
+       "      <td>122038.34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>102085.35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.195454</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>17.475000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5351</th>\n",
+       "      <td>558</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>41</td>\n",
+       "      <td>2</td>\n",
+       "      <td>124227.14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>1.117305</td>\n",
+       "      <td>0.048780</td>\n",
+       "      <td>13.609756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4314</th>\n",
+       "      <td>638</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>34</td>\n",
+       "      <td>5</td>\n",
+       "      <td>133501.36</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>155643.04</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.857741</td>\n",
+       "      <td>0.147059</td>\n",
+       "      <td>18.764706</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "<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>CreditScore</th>\n",
+       "      <th>Geography</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Tenure</th>\n",
+       "      <th>Balance</th>\n",
+       "      <th>NumOfProducts</th>\n",
+       "      <th>HasCrCard</th>\n",
+       "      <th>IsActiveMember</th>\n",
+       "      <th>EstimatedSalary</th>\n",
+       "      <th>Exited</th>\n",
+       "      <th>BalanceSalaryRatio</th>\n",
+       "      <th>TenureByAge</th>\n",
+       "      <th>CreditScoreGivenAge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8159</th>\n",
+       "      <td>461</td>\n",
+       "      <td>Spain</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>25</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>15306.29</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>18.440000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6332</th>\n",
+       "      <td>619</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>35</td>\n",
+       "      <td>4</td>\n",
+       "      <td>90413.12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20555.21</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4.398550</td>\n",
+       "      <td>0.114286</td>\n",
+       "      <td>17.685714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8895</th>\n",
+       "      <td>699</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>40</td>\n",
+       "      <td>8</td>\n",
+       "      <td>122038.34</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>102085.35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.195454</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>17.475000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5351</th>\n",
+       "      <td>558</td>\n",
+       "      <td>Germany</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>41</td>\n",
+       "      <td>2</td>\n",
+       "      <td>124227.14</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>111184.67</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.117305</td>\n",
+       "      <td>0.048780</td>\n",
+       "      <td>13.609756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4314</th>\n",
+       "      <td>638</td>\n",
+       "      <td>France</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>34</td>\n",
+       "      <td>5</td>\n",
+       "      <td>133501.36</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>155643.04</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.857741</td>\n",
+       "      <td>0.147059</td>\n",
+       "      <td>18.764706</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Resulting Data Frame\n",
+    "df_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.6.6",
+   "mimetype": "text/x-python",
+   "codemirror_mode": {
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+    "version": 3
+   },
+   "pygments_lexer": "ipython3",
+   "nbconvert_exporter": "python",
+   "file_extension": ".py"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}