2022.4 ML Bank Marketing Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9a924274",
   "metadata": {},
   "source": [
    "# <center> Predicting Customer Response to Telemarketing Campaigns- \n",
    "   # <center> A machine learning classification model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13884794",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "\n",
    "1. **[Project Background](#1)**\n",
    "2. **[Data Cleaning](#2)**\n",
    "3. **[Exploratory Data Analysis](#3)**\n",
    "4. **[Data Visualization](#4)**\n",
    "5. **[Machine Learning: Classification](#5)**\n",
    "6. **[Machine Learning: Regression](#6)**\n",
    "7. **[Conclusion & Recommendations](#7)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d71b48b",
   "metadata": {},
   "source": [
    "# Part 1. Project Background"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7747354c",
   "metadata": {},
   "source": [
    "This project preforms machine learning models on a portuguese credit card dataset in order to classify whether potential customers will be persuaded to become customers of a bank. Nowadays, marketing spending in the banking industry is massive. Machine learning optimizes the effectiveness of marketing plans, product designs and greater customer satisfaction. This is the way."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7680b4",
   "metadata": {},
   "source": [
    "### Main Objective: increase the effectiveness of the bank's telemarketing campaign\n",
    "This project will enable the bank to develop a more granular understanding of its customer base, predict customers' response to its telemarketing campaign and establish a target customer profile for future marketing plans. \n",
    "\n",
    "By analyzing customer features, such as demographics and transaction history, the bank will be able to predict customer saving behaviours and identify which type of customers is more likely to make term deposits. The bank can then focus its marketing efforts on those customers. This will not only allow the bank to secure deposits more effectively but also increase customer satisfaction by reducing undesirable advertisements for certain customers. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09292731",
   "metadata": {},
   "source": [
    "# Part 2. Data Cleaning <a id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e4933c",
   "metadata": {},
   "source": [
    "## Load the raw data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c34cf9",
   "metadata": {},
   "source": [
    "This dataset is about the direct phone call marketing campaigns, which aim to promote term deposits among existing customers, by a Portuguese banking institution from May 2008 to November 2010. It is publicly available here http://archive.ics.uci.edu/ml/datasets/Bank+Marketing#."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "475c2d54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['age', 'job', 'marital', 'education', 'default', 'balance', 'housing',\n",
      "       'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays',\n",
      "       'previous', 'poutcome', 'y'],\n",
      "      dtype='object')\n",
      "<bound method NDFrame.head of        age           job   marital  education default  balance housing loan  \\\n",
      "0       58    management   married   tertiary      no     2143     yes   no   \n",
      "1       44    technician    single  secondary      no       29     yes   no   \n",
      "2       33  entrepreneur   married  secondary      no        2     yes  yes   \n",
      "3       47   blue-collar   married    unknown      no     1506     yes   no   \n",
      "4       33       unknown    single    unknown      no        1      no   no   \n",
      "...    ...           ...       ...        ...     ...      ...     ...  ...   \n",
      "45206   51    technician   married   tertiary      no      825      no   no   \n",
      "45207   71       retired  divorced    primary      no     1729      no   no   \n",
      "45208   72       retired   married  secondary      no     5715      no   no   \n",
      "45209   57   blue-collar   married  secondary      no      668      no   no   \n",
      "45210   37  entrepreneur   married  secondary      no     2971      no   no   \n",
      "\n",
      "         contact  day month  duration  campaign  pdays  previous poutcome    y  \n",
      "0        unknown    5   may       261         1     -1         0  unknown   no  \n",
      "1        unknown    5   may       151         1     -1         0  unknown   no  \n",
      "2        unknown    5   may        76         1     -1         0  unknown   no  \n",
      "3        unknown    5   may        92         1     -1         0  unknown   no  \n",
      "4        unknown    5   may       198         1     -1         0  unknown   no  \n",
      "...          ...  ...   ...       ...       ...    ...       ...      ...  ...  \n",
      "45206   cellular   17   nov       977         3     -1         0  unknown  yes  \n",
      "45207   cellular   17   nov       456         2     -1         0  unknown  yes  \n",
      "45208   cellular   17   nov      1127         5    184         3  success  yes  \n",
      "45209  telephone   17   nov       508         4     -1         0  unknown   no  \n",
      "45210   cellular   17   nov       361         2    188        11    other   no  \n",
      "\n",
      "[45211 rows x 17 columns]>\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "      <td>45211.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.936210</td>\n",
       "      <td>1362.272058</td>\n",
       "      <td>15.806419</td>\n",
       "      <td>258.163080</td>\n",
       "      <td>2.763841</td>\n",
       "      <td>40.197828</td>\n",
       "      <td>0.580323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.618762</td>\n",
       "      <td>3044.765829</td>\n",
       "      <td>8.322476</td>\n",
       "      <td>257.527812</td>\n",
       "      <td>3.098021</td>\n",
       "      <td>100.128746</td>\n",
       "      <td>2.303441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>180.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>1428.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>319.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>4918.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>275.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                age        balance           day      duration      campaign  \\\n",
       "count  45211.000000   45211.000000  45211.000000  45211.000000  45211.000000   \n",
       "mean      40.936210    1362.272058     15.806419    258.163080      2.763841   \n",
       "std       10.618762    3044.765829      8.322476    257.527812      3.098021   \n",
       "min       18.000000   -8019.000000      1.000000      0.000000      1.000000   \n",
       "25%       33.000000      72.000000      8.000000    103.000000      1.000000   \n",
       "50%       39.000000     448.000000     16.000000    180.000000      2.000000   \n",
       "75%       48.000000    1428.000000     21.000000    319.000000      3.000000   \n",
       "max       95.000000  102127.000000     31.000000   4918.000000     63.000000   \n",
       "\n",
       "              pdays      previous  \n",
       "count  45211.000000  45211.000000  \n",
       "mean      40.197828      0.580323  \n",
       "std      100.128746      2.303441  \n",
       "min       -1.000000      0.000000  \n",
       "25%       -1.000000      0.000000  \n",
       "50%       -1.000000      0.000000  \n",
       "75%       -1.000000      0.000000  \n",
       "max      871.000000    275.000000  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import packages\n",
    "\n",
    "import numpy as np\n",
    "import math\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import scipy.stats as ss\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm  #Regression\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# Use inline magic command so plots appear in the data frame\n",
    "%matplotlib inline\n",
    "from IPython.display import Image\n",
    "#import data\n",
    "df = pd.read_csv('C:\\\\Users\\Owner\\Documents\\DataScience\\project4.csv')\n",
    "\n",
    "print(df.columns)\n",
    "print(df.head)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbfdaf35",
   "metadata": {},
   "source": [
    "Data Cleaning and Preparation: In this case the data is relatively clean, but you may still need some preprocessing, such as scaling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "029f3dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Delete the rows which column 'poutcome' contains 'other'\n",
    "#why? other does not tell us the decision if they became a customer or not, so it doesn't provide information for the model.\n",
    "condition = df.poutcome == 'other'\n",
    "dataset2 = df.drop(df[condition].index, axis = 0, inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1164b3c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Replace 'unknown' in job and education with 'other'\n",
    "dataset2[['job','education']] = dataset2[['job','education']].replace(['unknown'],'other')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b48a3cc",
   "metadata": {},
   "source": [
    "### 2.2 Drop outliers in the column 'balance'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc3c13c",
   "metadata": {},
   "source": [
    "In order to capture the general trend in the dataset, outliers in the column “balance” are dropped. Outliers are defined as the values which are more than three standard deviations away from the mean. In sum, 2556 rows of data were removed. We rename datasets as increaseing numbers in order to keep track of what changes were made."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b45e81b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore\n",
    "\n",
    "dataset2[['balance']].mean()\n",
    "\n",
    "\n",
    "dataset2['balance_outliers'] = dataset2['balance']\n",
    "dataset2['balance_outliers']= zscore(dataset2['balance_outliers'])\n",
    "\n",
    "condition1 = (dataset2['balance_outliers']>3) | (dataset2['balance_outliers']<-3 )\n",
    "dataset3 = dataset2.drop(dataset2[condition1].index, axis = 0, inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "edaed9fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset4 = dataset3.drop('balance_outliers', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e16c7516",
   "metadata": {},
   "source": [
    "### 2.3 Creating and transforming data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9a3b1e4",
   "metadata": {},
   "source": [
    "Some changes were made to the column name, units and data types for easier analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c5b406c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    37785\n",
       "1     4870\n",
       "Name: response_binary, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 1: Change column name: 'y' to 'response' This makes more sense and is more descriptive\n",
    "dataset4.rename(index=str, columns={'y': 'response'}, inplace = True)\n",
    "\n",
    "def convert(dataset4, new_column, old_column):\n",
    "    dataset4[new_column] = dataset4[old_column].apply(lambda x: 0 if x == 'no' else 1)\n",
    "    return dataset4[new_column].value_counts()\n",
    "\n",
    "convert(dataset4, \"response_binary\", \"response\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88763e4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Drop column \"contact\" which is useless\n",
    "dataset5 = dataset4.drop('contact', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6cca5243",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 3: Change the unit of 'duration' from seconds to minutes\n",
    "dataset5['duration'] = dataset5['duration'].apply(lambda n:n/60).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7daf6264",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 4: Change 'month' from words to numbers for easier analysis\n",
    "lst = [dataset5]\n",
    "for column in lst:\n",
    "    column.loc[column[\"month\"] == \"jan\", \"month_int\"] = 1\n",
    "    column.loc[column[\"month\"] == \"feb\", \"month_int\"] = 2\n",
    "    column.loc[column[\"month\"] == \"mar\", \"month_int\"] = 3\n",
    "    column.loc[column[\"month\"] == \"apr\", \"month_int\"] = 4\n",
    "    column.loc[column[\"month\"] == \"may\", \"month_int\"] = 5\n",
    "    column.loc[column[\"month\"] == \"jun\", \"month_int\"] = 6\n",
    "    column.loc[column[\"month\"] == \"jul\", \"month_int\"] = 7\n",
    "    column.loc[column[\"month\"] == \"aug\", \"month_int\"] = 8\n",
    "    column.loc[column[\"month\"] == \"sep\", \"month_int\"] = 9\n",
    "    column.loc[column[\"month\"] == \"oct\", \"month_int\"] = 10\n",
    "    column.loc[column[\"month\"] == \"nov\", \"month_int\"] = 11\n",
    "    column.loc[column[\"month\"] == \"dec\", \"month_int\"] = 12"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d31d795",
   "metadata": {},
   "source": [
    "### 2.4 Filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "42c03bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Drop rows that 'duration' < 5s\n",
    "condition2 = (dataset5['duration']<5/60)\n",
    "dataset6 = dataset5.drop(dataset5[condition2].index, axis = 0, inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d460666a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Drop customer values with 'other' education\n",
    "condition3 = (dataset6['education'] == 'other')\n",
    "dataset7 = dataset6.drop(dataset6[condition3].index, axis = 0, inplace = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c16f11a7",
   "metadata": {},
   "source": [
    "# Part 3. Exploratory Data Analysis <a id=\"3\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5024ad",
   "metadata": {},
   "source": [
    "   \n",
    "  ![](https://datos.gob.es/sites/default/files/u322/grafico.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "502fe90b",
   "metadata": {},
   "source": [
    "### 3.1 Visualize the distribution of 'age' and 'balance'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d67645e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_age_balance = plt.figure(figsize = (10,6))\n",
    "\n",
    "ra1 = dist_age_balance.add_subplot(1,2,1) \n",
    "ra2 = dist_age_balance.add_subplot(1,2,2)\n",
    "\n",
    "ra1.hist(dataset7['age'])\n",
    "ra1.set_title('The Distribution of Age')\n",
    "\n",
    "ra2.hist(dataset7['balance'], color = 'skyblue')\n",
    "ra2.set_title('The Distribution of Balance')\n",
    "\n",
    "plt.tight_layout() \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1dee215",
   "metadata": {},
   "source": [
    "**The distribution of age**: \n",
    "The distribuiton is right skewed, meaning that it has a longer tail towards the right and the median is to the right of the mean. It seems to be that most customers are under 40 and a very few amount are over 60. Distribution is relatively normal outside of that. \n",
    "\n",
    "**The distribution of balance**:\n",
    "\n",
    "After dropping outliers in balance, the distribution is also right-shifted (positively skewed). The distribution of balance has a huge standard deviation relative to the mean, suggesting large variabilities in customers' balance levels. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ae0418f",
   "metadata": {},
   "source": [
    "### 3.2 Visualize the relationship between 'age' and 'balance' \n",
    "Doing this to check if there is a relaitonship, but as you can see below, there isn't."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9acfd915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter_age_balance = dataset7.plot.scatter('age','balance',figsize = (7,5))\n",
    "\n",
    "plt.title('The Relationship between Age and Balance ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f259a03c",
   "metadata": {},
   "source": [
    "Based on this scatter plot, there is no clear relationship between client’s age and balance level.\n",
    "\n",
    "Nevertheless, over the age of 60, clients tend to have a significantly lower balance, mostly under 5,000 euros. This is due to the fact that most people retire after 60 and no longer have a reliable income source."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a7ec20",
   "metadata": {},
   "source": [
    "### 3.3 Visualize the distribution of 'duration' & 'campaign'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4c5cf54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_dur_cam = dataset7[['duration','campaign']].plot(kind = 'box', \n",
    "                                                      figsize = (8,8),\n",
    "                                                      subplots = True, layout = (1,2),\n",
    "                                                      sharex = False, sharey = False,\n",
    "                                                      title='The Distribution of Duration and Campaign')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1ab8cf3",
   "metadata": {},
   "source": [
    "**The distribution of duration**: As observed from the box plot, the duration of contact has a median of 3 minutes, with an interquartile range of 1.73 minutes to 5.3 minutes. The left-skewed boxplot indicates that most calls are relatively short. Also, there is a large number of outliers ranging from 10 minutes to 40 minutes, which are worth further study."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8dba818",
   "metadata": {},
   "source": [
    "**The distribution of campagin**: About half of the clients have been contacted by the bank for the second time, while 25% was first introduced to the term deposit. Most clients have been reached by the bank for one to three times, which is reasonable. However, some clients have been contacted by as high as 58 times, which is not normal. These clients may have some special needs that require frequent contact. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbffb981",
   "metadata": {},
   "source": [
    "### 3.4 Visualize the relationship between 'duration' & 'campaign': with response result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "78bdaf4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DLWiMacd/3/i+iJwpIvHgPnqLBB3Mg/3sALqCowH/WU6jRORUEdkv+FDOvceUep/5I/4Rz0L3A5ey+yjofX3+7muw+3w4vgWcIiKLGeC9RESmicjpQWGYwj81VLivU4GPBPftufiv2z8aYzbgH/H9crC+RfintAZNmIrIa0TkpOAxT+If+cxtc9jvLcF76TfwnxcDrktEThSRheKHCDrwv2zm2rAaeGewz0vx+4iV0u+9U0Rm4ffNeqScdo+GUS9Ogg/H0/A7I72K/03+BvxvdOX6X+DT+Ofhyl3XT/EP4W3C75zY90G4ETg4OJz2uyLLfxH/1MaT+B23VgXTyvFuYK34h2k/CFxQ5nJ9bcXvyLcZ/wX1QWPM8wPMfwv+t5Gt+B3APgIQvEjPwP9WtgO/Wv8Uu58P3wbOEb/H/XeCaf/E73uSO0ryLP6LNPd3Oet9D37H5WeD/bidEqeSyvBl/GKxTfqkn0r4Nv4377+KSCf+439UORsyxnTg9z1pLph2N37x8CR+p+zhfND19ftgXavxi4Ibg239Fv9b2i+D59DT+ImLcg323C/JGLMTOBf4Cn5xsz/wj0EWO09EuvAPEd8RLHeEMWZzsM41+MXy3/DTReVcuOtDwDXBY3cVBUcfg9MHXwL+ETwfeiUOgqNfp+KnhXbhv3ecGuzbkBhj7gGuxO9gvgW/w/M7h7D8tcBl+J2ac6+RS/GPqAJ8Er/w6sQ/GnBb/7UUtT/+/dmFf4Tm/0zpazRdj59qKzy6cD9+YfRAib/77seA9/lwBF9yforfj26g9xIL/7HcjH+q8PX4z4+cR/Hvj51BG88JngPg93ObHyz7W+B/gtfyYCL4r4Gd+O+nU4O2wR68twR+DMwVkdMGWdd0/PfMDvzTPfez+wvylfjPxVb8foC3DrC9Yu/v5wM3B0dzKoL0PjWnKpX4MbqfGWNmDzKrUkoNSERuBX5lSlworlqJf5HE5cHpF1WG4GjQE8AyY8z2sW5PTiVcsEkppdQoMsacP/hcaiIIjpbs9auu7ykd+E8ppZRSFUVP6yillFKqouiRE6WUUkpVlKroc/LmN7/Z/PnPfx7rZiillFJjoazxrMaTqjhysnPnkBN/SimllKpSVVGcKKWUUmri0OJEKaWUUhVFixOllFJKVRQtTpRSSilVUbQ4UUoppVRF0eJEKaWUUhVFixOllFJKVRQtTpRSSilVUbQ4UUoppVRF0eJEKaWUUhVFixOllFJKVRQtTpRSSilVUbQ4UUoppVRF0eJEKaWUUhVFixOllFJKVRQtTpRSSilVUbQ4UUoppVRF0eJEKaWUUhVFixOllFJKVRQtTpRSSilVUbQ4UUoppVRF0eJEKaWUUhVFixOllFJKVRQtTpRSSilVUbQ4UUoppVRF0eJEKaWUUhVFixOllFJKVRQtTpRSSilVUbQ4UUoppVRF0eJEKaWUUhVlRIsTEWkUkdtF5HkReU5EjhGRZhG5W0ReDH42jWQblFJKKVVdRvrIybeBPxtjDgQOA54DLgfuMcbsD9wT/K2UUkopBYxgcSIi9cAy4EYAY0zaGNMGnAHcHMx2M3DmSLVBKaWUUtVnJI+c7APsAH4iIv8WkRtEpAaYZozZAhD8nFpsYRG5RERWisjKHTt2jGAzlVJKKVVJRrI4cYAlwA+MMYcD3QzhFI4x5npjzFJjzNIpU6aMVBuVUkopVWFGsjjZCGw0xjwa/H07frGyTURmAAQ/t49gG5RSSilVZUasODHGbAU2iMhrgkknA88CdwAXBtMuBH4/Um1QSimlVPVxRnj9HwZ+LiJh4BXgvfgF0a9E5P3AeuDcEW6DUkopparIiBYnxpjVwNIiN508kttVSimlVPXSK8QqpZRSqqJocaKUUkqpiqLFiVJKKaUqihYnSimllKooWpwopZRSqqKMdJR4r9jc1sNFP/4Xc5rjnL1kFovn6kDGSiml1HhVFUdOsp5hWkOUlu401969htXrW8e6SUoppZQaIVVRnNiWYIlQHwtRFw2xYtWmsW6SUkoppUZIVRQnhWqjDhtaEmPdDKWUUkqNkKorTrqSWeY0x8e6GUoppZQaIVVRnLiewTOGjp4MnckMZy+ZNdZNUkoppdQIqYq0jmMJ29qTzGmOc/HxCzSto5RSSo1jVVGczGyMcdP7XjvWzVBKKaXUKKiK0zpKKaWUmji0OFFKKaVURdHiRCmllFIVRYsTpZRSSlUULU6UUkopVVG0OFFKKaVURdHiRCmllFIVRYsTpZRSSlUULU6UUkopVVG0OFFKKaVURdHiRCmllFIVRYsTpZRSSlUULU6UUkopVVGqYlRiNbGsXt/KilWb2NCSYE5znLOXzGLx3KaxbpZSSqlRokdOVEVZvb6Va+9eQ0t3mmkNUVq601x79xpWr28d66YppZQaJVqcqIqyYtUm6qIh6mMhLBHqYyHqoiFWrNo01k1TSik1SrQ4URVlQ0uC2mjvs421UYcNLYkxapFSSqnRpsWJqihzmuN0JbO9pnUls8xpjo9Ri5RSSo02LU5URTl7ySw6kxk6ejJ4xtDRk6EzmeHsJbPGumlKKaVGiRYnqqIsntvEZaccQHNNmG3tSZprwlx2ygGa1lFKqQlEo8Sq4iye26TFiFJKTWB65EQppZRSFUWLE6WUUkpVFC1OlFJKKVVRtDhRSimlVEXR4kQppZRSFUWLE6WUUkpVFC1OlFJKKVVRtDhRSimlVEXR4kQppZRSFUWLE6WUUkpVFC1OlFJKKVVRtDhRSimlVEXR4kQppZRSFUWLE6WUUkpVFC1OlFJKKVVRtDhRSimlVEXR4kQppZRSFUWLE6WUUkpVFC1OlFJKKVVRtDhRSimlVEVxxroBe8Pq9a2sWLWJDS0J5jTHOXvJLBbPbRrrZimllFJqGKr+yMnq9a1ce/caWrrTTGuI0tKd5tq717B6fetYN00ppZRSw1D1xcmKVZuoi4aoj4WwRKiPhaiLhlixatNYN00ppZRSw1D1xcmGlgS10d5np2qjDhtaEmPUIqWUUkrtiaovTuY0x+lKZntN60pmmdMcH6MWKaWUUmpPVH1xcvaSWXQmM3T0ZPCMoaMnQ2cyw9lLZo1105RSSik1DCNanIjIWhF5SkRWi8jKYFqziNwtIi8GP/coVrN4bhOXnXIAzTVhtrUnaa4Jc9kpB2haRymllKpSoxElPtEYs7Pg78uBe4wxXxGRy4O/PzPUlZYbH9aYsVJKKVVdxuK0zhnAzcHvNwNnDnUF5caHNWaslFJKVZ+RLk4M8FcReVxELgmmTTPGbAEIfk4d6krLjQ9rzFgppZSqPiN9WudYY8xmEZkK3C0iz5e7YFDMXAIwd+7cXrdtaEkwrSHaa1qx+HC58ymllFKqcozokRNjzObg53bgt8BrgW0iMgMg+Lm9xLLXG2OWGmOWTpkypddt5caHNWaslFJKVZ8RK05EpEZE6nK/A28EngbuAC4MZrsQ+P1Q111ufFhjxkoppVT1EWPMyKxYZB/8oyXgnz661RjzJRGZBPwKmAusB841xrQMtK6lS5ealStX9pqmaR2llFIThIx1A0bbiBUne1Ox4kQppZSaICZccVL1V4hVSiml1PiixYlSSimlKooWJ0oppZSqKFqcKKWUUqqiaHGilFJKqYqixYlSSimlKooWJ0oppZSqKFqcKKWUUqqiaHGilFJKqYqixYlSSimlKooWJ0oppZSqKFqcKKWUUqqiaHGilFJKqYqixYlSSimlKooWJ0oppZSqKFqcKKWUUqqiaHGilFJKqYqixYlSSimlKooWJ0oppZSqKM5YN2BvWr2+lRWrNrGhJcGc5jhnL5nF4rlNY90spZRSSg3BuDlysnp9K9fevYaW7jTTGqK0dKe59u41rF7fOtZNU0oppdQQjJviZMWqTdRFQ9THQlgi1MdC1EVDrFi1aaybppRSSqkhGDfFyYaWBLXR3mepaqMOG1oSY9QipZRSSg3HuClO5jTH6Upme03rSmaZ0xwfoxYppZRSajjGTXFy9pJZdCYzdPRk8IyhoydDZzLD2UtmjXXTlFJKKTUE46Y4WTy3ictOOYDmmjDb2pM014S57JQDNK2jlFJKVZlxFSVePLdJixGllFKqyo2bIydKKaWUGh+0OFFKKaVURdHiRCmllFIVRYsTpZRSSlUULU6UUkopVVG0OFFKKaVURamKKHEi7XLl757W0YaVUkqpCaAqjpxs60jqaMNKKaXUBFEVxYltiY42rJRSSk0QVVGcWCK9/tbRhpVSSqnxqyqKE8+YXn/raMNKKaXU+FUVxYnrGR1tWCmllJogqqI4mVYf1dGGlVJKqQmiKqLEhXZ0Jrnu/pdJZjyNFSullFLjUFUcOclFiUO2sHJtKyvXteLYorFipZRSahyqiuIkFyVeuytBPOIQCzms3ZXQWLFSSik1DlVFcZKLEnelsji2ELKF7lQW0FixUkopNd5URZ+TXJS4NuKQynoYAzURv+kaK1ZKKaXGl6o4cpKLEs+fFCeRytKTyTJ/UlxjxUoppdQ4VBVHTnJR4g0tCZbO95M5yYzHjIYwFx+/QNM6Siml1DhSFcVJPGzzhTMPHetmKKWUUmoUVMVpHaWUUkpNHFqcKKWUUqqiaHGilFJKqYqixYlSSimlKooWJ0oppZSqKFqcKKWUUqqiVEWUGGD1+lZWrNrEhpYE0ZBfU+nIxEoppdT4UxVHThJpl2vvXqMjEyullFITQFUUJ22JNHXRkI5MrJRSSk0AVVGcpLMetdFgoD8dmVgppZQa16qiOAk7Fl3JoBCJOGRdQ8Y1OjKxUkopNQ5VRXHSGA/TmczoyMRKKaXUBFAVxUk8bHPZKQfQXBMm4xqWzm9i6bwmsq6huSbMZaccoGkdpZRSapwY8SixiNjASmCTMeZUEWkGbgPmA2uBdxhjBo3aLJ7bpAWIUkopNQGMxpGTjwLPFfx9OXCPMWZ/4J7gb6WUUkopYISLExGZDbwNuKFg8hnAzcHvNwNnjmQblFJKKVVdRvrIybeATwNewbRpxpgtAMHPqcUWFJFLRGSliKzcsWPHCDdTKaWUUpVixIoTETkV2G6MeXw4yxtjrjfGLDXGLJ0yZcpebp1SSimlKtVIdog9FjhdRN4KRIF6EfkZsE1EZhhjtojIDGD7CLZBKaWUUlVmxI6cGGM+a4yZbYyZD7wT+Lsx5gLgDuDCYLYLgd+PVBuUUkopVX3GYlTirwC/EpH3A+uBc8tdsHBkYh2NWCmllBqfxBgz1m0Y1NKlS80Nv7mba+9eQ100RG3UoSuZpTOZ0QuwKaWUGu9krBsw2qriCrEAK1Ztyo9MbInoaMRKKaXUOFU1xcmGlkR+ZOIcHY1YKaWUGn+qpjiZ0xzPj0yco6MRK6WUUuNP1RQnZy+ZlR+Z2DNGRyNWSimlxqmqKU4Wz23Kj0y8rT2poxErpZRS49RYRImHrLU7zfFfvYftnWlsSzhsdoPGiJVSSqlxqiqOnGxuT7KtI4VjCcYYVq5r5dMrnmT1+taxbppSSiml9rKqKE4AQraFbQkh2yJkW2xu69EYsVJKKTUODVqciEiNiFjB7weIyOkiEhr5phUyWAWXoLEtIZ31NEaslFJKjUPlHDl5AIiKyCzgHuC9wE0j2aj+BK/gQrauZwg7lsaIlVJKqXGonOJEjDEJ4Czgu8aYtwMHj2yz+su4Hq5nyLgeGddjZmNMY8RKKaXUOFRWcSIixwDvAu4Kpo1qymdmQ5Rp9RGynkFEWDqvia+dvUjTOkoppdQ4VE6R8THgs8BvjTHPiMg+wL0j2qo+IiGbE14zTUcjVkoppSaAqhiVeNL8g8w7v/RzHY1YKaXURDThRiUueeRERP4AlKxcjDGnj0iLirAtfxRiIP9zxapNWpwopZRS49BAp3W+PmqtGIQlvYtGHY1YKaWUGr9KFifGmPtHsyED8fqcetLRiJVSSqnxa6DTOk8x8GmdRSPSoiJczx+FuLDPycXHLxitzSullFJqFA10WufUUWvFIKbVR2muCefTOhcfv0D7myillFLj1ECnddaNZkMGEg/bLJxVz6Ov7uLJjW3c98J26qM2rYksAItmN/DB1++rBYtSSik1DpQzts7RIvKYiHSJSFpEXBHpGI3G5bR2p/nyn56nM5klGvIH/XtmSxc9GRfbElaubeXqPzyjoxQrpZRS40A5V4j9HvAfwItADFgOfHckG9XXjq4U0ZBNPOzQlXIRESyBrlSWsGMTjzi0dmd0lGKllFJqHCinOMEY8xJgG2NcY8xPgBNHtlm9ZT1DNGTlfzcYBL+jLIBjC6msq/FipZRSahwo5/L1CREJA6tF5GvAFqBmZJvVm2MJyYxHPGzhWEIawcNgW/71T7KuIeLYGi9WSimlxoFyjpy8O5jvUqAbmAOcPZKN6mtKbYRkxiWRzlIbsTHG4BmojTiksy6JVJammpCOUqyUUkqNAyWLExGZIiIHG2PWGWOSxpgOY8zngR8D7aPXRGiqCfPZtxxIXdQhmfGY2RjjkBm1xEI2rmdYOr+Jq087RNM6Siml1Dgw0Gmd7wI/KDJ9FvDfwPkj0qIiWrvT3PDQq+zsTDG5LsIbD5pKe9LVUYqVUkqpcWig0zoLi13C3hjzF2DUrg4LsKUjSWcyS0M8xK6uFNc98ApPbGhjWkOUlu401969RmPESiml1DgxUHESGuZte50lQjzsYIlFxjU4tsW6lm4s8UcrrouGNEaslFJKjRMDFScvishb+04UkbcAr4xck/orHJQ445p8eidHRylWSimlxo+B+px8HLhTRN4BPB5MWwocwyiPu1M4KHHIFtKul7/uCegoxUoppdR4UvLIiTFmDbAQuB+YH/y/H1gU3DZqPGNIpLN4xiNkC1nXY15zDZ7xRyvuTGY0RqyUUkqNEwNehM0YkwJ+MkptKWlGfZS6qJNP6/zHkXN6pXV0lGKllFJq/CjnCrEVpSftsnJdK6msR3fapSOZyd+mBYpSSilV/coaW2es5aLE0ZDFto4kj61t5eXtXXQms2xq7eGVHV0aJ1ZKKaXGiYGuEHtP8POro9ec4nJR4q6US8i2AUhkPOJhh0jIZntnSuPESimlJjzxVcWBh4EMtAMzROT1wOkicriILCn8P1oNhN1R4qxnsCww+J1kwR+RuCuV1TixUkqpCUlE5ovIcyLyf8Aq4EoReUxEnhSRzwfz1IjIXSLyhIg8LSLnBdPXishXReRfwf/9gunzROSeYB33iMjcYPpNIvIdEfmniLwiIucE02eIyAMisjpY//HB9DeKyMMiskpEfi0iteXs00B9Tq4CLgdmA9f2uc0AJ5V7x+2pXJTYsQTXAwFEdo9IXBtxNE6slFJqInsN8F7gd8A5wGvxPy7vEJFlwBRgszHmbQAi0lCwbIcx5rUi8h7gW/iXC/ke8FNjzM0i8j7gO8CZwfwzgOOAA4E7gNvxh7T5izHmSyJiA3ERmQxcAbzBGNMtIp8BLgOuGWxnShYnxpjbgdtF5EpjzBcGvVtGUC5KXBux2dmVBiAesvx4sWeY0xSjM5nh4uMXjGUzlVJKqbGyzhjziIh8HXgj8O9gei2wP/Ag8PWgq8adxpgHC5b9RcHPbwa/HwOcFfx+C/C1gvl/Z4zxgGdFZFow7THgxyISCm5fHZx9ORj4R3BAIQw8XM7ODJrWMcZ8QUROB5YFk+4zxtxZzsr3lsIo8bT6KLMao/m0Tm3EYZ8ptTr4n1JKqYmsO/gpwJeNMT/sO4OIHAG8FfiyiPzVGJM7glFwqdNev1NieqpwtQDGmAeCIzRvA24Rkf8FWoG7jTH/MdSdGbQ4EZEv4x8e+nkw6aMicqwx5rND3dhwpV2PbR1JOnqytPVk2NGZZO6kGpYft4B3HDkXgNXrW7nyd0/rSMVKKaUmsr8AXxCRnxtjukRkFpDB/7xvMcb8TES6gIsKljkP+ErwM3dk45/AO/GPmrwLeGigjYrIPGCTMeZHIlIDLAG+BHxfRPYzxrwkInFgdjkXci3nOidvAxYHh3AQkZvxDxeNWnGyozNFOJnF4Pc/SWYNm1p6+PKfngfggGl1XHv3GuqioV4jFV92ygFaoCillJowjDF/FZGDgIeDUyldwAXAfsD/ioiHX6z8Z8FiERF5FD8kkzvK8RH80zSfAnbg92cZyAnAp0QkE2zzPcaYHSJyEfALEYkE810BDFqciDGljuAEM4g8CZxgjGkJ/m7GP7WzaLCV7y3RmfubmRd+C8+AJX6BIgLTgtM9Ry2YREt3mvrY7sGSO3oyNNeE+cKZh45WM5VSSqmRIIPPMswVi6wFlhpjdo7UNoajnCMnXwb+LSL34t9ByxjFoyYAguCZ3o+OZyAastjZmWJDS4JpDdFey2i0WCmllKpO5XSI/YWI3AcciV8ffMYYs3WkG9arDRgsoVeBYgkkMx6T6yLMaY73O3Ki0WKllFJqYMaY+WPdhmLKuoqcMWaLMeYOY8zvR7sw8RuwuyjxjN9lOObYJDMuy49bwNlLZtGZzNDRk9GRipVSSqkqVxWXuJ1SF6E26iD4R0yijjCrOcZn33Ig7zhyLovnNnHZKQfQXBNmW3uS5pqwdoZVSimlqlRVjEqczHiEbIuQLWRdg+v5oxMXWjy3qVcxMlC0ePX6Vlas2tTvtlLTq8LGx2H1rdC2DhrnweLzYfYRY90qpZRSasgGTOsEgwc9aYwZ08hLfOYBZsaF38QtaGpIIBq2ufLUg/PXOslZvb41Hy2ujfqXtu9MZrjslAMAit526qIZ3PnklqLLVHyBsvFxuPdLEK2HSB2kOiHZASd+TgsUpZSqfiOW1qlUA57WCa5t8kRuwJ+x4g/0F4ypE/w34o+zc8NDr/abf8WqTdRFQ9THQlgi1MdC+VGLS912w0Ovllym4q2+1S9Mog0glv8zWu9PV0oppapMOad1ZgDPiMi/2H15XIwxp49Yq/oxGHqXjgaDZzx2dqb6zT1YtLjYbTs7Uxze5whJ1cSR29ZB/Yze0yJ1/nSllFKqypRTnHx+xFsxKOl3TEsQLLGYXBfpN/dg0eJit02ui9CVzFZnHLlxHvTs8o+Y5KQ6/elKKaUmrPmX33Uk/qXqFwCvAjet/crbHtuTdYrIfOBP+Je0fx2wCTgDf2Tk64A48DLwPmNM63C2MWhaxxhzP7AWCAW/PwasGs7GhiuX0jHs/i/GYFuw/Lj+IxEPFC0uddvy4xZUbxx58fl+H5NkOxjP/5ns8KcrpZSakILC5BpgErAx+HlNMH1P7Q983xhzCNAGnA38FP9aaIuAp4D/Ge7KBy1ORORi4HYgN8LhLOB3w93gcNREHJpqwoRtwQJCljC9MVa0MywwYLS41G3vOHJu9caRZx/hd36NTYKOLf5P7QyrlFIT3UVAe/DfFPx+0V5Y96vGmNXB748D+wKNwUEMgJvxryg/LOWc1vkv/FGJHwUwxrwoIlOHu8HhSKSztHSn8YK0jucZtrQn+enDazlgWt2gEeG++saOx4XZR2gxopRSqtAC/CMmhTqC6XuqsMOnCzTuhXXmlXMRtpQxJp37Q0Qc/Aps1GQ9ky9MCqc9vbmTD/9iFavXt+bjwy3d6V4jE69eX97prj1dXimllKowrwL1fabVB9P3tnagVUSOD/5+N3D/APMPqJzi5H4R+W8gJiKnAL8G/jDcDQ5HqYC3LbClPTVgRLjcKPCeLq+UUkpVmJuAhuC/FPx+0wht70Lgf0XkSWAxfn+XYSmnOLkc2IHfueUDwB+BK4a7wb1JBFzPsKElwYaWBLXR3mephhIF3tPllVJKqUoSpHKuAnYBs4OfV+1pWscYs7bw4qzGmK8bY642xqw2xhxtjFlkjDlzuEkdKG9UYk9Ebsbvc2KAF8xAl5UdRcaAbcmAEeFyo8A6srFSSqnxJihE9qgYGQvlpHXehp9X/g7wPeAlEXnLSDesUKlKyDUwoyEyYES43CiwjmyslFJKVYYBx9YBEJHngVONMS8Ff+8L3GWMOXCQ5aLAA0AE/wjN7caY/xGRZuA2YD7+9VPeMdihn8iM/c2MC7/Vb3rUEabURZlcG+bQWY0snFXPU5s6hj1wX1UP/KeUUmq8mnBj65QTJd6eK0wCrwDby1guBZxkjOkSkRDwkIj8CTgLuMcY8xURuRy/T8tnhtpwCwjZFlnPsLk9SSzUxbpd3Xt0bZJxGTFWSimlqkzJ0zoicpaInIU/rs4fReQiEbkQP6kz6Pkr4+sK/gwF/w3+JW5vDqbfDJw5nIZ7QE/GIx52CNs227tSmq5RSimlxoGBjpycVvD7NuD1we87gLIOL4iIjX/luP3wL3P7qIhMM8ZsATDGbCl1QTcRuQS4BMCun1J0/V5wSipkC92prKZrlFJKqXGgZHFijHnvnq7cGOMCi0WkEfitiBw6yCKFy14PXA9+n5Ni81jin4bLuIaaiKPpGqWUUmocGLTPiYgsAD6M34E1P78x5vRyN2KMaROR+4A3A9tEZEZw1GQG5fVf6ccCYiGLRDqLZwxzmmJ0JjNcfPzeuCqvUkoppcZKOR1ifwfciN/XxCt3xSIyBcgEhUkMeAPwVeAO/KvIfSX4+ftBG2kJwu5IcXM8xAHTakm7hq5Ulpqwzb5TazVdo5RSShW6uuFI/IH+FuBftv4mrm4f9nVPROQLwE5jzLeDv7+E3/UjArwj+PnbIJ1bA/wK/wJwNvAFY8xtZW2njCjxo8aYo4axA4vwO7za+Ac6fmWMuUZEJgWNnQusB841xrQMtK4pCw42My/6Jh092XyBYgGxsEVzTYQpdRGm1kUASGa8fjHgciPCGiUeBRsfh9W3Qts6aJwHi8/XAQuVUmpgw4sS+4XJNfjj3nTgj6vTAFw13AJFROYDvzHGLBERC3gR+G/gZPyryAv+QYivAVOANxtjLg6WbTDGtJe1nTKKk/OB/YG/UjAKoTFm1RD3adhiMw8w097zzaK32QLxsI1tWTi2sHhOIxHHpjOZ4bJTDgDg2rvXUBcNURv1+6XkbissPHID/w02n9oDGx+He78E0XqI1EGqE5IdcOLntEBRSqnShlucfB+YhF+c5DQAu7i6/b+G3RiRu4FPA9OA5fjXLDsHaAtmqQW+DDwI/AX/gMSdxpgHy91GOad1FuKPLngSu0/rmODvUTFQAeUZP1IcDQl10RBrdyU4cn4zQD5WnBvQD8j/XLFqU6+io3Dgv4HmU3tg9a1+YRJt8P/O/Vx9qxYnSim19y0ANvaZ1hFM3xM34J8qmg78GP+oyZeNMT/sO6OIHAG8FfiyiPzVGFPWYIDlFCdvB/YxxqTLbfXeNtCxHYMfKXY9Lx8pht6D9k1riPZapljkeENLoqz51B5oWwf1M3pPi9T505VSSu1tr9L/yEl9MH1P/Bb/dFEIOB/IAl8QkZ8HF16dBWTwa4wWY8zPRKQLv6ApSznFyRNAI8NM1ewNAx3PEvxIsW1Z+Ugx9B60r5wB/XTgv1HQOA96du0+YgL+qZ3GeWPXJqWUGr9uwi8ioHefk2/syUqNMWkRuRdoCy4Z8lcROQh4WPxLfHQBF+Bf4+x/RcTDL1b+s9xtDDrwH/45pedF5C8ickfu/1B3Zk8EO1uUJX6k2LGEnkyW+ZPivQbtK3dAPx34bxQsPt/vY5JsB+P5P5Md/nSllFJ7l9/p9SpgF35iZhd70Bk2J+gIezR+khcAY8y3jTELg//HGGNeNsb8xRizyBiz2BhzpDFmZdnbKKND7OuLTTfG3F/uRvaUpnXGEU3rKKXUUFXMwH8icjBwJ35c+BMjtp3BipNKEJ2xv5neZ1RiAepjDu8+ai6ffPNBZa2nsPiIhvyDRsWKmXLXUQkFTKW1Ryml1F5XMcXJaBn0tI6IdIpIR/A/KSKuiHSMRuNyipVPBr9PyHUPvMLX//zcoOvIRYVbutM4trByXSsr17YSsoWW7jTX3r2G1etby17HtIZo2cuNlEprj1JKKbU3DFqcGGPqjDH1wf8ocDbwvZFv2uAM4NgWtzy6ftB5C6PCa3cliIUc4hGHtbsS1MdCZY1oXLgOS6Ts5UZKpbVHKaWU2hvK6RDbizHmd4ziNU4G4hn/0vbdKXfQeTe0JKiN+kme7lSWkC04ttBVJHpczjpyxjJuXGntUUoppfaGcgb+O6vgTwtYysCXHhk1lkDWM9RE7EHnLYwK10QcUhkPEagtEj0uZx05Yxk3rrT2KKWUUntDOUdOTiv4/yagEzhjJBtVLgGyrse7j5o76LyFUeH5k+L0ZLIkUv2jx+WuoxLixpXWHqWUUmpv0LSOpnWUUkpVtgmX1ilZnIjIVQMsZ4wxXxiZJvU3ZcHBZtZF36QrlUUQpjdEOOOwmTy+vo0nNrbjeoapdWE+fNL+vOPIwY+ilKPYhz6ghYBSSqnRpsVJ/gaRYhdXqQHeD0wyxtSOZMMKxWYeYKa/55vkLhQrBlz8EYnDjoUlQsb1CDsWV5168B4XKMVGKN7UmgCBWY1xHbVYKaXUaJpwxUnJPifGmG/k/gPXAzHgvcAvgX1GqX25tmBbgm0Jlkh+aGTPQMi2sC0hZFt4Bm54aE/HMyoe0W1NZGjtzmhsVymllBphA3aIFZFmEfki8CR+smeJMeYzxphRHQTQwO6jJuyOChUe87HEL2J2dqb2eHvFIrqprEcq2zuyrLFdpZRSau8rWZyIyP8Cj+GncxYaY642xozJpUcFyJ19Muw+vlV4nMsz/gCBk4MxdvbEnOY4Xclsr2kRxyLi9I4sa2xXKaWU2vsGOnLyCWAmcAWwueAS9p2jffl6EcH1DK5n8IzJN9oSyLgermfIuB6WwPLjFuzx9opFdJviIZpqQhrbVUoppUZYVUSJNa2jlFJqAptwHWKrojhpnneQafyPr+c7wgoQtgHxO8MeNruBtx8+i6c2dQxYOOg1QZRSSlUhLU4qUWTG/mZGn4uwgX9OKhKyyGT9GPHiOU3MmRQvGvMtFg/WKLBSSqkqMOGKkyEP/FdJPPwoMSKksh7bu1IlY746gq9SSilVHaq6OMkxxuAZf7ThnL4xXx3BVymllKoO46I4EREsgZrI7uKjb8y3WDxYo8BKKaVU5anq4sTCjxJjDBHHYmptpGTMV0fwVUoppapDVRQnNWGnV0MFiNgQcixEhCMXNHP16Yew79RatrUnaa4J9+vounhuE5edcgDNNeGS8yillFJq7DmDzzL2Euks9QV/GwARwrYwozHGrIYo3/37S2zvTGFbQjKTZc22+pKx4dz0XGdYLVDGH42NK6VU9arqKLEjELaFRNZgi3+JeYN/qidkCYfPbe4VLT510QzufHKLxonHOY2NK6XGGY0SVxPXQNL1iytjwLEtQraFMZDx6BctvuGhVzVOPAFobFwppapbVRcnBn/APyB/9dj8dM/0ixbv7ExpnHgC0Ni4UkpVt6ouTgR/8D+gX4dZy5J+0eLJdRGNE08AGhtXSqnqVtXFiS0Qtf3qRASyrkfG9RCBkEW/aPHy4xZonHgC0Ni4UkpVt6roEBudsb+Z3qdDbMQRwrbFjMYYi2bW8+ja1nxaZ9Gses5aMrvoQICa4pgY9HFWSo0jE65DbFVEiYtxPUNTXQiApzZ3AFAbsYk4No3xcH6+jmSGR1/dxdOb2jh0ViNnL5nFF848dEzarHYb6eJh8dym0S1GNj4Oq2+FtnXQOA8Wnw+zjxi97Sul1DhSFUdOSkWJAWrCNsmMiyWCZQkNUYe0a3A9j/mTatjemcKyBM8Y9p1ci2OLRkrH2LiL+m58HO79EkTrIVIHqU5IdsCJn9MCRSm1N0y4IydV3ecEoCfjIiIYwLGEZNYj6xlcD9a3JoiEbOJhh7Bts70rpZHSCjDuor6rb/ULk2gDiOX/jNb705VSSg1Z1RcnngET/LMEMsFRE88YejIeTtBhNmQL3amsRkorwLiL+rat84+YFIrU+dOVUkoNWdUXJ5aABP884xchtmVhiRALWWSDi7RlXENNxNFIaQUYd1Hfxnn+qZxCqU5/ulJKqSGr+uIkFrIxxiBA1jNEHQvHEmwL5jbFSWVcEuksaddlam1EI6UVYNxFfRef7/cxSbaD8fyfyQ5/ulJKqSGrig6xxaLEjgUzG6JEww4CJNJ+ERJxbBbNbuCkA6fy1KYOntncTlcqS03Yzqd1qrLT5Tgz7qK+mtZRSo2cCdchtiqKk75pnX0mxfngCfvy1KYOnt7Uxs6uFD0Zj2TGJZlxyXr+gIDTG6K8/oCpnL1kFmu2dXLDQ6+yszPF5LoIy49bwDuOnDt2OzUM4+4DXSmlVDm0OKlExaLEjsBBM+rZ2JagO+Xiega3yK7sN6UGEdjanqQ2GiIaskgGhcxn33Jg1RQo4y5+q5RSqlwTrjip2j4nWQPrWhOkXQjZVr/CxBKwLVjfkmBLewrXg3jYwRKLeNghGrK54aFXx6bxwzDu4rdKKaVUCVVbnAAkMx6e5+UH/ytkjJ/iybiGdNbF63OEKBqy2NmZGqWW7rlxF79VSimlSqjq4iQasrAsC6/I6RwR//onIVsIOzaW9K5gkhmPyXWRUWrpnht38VullFKqhKotThyBeU1xwjZkXA+7z9ETz4DrwdzmODMaItgWJNJZPOORSGdJZlyWH7dgbBo/DOMufquUUkqVUJUD/xWmdRxbBkzrHLPv5KJpnY+/Yf+q6QwL/kB2l51yQK+0zsXHL9DOsEoppcadqkjrFLvOiQXsN7WGo/aZzMJZ9flYcXfapTbiMDU4ZZPMeKMeux0o8runcWCNEyul1IQz4dI6VVGcDDQq8b6Ta2hJpJndGGN7VwpLhFTGxTPg2MLiOY1EHHvUYrcDRX6BPYoDa5xYKaUmpAlXnFRtn5OcDa0JoiGbda0JwrY/AnHaNWQ9QyzksHZXYlRjtwNFfvc0DqxxYqWUUhNB1RcnGdfkL6wWCnrFup4/MnFuJGIYvdjtQJHfPY0Da5xYKaXURFD1xUnIFpIZj2jIIhNcic22/JGJcyMRw+jFbgeK/O5pHFjjxEoppSaCqi9O5jTFSWZc5jXFSbv+4H9hW3AsoSeTZf6k+KjGbgeK/O5pHFjjxEoppSaCqugQq2md8tatlFJqXJpwHWKrojgpltaxAK/PfI7AGw6exgdfvy9A0Q/xXz22fsDRiX/269txnrmN6e42ttrTyB5yHhece87I7uAY0UJHKaWqghYnlWigKHFfFrDftFriIZtZTfFekdtFs+r5+b82EA3ZRUcn/tmvb2fuU9+mizjdEqfGJKglwfqFHx13BYrGkpVSqmpMuOKk6vuc9OUBm9t6aE1k+kVub3l0PdGQXXJ0YueZ2/zCxKoFsei2aukijvPMbWO7UyNAY8lKKaUq1bgrTgDSWY9UtvdJn9qoQ3fKJRrqvcuFoxNPd7fRLb2TL90SZ7q7bWQbPAY0lqyUUqpSjcviJOxYRJzeu9aVzFITsUlmehcthaMTb7WnUWN6fzjXmARb7Wkj2+AxoLFkpZRSlWrcFScWMLMxRlM81C9y++6j5pLMuCVHJ84ech61JKjxusB41Hhd1JIge8h5Y7tTI0BjyUoppSpV1XaI1bTOntO0jlJKVYUJ1yF2xIoTEZkD/BSYjl9HXG+M+baINAO3AfOBtcA7jDGtA62rVFrHscAWobEmzKzGGIfMbOh1zZOdXWk6khkSKRfXM9i20BQLse/UWiKOzdGRtZxtP8CU7FZonAeLz4fZRwz6oT3aH+rjpYgYL/uhlFKjTIuTvbZikRnADGPMKhGpAx4HzgQuAlqMMV8RkcuBJmPMZwZa10BR4twjVhe1mT+plg2tCWY3xtjc3kNHIkOmyO45AufO2M4FyZ/TaWLsN2cGk0NpSHbwwkEf5ktPxEtGbEc7gjteIr/jZT+UUmoMTLjiZMT6nBhjthhjVgW/dwLPAbOAM4Cbg9luxi9Y9ohtCYm0x7qW7vwIxcmMh5Hdj2evR1aEw1r+SDZURzZUz6u7khBtgGg9bQ/fPGDEdrQjuOMl8jte9kMppdTIG5UOsSIyHzgceBSYZozZAn4BA0wtscwlIrJSRFa6ifaS6zaACHjG5AcATGY8PGMwFD8qZIxhhredpFVDyLHoCkYuJlJHtHvjgBHb0Y7gjpfI73jZD6WUUiNvxIsTEakFVgAfM8Z0lLucMeZ6Y8xSY8xSO95Qev2AMWCJ5AuTaMjCEkFKHAkTEbZYU4l63WSyHrXByMWkOknWzB4wYjvaEdzxEvkdL/uhlFJq5I1ocSIiIfzC5OfGmN8Ek7cF/VFy/VK27+l2XM8QD1vMa67Jj1AcDVlIQX+aXsdQjOGJ5rfiZDpxMh0smBSFZDskO2g85sIBI7ajHcEdL5Hf8bIfSimlRt5IdogV/D4lLcaYjxVM/19gV0GH2GZjzKcHWpemdcZHymW87IdSSo2yCdchdiSLk+OAB4Gn2H1Jkv/G73fyK2AusB441xjTMtC6yh34L2ILtm0RsoX6qEMsZNPek6W9J4MBGqIOU+rCbGztoTvtIcD0higfPmm/Xtc6KSX34fqvV3extSOJ5xlmNMb6XStlsOUH+3Aezoe4fvArpdS4pcVJJRrKqMS5RzB3kTYTTAvZQtrdva+W+H1VHMvvq3LlqQcPWGDkorBtiQzPbe3AtvweLfGwjWfIj2w82PKDRWmHE7nVmK5SSo1rE644GXeXrzeAZUmvwsS2hKzXuwizLcG2BAO4HvmRiUvJRWHXtXQTsi3Ctt/pNuOaXiMbD7b8YFHa4URuNaarlFJqPBl3xQkECZ7g991R4yLzCRgMnjH5kYlLyUVhkxkP2/KLWEsIihOr7OULFYvSDidyqzFdpZRS48m4LE5yR0ygMGpcZD4DgmCJ5EcmLiUXhY2GLNyg0vGMf7qocGTjwZYvVCxKO5zIrcZ0lVJKjSfjrjgRwPMMFruPoLiewelTnbiewfVMcNqH/MjEpeSisPOaa8i4HmnXv9CbX5y4ZS8/WJR2OJFbjekqpZQaT8ZVh1hN62haRymlxqEJ1yG2qouTpphDMuPSky1I4QCWFZyyEX+Qv4zxO73aFkRtIRJymFwX4Y0HTaU96eY/0HPXSCn8gAcq+kNfixKllBr3tDipREOJEpcj7kAsEqK9J8NB0+s5eFYDG3YleG5rBwfPqGd20IdjU2sCBGY1xisyoqsRYqWUmhAmXHEy7vqclCOR9VM2jm2xvjWBJcL2rhTRkM32zlQ+jtuayNDananYiK5GiJVSSo1HE7I4AcgGnWR7Mv7Fa7tTfhInP0IxkMp6pLJur+UqKaKrEWKllFLj0YQtTpzgwmyxkH8X1ET8a5jkRygGIo5FxLF7LVdJEV2NECullBqPJmRxEnf865NkXY+5TXE8Y5haGyGZcZlaF8nHcZviIZpqQhUb0dUIsVJKqfGoqjvEalpH0zpKKTUBTLgOsVVdnPRlCzgWBN1IsMDvLGoJYdtibnOMxniYbR1JutMuM7ue5Q3pe5jFdrbZ08gech6HHnVy/sM+GpzySWa8oh/8q9e38tU/P88TG9vJZD0sC6Ihm5BlEQtbTKmLMjW4cmypdQwmV3w4m1fxZvfvHBhtpWHGfrD4fJh9xIDLFCtYKrqY2fg4rL4V2tZB47wB93HMVVNblVLVTouTSrSnUWJb/CKlK5klbEMk7LB/eg3/yW10mhjdEqeOBLX0cEvkfCILjiKVdVm9oQ0MHD63kbBj94rprl7fyqdXPMmrO7sR/PRP7p50hKAgEhzbxrGk6DoGk4sKH2Je4sy2m+gycdpNjMOnWTRZSTjxc/0+EAeKFwOVGz3e+Djc+yWI1kOkDlKdkOwouo9jrpraqpQaDyZccTIh+px4xk/jhGyLlAupjMep5l46TYxOqcEgdFJLJzFen7qH+liItbsSxEIO8YjD2l2JfjHdFas2sbmth5Bt0be8cw2EbItk1pBxvZLrGEwuKrys526Sdi3ZSD1hx+alDsf/YFx9a8llisWLKzp6vPpWf5+iDSCW/7PEPo65amqrUkpVoQlRnBj86LBlgWf8UYhnyw66iOeHLzYYOk2c2WwHcsWM4NiSjxcXxnQ3tCRIZ/0RinMjHkvB9izZva1S6xhMLircnNpC0qoBIOQEcedInX9KocQyhXLbrOjocds6f58KldjHMVdNbVVKqSo0IYoTwY8Oex5Y4o9CvNFMoZZEvqIQhDpJsJGpgB8tzriGrGvy8eLCmO6c5jhhxx+hODemoCnYnmd2b6vUOgaTiwq3RGYQ9boByGSDuHOq0+/rUGKZQrltVnT0uHGev0+FSuzjmKumtiqlVBWaEMWJJbliwyNiQyRkcaecSJ30UGe6EQx1dFFHD/dHTqajJ8P8SXF6MlkSqSzzJ8X7xXTPXjKLmY0xMq7X72SgLZBxPaKOELKtkusYTC4q/EDsFKJuF06qg3TWZb/6rN/HYfH5JZcpFi+u6Ojx4vP9fUq2g/H8nyX2ccxVU1uVUqoKjasOsZrW6b2MpnVGUDW1VSlV7SZch9iqL04E/3om3gC7EbFh3uRalh+3gPW7urnl0fV0pbIYj/xDHrYtFs9p4Kwls/nNqo08uakD1zNMrYvw4ZP2A+C7f3+J7Z0pbEtYNKuey99yEOB3Qn1mcztdqSw1YZtDZzUWvWbKoEVAnw+8F6afys82ThlwHWUXG/phWjUquoBUSo0FLU4q0d4YlTju+Jerz3pg20LG7b3fuSLHEv8CbmHHQvA70ooAxmAQHEv8DrauYVp9mMl1UWrCDi9t78KyJH+12Y1tPRw0vZ45k+LlRXb7xFNbW3exYfNW/jTl/bQ0LSy6jrJHJdboa9XQkaaVUkVMuOJkQvQ5AUi6howHXonbc6mbrAeI31fEsS3Cjk0qa0i7ELJtHNsiFPzf2pGmtTvD9s4UkZBNPOwQtm3Wtyb8EY67UuVHdvvEU1/qcMg4dSzrubvkOsqOBmv0tWpUdNxbKaVGyYQpTjyzO01T7GCRFNalBTPkphv8y+LnWBa4niGVdelKZXFsf8aQ7Y90HA1ZdBeMcDxoZLdPPLUrlSUbrqM5taXkOsqOBmv0tWpUdNxbKaVGyYQpTizZfVxMihwg61WwFMxgCq5h4hUcdvE8/2hLxLGpjThkg9NEGdcf6TiZ8agpGOF40Mhun3hqbcTBSXfSEplRch1lR4M1+lo1KjrurZRSo2TCFCdRWwhZpXc4d70SxwKMf2XXrOuRzrpEHCFsQ8Z1yboemeD/9PowTTUhptZFSGVcEuksaddlblPcH+G4NlJ+ZLdPPHW/+iyhbCcPxE4puY6yo8Eafa0aFR33VkqpUVL1HWI1raNpnfFG0zpKqT4mXIfYqi9OBuIEl251vd2D8lns7hRriV+UREMWYdsimXFJZj3Srsk/ExbKS5xjP8Ac2cE2exqvzjydfQ9/PU9t6uDpTW10p11qIw6HzGzIf7vt+8FSbFrZBQSMWlGxJx+Ke/UDVQsppZQqpMVJJdobUeLhWCQvcZlzO50mRhdxaknQaPfwfc6je9Jh+TSO5xn2m1rrd4AVmNUYz8dAN7UlwMCspvjQ476tG/zDQo2zRzwCvCcR1r0af9XYs1JK9TXhipMJ0+dkOM6xH/BHLiY3cnENbW6M0819rG9NELb9+HAkZLO9M0VrIkNrd6ZXDLS1O0NrIjO8uG/PLujeOSoR4D2JsO7V+KvGnpVSasLT4mQAc3IjFxfoIs4sdtCT8QgF8eHcqMOprEcq6/aaP5V1SWV7X12l7Livm/L/FxqhCPCeRFj3avxVY89KKTXhaXEygA25kYsL1JJgE1OIhaz8VWZzow5HHIuIY/eaP+LYRJzed3PZcV874v8vNEIR4D2JsO7V+KvGnpVSasLT4mQAt7vL/JGLyY1c3E2j3cMdcgJzm+KkXT8+nMq4TK2L0BQP0VQT6hUDbaoJ0RQPDS/uG5sENZNHJQK8JxHWvRp/1dizUkpNeOO6Q6ymdYZG0zpKKVWRJlyH2HFdnADUhASDkMjs7vdhA9GwPz5OPOywaHYDH3z9vr0+TL/+5+fy10MRBBuD7djEwzZL5zX1mn/1+lauu/9lntzYTjrrEQtbiAjtPRk8zzCjMcby4xbwjiPnDtrecj7k+85zwewdvGbrncU/zAf6oB+sCAhub9/yEs8nm/izfRLZmUsGLDz0Gh1KKbXXaXFSiUYqSixAYyyEbQmzm2NcfdohLJ7bxNf//BzXPfAKIkLW3X3URYCwLcTCDvMnx7n6tEMAuPoPz7CxpQdLoD2ZIesaXOOPs+NYQjxs4xn47FsOHLBAKSeS23ee5taneMuOG5kzczpNTZN6R2+hdCx3oNtmH5GP9LZ6Uf69zaNBeqiVBL9rvIhnZL+iMWEdUVcppUbEhCtOJnSfE9sSutNZ4hGH1u5MPvp6y6Prcez+d40BQo5N1jP5+Ves2kRrd4Z4xCGZ9XAsvxDJPZMsETKuIRqyueGhVwdsTzmR3L7zLOu5m4xTx0sdTv/o7UCx3MEiu8HtL3U4hB2bbKSepF3Lsp67S8aEdURdNZja2tpef990001ceumlAFx33XX89Kc/HXD5wvnHQt/27w3Lly/n2WefHXCem266ic2bNw9pmb1p7dq13Hrr3o/zj8T9OVT33Xcfp556KrD3n1/pdJpLLrmEAw44gAMPPJAVK1bstXWPd87gs4xfIpD1DI4tJFLZfPS1O+USDVn5wfz6LeP6keHc/KmsSzzikPUMtiW9Rj+2hKA4sdjZmeq3vkIbWhJMa4j2mlZsJOLCeZpTW2gLTyFRMAJyr+ht/e6BA4d0W9s6qJ9BV2oX8bCfQEpaNTSntpSMCZfTfqVK+eAHPzji23BdF9u2B59xlLiuyw033DDofDfddBOHHnooM2fOBChrmaHKZrM4TvGPhFxxcv752jF9MN3d3YRCIcLhMF/60peYOnUqa9aswfM8Wlpaxrp5VWNCHzkxxu80m3UNEcfOR19rIv7RkVKjF9uWlZ9/TnOciGOTdQ2OJXheMN4Pu8f8CdlCMuMxuS7Sf4UFyonk9p2nJTIDJ91JbcEIyPno7UCx3MEiu8HttRGHTHCdlqjXTUtkRsmYsI6oq/bE1Vdfzde//nUAHnvsMRYtWsQxxxzDpz71KQ499ND8fJs3b+bNb34z+++/P5/+9Kfz0//6179yzDHHsGTJEs4991y6uroAmD9/Ptdccw3HHXccv/71r3tt89e//jWHHnoohx12GMuWLQP6f3s+9dRTue+++/J/f+ITn2DJkiWcfPLJ7NixA4DvfOc7HHzwwSxatIh3vvOdAHR1dfHe976XhQsXsmjRovy35traWq666iqOOuooHn74YU444QRWrlyZv63v+m+//XZWrlzJu971LhYvXkxPT0+vZX7xi1+wcOFCDj30UD7zmc/k21lbW8vnPvc5DjvsMI4++mi2bdtW9D6/5JJLeOMb38h73vMe1q5dy/HHH8+SJUtYsmQJ//znPwG4/PLLefDBB1m8eDHf/OY3cV2XT33qUxx55JEsWrSIH/7wh/3W/bWvfY3vfOc7AHz84x/npJNOAuCee+7hggsuyM9XrI07duzg7LPP5sgjj+TII4/kH//4R76973vf+zjhhBPYZ5998uvv689//jNLlizhsMMO4+STTwbgX//6F6973es4/PDDed3rXscLL7xQdNmcYs+NgTz22GN84AMf4JBDDqG1tRWAH//4x3z2s58FwLIsJk+ePOh6lG9CFyeuZ6gJOyRSWZpqQvlkzbuPmkvW9frNL0Am6+JYkp//7CWzaKoJkUhliToWWc/FEvJHTzxjguLEZflxCwZsTzmR3L7zPBA7hVC2k/3qs/2jtwPFcgeL7Aa371efJZ11cVIdRN0uHoidUjImrCPqqsH09PSwePHi/P+rrrqq6Hzvfe97ue6663j44Yf7HelYvXo1t912G0899RS33XYbGzZsYOfOnXzxi1/kb3/7G6tWrWLp0qVce+21+WWi0SgPPfRQvnDIueaaa/jLX/7CE088wR133DFo+7u7u1myZAmrVq3i9a9/PZ///OcB+MpXvsK///1vnnzySa677joAvvCFL9DQ0MBTTz3Fk08+mf9w7u7u5tBDD+XRRx/luOOOG3T955xzDkuXLuXnP/85q1evJhaL5effvHkzn/nMZ/j73//O6tWreeyxx/jd736XX9fRRx/NE088wbJly/jRj35UdJ8ef/xxfv/733PrrbcydepU7r77blatWsVtt93GRz7ykfz+HX/88axevZqPf/zj3HjjjTQ0NPDYY4/x2GOP8aMf/YhXX+192nrZsmU8+OCDAKxcuZKuri4ymQwPPfQQxx9//IBt/OhHP8rHP/5xHnvsMVasWMHy5cvz633++ef5y1/+wr/+9S8+//nPk8lkem13x44dXHzxxaxYsYInnngiX5AeeOCBPPDAA/z73//mmmuu4b//+78HfKzLeW60tLTwne98h8MPP5wrrriCk046iRdeeIFp06bR1tYGwJVXXpkvmIsViKq4cV+c1ISEeKj3btpATdiiIeYQC9ssnd+U7wwL8Mk3H8QHl+1DPGxjWf7RlYgFsbBNXSzE0fs05+dfPNdfdun8Jhzboi4SYmZjlLnNMeJhG8cSJtVGBu0MC7B4bhOXnXIAzTVhtrUnaa4J9+tM2neersmHETnlSpomzYCOLf61UXKdWmcf4f8emzS02yB/e9OkGRzR1EM22sQv4++ma/JhJTu4ltN+NbHFYjFWr16d/3/NNdf0m6etrY3Ozk5e97rXAfQ7lXDyySfT0NBANBrl4IMPZt26dTzyyCM8++yzHHvssSxevJibb76Zdet2X1X4vPPOK9qeY489losuuogf/ehHuK5bdJ5ClmXl13XBBRfw0EMPAbBo0SLe9a538bOf/Sx/auRvf/sb//Vf/5VftqnJfx3Yts3ZZ589pPWX8thjj3HCCScwZcoUHMfhXe96Fw888AAA4XA435fiiCOOYO3atUXXcfrpp+cLnkwmw8UXX8zChQs599xzS/Zr+etf/8pPf/pTFi9ezFFHHcWuXbt48cUXe81zxBFH8Pjjj9PZ2UkkEuGYY45h5cqVPPjgg/nipFQb//a3v3HppZeyePFiTj/9dDo6Oujs9I/0vu1tbyMSiTB58mSmTp3a7wP/kUceYdmyZSxY4H8ZbG5uBqC9vZ1zzz2XQw89lI9//OM888wzA963gz03Nm/ezMyZM/n73//OHXfcwV/+8hfOO+88IhH/CHk2m2Xjxo0ce+yxrFq1imOOOYZPfvKTA25T7Tbu+5x0Zwy7j2P4PPxTLmHbojOZ4d4XtvO357bjBT1Zo7YwqS5KfXB6J531OMx+lffX/pOjm7pJh+aw4sFlfCs1n2jIYmNrgld2JnA9w9S6CGccNoOXdnTz5MZ2APaZXMMB0+qKxmwBrrv/ZVaubSWRcfNR5Y+9YX/A72T6rb+92CuWu9h6hcWpb0HbKmgDkkvguI/5O7f6Vrjvy72jwbOPyG/buWMVb3a/y4HRVhpm7McL00/lZ9kpPL2uje4Xu6iN/CN/zZbFc/1lG4Cjgv+DyRVs5Rir2HFFxZ31mi79DJYgzL35g/9Bn81mMcZwyimn8Itf/KLoMjU1NUWnX3fddTz66KPcddddLF68mNWrV+M4Dp63+8hpMpks2RYJzv3eddddPPDAA9xxxx184Qtf4JlnnsEYk7+9UDQaLbvfS7HlCw10X4VCofzyufupmML75pvf/CbTpk3jiSeewPM8otFo0WWMMXz3u9/lTW9604Dbnz9/Pj/5yU943etex6JFi7j33nt5+eWXOeiggwZso+d5PPzww72OEuUUe/z7tq3Y/XbllVdy4okn8tvf/pa1a9dywgknlGw7FH9uTJo0KX/7tGnTuPXWW7nxxhs57bTTeM973sMFF1zA1KlTAZg0aRLxeJy3v/3tAJx77rnceOONA25T7Tbuj5wUY4CulMf2rjRdKZdU1pD1DB5+H5FE1rChtYcNbSkSKZeDvRe5lNvIdu7ivi0Oz7/yKsdvup79Mi/w4Is7eXpzJ1nXwxbY2p7k+/e9woMv7sSyBNsSVq5t5dMrnuTqO56hpTvNtIYoLd1prv7DM3zq10/w8CstdKYyGGPoTGZ45JUWPnX7E/3mv/buNbyw8l7402dg/SNg2f7/DY/Abz/kT+/Z5Xd07dnlR4U3Pp6P+NbufIJ3Jm7BSbbyeGuM9Zs2kLr7C5gNK9ncnqQzmWVTaw+v7Oji2rvXsHp964g9Brk29d2/kdzmWG63qNwIzEUes4msqamJuro6HnnkEQB++ctfDrrM0UcfzT/+8Q9eeuklABKJBGvWrBl0uZdffpmjjjqKa665hsmTJ7Nhwwbmz5/P6tWr8TyPDRs28K9//Ss/v+d53H777QDceuutHHfccfn5TjzxRL72ta/R1tZGV1cXb3zjG/ne976XXzbXD2EgxdYPUFdXlz9yUOioo47i/vvvZ+fOnbiuyy9+8Qte//rXD7qdUtrb25kxYwaWZXHLLbfkjxj03f6b3vQmfvCDH+RPqaxZs4bu7u5+61u2bBlf//rXWbZsGccffzzXXXcdixcvHrTo6nvfrV69uux9OOaYY7j//vvzp5lynVDb29uZNcv/QnjTTTcNup5iz41Ctm1z1llncdddd3HXXXeRSCRYtmwZZ555Ju3t7YgIp512Wr6/0j333MPBBx9c9n5MdBOyOClUzlVeznEepNPE6ZIaEhlDqxcnG6pj4c67SGX9osQAjm2RO06TynpEHJuwYxOPOGxu6+k3OnFrd4YtHSmyrh9BDtkWIdsm43psaU8VHc247eGb/ZGKw3FwIv7/UBy6NpUcwTgX8V3WczdJu5ZspJ6wY/PULsg4dRzZ8ed+IyyPdAR4rGLHFRV31hGYS7rxxhu55JJLOOaYYzDG0NDQMOD8U6ZM4aabbuI//uM/WLRoEUcffTTPP//8oNv51Kc+le9MumzZMg477DCOPfZYFixYwMKFC/nkJz/JkiVL8vPX1NTwzDPPcMQRR/D3v/+dq666Ctd1ueCCC1i4cCGHH344H//4x2lsbOSKK66gtbU136ny3nvvHbQ9xdYPcNFFF/HBD34w3yE2Z8aMGXz5y1/mxBNP5LDDDmPJkiWcccYZg26nlA996EPcfPPNHH300axZsyZ/VGXRokU4jsNhhx3GN7/5TZYvX87BBx/MkiVLOPTQQ/nABz5Q9MjM8ccfz5YtWzjmmGOYNm0a0Wg0f0pnIN/5zndYuXIlixYt4uCDD8734ynHlClTuP766znrrLM47LDD8qfJPv3pT/PZz36WY489tqxTeMWeG6XMmjWLK664gueeey7fTwfgq1/9KldffTWLFi3illtu4Rvf+EbZ+zHRTeiLsJVDgJ+Ev8pWMwkrSPbEwjYzG6K4bRu5IPkp/Kvk+xdb605ncT0/Qrxgsp/hNxg2tiRorolw4oFT8+u+9/lttHRnsC2/sBH8wibrerie6Te/Zwznv3gZh4U2QaSG/NVUjIG29RCfBAe8cXfjjQcdW7go/WmmNUR536ufoi00hVwMaWNLgtlNMdy2jVxRczUigsHQk3ZZdsAUtrUnuel9rx2R+/WiH/+LaQ1RrIJvUJ4xI7rNsdxuUT87xz9iIgXfEYLHjAtuH922VJiurq78NTC+8pWvsGXLFr797W+PcatGXm1tbT5lpFQBvQib6m+DmUodCYzxzwHblhD1utnhTMcSCab780rwHCr88Mu6hrBj9Rud2D+yYmGJH0GG3LVRhHCJ0YyTNbP9kYrdgh7qbgaccMkRjHMR35bIDKKef+g1k/WIhm2cdCc7nOn9Rlge6QjwWMWOKyrurCMwl5Q7z3/ooYfy4IMPcsUVV4x1k5RSo2jCFyfllKO3Z4+nThLUmm7iIaHJSuBkOnlq8tuIOBZucEXYrOvlr3EScSxSWZd01iWRyjKzMdZvdOKmmhAz6iM4th9BzrgeGdclZFvMaIgUHc248ZgL/ZGK0wnIpvz/mQTUzio5gnEu4vtA7BSibhdOqoN01mXhJAhlO3ms/s39Rlge6QjwWMWOKyruPAFGYN6yZcuwLjx13nnnsXr1ap5++mnuuusupkyZMgKtqzx61EQpn57WKdOigtGJNzGV35plPOHtu/tUTHDkI2zB5Nowu7ozZDxD2LbYb6p/Cual7V2kXY+QJRw+t5Gzlszm789v57G1LXSlXAT/iqrHx9ZxdOdfmOpuY4OZwu/MMsJzj+Tytxzkp0o2Pg7/+BZsWkXK9dhoprE1G6Mx20Kt9EC4lvi8JUw57r355Ec+rbN5FWenVrB/1u8suCo7n2/2vI1V2QVEQjaTasJEQzY1YZtp9X5P/WTG65dqKSvxMkgSpdQ6RjpNo2md0dHZ2cmBBx5IMpnkmWeeYfr06WPdJKWq1YQ7raPFyR6w8PuWuMGVZmujDh09/umWSbVhIo5NRyJNyjVkXC9/CicTJHtmN8c5YFp9r0Hy3li/gXlPf5s2N0aXxKkjQR09fM+cy7lnnNnrWimr17fy2zvv4Kz2m9mejtBNnBoSTA2n+E3Dhbz91NOLFwzBgH7/2pxFUp3UWwludP6DxzL74HoeC2c1UhOx+ff6NhBYPKeRiGPnB/EDBh/gL5dEKTWwYAk6eOD4YIzh7W9/O7///e/Zd999efrpp0vGUpVSg5pwxcmEP62zJzz8wiQ3nk53bnwb8S9XH3ZsXIRU1kNECNkWtuX/zHiwpT3VLzXiPHMb7V6MLqlFsOiilk5inC4P9Bs4cMWqTZyUuocWN0bCqsVxbHrsWlrcGCel7imeQCkY0C/lGnrsWnqsWk4195H1DK4H27tSrN2VIB5xiIUc1u5K9Eq1lJV4GWYSpaLSNGrYfvSjH/H73/+ecDjMnXfeqYWJUmpItDgZplwZa/A7wxr8a6UYAOP/DuB6XhAv3n2EyhLwPEM62zvOVht1mO5uo9P07pzZRZzZbO83cOCGlgTT3K20u7vTJyJCuxtlmru1+IB7besgUkdXKovrGSwRuokz3duO63l4xtCdytKVyuLYQsiWfNGVG8RvQ0uC2mjv6/f1G+Av2E4vhQMLllDWulVFe/rpp/NXRr3++us58MADx7hFSqlqo8XJMOVKDcFP2AiCYwVZHfF/B3+QQL+TbGF0FSzLT+QU6kpm2WpPo056fxDXkmAjU/sNHDinOc42ezoNdhIvOD1njKHBTrLNnl48gVIwoJ9tCZ4x1JBgqzUV2/JPO9VEHGojDlnXkHENNcGggrlUS1mJl2EmUSoqTaOGLJFIcOqpp5LNZnnHO97BhRdeONZNUkpVIS1O9oAF2OIPICiQ/xDHGKIhi3TWxcYQcSyM8fuduJ7/M2TBjIZIv9RI9pDzaLB6qDVdGDxq6aKOHu4wy/oNHHj2kln8PXIyzXYPca+LbNYl5nbRbPfw98jJxRMoBQP6RWwh5nYR87q4U07AsQTbgqm1EeZPipNIZenJZJk/Kd4r1VJW4mWYSZSKStOoIbv44otZt24dc+bM0Ut1K6WGTTvEDlEuJhwLW/lTKY5t4QbJnLnN/lgQ61v8qzgumt3ASQdO5bf/3sQTG9uD8XfCfPik/TlgWl3R1Mjdf72L9kduZlJmKxvNFP4WPpm3veXUogMHrl7fyiMP3s28Db9lUnYbu0LTWTf7TI4+/pTSHUiDhEj7lpf4d2c9tySO5Tl7/3xbn9rUwYaWBNFgwMSRSuuUUlFpGlW2W265hfe85z04jsPjjz/OokWLxrpJSo0XE65D7IQsTgpjwRvMFG53l/Gc7B/0Gym9XNQWjgi9wlvce5mFHym+O3QS0+qjvNW7l4bkZl7KTOLX7jKe9vZhof0Kb5f72cfZRWjSPB6tfwt/aZvJzq40C1LPc5q5j9mynbbwDFY3v5Un2Y91u/xTOk1xhx2dadqTWQR6FUPxsMOUukjBAH1FPrgLCoMdznRWuMvY1p7kze7f84P+9SoWNj4OD30LNq/y/561BI79WP72F1beS9vDNxPt3kiyZjaNx1zIa5aeOPid3bdAmXk4bP530YKlZFEynCJnjCK6E7WwWrNmDQsXLiSdTvN///d//Od//udYN0mp8USLk0q0N4uTRfISlzm302lidBGnlgR10sO12XN40uw35GVnyi4sMWyVKbR7MX999PB792jOsB+hizg9dg1Rt4upoRTfNe8gkXH5qPXrYD011FsJ6khwrXsuG2MHkcq4tCf9zrJhW8i4fndaC7As/wqytRGHg2bUY1vSP2ZbEOPdmQnz0oYtTPF24FjCTmsq7SbG4dMsmqykH+0F+OOnoX09hIJRQDM9/of6W77KC1s7SN39BTJOHdlwHU66k1C2k8gpVw5coPSNE7euh61PwvSF0DSvV7x4tbdP0Qjx5w5L8Jrnvju0SPIwY8x7aqLGoJPJJIsWLeLFF1/k1FNP5Y477hh0YDel1JBMuBfUhOtzco79AJ0mRic1GIROaug0Mc6xHxjWss3SQaPppM3LTaulgxiX2H+kw8TooIasC91Sy45MlLe493KO/SAdJk4HtYgltHk1tHkxzrEeIJnxSGR2H77JpX7Ajy6D35E27ZrSA/QVxHhf3ZUkG6qnwXRQk23PD/r3UoezO9q7+lZ/RNxQwWCC4bg/kODqW2l7+Ga/MInUgwjZSD0Zp84fhHAgfePEnVv94qdra8nBCYsOdDjUSPIYDag3UWPQl156KS+++CLTp0/nZz/7mRYmSqk9NuGKkzmygy76R3XnyI5hLRsmQ0SymHxY2NBl4kyW9mBeg4dBBDpMjFnsYLZsp4sYYIIrzBo6TZw51o7gWiMFBUnwaz66bPx1uZ5HVypbPGZbEOPtSmUJORa2l8bx0gCEHIuuVHZ3tLdtHbgpsEO712GH/Glt64h2byQb7h0LzobriHZvHPgO6xsnTnf6xUmq4BLdQRtKRYij3RuHHkkeZox5T03EGPSKFSu48cYbsSyLP/zhD4OOHqyUUuWYcMXJBjOFWvpHdTeYwcfuKLZsmhAp4yD5sLBQKwl2moZgXsHCHxywXnrYxBQ2mqnU0gP4F28ThDpJsMGbEiRmdn/zzP2ajy4HAw3allV6gL6CGG9txCGT9XCtMFkrDPiD/tVGnN3R3sZ5xQcTtCPQOI9kzWycdO9YsJPu9AchHEjfOHG4zj9dFKndPa3P4ISF8gMdDjWSPEYD6k20GPSrr77KBRdcAMDXvvY1li5dOsYtUkqNFxOuOLndXUad9FBHN4Khjm7qpIfb3WXDWrbF1NMmdTRauWld1NPD9e5bqZce6unGsaHGdDEllORP9onc7h5PvSSopwvjGRqtbhqtHm73lhENWcRDux8Wp7BQAcC/eFvYltID9BXEeBdMiuJkOmiXerqdhvygf/vVZ3dHexefD7FJ/gCCucEE0wl/IMHF59N4zIWEsp04qQ4wBifVQSjb6Q9COJC+ceK66X5xUju95OCERQc6HGokeYwG1JtIMeh0Os1pp51GMpnkDW94A5dddtlYN0kpNY5MuA6xoGkdTeuMnImS1vnIRz7Cd7/7XSZNmsSaNWtobm4e6yYpNZ5NuI5cI1aciMiPgVOB7caYQ4NpzcBtwHxgLfAOY0zrYOsabnHStwhZ7e3DYuuVXkVJqYTOpLhNPBKmO5XFsfyxcrrS/iH7iC3s777Yr8B50uxHxBGiIZuQbREL2WA8OlIuxsD0hihvPGgqj69v49/r24qOUPzkxnYATpu0meX1jzIluzVfXDySmj/gtUcKrV7fyst/vZ7Xbv05TaYNq24a8WUfhSUXFL+zCj/MQzEw0N7ZwfPJJv5sn0R25pLBP2iHUBAUjpJcsmAaK6X2o0jB90hq/p4VIVU4KvFdd93Fqaeeiojw4IMPcuyxx451k5Qa77Q42WsrFlkGdAE/LShOvga0GGO+IiKXA03GmM8Mtq7hFCd9Y7+z2MYh9nqe9eazMeg7Uk6EuCFi057aPQaOAAvLiCPbfk9XXCBkgy1C2LboSrn5/iOW+Je+N0DIgngkRF3U4TXuGt6XvhU3XMfMadPYsXMHddLD7xou5LfbpxcdKbjwg3H1+lYe/c13OL/jBrJ2lKSJEPJ6aAi5hN70xf4FSmH0NpuGjY+R8TxWe/tjORFqJcHvGi/iGdmvdCx2CPHdXOT2EPMSZ7bdRJeJ9483j9UHdKn9OOQseOY3veLZddLD36cv53n7gOFFhsco8rwnNm7cyIEHHkh3dzfXXHMNV1555Vg3SamJYMIVJyPW58QY8wDQ0mfyGUAuf3ozcOZIbb9v7He6tJM0YaZJ65AixJ3p3oPzmSLrLrYuz+SivwCCY9v0ZD28YB22JViWYAWPQMbzY8Nhx+Y0cx89Vi2tXpznt3WRDdWTDdWxcOddJUcKLrRi1SZOTfyGrB0lbcWwbIuMHafLC8HD3+u/k4XR210vQzhORzbMLG8z2Ug9SbuWZT13DxyLHUJ8Nxe5XdZzN0m7tni8eayU2o+Hv9cvnp0N1bG0/c/DjwyPUeR5uLLZLGeccQbd3d0ce+yxfO5znxvrJimlxqnR7hA7zRizBSD4ObXUjCJyiYisFJGVbqJ9yBvqG/utlR56iFBDT35aORFir8iBpXLiyIbdCRsvH//dvbLCS0Hk5nU9v5yZ7m0nITW4niGZdgk5FkmrhsnZrSVHCi60oSVBg9tKmt0DBVqWkPDC0L29/w4VRm/TnWCHSHo2MePfV0mrhubUloFjsUOI7+Yit82pLSStGqBIvHmslNqP7u394tm5+wWGGRkeo8jzcF1xxRWsWrWKhoYGfvOb32BZE64/vVJqlFTsu4sx5npjzFJjzFI7PvRrJ/SN/XaZGDFSdBPLTysnQmwVOZhWThxZ2H0czsrHf3evrPBsWm5eO3iz32pNJW66sS0hGrbJZD2iXjc7neklRwouNKc5TrvdRJhUfprnGeJWGmqK1IOF0dtwHbgZopZLj/j3VdTrpiUyY+BY7BDiu7nIbUtkBlGvGygSbx4rpfajZmq/eHbufoFhRobHKPI8HH//+9/56le/CsBvfvMbpk4t+b1CKaX22GgXJ9tEZAZA8LPI1/i9o2/sd6tpICpptpmmIUWI68J2r7+lyLqLrcuSwjvXkHVdYo6FFazD9QyeZwgOlhCy/NhwOuvyBzmBmNdFk5XgwGm1OJkOnEwnT01+W8mRggudvWQWd8bPwnGThL0ePNcj5CaotTJwzKX9d7IwejtpX0gnqHfSbLJm4qQ6iLpdPBA7ZeBY7BDiu7nI7QOxU4i6XcXjzWOl1H4cc2m/eLaT6WRlw5uHHxkeo8jzUG3bto2zzjoLgMsvv5yTTjppjFuklBrvRjRKLCLzgTsLOsT+L7CroENsszHm04OtR9M6mtYZVZrWyfM8j+OPP55//vOfLF26lEceeQTbtgdfUCm1N024DrEjmdb5BXACMBnYBvwP8DvgV8BcYD1wrjGmb6fZfvb2dU6GI1fozLN2kK2fw/+1vpbH3d6FjWMx4HVSasIWn13YzQWxh+lat4qujlZasxFetBfwlLuAfd2XmOFtY703hRXeMp5mP14bfpVL6h/l8LoOGmbsxwvTT+VnG6fkr6OxcFY9L//7fg7Y+gdmmO2k6+YwY9n7eM3SE1m9vpXr7n+Zh1/aSXfGQwSm10f58En78Y4j5+bbVeraHMO+ZkeJD/GjI2s5234gX3A90DWHxranmC07aJ61P1OOe++Qrl+yxwXCqp/5HV27t/unbY65FKYeNLRiYRSKi7G8dso111zD//zP/1BbW8vzzz/PrFnj74Jyo67CC1JVkbQ4qURjXZzsyUjGfdfzCed2ptc5TO5ZhxEh63lsNw3MZgfP9Ik53xGMbJyw4ljRBg5qhraWnfxpyvtpaVrIxpYE7saVfFhuI2nV0UWcmNfN5FCSXUd+ku+vaeDF7V0k0i4SxJYdS4iGLK489WDeceTckiPpnrpoBnc+uWXoI+wWGRG5Tnp4uvFkDm27h04TY8rkKSS2vcz87Ctsie3Pdms6TraTg5uh/i1XlzXa8B7HeVf9DO6+0j9KFIr5V65NdULdLJh2YHnR3lGIAo/lSMf/+Mc/OP744zHGcOedd/K2t71tRLc3IVRhfFxVhAlXnFRsh9hKsicjGfdbDzGsrm14dphuomQJMddsp6dIzPkS+490EqfNi5NyDU/tgoxTx7Keu7FE2N6Z4nRzHx2mhh67Ftu2SDq1tHoxelbeQmsiQyrrYYnfGde2JEgFwQ0PvQqUHkn3hodeHd4Iu0VGRM6G6li26zayoTqyoXqe39bFJNNK1o7SkN1FKGSTDdWzrtspe7ThPY7zPvw9vygJ1/gx3nANeC50bCw/2jsKUeCxGul4165dnHHGGRhj+MhHPqKFyd5SZfFxpcaKFidl2JORjIutJ04PnhXC8wyuOEQlVTTmnBvZ2BjyseJsuC4fX+1KZZnFDjrN7gSSiNDhxWhObyWV9fIxZv82fwRkzxh2dvpJnlIj6e7sTA1vhN0iIyInrRrqsi0krRr/77RLzPSQJkLE9dcXcix2ZqJljza8x3He7u1+cVLIeOCme08bKNo7ClHgsRjp2BjDeeedx65du1i4cCFf//rXR2xbE06VxceVGitanJRhT0YyLraeBDEsL4NlCbbJkjSRojHn3MjGEhz5iIZtnHRnPr5aG3HYxBTqZHdRY4yh3uqhJTydiGPlY8z+bf4IyJYIk+v8a6CUGkl3cl1keCPsFhkROep10+k0E/W6/b/DNj0SI0yKlO2vL5P1mBxKlj3a8B7HeWum+qdyCokFdrj3tIGivaMQBR6LkY6/8Y1vcM899xCLxbjjjjsIhUIjtq0Jp4ri40qNJS1OyrAnIxn3Ww89eLXTsNw0NSRxyLBephIrEnO+3n0rdSRotBJEbGHhJAhlO3kgdgqeMUyti3CHnEC9dBNzu3Bdj2i2iyarh9jSd9MUDxFxLLzgyIvrmeB6KrD8uAVA6ZF0lx+3YHgj7BYZEdnJdPLApPNwMp04mQ4OnFbLLmnCcZO0O5PIZFycTAfzarJljza8x3HeYy71i5N0t3/EJN0Nlg31s8uP9o5CFHi0RzpeuXIln/mMP6LELbfcwvz580dkOxNWlcTHlRpr2iG2TJrW0bTOYO2p9rROe3s7Bx54IFu3bmX58uX86Ec/2uvbUGhaRw3HhOsQOy6Kk8LrmSSMf1h+mrRSS5IuYjxr5vW7pskieYkP2H/gMOsVAJ7w9uGH7mk8afbL37bUeoEaUnQTZaV3QP52gLOs+7jE/qPfL8RE2Wgmk5FwyeunLJKXONd5gIMirdheiknZ7dRIDy3SwE85leeysznLup99Q7toyzhkPUNc0mwwU1jhLuPF0AFcFPsnl4TuotFr2/1huuSC3W92W5+krW0X67tsnnLn8Xs5gcTkRdRFQ7y4vYuOngwZ12DwLxIXtgTbsVgoL3F++CGWNnQyc96B+TfLXz22nhseepXm9qc4k/tZaK1jUihNfTSE5faQ6ekihcPWmoMJLfs4r1l6YvkP6lDeoIvMu9rbZ3gFVRnXMBnRD4wK/WAyxnD66adz5513csABB/DEE08QjUbHullKKZ8WJ5VooOKkMOYbJsNi62UcsnhikTYhBMPLZhYuVj76u0he4n+cnzJbdpAkDAhRUmw0k/m5ezLvsu9hgWwlKmkMgmBIEuZVbzqfz17IfrKRz4VuJWnCeAiTpQMLj1XuvuykqV/MuLCNk2llif0yBosdph4LQ630sNU085KZjU2Ww+UlAFZ7+5ImRJ308G9vHy4K/Y0kYeLxehqcjH9aYun7YfMq8Fy6Nz9LezKLAK8yg6yx+XrmHJ42+4EUHydokbzEJ0O302PV0Emc185wmBvPcve09/PpR0Ical7kEveXCB77yiYi4lFHVzAekEWP3YAYl+3WVLw3f7W8AmUoccoi83a07eIbmXPY2bBwaPHnMkYcHtF4ZwXHSH/wgx/woQ99iHA4zNNPP83+++8/pu1RSvUy4YqTqu9zUhjzXSDb6CFCCI8IGRJEyRBimrT2iv6eYz9As3SQJEKaEGkceojQTCeX2H+kmU4cXFxssji42Di4NNPJOfYDXGL/kaQJkyBKnfSQxSaDzYHWpqIx48I2HmhtIhPMXyc9JIhiG5fptNBODfvIVpJE6CHCAtmWX9+F9t0kvDBJYrQls370NRSDx37kf9h1bqU9Y9FDlAwOU6WdThPnHPsBPIoXJrm2dZg4HdTgWBZP7QKi9fSsvIVoyOZt5j66pIbpVhsZQthkAb/vimeFCJskGTtGA520PXxz8Y30NZQ4ZZF513U7nJS6Z+jx5zJGHB7ReGeFxkiffPJJPvKRjwDw4x//WAsTpdSYq/ripDDmWys9ZLCxxcPG7/yRwaGGnl7R3zmygzAZMuyOaGawiUiWydJORLLY4uEFd4+HhY1HRLLMkR1MlnZ6ghF/Q7h4WLjYxMSP5/aNGRe2MSYpXGw8LEK4gB/xDUsGDNTQQwaHDDa1QQqnizg1kqSHCJbI7tGNQzG/I2ekDtKdJD0rvy819NBJbNC4s9+2GMYYbEtIpl2I1NGc3ko0ZDHTbKdL4vl2OcYvTvxQsoVtsrjiECJDtHtjeQ/aUOKURebdmYkyzd3aa1pZ8ecyRhwetD17YgxjpN///vfZuLH/49PV1cWpp55KNpvl/PPP513veteIt0UppQZT9cVJYcy3y8QI4eIaCzfYtRBZuon1iv5uMFNIEyLE7ohmCJeUcdhpGkgZB9dYWEGBY+HhYpEyDhvMFHaaBmLBiL8ZbCw8bFx6jF+w9I0ZF7axx0SwcbHwyOCPUWIMpE0IBLqJESJLCJeu4PoltSToNlFipPCCIsLfeI9/BCXVCeE6opaX35duYtTRM2jc2W9bDxIUPdGwDalOWsLTSWY8NstUak0i366sOOTGURY8XHGwTZYMIZI1s8t70IYSpywy7+RQkm329F7Tyoo/lzHi8KDt2RNjFCNNJBJ89KMf5ZBDDuHOO+/sddv73/9+NmzYwLx587QDrFKqYlR9cVIY833VTCNGigwWKULESRIiwzbT1Cv6e7u7jBZTT5QUYTKEyRIjRQt1XO++lRbqyGJj4+KQxcYli00LddzuLuN6961EJU2cJJ0mhoNLCJfnvVlFY8aFbXzem0UomL/TxIiTxBWbrTTTQDevmOlESREjxatmWn59N7unELfSROmhMer4R0wyPXDkxX6/hbrpNIQ8YiQJkWW7aaBOEtzuLsPC7wBb6v6rlwT1dJP1PBZOApIdxJa+m2TG5S45gVrTzVavkRAZXPzixACWlyEtUUJuD+3U0XjMheU9aEOJUxaZd15Nlr9HTh56/LmMEYdHNN45RjHSZ599lpqaGjo6OnjHO97Bhz/8YdLpNDfddBO/+tWvcByHu+66i3h85K6dopRSQ1H1HWJB0zqa1tG0zkB+8pOf8OEPf5ju7m4AYrEY8+bN45VXXiGdTvOjH/2I5cuXj2gblFJ7ZMJ1iB0XxQkMXGwUm7ewmGmgi/nWdkJk6DERdtKYL2iA/LwDFR7n2A9wsKyjlh66iPKsmc9qbx8WW6/0Whbg3GB96wum5Zavo4e0HedpM4/HMgtYbL3CwbKWWpIYBAuD2ELUsegmxlavkZjXzhwvaD8RdtHITmmmjg7mmO0APMO+7Fj4AcLzX8sf/3wXJ6f+xmzZwU5nOo3HXMgpb+w9dsoLK+9lywM/Jty5gS0ylVVNb2FX40KSGY85zXGW1/yDeWt+4vfXCNdBwxwIRf0P3JmHw+Z/ly4Atj5JoquN9kSGrOdhwnXE5h5e3nVOGPi6H6vXt/LIg3ezZOPN7J99kXjYJjJnKRz3sd7rLiwSQjHoboW2V/3L14fiUDOFF519+PbOI/lnzzwm10VYftwC//owGx+Hh77lp6SyKf/UWu1UmL6oZLExEtcqKXedl156Kd///vd7TbMsC2MMRx11FP/85z8RmXDvfUpVkwn3Ah0XxclA0eDPZy/sd8QkF+sNSZbXyvPUSJJOEyUuaSwMnSbK82Y+cXoAYbOZVHI04tz67CBq60ePYatXzzx7J88WjDQ8U3YBhs1mcn59M2UnICSIFl1+vTeVadKGQ5Z66SZhIsQlTYeJ+Uc/yBAhk2+/AElCWMYlKllaTB1pQkRJs9FM4RfeSZxmPUIHMbqJU2MSNFo9JI+7PF+gvLDyXrr/cg07M1F6rBpippuo28V11juJL3gtx3f9lbN2/oBIvJawbfsFijEw+0j/8u9bn4TpC6FpXv+4rueS3voc3ckUNaabpF1DFodN9hzmTakbeFRiBh6lF+C3d97Bhe0/YHJ2KykieEBzOEuoaT689Wu7i6RcpDebhnX/8E+Thesh0wUYeqwa/p2ZhYjDzZHzecLsRzLj8rWjM5yy9lpoXw8IpIJTNOF6mH6of5XZPtHgkRhZeCjrPPLII1m5cmXR9cTjcd75znfy/e9/X69rolTlmnDFSdX3OYGBo8F9Rw4ujPXuI1txxPWTMfgpmgwOEVymSSvNdNIsHQOORpxb3zRpJUOIBFHSOCywdpDsM9Jws3TQTGev9eW2UWr5+bKNNA5hyeLiUCNpsliEcQkbvzDxkz2pIPpsU0OKKFky2MRIkyZEkgjN0sFyyx/puJtawKJLamg3cXpW3pLfp7aHb/ZHNg5GOm43cbolzqnevazdleCNHb8mY0Vpy4Yh3ekXJHYItj8LnVv9IxFdW4vHdTu30unahCWLJza2yWLsMJNoGXxUYgYepXfFqk1+xNjrIGPH8JwInhWmIxuGnl27110Y6d31sj8asR2CTKf/0w7jZpJMp42kXctb3HuJhx2iIdu/n3p2+UdX3CRYjr//bsrf9yLR4JEYWXgo63zhhRdKrieRSPCLX/yChQsXsmbNmmG3Ryml9qZxUZwMFA3uG6UtjPXW0ION51/HRPxIsIdgi0cNPUQkS5hMr+VLxYRzUdvctmNFRhoOkyEivdMkuW2UWj4qKTLYhIJOuXZw/ZWQuNhidrefXPstP9gsXn4+f50OYTL5kY4LdRp/FOOcaPdGOrwYVnCo3xhDl9Qwix10p7LUZVvIWFEyrgE34xchlh2MVdPpFyeproKdLIjrpjtJejaOyeLh/8ziEPN6Bh+VmIFH6d3QkmCauxXHS5MN7kvLEpKe7RcPuXUXRnrTnWBcEBu8LFgWiH8f1tBDN3Gme/6psWjI8u8nN+UXMfl9t/x1pDuLRoNHYmThcte5fft20uk+Iy330dPTw8svv8zhhx/Ob3/722G3SSml9pZxUZwMFA3uG6UtjPV2E8PF8tM4xg4+2g2u8ftzpIxDmt4jspaKCeeitrlt9xQZaThNiJTp/YGS20ap5ZMmQgj/6E7uwnA2Lhlj4xrZ3X5y7ff8YLOx8vP568ySJpQf6bhQnfijGOcka2ZTb/XgBaf8RIRa080mplATceh0mgl5SUK2+B/SxvOPPoRifv+TTA9Eagt2siCuG64jarlkxcHC/+mQpceKDT4qMQOP0junOc42ezpZK4wT3JeeZ4haLtiR3esujPSG6/zCxLj+URDPA+Pfh93EqCHBVmuqf79kPP9+siN+YZLfd89fR7iuaDR4JEYWLnedTz31FJFIZND1RSIRRITW1tZht0kppfaWcVGcDBQN7jtycGGs9xUznazxj0p04V9/JESWFDbbTBMt1NFi6gccjTi3vm2miRAZ4iQJk+VVbwrRPiMNt5h6Wqjrtb7cNkotv9ZMI0yWtHGwydJtwjh4pLFJS4gUoeCaKJEg+uzSTYQkjl/kECZMhigpWkw9N3j+SMc1dAEetaabBkkQW/ru/D41HnOhP7JxMNJxgySoMQnutE5k/qQ4f60/l5CXpNFJ+x/Ibtr/sJ56MNRN94uT2unF47p106mzXdLGwTIurjiIm2YXzYOPSszAo/SevWSWHzG26gm5PVjZFJaXpt5JQ2zS7nUXRnon7esf9XEzEKrzf7pp7FCUrTQSdbv4k30iiXSWZMb176fYJMgkwI76R1vctF+w1E0vGg0eiZGFy13nk08+SSqVKrmeaDRKTU0Nn/jEJ9i0aRPve9/7ht0mpZTaW8ZFh1jQtI6mdTStU2yd73znO7ntttv6LRuJRLBtm0svvZTLL7+cpqa9P8qxUmqvmXAdYqu+OCm83shO08D17lt5ycwuWqjsJxt7zfsnbymTpCtfVHgYLIQwKSZLJxHJkDUWCRPGFv/USIIIK73X8EP3NCBXVKxlJjuZZPlj8ogBxL/Ie4tXwwtmLhkJ5wuhg6x11JEEgbSxSeOP01OqoFokL/Fp55cskRcJSxaMP7aNiP+MzT2CBtjoTeJZ9mEKbdTSg8G/XkqMNAaDi+BgQIQkYdqkiZb6A+mZtJCOVx9jqruNkEkzW3ZSZyXZaRr4kftWVrgnYFkQcWwOm93AZ958IIutV/wP+C1PQLqLLmK80F1DJrGTfbzNxCVJj0R5lVkY4DX4xZcNZKwQL3uz2MAUGpwsTbP2Y/9TPtAv5ZL78D06spbzUito2vm4f9QiXANzj4ZjPxbMHBQaTsy/UzI90DiPdZED2Pr8w0S7N2KH48xqitEUcoteL+XoyFrOth9gSnbrwNcg2fg4/ONbsO5hyCRI2zHWRA5lReRssjOXlF94jMI1T17zmtf06ugaiUSwLIv/vOAs/vuEeiZ520dm25Uy+nKltEOpPaPFSSUqVZycZd2XHx24B7+PRq0kafHixCXTK1acMA7NVg9dJkoPEerpolESvOpNpU6SQVS3h5SxqBW/A2FQY2ABHpAK+p/0mDDbaSJhInSbCAvttdSTKHmOLAs8585hltVCvXQH6xPs3AB6QIupwcNho5nC57Pv6RVV/mroevaVLdi4wYXjB5bCptPEMUCDJPLFiwVB/xTBYAFCl8TY7E1mluzgWW8+NhmOsF7Cw2JnMGpyVNJ8KXM+v/FOwBEQS3hT4yb+X+Md1IcFdq4h7UF3dze2SRElg4cE/WP8S/uLmF73T+6+7SbC4ywiicNhk4UZZ34RZh/RKyp7oLuGt276FlNTG4jbGWzLDvq4xP1TKaEaaJoDmSRsetzfwOwj6ehoQ7Y+ycbwfrihGuYknsEzBpl9JI31tb1GNz7QXcNJW2+g08TYb84MJofSxUcM3vg4/OkzsPNFcJNkjeC6WdISpSU6h19OupRnZL/BY8KjMEKx53nEYjHS6TThcBjbtrn44ou54n2nMeXJ/xu5bVfK6MuV0g6l9tyEK04wxlT8f3IjzRX5/5W3TTJrrjzQrLnyQHPN26aXnA8wnVdNzs972HSn5HzLl4RM9qoGk72qwfxrec2A61zx/vkmdVWTca+qN8uXhErOd/h027RfOc1kr6o32avqB1znRW853Mz7zJ1m3mfuNBe95fAB5/Wuqs//XzLDKjnf+5aETfaqBpO6qtE8urx2wHU+sLzRdFw11bReOd284/CGkvMdNLvBmO8dacw3F5lt/2/hgOv8wduixv2fepP9nwbzg7fFBpzXGGOu+O1T5kM/e9xM2+egkvNdvDRuzJdmGfOtw8zKa9854Dr//F/7mR1fPNBs/8JrzAVHNpec74DZTeZf33m3MXd8xJhfXTjgOn94RoNJfH6mSVw93Xz3tIEf00JLliwpvU9vWpifb+XKlQOuc+XKlfl5L7744pLziYj50Ic+ZLZu3WqM/4IqvU8//GF+nT/84Q/3zj698RBjfnWhMX+4bK/t05IlS3ptf8B9+tBJ/uN5x0fMDy85Zu/s08UX7/XHaUj7NBKPk+5Txe6TqYDP4dH+3zs6UoUyQ9gFf0SY4PdB61ATLDOwuKSwgiMaA25bDLa4vU7DlNIsnUV/3xP+ERd/JGEZpAW5SHIuhlxK1lh+ZDgUJ+OW7nS5e/uD73vOhpYE0xoGuyiY8WO9bmrQByrsJnGlEQTEy5ScTxC6UkEKpu8Iwv1mFjwDIhayt45A9h0YcC849NBD+10hdtTlItYzxrYZOIMnl5RSY6+qT+v8OfRp6iRBgt0fYjNkJyHjspVJpIPCJUSWGewiIzZbzGQAptBGWDJYxrCNZhrowpEsUZNBcp1G6F3QZHBI42CMP7XF1JEhxHxrq9/XpET7PSATRIZrJHfdE8mv2wD/v70zj4/squ7897ylNpWkltR723hfAGPaYAPGxjEQB0McYLCTDCSThXEIGQhxlsmEzDCB5MMnYWYg4TMwZByzhSWEmCXggMHBOA2eQNwGAwZs3F5736SWVFItbznzx32v6kkqqaVutaqq6379aaveq3fvPe/Wk+6pe+/vnBCPAzpKDHwjvpT/HhrVxJ96H+JVzr2JE6Q4yxjeFahogRiHktSa92JiopjlpChJjqiJHHmWPDMU2cAxfAlQHCJ1OMy6JMFhieuD/4EjUPBd3ia3cclIxLP8vRDUOVRVSvVD+NpI7kuaS23pcpTZh2M244iYHD8RLvucrTzAxaxzq1z77Ivhhnfzts8/yPhMg6Gizyv3vpsLpv+NUjCBAxTyvlnWSXt8YD2c+1Pw1L9CkDgqboGD0zX8cIaGNwCAFzdQVWI3z6ZLruUHjz7FuA5y51n/mVfufTelcJJpLZH3Xa44e8SoeYpjcMO7W517x+/Drq9B9QioUg0BjU1GaXeER4aex8dH38LoQI4/e/Uli39Id/y+CeZWGG6da9feqeBUt93Je+tGOyyWk6fvlnV6WkqczQ4sqMnwi8c+HVkgK96n64jwmtc2cPGIeVI34BM0j2eacU00+a/1jd8M7BEBDvsYY5xBDugwM+SXdBli4CfxFgIc4sw5aD1xU5pvyn3nS5X3MWYG9cSe49HApY5PI9nVEiftmZgoMWFy7BPScDx2s7Epe/6xnoGfSJKnMVmTC9Lg1ugVgHlggijm3vLLjPS3vBmiOuu8BgE+9cQhjAGPkBgx/yS9X206JgLU8Xgi3kwhrnDRcNyU4WalsjuHr2dShojUwXeiRO4bmLgkw2eYwaY2CSPnms2yjVkYO4/i6Db8uMYEIxzxz8CPqnhRlfzGCxZkN945fD1eMI0XTHHOWGHxjMHbX2ecIUzQNl8iHA0I1WXGG2ZH8brlyYQ7lKF4Tdru5L11ox0Wi2XF9PTMCVi1jlXr3JJcbNU6K+JUt90tKpluscNiOTn6buak552TlNc49/AW93Nsco4RqsOj8WYQh/NlHy4xE1qiQomtMo5LTEVzAJSlgQIzmseXgDJ1nMygL4C2VnloqEsNjwIhHrFZAlKIBSJ18IhxpFVGEQIVAvUpOSaxoCrM4lHTAkNOFY8IVajhMakDDEqdvASgJjy9k7Qdq1G3zGiBgoTkCJmhwCPxVspS43zZh09EDZejOoiDMCSzDEgVL5mrCdVln47xDb2UmjfIK7iXMZ0gUoejWmZEZshLQF199sfDrHemGaSGinBQxviw3MSGeD+v1a9SkhozWuDO+LmskyqXuY/jOcIRfytBGLEpPkhBZylIhEdIoA6TDHCQjQSljVyUH6dceYI4rBOp0sDjkLOJb/tXck65wTPlCcpSY7YREcyMUwoncIiM08conxr4FXJbn9l0KA57m7lt6vl88ehWXh58jde7/8RGZwp/aBNc+WYeWP+zfOY7e/H2fYfro7t5pmOcUnJl2PLspZ2RZIB7JBhdEPvkwk2Dy4phksZguWD/FzhDDjO67YLFY7ssNajuuZ/D3/ww43sfYY9u4JEtr+QFL7puQZurGltlEXtORfyWE8Y6IpbTF+ucdCPHc05e49zDn/gfI0dImCxdFAhQoGFcCIoEyTKC2blRSMKbN5Jw8O4psj3j1yz7mnTJZ7E1N02uCfAQNLlDzYSw16QeSe52blkFDuowG2SKGKFBjlwivDaSaY88IQ6t5SxTn6nZI6KBnyRJNEtns+pxlBFyBIzJJCLCLHnKVI1cWlubkKvkEZQiDWJcHKKmbbPkKRCy39nKAFWKbkQumEyyHhn7REHFpAM45Gxhf/5sNqzfwL6DB3Eb0zwk5/Ma/SpVcoRSYFMpRsIaHyr+OsfK5/PqYx8hjkI21p5ksJQj5wDrLzTLRO2kw4kc9dEph0d272NYqs1MxZVawOahAhduHloyO/ADT03wuTu+wC9UPkboDzIdF/HCaZ4xysJMzEtJYIGpL7+dH41D6A0y6FTxgmk+Xf4P/LsbXjknGN2qZUJexJ6Hn/7bvPN7pVXNtnzCWNmw5fSm75yTnt5zkvIG90u4avLLxLi4GaWNA2YZA5KBVfGTb9+KyWPT2pq6+izniZq/NJPafbwyTvOfKe0lC1Pp+2kclXZsksnkOiHGwcu0ncNsNk2PY9xkwcu8J0CAn5Q25QpiHJaiNExZhTK15j05yYsYhyINCgRESOKYSNPSEibr8qZ4P5HjEwaNZNdKZiYruYccAaPxYUJ/iIcOVpiIS1TdMjfqV6iTpy5FIoRjYY5K7HPD7Ge5pnoXNbfMaDxO7OZMZmU3b7Iot8konM1g/NDBCjWnPCdTcRTD/qn6cbMDpxmTQ3+QmlvG911Cf6h9JuZs1uRsZucHPgkPfJInZzxCfwjfd6m5ZUJ/kJfUvzanzVXNhLyIPcf+9aOrnm35hFmqzywWS89xWjgn62USETPwAc1v2enrucdzFS/m/OKD+FqznCBr2XuTjO1GKixzrlusLqd5jc47Tp2duX2iiQs3v850i2/ap34zUJy2XD6VNvVo8nquvNjMAHlmqcvxcQnn3kPGixPMPiDfc6g1IqJYmZUBilSpkU+W5JQgUmbjHMPRBKP1/dScAfLRLLHjm8zKrm8k0W0yCmczGNcaEa4jczIVxxrTCOM5RdplB04zJtecgeY533PaZ2LOZk1OSW079iRHggK+1/rVrTkDbIoOzGlzVTMhL2JPYWbPqmdbPmGW6jOLxdJznBbOyREdRrU1UMYZ9yON2dE6loyr0opm0i2LW+mMzvGugfQ+JTNez50DWqquuHmNzDsm6R9Z4DSkbWXPpw5h2qdBMm+lWbdJtE09krye6+wogkdIiIvEAVFz3qtZQfOnYpa2gjCmkHNxHaGkM1QpUqBu6hbBd4WS02DSHWE8v4VCPEPdLeHEgcmsHAUmi3KbjMLZDMaFnEsU65xMxY445Ly5v0btsgOnGZML8UzzXBDG7TMxZ7Mmp6S2rTuL9X6NIOMQFeIZDrqb57S5qpmQF7GnNnDGqmdbPmGW6jOLxdJznBbOya3RK4jExSPCIWrukUj3ZqTfvSV5HeA2ZwDMYHrqZk6W4/RkB+h0JiFe/PJmmbj5z5QOcRLhcGuvyGLtH9Th5DrFSeTFadsNXFK3xSzJRAhx8z2zHBZAxrmoqUuOgKrmTFmBShJ/Jt0rghgHskqOGj4uStxsy1g6i8m6fNDZghsHeH6uOdeVXdoBs+dk3NmAF0xx8aYyI84sxajCZ+Rl5KmT1youyjqvQdkJuKP0GnYUr6MQVRh3RnGihsmsHNWNJHox6XAiR714U5lCXJmTqdh1YMtQ/rjZgdOMyV4wTSGqEAQRXjDVPhPzUhLY7a/jrIEQL5giCCIKUQUvmObu/EvntLmqmZAXsWfdlb+66tmWTxgrG7ZYTitOiw2xYNU6Vq1j1TpWrWPVOpbTlm7ZebBm9KxzcqnsSmKMPMl6jjEi02YzpprZEZ8Id56TETedDEE1SX3X5iNv9y1d256k7SOTfVs1E49EE7UJNB2ObL2pLZqxdcG+kXnXzT8/37xYWXCf7Zyu9LiBAyL4GrUvQ8umtP35G3rr6gExeeKWrQqO01pqCpKNy2amx8X0jFnsEQQVYVoL7GE9W5lgmCljqpp7UoFIBRdwxDh8IQ4iZp5nVgYpl8sUwikIA/B8KG+B9RdAdQKOPGSCteUGqOQ3Ups6SikcxyMiwiGIHUJc6k6RQ94Wwtw61kVHyUUzxH6Z0lnPYfz8G/n6w4c5a/fnGAsPcsDZyFf9l7K39HQu2bZu7kDdbuCE5Q2m88o+vPkGPr5nQ9PJurgwwfCW8xeUTx2HH+6bpFIPGci5Lbucx47v4GTbnRc/pp2tJ+OoLFW2qxygtaKLHK2+7P/uwzon3ch85+RS2cXvebfjEnOxPME6mcVNlh2Ws6F0EZ/CssZo4ny1+0DmOGfHeUSzxWMy6iABFR9XI3BSsbgDGhlPSVyiKEA0bDqNmjhzaZNTmqckIQ3JUcdF3CKC8iRbKSazbE9GY0xpkUI0w4hb5Vb3tTQ2XYbnipHVOo8tlLke22MaGzlzaenrPInsxMRRdu87wHeHXsoLqvdQ0RKTWuSyTQ4jTq1ZPpUSR7Gy61AFxxFiVc5bX+aC8GHeGH/KqH4WkyNn252X7Rkvt8DWk5EuL1UWWD1JdK/QRbLoVZWkW06GvhuyenLPyU3uDqa1yCaZIE+EswLHhGVeYzn1LPo5pLM9S12zSD2mXGZqSaNk6krMXoQ06Z8qeDlEo2Y5MmUFE+5/UBqEeOS1QYGIiuaJnRzr4nHK0TFK0SSzTpkYYcYZYJISr+QeDlXqLVltO5nrzJFW3pelpK/zyu6a8gi8QX5m6h+MjDg/RM5z2TXlzSmfSokPTdfJ+y6lnEfOdTlUqfOS+td4eNJZWo6cbXfiMfBLkCvB0Ufb2noy0uWlyq6qJLpX6CJZdF/2v6Ur6Enn5Ew5TIUSA1RxpaW9sU5Hj5GV/rSZHZFFzi+n4nQ5SYhbjomm24jVHGcabrXVOlZMELtWcLuYOFYi8ShqFY8ATwMcMcuEIkJFi2zjEDP1sCWrbSdzTbMpZzmOlBmgUg8Jc4MMhuNNWbLvOSaTcqZ8KiWu1EM813SG7woz9ZBN0QGOBvml5cjZdusVI7d2fWhMt7X1ZKTLS5VdVUl0r9BFsui+7H9LV9CTzslu3UCZWWYoEqmz1Phm6WayUyOL7P05MY/TBIEzdTjJxh8n2XiTzMlINrJLtq3WsaDECB5RU5LuOIKrIVUpEuITik+cOCaqSlmq7GUjA3mvJattJ3N18+ZfluNImQHKeQ+vMc20N9qUJQdhTDnvzSmfSonLeY8wMp0RRMpA3uOgu5kxv760HDnbbr7cSraYSx2WubaejHR5qbKrKonuFbpIFt2X/W/pCnrSObk9uoZBqXJQR6gbPUtzU+ZypbuWzrPo5yCtvSMr/TxNOW3Wg7jGOUkdFCfJOi0CYQMVt1mOTFkFXGKmNYdHSF1y1HApSx0nbnDMGaXirmPWHaYUV3BQBuIZhpnlC1zLxnK+JattJ3MdWN/KpryU9HVe2fOHQvxwmq8O/byREdenaIQR5w+Fc8qnUuKNg3nqQcRsI6QRRWws57k7/1IuGo6XliNn252X7bmdrScjXV6q7KpKonuFLpJF92X/W7qCntwQC1atk143/7xV61i1TopV6/QwVq1jmUvf7VroaefkN90v8mLnAQoSzBmQU3rp00wT462l0ijdYjHfCZnjAM17f9F6MsbJvPdCcaiSJxcH5CVceJ1AFMPeeAQcnw0yQY6gOa2XJvtL+ycmcVBwEFFidTCRXDxyNMgR4s6JSkuzMeMcCaE6zEgBB5cSM7hxDNLaqxLhM+uUOVo8hzA3xOT0NMVwnLN0PwUCZrTA3c4VbMsHnBf8BIkbRDiUJKToRni5AXjalXD1LXPigWx45B/4uepnGdVjVHNj1C//Lc667jdbHZYMSpP7d7F7WpkNIoa90MREufAFsO+7cOD7Zpo/V+bw4MV8JrqGb9XPbj9wpIPc/u9Bw4ToP1y+iB2VMznzyA4uiB6hlHOZXX8pf5+7sX09e+6Hb/4V7PuOOd72HLjqlt6OIXIqB/95cWiOrXsW15R3syE80HFH46TpoNPUV07S/H7eedvzePvkfZ02ay3pSefkUtnFn3h/yzOdJ8gTLl7Q0nNEsKIM0dnZm5jlr1Om12bLz6lXWmkQprXIMRngLD2EArUkim2OkFlyTGuJUaeClzyLAT6+6+B6RRg7n4ef89945/dKvHDqTn5x8m+ok6NKnkGnQV7rjL/wbcZBSSSkE3GBXfvGuTB4CBF4LP8M3HCGi+VJ/LGzYfoAiEMjDHk43ILj+dy9+WYeci+cK/NMJalxCEd+YsoEIU+Fw2wJ96I4zLploigmT51juS3807Zb5tbjPAZf+kOYfAr8oumcdPbk5e/qzUH2VEp199w/J2v0xvgAW6qP8IR3LlvOvoj1fqN3syV3UOLcV5Lmdv38w8/dydsnX95p09aSntxzcpO7g1GZso7JachKHBOY61SsZEYpXVZbsGyWvSbZieIT8TQ9nORlcvEzWZKLBBRpkE0U4KAEsWtkzNWjzey9L5u+nQY56k4REYcZCgROgfzOD5iCiYR015THtngfgVskcItsifYyphNUYh/GHwcvD7kBpgOXMZ0g9Ae5fPLOhTLPVJJaOWA23+YGmI5ctkT7cIlwJULdHHV8quQZiqcW1vPAJ43k2S+Zdr28kRTPHOndjL+nUqo7L2v0cHiU0C0wphM8frTW29mSOyhx7itJc7t+hslOm7XW9KRzcqYcJkfQaTMsXcaqLuNpS16cpo5MZ1JSWbFpU/ElQrSlfBFiIhXjnET1Zvbe4XiCWjbfUKwEToFScNQUTCSklXpIUatE4pkYK9EsRa0yG+cgrBpJL1CLXYpapeYMMFrfD8yTeaaS1FQKnJTJaQNRxUlsVlUa6uHFjYX1HHvSSJ6T8oB5HdV7N+PvqZTqzssanY9maZCnqFUj917NttaaDkqc+0rS3K6fYaoTpnSSnnROdusGGvjHv9DSV6zqAqW05MXGFZHk/61M16ZNIVAXldavkuKYPS/igptvZu+ddEYoUGva6jiCH9eY9cdMwURCWs57VKWIq6FRCrklqlKk5DTMxtTIOOYFJ6IqRQrxDOP5LcA8mWcqSU2lwEmZhuRQEeLEZhEhJyGhk1tYz7qzzKxLlPkyEAXmXK9m/D2VUt15WaPrbokcdapSNHLv1WxrremgxLmvJM3t+hmGOmFKJ+lJ5+T26BrGdYg63vEvtvQU0Qqv10VeH4+4GQdl8XKpAxLg8pRswEFxiAgyWZKr+FTJkc1rHSP4TmSck+JYM3vvVwZvIkeDfFxFNWaAGn5co375b5mCiYT0/KGQvc5W/KiKH1XZ727jqIxQdgIYPQfCOjRmGPQjjsoIXjDNzuHrF8o8U0lqebOZ6WjMMOhG7He3EuESqYtEDfIEFKkz5QwtrGf764zkOZg17YZ1o3IaWN+7GX9PpVR3XtboSW8ML6pxVEY4Z6zQ29mSOyhx7itJc7t+huFOm7XW9OSGWLBqndXAqnWsWseqdaxaZ0VYtc7aYNU6vemcXCq7+J/u+znfOYgjyxtALWvD8T6Ldu9nz2VjsCxZzyL1Zsu2mxVJZ0zcdEer016xo2k9GTsCBcHBldhEwAcQoYHPo7INH+E8HsdNagwRYhx8jVExTpGb7FXRGGJxqEmecdlAxVvHZjnCkNTwi0OQH4bKvmYsFvLJxtZG1QSM8QpUnAHuq5/F+4Ofw3GEt+U+xdN5FC9qGKMdH0bOgivfzMPxNoIdf8nmmR/hOg7u2LlEuWGmj+xBGtPN2C2pAzS5fxcP1Ua4030J4dbncE3pSbb88FY2Tv+QKFYe5Fy+5VzBi8q7uWxwqhVnBVp/VP1i4p22j42SHWxekH+Clzb+mXj8yUXjrnz6vqe47ZuPc2S6zvrBPDdffQ6/cMXTFn9Ilsue++Hev4K9xvmaWHcJf59vOWq/fMZhLjpwx9oPyMt0BFZr0D5uPStxTE7SiVnJPfWJ09J3I1zPOSeXyi4+4P0vtjp9tz/IcippN/W2DFIHKC0uGBlyNpPyUhLnNBIuIsR4zDDAsMzgaJDsWclB2KC14GW8KgUmtUQDnwkGKNBgMxMgkCNqpS/MDxHGMUeCIkUnIHALuFGDgXiSBjki8Ym9PBrHjDPCWd4RqqPPYOfkEMNSpSyz3O1czQunvsiZcpgZ9dFYKMsMnkT8iAuYzG/leVs9RuoHjVO07gxj857ki96254JfmCM7zUpDL45+wjV7b2V/PYdfXMf6XH1BluRP3/cUf/7lhyj4LgXfoRbE1IKIt7784pNzUPbcD1/+L01nqhZG1GZnOOpv5kvbbmG2EfHywx/kzK2bGRkZWzv57DJlu6slsT1uPSuREZ+k5Hgl99RHEuO+c056bs/JTe4ONsm0DUFv6Qpk3r90WSzLYr9kqVNjsv0oseNTpkLcTAyk4OWYuxPHlIqBMlVq5NkiE2yWCQJcPI2T/S/J37KwShgGjDFO4BaJnBx56oS45KiTk4CGUyR2cmzW/VRin+r4XnKeS5gfouaWeVnldkaZYkZzNDRHID4+EQ7KRpmgHqnJilw9aiTGhWGTvThXMhLkiccWyE6z0tDLJ+9kPCpSc8pUw7htluTbvvk4hSS7siMOpZxHwXe57ZuPn9TnxwOfNDbnjFR6KnBpOAWG1ciqr6neReANmvtbS/nsMmW7qyWxPW49K5ERn6TkeCX31FcS4z6j55yTM+UwjljXxLLKnMQjdaKxVuY4Nmo24DqtuRRaGX/at+igBLjkCE3YfXGRNDuQJK5SHIKajbyRmA3kroatTMuJnDgSj1zcYDbO4YUzTSlszRlgXTxBjpCGeqQaa4cYUWVAa0SxGplsNtNyY7qVybheMefaZE0GGK3vZzIq4DpCGMXNdrNZko9M1yn4c/9cFXyHI9PzMjuvlHlS6SBS1PFxE1n1aH0/YW6wJQOedx+njGXKdldLYnvcelYiIz5JyfFK7qmvJMZ9Rs85J7t1A7H23QyX5VRzEo/UiSqGNPsvGfDjNGsysPScS6IKIqKBR4iLq1GSnYhW4iTHS5aMXFw1A2wkXivTciIndjWk4eQoOQ1Cb6AphS3EMxxzRmjgkZOQVGMd46AizIhxKsp5b26m5dxgK5NxvmzOtcmaDDCe38Kwa5wcz3Wa7WazJK8fzFML5jprtSBm/eC8zM4rZZ5U2ncFiQOiRFY9nt+C15huyYDn3ccpY5my3dWS2B63npXIiE9ScrySe+oriXGf0XPOye3RNRzUwf5bgLN0JTrvX1YZlLLU/IfSSnnoxAEVyjjpDtwkc/LcuLmmlANUKFKgzn4d4YCO4BMRipNEq03cJK+I5/kcZRQ/quLGDerk8YhokKehPrm4ihM3OCBbKDsBxdFtNMIIrz5FIarwlfJNjDPEgDTISQNfAwJcYoRDOkLeFZMVuThmJMa1SZO9uDFrJMgj5y6QnWaloTuHr2fUrVKIKxQ9p22W5JuvPodakl051pjZRkgtiLj56nNO6vNj++uMzQ0jlR7yI3JxjUkxsuodxevww2lzf2spn12mbHe1JLbHrWclMuKTlByv5J76SmLcZ/Tchliwap1uxqp1rFrHqnVWCavWsWqdFn03wvWEc3LJ1qL+681l8gS4osQIlbhAXuoUZbHvpZZeZCmnJOtEZBUyzDtWTZZJUocnNrMTjSRgWl19PIkJ8JkqnsFZ3hSlmceb9cRAXXPMap4Ss+QlMuobSeO2CHWK5KjjNZUxZqmjik+AxwABQkQcO3gS4MrcmZIY1yzgiBBrGhhfwcnhDm2FzZcydewwcuRh/HAGUGpS4kf+M9kVb+O6+F8Y0wkc18Pb+lz4mXfAGc/l4Z1fn+OETI8+i0+4r+aLR7fSiGKKvsuzZRcvqX+Nc6PHGKQKuTJD517O+Pk38vE9G+b8kQf48p13cOGBL3JB/ARjfp3h4RHKZz8XiiPw0B2EE08ShwEhDhP+Fu4auol7Bl7WdmB/ePMNC9rIDiTpQPPg3mPMNCK2y6Pc6O7g4sJEywHKDnKrNIB33QDXwXgia2JLps7D3ualY/WcJpzkM2adk25k+1Zf7/+N0pygXD23HmXpOJr5GeBS0SLrpGJmURa7+ATqF1pLOW2f00x7852s9NmOJU+sIWYJx2xyDXEIEQrJfpEQH0HJOzHOyHk8efHraXz7g2yMDxG4BaIoRqIa+2Uj73Ffz7dqZ/N0fYTfdW/HJeY8Zy8ky0B7nDNwXI+7Nt3M+MizqNRC9k7Msm32R/zS7MfxJOYs3UuM4AkMDK6jVHmSUPJINNO8i2mKKA6fG3sjh4vnzpHhTkwcZfe+A3x5w39stpGVfaay0DBSHj1S4ZnxLn4j/DsoDFF1Brhsk8OIU2tJUldJbtt1ctQOZv9dE1sydR4JcuzavZ9BqbbPrH2asArPWN85Jz0xxruJODKdpu8Joy1dR/a32wEGpdb+WTpBfz0rJ04lwiuxKT1WQLSe1KHNXD6OQlEjJPl9iHCJHZ9AXajsJb/zAwwz3ZQM1/CpkWdYJ3l59HU81+VG9xtMaZGNMkGAzywFQnyGo3Em4iLXVO9qSjInZgOunrmLqlNmoxwjlBx1KVJXD3/qKXA8JKqiuMTJHpQiDRrkefHEpxfIcHdNeQTe4Jw2srLPVBZ6qFIn57q8Su5h1h3gaFQk57mmnqwkdZXktl0nR+1g9t81sSVT5+NHa4T+0OKZtU8Tuu4Z6wHsOG/pK6T5U5Ph9NS0sax6deFhyz4SJ0SbeXvSY0muTt8J1YGwQSk4ik/QlAzHsRLgkSNkqx7CceBMDlGhRJkqgXigSoTHgFaZiovNrMQA9TBmqx5kVgYoapUQDxEz6+QQguMjxBmLwCWiRoHheGKBDLdSDwlzg3PayMo+U1noTD3Ed4XN8SFmZYAwivE9x9STlaSukty26+SoHcz+uya2ZOqs1EN8z1k8s/ZpQtc9Yz2AdU4sfUVraUeSMGKnpo1l1SsLD7NLT6krIslZbbon5ur0HU9i8HLM+mME+E3JsOMIPiENPPbJRuIYdrORMrNUKOJrCCK4hMxIkSGn2sxKDJD3HPbJJko6Q1WKeISogk9EjAdxYPIbNS0yszkFakw6IwtkuOW8h9eYntNGVvaZykIH8h5BpBxwNlLSGTzXIQhjU09WkrpKctuuk6N2MPvvmtiSqbOc9wjCePHM2qcJXfeM9QA94ZxEyZ+/9Bup3QJrORGyDkMMTGuh/bN0gtMpWTlxuvF1JTalxwKo5JM6JFnUiYkFquI2HRSXCCcO8CWC8jbql/8Wkww2JcMFAgrUmZRhvuy+mDCK+Ez0IoakyiEdwSegRA2PgEl3lBGnyo7idU1J5kjJ55sD11GMKxzSdXjaIK9V8hISDD0N4hB1i4mbZ1y9Kjly1Pn6yC8skOGePxTih9Nz2sjKPlNZ6MZynkYU8Y96LaVohjG3SiOMTD1ZSeoqyW27To7awey/a2JLps5zxgp4wdTimbVPE7ruGesBemJDrFXr9A9WrWPVOlatg1XrnEabYVOsWmdldMQ5EZHrgfdiokvdpqp/sdT1l19+ue7cuXNNbLNYLBaLpcvoO+dkzZd1RMQF3g+8HHgG8FoRecZa22GxWCwWi6U76cSek+cBu1T1MVVtAJ8CXtUBOywWi8VisXQh3vEvWXW2Abszx3uA58+/SETeALwhOayLyINrYFsvsR440mkjugzbJwuxfbIQ2ycLsX3Snm7plztV9fpOG7GWdMI5WVY8TlW9FbgVQER2qurlp9qwXsL2yUJsnyzE9slCbJ8sxPZJe2y/dI5OLOvsAc7MHJ8B7OuAHRaLxWKxWLqQTjgn9wEXiMg5IpID/j3whQ7YYbFYLBaLpQtZ82UdVQ1F5M3AVzBS4g+p6g+PU+zWU29Zz2H7ZCG2TxZi+2Qhtk8WYvukPbZfOkRPBGGzWCwWi8XSP/RE+HqLxWKxWCz9g3VOLBaLxWKxdBVd7ZyIyPUi8rCI7BKRP+q0PZ1CRD4kIoeysV5EZFRE7hKRR5Kfp18yiiUQkTNF5Osi8mMR+aGI/E5yvm/7RUQKIvJvIvK9pE/ekZzv2z4BE5VaRL4rInckx33dHwAi8oSI/EBEHhCRncm5vu4XEVknIreLyEPJ35Ur+71POknXOic2zP0cPgLMD8DzR8DXVPUC4GvJcT8RAr+vqk8HXgC8KXk++rlf6sBLVPXZwHbgehF5Af3dJwC/A/w4c9zv/ZHyYlXdnonj0e/98l5MsLOLgWdjnpl+75OO0bXOCTbMfRNV3QGMzzv9KuCjyeuPAq9eS5s6jaruV9XvJK+nMX9IttHH/aKGSnLoJ/+UPu4TETkD+Fngtszpvu2P49C3/SIiQ8A1wAcBVLWhqsfo4z7pNN3snLQLc7+tQ7Z0I5tUdT+YgRrY2GF7OoaInA1cBnybPu+XZAnjAeAQcJeq9nuf/BXwh0CcOdfP/ZGiwFdF5P4kVQj0d7+cCxwGPpwsAd4mIgP0d590lG52TpYV5t7S34hIGfgMcIuqTnXank6jqpGqbsdEXn6eiFzSYZM6hojcABxS1fs7bUsXcpWqPgezbP4mEbmm0wZ1GA94DvABVb0MmMEu4XSUbnZObJj7pTkoIlsAkp+HOmzPmiMiPsYx+YSqfjY53ff9ApBMSd+D2avUr31yFfBKEXkCsyz8EhH5OP3bH01UdV/y8xDwOcwyej/3yx5gTzLTCHA7xlnp5z7pKN3snNgw90vzBeBXk9e/CvxjB21Zc0REMOvDP1bV92Te6tt+EZENIrIueV0Efhp4iD7tE1V9q6qeoapnY/5+3K2qv0yf9keKiAyIyGD6GvgZ4EH6uF9U9QCwW0QuSk69FPgRfdwnnaarI8SKyCswa8ZpmPt3dtaiziAifwdci0nffRD4E+DzwKeBpwFPAT+vqvM3zZ62iMjVwDeAH9DaT/DHmH0nfdkvInIpZtOei/ni8WlV/VMRGaNP+yRFRK4F/kBVb+j3/hCRczGzJWCWMz6pqu+0/SLbMRunc8BjwK+T/B7Rp33SSbraObFYLBaLxdJ/dPOyjsVisVgslj7EOicWi8VisVi6CuucWCwWi8Vi6Sqsc2KxWCwWi6WrsM6JxWKxWCyWrsI6JxbLEohIlGRu/WGS7ff3RGTVfm9E5NdEZGvm+LZTmeBSRPIi8s/JPf1im/f/IMnK+mByv79ynPo+IiI3Ja/vEZHLl7p+Xtlb0vqTembT+BvJufeKiIrI+uT4/y237jZtXSsiLzzBshtE5M4Tbdtisawcr9MGWCxdTjUJB4+IbAQ+CQxjYs0sCxFxVTVa5O1fwwTASiN23nwyxi6DywA/vacsIvJG4Drgeao6JSLDnKJEZyLiAa/HROFM2YVJtPbxxAF8MbA3fVNVT8i5SLgWqAArdnBU9bCI7BeRq1T13pOwwWKxLBM7c2KxLJMk1PcbgDeL4ddE5H3p+yJyRxLsCxGpiMifisi3gStF5L+LyH3JjMStSfmbgMuBTyQzGcXs7IOIvFZEfpCUeVemnYqIvDOZ2fiWiGyab6uIjIrI50Xk+8k1lybO1ceB7Ul7580r9sfAf0pzFKnqpKp+NKlvgf2L9ZOY5IMfSa79gYj8bpvLXgJ8R1XDzLm/A9LZnGuBe4Hm+yJSSX5em/TT7ckszydSe0TkicxMy+XJdWcDbwR+N7nvFyWzIZ9J7uk+EbkqKfNTyTUPiEkAl87kfB74pcXu2WKxrC7WObFYVoCqPob5vTledtIB4EFVfb6qfhN4n6peoaqXAEXgBlW9HdgJ/JKqblfValo4Wep5F2YQ3w5cISKvztT9LVV9NrAD+I027b8D+K6qXopxOv42ca5uBr6RtPdopr1BYDB7bh4L7F/i3rcD21T1ElV9FvDhNtdcBcxPyPcIsEFERoDXYvLhLMZlwC3AMzAZZa9a7EJVfQL4a+Avk/v+BvDe5PgK4EZMZFCAPwDelMwsvQhIP5OdybHFYlkDrHNisaycRWcNMkSYpIQpLxaRb4vIDzAOxzOPU/4K4B5VPZzMLnwCSDPHNoA7ktf3A2e3KX818DEAVb0bGEuWaRZDWDrr90rsfww4V0T+t4hcD7TLFr0Fk6J+Pp/F5MF5PiY9wWL8m6ruUdUYeID2fbAUPw28T0QewORPGUoctHuB94jIW4B1mZmdQ8DWtjVZLJZVxzonFssKEJOXJMIMViFzf4cKmde1dJ+JiBSA/wPclMwk/M28a9s2tcR7gbbyTkS03zvWrvyizkeylDOT3N/cilZov6pOAM/GZEV+E61ZiSzVRer4FPBnwF2J47EY9czrbB9kP5Ol+tgBrkxmUrar6jZVnVbVv8DMLhWBb4nIxZm6qotVZrFYVhfrnFgsy0RENmCWB96XOAdPYPZvOCJyJibtfDvSQfKIiJSBmzLvTQODC4vwbeCnRGS9iLiYZY5/WYG5O0j2SCT7YI6ke0mW4M+B94vIUFJuSETecBz7F5Ds+XBU9TPA25i76TXlx8D580+q6lPAf8U4QyfCE8Bzk9c3Zs7P7+evAm/O2Lw9+Xmeqv5AVd+FWcpJnZMLMRuXLRbLGmDVOhbL0hSTqX8f8638Y8B7kvfuBR7HZEZ+EPhOuwpU9ZiI/E1y3RPAfZm3PwL8tYhUgSszZfaLyFuBr2NmQb6kqitJ1/524MMi8n1gllba96X4AFAG7hORAAiAdx/H/nZsS9pOv/y8tc01XyZZdpqPqv7fZdi6GO8APigiaYbqlC8Ct4vIq4DfBt6CccS+j/k7uAOzafYWEXkxZjbmR4mdYJRD/3QSdlkslhVgsxJbLJaOICKfA/5QVR/ptC3HQ0R2AK9KlqwsFsspxjonFoulI4jIRcAmVd3RaVuWIlnOu0pVP99pWyyWfsE6JxaLxWKxWLoKuyHWYrFYLBZLV2GdE4vFYrFYLF2FdU4sFovFYrF0FdY5sVgsFovF0lVY58RisVgsFktX8f8B4d7beEmNWswAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 557.25x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "dur_cam = sns.lmplot(x='duration', y='campaign',data = dataset7,\n",
    "                     hue = 'response',\n",
    "                     fit_reg = False,\n",
    "                     scatter_kws={'alpha':0.6}, height =7)\n",
    "\n",
    "plt.axis([0,65,0,65])\n",
    "plt.ylabel('Number of Calls')\n",
    "plt.xlabel('Duration of Calls (Minutes)')\n",
    "plt.title('The Relationship between the Number and Duration of Calls (with Response Result)')\n",
    "\n",
    "# Annotation\n",
    "plt.axhline(y=6, linewidth=2, color=\"k\", linestyle='--')\n",
    "plt.annotate('Higher subscription rate when calls <6',xytext = (35,13),\n",
    "             arrowprops=dict(color = 'k', width=1),xy=(30,6))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44991c8",
   "metadata": {},
   "source": [
    "In this scatter plot, clients subscribed to term deposits are denoted as \"yes\" while those did not are denoted as \"no\".\n",
    "\n",
    "As we can see from the plot, “yes” clients and “no” clients are forming two relatively separate clusters. Compared to “no” clients”, “yes” clients were contacted by fewer times and had longer call duration. More importantly, after five campaign calls, clients are more likely to reject the term deposit unless the duration is high. Most “yes” clients were approached by less than 10 times.\n",
    "\n",
    "This suggests that the bank should resist calling a client for more than five times. I mean, I would be really frustrated after two times. The negative press would be worse and it wouldn't set a good brand precedent. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c49594",
   "metadata": {},
   "source": [
    "### 3.5 Scatter matrix and Correlation matrix\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bd26ecca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "matrix = scatter_matrix(dataset7[['age','balance','duration','campaign']],figsize=(10,8))\n",
    "\n",
    "plt.suptitle('The Scatter Matrix of Age, Balance, Duration and Campaign')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b0ed9344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_data = dataset7[['age','balance','duration','campaign','month_int','previous','response_binary']]\n",
    "corr = corr_data.corr()\n",
    "\n",
    "cor_plot = sns.heatmap(corr,annot=True,cmap='RdYlGn',linewidths=0.2,annot_kws={'size':10})\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(6,5)\n",
    "plt.xticks(fontsize=10,rotation=-30)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "899bb516",
   "metadata": {},
   "source": [
    "The scatter matrix does not reveal any clear relationship among age, balance, duration and campaign.\n",
    "\n",
    "To investigate more about correlation, a correlation matrix was plotted with all qualitative variables. Clearly, “campaign outcome” has a strong correlation with “duration”, a moderate correlation with “previous contacts”, and mild correlations between “balance”, “month of contact” and “number of campaign”. Their influences on campaign outcome will be investigated further in the machine learning part."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39626bc2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6faa9b8b",
   "metadata": {},
   "source": [
    "Checking for Class imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "fd9790f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "no     39922\n",
       "yes     5289\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('y').mean()\n",
    "#checking for how many yes' or no\n",
    "\n",
    "df['y'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "64fd8d7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of \"No\": 88.302%\n",
      "Percentage of \"Yes\": 11.698%\n"
     ]
    }
   ],
   "source": [
    "#Balance in percentage form. \n",
    "countNo = len(df[df.y == 'no'])\n",
    "countYes = len(df[df.y == 'yes'])\n",
    "print('Percentage of \"No\": {:.3f}%'. format((countNo/(len(df.y))*100)))\n",
    "print('Percentage of \"Yes\": {:.3f}%'. format((countYes/(len(df.y))*100)))"
   ]
  },
  {
   "cell_type": "raw",
   "id": "87dc1ffe",
   "metadata": {},
   "source": [
    "Verifying Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a066b1bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age          0\n",
       "job          0\n",
       "marital      0\n",
       "education    0\n",
       "default      0\n",
       "balance      0\n",
       "housing      0\n",
       "loan         0\n",
       "contact      0\n",
       "day          0\n",
       "month        0\n",
       "duration     0\n",
       "campaign     0\n",
       "pdays        0\n",
       "previous     0\n",
       "poutcome     0\n",
       "y            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().any()  ##true or false for nulls\n",
    "\n",
    "df.isna().sum()  ##sum of nulls\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a011c8c",
   "metadata": {},
   "source": [
    "No nulls?! That's amazing and probably in real life. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e93c01e",
   "metadata": {},
   "source": [
    "# Part 4. Data Visualization <a id=\"4\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "232dfd40",
   "metadata": {},
   "source": [
    "With a sound knowledge of the distribution of key variables, further analysis of each customer characteristic can be carried out to investigate its influence on the subscription rate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a130c1b1",
   "metadata": {},
   "source": [
    "![qwe](https://media3.giphy.com/media/l378c04F2fjeZ7vH2/giphy.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6a21a1",
   "metadata": {},
   "source": [
    "### 4.1 Visualize the subscription and contact rate by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "863d603b",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = [dataset7]\n",
    "for column in lst:\n",
    "    column.loc[column[\"age\"] < 30,  'age_group'] = 20\n",
    "    column.loc[(column[\"age\"] >= 30) & (column[\"age\"] <= 39), 'age_group'] = 30\n",
    "    column.loc[(column[\"age\"] >= 40) & (column[\"age\"] <= 49), 'age_group'] = 40\n",
    "    column.loc[(column[\"age\"] >= 50) & (column[\"age\"] <= 59), 'age_group'] = 50\n",
    "    column.loc[column[\"age\"] >= 60, 'age_group'] = 60\n",
    "    \n",
    "    #we're grouping customers by age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "35f7c50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_age_response_pct = pd.crosstab(dataset7['response'],dataset7['age_group']).apply(lambda x: x/x.sum() * 100)\n",
    "count_age_response_pct = count_age_response_pct.transpose() #I'll have to come back to this code, but its basically response/age_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3bf2f69a",
   "metadata": {},
   "outputs": [],
   "source": [
    "age = pd.DataFrame(dataset7['age_group'].value_counts())\n",
    "age['% Contacted'] = age['age_group']*100/age['age_group'].sum()\n",
    "age['% Subscription'] = count_age_response_pct['yes']\n",
    "age.drop('age_group',axis = 1,inplace = True)\n",
    "\n",
    "age['age'] = [30,40,50,20,60]\n",
    "age = age.sort_values('age',ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1b6a6c77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_age = age[['% Subscription','% Contacted']].plot(kind = 'bar',\n",
    "                                              figsize=(8,6), color = ('green','red'))\n",
    "plt.xlabel('Age Group')\n",
    "plt.ylabel('Subscription Rate')\n",
    "plt.xticks(np.arange(5), ('<30', '30-39', '40-49', '50-59', '60+'),rotation = 'horizontal')\n",
    "plt.title('Subscription vs. Contact Rate by Age')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "704fdb03",
   "metadata": {},
   "source": [
    "**Insights: target the youngest and the oldest instead of the middle-aged**\n",
    "\n",
    "Green vertical bars indicate that clients with a age of 60+ have the highest subscription rate. About 17% of the subscriptions came from the clients aged between 18 to 29. More than 50% of the subscriptions are contributed by the youngest and the eldest clients. \n",
    "\n",
    "* It is not surprising to see such a pattern because the main investment objective of older people is saving for retirement while the middle-aged group tend to be more aggressive with a main objective of generating high investment income. Term deposits, as the least risky investment tool, are more preferable to the eldest. \n",
    "\n",
    "\n",
    "* The youngest may not have enough money or professional knowledge to engage in sophisticated investments, such as stocks and mutual funds. Term deposits are ideal investments for students.\n",
    "\n",
    "However, red vertical bars show that the bank focused its marketing efforts on the middle-aged group, which returned lower subscription rates than the younger and older groups. Thus, to make the marketing campaign more effective, the bank should target younger and older clients in the future. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5c27fa",
   "metadata": {},
   "source": [
    "### 4.2 Visualize the subscription rate by balance level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "305967a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = [dataset7]\n",
    "for column in lst:\n",
    "    column.loc[column[\"balance\"] <= 0,  'balance_group'] = 'no balance'\n",
    "    column.loc[(column[\"balance\"] > 0) & (column[\"balance\"] <= 1000), 'balance_group'] = 'low balance'\n",
    "    column.loc[(column[\"balance\"] > 1000) & (column[\"balance\"] <= 5000), 'balance_group'] = 'average balance'\n",
    "    column.loc[(column[\"balance\"] > 5000), 'balance_group'] = 'high balance'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "43639c5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_balance_response_pct = pd.crosstab(dataset7['response'],dataset7['balance_group']).apply(lambda x: x/x.sum() * 100)\n",
    "count_balance_response_pct = count_balance_response_pct.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "06328887",
   "metadata": {},
   "outputs": [],
   "source": [
    "bal = pd.DataFrame(dataset7['balance_group'].value_counts())\n",
    "bal['% Contacted'] = bal['balance_group']*100/bal['balance_group'].sum()\n",
    "bal['% Subscription'] = count_balance_response_pct['yes']\n",
    "bal.drop('balance_group',axis = 1,inplace = True)\n",
    "\n",
    "bal['bal'] = [1,2,0,3]\n",
    "bal = bal.sort_values('bal',ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "73e7e923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_balance = bal[['% Subscription','% Contacted']].plot(kind = 'bar',\n",
    "                                               color = ('royalblue','skyblue'),\n",
    "                                               figsize = (8,6))\n",
    "\n",
    "plt.title('Subscription vs Contact Rate by Balance Level')\n",
    "plt.ylabel('Subscription Rate')\n",
    "plt.xlabel('Balance Category')\n",
    "plt.xticks(rotation = 'horizontal')\n",
    "\n",
    "# label the bar\n",
    "for rec, label in zip(plot_balance.patches,\n",
    "                      bal['% Subscription'].round(1).astype(str)):\n",
    "    plot_balance.text(rec.get_x() + rec.get_width()/2, \n",
    "                      rec.get_height() + 1, \n",
    "                      label+'%',  \n",
    "                      ha = 'center', \n",
    "                      color = 'black')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3b71b21",
   "metadata": {},
   "source": [
    "**Insights: target clients with average or high balance** \n",
    "\n",
    "To identify the trend more easily, clients are categorized into four groups based on their levels of balance: \n",
    "* No Balance: clients with a negative balance.\n",
    "* Low Balance: clients with a balance between 0 and 1000 euros\n",
    "* Average Balance: clients with a balance between 1000 and 5000 euros.\n",
    "* High Balance: clients with a balance greater than 5000 euros.\n",
    "\n",
    "\n",
    "Unsurprisingly, this bar chart indicates a positive correlation between clients’ balance levels and subscription rate. Clients with negative balances only returned a subscription rate of 6.9% while clients with average or high balances had significantly higher subscription rates, nearly 15%. \n",
    "\n",
    "However, in this campaign, more than 50% of clients contacted only have a low balance level. In the future, the bank should shift its marketing focus to high-balance customers to secure more term deposits. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a88d29b1",
   "metadata": {},
   "source": [
    "### 4.3 Visualize the subscription rate by age and balance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cc1a47ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "age_balance1 = pd.DataFrame(dataset7.groupby(['age_group','balance_group'])['response_binary'].sum())\n",
    "age_balance2 = pd.DataFrame(dataset7.groupby(['age_group','balance_group'])['response'].count())\n",
    "\n",
    "age_balance1['response'] = age_balance2['response']\n",
    "age_balance1['response_rate'] = age_balance1['response_binary']/ (age_balance1['response'])\n",
    "age_balance1 = age_balance1.drop(['response_binary','response'],axis =1)\n",
    "\n",
    "age_balance1 = age_balance1.unstack()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8960b511",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "age_bal = age_balance1.plot(kind='bar',figsize = (10,6))\n",
    "\n",
    "# Set x ticks\n",
    "plt.xticks(np.arange(5),('<30', '30-39', '40-49', '50-59', '60+'),rotation = 'horizontal')\n",
    "\n",
    "# Set legend\n",
    "plt.legend(['Average Balance','High Balance','Low Balance','No Balance'],loc = 'best',ncol = 1)\n",
    "\n",
    "plt.ylabel('Subscription Rate')\n",
    "plt.xlabel('Age Group')\n",
    "plt.title('The Subscription Rate of Different Balance Levels in Each Age Group')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc648b0",
   "metadata": {},
   "source": [
    "**Insights: target older clients with high balance levels**\n",
    "\n",
    "While age represents a person’s life stage and balance represents a person’s financial condition, jointly evaluating the impact of these two factors enables us to investigate if there is a common trend across all ages, and to identify which combination of client features indicates the highest likelihood of subscription. \n",
    "\n",
    "In order to investigate the combined effect of age and balance on a client’s decision, we performed a two-layer grouping, segmenting customers according to their balance levels within each age group.\n",
    "\n",
    "* The graph tells the same story regarding the subscription rate for different age groups: **the willingness to subscribe is exceptionally high for people aged above 60 and younger people aged below 30** also have a distinguishable higher subscription rate than those of other age groups. \n",
    "\n",
    "\n",
    "* Furthermore, **the effect of balance levels on subscription decision is applicable to each individual age group**: every age group shares a common trend that the percentage of subscription increases with balance. \n",
    "\n",
    "In sum, the bank should **prioritize its telemarketing to clients who are above 60 years old and have positive balances**, because they have the highest acceptance rate of about 35%. The next group the bank should focus on is **young clients with positive balances**, who showed high subscription rates between 15% and 20%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af1a1e3",
   "metadata": {},
   "source": [
    "### 4.4 Visualize the subscription rate by job"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5e9d78f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_job_response_pct = pd.crosstab(dataset7['response'],dataset7['job']).apply(lambda x: x/x.sum() * 100)\n",
    "count_job_response_pct = count_job_response_pct.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3240ed7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XrqSsrIxVq1ZRWlrK1KlTueSSS6qIwizzmuOv4EcA3wNmAmcBXwYEzJI0ALgSyIuIAkglFZXa9wfyI+Jfkn4O/Dkizpd0ELBY0lPA94GSiMiXlE8qGdmFpIuAiwBatGm3J8/RzMzMdlNubi65ubkAtG7dmh49erBhwwYGDRqUrnPsscfyyCO7zjyuru2hhx7Ktm3biAhKS0vJzs5mwoQJjBkzhuzs7IY5MbPd1KxGEBJ/i4gXgUHJ33JSF/BHAt3q0P5PEfGvZHsQcKWkImABsB/QGRgA3A8QESuBlVV1FBH3RES/iOjXolXOpz4hMzMz27PWrVvH8uXLOeaYY3YqnzJlCqeeemqd27Zu3Zphw4bRu3dvunbtSk5ODkuWLGHIkCH1Gb7ZZ9IcRxA+TP4VcHNE/KbizipGDKprX97HsIhYU6kPgPhsYZqZmVkmbNmyhWHDhjFx4kTatGmTLr/pppto2bIl55133m61HTduHOPGjQNg9OjRFBYWMnnyZObPn09+fj7XXHNN/Z6Q2W5qjiMI5eYB50s6EEBSJ0ntgQ+A1rvRxw+VZASSeiflzwHnJWV5QP6eDNzMzMzqx/bt2xk2bBjnnXceQ4cOTZffd999PPHEEzzwwAPlPwTWuW255cuXA9C9e3emTZvGww8/zOrVq1m7dm39nIzZp9QcRxAAiIj5knoAi5Iv+hbgOxHxf5JeSBYm/xG4s4Zu/hOYCKxMkoR1wDeAXwP3SloJFAGL6+s8zMzMbM+ICC644AJ69OjBT3/603T53LlzueWWW3j22Wdp1arVbrWt6Nprr+Wee+5h+/btlJWVAZCVlUVJScmePxmzz0ARngnTGOyb2y1yR07MdBhmZmbNyrrxg9Pbzz//PMcffzy9evUiKys1yeLnP/85Y8aMYevWrRx88MFAaqHy3XffTXFxMaNHj+bJJ5+stu1pp50GwMyZM1mxYgXXXXcdAGPHjmXevHnk5+fzwAMPNOQpmyFpWURU+4wuJwiNhBMEMzOzhlcxQTBrLmpLEJrtFKPGplenHJb6/6TMzMzMLMOa8yJlMzMzMzOrxAmCmZmZmZmlOUEwMzMzM7M0JwhmZmZmZpbmBMHMzMzMzNKcIJiZmZmZWZoTBDMzMzMzS3OCYGZmZmZmaU4QzMzMzMwszQmCmZmZmZmlOUEwMzMzM7M0JwhmZmZmZpbmBMHMzMzMzNJaZjoAS1m1YTNdrpyT6TDMzJq8deMHZzoEM7MmzSMIZmZmZmaW5gTBzMzMzMzSmn2CIOlESU/U8zHWSfp8fR7DzMzMzGxPaPYJgpmZ7Z3OP/982rdvT15eXrrs+uuvp1OnThQUFFBQUMCTTz65S7s1a9ak9xcUFNCmTRsmTpwIwBVXXEF+fj4jRoxI158+fTqTJk2q9/MxM2soGUsQJHWRtLrC67GSrpe0QNItkhZL+h9Jxyf7W0iaIGmJpJWSvp+UnyjpWUkPJ/XHSzovab9K0uFJvamS7pb0l6TeN6qIqa2kmUn/L0rKl5Qlaa2kdkmdLEn/K+nzktpJejSJaYmkryZ1DpY0X9JySb8B1ABvqZmZVTBq1Cjmzp27S/lPfvITioqKKCoq4rTTTttl/xFHHJHev2zZMlq1asWZZ57J5s2bWbhwIStXrqSsrIxVq1ZRWlrK1KlTueSSSxrilMzMGkRjHUFoGRFfBn4MXJeUXQBsjoijgaOBCyV1TfYdBfwI6AV8F+ietJ8M/LBCv12AE4DBwN2S9qt03BuA5RGRD1wFTIuIj4H7gfOSOl8DVkTEu8Ak4LYkpmHJ8Uhifj4iegOzgM6f4b0wM7NPYcCAAbRt2/Yz9fH0009z+OGHc9hhh5GVlcW2bduICEpLS8nOzmbChAmMGTOG7OzsPRS1mVnmNdYE4bHk32WkLuoBBgEjJBUBfwUOBrol+5ZExFsRsRX4P2B+Ur6qQnuAhyPi44hYC7wOHFnpuMcB0wEi4s/AwZJygClA+Xjy+cC9yfbXgDuSmGYBbSS1BgaQSiqIiDnApqpOUtJFkpZKWlpWsrmWt8TMzPaEO+64g/z8fM4//3w2bary/57THnzwQYYPHw5A69atGTZsGL1796Zr167k5OSwZMkShgwZ0hBhm5k1mEwmCDsqHb/ir/lbk3/L+ORZDQJ+GBEFyV/XiJhfqT7AxxVef8zOz3qISjFUfl3VVKCIiPXA25L+DTgG+GOyLwvoXyGmThHxQTV9V9XxPRHRLyL6tWiVU1t1MzP7jH7wgx/wf//3fxQVFZGbm8tll11Wbd1t27Yxa9YsvvWtb6XLxo0bR1FREbfeeivXXnsthYWFTJ48mbPPPpsbb7yxIU7BzKzeZTJBeBton8zX3xfYZU1AJfOAH0jKBpDUXdIBu3nMbyVrCA4HvgCsqbT/OZKpRJJOBN6NiPeTfZNJjQo8HBFlSdl84NLyxpIKqujnVOBzuxmnmZnVgw4dOtCiRQuysrK48MILWbx4cbV1//jHP9KnTx86dOiwy77ly5cD0L17d6ZNm8bDDz/M6tWrWbt2bb3FbmbWUDL2JOWI2C6pkNR0oTeA12ppMpnUdKGXJAl4BzhjNw+7BngW6ABcHBEfpbpKux64V9JKoAQYWWHfLFJTi+6tUDYGuDOp35JUYnAxqbUMMyS9lBzv77sZp5mZ1YO33nqL3NxcAP7whz/sdIejymbMmJGeXlTZtddeyz333MP27dspK0v9ZpSVlUVJScmeD9rMrIEpotaZMHsFSVOBJyLikU/Zvh+pBcnH79HAEvvmdovckRPro2szs2Zl3fjBAAwfPpwFCxbw7rvv0qFDB2644QYWLFhAUVERkujSpQu/+c1vyM3Npbi4mNGjR6dve1pSUsKhhx7K66+/Tk7OzlNAZ86cyYoVK7juutQ9NMaOHcu8efPIz8/ngQceaNiTNTP7FCQti4h+1e53glCntlcCPwDOi4jn93Rs4ATBzGxPKU8QzMysak4Qmoh+/frF0qVLMx2GmZmZme3laksQGuttTs3MzMzMLAOcIJiZmZmZWZoTBDMzMzMzS3OCYGZmZmZmaU4QzMzMzMwszQmCmZmZmZmlOUEwMzMzM7M0JwhmZmZmZpbmBMHMzMzMzNKcIJiZmZmZWZoTBDMzMzMzS3OCYGZmZmZmaU4QzMzMzMwsrWWmA7CUVRs20+XKOZkOw8yaiXXjB2c6BDMza6Q8gmBmZmZmZmlOEMzMzMzMLM0JQj2T1EXSuZmOw8zMzMysLpwg1L8ugBMEM2uUzj//fNq3b09eXl667NprryU/P5+CggIGDRpEcXFxndsCXHHFFeTn5zNixIh02fTp05k0aVL9nISZme1RDZYgJL+kvyZpsqTVkh6Q9DVJL0haK+nLyd9CScuTf49I2o6S9JikuUnd/6rQ768lLZX0sqQbKpSflhzveUm3S3oiKT9A0hRJS5LjDKlwjJmSZkt6Q9Klkn6a1HlRUtuk3uFJHMsk/UXSkUn51OQ4CyW9LumsJJTxwPGSiiT9pGHebTOzuhk1ahRz587dqezyyy9n5cqVFBUV8Y1vfIPCwsI6t928eTMLFy5k5cqVlJWVsWrVKkpLS5k6dSqXXHJJvZ2HmZntOQ09gvBFYBKQDxxJ6pf144CxwFXAa8CAiOgN/Az4eYW2BcA5QC/gHEmHJuVXR0S/pM8TJOVL2g/4DXBqRBwHtKvQz9XAnyPiaOAkYIKkA5J9eUlMXwZuAkqSWBYB5T+F3QP8MCL6JnHfVaHv3OR8vkEqMQC4EvhLRBRExG27+X6ZmdWrAQMG0LZt253K2rRpk97+8MMPkVTntllZWWzbto2IoLS0lOzsbCZMmMCYMWPIzs7e8ydgZmZ7XEPf5vSNiFgFIOll4OmICEmrSE3FyQHuk9QNCKDif02ejojNSdtXgMOA9cDZki4idS65wJdIJT6vR8QbSdsZwEXJ9iDgdEljk9f7AZ2T7Wci4gPgA0mbgdlJ+SogX9KBwFeA31f4D+a+FWKcGREfA69I6lDbm5HEfRFAizbtaqltZtZwrr76aqZNm0ZOTg7PPPNMndu1bt2aYcOG0bt3b04++WRycnJYsmQJP/vZz+oxWjMz25MaegRha4Xtjyu8/pjUBf5/krpIzwO+Serivaq2ZUBLSV1J/Yp/ckTkA3OSNlX/3JUiYFjyi35BRHSOiFfrGF8W8F6FtgUR0aOaGGuKAYCIuCci+kVEvxatcmqrbmbWYG666SbWr1/Peeedxx133LFbbceNG0dRURG33nor1157LYWFhUyePJmzzz6bG2+8sZ4iNjOzPaWxLVLOATYk26PqUL8N8CGwOfnF/tSk/DXgC5K6JK/PqdBmHvBDJUMAknrXNbiIeB94Q9K3kraSdFQtzT4AWtf1GGZmjcm5557Lo48++qnaLl++HIDu3bszbdo0Hn74YVavXs3atWv3ZIhmZraHNbYE4b+AmyW9ALSorXJErACWAy8DU4AXkvJS4BJgrqTngbeBzUmz/yQ1dWmlpNXJ691xHnCBpBXJcYfUUn8lsEPSCi9SNrOmoOIF/KxZszjyyCM/VT/lowfbt2+nrKwMSK1RKCkp2SNxmplZ/VBEZDqGeiHpwIjYkowU3AmsbcyLhPfN7Ra5IydmOgwzaybWjR8MwPDhw1mwYAHvvvsuHTp04IYbbuDJJ59kzZo1ZGVlcdhhh3H33XfTqVMniouLGT16NE8++WS1bS+44AIAZs6cyYoVK7juuusAGDt2LPPmzSM/P58HHnggMydtZmYASFqW3OSn6v17cYLwE2AksA+pUYYLI6LR/mzlBMHMGlJ5gmBmZs1PbQlCQ9/FqMEkowWNdsTAzMzMzKwx2msThKamV6cclvoXPTMzMzPLsMa2SNnMzMzMzDLICYKZmZmZmaU5QTAzMzMzszQnCGZmZmZmluYEwczMzMzM0pwgmJmZmZlZmhMEMzMzMzNLc4JgZmZmZmZpThDMzMzMzCzNCYKZmZmZmaU5QTAzMzMzszQnCGZmZmZmltYy0wFYyqoNm+ly5ZxMh2FmGbZu/OBMh2BmZs2cRxDMzMzMzCzNCYKZmZmZmaU5QTAzMzMzszQnCAlJoyTdsZttnpR0UD2FZGbN3Pnnn0/79u3Jy8tLl11++eUceeSR5Ofnc+aZZ/Lee+9V2Xbu3LkcccQRfPGLX2T8+PHp8iuuuIL8/HxGjBiRLps+fTqTJk2qt/MwM7OmxQnCZxARp0XEe5mOw8z2TqNGjWLu3Lk7lQ0cOJDVq1ezcuVKunfvzs0337xLu7KyMv793/+dP/7xj7zyyivMmDGDV155hc2bN7Nw4UJWrlxJWVkZq1atorS0lKlTp3LJJZc01GmZmVkj12wSBEkzJS2T9LKki5Ky70n6H0nPAl+tUHeqpF9LekbS65JOkDRF0quSplaot07S5yV1Sfb9Nul/vqT9G/4szWxvMmDAANq2bbtT2aBBg2jZMnUDumOPPZY333xzl3aLFy/mi1/8Il/4whfYZ599+Pa3v83jjz9OVlYW27ZtIyIoLS0lOzubCRMmMGbMGLKzsxvknMzMrPFrNgkCcH5E9AX6AWMkdQJuIJUYDAS+VKn+54B/A34CzAZuA3oCvSQVVNF/N+DOiOgJvAcMqy0gSRdJWippaVnJ5k91UmbWfE2ZMoVTTz11l/INGzZw6KGHpl8fcsghbNiwgdatWzNs2DB69+5N165dycnJYcmSJQwZMqQhwzYzs0auOT0HYYykM5PtQ4HvAgsi4h0ASQ8B3SvUnx0RIWkV8HZErErqvQx0AYoq9f9GRJSXLUvq1Cgi7gHuAdg3t1vs/imZWXN100030bJlS84777xd9kXs+n8nkgAYN24c48aNA2D06NEUFhYyefJk5s+fT35+Ptdcc039Bm5mZo1esxhBkHQi8DWgf0QcBSwHXgNquijfmvz7cYXt8tdVJVYV65RVU8fM7DO77777eOKJJ3jggQfSF/4VHXLIIaxfvz79+s0336Rjx4471Vm+fDkA3bt3Z9q0aTz88MOsXr2atWvX1m/wZmbW6DWLBAHIATZFRImkI4Fjgf2BEyUdLCkb+FZGIzQzq4O5c+dyyy23MGvWLFq1alVlnaOPPpq1a9fyxhtvsG3bNh588EFOP/30nepce+21FBYWsn37dsrKygDIysqipKSk3s/BzMwat+aSIMwFWkpaCfwn8CLwFnA9sAh4CnipPg4s6WJJF9dH32a2dxs+fDj9+/dnzZo1HHLIIfzud7/j0ksv5YMPPmDgwIEUFBRw8cWp/3spLi7mtNNOA6Bly5bccccdfP3rX6dHjx6cffbZ9OzZM93vzJkzOfroo+nYsSMHHXQQ/fv3p1evXkjiqKOOysi5mplZ46Gq5qpaw9s3t1vkjpyY6TDMLMPWjR+c6RDMzGwvJ2lZRPSrbn9zGUEwMzMzM7M68ELaRqJXpxyW+pdDMzMzM8swjyCYmZmZmVmaEwQzMzMzM0urNUGQlNcQgZiZmZmZWebVZQThbkmLJV0i6aD6DsjMzMzMzDKn1gQhIo4DzgMOBZZK+m9JA+s9MjMzMzMza3B1WoMQEWuBa4ArgBOA2yW9JmlofQZnZmZmZmYNqy5rEPIl3Qa8Cvwb8M2I6JFs31bP8ZmZmZmZWQOqy3MQ7gB+C1wVEaXlhRFRLOmaeovMzMzMzMwaXI0JgqQWwPqImF7V/urKzczMzMysaapxilFElAEHS9qngeIxMzMzM7MMqssUo78BL0iaBXxYXhgRv6y3qMzMzMzMLCPqkiAUJ39ZQOv6DcfMzMzMzDJJEVG3ilJrICJiS/2G1Dztm9stckdOzHQYZvYZrRs/ONMhmJmZ1UjSsojoV93+utzmNE/ScmA18LKkZZJ67skgzczMzMyscajLg9LuAX4aEYdFxGHAZaRue2pmZmZmZnuZuiQIB0TEM+UvImIBcEC9RWRmZmZmZhlTlwThdUnXSuqS/F0DvLEng5A0VdJZyfbxkl6WVCRp/z15nBqOX6/rKiqen5k1H+effz7t27cnLy8vXfb73/+enj17kpWVxdKlS6tst379ek466SR69OhBz549mTRpUnrfFVdcQX5+PiNGjEiXTZ8+fac6ZmZmn0VdEoTzgXbAY8Afku3v1WNM5wG/iIiCik9uNjNrakaNGsXcuXN3KsvLy+Oxxx5jwIAB1bZr2bIlt956K6+++iovvvgid955J6+88gqbN29m4cKFrFy5krKyMlatWkVpaSlTp07lkksuqe/TMTOzZqLWBCEiNkXEmIjoExG9I+JHEbGptnaSDpA0R9IKSaslnSOpr6Rnk4XO8yTlVmozGjgb+JmkB6ro8zuSFiejC79JnvSMpC2Sbkn6fUrSlyUtkPS6pNOTOqMkPS5prqQ1kq6ron9JmpDEu0rSOUn5dElDKtR7QNLpklok9ZdIWinp+xX6uUPSK5LmAO1re7/MbO8zYMAA2rZtu1NZjx49OOKII2psl5ubS58+fQBo3bo1PXr0YMOGDWRlZbFt2zYigtLSUrKzs5kwYQJjxowhOzu73s7DzMyal1qfgyBpNlD5XqibgaXAbyLio2qangIUR8TgpJ8c4I/AkIh4J7n4vonUCAUAETFZ0nHAExHxSKU4egDnAF+NiO2S7iI12jCN1JqIBRFxhaQ/ADcCA4EvAfcBs5JuvgzkASXAEklzIqLiGP9QoAA4Cvh8Uuc5YDLwE+Dx5Dy+AowELgA2R8TRkvYl9UC5+UBv4AigF9ABeAWYUsV7exFwEUCLNu2qeRvNrDlbt24dy5cv55hjjqF169YMGzaM3r17c/LJJ5OTk8OSJUv42c9+lukwzcxsL1KXB6W9Tmpa0Yzk9TnA20B3Uncz+m417VYBv5B0C/AEsInUxfmfJAG0AN7ajVhPBvqSumgH2B/YmOzbBpSP468CtiZJxCqgS4U+/hQR/wSQ9BhwHKlEp9xxwIyIKAPelvQscHREzJJ0p6T2pJKIRyNih6RBQH6F9QU5QDdgQIV+iiX9uaoTioh7SN0lin1zu9XtgRRm1mxs2bKFYcOGMXHiRNq0aQPAuHHjGDduHACjR4+msLCQyZMnM3/+fPLz87nmmmsyGbKZme0F6pIg9I6IipNlZ0t6LiIGSHq5ukYR8T+S+gKnATcDfwJejoj+dQlM0qHA7OTl3YCA+yLiP6qovj0+eeLbx8DWJIaPJVU8x8oX4ZVfq4aQppMasfg2n4x6CPhhRMyrFPtpVfRtZlZn27dvZ9iwYZx33nkMHTp0l/3Lly8HoHv37vzoRz/iueee49vf/jZr166lW7duDR2umZntReqySLmdpM7lL5Ltzycvt1XXSFJHoCQi7gd+ARyT9NU/2Z9d0wPXImJ9slC5ICLuBp4Gzkp+xUdSW0mH1SH+igYm7fYHzgBeqLT/OeCcZG1BO1IjAYuTfVOBHyexlSdG84AfSMpOYuou6YCkn28n/eQCJ+1mnGbWjEUEF1xwAT169OCnP/1plXWuvfZaCgsL2b59O2VlZQBkZWVRUlLSkKGamdleqC4JwmXA85KekbQA+AtweXIhfF8N7XoBiyUVAVcDPwPOAm6RtAIoIjWXv04i4hXgGmC+pJWkRiRya261i+dJjQQUkZomVPkeg38AVgIrgD8D4yLiH8nx3wZeBe6tUH8yqfUFL0laDfyG1KjMH4C1pKY7/Rp4djfjNLO9wPDhw+nfvz9r1qzhkEMO4Xe/+x1/+MMfOOSQQ1i0aBGDBw/m61//OgDFxcWcdtppALzwwgtMnz6dP//5zxQUFFBQUMCTTz6Z7nfmzJkcffTRdOzYkYMOOoj+/fvTq1cvJHHUUUdl5FzNzGzvoU9m5tRQKbUA90hSU2peq2FhcqMlaRTQLyIu/ZTtW5G64O8TEZv3ZGyQWoOQO3Linu7WzBrYuvGDMx2CmZlZjSQti4h+1e2vdQQhuTC+HLg0IoqAQyV9Y8+F2PhJ+hrwGvCr+kgOzMzMzMwai1pHECQ9BCwDRkREXjJ/f1FEFDRAfM1Gv379orqnqpqZmZmZ7SmfeQQBODwi/gvYDpA83bimu/2YmZmZmVkTVZcEYVsyahAAkg4nuY2omZmZmZntXeryHITrST2E7FBJDwBfBb5Xn0GZmZmZmVlm1JogRMR8ScuAY0lNLfpRRLxb75GZmZmZmVmDq8tdjJ6OiH9GxJyIeCIi3pX0dEMEZ2ZmZmZmDavaEQRJ+wGtgM9L+hyfLExuA3RsgNjMzMzMzKyB1TTF6PvAj0klA8v4JEF4H7izfsMyMzMzM7NMqDZBiIhJwCRJP4yIXzVgTGZmZmZmliF1WaT8K0l5wJeA/SqUT6vPwMzMzMzMrOHVmiBIug44kVSC8CRwKvA84ATBzMzMzGwvU5cHpZ0FnAz8IyK+BxwF7FuvUZmZmZmZWUbUJUEojYiPgR2S2gAbgS/Ub1hmZmZmZpYJdXmS8lJJBwG/JXU3oy3A4voMyszMzMzMMkMRUffKUhegTUSsrLeImql9c7tF7siJmQ7DzGqxbvzgTIdgZmb2mUhaFhH9qttf7RQjSV+XdFbFsohYB/SSNHDPhWhmZmZmZo1FTWsQbgCeraL8aaCwfsIxMzMzM7NMqilBaBUR71QujIh/AAfUX0g7k3SQpEs+ZduplUdB6tBmsqQv1bD/dElXfpp4zMzMzMwau5oShP0k7bKIWVI2sH/9hbSLg4BPlSB8GhExOiJeqWH/rIgY31DxmFnjdf7559O+fXvy8vLSZf/6178YOHAg3bp1Y+DAgWzatKnKtrfddhs9e/YkLy+P4cOH89FHHwFwxRVXkJ+fz4gRI9J1p0+fzqRJk+r3ZMzMzBI1JQiPAb+VlB4tSLbvTvY1lPHA4ZKKJE2QdLmkJZJWSrqhQmwjkrIVkqZXaD9A0kJJr5ePJkg6UdICSY9Iek3SA5KU7FsgqV+yfYqkl5I+n07KRkm6I9n+pqS/Slou6SlJHZLy6yVNSfp6XdKYhnmrzKwhjRo1irlz5+5UNn78eE4++WTWrl3LySefzPjxu/6esGHDBm6//XaWLl3K6tWrKSsr48EHH2Tz5s0sXLiQlStXUlZWxqpVqygtLWXq1KlcckmD/U5iZmbNXE0JwjXA28DfJC2TtAxYB7yT7GsoVwL/FxEFwJ+AbsCXgQKgr6QBknoCVwP/FhFHAT+q0D4XOA74Bqlko1xv4MeknhD9BeCrFQ8qqR2pW7sOS/r8VhWxPQ8cGxG9gQeBcRX2HQl8PYn1umTkZSeSLpK0VNLSspLNtb8TZtaoDBgwgLZt2+5U9vjjjzNy5EgARo4cycyZM6tsu2PHDkpLS9mxYwclJSV07NiRrKwstm3bRkRQWlpKdnY2EyZMYMyYMWRn7/J/IWZmZvWi2gQhInZExJXAocCo5K9zRFwZEdsbJrxdDEr+lgMvkboI7wb8G/BIRLwLEBH/qtBmZkR8nEwb6lChfHFEvJk8BK4I6FLpWMcCz0XEG1X0We4QYJ6kVcDlQM8K++ZExNYkpo2Vjk3S5z0R0S8i+rVolVOnN8DMGre3336b3NxcAHJzc9m4ceMudTp16sTYsWPp3Lkzubm55OTkMGjQIFq3bs2wYcPo3bs3Xbt2JScnhyVLljBkyJCGPg0zM2vGan2SckSURsSq5K+0IYKqgYCbI6Ig+ftiRPwuKa/ugQ5bK7WvqryMXR8aV1Of5X4F3BERvYDvA/vtRv9m1kxt2rSJxx9/nDfeeIPi4mI+/PBD7r//fgDGjRtHUVERt956K9deey2FhYVMnjyZs88+mxtvvDHDkZuZWXNQa4LQCHwAtE625wHnSzoQQFInSe1J3Xr1bEkHJ+Vtq+xp9ywCTpDUtYY+c4ANyfbIPXBMM2viOnTowFtvvQXAW2+9Rfv27Xep89RTT9G1a1fatWtHdnY2Q4cOZeHChTvVWb58OQDdu3dn2rRpPPzww6xevZq1a9fW/0mYmVmz1ugThIj4J/CCpNXAQOC/gUXJtJ5HgNYR8TJwE/CspBXAL/fAcd8BLgIeS/p8qIpq1wO/l/QX4N3Pekwza/pOP/107rvvPgDuu+++KqcHde7cmRdffJGSkhIigqeffpoePXrsVKd89GD79u2UlZUBkJWVRUlJSf2fhJmZNWuKqG0WDUgaSmqhbwDPR8Qf6juw5mbf3G6RO3JipsMws1qsGz84vT18+HAWLFjAu+++S4cOHbjhhhs444wzOPvss/n73/9O586d+f3vf0/btm0pLi5m9OjRPPnkkwBcd911PPTQQ7Rs2ZLevXszefJk9t13XwBmzpzJihUruO666wAYO3Ys8+bNIz8/nwceeKDhT9rMzPYqkpZFRL9q99eWIEi6C/giMCMpOofUXYX+fY9FaU4QzJqIigmCmZlZU1RbglCXhbMnAHmRZBKS7gNW7aH4LNGrUw5LfeFhZmZmZhlWlzUIa4DOFV4fCqysn3DMzMzMzCyTqh1BkDSb1JqDHOBVSYuTXV8GFlbXzszMzMzMmq6aphj9osGiMDMzMzOzRqHaBCEini3fltQBODp5uTgidn00qJmZmZmZNXm1rkGQdDawGPgWcDbwV0ln1XdgZmZmZmbW8OpyF6OrgaPLRw0ktQOeIvWQMjMzMzMz24vU5S5GWZWmFP2zju3MzMzMzKyJqcsIwlxJ8/jkQWnfBv5YfyGZmZmZmVmm1JogRMTlkoYCXwUE3B0RM+s7MDMzMzMza3g1PQfhA1LPQYBUYlDuQkkfAf8HXB0RT9djfGZmZmZm1oBqus1p6+r2SWoB5AEPJP+amZmZmdle4FMtNo6IsohYAfxqD8djZmZmZmYZ9JnuRhQRv9lTgZiZmZmZWebV5S5G1gBWbdhMlyvnZDoMsyZl3fjBmQ7BzMxsr+PnGZiZmZmZWZoTBDMzMzMzS3OCUA1JB0m6pMLrEyU9kcmYzMzMzMzqmxOE6h0EXFJbpbqS5PUeZg1k0qRJ5OXl0bNnTyZOnLjL/gULFpCTk0NBQQEFBQUUFhYC8M4773DccceRl5fHzJkz0/WHDBlCcXFxA0VvZmaWWb5oTUj6KXB+8nIycCxwuKQi4E/AHOBASY+QevbDMuA7ERGS+gK/BA4E3gVGRcRbkhYAC0k9hXoWcGvDnZFZ87R69Wp++9vfsnjxYvbZZx9OOeUUBg8eTLdu3Xaqd/zxx/PEEzsPCs6YMYORI0fy7W9/m1NOOYUzzjiD2bNn06dPHzp27NiQp2FmZpYxThCA5AL/e8AxpJ4a/VfgO0BeRBQkdU4EegM9gWLgBeCrkv5K6nkQQyLiHUnnADfxSbJxUEScUM1xLwIuAmjRpl19nJpZs/Pqq69y7LHH0qpVKwBOOOEE/vCHPzBu3Lha22ZnZ1NaWsrWrVvJyspix44dTJw4kdmzZ9d32GZmZo2GpxilHAf8ISI+jIgtwGPA8VXUWxwRb0bEx0AR0AU4gtSIwp+S0YZrgEMqtHmouoNGxD0R0S8i+rVolbNHTsSsucvLy+O5557jn//8JyUlJTz55JOsX79+l3qLFi3iqKOO4tRTT+Xll18G4Nxzz2XevHmccsopXH/99dx1112MGDEinWyYmZk1Bx5BSFEd622tsF1G6v0T8HJE9K+mzYefJTAz2z09evTgiiuuYODAgRx44IEcddRRtGy58//V9enTh7/97W8ceOCBPPnkk5xxxhmsXbuWnJwc5sxJPY9k06ZN3HLLLTz22GNceOGFbNq0icsuu4z+/av7qpuZme0dPIKQ8hxwhqRWkg4AziQ1hah1HdquAdpJ6g8gKVtSz/oL1cxqc8EFF/DSSy/x3HPP0bZt213WH7Rp04YDDzwQgNNOO43t27fz7rvv7lSnsLCQq6++mhkzZtC3b1+mTJnCVVdd1WDnYGZmlilOEICIeAmYCiwmtf5gckQsA16QtFrShBrabgPOAm6RtILU1KOv1HvQZlatjRs3AvD3v/+dxx57jOHDh++0/x//+AcRAcDixYv5+OOPOfjgg9P7165dS3FxMSeccAIlJSVkZWUhiY8++qjhTsLMzCxDPMUoERG/JHUnoopl51aqtqDCvksrbBcBA6ro88Q9GaOZ1c2wYcP45z//SXZ2NnfeeSef+9znuPvuuwG4+OKLeeSRR/j1r39Ny5Yt2X///XnwwQeRPplpePXVV3PTTTcBMHz4cM444wwmTZqUvh2qmZnZ3kzlv6JZZu2b2y1yR07MdBhmTcq68YMzHYKZmVmTI2lZRPSrbr9HEBqJXp1yWOqLHTMzMzPLMK9BMDMzMzOzNCcIZmZmZmaW5gTBzMzMzMzSnCCYmZmZmVmaEwQzMzMzM0tzgmBmZmZmZmlOEMzMzMzMLM0JgpmZmZmZpTlBMDMzMzOzNCcIZmZmZmaW5gTBzMzMzMzSnCCYmZmZmVmaEwQzMzMzM0trmekALGXVhs10uXJOpsMwa3Drxg/OdAhmZmZWgUcQzMzMzMwszQmCmZmZmZmlNcoEQVIXSaszHcfukNRR0iPV7FsgqV9Dx2RmZmZmtrsaZYLQFEVEcUSclek4zPYWt912Gz179iQvL4/hw4fz0Ucf7bR/8+bNfPOb3+Soo46iZ8+e3HvvvQC88847HHfcceTl5TFz5sx0/SFDhlBcXNyQp2BmZtYkNeYEoYWk30p6WdJ8SftLKpD0oqSVkv4g6XOw8y/0kj4vaV2y3VPSYklFSZtuSfl3KpT/RlKLpHyLpFskLZP0lKQvJ32/Lun0pE4XSX+R9FLy95UK5auT7f0lPZgc8yFg/4Z+88yasg0bNnD77bezdOlSVq9eTVlZGQ8++OBOde68806+9KUvsWLFChYsWMBll13Gtm3bmDFjBiNHjmTRokVMmDABgNmzZ9OnTx86duyYidMxMzNrUhpzgtANuDMiegLvAcOAacAVEZEPrAKuq6WPi4FJEVEA9APelNQDOAf4alJeBpyX1D8AWBARfYEPgBuBgcCZQGFSZyMwMCL6JP3cXsVxfwCUJHHeBPTdrTM3M3bs2EFpaSk7duygpKRkl4t7SXzwwQdEBFu2bKFt27a0bNmS7OxsSktL2bp1K1lZWezYsYOJEydy+eWXZ+hMzMzMmpbGfJvTNyKiKNleBhwOHBQRzyZl9wG/r6WPRcDVkg4BHouItZJOJnXBvkQSpH7d35jU3wbMTbZXAVsjYrukVUCXpDwbuENSAankonsVxx1AkjhExEpJK6sKTtJFwEUALdq0q+VUzJqPTp06MXbsWDp37sz+++/PoEGDGDRo0E51Lr30Uk4//XQ6duzIBx98wEMPPURWVhbnnnsu5557LtOmTeOWW27hrrvuYsSIEbRq1SpDZ2NmZta0NOYRhK0VtsuAg2qou4NPzmW/8sKI+G/gdKAUmCfp3wAB90VEQfJ3RERcnzTZHhGRbH9cHkNEfMwnydRPgLeBo0iNSuxTTUxRTfknFSLuiYh+EdGvRauc2qqbNRubNm3i8ccf54033qC4uJgPP/yQ+++/f6c68+bNo6CggOLiYoqKirj00kt5//33ycnJYc6cOSxdupQ+ffrwxBNPMGzYMC688ELOOussFi1alKGzMjMzaxoac4JQ2WZgk6Tjk9ffBcpHE9bxyTSe9EJhSV8AXo+I24FZQD7wNHCWpPZJnbaSDtuNOHKAt5Kk4btAiyrqPEcybUlSXnJcM6ujp556iq5du9KuXTuys7MZOnQoCxcu3KnOvffey9ChQ5HEF7/4Rbp27cprr722U53CwkKuvvpqZsyYQd++fZkyZQpXXXVVQ56KmZlZk9OUEgSAkcCEZMpOAZ+sC/gF8ANJC4HPV6h/DrBaUhFwJDAtIl4BrgHmJ/38CcjdjRjuAkZKepHU9KIPq6jza+DApP9xwOLd6N+s2evcuTMvvvgiJSUlRARPP/00PXr02KXO008/DcDbb7/NmjVr+MIXvpDev3btWoqLiznhhBMoKSkhKysLSbvcDcnMzMx2pk9m1Fgm7ZvbLXJHTsx0GGYNbt34wVWWX3fddTz00EO0bNmS3r17M3ny5PStTC+++GKKi4sZNWoUb731FhHBlVdeyXe+8510+7PPPpubbrqJbt26sXHjRs444ww2b95MYWEhw4YNa5BzMzMza4wkLYuIap/R5QShkXCCYM1VdQmCmZmZ1Y/aEoTGfBejZqVXpxyW+kLJzMzMzDKsqa1BMDMzMzOzeuQEwczMzMzM0pwgmJmZmZlZmhMEMzMzMzNLc4JgZmZmZmZpThDMzMzMzCzNCYKZmZmZmaU5QTAzMzMzszQnCGZmZmZmluYEwczMzMzM0pwgmJmZmZlZmhMEMzMzMzNLc4JgZmZmZmZpLTMdgKWs2rCZLlfOyXQYZg1q3fjBmQ7BzMzMKvEIgpmZmZmZpTlBMDMzMzOzNCcItZBUKOlrmY7DzMzMzKwhOEEAJFW7FiMifhYRTzVkPGbN3W233UbPnj3Jy8tj+PDhfPTRRzvtnzBhAgUFBRQUFJCXl0eLFi3417/+xTvvvMNxxx1HXl4eM2fOTNcfMmQIxcXFDXwWZmZmTdNelSBIOkDSHEkrJK2WdI6kvpKelbRM0jxJuUndBZJ+LulZ4GpJ6yRlJftaSVovKVvSVElnJeVHS1qY9L9YUmtJLSRNkLRE0kpJ30/q5kp6TlJREsvxGXtjzJqQDRs2cPvtt7N06VJWr15NWVkZDz744E51Lr/8coqKiigqKuLmm2/mhBNOoG3btsyYMYORI0eyaNEiJkyYAMDs2bPp06cPHTt2zMTpmJmZNTl7212MTgGKI2IwgKQc4I/AkIh4R9I5wE3A+Un9gyLihKRuH+AE4Bngm8C8iNguiWT/PsBDwDkRsURSG6AUuADYHBFHS9oXeEHSfGBo0sdNkloArRriDTDbG+zYsYPS0lKys7MpKSmp8eJ+xowZDB8+HIDs7GxKS0vZunUrWVlZ7Nixg4kTJzJ79uyGCt3MzKzJ26tGEIBVwNck3ZL8Yn8okAf8SVIRcA1wSIX6D1XaPifZ/nalfQBHAG9FxBKAiHg/InYAg4ARSf9/BQ4GugFLgO9Juh7oFREfVA5W0kWSlkpaWlay+dOftdlepFOnTowdO5bOnTuTm5tLTk4OgwYNqrJuSUkJc+fOZdiwYQCce+65zJs3j1NOOYXrr7+eu+66ixEjRtCqlfNzMzOzutqrEoSI+B+gL6lE4WZgGPByRBQkf70iouKVxocVtmcBp0pqm/Tx50rdC4gqDivghxWO0TUi5kfEc8AAYAMwXdKIKuK9JyL6RUS/Fq1yPuVZm+1dNm3axOOPP84bb7xBcXExH374Iffff3+VdWfPns1Xv/pV2rZtC0BOTg5z5sxh6dKl9OnThyeeeIJhw4Zx4YUXctZZZ7Fo0aKGPBUzM7Mmaa9KECR1BEoi4n7gF8AxQDtJ/ZP92ZJ6VtU2IrYAi4FJwBMRUVapymtAR0lHJ321ThY3zwN+ICk7Ke+erIU4DNgYEb8Ffgf02dPna7Y3euqpp+jatSvt2rUjOzuboUOHsnDhwirrPvjgg+npRZUVFhZy9dVXM2PGDPr27cuUKVO46qqr6jN0MzOzvcLetgahFzBB0sfAduAHwA7g9mQ9QktgIvByNe0fAn4PnFh5R0RsS9Yw/ErS/qTWH3wNmAx0AV5SasHCO8AZSR+XS9oObAF2GUEws1117tyZF198kZKSEvbff3+efvpp+vXrt0u9zZs38+yzz1Y5urB27VqKi4s54YQTKCoqYv/990fSLndDMjMzs10poqpZM9bQ9s3tFrkjJ2Y6DLMGtW784CrLr7vuOh566CFatmxJ7969mTx5Mvfeey8AF198MQBTp05l7ty5u9zhCODss8/mpptuolu3bmzcuJEzzjiDzZs3U1hYmF6vYGZm1lxJWhYRu/76Vr7fCULj4ATBmqPqEgQzMzOrP7UlCHvVGgQzMzMzM/ts9rY1CE1Wr045LPWvqWZmZmaWYR5BMDMzMzOzNCcIZmZmZmaW5gTBzMzMzMzSnCCYmZmZmVmaEwQzMzMzM0tzgmBmZmZmZmlOEMzMzMzMLM0JgpmZmZmZpTlBMDMzMzOzNCcIZmZmZmaW5gTBzMzMzMzSnCCYmZmZmVlay0wHYCmrNmymy5VzMh2G2ae2bvzgTIdgZmZme4BHEMzMzMzMLM0JgpmZmZmZpTlBMDMzMzOztEabIEg6Q9KXMh2HmX02t912Gz179iQvL4/hw4fz0Ucf7bT/tddeo3///uy777784he/SJe/8847HHfcceTl5TFz5sx0+ZAhQyguLm6o8M3MzJqdRpsgAGcAVSYIkj7T4urP2r6pHNMs0zZs2MDtt9/O0qVLWb16NWVlZTz44IM71Wnbti233347Y8eO3al8xowZjBw5kkWLFjFhwgQAZs+eTZ8+fejYsWODnYOZmVlz06AJgqTvSFosqUjSbyS1kLRF0k2SVkh6UVIHSV8BTgcmJHUPl7RA0s8lPQv8SFJfSc9KWiZpnqTc5BgLJE2UtFDSaklfTsqvl3SPpPnANEntJD0qaUny99UK9aYk/bwuaUxN8SflWyrUOUvS1GR7qqRfSnoGuKVh3mWzxmXHjh2UlpayY8cOSkpKdrm4b9++PUcffTTZ2dk7lWdnZ1NaWsrWrVvJyspix44dTJw4kcsvv7whwzczM2t2GixBkNQDOAf4akQUAGXAecABwIsRcRTwHHBhRCwEZgGXR0RBRPxf0s1BEXECcDvwK+CsiOgLTAFuqnC4AyLiK8Alyb5yfYEhEXEuMAm4LSKOBoYBkyvUOxL4OvBl4DpJ2TXEX5vuwNci4rIq3pOLJC2VtLSsZHMdujJrWjp16sTYsWPp3Lkzubm55OTkMGjQoDq1Pffcc5k3bx6nnHIK119/PXfddRcjRoygVatW9Ry1mZlZ89aQ015OJnWBvkQSwP7ARmAb8ERSZxkwsIY+Hkr+PQLIA/6U9NUCeKtCvRkAEfGcpDaSDkrKZ0VEabL9NeBLSXuANpJaJ9tzImIrsFXSRqBDDfHX5vcRUVbVjoi4B7gHYN/cblGHvsyalE2bNvH444/zxhtvcNBBB/Gtb32L+++/n+985zu1ts3JyWHOnDnpfm655RYee+wxLrzwQjZt2sRll11G//796/sUzMzMmp2GTBAE3BcR/7FToTQ2IsovjstqienDCn29HBHVXR1Uvtguf/1hhbIsoH+FhKE8HoCtFYrKY6oy/iqOt181MZs1O0899RRdu3alXbt2AAwdOpSFCxfWKUGoqLCwkKuvvpoZM2bQt29fzj33XIYMGcIzzzxTH2GbmZk1aw25BuFp4CxJ7QEktZV0WA31PwBaV7NvDdBOUv+kr2xJPSvsPycpPw7YHBFVzd+ZD1xa/kJSwWeI/21JPSRlAWfW0o9Zs9G5c2defPFFSkpKiAiefvppevTosVt9rF27luLiYk444QRKSkrIyspC0i53QzIzM7M9o8EShIh4BbgGmC9pJfAnILeGJg8Cl0taLunwSn1tA84CbpG0AigCvlKhyiZJC4G7gQuq6X8M0E/SSkmvABd/hvivJDVN6s/sPNXJrFk75phjOOuss+jTpw+9evXi448/5qKLLuLuu+/m7rvvBuAf//gHhxxyCL/85S+58cYbOeSQQ3j//ffTfVx99dXceOONAAwfPpypU6dy7LHH7nLXIzMzM9sz9Mnsnr2DpAXA2IhYmulYdse+ud0id+TETIdh9qmtGz840yGYmZlZHUhaFhH9qtvfmJ+DYGZmZmZmDWyve3hXRJyY6Rg+jV6dcljqX2DNzMzMLMM8gmBmZmZmZmlOEMzMzMzMLM0JgpmZmZmZpTlBMDMzMzOzNCcIZmZmZmaW5gTBzMzMzMzSnCCYmZmZmVmaEwQzMzMzM0tzgmBmZmZmZmlOEMzMzMzMLM0JgpmZmZmZpTlBMDMzMzOztJaZDsBSVm3YTJcr52Q6DGtG1o0fnOkQzMzMrBHyCIKZmZmZmaU5QTAzMzMzszQnCGZmZmZmltboEwRJXSStrqJ8gaR+GYjnRElPJNujJN3R0DGYmZmZmdWXRp8g7E0keVG4NWpr1qyhoKAg/demTRsmTpy4U50HHniA/Px88vPz+cpXvsKKFSsAeOeddzjuuOPIy8tj5syZ6fpDhgyhuLi4Ac/CzMzMPoumkiC0lHSfpJWSHpHUquJOSVsqbJ8laWqy3U7So5KWJH9frapzSUdLWihphaTFklpL2k/SvZJWSVou6aSaApT0TUl/Teo+JalDUn69pHskzQemfdY3wqw+HXHEERQVFVFUVMSyZcto1aoVZ5555k51unbtyrPPPsvKlSu59tprueiiiwCYMWMGI0eOZNGiRUyYMAGA2bNn06dPHzp27Njg52JmZmafTlP5RfsI4IKIeEHSFOCSOrabBNwWEc9L6gzMA3pUrCBpH+Ah4JyIWCKpDVAK/AggInpJOhKYL6l7Dcd6Hjg2IkLSaGAccFmyry9wXESUVjr2RcBFAC3atKvjKZk1jKeffprDDz+cww47bKfyr3zlK+ntY489ljfffBOA7OxsSktL2bp1K1lZWezYsYOJEycye/bsBo3bzMzMPpumkiCsj4gXku37gTF1bPc14EuSyl+3kdQ6Ij6oUOcI4K2IWAIQEe8DSDoO+FVS9pqkvwE1JQiHAA9JygX2Ad6osG9W5eQg6fce4B6AfXO7RR3PyaxBPPjggwwfPrzGOr/73e849dRTATj33HM599xzmTZtGrfccgt33XUXI0aMoFWrVjX2YWZmZo1LU0kQKl881/R6vwrbWUD/Kn65nwd0AJYCt1fRH4CqKKvJr4BfRsQsSScC11fY9+Fu9mWWUdu2bWPWrFncfPPN1dZ55pln+N3vfsfzzz8PQE5ODnPmpB72t2nTJm655RYee+wxLrzwQjZt2sRll11G//79GyR+MzMz+/SayhqEzpLKryyGk5rOU9HbknpIygIqTpieD1xa/kJSAUBEfD0iCiJiNPAa0FHS0Umd1sli4ueA85Ky7kBnYE0NMeYAG5Ltkbt/imaNxx//+Ef69OlDhw4dqty/cuVKRo8ezeOPP87BBx+8y/7CwkKuvvpqZsyYQd++fZkyZQpXXXVVfYdtZmZme0BTSRBeBUZKWgm0BX5daf+VwBPAn4G3KpSPAfoli5tfAS6u3HFEbAPOAX4laQXwJ1KjEHcBLSStIrVGYVREbK0hxuuB30v6C/Du7p+iWeMxY8aMaqcX/f3vf2fo0KFMnz6d7t13nXW3du1aiouLOeGEEygpKSErKwtJfPTRR/UdtpmZme0BivDU98Zg39xukTtyYqbDsGZk3fjBVZaXlJRw6KGH8vrrr5OTkwPA3XffDcDFF1/M6NGjefTRR9OLl1u2bMnSpUvT7c8++2xuuukmunXrxsaNGznjjDPYvHkzhYWFDBs2rJ7PyszMzGojaVlEVPs8MScIjYQTBGto1SUIZmZmtnerLUFoKouU93q9OuWw1BdsZmZmZpZhTWUNgpmZmZmZNQAnCGZmZmZmluYEwczMzMzM0pwgmJmZmZlZmhMEMzMzMzNLc4JgZmZmZmZpfg5CIyHpA2BNpuOw3fJ5/NTspsSfV9Piz6tp8efV9Pgza1r29Od1WES0q26nn4PQeKyp6YEV1vhIWurPrOnw59W0+PNqWvx5NT3+zJqWhv68PMXIzMzMzMzSnCCYmZmZmVmaE4TG455MB2C7zZ9Z0+LPq2nx59W0+PNqevyZNS0N+nl5kbKZmZmZmaV5BMHMzMzMzNKcIDQCkk6RtEbS/0q6MtPxWM0krZO0SlKRpKWZjsd2JWmKpI2SVlcoayvpT5LWJv9+LpMx2ieq+byul7Qh+Z4VSTotkzHaJyQdKukZSa9KelnSj5Jyf8caoRo+L3/HGiFJ+0laLGlF8nndkJQ36PfLU4wyTFIL4H+AgcCbwBJgeES8ktHArFqS1gH9IsL3j26kJA0AtgDTIiIvKfsv4F8RMT5JxD8XEVdkMk5Lqebzuh7YEhG/yGRstitJuUBuRLwkqTWwDDgDGIW/Y41ODZ/X2fg71uhIEnBARGyRlA08D/wIGEoDfr88gpB5Xwb+NyJej4htwIPAkAzHZNakRcRzwL8qFQ8B7ku27yP1H0hrBKr5vKyRioi3IuKlZPsD4FWgE/6ONUo1fF7WCEXKluRldvIXNPD3ywlC5nUC1ld4/Sb+4jZ2AcyXtEzSRZkOxuqsQ0S8Ban/YALtMxyP1e5SSSuTKUiertIISeoC9Ab+ir9jjV6lzwv8HWuUJLWQVARsBP4UEQ3+/XKCkHmqoszzvhq3r0ZEH+BU4N+T6RFmtmf9GjgcKADeAm7NaDS2C0kHAo8CP46I9zMdj9Wsis/L37FGKiLKIqIAOAT4sqS8ho7BCULmvQkcWuH1IUBxhmKxOoiI4uTfjcAfSE0Ts8bv7WQubvmc3I0ZjsdqEBFvJ/+R/Bj4Lf6eNSrJ3OhHgQci4rGk2N+xRqqqz8vfscYvIt4DFgCn0MDfLycImbcE6Capq6R9gG8DszIck1VD0gHJIi8kHQAMAlbX3MoaiVnAyGR7JPB4BmOxWpT/hzBxJv6eNRrJIsrfAa9GxC8r7PJ3rBGq7vPyd6xxktRO0kHJ9v7A14DXaODvl+9i1AgktxabCLQApkTETZmNyKoj6QukRg0AWgL/7c+r8ZE0AzgR+DzwNnAdMBN4GOgM/B34VkR4YWwjUM3ndSKpqQ8BrAO+Xz7/1jJL0nHAX4BVwMdJ8VWk5rX7O9bI1PB5DcffsUZHUj6pRcgtSP2Q/3BEFEo6mAb8fjlBMDMzMzOzNE8xMjMzMzOzNCcIZmZmZmaW5gTBzMzMzMzSnCCYmZmZmVmaEwQzMzMzM0tzgmBmZjuRdLWklyWtlFQk6Zha6l8vaewejqGfpNtrqdNF0rm702Y3jr9O0qrkPXhW0mG7E4uZWVPmBMHMzNIk9Qe+AfSJiHxSD+lZ38AxtIyIpRExppaqXYD0RXkd2+yOk5L3YAFwze7EYmbWlDlBMDOzinKBdyNiK0BEvBsRxZD+Vf3zyXY/SQsqtDtK0p8lrZV0YVInV9JzySjEaknHJ+WnSHpJ0gpJTydl10u6R9J8YJqkEyU9UWHf9Mr9A+OB45P+f1KpTVtJM5MRgBeThw+V9zVF0gJJr0uqS0KxCOiUtO8i6S9J/C9J+ko1sbSQNEHSkiSG73+aD8PMLBNaZjoAMzNrVOYDP5P0P8BTwEMR8Wwd2uUDxwIHAMslzSH1pNZ5EXGTpBZAK0ntgN8CAyLiDUltK/TRFzguIkolnViH/q8ExkbENwAqtbkBWB4RZ0j6N2AaqafGAhwJnAS0BtZI+nVEbK/h3E4h9SRugI3AwIj4SFI3YAbQr4pYLgI2R8TRkvYFXpA0PyLeqOE4ZmaNghMEMzNLi4gtkvoCx5O6iH5I0pURMbWWpo9HRClQKukZ4MvAEmCKpGxgZkQUJRfxz5VfKEfEvyr0MSvpo679v1dDPMcBw5Jj/FnSwZJykn1zkhGSrZI2Ah2AN6vo4xlJHUglBeVTjLKBOyQVAGVA92qOPwjIl3RW8joH6AY4QTCzRs9TjMzMbCcRURYRCyLiOuBSkgttYAef/Hdjv8rNdu0mngMGABuA6ZJGAKqibrkPawqrlteVqYY+tlYoK6P6H8tOAg4DXgYKk7KfAG8DR5EaOdinhuP/MCIKkr+uETG/lpjNzBoFJwhmZpYm6Yhk6ky5AuBvyfY6UtOA4JOkodwQSftJOhg4EViS3PlnY0T8Fvgd0IfUfP4TJHVNjteWutmlf+ADUtOEqvIccF5yjBNJrat4v47HSktGLX4MjEhizQHeioiPge8CLZKqlWOZB/wgGT1BUndJB+zu8c3MMsFTjMzMrKIDgV9JOojUiMH/Ahcl+24AfifpKuCvldotBuYAnYH/jIhiSSOByyVtB7YAIyLinWR+/mOSskjm9Nchrqr6fwfYIWkFMBVYXqH+9cC9klYCJcDI3XgPdhIRb0maAfw7cBfwqKRvAc/wyajHykqxTCJ1Z6OXJAl4Bzjj08ZgZtaQFFHbKK2ZmVnmSLoe2BIRv8h0LGZmzYGnGJmZmZmZWZpHEMzMzMzMLM0jCGZmZmZmluYEwczMzMzM0pwgmJmZmZlZmhMEMzMzMzNLc4JgZmZmZmZpThDMzMzMzCzt/wMmahQwFp2Q6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_job = count_job_response_pct['yes'].sort_values(ascending = True).plot(kind ='barh',\n",
    "                                                                           figsize = (12,6))\n",
    "                                                                               \n",
    "plt.title('Subscription Rate by Job')\n",
    "plt.xlabel('Subscription Rate')\n",
    "plt.ylabel('Job Category')\n",
    "\n",
    "# Label each bar\n",
    "for rec, label in zip(plot_job.patches,\n",
    "                      count_job_response_pct['yes'].sort_values(ascending = True).round(1).astype(str)):\n",
    "    plot_job.text(rec.get_width()+0.8, \n",
    "                  rec.get_y()+ rec.get_height()-0.5, \n",
    "                  label+'%', \n",
    "                  ha = 'center', \n",
    "                  va='bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5842c94f",
   "metadata": {},
   "source": [
    "**Insights: target students and retired clients**\n",
    "\n",
    "This verifys our above analysis of targeting the very young and very old"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "282c95dd",
   "metadata": {},
   "source": [
    "### 4.5 Visualize the subscription and contact rate by month\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d858992d",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_month_response_pct = pd.crosstab(dataset7['response'],dataset7['month_int']).apply(lambda x: x/x.sum() * 100)\n",
    "count_month_response_pct = count_month_response_pct.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "45a61096",
   "metadata": {},
   "outputs": [],
   "source": [
    "month = pd.DataFrame(dataset7['month_int'].value_counts())\n",
    "month['% Contacted'] = month['month_int']*100/month['month_int'].sum()\n",
    "month['% Subscription'] = count_month_response_pct['yes']\n",
    "month.drop('month_int',axis = 1,inplace = True)\n",
    "\n",
    "month['Month'] = [5,7,8,6,11,4,2,1,10,9,3,12]\n",
    "month = month.sort_values('Month',ascending = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "622d4105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_month = month[['% Subscription','% Contacted']].plot(kind ='line',\n",
    "                                                          figsize = (10,6),\n",
    "                                                          marker = 'o')\n",
    "\n",
    "plt.title('Subscription vs. Contact Rate by Month')\n",
    "plt.ylabel('Subscription and Contact Rate')\n",
    "plt.xlabel('Month')\n",
    "\n",
    "ticks = np.arange(1,13,1)\n",
    "plt.xticks(ticks)\n",
    "\n",
    "# Annotation: peak of contact\n",
    "y = month['% Contacted'].max()\n",
    "x = month['% Contacted'].idxmax()\n",
    "plt.annotate('May: Peak of contact', xy=(x+0.1, y+0.1), xytext=(x+1,y+4), arrowprops=dict(facecolor='black', headwidth=6, width=1, headlength=4), horizontalalignment='left', verticalalignment='top')\n",
    "\n",
    "# Annotation: peak of subscription rate\n",
    "y = month['% Subscription'].max()\n",
    "x = month['% Subscription'].idxmax()\n",
    "plt.annotate('March: Peak Subscription rate', xy=(x+0.1, y+0.1), xytext=(x+1,y+1), arrowprops=dict(facecolor='black', headwidth=6, width=1, headlength=4), horizontalalignment='left', verticalalignment='top')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3a5305",
   "metadata": {},
   "source": [
    "**Insights: initiate the telemarketing campaign in fall or spring**\n",
    "\n",
    "Besides customer characteristics, external factors may also have an impact on the subscription rate, such as seasons and the time of calling. So the month of contact is also analyzed here.  \n",
    "\n",
    "This line chart displays the bank’s contact rate in each month as well as clients’ response rate in each month. One way to evaluate the effectiveness of the bank's marketing plan is to see whether these two lines have a similar trend over the same time horizon.\n",
    "* The bank **contacted most clients between May and August**. The highest contact rate is around 30%, which happened in May, while the contact rate is closer to 0 in March, September, October, and December. \n",
    "\n",
    "\n",
    "* However, the subscription rate showed a different trend. **The highest subscription rate occurred in March**, which is over 50%, and all subscription rates in **September, October, and December** are over 40%. \n",
    "\n",
    "Clearly, **these two lines move in different directions which strongly indicates the inappropriate timing of the bank’s marketing campaign**. To improve the marketing campaign, the bank should consider initiating the telemarketing campaign in fall and spring when the subscription rate tends to be higher.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b27f389",
   "metadata": {},
   "source": [
    "Visualizing and exploring the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "045d0567",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Age\n",
    "pd.crosstab(df.age,df.y).plot(kind=\"bar\",figsize=(20,6))\n",
    "plt.title('Client Subscribed Frequency for Ages')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c9814f93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1c7dbac3c10>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 593.25x540 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df, hue='y', vars= ['age', 'balance', 'duration'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9b7bcc15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='y', ylabel='count'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='y', data=df, label='Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7b39ba3a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='age', ylabel='Count'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df.age, bins = 20) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "feafe5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='balance', ylabel='Count'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9JMlXk9zflvf2bduSZCLJ3iQX9tUvSPJg23Zte2477dnut7X6riQrhjUWSdLRDfOI5CZg/YD6NVW1qi2fB0hyDr3nrZ/b2lyXZEHb/3pgM7CyLZOfuQl4tqrOBq4Brh7WQCRJRze0IKmqPwaemeHuFwG3VtWLVfU4MAGsSbIEOLmq7q2qAm4GLu5rs72t3w6smzxakSSNzlzMkXwwyQPt1NfiVlsK7OvbZ3+rLW3rU+tHtKmqw8BzwGmDvjDJ5iTjScYPHTo0eyORJI08SK4HvgtYBRwAfr3VBx1J1DT16dq8sli1rapWV9XqsbGxV9VhSdL0RhokVfV0Vb1UVd8CPgmsaZv2A8v7dl0GPNXqywbUj2iTZCFwCjM/lSZJmiUjDZI25zHpfcDkFV07gQ3tSqyz6E2q766qA8DzSda2+Y/LgTv62mxs65cA97R5FEnSCM3owVbHI8lngHcBb0uyH/gV4F1JVtE7BfUE8AGAqtqTZAfwMHAYuLKqXmofdQW9K8AWAXe2BeAG4JYkE/SORDYMayySpKMbWpBU1WUDyjdMs/9WYOuA+jgvP+a3v/4CcGmXPkqSuvOX7ZKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkTgwSSVInBokkqRODRJLUiUEiSerEIJEkdWKQSJI6MUgkSZ0YJJKkToYWJEluTHIwyUN9tVOT3JXksfa6uG/bliQTSfYmubCvfkGSB9u2a9uz22nPd7+t1XclWTGssUiSjm6YRyQ3Aeun1K4C7q6qlcDd7T1JzqH3zPVzW5vrkixoba4HNgMr2zL5mZuAZ6vqbOAa4OqhjUSSdFRDC5Kq+mPgmSnli4DtbX07cHFf/daqerGqHgcmgDVJlgAnV9W9VVXAzVPaTH7W7cC6yaMVSdLojHqO5IyqOgDQXk9v9aXAvr799rfa0rY+tX5Em6o6DDwHnDboS5NsTjKeZPzQoUOzNBRJErx2JtsHHUnUNPXp2ryyWLWtqlZX1eqxsbHj7KIkaZBRB8nT7XQV7fVgq+8Hlvfttwx4qtWXDagf0SbJQuAUXnkqTZI0ZKMOkp3Axra+Ebijr76hXYl1Fr1J9d3t9NfzSda2+Y/Lp7SZ/KxLgHvaPIokaYQWDuuDk3wGeBfwtiT7gV8BPgrsSLIJeBK4FKCq9iTZATwMHAaurKqX2kddQe8KsEXAnW0BuAG4JckEvSORDcMaiyTp6IYWJFV12VE2rTvK/luBrQPq48B5A+ov0IJIkjR3XiuT7ZKkecogkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6mROgiTJE0keTHJ/kvFWOzXJXUkea6+L+/bfkmQiyd4kF/bVL2ifM5Hk2vZcd0nSCM3lEckPVtWqqlrd3l8F3F1VK4G723uSnEPveeznAuuB65IsaG2uBzYDK9uyfoT9lyTx2jq1dRGwva1vBy7uq99aVS9W1ePABLAmyRLg5Kq6t6oKuLmvjSRpROYqSAr4oyT3JdncamdU1QGA9np6qy8F9vW13d9qS9v61LokaYQWztH3vrOqnkpyOnBXkken2XfQvEdNU3/lB/TCajPAmWee+Wr7KkmaxpwckVTVU+31IPD7wBrg6Xa6ivZ6sO2+H1je13wZ8FSrLxtQH/R926pqdVWtHhsbm82hSNLr3siDJMm3J/mOyXXgPcBDwE5gY9ttI3BHW98JbEjy5iRn0ZtU391Ofz2fZG27WuvyvjYnnjcsJMlxLUuXexQmaXjm4tTWGcDvtyt1FwK/U1V/mOQrwI4km4AngUsBqmpPkh3Aw8Bh4Mqqeql91hXATcAi4M62nJi+dZgf/8SfHlfT2z7wjlnujCS9bORBUlV/DXzvgPrXgXVHabMV2DqgPg6cN9t9lCTN3Gvp8l9J0jxkkEiSOjFIJEmdGCSSpE4MEklSJwaJJKkTg0SS1IlBIknqxCCRJHVikEiSOjFIJEmdGCSSpE4MEklSJwaJJKkTg0SS1IlBIknqxCCRJHVikLwe+Lx3SUM0F89sn1VJ1gP/BVgAfKqqPjrHXXrt8XnvkoZoXh+RJFkA/Ffgh4FzgMuSnDO3vZKk15d5HSTAGmCiqv66qv4euBW4aI77dGLpcFps4ZtOOu62nlaT5o9U1Vz34bgluQRYX1U/2d6/H/gnVfXBKfttBja3t98N7B1pR4/tbcDX5roTQ3Qij8+xzV8n8viGMbZ/WFVjgzbM9zmSDKi9IhmrahuwbfjdOT5Jxqtq9Vz3Y1hO5PE5tvnrRB7fqMc2309t7QeW971fBjw1R32RpNel+R4kXwFWJjkryZuADcDOOe6TJL2uzOtTW1V1OMkHgS/Qu/z3xqraM8fdOh6v2dNus+REHp9jm79O5PGNdGzzerJdkjT35vupLUnSHDNIJEmdGCRzLMn6JHuTTCS5aq77czRJlif5YpJHkuxJ8qFWPzXJXUkea6+L+9psaePam+TCvvoFSR5s265NklZ/c5LbWn1XkhUjHN+CJH+e5HMn0rja9781ye1JHm3//95+oowvyc+2P48PJflMkpPm89iS3JjkYJKH+mojGU+Sje07Hkuy8VV1vKpc5mihd4HAXwHfCbwJ+AvgnLnu11H6ugT4vrb+HcBf0rstzX8Grmr1q4Cr2/o5bTxvBs5q41zQtu0G3k7vd0B3Aj/c6j8N/FZb3wDcNsLx/RzwO8Dn2vsTYlztO7cDP9nW3wS89UQYH7AUeBxY1N7vAP71fB4b8M+A7wMe6qsNfTzAqcBft9fFbX3xjPs9yj/QLq/4Q/N24At977cAW+a6XzPs+x3AP6d3l4AlrbYE2DtoLPSurHt72+fRvvplwCf692nrC+n9MjcjGMsy4G7g3bwcJPN+XO37Tqb3j22m1Of9+OgFyb72j99C4HPAe+b72IAVHBkkQx9P/z5t2yeAy2baZ09tza3JvwiT9rfaa1o7HD4f2AWcUVUHANrr6W23o41taVufWj+iTVUdBp4DThvKII70MeAXgW/11U6EcUHvaPcQ8N/aqbtPJfl2ToDxVdVXgV8DngQOAM9V1R9xAoxtilGMp9O/RQbJ3JrRLV5eS5K8Bfg94MNV9Y3pdh1Qq2nq07UZmiQ/Ahysqvtm2mRA7TU3rj4L6Z0qub6qzgf+D73TI0czb8bX5gouonda5x8A357kJ6ZrMqD2mhzbDM3meDqN0yCZW/PqFi9J3kgvRD5dVZ9t5aeTLGnblwAHW/1oY9vf1qfWj2iTZCFwCvDM7I/kCO8EfjTJE/TuHv3uJL/N/B/XpP3A/qra1d7fTi9YToTx/RDweFUdqqpvAp8F3sGJMbZ+oxhPp3+LDJK5NW9u8dKu+rgBeKSqfqNv005g8gqPjfTmTibrG9pVImcBK4Hd7dD8+SRr22dePqXN5GddAtxT7YTtsFTVlqpaVlUr6P33v6eqfmK+j2tSVf1vYF+S726ldcDDnBjjexJYm+TbWp/WAY9wYoyt3yjG8wXgPUkWtyO997TazAx7QszlmBNr76V3BdRfAb881/2Zpp8/QO9Q9wHg/ra8l9751buBx9rrqX1tfrmNay/tqpFWXw081LZ9nJfvsHAS8LvABL2rTr5zxGN8Fy9Ptp9I41oFjLf/d/+d3lU5J8T4gF8FHm39uoXeFUzzdmzAZ+jN93yT3lHCplGNB/i3rT4B/JtX029vkSJJ6sRTW5KkTgwSSVInBokkqRODRJLUiUEiSerEIJE6SrKi/26tM9j/piSXDLNP0igZJJKkTgwSaXYsTLI9yQPpPfvj25L8pyRfac/K2Db5TIh+R9snyZeSXJ1kd5K/TPJPW31Bkl9rz5p4IMnPtPoFSb6c5L4kX5i8pYY0CgaJNDu+G9hWVf8Y+Aa95z58vKq+v6rOAxYBPzKg3XT7LKyqNcCHgV9ptc30blJ4fvuuT7d7oP0mcElVXQDcCGyd9RFKR7FwrjsgnSD2VdX/bOu/Dfx74PEkvwh8G71nZuwB/mBKux+cZp/JG2PeR+8ZFdC7UeFvVe8W4FTVM0nOA84D7moHNAvo3WZDGgmDRJodU+81VMB1wOqq2pfkI/Tuc/T/JTnpGPu82F5f4uW/qxnwXQH2VNXbuw5COh6e2pJmx5lJJv8hvwz4k7b+tfYMl0FXaZ00g32m+iPg37VbgJPkVHo37Bub/P4kb0xy7nGOQ3rVPCKRZscjwMYkn6B3l9br6d1l90HgCXqPDDhCVf1tkk9Ot88AnwL+EfBAkm8Cn6yqj7fLia9Ncgq9v9cfo3eaTBo67/4rSerEU1uSpE4MEklSJwaJJKkTg0SS1IlBIknqxCCRJHVikEiSOvl/kfYBPog3qz4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df.balance, bins = 20) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "68a710e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='duration', ylabel='Count'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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B/Ue0QJIkzbwj5aK8JGmWM1AkSZ0wUCRJnTBQJEmdMFAkSZ0wUCRJnTBQJEmdMFAkSZ0wUCRJnTBQJEmdMFAkSZ0wUCRJnTBQJEmdMFAkSZ0wUCRJnTBQJEmdMFAkSZ0wUCRJnTBQJEmdMFAkSZ0wUCRJnTBQJEmdMFAkSZ0wUCRJnTBQJEmdMFAkSZ2YkUBJsi3JQ0keTDLaaicmuTvJt9vrCX37X5Fka5LHk7yxr76qHWdrko8myUyMR5I0szOU11fVyqoaae8vBzZX1Qpgc3tPkjOBtcBZwBrgmiQLWptrgQ3Airasmcb+S5L6HEmnvM4HNrb1jcAFffWbq2pvVT0BbAXOSXIacFxV3VtVBdzY10aSNM1mKlAK+IskDyTZ0GqnVtVOgPZ6SqsvAZ7sa7u91Za09fH150myIcloktHdu3d3OAxJ0piFM/S551bVjiSnAHcneWzAvhNdF6kB9ecXq64DrgMYGRmZcB9J0uGZkRlKVe1or7uA24FzgKfaaSza6662+3ZgWV/zpcCOVl86QV2SNAOmPVCS/EySl4ytA78OPAxsAta13dYBd7T1TcDaJIuSnE7v4vv97bTYniSr291dl/S1kSRNs5k45XUqcHu7w3ch8KdV9YUkXwNuTbIe+B5wEUBVbUlyK/AIsA+4rKr2t2NdCtwAHAPc1RZJ0gyY9kCpqr8BfmmC+g+A8w7S5krgygnqo8DZXfdRkvTCHUm3DUuSZjEDRZLUCQNFktQJA0WS1AkDRZLUCQNFktQJA0WS1AkDZbY4aiFJprQsWfayme69pHlgph4OqRfqJ/u4+ONfnVLTW97x6o47I0nP5wxFktQJA0WS1AkDRZLUCQNFktQJA0WS1AkDRZLUCQNFktQJA0WS1AkDRZLUCQNFktQJA0WS1AkDRZLUCQNlPvBJxZKmgU8bng98UrGkaeAMRZLUCQNFktQJA0WS1AkDRYN5QV/SJM36i/JJ1gD/FVgAfKKqPjjDXZpbvKAvaZJm9QwlyQLgvwG/AZwJvDXJmTPbK/3UYcxunOFIs89sn6GcA2ytqr8BSHIzcD7wyIz2Sj2HMbsBuOXS15JkSm0XHL2I/T/eO6W2P7t0Gd9/8ntTaivNZ6mqme7DlCW5EFhTVf+mvX878M+r6p3j9tsAbGhvfx54fIofeTLwt1NsO5s57vnFcc8vkx33y6tq8aAdZvsMZaJ/vj4vIavqOuC6w/6wZLSqRg73OLON455fHPf80uW4Z/U1FGA7sKzv/VJgxwz1RZLmtdkeKF8DViQ5PcmLgLXAphnukyTNS7P6lFdV7UvyTuCL9G4b/mRVbRniRx72abNZynHPL457fuls3LP6orwk6cgx2095SZKOEAaKJKkTBsokJVmT5PEkW5NcPtP9OVxJPplkV5KH+2onJrk7ybfb6wl9265oY388yRv76quSPNS2fTRT/SbiNEiyLMmXkjyaZEuSd7X6XB/3i5Pcn+Sbbdz/udXn9LjHJFmQ5BtJPt/ez/lxJ9nW+vtgktFWG/64q8rlEAu9C/7fAX4OeBHwTeDMme7XYY7ptcAvAw/31f4AuLytXw5c1dbPbGNeBJze/iwWtG33A6+i952gu4DfmOmxDRjzacAvt/WXAP+3jW2ujzvAsW39aOA+YPVcH3ff+N8D/Cnw+fnw33nr7zbg5HG1oY/bGcrk/PQRL1X1j8DYI15mrar6CvD0uPL5wMa2vhG4oK9+c1XtraongK3AOUlOA46rqnur91/fjX1tjjhVtbOqvt7W9wCPAkuY++Ouqvr79vbothRzfNwASZYC/wL4RF95zo/7IIY+bgNlcpYAT/a9395qc82pVbUTev/zBU5p9YONf0lbH18/4iVZDryS3r/W5/y422mfB4FdwN1VNS/GDXwE+I/AT/pq82HcBfxFkgfao6dgGsY9q7+HMo0m9YiXOexg45+Vfy5JjgU+A7y7qn444LTwnBl3Ve0HViZ5KXB7krMH7D4nxp3kN4FdVfVAktdNpskEtVk37ubcqtqR5BTg7iSPDdi3s3E7Q5mc+fKIl6faNJf2uqvVDzb+7W19fP2IleRoemHyqar6bCvP+XGPqaq/A74MrGHuj/tc4LeSbKN3mvoNSf4nc3/cVNWO9roLuJ3eafuhj9tAmZz58oiXTcC6tr4OuKOvvjbJoiSnAyuA+9u0eU+S1e3uj0v62hxxWh+vBx6tqg/3bZrr417cZiYkOQb4NeAx5vi4q+qKqlpaVcvp/Z29p6r+NXN83El+JslLxtaBXwceZjrGPdN3I8yWBXgTvbuCvgO8b6b708F4Pg3sBH5M718i64GTgM3At9vriX37v6+N/XH67vQARtp/rN8B/pj29IUjcQFeQ2/K/i3gwba8aR6M+xeBb7RxPwz8p1af0+Me92fwOp67y2tOj5ve3ajfbMuWsf9fTce4ffSKJKkTnvKSJHXCQJEkdcJAkSR1wkCRJHXCQJEkdcJAkTqS5ANJ/kMHx3lpkt/pe/+zSW473ONKw2agSDMgyaDHHr0U+GmgVNWOqrpw6J2SDpOBIh2GJO9rvyHxl8DPt9qXk4y09ZPboz9I8ttJ/izJ5+g9uO/YJJuTfL395sTYE6w/CLyi/ZbFh5IsT/vdmvR+2+R/tP2/keT1fcf+bJIvtN+7+INp/qOQfDikNFVJVtF7pMcr6f1d+jrwwCGavQr4xap6us1S3ly9B1SeDPyfJJvo/VbF2VW1sn3O8r72lwFU1T9L8k/pBdM/adtWtr7sBR5P8rGq6n+KrDRUBoo0db8K3F5V/wDQwuBQ7q6qsd+hCfBfkryW3uPVlwCnHqL9a4CPAVTVY0m+C4wFyuaqerb15RHg5Rz4WHJpqAwU6fBM9OyifTx3OvnF47b9v771twGLgVVV9eN2amz8/uMN+gnWvX3r+/Hvt6aZ11CkqfsK8OYkx7Snu/7LVt8GrGrrgy6mH0/v9zp+3K6FvLzV99D7ieKDfebbANqprpfRe6CfNOMMFGmKqvdzwrfQe2rxZ4D/1Tb9IXBpkq8CJw84xKeAkSSj9ELisXbcHwD/O8nDST40rs01wIIkD7XP/u2q2ot0BPBpw5KkTjhDkSR1wkCRJHXCQJEkdcJAkSR1wkCRJHXCQJEkdcJAkSR14v8Dy4rO0/+mmFoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(df.duration, bins = 20) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7e97710",
   "metadata": {},
   "source": [
    "# Part 5. Machine Learning: Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "417f1e5e",
   "metadata": {},
   "source": [
    "![](https://www.wordstream.com/wp-content/uploads/2021/07/machine-learning1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46f9d9fa",
   "metadata": {},
   "source": [
    "The main objective of this project is to identify the most responsive customers before the marketing campaign so that the bank will be able to efficiently reach out to them, saving time and marketing resources. To achieve this objective, classification algorithms will be employed. By analyzing customer statistics, a classification model will be built to classify all clients into two groups: \"yes\" to term deposits and \"no\" to term deposits."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6620b1c",
   "metadata": {},
   "source": [
    "## Load the cleaned dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42c3ffb2",
   "metadata": {},
   "source": [
    "Dataset changes so far...\n",
    "<br>1)\tDefault\n",
    "<br>2)\tDelete the rows which column 'poutcome' contains 'other'\n",
    "<br>3)\tGetting ‘Balance’ outliers\n",
    "<br>4)\tDrop ‘balance’ outliers and Change column name: 'y' to 'response'\n",
    "<br>5)\tDrop column \"contact\" which is useless\n",
    "<br>6)\tDrop rows that 'duration' < 5s\n",
    "<br>7)\tDrop customer values with 'other' education\n",
    "\n",
    "\n",
    "<br>Still needs to be cleaned a little"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bd7e439b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response_binary</th>\n",
       "      <th>month_int</th>\n",
       "      <th>age_group</th>\n",
       "      <th>balance_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>2143</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>4.35</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>average balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>29</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>2.52</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>low balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>low balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>35</td>\n",
       "      <td>management</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>231</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>low balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>447</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>5</td>\n",
       "      <td>may</td>\n",
       "      <td>3.62</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>low balance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age           job  marital  education default  balance housing loan  day  \\\n",
       "0   58    management  married   tertiary      no     2143     yes   no    5   \n",
       "1   44    technician   single  secondary      no       29     yes   no    5   \n",
       "2   33  entrepreneur  married  secondary      no        2     yes  yes    5   \n",
       "5   35    management  married   tertiary      no      231     yes   no    5   \n",
       "6   28    management   single   tertiary      no      447     yes  yes    5   \n",
       "\n",
       "  month  duration  campaign  pdays  previous poutcome  response_binary  \\\n",
       "0   may      4.35         1     -1         0  unknown                0   \n",
       "1   may      2.52         1     -1         0  unknown                0   \n",
       "2   may      1.27         1     -1         0  unknown                0   \n",
       "5   may      2.32         1     -1         0  unknown                0   \n",
       "6   may      3.62         1     -1         0  unknown                0   \n",
       "\n",
       "   month_int  age_group    balance_group  \n",
       "0        5.0       50.0  average balance  \n",
       "1        5.0       40.0      low balance  \n",
       "2        5.0       30.0      low balance  \n",
       "5        5.0       30.0      low balance  \n",
       "6        5.0       20.0      low balance  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load 'cleaned' datset #7 \n",
    "dataset8 = dataset7.drop('response', axis=1)\n",
    "\n",
    "dataset8.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "40a7bab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>duration</th>\n",
       "      <th>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>response_binary</th>\n",
       "      <th>month_int</th>\n",
       "      <th>age_group</th>\n",
       "      <th>balance_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45205</th>\n",
       "      <td>25</td>\n",
       "      <td>technician</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>505</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>6.43</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>low balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>51</td>\n",
       "      <td>technician</td>\n",
       "      <td>married</td>\n",
       "      <td>tertiary</td>\n",
       "      <td>no</td>\n",
       "      <td>825</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>16.28</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>low balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45207</th>\n",
       "      <td>71</td>\n",
       "      <td>retired</td>\n",
       "      <td>divorced</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>1729</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>7.60</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>average balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45208</th>\n",
       "      <td>72</td>\n",
       "      <td>retired</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>5715</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>18.78</td>\n",
       "      <td>5</td>\n",
       "      <td>184</td>\n",
       "      <td>3</td>\n",
       "      <td>success</td>\n",
       "      <td>1</td>\n",
       "      <td>11.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>high balance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45209</th>\n",
       "      <td>57</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>668</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>17</td>\n",
       "      <td>nov</td>\n",
       "      <td>8.47</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>low balance</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          job   marital  education default  balance housing loan  \\\n",
       "45205   25   technician    single  secondary      no      505      no  yes   \n",
       "45206   51   technician   married   tertiary      no      825      no   no   \n",
       "45207   71      retired  divorced    primary      no     1729      no   no   \n",
       "45208   72      retired   married  secondary      no     5715      no   no   \n",
       "45209   57  blue-collar   married  secondary      no      668      no   no   \n",
       "\n",
       "       day month  duration  campaign  pdays  previous poutcome  \\\n",
       "45205   17   nov      6.43         2     -1         0  unknown   \n",
       "45206   17   nov     16.28         3     -1         0  unknown   \n",
       "45207   17   nov      7.60         2     -1         0  unknown   \n",
       "45208   17   nov     18.78         5    184         3  success   \n",
       "45209   17   nov      8.47         4     -1         0  unknown   \n",
       "\n",
       "       response_binary  month_int  age_group    balance_group  \n",
       "45205                1       11.0       20.0      low balance  \n",
       "45206                1       11.0       50.0      low balance  \n",
       "45207                1       11.0       60.0  average balance  \n",
       "45208                1       11.0       60.0     high balance  \n",
       "45209                0       11.0       50.0      low balance  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset8.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fed7bec",
   "metadata": {},
   "source": [
    "# 5.1 Prepare data for classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "dc14da79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['management' 'technician' 'entrepreneur' 'retired' 'admin.' 'services'\n",
      " 'blue-collar' 'self-employed' 'unemployed' 'housemaid' 'student' 'other']\n",
      "[ 4 10  2 ...  6  6  1]\n"
     ]
    }
   ],
   "source": [
    "##5.2 Create dummy variables from categorical features\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "import sklearn.model_selection as ms\n",
    "from sklearn import linear_model\n",
    "import sklearn.metrics as sklm\n",
    "import numpy as np\n",
    "import numpy.random as nr\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as ss\n",
    "import math\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "print(dataset8['job'].unique())\n",
    "Features = dataset8['job']\n",
    "enc = preprocessing.LabelEncoder()\n",
    "enc.fit(Features)\n",
    "Features = enc.transform(Features)\n",
    "print(Features)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "676c6cd3",
   "metadata": {},
   "source": [
    "Notice that this five original body style categories of this feature is now coded as integers in the set $\\{ 0,1,2,3,4 \\}$.\n",
    "\n",
    "For the next step in the process, the numerically coded categorical variable is converted to a set of dummy variables following these steps:\n",
    "1. A one hot encoder object is created using the `OneHotEncoder` method from the `sklearn.preprocessing` module.\n",
    "2. The numerically coded categorical feature is fit with the one hot encoder. \n",
    "3. The dummy variables are encoded using the `transform` method on the encodings.\n",
    "\n",
    "Execute the code in the cell below and examine the result. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b83ac9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ohe = preprocessing.OneHotEncoder()\n",
    "encoded = ohe.fit(Features.reshape(-1,1))\n",
    "Features = encoded.transform(Features.reshape(-1,1)).toarray()\n",
    "Features[:10,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc4ef0a",
   "metadata": {},
   "source": [
    "Notice that the body_style feature has been encoded as five columns. Each of these columns is a dummy variable representing one category. Each row has one and only one dummy variable with a 1, and the rest 0s. This is the one hot encoding.\n",
    "\n",
    "Now, you need to one hot encode all five categorical variables and append them as columns to the model matrix with the scaled numeric variables. The code in the cell below executes a for loop that calls the encode_string function and uses the numpy concatenate function to add the dummy variables to the model matrix. The encode_string function uses the same process discussed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "e304d442",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40841, 35)\n",
      "[[0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.\n",
      "  0. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
      "  0. 1. 0. 0. 1. 1. 0. 1. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "def encode_string(cat_feature):\n",
    "    ## First encode the strings to numeric categories\n",
    "    enc = preprocessing.LabelEncoder()\n",
    "    enc.fit(cat_feature)\n",
    "    enc_cat_feature = enc.transform(cat_feature)\n",
    "    ## Now, apply one hot encoding\n",
    "    ohe = preprocessing.OneHotEncoder()\n",
    "    encoded = ohe.fit(enc_cat_feature.reshape(-1,1))\n",
    "    return encoded.transform(enc_cat_feature.reshape(-1,1)).toarray()\n",
    "    \n",
    "\n",
    "categorical_columns = ['job', 'education', 'housing', 'loan', 'default', 'housing'] #maybe add poutcome?\n",
    "\n",
    "for col in categorical_columns:\n",
    "    temp = encode_string(dataset8[col])\n",
    "    Features = np.concatenate([Features, temp], axis = 1)\n",
    "\n",
    "print(Features.shape)\n",
    "print(Features[:2, :])    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3390c28d",
   "metadata": {},
   "source": [
    "Notice that the model matrix now has 54 features which encode the 6+ origianal categorical columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a611760",
   "metadata": {},
   "source": [
    "### 5.3 Add the numeric features\n",
    "\n",
    "To complete the model matrix, execute the code in the cell below to concatenate the three numeric features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9d4884d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,  1.000e+00,\n",
       "         0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,\n",
       "         0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,\n",
       "         0.000e+00,  1.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,\n",
       "         0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,\n",
       "         0.000e+00,  1.000e+00,  0.000e+00,  1.000e+00,  1.000e+00,\n",
       "         0.000e+00,  1.000e+00,  0.000e+00,  0.000e+00,  1.000e+00,\n",
       "         5.800e+01,  2.143e+03,  4.350e+00,  1.000e+00, -1.000e+00],\n",
       "       [ 0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,\n",
       "         0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,\n",
       "         1.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,\n",
       "         0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,  0.000e+00,\n",
       "         0.000e+00,  0.000e+00,  1.000e+00,  0.000e+00,  0.000e+00,\n",
       "         1.000e+00,  0.000e+00,  0.000e+00,  1.000e+00,  1.000e+00,\n",
       "         0.000e+00,  1.000e+00,  0.000e+00,  0.000e+00,  1.000e+00,\n",
       "         4.400e+01,  2.900e+01,  2.520e+00,  1.000e+00, -1.000e+00]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Features = np.concatenate([Features, np.array(dataset8[['age', 'balance', 'duration', 'campaign', 'pdays']])], axis = 1)\n",
    "Features[:2,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c9a0c62",
   "metadata": {},
   "source": [
    "##5.4 Split the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "561f95e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "##5.4 split the dataset\n",
    "## Randomly sample cases to create independent training and test data\n",
    "nr.seed(9988)\n",
    "labels = np.array(dataset8['response_binary'])\n",
    "\n",
    "indx = range(Features.shape[0])\n",
    "indx = ms.train_test_split(indx, test_size = 60)\n",
    "x_train = Features[indx[0],:]\n",
    "y_train = np.ravel(labels[indx[0]])\n",
    "x_test = Features[indx[1],:]\n",
    "y_test = np.ravel(labels[indx[1]])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18b5e0b9",
   "metadata": {},
   "source": [
    "##5.5 rescale the numeric features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c1df06a",
   "metadata": {},
   "source": [
    "Numeric features must be rescaled so they have a similar range of values. Rescaling prevents features from having an undue influence on model training simply because then have a larger range of numeric variables. \n",
    "\n",
    "The code in the cell below uses the `StandardScaler` function from the Scikit Learn preprocessing package to Zscore scale the numeric features. Notice that the scaler is fit only on the training data. The trained scaler is these applied to the test data. Test data should always be scaled using the parameters from the training data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4bda43bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40781, 40)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         1.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  1.        ,  0.        ,  0.        ,\n",
       "         0.        ,  1.        ,  0.        ,  1.        ,  0.        ,\n",
       "         1.        ,  1.        ,  0.        ,  0.        ,  1.        ,\n",
       "        -0.93450371, -0.95636958, -0.81024713,  3.94071641, -0.36632422],\n",
       "       [ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  1.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "         0.        ,  1.        ,  0.        ,  0.        ,  1.        ,\n",
       "         0.        ,  0.        ,  1.        ,  0.        ,  1.        ,\n",
       "         0.        ,  1.        ,  0.        ,  1.        ,  0.        ,\n",
       "        -1.88906966, -0.46296941, -0.95889669, -0.57147912, -0.36632422]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = preprocessing.StandardScaler().fit(x_train[:,35:]) #The \n",
    "x_train[:,35:] = scaler.transform(x_train[:,35:])\n",
    "x_test[:,35:] = scaler.transform(x_test[:,35:])\n",
    "print(x_train.shape)\n",
    "x_train[:2,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d5ff20",
   "metadata": {},
   "source": [
    "Logisitic Regression with weighted model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "41cdb7aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn.model_selection as ms\n",
    "from sklearn import linear_model\n",
    "import sklearn.metrics as sklm\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import numpy as np\n",
    "import numpy.random as nr\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "df267f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Randomly sample cases to create independent training and test data\n",
    "nr.seed(1115)\n",
    "\n",
    "Labels = np.array(dataset8['response_binary'])\n",
    "\n",
    "indx = range(Features.shape[0])\n",
    "indx = ms.train_test_split(indx, test_size = 300)\n",
    "X_train = Features[indx[0],:]\n",
    "y_train = np.ravel(Labels[indx[0]])\n",
    "X_test = Features[indx[1],:]\n",
    "y_test = np.ravel(Labels[indx[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "1cc007c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(class_weight={0: 0.45, 1: 0.55}, max_iter=1000)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_mod = linear_model.LogisticRegression(C = 1.0, class_weight = {0:0.45, 1:0.55}, max_iter=1000) \n",
    "logistic_mod.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "31ec4371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Confusion matrix\n",
      "                 Score positive    Score negative\n",
      "Actual positive       251                16\n",
      "Actual negative        15                18\n",
      "\n",
      "Accuracy        0.90\n",
      "AUC             0.87\n",
      "Macro precision 0.74\n",
      "Macro recall    0.74\n",
      " \n",
      "           Positive      Negative\n",
      "Num case      267            33\n",
      "Precision    0.94          0.53\n",
      "Recall       0.94          0.55\n",
      "F1           0.94          0.54\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def score_model(probs, threshold):\n",
    "    return np.array([1 if x > threshold else 0 for x in probs[:,1]])\n",
    "\n",
    "def print_metrics(labels, probs, threshold):\n",
    "    scores = score_model(probs, threshold)\n",
    "    metrics = sklm.precision_recall_fscore_support(labels, scores)\n",
    "    conf = sklm.confusion_matrix(labels, scores)\n",
    "    print('                 Confusion matrix')\n",
    "    print('                 Score positive    Score negative')\n",
    "    print('Actual positive    %6d' % conf[0,0] + '             %5d' % conf[0,1])\n",
    "    print('Actual negative    %6d' % conf[1,0] + '             %5d' % conf[1,1])\n",
    "    print('')\n",
    "    print('Accuracy        %0.2f' % sklm.accuracy_score(labels, scores))\n",
    "    print('AUC             %0.2f' % sklm.roc_auc_score(labels, probs[:,1]))\n",
    "    print('Macro precision %0.2f' % float((float(metrics[0][0]) + float(metrics[0][1]))/2.0))\n",
    "    print('Macro recall    %0.2f' % float((float(metrics[1][0]) + float(metrics[1][1]))/2.0))\n",
    "    print(' ')\n",
    "    print('           Positive      Negative')\n",
    "    print('Num case   %6d' % metrics[3][0] + '        %6d' % metrics[3][1])\n",
    "    print('Precision  %6.2f' % metrics[0][0] + '        %6.2f' % metrics[0][1])\n",
    "    print('Recall     %6.2f' % metrics[1][0] + '        %6.2f' % metrics[1][1])\n",
    "    print('F1         %6.2f' % metrics[2][0] + '        %6.2f' % metrics[2][1])\n",
    "\n",
    "def plot_auc(labels, probs):\n",
    "    ## Compute the false positive rate, true positive rate\n",
    "    ## and threshold along with the AUC\n",
    "    fpr, tpr, threshold = sklm.roc_curve(labels, probs[:,1])\n",
    "    auc = sklm.auc(fpr, tpr)\n",
    "    \n",
    "    ## Plot the result\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(fpr, tpr, color = 'orange', label = 'AUC = %0.2f' % auc)\n",
    "    plt.legend(loc = 'lower right')\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.xlim([0, 1])\n",
    "    plt.ylim([0, 1])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()    \n",
    "\n",
    "probabilities = logistic_mod.predict_proba(X_test)\n",
    "print_metrics(y_test, probabilities, 0.3)  \n",
    "plot_auc(y_test, probabilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bfdb136",
   "metadata": {},
   "source": [
    "        Cross validate model/optimize hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3890e785",
   "metadata": {},
   "outputs": [],
   "source": [
    "nr.seed(123)\n",
    "inside = ms.KFold(n_splits=10, shuffle = True)\n",
    "nr.seed(321)\n",
    "outside = ms.KFold(n_splits=10, shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1b793b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "nr.seed(3456)\n",
    "## Define the dictionary for the grid search and the model object to search on\n",
    "param_grid = {\"C\": [0.1, 1, 10, 100, 1000]}\n",
    "## Define the logistic regression model\n",
    "logistic_mod = linear_model.LogisticRegression(class_weight = {0:0.45, 0:0.55}, max_iter=1000) \n",
    "\n",
    "## Perform the grid search over the parameters\n",
    "clf = ms.GridSearchCV(estimator = logistic_mod, param_grid = param_grid, \n",
    "                      cv = inside, # Use the inside folds\n",
    "                      scoring = 'roc_auc',\n",
    "                      return_train_score = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "4815d736",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean performance metric = 0.866\n",
      "SDT of the metric       = 0.009\n",
      "Outcomes by cv fold\n",
      "Fold  1    0.871\n",
      "Fold  2    0.872\n",
      "Fold  3    0.868\n",
      "Fold  4    0.867\n",
      "Fold  5    0.844\n",
      "Fold  6    0.871\n",
      "Fold  7    0.861\n",
      "Fold  8    0.875\n",
      "Fold  9    0.862\n",
      "Fold 10    0.873\n"
     ]
    }
   ],
   "source": [
    "nr.seed(498)\n",
    "cv_estimate = cross_val_score(clf, Features, Labels, \n",
    "                                 cv = outside) # Use the outside folds\n",
    "print('Mean performance metric = %4.3f' % np.mean(cv_estimate))\n",
    "\n",
    "print('SDT of the metric       = %4.3f' % np.std(cv_estimate))\n",
    "print('Outcomes by cv fold')\n",
    "for i, x in enumerate(cv_estimate):\n",
    "    print('Fold %2d    %4.3f' % (i+1, x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec54aff2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "148d716b",
   "metadata": {},
   "source": [
    "Eliminate low variance features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "0e009db9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40841, 40)\n",
      "[False  True False False  True False False False False False False False\n",
      " False  True False False  True False False False False False False False\n",
      " False  True  True  True  True False False False False  True  True  True\n",
      "  True  True  True  True]\n",
      "(40841, 15)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import feature_selection as fs\n",
    "\n",
    "print(Features.shape)\n",
    "\n",
    "## Define the variance threhold and fit the threshold to the feature array. \n",
    "sel = fs.VarianceThreshold(threshold=(.8 * (1 - .8)))\n",
    "Features_reduced = sel.fit_transform(Features)\n",
    "\n",
    "## Print the support and shape for the transformed features\n",
    "print(sel.get_support())\n",
    "print(Features_reduced.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eeea8805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Reshape the Label array\n",
    "Labels = Labels.reshape(Labels.shape[0],)\n",
    "\n",
    "## Set folds for nested cross validation\n",
    "nr.seed(988)\n",
    "feature_folds = ms.KFold(n_splits=10, shuffle = True)\n",
    "\n",
    "## Define the model\n",
    "logistic_mod = linear_model.LogisticRegression(C = 10, class_weight = {0:0.45, 1:0.55}, max_iter=1000) \n",
    "\n",
    "## Perform feature selection by CV with high variance features only\n",
    "nr.seed(6677)\n",
    "selector = fs.RFECV(estimator = logistic_mod, cv = feature_folds,\n",
    "                      scoring = 'roc_auc')\n",
    "selector = selector.fit(Features_reduced, Labels)\n",
    "selector.support_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "dbb65b5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40841, 15)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selector.ranking_\n",
    "\n",
    "Features_reduced = selector.transform(Features_reduced)\n",
    "Features_reduced.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d1567c",
   "metadata": {},
   "source": [
    "Now features are at 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a535b7c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Number of features')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1, len(selector.grid_scores_) + 1), selector.grid_scores_)\n",
    "plt.title('Mean AUC by number of features')\n",
    "plt.ylabel('AUC')\n",
    "plt.xlabel('Number of features')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83345b0d",
   "metadata": {},
   "source": [
    "## Apply nested cross validation to create model\n",
    "\n",
    "The next step is to use nested cross validation to optimize the model hyperparameter and test the model performance. The model is constructed using the features selected. \n",
    "\n",
    "As a first step, construct the inside and outside folds for the nested cross validation by running the code in the cell below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d6beffc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Labels = Labels.reshape(Labels.shape[0],)\n",
    "scoring = ['precision_macro', 'recall_macro', 'roc_auc']\n",
    "logistic_mod = linear_model.LogisticRegression(C = 1.0, class_weight = {0:0.45, 1:0.55}, max_iter=1000) \n",
    "scores = ms.cross_validate(logistic_mod, Features, Labels, scoring=scoring,\n",
    "                        cv=10, return_train_score=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6aab3112",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Precision     Recall       AUC\n",
      "Fold  1    0.836        0.672      0.928\n",
      "Fold  2    0.879        0.676      0.939\n",
      "Fold  3    0.825        0.680      0.896\n",
      "Fold  4    0.712        0.588      0.765\n",
      "Fold  5    0.738        0.587      0.870\n",
      "Fold  6    0.704        0.574      0.793\n",
      "Fold  7    0.656        0.570      0.809\n",
      "Fold  8    0.612        0.551      0.812\n",
      "Fold  9    0.717        0.585      0.854\n",
      "Fold 10    0.632        0.636      0.779\n",
      "----------------------------------------\n",
      "Mean       0.731        0.612      0.844\n",
      "Std        0.085        0.047      0.059\n"
     ]
    }
   ],
   "source": [
    "def print_format(f,x,y,z):\n",
    "    print('Fold %2d    %4.3f        %4.3f      %4.3f' % (f, x, y, z))\n",
    "\n",
    "def print_cv(scores):\n",
    "    fold = [x + 1 for x in range(len(scores['test_precision_macro']))]\n",
    "    print('         Precision     Recall       AUC')\n",
    "    [print_format(f,x,y,z) for f,x,y,z in zip(fold, scores['test_precision_macro'], \n",
    "                                          scores['test_recall_macro'],\n",
    "                                          scores['test_roc_auc'])]\n",
    "    print('-' * 40)\n",
    "    print('Mean       %4.3f        %4.3f      %4.3f' % \n",
    "          (np.mean(scores['test_precision_macro']), np.mean(scores['test_recall_macro']), np.mean(scores['test_roc_auc'])))  \n",
    "    print('Std        %4.3f        %4.3f      %4.3f' % \n",
    "          (np.std(scores['test_precision_macro']), np.std(scores['test_recall_macro']), np.std(scores['test_roc_auc'])))\n",
    "\n",
    "print_cv(scores)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56644b7",
   "metadata": {},
   "source": [
    "Notice that there is considerable variability in each of the performance metrics from fold to fold. Even so, the standard deviations are at least an order of magnitude than the means. It is clear that any one fold does not provide a representative value of the performance metrics. The later is a key point as to why cross validation is important when evaluating a machine learning model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1b0cb84",
   "metadata": {},
   "source": [
    "Also copied\n",
    "\n",
    "nr.seed(123)\n",
    "inside = ms.KFold(n_splits=10, shuffle = True)\n",
    "nr.seed(321)\n",
    "outside = ms.KFold(n_splits=10, shuffle = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b8de0b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.87549534 0.87724633 0.87632169 0.87706478 0.87711361]\n",
      "[0.86625689 0.86537994 0.86693182 0.86477789 0.86509527]\n",
      "[0.85472195 0.8549382  0.85197708 0.85484065 0.85399151]\n",
      "[0.85702557 0.85957558 0.85678564 0.86015827 0.8593993 ]\n",
      "[0.85672817 0.8560817  0.85591407 0.85611206 0.85649665]\n",
      "[0.86983584 0.8704745  0.87075267 0.87091919 0.87053375]\n",
      "[0.86888163 0.86982519 0.86963139 0.87032422 0.87018917]\n",
      "[0.86053238 0.86097627 0.8605092  0.86114087 0.86217484]\n",
      "[0.88428456 0.88372479 0.88445748 0.88369567 0.88394703]\n",
      "[0.87025842 0.86853185 0.87116533 0.86918993 0.87115889]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Fit thhe cross validated grid search over the data \n",
    "clf.fit(Features, Labels)\n",
    "keys = list(clf.cv_results_.keys())\n",
    "for key in keys[6:16]:\n",
    "    print(clf.cv_results_[key])\n",
    "## And print the best parameter value\n",
    "clf.best_estimator_.C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4f62f3ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance metrics by parameter\n",
      "Parameter   Mean performance   STD performance\n",
      "    0.10        0.86640            0.00888\n",
      "    1.00        0.86668            0.00875\n",
      "   10.00        0.86644            0.00960\n",
      "  100.00        0.86682            0.00874\n",
      " 1000.00        0.86701            0.00891\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_cv(clf, params_grid, param = 'C'):\n",
    "    params = [x for x in params_grid[param]]\n",
    "  \n",
    "    keys = list(clf.cv_results_.keys())              \n",
    "    grid = np.array([clf.cv_results_[key] for key in keys[6:16]])\n",
    "    means = np.mean(grid, axis = 0)\n",
    "    stds = np.std(grid, axis = 0)\n",
    "    print('Performance metrics by parameter')\n",
    "    print('Parameter   Mean performance   STD performance')\n",
    "    for x,y,z in zip(params, means, stds):\n",
    "        print('%8.2f        %6.5f            %6.5f' % (x,y,z))\n",
    "    \n",
    "    params = [math.log10(x) for x in params]\n",
    "    \n",
    "    plt.scatter(params * grid.shape[0], grid.flatten())\n",
    "    p = plt.scatter(params, means, color = 'red', marker = '+', s = 300)\n",
    "    plt.plot(params, np.transpose(grid))\n",
    "    plt.title('Performance metric vs. log parameter value\\n from cross validation')\n",
    "    plt.xlabel('Log hyperparameter value')\n",
    "    plt.ylabel('Performance metric')\n",
    "    \n",
    "plot_cv(clf, param_grid)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d196a9f2",
   "metadata": {},
   "source": [
    "## Comparing classification algorithms (Currently still coding)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b949cc8",
   "metadata": {},
   "source": [
    "Four different classification algorithms (Logistic Regression, K-Neighbors Classifier, Decision Tree Classifier, and Gaussian NB) are run on the dataset and the best-performing one will be used to build the classification model.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "02a4d60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0163580a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "eaec8f69",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 20% of the data will be used for testing\n",
    "#test_size= 0.20\n",
    "#seed = 7\n",
    "#X_train, X_test, Y_train, Y_test= train_test_split(X, Y, test_size=test_size, random_state=seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "38056a68",
   "metadata": {},
   "outputs": [],
   "source": [
    "#models = []\n",
    "#models.append(('LR', LogisticRegression()))\n",
    "#models.append(('KNN', KNeighborsClassifier()))\n",
    "#models.append(('CART', DecisionTreeClassifier()))\n",
    "#models.append(('NB', GaussianNB()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59c7a107",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cf40833",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "222ad795",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copied clf code from here, but i think was originally pasted in wrong order, so pasting above\n",
    "\n",
    "nr.seed(3456)\n",
    "## Define the dictionary for the grid search and the model object to search on\n",
    "param_grid = {\"C\": [0.1, 1, 10, 100, 1000]}\n",
    "## Define the logistic regression model\n",
    "logistic_mod = linear_model.LogisticRegression(class_weight = {0:0.45, 0:0.55}) \n",
    "\n",
    "## Perform the grid search over the parameters\n",
    "clf = ms.GridSearchCV(estimator = logistic_mod, param_grid = param_grid, \n",
    "                      cv = inside, # Use the inside folds\n",
    "                      scoring = 'roc_auc',\n",
    "                      return_train_score = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4a199cee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Confusion matrix\n",
      "                 Score positive    Score negative\n",
      "Actual positive       250                17\n",
      "Actual negative        14                19\n",
      "\n",
      "Accuracy        0.90\n",
      "AUC             0.88\n",
      "Macro precision 0.74\n",
      "Macro recall    0.76\n",
      " \n",
      "           Positive      Negative\n",
      "Num case      267            33\n",
      "Precision    0.95          0.53\n",
      "Recall       0.94          0.58\n",
      "F1           0.94          0.55\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "logistic_mod = linear_model.LogisticRegression(C = 0.1, class_weight = {0:0.45, 1:0.55}, max_iter=1000) \n",
    "logistic_mod.fit(X_train, y_train)\n",
    "probabilities = logistic_mod.predict_proba(X_test)\n",
    "print_metrics(y_test, probabilities, 0.3)  \n",
    "plot_auc(y_test, probabilities)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a5984d",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "\n",
    "Then, I also balanced the data and optimized the features using hyperparameterization. \n",
    "\n",
    "The most important features the bank should focus on is Age(Under 30 or over 60), Job(Student or retired), Balance(more than $5,000 Euros)\n",
    "\n",
    "Logistic model was the best and had an accuracy of 0.88 with all features and 0.90 when features were reduced. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff64ce6",
   "metadata": {},
   "source": [
    "**The main objective of this project is to increase the effectiveness of the bank's telemarketing campaign, which was successfully met through data analysis,  visualization and analytical model building. A target customer profile was  established while classification and regression models were built to predict customers' response to the term deposit campaign.**\n",
    "\n",
    "## Conclusion:\n",
    "\n",
    "According to previous analysis, a target customer profile can be established. The most responsive customers possess these features:\n",
    "* Feature 1: age < 30 or age > 60\n",
    "* Feature 2: students or retired people\n",
    "* Feature 3: a balance of more than 5000 euros\n",
    "\n",
    "By applying logistic and ridge regression algorithms, classification and estimation model were successfully built. With these two models, the bank will be able to predict a customer's response to its telemarketing campaign before calling this customer. In this way, the bank can allocate more marketing efforts to the clients who are classified as highly likely to accept term deposits, and call less to those who are unlikely to make term deposits.\n",
    "\n",
    "In addition, predicting duration before calling and adjusting marketing plan benefit both the bank and its clients. On the one hand, it will increase the efficiency of the bank’s telemarketing campaign, saving time and efforts. On the other hand, it prevents some clients from receiving undesirable advertisements, raising customer satisfaction. With the aid of logistic and ridge regression models, the bank can enter a virtuous cycle of effective marketing, more investments and happier customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99f7f0b8",
   "metadata": {},
   "source": [
    "## Recommendations\n",
    "\n",
    "**1. More appropriate timing**\n",
    "\n",
    "When implementing a marketing strategy, external factors, such as the time of calling, should also be carefully considered. The previous analysis points out that March, September, October and December had the highest success rates. Nevertheless, more data should be collected and analyzed to make sure that this seasonal effect is constant over time. If the trend has the potential to continue in the future, the bank should consider initiating its telemarketing campaign in fall and spring. \n",
    "\n",
    "**2. Smarter marketing design**\n",
    "\n",
    "By targeting the right customers, the bank will have more and more positive responses, and the classification algorithms would ultimately eliminate the imbalance in the original dataset. Hence, more accurate information will be presented to the bank for improving the subscriptions. Meanwhile, to increase the likelihood of subscription, the bank should re-evaluate the content and design of its current campaign, making it more appealing to its target customers. \n",
    "\n",
    "**3. Better services provision**\n",
    "\n",
    "With a more granular understanding of its customer base, the bank has the ability to provide better banking services.\n",
    "For example, marital status and occupation reveal a customer's life stage while loan status indicates his/her overall risk profile. With this information, the bank can estimate when a customer might need to make an investment. In this way, the bank can better satisfy its customer demand by providing banking services for the right customer at the right time.\n",
    "\n",
    "**4. Compare with other algorithms**\n",
    "\n",
    "We want to make sure the current logarithmic algorithm is best, so we can compare it to KNeighborsClassifier, DecisionTreeClassifer, and GaussianNB. I'm currently still working on this, but have done so in other projects. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "806c91f0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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