notebook1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "67a30041-00ff-415a-983e-2b543b14753f",
   "metadata": {},
   "source": [
    "# Credit Card Offer Acceptance Trends in Banking\n",
    "\n",
    "Within this notebook, we meticulously analyze customer data to glean insights into the factors influencing their decision to accept or decline offered credit cards. Additionally, we construct predictive models to anticipate and understand customer behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9c6fce7b-3252-4d21-bb17-fb5f4d936102",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "importing necessary libraries\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "import warnings \n",
    "import pickle \n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mutual_info_score\n",
    "from sklearn.feature_extraction import DictVectorizer \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f6a7683f-5fd4-46d1-8d50-865a55a18720",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 18000\n",
      "Number of columns: 18\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "reading the data and checking the shape\n",
    "\"\"\"\n",
    "df = pd.read_csv(\"./data/creditcardmarketing-bbm.csv\")\n",
    "\n",
    "pd.set_option('display.max.columns', None)\n",
    "\n",
    "\n",
    "print(f\"Number of rows: {df.shape[0]}\")\n",
    "print(f\"Number of columns: {df.shape[1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbbbd837-f8c7-4b51-9ef8-6a59b2e4244d",
   "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>index</th>\n",
       "      <th>Customer Number</th>\n",
       "      <th>Offer Accepted</th>\n",
       "      <th>Reward</th>\n",
       "      <th>Mailer Type</th>\n",
       "      <th>Income Level</th>\n",
       "      <th># Bank Accounts Open</th>\n",
       "      <th>Overdraft Protection</th>\n",
       "      <th>Credit Rating</th>\n",
       "      <th># Credit Cards Held</th>\n",
       "      <th># Homes Owned</th>\n",
       "      <th>Household Size</th>\n",
       "      <th>Own Your Home</th>\n",
       "      <th>Average Balance</th>\n",
       "      <th>Q1 Balance</th>\n",
       "      <th>Q2 Balance</th>\n",
       "      <th>Q3 Balance</th>\n",
       "      <th>Q4 Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1160.75</td>\n",
       "      <td>1669.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>1095.0</td>\n",
       "      <td>1002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>147.25</td>\n",
       "      <td>39.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>366.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>276.50</td>\n",
       "      <td>367.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>242.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1219.00</td>\n",
       "      <td>1578.0</td>\n",
       "      <td>1760.0</td>\n",
       "      <td>1119.0</td>\n",
       "      <td>419.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1211.00</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>1357.0</td>\n",
       "      <td>982.0</td>\n",
       "      <td>365.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  Customer Number Offer Accepted     Reward Mailer Type Income Level  \\\n",
       "0      0                1             No  Air Miles      Letter         High   \n",
       "1      1                2             No  Air Miles      Letter       Medium   \n",
       "2      2                3             No  Air Miles    Postcard         High   \n",
       "3      3                4             No  Air Miles      Letter       Medium   \n",
       "4      4                5             No  Air Miles      Letter       Medium   \n",
       "\n",
       "   # Bank Accounts Open Overdraft Protection Credit Rating  \\\n",
       "0                     1                   No          High   \n",
       "1                     1                   No        Medium   \n",
       "2                     2                   No        Medium   \n",
       "3                     2                   No          High   \n",
       "4                     1                   No        Medium   \n",
       "\n",
       "   # Credit Cards Held  # Homes Owned  Household Size Own Your Home  \\\n",
       "0                    2              1               4            No   \n",
       "1                    2              2               5           Yes   \n",
       "2                    2              1               2           Yes   \n",
       "3                    1              1               4            No   \n",
       "4                    2              1               6           Yes   \n",
       "\n",
       "   Average Balance  Q1 Balance  Q2 Balance  Q3 Balance  Q4 Balance  \n",
       "0          1160.75      1669.0       877.0      1095.0      1002.0  \n",
       "1           147.25        39.0       106.0        78.0       366.0  \n",
       "2           276.50       367.0       352.0       145.0       242.0  \n",
       "3          1219.00      1578.0      1760.0      1119.0       419.0  \n",
       "4          1211.00      2140.0      1357.0       982.0       365.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Previewing the data\n",
    "\"\"\"\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3a11b5a-ea8d-46ab-bc24-b8a0add18e7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "index                     int64\n",
       "Customer Number           int64\n",
       "Offer Accepted           object\n",
       "Reward                   object\n",
       "Mailer Type              object\n",
       "Income Level             object\n",
       "# Bank Accounts Open      int64\n",
       "Overdraft Protection     object\n",
       "Credit Rating            object\n",
       "# Credit Cards Held       int64\n",
       "# Homes Owned             int64\n",
       "Household Size            int64\n",
       "Own Your Home            object\n",
       "Average Balance         float64\n",
       "Q1 Balance              float64\n",
       "Q2 Balance              float64\n",
       "Q3 Balance              float64\n",
       "Q4 Balance              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Checking the data types  of columns in the dataset \n",
    "\"\"\"\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "50cb3026-292f-4100-8ff0-bd507b4bd7b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Customer Number</th>\n",
       "      <th># Bank Accounts Open</th>\n",
       "      <th># Credit Cards Held</th>\n",
       "      <th># Homes Owned</th>\n",
       "      <th>Household Size</th>\n",
       "      <th>Average Balance</th>\n",
       "      <th>Q1 Balance</th>\n",
       "      <th>Q2 Balance</th>\n",
       "      <th>Q3 Balance</th>\n",
       "      <th>Q4 Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>17976.000000</td>\n",
       "      <td>17976.000000</td>\n",
       "      <td>17976.000000</td>\n",
       "      <td>17976.000000</td>\n",
       "      <td>17976.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8999.500000</td>\n",
       "      <td>9000.500000</td>\n",
       "      <td>1.255778</td>\n",
       "      <td>1.903500</td>\n",
       "      <td>1.203444</td>\n",
       "      <td>3.499056</td>\n",
       "      <td>940.515562</td>\n",
       "      <td>910.450656</td>\n",
       "      <td>999.392190</td>\n",
       "      <td>1042.033600</td>\n",
       "      <td>810.185803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5196.296758</td>\n",
       "      <td>5196.296758</td>\n",
       "      <td>0.472501</td>\n",
       "      <td>0.797009</td>\n",
       "      <td>0.427341</td>\n",
       "      <td>1.114182</td>\n",
       "      <td>350.297837</td>\n",
       "      <td>620.077060</td>\n",
       "      <td>457.402268</td>\n",
       "      <td>553.452599</td>\n",
       "      <td>559.001365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>48.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4499.750000</td>\n",
       "      <td>4500.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>787.500000</td>\n",
       "      <td>392.750000</td>\n",
       "      <td>663.000000</td>\n",
       "      <td>633.000000</td>\n",
       "      <td>363.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8999.500000</td>\n",
       "      <td>9000.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1007.000000</td>\n",
       "      <td>772.000000</td>\n",
       "      <td>1032.000000</td>\n",
       "      <td>945.500000</td>\n",
       "      <td>703.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13499.250000</td>\n",
       "      <td>13500.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1153.250000</td>\n",
       "      <td>1521.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1463.000000</td>\n",
       "      <td>1212.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17999.000000</td>\n",
       "      <td>18000.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>3366.250000</td>\n",
       "      <td>3450.000000</td>\n",
       "      <td>3421.000000</td>\n",
       "      <td>3823.000000</td>\n",
       "      <td>4215.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              index  Customer Number  # Bank Accounts Open  \\\n",
       "count  18000.000000     18000.000000          18000.000000   \n",
       "mean    8999.500000      9000.500000              1.255778   \n",
       "std     5196.296758      5196.296758              0.472501   \n",
       "min        0.000000         1.000000              1.000000   \n",
       "25%     4499.750000      4500.750000              1.000000   \n",
       "50%     8999.500000      9000.500000              1.000000   \n",
       "75%    13499.250000     13500.250000              1.000000   \n",
       "max    17999.000000     18000.000000              3.000000   \n",
       "\n",
       "       # Credit Cards Held  # Homes Owned  Household Size  Average Balance  \\\n",
       "count         18000.000000   18000.000000    18000.000000     17976.000000   \n",
       "mean              1.903500       1.203444        3.499056       940.515562   \n",
       "std               0.797009       0.427341        1.114182       350.297837   \n",
       "min               1.000000       1.000000        1.000000        48.250000   \n",
       "25%               1.000000       1.000000        3.000000       787.500000   \n",
       "50%               2.000000       1.000000        3.000000      1007.000000   \n",
       "75%               2.000000       1.000000        4.000000      1153.250000   \n",
       "max               4.000000       3.000000        9.000000      3366.250000   \n",
       "\n",
       "         Q1 Balance    Q2 Balance    Q3 Balance    Q4 Balance  \n",
       "count  17976.000000  17976.000000  17976.000000  17976.000000  \n",
       "mean     910.450656    999.392190   1042.033600    810.185803  \n",
       "std      620.077060    457.402268    553.452599    559.001365  \n",
       "min        0.000000      0.000000      0.000000      0.000000  \n",
       "25%      392.750000    663.000000    633.000000    363.000000  \n",
       "50%      772.000000   1032.000000    945.500000    703.000000  \n",
       "75%     1521.000000   1342.000000   1463.000000   1212.000000  \n",
       "max     3450.000000   3421.000000   3823.000000   4215.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Descriptive statisitics on numerical columns \n",
    "\"\"\"\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32580f71-e113-408c-853b-163d64f79f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Offer Accepted</th>\n",
       "      <th>Reward</th>\n",
       "      <th>Mailer Type</th>\n",
       "      <th>Income Level</th>\n",
       "      <th>Overdraft Protection</th>\n",
       "      <th>Credit Rating</th>\n",
       "      <th>Own Your Home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18000</td>\n",
       "      <td>18000</td>\n",
       "      <td>18000</td>\n",
       "      <td>18000</td>\n",
       "      <td>18000</td>\n",
       "      <td>18000</td>\n",
       "      <td>18000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>16977</td>\n",
       "      <td>6061</td>\n",
       "      <td>9147</td>\n",
       "      <td>9013</td>\n",
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       "      <td>6071</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Offer Accepted     Reward Mailer Type Income Level  \\\n",
       "count           18000      18000       18000        18000   \n",
       "unique              2          3           2            3   \n",
       "top                No  Air Miles    Postcard       Medium   \n",
       "freq            16977       6061        9147         9013   \n",
       "\n",
       "       Overdraft Protection Credit Rating Own Your Home  \n",
       "count                 18000         18000         18000  \n",
       "unique                    2             3             2  \n",
       "top                      No          High           Yes  \n",
       "freq                  15322          6071         11655  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Descriptive statisitics on object columns \n",
    "\"\"\"\n",
    "df.describe(include=\"object\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a8136a-1632-4d37-9ceb-8c98e98b02b3",
   "metadata": {},
   "source": [
    "# Data Preparation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "26d30bc0-b867-4cc9-83e1-e9dfdb2f46f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Creating a copy of the dataset to be used for analysis \n",
    "\"\"\"\n",
    "data = df.copy()\n",
    "\n",
    "\n",
    "data.columns = data.columns.str.lower().str.replace(\" \", '_')\n",
    "\n",
    "data.rename(columns={'#_bank_accounts_open':'bank_accounts_open', \"#_homes_owned\":\"homes_owned\", \n",
    "                     '#_credit_cards_held':'credit_cards_held'}, inplace=True)\n",
    "\n",
    "data['accepted_offer'] = (data['offer_accepted']==\"Yes\").astype(int)\n",
    "    \n",
    "del data['offer_accepted']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9b1b035f-2d41-4fd9-8781-c8c2764e0c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "dropping redundant columns\n",
    "\"\"\"\n",
    "data = data.drop(['index', 'customer_number'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c35c1d6-f3c6-4be0-bae5-b80ef58485ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reward                   0\n",
       "mailer_type              0\n",
       "income_level             0\n",
       "bank_accounts_open       0\n",
       "overdraft_protection     0\n",
       "credit_rating            0\n",
       "credit_cards_held        0\n",
       "homes_owned              0\n",
       "household_size           0\n",
       "own_your_home            0\n",
       "average_balance         24\n",
       "q1_balance              24\n",
       "q2_balance              24\n",
       "q3_balance              24\n",
       "q4_balance              24\n",
       "accepted_offer           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "checking for missing values\n",
    "\"\"\"\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f3b88483-1092-4f31-83a7-e93d6f9f9c8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reward                  0\n",
       "mailer_type             0\n",
       "income_level            0\n",
       "bank_accounts_open      0\n",
       "overdraft_protection    0\n",
       "credit_rating           0\n",
       "credit_cards_held       0\n",
       "homes_owned             0\n",
       "household_size          0\n",
       "own_your_home           0\n",
       "average_balance         0\n",
       "q1_balance              0\n",
       "q2_balance              0\n",
       "q3_balance              0\n",
       "q4_balance              0\n",
       "accepted_offer          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "dropping columns with missing values\n",
    "\"\"\"\n",
    "\n",
    "data.dropna(inplace=True)\n",
    "\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed1e9fc9-0349-4e40-8c87-4847fb9f621e",
   "metadata": {},
   "outputs": [],
   "source": [
    "numerical_cols = ['q1_balance', 'credit_cards_held',\n",
    "                  'q2_balance', 'q3_balance', 'q4_balance', 'average_balance', \n",
    "                  'household_size', 'bank_accounts_open', 'homes_owned']\n",
    "\n",
    "categorical_cols = ['reward', 'mailer_type', 'income_level', \n",
    "                    'overdraft_protection', 'credit_rating', 'own_your_home' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "58c31aac-2975-4870-9dc0-9b3217887679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['reward', array(['Air Miles', 'Cash Back', 'Points'], dtype=object)],\n",
       " ['mailer_type', array(['Letter', 'Postcard'], dtype=object)],\n",
       " ['income_level', array(['High', 'Medium', 'Low'], dtype=object)],\n",
       " ['overdraft_protection', array(['No', 'Yes'], dtype=object)],\n",
       " ['credit_rating', array(['High', 'Medium', 'Low'], dtype=object)],\n",
       " ['own_your_home', array(['No', 'Yes'], dtype=object)]]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def categorical_unique(data):\n",
    "    \"\"\"\n",
    "    This function takes in a dataframe and returns a list of the unique values in all columns with data type 'object'\n",
    "    :params: pandas dataframe \n",
    "    :return: Unique values in object type columns \n",
    "    :rtype: List \n",
    "    \"\"\"\n",
    "    unique_attributes = [[column, data[column].unique()] for column in data if data[column].dtype==\"object\"]\n",
    "    return unique_attributes\n",
    "\n",
    "categorical_unique(data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4a51a382-6ab8-408b-ab0d-c91acfb43cd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Splitting the data \n",
    "\"\"\"\n",
    "df_full_train, df_test = train_test_split(data, test_size=0.2, random_state=1)\n",
    "df_train, df_val = train_test_split(df_full_train, test_size=0.25, random_state=1)\n",
    "\n",
    "y_full_train = df_full_train.accepted_offer.values\n",
    "y_train = df_train.accepted_offer.values\n",
    "y_val = df_val.accepted_offer.values\n",
    "y_test = df_test.accepted_offer.values\n",
    "\n",
    "del df_train['accepted_offer']\n",
    "del df_val['accepted_offer']\n",
    "del df_test['accepted_offer']\n",
    "del df_full_train['accepted_offer']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8233c88-ca4a-4cae-a1d7-5417f736db60",
   "metadata": {},
   "source": [
    "## Exploratory DAta Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ba7a48e-c5dc-42f5-ad09-a00fb4305d04",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "checking distribution of binary columns\n",
    "\"\"\"\n",
    "plt.figure(figsize=(25,15))   \n",
    "plt.suptitle(\"Distribution of categorical columns\", fontsize=20)\n",
    "\n",
    "\n",
    "plt.subplot(2,3,1)\n",
    "sns.countplot(data.accepted_offer)\n",
    "plt.xlabel('Offer Accepted', fontsize=15)\n",
    "plt.ylabel('', fontsize=15)\n",
    "sns.despine()\n",
    "\n",
    "plt.subplot(2,3,2)\n",
    "sns.countplot(df_full_train.own_your_home)\n",
    "plt.xlabel('Reward', fontsize=15)\n",
    "plt.ylabel('', fontsize=15)\n",
    "sns.despine()\n",
    "\n",
    "plt.subplot(2,3,3)\n",
    "sns.countplot(df_full_train.mailer_type)\n",
    "plt.xlabel('Mailer Type', fontsize=15)\n",
    "plt.ylabel('', fontsize=15)\n",
    "sns.despine()\n",
    "\n",
    "plt.subplot(2,3,4)\n",
    "sns.countplot(df_full_train.income_level)\n",
    "plt.xlabel('Income Level', fontsize=15)\n",
    "plt.ylabel('', fontsize=15)\n",
    "sns.despine()\n",
    "\n",
    "plt.subplot(2,3,5)\n",
    "sns.countplot(df_full_train.overdraft_protection)\n",
    "plt.xlabel('Overdraft Protection', fontsize=15)\n",
    "plt.ylabel('', fontsize=15)\n",
    "sns.despine()\n",
    "\n",
    "plt.subplot(2,3,6)\n",
    "sns.countplot(df_full_train.credit_rating)\n",
    "plt.xlabel('Credit Rating', fontsize=15)\n",
    "plt.ylabel('', fontsize=15)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f409bfea-830e-4a56-8907-b706c545bb2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmoAAAG6CAYAAAC4BGrNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAXYElEQVR4nO3df7DldX3f8dfbXRQVqTCsFndJwWQnKfgjhB3EmKRt6MQ1SV1qxdCpulUbKkP9kTY/tM5Ua2ZbZ6JJ1Im2jAk/oo2DqBWdEXWwxlgRXIXyUyoVBrYSWGKagBNR8N0/zpdw3L27XnTP3s+9+3jMnDnnfM73e8777h93nvv9nnNPdXcAABjPo1Z6AAAAlibUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAa1fqUHWJStW7f2ZZddttJjAAAsRy21uGaPqN1zzz0rPQIAwA9lzYYaAMBqJ9QAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGtX6lBwBg325/89NXegQ4JP3If7hupUdI4ogaAMCwhBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoIQaAMCghBoAwKCEGgDAoBYaalX1a1V1Q1VdX1V/UlWHV9XRVfWpqvrqdH3U3Pavr6pbqurmqnru3PopVXXd9Ng7qqoWOTcAwAgWFmpVtTHJq5Ns6e6nJVmX5Kwkr0tyeXdvTnL5dD9VdeL0+ElJtiZ5V1Wtm57u3UnOTrJ5umxd1NwAAKNY9KnP9UkeW1XrkzwuydeTbEty4fT4hUnOmG5vS/L+7r6/u29NckuSU6vq2CRHdvcV3d1JLprbBwBgzVpYqHX3/03y1iS3J7kzyV919yeTPLm775y2uTPJk6ZdNia5Y+4pdk1rG6fbe67vparOrqqdVbVz9+7dB/LHAQA46BZ56vOozI6SnZDkKUkeX1Uv3t8uS6z1ftb3Xuw+r7u3dPeWDRs2PNKRAQCGsshTn/84ya3dvbu7v5PkQ0l+Osld0+nMTNd3T9vvSnLc3P6bMjtVumu6vec6AMCatshQuz3JaVX1uOlTmqcnuSnJpUm2T9tsT/KR6falSc6qqsdU1QmZfWjgqun06L1Vddr0PC+d2wcAYM1av6gn7u4rq+qSJF9O8kCSq5Ocl+SIJBdX1Ssyi7kzp+1vqKqLk9w4bX9udz84Pd05SS5I8tgkH58uAABr2sJCLUm6+41J3rjH8v2ZHV1bavsdSXYssb4zydMO+IAAAAPzzQQAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAg1poqFXVE6vqkqr6SlXdVFXPrqqjq+pTVfXV6fqoue1fX1W3VNXNVfXcufVTquq66bF3VFUtcm4AgBEs+oja25Nc1t0/keSZSW5K8rokl3f35iSXT/dTVScmOSvJSUm2JnlXVa2bnufdSc5Osnm6bF3w3AAAK25hoVZVRyb5uSR/mCTd/e3u/n9JtiW5cNrswiRnTLe3JXl/d9/f3bcmuSXJqVV1bJIju/uK7u4kF83tAwCwZi3yiNpTk+xOcn5VXV1V76mqxyd5cnffmSTT9ZOm7TcmuWNu/13T2sbp9p7re6mqs6tqZ1Xt3L1794H9aQAADrJFhtr6JD+V5N3dfXKSb2Y6zbkPS73vrPezvvdi93ndvaW7t2zYsOGRzgsAMJRFhtquJLu6+8rp/iWZhdtd0+nMTNd3z21/3Nz+m5J8fVrftMQ6AMCatrBQ6+4/T3JHVf34tHR6khuTXJpk+7S2PclHptuXJjmrqh5TVSdk9qGBq6bTo/dW1WnTpz1fOrcPAMCatX7Bz/+qJO+rqkcn+VqSl2UWhxdX1SuS3J7kzCTp7huq6uLMYu6BJOd294PT85yT5IIkj03y8ekCALCmLTTUuvuaJFuWeOj0fWy/I8mOJdZ3JnnaAR0OAGBwvpkAAGBQQg0AYFBCDQBgUEINAGBQQg0AYFBCDQBgUEINAGBQQg0AYFBCDQBgUEINAGBQQg0AYFBCDQBgUEINAGBQQg0AYFBCDQBgUEINAGBQQg0AYFBCDQBgUEINAGBQQg0AYFBCDQBgUEINAGBQQg0AYFBCDQBgUEINAGBQQg0AYFBCDQBgUMsKtaq6fDlrAAAcOOv392BVHZ7kcUmOqaqjktT00JFJnrLg2QAADmn7DbUk/zrJazOLsi/l4VD76yR/sLixAADYb6h199uTvL2qXtXd7zxIMwEAkO9/RC1J0t3vrKqfTnL8/D7dfdGC5lp1TvkN/xSwEr70Oy9d6REAFmZZoVZVf5zkR5Nck+TBabmTqBMAgAVZVqgl2ZLkxO7uRQ4DAMDDlvt31K5P8ncXOQgAAN9ruUfUjklyY1VdleT+hxa7+/kLmQoAgGWH2psWOQQAAHtb7qc+/3TRgwAA8L2W+6nPezP7lGeSPDrJYUm+2d1HLmowAIBD3XKPqD1h/n5VnZHk1EUMBADAzHI/9fk9uvu/J/n5AzsKAADzlnvq8wVzdx+V2d9V8zfVAAAWaLmf+vwnc7cfSHJbkm0HfBoAAP7Wct+j9rJFDwIAwPda1nvUqmpTVX24qu6uqruq6oNVtWnRwwEAHMqW+2GC85NcmuQpSTYm+ei0BgDAgiw31DZ09/nd/cB0uSDJhgXOBQBwyFtuqN1TVS+uqnXT5cVJ/mKRgwEAHOqWG2ovT/KiJH+e5M4kL0ziAwYAAAu03D/P8dtJtnf3XyZJVR2d5K2ZBRwAAAuw3CNqz3go0pKku7+R5OTFjAQAQLL8UHtUVR310J3piNpyj8YBAPADWG5svS3J56vqksy+OupFSXYsbCoAAJb9zQQXVdXOzL6IvZK8oLtvXOhkAACHuGWfvpzCTJwBABwky32PGgAAB5lQAwAYlFADABiUUAMAGJRQAwAYlFADABiUUAMAGJRQAwAYlFADABiUUAMAGJRQAwAYlFADABiUUAMAGJRQAwAY1MJDrarWVdXVVfWx6f7RVfWpqvrqdH3U3Lavr6pbqurmqnru3PopVXXd9Ng7qqoWPTcAwEo7GEfUXpPkprn7r0tyeXdvTnL5dD9VdWKSs5KclGRrkndV1bppn3cnOTvJ5umy9SDMDQCwohYaalW1KckvJXnP3PK2JBdOty9Mcsbc+vu7+/7uvjXJLUlOrapjkxzZ3Vd0dye5aG4fAIA1a9FH1H4/yW8m+e7c2pO7+84kma6fNK1vTHLH3Ha7prWN0+091/dSVWdX1c6q2rl79+4D8gMAAKyUhYVaVf1ykru7+0vL3WWJtd7P+t6L3ed195bu3rJhw4ZlviwAwJjWL/C5n5Pk+VX1i0kOT3JkVb03yV1VdWx33zmd1rx72n5XkuPm9t+U5OvT+qYl1gEA1rSFHVHr7td396buPj6zDwl8urtfnOTSJNunzbYn+ch0+9IkZ1XVY6rqhMw+NHDVdHr03qo6bfq050vn9gEAWLMWeURtX96S5OKqekWS25OcmSTdfUNVXZzkxiQPJDm3ux+c9jknyQVJHpvk49MFAGBNOyih1t2fSfKZ6fZfJDl9H9vtSLJjifWdSZ62uAkBAMbjmwkAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGJdQAAAYl1AAABiXUAAAGtbBQq6rjqup/VNVNVXVDVb1mWj+6qj5VVV+dro+a2+f1VXVLVd1cVc+dWz+lqq6bHntHVdWi5gYAGMUij6g9kOTfdfffT3JaknOr6sQkr0tyeXdvTnL5dD/TY2clOSnJ1iTvqqp103O9O8nZSTZPl60LnBsAYAgLC7XuvrO7vzzdvjfJTUk2JtmW5MJpswuTnDHd3pbk/d19f3ffmuSWJKdW1bFJjuzuK7q7k1w0tw8AwJp1UN6jVlXHJzk5yZVJntzddyazmEvypGmzjUnumNtt17S2cbq95/pSr3N2Ve2sqp27d+8+oD8DAMDBtvBQq6ojknwwyWu7+6/3t+kSa72f9b0Xu8/r7i3dvWXDhg2PfFgAgIEsNNSq6rDMIu193f2hafmu6XRmpuu7p/VdSY6b231Tkq9P65uWWAcAWNMW+anPSvKHSW7q7t+de+jSJNun29uTfGRu/ayqekxVnZDZhwaumk6P3ltVp03P+dK5fQAA1qz1C3zu5yR5SZLrquqaae3fJ3lLkour6hVJbk9yZpJ09w1VdXGSGzP7xOi53f3gtN85SS5I8tgkH58uAABr2sJCrbs/l6XfX5Ykp+9jnx1JdiyxvjPJ0w7cdAAA4/PNBAAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAIMSagAAgxJqAACDEmoAAINaNaFWVVur6uaquqWqXrfS8wAALNqqCLWqWpfkD5I8L8mJSf55VZ24slMBACzWqgi1JKcmuaW7v9bd307y/iTbVngmAICFWr/SAyzTxiR3zN3fleRZe25UVWcnOXu6e19V3XwQZmP1OybJPSs9BD+Yeuv2lR4B9sXvltXsjXWwX/Gy7t665+JqCbWl/rV6r4Xu85Kct/hxWEuqamd3b1npOYC1xe8WDoTVcupzV5Lj5u5vSvL1FZoFAOCgWC2h9sUkm6vqhKp6dJKzkly6wjMBACzUqjj12d0PVNW/SfKJJOuS/FF337DCY7F2OF0OLILfLfzQqnuvt3oBADCA1XLqEwDgkCPUAAAGJdQ4ZFRVV9Xb5u7/elW9aQVHAlaxmvlcVT1vbu1FVXXZSs7F2iLUOJTcn+QFVXXMSg8CrH49e5P3K5P8blUdXlWPT7IjybkrOxlriVDjUPJAZp/C+rU9H6iqv1dVl1fVtdP1jxz88YDVpruvT/LRJL+V5I1J3pvkDVX1xaq6uqq2JUlVnVRVV1XVNdPvmc0rODariE99csioqvuSPCXJtUmemeRXkxzR3W+qqo8muaS7L6yqlyd5fnefsXLTAqvFdCTty0m+neRjSW7o7vdW1ROTXJXk5CRvSfKF7n7f9PdA13X336zUzKweQo1DRlXd191HVNWbk3wnyd/k4VC7J8mx3f2dqjosyZ3d7RQpsCzT75X7krwoyeGZHcFPkqOTPDezWHtDkouSfKi7v7oSc7L6rIo/eAsH2O9n9r/f8/ezjf/BAI/Ed6dLJfln3X3zHo/fVFVXJvmlJJ+oqn/V3Z8+2EOy+niPGoec7v5GkouTvGJu+fOZfTVZkvyLJJ872HMBa8InkryqqipJqurk6fqpSb7W3e/I7CsQn7FyI7KaCDUOVW9LMn9q89VJXlZV1yZ5SZLXrMhUwGr320kOS3JtVV0/3U+SX0lyfVVdk+QnMjsFCt+X96gBAAzKETUAgEEJNQCAQQk1AIBBCTUAgEEJNQCAQQk1AIBBCTWABaqqf1hVH1vpOYDVSagBHABV5Sv5gANOqAHDqap/W1XXT5fXVtVvVtWrp8d+r6o+Pd0+vareO92+r6p2VNX/qqovVNWT9/HcT6iqW6vqsOn+kVV1W1UdVlU/Oe17bVV9uKqOmrb5TFVtmW4fU1W3Tbf/ZVV9oKo+muST+/mRjqiqS6rqK1X1vrmvFzq9qq6uquuq6o+q6jHT+m1V9Z+q6oqq2llVP1VVn6iq/1NVr5z7WX6jqr44zfsff6h/dGBIQg0YSlWdkuRlSZ6V5LQkv5rkz5L87LTJlszC57AkPzM9liSPT/KF7n5mks9O++2lu+9N8pnMvhw7mX3H6we7+zuZfa3Pb3X3M5Jcl+SNyxj52Um2d/fP72ebk5O8NsmJSZ6a5DlVdXiSC5L8Snc/Pcn6JOfM7XNHdz97+vkuSPLCzP493pwkVfULSTYnOTXJTyY5pap+bhnzAquIUANG8zNJPtzd3+zu+5J8KLMYOaWqnpDk/iRXZBZsP5uHQ+3bSR56L9iXkhy/n9d4T2YxmOn6/Kr6O0me2N1/Oq1fmGQ54fOp7v7G99nmqu7e1d3fTXLNNNuPJ7m1u//3Pl7v0un6uiRXdve93b07ybeq6olJfmG6XJ3ky5l9f+TmZcwLrCLeUwGMppZY6yS3ZRZVn09ybZJ/lORHk9w0bfOdfvjLix/Mfn6/dff/rKrjq+ofJFnX3ddPobYvD+Th/9gevsdj39zPfg+5f+72Q7Mt9XMutc9399j/u3P7/+fu/q/LeH1glXJEDRjNZ5OcUVWPq6rHJ/mnmR01+2ySX5+u/yzJK5NcMxdnj9RFSf4kyflJ0t1/leQvq+qhU6wvSfLQ0bXbkpwy3X7hD/h6e/pKkuOr6seWeL3l+ESSl1fVEUlSVRur6kkHaDZgEEINGEp3fzmz92RdleTKJO/p7qszi7Njk1zR3Xcl+VYePu35g3hfkqMyi7WHbE/yO1V1bWbv+3rztP7WJOdU1eeTHPNDvObf6u5vZXaE8ANVdV1mR8r+yyPY/5NJ/luSK6b9L0nyhAMxGzCO+sH/MwqwelXVC5Ns6+6XrPQsAPviPWrAIaeq3pnkeUl+caVnAdgfoQasWVX1hiRn7rH8ge5+1QJe6+lJ/niP5fu7+1kH+rWAQ4dTnwAAg/JhAgCAQQk1AIBBCTUAgEEJNQCAQf1/tHuT+9eMxHoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.title(' ')\n",
    "sns.countplot(df_full_train.own_your_home, )\n",
    "# plt.xlabel('Average Balance', fontsize=15)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "076f55f6-4e37-4e5b-8178-9bdb2beba3e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9dZI/Thc0/226oPuo1tpXtdaO67d79sG9okU70O/PoM/LMv2+AwBrkLAZADg9+4OJtx7Iiv0F0N7UWntxa21but6rP5Lk7nS9/F63Sk69nu/U9k39fE/2j109fT/pLsw1l68aUtQCpnt1b6yq+YZ8mH5tu5ewlplGe5zP995OP7YnY+xJ3F8c7f/1d59dVY+Yr/2I7x+5/dYZjw3Z32el+w7dnuTc1to/tNbun9Hmax+82gH5SD//xoHbGYc/SfKFdBd//I6qmkjy4/1jY+3VPIfpC4RunbF8etz1N7XWLuwvZDczXD+Y/fDd6XqlfzLJv22tvW364oMDt7sYu/r5xqo6kN7Ygz8vh9DvOwCwigibAWAd64OCl/V3P53kL4Zsr7X22dba65I8r190XB4Ydnzl9P2RHpHL4dsX8di7+xBz2nQ4euw8Ye+3zrPd6dd6sK/zln4+keS0edo9q5//w0E+z8H4p+x/fc+cp910bf88470dh+kxkB+e/b2G59RfaG06jPy71toHZzSZ3t9b5lj/sMw9Lvb0Ov88zxAVz5pjebK4z8qN/XxTVY374nYHpL9I3ev7u+cneW667/oXk/zpMpQw/UeMmcNKTO+Hd862Uv+bczC9cae3e8dsF8Pszbd/h7ipnx+e7qJ/izX2z8sift8BAITNALBeVdVD050aPj20wH9Z7EW9FtGbbbTX32jPws+M3H7UYp5rTJ5fVQ8a27eqvj5dr9Qk2T7j4X+ebpbk+2ZZ96FJfnae55x+rY86oEp7rbV3J7mtv/ufqurwmW2q6ruzP/B+/czHl0r/OXlTf/c/VtVRM9tU1ROT/EB/dylq+6vs7538i1X1vXM17IdB+LN0vZZbkv80S7Pp/f19c/wh5LzM3Yv70/38G2dbt6q+K93QC3NZzGflL7O/l+vvzPaez3jOmReaG7fX9PMzs3/oiu0DLwy4oP47Oz10yi0zHp7eD0+cY/XnZ/+QQQdieruPne2ihFX1pMwY0mNc+ot7/t/+7q9V1SMXuepBf14G/L4DAAibAWA9qarDquoJVfXSJLcm+aH+of+ZbhzOxTqlqt5dVT9bVd/Q9/pMdU7J/iBqKsl7Rtb7l+y/UNdPzhHqLYUjklxfVd8yUuez0gWmRybZkf3j0CZJWmtTSf6uv3tpVT1rOvCtqm9O8jeZ+6J8SfLefv7IqvrBg6z7F/r5v0lyTVVt7Z//iKr6kewPcW/KwF7pB+EXk3w5yb9K8qaq+sa+tsP6EPz/pOuV/YEswdAKrbWW7vP7wXT79w1V9Tv9+Mnpa/mqqjovXU/s6QDyV1prb5tlk9Pv5TckuaIPqFNVj6yqn033+ZjrgmjX9fPHJ3nVdHBXVQ+rqguSXJP5L745/Vn5N1X1uDle75eS/HS6sPwpSW6squeMBoNVtbWqLqiqm/u2S6a19o/pxvHdkP1/8LhiqZ6vqiaq6tuTvCH7L9J36Yxm0/vhu6rql6aHnaiqR1XVy5L8bg7sIqjT3pyu9/kxSV5bVZv67W7ov9tvzvwX/xvqxekuRHhSuv1+elUd0ddwVFV9a1Vd1v+mJRn8eTnY33cAAGEzAKxlVfWRkemT6cLB9yT5rXTjnX4syfNba/+uD+8OxDemC3tuS/KlqvpYuiD5xv6xzyT54dExU/tT0P9nf/e/JvlcVX24qj5UVb958K90QRekG1/25qr6bJLPJbk+ydcl+VSS72+tfWaW9X4mXYh0fN/+c1X1uXQ9Kh+T5MfmesK+R+IN/d3tVfWZ/nV+qKpespiiW2t/lW6IiJauB+kH+/34uXTDFTwy3f48e5axaZdUa+2d6V7//Un+dZJ3V9Wn0w1t8L+TnJAuxP+3rbXPLVENH0nytHRh30SSFyW5raq+2L9Pn0rXe38y3Wf/51trr5hjWzekG4s4SX4yycf6bXwy3ef8inS9Reda96r+7guSfLxf99PpQurbk8z6vL3/lW7M7aOT3F5Vu0c+K08beZ6/SPeefyHJk9KFq5+vqo9V1ZfSBe+XJfmWLM8FI18zcnucFwb8uRm/Xfem+1z9bbo/BuxJ8tLW2nUz1vuT7B/L+1eSfLaqPpEuYL4k3fv1mhyg1tr7k/xGf/f7k0xV1afSfQ+39/MXHeh2D+D535XkjHSfpyck+et0+/0T6d6Xt6f7jXv4jPX+Igf/eTng33cAgETYDABr3XH9dGy6MO4j6YKJ16QbPmJTa+1gep3+Q5If7Lfzj+lC669K1/vuXemC5G9orf2/Wda9MF3wNt2bczJd6PugYS7G6B3pxtv9k3SBzUSSe5L8jyTf2FqbeTp+kq+EPE9NFyTem+7Y6WNJXpUuvLlttvVGnJXkv6Xr0X1Eutf5dTmAoTVaa/+tr/1P04W3R6U7jf3t6YLop7bWdi52e+PUWtuerjfv5el6MB+ZLgh8V5KXJ3lCa+32Ja5hd2vtOenGjv79JHekC8UeNdJsV1/Lbzx4Cw/w79P1In1Xuvf4sHTh2jmttZ9ZYN0fSfKSJO9Ocl+6MXbfk+TiJKemCyTneg2fTPL0dJ+ze9J9l6Y/Kw+Z0fa16XqT/2q6P3p8Lt1rnf7u/V668YN/fYF6x+Ga7A8px9l7/WHZ/9t1XLrfhvvTvZ+/l+RJ/ffiAfpxwZ+d5JfTfee+nG4YnJvT/RHguTnIYR9aaxcl+Xf9tr6Y7vt8Z5JfSzcU0ZJ+B1trb07Xs/mSdGNSfzHJQ5N8KN0ZGhekC+Nnrncwn5chv+8AwDpXB96JCQAAVr+qOifd8BiV5Fdba7+0wiWtKVX1A+kC5y8mOWGpx2sGAGD107MZAIA1qe95/dL+7n+qqp+frz0HbLq39+sFzQAAJMJmAADWsNbabyeZHg/816tqSS+ct15U1flJTkt34byZF+oDAGCdMowGAACwoP5ihVelG7/3Uf3i31vEeNYAAKwTEytdAAAAcEh4SLqLFu5NcleSP053gTwAAEiiZzMAAAAAAGNgzGYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADDYxEoXkCSnn356u+6661a6DAAAxq9WugBWhmN8AIA1a85j/FXRs/ljH/vYSpcAAACMkWN8AID1Z1WEzQAAwPKpqq+vqneNTJ+pqpdU1TFVdX1Vvb+fHz2yzsVVdWdV3VFVz1nJ+gEAWJ2EzQAAsM601u5orT2ptfakJN+c5AtJ3pDkoiQ3tNZOSnJDfz9VdXKSc5M8PsnpSV5dVYevRO0AAKxewmYAAFjfnpnkA621Dyc5I8mV/fIrk5zZ3z4jyVWttftaa3cluTPJU5e7UAAAVjdhMwAArG/nJnl9f/u41tquJOnnx/bLNyXZMbLOVL/sAarq/Kq6papu2b179xKWDADAaiRsBgCAdaqqNiR5bpI/W6jpLMvagxa0dkVrbVtrbdvGjRvHUSIAAIcQYTMAAKxf35Xkn1prH+3vf7Sqjk+Sfn5vv3wqyZaR9TYn2blsVQIAcEgQNgMAwPr1Q9k/hEaSXJvkvP72eUneOLL83Ko6sqq2Jjkpyc3LViUAAIeEiZUuAAAAWH5VdVSS70xywcjiVya5uqqel+TuJGcnSWvt1qq6OsltSfYkubC1tneZSwYAYJUTNgMAwDrUWvtCkq+esezjSZ45R/tLklyyDKUBAHCIMowGAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbB6jTVsmU1VjnzZtmVzplwYAAMA6Nbn1xEwcsWHBaXLriStdKgArbGKlC1hLdk7tyDmX3zT27W6/4JSxbxMAAAAWY+fUVM561dsWbHfNhactQzUArGZ6NgMAAAAAMJiwGQAAAACAwQyjAQAAAOvQ5NYTs3NqasF2e/fuXYZqAFgLhM0wRpu2TGbn1I6xbvOEzVtyz467x7pNAACAxY7FvP35py5DNQCsBcJmGKOluEikC0QCAAAAcCgwZjMAAACMmNx6YiaO2DDvNLn1xJUuEwBWHT2bAQAAYMRihpe45sLTlqkaADh06NkMAAAAAMBgwmbGatOWyVTVWKdNWyZX+mUBAAAAAAswjAZj5QJ5AAAAALA+6dkMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMNrHSBbAyNm2ZzM6pHStdBgAAAGvEvpZMHLFhwXYnbN6cu+/64DJUBMByEzavUzunduScy28a+3a3X3DK2LeZwyZSVWPd5Ambt+SeHXePdZsAAADrWdu3J2ddduOC7a658LRlqAaAlSBsZvXbt2fswfiShOIAAAAAsI4ZsxkAAAAAgMGEzQAAAAAADGYYDdanJRgHGgAAAADWM2Ez69MSjAOdGAsaAAAAgPXLMBoAAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMq91hE6mqsU6btkyu9KsCAAAOwuTWEzNxxIZ5p8mtJ650mQCsUxMrXQCwgH17cs7lN411k9tf8PRU1Vi3mSQnbN6Se3bcPfbtAgAAnZ1TUznrVW+bt801F562TNUAwAMJmw8Ffc9WGJslCLCTZPsFp4x9mwAAAAAcGoTNh4Kl6NkqFAQAAAAAxsiYzQAAAAAADCZsBgAAAABgMMNoAAAAwAqb3Hpidk5NLdhu7969y1ANABwcYTMAAACssJ1TUznrVW9bsN3255+6DNUAwMExjAYAAKxDVfWoqrqmqt5XVbdX1bdV1TFVdX1Vvb+fHz3S/uKqurOq7qiq56xk7cChbV9LJo7YMO80ufXElS4TgIOgZzMAAKxPv5PkutbaWVW1IclRSV6W5IbW2iur6qIkFyX5hao6Ocm5SR6f5IQkf1NVj22tOZ8fOGBt356cddmN87a55sLTlqkaAMZJz2YAAFhnquqRSZ6e5A+SpLV2f2vtU0nOSHJl3+zKJGf2t89IclVr7b7W2l1J7kzy1OWsGQCA1U/YDAAA68+JSXYn+aOqemdV/X5VPSzJca21XUnSz4/t229KsmNk/al+2QNU1flVdUtV3bJ79+6lfQUAAKw6wmYAAFh/JpI8JclrWmtPTvL5dENmzKVmWdYetKC1K1pr21pr2zZu3DieSgEAOGQYsxkAANafqSRTrbV39PevSRc2f7Sqjm+t7aqq45PcO9J+y8j6m5PsXLZqgQMyfQG+hezda9h1AMZL2AwAAOtMa+0jVbWjqr6+tXZHkmcmua2fzkvyyn7+xn6Va5O8rqouTXeBwJOS3Lz8lQOLsZgL8CXJ9uefugzVALCeLCpsrqqfTfKT6U6Ve0+Sn0h3tertSR6d5ENJfrC19sm+/cVJnpdkb5IXtdbeNO7CAQCAQX4myWurakOSD6Y7xj8sydVV9bwkdyc5O0laa7dW1dXpwug9SS5srekSCQDAAywYNlfVpiQvSnJya+2L/UHmuUlOTnJDa+2VVXVRutPufqGqTu4ff3y6Xg9/U1WPdTAKAACrR2vtXUm2zfLQM+dof0mSS5ayJgAADm2LvUDgRJKHVtVEuh7NO5OckeTK/vErk5zZ3z4jyVWttftaa3cluTPJU8dWMQAAAKyw6XGRF5omt5640qUCwLJZsGdza+2eqvrNdKfRfTHJm1trb66q41pru/o2u6rq2H6VTUnePrKJqX7ZA1TV+UnOT5LJyclhrwIAAACW0WLHRb7mwtOWoRoAWB0W7NlcVUen6628Nd2wGA+rqh+db5VZlrUHLWjtitbattbato0bNy62XgAAAAAAVqHFDKPxrCR3tdZ2t9a+nOTPk5yS5KNVdXyS9PN7+/ZTSbaMrL853bAbAAAAAACsUYsJm+9O8rSqOqqqKt0FQ25Pcm2S8/o25yV5Y3/72iTnVtWRVbU1yUlJbh5v2QAAAAAArCaLGbP5HVV1TZJ/SrInyTuTXJHk4UmurqrnpQukz+7b31pVVye5rW9/YWtt7xLVf9A2bZnMzqkdK10GAAAAAMCasGDYnCSttZcnefmMxfel6+U8W/tLklwyrLSltXNqR865/KaxbnP7BaeMdXsAAAAAAIeKxQyjAQAAAAAA8xI2AwAAAAAw2KKG0QAAAAAO3L6WTByxYcF2e/euuksdAcABEzYDAADAEmn79uSsy25csN3255+6DNUAwNIyjAYAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMEmVroAAAAAgFH7WjJxxIYF252weXPuvuuDy1ARAIshbAYAAABWlbZvT8667MYF211z4WnLUA0Ai2UYDQAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADDYxEoXAAAAAMthcuuJ2Tk1tWC7vXv3LkM1ALD2CJuB8TlsIlU11k2esHlL7tlx91i3CQDA+rRzaipnveptC7bb/vxTl6EaAFh7hM3A+Ozbk3Muv2msm9x+wSlj3R4AAAAAS8OYzQAAAAAADCZsBgAAAABgMMNoAAAAAIekfS2ZOGLDgu1O2Lw5d9/1wWWoCGB9EzYDAMA6VFUfSvLZJHuT7GmtbauqY5JsT/LoJB9K8oOttU/27S9O8ry+/Ytaa29agbIBHqDt25OzLrtxwXbXXHjaMlQDgGE0AABg/fr21tqTWmvb+vsXJbmhtXZSkhv6+6mqk5Ocm+TxSU5P8uqqOnwlCgYAYPUSNgMAANPOSHJlf/vKJGeOLL+qtXZfa+2uJHcmeerylwcAwGombAYAgPWpJXlzVf1jVZ3fLzuutbYrSfr5sf3yTUl2jKw71S8DAICvMGYzAACsT6e21nZW1bFJrq+q983TtmZZ1h7UqAutz0+SycnJ8VQJAMAhQ89mAABYh1prO/v5vUnekG5YjI9W1fFJ0s/v7ZtPJdkysvrmJDtn2eYVrbVtrbVtGzduXMryAQBYhYTNAACwzlTVw6rqEdO3kzw7yXuTXJvkvL7ZeUne2N++Nsm5VXVkVW1NclKSm5e3agAAVjvDaAAAwPpzXJI3VFXS/Z/gda2166rqH5JcXVXPS3J3krOTpLV2a1VdneS2JHuSXNha27sypQMAsFoJmwEAYJ1prX0wyRNnWf7xJM+cY51LklyyxKUBAHAIM4wGAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZgHVl05bJVNXYp01bJlf6pQEAAMCKmljpAgBgOe2c2pFzLr9p7NvdfsEpY98mAAAAHEr0bAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNALBKbdoymaoa67Rpy+RKvywAAADWqImVLgAAmN3OqR055/KbxrrN7RecMtbtAQAAwDQ9mwFYtZaiZy8AAACwNPRsBmDV0rMXAAAADh16NgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMtKmyuqkdV1TVV9b6qur2qvq2qjqmq66vq/f386JH2F1fVnVV1R1U9Z+nKBwAAAABgNVhsz+bfSXJda+1xSZ6Y5PYkFyW5obV2UpIb+vupqpOTnJvk8UlOT/Lqqjp83IUDAAAAALB6LBg2V9Ujkzw9yR8kSWvt/tbap5KckeTKvtmVSc7sb5+R5KrW2n2ttbuS3JnkqeMtGwAAAACA1WQxPZtPTLI7yR9V1Tur6ver6mFJjmut7UqSfn5s335Tkh0j60/1yx6gqs6vqluq6pbdu3cPehEAAAAAAKysxYTNE0mekuQ1rbUnJ/l8+iEz5lCzLGsPWtDaFa21ba21bRs3blxUsQAAAIzX5NYTM3HEhgWnya0nrnSpAMAqN7GINlNJplpr7+jvX5MubP5oVR3fWttVVccnuXek/ZaR9Tcn2TmuggEAABifnVNTOetVb1uw3TUXnrYM1QAAh7IFeza31j6SZEdVfX2/6JlJbktybZLz+mXnJXljf/vaJOdW1ZFVtTXJSUluHmvVAAAAAACsKovp2ZwkP5PktVW1IckHk/xEuqD66qp6XpK7k5ydJK21W6vq6nSB9J4kF7bW9o69cgBYTQ6bSNVsI0kBAADA+rCosLm19q4k22Z56JlztL8kySUHXxYAHGL27ck5l9801k1uv+CUsW4PAAAAltJiLhAIAAAAAADzEjYDAAAAADCYsBkAAAAAgMGEzQAAAAAADLaoCwQCAADAajW59cTsnJpasN3evXuXoRoAWL+EzQAAABzSdk5N5axXvW3Bdtuff+oyVAMA65dhNAAAAAAAGEzYDAAAAADAYMJmAAAAxmZy64mZOGLDgtPk1hNXulQAYMyM2QwAAMDYLHb85GsuPG0ZqgEAlpOezQAAAAAADCZsBgAAAABgMGEzAACsU1V1eFW9s6r+qr9/TFVdX1Xv7+dHj7S9uKrurKo7quo5K1c1AACrlbAZAADWrxcnuX3k/kVJbmitnZTkhv5+qurkJOcmeXyS05O8uqoOX+ZaAQBY5YTNAIzFpi2TqaqxTgAsnaranOR7kvz+yOIzklzZ374yyZkjy69qrd3XWrsryZ1JnrpMpbKOTW49MRNHbFhw2rt370qXCgAkmVjpAgBYG3ZO7cg5l9801m1uv+CUsW4PgAf47SQ/n+QRI8uOa63tSpLW2q6qOrZfvinJ20faTfXLHqCqzk9yfpJMTk4uQcmsNzunpnLWq962YLvtzz91GaoBABaiZzMAAKwzVfW9Se5trf3jYleZZVl70ILWrmitbWutbdu4ceOgGgHGaV/LonrJT249caVLBTik6dkMAADrz6lJnltV353kIUkeWVV/muSjVXV836v5+CT39u2nkmwZWX9zkp3LWjFrznT4Nx/DYzAubd+enHXZjQu2u+bC05ahGoC1S9gMAADrTGvt4iQXJ0lVPSPJz7XWfrSqfiPJeUle2c/f2K9ybZLXVdWlSU5IclKSm5e5bNaYxYR/hscAgEOLsBkAAJj2yiRXV9Xzktyd5Owkaa3dWlVXJ7ktyZ4kF7bWdDkFAOABhM0AALCOtdbemuSt/e2PJ3nmHO0uSXLJshXGqrOYYS8SQ18AwHombAYAAGBBix3z1tAXALB+HbbSBQAAAAAAcOgTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAwAqZ3HpiJo7YsOA0ufXElS4VYEETK10AAAAAwHq1c2oqZ73qbQu2u+bC05ahGoBhhM0AAABr0OTWE7NzamrBdnv37l2GagCA9UDYDAAAsAYttrfk9uefugzVAADrgTGbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgsImVLgAAAABgrZncemJ2Tk0t2G7v3r3LUA3A8hA2AwAAAIzZzqmpnPWqty3YbvvzT12GagCWh2E0AAAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMNrHSBQAAAACsBvtaMnHEhnnbnLB5c+6+64PLVBHAoUXYDAAAAJCk7duTsy67cd4211x42jJVA3DoMYwGAACsM1X1kKq6uar+uapurapf7pcfU1XXV9X7+/nRI+tcXFV3VtUdVfWclaseAIDVStgMAADrz31JvqO19sQkT0pyelU9LclFSW5orZ2U5Ib+fqrq5CTnJnl8ktOTvLqqDl+JwgEAWL2EzQAAsM60zuf6u0f0U0tyRpIr++VXJjmzv31Gkqtaa/e11u5KcmeSpy5fxQAAHAoWHTZX1eFV9c6q+qv+vlPsAADgENUf378ryb1Jrm+tvSPJca21XUnSz4/tm29KsmNk9al+2cxtnl9Vt1TVLbt3717S+gEAWH0OpGfzi5PcPnLfKXYAAHCIaq3tba09KcnmJE+tqifM07xm28Qs27yitbattbZt48aNY6oUAIBDxaLC5qranOR7kvz+yGKn2AHAoeawiVTV2KdNWyZX+pUBB6m19qkkb03XUeSjVXV8kvTze/tmU0m2jKy2OcnO5asSYPXY15KJIzYsOO3du3elSwVYdhOLbPfbSX4+ySNGlj3gFLuqGj3F7u0j7WY9xQ4AWAH79uScy28a+2a3X3DK2LcJLJ2q2pjky621T1XVQ5M8K8mvJ7k2yXlJXtnP39ivcm2S11XVpUlOSHJSkpuXvXCAVaDt25OzLrtxwXbbn3/qMlQDsLosGDZX1fcmube19o9V9YxFbHNRp9hV1flJzk+SyUm9oQAAYBkdn+TKfri7w5Jc3Vr7q6r6+yRXV9Xzktyd5Owkaa3dWlVXJ7ktyZ4kF7bWdNkDAOABFtOz+dQkz62q707ykCSPrKo/TX+KXd+r+YBPsWutXZHkiiTZtm3bg8JoAABgabTW3p3kybMs/3iSZ86xziVJLlni0gAAOIQtOGZza+3i1trm1tqj0134729baz+a/afYJQ8+xe7cqjqyqrbGKXYAAAAAAGveYsdsns0r4xQ7AAAAAABygGFza+2t6a5U7RQ7AAAAAAC+YsFhNAAAAAAAYCHCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgEytdAAAAAADz29eSiSM2zNvmhM2bc/ddH1ymigAeTNgMAAAAsMq1fXty1mU3ztvmmgtPW6ZqAGZnGA0AAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAsMQmt56YiSM2LDhNbj1xpUsFADhoEytdAAAAwFq3c2oqZ73qbQu2u+bC05ahGgCApaFnM8A6s2nLZKpq7BMAAACwvunZDLDO7JzakXMuv2ns291+wSlj3yYAAABw6NCzGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAArAmTW0/MxBEbFpwmt5640qUCAKxJEytdAABz27RlMjundqx0GQBwSNg5NZWzXvW2Bdtdc+Fpy1ANAMD6I2wGWMV2Tu3IOZffNNZtbr/glLFuDwDWqsmtJ2bn1NS8bU7YvDl33/XBZaoIAGB1EzYDAADryr6WTByxYcF2e/fuzTmX/d28bfSSBgDYT9gMAACsK23fnpx12Y0Lttv+/FOXoZqDs5he13v37l2magAAOsJmAACAQ8xixqdezWE5ALA2HbbSBQCsFZu2TKaqxjoBAAAAHCr0bAYYExfzAwAAANYzPZsBAAAAABhM2AwAAAAAwGCG0QAAADhI+1oyccSGBdvt3bt3GaoBAFhZwmYAAICD1PbtyVmX3bhgu+3PP3UZqgEAWFmG0QAAAAAAYDBhMwAArDNVtaWq3lJVt1fVrVX14n75MVV1fVW9v58fPbLOxVV1Z1XdUVXPWbnqAQBYrYTNAACw/uxJ8h9aa9+Q5GlJLqyqk5NclOSG1tpJSW7o76d/7Nwkj09yepJXV9XhK1I5AACrljGbAQBgnWmt7Uqyq7/92aq6PcmmJGckeUbf7Mokb03yC/3yq1pr9yW5q6ruTPLUJH+/vJWvfS44CAAcyoTNAACwjlXVo5M8Ock7khzXB9Fpre2qqmP7ZpuSvH1ktal+2cxtnZ/k/CSZnJxcwqrXLhccBAAOZYbRAACAdaqqHp7kfyV5SWvtM/M1nWVZe9CC1q5orW1rrW3buHHjuMoEAOAQIWwGAIB1qKqOSBc0v7a19uf94o9W1fH948cnubdfPpVky8jqm5PsXK5aAQA4NAibAQBgnamqSvIHSW5vrV068tC1Sc7rb5+X5I0jy8+tqiOramuSk5LcvFz1AgBwaDBmMwAArD+nJvmxJO+pqnf1y16W5JVJrq6q5yW5O8nZSdJau7Wqrk5yW5I9SS5srblCHQAADyBsBgCAdaa19neZfRzmJHnmHOtckuSSJSsKAIBDnmE0AAAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABlswbK6qLVX1lqq6vapuraoX98uPqarrq+r9/fzokXUurqo7q+qOqnrOUr4AAAAAAABW3mJ6Nu9J8h9aa9+Q5GlJLqyqk5NclOSG1tpJSW7o76d/7Nwkj09yepJXV9XhS1E8AAAAAACrw4Jhc2ttV2vtn/rbn01ye5JNSc5IcmXf7MokZ/a3z0hyVWvtvtbaXUnuTPLUMdcNAAAAAMAqckBjNlfVo5M8Ock7khzXWtuVdIF0kmP7ZpuS7BhZbapfNnNb51fVLVV1y+7duw+idAAAAAAAVotFh81V9fAk/yvJS1prn5mv6SzL2oMWtHZFa21ba23bxo0bF1sGAAAAAACr0KLC5qo6Il3Q/NrW2p/3iz9aVcf3jx+f5N5++VSSLSOrb06yczzlAgAAAACwGi0YNldVJfmDJLe31i4deejaJOf1t89L8saR5edW1ZFVtTXJSUluHl/JAAAAAACsNhOLaHNqkh9L8p6qele/7GVJXpnk6qp6XpK7k5ydJK21W6vq6iS3JdmT5MLW2t5xFw4AAAAAwOqxYNjcWvu7zD4Oc5I8c451LklyyYC6AAAAAAA4hCz6AoEAAAAAADAXYTOwuh02kaoa67Rpy+RKvyoAAACANWcxYzYDrJx9e3LO5TeNdZPbLzhlrNsDAAAAQM9mAAAAAADGQNgMAAAAAMBgwmYAYDjjqwMAAKx7xmwGAIYzvjoAAMC6p2czAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQBgnamqP6yqe6vqvSPLjqmq66vq/f386JHHLq6qO6vqjqp6zspUDQDAaidsBgCA9eePk5w+Y9lFSW5orZ2U5Ib+fqrq5CTnJnl8v86rq+rw5SsVAIBDhbAZAADWmdba/03yiRmLz0hyZX/7yiRnjiy/qrV2X2vtriR3JnnqctQJAMChRdgMAAAkyXGttV1J0s+P7ZdvSrJjpN1Uv+xBqur8qrqlqm7ZvXv3khYLAMDqI2wG1p/DJlJVY5+AMVui7+qmLZMr/crgUDPbP3JttoattStaa9taa9s2bty4xGUBALDaTKx0AQDLbt+enHP5TWPf7PYLThn7NmFd812F5fbRqjq+tbarqo5Pcm+/fCrJlpF2m5PsXPbqAABY9fRsBgAAkuTaJOf1t89L8saR5edW1ZFVtTXJSUluXoH6AFjAvpZMHLFhwWly64krXSqwRunZDAAA60xVvT7JM5J8TVVNJXl5klcmubqqnpfk7iRnJ0lr7daqujrJbUn2JLmwtbZ3RQoHYF5t356cddmNC7a75sLTlqEaYD0SNgMAwDrTWvuhOR565hztL0lyydJVBADAWmAYDQAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMNjEShcAAAAAwPLZ15KJIzYs2O6EzZtz910fXIaKgLVC2AwAAACwjrR9e3LWZTcu2O6aC09bhmqAtcQwGgAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGCwiZUuAAAAAIDVZ19LJo7YsGC7EzZvzt13fXAZKgJWO2EzAAAAAA/S9u3JWZfduGC7ay48bRmqAQ4FhtEAAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADDax0gUAAAAAcOja15KJIzYs2O6EzZtz910fXIaKgJUibAYAAADgoLV9e3LWZTcu2O6aC09bhmqAlWQYDQAAAAAABluysLmqTq+qO6rqzqq6aKmeBwAAWHqO7wEAWMiShM1VdXiSVyX5riQnJ/mhqjp5KZ4LAABYWo7vAQBYjKXq2fzUJHe21j7YWrs/yVVJzlii5wIAWHM2bZlMVY192rRlcqVfGocmx/cAACyoWmvj32jVWUlOb639ZH//x5J8a2vthSNtzk9yfn/365PcMfZC5vc1ST62zM/J0rAv1w77cu2wL9cO+3LtWKl9+bHW2ukr8LyM0WKO7/vljvHXPu/x0vMeLz3v8dLzHi897/HS8x7Pbc5j/IklesKaZdkDUu3W2hVJrlii519QVd3SWtu2Us/P+NiXa4d9uXbYl2uHfbl22JcMtODxfeIYfz3wHi897/HS8x4vPe/x0vMeLz3v8cFZqmE0ppJsGbm/OcnOJXouAABgaTm+BwBgQUsVNv9DkpOqamtVbUhybpJrl+i5AACApeX4HgCABS3JMBqttT1V9cIkb0pyeJI/bK3duhTPNcCKnd7H2NmXa4d9uXbYl2uHfbl22JcctEPk+D7xOV8O3uOl5z1eet7jpec9Xnre46XnPT4IS3KBQAAAAAAA1pelGkYDAAAAAIB1RNgMAAAAAMBg6zJsrqrTq+qOqrqzqi5a6XqYX1V9qKreU1Xvqqpb+mXHVNX1VfX+fn70SPuL+317R1U9Z+Uqp6r+sKrurar3jiw74H1XVd/cfwburKr/XlW13K9lvZtjX76iqu7pv5vvqqrvHnnMvlylqmpLVb2lqm6vqlur6sX9ct/NQ8w8+9J3k3XH8f34OPYeP8fES8+x6tJzDLn0HNstvap6SFXdXFX/3L/Hv9wv9zkep9bauprSXdDkA0lOTLIhyT8nOXml6zLNu88+lORrZiz7r0ku6m9flOTX+9sn9/v0yCRb+319+Eq/hvU6JXl6kqckee+QfZfk5iTflqSS/HWS71rp17bepjn25SuS/Nwsbe3LVTwlOT7JU/rbj0jyL/0+8908xKZ59qXvpmldTY7vx/5+OvYe/3vqmHhl3mP/Ho73PXYMuXLvsc/y+N7jSvLw/vYRSd6R5Gk+x+Od1mPP5qcmubO19sHW2v1JrkpyxgrXxIE7I8mV/e0rk5w5svyq1tp9rbW7ktyZbp+zAlpr/zfJJ2YsPqB9V1XHJ3lka+3vW/eL/icj67BM5tiXc7EvV7HW2q7W2j/1tz+b5PYkm+K7eciZZ1/Oxb5krXJ8v/Qcew/gmHjpOVZdeo4hl55ju6XXOp/r7x7RTy0+x2O1HsPmTUl2jNyfyvxfXlZeS/LmqvrHqjq/X3Zca21X0v0gJzm2X27/rn4Huu829bdnLmd1eGFVvbs/dXH6VCP78hBRVY9O8uR0f9H33TyEzdiXie8m64vjv/Fy7L08/Lu7PPx7uAQcQy49x3ZLp6oOr6p3Jbk3yfWtNZ/jMVuPYfNsY6i0Za+CA3Fqa+0pSb4ryYVV9fR52tq/h6659p19unq9Jsljkjwpya4kv9Uvty8PAVX18CT/K8lLWmufma/pLMvsz1Vkln3pu8l64zM8Xo69V5bf6vHx7+EScAy59BzbLa3W2t7W2pOSbE7XS/kJ8zT3Hh+E9Rg2TyXZMnJ/c5KdK1QLi9Ba29nP703yhnSn5n20P20h/fzevrn9u/od6L6b6m/PXM4Ka619tP+Hel+S/5H9p83al6tcVR2R7gD2ta21P+8X+24egmbbl76brEOO/8bIsfey8e/uEvPv4fg5hlx6ju2WT2vtU0nemuT0+ByP1XoMm/8hyUlVtbWqNiQ5N8m1K1wTc6iqh1XVI6ZvJ3l2kvem22fn9c3OS/LG/va1Sc6tqiOramuSk9IN2s7qcUD7rj+F5bNV9bT+6q7/bmQdVtD0P8a970v33Uzsy1Wtf+//IMntrbVLRx7y3TzEzLUvfTdZhxzfj4lj72Xl390l5t/D8XIMufQc2y29qtpYVY/qbz80ybOSvC8+x2M1sdIFLLfW2p6qemGSN6W7cvUfttZuXeGymNtxSd7QfXczkeR1rbXrquofklxdVc9LcneSs5OktXZrVV2d5LYke5Jc2FrbuzKlU1WvT/KMJF9TVVNJXp7klTnwffeCJH+c5KHprvL618v4Msic+/IZVfWkdKcLfSjJBYl9eQg4NcmPJXlPP1ZZkrwsvpuHorn25Q/5brKeOL4fK8feS8Ax8dJzrLosHEMuPcd2S+/4JFdW1eHpOuBe3Vr7q6r6+/gcj011F00EAAAAAICDtx6H0QAAAAAAYMyEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AysmKr68ap6R1V9vqo+U1VvqarvWcLn21BVr6iqJy3Vc4w813dW1eur6kNV1arqFYtc7xV9++npC1X1nqo6/yDraFX1woNZFwAADtRaPcavqsOr6heq6v9V1cf76c1V9S2LWNcxPrBuCJuBFVFVr0ny+0nekeTMJOckuTvJX1XVzy7R025I8vIkT1qi7Y86Pck3JbkhyRcOcN1PJ/m2fvq3/TYur6ofHmuFAAAwRmv8GP+hSS5K8g9JfizJjyb5cpK/q6pvXsT6jvGBdWFipQsA1p+qOjPJ85O8oLV22chDf11VH0nyG1V1Q2vt3StS4CJV1UNba1+c4+H/2Fr7D327Mw5w03taa28fuX9DVZ2S7oD9dQdeKQAALK11cIz/xSQnttY+OdL2hiT/kuSFSX5igU07xgfWBT2bgZXw4iR3Jvkfszz2a0k+l+6ALUnSD0Xxm6ON+tPzWlU9vL//sKr6vaq6oz8t7a6qelVVPXJktc/28z8aOYXt0f36D6mq/1pVO6rqvqr656r67hnP+aGq+q2q+qWqmkrymbleYGtt36LfjcX5bJIjRmpZzOt9kKr6nqq6vqru7U9rfHtVPXtGm1dU1ceq6sn941+oqndW1b+ZZXs/1Z8C+KWq+mhVXVNVXzXy+L+uqrf12/h4Vf2PqnrEGN4PAABWlzV9jN9a2zsaNPfL7k9ya5JjF3x3ZucYH1hzhM3AsqqqiXSnjv1la23vzMdba59O8pYkpx3gpo9KcniSX0zyXUl+Kcl3JPmzkTbf0c9/NftPYdvVL7smyY+nOxD+t+lOj7u2Hjz22w/3tf10utMCl0RVTfTTI6vqR/vnfMNIk8W83tlsTfKX6U79+4EkN6XrbXLqjHZHJbkyyeV9u/uSvKGqjhqp8T/1j78tXY+MF6Q7PXD6Pwenpjs98CNJzkrykiTfneSPFvk2AABwCFivx/hVdWSSb05y2yLbO8YH1jzDaADL7WuSHJnkw/O0+XCSZ8/z+IO01nanOxBK8pUD3rvSjaE22Vq7O93BZZJ8YPQUtqp6ZpLvSfKM1trb+sVvrqrHpjvQO3vG031va+1LB1LfAfrqdOO/jfrvrbU/mb6zyNf7IK213xtZ57B0B/2PT/K8JDeONH1okpe01v62b7sryTuTPD3JdVX1qCQvS/LbrbWXjqz35yO3X5nkptbaVw7Yq+qedKcMPqG19t553wUAAA4V6/UY/xeTHJ1unOqFOMYH1gU9m4HVqh3oClX1Y/2pYJ9Lf7GO/qHHLrDqs9L9Zf7Gkd4GE+n+Yr9tRtsbljhoTrqeA9/ST/863SmJ51XVy0cbHczrrarNVXVlf0C4p1/v2bOs8+Ukbx25P91bY3M//7Z0B6uz9mDoe0d8W5KrZ7ynf9dvezEXUQEAYG1ZM8f4VfU96cLmX2it3bGIVRzjA+uCns3AcvtYutO1vm6eNl+X/ae+LUpVfV+SP0nymnR/jf9EkuPTnZb2kAVW/5okX5sH9zRIkpmnAX70QOo6SHtaa7eM3L+xqo5I8mtV9buttU8czOvtezlcm+QRSf6/dGPqfT7Jr+TB48x9ZnTc6dba/VWVkW1/dT+faz8dne4UwFf300xb5lgPAIBDz7o6xq+qb0myPcnlrbXfXuRqjvGBdUHYDCyr1tqeqropyfdU1c/NvJBef/GLZ6Q7YJr2pSQbZmzqmBn3z07yjtbaT49sa7Fjwn0iyT3pxiRbyAH3xhiT29K9B49JV+/BvN5/leTJSb6rtXbdyHoPPYh6Pt7Pj0/3n4uZPpXuvXpFkv8zy+M7D+I5AQBYhdbTMX4/DMf/TtdD+mcWu94cHOMDa46wGVgJ/z3dX+d/MskVMx67KMmj8sBTt6aSfMOMdt854/5D0/WmGPUjM+7f389n9gq4Icl/SPK51tr75it8BT2hn+/o54t5vTNNH3B+Zb2q+rokpyZ59wHW8/dJvpjkvCQ/N/PB1trnq+rtSb6+tfYrB7htAAAOPWv+GL+qjk/ypiQfSPJDs10M8QA5xgfWHGEzsOxaa39RVZcleVVVnZzkr9L9Hp2T7mrRv9dae+vIKm9I8rtV9bJ0FwD5/nQXvBh1fb+9X0zyjnRXRH7mjOe9v6ruSvKDVfXedL0p3t2v+6Yk11fVrye5NckjkzwpyUNaaxcf6GvsD/C+pb+7IcnJVXVWks+31v56gdUnquppI+t+c5L/lOSNrbWPLPb1zuJ96Q7qf6uqfindqXa/nK7HxwFprX2qqv5zkkuqakO6ng1HprsIyy+31u5J8vPpLhSyL92VwD+bZLJv84uttX850OcFAGB1WuvH+H1P4b9ON5TEC5N8Uz8ERZLc11p75wKbcIwPrAvCZmCl/HS6A6gXJPmpJEf1yy9O8usz2l6R7tSyF6U72PmTJL+a5PKRNpcnOTHdhTYeku5A7YeTvD0P9Pwkv5nkb/ptbW2tfaiqvj/duGgvSXew9Ikk70ryuwf5+r49D+y5cXY/fTjJoxdY96vS9SpIujHmPpzksnSvedpiX+9XtNbu61/nq9IdGE4luSTdKY1PmGu9ebb3X6rqE30NFyT5ZJL/m+6AM621v6uqp6c72P2f6cZ3+3CS67I8Y18DALC81vIx/nFJntjf/qsZjznGd4wP9Kq1lRp+FGC/qtqcrkfDO5P82zGckgYAAKwgx/gA689hK10AQJK01qaSfF+S70g33hsAAHAIc4wPsP7o2QwAAAAAwGB6NgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDTax0AUly+umnt+uuu26lywAAYPxqpQtgZTjGBwBYs+Y8xl8VPZs/9rGPrXQJAADAGDnGBwBYf1ZF2AwAAAAAwKFN2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJm2HEpi2TqaoFp01bJle6VAAA1qHHPPZxOerhj5h3esxjH7fSZQIA69TEShcAy2HTlsnsnNqxqLbnXH7Tgm22X3DK0JIAAOCA7dp5T5576ZvnbXPtS5+9TNUAADyQsJl1YefUDiEyAAAAACwhw2gAAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYIsKm6vqZ6vq1qp6b1W9vqoeUlXHVNX1VfX+fn70SPuLq+rOqrqjqp6zdOUDAAAAALAaLBg2V9WmJC9Ksq219oQkhyc5N8lFSW5orZ2U5Ib+fqrq5P7xxyc5Pcmrq+rwpSkfAAAAAIDVYLHDaEwkeWhVTSQ5KsnOJGckubJ//MokZ/a3z0hyVWvtvtbaXUnuTPLUsVUMAAAAAMCqs2DY3Fq7J8lvJrk7ya4kn26tvTnJca21XX2bXUmO7VfZlGTHyCam+mUPUFXnV9UtVXXL7t27h70KAAAAAABW1GKG0Tg6XW/lrUlOSPKwqvrR+VaZZVl70ILWrmitbWutbdu4ceNi6wUAAAAAYBVazDAaz0pyV2ttd2vty0n+PMkpST5aVccnST+/t28/lWTLyPqb0w27AQAAAADAGrWYsPnuJE+rqqOqqpI8M8ntSa5Ncl7f5rwkb+xvX5vk3Ko6sqq2Jjkpyc3jLRsAAAAAgNVkYqEGrbV3VNU1Sf4pyZ4k70xyRZKHJ7m6qp6XLpA+u29/a1VdneS2vv2FrbW9S1Q/AAAAAACrwIJhc5K01l6e5OUzFt+XrpfzbO0vSXLJsNIAAAAAADhULGYYDQAAAAAAmJewGQAA1qGq+tmqurWq3ltVr6+qh1TVMVV1fVW9v58fPdL+4qq6s6ruqKrnrGTtAACsTsJmAABYZ6pqU5IXJdnWWntCksOTnJvkoiQ3tNZOSnJDfz9VdXL/+OOTnJ7k1VV1+ErUDgDA6iVsBgCA9WkiyUOraiLJUUl2JjkjyZX941cmObO/fUaSq1pr97XW7kpyZ5KnLm+5AACsdsJmAABYZ1pr9yT5zSR3J9mV5NOttTcnOa61tqtvsyvJsf0qm5LsGNnEVL/sAarq/Kq6papu2b1791K+BAAAViFhM4esTVsmU1WLmgAA2K8fi/mMJFuTnJDkYVX1o/OtMsuy9qAFrV3RWtvWWtu2cePG8RQLAMAhY2KlC4CDtXNqR865/KZFtd1+wSlLXA0AwCHlWUnuaq3tTpKq+vMkpyT5aFUd31rbVVXHJ7m3bz+VZMvI+pvTDbsBAABfoWczAACsP3cneVpVHVXdaWDPTHJ7kmuTnNe3OS/JG/vb1yY5t6qOrKqtSU5KcvMy1wwAwCqnZzMAAKwzrbV3VNU1Sf4pyZ4k70xyRZKHJ7m6qp6XLpA+u29/a1VdneS2vv2FrbW9K1I8AACrlrAZAADWodbay5O8fMbi+9L1cp6t/SVJLlnqugAAOHQZRgMAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYG2bRlMlW1qGnTlsmVLhcAAAAAWCITK10Ah7adUztyzuU3Lart9gtOWeJqltFhE6mqRTU9YfOW3LPj7iUuCAAAAABWlrAZDsa+PeszZAcAAACAORhGg+XT9wY23AYAAAAArD16NrN8FtkbWE9gAADWm8c89nHZtfOeBdt96Uv3LUM1AAAHR9gMAACwwnbtvCfPvfTNC7bb/oKnL0M1AAAHxzAaAAAAAAAMpmczAADAGnLf/V/OUQ9/xILtjj9hUz7wL+9bhooAgPVC2AwAALCG7Nu7N8+99C0Ltrv2pc9ehmoAgPVE2AwAADDDYi7Yp2cwAMADCZsBAABmWMwF+/QMBgB4IGEzAADAQTA2MgDAAwmbAQAADoKxkQEAHuiwlS4AAAAAAIBDn7AZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAg02sdAEAAABr2X33fzlHPfwR87b50pfuW6ZqAACWjrAZAABgCe3buzfPvfQt87bZ/oKnL1M1AABLR9jM6nPYRKpqpasAAAAAAA6AsJnVZ9+enHP5TQs2237BKctQDAAAAACwGC4QCAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDTax0AQAAAJAkj3ns47Jr5z0Ltjv+hE35wL+8bxkqAgAOhLAZAACAVWHXznvy3EvfvGC7a1/67GWoBgA4UIbRAAAAAABgMGEzAAAAAACDCZsBAAAAABjMmM0AAMC6spiL0H3pS/ctUzUAAGuHsBkAAFhXFnMRuu0vePoyVQMAsHYYRgMAAAAAgMH0bAYAAFiH7rv/yznq4Y+Yt83xJ2zKB/7lfctUEQBwqBM2AwAArEP79u7Ncy99y7xtrn3ps5epGgBgLTCMBgAAAAAAg+nZDAAAwKwWM9RGYrgNAKAjbAYAAGBWixlqI0n+7IXfbvxnAEDYDAAAwDCLCaUXE0h/6Uv3jbMsAGCZCZsBAABYcosJpLe/4OnLVA0AsBRcIBAAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDLSpsrqpHVdU1VfW+qrq9qr6tqo6pquur6v39/OiR9hdX1Z1VdUdVPWfpygcAAAAAYDVYbM/m30lyXWvtcUmemOT2JBcluaG1dlKSG/r7qaqTk5yb5PFJTk/y6qo6fNyFAwAAAACweiwYNlfVI5M8PckfJElr7f7W2qeSnJHkyr7ZlUnO7G+fkeSq1tp9rbW7ktyZ5KnjLRsAAAAAgNVkMT2bT0yyO8kfVdU7q+r3q+phSY5rre1Kkn5+bN9+U5IdI+tP9cseoKrOr6pbquqW3bt3D3oRAAAAAACsrMWEzRNJnpLkNa21Jyf5fPohM+ZQsyxrD1rQ2hWttW2ttW0bN25cVLEAAAAAAKxOiwmbp5JMtdbe0d+/Jl34/NGqOj5J+vm9I+23jKy/OcnO8ZQLAAAAAMBqtGDY3Fr7SJIdVfX1/aJnJrktybVJzuuXnZfkjf3ta5OcW1VHVtXWJCcluXmsVQMAAAAAsKpMLLLdzyR5bVVtSPLBJD+RLqi+uqqel+TuJGcnSWvt1qq6Ol0gvSfJha21vWOvHAAAOGhV9agkv5/kCemGvfv3Se5Isj3Jo5N8KMkPttY+2be/OMnzkuxN8qLW2puWvWgAAFa1RYXNrbV3Jdk2y0PPnKP9JUkuOfiyAACAJfY7Sa5rrZ3Vdyo5KsnLktzQWntlVV2U7lotv1BVJyc5N8njk5yQ5G+q6rE6lQAAMGoxYzYDAABrSFU9MsnTk/xBkrTW7m+tfSrJGUmu7JtdmeTM/vYZSa5qrd3XWrsryZ1JnrqcNQMAsPotdhgNAABg7Tgxye4kf1RVT0zyj0lenOS41tquJGmt7aqqY/v2m5K8fWT9qX7ZA1TV+UnOT5LJycmlq5517777v5yjHv6Iedscf8KmfOBf3rdMFQEAibCZOWzaMpmdUztWugwAAJbGRJKnJPmZ1to7qup30g2ZMZeaZVl70ILWrkhyRZJs27btQY/DuOzbuzfPvfQt87a59qXPXqZqAIBpwmZmtXNqR865/KYF222/4JRlqAYAgDGbSjLVWntHf/+adGHzR6vq+L5X8/FJ7h1pv2Vk/c1Jdi5btYv0mMc+Lrt23rNguy996b5lqAYAYP0RNgMAwDrTWvtIVe2oqq9vrd2R7sLft/XTeUle2c/f2K9ybZLXVdWl6S4QeFKSm5e/8vnt2nlPnnvpmxdst/0FT1+GagAA1h9hMwAArE8/k+S1VbUhyQeT/ES6C4hfXVXPS3J3krOTpLV2a1VdnS6M3pPkwtba3pUpGwCA1UrYDAAA61Br7V1Jts3y0DPnaH9JkkuWsiYAAA5th610AQAAAAAAHPqEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM0AAAAAAAwmbAYAAAAAYDBhMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGAwYTMAAAAAAIMJmwEAAAAAGEzYDAAAAADAYMJmAAAAAAAGEzYDAAAAADCYsBkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZiCbtkymqhacNm2ZXOlSAQAAAFilJla6AGDl7ZzakXMuv2nBdtsvOGUZqgEAAADgUKRnMwAAAAAAgwmbAQAAAAAYTNgMAAAAAMBgwmYAAAAAAAYTNgMAAAAAMJiwGQAAAACAwYTNAAAAAAAMJmwGAAAAAGCwiZUuAAAAAMbtvvu/nKMe/ogF2x1/wqZ84F/etwwVAcDaJ2wGAABgzdm3d2+ee+lbFmx37UufvQzVAMD6YBgNAAAAAAAGEzbDGrVpy2SqalETAAAAAAxlGA1Yo3ZO7cg5l9+0qLbbLzhliasBAAAAYK3TsxkAAAAAgMGEzQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZgAAAAAABhM2AwAAAAAwmLAZAAAAAIDBhM3A4h02kapacNq0ZXKlKwUAAABgmU2sdAHAIWTfnpxz+U0LNtt+wSnLUAwAAAAAq4mezQAAAAAADCZsBgAAAABgMGEzAAAAAACDCZsBAAAAABhM2AwAAAAAwGDCZuCQsGnLZKpqwWliw0MW1W7TlsmVfkkAAAAAa8rEShfA8tq0ZTI7p3asdBlwwHZO7cg5l9+0YLvtF5yy6HYAAAAAjI+weZ05kMCO1ckfDAAAAABYjYTNcIjxBwMAAAAAVqNFj9lcVYdX1Tur6q/6+8dU1fVV9f5+fvRI24ur6s6quqOqnrMUhQMAAAAAsHocyAUCX5zk9pH7FyW5obV2UpIb+vupqpOTnJvk8UlOT/Lqqjp8POUCAAAAALAaLSpsrqrNSb4nye+PLD4jyZX97SuTnDmy/KrW2n2ttbuS3JnkqWOpFgAAAACAVWmxPZt/O8nPJ9k3suy41tquJOnnx/bLNyUZvXrZVL/sAarq/Kq6papu2b1794HWDQAAAADAKrJg2FxV35vk3tbaPy5ymzXLsvagBa39/+3de5RlV10n8O8vqTyIaUQlOp1+SMwQEfkDNDqQOBkGJPLQgI5M4gPDLDQxkjUy0XEC6giOONGRXiwEIfEZVKTjA2lZIybGoAvDKwICISEmEpJ+SEAGCWg66e49f5zT8VJdVfdWn6q61XU/n7Xuuveeu889++ze6+aXX+3HNa21s1trZ5922mkTfjUAAAAAAOvRJCObz01yQVXdneTNSZ5WVb+T5JNVtTlJ+uf7+vK7k2wbOX9rkr0rVmMAAGBF2AQcAICVNDbZ3Fp7aWtta2vtMek2/vuL1tr3J9mV5OK+2MVJ3tq/3pXkoqo6qarOSPLYJO9d8ZoDAABD2QQcAIAVM+mazQu5Kskzqurvkjyjf5/W2q1Jrkvy0SRvT/Li1trBoRUFAABWjk3AAQBYaXPLKdxae0eSd/Sv/zHJ0xcp98okrxxYN4DVc9xcqhZaYv5Ip2/dlj333rPKFQKANffqdJuAbxo59kWbgFfV6Cbg7x4pt+gm4EkuSZLt27evQpVhdpx51uOyb++eJctsPn1L7rrj9jWqEQCMt6xkM8CGcehALrz65omK7rz0nFWuDACsrdFNwKvqqZOcssCxBTcBT3JNkpx99tlHfA5Mbt/ePblgx/VLltl1xflrVBsAmIxkMwAAzJ7Dm4A/O8nJSR45ugl4P6rZJuAAACzLkDWbgUn0yzWMe8ydePJE5QAAhrIJOAAAq8HIZlhtEy7XsPPScyYuBwCwSq5Kcl1VvSjJPUmen3SbgFfV4U3AD8Qm4AAALECyGQAAZphNwGFtTbLxX5I88MD+NagNAKwsyWZgqrZs2569u++ddjUAAGBNTLLxX5LsvOy8NagNAKwsyWZgqvbuvtfyIQAAAAAbgA0CAQAAAAAYTLIZAAAAAIDBJJsBAAAAABjMms3AyjtuLlU17VoAAAAAsIYkm4GVd+jARJv+JTb+AwAAANgoLKMBAAAAAMBgks0AAAAAAAwm2QwAAAAAwGCSzQAAAAAADCbZDAAAAADAYJLNAAAAAAAMJtkMAAAAAMBgks0A4xw3l6oa+9iybfu0awoAAAAwNXPTrgDAunfoQC68+uaxxXZees4aVAYAAABgfTKyGQAAAACAwSSbAQAAAAAYTLIZAAAAAIDBJJsBAAAAABhMshkAAAAAgMEkmwEAAAAAGEyyGQAAAACAweamXQEAAADYCM4863HZt3fPkmUeeGD/GtUGANaeZDMAAACsgH179+SCHdcvWWbnZeetUW0AYO1ZRgMAAAAAgMEkmwEAAAAAGMwyGgAAAHAM2v/gQznl1E1jy20+fUvuuuP2NagRALNOshkAAACOQYcOHswFO24aW27XFeevQW0AwDIaAAAAAACsAMlmAAAAAAAGk2wGAAAAAGAwyeYVtGXb9lTV2MeWbdundm0AAAAAgNVgg8AVtHf3vbnw6pvHltt56Tkb6toAAAAAAEY2jzHpiGGjhoFJLed3ZTVmQgAAAACsBiObx5h0xHBi1DAwGb8rAAAAwEY0syObrXEMAAAAALByZnZk81TXOD5ubqJE9ulbt2XPvfes/PUBAABglZx51uOyb++eJctsPn1L7rrj9jWqEQBrZWaTzVN16IDN/AAAANiQ9u3dkwt2XL9kmV1XnL9GtQFgLUk2r2cTjoAGAAAAAJg2yeb1bMIR0IlR0AAAAKyuSZbHSJIHHti/BrUBYD2SbAYAAADGmmR5jCTZedl5Y8vsf/ChnHLqprHlDhw8lLnjj1uyjPWfAdYPyWYAAABgTR06eDAX7LhpbLmdl52X73r9Xy1ZxvrPAOvH0n8eBAAAAACACUg2AwAAAAAwmGU0AFbKcXOpqmnXAgAAAGAqJJsBVsqhA7nw6pvHFtt56TlrUBkAAACAtWUZDQAAAAAABpNsBljP+qU5xj22bNs+7ZoCAAAAM84yGgDr2aRLc1x23kTrRZ++dVv23HvPStQMAAAA4ItINgNsBNaLBgBggDPPelz27d2zZJkHHti/RrUB4Fgl2QwAAAAzbt/ePblgx/VLltl52XlrVBsAjlXWbAYAAAAAYDDJZgAAAAAABpNsBgAAAABgMMlmAAAAAAAGk2wGAAAAAGAwyWYAAAAAAAaTbAYAAAAAYDDJZgAAAAAABpubdgUAAABgWvY/+FBOOXXTkmU2n74ld91x+xrVaOVNco8PPLB/jWoDwEYm2QwAAMDMOnTwYC7YcdOSZXZdcf4a1WZ1THKPOy87b41qA8BGZhkNAAAAAAAGM7IZAAAAljDJMhSJpSgAQLIZAAAAljDJMhSJpSgAwDIaAAAAAAAMJtkMAAAAAMBgY5PNVbWtqm6qqtuq6taq+tH++JdX1Q1V9Xf985eNnPPSqrqzqj5WVd+2mjcAAAAAAMD0TTKy+UCSH2utfV2SJyd5cVU9PsmVSW5srT02yY39+/SfXZTk65M8M8mvVNXxq1F5AAAAAADWh7HJ5tbavtba+/vX9ye5LcmWJM9Ncm1f7Nokz+tfPzfJm1tr+1trH09yZ5JvXuF6A3A0jptLVU302LJt+7RrC8AqMXsR2Ej2P/hQTjl109jHmWc9btpVBdjw5pZTuKoek+RJSd6T5Ktaa/uSLiFdVV/ZF9uS5N0jp+3uj83/rkuSXJIk27dLaACsiUMHcuHVN09UdOel56xyZQCYosOzF99fVZuS/E1V3ZDkhelmL15VVVemm734P+bNXjw9yZ9X1VmttYNTqj/Aww4dPJgLdtw0ttyuK85fg9oAzLaJNwisqlOT/GGSl7TWPrdU0QWOtSMOtHZNa+3s1trZp5122qTVAAAABjJ7EQCA1TBRsrmqTkiXaP7d1tof9Yc/WVWb+883J7mvP747ybaR07cm2bsy1QUAAFbSUrMXk4zOXrx35LRFZy9W1S1VdcunPvWpVa03AADrz9hkc1VVkl9PcltrbcfIR7uSXNy/vjjJW0eOX1RVJ1XVGUkem+S9K1dlAABgJZi9CADASppkzeZzk7wgyYer6oP9sZcluSrJdVX1oiT3JHl+krTWbq2q65J8NN1acC+2lhsAAKwvS81e7PdkMXsRAIBlGZtsbq29MwuPZEiSpy9yziuTvHJAvQAAgFUywezFq3Lk7MU3VdWOdBsEmr0IAMARJhnZDAAAbCxmLwIAsOIkmwEAYMaYvQgAwGoYu0EgAAAAAACMI9kMAAAAAMBgks0AAAAAAAwm2QwAAAAAwGCSzQAAAAAADCbZDAAAAADAYJLNAAAAAAAMJtkMAAAAAMBgks0AAAAAAAwm2QwAAAAAwGCSzQAAAAAADCbZDAAAAADAYJLNAAAAAAAMNjftCgAAAACstv0PPpRTTt20ZJnNp2/JXXfcvkY1Ath4JJsBAACADe/QwYO5YMdNS5bZdcX5a1QbgI3JMhoAAAAAAAwm2QwAAAAAwGCSzQAAAAAADCbZDAAAAADAYDYIBAAAAEiy/8GHcsqpm8aW23z6ltx1x+1rUCOAY4tkMwAAAECSQwcP5oIdN40t9/uX/8exSWkJaWAWSTYDAAAALMMkSeldV5y/RrUBWD+s2QwAAAAAwGCSzQAAAAAADCbZDAAAAADAYJLNAAAAAAAMJtkMwMKOm0tVjX1s2bZ92jUFAAAA1oG5aVcAgHXq0IFcePXNY4vtvPScNagMAAAAsN4Z2QwAAAAAwGCSzQAAAAAADCbZDAAAAADAYJLNAAAAAAAMZoNAAAAAgBW2/8GHcsqpm5Yss/n0LbnrjtvXqEYAq0+yGQAAAGCFHTp4MBfsuGnJMruuOH+NagOwNiyjAQAAAADAYJLNAAAAAAAMJtkMAAAAAMBgks0AAAAAAAwm2QwAAAAAwGCSzQAAAAAADCbZDAAAAADAYJLNAAxz3Fyqauxjy7bt064pAAAAsIrmpl0BAI5xhw7kwqtvHlts56XnrEFlAAAAgGkxshkAAAAAgMEkmwEAAAAAGEyyGQAAAACAwSSbAQAAAAAYTLIZAAAAAIDBJJsBAAAAABhMshkAAAAAgMEkmwEAAAAAGEyyGQAAAACAwSSbAQAAAAAYTLIZAAAAAIDBJJsBAAAAABhMshkAAAAAgMEkmwEAAAAAGEyyGQAAAACAwSSbAQAAAAAYTLIZAAAAAIDBJJsBAAAAABhMshkAAAAAgMEkmwEAAAAAGEyyGQAAAACAwSSbAQAAAAAYTLIZAAAAAIDBJJsBAAAAABhMshkAAAAAgMEkmwEAAAAAGEyyGQAAAACAwVYt2VxVz6yqj1XVnVV15WpdBwAAWH3iewAAxlmVZHNVHZ/kdUmeleTxSb6nqh6/GtcCAABWl/geAIBJzK3S935zkjtba3+fJFX15iTPTfLRVboeACxqy7bt2bv73rHlTt+6LXvuvWfmrn38CSfl4EP7x5ZbjTqyMib9t078O3LUxPcAq2D/gw/llFM3jS134OChzB2/9HjBzadvyV133D72u84863HZt3fPinwXk5uk3ZOV/bfm2Ddpv1lPfaJaayv/pVXfneSZrbUf7N+/IMm/a61dPlLmkiSX9G+/NsnHVrwiS3t0kk+v8TWPZdprctpqctpqebTX5LTV5LTV8mivyR1uq0+31p457cowzCTxfX9cjM96p48wjj7CUvQPxpmVPrJojL9aI5trgWNflNVurV2T5JpVuv5YVXVLa+3saV3/WKO9JqetJqetlkd7TU5bTU5bLY/2mpy22nDGxveJGJ/1Tx9hHH2EpegfjKOPrN4GgbuTbBt5vzXJ3lW6FgAAsLrE9wAAjLVayeb3JXlsVZ1RVScmuSjJrlW6FgAAsLrE9wAAjLUqy2i01g5U1eVJ/izJ8Ul+o7V262pca4CpTe87RmmvyWmryWmr5dFek9NWk9NWy6O9JqetNpBjJL5P9DvG00cYRx9hKfoH48x8H1mVDQIBAAAAAJgtq7WMBgAAAAAAM0SyGQAAAACAwWYy2VxVz6yqj1XVnVV15bTrsx5U1d1V9eGq+mBV3dIf+/KquqGq/q5//rKR8i/t2+9jVfVt06v56quq36iq+6rqIyPHlt02VfWNfRvfWVWvqapa63tZC4u018urak/fvz5YVc8e+Wxm26uqtlXVTVV1W1XdWlU/2h/Xv+ZZoq30rXmq6uSqem9V/W3fVq/oj+tXC1iivfStRVTV8VX1gap6W/9e32LqSnw/sxaJPf0u8TAxN0sROzMpMfAytNZm6pFuQ5O7knxNkhOT/G2Sx0+7XtN+JLk7yaPnHfvFJFf2r69M8gv968f37XZSkjP69jx+2vewim1zXpJvSPKRIW2T5L1JnpKkkvxpkmdN+97WsL1enuTHFyg70+2VZHOSb+hfb0pyR98m+tfkbaVvHXnvleTU/vUJSd6T5Mn61bLbS99avM2uSPKmJG/r3+tbHlN9RHw/04+I1T3G9xExt8dS/UPs7DFpXxEDT/iYxZHN35zkztba37fWHkzy5iTPnXKd1qvnJrm2f31tkueNHH9za21/a+3jSe5M164bUmvtr5J8Zt7hZbVNVW1O8sjW2rta9wvzxpFzNpRF2msxM91erbV9rbX396/vT3Jbki3Rv46wRFstZpbbqrXWPt+/PaF/tOhXC1qivRYz0+1VVVuTPCfJr40c1reYNvH9DBOrM46Ym6WInZmEGHh5ZjHZvCXJvSPvd2fphMWsaEmur6q/qapL+mNf1Vrbl3T/gU7ylf1xbbj8ttnSv55/fJZcXlUf6qc6Hp5eor16VfWYJE9K95d0/WsJ89oq0beO0E/x+mCS+5Lc0FrTr5awSHsl+tZCXp3kJ5IcGjmmbzFtYlPm87vEgsTcLETszAReHTHwxGYx2bzQeihLjWCaFee21r4hybOSvLiqzluirDZc3GJtM+tt9vokZyZ5YpJ9SV7VH9deSarq1CR/mOQlrbXPLVV0gWMz1V4LtJW+tYDW2sHW2hOTbE33V/QnLFF8ptsqWbS99K15qurbk9zXWvubSU9Z4NhMtBVrTp9iUn6XZpiYm8WInVmKGHj5ZjHZvDvJtpH3W5PsnVJd1o3W2t7++b4kb0k3HfGT/TD/9M/39cW14fLbZnf/ev7xmdBa+2T/H/BDSX41/7rsysy3V1WdkC7o/d3W2h/1h/WvBSzUVvrW0lprn03yjiTPjH411mh76VsLOjfJBVV1d7plCp5WVb8TfYvpE5syn98lvoiYm0mInVmEGHiZZjHZ/L4kj62qM6rqxCQXJdk15TpNVVV9SVVtOvw6yflJPpKuXS7ui12c5K39611JLqqqk6rqjCSPTbfI+SxZVtv0Uyrur6on97uN/sDIORve4R/g3nem61/JjLdXf2+/nuS21tqOkY/0r3kWayt960hVdVpVPap//Ygk35rk9uhXC1qsvfStI7XWXtpa29pae0y6+OkvWmvfH32L6RPfM5/fJR4m5mYpYmfGEQMv39y0K7DWWmsHquryJH+Wbufq32it3Trlak3bVyV5S9fXM5fkTa21t1fV+5JcV1UvSnJPkucnSWvt1qq6LslHkxxI8uLW2sHpVH31VdXvJXlqkkdX1e4kP5Pkqiy/bS5L8ltJHpFu19E/XcPbWDOLtNdTq+qJ6aaI3J3k0kR7pfsL6QuSfLi6NcKS5GXRvxayWFt9j751hM1Jrq2q49P9Ufm61trbqupd0a8Wslh7/ba+NTG/WUyV+H62idWZgJibpYidOVp+QxZR3QaIAAAAAABw9GZxGQ0AAAAAAFaYZDMAAAAAAINJNgMAAAAAMJhkMwAAAAAAg0k2AwAAAAAwmGQzMDVV9cKqek9VfaGqPldVN1XVc1bxeidW1cur6omrdY2Ra72iqj7c39f9VXVLVV04wXkvr6o28vjn/nsuOcp6tKq6/GjOBQCA5drIMf686z6vj7VvmaCsGB+YGZLNwFRU1euT/FqS9yR5XpILk9yT5G1V9d9W6bInJvmZJE9cpe8f9cgkv5Xuvv5TkvcneXNVffcE5/5Tkqf0j+9IcmOSq6vqe1enqgAAMNwMxPhJkqo6OcmOJJ9cxmlifGAmzE27AsDsqarnJfnhJJe11t4w8tGfVtU/JPk/VXVja+1DU6nghKrqEa21f1nos9ba/GD6+qr6+iQ/kOQPxnz1gdbau0fe31hV56QL2N90tPUFAIDVMgsx/oj/nmRPkruSPGHCrxbjAzPByGZgGn40yZ1JfnWBz34+yeeTPDwtrKrurqpfGi3UT89rVXVq//5Lquq1VfWxflrax6vqdVX1yJHT7u+ff3NkCttj+vNPrqpfrKp7q2p/Vf1tVT173jXvrqpXVdVPV9XuJJ9b5n3/Y7qRF0fj/iQnjNRlkvs9QlU9p6puqKr7+mmN766q8+eVeXlVfbqqntR//s9V9YGq+vcLfN8P9VMAH6iqT1bVH1TVl458/i1V9Zf9d/xjVf1qVW06yjYAAGD9mokYv6q2J/mJ/n6HEuMDG45kM7Cmqmou3dSxP2mtHZz/eWvtn5LclOQ/LPOrT0lyfJKfTPKsJD+d5GlJfn+kzNP655/Lv05h29cf+4MkL0wXCH9Hkvcl2VVHrv32vX3dfiTdtMAlVdVcVT2qqr4vyflJ3jDunJHz5qrqkVX1/f0137LM+13IGUn+JMkL0i3vcXO60Sbnzit3SpJrk1zdl9uf5C1VdcpIHX+q//wv043IuCzd9MDD/3Nwbrrpgf+Q5LuTvCTJs5P85iRtAADAsWHGYvxXJbmutfb+Zd6LGB+YCZbRANbao5OclOQTS5T5RLrE7MRaa59KFwgleTjg/XiSd1bV9tbaPemCyyS5a3QKW1U9Pclzkjy1tfaX/eHrq+qsdIHe8+dd7ttbaw+Mq1NVPTnJu/q3B5Jc3lr74wlu5yuSPDTv2Gtaa288/GbC+z1Ca+21I+ccly7o//okL0ry1yNFH5HkJa21v+jL7kvygSTnJXl7VT0qycuSvLq1dsXIeX808vqqJDe31h4O2KtqT7opg09orX1kyVYAAOBYMRMxflX9xyTfluSs5dxHT4wPzAQjm4H1qi33hKp6QT8V7PPpArl39h+NCwa/Nd1f5v96ZLTBXLq/2J89r+yNkySaex9O8k1JnpHktUleW1XfM8F5/9Sf901JviXdFL2Lq+pnRgsdzf1W1daqurYPCA/0552/wDkPJXnHyPuP9s9b++enpAtWFxzB0I+OeEqS6+a16Tv77/7GxeoIAMCGdczG+P25r0nyc621f1jufUSMD8wII5uBtfbpdNO1vnqJMl+df536NpGq+s4kb0zy+nR/jf9Mks3ppqWdPOb0Ryf5NzlypEGSzJ8GOPGO0621LyS5pX/75/06Z7+Q5PfGnHqgtXbLyPu/rqoTkvx8Vf1ya+0zR3O//SiHXUk2Jfmf6dbU+0KSn03ylfOKf661dmjkXh6sqox891f0z4v9O31ZuimAv9I/5tu2yHkAABx7ZiHG/6Ekj0pybT8COOn2Yzm+f/+F1tpC1zpMjA/MBMlmYE211g5U1c1JnlNVPz4a7CRJdZtfPDVdwHTYAzlyY70vn/f++Une01r7kZHvmnRNuM+k2036eROUXfZojBHvT/JfquqEMYHoQj6arg3OTFffo7nff5vkSUme1Vp7+8h5j1hmXZJus8OkC34/vcDnn03XVi9P8n8X+HzvUVwTAIB1aEZi/K9NNwJ4oVHN/y/desm/M2HdDhPjAxuOZDMwDa9J99f5H0xyzbzPrkw3YmB06tbuJF83r9wz5r1/RLrRFKO+b977B/vn+aMCbkzyY0k+31q7famKD3Rukt1HkWhOkif0z/f2z5Pc73yHA86Hz6uqr+7r9aFl1uddSf4lycVJfnz+h621L1TVu5N8bWvtZ5f53QAAHHs2eoz/2iR/PO/Ylek257s0yW1H8Z1ifGDDkWwG1lxr7Y+r6g1JXldVj0/ytnS/Rxem2y36ta21d4yc8pYkv1xVL0u3Ach3pdvwYtQN/ff9ZJL3pNsR+enzrvtgVX08yX+uqo+kG03xof7cP0tyQ1X9QpJbkzwyyROTnNxae+ly7q8P7n4zyZuS/H26nZu/M8lFGdnwYwlz/eaCSTfS4RuT/FSSt46sDzf2fhdwe7qg/lVV9dPpptq9It2Ij2VprX22qv5XkldW1YnpRjaclG4Tlle01vYk+Yl0G4UcSrcT+P1JtvdlfrK1dsdyrwsAwPq00WP81tqd6ZaoeFhVvTDJo+fd12LE+MBMkGwGpuVH0gVQl6Vb/+yU/vhL061rPOqadFPL/mu6YOeNSX4uydUjZa5O8jXpNto4OV2g9r1J3p0v9sNJfinJn/ffdUZr7e6q+q5066K9JF2w9JkkH0zyy0dxb59NN4Xsp9KtE/fZdFPkntNaW2i62Xxfmm5UQdKtMfeJJG9Id8+HTXq/D2ut7e/v83XpAsPdSV6ZbkrjExY7b4nv+99V9Zm+Dpemmz74V+kCzrTW3llV56ULdn873fpun0jy9ixj7WsAAI4ZGznGH0qMD8yEam3I8qMAK6OqtqYb0fCBJN/RWpu/aQcAAHAMEeMDzJ7jpl0BgCRpre1Ot9TE09Kt9wYAABzDxPgAs8fIZgAAAAAABjOyGQAAAACAwSSbAQAAAAAYTLIZAAAAAIDBJJsBAAAAABhMshkAAAAAgMEkmwEAAAAAGOz/A+duNTWpGxlDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,15))   \n",
    "plt.suptitle(\"Distribution of Quaterly Balances\", fontsize=25)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "sns.histplot(df_full_train.q1_balance)\n",
    "plt.xlabel('Quater 1 Balance', fontsize=15)\n",
    "plt.ylabel('')\n",
    "sns.despine()\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.histplot(df_full_train.q2_balance)\n",
    "plt.xlabel('Quater 2 Balance', fontsize=15)\n",
    "plt.ylabel('')\n",
    "sns.despine()\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.histplot(df_full_train.q3_balance)\n",
    "plt.xlabel('Quater 3 Balance', fontsize=15)\n",
    "plt.ylabel('')\n",
    "sns.despine()\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.histplot(df_full_train.q4_balance)\n",
    "plt.xlabel('Quater 4 Balance', fontsize=15)\n",
    "plt.ylabel('')\n",
    "sns.despine()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "af7270d2-8f75-451b-b687-25cb8c68ddf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kl9b/jcDJwMHtNt0+JUmS5m1sAaiqPgHcOM/uxwHnVdUtVfU14GrgkUlWAXtW1aeqqoBzgCePpWBJktSNpTgH6DlJvtCWyPZpbfsB14z0uba17dfuT22fVpKTk6xLsm7jxo0LXbckSVomFjsAvRF4MHAYsAF4VWuf7ryemqV9WlV1VlUdXlWHr1y58i6WKkmSlqtFDUBVdV1V3V5VW4G/AR7ZNl0LHDDSdX9gorXvP027JEnSdlvUANTO6Zn0FGDyCrELgBOS7J7kIIaTnS+tqg3AzUke1a7+OhF432LWLEmSlp9dx7XjJOcCRwD7JrkWeBlwRJLDGJaxvg78NkBVXZlkLXAVcBtwSlXd3nb1bIYryu4JfKDdJEmSttvYAlBVPWOa5jfP0v8M4Ixp2tcBhy5gaZIkqXN+ErQkSeqOAUiSJHXHACRJkrpjAJIkSd0xAEmSpO4YgCRJUncMQJIkqTsGIEmS1B0DkCRJ6o4BSJIkdccAJEmSumMAkiRJ3TEASZKk7hiAJElSdwxAkiSpOwYgSZLUHQOQJEnqjgFIkiR1Z9elLkDS3DZt2cKee6+Ytc+q1atZf9UVi1SRJO3cDEDSTqC2buWYMy+ctc9Fpx67SNVI0s7PJTBJktQdA5AkSeqOAUiSJHXHACRJkrpjAJIkSd0xAEmSpO4YgCRJUncMQJIkqTsGIEmS1B0DkCRJ6o4BSJIkdccAJEmSumMAkiRJ3TEASZKk7hiAJElSdwxAkiSpOwYgSZLUHQOQJEnqjgFIkiR1xwAkSZK6YwCSJEndMQBJkqTuGIAkSVJ35hWAkjx2Pm2SJEk7g/nOAL1+nm2SJEk7vF1n25jk0cBjgJVJnj+yaU9gl3EWJkmSNC6zBiBgN2CP1u8+I+03AU8dV1GSJEnjNGsAqqqPAx9P8taq+sYi1SRJkjRWc80ATdo9yVnAgaPPqapfHkdRkiRJ4zTfAPQPwJuAvwVuH185kiRJ4zffAHRbVb1xrJVIkiQtkvleBv/+JL+bZFWSFZO3sVYmSZI0JvOdATqp/fzDkbYCHrSw5UiSJI3fvAJQVR007kIkSZIWy7wCUJITp2uvqnMWthxJkqTxm+8S2CNG7t8DOBL4V8AAJEmSdjrzXQL7vdHHSfYC3jaWiiRJksZsvleBTbUZOHghC5EkSVos8z0H6P0MV33B8CWoDwXWjqsoSZKkcZrvOUCvHLl/G/CNqrp2DPVIkiSN3byWwNqXon6J4Rvh9wF+OM6iJEmSxmleASjJ04BLgeOBpwGfSfLUcRYmSZI0LvNdAnsx8Iiquh4gyUrgw8C7xlWYJEnSuMz3KrC7TYaf5tvb8FxJkqQdynxngD6Y5J+Ac9vjpwMXjackSZKk8Zo1ACV5CHC/qvrDJP8N+HkgwKeAdyxCfZIkSQturmWs1wA3A1TVe6rq+VX1PIbZn9eMtzRJkqTxmCsAHVhVX5jaWFXrgAPHUpEkSdKYzRWA7jHLtnsuZCGSJEmLZa4A9Nkk/3NqY5JnAZ8bT0mSJEnjNddVYM8Fzk/yTH4ceA4HdgOeMsa6JEmSxmbWAFRV1wGPSfI44NDWfGFVfWTslUmSJI3JvD4HqKo+Cnx0zLVIY3XImkPZMDExZ79NmzcvQjWSpKU03w9ClHZ6GyYmOObMC+fst/aUI8ZfjCRpSfl1FpIkqTtjC0BJ3pLk+iRXjLStSHJxki+3n/uMbHtRkquTrE/yhJH2hye5vG17XZKMq2ZJktSHcc4AvRU4akrbacAlVXUwcEl7TJI1wAnAw9pz3pBkl/acNwInAwe329R9SpIkbZOxBaCq+gRw45Tm44Cz2/2zgSePtJ9XVbdU1deAq4FHJlkF7FlVn6qqAs4ZeY4kSdJ2WexzgO5XVRsA2s/7tvb9gGtG+l3b2vZr96e2TyvJyUnWJVm3cePGBS1ckiQtHzvKSdDTnddTs7RPq6rOqqrDq+rwlStXLlhxkiRpeVnsy+CvS7Kqqja05a3rW/u1wAEj/fYHJlr7/tO0S5pi05Yt7Ln3iln7rFq9mvVXXTFrH0nqwWIHoAuAk4BXtJ/vG2l/Z5JXA6sZTna+tKpuT3JzkkcBnwFOBF6/yDVLO4XaunXOzzm66NRjF6kaSdqxjS0AJTkXOALYN8m1wMsYgs/a9mWq3wSOB6iqK5OsBa4CbgNOqarb266ezXBF2T2BD7SbJEnSdhtbAKqqZ8yw6cgZ+p8BnDFN+zp+/D1kkiRJd9mOchK0JEnSovG7wLTDm8+XmHpyryRpWxiAtMObz5eYenKvJGlbGIC0LMznEvBNmzcvUjWSpB2dAUjLwnwuAV97yhGLU4wkaYfnSdCSJKk7BiBJktQdA5AkSeqOAUiSJHXHACRJkrpjAJIkSd0xAEmSpO4YgCRJUncMQJIkqTsGIEmS1B0DkCRJ6o4BSJIkdccAJEmSumMAkiRJ3TEASZKk7hiAJElSdwxAkiSpOwYgSZLUHQOQJEnqjgFIkiR1xwAkSZK6YwCSJEndMQBJkqTuGIAkSVJ3dl3qAjS7Q9YcyoaJiTn7rVq9mvVXXbEIFUmStPMzAC2h+YSbTZs3c/xffnTOfV106rELVZYkScueAWgJbZiY4JgzL5y1z9pTjlicYiRJ6ojnAEmSpO4YgCRJUncMQJIkqTsGIEmS1B0DkCRJ6o4BSJIkdccAJEmSumMAkiRJ3TEASZKk7hiAJElSdwxAkiSpOwYgSZLUHQOQJEnqjgFIkiR1xwAkSZK6YwCSJEndMQBJkqTuGIAkSVJ3DECSJKk7BiBJktQdA5AkSeqOAUiSJHXHACRJkrpjAJIkSd0xAEmSpO7sutQFqG+HrDmUDRMTs/bZtHnzIlUjSeqFAUhLasPEBMeceeGsfdaecsTiFCNJ6oZLYJIkqTsGIEmS1B0DkCRJ6o4BSJIkdccAJEmSumMAkiRJ3fEyeKkjm7ZsYc+9V8zaZ9Xq1ay/6opFqkiSloYBSOpIbd065+cuXXTqsYtUjSQtHZfAJElSdwxAkiSpOwYgSZLUHQOQJEnqjgFIkiR1xwAkSZK6YwCSJEndMQBJkqTuLEkASvL1JJcnuSzJuta2IsnFSb7cfu4z0v9FSa5Osj7JE5aiZkmStHws5QzQ46rqsKo6vD0+Dbikqg4GLmmPSbIGOAF4GHAU8IYkuyxFwZIkaXnYkZbAjgPObvfPBp480n5eVd1SVV8DrgYeufjlSZKk5WKpAlABH0ryuSQnt7b7VdUGgPbzvq19P+Cakede29ruJMnJSdYlWbdx48YxlS5JknZ2S/VlqI+tqokk9wUuTvKlWfpmmraarmNVnQWcBXD44YdP20eSJGlJZoCqaqL9vB44n2FJ67okqwDaz+tb92uBA0aevj8wsXjVSpKk5WbRA1CSeye5z+R94FeAK4ALgJNat5OA97X7FwAnJNk9yUHAwcCli1u1JElaTpZiCex+wPlJJl//nVX1wSSfBdYmeRbwTeB4gKq6Msla4CrgNuCUqrp9CeqWJEnLxKIHoKr6KvDT07R/GzhyhuecAZwx5tIkSVIndqTL4CVJkhaFAUiSJHXHACRJkrpjAJIkSd0xAEmSpO4YgCRJUncMQJIkqTsGIEmS1J2l+jLUZe+QNYeyYWL2ryzbtHnzIlUjSZJGGYDGZMPEBMeceeGsfdaecsTiFCNJku7AJTBJktQdA5AkSeqOS2DLxKYtW9hz7xWz9lm1ejXrr7pikSqSJGnHZQBaJmrr1jnPObro1GMXqRpJknZsLoFJkqTuGIAkSVJ3DECSJKk7BiBJktQdA5AkSeqOAUiSJHXHACRJkrpjAJIkSd3xgxAl3YGfKi6pBwYgSXfgp4pL6oFLYJIkqTsGIEmS1B0DkCRJ6o4BSJIkdccAJEmSumMAkiRJ3TEASZKk7hiAJElSdwxAkiSpOwYgSZLUHQOQJEnqjgFIkiR1xwAkSZK6YwCSJEndMQBJkqTuGIAkSVJ3DECSJKk7BiBJktQdA5AkSeqOAUiSJHXHACRJkrpjAJIkSd0xAEmSpO7sutQFaPk6ZM2hbJiYmLXPps2bF6kaLaRNW7aw594r5uy3avVq1l91xSJUJEnbxgCksdkwMcExZ144a5+1pxyxOMVoQdXWrXP+2QJcdOqxi1CNJG07l8AkSVJ3DECSJKk7BiBJktQdA5AkSeqOAUiSJHXHq8AkLQvz+dgF8NJ8SQMDkKRlYT4fuwBemi9p4BKYJEnqjgFIkiR1xwAkSZK6YwCSJEndMQBJkqTueBWYpB3efC5x37R58yJVI2k5MABJ2uHN5xL3taccMa99bdqyhT33XjFrHz8rSFr+DECSulJbt84ZpvysIGn5MwB1xP/5arH5npO0ozIAdcT/+Wqx+Z6TtKPyKjBJktQdA5AkSeqOS2DbwUtyJUnauRmAtsNCXpIrSZIWnwFI0pKaz5VizqhKWmgGIElLaj5XijmjKmmheRK0JEnqjgFIkiR1xyUwbbP5XAUHnrehnZefYC0tfztNAEpyFPBaYBfgb6vqFUtcUrfmcxUceN6Gdl5+grW0/O0US2BJdgH+CjgaWAM8I8mapa1KkiTtrHaWGaBHAldX1VcBkpwHHAdctaRVSerWcl4mm+8y9856fBJAqmqpa5hTkqcCR1XVb7XHvw78XFU9Z0q/k4GT28NDgPV34WX3BW64C89frhyXO3NMpue43JljMj3H5c4ck+lNHZcbquqo7dnRzjIDlGna7pTcquos4KwFecFkXVUdvhD7Wk4clztzTKbnuNyZYzI9x+XOHJPpLeS47BTnAAHXAgeMPN4fmHt+VpIkaRo7SwD6LHBwkoOS7AacAFywxDVJkqSd1E6xBFZVtyV5DvBPDJfBv6Wqrhzzyy7IUtoy5LjcmWMyPcflzhyT6Tkud+aYTG/BxmWnOAlakiRpIe0sS2CSJEkLxgAkSZK6YwCaIslRSdYnuTrJaUtdz2JL8vUklye5LMm61rYiycVJvtx+7jPS/0VtrNYnecLSVb5wkrwlyfVJrhhp2+YxSPLwNpZXJ3ldkuk+zmGnMcO4nJ7kW+39clmSY0a2LftxSXJAko8m+WKSK5P8QWvv+v0yy7h0+35Jco8klyb5fBuT/93au32vzDImi/M+qSpv7cZwgvVXgAcBuwGfB9YsdV2LPAZfB/ad0nYmcFq7fxrw5+3+mjZGuwMHtbHbZamPYQHG4BeBnwWuuCtjAFwKPJrhc6w+ABy91Mc2hnE5HXjhNH27GBdgFfCz7f59gH9vx971+2WWcen2/dLq36PdvzvwGeBRPb9XZhmTRXmfOAN0Rz/6yo2q+iEw+ZUbvTsOOLvdPxt48kj7eVV1S1V9DbiaYQx3alX1CeDGKc3bNAZJVgF7VtWnavjbec7Ic3ZKM4zLTLoYl6raUFX/2u7fDHwR2I/O3y+zjMtMlv241OD77eHd263o+L0yy5jMZEHHxAB0R/sB14w8vpbZ/9IuRwV8KMnnMny1CMD9qmoDDP+wAfdt7T2N17aOwX7t/tT25eg5Sb7Qlsgmp++7G5ckBwI/w/C/WN8vzZRxgY7fL0l2SXIZcD1wcVV1/16ZYUxgEd4nBqA7mtdXbixzj62qnwWOBk5J8ouz9HW8Zh6DXsbmjcCDgcOADcCrWntX45JkD+DdwHOr6qbZuk7T1tO4dP1+qarbq+owhm8zeGSSQ2fp3vOYLMr7xAB0R91/5UZVTbSf1wPnMyxpXdemGGk/r2/dexqvbR2Da9v9qe3LSlVd1/4B2wr8DT9eAu1mXJLcneGX/Duq6j2tufv3y3Tj4vtlUFXfBT4GHIXvFeCOY7JY7xMD0B11/ZUbSe6d5D6T94FfAa5gGIOTWreTgPe1+xcAJyTZPclBwMEMJ6ItR9s0Bm0q++Ykj2pXI5w48pxlY/If7uYpDO8X6GRc2jG8GfhiVb16ZFPX75eZxqXn90uSlUn2bvfvCTwe+BIdv1dmGpNFe5+M6+zunfUGHMNwxcJXgBcvdT2LfOwPYjjD/vPAlZPHD/wEcAnw5fZzxchzXtzGaj076ZUI04zDuQzTrrcy/M/iWdszBsDh7S/uV4C/pH3y+s56m2Fc3gZcDnyh/eO0qqdxAX6eYar9C8Bl7XZM7++XWcal2/cL8FPAv7VjvwJ4aWvv9r0yy5gsyvvEr8KQJEndcQlMkiR1xwAkSZK6YwCSJEndMQBJkqTuGIAkSVJ3DEDSMpXB15JUkocsdT1LIcnX2/FP3jYmuSjJT2/Hvn6j7WOPcdQqaXEZgKTl69HAge3+CUtYx1J7J8NYPBr4bWAl8E8j3y8kqUMGIGn5egawieFLKJ+xWC/aPtF1R7Khqj7dbu9h+LTd+zEEIkmdMgBJy1CSXYDjGT5F9S3AmiQ/1bbdO8mmJL87zfPWJXnbyOMHJDkvyY1JNif5pySHjGw/sC0LPTPJOUm+C7y/bTsxySfbc7+T5KNJDp/mNZ+T5JpW03uTHNn2ecRIn7slOS3J1UluSfLvSU6auq95urn9vPvI/h+d5IIkE62Oy5I8c64dJXlFksuTfD/JtUnekeT+U/p8Pckrkzyv9flOG9O9p/T7iSR/nWRDkh8kWZ/kuWMaA6l7BiBpefplhlmO84B3MXx9xTMAqmoT8I/A00efkORBwMOBv2+PVwCfBA4Bfgd4GnBv4MPTzPK8kiFYHA/8WWs7EDintf0qw9dnfKK9zuRrPgV4PUNQewrDR9+/eZrjeT3wEuAs4FiGL+p9S5InzmMskmTXdjsAOBO4Efj4SJ8HAv8P+C3gSQxf4vl3SeaaObtvO95jgecyfJ3MR1oAHfU04EjgZOCPgCfy43GanDX7GPBk4OUMXxvxKmD1yD7uyhhImmqpvwvEmzdvC39jmPX5DrBbe3wh8DX40dffPAW4HVg98pwXMQSDyee8HPg2d/xuon2A7wGntMcHMnzn0/lz1HM3YFeGL3986Uj7Z4ELp/R9Q9vnEe3xQ4CtwElT+p0DfHaO1/1629fo7TvA42Z5Tlqtfw18ZKT9N9rz95jhebsA+7U+vzilhq8Au460vQb4j5HHv92O8bAZ9r3dY+DNm7fpb84ASctMkt0ZAs75VfXD1nwuQ1h5VHv8AeD7DLMzk54+5TmPBy4GbpqcQWGY5fkcwxcPjrpwmjoemuT8JNcxhK1bGWaTfrJt3wU4jGH2Z9TUx0cy/PI/f2QmZ1eGL448bJrZlqneDjyi3Z7A8C3R508uCbZa9knyuiTfaHXeyjBb85Oz7TjJ0Un+Jcn3gNsYZrmY5nkfrarbRh5fBdw3yW7t8S8D/1ZVl83wUnd1DCRNsetSFyBpwR0N7A1cNHKeyceAWxiWwT5VVT9I8j6G0PPadl7PTwN/OLKffRkC0x2WyppLpjy+bvRBkvsAH2rtzwe+AfwA+FvgHq3bSoZ/gzZO2dfUx/syzK58b7qDBVbx4+Axneuqat1IbRcDPwO8FHhqa34rw7G+nCGc3AQ8Gzhupp0meQRDWDsfeAVwPcPsz6f58TFO+u6Uxz9kmGnard3/CWDDLMdwV8dA0hQGIGn5mTxv5R+m2fa0JM+rqtsZzvV5f5IHMIScjcBHRvreyPAL/uXT7OfmKY9ryuNHA/sD/6WqvjTZmGSvkT4bGWZNVk557tTHN7Z+j2WYBZnq+mnaZlRVleRLwKGtpnswnFPznKp600itc82QP4XhGJ5eVdWe88BtqWXEtxmWuWayoGMgyQAkLSvtQ/qeyLDkddaUzT8DvBp4HPBhhhma7zCcoPt04F0tGE26pG27sqq2bGMpkydJ3zJS22MYluE+B1BVtye5jGGW5a9Hnvtfp+zrIwyzH3tV1cXbWMedJAmwBrimNe3e9j9a631aHVOD3ah7ArdOhp9mzivHZnAJcHySn6qqL0yzfUHHQJIBSFpujgPuBby2qj4zuiHJ/wNezDBD9OGqujXJ+QxLVKuAqZfFvxr4NYarml4PfIvhyrJfAj5ZVefOUsenGc4x+pskZzLMBp3e9jHqz4D3JPlLhtmmxzLMxkCb6aiq9UneBJzX9rWOYYnpYcBPVtVvzTEmq5JMnvu0D8MVaYcyLIFRVd9L8lngpUluaq97GsNy056z7Pdi4LlJXsNw6f9jGMZre5wDnAJ8KMnpwHrgIIbjO20BxkDSFJ4ELS0vzwC+PDX8AFTVrcBa4L+1E6VhuEx+FTAB/POU/jcwnBfzJeAvGGaMzgT2YrhcfUZVdR3DCdb3Zzjp+LkMl9JfPaXf+cDvM1z+/V6GE5Vf2DbfNNL1FIaluBOBixjO2TkW+MRsdTS/Cnyq3c4FDgae2l57tM/XGILIaxkugz9njmO8iOGS9v/OEN5+iWH2bZtV1Q8YToR+P/AnDCepn8rw5zLproyBpClyx9lbSVpaSV7CMFO1YjuW3iRpXlwCk7Rkkqxk+PyhjwKbgV9gmFV5s+FH0jgZgCQtpR8C/4lhWWcvhkvBXwv88VIWJWn5cwlMkiR1x5OgJUlSdwxAkiSpOwYgSZLUHQOQJEnqjgFIkiR15/8Dt3tkWcD1S3QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title('Distribution of Average Balance ')\n",
    "sns.histplot(data.average_balance, bins=50)\n",
    "plt.xlabel('Average Balance', fontsize=15)\n",
    "sns.despine()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "90cba3c5-86f8-497a-bffb-f966b9b3bdd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Creating a bar chart to check how Education Role affects monthly income \n",
    "\"\"\"\n",
    "plt.figure(figsize=(20,15))\n",
    "\"\"\"\n",
    "Creating a bar chart to check how Education Field affects monthly income \n",
    "\"\"\"\n",
    "plt.subplot(2,2,1)\n",
    "# plt.title(\"Monthly Income Education Field\", fontsize=15)\n",
    "sns.barplot(x=y_full_train, y=df_full_train[\"credit_cards_held\"], color=\"#004481\")\n",
    "sns.despine()\n",
    "\n",
    "\"\"\"\n",
    "Creating a bar chart to check how Education Field affects monthly income \n",
    "\"\"\"\n",
    "plt.subplot(2,2,2)\n",
    "# plt.title(\"Monthly Income Education Field\", fontsize=15)\n",
    "sns.barplot(x=y_full_train, y=df_full_train[\"homes_owned\"], color=\"#004481\")\n",
    "sns.despine()\n",
    "\n",
    "\"\"\"\n",
    "Creating a bar chart to check how Education Field affects monthly income \n",
    "\"\"\"\n",
    "plt.subplot(2,2,3)\n",
    "# plt.title(\"Monthly Income Education Field\", fontsize=15)\n",
    "sns.barplot(x=y_full_train, y=df_full_train[\"average_balance\"], color=\"#004481\")\n",
    "sns.despine()\n",
    "\n",
    "\"\"\"\n",
    "Creating a bar chart to check how Education Field affects monthly income \n",
    "\"\"\"\n",
    "plt.subplot(2,2,4)\n",
    "# plt.title(\"Monthly Income Education Field\", fontsize=15)\n",
    "sns.barplot(x=y_full_train, y=df_full_train[\"bank_accounts_open\"], color=\"#004481\")\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d61dd29a-86c6-4f42-8e0a-254f7602760c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "reward                  0.002738\n",
       "mailer_type             0.004582\n",
       "income_level            0.003115\n",
       "overdraft_protection    0.000008\n",
       "credit_rating           0.012787\n",
       "own_your_home           0.000008\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def find_mi(series):\n",
    "    \"\"\"\n",
    "    This function computes the mutual info score,\n",
    "    between the categorical columns and the target column\n",
    "    \"\"\"\n",
    "    return mutual_info_score(series, y_full_train)\n",
    "\n",
    "\n",
    "df_full_train[categorical_cols].apply(find_mi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c39fb8bc-0534-415c-a743-3b6e85668bb1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Heatmap \n",
    "\"\"\"\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.heatmap(df_full_train.corr(), cmap=\"viridis\", annot=True)\n",
    "# plt.title(\"Heatmap of top correlated columns\", fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ecfcfcb-b61e-4ad2-9e02-53794f50d2b5",
   "metadata": {},
   "source": [
    "## Model Building and Parameter Tuning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d9a3ceb9-c4fc-4576-b5a8-eb60a1aca08a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Setting random state \n",
    "\"\"\"\n",
    "r = 1\n",
    "\n",
    "\"\"\"\n",
    "Creating a dictionary to store models and their results \n",
    "\"\"\"\n",
    "models = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb486ea1-cebe-47c1-8c07-d5cfec9d23a0",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cb179b0f-96d4-4d35-91bf-fb4c24bf7009",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.94\n"
     ]
    }
   ],
   "source": [
    "train_dict = df_train[categorical_cols + numerical_cols].to_dict(orient='records')\n",
    "dv = DictVectorizer(sparse=False)\n",
    "X_train = dv.fit_transform(train_dict)\n",
    "\n",
    "lr = LogisticRegression()\n",
    "\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "val_dict = df_val[categorical_cols + numerical_cols].to_dict(orient='records')\n",
    "X_val = dv.transform(val_dict)\n",
    "\n",
    "y_pred = lr.predict(X_val)\n",
    "\n",
    "#checking the accuracy\n",
    "score = round(accuracy_score(y_pred, y_val),2)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "40815cce-146f-4cbb-b15c-15f81442f8ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.94\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "training and testing the model\n",
    "\"\"\"\n",
    "#DictVectorizer performs one hot enconding on categorical columns\n",
    "dv = DictVectorizer(sparse=False)\n",
    "\n",
    "#creating an instance of the logistic regression model\n",
    "lr = LogisticRegression(solver=\"liblinear\", C=1.0)\n",
    "\n",
    "#convert train data to dictionary\n",
    "train_dict = df_train[categorical_cols + numerical_cols].to_dict(orient='records')\n",
    "\n",
    "#one hot encoding\n",
    "X_train = dv.fit_transform(train_dict)\n",
    "\n",
    "#Training the model \n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "#convert validation data to dictionary\n",
    "val_dict = df_val[categorical_cols + numerical_cols].to_dict(orient='records')\n",
    "\n",
    "#one hot encoding\n",
    "X_val = dv.transform(val_dict)\n",
    "\n",
    "#predicting for the validation data\n",
    "y_pred = lr.predict(X_val)\n",
    "\n",
    "#checking the accuracy\n",
    "score = round(accuracy_score(y_pred, y_val),2)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cc9ac06-c090-4bda-b251-48fb7e94d1f8",
   "metadata": {},
   "source": [
    "### Tuning Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16a1b6c3-712f-460a-87b3-7e692f936fbc",
   "metadata": {},
   "source": [
    "Here we try to find the best regularization value for our model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a49efb7b-7163-48ba-90a6-4ef3b9cc6e30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 0.937,\n",
       " 5: 0.937,\n",
       " 7: 0.937,\n",
       " 10: 0.937,\n",
       " 15: 0.937,\n",
       " 30: 0.937,\n",
       " 50: 0.937,\n",
       " 100: 0.937}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_dict = {}\n",
    "for c in [1, 5, 7, 10, 15, 30, 50, 100]:\n",
    "    \n",
    "    dv = DictVectorizer(sparse=False)\n",
    "    \n",
    "    train_dict = df_train.to_dict(orient='records')\n",
    "    X_train = dv.fit_transform(train_dict)\n",
    "    \n",
    "    lr = LogisticRegression(C=c, random_state=42)\n",
    "    lr.fit(X_train, y_train)\n",
    "    \n",
    "    val_dict = df_val.to_dict(orient='records')\n",
    "    X_val = dv.transform(val_dict)\n",
    "\n",
    "    y_pred = lr.predict(X_val)\n",
    "\n",
    "    score = np.round(accuracy_score(y_val, y_pred), 3)\n",
    "    scores_dict[c] = score \n",
    "scores_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91a5a832-2b9e-4103-9101-17fe8f18c921",
   "metadata": {},
   "source": [
    "We can see that tuning our model does not lead to significant change in accuracy, <br> Therefore we use the original parameters for the final model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f999ca54-cf44-4d84-8800-45de097f8151",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.937\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Training the final model\n",
    "\"\"\"\n",
    "\n",
    "lr = LogisticRegression()\n",
    "\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "y_pred = lr.predict(X_val)\n",
    "\n",
    "#checking the accuracy\n",
    "\n",
    "lr_score = round(accuracy_score(y_pred, y_val), 3)\n",
    "\n",
    "models['Logistic Regression'] = lr_score \n",
    "\n",
    "print(lr_score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3f35466-724f-4e80-8604-e3fdb710adfd",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Decision Trees"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "196a4ff0-83fa-4113-b373-ce25e40f84d3",
   "metadata": {},
   "source": [
    "We use decison trees with default parameters then with tuning. <br> \n",
    "parameters to be tuned:\n",
    "- max_depth \n",
    "- min_samples_leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a3e9c8b7-2f40-4746-88e9-434da606f679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8840055632823366"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "with default parameters\n",
    "\"\"\"\n",
    "dt = DecisionTreeClassifier(random_state=r)\n",
    "dt.fit(X_train, y_train)\n",
    "y_pred_dt = dt.predict(X_val)\n",
    "\n",
    "accuracy_score(y_val, y_pred_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f2c6336-8652-4bb0-ae9d-dae8525508b3",
   "metadata": {},
   "source": [
    "#### Tuning max_depth parameter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f528af7f-8a46-4a1a-a111-8630609e6a72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -> 0.937134909596662\n",
      "3 -> 0.937134909596662\n",
      "5 -> 0.9360222531293463\n",
      "10 -> 0.9162726008344924\n",
      "20 -> 0.8803894297635605\n",
      "None -> 0.8840055632823366\n"
     ]
    }
   ],
   "source": [
    "for m in [1,3, 5, 10, 20, None]:\n",
    "    dt = DecisionTreeClassifier(max_depth=m, random_state=r)\n",
    "    dt.fit(X_train, y_train)\n",
    "    y_pred_dt = dt.predict(X_val)\n",
    "\n",
    "    score = accuracy_score(y_val, y_pred_dt)\n",
    "    print(f\"{m} -> {score}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "368e0074-49f6-4f12-911a-50cb5771c4b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 -> 0.937134909596662\n",
      "4 -> 0.937134909596662\n",
      "5 -> 0.9360222531293463\n",
      "6 -> 0.9337969401947149\n",
      "7 -> 0.9301808066759388\n"
     ]
    }
   ],
   "source": [
    "for m in [3, 4, 5, 6, 7]:\n",
    "    dt = DecisionTreeClassifier(max_depth=m, random_state=r)\n",
    "    dt.fit(X_train, y_train)\n",
    "    y_pred_dt = dt.predict(X_val)\n",
    "\n",
    "    score = accuracy_score(y_val, y_pred_dt)\n",
    "    print(f\"{m} -> {score}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79dc0754-19d3-4406-b6d6-39d9b24b3880",
   "metadata": {},
   "source": [
    "Let's choose 3 as our max_depth "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4b51357-d5d5-46da-afb4-8ccb0edc03b2",
   "metadata": {},
   "source": [
    "#### Tuning min_samples_leaf parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "00095dae-9bb0-49e0-9d36-41d809b822ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 -> 0.937134909596662\n",
      "3 -> 0.937134909596662\n",
      "5 -> 0.937134909596662\n",
      "10 -> 0.937134909596662\n",
      "20 -> 0.937134909596662\n"
     ]
    }
   ],
   "source": [
    "for l in [1, 3, 5, 10, 20]:\n",
    "    dt = DecisionTreeClassifier(max_depth=1, min_samples_leaf=l, random_state=r)\n",
    "    dt.fit(X_train, y_train)\n",
    "    y_pred_dt = dt.predict(X_val)\n",
    "\n",
    "    score = accuracy_score(y_val, y_pred_dt)\n",
    "    print(f\"{l} -> {score}\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eacf5b50-b386-4d78-a1aa-e4adfa166772",
   "metadata": {},
   "source": [
    "We can see that the min_samples_leaf value used has no effect on the accuracy of our model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd3b7a87-61c5-4b87-9646-57c16d6aa64b",
   "metadata": {},
   "source": [
    "Trying different combinations of max_depth and min_samples_leaf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1db5c21b-932c-4d22-bf55-0f3c8dfeaf59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1 -> 0.937134909596662\n",
      "3 -> 0.937134909596662\n",
      "5 -> 0.937134909596662\n",
      "10 -> 0.937134909596662\n",
      "20 -> 0.937134909596662\n",
      "\n",
      "3\n",
      "1 -> 0.937134909596662\n",
      "3 -> 0.937134909596662\n",
      "5 -> 0.937134909596662\n",
      "10 -> 0.937134909596662\n",
      "20 -> 0.937134909596662\n",
      "\n",
      "5\n",
      "1 -> 0.9360222531293463\n",
      "3 -> 0.9363004172461753\n",
      "5 -> 0.9363004172461753\n",
      "10 -> 0.9354659248956885\n",
      "20 -> 0.9337969401947149\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for m in [1, 3, 5]:\n",
    "    print(m)\n",
    "    for l in [1, 3, 5, 10, 20]:\n",
    "        dt = DecisionTreeClassifier(max_depth=m, min_samples_leaf=l)\n",
    "        dt.fit(X_train, y_train)\n",
    "        y_pred_dt = dt.predict(X_val)\n",
    "\n",
    "        score = accuracy_score(y_val, y_pred_dt)\n",
    "        print(f\"{l} -> {score}\")  \n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "825b4fe8-eaf3-4224-8f9c-fcc62b25dee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.937\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Training final Model\n",
    "\"\"\"\n",
    "dt = DecisionTreeClassifier(max_depth=3, min_samples_leaf=1)\n",
    "dt.fit(X_train, y_train)\n",
    "y_pred_dt = dt.predict(X_val)\n",
    "\n",
    "dt_score = round(accuracy_score(y_val, y_pred_dt), 3)\n",
    "print(dt_score)\n",
    "\n",
    "models['Decision Trees'] = dt_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f534a48a-d115-4944-bcbf-e5aa127a7a9e",
   "metadata": {},
   "source": [
    "#### Feature Importance "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0434625d-2c01-4b58-b8d1-72de7128fc58",
   "metadata": {},
   "source": [
    "We can check the features that were most important for our decision tree classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "121afcd4-b936-43f7-9bdd-de2699b33627",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "dt_imp = pd.Series(dt.feature_importances_, index=[dv.get_feature_names()]).sort_values(ascending=True)\n",
    "dt_imp.plot(kind='barh')\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d4593f-933a-4f02-835f-29a792bda669",
   "metadata": {},
   "source": [
    "The chart shows that there are a few high impact columns, the most important of which ccustomers with low credit rating "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82371959-0ed0-4f9e-bf1d-c73695a62a82",
   "metadata": {},
   "source": [
    "## Selecting the best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9efe2dd6-42cb-47df-b765-3c19494f7d10",
   "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>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Decision Trees</td>\n",
       "      <td>0.937</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Score\n",
       "0  Logistic Regression  0.937\n",
       "1       Decision Trees  0.937"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores_df = pd.DataFrame(models.items(), columns=['Model', 'Score'])\n",
    "scores_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf9bbcb7-6df6-4c09-94f6-14d478348325",
   "metadata": {},
   "source": [
    "Since we have the same performance, we select logistic rgression because it is less complex to implement. <br><br> \n",
    "Now we can train our final model <br><br>\n",
    "First we create functions for training and testing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "42fdf466-ed1d-498a-8e99-d1b339cf05d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(df, y):\n",
    "    \"\"\"\n",
    "    This function takes in a set of features and targets, \n",
    "    and fits them on a logistic regression model\n",
    "    params: features, target\n",
    "    returns: dict vectorizer, model objects\n",
    "    rtype: object\n",
    "    \"\"\"\n",
    "    train_dict = df.to_dict(orient='records')\n",
    "    dv = DictVectorizer()\n",
    "    X = dv.fit_transform(train_dict)\n",
    "\n",
    "    model = LogisticRegression(random_state=1)\n",
    "    model.fit(X, y)\n",
    "    \n",
    "    return dv, model\n",
    "\n",
    "def predict(df, dv, model):\n",
    "    \"\"\"\n",
    "    This function predicts a target class for a set of features\n",
    "    params: features, standard scaler and model objects\n",
    "    returns: target class\n",
    "    rtype: integer\n",
    "    \"\"\"\n",
    "    test_dict = df.to_dict(orient=\"records\")    \n",
    "    X = dv.transform(test_dict)\n",
    "    y_pred = model.predict(X)\n",
    "\n",
    "    return y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "50082eb7-844a-480f-aa08-684a346b3459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.948"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Evaluating model on test data \n",
    "\"\"\"\n",
    "# df_full_train.drop(columns=['accepted_offer'], inplace=True)\n",
    "\n",
    "dv, model = train(df_full_train, y_full_train)\n",
    "\n",
    "y_pred = predict(df_test, dv, model)\n",
    "\n",
    "round(accuracy_score(y_test, y_pred), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21db7e4c-fb61-4a4c-b77e-e9742097d413",
   "metadata": {},
   "source": [
    "## Saving the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c5acb149-9b9f-4683-bf65-a2ef963a94e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_file = \"model.bin\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "77fd22b3-0911-4b01-b2e5-8a233aaf8e0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(output_file, 'wb') as output_file:\n",
    "    pickle.dump((dv, model), output_file) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4e3218e7-deca-4ca8-8135-07acbb76c7bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "input_file = \"model.bin\"\n",
    "\n",
    "with open(input_file, 'rb') as file:\n",
    "    dv, model = pickle.load(file)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f51595fa-b510-4b66-924a-4fc020ae2ce2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = {\n",
    "         'reward': 'Air Miles',\n",
    "         'mailer_type': 'Postcard',\n",
    "         'income_level': 'Medium',\n",
    "         'bank_accounts_open': 2,\n",
    "         'overdraft_protection': 'No',\n",
    "         'credit_rating': 'Medium',\n",
    "         'credit_cards_held': 2,\n",
    "         'homes_owned': 1,\n",
    "         'household_size': 4,\n",
    "         'own_your_home': 'Yes',\n",
    "         'average_balance': 745.5,\n",
    "         'q1_balance': 505.0,\n",
    "         'q2_balance': 877.0,\n",
    "         'q3_balance': 632.0,\n",
    "         'q4_balance': 968.0\n",
    "       }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "567f632c-31ec-41f4-bf79-a5107bdffa0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.DataFrame(data, index=[0])\n",
    "\n",
    "X = dv.transform([data])\n",
    "\n",
    "y_pred = model.predict_proba(X)[0, 1]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "527185d3-a06d-47ab-9350-6c1279ca3d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05579647625971715"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0316b834-8bf2-4aef-8e01-d74772fedb54",
   "metadata": {},
   "outputs": [],
   "source": [
    "decision = (y_pred >= 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c23ac2d-0128-4657-b9ff-5faac15fde98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision"
   ]
  }
 ],
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