python_files/case_study_classification_bins_coulmn_sum.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Table of Contents\n",
    "\n",
    "* [Case study classification project](#casestudy)\n",
    "* [EDA - exploratory data analysis - get to know the data](#eda)\n",
    "* [Get the sweet summary stats on each column to understand the distribution better](#summary)\n",
    "* [Get a  representation of the numbers](#numbersrep)\n",
    "* [Outliers](#outliers)\n",
    "* [Box Plots](#boxplots)\n",
    "* [Outliers](#outliers)\n",
    "* [Pre-processing](#preprocessing)\n",
    "* [Split off the dependant variable (label) ](#split)\n",
    "* [TT split and get model](#ttsplit)\n",
    "* [Apply model, train model](#applymoded)\n",
    "* [Evaluate accuracy and test](#evaluate)\n",
    "* [Visualising accuracy - ROC / AUC](#rocauc)\n",
    "* [Data imbalanced -  SMOTE (oversample the minority class of the target label)](#smote)\n",
    "\n",
    "\n",
    "* [sample](#sample)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Case study classification project <a class=\"anchor\" id=\"casestudy\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import pandas as pd \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# conda install -c conda-forge jupyter_contrib_nbextensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.read_csv('creditcardmarketing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_original = pd.read_excel('creditcardmarketing.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save as a csv file to use in the SQL task\n",
    "#import csv\n",
    "#df_original.to_csv(r'new_creditcardmarketing_original.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# create a copy of the dataaet\n",
    "df = df_original"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  EDA - exploratory data analysis - get to know the data <a class=\"anchor\" id=\"eda\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check information about the dataset\n",
    "#df.dtypes\n",
    "#df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cheack shape\n",
    "#df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print the sum of null values \n",
    "#print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standize the readers: replace spaces by _, put in lowercase, remove #\n",
    "df.columns = df.columns.str.replace(' ', '_')\n",
    "df.columns= df.columns.str.lower()\n",
    "df.columns= df.columns.str.replace('#_', '')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the percentage of missing values\n",
    "#pct_missing=24/18000\n",
    "#pct_missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>We found few missing values (less than 20%). so we are not droping them.<br/><b>\n",
    "Since the balances columns are float, we will fill them with the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # fill nulls with the means\n",
    "\n",
    "# col_miss_values= ['average_balance', 'q1_balance', 'q2_balance', 'q3_balance', 'q4_balance']\n",
    "\n",
    "# mean_average_balance = df['average_balance'].mean()\n",
    "# df['average_balance'] = df['average_balance'].fillna(mean_average_balance)\n",
    "\n",
    "# mean_average_balance = df['q1_balance'].mean()\n",
    "# df['q1_balance'] = df['q1_balance'].fillna(mean_average_balance)\n",
    "\n",
    "# mean_q2_balance = df['q2_balance'].mean()\n",
    "# df['q2_balance'] = df['q2_balance'].fillna(mean_q2_balance)\n",
    "\n",
    "# mean_q3_balance = df['q3_balance'].mean()\n",
    "# df['q3_balance'] = df['q3_balance'].fillna(mean_q3_balance)\n",
    "\n",
    "# mean_q4_balance = df['q4_balance'].mean()\n",
    "# df['q4_balance'] = df['q4_balance'].fillna(mean_q4_balance)\n",
    "                                           \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_miss_values= [ 'q2_balance', 'q3_balance', 'q4_balance']\n",
    "\n",
    "\n",
    "mean_q2_balance = df['q2_balance'].mean()\n",
    "df['q2_balance'] = df['q2_balance'].fillna(mean_q2_balance)\n",
    "df['q2_balance'] = pd.to_numeric(df['q2_balance'], errors = 'coerce')\n",
    "\n",
    "mean_q3_balance = df['q3_balance'].mean()\n",
    "df['q3_balance'] = df['q3_balance'].fillna(mean_q3_balance)\n",
    "df['q3_balance'] = pd.to_numeric(df['q3_balance'], errors = 'coerce')\n",
    "\n",
    "mean_q4_balance = df['q4_balance'].mean()\n",
    "df['q4_balance'] = df['q4_balance'].fillna(mean_q4_balance)\n",
    "df['q4_balance'] = pd.to_numeric(df['q4_balance'], errors = 'coerce')\n",
    "\n",
    "#df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4643.0\n",
       "1         589.0\n",
       "2        1106.0\n",
       "3        4876.0\n",
       "4        4844.0\n",
       "          ...  \n",
       "17995     670.0\n",
       "17996    3402.0\n",
       "17997    4349.0\n",
       "17998    4089.0\n",
       "17999    4224.0\n",
       "Name: q_sum, Length: 18000, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a new column that gives the difference between q2 and q1\n",
    "df['q_sum']= df['q1_balance']+df['q2_balance']+df['q3_balance']+df['q4_balance']\n",
    "df['q_sum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customer_number', 'offer_accepted', 'reward', 'mailer_type',\n",
       "       'income_level', 'bank_accounts_open', 'overdraft_protection',\n",
       "       'credit_rating', 'credit_cards_held', 'homes_owned', 'household_size',\n",
       "       'own_your_home', 'average_balance', 'q1_balance', 'q2_balance',\n",
       "       'q3_balance', 'q4_balance', 'q_sum'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    5712\n",
       "4    5543\n",
       "5    2853\n",
       "2    2813\n",
       "1     539\n",
       "6     538\n",
       "9       1\n",
       "8       1\n",
       "Name: household_size, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['household_size'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the sweet summary stats on each column to understand the distribution better <a class=\"anchor\" id=\"summary\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#statistical distribution\n",
    "#df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get a  representation of the numbers  <a class=\"anchor\" id=\"numbersrep\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using pandas\n",
    "# hist = df.hist(figsize=(15,10),layout=(3,4), grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # check relationships with seaborn\n",
    "# sns.pairplot(df)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<B>Only the household_size has a normal distribution.<B>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # check for multicollinearity \n",
    "# corr_matrix=df.corr(method='pearson')  # default\n",
    "# fig, ax = plt.subplots(figsize=(10, 8))\n",
    "# ax = sns.heatmap(corr_matrix, annot=True)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The graph proves the absence of multicollinearity. \n",
    "\n",
    "\"The linear model makes the following assumptions\n",
    "- Require a linear relationship between the dependent and independent variables.\n",
    "- The error terms (residuals) need to be normally distributed. \n",
    "- If not weights can be introduced to penalize some observations in the error metric.\n",
    "Independent variables are normally distributed.\n",
    "If not (apply transformation Box-Cox)\n",
    "- Absence of multicollinearity.\n",
    "- Independent observations (not repeated)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>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>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>q_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>4643.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>Yes</td>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>4876.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>High</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>4459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>1135.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No</td>\n",
       "      <td>Cash Back</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Low</td>\n",
       "      <td>1</td>\n",
       "      <td>Medium</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>1</td>\n",
       "      <td>Low</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>Low</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3897.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  offer_accepted     reward mailer_type income_level  bank_accounts_open  \\\n",
       "0             No  Air Miles      Letter         High                   1   \n",
       "1             No  Air Miles      Letter       Medium                   1   \n",
       "2             No  Air Miles    Postcard         High                   2   \n",
       "3             No  Air Miles      Letter       Medium                   2   \n",
       "4             No  Air Miles      Letter       Medium                   1   \n",
       "5             No  Air Miles      Letter       Medium                   1   \n",
       "6             No  Air Miles      Letter       Medium                   1   \n",
       "7             No  Cash Back    Postcard          Low                   1   \n",
       "8             No  Air Miles    Postcard       Medium                   1   \n",
       "9             No  Air Miles      Letter         High                   2   \n",
       "\n",
       "  credit_rating  credit_cards_held  homes_owned  household_size own_your_home  \\\n",
       "0          High                  2            1               4            No   \n",
       "1        Medium                  2            2               5           Yes   \n",
       "2        Medium                  2            1               2           Yes   \n",
       "3          High                  1            1               4            No   \n",
       "4        Medium                  2            1               6           Yes   \n",
       "5          High                  3            1               4            No   \n",
       "6        Medium                  2            1               3            No   \n",
       "7        Medium                  4            1               4           Yes   \n",
       "8           Low                  2            1               4           Yes   \n",
       "9           Low                  3            2               4           Yes   \n",
       "\n",
       "    q_sum  \n",
       "0  4643.0  \n",
       "1   589.0  \n",
       "2  1106.0  \n",
       "3  4876.0  \n",
       "4  4844.0  \n",
       "5  4459.0  \n",
       "6  1135.0  \n",
       "7  1114.0  \n",
       "8  4020.0  \n",
       "9  3897.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for more claning\n",
    "# df.head(20)\n",
    "# df.tail(20)\n",
    "# drop customer_number\n",
    "df=df.drop(['customer_number','average_balance','q1_balance', 'q2_balance',\n",
    "       'q3_balance', 'q4_balance','overdraft_protection',], axis=1)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that there is no need to make more cleaning. Neither for Bucketing features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['offer_accepted', 'reward', 'mailer_type', 'income_level',\n",
       "       'bank_accounts_open', 'credit_rating', 'credit_cards_held',\n",
       "       'homes_owned', 'household_size', 'own_your_home', 'q_sum'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  New dataframe for binning <a class=\"anchor\" id=\"eda\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.groupby(['offer_accepted', 'reward', 'mailer_type', 'income_level','household_size','credit_rating', 'credit_cards_held',\n",
    "       'homes_owned', 'own_your_home', 'q_sum'])['bank_accounts_open'].sum().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# common practise to keep the orginal data\n",
    "df['bank_accounts_open'] = pd.qcut(df['bank_accounts_open'],duplicates='drop', q=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.999, 2.0], (2.0, 4.0]]\n",
       "Categories (2, interval[float64]): [(0.999, 2.0] < (2.0, 4.0]]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bank_accounts_open'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>mailer_type</th>\n",
       "      <th>income_level</th>\n",
       "      <th>household_size</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>own_your_home</th>\n",
       "      <th>q_sum</th>\n",
       "      <th>bank_accounts_open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2833.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5062.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>3248.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>4494.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>1</td>\n",
       "      <td>High</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3740.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17937</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>5</td>\n",
       "      <td>Low</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3823.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17938</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>5</td>\n",
       "      <td>Low</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4593.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17939</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>6</td>\n",
       "      <td>Low</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>4314.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17940</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>6</td>\n",
       "      <td>Low</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1069.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17941</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5204.0</td>\n",
       "      <td>(0.999, 2.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17942 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      offer_accepted     reward mailer_type income_level  household_size  \\\n",
       "0                 No  Air Miles      Letter         High               1   \n",
       "1                 No  Air Miles      Letter         High               1   \n",
       "2                 No  Air Miles      Letter         High               1   \n",
       "3                 No  Air Miles      Letter         High               1   \n",
       "4                 No  Air Miles      Letter         High               1   \n",
       "...              ...        ...         ...          ...             ...   \n",
       "17937            Yes     Points    Postcard       Medium               5   \n",
       "17938            Yes     Points    Postcard       Medium               5   \n",
       "17939            Yes     Points    Postcard       Medium               6   \n",
       "17940            Yes     Points    Postcard       Medium               6   \n",
       "17941            Yes     Points    Postcard       Medium               6   \n",
       "\n",
       "      credit_rating  credit_cards_held  homes_owned own_your_home   q_sum  \\\n",
       "0              High                  1            1           Yes  2833.0   \n",
       "1              High                  1            2           Yes  5062.0   \n",
       "2              High                  2            1            No  3248.0   \n",
       "3              High                  2            1            No  4494.0   \n",
       "4              High                  2            1           Yes  3740.0   \n",
       "...             ...                ...          ...           ...     ...   \n",
       "17937           Low                  2            1           Yes  3823.0   \n",
       "17938           Low                  2            2           Yes  4593.0   \n",
       "17939           Low                  1            1            No  4314.0   \n",
       "17940           Low                  1            1           Yes  1069.0   \n",
       "17941        Medium                  3            1           Yes  5204.0   \n",
       "\n",
       "      bank_accounts_open  \n",
       "0           (0.999, 2.0]  \n",
       "1           (0.999, 2.0]  \n",
       "2           (0.999, 2.0]  \n",
       "3           (0.999, 2.0]  \n",
       "4           (0.999, 2.0]  \n",
       "...                  ...  \n",
       "17937       (0.999, 2.0]  \n",
       "17938       (0.999, 2.0]  \n",
       "17939       (0.999, 2.0]  \n",
       "17940       (0.999, 2.0]  \n",
       "17941       (0.999, 2.0]  \n",
       "\n",
       "[17942 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outliers <a class=\"anchor\" id=\"outliers\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Plots <a class=\"anchor\" id=\"boxplots\"></a>\n",
    "A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile (Q1), median, third quartile (Q3), and “maximum”). It can tell you about your outliers and what their values are. It can also tell you if your data is symmetrical, how tightly your data is grouped, and if and how your data is skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q1 of the column  q_sum : 3150.0\n",
      "Q3 of the column  q_sum : 4613.75\n",
      "IRQ of the column  q_sum : 1463.75\n",
      "lower_whisker:  954.375 , upper whisker:  6809.375\n"
     ]
    }
   ],
   "source": [
    "# find quartiles and clean up outliers\n",
    "cols_outliers = ['q_sum']\n",
    "\n",
    "for col in cols_outliers:\n",
    "    Q1 = df[col].quantile(0.25)\n",
    "    Q3 = df[col].quantile(0.75)\n",
    "    IQR = Q3 - Q1\n",
    "    print('Q1 of the column ', col,':', Q1)\n",
    "    print('Q3 of the column ', col,':', Q3)\n",
    "    print('IRQ of the column ', col,':', IQR)\n",
    "    Lower_Whisker = Q1 - 1.5 * IQR\n",
    "    Upper_Whisker = Q3 + 1.5 * IQR\n",
    "    print('lower_whisker: ', Lower_Whisker, ',' , 'upper whisker: ', Upper_Whisker)\n",
    "    #  Outlier Treatment \n",
    "    df = df[df[col]< Upper_Whisker]\n",
    "    df = df[df[col]> Lower_Whisker]\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#check outliers\n",
    "df.boxplot(grid=False, figsize= (15,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # check for multicollinearity \n",
    "corr_matrix=df.corr(method='pearson')  # default\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "ax = sns.heatmap(corr_matrix, annot=True)\n",
    "plt.show()\n",
    "# q1, q2 ,q3 have collinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>offer_accepted</th>\n",
       "      <th>reward</th>\n",
       "      <th>mailer_type</th>\n",
       "      <th>income_level</th>\n",
       "      <th>credit_rating</th>\n",
       "      <th>own_your_home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>Air Miles</td>\n",
       "      <td>Letter</td>\n",
       "      <td>High</td>\n",
       "      <td>High</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17937</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17938</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17939</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17940</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Low</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17941</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Points</td>\n",
       "      <td>Postcard</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16505 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      offer_accepted     reward mailer_type income_level credit_rating  \\\n",
       "0                 No  Air Miles      Letter         High          High   \n",
       "1                 No  Air Miles      Letter         High          High   \n",
       "2                 No  Air Miles      Letter         High          High   \n",
       "3                 No  Air Miles      Letter         High          High   \n",
       "4                 No  Air Miles      Letter         High          High   \n",
       "...              ...        ...         ...          ...           ...   \n",
       "17937            Yes     Points    Postcard       Medium           Low   \n",
       "17938            Yes     Points    Postcard       Medium           Low   \n",
       "17939            Yes     Points    Postcard       Medium           Low   \n",
       "17940            Yes     Points    Postcard       Medium           Low   \n",
       "17941            Yes     Points    Postcard       Medium        Medium   \n",
       "\n",
       "      own_your_home  \n",
       "0               Yes  \n",
       "1               Yes  \n",
       "2                No  \n",
       "3                No  \n",
       "4               Yes  \n",
       "...             ...  \n",
       "17937           Yes  \n",
       "17938           Yes  \n",
       "17939            No  \n",
       "17940           Yes  \n",
       "17941           Yes  \n",
       "\n",
       "[16505 rows x 6 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label / encode any categorical columns \n",
    "\n",
    "cat = df.select_dtypes(include = np.object)\n",
    "cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # checking all the categorical columns\n",
    "# cols_cat = ['offer_accepted', 'reward', 'mailer_type', 'income_level', 'overdraft_protection', 'credit_rating', 'own_your_home']\n",
    "# for col in cols_cat:\n",
    "#     print(\"Frequency analysis of column: \",col)\n",
    "#     my_data = df[col].value_counts().reset_index()\n",
    "#     my_data.drop(my_data.index[0])\n",
    "#     ax = sns.barplot(x=col, y=\"index\", data = my_data).set_title(col.upper())\n",
    "#     plt.figure()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#a =df['average_balance'].value_counts().reset_index()\n",
    "#a.drop(a.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# locate the target column in the last position\n",
    "cols = list(df.columns)\n",
    "cols = cols[1:]+[cols[0]]\n",
    "df = df[cols]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert balances to float\n",
    "\n",
    "#df['q2_balance'] = pd.to_numeric(df.q2_balance, errors='coerce')\n",
    "#df['q3_balance'] = pd.to_numeric(df.q3_balance, errors='coerce')\n",
    "#df['q4_balance'] = pd.to_numeric(df.q4_balance, errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Density Plots\n",
    "\n",
    "A density plot is a representation of the distribution of a numeric variable. It uses a kernel density estimate to show the probability density function of the variable. It is a smoothed version of the histogram and is used in the same concept.\n",
    "\n",
    "Kernel Density Estimation(KDE): Kernel density estimation is a really useful statistical tool with an intimidating name. Often shortened to KDE, it’s a technique that let’s you create a smooth curve given a set of data. Kernel Density Estimation (KDE) is a way to estimate the probability density function of a continuous random variable. It is used for non-parametric analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>household_size</th>\n",
       "      <th>credit_cards_held</th>\n",
       "      <th>homes_owned</th>\n",
       "      <th>q_sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2833.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5062.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3740.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17937</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3823.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17938</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4593.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17939</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4314.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17940</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17941</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>5204.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16505 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       household_size  credit_cards_held  homes_owned   q_sum\n",
       "0                   1                  1            1  2833.0\n",
       "1                   1                  1            2  5062.0\n",
       "2                   1                  2            1  3248.0\n",
       "3                   1                  2            1  4494.0\n",
       "4                   1                  2            1  3740.0\n",
       "...               ...                ...          ...     ...\n",
       "17937               5                  2            1  3823.0\n",
       "17938               5                  2            2  4593.0\n",
       "17939               6                  1            1  4314.0\n",
       "17940               6                  1            1  1069.0\n",
       "17941               6                  3            1  5204.0\n",
       "\n",
       "[16505 rows x 4 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerics = ['int64', 'float64']\n",
    "df_num = df.select_dtypes(include=numerics)\n",
    "df_num\n",
    "# for col in df_num.columns:\n",
    "#     ax = sns.kdeplot(data= df_num, x= col, shade= True)\n",
    "#     plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for col in df_num.columns:\n",
    "#     plt.figure(figsize=(10,6)) \n",
    "#     sns.kdeplot(df[col][df.offer_accepted  == 'Yes'], shade=True)\n",
    "#     sns.kdeplot(df[col][df.offer_accepted  == 'No'], shade=True)\n",
    "#     plt.legend(['offer_accepted ', 'offer_rejected'])\n",
    "#     plt.title(col)\n",
    "#     plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check outliers\n",
    "#df.boxplot(grid=False, figsize= (15,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the quartiles didn't removed all the outliers for average_balance. It could be improved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'household_size'}>,\n",
       "        <AxesSubplot:title={'center':'credit_cards_held'}>,\n",
       "        <AxesSubplot:title={'center':'homes_owned'}>,\n",
       "        <AxesSubplot:title={'center':'q_sum'}>],\n",
       "       [<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>],\n",
       "       [<AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uaSvb9OT07SQ5Kcn6JOu3bt06YBYlSZIkafkY9MncoVV1W5LHApckuXGaZfv1g6tp0rdPrDoDOANgzZo1fZeRJEmSpOVsoCdzVXVbe90C/D1wMPCdJPsAtNctbfHNwH49q68Cbmvpq/qkS5IkSZJmacbKXJJHJHnUxDTwK8B1wPnACW2xE4BPtunzgeOTPDTJ4+kCnVzVmmLek+SQFsXylT3rSJIkSZJmYZAnc3sDX0zyVeAq4IKq+jSwFjgiyc3AEW2eqroeOBf4OvBp4LVVdX/7rNcA7wU2AN/A4CeSpEWW5H1JtiS5riftfya5McnXkvx9kt173pvVcDvtZuZHW/qVSVYv5vZJkpaPGfvMVdU3gWf0Sb8DOHyKdU4HTu+Tvh54+uyzKUnSvDkL+Bvg7J60S4DTqmpbkrcBpwGnTBpuZ1/gM0me1G5STgy3cwVwId1wOxcBJwLfq6onJjkeeBvwkkXZMknSsjKbceYkSRp7VfUF4M5JaRdX1bY2ewU/6eP9wHA7VXULXcuSg1tf8d2q6vKqKrqK4bE966xr0+cBh088tZMkaT5ZmZMk6cF+g590A5jLcDsPrNMqiN8HHtPvixyKR5K0I6zMSZLUJPkfwDbgQxNJfRababidWQ3FU1VrqmrNihUrZptdSdIyZ2VOkiQgyQnA0cDLWtNJmNtwOw+sk2Rn4NFMatYpSdJ8sDInSVr2khwJnAK8qKp+2PPWXIbb6R2659eAz/ZUDiX1kWSnJF9J8qk2v2eSS5Lc3F736Fl2VhFmpaXMypwkaVlJ8hHgcuDJSTYnOZEuuuWjgEuSXJPkb2HOw+2cCTwmyQbg9cCpi7Nl0lg7GbihZ/5U4NKqOgC4tM0zKcLskcC7kuzU1pmIMHtA+ztycbIuDc+MQxNIkrSUVNVL+ySfOc3ysxpup6p+BBy3I3mUlpMkq4Cj6MrZ61vyMcBhbXodcBnd0/MHIswCt7SbJgcn2UiLMNs+cyLCrGMaa0nzyZwkSZKG6R3AG4Ef96Tt3Zoz014f29LnEmFWWrKszEmSJGkokhwNbKmqqwddpU/aTBFmJ3+nQ4JoybAyJ42oJLsnOS/JjUluSPJsO4RLkpaYQ4EXtWaS5wDPTfJB4DtJ9gFor1va8nOJMPsgDgmipcTKnDS6/gr4dFX9LPAMuo7hdgiXJC0ZVXVaVa2qqtV057HPVtXLeXBU2BN4cLTY2UaYlZYsK3PSCEqyG/DLtKAMVfXvVXUXXcfvdW2xdXSdu6GnQ3hV3UIXXe/gdjdzt6q6vIVGP7tnHUmSRtVa4IgkNwNHtPm5RpiVliyjWUqj6QnAVuD9SZ4BXE0XtvlBHcKT9HYIv6Jn/YmO3/dhh3BJ0hioqsvoolZSVXcAh0+x3KwizEpLmU/mpNG0M/BM4N1VdRDwr0w/VpUdwiVJkpYZK3PSaNoMbK6qK9v8eXSVOzuES5IkCbAyJ42kqvo2sCnJk1vS4XT9A+wQLkmSJMA+c9Io+x3gQ0keAnwTeDXdDZhzk5wI3AocB12H8CQTHcK3sX2H8LOAXek6g9shXJIkaQmwMieNqKq6BljT5y07hEuSJGnwZpZJdkrylSSfavMOXixJkiRJQzKbPnMn0w1aPMHBiyVJYyfJ+5JsSXJdT9q83aBsfVc/2tKvTLJ6UTdQkrRsDNTMMskq4Ci6Jlyvb8nHAIe16XV044KcQs/gxcAtSSYGL95IG7y4febE4MX235HGzOpTL5jzuhvXHjWPOZHm5Czgb4Cze9ImblCuTXJqmz9l0g3KfYHPJHlS65M6cYPyCuBCuhuUFwEnAt+rqicmOR54G/CSRdkySdKyMuiTuXcAbwR+3JP2oMGLgd7Bizf1LDcxSPFKHLxYkjRkVfUF4M5JycfQ3ZikvR7bk35OVd1bVbcAEzco96HdoKyqoqsYHtvns84DDrdbgSRpIcxYmUtyNLClqq4e8DMdvFiSNG7m8wblA+tU1Tbg+8BjFiznkqRla5Anc4cCL2rNJM8Bnpvkgzh4sSRp6ZvLDUpvXkqSFsWMlbmqOq2qVlXVarp+A5+tqpfj4MWSpKVjPm9QPrBOkp2BR7N9s07Am5eSpB0zm2iWk60FjkhyM3BEm6eqrgcmBi/+NNsPXvxeuj4H38DgJ5Kk0TCfNyh7P+vX6G6C9n0yJ0nSjpjVoOFVdRld1Eqq6g4cvFiSNGaSfIQuGvNeSTYDb6a7IXlukhOBW4HjoLtBmWTiBuU2tr9BeRawK93NyYkblGcCH2jRnO+ka9UiSdK8m1VlTpKkcVdVL53irXm5QVlVP6JVBiVJWkg70sxSkiRJkjQkVuYkSZIkaQxZmZMkSZKkMWSfOUmSJEkjYfWpF8x53Y1rj5rHnIwHn8xJkiRJ0hiyMidJkiRJY8jKnCRJkiSNIStzkiRJGookD0tyVZKvJrk+yVta+p5JLklyc3vdo2ed05JsSHJTkuf3pD8rybXtvXcmyTC2SVpMVuYkSZI0LPcCz62qZwAHAkcmOQQ4Fbi0qg4ALm3zJHkqcDzwNOBI4F1Jdmqf9W7gJOCA9nfkIm6HNBRW5iRJkjQU1flBm92l/RVwDLCupa8Djm3TxwDnVNW9VXULsAE4OMk+wG5VdXlVFXB2zzrSkmVlTpIkSUOTZKck1wBbgEuq6kpg76q6HaC9PrYtvhLY1LP65pa2sk1PTu/3fSclWZ9k/datW+d1W6TFZmVOkiRJQ1NV91fVgcAquqdsT59m8X794Gqa9H7fd0ZVramqNStWrJh1fqVRYmVOkiRJQ1dVdwGX0fV1+05rOkl73dIW2wzs17PaKuC2lr6qT7q0pFmZkySpSfJ7LaLedUk+0iLtGVVPWiBJViTZvU3vCjwPuBE4HzihLXYC8Mk2fT5wfJKHJnk8XaCTq1pTzHuSHNLK2yt71pGWLCtzkiQBSVYCvwusqaqnAzvRRc0zqp60cPYBPpfka8CX6PrMfQpYCxyR5GbgiDZPVV0PnAt8Hfg08Nqqur991muA99IFRfkGcNFibog0DDsPOwOSJI2QnYFdk9wHPJyumdZpwGHt/XV0zcBOoSeqHnBLkomoehtpUfUAkkxE1fPCUpqkqr4GHNQn/Q7g8CnWOR04vU/6emC6/nbSkmNlTpIkoKr+JclfALcC/wZcXFUXJ3lQVL0kvVH1ruj5iInoefcxi6h6dE/w2H///edzcyRpzlafesGws6ABzdjMsvUXuCrJV1s/gre0dPsQSJKWjHYeOwZ4PLAv8IgkL59ulT5pRtWTJC2aQfrM3Qs8t6qeARwIHJnkEOxDIElaWp4H3FJVW6vqPuDjwHMwqp4kaUTNWJmrzg/a7C7tr+juXq5r6evo+gNATx+CqrqFrhPqwe0EuFtVXV5VBZzds44kScN2K3BIkoe3liOHAzdgVD1J0ogaKJplkp2SXEN3N/KSqroSeFAfAqC3D8GmntUn+gqsZMA+BJI6rex9Jcmn2rzNm6UF0s5t5wFfBq6lO0eegVH1JEkjaqAAKO3kdGAbB+Tvk0wXKWiH+xDYIVx6wMl0TwZ2a/MTzZvXJjm1zZ8yqXnzvsBnkjypld2J5s1XABfSNW/2wlLqo6reDLx5UvK9GFVPkjSCZjXOXFXdRReS+UgWsA+BHcIlSLIKOIru7v4EmzdLkiQJGCya5Yr2RI4ku9J1EL8R+xBIC+0dwBuBH/ek2bxZkiRJwGDNLPcB1rWIlD8FnFtVn0pyOXBukhPpOo0fB10fgiQTfQi2sX0fgrOAXemaednUS+ojydHAlqq6Oslhg6zSJ83mzZIkSUvYjJW5qvoacFCf9DuwD4G0UA4FXpTkhcDDgN2SfJDWvLkNXDzvzZvpgj2wZs2avhU+SZIkjY5Z9ZmTtDiq6rSqWlVVq+kCm3y2ql6OzZslSZLUDBTNUtLIWIvNmyVJkoSVOWnkVdVldFFkbd4sSZKkB9jMUpIkSZLGkJU5SZIkSRpDNrNchlafesEOrb9x7VHzlBNJkiRJc+WTOUmSJEkaQ8v6ydyOPqGSJEmSpGHxyZwkSU2S3ZOcl+TGJDckeXaSPZNckuTm9rpHz/KnJdmQ5KYkz+9Jf1aSa9t772zjPEqSNK+szEmS9BN/BXy6qn4WeAZwA3AqcGlVHQBc2uZJ8lTgeOBpwJHAu5Ls1D7n3cBJwAHt78jF3AhJ0vJgZU6SJCDJbsAvA2cCVNW/V9VdwDHAurbYOuDYNn0McE5V3VtVtwAbgIOT7APsVlWXV1UBZ/esI0nSvLEyJ0lS5wnAVuD9Sb6S5L1JHgHsXVW3A7TXx7blVwKbetbf3NJWtunJ6ZIkzSsrc5IkdXYGngm8u6oOAv6V1qRyCv36wdU06dt/QHJSkvVJ1m/dunW2+ZXGXpL9knyu9VG9PsnJLd2+qtIArMxJktTZDGyuqivb/Hl0lbvvtKaTtNctPcvv17P+KuC2lr6qT/p2quqMqlpTVWtWrFgxbxsijZFtwBuq6inAIcBrW39U+6pKA7AyJ0kSUFXfBjYleXJLOhz4OnA+cEJLOwH4ZJs+Hzg+yUOTPJ7u4vGq1hTzniSHtCcDr+xZR1KPqrq9qr7cpu+hCzq0EvuqSgNZ1uPMSZI0ye8AH0ryEOCbwKvpbnyem+RE4FbgOICquj7JuXQVvm3Aa6vq/vY5rwHOAnYFLmp/kqaRZDVwEHAlk/qqJuntq3pFz2oTfVLvY8C+qklOonuCx/777z+PWzA6HEt5+bAyJ0ljYEdOzBvXHjWPOVnaquoaYE2ftw6fYvnTgdP7pK8Hnj6vmZOWsCSPBD4GvK6q7p6mu9sO91WtqjOAMwDWrFnTdxlpXNjMUpIkSUOTZBe6ityHqurjLXnB+qpKS4mVOUmSJA1F61d6JnBDVb295y37qkoDmLEyZ8hYSZIkLZBDgVcAz01yTft7IbAWOCLJzcARbZ6quh6Y6Kv6abbvq/peuqAo38C+qloGBukzNxEy9stJHgVcneQS4FV0IWPXJjmVLmTsKZNCxu4LfCbJk1pBmwgZewVwIV3IWAuaJEnSMlRVX6R/fzewr6o0oxmfzBkyVpIkSZJGz6z6zE0XMhboDRm7qWe1idCwK5lFyNgk65Os37p162yyKEmSJEnLwsCVuckhY6dbtE/arEPGVtWaqlqzYsWKQbMoSZIkScvGQJU5Q8ZKkiRJ0mgZJJqlIWMlSZIkacQMEs1yImTstUmuaWlvogsRe26SE4FbgeOgCxmbZCJk7Da2Dxl7FrArXRRLI1lKkiRJ0hzMWJkzZKwkSZIkjZ5ZRbOUJEmSJI0GK3OSJEmSNIaszEmS1CPJTkm+kuRTbX7PJJckubm97tGz7GlJNiS5Kcnze9KfleTa9t47W+AvSZLmlZU5SZIe7GTghp75U4FLq+oA4NI2T5KnAscDTwOOBN6VZKe2zruBk+giOh/Q3pckaV5ZmZMkqUmyCjgKeG9P8jHAuja9Dji2J/2cqrq3qm4BNgAHt7FXd6uqy6uqgLN71pEkad4MMjSBJEnLxTuANwKP6knbu42VSlXdnuSxLX0lcEXPcptb2n1tenK6JGkBrT71gh1af+Pao+YpJ4vHJ3PSCEqyX5LPJbkhyfVJTm7p9t2RFkiSo4EtVXX1oKv0Satp0vt950lJ1idZv3Xr1gG/VpKkjpU5aTRtA95QVU8BDgFe2/rn2HdHWjiHAi9KshE4B3hukg8C32lNJ2mvW9rym4H9etZfBdzW0lf1Sd9OVZ1RVWuqas2KFSvmc1skScuAlTlpBFXV7VX15TZ9D10whpXYd0daMFV1WlWtqqrVdDdHPltVLwfOB05oi50AfLJNnw8cn+ShSR5Pd7PkqtYk854kh7Qn4a/sWUeSpHljnzlpxCVZDRwEXMkC9t1JchLdEzz233//edwCaeytBc5NciJwK3AcQFVdn+Rc4Ot0T9NfW1X3t3VeA5wF7Apc1P4kjZkd6YM1jv2vNH6szEkjLMkjgY8Br6uqu6fp7rbDfXeq6gzgDIA1a9b0XUZaLqrqMuCyNn0HcPgUy50OnN4nfT3w9IXLoSRJNrOURlaSXegqch+qqo+35AXruyNJkqTxYmVOGkGtn82ZwA1V9faet+y7I0mSJMBmltKoOhR4BXBtkmta2puw744kSZIaK3PSCKqqL9K/vxvYd0eStIQkeR8wMc7j01vansBHgdXARuDFVfW99t5pwInA/cDvVtU/tPRn8ZOblxcCJ7dIztKSZTNLSZIkDdNZbD8GquOqSgPwyZwkSVqSdiSsPBhafrFU1RfaMDy9jgEOa9Pr6KLLnkLPuKrALUkmxlXdSBtXFSDJxLiqdi3QkuaTOUmSJI2aB42rCvSOq7qpZ7mJ8VNXMuC4qtJSMmNlLsn7kmxJcl1P2p5JLklyc3vdo+e905JsSHJTkuf3pD8rybXtvXdmmgGzJEmSpD52eFzVJCclWZ9k/datW+c1c9JiG+TJ3FnYjlmSJEmLZ8HGVa2qM6pqTVWtWbFixbxnXFpMM1bmquoLwJ2Tko+ha79Mez22J/2cqrq3qm4BJtox70Nrx9yiCp3ds44kSZLUy3FVpQHMNQDKg9oxJ+ltx3xFz3IT7ZXvw3bMkiRJmiTJR+iCneyVZDPwZhxXVRrIfEez3OF2zNC1ZaZrksn+++8/PzmTJEnSyKmql07xluOqSjOYazTLBWvHDLZlliRJkqSZzLUyZztmSdKSkmS/JJ9LckOS65Oc3NKN4CxJGkmDDE3wEeBy4MlJNre2y2uBI5LcDBzR5qmq64GJdsyfZvt2zO+lC4ryDWzHLEkaLduAN1TVU4BDgNe2KM1GcJYkjaQZ+8zZjlmStBy0ViQTwb3uSXIDXbCuY+iCM0AXwfky4BR6IjgDtySZiOC8kRbBGSDJRARnb2JKkubVXJtZSpK0ZCVZDRwEXMmkCM5AbwTnTT2rTURqXsmAEZwdvFiStCOszEmS1CPJI4GPAa+rqrunW7RP2qwiOBvwS5K0I6zMSZLUJNmFriL3oar6eEte0AjOkiTNlZU5SZKAFnHyTOCGqnp7z1tGcJYkjaT5HjRckqRxdSjwCuDaJNe0tDfRRWw+t0VzvhU4DroIzkkmIjhvY/sIzmcBu9IFPjH4iSRp3lmZkyQJqKov0r+/GxjBWZI0gmxmKUmSJEljyMqcJEmSJI2hsW5mufrUC4adBc3Sjv7PNq49ap5yIkmSNLq8ztUgfDInSZIkSWNorJ/MSZIkSdJ82JGnocNqPeaTOUmSJEkaQz6ZkyQtGPvJSpK0cHwyJ0mSJEljyMqcJEmSJI0hK3OSJEmSNIaszEmSJEnSGDIAiiRJkiTtgGEF/Fr0J3NJjkxyU5INSU5d7O+XliPLnbT4LHfS4rPcablZ1CdzSXYC/j/gCGAz8KUk51fV1xczH9JyYrmTFp/lTlp8o1budvRJjTSIxW5meTCwoaq+CZDkHOAYwJObtHAsd9Lis9xpaJbx+I6WOy07i93MciWwqWd+c0uTtHAsd9Lis9xJi89yp2VnsZ/MpU9abbdQchJwUpv9QZKbFjRX/e0FfHehPjxvm/ePXND89pqnvM8pvwvwu83GTHl+3GJlZJYWotzNeX8b8v+wn4G3ZQTz3s922zMm+e4rb7Pc9RjVcrdo55852KG8LdffzXI3NCO7T+wAt2lAAxxv+pa7xa7MbQb265lfBdw2eaGqOgM4Y7Ey1U+S9VW1Zph5mA3zu/DGMc/NvJe7Mf4ttrOUtgXcnhGybMrdqOYLzNtcjXLeZjA215n9jPHvPiW3aeEtdjPLLwEHJHl8kocAxwPnL3IepOXGcictPsudtPgsd1p2FvXJXFVtS/LfgH8AdgLeV1XXL2YepOXGcictPsudtPgsd1qOFn3Q8Kq6ELhwsb93Dkbu8fsMzO/CG8c8AwtS7sb2t+hjKW0LuD0jYxmVu1HNF5i3uRrlvE1rjK4z+xnb330abtMCS9V2/UIlSZIkSSNusfvMSZIkSZLmgZW5Hkn2S/K5JDckuT7JycPO0yCS7JTkK0k+Ney8DCLJ7knOS3Jj+62fPew8TSfJ77X94bokH0nysGHnaRiSvC/JliTXDTsv82Fcy/tUkjwsyVVJvtq25y3DztOOGrdj23yYqZyl884kG5J8LckzRyhvhyX5fpJr2t8fLVK+ZizLw/rdBszbsH63GY8Zw9zfloKp/v9J9kxySZKb2+sePeuc1n7vm5I8vyf9WUmube+9M0m/YRgWzeTj87hvU79r07HZpqryr/0B+wDPbNOPAv4ZeOqw8zVAvl8PfBj41LDzMmB+1wG/2aYfAuw+7DxNk9eVwC3Arm3+XOBVw87XkH6LXwaeCVw37LzM0/aMZXmfZnsCPLJN7wJcCRwy7Hzt4DaN1bFtnrZ52nIGvBC4qP2/DwGuHKG8HTaM/9UgZXlYv9uAeRvW7zbjMWOY+9tS+Jvq/w/8OXBqSz8VeFubfirwVeChwOOBbwA7tfeuAp7d/hcXAS8Y8rY96Pg87ttEn2vTcdkmn8z1qKrbq+rLbfoe4Aa6i/mRlWQVcBTw3mHnZRBJdqO7IDgToKr+varuGmqmZrYzsGuSnYGH02fMmuWgqr4A3DnsfMyXcSzv06nOD9rsLu1vbDtFj9uxbb4MUM6OAc5u/+8rgN2T7DMieRuKAcvyUH63UT7ODHjMGNr+thRM8/8/hq7yQHs9tk0fA5xTVfdW1S3ABuDg9pvvVlWXV1djOLtnnUU3xfF5bLdpmmvTsdgmK3NTSLIaOIjuTtUoewfwRuDHQ87HoJ4AbAXe3x7PvzfJI4adqalU1b8AfwHcCtwOfL+qLh5urjTfxqi8T6s1e7kG2AJcUlXjvD3vYLyObYtlJbCpZ34zI1I5aJ7dmu1dlORpi/3l05Tlof9uMxxnhvK7DXDMGPrvtlRM+v/vXVW3Q1fhAx7bFpvq917ZpienD8s72P74PM7bNNW16Vhsk5W5PpI8EvgY8LqqunvY+ZlKkqOBLVV19bDzMgs70zXTeXdVHQT8K92j65HU2kcfQ/cYfV/gEUlePtxcaT6NS3kfRFXdX1UHAqvo7hI+fchZmpMxPbYtln79L0blCeyXgcdV1TOAvwY+sZhfPkNZHurvNkPehva7DXDMGOX9bWzM4jwz1e89Mv+HORyfR36bmP216Uhtk5W5SZLsQlfgPlRVHx92fmZwKPCiJBuBc4DnJvngcLM0o83A5p67f+fRFaBR9TzglqraWlX3AR8HnjPkPGmejFl5H1hrHnIZcORwczJn43hsWyybgf165lcxIk2/q+ruiWZ71Y31tUuSvRbjuwcoy0P73WbK2zB/t5483EX/Y8bI7m/jYor//3cmmqu21y0tfarfe3Obnpw+DFMdn8d5m6a6Nh2LbbIy16NFnDkTuKGq3j7s/Mykqk6rqlVVtRo4HvhsVY30U6Oq+jawKcmTW9LhwNeHmKWZ3AockuThbf84nK7Nu8bcuJX3mSRZkWT3Nr0r3Y2IG4eaqTkax2PbIjofeGU6h9A1/b592JkCSPLTE5HbkhxMd41xxyJ87yBleSi/2yB5G+LvNsgxY2T3t3Ewzf//fOCENn0C8Mme9OOTPDTJ44EDgKvab35PkkPaZ76yZ51FNc3xeZy3aapr07HYpp0X+gvGzKHAK4BrWxtygDe1O2WaP78DfCjJQ4BvAq8ecn6mVFVXJjmPrhnMNuArwBnDzdVwJPkIXdS1vZJsBt5cVWcON1c7ZKmV932AdUl2orsYPLeqlk1I/6WiXzmjC0xBVf0tcCFdhMENwA9ZxOPnAHn7NeA1SbYB/wYc34IALLS+ZRnYvydvw/rdBsnbsH63vseMJL/dk7eh7W9LxFT//7XAuUlOpLtpfBxAVV2f5Fy6isQ24LVVdX9b7zXAWcCudFESL1qkbRjUuG9Tv2vTn2IMtimLc7yQJEmSJM0nm1lKkiRJ0hiyMidJkiRJY8jKnCRJkiSNIStzkiRJkjSGrMxJkiRJ0hiyMidJkiRJY8jKnCRJkiSNIStzkiRJkjSG/i9BvydGEJaCHQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(figsize=(15,10),layout=(3,4), grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pre-processing <a class=\"anchor\" id=\"preprocessing\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset index\n",
    "df = df.reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16505 entries, 0 to 16504\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   reward              16505 non-null  object  \n",
      " 1   mailer_type         16505 non-null  object  \n",
      " 2   income_level        16505 non-null  object  \n",
      " 3   household_size      16505 non-null  int64   \n",
      " 4   credit_rating       16505 non-null  object  \n",
      " 5   credit_cards_held   16505 non-null  int64   \n",
      " 6   homes_owned         16505 non-null  int64   \n",
      " 7   own_your_home       16505 non-null  object  \n",
      " 8   bank_accounts_open  16505 non-null  category\n",
      " 9   offer_accepted      16505 non-null  object  \n",
      "dtypes: category(1), int64(3), object(6)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# categorical = pd.get_dummies(cat, columns =['reward', 'mailer_type', 'income_level',\n",
    "#        'overdraft_protection', 'credit_rating', 'own_your_home',\n",
    "#        'average_balance', 'offer_accepted'],drop_first=True)\n",
    "\n",
    "cat = df.select_dtypes(exclude = ['float64'])\n",
    "cat.info()\n",
    "\n",
    "# categorical.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['reward',\n",
       " 'mailer_type',\n",
       " 'income_level',\n",
       " 'household_size',\n",
       " 'credit_rating',\n",
       " 'credit_cards_held',\n",
       " 'homes_owned',\n",
       " 'own_your_home',\n",
       " 'bank_accounts_open',\n",
       " 'offer_accepted']"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a list of the column names\n",
    "cat_col=cat.columns.to_list()\n",
    "cat_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = pd.get_dummies(cat, columns =['reward',\n",
    " 'mailer_type',\n",
    " 'income_level',\n",
    " 'bank_accounts_open',\n",
    " 'credit_rating',\n",
    " 'credit_cards_held',\n",
    " 'homes_owned',\n",
    " 'own_your_home',\n",
    " 'household_size',\n",
    " 'offer_accepted'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#categorical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#categorical['offer_accepted_Yes'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# percentage\n",
    "#pct_offer_accepted = 1023/18000\n",
    "#pct_offer_accepted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numerical scaling \n",
    "from sklearn.preprocessing import Normalizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_num are the numerical\n",
    "\n",
    "X = df_num.drop(['household_size', 'credit_cards_held', 'homes_owned'], axis=1)\n",
    "transformer = Normalizer().fit(X)\n",
    "X_normalised= transformer.transform(X)\n",
    "X_normalised = pd.DataFrame(X_normalised)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16500</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16501</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16502</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16503</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16504</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16505 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         0\n",
       "0      1.0\n",
       "1      1.0\n",
       "2      1.0\n",
       "3      1.0\n",
       "4      1.0\n",
       "...    ...\n",
       "16500  1.0\n",
       "16501  1.0\n",
       "16502  1.0\n",
       "16503  1.0\n",
       "16504  1.0\n",
       "\n",
       "[16505 rows x 1 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_normalised\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split off the dependant variable (label) <a class=\"anchor\" id=\"split\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sort out the categoricals \n",
    "y= categorical['offer_accepted_Yes']\n",
    "categorical=categorical.drop(['offer_accepted_Yes'], axis=1)\n",
    "#categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# bring the numerical data (scaled)in with the x cat\n",
    "X = np.concatenate((X_normalised, categorical),axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TT split, get model <a class=\"anchor\" id=\"ttsplit\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test =train_test_split(X, y, test_size= 0.4, random_state=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply model and train model <a class=\"anchor\" id=\"applymodel\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "classification = LogisticRegression(random_state=0, solver='lbfgs',max_iter=400,\n",
    "                  multi_class='ovr').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate accuracy and test <a class=\"anchor\" id=\"evaluate\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score \n",
    "predictions = classification.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9453196001211754"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# next steps would be to visualise the accuracy of the predictions in some ways \n",
    "\n",
    "# can i improve my model ?? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising accuracy - ROC / AUC  <a class=\"anchor\" id=\"rocauc\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.750780625485741\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics \n",
    "probs = classification.predict_proba(X_test)\n",
    "preds =probs[:,1]\n",
    "fpr, tpr, threshold = metrics.roc_curve(y_test, preds)\n",
    "roc_auc = metrics.auc(fpr, tpr)\n",
    "print(roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#definitions \n",
    "# tpr = true positive rate \n",
    "# fpr = false positive rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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T159/Qo8e8OSTNjvT5ZcnRfmNouCJwrkk9tdfVqX1hResrAbYl9TDDrNeS0cdlfQ9KzPDmDF2LeKXX+y2bduwIypS/ifmXBKaN89K/bz6qi0ffDDce6/NeukXp5PM//2fnUnUqgVffgmtWoUdUZHzROFcSFStSOi0aX9//MMPc5YfecR6VqboddD0lV3Er1kzm9KvZ08oWzbsqOLCE4VzCaZqI6QnT7ZrDocfbpVZIzVubHXiOnb06w9JZ/VqSwx16liXs7PPDn2+iHjzROFcgsycaT2WXnnFiu0BnHaaDZDby+s4Jz9VG+repYt1N3voobAjShhPFM4lwKBB1gkmW7VqNharXDlvUkoJy5bBTTdZm2CzZnYtokGDsKNKGE8UzsVZr152jaFMGfjiC0sM9etD+fJhR+ZitmIFjBoFTzwBXbtmXFezzPq0ziXIzJlwxRXw2282fSjYl1GvsZRC5s+H4cMtMTRpAosXQ8WKYUcVCk8UzhURVfj8c6vWMHGiFeVr0waOPx5OP91+XArYvh2efRbuv98mBO/QwWqjZGiSAE8UzsUkK8suQKvmv86771op71KlrEPM66+n3bir9DdjBlxzjZXjPfts65aWgkX8iponCuei+Plnm4zsllvsGFKQ5s3hk098XoeUtGkTtGxpF5HeesvOJLynAeCJwrk8DR5sZwTZczsUL25zPRRUuqdsWT+2pJyZM6FuXSviN2SIFfGrUiXsqJKKJwrnIuzYYeMcunaFjRvhzjut+ejgg6F27bCjc0Vq0yYbC/H00/Daa9b74LTTwo4qKXmicBlt6VLr3LJiBXTvbrNTbtpkz331FZx6arjxuTgZPRquuw7mzoXrr4dzzw07oqTmicJlBFXYvDln+f/+z5LEm2/uul7Llnbm8J//wD77JDZGlyAPPQQPP2y/6FGj4JRTwo4o6XmicGlv1Ci46irrBp9bjRpW7POyy6BSJWjUKOHhuUTJLuLXvLm1KT78sE8FGKO4JgoRaQ30AYoBA1W1d67nKwBvAjWCWJ5U1VfjGZPLDFu3Wg/Hq6+2KQLArk/eeafdL17cCu75NcsMsGoV3HabVV986KGMKOJX1OKWKESkGNAXOB3IAn4QkeGqOjNitZuBmap6johUAWaLyCBV3RKvuFz6++ADmztm7VpbLlnSeiydfbb3SMooqtZ97dZbbYrA//u/sCNKWfE8o2gOzFXV+QAiMgQ4D4hMFAqUFxEBygFrgW1xjMmloalTbYIxgEWLbCZKsHpKzz9vZTNKlAgvPheCrCy48Ub4+GM45hh4+WX7g3CFEs9EUQ1YErGcBRyTa53ngeHAMqA8cLGq7sj9RiLSGegMUKNGjbgE65LT5s2WCKZNg2eeybsc99Spf3/slVfsuoTLUKtW2beHp5+2M4pixcKOKKXFM1HkdZKfuwDCmcAU4FSgNvCFiIxV1T92eZFqf6A/QLNmzaIUUXDp5s47bb7obKef/vdJxGrVsscvvtiWS5XyyqwZae5c+Ogjm1SocWPr6+xd14pEPBNFFnBwxHJ17Mwh0lVAb1VVYK6ILACOACbEMS6XIr7+2pLE4Ydbd9Vq1TJqCgAXq23b7HSze3f7lnDppVC1qieJIhTPebV+AOqISC0RKQl0wJqZIi0GWgGISFXgcGB+HGNyKeTrr+323XfhrLM8Sbg8TJtm5Xnvvtvqq8yYYUnCFam4nVGo6jYR6QKMwLrHvqKqM0TkhuD5fkBP4DURmYY1Vd2jqqvjFZNLDfPn2wXpd9+FAw+Eo44KOyKXlDZtssFye+1lNZouusi7tcVJXMdRqOqnwKe5HusXcX8ZUECZNZcJliyxZuU1a3Ieq1wZHnkkvJhckpo+3Xow7b231X5v2NDL9caZT+nukkKPHpYk9tnHxkS9+qp1XLn66rAjc0lj40a44w5rg8yuvdKqlSeJBPASHi50qlbS+8ADrft7Xl1gXYb76isr4rdgAdx0E5x3XtgRZRT/l3ShW7rUZp887DBPEi4P3btb+e/ixeGbb6BvX+/RlGD+b+lCN3as3XbvHm4cLsnsCMbeHn88/Otf8NNP0KJFuDFlKE8ULlRz5tgI6gMOgKOPDjsalxR+/dWmIc2uzXTWWfD441CmTLhxZTBPFC4Uv/5qyaFuXWt2+uILb03IeKp2kbpuXavs6CXAk4YnCpdQW7fClCk2QdDKlXY28dVXcOSRYUfmQrVkic05e8UVNhR/8mS4556wo3IB7/XkEuatt2yCoGxnnmnXJUuVCi8mlyTWrIFx46BPH7j5Zi/il2Q8UbiEWLQoJ0lUqwZDh8Kxx4YbkwvZnDkwfDjcdZdNLbhkiVdzTFLe9OTiavt2m7e+Zk1b/v57GyvhSSKDbdtmF6cbNIBHH7U2SPAkkcQ8Ubi4GTDApif+6CO7Ltm+PTRtGnZULlQ//WQTCXXrBm3awMyZXsQvBXjTkytyK1fCsGFwww22fM018OKLPstcxtu0yUpuFC9ubY/t24cdkYuRJwpXZHbssMqv3btbMU+wmSh9HvsMN3WqlQDee28rCdywIey3X9hRud3gTU+uyLRrB3XqWJI4/ni7NulJIoNt2AC33WYXqt94wx475RRPEinIzyjcHtm6FerVgxUr7LgANmbquOOgevVwY3Mh+uIL6NwZFi6ELl3g/PPDjsjtAT+jcIX2119WaWHuXBtUe/vt8OOP1g32kEPCjs6F5v77bba5UqWskNdzz3mPphQX8xmFiJRV1Y3xDMallgsusGsQp5xiXyB9jFSG27HDyv+eeCLcey88+CCULh12VK4IFHhGISLHi8hMYFaw3FBEXoh7ZC6p/fqrJYmyZa352ZNEBluxwr419Ohhy2edBY895kkijcTS9PQf4ExgDYCq/gR4rd8MpmoXqwEGDbKR1i4DqcJrr9lFqo8/9qqOaSymaxSquiTXQ9vjEItLAVu32jipefOs+qtPNJahFi2C1q2tqmP9+jaQ7q67wo7KxUks1yiWiMjxgIpISeBWgmYol1n++svmjJg2zQbPzfK/gsz122/www/w/PNw440+NWGai+W3ewNwM1ANyAIaATfFMSaXhLZtg2bNLEk8/TRs2QIVK4YdlUuo2bPhiSfsfsOGsHixVXr1JJH2YvkNH66ql6lqVVXdX1UvB+rGOzCXPNavt4F006dbcb+uXcOOyCXU1q3Qq5clh969rScDQLly4cblEiaWRPFcjI+5NPPbb1bQ7+qrbdxUiRIwYwaIhB2ZS5jJk62I3333wTnnWBG//fcPOyqXYPleoxCR44DjgSoickfEU/sA3hkyzS1YYF8g16+35QcfzJnC2GWITZvg9NPtG8J778E//xl2RC4k0S5mlwTKBetEDqv8A7ggnkG58LVta0nihhvglltsGmOXISZPtvpMe+9tVV4bNoR99w07KhciUdXoK4j8Q1UXJSieAjVr1kwnTpwYdhhpbcsWOy5s3Wr3XYZYv95GVPftC6+/Dh07hh2RK0IiMklVmxXmtbF0j90kIk8A9YGdQy1V9dTCbNAlr/nz4Y47rLjfpk1WEdpliM8/h+uvt5K/t93mzUxuF7EkikHA20BbrKvslcCqeAblEm/FCqhd2+6XLQsnn2xzzLgMcO+91pupbl0YN85K/zoXIZZEUUlVXxaR21T1G+AbEfkm3oG5xJoyxW47dIC33vKeTRlh+3Yr0nXyyTbr3AMPWMVX53KJJVFsDW6Xi8jZwDLAZxpII4sXQ6dOdqz49789SaS95cttoFz9+tCzJ5x5pv04l49YxlE8IiIVgDuBu4CBQNd4BuUS6+67YeNG+PJLOPjgsKNxcaMKr75qRfw++8x7MrmYFXhGoaofB3d/B04BEJET4hmUS4wdO+D77+Hrr22e+5Ytw47Ixc3ChXDddfZt4KSTYOBAOOywsKNyKSLfMwoRKSYil4jIXSJyZPBYWxEZDzyfsAhd3Nxzj5ULX7XK5ppxaez33236wRdegNGjPUm43RKt6ell4FqgEvCsiLwKPAn8W1Ubx/LmItJaRGaLyFwR6ZbPOieLyBQRmeEXyRPrjTfg7LPtrKJTp7CjcUVu5kzrzQQ5Rfy80qsrhGhNT82ABqq6Q0RKA6uBQ1V1RSxvLCLFgL7A6VjV2R9EZLiqzoxYpyLwAtBaVReLiBeRSZCtW+1MonFjaN487GhckdqyxXol9Oxpc1VffbXVZypbNuzIXIqK9tVii6ruAFDVzcCcWJNEoDkwV1Xnq+oWYAiQe5qbS4H3VXVxsJ1fd+P93R4YNcquUTRtGnYkrkhNnGiThnTvboPmvIifKwLRziiOEJGpwX0BagfLAqiqNijgvasBkTPjZQHH5FrnMKCEiIzG6kn1UdX/5n4jEekMdAaoUaNGAZt1BVGFa66xL5jeKzKNbNxov9DSpeHDD+Hcc8OOyKWJaIliT8vA5dUbP3dhqeJAU6AVUAb4n4h8p6pzdnmRan+gP1itpz2MK+N98w0sXWoDcsuUCTsat8d+/NGK+JUtCx98AA0a+KxSrkjl2/Skqoui/cTw3llAZK/86thgvdzrfK6qG1V1NTAGaLi7H8LtnmHDbADuAw+EHYnbI3/8ATfdZO2Hb75pj7Vo4UnCFbl4dn/4AagjIrWCubY7AMNzrfMhcJKIFBeRvbGmKZ+JOY5++82mOT7pJKsi7VLUp5/ayOqXXrJKju3bhx2RS2OxlPAoFFXdJiJdgBHYREevqOoMEbkheL6fqs4Skc+BqcAOYKCqTo9XTJlu61Y45BAr8XP77WFH4wrtnnusV1O9ejZfxDG5L/05V7RiShQiUgaooaqzd+fNVfVT4NNcj/XLtfwE8MTuvK/bfTt2QJ8+sG4dnHACtG4ddkRut6jaL7FYMSvrW7q0TU/qRfxcAhTY9CQi5wBTgM+D5UYikrsJySWp1attMG6ZMlbTCWxgro+5SiFLl0K7dvDQQ7Z8xhk2L60nCZcgsRwuemBjIn4DUNUpQM14BeSKzrhxUKWKFQrdssVqOY0caVViXQpQhQEDrIlp5EioXDnsiFyGiuWQsU1VfxevPZ1yXnnFbh96CLp29c4wKWXBAhvs8vXXNl/EgAFw6KFhR+UyVCyJYrqIXAoUE5E6wK3A+PiG5fbUypWWKNq2hR49wo7G7bYNG2DqVOvVdO213lboQhXLX98t2HzZfwFvYeXGu8YxJreHtmyBo46y+1deGW4sbjdMnw6PPWb3jzrKivh17uxJwoUulr/Aw1X1flU9Ovh5IKj95JLU4MFW8O+886zcj0tyW7bYxekmTeA//4Ffg5JnPtDFJYlYEsXTIvKziPQUkfpxj8jtsY8+gmrVrJqDfxlNcj/8YCOre/SACy/0In4uKRV4GFHVU4CTgVVAfxGZJiJe/CEJ7dhhZxPvvQdt2vjc10lv40Yb0LJuHQwfDoMGWTc155JMTN83VXWFqj4L3ICNqXgwnkG5whk0CC691HpR3nxz2NG4fE2caFm9bFmr8jpjBpxzTthROZevWAbc1RWRHiIyHZsCdTxW4M8lEVV46im7/8svNqGZSzK//w7XX2/zRWQX8TvxRKhQIdy4nCtALN1jXwUGA2eoau7qry5JvPkm/PSTzVjn4yWS0EcfwQ03wIoVcNddcMEFYUfkXMwKTBSqemwiAnGF9+efNjYLYMyYcGNxebj7bnjySevyOmyYnVE4l0LyTRQi8o6qXiQi09h1wqFYZ7hzCaAK559vlWHbtIFy5cKOyAH2i9m+3eqlnHEG7LOPVX0tWTLsyJzbbdHOKG4LbtsmIhBXOPfdByNGWM24oUPDjsYBkJUFN95oM809+iicfrr9OJeios1wtzy4e1Mes9vdlJjwXDTLlkHv3lYzrm9fq0DtQrRjh5XcqFcPRo2CAw4IOyLnikQs3WPz+ip0VlEH4nbfL7/Ybc+ecNBB4caS8ebPh1NPtQvWzZvDtGlwyy1hR+VckYh2jeJG7MzhEBGZGvFUeWBcvANzBbv+erutVi3cOBw2eG7mTBg4EK6+2kc7urQS7RrFW8BnQC+gW8Tj61V1bVyjcgV66imYPdvGS/hMmCGZNs0GzD3wgPVoWrTIZohyLs1Ea3pSVV0I3Aysj/hBRPaLf2guP+vXW1f8SpWsKdwl2F9/wYMPWhG/Z5/NKeLnScKlqYLOKNoCk7DusZHn0gocEse4XD42bYLTTrP7Tz0F+3nKTqzvvrNBKzNnwhVXWLXXSpXCjsq5uMo3Uahq2+C2VuLCcdF88QV06waTJ9ukRB07hh1Rhtm4Ec4+22o0ffopnOV9OlxmiKXW0wkiUja4f7mIPC0iNeIfmsu2fbs1g59xBvz4I7z4Ilx1lV8vTZjvv88p4vfRR1bEz5OEyyCxdI99EdgkIg2BfwGLgDfiGpXbxVtv2bgtgC+/zOnt5OLst99sGtJjj80p4nf88VC+fKhhOZdosSSKbaqqwHlAH1Xtg3WRdQnw2WfWJL7//rBmDbRqFXZEGWLYMBs499prVnrjwgvDjsi50MSSKNaLyL3AFcAnIlIMKBHfsBxAly5Wv0kVHn7YL1wnzB13WAGt/fe3Zqfevb1Hk8tosZQZvxi4FLhaVVcE1yeeiG9YmW31apsZs29fm4Ro6VKvJRd3kUX82rSxnkz/+heU8O9Ezom1KhWwkkhVILs28gRV/TWuUUXRrFkznThxYlibj7tp02xOie3breDoF19YRQgXR4sXW+mNxo1zLgY5l2ZEZJKqNivMa2Pp9XQRMAG4ELgI+F5EfNaVIvbbb3DCCTZVwfbt8PjjNseNJ4k42rEDXngB6teHb77xglnO5SOWpqf7gaOzzyJEpArwJeBFrYvQs8/C+PFw3nnQqxfUrRt2RGlu7lyryTR2rJUA798fatYMOyrnklIsiWKvXE1Na4jtIriL0bZt1vuyRg3rbOMSYPNmmDMHXn0VrrzSB6U4F0UsieJzERmBzZsNdnH70/iFlHnefddKhv/3v2FHkuamTLEifg89BEceCQsXQunSYUflXNIr8MxAVe8GXgIaAA2B/qp6T7wDyxSTJ8Oll9p9rwIbJ5s3w/33Q7NmNqw9u4ifJwnnYhJtPoo6wJNAbWAacJeqLk1UYJlAFa67zu4PHgyHHRZuPGlp/Hgbsfjzz9bE9PTTPiDFud0U7YziFeBjoD1WQfa5hESUIebOha5dYdIkuPlm6NAh7IjS0MaNcM45VnL3889tlLUnCed2W7RrFOVVdUBwf7aI/JiIgDLBX39BixawPJiV/IEHwo0n7fzvf9aOV7YsfPyxXY/w+kzOFVq0M4rSItJYRJqISBOgTK7lAolIaxGZLSJzRaRblPWOFpHtmTI+o3VrSxL33ANZWXDAAWFHlCbWrbMur8cfD28EdSuPO86ThHN7KNoZxXLg6YjlFRHLCpwa7Y2DmlB9gdOBLOAHERmuqjPzWO9xYMTuhZ6aJk6E0aOtqal377CjSSPvv29teKtWwb33wsUXhx2Rc2kj2sRFp+zhezcH5qrqfAARGYJVoJ2Za71bgPfIKRGS1tq0sdtevcKNI63cfjs88ww0amQTCjVuHHZEzqWVWMZRFFY1YEnEchawSwdQEakGnI+dneSbKESkM9AZoEaN1J0z6fvv7QvvBRf4IOA9FlnEr21bq/R6111exM+5OIjnCOu8hrrmrkD4DHCPqm6P9kaq2l9Vm6lqsypVqhRVfAm1dasdx/be26YxdXtg4UK70NO9uy23amXNTZ4knIuLeCaKLODgiOXqwLJc6zQDhojIQuAC4AURaRfHmELzxhvw7bfW28mvrRbSjh3w3HPWi2n8ePjHP8KOyLmMUGDTk4gIcBlwiKo+HMxHcYCqTijgpT8AdUSkFrAU6IDNa7GTqtaK2M5rwMeqOmy3PkEK2LIFHnzQ7g8eHH1dl49ffrGJwseNs7OJfv08UTiXILGcUbwAHAdcEiyvx3ozRaWq24AuWG+mWcA7qjpDRG4QkRsKGW9K2bwZnnrKmpuWLrVrrRUrhh1VitqyBebNs4JYn37qScK5BIrlYvYxqtpERCYDqOo6EYlpvjVV/ZRcBQRVtV8+63aK5T1TycMP5/Ru6tXLJkxzu2HyZCvi16OHzRmxcCGUKhV2VM5lnFjOKLYGYx0Uds5HsSOuUaUBVWtmOvJImDULunWDvbw4e2w2b7aL00cfDS+9ZF3FwJOEcyGJ5dD1LPABsL+IPAp8CzwW16jSwOzZ9gX45pvhiCPCjiaFfPstNGxooxE7doSZMyFFe7o5ly4KbHpS1UEiMglohXV5baeqs+IeWYr68kvr/rpwoS2fdVao4aSWDRtsir999oGRI23mOedc6GLp9VQD2AR8FPmYqi6OZ2Cp6oEHbGBdnTo2z4Rfc43Bt99afaZy5eCTT6y9rly5sKNyzgViaXr6BCs3/gnwFTAf+CyeQaWyefOgc2ebZXPQoLCjSXJr1ljz0kkn5RTxO/ZYTxLOJZlYmp6OilwOKsdeH7eIUtjIkbB6tXXQcVGowtCh0KULrF1rI6x9Qg7nktZu13pS1R9FJCMK+O2uO++02/PPDzeOpHf77dCnDzRtatm1YcOwI3LORRHLNYo7Ihb3ApoAq+IWUYpaswamT7cJ1Q4+uOD1M44qbNtm9ZjOPRcOOgjuuMOK+jnnklos1yjKR/yUwq5VnBfPoFLROefYrfdyysOCBXDGGTlF/E491UYfepJwLiVE/U8NBtqVU9W7ExRPSlqzxmbfPOEEuOaasKNJItu3w/PPw333QbFicOGFYUfknCuEfM8oRKR4UP47pmlPM9WkSVYRFmwWzpIxFTfJAHPmWG+mrl2hZUuYMcO6gznnUk60M4oJWJKYIiLDgXeBjdlPqur7cY4tJdx0EyxaBFdcAf/8Z9jRJJFt22zHvPmmDSiRvKYncc6lglgaifcD1mCz0Ck2OluBjE8UXbrAhAnQvr0VNc14EydaEb+ePaFePZg/3+szOZcGoiWK/YMeT9PJSRDZcs9Ul3H69IG+QbH1AQPCjSV0f/4JDz1kNdUPOABuvdXqM3mScC4tROv1VAwoF/yUj7if/ZOxVq+2pnewidb23TfUcML1zTfQoAE88YRdyZ8xw4v4OZdmop1RLFfVhxMWSQr5LChgctVVcNxx4cYSqg0b7MJMxYrw1VfW7dU5l3aiJQq/+piPH36wcWP98pyCKQOMHWt9gcuVs6xZvz6ULRt2VM65OInW9NQqYVGkkDFj7NrEBRdkYFfY1avh8sutP3B2Eb/mzT1JOJfm8k0Uqro2kYEkux077Fpty5aWIF56KeyIEkgV3n7bejK9/bZduPYifs5lDK+hEKMxY+Cuu+x+795Qvny48STUbbfBc8/Z1KRffQVHHVXwa5xzacMTRYyyzyBmzoS6dcONJSFUYetWO306/3ybgalrVyvF4ZzLKLEUBcx4qjBkiBU8zYgkMW8etGpl0/UBnHKK1VD3JOFcRvJEEYOZM+22Tp1w44i77dvh6aetaWnSJDj88LAjcs4lAW96ikH2uIlu3cKNI65+/hmuvNJqkpxzDrz4IlSrFnZUzrkk4ImiAI89Bvffb031af0Fe8cOWLYMBg+Giy/2In7OuZ08UUTxxx+WJMAKoR5wQLjxFLkJE6yI36OPWtfXefMycHCIc64gfo0iijZt7LZ37zRLEps2WV/f446D11+HVcHMtp4knHN58ESRjylTYNw4m0rhX/8KO5oi9PXXdrH6qafguuu8iJ9zrkDe9JSH996Diy6yUkZ9+6ZRc/2GDTYdacWKljBOPjnsiJxzKcDPKHIZNcrqOO3YAb162TE15Y0ebR8ou4jf1KmeJJxzMfNEkcvQoXb71Vc2g11KW7UKLrnEBsy9+aY9dvTRsPfe4cblnEsp3vQUQRU+/hjatUvxqRVUrZvrrbfC+vU2NakX8XPOFZKfUUSYOhWWLIG2bcOOZA/dcgtcdpkNJZ882UpxeI8m51wh+RlFYN06+9JdrlyKJoodO2DbNksIF1wAhx5qCcPrMznn9lBczyhEpLWIzBaRuSLytwIYInKZiEwNfsaLSMN4xhPNffdZFYtBg6Bq1bCiKKRffrG2suzRgSef7JVenXNFJm6JQkSKAX2Bs4B6wCUiUi/XaguAlqraAOgJ9I9XPNFMmmTTml52GZx7bhgRFNK2bfDkk9CggQ38yIjSts65RItn01NzYK6qzgcQkSHAecDM7BVUdXzE+t8B1eMYT74eeshus6tqp4RZs6BjR5g4Ec47D154weqgO+dcEYtn01M1YEnEclbwWH6uAT7L6wkR6SwiE0Vk4qrschNFZNIkG1rQrRsccUSRvnX8rVxpU5N+8IEnCedc3MQzUeQ1nlnzXFHkFCxR3JPX86raX1WbqWqzKkVYbmL1artwrZoiZTq++w7uvdfu161rRfwuuiiNho4755JRPBNFFnBwxHJ1YFnulUSkATAQOE9V18Qxnl107GgljlasgNNOg333TdSWC2HjRrj9djj+eLvann1WVaJEuHE55zJCPBPFD0AdEaklIiWBDsDwyBVEpAbwPnCFqs6JYyy7+OUXeOMN6yjUvz+MHJmoLRfCl1/CkUfCM8/ATTd5ET/nXMLF7WK2qm4TkS7ACKAY8IqqzhCRG4Ln+wEPApWAF8SaT7aparN4xQQ222d2z6Y33kjypv0NG2xwx377wZgxcNJJYUfknMtAcR1wp6qfAp/meqxfxP1rgWvjGUNuPXrYeIl69ZI4SYwaBS1b2ui/ESMs2DJlwo7KOZehMqqEx/btMGAANGxoLThJZ+VKuzjdqlVOEb+mTT1JOOdClVGJ4thj7Vh89dVhR5KLqrWD1auXMzXppZeGHZVzzgEZVOupTRsbmwZwzTXhxvI3N98ML75oU5O+/LKPsHbOJZWMSBTjx9ugOoAff4SyZcONB7Aiflu3QqlScPHFlhxuusnrMznnkk5GND29957dTpoEjRuHGwsAs2fbxersIn4tW3qlV+dc0kr7RPHUU/D003Z9okmTkIPZuhV697ar6dOnw1FHhRyQc84VLO2bnl5/3W6zC/+FZsYMuOIKm0jon/+Evn3hgANCDso55wqW1oli0yY7PnfvDq1bhxxMsWKwdq1Nyt2+fcjBOOdc7NK66WnKFLtm3CyuY72jGD8e7gnqHB5xBMyd60nCOZdy0jpRDBxotwlPFBs2wK23woknWhnw1avt8eJpfQLnnEtTaZsopk2DV1+1+wkt1TFypBXxe/556NLFLlpXrpzAAJxzrmil7Vfc3r3t9uWXE7jRDRtsPtVKlWDsWDjhhARu3Dnn4iMtzygWLoS33oKuXRNUruOLL6yQVLlydkYxZYonCedc2kjLRDE8mPXippvivKHly+3i9Bln2IRCYCP6SpeO84adcy5x0i5RbN4Mzz5rFTHq1InTRlThtdesiN8nn1g7lxfxc86lqbS6RvHjj1Y2ad48uO++OG7oxhvhpZesV9PAgXD44XHcmHOpa+vWrWRlZbF58+awQ8kYpUuXpnr16pQowqmS0yZRLFliUzeAXZ9o166INxBZxO/SS6FBA7jhBtgr7U7KnCsyWVlZlC9fnpo1axLMYuniSFVZs2YNWVlZ1KpVq8jeN22Oct265dxeckkRz/Uza5ZNQ5p9mtKihV0A8SThXFSbN2+mUqVKniQSRESoVKlSkZ/BpcWRbs4cO4s44gjo1asI33jrVnjsMWjUyOZPTYrSs86lFk8SiRWP/Z0WTU9vvWW3ffsW4ZvOmAGXX25dXS+8EJ57DqpWLcINOOdcakiLM4phw6BmTTj11CJ80+LF4fff4f334Z13PEk4l8I++OADRISff/5552OjR4+mbdu2u6zXqVMnhg4dCtiF+G7dulGnTh2OPPJImjdvzmfZM6DtgV69enHooYdy+OGHM2LEiDzXufjii2nUqBGNGjWiZs2aNGrUCICFCxdSpkyZnc/dcMMNexxPLFL+jELVjudFcrY1dqzNWf3kk9aTac4cr8/kXBoYPHgwJ554IkOGDKFHjx4xvaZ79+4sX76c6dOnU6pUKVauXMk333yzR3HMnDmTIUOGMGPGDJYtW8Zpp53GnDlzKJZr0rK333575/0777yTChUq7FyuXbs2U6ZM2aM4dlfKHwVnzLCR2C+8sAdvsn69XQV/4QWoVcvuV67sScK5ItS1q7XkFqVGjeCZZ6Kvs2HDBsaNG8fXX3/NueeeG1Oi2LRpEwMGDGDBggWUKlUKgKpVq3LRRRftUbwffvghHTp0oFSpUtSqVYtDDz2UCRMmcNxxx+W5vqryzjvvMGrUqD3a7p5K6aanTZusAxLYXECF8tlnUL8+vPii/SVPm+ZF/JxLI8OGDaN169Ycdthh7Lfffvz4448Fvmbu3LnUqFGDffbZp8B1b7/99p1NQZE/vbMLzkVYunQpBx988M7l6tWrs3Tp0nzfe+zYsVStWpU6EaOHFyxYQOPGjWnZsiVjx44tML6ikNJfme+9F9atg2OOKeQlhPXroWNH2H9/mzvi2GOLPEbnnCnom3+8DB48mK5duwLQoUMHBg8eTJMmTfLtHbS7vYb+85//xLyuqu7W9gYPHswll1yyc/nAAw9k8eLFVKpUiUmTJtGuXTtmzJgRU0LbEymbKLZts1IdYIOkY6YKI0bA6adD+fLw5ZfWrzY4vXTOpY81a9YwatQopk+fjoiwfft2RIR///vfVKpUiXXr1u2y/tq1a6lcuTKHHnooixcvZv369ZQvXz7qNm6//Xa+/vrrvz3eoUMHumUP8ApUr16dJUuW7FzOysrioHzmQdi2bRvvv/8+kyZN2vlYqVKldjaFNW3alNq1azNnzhyaxXvSHVVNqZ+mTZuqqurrr6uC6rBhGrtly1TbtbMXvv76brzQOVcYM2fODHX7/fr1086dO+/yWIsWLXTMmDG6efNmrVmz5s4YFy5cqDVq1NDffvtNVVXvvvtu7dSpk/7111+qqrps2TJ944039iie6dOna4MGDXTz5s06f/58rVWrlm7bti3PdT/77DNt0aLFLo/9+uuvO9efN2+eHnTQQbpmzZq/vTav/Q5M1EIed1P2GsXdd9s01G3axLCyKrzyilUK/Pxz+Pe/vYifcxlg8ODBnH/++bs81r59e9566y1KlSrFm2++yVVXXUWjRo244IILGDhw4M4eRo888ghVqlShXr16HHnkkbRr144qVarsUTz169fnoosuol69erRu3Zq+ffvu7PF07bXXMnHixJ3rDhkyZJdmJ4AxY8bQoEEDGjZsyAUXXEC/fv3Yb7/99iimWIjm0WaWzJo1a6Z33TWRSy6xcXDvvBPDi66/Hvr3tyvfAwfGsayscy7SrFmzqFu3bthhZJy89ruITFLVQrVRpeQ1iieftNvHH4+y0vbtVoKjdGkbYd24MXTu7PWZnHNuN6XcUXPbNpg0Cc4/34Y85GnGDJthLruI30kneaVX55wrpJQ7cm7aZLcXX5zHk1u2QM+edvYwdy4cfXRCY3PO/V2qNW+nunjs75RrevrzT7s95ZRcT0ybBpddZrcdOljf2T288OSc2zOlS5dmzZo1Xmo8QTSYj6J0EU/HnHKJYvNmGzi9//65nihZ0k43PvwQzj03lNicc7uqXr06WVlZrFq1KuxQMkb2DHdFKeUSxerV0LJlsPDNNzB8ODz1lBXxmz3b+sw655JCiRIlinSmNReOuF6jEJHWIjJbROaKSLc8nhcReTZ4fqqINInlfU9q+IfNW33yyVZjfPVqe8KThHPOFbm4JQoRKQb0Bc4C6gGXiEi9XKudBdQJfjoDLxb0vvvwOw+/X9/GRdxxhxfxc865OIvnGUVzYK6qzlfVLcAQ4Lxc65wH/DcYYf4dUFFEDoz2prVYCBUqWBG/p56CvfeOS/DOOedMPK9RVAOWRCxnAcfEsE41YHnkSiLSGTvjAPhrrxkzpnulVwAqA6vDDiJJ+L7I4fsih++LHIcX9oXxTBR59YXL3cE3lnVQ1f5AfwARmVjYYejpxvdFDt8XOXxf5PB9kUNEJha8Vt7i2fSUBRwcsVwdWFaIdZxzzoUononiB6COiNQSkZJAB2B4rnWGAx2D3k/HAr+r6vLcb+Sccy48cWt6UtVtItIFGAEUA15R1RkickPwfD/gU6ANMBfYBFwVw1v3j1PIqcj3RQ7fFzl8X+TwfZGj0Psi5cqMO+ecS6yUKwronHMusTxROOeciyppE0W8yn+kohj2xWXBPpgqIuNFpGEYcSZCQfsiYr2jRWS7iFyQyPgSKZZ9ISIni8gUEZkhIt8kOsZEieF/pIKIfCQiPwX7IpbroSlHRF4RkV9FZHo+zxfuuFnYybbj+YNd/J4HHAKUBH4C6uVapw3wGTYW41jg+7DjDnFfHA/sG9w/K5P3RcR6o7DOEheEHXeIfxcVgZlAjWB5/7DjDnFf3Ac8HtyvAqwFSoYdexz2RQugCTA9n+cLddxM1jOKuJT/SFEF7gtVHa+q64LF77DxKOkolr8LgFuA94BfExlcgsWyLy4F3lfVxQCqmq77I5Z9oUB5sUkxymGJYltiw4w/VR2Dfbb8FOq4mayJIr/SHru7TjrY3c95DfaNIR0VuC9EpBpwPtAvgXGFIZa/i8OAfUVktIhMEpGOCYsusWLZF88DdbEBvdOA21R1R2LCSyqFOm4m63wURVb+Iw3E/DlF5BQsUZwY14jCE8u+eAa4R1W3p/mMarHsi+JAU6AVUAb4n4h8p6pz4h1cgsWyL84EpgCnArWBL0RkrKr+EefYkk2hjpvJmii8/EeOmD6niDQABgJnqeqaBMWWaLHsi2bAkCBJVAbaiMg2VR2WkAgTJ9b/kdWquhHYKCJjgIZAuiWKWPbFVUBvtYb6uSKyADgCmJCYEJNGoY6bydr05OU/chS4L0SkBvA+cEUafluMVOC+UNVaqlpTVWsCQ4Gb0jBJQGz/Ix8CJ4lIcRHZG6vePCvBcSZCLPtiMXZmhYhUxSqpzk9olMmhUMfNpDyj0PiV/0g5Me6LB4FKwAvBN+ltmoYVM2PcFxkhln2hqrNE5HNgKrADGKiqeXabTGUx/l30BF4TkWlY88s9qpp25cdFZDBwMlBZRLKAh4ASsGfHTS/h4ZxzLqpkbXpyzjmXJDxROOeci8oThXPOuag8UTjnnIvKE4VzzrmoPFG4pBRUfp0S8VMzyrobimB7r4nIgmBbP4rIcYV4j4EiUi+4f1+u58bvaYzB+2Tvl+lBNdSKBazfSETaFMW2Xeby7rEuKYnIBlUtV9TrRnmP14CPVXWoiJwBPKmqDfbg/fY4poLeV0ReB+ao6qNR1u8ENFPVLkUdi8scfkbhUoKIlBORr4Jv+9NE5G9VY0XkQBEZE/GN+6Tg8TNE5H/Ba98VkYIO4GOAQ4PX3hG813QR6Ro8VlZEPgnmNpguIhcHj48WkWYi0hsoE8QxKHhuQ3D7duQ3/OBMpr2IFBORJ0TkB7F5Aq6PYbf8j6Cgm4g0F5uLZHJwe3gwSvlh4OIglouD2F8JtjM5r/3o3N+EXT/df/wnrx9gO1bEbQrwAVZFYJ/gucrYyNLsM+INwe2dwP3B/WJA+WDdMUDZ4PF7gAfz2N5rBHNXABcC32MF9aYBZbHS1DOAxkB7YEDEaysEt6Oxb+87Y4pYJzvG84HXg/slsUqeZYDOwAPB46WAiUCtPOLcEPH53gVaB8v7AMWD+6cB7wX3OwHPR7z+MeDy4H5FrO5T2bB/3/6T3D9JWcLDOeBPVW2UvSAiJYDHRKQFVo6iGlAVWBHxmh+AV4J1h6nqFBFpCdQDxgXlTUpi38Tz8oSIPACswqrwtgI+UCuqh4i8D5wEfA48KSKPY81VY3fjc30GPCsipYDWwBhV/TNo7mogOTPyVQDqAAtyvb6MiEwBagKTgC8i1n9dROpg1UBL5LP9M4BzReSuYLk0UIP0rAHliognCpcqLsNmJmuqqltFZCF2kNtJVccEieRs4A0ReQJYB3yhqpfEsI27VXVo9oKInJbXSqo6R0SaYjVzeonISFV9OJYPoaqbRWQ0Vvb6YmBw9uaAW1R1RAFv8aeqNhKRCsDHwM3As1gto69V9fzgwv/ofF4vQHtVnR1LvM6BX6NwqaMC8GuQJE4B/pF7BRH5R7DOAOBlbErI74ATRCT7msPeInJYjNscA7QLXlMWazYaKyIHAZtU9U3gyWA7uW0NzmzyMgQrxnYSVsiO4PbG7NeIyGHBNvOkqr8DtwJ3Ba+pACwNnu4Usep6rAku2wjgFglOr0SkcX7bcC6bJwqXKgYBzURkInZ28XMe65wMTBGRydh1hD6qugo7cA4WkalY4jgilg2q6o/YtYsJ2DWLgao6GTgKmBA0Ad0PPJLHy/sDU7MvZucyEpvb+Eu1qTvB5hKZCfwoItOBlyjgjD+I5SesrPa/sbObcdj1i2xfA/WyL2ZjZx4lgtimB8vOReXdY51zzkXlZxTOOeei8kThnHMuKk8UzjnnovJE4ZxzLipPFM4556LyROGccy4qTxTOOeei+n/cW/fYY5P6IgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#method to plot AUC visually \n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# confusion matrix \n",
    "\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "\n",
    "#accuracy_score(y_test,predictions)\n",
    "#confusion_matrix(y_test,predictions)\n",
    "#plot_confusion_matrix(classification, X_test, y_test)\n",
    "#plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data imbalanced -  SMOTE (oversample the minority class of the target label) <a class=\"anchor\" id=\"smote\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    15572\n",
       "0    15572\n",
       "Name: offer_accepted_Yes, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_sm, y_sm = smote.fit_resample(X, y)\n",
    "y_sm.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#re do train test split It2\n",
    "\n",
    "X_train, X_test, y_train, y_test =train_test_split(X_sm, y_sm, test_size= 0.4, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply model It2 \n",
    "classification = LogisticRegression(random_state=0, solver='lbfgs',max_iter=400,\n",
    "                  multi_class='ovr').fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6982661743458018"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accuracy assessment It2\n",
    "predictions = classification.predict(X_test)\n",
    "accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#method to plot AUC visually \n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Conf matrix It2 \n",
    "confusion_matrix(y_test,predictions)\n",
    "plot_confusion_matrix(classification, X_test, y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### conclusion on imbalance:\n",
    "\n",
    "- The SMOTE technique solved the imbalanced but the accuracy got too low (71%), but the TOMEK doesn't.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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    "### References\n",
    "- lectures\n",
    "- women who code: munibootcamp of data science\n",
    "- https://machinelearningmastery.com/confusion-matrix-machine-learning/\n",
    "- https://medium.com/analytics-vidhya/outlier-treatment-9bbe87384d02\n",
    "    "
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