credit_rating_analytics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing borrowers’ risk of defaulting\n",
    "\n",
    "Your project is to prepare a report for a bank’s loan division. You’ll need to find out if a customer’s marital status and number of children has an impact on whether they will default on a loan. The bank already has some data on customers’ credit worthiness.\n",
    "\n",
    "Your report will be considered when building a **credit scoring** of a potential customer. A **credit scoring** is used to evaluate the ability of a potential borrower to repay their loan."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objectives\n",
    "\n",
    "The objective of this project is to:\n",
    "- Prepare a report for a bank's loan division by analyze a borrower's risk of defaulting.\n",
    "- Apply Data Preprocessing to a real-life analytical case study."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    " # Table of contents\n",
    "\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ol>\n",
    "        <li><a href=\"#open_the_data\">Open the data file and have a look at the general information</a></li>\n",
    "        <li><a href=\"#data_preprocessing\">Data preprocessing</a></li>\n",
    "        <li><a href=\"#answer_questions\">Answer these questions</a></li>\n",
    "        <li><a href=\"#general_conclusion\">General conclusion</a></li>\n",
    "        <li><a href=\"#project_readiness_checklist\">Project Readiness Checklist</a></li>\n",
    "    </ol>\n",
    "</div>\n",
    "<br>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"open_the_data\">\n",
    "    <h2>Open the data file and have a look at the general information.</h2> \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We require the following libraries: *pandas* and *numpy* for data manipulation, *nlkt* for lemmatization, *seaborn* for visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in c:\\users\\hotty\\anaconda3\\lib\\site-packages (3.5)\n",
      "Requirement already satisfied: click in c:\\users\\hotty\\anaconda3\\lib\\site-packages (from nltk) (7.1.2)\n",
      "Requirement already satisfied: tqdm in c:\\users\\hotty\\anaconda3\\lib\\site-packages (from nltk) (4.50.2)\n",
      "Requirement already satisfied: joblib in c:\\users\\hotty\\anaconda3\\lib\\site-packages (from nltk) (0.17.0)\n",
      "Requirement already satisfied: regex in c:\\users\\hotty\\anaconda3\\lib\\site-packages (from nltk) (2020.10.15)\n",
      "Required packages has been correctly loaded!\n"
     ]
    }
   ],
   "source": [
    "# import pandas and numpy for data preprocessing and manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# import WordNet Lemmatizer for dealing with categorical variables\n",
    "!pip install nltk\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "# import Snowball stemmer\n",
    "from nltk.stem import SnowballStemmer\n",
    "\n",
    "# Seaborn for visualization\n",
    "import seaborn as sns\n",
    "sns.set(font_scale = 1)\n",
    "\n",
    "# Matplotlib for visualization\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "print('Required packages has been correctly loaded!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data has been read correctly!\n"
     ]
    }
   ],
   "source": [
    "# read in the data\n",
    "try:\n",
    "    df = pd.read_csv('https://code.s3.yandex.net/datasets/credit_scoring_eng.csv')\n",
    "except:\n",
    "    df = pd.read_csv('credit_scoring_eng_eng.csv')\n",
    "print('Data has been read correctly!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-8437.673028</td>\n",
       "      <td>42</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>40620.102</td>\n",
       "      <td>purchase of the house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>-4024.803754</td>\n",
       "      <td>36</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>17932.802</td>\n",
       "      <td>car purchase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-5623.422610</td>\n",
       "      <td>33</td>\n",
       "      <td>Secondary Education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>23341.752</td>\n",
       "      <td>purchase of the house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>-4124.747207</td>\n",
       "      <td>32</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>42820.568</td>\n",
       "      <td>supplementary education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>retiree</td>\n",
       "      <td>0</td>\n",
       "      <td>25378.572</td>\n",
       "      <td>to have a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>-926.185831</td>\n",
       "      <td>27</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "      <td>40922.170</td>\n",
       "      <td>purchase of the house</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>-2879.202052</td>\n",
       "      <td>43</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>0</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>business</td>\n",
       "      <td>0</td>\n",
       "      <td>38484.156</td>\n",
       "      <td>housing transactions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>-152.779569</td>\n",
       "      <td>50</td>\n",
       "      <td>SECONDARY EDUCATION</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>21731.829</td>\n",
       "      <td>education</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>-6929.865299</td>\n",
       "      <td>35</td>\n",
       "      <td>BACHELOR'S DEGREE</td>\n",
       "      <td>0</td>\n",
       "      <td>civil partnership</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>15337.093</td>\n",
       "      <td>having a wedding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>-2188.756445</td>\n",
       "      <td>41</td>\n",
       "      <td>secondary education</td>\n",
       "      <td>1</td>\n",
       "      <td>married</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>employee</td>\n",
       "      <td>0</td>\n",
       "      <td>23108.150</td>\n",
       "      <td>purchase of the house for my family</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years            education  education_id  \\\n",
       "0         1   -8437.673028         42    bachelor's degree             0   \n",
       "1         1   -4024.803754         36  secondary education             1   \n",
       "2         0   -5623.422610         33  Secondary Education             1   \n",
       "3         3   -4124.747207         32  secondary education             1   \n",
       "4         0  340266.072047         53  secondary education             1   \n",
       "5         0    -926.185831         27    bachelor's degree             0   \n",
       "6         0   -2879.202052         43    bachelor's degree             0   \n",
       "7         0    -152.779569         50  SECONDARY EDUCATION             1   \n",
       "8         2   -6929.865299         35    BACHELOR'S DEGREE             0   \n",
       "9         0   -2188.756445         41  secondary education             1   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0            married                 0      F    employee     0     40620.102   \n",
       "1            married                 0      F    employee     0     17932.802   \n",
       "2            married                 0      M    employee     0     23341.752   \n",
       "3            married                 0      M    employee     0     42820.568   \n",
       "4  civil partnership                 1      F     retiree     0     25378.572   \n",
       "5  civil partnership                 1      M    business     0     40922.170   \n",
       "6            married                 0      F    business     0     38484.156   \n",
       "7            married                 0      M    employee     0     21731.829   \n",
       "8  civil partnership                 1      F    employee     0     15337.093   \n",
       "9            married                 0      M    employee     0     23108.150   \n",
       "\n",
       "                               purpose  \n",
       "0                purchase of the house  \n",
       "1                         car purchase  \n",
       "2                purchase of the house  \n",
       "3              supplementary education  \n",
       "4                    to have a wedding  \n",
       "5                purchase of the house  \n",
       "6                 housing transactions  \n",
       "7                            education  \n",
       "8                     having a wedding  \n",
       "9  purchase of the house for my family  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use the head() method to view the first 10 rows\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21525 entries, 0 to 21524\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   children          21525 non-null  int64  \n",
      " 1   days_employed     19351 non-null  float64\n",
      " 2   dob_years         21525 non-null  int64  \n",
      " 3   education         21525 non-null  object \n",
      " 4   education_id      21525 non-null  int64  \n",
      " 5   family_status     21525 non-null  object \n",
      " 6   family_status_id  21525 non-null  int64  \n",
      " 7   gender            21525 non-null  object \n",
      " 8   income_type       21525 non-null  object \n",
      " 9   debt              21525 non-null  int64  \n",
      " 10  total_income      19351 non-null  float64\n",
      " 11  purpose           21525 non-null  object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# check the datatype with info() method\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21525, 12)"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the structure of our dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the initial look at the data, we can see that `days_employed` and `total_income` has some missing data or 'NaNs'. We can see that the columns with missing values is of the float datatype. From the shape of the dataset, there are 21525 rows and 12 columns. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By initially looking at the dataset and general information about the data, we can get a picture of the structure of the data, the datatypes, number of rows and columns in the data. Now we proceed to determine the number of missing values and see what significance their absence is absence is for the dataset.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"data_preprocessing\">\n",
    "    <h2>Data preprocessing</h2> \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children               0\n",
       "days_employed       2174\n",
       "dob_years              0\n",
       "education              0\n",
       "education_id           0\n",
       "family_status          0\n",
       "family_status_id       0\n",
       "gender                 0\n",
       "income_type            0\n",
       "debt                   0\n",
       "total_income        2174\n",
       "purpose                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the number of missing values per column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children            0.000000\n",
       "days_employed       0.100999\n",
       "dob_years           0.000000\n",
       "education           0.000000\n",
       "education_id        0.000000\n",
       "family_status       0.000000\n",
       "family_status_id    0.000000\n",
       "gender              0.000000\n",
       "income_type         0.000000\n",
       "debt                0.000000\n",
       "total_income        0.100999\n",
       "purpose             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for N/As\n",
    "df.isnull().sum()/df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to calculate the percentage of missing values\n",
    "def missing_values_table(df):\n",
    "        # Total missing values\n",
    "        mis_val = df.isnull().sum()\n",
    "        \n",
    "        # Percentage of missing values\n",
    "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
    "        \n",
    "        # Make a table with the results\n",
    "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
    "        \n",
    "        # Rename the columns\n",
    "        mis_val_table_ren_columns = mis_val_table.rename(\n",
    "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
    "        \n",
    "        # Sort the table by percentage of missing descending\n",
    "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
    "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
    "        '% of Total Values', ascending=False).round(1)\n",
    "        \n",
    "        # Print some summary information\n",
    "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
    "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
    "              \" columns that have missing values.\")\n",
    "        \n",
    "        # Return the dataframe with missing information\n",
    "        return mis_val_table_ren_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 12 columns.\n",
      "There are 2 columns that have missing values.\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>days_employed</th>\n",
       "      <td>2174</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_income</th>\n",
       "      <td>2174</td>\n",
       "      <td>10.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Missing Values  % of Total Values\n",
       "days_employed            2174               10.1\n",
       "total_income             2174               10.1"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_values_table(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above processing of missing values, we can see that about 10% of data on how long the customer has been working <em>\"days_employed\"</em> and monthly income <em>\"total_income\"</em> is missing. We need to decide whether the missing data is low enough for rows with missing data to be dropped or replacing the missing data with the mean or median is necessary. We proceed to determine whether the data has significant outliers by determining summary statistics for the rows with missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     19351.000000\n",
      "mean      63046.497661\n",
      "std      140827.311974\n",
      "min      -18388.949901\n",
      "25%       -2747.423625\n",
      "50%       -1203.369529\n",
      "75%        -291.095954\n",
      "max      401755.400475\n",
      "Name: days_employed, dtype: float64\n",
      "The mean for days_employed is 63046.50 and median is -1203.37\n",
      "\n",
      "count     19351.000000\n",
      "mean      26787.568355\n",
      "std       16475.450632\n",
      "min        3306.762000\n",
      "25%       16488.504500\n",
      "50%       23202.870000\n",
      "75%       32549.611000\n",
      "max      362496.645000\n",
      "Name: total_income, dtype: float64\n",
      "The mean for total_income is 26787.57 and median is 23202.87\n",
      "\n",
      "count     19351.000000\n",
      "mean      66914.728907\n",
      "std      139030.880527\n",
      "min          24.141633\n",
      "25%         927.009265\n",
      "50%        2194.220567\n",
      "75%        5537.882441\n",
      "max      401755.400475\n",
      "Name: days_employed, dtype: float64\n",
      "Taking the absolute value for days_employed gives a mean of 66914.73 and median of 2194.22\n"
     ]
    }
   ],
   "source": [
    "# use describe() to check for outliers\n",
    "print(df['days_employed'].describe())\n",
    "print('The mean for days_employed is {:.2f} and median is {:.2f}'.format(df['days_employed'].mean(), df['days_employed'].median()))\n",
    "print()\n",
    "print(df['total_income'].describe())\n",
    "print('The mean for total_income is {:.2f} and median is {:.2f}'.format(df['total_income'].mean(), df['total_income'].median()))\n",
    "print()\n",
    "\n",
    "# what if we get absolute value for day_employed, check for outliers  \n",
    "days_employed_absolute = abs(df['days_employed'])\n",
    "print(days_employed_absolute.describe())\n",
    "print('Taking the absolute value for days_employed gives a mean of {:.2f} and median of {:.2f}'.format(days_employed_absolute.mean(), days_employed_absolute.median()))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the `describe()` method, for the column `days_employed`, the mean is 63046 and the median is -1203.36. This means that the dataset contains significant outliers. For the column `total_income`, the mean is 26787 and the median is 23202. We could likely replace missing values in this column with the mean or median. Checking through the missing values, we observe that the missing values exhibit a 'systematic' pattern. For each rows with missing data, the `days_employed` and `total_income` values are all missing. This means the data is \"Missing not at random (MNAR)\". That means those who refused to give information about the days employed will likely not give information about their total income. A quick check on using the absolute values in the `days_employed` column gives a mean of 66914 and a median of 2194. With this result, and high standard deviation, the best option would be to drop those rows with missing value to avoid introducing \"variance\" or \"bias\" into our analysis. Moreover, since the sample size is large enough, we can drop the 10% of the missing data without substantial loss of statistical power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of missing days employed by gender\n",
      "F    1484\n",
      "M     690\n",
      "Name: gender, dtype: int64\n",
      "\n",
      "Count of total income missing by gender\n",
      "F    1484\n",
      "M     690\n",
      "Name: gender, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count of missing data by gender\n",
    "print('Count of missing days employed by gender')\n",
    "print(df[df['days_employed'].isnull()]['gender'].value_counts())\n",
    "\n",
    "print()\n",
    "print('Count of total income missing by gender')\n",
    "print(df[df['total_income'].isnull()]['gender'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The count of missing data by gender shows that the data is missing not at random. i.e. same number of both gender are missing in the `days_employed` and `total_income` columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count of missing days employed by income type\n",
      "employee         1105\n",
      "business          508\n",
      "retiree           413\n",
      "civil servant     147\n",
      "entrepreneur        1\n",
      "Name: income_type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# count of missing data by income type\n",
    "print('Count of missing days employed by income type')\n",
    "print(df[df['days_employed'].isnull()]['income_type'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping rows with missing values\n",
    "credit_score_df = df.dropna().reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your selected dataframe has 12 columns.\n",
      "There are 0 columns that have missing values.\n"
     ]
    },
    {
     "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>Missing Values</th>\n",
       "      <th>% of Total Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Missing Values, % of Total Values]\n",
       "Index: []"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to see if missing data still exist after dropping rows\n",
    "missing_values_table(credit_score_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19351 entries, 0 to 19350\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   children          19351 non-null  int64  \n",
      " 1   days_employed     19351 non-null  float64\n",
      " 2   dob_years         19351 non-null  int64  \n",
      " 3   education         19351 non-null  object \n",
      " 4   education_id      19351 non-null  int64  \n",
      " 5   family_status     19351 non-null  object \n",
      " 6   family_status_id  19351 non-null  int64  \n",
      " 7   gender            19351 non-null  object \n",
      " 8   income_type       19351 non-null  object \n",
      " 9   debt              19351 non-null  int64  \n",
      " 10  total_income      19351 non-null  float64\n",
      " 11  purpose           19351 non-null  object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# check new dataframe info\n",
    "credit_score_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After dropping 'Nans' from the rows, we then proceed to check for unique entry of data in each column. This way, we can know at a glance which columns to fix when processing for duplicates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  0,  3,  2, -1,  4, 20,  5], dtype=int64)"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'children' column\n",
    "credit_score_df['children'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8437.67302776, -4024.80375385, -5623.42261023, ...,\n",
       "       -2113.3468877 , -3112.4817052 , -1984.50758853])"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'day_employed' column\n",
    "credit_score_df['days_employed'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"bachelor's degree\", 'secondary education', 'Secondary Education',\n",
       "       'SECONDARY EDUCATION', \"BACHELOR'S DEGREE\", 'some college',\n",
       "       'primary education', \"Bachelor's Degree\", 'SOME COLLEGE',\n",
       "       'Some College', 'PRIMARY EDUCATION', 'Primary Education',\n",
       "       'Graduate Degree', 'GRADUATE DEGREE', 'graduate degree'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'education' column\n",
    "credit_score_df['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique enrty in the 'education_id' column\n",
    "credit_score_df['education_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['married', 'civil partnership', 'widow / widower', 'divorced',\n",
       "       'unmarried'], dtype=object)"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'family_status' column\n",
    "credit_score_df['family_status'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4], dtype=int64)"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'family_status_id' column\n",
    "credit_score_df['family_status_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['F', 'M', 'XNA'], dtype=object)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'gender' column\n",
    "credit_score_df['gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['employee', 'retiree', 'business', 'civil servant', 'unemployed',\n",
       "       'student', 'entrepreneur', 'paternity / maternity leave'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'income_type' column\n",
    "credit_score_df['income_type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=int64)"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'debt' column\n",
    "credit_score_df['debt'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([40620.102, 17932.802, 23341.752, ..., 14347.61 , 39054.888,\n",
       "       13127.587])"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'total_income' column\n",
    "credit_score_df['total_income'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['purchase of the house', 'car purchase', 'supplementary education',\n",
       "       'to have a wedding', 'housing transactions', 'education',\n",
       "       'having a wedding', 'purchase of the house for my family',\n",
       "       'buy real estate', 'buy commercial real estate',\n",
       "       'buy residential real estate', 'construction of own property',\n",
       "       'property', 'building a property', 'buying a second-hand car',\n",
       "       'buying my own car', 'transactions with commercial real estate',\n",
       "       'housing', 'transactions with my real estate', 'cars',\n",
       "       'to become educated', 'getting an education',\n",
       "       'second-hand car purchase', 'car', 'wedding ceremony',\n",
       "       'to get a supplementary education', 'purchase of my own house',\n",
       "       'real estate transactions', 'getting higher education',\n",
       "       'to own a car', 'purchase of a car', 'profile education',\n",
       "       'university education', 'buying property for renting out',\n",
       "       'to buy a car', 'building a real estate', 'housing renovation',\n",
       "       'going to university'], dtype=object)"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for unique entry in the 'purpose' column\n",
    "credit_score_df['purpose'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the Data Preprocessing section, we computed the number of missing values per column and the percentage of missing values per column. We can see that 10% of rows are missing `days_employed` and `total_income`. Since the type of data with missing values are quantitative, and the data are \"Missing not at random\", we proceed to drop those rows with missing values. We drop missing rows because 10% of missing values would not really skew our analysis. We also checked for uniqueness in our dataset. We found out that:\n",
    "- `children` has a negative value of -1 so we have to deal with it \n",
    "- `days_employed` have negative value of days. We have to take the absolute value for our analysis to be accurate\n",
    "- `education` has duplicate data\n",
    "- `gender` has an 'XNA' entry. Since there is no logical way to decide whether that entry is for a male or female, we would have to drop that row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data type replacement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After dropping rows, we still observe that the `days_employed` and `total_income` are of float datatype. We need to replace data type and convert from 'float' to 'int'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19351 entries, 0 to 19350\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   children          19351 non-null  int64 \n",
      " 1   days_employed     19351 non-null  int32 \n",
      " 2   dob_years         19351 non-null  int64 \n",
      " 3   education         19351 non-null  object\n",
      " 4   education_id      19351 non-null  int64 \n",
      " 5   family_status     19351 non-null  object\n",
      " 6   family_status_id  19351 non-null  int64 \n",
      " 7   gender            19351 non-null  object\n",
      " 8   income_type       19351 non-null  object\n",
      " 9   debt              19351 non-null  int64 \n",
      " 10  total_income      19351 non-null  int32 \n",
      " 11  purpose           19351 non-null  object\n",
      "dtypes: int32(2), int64(5), object(5)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# change float to int in days_employed column\n",
    "credit_score_df['days_employed'] = credit_score_df['days_employed'].astype('int')\n",
    "\n",
    "# change float to int in total_income column\n",
    "credit_score_df['total_income'] = credit_score_df['total_income'].astype('int')\n",
    "\n",
    "credit_score_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are able to change the `days_employed` and `total_income` from floating point value to integer datatype by using the `astype()` method and the `('int')` argument."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Processing duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     12754\n",
       "1      4343\n",
       "2      1851\n",
       "3       294\n",
       "20       67\n",
       "4        34\n",
       "5         8\n",
       "Name: children, dtype: int64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert rows with '-1' values for children to '0'\n",
    "credit_score_df.loc[credit_score_df['children'] == -1, 'children'] = 0\n",
    "\n",
    "# check values after convertion\n",
    "credit_score_df['children'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique gender are: ['F' 'M']\n",
      "\n",
      "New dataframe shape is: (19350, 12)\n"
     ]
    }
   ],
   "source": [
    "# locate index of 'gender' row with 'XNA'\n",
    "i = credit_score_df[(credit_score_df['gender'] == 'XNA')].index\n",
    "\n",
    "# drop row in 'gender' column with 'XNA'\n",
    "credit_score_df = credit_score_df.drop(i)\n",
    "print('Unique gender are:', credit_score_df['gender'].unique())\n",
    "print()\n",
    "print('New dataframe shape is:', credit_score_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      8437\n",
       "1      4024\n",
       "2      5623\n",
       "3      4124\n",
       "4    340266\n",
       "5       926\n",
       "6      2879\n",
       "7       152\n",
       "8      6929\n",
       "9      2188\n",
       "Name: days_employed, dtype: int32"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert negative 'days_employed' to positive values\n",
    "credit_score_df['days_employed'] = abs(credit_score_df['days_employed'])\n",
    "credit_score_df['days_employed'].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "secondary education    12342\n",
       "bachelor's degree       4222\n",
       "SECONDARY EDUCATION      705\n",
       "Secondary Education      646\n",
       "some college             612\n",
       "BACHELOR'S DEGREE        251\n",
       "Bachelor's Degree        243\n",
       "primary education        231\n",
       "Some College              40\n",
       "SOME COLLEGE              22\n",
       "PRIMARY EDUCATION         16\n",
       "Primary Education         14\n",
       "graduate degree            4\n",
       "Graduate Degree            1\n",
       "GRADUATE DEGREE            1\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for duplicates\n",
    "credit_score_df['education'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "secondary education    13693\n",
       "bachelor's degree       4716\n",
       "some college             674\n",
       "primary education        261\n",
       "graduate degree            6\n",
       "Name: education, dtype: int64"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# processing duplicates in 'education' column\n",
    "credit_score_df['education'] = credit_score_df['education'].str.lower()\n",
    "credit_score_df['education'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"bachelor's degree\", 'secondary education', 'some college',\n",
       "       'primary education', 'graduate degree'], dtype=object)"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check updated table\n",
    "credit_score_df['education'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "wedding                                     721\n",
       "to have a wedding                           693\n",
       "having a wedding                            685\n",
       "real estate transactions                    615\n",
       "buy commercial real estate                  597\n",
       "purchase of the house                       595\n",
       "buying property for renting out             588\n",
       "housing                                     587\n",
       "transactions with commercial real estate    581\n",
       "building a real estate                      580\n",
       "housing transactions                        579\n",
       "purchase of my own house                    574\n",
       "property                                    572\n",
       "purchase of the house for my family         570\n",
       "building a property                         561\n",
       "construction of own property                560\n",
       "transactions with my real estate            559\n",
       "buy real estate                             551\n",
       "buy residential real estate                 546\n",
       "housing renovation                          542\n",
       "car                                         454\n",
       "buying my own car                           452\n",
       "to buy a car                                442\n",
       "going to university                         440\n",
       "buying a second-hand car                    437\n",
       "second-hand car purchase                    435\n",
       "to own a car                                428\n",
       "cars                                        421\n",
       "car purchase                                419\n",
       "supplementary education                     414\n",
       "university education                        413\n",
       "purchase of a car                           409\n",
       "education                                   405\n",
       "to get a supplementary education            396\n",
       "getting an education                        393\n",
       "getting higher education                    390\n",
       "profile education                           389\n",
       "to become educated                          357\n",
       "Name: purpose, dtype: int64"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# processing duplicates in 'purpose' column\n",
    "credit_score_df['purpose'] = credit_score_df['purpose'].str.lower()\n",
    "credit_score_df['purpose'] = credit_score_df['purpose'].replace(['wedding ceremony'], 'wedding')\n",
    "credit_score_df['purpose'].value_counts().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'a': 4631, 'real': 4029, 'estate': 4029, 'car': 3897, 'purchase': 3002, 'education': 2800, 'to': 2756, 'of': 2708, 'transaction': 2334, 'property': 2281, 'my': 2155, 'buy': 2136, 'wedding': 2099, 'own': 2014, 'house': 1739, 'housing': 1708, 'buying': 1477, 'commercial': 1178, 'the': 1165, 'for': 1158, 'building': 1141, 'with': 1140, 'second-hand': 872, 'university': 853, 'supplementary': 810, 'getting': 783, 'have': 693, 'having': 685, 'renting': 588, 'out': 588, 'family': 570, 'construction': 560, 'residential': 546, 'renovation': 542, 'going': 440, 'get': 396, 'an': 393, 'higher': 390, 'profile': 389, 'become': 357, 'educated': 357})\n"
     ]
    }
   ],
   "source": [
    "# checking frequency of words in 'purpose' column\n",
    "allwords = ' '.join(map(str, credit_score_df['purpose']))\n",
    "allwords = allwords.lower()\n",
    "\n",
    "wordnet_lemma = WordNetLemmatizer()\n",
    "\n",
    "# change allwords to a list or words\n",
    "allwords = nltk.word_tokenize(allwords)\n",
    "\n",
    "# find and save the lemma of each word\n",
    "lemmas = [wordnet_lemma.lemmatize(w, pos = 'n') for w in allwords]\n",
    "\n",
    "# print lemmas\n",
    "#print(lemmas)\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "# print the frequency of each lemma\n",
    "print(Counter(lemmas))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have observed, the duplicate data in the `education` column may have occured when more than one analysts is involved in data collection and entry. One analyst prefers to write item names using **ALL CAPS**, another uses maybe **Lower case**. We have been able to process the duplicates in the `education` column by using `str.lower()` method in pandas. We also carried out lemmatization on the `purpose` column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorizing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recategorize words in 'purpose'\n",
    "credit_score_df['purpose'] = credit_score_df['purpose'].replace(\n",
    "    ['supplementary education', 'university education', 'education', 'to get a supplementary education', 'getting an education', \n",
    "     'getting higher education', 'profile education', 'to become educated', 'going to university'], 'education'\n",
    ")\n",
    "\n",
    "credit_score_df['purpose'] = credit_score_df['purpose'].replace(\n",
    "    ['wedding', 'to have a wedding', 'having a wedding'], 'wedding'\n",
    ")\n",
    "\n",
    "credit_score_df['purpose'] = credit_score_df['purpose'].replace(\n",
    "    ['real estate transactions', 'buy commercial real estate', 'building a real estate', 'transactions with commercial real estate', \n",
    "     'transactions with my real estate', 'buy real estate', 'buy residential real estate'], 'real estate'\n",
    ")\n",
    "\n",
    "credit_score_df['purpose'] = credit_score_df['purpose'].replace(\n",
    "    ['buying property for renting out', 'construction of own property', 'property', 'building a property'], 'property'\n",
    ")\n",
    "\n",
    "credit_score_df['purpose'] = credit_score_df['purpose'].replace(\n",
    "    ['purchase of the house', 'housing', 'housing transactions', 'purchase of my own house', \n",
    "     'purchase of the house for my family', 'housing renovation'], 'house purchase'\n",
    ")\n",
    "\n",
    "credit_score_df['purpose'] = credit_score_df['purpose'].replace(\n",
    "    ['car', 'buying my own car', 'to buy a car', 'buying a second-hand car', 'second-hand car purchase', 'to own a car', \n",
    "     'cars', 'car purchase', 'purchase of a car'], 'car purchase'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   children  days_employed  dob_years            education  education_id  \\\n",
      "0         1           8437         42    bachelor's degree             0   \n",
      "1         1           4024         36  secondary education             1   \n",
      "2         0           5623         33  secondary education             1   \n",
      "3         3           4124         32  secondary education             1   \n",
      "4         0         340266         53  secondary education             1   \n",
      "5         0            926         27    bachelor's degree             0   \n",
      "6         0           2879         43    bachelor's degree             0   \n",
      "7         0            152         50  secondary education             1   \n",
      "8         2           6929         35    bachelor's degree             0   \n",
      "9         0           2188         41  secondary education             1   \n",
      "\n",
      "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
      "0            married                 0      F    employee     0         40620   \n",
      "1            married                 0      F    employee     0         17932   \n",
      "2            married                 0      M    employee     0         23341   \n",
      "3            married                 0      M    employee     0         42820   \n",
      "4  civil partnership                 1      F     retiree     0         25378   \n",
      "5  civil partnership                 1      M    business     0         40922   \n",
      "6            married                 0      F    business     0         38484   \n",
      "7            married                 0      M    employee     0         21731   \n",
      "8  civil partnership                 1      F    employee     0         15337   \n",
      "9            married                 0      M    employee     0         23108   \n",
      "\n",
      "          purpose  \n",
      "0  house purchase  \n",
      "1    car purchase  \n",
      "2  house purchase  \n",
      "3       education  \n",
      "4         wedding  \n",
      "5  house purchase  \n",
      "6  house purchase  \n",
      "7       education  \n",
      "8         wedding  \n",
      "9  house purchase  \n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 19350 entries, 0 to 19350\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   children          19350 non-null  int64 \n",
      " 1   days_employed     19350 non-null  int32 \n",
      " 2   dob_years         19350 non-null  int64 \n",
      " 3   education         19350 non-null  object\n",
      " 4   education_id      19350 non-null  int64 \n",
      " 5   family_status     19350 non-null  object\n",
      " 6   family_status_id  19350 non-null  int64 \n",
      " 7   gender            19350 non-null  object\n",
      " 8   income_type       19350 non-null  object\n",
      " 9   debt              19350 non-null  int64 \n",
      " 10  total_income      19350 non-null  int32 \n",
      " 11  purpose           19350 non-null  object\n",
      "dtypes: int32(2), int64(5), object(5)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# check info \n",
    "print(credit_score_df.head(10))\n",
    "print()\n",
    "credit_score_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now try to encode categorical data to numeric quantities by simply replacing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "secondary education    13693\n",
      "bachelor's degree       4716\n",
      "some college             674\n",
      "primary education        261\n",
      "graduate degree            6\n",
      "Name: education, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(credit_score_df['education'].value_counts())   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we create a copy of the dataframe to use for encoding\n",
    "credit_score_df_encode = credit_score_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'education': {'graduate degree': 1, \"bachelor's degree\": 2, 'some college': 3, 'secondary education': 4, 'primary education': 5}}\n"
     ]
    }
   ],
   "source": [
    "# encoding 'education' column\n",
    "education_encode = {'education':{'graduate degree': 1, \"bachelor's degree\": 2, 'some college': 3,\n",
    "                            'secondary education': 4, 'primary education': 5}}\n",
    "print(education_encode)\n",
    "credit_score_df_encode.replace(education_encode, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'family_status': {'civil partnership': 1, 'divorced': 2, 'married': 3, 'unmarried': 4, 'widow / widower': 5}}\n"
     ]
    }
   ],
   "source": [
    "# encoding 'family_status' column\n",
    "labels = credit_score_df_encode['family_status'].astype('category').cat.categories.tolist()\n",
    "family_status_encode = {'family_status' : {k: v for k,v in zip(labels, list(range(1, len(labels)+1)))}}\n",
    "\n",
    "print(family_status_encode)\n",
    "credit_score_df_encode.replace(family_status_encode, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'income_type': {'business': 1, 'civil servant': 2, 'employee': 3, 'entrepreneur': 4, 'paternity / maternity leave': 5, 'retiree': 6, 'student': 7, 'unemployed': 8}}\n"
     ]
    }
   ],
   "source": [
    "# encoding 'income_type' column\n",
    "labels = credit_score_df_encode['income_type'].astype('category').cat.categories.tolist()\n",
    "income_type_encode = {'income_type' : {k: v for k,v in zip(labels, list(range(1, len(labels)+1)))}}\n",
    "\n",
    "print(income_type_encode)\n",
    "credit_score_df_encode.replace(income_type_encode, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'purpose': {'car purchase': 1, 'education': 2, 'house purchase': 3, 'property': 4, 'real estate': 5, 'wedding': 6}}\n"
     ]
    }
   ],
   "source": [
    "# encoding 'purpose' column\n",
    "labels = credit_score_df_encode['purpose'].astype('category').cat.categories.tolist()\n",
    "purpose_encode = {'purpose' : {k: v for k,v in zip(labels, list(range(1, len(labels)+1)))}}\n",
    "\n",
    "print(purpose_encode)\n",
    "credit_score_df_encode.replace(purpose_encode, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'gender': {'F': 1, 'M': 2}}\n"
     ]
    }
   ],
   "source": [
    "# encoding 'gender' column\n",
    "labels = credit_score_df_encode['gender'].astype('category').cat.categories.tolist()\n",
    "gender_encode = {'gender' : {k: v for k,v in zip(labels, list(range(1, len(labels)+1)))}}\n",
    "\n",
    "print(gender_encode)\n",
    "credit_score_df_encode.replace(gender_encode, inplace =True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>17932</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>23341</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>42820</td>\n",
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       "      <th>4</th>\n",
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>25378</td>\n",
       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>926</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>40922</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>2879</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>38484</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>50</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>21731</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>6929</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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       "      <td>15337</td>\n",
       "      <td>6</td>\n",
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       "      <th>9</th>\n",
       "      <td>0</td>\n",
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       "      <td>41</td>\n",
       "      <td>4</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
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       "      <td>23108</td>\n",
       "      <td>3</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years  education  education_id  family_status  \\\n",
       "0         1           8437         42          2             0              3   \n",
       "1         1           4024         36          4             1              3   \n",
       "2         0           5623         33          4             1              3   \n",
       "3         3           4124         32          4             1              3   \n",
       "4         0         340266         53          4             1              1   \n",
       "5         0            926         27          2             0              1   \n",
       "6         0           2879         43          2             0              3   \n",
       "7         0            152         50          4             1              3   \n",
       "8         2           6929         35          2             0              1   \n",
       "9         0           2188         41          4             1              3   \n",
       "\n",
       "   family_status_id  gender  income_type  debt  total_income  purpose  \n",
       "0                 0       1            3     0         40620        3  \n",
       "1                 0       1            3     0         17932        1  \n",
       "2                 0       2            3     0         23341        3  \n",
       "3                 0       2            3     0         42820        2  \n",
       "4                 1       1            6     0         25378        6  \n",
       "5                 1       2            1     0         40922        3  \n",
       "6                 0       1            1     0         38484        3  \n",
       "7                 0       2            3     0         21731        2  \n",
       "8                 1       1            3     0         15337        6  \n",
       "9                 0       2            3     0         23108        3  "
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show all categories with encoding\n",
    "credit_score_df_encode.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have been able to categorize the data after lemmatization. We also encode categorical variables. This will be useful for future analysis. From the categorizing step, we observe that categorizing with encoding changes the categorical variable to quantitative variables. This is a useful step if we plan to do feature engineering for Machine Learning application. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"answer_questions\">\n",
    "    <h2>Answer these questions</h2> \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Is there a relation between having kids and repaying a loan on time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do an initial Exploratory data analysis to see if we can visualize a relation between having kids and repaying a loan on time. We start by plotting a correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "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": [
    "# correlation matrix \n",
    "from IPython.core.pylabtools import figsize\n",
    "figsize(10, 8)\n",
    "\n",
    "corrMatrix = credit_score_df_encode.corr()\n",
    "sns.heatmap(corrMatrix, annot=True)\n",
    "plt.title('Correlation Matrix Plot')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation between having kids and on time loan repayment')"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# having kids as a potential predictor of repaying a loan on time\n",
    "figsize(8, 6)\n",
    "sns.regplot(x='children', y='debt', data=credit_score_df_encode)\n",
    "plt.ylim(0,)\n",
    "plt.title('Correlation between having kids and on time loan repayment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we start to analyze the data by creating a correlation matrix. Our correlation matrix does show that the linear relationship isn't extremely strong (~0.021) across the dataset. Ofcourse, we need to understand that **correlation does not imply causation** thus we need to look for another way to extract insight from the data. One such way is by using a pivot table and plotting the characteristics of the table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <th>family_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>civil partnership</th>\n",
       "      <td>202</td>\n",
       "      <td>2468</td>\n",
       "      <td>0.081848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>49</td>\n",
       "      <td>717</td>\n",
       "      <td>0.068340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>462</td>\n",
       "      <td>6768</td>\n",
       "      <td>0.068262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>192</td>\n",
       "      <td>2038</td>\n",
       "      <td>0.094210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>48</td>\n",
       "      <td>762</td>\n",
       "      <td>0.062992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>civil partnership</th>\n",
       "      <td>102</td>\n",
       "      <td>897</td>\n",
       "      <td>0.113712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>19</td>\n",
       "      <td>280</td>\n",
       "      <td>0.067857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>230</td>\n",
       "      <td>2683</td>\n",
       "      <td>0.085725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>51</td>\n",
       "      <td>407</td>\n",
       "      <td>0.125307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>6</td>\n",
       "      <td>76</td>\n",
       "      <td>0.078947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2</th>\n",
       "      <th>civil partnership</th>\n",
       "      <td>26</td>\n",
       "      <td>305</td>\n",
       "      <td>0.085246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>6</td>\n",
       "      <td>74</td>\n",
       "      <td>0.081081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>135</td>\n",
       "      <td>1389</td>\n",
       "      <td>0.097192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>8</td>\n",
       "      <td>66</td>\n",
       "      <td>0.121212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>0.117647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">3</th>\n",
       "      <th>civil partnership</th>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>14</td>\n",
       "      <td>226</td>\n",
       "      <td>0.061947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">4</th>\n",
       "      <th>civil partnership</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>civil partnership</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20</th>\n",
       "      <th>civil partnership</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>3</td>\n",
       "      <td>46</td>\n",
       "      <td>0.065217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            sum   len      mean\n",
       "                           debt  debt      debt\n",
       "children family_status                         \n",
       "0        civil partnership  202  2468  0.081848\n",
       "         divorced            49   717  0.068340\n",
       "         married            462  6768  0.068262\n",
       "         unmarried          192  2038  0.094210\n",
       "         widow / widower     48   762  0.062992\n",
       "1        civil partnership  102   897  0.113712\n",
       "         divorced            19   280  0.067857\n",
       "         married            230  2683  0.085725\n",
       "         unmarried           51   407  0.125307\n",
       "         widow / widower      6    76  0.078947\n",
       "2        civil partnership   26   305  0.085246\n",
       "         divorced             6    74  0.081081\n",
       "         married            135  1389  0.097192\n",
       "         unmarried            8    66  0.121212\n",
       "         widow / widower      2    17  0.117647\n",
       "3        civil partnership    6    45  0.133333\n",
       "         divorced             1    10  0.100000\n",
       "         married             14   226  0.061947\n",
       "         unmarried            1     7  0.142857\n",
       "         widow / widower      0     6  0.000000\n",
       "4        civil partnership    0     7  0.000000\n",
       "         divorced             0     1  0.000000\n",
       "         married              2    24  0.083333\n",
       "         unmarried            1     1  1.000000\n",
       "         widow / widower      0     1  0.000000\n",
       "5        civil partnership    0     1  0.000000\n",
       "         married              0     7  0.000000\n",
       "20       civil partnership    3    11  0.272727\n",
       "         divorced             1     1  1.000000\n",
       "         married              3    46  0.065217\n",
       "         unmarried            1     6  0.166667\n",
       "         widow / widower      0     3  0.000000"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate pivot table\n",
    "loan_table = pd.pivot_table(credit_score_df, index = ['children', 'family_status'],\n",
    "                            values = 'debt',\n",
    "                            aggfunc = ['sum', len, 'mean']\n",
    "                           )\n",
    "loan_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot of debt vs number of children')"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of debt vs number of children\n",
    "sns.set()\n",
    "pd.pivot_table(credit_score_df, index = 'children', values = 'debt').plot(kind= 'bar')\n",
    "plt.ylabel('debt')\n",
    "plt.title('Plot of debt vs number of children')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, we can see that people with more kids tends to have more debt. Statistically speaking, people with more than 5 kids and up to 20 kids are ~37% more likely to be in debt than people with no kid. This is a reasonable figure when you consider the fact that having more kid means you end up with more responsibility. We can continue to dissect the data to generate more insight by looking at `debt` vs `number of children` with `family status` consideration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot of debt vs number of children with family status consideration')"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of debt vs number of children with family status consideration\n",
    "sns.set()\n",
    "pd.pivot_table(credit_score_df, index = 'children', columns= 'family_status', values = 'debt').plot(kind= 'bar')\n",
    "plt.ylabel('debt')\n",
    "plt.title('Plot of debt vs number of children with family status consideration')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, we can observe that unmarried people with up to 4 kids and divorced people with up to 20 kids are ~75% more likely to be in debt than any other family status, and about 80% more likely to be in debt than people with 3 or less number of children."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <th>family_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>civil partnership</th>\n",
       "      <td>202</td>\n",
       "      <td>2468</td>\n",
       "      <td>0.081848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>49</td>\n",
       "      <td>717</td>\n",
       "      <td>0.068340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>462</td>\n",
       "      <td>6768</td>\n",
       "      <td>0.068262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>192</td>\n",
       "      <td>2038</td>\n",
       "      <td>0.094210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>48</td>\n",
       "      <td>762</td>\n",
       "      <td>0.062992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            sum   len      mean\n",
       "                           debt  debt      debt\n",
       "children family_status                         \n",
       "0        civil partnership  202  2468  0.081848\n",
       "         divorced            49   717  0.068340\n",
       "         married            462  6768  0.068262\n",
       "         unmarried          192  2038  0.094210\n",
       "         widow / widower     48   762  0.062992"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query table to extract more insight\n",
    "loan_table.query('children == 0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>children</th>\n",
       "      <th>family_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">20</th>\n",
       "      <th>civil partnership</th>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>3</td>\n",
       "      <td>46</td>\n",
       "      <td>0.065217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            sum  len      mean\n",
       "                           debt debt      debt\n",
       "children family_status                        \n",
       "20       civil partnership    3   11  0.272727\n",
       "         divorced             1    1  1.000000\n",
       "         married              3   46  0.065217\n",
       "         unmarried            1    6  0.166667\n",
       "         widow / widower      0    3  0.000000"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# query table to extract more insight\n",
    "loan_table.query('children == 20')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From our result above, the data tells us that people with more than 5 kids in general tend to be in more debt than people with less kids. People with more than 5 kids and up to 20 kids are ~37% more likely to be in debt than people with no kid thus, there is a relationship between having kids and repaying a loan on time. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Is there a relation between marital status and repaying a loan on time?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by looking at the correlation matrix. We see a weak correlation between `family_status` and repaying a `loan` on time across the data. We can slice the data by using pivot table to generate more insight."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation between Marital status and on time loan repayment')"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# marital status as a potential predictor of repaying a loan on time\n",
    "figsize(8, 6)\n",
    "sns.regplot(x='family_status', y='debt', data=credit_score_df_encode)\n",
    "plt.ylim(0,)\n",
    "plt.title('Correlation between Marital status and on time loan repayment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>civil partnership</th>\n",
       "      <td>339</td>\n",
       "      <td>3734</td>\n",
       "      <td>0.090787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>divorced</th>\n",
       "      <td>76</td>\n",
       "      <td>1083</td>\n",
       "      <td>0.070175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>846</td>\n",
       "      <td>11143</td>\n",
       "      <td>0.075922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unmarried</th>\n",
       "      <td>254</td>\n",
       "      <td>2525</td>\n",
       "      <td>0.100594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>widow / widower</th>\n",
       "      <td>56</td>\n",
       "      <td>865</td>\n",
       "      <td>0.064740</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   sum    len      mean\n",
       "                  debt   debt      debt\n",
       "family_status                          \n",
       "civil partnership  339   3734  0.090787\n",
       "divorced            76   1083  0.070175\n",
       "married            846  11143  0.075922\n",
       "unmarried          254   2525  0.100594\n",
       "widow / widower     56    865  0.064740"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate pivot table\n",
    "pd.pivot_table(credit_score_df, index = ['family_status'],\n",
    "                            values = 'debt',\n",
    "                            aggfunc = ['sum', len, 'mean']\n",
    "                           )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot of debt vs family status')"
      ]
     },
     "execution_count": 273,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of family status vs debt\n",
    "sns.set()\n",
    "pd.pivot_table(credit_score_df, index = 'family_status', values = 'debt').plot(kind= 'bar')\n",
    "plt.ylabel('debt')\n",
    "plt.title('Plot of debt vs family status')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, unmarried people are more than 2% likely to be in debt than married people. Widow/widower are least likely to be in debt than any of the other groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>family_status</th>\n",
       "      <th>education</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">civil partnership</th>\n",
       "      <th>bachelor's degree</th>\n",
       "      <td>854</td>\n",
       "      <td>0.055035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary education</th>\n",
       "      <td>50</td>\n",
       "      <td>0.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary education</th>\n",
       "      <td>2679</td>\n",
       "      <td>0.100411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some college</th>\n",
       "      <td>151</td>\n",
       "      <td>0.092715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">divorced</th>\n",
       "      <th>bachelor's degree</th>\n",
       "      <td>292</td>\n",
       "      <td>0.047945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>graduate degree</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary education</th>\n",
       "      <td>20</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary education</th>\n",
       "      <td>744</td>\n",
       "      <td>0.077957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some college</th>\n",
       "      <td>26</td>\n",
       "      <td>0.076923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">married</th>\n",
       "      <th>bachelor's degree</th>\n",
       "      <td>2740</td>\n",
       "      <td>0.051825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>graduate degree</th>\n",
       "      <td>4</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary education</th>\n",
       "      <td>131</td>\n",
       "      <td>0.106870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary education</th>\n",
       "      <td>7957</td>\n",
       "      <td>0.083072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some college</th>\n",
       "      <td>311</td>\n",
       "      <td>0.093248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">unmarried</th>\n",
       "      <th>bachelor's degree</th>\n",
       "      <td>706</td>\n",
       "      <td>0.060907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>graduate degree</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary education</th>\n",
       "      <td>32</td>\n",
       "      <td>0.093750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary education</th>\n",
       "      <td>1613</td>\n",
       "      <td>0.117793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some college</th>\n",
       "      <td>173</td>\n",
       "      <td>0.104046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">widow / widower</th>\n",
       "      <th>bachelor's degree</th>\n",
       "      <td>124</td>\n",
       "      <td>0.048387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>primary education</th>\n",
       "      <td>28</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secondary education</th>\n",
       "      <td>700</td>\n",
       "      <td>0.068571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some college</th>\n",
       "      <td>13</td>\n",
       "      <td>0.153846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        len      mean\n",
       "                                       debt      debt\n",
       "family_status     education                          \n",
       "civil partnership bachelor's degree     854  0.055035\n",
       "                  primary education      50  0.180000\n",
       "                  secondary education  2679  0.100411\n",
       "                  some college          151  0.092715\n",
       "divorced          bachelor's degree     292  0.047945\n",
       "                  graduate degree         1  0.000000\n",
       "                  primary education      20  0.100000\n",
       "                  secondary education   744  0.077957\n",
       "                  some college           26  0.076923\n",
       "married           bachelor's degree    2740  0.051825\n",
       "                  graduate degree         4  0.000000\n",
       "                  primary education     131  0.106870\n",
       "                  secondary education  7957  0.083072\n",
       "                  some college          311  0.093248\n",
       "unmarried         bachelor's degree     706  0.060907\n",
       "                  graduate degree         1  0.000000\n",
       "                  primary education      32  0.093750\n",
       "                  secondary education  1613  0.117793\n",
       "                  some college          173  0.104046\n",
       "widow / widower   bachelor's degree     124  0.048387\n",
       "                  primary education      28  0.000000\n",
       "                  secondary education   700  0.068571\n",
       "                  some college           13  0.153846"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table with family_status, debt and education\n",
    "loan_table1 = pd.pivot_table(credit_score_df, \n",
    "                             index = ['family_status', 'education'], \n",
    "                             values= 'debt', \n",
    "                             aggfunc = [len, 'mean']\n",
    "                            )\n",
    "loan_table1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot of debt vs family status with education level consideration')"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of debt vs family status with education level consideration\n",
    "sns.set()\n",
    "pd.pivot_table(credit_score_df, index = 'family_status', columns= 'education', values = 'debt').plot(kind= 'bar')\n",
    "plt.ylabel('debt')\n",
    "plt.title('Plot of debt vs family status with education level consideration')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that when we consider family status across education levels, the data tells us that people with graduate degrees are less likely to be in debt than any other group. Widow/widowers with some college degree are more likely to be in debt than any other level of family status with same degree level. Civil partnership with primary education are more than likely to be in debt than any other level of family status with same degree level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, unmarried people are more than 2% likely to be in debt than married people. Widow/widower are least likely to be in debt than any of the other groups. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Is there a relation between income level and repaying a loan on time?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation between total income and on time loan repayment')"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total income as a potential predictor of repaying a loan on time\n",
    "figsize(8, 6)\n",
    "sns.regplot(x='total_income', y='debt', data=credit_score_df_encode)\n",
    "plt.ylim(0,)\n",
    "plt.title('Correlation between total income and on time loan repayment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>debt</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_income</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>0.0</td>\n",
       "      <td>359219.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>0.0</td>\n",
       "      <td>369708.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3418</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3642.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3471</th>\n",
       "      <td>0.0</td>\n",
       "      <td>359726.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3503</th>\n",
       "      <td>0.0</td>\n",
       "      <td>346602.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273809</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5734.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274402</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4719.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276204</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5248.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352136</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2577.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362496</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1477.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15388 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              children  days_employed  debt  dob_years  education_id  \\\n",
       "total_income                                                           \n",
       "3306               0.0       359219.0   1.0       57.0           1.0   \n",
       "3392               0.0       369708.0   0.0       37.0           1.0   \n",
       "3418               1.0         3642.0   0.0       52.0           1.0   \n",
       "3471               0.0       359726.0   0.0       68.0           1.0   \n",
       "3503               0.0       346602.0   0.0       61.0           1.0   \n",
       "...                ...            ...   ...        ...           ...   \n",
       "273809             0.0         5734.0   0.0       42.0           0.0   \n",
       "274402             0.0         4719.0   0.0       61.0           1.0   \n",
       "276204             1.0         5248.0   0.0       35.0           1.0   \n",
       "352136             1.0         2577.0   1.0       39.0           0.0   \n",
       "362496             0.0         1477.0   0.0       44.0           0.0   \n",
       "\n",
       "              family_status_id  \n",
       "total_income                    \n",
       "3306                       0.0  \n",
       "3392                       1.0  \n",
       "3418                       0.0  \n",
       "3471                       1.0  \n",
       "3503                       0.0  \n",
       "...                        ...  \n",
       "273809                     1.0  \n",
       "274402                     4.0  \n",
       "276204                     1.0  \n",
       "352136                     0.0  \n",
       "362496                     0.0  \n",
       "\n",
       "[15388 rows x 6 columns]"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate pivot table\n",
    "pd.pivot_table(credit_score_df, index = ['total_income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot of total income vs debt')"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of total income vs debt\n",
    "sns.set()\n",
    "pd.pivot_table(credit_score_df, index = 'debt', values = 'total_income').plot(kind= 'bar')\n",
    "plt.ylabel('total_income')\n",
    "plt.title('Plot of total income vs debt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_income</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3306</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3392</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3418</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3471</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3503</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273809</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274402</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276204</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352136</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362496</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15388 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             mean  min  max\n",
       "             debt debt debt\n",
       "total_income               \n",
       "3306          1.0    1    1\n",
       "3392          0.0    0    0\n",
       "3418          0.0    0    0\n",
       "3471          0.0    0    0\n",
       "3503          0.0    0    0\n",
       "...           ...  ...  ...\n",
       "273809        0.0    0    0\n",
       "274402        0.0    0    0\n",
       "276204        0.0    0    0\n",
       "352136        1.0    1    1\n",
       "362496        0.0    0    0\n",
       "\n",
       "[15388 rows x 3 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table with total_income vs debt\n",
    "loan_table2 = pd.pivot_table(credit_score_df, \n",
    "                             index = 'total_income', \n",
    "                             values= 'debt', \n",
    "                             aggfunc = ['mean', min, max]\n",
    "                            )\n",
    "loan_table2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate scatter plot \n",
    "ax = sns.scatterplot(x='total_income', y='debt', data=credit_score_df )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the correlation matrix, we have a weak negative correlation between `total_income` and `debt`. Looking further at the pivot table, we cannot derive any relationship between `total_income` and `debt`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- How do different loan purposes affect on-time repayment of the loan?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Correlation between loan purpose and on time loan repayment')"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# different loan purposes as a potential predictor of repaying a loan on time\n",
    "figsize(8, 6)\n",
    "sns.regplot(x='purpose', y='debt', data=credit_score_df_encode)\n",
    "plt.ylim(0,)\n",
    "plt.title('Correlation between loan purpose and on time loan repayment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "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>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>debt</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>total_income</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>car purchase</th>\n",
       "      <td>0.547344</td>\n",
       "      <td>68927.162176</td>\n",
       "      <td>0.094175</td>\n",
       "      <td>43.465230</td>\n",
       "      <td>0.821914</td>\n",
       "      <td>0.977675</td>\n",
       "      <td>26771.863485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>0.542118</td>\n",
       "      <td>68308.709480</td>\n",
       "      <td>0.092021</td>\n",
       "      <td>43.355852</td>\n",
       "      <td>0.833194</td>\n",
       "      <td>0.961079</td>\n",
       "      <td>26310.595218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>house purchase</th>\n",
       "      <td>0.543661</td>\n",
       "      <td>64517.912388</td>\n",
       "      <td>0.068755</td>\n",
       "      <td>43.057441</td>\n",
       "      <td>0.808239</td>\n",
       "      <td>0.977372</td>\n",
       "      <td>27282.716565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property</th>\n",
       "      <td>0.551512</td>\n",
       "      <td>64837.736081</td>\n",
       "      <td>0.075844</td>\n",
       "      <td>42.933363</td>\n",
       "      <td>0.814993</td>\n",
       "      <td>0.951776</td>\n",
       "      <td>26645.213941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>real estate</th>\n",
       "      <td>0.522959</td>\n",
       "      <td>66961.589724</td>\n",
       "      <td>0.075701</td>\n",
       "      <td>43.372797</td>\n",
       "      <td>0.822288</td>\n",
       "      <td>0.969720</td>\n",
       "      <td>27018.844378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wedding</th>\n",
       "      <td>0.534064</td>\n",
       "      <td>66918.987613</td>\n",
       "      <td>0.075274</td>\n",
       "      <td>43.151977</td>\n",
       "      <td>0.805145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>26524.367794</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                children  days_employed      debt  dob_years  education_id  \\\n",
       "purpose                                                                      \n",
       "car purchase    0.547344   68927.162176  0.094175  43.465230      0.821914   \n",
       "education       0.542118   68308.709480  0.092021  43.355852      0.833194   \n",
       "house purchase  0.543661   64517.912388  0.068755  43.057441      0.808239   \n",
       "property        0.551512   64837.736081  0.075844  42.933363      0.814993   \n",
       "real estate     0.522959   66961.589724  0.075701  43.372797      0.822288   \n",
       "wedding         0.534064   66918.987613  0.075274  43.151977      0.805145   \n",
       "\n",
       "                family_status_id  total_income  \n",
       "purpose                                         \n",
       "car purchase            0.977675  26771.863485  \n",
       "education               0.961079  26310.595218  \n",
       "house purchase          0.977372  27282.716565  \n",
       "property                0.951776  26645.213941  \n",
       "real estate             0.969720  27018.844378  \n",
       "wedding                 1.000000  26524.367794  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table\n",
    "pd.pivot_table(credit_score_df, index = ['purpose'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "      <th>len</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "      <th>debt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>car purchase</th>\n",
       "      <td>367</td>\n",
       "      <td>3897</td>\n",
       "      <td>0.094175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education</th>\n",
       "      <td>331</td>\n",
       "      <td>3597</td>\n",
       "      <td>0.092021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>house purchase</th>\n",
       "      <td>237</td>\n",
       "      <td>3447</td>\n",
       "      <td>0.068755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property</th>\n",
       "      <td>173</td>\n",
       "      <td>2281</td>\n",
       "      <td>0.075844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>real estate</th>\n",
       "      <td>305</td>\n",
       "      <td>4029</td>\n",
       "      <td>0.075701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wedding</th>\n",
       "      <td>158</td>\n",
       "      <td>2099</td>\n",
       "      <td>0.075274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sum   len      mean\n",
       "               debt  debt      debt\n",
       "purpose                            \n",
       "car purchase    367  3897  0.094175\n",
       "education       331  3597  0.092021\n",
       "house purchase  237  3447  0.068755\n",
       "property        173  2281  0.075844\n",
       "real estate     305  4029  0.075701\n",
       "wedding         158  2099  0.075274"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pivot table with purpose vs debt\n",
    "loan_table3 = pd.pivot_table(credit_score_df, \n",
    "                             index = ['purpose'], \n",
    "                             values= 'debt', \n",
    "                             aggfunc = ['sum', len, 'mean']\n",
    "                            )\n",
    "loan_table3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plot of debt vs purpose')"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bzeZYNgyj0PK1tg8fPlzjx49XixYttGbNGj3//PNasWJFoa+/lhMnsmS3Gzf8Htz1f2Ra2mmzIxTCON18fn5lLf3+ihJj5RzGyTnuOE4eHrar7ty67HB9lSpVlJaW5lhOS0uTv7//VbcfP35c/v7+Sk9P188//6wWLVpIkoKDg5WWlqaMjAxXRQUAwJJcVvKNGjVSQkKC0tPTlZ2drbVr1yooKMix/Y477pC3t7d27dolSYqLi1NQUJDKly8vb29v7dy5U5K0a9culSlTRhUqVHBVVAAALMllh+srV66sIUOGKDIyUnl5eYqIiFDt2rXVt29fvfDCC3rooYcUHR2t0aNHKysrS7Vq1VJkZKRsNptmzZqlqKgo5eTkqEyZMpo5c6arYgIAYFkuK3lJCg0NVWhoaKF1c+bMcfw7ICBAS5YsuexxtWvX1qeffurKaAAAWB53vAMAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALMqlN8MBgOtR9m+l5ONddL+eimISpZxz+Tp9KrsI0hQddxwnyT3H6lZFyQNwOz7eXgp9Mc7sGIWsnBom95p7zD3HSXLPsbpVUfIAAMsryqMexemIByUPALA8dzzqcTOOeHDhHQAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFuVldgDgVlL2b6Xk4100P3Z+fmWL5HlyzuXr9KnsInkuAO6FkgduIh9vL4W+GGd2jEJWTg3TabNDAHAJDtcDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAW5dKSX7lypdq0aaNWrVppwYIFl21PTExUeHi4goODNWrUKOXn50uSUlNT1a9fP7Vv315dunRRUlKSK2MCAGBJLiv5lJQUTZs2TQsXLlRsbKxiYmJ06NChQl8zbNgwjR07VmvWrJFhGFq8eLEk6eWXX1azZs0UGxursLAwRUdHuyomAACW5bKS37p1qwIDA1WuXDmVLl1awcHBio+Pd2w/cuSIcnJyVKdOHUlSeHi44uPjlZ6ern379qlLly6SpI4dO2rw4MGuigkAgGV5ueqJU1NT5efn51j29/fXnj17rrrdz89PKSkp+v3333X77bdr0qRJ2rlzp/z8/DRmzJi/9NoVK/re+BtwY35+Zc2OUCwwTs5jrJzDODmPsXKOq8fJZSVvt9tls9kcy4ZhFFq+2vb8/Hz9+OOPGjhwoEaOHKlPP/1UI0aM0Pz5851+7RMnsmS3Gzf8Htz1mzQt7bTZEQphnJzHWDmHcXKOu46TxFg5qyjGycPDdtWdW5cdrq9SpYrS0tIcy2lpafL397/q9uPHj8vf319+fn4qU6aMmjVrJkkKCQkpdAQAAAA4x2Ul36hRIyUkJCg9PV3Z2dlau3atgoKCHNvvuOMOeXt7a9euXZKkuLg4BQUF6e6771aVKlX01VdfSZI2btyoWrVquSomAACW5bKSr1y5soYMGaLIyEi1b99eISEhql27tvr27au9e/dKkqKjozVx4kS1bt1aZ8+eVWRkpCRp5syZ+uCDDxQSEqKPP/5Yb7zxhqtiAgBgWS47Jy9JoaGhCg0NLbRuzpw5jn8HBARoyZIllz2uevXqf+kcPAAAuBx3vAMAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKKcKvmUlJTL1h06dKjIwwAAgKJzzZI/efKkTp48qb59+yozM9OxfPz4cT3//PM3KyMAALgOXtfa+OKLL2rLli2SpAYNGvzxIC8vBQcHuzYZAAC4Idcs+Q8//FCSNHLkSE2cOPGmBAIAAEXjmiV/wcSJE/XTTz8pISFBXl5eatSoke6++25XZwMAADfAqQvvli5dqsjISO3Zs0c7d+5U165dtWbNGldnAwAAN8CpPfl58+Zp+fLl8vf3lyQlJyerf//+nJcHAMCNObUnX6JECUfBS9Ltt9+uEiVKuCwUAAC4cdfck//hhx8kSTVr1tT48ePVuXNneXp6atmyZXr00UdvSkAAAHB9rlnyAwcOLLT85ZdfOv5ts9k0evRol4QCAAA37polv2HDhpuVAwAAFDGnzsmfOXNG48ePV8+ePXXy5EmNHTtWZ86ccXU2AABwA5wq+ddff11ly5bViRMn5O3traysLI0dO9bV2QAAwA1wquQTExM1ZMgQeXl5qVSpUoqOjlZiYqKrswEAgBvgVMl7eBT+soKCgsvWAQAA9+LUzXDq1aunKVOmKCcnR5s3b9Ynn3yi+vXruzobAAC4AU7tjr/00ksqXbq0ypYtq+nTpysgIEAjRoxwdTYAAHADrrkn36NHD9lsNseyj4+PJGn37t3q06ePPv74Y9emAwAA1+2aJd+9e3dJ0rp165SVlaWOHTvK09NTcXFx+tvf/nZTAgIAgOtzzZK/MAHNhx9+qP/7v/9zXGzXtGlTde7c2fXpAADAdXPqnHxGRobOnTvnWD5z5owyMzNdFgoAANw4p66uDwkJUadOndSyZUsZhqH4+Hh16tTJ1dkAAMANcKrkBw0apFq1amnbtm2SpBEjRujxxx93aTAAAHBjnCp5SWrRooVatGjhyiwAAKAIcds6AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsyqUlv3LlSrVp00atWrXSggULLtuemJio8PBwBQcHa9SoUcrPzy+0/ccff9SDDz7oyogAAFiWy0o+JSVF06ZN08KFCxUbG6uYmBgdOnSo0NcMGzZMY8eO1Zo1a2QYhhYvXuzYlp2draioKOXl5bkqIgAAluaykt+6dasCAwNVrlw5lS5dWsHBwYqPj3dsP3LkiHJyclSnTh1JUnh4eKHtkyZNUs+ePV0VDwAAy3NZyaempsrPz8+x7O/vr5SUlKtu9/Pzc2z/4osvlJOTo9atW7sqHgAAluflqie22+2y2WyOZcMwCi1fbXtaWppmz56tefPmXfdrV6zoe92PLQ78/MqaHaFYYJycx1g5h3FyHmPlHFePk8tKvkqVKtq5c6djOS0tTf7+/oW2p6WlOZaPHz8uf39/ffnllzp58qS6devm2BYWFqYFCxbI19e58j5xIkt2u3HD78Fdv0nT0k6bHaEQxsl5jJVzGCfnuOs4SYyVs4pinDw8bFfduXXZ4fpGjRopISFB6enpys7O1tq1axUUFOTYfscdd8jb21u7du2SJMXFxSkoKEhPPfWU1q9fr7i4OMXFxTm2OVvwAADgPJeVfOXKlTVkyBBFRkaqffv2CgkJUe3atdW3b1/t3btXkhQdHa2JEyeqdevWOnv2rCIjI10VBwCAW47LDtdLUmhoqEJDQwutmzNnjuPfAQEBWrJkyTWfY//+/S7JBgCA1XHHOwAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKIoeQAALIqSBwDAoih5AAAsyqUlv3LlSrVp00atWrXSggULLtuemJio8PBwBQcHa9SoUcrPz5ck7dq1SxEREQoLC1PPnj115MgRV8YEAMCSXFbyKSkpmjZtmhYuXKjY2FjFxMTo0KFDhb5m2LBhGjt2rNasWSPDMLR48WLH+tdff11xcXEKDQ3V66+/7qqYAABYlstKfuvWrQoMDFS5cuVUunRpBQcHKz4+3rH9yJEjysnJUZ06dSRJ4eHhio+PV25urgYNGqSAgABJUs2aNXX06FFXxQQAwLJcVvKpqany8/NzLPv7+yslJeWq2/38/JSSkqKSJUsqLCxMkmS32zVr1iy1aNHCVTEBALAsL1c9sd1ul81mcywbhlFo+c+25+bmasSIEcrPz1f//v3/0mtXrOh7A8ndn59fWbMjFAuMk/MYK+cwTs5jrJzj6nFyWclXqVJFO3fudCynpaXJ39+/0Pa0tDTH8vHjxx3bz5w5o2effVblypXT7NmzVaJEib/02idOZMluN27wHbjvN2la2mmzIxTCODmPsXIO4+Qcdx0nibFyVlGMk4eH7ao7ty47XN+oUSMlJCQoPT1d2dnZWrt2rYKCghzb77jjDnl7e2vXrl2SpLi4OMf2YcOGqVq1apo+fbpKlizpqogAAFiay/bkK1eurCFDhigyMlJ5eXmKiIhQ7dq11bdvX73wwgt66KGHFB0drdGjRysrK0u1atVSZGSkfvzxR33xxRe699571aFDB0nnz+fPmTPHVVEBALAkl5W8JIWGhio0NLTQuovLOiAgQEuWLCm0/R//+If279/vylgAANwSuOMdAAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABZFyQMAYFGUPAAAFkXJAwBgUZQ8AAAWRckDAGBRlDwAABbl0pJfuXKl2rRpo1atWmnBggWXbU9MTFR4eLiCg4M1atQo5efnS5KSk5PVrVs3tW7dWs8++6zOnDnjypgAAFiSy0o+JSVF06ZN08KFCxUbG6uYmBgdOnSo0NcMGzZMY8eO1Zo1a2QYhhYvXixJeu2119S1a1fFx8frwQcf1DvvvOOqmAAAWJbLSn7r1q0KDAxUuXLlVLp0aQUHBys+Pt6x/ciRI8rJyVGdOnUkSeHh4YqPj1deXp6++eYbBQcHF1oPAAD+Gi9XPXFqaqr8/Pwcy/7+/tqzZ89Vt/v5+SklJUUZGRny9fWVl5dXofV/hYeH7QbT/8G/fKkie66iUpTvr6gwTs5jrJzDODnHHcdJYqycVRTjdK3ncFnJ2+122Wx/vLBhGIWWr7b90q+TdNnynylfvsx1pr7ch6NbFdlzFZWKFX3NjnAZxsl5jJVzGCfnuOM4SYyVs1w9Ti47XF+lShWlpaU5ltPS0uTv73/V7cePH5e/v78qVKig06dPq6Cg4IqPAwAAznFZyTdq1EgJCQlKT09Xdna21q5dq6CgIMf2O+64Q97e3tq1a5ckKS4uTkFBQSpRooTq1q2r1atXS5JiY2MLPQ4AADjHZhiG4aonX7lypd577z3l5eUpIiJCffv2Vd++ffXCCy/ooYce0r59+zR69GhlZWWpVq1amjhxokqWLKkjR45oxIgROnHihKpWraq33npLt912m6tiAgBgSS4teQAAYB7ueAcAgEVR8gAAWBQlDwCARVHyAABYFCUPAIBFUfIAAFgUJe9CmZmZZkeAhaxcuVLTpk1Tdna2YmNjzY6DYu7IkSN6+umn1apVK6WmpioyMlJJSUlmx0IRo+RdIDExUa1bt1ZYWJhSUlLUsmVL/fDDD2bHcltZWVk6evSokpOTHf+hsOjoaH311Vdau3atCgoKtHTpUk2aNMnsWG7p888/V25urtkx3N7YsWP1zDPPqEyZMvLz81NISIiGDx9udiy3Exsbe9l/8fHxOnDggNnRnELJu8Drr7+u//znPypXrpwqV66scePG6dVXXzU7llt69913FRQUpG7duql79+7q3r27evToYXYst/P1119rypQp8vb2lq+vr+bOnatNmzaZHcstbdq0Sa1bt9Zrr71WaOZLFJaRkaHGjRs7JgXr1KmTsrKyzI7ldr744gvNnDlTiYmJSkxM1OzZs7VgwQKNHDlS8+bNMzven3LZLHS3suzsbNWoUcOx/Nhjj2ny5MkmJnJfS5Ys0fr161WhQgWzo7g1D4/zf49fmJExNzfXsQ6FTZw4UTk5OVqzZo1mzpypEydOqG3btmrfvr0qVqxodjy34ePjo2PHjjm+p3bu3KmSJUuanMr9pKWlafny5frb3/4mSRo4cKAGDBigmJgYhYeHq1evXuYG/BOUvAuUK1dO+/btc/zwrFixgnvvX0XVqlUZGye0bt1agwcPVmZmpubNm6e4uDiFhISYHctt+fj46I477lDVqlV1+PBh7d+/X7169VLnzp3VvXt3s+O5hREjRqh///767bffFBYWpszMTM2YMcPsWG4nIyNDZcr8MX25t7e3MjMz5eXl9ZenQTcD9653gd9++03Dhw/X3r175e3trb///e+aMmWKqlevbnY0tzNmzBgdOHBADRo0KLQX8fzzz5uYyj1t3rxZW7duld1uV8OGDdW0aVOzI7mladOm6bPPPtOdd96pjh07Kjg4WN7e3srKytITTzyh7du3mx3RLWRmZqp06dL69ddfVVBQoOrVqystLU133HGH2dHcytSpU7V79249+eSTstvtWrt2rf75z3/q73//uz777DN98MEHZke8Jkrehc6ePSu73S5J8vX1NTmNe5o1a9YV11PyheXm5urnn39WQECAVqxYocTERPXt25fTHFcwY8YMhYeH66677rps2549e1S7dm0TUrmPo0ePyjAM9evXT3PmzNGFCigoKFDfvn0VHx9vckL3s3HjRm3ZskWenp5q1KiRHn/8cX333Xe655573P5IJCXvAhs3btTOnTv173//WxEREUpPT9fw4cMVHh5udjS3lJ6erv/9738qKChQnTp1VKlSJbMjuZ1BgwbpzjvvVHBwsIYNG6Z27dppz549eu+998yO5nYGDhyomTNnFlrXs2dP/fe//zUpkXsZOXKktm/frtTUVPn7+zvWe3l5qWnTpnrllVdMTOeeDh48qMzMTF1cl/Xq1TMxkfMoeRfo2LGjJkyYoL1792rnzp0aO3asevTooWXLlpkdze1s3rxZr7zyiurUqSO73a7du3drwoQJatasmdnR3ErHjh21dOlSTZkyRbfddpv69evnWIfznn/+eSUmJl5WXgUFBapataoWLVpkYjr38/7776tfv35mx3B7r732mjZu3FjoyJDNZtPHH39sYirnceGdiwQEBGjmzJlq166dypQpo7y8PLMjuaVp06Zp4cKFjh+g33//Xc8//zwlf4mCggKlp6dr/fr1mjlzptLS0nTu3DmzY7mVSZMm6eTJk3rttdc0btw4x3ovLy+uqr+Cjh07at68eTpz5owMw5DdbldSUpLefPNNs6O5lS1btig+Pl4+Pj5mR7kulLwLVKpUSVFRUfr+++81ZcoUTZo0SbfffrvZsdxSfn5+ob+Q77rrLsd1DPjDM888o06dOql58+a6//77FRwcrEGDBpkdy634+vrK19dXx48f5+IxJwwZMkRVq1bVd999pxYtWujLL7/UQw89ZHYst3PXXXepOB/wpuRdYOrUqVq/fr0iIyNVunRp3XXXXRo4cKDZsdzS7bffrnnz5ikiIkLS+c/N8wv6cqGhoQoNDXUsr169mqNDV1GpUiXt3LlTtWvX5nPf15CamqqPP/5YkydPVqtWrdSnTx/17NnT7Fhu57bbblPbtm31yCOPFPp+mjhxoompnEfJu0DJkiVVpkwZ7d69W7t375aPj48++OAD9ryuYMKECYqKitK7774rwzAUGBio8ePHmx3L7WzYsEHTp0/X2bNnHYdWs7OztW3bNrOjuZ29e/c6Pgtvs9kcd3RLTEw0OZl7uXBV+D333KN9+/bp4YcfNjmRe2rSpImaNGlidozrRsm7wNChQ5WZmanffvtNdevW1fbt2/Xoo4+aHcstVaxYUdOnTzc7htubOHGioqKiNHfuXA0YMEDr169Xdna22bHcEn/4OCcwMFAvvPCChg8frt69e+uHH34otuedXSEtLU1+fn5q0KCB2VFuCFfXu0DLli21du1aTZgwQR07dpSvr68GDx7MldAX6d+/v9577z01b978ineN+uKLL0xI5b7Cw8O1bNkyvfPOO3rwwQcVFBSkNm3aaPXq1WZHczu5ubn66KOP9Msvv2jMmDGaN2+e+vXrx6H7K/jtt99099136/vvv9fOnTvVpk2bQp9MuJVd+jvq4qq02WzF5ncUe/IuULFiRdlsNt1zzz3av3+/2rdvz/nTS0RFRUmS5s+fb3KS4sHHx0e//PKLatSooR07digwMJDvqasYP368KlSooB9++EGenp46fPiwXnnlFUVHR5sdza1cfD+BBx98UA8++CD3E7jIhXtQbNiwweQkN4aSd4H77rtPUVFR+te//qWXXnpJqampxfrqTFe4sLcwadIkblzihMGDB2v69OmaMmWK3n//fcXExKhjx45mx3JLP/zwg5YvX65NmzapVKlSevPNNwtdtHiru3A/gZSUFD3xxBOO9QUFBapSpYqJydzLyJEjr7mdC+9uYePGjdPu3bt177336oUXXtDWrVs1depUs2O5lYtvXMIvmj9Xv3591a9fX5K0dOlSZWZmuv3tNM1is9mUm5vrOA2UkZFRLCYSuVku3E9gwoQJGj16tGM99xMo7MLP28aNG3XmzBm1a9dOXl5eWr16tcqWLWtyOudxTt5FfvrpJ2VkZBTL2yDeDFlZWdf8RePlxd+fF9uzZ48++uijy76nistdt26m2NhYffrppzp8+LCefPJJrV+/Xs8995zjY5o47+L5EFauXKkff/yR+RCu4KmnnlJMTIxjame73a5OnTppyZIlJidzDiXvAmPGjNGmTZt09913O9YVp9sg3mw//vij46NhBQUFSkpK4hfyJZ588kl1795d9957b6G90gt7Gyjs0KFD2r59u+x2u+rVq6eAgACzI7mdC/MhtGrVSsOGDVNYWBjzIVxB69attXDhQscfP6mpqerVq1exueiV3SUXSEhI0Lp167ia1wmjR4/Wjh07lJmZqerVq2vfvn169NFHKflL+Pj4qFu3bmbHKBby8vL09ddfa9u2bfLy8pK3t7dq1qzJIftLJCUlacaMGZoyZYoiIiIc8yGgsAEDBqhdu3Z69NFHZRiGvvvuu0JHH90dJe8CVatW1blz5yh5J2zdulVr1qxRVFSUIiMjlZ2drUmTJpkdy20kJydLkh544AHNmzdPTzzxhDw9PR3buV3y5UaPHq2cnBx16tRJdrtdcXFxOnjwoEaNGmV2NLfCfAjOad++vRo1aqTdu3fLZrNp3LhxxeraBUq+CF24GrOgoEBhYWGqW7duoV/IxeVqzJvJ399fJUqUUI0aNbR//361bdtWp0+fNjuW2+jevbvjM7rbtm0rdMqnOH1W92b63//+V2hO9ObNmyskJMTERO6J+RCubdasWVdcf+DAAUnnLx4uDij5InTh/CjnSZ1XuXJlvffee2rYsKGmTJki6fwFQTjv4s/o5uXlqUSJEsrLy1Nubq7KlCljYjL3deedd+rw4cOqVq2aJOn48eOqXLmyyancz4X5EDIzMyVJq1at4oLXK9izZ4+OHTum1q1by8vLS+vWrStW82tw4Z0LZGVlKS4uTt26dVNKSor+7//+T/369VOpUqXMjuZ2srKy9NVXX6lt27aaP3++EhISFBkZqcDAQLOjuZXPP/9c77zzjlauXKnffvtNPXr00JgxY9SiRQuzo7mdXr166bvvvnMcSdu1a5f8/f1VqVIlSXwi4YJ9+/Zp8ODBysnJUUxMjLp3767p06erVq1aZkdzK126dNHcuXMdv7/PnTunyMhIxcTEmJzMOfzZ5gIvvfSSatasKUkqU6aM7Ha7Xn755ctu+oLzh5xPnjwpSWrVqpVOnDjBRBlX8M4772ju3LmSpLvvvlvLli1T7969Kfkr+Pe//11o+ZlnnjEpiXuLiorSf/7zH7344ouqXLmyxo0bp1dffbXYfDTsZrn0Pgt5eXmO31nFgYfZAawoOTlZQ4YMkXR+jushQ4bot99+MzmVe3rxxReVmpoq6fwfRIZh6OWXXzY5lfvJy8tz7IlK52+dzEG4K6tfv76ys7O1ceNGrVu3TqdOnXLcTIhTaX/Izs5WjRo1HMuPPfYYp8qu4KmnnlLHjh01efJkTZ48WR07dixWU/KyJ+8CNptN+/fvd+zN//TTT5zruork5GS9++67kv74gygsLMzkVO7n0Ucf1dChQxUaGiqbzabVq1erTp06ZsdyS3PmzNHatWsVGhoqwzD07rvv6uDBg3r22WfNjuZWypUrp3379jn2UlesWMFdFK+gT58+CgwM1I4dO2Sz2TRjxoxidd8FmscFRowYod69ezsu9snIyHBcVIbC+IPIOePGjdP8+fMVExMjLy8v1a1bV127djU7lltasWKFPv30U8e0qZ06dVJ4eDglf4lx48Zp+PDhOnjwoOrWratq1aoxic9V/PLLL8rMzFT//v21du1aSv5Wd+7cOW3cuFEHDhyQl5eXqlevzmfmr+LCXNYX/0H05ptvmpzK/Tz77LP68MMPOb/sBMMwCs2L7u3tzR+OV3D33Xdr0aJFOnv2rOx2u3x9fc2O5Jaio6N17Ngx/fDDD+rbt6+WLl2qffv2acSIEWZHcwpX17tA27ZttWrVKrNjFBu5ubn8QfQnunbtqqlTp6pq1apmR3F7r7/+ulJSUtShQwdJ5+9l7+/vX6zuUgb30b59ey1fvlwdOnRQbGys8vPz1a5dO25reyu76667NHLkSD388MOF9ijat29vXig3dbXpHLlxUGHp6elq3ry5KlasKG9vbxmGwc1wrmLUqFFatGiRYmNjZRiGAgMD1blzZ7NjoZi6MDHNhWsXcnNzHeuKA0reBcqXLy/p/J23LkbJX+7iq53z8/P1xRdfqHr16iYmck8ffvih2RGKjT59+ujDDz/kmgUUidatW2vw4MHKzMzUvHnzFBcXV6zuoEjJuwB7oc67cEj1goiICP3rX/8yKY37+uabb664vjjdeetmyc7O1tGjRzm1cRU9evS45mQ93CyosF27dqlp06YqU6aMjh07phdeeEHNmjUzO5bTKHkXaN68+RV/iDi0+ud++uknx+fm8Yft27c7/p2Xl6ddu3apbt26HB26gktPbVzAz995AwcONDtCsfLss89q8+bNOnjwoAoKCuTj46OKFSuqdu3aZkdzChfeucCRI0cc/87Pz9e6deuUm5t72Z24IAUEBDgmYJGkChUqaOjQoUw1+ydOnjypIUOGOO6Chz/88ssv+uqrr7Rt2zZ5enrq8ccfV8OGDXXXXXeZHc3t/Pjjjzp79qwMw1BBQYGSkpL42buK9PR0xcfH691331V6erq+//57syM5hZK/ScLDw7Vs2TKzY8AicnNzFRISorVr15odxe0MHz5c586dU7t27RxTzVapUoWpZi8xevRo7dixQ5mZmapevbr27dunRx99lOs/LvHaa69p165d8vT0VL169dSgQQPVr19fZcuWNTuaUzhc7wIXnz81DEMHDx5knuZLXG0axwuKyzSON8vF51ENw1BSUpIef/xxk1O5J6aadc7WrVu1Zs0aRUVFKTIyUtnZ2Zo0aZLZsdzOqVOnZBiG7rnnHtWoUUPVq1cvNgUvUfIu8fbbbzv+bbPZVL58eX54rqK4T+N4s1x8HvXC99S9995rYiL3xVSzzvH391eJEiVUo0YN7d+/X23bttXp06fNjuV2pk6dKun89UIJCQkaMGCAzp49q82bN5uczDmUvAvMnz9f0vnzpp6ensXqr76b5cKeepcuXRQTE+OYxrFnz56KjIw0M5pbql+/vtavX+84zxwUFETJX0V+fr7CwsJUt25deXl5adeuXfLz83N8X3H1+HmVK1fWe++9p4YNGzpuu80ENZf7+eeflZCQoISEBO3bt0+1a9cuVkfRKHkX2Ldvn15++WWlpKTIMAxVr15dkydPduxZ4A/FfRrHm2Xy5MnavXu32rZtK7vdrhkzZmjv3r0aMGCA2dHczqUXuPbu3dukJO5twoQJ+uqrr1S7dm21atVKn332mcaNG2d2LLczaNAgNWvWTL169dIjjzwiT09PsyP9JVx45wLh4eEaOHCg47OU69at09y5c7Vw4UKTk7mfDz74QMuXL1dQUJAkacOGDYqMjFS3bt1MTuZegoODtWrVKsc92M+dO6f27dvr888/NzkZirOkpCQdOnRIjRs31tGjR/kEggUVn3vzFSOGYRS6WULLli119uxZExO5rz59+igkJESLFi1SmTJl1K1bNwr+Cvz8/HTq1CnHcl5enuPOisD1WL16tZ599lm9/vrryszMVJcuXRQXF2d2LBQxSt4FGjVqpHfeeUfHjx9XRkaGFixYoBo1aig5OVnJyclmx3Mr0dHR+umnn1S1alX17NlT69at4yLFK6hQoYLatWunV199VVFRUQoPD5fdbtfIkSOvev9/4FrmzJmjRYsWydfXVxUrVtTy5cv1/vvvmx0LRYxz8i5w4RDqkiVLCq3v3r07k4pc4uuvv3bM8FS2bFnNnTtX7dq1KzbTON4szZo1K3R06MEHHzQxDazAw8Oj0PSy/v7+xWriFTiHkneBDRs2mB2h2CjuMzzdLJfe4x+4Uffdd58++eQT5efnKzExUQsXLlRAQIDZsVDE+G0KU106w1P37t25cQlwE4wdO1YpKSny9vbWK6+8Il9fX7366qtmx0IR4+p6mG7z5s3aunWr7Ha7AgMDi9UMT0BxNXLkSGbMvAVwuN4FvvzySzVt2tTsGMVGkyZN1KRJE7NjuL0LH3dq0qSJkpOT+bgTbsiBAwd05swZlSlTxuwocCFK3gWmTJlCyaNIrV69WrNnz1Z2drZiYmLUpUsXvfzyywoLCzM7GoopDw8PNWvWTPfcc0+hKXm5I6C1cLjeBQYMGKDy5cvr4Ycflo+Pj2M9c3/jenXo0EHz589X9+7dFRsbq9TUVD399NNatWqV2dFQTO3YseOK6+vXr3+Tk8CV2JN3gQs3Kfnf//5XaD0lj+vFx51Q1CjzWwMl7wJXupglJyfHhCSwCj7uBOB6cLjeBTZs2KDp06fr7NmzMgxDdrtdOTk5SkhIMDsaiqmzZ89q9uzZhT6F8NxzzxXauweAS1HyLtCyZUtFRUVp7ty5GjBggNavX6/s7GyNHTvW7GgoxnJzc1WyZEn9+uuv+vXXXxUUFMQhewDXxG8IFyhbtqwCAwP18MMP6/Tp0xo2bJi2bdtmdiwUY7NmzdKIESOUnJys7t2767///a/eeOMNs2MBcHOUvAv4+Pjol19+UY0aNbRjxw7l5uYqLy/P7FgoxjZs2KA33nhDn332mUJDQzV37lx9++23ZscC4OYoeRcYPHiwpk+frmbNmikhIUGPPfaYWrRoYXYsFGN2u10+Pj7auHGjmjZtKrvdruzsbLNjAXBzXF3vAvXr11eNGjVUsmRJffLJJzp48KBq165tdiwUYw0bNlRISIh8fHxUr149de/endv/AvhTXHjnAh9//LGWL1+u5cuX68iRI+rTp4969eqlzp07mx0NxVhycrKqVKkiDw8PJSYm6oEHHjA7EgA3R8m7QEhIiBYvXqzSpUtLkrKzs9WpUyetXLnS5GQorkaOHHnF9UwwAuBaOFzvAnl5eSpZsqRjuUSJEiamgRVcfHey/Px8ffHFF6pevbqJiQAUB5S8C7Ro0UI9e/bUk08+KZvNpjVr1uiJJ54wOxaKsQ4dOhRajoiI0L/+9S+T0gAoLjhc7yLx8fH65ptv5OXlpXr16nF1PYrUoUOH1K9fP23YsMHsKADcGCUPFAMBAQGy2Wy68ONaoUIFDR06VBERESYnA+DOKHkAACyKc/JAMZCdna1Zs2YpISFBBQUFCgwM1KBBgxyf4ACAK2FPHigGRo4cqVKlSqlTp06SpMWLF+v06dOaMmWKyckAuDNKHigG2rVrpxUrVhRa16ZNG61evdqkRACKA+5dDxQDhmHo1KlTjuVTp07J09PTxEQAigPOyQPFQK9evRQREaHmzZvLMAxt3LhR/fr1MzsWADfH4XqgmDhw4IC++eYb2e121a9fXzVr1jQ7EgA3R8kDxUBeXp62bt2qjIyMQuvbt29vTiAAxQKH64FiYNCgQUpLS1ONGjVks9kc6yl5ANdCyQPFwM8//6z4+HizYwAoZri6HigG7r77biUnJ5sdA0Axwzl5wI316NFDNptN6enpOnr0qAICAgp9dO7jjz82MR0Ad8fhesCNDRw40OwIAIox9uQBALAozskDAGBRlDwAABZFyQMAYFGUPAAAFsXV9cAtaPv27YqOjtbtt9+un3/+WT4+Ppo0aZLmzJmj++67T88884wkacSIEY7l5s2bq3bt2tq/f7+GDh2qiRMnqm3bttqyZYtOnz6tp59+Wl27dpUkxcTEaP78+fLw8FClSpU0ZswY3XPPPdq5c6cmTZoku90uSerfv7+Cg4OVm5ur6OhoffPNNyooKNA//vEPjR49Wr6+vqaNEWAF7MkDt6jvv/9ePXr00MqVKxUeHq5hw4b96WPuu+8+ff7552rZsqUkKTMzU0uXLtX8+fP19ttva//+/UpISNAHH3ygjz/+WCtWrFBISIiee+45GYahmTNn6umnn9ayZcv0xhtvaNu2bZKk999/X56enlq2bJlWrFghf39/RUdHu/T9A7cC9uSBW1RAQIDq1q0rSerYsaPGjx8vf3//az7mwtdf0LVrV9lsNlWpUkVNmjTRli1bdPz4cbVp00YVKlSQJIWHh2vChAlKSkrSk08+qfHjx2vDhg1q1KiRhg4dKkn68ssvdfr0aW3dulXS+Ql5KlasWNRvGbjlUPLALeriO+ddUL58eV1864y8vLxC20uXLl1o2cvrj18hdrtdHh4ejkPxFzMMQ/n5+erSpYuaNWumLVu2aPPmzZo1a5bi4+Nlt9v1yiuv6PHHH5cknTlzRufOnbuh9weAw/XALWvfvn3at2+fpPPn0B955BGVL19e33//vSQpJSVFO3bsuOZzxMbGSpKSk5O1ZcsWBQUFqUmTJlq9erXS09MlSUuXLlW5cuVUrVo1denSRYmJiQoPD1dUVJROnTqltLQ0NW7cWAsWLFBubq7sdrvGjBmjt956y3VvHrhFsCcP3KIqVaqk6dOn68iRI6pQoYLefPNNeXp66qWXXlJwcLDuvPNOBQYGXvM5kpKSFB4erpycHI0ePVrVq1dX9erV1atXL/Xs2VN2u10VKlTQe++9Jw8PD7300kt64403NH36dNlsNj3//PO688479e9//1uTJ09Whw4dVFBQoAceeEAjRoy4SSMBWBe3tQVuQdu3b1dUVJQ+++yz636O5s2ba8aMGXrooYeKMBmAosThegAALIo9eQAALIo9eQAALIqSBwDAoih5AAAsipIHAMCiKHkAACyKkgcAwKL+H9c+SqoQayCmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot of purpose vs debt\n",
    "sns.set()\n",
    "pd.pivot_table(credit_score_df, index = 'purpose', values = 'debt').plot(kind= 'bar')\n",
    "plt.ylabel('debt')\n",
    "plt.title('Plot of debt vs purpose')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
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    "From the correlation matrix, there is a weak negative correlation between different loan purposes and loan repayment. As we observe, more people requesting a loan for car purchase and education purposes will likely not be able to make on-time repayment of the loan. People requesting loan for house purchase are 20% more likely to make loan repayment than people requesting loan for car purchase."
   ]
  },
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    "<div id=\"general_conclusion\">\n",
    "    <h2>General conclusion</h2> \n",
    "</div>"
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    "At the end of this project, we have been able to carry out data preprocessing for our client who is trying to analyze a borrower's risk of defaulting in loan repayment. In this project, we cleaned the data, did some type conversion, categorized data, process duplicates in the data and prepared the data for analysis. Using pivot table and a little bit of exploratory data analysis, we were able to arrive at the following conclusions about the bank's customer:\n",
    " - People with more than 5 kids and up to 20 kids are ~37% more likely to be in debt than people with no kid thus, there is a relationship between having kids and repaying a loan on time. This means that people with kids are likely to default on loan repayment.\n",
    " - Unmarried people with up to 4 kids and divorced people with up to 20 kids are ~75% more likely to be in debt than any other family status, and about 80% more likely to be in debt than people with 3 or less number of children. \n",
    " - Unmarried people are more than 2% likely to be in debt than married people. Widow/widower are least likely to be in debt than any of the other groups. This means unmarried people are more likely to default on loan repayment.\n",
    " - There is no correlation between income level and defaulting on loan payment. \n",
    " - People requesting a loan for car purchase and education purposes will most likely default on loan repayment. People requesting loan for house purchase make on time payment than any other category."
   ]
  },
  {
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   "metadata": {},
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    "<div id=\"project_readiness_checklist\">\n",
    "    <h2>Project Readiness Checklist</h2> \n",
    "</div>\n",
    "\n",
    "Put 'x' in the completed points. Then press Shift + Enter."
   ]
  },
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    "- [x]  file open;\n",
    "- [ ]  file examined;\n",
    "- [ ]  missing values defined;\n",
    "- [ ]  missing values are filled;\n",
    "- [ ]  an explanation of which missing value types were detected;\n",
    "- [ ]  explanation for the possible causes of missing values;\n",
    "- [ ]  an explanation of how the blanks are filled;\n",
    "- [ ]  replaced the real data type with an integer;\n",
    "- [ ]  an explanation of which method is used to change the data type and why;\n",
    "- [ ]  duplicates deleted;\n",
    "- [ ]  an explanation of which method is used to find and remove duplicates;\n",
    "- [ ]  description of the possible reasons for the appearance of duplicates in the data;\n",
    "- [ ]  data is categorized;\n",
    "- [ ]  an explanation of the principle of data categorization;\n",
    "- [ ]  an answer to the question \"Is there a relation between having kids and repaying a loan on time?\";\n",
    "- [ ]  an answer to the question \" Is there a relation between marital status and repaying a loan on time?\";\n",
    "- [ ]   an answer to the question \" Is there a relation between income level and repaying a loan on time?\";\n",
    "- [ ]  an answer to the question \" How do different loan purposes affect on-time repayment of the loan?\"\n",
    "- [ ]  conclusions are present on each stage;\n",
    "- [ ]  a general conclusion is made."
   ]
  }
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