Step1_Explore&Clean.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "32d003f1",
   "metadata": {},
   "source": [
    "#### The objective is to build a publishable and fully reproducible project that uses Machine Learning (4-5 different models). The deliverable of this project is a folder with all the following components:\n",
    "\n",
    "- A README file (.txt text file) to briefly introduce the project idea and how it is organized.\n",
    "- “.HTML” and “.IPYNB” file\n",
    "    - STEP 1: Well documented Python code to read, explore and clean data (automated processes and no hand edit on raw data).\n",
    "    - STEP 2: Well documented Python code to manipulate, wrangle and visualize data.\n",
    "    - STEP 3: Well documented Python code to run ML models and make publication-worthy figures or tables.\n",
    "- Final report should be in .pdf format (or .docx). This file should be structured with Introduction, Methods and Materials, Results, Discussion, and Conclusions.\n",
    "\n",
    "Due Date : Aug 25, 11:55 PM IST"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dad6aec1",
   "metadata": {},
   "source": [
    "# Dataset Domain: Banking\n",
    "\n",
    "This data set contains historical data of the customers who have taken loans from a German bank and the bank is facing issues with loan defaulters. The bank intends to build a machine learning model to predict whether the customer will default or not based on historical data. The bank has historical information on relevant features for each customer such as employment duration, existing loans count, saving balance, percentage of income, age, default status.\n",
    "\n",
    "The data set has 17 columns and 1000 rows. Columns are described below and each row is a customer. \n",
    "\n",
    "- checking_balance - refers to the amount of money available in a 'checking account' (a.k.a current account) of the customer for everyday financial transactions (\"unknown\", \"< 0 DM\", \"1 - 200 DM\" and \"> 200 DM\")\n",
    "- months_loan_duration - The duration, in months, since the loan was taken\n",
    "- credit_history - The credit history of each customer (\"good\", \"critical\", \"poor\", \"very good\" and \"perfect\")\n",
    "- purpose - The purpose for which the loan was taken (\"furniture/appliances\", \"car\", \"business\", \"education\" and \"renovations\")\n",
    "- amount - The amount of loan taken by the customer\n",
    "- savings_balance - refers to the amount of money available in a 'savings account' of the customer for accumulating funds over time and earning interest (\"< 100 DM\", \"unknown\", \"100 - 500 DM\", \"500 - 1000 DM\" and \"> 1000 DM\")\n",
    "- employment_duration - The duration of the customer's employment (\"1 - 4 years\", \"> 7 years\", \"4 - 7 years\", \"< 1 year\" and \"unemployed\")\n",
    "- percent_of_income - The installment rate, expressed as a percentage of disposable income, indicates the portion of income being utilized to make loan payments\n",
    "- years_at_residence - The duration of the customer's current residence\n",
    "- age - The age of the customer\n",
    "- other_credit - Whether the customer has taken any other credits (\"none\", \"bank\" and \"store\")\n",
    "- housing - The type of housing the customer has (\"own\", \"rent\" and \"other\")\n",
    "- existing_loans_count - signifies the quantity of ongoing loans (currently active credit lines) already held by a customer with this bank\n",
    "- job - The job type of the customer (\"skilled\", \"unskilled\", \"management\" and \"unemployed\")\n",
    "- dependents - Whether the customer has any dependents\n",
    "- phone - Whether the customer has a phone (\"no\" and \"yes\")\n",
    "- default - Default status (Target column - \"no\" and \"yes\"): The target variable indicating whether the customer defaulted on the loan or not.\n",
    "\n",
    "NOTE: \"DM\" stands for \"Deutsche Mark\" (previously legal currency of Germany)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30cab7a4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['checking_balance', 'months_loan_duration', 'credit_history', 'purpose',\n",
      "       'amount', 'savings_balance', 'employment_duration', 'percent_of_income',\n",
      "       'years_at_residence', 'age', 'other_credit', 'housing',\n",
      "       'existing_loans_count', 'job', 'dependents', 'phone', 'default'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   checking_balance      1000 non-null   object\n",
      " 1   months_loan_duration  1000 non-null   int64 \n",
      " 2   credit_history        1000 non-null   object\n",
      " 3   purpose               1000 non-null   object\n",
      " 4   amount                1000 non-null   int64 \n",
      " 5   savings_balance       1000 non-null   object\n",
      " 6   employment_duration   1000 non-null   object\n",
      " 7   percent_of_income     1000 non-null   int64 \n",
      " 8   years_at_residence    1000 non-null   int64 \n",
      " 9   age                   1000 non-null   int64 \n",
      " 10  other_credit          1000 non-null   object\n",
      " 11  housing               1000 non-null   object\n",
      " 12  existing_loans_count  1000 non-null   int64 \n",
      " 13  job                   1000 non-null   object\n",
      " 14  dependents            1000 non-null   int64 \n",
      " 15  phone                 1000 non-null   object\n",
      " 16  default               1000 non-null   object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 132.9+ KB\n",
      "None\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>checking_balance</th>\n",
       "      <th>months_loan_duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>amount</th>\n",
       "      <th>savings_balance</th>\n",
       "      <th>employment_duration</th>\n",
       "      <th>percent_of_income</th>\n",
       "      <th>years_at_residence</th>\n",
       "      <th>age</th>\n",
       "      <th>other_credit</th>\n",
       "      <th>housing</th>\n",
       "      <th>existing_loans_count</th>\n",
       "      <th>job</th>\n",
       "      <th>dependents</th>\n",
       "      <th>phone</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>6</td>\n",
       "      <td>critical</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>1169</td>\n",
       "      <td>unknown</td>\n",
       "      <td>&gt; 7 years</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 - 200 DM</td>\n",
       "      <td>48</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>5951</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>12</td>\n",
       "      <td>critical</td>\n",
       "      <td>education</td>\n",
       "      <td>2096</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>4 - 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>42</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>7882</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>4 - 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>24</td>\n",
       "      <td>poor</td>\n",
       "      <td>car</td>\n",
       "      <td>4870</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>none</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>unknown</td>\n",
       "      <td>12</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>1736</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>4 - 7 years</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>30</td>\n",
       "      <td>good</td>\n",
       "      <td>car</td>\n",
       "      <td>3857</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>management</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>unknown</td>\n",
       "      <td>12</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>804</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>&gt; 7 years</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>45</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>1845</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>none</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1 - 200 DM</td>\n",
       "      <td>45</td>\n",
       "      <td>critical</td>\n",
       "      <td>car</td>\n",
       "      <td>4576</td>\n",
       "      <td>100 - 500 DM</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    checking_balance  months_loan_duration credit_history  \\\n",
       "0             < 0 DM                     6       critical   \n",
       "1         1 - 200 DM                    48           good   \n",
       "2            unknown                    12       critical   \n",
       "3             < 0 DM                    42           good   \n",
       "4             < 0 DM                    24           poor   \n",
       "..               ...                   ...            ...   \n",
       "995          unknown                    12           good   \n",
       "996           < 0 DM                    30           good   \n",
       "997          unknown                    12           good   \n",
       "998           < 0 DM                    45           good   \n",
       "999       1 - 200 DM                    45       critical   \n",
       "\n",
       "                  purpose  amount savings_balance employment_duration  \\\n",
       "0    furniture/appliances    1169         unknown           > 7 years   \n",
       "1    furniture/appliances    5951        < 100 DM         1 - 4 years   \n",
       "2               education    2096        < 100 DM         4 - 7 years   \n",
       "3    furniture/appliances    7882        < 100 DM         4 - 7 years   \n",
       "4                     car    4870        < 100 DM         1 - 4 years   \n",
       "..                    ...     ...             ...                 ...   \n",
       "995  furniture/appliances    1736        < 100 DM         4 - 7 years   \n",
       "996                   car    3857        < 100 DM         1 - 4 years   \n",
       "997  furniture/appliances     804        < 100 DM           > 7 years   \n",
       "998  furniture/appliances    1845        < 100 DM         1 - 4 years   \n",
       "999                   car    4576    100 - 500 DM          unemployed   \n",
       "\n",
       "     percent_of_income  years_at_residence  age other_credit housing  \\\n",
       "0                    4                   4   67         none     own   \n",
       "1                    2                   2   22         none     own   \n",
       "2                    2                   3   49         none     own   \n",
       "3                    2                   4   45         none   other   \n",
       "4                    3                   4   53         none   other   \n",
       "..                 ...                 ...  ...          ...     ...   \n",
       "995                  3                   4   31         none     own   \n",
       "996                  4                   4   40         none     own   \n",
       "997                  4                   4   38         none     own   \n",
       "998                  4                   4   23         none   other   \n",
       "999                  3                   4   27         none     own   \n",
       "\n",
       "     existing_loans_count         job  dependents phone default  \n",
       "0                       2     skilled           1   yes      no  \n",
       "1                       1     skilled           1    no     yes  \n",
       "2                       1   unskilled           2    no      no  \n",
       "3                       1     skilled           2    no      no  \n",
       "4                       2     skilled           2    no     yes  \n",
       "..                    ...         ...         ...   ...     ...  \n",
       "995                     1   unskilled           1    no      no  \n",
       "996                     1  management           1   yes      no  \n",
       "997                     1     skilled           1    no      no  \n",
       "998                     1     skilled           1   yes     yes  \n",
       "999                     1     skilled           1    no      no  \n",
       "\n",
       "[1000 rows x 17 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# path to the bank dataset CSV file\n",
    "file_path = 'German_bank.csv'\n",
    "\n",
    "# Load the CSV file into a pandas DataFrame\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "# Understanding the type and contents of the dataset\n",
    "print(df.columns)\n",
    "print(df.info())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "382f2b3e",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>months_loan_duration</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>20.903</td>\n",
       "      <td>12.058814</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.00</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3271.258</td>\n",
       "      <td>2822.736876</td>\n",
       "      <td>250.0</td>\n",
       "      <td>1365.5</td>\n",
       "      <td>2319.5</td>\n",
       "      <td>3972.25</td>\n",
       "      <td>18424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>percent_of_income</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.973</td>\n",
       "      <td>1.118715</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>years_at_residence</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.845</td>\n",
       "      <td>1.103718</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>35.546</td>\n",
       "      <td>11.375469</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>42.00</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>existing_loans_count</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.407</td>\n",
       "      <td>0.577654</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dependents</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.155</td>\n",
       "      <td>0.362086</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count      mean          std    min     25%     50%  \\\n",
       "months_loan_duration  1000.0    20.903    12.058814    4.0    12.0    18.0   \n",
       "amount                1000.0  3271.258  2822.736876  250.0  1365.5  2319.5   \n",
       "percent_of_income     1000.0     2.973     1.118715    1.0     2.0     3.0   \n",
       "years_at_residence    1000.0     2.845     1.103718    1.0     2.0     3.0   \n",
       "age                   1000.0    35.546    11.375469   19.0    27.0    33.0   \n",
       "existing_loans_count  1000.0     1.407     0.577654    1.0     1.0     1.0   \n",
       "dependents            1000.0     1.155     0.362086    1.0     1.0     1.0   \n",
       "\n",
       "                          75%      max  \n",
       "months_loan_duration    24.00     72.0  \n",
       "amount                3972.25  18424.0  \n",
       "percent_of_income        4.00      4.0  \n",
       "years_at_residence       4.00      4.0  \n",
       "age                     42.00     75.0  \n",
       "existing_loans_count     2.00      4.0  \n",
       "dependents               1.00      2.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary Statistics of the numerical columns\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2cafbce0",
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>checking_balance</th>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_history</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>good</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>1000</td>\n",
       "      <td>6</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>savings_balance</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_duration</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other_credit</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>none</td>\n",
       "      <td>814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>own</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phone</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count unique                   top freq\n",
       "checking_balance     1000      4               unknown  394\n",
       "credit_history       1000      5                  good  530\n",
       "purpose              1000      6  furniture/appliances  473\n",
       "savings_balance      1000      5              < 100 DM  603\n",
       "employment_duration  1000      5           1 - 4 years  339\n",
       "other_credit         1000      3                  none  814\n",
       "housing              1000      3                   own  713\n",
       "job                  1000      4               skilled  630\n",
       "phone                1000      2                    no  596\n",
       "default              1000      2                    no  700"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistics for the qualitative categorical columns\n",
    "df.describe(include=['O']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "94c2a45b",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical variables =  ['checking_balance', 'credit_history', 'purpose', 'savings_balance', 'employment_duration', 'other_credit', 'housing', 'job', 'phone', 'default']\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>Column</th>\n",
       "      <th>Category</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>checking_balance</td>\n",
       "      <td>unknown</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>checking_balance</td>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>checking_balance</td>\n",
       "      <td>1 - 200 DM</td>\n",
       "      <td>269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>checking_balance</td>\n",
       "      <td>&gt; 200 DM</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>credit_history</td>\n",
       "      <td>good</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>credit_history</td>\n",
       "      <td>critical</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>credit_history</td>\n",
       "      <td>poor</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>credit_history</td>\n",
       "      <td>very good</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>credit_history</td>\n",
       "      <td>perfect</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>default</td>\n",
       "      <td>no</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>default</td>\n",
       "      <td>yes</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>employment_duration</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>employment_duration</td>\n",
       "      <td>&gt; 7 years</td>\n",
       "      <td>253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>employment_duration</td>\n",
       "      <td>4 - 7 years</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>employment_duration</td>\n",
       "      <td>&lt; 1 year</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>employment_duration</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>housing</td>\n",
       "      <td>own</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>housing</td>\n",
       "      <td>rent</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>housing</td>\n",
       "      <td>other</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>job</td>\n",
       "      <td>skilled</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>job</td>\n",
       "      <td>unskilled</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>job</td>\n",
       "      <td>management</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>job</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>other_credit</td>\n",
       "      <td>none</td>\n",
       "      <td>814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>other_credit</td>\n",
       "      <td>bank</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>other_credit</td>\n",
       "      <td>store</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>phone</td>\n",
       "      <td>no</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>phone</td>\n",
       "      <td>yes</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>purpose</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>purpose</td>\n",
       "      <td>car</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>purpose</td>\n",
       "      <td>business</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>purpose</td>\n",
       "      <td>education</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>purpose</td>\n",
       "      <td>renovations</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>purpose</td>\n",
       "      <td>car0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>savings_balance</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>savings_balance</td>\n",
       "      <td>unknown</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>savings_balance</td>\n",
       "      <td>100 - 500 DM</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>savings_balance</td>\n",
       "      <td>500 - 1000 DM</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>savings_balance</td>\n",
       "      <td>&gt; 1000 DM</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Column              Category  Frequency\n",
       "0      checking_balance               unknown        394\n",
       "1      checking_balance                < 0 DM        274\n",
       "2      checking_balance            1 - 200 DM        269\n",
       "3      checking_balance              > 200 DM         63\n",
       "4        credit_history                  good        530\n",
       "5        credit_history              critical        293\n",
       "6        credit_history                  poor         88\n",
       "7        credit_history             very good         49\n",
       "8        credit_history               perfect         40\n",
       "9               default                    no        700\n",
       "10              default                   yes        300\n",
       "11  employment_duration           1 - 4 years        339\n",
       "12  employment_duration             > 7 years        253\n",
       "13  employment_duration           4 - 7 years        174\n",
       "14  employment_duration              < 1 year        172\n",
       "15  employment_duration            unemployed         62\n",
       "16              housing                   own        713\n",
       "17              housing                  rent        179\n",
       "18              housing                 other        108\n",
       "19                  job               skilled        630\n",
       "20                  job             unskilled        200\n",
       "21                  job            management        148\n",
       "22                  job            unemployed         22\n",
       "23         other_credit                  none        814\n",
       "24         other_credit                  bank        139\n",
       "25         other_credit                 store         47\n",
       "26                phone                    no        596\n",
       "27                phone                   yes        404\n",
       "28              purpose  furniture/appliances        473\n",
       "29              purpose                   car        337\n",
       "30              purpose              business         97\n",
       "31              purpose             education         59\n",
       "32              purpose           renovations         22\n",
       "33              purpose                  car0         12\n",
       "34      savings_balance              < 100 DM        603\n",
       "35      savings_balance               unknown        183\n",
       "36      savings_balance          100 - 500 DM        103\n",
       "37      savings_balance         500 - 1000 DM         63\n",
       "38      savings_balance             > 1000 DM         48"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify qualitative (categorical) columns based on data types\n",
    "qualitative_columns = [column for column in df.columns if df[column].dtype == 'object']\n",
    "print(\"Categorical variables = \", qualitative_columns)\n",
    "\n",
    "# List to store dictionaries of categories and frequencies\n",
    "category_frequency_list = []\n",
    "\n",
    "# Iterate through qualitative columns\n",
    "for column in qualitative_columns:\n",
    "    unique_categories = df[column].unique()\n",
    "    for category in unique_categories:\n",
    "        frequency = (df[column] == category).sum()\n",
    "        category_frequency_list.append({'Column': column, 'Category': category, 'Frequency': frequency})\n",
    "\n",
    "# Create a DataFrame and sort in descending order of frequency under each column\n",
    "category_frequency_df = pd.DataFrame(category_frequency_list)\n",
    "category_frequency_df.sort_values(by=['Column', 'Frequency'], ascending=[True, False], inplace=True)\n",
    "\n",
    "# Reset index for the final DataFrame\n",
    "category_frequency_df.reset_index(drop=True, inplace=True)\n",
    "category_frequency_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1f4df5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the category 'car0' with 'car' as it seems to be a typo\n",
    "\n",
    "df['purpose'] = df['purpose'].str.replace('car0', 'car')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec647a72",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Values:\n",
      "checking_balance        0\n",
      "months_loan_duration    0\n",
      "credit_history          0\n",
      "purpose                 0\n",
      "amount                  0\n",
      "savings_balance         0\n",
      "employment_duration     0\n",
      "percent_of_income       0\n",
      "years_at_residence      0\n",
      "age                     0\n",
      "other_credit            0\n",
      "housing                 0\n",
      "existing_loans_count    0\n",
      "job                     0\n",
      "dependents              0\n",
      "phone                   0\n",
      "default                 0\n",
      "dtype: int64\n",
      "\n",
      "Duplicate Rows =  0\n"
     ]
    }
   ],
   "source": [
    "# Check for missing values\n",
    "missing_values = df.isnull().sum()\n",
    "\n",
    "# Check for duplicate rows\n",
    "duplicate_rows = df.duplicated().sum()\n",
    "\n",
    "print(\"Missing Values:\")\n",
    "print(missing_values)\n",
    "\n",
    "print(\"\\nDuplicate Rows = \", duplicate_rows)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7d35effa",
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>checking_balance</th>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>unknown</td>\n",
       "      <td>394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_history</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>good</td>\n",
       "      <td>530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>savings_balance</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>employment_duration</th>\n",
       "      <td>1000</td>\n",
       "      <td>5</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other_credit</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>none</td>\n",
       "      <td>814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>own</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job</th>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>skilled</td>\n",
       "      <td>630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>phone</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>default</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count unique                   top freq\n",
       "checking_balance     1000      4               unknown  394\n",
       "credit_history       1000      5                  good  530\n",
       "purpose              1000      5  furniture/appliances  473\n",
       "savings_balance      1000      5              < 100 DM  603\n",
       "employment_duration  1000      5           1 - 4 years  339\n",
       "other_credit         1000      3                  none  814\n",
       "housing              1000      3                   own  713\n",
       "job                  1000      4               skilled  630\n",
       "phone                1000      2                    no  596\n",
       "default              1000      2                    no  700"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistics for the qualitative categorical columns (after corrections to the \"purpose\" column)\n",
    "df.describe(include=['O']).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "916872b2",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['checking_balance', 'months_loan_duration', 'credit_history', 'purpose',\n",
      "       'amount', 'savings_balance', 'employment_duration', 'percent_of_income',\n",
      "       'years_at_residence', 'age', 'other_credit', 'housing',\n",
      "       'existing_loans_count', 'job', 'dependents', 'phone', 'default'],\n",
      "      dtype='object')\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 17 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   checking_balance      1000 non-null   object\n",
      " 1   months_loan_duration  1000 non-null   int64 \n",
      " 2   credit_history        1000 non-null   object\n",
      " 3   purpose               1000 non-null   object\n",
      " 4   amount                1000 non-null   int64 \n",
      " 5   savings_balance       1000 non-null   object\n",
      " 6   employment_duration   1000 non-null   object\n",
      " 7   percent_of_income     1000 non-null   int64 \n",
      " 8   years_at_residence    1000 non-null   int64 \n",
      " 9   age                   1000 non-null   int64 \n",
      " 10  other_credit          1000 non-null   object\n",
      " 11  housing               1000 non-null   object\n",
      " 12  existing_loans_count  1000 non-null   int64 \n",
      " 13  job                   1000 non-null   object\n",
      " 14  dependents            1000 non-null   int64 \n",
      " 15  phone                 1000 non-null   object\n",
      " 16  default               1000 non-null   object\n",
      "dtypes: int64(7), object(10)\n",
      "memory usage: 132.9+ KB\n",
      "None\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>checking_balance</th>\n",
       "      <th>months_loan_duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>amount</th>\n",
       "      <th>savings_balance</th>\n",
       "      <th>employment_duration</th>\n",
       "      <th>percent_of_income</th>\n",
       "      <th>years_at_residence</th>\n",
       "      <th>age</th>\n",
       "      <th>other_credit</th>\n",
       "      <th>housing</th>\n",
       "      <th>existing_loans_count</th>\n",
       "      <th>job</th>\n",
       "      <th>dependents</th>\n",
       "      <th>phone</th>\n",
       "      <th>default</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>6</td>\n",
       "      <td>critical</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>1169</td>\n",
       "      <td>unknown</td>\n",
       "      <td>&gt; 7 years</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1 - 200 DM</td>\n",
       "      <td>48</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>5951</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>unknown</td>\n",
       "      <td>12</td>\n",
       "      <td>critical</td>\n",
       "      <td>education</td>\n",
       "      <td>2096</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>4 - 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>42</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>7882</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>4 - 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>24</td>\n",
       "      <td>poor</td>\n",
       "      <td>car</td>\n",
       "      <td>4870</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>none</td>\n",
       "      <td>other</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>unknown</td>\n",
       "      <td>12</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>1736</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>4 - 7 years</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>30</td>\n",
       "      <td>good</td>\n",
       "      <td>car</td>\n",
       "      <td>3857</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>management</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>unknown</td>\n",
       "      <td>12</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>804</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>&gt; 7 years</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>45</td>\n",
       "      <td>good</td>\n",
       "      <td>furniture/appliances</td>\n",
       "      <td>1845</td>\n",
       "      <td>&lt; 100 DM</td>\n",
       "      <td>1 - 4 years</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>none</td>\n",
       "      <td>other</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1 - 200 DM</td>\n",
       "      <td>45</td>\n",
       "      <td>critical</td>\n",
       "      <td>car</td>\n",
       "      <td>4576</td>\n",
       "      <td>100 - 500 DM</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    checking_balance  months_loan_duration credit_history  \\\n",
       "0             < 0 DM                     6       critical   \n",
       "1         1 - 200 DM                    48           good   \n",
       "2            unknown                    12       critical   \n",
       "3             < 0 DM                    42           good   \n",
       "4             < 0 DM                    24           poor   \n",
       "..               ...                   ...            ...   \n",
       "995          unknown                    12           good   \n",
       "996           < 0 DM                    30           good   \n",
       "997          unknown                    12           good   \n",
       "998           < 0 DM                    45           good   \n",
       "999       1 - 200 DM                    45       critical   \n",
       "\n",
       "                  purpose  amount savings_balance employment_duration  \\\n",
       "0    furniture/appliances    1169         unknown           > 7 years   \n",
       "1    furniture/appliances    5951        < 100 DM         1 - 4 years   \n",
       "2               education    2096        < 100 DM         4 - 7 years   \n",
       "3    furniture/appliances    7882        < 100 DM         4 - 7 years   \n",
       "4                     car    4870        < 100 DM         1 - 4 years   \n",
       "..                    ...     ...             ...                 ...   \n",
       "995  furniture/appliances    1736        < 100 DM         4 - 7 years   \n",
       "996                   car    3857        < 100 DM         1 - 4 years   \n",
       "997  furniture/appliances     804        < 100 DM           > 7 years   \n",
       "998  furniture/appliances    1845        < 100 DM         1 - 4 years   \n",
       "999                   car    4576    100 - 500 DM          unemployed   \n",
       "\n",
       "     percent_of_income  years_at_residence  age other_credit housing  \\\n",
       "0                    4                   4   67         none     own   \n",
       "1                    2                   2   22         none     own   \n",
       "2                    2                   3   49         none     own   \n",
       "3                    2                   4   45         none   other   \n",
       "4                    3                   4   53         none   other   \n",
       "..                 ...                 ...  ...          ...     ...   \n",
       "995                  3                   4   31         none     own   \n",
       "996                  4                   4   40         none     own   \n",
       "997                  4                   4   38         none     own   \n",
       "998                  4                   4   23         none   other   \n",
       "999                  3                   4   27         none     own   \n",
       "\n",
       "     existing_loans_count         job  dependents phone default  \n",
       "0                       2     skilled           1   yes      no  \n",
       "1                       1     skilled           1    no     yes  \n",
       "2                       1   unskilled           2    no      no  \n",
       "3                       1     skilled           2    no      no  \n",
       "4                       2     skilled           2    no     yes  \n",
       "..                    ...         ...         ...   ...     ...  \n",
       "995                     1   unskilled           1    no      no  \n",
       "996                     1  management           1   yes      no  \n",
       "997                     1     skilled           1    no      no  \n",
       "998                     1     skilled           1   yes     yes  \n",
       "999                     1     skilled           1    no      no  \n",
       "\n",
       "[1000 rows x 17 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.columns)\n",
    "print(df.info())\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "568e6867",
   "metadata": {},
   "outputs": [],
   "source": [
    "csv_filename = 'Step1output.csv'\n",
    "df.to_csv(csv_filename, index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}