finML - Credit Scoring.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "78244359-3c57-4207-9818-62ef064b84f7",
   "metadata": {},
   "source": [
    "### **Credit Score (rating a consumer's creditworthiness)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e86df732-ece1-4b83-a888-f24cec8300b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing Necessary Libraries (Data Manipulation & Visualization)\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Importing Necessary Libraries (Machine Learning)\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestClassifier   # for classification problem\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression   # for classification problem\n",
    "from sklearn import preprocessing\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, explained_variance_score    # for statistics\n",
    "from sklearn.metrics import classification_report   # for classification problem\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_context(\"paper\", font_scale=1.0)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "# Display option settings for writing\n",
    "pd.options.display.max_rows = 1000\n",
    "pd.options.display.max_columns = 100\n",
    "pd.options.display.float_format = '(:.2f)'.format\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b90eb9-e074-400f-9063-9fee73cbb70a",
   "metadata": {},
   "source": [
    "### **Importing Dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4f8a204b-b8a0-4dd5-8440-f12920f67bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/victoriaoluwakemibabalola/Documents/Personal Development/fin-ML'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0baae798-283e-4cee-95d3-d7de43169e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save to a CSV file for future use\n",
    "df_train = pd.read_csv(\"/Users/victoriaoluwakemibabalola/Documents/Personal Development/fin-ML/credit_scoring/train.csv\", sep = \",\")\n",
    "df_test = pd.read_csv(\"/Users/victoriaoluwakemibabalola/Documents/Personal Development/fin-ML/credit_scoring/test.csv\", sep = \",\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4bd38d8-1cbd-4b9a-9430-ef62a747e34a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Customer_ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>SSN</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Annual_Income</th>\n",
       "      <th>Monthly_Inhand_Salary</th>\n",
       "      <th>Num_Bank_Accounts</th>\n",
       "      <th>Num_Credit_Card</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Num_of_Loan</th>\n",
       "      <th>Type_of_Loan</th>\n",
       "      <th>Delay_from_due_date</th>\n",
       "      <th>Num_of_Delayed_Payment</th>\n",
       "      <th>Changed_Credit_Limit</th>\n",
       "      <th>Num_Credit_Inquiries</th>\n",
       "      <th>Credit_Mix</th>\n",
       "      <th>Outstanding_Debt</th>\n",
       "      <th>Credit_Utilization_Ratio</th>\n",
       "      <th>Credit_History_Age</th>\n",
       "      <th>Payment_of_Min_Amount</th>\n",
       "      <th>Total_EMI_per_month</th>\n",
       "      <th>Amount_invested_monthly</th>\n",
       "      <th>Payment_Behaviour</th>\n",
       "      <th>Monthly_Balance</th>\n",
       "      <th>Credit_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0x1602</td>\n",
       "      <td>CUS_0xd40</td>\n",
       "      <td>January</td>\n",
       "      <td>Aaron Maashoh</td>\n",
       "      <td>23</td>\n",
       "      <td>821-00-0265</td>\n",
       "      <td>Scientist</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto Loan, Credit-Builder Loan, Personal Loan,...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>11.27</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>_</td>\n",
       "      <td>809.98</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>22 Years and 1 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>80.41529543900253</td>\n",
       "      <td>High_spent_Small_value_payments</td>\n",
       "      <td>312.49408867943663</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0x1603</td>\n",
       "      <td>CUS_0xd40</td>\n",
       "      <td>February</td>\n",
       "      <td>Aaron Maashoh</td>\n",
       "      <td>23</td>\n",
       "      <td>821-00-0265</td>\n",
       "      <td>Scientist</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto Loan, Credit-Builder Loan, Personal Loan,...</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.27</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Good</td>\n",
       "      <td>809.98</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>118.28022162236736</td>\n",
       "      <td>Low_spent_Large_value_payments</td>\n",
       "      <td>284.62916249607184</td>\n",
       "      <td>Good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID Customer_ID     Month           Name Age          SSN Occupation  \\\n",
       "0  0x1602   CUS_0xd40   January  Aaron Maashoh  23  821-00-0265  Scientist   \n",
       "1  0x1603   CUS_0xd40  February  Aaron Maashoh  23  821-00-0265  Scientist   \n",
       "\n",
       "  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  Num_Credit_Card  \\\n",
       "0      19114.12                 (:.2f)                  3                4   \n",
       "1      19114.12                    NaN                  3                4   \n",
       "\n",
       "   Interest_Rate Num_of_Loan  \\\n",
       "0              3           4   \n",
       "1              3           4   \n",
       "\n",
       "                                        Type_of_Loan  Delay_from_due_date  \\\n",
       "0  Auto Loan, Credit-Builder Loan, Personal Loan,...                    3   \n",
       "1  Auto Loan, Credit-Builder Loan, Personal Loan,...                   -1   \n",
       "\n",
       "  Num_of_Delayed_Payment Changed_Credit_Limit  Num_Credit_Inquiries  \\\n",
       "0                      7                11.27                (:.2f)   \n",
       "1                    NaN                11.27                (:.2f)   \n",
       "\n",
       "  Credit_Mix Outstanding_Debt  Credit_Utilization_Ratio  \\\n",
       "0          _           809.98                    (:.2f)   \n",
       "1       Good           809.98                    (:.2f)   \n",
       "\n",
       "      Credit_History_Age Payment_of_Min_Amount  Total_EMI_per_month  \\\n",
       "0  22 Years and 1 Months                    No               (:.2f)   \n",
       "1                    NaN                    No               (:.2f)   \n",
       "\n",
       "  Amount_invested_monthly                Payment_Behaviour  \\\n",
       "0       80.41529543900253  High_spent_Small_value_payments   \n",
       "1      118.28022162236736   Low_spent_Large_value_payments   \n",
       "\n",
       "      Monthly_Balance Credit_Score  \n",
       "0  312.49408867943663         Good  \n",
       "1  284.62916249607184         Good  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head(2)   # make 2 to see properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cfc14727-cf28-4585-8e59-f6e2d70cb1ed",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Customer_ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>SSN</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Annual_Income</th>\n",
       "      <th>Monthly_Inhand_Salary</th>\n",
       "      <th>Num_Bank_Accounts</th>\n",
       "      <th>Num_Credit_Card</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Num_of_Loan</th>\n",
       "      <th>Type_of_Loan</th>\n",
       "      <th>Delay_from_due_date</th>\n",
       "      <th>Num_of_Delayed_Payment</th>\n",
       "      <th>Changed_Credit_Limit</th>\n",
       "      <th>Num_Credit_Inquiries</th>\n",
       "      <th>Credit_Mix</th>\n",
       "      <th>Outstanding_Debt</th>\n",
       "      <th>Credit_Utilization_Ratio</th>\n",
       "      <th>Credit_History_Age</th>\n",
       "      <th>Payment_of_Min_Amount</th>\n",
       "      <th>Total_EMI_per_month</th>\n",
       "      <th>Amount_invested_monthly</th>\n",
       "      <th>Payment_Behaviour</th>\n",
       "      <th>Monthly_Balance</th>\n",
       "      <th>Credit_Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99995</th>\n",
       "      <td>0x25fe9</td>\n",
       "      <td>CUS_0x942c</td>\n",
       "      <td>April</td>\n",
       "      <td>Nicks</td>\n",
       "      <td>25</td>\n",
       "      <td>078-73-5990</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Auto Loan, and Student Loan</td>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>_</td>\n",
       "      <td>502.38</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>31 Years and 6 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>60.97133255718485</td>\n",
       "      <td>High_spent_Large_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99996</th>\n",
       "      <td>0x25fea</td>\n",
       "      <td>CUS_0x942c</td>\n",
       "      <td>May</td>\n",
       "      <td>Nicks</td>\n",
       "      <td>25</td>\n",
       "      <td>078-73-5990</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Auto Loan, and Student Loan</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>11.5</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>_</td>\n",
       "      <td>502.38</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>31 Years and 7 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>54.18595028760385</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99997</th>\n",
       "      <td>0x25feb</td>\n",
       "      <td>CUS_0x942c</td>\n",
       "      <td>June</td>\n",
       "      <td>Nicks</td>\n",
       "      <td>25</td>\n",
       "      <td>078-73-5990</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5729</td>\n",
       "      <td>2</td>\n",
       "      <td>Auto Loan, and Student Loan</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>11.5</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Good</td>\n",
       "      <td>502.38</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>31 Years and 8 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>24.02847744864441</td>\n",
       "      <td>High_spent_Large_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99998</th>\n",
       "      <td>0x25fec</td>\n",
       "      <td>CUS_0x942c</td>\n",
       "      <td>July</td>\n",
       "      <td>Nicks</td>\n",
       "      <td>25</td>\n",
       "      <td>078-73-5990</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Auto Loan, and Student Loan</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.5</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Good</td>\n",
       "      <td>502.38</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>31 Years and 9 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>251.67258219721603</td>\n",
       "      <td>Low_spent_Large_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Standard</td>\n",
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       "      <th>99999</th>\n",
       "      <td>0x25fed</td>\n",
       "      <td>CUS_0x942c</td>\n",
       "      <td>August</td>\n",
       "      <td>Nicks</td>\n",
       "      <td>25</td>\n",
       "      <td>078-73-5990</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99_</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Auto Loan, and Student Loan</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>11.5</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Good</td>\n",
       "      <td>502.38</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>31 Years and 10 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>167.1638651610451</td>\n",
       "      <td>!@9#%8</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            ID Customer_ID   Month   Name Age          SSN Occupation  \\\n",
       "99995  0x25fe9  CUS_0x942c   April  Nicks  25  078-73-5990   Mechanic   \n",
       "99996  0x25fea  CUS_0x942c     May  Nicks  25  078-73-5990   Mechanic   \n",
       "99997  0x25feb  CUS_0x942c    June  Nicks  25  078-73-5990   Mechanic   \n",
       "99998  0x25fec  CUS_0x942c    July  Nicks  25  078-73-5990   Mechanic   \n",
       "99999  0x25fed  CUS_0x942c  August  Nicks  25  078-73-5990   Mechanic   \n",
       "\n",
       "      Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  \\\n",
       "99995      39628.99                 (:.2f)                  4   \n",
       "99996      39628.99                 (:.2f)                  4   \n",
       "99997      39628.99                 (:.2f)                  4   \n",
       "99998      39628.99                 (:.2f)                  4   \n",
       "99999     39628.99_                 (:.2f)                  4   \n",
       "\n",
       "       Num_Credit_Card  Interest_Rate Num_of_Loan  \\\n",
       "99995                6              7           2   \n",
       "99996                6              7           2   \n",
       "99997                6           5729           2   \n",
       "99998                6              7           2   \n",
       "99999                6              7           2   \n",
       "\n",
       "                      Type_of_Loan  Delay_from_due_date  \\\n",
       "99995  Auto Loan, and Student Loan                   23   \n",
       "99996  Auto Loan, and Student Loan                   18   \n",
       "99997  Auto Loan, and Student Loan                   27   \n",
       "99998  Auto Loan, and Student Loan                   20   \n",
       "99999  Auto Loan, and Student Loan                   18   \n",
       "\n",
       "      Num_of_Delayed_Payment Changed_Credit_Limit  Num_Credit_Inquiries  \\\n",
       "99995                      7                 11.5                (:.2f)   \n",
       "99996                      7                 11.5                (:.2f)   \n",
       "99997                      6                 11.5                (:.2f)   \n",
       "99998                    NaN                 11.5                (:.2f)   \n",
       "99999                      6                 11.5                (:.2f)   \n",
       "\n",
       "      Credit_Mix Outstanding_Debt  Credit_Utilization_Ratio  \\\n",
       "99995          _           502.38                    (:.2f)   \n",
       "99996          _           502.38                    (:.2f)   \n",
       "99997       Good           502.38                    (:.2f)   \n",
       "99998       Good           502.38                    (:.2f)   \n",
       "99999       Good           502.38                    (:.2f)   \n",
       "\n",
       "           Credit_History_Age Payment_of_Min_Amount  Total_EMI_per_month  \\\n",
       "99995   31 Years and 6 Months                    No               (:.2f)   \n",
       "99996   31 Years and 7 Months                    No               (:.2f)   \n",
       "99997   31 Years and 8 Months                    No               (:.2f)   \n",
       "99998   31 Years and 9 Months                    No               (:.2f)   \n",
       "99999  31 Years and 10 Months                    No               (:.2f)   \n",
       "\n",
       "      Amount_invested_monthly                 Payment_Behaviour  \\\n",
       "99995       60.97133255718485   High_spent_Large_value_payments   \n",
       "99996       54.18595028760385  High_spent_Medium_value_payments   \n",
       "99997       24.02847744864441   High_spent_Large_value_payments   \n",
       "99998      251.67258219721603    Low_spent_Large_value_payments   \n",
       "99999       167.1638651610451                            !@9#%8   \n",
       "\n",
       "      Monthly_Balance Credit_Score  \n",
       "99995          (:.2f)         Poor  \n",
       "99996          (:.2f)         Poor  \n",
       "99997          (:.2f)         Poor  \n",
       "99998          (:.2f)     Standard  \n",
       "99999          (:.2f)         Poor  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a007ba7c-dba0-4f83-96fd-c382955f3e71",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Customer_ID</th>\n",
       "      <th>Month</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>SSN</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Annual_Income</th>\n",
       "      <th>Monthly_Inhand_Salary</th>\n",
       "      <th>Num_Bank_Accounts</th>\n",
       "      <th>Num_Credit_Card</th>\n",
       "      <th>Interest_Rate</th>\n",
       "      <th>Num_of_Loan</th>\n",
       "      <th>Type_of_Loan</th>\n",
       "      <th>Delay_from_due_date</th>\n",
       "      <th>Num_of_Delayed_Payment</th>\n",
       "      <th>Changed_Credit_Limit</th>\n",
       "      <th>Num_Credit_Inquiries</th>\n",
       "      <th>Credit_Mix</th>\n",
       "      <th>Outstanding_Debt</th>\n",
       "      <th>Credit_Utilization_Ratio</th>\n",
       "      <th>Credit_History_Age</th>\n",
       "      <th>Payment_of_Min_Amount</th>\n",
       "      <th>Total_EMI_per_month</th>\n",
       "      <th>Amount_invested_monthly</th>\n",
       "      <th>Payment_Behaviour</th>\n",
       "      <th>Monthly_Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0x160a</td>\n",
       "      <td>CUS_0xd40</td>\n",
       "      <td>September</td>\n",
       "      <td>Aaron Maashoh</td>\n",
       "      <td>23</td>\n",
       "      <td>821-00-0265</td>\n",
       "      <td>Scientist</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto Loan, Credit-Builder Loan, Personal Loan,...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>11.27</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Good</td>\n",
       "      <td>809.98</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>22 Years and 9 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>236.64268203272135</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>186.26670208571772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0x160b</td>\n",
       "      <td>CUS_0xd40</td>\n",
       "      <td>October</td>\n",
       "      <td>Aaron Maashoh</td>\n",
       "      <td>24</td>\n",
       "      <td>821-00-0265</td>\n",
       "      <td>Scientist</td>\n",
       "      <td>19114.12</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Auto Loan, Credit-Builder Loan, Personal Loan,...</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>13.27</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Good</td>\n",
       "      <td>809.98</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>22 Years and 10 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>21.465380264657146</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>361.44400385378196</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID Customer_ID      Month           Name Age          SSN Occupation  \\\n",
       "0  0x160a   CUS_0xd40  September  Aaron Maashoh  23  821-00-0265  Scientist   \n",
       "1  0x160b   CUS_0xd40    October  Aaron Maashoh  24  821-00-0265  Scientist   \n",
       "\n",
       "  Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  Num_Credit_Card  \\\n",
       "0      19114.12                 (:.2f)                  3                4   \n",
       "1      19114.12                 (:.2f)                  3                4   \n",
       "\n",
       "   Interest_Rate Num_of_Loan  \\\n",
       "0              3           4   \n",
       "1              3           4   \n",
       "\n",
       "                                        Type_of_Loan  Delay_from_due_date  \\\n",
       "0  Auto Loan, Credit-Builder Loan, Personal Loan,...                    3   \n",
       "1  Auto Loan, Credit-Builder Loan, Personal Loan,...                    3   \n",
       "\n",
       "  Num_of_Delayed_Payment Changed_Credit_Limit  Num_Credit_Inquiries  \\\n",
       "0                      7                11.27                (:.2f)   \n",
       "1                      9                13.27                (:.2f)   \n",
       "\n",
       "  Credit_Mix Outstanding_Debt  Credit_Utilization_Ratio  \\\n",
       "0       Good           809.98                    (:.2f)   \n",
       "1       Good           809.98                    (:.2f)   \n",
       "\n",
       "       Credit_History_Age Payment_of_Min_Amount  Total_EMI_per_month  \\\n",
       "0   22 Years and 9 Months                    No               (:.2f)   \n",
       "1  22 Years and 10 Months                    No               (:.2f)   \n",
       "\n",
       "  Amount_invested_monthly                 Payment_Behaviour  \\\n",
       "0      236.64268203272135    Low_spent_Small_value_payments   \n",
       "1      21.465380264657146  High_spent_Medium_value_payments   \n",
       "\n",
       "      Monthly_Balance  \n",
       "0  186.26670208571772  \n",
       "1  361.44400385378196  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df_test.head(2)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "28504824-fc2b-4569-8eab-78e42b7e5550",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Payment_of_Min_Amount</th>\n",
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       "      <th>Monthly_Balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>0x25fe5</td>\n",
       "      <td>CUS_0x8600</td>\n",
       "      <td>December</td>\n",
       "      <td>Sarah McBridec</td>\n",
       "      <td>4975</td>\n",
       "      <td>031-35-0942</td>\n",
       "      <td>Architect</td>\n",
       "      <td>20002.88</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>5</td>\n",
       "      <td>Personal Loan, Auto Loan, Mortgage Loan, Stude...</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>18.31</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>_</td>\n",
       "      <td>3571.7</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>146.48632477751087</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>275.53956951573343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>0x25fee</td>\n",
       "      <td>CUS_0x942c</td>\n",
       "      <td>September</td>\n",
       "      <td>Nicks</td>\n",
       "      <td>25</td>\n",
       "      <td>078-73-5990</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
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       "      <td>Auto Loan, and Student Loan</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.5</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Good</td>\n",
       "      <td>502.38</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>31 Years and 11 Months</td>\n",
       "      <td>NM</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>181.44299902757518</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>409.39456169535066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>0x25fef</td>\n",
       "      <td>CUS_0x942c</td>\n",
       "      <td>October</td>\n",
       "      <td>Nicks</td>\n",
       "      <td>25</td>\n",
       "      <td>078-73-5990</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Auto Loan, and Student Loan</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Good</td>\n",
       "      <td>502.38</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>32 Years and 0 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>__10000__</td>\n",
       "      <td>Low_spent_Large_value_payments</td>\n",
       "      <td>349.7263321025098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>0x25ff0</td>\n",
       "      <td>CUS_0x942c</td>\n",
       "      <td>November</td>\n",
       "      <td>Nicks</td>\n",
       "      <td>25</td>\n",
       "      <td>078-73-5990</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2_</td>\n",
       "      <td>Auto Loan, and Student Loan</td>\n",
       "      <td>21</td>\n",
       "      <td>6_</td>\n",
       "      <td>11.5</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Good</td>\n",
       "      <td>502.38</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>32 Years and 1 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>97.59857973344877</td>\n",
       "      <td>High_spent_Small_value_payments</td>\n",
       "      <td>463.23898098947717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>0x25ff1</td>\n",
       "      <td>CUS_0x942c</td>\n",
       "      <td>December</td>\n",
       "      <td>Nicks</td>\n",
       "      <td>25</td>\n",
       "      <td>078-73-5990</td>\n",
       "      <td>Mechanic</td>\n",
       "      <td>39628.99</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Auto Loan, and Student Loan</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>_</td>\n",
       "      <td>502.38</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>32 Years and 2 Months</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>220.45787812168732</td>\n",
       "      <td>Low_spent_Medium_value_payments</td>\n",
       "      <td>360.37968260123847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ID Customer_ID      Month            Name   Age          SSN  \\\n",
       "49995  0x25fe5  CUS_0x8600   December  Sarah McBridec  4975  031-35-0942   \n",
       "49996  0x25fee  CUS_0x942c  September           Nicks    25  078-73-5990   \n",
       "49997  0x25fef  CUS_0x942c    October           Nicks    25  078-73-5990   \n",
       "49998  0x25ff0  CUS_0x942c   November           Nicks    25  078-73-5990   \n",
       "49999  0x25ff1  CUS_0x942c   December           Nicks    25  078-73-5990   \n",
       "\n",
       "      Occupation Annual_Income  Monthly_Inhand_Salary  Num_Bank_Accounts  \\\n",
       "49995  Architect      20002.88                 (:.2f)                 10   \n",
       "49996   Mechanic      39628.99                    NaN                  4   \n",
       "49997   Mechanic      39628.99                 (:.2f)                  4   \n",
       "49998   Mechanic      39628.99                    NaN                  4   \n",
       "49999   Mechanic      39628.99                 (:.2f)                  4   \n",
       "\n",
       "       Num_Credit_Card  Interest_Rate Num_of_Loan  \\\n",
       "49995                8             29           5   \n",
       "49996                6              7          2_   \n",
       "49997                6              7           2   \n",
       "49998                6              7          2_   \n",
       "49999                6              7           2   \n",
       "\n",
       "                                            Type_of_Loan  Delay_from_due_date  \\\n",
       "49995  Personal Loan, Auto Loan, Mortgage Loan, Stude...                   33   \n",
       "49996                        Auto Loan, and Student Loan                   20   \n",
       "49997                        Auto Loan, and Student Loan                   23   \n",
       "49998                        Auto Loan, and Student Loan                   21   \n",
       "49999                        Auto Loan, and Student Loan                   22   \n",
       "\n",
       "      Num_of_Delayed_Payment Changed_Credit_Limit  Num_Credit_Inquiries  \\\n",
       "49995                     25                18.31                (:.2f)   \n",
       "49996                    NaN                 11.5                (:.2f)   \n",
       "49997                      5                 13.5                (:.2f)   \n",
       "49998                     6_                 11.5                (:.2f)   \n",
       "49999                      5                 11.5                (:.2f)   \n",
       "\n",
       "      Credit_Mix Outstanding_Debt  Credit_Utilization_Ratio  \\\n",
       "49995          _           3571.7                    (:.2f)   \n",
       "49996       Good           502.38                    (:.2f)   \n",
       "49997       Good           502.38                    (:.2f)   \n",
       "49998       Good           502.38                    (:.2f)   \n",
       "49999          _           502.38                    (:.2f)   \n",
       "\n",
       "           Credit_History_Age Payment_of_Min_Amount  Total_EMI_per_month  \\\n",
       "49995                     NaN                   Yes               (:.2f)   \n",
       "49996  31 Years and 11 Months                    NM               (:.2f)   \n",
       "49997   32 Years and 0 Months                    No               (:.2f)   \n",
       "49998   32 Years and 1 Months                    No               (:.2f)   \n",
       "49999   32 Years and 2 Months                    No               (:.2f)   \n",
       "\n",
       "      Amount_invested_monthly                Payment_Behaviour  \\\n",
       "49995      146.48632477751087   Low_spent_Small_value_payments   \n",
       "49996      181.44299902757518   Low_spent_Small_value_payments   \n",
       "49997               __10000__   Low_spent_Large_value_payments   \n",
       "49998       97.59857973344877  High_spent_Small_value_payments   \n",
       "49999      220.45787812168732  Low_spent_Medium_value_payments   \n",
       "\n",
       "          Monthly_Balance  \n",
       "49995  275.53956951573343  \n",
       "49996  409.39456169535066  \n",
       "49997   349.7263321025098  \n",
       "49998  463.23898098947717  \n",
       "49999  360.37968260123847  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "84a58cf5-22a3-4d99-b000-127794d0987f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.columns = [x.lower() for x in df_train.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "c0ce18b0-32e1-47c0-a53e-38241d3505a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'customer_id', 'month', 'name', 'age', 'ssn', 'occupation',\n",
       "       'annual_income', 'monthly_inhand_salary', 'num_bank_accounts',\n",
       "       'num_credit_card', 'interest_rate', 'num_of_loan', 'type_of_loan',\n",
       "       'delay_from_due_date', 'num_of_delayed_payment', 'changed_credit_limit',\n",
       "       'num_credit_inquiries', 'credit_mix', 'outstanding_debt',\n",
       "       'credit_utilization_ratio', 'credit_history_age',\n",
       "       'payment_of_min_amount', 'total_emi_per_month',\n",
       "       'amount_invested_monthly', 'payment_behaviour', 'monthly_balance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "07854eb4-6371-4056-ba20-39294f3787db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# By inspection, VVIPS\n",
    "# credit history age\n",
    "# payment of minimum amount"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc62202e-7b17-4f87-8dcd-3147bd0c5a58",
   "metadata": {},
   "source": [
    "### **Data Quality Check**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "f696e325-edf6-4897-bc9c-ccd1b842965a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 27)"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape   # not all of these columns will be used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "329c53b4-de8b-469b-9d59-5b9072c411c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping not needed columns\n",
    "df.drop([\"id\", \"customer_id\", \"month\", \"name\", \"ssn\", \"type_of_loan\", \"credit_history_age\"], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "8bd2760a-e5bc-4100-898a-3ce33a46faa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 20 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       50000 non-null  int64  \n",
      " 1   occupation                50000 non-null  object \n",
      " 2   annual_income             50000 non-null  float64\n",
      " 3   monthly_inhand_salary     50000 non-null  float64\n",
      " 4   num_bank_accounts         50000 non-null  int64  \n",
      " 5   num_credit_card           50000 non-null  int64  \n",
      " 6   interest_rate             50000 non-null  int64  \n",
      " 7   num_of_loan               50000 non-null  int64  \n",
      " 8   delay_from_due_date       50000 non-null  int64  \n",
      " 9   num_of_delayed_payment    50000 non-null  int64  \n",
      " 10  changed_credit_limit      50000 non-null  float64\n",
      " 11  num_credit_inquiries      50000 non-null  int64  \n",
      " 12  credit_mix                50000 non-null  object \n",
      " 13  outstanding_debt          50000 non-null  float64\n",
      " 14  credit_utilization_ratio  50000 non-null  float64\n",
      " 15  payment_of_min_amount     50000 non-null  object \n",
      " 16  total_emi_per_month       50000 non-null  float64\n",
      " 17  amount_invested_monthly   50000 non-null  float64\n",
      " 18  payment_behaviour         50000 non-null  object \n",
      " 19  monthly_balance           50000 non-null  float64\n",
      "dtypes: float64(8), int64(8), object(4)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "c4226a55-3154-435b-ab47-3ddbbc5fdafa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    50000\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().value_counts()   # how many duplicated values do we have in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "02ddcb22-941a-4218-9889-85db26bb09aa",
   "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>feature_name</th>\n",
       "      <th>null_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>age</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>occupation</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>annual_income</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>monthly_inhand_salary</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>num_bank_accounts</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>num_credit_card</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>interest_rate</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>num_of_loan</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>delay_from_due_date</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>num_of_delayed_payment</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>changed_credit_limit</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>num_credit_inquiries</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>credit_mix</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>outstanding_debt</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>credit_utilization_ratio</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>payment_of_min_amount</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>total_emi_per_month</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>amount_invested_monthly</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>payment_behaviour</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>monthly_balance</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                feature_name  null_counts\n",
       "0                        age            0\n",
       "1                 occupation            0\n",
       "2              annual_income            0\n",
       "3      monthly_inhand_salary            0\n",
       "4          num_bank_accounts            0\n",
       "5            num_credit_card            0\n",
       "6              interest_rate            0\n",
       "7                num_of_loan            0\n",
       "8        delay_from_due_date            0\n",
       "9     num_of_delayed_payment            0\n",
       "10      changed_credit_limit            0\n",
       "11      num_credit_inquiries            0\n",
       "12                credit_mix            0\n",
       "13          outstanding_debt            0\n",
       "14  credit_utilization_ratio            0\n",
       "15     payment_of_min_amount            0\n",
       "16       total_emi_per_month            0\n",
       "17   amount_invested_monthly            0\n",
       "18         payment_behaviour            0\n",
       "19           monthly_balance            0"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().reset_index().rename(columns={\"index\": \"feature_name\", 0: \"null_counts\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "3b13f9b6-888b-4c86-9f89-e5d0638067b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50000"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "d1bdc063-1b57-4999-a48f-7333e7fa4356",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 Records dropped\n"
     ]
    }
   ],
   "source": [
    "# Dropping the null values (with threshold) should there be missing values\n",
    "size_before_cleaning = df.shape   # dimension of the dataframe\n",
    "df = df[df.isnull().sum(axis=1) < 3]\n",
    "print(\"{} Records dropped\".format(size_before_cleaning[0] - df.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "a50c167c-ec05-4853-97ed-76e47a7a48bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>monthly_inhand_salary</th>\n",
       "      <th>num_bank_accounts</th>\n",
       "      <th>num_credit_card</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>num_of_loan</th>\n",
       "      <th>delay_from_due_date</th>\n",
       "      <th>num_of_delayed_payment</th>\n",
       "      <th>changed_credit_limit</th>\n",
       "      <th>num_credit_inquiries</th>\n",
       "      <th>credit_mix</th>\n",
       "      <th>outstanding_debt</th>\n",
       "      <th>credit_utilization_ratio</th>\n",
       "      <th>payment_of_min_amount</th>\n",
       "      <th>total_emi_per_month</th>\n",
       "      <th>amount_invested_monthly</th>\n",
       "      <th>payment_behaviour</th>\n",
       "      <th>monthly_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>9</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>Good</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>7</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>5</td>\n",
       "      <td>Good</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51</td>\n",
       "      <td>Banker</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>3</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age occupation  annual_income  monthly_inhand_salary  num_bank_accounts  \\\n",
       "0   31     Lawyer         (:.2f)                 (:.2f)                  9   \n",
       "1   56   Engineer         (:.2f)                 (:.2f)                  6   \n",
       "2   23     Doctor         (:.2f)                 (:.2f)                  5   \n",
       "3   47     Lawyer         (:.2f)                 (:.2f)                  6   \n",
       "4   51     Banker         (:.2f)                 (:.2f)                  7   \n",
       "\n",
       "   num_credit_card  interest_rate  num_of_loan  delay_from_due_date  \\\n",
       "0                1             10            1                    7   \n",
       "1                5             13            4                    7   \n",
       "2                3              6            4                    8   \n",
       "3                1             13            6                   19   \n",
       "4                3             16            4                    4   \n",
       "\n",
       "   num_of_delayed_payment  changed_credit_limit  num_credit_inquiries  \\\n",
       "0                      14                (:.2f)                     9   \n",
       "1                       1                (:.2f)                     6   \n",
       "2                      17                (:.2f)                     7   \n",
       "3                      18                (:.2f)                     5   \n",
       "4                       9                (:.2f)                     3   \n",
       "\n",
       "  credit_mix  outstanding_debt  credit_utilization_ratio  \\\n",
       "0       Fair            (:.2f)                    (:.2f)   \n",
       "1       Good            (:.2f)                    (:.2f)   \n",
       "2       Fair            (:.2f)                    (:.2f)   \n",
       "3       Good            (:.2f)                    (:.2f)   \n",
       "4       Fair            (:.2f)                    (:.2f)   \n",
       "\n",
       "  payment_of_min_amount  total_emi_per_month  amount_invested_monthly  \\\n",
       "0                   Yes               (:.2f)                   (:.2f)   \n",
       "1                    No               (:.2f)                   (:.2f)   \n",
       "2                    No               (:.2f)                   (:.2f)   \n",
       "3                    No               (:.2f)                   (:.2f)   \n",
       "4                   Yes               (:.2f)                   (:.2f)   \n",
       "\n",
       "                  payment_behaviour  monthly_balance  \n",
       "0  High_spent_Medium_value_payments           (:.2f)  \n",
       "1  High_spent_Medium_value_payments           (:.2f)  \n",
       "2    Low_spent_Small_value_payments           (:.2f)  \n",
       "3    Low_spent_Small_value_payments           (:.2f)  \n",
       "4  High_spent_Medium_value_payments           (:.2f)  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "2788738d-fcc2-46ce-b326-c3937eb1bb82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>monthly_inhand_salary</th>\n",
       "      <th>num_bank_accounts</th>\n",
       "      <th>num_credit_card</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>num_of_loan</th>\n",
       "      <th>delay_from_due_date</th>\n",
       "      <th>num_of_delayed_payment</th>\n",
       "      <th>changed_credit_limit</th>\n",
       "      <th>num_credit_inquiries</th>\n",
       "      <th>credit_mix</th>\n",
       "      <th>outstanding_debt</th>\n",
       "      <th>credit_utilization_ratio</th>\n",
       "      <th>payment_of_min_amount</th>\n",
       "      <th>total_emi_per_month</th>\n",
       "      <th>amount_invested_monthly</th>\n",
       "      <th>payment_behaviour</th>\n",
       "      <th>monthly_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>57</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>8</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>26</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Medium_spent_Large_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>40</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>5</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>50</td>\n",
       "      <td>Artist</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>25</td>\n",
       "      <td>18</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>5</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Medium_spent_Large_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>27</td>\n",
       "      <td>Scientist</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>15</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>Good</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Medium_spent_Large_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       age occupation  annual_income  monthly_inhand_salary  \\\n",
       "49995   57     Lawyer         (:.2f)                 (:.2f)   \n",
       "49996   26     Lawyer         (:.2f)                 (:.2f)   \n",
       "49997   40   Engineer         (:.2f)                 (:.2f)   \n",
       "49998   50     Artist         (:.2f)                 (:.2f)   \n",
       "49999   27  Scientist         (:.2f)                 (:.2f)   \n",
       "\n",
       "       num_bank_accounts  num_credit_card  interest_rate  num_of_loan  \\\n",
       "49995                  7                5             17            3   \n",
       "49996                  6                3              5            5   \n",
       "49997                  5                4             13            6   \n",
       "49998                  3                2             12            5   \n",
       "49999                  3                1             12            6   \n",
       "\n",
       "       delay_from_due_date  num_of_delayed_payment  changed_credit_limit  \\\n",
       "49995                   18                      15                (:.2f)   \n",
       "49996                    3                      13                (:.2f)   \n",
       "49997                   10                       0                (:.2f)   \n",
       "49998                   25                      18                (:.2f)   \n",
       "49999                   13                      15                (:.2f)   \n",
       "\n",
       "       num_credit_inquiries credit_mix  outstanding_debt  \\\n",
       "49995                     8       Fair            (:.2f)   \n",
       "49996                     4       Fair            (:.2f)   \n",
       "49997                     5       Fair            (:.2f)   \n",
       "49998                     5       Fair            (:.2f)   \n",
       "49999                     4       Good            (:.2f)   \n",
       "\n",
       "       credit_utilization_ratio payment_of_min_amount  total_emi_per_month  \\\n",
       "49995                    (:.2f)                    No               (:.2f)   \n",
       "49996                    (:.2f)                    No               (:.2f)   \n",
       "49997                    (:.2f)                   Yes               (:.2f)   \n",
       "49998                    (:.2f)                    No               (:.2f)   \n",
       "49999                    (:.2f)                   Yes               (:.2f)   \n",
       "\n",
       "       amount_invested_monthly                  payment_behaviour  \\\n",
       "49995                   (:.2f)   High_spent_Medium_value_payments   \n",
       "49996                   (:.2f)  Medium_spent_Large_value_payments   \n",
       "49997                   (:.2f)   High_spent_Medium_value_payments   \n",
       "49998                   (:.2f)  Medium_spent_Large_value_payments   \n",
       "49999                   (:.2f)  Medium_spent_Large_value_payments   \n",
       "\n",
       "       monthly_balance  \n",
       "49995           (:.2f)  \n",
       "49996           (:.2f)  \n",
       "49997           (:.2f)  \n",
       "49998           (:.2f)  \n",
       "49999           (:.2f)  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "cc35274f-ccc9-4500-8a28-392e6070690d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([31, 56, 23, 47, 51, 35, 30, 49, 39, 38, 20, 53, 64, 61, 22, 37, 19,\n",
       "       62, 54, 60, 26, 24, 58, 32, 27, 18, 44, 63, 21, 45, 25, 34, 33, 55,\n",
       "       52, 28, 29, 46, 41, 40, 48, 36, 50, 57, 42, 59, 43])"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insoect each column for possible cleaning\n",
    "df[\"age\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "d8f0eebb-a142-4006-9ba4-1e47726043aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age\n",
       "59    1150\n",
       "40    1133\n",
       "44    1130\n",
       "49    1126\n",
       "29    1120\n",
       "23    1108\n",
       "31    1105\n",
       "52    1101\n",
       "58    1095\n",
       "39    1092\n",
       "30    1092\n",
       "34    1092\n",
       "61    1085\n",
       "54    1084\n",
       "56    1083\n",
       "18    1080\n",
       "64    1078\n",
       "28    1078\n",
       "46    1077\n",
       "50    1076\n",
       "45    1073\n",
       "25    1072\n",
       "37    1071\n",
       "43    1067\n",
       "53    1067\n",
       "26    1059\n",
       "47    1055\n",
       "63    1054\n",
       "33    1052\n",
       "62    1049\n",
       "48    1048\n",
       "57    1044\n",
       "32    1041\n",
       "38    1040\n",
       "20    1039\n",
       "60    1039\n",
       "22    1030\n",
       "55    1025\n",
       "36    1023\n",
       "21    1021\n",
       "24    1020\n",
       "51    1020\n",
       "41    1014\n",
       "42    1013\n",
       "27    1008\n",
       "19     988\n",
       "35     983\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"age\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "e87dde58-ddfd-4967-a05b-ee1257885299",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a function to clean some of the columns\n",
    "def amount_invested_monthly(col):\n",
    "    if \"__\" in str(col):\n",
    "        return str(col).split(\"__\")[1]\n",
    "    else:\n",
    "        return str(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "fe964009-2a08-4ff9-9602-4381eac8ad95",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"amount_invested_monthly\"] = df[\"amount_invested_monthly\"].apply(amount_invested_monthly)\n",
    "df[\"amount_invested_monthly\"] = df[\"amount_invested_monthly\"].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "1d00aed0-0812-41bf-8e89-7742a8889f67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>monthly_inhand_salary</th>\n",
       "      <th>num_bank_accounts</th>\n",
       "      <th>num_credit_card</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>num_of_loan</th>\n",
       "      <th>delay_from_due_date</th>\n",
       "      <th>num_of_delayed_payment</th>\n",
       "      <th>changed_credit_limit</th>\n",
       "      <th>num_credit_inquiries</th>\n",
       "      <th>credit_mix</th>\n",
       "      <th>outstanding_debt</th>\n",
       "      <th>credit_utilization_ratio</th>\n",
       "      <th>payment_of_min_amount</th>\n",
       "      <th>total_emi_per_month</th>\n",
       "      <th>amount_invested_monthly</th>\n",
       "      <th>payment_behaviour</th>\n",
       "      <th>monthly_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>9</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>56</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>Good</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>17</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>7</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>5</td>\n",
       "      <td>Good</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51</td>\n",
       "      <td>Banker</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>3</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age occupation  annual_income  monthly_inhand_salary  num_bank_accounts  \\\n",
       "0   31     Lawyer         (:.2f)                 (:.2f)                  9   \n",
       "1   56   Engineer         (:.2f)                 (:.2f)                  6   \n",
       "2   23     Doctor         (:.2f)                 (:.2f)                  5   \n",
       "3   47     Lawyer         (:.2f)                 (:.2f)                  6   \n",
       "4   51     Banker         (:.2f)                 (:.2f)                  7   \n",
       "\n",
       "   num_credit_card  interest_rate  num_of_loan  delay_from_due_date  \\\n",
       "0                1             10            1                    7   \n",
       "1                5             13            4                    7   \n",
       "2                3              6            4                    8   \n",
       "3                1             13            6                   19   \n",
       "4                3             16            4                    4   \n",
       "\n",
       "   num_of_delayed_payment  changed_credit_limit  num_credit_inquiries  \\\n",
       "0                      14                (:.2f)                     9   \n",
       "1                       1                (:.2f)                     6   \n",
       "2                      17                (:.2f)                     7   \n",
       "3                      18                (:.2f)                     5   \n",
       "4                       9                (:.2f)                     3   \n",
       "\n",
       "  credit_mix  outstanding_debt  credit_utilization_ratio  \\\n",
       "0       Fair            (:.2f)                    (:.2f)   \n",
       "1       Good            (:.2f)                    (:.2f)   \n",
       "2       Fair            (:.2f)                    (:.2f)   \n",
       "3       Good            (:.2f)                    (:.2f)   \n",
       "4       Fair            (:.2f)                    (:.2f)   \n",
       "\n",
       "  payment_of_min_amount  total_emi_per_month  amount_invested_monthly  \\\n",
       "0                   Yes               (:.2f)                   (:.2f)   \n",
       "1                    No               (:.2f)                   (:.2f)   \n",
       "2                    No               (:.2f)                   (:.2f)   \n",
       "3                    No               (:.2f)                   (:.2f)   \n",
       "4                   Yes               (:.2f)                   (:.2f)   \n",
       "\n",
       "                  payment_behaviour  monthly_balance  \n",
       "0  High_spent_Medium_value_payments           (:.2f)  \n",
       "1  High_spent_Medium_value_payments           (:.2f)  \n",
       "2    Low_spent_Small_value_payments           (:.2f)  \n",
       "3    Low_spent_Small_value_payments           (:.2f)  \n",
       "4  High_spent_Medium_value_payments           (:.2f)  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "db7faa0d-deb0-458c-b7ee-f5f5eeac4453",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_delayed_payments(value):\n",
    "    if \"__\" in str(value):\n",
    "        return str(value).split(\"__\")[1]\n",
    "    elif \"_\" in str(value):\n",
    "        return str(value).replace(\"_\", \"\")\n",
    "    elif str(value) == \"_\":\n",
    "        return str(value)\n",
    "    else:\n",
    "        return str(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "id": "85c22c7a-eb44-4650-9aa4-8b3ca16679b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"num_of_delayed_payment\"] = df[\"num_of_delayed_payment\"].apply(filter_delayed_payments)\n",
    "df[\"num_of_delayed_payment\"] = df[\"num_of_delayed_payment\"].astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "1c464939-d44e-4315-99f9-22c9f79d762f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_general(value):\n",
    "    if \"-\" in str(value):\n",
    "        return str(value).split(\"-\")[1]\n",
    "    elif \"_\" in str(value):\n",
    "        return str(value).split(\"_\")[1]\n",
    "    else:\n",
    "        return str(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "id": "629117aa-1966-41ce-bdab-5322397d95b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "age: Successfully Cleaned\n",
      "annual_income: Successfully Cleaned\n",
      "num_of_loan: Successfully Cleaned\n",
      "outstanding_debt: Successfully Cleaned\n",
      "monthly_balance: Successfully Cleaned\n"
     ]
    }
   ],
   "source": [
    "df.drop(df[df[\"monthly_balance\"] == \"__-3333333333333333__\"].index, inplace = True)\n",
    "for i in [\"age\", \"annual_income\", \"num_of_loan\", \"outstanding_debt\", \"monthly_balance\"]:\n",
    "    df[i] = df[i].apply(filter_general)\n",
    "    df[i] = df[i].astype(np.float64)\n",
    "    print(i + \": Successfully Cleaned\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "43dbc07c-f667-43c4-a47f-52a31d9d7560",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"changed_credit_limit\"] = df[\"changed_credit_limit\"].apply(lambda x:str(x).split(\"-\")[-1])\n",
    "df.drop(df[df[\"changed_credit_limit\"] == \"_\"].index, inplace = True)\n",
    "df[\"changed_credit_limit\"] = df[\"changed_credit_limit\"].astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "cc1e84ab-8832-4777-bbe3-fdf5da514915",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['occupation'] = df['occupation'].replace('______', np.nan)\n",
    "df['occupation'] = df['occupation'].fillna(np.random.choice(pd.Series([\n",
    "    'Scientist', 'Teacher', 'Engineer', 'Entrepreneur',\n",
    "    'Developer', 'Lawyer', 'Media_Manager', 'Doctor',\n",
    "    'Journalist', 'Manager', 'Accountant', 'Musician',\n",
    "    'Mechanic', 'Writer', 'Architect'\n",
    "])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "b8bee086-0afd-4ddf-852d-0cabf713a564",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['credit_mix'] = df['credit_mix'].replace('_', np.nan)\n",
    "df['credit_mix'] = df['credit_mix'].fillna(np.random.choice(pd.Series([\n",
    "    'Standard', 'Good', 'Bad'\n",
    "])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "2aa2fe4d-a815-41a1-8949-d608fc79f882",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['payment_of_min_amount'] = df['payment_of_min_amount'].replace('NM', np.nan)\n",
    "df['payment_of_min_amount'] = df['payment_of_min_amount'].fillna(np.random.choice(pd.Series(['Yes', 'No'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "b35beafe-211d-458b-9212-d40adf10a6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['payment_behaviour'] = df['payment_behaviour'].replace('@#%$', np.nan)\n",
    "df['payment_behaviour'] = df['payment_behaviour'].fillna(np.random.choice(pd.Series([\n",
    "    'High_spent_Small_value_payments', 'Low_spent_Small_value_payments',\n",
    "    'High_spent_Medium_value_payments', 'Low_spent_Large_value_payments',\n",
    "    'High_spent_Large_value_payments', 'Low_spent_Medium_value_payments'\n",
    "])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "b35a2cf0-9b83-4831-a508-4c6282e59c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in ['monthly_inhand_salary', 'num_of_delayed_payment', 'num_credit_inquiries',\n",
    "          'amount_invested_monthly', 'monthly_balance']:\n",
    "    df[i].fillna(df[i].median(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "15d3ac21-246b-449b-9587-2c438da94f2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>occupation</th>\n",
       "      <th>annual_income</th>\n",
       "      <th>monthly_inhand_salary</th>\n",
       "      <th>num_bank_accounts</th>\n",
       "      <th>num_credit_card</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>num_of_loan</th>\n",
       "      <th>delay_from_due_date</th>\n",
       "      <th>num_of_delayed_payment</th>\n",
       "      <th>changed_credit_limit</th>\n",
       "      <th>num_credit_inquiries</th>\n",
       "      <th>credit_mix</th>\n",
       "      <th>outstanding_debt</th>\n",
       "      <th>credit_utilization_ratio</th>\n",
       "      <th>payment_of_min_amount</th>\n",
       "      <th>total_emi_per_month</th>\n",
       "      <th>amount_invested_monthly</th>\n",
       "      <th>payment_behaviour</th>\n",
       "      <th>monthly_balance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>7</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>9</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Engineer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>7</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>Good</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>8</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>7</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>19</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>5</td>\n",
       "      <td>Good</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>No</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Low_spent_Small_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Banker</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>4</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>3</td>\n",
       "      <td>Fair</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>Yes</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>(:.2f)</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age occupation  annual_income  monthly_inhand_salary  num_bank_accounts  \\\n",
       "0 (:.2f)     Lawyer         (:.2f)                 (:.2f)                  9   \n",
       "1 (:.2f)   Engineer         (:.2f)                 (:.2f)                  6   \n",
       "2 (:.2f)     Doctor         (:.2f)                 (:.2f)                  5   \n",
       "3 (:.2f)     Lawyer         (:.2f)                 (:.2f)                  6   \n",
       "4 (:.2f)     Banker         (:.2f)                 (:.2f)                  7   \n",
       "\n",
       "   num_credit_card  interest_rate  num_of_loan  delay_from_due_date  \\\n",
       "0                1             10       (:.2f)                    7   \n",
       "1                5             13       (:.2f)                    7   \n",
       "2                3              6       (:.2f)                    8   \n",
       "3                1             13       (:.2f)                   19   \n",
       "4                3             16       (:.2f)                    4   \n",
       "\n",
       "   num_of_delayed_payment  changed_credit_limit  num_credit_inquiries  \\\n",
       "0                  (:.2f)                (:.2f)                     9   \n",
       "1                  (:.2f)                (:.2f)                     6   \n",
       "2                  (:.2f)                (:.2f)                     7   \n",
       "3                  (:.2f)                (:.2f)                     5   \n",
       "4                  (:.2f)                (:.2f)                     3   \n",
       "\n",
       "  credit_mix  outstanding_debt  credit_utilization_ratio  \\\n",
       "0       Fair            (:.2f)                    (:.2f)   \n",
       "1       Good            (:.2f)                    (:.2f)   \n",
       "2       Fair            (:.2f)                    (:.2f)   \n",
       "3       Good            (:.2f)                    (:.2f)   \n",
       "4       Fair            (:.2f)                    (:.2f)   \n",
       "\n",
       "  payment_of_min_amount  total_emi_per_month  amount_invested_monthly  \\\n",
       "0                   Yes               (:.2f)                   (:.2f)   \n",
       "1                    No               (:.2f)                   (:.2f)   \n",
       "2                    No               (:.2f)                   (:.2f)   \n",
       "3                    No               (:.2f)                   (:.2f)   \n",
       "4                   Yes               (:.2f)                   (:.2f)   \n",
       "\n",
       "                  payment_behaviour  monthly_balance  \n",
       "0  High_spent_Medium_value_payments           (:.2f)  \n",
       "1  High_spent_Medium_value_payments           (:.2f)  \n",
       "2    Low_spent_Small_value_payments           (:.2f)  \n",
       "3    Low_spent_Small_value_payments           (:.2f)  \n",
       "4  High_spent_Medium_value_payments           (:.2f)  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "d9853d71-c140-47ac-9c96-37e7a129475b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 20 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   age                       50000 non-null  float64\n",
      " 1   occupation                50000 non-null  object \n",
      " 2   annual_income             50000 non-null  float64\n",
      " 3   monthly_inhand_salary     50000 non-null  float64\n",
      " 4   num_bank_accounts         50000 non-null  int64  \n",
      " 5   num_credit_card           50000 non-null  int64  \n",
      " 6   interest_rate             50000 non-null  int64  \n",
      " 7   num_of_loan               50000 non-null  float64\n",
      " 8   delay_from_due_date       50000 non-null  int64  \n",
      " 9   num_of_delayed_payment    50000 non-null  float64\n",
      " 10  changed_credit_limit      50000 non-null  float64\n",
      " 11  num_credit_inquiries      50000 non-null  int64  \n",
      " 12  credit_mix                50000 non-null  object \n",
      " 13  outstanding_debt          50000 non-null  float64\n",
      " 14  credit_utilization_ratio  50000 non-null  float64\n",
      " 15  payment_of_min_amount     50000 non-null  object \n",
      " 16  total_emi_per_month       50000 non-null  float64\n",
      " 17  amount_invested_monthly   50000 non-null  float64\n",
      " 18  payment_behaviour         50000 non-null  object \n",
      " 19  monthly_balance           50000 non-null  float64\n",
      "dtypes: float64(11), int64(5), object(4)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98d475b5-316b-4213-ae63-3ef28644e87c",
   "metadata": {},
   "source": [
    "### **Exploratory Data Analysis (Categorical Variables)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "8aa583cc-8416-4dbb-a649-11f31e6ab8c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat = df.select_dtypes(include = \"object\") \n",
    "df_num = df.select_dtypes(include = \"number\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "134292a1-17e4-4596-910f-2909d98c7c07",
   "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>occupation</th>\n",
       "      <td>50000</td>\n",
       "      <td>7</td>\n",
       "      <td>Doctor</td>\n",
       "      <td>7208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_mix</th>\n",
       "      <td>50000</td>\n",
       "      <td>3</td>\n",
       "      <td>Bad</td>\n",
       "      <td>16753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_of_min_amount</th>\n",
       "      <td>50000</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>25024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>payment_behaviour</th>\n",
       "      <td>50000</td>\n",
       "      <td>3</td>\n",
       "      <td>High_spent_Medium_value_payments</td>\n",
       "      <td>16950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       count unique                               top   freq\n",
       "occupation             50000      7                            Doctor   7208\n",
       "credit_mix             50000      3                               Bad  16753\n",
       "payment_of_min_amount  50000      2                               Yes  25024\n",
       "payment_behaviour      50000      3  High_spent_Medium_value_payments  16950"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "8cfe33d1-e046-4742-a8de-ae2aec24438c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "occupation\n",
      "Doctor       7208\n",
      "Teacher      7180\n",
      "Artist       7167\n",
      "Lawyer       7117\n",
      "Banker       7114\n",
      "Scientist    7112\n",
      "Engineer     7102\n",
      "Name: count, dtype: int64\n",
      "\n",
      "-----------------\n",
      "credit_mix\n",
      "Bad     16753\n",
      "Good    16630\n",
      "Fair    16617\n",
      "Name: count, dtype: int64\n",
      "\n",
      "-----------------\n",
      "payment_of_min_amount\n",
      "Yes    25024\n",
      "No     24976\n",
      "Name: count, dtype: int64\n",
      "\n",
      "-----------------\n",
      "payment_behaviour\n",
      "High_spent_Medium_value_payments     16950\n",
      "Medium_spent_Large_value_payments    16570\n",
      "Low_spent_Small_value_payments       16480\n",
      "Name: count, dtype: int64\n",
      "\n",
      "-----------------\n"
     ]
    }
   ],
   "source": [
    "for cat_col in df_cat:\n",
    "    print(df_cat[cat_col].value_counts())\n",
    "    print(\"\\n-----------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "dc21d1b3-a415-438d-abc0-f286a4e59e79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the data\n",
    "# Occupation\n",
    "plt.figure(figsize = (10, 4))\n",
    "ax = sns.countplot(x = \"occupation\", data = df_cat, order = df[\"occupation\"].value_counts().index)\n",
    "plt.title(\"Distribution of Counts by Occupation\")\n",
    "plt.xlabel(\"Occupation\")\n",
    "plt.ylabel(\"Occuppation Counts\")\n",
    "for p in ax.patches:\n",
    "    ax.annotate(\"{}\".format(p.get_height()), (p.get_x() + 0.1, p.get_height() + 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "d24f2803-3f29-42b7-a3db-8b10bae31c17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Credit Mix\n",
    "plt.figure(figsize=(10,4))\n",
    "ax = sns.countplot(x='credit_mix', data=df_cat, order=df['credit_mix'].value_counts().index)\n",
    "plt.title('Distribution of Counts by Credit Mix')\n",
    "plt.xlabel('Credit Mix')\n",
    "plt.ylabel('Credit Mix Counts')\n",
    "for p in ax.patches:\n",
    "    ax.annotate('{}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "fb69b356-7c7e-4c6b-81aa-600e0d039006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Payment Behaviour\n",
    "plt.figure(figsize=(10,4))\n",
    "ax = sns.countplot(x='payment_behaviour', data=df_cat, order=df['payment_behaviour'].value_counts().index)\n",
    "plt.title('Distribution of Counts by Payment Behavior')\n",
    "plt.xlabel('Payment Behavior')\n",
    "plt.ylabel('Payment Behavior Counts')\n",
    "for p in ax.patches:\n",
    "    ax.annotate('{}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "fbbdaa0f-2806-44a8-bee5-e815c7ffeb9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['occupation', 'credit_mix', 'payment_of_min_amount',\n",
       "       'payment_behaviour'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "25a0ada8-25c3-4a3d-8244-9d4b00c05fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Changed Credit Limit\n",
    "# plt.figure(figsize=(10,4))\n",
    "# ax = sns.countplot(x = \"changed_credit_Limit\", data = df_cat, order = df[\"changed_credit_Limit\"].value_counts().iloc[:20].index)\n",
    "# plt.title(\"Distribution of Counts by Changed Credit Limit\")\n",
    "# plt.xlabel(\"Changed Credit Limit\")\n",
    "# plt.ylabel(\"Changed Credit Limit Counts\")\n",
    "# for p in ax.patches:\n",
    "#     ax.annotate(\"{}\".format(p.get_height()), (p.get_x() + 0.1, p.get_height() + 50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "5f67551c-95d3-4c7c-9832-9ffaeed9781f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Payment of Min Amount\n",
    "plt.figure(figsize=(10,4))\n",
    "ax = sns.countplot(x='payment_of_min_amount', data=df_cat, order=df['payment_of_min_amount'].value_counts().index)\n",
    "plt.title('Distribution of Counts by Payment of Min Amount')\n",
    "plt.xlabel('Payment of Min Amount')\n",
    "plt.ylabel('Payment of Min Amount Counts')\n",
    "for p in ax.patches:\n",
    "    ax.annotate('{}'.format(p.get_height()), (p.get_x()+0.1, p.get_height()+50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "8782584a-dba1-42f7-afb4-51148d03cfb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['occupation', 'credit_mix', 'payment_of_min_amount',\n",
       "       'payment_behaviour'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "daf10b21-c4dd-482f-8060-10a194800670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'occupation', 'annual_income', 'monthly_inhand_salary',\n",
       "       'num_bank_accounts', 'num_credit_card', 'interest_rate', 'num_of_loan',\n",
       "       'delay_from_due_date', 'num_of_delayed_payment', 'changed_credit_limit',\n",
       "       'num_credit_inquiries', 'credit_mix', 'outstanding_debt',\n",
       "       'credit_utilization_ratio', 'payment_of_min_amount',\n",
       "       'total_emi_per_month', 'amount_invested_monthly', 'payment_behaviour',\n",
       "       'monthly_balance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "f2ecf259-f53c-4969-ba90-9b68d2feefc0",
   "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>changed_credit_limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>(:.2f)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       changed_credit_limit\n",
       "0                    (:.2f)\n",
       "1                    (:.2f)\n",
       "2                    (:.2f)\n",
       "3                    (:.2f)\n",
       "4                    (:.2f)\n",
       "...                     ...\n",
       "49995                (:.2f)\n",
       "49996                (:.2f)\n",
       "49997                (:.2f)\n",
       "49998                (:.2f)\n",
       "49999                (:.2f)\n",
       "\n",
       "[50000 rows x 1 columns]"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"changed_credit_limit\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "b6ad14fb-a72e-49cc-8a5f-c55de486d2dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'occupation', 'annual_income', 'monthly_inhand_salary',\n",
       "       'num_bank_accounts', 'num_credit_card', 'interest_rate', 'num_of_loan',\n",
       "       'delay_from_due_date', 'num_of_delayed_payment', 'changed_credit_limit',\n",
       "       'num_credit_inquiries', 'credit_mix', 'outstanding_debt',\n",
       "       'credit_utilization_ratio', 'payment_of_min_amount',\n",
       "       'total_emi_per_month', 'amount_invested_monthly', 'payment_behaviour',\n",
       "       'monthly_balance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb65c3ba-5fbe-4f64-b7dc-54098d0e0fb8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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