Predicting Default Risk code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3290d3bb",
   "metadata": {},
   "source": [
    "# Data Preprocessing and Data Cleaning\n",
    "\n",
    "1. Are any of the numerical data fields highly-correlated with each other?\n",
    "2. Are there any missing data for each of the data fields?\n",
    "3. Are there only a few values in a subset of your data field? Does the data field look very uniform (remove fields having low variability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5baf19f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "164fc1dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit-Application-Result</th>\n",
       "      <th>Account-Balance</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Value-Savings-Stocks</th>\n",
       "      <th>Length-of-current-employment</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Guarantors</th>\n",
       "      <th>Duration-in-Current-address</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Concurrent-Credits</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>No-of-Credits-at-this-Bank</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>No-of-dependents</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign-Worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Other</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1544</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>More than 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3380</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Creditworthy</td>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>343</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Credit-Application-Result Account-Balance  Duration-of-Credit-Month  \\\n",
       "0              Creditworthy    Some Balance                         4   \n",
       "1              Creditworthy    Some Balance                         4   \n",
       "2              Creditworthy    Some Balance                         4   \n",
       "3              Creditworthy    Some Balance                         4   \n",
       "4              Creditworthy      No Account                         6   \n",
       "\n",
       "  Payment-Status-of-Previous-Credit       Purpose  Credit-Amount  \\\n",
       "0                           Paid Up         Other           1494   \n",
       "1                           Paid Up  Home Related           1494   \n",
       "2        No Problems (in this bank)  Home Related           1544   \n",
       "3        No Problems (in this bank)  Home Related           3380   \n",
       "4                           Paid Up  Home Related            343   \n",
       "\n",
       "  Value-Savings-Stocks Length-of-current-employment  Instalment-per-cent  \\\n",
       "0           £100-£1000                        < 1yr                    1   \n",
       "1           £100-£1000                        < 1yr                    1   \n",
       "2                 None                      1-4 yrs                    2   \n",
       "3                 None                      1-4 yrs                    1   \n",
       "4                 None                        < 1yr                    4   \n",
       "\n",
       "  Guarantors  Duration-in-Current-address  Most-valuable-available-asset  \\\n",
       "0       None                          2.0                              1   \n",
       "1       None                          2.0                              1   \n",
       "2       None                          1.0                              1   \n",
       "3       None                          1.0                              1   \n",
       "4       None                          1.0                              1   \n",
       "\n",
       "   Age-years Concurrent-Credits  Type-of-apartment No-of-Credits-at-this-Bank  \\\n",
       "0        NaN  Other Banks/Depts                  2                          1   \n",
       "1       29.0  Other Banks/Depts                  2                          1   \n",
       "2       42.0  Other Banks/Depts                  2                More than 1   \n",
       "3       37.0  Other Banks/Depts                  2                          1   \n",
       "4       27.0  Other Banks/Depts                  2                          1   \n",
       "\n",
       "   Occupation  No-of-dependents  Telephone  Foreign-Worker  \n",
       "0           1                 2          1               2  \n",
       "1           1                 2          1               2  \n",
       "2           1                 2          1               1  \n",
       "3           1                 2          1               1  \n",
       "4           1                 1          1               1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('credit-data-training.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d53abf8",
   "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>Duration-of-Credit-Month</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Duration-in-Current-address</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>No-of-dependents</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign-Worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>488.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>21.43400</td>\n",
       "      <td>3199.980000</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>2.660256</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>35.637295</td>\n",
       "      <td>1.928000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.14600</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.038000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.30742</td>\n",
       "      <td>2831.386861</td>\n",
       "      <td>1.113724</td>\n",
       "      <td>1.150017</td>\n",
       "      <td>1.064268</td>\n",
       "      <td>11.501522</td>\n",
       "      <td>0.539814</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.35346</td>\n",
       "      <td>0.490389</td>\n",
       "      <td>0.191388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.00000</td>\n",
       "      <td>1357.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.00000</td>\n",
       "      <td>2236.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.00000</td>\n",
       "      <td>3941.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.00000</td>\n",
       "      <td>18424.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Duration-of-Credit-Month  Credit-Amount  Instalment-per-cent  \\\n",
       "count                 500.00000     500.000000           500.000000   \n",
       "mean                   21.43400    3199.980000             3.010000   \n",
       "std                    12.30742    2831.386861             1.113724   \n",
       "min                     4.00000     276.000000             1.000000   \n",
       "25%                    12.00000    1357.250000             2.000000   \n",
       "50%                    18.00000    2236.500000             3.000000   \n",
       "75%                    24.00000    3941.500000             4.000000   \n",
       "max                    60.00000   18424.000000             4.000000   \n",
       "\n",
       "       Duration-in-Current-address  Most-valuable-available-asset   Age-years  \\\n",
       "count                   156.000000                     500.000000  488.000000   \n",
       "mean                      2.660256                       2.360000   35.637295   \n",
       "std                       1.150017                       1.064268   11.501522   \n",
       "min                       1.000000                       1.000000   19.000000   \n",
       "25%                       2.000000                       1.000000   27.000000   \n",
       "50%                       2.000000                       3.000000   33.000000   \n",
       "75%                       4.000000                       3.000000   42.000000   \n",
       "max                       4.000000                       4.000000   75.000000   \n",
       "\n",
       "       Type-of-apartment  Occupation  No-of-dependents   Telephone  \\\n",
       "count         500.000000       500.0         500.00000  500.000000   \n",
       "mean            1.928000         1.0           1.14600    1.400000   \n",
       "std             0.539814         0.0           0.35346    0.490389   \n",
       "min             1.000000         1.0           1.00000    1.000000   \n",
       "25%             2.000000         1.0           1.00000    1.000000   \n",
       "50%             2.000000         1.0           1.00000    1.000000   \n",
       "75%             2.000000         1.0           1.00000    2.000000   \n",
       "max             3.000000         1.0           2.00000    2.000000   \n",
       "\n",
       "       Foreign-Worker  \n",
       "count      500.000000  \n",
       "mean         1.038000  \n",
       "std          0.191388  \n",
       "min          1.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          2.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e82343eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Creditworthy', 'Non-Creditworthy'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#change credit-result into numbers\n",
    "df['Credit-Application-Result'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a564cec",
   "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>Credit-Application-Result</th>\n",
       "      <th>Account-Balance</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Value-Savings-Stocks</th>\n",
       "      <th>Length-of-current-employment</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Guarantors</th>\n",
       "      <th>Duration-in-Current-address</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Concurrent-Credits</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>No-of-Credits-at-this-Bank</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>No-of-dependents</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign-Worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Other</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1544</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>More than 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3380</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>343</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>4</td>\n",
       "      <td>None</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit-Application-Result Account-Balance  Duration-of-Credit-Month  \\\n",
       "0                          1    Some Balance                         4   \n",
       "1                          1    Some Balance                         4   \n",
       "2                          1    Some Balance                         4   \n",
       "3                          1    Some Balance                         4   \n",
       "4                          1      No Account                         6   \n",
       "\n",
       "  Payment-Status-of-Previous-Credit       Purpose  Credit-Amount  \\\n",
       "0                           Paid Up         Other           1494   \n",
       "1                           Paid Up  Home Related           1494   \n",
       "2        No Problems (in this bank)  Home Related           1544   \n",
       "3        No Problems (in this bank)  Home Related           3380   \n",
       "4                           Paid Up  Home Related            343   \n",
       "\n",
       "  Value-Savings-Stocks Length-of-current-employment  Instalment-per-cent  \\\n",
       "0           £100-£1000                        < 1yr                    1   \n",
       "1           £100-£1000                        < 1yr                    1   \n",
       "2                 None                      1-4 yrs                    2   \n",
       "3                 None                      1-4 yrs                    1   \n",
       "4                 None                        < 1yr                    4   \n",
       "\n",
       "  Guarantors  Duration-in-Current-address  Most-valuable-available-asset  \\\n",
       "0       None                          2.0                              1   \n",
       "1       None                          2.0                              1   \n",
       "2       None                          1.0                              1   \n",
       "3       None                          1.0                              1   \n",
       "4       None                          1.0                              1   \n",
       "\n",
       "   Age-years Concurrent-Credits  Type-of-apartment No-of-Credits-at-this-Bank  \\\n",
       "0        NaN  Other Banks/Depts                  2                          1   \n",
       "1       29.0  Other Banks/Depts                  2                          1   \n",
       "2       42.0  Other Banks/Depts                  2                More than 1   \n",
       "3       37.0  Other Banks/Depts                  2                          1   \n",
       "4       27.0  Other Banks/Depts                  2                          1   \n",
       "\n",
       "   Occupation  No-of-dependents  Telephone  Foreign-Worker  \n",
       "0           1                 2          1               2  \n",
       "1           1                 2          1               2  \n",
       "2           1                 2          1               1  \n",
       "3           1                 2          1               1  \n",
       "4           1                 1          1               1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Credit-Application-Result'] = df['Credit-Application-Result'].replace({'Creditworthy':1,'Non-Creditworthy':0})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bb0b8c76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Some Balance' 'No Account']\n",
      "['Paid Up' 'No Problems (in this bank)' 'Some Problems']\n",
      "['Other' 'Home Related' 'Used car' 'New car']\n",
      "['£100-£1000' 'None' '< £100']\n",
      "['< 1yr' '1-4 yrs' '4-7 yrs']\n",
      "['None' 'Yes']\n",
      "['Other Banks/Depts']\n",
      "['1' 'More than 1']\n"
     ]
    }
   ],
   "source": [
    "print(df['Account-Balance'].unique())\n",
    "print(df['Payment-Status-of-Previous-Credit'].unique())\n",
    "print(df['Purpose'].unique())\n",
    "print(df['Value-Savings-Stocks'].unique())\n",
    "print(df['Length-of-current-employment'].unique())\n",
    "print(df['Guarantors'].unique())\n",
    "print(df['Concurrent-Credits'].unique())\n",
    "print(df['No-of-Credits-at-this-Bank'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56f2bcb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No Account      262\n",
      "Some Balance    238\n",
      "Name: Account-Balance, dtype: int64\n",
      "24    108\n",
      "12     92\n",
      "18     45\n",
      "36     41\n",
      "6      34\n",
      "48     27\n",
      "9      27\n",
      "15     24\n",
      "30     18\n",
      "21     13\n",
      "10     11\n",
      "60      8\n",
      "42      5\n",
      "27      5\n",
      "11      5\n",
      "8       5\n",
      "45      4\n",
      "4       4\n",
      "20      4\n",
      "28      3\n",
      "33      3\n",
      "39      3\n",
      "7       3\n",
      "14      2\n",
      "26      1\n",
      "16      1\n",
      "40      1\n",
      "13      1\n",
      "47      1\n",
      "22      1\n",
      "Name: Duration-of-Credit-Month, dtype: int64\n",
      "Paid Up                       260\n",
      "No Problems (in this bank)    204\n",
      "Some Problems                  36\n",
      "Name: Payment-Status-of-Previous-Credit, dtype: int64\n",
      "Home Related    355\n",
      "Used car         83\n",
      "New car          47\n",
      "Other            15\n",
      "Name: Purpose, dtype: int64\n",
      "None          298\n",
      "£100-£1000    154\n",
      "< £100         48\n",
      "Name: Value-Savings-Stocks, dtype: int64\n",
      "< 1yr      279\n",
      "4-7 yrs    124\n",
      "1-4 yrs     97\n",
      "Name: Length-of-current-employment, dtype: int64\n",
      "4    246\n",
      "2    109\n",
      "3     79\n",
      "1     66\n",
      "Name: Instalment-per-cent, dtype: int64\n",
      "None    457\n",
      "Yes      43\n",
      "Name: Guarantors, dtype: int64\n",
      "4.0    56\n",
      "2.0    51\n",
      "1.0    29\n",
      "3.0    20\n",
      "Name: Duration-in-Current-address, dtype: int64\n",
      "3    174\n",
      "1    148\n",
      "2    101\n",
      "4     77\n",
      "Name: Most-valuable-available-asset, dtype: int64\n",
      "Other Banks/Depts    500\n",
      "Name: Concurrent-Credits, dtype: int64\n",
      "2    352\n",
      "1     92\n",
      "3     56\n",
      "Name: Type-of-apartment, dtype: int64\n",
      "1              320\n",
      "More than 1    180\n",
      "Name: No-of-Credits-at-this-Bank, dtype: int64\n",
      "1    500\n",
      "Name: Occupation, dtype: int64\n",
      "1    427\n",
      "2     73\n",
      "Name: No-of-dependents, dtype: int64\n",
      "1    481\n",
      "2     19\n",
      "Name: Foreign-Worker, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df['Account-Balance'].value_counts())\n",
    "print(df['Duration-of-Credit-Month'].value_counts())\n",
    "print(df['Payment-Status-of-Previous-Credit'].value_counts())\n",
    "print(df['Purpose'].value_counts())\n",
    "print(df['Value-Savings-Stocks'].value_counts())\n",
    "print(df['Length-of-current-employment'].value_counts())\n",
    "print(df['Instalment-per-cent'].value_counts())\n",
    "print(df['Guarantors'].value_counts())\n",
    "print(df['Duration-in-Current-address'].value_counts())\n",
    "print(df['Most-valuable-available-asset'].value_counts())\n",
    "print(df['Concurrent-Credits'].value_counts())\n",
    "print(df['Type-of-apartment'].value_counts())\n",
    "print(df['No-of-Credits-at-this-Bank'].value_counts())\n",
    "print(df['Occupation'].value_counts())\n",
    "print(df['No-of-dependents'].value_counts())\n",
    "print(df['Foreign-Worker'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "140a2d59",
   "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>Credit-Application-Result</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Duration-in-Current-address</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>No-of-dependents</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign-Worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>156.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>488.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.716000</td>\n",
       "      <td>21.43400</td>\n",
       "      <td>3199.980000</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>2.660256</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>35.637295</td>\n",
       "      <td>1.928000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.14600</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>1.038000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.451388</td>\n",
       "      <td>12.30742</td>\n",
       "      <td>2831.386861</td>\n",
       "      <td>1.113724</td>\n",
       "      <td>1.150017</td>\n",
       "      <td>1.064268</td>\n",
       "      <td>11.501522</td>\n",
       "      <td>0.539814</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.35346</td>\n",
       "      <td>0.490389</td>\n",
       "      <td>0.191388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>1357.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>2236.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>3941.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>18424.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Credit-Application-Result  Duration-of-Credit-Month  Credit-Amount  \\\n",
       "count                 500.000000                 500.00000     500.000000   \n",
       "mean                    0.716000                  21.43400    3199.980000   \n",
       "std                     0.451388                  12.30742    2831.386861   \n",
       "min                     0.000000                   4.00000     276.000000   \n",
       "25%                     0.000000                  12.00000    1357.250000   \n",
       "50%                     1.000000                  18.00000    2236.500000   \n",
       "75%                     1.000000                  24.00000    3941.500000   \n",
       "max                     1.000000                  60.00000   18424.000000   \n",
       "\n",
       "       Instalment-per-cent  Duration-in-Current-address  \\\n",
       "count           500.000000                   156.000000   \n",
       "mean              3.010000                     2.660256   \n",
       "std               1.113724                     1.150017   \n",
       "min               1.000000                     1.000000   \n",
       "25%               2.000000                     2.000000   \n",
       "50%               3.000000                     2.000000   \n",
       "75%               4.000000                     4.000000   \n",
       "max               4.000000                     4.000000   \n",
       "\n",
       "       Most-valuable-available-asset   Age-years  Type-of-apartment  \\\n",
       "count                     500.000000  488.000000         500.000000   \n",
       "mean                        2.360000   35.637295           1.928000   \n",
       "std                         1.064268   11.501522           0.539814   \n",
       "min                         1.000000   19.000000           1.000000   \n",
       "25%                         1.000000   27.000000           2.000000   \n",
       "50%                         3.000000   33.000000           2.000000   \n",
       "75%                         3.000000   42.000000           2.000000   \n",
       "max                         4.000000   75.000000           3.000000   \n",
       "\n",
       "       Occupation  No-of-dependents   Telephone  Foreign-Worker  \n",
       "count       500.0         500.00000  500.000000      500.000000  \n",
       "mean          1.0           1.14600    1.400000        1.038000  \n",
       "std           0.0           0.35346    0.490389        0.191388  \n",
       "min           1.0           1.00000    1.000000        1.000000  \n",
       "25%           1.0           1.00000    1.000000        1.000000  \n",
       "50%           1.0           1.00000    1.000000        1.000000  \n",
       "75%           1.0           1.00000    2.000000        1.000000  \n",
       "max           1.0           2.00000    2.000000        2.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827c60b4",
   "metadata": {},
   "source": [
    "Based on the value counts and describe above, remove the following columns:\n",
    "1. Guarantors : low variability, the data is skewed towards 'None'.\n",
    "2. Duration-in-Current-address : too much missing data.\n",
    "3. Concurrent-Credits: the data is uniform.\n",
    "4. Occupation: the data is uniform.\n",
    "5. No-of-dependents: low variability, the data is skewed towards 1.\n",
    "6. Foreign-Worker: low variability, the data is skewed towards 1.\n",
    "\n",
    "Also, we need to remove Telephone column because it's irrelevant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f7cf59cd",
   "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>Credit-Application-Result</th>\n",
       "      <th>Account-Balance</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Value-Savings-Stocks</th>\n",
       "      <th>Length-of-current-employment</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>No-of-Credits-at-this-Bank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Other</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1544</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>More than 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3380</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>343</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit-Application-Result Account-Balance  Duration-of-Credit-Month  \\\n",
       "0                          1    Some Balance                         4   \n",
       "1                          1    Some Balance                         4   \n",
       "2                          1    Some Balance                         4   \n",
       "3                          1    Some Balance                         4   \n",
       "4                          1      No Account                         6   \n",
       "\n",
       "  Payment-Status-of-Previous-Credit       Purpose  Credit-Amount  \\\n",
       "0                           Paid Up         Other           1494   \n",
       "1                           Paid Up  Home Related           1494   \n",
       "2        No Problems (in this bank)  Home Related           1544   \n",
       "3        No Problems (in this bank)  Home Related           3380   \n",
       "4                           Paid Up  Home Related            343   \n",
       "\n",
       "  Value-Savings-Stocks Length-of-current-employment  Instalment-per-cent  \\\n",
       "0           £100-£1000                        < 1yr                    1   \n",
       "1           £100-£1000                        < 1yr                    1   \n",
       "2                 None                      1-4 yrs                    2   \n",
       "3                 None                      1-4 yrs                    1   \n",
       "4                 None                        < 1yr                    4   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1        NaN                  2   \n",
       "1                              1       29.0                  2   \n",
       "2                              1       42.0                  2   \n",
       "3                              1       37.0                  2   \n",
       "4                              1       27.0                  2   \n",
       "\n",
       "  No-of-Credits-at-this-Bank  \n",
       "0                          1  \n",
       "1                          1  \n",
       "2                More than 1  \n",
       "3                          1  \n",
       "4                          1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean = df.drop(['Guarantors','Duration-in-Current-address','Concurrent-Credits','Occupation','No-of-dependents','Foreign-Worker','Telephone'],\n",
    "                  axis = 1)\n",
    "df_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d17456d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median = df_clean['Age-years'].median()\n",
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2707aa84",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Credit-Application-Result</th>\n",
       "      <th>Account-Balance</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Value-Savings-Stocks</th>\n",
       "      <th>Length-of-current-employment</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>No-of-Credits-at-this-Bank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Other</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1544</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>More than 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3380</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>343</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit-Application-Result Account-Balance  Duration-of-Credit-Month  \\\n",
       "0                          1    Some Balance                         4   \n",
       "1                          1    Some Balance                         4   \n",
       "2                          1    Some Balance                         4   \n",
       "3                          1    Some Balance                         4   \n",
       "4                          1      No Account                         6   \n",
       "\n",
       "  Payment-Status-of-Previous-Credit       Purpose  Credit-Amount  \\\n",
       "0                           Paid Up         Other           1494   \n",
       "1                           Paid Up  Home Related           1494   \n",
       "2        No Problems (in this bank)  Home Related           1544   \n",
       "3        No Problems (in this bank)  Home Related           3380   \n",
       "4                           Paid Up  Home Related            343   \n",
       "\n",
       "  Value-Savings-Stocks Length-of-current-employment  Instalment-per-cent  \\\n",
       "0           £100-£1000                        < 1yr                    1   \n",
       "1           £100-£1000                        < 1yr                    1   \n",
       "2                 None                      1-4 yrs                    2   \n",
       "3                 None                      1-4 yrs                    1   \n",
       "4                 None                        < 1yr                    4   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1       33.0                  2   \n",
       "1                              1       29.0                  2   \n",
       "2                              1       42.0                  2   \n",
       "3                              1       37.0                  2   \n",
       "4                              1       27.0                  2   \n",
       "\n",
       "  No-of-Credits-at-this-Bank  \n",
       "0                          1  \n",
       "1                          1  \n",
       "2                More than 1  \n",
       "3                          1  \n",
       "4                          1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# impute Age-years column with median because only a few data are missing and the data is not symmetrical.\n",
    "df_clean_2 = df_clean.fillna({'Age-years':median})\n",
    "df_clean_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1e6b2b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit-Application-Result</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.716000</td>\n",
       "      <td>21.43400</td>\n",
       "      <td>3199.980000</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>35.574000</td>\n",
       "      <td>1.928000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.451388</td>\n",
       "      <td>12.30742</td>\n",
       "      <td>2831.386861</td>\n",
       "      <td>1.113724</td>\n",
       "      <td>1.064268</td>\n",
       "      <td>11.369567</td>\n",
       "      <td>0.539814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>1357.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>2236.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>3941.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>18424.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Credit-Application-Result  Duration-of-Credit-Month  Credit-Amount  \\\n",
       "count                 500.000000                 500.00000     500.000000   \n",
       "mean                    0.716000                  21.43400    3199.980000   \n",
       "std                     0.451388                  12.30742    2831.386861   \n",
       "min                     0.000000                   4.00000     276.000000   \n",
       "25%                     0.000000                  12.00000    1357.250000   \n",
       "50%                     1.000000                  18.00000    2236.500000   \n",
       "75%                     1.000000                  24.00000    3941.500000   \n",
       "max                     1.000000                  60.00000   18424.000000   \n",
       "\n",
       "       Instalment-per-cent  Most-valuable-available-asset   Age-years  \\\n",
       "count           500.000000                     500.000000  500.000000   \n",
       "mean              3.010000                       2.360000   35.574000   \n",
       "std               1.113724                       1.064268   11.369567   \n",
       "min               1.000000                       1.000000   19.000000   \n",
       "25%               2.000000                       1.000000   27.000000   \n",
       "50%               3.000000                       3.000000   33.000000   \n",
       "75%               4.000000                       3.000000   41.000000   \n",
       "max               4.000000                       4.000000   75.000000   \n",
       "\n",
       "       Type-of-apartment  \n",
       "count         500.000000  \n",
       "mean            1.928000  \n",
       "std             0.539814  \n",
       "min             1.000000  \n",
       "25%             2.000000  \n",
       "50%             2.000000  \n",
       "75%             2.000000  \n",
       "max             3.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "793a4460",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit-Application-Result</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Instalment-per-cent</th>\n",
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       "  <tbody>\n",
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       "      <th>Credit-Application-Result</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.202504</td>\n",
       "      <td>-0.201946</td>\n",
       "      <td>-0.062107</td>\n",
       "      <td>-0.141332</td>\n",
       "      <td>0.052914</td>\n",
       "      <td>-0.026516</td>\n",
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       "    <tr>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <td>-0.202504</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.573980</td>\n",
       "      <td>0.068106</td>\n",
       "      <td>0.299855</td>\n",
       "      <td>-0.064197</td>\n",
       "      <td>0.152516</td>\n",
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       "    <tr>\n",
       "      <th>Credit-Amount</th>\n",
       "      <td>-0.201946</td>\n",
       "      <td>0.573980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.288852</td>\n",
       "      <td>0.325545</td>\n",
       "      <td>0.069316</td>\n",
       "      <td>0.170071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <td>-0.062107</td>\n",
       "      <td>0.068106</td>\n",
       "      <td>-0.288852</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.081493</td>\n",
       "      <td>0.039270</td>\n",
       "      <td>0.074533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <td>-0.141332</td>\n",
       "      <td>0.299855</td>\n",
       "      <td>0.325545</td>\n",
       "      <td>0.081493</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.086233</td>\n",
       "      <td>0.373101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age-years</th>\n",
       "      <td>0.052914</td>\n",
       "      <td>-0.064197</td>\n",
       "      <td>0.069316</td>\n",
       "      <td>0.039270</td>\n",
       "      <td>0.086233</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <td>-0.026516</td>\n",
       "      <td>0.152516</td>\n",
       "      <td>0.170071</td>\n",
       "      <td>0.074533</td>\n",
       "      <td>0.373101</td>\n",
       "      <td>0.329350</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Credit-Application-Result  \\\n",
       "Credit-Application-Result                       1.000000   \n",
       "Duration-of-Credit-Month                       -0.202504   \n",
       "Credit-Amount                                  -0.201946   \n",
       "Instalment-per-cent                            -0.062107   \n",
       "Most-valuable-available-asset                  -0.141332   \n",
       "Age-years                                       0.052914   \n",
       "Type-of-apartment                              -0.026516   \n",
       "\n",
       "                               Duration-of-Credit-Month  Credit-Amount  \\\n",
       "Credit-Application-Result                     -0.202504      -0.201946   \n",
       "Duration-of-Credit-Month                       1.000000       0.573980   \n",
       "Credit-Amount                                  0.573980       1.000000   \n",
       "Instalment-per-cent                            0.068106      -0.288852   \n",
       "Most-valuable-available-asset                  0.299855       0.325545   \n",
       "Age-years                                     -0.064197       0.069316   \n",
       "Type-of-apartment                              0.152516       0.170071   \n",
       "\n",
       "                               Instalment-per-cent  \\\n",
       "Credit-Application-Result                -0.062107   \n",
       "Duration-of-Credit-Month                  0.068106   \n",
       "Credit-Amount                            -0.288852   \n",
       "Instalment-per-cent                       1.000000   \n",
       "Most-valuable-available-asset             0.081493   \n",
       "Age-years                                 0.039270   \n",
       "Type-of-apartment                         0.074533   \n",
       "\n",
       "                               Most-valuable-available-asset  Age-years  \\\n",
       "Credit-Application-Result                          -0.141332   0.052914   \n",
       "Duration-of-Credit-Month                            0.299855  -0.064197   \n",
       "Credit-Amount                                       0.325545   0.069316   \n",
       "Instalment-per-cent                                 0.081493   0.039270   \n",
       "Most-valuable-available-asset                       1.000000   0.086233   \n",
       "Age-years                                           0.086233   1.000000   \n",
       "Type-of-apartment                                   0.373101   0.329350   \n",
       "\n",
       "                               Type-of-apartment  \n",
       "Credit-Application-Result              -0.026516  \n",
       "Duration-of-Credit-Month                0.152516  \n",
       "Credit-Amount                           0.170071  \n",
       "Instalment-per-cent                     0.074533  \n",
       "Most-valuable-available-asset           0.373101  \n",
       "Age-years                               0.329350  \n",
       "Type-of-apartment                       1.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check correlations\n",
    "corr = df_clean_2.corr()\n",
    "display(corr)\n",
    "\n",
    "# plot a heatmap\n",
    "sns.heatmap(corr, xticklabels = corr.columns, yticklabels = corr.columns, cmap = 'Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "da938059",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make columns into dummy variables\n",
    "account_balance_dummies = pd.get_dummies(df_clean_2['Account-Balance'],prefix = 'Account-Balance')\n",
    "payment_status_of_previous_credit = pd.get_dummies(df_clean_2['Payment-Status-of-Previous-Credit'], prefix = 'Payment-Status-of-Previous-Credit')\n",
    "purpose = pd.get_dummies(df_clean_2['Purpose'], prefix = 'Purpose')\n",
    "value_savings_stocks = pd.get_dummies(df_clean_2['Value-Savings-Stocks'], prefix = 'Value-Savings-Stocks')\n",
    "length_of_current_employment = pd.get_dummies(df_clean_2['Length-of-current-employment'], prefix = 'Length-of-current-employment')\n",
    "no_of_credits_at_this_bank = pd.get_dummies(df_clean_2['No-of-Credits-at-this-Bank'], prefix = 'No-of-Credits-at-this-Bank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "69e94af9",
   "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>Credit-Application-Result</th>\n",
       "      <th>Account-Balance</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Value-Savings-Stocks</th>\n",
       "      <th>Length-of-current-employment</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>...</th>\n",
       "      <th>Purpose_Other</th>\n",
       "      <th>Purpose_Used car</th>\n",
       "      <th>Value-Savings-Stocks_&lt; £100</th>\n",
       "      <th>Value-Savings-Stocks_None</th>\n",
       "      <th>Value-Savings-Stocks_£100-£1000</th>\n",
       "      <th>Length-of-current-employment_1-4 yrs</th>\n",
       "      <th>Length-of-current-employment_4-7 yrs</th>\n",
       "      <th>Length-of-current-employment_&lt; 1yr</th>\n",
       "      <th>No-of-Credits-at-this-Bank_1</th>\n",
       "      <th>No-of-Credits-at-this-Bank_More than 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Other</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1544</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3380</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>343</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit-Application-Result Account-Balance  Duration-of-Credit-Month  \\\n",
       "0                          1    Some Balance                         4   \n",
       "1                          1    Some Balance                         4   \n",
       "2                          1    Some Balance                         4   \n",
       "3                          1    Some Balance                         4   \n",
       "4                          1      No Account                         6   \n",
       "\n",
       "  Payment-Status-of-Previous-Credit       Purpose  Credit-Amount  \\\n",
       "0                           Paid Up         Other           1494   \n",
       "1                           Paid Up  Home Related           1494   \n",
       "2        No Problems (in this bank)  Home Related           1544   \n",
       "3        No Problems (in this bank)  Home Related           3380   \n",
       "4                           Paid Up  Home Related            343   \n",
       "\n",
       "  Value-Savings-Stocks Length-of-current-employment  Instalment-per-cent  \\\n",
       "0           £100-£1000                        < 1yr                    1   \n",
       "1           £100-£1000                        < 1yr                    1   \n",
       "2                 None                      1-4 yrs                    2   \n",
       "3                 None                      1-4 yrs                    1   \n",
       "4                 None                        < 1yr                    4   \n",
       "\n",
       "   Most-valuable-available-asset  ...  Purpose_Other  Purpose_Used car  \\\n",
       "0                              1  ...              1                 0   \n",
       "1                              1  ...              0                 0   \n",
       "2                              1  ...              0                 0   \n",
       "3                              1  ...              0                 0   \n",
       "4                              1  ...              0                 0   \n",
       "\n",
       "  Value-Savings-Stocks_< £100  Value-Savings-Stocks_None  \\\n",
       "0                           0                          0   \n",
       "1                           0                          0   \n",
       "2                           0                          1   \n",
       "3                           0                          1   \n",
       "4                           0                          1   \n",
       "\n",
       "   Value-Savings-Stocks_£100-£1000  Length-of-current-employment_1-4 yrs  \\\n",
       "0                                1                                     0   \n",
       "1                                1                                     0   \n",
       "2                                0                                     1   \n",
       "3                                0                                     1   \n",
       "4                                0                                     0   \n",
       "\n",
       "   Length-of-current-employment_4-7 yrs  Length-of-current-employment_< 1yr  \\\n",
       "0                                     0                                   1   \n",
       "1                                     0                                   1   \n",
       "2                                     0                                   0   \n",
       "3                                     0                                   0   \n",
       "4                                     0                                   1   \n",
       "\n",
       "   No-of-Credits-at-this-Bank_1  No-of-Credits-at-this-Bank_More than 1  \n",
       "0                             1                                       0  \n",
       "1                             1                                       0  \n",
       "2                             0                                       1  \n",
       "3                             1                                       0  \n",
       "4                             1                                       0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_dummies = pd.concat([df_clean_2, account_balance_dummies, payment_status_of_previous_credit, purpose, \n",
    "                             value_savings_stocks, length_of_current_employment, no_of_credits_at_this_bank],axis = 1)\n",
    "df_clean_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fca046b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Credit-Application-Result</th>\n",
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       "      <th>Length-of-current-employment_1-4 yrs</th>\n",
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       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
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       "      <td>1544</td>\n",
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       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>343</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit-Application-Result  Duration-of-Credit-Month  Credit-Amount  \\\n",
       "0                          1                         4           1494   \n",
       "1                          1                         4           1494   \n",
       "2                          1                         4           1544   \n",
       "3                          1                         4           3380   \n",
       "4                          1                         6            343   \n",
       "\n",
       "   Instalment-per-cent  Most-valuable-available-asset  Age-years  \\\n",
       "0                    1                              1       33.0   \n",
       "1                    1                              1       29.0   \n",
       "2                    2                              1       42.0   \n",
       "3                    1                              1       37.0   \n",
       "4                    4                              1       27.0   \n",
       "\n",
       "   Type-of-apartment  Account-Balance_No Account  \\\n",
       "0                  2                           0   \n",
       "1                  2                           0   \n",
       "2                  2                           0   \n",
       "3                  2                           0   \n",
       "4                  2                           1   \n",
       "\n",
       "   Account-Balance_Some Balance  \\\n",
       "0                             1   \n",
       "1                             1   \n",
       "2                             1   \n",
       "3                             1   \n",
       "4                             0   \n",
       "\n",
       "   Payment-Status-of-Previous-Credit_No Problems (in this bank)  ...  \\\n",
       "0                                                  0             ...   \n",
       "1                                                  0             ...   \n",
       "2                                                  1             ...   \n",
       "3                                                  1             ...   \n",
       "4                                                  0             ...   \n",
       "\n",
       "   Purpose_Other  Purpose_Used car  Value-Savings-Stocks_< £100  \\\n",
       "0              1                 0                            0   \n",
       "1              0                 0                            0   \n",
       "2              0                 0                            0   \n",
       "3              0                 0                            0   \n",
       "4              0                 0                            0   \n",
       "\n",
       "   Value-Savings-Stocks_None  Value-Savings-Stocks_£100-£1000  \\\n",
       "0                          0                                1   \n",
       "1                          0                                1   \n",
       "2                          1                                0   \n",
       "3                          1                                0   \n",
       "4                          1                                0   \n",
       "\n",
       "   Length-of-current-employment_1-4 yrs  Length-of-current-employment_4-7 yrs  \\\n",
       "0                                     0                                     0   \n",
       "1                                     0                                     0   \n",
       "2                                     1                                     0   \n",
       "3                                     1                                     0   \n",
       "4                                     0                                     0   \n",
       "\n",
       "   Length-of-current-employment_< 1yr  No-of-Credits-at-this-Bank_1  \\\n",
       "0                                   1                             1   \n",
       "1                                   1                             1   \n",
       "2                                   0                             0   \n",
       "3                                   0                             1   \n",
       "4                                   1                             1   \n",
       "\n",
       "   No-of-Credits-at-this-Bank_More than 1  \n",
       "0                                       0  \n",
       "1                                       0  \n",
       "2                                       1  \n",
       "3                                       0  \n",
       "4                                       0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_dummies_2 = df_clean_dummies.drop(['Account-Balance','Payment-Status-of-Previous-Credit','Purpose','Value-Savings-Stocks','Length-of-current-employment','No-of-Credits-at-this-Bank'],\n",
    "                                          axis = 1)\n",
    "df_clean_dummies_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8af5c5e4",
   "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>Credit-Application-Result</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
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       "      <th>Instalment-per-cent</th>\n",
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       "      <th>Value-Savings-Stocks_£100-£1000</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.00000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
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       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.716000</td>\n",
       "      <td>21.43400</td>\n",
       "      <td>3199.980000</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>35.574000</td>\n",
       "      <td>1.928000</td>\n",
       "      <td>0.524000</td>\n",
       "      <td>0.476000</td>\n",
       "      <td>0.408000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.166000</td>\n",
       "      <td>0.096000</td>\n",
       "      <td>0.596000</td>\n",
       "      <td>0.308000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>0.248000</td>\n",
       "      <td>0.558000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.360000</td>\n",
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       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.451388</td>\n",
       "      <td>12.30742</td>\n",
       "      <td>2831.386861</td>\n",
       "      <td>1.113724</td>\n",
       "      <td>1.064268</td>\n",
       "      <td>11.369567</td>\n",
       "      <td>0.539814</td>\n",
       "      <td>0.499924</td>\n",
       "      <td>0.499924</td>\n",
       "      <td>0.491955</td>\n",
       "      <td>...</td>\n",
       "      <td>0.170758</td>\n",
       "      <td>0.372453</td>\n",
       "      <td>0.294886</td>\n",
       "      <td>0.491189</td>\n",
       "      <td>0.462129</td>\n",
       "      <td>0.395825</td>\n",
       "      <td>0.432284</td>\n",
       "      <td>0.497122</td>\n",
       "      <td>0.480481</td>\n",
       "      <td>0.480481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>276.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>1357.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.00000</td>\n",
       "      <td>2236.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>24.00000</td>\n",
       "      <td>3941.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>18424.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Credit-Application-Result  Duration-of-Credit-Month  Credit-Amount  \\\n",
       "count                 500.000000                 500.00000     500.000000   \n",
       "mean                    0.716000                  21.43400    3199.980000   \n",
       "std                     0.451388                  12.30742    2831.386861   \n",
       "min                     0.000000                   4.00000     276.000000   \n",
       "25%                     0.000000                  12.00000    1357.250000   \n",
       "50%                     1.000000                  18.00000    2236.500000   \n",
       "75%                     1.000000                  24.00000    3941.500000   \n",
       "max                     1.000000                  60.00000   18424.000000   \n",
       "\n",
       "       Instalment-per-cent  Most-valuable-available-asset   Age-years  \\\n",
       "count           500.000000                     500.000000  500.000000   \n",
       "mean              3.010000                       2.360000   35.574000   \n",
       "std               1.113724                       1.064268   11.369567   \n",
       "min               1.000000                       1.000000   19.000000   \n",
       "25%               2.000000                       1.000000   27.000000   \n",
       "50%               3.000000                       3.000000   33.000000   \n",
       "75%               4.000000                       3.000000   41.000000   \n",
       "max               4.000000                       4.000000   75.000000   \n",
       "\n",
       "       Type-of-apartment  Account-Balance_No Account  \\\n",
       "count         500.000000                  500.000000   \n",
       "mean            1.928000                    0.524000   \n",
       "std             0.539814                    0.499924   \n",
       "min             1.000000                    0.000000   \n",
       "25%             2.000000                    0.000000   \n",
       "50%             2.000000                    1.000000   \n",
       "75%             2.000000                    1.000000   \n",
       "max             3.000000                    1.000000   \n",
       "\n",
       "       Account-Balance_Some Balance  \\\n",
       "count                    500.000000   \n",
       "mean                       0.476000   \n",
       "std                        0.499924   \n",
       "min                        0.000000   \n",
       "25%                        0.000000   \n",
       "50%                        0.000000   \n",
       "75%                        1.000000   \n",
       "max                        1.000000   \n",
       "\n",
       "       Payment-Status-of-Previous-Credit_No Problems (in this bank)  ...  \\\n",
       "count                                         500.000000             ...   \n",
       "mean                                            0.408000             ...   \n",
       "std                                             0.491955             ...   \n",
       "min                                             0.000000             ...   \n",
       "25%                                             0.000000             ...   \n",
       "50%                                             0.000000             ...   \n",
       "75%                                             1.000000             ...   \n",
       "max                                             1.000000             ...   \n",
       "\n",
       "       Purpose_Other  Purpose_Used car  Value-Savings-Stocks_< £100  \\\n",
       "count     500.000000        500.000000                   500.000000   \n",
       "mean        0.030000          0.166000                     0.096000   \n",
       "std         0.170758          0.372453                     0.294886   \n",
       "min         0.000000          0.000000                     0.000000   \n",
       "25%         0.000000          0.000000                     0.000000   \n",
       "50%         0.000000          0.000000                     0.000000   \n",
       "75%         0.000000          0.000000                     0.000000   \n",
       "max         1.000000          1.000000                     1.000000   \n",
       "\n",
       "       Value-Savings-Stocks_None  Value-Savings-Stocks_£100-£1000  \\\n",
       "count                 500.000000                       500.000000   \n",
       "mean                    0.596000                         0.308000   \n",
       "std                     0.491189                         0.462129   \n",
       "min                     0.000000                         0.000000   \n",
       "25%                     0.000000                         0.000000   \n",
       "50%                     1.000000                         0.000000   \n",
       "75%                     1.000000                         1.000000   \n",
       "max                     1.000000                         1.000000   \n",
       "\n",
       "       Length-of-current-employment_1-4 yrs  \\\n",
       "count                            500.000000   \n",
       "mean                               0.194000   \n",
       "std                                0.395825   \n",
       "min                                0.000000   \n",
       "25%                                0.000000   \n",
       "50%                                0.000000   \n",
       "75%                                0.000000   \n",
       "max                                1.000000   \n",
       "\n",
       "       Length-of-current-employment_4-7 yrs  \\\n",
       "count                            500.000000   \n",
       "mean                               0.248000   \n",
       "std                                0.432284   \n",
       "min                                0.000000   \n",
       "25%                                0.000000   \n",
       "50%                                0.000000   \n",
       "75%                                0.000000   \n",
       "max                                1.000000   \n",
       "\n",
       "       Length-of-current-employment_< 1yr  No-of-Credits-at-this-Bank_1  \\\n",
       "count                          500.000000                    500.000000   \n",
       "mean                             0.558000                      0.640000   \n",
       "std                              0.497122                      0.480481   \n",
       "min                              0.000000                      0.000000   \n",
       "25%                              0.000000                      0.000000   \n",
       "50%                              1.000000                      1.000000   \n",
       "75%                              1.000000                      1.000000   \n",
       "max                              1.000000                      1.000000   \n",
       "\n",
       "       No-of-Credits-at-this-Bank_More than 1  \n",
       "count                              500.000000  \n",
       "mean                                 0.360000  \n",
       "std                                  0.480481  \n",
       "min                                  0.000000  \n",
       "25%                                  0.000000  \n",
       "50%                                  0.000000  \n",
       "75%                                  1.000000  \n",
       "max                                  1.000000  \n",
       "\n",
       "[8 rows x 24 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_clean_dummies_2.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9d200f7",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "bc940b9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "24ec5a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_logistic_data = df_clean_dummies_2.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "25123021",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Credit-Application-Result, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = df_logistic_data['Credit-Application-Result']\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b0ccf70f",
   "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>Duration-of-Credit-Month</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>Account-Balance_No Account</th>\n",
       "      <th>Account-Balance_Some Balance</th>\n",
       "      <th>Payment-Status-of-Previous-Credit_No Problems (in this bank)</th>\n",
       "      <th>Payment-Status-of-Previous-Credit_Paid Up</th>\n",
       "      <th>...</th>\n",
       "      <th>Purpose_Other</th>\n",
       "      <th>Purpose_Used car</th>\n",
       "      <th>Value-Savings-Stocks_&lt; £100</th>\n",
       "      <th>Value-Savings-Stocks_None</th>\n",
       "      <th>Value-Savings-Stocks_£100-£1000</th>\n",
       "      <th>Length-of-current-employment_1-4 yrs</th>\n",
       "      <th>Length-of-current-employment_4-7 yrs</th>\n",
       "      <th>Length-of-current-employment_&lt; 1yr</th>\n",
       "      <th>No-of-Credits-at-this-Bank_1</th>\n",
       "      <th>No-of-Credits-at-this-Bank_More than 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1494</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>1494</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1544</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3380</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>343</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration-of-Credit-Month  Credit-Amount  Instalment-per-cent  \\\n",
       "0                         4           1494                    1   \n",
       "1                         4           1494                    1   \n",
       "2                         4           1544                    2   \n",
       "3                         4           3380                    1   \n",
       "4                         6            343                    4   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1       33.0                  2   \n",
       "1                              1       29.0                  2   \n",
       "2                              1       42.0                  2   \n",
       "3                              1       37.0                  2   \n",
       "4                              1       27.0                  2   \n",
       "\n",
       "   Account-Balance_No Account  Account-Balance_Some Balance  \\\n",
       "0                           0                             1   \n",
       "1                           0                             1   \n",
       "2                           0                             1   \n",
       "3                           0                             1   \n",
       "4                           1                             0   \n",
       "\n",
       "   Payment-Status-of-Previous-Credit_No Problems (in this bank)  \\\n",
       "0                                                  0              \n",
       "1                                                  0              \n",
       "2                                                  1              \n",
       "3                                                  1              \n",
       "4                                                  0              \n",
       "\n",
       "   Payment-Status-of-Previous-Credit_Paid Up  ...  Purpose_Other  \\\n",
       "0                                          1  ...              1   \n",
       "1                                          1  ...              0   \n",
       "2                                          0  ...              0   \n",
       "3                                          0  ...              0   \n",
       "4                                          1  ...              0   \n",
       "\n",
       "   Purpose_Used car  Value-Savings-Stocks_< £100  Value-Savings-Stocks_None  \\\n",
       "0                 0                            0                          0   \n",
       "1                 0                            0                          0   \n",
       "2                 0                            0                          1   \n",
       "3                 0                            0                          1   \n",
       "4                 0                            0                          1   \n",
       "\n",
       "   Value-Savings-Stocks_£100-£1000  Length-of-current-employment_1-4 yrs  \\\n",
       "0                                1                                     0   \n",
       "1                                1                                     0   \n",
       "2                                0                                     1   \n",
       "3                                0                                     1   \n",
       "4                                0                                     0   \n",
       "\n",
       "   Length-of-current-employment_4-7 yrs  Length-of-current-employment_< 1yr  \\\n",
       "0                                     0                                   1   \n",
       "1                                     0                                   1   \n",
       "2                                     0                                   0   \n",
       "3                                     0                                   0   \n",
       "4                                     0                                   1   \n",
       "\n",
       "   No-of-Credits-at-this-Bank_1  No-of-Credits-at-this-Bank_More than 1  \n",
       "0                             1                                       0  \n",
       "1                             1                                       0  \n",
       "2                             0                                       1  \n",
       "3                             1                                       0  \n",
       "4                             1                                       0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_logistic_data.drop('Credit-Application-Result', axis = 1)\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b377a36d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#split dataset\n",
    "X_train_lr, X_test_lr, Y_train_lr, Y_test_lr = train_test_split(X, Y, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "1c0db25c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f790d4b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#build model\n",
    "logistic_model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "55da15be",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xuejunsui/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_model.fit(X_train_lr, Y_train_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6773117e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the score of the model\n",
    "logistic_model.score(X_test_lr, Y_test_lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6ef5ef",
   "metadata": {},
   "source": [
    "# Change customers to score file into the same format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7b11b262",
   "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>Account-Balance</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Value-Savings-Stocks</th>\n",
       "      <th>Length-of-current-employment</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Guarantors</th>\n",
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       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Concurrent-Credits</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>No-of-Credits-at-this-Bank</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>No-of-dependents</th>\n",
       "      <th>Telephone</th>\n",
       "      <th>Foreign-Worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Account</td>\n",
       "      <td>9</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>2799</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>1</td>\n",
       "      <td>More than 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No Account</td>\n",
       "      <td>12</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>2122</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>1</td>\n",
       "      <td>More than 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Account</td>\n",
       "      <td>24</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3758</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Account</td>\n",
       "      <td>11</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3905</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>1</td>\n",
       "      <td>More than 1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1957</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>Other Banks/Depts</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Account-Balance  Duration-of-Credit-Month Payment-Status-of-Previous-Credit  \\\n",
       "0      No Account                         9        No Problems (in this bank)   \n",
       "1      No Account                        12        No Problems (in this bank)   \n",
       "2      No Account                        24                           Paid Up   \n",
       "3      No Account                        11        No Problems (in this bank)   \n",
       "4      No Account                         6        No Problems (in this bank)   \n",
       "\n",
       "        Purpose  Credit-Amount Value-Savings-Stocks  \\\n",
       "0  Home Related           2799                 None   \n",
       "1  Home Related           2122                 None   \n",
       "2  Home Related           3758           £100-£1000   \n",
       "3  Home Related           3905                 None   \n",
       "4  Home Related           1957                 None   \n",
       "\n",
       "  Length-of-current-employment  Instalment-per-cent Guarantors  \\\n",
       "0                        < 1yr                    2       None   \n",
       "1                        < 1yr                    3       None   \n",
       "2                        < 1yr                    1       None   \n",
       "3                        < 1yr                    2       None   \n",
       "4                      1-4 yrs                    1       None   \n",
       "\n",
       "   Duration-in-Current-address  Most-valuable-available-asset  Age-years  \\\n",
       "0                            2                              1         36   \n",
       "1                            2                              1         39   \n",
       "2                            4                              4         23   \n",
       "3                            2                              1         36   \n",
       "4                            4                              3         31   \n",
       "\n",
       "  Concurrent-Credits  Type-of-apartment No-of-Credits-at-this-Bank  \\\n",
       "0  Other Banks/Depts                  1                More than 1   \n",
       "1  Other Banks/Depts                  1                More than 1   \n",
       "2  Other Banks/Depts                  1                          1   \n",
       "3  Other Banks/Depts                  1                More than 1   \n",
       "4  Other Banks/Depts                  2                          1   \n",
       "\n",
       "   Occupation  No-of-dependents  Telephone  Foreign-Worker  \n",
       "0           1                 2          1               1  \n",
       "1           1                 2          1               2  \n",
       "2           1                 1          1               1  \n",
       "3           1                 2          1               1  \n",
       "4           1                 1          1               1  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers = pd.read_excel('customers-to-score.xlsx')\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2b13c291",
   "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>Duration-of-Credit-Month</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Duration-in-Current-address</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
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       "      <th>No-of-dependents</th>\n",
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       "      <th>Foreign-Worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.0</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.254000</td>\n",
       "      <td>3300.400000</td>\n",
       "      <td>2.954000</td>\n",
       "      <td>2.858000</td>\n",
       "      <td>2.338</td>\n",
       "      <td>35.626000</td>\n",
       "      <td>1.926000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.166000</td>\n",
       "      <td>1.410000</td>\n",
       "      <td>1.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.766664</td>\n",
       "      <td>2806.153898</td>\n",
       "      <td>1.119997</td>\n",
       "      <td>1.108284</td>\n",
       "      <td>1.044</td>\n",
       "      <td>11.296608</td>\n",
       "      <td>0.526382</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.372453</td>\n",
       "      <td>0.492326</td>\n",
       "      <td>0.196155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1381.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>2370.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>3967.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>15945.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Duration-of-Credit-Month  Credit-Amount  Instalment-per-cent  \\\n",
       "count                500.000000     500.000000           500.000000   \n",
       "mean                  20.254000    3300.400000             2.954000   \n",
       "std                   11.766664    2806.153898             1.119997   \n",
       "min                    4.000000     250.000000             1.000000   \n",
       "25%                   12.000000    1381.750000             2.000000   \n",
       "50%                   18.000000    2370.500000             3.000000   \n",
       "75%                   24.000000    3967.500000             4.000000   \n",
       "max                   72.000000   15945.000000             4.000000   \n",
       "\n",
       "       Duration-in-Current-address  Most-valuable-available-asset   Age-years  \\\n",
       "count                   500.000000                        500.000  500.000000   \n",
       "mean                      2.858000                          2.338   35.626000   \n",
       "std                       1.108284                          1.044   11.296608   \n",
       "min                       1.000000                          1.000   20.000000   \n",
       "25%                       2.000000                          1.000   27.000000   \n",
       "50%                       3.000000                          2.000   33.000000   \n",
       "75%                       4.000000                          3.000   42.000000   \n",
       "max                       4.000000                          4.000   74.000000   \n",
       "\n",
       "       Type-of-apartment  Occupation  No-of-dependents   Telephone  \\\n",
       "count         500.000000       500.0        500.000000  500.000000   \n",
       "mean            1.926000         1.0          1.166000    1.410000   \n",
       "std             0.526382         0.0          0.372453    0.492326   \n",
       "min             1.000000         1.0          1.000000    1.000000   \n",
       "25%             2.000000         1.0          1.000000    1.000000   \n",
       "50%             2.000000         1.0          1.000000    1.000000   \n",
       "75%             2.000000         1.0          1.000000    2.000000   \n",
       "max             3.000000         1.0          2.000000    2.000000   \n",
       "\n",
       "       Foreign-Worker  \n",
       "count      500.000000  \n",
       "mean         1.040000  \n",
       "std          0.196155  \n",
       "min          1.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          2.000000  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c284524a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Duration-of-Credit-Month</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Account</td>\n",
       "      <td>9</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>2799</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>No Account</td>\n",
       "      <td>12</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>2122</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>More than 1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Account</td>\n",
       "      <td>24</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3758</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Account</td>\n",
       "      <td>11</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3905</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>More than 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1957</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Account-Balance  Duration-of-Credit-Month Payment-Status-of-Previous-Credit  \\\n",
       "0      No Account                         9        No Problems (in this bank)   \n",
       "1      No Account                        12        No Problems (in this bank)   \n",
       "2      No Account                        24                           Paid Up   \n",
       "3      No Account                        11        No Problems (in this bank)   \n",
       "4      No Account                         6        No Problems (in this bank)   \n",
       "\n",
       "        Purpose  Credit-Amount Value-Savings-Stocks  \\\n",
       "0  Home Related           2799                 None   \n",
       "1  Home Related           2122                 None   \n",
       "2  Home Related           3758           £100-£1000   \n",
       "3  Home Related           3905                 None   \n",
       "4  Home Related           1957                 None   \n",
       "\n",
       "  Length-of-current-employment  Instalment-per-cent  \\\n",
       "0                        < 1yr                    2   \n",
       "1                        < 1yr                    3   \n",
       "2                        < 1yr                    1   \n",
       "3                        < 1yr                    2   \n",
       "4                      1-4 yrs                    1   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1         36                  1   \n",
       "1                              1         39                  1   \n",
       "2                              4         23                  1   \n",
       "3                              1         36                  1   \n",
       "4                              3         31                  2   \n",
       "\n",
       "  No-of-Credits-at-this-Bank  \n",
       "0                More than 1  \n",
       "1                More than 1  \n",
       "2                          1  \n",
       "3                More than 1  \n",
       "4                          1  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers2 = customers.drop(['Guarantors','Duration-in-Current-address','Concurrent-Credits','Occupation','No-of-dependents','Foreign-Worker','Telephone'],\n",
    "                  axis = 1)\n",
    "customers2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "1c798f9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make columns into dummy variables\n",
    "account_balance_dummies = pd.get_dummies(customers2['Account-Balance'],prefix = 'Account-Balance')\n",
    "payment_status_of_previous_credit = pd.get_dummies(customers2['Payment-Status-of-Previous-Credit'], prefix = 'Payment-Status-of-Previous-Credit')\n",
    "purpose = pd.get_dummies(customers2['Purpose'], prefix = 'Purpose')\n",
    "value_savings_stocks = pd.get_dummies(customers2['Value-Savings-Stocks'], prefix = 'Value-Savings-Stocks')\n",
    "length_of_current_employment = pd.get_dummies(customers2['Length-of-current-employment'], prefix = 'Length-of-current-employment')\n",
    "no_of_credits_at_this_bank = pd.get_dummies(customers2['No-of-Credits-at-this-Bank'], prefix = 'No-of-Credits-at-this-Bank')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5ab99c5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Account-Balance</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
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       "      <th>Age-years</th>\n",
       "      <th>...</th>\n",
       "      <th>Purpose_New car</th>\n",
       "      <th>Purpose_Used car</th>\n",
       "      <th>Value-Savings-Stocks_&lt; £100</th>\n",
       "      <th>Value-Savings-Stocks_None</th>\n",
       "      <th>Value-Savings-Stocks_£100-£1000</th>\n",
       "      <th>Length-of-current-employment_1-4 yrs</th>\n",
       "      <th>Length-of-current-employment_4-7 yrs</th>\n",
       "      <th>Length-of-current-employment_&lt; 1yr</th>\n",
       "      <th>No-of-Credits-at-this-Bank_1</th>\n",
       "      <th>No-of-Credits-at-this-Bank_More than 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Account</td>\n",
       "      <td>9</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>2799</td>\n",
       "      <td>None</td>\n",
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       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No Account</td>\n",
       "      <td>12</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>2122</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>24</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3758</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Account</td>\n",
       "      <td>11</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3905</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1957</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Account-Balance  Duration-of-Credit-Month Payment-Status-of-Previous-Credit  \\\n",
       "0      No Account                         9        No Problems (in this bank)   \n",
       "1      No Account                        12        No Problems (in this bank)   \n",
       "2      No Account                        24                           Paid Up   \n",
       "3      No Account                        11        No Problems (in this bank)   \n",
       "4      No Account                         6        No Problems (in this bank)   \n",
       "\n",
       "        Purpose  Credit-Amount Value-Savings-Stocks  \\\n",
       "0  Home Related           2799                 None   \n",
       "1  Home Related           2122                 None   \n",
       "2  Home Related           3758           £100-£1000   \n",
       "3  Home Related           3905                 None   \n",
       "4  Home Related           1957                 None   \n",
       "\n",
       "  Length-of-current-employment  Instalment-per-cent  \\\n",
       "0                        < 1yr                    2   \n",
       "1                        < 1yr                    3   \n",
       "2                        < 1yr                    1   \n",
       "3                        < 1yr                    2   \n",
       "4                      1-4 yrs                    1   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  ...  Purpose_New car  \\\n",
       "0                              1         36  ...                0   \n",
       "1                              1         39  ...                0   \n",
       "2                              4         23  ...                0   \n",
       "3                              1         36  ...                0   \n",
       "4                              3         31  ...                0   \n",
       "\n",
       "  Purpose_Used car  Value-Savings-Stocks_< £100  Value-Savings-Stocks_None  \\\n",
       "0                0                            0                          1   \n",
       "1                0                            0                          1   \n",
       "2                0                            0                          0   \n",
       "3                0                            0                          1   \n",
       "4                0                            0                          1   \n",
       "\n",
       "   Value-Savings-Stocks_£100-£1000  Length-of-current-employment_1-4 yrs  \\\n",
       "0                                0                                     0   \n",
       "1                                0                                     0   \n",
       "2                                1                                     0   \n",
       "3                                0                                     0   \n",
       "4                                0                                     1   \n",
       "\n",
       "   Length-of-current-employment_4-7 yrs  Length-of-current-employment_< 1yr  \\\n",
       "0                                     0                                   1   \n",
       "1                                     0                                   1   \n",
       "2                                     0                                   1   \n",
       "3                                     0                                   1   \n",
       "4                                     0                                   0   \n",
       "\n",
       "   No-of-Credits-at-this-Bank_1  No-of-Credits-at-this-Bank_More than 1  \n",
       "0                             0                                       1  \n",
       "1                             0                                       1  \n",
       "2                             1                                       0  \n",
       "3                             0                                       1  \n",
       "4                             1                                       0  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_with_dummies = pd.concat([customers2, account_balance_dummies, payment_status_of_previous_credit, purpose, \n",
    "                             value_savings_stocks, length_of_current_employment, no_of_credits_at_this_bank], axis = 1)\n",
    "customers_with_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2aabfeac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1957</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration-of-Credit-Month  Credit-Amount  Instalment-per-cent  \\\n",
       "0                         9           2799                    2   \n",
       "1                        12           2122                    3   \n",
       "2                        24           3758                    1   \n",
       "3                        11           3905                    2   \n",
       "4                         6           1957                    1   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1         36                  1   \n",
       "1                              1         39                  1   \n",
       "2                              4         23                  1   \n",
       "3                              1         36                  1   \n",
       "4                              3         31                  2   \n",
       "\n",
       "   Account-Balance_No Account  Account-Balance_Some Balance  \\\n",
       "0                           1                             0   \n",
       "1                           1                             0   \n",
       "2                           1                             0   \n",
       "3                           1                             0   \n",
       "4                           1                             0   \n",
       "\n",
       "   Payment-Status-of-Previous-Credit_No Problems (in this bank)  \\\n",
       "0                                                  1              \n",
       "1                                                  1              \n",
       "2                                                  0              \n",
       "3                                                  1              \n",
       "4                                                  1              \n",
       "\n",
       "   Payment-Status-of-Previous-Credit_Paid Up  ...  Purpose_New car  \\\n",
       "0                                          0  ...                0   \n",
       "1                                          0  ...                0   \n",
       "2                                          1  ...                0   \n",
       "3                                          0  ...                0   \n",
       "4                                          0  ...                0   \n",
       "\n",
       "   Purpose_Used car  Value-Savings-Stocks_< £100  Value-Savings-Stocks_None  \\\n",
       "0                 0                            0                          1   \n",
       "1                 0                            0                          1   \n",
       "2                 0                            0                          0   \n",
       "3                 0                            0                          1   \n",
       "4                 0                            0                          1   \n",
       "\n",
       "   Value-Savings-Stocks_£100-£1000  Length-of-current-employment_1-4 yrs  \\\n",
       "0                                0                                     0   \n",
       "1                                0                                     0   \n",
       "2                                1                                     0   \n",
       "3                                0                                     0   \n",
       "4                                0                                     1   \n",
       "\n",
       "   Length-of-current-employment_4-7 yrs  Length-of-current-employment_< 1yr  \\\n",
       "0                                     0                                   1   \n",
       "1                                     0                                   1   \n",
       "2                                     0                                   1   \n",
       "3                                     0                                   1   \n",
       "4                                     0                                   0   \n",
       "\n",
       "   No-of-Credits-at-this-Bank_1  No-of-Credits-at-this-Bank_More than 1  \n",
       "0                             0                                       1  \n",
       "1                             0                                       1  \n",
       "2                             1                                       0  \n",
       "3                             0                                       1  \n",
       "4                             1                                       0  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_clean = customers_with_dummies.drop(['Account-Balance','Payment-Status-of-Previous-Credit','Purpose','Value-Savings-Stocks','Length-of-current-employment','No-of-Credits-at-this-Bank'],\n",
    "                                          axis = 1)\n",
    "customers_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ce4f63b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: Purpose_Other, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the missing column because lack of 1 value during dummy variable split\n",
    "customers_clean['Purpose_Other'] = 0\n",
    "customers_clean['Purpose_Other'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "fc50d8eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 23)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb0ad02a",
   "metadata": {},
   "source": [
    "## Logistic regression model prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "8baa8b97",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xuejunsui/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:488: FutureWarning: The feature names should match those that were passed during fit. Starting version 1.2, an error will be raised.\n",
      "Feature names must be in the same order as they were in fit.\n",
      "\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,\n",
       "       0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1,\n",
       "       1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0,\n",
       "       0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logistic_predict = logistic_model.predict(customers_clean)\n",
    "logistic_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d44ce2",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fc427ed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn import tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "28e4232b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit-Application-Result</th>\n",
       "      <th>Account-Balance</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Value-Savings-Stocks</th>\n",
       "      <th>Length-of-current-employment</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>No-of-Credits-at-this-Bank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Other</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1544</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>More than 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3380</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>343</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Credit-Application-Result Account-Balance  Duration-of-Credit-Month  \\\n",
       "0                          1    Some Balance                         4   \n",
       "1                          1    Some Balance                         4   \n",
       "2                          1    Some Balance                         4   \n",
       "3                          1    Some Balance                         4   \n",
       "4                          1      No Account                         6   \n",
       "\n",
       "  Payment-Status-of-Previous-Credit       Purpose  Credit-Amount  \\\n",
       "0                           Paid Up         Other           1494   \n",
       "1                           Paid Up  Home Related           1494   \n",
       "2        No Problems (in this bank)  Home Related           1544   \n",
       "3        No Problems (in this bank)  Home Related           3380   \n",
       "4                           Paid Up  Home Related            343   \n",
       "\n",
       "  Value-Savings-Stocks Length-of-current-employment  Instalment-per-cent  \\\n",
       "0           £100-£1000                        < 1yr                    1   \n",
       "1           £100-£1000                        < 1yr                    1   \n",
       "2                 None                      1-4 yrs                    2   \n",
       "3                 None                      1-4 yrs                    1   \n",
       "4                 None                        < 1yr                    4   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1       33.0                  2   \n",
       "1                              1       29.0                  2   \n",
       "2                              1       42.0                  2   \n",
       "3                              1       37.0                  2   \n",
       "4                              1       27.0                  2   \n",
       "\n",
       "  No-of-Credits-at-this-Bank  \n",
       "0                          1  \n",
       "1                          1  \n",
       "2                More than 1  \n",
       "3                          1  \n",
       "4                          1  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_data = df_clean_2.copy()\n",
    "decision_tree_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "0149a3c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = decision_tree_data.drop(\"Credit-Application-Result\", axis = 1)\n",
    "target = decision_tree_data['Credit-Application-Result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "92935448",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Value-Savings-Stocks</th>\n",
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       "      <th>Type-of-apartment</th>\n",
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       "    <tr>\n",
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       "      <td>1494</td>\n",
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       "      <td>Some Balance</td>\n",
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       "      <td>Home Related</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1544</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>More than 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3380</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>343</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Account-Balance  Duration-of-Credit-Month Payment-Status-of-Previous-Credit  \\\n",
       "0    Some Balance                         4                           Paid Up   \n",
       "1    Some Balance                         4                           Paid Up   \n",
       "2    Some Balance                         4        No Problems (in this bank)   \n",
       "3    Some Balance                         4        No Problems (in this bank)   \n",
       "4      No Account                         6                           Paid Up   \n",
       "\n",
       "        Purpose  Credit-Amount Value-Savings-Stocks  \\\n",
       "0         Other           1494           £100-£1000   \n",
       "1  Home Related           1494           £100-£1000   \n",
       "2  Home Related           1544                 None   \n",
       "3  Home Related           3380                 None   \n",
       "4  Home Related            343                 None   \n",
       "\n",
       "  Length-of-current-employment  Instalment-per-cent  \\\n",
       "0                        < 1yr                    1   \n",
       "1                        < 1yr                    1   \n",
       "2                      1-4 yrs                    2   \n",
       "3                      1-4 yrs                    1   \n",
       "4                        < 1yr                    4   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1       33.0                  2   \n",
       "1                              1       29.0                  2   \n",
       "2                              1       42.0                  2   \n",
       "3                              1       37.0                  2   \n",
       "4                              1       27.0                  2   \n",
       "\n",
       "  No-of-Credits-at-this-Bank  \n",
       "0                          1  \n",
       "1                          1  \n",
       "2                More than 1  \n",
       "3                          1  \n",
       "4                          1  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ba8ec879",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Credit-Application-Result, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "7ec424df",
   "metadata": {},
   "outputs": [],
   "source": [
    "account_balance = LabelEncoder()\n",
    "duration_of_credit_month = LabelEncoder()\n",
    "payment_status_of_previous_credit = LabelEncoder()\n",
    "purpose = LabelEncoder()\n",
    "credit_amount = LabelEncoder()\n",
    "value_savings_stocks = LabelEncoder()\n",
    "length_of_current_employment = LabelEncoder()\n",
    "instalment_percent = LabelEncoder()\n",
    "most_valuable_available_asset = LabelEncoder()\n",
    "age_years = LabelEncoder()\n",
    "type_of_apartment = LabelEncoder()\n",
    "no_of_credits_at_this_bank = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "948d6c57",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs['Account-Balance_n'] = account_balance.fit_transform(inputs['Account-Balance'])\n",
    "inputs['Payment-Status-of-Previous-Credit_n'] = payment_status_of_previous_credit.fit_transform(inputs['Payment-Status-of-Previous-Credit'])\n",
    "inputs['Purpose_n'] = purpose.fit_transform(inputs['Purpose'])\n",
    "inputs['Value-Savings-Stocks_n'] = value_savings_stocks.fit_transform(inputs['Value-Savings-Stocks'])\n",
    "inputs['Length-of-current-employment_n'] = length_of_current_employment.fit_transform(inputs['Length-of-current-employment'])\n",
    "inputs['No-of-Credits-at-this-Bank_n'] = no_of_credits_at_this_bank.fit_transform(inputs['No-of-Credits-at-this-Bank'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "19f117e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
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       "      <th>Credit-Amount</th>\n",
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       "      <th>Type-of-apartment</th>\n",
       "      <th>No-of-Credits-at-this-Bank</th>\n",
       "      <th>Account-Balance_n</th>\n",
       "      <th>Payment-Status-of-Previous-Credit_n</th>\n",
       "      <th>Purpose_n</th>\n",
       "      <th>Value-Savings-Stocks_n</th>\n",
       "      <th>Length-of-current-employment_n</th>\n",
       "      <th>No-of-Credits-at-this-Bank_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Other</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1494</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1544</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>More than 1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Some Balance</td>\n",
       "      <td>4</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3380</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>343</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Account-Balance  Duration-of-Credit-Month Payment-Status-of-Previous-Credit  \\\n",
       "0    Some Balance                         4                           Paid Up   \n",
       "1    Some Balance                         4                           Paid Up   \n",
       "2    Some Balance                         4        No Problems (in this bank)   \n",
       "3    Some Balance                         4        No Problems (in this bank)   \n",
       "4      No Account                         6                           Paid Up   \n",
       "\n",
       "        Purpose  Credit-Amount Value-Savings-Stocks  \\\n",
       "0         Other           1494           £100-£1000   \n",
       "1  Home Related           1494           £100-£1000   \n",
       "2  Home Related           1544                 None   \n",
       "3  Home Related           3380                 None   \n",
       "4  Home Related            343                 None   \n",
       "\n",
       "  Length-of-current-employment  Instalment-per-cent  \\\n",
       "0                        < 1yr                    1   \n",
       "1                        < 1yr                    1   \n",
       "2                      1-4 yrs                    2   \n",
       "3                      1-4 yrs                    1   \n",
       "4                        < 1yr                    4   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1       33.0                  2   \n",
       "1                              1       29.0                  2   \n",
       "2                              1       42.0                  2   \n",
       "3                              1       37.0                  2   \n",
       "4                              1       27.0                  2   \n",
       "\n",
       "  No-of-Credits-at-this-Bank  Account-Balance_n  \\\n",
       "0                          1                  1   \n",
       "1                          1                  1   \n",
       "2                More than 1                  1   \n",
       "3                          1                  1   \n",
       "4                          1                  0   \n",
       "\n",
       "   Payment-Status-of-Previous-Credit_n  Purpose_n  Value-Savings-Stocks_n  \\\n",
       "0                                    1          2                       2   \n",
       "1                                    1          0                       2   \n",
       "2                                    0          0                       1   \n",
       "3                                    0          0                       1   \n",
       "4                                    1          0                       1   \n",
       "\n",
       "   Length-of-current-employment_n  No-of-Credits-at-this-Bank_n  \n",
       "0                               2                             0  \n",
       "1                               2                             0  \n",
       "2                               0                             1  \n",
       "3                               0                             0  \n",
       "4                               2                             0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e302138f",
   "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",
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       "    }\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>Duration-of-Credit-Month</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>Account-Balance_n</th>\n",
       "      <th>Payment-Status-of-Previous-Credit_n</th>\n",
       "      <th>Purpose_n</th>\n",
       "      <th>Value-Savings-Stocks_n</th>\n",
       "      <th>Length-of-current-employment_n</th>\n",
       "      <th>No-of-Credits-at-this-Bank_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>1494</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>1494</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>1544</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3380</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>343</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration-of-Credit-Month  Credit-Amount  Instalment-per-cent  \\\n",
       "0                         4           1494                    1   \n",
       "1                         4           1494                    1   \n",
       "2                         4           1544                    2   \n",
       "3                         4           3380                    1   \n",
       "4                         6            343                    4   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1       33.0                  2   \n",
       "1                              1       29.0                  2   \n",
       "2                              1       42.0                  2   \n",
       "3                              1       37.0                  2   \n",
       "4                              1       27.0                  2   \n",
       "\n",
       "   Account-Balance_n  Payment-Status-of-Previous-Credit_n  Purpose_n  \\\n",
       "0                  1                                    1          2   \n",
       "1                  1                                    1          0   \n",
       "2                  1                                    0          0   \n",
       "3                  1                                    0          0   \n",
       "4                  0                                    1          0   \n",
       "\n",
       "   Value-Savings-Stocks_n  Length-of-current-employment_n  \\\n",
       "0                       2                               2   \n",
       "1                       2                               2   \n",
       "2                       1                               0   \n",
       "3                       1                               0   \n",
       "4                       1                               2   \n",
       "\n",
       "   No-of-Credits-at-this-Bank_n  \n",
       "0                             0  \n",
       "1                             0  \n",
       "2                             1  \n",
       "3                             0  \n",
       "4                             0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_n = inputs.drop(['Account-Balance','Payment-Status-of-Previous-Credit','Purpose','Value-Savings-Stocks','Length-of-current-employment','No-of-Credits-at-this-Bank'],\n",
    "                      axis = 1)\n",
    "inputs_n.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "94d78cd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_dt, X_test_dt, Y_train_dt, Y_test_dt = train_test_split(inputs_n, target, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "2c0fab46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "c42cb85a",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_model = tree.DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "d958f45d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_model.fit(X_train_dt, Y_train_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7ceb2d77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_model.score(X_test_dt, Y_test_dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6232a91",
   "metadata": {},
   "source": [
    "## make decision tree prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "3ceb658e",
   "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>Account-Balance</th>\n",
       "      <th>Duration-of-Credit-Month</th>\n",
       "      <th>Payment-Status-of-Previous-Credit</th>\n",
       "      <th>Purpose</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Value-Savings-Stocks</th>\n",
       "      <th>Length-of-current-employment</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>No-of-Credits-at-this-Bank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Account</td>\n",
       "      <td>9</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>2799</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>More than 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No Account</td>\n",
       "      <td>12</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>2122</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>More than 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Account</td>\n",
       "      <td>24</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3758</td>\n",
       "      <td>£100-£1000</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Account</td>\n",
       "      <td>11</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3905</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>More than 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1957</td>\n",
       "      <td>None</td>\n",
       "      <td>1-4 yrs</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Account-Balance  Duration-of-Credit-Month Payment-Status-of-Previous-Credit  \\\n",
       "0      No Account                         9        No Problems (in this bank)   \n",
       "1      No Account                        12        No Problems (in this bank)   \n",
       "2      No Account                        24                           Paid Up   \n",
       "3      No Account                        11        No Problems (in this bank)   \n",
       "4      No Account                         6        No Problems (in this bank)   \n",
       "\n",
       "        Purpose  Credit-Amount Value-Savings-Stocks  \\\n",
       "0  Home Related           2799                 None   \n",
       "1  Home Related           2122                 None   \n",
       "2  Home Related           3758           £100-£1000   \n",
       "3  Home Related           3905                 None   \n",
       "4  Home Related           1957                 None   \n",
       "\n",
       "  Length-of-current-employment  Instalment-per-cent  \\\n",
       "0                        < 1yr                    2   \n",
       "1                        < 1yr                    3   \n",
       "2                        < 1yr                    1   \n",
       "3                        < 1yr                    2   \n",
       "4                      1-4 yrs                    1   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1         36                  1   \n",
       "1                              1         39                  1   \n",
       "2                              4         23                  1   \n",
       "3                              1         36                  1   \n",
       "4                              3         31                  2   \n",
       "\n",
       "  No-of-Credits-at-this-Bank  \n",
       "0                More than 1  \n",
       "1                More than 1  \n",
       "2                          1  \n",
       "3                More than 1  \n",
       "4                          1  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers3 = customers2.copy()\n",
    "customers3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "593c89e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "customers3['Account-Balance_n'] = account_balance.fit_transform(customers3['Account-Balance'])\n",
    "customers3['Payment-Status-of-Previous-Credit_n'] = payment_status_of_previous_credit.fit_transform(customers3['Payment-Status-of-Previous-Credit'])\n",
    "customers3['Purpose_n'] = purpose.fit_transform(customers3['Purpose'])\n",
    "customers3['Value-Savings-Stocks_n'] = value_savings_stocks.fit_transform(customers3['Value-Savings-Stocks'])\n",
    "customers3['Length-of-current-employment_n'] = length_of_current_employment.fit_transform(customers3['Length-of-current-employment'])\n",
    "customers3['No-of-Credits-at-this-Bank_n'] = no_of_credits_at_this_bank.fit_transform(customers3['No-of-Credits-at-this-Bank'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "5642d898",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>9</td>\n",
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       "      <td>Home Related</td>\n",
       "      <td>2122</td>\n",
       "      <td>None</td>\n",
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       "      <td>3</td>\n",
       "      <td>1</td>\n",
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       "      <td>No Account</td>\n",
       "      <td>24</td>\n",
       "      <td>Paid Up</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3758</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Account</td>\n",
       "      <td>11</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>3905</td>\n",
       "      <td>None</td>\n",
       "      <td>&lt; 1yr</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
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       "      <td>No Account</td>\n",
       "      <td>6</td>\n",
       "      <td>No Problems (in this bank)</td>\n",
       "      <td>Home Related</td>\n",
       "      <td>1957</td>\n",
       "      <td>None</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Account-Balance  Duration-of-Credit-Month Payment-Status-of-Previous-Credit  \\\n",
       "0      No Account                         9        No Problems (in this bank)   \n",
       "1      No Account                        12        No Problems (in this bank)   \n",
       "2      No Account                        24                           Paid Up   \n",
       "3      No Account                        11        No Problems (in this bank)   \n",
       "4      No Account                         6        No Problems (in this bank)   \n",
       "\n",
       "        Purpose  Credit-Amount Value-Savings-Stocks  \\\n",
       "0  Home Related           2799                 None   \n",
       "1  Home Related           2122                 None   \n",
       "2  Home Related           3758           £100-£1000   \n",
       "3  Home Related           3905                 None   \n",
       "4  Home Related           1957                 None   \n",
       "\n",
       "  Length-of-current-employment  Instalment-per-cent  \\\n",
       "0                        < 1yr                    2   \n",
       "1                        < 1yr                    3   \n",
       "2                        < 1yr                    1   \n",
       "3                        < 1yr                    2   \n",
       "4                      1-4 yrs                    1   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1         36                  1   \n",
       "1                              1         39                  1   \n",
       "2                              4         23                  1   \n",
       "3                              1         36                  1   \n",
       "4                              3         31                  2   \n",
       "\n",
       "  No-of-Credits-at-this-Bank  Account-Balance_n  \\\n",
       "0                More than 1                  0   \n",
       "1                More than 1                  0   \n",
       "2                          1                  0   \n",
       "3                More than 1                  0   \n",
       "4                          1                  0   \n",
       "\n",
       "   Payment-Status-of-Previous-Credit_n  Purpose_n  Value-Savings-Stocks_n  \\\n",
       "0                                    0          0                       1   \n",
       "1                                    0          0                       1   \n",
       "2                                    1          0                       2   \n",
       "3                                    0          0                       1   \n",
       "4                                    0          0                       1   \n",
       "\n",
       "   Length-of-current-employment_n  No-of-Credits-at-this-Bank_n  \n",
       "0                               2                             1  \n",
       "1                               2                             1  \n",
       "2                               2                             0  \n",
       "3                               2                             1  \n",
       "4                               0                             0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1fe7bf1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>11</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration-of-Credit-Month  Credit-Amount  Instalment-per-cent  \\\n",
       "0                         9           2799                    2   \n",
       "1                        12           2122                    3   \n",
       "2                        24           3758                    1   \n",
       "3                        11           3905                    2   \n",
       "4                         6           1957                    1   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1         36                  1   \n",
       "1                              1         39                  1   \n",
       "2                              4         23                  1   \n",
       "3                              1         36                  1   \n",
       "4                              3         31                  2   \n",
       "\n",
       "   Account-Balance_n  Payment-Status-of-Previous-Credit_n  Purpose_n  \\\n",
       "0                  0                                    0          0   \n",
       "1                  0                                    0          0   \n",
       "2                  0                                    1          0   \n",
       "3                  0                                    0          0   \n",
       "4                  0                                    0          0   \n",
       "\n",
       "   Value-Savings-Stocks_n  Length-of-current-employment_n  \\\n",
       "0                       1                               2   \n",
       "1                       1                               2   \n",
       "2                       2                               2   \n",
       "3                       1                               2   \n",
       "4                       1                               0   \n",
       "\n",
       "   No-of-Credits-at-this-Bank_n  \n",
       "0                             1  \n",
       "1                             1  \n",
       "2                             0  \n",
       "3                             1  \n",
       "4                             0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers3_n = customers3.drop(['Account-Balance','Payment-Status-of-Previous-Credit','Purpose','Value-Savings-Stocks','Length-of-current-employment','No-of-Credits-at-this-Bank'],\n",
    "                      axis = 1)\n",
    "customers3_n.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "fd981d2c",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_tree_predict = tree_model.predict(customers3_n)\n",
    "decision_tree_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dcb2402",
   "metadata": {},
   "source": [
    "# Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4e43ecaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f299b4fe",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3380</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>343</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration-of-Credit-Month  Credit-Amount  Instalment-per-cent  \\\n",
       "0                         4           1494                    1   \n",
       "1                         4           1494                    1   \n",
       "2                         4           1544                    2   \n",
       "3                         4           3380                    1   \n",
       "4                         6            343                    4   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1       33.0                  2   \n",
       "1                              1       29.0                  2   \n",
       "2                              1       42.0                  2   \n",
       "3                              1       37.0                  2   \n",
       "4                              1       27.0                  2   \n",
       "\n",
       "   Account-Balance_n  Payment-Status-of-Previous-Credit_n  Purpose_n  \\\n",
       "0                  1                                    1          2   \n",
       "1                  1                                    1          0   \n",
       "2                  1                                    0          0   \n",
       "3                  1                                    0          0   \n",
       "4                  0                                    1          0   \n",
       "\n",
       "   Value-Savings-Stocks_n  Length-of-current-employment_n  \\\n",
       "0                       2                               2   \n",
       "1                       2                               2   \n",
       "2                       1                               0   \n",
       "3                       1                               0   \n",
       "4                       1                               2   \n",
       "\n",
       "   No-of-Credits-at-this-Bank_n  \n",
       "0                             0  \n",
       "1                             0  \n",
       "2                             1  \n",
       "3                             0  \n",
       "4                             0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs_n.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1a735c57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Credit-Application-Result, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "a61a1d1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_f, X_test_f, Y_train_f, Y_test_f = train_test_split(inputs_n, target, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "76c857a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "350"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "afd048aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest_model = RandomForestClassifier(n_estimators = 40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "ed173ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=40)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_model.fit(X_train_f, Y_train_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "ef6a96a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6933333333333334"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_model.score(X_test_f, Y_test_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1583412c",
   "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>Duration-of-Credit-Month</th>\n",
       "      <th>Credit-Amount</th>\n",
       "      <th>Instalment-per-cent</th>\n",
       "      <th>Most-valuable-available-asset</th>\n",
       "      <th>Age-years</th>\n",
       "      <th>Type-of-apartment</th>\n",
       "      <th>Account-Balance_n</th>\n",
       "      <th>Payment-Status-of-Previous-Credit_n</th>\n",
       "      <th>Purpose_n</th>\n",
       "      <th>Value-Savings-Stocks_n</th>\n",
       "      <th>Length-of-current-employment_n</th>\n",
       "      <th>No-of-Credits-at-this-Bank_n</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9</td>\n",
       "      <td>2799</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>2122</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>3758</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>3905</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1957</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Duration-of-Credit-Month  Credit-Amount  Instalment-per-cent  \\\n",
       "0                         9           2799                    2   \n",
       "1                        12           2122                    3   \n",
       "2                        24           3758                    1   \n",
       "3                        11           3905                    2   \n",
       "4                         6           1957                    1   \n",
       "\n",
       "   Most-valuable-available-asset  Age-years  Type-of-apartment  \\\n",
       "0                              1         36                  1   \n",
       "1                              1         39                  1   \n",
       "2                              4         23                  1   \n",
       "3                              1         36                  1   \n",
       "4                              3         31                  2   \n",
       "\n",
       "   Account-Balance_n  Payment-Status-of-Previous-Credit_n  Purpose_n  \\\n",
       "0                  0                                    0          0   \n",
       "1                  0                                    0          0   \n",
       "2                  0                                    1          0   \n",
       "3                  0                                    0          0   \n",
       "4                  0                                    0          0   \n",
       "\n",
       "   Value-Savings-Stocks_n  Length-of-current-employment_n  \\\n",
       "0                       1                               2   \n",
       "1                       1                               2   \n",
       "2                       2                               2   \n",
       "3                       1                               2   \n",
       "4                       1                               0   \n",
       "\n",
       "   No-of-Credits-at-this-Bank_n  \n",
       "0                             1  \n",
       "1                             1  \n",
       "2                             0  \n",
       "3                             1  \n",
       "4                             0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers3_n.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "f87e055a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
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       "       1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0,\n",
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       "       1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n",
       "       1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_predict = forest_model.predict(customers3_n)\n",
    "forest_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05ca07f4",
   "metadata": {},
   "source": [
    "# Boosted Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0ab2e6bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "6883a2b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_bf, X_test_bf, Y_train_bf, Y_test_bf = train_test_split(inputs_n, target, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "6c2ad19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "boost_model = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "de7b5b4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xuejunsui/opt/anaconda3/lib/python3.8/site-packages/xgboost/sklearn.py:1224: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13:09:54] WARNING: /Users/runner/miniforge3/conda-bld/xgboost-split_1645117948562/work/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.300000012,\n",
       "              max_delta_step=0, max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "              num_parallel_tree=1, predictor='auto', random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boost_model.fit(X_train_bf, Y_train_bf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "7b460cf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7733333333333333"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boost_model.score(X_test_bf, Y_test_bf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7ced47b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1,\n",
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1,\n",
       "       1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1,\n",
       "       1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0,\n",
       "       0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boost_predict = boost_model.predict(customers3_n)\n",
    "boost_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43065f89",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1072acbf",
   "metadata": {},
   "source": [
    "## ROC curve"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75e23b6",
   "metadata": {},
   "source": [
    "The ROC curve summarizes the prediction performance of a classification model at all classification thresholds. It plots the False Positive Rate (FPR) on the X-axis and the True Positive Rate (TPR) on Y- axis. \n",
    "\n",
    "ROC is the receiver operating characteristic.\n",
    "AUROC is the area under the ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "01043a88",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6fed924f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the prediction probabilities\n",
    "lr_probs = logistic_model.predict_proba(X_test_lr)\n",
    "dt_probs = tree_model.predict_proba(X_test_dt)\n",
    "f_probs = forest_model.predict_proba(X_test_f)\n",
    "bf_probs = boost_model.predict_proba(X_test_bf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "a799bcdc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate AUROC score\n",
    "lr_auc = roc_auc_score(Y_test_lr, lr_probs[:,1])\n",
    "dt_auc = roc_auc_score(Y_test_dt, dt_probs[:,1])\n",
    "f_auc = roc_auc_score(Y_test_f, f_probs[:,1])\n",
    "bf_auc = roc_auc_score(Y_test_bf,bf_probs[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "3b3474bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression AUROC = 0.742\n",
      "Decision Tree AUROC = 0.600\n",
      "Forest Model AUROC = 0.689\n",
      "Boosted Forest AUROC = 0.720\n"
     ]
    }
   ],
   "source": [
    "print('Logistic Regression AUROC = {:.3f}'.format(lr_auc))\n",
    "print('Decision Tree AUROC = {:.3f}'.format(dt_auc))\n",
    "print('Forest Model AUROC = {:.3f}'.format(f_auc))\n",
    "print('Boosted Forest AUROC = {:.3f}'.format(bf_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "0963dd82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate ROC curve\n",
    "lr_fpr, lr_tpr, _ = roc_curve(Y_test_lr, lr_probs[:,1])\n",
    "dt_fpr, dt_tpr, _ = roc_curve(Y_test_dt, dt_probs[:,1])\n",
    "f_fpr, f_tpr, _ = roc_curve(Y_test_f, f_probs[:,1])\n",
    "bf_fpr, bf_tpr, _ = roc_curve(Y_test_bf, bf_probs[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "7bc967f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(lr_fpr, lr_tpr, linestyle = '--', label = 'Logistic Regression(AUROC = {:.3f})'.format(lr_auc))\n",
    "plt.plot(dt_fpr, dt_tpr, linestyle = '--', label = 'Decision Tree(AUROC = {:.3f})'.format(dt_auc))\n",
    "plt.plot(f_fpr, f_tpr, linestyle = '--', label = 'Forest Model(AUROC = {:.3f})'.format(f_auc))\n",
    "plt.plot(bf_fpr, bf_tpr, linestyle = '--', label = 'Boosted Forest(AUROC = {:.3f})'.format(bf_auc))\n",
    "\n",
    "#Title\n",
    "plt.title('ROC Plot')\n",
    "#Axis labels\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "#Show legend\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4cb0b13",
   "metadata": {},
   "source": [
    "Classifiers that give curves closer to the top-left corner indicate a better performance. As a baseline, a random classifier is expected to give points lying along the diagonal. From the graph above, logistic regression model has the best performance. It also has the highest AUROC score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1f463dd",
   "metadata": {},
   "source": [
    "## Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "971a4e60",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa73c75a",
   "metadata": {},
   "source": [
    "## Precision, Recall, F1 Score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa23177",
   "metadata": {},
   "source": [
    "Precision: out of all positive/negative **predictions** how many you got it right.\n",
    "\n",
    "Recall: out of all positive/negative **truth** how many you got it right.\n",
    "\n",
    "F1 Score: 2*(precision*recall)/(precision + recall)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "fd6558d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Source code credit for this function: https://gist.github.com/shaypal5/94c53d765083101efc0240d776a23823\n",
    "def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize = 14):\n",
    "    \"\"\"Prints a confusion matrix, as returned by sklearn.metrics.confusion_matrix, as a heatmap.\n",
    "    \n",
    "    Arguments\n",
    "    ---------\n",
    "    confusion_matrix: numpy.ndarray\n",
    "        The numpy.ndarray object returned from a call to sklearn.metrics.confusion_matrix. \n",
    "        Similarly constructed ndarrays can also be used.\n",
    "    class_names: list\n",
    "        An ordered list of class names, in the order they index the given confusion matrix.\n",
    "    figsize: tuple\n",
    "        A 2-long tuple, the first value determining the horizontal size of the ouputted figure,\n",
    "        the second determining the vertical size. Defaults to (10,7).\n",
    "    fontsize: int\n",
    "        Font size for axes labels. Defaults to 14.\n",
    "        \n",
    "    Returns\n",
    "    -------\n",
    "    matplotlib.figure.Figure\n",
    "        The resulting confusion matrix figure\n",
    "    \"\"\"\n",
    "    df_cm = pd.DataFrame(confusion_matrix,\n",
    "                        index=class_names, columns = class_names)\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    try:\n",
    "        heatmap = sns.heatmap(df_cm, annot = True, fmt = 'd')\n",
    "    except ValueError:\n",
    "        raise ValueError('Confusion matrix values must be integers.')\n",
    "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation = 0, ha = 'right', fontsize = fontsize)\n",
    "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation = 45, ha = 'right', fontsize = fontsize)\n",
    "    plt.ylabel('Truth')\n",
    "    plt.xlabel('Prediction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "b7e4faa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_truth = Y_test_lr\n",
    "lr_prediction = logistic_model.predict(X_test_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "3b5aeef8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_lr = confusion_matrix(lr_truth, lr_prediction)\n",
    "print_confusion_matrix(cm_lr,['Non-creditworthy','Creditworthy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "61d9f3e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.45      0.53        38\n",
      "           1       0.83      0.92      0.87       112\n",
      "\n",
      "    accuracy                           0.80       150\n",
      "   macro avg       0.74      0.68      0.70       150\n",
      "weighted avg       0.79      0.80      0.79       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(lr_truth, lr_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "27fa3a77",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_truth = Y_test_dt\n",
    "dt_prediction = tree_model.predict(X_test_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "51cdb737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_dt = confusion_matrix(dt_truth, dt_prediction)\n",
    "print_confusion_matrix(cm_dt,['Non-creditworthy','Creditworthy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "4a356bca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.32      0.43      0.37        30\n",
      "           1       0.84      0.77      0.80       120\n",
      "\n",
      "    accuracy                           0.70       150\n",
      "   macro avg       0.58      0.60      0.58       150\n",
      "weighted avg       0.74      0.70      0.72       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(dt_truth, dt_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "4b080c52",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_truth = Y_test_f\n",
    "f_prediction = forest_model.predict(X_test_f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "6f0ceb02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_f = confusion_matrix(f_truth, f_prediction)\n",
    "print_confusion_matrix(cm_f,['Non-creditworthy','Creditworthy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "7ec3ff3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.29      0.38        49\n",
      "           1       0.72      0.89      0.80       101\n",
      "\n",
      "    accuracy                           0.69       150\n",
      "   macro avg       0.64      0.59      0.59       150\n",
      "weighted avg       0.67      0.69      0.66       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(f_truth, f_prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "db7c1da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "bf_truth = Y_test_bf\n",
    "bf_prediction = boost_model.predict(X_test_bf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "8142e116",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_bf = confusion_matrix(bf_truth, bf_prediction)\n",
    "print_confusion_matrix(cm_bf,['Non-creditworthy','Creditworthy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "622669a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.43      0.53        44\n",
      "           1       0.80      0.92      0.85       106\n",
      "\n",
      "    accuracy                           0.77       150\n",
      "   macro avg       0.74      0.67      0.69       150\n",
      "weighted avg       0.76      0.77      0.76       150\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(bf_truth, bf_prediction))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9911a7d3",
   "metadata": {},
   "source": [
    "The logistic regression model has an overall highest performance. In this way, we choose logistic regression model results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30368ab3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}