Credit Card Approval Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Card Approval Prediction Using Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement:\n",
    "\n",
    "Banking industries received so many applications for credit card request. Going through each request manually can be very time consuming, also prone to human errors. However, if we can use the historical data to build a model which can shortlist the candidates for approval that can be great."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Importing Libraries and Loading Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, loading and viewing the dataset. We find that since this data is confidential, the contributor of the dataset has anonymized the feature names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loading the neccessary libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>11</th>\n",
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       "      <th>14</th>\n",
       "      <th>15</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
       "      <td>0.000</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.25</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00202</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>3.04</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>6</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00043</td>\n",
       "      <td>560</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>a</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>q</td>\n",
       "      <td>h</td>\n",
       "      <td>1.50</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00280</td>\n",
       "      <td>824</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>00120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  0      1      2  3  4  5  6     7  8  9   10 11 12     13   14 15\n",
       "0  b  30.83  0.000  u  g  w  v  1.25  t  t   1  f  g  00202    0  +\n",
       "1  a  58.67  4.460  u  g  q  h  3.04  t  t   6  f  g  00043  560  +\n",
       "2  a  24.50  0.500  u  g  q  h  1.50  t  f   0  f  g  00280  824  +\n",
       "3  b  27.83  1.540  u  g  w  v  3.75  t  t   5  t  g  00100    3  +\n",
       "4  b  20.17  5.625  u  g  w  v  1.71  t  f   0  f  s  00120    0  +"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"C:/Users/Nantha/Desktop/Nantha Kumar/SLRC/proj/credit card/crx.data\",header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspecting the applications\n",
    "The features of this dataset have been anonymized to protect the privacy, but this blog gives us a pretty good overview of the probable features. The probable features in a typical credit card application are Gender, Age, Debt, Married, BankCustomer, EducationLevel, Ethnicity, YearsEmployed, PriorDefault, Employed, CreditScore, DriversLicense, Citizen, ZipCode, Income and finally the ApprovalStatus. This gives us a pretty good starting point, and we can map these features with respect to the columns in the output.\n",
    "\n",
    "As we can see from our first glance at the data, the dataset has a mixture of numerical and non-numerical features. let's learn about the dataset a bit more to see if there are other dataset issues that need to be fixed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['Gender', 'Age', 'Debt', 'Married', 'BankCustomer', 'EducationLevel', 'Ethnicity', 'YearsEmployed', 'PriorDefault', 'Employed', 'CreditScore', 'DriversLicense', 'Citizen', 'ZipCode', 'Income', 'ApprovalStatus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(690, 16)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that there are **690** rows and **16** features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Gender</th>\n",
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       "      <th>Debt</th>\n",
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       "      <th>Ethnicity</th>\n",
       "      <th>YearsEmployed</th>\n",
       "      <th>PriorDefault</th>\n",
       "      <th>Employed</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>DriversLicense</th>\n",
       "      <th>Citizen</th>\n",
       "      <th>ZipCode</th>\n",
       "      <th>Income</th>\n",
       "      <th>ApprovalStatus</th>\n",
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       "  </thead>\n",
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       "      <td>0</td>\n",
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       "      <td>30.83</td>\n",
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       "      <td>u</td>\n",
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       "      <td>2</td>\n",
       "      <td>a</td>\n",
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       "      <td>0.500</td>\n",
       "      <td>u</td>\n",
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       "      <td>824</td>\n",
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       "      <td>3</td>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.540</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>3.75</td>\n",
       "      <td>t</td>\n",
       "      <td>t</td>\n",
       "      <td>5</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00100</td>\n",
       "      <td>3</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>b</td>\n",
       "      <td>20.17</td>\n",
       "      <td>5.625</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>w</td>\n",
       "      <td>v</td>\n",
       "      <td>1.71</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>00120</td>\n",
       "      <td>0</td>\n",
       "      <td>+</td>\n",
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      ],
      "text/plain": [
       "  Gender    Age   Debt Married BankCustomer EducationLevel Ethnicity  \\\n",
       "0      b  30.83  0.000       u            g              w         v   \n",
       "1      a  58.67  4.460       u            g              q         h   \n",
       "2      a  24.50  0.500       u            g              q         h   \n",
       "3      b  27.83  1.540       u            g              w         v   \n",
       "4      b  20.17  5.625       u            g              w         v   \n",
       "\n",
       "   YearsEmployed PriorDefault Employed  CreditScore DriversLicense Citizen  \\\n",
       "0           1.25            t        t            1              f       g   \n",
       "1           3.04            t        t            6              f       g   \n",
       "2           1.50            t        f            0              f       g   \n",
       "3           3.75            t        t            5              t       g   \n",
       "4           1.71            t        f            0              f       s   \n",
       "\n",
       "  ZipCode  Income ApprovalStatus  \n",
       "0   00202       0              +  \n",
       "1   00043     560              +  \n",
       "2   00280     824              +  \n",
       "3   00100       3              +  \n",
       "4   00120       0              +  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Descriptive Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Debt</th>\n",
       "      <th>YearsEmployed</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.000000</td>\n",
       "      <td>690.00000</td>\n",
       "      <td>690.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>4.758725</td>\n",
       "      <td>2.223406</td>\n",
       "      <td>2.40000</td>\n",
       "      <td>1017.385507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>4.978163</td>\n",
       "      <td>3.346513</td>\n",
       "      <td>4.86294</td>\n",
       "      <td>5210.102598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>7.207500</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>395.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>67.00000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Debt  YearsEmployed  CreditScore         Income\n",
       "count  690.000000     690.000000    690.00000     690.000000\n",
       "mean     4.758725       2.223406      2.40000    1017.385507\n",
       "std      4.978163       3.346513      4.86294    5210.102598\n",
       "min      0.000000       0.000000      0.00000       0.000000\n",
       "25%      1.000000       0.165000      0.00000       0.000000\n",
       "50%      2.750000       1.000000      0.00000       5.000000\n",
       "75%      7.207500       2.625000      3.00000     395.500000\n",
       "max     28.000000      28.500000     67.00000  100000.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " We can observe that average income for all the users is 1017.4, the average year of employment 2.4 years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 690 entries, 0 to 689\n",
      "Data columns (total 16 columns):\n",
      "Gender            690 non-null object\n",
      "Age               690 non-null object\n",
      "Debt              690 non-null float64\n",
      "Married           690 non-null object\n",
      "BankCustomer      690 non-null object\n",
      "EducationLevel    690 non-null object\n",
      "Ethnicity         690 non-null object\n",
      "YearsEmployed     690 non-null float64\n",
      "PriorDefault      690 non-null object\n",
      "Employed          690 non-null object\n",
      "CreditScore       690 non-null int64\n",
      "DriversLicense    690 non-null object\n",
      "Citizen           690 non-null object\n",
      "ZipCode           690 non-null object\n",
      "Income            690 non-null int64\n",
      "ApprovalStatus    690 non-null object\n",
      "dtypes: float64(2), int64(2), object(12)\n",
      "memory usage: 86.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "We will analyse the data using visualizations. The Target variable is ApprovalStatus. We will do univariate, bivariate and multivariate analysis to find which features affect the target variable the most."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-    383\n",
       "+    307\n",
       "Name: ApprovalStatus, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ApprovalStatus'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "sns.countplot(df['ApprovalStatus'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe that the no of application rejected is more than the no of approved applications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,5))\n",
    "sns.distplot(df['Income'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that income is highly right skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(15,5))\n",
    "sns.distplot(df['Debt'],color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pairplot showing the correlation between elements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling the missing values\n",
    "\n",
    "Our dataset contains both numeric and non-numeric data (specifically data that are of float64, int64 and object types). Specifically, the features 2, 7, 10 and 14 contain numeric values (of types float64, float64, int64 and int64 respectively) and all the other features contain non-numeric values.\n",
    "The dataset also contains values from several ranges. Some features have a value range of 0 - 28, some have a range of 2 - 67, and some have a range of 1017 - 100000. Apart from these, we can get useful statistical information (like mean, max, and min) about the features that have numerical values.\n",
    "Finally, the dataset has missing values. The missing values in the dataset are labeled with '?', which can be seen in the last cell's output.\n",
    "Now, let's temporarily replace these missing value question marks with NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender            0\n",
       "Age               0\n",
       "Debt              0\n",
       "Married           0\n",
       "BankCustomer      0\n",
       "EducationLevel    0\n",
       "Ethnicity         0\n",
       "YearsEmployed     0\n",
       "PriorDefault      0\n",
       "Employed          0\n",
       "CreditScore       0\n",
       "DriversLicense    0\n",
       "Citizen           0\n",
       "ZipCode           0\n",
       "Income            0\n",
       "ApprovalStatus    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that there is no null values in the dataset. But on closely examining the data we may find null values in the form of symbols or numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 690 entries, 0 to 689\n",
      "Data columns (total 16 columns):\n",
      "Gender            690 non-null object\n",
      "Age               690 non-null object\n",
      "Debt              690 non-null float64\n",
      "Married           690 non-null object\n",
      "BankCustomer      690 non-null object\n",
      "EducationLevel    690 non-null object\n",
      "Ethnicity         690 non-null object\n",
      "YearsEmployed     690 non-null float64\n",
      "PriorDefault      690 non-null object\n",
      "Employed          690 non-null object\n",
      "CreditScore       690 non-null int64\n",
      "DriversLicense    690 non-null object\n",
      "Citizen           690 non-null object\n",
      "ZipCode           690 non-null object\n",
      "Income            690 non-null int64\n",
      "ApprovalStatus    690 non-null object\n",
      "dtypes: float64(2), int64(2), object(12)\n",
      "memory usage: 86.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Closely looking at the object columns and checking the values in it we can find if there is any null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['b', 'a', '?'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find that there is '?' in the dataset which is a null value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['u', 'y', '?', 'l'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Married'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Married column has a null value also. We will replace the '?' for the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the '?'s with NaN\n",
    "df = df.replace({\"?\":np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender            12\n",
       "Age               12\n",
       "Debt               0\n",
       "Married            6\n",
       "BankCustomer       6\n",
       "EducationLevel     9\n",
       "Ethnicity          9\n",
       "YearsEmployed      0\n",
       "PriorDefault       0\n",
       "Employed           0\n",
       "CreditScore        0\n",
       "DriversLicense     0\n",
       "Citizen            0\n",
       "ZipCode           13\n",
       "Income             0\n",
       "ApprovalStatus     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we found there are null values in Gender,Age,Married,BankCustomer,EducationLevel,Ethnicity and Zipcode."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling the missing values for the numerical columns\n",
    "\n",
    "We replaced all the question marks with NaNs. This is going to help us in the next missing value treatment that we are going to perform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender            12\n",
       "Age               12\n",
       "Debt               0\n",
       "Married            6\n",
       "BankCustomer       6\n",
       "EducationLevel     9\n",
       "Ethnicity          9\n",
       "YearsEmployed      0\n",
       "PriorDefault       0\n",
       "Employed           0\n",
       "CreditScore        0\n",
       "DriversLicense     0\n",
       "Citizen            0\n",
       "ZipCode           13\n",
       "Income             0\n",
       "ApprovalStatus     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Impute the missing values with mean imputation for the numeric columns\n",
    "df.fillna(df.mean(), inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handling the missing values for categorical variables\n",
    "We have successfully taken care of the missing values present in the numeric columns. There are still some missing values to be imputed for columns Gender,Age,Married,BankCustomer,EducationLevel,Ethnicity and Zipcode. All of these columns contain non-numeric data and this why the mean imputation strategy would not work here. This needs a different treatment.\n",
    "\n",
    "We are going to impute these missing values with the most frequent values as present in the respective columns. This is good practice when it comes to imputing missing values for categorical data in general."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender            0\n",
       "Age               0\n",
       "Debt              0\n",
       "Married           0\n",
       "BankCustomer      0\n",
       "EducationLevel    0\n",
       "Ethnicity         0\n",
       "YearsEmployed     0\n",
       "PriorDefault      0\n",
       "Employed          0\n",
       "CreditScore       0\n",
       "DriversLicense    0\n",
       "Citizen           0\n",
       "ZipCode           0\n",
       "Income            0\n",
       "ApprovalStatus    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterate over each column of df\n",
    "for col in df:\n",
    "    # Check if the column is of object type\n",
    "    if (df[col].dtypes == 'object'):\n",
    "        # Impute with the most frequent value / mode\n",
    "        df = df.fillna(df[col].value_counts().index[0])\n",
    "\n",
    "# Count the number of NaNs in the dataset and print the counts to verify\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The missing values are now successfully handled. Now Let's see the heatmap for the dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(8,8))\n",
    "sns.heatmap(df.corr(),annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing the data\n",
    "\n",
    "There is still some minor but essential data preprocessing needed before we proceed towards building our machine learning model. We are going to divide these remaining preprocessing steps into three main tasks:\n",
    "\n",
    "Convert the non-numeric data into numeric.\n",
    "Split the data into train and test sets.\n",
    "Scale the feature values to a uniform range.\n",
    "First, we will be converting all the non-numeric values into numeric ones. We do this because not only it results in a faster computation but also many machine learning models (like XGBoost) (and especially the ones developed using scikit-learn) require the data to be in a strictly numeric format. We will do this by using a technique called label encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 690 entries, 0 to 689\n",
      "Data columns (total 16 columns):\n",
      "Gender            690 non-null object\n",
      "Age               690 non-null object\n",
      "Debt              690 non-null float64\n",
      "Married           690 non-null object\n",
      "BankCustomer      690 non-null object\n",
      "EducationLevel    690 non-null object\n",
      "Ethnicity         690 non-null object\n",
      "YearsEmployed     690 non-null float64\n",
      "PriorDefault      690 non-null object\n",
      "Employed          690 non-null object\n",
      "CreditScore       690 non-null int64\n",
      "DriversLicense    690 non-null object\n",
      "Citizen           690 non-null object\n",
      "ZipCode           690 non-null object\n",
      "Income            690 non-null int64\n",
      "ApprovalStatus    690 non-null object\n",
      "dtypes: float64(2), int64(2), object(12)\n",
      "memory usage: 86.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_categorical=[]\n",
    "for col in df:\n",
    "    \n",
    "    # Compare if the dtype is object\n",
    "    if df[col].dtype=='object':\n",
    "        df_categorical.append(col) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Gender',\n",
       " 'Age',\n",
       " 'Married',\n",
       " 'BankCustomer',\n",
       " 'EducationLevel',\n",
       " 'Ethnicity',\n",
       " 'PriorDefault',\n",
       " 'Employed',\n",
       " 'DriversLicense',\n",
       " 'Citizen',\n",
       " 'ZipCode',\n",
       " 'ApprovalStatus']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ApprovalStatus'] = df['ApprovalStatus'].map({\"+\":1,\"-\":0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Married</th>\n",
       "      <th>BankCustomer</th>\n",
       "      <th>EducationLevel</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>PriorDefault</th>\n",
       "      <th>Employed</th>\n",
       "      <th>DriversLicense</th>\n",
       "      <th>Citizen</th>\n",
       "      <th>ZipCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>328</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender  Age  Married  BankCustomer  EducationLevel  Ethnicity  \\\n",
       "0       1  156        2             1              13          8   \n",
       "1       0  328        2             1              11          4   \n",
       "2       0   89        2             1              11          4   \n",
       "3       1  125        2             1              13          8   \n",
       "4       1   43        2             1              13          8   \n",
       "\n",
       "   PriorDefault  Employed  DriversLicense  Citizen  ZipCode  \n",
       "0             1         1               0        0       68  \n",
       "1             1         1               0        0       11  \n",
       "2             1         0               0        0       96  \n",
       "3             1         1               1        0       31  \n",
       "4             1         0               0        2       37  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_categorical_data = df[['Gender',\n",
    " 'Age',\n",
    " 'Married',\n",
    " 'BankCustomer',\n",
    " 'EducationLevel',\n",
    " 'Ethnicity',\n",
    " 'PriorDefault',\n",
    " 'Employed',\n",
    " 'DriversLicense',\n",
    " 'Citizen',\n",
    " 'ZipCode']]\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "df_categorical_data = df_categorical_data.apply(le.fit_transform)\n",
    "df_categorical_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Debt</th>\n",
       "      <th>YearsEmployed</th>\n",
       "      <th>CreditScore</th>\n",
       "      <th>Income</th>\n",
       "      <th>ApprovalStatus</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Married</th>\n",
       "      <th>BankCustomer</th>\n",
       "      <th>EducationLevel</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>PriorDefault</th>\n",
       "      <th>Employed</th>\n",
       "      <th>DriversLicense</th>\n",
       "      <th>Citizen</th>\n",
       "      <th>ZipCode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.460</td>\n",
       "      <td>3.04</td>\n",
       "      <td>6</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>328</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.50</td>\n",
       "      <td>0</td>\n",
       "      <td>824</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.540</td>\n",
       "      <td>3.75</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.625</td>\n",
       "      <td>1.71</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Debt  YearsEmployed  CreditScore  Income  ApprovalStatus  Gender  Age  \\\n",
       "0  0.000           1.25            1       0               1       1  156   \n",
       "1  4.460           3.04            6     560               1       0  328   \n",
       "2  0.500           1.50            0     824               1       0   89   \n",
       "3  1.540           3.75            5       3               1       1  125   \n",
       "4  5.625           1.71            0       0               1       1   43   \n",
       "\n",
       "   Married  BankCustomer  EducationLevel  Ethnicity  PriorDefault  Employed  \\\n",
       "0        2             1              13          8             1         1   \n",
       "1        2             1              11          4             1         1   \n",
       "2        2             1              11          4             1         0   \n",
       "3        2             1              13          8             1         1   \n",
       "4        2             1              13          8             1         0   \n",
       "\n",
       "   DriversLicense  Citizen  ZipCode  \n",
       "0               0        0       68  \n",
       "1               0        0       11  \n",
       "2               0        0       96  \n",
       "3               1        0       31  \n",
       "4               0        2       37  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat df_categorical with original df\n",
    "df = df.drop(df_categorical_data.columns, axis=1)\n",
    "df = pd.concat([df, df_categorical_data], axis=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting the dataset into train and test sets\n",
    "We have successfully converted all the non-numeric values to numeric ones.\n",
    "\n",
    "Now, we will split our data into train set and test set to prepare our data for two different phases of machine learning modeling: training and testing. Ideally, no information from the test data should be used to scale the training data or should be used to direct the training process of a machine learning model. Hence, we first split the data and then apply the scaling.\n",
    "\n",
    "Also, features like DriversLicense and ZipCode are not as important as the other features in the dataset for predicting credit card approvals. We should drop them to design our machine learning model with the best set of features. In Data Science literature, this is often referred to as feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Drop the features 11 and 13 and convert the DataFrame to a NumPy array\n",
    "df = df.drop(['DriversLicense', 'ZipCode'], axis=1)\n",
    "\n",
    "# Segregate features and labels into separate variables\n",
    "X = df.drop('ApprovalStatus',axis=1)\n",
    "y = df['ApprovalStatus']\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                y,\n",
    "                                test_size=0.3,\n",
    "                                random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scaling the data\n",
    "The data is now split into two separate sets - train and test sets respectively. We are only left with one final preprocessing step of scaling before we can fit a machine learning model to the data.\n",
    "\n",
    "Now, let's try to understand what these scaled values mean in the real world. Let's use CreditScore as an example. The credit score of a person is their creditworthiness based on their credit history. The higher this number, the more financially trustworthy a person is considered to be. So, a CreditScore of 1 is the highest since we're rescaling all the values to the range of 0-1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import MinMaxScaler\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "\n",
    "# Instantiate MinMaxScaler and use it to rescale X_train and X_test\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "rescaledX_train = scaler.fit_transform(X_train)\n",
    "rescaledX_test = scaler.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fitting a logistic regression model to the train set\n",
    "Essentially, predicting if a credit card application will be approved or not is a classification task. According to UCI, our dataset contains more instances that correspond to \"Denied\" status than instances corresponding to \"Approved\" status. Specifically, out of 690 instances, there are 383 (55.5%) applications that got denied and 307 (44.5%) applications that got approved.\n",
    "\n",
    "This gives us a benchmark. A good machine learning model should be able to accurately predict the status of the applications with respect to these statistics.\n",
    "\n",
    "Let's start our machine learning modeling with a Logistic Regression model (a generalized linear model)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import LogisticRegression\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "# Instantiate a LogisticRegression classifier with default parameter values\n",
    "logreg = LogisticRegression()\n",
    "\n",
    "# Fit logreg to the train set\n",
    "logreg.fit(rescaledX_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Making predictions and evaluating performance\n",
    "\n",
    "We will now evaluate our model on the test set with respect to classification accuracy. But we will also take a look the model's confusion matrix. In the case of predicting credit card applications, it is equally important to see if our machine learning model is able to predict the approval status of the applications as denied that originally got denied. If our model is not performing well in this aspect, then it might end up approving the application that should have been approved. The confusion matrix helps us to view our model's performance from these aspects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier:  0.8599033816425121\n",
      "Confusion Matrix\n",
      " [[98 24]\n",
      " [ 5 80]]\n"
     ]
    }
   ],
   "source": [
    "# Import confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Use logreg to predict instances from the test set and store it\n",
    "y_pred = logreg.predict(rescaledX_test)\n",
    "\n",
    "# Get the accuracy score of logreg model and print it\n",
    "print(\"Accuracy of logistic regression classifier: \", logreg.score(rescaledX_test,y_test))\n",
    "\n",
    "# Print the confusion matrix of the logreg model\n",
    "print(\"Confusion Matrix\\n\", confusion_matrix(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.80      0.87       122\n",
      "           1       0.77      0.94      0.85        85\n",
      "\n",
      "    accuracy                           0.86       207\n",
      "   macro avg       0.86      0.87      0.86       207\n",
      "weighted avg       0.88      0.86      0.86       207\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for the model: 0.8722275795564127\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "\n",
    "print('AUC for the model:',roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid searching and making the model perform better\n",
    "The model  was able to yield an accuracy score of almost 84%.\n",
    "\n",
    "For the confusion matrix, the first element of the of the first row of the confusion matrix denotes the true negatives meaning the number of negative instances (denied applications) predicted by the model correctly. And the last element of the second row of the confusion matrix denotes the true positives meaning the number of positive instances (approved applications) predicted by the model correctly.\n",
    "\n",
    "We can perform a grid search of the model parameters to improve the model's ability to predict credit card approvals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import GridSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the grid of values for tol and max_iter\n",
    "tol = [0.01,0.001,0.0001]\n",
    "max_iter = [100,150,200]\n",
    "\n",
    "# Create a dictionary where tol and max_iter are keys and the lists of their values are corresponding values\n",
    "param_grid = {\"tol\":tol, \"max_iter\":max_iter}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Finding the best performing model\n",
    "We have defined the grid of hyperparameter values and converted them into a single dictionary format which GridSearchCV() expects as one of its parameters. Now, we will begin the grid search to see which values perform best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.853623 using {'max_iter': 100, 'tol': 0.01}\n"
     ]
    }
   ],
   "source": [
    "# Instantiate GridSearchCV with the required parameters\n",
    "grid_model = GridSearchCV(estimator=logreg, param_grid=param_grid, cv=5)\n",
    "\n",
    "# Use scaler to rescale X and assign it to rescaledX\n",
    "rescaledX = scaler.fit_transform(X)\n",
    "\n",
    "# Fit data to grid_model\n",
    "grid_model_result = grid_model.fit(rescaledX, y)\n",
    "\n",
    "# Summarize results\n",
    "best_score, best_params = grid_model_result.best_score_, grid_model_result.best_params_\n",
    "print(\"Best: %f using %s\" % (best_score, best_params))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8536231884057971"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model_result.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have achieved a score of 85% accuracy after using GridSearchCV"
   ]
  }
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