Credit Approval - Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(action = \"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Debt</th>\n",
       "      <th>Married</th>\n",
       "      <th>Bank Costumer</th>\n",
       "      <th>Educational Level</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Years employed</th>\n",
       "      <th>Prior default</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Credit score</th>\n",
       "      <th>Drivers Licence</th>\n",
       "      <th>Citizen</th>\n",
       "      <th>Zip code</th>\n",
       "      <th>Income</th>\n",
       "      <th>Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>6.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43</td>\n",
       "      <td>560.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.5</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>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280</td>\n",
       "      <td>824.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>5.0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex    Age   Debt Married Bank Costumer Educational Level 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   24.5  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",
       "   Years employed Prior default Employed  Credit score Drivers Licence  \\\n",
       "0            1.25             t        t           1.0               f   \n",
       "1            3.04             t        t           6.0               f   \n",
       "2            1.50             t        f           0.0               f   \n",
       "3            3.75             t        t           5.0               t   \n",
       "4            1.71             t        f           0.0               f   \n",
       "\n",
       "  Citizen Zip code  Income Approved  \n",
       "0       g      202     0.0        +  \n",
       "1       g       43   560.0        +  \n",
       "2       g      280   824.0        +  \n",
       "3       g      100     3.0        +  \n",
       "4       s      120     0.0        +  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('credit_approval.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Married</th>\n",
       "      <th>Bank Costumer</th>\n",
       "      <th>Educational Level</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Prior default</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Drivers Licence</th>\n",
       "      <th>Citizen</th>\n",
       "      <th>Zip code</th>\n",
       "      <th>Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "      <td>690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "      <td>350</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>171</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>b</td>\n",
       "      <td>?</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>t</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>468</td>\n",
       "      <td>12</td>\n",
       "      <td>519</td>\n",
       "      <td>519</td>\n",
       "      <td>137</td>\n",
       "      <td>399</td>\n",
       "      <td>361</td>\n",
       "      <td>395</td>\n",
       "      <td>374</td>\n",
       "      <td>625</td>\n",
       "      <td>132</td>\n",
       "      <td>383</td>\n",
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      "text/plain": [
       "        Sex  Age Married Bank Costumer Educational Level Ethnicity  \\\n",
       "count   690  690     690           690               690       690   \n",
       "unique    3  350       4             4                15        10   \n",
       "top       b    ?       u             g                 c         v   \n",
       "freq    468   12     519           519               137       399   \n",
       "\n",
       "       Prior default Employed Drivers Licence Citizen Zip code Approved  \n",
       "count            690      690             690     690      690      690  \n",
       "unique             2        2               2       3      171        2  \n",
       "top                t        f               f       g        0        -  \n",
       "freq             361      395             374     625      132      383  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include=['O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 692 entries, 0 to 691\n",
      "Data columns (total 16 columns):\n",
      "Sex                  692 non-null int32\n",
      "Age                  692 non-null int32\n",
      "Debt                 692 non-null float64\n",
      "Married              692 non-null int32\n",
      "Bank Costumer        692 non-null int32\n",
      "Educational Level    692 non-null int32\n",
      "Ethnicity            692 non-null int32\n",
      "Years employed       692 non-null float64\n",
      "Prior default        692 non-null int32\n",
      "Employed             692 non-null int32\n",
      "Credit score         692 non-null float64\n",
      "Drivers Licence      692 non-null int32\n",
      "Citizen              692 non-null int32\n",
      "Zip code             692 non-null int32\n",
      "Income               692 non-null float64\n",
      "Approved             692 non-null int32\n",
      "dtypes: float64(4), int32(12)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n"
     ]
    }
   ],
   "source": [
    "print(data.isnull().values.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "# Impute the missing values with mean imputation\n",
    "data = data.fillna(data.mean())\n",
    "\n",
    "# Count the number of NaNs in the dataset to verify\n",
    "\n",
    "print(data.isnull().values.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Debt</th>\n",
       "      <th>Married</th>\n",
       "      <th>Bank Costumer</th>\n",
       "      <th>Educational Level</th>\n",
       "      <th>Ethnicity</th>\n",
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       "      <th>Prior default</th>\n",
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       "      <th>Credit score</th>\n",
       "      <th>Drivers Licence</th>\n",
       "      <th>Citizen</th>\n",
       "      <th>Zip code</th>\n",
       "      <th>Income</th>\n",
       "      <th>Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\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",
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       "    <tr>\n",
       "      <th>1</th>\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>6.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43</td>\n",
       "      <td>560.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.5</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>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280</td>\n",
       "      <td>824.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>5.0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  Sex    Age   Debt Married Bank Costumer Educational Level 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   24.5  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",
       "   Years employed Prior default Employed  Credit score Drivers Licence  \\\n",
       "0            1.25             t        t           1.0               f   \n",
       "1            3.04             t        t           6.0               f   \n",
       "2            1.50             t        f           0.0               f   \n",
       "3            3.75             t        t           5.0               t   \n",
       "4            1.71             t        f           0.0               f   \n",
       "\n",
       "  Citizen Zip code  Income Approved  \n",
       "0       g      202     0.0        +  \n",
       "1       g       43   560.0        +  \n",
       "2       g      280   824.0        +  \n",
       "3       g      100     3.0        +  \n",
       "4       s      120     0.0        +  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 692 entries, 0 to 691\n",
      "Data columns (total 16 columns):\n",
      "Sex                  690 non-null object\n",
      "Age                  690 non-null object\n",
      "Debt                 692 non-null float64\n",
      "Married              690 non-null object\n",
      "Bank Costumer        690 non-null object\n",
      "Educational Level    690 non-null object\n",
      "Ethnicity            690 non-null object\n",
      "Years employed       692 non-null float64\n",
      "Prior default        690 non-null object\n",
      "Employed             690 non-null object\n",
      "Credit score         692 non-null float64\n",
      "Drivers Licence      690 non-null object\n",
      "Citizen              690 non-null object\n",
      "Zip code             690 non-null object\n",
      "Income               692 non-null float64\n",
      "Approved             690 non-null object\n",
      "dtypes: float64(4), object(12)\n",
      "memory usage: 86.6+ KB\n",
      "None\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#missing value in non numeric column\n",
    "\n",
    "print(data.info())\n",
    "for col in data.columns:\n",
    "    #check if col is obj type\n",
    "    if data[col].dtypes == 'object':\n",
    "        #impute with most freq value\n",
    "        data[col] = data[col].fillna(data[col].value_counts().index[0])\n",
    "        #count nans in dataset and print count to verify\n",
    "print(data.isnull().values.sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Debt</th>\n",
       "      <th>Married</th>\n",
       "      <th>Bank Costumer</th>\n",
       "      <th>Educational Level</th>\n",
       "      <th>Ethnicity</th>\n",
       "      <th>Years employed</th>\n",
       "      <th>Prior default</th>\n",
       "      <th>Employed</th>\n",
       "      <th>Credit score</th>\n",
       "      <th>Drivers Licence</th>\n",
       "      <th>Citizen</th>\n",
       "      <th>Zip code</th>\n",
       "      <th>Income</th>\n",
       "      <th>Approved</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>1.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>202</td>\n",
       "      <td>0.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>6.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>43</td>\n",
       "      <td>560.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>24.5</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>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>280</td>\n",
       "      <td>824.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>5.0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>100</td>\n",
       "      <td>3.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>s</td>\n",
       "      <td>120</td>\n",
       "      <td>0.0</td>\n",
       "      <td>+</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex    Age   Debt Married Bank Costumer Educational Level 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   24.5  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",
       "   Years employed Prior default Employed  Credit score Drivers Licence  \\\n",
       "0            1.25             t        t           1.0               f   \n",
       "1            3.04             t        t           6.0               f   \n",
       "2            1.50             t        f           0.0               f   \n",
       "3            3.75             t        t           5.0               t   \n",
       "4            1.71             t        f           0.0               f   \n",
       "\n",
       "  Citizen Zip code  Income Approved  \n",
       "0       g      202     0.0        +  \n",
       "1       g       43   560.0        +  \n",
       "2       g      280   824.0        +  \n",
       "3       g      100     3.0        +  \n",
       "4       s      120     0.0        +  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import LabelEncoder\n",
    "# encoding object data type (conv non numeric to numeric)\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Instantiate LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "# Iterate over all the values of each column and extract their dtypes\n",
    "for col in data.columns:\n",
    "    # Compare if the dtype is object\n",
    "    if data[col].dtype=='object':\n",
    "    # Use LabelEncoder to do the numeric transformation\n",
    "        data[col]=le.fit_transform(data[col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>118</td>\n",
       "      <td>560.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>0.500</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>5.625</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
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      "text/plain": [
       "   Sex  Age   Debt  Married  Bank Costumer  Educational Level  Ethnicity  \\\n",
       "0    2  156  0.000        2              1                 13          8   \n",
       "1    1  328  4.460        2              1                 11          4   \n",
       "2    1   89  0.500        2              1                 11          4   \n",
       "3    2  125  1.540        2              1                 13          8   \n",
       "4    2   43  5.625        2              1                 13          8   \n",
       "\n",
       "   Years employed  Prior default  Employed  Credit score  Drivers Licence  \\\n",
       "0            1.25              1         1           1.0                0   \n",
       "1            3.04              1         1           6.0                0   \n",
       "2            1.50              1         0           0.0                0   \n",
       "3            3.75              1         1           5.0                1   \n",
       "4            1.71              1         0           0.0                0   \n",
       "\n",
       "   Citizen  Zip code  Income  Approved  \n",
       "0        0        42     0.0         0  \n",
       "1        0       118   560.0         0  \n",
       "2        0        74   824.0         0  \n",
       "3        0         1     3.0         0  \n",
       "4        2         8     0.0         0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "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. This is often called feature engineering or, more specifically, feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop features 10 and 13 and convert the DataFrame to a NumPy array\n",
    "df=data.drop(['Drivers Licence', 'Zip code'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>560.0</td>\n",
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       "      <th>2</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>824.0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>8</td>\n",
       "      <td>1.71</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sex  Age   Debt  Married  Bank Costumer  Educational Level  Ethnicity  \\\n",
       "0    2  156  0.000        2              1                 13          8   \n",
       "1    1  328  4.460        2              1                 11          4   \n",
       "2    1   89  0.500        2              1                 11          4   \n",
       "3    2  125  1.540        2              1                 13          8   \n",
       "4    2   43  5.625        2              1                 13          8   \n",
       "\n",
       "   Years employed  Prior default  Employed  Credit score  Citizen  Income  \\\n",
       "0            1.25              1         1           1.0        0     0.0   \n",
       "1            3.04              1         1           6.0        0   560.0   \n",
       "2            1.50              1         0           0.0        0   824.0   \n",
       "3            3.75              1         1           5.0        0     3.0   \n",
       "4            1.71              1         0           0.0        2     0.0   \n",
       "\n",
       "   Approved  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Sex', 'Age', 'Debt', 'Married', 'Bank Costumer', 'Educational Level',\n",
       "       'Ethnicity', 'Years employed', 'Prior default', 'Employed',\n",
       "       'Credit score', 'Citizen', 'Income', 'Approved'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('cleaned_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:13]\n",
    "y= df.iloc[:,13]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Arjun\\Miniconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:323: DataConversionWarning: Data with input dtype int32, float64 were all converted to float64 by MinMaxScaler.\n",
      "  return self.partial_fit(X, y)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "# Instantiate MinMaxScaler and use it to rescale\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "rescaledX = scaler.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Splitting the dataset into train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(rescaledX,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import LogisticRegression\n",
    "\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(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making predictions and evaluating performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier:  0.8439306358381503\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[72,  7],\n",
       "       [20, 74]], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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(X_test)\n",
    "\n",
    "# Get the accuracy score of logreg model and print it\n",
    "print(\"Accuracy of logistic regression classifier: \", logreg.score(X_test, y_test))\n",
    "\n",
    "# Print the confusion matrix of the logreg model\n",
    "confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid searching and making the model perform better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'tol': [0.01, 0.001, 0.0001], 'max_iter': [100, 150, 200]}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# 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 = dict(tol=tol, max_iter=max_iter)\n",
    "print(param_grid)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the best performing model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.849711 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",
    "# 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": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "train = []\n",
    "test = []\n",
    "index = []\n",
    "\n",
    "for C in [0.001, 0.01,0.1, 1, 10, 100]:\n",
    "    for gamma in [0.001, 0.01,0.1, 1, 10, 100]:\n",
    "        #create the model\n",
    "        svc = SVC(kernel = 'rbf', C = C, gamma = gamma)\n",
    "        \n",
    "        #train the model\n",
    "        svc.fit(X_train, y_train)\n",
    "        \n",
    "        #evaluate the model\n",
    "        train.append(svc.score(X_train, y_train))\n",
    "        test.append(svc.score(X_test, y_test))\n",
    "        index.append((C,gamma))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x206d58b1898>,\n",
       "  <matplotlib.axis.XTick at 0x206d58b1208>,\n",
       "  <matplotlib.axis.XTick at 0x206d5974b70>,\n",
       "  <matplotlib.axis.XTick at 0x206d59a28d0>,\n",
       "  <matplotlib.axis.XTick at 0x206d59a2d68>,\n",
       "  <matplotlib.axis.XTick at 0x206d59ad2b0>,\n",
       "  <matplotlib.axis.XTick at 0x206d59ad7f0>,\n",
       "  <matplotlib.axis.XTick at 0x206d59add30>,\n",
       "  <matplotlib.axis.XTick at 0x206d59b22b0>,\n",
       "  <matplotlib.axis.XTick at 0x206d59ad668>,\n",
       "  <matplotlib.axis.XTick at 0x206d59a2828>,\n",
       "  <matplotlib.axis.XTick at 0x206d59b2128>,\n",
       "  <matplotlib.axis.XTick at 0x206d59b2f60>,\n",
       "  <matplotlib.axis.XTick at 0x206d59bd4e0>,\n",
       "  <matplotlib.axis.XTick at 0x206d59bda20>,\n",
       "  <matplotlib.axis.XTick at 0x206d59bdf60>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b544e0>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b54a20>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b54f60>,\n",
       "  <matplotlib.axis.XTick at 0x206d59bdac8>,\n",
       "  <matplotlib.axis.XTick at 0x206d59b29b0>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b594e0>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b59a20>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b59f60>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b5d4e0>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b5da20>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b5df60>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b624e0>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b5d828>,\n",
       "  <matplotlib.axis.XTick at 0x206d59a20b8>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b628d0>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b62e10>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b66390>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b668d0>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b66e10>,\n",
       "  <matplotlib.axis.XTick at 0x206d5b6a390>],\n",
       " <a list of 36 Text xticklabel objects>)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(figsize = (15,5))\n",
    "\n",
    "plt.plot(range(len(index)), train, label = 'Train score')\n",
    "plt.plot(range(len(index)), test, label = 'Test score')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.xlabel('(C, gamma)')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xticks(range(len(index)), index, rotation = 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel = 'rbf', C = 1, gamma = 0.01)\n",
    "svc.fit(X_train,y_train)\n",
    "train_score = svc.score(X_train, y_train)\n",
    "test_score = svc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8574181117533719\n",
      "0.838150289017341\n"
     ]
    }
   ],
   "source": [
    "print(train_score)\n",
    "print(test_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.plotting import plot_decision_regions\n",
    "\n",
    "X_2d = X[['Debt', 'Age']].iloc[50:100]\n",
    "y_2d = y[50:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(kernel = 'rbf', C = 1, gamma = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma=1, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_2d, y_2d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x206d60a7438>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plot_decision_regions(X = np.array(X_2d), y = np.array(y_2d), clf = svc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}