predicting_credit_card_approvals.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Credit card applications\n",
    "<p>Commercial banks on a daily basis receive <em>a lot</em> of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low income levels, or too many inquiries on an individual's credit report. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated using machine learning and pretty much every commercial bank does so nowadays. In this notebook, we will build a prediction system for automatic credit card approvals using machine learning techniques, just like the real banks do.</p>\n",
    "<p><img src=\"https://assets.datacamp.com/production/project_558/img/credit_card.jpg\" alt=\"Credit card being held in hand\"></p>\n",
    "<p>We'll use the <a href=\"http://archive.ics.uci.edu/ml/datasets/credit+approval\">Credit Card Approval dataset</a> from the UCI Machine Learning Repository. The structure of this notebook is as follows:</p>\n",
    "<ul>\n",
    "<li>First, we will start by loading and viewing the dataset.</li>\n",
    "<li>We will analyze the dataset for numerical and non-numerical values, if it contains values between different ranges and if it has any missing values.</li>\n",
    "<li>We will then preprocess the dataset to ensure that we are only feeding numerical values to the machine learning model for predicitons.</li>\n",
    "<li>Finally, we will build a machine learning model that can predict if an individual's application for a credit card will be accepted or not.</li>\n",
    "<li> Source: <a href=\"https://projects.datacamp.com/projects/558\">Datacamp Project</a></li>  \n",
    "<li> I have extended the project by including visualizations and correlation analysis.\n",
    "</ul>\n",
    "<p>First, loading and viewing the dataset. We find that since this data is confidential, the contributor of the dataset has anonymized the feature names.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:10.747930Z",
     "start_time": "2020-03-30T03:25:10.744866Z"
    }
   },
   "outputs": [],
   "source": [
    "# !pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:11.703494Z",
     "start_time": "2020-03-30T03:25:10.750303Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from pylab import rcParams\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:11.738051Z",
     "start_time": "2020-03-30T03:25:11.709196Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b</td>\n",
       "      <td>30.83</td>\n",
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       "      <td>g</td>\n",
       "      <td>w</td>\n",
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       "      <td>1.25</td>\n",
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       "      <td>+</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</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00043</td>\n",
       "      <td>560</td>\n",
       "      <td>+</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\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",
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       "      <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</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",
       "      <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</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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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loading the dataset\n",
    "cc_apps = pd.read_csv('dataset/cc_approvals.data', header=None)\n",
    "\n",
    "# Inspect data\n",
    "cc_apps.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:11.746322Z",
     "start_time": "2020-03-30T03:25:11.740043Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-    383\n",
       "+    307\n",
       "Name: 15, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_apps[15].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:11.826420Z",
     "start_time": "2020-03-30T03:25:11.748409Z"
    }
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets visualize the target with a pie chart\n",
    "# '+' represents approved and '-' represents rejection\n",
    "data_size = cc_apps[15].value_counts(sort=True)\n",
    "colors = ['red','orange']\n",
    "rcParams['figure.figsize'] = 5,5\n",
    "\n",
    "# For plotting\n",
    "labels = '+', '-'\n",
    "explode = (0, 0.1) \n",
    "plt.pie(data_size, explode=explode, labels=labels, colors=colors,\n",
    "        autopct='%1.1f%%', shadow=True, startangle=90,)\n",
    "plt.title('Percentage of Credit Card Approvals in dataset')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Inspecting the applications\n",
    "<p>The features of this dataset have been anonymized to protect the privacy, but <a href=\"http://rstudio-pubs-static.s3.amazonaws.com/73039_9946de135c0a49daa7a0a9eda4a67a72.html\">this blog</a> gives us a pretty good overview of the probable features. The features in a typical credit card application are <code>Gender</code>, <code>Age</code>, <code>Debt</code>, <code>Married</code>, <code>BankCustomer</code>, <code>EducationLevel</code>, <code>Ethnicity</code>, <code>YearsEmployed</code>, <code>PriorDefault</code>, <code>Employed</code>, <code>CreditScore</code>, <code>DriversLicense</code>, <code>Citizen</code>, <code>ZipCode</code>, <code>Income</code> and finally the <code>ApprovalStatus</code>. This gives us a pretty good starting point, and we can map these features with respect to the columns in the output.   </p>\n",
    "<p>We see that there are numeric as well as non-numeric columns in the data. We would need to preprocess them to convert them to have numeric values. We would also need to see if there are any other issues which needs to be fixed. </p>\n",
    "<p>Important thing to note is the target variable is ApprovalStatus whose values are '+' and '-' denoting approved and denied credit card status. We would need to convert the symbols to numerical values 1 and 0.  </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:11.880286Z",
     "start_time": "2020-03-30T03:25:11.828298Z"
    },
    "dc": {
     "key": "10"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               2           7          10             14\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\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 690 entries, 0 to 689\n",
      "Data columns (total 16 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   0       690 non-null    object \n",
      " 1   1       690 non-null    object \n",
      " 2   2       690 non-null    float64\n",
      " 3   3       690 non-null    object \n",
      " 4   4       690 non-null    object \n",
      " 5   5       690 non-null    object \n",
      " 6   6       690 non-null    object \n",
      " 7   7       690 non-null    float64\n",
      " 8   8       690 non-null    object \n",
      " 9   9       690 non-null    object \n",
      " 10  10      690 non-null    int64  \n",
      " 11  11      690 non-null    object \n",
      " 12  12      690 non-null    object \n",
      " 13  13      690 non-null    object \n",
      " 14  14      690 non-null    int64  \n",
      " 15  15      690 non-null    object \n",
      "dtypes: float64(2), int64(2), object(12)\n",
      "memory usage: 86.4+ KB\n",
      "None\n",
      "\n",
      "\n"
     ]
    },
    {
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       "      <td>7.250</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>m</td>\n",
       "      <td>v</td>\n",
       "      <td>0.040</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00100</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>a</td>\n",
       "      <td>17.92</td>\n",
       "      <td>10.210</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>0.000</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00000</td>\n",
       "      <td>50</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>a</td>\n",
       "      <td>20.08</td>\n",
       "      <td>1.250</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>0.000</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00000</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>b</td>\n",
       "      <td>19.50</td>\n",
       "      <td>0.290</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>k</td>\n",
       "      <td>v</td>\n",
       "      <td>0.290</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00280</td>\n",
       "      <td>364</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>b</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.000</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>d</td>\n",
       "      <td>h</td>\n",
       "      <td>3.000</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00176</td>\n",
       "      <td>537</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>b</td>\n",
       "      <td>17.08</td>\n",
       "      <td>3.290</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>i</td>\n",
       "      <td>v</td>\n",
       "      <td>0.335</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00140</td>\n",
       "      <td>2</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>b</td>\n",
       "      <td>36.42</td>\n",
       "      <td>0.750</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>d</td>\n",
       "      <td>v</td>\n",
       "      <td>0.585</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00240</td>\n",
       "      <td>3</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>b</td>\n",
       "      <td>40.58</td>\n",
       "      <td>3.290</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>m</td>\n",
       "      <td>v</td>\n",
       "      <td>3.500</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>s</td>\n",
       "      <td>00400</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>b</td>\n",
       "      <td>21.08</td>\n",
       "      <td>10.085</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>e</td>\n",
       "      <td>h</td>\n",
       "      <td>1.250</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00260</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>a</td>\n",
       "      <td>22.67</td>\n",
       "      <td>0.750</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>v</td>\n",
       "      <td>2.000</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>2</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00200</td>\n",
       "      <td>394</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>a</td>\n",
       "      <td>25.25</td>\n",
       "      <td>13.500</td>\n",
       "      <td>y</td>\n",
       "      <td>p</td>\n",
       "      <td>ff</td>\n",
       "      <td>ff</td>\n",
       "      <td>2.000</td>\n",
       "      <td>f</td>\n",
       "      <td>t</td>\n",
       "      <td>1</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00200</td>\n",
       "      <td>1</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>b</td>\n",
       "      <td>17.92</td>\n",
       "      <td>0.205</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>aa</td>\n",
       "      <td>v</td>\n",
       "      <td>0.040</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>f</td>\n",
       "      <td>g</td>\n",
       "      <td>00280</td>\n",
       "      <td>750</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>689</th>\n",
       "      <td>b</td>\n",
       "      <td>35.00</td>\n",
       "      <td>3.375</td>\n",
       "      <td>u</td>\n",
       "      <td>g</td>\n",
       "      <td>c</td>\n",
       "      <td>h</td>\n",
       "      <td>8.290</td>\n",
       "      <td>f</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>t</td>\n",
       "      <td>g</td>\n",
       "      <td>00000</td>\n",
       "      <td>0</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0      1       2  3  4   5   6      7  8  9   10 11 12     13   14 15\n",
       "673  ?  29.50   2.000  y  p   e   h  2.000  f  f   0  f  g  00256   17  -\n",
       "674  a  37.33   2.500  u  g   i   h  0.210  f  f   0  f  g  00260  246  -\n",
       "675  a  41.58   1.040  u  g  aa   v  0.665  f  f   0  f  g  00240  237  -\n",
       "676  a  30.58  10.665  u  g   q   h  0.085  f  t  12  t  g  00129    3  -\n",
       "677  b  19.42   7.250  u  g   m   v  0.040  f  t   1  f  g  00100    1  -\n",
       "678  a  17.92  10.210  u  g  ff  ff  0.000  f  f   0  f  g  00000   50  -\n",
       "679  a  20.08   1.250  u  g   c   v  0.000  f  f   0  f  g  00000    0  -\n",
       "680  b  19.50   0.290  u  g   k   v  0.290  f  f   0  f  g  00280  364  -\n",
       "681  b  27.83   1.000  y  p   d   h  3.000  f  f   0  f  g  00176  537  -\n",
       "682  b  17.08   3.290  u  g   i   v  0.335  f  f   0  t  g  00140    2  -\n",
       "683  b  36.42   0.750  y  p   d   v  0.585  f  f   0  f  g  00240    3  -\n",
       "684  b  40.58   3.290  u  g   m   v  3.500  f  f   0  t  s  00400    0  -\n",
       "685  b  21.08  10.085  y  p   e   h  1.250  f  f   0  f  g  00260    0  -\n",
       "686  a  22.67   0.750  u  g   c   v  2.000  f  t   2  t  g  00200  394  -\n",
       "687  a  25.25  13.500  y  p  ff  ff  2.000  f  t   1  t  g  00200    1  -\n",
       "688  b  17.92   0.205  u  g  aa   v  0.040  f  f   0  f  g  00280  750  -\n",
       "689  b  35.00   3.375  u  g   c   h  8.290  f  f   0  t  g  00000    0  -"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print summary statistics\n",
    "cc_apps_description = cc_apps.describe()\n",
    "print(cc_apps_description)\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "# Print DataFrame information\n",
    "cc_apps_info = cc_apps.info()\n",
    "print(cc_apps_info)\n",
    "\n",
    "print(\"\\n\")\n",
    "\n",
    "# Inspect missing values in the dataset\n",
    "cc_apps.tail(17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Handling the missing values (part i)\n",
    "<p>As we see above:</p>\n",
    "<ul>\n",
    "<li>This dataset contains both numeric and non-numeric data (specifically data that are of <code>float64</code>, <code>int64</code> and <code>object</code> 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.</li>\n",
    "<li>The dataset also contains values between different 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 <code>mean</code>, <code>max</code>, and <code>min</code>) about the features that have numerical values. </li>\n",
    "<li>Finally, the dataset has missing values, which we'll take care of in this task. The missing values in the dataset are labeled with '?', which can be seen in the last cell's output.</li>\n",
    "</ul>\n",
    "<p>We've uncovered some issues that will affect the performance of our machine learning model(s) if they go unchanged.</p>\n",
    "<p>Now, let's temporarily replace these missing value question marks with NaN.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:12.084332Z",
     "start_time": "2020-03-30T03:25:12.059331Z"
    },
    "dc": {
     "key": "17"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before     0      1       2  3  4   5   6      7  8  9   10 11 12     13   14 15\n",
      "673  ?  29.50   2.000  y  p   e   h  2.000  f  f   0  f  g  00256   17  -\n",
      "674  a  37.33   2.500  u  g   i   h  0.210  f  f   0  f  g  00260  246  -\n",
      "675  a  41.58   1.040  u  g  aa   v  0.665  f  f   0  f  g  00240  237  -\n",
      "676  a  30.58  10.665  u  g   q   h  0.085  f  t  12  t  g  00129    3  -\n",
      "677  b  19.42   7.250  u  g   m   v  0.040  f  t   1  f  g  00100    1  -\n",
      "678  a  17.92  10.210  u  g  ff  ff  0.000  f  f   0  f  g  00000   50  -\n",
      "679  a  20.08   1.250  u  g   c   v  0.000  f  f   0  f  g  00000    0  -\n",
      "680  b  19.50   0.290  u  g   k   v  0.290  f  f   0  f  g  00280  364  -\n",
      "681  b  27.83   1.000  y  p   d   h  3.000  f  f   0  f  g  00176  537  -\n",
      "682  b  17.08   3.290  u  g   i   v  0.335  f  f   0  t  g  00140    2  -\n",
      "683  b  36.42   0.750  y  p   d   v  0.585  f  f   0  f  g  00240    3  -\n",
      "684  b  40.58   3.290  u  g   m   v  3.500  f  f   0  t  s  00400    0  -\n",
      "685  b  21.08  10.085  y  p   e   h  1.250  f  f   0  f  g  00260    0  -\n",
      "686  a  22.67   0.750  u  g   c   v  2.000  f  t   2  t  g  00200  394  -\n",
      "687  a  25.25  13.500  y  p  ff  ff  2.000  f  t   1  t  g  00200    1  -\n",
      "688  b  17.92   0.205  u  g  aa   v  0.040  f  f   0  f  g  00280  750  -\n",
      "689  b  35.00   3.375  u  g   c   h  8.290  f  f   0  t  g  00000    0  -\n",
      "After       0      1       2  3  4   5   6      7  8  9   10 11 12     13   14 15\n",
      "673  NaN  29.50   2.000  y  p   e   h  2.000  f  f   0  f  g  00256   17  -\n",
      "674    a  37.33   2.500  u  g   i   h  0.210  f  f   0  f  g  00260  246  -\n",
      "675    a  41.58   1.040  u  g  aa   v  0.665  f  f   0  f  g  00240  237  -\n",
      "676    a  30.58  10.665  u  g   q   h  0.085  f  t  12  t  g  00129    3  -\n",
      "677    b  19.42   7.250  u  g   m   v  0.040  f  t   1  f  g  00100    1  -\n",
      "678    a  17.92  10.210  u  g  ff  ff  0.000  f  f   0  f  g  00000   50  -\n",
      "679    a  20.08   1.250  u  g   c   v  0.000  f  f   0  f  g  00000    0  -\n",
      "680    b  19.50   0.290  u  g   k   v  0.290  f  f   0  f  g  00280  364  -\n",
      "681    b  27.83   1.000  y  p   d   h  3.000  f  f   0  f  g  00176  537  -\n",
      "682    b  17.08   3.290  u  g   i   v  0.335  f  f   0  t  g  00140    2  -\n",
      "683    b  36.42   0.750  y  p   d   v  0.585  f  f   0  f  g  00240    3  -\n",
      "684    b  40.58   3.290  u  g   m   v  3.500  f  f   0  t  s  00400    0  -\n",
      "685    b  21.08  10.085  y  p   e   h  1.250  f  f   0  f  g  00260    0  -\n",
      "686    a  22.67   0.750  u  g   c   v  2.000  f  t   2  t  g  00200  394  -\n",
      "687    a  25.25  13.500  y  p  ff  ff  2.000  f  t   1  t  g  00200    1  -\n",
      "688    b  17.92   0.205  u  g  aa   v  0.040  f  f   0  f  g  00280  750  -\n",
      "689    b  35.00   3.375  u  g   c   h  8.290  f  f   0  t  g  00000    0  -\n"
     ]
    }
   ],
   "source": [
    "# Inspecting missing values in the dataset\n",
    "print('Before', cc_apps.tail(17))\n",
    "\n",
    "# Replace the '?'s with NaN\n",
    "cc_apps = cc_apps.replace('?', np.nan)\n",
    "\n",
    "# Inspect the missing values again\n",
    "print('After', cc_apps.tail(17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:12.242802Z",
     "start_time": "2020-03-30T03:25:12.237307Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    468\n",
       "a    210\n",
       "Name: 0, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of a, b values in 0th column\n",
    "cc_apps[0].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:12.419448Z",
     "start_time": "2020-03-30T03:25:12.412453Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     12\n",
       "1     12\n",
       "2      0\n",
       "3      6\n",
       "4      6\n",
       "5      9\n",
       "6      9\n",
       "7      0\n",
       "8      0\n",
       "9      0\n",
       "10     0\n",
       "11     0\n",
       "12     0\n",
       "13    13\n",
       "14     0\n",
       "15     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_apps.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Handling the missing values (part ii)\n",
    "<p>We have replaced all the question marks with NaNs. \n",
    "<p>An important question here is <em>why are we giving so much importance to missing values</em>? Can't they be just ignored? Answer is: Ignoring missing values can heavily affect the performance of a machine learning model. While ignoring the missing values our machine learning model may miss out on information about the dataset that may be useful for its training. Then, there are many models which cannot handle missing values implicitly such as LDA. </p>\n",
    "<p> We see above that columns 0, 1, 3, 4, 5, 6 and 13 have non-numeric values and we would need to impute the missing values in those columns with the most frequent values present in the respective columns. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:12.820066Z",
     "start_time": "2020-03-30T03:25:12.768529Z"
    },
    "dc": {
     "key": "31"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "5     0\n",
       "6     0\n",
       "7     0\n",
       "8     0\n",
       "9     0\n",
       "10    0\n",
       "11    0\n",
       "12    0\n",
       "13    0\n",
       "14    0\n",
       "15    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterate over each column of cc_apps\n",
    "for col in cc_apps:\n",
    "    # Check if the column is of object type\n",
    "    if cc_apps[col].dtypes == 'object':\n",
    "        # Impute with the most frequent value\n",
    "        cc_apps = cc_apps.fillna(cc_apps[col].value_counts().index[0])\n",
    "\n",
    "# Count the number of NaNs in the dataset and print the counts to verify\n",
    "cc_apps.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Preprocessing the data (part i)\n",
    "<p>We see from above that we don't have any missing values and that's great.</p>\n",
    "<p>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:</p>\n",
    "<ol>\n",
    "<li>Convert the non-numeric data into numeric.</li>\n",
    "<li>Split the data into train and test sets. </li>\n",
    "<li>Scale the feature values to a uniform range.</li>\n",
    "</ol>\n",
    "<p>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 random forest, XGBoost,etc) (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 <a href=\"http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html\">label encoding</a>.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:13.099640Z",
     "start_time": "2020-03-30T03:25:13.075826Z"
    }
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>156</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>328</td>\n",
       "      <td>93</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>89</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>125</td>\n",
       "      <td>46</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>73</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>109</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1    2   3   4   5   6   7   8   9   10  11  12  13   14  15\n",
       "0   1  156    0   2   1  13   8  30   1   1   1   0   0  68    0   0\n",
       "1   0  328   93   2   1  11   4  65   1   1   6   0   0  11  119   0\n",
       "2   0   89   16   2   1  11   4  36   1   0   0   0   0  96  140   0\n",
       "3   1  125   46   2   1  13   8  73   1   1   5   1   0  31    3   0\n",
       "4   1   43  109   2   1  13   8  41   1   0   0   0   2  37    0   0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_apps = cc_apps.apply(LabelEncoder().fit_transform)\n",
    "cc_apps.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:14.406830Z",
     "start_time": "2020-03-30T03:25:13.401981Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b009550>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To see the correlation between variables, we will use Pearson coefficient and use heatmap for visualization\n",
    "# Correlation Plot\n",
    "corr_matrix = cc_apps.corr()\n",
    "f, ax = plt.subplots(figsize=(20, 10))\n",
    "\n",
    "#Draw the heatmap using seaborn\n",
    "sns.heatmap(corr_matrix, linewidths=0.1, cmap = plt.cm.Blues, linecolor = 'White', vmax=0.8, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:25:14.414748Z",
     "start_time": "2020-03-30T03:25:14.409116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.028934\n",
       "1    -0.133304\n",
       "2    -0.202400\n",
       "3     0.191431\n",
       "4     0.187520\n",
       "5    -0.130026\n",
       "6    -0.000877\n",
       "7    -0.385006\n",
       "8    -0.720407\n",
       "9    -0.458301\n",
       "10   -0.462489\n",
       "11   -0.031625\n",
       "12    0.100867\n",
       "13    0.094851\n",
       "14   -0.381625\n",
       "15    1.000000\n",
       "Name: 15, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_apps.corr()[15] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:02.690791Z",
     "start_time": "2020-03-30T03:31:02.684792Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2      True\n",
       "3      True\n",
       "4      True\n",
       "5     False\n",
       "6     False\n",
       "7      True\n",
       "8      True\n",
       "9      True\n",
       "10     True\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14     True\n",
       "15     True\n",
       "Name: 15, dtype: bool"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cc_apps.corr()[15].abs() > 0.15"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will select the features with absolute correlation value greater than 0.15 wrt to the target variable.\n",
    "Hence, features for training will be 2,3,4,7,8,9,10,14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Splitting the dataset into train and test sets\n",
    "<p>We have successfully converted all the non-numeric values to numeric ones.</p>\n",
    "<p>Now, we will split our data into training set and testing set. We will use stratified split to keep the same percentage of approved and rejected applications as in the full dataset. 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.</p>\n",
    "<p>Also, features like <code>DriversLicense</code> and <code>ZipCode</code> 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 <em>feature selection</em>. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:08.719763Z",
     "start_time": "2020-03-30T03:31:08.717503Z"
    }
   },
   "outputs": [],
   "source": [
    "feature_names = [2,3,4, 7,8,9,10,14]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:09.154714Z",
     "start_time": "2020-03-30T03:31:09.143989Z"
    },
    "dc": {
     "key": "45"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "# We will not consider the features like 11, 13 which are not important and do not help in model prediction.\n",
    "# Segregate features and labels into separate variables\n",
    "X = cc_apps[feature_names]\n",
    "y = cc_apps[[15]]\n",
    "\n",
    "# Split into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,\n",
    "                                y, stratify = y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:09.340235Z",
     "start_time": "2020-03-30T03:31:09.332615Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
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       "      <th>411</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>462 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     15\n",
       "41    0\n",
       "675   1\n",
       "31    0\n",
       "624   1\n",
       "303   1\n",
       "..   ..\n",
       "101   1\n",
       "416   1\n",
       "569   0\n",
       "290   1\n",
       "411   1\n",
       "\n",
       "[462 rows x 1 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:09.512304Z",
     "start_time": "2020-03-30T03:31:09.507945Z"
    },
    "dc": {
     "key": "45"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(y_train == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:09.690721Z",
     "start_time": "2020-03-30T03:31:09.686459Z"
    },
    "dc": {
     "key": "45"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(y_train == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:09.854160Z",
     "start_time": "2020-03-30T03:31:09.850431Z"
    },
    "dc": {
     "key": "45"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(y_test==1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:09.980221Z",
     "start_time": "2020-03-30T03:31:09.975925Z"
    },
    "dc": {
     "key": "45"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(y_test==0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Preprocessing the data (part ii)\n",
    "<p>Training and testing sets are ready now. We are only left with one final preprocessing step of scaling before we can fit a machine learning model to the data. </p>\n",
    "<p>Now, let's try to understand what these scaled values mean in the real world. Let's use <code>CreditScore</code> 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 <code>CreditScore</code> of 1 is the highest since we're rescaling all the values to the range of 0-1.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:10.226504Z",
     "start_time": "2020-03-30T03:31:10.221927Z"
    },
    "dc": {
     "key": "52"
    },
    "tags": [
     "sample_code"
    ]
   },
   "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": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:10.370129Z",
     "start_time": "2020-03-30T03:31:10.365923Z"
    },
    "dc": {
     "key": "52"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.92417062, 0.66666667, 0.33333333, ..., 1.        , 0.5       ,\n",
       "        0.79497908],\n",
       "       [0.1563981 , 0.66666667, 0.33333333, ..., 0.        , 0.        ,\n",
       "        0.34728033],\n",
       "       [0.7014218 , 0.66666667, 0.33333333, ..., 1.        , 0.36363636,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.14218009, 0.66666667, 0.33333333, ..., 1.        , 0.18181818,\n",
       "        0.72803347],\n",
       "       [0.11848341, 0.66666667, 0.33333333, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.34123223, 0.66666667, 0.33333333, ..., 1.        , 0.04545455,\n",
       "        0.09205021]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rescaledX_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:10.495410Z",
     "start_time": "2020-03-30T03:31:10.491155Z"
    },
    "dc": {
     "key": "52"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.55140187, 0.66666667, 0.33333333, ..., 0.        , 0.        ,\n",
       "        0.21212121],\n",
       "       [0.02336449, 0.66666667, 0.33333333, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       [0.09813084, 0.66666667, 0.33333333, ..., 0.        , 0.        ,\n",
       "        0.        ],\n",
       "       ...,\n",
       "       [0.91121495, 0.66666667, 0.33333333, ..., 1.        , 0.66666667,\n",
       "        0.80952381],\n",
       "       [0.40186916, 0.66666667, 0.33333333, ..., 1.        , 0.14285714,\n",
       "        0.81818182],\n",
       "       [0.03738318, 0.66666667, 0.33333333, ..., 1.        , 0.52380952,\n",
       "        0.20779221]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rescaledX_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Fitting a logistic regression model to the train set\n",
    "<p>Essentially, predicting if a credit card application will be approved or not is a <a href=\"https://en.wikipedia.org/wiki/Statistical_classification\">classification</a> task. <a href=\"http://archive.ics.uci.edu/ml/machine-learning-databases/credit-screening/crx.names\">According to UCI</a>, 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. </p>\n",
    "<p>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.</p>\n",
    "<p>Which model should we pick? Because of this correlation between the features, we'll take advantage of the fact that generalized linear models perform well in these cases. Let's start our machine learning modeling with a Logistic Regression model (a generalized linear model).</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:10.767551Z",
     "start_time": "2020-03-30T03:31:10.764528Z"
    }
   },
   "outputs": [],
   "source": [
    "rescaledX_train = pd.DataFrame(rescaledX_train, columns = feature_names)\n",
    "rescaledX_test = pd.DataFrame(rescaledX_test, columns = feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:10.890813Z",
     "start_time": "2020-03-30T03:31:10.877349Z"
    }
   },
   "outputs": [
    {
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       "           2         3         4         7    8    9         10        14\n",
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       "459  0.142180  0.666667  0.333333  0.146154  1.0  1.0  0.181818  0.728033\n",
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       "\n",
       "[462 rows x 8 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rescaledX_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:11.197994Z",
     "start_time": "2020-03-30T03:31:11.192264Z"
    },
    "dc": {
     "key": "59"
    },
    "tags": [
     "sample_code"
    ]
   },
   "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='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 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.values.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "66"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 10. Making predictions and evaluating performance\n",
    "<p>But how well does our model perform? </p>\n",
    "<p>We will now evaluate our model on the test set with respect to <a href=\"https://developers.google.com/machine-learning/crash-course/classification/accuracy\">classification accuracy</a>. But we will also take a look the model's <a href=\"http://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/\">confusion matrix</a>. 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.  </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:11.639976Z",
     "start_time": "2020-03-30T03:31:11.633905Z"
    },
    "dc": {
     "key": "66"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier:  0.8464912280701754\n",
      "Confusion matrix:  [[ 88  13]\n",
      " [ 22 105]]\n"
     ]
    }
   ],
   "source": [
    "# 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: \", accuracy_score(y_test,y_pred))\n",
    "\n",
    "# Print the confusion matrix of the logreg model\n",
    "print('Confusion matrix: ', confusion_matrix(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 11. Grid searching and making the model perform better\n",
    "<p>Our model was pretty good! It was able to yield an accuracy score of almost 85%.</p>\n",
    "<p>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.</p>\n",
    "<p>Let's see if we can do better. We can perform a <a href=\"https://machinelearningmastery.com/how-to-tune-algorithm-parameters-with-scikit-learn/\">grid search</a> of the model parameters to improve the model's ability to predict credit card approvals.</p>\n",
    "<p><a href=\"http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html\">scikit-learn's implementation of logistic regression</a> consists of different hyperparameters but we will grid search over the following two:</p>\n",
    "<ul>\n",
    "<li>tol</li>\n",
    "<li>max_iter</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:13.966681Z",
     "start_time": "2020-03-30T03:31:13.963397Z"
    }
   },
   "outputs": [],
   "source": [
    "# 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 12. Finding the best performing model\n",
    "<p>We have defined the grid of hyperparameter values and converted them into a single dictionary format which <code>GridSearchCV()</code> expects as one of its parameters. Now, we will begin the grid search to see which values perform best.</p>\n",
    "<p>We will instantiate <code>GridSearchCV()</code> with our earlier <code>logreg</code> model with all the data we have. Instead of passing train and test sets separately, we will supply <code>X</code> (scaled version) and <code>y</code>. We will also instruct <code>GridSearchCV()</code> to perform a <a href=\"https://www.dataschool.io/machine-learning-with-scikit-learn/\">cross-validation</a> of five folds.</p>\n",
    "<p>We'll end the notebook by storing the best-achieved score and the respective best parameters.</p>\n",
    "<p>While building this credit card predictor, we tackled some of the most widely-known preprocessing steps such as <strong>scaling</strong>, <strong>label encoding</strong>, and <strong>missing value imputation</strong>, correlation analysis. We finished with some <strong>machine learning</strong> to predict if a person's application for a credit card would get approved or not given some information about that person.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-30T03:31:14.888940Z",
     "start_time": "2020-03-30T03:31:14.799684Z"
    },
    "dc": {
     "key": "80"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.840580 using {'max_iter': 100, 'tol': 0.001}\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.values.ravel())\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": "markdown",
   "metadata": {},
   "source": [
    "### Few ideas to try:\n",
    "1. We can try random forest and xgboost.\n",
    "2. We can use ensemble method called majority voting to predict the final label using the predictions from the three\n",
    "machine learning methods."
   ]
  }
 ],
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