Mutual Information Classification -  Loan Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ec7f176a",
   "metadata": {},
   "source": [
    "### Problem Statement: Mutual Information Classification\n",
    "i have Write Python script that performs model-based feature selection using Mutual Information\n",
    "Classification on the \"loan\" dataset which is downloaded from kaggle."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2db2898a",
   "metadata": {},
   "source": [
    "#### ● Load the dataset using the pandas library or any other suitable method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e7fc17fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "04a03c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_selection import mutual_info_classif\n",
    "from sklearn.feature_selection import SelectKBest, mutual_info_classif\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51490f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "d2e0685c",
   "metadata": {},
   "outputs": [],
   "source": [
    "LoanDataSet = pd.read_csv(\"data/loan.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "041eb772",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loan_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Property_Area</th>\n",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001002</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married Dependents     Education Self_Employed  \\\n",
       "0  LP001002   Male      No          0      Graduate            No   \n",
       "1  LP001003   Male     Yes          1      Graduate            No   \n",
       "2  LP001005   Male     Yes          0      Graduate           Yes   \n",
       "3  LP001006   Male     Yes          0  Not Graduate            No   \n",
       "4  LP001008   Male      No          0      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0         NaN             360.0   \n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Loan_Status  \n",
       "0             1.0         Urban           Y  \n",
       "1             1.0         Rural           N  \n",
       "2             1.0         Urban           Y  \n",
       "3             1.0         Urban           Y  \n",
       "4             1.0         Urban           Y  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoanDataSet.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "20ed2761",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 614 entries, 0 to 613\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Loan_ID            614 non-null    object \n",
      " 1   Gender             601 non-null    object \n",
      " 2   Married            611 non-null    object \n",
      " 3   Dependents         599 non-null    object \n",
      " 4   Education          614 non-null    object \n",
      " 5   Self_Employed      582 non-null    object \n",
      " 6   ApplicantIncome    614 non-null    int64  \n",
      " 7   CoapplicantIncome  614 non-null    float64\n",
      " 8   LoanAmount         592 non-null    float64\n",
      " 9   Loan_Amount_Term   600 non-null    float64\n",
      " 10  Credit_History     564 non-null    float64\n",
      " 11  Property_Area      614 non-null    object \n",
      " 12  Loan_Status        614 non-null    object \n",
      "dtypes: float64(4), int64(1), object(8)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "LoanDataSet.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4543f2b4",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoanDataSet.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7bb8c2cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoanDataSet.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35803800",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "31b90dcf",
   "metadata": {},
   "source": [
    "####  ● Apply necessary processing on it (Hint : label_encoding , null value handling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f37ca20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, drop the unneccesary column, and compute the mean of numerial column to replace\n",
    "# null values... then drop rows which has minimum number of null values...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2d671dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "LoanDataSet.drop('Loan_ID', axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae5f79ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoanDataSet['LoanAmount'] = LoanDataSet['LoanAmount'].fillna(LoanDataSet['LoanAmount'].mean())\n",
    "LoanDataSet['LoanAmount'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b1302959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoanDataSet['Loan_Amount_Term'] = LoanDataSet['Loan_Amount_Term'].fillna(LoanDataSet['Loan_Amount_Term'].mean())\n",
    "LoanDataSet['Loan_Amount_Term'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a53817da",
   "metadata": {},
   "outputs": [
    {
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       "      <th>CoapplicantIncome</th>\n",
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       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
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       "      <th>Loan_Status</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
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       "      <td>Graduate</td>\n",
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       "      <td>Urban</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Graduate</td>\n",
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       "      <td>Graduate</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0      Male      No          0      Graduate            No             5849   \n",
       "1      Male     Yes          1      Graduate            No             4583   \n",
       "2      Male     Yes          0      Graduate           Yes             3000   \n",
       "3      Male     Yes          0  Not Graduate            No             2583   \n",
       "4      Male      No          0      Graduate            No             6000   \n",
       "..      ...     ...        ...           ...           ...              ...   \n",
       "609  Female      No          0      Graduate            No             2900   \n",
       "610    Male     Yes         3+      Graduate            No             4106   \n",
       "611    Male     Yes          1      Graduate            No             8072   \n",
       "612    Male     Yes          2      Graduate            No             7583   \n",
       "613  Female      No          0      Graduate           Yes             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                  0.0  146.412162             360.0             1.0   \n",
       "1               1508.0  128.000000             360.0             1.0   \n",
       "2                  0.0   66.000000             360.0             1.0   \n",
       "3               2358.0  120.000000             360.0             1.0   \n",
       "4                  0.0  141.000000             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609                0.0   71.000000             360.0             1.0   \n",
       "610                0.0   40.000000             180.0             1.0   \n",
       "611              240.0  253.000000             360.0             1.0   \n",
       "612                0.0  187.000000             360.0             1.0   \n",
       "613                0.0  133.000000             360.0             0.0   \n",
       "\n",
       "    Property_Area Loan_Status  \n",
       "0           Urban           Y  \n",
       "1           Rural           N  \n",
       "2           Urban           Y  \n",
       "3           Urban           Y  \n",
       "4           Urban           Y  \n",
       "..            ...         ...  \n",
       "609         Rural           Y  \n",
       "610         Rural           Y  \n",
       "611         Urban           Y  \n",
       "612         Urban           Y  \n",
       "613     Semiurban           N  \n",
       "\n",
       "[614 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoanDataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a1e80982",
   "metadata": {},
   "outputs": [],
   "source": [
    "LoanDataSet = LoanDataSet.dropna()\n",
    "# i have droped the another rows with Null values because there count was minimum.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d1097ba",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
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       "      <td>71.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
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       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
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       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
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       "      <td>253.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>133.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>511 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents     Education Self_Employed  ApplicantIncome  \\\n",
       "0      Male      No          0      Graduate            No             5849   \n",
       "1      Male     Yes          1      Graduate            No             4583   \n",
       "2      Male     Yes          0      Graduate           Yes             3000   \n",
       "3      Male     Yes          0  Not Graduate            No             2583   \n",
       "4      Male      No          0      Graduate            No             6000   \n",
       "..      ...     ...        ...           ...           ...              ...   \n",
       "609  Female      No          0      Graduate            No             2900   \n",
       "610    Male     Yes         3+      Graduate            No             4106   \n",
       "611    Male     Yes          1      Graduate            No             8072   \n",
       "612    Male     Yes          2      Graduate            No             7583   \n",
       "613  Female      No          0      Graduate           Yes             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "0                  0.0  146.412162             360.0             1.0   \n",
       "1               1508.0  128.000000             360.0             1.0   \n",
       "2                  0.0   66.000000             360.0             1.0   \n",
       "3               2358.0  120.000000             360.0             1.0   \n",
       "4                  0.0  141.000000             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609                0.0   71.000000             360.0             1.0   \n",
       "610                0.0   40.000000             180.0             1.0   \n",
       "611              240.0  253.000000             360.0             1.0   \n",
       "612                0.0  187.000000             360.0             1.0   \n",
       "613                0.0  133.000000             360.0             0.0   \n",
       "\n",
       "    Property_Area Loan_Status  \n",
       "0           Urban           Y  \n",
       "1           Rural           N  \n",
       "2           Urban           Y  \n",
       "3           Urban           Y  \n",
       "4           Urban           Y  \n",
       "..            ...         ...  \n",
       "609         Rural           Y  \n",
       "610         Rural           Y  \n",
       "611         Urban           Y  \n",
       "612         Urban           Y  \n",
       "613     Semiurban           N  \n",
       "\n",
       "[511 rows x 12 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LoanDataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6eedabbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform label encoding on categorical columns as a part of data preprocessing\n",
    "# so that data converted into numberical form, and understandable for ML algorithms..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98c18516",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
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       "</table>\n",
       "<p>511 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender Married Dependents     Education Self_Employed Property_Area  \\\n",
       "0      Male      No          0      Graduate            No         Urban   \n",
       "1      Male     Yes          1      Graduate            No         Rural   \n",
       "2      Male     Yes          0      Graduate           Yes         Urban   \n",
       "3      Male     Yes          0  Not Graduate            No         Urban   \n",
       "4      Male      No          0      Graduate            No         Urban   \n",
       "..      ...     ...        ...           ...           ...           ...   \n",
       "609  Female      No          0      Graduate            No         Rural   \n",
       "610    Male     Yes         3+      Graduate            No         Rural   \n",
       "611    Male     Yes          1      Graduate            No         Urban   \n",
       "612    Male     Yes          2      Graduate            No         Urban   \n",
       "613  Female      No          0      Graduate           Yes     Semiurban   \n",
       "\n",
       "    Loan_Status  \n",
       "0             Y  \n",
       "1             N  \n",
       "2             Y  \n",
       "3             Y  \n",
       "4             Y  \n",
       "..          ...  \n",
       "609           Y  \n",
       "610           Y  \n",
       "611           Y  \n",
       "612           Y  \n",
       "613           N  \n",
       "\n",
       "[511 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_loan_columns_data = LoanDataSet.select_dtypes(include = object)\n",
    "object_loan_columns_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f0b9e599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Male', 'No', '0', ..., 'No', 'Urban', 'Y'],\n",
       "       ['Male', 'Yes', '1', ..., 'No', 'Rural', 'N'],\n",
       "       ['Male', 'Yes', '0', ..., 'Yes', 'Urban', 'Y'],\n",
       "       ...,\n",
       "       ['Male', 'Yes', '1', ..., 'No', 'Urban', 'Y'],\n",
       "       ['Male', 'Yes', '2', ..., 'No', 'Urban', 'Y'],\n",
       "       ['Female', 'No', '0', ..., 'Yes', 'Semiurban', 'N']], dtype=object)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_loan_columns_array = object_loan_columns_data.values\n",
    "object_loan_columns_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "73b854e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for i in range(object_loan_columns_array.shape[1]): \n",
    "    le = LabelEncoder()\n",
    "    Unique_labels = set(object_loan_columns_array[:,i])\n",
    "    Unique_labels = list(Unique_labels)\n",
    "    Unique_labels = np.array(Unique_labels)\n",
    "    le.fit(Unique_labels)\n",
    "    object_loan_columns_array[:,i] = le.transform(object_loan_columns_array[:,i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "36a638de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, ..., 0, 2, 1],\n",
       "       [1, 1, 1, ..., 0, 0, 0],\n",
       "       [1, 1, 0, ..., 1, 2, 1],\n",
       "       ...,\n",
       "       [1, 1, 1, ..., 0, 2, 1],\n",
       "       [1, 1, 2, ..., 0, 2, 1],\n",
       "       [0, 0, 0, ..., 1, 1, 0]], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "object_loan_columns_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "de9562bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 511 entries, 0 to 613\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   Gender         511 non-null    object\n",
      " 1   Married        511 non-null    object\n",
      " 2   Dependents     511 non-null    object\n",
      " 3   Education      511 non-null    object\n",
      " 4   Self_Employed  511 non-null    object\n",
      " 5   Property_Area  511 non-null    object\n",
      " 6   Loan_Status    511 non-null    object\n",
      "dtypes: object(7)\n",
      "memory usage: 31.9+ KB\n"
     ]
    }
   ],
   "source": [
    "object_loan_columns_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3affe708",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convertion of Object Datatype column to Int datatype\n",
    "for colname in object_loan_columns_data.columns:\n",
    "    object_loan_columns_data[colname] = object_loan_columns_data[colname].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8b9a4259",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 511 entries, 0 to 613\n",
      "Data columns (total 7 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   Gender         511 non-null    int64\n",
      " 1   Married        511 non-null    int64\n",
      " 2   Dependents     511 non-null    int64\n",
      " 3   Education      511 non-null    int64\n",
      " 4   Self_Employed  511 non-null    int64\n",
      " 5   Property_Area  511 non-null    int64\n",
      " 6   Loan_Status    511 non-null    int64\n",
      "dtypes: int64(7)\n",
      "memory usage: 31.9 KB\n"
     ]
    }
   ],
   "source": [
    "object_loan_columns_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a1919aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select Only int and float data type columns from dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa661139",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>511 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0  146.412162             360.0   \n",
       "1               4583             1508.0  128.000000             360.0   \n",
       "2               3000                0.0   66.000000             360.0   \n",
       "3               2583             2358.0  120.000000             360.0   \n",
       "4               6000                0.0  141.000000             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0   71.000000             360.0   \n",
       "610             4106                0.0   40.000000             180.0   \n",
       "611             8072              240.0  253.000000             360.0   \n",
       "612             7583                0.0  187.000000             360.0   \n",
       "613             4583                0.0  133.000000             360.0   \n",
       "\n",
       "     Credit_History  \n",
       "0               1.0  \n",
       "1               1.0  \n",
       "2               1.0  \n",
       "3               1.0  \n",
       "4               1.0  \n",
       "..              ...  \n",
       "609             1.0  \n",
       "610             1.0  \n",
       "611             1.0  \n",
       "612             1.0  \n",
       "613             0.0  \n",
       "\n",
       "[511 rows x 5 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_float_loan_columns_data = LoanDataSet.select_dtypes(include = [int, float])\n",
    "int_float_loan_columns_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "af599278",
   "metadata": {},
   "outputs": [],
   "source": [
    "FinalLoanDataSet = pd.concat([int_float_loan_columns_data,\n",
    "                              object_loan_columns_data], axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3256e1ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>Education</th>\n",
       "      <th>Self_Employed</th>\n",
       "      <th>Property_Area</th>\n",
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       "      <th>611</th>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>511 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0  146.412162             360.0   \n",
       "1               4583             1508.0  128.000000             360.0   \n",
       "2               3000                0.0   66.000000             360.0   \n",
       "3               2583             2358.0  120.000000             360.0   \n",
       "4               6000                0.0  141.000000             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0   71.000000             360.0   \n",
       "610             4106                0.0   40.000000             180.0   \n",
       "611             8072              240.0  253.000000             360.0   \n",
       "612             7583                0.0  187.000000             360.0   \n",
       "613             4583                0.0  133.000000             360.0   \n",
       "\n",
       "     Credit_History  Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "0               1.0       1        0           0          0              0   \n",
       "1               1.0       1        1           1          0              0   \n",
       "2               1.0       1        1           0          0              1   \n",
       "3               1.0       1        1           0          1              0   \n",
       "4               1.0       1        0           0          0              0   \n",
       "..              ...     ...      ...         ...        ...            ...   \n",
       "609             1.0       0        0           0          0              0   \n",
       "610             1.0       1        1           3          0              0   \n",
       "611             1.0       1        1           1          0              0   \n",
       "612             1.0       1        1           2          0              0   \n",
       "613             0.0       0        0           0          0              1   \n",
       "\n",
       "     Property_Area  Loan_Status  \n",
       "0                2            1  \n",
       "1                0            0  \n",
       "2                2            1  \n",
       "3                2            1  \n",
       "4                2            1  \n",
       "..             ...          ...  \n",
       "609              0            1  \n",
       "610              0            1  \n",
       "611              2            1  \n",
       "612              2            1  \n",
       "613              1            0  \n",
       "\n",
       "[511 rows x 12 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FinalLoanDataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6381b802",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8f330977",
   "metadata": {},
   "source": [
    "#### ● Separate the features (X) and the target variable (y : Loan_Status) from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8078c28e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0, 1}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Then implement Mutual information Classification on Cleaned Housing Data Set\n",
    "set(FinalLoanDataSet.Loan_Status)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "386ed88d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Loan = FinalLoanDataSet.drop('Loan_Status', axis = 1).values\n",
    "# Independent Variable contains all column values except Loan Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "81f8beea",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_Loan = FinalLoanDataSet.Loan_Status.values\n",
    "# dependent variable loan status...  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c754b020",
   "metadata": {},
   "outputs": [],
   "source": [
    "MicLoanDataset = mutual_info_classif(X_Loan, Y_Loan, random_state = 1)\n",
    "# pass X, Y values to mutual_info_classifier Object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b2d2a008",
   "metadata": {},
   "outputs": [],
   "source": [
    "#help(mutual_info_classif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6695ea47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11,)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MicLoanDataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7db8df02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.009821\n",
       "1     0.000000\n",
       "2     0.008270\n",
       "3     0.012135\n",
       "4     0.124372\n",
       "5     0.000000\n",
       "6     0.000000\n",
       "7     0.000000\n",
       "8     0.005962\n",
       "9     0.000000\n",
       "10    0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanDataSeries = pd.Series(MicLoanDataset) \n",
    "# convert 1d data into series... \n",
    "loanDataSeries \n",
    "# this series contains MIC score value of independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0ed258de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Gender', 'Married', 'Dependents',\n",
       "       'Education', 'Self_Employed', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FinalLoanDataSet.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3bec99f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Gender', 'Married', 'Dependents',\n",
       "       'Education', 'Self_Employed', 'Property_Area'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FinalLoanDataSet.columns[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2f7ebe4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "IndependentLoanVariables = FinalLoanDataSet.columns[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b0808d1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Gender', 'Married', 'Dependents',\n",
       "       'Education', 'Self_Employed', 'Property_Area'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IndependentLoanVariables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b6390e86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ApplicantIncome      0.009821\n",
       "CoapplicantIncome    0.000000\n",
       "LoanAmount           0.008270\n",
       "Loan_Amount_Term     0.012135\n",
       "Credit_History       0.124372\n",
       "Gender               0.000000\n",
       "Married              0.000000\n",
       "Dependents           0.000000\n",
       "Education            0.005962\n",
       "Self_Employed        0.000000\n",
       "Property_Area        0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanDataSeries.index = IndependentLoanVariables\n",
    "# provide index name to series object from independent variable \n",
    "# columns for more readability\n",
    "loanDataSeries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "88abe4a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CoapplicantIncome    0.000000\n",
       "Gender               0.000000\n",
       "Married              0.000000\n",
       "Dependents           0.000000\n",
       "Self_Employed        0.000000\n",
       "Property_Area        0.000000\n",
       "Education            0.005962\n",
       "LoanAmount           0.008270\n",
       "ApplicantIncome      0.009821\n",
       "Loan_Amount_Term     0.012135\n",
       "Credit_History       0.124372\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanDataSeries.sort_values(ascending= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "685950d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Gender', 'Married', 'Dependents',\n",
       "       'Education', 'Self_Employed', 'Property_Area'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanDataSeries.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7e91eb64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Features</th>\n",
       "      <th>MicScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ApplicantIncome</td>\n",
       "      <td>0.009821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CoapplicantIncome</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LoanAmount</td>\n",
       "      <td>0.008270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Loan_Amount_Term</td>\n",
       "      <td>0.012135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Credit_History</td>\n",
       "      <td>0.124372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gender</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Married</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Dependents</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Education</td>\n",
       "      <td>0.005962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Self_Employed</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Property_Area</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Features  MicScore\n",
       "0     ApplicantIncome  0.009821\n",
       "1   CoapplicantIncome  0.000000\n",
       "2          LoanAmount  0.008270\n",
       "3    Loan_Amount_Term  0.012135\n",
       "4      Credit_History  0.124372\n",
       "5              Gender  0.000000\n",
       "6             Married  0.000000\n",
       "7          Dependents  0.000000\n",
       "8           Education  0.005962\n",
       "9       Self_Employed  0.000000\n",
       "10      Property_Area  0.000000"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert data inside series to dataframe. so that we can plot graph from it... \n",
    "MicScoresDataset = pd.DataFrame({'Features' : loanDataSeries.index,\n",
    "                                'MicScore' : loanDataSeries.values})\n",
    "MicScoresDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1e1223a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10]),\n",
       " [Text(0, 0, 'ApplicantIncome'),\n",
       "  Text(1, 0, 'CoapplicantIncome'),\n",
       "  Text(2, 0, 'LoanAmount'),\n",
       "  Text(3, 0, 'Loan_Amount_Term'),\n",
       "  Text(4, 0, 'Credit_History'),\n",
       "  Text(5, 0, 'Gender'),\n",
       "  Text(6, 0, 'Married'),\n",
       "  Text(7, 0, 'Dependents'),\n",
       "  Text(8, 0, 'Education'),\n",
       "  Text(9, 0, 'Self_Employed'),\n",
       "  Text(10, 0, 'Property_Area')])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "sns.barplot(x = 'Features' , y = 'MicScore', data = MicScoresDataset, palette = 'viridis')\n",
    "sns.despine()\n",
    "plt.title('MIC for Loan application approval')\n",
    "plt.xlabel('Feature' , fontsize = 14)\n",
    "plt.ylabel(\"MIC Score\" , fontsize = 14)\n",
    "plt.xticks(rotation = 90, fontsize = 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "421fc843",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "11587ced",
   "metadata": {},
   "source": [
    "#### ● Use the SelectKBest class from the sklearn.feature_selection module to select the top K features based on mutual information scores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f1027914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Gender', 'Married', 'Dependents',\n",
       "       'Education', 'Self_Employed', 'Property_Area', 'Loan_Status'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "FinalLoanDataSet.columns # display columns from Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "865c2054",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Loan = FinalLoanDataSet[['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
    "       'Loan_Amount_Term', 'Credit_History', 'Gender', 'Married', 'Dependents',\n",
    "       'Education', 'Self_Employed', 'Property_Area']]\n",
    "# independent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "36d2c6cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_Loan = FinalLoanDataSet['Loan_Status']\n",
    "# dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1c92e4e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data in training and test set with random state 42 and 80%- 20%\n",
    "X_train_Loan, X_test_Loan, y_train_Loan, y_test_Loan = train_test_split(\n",
    "    X_Loan, y_Loan, test_size=0.2, random_state=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "013c31eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Features before SelectKBest: 11\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Features before SelectKBest:\", X_train_Loan.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86c91d3c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8344a0e8",
   "metadata": {},
   "source": [
    "####  ● Choose an appropriate value of K (number of features to select) for the analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8c9e28",
   "metadata": {},
   "source": [
    "####  ● Fit the feature selection model on the dataset and transform the feature matrix accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f063e44e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectKBest(k=5, score_func=&lt;function mutual_info_classif at 0x11031cc20&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=5, score_func=&lt;function mutual_info_classif at 0x11031cc20&gt;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectKBest(k=5, score_func=<function mutual_info_classif at 0x11031cc20>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "selectKLoanBestObj = SelectKBest(k=5, score_func=mutual_info_classif)\n",
    "selectKLoanBestObj.fit(X_train_Loan, y_train_Loan)\n",
    "# i have selected 4 Best columns with scoreing function values as mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b7e3a9dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# help(SelectKBest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0975aeb3",
   "metadata": {},
   "source": [
    "● Print the names or indices of the selected features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "aadcce18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4,  5,  6,  8, 10])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kBestLoanColumnIndex = selectKLoanBestObj.get_support(indices=True)\n",
    "kBestLoanColumnIndex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "81458aaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Education</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>511 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Credit_History  Gender  Married  Education  Property_Area\n",
       "0               1.0       1        0          0              2\n",
       "1               1.0       1        1          0              0\n",
       "2               1.0       1        1          0              2\n",
       "3               1.0       1        1          1              2\n",
       "4               1.0       1        0          0              2\n",
       "..              ...     ...      ...        ...            ...\n",
       "609             1.0       0        0          0              0\n",
       "610             1.0       1        1          0              0\n",
       "611             1.0       1        1          0              2\n",
       "612             1.0       1        1          0              2\n",
       "613             0.0       0        0          0              1\n",
       "\n",
       "[511 rows x 5 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameWithNewLoanColumns = FinalLoanDataSet.iloc[:,kBestLoanColumnIndex]\n",
    "dataFrameWithNewLoanColumns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6e7dcbb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '331'), Text(0, 0, '180')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    " #plot count plot of Self_Employed column to display number of emloyeed or not count\n",
    "ax = sns.countplot(x=dataFrameWithNewLoanColumns['Married'],\n",
    "                  order=dataFrameWithNewLoanColumns['Married'].value_counts(ascending=False).index);\n",
    "\n",
    "abs_values = dataFrameWithNewLoanColumns['Married'].value_counts(ascending=False).values\n",
    "\n",
    "ax.bar_label(container=ax.containers[0], labels=abs_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "656b378d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '401'), Text(0, 0, '110')]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# then count plot to show number of Credit_History count of Credit_History column\n",
    "ax = sns.countplot(x=dataFrameWithNewLoanColumns['Education'],\n",
    "                   order=dataFrameWithNewLoanColumns['Education'].value_counts(ascending=False).index);\n",
    "\n",
    "abs_values = dataFrameWithNewLoanColumns['Education'].value_counts(ascending=False).values\n",
    "\n",
    "ax.bar_label(container=ax.containers[0], labels=abs_values)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "2d1295f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# implememting different ML algorithms for checking the accouracy of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5a06aa57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Credit_History', 'Gender', 'Married', 'Education', 'Property_Area'], dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameWithNewLoanColumns.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a91df1e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Loan_model = dataFrameWithNewLoanColumns[['Credit_History', 'Gender', 'Married', 'Education', 'Property_Area']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "89f82070",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_Loan_model =  FinalLoanDataSet['Loan_Status']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "8f9639e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_Loan , X_test_Loan , y_train_Loan , y_test_Loan = train_test_split(X_Loan_model, Y_Loan_model, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "8957e6be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Credit_History</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Married</th>\n",
       "      <th>Education</th>\n",
       "      <th>Property_Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "</div>"
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      "text/plain": [
       "     Credit_History  Gender  Married  Education  Property_Area\n",
       "484             1.0       1        1          0              1\n",
       "400             0.0       1        1          1              2\n",
       "319             1.0       1        1          0              0\n",
       "164             1.0       1        1          0              2\n",
       "72              1.0       1        0          0              1\n",
       "..              ...     ...      ...        ...            ...\n",
       "159             1.0       1        1          0              1\n",
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       "250             0.0       1        1          1              1\n",
       "290             1.0       1        1          0              0\n",
       "\n",
       "[408 rows x 5 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "82d37224",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "     Credit_History  Gender  Married  Education  Property_Area\n",
       "457             1.0       1        1          0              2\n",
       "548             0.0       0        0          0              1\n",
       "87              1.0       1        1          0              1\n",
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     "execution_count": 64,
     "metadata": {},
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   ],
   "source": [
    "X_test_Loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f2d7029c",
   "metadata": {},
   "outputs": [
    {
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       "0.5533980582524272"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "K_CLF_Loan = KNeighborsClassifier(n_neighbors=2)\n",
    "K_CLF_Loan.fit(X_train_Loan,y_train_Loan)\n",
    "y_pred_Loan_CLF = K_CLF_Loan.predict(X_test_Loan)\n",
    "accuracy_score(y_test_Loan,y_pred_Loan_CLF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "88a9655e",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_tree_Loan =  DecisionTreeClassifier(criterion=\"entropy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "18174290",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_tree_Loan.fit(X_train_Loan,y_train_Loan) #training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "13b81406",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_Loan_tree = clf_tree_Loan.predict(X_test_Loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "43abefcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8252427184466019"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred_Loan_tree,y_test_Loan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29211173",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1080934",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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