Medina_Classification__Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VZKP0katDPx6"
   },
   "source": [
    "# Project: Classification - Loan Eligibility Prediction\n",
    "\n",
    "----------------\n",
    "## Context: \n",
    "----------------\n",
    "\n",
    "Credit risk is the risk of loss resulting from the failure by a borrower to repay the principal and interest owed to the lender. The lender uses the interest payments from the loan to compensate for the risk of potential losses. When the borrower defaults on his/her obligations, it causes an interruption in the cash flow of the lender.\n",
    "\n",
    "In the banking sector, this is an important factor to be considered before approving the loan of an applicant in order to cushion the lender from loss of cash flow and reduce the severity of losses. \n",
    "\n",
    "------------------\n",
    "## Objective:\n",
    "------------------\n",
    "\n",
    "Easy House is a finance company that deals in several varieties of home loans. They have a presence across urban, semi-urban, and rural areas. Currently, the customer first applies for a home loan post which the company validates the customer's eligibility for that loan. \n",
    "\n",
    "Now, the company wants to automate the loan eligibility process. They want to harness their past customers' data to build a model to predict whether the loan should be approved or not. This would help the company prevent potential losses, save time and focus more on eligible customers.\n",
    "\n",
    "--------------------------\n",
    "## Data Dictionary:\n",
    "--------------------------\n",
    "\n",
    "The data contains the following attributes:\n",
    "\n",
    "* **Loan_ID**: Unique Loan ID\n",
    "* **Gender**: Gender of the applicant - Male/Female\n",
    "* **Married**: Whether the applicant is married or not (Yes/No)\n",
    "* **Dependents**: Number of dependents of the applicant\n",
    "* **Education**: Applicant's education (Graduate/Not Graduate)\n",
    "* **Self_Employed**: Whether the applicant is self-employed (Yes/No)\n",
    "* **ApplicantIncome**: The income of the applicant (\\$)\n",
    "* **CoapplicantIncome**: The co-applicant's income in case of a joint loan and 0 otherwise (\\$)\n",
    "* **LoanAmount**: Loan amount (dollars in thousands) \n",
    "* **Loan_Amount_Term**: Term of loan in months\n",
    "* **Credit_History**: Whether the applicant's credit history meets required guidelines\n",
    "* **Property_Area**: The area the property pertaining to the loan belongs to - Urban/Semi-Urban/Rural\n",
    "* **Loan_Status**: Loan approved (1 - Yes, 0 - No)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GLmC-0-fDPyI"
   },
   "source": [
    "## Importing necessary libraries and overview of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "id": "zeF8YaNKDPyK"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "#To scale the data using z-score \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "#Algorithms to use\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#Metrics to evaluate the model\n",
    "from sklearn.metrics import confusion_matrix, classification_report, precision_recall_curve\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "from scipy.stats import shapiro\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xtgae1VCDPyO"
   },
   "source": [
    "#### Loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "id": "4VRuDPneDPyQ",
    "outputId": "1890a81a-61ce-449c-eb76-4096cb81569d"
   },
   "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>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>0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</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</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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           0             360.0   \n",
       "1             4583             1508.0         128             360.0   \n",
       "2             3000                0.0          66             360.0   \n",
       "3             2583             2358.0         120             360.0   \n",
       "4             6000                0.0         141             360.0   \n",
       "\n",
       "   Credit_History Property_Area  Loan_Status  \n",
       "0             1.0         Urban            1  \n",
       "1             1.0         Rural            0  \n",
       "2             1.0         Urban            1  \n",
       "3             1.0         Urban            1  \n",
       "4             1.0         Urban            1  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"CreditRisk.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7zmvKC9aDPya"
   },
   "source": [
    "#### Check the info of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "id": "WFXzpvckDPyc",
    "outputId": "da1d04ad-dc46-418e-bfcd-ca600b2beb83"
   },
   "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         614 non-null    int64  \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    int64  \n",
      "dtypes: float64(3), int64(3), object(7)\n",
      "memory usage: 62.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>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>0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>0</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</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</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</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LP001013</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2333</td>\n",
       "      <td>1516.0</td>\n",
       "      <td>95</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LP001014</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3036</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>158</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>LP001018</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4006</td>\n",
       "      <td>1526.0</td>\n",
       "      <td>168</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>LP001020</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>12841</td>\n",
       "      <td>10968.0</td>\n",
       "      <td>349</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>0</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",
       "5  LP001011   Male     Yes          2      Graduate           Yes   \n",
       "6  LP001013   Male     Yes          0  Not Graduate            No   \n",
       "7  LP001014   Male     Yes         3+      Graduate            No   \n",
       "8  LP001018   Male     Yes          2      Graduate            No   \n",
       "9  LP001020   Male     Yes          1      Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5849                0.0           0             360.0   \n",
       "1             4583             1508.0         128             360.0   \n",
       "2             3000                0.0          66             360.0   \n",
       "3             2583             2358.0         120             360.0   \n",
       "4             6000                0.0         141             360.0   \n",
       "5             5417             4196.0         267             360.0   \n",
       "6             2333             1516.0          95             360.0   \n",
       "7             3036             2504.0         158             360.0   \n",
       "8             4006             1526.0         168             360.0   \n",
       "9            12841            10968.0         349             360.0   \n",
       "\n",
       "   Credit_History Property_Area  Loan_Status  \n",
       "0             1.0         Urban            1  \n",
       "1             1.0         Rural            0  \n",
       "2             1.0         Urban            1  \n",
       "3             1.0         Urban            1  \n",
       "4             1.0         Urban            1  \n",
       "5             1.0         Urban            1  \n",
       "6             1.0         Urban            1  \n",
       "7             0.0     Semiurban            0  \n",
       "8             1.0         Urban            1  \n",
       "9             1.0     Semiurban            0  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zVm4D1InDPye"
   },
   "source": [
    "**Observations:**\n",
    "\n",
    "- There are 614 observations and 13 columns in the data.\n",
    "- ApplicantIncome, CoapplicantIncome, Loan_Amount_Term, Credit_History, and Loan_Status are numeric data types. The rest of the variables are of the object data type.\n",
    "- There are several columns with less than 614 non-null entries i.e. these columns have missing values.\n",
    "- Loan_ID column is an identifier. Let's check if each entry of the column is unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "id": "LypUFA5xb0jR",
    "outputId": "e43d6225-5f92-41cd-8291-4a8add7d40f6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "614"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.Loan_ID.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xD9aPH3ub0jS"
   },
   "source": [
    "**Observations:**\n",
    "- We can see that all the entries of this column are unique. Hence, this column would not add any value to our analysis. \n",
    "- Let's drop this column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "__7ciGcIDPyk"
   },
   "source": [
    "## Exploratory Data Analysis and Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lDXcPkLkDPym"
   },
   "source": [
    "#### Dropping Loan_ID column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "id": "_ei57w6EDPym"
   },
   "outputs": [],
   "source": [
    "data.drop(columns=['Loan_ID'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wU2dvaXZDPyn"
   },
   "source": [
    "#### Summary Statistics for numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "id": "hWxLHPlVDPyp"
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ApplicantIncome</th>\n",
       "      <td>614.0</td>\n",
       "      <td>5403.459283</td>\n",
       "      <td>6109.041673</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2877.5</td>\n",
       "      <td>3812.5</td>\n",
       "      <td>5795.00</td>\n",
       "      <td>81000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <td>614.0</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1188.5</td>\n",
       "      <td>2297.25</td>\n",
       "      <td>41667.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>614.0</td>\n",
       "      <td>141.166124</td>\n",
       "      <td>88.340630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>164.75</td>\n",
       "      <td>700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>600.0</td>\n",
       "      <td>342.000000</td>\n",
       "      <td>65.120410</td>\n",
       "      <td>12.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>360.00</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   count         mean          std    min     25%     50%  \\\n",
       "ApplicantIncome    614.0  5403.459283  6109.041673  150.0  2877.5  3812.5   \n",
       "CoapplicantIncome  614.0  1621.245798  2926.248369    0.0     0.0  1188.5   \n",
       "LoanAmount         614.0   141.166124    88.340630    0.0    98.0   125.0   \n",
       "Loan_Amount_Term   600.0   342.000000    65.120410   12.0   360.0   360.0   \n",
       "\n",
       "                       75%      max  \n",
       "ApplicantIncome    5795.00  81000.0  \n",
       "CoapplicantIncome  2297.25  41667.0  \n",
       "LoanAmount          164.75    700.0  \n",
       "Loan_Amount_Term    360.00    480.0  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a list of numerical columns\n",
    "num_cols = ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term']\n",
    "\n",
    "\n",
    "# print summary statistics \n",
    "data[num_cols].describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ApplicantIncome\n",
      "Skew : 6.54\n",
      "Mean: 5403.46 | Median: 3812.5 | 2nd Qrt: 2877.5 | 3rd Qrt: 5795.0\n",
      "Stdev: 6109.04\n",
      "p-value: 0.0, stat: 0.471 and probably NOT Gaussian \n"
     ]
    },
    {
     "data": {
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mVeOrVq1o1Pq1tbVXTV/+7f2V5fBali1borlzf3bdZbKyNno+rdztdiszc4PGjZvQqGytrSWy1reNK8dray9dbtucx/EV/vT81scK+9CaWut4NeqVvvnz53v+vP7661q3bp1KSkpaPAwAAEBbcrlq6yln5h9/msooP/8L5ed/IWOuv/6JE0UNbi0//wtPPperVvn5X9xAptbRElnr28aV45eOq2nW4/gKf3p+62OFfWhNrXW8GvVK39f16NFDRUUN/8cEAAD8W2hoqG66qYteeumVRi2/cOGrOnjsdKO3HxDUUbeH39ro7V/PxImP1nt5Z2MFBl760ejq4mf7x99NLX42RUX9QJK0Zcum6xa/224La3BrUVE/0B/+sEUuV60CA4M82/ZFLZG1vm1cOW6z2f7xSp/x+WPSEH96futjhX1oTa11vJr8nr4VK1bo+eefV/fu3b0SCAAA4EaEh4c3exsBAQGaOHHyVePJyRMUFNTwZatBQUEKCgqqM/3QQ0lKTBzpKZT1mTTp2Qa3n5g4UgEBNk/Whx5KanCdttISWevbxpXjl455YLMex1f40/NbHyvsQ2tqrePVqNK3f/9+z58DBw6oV69eSktL80ogAACAG9GlSxcFBFz7R5tOnW6q92ubzabbbguTzWZT//7Ruv/+qDrLBAYG6Yc/HKwBA354zW1fWeYGDPhhneUGDIjRLbeEKjS0qwYMiPGM33TTTXXWve22sEbdsiE0tKv694/xZPXlj8Nviaz1baPueIwGDPihXxyThvjT81sfK+xDa2qt49Woyzvnz58vSSoqKlJtba16927cfWQAAABaU69et+nEiaKr7tP39NNTPffpmzx5quc+fU8/PbXOffou/5Z98uQpnvv0Pfnk05Iu/Ua+sLDgqvv0PfnkZM99+h56KEnGGBUWFkhSnd/aX15fuvo+fY15le/K7RQVHfeLV1BaImt927hy3BijU6dO+sUxaYg/Pb/1scI+tKbWOF4209C7inXp07CefvppnTp1Sm63W127dtWyZct0xx13eC1YU5WUlHs++eZG2O1dlPhCRgsmunFZrw+X01lW73y7vct15/sKf8kpkdVb/CWrv+SUyBoQYFP37p1bdJvtQXPOkT//+TzV1Lia/J6+Tr0HS5IqCi/d1/fy9NdVFG7SnS30nr6mZm1L7f172Rv8JadEVm9pz1kbOj826vLOOXPmaOLEidqxY4d27typyZMna/bs2S0WEgAAAADgHY0qfSUlJRo5cqRnetSoUTp79qzXQgEAAAAAWkajSp/L5VJpaaln+syZM97KAwAAAABoQY36IJdHH31UDz/8sIYMGSKbzaacnBw99thj3s4GAAAAAGimRr3SFxNz6SOGa2pqdOjQIRUXF+vBBx/0ajAAAAAAQPM16pW+GTNm6JFHHlFycrKqqqr0wQcfKCUlRb/+9a+9nQ8AAAAA0AyNeqXv7NmzSk5OliSFhIRo/PjxcjqdXg0GAAAAAGi+Rn+QS3FxsWf69OnTasTt/QAAAAAAbaxRl3eOHz9eI0aM0IABA2Sz2ZSXl6cXX3zR29kAAAAAAM3UqNI3evRo3XPPPfrjH/+owMBAPfHEE/r2t7/t7WwAAAAAgGZqVOmTpIiICEVERHgzCwAAAACghTXqPX0AAAAAAP9E6QMAAAAAC6P0AQAAAICFUfoAAAAAwMIofQAAAABgYZQ+AAAAALAwSh8AAAAAWBilDwAAAAAsjNIHAAAAABZG6QMAAAAAC/Nq6VuyZIkSEhKUkJCgRYsWSZLy8vKUmJio2NhYLV682LPs3r17lZSUpLi4OM2cOVO1tbXejAYAAAAA7YLXSl9eXp4+//xzbdy4UR999JH++te/Kjs7WykpKVq6dKlycnK0Z88ebd26VZI0ffp0zZo1S5988omMMVq7dq23ogEAAABAu+G10me32zVjxgx16NBBwcHBuuOOO1RQUKDevXsrPDxcQUFBSkxMVG5uroqKilRZWanIyEhJUlJSknJzc70VDQAAAADaDa+VvrvuustT4goKCvTxxx/LZrPJbrd7lnE4HCouLtapU6fqjNvtdhUXF3srGgAAAAC0G0HefoADBw5o0qRJevHFFxUYGKiCggLPPGOMbDab3G63bDbbVeNN0b1755aK7BPs9i7Nmu8r/CWnRFZv8Zes/pJTIisAAGgar5a+nTt3aurUqUpJSVFCQoK+/PJLOZ1Oz3yn0ymHw6GePXvWGT99+rQcDkeTHqukpFxut7nhrL72g4nTWVbvPLu9y3Xn+wp/ySmR1Vv8Jau/5JTIGhBgs9wv+QAA8DavXd558uRJPfPMM0pLS1NCQoIk6d5779WRI0dUWFgol8ul7OxsRUdHKywsTCEhIdq5c6ckKSMjQ9HR0d6KBgAAAADthtde6Vu+fLmqqqq0YMECz9iYMWO0YMECTZkyRVVVVYqJiVF8fLwkKS0tTampqSovL1efPn2UnJzsrWgAAAAA0G54rfSlpqYqNTX1mvMyMzOvGouIiND69eu9FQcAAAAA2iWv3pwdAAAAANC2KH0AAAAAYGGUPgAAAACwMEofAAAAAFgYpQ8AAAAALIzSBwAAAAAWRukDAAAAAAvz2n36AACA/3vwwQd1/vzFto7RKP6UFQBaE6UPAADUa9CgQXI6y9o6RqP4U1YAaE1c3gkAAAAAFkbpAwAAAAALo/QBAAAAgIVR+gAAAADAwih9AAAAAGBhlD4AAAAAsDBKHwAAAABYGKUPAAAAACyM0gcAAAAAFkbpAwAAAAALo/QBAAAAgIVR+gAAAADAwih9AAAAAGBhlD4AAAAAsDBKHwAAAABYGKUPAAAAACyM0gcAAAAAFkbpAwAAAAALo/QBAAAAgIVR+gAAAADAwih9AAAAAGBhXi195eXlGjZsmI4fPy5JysvLU2JiomJjY7V48WLPcnv37lVSUpLi4uI0c+ZM1dbWejMWAAAAALQbXit9u3bt0o9+9CMVFBRIkiorK5WSkqKlS5cqJydHe/bs0datWyVJ06dP16xZs/TJJ5/IGKO1a9d6KxYAAAAAtCteK31r167VT3/6UzkcDknS7t271bt3b4WHhysoKEiJiYnKzc1VUVGRKisrFRkZKUlKSkpSbm6ut2IBAAAAQLsS5K0Nv/baa3WmT506Jbvd7pl2OBwqLi6+atxut6u4uLjJj9e9e+cbD+uD7PYuzZrvK/wlp0RWb/GXrP6SUyIrAABoGq+Vvq9zu92y2WyeaWOMbDZbveNNVVJSLrfb3HA+X/vBxOksq3ee3d7luvN9hb/klMjqLf6S1V9ySmQNCLBZ7pd8AAB4W6t9emfPnj3ldDo9006nUw6H46rx06dPey4JBQAAAAA0T6uVvnvvvVdHjhxRYWGhXC6XsrOzFR0drbCwMIWEhGjnzp2SpIyMDEVHR7dWLAAAAACwtFa7vDMkJEQLFizQlClTVFVVpZiYGMXHx0uS0tLSlJqaqvLycvXp00fJycmtFQsAAAAALM3rpW/z5s2er6OiopSZmXnVMhEREVq/fr23owAAAABAu9Nql3cCAAAAAFofpQ8AAAAALIzSBwAAAAAWRukDAAAAAAuj9AEAAACAhVH6AAAAAMDCKH0AAAAAYGGUPgAAAACwMEofAAAAAFgYpQ8AAAAALCyorQPgatU1LtntXa67TEPzW0plVa3Kzl9slccCAAAA0PIofT6oQ3CgEl/IaOsYkqSs14errK1DAAD8iquyVBWFmzxfS/JMX2tZ6dbWCQYA7RSlDwAAtJjw8N51ps+du/Sjxi23hNazxq1XrQMAaFmUPgAA0GLGjk1u6wgAgK/hg1wAAAAAwMIofQAAAABgYZQ+AAAAALAwSh8AAAAAWBilDwAAAAAsjNIHAAAAABZG6QMAAAAAC6P0AQAAAICFUfoAAAAAwMIofQAAAABgYZQ+AAAAALAwSh8AAAAAWBilDwAAAAAsLKitA8C3Vde4ZLd3ueH1m7PulSqralV2/mKLbAsAAABoTyh9uK4OwYFKfCGjrWMo6/XhKmvrEAAAAIAf4vJOAAAAALAwnyp9WVlZGjp0qGJjY/Xee++1dRwAAAAA8Hs+c3lncXGxFi9erA0bNqhDhw4aM2aM7r//ft15551tHQ0AAAAA/JbPlL68vDx9//vfV2hoqCQpLi5Oubm5evbZZxu1fkCArdkZHF2/0exttBSyXK0lnuOW2H7nzh0VEtK23zp2exdVVdWqvLyyTXM0hreft5biLzml9p3Vn/bdlzT3uPnTcSerd/hLVn/JKZHVW9pr1oa2ZTPGmBZ7tGZYtmyZKioqNG3aNEnSunXrtHv3br366qttnAwAAAAA/JfPvKfP7XbLZvv/hmqMqTMNAAAAAGg6nyl9PXv2lNPp9Ew7nU45HI42TAQAAAAA/s9nSt8DDzyg/Px8nTlzRhcvXtSnn36q6Ojoto4FAAAAAH7NZz7IpUePHpo2bZqSk5NVU1Oj0aNH65//+Z/bOhYAAAAA+DWf+SAXAAAAAEDL85nLOwEAAAAALY/SBwAAAAAWRukDAAAAAAuj9AEAAACAhbX70peVlaWhQ4cqNjZW7733Xqs+dnl5uYYNG6bjx49LkvLy8pSYmKjY2FgtXrzYs9zevXuVlJSkuLg4zZw5U7W1tZKkEydO6JFHHlF8fLwmT56sCxcuSJLOnz+vJ598UkOGDNEjjzxS5/6HN2LJkiVKSEhQQkKCFi1a5NNZf/GLX2jo0KFKSEjQypUrfTrrZQsXLtSMGTN8Ouu4ceOUkJCg4cOHa/jw4dq1a5dPZt28ebOSkpI0ZMgQzZ07V5JvHtN169Z5juXw4cN13333ac6cOT6ZVZIyMjI8/wcsXLhQkm8eV7Q8zpEN4xzpve9lzo8tm5VzZDs/R5p27KuvvjIDBw40Z8+eNRcuXDCJiYnmwIEDrfLYf/nLX8ywYcNMnz59zLFjx8zFixdNTEyMOXr0qKmpqTETJkwwW7ZsMcYYk5CQYP785z8bY4x5+eWXzXvvvWeMMebJJ5802dnZxhhjlixZYhYtWmSMMWb27Nlm2bJlxhhjNm7caH7yk5/ccM4vvvjCPPzww6aqqspUV1eb5ORkk5WV5ZNZt2/fbsaMGWNqamrMxYsXzcCBA83evXt9MutleXl55v777zcvvfSSz/4bcLvdpn///qampsYz5otZjx49avr3729OnjxpqqurzY9+9COzZcsWn8v5dfv37zcPPvigOXHihE9mraioMN/73vdMSUmJqampMaNHjzabNm3yyaxoWZwjG8Y50nvfy5wfWzYr50jOke269G3YsMG8/PLLnuklS5aYN954o1UeOyUlxezYscMMHDjQHDt2zGzfvt0kJyd75m/cuNHMmDHDHD9+3AwePNgzvmPHDjNu3DhTXV1t+vbt6/mP5sSJE2bQoEHGGGMGDhxoTpw4YYwxpqamxvTt29dUV1ffUM79+/d7/nEac+kf3xtvvOGTWY0xnnWPHz9uoqOjffa4GmPM2bNnzb//+7+blStXmpdeeslnsx48eND079/fjBs3ziQmJpp3333XJ7MuX77czJs3zzP91Vdf+WTOrxs7dqzJycnx2axlZWXmvvvuM8ePHzcXL140I0aM8NmsaFmcIxvGOdI7WTk/tnxWzpGcI9v15Z2nTp2S3W73TDscDhUXF7fKY7/22mvq169fg1m+Pm6321VcXKyzZ8+qc+fOCgoKqjP+9W0FBQWpc+fOOnPmzA3lvOuuuxQZGSlJKigo0McffyybzeaTWSUpODhY6enpSkhIUFRUlM8eV0maNWuWpk2bpptvvvmq7ftS1vPnzysqKkq//OUv9c4772j16tU6ceKEz2UtLCyUy+XSU089peHDh+v999/32WN6WV5eniorKzVkyBCfzdq5c2f95Cc/0ZAhQxQTE6OwsDCfzYqWxTmyYZwjvZOV82PLZ+UcyTmyXZc+t9stm83mmTbG1Jn2hSz1jV8ra33ZjTEKCGjeU33gwAFNmDBBL774osLDw30669SpU5Wfn6+TJ0+qoKDAJ7OuW7dOvXr1UlRUlGfMV/8N9O3bV4sWLVKXLl3UrVs3jR49Wunp6T6X1eVyKT8/X/PmzdOaNWu0e/duHTt2zOdyXmn16tV6/PHHJfnu879v3z59+OGH+uyzz7Rt2zYFBAT47PcVWhbnyMbjHNlyWTk/eicr50jOke36zNqzZ886b4h0Op1yOBw+leXr46dPn5bD4VC3bt1UVlYml8tVZ3np0m8UTp8+LUmqra3VhQsXFBoaesPZdu7cqfHjx+uFF17QyJEjfTbroUOHtHfvXknSN77xDcXGxmr79u0+mTUnJ0dffPGFhg8frvT0dG3evFnr1q3zyax/+tOflJ+f75k2xigsLMznst56662KiopSt27d1LFjR/3bv/2b8vLyfC7nZdXV1dqxY4cGDRokyXf/D/j8888VFRWl7t27q0OHDkpKSvLZ7yu0LM6RjcM5smWzcn70TlbOkZwj23Xpe+CBB5Sfn68zZ87o4sWL+vTTTxUdHd0mWe69914dOXLE8/J7dna2oqOjFRYWppCQEO3cuVPSpU8Iio6OVnBwsPr166ecnBxJ0kcffeTJHhMTo48++kjSpf88+/Xrp+Dg4BvKdfLkST3zzDNKS0tTQkKCT2c9fvy4UlNTVV1drerqam3atEljxozxyawrV65Udna2MjIyNHXqVA0aNEhvv/22T2YtKyvTokWLVFVVpfLycm3cuFHPP/+8z2UdOHCgPv/8c50/f14ul0vbtm1TfHy8z+W87O9//7u+9a1vqVOnTpJ89/sqIiJCeXl5qqiokDFGmzdv9tmsaFmcIxvGObLls3J+9E5WzpGcI9v1B7kYY0xmZqZJSEgwsbGx5q233mr1x7/8JnVjLn1SVWJioomNjTWvvfaacbvdxhhj9u7da0aNGmXi4uLM888/b6qqqowxl96I/eijj5ohQ4aYCRMmmNLSUmPMpTdAT5o0yQwdOtQ8/PDDnu3fiFdffdVERkaahx56yPPn/fff98msxhiTnp5uhgwZYoYNG2bS09ONMb55XK/04Ycfmpdeesmnsy5evNjEx8eb2NhY88477/hs1nXr1nm+n2fPnm1cLpdP5jTGmN/97nfmueeeqzPmq1mXLVtm4uLizLBhw8zLL79sKisrfTYrWhbnyOvjHOnd72XOjy2blXNk+z5H2owx5sbqIgAAAADA17XryzsBAAAAwOoofQAAAABgYZQ+AAAAALAwSh8AAAAAWBilDwAAAAAsjNIHAACARqupqVH//v01ceLEZm/r7rvv1pkzZ7Rp0ybNnTu3BdLVdezYMU2ZMsUzPW7cOOXm5rb44wC+LqitAwAAAMB//P73v1dERIT27NmjQ4cO6Y477mj2NgcPHqzBgwe3QLq6Tpw4oSNHjrT4dgF/Q+kDAABAo33wwQcaOnSobr/9dv3mN7/RnDlztH37dqWlpem2227T4cOH1bFjRy1YsEB33HGHZsyYoZCQEO3bt08lJSX6wQ9+oNTUVAUHB3u2uWHDBn3yySdatmyZnE6nfvrTn+rw4cMKCAjQmDFjlJycrL/85S/62c9+purqajmdTj3wwAOaN2+ejh8/rvHjxysmJka7du3S+fPnNX36dA0aNEipqakqLi7WE088oeXLl9fZj0GDBmnkyJHKz8/XyZMnNXz4cD333HOSpPXr12vlypUKCAhQ165dtXDhQvXq1Utr1qzRu+++q4CAAN1666165ZVX9E//9E+aMWOGOnbsqP3796ukpESDBg1SaGioPvvsMzmdTs2dO1dRUVGqrq5WWlqaduzYIZfLpe9+97tKTU1V586dW/MpRDvE5Z0AAABolIMHD+rPf/6z4uPjNWLECGVkZOjs2bOSpD179mjcuHHKyspSUlKSpk+f7llv9+7dWrFihXJycnTo0CGtWbOm3seYPXu2vvWtbyk3N1dr1qzR2rVrVVhYqFWrVmnq1Klat26dfve732nz5s3as2ePpEuXcfbv31/r16/XCy+8oHnz5ikwMFBz587V7bffflXhu6yiokLvv/++Vq9erRUrVujYsWPat2+f0tLS9PbbbysrK0uDBg3Sm2++qfz8fL399ttatWqVMjMzNWzYMD3zzDMyxkiS/va3v+k3v/mNfvvb32rFihXq1KmTVq9ereTkZP3617+WJL311lsKDAzUhg0blJmZKYfDobS0tBZ5boDr4ZU+AAAANMoHH3yggQMHqmvXruratau++c1vau3atYqMjFRERIT69esnSRo1apTmzJnjKYQjR47UTTfdJEkaPny4Nm3apEcfffSaj5GXl+cpjF26dFF2drYkacGCBfrDH/6gX/3qVzp8+LCqqqpUUVGh0NBQBQcHKyYmRpL03e9+V6WlpY3an8uXlPbo0UPdu3fXuXPntGPHDvXv31+9evWSJI0fP16StGjRIg0dOlTdunWTJCUlJem1117T8ePHJUkDBw5UcHCw7Ha7OnXqpAEDBkiSbr/9dk+eLVu2qKysTHl5eZIuvT+ye/fujcoKNAelDwAAAA2qqKhQRkaGOnTooEGDBkmSysvL9dvf/lb33HOPAgMDr1rn8tiV84wxCgio/2KzoKAg2Ww2z/SxY8fUtWtXTZgwQXfffbcGDBigIUOGaNeuXZ5X2YKDgz3bvHLdhoSEhHi+ttlsMsYoMDCwzjYqKytVVFQkt9t91frGGNXW1kqSOnTocNV+fJ3b7VZKSoqnoF64cEFVVVWNzgvcKC7vBAAAQIOysrIUGhqqbdu2afPmzdq8ebP+53/+RxUVFTpz5oz27dunffv2SZLWrFmjvn376uabb5Ykffzxx6qurlZVVZU2btyogQMH1vs4UVFR+vDDDyVJZWVleuyxx1RQUKD//d//1X/+538qNjZWX331lY4ePXrNInalwMBA1dTUNGk/77//fuXn5+vUqVOSpNWrV+tnP/uZBgwYoJycHJ05c0aS9OGHHyo0NFS9e/du9Lb79++v9957T9XV1XK73XrllVf085//vEn5gBvBK30AAABo0AcffKDHH3+8zqt2N998s8aNG6d33nlHt956q/77v/9bRUVF6tatmxYtWuRZrmPHjho7dqzOnz+vuLg4jRo1qt7HmTVrlv7rv/5LiYmJMsZo0qRJuueee/Tkk09q5MiR6tSpk3r06KF/+Zd/UWFhocLDw+vd1p133qmQkBCNHj1a69ata9R+3n333Zo+fbrnlhR2u13z5s1Tjx49NH78eD322GNyu93q1q2bli1bdt1XLb/u6aef1sKFCzVy5Ei5XC595zvf0YwZMxq9PnCjbOby6+IAAADADdi+fbteffVVz/vvrjRjxgzdddddeuKJJ9ogGQCJyzsBAAAAwNJ4pQ8AAAAALIxX+gAAAADAwih9AAAAAGBhlD4AAAAAsDBKHwAAAABYGKUPAAAAACyM0gcAAAAAFvZ/9jEus3I/OUMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CoapplicantIncome\n",
      "Skew : 7.49\n",
      "Mean: 1621.25 | Median: 1188.5 | 2nd Qrt: 0.0 | 3rd Qrt: 2297.25\n",
      "Stdev: 2926.25\n",
      "p-value: 0.0, stat: 0.482 and probably NOT Gaussian \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LoanAmount\n",
      "Skew : 2.36\n",
      "Mean: 141.17 | Median: 125.0 | 2nd Qrt: 98.0 | 3rd Qrt: 164.75\n",
      "Stdev: 88.34\n",
      "p-value: 0.0, stat: 0.806 and probably NOT Gaussian \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loan_Amount_Term\n",
      "Skew : -2.36\n",
      "Mean: 342.0 | Median: 360.0 | 2nd Qrt: 360.0 | 3rd Qrt: 360.0\n",
      "Stdev: 65.12\n",
      "p-value: 1.0, stat: nan and probably Gaussian \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def fn_guassian(dataIN):\n",
    "    \n",
    "    stat, p = shapiro(dataIN)\n",
    "    stat = round(stat,3)\n",
    "    p = round(p,3)\n",
    "    normtxt = 'probably NOT Gaussian'\n",
    "    if p > .05:\n",
    "        normtxt = 'probably Gaussian'\n",
    "    print(f'p-value: {p}, stat: {stat} and {normtxt} ')\n",
    "\n",
    "df = data\n",
    "for col in df[num_cols].columns:\n",
    "    print(col)\n",
    "    print('Skew :',round(df[col].skew(),2))\n",
    "    plt.figure(figsize=(15,4))\n",
    "    plt.subplot(1,2,1)\n",
    "    df[col].hist()\n",
    "    print(f'Mean: {round(df[col].mean(),2)} | Median: {df[col].median()} | 2nd Qrt: {df[col].quantile(0.25)} | 3rd Qrt: {df[col].quantile(0.75)}')\n",
    "    print(f'Stdev: {round(df[col].std(),2)}')\n",
    "    \n",
    "    fn_guassian(df[col])\n",
    "    plt.ylabel('count')\n",
    "    plt.subplot(1,2,2)\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hy8l__giuqA_"
   },
   "source": [
    "**Observations:__________**\n",
    "\n",
    "Jose Medina Observations\n",
    "> - 'ApplicantIncome' is skewed left - with many outliers on the higher end. **Mean: 5403.46 | Median: 3812.5**\n",
    "> - 'CoapplicantIncome' is skewed left - with many outliers on the higher end. **Mean: 1621.25 | Median: 1188.5**\n",
    "> - 'LoanAmount' is more evenly distributed with **Mean: 141.17 | Median: 125.0**\n",
    "> - 'Loan_Amount_Term' reveals that most people opt for the 30 yr term, with the next biggest preference being 15 year, then a few unique others that occur less frequently. **Mean: 28.5 | Median: 30.0**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aIVFJ11IDPyu"
   },
   "source": [
    "#### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "id": "iA9yPLMGDPyu"
   },
   "outputs": [],
   "source": [
    "#Converting the scale of loan term from months to years\n",
    "data['Loan_Amount_Term']=data['Loan_Amount_Term']/12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "id": "7FQ6Oo6UDPyv"
   },
   "outputs": [],
   "source": [
    "#Adding the applicant and co-applicant income to get the total income per application\n",
    "data['total_income']=data['ApplicantIncome'] + data['CoapplicantIncome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "id": "-VIN5xybDPyw"
   },
   "outputs": [],
   "source": [
    "#Dropping the columns as we created a new column which captures the same information\n",
    "data.drop(columns=['ApplicantIncome', 'CoapplicantIncome'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BRkQPSCQDPyx"
   },
   "source": [
    "#### Check the distribution and outliers for each column in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "id": "Ep2s-qpXDPyx",
    "outputId": "fbcf3ff8-2fe2-43da-beb1-83c5d05756cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LoanAmount\n",
      "Skew : 2.36\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total_income\n",
      "Skew : 5.63\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['LoanAmount', 'total_income']:\n",
    "    print(col)\n",
    "    print('Skew :',round(data[col].skew(),2))\n",
    "    plt.figure(figsize=(15,4))\n",
    "    plt.subplot(1,2,1)\n",
    "    data[col].hist(bins=10, grid=False)\n",
    "    plt.ylabel('count')\n",
    "    plt.subplot(1,2,2)\n",
    "    sns.boxplot(x=data[col])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bIaj6W_MDPyz"
   },
   "source": [
    "**Observations:**\n",
    "\n",
    "- Both the variables are highly skewed to the right and have many outliers which can be expected as the data contains different types of areas - Rural, Urban & Semi-Urban. \n",
    "- We can observe from the histogram that majority of values for total income are less than 10,000K dollars.\n",
    "\n",
    "Now, let's check the percentage of observations in each category for all the categorical variables."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-Rlb274WDPy0"
   },
   "source": [
    "#### Checking percentage of each category for categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "id": "x0l5pOwZDPy1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male      0.796417\n",
      "Female    0.182410\n",
      "Name: Gender, dtype: float64\n",
      "****************************************\n",
      "Yes    0.648208\n",
      "No     0.346906\n",
      "Name: Married, dtype: float64\n",
      "****************************************\n",
      "0     0.561889\n",
      "1     0.166124\n",
      "2     0.164495\n",
      "3+    0.083062\n",
      "Name: Dependents, dtype: float64\n",
      "****************************************\n",
      "Graduate        0.781759\n",
      "Not Graduate    0.218241\n",
      "Name: Education, dtype: float64\n",
      "****************************************\n",
      "No     0.814332\n",
      "Yes    0.133550\n",
      "Name: Self_Employed, dtype: float64\n",
      "****************************************\n",
      "1.0    0.773616\n",
      "0.0    0.144951\n",
      "Name: Credit_History, dtype: float64\n",
      "****************************************\n",
      "Semiurban    0.379479\n",
      "Urban        0.328990\n",
      "Rural        0.291531\n",
      "Name: Property_Area, dtype: float64\n",
      "****************************************\n",
      "1    0.687296\n",
      "0    0.312704\n",
      "Name: Loan_Status, dtype: float64\n",
      "****************************************\n"
     ]
    }
   ],
   "source": [
    "#Creating list of categorical columns\n",
    "cat_col= ['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed', 'Credit_History','Property_Area', 'Loan_Status']\n",
    "\n",
    "for col in cat_col:\n",
    "    print(data[col].value_counts() / len(data)) #Write your code here\n",
    "    print('*'*40)                #Print the * 40 times to separate different variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fUwYF11gwy-5"
   },
   "source": [
    "**Observations:___________**\n",
    "\n",
    "Jose Medina Observations\n",
    "\n",
    "> - Gender: males represents a majority (79.6%)\n",
    "> - Married: married represents a majority (64.8%)\n",
    "> - Dependents: 0 depends represents majority (56.2%), with roughly even representation in 1 (16.6%) and 2 (16.4%) dependents, and slightly less for 3+ dependants (8.3%)\n",
    "> - Graduate: graduate represents a majority (78.1%)\n",
    "> - Self-employed: most applicants are NOT self-employeed (self-employed=No is 81.4%)\n",
    "> - Credity History: most applicants (77%) meet the required Credit_History guidelines (Credit_History=1)\n",
    "> - Property_Area is relatively evently distributed among the 3 possible values - with ~38% Semiurban, ~33% for Urban, and ~29% for Rural\n",
    "> - Loan_Status = 1 (approved) is a majority (~68.7%)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ukUAlvRtDPy3"
   },
   "source": [
    "#### Imputing missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "id": "G-QGGBGJDPy4"
   },
   "outputs": [],
   "source": [
    "#Imputing missing values with mode for the categorical variables \n",
    "for col in ['Gender', 'Married', 'Dependents', 'Self_Employed', 'Loan_Amount_Term', 'Credit_History']:\n",
    "    data[col].fillna(value=data[col].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "id": "rS94M4lcDPy5"
   },
   "outputs": [],
   "source": [
    "#Replacing 0's with null values in loan amount \n",
    "data.LoanAmount.replace(0, np.nan, inplace=True)\n",
    "\n",
    "#Imputing null values in loan amount with the median\n",
    "data.LoanAmount.fillna(value=data.LoanAmount.median(), inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EhDZ1oRaDPy6"
   },
   "source": [
    "**We are done with the data preprocessing. Let's explore the data a bit more with bivariate analysis.**\n",
    "\n",
    "Let's check the relationship of loan amount with the total income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "id": "Nt-knNhSDPy7",
    "outputId": "248c4da7-ca2b-48dd-e118-cc2036c10d02"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.regplot(x='LoanAmount', y='total_income', data=data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "T2Snl5awDPy8"
   },
   "source": [
    "**Observations:**\n",
    "- The plot shows that the loan amount is positively correlated with total income. This implies that the loan amount for higher-income applicants is progressively higher.\n",
    "- There are some outliers visible, showing applicants with low income having been given loans of a higher amount."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PVbble7sb0jp"
   },
   "source": [
    "Let's check the relationship of total income with loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "id": "_zfwsl2MDPy8",
    "outputId": "0abc61e1-89ec-4cbc-c97c-74aeb1f4a813"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data.Loan_Status, y=data.total_income)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4cY1RUPpDPy-"
   },
   "source": [
    "**Observations:**\n",
    "- Except for some outliers, there is no major difference between the income of those customers who are eligible versus those who are not. \n",
    "- This implies that income alone might not be a good indicator of a customer's loan eligibility."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1jI1J2bkb0jq"
   },
   "source": [
    "Let's check how credit history is related to loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "id": "-rQnVxvdDPy_",
    "outputId": "9493c640-57f9-4589-80be-9e206b0227a6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=data.Credit_History, hue=data.Loan_Status)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UJxePuBoDPzA"
   },
   "source": [
    "**Observations:**\n",
    "- The plot shows that credit history is an important factor while checking loan eligibility. There are very few customers whose loan was approved even when their credit history did not meet required guidelines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hGn6jFYgb0kG"
   },
   "source": [
    "Let's check the relationship between education and loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "id": "aGrFWboqDPzB",
    "outputId": "176f49f1-1975-42bd-daff-c25ad7e1cf10"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Education', ylabel='count'>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AAACMRxQDAADAeEQxAAAAjEcUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMRxQDAADAeEQxAAAAjEcUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMRxQDAADAeEQxAAAAjEcUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMRxQDAADAeEQxAAAAjEcUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMRxQDAADAeHZvnvzFF1/U2rVrJUmRkZFKSkrSxIkTlZOTo3r16kmSxo4dq+7du2vnzp2aNGmSTp06pQ4dOmjatGmy2706HgAAACDJi1G8efNmffTRR1qxYoVsNptGjBih9evXKzc3V2+88YbCwsKqHD9hwgRNnz5d7du3V0pKijIyMjR48GBvjQcAAAB4eG35hMPhUHJysgIDAxUQEKDw8HAdOnRIhw4dUkpKimJjY5Weni63262DBw+qtLRU7du3lyTFxcUpOzvbW6MBAAAAVXjtneJWrVp5fs7Ly9PatWu1aNEibdu2TVOnTlWDBg00atQoLV26VK1atZLD4fAc73A45HQ6z+vxGjWqX2OzA0BNcDga+HoEAEA1eX3R7p49ezRq1CglJSXpj3/8o2bPnu25LT4+XpmZmQoPD5fNZvPstyyrynZ1HDlSIrfbqrG5gQtBBOHHCgtP+noEAMD3/Pxs53wT1atXn8jJydGwYcP02GOPqV+/ftq9e7fWrVvnud2yLNntdjVt2lSFhYWe/UVFRWetOQYAAAC8xWtRfPjwYY0ZM0YzZ85Ur169JJ2J4KeeekrHjx9XRUWFFi9erO7du6t58+YKCgpSTk6OJCkrK0sRERHeGg0AAACowmvLJ+bPn6+ysjLNmDHDs+/ee+/Vgw8+qEGDBsnlcikqKkq9e/eWJM2cOVOpqakqKSlRu3btlJCQ4K3RAAAAgCpslmVdFAtxWVOMusDhaKCctBG+HgN1wE1Jr7KmGADqEJ+uKQYAAAB+C4hiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYz6tR/OKLL6pXr17q1auX0tLSJEmbN29WbGysoqKi9Pzzz3uO3blzp+Li4hQdHa1JkybJ5XJ5czQAAADAw2tRvHnzZn300UdasWKFMjMz9cUXX2j16tVKSUnRnDlztGbNGuXm5mrjxo2SpAkTJmjKlClat26dLMtSRkaGt0YDAAAAqvBaFDscDiUnJyswMFABAQEKDw9XXl6eWrZsqRYtWshutys2NlbZ2dk6ePCgSktL1b59e0lSXFycsrOzvTUaAAAAUIXdWydu1aqV5+e8vDytXbtW9913nxwOh2d/WFiYnE6nCgoKqux3OBxyOp3n9XiNGtX/74cGgBrkcDTw9QgAgGryWhT/YM+ePRo1apSSkpLk7++vvLw8z22WZclms8ntdstms521/3wcOVIit9uqqbGBC0IE4ccKC0/6egQAwPf8/GznfBPVqx+0y8nJ0bBhw/TYY4+pX79+atq0qQoLCz23FxYWKiws7Kz9RUVFCgsL8+ZoAAAAgIfXovjw4cMaM2aMZs6cqV69ekmSrr/+eu3bt0/79+9XZWWlVq9erYiICDVv3lxBQUHKycmRJGVlZSkiIsJbowEAAABVeG35xPz581VWVqYZM2Z49t17772aMWOGxo0bp7KyMkVGRiomJkaSNHPmTKWmpqqkpETt2rVTQkKCt0YDAAAAqrBZlnVRLMRlTTHqAoejgXLSRvh6DNQBNyW9yppiAKhDfLqmGAAAAPgtIIoBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxqtWFDudzrP2ff311zU+DAAAAOAL54ziY8eO6dixYxo5cqSOHz/u2S4qKtLYsWNra0YAAADAq+znuvGxxx7Tpk2bJEmdOnX6vzvZ7YqOjvbuZAAAAEAtOWcUz58/X5I0ceJEPf3007UyEAAAAFDbzhnFP3j66ad18OBBHT9+XJZlefa3a9fOa4MBAAAAtaVaUZyenq758+erUaNGnn02m03vvfee1wYDAAAAaku1ojgzM1PvvPOOmjRp4u15AAAAgFpXrUuyNWvWjCAGAADARata7xR36dJFaWlpuuOOOxQcHOzZz5piAAAAXAyqFcXLly+XJGVnZ3v2saYYAAAAF4tqRfGGDRu8PQcAAADgM9WK4tdee+1n999///01OgwAAADgC9WK4q+++srzc3l5uT755BN16dLFa0MBAAAAtanaX97xY06nU5MmTfLKQAAAAEBtq9Yl2X6qSZMmOnjwYE3PAgAAAPjEea8ptixLubm5Vb7dDgAAAPgtO+81xdKZL/NISkryykAAAABAbTuvNcUHDx6Uy+VSy5YtvToUAAAAUJuqFcX79+/Xww8/rIKCArndboWGhmrevHkKDw/39nwAAACA11Xrg3ZPPPGERowYoU8++UQ5OTl66KGHNG3aNG/PBgAAANSKakXxkSNH1K9fP892//79dfToUa8NBQAAANSmakVxZWWljh075tkuLi721jwAAABAravWmuL77rtP99xzj3r06CGbzaY1a9Zo6NCh3p4NAAAAqBXVeqc4MjJSklRRUaG9e/fK6XSqe/fuXh0MAAAAqC3Veqc4OTlZQ4YMUUJCgsrKyvTWW28pJSVFr7zyirfnAwAAALyuWu8UHz16VAkJCZKkoKAgDRs2TIWFhV4dDAAAAKgt1f6gndPp9GwXFRXJsiyvDQUAAADUpmotnxg2bJj69u2rrl27ymazafPmzXzNMwAAAC4a1YriAQMG6JprrtHWrVvl7++v4cOHq3Xr1t6eDQAAAKgV1YpiSWrbtq3atm3rzVkAAAAAn6jWmmIAAADgYkYUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMRxQDAADAeEQxAAAAjEcUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMRxQDAADAeEQxAAAAjEcUAwAAwHhEMQAAAIzn1SguKSlR7969lZ+fL0maOHGioqKi1KdPH/Xp00fr16+XJO3cuVNxcXGKjo7WpEmT5HK5vDkWAAAAUIXXonj79u0aNGiQ8vLyPPtyc3P1xhtvKCsrS1lZWerevbskacKECZoyZYrWrVsny7KUkZHhrbEAAACAs3gtijMyMjR16lSFhYVJkk6fPq1Dhw4pJSVFsbGxSk9Pl9vt1sGDB1VaWqr27dtLkuLi4pSdne2tsQAAAICz2L114ieffLLKdlFRkTp37qypU6eqQYMGGjVqlJYuXapWrVrJ4XB4jnM4HHI6nef9eI0a1f+vZwaAmuRwNPD1CACAavJaFP9UixYtNHv2bM92fHy8MjMzFR4eLpvN5tlvWVaV7eo6cqREbrdVI7MCF4oIwo8VFp709QgAgO/5+dnO+SZqrV19Yvfu3Vq3bp1n27Is2e12NW3aVIWFhZ79RUVFniUXAAAAQG2otSi2LEtPPfWUjh8/roqKCi1evFjdu3dX8+bNFRQUpJycHElSVlaWIiIiamssAAAAoPaWT7Rt21YPPvigBg0aJJfLpaioKPXu3VuSNHPmTKWmpqqkpETt2rVTQkJCbY0FAAAAyGZZ1kWxEJc1xagLHI4Gykkb4esxUAfclPQqa4oBoA6pM2uKAQAAgLqKKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiv1r6842LXoGGwgoMCfD0GAAAALgBRXEOCgwI0OGmRr8eAj72ZNsTXIwAAgAvA8gkAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGAACA8YhiAAAAGM+rUVxSUqLevXsrPz9fkrR582bFxsYqKipKzz//vOe4nTt3Ki4uTtHR0Zo0aZJcLpc3xwIAAACq8FoUb9++XYMGDVJeXp4kqbS0VCkpKZozZ47WrFmj3Nxcbdy4UZI0YcIETZkyRevWrZNlWcrIyPDWWAAAAMBZvBbFGRkZmjp1qsLCwiRJO3bsUMuWLdWiRQvZ7XbFxsYqOztbBw8eVGlpqdq3by9JiouLU3Z2trfGAgAAAM5i99aJn3zyySrbBQUFcjgcnu2wsDA5nc6z9jscDjmdzvN+vEaN6l/4sADgBQ5HA1+PAACoJq9F8U+53W7ZbDbPtmVZstlsv7j/fB05UiK326qRWS8Ef/kB+KnCwpO+HgEA8D0/P9s530SttatPNG3aVIWFhZ7twsJChYWFnbW/qKjIs+QCAAAAqA21FsXXX3+99u3bp/3796uyslKrV69WRESEmjdvrqCgIOXk5EiSsrKyFBERUVtjAQAAALW3fCIoKEgzZszQuHHjVFZWpsjISMXExEiSZs6cqdTUVJWUlKhdu3ZKSEiorbEAAAAA70fxhg0bPD936dJFK1euPOuYtm3baunSpd4eBQAAAPhZfKMdAAAAjEcUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMRxQDAADAeEQxAAAAjEcUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMRxQDAADAeEQxAAAAjEcUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMZ/f1AAAA4OIWekmg7IFBvh4DPuYqL9PR4+W+HuMXEcUAAMCr7IFBykkb4esx4GM3Jb0qqe5GMcsnAAAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPLsvHjQ+Pl7FxcWy2888/BNPPKFTp07p6aefVllZmXr06KFHHnnEF6MBAADAQLUexZZlKS8vT++//74niktLSxUTE6OFCxeqWbNmGjVqlDZu3KjIyMjaHg8AUEMaNAxWcFCAr8cAgGqp9Sj+5ptvJEkPPPCAjh07poEDB6p169Zq2bKlWrRoIUmKjY1VdnY2UQwAv2HBQQEanLTI12OgDngzbYivRwB+Va1H8YkTJ9SlSxdNnjxZFRUVSkhI0IgRI+RwODzHhIWFyel0ntd5GzWqX9OjAsB/xeFo4OsRAKBOqcuvi7UexTfccINuuOEGz/aAAQOUnp6um266ybPPsizZbLbzOu+RIyVyu60am/N81eV/yQB8o7DwpK9H8CleFwH8lC9fF/38bOd8E7XWrz7x6aefasuWLZ5ty7LUvHlzFRYWevYVFhYqLCystkcDAACAoWo9ik+ePKm0tDSVlZWppKREK1as0KOPPqp9+/Zp//79qqys1OrVqxUREVHbowEAAMBQtb58olu3btq+fbv69u0rt9utwYMH64YbbtCMGTM0btw4lZWVKTIyUjExMbU9GgAAAAzlk+sUJyYmKjExscq+Ll26aOXKlb4YBwAAAIbjG+0AAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGI4oBAABgPKIYAAAAxiOKAQAAYDyiGAAAAMYjigEAAGA8ohgAAADGq1NRvGrVKvXs2VNRUVFatGiRr8cBAACAIey+HuAHTqdTzz//vJYvX67AwEDde++96tSpk6688kpfjwYAAICLXJ2J4s2bN6tz584KCQmRJEVHRys7O1tjx46t1v39/GxenK56Gof+3tcjoA4IbNjI1yOgjqgLr0u+xusifsBrIyTfvi7+2mPbLMuyammWc5o3b56+++47PfLII5KkJUuWaMeOHfrrX//q48kAAABwsasza4rdbrdstv8reMuyqmwDAAAA3lJnorhp06YqLCz0bBcWFiosLMyHEwEAAMAUdSaKb7nlFm3ZskXFxcU6ffq03nnnHUVERPh6LAAAABigznzQrkmTJnrkkUeUkJCgiooKDRgwQNddd52vxwIAAIAB6swH7QAAAABfqTPLJwAAAABfIYoBAABgPKIYAAAAxiOKAQAAYDyiGMZyuVx66aWX1KNHD/Xs2VPR0dGaO3euLvSzp/n5+br99tsv6L4HDhxQSkrKBd0XAM4lPz9fbdq00aZNm6rsv/3225Wfn3/O+8bHx//ibWvXrtWAAQPUo0cP3XnnnUpJSdHJkyf/q1mrM9MvOdesQHUQxTDWtGnTtGPHDi1evFhr1qzRsmXLtGXLFr355pu1PsuhQ4d04MCBWn9cAGYICAjQ5MmTVVJScl7327Zt28/uX7VqlV544QU988wzWrt2rdavX6+QkBBNmjSpJsa9IL80K1BdRDGM9O2332rlypWaMWOGGjZsKEmqX7++pkyZosaNGys5OVmjR49Wjx49tGHDBq1du1YDBw7UXXfdpZiYGH322WeSpC+//FL9+vVTv379NHv2bM/5k5OTtXz5cs92mzZtJElOp1PDhw/XwIEDddttt2nWrFmSpOnTpys3N1fTpk2TJL388svq16+f7rrrLqWlpV3wu9cAIElhYWG65ZZb9Mwzz/zs7XPnzlXPnj0VGxurGTNmqLKyUtOnT5ck3X333Wcd/+KLLyolJUXh4eGSJJvNpkceeUTXXnutJGn58uWKj49XbGys/va3v+mrr75SfHy8+vfvr27duumtt96SJB07dkwjR45UbGysEhMTVVZW5rl/cnKy5/Hi4+P18ccfy+VyKTU1Vffcc4/uuOMOPfzwwyotLT1r1g8//FADBgxQ3759NXbsWB09erQmnkZc5IhiGGnHjh0KDw/XJZdcUmV/eHi4oqOjJUkhISFau3atbrvtNv3zn//U3LlztXLlSo0YMUIvv/yyJOnPf/6zHn/8ca1YsUKXX375rz7u6tWr1bt3b2VkZGjVqlV6/fXXVVxcrNTUVF1zzTWaOnWqPvzwQ+Xm5mrp0qXKzMyU0+nUypUra/5JAGCU5ORkffTRR2cto9i4caM2bNigZcuWacWKFdq/f7/++c9/KjU1VZK0ZMmSKscfO3ZMeXl56tChQ5X9AQEBGjlypGfb6XRqxYoVevTRR7VkyRI9/PDDWrZsmRYsWKC0tDRJUnp6uq6++mqtWrVKQ4YMUVFR0Tn/DP/+978VEBCgxYsXa/369Tp58qQ2btxYZdbi4mI999xzmj9/vjIzM3Xrrbdq5syZF/akwSh15hvtgNpms9k8P2dnZ+ull16S2+1WYGCgWrVq5flGRT8/P82ePVsbNmzQvn37tG3bNvn5+am4uFgFBQX605/+JEmKi4vTsmXLzvmYw4cP19atWzV//nzt2bNHFRUVOn36dJVjtmzZoh07diguLk6SVFpaqssuu6wm/+gADFS/fn399a9/1eTJk6v8or1161b16tVL9erVkyT1799fmZmZGjJkyDnP98NraH5+vsaMGSNJKi4uVkZGhiTp6quvlt1+JjOSk5P1r3/9S/PmzdNXX32l7777TtKZJQ/PPfecJKljx45q0aLFOR+zY8eOCgkJ0aJFi/TNN98oLy/Pc64fbN++XYcPH1ZCQoIkye12n/UGCPBziGIY6ZprrtHevXtVUlKi+vXrKyYmRjExMcrPz/e8kAYHB0uSTp06pQEDBuiuu+5Sx44d1aZNGy1atEg2m63KsgZ/f3/Pzz++raKiwrN/xowZOnDggHr37q0777xTmzdvPmtpRGVlpYYOHar7779fknTixIkq5waAC3XrrbeetYzC7XafdZzL5frFc4SEhKhFixb67LPPdOutt+ryyy9XVlaWpDMflKusrJT0f6+hkpSYmKiGDRuqW7du6tmzp1avXi1Jv/g6+tP9P7yOvvfee0pPT1dCQoLi4uJ09OjRn30NvfHGGzV37lxJUllZmU6dOlWNZwemY/kEjHTZZZfprrvu0p///GedOHFC0pm/BD744AP5+VX9zyIvL082m02jR49Wp06dtH79elVWVio0NFSXXXaZPvjgA0nyvMhLZ/7S+PrrryVJ7777rmf/pk2bNHz4cPXo0UP79u2T0+mU2+2Wv7+/5y+hzp07KysrS6dOnZLL5dKYMWO0bt06bz4dAAzywzKKgoICSWdec95++22VlpbK5XJp2bJl6ty5syRVeW36scTERE2fPl179+717Pv000917Nixn/0lftOmTRo/frzuvPNOffjhh5LOxGuXLl08Qb1jxw795z//kSSFhoZq7969sixLBw4c0O7duyWd+T9pPXr0UP/+/dWwYUN9/PHHngj/Ydbrr79en3/+ufbt2ydJmjNnjme5BnAuvFMMY/3lL3/Ra6+9poSEBFVWVurUqVPq1KmTXnnlFc2bN89zXNu2bXXVVVepR48estlsuvXWW5WTkyNJevbZZzVx4kS98MILat++vec+gwYNUmJiomJjY9W5c2c5HA5J0qhRo5SUlKTg4GA1bdpU11xzjfLz83XVVVfp5MmTmjBhgp599lnt2rVLAwcOVGVlpbp27ap+/frV6nMD4OL1wzKK4cOHS5K6deumnTt3qn///nK5XLr11lt13333SZLuuOMO9enTR8uXL1dQUJDnHL1799bvfvc7paam6tSpUyopKVF4eLhefPFFNWvW7KzHHDdunAYPHqygoCC1bdtWzZs3V35+vsaPH6/k5GT16tVLf/zjHz3LJ2655RYtW7ZMMTExuuKKK3TTTTdJOvNBuscff1xvv/22AgICdOONN3ou4fbjWZ966iklJibK7XarSZMmevbZZ736nOLiYLP4WDsAAAAMx/IJAAAAGI8oBgAAgPGIYgAAABiPKAYAAIDxiGIAAAAYjygGgFrWpk0bxcbGqk+fPlX++eHSUj+WnZ2t+Ph4r8zxwAMPqLi4WJI0cuRIz7W1AcBEXKcYAHzg9ddf16WXXurTGTZt2uT5+ZVXXvHhJADge0QxANQxs2bN0qpVqxQSEqKWLVt69icnJ6tVq1aeL1348fa+ffs0ZcoUFRcXy8/PTw899JB69uyp999/X/PmzVN5ebmKi4vVt29fJSYmauLEiZKkoUOH6uWXX9aQIUM0a9YsXXvttVq8eLEWLlwoPz8/NW7cWJMnT9YVV1yh5ORk1a9fX7t379a3336rNm3a6JlnntHvf/97nzxPAFCTiGIA8IGhQ4dW+Urxyy+/XLNnz9a7776rd955R5mZmQoODtaYMWOqdb5HH31UAwYM0JAhQ3T48GHFx8era9eu+sc//qEZM2boD3/4g5xOp7p166aEhAQ9/fTTWr58+VnvWG/ZskWvvvqqFi9erEsvvVTLly/XmDFj9Pbbb0uScnNztWDBAtlsNg0cOFDZ2dnq379/zT45AOADRDEA+MAvLZ/YsmWLunfvrvr160uS+vfvr4ULF57zXMeOHdOuXbt09913S5KaNWumd999V5I0d+5cffDBB1q9erX27t0ry7J0+vTpXzzXv/71L/Xs2dMzW1xcnJ588knPeueuXbsqMDBQktS6dWsdP378PP/kAFA38UE7AKhjLMvy/Ozv7+/52WazVbmtoqJCkmS32z23/+Cbb77Rd999p379+umLL77Q1VdfraSkJNnt9irn+Cm32/2z87hcLklScHDwL84DAL9lRDEA1CERERHKzs7WiRMn5Ha7lZWV5bktNDRUubm5kiSn06lt27ZJkurXr6927dopMzNTknT48GENGjRIX331lUpKSpSYmKjbb79dH3/8scrLyz3h6+/v74ndH3Tt2lVr1qzxXJVi2bJlZ61tBoCLEcsnAMAHfrqmWDqzLjgyMlK7d+9W//791bBhQ7Vt21ZHjx6VJMXHx+vxxx9XdHS0Lr/8cnXu3Nlz3+eee07Tpk3TwoULZbPZ9OSTT+q6667Tbbfdph49eigwMFCtW7fWlVdeqf379+t//ud/FBMTo/j4eP3973/3nOdPf/qThg0bpqFDh8rtduvSSy/VvHnzzpoVAC42Nov/9wUAAADD8as/AAAAjEcUAwAAwHhEMQAAAIxHFAMAAMB4RDEAAACMRxQDAADAeEQxAAAAjEcUAwAAwHj/H+5R7iqcKKGyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=data.Education, hue=data.Loan_Status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bkbSi9QdDPzC"
   },
   "source": [
    "**Observations:**\n",
    "- We can see that graduate customers are more likely to get loans.\n",
    "- This might be because, in general, graduate customers have higher income when compared to non-graduates. Let's check if this is true."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "id": "HjP1fFroDPzD",
    "outputId": "8664a005-19b2-477c-d9ae-6c32285a6a9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Education', ylabel='total_income'>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=data.Education, y=data.total_income)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xOVzdKwxDPzE"
   },
   "source": [
    "**Observations:**\n",
    "- We can see that this does appear to be true; graduate customers seem to earn more on average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uJpSDK7pb0kN"
   },
   "source": [
    "Let's check how property area is related with loan status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "id": "uW4DfezQDPzF",
    "outputId": "9d4f8e25-35c4-4f23-d0d0-9101a2a551a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Property_Area', ylabel='count'>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=data.Property_Area, hue=data.Loan_Status)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Od0NdHRDPzG"
   },
   "source": [
    "**Observations:**\n",
    "\n",
    "- The plot shows that more loans are approved for properties in semi-urban areas. \n",
    "- This could be due to several reasons. The bank might be charging higher interest rates for semi-urban areas or the current customer base of the company from semi-urban areas might actually be more eligible for home loans based on loan applicant features. We cannot be certain as we don't have the data to support this claim.\n",
    "\n",
    "**Now that we have processed and explored our data, let's prepare it for modeling.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3NtIHppoDPzH"
   },
   "source": [
    "## Preparing data for modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N6RcR8FrDPzH"
   },
   "source": [
    "- Models cannot take non-numeric inputs. So, we will first create dummy variables for all the categorical variables.\n",
    "- We will then split the data into train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "id": "mnL99NcgDPzI"
   },
   "outputs": [],
   "source": [
    "#Separating target variable and other variables\n",
    "\n",
    "X= data.drop(columns=['Loan_Status'])#write your code here\n",
    "Y= data['Loan_Status']#write your code here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "id": "AGlNuXAkDPzJ"
   },
   "outputs": [],
   "source": [
    "#Creating dummy variables \n",
    "#drop_first=True is used to avoid redundant variables\n",
    "X = pd.get_dummies(X, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "id": "UR6NWZNnDPzK",
    "outputId": "2a14f95d-8b8e-44ec-9add-2fdc204f8182"
   },
   "outputs": [],
   "source": [
    "#Splitting the data into train and test sets\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,Y,test_size=0.30,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x9Ycp1rkDPzL"
   },
   "source": [
    "## Building Classification Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fajKKlPmDPzM"
   },
   "source": [
    "**Before training the model, let's choose the appropriate model evaluation criterion as per the problem on hand.**\n",
    "\n",
    "### Model evaluation criterion:\n",
    "\n",
    "#### Since this is a binary classification problem, the model can make wrong predictions in one of two ways:\n",
    "1. Predicting a customer is not eligible for a loan when the customer actually is eligible - Loss of opportunity\n",
    "2. Predicting a customer is eligible for a loan when the customer is actually not - Financial loss\n",
    "\n",
    "#### Which case is more important? \n",
    "* Predicting that the customer is eligible when he is not, because the person might default or not be able to return the loan, which would result in financial loss to the company.\n",
    "\n",
    "#### How to reduce this loss i.e need to reduce False Negatives?\n",
    "* In classification, the class of interest is considered the positive class. Here, the class of interest is 0 i.e. identifying non-eligible customers. So, the company wants to minimize the number of false negatives, in other words **Recall** is the evaluation criterion to be maximized."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3hUiY1AgDPzN"
   },
   "source": [
    "Also, let's create a function to calculate and print the classification report and confusion matrix so that we don't have to rewrite the same code repeatedly for each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "id": "VxiT1LhpDPzN"
   },
   "outputs": [],
   "source": [
    "#function to print classification report and get confusion matrix in a proper format\n",
    "\n",
    "def metrics_score(actual, predicted):\n",
    "    print(classification_report(actual, predicted))\n",
    "    cm = confusion_matrix(actual, predicted)\n",
    "    plt.figure(figsize=(8,5))\n",
    "    sns.heatmap(cm, annot=True,  fmt='.2f', xticklabels=['Not Eligible', 'Eligible'], yticklabels=['Not Eligible', 'Eligible'])\n",
    "    plt.ylabel('Actual')\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KtY-QagUDPzO"
   },
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "id": "ZQCUzmrdDPzO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(random_state=1)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#define logistic regression model \n",
    "log_reg= LogisticRegression(random_state=1)\n",
    "\n",
    "#fit the model\n",
    "log_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-GGajC53DPzP"
   },
   "source": [
    "**Let's check the coefficient of each dependent variable in the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "id": "DUs6dFjdDPzQ",
    "outputId": "271ffac2-c77f-4d94-8ccf-c8e7f57687d6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Credit_History             3.026274\n",
       "Property_Area_Semiurban    0.821581\n",
       "Married_Yes                0.189397\n",
       "Dependents_2               0.154733\n",
       "Dependents_3+              0.113794\n",
       "total_income              -0.000010\n",
       "LoanAmount                -0.000475\n",
       "Property_Area_Urban       -0.006431\n",
       "Loan_Amount_Term          -0.065875\n",
       "Self_Employed_Yes         -0.098513\n",
       "Gender_Male               -0.106018\n",
       "Dependents_1              -0.145736\n",
       "Education_Not Graduate    -0.491753\n",
       "dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(log_reg.coef_[0], index=X_train.columns).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IpPP1F8xxCSy"
   },
   "source": [
    "**Observations:_________**\n",
    "\n",
    "Jose Medina Observations\n",
    "\n",
    "> **Some features that positively affect LoanStatus**\n",
    "> - Credit History (largest coefficient) - this we noted was a prominent feature in the bar charts above - with more loans being approved, generally, when Credit_History met requirements (Credit_History=1)\n",
    "> - Property_Area_Semiurban - again, this was also noteworth in the bar charts above, and more approved loans seemed to be associated with Semiurban\n",
    "> - Maried_Yes\n",
    "> - Dependents_2              \n",
    "> - Dependents_3+\n",
    "\n",
    "> **Some features that negatively affect LoanStatus**\n",
    "> - Loan_Amount_Term\n",
    "> - Education_Not Graduate\n",
    "> - Dependents_1  \n",
    "> - Gender_Male (<-- but we need keep in mind that majority of applicants are male)      \n",
    "> - Self_Employed_Yes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "id": "SdRYNh2sb0ke",
    "outputId": "a21068c9-cbb9-41c9-f712-039492353a36"
   },
   "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>odds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Credit_History</th>\n",
       "      <td>20.620255</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property_Area_Semiurban</th>\n",
       "      <td>2.274092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Married_Yes</th>\n",
       "      <td>1.208521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents_2</th>\n",
       "      <td>1.167346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents_3+</th>\n",
       "      <td>1.120521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_income</th>\n",
       "      <td>0.999990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LoanAmount</th>\n",
       "      <td>0.999526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Property_Area_Urban</th>\n",
       "      <td>0.993590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <td>0.936248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Self_Employed_Yes</th>\n",
       "      <td>0.906184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender_Male</th>\n",
       "      <td>0.899408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents_1</th>\n",
       "      <td>0.864386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education_Not Graduate</th>\n",
       "      <td>0.611553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              odds\n",
       "Credit_History           20.620255\n",
       "Property_Area_Semiurban   2.274092\n",
       "Married_Yes               1.208521\n",
       "Dependents_2              1.167346\n",
       "Dependents_3+             1.120521\n",
       "total_income              0.999990\n",
       "LoanAmount                0.999526\n",
       "Property_Area_Urban       0.993590\n",
       "Loan_Amount_Term          0.936248\n",
       "Self_Employed_Yes         0.906184\n",
       "Gender_Male               0.899408\n",
       "Dependents_1              0.864386\n",
       "Education_Not Graduate    0.611553"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "odds = np.exp(log_reg.coef_[0]) #finding the odds\n",
    "\n",
    "#adding the odds to a dataframe and sorting the values\n",
    "pd.DataFrame(odds, X_train.columns, columns=['odds']).sort_values(by='odds', ascending=False) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yKfopkPjxID_"
   },
   "source": [
    "**Observations:_____**\n",
    "\n",
    "Jose Medina Observations\n",
    "\n",
    "Since the objective is to understand which loans are likely to be approved, we shall focus our observations on a few notable odds for approval of loans are as follows:\n",
    "\n",
    "**An applicant that has:**\n",
    "> - the required Credit_History (=1) is 21.3 times more likely to be approved for a loan than those are do NOT have the required credit history, per guidelines.\n",
    "> - an application for a Semiurban property area is 2.07 times more likely to be approved for a loan than another property area type\n",
    "> - Married_Yes (married inviduals) are 1.38 times more likely to get approved for a loan than those with other marital status\n",
    "> - Dependents_2 (applicants with 2 dependants) are 1.22 times more likely to be approved for a loan than other dependent levels\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jdRHileGDPzT"
   },
   "source": [
    "**Now, let's check the performance of the model on the training set**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "id": "AVAZY2p-DPzU"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.44      0.59       131\n",
      "           1       0.80      0.98      0.88       298\n",
      "\n",
      "    accuracy                           0.82       429\n",
      "   macro avg       0.85      0.71      0.74       429\n",
      "weighted avg       0.83      0.82      0.79       429\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#predict on the training data \n",
    "y_pred_train = log_reg.predict(X_train) #Write your code here\n",
    "\n",
    "# Checking performance on the training data\n",
    "metrics_score(y_train, y_pred_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lb9dV9I4DPzW"
   },
   "source": [
    "**Reading confusion matrix (clockwise):**\n",
    "\n",
    "- **True Positive**: Predicting the customer is not eligible and the customer is actually not eligible\n",
    "- **False Negative**: Predicting the customer is eligible but the customer is actually not eligible\n",
    "- **True Negative**: Predicting the customer is eligible and the customer is actually eligible\n",
    "- **False Positive**: Predicting the customer is not eligible but the customer is actually eligible"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M9cN7fTq7RGC"
   },
   "source": [
    "**Observations:_____**\n",
    "\n",
    "Jose Medina Observations:\n",
    "\n",
    "> - We are getting an accuracy of around 82% on train and test dataset.\n",
    "> - Our recall is quite high (98%) for class=1 (Loan Approved) which is reflected in the confusion matrix (we caught 292 approve loans out of a total of 298)\n",
    "> - Precision for class=1 is 80% which is fairly good, though there's room for improvement as in some cases we are still falsely predicting that individuals are eligible when in actuality they are not (73)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CEvDJFiFb0kl"
   },
   "source": [
    "**Precision-Recall Curve for Logistic Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "id": "xd2N6ZdpDPzW",
    "outputId": "9238370b-8334-4a4a-c098-34cadd49301f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_scores=log_reg.predict_proba(X_train) #predict_proba gives the probability of each observation belonging to each class\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train, y_scores[:,1])\n",
    "\n",
    "#Plot values of precisions, recalls, and thresholds\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.plot(thresholds, precisions[:-1], 'b--', label='precision')\n",
    "plt.plot(thresholds, recalls[:-1], 'g--', label = 'recall')\n",
    "plt.xlabel('Threshold')\n",
    "plt.legend(loc='upper left')\n",
    "plt.ylim([0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QWO8v_IPb0ko"
   },
   "source": [
    "**Observations:**\n",
    "- We can see that precision and recall are balanced for threshold of about 0.7.\n",
    "- Let's try to calculate the exact threshold where precision and recall are equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "id": "25j4jYZCb0ko",
    "outputId": "81153045-7b58-47f2-aca3-f122af05cad1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7221983510536458\n"
     ]
    }
   ],
   "source": [
    "#calculating the exact threshold where precision and recall are equal.\n",
    "for i in np.arange(len(thresholds)):\n",
    "    if precisions[i]==recalls[i]:\n",
    "        print(thresholds[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4gxy8CC7b0k2"
   },
   "source": [
    "- The threshold of 0.72 would give a balanced precision and recall.\n",
    "- We can choose the threshold to be a bit higher to make the recall higher but then the precision would drop.\n",
    "- **This decision depends on the stakeholders and other business driven factors.** For example, if the company can bear the cost of some false positives and be more conservative while approving loans then we can choose for the recall to be higher.\n",
    "- Let's choose the recall to be 0.75 for the current scenario, since we have mentioned that the company wants to optimize recall.\n",
    "- **Note:** We also don't want the precision to be so poor that it outweighs the benefits of a higher recall. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "id": "9UmTsFiuDPzX",
    "outputId": "c3a455bb-7ea7-422f-bda1-6f8f5b5902d9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.51      0.73      0.60       131\n",
      "           1       0.85      0.69      0.76       298\n",
      "\n",
      "    accuracy                           0.70       429\n",
      "   macro avg       0.68      0.71      0.68       429\n",
      "weighted avg       0.75      0.70      0.71       429\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimal_threshold1 = 0.75\n",
    "metrics_score(y_train, y_scores[:,1]>optimal_threshold1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "id": "tZZ_ZG2yDPza",
    "outputId": "0ce358bd-3cac-4340-ee6d-30c40cb24f4b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.56      0.66      0.60        61\n",
      "           1       0.81      0.74      0.78       124\n",
      "\n",
      "    accuracy                           0.71       185\n",
      "   macro avg       0.68      0.70      0.69       185\n",
      "weighted avg       0.73      0.71      0.72       185\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Checking performance on the testing data\n",
    "y_pred_test = log_reg.predict_proba(X_test)\n",
    "metrics_score(y_test, y_pred_test[:,1]>optimal_threshold1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8axwm450vf8O"
   },
   "source": [
    "**Observations:____**\n",
    "\n",
    "Jose Medina Observations\n",
    "\n",
    "> Comparing performance on Training Data **BEFORE threshold adjustment** to performance on Training Data **AFTER threshold adjustments** we can observe the following:\n",
    "> - As we would have expected by observing the ROC curve, after adjusting the threshold to 0.75, we see that we have increased our precision from 80% (for class=1) to 84% and sacrificed recall which has dropped from 98% to 70%.\n",
    "\n",
    "> Comparing performance on Training Data **AFTER threshold adjustment** to performance on **TEST** Data **AFTER threshold adjustments** we can observe the following:\n",
    "> - we see that the model performs slightly worse in terms of precision on the TEST data with a precision of 81% (class=1) in TEST vs. 84% in Training. In contrast, recall performed a bit better in TEST (73%) vs. 70% in TRAIN. These results would give us some confidence that the model would perform at this level in production new Loan Application data.\n",
    "\n",
    "**We cannot yet say, however, if this level of performance is acceptable to the Easy House finance company. Though it appears optimal given the circumstances of the data provided to us, Easy House finance company might be interested in exploring whether additional data sets could offer opportunities to increase recall and precision - as many loans that would have been approved would not be reflected as such - in TEST set 34 (27%) out 124 Eligible Loans would have been flagged as ineligble by our model.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "t_mZdh8VDPzb"
   },
   "source": [
    "### K - Nearest Neighbors (KNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-UbHz9RuDPzc"
   },
   "source": [
    "- KNN is a distance based algorithm and all distance based algorithms are affected by the scale of the data.\n",
    "- We will scale the attributes (dataframe X defined above) before building the KNN model.\n",
    "\n",
    "**But before actually building the model, we need to identify the value of K to be used in KNN. We will perform the following steps for the same.**\n",
    "\n",
    "- For every value of K (from 1 to 20), split the train set into new train and validation set (30 times)\n",
    "- Scale the training data and validation data\n",
    "- Take the average of the error on these train and the validation sets for each K\n",
    "- Plot the average train vs the validation set error for all Ks \n",
    "- Choose a suitable K from the plot where the two errors are comparable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "id": "AGOzbhU0DPzd",
    "outputId": "862b2a67-36a2-4a1c-eeab-cc492df058d0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: [0.0, 0.27868217054263567],\n",
       " 2: [0.11671525753158404, 0.3426356589147287],\n",
       " 3: [0.15753158406219633, 0.23992248062015506],\n",
       " 4: [0.16297376093294463, 0.26046511627906976],\n",
       " 5: [0.186977648202138, 0.222093023255814],\n",
       " 6: [0.18950437317784263, 0.24418604651162787],\n",
       " 7: [0.19465500485908646, 0.21046511627906975],\n",
       " 8: [0.18969873663751216, 0.21085271317829468],\n",
       " 9: [0.19883381924198254, 0.2038759689922481],\n",
       " 10: [0.1949465500485909, 0.23604651162790702],\n",
       " 11: [0.19951409135082604, 0.21937984496124036],\n",
       " 12: [0.1992225461613217, 0.21046511627906966],\n",
       " 13: [0.2006802721088436, 0.21976744186046512],\n",
       " 14: [0.1947521865889213, 0.20658914728682173],\n",
       " 15: [0.20204081632653062, 0.20271317829457375],\n",
       " 16: [0.1936831875607386, 0.21317829457364343],\n",
       " 17: [0.2007774538386783, 0.21085271317829457],\n",
       " 18: [0.19494655004859088, 0.20930232558139542],\n",
       " 19: [0.20233236151603498, 0.20310077519379852],\n",
       " 20: [0.1994169096209913, 0.20736434108527127]}"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier()\n",
    "ss = StandardScaler()\n",
    "\n",
    "# We select the best value of k for which the error rate is the least in the validation data\n",
    "# Let us loop over a few values of the k to determine the best k\n",
    "\n",
    "train_error = []\n",
    "test_error = []\n",
    "knn_many_split = {}\n",
    "\n",
    "error_df_knn = pd.DataFrame()\n",
    "features = X.columns\n",
    "\n",
    "for k in range(1,21):\n",
    "    train_error = []\n",
    "    test_error = []\n",
    "    lista = []\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    for i in range(30):\n",
    "        x_train_new, x_val, y_train_new, y_val = train_test_split(X_train, y_train, test_size = 0.20)\n",
    "    \n",
    "        #let us scale the data\n",
    "        x_train_new = ss.fit_transform(x_train_new)  #fit_transform the training data\n",
    "        x_val = ss.transform(x_val) #transform the validation set\n",
    "        #Fitting knn on training data\n",
    "        knn.fit(x_train_new, y_train_new)\n",
    "        #Calculating error on training and validation data\n",
    "        train_error.append(1 - knn.score(x_train_new, y_train_new)) \n",
    "        test_error.append(1 - knn.score(x_val, y_val))\n",
    "    lista.append(sum(train_error)/len(train_error))\n",
    "    lista.append(sum(test_error)/len(test_error))\n",
    "    knn_many_split[k] = lista\n",
    "\n",
    "knn_many_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "id": "5eaW0nGbDPzh",
    "outputId": "860fc081-4f5c-4bde-f4a7-0708375090d6"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extracting the train and the test error for each k in a list for easy plotting\n",
    "\n",
    "kltest = []\n",
    "vltest = []\n",
    "for k, v in knn_many_split.items():\n",
    "    kltest.append(k)\n",
    "    vltest.append(knn_many_split[k][1])\n",
    "\n",
    "kltrain = []\n",
    "vltrain = []\n",
    "\n",
    "for k, v in knn_many_split.items():\n",
    "    kltrain.append(k)\n",
    "    vltrain.append(knn_many_split[k][0])\n",
    "\n",
    "# Plotting k vs error \n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(kltest,vltest, label = 'test' )\n",
    "plt.plot(kltrain,vltrain, label = 'train')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Dw7qtTmODPzj"
   },
   "source": [
    "**Observations:**\n",
    "- We can see that the train error and the test error are more or less similar for K more than or equal to 7.\n",
    "- This implies that we would get generalized results if we choose K=7.\n",
    "- So, let's scale the full data and fit the model on the entire training set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "id": "vbm0fsfIDPzk"
   },
   "outputs": [],
   "source": [
    "# scaling the data\n",
    "scaler=StandardScaler()\n",
    "X_train_scaled=pd.DataFrame(scaler.fit_transform(X_train), columns=X_train.columns)  #fit_transform the training data\n",
    "X_test_scaled=pd.DataFrame(scaler.transform(X_test), columns=X_test.columns) #transform the testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "id": "cLMS_4Z6b0lB",
    "outputId": "7755c3b6-2556-4f39-eb0b-5cf1b94966f4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=7, weights='distance')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting the model on the scaled data\n",
    "knn = KNeighborsClassifier(n_neighbors = 7, weights='distance')\n",
    "knn.fit(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EVnW7sjwDPzm"
   },
   "source": [
    "**Now, let's check the performance of the model on the training and testing data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "id": "cKgxffwiDPzn"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       131\n",
      "           1       1.00      1.00      1.00       298\n",
      "\n",
      "    accuracy                           1.00       429\n",
      "   macro avg       1.00      1.00      1.00       429\n",
      "weighted avg       1.00      1.00      1.00       429\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#predict on train data\n",
    "y_pred_train_knn =  knn.predict(X_train_scaled)# write your code here\n",
    "\n",
    "#checking performance of the model\n",
    "metrics_score(y_train, y_pred_train_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dWPmkx7SyANS"
   },
   "source": [
    "**Observations:____**\n",
    "\n",
    "Jose Medina Observations\n",
    "\n",
    "> - The model predicts extremely well with respect to the training set. We achieve 100% preciion and recall.\n",
    "> - With performance like this, we need to be leery of over-fitting so we must check the results agains the TEST data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "id": "0GYw-kr6DPzo"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.61      0.31      0.41        61\n",
      "           1       0.73      0.90      0.81       124\n",
      "\n",
      "    accuracy                           0.71       185\n",
      "   macro avg       0.67      0.61      0.61       185\n",
      "weighted avg       0.69      0.71      0.68       185\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#predict on test data\n",
    "y_pred_test_knn = knn.predict(X_test_scaled)# Write your code here\n",
    "\n",
    "#checking performance of the model\n",
    "metrics_score(y_test, y_pred_test_knn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qvhsZIbAyOhY"
   },
   "source": [
    "**Observation:____**\n",
    "\n",
    "Jose Medina Observation\n",
    "\n",
    "> **Test Data** performance indicates 73% precision, 90% recall, and 81% f1-score. This performance is worse than the results of the **Train Data** performance assessment.However we know that the TRAIN results point to overfitting.\n",
    "\n",
    "We know from our earlier analysis that the typical loan has a mean of 141K. If we simplistically assume these loans would be approved blindly as a result of the model we'd be exposing the company to potential for 5.9MM in loan loss/risk (42 * 5.9MM). Additionally, we would be disqualifying 12 applications that would have been qualified. This is not only an opportunity cost ( we wold not be collecting interest on these loans), but also a risk for litigtation under fair lending laws and practices. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Conclusion:"
   ]
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   "source": [
    "Jose Medina Conclusions\n",
    "\n",
    "We have tried multiple models and were able to identify the key factors affecting loan eligibility (Loan_Status: Loan approved (1 - Yes, 0 - No). \n",
    "\n",
    "We noted that top factors increasing the odds of loan elegibility includ\n",
    "> - the required Credit_History (=1) is 21.3 times more likely to be approved for a loan than those are do NOT have the required credit history, per guidelines.\n",
    "> - an application for a Semiurban property area is 2.07 times more likely to be approved for a loan than another property area type\n",
    "> - Married_Yes (married inviduals) are 1.38 times more likely to get approved for a loan than those with other marital status\n",
    "> - Dependents_2 (applicants with 2 dependants) are 1.22 times more likely to be approved for a loan than other dependent levels\n",
    "\n",
    "Our final model, a K - Nearest Neighbors (KNN), achieves a precision of 73%, recall of 90%, and F1-Score of 81% for class=1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2BkZh6eHluZK"
   },
   "source": [
    "## Recommendations:"
   ]
  },
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   "source": [
    "\n",
    "**The final model selection depends on a discussion with Easy House Finance Company.** However, if we assume that it is vitally important to them to be sensitive - and not let any potential loan slip away, they should use the model with highest recall, balancing precision (i.e. our model from Question 9 - K - Nearest Neighbors).\n",
    "\n",
    "In loan application screening, it is vitally important to identify all elegible loans. However, as with any model, that will result in some loans that should have been approved to be denied, and some that should not have been approved to be approved.\n",
    "\n",
    "I would recommend that this model be implemented with a few potential additions:\n",
    "- Explore implementing an ensembling approach:\n",
    "  -- implement the KNN\n",
    "  -- then for both approved and declined loans that are near the threshold, further qualify these applicants with a model that uses augmented data sets to further qualify those loans (for example, ask the applicant for more detail on their finances)\n",
    "- a \"human-in-the-loop\" to validate a random sample of denied and/or approved applicants. This would stil require far less manual effort than they were originally putting forth without our KNN model. This would serve a dual purpose of helping us create more feedback and training data for future model fine-tuning.\n",
    "\n",
    "  \n"
   ]
  }
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