loan_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LOAN PREDICTION: Decision Tree, SVM and  Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import seaborn as sns  \n",
    "import matplotlib.pyplot as plt  \n",
    "from sklearn.model_selection import train_test_split  \n",
    "from sklearn import tree  \n",
    "from sklearn.tree import plot_tree  \n",
    "from sklearn.metrics import accuracy_score \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PART 1 - Data Handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing data with pandas\n",
    "data = pd.read_csv(\"loan_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>NaN</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>LP002978</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>LP002979</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3+</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>LP002983</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>LP002984</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>LP002990</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Semiurban</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>614 rows × 13 columns</p>\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",
       "609  LP002978  Female      No          0      Graduate            No   \n",
       "610  LP002979    Male     Yes         3+      Graduate            No   \n",
       "611  LP002983    Male     Yes          1      Graduate            No   \n",
       "612  LP002984    Male     Yes          2      Graduate            No   \n",
       "613  LP002990  Female      No          0      Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5849                0.0         NaN             360.0   \n",
       "1               4583             1508.0       128.0             360.0   \n",
       "2               3000                0.0        66.0             360.0   \n",
       "3               2583             2358.0       120.0             360.0   \n",
       "4               6000                0.0       141.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "609             2900                0.0        71.0             360.0   \n",
       "610             4106                0.0        40.0             180.0   \n",
       "611             8072              240.0       253.0             360.0   \n",
       "612             7583                0.0       187.0             360.0   \n",
       "613             4583                0.0       133.0             360.0   \n",
       "\n",
       "     Credit_History Property_Area Loan_Status  \n",
       "0               1.0         Urban           Y  \n",
       "1               1.0         Rural           N  \n",
       "2               1.0         Urban           Y  \n",
       "3               1.0         Urban           Y  \n",
       "4               1.0         Urban           Y  \n",
       "..              ...           ...         ...  \n",
       "609             1.0         Rural           Y  \n",
       "610             1.0         Rural           Y  \n",
       "611             1.0         Urban           Y  \n",
       "612             1.0         Urban           Y  \n",
       "613             0.0     Semiurban           N  \n",
       "\n",
       "[614 rows x 13 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Showing an overview of our data\n",
    "data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 13)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The shape property returns a tuple representing the dimensionality of the DataFrame. \n",
    "# The format of shape is (rows, columns).\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>ApplicantIncome</th>\n",
       "      <th>CoapplicantIncome</th>\n",
       "      <th>LoanAmount</th>\n",
       "      <th>Loan_Amount_Term</th>\n",
       "      <th>Credit_History</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>614.000000</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>592.000000</td>\n",
       "      <td>600.00000</td>\n",
       "      <td>564.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5403.459283</td>\n",
       "      <td>1621.245798</td>\n",
       "      <td>146.412162</td>\n",
       "      <td>342.00000</td>\n",
       "      <td>0.842199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6109.041673</td>\n",
       "      <td>2926.248369</td>\n",
       "      <td>85.587325</td>\n",
       "      <td>65.12041</td>\n",
       "      <td>0.364878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2877.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3812.500000</td>\n",
       "      <td>1188.500000</td>\n",
       "      <td>128.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5795.000000</td>\n",
       "      <td>2297.250000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>360.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>81000.000000</td>\n",
       "      <td>41667.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>480.00000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "count       614.000000         614.000000  592.000000         600.00000   \n",
       "mean       5403.459283        1621.245798  146.412162         342.00000   \n",
       "std        6109.041673        2926.248369   85.587325          65.12041   \n",
       "min         150.000000           0.000000    9.000000          12.00000   \n",
       "25%        2877.500000           0.000000  100.000000         360.00000   \n",
       "50%        3812.500000        1188.500000  128.000000         360.00000   \n",
       "75%        5795.000000        2297.250000  168.000000         360.00000   \n",
       "max       81000.000000       41667.000000  700.000000         480.00000   \n",
       "\n",
       "       Credit_History  \n",
       "count      564.000000  \n",
       "mean         0.842199  \n",
       "std          0.364878  \n",
       "min          0.000000  \n",
       "25%          1.000000  \n",
       "50%          1.000000  \n",
       "75%          1.000000  \n",
       "max          1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas describe() is used to view some basic statistical details like percentile, mean, std etc. of a data frame or a series of numeric values. W\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID               0\n",
       "Gender               13\n",
       "Married               3\n",
       "Dependents           15\n",
       "Education             0\n",
       "Self_Employed        32\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount           22\n",
       "Loan_Amount_Term     14\n",
       "Credit_History       50\n",
       "Property_Area         0\n",
       "Loan_Status           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Taking care of missing values\n",
    "# Within pandas, a null value is considered missing and is denoted by NaN.\n",
    "# First, I need to find out the number of missing values in each column.\n",
    "# To see how many missing values exist in the collection I use .sum() chained on the function isnull(). \n",
    "data.isnull().sum()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The dropna() function removes the empty fields from the data\n",
    "data = data.dropna()  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Loan_ID              0\n",
       "Gender               0\n",
       "Married              0\n",
       "Dependents           0\n",
       "Education            0\n",
       "Self_Employed        0\n",
       "ApplicantIncome      0\n",
       "CoapplicantIncome    0\n",
       "LoanAmount           0\n",
       "Loan_Amount_Term     0\n",
       "Credit_History       0\n",
       "Property_Area        0\n",
       "Loan_Status          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking whether any value is still missing\n",
    "data.isnull().sum()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\alice\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexing.py:1743: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(ilocs[0], value)\n"
     ]
    }
   ],
   "source": [
    "# With the replace method all Y values will be replaced with boolean values\n",
    "# With the implance method the table will be changed here\n",
    "data.replace({\"Loan_Status\":{\"N\":0,\"Y\":1}},inplace=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "      <td>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>66.0</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>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>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>141.0</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>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>267.0</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",
       "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",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "5             5417             4196.0       267.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  Loan_Status  \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  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking te table again\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     274\n",
       "2      85\n",
       "1      80\n",
       "3+     41\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking how many lines contains dependents  \n",
    "data[\"Dependents\"].value_counts()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We cannot have \"+\", so we fix this issue replacing \"3+\". I will call it \" \"4\" \n",
    "data = data.replace(to_replace=\"3+\",value=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    274\n",
       "2     85\n",
       "1     80\n",
       "4     41\n",
       "Name: Dependents, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, I should count again\n",
    "data[\"Dependents\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Education', ylabel='count'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploring Data through Visualizations with Seaborn\n",
    "# Verifying if the Y is related to the level of education\n",
    "sns.countplot(x=\"Education\",hue=\"Loan_Status\", data=data)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Married', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    " # Checking if the y is related to marital status\n",
    "sns.countplot(x=\"Married\",hue=\"Loan_Status\", data=data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Dependents', ylabel='count'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Dependents\",hue=\"Loan_Status\",data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Semiurban    191\n",
       "Urban        150\n",
       "Rural        139\n",
       "Name: Property_Area, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"Property_Area\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=data.replace({\"Married\":{\"No\":0,\"Yes\":1},\n",
    "                  \"Gender\":{\"Female\":0,\"Male\":1},\n",
    "                   \"Self_Employed\":{\"No\":0,\"Yes\":1},\n",
    "                   \"Property_Area\":{\"Rural\":0,\"Semiurban\":1,\"Urban\":2},\n",
    "                   \"Education\":{\"Not Graduate\":0,\"Graduate\":1}\n",
    "                  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>1</th>\n",
       "      <td>LP001003</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001006</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001008</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LP001011</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID  Gender  Married Dependents  Education  Self_Employed  \\\n",
       "1  LP001003       1        1          1          1              0   \n",
       "2  LP001005       1        1          0          1              1   \n",
       "3  LP001006       1        1          0          0              0   \n",
       "4  LP001008       1        0          0          1              0   \n",
       "5  LP001011       1        1          2          1              1   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "1             4583             1508.0       128.0             360.0   \n",
       "2             3000                0.0        66.0             360.0   \n",
       "3             2583             2358.0       120.0             360.0   \n",
       "4             6000                0.0       141.0             360.0   \n",
       "5             5417             4196.0       267.0             360.0   \n",
       "\n",
       "   Credit_History  Property_Area  Loan_Status  \n",
       "1             1.0              0            0  \n",
       "2             1.0              2            1  \n",
       "3             1.0              2            1  \n",
       "4             1.0              2            1  \n",
       "5             1.0              2            1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking how it looks \n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PART 2 - Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gender</th>\n",
       "      <th>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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4583</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2583</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5417</td>\n",
       "      <td>4196.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4106</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8072</td>\n",
       "      <td>240.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>612</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4583</td>\n",
       "      <td>0.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>480 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       "1         1        1          1          1              0             4583   \n",
       "2         1        1          0          1              1             3000   \n",
       "3         1        1          0          0              0             2583   \n",
       "4         1        0          0          1              0             6000   \n",
       "5         1        1          2          1              1             5417   \n",
       "..      ...      ...        ...        ...            ...              ...   \n",
       "609       0        0          0          1              0             2900   \n",
       "610       1        1          4          1              0             4106   \n",
       "611       1        1          1          1              0             8072   \n",
       "612       1        1          2          1              0             7583   \n",
       "613       0        0          0          1              1             4583   \n",
       "\n",
       "     CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       "1               1508.0       128.0             360.0             1.0   \n",
       "2                  0.0        66.0             360.0             1.0   \n",
       "3               2358.0       120.0             360.0             1.0   \n",
       "4                  0.0       141.0             360.0             1.0   \n",
       "5               4196.0       267.0             360.0             1.0   \n",
       "..                 ...         ...               ...             ...   \n",
       "609                0.0        71.0             360.0             1.0   \n",
       "610                0.0        40.0             180.0             1.0   \n",
       "611              240.0       253.0             360.0             1.0   \n",
       "612                0.0       187.0             360.0             1.0   \n",
       "613                0.0       133.0             360.0             0.0   \n",
       "\n",
       "     Property_Area  \n",
       "1                0  \n",
       "2                2  \n",
       "3                2  \n",
       "4                2  \n",
       "5                2  \n",
       "..             ...  \n",
       "609              0  \n",
       "610              0  \n",
       "611              2  \n",
       "612              2  \n",
       "613              1  \n",
       "\n",
       "[480 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Defining X by removing Loan_ID and Loan_Status (which is Y)\n",
    "X  = data.drop(columns=[\"Loan_ID\",\"Loan_Status\"],axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      0\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "5      1\n",
       "      ..\n",
       "609    1\n",
       "610    1\n",
       "611    1\n",
       "612    1\n",
       "613    0\n",
       "Name: Loan_Status, Length: 480, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Y will be defined as Loan_Status\n",
    "Y = data[\"Loan_Status\"]\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[     Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       " 426       0        0          1          0              0             4606   \n",
       " 15        1        0          0          1              0             4950   \n",
       " 607       1        1          2          0              0             3987   \n",
       " 167       1        1          0          1              0             2439   \n",
       " 453       1        1          0          1              0             2483   \n",
       " ..      ...      ...        ...        ...            ...              ...   \n",
       " 390       1        0          4          1              0             9167   \n",
       " 152       1        0          0          1              0             9166   \n",
       " 406       1        1          0          0              0             2167   \n",
       " 598       1        1          0          1              1             9963   \n",
       " 354       0        1          0          1              0             2423   \n",
       " \n",
       "      CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       " 426                0.0        81.0             360.0             1.0   \n",
       " 15                 0.0       125.0             360.0             1.0   \n",
       " 607             1411.0       157.0             360.0             1.0   \n",
       " 167             3333.0       129.0             360.0             1.0   \n",
       " 453             2466.0        90.0             180.0             0.0   \n",
       " ..                 ...         ...               ...             ...   \n",
       " 390                0.0       185.0             360.0             1.0   \n",
       " 152                0.0       244.0             360.0             1.0   \n",
       " 406             2400.0       115.0             360.0             1.0   \n",
       " 598                0.0       180.0             360.0             1.0   \n",
       " 354              505.0       130.0             360.0             1.0   \n",
       " \n",
       "      Property_Area  \n",
       " 426              0  \n",
       " 15               2  \n",
       " 607              0  \n",
       " 167              0  \n",
       " 453              0  \n",
       " ..             ...  \n",
       " 390              0  \n",
       " 152              2  \n",
       " 406              2  \n",
       " 598              0  \n",
       " 354              1  \n",
       " \n",
       " [384 rows x 11 columns],\n",
       "      Gender  Married Dependents  Education  Self_Employed  ApplicantIncome  \\\n",
       " 192       1        1          0          0              0             6033   \n",
       " 547       1        0          0          1              0             2526   \n",
       " 487       1        1          1          1              0            18333   \n",
       " 321       1        1          4          1              0             4342   \n",
       " 245       1        0          0          1              1             6050   \n",
       " ..      ...      ...        ...        ...            ...              ...   \n",
       " 337       1        1          2          1              1             2500   \n",
       " 199       1        0          0          1              1            11000   \n",
       " 324       1        1          4          1              0            15000   \n",
       " 47        1        1          0          1              0             5821   \n",
       " 249       1        1          0          1              0             1809   \n",
       " \n",
       "      CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
       " 192                0.0       160.0             360.0             1.0   \n",
       " 547             1783.0       145.0             360.0             1.0   \n",
       " 487                0.0       500.0             360.0             1.0   \n",
       " 321              189.0       124.0             360.0             1.0   \n",
       " 245             4333.0       120.0             180.0             1.0   \n",
       " ..                 ...         ...               ...             ...   \n",
       " 337             4600.0       176.0             360.0             1.0   \n",
       " 199                0.0        83.0             360.0             1.0   \n",
       " 324                0.0       300.0             360.0             1.0   \n",
       " 47                 0.0       144.0             360.0             1.0   \n",
       " 249             1868.0        90.0             360.0             1.0   \n",
       " \n",
       "      Property_Area  \n",
       " 192              2  \n",
       " 547              0  \n",
       " 487              2  \n",
       " 321              1  \n",
       " 245              2  \n",
       " ..             ...  \n",
       " 337              0  \n",
       " 199              2  \n",
       " 324              0  \n",
       " 47               2  \n",
       " 249              2  \n",
       " \n",
       " [96 rows x 11 columns],\n",
       " 426    0\n",
       " 15     1\n",
       " 607    1\n",
       " 167    1\n",
       " 453    1\n",
       "       ..\n",
       " 390    1\n",
       " 152    0\n",
       " 406    1\n",
       " 598    1\n",
       " 354    1\n",
       " Name: Loan_Status, Length: 384, dtype: int64,\n",
       " 192    0\n",
       " 547    1\n",
       " 487    0\n",
       " 321    1\n",
       " 245    0\n",
       "       ..\n",
       " 337    1\n",
       " 199    0\n",
       " 324    1\n",
       " 47     1\n",
       " 249    1\n",
       " Name: Loan_Status, Length: 96, dtype: int64]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " # Separating data into training and testing sets\n",
    "train_test_split(X,Y,test_size=0.2) # wih the third parameter being the test_size, it automatically recognizes the training as 80%\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We have the data above in the following order: x training / x test / y training / y teste\n",
    "# Now I store this in 4 variables:\n",
    "X_train,X_test,Y_train,Y_real=train_test_split(X,Y,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(384, 11)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the size of X training and x test\n",
    "X_train.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(96, 11)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating the model  \n",
    "model = tree.DecisionTreeClassifier(max_depth=3) \n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=3)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Passing our training set to the fit method \n",
    "# In fit we fit\\train the weights using the training data. Note: Before you can make predictions, you must train a final model.\n",
    "model.fit(X_train,Y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making a prediction to test the training \n",
    "model.predict(X_test) # It receives X as a parameter to predict Y  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Storing the result in a variable \n",
    "Y_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84375"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking model accuracy\n",
    "accuracy_score(Y_pred,Y_real)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed',\n",
       "       'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
       "       'Loan_Amount_Term', 'Credit_History', 'Property_Area'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the columns names\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(1534.5, 1902.6000000000001, 'Credit_History <= 0.5\\ngini = 0.428\\nsamples = 384\\nvalue = [119, 265]\\nclass = Accepted'),\n",
       " Text(837.0, 1359.0, 'LoanAmount <= 547.5\\ngini = 0.174\\nsamples = 52\\nvalue = [47, 5]\\nclass = Denied'),\n",
       " Text(558.0, 815.4000000000001, 'LoanAmount <= 136.5\\ngini = 0.145\\nsamples = 51\\nvalue = [47, 4]\\nclass = Denied'),\n",
       " Text(279.0, 271.79999999999995, 'gini = 0.0\\nsamples = 32\\nvalue = [32, 0]\\nclass = Denied'),\n",
       " Text(837.0, 271.79999999999995, 'gini = 0.332\\nsamples = 19\\nvalue = [15, 4]\\nclass = Denied'),\n",
       " Text(1116.0, 815.4000000000001, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]\\nclass = Accepted'),\n",
       " Text(2232.0, 1359.0, 'LoanAmount <= 159.0\\ngini = 0.34\\nsamples = 332\\nvalue = [72, 260]\\nclass = Accepted'),\n",
       " Text(1674.0, 815.4000000000001, 'Loan_Amount_Term <= 48.0\\ngini = 0.284\\nsamples = 228\\nvalue = [39, 189]\\nclass = Accepted'),\n",
       " Text(1395.0, 271.79999999999995, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]\\nclass = Denied'),\n",
       " Text(1953.0, 271.79999999999995, 'gini = 0.274\\nsamples = 226\\nvalue = [37, 189]\\nclass = Accepted'),\n",
       " Text(2790.0, 815.4000000000001, 'ApplicantIncome <= 17916.5\\ngini = 0.433\\nsamples = 104\\nvalue = [33, 71]\\nclass = Accepted'),\n",
       " Text(2511.0, 271.79999999999995, 'gini = 0.418\\nsamples = 101\\nvalue = [30, 71]\\nclass = Accepted'),\n",
       " Text(3069.0, 271.79999999999995, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\nclass = Denied')]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 4320x2880 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating the tree with the plot tree function\n",
    "# Use the figsize of plt.figure to control the size of the rendering \n",
    "plt.figure(figsize=(60,40))\n",
    "plot_tree(model,\n",
    "         feature_names = ['Gender', 'Married', 'Dependents', 'Education', 'Self_Employed',\n",
    "       'ApplicantIncome', 'CoapplicantIncome', 'LoanAmount',\n",
    "       'Loan_Amount_Term', 'Credit_History', 'Property_Area'],\n",
    "          class_names = [\"Denied\",\"Accepted\"], filled=True, rounded=True, fontsize=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Suport Vector Machine (SVC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import svm \n",
    "model_SVC = svm.SVC(kernel=\"linear\") # defining the method. I tested poli and rff but linear had the best accuracy (poli = 0,6875 /rff = 0,6875 )\n",
    "model_SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(kernel='linear')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_SVC.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Testing\n",
    "Y_pred = model_SVC.predict(X_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78125"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Comparing the predicted y with the real y find the accuracy\n",
    "accuracy_score(Y_pred,Y_real)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ and ‘saga’ are faster for large ones.\n",
    "log_reg = LogisticRegression(solver = 'liblinear')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#training the model \n",
    "log_reg.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#salvando o teste do modelo numa variável \n",
    "y_pred = log_reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred #note the large amount of zeros. Means that the models is predicting more situations where the result is False. Note q por isso o modelo ficará mais biased para rejeitar do que aceitar  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.84375"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " \n",
    "# Accuracy = true negatives + true positives / true positives + false positives + true negatives + false negatives\n",
    "acc = accuracy_score(Y_real,y_pred)  \n",
    "acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PART 3 - Valuation Analysis: using kfold, a model validation technique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance Measurement (accuracy score) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# I have found the accuracy score for the Decision Tree model (line 130) and SVM model (line 136). \n",
    "# Here are the results: \n",
    "\n",
    "# -> Decision Tree model  - accuracy score:  0.84375\n",
    "# -> SVM model - accuracy score:  0.78125\n",
    "# -> Logistic Regression model - accuracy score: 0.84375"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kfold -> Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now using kfold, a model validation technique where it's not using your pre-trained model\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "kf = KFold(n_splits=5,shuffle=True)  \n",
    "cv_r = cross_val_score(model, X, Y, cv=kf)\n",
    "np.mean(cv_r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kfold -> Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf_svm = KFold(n_splits=5,shuffle=True)  \n",
    "# first parameter = Number of folds. Must be at least 2.\n",
    "# second parameter = Whether to shuffle the data before splitting into batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.73958333, 0.8125    , 0.78125   , 0.79166667, 0.84375   ])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Evaluating a score by cross-validation  \n",
    "cv_svm = cross_val_score(model_SVC, X, Y, cv=kf_svm)  \n",
    "# first parameter = The object to use to fit the data.\n",
    "# second parameter = X\n",
    "# second parameter = Y\n",
    "# third parameter = cross validation\n",
    "# NOTE: it took the empty model, trained it, tested it and compared the test result (predicted y) with the real y.\n",
    "# after making this comparison it returns the accuracy of each fold (5)\n",
    "cv_svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.79375"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean accuracy \n",
    "np.mean(cv_svm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kfold -> Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "kf_lg = KFold(n_splits=5,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_lg = cross_val_score(model_SVC, X, Y, cv=kf_lg)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7916666666666666"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean accuracy \n",
    "np.mean(cv_lg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}