SVM_Py.py

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71320d9a",
   "metadata": {},
   "source": [
    "# <font color= black>Support Vector Machine (SVM)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf49eb3",
   "metadata": {},
   "source": [
    "**<font color = blue>1. When to use SVM?</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f470e1",
   "metadata": {},
   "source": [
    "The major limitation of K-NN (K nearest neighbour) is that it is difficult to categorize data when it is dense. \n",
    "For instance, if the co-ordinates are closely spaced to each other, the machine finds it very difficult to categroize it\n",
    "into one class.\n",
    "This limitation of K-NN is then overcomed using SVM (i.e. Space Vector Machine) algorithm.\n",
    "SVM is used for both classification & regression problems.\n",
    "Primarily, SVM is used for binary & multi-class Classification.\n",
    "\n",
    "<font color = red>**SVM is suited when we have more number of columns than the number of rows.**</font>\n",
    "Such a data is called high-dimensional data. It is the data where number of variables are high as compared to the number\n",
    "of observations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c7ca110",
   "metadata": {},
   "source": [
    "<font color = blue>**2. Linearly and Non-linearly separable data**</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63891ce2",
   "metadata": {},
   "source": [
    "Linearly separable data is the data where you can partition the data into classes simply by putting a line between them.\n",
    "\n",
    "However, there are scenarios where one cannot separate the data by simply putting a linear line. Part of the reason might\n",
    "be due to it's non-linear nature, such data is called as Non-linearly separable data.\n",
    "One example of a Non-linearly separable data is data in concentric patches.\n",
    "\n",
    "SVM works on both Linearly & Non-linearly separable data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1de6ca8e",
   "metadata": {},
   "source": [
    "**<font color = blue>3. What does one mean by Support Vector?</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4521ad1d",
   "metadata": {},
   "source": [
    "**<font color = red>The datapoint of a class which is at the extreme ends of the class as well as the one which is closest to another class \n",
    "is called as the Support Vector of that class.</font>**\n",
    "Once we find the Support vectors we draw a hyperplane passing through both these co-ordinates and then draw a margin throughthe centre of this hyperplane, which we define as maximum margin.\n",
    "\n",
    "So in the case of a densely populated data, even if the data points are closely packed we could easily differentiate with\n",
    "the help of this maximum margin. This makes the classification a lot simpler.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f37af80f",
   "metadata": {},
   "source": [
    "<img src = \"SVM_image.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac1b09f7",
   "metadata": {},
   "source": [
    "If we look at the above figure,it tells us clearly how support vectors can be used to create hyperplane & maximum\n",
    "margin which can further classify the data better.\n",
    "The dimensions of the hyperplane depends upon the features present in the dataset, which means if there are 2 features,\n",
    "then the hyperplane will be a straight line, if there are 3 features then the hyperplane will be a 2 dimensional plane.\n",
    "\n",
    "We always create a hyperplane that has maximum margin, which means maximum distance between the data points."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cc3f38e",
   "metadata": {},
   "source": [
    "**<font color = blue>4. Hyperparameter</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f655725",
   "metadata": {},
   "source": [
    "In Machine Learning it is difficult to tune the algorithm based on different datasets. To make it impossible, we tune\n",
    "the model using a hyperparameter, which is basically a user defined variable which can be used to tune the model as per\n",
    "the different datasets, so that we achieve the desired accuracy for that model.\n",
    "\n",
    "In SVM, we have 3 hyperparameters alpha, beta & gamma."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fff74578",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86248157",
   "metadata": {},
   "source": [
    "# Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4af978c6",
   "metadata": {},
   "source": [
    "### **<font color = blue>1. Importing the Libraries</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "618bc7d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92310d76",
   "metadata": {},
   "source": [
    "### **<font color = blue>2. Reading the training & testing data</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a92931d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = pd.read_csv(r'risk_analytics_train.csv',index_col=0,header=0)\n",
    "test_data = pd.read_csv(r'risk_analytics_test.csv',index_col=0,header=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e14539e1",
   "metadata": {},
   "source": [
    "### **<font color = blue>3. Preprocessing</font>**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14d3b0c8",
   "metadata": {},
   "source": [
    "#### **<font color = green>3.1 Checking the dimensions</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "60b29221",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(614, 12)\n",
      "(367, 11)\n"
     ]
    }
   ],
   "source": [
    "print(train_data.shape)\n",
    "print(test_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "b080ab9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001002</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</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>LP001003</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</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>LP001005</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</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>LP001006</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</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>LP001008</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>6000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Gender Married  Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001002   Male      No         0.0      Graduate            No   \n",
       "LP001003   Male     Yes         1.0      Graduate            No   \n",
       "LP001005   Male     Yes         0.0      Graduate           Yes   \n",
       "LP001006   Male     Yes         0.0  Not Graduate            No   \n",
       "LP001008   Male      No         0.0      Graduate            No   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001002             5849                0.0         NaN             360.0   \n",
       "LP001003             4583             1508.0       128.0             360.0   \n",
       "LP001005             3000                0.0        66.0             360.0   \n",
       "LP001006             2583             2358.0       120.0             360.0   \n",
       "LP001008             6000                0.0       141.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area Loan_Status  \n",
       "Loan_ID                                             \n",
       "LP001002             1.0         Urban           Y  \n",
       "LP001003             1.0         Rural           N  \n",
       "LP001005             1.0         Urban           Y  \n",
       "LP001006             1.0         Urban           Y  \n",
       "LP001008             1.0         Urban           Y  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Preview the train & test data\n",
    "\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4e7aaaa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<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",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001015</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001022</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001031</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001035</th>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001051</th>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Gender Married  Dependents     Education Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001015   Male     Yes         0.0      Graduate            No   \n",
       "LP001022   Male     Yes         1.0      Graduate            No   \n",
       "LP001031   Male     Yes         2.0      Graduate            No   \n",
       "LP001035   Male     Yes         2.0      Graduate            No   \n",
       "LP001051   Male      No         0.0  Not Graduate            No   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001015             5720                  0       110.0             360.0   \n",
       "LP001022             3076               1500       126.0             360.0   \n",
       "LP001031             5000               1800       208.0             360.0   \n",
       "LP001035             2340               2546       100.0             360.0   \n",
       "LP001051             3276                  0        78.0             360.0   \n",
       "\n",
       "          Credit_History Property_Area  \n",
       "Loan_ID                                 \n",
       "LP001015             1.0         Urban  \n",
       "LP001022             1.0         Urban  \n",
       "LP001031             1.0         Urban  \n",
       "LP001035             NaN         Urban  \n",
       "LP001051             1.0         Urban  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82c774c7",
   "metadata": {},
   "source": [
    "#### **<font color = green>3.2 Null value & special characters treatment</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "73000468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for null values\n",
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "4104fcfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender               11\n",
       "Married               0\n",
       "Dependents           10\n",
       "Education             0\n",
       "Self_Employed        23\n",
       "ApplicantIncome       0\n",
       "CoapplicantIncome     0\n",
       "LoanAmount            5\n",
       "Loan_Amount_Term      6\n",
       "Credit_History       29\n",
       "Property_Area         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "838b2de4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gender                0\n",
      "Married               0\n",
      "Dependents            0\n",
      "Education             0\n",
      "Self_Employed         0\n",
      "ApplicantIncome       0\n",
      "CoapplicantIncome     0\n",
      "LoanAmount           22\n",
      "Loan_Amount_Term      0\n",
      "Credit_History       50\n",
      "Property_Area         0\n",
      "Loan_Status           0\n",
      "dtype: int64\n",
      "Gender                0\n",
      "Married               0\n",
      "Dependents            0\n",
      "Education             0\n",
      "Self_Employed         0\n",
      "ApplicantIncome       0\n",
      "CoapplicantIncome     0\n",
      "LoanAmount            5\n",
      "Loan_Amount_Term      0\n",
      "Credit_History       29\n",
      "Property_Area         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Treating the null values\n",
    "# Inputing Categorical missing data with \"mode\" value\n",
    "\n",
    "colname1 = [\"Gender\",\"Married\",\"Dependents\",\"Self_Employed\",\"Loan_Amount_Term\"]\n",
    "\n",
    "for x in colname1:\n",
    "    train_data[x].fillna(train_data[x].mode()[0],inplace=True)\n",
    "    test_data[x].fillna(test_data[x].mode()[0],inplace=True)\n",
    "    \n",
    "print(train_data.isnull().sum())\n",
    "print(test_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5286092a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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       50\n",
      "Property_Area         0\n",
      "Loan_Status           0\n",
      "dtype: int64\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       29\n",
      "Property_Area         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Inputing the numeric values with \"mean\" value\n",
    "\n",
    "train_data[\"LoanAmount\"].fillna(round(train_data[\"LoanAmount\"].mean(),0),inplace = True)\n",
    "test_data[\"LoanAmount\"].fillna(round(test_data[\"LoanAmount\"].mean(),0),inplace = True)\n",
    "\n",
    "print(train_data.isnull().sum())\n",
    "print(test_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a0c0084f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\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",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# So from above we got to know that we are still yet to replace the null values from credit history\n",
    "\n",
    "train_data[\"Credit_History\"].fillna(value=0,inplace = True)\n",
    "test_data[\"Credit_History\"].fillna(value=0,inplace = True)\n",
    "\n",
    "print(train_data.isnull().sum())\n",
    "print(test_data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab10b96b",
   "metadata": {},
   "source": [
    "#### **<font color = green>3.3 Label Encoding</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2503a1d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting all the categorical data into numerical for train_data\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "colname = [\"Gender\",\"Married\",\"Education\",\"Self_Employed\",\"Dependents\",\"Self_Employed\",\"Property_Area\",\"Loan_Status\"]\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "for x in colname:\n",
    "    train_data[x]=le.fit_transform(train_data[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8858dee9",
   "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",
       "      <th>Loan_Status</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001002</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5849</td>\n",
       "      <td>0.0</td>\n",
       "      <td>146.0</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>LP001003</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</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>LP001005</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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>LP001006</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</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>LP001008</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001002       1        0           0          0              0   \n",
       "LP001003       1        1           1          0              0   \n",
       "LP001005       1        1           0          0              1   \n",
       "LP001006       1        1           0          1              0   \n",
       "LP001008       1        0           0          0              0   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001002             5849                0.0       146.0             360.0   \n",
       "LP001003             4583             1508.0       128.0             360.0   \n",
       "LP001005             3000                0.0        66.0             360.0   \n",
       "LP001006             2583             2358.0       120.0             360.0   \n",
       "LP001008             6000                0.0       141.0             360.0   \n",
       "\n",
       "          Credit_History  Property_Area  Loan_Status  \n",
       "Loan_ID                                               \n",
       "LP001002             1.0              2            1  \n",
       "LP001003             1.0              0            0  \n",
       "LP001005             1.0              2            1  \n",
       "LP001006             1.0              2            1  \n",
       "LP001008             1.0              2            1  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "af9073f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting all the categorical data into numerical for test_data\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "colname = [\"Gender\",\"Married\",\"Education\",\"Self_Employed\",\"Property_Area\"]\n",
    "\n",
    "le = LabelEncoder()\n",
    "\n",
    "for x in colname:\n",
    "    test_data[x]=le.fit_transform(test_data[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3dd8506b",
   "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",
       "    <tr>\n",
       "      <th>Loan_ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LP001015</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001022</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001031</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001035</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LP001051</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Gender  Married  Dependents  Education  Self_Employed  \\\n",
       "Loan_ID                                                           \n",
       "LP001015       1        1         0.0          0              0   \n",
       "LP001022       1        1         1.0          0              0   \n",
       "LP001031       1        1         2.0          0              0   \n",
       "LP001035       1        1         2.0          0              0   \n",
       "LP001051       1        0         0.0          1              0   \n",
       "\n",
       "          ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "Loan_ID                                                                      \n",
       "LP001015             5720                  0       110.0             360.0   \n",
       "LP001022             3076               1500       126.0             360.0   \n",
       "LP001031             5000               1800       208.0             360.0   \n",
       "LP001035             2340               2546       100.0             360.0   \n",
       "LP001051             3276                  0        78.0             360.0   \n",
       "\n",
       "          Credit_History  Property_Area  \n",
       "Loan_ID                                  \n",
       "LP001015             1.0              2  \n",
       "LP001022             1.0              2  \n",
       "LP001031             1.0              2  \n",
       "LP001035             0.0              2  \n",
       "LP001051             1.0              2  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a3ab19",
   "metadata": {},
   "source": [
    "#### **<font color = green>3.4 Checking Correlation</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "5ceaf1a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x3000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20,30))\n",
    "sns.heatmap(train_data.corr(),vmin=-1.0,vmax=1.0,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "457dc9b3",
   "metadata": {},
   "source": [
    "### **<font color = blue>4. Splitting Training & Testing data</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "f2f67f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"One thing we have to remember here is that the train & test data are already split and given to us.\n",
    "    So here instead of splitting in X & Y and then applying train test split, we are directly defining our \n",
    "    X_train & Y_train.\n",
    "    In the code below astype(int) will retuen the integer value of the last column of train data\n",
    "    (since Y_train = train_data.values[:,-1] )\"\"\"\n",
    "\n",
    "X_train = train_data.values[:,0:-1]\n",
    "Y_train = train_data.values[:,-1]\n",
    "Y_train = Y_train.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "3b41af5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 11)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "e593069b",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"We are NOT splitting the test data here since we do not have any dependent column y present.\n",
    "    Here we are keeping the test data as it is\"\"\"\n",
    "\n",
    "X_test = test_data.values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6df743dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(367, 11)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7a0dfc3",
   "metadata": {},
   "source": [
    "### **<font color = blue>5. Scaling the data</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e3f90a96",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(X_train)\n",
    "X_train = scaler.transform(X_train)\n",
    "X_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "05744265",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.47234264 -1.37208932 -0.73780632 ...  0.2732313   0.54095432\n",
      "   1.22329839]\n",
      " [ 0.47234264  0.72881553  0.25346957 ...  0.2732313   0.54095432\n",
      "  -1.31851281]\n",
      " [ 0.47234264  0.72881553 -0.73780632 ...  0.2732313   0.54095432\n",
      "   1.22329839]\n",
      " ...\n",
      " [ 0.47234264  0.72881553  0.25346957 ...  0.2732313   0.54095432\n",
      "   1.22329839]\n",
      " [ 0.47234264  0.72881553  1.24474546 ...  0.2732313   0.54095432\n",
      "   1.22329839]\n",
      " [-2.11710719 -1.37208932 -0.73780632 ...  0.2732313  -1.84858491\n",
      "  -0.04760721]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "6890c7c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.47234264  0.72881553 -0.73780632 ...  0.2732313   0.54095432\n",
      "   1.22329839]\n",
      " [ 0.47234264  0.72881553  0.25346957 ...  0.2732313   0.54095432\n",
      "   1.22329839]\n",
      " [ 0.47234264  0.72881553  1.24474546 ...  0.2732313   0.54095432\n",
      "   1.22329839]\n",
      " ...\n",
      " [ 0.47234264 -1.37208932 -0.73780632 ...  0.2732313  -1.84858491\n",
      "  -0.04760721]\n",
      " [ 0.47234264  0.72881553 -0.73780632 ...  0.2732313   0.54095432\n",
      "  -1.31851281]\n",
      " [ 0.47234264 -1.37208932 -0.73780632 ... -2.52283563  0.54095432\n",
      "  -1.31851281]]\n"
     ]
    }
   ],
   "source": [
    "print(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99139d53",
   "metadata": {},
   "source": [
    "### **<font color = blue>6. SVM fitting the model</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "0f2bfa67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(C=20, gamma=0.01)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=20, gamma=0.01)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=20, gamma=0.01)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"In SVM we have 3 hyperparameters: C (Cost penalty), Kernel & gamma.\n",
    "    The values .... are base values of the model & if one is changing these base values, then one is performing\n",
    "    Hyperparameter tuning. Hyperparameter tuning improves the accuracy of the model.\"\"\"\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "svc_model = SVC(kernel ='rbf',C=20, gamma =0.01)\n",
    "svc_model.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df8379ec",
   "metadata": {},
   "source": [
    "### **<font color = blue>7. Predicting Y based on X_test</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "481d31af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "Y_pred = svc_model.predict(X_test)\n",
    "print(list(Y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d005104a",
   "metadata": {},
   "source": [
    "### **<font color = blue>8. Evaluation of the model</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5be3f9b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7768729641693811"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc_model.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "080ea43a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.51      0.59       192\n",
      "           1       0.80      0.90      0.85       422\n",
      "\n",
      "    accuracy                           0.78       614\n",
      "   macro avg       0.75      0.70      0.72       614\n",
      "weighted avg       0.77      0.78      0.77       614\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "Y_pred_new=svc_model.predict(X_train)\n",
    "confusion_matrix(Y_train, Y_pred_new)\n",
    "\n",
    "print (classification_report(Y_train, Y_pred_new) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f95acddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\"Here we are not getting any accuracy score, because our test data do not consist of any Y-values\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f0cd10",
   "metadata": {},
   "source": [
    "### **<font color = blue>9. Adding Y-predictions column to the test data to display Eligibilty/Non-Eligbility</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "44ebd86f",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>LP002971</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>4009</td>\n",
       "      <td>1777</td>\n",
       "      <td>113.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>LP002975</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>4158</td>\n",
       "      <td>709</td>\n",
       "      <td>115.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>LP002980</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3250</td>\n",
       "      <td>1993</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Semiurban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>LP002986</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>2393</td>\n",
       "      <td>158.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>LP002989</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>Yes</td>\n",
       "      <td>9200</td>\n",
       "      <td>0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>367 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Loan_ID Gender Married  Dependents     Education Self_Employed  \\\n",
       "0    LP001015   Male     Yes         0.0      Graduate            No   \n",
       "1    LP001022   Male     Yes         1.0      Graduate            No   \n",
       "2    LP001031   Male     Yes         2.0      Graduate            No   \n",
       "3    LP001035   Male     Yes         2.0      Graduate            No   \n",
       "4    LP001051   Male      No         0.0  Not Graduate            No   \n",
       "..        ...    ...     ...         ...           ...           ...   \n",
       "362  LP002971   Male     Yes         3.0  Not Graduate           Yes   \n",
       "363  LP002975   Male     Yes         0.0      Graduate            No   \n",
       "364  LP002980   Male      No         0.0      Graduate            No   \n",
       "365  LP002986   Male     Yes         0.0      Graduate            No   \n",
       "366  LP002989   Male      No         0.0      Graduate           Yes   \n",
       "\n",
       "     ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0               5720                  0       110.0             360.0   \n",
       "1               3076               1500       126.0             360.0   \n",
       "2               5000               1800       208.0             360.0   \n",
       "3               2340               2546       100.0             360.0   \n",
       "4               3276                  0        78.0             360.0   \n",
       "..               ...                ...         ...               ...   \n",
       "362             4009               1777       113.0             360.0   \n",
       "363             4158                709       115.0             360.0   \n",
       "364             3250               1993       126.0             360.0   \n",
       "365             5000               2393       158.0             360.0   \n",
       "366             9200                  0        98.0             180.0   \n",
       "\n",
       "     Credit_History Property_Area  \n",
       "0               1.0         Urban  \n",
       "1               1.0         Urban  \n",
       "2               1.0         Urban  \n",
       "3               NaN         Urban  \n",
       "4               1.0         Urban  \n",
       "..              ...           ...  \n",
       "362             1.0         Urban  \n",
       "363             1.0         Urban  \n",
       "364             NaN     Semiurban  \n",
       "365             1.0         Rural  \n",
       "366             1.0         Rural  \n",
       "\n",
       "[367 rows x 12 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data=pd.read_csv(r'risk_analytics_test.csv', header=0)\n",
    "test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "f04553ff",
   "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>Y_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</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>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</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>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</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>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</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",
       "0  LP001015   Male     Yes         0.0      Graduate            No   \n",
       "1  LP001022   Male     Yes         1.0      Graduate            No   \n",
       "2  LP001031   Male     Yes         2.0      Graduate            No   \n",
       "3  LP001035   Male     Yes         2.0      Graduate            No   \n",
       "4  LP001051   Male      No         0.0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area  Y_predictions  \n",
       "0             1.0         Urban              1  \n",
       "1             1.0         Urban              1  \n",
       "2             1.0         Urban              1  \n",
       "3             NaN         Urban              0  \n",
       "4             1.0         Urban              1  "
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"Y_predictions\"]=Y_pred\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b94a1215",
   "metadata": {},
   "source": [
    "### **<font color = blue>10. Mapping Eligibilty: \"1\"/Non-Eligbility: \"0\" in Y_predictions col</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "f1f462f9",
   "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>Y_predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LP001015</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5720</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LP001022</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3076</td>\n",
       "      <td>1500</td>\n",
       "      <td>126.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LP001031</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>5000</td>\n",
       "      <td>1800</td>\n",
       "      <td>208.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LP001035</td>\n",
       "      <td>Male</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>2340</td>\n",
       "      <td>2546</td>\n",
       "      <td>100.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Not Eligible</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LP001051</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Not Graduate</td>\n",
       "      <td>No</td>\n",
       "      <td>3276</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Urban</td>\n",
       "      <td>Eligible</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Loan_ID Gender Married  Dependents     Education Self_Employed  \\\n",
       "0  LP001015   Male     Yes         0.0      Graduate            No   \n",
       "1  LP001022   Male     Yes         1.0      Graduate            No   \n",
       "2  LP001031   Male     Yes         2.0      Graduate            No   \n",
       "3  LP001035   Male     Yes         2.0      Graduate            No   \n",
       "4  LP001051   Male      No         0.0  Not Graduate            No   \n",
       "\n",
       "   ApplicantIncome  CoapplicantIncome  LoanAmount  Loan_Amount_Term  \\\n",
       "0             5720                  0       110.0             360.0   \n",
       "1             3076               1500       126.0             360.0   \n",
       "2             5000               1800       208.0             360.0   \n",
       "3             2340               2546       100.0             360.0   \n",
       "4             3276                  0        78.0             360.0   \n",
       "\n",
       "   Credit_History Property_Area Y_predictions  \n",
       "0             1.0         Urban      Eligible  \n",
       "1             1.0         Urban      Eligible  \n",
       "2             1.0         Urban      Eligible  \n",
       "3             NaN         Urban  Not Eligible  \n",
       "4             1.0         Urban      Eligible  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data[\"Y_predictions\"]=test_data[\"Y_predictions\"]. map({1:\"Eligible\", 0:\"Not Eligible\"})\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c644f08",
   "metadata": {},
   "source": [
    "### **<font color = blue>11. Outsourcing the Loan elibilty test data to the client</font>**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "8f94f143",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data.to_csv(r'test_data_output.csv',index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8d33ccfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y_predictions\n",
       "Eligible        280\n",
       "Not Eligible     87\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.Y_predictions.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b6ba1ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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