From 1272fcf3a16bec8a600025321740ff61d38ec22a Mon Sep 17 00:00:00 2001
From: Ananya Gupta <145869907+ananyag309@users.noreply.github.com>
Date: Wed, 31 Jul 2024 16:38:06 +0530
Subject: [PATCH] Loan eligible prediction

---
 .../Loan_Eligibility_prediction.ipynb         | 2605 +++++++++++++++++
 .../Loan eligible prediction/README.md        |    3 +
 2 files changed, 2608 insertions(+)
 create mode 100644 Loan Status Prediction/Loan eligible prediction/Loan_Eligibility_prediction.ipynb
 create mode 100644 Loan Status Prediction/Loan eligible prediction/README.md

diff --git a/Loan Status Prediction/Loan eligible prediction/Loan_Eligibility_prediction.ipynb b/Loan Status Prediction/Loan eligible prediction/Loan_Eligibility_prediction.ipynb
new file mode 100644
index 00000000..924fae6c
--- /dev/null
+++ b/Loan Status Prediction/Loan eligible prediction/Loan_Eligibility_prediction.ipynb	
@@ -0,0 +1,2605 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Uu62u0KdsDzr"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd \n",
+        "from scipy import stats\n",
+        "import numpy as np \n",
+        "import matplotlib.pyplot as plt \n",
+        "import seaborn as sns\n",
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mATJJ5yusK5E"
+      },
+      "outputs": [],
+      "source": [
+        "data=pd.read_csv(\"/content/loan_data_set.csv\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hg2RS6uGEM6E"
+      },
+      "outputs": [],
+      "source": [
+        "#Encoding\n",
+        "data=pd.get_dummies(data,columns=['Property_Area', 'Dependents'])\n",
+        "#data.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8qdeJMAFxVEX"
+      },
+      "outputs": [],
+      "source": [
+        "#converting categorical values to numerical\n",
+        "data['Education']=data['Education'].replace(\"Graduate\",1)\n",
+        "data['Education']=data['Education'].replace(\"Not Graduate\",0)\n",
+        "data['Self_Employed']=data['Self_Employed'].replace(\"Yes\",1)\n",
+        "data['Self_Employed']=data['Self_Employed'].replace(\"No\",0)\n",
+        "data['Married']=data['Married'].replace(\"Yes\",1)\n",
+        "data['Married']=data['Married'].replace(\"No\",0)\n",
+        "data['Gender']=data['Gender'].replace(\"Male\",1)\n",
+        "data['Gender']=data['Gender'].replace(\"Female\",0)\n",
+        "data['Loan_Status']=data['Loan_Status'].replace(\"Y\",1)\n",
+        "data['Loan_Status']=data['Loan_Status'].replace(\"N\",0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2Oa5d-M51IFr",
+        "outputId": "231abe82-f05f-45a1-cbff-811c9c8a5cd1"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ],
+      "source": [
+        "#make sure there are no duplicated records\n",
+        "sum(data.duplicated())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f7APuHdN12sp",
+        "outputId": "fdbf3fbb-7637-42ff-f97c-5c0d649c503e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loan_ID                     0\n",
+            "Gender                     13\n",
+            "Married                     3\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",
+            "Loan_Status                 0\n",
+            "Property_Area_Rural         0\n",
+            "Property_Area_Semiurban     0\n",
+            "Property_Area_Urban         0\n",
+            "Dependents_0                0\n",
+            "Dependents_1                0\n",
+            "Dependents_2                0\n",
+            "Dependents_3+               0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "#checking for NULLS\n",
+        "print (data.isnull().sum())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 364
+        },
+        "id": "2JqF7S8k6nHM",
+        "outputId": "7c63c366-72b2-47ef-f0e3-7207ef7dc07d"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "           Gender     Married   Education  Self_Employed  ApplicantIncome  \\\n",
+              "count  601.000000  611.000000  614.000000     582.000000       614.000000   \n",
+              "mean     0.813644    0.651391    0.781759       0.140893      5403.459283   \n",
+              "std      0.389718    0.476920    0.413389       0.348211      6109.041673   \n",
+              "min      0.000000    0.000000    0.000000       0.000000       150.000000   \n",
+              "25%      1.000000    0.000000    1.000000       0.000000      2877.500000   \n",
+              "50%      1.000000    1.000000    1.000000       0.000000      3812.500000   \n",
+              "75%      1.000000    1.000000    1.000000       0.000000      5795.000000   \n",
+              "max      1.000000    1.000000    1.000000       1.000000     81000.000000   \n",
+              "\n",
+              "       CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
+              "count         614.000000  592.000000         600.00000      564.000000   \n",
+              "mean         1621.245798  146.412162         342.00000        0.842199   \n",
+              "std          2926.248369   85.587325          65.12041        0.364878   \n",
+              "min             0.000000    9.000000          12.00000        0.000000   \n",
+              "25%             0.000000  100.000000         360.00000        1.000000   \n",
+              "50%          1188.500000  128.000000         360.00000        1.000000   \n",
+              "75%          2297.250000  168.000000         360.00000        1.000000   \n",
+              "max         41667.000000  700.000000         480.00000        1.000000   \n",
+              "\n",
+              "       Loan_Status  Property_Area_Rural  Property_Area_Semiurban  \\\n",
+              "count   614.000000           614.000000               614.000000   \n",
+              "mean      0.687296             0.291531                 0.379479   \n",
+              "std       0.463973             0.454838                 0.485653   \n",
+              "min       0.000000             0.000000                 0.000000   \n",
+              "25%       0.000000             0.000000                 0.000000   \n",
+              "50%       1.000000             0.000000                 0.000000   \n",
+              "75%       1.000000             1.000000                 1.000000   \n",
+              "max       1.000000             1.000000                 1.000000   \n",
+              "\n",
+              "       Property_Area_Urban  Dependents_0  Dependents_1  Dependents_2  \\\n",
+              "count           614.000000    614.000000    614.000000    614.000000   \n",
+              "mean              0.328990      0.561889      0.166124      0.164495   \n",
+              "std               0.470229      0.496559      0.372495      0.371027   \n",
+              "min               0.000000      0.000000      0.000000      0.000000   \n",
+              "25%               0.000000      0.000000      0.000000      0.000000   \n",
+              "50%               0.000000      1.000000      0.000000      0.000000   \n",
+              "75%               1.000000      1.000000      0.000000      0.000000   \n",
+              "max               1.000000      1.000000      1.000000      1.000000   \n",
+              "\n",
+              "       Dependents_3+  \n",
+              "count     614.000000  \n",
+              "mean        0.083062  \n",
+              "std         0.276201  \n",
+              "min         0.000000  \n",
+              "25%         0.000000  \n",
+              "50%         0.000000  \n",
+              "75%         0.000000  \n",
+              "max         1.000000  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-f248acc5-609b-47cb-8b28-579e3afb3fa5\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <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>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>Loan_Status</th>\n",
+              "      <th>Property_Area_Rural</th>\n",
+              "      <th>Property_Area_Semiurban</th>\n",
+              "      <th>Property_Area_Urban</th>\n",
+              "      <th>Dependents_0</th>\n",
+              "      <th>Dependents_1</th>\n",
+              "      <th>Dependents_2</th>\n",
+              "      <th>Dependents_3+</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>601.000000</td>\n",
+              "      <td>611.000000</td>\n",
+              "      <td>614.000000</td>\n",
+              "      <td>582.000000</td>\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",
+              "      <td>614.000000</td>\n",
+              "      <td>614.000000</td>\n",
+              "      <td>614.000000</td>\n",
+              "      <td>614.000000</td>\n",
+              "      <td>614.000000</td>\n",
+              "      <td>614.000000</td>\n",
+              "      <td>614.000000</td>\n",
+              "      <td>614.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.813644</td>\n",
+              "      <td>0.651391</td>\n",
+              "      <td>0.781759</td>\n",
+              "      <td>0.140893</td>\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",
+              "      <td>0.687296</td>\n",
+              "      <td>0.291531</td>\n",
+              "      <td>0.379479</td>\n",
+              "      <td>0.328990</td>\n",
+              "      <td>0.561889</td>\n",
+              "      <td>0.166124</td>\n",
+              "      <td>0.164495</td>\n",
+              "      <td>0.083062</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.389718</td>\n",
+              "      <td>0.476920</td>\n",
+              "      <td>0.413389</td>\n",
+              "      <td>0.348211</td>\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",
+              "      <td>0.463973</td>\n",
+              "      <td>0.454838</td>\n",
+              "      <td>0.485653</td>\n",
+              "      <td>0.470229</td>\n",
+              "      <td>0.496559</td>\n",
+              "      <td>0.372495</td>\n",
+              "      <td>0.371027</td>\n",
+              "      <td>0.276201</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\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",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\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",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\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",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\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",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\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",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f248acc5-609b-47cb-8b28-579e3afb3fa5')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "        \n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-f248acc5-609b-47cb-8b28-579e3afb3fa5 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-f248acc5-609b-47cb-8b28-579e3afb3fa5');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ],
+      "source": [
+        "data.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3tFOlTB0EZFT",
+        "outputId": "e919111c-bc7a-4a70-da85-13970a63e23c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<bound method Series.mode of 0      1.0\n",
+            "1      1.0\n",
+            "2      1.0\n",
+            "3      1.0\n",
+            "4      1.0\n",
+            "      ... \n",
+            "609    1.0\n",
+            "610    1.0\n",
+            "611    1.0\n",
+            "612    1.0\n",
+            "613    0.0\n",
+            "Name: Credit_History, Length: 614, dtype: float64>\n",
+            "<bound method Series.mode of 0      0.0\n",
+            "1      1.0\n",
+            "2      1.0\n",
+            "3      1.0\n",
+            "4      0.0\n",
+            "      ... \n",
+            "609    0.0\n",
+            "610    1.0\n",
+            "611    1.0\n",
+            "612    1.0\n",
+            "613    0.0\n",
+            "Name: Married, Length: 614, dtype: float64>\n",
+            "<bound method Series.mode of 0      0.0\n",
+            "1      0.0\n",
+            "2      1.0\n",
+            "3      0.0\n",
+            "4      0.0\n",
+            "      ... \n",
+            "609    0.0\n",
+            "610    0.0\n",
+            "611    0.0\n",
+            "612    0.0\n",
+            "613    1.0\n",
+            "Name: Self_Employed, Length: 614, dtype: float64>\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(data['Credit_History'].mode)\n",
+        "print(data['Married'].mode)\n",
+        "print(data['Self_Employed'].mode)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sCUN9q8F7DbJ"
+      },
+      "outputs": [],
+      "source": [
+        "#replacing nulls with mean or mode\n",
+        "data['LoanAmount'].fillna(146.412162,inplace=True) \n",
+        "data['Married'].fillna(0,inplace=True)\n",
+        "data['Self_Employed'].fillna(0,inplace=True)\n",
+        "data['Gender'].fillna(1,inplace=True)\n",
+        "data['Loan_Amount_Term'].fillna(360.00000,inplace=True)\n",
+        "data['Credit_History'].fillna(1,inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "DAXZBD4yE1vy",
+        "outputId": "10de5fea-54db-4b49-a2be-b19b000e589a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Loan_ID                    0\n",
+            "Gender                     0\n",
+            "Married                    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",
+            "Loan_Status                0\n",
+            "Property_Area_Rural        0\n",
+            "Property_Area_Semiurban    0\n",
+            "Property_Area_Urban        0\n",
+            "Dependents_0               0\n",
+            "Dependents_1               0\n",
+            "Dependents_2               0\n",
+            "Dependents_3+              0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print (data.isnull().sum())"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UGQ4jApWGjC3",
+        "outputId": "6704f6b1-a160-49ee-9962-275b7ce08e4c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 614 entries, 0 to 613\n",
+            "Data columns (total 18 columns):\n",
+            " #   Column                   Non-Null Count  Dtype  \n",
+            "---  ------                   --------------  -----  \n",
+            " 0   Loan_ID                  614 non-null    object \n",
+            " 1   Gender                   614 non-null    float64\n",
+            " 2   Married                  614 non-null    float64\n",
+            " 3   Education                614 non-null    int64  \n",
+            " 4   Self_Employed            614 non-null    float64\n",
+            " 5   ApplicantIncome          614 non-null    int64  \n",
+            " 6   CoapplicantIncome        614 non-null    float64\n",
+            " 7   LoanAmount               614 non-null    float64\n",
+            " 8   Loan_Amount_Term         614 non-null    float64\n",
+            " 9   Credit_History           614 non-null    float64\n",
+            " 10  Loan_Status              614 non-null    int64  \n",
+            " 11  Property_Area_Rural      614 non-null    uint8  \n",
+            " 12  Property_Area_Semiurban  614 non-null    uint8  \n",
+            " 13  Property_Area_Urban      614 non-null    uint8  \n",
+            " 14  Dependents_0             614 non-null    uint8  \n",
+            " 15  Dependents_1             614 non-null    uint8  \n",
+            " 16  Dependents_2             614 non-null    uint8  \n",
+            " 17  Dependents_3+            614 non-null    uint8  \n",
+            "dtypes: float64(7), int64(3), object(1), uint8(7)\n",
+            "memory usage: 57.1+ KB\n"
+          ]
+        }
+      ],
+      "source": [
+        "data.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OzTP78xf5mWf"
+      },
+      "outputs": [],
+      "source": [
+        "#dropping loan id column as it is irrelevant / costing features \n",
+        "# costing feature \n",
+        "data.drop('Loan_ID', axis=1, inplace=True)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 619
+        },
+        "id": "J5tOUpQVghjP",
+        "outputId": "84a70b3e-cdbf-49f1-f61a-347c5cc87874"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 50
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#Create correlation matrix and visualize it\n",
+        "corr_matrix = data.corr()\n",
+        "sns.heatmap(corr_matrix, cmap='magma')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 637
+        },
+        "id": "5nPdIH5KhclZ",
+        "outputId": "a0c616fe-47d2-4b6c-9d3c-cbbeebab93ce"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-51-46866af64ca6>:3: FutureWarning: this method is deprecated in favour of `Styler.format(precision=..)`\n",
+            "  corr_table.style.background_gradient(cmap='magma').set_precision(2)\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<pandas.io.formats.style.Styler at 0x7f137ca25300>"
+            ],
+            "text/html": [
+              "<style type=\"text/css\">\n",
+              "#T_5bf80_row0_col0, #T_5bf80_row1_col1, #T_5bf80_row2_col2, #T_5bf80_row3_col3, #T_5bf80_row4_col4, #T_5bf80_row5_col5, #T_5bf80_row6_col6, #T_5bf80_row7_col7, #T_5bf80_row8_col8, #T_5bf80_row9_col9, #T_5bf80_row10_col10, #T_5bf80_row11_col11, #T_5bf80_row12_col12, #T_5bf80_row13_col13, #T_5bf80_row14_col14, #T_5bf80_row15_col15, #T_5bf80_row16_col16 {\n",
+              "  background-color: #fcfdbf;\n",
+              "  color: #000000;\n",
+              "}\n",
+              "#T_5bf80_row0_col1 {\n",
+              "  background-color: #c23b75;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col2, #T_5bf80_row10_col11 {\n",
+              "  background-color: #03030f;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col3, #T_5bf80_row3_col2, #T_5bf80_row8_col3, #T_5bf80_row11_col5 {\n",
+              "  background-color: #0e0b2b;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col4, #T_5bf80_row10_col6, #T_5bf80_row12_col0 {\n",
+              "  background-color: #29115a;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col5, #T_5bf80_row1_col5, #T_5bf80_row4_col0, #T_5bf80_row7_col1 {\n",
+              "  background-color: #311165;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col6, #T_5bf80_row3_col4, #T_5bf80_row11_col9 {\n",
+              "  background-color: #420f75;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col7, #T_5bf80_row13_col4, #T_5bf80_row16_col7 {\n",
+              "  background-color: #02020d;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col8, #T_5bf80_row1_col8, #T_5bf80_row3_col7, #T_5bf80_row5_col3, #T_5bf80_row7_col4, #T_5bf80_row10_col12, #T_5bf80_row14_col8, #T_5bf80_row15_col8 {\n",
+              "  background-color: #0a0822;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col9, #T_5bf80_row1_col13, #T_5bf80_row10_col5, #T_5bf80_row11_col2, #T_5bf80_row16_col13 {\n",
+              "  background-color: #160f3b;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col10, #T_5bf80_row3_col14 {\n",
+              "  background-color: #882781;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col11, #T_5bf80_row2_col10, #T_5bf80_row5_col16, #T_5bf80_row10_col16 {\n",
+              "  background-color: #5d177f;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col12, #T_5bf80_row2_col12, #T_5bf80_row6_col14, #T_5bf80_row8_col11, #T_5bf80_row9_col15, #T_5bf80_row10_col13 {\n",
+              "  background-color: #832681;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col13, #T_5bf80_row2_col1, #T_5bf80_row15_col0 {\n",
+              "  background-color: #4c117a;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col14, #T_5bf80_row1_col10, #T_5bf80_row3_col12, #T_5bf80_row6_col12 {\n",
+              "  background-color: #721f81;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col15 {\n",
+              "  background-color: #942c80;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row0_col16, #T_5bf80_row7_col15, #T_5bf80_row9_col12, #T_5bf80_row11_col15 {\n",
+              "  background-color: #6e1e81;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col0 {\n",
+              "  background-color: #a02f7f;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col2, #T_5bf80_row3_col8, #T_5bf80_row6_col8, #T_5bf80_row6_col9, #T_5bf80_row14_col9 {\n",
+              "  background-color: #08071e;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col3, #T_5bf80_row2_col0, #T_5bf80_row3_col9, #T_5bf80_row4_col9, #T_5bf80_row8_col4, #T_5bf80_row8_col7, #T_5bf80_row9_col6, #T_5bf80_row11_col3 {\n",
+              "  background-color: #110c2f;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col4, #T_5bf80_row9_col2 {\n",
+              "  background-color: #251255;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col6, #T_5bf80_row16_col14 {\n",
+              "  background-color: #4f127b;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col7, #T_5bf80_row4_col5, #T_5bf80_row5_col4, #T_5bf80_row10_col2, #T_5bf80_row10_col9, #T_5bf80_row11_col10, #T_5bf80_row11_col12, #T_5bf80_row12_col11, #T_5bf80_row13_col0, #T_5bf80_row13_col1, #T_5bf80_row13_col3, #T_5bf80_row13_col6, #T_5bf80_row13_col14, #T_5bf80_row13_col15, #T_5bf80_row13_col16, #T_5bf80_row14_col13, #T_5bf80_row15_col13, #T_5bf80_row16_col8 {\n",
+              "  background-color: #000004;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col9, #T_5bf80_row2_col9 {\n",
+              "  background-color: #2d1161;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col11, #T_5bf80_row1_col12, #T_5bf80_row1_col16, #T_5bf80_row4_col12, #T_5bf80_row16_col1 {\n",
+              "  background-color: #792282;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col14 {\n",
+              "  background-color: #932b80;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row1_col15 {\n",
+              "  background-color: #b73779;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col3, #T_5bf80_row3_col5, #T_5bf80_row11_col4, #T_5bf80_row11_col8, #T_5bf80_row12_col6 {\n",
+              "  background-color: #120d31;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col4 {\n",
+              "  background-color: #491078;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col5, #T_5bf80_row2_col7 {\n",
+              "  background-color: #2a115c;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col6, #T_5bf80_row9_col10 {\n",
+              "  background-color: #57157e;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col8, #T_5bf80_row3_col0, #T_5bf80_row6_col7 {\n",
+              "  background-color: #1d1147;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col11, #T_5bf80_row12_col14 {\n",
+              "  background-color: #842681;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col13, #T_5bf80_row8_col13, #T_5bf80_row15_col12 {\n",
+              "  background-color: #7e2482;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col14, #T_5bf80_row4_col10, #T_5bf80_row4_col11, #T_5bf80_row6_col15, #T_5bf80_row12_col15, #T_5bf80_row15_col11 {\n",
+              "  background-color: #762181;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col15, #T_5bf80_row16_col12 {\n",
+              "  background-color: #6d1d81;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row2_col16 {\n",
+              "  background-color: #400f74;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row3_col1, #T_5bf80_row8_col1, #T_5bf80_row11_col16 {\n",
+              "  background-color: #54137d;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row3_col6, #T_5bf80_row6_col0, #T_5bf80_row12_col16 {\n",
+              "  background-color: #440f76;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row3_col10, #T_5bf80_row6_col11, #T_5bf80_row13_col11 {\n",
+              "  background-color: #782281;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row3_col11, #T_5bf80_row4_col14, #T_5bf80_row6_col1, #T_5bf80_row6_col10, #T_5bf80_row14_col11, #T_5bf80_row16_col10 {\n",
+              "  background-color: #7c2382;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row3_col13, #T_5bf80_row10_col14, #T_5bf80_row14_col10 {\n",
+              "  background-color: #5c167f;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row3_col15, #T_5bf80_row7_col10, #T_5bf80_row16_col11 {\n",
+              "  background-color: #7b2382;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row3_col16, #T_5bf80_row10_col1, #T_5bf80_row11_col1, #T_5bf80_row12_col1, #T_5bf80_row16_col6 {\n",
+              "  background-color: #52137c;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row4_col1, #T_5bf80_row7_col12 {\n",
+              "  background-color: #601880;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row4_col2, #T_5bf80_row14_col15 {\n",
+              "  background-color: #3b0f70;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row4_col3, #T_5bf80_row5_col0, #T_5bf80_row7_col16, #T_5bf80_row10_col0 {\n",
+              "  background-color: #390f6e;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row4_col6 {\n",
+              "  background-color: #e44f64;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row4_col7, #T_5bf80_row7_col5, #T_5bf80_row7_col8, #T_5bf80_row9_col5, #T_5bf80_row12_col3, #T_5bf80_row12_col9, #T_5bf80_row15_col2 {\n",
+              "  background-color: #07061c;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row4_col8, #T_5bf80_row5_col7, #T_5bf80_row12_col8 {\n",
+              "  background-color: #050416;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row4_col13, #T_5bf80_row7_col14 {\n",
+              "  background-color: #5a167e;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row4_col15, #T_5bf80_row9_col14 {\n",
+              "  background-color: #681c81;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row4_col16, #T_5bf80_row5_col12, #T_5bf80_row6_col16 {\n",
+              "  background-color: #802582;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row5_col1, #T_5bf80_row5_col14 {\n",
+              "  background-color: #6a1c81;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row5_col2 {\n",
+              "  background-color: #1e1149;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row5_col6 {\n",
+              "  background-color: #5f187f;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row5_col8, #T_5bf80_row16_col9 {\n",
+              "  background-color: #0b0924;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row5_col9, #T_5bf80_row10_col8 {\n",
+              "  background-color: #040414;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row5_col10, #T_5bf80_row5_col11, #T_5bf80_row5_col13, #T_5bf80_row8_col15 {\n",
+              "  background-color: #732081;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row5_col15, #T_5bf80_row8_col12, #T_5bf80_row8_col14, #T_5bf80_row9_col13, #T_5bf80_row11_col14, #T_5bf80_row14_col1 {\n",
+              "  background-color: #752181;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row6_col2 {\n",
+              "  background-color: #471078;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row6_col3 {\n",
+              "  background-color: #341069;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row6_col4 {\n",
+              "  background-color: #e24d66;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row6_col5 {\n",
+              "  background-color: #59157e;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row6_col13, #T_5bf80_row16_col4, #T_5bf80_row16_col15 {\n",
+              "  background-color: #4e117b;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row7_col0 {\n",
+              "  background-color: #090720;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row7_col2, #T_5bf80_row8_col2, #T_5bf80_row15_col6 {\n",
+              "  background-color: #21114e;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row7_col3 {\n",
+              "  background-color: #06051a;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row7_col6, #T_5bf80_row14_col3, #T_5bf80_row14_col16, #T_5bf80_row15_col16 {\n",
+              "  background-color: #271258;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row7_col9, #T_5bf80_row9_col7, #T_5bf80_row15_col4 {\n",
+              "  background-color: #0c0926;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row7_col11 {\n",
+              "  background-color: #892881;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row7_col13 {\n",
+              "  background-color: #902a81;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row8_col0 {\n",
+              "  background-color: #20114b;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row8_col5, #T_5bf80_row11_col6, #T_5bf80_row15_col5 {\n",
+              "  background-color: #19103f;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row8_col6, #T_5bf80_row10_col4 {\n",
+              "  background-color: #1a1042;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row8_col9 {\n",
+              "  background-color: #d8456c;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row8_col10, #T_5bf80_row9_col1, #T_5bf80_row13_col12 {\n",
+              "  background-color: #6b1d81;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row8_col16, #T_5bf80_row16_col0 {\n",
+              "  background-color: #3f0f72;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row9_col0, #T_5bf80_row14_col4, #T_5bf80_row16_col5 {\n",
+              "  background-color: #221150;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row9_col3, #T_5bf80_row14_col5 {\n",
+              "  background-color: #0d0a29;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row9_col4, #T_5bf80_row10_col3, #T_5bf80_row13_col8 {\n",
+              "  background-color: #140e36;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row9_col8 {\n",
+              "  background-color: #d2426f;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row9_col11, #T_5bf80_row15_col1 {\n",
+              "  background-color: #9e2f7f;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row9_col16 {\n",
+              "  background-color: #4a1079;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row10_col7, #T_5bf80_row12_col5, #T_5bf80_row14_col0 {\n",
+              "  background-color: #1c1044;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row10_col15, #T_5bf80_row11_col13, #T_5bf80_row15_col10 {\n",
+              "  background-color: #701f81;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row11_col0, #T_5bf80_row12_col10 {\n",
+              "  background-color: #030312;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row11_col7, #T_5bf80_row15_col9 {\n",
+              "  background-color: #241253;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row12_col2, #T_5bf80_row12_col4, #T_5bf80_row13_col5 {\n",
+              "  background-color: #150e38;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row12_col7 {\n",
+              "  background-color: #010106;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row12_col13 {\n",
+              "  background-color: #651a80;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row13_col2, #T_5bf80_row15_col3 {\n",
+              "  background-color: #180f3d;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row13_col7 {\n",
+              "  background-color: #36106b;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row13_col9 {\n",
+              "  background-color: #130d34;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row13_col10 {\n",
+              "  background-color: #812581;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row14_col2, #T_5bf80_row15_col7, #T_5bf80_row16_col3 {\n",
+              "  background-color: #100b2d;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row14_col6 {\n",
+              "  background-color: #2f1163;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row14_col7 {\n",
+              "  background-color: #010108;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row14_col12 {\n",
+              "  background-color: #8c2981;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row15_col14 {\n",
+              "  background-color: #3d0f71;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "#T_5bf80_row16_col2 {\n",
+              "  background-color: #02020b;\n",
+              "  color: #f1f1f1;\n",
+              "}\n",
+              "</style>\n",
+              "<table id=\"T_5bf80\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr>\n",
+              "      <th class=\"blank level0\" >&nbsp;</th>\n",
+              "      <th id=\"T_5bf80_level0_col0\" class=\"col_heading level0 col0\" >Gender</th>\n",
+              "      <th id=\"T_5bf80_level0_col1\" class=\"col_heading level0 col1\" >Married</th>\n",
+              "      <th id=\"T_5bf80_level0_col2\" class=\"col_heading level0 col2\" >Education</th>\n",
+              "      <th id=\"T_5bf80_level0_col3\" class=\"col_heading level0 col3\" >Self_Employed</th>\n",
+              "      <th id=\"T_5bf80_level0_col4\" class=\"col_heading level0 col4\" >ApplicantIncome</th>\n",
+              "      <th id=\"T_5bf80_level0_col5\" class=\"col_heading level0 col5\" >CoapplicantIncome</th>\n",
+              "      <th id=\"T_5bf80_level0_col6\" class=\"col_heading level0 col6\" >LoanAmount</th>\n",
+              "      <th id=\"T_5bf80_level0_col7\" class=\"col_heading level0 col7\" >Loan_Amount_Term</th>\n",
+              "      <th id=\"T_5bf80_level0_col8\" class=\"col_heading level0 col8\" >Credit_History</th>\n",
+              "      <th id=\"T_5bf80_level0_col9\" class=\"col_heading level0 col9\" >Loan_Status</th>\n",
+              "      <th id=\"T_5bf80_level0_col10\" class=\"col_heading level0 col10\" >Property_Area_Rural</th>\n",
+              "      <th id=\"T_5bf80_level0_col11\" class=\"col_heading level0 col11\" >Property_Area_Semiurban</th>\n",
+              "      <th id=\"T_5bf80_level0_col12\" class=\"col_heading level0 col12\" >Property_Area_Urban</th>\n",
+              "      <th id=\"T_5bf80_level0_col13\" class=\"col_heading level0 col13\" >Dependents_0</th>\n",
+              "      <th id=\"T_5bf80_level0_col14\" class=\"col_heading level0 col14\" >Dependents_1</th>\n",
+              "      <th id=\"T_5bf80_level0_col15\" class=\"col_heading level0 col15\" >Dependents_2</th>\n",
+              "      <th id=\"T_5bf80_level0_col16\" class=\"col_heading level0 col16\" >Dependents_3+</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row0\" class=\"row_heading level0 row0\" >Gender</th>\n",
+              "      <td id=\"T_5bf80_row0_col0\" class=\"data row0 col0\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row0_col1\" class=\"data row0 col1\" >0.37</td>\n",
+              "      <td id=\"T_5bf80_row0_col2\" class=\"data row0 col2\" >-0.05</td>\n",
+              "      <td id=\"T_5bf80_row0_col3\" class=\"data row0 col3\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row0_col4\" class=\"data row0 col4\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row0_col5\" class=\"data row0 col5\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row0_col6\" class=\"data row0 col6\" >0.11</td>\n",
+              "      <td id=\"T_5bf80_row0_col7\" class=\"data row0 col7\" >-0.07</td>\n",
+              "      <td id=\"T_5bf80_row0_col8\" class=\"data row0 col8\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row0_col9\" class=\"data row0 col9\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row0_col10\" class=\"data row0 col10\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row0_col11\" class=\"data row0 col11\" >-0.11</td>\n",
+              "      <td id=\"T_5bf80_row0_col12\" class=\"data row0 col12\" >0.03</td>\n",
+              "      <td id=\"T_5bf80_row0_col13\" class=\"data row0 col13\" >-0.15</td>\n",
+              "      <td id=\"T_5bf80_row0_col14\" class=\"data row0 col14\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row0_col15\" class=\"data row0 col15\" >0.13</td>\n",
+              "      <td id=\"T_5bf80_row0_col16\" class=\"data row0 col16\" >0.10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row1\" class=\"row_heading level0 row1\" >Married</th>\n",
+              "      <td id=\"T_5bf80_row1_col0\" class=\"data row1 col0\" >0.37</td>\n",
+              "      <td id=\"T_5bf80_row1_col1\" class=\"data row1 col1\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row1_col2\" class=\"data row1 col2\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row1_col3\" class=\"data row1 col3\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row1_col4\" class=\"data row1 col4\" >0.05</td>\n",
+              "      <td id=\"T_5bf80_row1_col5\" class=\"data row1 col5\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row1_col6\" class=\"data row1 col6\" >0.15</td>\n",
+              "      <td id=\"T_5bf80_row1_col7\" class=\"data row1 col7\" >-0.10</td>\n",
+              "      <td id=\"T_5bf80_row1_col8\" class=\"data row1 col8\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row1_col9\" class=\"data row1 col9\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row1_col10\" class=\"data row1 col10\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row1_col11\" class=\"data row1 col11\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row1_col12\" class=\"data row1 col12\" >0.00</td>\n",
+              "      <td id=\"T_5bf80_row1_col13\" class=\"data row1 col13\" >-0.34</td>\n",
+              "      <td id=\"T_5bf80_row1_col14\" class=\"data row1 col14\" >0.12</td>\n",
+              "      <td id=\"T_5bf80_row1_col15\" class=\"data row1 col15\" >0.25</td>\n",
+              "      <td id=\"T_5bf80_row1_col16\" class=\"data row1 col16\" >0.14</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row2\" class=\"row_heading level0 row2\" >Education</th>\n",
+              "      <td id=\"T_5bf80_row2_col0\" class=\"data row2 col0\" >-0.05</td>\n",
+              "      <td id=\"T_5bf80_row2_col1\" class=\"data row2 col1\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row2_col2\" class=\"data row2 col2\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row2_col3\" class=\"data row2 col3\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row2_col4\" class=\"data row2 col4\" >0.14</td>\n",
+              "      <td id=\"T_5bf80_row2_col5\" class=\"data row2 col5\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row2_col6\" class=\"data row2 col6\" >0.17</td>\n",
+              "      <td id=\"T_5bf80_row2_col7\" class=\"data row2 col7\" >0.07</td>\n",
+              "      <td id=\"T_5bf80_row2_col8\" class=\"data row2 col8\" >0.07</td>\n",
+              "      <td id=\"T_5bf80_row2_col9\" class=\"data row2 col9\" >0.09</td>\n",
+              "      <td id=\"T_5bf80_row2_col10\" class=\"data row2 col10\" >-0.08</td>\n",
+              "      <td id=\"T_5bf80_row2_col11\" class=\"data row2 col11\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row2_col12\" class=\"data row2 col12\" >0.03</td>\n",
+              "      <td id=\"T_5bf80_row2_col13\" class=\"data row2 col13\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row2_col14\" class=\"data row2 col14\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row2_col15\" class=\"data row2 col15\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row2_col16\" class=\"data row2 col16\" >-0.06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row3\" class=\"row_heading level0 row3\" >Self_Employed</th>\n",
+              "      <td id=\"T_5bf80_row3_col0\" class=\"data row3 col0\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row3_col1\" class=\"data row3 col1\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row3_col2\" class=\"data row3 col2\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row3_col3\" class=\"data row3 col3\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row3_col4\" class=\"data row3 col4\" >0.13</td>\n",
+              "      <td id=\"T_5bf80_row3_col5\" class=\"data row3 col5\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row3_col6\" class=\"data row3 col6\" >0.12</td>\n",
+              "      <td id=\"T_5bf80_row3_col7\" class=\"data row3 col7\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row3_col8\" class=\"data row3 col8\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row3_col9\" class=\"data row3 col9\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row3_col10\" class=\"data row3 col10\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row3_col11\" class=\"data row3 col11\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row3_col12\" class=\"data row3 col12\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row3_col13\" class=\"data row3 col13\" >-0.09</td>\n",
+              "      <td id=\"T_5bf80_row3_col14\" class=\"data row3 col14\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row3_col15\" class=\"data row3 col15\" >0.03</td>\n",
+              "      <td id=\"T_5bf80_row3_col16\" class=\"data row3 col16\" >0.00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row4\" class=\"row_heading level0 row4\" >ApplicantIncome</th>\n",
+              "      <td id=\"T_5bf80_row4_col0\" class=\"data row4 col0\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row4_col1\" class=\"data row4 col1\" >0.05</td>\n",
+              "      <td id=\"T_5bf80_row4_col2\" class=\"data row4 col2\" >0.14</td>\n",
+              "      <td id=\"T_5bf80_row4_col3\" class=\"data row4 col3\" >0.13</td>\n",
+              "      <td id=\"T_5bf80_row4_col4\" class=\"data row4 col4\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row4_col5\" class=\"data row4 col5\" >-0.12</td>\n",
+              "      <td id=\"T_5bf80_row4_col6\" class=\"data row4 col6\" >0.57</td>\n",
+              "      <td id=\"T_5bf80_row4_col7\" class=\"data row4 col7\" >-0.05</td>\n",
+              "      <td id=\"T_5bf80_row4_col8\" class=\"data row4 col8\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row4_col9\" class=\"data row4 col9\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row4_col10\" class=\"data row4 col10\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row4_col11\" class=\"data row4 col11\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row4_col12\" class=\"data row4 col12\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row4_col13\" class=\"data row4 col13\" >-0.09</td>\n",
+              "      <td id=\"T_5bf80_row4_col14\" class=\"data row4 col14\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row4_col15\" class=\"data row4 col15\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row4_col16\" class=\"data row4 col16\" >0.16</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row5\" class=\"row_heading level0 row5\" >CoapplicantIncome</th>\n",
+              "      <td id=\"T_5bf80_row5_col0\" class=\"data row5 col0\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row5_col1\" class=\"data row5 col1\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row5_col2\" class=\"data row5 col2\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row5_col3\" class=\"data row5 col3\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row5_col4\" class=\"data row5 col4\" >-0.12</td>\n",
+              "      <td id=\"T_5bf80_row5_col5\" class=\"data row5 col5\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row5_col6\" class=\"data row5 col6\" >0.19</td>\n",
+              "      <td id=\"T_5bf80_row5_col7\" class=\"data row5 col7\" >-0.06</td>\n",
+              "      <td id=\"T_5bf80_row5_col8\" class=\"data row5 col8\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row5_col9\" class=\"data row5 col9\" >-0.06</td>\n",
+              "      <td id=\"T_5bf80_row5_col10\" class=\"data row5 col10\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row5_col11\" class=\"data row5 col11\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row5_col12\" class=\"data row5 col12\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row5_col13\" class=\"data row5 col13\" >0.00</td>\n",
+              "      <td id=\"T_5bf80_row5_col14\" class=\"data row5 col14\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row5_col15\" class=\"data row5 col15\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row5_col16\" class=\"data row5 col16\" >0.04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row6\" class=\"row_heading level0 row6\" >LoanAmount</th>\n",
+              "      <td id=\"T_5bf80_row6_col0\" class=\"data row6 col0\" >0.11</td>\n",
+              "      <td id=\"T_5bf80_row6_col1\" class=\"data row6 col1\" >0.15</td>\n",
+              "      <td id=\"T_5bf80_row6_col2\" class=\"data row6 col2\" >0.17</td>\n",
+              "      <td id=\"T_5bf80_row6_col3\" class=\"data row6 col3\" >0.12</td>\n",
+              "      <td id=\"T_5bf80_row6_col4\" class=\"data row6 col4\" >0.57</td>\n",
+              "      <td id=\"T_5bf80_row6_col5\" class=\"data row6 col5\" >0.19</td>\n",
+              "      <td id=\"T_5bf80_row6_col6\" class=\"data row6 col6\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row6_col7\" class=\"data row6 col7\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row6_col8\" class=\"data row6 col8\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row6_col9\" class=\"data row6 col9\" >-0.04</td>\n",
+              "      <td id=\"T_5bf80_row6_col10\" class=\"data row6 col10\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row6_col11\" class=\"data row6 col11\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row6_col12\" class=\"data row6 col12\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row6_col13\" class=\"data row6 col13\" >-0.14</td>\n",
+              "      <td id=\"T_5bf80_row6_col14\" class=\"data row6 col14\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row6_col15\" class=\"data row6 col15\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row6_col16\" class=\"data row6 col16\" >0.15</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row7\" class=\"row_heading level0 row7\" >Loan_Amount_Term</th>\n",
+              "      <td id=\"T_5bf80_row7_col0\" class=\"data row7 col0\" >-0.07</td>\n",
+              "      <td id=\"T_5bf80_row7_col1\" class=\"data row7 col1\" >-0.10</td>\n",
+              "      <td id=\"T_5bf80_row7_col2\" class=\"data row7 col2\" >0.07</td>\n",
+              "      <td id=\"T_5bf80_row7_col3\" class=\"data row7 col3\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row7_col4\" class=\"data row7 col4\" >-0.05</td>\n",
+              "      <td id=\"T_5bf80_row7_col5\" class=\"data row7 col5\" >-0.06</td>\n",
+              "      <td id=\"T_5bf80_row7_col6\" class=\"data row7 col6\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row7_col7\" class=\"data row7 col7\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row7_col8\" class=\"data row7 col8\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row7_col9\" class=\"data row7 col9\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row7_col10\" class=\"data row7 col10\" >0.03</td>\n",
+              "      <td id=\"T_5bf80_row7_col11\" class=\"data row7 col11\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row7_col12\" class=\"data row7 col12\" >-0.09</td>\n",
+              "      <td id=\"T_5bf80_row7_col13\" class=\"data row7 col13\" >0.11</td>\n",
+              "      <td id=\"T_5bf80_row7_col14\" class=\"data row7 col14\" >-0.09</td>\n",
+              "      <td id=\"T_5bf80_row7_col15\" class=\"data row7 col15\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row7_col16\" class=\"data row7 col16\" >-0.08</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row8\" class=\"row_heading level0 row8\" >Credit_History</th>\n",
+              "      <td id=\"T_5bf80_row8_col0\" class=\"data row8 col0\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row8_col1\" class=\"data row8 col1\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row8_col2\" class=\"data row8 col2\" >0.07</td>\n",
+              "      <td id=\"T_5bf80_row8_col3\" class=\"data row8 col3\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row8_col4\" class=\"data row8 col4\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row8_col5\" class=\"data row8 col5\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row8_col6\" class=\"data row8 col6\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row8_col7\" class=\"data row8 col7\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row8_col8\" class=\"data row8 col8\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row8_col9\" class=\"data row8 col9\" >0.54</td>\n",
+              "      <td id=\"T_5bf80_row8_col10\" class=\"data row8 col10\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row8_col11\" class=\"data row8 col11\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row8_col12\" class=\"data row8 col12\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row8_col13\" class=\"data row8 col13\" >0.05</td>\n",
+              "      <td id=\"T_5bf80_row8_col14\" class=\"data row8 col14\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row8_col15\" class=\"data row8 col15\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row8_col16\" class=\"data row8 col16\" >-0.06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row9\" class=\"row_heading level0 row9\" >Loan_Status</th>\n",
+              "      <td id=\"T_5bf80_row9_col0\" class=\"data row9 col0\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row9_col1\" class=\"data row9 col1\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row9_col2\" class=\"data row9 col2\" >0.09</td>\n",
+              "      <td id=\"T_5bf80_row9_col3\" class=\"data row9 col3\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row9_col4\" class=\"data row9 col4\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row9_col5\" class=\"data row9 col5\" >-0.06</td>\n",
+              "      <td id=\"T_5bf80_row9_col6\" class=\"data row9 col6\" >-0.04</td>\n",
+              "      <td id=\"T_5bf80_row9_col7\" class=\"data row9 col7\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row9_col8\" class=\"data row9 col8\" >0.54</td>\n",
+              "      <td id=\"T_5bf80_row9_col9\" class=\"data row9 col9\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row9_col10\" class=\"data row9 col10\" >-0.10</td>\n",
+              "      <td id=\"T_5bf80_row9_col11\" class=\"data row9 col11\" >0.14</td>\n",
+              "      <td id=\"T_5bf80_row9_col12\" class=\"data row9 col12\" >-0.04</td>\n",
+              "      <td id=\"T_5bf80_row9_col13\" class=\"data row9 col13\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row9_col14\" class=\"data row9 col14\" >-0.04</td>\n",
+              "      <td id=\"T_5bf80_row9_col15\" class=\"data row9 col15\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row9_col16\" class=\"data row9 col16\" >-0.03</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row10\" class=\"row_heading level0 row10\" >Property_Area_Rural</th>\n",
+              "      <td id=\"T_5bf80_row10_col0\" class=\"data row10 col0\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row10_col1\" class=\"data row10 col1\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row10_col2\" class=\"data row10 col2\" >-0.08</td>\n",
+              "      <td id=\"T_5bf80_row10_col3\" class=\"data row10 col3\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row10_col4\" class=\"data row10 col4\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row10_col5\" class=\"data row10 col5\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row10_col6\" class=\"data row10 col6\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row10_col7\" class=\"data row10 col7\" >0.03</td>\n",
+              "      <td id=\"T_5bf80_row10_col8\" class=\"data row10 col8\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row10_col9\" class=\"data row10 col9\" >-0.10</td>\n",
+              "      <td id=\"T_5bf80_row10_col10\" class=\"data row10 col10\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row10_col11\" class=\"data row10 col11\" >-0.50</td>\n",
+              "      <td id=\"T_5bf80_row10_col12\" class=\"data row10 col12\" >-0.45</td>\n",
+              "      <td id=\"T_5bf80_row10_col13\" class=\"data row10 col13\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row10_col14\" class=\"data row10 col14\" >-0.08</td>\n",
+              "      <td id=\"T_5bf80_row10_col15\" class=\"data row10 col15\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row10_col16\" class=\"data row10 col16\" >0.04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row11\" class=\"row_heading level0 row11\" >Property_Area_Semiurban</th>\n",
+              "      <td id=\"T_5bf80_row11_col0\" class=\"data row11 col0\" >-0.11</td>\n",
+              "      <td id=\"T_5bf80_row11_col1\" class=\"data row11 col1\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row11_col2\" class=\"data row11 col2\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row11_col3\" class=\"data row11 col3\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row11_col4\" class=\"data row11 col4\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row11_col5\" class=\"data row11 col5\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row11_col6\" class=\"data row11 col6\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row11_col7\" class=\"data row11 col7\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row11_col8\" class=\"data row11 col8\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row11_col9\" class=\"data row11 col9\" >0.14</td>\n",
+              "      <td id=\"T_5bf80_row11_col10\" class=\"data row11 col10\" >-0.50</td>\n",
+              "      <td id=\"T_5bf80_row11_col11\" class=\"data row11 col11\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row11_col12\" class=\"data row11 col12\" >-0.55</td>\n",
+              "      <td id=\"T_5bf80_row11_col13\" class=\"data row11 col13\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row11_col14\" class=\"data row11 col14\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row11_col15\" class=\"data row11 col15\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row11_col16\" class=\"data row11 col16\" >0.01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row12\" class=\"row_heading level0 row12\" >Property_Area_Urban</th>\n",
+              "      <td id=\"T_5bf80_row12_col0\" class=\"data row12 col0\" >0.03</td>\n",
+              "      <td id=\"T_5bf80_row12_col1\" class=\"data row12 col1\" >0.00</td>\n",
+              "      <td id=\"T_5bf80_row12_col2\" class=\"data row12 col2\" >0.03</td>\n",
+              "      <td id=\"T_5bf80_row12_col3\" class=\"data row12 col3\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row12_col4\" class=\"data row12 col4\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row12_col5\" class=\"data row12 col5\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row12_col6\" class=\"data row12 col6\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row12_col7\" class=\"data row12 col7\" >-0.09</td>\n",
+              "      <td id=\"T_5bf80_row12_col8\" class=\"data row12 col8\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row12_col9\" class=\"data row12 col9\" >-0.04</td>\n",
+              "      <td id=\"T_5bf80_row12_col10\" class=\"data row12 col10\" >-0.45</td>\n",
+              "      <td id=\"T_5bf80_row12_col11\" class=\"data row12 col11\" >-0.55</td>\n",
+              "      <td id=\"T_5bf80_row12_col12\" class=\"data row12 col12\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row12_col13\" class=\"data row12 col13\" >-0.05</td>\n",
+              "      <td id=\"T_5bf80_row12_col14\" class=\"data row12 col14\" >0.07</td>\n",
+              "      <td id=\"T_5bf80_row12_col15\" class=\"data row12 col15\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row12_col16\" class=\"data row12 col16\" >-0.05</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row13\" class=\"row_heading level0 row13\" >Dependents_0</th>\n",
+              "      <td id=\"T_5bf80_row13_col0\" class=\"data row13 col0\" >-0.15</td>\n",
+              "      <td id=\"T_5bf80_row13_col1\" class=\"data row13 col1\" >-0.34</td>\n",
+              "      <td id=\"T_5bf80_row13_col2\" class=\"data row13 col2\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row13_col3\" class=\"data row13 col3\" >-0.09</td>\n",
+              "      <td id=\"T_5bf80_row13_col4\" class=\"data row13 col4\" >-0.09</td>\n",
+              "      <td id=\"T_5bf80_row13_col5\" class=\"data row13 col5\" >0.00</td>\n",
+              "      <td id=\"T_5bf80_row13_col6\" class=\"data row13 col6\" >-0.14</td>\n",
+              "      <td id=\"T_5bf80_row13_col7\" class=\"data row13 col7\" >0.11</td>\n",
+              "      <td id=\"T_5bf80_row13_col8\" class=\"data row13 col8\" >0.05</td>\n",
+              "      <td id=\"T_5bf80_row13_col9\" class=\"data row13 col9\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row13_col10\" class=\"data row13 col10\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row13_col11\" class=\"data row13 col11\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row13_col12\" class=\"data row13 col12\" >-0.05</td>\n",
+              "      <td id=\"T_5bf80_row13_col13\" class=\"data row13 col13\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row13_col14\" class=\"data row13 col14\" >-0.51</td>\n",
+              "      <td id=\"T_5bf80_row13_col15\" class=\"data row13 col15\" >-0.50</td>\n",
+              "      <td id=\"T_5bf80_row13_col16\" class=\"data row13 col16\" >-0.34</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row14\" class=\"row_heading level0 row14\" >Dependents_1</th>\n",
+              "      <td id=\"T_5bf80_row14_col0\" class=\"data row14 col0\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row14_col1\" class=\"data row14 col1\" >0.12</td>\n",
+              "      <td id=\"T_5bf80_row14_col2\" class=\"data row14 col2\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row14_col3\" class=\"data row14 col3\" >0.08</td>\n",
+              "      <td id=\"T_5bf80_row14_col4\" class=\"data row14 col4\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row14_col5\" class=\"data row14 col5\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row14_col6\" class=\"data row14 col6\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row14_col7\" class=\"data row14 col7\" >-0.09</td>\n",
+              "      <td id=\"T_5bf80_row14_col8\" class=\"data row14 col8\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row14_col9\" class=\"data row14 col9\" >-0.04</td>\n",
+              "      <td id=\"T_5bf80_row14_col10\" class=\"data row14 col10\" >-0.08</td>\n",
+              "      <td id=\"T_5bf80_row14_col11\" class=\"data row14 col11\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row14_col12\" class=\"data row14 col12\" >0.07</td>\n",
+              "      <td id=\"T_5bf80_row14_col13\" class=\"data row14 col13\" >-0.51</td>\n",
+              "      <td id=\"T_5bf80_row14_col14\" class=\"data row14 col14\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row14_col15\" class=\"data row14 col15\" >-0.20</td>\n",
+              "      <td id=\"T_5bf80_row14_col16\" class=\"data row14 col16\" >-0.13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row15\" class=\"row_heading level0 row15\" >Dependents_2</th>\n",
+              "      <td id=\"T_5bf80_row15_col0\" class=\"data row15 col0\" >0.13</td>\n",
+              "      <td id=\"T_5bf80_row15_col1\" class=\"data row15 col1\" >0.25</td>\n",
+              "      <td id=\"T_5bf80_row15_col2\" class=\"data row15 col2\" >-0.02</td>\n",
+              "      <td id=\"T_5bf80_row15_col3\" class=\"data row15 col3\" >0.03</td>\n",
+              "      <td id=\"T_5bf80_row15_col4\" class=\"data row15 col4\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row15_col5\" class=\"data row15 col5\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row15_col6\" class=\"data row15 col6\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row15_col7\" class=\"data row15 col7\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row15_col8\" class=\"data row15 col8\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row15_col9\" class=\"data row15 col9\" >0.06</td>\n",
+              "      <td id=\"T_5bf80_row15_col10\" class=\"data row15 col10\" >-0.00</td>\n",
+              "      <td id=\"T_5bf80_row15_col11\" class=\"data row15 col11\" >-0.01</td>\n",
+              "      <td id=\"T_5bf80_row15_col12\" class=\"data row15 col12\" >0.02</td>\n",
+              "      <td id=\"T_5bf80_row15_col13\" class=\"data row15 col13\" >-0.50</td>\n",
+              "      <td id=\"T_5bf80_row15_col14\" class=\"data row15 col14\" >-0.20</td>\n",
+              "      <td id=\"T_5bf80_row15_col15\" class=\"data row15 col15\" >1.00</td>\n",
+              "      <td id=\"T_5bf80_row15_col16\" class=\"data row15 col16\" >-0.13</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th id=\"T_5bf80_level0_row16\" class=\"row_heading level0 row16\" >Dependents_3+</th>\n",
+              "      <td id=\"T_5bf80_row16_col0\" class=\"data row16 col0\" >0.10</td>\n",
+              "      <td id=\"T_5bf80_row16_col1\" class=\"data row16 col1\" >0.14</td>\n",
+              "      <td id=\"T_5bf80_row16_col2\" class=\"data row16 col2\" >-0.06</td>\n",
+              "      <td id=\"T_5bf80_row16_col3\" class=\"data row16 col3\" >0.00</td>\n",
+              "      <td id=\"T_5bf80_row16_col4\" class=\"data row16 col4\" >0.16</td>\n",
+              "      <td id=\"T_5bf80_row16_col5\" class=\"data row16 col5\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row16_col6\" class=\"data row16 col6\" >0.15</td>\n",
+              "      <td id=\"T_5bf80_row16_col7\" class=\"data row16 col7\" >-0.08</td>\n",
+              "      <td id=\"T_5bf80_row16_col8\" class=\"data row16 col8\" >-0.06</td>\n",
+              "      <td id=\"T_5bf80_row16_col9\" class=\"data row16 col9\" >-0.03</td>\n",
+              "      <td id=\"T_5bf80_row16_col10\" class=\"data row16 col10\" >0.04</td>\n",
+              "      <td id=\"T_5bf80_row16_col11\" class=\"data row16 col11\" >0.01</td>\n",
+              "      <td id=\"T_5bf80_row16_col12\" class=\"data row16 col12\" >-0.05</td>\n",
+              "      <td id=\"T_5bf80_row16_col13\" class=\"data row16 col13\" >-0.34</td>\n",
+              "      <td id=\"T_5bf80_row16_col14\" class=\"data row16 col14\" >-0.13</td>\n",
+              "      <td id=\"T_5bf80_row16_col15\" class=\"data row16 col15\" >-0.13</td>\n",
+              "      <td id=\"T_5bf80_row16_col16\" class=\"data row16 col16\" >1.00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 51
+        }
+      ],
+      "source": [
+        "#Correlation table\n",
+        "corr_table = data.corr()\n",
+        "corr_table.style.background_gradient(cmap='magma').set_precision(2)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Checking for outliers"
+      ],
+      "metadata": {
+        "id": "kDLb0aJjunte"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "1qjI_GLA6Uwf",
+        "outputId": "8b65a216-19e6-41f5-cff2-cbfa135b3dca"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "sns.boxplot(data=data, palette='rainbow', orient='h')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4dbkVfwLIUyv"
+      },
+      "source": [
+        "We noticed presence of outliers in \"ApplicantIncome\" and \"CoapplicantIncome\" columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7usWPmnfIYz7",
+        "outputId": "7a8e76d8-9791-44db-9976-b9b1e62fcc35"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "20833\n"
+          ]
+        }
+      ],
+      "source": [
+        "#calculating maximum non-outlier\n",
+        "z_scores = stats.zscore(data['ApplicantIncome'])\n",
+        "outliers = (z_scores > 3) | (z_scores < -3)\n",
+        "max_non_outlier = np.max(data['ApplicantIncome'][~outliers])\n",
+        "print(max_non_outlier)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "a9Iyoea_Ia5I"
+      },
+      "outputs": [],
+      "source": [
+        "#Replacing outliers with maximum non-outlier\n",
+        "data['ApplicantIncome'] = np.where(data['ApplicantIncome'] > 20833, 20833, data['ApplicantIncome'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "O43OhzFUI2Xf",
+        "outputId": "6dd126c7-7e1f-4509-8299-04203c5ed6d5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 55
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "sns.boxplot(data=data, palette='rainbow', orient='h')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ye1MqcnDI4ZF"
+      },
+      "source": [
+        "We notice the change in the box plot"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N8kh0vOmI8q_",
+        "outputId": "d63a1cc0-934b-4405-ed30-4adaf0f43e2a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "8980.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Repeat same steps with \"CoapplicantIncome\"\n",
+        "z2_scores = stats.zscore(data['CoapplicantIncome'])\n",
+        "outliers2 = (z2_scores > 3) | (z2_scores < -3)\n",
+        "max2_non_outlier = np.max(data['CoapplicantIncome'][~outliers2])\n",
+        "print(max2_non_outlier)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2cHNwDn3Lycu"
+      },
+      "outputs": [],
+      "source": [
+        "data['CoapplicantIncome'] = np.where(data['CoapplicantIncome'] > 8980, 8980, data['CoapplicantIncome'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "LEnmmkUyL1t1",
+        "outputId": "530cfd1f-3bf9-4820-954e-b0e304fa67e6"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 58
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "sns.boxplot(data=data, palette='rainbow', orient='h')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p20P91vAL786"
+      },
+      "source": [
+        "We notice the change in the box plot"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Data visualization"
+      ],
+      "metadata": {
+        "id": "wqD2oUk3uxWs"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "MwLiIUGPP2wS",
+        "outputId": "d0385f15-b110-47e9-962d-2aa96eb8c376"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='LoanAmount', ylabel='Count'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 59
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#plot the 'LoanAmount' column with its density\n",
+        "sns.histplot(data['LoanAmount'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 466
+        },
+        "id": "Ce3yyBprP9qv",
+        "outputId": "71f23f03-f3f2-4318-c191-5003b54ae537"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='Gender', ylabel='LoanAmount'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "#bar plot (density)\n",
+        "#note that 1(orange): male, 0(blue):female\n",
+        "sns.barplot(x='Gender',y='LoanAmount',data=data)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 270
+        },
+        "id": "ojizMgpHg9ms",
+        "outputId": "18ece7d1-ac79-492a-bf44-9b14a4b91f70"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Gender  Married  Education  Self_Employed  ApplicantIncome  \\\n",
+              "0     1.0      0.0        1.0            0.0         0.275540   \n",
+              "1     1.0      1.0        1.0            0.0         0.214331   \n",
+              "2     1.0      1.0        1.0            1.0         0.137794   \n",
+              "3     1.0      1.0        0.0            0.0         0.117633   \n",
+              "4     1.0      0.0        1.0            0.0         0.282841   \n",
+              "\n",
+              "   CoapplicantIncome  LoanAmount  Loan_Amount_Term  Credit_History  \\\n",
+              "0           0.000000    0.198860           0.74359             1.0   \n",
+              "1           0.167929    0.172214           0.74359             1.0   \n",
+              "2           0.000000    0.082489           0.74359             1.0   \n",
+              "3           0.262584    0.160637           0.74359             1.0   \n",
+              "4           0.000000    0.191027           0.74359             1.0   \n",
+              "\n",
+              "   Loan_Status  Property_Area_Rural  Property_Area_Semiurban  \\\n",
+              "0          1.0                  0.0                      0.0   \n",
+              "1          0.0                  1.0                      0.0   \n",
+              "2          1.0                  0.0                      0.0   \n",
+              "3          1.0                  0.0                      0.0   \n",
+              "4          1.0                  0.0                      0.0   \n",
+              "\n",
+              "   Property_Area_Urban  Dependents_0  Dependents_1  Dependents_2  \\\n",
+              "0                  1.0           1.0           0.0           0.0   \n",
+              "1                  0.0           0.0           1.0           0.0   \n",
+              "2                  1.0           1.0           0.0           0.0   \n",
+              "3                  1.0           1.0           0.0           0.0   \n",
+              "4                  1.0           1.0           0.0           0.0   \n",
+              "\n",
+              "   Dependents_3+  \n",
+              "0            0.0  \n",
+              "1            0.0  \n",
+              "2            0.0  \n",
+              "3            0.0  \n",
+              "4            0.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e2756813-a9a0-4aa8-b32d-fd7035085af7\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "  <thead>\n",
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+              "      <th></th>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Married</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>Loan_Status</th>\n",
+              "      <th>Property_Area_Rural</th>\n",
+              "      <th>Property_Area_Semiurban</th>\n",
+              "      <th>Property_Area_Urban</th>\n",
+              "      <th>Dependents_0</th>\n",
+              "      <th>Dependents_1</th>\n",
+              "      <th>Dependents_2</th>\n",
+              "      <th>Dependents_3+</th>\n",
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+              "      <th>0</th>\n",
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+              "      <th>2</th>\n",
+              "      <td>1.0</td>\n",
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+              "      <td>0.0</td>\n",
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+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
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+              "      <th>4</th>\n",
+              "      <td>1.0</td>\n",
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+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2756813-a9a0-4aa8-b32d-fd7035085af7')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
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+              "      </button>\n",
+              "      \n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-e2756813-a9a0-4aa8-b32d-fd7035085af7 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-e2756813-a9a0-4aa8-b32d-fd7035085af7');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n",
+              "  "
+            ]
+          },
+          "metadata": {},
+          "execution_count": 61
+        }
+      ],
+      "source": [
+        "#feature scaling normalization\n",
+        "from sklearn.preprocessing import MinMaxScaler \n",
+        "#to take an object from class MinMaxScaler\n",
+        "scaler = MinMaxScaler() \n",
+        "#this code normalizes values in dataframe , but it returns an array\n",
+        "normalized_data = scaler.fit_transform(data)\n",
+        "\n",
+        "#converting array to dataframe\n",
+        "df = pd.DataFrame(normalized_data, columns=data.columns)\n",
+        "\n",
+        "#now we have df as a normalized dataframe with all values in range [0,1]\n",
+        "df.head()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define your data and labels\n",
+        "X =df.drop(['Loan_Status'],axis=1)\n",
+        "y =df['Loan_Status']"
+      ],
+      "metadata": {
+        "id": "6BeSlryE7hl4"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mxan00OA3vYg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b0bddff0-b140-4050-f566-f455be6f6bc8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best random_state: 8\n",
+            "Best accuracy: 0.8861788617886179\n"
+          ]
+        }
+      ],
+      "source": [
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "\n",
+        "# Define the range of random_state values to test\n",
+        "random_state_values = range(1, 101)\n",
+        "\n",
+        "best_random_state = None\n",
+        "best_accuracy = 0\n",
+        "\n",
+        "# Iterate over the random_state values\n",
+        "for random_state in random_state_values:\n",
+        "    # Split the data into training and testing sets\n",
+        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)\n",
+        "\n",
+        "    # Initialize and train your model\n",
+        "    model = LogisticRegression()\n",
+        "    model.fit(X_train, y_train)\n",
+        "\n",
+        "    # Evaluate the accuracy\n",
+        "    accuracy = model.score(X_test, y_test)\n",
+        "\n",
+        "    # Update the best accuracy and random_state value if necessary\n",
+        "    if accuracy > best_accuracy:\n",
+        "        best_accuracy = accuracy\n",
+        "        best_random_state = random_state\n",
+        "\n",
+        "# Print the best random_state value and corresponding accuracy\n",
+        "print(\"Best random_state:\", best_random_state)\n",
+        "print(\"Best accuracy:\", best_accuracy)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.metrics import accuracy_score, mean_squared_error, confusion_matrix, classification_report\n",
+        "x_train, x_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)"
+      ],
+      "metadata": {
+        "id": "kiuL_Eqf70VZ"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Logistic Regression\n",
+        "lr_model = LogisticRegression(solver='liblinear', C=10.0, random_state=8)\n",
+        "lr_model.fit(x_train, y_train)\n",
+        "lr_pred = lr_model.predict(x_test)\n",
+        "lr_acc = accuracy_score(y_test, lr_pred)\n",
+        "lr_mse = mean_squared_error(y_test, lr_pred)\n",
+        "lr_cm = confusion_matrix(y_test, lr_pred)\n",
+        "lr_cr = classification_report(y_test, lr_pred)\n",
+        "print(\"\\nLogistic Regression model results:\")\n",
+        "print(\"Logistic Regression accuracy:\", lr_acc)\n",
+        "print(\"Mean Squared Error:\", lr_mse)\n",
+        "print(\"Confusion Matrix:\\n\", lr_cm)\n",
+        "print(\"Classification Report:\\n\", lr_cr)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "U5mstPWk5SSB",
+        "outputId": "c21cac59-a717-4d41-d3b4-696cb4848382"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Logistic Regression model results:\n",
+            "Logistic Regression accuracy: 0.8861788617886179\n",
+            "Mean Squared Error: 0.11382113821138211\n",
+            "Confusion Matrix:\n",
+            " [[19 14]\n",
+            " [ 0 90]]\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "         0.0       1.00      0.58      0.73        33\n",
+            "         1.0       0.87      1.00      0.93        90\n",
+            "\n",
+            "    accuracy                           0.89       123\n",
+            "   macro avg       0.93      0.79      0.83       123\n",
+            "weighted avg       0.90      0.89      0.87       123\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n",
+        "cm=confusion_matrix(y_test,lr_pred)\n",
+        "plt.title('Heatmap of confusion_matrix',fontsize=15)\n",
+        "sns.heatmap(cm,annot=True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 454
+        },
+        "id": "ToSXlDnY5b8r",
+        "outputId": "faada0a0-5dbd-4c6f-c15c-e7880cae7360"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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8L57w+eef6+zZsxo5cqSefvrpUss99fc5YsQIbdu2TX369NGyZcs0cuRIdevWjWcD+Dju8/eQhg0bKi4uTv/973+1bdu2cm1z7Ngxx7ZlLbvYPfQl/9ifP3/+CqMtv+PHj6u4uFhRUVGlEv+5c+c0f/78S27/ySeflJp39OhR/fvf/3aMg7ic22+/XdIv92aXde/47Nmznda7GomJiZKkd99997LrxsfHKzAwUNnZ2WWOeSiJq2PHjhVa6UZFRUlSmb93X331lc6dO1eu/TRu3FjPPvusJOmHH3645LoxMTGKiYnRoUOHlJGRUWr5Z599pmPHjql58+alWv6/Fpf6+/zmm2+Um5tban5F/20uXLhQ7777rpo3b67Zs2frzTff1OnTp/Xwww+X+32GdyL5e9Do0aNVXFyspKSkMh/EceTIEU2fPt3xc/PmzVWtWjUtW7bMKXmcOXNGQ4YM0dGjR8s8TknVtXXrVveeQBnq16+vkJAQ/fDDD07jHIqKivTss89e9oPOxx9/rCVLljh+Pn/+vEaMGKGCggLdfffdiomJuWwMXbp00fXXX6+ffvpJY8aMcWqzzp8/X59++qmCg4M1aNCgKzhDZ4MHD1Z4eLi++OILTZo0qVRLd9WqVTp48KAkKSgoSIMGDVJxcbFSUlJUUFDgWG/btm166aWXJEnDhg276rgupVOnTpJ++bBx4diRXbt2XfTYEydOLLMj8Pnnn0v6pQNyOUOHDpUkpaam6tChQ475OTk5euaZZySpzIr516Jk0ODs2bOd3tt9+/ZpyJAhZW4THh4uf39/7dy5s8wPqlcjNzdXgwcPlp+fn2bPnq3g4GANHDhQ9957r7777juNHj3arcfDrwttfw/67W9/q//85z965ZVXFB8fr3bt2qlZs2Yyxmjnzp3auHGjgoOD9fjjj0v6JbE+9thjmj59utq2batu3bopMDBQy5cvV1FRkR599NEyLyH07t1bmZmZuuOOO9S1a1cFBQUpPDxcr776qtvPyc/PT6NGjdLzzz+vzp07q1u3bgoNDdXq1auVm5urlJQUvfPOOxfd/oknnlCvXr3UqVMnRUVFafXq1dq1a5eio6P19ttvlysGm82mDz/8UF27dtUrr7yi+fPnq127dtqzZ49WrFghPz8/zZgxw1EBX43Q0FDNnTtXvXv31ogRIzR58mTddNNNOn36tDZv3qwdO3bo+++/V/369SVJ6enpWrVqlZYuXaqmTZuqc+fOKigo0LJly3TmzBkNGzbsikZ1u6JZs2b63e9+p/fff1/t2rVTp06ddOrUKa1atUp33nmnTp06Vao9P378eI0cOVJt27ZVixYtZIzRhg0btG3bNoWGhmrkyJGXPe6IESO0bNkyffHFF2rRooW6desmY4wyMjJ04sQJ9e3bV0899VRFnXaF6927t1q3bq1169apefPm6tixo86cOaOvvvpK7dq106233qqVK1c6bRMQEKCePXtq0aJFatu2rW688UYFBASoY8eOGjhw4FXFM2jQIB06dEjjx493usY/ffp0rVq1Sq+//rruvPNOx4dB+BYqfw97+eWXlZmZqaSkJOXk5GjBggX66quvVFRUpN///vdauHCh0/pTp07VX//6V8XGxiojI0PLly9XYmKi1q1bd9GR1MOGDdMLL7yg4OBg/e///q9mzJhx0UfJusOf/vQnvffee2rTpo1WrFihL7/8Um3bttWqVavUvn37S247cuRI/f3vf9fx48e1YMEC5efna8CAAVq9enW5qv4S119/vb777js9/vjjOnnypObNm6etW7eqb9++WrFihR544IGrPU2HLl26aMOGDRoyZIiMMVqwYIFWrFihkJAQ/fnPf3a6E6BWrVrKzMzU+PHjFR4eroULF2r58uVq37695syZozfffNNtcV3K9OnT9dxzz6l27dpasmSJfvrpJ6Wlpemjjz4qc/233npL/fv316lTp/TFF19o8eLF8vPzU2pqqjZu3KgWLVpc9pjVq1fXwoUL9eabb6pp06ZasmSJ/v3vf6tly5Z65513NG/evEob1FgRAgICtHz5cv3+979XjRo19K9//UubN2/W0KFDtXTp0jJvO5V+eWTzgAEDdOTIEc2ZM0czZsxQZmbmVcUyZcoUff7550pISNDzzz/vtCwsLEwzZ86UMUYDBgzQ8ePHr+pY+HWyGU8Ouwb+T5cuXZSZmaldu3Zd9FY4AIB7/Ho/ZgMAgCtC8gcAwMcw4A+AV3j11Ve1ZcuWcq37l7/8ReHh4RUcEVB1cc0fgFcoGTdSHowtQVV14sQJjR49WvPnz9fBgwd1ww036M0339RNN90k6ZcnvY4dO1bTp09XXl6eOnbsqKlTp5Zr0O2FaPsD8Apff/21zC/fVHrZicSPqmrw4MFaunSpPvjgA23atEndu3dXYmKi47tIJkyYoMmTJ2vatGlavXq1goKC1KNHD505c8al41D5AwBQBZw+fVq1atXSP//5T911112O+fHx8erVq5defPFFRUdH649//KPj2RrHjx9XRESEZs2aVea3ol4MlT8AABWosLBQ+fn5TlNZ30Z5/vx5FRUVlfoip8DAQH377bfatWuXcnJyHI8Vl6SQkBB16NBBWVlZLsVUZQb89Wvc29MhAFVO7vmLfzUs4MtW7FtWofs/d9h9X8SU/vb7Gj9+vNO8sWPHaty4cU7zatWqpYSEBL344otq1aqVIiIi9NFHHykrK0vNmzd3PGI7IiLCabuIiIiLfiHXxVSZ5A8AQJVR7L7vWkhLS1NqaqrTPLvdXua6H3zwgQYNGqQGDRqoevXquvHGG/XQQw8pOzvbbfFItP0BAKhQdrtdtWvXdpoulvybNWumzMxMnTx5Unv37tWaNWt07tw5NW3a1PGNl9ZviMzNzXX52zBJ/gAAWJli901XICgoSFFRUTp27JiWLFmiPn36KDY2VpGRkU5fi52fn6/Vq1crISHBpf3T9gcAwKr4ypL21VqyZImMMWrZsqV27NihZ555RnFxcRo4cKBsNpuGDx+ul156SS1atFBsbKxGjx6t6Oho9e3b16XjkPwBALAwV1ixX63jx48rLS1NP//8s0JDQ5WUlKSXX37Z8a2Qo0aNUkFBgZ544gnl5eXptttu0+LFi0vdIXA5VeY+f0b7A6Ux2h8oW0WP9j+7/z9u21dAdGu37ctdqPwBALDyUNu/spD8AQCw8lDbv7Iw2h8AAB9D5Q8AgJUbH/JTFZH8AQCwou0PAAC8CZU/AABWjPYHAMC3eOohP5WFtj8AAD6Gyh8AACva/gAA+Bgvb/uT/AEAsPLy+/y55g8AgI+h8gcAwIq2PwAAPsbLB/zR9gcAwMdQ+QMAYEXbHwAAH0PbHwAAeBMqfwAALIzx7vv8Sf4AAFh5+TV/2v4AAPgYKn8AAKy8fMAfyR8AACsvb/uT/AEAsOKLfQAAgDeh8gcAwIq2PwAAPsbLB/zR9gcAwMdQ+QMAYOXlbX8qfwAArIqL3Te5oKioSKNHj1ZsbKwCAwPVrFkzvfjiizLGONYxxmjMmDGKiopSYGCgEhMTtX37dpeOQ/IHAKCKeO211zR16lS9/fbb2rx5s1577TVNmDBBb731lmOdCRMmaPLkyZo2bZpWr16toKAg9ejRQ2fOnCn3cWj7AwBg5aEBfytXrlSfPn101113SZKaNGmijz76SGvWrJH0S9U/adIkvfDCC+rTp48k6f3331dERIQWLFig/v37l+s4VP4AAFgYU+S2qbCwUPn5+U5TYWFhmce99dZblZGRoW3btkmSNmzYoG+//Va9evWSJO3atUs5OTlKTEx0bBMSEqIOHTooKyur3OdH8gcAoAKlp6crJCTEaUpPTy9z3eeee079+/dXXFyc/P39dcMNN2j48OF6+OGHJUk5OTmSpIiICKftIiIiHMvKg7Y/AABWbmz7p6WlKTU11Wme3W4vc91PPvlEH374oebMmaPWrVtr/fr1Gj58uKKjo5WcnOy2mEj+AABYufFWP7vdftFkb/XMM884qn9Juv7667V7926lp6crOTlZkZGRkqTc3FxFRUU5tsvNzVW7du3KHRNtfwAArDx0q9+pU6dUrZpzaq5evbqK/28/sbGxioyMVEZGhmN5fn6+Vq9erYSEhHIfh8ofAIAq4p577tHLL7+smJgYtW7dWt9//73eeOMNDRo0SJJks9k0fPhwvfTSS2rRooViY2M1evRoRUdHq2/fvuU+DskfAAArDz3h76233tLo0aP11FNP6eDBg4qOjtaTTz6pMWPGONYZNWqUCgoK9MQTTygvL0+33XabFi9erBo1apT7ODZz4WODPKhf496eDgGocnLPn/R0CECVtGLfsgrd/+l/T3HbvgK7P+W2fbkL1/wBAPAxtP0BALDy8i/2IfkDAGDlocf7Vhba/gAA+BgqfwAArLy88if5AwBg5eXX/Gn7AwDgY6j8AQCwou0PAICP8fK2P8kfAAArL6/8ueYPAICPofIHAMCKtj8AAD6Gtj8AAPAmVP4AAFh5eeVP8gcAwMoYT0dQoWj7AwDgY6j8AQCwou0PAICP8fLkT9sfAAAfQ+UPAIAVD/kBAMDHeHnbn+QPAIAVt/oBAABvQuUPAIAVbX8AAHyMlyd/2v4AAPgYKn8AAKy41Q8AAN9iihntDwAAvAjJHwAAq+Ji900uaNKkiWw2W6kpJSVFknTmzBmlpKQoLCxMwcHBSkpKUm5ursunR/IHAMDKFLtvcsHatWt14MABx7R06VJJ0v333y9JGjFihBYtWqS5c+cqMzNT+/fvV79+/Vw+Pa75AwBQRdSrV8/p51dffVXNmjVT586ddfz4cc2YMUNz5sxRt27dJEkzZ85Uq1attGrVKt1yyy3lPg6VPwAAVsXGbVNhYaHy8/OdpsLCwsuGcPbsWc2ePVuDBg2SzWZTdna2zp07p8TERMc6cXFxiomJUVZWlkunR/IHAMDKjdf809PTFRIS4jSlp6dfNoQFCxYoLy9Pjz76qCQpJydHAQEBqlOnjtN6ERERysnJcen0aPsDAGDlxif8paWlKTU11Wme3W6/7HYzZsxQr169FB0d7bZYSpD8AQCoQHa7vVzJ/kK7d+/Wl19+qU8//dQxLzIyUmfPnlVeXp5T9Z+bm6vIyEiX9k/bHwAAK2PcN12BmTNnqn79+rrrrrsc8+Lj4+Xv76+MjAzHvK1bt2rPnj1KSEhwaf9U/gAAWHnwi32Ki4s1c+ZMJScny8/v/6fpkJAQPfbYY0pNTVVoaKhq166toUOHKiEhwaWR/hKVv8+69ubWSpvxgv62ZqY+3b1QN3fv4LQ8JLyO/vCXp/W3NTP10Za5Gv3eOEU1ifJQtEDlaNuhjV6b9bL+mf2JVuxbptt7dLzous+8Olwr9i3TA4OTKjFC+IIvv/xSe/bs0aBBg0otmzhxou6++24lJSWpU6dOioyMdLo0UF4kfx9lr2nXT5t3afro/ylz+XPT/6SImEi9Ovhl/fHO4Tq076DGffii7IGuXbcCfk0Ca9bQjv/u1F+fn3zJ9Tr1vE2tb7xWhw4crqTIUOnceKufq7p37y5jjK655ppSy2rUqKF33nlHR48eVUFBgT799FOXr/dLtP191vdff6fvv/6uzGVRsdFqeWOcnk5M0d7teyVJ//P8VP193Xu6vU8nffmPpZUZKlBpVn21Rqu+WnPJdcIjwzXipaFK/e2zev39VyopMlQ6vtXP2eHDh/X3v/9dWVlZjvsKIyMjdeutt+rRRx8t9XQi/Pr4B/hLks4WnnPMM8bo3Nlzimt/LckfPstms2nM5DTNmfqxdm37ydPhAFfMpbb/2rVrdc0112jy5MkKCQlRp06d1KlTJ4WEhGjy5MmKi4vTunXrLrufsp52VGSKrvgk4F77dv6sQz8f1CPP/k5BtYPk5++ne4f0U3h0PdWtX9fT4QEe80hKfxWdL9LcGa5fY8WvjAfb/pXBpcp/6NChuv/++zVt2jTZbDanZcYYDRkyREOHDr3sYwbT09M1fvx4p3lxta9RqzotXQkHFaTofJFeezJdKROG6oNNH6nofJE2frtB2V+tK/W+A76i5fUtdP9jSRrU80lPh4JKYDw42r8yuJT8N2zYoFmzZpWZAGw2m0aMGKEbbrjhsvsp62lHA657yJVQUMF+/GGn/njncNWsVVN+/n7KP5qvVxe8rp2bdng6NMAj2nZoo7rhdfS/a/7hmOfnV11/GDNEDwxO0n23/NaD0QGucSn5R0ZGas2aNYqLiytz+Zo1axQREXHZ/ZT1tKPqtuquhIJKcurEKUlSVJMoNWvTXB/99UMPRwR4xuL/Xaq1y7Od5k38cIIW/+9Sff7JYg9FhQpTRdv17uJS8h85cqSeeOIJZWdn64477nAk+tzcXGVkZGj69On6y1/+UiGBwr1q1KyhyAvu26/fKEJNro3VybwTOrz/sBLu7Kj8o8d1eN8hxcQ10WNjB2vNv1drw/L1ngsaqGCBNWuoYWwDx8/RMVFq0bqZ8o+dUO7+g8o/lu+0/vnz53X00FHt2bm3skNFRWO0//+XkpKi8PBwTZw4UVOmTFFR0S+D9KpXr674+HjNmjVLDzzwQIUECvdq1qa5Xvz4/9+mNGjMYEnSsrkZenvkm6pbv64Gjh6kkPA6yjt4TF9/+pXmTv7YU+EClSKubUu9PW+i4+dh456SJH3+yWK9PGKCp8KCJ3h55W8z5soePHzu3DkdPvzLAy7Cw8Pl7+9/VYH0a9z7qrYHvFHu+ZOeDgGoklbsW1ah+y/488Nu21fQmKp3ufSKH/Lj7++vqCge9woA8EKM9gcAwMd4edufZ/sDAOBjqPwBALBitD8AAD6Gtj8AAPAmVP4AAFjwbH8AAHwNbX8AAOBNqPwBALDy8sqf5A8AgBW3+gEA4GO8vPLnmj8AAD6Gyh8AAAvj5ZU/yR8AACsvT/60/QEA8DFU/gAAWPGEPwAAfAxtfwAA4E2o/AEAsPLyyp/kDwCAhTHenfxp+wMAUIXs27dPjzzyiMLCwhQYGKjrr79e69atcyw3xmjMmDGKiopSYGCgEhMTtX37dpeOQfIHAMCq2LhvcsGxY8fUsWNH+fv764svvtB///tf/fWvf1XdunUd60yYMEGTJ0/WtGnTtHr1agUFBalHjx46c+ZMuY9D2x8AACsPXfN/7bXX1KhRI82cOdMxLzY21vHfxhhNmjRJL7zwgvr06SNJev/99xUREaEFCxaof//+5ToOlT8AABam2LhtKiwsVH5+vtNUWFhY5nEXLlyo9u3b6/7771f9+vV1ww03aPr06Y7lu3btUk5OjhITEx3zQkJC1KFDB2VlZZX7/Ej+AABUoPT0dIWEhDhN6enpZa77448/aurUqWrRooWWLFmi3//+9xo2bJjee+89SVJOTo4kKSIiwmm7iIgIx7LyoO0PAICVG9v+aWlpSk1NdZpnt9vLPmxxsdq3b69XXnlFknTDDTfohx9+0LRp05ScnOy2mKj8AQCwKnbfZLfbVbt2bafpYsk/KipK1157rdO8Vq1aac+ePZKkyMhISVJubq7TOrm5uY5l5UHyBwCgiujYsaO2bt3qNG/btm1q3LixpF8G/0VGRiojI8OxPD8/X6tXr1ZCQkK5j0PbHwAAC+Oh0f4jRozQrbfeqldeeUUPPPCA1qxZo3fffVfvvvuuJMlms2n48OF66aWX1KJFC8XGxmr06NGKjo5W3759y30ckj8AAFYeSv433XST5s+fr7S0NP35z39WbGysJk2apIcfftixzqhRo1RQUKAnnnhCeXl5uu2227R48WLVqFGj3MexmSryDMN+jXt7OgSgysk9f9LTIQBV0op9yyp0/3kPdXXbvup89JXb9uUuVP4AAFgVezqAikXyBwDAwlPX/CsLo/0BAPAxVP4AAFjR9gcAwLd4e9uf5A8AgJWXV/5c8wcAwMdQ+QMAYGG8vPIn+QMAYOXlyZ+2PwAAPobKHwAAC9r+AAD4Gi9P/rT9AQDwMVT+AABY0PYHAMDHkPwBAPAx3p78ueYPAICPofIHAMDK2DwdQYUi+QMAYEHbHwAAeBUqfwAALEwxbX8AAHwKbX8AAOBVqPwBALAwjPYHAMC30PYHAABehcofAAALRvsDAOBjjPF0BBWL5A8AgIW3V/5c8wcAwMeQ/AEAsDDFNrdNrhg3bpxsNpvTFBcX51h+5swZpaSkKCwsTMHBwUpKSlJubq7L50fyBwDAwhj3Ta5q3bq1Dhw44Ji+/fZbx7IRI0Zo0aJFmjt3rjIzM7V//37169fP5WNwzR8AgCrEz89PkZGRpeYfP35cM2bM0Jw5c9StWzdJ0syZM9WqVSutWrVKt9xyS7mPQeUPAICFO9v+hYWFys/Pd5oKCwsveuzt27crOjpaTZs21cMPP6w9e/ZIkrKzs3Xu3DklJiY61o2Li1NMTIyysrJcOj+SPwAAFsbY3Dalp6crJCTEaUpPTy/zuB06dNCsWbO0ePFiTZ06Vbt27dLtt9+uEydOKCcnRwEBAapTp47TNhEREcrJyXHp/Gj7AwBQgdLS0pSamuo0z263l7lur169HP/dpk0bdejQQY0bN9Ynn3yiwMBAt8VE8gcAwMKdz/a32+0XTfaXU6dOHV1zzTXasWOHfvOb3+js2bPKy8tzqv5zc3PLHCNwKbT9AQCwKDY2t01X4+TJk9q5c6eioqIUHx8vf39/ZWRkOJZv3bpVe/bsUUJCgkv7pfIHAKCKGDlypO655x41btxY+/fv19ixY1W9enU99NBDCgkJ0WOPPabU1FSFhoaqdu3aGjp0qBISElwa6S+R/AEAKMVcZcV+pX7++Wc99NBDOnLkiOrVq6fbbrtNq1atUr169SRJEydOVLVq1ZSUlKTCwkL16NFDU6ZMcfk4NmOqxtcX9Gvc29MhAFVO7vmTng4BqJJW7FtWofvfcs2dbttX3LbP3bYvd6HyBwDAomqUxRWHAX8AAPgYKn8AACy8/St9Sf4AAFhc7S16VR1tfwAAfAyVPwAAFp661a+ykPwBALBgtD8AAPAqVP4AAFh4+4A/kj8AABbefs2ftj8AAD6Gyh8AAAtvH/BH8gcAwIJr/pVk4YFsT4cAVDmn9y/3dAiAT+KaPwAA8CpVpvIHAKCqoO0PAICP8fLxfrT9AQDwNVT+AABY0PYHAMDHMNofAAB4FSp/AAAsij0dQAUj+QMAYGFE2x8AAHgRKn8AACyKvfxGf5I/AAAWxV7e9if5AwBgwTV/AADgVaj8AQCw4FY/AAB8DG1/AABQ6V599VXZbDYNHz7cMe/MmTNKSUlRWFiYgoODlZSUpNzcXJf3TfIHAMCi2I3TlVi7dq3+53/+R23atHGaP2LECC1atEhz585VZmam9u/fr379+rm8f5I/AAAWnkz+J0+e1MMPP6zp06erbt26jvnHjx/XjBkz9MYbb6hbt26Kj4/XzJkztXLlSq1atcqlY5D8AQCoQIWFhcrPz3eaCgsLL7p+SkqK7rrrLiUmJjrNz87O1rlz55zmx8XFKSYmRllZWS7FRPIHAMDCyOa2KT09XSEhIU5Tenp6mcf9xz/+oe+++67M5Tk5OQoICFCdOnWc5kdERCgnJ8el82O0PwAAFsVuHOyflpam1NRUp3l2u73Uenv37tXTTz+tpUuXqkaNGu4LoAwkfwAAKpDdbi8z2VtlZ2fr4MGDuvHGGx3zioqK9M033+jtt9/WkiVLdPbsWeXl5TlV/7m5uYqMjHQpJpI/AAAWnni2/x133KFNmzY5zRs4cKDi4uL07LPPqlGjRvL391dGRoaSkpIkSVu3btWePXuUkJDg0rFI/gAAWHjiS/1q1aql6667zmleUFCQwsLCHPMfe+wxpaamKjQ0VLVr19bQoUOVkJCgW265xaVjkfwBALCoqo/3nThxoqpVq6akpCQVFhaqR48emjJlisv7sRljqsS3FvsFNPB0CECVc3r/ck+HAFRJ/uFNK3T/n0b+1m376pczx237chcqfwAALIpt3v1sf5I/AAAWVaIlXoF4yA8AAD6Gyh8AAIuqOuDPXUj+AABYuPMJf1URbX8AAHwMlT8AABaeeMJfZSL5AwBgwWh/AADgVaj8AQCw8PYBfyR/AAAsuNUPAAAfwzV/AADgVaj8AQCw4Jo/AAA+xtuv+dP2BwDAx1D5AwBg4e2VP8kfAAAL4+XX/Gn7AwDgY6j8AQCwoO0PAICP8fbkT9sfAAAfQ+UPAICFtz/el+QPAIAFT/gDAMDHcM0fAAB4FSp/AAAsvL3yJ/kDAGDh7QP+aPsDAOBjqPwBALDw9tH+VP4AAFgUu3FyxdSpU9WmTRvVrl1btWvXVkJCgr744gvH8jNnziglJUVhYWEKDg5WUlKScnNzXT4/kj8AAFVEw4YN9eqrryo7O1vr1q1Tt27d1KdPH/3nP/+RJI0YMUKLFi3S3LlzlZmZqf3796tfv34uH8dmjKkS4xr8Ahp4OgSgyjm9f7mnQwCqJP/wphW6//TGj7htX2m7Z1/V9qGhoXr99dd13333qV69epozZ47uu+8+SdKWLVvUqlUrZWVl6ZZbbin3PrnmDwCARbEbx/sXFhaqsLDQaZ7dbpfdbr/kdkVFRZo7d64KCgqUkJCg7OxsnTt3TomJiY514uLiFBMT43Lyp+0PAEAFSk9PV0hIiNOUnp5+0fU3bdqk4OBg2e12DRkyRPPnz9e1116rnJwcBQQEqE6dOk7rR0REKCcnx6WYqPwBALBw50N+0tLSlJqa6jTvUlV/y5YttX79eh0/flzz5s1TcnKyMjMz3RgRyR8AgFLcORiuPC3+CwUEBKh58+aSpPj4eK1du1ZvvvmmHnzwQZ09e1Z5eXlO1X9ubq4iIyNdiom2PwAAFp661a/MWIqLVVhYqPj4ePn7+ysjI8OxbOvWrdqzZ48SEhJc2ieVPwAAVURaWpp69eqlmJgYnThxQnPmzNHXX3+tJUuWKCQkRI899phSU1MVGhqq2rVra+jQoUpISHBpsJ9E8gcAoBRPPeHv4MGD+t3vfqcDBw4oJCREbdq00ZIlS/Sb3/xGkjRx4kRVq1ZNSUlJKiwsVI8ePTRlyhSXj8N9/kAVxn3+QNkq+j7/F5r81m37eumnOW7bl7twzR8AAB9D2x8AAIsq0RKvQCR/AAAs3Hmff1VE2x8AAB9D5Q8AgIU7n+1fFZH8AQCw8O7UT9sfAACfQ+UPAICFtw/4I/kDAGDBNX8AAHyMd6d+rvkDAOBzqPwBALDgmj8AAD7GeHnjn7Y/AAA+hsofAAAL2v4AAPgYb7/Vj7Y/AAA+hsofAAAL7677Sf4AAJRC2x8+4/dDkrVj2yqdzN+pld8u0k3t23k6JKBSFRSc0quTpuk3/ZIV37WPHn4yVZs2b3UsN8bo7envq0vv3yq+ax8NfjpNu/fu82DEwJUh+UOSdP/9vfWX18fqxZfe0E0demrDxv/q888+VL16YZ4ODag0Y159U1lrv1f6mJGa/8FU3XrzjXr86T8p99BhSdLfP5yrD+ct1JhnhmrO9EkKrFFDT6a+oMLCsx6OHO5W7MapKiL5Q5I04unH9bcZc/Te+59o8+bteirlOZ06dVoDH+3v6dCASnGmsFBfZn6r1JTH1L7d9YppGK2Uxx5RTMNofTz/Mxlj9MEnC/REcn91uz1BLZvH6pXRI3Xw8BFlLF/p6fDhZsaN/6uKSP6Qv7+/bryxjTKWLXfMM8YoY9m3uuWWeA9GBlSeovNFKioqlj3A32m+3R6g7zb+Rz/vz9HhI8eU0P4Gx7JawUFqc21LbfhhS2WHiwpG5e+ivXv3atCgQZdcp7CwUPn5+U6TMVXz05EvCA8PlZ+fnw7mHnaaf/DgIUVG1PNQVEDlCgqqqbbXtdK0WR/p4KEjKioq0qIly7Thhy06fPioDh89JkkKC63rtF1YaF0dPnLMEyEDV8ztyf/o0aN67733LrlOenq6QkJCnCZTfMLdoQCAS9JHj5SMUbe+j+jGrr314dx/qldiZ9mq0ST1Nd7e9nf5Vr+FCxdecvmPP/542X2kpaUpNTXVaV7dsDhXQ4GbHD58VOfPn1f9iHCn+fXr11NO7iEPRQVUvpiG0Zr1zus6dfqMCgpOqV54qP44Ol0NoyMV/n8V/5Gjx1QvPNSxzZGjx9SyRTNPhYwKUlXb9e7icvLv27evbDbbJdv0Npvtkvuw2+2y2+0ubYOKc+7cOX333UZ163qbFi5cIumX96Nb19s0ZepMD0cHVL6agTVUM7CGjuef0Mo12Up9atAvHwDC6mpV9nrFXfNLsj9ZUKCN/92qB+69y8MRA65xOflHRUVpypQp6tOnT5nL169fr/h4Bon92kx8c7pmzpio7O82au3a7zVs6OMKCgrUrPc+9nRoQKVZsTpbxhg1iWmoPT/v11/fmaHYmIbqe1d32Ww2DXigr9597x9q3LCBGkRH6O3pH6h+eJjuuP1WT4cONyv28nFoLif/+Ph4ZWdnXzT5X64rgKpp7tyFqhceqnFjRioysp42bPiP7rr7ER08ePjyGwNe4sTJAk2aNlO5hw4rpHYt/abzbRr2ZLL8/X75p3LQw/fr9OkzGjdhsk6cPKkb27TWtL++KLs9wMORw928PYvZjIuZevny5SooKFDPnj3LXF5QUKB169apc+fOLgXiF9DApfUBX3B6//LLrwT4IP/wphW6/0ca93Pbvmbv/tRt+3IXl4ew3n777RdN/JIUFBTkcuIHAKAqKZZx2+SK9PR03XTTTapVq5bq16+vvn37auvWrU7rnDlzRikpKQoLC1NwcLCSkpKUm5vr0nG4fwUAAAtP3eqXmZmplJQUrVq1SkuXLtW5c+fUvXt3FRQUONYZMWKEFi1apLlz5yozM1P79+9Xv36udSpcbvtXFNr+QGm0/YGyVXTb/6HGfd22r492L7jibQ8dOqT69esrMzNTnTp10vHjx1WvXj3NmTNH9913nyRpy5YtatWqlbKysnTLLbeUa79U/gAAWLjz8b5lPdW2sLCwXHEcP35ckhQa+suzJbKzs3Xu3DklJiY61omLi1NMTIyysrLKfX4kfwAALNx5zb+sp9qmp6dfPobiYg0fPlwdO3bUddddJ0nKyclRQECA6tSp47RuRESEcnJyyn1+Lt/qBwCAt3PnY3nLeqqt9UF3ZUlJSdEPP/ygb7/91m2xlCD5AwBQgcp6qu3l/OEPf9C//vUvffPNN2rYsKFjfmRkpM6ePau8vDyn6j83N1eRkZHl3j9tfwAALDz1lb7GGP3hD3/Q/PnztWzZMsXGxjotj4+Pl7+/vzIyMhzztm7dqj179ighIaHcx6HyBwDAwlM3wqWkpGjOnDn65z//qVq1ajmu44eEhCgwMFAhISF67LHHlJqaqtDQUNWuXVtDhw5VQkJCuUf6SyR/AACqjKlTp0qSunTp4jR/5syZevTRRyVJEydOVLVq1ZSUlKTCwkL16NFDU6ZMcek43OcPVGHc5w+UraLv8+8Tc7fb9vXPPf9y277chcofAAALV6/V/9ow4A8AAB9D5Q8AgIU77/Ovikj+AABYuPptfL82tP0BAPAxVP4AAFhUkRvhKgzJHwAAC28f7U/yBwDAwtsH/HHNHwAAH0PlDwCAhbeP9if5AwBg4e0D/mj7AwDgY6j8AQCwoO0PAICPYbQ/AADwKlT+AABYFHv5gD+SPwAAFt6d+mn7AwDgc6j8AQCwYLQ/AAA+huQPAICP4Ql/AADAq1D5AwBgQdsfAAAfwxP+AACAV6HyBwDAwtsH/JH8AQCw8PZr/rT9AQDwMVT+AABY0PYHAMDH0PYHAACV4ptvvtE999yj6Oho2Ww2LViwwGm5MUZjxoxRVFSUAgMDlZiYqO3bt7t8HJI/AAAWxo3/c0VBQYHatm2rd955p8zlEyZM0OTJkzVt2jStXr1aQUFB6tGjh86cOePScWj7AwBgUeyha/69evVSr169ylxmjNGkSZP0wgsvqE+fPpKk999/XxEREVqwYIH69+9f7uNQ+QMAYOHOyr+wsFD5+flOU2Fhocsx7dq1Szk5OUpMTHTMCwkJUYcOHZSVleXSvkj+AABUoPT0dIWEhDhN6enpLu8nJydHkhQREeE0PyIiwrGsvGj7AwBg4c62f1pamlJTU53m2e12t+3/SpD8AQCwcOcX+9jtdrck+8jISElSbm6uoqKiHPNzc3PVrl07l/ZF2x8AgF+B2NhYRUZGKiMjwzEvPz9fq1evVkJCgkv7ovIHAMDCU6P9T548qR07djh+3rVrl9avX6/Q0FDFxMRo+PDheumll9SiRQvFxsZq9OjRio6OVt++fV06DskfAAALd7b9XbFu3Tp17drV8XPJWIHk5GTNmjVLo0aNUkFBgZ544gnl5eXptttu0+LFi1WjRg2XjmMzVeQBxn4BDTwdAlDlnN6/3NMhAFWSf3jTCt1/i3rxbtvX9kPZbtuXu1D5AwBg4am2f2Uh+QMAYOGptn9lYbQ/AAA+hsofAAALY4o9HUKFIvkDAGBR7OVtf5I/AAAWVeRGuArDNX8AAHwMlT8AABa0/QEA8DG0/QEAgFeh8gcAwIIn/AEA4GN4wh8AAPAqVP4AAFh4+4A/kj8AABbefqsfbX8AAHwMlT8AABa0/QEA8DHc6gcAgI/x9sqfa/4AAPgYKn8AACy8fbQ/yR8AAAva/gAAwKtQ+QMAYMFofwAAfAxf7AMAALwKlT8AABa0/QEA8DGM9gcAAF6Fyh8AAAsG/AEA4GOMMW6bXPXOO++oSZMmqlGjhjp06KA1a9a4/fxI/gAAWHgq+X/88cdKTU3V2LFj9d1336lt27bq0aOHDh486NbzI/kDAFBFvPHGG3r88cc1cOBAXXvttZo2bZpq1qypv//97249DskfAAAL48apsLBQ+fn5TlNhYWGpY549e1bZ2dlKTEx0zKtWrZoSExOVlZXl1vOrMgP+zp/d5+kQoF9+SdPT05WWlia73e7pcIAqgb8L3+POnDRu3DiNHz/ead7YsWM1btw4p3mHDx9WUVGRIiIinOZHRERoy5YtbotHkmzG229mhEvy8/MVEhKi48ePq3bt2p4OB6gS+LvA1SgsLCxV6dvt9lIfJPfv368GDRpo5cqVSkhIcMwfNWqUMjMztXr1arfFVGUqfwAAvFFZib4s4eHhql69unJzc53m5+bmKjIy0q0xcc0fAIAqICAgQPHx8crIyHDMKy4uVkZGhlMnwB2o/AEAqCJSU1OVnJys9u3b6+abb9akSZNUUFCggQMHuvU4JH84sdvtGjt2LIOagAvwd4HK8uCDD+rQoUMaM2aMcnJy1K5dOy1evLjUIMCrxYA/AAB8DNf8AQDwMSR/AAB8DMkfAAAfQ/IHAMDHkPwBAPAxJH84VMZ3SAO/Jt98843uueceRUdHy2azacGCBZ4OCXALkj8kVd53SAO/JgUFBWrbtq3eeecdT4cCuBX3+UOS1KFDB9100016++23Jf3ySMlGjRpp6NCheu655zwcHeB5NptN8+fPV9++fT0dCnDVqPxRqd8hDQDwPJI/Lvkd0jk5OR6KCgBQUUj+AAD4GJI/KvU7pAEAnkfyR6V+hzQAwPP4Sl9IqrzvkAZ+TU6ePKkdO3Y4ft61a5fWr1+v0NBQxcTEeDAy4Opwqx8c3n77bb3++uuO75CePHmyOnTo4OmwAI/5+uuv1bVr11Lzk5OTNWvWrMoPCHATkj8AAD6Ga/4AAPgYkj8AAD6G5A8AgI8h+QMA4GNI/gAA+BiSPwAAPobkDwCAjyH5AwDgY0j+AAD4GJI/AAA+huQPAICP+X/YnYxITI7+kwAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "\n",
+        "# Choose a range of random_state values to test\n",
+        "random_state_values = range(1, 101)\n",
+        "\n",
+        "best_random_state = None\n",
+        "best_accuracy = 0.0\n",
+        "\n",
+        "# Iterate over the random_state values\n",
+        "for random_state in random_state_values:\n",
+        "    # Split the data into training and testing sets\n",
+        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)\n",
+        "\n",
+        "    # Initialize and train the SVM model\n",
+        "    svm_classifier = SVC(C=1.0, kernel='linear', random_state=random_state)\n",
+        "    svm_classifier.fit(X_train, y_train)\n",
+        "\n",
+        "    # Predict the labels for the test set\n",
+        "    y_predicted = svm_classifier.predict(X_test)\n",
+        "\n",
+        "    # Calculate the accuracy\n",
+        "    test_accuracy = accuracy_score(y_test, y_predicted)\n",
+        "\n",
+        "    # Update the best accuracy and random_state value if necessary\n",
+        "    if test_accuracy > best_accuracy:\n",
+        "        best_accuracy = test_accuracy\n",
+        "        best_random_state = random_state\n",
+        "\n",
+        "# Print the best random_state value and corresponding accuracy\n",
+        "print(\"Best random_state:\", best_random_state)\n",
+        "print(\"Best accuracy:\", best_accuracy)"
+      ],
+      "metadata": {
+        "id": "Djv1dnhcqdXw",
+        "outputId": "44d48885-3df7-4c5d-90df-07292a564c2c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best random_state: 8\n",
+            "Best accuracy: 0.8861788617886179\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#SVM\n",
+        "from sklearn.svm import SVC\n",
+        "\n",
+        "# Define your data and labels\n",
+        "X =df.drop(['Loan_Status'],axis=1)\n",
+        "y =df['Loan_Status']\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=8)\n",
+        "\n",
+        "svm_model = SVC(C=1.0, kernel='linear', random_state=8)\n",
+        "svm_model.fit(X_train, y_train)\n",
+        "svm_pred = svm_model.predict(X_test)\n",
+        "svm_acc = accuracy_score(y_test, svm_pred)\n",
+        "svm_mse = mean_squared_error(y_test, svm_pred)\n",
+        "svm_cm = confusion_matrix(y_test, svm_pred)\n",
+        "svm_cr = classification_report(y_test, svm_pred)\n",
+        "print(\"SVM model results:\")\n",
+        "print(\"SVM accuracy:\", svm_acc)\n",
+        "print(\"Mean Squared Error:\", svm_mse)\n",
+        "print(\"Confusion Matrix:\\n\", svm_cm)\n",
+        "print(\"Classification Report:\\n\", svm_cr)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QZVVJeHR8Wqp",
+        "outputId": "24e10e5c-2bb3-4c35-9ddf-301e5a88308a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "SVM model results:\n",
+            "SVM accuracy: 0.8861788617886179\n",
+            "Mean Squared Error: 0.11382113821138211\n",
+            "Confusion Matrix:\n",
+            " [[19 14]\n",
+            " [ 0 90]]\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "         0.0       1.00      0.58      0.73        33\n",
+            "         1.0       0.87      1.00      0.93        90\n",
+            "\n",
+            "    accuracy                           0.89       123\n",
+            "   macro avg       0.93      0.79      0.83       123\n",
+            "weighted avg       0.90      0.89      0.87       123\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n",
+        "cm=confusion_matrix(y_test,svm_pred)\n",
+        "plt.title('Heatmap of confusion_matrix',fontsize=15)\n",
+        "sns.heatmap(cm,annot=True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 454
+        },
+        "id": "jsiAez_h8djc",
+        "outputId": "8c95c1ae-5065-41a8-9e5e-48f79f2c5018"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x=df.drop(['Loan_Status'],axis=1)\n",
+        "y=df['Loan_Status']"
+      ],
+      "metadata": {
+        "id": "NvoOl4BU-j7v"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "\n",
+        "random_state_values = range(1, 101)\n",
+        "\n",
+        "best_random_state = None\n",
+        "best_accuracy = 0.0\n",
+        "\n",
+        "# Iterate over the random_state values\n",
+        "for random_state in random_state_values:\n",
+        "    # Split the data into training and testing sets\n",
+        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)\n",
+        "\n",
+        "    # Initialize and train your Decision Tree model\n",
+        "    dt_model = DecisionTreeClassifier(random_state=random_state)\n",
+        "    dt_model.fit(X_train, y_train)\n",
+        "\n",
+        "    # Evaluate the accuracy\n",
+        "    test_accuracy = dt_model.score(X_test, y_test)\n",
+        "\n",
+        "    # Update the best accuracy and random_state value if necessary\n",
+        "    if test_accuracy > best_accuracy:\n",
+        "        best_accuracy = test_accuracy\n",
+        "        best_random_state = random_state\n",
+        "\n",
+        "# Print the best random_state value and corresponding accuracy\n",
+        "print(\"Best random_state:\", best_random_state)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_l4bjB9BAo8R",
+        "outputId": "c2f64d05-6fbb-4797-fd40-d6c0a7f49032"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Best random_state: 7\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.metrics import accuracy_score\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "# Define your features and target\n",
+        "X =df.drop(['Loan_Status'],axis=1)\n",
+        "y =df['Loan_Status']\n",
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=7)\n",
+        "\n",
+        "# Initialize and train the Decision Tree model\n",
+        "dt_model = DecisionTreeClassifier(random_state=7, criterion = \"entropy\",max_depth=1)\n",
+        "dt_model.fit(X_train, y_train)\n",
+        "\n",
+        "# Predict the labels for the test set\n",
+        "y_pred = dt_model.predict(X_test)\n",
+        "\n",
+        "# Calculate the accuracy of the model\n",
+        "accuracy = accuracy_score(y_test, y_pred)\n",
+        "\n",
+        "# Print the accuracy\n",
+        "\n",
+        "dt_mse = mean_squared_error(y_test, y_pred)\n",
+        "dt_cm = confusion_matrix(y_test, y_pred)\n",
+        "dt_cr = classification_report(y_test, y_pred)\n",
+        "print(\"\\nDecision Tree model results:\")\n",
+        "print(\"Decision Tree Accuracy:\", accuracy)\n",
+        "print(\"Mean Squared Error:\", dt_mse)\n",
+        "print(\"Confusion Matrix:\\n\", dt_cm)\n",
+        "print(\"Classification Report:\\n\", dt_cr)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lRs16qLL8jBg",
+        "outputId": "b2b1492a-b7b4-42d5-c66a-ec7594583535"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Decision Tree model results:\n",
+            "Decision Tree Accuracy: 0.8292682926829268\n",
+            "Mean Squared Error: 0.17073170731707318\n",
+            "Confusion Matrix:\n",
+            " [[20 19]\n",
+            " [ 2 82]]\n",
+            "Classification Report:\n",
+            "               precision    recall  f1-score   support\n",
+            "\n",
+            "         0.0       0.91      0.51      0.66        39\n",
+            "         1.0       0.81      0.98      0.89        84\n",
+            "\n",
+            "    accuracy                           0.83       123\n",
+            "   macro avg       0.86      0.74      0.77       123\n",
+            "weighted avg       0.84      0.83      0.81       123\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import confusion_matrix\n",
+        "cm=confusion_matrix(y_test,y_pred)\n",
+        "plt.title('Heatmap of confusion_matrix',fontsize=15)\n",
+        "sns.heatmap(cm,annot=True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 454
+        },
+        "id": "NvB4GYytEIzC",
+        "outputId": "0a046a0c-0379-4abf-889c-664ba4752fb6"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Loan Status Prediction/Loan eligible prediction/README.md b/Loan Status Prediction/Loan eligible prediction/README.md
new file mode 100644
index 00000000..6bacc116
--- /dev/null
+++ b/Loan Status Prediction/Loan eligible prediction/README.md	
@@ -0,0 +1,3 @@
+# Loan-Eligibility-Prediction
+
+Loan eligibility prediction project using python. It includes implementation of popular machine learning algorithms such as decision tree, support vector machine (SVM), and logistic regression. These models are utilized to determine whether a loan applicant is eligible for a loan based on various factors.
\ No newline at end of file