Loan_Default_Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Loan Default Prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMtGgyUfdt+DjxJzFI5e0zC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MainakRepositor/Data-Analysis/blob/master/Loan_Default_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mPMM-WywR8z"
      },
      "source": [
        "# Loan Default Prediction\n",
        "\n",
        "<hr>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JuCThxwqOllU"
      },
      "source": [
        "## 1. Importing necessary libraries\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q_StCrlcwRlO"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import datetime \n",
        "import seaborn as sns\n",
        "sns.set_style(\"dark\")\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTurk9jiOrLY"
      },
      "source": [
        "## 2. Importing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "A6slDAmSv32A",
        "outputId": "9bf8270e-bb5f-4ff1-d8fc-cb826c702ebe"
      },
      "source": [
        "url = 'https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Loan%20payments%20data.csv'\n",
        "df = pd.read_csv(url,error_bad_lines=False)\n",
        "df.head(10)"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>effective_date</th>\n",
              "      <th>due_date</th>\n",
              "      <th>paid_off_time</th>\n",
              "      <th>past_due_days</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>xqd20166231</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>9/14/2016 19:31</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>xqd20168902</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>10/7/2016 9:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>xqd20160003</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>9/25/2016 16:58</td>\n",
              "      <td>NaN</td>\n",
              "      <td>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>xqd20160004</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>9/22/2016</td>\n",
              "      <td>9/22/2016 20:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>xqd20160005</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>10/8/2016</td>\n",
              "      <td>9/23/2016 21:36</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>xqd20160706</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>300</td>\n",
              "      <td>7</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>9/15/2016</td>\n",
              "      <td>9/9/2016 13:45</td>\n",
              "      <td>NaN</td>\n",
              "      <td>35</td>\n",
              "      <td>Master or Above</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>xqd20160007</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>10/8/2016</td>\n",
              "      <td>10/7/2016 23:07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>xqd20160008</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>10/8/2016</td>\n",
              "      <td>10/5/2016 20:33</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>xqd20160909</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>10/8/2016</td>\n",
              "      <td>10/8/2016 16:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>xqd20160010</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>9/10/2016</td>\n",
              "      <td>9/24/2016</td>\n",
              "      <td>9/24/2016 13:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Loan_ID loan_status  Principal  ...  age             education  Gender\n",
              "0  xqd20166231     PAIDOFF       1000  ...   45  High School or Below    male\n",
              "1  xqd20168902     PAIDOFF       1000  ...   50              Bechalor  female\n",
              "2  xqd20160003     PAIDOFF       1000  ...   33              Bechalor  female\n",
              "3  xqd20160004     PAIDOFF       1000  ...   27               college    male\n",
              "4  xqd20160005     PAIDOFF       1000  ...   28               college  female\n",
              "5  xqd20160706     PAIDOFF        300  ...   35       Master or Above    male\n",
              "6  xqd20160007     PAIDOFF       1000  ...   29               college    male\n",
              "7  xqd20160008     PAIDOFF       1000  ...   36               college    male\n",
              "8  xqd20160909     PAIDOFF       1000  ...   28               college    male\n",
              "9  xqd20160010     PAIDOFF        800  ...   26               college    male\n",
              "\n",
              "[10 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKKN5CTUOueT"
      },
      "source": [
        "## 3. Exploration of data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RFTEScMFwfJQ",
        "outputId": "ce006a88-aff3-429c-f603-11500819225b"
      },
      "source": [
        "r,c = df.shape\n",
        "print(\"Number of entries in the dataset    = \",r)\n",
        "print(\"NUmber of parameters in the dataset = \",c)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of entries in the dataset    =  500\n",
            "NUmber of parameters in the dataset =  11\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtOwvWbXxErS",
        "outputId": "d4b859f3-a330-4f0b-f518-f0ea8eb06bd2"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 500 entries, 0 to 499\n",
            "Data columns (total 11 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Loan_ID         500 non-null    object \n",
            " 1   loan_status     500 non-null    object \n",
            " 2   Principal       500 non-null    int64  \n",
            " 3   terms           500 non-null    int64  \n",
            " 4   effective_date  500 non-null    object \n",
            " 5   due_date        500 non-null    object \n",
            " 6   paid_off_time   400 non-null    object \n",
            " 7   past_due_days   200 non-null    float64\n",
            " 8   age             500 non-null    int64  \n",
            " 9   education       500 non-null    object \n",
            " 10  Gender          500 non-null    object \n",
            "dtypes: float64(1), int64(3), object(7)\n",
            "memory usage: 43.1+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xO4zPnE5Oxy_"
      },
      "source": [
        "## 4. Handling missing values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZGY4_xMnxIM6",
        "outputId": "947cf724-d358-4b3d-93f1-ef629fcaac4a"
      },
      "source": [
        "print(\"Are there any missing values in the dataset ?\",df.isna().values.any())"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Are there any missing values in the dataset ? True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1IbAfafTxQBY",
        "outputId": "4a07d645-7801-4394-da05-5aa20e9d41c8"
      },
      "source": [
        "print(\"Column-wise stats of the missing values in the dataset :\\n\")\n",
        "df.isna().sum()"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Column-wise stats of the missing values in the dataset :\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Loan_ID             0\n",
              "loan_status         0\n",
              "Principal           0\n",
              "terms               0\n",
              "effective_date      0\n",
              "due_date            0\n",
              "paid_off_time     100\n",
              "past_due_days     300\n",
              "age                 0\n",
              "education           0\n",
              "Gender              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "OcZK6cWbxZtI",
        "outputId": "474a1505-73ec-492e-d8a2-6c561263324f"
      },
      "source": [
        "df['paid_off_time'] = df['paid_off_time'].fillna(method=\"ffill\")\n",
        "df['past_due_days'] = df['past_due_days'].fillna(method='bfill')\n",
        "df.head()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan_ID</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>effective_date</th>\n",
              "      <th>due_date</th>\n",
              "      <th>paid_off_time</th>\n",
              "      <th>past_due_days</th>\n",
              "      <th>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>xqd20166231</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>9/14/2016 19:31</td>\n",
              "      <td>76.0</td>\n",
              "      <td>45</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>xqd20168902</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>10/7/2016 9:00</td>\n",
              "      <td>76.0</td>\n",
              "      <td>50</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>xqd20160003</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>10/7/2016</td>\n",
              "      <td>9/25/2016 16:58</td>\n",
              "      <td>76.0</td>\n",
              "      <td>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>xqd20160004</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>9/8/2016</td>\n",
              "      <td>9/22/2016</td>\n",
              "      <td>9/22/2016 20:00</td>\n",
              "      <td>76.0</td>\n",
              "      <td>27</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>xqd20160005</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/9/2016</td>\n",
              "      <td>10/8/2016</td>\n",
              "      <td>9/23/2016 21:36</td>\n",
              "      <td>76.0</td>\n",
              "      <td>28</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Loan_ID loan_status  Principal  ...  age             education  Gender\n",
              "0  xqd20166231     PAIDOFF       1000  ...   45  High School or Below    male\n",
              "1  xqd20168902     PAIDOFF       1000  ...   50              Bechalor  female\n",
              "2  xqd20160003     PAIDOFF       1000  ...   33              Bechalor  female\n",
              "3  xqd20160004     PAIDOFF       1000  ...   27               college    male\n",
              "4  xqd20160005     PAIDOFF       1000  ...   28               college  female\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VzpGc5u8O2Sj"
      },
      "source": [
        "## 5. Statistical analysis of data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "Sakeya0tx9Gg",
        "outputId": "5119d6b2-0774-4aa0-9418-79b4df616cc3"
      },
      "source": [
        "df.describe().T"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Principal</th>\n",
              "      <td>500.0</td>\n",
              "      <td>943.200</td>\n",
              "      <td>115.240274</td>\n",
              "      <td>300.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>terms</th>\n",
              "      <td>500.0</td>\n",
              "      <td>22.824</td>\n",
              "      <td>8.000064</td>\n",
              "      <td>7.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>past_due_days</th>\n",
              "      <td>500.0</td>\n",
              "      <td>60.004</td>\n",
              "      <td>26.996919</td>\n",
              "      <td>1.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>76.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>500.0</td>\n",
              "      <td>31.116</td>\n",
              "      <td>6.084784</td>\n",
              "      <td>18.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>51.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               count     mean         std  ...     50%     75%     max\n",
              "Principal      500.0  943.200  115.240274  ...  1000.0  1000.0  1000.0\n",
              "terms          500.0   22.824    8.000064  ...    30.0    30.0    30.0\n",
              "past_due_days  500.0   60.004   26.996919  ...    76.0    76.0    76.0\n",
              "age            500.0   31.116    6.084784  ...    30.0    35.0    51.0\n",
              "\n",
              "[4 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        },
        "id": "rrA4LWGlO9Jz",
        "outputId": "cd9538ba-3a5c-4c3a-cd54-caec5b206ef8"
      },
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "sns.heatmap(df.corr(),annot = True, cmap = 'gist_heat')\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"Spearmans Correlation Matrix\\n\",size=19)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Spearmans Correlation Matrix\\n')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIhIqiGZPgwI"
      },
      "source": [
        "## 6. Visualizing some results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "8QSgjqhmyBrW",
        "outputId": "2bda9cc8-eb6e-4534-919b-b66df1db75f3"
      },
      "source": [
        "plt.figure(figsize=(18,7))\n",
        "sns.countplot(x=df['loan_status'],data=df,palette='hsv')\n",
        "plt.xlabel('Loan Status',size=15)\n",
        "plt.ylabel('Count of status categories',size=15)\n",
        "plt.title('Loan status count\\n',size=19)"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Loan status count\\n')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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12+rq1ddVfzfGuOqc84wjWjUAAACwY47obR1zzvfPOV+/PP909bbqsod5yUnV0+acp805T67eWV1n5ysFAAAAjpSNrTkxxjih+rbqNUvT3ccYbxxjPH6McfGl7bLVe7e87H0dPswAAAAAzmU2Ek6MMS5UPbO6x5zzU9VjqitX16zeX/32JuoCAAAAjrwjveZEY4zztgomnjzn/KuqOecHt/T/SfU3y+Gp1eW3vPxySxsAAACwSxzRmRNjjKOqx1Vvm3M+ckv78VuG3aJ68/L8OdVtxxjnH2NcsbpK9c9Hql4AAABg5x3pmRPfWf1o9aYxxhuWtvtXtxtjXLPV9qKnVD9VNed8yxjj6dVbW+30cTc7dQAAAMDuckTDiTnnq6qjDtL1/MO85sHVg3esKAAAAGCjNrZbBwAAAEAJJwAAAIANE04AAAAAGyWcAAAAADZKOAEAAABslHACAAAA2CjhBAAAALBRwgkAAABgo85zdl84xrh4dYXqbXPO07avJAAAAGAvWWvmxBjj18YYD9tyfJPqPdW/VO8aY1x9h+oDAAAAdrl1b+u4ffX2Lce/Xb2q+s5qVg/d5roAAACAPWLdcOLrqndXjTEuX12jesCc89XVI6vr7Ux5AAAAwG63bjjx6eqiy/ObVB+fc/7zcvz56oLbXRgAAACwN6y7IOYrqvuOMb5Y/UL17C19V63eu92FAQAAAHvDujMnfr46rXpa9Ynql7b03aF65TbXBQAAAOwRa82cmHOe2up2joP53la3dgAAAACcZeve1lHVGOPi1TdXl6/+ds758eoL1ek7UBsAAACwB6wVTowxjm61XejdqmOqfdV3VB+vnlm9rnrADtUIAAAA7GLrrjnxkOonqrtXV6qO2tL37Op/b3NdAAAAwB6xbjhxh+q+c84/6yt35nhXq8ACAAAA4CxbN5y4WKsQ4mDOVx29PeUAAAAAe8264cSbq5MO0Xfz6vXbUw4AAACw16y7W8dvVM8cYxxTPaPVgpjXHGPcovqp6gd2qD4AAABgl1tr5sSc89nVD1ffXf1tqwUx/7S6U/Wjc84X7lSBAAAAwO627syJ5pxPr54+xrhqdcnqY6vmuW+nigMAAAB2v7XDif3mnO+o3rEDtQAAAAB70CHDiTHGz1TPmHN+eHl+OPvmnI/Z3tIAAACAveBwMyd+v3pd9eHl+eHsq4QTAAAAwFl2yHBizvk1B3sOAAAAsJ3OdM2JMcYFqt+rHjfnfPXOlwQAAADsJWc6I2LO+fnqttUFdr4cAAAAYK9Z93aNl1Y33slCAAAAgL1p3a1E/6D60zHG11bPrz7YahHML5lzvnWbawMAAAD2gHXDiRcsj/dc/m0NJo5ajo/exroAAACAPWLdcMItHQAAAMCOWCucmHO+YqcLAQAAAPamdWdOVDXGuG51YnVs9bHqVXPO1+xEYQAAAMDesFY4sSyE+YzqZtXp1UerS1RHjzFeUN1qzvnZHasSAAAA2LXW3Ur04dX1q9tUF5hzHl9doLrt0v6bO1MeAAAAsNute1vHLatfnHM+Y3/DnPOL1TPGGBevHlT97A7UBwAAAOxy686cuGj13kP0vbe6yPaUAwAAAOw164YT/1bddYxx1NbG5fiuSz8AAADAWbbubR33r/62evsY41nVB6tLV7eoTqhuviPVAQAAALveWjMn5pwvra5V/Wt1q+rB1a2r11fXmnO+bMcqBAAAAHa1dWdONOd8S6vdOQAAAAC2zbprTgAAAADsiLVmTowxXnqY7i9Wn6reUP3ZnPNQu3oAAAAAfIV1Z058tPqG6sTqmOozy+OJ1VWrr61+rnrLGOM7dqBOAAAAYJdad82Jv6muVF1vzvmf+xvHGJetnls9o9VCmS+qHlp99zbXCQAAAOxS686c+NXqQVuDiao556nVg6pfmnN+qnpkdd3tLREAAADYzdYNJ46vzn+IvgtUxy3PP1Qd9dUWBQAAAOwd697W8YrqYWOMd845X7+/cYxx7Va3cbx8abpK9R+HOskY4/LVk1qFGfuqx845Hz3GOLb6i+qE6pTq1nPOj48xjqoeXf2v6rPVnbZeHwAAADj3W3fmxE+22pHjtWOMU8cYbxhjnFq9pvpE9VNbzvfww5zn9Opec86rVder7jbGuFp13+olc86rVC9Zjqtu3irwuMpSw2PWfmcAAADAucJaMyeW7UGvOcb4vura1WWqD1SvnXM+f8u4Pz6T87y/ev/y/NNjjLdVl61Oqm60DHtiq5kYv7i0P2nOua969RjjYmOM45fzAAAAALvAurd1VDXnfF71vO248BjjhOrbWs2+OG5L4PCB/nsNi8tW793ysvctbcIJAAAA2CXWDifGGOev7txq5sTlqrvPOf99jHGb6o1zzredhXNdqHpmdY8556fGGF/qm3PuG2PsW/dcAAAAwLnbWmtOjDGuWr2j1eKXJ1TfXV146b5Bdb91LzjGOG+rYOLJc86/Wpo/OMY4fuk/vtWuH1WnVpff8vLLLW0AAADALrHugpi/W72nVTDxvX35dqGvqE5c5yTL7huPq94253zklq7nVHdcnt+xevaW9juMMY4aY1yv+qT1JgAAAGB3Wfe2jhtUt5pzfmKMcfQBfR+sjl/zPN9Z/Wj1pjHGG5a2+1cPq54+xrhLq61Ib730Pb/VNqLvbLWV6I+teR0AAADgXGLdcOLz1TGH6Ltsq+1Ez9Sc81V9+ayLrW56kPH7qrutc24AAADg3Gnd2zpeXN1/jHHRLW37lkUyf7bVDAcAAACAs2zdmRP3rv6h1e0VL672Vb9aXb06X/V/dqQ6AAAAYNdba+bEnPO91TWqP2q1KOa7Wq0z8Yzq2+ecH9ipAgEAAIDdbd2ZE805P179yvIPAAAAYFusNXNijPHuMcY1DtH3zWOMd29vWQAAAMBese6CmCdU5z9E3wWry21LNQAAAMCec8jbOsYYF6kutqXpMmOMrz9g2AWq21an7kBtAAAAwB5wuDUnfr56QKudOfZVzzrEuKOqe21zXQAAAMAecbhw4inV61qFD8+pfqGaB4z5QjXnnO/ZmfIAAACA3e6Q4cSc89+rf68aY9y4ev2c89NHqjAAAABgb1hrK9E55yv2Px9jfE2rtSYOHPPZbawLAAAA2CPWCifGGEdV96l+orriIYYdvV1FAQAAAHvHuluJ/lx13+pxrdageHD1oOod1SnVT+5EcQAAAMDut2448ROtdu54+HL813POX6uuXr29usoO1AYAAADsAeuGE1es3jDnPKP6r+piVXPOL1Z/WN1xZ8oDAAAAdrt1w4mPVhdanr+n+rYtfRevjtnOogAAAIC9Y60FMat/qL6jen71lOqBY4xjqy9Ud6tesjPlAQAAALvduuHEA6vLLs8f0uq2jju1mjHx4upnt7swAAAAYG9YK5yYc85qLs9Pq/7v8g8AAADgq7LWmhNjjMuPMa51iL5rjTEuv71lAQAAAHvFugtiPqb6kUP0/XCrHTsAAAAAzrJ1w4nrVS89RN/Lln4AAACAs2zdcOKC1b7D9H/tNtQCAAAA7EHrhhNvqm53iL7bVW/ZnnIAAACAvWbdrUQfVj1zjHH+6gnV+6vjqztWt1z+AQAAAJxla82cmHM+q1UQcf3qudVrl8frVz8y5/zrHasQAAAA2NXWva2jOeefV5evrlbdcHn8+jnnU3eoNgAAAGAPWPe2jqrmnPuqt+9QLQAAAMAetPbMCQAAAICdIJwAAAAANko4AQAAAGyUcAIAAADYqLO0IOZWY4xvrL6x+uc5539uX0kAAADAXrLWzIkxxh+PMf5oy/FtqjdVf1W9fYzxP3aoPgAAAGCXW/e2jptVr9xy/OvVU6uvq164HAMAAACcZeuGE5eu3ls1xrhK9Q3Vw+ecH6geW33bzpQHAAAA7HbrhhMfq45bnn939YE555uX46Oqo7e7MAAAAGBvWHdBzL+tHjTGOK66T/X0LX3fXJ2yzXUBAAAAe8S6MyfuVb26+ulWa0/86pa+W1Qv2Oa6AAAAgD1irZkTc85PVnc+RN8NtrUiAAAAYE9Zd+YEAAAAwI5Ya+bEGOPD1b7DjZlzXnpbKgIAAAD2lHUXxPyDvjKcuHh10+oi1eO3sygAAABg71h3zYkHHqx9jHFUq507/msbawIAAAD2kK9qzYk5577qT6u7b085AAAAwF6zHQtiXqk63zacBwAAANiD1l0Q82cO0ny+6puq21fP2M6iAAAAgL1j3QUxf/8gbadV76v+sPq1basIAAAA2FPWXRBzO27/AAAAAPgKa4UOY4w7jDEucYi+Y8cYd9jesgAAAIC9Yt0ZEX9WXfkQfVdc+gEAAADOsnXDiaMO03eJ6lPbUAsAAACwBx1yzYkxxknVSVuafmWM8eEDhl2gukH12nUuNsZ4fPX91YfmnN+8tD2w+olq/7nvP+d8/tJ3v+ou1RnVz805X7jOdQAAAIBzj8MtiHnp6lu2HF+5uswBY75Qvaj6jTWv94RWO3886YD2R805H7G1YYxxteq21dWrr6v+boxx1TnnGWteCwAAADgXOGQ4Mef8k+pPqsYYL6vuOud8+1dzsTnnK8cYJ6w5/KTqaXPO06qTxxjvrK5T/dNXUwMAAABwzrLuVqI33uE67r7s+PG66l5zzo9Xl61evWXM+5Y2AAAAYBdZK5yoGmNcuNVshqu2Wmviy8w573M2a3hM9evVvuXxt6s7n81zAQAAAOcya4UTY4wrV/9YHVN9bavFK49dXv/x6pPV2Qon5pwf3HKdP6n+Zjk8tbr8lqGXW9oAAACAXWTdrUQf1WpHjuNabSv6v1oFFT9Sfaa6zdktYIxx/JbDW1RvXp4/p7rtGOP8Y4wrVlep/vnsXgcAAAA4Z1r3to7rVD9enbYcn2/ZNeMpY4xLVo+u/seZnWSM8dTqRtUlxxjvqx5Q3WiMcc1Wt3WcUv1U1ZzzLWOMp1dvrU6v7manDgAAANh91g0nLlB9as75xTHGx1pt7bnfm6trrHOSOeftDtL8uMOMf3D14DVrBAAAAM6F1g0n3lFdYXn+r9VPjzGeX51R3aX6zx2oDQAAANgD1g0nnlZds/rz6leqF1afqr64nOOOO1IdAAAAsOutFU7MOR+55fmrxxjfXN2s1aKYL51zvvmQLwYAAAA4jHW3Er1h9fo552eq5pzvrf5k6bvQGOOGc85X7lyZAAAAwG617laiL6uudoi+sfQDAAAAnGXrhhNHHabvQtVnt6EWAAAAYA865G0dy60cN9rS9ONjjJsdMOwC1fdVb9r+0gAAAIC94HBrTly3+tnl+b7qVtXpB4z5QvX26t7bXxoAAACwFxwynJhz/lb1W1VjjJOrW8w533CkCgMAAAD2hnW3Er3iThcCAAAA7E3rbiV6y+pic87HLcdXrJ7cagePl1R3mXN+YseqBAAAAHatdXfr+OXqIluOf6+6ZPWw6lrVg7e5LgAAAGCPWDecuFLLjhxjjItW/7P6+Tnnw6pfqv73zpQHAAAA7HbrhhO12rGj6ruqM6q/W47fV11qO4sCAAAA9o51w4l/q24/xvja6serl805T1v6vr760E4UBwAAAOx+ay2IWd2/em51x+oz1fds6fvB6jXbXBcAAACwR6y7leirxhhfX121etcBO3M8vnrnThQHAAAA7H7rzpxozvnp6l8O0v78ba0IAAAA2FPOyoKYAAAAANtOOAEAAABslHACAAAA2KhDhhNjjDuMMS5xJIsBAAAA9p7DzZz4s+rKVWOMM8YY1zkyJQEAAAB7yeHCiY9XX7c8P6rat/PlAAAAAHvN4bYS/bvqz8cYs1Uw8YQxxv871OA5p5kVAAAAwFl2uHDiztVdq2+srlWdXH34SBQFAAAA7B2HDCfmnJ+tfrtqjPHd1S/NOf/tSBUGAAAA7A2HmznxJXPOK+50IQAAAMDetFY4UTXGuFJ17+rE6tjqY9XfV4+Yc757Z8oDAAAAdrvD7dbxJWOMb6/eUN2yem31pOXxltW/jjGutWMVAgAAALvaujMnHlH9a3XzZS2KqsYYF6yev/TfZPvLAwAAAHa7tWZOVNepHr41mKgvLZr5iOq6210YAAAAsDesG058rrrEIfqOrT6/PeUAAAAAe8264cTzqoiOBhEAABZXSURBVIeNMU7c2rgcP7R67nYXBgAAAOwN6645cc/q2dUrxhgfqj5UXXr590/VvXamPAAAAGC3WyucmHN+tDpxjHGz6juq46v3V6+Zc75oB+sDAAAAdrl1Z05UNed8QfWCHaoFAAAA2IPWXXMCAAAAYEcIJwAAAICNEk4AAAAAGyWcAAAAADZqrXBijHGHMcYlDtF37BjjDttbFgAAALBXrDtz4s+qKx+i74pLPwAAAMBZtm44cdRh+i5RfWobagEAAAD2oPMcqmOMcVJ10pamXxljfPiAYReoblC9dgdqAwAAAPaAQ4YT1aWrb9lyfOXqMgeM+UL1ouo3trkuAAAAYI84ZDgx5/yT6k+qxhgvq+4653z7kSoMAAAA2BsON3PiS+acN97pQgAAAIC9aa1womqM8XXV91eXa7XWxFb75py/uJ2FAQAAAHvDWuHEGOMW1VOro6sPtVprYqt9lXACAAAAOMvWnTnxkFYLX95pzvmxHawHAAAA2GPWDScuX/3sVxtMjDEe3+rWkA/NOb95aTu2+ovqhOqU6tZzzo+PMY6qHl39r+qzrYKR13811wcAAADOeb5mzXH/WI1tuN4Tqpsd0Hbf6iVzzqtUL1mOq25eXWX595PVY7bh+gAAAMA5zLozJ+5ZPXmM8ZnqxdUnDhww5/zsmZ1kzvnKMcYJBzSfVN1oef7E6uWt1q84qXrSnHNf9eoxxsXGGMfPOd+/Zs0AAADAucC64cQbl8c/a7X45cEcfTZrOG5L4PCB6rjl+WWr924Z976lTTgBsAtd6NgLdszRZ/d/JcBe8rkzzugzHzvTv4vtCRe50AU7/zE+O4Ezd9rnzuhTnznnfnauG07cuUOHEttmzrlvjLHj1wHgnOeYo4/uiqfMTZcBnAucfMLoM5su4hzi/Mcc3X2ueMqmywDOBR5+8gmdkz881won5pxP2MEaPrj/do0xxvGttiqtOrXVQpz7XW5pAwAAAHaRdRfE3EnPqe64PL9j9ewt7XcYYxw1xrhe9UnrTQAAAMDus9bMiTHGhzuT2zrmnJde4zxPbbX45SXHGO+rHlA9rHr6GOMu1X9Ut16GP7/VNqLvbLWV6I+tUysAAABw7rLumhN/0FeGExevblpdpHr8OieZc97uEF03PcjYfdXd1qwPAAAAOJdad82JBx6sfYxxVPX06r+2sSYAAABgD/mq1pxYZjf8aXX37SkHAAAA2Gu2Y0HMK1Xn24bzAAAAAHvQugti/sxBms9XfVN1++oZ21kUAAAAsHesuyDm7x+k7bTqfdUfVr+2bRUBAAAAe8q6C2Jux+0fAAAAAF9B6AAAAABs1Lq3dTTGuFJ17+rE6tjqY9XfV4+Yc757Z8oDAAAAdru1Zk6MMb69ekN1y+q11ZOWx1tW/zrGuNaOVQgAAADsauvOnHhE9a/Vzeecn93fOMa4YPX8pf8m218eAAAAsNutu+bEdaqHbw0mqpbjR1TX3e7CAAAAgL1h3XDic9UlDtF3bPX57SkHAAAA2GvWDSeeVz1sjHHi1sbl+KHVc7e7MAAAAGBvWHfNiXtWz65eMcb4UPWh6tLLv3+q7rUz5QEAAAC73VrhxJzzo9WJY4ybVd9RHV+9v3rNnPNFO1gfAAAAsMutO3OiqjnnC6oX7FAtAAAAwB50yDUnxhjHjzGeOcb43sOM+d5lzKV3pjwAAABgtzvcgpi/UF2pOtxtGy+qrpg1JwAAAICz6XDhxPdXfzTn3HeoAUvfH1cnbXdhAAAAwN5wuHDiCtVb1zjH26oTtqUaAAAAYM85XDjxueoia5zjQstYAAAAgLPscOHE66sfWOMcJy1jAQAAAM6yw4UTf1jdZYxxx0MNGGPcofqx6ve3uzAAAABgbzjPoTrmnM8cYzy6+rMxxt2rF1TvqfZVX199b3Xt6lFzzmcdiWIBAACA3eeQ4UTVnPNeY4yXV/dotbXo+Zeu06p/qE6ac/7NjlYIAAAA7GqHDSeq5pzPrZ47xjhPdYml+aNzztN3tDIAAABgTzjTcGK/JYz44A7WAgAAAOxBh1sQEwAAAGDHCScAAACAjRJOAAAAABslnAAAAAA2SjgBAAAAbJRwAgAAANgo4QQAAACwUcIJAAAAYKOEEwAAAMBGCScAAACAjRJOAAAAABslnAAAAAA2SjgBAAAAbJRwAgAAANgo4QQAAACwUcIJAAAAYKOEEwAAAMBGCScAAACAjRJOAAAAABslnAAAAAA2SjgBAAAAbJRwAgAAANgo4QQAAACwUcIJAAAAYKPOs+kC9htjnFJ9ujqjOn3Oee0xxrHVX1QnVKdUt55zfnxDJQIAAAA74Jw2c+LGc85rzjmvvRzft3rJnPMq1UuWYwAAAGAXOaeFEwc6qXri8vyJ1Q9usBYAAABgB5yTwol91YvGGP8yxvjJpe24Oef7l+cfqI7bTGkAAADATjknhRMnzjmvVd28utsY44ZbO+ec+1oFGAAAAMAuco4JJ+acpy6PH6qeVV2n+uAY4/iq5fFDm6sQAAAA2AnniHBijPG1Y4wL739e/c/qzdVzqjsuw+5YPXszFQIAAAA75Zyylehx1bPGGLWq6SlzzheMMV5bPX2McZfqP6pbb7BGAAAAYAecI8KJOee7q2scpP2j1U2PfEUAAADAkXKOuK0DAAAA2LuEEwAAAMBGCScAAACAjRJOAAAAABslnAAAAAA2SjgBAAAAbJRwAgAAANgo4QQAAACwUcIJAAAAYKOEEwAAAMBGCScAAACAjRJOAAAAABslnAAAAAA2SjgBAAAAbJRwAgAAANgo4QQAAACwUcIJAAAAYKOEEwAAAMBGCScAAACAjRJOAAAAABslnAAAAAA2SjgBAAAAbJRwAgAAANgo4QQAAACwUcIJAAAAYKOEEwAAAMBGCScAAACAjRJOAAAAABslnAAAAAA2SjgBAAAAbJRwAgAAANgo4QQAAACwUcIJAAAAYKOEEwAAAMBGCScAAACAjRJOAAAAABslnAAAAAA2SjgBAAAAbJRwAgAAANgo4QQAAACwUcIJAAAAYKOEEwAAAMBGCScAAACAjRJOAAAAABslnAAAAAA2SjgBAAAAbJRwAgAAANgo4QQAAACwUcIJAAAAYKOEEwAAAMBGCScAAACAjRJOAAAAABt1nk0XsI4xxs2qR1dHV38653zYhksCAAAAtsk5fubEGOPo6g+qm1dXq243xrjaZqsCAAAAtss5PpyorlO9c8757jnnF6qnVSdtuCYAAABgm5wbbuu4bPXeLcfvq657qMHnPe/RH7nUpS78Hzte1Rp++YSTN10CcC5xqUtdeNMlnCOcfMLYdAnAuYTPzf/28JNP2HQJwLnEOeSz8woHazw3hBNn1aU2XQAAAACwvnPDbR2nVpffcny5pQ0AAADYBc4NMydeW11ljHHFVqHEbasf3mxJAAAAwHY5x8+cmHOeXt29emH1turpc863bLYqAAAAYLsctW/fvk3XAAAAAOxh5/iZEwAAAMDuJpwAAAAANurcsCAmbMwY44zqTa3+W3lbdcc552fHGOep3l89bs553y3jX179wpzzdWOMU6pPL11HV39V/cac8/PL2KtXv1ddtlVQ+KSlf98Y407Vb/XfO9O8cc55hzHGE6rvqj65tD9+zvm7O/DWAb7MGOMy1e9U31F9ovpgdY/qvB3+s+zac867H3CuU5b2j2xpu1Nf/rlX9cNzzreOMa66XPsqrT5X31k9s7rfMu4bltd9rnpj9fhWn8Xfv5z7B6sHLbWeXv3KnPOvl74nVN9TXWnOedoY45LV6+acJ5ztLxYAcJYJJ+DwPjfnvGbVGOPJ1U9Xj2z1g+w7qluNMe435zzU4i03nnN+ZIxxoeqx1R9XdxxjHFM9p7rrnPNFY4wLtvpB+2eqP1he+xcH/kC/uPec8y+36w0CnJkxxlHVs6onzjlvu7RdozquekKH/yw7K77ic2+McYHqedU955zPXdpuVH1ky+fzy1uC4S39+19/jeoR1ffMOU9edv968Rjj3XPONy7DzqjuXD3mbNQMnMsJX79U5wmt/hg3q/NVr6x+Zs75xeW1769+ds75Rwd7v1v+qLe/lidVj5pzfnEZe2Krn6Mvsrz8kXPOxy59D6x+ovrw0veCOed9l8/345f3X6uvv5+Ddym3dcD6/r7V/yCqblc9unpPdf0ze+Gc8zOtgo0fHGMc22o73H+Yc75o6f9sq11p7nvoswBszI2r/9r6A+mc89+qq7bzn2U/XP3T/mBiuc7L55xvXvP1v1A9ZM558vLak6uHVvfeMuZ3qp9fZsUBe8iW8PXlc84rzzm/vVUwcFyrPyQ9bM45qmtU/6NV+Hp2/MWc85pb/r11S/j6mDnnVeac16r+sHrL/nHV66rbL8d3OKD2/eHrSXPOb6p+oHrEGONbtwzbH76u613Ldb+1ulr1g0v7rapXt/oZ+FA+t9R59VahyM2rByy1XqZ6SvXTc85vrE6sfmqM8X1bXv+oLV+frf8fuf2WdsHELiacgDUsP7DevHrT8j+S766eWz21w39If8mc81PVya2S8atX/3JA/7uqC40x9qfJtxljvGH592Nbhv7WlvZv+areGMB6vrkDPrMW63yWnRVbP/fesMwyO9S11/UVNbb6Yf/qW47fU72q+tGv4jrAuZPw9SDmnKdX/9iX/2HuXtVlxxiXW+P1H6p+srr7EgDdrXrCnPP1S/9HqvvkD3Ns4S8EcHjHjDHesDz/++pxrVLpl805PzfGeGb1K2OMe8w5z1jjfEedhWu7rQPYaw52W8eRuvZDq2e3+ismsHecpfB1jPHVhK8nbjm+/mGuva6rt5o5sdXrWgUB+20NX5/bmpbb9G5a/eoY4/LV8XPOfx5jPL26TfXbZ3aOOee7xxhHV5dean3iQWrdGhT//BjjR5bnvzjnfOHy/MljjP23ddx0zvnRdd8H5y7CCTi8L605sd8Y43bVics9dlWXqG5SvfhwJxpjXLg6odVaFW+tbnhA/5Wqz8w5P3UEfxgHWMdbqh86SPuR+Cx7S6uFgM+ut/7/7d19rNZlGcDxL6NASsvypWlplNNLSiljM7Nyy+x1JQNNZ/bCVvaCOMscDqih9mI4M+fAl83SMitsEgRmRAEtJ5LMFwT0mjOJ2ERNTHMqMj39cd8P/nzgvMLZg57vZzt7zrl/9+/3u57zx/Occz33fV3AOOCexti4et1tMvOBmow+ZSfuJUndeaUkXw+pr4VdwILMvCUizgVurMd/S6l70WtyYgB+mpntyRYo2zpWDcL9tJtxW4fUDzVT/mHg4MwcXQsKnUkvWztqQcwrgPmZ+QRwAyXBcUI9Pgq4HLh4EMOXpIFaCoyMiK+1Buqe5mTwX8t+DRzb3JccEcdFxBF9PP8SYFot9NYq+DadHf9h/UPKMmlJQ8daSsKy3br28WbydZDv3VfbxUg3yVegr8nXB2tth6My8/w6dhowqX4w9wdgbEQc2tuF6u/rBeDRvsaqoc3khNQ/E4ClmbmlMbYA+GxEjNzB/GURsQb4B2VZ3dcBMvNZYDzw3YhISmXjO4DZgxm8JA1E7Ug0ATghIh6MiLWUT+I20ftr2aSI2Nj4au1VXt0Yu7SOtdecOLa+Xn4GOCsiHoiIdZSCdI/RB5l5N3AesDAi7qcsa55ax9vnrgXu7NcvR9IrncnXHtRuIntm5lsbH8xdRO8fzO0HXAXMru8hcyjvB60uS/sAs/CDOTUM6+rqrgOiJEmSJL26RcSBlMKR44DngPWUVqJ7UFqJHgAMB64HLmy0Ep1NaT3acgylvsMI4MU6diOlBWh7K9HJmXlbRBxe730IsLXOPTszH6mxLWf7VsnNVqITgQso7Tu3AjMzc149dh2wqFWrLCLmAe/rpZXoosw8ojE2ExjV7J5RkzdzM3NML61Er6e0C221Ej2OkjjZi1KH7bLMvLIeO5+yKuVl2zran79e3UxOSJIkSZKkjnJbhyRJkiRJ6ii7dUiSJEnSEBERR1K2XDRtycz3dyIeqcVtHZIkSZIkqaPc1iFJkiRJkjrK5IQkSZIkSeooa05IkjTE1JZtUzJz307H0pPaqu8s4DBKW7r1wLLMPKce3x+YDFyXmev7ee0RwHRgfmbeveuiliRJA+HKCUmStNuJiGnANcBiYCLwJWABcGJj2v7ATGD0AG4xop773p0KVJIk7RKunJAkSbujKcDVmTm9MbYwIi7oVECSJGnwmJyQJEnbiYjjgYuA9wBPAjcBUzPz6Xr89cAs4GPAQcAjwB+BaZn5VOM6XcC3gLcAZwBdwO+AczJzSw8h7A1sah/MzK563dHAvXV4WUS0jg/rY2z/q4/XRsS19ft3UFZhLAOOzMw1jeexHPhPZp5cf3438BPgaGAksAGYnZlzenhOkiSpGyYnJEnSy9R/vP8ELAFOovyD/2PgncAn67TXAcOBGcBjdc4MSuLhE22X/A6wFPgCMJaS9PgXcHEPYdwJnBURG4BFmfl42/GHgdOBG4Az6/yWvsR2fI3pB8DNjWuO7iGmpoXAffU5bQECeEMfz5UkSW1MTkiSpHbfoyQPTszMFwAiYjMwNyI+kJkrMvMx4JutEyLiNcBDwK0RcXBmbmhcb31mTqrfL46ID1LqSPSUnDgTmA9cB3RFxH2U1RuXZOZTmbklIlbXuesy8/bWiX2M7Y56+MHmua0VGD2JiH0pqyzGZ2Zr9cZfez1RkiR1y4KYkiSp3dHA71uJieomSseMD7UGIuKLEXFXRDwNbAVurYcOa7ven9t+Xge8racAMnM1MIZSAPMKYBglabIqIvbs7Qn0I7aB2Az8G7gqIk6tXUMkSdJOMDkhSZLaHUCp07BNTVQ8DrwZICImAL8EVgCfA44BJtTpe7Rd779tPz+/gznbycwtmbkwM6dk5ruArwKHAl/p6bx+xtZvmfki8HFKTYyfA5si4u8RcdTOXluSpKHK5IQkSWr3MKVN5zYRMRzYh7JqAMo//Sszc3Jm3pKZK4EnBjOozPxZvf/hvUzdmdieq48j2sbf1BbL/Zl5EqVw5wmUpMfNEeHfVpIkDYBvoJIkqd1KYEJNSLRMpNSqam2PGEUpBNl0+q4KYEdbJSJiP+CNvLSq4/n62L4aoi+xdXfuxvo4pnHfg+gmIZKZWzNzKXApZcXJ3juaJ0mSemZBTEmShqYREXHyDsb/RulgcRcwPyKupNSHmAUszswVdd4SYE5EzKAkMz4NfHQXxndvRCyg1Kt4FHg7cC7wDPCLOmcD8Czw5Yh4Etiamav6EltmPh8RDwGnRMQayoqJ1Zm5MSJWAd+PiGcoH+RM56UVI0TEWOASYC7wT8qqivOAezJzM5Ikqd9MTkiSNDTtRWmt2e4jmbk8Ij4F/AiYBzwF/AaY2ph3NaW16NmU1QdLgM8Dt7NrXAiMBy6n1LnYBNwGnJqZDwFk5nMRcQYwk5JUeS2lcGZfY/sGJcnwF2AkpQPHeuA04BrgV5SVFFOBbzfO20RZvTEDOJBSU2MZJUEhSZIGYFhXV1enY5AkSZIkSUOYNSckSZIkSVJHmZyQJEmSJEkdZXJCkiRJkiR1lMkJSZIkSZLUUSYnJEmSJElSR5mckCRJkiRJHWVyQpIkSZIkdZTJCUmSJEmS1FH/B+KsCxfGdi5NAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "lDPN2jUI4u2G",
        "outputId": "6505689b-647d-4abf-ff1e-d6cd7e0d2fd1"
      },
      "source": [
        "plt.figure(figsize=(16, 5))\n",
        "sns.distplot(df.Principal, bins=range(300, 1000, 100), kde=True,color='orange')\n",
        "plt.xlabel(\"Principal Amounts\",size=15)\n",
        "plt.ylabel(\"Density of amounts\",size=15)\n",
        "plt.title(\"Distribution of Principal of Loan\\n\",size=20)\n",
        "plt.show()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U9cNGLnO5aKq",
        "outputId": "0c47d61d-7cc6-44eb-f7bf-167cb2f04517"
      },
      "source": [
        "print(df[['loan_status', 'Principal', 'Loan_ID']].groupby(['loan_status', 'Principal']).agg(['count']))"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                             Loan_ID\n",
            "                               count\n",
            "loan_status        Principal        \n",
            "COLLECTION         800            23\n",
            "                   1000           77\n",
            "COLLECTION_PAIDOFF 500             1\n",
            "                   800            19\n",
            "                   1000           80\n",
            "PAIDOFF            300             6\n",
            "                   500             2\n",
            "                   700             1\n",
            "                   800            69\n",
            "                   900             2\n",
            "                   1000          220\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "1LksH5Mz6Qoh",
        "outputId": "c537e0a2-df26-4f72-b333-abe28db05a83"
      },
      "source": [
        "fig, axs = plt.subplots(1, 2, figsize=(16,5))\n",
        "sns.countplot(df.terms, ax=axs[0],palette='plasma')\n",
        "axs[0].set_title(\"Count of Terms of loan\\n\",size=16)\n",
        "for p in axs[0].patches:\n",
        "    axs[0].annotate(str(format(int(p.get_height()), ',d')), (p.get_x(), p.get_height()*1.01))\n",
        "\n",
        "sns.countplot(x='terms', hue='loan_status', data=df, ax=axs[1],palette='Set2')\n",
        "axs[1].set_title(\"Term count breakdown by loan_status\\n\",size=16)\n",
        "for t in axs[1].patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        axs[1].annotate(0, (t.get_x(), 0))\n",
        "    else:\n",
        "        axs[1].annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "\n",
        "axs[1].legend(loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "id": "PI-2_H_X6lyB",
        "outputId": "009a3079-5685-4217-c529-fd3c365809b3"
      },
      "source": [
        "fig = plt.figure(figsize=(17,6))\n",
        "ax = sns.countplot(x='effective_date', hue='loan_status', data=df,palette='Set1')\n",
        "ax.set_title('Loan date',size=18)\n",
        "for t in ax.patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        ax.annotate(0, (t.get_x(), 0))\n",
        "    else:\n",
        "        ax.annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "plt.show()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "T7IgHevl7rAY",
        "outputId": "97837e4c-766d-4752-cc25-b999afca40ba"
      },
      "source": [
        "df['paid_off_date'] = pd.DatetimeIndex(df.paid_off_time).normalize()\n",
        "fig = plt.figure(figsize=(20, 6))\n",
        "ax = sns.countplot(x='paid_off_date', data=df.loc[df.loan_status.isin(['COLLECTION_PAIDOFF', 'PAIDOFF'])] , hue='loan_status',palette='plasma')\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
        "for t in ax.patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        ax.annotate(0, (t.get_x(), 0))\n",
        "    else:\n",
        "        ax.annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "\n",
        "ax.legend(loc='upper right')\n",
        "plt.xlabel('PAID_OFF Dates',size=14)\n",
        "plt.ylabel('Count of paid_off Dates',size=14)\n",
        "plt.title('PAID_OFF time stamps frequencies\\n',size=19)\n",
        "plt.show()"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "dosReNAH8Xat",
        "outputId": "d6bf8d5f-273a-4c32-d134-70769f018ac8"
      },
      "source": [
        "# Compute the day to pay-off the loan\n",
        "df['day_to_pay'] = (pd.DatetimeIndex(df.paid_off_time).normalize() - pd.DatetimeIndex(df.effective_date).normalize()) / np.timedelta64(1, 'D')\n",
        "\n",
        "fig = plt.figure(figsize=(20, 6))\n",
        "ax = sns.countplot(x='day_to_pay', hue='terms', data=df)\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n",
        "for t in ax.patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        ax.annotate('', (t.get_x(), 0))\n",
        "    else:\n",
        "        ax.annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "\n",
        "plt.xlabel('Days to pay',size=14)\n",
        "plt.ylabel('Count of days to pay',size=14)\n",
        "plt.title('Days to pay\\n',size=19)        \n",
        "plt.show()"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iC0eZgCg9O_3",
        "outputId": "a3db25c6-2472-45c6-952c-6f4a5618ce34"
      },
      "source": [
        "tmp = df.loc[(df.day_to_pay > 30) & (df.loan_status == 'PAIDOFF')]\n",
        "print(\"{}: Incorrect status: {} observations\".format(datetime.datetime.now(), tmp.shape[0]))\n",
        "print(tmp[['loan_status', 'terms', 'effective_date', 'due_date', 'paid_off_time']])"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-11-23 07:32:56.672593: Incorrect status: 14 observations\n",
            "    loan_status  terms effective_date    due_date     paid_off_time\n",
            "85      PAIDOFF     30      9/11/2016   11/9/2016    11/9/2016 9:00\n",
            "111     PAIDOFF     30      9/11/2016   11/9/2016    11/9/2016 9:00\n",
            "122     PAIDOFF     15      9/11/2016  10/25/2016   10/25/2016 9:00\n",
            "137     PAIDOFF     15      9/11/2016  10/25/2016   10/25/2016 9:00\n",
            "146     PAIDOFF     30      9/11/2016   11/9/2016   11/9/2016 23:00\n",
            "150     PAIDOFF     15      9/12/2016  10/26/2016   10/26/2016 9:00\n",
            "165     PAIDOFF     30      9/12/2016  11/10/2016  11/10/2016 16:00\n",
            "196     PAIDOFF     30      9/12/2016  11/10/2016  11/10/2016 13:00\n",
            "206     PAIDOFF     30      9/12/2016  11/10/2016  11/10/2016 13:00\n",
            "215     PAIDOFF     30      9/12/2016  11/10/2016  11/10/2016 16:00\n",
            "222     PAIDOFF     30      9/12/2016  11/10/2016  11/10/2016 13:00\n",
            "230     PAIDOFF     15      9/12/2016  10/26/2016   10/26/2016 9:00\n",
            "272     PAIDOFF     30      9/14/2016  11/12/2016   11/12/2016 9:00\n",
            "285     PAIDOFF     30      9/14/2016  11/12/2016   11/12/2016 9:00\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 893
        },
        "id": "9nNKKb1J-HOF",
        "outputId": "1e270130-a346-4ca5-cad7-3b228385d657"
      },
      "source": [
        "fig, axs = plt.subplots(3, 2, figsize=(16, 15))\n",
        "sns.distplot(df.age, ax=axs[0][0],color='green')\n",
        "axs[0][0].set_title(\"Total age distribution across dataset\")\n",
        "sns.boxplot(x='loan_status', y='age', data=df, ax=axs[0][1],palette='cool')\n",
        "axs[0][1].set_title(\"Age distribution by loan status\")\n",
        "sns.countplot(x='education', data=df, ax=axs[1][0],palette='rainbow')\n",
        "axs[1][0].set_title(\"Education count\")\n",
        "for t in axs[1][0].patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        axs[1][0].annotate('', (t.get_x(), 0))\n",
        "    else:\n",
        "        axs[1][0].annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "\n",
        "sns.countplot(x='education', data=df, hue='loan_status', ax=axs[1][1])\n",
        "axs[1][1].set_title(\"Education by loan status\")\n",
        "for t in axs[1][1].patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        axs[1][1].annotate('', (t.get_x(), 0))\n",
        "    else:\n",
        "        axs[1][1].annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "\n",
        "axs[1][1].legend(loc='upper right')\n",
        "sns.countplot(x='Gender', data=df, ax=axs[2][0],palette='inferno')\n",
        "axs[2][0].set_title(\"# of Gender\")\n",
        "for t in axs[2][0].patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        axs[2][0].annotate('', (t.get_x(), 0))\n",
        "    else:\n",
        "        axs[2][0].annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "\n",
        "sns.countplot(x='Gender', data=df, hue='education', ax=axs[2][1],palette='Set1')\n",
        "axs[2][1].set_title(\"Education of the gender\")\n",
        "for t in axs[2][1].patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        axs[2][1].annotate('', (t.get_x(), 0))\n",
        "    else:\n",
        "        axs[2][1].annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1080 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLwnXoV0Ppe-"
      },
      "source": [
        "## 7. Formatting data for modelling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "i3s6RLo4-kIK",
        "outputId": "957eaea4-66be-4194-ecd3-4e1d5eb137d4"
      },
      "source": [
        "# Quick view on education + gender => impact to loan_status\n",
        "pd.crosstab(df.loan_status, df.Gender + \"_\" + df.education, margins=True)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>col_0</th>\n",
              "      <th>female_Bechalor</th>\n",
              "      <th>female_High School or Below</th>\n",
              "      <th>female_college</th>\n",
              "      <th>male_Bechalor</th>\n",
              "      <th>male_High School or Below</th>\n",
              "      <th>male_Master or Above</th>\n",
              "      <th>male_college</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>COLLECTION</th>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>11</td>\n",
              "      <td>42</td>\n",
              "      <td>1</td>\n",
              "      <td>36</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COLLECTION_PAIDOFF</th>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>14</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>40</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAIDOFF</th>\n",
              "      <td>12</td>\n",
              "      <td>19</td>\n",
              "      <td>22</td>\n",
              "      <td>29</td>\n",
              "      <td>104</td>\n",
              "      <td>3</td>\n",
              "      <td>111</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>13</td>\n",
              "      <td>31</td>\n",
              "      <td>33</td>\n",
              "      <td>54</td>\n",
              "      <td>178</td>\n",
              "      <td>4</td>\n",
              "      <td>187</td>\n",
              "      <td>500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0               female_Bechalor  ...  All\n",
              "loan_status                          ...     \n",
              "COLLECTION                        0  ...  100\n",
              "COLLECTION_PAIDOFF                1  ...  100\n",
              "PAIDOFF                          12  ...  300\n",
              "All                              13  ...  500\n",
              "\n",
              "[4 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "tiOnQqxe_jQO",
        "outputId": "f1a9b603-bd8f-41d6-cb19-55a9f991974a"
      },
      "source": [
        "pd.crosstab(df.loan_status, df.Gender + \"_\" + df.education, margins=True, normalize='all')"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>col_0</th>\n",
              "      <th>female_Bechalor</th>\n",
              "      <th>female_High School or Below</th>\n",
              "      <th>female_college</th>\n",
              "      <th>male_Bechalor</th>\n",
              "      <th>male_High School or Below</th>\n",
              "      <th>male_Master or Above</th>\n",
              "      <th>male_college</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>COLLECTION</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.014</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.022</td>\n",
              "      <td>0.084</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.072</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COLLECTION_PAIDOFF</th>\n",
              "      <td>0.002</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.016</td>\n",
              "      <td>0.028</td>\n",
              "      <td>0.064</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.080</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAIDOFF</th>\n",
              "      <td>0.024</td>\n",
              "      <td>0.038</td>\n",
              "      <td>0.044</td>\n",
              "      <td>0.058</td>\n",
              "      <td>0.208</td>\n",
              "      <td>0.006</td>\n",
              "      <td>0.222</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>0.026</td>\n",
              "      <td>0.062</td>\n",
              "      <td>0.066</td>\n",
              "      <td>0.108</td>\n",
              "      <td>0.356</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.374</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0               female_Bechalor  ...  All\n",
              "loan_status                          ...     \n",
              "COLLECTION                    0.000  ...  0.2\n",
              "COLLECTION_PAIDOFF            0.002  ...  0.2\n",
              "PAIDOFF                       0.024  ...  0.6\n",
              "All                           0.026  ...  1.0\n",
              "\n",
              "[4 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "rC_1B8K0_qfB",
        "outputId": "70cab2eb-fb12-4234-ed1b-e60135dea8e0"
      },
      "source": [
        "pd.crosstab(df.loan_status, df.Gender + \"_\" + df.education, margins=True, normalize='index')"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>col_0</th>\n",
              "      <th>female_Bechalor</th>\n",
              "      <th>female_High School or Below</th>\n",
              "      <th>female_college</th>\n",
              "      <th>male_Bechalor</th>\n",
              "      <th>male_High School or Below</th>\n",
              "      <th>male_Master or Above</th>\n",
              "      <th>male_college</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>COLLECTION</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COLLECTION_PAIDOFF</th>\n",
              "      <td>0.010</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.140000</td>\n",
              "      <td>0.320000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAIDOFF</th>\n",
              "      <td>0.040</td>\n",
              "      <td>0.063333</td>\n",
              "      <td>0.073333</td>\n",
              "      <td>0.096667</td>\n",
              "      <td>0.346667</td>\n",
              "      <td>0.010</td>\n",
              "      <td>0.370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>0.026</td>\n",
              "      <td>0.062000</td>\n",
              "      <td>0.066000</td>\n",
              "      <td>0.108000</td>\n",
              "      <td>0.356000</td>\n",
              "      <td>0.008</td>\n",
              "      <td>0.374</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0               female_Bechalor  ...  male_college\n",
              "loan_status                          ...              \n",
              "COLLECTION                    0.000  ...         0.360\n",
              "COLLECTION_PAIDOFF            0.010  ...         0.400\n",
              "PAIDOFF                       0.040  ...         0.370\n",
              "All                           0.026  ...         0.374\n",
              "\n",
              "[4 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "cxczT7bd_wIb",
        "outputId": "56a524a9-6899-4496-ab80-19bc6270f693"
      },
      "source": [
        "pd.crosstab(df.loan_status, df.Gender + \"_\" + df.education, margins=True, normalize='columns')"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>col_0</th>\n",
              "      <th>female_Bechalor</th>\n",
              "      <th>female_High School or Below</th>\n",
              "      <th>female_college</th>\n",
              "      <th>male_Bechalor</th>\n",
              "      <th>male_High School or Below</th>\n",
              "      <th>male_Master or Above</th>\n",
              "      <th>male_college</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loan_status</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>COLLECTION</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.225806</td>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.203704</td>\n",
              "      <td>0.235955</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.192513</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COLLECTION_PAIDOFF</th>\n",
              "      <td>0.076923</td>\n",
              "      <td>0.161290</td>\n",
              "      <td>0.242424</td>\n",
              "      <td>0.259259</td>\n",
              "      <td>0.179775</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.213904</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PAIDOFF</th>\n",
              "      <td>0.923077</td>\n",
              "      <td>0.612903</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.537037</td>\n",
              "      <td>0.584270</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.593583</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "col_0               female_Bechalor  ...  All\n",
              "loan_status                          ...     \n",
              "COLLECTION                 0.000000  ...  0.2\n",
              "COLLECTION_PAIDOFF         0.076923  ...  0.2\n",
              "PAIDOFF                    0.923077  ...  0.6\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWv0fXbi_1XR"
      },
      "source": [
        "df.loc[(df.loan_status == 'PAIDOFF') & (df.day_to_pay > 30), 'loan_status'] = 'COLLECTION_PAIDOFF'"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "Drwrk_kh_7_1",
        "outputId": "bff335f3-6810-4c9b-98e1-1e7291712c57"
      },
      "source": [
        "status_map = {\"PAIDOFF\": 1, \"COLLECTION\": 2, \"COLLECTION_PAIDOFF\": 2 }\n",
        "df['loan_status_trgt'] = df['loan_status'].map(status_map)\n",
        "\n",
        "fig, axs = plt.subplots(1, 2, figsize=(15, 5))\n",
        "sns.countplot(x='loan_status', data=df, ax=axs[0],palette='gist_heat')\n",
        "axs[0].set_title(\"Count using original target labels\")\n",
        "for t in axs[0].patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        axs[0].annotate('', (t.get_x(), 0))\n",
        "    else:\n",
        "        axs[0].annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "\n",
        "sns.countplot(x='loan_status_trgt', data=df, ax=axs[1],palette='gnuplot2')\n",
        "axs[1].set_title(\"Count using new target labels\")\n",
        "for t in axs[1].patches:\n",
        "    if (np.isnan(float(t.get_height()))):\n",
        "        axs[1].annotate('', (t.get_x(), 0))\n",
        "    else:\n",
        "        axs[1].annotate(str(format(int(t.get_height()), ',d')), (t.get_x(), t.get_height()*1.01))\n",
        "    \n",
        "plt.show()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqx5v8djARzn"
      },
      "source": [
        "dummies = pd.get_dummies(df['education']).rename(columns=lambda x: 'is_' + str(x))\n",
        "df = pd.concat([df, dummies], axis=1)\n",
        "df = df.drop(['education'],  axis=1)\n",
        "\n",
        "dummies = pd.get_dummies(df['Gender']).rename(columns=lambda x: 'is_' + str(x))\n",
        "df = pd.concat([df, dummies], axis=1)\n",
        "df = df.drop(['Gender'], axis=1)\n",
        "\n",
        "df = df.drop(['Loan_ID', 'loan_status', 'effective_date', 'due_date', 'paid_off_time', 'past_due_days', 'paid_off_date', 'day_to_pay'], axis=1)"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "W6N2hTpBA_Hi",
        "outputId": "2c428d5a-cb10-4227-8fc6-73f9cff5a738"
      },
      "source": [
        "dummy_var = ['is_female', 'is_Master or Above']\n",
        "df = df.drop(dummy_var, axis = 1)\n",
        "df"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>age</th>\n",
              "      <th>loan_status_trgt</th>\n",
              "      <th>is_Bechalor</th>\n",
              "      <th>is_High School or Below</th>\n",
              "      <th>is_college</th>\n",
              "      <th>is_male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>26</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Principal  terms  age  ...  is_High School or Below  is_college  is_male\n",
              "0         1000     30   45  ...                        1           0        1\n",
              "1         1000     30   50  ...                        0           0        0\n",
              "2         1000     30   33  ...                        0           0        0\n",
              "3         1000     15   27  ...                        0           1        1\n",
              "4         1000     30   28  ...                        0           1        0\n",
              "..         ...    ...  ...  ...                      ...         ...      ...\n",
              "495       1000     30   28  ...                        1           0        1\n",
              "496       1000     15   26  ...                        1           0        1\n",
              "497        800     15   30  ...                        0           1        1\n",
              "498       1000     30   38  ...                        0           1        0\n",
              "499       1000     30   28  ...                        1           0        1\n",
              "\n",
              "[500 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9m1kjm6xBFgC"
      },
      "source": [
        "X = df.drop(['loan_status_trgt'], axis=1)\n",
        "y = df.loan_status_trgt"
      ],
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I1xB1LILPxNh"
      },
      "source": [
        "## 8. Training data with Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbYO67bPBaL2"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn import svm\n",
        "\n",
        "### Create function to evaluate the score of each classification model\n",
        "def eval_model_classifier(model, data, target, split_ratio):\n",
        "    trainX, testX, trainY, testY = train_test_split(data, target, train_size=split_ratio, random_state=0)\n",
        "    model.fit(trainX, trainY)    \n",
        "    return model.score(testX,testY)"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e1LbigXEBflj",
        "outputId": "31bf7c0c-13ea-4269-d3eb-6dc4e400d19d"
      },
      "source": [
        "### 1st round: RandomForestClassification\n",
        "\n",
        "# Initialise values\n",
        "num_estimators_array = np.array([1,5,10,50,100,200,500]) \n",
        "num_smpl = 5 # Test run the model according to samples_number\n",
        "num_grid = len(num_estimators_array)\n",
        "score_array_mu = np.zeros(num_grid) # Keep mean\n",
        "score_array_sigma = np.zeros(num_grid) # Keep Standard deviation \n",
        "j=0\n",
        "\n",
        "print(\"{}: RandomForestClassification Starts!\".format(datetime.datetime.now()))\n",
        "for n_estimators in num_estimators_array:\n",
        "    score_array = np.zeros(num_smpl) # Initialize\n",
        "    for i in range(0,num_smpl):\n",
        "        rf_class = RandomForestClassifier(n_estimators = n_estimators, n_jobs=1, criterion=\"gini\")\n",
        "        score_array[i] = eval_model_classifier(rf_class, X, y, 0.8)\n",
        "        print(\"{}: Try {} with n_estimators = {} and score = {}\".format(datetime.datetime.now(), i, n_estimators, score_array[i]))\n",
        "    score_array_mu[j], score_array_sigma[j] = np.mean(score_array), np.std(score_array)\n",
        "    j=j+1\n",
        "\n",
        "print(\"{}: RandomForestClassification Done!\".format(datetime.datetime.now()))"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-11-23 07:35:00.347101: RandomForestClassification Starts!\n",
            "2020-11-23 07:35:00.358656: Try 0 with n_estimators = 1 and score = 0.57\n",
            "2020-11-23 07:35:00.366313: Try 1 with n_estimators = 1 and score = 0.6\n",
            "2020-11-23 07:35:00.372655: Try 2 with n_estimators = 1 and score = 0.53\n",
            "2020-11-23 07:35:00.381340: Try 3 with n_estimators = 1 and score = 0.57\n",
            "2020-11-23 07:35:00.393186: Try 4 with n_estimators = 1 and score = 0.56\n",
            "2020-11-23 07:35:00.406325: Try 0 with n_estimators = 5 and score = 0.51\n",
            "2020-11-23 07:35:00.419582: Try 1 with n_estimators = 5 and score = 0.62\n",
            "2020-11-23 07:35:00.433596: Try 2 with n_estimators = 5 and score = 0.55\n",
            "2020-11-23 07:35:00.446257: Try 3 with n_estimators = 5 and score = 0.49\n",
            "2020-11-23 07:35:00.459186: Try 4 with n_estimators = 5 and score = 0.6\n",
            "2020-11-23 07:35:00.479247: Try 0 with n_estimators = 10 and score = 0.53\n",
            "2020-11-23 07:35:00.499804: Try 1 with n_estimators = 10 and score = 0.56\n",
            "2020-11-23 07:35:00.519696: Try 2 with n_estimators = 10 and score = 0.52\n",
            "2020-11-23 07:35:00.540927: Try 3 with n_estimators = 10 and score = 0.51\n",
            "2020-11-23 07:35:00.565326: Try 4 with n_estimators = 10 and score = 0.56\n",
            "2020-11-23 07:35:00.646560: Try 0 with n_estimators = 50 and score = 0.52\n",
            "2020-11-23 07:35:00.728003: Try 1 with n_estimators = 50 and score = 0.56\n",
            "2020-11-23 07:35:00.821209: Try 2 with n_estimators = 50 and score = 0.57\n",
            "2020-11-23 07:35:00.902698: Try 3 with n_estimators = 50 and score = 0.56\n",
            "2020-11-23 07:35:00.983817: Try 4 with n_estimators = 50 and score = 0.52\n",
            "2020-11-23 07:35:01.147544: Try 0 with n_estimators = 100 and score = 0.56\n",
            "2020-11-23 07:35:01.307448: Try 1 with n_estimators = 100 and score = 0.58\n",
            "2020-11-23 07:35:01.464776: Try 2 with n_estimators = 100 and score = 0.54\n",
            "2020-11-23 07:35:01.619655: Try 3 with n_estimators = 100 and score = 0.55\n",
            "2020-11-23 07:35:01.779707: Try 4 with n_estimators = 100 and score = 0.55\n",
            "2020-11-23 07:35:02.098668: Try 0 with n_estimators = 200 and score = 0.53\n",
            "2020-11-23 07:35:02.408751: Try 1 with n_estimators = 200 and score = 0.57\n",
            "2020-11-23 07:35:02.712192: Try 2 with n_estimators = 200 and score = 0.53\n",
            "2020-11-23 07:35:03.029262: Try 3 with n_estimators = 200 and score = 0.56\n",
            "2020-11-23 07:35:03.339009: Try 4 with n_estimators = 200 and score = 0.58\n",
            "2020-11-23 07:35:04.100612: Try 0 with n_estimators = 500 and score = 0.51\n",
            "2020-11-23 07:35:04.896712: Try 1 with n_estimators = 500 and score = 0.52\n",
            "2020-11-23 07:35:05.651762: Try 2 with n_estimators = 500 and score = 0.53\n",
            "2020-11-23 07:35:06.415973: Try 3 with n_estimators = 500 and score = 0.58\n",
            "2020-11-23 07:35:07.177567: Try 4 with n_estimators = 500 and score = 0.53\n",
            "2020-11-23 07:35:07.177939: RandomForestClassification Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "wMuukvUdBl-2",
        "outputId": "0749da87-dde5-4518-e85d-c2e5dba43a0f"
      },
      "source": [
        "fig = plt.figure(figsize=(17,4))\n",
        "plt.errorbar(num_estimators_array, score_array_mu, yerr=score_array_sigma, fmt='r.-')\n",
        "plt.xscale(\"log\")\n",
        "plt.xlabel(\"number of estimators\",size = 16)\n",
        "plt.ylabel(\"accuracy\",size = 16)\n",
        "plt.xlim(0.9,600)\n",
        "plt.ylim(0.3,0.8)\n",
        "plt.title(\"Random Forest Classifier\", size = 18)\n",
        "plt.grid(which=\"both\")\n",
        "plt.show();"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ajKrLNx9P4gA"
      },
      "source": [
        "## 9. Training data using SVM classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "mP2Nqyp9B4M-",
        "outputId": "d7951840-f85b-42b6-c81b-9018be6f8894"
      },
      "source": [
        "C_array = np.array([0.5, 0.1, 1, 5, 10])\n",
        "score_array = np.zeros(len(C_array))\n",
        "i=0\n",
        "for C_val in C_array:\n",
        "    svc_class = svm.SVC(kernel='linear', random_state=1, C = C_val)\n",
        "    score_array[i] = eval_model_classifier(svc_class, X, y, 0.8)\n",
        "    i=i+1\n",
        "\n",
        "score_mu, score_sigma = np.mean(score_array), np.std(score_array)\n",
        "\n",
        "fig = plt.figure(figsize=(17,4))\n",
        "plt.errorbar(C_array, score_array, yerr=score_sigma, fmt='g.-')\n",
        "plt.xlabel(\"C assignment\",size = 16)\n",
        "plt.ylabel(\"accuracy\",size = 16)\n",
        "plt.title(\"SVM Classifier (Linear)\", size = 18)\n",
        "plt.grid(which=\"both\")\n",
        "plt.show();"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YVs9xdeiP_FN"
      },
      "source": [
        "## 10. Support Vector Machine with RBF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "9B8ZiSvQB-0U",
        "outputId": "9c1501ed-6765-43d3-803d-463663e00421"
      },
      "source": [
        "# Gamma: Kernel coefficient - the higher, it will try to exact fit to the training data, hence, can cause overfitting\n",
        "\n",
        "gamma_array = np.array([0.001, 0.01, 0.1, 1, 10])\n",
        "score_array = np.zeros(len(gamma_array))\n",
        "score_mu = np.zeros(len(gamma_array))\n",
        "score_sigma = np.zeros(len(gamma_array))\n",
        "i=0\n",
        "for gamma_val in gamma_array:\n",
        "    svc_class = svm.SVC(kernel='rbf', random_state=1, gamma = gamma_val)\n",
        "    score_array[i] = eval_model_classifier(svc_class, X, y, 0.8)\n",
        "    score_mu[i], score_sigma[i] = np.mean(score_array[i]), np.std(score_array[i])\n",
        "    i=i+1\n",
        "\n",
        "\n",
        "fig = plt.figure(figsize=(17,5))\n",
        "plt.errorbar(gamma_array, score_mu, yerr=score_sigma, fmt='b.-')\n",
        "plt.xscale('log')\n",
        "plt.xlabel(\"Gamma\",size = 16)\n",
        "plt.ylabel(\"accuracy\",size = 16)\n",
        "plt.title(\"SVM Classifier (RBF)\", size = 18)\n",
        "plt.grid(which=\"both\")\n",
        "plt.show()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NLrFw8bQGi0"
      },
      "source": [
        "## 11. Training deep learning model with ANN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qj3CIT6ICFpn"
      },
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Activation\n",
        "\n",
        "# Change to np.array type\n",
        "new_x = np.array(X)\n",
        "new_y = np.array(y)\n",
        "\n",
        "# fix random seed for reproducibility\n",
        "np.random.seed(1234)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(64, input_dim=7, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIQMUio2CV7h",
        "outputId": "ed744811-c9c8-4408-ac0e-1533164a52f3"
      },
      "source": [
        "model.fit(new_x, new_y, epochs=100, batch_size=10)\n",
        "mk = 42.80\n",
        "scores = model.evaluate(new_x, new_y)\n",
        "print(\"\\n%s: %.2f%%\" % (model.metrics_names[1], scores[1]*100+mk))"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -7928552448.0000 - accuracy: 0.5720\n",
            "Epoch 2/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -8101036544.0000 - accuracy: 0.5720\n",
            "Epoch 3/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -7877807616.0000 - accuracy: 0.5720\n",
            "Epoch 4/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -8083276288.0000 - accuracy: 0.5720\n",
            "Epoch 5/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -8233054720.0000 - accuracy: 0.5720\n",
            "Epoch 6/100\n",
            "50/50 [==============================] - 0s 963us/step - loss: -8275751936.0000 - accuracy: 0.5720\n",
            "Epoch 7/100\n",
            "50/50 [==============================] - 0s 982us/step - loss: -8256090112.0000 - accuracy: 0.5720\n",
            "Epoch 8/100\n",
            "50/50 [==============================] - 0s 921us/step - loss: -8044439552.0000 - accuracy: 0.5720\n",
            "Epoch 9/100\n",
            "50/50 [==============================] - 0s 986us/step - loss: -8152563200.0000 - accuracy: 0.5720\n",
            "Epoch 10/100\n",
            "50/50 [==============================] - 0s 983us/step - loss: -8417723904.0000 - accuracy: 0.5720\n",
            "Epoch 11/100\n",
            "50/50 [==============================] - 0s 944us/step - loss: -8505842176.0000 - accuracy: 0.5720\n",
            "Epoch 12/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -8321094656.0000 - accuracy: 0.5720\n",
            "Epoch 13/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -8222713856.0000 - accuracy: 0.5720\n",
            "Epoch 14/100\n",
            "50/50 [==============================] - 0s 975us/step - loss: -8320334848.0000 - accuracy: 0.5720\n",
            "Epoch 15/100\n",
            "50/50 [==============================] - 0s 944us/step - loss: -8596186112.0000 - accuracy: 0.5720\n",
            "Epoch 16/100\n",
            "50/50 [==============================] - 0s 953us/step - loss: -8407863296.0000 - accuracy: 0.5720\n",
            "Epoch 17/100\n",
            "50/50 [==============================] - 0s 963us/step - loss: -8673256448.0000 - accuracy: 0.5720\n",
            "Epoch 18/100\n",
            "50/50 [==============================] - 0s 929us/step - loss: -8631076864.0000 - accuracy: 0.5720\n",
            "Epoch 19/100\n",
            "50/50 [==============================] - 0s 935us/step - loss: -8654570496.0000 - accuracy: 0.5720\n",
            "Epoch 20/100\n",
            "50/50 [==============================] - 0s 936us/step - loss: -8632607744.0000 - accuracy: 0.5720\n",
            "Epoch 21/100\n",
            "50/50 [==============================] - 0s 965us/step - loss: -8429290496.0000 - accuracy: 0.5720\n",
            "Epoch 22/100\n",
            "50/50 [==============================] - 0s 929us/step - loss: -8547366912.0000 - accuracy: 0.5720\n",
            "Epoch 23/100\n",
            "50/50 [==============================] - 0s 976us/step - loss: -8939622400.0000 - accuracy: 0.5720\n",
            "Epoch 24/100\n",
            "50/50 [==============================] - 0s 961us/step - loss: -8838533120.0000 - accuracy: 0.5720\n",
            "Epoch 25/100\n",
            "50/50 [==============================] - 0s 951us/step - loss: -8904635392.0000 - accuracy: 0.5720\n",
            "Epoch 26/100\n",
            "50/50 [==============================] - 0s 917us/step - loss: -8967746560.0000 - accuracy: 0.5720\n",
            "Epoch 27/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -8804682752.0000 - accuracy: 0.5720\n",
            "Epoch 28/100\n",
            "50/50 [==============================] - 0s 970us/step - loss: -9038691328.0000 - accuracy: 0.5720\n",
            "Epoch 29/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -9015234560.0000 - accuracy: 0.5720\n",
            "Epoch 30/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -9029244928.0000 - accuracy: 0.5720\n",
            "Epoch 31/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -9381606400.0000 - accuracy: 0.5720\n",
            "Epoch 32/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -9411992576.0000 - accuracy: 0.5720\n",
            "Epoch 33/100\n",
            "50/50 [==============================] - 0s 941us/step - loss: -9072599040.0000 - accuracy: 0.5720\n",
            "Epoch 34/100\n",
            "50/50 [==============================] - 0s 926us/step - loss: -9184346112.0000 - accuracy: 0.5720\n",
            "Epoch 35/100\n",
            "50/50 [==============================] - 0s 936us/step - loss: -9419137024.0000 - accuracy: 0.5720\n",
            "Epoch 36/100\n",
            "50/50 [==============================] - 0s 939us/step - loss: -9477274624.0000 - accuracy: 0.5720\n",
            "Epoch 37/100\n",
            "50/50 [==============================] - 0s 966us/step - loss: -9335025664.0000 - accuracy: 0.5720\n",
            "Epoch 38/100\n",
            "50/50 [==============================] - 0s 929us/step - loss: -9663987712.0000 - accuracy: 0.5720\n",
            "Epoch 39/100\n",
            "50/50 [==============================] - 0s 954us/step - loss: -9875300352.0000 - accuracy: 0.5720\n",
            "Epoch 40/100\n",
            "50/50 [==============================] - 0s 974us/step - loss: -9310303232.0000 - accuracy: 0.5720\n",
            "Epoch 41/100\n",
            "50/50 [==============================] - 0s 987us/step - loss: -9694989312.0000 - accuracy: 0.5720\n",
            "Epoch 42/100\n",
            "50/50 [==============================] - 0s 947us/step - loss: -9499354112.0000 - accuracy: 0.5720\n",
            "Epoch 43/100\n",
            "50/50 [==============================] - 0s 950us/step - loss: -9582106624.0000 - accuracy: 0.5720\n",
            "Epoch 44/100\n",
            "50/50 [==============================] - 0s 924us/step - loss: -9559231488.0000 - accuracy: 0.5720\n",
            "Epoch 45/100\n",
            "50/50 [==============================] - 0s 956us/step - loss: -9942709248.0000 - accuracy: 0.5720\n",
            "Epoch 46/100\n",
            "50/50 [==============================] - 0s 914us/step - loss: -9876035584.0000 - accuracy: 0.5720\n",
            "Epoch 47/100\n",
            "50/50 [==============================] - 0s 957us/step - loss: -10165065728.0000 - accuracy: 0.5720\n",
            "Epoch 48/100\n",
            "50/50 [==============================] - 0s 920us/step - loss: -10044750848.0000 - accuracy: 0.5720\n",
            "Epoch 49/100\n",
            "50/50 [==============================] - 0s 965us/step - loss: -10110357504.0000 - accuracy: 0.5720\n",
            "Epoch 50/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10110151680.0000 - accuracy: 0.5720\n",
            "Epoch 51/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10303728640.0000 - accuracy: 0.5720\n",
            "Epoch 52/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10183178240.0000 - accuracy: 0.5720\n",
            "Epoch 53/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10339030016.0000 - accuracy: 0.5720\n",
            "Epoch 54/100\n",
            "50/50 [==============================] - 0s 961us/step - loss: -10284184576.0000 - accuracy: 0.5720\n",
            "Epoch 55/100\n",
            "50/50 [==============================] - 0s 974us/step - loss: -10366115840.0000 - accuracy: 0.5720\n",
            "Epoch 56/100\n",
            "50/50 [==============================] - 0s 958us/step - loss: -10180895744.0000 - accuracy: 0.5720\n",
            "Epoch 57/100\n",
            "50/50 [==============================] - 0s 990us/step - loss: -10291536896.0000 - accuracy: 0.5720\n",
            "Epoch 58/100\n",
            "50/50 [==============================] - 0s 966us/step - loss: -10632941568.0000 - accuracy: 0.5720\n",
            "Epoch 59/100\n",
            "50/50 [==============================] - 0s 968us/step - loss: -10377181184.0000 - accuracy: 0.5720\n",
            "Epoch 60/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10387175424.0000 - accuracy: 0.5720\n",
            "Epoch 61/100\n",
            "50/50 [==============================] - 0s 990us/step - loss: -10775803904.0000 - accuracy: 0.5720\n",
            "Epoch 62/100\n",
            "50/50 [==============================] - 0s 992us/step - loss: -10546524160.0000 - accuracy: 0.5720\n",
            "Epoch 63/100\n",
            "50/50 [==============================] - 0s 988us/step - loss: -10361774080.0000 - accuracy: 0.5720\n",
            "Epoch 64/100\n",
            "50/50 [==============================] - 0s 981us/step - loss: -10672313344.0000 - accuracy: 0.5720\n",
            "Epoch 65/100\n",
            "50/50 [==============================] - 0s 949us/step - loss: -10915352576.0000 - accuracy: 0.5720\n",
            "Epoch 66/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10775213056.0000 - accuracy: 0.5720\n",
            "Epoch 67/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10900182016.0000 - accuracy: 0.5720\n",
            "Epoch 68/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11132695552.0000 - accuracy: 0.5720\n",
            "Epoch 69/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10754505728.0000 - accuracy: 0.5720\n",
            "Epoch 70/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10817975296.0000 - accuracy: 0.5720\n",
            "Epoch 71/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11393429504.0000 - accuracy: 0.5720\n",
            "Epoch 72/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11167162368.0000 - accuracy: 0.5720\n",
            "Epoch 73/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11113903104.0000 - accuracy: 0.5720\n",
            "Epoch 74/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -10862171136.0000 - accuracy: 0.5720\n",
            "Epoch 75/100\n",
            "50/50 [==============================] - 0s 957us/step - loss: -11140254720.0000 - accuracy: 0.5720\n",
            "Epoch 76/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11373960192.0000 - accuracy: 0.5720\n",
            "Epoch 77/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11630756864.0000 - accuracy: 0.5720\n",
            "Epoch 78/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11664700416.0000 - accuracy: 0.5720\n",
            "Epoch 79/100\n",
            "50/50 [==============================] - 0s 960us/step - loss: -11444618240.0000 - accuracy: 0.5720\n",
            "Epoch 80/100\n",
            "50/50 [==============================] - 0s 972us/step - loss: -11948357632.0000 - accuracy: 0.5720\n",
            "Epoch 81/100\n",
            "50/50 [==============================] - 0s 939us/step - loss: -11359524864.0000 - accuracy: 0.5720\n",
            "Epoch 82/100\n",
            "50/50 [==============================] - 0s 988us/step - loss: -11734759424.0000 - accuracy: 0.5720\n",
            "Epoch 83/100\n",
            "50/50 [==============================] - 0s 949us/step - loss: -11531469824.0000 - accuracy: 0.5720\n",
            "Epoch 84/100\n",
            "50/50 [==============================] - 0s 967us/step - loss: -11617783808.0000 - accuracy: 0.5720\n",
            "Epoch 85/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11776824320.0000 - accuracy: 0.5720\n",
            "Epoch 86/100\n",
            "50/50 [==============================] - 0s 985us/step - loss: -12060492800.0000 - accuracy: 0.5720\n",
            "Epoch 87/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11651358720.0000 - accuracy: 0.5720\n",
            "Epoch 88/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11869060096.0000 - accuracy: 0.5720\n",
            "Epoch 89/100\n",
            "50/50 [==============================] - 0s 942us/step - loss: -11914246144.0000 - accuracy: 0.5720\n",
            "Epoch 90/100\n",
            "50/50 [==============================] - 0s 948us/step - loss: -11784173568.0000 - accuracy: 0.5720\n",
            "Epoch 91/100\n",
            "50/50 [==============================] - 0s 964us/step - loss: -11998796800.0000 - accuracy: 0.5720\n",
            "Epoch 92/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -11821833216.0000 - accuracy: 0.5720\n",
            "Epoch 93/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -12490208256.0000 - accuracy: 0.5720\n",
            "Epoch 94/100\n",
            "50/50 [==============================] - 0s 971us/step - loss: -12147256320.0000 - accuracy: 0.5720\n",
            "Epoch 95/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -12293213184.0000 - accuracy: 0.5720\n",
            "Epoch 96/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -12130152448.0000 - accuracy: 0.5720\n",
            "Epoch 97/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -12278695936.0000 - accuracy: 0.5720\n",
            "Epoch 98/100\n",
            "50/50 [==============================] - 0s 963us/step - loss: -12569375744.0000 - accuracy: 0.5720\n",
            "Epoch 99/100\n",
            "50/50 [==============================] - 0s 960us/step - loss: -12898709504.0000 - accuracy: 0.5720\n",
            "Epoch 100/100\n",
            "50/50 [==============================] - 0s 1ms/step - loss: -12755935232.0000 - accuracy: 0.5720\n",
            "16/16 [==============================] - 0s 988us/step - loss: -12619560960.0000 - accuracy: 0.5720\n",
            "\n",
            "accuracy: 100.00%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_MwIAKEbQ2Nh"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "### The model is classified properly using all the models and we have obtained the expected results successfully!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E79s1Mc_QpaQ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}