ML0101EN_Proj_Loan_py_v1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vamystry-suvam/trial/blob/main/ML0101EN_Proj_Loan_py_v1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "hE77YpHwr72d"
      },
      "source": [
        "<p style=\"text-align:center\">\n",
        "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\" target=\"_blank\">\n",
        "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
        "    </a>\n",
        "</p>\n",
        "\n",
        "<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "_rIQJzhlr72q"
      },
      "source": [
        "In this notebook we try to practice all the classification algorithms that we have learned in this course.\n",
        "\n",
        "We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n",
        "\n",
        "Let's first load required libraries:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "4DLF4gNPr72x"
      },
      "outputs": [],
      "source": [
        "import itertools\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.ticker import NullFormatter\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.ticker as ticker\n",
        "from sklearn import preprocessing\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "uqy5R40pr723"
      },
      "source": [
        "### About dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "QrKEsLy6r727"
      },
      "source": [
        "This dataset is about past loans. The **Loan_train.csv** data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n",
        "\n",
        "| Field          | Description                                                                           |\n",
        "| -------------- | ------------------------------------------------------------------------------------- |\n",
        "| Loan_status    | Whether a loan is paid off on in collection                                           |\n",
        "| Principal      | Basic principal loan amount at the                                                    |\n",
        "| Terms          | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n",
        "| Effective_date | When the loan got originated and took effects                                         |\n",
        "| Due_date       | Since it’s one-time payoff schedule, each loan has one single due date                |\n",
        "| Age            | Age of applicant                                                                      |\n",
        "| Education      | Education of applicant                                                                |\n",
        "| Gender         | The gender of applicant                                                               |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "9NjivGuUr72_"
      },
      "source": [
        "Let's download the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvVUANZYr73D",
        "outputId": "1ae8f57f-0851-4dcd-c315-5e0bd1fb0e5b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-29 08:45:22--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv\n",
            "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
            "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 23101 (23K) [text/csv]\n",
            "Saving to: ‘loan_train.csv’\n",
            "\n",
            "loan_train.csv      100%[===================>]  22.56K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-08-29 08:45:22 (262 MB/s) - ‘loan_train.csv’ saved [23101/23101]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O loan_train.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "3yFHtO2ur73G"
      },
      "source": [
        "### Load Data From CSV File\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AjVXMLdKr73J",
        "outputId": "25f5f6d1-75b5-4c20-add7-976f0579940e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
              "0           0             0     PAIDOFF       1000     30       9/8/2016   \n",
              "1           2             2     PAIDOFF       1000     30       9/8/2016   \n",
              "2           3             3     PAIDOFF       1000     15       9/8/2016   \n",
              "3           4             4     PAIDOFF       1000     30       9/9/2016   \n",
              "4           6             6     PAIDOFF       1000     30       9/9/2016   \n",
              "\n",
              "    due_date  age             education  Gender  \n",
              "0  10/7/2016   45  High School or Below    male  \n",
              "1  10/7/2016   33              Bechalor  female  \n",
              "2  9/22/2016   27               college    male  \n",
              "3  10/8/2016   28               college  female  \n",
              "4  10/8/2016   29               college    male  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8fc3f0f9-4a01-4ecf-8195-b318bbc842ea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</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>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</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>45</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</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>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</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>27</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</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>28</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>6</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>29</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8fc3f0f9-4a01-4ecf-8195-b318bbc842ea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8fc3f0f9-4a01-4ecf-8195-b318bbc842ea button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8fc3f0f9-4a01-4ecf-8195-b318bbc842ea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df = pd.read_csv('loan_train.csv')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Ow868PPr73Q",
        "outputId": "bfbd1949-8545-4ccb-d610-8a5615fbe0cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(346, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "Yln4ozBrr73T"
      },
      "source": [
        "### Convert to date time object\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wQ6iOxxDr73W",
        "outputId": "ba269322-a6ad-4a6d-ec90-5e75e2475052"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
              "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
              "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
              "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
              "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
              "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
              "\n",
              "    due_date  age             education  Gender  \n",
              "0 2016-10-07   45  High School or Below    male  \n",
              "1 2016-10-07   33              Bechalor  female  \n",
              "2 2016-09-22   27               college    male  \n",
              "3 2016-10-08   28               college  female  \n",
              "4 2016-10-08   29               college    male  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-38f3af46-af67-4b9a-a3b9-694f393b743b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</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>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</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>2</td>\n",
              "      <td>2</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</td>\n",
              "      <td>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-09-22</td>\n",
              "      <td>27</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016-10-08</td>\n",
              "      <td>28</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016-10-08</td>\n",
              "      <td>29</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-38f3af46-af67-4b9a-a3b9-694f393b743b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-38f3af46-af67-4b9a-a3b9-694f393b743b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-38f3af46-af67-4b9a-a3b9-694f393b743b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df['due_date'] = pd.to_datetime(df['due_date'])\n",
        "df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "WcMFf8PAr73Y"
      },
      "source": [
        "# Data visualization and pre-processing\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "1HVKShFFr73a"
      },
      "source": [
        "Let’s see how many of each class is in our data set\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ho9tc1dhr73b",
        "outputId": "1720e37e-6777-4d8b-a339-ff11d89e2bef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PAIDOFF       260\n",
              "COLLECTION     86\n",
              "Name: loan_status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df['loan_status'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "_eZkn0a5r73e"
      },
      "source": [
        "260 people have paid off the loan on time while 86 have gone into collection\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQRt6plEr73f"
      },
      "source": [
        "Let's plot some columns to underestand data better:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OnRTJYlr73h",
        "outputId": "83f03037-9e17-477f-e4be-8d64cb54d3ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: conda: command not found\n"
          ]
        }
      ],
      "source": [
        "# notice: installing seaborn might takes a few minutes\n",
        "!conda install -c anaconda seaborn -y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "d0JcJcYZr73k",
        "outputId": "e364281c-502b-4fca-b7a6-7971a82650ea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n",
        "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
        "g.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n",
        "\n",
        "g.axes[-1].legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "b1EHIEyUr73n",
        "outputId": "2cdd3d8a-f343-4ce8-bfbf-021612d6ff0d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "bins = np.linspace(df.age.min(), df.age.max(), 10)\n",
        "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
        "g.map(plt.hist, 'age', bins=bins, ec=\"k\")\n",
        "\n",
        "g.axes[-1].legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "wDQQ2b_mr73p"
      },
      "source": [
        "# Pre-processing:  Feature selection/extraction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "N0tCcNaWr73r"
      },
      "source": [
        "### Let's look at the day of the week people get the loan\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "pzGDOZa7r73t",
        "outputId": "d86baaf0-a8f4-44c3-f625-0123416bb120"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x216 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df['dayofweek'] = df['effective_date'].dt.dayofweek\n",
        "bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n",
        "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
        "g.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\n",
        "g.axes[-1].legend()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "1qDdQ61Rr73v"
      },
      "source": [
        "We see that people who get the loan at the end of the week don't pay it off, so let's use Feature binarization to set a threshold value less than day 4\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5FAYb7jlr73x",
        "outputId": "010a4342-5f57-4435-8a0f-674fd3b67409"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
              "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
              "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
              "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
              "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
              "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
              "\n",
              "    due_date  age             education  Gender  dayofweek  weekend  \n",
              "0 2016-10-07   45  High School or Below    male          3        0  \n",
              "1 2016-10-07   33              Bechalor  female          3        0  \n",
              "2 2016-09-22   27               college    male          3        0  \n",
              "3 2016-10-08   28               college  female          4        1  \n",
              "4 2016-10-08   29               college    male          4        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-41fb6ad9-41cf-40f0-9043-c421cdc57853\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</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>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</td>\n",
              "      <td>45</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>male</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</td>\n",
              "      <td>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-09-22</td>\n",
              "      <td>27</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016-10-08</td>\n",
              "      <td>28</td>\n",
              "      <td>college</td>\n",
              "      <td>female</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016-10-08</td>\n",
              "      <td>29</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41fb6ad9-41cf-40f0-9043-c421cdc57853')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-41fb6ad9-41cf-40f0-9043-c421cdc57853 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-41fb6ad9-41cf-40f0-9043-c421cdc57853');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "k2m4eZ_ir73z"
      },
      "source": [
        "## Convert Categorical features to numerical values\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "J3bod7Rmr730"
      },
      "source": [
        "Let's look at gender:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ksNAfggFr731",
        "outputId": "e121261f-3d1c-489a-d58d-359c0e2fa1d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender  loan_status\n",
              "0       PAIDOFF        0.731293\n",
              "        COLLECTION     0.268707\n",
              "1       PAIDOFF        0.865385\n",
              "        COLLECTION     0.134615\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "0YtXUafYr732"
      },
      "source": [
        "86 % of female pay there loans while only 73 % of males pay there loan\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "dduDEjIkr734"
      },
      "source": [
        "Let's convert male to 0 and female to 1:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "CTOuE2a9r735",
        "outputId": "2c9f1aa7-fd44-4e33-85d6-78ccfe054b28"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
              "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
              "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
              "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
              "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
              "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
              "\n",
              "    due_date  age             education  Gender  dayofweek  weekend  \n",
              "0 2016-10-07   45  High School or Below       0          3        0  \n",
              "1 2016-10-07   33              Bechalor       1          3        0  \n",
              "2 2016-09-22   27               college       0          3        0  \n",
              "3 2016-10-08   28               college       1          4        1  \n",
              "4 2016-10-08   29               college       0          4        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6309d89-861a-4022-aeac-1c18088b3d71\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</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>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "      <th>dayofweek</th>\n",
              "      <th>weekend</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</td>\n",
              "      <td>45</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-10-07</td>\n",
              "      <td>33</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>2016-09-08</td>\n",
              "      <td>2016-09-22</td>\n",
              "      <td>27</td>\n",
              "      <td>college</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016-10-08</td>\n",
              "      <td>28</td>\n",
              "      <td>college</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>2016-09-09</td>\n",
              "      <td>2016-10-08</td>\n",
              "      <td>29</td>\n",
              "      <td>college</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6309d89-861a-4022-aeac-1c18088b3d71')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b6309d89-861a-4022-aeac-1c18088b3d71 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b6309d89-861a-4022-aeac-1c18088b3d71');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "DE2Mx7nEr736"
      },
      "source": [
        "## One Hot Encoding\n",
        "\n",
        "#### How about education?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "muha3z7Qr737",
        "outputId": "feeea480-cd13-4788-bc7f-07bad6f98f13"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "education             loan_status\n",
              "Bechalor              PAIDOFF        0.750000\n",
              "                      COLLECTION     0.250000\n",
              "High School or Below  PAIDOFF        0.741722\n",
              "                      COLLECTION     0.258278\n",
              "Master or Above       COLLECTION     0.500000\n",
              "                      PAIDOFF        0.500000\n",
              "college               PAIDOFF        0.765101\n",
              "                      COLLECTION     0.234899\n",
              "Name: loan_status, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "ApUCG8fqr739"
      },
      "source": [
        "#### Features before One Hot Encoding\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7j8S-Q-Kr73-",
        "outputId": "199130e6-fad8-474e-9866-2fe54122ba6b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Principal  terms  age  Gender             education\n",
              "0       1000     30   45       0  High School or Below\n",
              "1       1000     30   33       1              Bechalor\n",
              "2       1000     15   27       0               college\n",
              "3       1000     30   28       1               college\n",
              "4       1000     30   29       0               college"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-32700284-4aaf-49e9-9014-78b64f0cc118\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>education</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>0</td>\n",
              "      <td>High School or Below</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>Bechalor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>college</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>college</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>college</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-32700284-4aaf-49e9-9014-78b64f0cc118')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-32700284-4aaf-49e9-9014-78b64f0cc118 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-32700284-4aaf-49e9-9014-78b64f0cc118');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "df[['Principal','terms','age','Gender','education']].head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "VzgNjGIBr74A"
      },
      "source": [
        "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a4s2E3qRr74B",
        "outputId": "1cb68e51-5629-4927-a0a3-426687cbaf32"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
              "0       1000     30   45       0        0         0                     1   \n",
              "1       1000     30   33       1        0         1                     0   \n",
              "2       1000     15   27       0        0         0                     0   \n",
              "3       1000     30   28       1        1         0                     0   \n",
              "4       1000     30   29       0        1         0                     0   \n",
              "\n",
              "   college  \n",
              "0        0  \n",
              "1        0  \n",
              "2        1  \n",
              "3        1  \n",
              "4        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a3597cd-0473-4eac-a6a6-79cea6992439\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>weekend</th>\n",
              "      <th>Bechalor</th>\n",
              "      <th>High School or Below</th>\n",
              "      <th>college</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0a3597cd-0473-4eac-a6a6-79cea6992439')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a3597cd-0473-4eac-a6a6-79cea6992439 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0a3597cd-0473-4eac-a6a6-79cea6992439');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
        "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
        "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
        "Feature.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "PxxKlplsr74C"
      },
      "source": [
        "### Feature Selection\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "wI9sIluqr74E"
      },
      "source": [
        "Let's define feature sets, X:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "DpgpAuHGr74F",
        "outputId": "e411f5ea-74df-4033-a313-a052ae6892ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
              "0       1000     30   45       0        0         0                     1   \n",
              "1       1000     30   33       1        0         1                     0   \n",
              "2       1000     15   27       0        0         0                     0   \n",
              "3       1000     30   28       1        1         0                     0   \n",
              "4       1000     30   29       0        1         0                     0   \n",
              "\n",
              "   college  \n",
              "0        0  \n",
              "1        0  \n",
              "2        1  \n",
              "3        1  \n",
              "4        1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91d2583e-c622-402b-aa2d-f2206b32114a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Principal</th>\n",
              "      <th>terms</th>\n",
              "      <th>age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>weekend</th>\n",
              "      <th>Bechalor</th>\n",
              "      <th>High School or Below</th>\n",
              "      <th>college</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>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1000</td>\n",
              "      <td>15</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91d2583e-c622-402b-aa2d-f2206b32114a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91d2583e-c622-402b-aa2d-f2206b32114a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-91d2583e-c622-402b-aa2d-f2206b32114a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "X = Feature\n",
        "X[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "DNj7c7fdr74H"
      },
      "source": [
        "What are our lables?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pF-KKli_r74I",
        "outputId": "93d60a56-2c8b-4f62-c7e6-e2b8af9d336e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "y = df['loan_status'].values\n",
        "y[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "3pYvtb75r74J"
      },
      "source": [
        "## Normalize Data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "oGn4wvsNr74K"
      },
      "source": [
        "Data Standardization give data zero mean and unit variance (technically should be done after train test split)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-9YmSBvr74L",
        "outputId": "23ab4f66-525a-4ab3-806c-6771440ed97e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n",
              "        -0.38170062,  1.13639374, -0.86968108],\n",
              "       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n",
              "         2.61985426, -0.87997669, -0.86968108],\n",
              "       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n",
              "        -0.38170062, -0.87997669,  1.14984679],\n",
              "       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n",
              "        -0.38170062, -0.87997669,  1.14984679],\n",
              "       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n",
              "        -0.38170062, -0.87997669,  1.14984679]])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
        "X[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "xgXcL3nSr74M"
      },
      "source": [
        "# Classification\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "fzCXJVOur74N"
      },
      "source": [
        "Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\n",
        "You should use the following algorithm:\n",
        "\n",
        "*   K Nearest Neighbor(KNN)\n",
        "*   Decision Tree\n",
        "*   Support Vector Machine\n",
        "*   Logistic Regression\n",
        "\n",
        "\\__ Notice:\\__\n",
        "\n",
        "*   You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n",
        "*   You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n",
        "*   You should include the code of the algorithm in the following cells.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBwj3Norr74P"
      },
      "source": [
        "# K Nearest Neighbor(KNN)\n",
        "\n",
        "Notice: You should find the best k to build the model with the best accuracy.\\\n",
        "**warning:** You should not use the **loan_test.csv** for finding the best k, however, you can split your train_loan.csv into train and test to find the best **k**.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fItcGXPKr74R",
        "outputId": "d6c3f793-a3e1-4425-f679-d0d66ab8305c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set:  (276, 8) (276,)\n",
            "Test set:  (70, 8) (70,)\n"
          ]
        }
      ],
      "source": [
        "#Data Sets:\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\n",
        "print(\"Train set: \", X_train.shape, y_train.shape)\n",
        "print(\"Test set: \", X_test.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0tyem3fr74S",
        "outputId": "0c7b64e5-ba49-4fb3-d7ee-b746a95c3a7d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 0.6428571428571429\n",
            "2 0.5857142857142857\n",
            "3 0.7428571428571429\n",
            "4 0.7\n",
            "5 0.7428571428571429\n",
            "6 0.7142857142857143\n",
            "7 0.8\n",
            "8 0.7571428571428571\n",
            "9 0.7428571428571429\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "k = 3\n",
        "#We fit the model:\n",
        "kNN_model = KNeighborsClassifier(n_neighbors=k).fit(X_train, y_train)\n",
        "kNN_model\n",
        "y_pred = kNN_model.predict( X_test )\n",
        "#Find the max value\n",
        "accuracies = {}\n",
        "k_max = 1\n",
        "acc_max = 0\n",
        "for k in range(1, 10):\n",
        "    kNN_model = KNeighborsClassifier(n_neighbors=k).fit(X_train, y_train)\n",
        "    y_pred = kNN_model.predict( X_test )\n",
        "    accuracies[k] = accuracy_score(y_test, y_pred)\n",
        "    print(k, accuracies[k])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TOBhwfg6r74T",
        "outputId": "ca02060c-920d-4366-a57c-3118983fa7fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We take k = 7\n",
            "Train set Accuracy (Jaccard):  0.8043478260869565\n",
            "Test set Accuracy (Jaccard):  0.8\n",
            "Train set Accuracy (F1):  0.7942614463042823\n",
            "Test set Accuracy (F1):  0.8\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import jaccard_score\n",
        "from sklearn import metrics\n",
        "\n",
        "print(\"We take k = 7\")\n",
        "knn_model = KNeighborsClassifier(n_neighbors = 7).fit(X_train, y_train)\n",
        "print(\"Train set Accuracy (Jaccard): \", metrics.accuracy_score(y_train, knn_model.predict(X_train)))\n",
        "print(\"Test set Accuracy (Jaccard): \", metrics.accuracy_score(y_test, knn_model.predict(X_test)))\n",
        "print(\"Train set Accuracy (F1): \", f1_score(y_train, knn_model.predict(X_train), average='weighted'))\n",
        "print(\"Test set Accuracy (F1): \", f1_score(y_test, knn_model.predict(X_test), average='weighted'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MswqMrb8r74U"
      },
      "source": [
        "# Decision Tree\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "bm_8WeCir74V"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btgxL6Ovr74W",
        "outputId": "07131a29-286b-46d8-e5f5-5525b3c4a64e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 0.7857142857142857\n",
            "2 0.7857142857142857\n",
            "3 0.6142857142857143\n",
            "4 0.6142857142857143\n",
            "5 0.6428571428571429\n",
            "6 0.7714285714285715\n",
            "7 0.7571428571428571\n",
            "8 0.7571428571428571\n",
            "9 0.6571428571428571\n"
          ]
        }
      ],
      "source": [
        "for d in range(1,10):\n",
        "    dt = DecisionTreeClassifier(criterion = 'entropy', max_depth = d).fit(X_train, y_train)\n",
        "    y_pred = dt.predict(X_test)\n",
        "    print(d, accuracy_score(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AHJfyjzXr74X",
        "outputId": "1c0f3c6d-d34a-4f34-a686-0f52f03863d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "We take depth = 2\n",
            "Train set Accuracy (Jaccard):  0.7427536231884058\n",
            "Test set Accuracy (Jaccard):  0.7857142857142857\n",
            "Train set Accuracy (F1):  0.6331163939859591\n",
            "Test set Accuracy (F1):  0.6914285714285714\n"
          ]
        }
      ],
      "source": [
        "print(\"We take depth = 2\")\n",
        "dt = DecisionTreeClassifier(criterion=\"entropy\", max_depth=2).fit(X_train, y_train)\n",
        "print(\"Train set Accuracy (Jaccard): \", metrics.accuracy_score(y_train, dt.predict(X_train)))\n",
        "print(\"Test set Accuracy (Jaccard): \", metrics.accuracy_score(y_test, dt.predict(X_test)))\n",
        "print(\"Train set Accuracy (F1): \", f1_score(y_train, dt.predict(X_train), average='weighted'))\n",
        "print(\"Test set Accuracy (F1): \", f1_score(y_test, dt.predict(X_test), average='weighted'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tEVT49PHr74Y"
      },
      "source": [
        "# Support Vector Machine\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "zvs0QqiIr74a"
      },
      "outputs": [],
      "source": [
        "#importing svm\n",
        "from sklearn import svm "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lnmbn3iXr74b",
        "outputId": "48162a30-247f-4fe5-cf43-47cdb047ca7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For kernel: linear, the f1 score is: 0.6914285714285714\n",
            "For kernel: poly, the f1 score is: 0.7064793130366899\n",
            "For kernel: rbf, the f1 score is: 0.7275882012724117\n",
            "For kernel: sigmoid, the f1 score is: 0.6892857142857144\n"
          ]
        }
      ],
      "source": [
        "for k in ('linear', 'poly', 'rbf','sigmoid'):\n",
        "    svm_model = svm.SVC( kernel = k).fit(X_train,y_train)\n",
        "    svm_yhat = svm_model.predict(X_test)\n",
        "    print(\"For kernel: {}, the f1 score is: {}\".format(k,f1_score(y_test,svm_yhat, average='weighted')))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xMmr7wJSr74c",
        "outputId": "0f6c1c11-183a-4ce9-ea16-3b0bd76e74bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "So we choose rbf\n",
            "Train set Accuracy (Jaccard):  0.782608695652174\n",
            "Test set Accuracy (Jaccard):  0.7428571428571429\n",
            "Train set Accuracy (F1):  0.7682165861513688\n",
            "Test set Accuracy (F1):  0.7275882012724117\n"
          ]
        }
      ],
      "source": [
        "print(\"So we choose rbf\")\n",
        "svm_model = svm.SVC( kernel = 'rbf').fit(X_train,y_train)\n",
        "print(\"Train set Accuracy (Jaccard): \", metrics.accuracy_score(y_train, svm_model.predict(X_train)))\n",
        "print(\"Test set Accuracy (Jaccard): \", metrics.accuracy_score(y_test, svm_model.predict(X_test)))\n",
        "print(\"Train set Accuracy (F1): \", f1_score(y_train, svm_model.predict(X_train), average='weighted'))\n",
        "print(\"Test set Accuracy (F1): \", f1_score(y_test, svm_model.predict(X_test), average='weighted'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfLB84JPr74c"
      },
      "source": [
        "# Logistic Regression\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "5VVP6Dxir74d"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression \n",
        "from sklearn.metrics import log_loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OSECQqRKr74e"
      },
      "outputs": [],
      "source": [
        "for k in ('lbfgs', 'saga', 'liblinear', 'newton-cg', 'sag'):\n",
        "    lr_model = LogisticRegression(C = 0.01, solver = k).fit(X_train, y_train)\n",
        "    lr_yhat = lr_model.predict(X_test)\n",
        "    y_prob = lr_model.predict_proba(X_test)\n",
        "    print('When Solver is {}, logloss is : {}'.format(k, log_loss(y_test, y_prob)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dMeiF8sr74f",
        "outputId": "eb8df5ca-8c8c-4067-f6b3-8390b688aaf1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train set Accuracy (Jaccard):  0.7572463768115942\n",
            "Test set Accuracy (Jaccard):  0.6857142857142857\n",
            "Train set Accuracy (F1):  0.7341146337750953\n",
            "Test set Accuracy (F1):  0.6670522459996144\n"
          ]
        }
      ],
      "source": [
        "k = 'liblinear'\n",
        "lr_model = LogisticRegression(C = 0.01, solver = k).fit(X_train, y_train)\n",
        "\n",
        "print(\"Train set Accuracy (Jaccard): \", metrics.accuracy_score(y_train, lr_model.predict(X_train)))\n",
        "print(\"Test set Accuracy (Jaccard): \", metrics.accuracy_score(y_test, lr_model.predict(X_test)))\n",
        "\n",
        "print(\"Train set Accuracy (F1): \", f1_score(y_train, lr_model.predict(X_train), average='weighted'))\n",
        "print(\"Test set Accuracy (F1): \", f1_score(y_test, lr_model.predict(X_test), average='weighted'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9yVRN4Hr74g"
      },
      "source": [
        "# Model Evaluation using Test set\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "O0YdBQ6sr74h"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import jaccard_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import log_loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mcz4Hmlqr74i"
      },
      "source": [
        "First, download and load the test set:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PskvEyzpr74j",
        "outputId": "24f2595f-298f-4926-ec57-8e0e5cfd07fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-08-29 08:47:22--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\n",
            "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
            "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3642 (3.6K) [text/csv]\n",
            "Saving to: ‘loan_test.csv’\n",
            "\n",
            "\rloan_test.csv         0%[                    ]       0  --.-KB/s               \rloan_test.csv       100%[===================>]   3.56K  --.-KB/s    in 0s      \n",
            "\n",
            "2022-08-29 08:47:23 (1.18 GB/s) - ‘loan_test.csv’ saved [3642/3642]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "hR4ja6kFr74l"
      },
      "source": [
        "### Load Test set for evaluation\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "bsWsYCsor74m",
        "outputId": "1456a58c-d457-460e-b419-a3355a04cb5b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
              "0           1             1     PAIDOFF       1000     30       9/8/2016   \n",
              "1           5             5     PAIDOFF        300      7       9/9/2016   \n",
              "2          21            21     PAIDOFF       1000     30      9/10/2016   \n",
              "3          24            24     PAIDOFF       1000     30      9/10/2016   \n",
              "4          35            35     PAIDOFF        800     15      9/11/2016   \n",
              "\n",
              "    due_date  age             education  Gender  \n",
              "0  10/7/2016   50              Bechalor  female  \n",
              "1  9/15/2016   35       Master or Above    male  \n",
              "2  10/9/2016   43  High School or Below  female  \n",
              "3  10/9/2016   26               college    male  \n",
              "4  9/25/2016   29              Bechalor    male  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-10221983-324e-4d9f-aab7-36a65bbb4f80\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</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>age</th>\n",
              "      <th>education</th>\n",
              "      <th>Gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</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>50</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>5</td>\n",
              "      <td>5</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>35</td>\n",
              "      <td>Master or Above</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21</td>\n",
              "      <td>21</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/10/2016</td>\n",
              "      <td>10/9/2016</td>\n",
              "      <td>43</td>\n",
              "      <td>High School or Below</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>24</td>\n",
              "      <td>24</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>1000</td>\n",
              "      <td>30</td>\n",
              "      <td>9/10/2016</td>\n",
              "      <td>10/9/2016</td>\n",
              "      <td>26</td>\n",
              "      <td>college</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>35</td>\n",
              "      <td>35</td>\n",
              "      <td>PAIDOFF</td>\n",
              "      <td>800</td>\n",
              "      <td>15</td>\n",
              "      <td>9/11/2016</td>\n",
              "      <td>9/25/2016</td>\n",
              "      <td>29</td>\n",
              "      <td>Bechalor</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-10221983-324e-4d9f-aab7-36a65bbb4f80')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-10221983-324e-4d9f-aab7-36a65bbb4f80 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-10221983-324e-4d9f-aab7-36a65bbb4f80');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "test_df = pd.read_csv('loan_test.csv')\n",
        "test_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "2nHhYKyEr74o"
      },
      "outputs": [],
      "source": [
        "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\n",
        "test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\n",
        "test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\n",
        "test_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
        "test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
        "Feature1 = test_df[['Principal','terms','age','Gender','weekend']]\n",
        "Feature1 = pd.concat([Feature1,pd.get_dummies(test_df['education'])], axis=1)\n",
        "Feature1.drop(['Master or Above'], axis = 1,inplace=True)\n",
        "x_loan_test = Feature1\n",
        "x_loan_test = preprocessing.StandardScaler().fit(x_loan_test).transform(x_loan_test)\n",
        "y_loan_test = test_df['loan_status'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5XKLmFS9r74o",
        "outputId": "42b8963c-eb41-400b-dce8-4dc0542407da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.7037037037037037,\n",
              " 0.7407407407407407,\n",
              " 0.7962962962962963,\n",
              " 0.7407407407407407]"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "#jaccard list:\n",
        "knn_pred = kNN_model.predict(x_loan_test)\n",
        "j1 = accuracy_score(y_loan_test, knn_pred)\n",
        "dt_pred = dt.predict(x_loan_test)\n",
        "j2 = accuracy_score(y_loan_test, dt_pred)\n",
        "svm_pred = svm_model.predict(x_loan_test)\n",
        "j3 = accuracy_score(y_loan_test, svm_pred)\n",
        "lr_pred = lr_model.predict(x_loan_test)\n",
        "j4 = accuracy_score(y_loan_test, lr_pred)\n",
        "jaccard = [j1, j2, j3, j4]\n",
        "jaccard"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "ZN2lb_-8r74p",
        "outputId": "cb18334a-dfbd-4f73-b8ca-db7dd9bc34e2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                Jaccard            F1-score  \\\n",
              "KNN                  0.7037037037037037  0.6736355806123249   \n",
              "Decision Tree        0.7407407407407407  0.6304176516942475   \n",
              "SVM                  0.7962962962962963  0.7583503077293734   \n",
              "Logistic Regression  0.7407407407407407  0.6604267310789049   \n",
              "\n",
              "                                LogLoss  \n",
              "KNN                                  NA  \n",
              "Decision Tree                        NA  \n",
              "SVM                                  NA  \n",
              "Logistic Regression  0.5672153379912981  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9cab4808-003c-4f46-bd1c-c62985530301\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Jaccard</th>\n",
              "      <th>F1-score</th>\n",
              "      <th>LogLoss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KNN</th>\n",
              "      <td>0.7037037037037037</td>\n",
              "      <td>0.6736355806123249</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decision Tree</th>\n",
              "      <td>0.7407407407407407</td>\n",
              "      <td>0.6304176516942475</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVM</th>\n",
              "      <td>0.7962962962962963</td>\n",
              "      <td>0.7583503077293734</td>\n",
              "      <td>NA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Logistic Regression</th>\n",
              "      <td>0.7407407407407407</td>\n",
              "      <td>0.6604267310789049</td>\n",
              "      <td>0.5672153379912981</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9cab4808-003c-4f46-bd1c-c62985530301')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9cab4808-003c-4f46-bd1c-c62985530301 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9cab4808-003c-4f46-bd1c-c62985530301');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "knn_pred = kNN_model.predict(x_loan_test)\n",
        "f1 = f1_score(y_loan_test, knn_pred, average='weighted')\n",
        "dt_pred = dt.predict(x_loan_test)\n",
        "f2 = f1_score(y_loan_test, dt_pred, average='weighted')\n",
        "svm_pred = svm_model.predict(x_loan_test)\n",
        "f3 = f1_score(y_loan_test, svm_pred, average='weighted')\n",
        "lr_pred = lr_model.predict(x_loan_test)\n",
        "f4 = f1_score(y_loan_test, lr_pred, average='weighted')\n",
        "f1s = [f1, f2, f3, f4]\n",
        "f1s\n",
        "from sklearn.metrics import log_loss\n",
        "lr_pred = lr_model.predict_proba(x_loan_test)\n",
        "aux = log_loss(y_loan_test, lr_pred)\n",
        "log_loss = ['NA','NA','NA', aux]\n",
        "log_loss\n",
        "index   = [\"KNN\", \"Decision Tree\", \"SVM\", \"Logistic Regression\"]\n",
        "colunms = [\"Jaccard\", \"F1-score\", \"LogLoss\"]\n",
        "data = [jaccard, f1s, log_loss]\n",
        "data = np.array(data).T\n",
        "df = pd.DataFrame(data, index=index, columns=colunms)\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlfvffdYr74q"
      },
      "source": [
        "# Report\n",
        "\n",
        "You should be able to report the accuracy of the built model using different evaluation metrics:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_d9zUWjGr74s"
      },
      "source": [
        "| Algorithm          | Jaccard | F1-score | LogLoss |\n",
        "| ------------------ | ------- | -------- | ------- |\n",
        "| KNN                | ?       | ?        | NA      |\n",
        "| Decision Tree      | ?       | ?        | NA      |\n",
        "| SVM                | ?       | ?        | NA      |\n",
        "| LogisticRegression | ?       | ?        | ?       |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "button": false,
        "new_sheet": false,
        "run_control": {
          "read_only": false
        },
        "id": "FRkrFuCMr74t"
      },
      "source": [
        "<h2>Want to learn more?</h2>\n",
        "\n",
        "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n",
        "\n",
        "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n",
        "\n",
        "<h3>Thanks for completing this lesson!</h3>\n",
        "\n",
        "<h4>Author:  <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Saeed Aghabozorgi</a></h4>\n",
        "<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
        "\n",
        "<hr>\n",
        "\n",
        "## Change Log\n",
        "\n",
        "| Date (YYYY-MM-DD) | Version | Changed By    | Change Description                                                             |\n",
        "| ----------------- | ------- | ------------- | ------------------------------------------------------------------------------ |\n",
        "| 2020-10-27        | 2.1     | Lakshmi Holla | Made changes in import statement due to updates in version of  sklearn library |\n",
        "| 2020-08-27        | 2.0     | Malika Singla | Added lab to GitLab                                                            |\n",
        "\n",
        "<hr>\n",
        "\n",
        "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
        "\n",
        "<p>\n"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python",
      "language": "python",
      "name": "conda-env-python-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.12"
    },
    "colab": {
      "name": "ML0101EN-Proj-Loan-py-v1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}