Phishing_Website_Detection_Models_&_Training.ipynb

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      "name": "Phishing Website Detection_Models & Training.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WTVY5lz4vJQM"
      },
      "source": [
        "# **Phishing Website Detection by Machine Learning Techniques**\n",
        "\n",
        "*Final project of Data Mining Course(CSE3054)*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_cJA-yD-vNcS"
      },
      "source": [
        "## **1. Objective:**\n",
        "A phishing website is a common social engineering method that mimics trustful uniform resource locators (URLs) and webpages.Phishing websites are created to dupe unsuspecting users into thinking they are on a legitimate site. The criminals will spend a lot of time making the site seem as credible as possible and many sites will appear almost indistinguishable from the real thing.The objective of this project is to train machine learning models and deep neural nets on the dataset created to predict phishing websites. Both phishing and benign URLs of websites are gathered to form a dataset and from them required URL and website content-based features are extracted. The performance level of each model is measures and compared.\n",
        "\n",
        "\n",
        "The Members of this project are \n",
        "*   K Seshank (20BDS0136)\n",
        "*   Jehan Sharukh Bhathena (20BDS0186)\n",
        "*   Aaryan Bhatia Ghosh (20BDS0187)\n",
        "*   Harsh Avinash (20BDS0190)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*This project is worked on Google Collaboratory.*<br>\n",
        "*The required packages for this notebook are imported when needed.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NOq598WCqIol"
      },
      "source": [
        "## **2. Loading the Data:**\n",
        "\n",
        "The features are extracted and store in the csv file. The working of this can be seen in the 'Phishing Website Detection_Feature Extraction.ipynb' file.\n",
        "(change file name)\n",
        "\n",
        "The reulted csv file is uploaded to this notebook and stored in the pandas dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C297HhYulXcb"
      },
      "source": [
        "#importing basic packages\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fVPglpaf4REa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "outputId": "3db2dc91-798b-48c2-e983-1a45eb59310b"
      },
      "source": [
        "#Loading the data\n",
        "data0 = pd.read_csv('5.urldata.csv')\n",
        "data0.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Domain</th>\n",
              "      <th>Have_IP</th>\n",
              "      <th>Have_At</th>\n",
              "      <th>URL_Length</th>\n",
              "      <th>URL_Depth</th>\n",
              "      <th>Redirection</th>\n",
              "      <th>https_Domain</th>\n",
              "      <th>TinyURL</th>\n",
              "      <th>Prefix/Suffix</th>\n",
              "      <th>DNS_Record</th>\n",
              "      <th>Web_Traffic</th>\n",
              "      <th>Domain_Age</th>\n",
              "      <th>Domain_End</th>\n",
              "      <th>iFrame</th>\n",
              "      <th>Mouse_Over</th>\n",
              "      <th>Right_Click</th>\n",
              "      <th>Web_Forwards</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>graphicriver.net</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ecnavi.jp</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</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>hubpages.com</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>extratorrent.cc</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>icicibank.com</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             Domain  Have_IP  Have_At  ...  Right_Click  Web_Forwards  Label\n",
              "0  graphicriver.net        0        0  ...            1             0      0\n",
              "1         ecnavi.jp        0        0  ...            1             0      0\n",
              "2      hubpages.com        0        0  ...            1             0      0\n",
              "3   extratorrent.cc        0        0  ...            1             0      0\n",
              "4     icicibank.com        0        0  ...            1             0      0\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMXihulvMcAj"
      },
      "source": [
        "## **3. Displaying with Data**\n",
        "In this step, few dataframe methods are used to look into the data and its features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hARIXyGKorc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f79a693-a4dd-4224-cd2b-77089efb9519"
      },
      "source": [
        "#Checking the shape of the dataset\n",
        "data0.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q3uBm9ObMyaG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5b091562-19f2-43c2-8baa-3c2a158370f9"
      },
      "source": [
        "#Listing the features of the dataset\n",
        "data0.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Domain', 'Have_IP', 'Have_At', 'URL_Length', 'URL_Depth',\n",
              "       'Redirection', 'https_Domain', 'TinyURL', 'Prefix/Suffix', 'DNS_Record',\n",
              "       'Web_Traffic', 'Domain_Age', 'Domain_End', 'iFrame', 'Mouse_Over',\n",
              "       'Right_Click', 'Web_Forwards', 'Label'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gBMqupCMM74V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ca86e7c-836d-4519-b184-ab25e78b5a08"
      },
      "source": [
        "#Information about the dataset\n",
        "data0.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 18 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Domain         10000 non-null  object\n",
            " 1   Have_IP        10000 non-null  int64 \n",
            " 2   Have_At        10000 non-null  int64 \n",
            " 3   URL_Length     10000 non-null  int64 \n",
            " 4   URL_Depth      10000 non-null  int64 \n",
            " 5   Redirection    10000 non-null  int64 \n",
            " 6   https_Domain   10000 non-null  int64 \n",
            " 7   TinyURL        10000 non-null  int64 \n",
            " 8   Prefix/Suffix  10000 non-null  int64 \n",
            " 9   DNS_Record     10000 non-null  int64 \n",
            " 10  Web_Traffic    10000 non-null  int64 \n",
            " 11  Domain_Age     10000 non-null  int64 \n",
            " 12  Domain_End     10000 non-null  int64 \n",
            " 13  iFrame         10000 non-null  int64 \n",
            " 14  Mouse_Over     10000 non-null  int64 \n",
            " 15  Right_Click    10000 non-null  int64 \n",
            " 16  Web_Forwards   10000 non-null  int64 \n",
            " 17  Label          10000 non-null  int64 \n",
            "dtypes: int64(17), object(1)\n",
            "memory usage: 1.4+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfrxhE3pNfiw"
      },
      "source": [
        "## **4. Visualizing the data**\n",
        "We make and display plots of the data and find the relation between the variables."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9K0yAdAM70w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "28d07021-c2a2-481c-c75f-4ffffa5f400e"
      },
      "source": [
        "#Plotting the data distribution\n",
        "data0.hist(bins = 50,figsize = (24,18))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x1296 with 20 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jy9fjgj3M7zc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 929
        },
        "outputId": "3a9727ee-a564-4586-df64-25f2fab391af"
      },
      "source": [
        "#Correlation heatmap\n",
        "#Plotting a Heatmap of the data (a darker region indicates a lower value,and a lighter region indicates a higher value)\n",
        "plt.figure(figsize=(15,15))\n",
        "sns.heatmap(data0.corr())\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9cbVPMe8NxFN"
      },
      "source": [
        "## **5. Data Preprocessing & EDA**\n",
        "Data Preprocessing and EDA are essential tasks to be performed on the data before making the model.\n",
        "We make the data ready to be fed into the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_HKPelgnmf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "7184ba6c-7e91-4c4d-8dee-da11b53316a5"
      },
      "source": [
        "data0.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Have_IP</th>\n",
              "      <th>Have_At</th>\n",
              "      <th>URL_Length</th>\n",
              "      <th>URL_Depth</th>\n",
              "      <th>Redirection</th>\n",
              "      <th>https_Domain</th>\n",
              "      <th>TinyURL</th>\n",
              "      <th>Prefix/Suffix</th>\n",
              "      <th>DNS_Record</th>\n",
              "      <th>Web_Traffic</th>\n",
              "      <th>Domain_Age</th>\n",
              "      <th>Domain_End</th>\n",
              "      <th>iFrame</th>\n",
              "      <th>Mouse_Over</th>\n",
              "      <th>Right_Click</th>\n",
              "      <th>Web_Forwards</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.0000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>10000.00000</td>\n",
              "      <td>10000.000000</td>\n",
              "      <td>10000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.005500</td>\n",
              "      <td>0.022600</td>\n",
              "      <td>0.773400</td>\n",
              "      <td>3.072000</td>\n",
              "      <td>0.013500</td>\n",
              "      <td>0.000200</td>\n",
              "      <td>0.090300</td>\n",
              "      <td>0.093200</td>\n",
              "      <td>0.100800</td>\n",
              "      <td>0.845700</td>\n",
              "      <td>0.413700</td>\n",
              "      <td>0.8099</td>\n",
              "      <td>0.090900</td>\n",
              "      <td>0.06660</td>\n",
              "      <td>0.99930</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.073961</td>\n",
              "      <td>0.148632</td>\n",
              "      <td>0.418653</td>\n",
              "      <td>2.128631</td>\n",
              "      <td>0.115408</td>\n",
              "      <td>0.014141</td>\n",
              "      <td>0.286625</td>\n",
              "      <td>0.290727</td>\n",
              "      <td>0.301079</td>\n",
              "      <td>0.361254</td>\n",
              "      <td>0.492521</td>\n",
              "      <td>0.3924</td>\n",
              "      <td>0.287481</td>\n",
              "      <td>0.24934</td>\n",
              "      <td>0.02645</td>\n",
              "      <td>0.306955</td>\n",
              "      <td>0.500025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Have_IP       Have_At  ...  Web_Forwards         Label\n",
              "count  10000.000000  10000.000000  ...  10000.000000  10000.000000\n",
              "mean       0.005500      0.022600  ...      0.105300      0.500000\n",
              "std        0.073961      0.148632  ...      0.306955      0.500025\n",
              "min        0.000000      0.000000  ...      0.000000      0.000000\n",
              "25%        0.000000      0.000000  ...      0.000000      0.000000\n",
              "50%        0.000000      0.000000  ...      0.000000      0.500000\n",
              "75%        0.000000      0.000000  ...      0.000000      1.000000\n",
              "max        1.000000      1.000000  ...      1.000000      1.000000\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XBqTcpqen_fB"
      },
      "source": [
        "The above obtained result shows that the most of the data is made of Binary Values ie. 0's & 1's except 'Domain' & 'URL_Depth' columns. The Domain column doesnt have any significance to the machine learning model training.\n",
        "So we need to drop the column *'Domain'* from the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tdpRw0Bcn_K1"
      },
      "source": [
        "#Dropping the Domain column\n",
        "data = data0.drop(['Domain'], axis = 1).copy()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "KGJa7QXp1acb",
        "outputId": "249cad48-451f-4cfc-84d7-1ce684db57f1"
      },
      "source": [
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Have_IP</th>\n",
              "      <th>Have_At</th>\n",
              "      <th>URL_Length</th>\n",
              "      <th>URL_Depth</th>\n",
              "      <th>Redirection</th>\n",
              "      <th>https_Domain</th>\n",
              "      <th>TinyURL</th>\n",
              "      <th>Prefix/Suffix</th>\n",
              "      <th>DNS_Record</th>\n",
              "      <th>Web_Traffic</th>\n",
              "      <th>Domain_Age</th>\n",
              "      <th>Domain_End</th>\n",
              "      <th>iFrame</th>\n",
              "      <th>Mouse_Over</th>\n",
              "      <th>Right_Click</th>\n",
              "      <th>Web_Forwards</th>\n",
              "      <th>Label</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 17 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      Have_IP  Have_At  URL_Length  ...  Right_Click  Web_Forwards  Label\n",
              "0           0        0           1  ...            1             0      0\n",
              "1           0        0           1  ...            1             0      0\n",
              "2           0        0           1  ...            1             0      0\n",
              "3           0        0           1  ...            1             0      0\n",
              "4           0        0           1  ...            1             0      0\n",
              "...       ...      ...         ...  ...          ...           ...    ...\n",
              "9995        0        0           1  ...            1             0      1\n",
              "9996        0        0           1  ...            1             0      1\n",
              "9997        0        1           1  ...            1             0      1\n",
              "9998        0        0           1  ...            1             0      1\n",
              "9999        0        1           1  ...            1             0      1\n",
              "\n",
              "[10000 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6nOAic4O1zdT",
        "outputId": "bf98e33f-10e2-422b-f483-2c3c1ee4e94b"
      },
      "source": [
        "data.URL_Depth.max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "peN2Q-WdowNL"
      },
      "source": [
        "\n",
        "We now have 17 columns which includes 16 features and 1 target column.The 'URL_Depth'* maximum value is 20.Most of the other models have  Values of 0s and 1s only"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGO3nWV1nvW1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4270bd75-fb44-426c-a30d-71e938ae201f"
      },
      "source": [
        "#checking the data for null or missing values\n",
        "data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Have_IP          0\n",
              "Have_At          0\n",
              "URL_Length       0\n",
              "URL_Depth        0\n",
              "Redirection      0\n",
              "https_Domain     0\n",
              "TinyURL          0\n",
              "Prefix/Suffix    0\n",
              "DNS_Record       0\n",
              "Web_Traffic      0\n",
              "Domain_Age       0\n",
              "Domain_End       0\n",
              "iFrame           0\n",
              "Mouse_Over       0\n",
              "Right_Click      0\n",
              "Web_Forwards     0\n",
              "Label            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xo6cAqAqJOq"
      },
      "source": [
        "In the feature extraction file, the extracted features of legitmate & phishing url datasets are just concatenated without any shuffling. This resulted in top 5000 rows of legitimate url data & bottom 5000 of phishing url data. **(Subject to change)**\n",
        "\n",
        "To even out the distribution while splitting the data into training & testing sets, we need to shuffle it. This even evades the case of overfitting while model training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4LZnaoU_qBsz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 205
        },
        "outputId": "caddbb79-c1a4-4671-cda4-80b7f1159163"
      },
      "source": [
        "# shuffling the rows in the dataset so that when splitting the train and test set are equally distributed\n",
        "data = data.sample(frac=1).reset_index(drop=True)\n",
        "data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Have_IP</th>\n",
              "      <th>Have_At</th>\n",
              "      <th>URL_Length</th>\n",
              "      <th>URL_Depth</th>\n",
              "      <th>Redirection</th>\n",
              "      <th>https_Domain</th>\n",
              "      <th>TinyURL</th>\n",
              "      <th>Prefix/Suffix</th>\n",
              "      <th>DNS_Record</th>\n",
              "      <th>Web_Traffic</th>\n",
              "      <th>Domain_Age</th>\n",
              "      <th>Domain_End</th>\n",
              "      <th>iFrame</th>\n",
              "      <th>Mouse_Over</th>\n",
              "      <th>Right_Click</th>\n",
              "      <th>Web_Forwards</th>\n",
              "      <th>Label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Have_IP  Have_At  URL_Length  ...  Right_Click  Web_Forwards  Label\n",
              "0        0        0           1  ...            1             0      1\n",
              "1        0        0           0  ...            1             0      1\n",
              "2        0        0           1  ...            1             0      1\n",
              "3        0        0           1  ...            1             0      0\n",
              "4        0        0           1  ...            1             0      0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FsQosd_nycx"
      },
      "source": [
        "From the above execution, it is clear that the data doesnot have any missing values.\n",
        "\n",
        "By this, the data is throughly preprocessed & is ready for training."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fs5hu2UR4SIF"
      },
      "source": [
        "## **6. Splitting the Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzEU-wcLN8K7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77670317-44bb-48df-9872-e54d68905e01"
      },
      "source": [
        "# Sepratating & assigning features and target columns to X & y\n",
        "y = data['Label']\n",
        "X = data.drop('Label',axis=1)\n",
        "X.shape, y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((10000, 16), (10000,))"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84xKobSqAV3U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "363dc53c-11c8-4119-d8b2-b517015e95ea"
      },
      "source": [
        "# Splitting the dataset into train and test sets: 80-20 split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
        "                                                    test_size = 0.2, random_state = 12)\n",
        "X_train.shape, X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((8000, 16), (2000, 16))"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ah9B035xOjs1"
      },
      "source": [
        "## **7. Machine Learning Models & Training**\n",
        "\n",
        "From the dataset above, it is clear that this is a supervised machine learning task. There are two major types of supervised machine learning problems, called classification and regression. \n",
        "\n",
        "This data set comes under **classification** problem, as the input URL is classified as phishing (1) or legitimate (0),ie one of the two. \n",
        "The supervised machine learning models algorithms considered to train the models in this notebook are:\n",
        "* Decision Tree\n",
        "* Random Forest\n",
        "* Multilayer Perceptrons\n",
        "* XGBoost\n",
        "* Autoencoder Neural Network\n",
        "* Support Vector Machines"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D5Tg_ei0-xPU"
      },
      "source": [
        "#importing packages\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPBHdBikSXHv"
      },
      "source": [
        "# Creating holders to store the model performance results\n",
        "ML_Model = []\n",
        "acc_train = []\n",
        "acc_test = []\n",
        "\n",
        "#function to call for storing the results\n",
        "def storeResults(model, a,b):\n",
        "  ML_Model.append(model)\n",
        "  acc_train.append(round(a, 3))\n",
        "  acc_test.append(round(b, 3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YOolxQWs-VbB"
      },
      "source": [
        "### **7.a. Decision Tree Classifier**\n",
        " Decision trees are widely used models for classification and regression tasks. Essentially, they learn a hierarchy of if/else questions, leading to a decision. Learning a decision tree means learning the sequence of if/else questions that gets us to the true answer most quickly.\n",
        "It uses an iterative process of splitting the data into partitions, and then splitting it up further on each of the branches. \n",
        "In the machine learning setting, these questions are called tests (not to be confused with the test set, which is the data we use to test to see how generalizable our model is). To build a tree, the algorithm searches over all possible tests and finds the one that is most informative about the target variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1kzsjtudy-0w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e11771f6-c0af-4664-a37d-5d00551277b0"
      },
      "source": [
        "# Decision Tree model \n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# instantiate the model \n",
        "tree = DecisionTreeClassifier(max_depth = 5)\n",
        "# fit the model \n",
        "tree.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cpPk7O-MrTZi"
      },
      "source": [
        "#predicting the target value from the model for the samples\n",
        "y_test_tree = tree.predict(X_test)\n",
        "y_train_tree = tree.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLn-_qOuS_9Y"
      },
      "source": [
        "**Performance Evaluation:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4wDTnFZrz3q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25f1a524-bc5d-49a2-b8f7-b4d427497537"
      },
      "source": [
        "#computing the accuracy of the model performance\n",
        "acc_train_tree = accuracy_score(y_train,y_train_tree)\n",
        "acc_test_tree = accuracy_score(y_test,y_test_tree)\n",
        "\n",
        "print(\"Decision Tree: Accuracy on training Data: {:.3f}\".format(acc_train_tree))\n",
        "print(\"Decision Tree: Accuracy on test Data: {:.3f}\".format(acc_test_tree))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree: Accuracy on training Data: 0.815\n",
            "Decision Tree: Accuracy on test Data: 0.806\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LITrJdVGWwTl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "82ca27d7-c32d-499c-94c3-610e03bbb6e9"
      },
      "source": [
        "#checking the importance of each column on the model\n",
        "plt.figure(figsize=(9,7))\n",
        "n_features = X_train.shape[1]\n",
        "plt.barh(range(n_features), tree.feature_importances_, align='center')\n",
        "plt.yticks(np.arange(n_features), X_train.columns)\n",
        "plt.xlabel(\"Feature importance\")\n",
        "plt.ylabel(\"Feature\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "XBy26O3LrvEh",
        "outputId": "0a6e8122-06bd-4e5e-cb3a-dd75e5e285df"
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "cm = confusion_matrix(y_test,y_test_tree)\n",
        "plt.figure(figsize=(12,9))\n",
        "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Blues')\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "all_sample_title = 'Accuracy Score: {0}'.format(tree.score(X_test, y_test))\n",
        "plt.title(all_sample_title, size = 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Score: 0.806')"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XpC9PAn5RTfY"
      },
      "source": [
        "**Storing the results:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5XKvXxr9RSxl"
      },
      "source": [
        "#storing the results. The below mentioned order of parameter passing is important.\n",
        "#Caution: Execute only once to avoid duplications.\n",
        "storeResults('Decision Tree', acc_train_tree, acc_test_tree)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LuDTMcmb-n5x"
      },
      "source": [
        "### **7.b. Random Forest Classifier**\n",
        "Random forests for regression and classification are currently among the most widely used machine learning methods.A random forest is essentially a collection of decision trees, where each tree is slightly different from the others. The idea behind random forests is that each tree might do a relatively good job of predicting, but will likely overfit on part of the data.\n",
        "We obtain a more accurate and stable prediction, it uses the ensemble method of 'bagging' and thus gives an accurate result.Random forest adds randomness to the model while growing the trees.\n",
        "If we build many trees, all of which work well and overfit in different ways, we can reduce the amount of overfitting by averaging their results. To build a random forest model, you need to decide on the number of trees to build (the n_estimators parameter of RandomForestRegressor or RandomForestClassifier). They are very powerful, often work well without heavy tuning of the parameters, and don’t require scaling of the data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fmB9rPSsR6y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10b1001f-a0c2-451f-ed8e-9f0f85d9effa"
      },
      "source": [
        "# Random Forest model\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# instantiate the model\n",
        "forest = RandomForestClassifier(max_depth=5)\n",
        "\n",
        "# fit the model \n",
        "forest.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J1Qck-wrsabB"
      },
      "source": [
        "#predicting the target value from the model for the samples\n",
        "y_test_forest = forest.predict(X_test)\n",
        "y_train_forest = forest.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i8TybBPHT1ao"
      },
      "source": [
        "**Performance Evaluation:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oguf-37tsboO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "910c2401-e5ec-4082-d62d-b3cffbce6ac6"
      },
      "source": [
        "#computing the accuracy of the model performance\n",
        "acc_train_forest = accuracy_score(y_train,y_train_forest)\n",
        "acc_test_forest = accuracy_score(y_test,y_test_forest)\n",
        "\n",
        "print(\"Random forest: Accuracy on training Data: {:.3f}\".format(acc_train_forest))\n",
        "print(\"Random forest: Accuracy on test Data: {:.3f}\".format(acc_test_forest))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random forest: Accuracy on training Data: 0.823\n",
            "Random forest: Accuracy on test Data: 0.812\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9GZGxvZ9jnB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "303612f9-a5a3-458e-ef06-5832985e6766"
      },
      "source": [
        "#checking the feature improtance in the model\n",
        "plt.figure(figsize=(9,7))\n",
        "n_features = X_train.shape[1]\n",
        "plt.barh(range(n_features), forest.feature_importances_, align='center')\n",
        "plt.yticks(np.arange(n_features), X_train.columns)\n",
        "plt.xlabel(\"Feature importance\")\n",
        "plt.ylabel(\"Feature\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "v1wRrz10xZ8a",
        "outputId": "e1c37471-9a52-4dd0-82ec-99700183bb16"
      },
      "source": [
        "cm = confusion_matrix(y_test,y_test_forest)\n",
        "plt.figure(figsize=(12,9))\n",
        "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Blues')\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "all_sample_title = 'Accuracy Score: {0}'.format(forest.score(X_test, y_test))\n",
        "plt.title(all_sample_title, size = 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Score: 0.8125')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6U_BEF8W-FS"
      },
      "source": [
        "**Storing the results:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNf4EXHUW-FU"
      },
      "source": [
        "#storing the results. The below mentioned order of parameter passing is important.\n",
        "#Caution: Execute only once to avoid duplications.\n",
        "storeResults('Random Forest', acc_train_forest, acc_test_forest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJ_EW8QUC0bn"
      },
      "source": [
        "### **7.c. Multilayer Perceptrons (MLPs): Deep Learning**\n",
        "Multilayer perceptrons (MLPs) are also known as  feed-forward artificial neural networks, or sometimes just neural networks. Multilayer perceptrons can be applied for both classification and regression problems.\n",
        "MLPs can be viewed as generalizations of linear models that perform multiple stages of processing to come to a decision.A Multilayer Perceptron has input and output layers, and one or more hidden layers with many neurons stacked together. Multilayer Perceptron can use any arbitrary activation function.They consist of multiple layers of neurons each fully connected to those in the layer below (from which they receive input) and those above (which they, in turn, influence). MLP utilizes a supervised learning technique called backpropagation for training.Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.\n",
        "An MLP consists of many perceptrons organized into layers.\n",
        "A true perceptron performs binary classification, an MLP neuron is free to either perform classification or regression, depending upon its activation function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JSFAbsgnAxqv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60618762-fa44-4c17-8f96-afc9d3535a50"
      },
      "source": [
        "# Multilayer Perceptrons model\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "# instantiate the model\n",
        "mlp = MLPClassifier(alpha=0.001, hidden_layer_sizes=([100,100,100]))\n",
        "\n",
        "# fit the model \n",
        "mlp.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(alpha=0.001, hidden_layer_sizes=[100, 100, 100])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gyuSg6w_A4pN"
      },
      "source": [
        "#predicting the target value from the model for the samples\n",
        "y_test_mlp = mlp.predict(X_test)\n",
        "y_train_mlp = mlp.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UlDx0rDXatCl"
      },
      "source": [
        "**Performance Evaluation:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2ndgKQbA64_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c4e1f786-b908-42bd-99fd-cb20919d7fdb"
      },
      "source": [
        "#computing the accuracy of the model performance\n",
        "acc_train_mlp = accuracy_score(y_train,y_train_mlp)\n",
        "acc_test_mlp = accuracy_score(y_test,y_test_mlp)\n",
        "\n",
        "print(\"Multilayer Perceptrons: Accuracy on training Data: {:.3f}\".format(acc_train_mlp))\n",
        "print(\"Multilayer Perceptrons: Accuracy on test Data: {:.3f}\".format(acc_test_mlp))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Multilayer Perceptrons: Accuracy on training Data: 0.863\n",
            "Multilayer Perceptrons: Accuracy on test Data: 0.845\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "-Sh5EaHAyP2U",
        "outputId": "9ac5fcd6-b99a-480c-be5a-e2bd6e19d896"
      },
      "source": [
        "cm = confusion_matrix(y_test,y_test_mlp)\n",
        "plt.figure(figsize=(12,9))\n",
        "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Blues')\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "all_sample_title = 'Accuracy Score: {0}'.format(mlp.score(X_test, y_test))\n",
        "plt.title(all_sample_title, size = 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Score: 0.8445')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjBgfI64Xubd"
      },
      "source": [
        "**Storing the results:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N0fsq4yEXubk"
      },
      "source": [
        "#storing the results. The below mentioned order of parameter passing is important.\n",
        "#Caution: Execute only once to avoid duplications.\n",
        "storeResults('Multilayer Perceptrons', acc_train_mlp, acc_test_mlp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lVuTty-KaS4c"
      },
      "source": [
        "### **7.d. XGBoost Classifier**\n",
        "XGBoost is one of the most popular machine learning algorithms these days. XGBoost stands for eXtreme Gradient Boosting. Regardless of the type of prediction task at hand; regression or classification. XGBoost is an implementation of gradient boosted decision trees designed for speed and performance.\n",
        "It is a decision-tree-based ensemble Machine Learning Algorithm that uses a gradient boosting framework.It tends to outperform all other algorithms or frameworks.XGBoost applies the principle of boosting weak learners(CARTs generally)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oIIQGzxgAREc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a76110d1-c5d5-4136-c284-61221ad7786a"
      },
      "source": [
        "#XGBoost Classification model\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "# instantiate the model\n",
        "xgb = XGBClassifier(learning_rate=0.4,max_depth=7)\n",
        "#fit the model\n",
        "xgb.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(learning_rate=0.4, max_depth=7)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fx9xbzfAUO-"
      },
      "source": [
        "#predicting the target value from the model for the samples\n",
        "y_test_xgb = xgb.predict(X_test)\n",
        "y_train_xgb = xgb.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pwoDNqDIaxB9"
      },
      "source": [
        "**Performance Evaluation:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x1NNeI-NaxCA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9486afa2-0795-4a6d-a03e-98f36b4e4d02"
      },
      "source": [
        "#computing the accuracy of the model performance\n",
        "acc_train_xgb = accuracy_score(y_train,y_train_xgb)\n",
        "acc_test_xgb = accuracy_score(y_test,y_test_xgb)\n",
        "\n",
        "print(\"XGBoost: Accuracy on training Data: {:.3f}\".format(acc_train_xgb))\n",
        "print(\"XGBoost : Accuracy on test Data: {:.3f}\".format(acc_test_xgb))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "XGBoost: Accuracy on training Data: 0.870\n",
            "XGBoost : Accuracy on test Data: 0.848\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "V0fMduGqzq48",
        "outputId": "743f3d8c-924b-426c-80f4-8d8840dd0b86"
      },
      "source": [
        "cm = confusion_matrix(y_test,y_test_xgb)\n",
        "plt.figure(figsize=(12,9))\n",
        "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Blues')\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "all_sample_title = 'Accuracy Score: {0}'.format(xgb.score(X_test, y_test))\n",
        "plt.title(all_sample_title, size = 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Score: 0.848')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_g2HQNotaxCQ"
      },
      "source": [
        "**Storing the results:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFNo8jskaxCS"
      },
      "source": [
        "#storing the results. The below mentioned order of parameter passing is important.\n",
        "#Caution: Execute only once to avoid duplications.\n",
        "storeResults('XGBoost', acc_train_xgb, acc_test_xgb)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EwQ8DP4OsmcG"
      },
      "source": [
        "### **7.e. Autoencoder Neural Network**\n",
        "An auto encoder is a neural network that has the same number of input neurons as it does outputs.An autoencoder has two main parts: an encoder that maps the input into the code, and a decoder that maps the code to a reconstruction of the input.\n",
        "The hidden layers of the neural network will have fewer neurons than the input/output neurons. Because there are fewer neurons, the auto-encoder must learn to encode the input to the fewer hidden neurons. The predictors (x) and output (y) are exactly the same in an auto encoder.\n",
        "Autoencoder reduces data dimensions by learning how to ignore the noise in the data.It works very well on unlabelled data.Examples are regularized autoencoders (Sparse, Denoising and Contractive).Autoencoders can randomly generate new data is similar to the input data(training data)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cy28vy3fDab"
      },
      "source": [
        "#importing required packages\n",
        "import keras\n",
        "from keras.layers import Input, Dense\n",
        "from keras import regularizers\n",
        "import tensorflow as tf\n",
        "from keras.models import Model\n",
        "from sklearn import metrics"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Rca6ZpShnRN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfe8ab6a-ae7d-4b14-9f49-563317d6853a"
      },
      "source": [
        "#building autoencoder model\n",
        "\n",
        "input_dim = X_train.shape[1]\n",
        "encoding_dim = input_dim\n",
        "\n",
        "input_layer = Input(shape=(input_dim, ))\n",
        "encoder = Dense(encoding_dim, activation=\"relu\",\n",
        "                activity_regularizer=regularizers.l1(10e-4))(input_layer)\n",
        "encoder = Dense(int(encoding_dim), activation=\"relu\")(encoder)\n",
        "\n",
        "encoder = Dense(int(encoding_dim-2), activation=\"relu\")(encoder)\n",
        "code = Dense(int(encoding_dim-4), activation='relu')(encoder)\n",
        "decoder = Dense(int(encoding_dim-2), activation='relu')(code)\n",
        "\n",
        "decoder = Dense(int(encoding_dim), activation='relu')(encoder)\n",
        "decoder = Dense(input_dim, activation='relu')(decoder)\n",
        "autoencoder = Model(inputs=input_layer, outputs=decoder)\n",
        "autoencoder.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 16)]              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 16)                272       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 16)                272       \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 14)                238       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 16)                240       \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 16)                272       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,294\n",
            "Trainable params: 1,294\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FFet6_03sBtK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec4b778f-4b72-4370-84c4-f5a9cf2dd79d"
      },
      "source": [
        "#compiling the model\n",
        "autoencoder.compile(optimizer='adam',\n",
        "                    loss='binary_crossentropy',\n",
        "                    metrics=['accuracy'])\n",
        "\n",
        "#Training the model\n",
        "history = autoencoder.fit(X_train, X_train, epochs=10, batch_size=64, shuffle=True, validation_split=0.2) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "100/100 [==============================] - 1s 4ms/step - loss: 2.8605 - accuracy: 0.0781 - val_loss: 1.4807 - val_accuracy: 0.0794\n",
            "Epoch 2/10\n",
            "100/100 [==============================] - 0s 2ms/step - loss: 0.4972 - accuracy: 0.0933 - val_loss: -0.8172 - val_accuracy: 0.4313\n",
            "Epoch 3/10\n",
            "100/100 [==============================] - 0s 2ms/step - loss: -0.7422 - accuracy: 0.5302 - val_loss: -0.8361 - val_accuracy: 0.3325\n",
            "Epoch 4/10\n",
            "100/100 [==============================] - 0s 2ms/step - loss: -0.8802 - accuracy: 0.1822 - val_loss: -0.9416 - val_accuracy: 0.0463\n",
            "Epoch 5/10\n",
            "100/100 [==============================] - 0s 2ms/step - loss: -0.9300 - accuracy: 0.0455 - val_loss: -0.9778 - val_accuracy: 0.0456\n",
            "Epoch 6/10\n",
            "100/100 [==============================] - 0s 2ms/step - loss: -0.9636 - accuracy: 0.0270 - val_loss: -1.0004 - val_accuracy: 0.0137\n",
            "Epoch 7/10\n",
            "100/100 [==============================] - 0s 2ms/step - loss: -0.9940 - accuracy: 0.0044 - val_loss: -1.0306 - val_accuracy: 0.0012\n",
            "Epoch 8/10\n",
            "100/100 [==============================] - 0s 3ms/step - loss: -1.0163 - accuracy: 0.0016 - val_loss: -1.0478 - val_accuracy: 0.0012\n",
            "Epoch 9/10\n",
            "100/100 [==============================] - 0s 3ms/step - loss: -1.0309 - accuracy: 0.0016 - val_loss: -1.0677 - val_accuracy: 0.0012\n",
            "Epoch 10/10\n",
            "100/100 [==============================] - 0s 3ms/step - loss: -1.0586 - accuracy: 0.0016 - val_loss: -1.0953 - val_accuracy: 0.0012\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q1ONIVqSsXSP"
      },
      "source": [
        "**Performance Evaluation:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQa0u7vyj2Ml",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d8b1292-9f11-40fb-ae3f-db10b377b343"
      },
      "source": [
        "acc_train_auto = autoencoder.evaluate(X_train, X_train)[1]\n",
        "acc_test_auto = autoencoder.evaluate(X_test, X_test)[1]\n",
        "\n",
        "print('\\nAutoencoder: Accuracy on training Data: {:.3f}' .format(acc_train_auto))\n",
        "print('Autoencoder: Accuracy on test Data: {:.3f}' .format(acc_test_auto))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "250/250 [==============================] - 1s 2ms/step - loss: -1.0761 - accuracy: 0.0015\n",
            "63/63 [==============================] - 0s 2ms/step - loss: -0.9596 - accuracy: 0.0015\n",
            "\n",
            "Autoencoder: Accuracy on training Data: 0.002\n",
            "Autoencoder: Accuracy on test Data: 0.002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YG5oxuvJsflM"
      },
      "source": [
        "**Storing the results:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqjgA0aCsflO"
      },
      "source": [
        "#storing the results. The below mentioned order of parameter passing is important.\n",
        "#Caution: Execute only once to avoid duplications.\n",
        "storeResults('AutoEncoder', acc_train_auto, acc_test_auto)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "plQeFh4fwB3a"
      },
      "source": [
        "### **7.f. Support Vector Machines**\n",
        "\n",
        "In machine learning, support-vector machines (SVMs, also support-vector networks) are supervised learning models which are used for classification and regression problems however they are primarily used for classification problems in machine learning.SVM aims to create a decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category.The Ideal decision boundary is called a hyperplane. The data points or vectors that are the closest to the hyperplane and which affect the position of the hyperplane are termed as support vector.Since these vectors support the hyperplane,Hence called a support Vector."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBnV71QowZ07",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ee7a4c2-652c-4edf-d5d2-7a09990a4022"
      },
      "source": [
        "#Support vector machine model\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "# instantiate the model\n",
        "svm = SVC(kernel='linear', C=1.0, random_state=12)\n",
        "#fit the model\n",
        "svm.fit(X_train, y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC(kernel='linear', random_state=12)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Nlrfq-pwZ1G"
      },
      "source": [
        "#predicting the target value from the model for the samples\n",
        "y_test_svm = svm.predict(X_test)\n",
        "y_train_svm = svm.predict(X_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qb2FHrUpwZ1P"
      },
      "source": [
        "**Performance Evaluation:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ofewcVHwZ1R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e37eba00-d825-4b53-e764-7784ee79d28f"
      },
      "source": [
        "#computing the accuracy of the model performance\n",
        "acc_train_svm = accuracy_score(y_train,y_train_svm)\n",
        "acc_test_svm = accuracy_score(y_test,y_test_svm)\n",
        "\n",
        "print(\"SVM: Accuracy on training Data: {:.3f}\".format(acc_train_svm))\n",
        "print(\"SVM : Accuracy on test Data: {:.3f}\".format(acc_test_svm))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM: Accuracy on training Data: 0.803\n",
            "SVM : Accuracy on test Data: 0.796\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 586
        },
        "id": "dNuzuoYRMrm8",
        "outputId": "3bd7d429-20d2-464e-adba-8e4d3bfd114a"
      },
      "source": [
        "cm = confusion_matrix(y_test,y_test_svm)\n",
        "plt.figure(figsize=(12,9))\n",
        "sns.heatmap(data=cm,linewidths=.5, annot=True,square = True,  cmap = 'Blues')\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "all_sample_title = 'Accuracy Score: {0}'.format(svm.score(X_test, y_test))\n",
        "plt.title(all_sample_title, size = 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Score: 0.7965')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ToF9fRNSwZ1b"
      },
      "source": [
        "**Storing the results:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZAc00sUwZ1d"
      },
      "source": [
        "#storing the results. The below mentioned order of parameter passing is important.\n",
        "#Caution: Execute only once to avoid duplications.\n",
        "storeResults('SVM', acc_train_svm, acc_test_svm)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k3vsRppPv3rs"
      },
      "source": [
        "## **8. Comparision of Models**\n",
        "To compare the models performance, a dataframe is created. The columns of this dataframe are the lists created to store the results of the model.\n",
        "\n",
        "The columns are named:'ML Model' which is the name of the model\n",
        "\n",
        "'Train Accuracy':Training accuracy\n",
        "\n",
        "'Test Accuracy':Testing accuracy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RkOSzcfsv8Xl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "e15d2203-c4b5-4a3b-8247-b7b4b5a3adf3"
      },
      "source": [
        "#creating dataframe\n",
        "results = pd.DataFrame({ 'ML Model': ML_Model,    \n",
        "    'Train Accuracy': acc_train,\n",
        "    'Test Accuracy': acc_test})\n",
        "results"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ML Model</th>\n",
              "      <th>Train Accuracy</th>\n",
              "      <th>Test Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.823</td>\n",
              "      <td>0.812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Multilayer Perceptrons</td>\n",
              "      <td>0.863</td>\n",
              "      <td>0.844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.870</td>\n",
              "      <td>0.848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AutoEncoder</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.796</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 ML Model  Train Accuracy  Test Accuracy\n",
              "0           Decision Tree           0.815          0.806\n",
              "1           Random Forest           0.823          0.812\n",
              "2  Multilayer Perceptrons           0.863          0.844\n",
              "3                 XGBoost           0.870          0.848\n",
              "4             AutoEncoder           0.002          0.002\n",
              "5                     SVM           0.804          0.796"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eKheGBiHwDfK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "f24d9f49-3eee-4fdb-9b2a-2885a06a8885"
      },
      "source": [
        "#Sorting the datafram on accuracy\n",
        "results.sort_values(by=['Test Accuracy', 'Train Accuracy'], ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ML Model</th>\n",
              "      <th>Train Accuracy</th>\n",
              "      <th>Test Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.870</td>\n",
              "      <td>0.848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Multilayer Perceptrons</td>\n",
              "      <td>0.863</td>\n",
              "      <td>0.844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.823</td>\n",
              "      <td>0.812</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.815</td>\n",
              "      <td>0.806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SVM</td>\n",
              "      <td>0.804</td>\n",
              "      <td>0.796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AutoEncoder</td>\n",
              "      <td>0.002</td>\n",
              "      <td>0.002</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 ML Model  Train Accuracy  Test Accuracy\n",
              "3                 XGBoost           0.870          0.848\n",
              "2  Multilayer Perceptrons           0.863          0.844\n",
              "1           Random Forest           0.823          0.812\n",
              "0           Decision Tree           0.815          0.806\n",
              "5                     SVM           0.804          0.796\n",
              "4             AutoEncoder           0.002          0.002"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-puFA0EfHckr"
      },
      "source": [
        "y=np.arange(0,105,5)\n",
        "x1=results['Train Accuracy']*100\n",
        "x2=results['Test Accuracy']*100"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "MVsg5rWtIFqu",
        "outputId": "04027edb-f6f5-4ce6-84de-02fe86450807"
      },
      "source": [
        "plt.figure(figsize=(17,13))\n",
        "plt.bar(results['ML Model'], x1,color=['yellow', 'red', 'green', 'cyan', 'orange','purple'])\n",
        "plt.title('Accuracy per Model(Training)',fontsize=14)\n",
        "plt.yticks(y)\n",
        "plt.ylabel('Train Accuracy',rotation=0,fontsize=14, labelpad=50)\n",
        "\n",
        "plt.xlabel('Model',fontsize=14)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Model')"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "A1ZvKuYDF9eo",
        "outputId": "95cb2ef2-7f75-409c-9ac3-61c7aca455fb"
      },
      "source": [
        "plt.figure(figsize=(17,13))\n",
        "plt.bar(results['ML Model'], x2,color=['yellow', 'red', 'green', 'cyan', 'orange','purple'])\n",
        "plt.title('Accuracy per Model(Testing)',fontsize=14)\n",
        "plt.yticks(y)\n",
        "plt.ylabel('Accuracy',rotation=0,fontsize=14, labelpad=50)\n",
        "\n",
        "plt.xlabel('Model',fontsize=14)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Model')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1224x936 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5t9806vn601b"
      },
      "source": [
        "For the above comparision, it is clear that the XGBoost Classifier works well with this dataset.\n",
        "\n",
        "So, saving the model for future use."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCIIkZ7V3AFN"
      },
      "source": [
        "# save XGBoost model to file\n",
        "import pickle\n",
        "pickle.dump(xgb, open(\"XGBoostClassifier.pickle.dat\", \"wb\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PbrNHP0o3QrD"
      },
      "source": [
        "**Testing the saved model:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZEm_PS33QD-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9379b71-f6fd-4d65-8edd-005485d1c412"
      },
      "source": [
        "# load model from file\n",
        "loaded_model = pickle.load(open(\"XGBoostClassifier.pickle.dat\", \"rb\"))\n",
        "loaded_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(learning_rate=0.4, max_depth=7, missing=nan)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vy2egEdwkqZ"
      },
      "source": [
        "## **9. References**\n",
        "* https://blog.keras.io/building-autoencoders-in-keras.html\n",
        "* https://en.wikipedia.org/wiki/Autoencoder\n",
        "* https://mc.ai/a-beginners-guide-to-build-stacked-autoencoder-and-tying-weights-with-it/\n",
        "* https://github.com/shreyagopal/t81_558_deep_learning/blob/master/t81_558_class_14_03_anomaly.ipynb\n",
        "* https://machinelearningmastery.com/save-gradient-boosting-models-xgboost-python/"
      ]
    }
  ]
}