Day1_Cybersecurity.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNt0Hi9pEoZoJHcIV3IY872",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/vishymails/cyber_security_AIML/blob/main/Day1_Cybersecurity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "OWq-BYmmvMBO"
      },
      "outputs": [],
      "source": [
        "# Analyzing Time Series Using Statsmodels"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from random import random\n",
        "\n",
        "time_series = [2 * x + random() for x in range(1, 100)]"
      ],
      "metadata": {
        "id": "tKMhImYo9kNr"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(time_series)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "6b1ZGNlj95-c",
        "outputId": "8513e45d-4202-43f9-eca1-e2a960ff2375"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.ar_model import AutoReg\n",
        "\n",
        "model = AutoReg(time_series , 2)\n",
        "model_fit = model.fit()\n",
        "\n",
        "y = model_fit.predict(len(time_series), len(time_series))\n",
        "\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zjJJUGoD-GSk",
        "outputId": "b300c0b6-51ab-47ee-8854-3a9f736f2158"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[200.2815657]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwv_NHFZ-juG",
        "outputId": "910b5037-fe99-4a2a-d768-97a279e97883"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2.7503246 , 0.6198164 , 0.38019616])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "\n",
        "model = ARIMA(time_series, order = (0, 1, 2))\n",
        "\n",
        "model_fit  = model.fit()\n",
        "\n",
        "y = model_fit.predict(len(time_series), len(time_series))\n",
        "\n",
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XX1rKKcG-zwj",
        "outputId": "f82053f1-e8c1-459d-f343-6c2cd947fbd0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[198.16737033]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2DX--2F_1QB",
        "outputId": "73304f0c-f8a1-4291-99d6-fff9417d226b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.89715642, 0.57444483, 1.23680021])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_fit.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "4d-du988AuOh",
        "outputId": "ffe79124-f83e-4e80-cc34-a51ea0b0a430"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                               SARIMAX Results                                \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   No. Observations:                   99\n",
              "Model:                 ARIMA(0, 1, 2)   Log Likelihood                -150.067\n",
              "Date:                Mon, 15 Apr 2024   AIC                            306.134\n",
              "Time:                        09:09:54   BIC                            313.889\n",
              "Sample:                             0   HQIC                           309.271\n",
              "                                 - 99                                         \n",
              "Covariance Type:                  opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ma.L1          0.8972      0.092      9.718      0.000       0.716       1.078\n",
              "ma.L2          0.5744      0.095      6.069      0.000       0.389       0.760\n",
              "sigma2         1.2368      0.210      5.881      0.000       0.825       1.649\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                  33.54   Jarque-Bera (JB):                 0.56\n",
              "Prob(Q):                              0.00   Prob(JB):                         0.76\n",
              "Heteroskedasticity (H):               0.69   Skew:                             0.15\n",
              "Prob(H) (two-sided):                  0.30   Kurtosis:                         3.20\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>99</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>            <td>ARIMA(0, 1, 2)</td>  <th>  Log Likelihood     </th> <td>-150.067</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>            <td>Mon, 15 Apr 2024</td> <th>  AIC                </th>  <td>306.134</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                <td>09:09:54</td>     <th>  BIC                </th>  <td>313.889</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th>  <td>309.271</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                       <td> - 99</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>  <td>    0.8972</td> <td>    0.092</td> <td>    9.718</td> <td> 0.000</td> <td>    0.716</td> <td>    1.078</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2</th>  <td>    0.5744</td> <td>    0.095</td> <td>    6.069</td> <td> 0.000</td> <td>    0.389</td> <td>    0.760</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th> <td>    1.2368</td> <td>    0.210</td> <td>    5.881</td> <td> 0.000</td> <td>    0.825</td> <td>    1.649</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>33.54</td> <th>  Jarque-Bera (JB):  </th> <td>0.56</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.00</td>  <th>  Prob(JB):          </th> <td>0.76</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>0.69</td>  <th>  Skew:              </th> <td>0.15</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.30</td>  <th>  Kurtosis:          </th> <td>3.20</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}          &        y         & \\textbf{  No. Observations:  } &     99      \\\\\n\\textbf{Model:}                  &  ARIMA(0, 1, 2)  & \\textbf{  Log Likelihood     } &  -150.067   \\\\\n\\textbf{Date:}                   & Mon, 15 Apr 2024 & \\textbf{  AIC                } &  306.134    \\\\\n\\textbf{Time:}                   &     09:09:54     & \\textbf{  BIC                } &  313.889    \\\\\n\\textbf{Sample:}                 &        0         & \\textbf{  HQIC               } &  309.271    \\\\\n\\textbf{}                        &       - 99       & \\textbf{                     } &             \\\\\n\\textbf{Covariance Type:}        &       opg        & \\textbf{                     } &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{ma.L1}  &       0.8972  &        0.092     &     9.718  &         0.000        &        0.716    &        1.078     \\\\\n\\textbf{ma.L2}  &       0.5744  &        0.095     &     6.069  &         0.000        &        0.389    &        0.760     \\\\\n\\textbf{sigma2} &       1.2368  &        0.210     &     5.881  &         0.000        &        0.825    &        1.649     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Ljung-Box (L1) (Q):}     & 33.54 & \\textbf{  Jarque-Bera (JB):  } & 0.56  \\\\\n\\textbf{Prob(Q):}                &  0.00 & \\textbf{  Prob(JB):          } & 0.76  \\\\\n\\textbf{Heteroskedasticity (H):} &  0.69 & \\textbf{  Skew:              } & 0.15  \\\\\n\\textbf{Prob(H) (two-sided):}    &  0.30 & \\textbf{  Kurtosis:          } & 3.20  \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{SARIMAX Results}\n\\end{center}\n\nWarnings: \\newline\n [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Anomally Detection with Isolation Forest"
      ],
      "metadata": {
        "id": "NCNwyG_WBR4s"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "random_seed = np.random.RandomState(12)"
      ],
      "metadata": {
        "id": "6tQ12p5sOJw-"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = 0.5 * random_seed.randn(500, 2)\n",
        "\n",
        "X_train = np.r_[X_train + 3 , X_train]\n",
        "\n",
        "\n",
        "X_train = pd.DataFrame(X_train, columns = [\"x\", \"y\"])\n",
        "\n",
        "\n",
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0wz52_oPOVQ0",
        "outputId": "0b60908a-6b24-43d9-d3d2-319e897b4df0"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
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              "3  3.002564  2.939886\n",
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              "  @keyframes spin {\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = 0.5 * random_seed.randn(500, 2)\n",
        "\n",
        "X_test = np.r_[X_test + 3 , X_test]\n",
        "\n",
        "\n",
        "X_test = pd.DataFrame(X_test, columns = [\"x\", \"y\"])\n",
        "\n",
        "\n",
        "X_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "Gs0y9-CAPZG_",
        "outputId": "29e3c6b5-d491-4004-e25b-646da02c0a64"
      },
      "execution_count": 34,
      "outputs": [
        {
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              "          x         y\n",
              "0  3.944575  3.866919\n",
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              "    .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>x</th>\n",
              "      <th>y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.944575</td>\n",
              "      <td>3.866919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.984853</td>\n",
              "      <td>3.142150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.501735</td>\n",
              "      <td>2.168262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.906300</td>\n",
              "      <td>3.233826</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.273225</td>\n",
              "      <td>3.261790</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e503a84f-a01f-4adb-9496-fec1b7578e55')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e503a84f-a01f-4adb-9496-fec1b7578e55 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e503a84f-a01f-4adb-9496-fec1b7578e55');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-fdffebe9-42f2-43f4-8f0a-73d325fb8f29\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fdffebe9-42f2-43f4-8f0a-73d325fb8f29')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fdffebe9-42f2-43f4-8f0a-73d325fb8f29 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test",
              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5772935929975855,\n        \"min\": -1.3731293800372435,\n        \"max\": 4.54376962364104,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.48453152568657026,\n          -0.711844853277527,\n          -0.014060895097489602\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.576443271063109,\n        \"min\": -1.450903590300363,\n        \"max\": 4.672324519043446,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.5144772611802306,\n          -0.1564771034436885,\n          -0.1013763516154491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_outliers = random_seed.uniform(low=-5, high=5, size=(50,2))\n",
        "\n",
        "X_outliers = pd.DataFrame(X_outliers, columns=[\"x\", \"y\"])\n",
        "\n",
        "X_outliers.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7ZVu1yWqPo-g",
        "outputId": "ca5ac29c-34ee-42e8-8d2e-68d6c7d0f070"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          x         y\n",
              "0 -2.763810 -4.040638\n",
              "1  3.716652 -2.261580\n",
              "2  4.168199  3.066528\n",
              "3  3.686773 -1.612318\n",
              "4  0.271531  2.587032"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-943e6369-2b1a-4f76-9894-3a8aefe43adb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <td>-2.763810</td>\n",
              "      <td>-4.040638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.716652</td>\n",
              "      <td>-2.261580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.168199</td>\n",
              "      <td>3.066528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.686773</td>\n",
              "      <td>-1.612318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.271531</td>\n",
              "      <td>2.587032</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-943e6369-2b1a-4f76-9894-3a8aefe43adb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-943e6369-2b1a-4f76-9894-3a8aefe43adb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-943e6369-2b1a-4f76-9894-3a8aefe43adb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
              "<div id=\"df-34448ba7-4176-4e4b-a81a-389eca9347af\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-34448ba7-4176-4e4b-a81a-389eca9347af')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-34448ba7-4176-4e4b-a81a-389eca9347af button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_outliers",
              "summary": "{\n  \"name\": \"X_outliers\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0262232027317917,\n        \"min\": -4.7896291847691765,\n        \"max\": 4.92329849531902,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          -3.0833759589462515,\n          0.717045334886226,\n          -2.0565694561753824\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.894360341193729,\n        \"min\": -4.684175048924714,\n        \"max\": 4.349646530858459,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          3.2327741462530444,\n          -2.610746627079259,\n          2.1223605158174665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "p1 = plt.scatter(X_train.x, X_train.y, c = \"red\", s= 50, edgecolor=\"black\")\n",
        "p2 = plt.scatter(X_test.x, X_test.y, c = \"green\", s= 50, edgecolor=\"black\")\n",
        "p3 = plt.scatter(X_outliers.x, X_outliers.y, c = \"blue\", s= 50, edgecolor=\"black\")\n",
        "\n",
        "\n",
        "plt.xlim((-6, 6))\n",
        "plt.ylim((-6, 6))\n",
        "\n",
        "plt.legend([p1, p2, p3],\n",
        "      [\"training set\", \"normal testing set\", \"anomalous testing set\"],\n",
        "      loc = \"lower right\",)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "l_j3DwtdQUvE",
        "outputId": "adfc63b7-f957-458d-f991-f0bbc258f1fe"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import IsolationForest\n",
        "\n",
        "clf = IsolationForest()\n",
        "\n",
        "clf.fit(X_train)\n",
        "\n",
        "y_pred_train = clf.predict(X_train)\n",
        "y_pred_test = clf.predict(X_test)\n",
        "y_pred_outliers = clf.predict(X_outliers)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "MUq06F5lRk7_"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_outliers = X_outliers.assign(pred = y_pred_outliers)\n",
        "\n",
        "X_outliers.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "lDTibXmqS2iH",
        "outputId": "d6a7c115-e711-4091-8d2b-fa323c39ffba"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          x         y  pred\n",
              "0 -2.763810 -4.040638    -1\n",
              "1  3.716652 -2.261580    -1\n",
              "2  4.168199  3.066528    -1\n",
              "3  3.686773 -1.612318    -1\n",
              "4  0.271531  2.587032    -1"
            ],
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              "\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_outliers",
              "summary": "{\n  \"name\": \"X_outliers\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0262232027317917,\n        \"min\": -4.7896291847691765,\n        \"max\": 4.92329849531902,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          -3.0833759589462515,\n          0.717045334886226,\n          -2.0565694561753824\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.894360341193729,\n        \"min\": -4.684175048924714,\n        \"max\": 4.349646530858459,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          3.2327741462530444,\n          -2.610746627079259,\n          2.1223605158174665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p1 = plt.scatter(X_train.x, X_train.y, c = \"red\", s= 50, edgecolor=\"black\")\n",
        "\n",
        "p2 = plt.scatter( X_outliers.loc[X_outliers.pred == -1, [\"x\"]],\n",
        "                  X_outliers.loc[X_outliers.pred == -1, [\"y\"]],\n",
        "                  c = \"blue\", s= 50, edgecolor=\"black\")\n",
        "\n",
        "p3 = plt.scatter( X_outliers.loc[X_outliers.pred == 1, [\"x\"]],\n",
        "                  X_outliers.loc[X_outliers.pred == 1, [\"y\"]],\n",
        "                  c = \"green\", s= 50, edgecolor=\"black\")\n",
        "\n",
        "plt.xlim((-6, 6))\n",
        "plt.ylim((-6, 6))\n",
        "\n",
        "plt.legend([p1, p2, p3],\n",
        "      [\"training Observations \", \"detected outliers\", \"incorrectly labeled outliers\"],\n",
        "      loc = \"lower right\",)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "Zc7a-NpHTBoo",
        "outputId": "ad0bbc88-eeeb-4c61-c245-515556dc29cd"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = X_test.assign(pred = y_pred_test)\n",
        "\n",
        "X_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "fBf_BglzUGdg",
        "outputId": "25f7b2ae-888d-41d0-ad20-e12a32c26441"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          x         y  pred\n",
              "0  3.944575  3.866919    -1\n",
              "1  2.984853  3.142150     1\n",
              "2  3.501735  2.168262    -1\n",
              "3  2.906300  3.233826     1\n",
              "4  3.273225  3.261790     1"
            ],
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e5566694-9f46-464f-9bfa-bffeb5fab420 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e5566694-9f46-464f-9bfa-bffeb5fab420');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c46b6b3b-6c18-46dd-95d1-1a55c15c536a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c46b6b3b-6c18-46dd-95d1-1a55c15c536a')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c46b6b3b-6c18-46dd-95d1-1a55c15c536a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test",
              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"x\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5772935929975855,\n        \"min\": -1.3731293800372435,\n        \"max\": 4.54376962364104,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.48453152568657026,\n          -0.711844853277527,\n          -0.014060895097489602\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.576443271063109,\n        \"min\": -1.450903590300363,\n        \"max\": 4.672324519043446,\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          0.5144772611802306,\n          -0.1564771034436885,\n          -0.1013763516154491\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pred\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": -1,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          -1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p1 = plt.scatter(X_train.x, X_train.y, c = \"red\", s= 50, edgecolor=\"black\")\n",
        "\n",
        "p2 = plt.scatter( X_test.loc[X_test.pred == 1, [\"x\"]],\n",
        "                  X_test.loc[X_test.pred == 1, [\"y\"]],\n",
        "                  c = \"blue\", s= 50, edgecolor=\"black\")\n",
        "\n",
        "p3 = plt.scatter( X_test.loc[X_test.pred == -1, [\"x\"]],\n",
        "                  X_test.loc[X_test.pred == -1, [\"y\"]],\n",
        "                  c = \"green\", s= 50, edgecolor=\"black\")\n",
        "\n",
        "plt.xlim((-6, 6))\n",
        "plt.ylim((-6, 6))\n",
        "\n",
        "plt.legend([p1, p2, p3],\n",
        "      [\"training Observations \", \"Correctly labeled test observations\", \"incorrectly labeled Test observations\"],\n",
        "      loc = \"lower right\",)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "y9B2HeC8VAM2",
        "outputId": "231ee9c8-211c-44e6-cc59-ce74f06d0781"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Standardizing Your data"
      ],
      "metadata": {
        "id": "qmq8MY1zVXRU"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv(\"file_pe_headers.csv\", sep=\",\")\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "8c40qkidgKlB",
        "outputId": "45d461f5-9278-40bf-927a-a0229cf77a7f"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          Name  e_magic  e_cblp  e_cp  e_crlc  \\\n",
              "0  VirusShare_a878ba26000edaac5c98eff4432723b3    23117     144     3       0   \n",
              "1  VirusShare_ef9130570fddc174b312b2047f5f4cf0    23117     144     3       0   \n",
              "2  VirusShare_ef84cdeba22be72a69b198213dada81a    23117     144     3       0   \n",
              "3  VirusShare_6bf3608e60ebc16cbcff6ed5467d469e    23117     144     3       0   \n",
              "4  VirusShare_2cc94d952b2efb13c7d6bbe0dd59d3fb    23117     144     3       0   \n",
              "\n",
              "   e_cparhdr  e_minalloc  e_maxalloc  e_ss  e_sp  ...  SectionMaxChar  \\\n",
              "0          4           0       65535     0   184  ...      3758096608   \n",
              "1          4           0       65535     0   184  ...      3791650880   \n",
              "2          4           0       65535     0   184  ...      3221225536   \n",
              "3          4           0       65535     0   184  ...      3224371328   \n",
              "4          4           0       65535     0   184  ...      3227516992   \n",
              "\n",
              "   SectionMainChar  DirectoryEntryImport  DirectoryEntryImportSize  \\\n",
              "0                0                     7                       152   \n",
              "1                0                    16                       311   \n",
              "2                0                     6                       176   \n",
              "3                0                     8                       155   \n",
              "4                0                     2                        43   \n",
              "\n",
              "   DirectoryEntryExport  ImageDirectoryEntryExport  ImageDirectoryEntryImport  \\\n",
              "0                     0                          0                      54440   \n",
              "1                     0                          0                     262276   \n",
              "2                     0                          0                      36864   \n",
              "3                     0                          0                     356352   \n",
              "4                     0                          0                      61440   \n",
              "\n",
              "   ImageDirectoryEntryResource  ImageDirectoryEntryException  \\\n",
              "0                        77824                         73728   \n",
              "1                       294912                             0   \n",
              "2                        40960                             0   \n",
              "3                      1003520                             0   \n",
              "4                        73728                             0   \n",
              "\n",
              "   ImageDirectoryEntrySecurity  \n",
              "0                            0  \n",
              "1                       346112  \n",
              "2                            0  \n",
              "3                     14109472  \n",
              "4                        90624  \n",
              "\n",
              "[5 rows x 79 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ff01b3e-a9d0-4cb5-9787-2dd8a31663d4\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>e_magic</th>\n",
              "      <th>e_cblp</th>\n",
              "      <th>e_cp</th>\n",
              "      <th>e_crlc</th>\n",
              "      <th>e_cparhdr</th>\n",
              "      <th>e_minalloc</th>\n",
              "      <th>e_maxalloc</th>\n",
              "      <th>e_ss</th>\n",
              "      <th>e_sp</th>\n",
              "      <th>...</th>\n",
              "      <th>SectionMaxChar</th>\n",
              "      <th>SectionMainChar</th>\n",
              "      <th>DirectoryEntryImport</th>\n",
              "      <th>DirectoryEntryImportSize</th>\n",
              "      <th>DirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryImport</th>\n",
              "      <th>ImageDirectoryEntryResource</th>\n",
              "      <th>ImageDirectoryEntryException</th>\n",
              "      <th>ImageDirectoryEntrySecurity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VirusShare_a878ba26000edaac5c98eff4432723b3</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3758096608</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>152</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>54440</td>\n",
              "      <td>77824</td>\n",
              "      <td>73728</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>VirusShare_ef9130570fddc174b312b2047f5f4cf0</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3791650880</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>311</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>262276</td>\n",
              "      <td>294912</td>\n",
              "      <td>0</td>\n",
              "      <td>346112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>VirusShare_ef84cdeba22be72a69b198213dada81a</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3221225536</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>176</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36864</td>\n",
              "      <td>40960</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VirusShare_6bf3608e60ebc16cbcff6ed5467d469e</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3224371328</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>155</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>356352</td>\n",
              "      <td>1003520</td>\n",
              "      <td>0</td>\n",
              "      <td>14109472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>VirusShare_2cc94d952b2efb13c7d6bbe0dd59d3fb</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3227516992</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>61440</td>\n",
              "      <td>73728</td>\n",
              "      <td>0</td>\n",
              "      <td>90624</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 79 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ff01b3e-a9d0-4cb5-9787-2dd8a31663d4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3ff01b3e-a9d0-4cb5-9787-2dd8a31663d4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3ff01b3e-a9d0-4cb5-9787-2dd8a31663d4');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-2cdcb794-3249-43fe-831d-7f409a04ba3c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2cdcb794-3249-43fe-831d-7f409a04ba3c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-2cdcb794-3249-43fe-831d-7f409a04ba3c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data"
            }
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop([\"Name\", \"Malware\"], axis=1).to_numpy()\n",
        "\n",
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqFTqp4PgW5S",
        "outputId": "699f3fe2-494d-4ed9-d7dc-7c0d6c562e19"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2.31170e+04 1.44000e+02 3.00000e+00 ... 7.78240e+04 7.37280e+04\n",
            "  0.00000e+00]\n",
            " [2.31170e+04 1.44000e+02 3.00000e+00 ... 2.94912e+05 0.00000e+00\n",
            "  3.46112e+05]\n",
            " [2.31170e+04 1.44000e+02 3.00000e+00 ... 4.09600e+04 0.00000e+00\n",
            "  0.00000e+00]\n",
            " ...\n",
            " [2.31170e+04 0.00000e+00 0.00000e+00 ... 6.14400e+04 0.00000e+00\n",
            "  0.00000e+00]\n",
            " [2.31170e+04 1.44000e+02 3.00000e+00 ... 1.02400e+05 0.00000e+00\n",
            "  0.00000e+00]\n",
            " [2.31170e+04 1.44000e+02 3.00000e+00 ... 5.57056e+05 0.00000e+00\n",
            "  0.00000e+00]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "X_standardized = StandardScaler().fit_transform(X)\n",
        "\n",
        "print(X_standardized)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IB4-YRFNhDHM",
        "outputId": "1a09b173-c9a8-4057-94d3-5a75021842ca"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.         -0.03506542 -0.04751096 ... -0.07054894 -0.0198525\n",
            "  -0.04066791]\n",
            " [ 0.         -0.03506542 -0.04751096 ... -0.03849221 -0.02110877\n",
            "  -0.02469983]\n",
            " [ 0.         -0.03506542 -0.04751096 ... -0.07599254 -0.02110877\n",
            "  -0.04066791]\n",
            " ...\n",
            " [ 0.         -0.18093613 -0.04958686 ... -0.07296832 -0.02110877\n",
            "  -0.04066791]\n",
            " [ 0.         -0.03506542 -0.04751096 ... -0.06691988 -0.02110877\n",
            "  -0.04066791]\n",
            " [ 0.         -0.03506542 -0.04751096 ...  0.00021781 -0.02110877\n",
            "  -0.04066791]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qLIYSjGhqSx",
        "outputId": "b60b3df1-74de-4343-9351-86681ef99854"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19611, 79)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "3rH7y7t1jxoN",
        "outputId": "72cd4d23-5881-457e-d5c5-433c570d0e23"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       e_magic        e_cblp          e_cp        e_crlc     e_cparhdr  \\\n",
              "count  19611.0  19611.000000  19611.000000  19611.000000  19611.000000   \n",
              "mean   23117.0    178.615726     71.660752     49.146958     37.370710   \n",
              "std        0.0    987.200729   1445.192977   1212.201919    864.515405   \n",
              "min    23117.0      0.000000      0.000000      0.000000      0.000000   \n",
              "25%    23117.0    144.000000      3.000000      0.000000      4.000000   \n",
              "50%    23117.0    144.000000      3.000000      0.000000      4.000000   \n",
              "75%    23117.0    144.000000      3.000000      0.000000      4.000000   \n",
              "max    23117.0  59448.000000  63200.000000  64613.000000  43690.000000   \n",
              "\n",
              "         e_minalloc    e_maxalloc          e_ss         e_sp        e_csum  \\\n",
              "count  19611.000000  19611.000000  19611.000000  19611.00000  19611.000000   \n",
              "mean      37.032635  64178.739687     10.418490    226.46530     29.689103   \n",
              "std      915.833139   9110.755873    637.116265   1249.68033   1015.303419   \n",
              "min        0.000000      0.000000      0.000000      0.00000      0.000000   \n",
              "25%        0.000000  65535.000000      0.000000    184.00000      0.000000   \n",
              "50%        0.000000  65535.000000      0.000000    184.00000      0.000000   \n",
              "75%        0.000000  65535.000000      0.000000    184.00000      0.000000   \n",
              "max    43690.000000  65535.000000  61436.000000  65464.00000  63262.000000   \n",
              "\n",
              "       ...  SectionMaxChar  SectionMainChar  DirectoryEntryImport  \\\n",
              "count  ...    1.961100e+04          19611.0          19611.000000   \n",
              "mean   ...    3.163632e+09              0.0              6.112437   \n",
              "std    ...    5.860332e+08              0.0              7.525158   \n",
              "min    ...    1.073742e+09              0.0              0.000000   \n",
              "25%    ...    3.221226e+09              0.0              2.000000   \n",
              "50%    ...    3.221226e+09              0.0              4.000000   \n",
              "75%    ...    3.221226e+09              0.0              8.000000   \n",
              "max    ...    4.294967e+09              0.0            588.000000   \n",
              "\n",
              "       DirectoryEntryImportSize  DirectoryEntryExport  \\\n",
              "count              19611.000000          19611.000000   \n",
              "mean                 101.912804             14.131865   \n",
              "std                  127.076767            154.958102   \n",
              "min                    0.000000              0.000000   \n",
              "25%                   17.000000              0.000000   \n",
              "50%                   80.000000              0.000000   \n",
              "75%                  135.000000              0.000000   \n",
              "max                 4016.000000           7319.000000   \n",
              "\n",
              "       ImageDirectoryEntryExport  ImageDirectoryEntryImport  \\\n",
              "count               1.961100e+04               1.961100e+04   \n",
              "mean                3.368566e+05               4.047213e+05   \n",
              "std                 2.181191e+07               4.704601e+06   \n",
              "min                 0.000000e+00               0.000000e+00   \n",
              "25%                 0.000000e+00               2.960400e+04   \n",
              "50%                 0.000000e+00               8.222400e+04   \n",
              "75%                 0.000000e+00               2.477080e+05   \n",
              "max                 2.147484e+09               5.368914e+08   \n",
              "\n",
              "       ImageDirectoryEntryResource  ImageDirectoryEntryException  \\\n",
              "count                 1.961100e+04                  1.961100e+04   \n",
              "mean                  5.555810e+05                  1.238834e+06   \n",
              "std                   6.772167e+06                  5.868961e+07   \n",
              "min                   0.000000e+00                  0.000000e+00   \n",
              "25%                   4.096000e+04                  0.000000e+00   \n",
              "50%                   1.310720e+05                  0.000000e+00   \n",
              "75%                   3.870720e+05                  0.000000e+00   \n",
              "max                   8.304108e+08                  2.906159e+09   \n",
              "\n",
              "       ImageDirectoryEntrySecurity  \n",
              "count                 1.961100e+04  \n",
              "mean                  8.814868e+05  \n",
              "std                   2.167579e+07  \n",
              "min                   0.000000e+00  \n",
              "25%                   0.000000e+00  \n",
              "50%                   0.000000e+00  \n",
              "75%                   3.184640e+05  \n",
              "max                   2.415919e+09  \n",
              "\n",
              "[8 rows x 78 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f85eafe-2eb1-4d7a-9e69-a370d52f92f0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>e_magic</th>\n",
              "      <th>e_cblp</th>\n",
              "      <th>e_cp</th>\n",
              "      <th>e_crlc</th>\n",
              "      <th>e_cparhdr</th>\n",
              "      <th>e_minalloc</th>\n",
              "      <th>e_maxalloc</th>\n",
              "      <th>e_ss</th>\n",
              "      <th>e_sp</th>\n",
              "      <th>e_csum</th>\n",
              "      <th>...</th>\n",
              "      <th>SectionMaxChar</th>\n",
              "      <th>SectionMainChar</th>\n",
              "      <th>DirectoryEntryImport</th>\n",
              "      <th>DirectoryEntryImportSize</th>\n",
              "      <th>DirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryExport</th>\n",
              "      <th>ImageDirectoryEntryImport</th>\n",
              "      <th>ImageDirectoryEntryResource</th>\n",
              "      <th>ImageDirectoryEntryException</th>\n",
              "      <th>ImageDirectoryEntrySecurity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>19611.0</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.00000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>19611.0</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>19611.000000</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "      <td>1.961100e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>178.615726</td>\n",
              "      <td>71.660752</td>\n",
              "      <td>49.146958</td>\n",
              "      <td>37.370710</td>\n",
              "      <td>37.032635</td>\n",
              "      <td>64178.739687</td>\n",
              "      <td>10.418490</td>\n",
              "      <td>226.46530</td>\n",
              "      <td>29.689103</td>\n",
              "      <td>...</td>\n",
              "      <td>3.163632e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.112437</td>\n",
              "      <td>101.912804</td>\n",
              "      <td>14.131865</td>\n",
              "      <td>3.368566e+05</td>\n",
              "      <td>4.047213e+05</td>\n",
              "      <td>5.555810e+05</td>\n",
              "      <td>1.238834e+06</td>\n",
              "      <td>8.814868e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>987.200729</td>\n",
              "      <td>1445.192977</td>\n",
              "      <td>1212.201919</td>\n",
              "      <td>864.515405</td>\n",
              "      <td>915.833139</td>\n",
              "      <td>9110.755873</td>\n",
              "      <td>637.116265</td>\n",
              "      <td>1249.68033</td>\n",
              "      <td>1015.303419</td>\n",
              "      <td>...</td>\n",
              "      <td>5.860332e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.525158</td>\n",
              "      <td>127.076767</td>\n",
              "      <td>154.958102</td>\n",
              "      <td>2.181191e+07</td>\n",
              "      <td>4.704601e+06</td>\n",
              "      <td>6.772167e+06</td>\n",
              "      <td>5.868961e+07</td>\n",
              "      <td>2.167579e+07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>23117.0</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>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.073742e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>184.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.221226e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.960400e+04</td>\n",
              "      <td>4.096000e+04</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>184.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.221226e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>8.222400e+04</td>\n",
              "      <td>1.310720e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>144.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>184.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>3.221226e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.477080e+05</td>\n",
              "      <td>3.870720e+05</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.184640e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>23117.0</td>\n",
              "      <td>59448.000000</td>\n",
              "      <td>63200.000000</td>\n",
              "      <td>64613.000000</td>\n",
              "      <td>43690.000000</td>\n",
              "      <td>43690.000000</td>\n",
              "      <td>65535.000000</td>\n",
              "      <td>61436.000000</td>\n",
              "      <td>65464.00000</td>\n",
              "      <td>63262.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.294967e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>588.000000</td>\n",
              "      <td>4016.000000</td>\n",
              "      <td>7319.000000</td>\n",
              "      <td>2.147484e+09</td>\n",
              "      <td>5.368914e+08</td>\n",
              "      <td>8.304108e+08</td>\n",
              "      <td>2.906159e+09</td>\n",
              "      <td>2.415919e+09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 78 columns</p>\n",
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              "        document.querySelector('#df-3f85eafe-2eb1-4d7a-9e69-a370d52f92f0 button.colab-df-convert');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c5e007c9-f0db-4bf6-bf1c-75703a08976b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training an XGBoost Classifier\n",
        "\n"
      ],
      "metadata": {
        "id": "mt1AO7UvkzzK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv(\"file_pe_headers.csv\", sep=\",\")\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "Yk7_jnGkj2jO",
        "outputId": "9c9a9c7b-71f5-4493-e443-2355b3dfa395"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          Name  e_magic  e_cblp  e_cp  e_crlc  \\\n",
              "0  VirusShare_a878ba26000edaac5c98eff4432723b3    23117     144     3       0   \n",
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              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3758096608</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>152</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>54440</td>\n",
              "      <td>77824</td>\n",
              "      <td>73728</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>VirusShare_ef9130570fddc174b312b2047f5f4cf0</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3791650880</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>311</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>262276</td>\n",
              "      <td>294912</td>\n",
              "      <td>0</td>\n",
              "      <td>346112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>VirusShare_ef84cdeba22be72a69b198213dada81a</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3221225536</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>176</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36864</td>\n",
              "      <td>40960</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VirusShare_6bf3608e60ebc16cbcff6ed5467d469e</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3224371328</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>155</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>356352</td>\n",
              "      <td>1003520</td>\n",
              "      <td>0</td>\n",
              "      <td>14109472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>VirusShare_2cc94d952b2efb13c7d6bbe0dd59d3fb</td>\n",
              "      <td>23117</td>\n",
              "      <td>144</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>65535</td>\n",
              "      <td>0</td>\n",
              "      <td>184</td>\n",
              "      <td>...</td>\n",
              "      <td>3227516992</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>61440</td>\n",
              "      <td>73728</td>\n",
              "      <td>0</td>\n",
              "      <td>90624</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 79 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4504d49f-b649-4d76-bd19-05d6158788c1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4504d49f-b649-4d76-bd19-05d6158788c1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4504d49f-b649-4d76-bd19-05d6158788c1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a9ee471e-284d-4c75-8bbe-57b0d844a342\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a9ee471e-284d-4c75-8bbe-57b0d844a342')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a9ee471e-284d-4c75-8bbe-57b0d844a342 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df[\"Malware\"]"
      ],
      "metadata": {
        "id": "OsRZ29wwlFC4"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VJSXyf2lJpE",
        "outputId": "99eb496a-0055-4900-cbe7-67fe5558d999"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        1\n",
              "1        1\n",
              "2        1\n",
              "3        1\n",
              "4        1\n",
              "        ..\n",
              "19606    0\n",
              "19607    0\n",
              "19608    0\n",
              "19609    0\n",
              "19610    0\n",
              "Name: Malware, Length: 19611, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop([\"Name\", \"Malware\"], axis = 1).to_numpy()"
      ],
      "metadata": {
        "id": "FBd1PHRYlLeD"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)"
      ],
      "metadata": {
        "id": "RDWca6Cflac6"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from xgboost import XGBClassifier\n",
        "\n",
        "XGB_model_instance = XGBClassifier()\n",
        "\n",
        "XGB_model_instance.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "TV5_7KlMl0sE",
        "outputId": "827d36f4-492c-46dc-e18c-3ecbcf1390ce"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_pred = XGB_model_instance.predict(X_test)\n",
        "\n",
        "y_test_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ISlWirSmNfv",
        "outputId": "f432d50c-c246-427c-b8e9-c4b1b8c37c32"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_test_pred)\n",
        "\n",
        "print(\"Accuracy : %.2f%%\" % (accuracy * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8FA0yCMlmXIW",
        "outputId": "ac5aa3a6-de87-4de4-a2ad-31829939d82f"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy : 99.39%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "870GpcuXmw0-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}