preprocessing.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Importing libraries"
      ],
      "metadata": {
        "id": "GqF8J5dg1mpV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3kuw_QKv_jly"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYTOBe1zF9kt",
        "outputId": "ede2c085-6133-42f8-8a08-3eb2428dc568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/DATA_MINING/CTU-IoT-Malware-Capture-1-1conn.log.labeled.csv', sep='|')"
      ],
      "metadata": {
        "id": "upFtR5oeA8y7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "bgFGSQp2J0yQ",
        "outputId": "7c19bd22-41b7-4ec5-86dc-e1d9fea4b1da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   ts                 uid        id.orig_h  id.orig_p  \\\n",
              "0        1.525880e+09  CUmrqr4svHuSXJy5z7  192.168.100.103    51524.0   \n",
              "1        1.525880e+09  CH98aB3s1kJeq6SFOc  192.168.100.103    56305.0   \n",
              "2        1.525880e+09   C3GBTkINvXNjVGtN5  192.168.100.103    41101.0   \n",
              "3        1.525880e+09   CDe43c1PtgynajGI6  192.168.100.103    60905.0   \n",
              "4        1.525880e+09  CJaDcG3MZzvf1YVYI4  192.168.100.103    44301.0   \n",
              "...               ...                 ...              ...        ...   \n",
              "1008743  1.526283e+09   CUyBg1WA04SBmf3ah  192.168.100.103    43763.0   \n",
              "1008744  1.526283e+09  CvaIoX2b9VynuYieBf  192.168.100.103    43763.0   \n",
              "1008745  1.526283e+09   CVHrUzIUN0mW5lQsl  192.168.100.103    43763.0   \n",
              "1008746  1.526283e+09  CkGlnL2pxzlusrFlLj  192.168.100.103    43763.0   \n",
              "1008747  1.526283e+09  CoOjVZ1zw3pWf7a9n5  192.168.100.103    43763.0   \n",
              "\n",
              "               id.resp_h  id.resp_p proto service  duration orig_bytes  ...  \\\n",
              "0         65.127.233.163       23.0   tcp       -  2.999051          0  ...   \n",
              "1          63.150.16.171       23.0   tcp       -         -          -  ...   \n",
              "2           111.40.23.49       23.0   tcp       -         -          -  ...   \n",
              "3        131.174.215.147       23.0   tcp       -  2.998796          0  ...   \n",
              "4            91.42.47.63       23.0   tcp       -         -          -  ...   \n",
              "...                  ...        ...   ...     ...       ...        ...  ...   \n",
              "1008743    16.219.83.137    64906.0   udp       -         -          -  ...   \n",
              "1008744   100.57.245.196    39435.0   udp       -         -          -  ...   \n",
              "1008745     249.99.119.9    26169.0   udp       -         -          -  ...   \n",
              "1008746  205.103.167.192    18241.0   udp       -         -          -  ...   \n",
              "1008747    23.70.168.160    16854.0   udp       -         -          -  ...   \n",
              "\n",
              "        local_resp missed_bytes history orig_pkts  orig_ip_bytes resp_pkts  \\\n",
              "0                -          0.0       S       3.0          180.0       0.0   \n",
              "1                -          0.0       S       1.0           60.0       0.0   \n",
              "2                -          0.0       S       1.0           60.0       0.0   \n",
              "3                -          0.0       S       3.0          180.0       0.0   \n",
              "4                -          0.0       S       1.0           60.0       0.0   \n",
              "...            ...          ...     ...       ...            ...       ...   \n",
              "1008743          -          0.0       D       1.0           40.0       0.0   \n",
              "1008744          -          0.0       D       1.0           40.0       0.0   \n",
              "1008745          -          0.0       D       1.0           40.0       0.0   \n",
              "1008746          -          0.0       D       1.0           40.0       0.0   \n",
              "1008747          -          0.0       D       1.0           40.0       0.0   \n",
              "\n",
              "         resp_ip_bytes  tunnel_parents      label             detailed-label  \n",
              "0                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "1                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "2                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "3                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "4                  0.0               -  Malicious  PartOfAHorizontalPortScan  \n",
              "...                ...             ...        ...                        ...  \n",
              "1008743            0.0               -     Benign                          -  \n",
              "1008744            0.0               -     Benign                          -  \n",
              "1008745            0.0               -     Benign                          -  \n",
              "1008746            0.0               -     Benign                          -  \n",
              "1008747            0.0               -     Benign                          -  \n",
              "\n",
              "[1008748 rows x 23 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ts</th>\n",
              "      <th>uid</th>\n",
              "      <th>id.orig_h</th>\n",
              "      <th>id.orig_p</th>\n",
              "      <th>id.resp_h</th>\n",
              "      <th>id.resp_p</th>\n",
              "      <th>proto</th>\n",
              "      <th>service</th>\n",
              "      <th>duration</th>\n",
              "      <th>orig_bytes</th>\n",
              "      <th>...</th>\n",
              "      <th>local_resp</th>\n",
              "      <th>missed_bytes</th>\n",
              "      <th>history</th>\n",
              "      <th>orig_pkts</th>\n",
              "      <th>orig_ip_bytes</th>\n",
              "      <th>resp_pkts</th>\n",
              "      <th>resp_ip_bytes</th>\n",
              "      <th>tunnel_parents</th>\n",
              "      <th>label</th>\n",
              "      <th>detailed-label</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CUmrqr4svHuSXJy5z7</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>51524.0</td>\n",
              "      <td>65.127.233.163</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>2.999051</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CH98aB3s1kJeq6SFOc</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>56305.0</td>\n",
              "      <td>63.150.16.171</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>C3GBTkINvXNjVGtN5</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>41101.0</td>\n",
              "      <td>111.40.23.49</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CDe43c1PtgynajGI6</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>60905.0</td>\n",
              "      <td>131.174.215.147</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>2.998796</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.525880e+09</td>\n",
              "      <td>CJaDcG3MZzvf1YVYI4</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>44301.0</td>\n",
              "      <td>91.42.47.63</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Malicious</td>\n",
              "      <td>PartOfAHorizontalPortScan</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1008743</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CUyBg1WA04SBmf3ah</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>16.219.83.137</td>\n",
              "      <td>64906.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
              "      <td>-</td>\n",
              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
              "      <td>-</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008744</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CvaIoX2b9VynuYieBf</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>100.57.245.196</td>\n",
              "      <td>39435.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
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              "    <tr>\n",
              "      <th>1008745</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CVHrUzIUN0mW5lQsl</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
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              "    <tr>\n",
              "      <th>1008746</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CkGlnL2pxzlusrFlLj</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>205.103.167.192</td>\n",
              "      <td>18241.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
              "      <td>-</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>1008747</th>\n",
              "      <td>1.526283e+09</td>\n",
              "      <td>CoOjVZ1zw3pWf7a9n5</td>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-</td>\n",
              "      <td>Benign</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2d9cdd01-66ff-4dbd-8948-6fd0a247a4ae');\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-c968b6dd-b5a6-4feb-ab1b-8b85312c7cdf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c968b6dd-b5a6-4feb-ab1b-8b85312c7cdf')\"\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-c968b6dd-b5a6-4feb-ab1b-8b85312c7cdf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Description"
      ],
      "metadata": {
        "id": "LtW7d4ARPAAT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking na values\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ynxpmEwePghH",
        "outputId": "b0836e66-433b-46ac-fbb7-a350f79f8ad0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ts                0\n",
              "uid               0\n",
              "id.orig_h         0\n",
              "id.orig_p         0\n",
              "id.resp_h         0\n",
              "id.resp_p         0\n",
              "proto             0\n",
              "service           0\n",
              "duration          0\n",
              "orig_bytes        0\n",
              "resp_bytes        0\n",
              "conn_state        0\n",
              "local_orig        0\n",
              "local_resp        0\n",
              "missed_bytes      0\n",
              "history           0\n",
              "orig_pkts         0\n",
              "orig_ip_bytes     0\n",
              "resp_pkts         0\n",
              "resp_ip_bytes     0\n",
              "tunnel_parents    0\n",
              "label             0\n",
              "detailed-label    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We don't have na values in our dataset but we have '-'values in our data which is null"
      ],
      "metadata": {
        "id": "_1eBBoy2P2QZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking all the unique values in the data\n",
        "col = df.columns\n",
        "for i in col:\n",
        "  print(f\"Total unique values sum for column {i} is:\")\n",
        "  print(df[i].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yxBEG0w2MnNT",
        "outputId": "fd293784-241c-4c81-8017-5f4bc7e56af1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total unique values sum for column ts is:\n",
            "1.525880e+09    1\n",
            "1.526138e+09    1\n",
            "1.526138e+09    1\n",
            "1.526138e+09    1\n",
            "1.526138e+09    1\n",
            "               ..\n",
            "1.526007e+09    1\n",
            "1.526007e+09    1\n",
            "1.526007e+09    1\n",
            "1.526007e+09    1\n",
            "1.526283e+09    1\n",
            "Name: ts, Length: 1008748, dtype: int64\n",
            "Total unique values sum for column uid is:\n",
            "CUmrqr4svHuSXJy5z7    1\n",
            "COj4Eq4lmR86amgfI6    1\n",
            "CBqL9l4KOG0Y3zauml    1\n",
            "CLIXVIOgCuf9Pv6j      1\n",
            "CImdWC3kB3Zhudx4q3    1\n",
            "                     ..\n",
            "CIEU4w1lSTwgbUFwHe    1\n",
            "CaXPwRJbmRa0E2N32     1\n",
            "CHMbUQ2DPEF61n2Chf    1\n",
            "CwWZ3m2uWO6qokLuu4    1\n",
            "CoOjVZ1zw3pWf7a9n5    1\n",
            "Name: uid, Length: 1008748, dtype: int64\n",
            "Total unique values sum for column id.orig_h is:\n",
            "192.168.100.103    991061\n",
            "192.168.100.1        1651\n",
            "4.68.110.10            43\n",
            "194.70.98.42           23\n",
            "218.248.235.161        13\n",
            "                    ...  \n",
            "203.233.35.193          1\n",
            "84.61.235.57            1\n",
            "209.180.197.181         1\n",
            "119.166.138.30          1\n",
            "255.176.39.22           1\n",
            "Name: id.orig_h, Length: 15004, dtype: int64\n",
            "Total unique values sum for column id.orig_p is:\n",
            "43763.0    399650\n",
            "3.0         14516\n",
            "123.0        8551\n",
            "11.0         2903\n",
            "23.0          158\n",
            "            ...  \n",
            "41861.0         1\n",
            "33950.0         1\n",
            "47075.0         1\n",
            "4.0             1\n",
            "448.0           1\n",
            "Name: id.orig_p, Length: 28243, dtype: int64\n",
            "Total unique values sum for column id.resp_h is:\n",
            "192.168.100.103    17687\n",
            "147.231.100.5       4313\n",
            "213.239.154.12      1428\n",
            "37.187.104.44       1408\n",
            "89.221.214.130      1402\n",
            "                   ...  \n",
            "48.19.58.31            1\n",
            "213.71.0.16            1\n",
            "241.61.159.121         1\n",
            "88.236.105.181         1\n",
            "23.70.168.160          1\n",
            "Name: id.resp_h, Length: 597107, dtype: int64\n",
            "Total unique values sum for column id.resp_p is:\n",
            "23.0       271561\n",
            "8080.0     135382\n",
            "2323.0      88184\n",
            "9527.0      44338\n",
            "123.0        8553\n",
            "            ...  \n",
            "10962.0         1\n",
            "43527.0         1\n",
            "14119.0         1\n",
            "45660.0         1\n",
            "688.0           1\n",
            "Name: id.resp_p, Length: 65426, dtype: int64\n",
            "Total unique values sum for column proto is:\n",
            "tcp     583134\n",
            "udp     408193\n",
            "icmp     17421\n",
            "Name: proto, dtype: int64\n",
            "Total unique values sum for column service is:\n",
            "-       1005507\n",
            "http       3238\n",
            "dhcp          1\n",
            "ssh           1\n",
            "dns           1\n",
            "Name: service, dtype: int64\n",
            "Total unique values sum for column duration is:\n",
            "-            796300\n",
            "2.998791       4494\n",
            "2.998792       4342\n",
            "2.998790       4167\n",
            "2.998793       3836\n",
            "              ...  \n",
            "11.675080         1\n",
            "11.743790         1\n",
            "11.780521         1\n",
            "11.847977         1\n",
            "6.339738          1\n",
            "Name: duration, Length: 16650, dtype: int64\n",
            "Total unique values sum for column orig_bytes is:\n",
            "-      796300\n",
            "0      195511\n",
            "48       6518\n",
            "18       3315\n",
            "180       834\n",
            "        ...  \n",
            "172         1\n",
            "155         1\n",
            "560         1\n",
            "540         1\n",
            "640         1\n",
            "Name: orig_bytes, Length: 171, dtype: int64\n",
            "Total unique values sum for column resp_bytes is:\n",
            "-      796300\n",
            "0      198819\n",
            "48       6487\n",
            "144       766\n",
            "27        458\n",
            "        ...  \n",
            "451         1\n",
            "554         1\n",
            "440         1\n",
            "552         1\n",
            "465         1\n",
            "Name: resp_bytes, Length: 479, dtype: int64\n",
            "Total unique values sum for column conn_state is:\n",
            "S0        971229\n",
            "OTH        17421\n",
            "SF         13881\n",
            "REJ         4346\n",
            "RSTR        1465\n",
            "RSTOS0       197\n",
            "RSTRH         66\n",
            "RSTO          47\n",
            "S2            40\n",
            "SH            29\n",
            "S1            27\n",
            "Name: conn_state, dtype: int64\n",
            "Total unique values sum for column local_orig is:\n",
            "-    1008748\n",
            "Name: local_orig, dtype: int64\n",
            "Total unique values sum for column local_resp is:\n",
            "-    1008748\n",
            "Name: local_resp, dtype: int64\n",
            "Total unique values sum for column missed_bytes is:\n",
            "0.0    1008748\n",
            "Name: missed_bytes, dtype: int64\n",
            "Total unique values sum for column history is:\n",
            "S           569605\n",
            "D           401606\n",
            "-            17421\n",
            "Dd            6569\n",
            "Sr            4346\n",
            "             ...  \n",
            "ShADdfR          1\n",
            "FfA              1\n",
            "ShAFafR          1\n",
            "HaDdAFTf         1\n",
            "^hADr            1\n",
            "Name: history, Length: 126, dtype: int64\n",
            "Total unique values sum for column orig_pkts is:\n",
            "1.0     806724\n",
            "3.0     192106\n",
            "5.0       2237\n",
            "2.0       1187\n",
            "4.0       1072\n",
            "8.0        571\n",
            "28.0       416\n",
            "29.0       320\n",
            "27.0       292\n",
            "24.0       271\n",
            "11.0       245\n",
            "12.0       244\n",
            "26.0       237\n",
            "23.0       236\n",
            "25.0       210\n",
            "15.0       202\n",
            "13.0       189\n",
            "9.0        186\n",
            "6.0        165\n",
            "7.0        162\n",
            "22.0       131\n",
            "18.0       131\n",
            "30.0       128\n",
            "19.0       118\n",
            "10.0       105\n",
            "45.0       101\n",
            "14.0        94\n",
            "21.0        92\n",
            "20.0        81\n",
            "31.0        68\n",
            "44.0        64\n",
            "16.0        56\n",
            "0.0         45\n",
            "17.0        32\n",
            "32.0        31\n",
            "34.0        25\n",
            "35.0        22\n",
            "33.0        21\n",
            "37.0        20\n",
            "43.0        20\n",
            "38.0        17\n",
            "36.0        16\n",
            "46.0        14\n",
            "39.0        11\n",
            "40.0         9\n",
            "41.0         7\n",
            "42.0         4\n",
            "47.0         4\n",
            "50.0         3\n",
            "48.0         2\n",
            "51.0         1\n",
            "49.0         1\n",
            "52.0         1\n",
            "60.0         1\n",
            "Name: orig_pkts, dtype: int64\n",
            "Total unique values sum for column orig_ip_bytes is:\n",
            "40.0      399801\n",
            "60.0      382628\n",
            "180.0     190632\n",
            "68.0        8678\n",
            "76.0        8462\n",
            "           ...  \n",
            "1209.0         1\n",
            "811.0          1\n",
            "1073.0         1\n",
            "394.0          1\n",
            "948.0          1\n",
            "Name: orig_ip_bytes, Length: 1249, dtype: int64\n",
            "Total unique values sum for column resp_pkts is:\n",
            "0.0     988854\n",
            "1.0      10885\n",
            "5.0       1468\n",
            "4.0       1410\n",
            "6.0        658\n",
            "         ...  \n",
            "49.0         1\n",
            "72.0         1\n",
            "51.0         1\n",
            "45.0         1\n",
            "46.0         1\n",
            "Name: resp_pkts, Length: 69, dtype: int64\n",
            "Total unique values sum for column resp_ip_bytes is:\n",
            "0.0       988854\n",
            "76.0        6487\n",
            "40.0        4208\n",
            "216.0        953\n",
            "412.0        603\n",
            "           ...  \n",
            "604.0          1\n",
            "2438.0         1\n",
            "2272.0         1\n",
            "4008.0         1\n",
            "345.0          1\n",
            "Name: resp_ip_bytes, Length: 1141, dtype: int64\n",
            "Total unique values sum for column tunnel_parents is:\n",
            "-    1008748\n",
            "Name: tunnel_parents, dtype: int64\n",
            "Total unique values sum for column label is:\n",
            "Malicious    539473\n",
            "Benign       469275\n",
            "Name: label, dtype: int64\n",
            "Total unique values sum for column detailed-label is:\n",
            "PartOfAHorizontalPortScan    539465\n",
            "-                            469275\n",
            "C&C                               8\n",
            "Name: detailed-label, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NZPXxZUjjs_",
        "outputId": "32ba8878-f368-46a3-9391-60d6b4b85ae9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1008748 entries, 0 to 1008747\n",
            "Data columns (total 23 columns):\n",
            " #   Column          Non-Null Count    Dtype  \n",
            "---  ------          --------------    -----  \n",
            " 0   ts              1008748 non-null  float64\n",
            " 1   uid             1008748 non-null  object \n",
            " 2   id.orig_h       1008748 non-null  object \n",
            " 3   id.orig_p       1008748 non-null  float64\n",
            " 4   id.resp_h       1008748 non-null  object \n",
            " 5   id.resp_p       1008748 non-null  float64\n",
            " 6   proto           1008748 non-null  object \n",
            " 7   service         1008748 non-null  object \n",
            " 8   duration        1008748 non-null  object \n",
            " 9   orig_bytes      1008748 non-null  object \n",
            " 10  resp_bytes      1008748 non-null  object \n",
            " 11  conn_state      1008748 non-null  object \n",
            " 12  local_orig      1008748 non-null  object \n",
            " 13  local_resp      1008748 non-null  object \n",
            " 14  missed_bytes    1008748 non-null  float64\n",
            " 15  history         1008748 non-null  object \n",
            " 16  orig_pkts       1008748 non-null  float64\n",
            " 17  orig_ip_bytes   1008748 non-null  float64\n",
            " 18  resp_pkts       1008748 non-null  float64\n",
            " 19  resp_ip_bytes   1008748 non-null  float64\n",
            " 20  tunnel_parents  1008748 non-null  object \n",
            " 21  label           1008748 non-null  object \n",
            " 22  detailed-label  1008748 non-null  object \n",
            "dtypes: float64(8), object(15)\n",
            "memory usage: 177.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Observation( Data Understanding)\n",
        "\n",
        "a)--> Columns ts(timestamps) and uid(unique id ) contains all the unique values which will not be usefull for our model.\n",
        "\n",
        "b)--> Most of the columns contains 0.0 in many of the instances the instances.\n",
        "\n",
        "c)-->All the columns which contains \"-\" in their column are null.\n",
        "\n",
        "d)-->Column 'detailed-label' is the part of our target variable we will not consider this variable.\n"
      ],
      "metadata": {
        "id": "zMEbnCVrPY1n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Missing Values Handling"
      ],
      "metadata": {
        "id": "R5WxPyox1cVz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#a)Dropping ts and uid\n",
        "df = df.drop(columns=['ts','uid'])"
      ],
      "metadata": {
        "id": "u7k0PoZKbfge"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#b) Further working for columns which contains 0.0\n",
        "missing_val_col = []\n",
        "\n",
        "# Iterate through each column in the DataFrame\n",
        "for column in df.columns:\n",
        "    # Check if the column contains 0.0\n",
        "    if 0.0 in df[column].unique():\n",
        "        missing_val_col.append(column)\n",
        "\n",
        "# Print the list of column names that contain 0.0\n",
        "print(\"Columns with 0.0 \")\n",
        "print(missing_val_col)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oubLLc-AjGOY",
        "outputId": "4900cb18-ea2a-455e-de5c-d3884a8fa68b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns with 0.0 \n",
            "['id.resp_p', 'missed_bytes', 'orig_pkts', 'orig_ip_bytes', 'resp_pkts', 'resp_ip_bytes']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize a dictionary to store the percentage of 0.0 values for each column\n",
        "percentage_dict = {}\n",
        "\n",
        "# Calculate the percentage of \"-\" values for each column\n",
        "for column in missing_val_col:\n",
        "    percentage = (df[column] == 0.0).mean() * 100\n",
        "    percentage_dict[column] = percentage\n",
        "\n",
        "# Print the percentage of \"-\" values for each column\n",
        "for column, percentage in percentage_dict.items():\n",
        "    print(f\"Column '{column}' has {percentage:.2f}% 0.0 values.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_mR_qHDjGHh",
        "outputId": "1b9a724f-525f-40c2-c4e9-a836b47a47ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column 'id.resp_p' has 0.33% 0.0 values.\n",
            "Column 'missed_bytes' has 100.00% 0.0 values.\n",
            "Column 'orig_pkts' has 0.00% 0.0 values.\n",
            "Column 'orig_ip_bytes' has 0.00% 0.0 values.\n",
            "Column 'resp_pkts' has 98.03% 0.0 values.\n",
            "Column 'resp_ip_bytes' has 98.03% 0.0 values.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping all the columns which higher amount of 0.0 values\n",
        "df = df.drop(columns=['missed_bytes','resp_pkts','resp_ip_bytes'])"
      ],
      "metadata": {
        "id": "IWpmHoCLmEe0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#c) Further working for columns which contains \"-\"\n",
        "missing_val_col_2 = []\n",
        "\n",
        "# Iterate through each column in the DataFrame\n",
        "for column in df.columns:\n",
        "    # Check if the column contains \"-\"\n",
        "    if '-' in df[column].unique():\n",
        "        missing_val_col_2.append(column)\n",
        "\n",
        "# Print the list of column names that contain \"-\"\n",
        "print(\"Columns with '-' character:\")\n",
        "print(missing_val_col_2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZ82zPS3QDB4",
        "outputId": "1cf6aa59-23cb-4bb3-8007-d48080d2c27d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-82-eaf727ef3804>:7: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
            "  if '-' in df[column].unique():\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns with '-' character:\n",
            "['service', 'duration', 'orig_bytes', 'resp_bytes', 'local_orig', 'local_resp', 'history', 'tunnel_parents', 'detailed-label']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize a dictionary to store the percentage of \"-\" values for each column\n",
        "percentage_dict_2 = {}\n",
        "\n",
        "# Calculate the percentage of \"-\" values for each column\n",
        "for column in missing_val_col_2:\n",
        "    percentage = (df[column] == '-').mean() * 100\n",
        "    percentage_dict_2[column] = percentage\n",
        "\n",
        "# Print the percentage of \"-\" values for each column\n",
        "for column, percentage in percentage_dict_2.items():\n",
        "    print(f\"Column '{column}' has {percentage:.2f}% '-' values.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EpJ8bCQCQ9yG",
        "outputId": "27ecad65-6ba0-4bb6-c9b2-5f069438d7f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column 'service' has 99.68% '-' values.\n",
            "Column 'duration' has 78.94% '-' values.\n",
            "Column 'orig_bytes' has 78.94% '-' values.\n",
            "Column 'resp_bytes' has 78.94% '-' values.\n",
            "Column 'local_orig' has 100.00% '-' values.\n",
            "Column 'local_resp' has 100.00% '-' values.\n",
            "Column 'history' has 1.73% '-' values.\n",
            "Column 'tunnel_parents' has 100.00% '-' values.\n",
            "Column 'detailed-label' has 46.52% '-' values.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropping all the columns contains \"-\" and 0.0 values except 'history'"
      ],
      "metadata": {
        "id": "Ei1WSsZKTbV9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping all the columns which have \"-\" values\n",
        "\n",
        "# Keep only the \"history\" column and drop all other columns in 'missing_val_col'\n",
        "columns_to_drop = [col for col in missing_val_col_2 if col != \"history\"]\n",
        "df = df.drop(columns=columns_to_drop)\n",
        "\n",
        "# Now 'df' will contain only the \"history\" column"
      ],
      "metadata": {
        "id": "nRhsAZHvTrBO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Imputing value"
      ],
      "metadata": {
        "id": "RuG2mGpGY_6i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the mode of the \"history\" column\n",
        "mode_value = df['history'].mode().iloc[0]\n",
        "\n",
        "# Replace \"-\" values with the mode\n",
        "df['history'].replace('-', mode_value, inplace=True)"
      ],
      "metadata": {
        "id": "6dj9hvvsY3Qp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "tHJVLSgpZWNn",
        "outputId": "b1535d80-22df-448f-bfca-57c2d4022f9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               id.orig_h  id.orig_p        id.resp_h  id.resp_p proto  \\\n",
              "0        192.168.100.103    51524.0   65.127.233.163       23.0   tcp   \n",
              "1        192.168.100.103    56305.0    63.150.16.171       23.0   tcp   \n",
              "2        192.168.100.103    41101.0     111.40.23.49       23.0   tcp   \n",
              "3        192.168.100.103    60905.0  131.174.215.147       23.0   tcp   \n",
              "4        192.168.100.103    44301.0      91.42.47.63       23.0   tcp   \n",
              "...                  ...        ...              ...        ...   ...   \n",
              "1008743  192.168.100.103    43763.0    16.219.83.137    64906.0   udp   \n",
              "1008744  192.168.100.103    43763.0   100.57.245.196    39435.0   udp   \n",
              "1008745  192.168.100.103    43763.0     249.99.119.9    26169.0   udp   \n",
              "1008746  192.168.100.103    43763.0  205.103.167.192    18241.0   udp   \n",
              "1008747  192.168.100.103    43763.0    23.70.168.160    16854.0   udp   \n",
              "\n",
              "        conn_state history  orig_pkts  orig_ip_bytes      label  \n",
              "0               S0       S        3.0          180.0  Malicious  \n",
              "1               S0       S        1.0           60.0  Malicious  \n",
              "2               S0       S        1.0           60.0  Malicious  \n",
              "3               S0       S        3.0          180.0  Malicious  \n",
              "4               S0       S        1.0           60.0  Malicious  \n",
              "...            ...     ...        ...            ...        ...  \n",
              "1008743         S0       D        1.0           40.0     Benign  \n",
              "1008744         S0       D        1.0           40.0     Benign  \n",
              "1008745         S0       D        1.0           40.0     Benign  \n",
              "1008746         S0       D        1.0           40.0     Benign  \n",
              "1008747         S0       D        1.0           40.0     Benign  \n",
              "\n",
              "[1008748 rows x 10 columns]"
            ],
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              "      <th></th>\n",
              "      <th>id.orig_h</th>\n",
              "      <th>id.orig_p</th>\n",
              "      <th>id.resp_h</th>\n",
              "      <th>id.resp_p</th>\n",
              "      <th>proto</th>\n",
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              "      <th>history</th>\n",
              "      <th>orig_pkts</th>\n",
              "      <th>orig_ip_bytes</th>\n",
              "      <th>label</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>51524.0</td>\n",
              "      <td>65.127.233.163</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>Malicious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>56305.0</td>\n",
              "      <td>63.150.16.171</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Malicious</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>41101.0</td>\n",
              "      <td>111.40.23.49</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Malicious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>60905.0</td>\n",
              "      <td>131.174.215.147</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>3.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>Malicious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>44301.0</td>\n",
              "      <td>91.42.47.63</td>\n",
              "      <td>23.0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>S0</td>\n",
              "      <td>S</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>Malicious</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008743</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>16.219.83.137</td>\n",
              "      <td>64906.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008744</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>100.57.245.196</td>\n",
              "      <td>39435.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008745</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>249.99.119.9</td>\n",
              "      <td>26169.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1008746</th>\n",
              "      <td>192.168.100.103</td>\n",
              "      <td>43763.0</td>\n",
              "      <td>205.103.167.192</td>\n",
              "      <td>18241.0</td>\n",
              "      <td>udp</td>\n",
              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
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              "    <tr>\n",
              "      <th>1008747</th>\n",
              "      <td>192.168.100.103</td>\n",
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              "      <td>S0</td>\n",
              "      <td>D</td>\n",
              "      <td>1.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>Benign</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>1008748 rows × 10 columns</p>\n",
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              "        <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-232513bb-256b-45b6-8398-73f8d92b542c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Attack Class Distribution\n",
        "df['label'].value_counts()"
      ],
      "metadata": {
        "id": "ZvUUr1LmcAS4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d97e7230-d2eb-4ea8-ea17-97c9d493d8db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Malicious    539473\n",
              "Benign       469275\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Encoding categorical class"
      ],
      "metadata": {
        "id": "O7e_CZ7AqGf9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frpR0boAq8yQ",
        "outputId": "9cb3ea12-2369-42b3-9b8b-e02a6cc1ead8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id.orig_h', 'id.orig_p', 'id.resp_h', 'id.resp_p', 'proto',\n",
              "       'conn_state', 'history', 'orig_pkts', 'orig_ip_bytes', 'label'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoder = LabelEncoder()\n",
        "\n",
        "# extract categorical attributes\n",
        "cat_df = df.select_dtypes(include=['object']).copy()\n",
        "\n",
        "# encode the categorical attributes\n",
        "for column in cat_df.columns:\n",
        "    df[column] = encoder.fit_transform(df[column])"
      ],
      "metadata": {
        "id": "ykp2B-oxprWE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "a0BGlRqfqqIh",
        "outputId": "b929d7bf-b1fc-4fef-bbb4-9d0d0b98ecdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id.orig_h  id.orig_p  id.resp_h  id.resp_p  proto  conn_state  \\\n",
              "0             6478    51524.0     501746       23.0      1           6   \n",
              "1             6478    56305.0     496872       23.0      1           6   \n",
              "2             6478    41101.0      36215       23.0      1           6   \n",
              "3             6478    60905.0      88035       23.0      1           6   \n",
              "4             6478    44301.0     576062       23.0      1           6   \n",
              "...            ...        ...        ...        ...    ...         ...   \n",
              "1008743       6478    43763.0     165796    64906.0      2           6   \n",
              "1008744       6478    43763.0       7220    39435.0      2           6   \n",
              "1008745       6478    43763.0     389190    26169.0      2           6   \n",
              "1008746       6478    43763.0     293502    18241.0      2           6   \n",
              "1008747       6478    43763.0     354657    16854.0      2           6   \n",
              "\n",
              "         history  orig_pkts  orig_ip_bytes  label  \n",
              "0             21        3.0          180.0      1  \n",
              "1             21        1.0           60.0      1  \n",
              "2             21        1.0           60.0      1  \n",
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              "4             21        1.0           60.0      1  \n",
              "...          ...        ...            ...    ...  \n",
              "1008743        1        1.0           40.0      0  \n",
              "1008744        1        1.0           40.0      0  \n",
              "1008745        1        1.0           40.0      0  \n",
              "1008746        1        1.0           40.0      0  \n",
              "1008747        1        1.0           40.0      0  \n",
              "\n",
              "[1008748 rows x 10 columns]"
            ],
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              "      <th>1008743</th>\n",
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              "      <td>6478</td>\n",
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              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>1008746</th>\n",
              "      <td>6478</td>\n",
              "      <td>43763.0</td>\n",
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              "      <td>18241.0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
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              "  </tbody>\n",
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              "<p>1008748 rows × 10 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-d5ea620e-f151-4327-80b3-e47e123d0dd2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\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",
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              "\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-d5ea620e-f151-4327-80b3-e47e123d0dd2');\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",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "\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-c336100a-781c-4671-b2a6-7d7e5ef84300 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Feature Selection"
      ],
      "metadata": {
        "id": "DYFM3FwWs3qg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To find important features for further machine learning modeling, we can use feature selection techniques or tree-based algorithms that provide feature importance scores."
      ],
      "metadata": {
        "id": "W20mvHeHs5__"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Feature importance"
      ],
      "metadata": {
        "id": "oo0K2WOX0rBp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Separate the features and the target variable\n",
        "X = df.drop('label', axis=1)\n",
        "y = df['label']\n",
        "\n",
        "# Create a Random Forest classifier\n",
        "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Fit the model to your data\n",
        "rf_classifier.fit(X, y)\n",
        "\n",
        "# Get feature importances\n",
        "feature_importances = rf_classifier.feature_importances_\n",
        "\n",
        "# Create a DataFrame to display feature names and their importance scores\n",
        "feature_importance_df = pd.DataFrame({'Feature': X.columns,\n",
        "                                      'Importance': feature_importances})\n",
        "\n",
        "# Sort the features by importance in descending order\n",
        "feature_importance_df = feature_importance_df.sort_values(by='Importance',\n",
        "                                                          ascending=False)\n",
        "\n",
        "# Create a bar graph\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'])\n",
        "plt.xlabel('Importance')\n",
        "plt.title('Feature Importance')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "hcLTP541qfbN",
        "outputId": "512bba83-8cea-4b9f-f832-7a47045b6ff1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}