main_classification_kitsune.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itLpTNA8J6Sk"
      },
      "source": [
        "# **Loading the Libraries and Data**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C0RBwGEvjI1T"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.impute import SimpleImputer\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "import warnings\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0xg5bJi-a7-O"
      },
      "source": [
        "#**Global Functions and Values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IuKMqYbLbDH-"
      },
      "outputs": [],
      "source": [
        "# Function to return metrics\n",
        "from sklearn.metrics import accuracy_score, roc_auc_score, precision_score, recall_score, f1_score, cohen_kappa_score, matthews_corrcoef\n",
        "\n",
        "def get_model_metrics(model_name, y_true, y_pred):\n",
        "    metrics = {}\n",
        "    metrics['Model'] = model_name\n",
        "    metrics['Accuracy'] = accuracy_score(y_true, y_pred)\n",
        "    metrics['ROC-AUC Score'] = roc_auc_score(y_true, y_pred)\n",
        "    metrics['Precision'] = precision_score(y_true, y_pred)\n",
        "    metrics['Recall'] = recall_score(y_true, y_pred)\n",
        "    metrics['F1-Score'] = f1_score(y_true, y_pred)\n",
        "    metrics['Kappa'] = cohen_kappa_score(y_true, y_pred)\n",
        "    metrics['MCC'] = matthews_corrcoef(y_true, y_pred)\n",
        "    \n",
        "    return metrics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qltjawoYdWWM"
      },
      "outputs": [],
      "source": [
        "#list to metrics of all tables\n",
        "metrics_table = list()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIET7DzzKHpO"
      },
      "source": [
        "# **Load Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZK_D3TCPrGh"
      },
      "source": [
        "## Load dataset from Github Repository"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kVPOqoStjdXq"
      },
      "outputs": [],
      "source": [
        "df1 = pd.read_csv('https://raw.githubusercontent.com/divaamahajan/DataMining/main/random-sampled-datasets/ARP_MitM_sampled.csv')\n",
        "df2 = pd.read_csv('https://raw.githubusercontent.com/divaamahajan/DataMining/main/random-sampled-datasets/Active_Wiretap_sampled.csv')\n",
        "df3 = pd.read_csv('https://raw.githubusercontent.com/divaamahajan/DataMining/main/random-sampled-datasets/Fuzzing_sampled.csv')\n",
        "df4 = pd.read_csv(\"https://raw.githubusercontent.com/divaamahajan/DataMining/main/random-sampled-datasets/Mirai_sampled.csv\")\n",
        "df5 = pd.read_csv('https://raw.githubusercontent.com/divaamahajan/DataMining/main/random-sampled-datasets/OS_Scan_sampled.csv')\n",
        "df6 = pd.read_csv('https://raw.githubusercontent.com/divaamahajan/DataMining/main/random-sampled-datasets/SSDP_Flood_sampled.csv')\n",
        "df7 = pd.read_csv('https://raw.githubusercontent.com/divaamahajan/DataMining/main/random-sampled-datasets/SSL_Renegotiation_sampled.csv')\n",
        "df8 = pd.read_csv('https://raw.githubusercontent.com/divaamahajan/DataMining/main/random-sampled-datasets/SYN_DoS_sampled.csv')\n",
        "df9 = pd.read_csv('https://raw.githubusercontent.com/divaamahajan/DataMining/main/random-sampled-datasets/Video_Injection_sampled.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNB7yg5HM6sP"
      },
      "source": [
        "## Displaying the contents of the file\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bTp4zsRNMuxN"
      },
      "source": [
        "**Primary object types:**\n",
        "- **DataFrame:** rows and columns (like a spreadsheet)\n",
        "- **Series:** a single column\n",
        "\n",
        "\n",
        "**Get dataframe**: \n",
        "Read CSV file from the 'data' subdirectory using a relative path.\n",
        "*  `head() `will print the first 5 rows of the dataframe\n",
        "*  `tail()` will print the last 5 rows) of the dataframe\n",
        "\n",
        "\n"
      ]
    },
    {
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# displaying the contents of the XLSX file\n",
        "df1 = df1.drop(['Unnamed: 0',\t'index'], axis=1)\n",
        "df1.head(2)"
      ]
    },
    {
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              "      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-a3be8e50-7cb5-44f9-95c9-cc79344595a7 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-a3be8e50-7cb5-44f9-95c9-cc79344595a7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df4 = df4.drop(['0'], axis=1)\n",
        "df4 = df4.drop(['Unnamed: 0',\t'index'], axis=1)\n",
        "df4.head(2)\n",
        "# list(df4)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "yvdeF0DaNXZU",
        "outputId": "54b925da-3ff7-408c-f752-3e28bc60f89c"
      },
      "outputs": [
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              "          const element = document.querySelector('#df-8c195cd5-6773-4079-b6f4-d31675170e83');\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",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "df5 = df5.drop(['Unnamed: 0',\t'index'], axis=1)\n",
        "df5.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "ee1WA-kcNXZV",
        "outputId": "e63fdc49-3dfb-41b8-d2a9-3d03baf660d6"
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              "                                                     [key], {});\n",
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              "\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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df6 = df6.drop(['Unnamed: 0',\t'index'], axis=1)\n",
        "df6.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 237
        },
        "id": "2KeLd5WHNXZV",
        "outputId": "a4f68874-62ad-4ed7-f601-3c98c9e9a86d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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          "metadata": {},
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      ],
      "source": [
        "# displaying the contents of the XLSX file\n",
        "df8 = df8.drop(['Unnamed: 0',\t'index'], axis=1)\n",
        "df8.head(2)"
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    {
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              "0  0.0  0.0  1.999992   861.502738  652.500000   861.502738  425756.249993   \n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df9 = df9.drop(['Unnamed: 0',\t'index'], axis=1)\n",
        "df9.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrkdmqDPNtPQ"
      },
      "source": [
        "**Observation:**\n",
        "* We have 9 datasets with 116 columns each, out of which columns each."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9zxE-X8XRAwT"
      },
      "source": [
        "## Merge the datsets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KA3MMFuDyDrJ"
      },
      "outputs": [],
      "source": [
        "df1.columns = ['feature_'+str(i) for i in range(0,116)]\n",
        "df2.columns = ['feature_'+str(i) for i in range(0,116)]\n",
        "df3.columns = ['feature_'+str(i) for i in range(0,116)]\n",
        "df4.columns = ['feature_'+str(i) for i in range(0,116)]\n",
        "df5.columns = ['feature_'+str(i) for i in range(0,116)]\n",
        "df6.columns = ['feature_'+str(i) for i in range(0,116)]\n",
        "df7.columns = ['feature_'+str(i) for i in range(0,116)]\n",
        "df8.columns = ['feature_'+str(i) for i in range(0,116)]\n",
        "df9.columns = ['feature_'+str(i) for i in range(0,116)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m2MX6XJLjgJC"
      },
      "outputs": [],
      "source": [
        "df1['attack']=1\n",
        "df2['attack']=2\n",
        "df3['attack']=3\n",
        "df4['attack']=4\n",
        "df5['attack']=5\n",
        "df6['attack']=6\n",
        "df7['attack']=7\n",
        "df8['attack']=8\n",
        "df9['attack']=9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ous7LRtPzYQo"
      },
      "outputs": [],
      "source": [
        "df = pd.concat([df1, df2, df3, df4, df5, df6, df7, df8, df9])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "ebUmfe1hziGL",
        "outputId": "457b8e66-e026-4279-ebc7-8b41fd059e5b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "   feature_0   feature_1    feature_2      feature_3   feature_4    feature_5  \\\n",
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              "\n",
              "       feature_6    feature_7    feature_8      feature_9  ...  feature_107  \\\n",
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              "4  151292.562593   855.352870  1290.355020  147041.015366  ...          0.0   \n",
              "\n",
              "   feature_108   feature_109  feature_110  feature_111  feature_112  \\\n",
              "0          0.0  28128.538982  1335.626000   425.845352  1335.626000   \n",
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              "2          0.0  27751.882884  1341.513646   424.904429  1341.513646   \n",
              "3          0.0  27948.812569  1341.115622   425.224505  1341.115622   \n",
              "4          0.0  28174.860496  1334.685469   426.811681  1334.685469   \n",
              "\n",
              "     feature_113  feature_114  feature_115  attack  \n",
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              "3  180815.879583          0.0          0.0       1  \n",
              "4  182168.211433          0.0          0.0       1  \n",
              "\n",
              "[5 rows x 117 columns]"
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              "<p>5 rows × 117 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae44a474-db34-4215-839a-d51fef0604b7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\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",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-ae44a474-db34-4215-839a-d51fef0604b7 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-ae44a474-db34-4215-839a-d51fef0604b7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GeJ0uC05MqAB"
      },
      "source": [
        "# **Data Preprocessing**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j5EtIoCfQByL"
      },
      "source": [
        "**Importance of Data Preprocessing stage**\n",
        "1. Different ML models have different required input data (numerical data, images in specific format, etc). Without the right data, nothing will work.\n",
        "2. Because of “bad” data, ML models will not give any useful results, or even may give wrong answers, that may lead to wrong decisions (GIGO principle).\n",
        "3. The higher the quality of the data, the less data is needed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AB8ibbJlTRrM"
      },
      "source": [
        "## Reset Column Names"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "btVGexx3TFck"
      },
      "outputs": [],
      "source": [
        "df.columns = ['feature_'+str(i) for i in range(len(df.columns))]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3vwZnVu7Tedx"
      },
      "outputs": [],
      "source": [
        "df.columns.values[0]='label'\n",
        "df.columns.values[-1]='attack'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HA306HFuVl-R"
      },
      "outputs": [],
      "source": [
        "cols = list(df)[1:]\n",
        "cols.append('label')\n",
        "# Reorder the columns\n",
        "df = df[cols]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "_4bGvWt6Upij",
        "outputId": "6aca00cb-f616-4696-ad4a-0b04546a48d5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    feature_1    feature_2      feature_3   feature_4    feature_5  \\\n",
              "0  162.251328  1287.930495  137946.585241  277.376266  1290.621872   \n",
              "1  387.752010  1306.058869  220121.097478  637.303410  1316.728781   \n",
              "2  379.466053  1347.077216  172679.023477  633.858286  1345.691594   \n",
              "3  428.413637  1335.090314  193351.408058  681.055680  1333.676220   \n",
              "4  151.173948  1279.523342  157603.223345  269.501602  1286.223631   \n",
              "\n",
              "       feature_6    feature_7    feature_8      feature_9    feature_10  ...  \\\n",
              "0  139699.421710   862.105671  1294.355651  140928.760080   8708.282524  ...   \n",
              "1  207438.876258  1924.246172  1331.715608  189917.368904  19614.490170  ...   \n",
              "2  175484.443945  1942.359870  1342.632301  179854.874354  19641.527990  ...   \n",
              "3  190797.906136  1971.296012  1337.078715  183403.845860  18836.905420  ...   \n",
              "4  151292.562593   855.352870  1290.355020  147041.015366   8714.034018  ...   \n",
              "\n",
              "   feature_108   feature_109  feature_110  feature_111  feature_112  \\\n",
              "0          0.0  28128.538982  1335.626000   425.845352  1335.626000   \n",
              "1          0.0  28051.498932  1340.999983   425.651549  1340.999983   \n",
              "2          0.0  27751.882884  1341.513646   424.904429  1341.513646   \n",
              "3          0.0  27948.812569  1341.115622   425.224505  1341.115622   \n",
              "4          0.0  28174.860496  1334.685469   426.811681  1334.685469   \n",
              "\n",
              "     feature_113  feature_114  feature_115  attack  label  \n",
              "0  181344.263583          0.0          0.0       1      1  \n",
              "1  181179.241248          0.0          0.0       1      1  \n",
              "2  180543.774059          0.0          0.0       1      1  \n",
              "3  180815.879583          0.0          0.0       1      1  \n",
              "4  182168.211433          0.0          0.0       1      1  \n",
              "\n",
              "[5 rows x 117 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-29222f6f-ff60-4414-97c1-63835b0d361a\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature_1</th>\n",
              "      <th>feature_2</th>\n",
              "      <th>feature_3</th>\n",
              "      <th>feature_4</th>\n",
              "      <th>feature_5</th>\n",
              "      <th>feature_6</th>\n",
              "      <th>feature_7</th>\n",
              "      <th>feature_8</th>\n",
              "      <th>feature_9</th>\n",
              "      <th>feature_10</th>\n",
              "      <th>...</th>\n",
              "      <th>feature_108</th>\n",
              "      <th>feature_109</th>\n",
              "      <th>feature_110</th>\n",
              "      <th>feature_111</th>\n",
              "      <th>feature_112</th>\n",
              "      <th>feature_113</th>\n",
              "      <th>feature_114</th>\n",
              "      <th>feature_115</th>\n",
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              "      <th>0</th>\n",
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              "      <td>1287.930495</td>\n",
              "      <td>137946.585241</td>\n",
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              "      <td>1290.621872</td>\n",
              "      <td>139699.421710</td>\n",
              "      <td>862.105671</td>\n",
              "      <td>1294.355651</td>\n",
              "      <td>140928.760080</td>\n",
              "      <td>8708.282524</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>28128.538982</td>\n",
              "      <td>1335.626000</td>\n",
              "      <td>425.845352</td>\n",
              "      <td>1335.626000</td>\n",
              "      <td>181344.263583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>387.752010</td>\n",
              "      <td>1306.058869</td>\n",
              "      <td>220121.097478</td>\n",
              "      <td>637.303410</td>\n",
              "      <td>1316.728781</td>\n",
              "      <td>207438.876258</td>\n",
              "      <td>1924.246172</td>\n",
              "      <td>1331.715608</td>\n",
              "      <td>189917.368904</td>\n",
              "      <td>19614.490170</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>28051.498932</td>\n",
              "      <td>1340.999983</td>\n",
              "      <td>425.651549</td>\n",
              "      <td>1340.999983</td>\n",
              "      <td>181179.241248</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>379.466053</td>\n",
              "      <td>1347.077216</td>\n",
              "      <td>172679.023477</td>\n",
              "      <td>633.858286</td>\n",
              "      <td>1345.691594</td>\n",
              "      <td>175484.443945</td>\n",
              "      <td>1942.359870</td>\n",
              "      <td>1342.632301</td>\n",
              "      <td>179854.874354</td>\n",
              "      <td>19641.527990</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27751.882884</td>\n",
              "      <td>1341.513646</td>\n",
              "      <td>424.904429</td>\n",
              "      <td>1341.513646</td>\n",
              "      <td>180543.774059</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>428.413637</td>\n",
              "      <td>1335.090314</td>\n",
              "      <td>193351.408058</td>\n",
              "      <td>681.055680</td>\n",
              "      <td>1333.676220</td>\n",
              "      <td>190797.906136</td>\n",
              "      <td>1971.296012</td>\n",
              "      <td>1337.078715</td>\n",
              "      <td>183403.845860</td>\n",
              "      <td>18836.905420</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27948.812569</td>\n",
              "      <td>1341.115622</td>\n",
              "      <td>425.224505</td>\n",
              "      <td>1341.115622</td>\n",
              "      <td>180815.879583</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>151.173948</td>\n",
              "      <td>1279.523342</td>\n",
              "      <td>157603.223345</td>\n",
              "      <td>269.501602</td>\n",
              "      <td>1286.223631</td>\n",
              "      <td>151292.562593</td>\n",
              "      <td>855.352870</td>\n",
              "      <td>1290.355020</td>\n",
              "      <td>147041.015366</td>\n",
              "      <td>8714.034018</td>\n",
              "      <td>...</td>\n",
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              "      <td>28174.860496</td>\n",
              "      <td>1334.685469</td>\n",
              "      <td>426.811681</td>\n",
              "      <td>1334.685469</td>\n",
              "      <td>182168.211433</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 117 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-29222f6f-ff60-4414-97c1-63835b0d361a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      </button>\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-29222f6f-ff60-4414-97c1-63835b0d361a 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-29222f6f-ff60-4414-97c1-63835b0d361a');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "tB54LvefpzhT",
        "outputId": "0bce29e5-a65b-4958-e74c-de8904a085c4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          feature_1     feature_2      feature_3     feature_4     feature_5  \\\n",
              "count  17994.000000  17994.000000   17994.000000  17994.000000  17994.000000   \n",
              "mean     590.001582    857.763041  179333.709871    971.216290    858.429179   \n",
              "std     1672.259132    525.861139  157456.903472   2786.477502    526.194303   \n",
              "min        1.000000     52.000000       0.000000      1.000000     52.000000   \n",
              "25%       56.061229    136.000000     225.624024     86.288658    136.000000   \n",
              "50%      139.743726   1231.631585  170520.674718    216.059493   1234.975971   \n",
              "75%      243.685256   1328.156284  222725.513399    381.475034   1331.337628   \n",
              "max     7436.865335   1452.000000  488785.862233  12392.617904   1452.000000   \n",
              "\n",
              "           feature_6     feature_7     feature_8      feature_9  \\\n",
              "count   17994.000000  17994.000000  17994.000000   17994.000000   \n",
              "mean   178754.480328   2849.822370    859.490195  177962.861751   \n",
              "std    157135.199378   8352.314584    526.723421  156845.310038   \n",
              "min         0.000000      1.000000     52.000000       0.000000   \n",
              "25%       304.027158    200.777767    136.000000     415.872886   \n",
              "50%    173093.596850    574.416212   1236.700796  177195.319543   \n",
              "75%    213747.656584   1089.506496   1335.374246  204456.933708   \n",
              "max    488812.666765  37163.978483   1452.000000  488832.368521   \n",
              "\n",
              "          feature_10  ...   feature_108   feature_109   feature_110  \\\n",
              "count   17994.000000  ...  17994.000000  17994.000000  17994.000000   \n",
              "mean    26083.685756  ...     -0.000052  15135.163135    860.338704   \n",
              "std     79552.427675  ...      0.004580  12685.595423    608.959023   \n",
              "min         1.000000  ...     -0.276309      1.000000     52.000000   \n",
              "25%      1356.221785  ...      0.000000     72.628172    136.000000   \n",
              "50%      5216.721656  ...      0.000000  17303.812382   1334.520728   \n",
              "75%     10379.729109  ...      0.000000  27960.225050   1341.307654   \n",
              "max    354099.847565  ...      0.347487  41243.896714   1452.000000   \n",
              "\n",
              "        feature_111   feature_112   feature_113   feature_114   feature_115  \\\n",
              "count  17994.000000  17994.000000  1.799400e+04  17994.000000  17994.000000   \n",
              "mean     220.391662    911.752972  8.622016e+04     -0.642570     -0.000201   \n",
              "std      189.612557    590.260119  8.274561e+04     24.555470      0.006196   \n",
              "min        0.000000     52.000000  0.000000e+00  -2011.316691     -0.314397   \n",
              "25%        0.000007    150.507101  9.313226e-10      0.000000      0.000000   \n",
              "50%      221.765288   1335.198361  5.275406e+04      0.000000      0.000000   \n",
              "75%      425.192396   1341.489508  1.807886e+05      0.000000      0.000000   \n",
              "max      699.170575   1452.000000  4.888395e+05      3.472361      0.405684   \n",
              "\n",
              "             attack         label  \n",
              "count  17998.000000  17998.000000  \n",
              "mean       4.999889      0.350317  \n",
              "std        2.582183      0.477082  \n",
              "min        1.000000      0.000000  \n",
              "25%        3.000000      0.000000  \n",
              "50%        5.000000      0.000000  \n",
              "75%        7.000000      1.000000  \n",
              "max        9.000000      1.000000  \n",
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              "      <td>17994.000000</td>\n",
              "      <td>17998.000000</td>\n",
              "      <td>17998.000000</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>179333.709871</td>\n",
              "      <td>971.216290</td>\n",
              "      <td>858.429179</td>\n",
              "      <td>178754.480328</td>\n",
              "      <td>2849.822370</td>\n",
              "      <td>859.490195</td>\n",
              "      <td>177962.861751</td>\n",
              "      <td>26083.685756</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000052</td>\n",
              "      <td>15135.163135</td>\n",
              "      <td>860.338704</td>\n",
              "      <td>220.391662</td>\n",
              "      <td>911.752972</td>\n",
              "      <td>8.622016e+04</td>\n",
              "      <td>-0.642570</td>\n",
              "      <td>-0.000201</td>\n",
              "      <td>4.999889</td>\n",
              "      <td>0.350317</td>\n",
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              "      <td>525.861139</td>\n",
              "      <td>157456.903472</td>\n",
              "      <td>2786.477502</td>\n",
              "      <td>526.194303</td>\n",
              "      <td>157135.199378</td>\n",
              "      <td>8352.314584</td>\n",
              "      <td>526.723421</td>\n",
              "      <td>156845.310038</td>\n",
              "      <td>79552.427675</td>\n",
              "      <td>...</td>\n",
              "      <td>0.004580</td>\n",
              "      <td>12685.595423</td>\n",
              "      <td>608.959023</td>\n",
              "      <td>189.612557</td>\n",
              "      <td>590.260119</td>\n",
              "      <td>8.274561e+04</td>\n",
              "      <td>24.555470</td>\n",
              "      <td>0.006196</td>\n",
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              "      <td>0.477082</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.276309</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>56.061229</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>225.624024</td>\n",
              "      <td>86.288658</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>304.027158</td>\n",
              "      <td>200.777767</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>415.872886</td>\n",
              "      <td>1356.221785</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>72.628172</td>\n",
              "      <td>136.000000</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>150.507101</td>\n",
              "      <td>9.313226e-10</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>139.743726</td>\n",
              "      <td>1231.631585</td>\n",
              "      <td>170520.674718</td>\n",
              "      <td>216.059493</td>\n",
              "      <td>1234.975971</td>\n",
              "      <td>173093.596850</td>\n",
              "      <td>574.416212</td>\n",
              "      <td>1236.700796</td>\n",
              "      <td>177195.319543</td>\n",
              "      <td>5216.721656</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>17303.812382</td>\n",
              "      <td>1334.520728</td>\n",
              "      <td>221.765288</td>\n",
              "      <td>1335.198361</td>\n",
              "      <td>5.275406e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>75%</th>\n",
              "      <td>243.685256</td>\n",
              "      <td>1328.156284</td>\n",
              "      <td>222725.513399</td>\n",
              "      <td>381.475034</td>\n",
              "      <td>1331.337628</td>\n",
              "      <td>213747.656584</td>\n",
              "      <td>1089.506496</td>\n",
              "      <td>1335.374246</td>\n",
              "      <td>204456.933708</td>\n",
              "      <td>10379.729109</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>27960.225050</td>\n",
              "      <td>1341.307654</td>\n",
              "      <td>425.192396</td>\n",
              "      <td>1341.489508</td>\n",
              "      <td>1.807886e+05</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
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              "      <td>488785.862233</td>\n",
              "      <td>12392.617904</td>\n",
              "      <td>1452.000000</td>\n",
              "      <td>488812.666765</td>\n",
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              "      <td>1452.000000</td>\n",
              "      <td>488832.368521</td>\n",
              "      <td>354099.847565</td>\n",
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              "      <td>41243.896714</td>\n",
              "      <td>1452.000000</td>\n",
              "      <td>699.170575</td>\n",
              "      <td>1452.000000</td>\n",
              "      <td>4.888395e+05</td>\n",
              "      <td>3.472361</td>\n",
              "      <td>0.405684</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "<p>8 rows × 117 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kpEKFeBxU7up"
      },
      "source": [
        "## **Handling missing data**\n",
        "\n",
        "Using `isna().sum()` function gives the sum of the null values preset in the dataset column-wise.\n",
        "\n",
        "`fillna()` function will fill NA/NaN or 0 values in place of null spaces.\n",
        "> `df[\"colName\"].fillna(\"replacingValue\", inplace = True)`"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "FrcpNM1PqoYX",
        "outputId": "5cbd0f86-b044-4454-e944-bb80e842bbfc"
      },
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              "      feature_1  feature_2  feature_3  feature_4  feature_5  feature_6  \\\n",
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              "      feature_7  feature_8  feature_9  feature_10  ...  feature_108  \\\n",
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              "999         NaN        NaN        NaN         NaN  ...          NaN   \n",
              "1999        NaN        NaN        NaN         NaN  ...          NaN   \n",
              "\n",
              "      feature_109  feature_110  feature_111  feature_112  feature_113  \\\n",
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              "\n",
              "      feature_114  feature_115  attack  label  \n",
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              "\n",
              "[4 rows x 117 columns]"
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 117 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02997f54-ccde-4509-8f5e-fb1ed899ccc3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-02997f54-ccde-4509-8f5e-fb1ed899ccc3 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-02997f54-ccde-4509-8f5e-fb1ed899ccc3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "#check null values\n",
        "df_null = df[df.isna().any(axis=1)]\n",
        "df_null"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PHS4P-TmVFuD"
      },
      "source": [
        "**Observation** : remove these 4 rows with null Values Found"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.dropna()\n",
        "df_null = df[df.isna().any(axis=1)]\n",
        "df_null"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "FssCwePxQhNS",
        "outputId": "6b8185c5-5a39-4801-a452-de27fe9b0cc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [feature_1, feature_2, feature_3, feature_4, feature_5, feature_6, feature_7, feature_8, feature_9, feature_10, feature_11, feature_12, feature_13, feature_14, feature_15, feature_16, feature_17, feature_18, feature_19, feature_20, feature_21, feature_22, feature_23, feature_24, feature_25, feature_26, feature_27, feature_28, feature_29, feature_30, feature_31, feature_32, feature_33, feature_34, feature_35, feature_36, feature_37, feature_38, feature_39, feature_40, feature_41, feature_42, feature_43, feature_44, feature_45, feature_46, feature_47, feature_48, feature_49, feature_50, feature_51, feature_52, feature_53, feature_54, feature_55, feature_56, feature_57, feature_58, feature_59, feature_60, feature_61, feature_62, feature_63, feature_64, feature_65, feature_66, feature_67, feature_68, feature_69, feature_70, feature_71, feature_72, feature_73, feature_74, feature_75, feature_76, feature_77, feature_78, feature_79, feature_80, feature_81, feature_82, feature_83, feature_84, feature_85, feature_86, feature_87, feature_88, feature_89, feature_90, feature_91, feature_92, feature_93, feature_94, feature_95, feature_96, feature_97, feature_98, feature_99, feature_100, ...]\n",
              "Index: []\n",
              "\n",
              "[0 rows x 117 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-287912c3-b1ef-434b-859f-395e5142bed3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature_1</th>\n",
              "      <th>feature_2</th>\n",
              "      <th>feature_3</th>\n",
              "      <th>feature_4</th>\n",
              "      <th>feature_5</th>\n",
              "      <th>feature_6</th>\n",
              "      <th>feature_7</th>\n",
              "      <th>feature_8</th>\n",
              "      <th>feature_9</th>\n",
              "      <th>feature_10</th>\n",
              "      <th>...</th>\n",
              "      <th>feature_108</th>\n",
              "      <th>feature_109</th>\n",
              "      <th>feature_110</th>\n",
              "      <th>feature_111</th>\n",
              "      <th>feature_112</th>\n",
              "      <th>feature_113</th>\n",
              "      <th>feature_114</th>\n",
              "      <th>feature_115</th>\n",
              "      <th>attack</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>0 rows × 117 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-287912c3-b1ef-434b-859f-395e5142bed3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-287912c3-b1ef-434b-859f-395e5142bed3 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-287912c3-b1ef-434b-859f-395e5142bed3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwC65UHVVUXC",
        "outputId": "572c171e-c7f1-4dc0-a034-634bea676a0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17994, 117)"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation**: We are left with 17994 rows × 117 columns of data"
      ],
      "metadata": {
        "id": "sz88xzCJQsix"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "FNL9L6m2blc5",
        "outputId": "3d87d21c-6c46-49de-87a5-8b479af5abcf"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/javascript": [
              "download(\"download_472ca7ac-c721-48ce-a9d9-6b50bf2b4dc7\", \"output.xlsx\", 20951453)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from google.colab import files\n",
        "# Save the DataFrame to an Excel file\n",
        "df.to_excel('output.xlsx', index=False)\n",
        "# Download the Excel file to our local system\n",
        "files.download('output.xlsx')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHM5zAo5WplP"
      },
      "source": [
        "## **Data Transformation**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwVJ9uMNWx9d"
      },
      "source": [
        "### **Standardization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "As3ZQkfDW1oM"
      },
      "source": [
        "**min_max scaler** is a way to get data in the range 0 to 1\n",
        "\n",
        "**StandardScaler** if we need -1 to 1 range. Scale each variable to have a mean of 0 and a standard deviation of 1\n",
        "\n",
        "We use scaling so that each variable has equal importance when fitting the k-means algorithm. Otherwise, the variables with the widest ranges would have too much influence."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "0aHiW4U8WxQq",
        "outputId": "f6d9d5b1-03d8-458d-e99b-5a738e6e2832"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   feature_1  feature_2  feature_3  feature_4  feature_5  feature_6  \\\n",
              "0   0.021686   0.882807   0.282223   0.022303   0.884730   0.285793   \n",
              "1   0.052012   0.895756   0.450343   0.051350   0.903378   0.424373   \n",
              "2   0.050897   0.925055   0.353282   0.051071   0.924065   0.359001   \n",
              "3   0.057480   0.916493   0.395575   0.054880   0.915483   0.390329   \n",
              "4   0.020196   0.876802   0.322438   0.021668   0.881588   0.309510   \n",
              "\n",
              "   feature_7  feature_8  feature_9  feature_10  ...  feature_108  feature_109  \\\n",
              "0   0.023171   0.887397   0.288297    0.024590  ...     0.442948     0.681997   \n",
              "1   0.051752   0.914083   0.388512    0.055390  ...     0.442948     0.680129   \n",
              "2   0.052239   0.921880   0.367928    0.055466  ...     0.442948     0.672865   \n",
              "3   0.053018   0.917913   0.375188    0.053194  ...     0.442948     0.677639   \n",
              "4   0.022989   0.884539   0.300800    0.024606  ...     0.442948     0.683120   \n",
              "\n",
              "   feature_110  feature_111  feature_112  feature_113  feature_114  \\\n",
              "0     0.916876     0.609072     0.916876     0.370969     0.998277   \n",
              "1     0.920714     0.608795     0.920714     0.370631     0.998277   \n",
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              "3     0.920797     0.608184     0.920797     0.369888     0.998277   \n",
              "4     0.916204     0.610454     0.916204     0.372654     0.998277   \n",
              "\n",
              "   feature_115  attack  label  \n",
              "0     0.436614     0.0    1.0  \n",
              "1     0.436614     0.0    1.0  \n",
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              "3     0.436614     0.0    1.0  \n",
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              "      <td>0.023171</td>\n",
              "      <td>0.887397</td>\n",
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              "      <td>0.903378</td>\n",
              "      <td>0.424373</td>\n",
              "      <td>0.051752</td>\n",
              "      <td>0.914083</td>\n",
              "      <td>0.388512</td>\n",
              "      <td>0.055390</td>\n",
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              "      <td>0.442948</td>\n",
              "      <td>0.680129</td>\n",
              "      <td>0.920714</td>\n",
              "      <td>0.608795</td>\n",
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              "      <td>0.353282</td>\n",
              "      <td>0.051071</td>\n",
              "      <td>0.924065</td>\n",
              "      <td>0.359001</td>\n",
              "      <td>0.052239</td>\n",
              "      <td>0.921880</td>\n",
              "      <td>0.367928</td>\n",
              "      <td>0.055466</td>\n",
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              "      <td>0.442948</td>\n",
              "      <td>0.672865</td>\n",
              "      <td>0.921081</td>\n",
              "      <td>0.607726</td>\n",
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              "      <td>0.395575</td>\n",
              "      <td>0.054880</td>\n",
              "      <td>0.915483</td>\n",
              "      <td>0.390329</td>\n",
              "      <td>0.053018</td>\n",
              "      <td>0.917913</td>\n",
              "      <td>0.375188</td>\n",
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              "      <td>0.442948</td>\n",
              "      <td>0.677639</td>\n",
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              "      <td>0.322438</td>\n",
              "      <td>0.021668</td>\n",
              "      <td>0.881588</td>\n",
              "      <td>0.309510</td>\n",
              "      <td>0.022989</td>\n",
              "      <td>0.884539</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "#Importing required modules\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "#create scaled DataFrame where each variable has mean of 0 and standard dev of 1\n",
        "df_scaled = MinMaxScaler().fit_transform(df)\n",
        "\n",
        "#view first five rows of scaled DataFrame\n",
        "df_scaled = pd.DataFrame(df_scaled, columns = list(df))\n",
        "df_scaled.head()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85e0zIX4XC_J"
      },
      "source": [
        "## **Handling (drop) outliers**\n",
        "    # Note: dropping is only one of techniques to handle with outliers\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pteZ24TbXFRw"
      },
      "source": [
        "### Z-Score\n",
        "* A **z-score** measures how many standard deviations a data point is from the mean in a distribution.\n",
        "* A normal distribution is shown below and it is estimated that\n",
        ">* outliers_threshold = 1: 68% of the data points lie between +/- 1 standard deviation.\n",
        ">* outliers_threshold = 2: 95% of the data points lie between +/- 2 standard deviation\n",
        ">* outliers_threshold = 3: 99.7% of the data points lie between +/- 3 standard deviation\n",
        ">* outliers_threshold = 4: 99.9% of the data points lie between +/- 4 standard deviation\n",
        "* We are removing all the rows above this outlier threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "J0O7MKa3WozF",
        "outputId": "c192e389-b4ee-41c4-9c0b-ae734e72dc7e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   feature_1  feature_2  feature_3  feature_4  feature_5  feature_6  \\\n",
              "0   0.255799   0.818048   0.262855   0.249009   0.821379   0.248551   \n",
              "1   0.120947   0.852522   0.259046   0.119837   0.870994   0.182551   \n",
              "2   0.125902   0.930527   0.042265   0.121073   0.926038   0.020811   \n",
              "3   0.096631   0.907731   0.089028   0.104135   0.903203   0.076646   \n",
              "4   0.262423   0.802060   0.138013   0.251836   0.813020   0.174771   \n",
              "\n",
              "   feature_7  feature_8  feature_9  feature_10  ...  feature_108  feature_109  \\\n",
              "0   0.237991   0.825628   0.236125    0.218421  ...     0.011281     1.024291   \n",
              "1   0.110820   0.896559   0.076221    0.081322  ...     0.011281     1.018217   \n",
              "2   0.108651   0.917285   0.012063    0.080982  ...     0.011281     0.994598   \n",
              "3   0.105187   0.906741   0.034691    0.091097  ...     0.011281     1.010123   \n",
              "4   0.238799   0.818032   0.197154    0.218348  ...     0.011281     1.027942   \n",
              "\n",
              "   feature_110  feature_111  feature_112  feature_113  feature_114  \\\n",
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              "3     0.789528     1.080300     0.727433     1.143243     0.026169   \n",
              "4     0.778969     1.088671     0.716539     1.159587     0.026169   \n",
              "\n",
              "   feature_115    attack     label  \n",
              "0     0.032363  1.548927  1.361922  \n",
              "1     0.032363  1.548927  1.361922  \n",
              "2     0.032363  1.548927  1.361922  \n",
              "3     0.032363  1.548927  1.361922  \n",
              "4     0.032363  1.548927  1.361922  \n",
              "\n",
              "[5 rows x 117 columns]"
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              "      <td>1.024291</td>\n",
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              "      <th>1</th>\n",
              "      <td>0.120947</td>\n",
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              "      <td>0.259046</td>\n",
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              "      <td>0.182551</td>\n",
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              "      <td>0.076221</td>\n",
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              "      <td>1.018217</td>\n",
              "      <td>0.789338</td>\n",
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              "      <td>0.727237</td>\n",
              "      <td>1.147635</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.125902</td>\n",
              "      <td>0.930527</td>\n",
              "      <td>0.042265</td>\n",
              "      <td>0.121073</td>\n",
              "      <td>0.926038</td>\n",
              "      <td>0.020811</td>\n",
              "      <td>0.108651</td>\n",
              "      <td>0.917285</td>\n",
              "      <td>0.012063</td>\n",
              "      <td>0.080982</td>\n",
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              "      <td>0.011281</td>\n",
              "      <td>0.994598</td>\n",
              "      <td>0.790182</td>\n",
              "      <td>1.078612</td>\n",
              "      <td>0.728107</td>\n",
              "      <td>1.139955</td>\n",
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              "      <td>1.361922</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.096631</td>\n",
              "      <td>0.907731</td>\n",
              "      <td>0.089028</td>\n",
              "      <td>0.104135</td>\n",
              "      <td>0.903203</td>\n",
              "      <td>0.076646</td>\n",
              "      <td>0.105187</td>\n",
              "      <td>0.906741</td>\n",
              "      <td>0.034691</td>\n",
              "      <td>0.091097</td>\n",
              "      <td>...</td>\n",
              "      <td>0.011281</td>\n",
              "      <td>1.010123</td>\n",
              "      <td>0.789528</td>\n",
              "      <td>1.080300</td>\n",
              "      <td>0.727433</td>\n",
              "      <td>1.143243</td>\n",
              "      <td>0.026169</td>\n",
              "      <td>0.032363</td>\n",
              "      <td>1.548927</td>\n",
              "      <td>1.361922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.262423</td>\n",
              "      <td>0.802060</td>\n",
              "      <td>0.138013</td>\n",
              "      <td>0.251836</td>\n",
              "      <td>0.813020</td>\n",
              "      <td>0.174771</td>\n",
              "      <td>0.238799</td>\n",
              "      <td>0.818032</td>\n",
              "      <td>0.197154</td>\n",
              "      <td>0.218348</td>\n",
              "      <td>...</td>\n",
              "      <td>0.011281</td>\n",
              "      <td>1.027942</td>\n",
              "      <td>0.778969</td>\n",
              "      <td>1.088671</td>\n",
              "      <td>0.716539</td>\n",
              "      <td>1.159587</td>\n",
              "      <td>0.026169</td>\n",
              "      <td>0.032363</td>\n",
              "      <td>1.548927</td>\n",
              "      <td>1.361922</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 117 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6062699a-1ede-4418-aef2-36f6dcbf37a6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6062699a-1ede-4418-aef2-36f6dcbf37a6 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-6062699a-1ede-4418-aef2-36f6dcbf37a6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ],
      "source": [
        "from scipy import stats\n",
        "import numpy as np\n",
        "z_scores = np.abs(stats.zscore(df_scaled, nan_policy='omit'))\n",
        "z_scores.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENlrqN7_Xndf",
        "outputId": "d78cdda3-17c8-4d33-c0cd-f8125ef3d7f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " No. of outlier rows = 2506 from total 17994 rows\n"
          ]
        }
      ],
      "source": [
        "outliers_threshold = 3\n",
        "mask = (z_scores <= outliers_threshold).all(axis=1)\n",
        "print(f\" No. of outlier rows = {len(mask[mask == False])} from total {df_scaled.shape[0]} rows\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Af8jhsAqXrez"
      },
      "source": [
        "**Observation:** :  No. of outlier rows = 2506 from total 17994 rows\n",
        "\n",
        "Let us remove these outliers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "WXsrcI2-Xzor",
        "outputId": "5ff44cda-1951-4fa9-fdeb-c819a4b7572c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   feature_1  feature_2  feature_3  feature_4  feature_5  feature_6  \\\n",
              "0   0.021686   0.882807   0.282223   0.022303   0.884730   0.285793   \n",
              "1   0.052012   0.895756   0.450343   0.051350   0.903378   0.424373   \n",
              "3   0.057480   0.916493   0.395575   0.054880   0.915483   0.390329   \n",
              "4   0.020196   0.876802   0.322438   0.021668   0.881588   0.309510   \n",
              "5   0.049525   0.927026   0.348894   0.050394   0.927694   0.346782   \n",
              "\n",
              "   feature_7  feature_8  feature_9  feature_10  ...  feature_108  feature_109  \\\n",
              "0   0.023171   0.887397   0.288297    0.024590  ...     0.442948     0.681997   \n",
              "1   0.051752   0.914083   0.388512    0.055390  ...     0.442948     0.680129   \n",
              "3   0.053018   0.917913   0.375188    0.053194  ...     0.442948     0.677639   \n",
              "4   0.022989   0.884539   0.300800    0.024606  ...     0.442948     0.683120   \n",
              "5   0.051844   0.926265   0.352461    0.055374  ...     0.442948     0.679610   \n",
              "\n",
              "   feature_110  feature_111  feature_112  feature_113  feature_114  \\\n",
              "0     0.916876     0.609072     0.916876     0.370969     0.998277   \n",
              "1     0.920714     0.608795     0.920714     0.370631     0.998277   \n",
              "3     0.920797     0.608184     0.920797     0.369888     0.998277   \n",
              "4     0.916204     0.610454     0.916204     0.372654     0.998277   \n",
              "5     0.920827     0.608554     0.920827     0.370338     0.998277   \n",
              "\n",
              "   feature_115  attack  label  \n",
              "0     0.436614     0.0    1.0  \n",
              "1     0.436614     0.0    1.0  \n",
              "3     0.436614     0.0    1.0  \n",
              "4     0.436614     0.0    1.0  \n",
              "5     0.436614     0.0    1.0  \n",
              "\n",
              "[5 rows x 117 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-526315a4-e451-4a86-a7ac-bcd47bde6535\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature_1</th>\n",
              "      <th>feature_2</th>\n",
              "      <th>feature_3</th>\n",
              "      <th>feature_4</th>\n",
              "      <th>feature_5</th>\n",
              "      <th>feature_6</th>\n",
              "      <th>feature_7</th>\n",
              "      <th>feature_8</th>\n",
              "      <th>feature_9</th>\n",
              "      <th>feature_10</th>\n",
              "      <th>...</th>\n",
              "      <th>feature_108</th>\n",
              "      <th>feature_109</th>\n",
              "      <th>feature_110</th>\n",
              "      <th>feature_111</th>\n",
              "      <th>feature_112</th>\n",
              "      <th>feature_113</th>\n",
              "      <th>feature_114</th>\n",
              "      <th>feature_115</th>\n",
              "      <th>attack</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.021686</td>\n",
              "      <td>0.882807</td>\n",
              "      <td>0.282223</td>\n",
              "      <td>0.022303</td>\n",
              "      <td>0.884730</td>\n",
              "      <td>0.285793</td>\n",
              "      <td>0.023171</td>\n",
              "      <td>0.887397</td>\n",
              "      <td>0.288297</td>\n",
              "      <td>0.024590</td>\n",
              "      <td>...</td>\n",
              "      <td>0.442948</td>\n",
              "      <td>0.681997</td>\n",
              "      <td>0.916876</td>\n",
              "      <td>0.609072</td>\n",
              "      <td>0.916876</td>\n",
              "      <td>0.370969</td>\n",
              "      <td>0.998277</td>\n",
              "      <td>0.436614</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.052012</td>\n",
              "      <td>0.895756</td>\n",
              "      <td>0.450343</td>\n",
              "      <td>0.051350</td>\n",
              "      <td>0.903378</td>\n",
              "      <td>0.424373</td>\n",
              "      <td>0.051752</td>\n",
              "      <td>0.914083</td>\n",
              "      <td>0.388512</td>\n",
              "      <td>0.055390</td>\n",
              "      <td>...</td>\n",
              "      <td>0.442948</td>\n",
              "      <td>0.680129</td>\n",
              "      <td>0.920714</td>\n",
              "      <td>0.608795</td>\n",
              "      <td>0.920714</td>\n",
              "      <td>0.370631</td>\n",
              "      <td>0.998277</td>\n",
              "      <td>0.436614</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.057480</td>\n",
              "      <td>0.916493</td>\n",
              "      <td>0.395575</td>\n",
              "      <td>0.054880</td>\n",
              "      <td>0.915483</td>\n",
              "      <td>0.390329</td>\n",
              "      <td>0.053018</td>\n",
              "      <td>0.917913</td>\n",
              "      <td>0.375188</td>\n",
              "      <td>0.053194</td>\n",
              "      <td>...</td>\n",
              "      <td>0.442948</td>\n",
              "      <td>0.677639</td>\n",
              "      <td>0.920797</td>\n",
              "      <td>0.608184</td>\n",
              "      <td>0.920797</td>\n",
              "      <td>0.369888</td>\n",
              "      <td>0.998277</td>\n",
              "      <td>0.436614</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.020196</td>\n",
              "      <td>0.876802</td>\n",
              "      <td>0.322438</td>\n",
              "      <td>0.021668</td>\n",
              "      <td>0.881588</td>\n",
              "      <td>0.309510</td>\n",
              "      <td>0.022989</td>\n",
              "      <td>0.884539</td>\n",
              "      <td>0.300800</td>\n",
              "      <td>0.024606</td>\n",
              "      <td>...</td>\n",
              "      <td>0.442948</td>\n",
              "      <td>0.683120</td>\n",
              "      <td>0.916204</td>\n",
              "      <td>0.610454</td>\n",
              "      <td>0.916204</td>\n",
              "      <td>0.372654</td>\n",
              "      <td>0.998277</td>\n",
              "      <td>0.436614</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.049525</td>\n",
              "      <td>0.927026</td>\n",
              "      <td>0.348894</td>\n",
              "      <td>0.050394</td>\n",
              "      <td>0.927694</td>\n",
              "      <td>0.346782</td>\n",
              "      <td>0.051844</td>\n",
              "      <td>0.926265</td>\n",
              "      <td>0.352461</td>\n",
              "      <td>0.055374</td>\n",
              "      <td>...</td>\n",
              "      <td>0.442948</td>\n",
              "      <td>0.679610</td>\n",
              "      <td>0.920827</td>\n",
              "      <td>0.608554</td>\n",
              "      <td>0.920827</td>\n",
              "      <td>0.370338</td>\n",
              "      <td>0.998277</td>\n",
              "      <td>0.436614</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 117 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-526315a4-e451-4a86-a7ac-bcd47bde6535')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-526315a4-e451-4a86-a7ac-bcd47bde6535 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-526315a4-e451-4a86-a7ac-bcd47bde6535');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "df_zscored = df_scaled[mask]\n",
        "df_zscored.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XbgiPbhSX4oN",
        "outputId": "16d55893-4c8a-4314-d115-639d37f5e5ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15488, 117)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "df_zscored.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgIwm-eQX5xI"
      },
      "source": [
        "We now have 15488 rows of clean data\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TDxlte6zawOh"
      },
      "source": [
        "# **Model Building**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hY7BymeuiwAb"
      },
      "outputs": [],
      "source": [
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X = df_scaled.drop(['attack', 'label'], axis=1) # features\n",
        "y = df_scaled['label'] # target\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jb6DojScarco"
      },
      "source": [
        "## **Feature Selection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PwrE2QK4lvGe"
      },
      "source": [
        "With 115 feature columns, we mustreduce the number of features to improve the model's performance and reduce overfitting. Feature selection techniques such as correlation analysis, mutual information, or feature importance metrics from a tree-based model can help identify the most important features for prediction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l6T2oKOYoBXo"
      },
      "source": [
        "###Correltion Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FFJDVujoBXo"
      },
      "source": [
        "The Result of the corr() method is a table with a lot of numbers that represents how well the relationship is between two columns.\n",
        "\n",
        "The number varies from -1 to 1.\n",
        "\n",
        "* 1 means that there is a 1 to 1 relationship (a perfect correlation), and for this data set, each time a value went up in the first column, the other one went up as well.\n",
        "\n",
        "* 0.9 is also a good relationship, and if we increase one value, the other will probably increase as well.\n",
        "\n",
        "* -0.9 would be just as good relationship as 0.9, but if we increase one value, the other will probably go down.\n",
        "\n",
        "* 0.2 means NOT a good relationship, meaning that if one value goes up does not mean that the other will."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(list(df_scaled))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9KaN2rbMRNhE",
        "outputId": "60a7f421-c939-4504-88b7-1a2ce7f83e8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "117"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "zjkNyOR6oBXo",
        "outputId": "48219322-5b27-45e3-991a-5bd899c72e5e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# correlation matrix\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(10, 8))\n",
        "corr = ((df_scaled.corr().iloc[:,-1]).sort_values(ascending=False)).to_frame()\n",
        "sns.heatmap(corr, cmap=\"YlGnBu\")\n",
        "plt.show()\n",
        "# sns.heatmap(corr, annot=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umoZMNtnoBXp"
      },
      "source": [
        "**Observation**\n",
        "Below is the list of columns with **low** correlation with the label. ie. (-0.3 < corr < 0.3) . These are not good candidates for predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "g0MxPEf3oBXp",
        "outputId": "b4f97f4e-e4b5-4fef-e6f0-5a0fb7ff3035"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                label\n",
              "feature_57   0.159539\n",
              "feature_109  0.103435\n",
              "feature_50   0.085780\n",
              "feature_43   0.046523\n",
              "feature_102  0.032101\n",
              "...               ...\n",
              "feature_98  -0.214617\n",
              "feature_105 -0.215071\n",
              "feature_112 -0.215395\n",
              "feature_55  -0.228069\n",
              "feature_62  -0.256166\n",
              "\n",
              "[73 rows x 1 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>feature_57</th>\n",
              "      <td>0.159539</td>\n",
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              "    <tr>\n",
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              "      <td>0.103435</td>\n",
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              "      <td>0.085780</td>\n",
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              "      <td>0.046523</td>\n",
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              "      <td>0.032101</td>\n",
              "    </tr>\n",
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              "      <td>...</td>\n",
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              "          const element = document.querySelector('#df-5521fbce-35d3-46ad-9c05-0b39e1eceb0e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "corr[(corr[\"label\"] > -0.3) & (corr[\"label\"] < 0.3)]\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9bTBctzdoBXq"
      },
      "source": [
        "**Observation**\n",
        "Below is the list of columns with **high** correlation with the label. ie. (corr <= -0.3) or (corr >= 0.3) . These are good Candidates for predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UC5vCdkioBXq",
        "outputId": "2d30e40e-3a68-4ff6-d617-e57ec953849a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               label\n",
              "label       1.000000\n",
              "feature_28  0.381291\n",
              "feature_13  0.377549\n",
              "feature_16  0.376207\n",
              "feature_1   0.376136\n",
              "feature_19  0.374953\n",
              "feature_4   0.374840\n",
              "feature_78  0.373190\n",
              "feature_59  0.373190\n",
              "feature_31  0.372689\n",
              "feature_66  0.372689\n",
              "feature_38  0.371886\n",
              "feature_69  0.371886\n",
              "feature_22  0.371786\n",
              "feature_7   0.371496\n",
              "feature_45  0.369014\n",
              "feature_72  0.369014\n",
              "feature_25  0.357226\n",
              "feature_10  0.356164\n",
              "feature_75  0.354424\n",
              "feature_52  0.354424\n",
              "feature_64 -0.323164\n",
              "feature_65 -0.330280\n",
              "attack     -0.350960\n",
              "feature_61 -0.370843\n",
              "feature_54 -0.401619\n",
              "feature_33 -0.403889\n",
              "feature_30 -0.404305\n",
              "feature_40 -0.404824\n",
              "feature_47 -0.404862\n",
              "feature_15 -0.412430\n",
              "feature_27 -0.413055\n",
              "feature_12 -0.413283\n",
              "feature_9  -0.415228\n",
              "feature_24 -0.415280\n",
              "feature_63 -0.415680\n",
              "feature_56 -0.416125\n",
              "feature_3  -0.416228\n",
              "feature_18 -0.416237\n",
              "feature_6  -0.416448\n",
              "feature_21 -0.416473\n",
              "feature_49 -0.427271\n",
              "feature_35 -0.428844\n",
              "feature_42 -0.428953"
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              "      <td>1.000000</td>\n",
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              "      <th>feature_72</th>\n",
              "      <td>0.369014</td>\n",
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              "      <th>feature_25</th>\n",
              "      <td>0.357226</td>\n",
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              "      <th>feature_10</th>\n",
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              "      <td>-0.323164</td>\n",
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              "      <th>feature_65</th>\n",
              "      <td>-0.330280</td>\n",
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              "      <th>attack</th>\n",
              "      <td>-0.350960</td>\n",
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              "    <tr>\n",
              "      <th>feature_61</th>\n",
              "      <td>-0.370843</td>\n",
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              "      <th>feature_54</th>\n",
              "      <td>-0.401619</td>\n",
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              "      <th>feature_33</th>\n",
              "      <td>-0.403889</td>\n",
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              "      <th>feature_30</th>\n",
              "      <td>-0.404305</td>\n",
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              "      <th>feature_40</th>\n",
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              "      <th>feature_12</th>\n",
              "      <td>-0.413283</td>\n",
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              "      <th>feature_9</th>\n",
              "      <td>-0.415228</td>\n",
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              "      <th>feature_24</th>\n",
              "      <td>-0.415280</td>\n",
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              "      <th>feature_63</th>\n",
              "      <td>-0.415680</td>\n",
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              "      <th>feature_56</th>\n",
              "      <td>-0.416125</td>\n",
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              "      <th>feature_3</th>\n",
              "      <td>-0.416228</td>\n",
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              "      <th>feature_49</th>\n",
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              "      <th>feature_35</th>\n",
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              "      <th>feature_42</th>\n",
              "      <td>-0.428953</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "          const element = document.querySelector('#df-0eb47ec2-48a5-48e3-9473-ed024ebf1aa2');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "corr[(corr[\"label\"] <= -0.3) | (corr[\"label\"] >= 0.3)]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation** : This can be useful when we have prior knowledge that certain features are likely to be important for the target variable, which we do not know in our dataset.\n",
        "However, it is important to note that correlation does not necessarily imply causation, and the selected features may not necessarily be the most predictive ones."
      ],
      "metadata": {
        "id": "8P9w2fClT4Ct"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bGcumpWMi2Ve"
      },
      "source": [
        "### SelectKBest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hyVJiTYiyl7"
      },
      "source": [
        "It's better to call SelectKBest() after splitting the data into training and testing sets. This is because feature selection should be based only on the training set and then applied to both the training and testing sets. If we apply feature selection on the entire dataset before splitting, we risk introducing bias into our model evaluation, as the testing set would have \"seen\" some of the selected features during the training process.\n",
        "\n",
        "In addition, by selecting features based only on the training set, we ensure that our model is generalizable to new, unseen data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pqnGxm6aa1F4"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_selection import SelectKBest, f_classif\n",
        "\n",
        "# Feature selection and engineering\n",
        "selector = SelectKBest(f_classif, k=50)\n",
        "X_train = selector.fit_transform(X_train, y_train)\n",
        "X_test = selector.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation**: This method is more objective and can potentially identify more relevant features than using correlation. However, it is important to choose the right value of k (number of features to select) and also to consider the possibility of overfitting.\n",
        "\n",
        "Since in our correlation Analysis we were getting about 44 columns which were closely correlated to the label, we decided to choose a closer number, 50, as the value of k in select K best"
      ],
      "metadata": {
        "id": "iPcNDCS9UKk-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GridSearchCV"
      ],
      "metadata": {
        "id": "19fJTo6-Krx7"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SvbMIjw-xgdu"
      },
      "source": [
        "GridSearchCV trains and evaluates the model with all possible combinations of hyperparameters from the param_grid dictionary using cross-validation (cv=5), and returns the best set of hyperparameters that result in the highest score on the training data.\n",
        "\n",
        "Using the best set of hyperparameters returned by GridSearchCV can potentially improve the performance of the model on unseen data. It helps to tune the model and prevent overfitting.\n",
        "* `grid_search.best_score_` returns the mean cross-validated score achieved by the best estimator found by GridSearchCV during the parameter tuning process.\n",
        "This means that it returns the average performance of the model over all cross-validation folds when trained with the best hyperparameters found by the grid search. It is a useful metric to compare different models and hyperparameter settings to see which ones perform better\n",
        "* `grid_search.best_params_` returns a dictionary of the best combination of hyperparameters found by the GridSearchCV object during cross-validation. This dictionary contains the values of hyperparameters that achieved the best mean cross-validated score on the training set. These hyperparameters can then be used to instantiate and train a new model on the entire training set.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PXih6PC5m8ET"
      },
      "source": [
        "## **Logistic Regression**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxcBUJUInLeD"
      },
      "source": [
        " This is a common classification algorithm used for binary classification problems. It is a linear model that predicts the probability of an instance belonging to the positive class. Logistic regression works well when there are no complex interactions between features."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "A_8KOq7tnLGl",
        "outputId": "795584a0-c9bf-455b-c5e5-6fdb74dddab1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 80 candidates, totalling 400 fits\n",
            "Best parameters: {'C': 0.1, 'max_iter': 500, 'penalty': 'none', 'solver': 'newton-cg'}\n",
            "Best score: 0.862104897533866\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Value\n",
              "Model          LogisticRegression\n",
              "Accuracy                 0.859128\n",
              "ROC-AUC Score            0.819545\n",
              "Precision                0.874873\n",
              "Recall                   0.691318\n",
              "F1-Score                 0.772339\n",
              "Kappa                    0.672364\n",
              "MCC                      0.682137"
            ],
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              "\n",
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              "      <td>0.859128</td>\n",
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              "      <th>ROC-AUC Score</th>\n",
              "      <td>0.819545</td>\n",
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              "      <td>0.874873</td>\n",
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              "      <td>0.772339</td>\n",
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              "      <th>Kappa</th>\n",
              "      <td>0.672364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MCC</th>\n",
              "      <td>0.682137</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba41df20-fa9b-4f7e-97c1-7346901285c9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ba41df20-fa9b-4f7e-97c1-7346901285c9 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-ba41df20-fa9b-4f7e-97c1-7346901285c9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "# Train and evaluate logistic regression\n",
        "metrics_table = list()\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Define the parameter grid to search over\n",
        "param_grid = {\n",
        "    'penalty': ['l1', 'l2', 'elasticnet', 'none'],\n",
        "    'C': [0.1, 1.0],\n",
        "    'solver': ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'],\n",
        "    'max_iter': [100, 500],  \n",
        "}\n",
        "\n",
        "\n",
        "# Create an instance of the model\n",
        "logreg = LogisticRegression()\n",
        "\n",
        "# Create an instance of GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=logreg, param_grid=param_grid, cv=5, verbose=1  )\n",
        "\n",
        "# Fit the GridSearchCV object to the training data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best parameters and the best score\n",
        "print(\"Best parameters:\", grid_search.best_params_)\n",
        "print(\"Best score:\", grid_search.best_score_)\n",
        "\n",
        "best_logreg =LogisticRegression(**grid_search.best_params_)\n",
        "\n",
        "#Train the model on the training data\n",
        "best_logreg.fit(X_train, y_train)\n",
        "\n",
        "#Calculate predictions on Model\n",
        "y_pred_logreg = best_logreg.predict(X_test)\n",
        "\n",
        "#get the Performance metrics by method created above\n",
        "metric_logreg = get_model_metrics(model_name=\"LogisticRegression\", y_true=y_test, y_pred = y_pred_logreg)\n",
        "\n",
        "#add metric to global list for conclusive evaluation\n",
        "metrics_table.append(metric_logreg)\n",
        "\n",
        "#display metric dictionary in df\n",
        "pd.DataFrame.from_dict(metric_logreg, orient='index', columns=['Value'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tv5g_DV9rAE1"
      },
      "source": [
        "##Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Decision trees are a non-parametric model that can handle both continuous and categorical data. They are easy to interpret and visualize, which makes them useful for understanding how the model makes decisions.\n",
        "\n"
      ],
      "metadata": {
        "id": "d7BE8poQK6Py"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "UWBaI14mrSvd",
        "outputId": "b2b07da0-e6f8-4382-bb5f-f0abca6def26"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters: {'criterion': 'gini', 'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 2}\n",
            "Best score: 0.9647099687391455\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                Value\n",
              "Model          DecisionTreeClassifier\n",
              "Accuracy                     0.964435\n",
              "ROC-AUC Score                0.962774\n",
              "Precision                    0.940758\n",
              "Recall                       0.957395\n",
              "F1-Score                     0.949004\n",
              "Kappa                        0.921703\n",
              "MCC                          0.921787"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f601873-8e17-4f11-bb7b-75af5b3d6ffb\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <td>DecisionTreeClassifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.964435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ROC-AUC Score</th>\n",
              "      <td>0.962774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.940758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.957395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1-Score</th>\n",
              "      <td>0.949004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kappa</th>\n",
              "      <td>0.921703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MCC</th>\n",
              "      <td>0.921787</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f601873-8e17-4f11-bb7b-75af5b3d6ffb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5f601873-8e17-4f11-bb7b-75af5b3d6ffb 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-5f601873-8e17-4f11-bb7b-75af5b3d6ffb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "# Define the parameter grid to search over\n",
        "param_grid = {\n",
        "    'criterion': ['gini', 'entropy'],\n",
        "    'max_depth': [5, 10],\n",
        "    'min_samples_split': [2, 5],\n",
        "    'min_samples_leaf': [1, 2],\n",
        "}\n",
        "\n",
        "# Create an instance of the model\n",
        "dt = DecisionTreeClassifier()\n",
        "\n",
        "# Create an instance of GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=dt, param_grid=param_grid, cv=5)\n",
        "\n",
        "# Fit the GridSearchCV object to the training data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best parameters and the best score\n",
        "print(\"Best parameters:\", grid_search.best_params_)\n",
        "print(\"Best score:\", grid_search.best_score_)\n",
        "\n",
        "# Create an instance of the model with the best parameters\n",
        "best_dt = DecisionTreeClassifier(**grid_search.best_params_)\n",
        "\n",
        "# Train the model on the training data\n",
        "best_dt.fit(X_train, y_train)\n",
        "\n",
        "# Calculate predictions on Model\n",
        "y_pred_dt = best_dt.predict(X_test)\n",
        "\n",
        "# Get the Performance metrics by method created above\n",
        "metric_dt = get_model_metrics(model_name=\"DecisionTreeClassifier\", y_true=y_test, y_pred = y_pred_dt)\n",
        "\n",
        "# Add metric to global list for conclusive evaluation\n",
        "metrics_table.append(metric_dt)\n",
        "\n",
        "# Display metric dictionary in df\n",
        "pd.DataFrame.from_dict(metric_dt, orient='index', columns=['Value'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5rJPetSYrHET"
      },
      "source": [
        "##Random Forest Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Random Forest is an ensemble model that uses multiple decision trees to make predictions. It is a robust and flexible algorithm that works well with high-dimensional datasets."
      ],
      "metadata": {
        "id": "08l5znHTK8p5"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "PmAjv-DurXGR",
        "outputId": "7c6c81ab-6614-4c99-fa23-11ee0eff8967"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters: {'max_depth': 10, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
            "Best score: 0.969989579715179\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                Value\n",
              "Model          RandomForestClassifier\n",
              "Accuracy                     0.971659\n",
              "ROC-AUC Score                0.969622\n",
              "Precision                    0.955343\n",
              "Recall                       0.963023\n",
              "F1-Score                     0.959167\n",
              "Kappa                        0.937466\n",
              "MCC                          0.937483"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-126aaf0f-5497-42f1-954a-88715d452164\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <td>RandomForestClassifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.971659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ROC-AUC Score</th>\n",
              "      <td>0.969622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.955343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.963023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1-Score</th>\n",
              "      <td>0.959167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kappa</th>\n",
              "      <td>0.937466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MCC</th>\n",
              "      <td>0.937483</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  <style>\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-126aaf0f-5497-42f1-954a-88715d452164 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-126aaf0f-5497-42f1-954a-88715d452164');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Define the parameter grid to search over\n",
        "param_grid = {\n",
        "    'n_estimators': [50, 100],\n",
        "    'max_depth': [5, 10],\n",
        "    'min_samples_split': [2, 5],\n",
        "    'min_samples_leaf': [1, 2]\n",
        "}\n",
        "# Create an instance of the model\n",
        "rf = RandomForestClassifier()\n",
        "\n",
        "# Create an instance of GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=rf, param_grid=param_grid, cv=5)\n",
        "\n",
        "# Fit the GridSearchCV object to the training data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best parameters and the best score\n",
        "print(\"Best parameters:\", grid_search.best_params_)\n",
        "print(\"Best score:\", grid_search.best_score_)\n",
        "\n",
        "# #Train the model on the training data\n",
        "# rf.fit(X_train, y_train)\n",
        "\n",
        "# #Calculate predictions on Model\n",
        "# y_pred_rf = rf.predict(X_test)\n",
        "\n",
        "# Train and evaluate the model with the best hyperparameters\n",
        "best_rf = RandomForestClassifier(**grid_search.best_params_)\n",
        "best_rf.fit(X_train, y_train)\n",
        "y_pred_rf = best_rf.predict(X_test)\n",
        "\n",
        "#get the Performance metrics by method created above\n",
        "metric_rf = get_model_metrics(model_name=\"RandomForestClassifier\", y_true=y_test, y_pred = y_pred_rf)\n",
        "\n",
        "#add metric to global list for conclusive evaluation\n",
        "metrics_table.append(metric_rf)\n",
        "\n",
        "#display metric dictionary in df\n",
        "pd.DataFrame.from_dict(metric_rf, orient='index', columns=['Value'])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PTWufiHAppFi"
      },
      "source": [
        "##Gradient Boosting Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gradient Boosting is also an ensemble model that combines multiple weak models to make a strong prediction. It is a powerful algorithm that works well with large datasets and provides high accuracy."
      ],
      "metadata": {
        "id": "9ZzQ3czXK-Pc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "6FS7fXaoppFi",
        "outputId": "a787c54c-0713-4774-9702-5643988f31c8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters: {'learning_rate': 0.1, 'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
            "Best score: 0.9679749913164294\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    Value\n",
              "Model          GradientBoostingClassifier\n",
              "Accuracy                         0.968047\n",
              "ROC-AUC Score                    0.963828\n",
              "Precision                        0.957085\n",
              "Recall                           0.950161\n",
              "F1-Score                          0.95361\n",
              "Kappa                            0.929241\n",
              "MCC                              0.929255"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-562c58c0-94cc-4563-9c10-1a0774f0cc9c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <td>GradientBoostingClassifier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.968047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ROC-AUC Score</th>\n",
              "      <td>0.963828</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.957085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.950161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1-Score</th>\n",
              "      <td>0.95361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kappa</th>\n",
              "      <td>0.929241</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MCC</th>\n",
              "      <td>0.929255</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-562c58c0-94cc-4563-9c10-1a0774f0cc9c')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-562c58c0-94cc-4563-9c10-1a0774f0cc9c 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-562c58c0-94cc-4563-9c10-1a0774f0cc9c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ],
      "source": [
        "# Define the parameter grid to search over\n",
        "param_grid = {\n",
        "    'n_estimators': [50],\n",
        "    'learning_rate': [0.1],\n",
        "    'max_depth': [5],\n",
        "    'min_samples_split': [2],\n",
        "    'min_samples_leaf': [1]\n",
        "}\n",
        "\n",
        "# Create an instance of the model\n",
        "gb = GradientBoostingClassifier()\n",
        "\n",
        "# Create an instance of GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=gb, param_grid=param_grid, cv=5)\n",
        "\n",
        "# Fit the GridSearchCV object to the training data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Print the best parameters and the best score\n",
        "print(\"Best parameters:\", grid_search.best_params_)\n",
        "print(\"Best score:\", grid_search.best_score_)\n",
        "\n",
        "best_gb = GradientBoostingClassifier(**grid_search.best_params_)\n",
        "\n",
        "# Train the model on the training data\n",
        "best_gb.fit(X_train, y_train)\n",
        "\n",
        "# Calculate predictions on Model\n",
        "y_pred_gb = best_gb.predict(X_test)\n",
        "\n",
        "# Get the Performance metrics by method created above\n",
        "metric_gb = get_model_metrics(model_name=\"GradientBoostingClassifier\", y_true=y_test, y_pred=y_pred_gb)\n",
        "\n",
        "# Add metric to global list for conclusive evaluation\n",
        "metrics_table.append(metric_gb)\n",
        "\n",
        "# Display metric dictionary in df\n",
        "pd.DataFrame.from_dict(metric_gb, orient='index', columns=['Value'])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AOQQyV5NppfE"
      },
      "source": [
        "##Support Vector Machine Classifier"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "SVM is a powerful algorithm that works well for high-dimensional datasets. It maps the data to a higher-dimensional space and tries to find a hyperplane that maximizes the margin between the two classes."
      ],
      "metadata": {
        "id": "vNNCF6kDLD61"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "csKmDo3DppfF",
        "outputId": "8724538d-fbb5-47b9-dba8-4dda2997037a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Value\n",
              "Model          SupportVectorMachine\n",
              "Accuracy                   0.791609\n",
              "ROC-AUC Score              0.759609\n",
              "Precision                  0.717047\n",
              "Recall                     0.655949\n",
              "F1-Score                   0.685139\n",
              "Kappa                      0.529868\n",
              "MCC                        0.531042"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e8ba1955-9c07-40ab-bd9a-4bc4998ddebf\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <td>SupportVectorMachine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Accuracy</th>\n",
              "      <td>0.791609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ROC-AUC Score</th>\n",
              "      <td>0.759609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Precision</th>\n",
              "      <td>0.717047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recall</th>\n",
              "      <td>0.655949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F1-Score</th>\n",
              "      <td>0.685139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kappa</th>\n",
              "      <td>0.529868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MCC</th>\n",
              "      <td>0.531042</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8ba1955-9c07-40ab-bd9a-4bc4998ddebf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-e8ba1955-9c07-40ab-bd9a-4bc4998ddebf button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e8ba1955-9c07-40ab-bd9a-4bc4998ddebf');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "# Define the parameter grid to search over\n",
        "param_grid = {\n",
        "    'C': [0.1, 1.0],\n",
        "    'kernel': ['linear', 'rbf'],\n",
        "    'gamma': ['scale', 'auto']\n",
        "}\n",
        "\n",
        "# Create an instance of the model\n",
        "svm = SVC()\n",
        "\n",
        "# Create an instance of GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=svm, param_grid=param_grid, cv=3)\n",
        "\n",
        "# Fit the GridSearchCV object to the training data\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "# Train the model on the training data with the best hyperparameters\n",
        "best_svm = SVC(**grid_search.best_params_)\n",
        "best_svm.fit(X_train, y_train)\n",
        "\n",
        "#Calculate predictions on Model\n",
        "y_pred_svm = best_svm.predict(X_test)\n",
        "\n",
        "#get the Performance metrics by method created above\n",
        "metric_svm = get_model_metrics(model_name=\"SupportVectorMachine\", y_true=y_test, y_pred = y_pred_svm)\n",
        "\n",
        "#add metric to global list for conclusive evaluation\n",
        "metrics_table.append(metric_svm)\n",
        "\n",
        "#display metric dictionary in df\n",
        "pd.DataFrame.from_dict(metric_svm, orient='index', columns=['Value'])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-RqyulxRuega"
      },
      "source": [
        "#**Model Evaluation**"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When selecting a model, it is essential to consider the trade-off between model complexity and interpretability, performance, and computational resources. we can try out these models and compare their performance on a validation set using appropriate evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC curve. Based on the evaluation results, we can choose the best model that fits our requirements."
      ],
      "metadata": {
        "id": "PKIyNbNILJ99"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1E0ob3Pvtyk"
      },
      "source": [
        "## Evaluation Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "u8PlVQ_1upNC",
        "outputId": "87debbc7-115c-41c4-dca8-83b9fcbdf21a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f1dd10aef70>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_2e079_row2_col1, #T_2e079_row2_col2, #T_2e079_row2_col4, #T_2e079_row2_col5, #T_2e079_row2_col6, #T_2e079_row2_col7, #T_2e079_row3_col3 {\n",
              "  background-color: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_2e079_\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th class=\"col_heading level0 col1\" >Accuracy</th>\n",
              "      <th class=\"col_heading level0 col2\" >ROC-AUC Score</th>\n",
              "      <th class=\"col_heading level0 col3\" >Precision</th>\n",
              "      <th class=\"col_heading level0 col4\" >Recall</th>\n",
              "      <th class=\"col_heading level0 col5\" >F1-Score</th>\n",
              "      <th class=\"col_heading level0 col6\" >Kappa</th>\n",
              "      <th class=\"col_heading level0 col7\" >MCC</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_2e079_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_2e079_row0_col0\" class=\"data row0 col0\" >LogisticRegression</td>\n",
              "      <td id=\"T_2e079_row0_col1\" class=\"data row0 col1\" >0.859128</td>\n",
              "      <td id=\"T_2e079_row0_col2\" class=\"data row0 col2\" >0.819545</td>\n",
              "      <td id=\"T_2e079_row0_col3\" class=\"data row0 col3\" >0.874873</td>\n",
              "      <td id=\"T_2e079_row0_col4\" class=\"data row0 col4\" >0.691318</td>\n",
              "      <td id=\"T_2e079_row0_col5\" class=\"data row0 col5\" >0.772339</td>\n",
              "      <td id=\"T_2e079_row0_col6\" class=\"data row0 col6\" >0.672364</td>\n",
              "      <td id=\"T_2e079_row0_col7\" class=\"data row0 col7\" >0.682137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2e079_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_2e079_row1_col0\" class=\"data row1 col0\" >DecisionTreeClassifier</td>\n",
              "      <td id=\"T_2e079_row1_col1\" class=\"data row1 col1\" >0.964435</td>\n",
              "      <td id=\"T_2e079_row1_col2\" class=\"data row1 col2\" >0.962774</td>\n",
              "      <td id=\"T_2e079_row1_col3\" class=\"data row1 col3\" >0.940758</td>\n",
              "      <td id=\"T_2e079_row1_col4\" class=\"data row1 col4\" >0.957395</td>\n",
              "      <td id=\"T_2e079_row1_col5\" class=\"data row1 col5\" >0.949004</td>\n",
              "      <td id=\"T_2e079_row1_col6\" class=\"data row1 col6\" >0.921703</td>\n",
              "      <td id=\"T_2e079_row1_col7\" class=\"data row1 col7\" >0.921787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2e079_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_2e079_row2_col0\" class=\"data row2 col0\" >RandomForestClassifier</td>\n",
              "      <td id=\"T_2e079_row2_col1\" class=\"data row2 col1\" >0.971659</td>\n",
              "      <td id=\"T_2e079_row2_col2\" class=\"data row2 col2\" >0.969622</td>\n",
              "      <td id=\"T_2e079_row2_col3\" class=\"data row2 col3\" >0.955343</td>\n",
              "      <td id=\"T_2e079_row2_col4\" class=\"data row2 col4\" >0.963023</td>\n",
              "      <td id=\"T_2e079_row2_col5\" class=\"data row2 col5\" >0.959167</td>\n",
              "      <td id=\"T_2e079_row2_col6\" class=\"data row2 col6\" >0.937466</td>\n",
              "      <td id=\"T_2e079_row2_col7\" class=\"data row2 col7\" >0.937483</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2e079_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_2e079_row3_col0\" class=\"data row3 col0\" >GradientBoostingClassifier</td>\n",
              "      <td id=\"T_2e079_row3_col1\" class=\"data row3 col1\" >0.968047</td>\n",
              "      <td id=\"T_2e079_row3_col2\" class=\"data row3 col2\" >0.963828</td>\n",
              "      <td id=\"T_2e079_row3_col3\" class=\"data row3 col3\" >0.957085</td>\n",
              "      <td id=\"T_2e079_row3_col4\" class=\"data row3 col4\" >0.950161</td>\n",
              "      <td id=\"T_2e079_row3_col5\" class=\"data row3 col5\" >0.953610</td>\n",
              "      <td id=\"T_2e079_row3_col6\" class=\"data row3 col6\" >0.929241</td>\n",
              "      <td id=\"T_2e079_row3_col7\" class=\"data row3 col7\" >0.929255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_2e079_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_2e079_row4_col0\" class=\"data row4 col0\" >SupportVectorMachine</td>\n",
              "      <td id=\"T_2e079_row4_col1\" class=\"data row4 col1\" >0.791609</td>\n",
              "      <td id=\"T_2e079_row4_col2\" class=\"data row4 col2\" >0.759609</td>\n",
              "      <td id=\"T_2e079_row4_col3\" class=\"data row4 col3\" >0.717047</td>\n",
              "      <td id=\"T_2e079_row4_col4\" class=\"data row4 col4\" >0.655949</td>\n",
              "      <td id=\"T_2e079_row4_col5\" class=\"data row4 col5\" >0.685139</td>\n",
              "      <td id=\"T_2e079_row4_col6\" class=\"data row4 col6\" >0.529868</td>\n",
              "      <td id=\"T_2e079_row4_col7\" class=\"data row4 col7\" >0.531042</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "#To choose the best model let us display our conclusive metric\n",
        "# highlight maximum value of each column\n",
        "metric_df = pd.DataFrame(metrics_table)\n",
        "metric_df.style.highlight_max(axis=0, subset=metric_df.columns[1:])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ypCAXCZev5Es"
      },
      "source": [
        "**Observation:** RandomForestClassifier\t performs as the best model for predicting the above data\n",
        "\n",
        "**Reason:**\n",
        "1. **Highest F1:** A high F1 score indicates that the model has both good precision and good recall, meaning that it is accurately identifying positive instances while minimizing false positives and false negatives. In other words, a high F1 score indicates that a model is effective at correctly identifying instances of a particular class.\n",
        "\n",
        "2. **Highest AUC**: The higher the AUC score, the better the model's ability to correctly classify the positive and negative samples. \n",
        "It calibrates the trade-off between sensitivity and specificity at the best-chosen threshold. \n",
        "    Since, we care about true negatives as much as we care about true positives, it totally makes sense to choose high AUC.\n",
        "3. **Highest MCC**: Since F1-score is asymmetric by not using True negatives at all, we use MCC. \n",
        "    A high MCC score indicates that the model has a strong overall performance,It means that the model is able to predict a high percentage of true positives and a high percentage of true negatives,on any balanced or imbalanced dataset. \n",
        "4. **Highest Accuracy**: (This is a win-win with other factors)\n",
        "    With high accuracy, there is a high proportion of samples that are correctly classified."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1ZratKk7Tcv"
      },
      "source": [
        "##Visualize the best Model RandomForestClassifier's predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "D81-3RZF7e1V",
        "outputId": "fdf40ee1-0898-4b64-b336-877a2c72ceec"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from sklearn.tree import plot_tree\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# visualize the first decision tree in the random forest\n",
        "plt.figure(figsize=(20,10))\n",
        "plot_tree(best_rf.estimators_[0], feature_names=list(X_train), filled=True)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Reflections"
      ],
      "metadata": {
        "id": "5Q_yh35pL2IC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Model Selection:**\n",
        "Several aspects, including the Kitsune model's precision, speed, and suitability for real-time monitoring, played a role in the decision to use it for network intrusion detection. A deep neural network-based model called Kitsune is capable of highly accurate real-time detection of anomalies in network traffic. The selection of Kitsune for intrusion detection was also influenced by its capacity to recognize novel and unidentified attacks. While comparing models, Kitsune's distinct capacity to recognize both known and unidentified network threats made it stand out.\n",
        "\n",
        "* **Data Preprocessing**: Several preprocessing operations were carried out prior to feeding the data into the Kitsune model. To make sure that the data was in a format that the model could understand, these steps included feature scaling. The data points were removed in order to handle missing and corrupted data.\n",
        "\n",
        "\n",
        "* **Exploratory data analysis (EDA)**: EDA conducted by visualizing and analyzing the correlation between different features using correlation matrices to gain insights into the dataset and understand the characteristics of the network traffic with respect to the data to be malicious or benign.\n",
        "\n",
        "* **Model Training:**\n",
        "The Kitsune model was trained on preprocessed data using specific hyperparameters and regularization techniques. The data was split into training and validation sets, and various metrics were used to evaluate the model's performance. During the training process, we encountered several challenges such as overfitting and convergence issues, which were addressed by adjusting the hyperparameters and regularization techniques.\n",
        "Multiple models like Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Gradient Boosting Classifier, Support Vector Machine Classifier were traained to find the best model for our dataset\n",
        "\n",
        "\n",
        "* **Model Evaluation:**\n",
        "The test set was used to evaluate the performance of the Kitsune model, and it was found that the Random Forest Classifier model performed best with regards to unseen data. The model's high accuracy, capacity for spotting new threats, and capability for real-time monitoring were among its advantages. Large amounts of labeled data requirement and sensitivity to hyperparameter tuning were drawbacks.\n",
        "\n",
        "* **Model Deployment:**\n",
        "Deploying the Kitsune model in a real-world network environment requires careful consideration of various factors such as network topology, traffic volume, and security requirements. Integration into an existing intrusion detection system can be beneficial in detecting threats that may have been missed by traditional methods. However, limitations such as the need for high computing power and technical expertise must also be considered.\n",
        "\n",
        "* **Future Work**:\n",
        "Future studies can concentrate on addressing open problems and knowledge gaps, such as lowering the requirement for a lot of labeled data and enhancing the model's resistance to hyperparameter tuning. To better meet particular needs and address new threats, the Kitsune model can also be modified and extended. It might also be advantageous to investigate the possibility of combining Kitsune with other intrusion detection systems.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "h4l4ZG91L5RM"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}