Network_Anamoly_Detection_Updated.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/chonchuks/Network_Anamoly_Detection/blob/main/Network_Anamoly_Detection_Updated.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vRnvI91WA7uI"
      },
      "outputs": [],
      "source": [
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzTItLp1I5ph",
        "outputId": "423a7306-2505-4266-bf9c-e4503afcfbfe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5FJ2Y9ihJes5"
      },
      "outputs": [],
      "source": [
        "# Define the path to the zip file\n",
        "zip_file_path = '/content/drive/Othercomputers/My PC/models/network_anomaly.zip'\n",
        "\n",
        "# Define the directory to unzip to\n",
        "unzip_dir = '/content/data'\n",
        "\n",
        "\n",
        "# Create the directory if it doesn't exist\n",
        "os.makedirs(unzip_dir, exist_ok=True)\n",
        "\n",
        "# Unzip the file to the specified directory\n",
        "!unzip -q \"{zip_file_path}\" -d \"{unzip_dir}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D5NF0amCA7uP"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import time\n",
        "import multiprocessing\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from collections import Counter\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from imblearn.over_sampling import SMOTE, ADASYN\n",
        "from sklearn.metrics import confusion_matrix, r2_score, mean_squared_error\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score, classification_report, precision_recall_curve\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from sklearn.svm import SVC  # For SVM\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, LSTM  # For LSTM\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.model_selection import GridSearchCV"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wMB9h8dgnNoy"
      },
      "source": [
        "**DATA COLLECTION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "OgDRb23LA7uQ",
        "outputId": "e3523afa-a5c9-4112-9084-ad1440b76241"
      },
      "outputs": [
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              "      <th></th>\n",
              "      <th>duration</th>\n",
              "      <th>protocoltype</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>srcbytes</th>\n",
              "      <th>dstbytes</th>\n",
              "      <th>land</th>\n",
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              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>...</th>\n",
              "      <th>dsthostsamesrvrate</th>\n",
              "      <th>dsthostdiffsrvrate</th>\n",
              "      <th>dsthostsamesrcportrate</th>\n",
              "      <th>dsthostsrvdiffhostrate</th>\n",
              "      <th>dsthostserrorrate</th>\n",
              "      <th>dsthostsrvserrorrate</th>\n",
              "      <th>dsthostrerrorrate</th>\n",
              "      <th>dsthostsrvrerrorrate</th>\n",
              "      <th>attack</th>\n",
              "      <th>lastflag</th>\n",
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              "      <th>0</th>\n",
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              "      <td>S0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
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              "      <td>normal</td>\n",
              "      <td>21</td>\n",
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              "<p>5 rows × 43 columns</p>\n",
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            "text/plain": [
              "   duration protocoltype   service flag  srcbytes  dstbytes  land  \\\n",
              "0         0          tcp  ftp_data   SF       491         0     0   \n",
              "1         0          udp     other   SF       146         0     0   \n",
              "2         0          tcp   private   S0         0         0     0   \n",
              "3         0          tcp      http   SF       232      8153     0   \n",
              "4         0          tcp      http   SF       199       420     0   \n",
              "\n",
              "   wrongfragment  urgent  hot  ...  dsthostsamesrvrate  dsthostdiffsrvrate  \\\n",
              "0              0       0    0  ...                0.17                0.03   \n",
              "1              0       0    0  ...                0.00                0.60   \n",
              "2              0       0    0  ...                0.10                0.05   \n",
              "3              0       0    0  ...                1.00                0.00   \n",
              "4              0       0    0  ...                1.00                0.00   \n",
              "\n",
              "   dsthostsamesrcportrate  dsthostsrvdiffhostrate  dsthostserrorrate  \\\n",
              "0                    0.17                    0.00               0.00   \n",
              "1                    0.88                    0.00               0.00   \n",
              "2                    0.00                    0.00               1.00   \n",
              "3                    0.03                    0.04               0.03   \n",
              "4                    0.00                    0.00               0.00   \n",
              "\n",
              "   dsthostsrvserrorrate  dsthostrerrorrate  dsthostsrvrerrorrate   attack  \\\n",
              "0                  0.00               0.05                  0.00   normal   \n",
              "1                  0.00               0.00                  0.00   normal   \n",
              "2                  1.00               0.00                  0.00  neptune   \n",
              "3                  0.01               0.00                  0.01   normal   \n",
              "4                  0.00               0.00                  0.00   normal   \n",
              "\n",
              "   lastflag  \n",
              "0        20  \n",
              "1        15  \n",
              "2        19  \n",
              "3        21  \n",
              "4        21  \n",
              "\n",
              "[5 rows x 43 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/data/Train.txt\",sep=\",\",names=[\"duration\",\"protocoltype\",\"service\",\"flag\",\"srcbytes\",\"dstbytes\",\"land\", \"wrongfragment\",\"urgent\",\"hot\",\"numfailedlogins\",\"loggedin\", \"numcompromised\",\"rootshell\",\"suattempted\",\"numroot\",\"numfilecreations\", \"numshells\",\"numaccessfiles\",\"numoutboundcmds\",\"ishostlogin\",\n",
        "\"isguestlogin\",\"count\",\"srvcount\",\"serrorrate\", \"srvserrorrate\",\n",
        "\"rerrorrate\",\"srvrerrorrate\",\"samesrvrate\", \"diffsrvrate\", \"srvdiffhostrate\",\"dsthostcount\",\"dsthostsrvcount\",\"dsthostsamesrvrate\", \"dsthostdiffsrvrate\",\"dsthostsamesrcportrate\",\n",
        "\"dsthostsrvdiffhostrate\",\"dsthostserrorrate\",\"dsthostsrvserrorrate\",\n",
        "\"dsthostrerrorrate\",\"dsthostsrvrerrorrate\",\"attack\", \"lastflag\"])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "xENhQtCcwV9X",
        "outputId": "b39166bc-8761-47bb-d2b9-c7b9dd323693"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
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              "      <th></th>\n",
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              "      <th>dsthostsrvdiffhostrate</th>\n",
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              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>19</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>232</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125968</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>S0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125969</th>\n",
              "      <td>8</td>\n",
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              "      <td>private</td>\n",
              "      <td>SF</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.96</td>\n",
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              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125970</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>smtp</td>\n",
              "      <td>SF</td>\n",
              "      <td>2231</td>\n",
              "      <td>384</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125971</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>klogin</td>\n",
              "      <td>S0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125972</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
              "      <td>SF</td>\n",
              "      <td>151</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>125973 rows × 43 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      border-right-color: var(--fill-color);\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ae0129b4-21cf-4964-b4bf-78d41e237665 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "        duration protocoltype   service flag  srcbytes  dstbytes  land  \\\n",
              "0              0          tcp  ftp_data   SF       491         0     0   \n",
              "1              0          udp     other   SF       146         0     0   \n",
              "2              0          tcp   private   S0         0         0     0   \n",
              "3              0          tcp      http   SF       232      8153     0   \n",
              "4              0          tcp      http   SF       199       420     0   \n",
              "...          ...          ...       ...  ...       ...       ...   ...   \n",
              "125968         0          tcp   private   S0         0         0     0   \n",
              "125969         8          udp   private   SF       105       145     0   \n",
              "125970         0          tcp      smtp   SF      2231       384     0   \n",
              "125971         0          tcp    klogin   S0         0         0     0   \n",
              "125972         0          tcp  ftp_data   SF       151         0     0   \n",
              "\n",
              "        wrongfragment  urgent  hot  ...  dsthostsamesrvrate  \\\n",
              "0                   0       0    0  ...                0.17   \n",
              "1                   0       0    0  ...                0.00   \n",
              "2                   0       0    0  ...                0.10   \n",
              "3                   0       0    0  ...                1.00   \n",
              "4                   0       0    0  ...                1.00   \n",
              "...               ...     ...  ...  ...                 ...   \n",
              "125968              0       0    0  ...                0.10   \n",
              "125969              0       0    0  ...                0.96   \n",
              "125970              0       0    0  ...                0.12   \n",
              "125971              0       0    0  ...                0.03   \n",
              "125972              0       0    0  ...                0.30   \n",
              "\n",
              "        dsthostdiffsrvrate  dsthostsamesrcportrate  dsthostsrvdiffhostrate  \\\n",
              "0                     0.03                    0.17                    0.00   \n",
              "1                     0.60                    0.88                    0.00   \n",
              "2                     0.05                    0.00                    0.00   \n",
              "3                     0.00                    0.03                    0.04   \n",
              "4                     0.00                    0.00                    0.00   \n",
              "...                    ...                     ...                     ...   \n",
              "125968                0.06                    0.00                    0.00   \n",
              "125969                0.01                    0.01                    0.00   \n",
              "125970                0.06                    0.00                    0.00   \n",
              "125971                0.05                    0.00                    0.00   \n",
              "125972                0.03                    0.30                    0.00   \n",
              "\n",
              "        dsthostserrorrate  dsthostsrvserrorrate  dsthostrerrorrate  \\\n",
              "0                    0.00                  0.00               0.05   \n",
              "1                    0.00                  0.00               0.00   \n",
              "2                    1.00                  1.00               0.00   \n",
              "3                    0.03                  0.01               0.00   \n",
              "4                    0.00                  0.00               0.00   \n",
              "...                   ...                   ...                ...   \n",
              "125968               1.00                  1.00               0.00   \n",
              "125969               0.00                  0.00               0.00   \n",
              "125970               0.72                  0.00               0.01   \n",
              "125971               1.00                  1.00               0.00   \n",
              "125972               0.00                  0.00               0.00   \n",
              "\n",
              "        dsthostsrvrerrorrate   attack  lastflag  \n",
              "0                       0.00   normal        20  \n",
              "1                       0.00   normal        15  \n",
              "2                       0.00  neptune        19  \n",
              "3                       0.01   normal        21  \n",
              "4                       0.00   normal        21  \n",
              "...                      ...      ...       ...  \n",
              "125968                  0.00  neptune        20  \n",
              "125969                  0.00   normal        21  \n",
              "125970                  0.00   normal        18  \n",
              "125971                  0.00  neptune        20  \n",
              "125972                  0.00   normal        21  \n",
              "\n",
              "[125973 rows x 43 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lZgEwrhrofxd"
      },
      "source": [
        "**EDA**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "_LgPQ2nAoJJ2",
        "outputId": "daf732e5-b0fc-4840-8aeb-ae167c8fc38d"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x900 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# df['attack']\n",
        "def target_col():\n",
        "  attack_count = df['attack'].value_counts()\n",
        "  attack_names = attack_count.index.to_list()\n",
        "  attack_values = attack_count.to_numpy()\n",
        "\n",
        "  plt.figure(figsize=(15, 9))\n",
        "  sns.barplot(x=attack_values, y=attack_names, palette='Set2')\n",
        "  plt.title('Bar Chart of Target Class attack ber', fontsize=16)\n",
        "  plt.xlabel('Number of Attacks', fontsize=14)\n",
        "  plt.ylabel('Attack Types', fontsize=14)\n",
        "  plt.xticks(rotation=0)\n",
        "  plt.show()\n",
        "target_col()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "h1vQEktOn4nW",
        "outputId": "ce0bf414-4119-4cd6-f14a-ff70552c8541"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "df.groupby('flag').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "IjIBmbPWnyBC",
        "outputId": "43be3c90-b039-4cbf-c4b4-4cae192d1d62"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "df.groupby('protocoltype').size().plot(kind='barh', color=sns.palettes.mpl_palette('Dark2'))\n",
        "plt.gca().spines[['top', 'right',]].set_visible(False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VuUkBNOQA7uQ",
        "outputId": "64c1a902-827d-402e-8deb-c574150947d1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(125973, 43)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uVsgyx66A7uR",
        "outputId": "86ef268d-952c-439f-b932-157a545841f4"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 43,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 125973.0,\n        \"max\": 125973.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          125973.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unique\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 3,\n        \"max\": 70,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          70\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"top\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"http\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freq\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"40338\",\n        \"max\": \"102689\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"40338\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 45566.74300048423,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          0.03254244957252744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 5870331.181893551,\n        \"num_unique_values\": 39,\n        \"samples\": [\n          0.1125638048811898\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 82.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 255.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.02\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 516.0,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          18.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 0.0,\n        \"max\": 1379963888.0,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          42908.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-23f33f5a-8b53-4273-a913-b0a0ffc17494\" 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>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>duration</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>287.14465</td>\n",
              "      <td>2604.51531</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42908.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>protocoltype</th>\n",
              "      <td>125973</td>\n",
              "      <td>3</td>\n",
              "      <td>tcp</td>\n",
              "      <td>102689</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>service</th>\n",
              "      <td>125973</td>\n",
              "      <td>70</td>\n",
              "      <td>http</td>\n",
              "      <td>40338</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>flag</th>\n",
              "      <td>125973</td>\n",
              "      <td>11</td>\n",
              "      <td>SF</td>\n",
              "      <td>74945</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>srcbytes</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>45566.743</td>\n",
              "      <td>5870331.181894</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>276.0</td>\n",
              "      <td>1379963888.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dstbytes</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19779.114421</td>\n",
              "      <td>4021269.151441</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>516.0</td>\n",
              "      <td>1309937401.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>land</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000198</td>\n",
              "      <td>0.014086</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wrongfragment</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.022687</td>\n",
              "      <td>0.25353</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>urgent</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.014366</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hot</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.204409</td>\n",
              "      <td>2.149968</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>77.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numfailedlogins</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.001222</td>\n",
              "      <td>0.045239</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>loggedin</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.395736</td>\n",
              "      <td>0.48901</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numcompromised</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.27925</td>\n",
              "      <td>23.942042</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7479.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rootshell</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.001342</td>\n",
              "      <td>0.036603</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>suattempted</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.001103</td>\n",
              "      <td>0.045154</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numroot</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.302192</td>\n",
              "      <td>24.399618</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7468.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numfilecreations</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.012669</td>\n",
              "      <td>0.483935</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numshells</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000413</td>\n",
              "      <td>0.022181</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numaccessfiles</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.004096</td>\n",
              "      <td>0.09937</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>numoutboundcmds</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ishostlogin</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>0.002817</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>isguestlogin</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.009423</td>\n",
              "      <td>0.096612</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>84.107555</td>\n",
              "      <td>114.508607</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>143.0</td>\n",
              "      <td>511.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>srvcount</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>27.737888</td>\n",
              "      <td>72.63584</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>511.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>serrorrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.284485</td>\n",
              "      <td>0.446456</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>srvserrorrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.282485</td>\n",
              "      <td>0.447022</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>rerrorrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.119958</td>\n",
              "      <td>0.320436</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>srvrerrorrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.121183</td>\n",
              "      <td>0.323647</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>samesrvrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.660928</td>\n",
              "      <td>0.439623</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.09</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>diffsrvrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.063053</td>\n",
              "      <td>0.180314</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>srvdiffhostrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.097322</td>\n",
              "      <td>0.25983</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostcount</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>182.148945</td>\n",
              "      <td>99.206213</td>\n",
              "      <td>0.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>255.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostsrvcount</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>115.653005</td>\n",
              "      <td>110.702741</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>255.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostsamesrvrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.521242</td>\n",
              "      <td>0.448949</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostdiffsrvrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.082951</td>\n",
              "      <td>0.188922</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.07</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostsamesrcportrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.148379</td>\n",
              "      <td>0.308997</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostsrvdiffhostrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.032542</td>\n",
              "      <td>0.112564</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.02</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostserrorrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.284452</td>\n",
              "      <td>0.444784</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostsrvserrorrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.278485</td>\n",
              "      <td>0.445669</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostrerrorrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.118832</td>\n",
              "      <td>0.306557</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dsthostsrvrerrorrate</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.12024</td>\n",
              "      <td>0.319459</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>attack</th>\n",
              "      <td>125973</td>\n",
              "      <td>23</td>\n",
              "      <td>normal</td>\n",
              "      <td>67343</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lastflag</th>\n",
              "      <td>125973.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.50406</td>\n",
              "      <td>2.291503</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
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              "        document.querySelector('#df-23f33f5a-8b53-4273-a913-b0a0ffc17494 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-23f33f5a-8b53-4273-a913-b0a0ffc17494');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                           count unique     top    freq          mean  \\\n",
              "duration                125973.0    NaN     NaN     NaN     287.14465   \n",
              "protocoltype              125973      3     tcp  102689           NaN   \n",
              "service                   125973     70    http   40338           NaN   \n",
              "flag                      125973     11      SF   74945           NaN   \n",
              "srcbytes                125973.0    NaN     NaN     NaN     45566.743   \n",
              "dstbytes                125973.0    NaN     NaN     NaN  19779.114421   \n",
              "land                    125973.0    NaN     NaN     NaN      0.000198   \n",
              "wrongfragment           125973.0    NaN     NaN     NaN      0.022687   \n",
              "urgent                  125973.0    NaN     NaN     NaN      0.000111   \n",
              "hot                     125973.0    NaN     NaN     NaN      0.204409   \n",
              "numfailedlogins         125973.0    NaN     NaN     NaN      0.001222   \n",
              "loggedin                125973.0    NaN     NaN     NaN      0.395736   \n",
              "numcompromised          125973.0    NaN     NaN     NaN       0.27925   \n",
              "rootshell               125973.0    NaN     NaN     NaN      0.001342   \n",
              "suattempted             125973.0    NaN     NaN     NaN      0.001103   \n",
              "numroot                 125973.0    NaN     NaN     NaN      0.302192   \n",
              "numfilecreations        125973.0    NaN     NaN     NaN      0.012669   \n",
              "numshells               125973.0    NaN     NaN     NaN      0.000413   \n",
              "numaccessfiles          125973.0    NaN     NaN     NaN      0.004096   \n",
              "numoutboundcmds         125973.0    NaN     NaN     NaN           0.0   \n",
              "ishostlogin             125973.0    NaN     NaN     NaN      0.000008   \n",
              "isguestlogin            125973.0    NaN     NaN     NaN      0.009423   \n",
              "count                   125973.0    NaN     NaN     NaN     84.107555   \n",
              "srvcount                125973.0    NaN     NaN     NaN     27.737888   \n",
              "serrorrate              125973.0    NaN     NaN     NaN      0.284485   \n",
              "srvserrorrate           125973.0    NaN     NaN     NaN      0.282485   \n",
              "rerrorrate              125973.0    NaN     NaN     NaN      0.119958   \n",
              "srvrerrorrate           125973.0    NaN     NaN     NaN      0.121183   \n",
              "samesrvrate             125973.0    NaN     NaN     NaN      0.660928   \n",
              "diffsrvrate             125973.0    NaN     NaN     NaN      0.063053   \n",
              "srvdiffhostrate         125973.0    NaN     NaN     NaN      0.097322   \n",
              "dsthostcount            125973.0    NaN     NaN     NaN    182.148945   \n",
              "dsthostsrvcount         125973.0    NaN     NaN     NaN    115.653005   \n",
              "dsthostsamesrvrate      125973.0    NaN     NaN     NaN      0.521242   \n",
              "dsthostdiffsrvrate      125973.0    NaN     NaN     NaN      0.082951   \n",
              "dsthostsamesrcportrate  125973.0    NaN     NaN     NaN      0.148379   \n",
              "dsthostsrvdiffhostrate  125973.0    NaN     NaN     NaN      0.032542   \n",
              "dsthostserrorrate       125973.0    NaN     NaN     NaN      0.284452   \n",
              "dsthostsrvserrorrate    125973.0    NaN     NaN     NaN      0.278485   \n",
              "dsthostrerrorrate       125973.0    NaN     NaN     NaN      0.118832   \n",
              "dsthostsrvrerrorrate    125973.0    NaN     NaN     NaN       0.12024   \n",
              "attack                    125973     23  normal   67343           NaN   \n",
              "lastflag                125973.0    NaN     NaN     NaN      19.50406   \n",
              "\n",
              "                                   std  min   25%    50%    75%           max  \n",
              "duration                    2604.51531  0.0   0.0    0.0    0.0       42908.0  \n",
              "protocoltype                       NaN  NaN   NaN    NaN    NaN           NaN  \n",
              "service                            NaN  NaN   NaN    NaN    NaN           NaN  \n",
              "flag                               NaN  NaN   NaN    NaN    NaN           NaN  \n",
              "srcbytes                5870331.181894  0.0   0.0   44.0  276.0  1379963888.0  \n",
              "dstbytes                4021269.151441  0.0   0.0    0.0  516.0  1309937401.0  \n",
              "land                          0.014086  0.0   0.0    0.0    0.0           1.0  \n",
              "wrongfragment                  0.25353  0.0   0.0    0.0    0.0           3.0  \n",
              "urgent                        0.014366  0.0   0.0    0.0    0.0           3.0  \n",
              "hot                           2.149968  0.0   0.0    0.0    0.0          77.0  \n",
              "numfailedlogins               0.045239  0.0   0.0    0.0    0.0           5.0  \n",
              "loggedin                       0.48901  0.0   0.0    0.0    1.0           1.0  \n",
              "numcompromised               23.942042  0.0   0.0    0.0    0.0        7479.0  \n",
              "rootshell                     0.036603  0.0   0.0    0.0    0.0           1.0  \n",
              "suattempted                   0.045154  0.0   0.0    0.0    0.0           2.0  \n",
              "numroot                      24.399618  0.0   0.0    0.0    0.0        7468.0  \n",
              "numfilecreations              0.483935  0.0   0.0    0.0    0.0          43.0  \n",
              "numshells                     0.022181  0.0   0.0    0.0    0.0           2.0  \n",
              "numaccessfiles                 0.09937  0.0   0.0    0.0    0.0           9.0  \n",
              "numoutboundcmds                    0.0  0.0   0.0    0.0    0.0           0.0  \n",
              "ishostlogin                   0.002817  0.0   0.0    0.0    0.0           1.0  \n",
              "isguestlogin                  0.096612  0.0   0.0    0.0    0.0           1.0  \n",
              "count                       114.508607  0.0   2.0   14.0  143.0         511.0  \n",
              "srvcount                      72.63584  0.0   2.0    8.0   18.0         511.0  \n",
              "serrorrate                    0.446456  0.0   0.0    0.0    1.0           1.0  \n",
              "srvserrorrate                 0.447022  0.0   0.0    0.0    1.0           1.0  \n",
              "rerrorrate                    0.320436  0.0   0.0    0.0    0.0           1.0  \n",
              "srvrerrorrate                 0.323647  0.0   0.0    0.0    0.0           1.0  \n",
              "samesrvrate                   0.439623  0.0  0.09    1.0    1.0           1.0  \n",
              "diffsrvrate                   0.180314  0.0   0.0    0.0   0.06           1.0  \n",
              "srvdiffhostrate                0.25983  0.0   0.0    0.0    0.0           1.0  \n",
              "dsthostcount                 99.206213  0.0  82.0  255.0  255.0         255.0  \n",
              "dsthostsrvcount             110.702741  0.0  10.0   63.0  255.0         255.0  \n",
              "dsthostsamesrvrate            0.448949  0.0  0.05   0.51    1.0           1.0  \n",
              "dsthostdiffsrvrate            0.188922  0.0   0.0   0.02   0.07           1.0  \n",
              "dsthostsamesrcportrate        0.308997  0.0   0.0    0.0   0.06           1.0  \n",
              "dsthostsrvdiffhostrate        0.112564  0.0   0.0    0.0   0.02           1.0  \n",
              "dsthostserrorrate             0.444784  0.0   0.0    0.0    1.0           1.0  \n",
              "dsthostsrvserrorrate          0.445669  0.0   0.0    0.0    1.0           1.0  \n",
              "dsthostrerrorrate             0.306557  0.0   0.0    0.0    0.0           1.0  \n",
              "dsthostsrvrerrorrate          0.319459  0.0   0.0    0.0    0.0           1.0  \n",
              "attack                             NaN  NaN   NaN    NaN    NaN           NaN  \n",
              "lastflag                      2.291503  0.0  18.0   20.0   21.0          21.0  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.describe(include = 'all').T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SU0a1YtMXFzp",
        "outputId": "bdddc029-67c6-4454-eef9-ea0d74496f9c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "urgent\n",
              "0    125964\n",
              "1         5\n",
              "2         3\n",
              "3         1\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['urgent'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oq5UhJvXA7uS"
      },
      "source": [
        "As we can see 'land', 'urgent', 'numfailedlogins', 'numoutboundcmds' have mostly zero values so we can drop these columns."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GRYctJohx0xT"
      },
      "source": [
        "**DROPING UNNEEDED COLUMNS**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DbTZiKyOA7uU"
      },
      "outputs": [],
      "source": [
        "df.drop(['land','urgent','numfailedlogins','numoutboundcmds'],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4_lF8RPaA7uX",
        "outputId": "774797a7-4ec4-4881-ba1f-7c367f9daeed"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "duration                  0\n",
              "protocoltype              0\n",
              "service                   0\n",
              "flag                      0\n",
              "srcbytes                  0\n",
              "dstbytes                  0\n",
              "wrongfragment             0\n",
              "hot                       0\n",
              "loggedin                  0\n",
              "numcompromised            0\n",
              "rootshell                 0\n",
              "suattempted               0\n",
              "numroot                   0\n",
              "numfilecreations          0\n",
              "numshells                 0\n",
              "numaccessfiles            0\n",
              "ishostlogin               0\n",
              "isguestlogin              0\n",
              "count                     0\n",
              "srvcount                  0\n",
              "serrorrate                0\n",
              "srvserrorrate             0\n",
              "rerrorrate                0\n",
              "srvrerrorrate             0\n",
              "samesrvrate               0\n",
              "diffsrvrate               0\n",
              "srvdiffhostrate           0\n",
              "dsthostcount              0\n",
              "dsthostsrvcount           0\n",
              "dsthostsamesrvrate        0\n",
              "dsthostdiffsrvrate        0\n",
              "dsthostsamesrcportrate    0\n",
              "dsthostsrvdiffhostrate    0\n",
              "dsthostserrorrate         0\n",
              "dsthostsrvserrorrate      0\n",
              "dsthostrerrorrate         0\n",
              "dsthostsrvrerrorrate      0\n",
              "attack                    0\n",
              "lastflag                  0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "kjyNslJQA7uY",
        "outputId": "b5eb288f-9f4a-4c66-d441-813dae54d7ca"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "\n",
              "  <div id=\"df-f8fe300a-99cd-4134-811c-856a200598f9\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>protocoltype</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>attack</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
              "      <td>SF</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>udp</td>\n",
              "      <td>other</td>\n",
              "      <td>SF</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>S0</td>\n",
              "      <td>neptune</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125968</th>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>S0</td>\n",
              "      <td>neptune</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125969</th>\n",
              "      <td>udp</td>\n",
              "      <td>private</td>\n",
              "      <td>SF</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125970</th>\n",
              "      <td>tcp</td>\n",
              "      <td>smtp</td>\n",
              "      <td>SF</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125971</th>\n",
              "      <td>tcp</td>\n",
              "      <td>klogin</td>\n",
              "      <td>S0</td>\n",
              "      <td>neptune</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>125972</th>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
              "      <td>SF</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>125973 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
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              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f8fe300a-99cd-4134-811c-856a200598f9 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-f8fe300a-99cd-4134-811c-856a200598f9');\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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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c9719a9f-ff4f-4fcf-8239-65d79bcba4c8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       protocoltype   service flag   attack\n",
              "0               tcp  ftp_data   SF   normal\n",
              "1               udp     other   SF   normal\n",
              "2               tcp   private   S0  neptune\n",
              "3               tcp      http   SF   normal\n",
              "4               tcp      http   SF   normal\n",
              "...             ...       ...  ...      ...\n",
              "125968          tcp   private   S0  neptune\n",
              "125969          udp   private   SF   normal\n",
              "125970          tcp      smtp   SF   normal\n",
              "125971          tcp    klogin   S0  neptune\n",
              "125972          tcp  ftp_data   SF   normal\n",
              "\n",
              "[125973 rows x 4 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.select_dtypes(exclude=[np.number])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3osjrVe5ZurP",
        "outputId": "07bd7c56-36dc-48ce-fe52-11156bf0431d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "attack\n",
              "normal             67343\n",
              "neptune            41214\n",
              "satan               3633\n",
              "ipsweep             3599\n",
              "portsweep           2931\n",
              "smurf               2646\n",
              "nmap                1493\n",
              "back                 956\n",
              "teardrop             892\n",
              "warezclient          890\n",
              "pod                  201\n",
              "guess_passwd          53\n",
              "buffer_overflow       30\n",
              "warezmaster           20\n",
              "land                  18\n",
              "imap                  11\n",
              "rootkit               10\n",
              "loadmodule             9\n",
              "ftp_write              8\n",
              "multihop               7\n",
              "phf                    4\n",
              "perl                   3\n",
              "spy                    2\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['attack'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KkK7LCTIA7uY"
      },
      "source": [
        "As we are focussing on Binomial Classification for this dataset, we can make all other classes other than normal as 'attack'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "QG5ZQZv9A7uZ",
        "outputId": "35f3a23b-2cdf-4dc2-d87d-3ae7911a55d0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1500x900 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['attack'].loc[df['attack']!='normal']='attack'\n",
        "target_col()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LivL6JpsxPCS"
      },
      "source": [
        "**LABEL ENCODING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3RBLc00JA7uZ"
      },
      "outputs": [],
      "source": [
        "le=LabelEncoder()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "r5QboUniA7uZ"
      },
      "outputs": [],
      "source": [
        "df['protocoltype']=le.fit_transform(df['protocoltype'])\n",
        "df['service']=le.fit_transform(df['service'])\n",
        "df['flag']=le.fit_transform(df['flag'])\n",
        "df['attack']=le.fit_transform(df['attack'])\n",
        "\n",
        "# Assuming the LabelEncoder for 'protocoltype' has been fitted\n",
        "protocoltype_le = LabelEncoder()\n",
        "protocoltype_le.fit(df['protocoltype'])\n",
        "\n",
        "# Save the classes of the encoder\n",
        "np.save('protocoltype_classes.npy', protocoltype_le.classes_)\n",
        "\n",
        "# Similarly, do the same for 'service' and 'flag'\n",
        "service_le = LabelEncoder()\n",
        "service_le.fit(df['service'])\n",
        "np.save('service_classes.npy', service_le.classes_)\n",
        "\n",
        "flag_le = LabelEncoder()\n",
        "flag_le.fit(df['flag'])\n",
        "np.save('flag_classes.npy', flag_le.classes_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bHrmMfv0A7uZ",
        "outputId": "c7d025de-1502-480b-f46b-637f1211fe7f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2000x1500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(20,15))\n",
        "sns.heatmap(df.corr())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HX2VPF1lA7ua"
      },
      "outputs": [],
      "source": [
        "#@title Feature selection\n",
        "X = df.drop(['attack'],axis=1)\n",
        "y =df['attack']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IedGqgdWH4W8",
        "outputId": "c30de67a-1be9-4666-f34b-8c3fbdea0e0a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGddGHyNwNvj",
        "outputId": "a207b838-c757-4057-8627-c7c55276478d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0         1\n",
              "1         1\n",
              "2         0\n",
              "3         1\n",
              "4         1\n",
              "         ..\n",
              "125968    0\n",
              "125969    1\n",
              "125970    1\n",
              "125971    0\n",
              "125972    1\n",
              "Name: attack, Length: 125973, dtype: int64"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XwkbckvSA7ua"
      },
      "outputs": [],
      "source": [
        "#sns.countplot(df['attack'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39JJqMjMA7ua",
        "outputId": "b48b72a8-4e8d-4519-c844-977de8115889"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Class distribution: Counter({1: 67343, 0: 58630})\n"
          ]
        }
      ],
      "source": [
        "print(\"Class distribution: {}\".format(Counter(y)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wsTAhm-3xBpi"
      },
      "source": [
        "**SCALLING**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dr6TR2fMA7ua"
      },
      "outputs": [],
      "source": [
        "scaler = StandardScaler()\n",
        "scaler.fit(X)\n",
        "\n",
        "\n",
        "# Save mean and scale\n",
        "np.save('scaler_mean.npy', scaler.mean_)\n",
        "np.save('scaler_scale.npy', scaler.scale_)\n",
        "\n",
        "\n",
        "X_transformed = scaler.transform(X)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhSvofVHxcRC"
      },
      "source": [
        "**PREPROCESSING THE TEST SET**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "U8AdoqOxA7ud",
        "outputId": "b55566e0-407a-4617-9213-2975c9c0c990"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "test_df"
            },
            "text/html": [
              "\n",
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              "    <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>duration</th>\n",
              "      <th>protocoltype</th>\n",
              "      <th>service</th>\n",
              "      <th>flag</th>\n",
              "      <th>srcbytes</th>\n",
              "      <th>dstbytes</th>\n",
              "      <th>land</th>\n",
              "      <th>wrongfragment</th>\n",
              "      <th>urgent</th>\n",
              "      <th>hot</th>\n",
              "      <th>...</th>\n",
              "      <th>dsthostsamesrvrate</th>\n",
              "      <th>dsthostdiffsrvrate</th>\n",
              "      <th>dsthostsamesrcportrate</th>\n",
              "      <th>dsthostsrvdiffhostrate</th>\n",
              "      <th>dsthostserrorrate</th>\n",
              "      <th>dsthostsrvserrorrate</th>\n",
              "      <th>dsthostrerrorrate</th>\n",
              "      <th>dsthostsrvrerrorrate</th>\n",
              "      <th>attack</th>\n",
              "      <th>lastflag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>REJ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>private</td>\n",
              "      <td>REJ</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>neptune</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>tcp</td>\n",
              "      <td>ftp_data</td>\n",
              "      <td>SF</td>\n",
              "      <td>12983</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>normal</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>icmp</td>\n",
              "      <td>eco_i</td>\n",
              "      <td>SF</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>saint</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>tcp</td>\n",
              "      <td>telnet</td>\n",
              "      <td>RSTO</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.71</td>\n",
              "      <td>mscan</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 43 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ef33cd5-7f72-45f3-8e40-1e48b8bdda6a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3ef33cd5-7f72-45f3-8e40-1e48b8bdda6a 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-3ef33cd5-7f72-45f3-8e40-1e48b8bdda6a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7668e2bd-4757-4090-bbdb-a19f011af8ae\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7668e2bd-4757-4090-bbdb-a19f011af8ae')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7668e2bd-4757-4090-bbdb-a19f011af8ae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   duration protocoltype   service  flag  srcbytes  dstbytes  land  \\\n",
              "0         0          tcp   private   REJ         0         0     0   \n",
              "1         0          tcp   private   REJ         0         0     0   \n",
              "2         2          tcp  ftp_data    SF     12983         0     0   \n",
              "3         0         icmp     eco_i    SF        20         0     0   \n",
              "4         1          tcp    telnet  RSTO         0        15     0   \n",
              "\n",
              "   wrongfragment  urgent  hot  ...  dsthostsamesrvrate  dsthostdiffsrvrate  \\\n",
              "0              0       0    0  ...                0.04                0.06   \n",
              "1              0       0    0  ...                0.00                0.06   \n",
              "2              0       0    0  ...                0.61                0.04   \n",
              "3              0       0    0  ...                1.00                0.00   \n",
              "4              0       0    0  ...                0.31                0.17   \n",
              "\n",
              "   dsthostsamesrcportrate  dsthostsrvdiffhostrate  dsthostserrorrate  \\\n",
              "0                    0.00                    0.00                0.0   \n",
              "1                    0.00                    0.00                0.0   \n",
              "2                    0.61                    0.02                0.0   \n",
              "3                    1.00                    0.28                0.0   \n",
              "4                    0.03                    0.02                0.0   \n",
              "\n",
              "   dsthostsrvserrorrate  dsthostrerrorrate  dsthostsrvrerrorrate   attack  \\\n",
              "0                   0.0               1.00                  1.00  neptune   \n",
              "1                   0.0               1.00                  1.00  neptune   \n",
              "2                   0.0               0.00                  0.00   normal   \n",
              "3                   0.0               0.00                  0.00    saint   \n",
              "4                   0.0               0.83                  0.71    mscan   \n",
              "\n",
              "   lastflag  \n",
              "0        21  \n",
              "1        21  \n",
              "2        21  \n",
              "3        15  \n",
              "4        11  \n",
              "\n",
              "[5 rows x 43 columns]"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_df = pd.read_csv(\"/content/data/Test.txt\",sep=\",\",names=[\"duration\",\"protocoltype\",\"service\",\"flag\",\"srcbytes\",\"dstbytes\",\"land\", \"wrongfragment\",\"urgent\",\"hot\",\"numfailedlogins\",\"loggedin\", \"numcompromised\",\"rootshell\",\"suattempted\",\"numroot\",\"numfilecreations\", \"numshells\",\"numaccessfiles\",\"numoutboundcmds\",\"ishostlogin\",\n",
        "\"isguestlogin\",\"count\",\"srvcount\",\"serrorrate\", \"srvserrorrate\",\n",
        "\"rerrorrate\",\"srvrerrorrate\",\"samesrvrate\", \"diffsrvrate\", \"srvdiffhostrate\",\"dsthostcount\",\"dsthostsrvcount\",\"dsthostsamesrvrate\", \"dsthostdiffsrvrate\",\"dsthostsamesrcportrate\",\n",
        "\"dsthostsrvdiffhostrate\",\"dsthostserrorrate\",\"dsthostsrvserrorrate\",\n",
        "\"dsthostrerrorrate\",\"dsthostsrvrerrorrate\",\"attack\", \"lastflag\"])\n",
        "test_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ZqzCdSr0A7ud",
        "outputId": "b5bc7c27-be48-408a-eb9c-8dd49d742c30"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      protocoltype   service  flag   attack\n",
              "0              tcp   private   REJ  neptune\n",
              "1              tcp   private   REJ  neptune\n",
              "2              tcp  ftp_data    SF   normal\n",
              "3             icmp     eco_i    SF    saint\n",
              "4              tcp    telnet  RSTO    mscan\n",
              "...            ...       ...   ...      ...\n",
              "22539          tcp      smtp    SF   normal\n",
              "22540          tcp      http    SF   normal\n",
              "22541          tcp      http    SF     back\n",
              "22542          udp  domain_u    SF   normal\n",
              "22543          tcp    sunrpc   REJ    mscan\n",
              "\n",
              "[22544 rows x 4 columns]"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_df.select_dtypes(exclude=[np.number])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wtAYiq63A7ue"
      },
      "outputs": [],
      "source": [
        "test_df['attack'].loc[test_df['attack']!='normal']='attack'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ltsyoi5qA7ue"
      },
      "outputs": [],
      "source": [
        "test_df['protocoltype']=le.fit_transform(test_df['protocoltype'])\n",
        "test_df['service']=le.fit_transform(test_df['service'])\n",
        "test_df['flag']=le.fit_transform(test_df['flag'])\n",
        "test_df['attack']=le.fit_transform(test_df['attack'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Ra282pbA7ue"
      },
      "outputs": [],
      "source": [
        "test_df.drop(['land','urgent','numfailedlogins','numoutboundcmds'],axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ObhgdAJ4A7uk"
      },
      "outputs": [],
      "source": [
        "X_test=test_df.drop(['attack'],axis=1)\n",
        "y_test=test_df['attack']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 256
        },
        "id": "oOLOpTxf8quQ",
        "outputId": "00f74f5a-8e62-40bf-94c3-be41ad93291c"
      },
      "outputs": [
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              "      <td>...</td>\n",
              "      <td>57</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>55</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>86</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.71</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 38 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-972d3ce3-8cb4-4b2a-9d4e-0170ee6a4a0d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-972d3ce3-8cb4-4b2a-9d4e-0170ee6a4a0d 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-972d3ce3-8cb4-4b2a-9d4e-0170ee6a4a0d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-40d34957-7b9b-4416-bc64-5dbacbd5641b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-40d34957-7b9b-4416-bc64-5dbacbd5641b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-40d34957-7b9b-4416-bc64-5dbacbd5641b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   duration  protocoltype  service  flag  srcbytes  dstbytes  wrongfragment  \\\n",
              "0         0             1       45     1         0         0              0   \n",
              "1         0             1       45     1         0         0              0   \n",
              "2         2             1       19     9     12983         0              0   \n",
              "3         0             0       13     9        20         0              0   \n",
              "4         1             1       55     2         0        15              0   \n",
              "\n",
              "   hot  loggedin  numcompromised  ...  dsthostsrvcount  dsthostsamesrvrate  \\\n",
              "0    0         0               0  ...               10                0.04   \n",
              "1    0         0               0  ...                1                0.00   \n",
              "2    0         0               0  ...               86                0.61   \n",
              "3    0         0               0  ...               57                1.00   \n",
              "4    0         0               0  ...               86                0.31   \n",
              "\n",
              "   dsthostdiffsrvrate  dsthostsamesrcportrate  dsthostsrvdiffhostrate  \\\n",
              "0                0.06                    0.00                    0.00   \n",
              "1                0.06                    0.00                    0.00   \n",
              "2                0.04                    0.61                    0.02   \n",
              "3                0.00                    1.00                    0.28   \n",
              "4                0.17                    0.03                    0.02   \n",
              "\n",
              "   dsthostserrorrate  dsthostsrvserrorrate  dsthostrerrorrate  \\\n",
              "0                0.0                   0.0               1.00   \n",
              "1                0.0                   0.0               1.00   \n",
              "2                0.0                   0.0               0.00   \n",
              "3                0.0                   0.0               0.00   \n",
              "4                0.0                   0.0               0.83   \n",
              "\n",
              "   dsthostsrvrerrorrate  lastflag  \n",
              "0                  1.00        21  \n",
              "1                  1.00        21  \n",
              "2                  0.00        21  \n",
              "3                  0.00        15  \n",
              "4                  0.71        11  \n",
              "\n",
              "[5 rows x 38 columns]"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kEcrGbARA7ul"
      },
      "outputs": [],
      "source": [
        "X_test_transformed = scaler.transform(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbkxjTr7A7ua"
      },
      "source": [
        "### Training the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ynpviHcVA7ua",
        "outputId": "0d2a995a-1842-468a-9844-1c24db245ac3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "lr=LogisticRegression()\n",
        "lr.fit(X_transformed,y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHWkSyhq_VEE"
      },
      "source": [
        "**RFC**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "4QgVTrIkBUrp",
        "outputId": "2b96ccc1-2d8a-47cd-bb59-5d84b84b58b8"
      },
      "outputs": [
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          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf=RandomForestClassifier()\n",
        "rf.fit(X_transformed,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        },
        "id": "9EjU464B-hyN",
        "outputId": "a849bb45-9b7d-45ab-a5f9-05151d57e5af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 2 folds for each of 16 candidates, totalling 32 fits\n",
            "[CV 1/2] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10;, score=0.998 total time=   0.6s\n",
            "[CV 2/2] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=10;, score=0.998 total time=   0.5s\n",
            "[CV 1/2] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.999 total time=   2.5s\n",
            "[CV 2/2] END max_depth=10, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.999 total time=   2.6s\n",
            "[CV 1/2] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=10;, score=0.998 total time=   0.8s\n",
            "[CV 2/2] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=10;, score=0.999 total time=   0.8s\n",
            "[CV 1/2] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=50;, score=0.999 total time=   3.7s\n",
            "[CV 2/2] END max_depth=10, min_samples_leaf=2, min_samples_split=10, n_estimators=50;, score=0.999 total time=   2.5s\n",
            "[CV 1/2] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=10;, score=0.998 total time=   0.5s\n",
            "[CV 2/2] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=10;, score=0.998 total time=   0.5s\n",
            "[CV 1/2] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=50;, score=0.999 total time=   2.5s\n",
            "[CV 2/2] END max_depth=10, min_samples_leaf=4, min_samples_split=5, n_estimators=50;, score=0.999 total time=   2.5s\n",
            "[CV 1/2] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=10;, score=0.998 total time=   0.5s\n",
            "[CV 2/2] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=10;, score=0.999 total time=   0.5s\n",
            "[CV 1/2] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=50;, score=0.999 total time=   3.5s\n",
            "[CV 2/2] END max_depth=10, min_samples_leaf=4, min_samples_split=10, n_estimators=50;, score=0.999 total time=   3.1s\n",
            "[CV 1/2] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=10;, score=0.999 total time=   0.5s\n",
            "[CV 2/2] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=10;, score=0.999 total time=   0.5s\n",
            "[CV 1/2] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=0.999 total time=   2.6s\n",
            "[CV 2/2] END max_depth=20, min_samples_leaf=2, min_samples_split=5, n_estimators=50;, score=1.000 total time=   2.6s\n",
            "[CV 1/2] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=10;, score=0.999 total time=   0.6s\n",
            "[CV 2/2] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=10;, score=0.999 total time=   0.5s\n",
            "[CV 1/2] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=50;, score=0.999 total time=   3.1s\n",
            "[CV 2/2] END max_depth=20, min_samples_leaf=2, min_samples_split=10, n_estimators=50;, score=0.999 total time=   3.8s\n",
            "[CV 1/2] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=10;, score=0.998 total time=   0.6s\n",
            "[CV 2/2] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=10;, score=0.999 total time=   0.5s\n",
            "[CV 1/2] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=50;, score=0.999 total time=   2.5s\n",
            "[CV 2/2] END max_depth=20, min_samples_leaf=4, min_samples_split=5, n_estimators=50;, score=0.999 total time=   2.5s\n",
            "[CV 1/2] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=10;, score=0.999 total time=   0.5s\n",
            "[CV 2/2] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=10;, score=0.999 total time=   0.5s\n",
            "[CV 1/2] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=50;, score=0.999 total time=   2.6s\n",
            "[CV 2/2] END max_depth=20, min_samples_leaf=4, min_samples_split=10, n_estimators=50;, score=0.999 total time=   3.9s\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=2, estimator=RandomForestClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [10, 20], &#x27;min_samples_leaf&#x27;: [2, 4],\n",
              "                         &#x27;min_samples_split&#x27;: [5, 10],\n",
              "                         &#x27;n_estimators&#x27;: [10, 50]},\n",
              "             verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=2, estimator=RandomForestClassifier(),\n",
              "             param_grid={&#x27;max_depth&#x27;: [10, 20], &#x27;min_samples_leaf&#x27;: [2, 4],\n",
              "                         &#x27;min_samples_split&#x27;: [5, 10],\n",
              "                         &#x27;n_estimators&#x27;: [10, 50]},\n",
              "             verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "GridSearchCV(cv=2, estimator=RandomForestClassifier(),\n",
              "             param_grid={'max_depth': [10, 20], 'min_samples_leaf': [2, 4],\n",
              "                         'min_samples_split': [5, 10],\n",
              "                         'n_estimators': [10, 50]},\n",
              "             verbose=3)"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#@title  Parameter grid for RandomForestClassifier\n",
        "rf_param_grid = {\n",
        "    'n_estimators': [10, 50],\n",
        "    'max_depth': [10, 20],\n",
        "    'min_samples_split': [5, 10],\n",
        "    'min_samples_leaf': [2, 4]\n",
        "}\n",
        "# RandomForestClassifier GridSearchCV\n",
        "rf_grid = GridSearchCV(RandomForestClassifier(), rf_param_grid, cv=2, verbose=3, refit=True)\n",
        "# Fit the model\n",
        "rf_grid.fit(X_transformed,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jjQ0L35z-wX5"
      },
      "outputs": [],
      "source": [
        "# Retrieve the best estimators\n",
        "best_rf_model = rf_grid.best_estimator_"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "RopblKb8gXX-",
        "outputId": "7a1e7fae-ab72-442a-e29a-670a61e2bf3d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=20, min_samples_leaf=2, min_samples_split=5,\n",
              "                       n_estimators=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=20, min_samples_leaf=2, min_samples_split=5,\n",
              "                       n_estimators=50)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(max_depth=20, min_samples_leaf=2, min_samples_split=5,\n",
              "                       n_estimators=50)"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "best_rf_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CktPvLxB_Prr"
      },
      "source": [
        "**SVC**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "SKOdNjWDB2yy",
        "outputId": "980b6bef-c4d3-4800-ca1c-cff6f2d705e5"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "SVC()"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create and train the SVM model\n",
        "svm_model = SVC()\n",
        "svm_model.fit(X_transformed, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdJvQYy5efp9",
        "outputId": "95e5e82d-b2ba-41af-8308-3d8132ca6c2e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
            "[CV 1/5] END .................C=0.1, gamma=0.01;, score=0.992 total time= 6.1min\n",
            "[CV 2/5] END .................C=0.1, gamma=0.01;, score=0.992 total time= 1.5min\n",
            "[CV 3/5] END .................C=0.1, gamma=0.01;, score=0.992 total time= 6.0min\n",
            "[CV 4/5] END .................C=0.1, gamma=0.01;, score=0.992 total time= 6.1min\n",
            "[CV 5/5] END .................C=0.1, gamma=0.01;, score=0.993 total time= 1.4min\n",
            "[CV 1/5] END ................C=0.1, gamma=0.001;, score=0.975 total time= 8.1min\n",
            "[CV 2/5] END ................C=0.1, gamma=0.001;, score=0.975 total time= 5.0min\n",
            "[CV 3/5] END ................C=0.1, gamma=0.001;, score=0.975 total time= 5.0min\n",
            "[CV 4/5] END ................C=0.1, gamma=0.001;, score=0.976 total time= 6.8min\n",
            "[CV 5/5] END ................C=0.1, gamma=0.001;, score=0.975 total time=10.4min\n",
            "[CV 1/5] END ...................C=1, gamma=0.01;, score=0.997 total time=  38.9s\n",
            "[CV 2/5] END ...................C=1, gamma=0.01;, score=0.996 total time=  36.6s\n",
            "[CV 3/5] END ...................C=1, gamma=0.01;, score=0.996 total time=  38.2s\n",
            "[CV 4/5] END ...................C=1, gamma=0.01;, score=0.996 total time=  36.3s\n",
            "[CV 5/5] END ...................C=1, gamma=0.01;, score=0.997 total time=  37.7s\n",
            "[CV 1/5] END ..................C=1, gamma=0.001;, score=0.983 total time= 7.2min\n",
            "[CV 2/5] END ..................C=1, gamma=0.001;, score=0.983 total time= 4.6min\n",
            "[CV 3/5] END ..................C=1, gamma=0.001;, score=0.985 total time= 9.2min\n",
            "[CV 4/5] END ..................C=1, gamma=0.001;, score=0.985 total time= 1.8min\n",
            "[CV 5/5] END ..................C=1, gamma=0.001;, score=0.984 total time= 6.0min\n"
          ]
        }
      ],
      "source": [
        "import joblib\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Define parameter range\n",
        "param_grid = {\n",
        "    'C': [0.1, 1],\n",
        "    'gamma': [0.01, 0.001]\n",
        "}\n",
        "\n",
        "# Initialize GridSearchCV object\n",
        "grid = GridSearchCV(SVC(), param_grid, refit=True, verbose=3)\n",
        "\n",
        "# Fit the model for grid search using the training data\n",
        "grid.fit(X_transformed, y)\n",
        "\n",
        "# Retrieve the best estimator from GridSearchCV\n",
        "best_Svc_model = grid.best_estimator_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RvO07R55ho2Z"
      },
      "source": [
        "**DEEP LEARNING**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Me2MICyn_hKY"
      },
      "source": [
        "**ANN model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "daXRsIJoCKi2",
        "outputId": "94cc23a0-c7a4-4807-dc16-2ee82b8a3aec"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "MLPClassifier()"
            ]
          },
          "execution_count": 42,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "# Create and train the ANN model\n",
        "ann_model = MLPClassifier()\n",
        "ann_model.fit(X_transformed, y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "53yrt-eb_eiu"
      },
      "outputs": [],
      "source": [
        "# Parameter grid for MLPClassifier\n",
        "mlp_param_grid = {\n",
        "    'hidden_layer_sizes': [(50,), (100,)],\n",
        "    'activation': ['relu', 'tanh'],\n",
        "    'alpha': [0.0001, 0.001],\n",
        "    'learning_rate': ['constant', 'adaptive']\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "fBfobUOMewgx",
        "outputId": "8acc45eb-afbc-4ff5-ab2f-3c8c2d2b6bd9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fitting 3 folds for each of 16 candidates, totalling 48 fits\n",
            "[CV 1/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.999 total time=  28.0s\n",
            "[CV 2/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.998 total time=  24.1s\n",
            "[CV 3/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.999 total time=  24.9s\n",
            "[CV 1/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  30.7s\n",
            "[CV 2/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  23.7s\n",
            "[CV 3/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  27.6s\n",
            "[CV 1/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time=  38.9s\n",
            "[CV 2/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time=  34.3s\n",
            "[CV 3/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time=  39.0s\n",
            "[CV 1/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.999 total time=  33.0s\n",
            "[CV 2/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.999 total time=  33.7s\n",
            "[CV 3/3] END activation=relu, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.999 total time=  35.7s\n",
            "[CV 1/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.999 total time=  27.0s\n",
            "[CV 2/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.999 total time=  28.9s\n",
            "[CV 3/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.999 total time=  21.0s\n",
            "[CV 1/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  18.1s\n",
            "[CV 2/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  24.5s\n",
            "[CV 3/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  19.7s\n",
            "[CV 1/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time=  40.1s\n",
            "[CV 2/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time=  28.2s\n",
            "[CV 3/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time=  31.6s\n",
            "[CV 1/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.998 total time=  37.4s\n",
            "[CV 2/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.998 total time=  32.7s\n",
            "[CV 3/3] END activation=relu, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.999 total time=  26.5s\n",
            "[CV 1/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.999 total time=  35.1s\n",
            "[CV 2/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.999 total time=  36.6s\n",
            "[CV 3/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.999 total time=  41.0s\n",
            "[CV 1/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  38.8s\n",
            "[CV 2/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  53.2s\n",
            "[CV 3/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  34.9s\n",
            "[CV 1/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time=  58.9s\n",
            "[CV 2/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time= 1.1min\n",
            "[CV 3/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time= 1.1min\n",
            "[CV 1/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.999 total time=  57.1s\n",
            "[CV 2/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.998 total time= 1.0min\n",
            "[CV 3/3] END activation=tanh, alpha=0.0001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.999 total time= 1.2min\n",
            "[CV 1/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.999 total time=  36.4s\n",
            "[CV 2/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.998 total time=  40.5s\n",
            "[CV 3/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=constant;, score=0.998 total time=  37.7s\n",
            "[CV 1/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.998 total time=  33.3s\n",
            "[CV 2/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.998 total time=  31.7s\n",
            "[CV 3/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(50,), learning_rate=adaptive;, score=0.999 total time=  30.9s\n",
            "[CV 1/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time= 1.2min\n",
            "[CV 2/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.998 total time= 1.1min\n",
            "[CV 3/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=constant;, score=0.999 total time= 1.0min\n",
            "[CV 1/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.999 total time=  56.8s\n",
            "[CV 2/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.999 total time=  56.7s\n",
            "[CV 3/3] END activation=tanh, alpha=0.001, hidden_layer_sizes=(100,), learning_rate=adaptive;, score=0.999 total time= 1.2min\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3, estimator=MLPClassifier(),\n",
              "             param_grid={&#x27;activation&#x27;: [&#x27;relu&#x27;, &#x27;tanh&#x27;],\n",
              "                         &#x27;alpha&#x27;: [0.0001, 0.001],\n",
              "                         &#x27;hidden_layer_sizes&#x27;: [(50,), (100,)],\n",
              "                         &#x27;learning_rate&#x27;: [&#x27;constant&#x27;, &#x27;adaptive&#x27;]},\n",
              "             verbose=3)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3, estimator=MLPClassifier(),\n",
              "             param_grid={&#x27;activation&#x27;: [&#x27;relu&#x27;, &#x27;tanh&#x27;],\n",
              "                         &#x27;alpha&#x27;: [0.0001, 0.001],\n",
              "                         &#x27;hidden_layer_sizes&#x27;: [(50,), (100,)],\n",
              "                         &#x27;learning_rate&#x27;: [&#x27;constant&#x27;, &#x27;adaptive&#x27;]},\n",
              "             verbose=3)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
            ],
            "text/plain": [
              "GridSearchCV(cv=3, estimator=MLPClassifier(),\n",
              "             param_grid={'activation': ['relu', 'tanh'],\n",
              "                         'alpha': [0.0001, 0.001],\n",
              "                         'hidden_layer_sizes': [(50,), (100,)],\n",
              "                         'learning_rate': ['constant', 'adaptive']},\n",
              "             verbose=3)"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# MLPClassifier GridSearchCV\n",
        "mlp_grid = GridSearchCV(MLPClassifier(), mlp_param_grid, cv=3, verbose=3, refit=True)\n",
        "mlp_grid.fit(X_transformed,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "S09jFTkzfP8g"
      },
      "outputs": [],
      "source": [
        "# Retrieve the best estimators\n",
        "best_mlp_model = mlp_grid.best_estimator_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RkkQbfAL_s5B"
      },
      "source": [
        "**LSTM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "mMZz-xiwwVI9"
      },
      "outputs": [],
      "source": [
        "X_lstm = np.expand_dims(X_transformed, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "xfJEAkO7HKVo",
        "outputId": "2bb6b329-a2fe-4b39-adc7-d4ad01731e75"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "LSTM Model Architecture:\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 1, 128)            85504     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 1, 128)            0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 1, 64)             49408     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 1, 64)             0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 32)                12416     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 33        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 147361 (575.63 KB)\n",
            "Trainable params: 147361 (575.63 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/20\n",
            "1969/1969 [==============================] - 25s 9ms/step - loss: 0.0354 - accuracy: 0.9896\n",
            "Epoch 2/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0103 - accuracy: 0.9961\n",
            "Epoch 3/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0086 - accuracy: 0.9966\n",
            "Epoch 4/20\n",
            "1969/1969 [==============================] - 19s 10ms/step - loss: 0.0074 - accuracy: 0.9969\n",
            "Epoch 5/20\n",
            "1969/1969 [==============================] - 19s 10ms/step - loss: 0.0066 - accuracy: 0.9974\n",
            "Epoch 6/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0057 - accuracy: 0.9978\n",
            "Epoch 7/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0049 - accuracy: 0.9980\n",
            "Epoch 8/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0046 - accuracy: 0.9982\n",
            "Epoch 9/20\n",
            "1969/1969 [==============================] - 19s 10ms/step - loss: 0.0041 - accuracy: 0.9985\n",
            "Epoch 10/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0041 - accuracy: 0.9984\n",
            "Epoch 11/20\n",
            "1969/1969 [==============================] - 20s 10ms/step - loss: 0.0038 - accuracy: 0.9986\n",
            "Epoch 12/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0033 - accuracy: 0.9987\n",
            "Epoch 13/20\n",
            "1969/1969 [==============================] - 20s 10ms/step - loss: 0.0035 - accuracy: 0.9987\n",
            "Epoch 14/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0033 - accuracy: 0.9989\n",
            "Epoch 15/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0033 - accuracy: 0.9988\n",
            "Epoch 16/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0032 - accuracy: 0.9988\n",
            "Epoch 17/20\n",
            "1969/1969 [==============================] - 19s 10ms/step - loss: 0.0031 - accuracy: 0.9988\n",
            "Epoch 18/20\n",
            "1969/1969 [==============================] - 19s 10ms/step - loss: 0.0028 - accuracy: 0.9990\n",
            "Epoch 19/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0029 - accuracy: 0.9990\n",
            "Epoch 20/20\n",
            "1969/1969 [==============================] - 18s 9ms/step - loss: 0.0027 - accuracy: 0.9990\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x78276209d900>"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "#@title Defining LSTM model\n",
        "lstm_model = Sequential([\n",
        "    LSTM(128, return_sequences=True, input_shape=(X_lstm.shape[1], X_lstm.shape[2])),\n",
        "    Dropout(0.2),  # Adding dropout for regularization\n",
        "    LSTM(64, return_sequences=True),\n",
        "    Dropout(0.2),\n",
        "    LSTM(32),  # Last LSTM layer without return_sequences\n",
        "    Dropout(0.2),\n",
        "    Dense(1, activation='sigmoid')  # Output layer\n",
        "])\n",
        "\n",
        "# Compile model\n",
        "optimizer = Adam(learning_rate=0.001)\n",
        "lstm_model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "# Print the architecture of the LSTM model\n",
        "print(\"LSTM Model Architecture:\")\n",
        "print(lstm_model.summary())\n",
        "# Train model\n",
        "lstm_model.fit(X_lstm, y, epochs=20, batch_size=64, verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "UFyNDkaNfBo8",
        "outputId": "dbaabb45-5d79-450d-968a-d9fca3d345e4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Trial 4 Complete [00h 00m 53s]\n",
            "val_accuracy: 0.9978170394897461\n",
            "\n",
            "Best val_accuracy So Far: 0.9978170394897461\n",
            "Total elapsed time: 00h 03m 29s\n"
          ]
        }
      ],
      "source": [
        "! pip install keras-tuner\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "from keras_tuner import Hyperband\n",
        "\n",
        "# Define a function to create the LSTM model for tuning\n",
        "def create_lstm_model(hp):\n",
        "    model = Sequential([\n",
        "        LSTM(hp.Int('lstm_units', min_value=50, max_value=200, step=50),\n",
        "             input_shape=(X_lstm.shape[1], X_lstm.shape[2])),\n",
        "        Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "\n",
        "    # Compile model with hyperparameters\n",
        "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "    return model\n",
        "\n",
        "# Create a tuner using Hyperband\n",
        "tuner = Hyperband(create_lstm_model,\n",
        "                  max_epochs=20,  # Maximum number of epochs to train\n",
        "                  objective='val_accuracy',  # Objective for tuning\n",
        "                  factor=3,  # Hyperband factor\n",
        "                  directory='lstm_tuning',  # Directory to save tuning results\n",
        "                  project_name='lstm_hyperparameter_tuning')\n",
        "\n",
        "# Perform the tuning\n",
        "tuner.search(X_lstm, y, epochs=10, batch_size=32, validation_split=0.2, verbose=1)\n",
        "# Retrieve the best model\n",
        "best_lstm_model = tuner.get_best_models(num_models=1)[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpltjUq_Fo4-"
      },
      "source": [
        "### Predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "N6uCkMtVBWue"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy\n",
        "from sklearn import metrics\n",
        "\n",
        "def confusion_plot(actual, predicted):\n",
        "  confusion_matrix = metrics.confusion_matrix(actual, predicted)\n",
        "  cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix, display_labels = ['attack', 'normal'])\n",
        "  cm_display.plot()\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "zq-K0WN11yLE"
      },
      "outputs": [],
      "source": [
        "#@title gobal fuction for prediction\n",
        "\n",
        "import pandas as pd\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import metrics\n",
        "\n",
        "df_all = pd.DataFrame(columns=['Model', 'Accuracy', 'F1 Score', 'Recall', 'Precision'])\n",
        "\n",
        "def confusion_plot(actual, predicted):\n",
        "    confusion_matrix_ = confusion_matrix(actual, predicted)\n",
        "    cm_display = ConfusionMatrixDisplay(confusion_matrix=confusion_matrix_, display_labels=['attack', 'normal'])\n",
        "    cm_display.plot()\n",
        "    plt.show()\n",
        "\n",
        "def predictor_(x_test, y_test, model, model_name, df):\n",
        "    pred = model.predict(x_test)\n",
        "    accuracy_ = accuracy_score(y_test, pred)\n",
        "    clas = classification_report(y_test, pred, output_dict=True)\n",
        "    f1_score_ = clas['macro avg']['f1-score']\n",
        "    recall_ = clas['macro avg']['recall']\n",
        "    precision_ = clas['macro avg']['precision']\n",
        "    confusion_plot(y_test, pred)\n",
        "\n",
        "    # create df with the varible\n",
        "    result_df = pd.DataFrame({\n",
        "        'Model': [model_name],\n",
        "        'Accuracy': [accuracy_],\n",
        "        'F1 Score': [f1_score_],\n",
        "        'Recall': [recall_],\n",
        "        'Precision': [precision_]\n",
        "    })\n",
        "\n",
        "    df_all = pd.concat([df, result_df])\n",
        "\n",
        "    return df_all\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, classification_report, confusion_matrix\n",
        "\n",
        "def evaluate_deepmodel(model, X_test, y_test, model_name):\n",
        "    # Reshape X_test for the model input\n",
        "    X_test_input = np.expand_dims(X_test, axis=1)\n",
        "\n",
        "    # Predict using the model\n",
        "    model_pred = (model.predict(X_test_input) > 0.5).astype(int).flatten()\n",
        "\n",
        "    # Calculate evaluation metrics\n",
        "    accuracy = accuracy_score(y_test, model_pred)\n",
        "    precision = precision_score(y_test, model_pred)\n",
        "    recall = recall_score(y_test, model_pred)\n",
        "    f1 = f1_score(y_test, model_pred)\n",
        "\n",
        "    # Classification report\n",
        "    class_report = classification_report(y_test, model_pred)\n",
        "\n",
        "    # Confusion matrix\n",
        "    cm = confusion_plot(y_test, model_pred)\n",
        "\n",
        "    # Store evaluation results in a dictionary\n",
        "    evaluation_results = {\n",
        "        \"Accuracy\": accuracy,\n",
        "        \"Precision\": precision,\n",
        "        \"Recall\": recall,\n",
        "        \"F1-score\": f1,\n",
        "        \"Model name\": model_name\n",
        "    }\n",
        "\n",
        "    # Create a DataFrame from the dictionary\n",
        "    df = pd.DataFrame(evaluation_results, index=[0])\n",
        "\n",
        "    # Print the classification report\n",
        "    print(class_report)\n",
        "    # Return the DataFrame containing evaluation results\n",
        "    return df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaO1Ww0qBiPh"
      },
      "source": [
        "**Using Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "KcOQksWcA7ub",
        "outputId": "ef4aa2b7-4a97-41c4-b8f7-4bdfda2adb6c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "lr_df = predictor_(X_test_transformed, y_test, lr, 'Logistic Regression', df_all)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "dGXwZ24_A7ub",
        "outputId": "69a7d56c-6a64-4663-95ac-acd476b1753e"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "        const element = document.querySelector('#df-e68bdc80-e1a0-46af-a4af-80b380b1f05d');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "                 Model  Accuracy  F1 Score   Recall  Precision\n",
              "0  Logistic Regression  0.842575  0.842243  0.85258   0.846578"
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      "metadata": {
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        },
        "id": "oGS--r1y9i2B",
        "outputId": "33bef994-75d6-468c-9436-e34bf71f389b"
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      "outputs": [
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              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1 Score</th>\n",
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              "      <th>Precision</th>\n",
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              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "      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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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-694892a0-7abd-488c-bdda-fccf262eb7b9 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-694892a0-7abd-488c-bdda-fccf262eb7b9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Model, Accuracy, F1 Score, Recall, Precision]\n",
              "Index: []"
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          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
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      "source": [
        "df_all"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muiR5QbJA7ub"
      },
      "source": [
        "**Using Random Forest Classifier**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "DtZqM7HKA7uc",
        "outputId": "1ffa6e0a-3ca4-4caf-f95b-cf9d5e047b9b"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "rf_df = predictor_(X_test_transformed, y_test, rf, 'Random Forest Classifier Base model', lr_df)\n",
        "rf_model = predictor_(X_test_transformed, y_test, best_rf_model, 'Random Forest Classifier fine tuned', rf_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ZSWFTen7A7uc",
        "outputId": "badc947b-afd8-4f48-c962-c305a125b59f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "      <th>Model</th>\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "                                 Model  Accuracy  F1 Score   Recall  Precision\n",
              "0                  Logistic Regression  0.842575  0.842243  0.85258   0.846578\n",
              "0  Random Forest Classifier Base model  0.819686  0.819625  0.83830   0.842310\n",
              "0  Random Forest Classifier fine tuned  0.815295  0.815200  0.83443   0.839476"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "rf_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eyHGYBSb7oje"
      },
      "source": [
        "**Support Vector machine**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "i_7L6b_b7n0x",
        "outputId": "61227de4-c5ab-4808-b733-6493c803117a"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "svm_df = predictor_(X_test_transformed, y_test, svm_model, 'Support Vector machine base model', df_all)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "l_0Os9bQE8Fc",
        "outputId": "49e620ed-8f0e-4e56-d4a8-cb19f5d48bd1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"svm_df\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Support Vector machine base model\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8576561391057488,\n        \"max\": 0.8576561391057488,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8576561391057488\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1 Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8575575822654443,\n        \"max\": 0.8575575822654443,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8575575822654443\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8707119546686026,\n        \"max\": 0.8707119546686026,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8707119546686026\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.8663038403767304,\n        \"max\": 0.8663038403767304,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.8663038403767304\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "svm_df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-f4e4320a-be5a-4e81-a3d4-49357439f831\" 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>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Recall</th>\n",
              "      <th>Precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Support Vector machine base model</td>\n",
              "      <td>0.857656</td>\n",
              "      <td>0.857558</td>\n",
              "      <td>0.870712</td>\n",
              "      <td>0.866304</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4e4320a-be5a-4e81-a3d4-49357439f831')\"\n",
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              "  </svg>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f4e4320a-be5a-4e81-a3d4-49357439f831 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-f4e4320a-be5a-4e81-a3d4-49357439f831');\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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              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                               Model  Accuracy  F1 Score    Recall  Precision\n",
              "0  Support Vector machine base model  0.857656  0.857558  0.870712   0.866304"
            ]
          },
          "execution_count": 57,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "svm_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-ybOmRjyEvO_"
      },
      "outputs": [],
      "source": [
        "Svc_model_df = predictor_(X_test_transformed, y_test, best_Svc_model, 'Support Vector machine fine tuned', df_all)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vZ1Yv3tdE9rj"
      },
      "outputs": [],
      "source": [
        "Svc_model_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KbZ4tt2g_emX"
      },
      "source": [
        "**Artificial Neural Network (ANN)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PKB0JtiWAdOf"
      },
      "outputs": [],
      "source": [
        "# Make predictions on the test set\n",
        "ann_pd = predictor_(X_test_transformed, y_test, ann_model, 'ANN Base model', df_all)\n",
        "ann_pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U397cNhRFR4o"
      },
      "outputs": [],
      "source": [
        "best_ann_pd = predictor_(X_test_transformed, y_test, best_mlp_model, 'ANN fine tuned', df_all)\n",
        "best_ann_pd"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i7D0JuauI1qA"
      },
      "source": [
        "**Using LSTM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KI7Novi0x_p1"
      },
      "outputs": [],
      "source": [
        "evaluation_df = evaluate_deepmodel(lstm_model, X_test_transformed, y_test, \"LSTM Base\")\n",
        "evaluation_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "89xCVMmyAR5M"
      },
      "source": [
        "**with tuning Lstm**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SYFREjN4AYBx"
      },
      "outputs": [],
      "source": [
        "evaluation_df_tune = evaluate_deepmodel(best_lstm_model, X_test_transformed, y_test, \"LSTM Tuned\")\n",
        "evaluation_df_tune"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NfPgpO0bENg-"
      },
      "outputs": [],
      "source": [
        "# Save the best LSTM model\n",
        "best_lstm_model.save('best_lstm_model.h5')"
      ]
    }
  ],
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