cyberattacks.ipynb

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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Detection of Cyber Attacks using ML**"
      ],
      "metadata": {
        "id": "LLzs9IdXVI6K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install catboost"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2rLvaLlkYxcr",
        "outputId": "25fd4601-a2f5-4c0f-c5ac-2a9e5ea5f7f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: catboost in /usr/local/lib/python3.9/dist-packages (1.1.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.9/dist-packages (from catboost) (1.16.0)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.9/dist-packages (from catboost) (1.4.4)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.9/dist-packages (from catboost) (0.20.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.9/dist-packages (from catboost) (1.22.4)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.9/dist-packages (from catboost) (5.13.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (from catboost) (1.10.1)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.24.0->catboost) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas>=0.24.0->catboost) (2022.7.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (1.0.7)\n",
            "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (5.12.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (0.11.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (23.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (1.4.4)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (4.39.2)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (8.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->catboost) (3.0.9)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.9/dist-packages (from plotly->catboost) (8.2.2)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources>=3.2.0->matplotlib->catboost) (3.15.0)\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "#importing basic packages\n",
        "\n",
        "import os \n",
        "import timeit\n",
        "import warnings \n",
        "from collections import defaultdict\n",
        "\n",
        "import catboost as cb \n",
        "import lightgbm as lgb\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np \n",
        "import pandas as pd\n",
        "import xgboost as xgb\n",
        "import copy\n",
        "from imblearn.under_sampling import CondensedNearestNeighbour\n",
        "from sklearn.ensemble import AdaBoostClassifier \n",
        "from sklearn.ensemble import RandomForestClassifier, VotingClassifier\n",
        "from sklearn.linear_model import LogisticRegression \n",
        "from sklearn.linear_model import SGDClassifier \n",
        "from sklearn.metrics import accuracy_score, classification_report\n",
        "#plot_confusion_matrix \n",
        "from sklearn.metrics import confusion_matrix, zero_one_loss\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import SVC \n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from termcolor import colored"
      ],
      "metadata": {
        "id": "OAaWoN6xFfkr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('KDDTrain+.txt', sep=\",\", header=None)\n",
        "df_train = df_train[df_train.columns[:-1]]\n",
        "titles = pd.read_csv('Field Names.csv', header=None)\n",
        "label = pd.Series(['label'], index=[41])\n",
        "titles = titles[0].append(label)\n",
        "df_train.columns = titles.to_list()\n",
        "y_train = df_train['label']\n",
        "df_train = df_train.drop(['num_outbound_cmds'],axis=1)\n",
        "df_train_original = df_train\n",
        "df_train_original"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "hgMPtRwRdtzj",
        "outputId": "3c9dc265-51fc-4512-a1af-2ba32d7b3721"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-ac049c3821e4>:5: FutureWarning: The series.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
            "  titles = titles[0].append(label)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        duration protocol_type   service flag  src_bytes  dst_bytes  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",
              "        wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
              "0                    0       0    0  ...                  25   \n",
              "1                    0       0    0  ...                   1   \n",
              "2                    0       0    0  ...                  26   \n",
              "3                    0       0    0  ...                 255   \n",
              "4                    0       0    0  ...                 255   \n",
              "...                ...     ...  ...  ...                 ...   \n",
              "125968               0       0    0  ...                  25   \n",
              "125969               0       0    0  ...                 244   \n",
              "125970               0       0    0  ...                  30   \n",
              "125971               0       0    0  ...                   8   \n",
              "125972               0       0    0  ...                  77   \n",
              "\n",
              "        dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                         0.17                    0.03   \n",
              "1                         0.00                    0.60   \n",
              "2                         0.10                    0.05   \n",
              "3                         1.00                    0.00   \n",
              "4                         1.00                    0.00   \n",
              "...                        ...                     ...   \n",
              "125968                    0.10                    0.06   \n",
              "125969                    0.96                    0.01   \n",
              "125970                    0.12                    0.06   \n",
              "125971                    0.03                    0.05   \n",
              "125972                    0.30                    0.03   \n",
              "\n",
              "        dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                              0.17                         0.00   \n",
              "1                              0.88                         0.00   \n",
              "2                              0.00                         0.00   \n",
              "3                              0.03                         0.04   \n",
              "4                              0.00                         0.00   \n",
              "...                             ...                          ...   \n",
              "125968                         0.00                         0.00   \n",
              "125969                         0.01                         0.00   \n",
              "125970                         0.00                         0.00   \n",
              "125971                         0.00                         0.00   \n",
              "125972                         0.30                         0.00   \n",
              "\n",
              "        dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\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",
              "        dst_host_srv_rerror_rate    label  \n",
              "0                           0.00   normal  \n",
              "1                           0.00   normal  \n",
              "2                           0.00  neptune  \n",
              "3                           0.01   normal  \n",
              "4                           0.00   normal  \n",
              "...                          ...      ...  \n",
              "125968                      0.00  neptune  \n",
              "125969                      0.00   normal  \n",
              "125970                      0.00   normal  \n",
              "125971                      0.00  neptune  \n",
              "125972                      0.00   normal  \n",
              "\n",
              "[125973 rows x 41 columns]"
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              "      <td>SF</td>\n",
              "      <td>151</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>77</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",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>125973 rows × 41 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('KDDTest+.txt', sep=\",\", header=None)\n",
        "df_test = df_test[df_test.columns[:-1]]\n",
        "df_test.columns = titles.to_list()\n",
        "y_test = df_test['label']\n",
        "df_test = df_test.drop(['num_outbound_cmds'],axis=1)\n",
        "df_test_original = df_test\n",
        "df_test_original"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "WmUhc4u5dt8q",
        "outputId": "42c39145-5e26-4c50-8ec1-07916049d107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       duration protocol_type   service  flag  src_bytes  dst_bytes  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",
              "22539         0           tcp      smtp    SF        794        333     0   \n",
              "22540         0           tcp      http    SF        317        938     0   \n",
              "22541         0           tcp      http    SF      54540       8314     0   \n",
              "22542         0           udp  domain_u    SF         42         42     0   \n",
              "22543         0           tcp    sunrpc   REJ          0          0     0   \n",
              "\n",
              "       wrong_fragment  urgent  hot  ...  dst_host_srv_count  \\\n",
              "0                   0       0    0  ...                  10   \n",
              "1                   0       0    0  ...                   1   \n",
              "2                   0       0    0  ...                  86   \n",
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              "22543               0       0    0  ...                  21   \n",
              "\n",
              "       dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                        0.04                    0.06   \n",
              "1                        0.00                    0.06   \n",
              "2                        0.61                    0.04   \n",
              "3                        1.00                    0.00   \n",
              "4                        0.31                    0.17   \n",
              "...                       ...                     ...   \n",
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              "22540                    1.00                    0.00   \n",
              "22541                    1.00                    0.00   \n",
              "22542                    0.99                    0.01   \n",
              "22543                    0.08                    0.03   \n",
              "\n",
              "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                             0.00                         0.00   \n",
              "1                             0.00                         0.00   \n",
              "2                             0.61                         0.02   \n",
              "3                             1.00                         0.28   \n",
              "4                             0.03                         0.02   \n",
              "...                            ...                          ...   \n",
              "22539                         0.01                         0.01   \n",
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              "22541                         0.00                         0.00   \n",
              "22542                         0.00                         0.00   \n",
              "22543                         0.00                         0.00   \n",
              "\n",
              "       dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "0                      0.00                       0.0                  1.00   \n",
              "1                      0.00                       0.0                  1.00   \n",
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              "4                      0.00                       0.0                  0.83   \n",
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              "22542                  0.00                       0.0                  0.00   \n",
              "22543                  0.00                       0.0                  0.44   \n",
              "\n",
              "       dst_host_srv_rerror_rate    label  \n",
              "0                          1.00  neptune  \n",
              "1                          1.00  neptune  \n",
              "2                          0.00   normal  \n",
              "3                          0.00    saint  \n",
              "4                          0.71    mscan  \n",
              "...                         ...      ...  \n",
              "22539                      0.00   normal  \n",
              "22540                      0.00   normal  \n",
              "22541                      0.07     back  \n",
              "22542                      0.00   normal  \n",
              "22543                      1.00    mscan  \n",
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              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1.00</td>\n",
              "      <td>mscan</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22544 rows × 41 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c17ac4eb-fc24-4d50-8fa7-09c93b885bcf')\"\n",
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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = copy.deepcopy(df_train_original)\n",
        "df_test = copy.deepcopy(df_test_original)"
      ],
      "metadata": {
        "id": "FmgEq8tRdt_N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dos_probe_list = ['apache2','back','land','neptune','mailbomb','pod','processtable','smurf','teardrop','udpstorm','worm','ipsweep','mscan','nmap','portsweep','saint','satan']\n",
        "\n",
        "y_train = np.array([1 if x in dos_probe_list else 0 for x in df_train['label']])\n",
        "\n",
        "df_train = df_train.drop(['label'],axis=1)\n",
        "df_train = df_train.reset_index().drop(['index'], axis=1)\n",
        "df_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "pMqUuR7-duBS",
        "outputId": "1a9ba138-0121-4643-d262-a2ac9489f768"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        duration protocol_type   service flag  src_bytes  dst_bytes  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",
              "        wrong_fragment  urgent  hot  ...  dst_host_count  dst_host_srv_count  \\\n",
              "0                    0       0    0  ...             150                  25   \n",
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              "125971               0       0    0  ...             255                   8   \n",
              "125972               0       0    0  ...             255                  77   \n",
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              "        dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                         0.17                    0.03   \n",
              "1                         0.00                    0.60   \n",
              "2                         0.10                    0.05   \n",
              "3                         1.00                    0.00   \n",
              "4                         1.00                    0.00   \n",
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              "125970                    0.12                    0.06   \n",
              "125971                    0.03                    0.05   \n",
              "125972                    0.30                    0.03   \n",
              "\n",
              "        dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                              0.17                         0.00   \n",
              "1                              0.88                         0.00   \n",
              "2                              0.00                         0.00   \n",
              "3                              0.03                         0.04   \n",
              "4                              0.00                         0.00   \n",
              "...                             ...                          ...   \n",
              "125968                         0.00                         0.00   \n",
              "125969                         0.01                         0.00   \n",
              "125970                         0.00                         0.00   \n",
              "125971                         0.00                         0.00   \n",
              "125972                         0.30                         0.00   \n",
              "\n",
              "        dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "0                       0.00                      0.00                  0.05   \n",
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              "4                       0.00                      0.00                  0.00   \n",
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              "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",
              "        dst_host_srv_rerror_rate  \n",
              "0                           0.00  \n",
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              "2                           0.00  \n",
              "3                           0.01  \n",
              "4                           0.00  \n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>125973 rows × 40 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e8f5307c-2b58-4077-82b0-d513201e62a1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e8f5307c-2b58-4077-82b0-d513201e62a1 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-e8f5307c-2b58-4077-82b0-d513201e62a1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import matthews_corrcoef\n",
        "from sklearn.feature_selection import SelectKBest, chi2, f_classif, mutual_info_classif\n",
        "X_train = df_train.drop(['protocol_type','service','flag'],axis=1)\n",
        "mutual_info = mutual_info_classif(X_train, y_train)\n",
        "mutual_info = pd.Series(mutual_info)\n",
        "mutual_info.index = X_train.columns\n",
        "mutual_info.sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtcGq5HNduCz",
        "outputId": "e6cbe51c-3d0f-4164-9f99-581ab205f72f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "src_bytes                      0.575780\n",
              "dst_bytes                      0.436269\n",
              "same_srv_rate                  0.381445\n",
              "diff_srv_rate                  0.371770\n",
              "dst_host_srv_count             0.331658\n",
              "dst_host_same_srv_rate         0.320802\n",
              "logged_in                      0.318627\n",
              "dst_host_diff_srv_rate         0.293879\n",
              "dst_host_serror_rate           0.293492\n",
              "dst_host_srv_serror_rate       0.288411\n",
              "serror_rate                    0.286846\n",
              "srv_serror_rate                0.277032\n",
              "count                          0.273861\n",
              "dst_host_srv_diff_host_rate    0.192514\n",
              "dst_host_count                 0.150179\n",
              "dst_host_same_src_port_rate    0.126397\n",
              "srv_diff_host_rate             0.095213\n",
              "srv_count                      0.069319\n",
              "dst_host_srv_rerror_rate       0.066643\n",
              "rerror_rate                    0.041684\n",
              "dst_host_rerror_rate           0.037650\n",
              "srv_rerror_rate                0.035912\n",
              "duration                       0.027572\n",
              "hot                            0.009803\n",
              "wrong_fragment                 0.006909\n",
              "is_guest_login                 0.005588\n",
              "num_root                       0.005080\n",
              "num_compromised                0.003664\n",
              "num_failed_logins              0.003266\n",
              "root_shell                     0.003107\n",
              "num_shells                     0.003051\n",
              "num_file_creations             0.001996\n",
              "urgent                         0.001629\n",
              "is_host_login                  0.000914\n",
              "land                           0.000372\n",
              "num_access_files               0.000066\n",
              "su_attempted                   0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mutual_info.sort_values(ascending=False).plot.bar(figsize=(20, 8));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "ItNaq_LKduE4",
        "outputId": "fd21a3ce-7735-4450-b4f2-120e9ca6814e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sel_five_cols = SelectKBest(mutual_info_classif, k=20)\n",
        "sel_five_cols.fit(X_train, y_train)\n",
        "X_train.columns[sel_five_cols.get_support()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EuFUognoduG-",
        "outputId": "3288cb7e-1681-466d-84a5-f80fa030b167"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['src_bytes', 'dst_bytes', 'logged_in', 'count', 'srv_count',\n",
              "       'serror_rate', 'srv_serror_rate', 'rerror_rate', 'same_srv_rate',\n",
              "       'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count',\n",
              "       'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
              "       'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
              "       'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
              "       'dst_host_srv_serror_rate', 'dst_host_srv_rerror_rate'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df_train[['logged_in', 'count', 'serror_rate', 'rerror_rate', 'same_srv_rate',\n",
        "       'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
        "       'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
        "       'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
        "       'dst_host_srv_serror_rate', 'dst_host_srv_rerror_rate']]"
      ],
      "metadata": {
        "id": "ZsPj-swGgSO6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler1 = MinMaxScaler()\n",
        "df_minmax = scaler1.fit_transform(X_train)\n",
        "X_train = pd.DataFrame(df_minmax, columns=(X_train.columns))\n",
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "DWojsxrZgSRF",
        "outputId": "308fb044-8020-4333-ec65-2b943bff1f95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        logged_in     count  serror_rate  rerror_rate  same_srv_rate  \\\n",
              "0             0.0  0.003914          0.0          0.0           1.00   \n",
              "1             0.0  0.025440          0.0          0.0           0.08   \n",
              "2             0.0  0.240705          1.0          0.0           0.05   \n",
              "3             1.0  0.009785          0.2          0.0           1.00   \n",
              "4             1.0  0.058708          0.0          0.0           1.00   \n",
              "...           ...       ...          ...          ...            ...   \n",
              "125968        0.0  0.360078          1.0          0.0           0.14   \n",
              "125969        0.0  0.003914          0.0          0.0           1.00   \n",
              "125970        1.0  0.001957          0.0          0.0           1.00   \n",
              "125971        0.0  0.281800          1.0          0.0           0.06   \n",
              "125972        1.0  0.001957          0.0          0.0           1.00   \n",
              "\n",
              "        diff_srv_rate  srv_diff_host_rate  dst_host_srv_count  \\\n",
              "0                0.00                0.00            0.098039   \n",
              "1                0.15                0.00            0.003922   \n",
              "2                0.07                0.00            0.101961   \n",
              "3                0.00                0.00            1.000000   \n",
              "4                0.00                0.09            1.000000   \n",
              "...               ...                 ...                 ...   \n",
              "125968           0.06                0.00            0.098039   \n",
              "125969           0.00                0.00            0.956863   \n",
              "125970           0.00                0.00            0.117647   \n",
              "125971           0.05                0.00            0.031373   \n",
              "125972           0.00                0.00            0.301961   \n",
              "\n",
              "        dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                         0.17                    0.03   \n",
              "1                         0.00                    0.60   \n",
              "2                         0.10                    0.05   \n",
              "3                         1.00                    0.00   \n",
              "4                         1.00                    0.00   \n",
              "...                        ...                     ...   \n",
              "125968                    0.10                    0.06   \n",
              "125969                    0.96                    0.01   \n",
              "125970                    0.12                    0.06   \n",
              "125971                    0.03                    0.05   \n",
              "125972                    0.30                    0.03   \n",
              "\n",
              "        dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                              0.17                         0.00   \n",
              "1                              0.88                         0.00   \n",
              "2                              0.00                         0.00   \n",
              "3                              0.03                         0.04   \n",
              "4                              0.00                         0.00   \n",
              "...                             ...                          ...   \n",
              "125968                         0.00                         0.00   \n",
              "125969                         0.01                         0.00   \n",
              "125970                         0.00                         0.00   \n",
              "125971                         0.00                         0.00   \n",
              "125972                         0.30                         0.00   \n",
              "\n",
              "        dst_host_serror_rate  dst_host_srv_serror_rate  \\\n",
              "0                       0.00                      0.00   \n",
              "1                       0.00                      0.00   \n",
              "2                       1.00                      1.00   \n",
              "3                       0.03                      0.01   \n",
              "4                       0.00                      0.00   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "ohe = OneHotEncoder(handle_unknown='ignore')\n",
        "label_enc = ohe.fit_transform(df_train.iloc[:,1:4])\n",
        "label_enc.toarray()\n",
        "new_labels = ohe.get_feature_names_out(['protocol_type', 'service', 'flag'])\n",
        "df_enc = pd.DataFrame(data=label_enc.toarray(), columns=new_labels)\n",
        "X_train = pd.concat([X_train, df_enc], axis=1)\n",
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "bmt6fGg8gSUb",
        "outputId": "117ce494-4ddf-4c99-de7d-87d2d2cd3b18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        logged_in     count  serror_rate  rerror_rate  same_srv_rate  \\\n",
              "0             0.0  0.003914          0.0          0.0           1.00   \n",
              "1             0.0  0.025440          0.0          0.0           0.08   \n",
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              "\n",
              "        diff_srv_rate  srv_diff_host_rate  dst_host_srv_count  \\\n",
              "0                0.00                0.00            0.098039   \n",
              "1                0.15                0.00            0.003922   \n",
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              "...               ...                 ...                 ...   \n",
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              "125972           0.00                0.00            0.301961   \n",
              "\n",
              "        dst_host_same_srv_rate  dst_host_diff_srv_rate  ...  flag_REJ  \\\n",
              "0                         0.17                    0.03  ...       0.0   \n",
              "1                         0.00                    0.60  ...       0.0   \n",
              "2                         0.10                    0.05  ...       0.0   \n",
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              "125972                    0.30                    0.03  ...       0.0   \n",
              "\n",
              "        flag_RSTO  flag_RSTOS0  flag_RSTR  flag_S0  flag_S1  flag_S2  flag_S3  \\\n",
              "0             0.0          0.0        0.0      0.0      0.0      0.0      0.0   \n",
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              "\n",
              "[125973 rows x 99 columns]"
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              "      <th></th>\n",
              "      <th>logged_in</th>\n",
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              "      <th>flag_RSTO</th>\n",
              "      <th>flag_RSTOS0</th>\n",
              "      <th>flag_RSTR</th>\n",
              "      <th>flag_S0</th>\n",
              "      <th>flag_S1</th>\n",
              "      <th>flag_S2</th>\n",
              "      <th>flag_S3</th>\n",
              "      <th>flag_SF</th>\n",
              "      <th>flag_SH</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003914</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.098039</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>...</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",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.025440</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.003922</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.60</td>\n",
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              "      <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",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>0.240705</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.101961</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.05</td>\n",
              "      <td>...</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",
              "      <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>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.009785</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</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",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.058708</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.09</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>125968</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.360078</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.098039</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.06</td>\n",
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              "      <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",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>125969</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.003914</td>\n",
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              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.956863</td>\n",
              "      <td>0.96</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>125970</th>\n",
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              "      <th>125972</th>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.301961</td>\n",
              "      <td>0.30</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>125973 rows × 99 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test = np.array([1 if x in dos_probe_list else 0 for x in df_test['label']])\n",
        "\n",
        "df_test = df_test.drop(['label'],axis=1)\n",
        "df_test = df_test.reset_index().drop(['index'], axis=1)\n",
        "df_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
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        "id": "a3Wv_2WbgeXM",
        "outputId": "cbdc98fa-77be-46fc-d25a-96d987b8315b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       duration protocol_type   service  flag  src_bytes  dst_bytes  land  \\\n",
              "0             0           tcp   private   REJ          0          0     0   \n",
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              "       wrong_fragment  urgent  hot  ...  dst_host_count  dst_host_srv_count  \\\n",
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              "22542                         0.00                         0.00   \n",
              "22543                         0.00                         0.00   \n",
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              "       dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
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              "4                          0.71  \n",
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              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_rerror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</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>255</td>\n",
              "      <td>10</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</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>255</td>\n",
              "      <td>1</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</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>134</td>\n",
              "      <td>86</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</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>3</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>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</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>29</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>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22539</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>smtp</td>\n",
              "      <td>SF</td>\n",
              "      <td>794</td>\n",
              "      <td>333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>100</td>\n",
              "      <td>141</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22540</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>317</td>\n",
              "      <td>938</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>197</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22541</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>54540</td>\n",
              "      <td>8314</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.07</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22542</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>domain_u</td>\n",
              "      <td>SF</td>\n",
              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>252</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22543</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>sunrpc</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>255</td>\n",
              "      <td>21</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.44</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22544 rows × 40 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d531927b-31e2-40ad-a70a-7f3a15fb5692')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = df_test[['logged_in', 'count', 'serror_rate', 'rerror_rate', 'same_srv_rate',\n",
        "       'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
        "       'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
        "       'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
        "       'dst_host_srv_serror_rate', 'dst_host_srv_rerror_rate']]"
      ],
      "metadata": {
        "id": "u2JDr86SgecW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_minmax = scaler1.transform(X_test)\n",
        "X_test = pd.DataFrame(df_minmax, columns=(X_test.columns))\n",
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "E1ZmaJUVgnZ2",
        "outputId": "3ffa4d5d-5cc7-4ae1-c21c-c6e1f9fe79b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       logged_in     count  serror_rate  rerror_rate  same_srv_rate  \\\n",
              "0            0.0  0.448141          0.0          1.0           0.04   \n",
              "1            0.0  0.266145          0.0          1.0           0.01   \n",
              "2            0.0  0.001957          0.0          0.0           1.00   \n",
              "3            0.0  0.001957          0.0          0.0           1.00   \n",
              "4            0.0  0.001957          0.0          1.0           1.00   \n",
              "...          ...       ...          ...          ...            ...   \n",
              "22539        1.0  0.001957          0.0          0.0           1.00   \n",
              "22540        1.0  0.003914          0.0          0.0           1.00   \n",
              "22541        1.0  0.009785          0.0          0.0           1.00   \n",
              "22542        0.0  0.007828          0.0          0.0           1.00   \n",
              "22543        0.0  0.007828          0.0          1.0           0.25   \n",
              "\n",
              "       diff_srv_rate  srv_diff_host_rate  dst_host_srv_count  \\\n",
              "0               0.06                0.00            0.039216   \n",
              "1               0.06                0.00            0.003922   \n",
              "2               0.00                0.00            0.337255   \n",
              "3               0.00                1.00            0.223529   \n",
              "4               0.00                0.75            0.337255   \n",
              "...              ...                 ...                 ...   \n",
              "22539           0.00                0.00            0.552941   \n",
              "22540           0.00                0.18            1.000000   \n",
              "22541           0.00                0.20            1.000000   \n",
              "22542           0.00                0.33            0.988235   \n",
              "22543           1.00                1.00            0.082353   \n",
              "\n",
              "       dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                        0.04                    0.06   \n",
              "1                        0.00                    0.06   \n",
              "2                        0.61                    0.04   \n",
              "3                        1.00                    0.00   \n",
              "4                        0.31                    0.17   \n",
              "...                       ...                     ...   \n",
              "22539                    0.72                    0.06   \n",
              "22540                    1.00                    0.00   \n",
              "22541                    1.00                    0.00   \n",
              "22542                    0.99                    0.01   \n",
              "22543                    0.08                    0.03   \n",
              "\n",
              "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                             0.00                         0.00   \n",
              "1                             0.00                         0.00   \n",
              "2                             0.61                         0.02   \n",
              "3                             1.00                         0.28   \n",
              "4                             0.03                         0.02   \n",
              "...                            ...                          ...   \n",
              "22539                         0.01                         0.01   \n",
              "22540                         0.01                         0.01   \n",
              "22541                         0.00                         0.00   \n",
              "22542                         0.00                         0.00   \n",
              "22543                         0.00                         0.00   \n",
              "\n",
              "       dst_host_serror_rate  dst_host_srv_serror_rate  \\\n",
              "0                      0.00                       0.0   \n",
              "1                      0.00                       0.0   \n",
              "2                      0.00                       0.0   \n",
              "3                      0.00                       0.0   \n",
              "4                      0.00                       0.0   \n",
              "...                     ...                       ...   \n",
              "22539                  0.01                       0.0   \n",
              "22540                  0.01                       0.0   \n",
              "22541                  0.00                       0.0   \n",
              "22542                  0.00                       0.0   \n",
              "22543                  0.00                       0.0   \n",
              "\n",
              "       dst_host_srv_rerror_rate  \n",
              "0                          1.00  \n",
              "1                          1.00  \n",
              "2                          0.00  \n",
              "3                          0.00  \n",
              "4                          0.71  \n",
              "...                         ...  \n",
              "22539                      0.00  \n",
              "22540                      0.00  \n",
              "22541                      0.07  \n",
              "22542                      0.00  \n",
              "22543                      1.00  \n",
              "\n",
              "[22544 rows x 15 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>logged_in</th>\n",
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              "      <th>dst_host_diff_srv_rate</th>\n",
              "      <th>dst_host_same_src_port_rate</th>\n",
              "      <th>dst_host_srv_diff_host_rate</th>\n",
              "      <th>dst_host_serror_rate</th>\n",
              "      <th>dst_host_srv_serror_rate</th>\n",
              "      <th>dst_host_srv_rerror_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.448141</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.039216</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.266145</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.003922</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.337255</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.223529</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.337255</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.17</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.71</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>22539</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.001957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.552941</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
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              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>22540</th>\n",
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              "      <td>0.003914</td>\n",
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              "      <td>0.18</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>22541</th>\n",
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              "      <td>0.20</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "      <th>22542</th>\n",
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              "      <td>0.99</td>\n",
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              "      <td>0.00</td>\n",
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              "      <th>22543</th>\n",
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              "      <td>0.08</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
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              "<p>22544 rows × 15 columns</p>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_enc = ohe.transform(df_test.iloc[:,1:4])\n",
        "label_enc.toarray()\n",
        "new_labels = ohe.get_feature_names_out(['protocol_type', 'service', 'flag'])\n",
        "df_enc = pd.DataFrame(data=label_enc.toarray(), columns=new_labels)\n",
        "X_test = pd.concat([X_test, df_enc], axis=1)\n",
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "kA3s4_V6gncj",
        "outputId": "0edcffc1-526b-4731-f8a1-63f61e5dc59d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       logged_in     count  serror_rate  rerror_rate  same_srv_rate  \\\n",
              "0            0.0  0.448141          0.0          1.0           0.04   \n",
              "1            0.0  0.266145          0.0          1.0           0.01   \n",
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              "...          ...       ...          ...          ...            ...   \n",
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              "22543        0.0  0.007828          0.0          1.0           0.25   \n",
              "\n",
              "       diff_srv_rate  srv_diff_host_rate  dst_host_srv_count  \\\n",
              "0               0.06                0.00            0.039216   \n",
              "1               0.06                0.00            0.003922   \n",
              "2               0.00                0.00            0.337255   \n",
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              "4               0.00                0.75            0.337255   \n",
              "...              ...                 ...                 ...   \n",
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              "22542           0.00                0.33            0.988235   \n",
              "22543           1.00                1.00            0.082353   \n",
              "\n",
              "       dst_host_same_srv_rate  dst_host_diff_srv_rate  ...  flag_REJ  \\\n",
              "0                        0.04                    0.06  ...       1.0   \n",
              "1                        0.00                    0.06  ...       1.0   \n",
              "2                        0.61                    0.04  ...       0.0   \n",
              "3                        1.00                    0.00  ...       0.0   \n",
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              "22542                    0.99                    0.01  ...       0.0   \n",
              "22543                    0.08                    0.03  ...       1.0   \n",
              "\n",
              "       flag_RSTO  flag_RSTOS0  flag_RSTR  flag_S0  flag_S1  flag_S2  flag_S3  \\\n",
              "0            0.0          0.0        0.0      0.0      0.0      0.0      0.0   \n",
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              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.337255</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.04</td>\n",
              "      <td>...</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",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.223529</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</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",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.001957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.337255</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.17</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</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",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22539</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.001957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.552941</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.06</td>\n",
              "      <td>...</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",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22540</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.003914</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.18</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</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",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22541</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.009785</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</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",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22542</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.007828</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.988235</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.01</td>\n",
              "      <td>...</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",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22543</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.007828</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.082353</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.03</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</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",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>22544 rows × 99 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb15215d-70c2-4ba7-8e85-e05d409f0430')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cb15215d-70c2-4ba7-8e85-e05d409f0430 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-cb15215d-70c2-4ba7-8e85-e05d409f0430');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "npU4w8Lkgtfb",
        "outputId": "ed59e21c-81fb-46ee-d5bb-b7370c79c118"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(125973, 99)\n",
            "(125973,)\n",
            "(22544, 99)\n",
            "(22544,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, f1_score\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import matthews_corrcoef, precision_score, recall_score\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.decomposition import KernelPCA\n",
        "pca_dos_probe = PCA(n_components=0.95)\n",
        "X_train_dos_probe = pca_dos_probe.fit_transform(X_train)\n",
        "X_test_dos_probe = pca_dos_probe.transform(X_test)\n",
        "\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "dos_probe_classifier = GaussianNB()\n",
        "# dos_probe_classifier = LogisticRegression()\n",
        "\n",
        "dos_probe_classifier.fit(X_train_dos_probe, y_train)\n",
        "predicted = dos_probe_classifier.predict(X_test_dos_probe)\n",
        "\n",
        "print(confusion_matrix(y_test,predicted))\n",
        "print('Accuracy = ', accuracy_score(y_test,predicted))\n",
        "print('F1 Score = ', f1_score(y_test,predicted))\n",
        "print('Precision = ', precision_score(y_test,predicted))\n",
        "print('Recall = ', recall_score(y_test,predicted))\n",
        "print(X_train_dos_probe.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SzET3wgtgtht",
        "outputId": "22ff262f-6c0f-4865-a2cf-27c770cda7e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[9544 3119]\n",
            " [1234 8647]]\n",
            "Accuracy =  0.8069109297374024\n",
            "F1 Score =  0.7989097796461403\n",
            "Precision =  0.7349141594424613\n",
            "Recall =  0.8751138548729885\n",
            "(125973, 29)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = copy.deepcopy(df_train_original)\n",
        "df_test = copy.deepcopy(df_test_original)"
      ],
      "metadata": {
        "id": "UUznSSm-gtjy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train[(df_train['label']=='normal') | (df_train['label']=='guess_passwd') \n",
        "                    | (df_train['label']=='ftp_write')\n",
        "                    | (df_train['label']=='imap') | (df_train['label']=='xsnoop') | (df_train['label']=='phf')\n",
        "                    | (df_train['label']=='multihop') | (df_train['label']=='warezmaster') \n",
        "                    | (df_train['label']=='xlock')\n",
        "                    | (df_train['label']=='snmpguess') | (df_train['label']=='snmpgetattack') \n",
        "                    | (df_train['label']=='httptunnel')\n",
        "                    | (df_train['label']=='sendmail') | (df_train['label']=='named') \n",
        "                    | (df_train['label']=='spy') \n",
        "                    | (df_train['label']=='warezclient') | (df_train['label']=='buffer_overflow') \n",
        "                    | (df_train['label']=='loadmodule') | (df_train['label']=='rootkit')\n",
        "                    | (df_train['label']=='perl') | (df_train['label']=='xterm') \n",
        "                    | (df_train['label']=='sqlattack')\n",
        "                    | (df_train['label']=='ps')]\n",
        "\n",
        "y_train = np.array([0 if x=='normal' else 1 for x in df_train['label']])\n",
        "df_train = df_train.drop(['label'],axis=1)\n",
        "df_train = df_train.reset_index().drop(['index'], axis=1)\n",
        "df_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "LciMFVC0hB_l",
        "outputId": "76368667-734b-4703-f21b-b47726848e79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
              "0             0           tcp  ftp_data   SF        491          0     0   \n",
              "1             0           udp     other   SF        146          0     0   \n",
              "2             0           tcp      http   SF        232       8153     0   \n",
              "3             0           tcp      http   SF        199        420     0   \n",
              "4             0           tcp      http   SF        287       2251     0   \n",
              "...         ...           ...       ...  ...        ...        ...   ...   \n",
              "68385         0           tcp      smtp   SF       2233        365     0   \n",
              "68386         0           tcp      http   SF        359        375     0   \n",
              "68387         8           udp   private   SF        105        145     0   \n",
              "68388         0           tcp      smtp   SF       2231        384     0   \n",
              "68389         0           tcp  ftp_data   SF        151          0     0   \n",
              "\n",
              "       wrong_fragment  urgent  hot  ...  dst_host_count  dst_host_srv_count  \\\n",
              "0                   0       0    0  ...             150                  25   \n",
              "1                   0       0    0  ...             255                   1   \n",
              "2                   0       0    0  ...              30                 255   \n",
              "3                   0       0    0  ...             255                 255   \n",
              "4                   0       0    0  ...               8                 219   \n",
              "...               ...     ...  ...  ...             ...                 ...   \n",
              "68385               0       0    0  ...               1                   2   \n",
              "68386               0       0    0  ...               3                 255   \n",
              "68387               0       0    0  ...             255                 244   \n",
              "68388               0       0    0  ...             255                  30   \n",
              "68389               0       0    0  ...             255                  77   \n",
              "\n",
              "       dst_host_same_srv_rate  dst_host_diff_srv_rate  \\\n",
              "0                        0.17                    0.03   \n",
              "1                        0.00                    0.60   \n",
              "2                        1.00                    0.00   \n",
              "3                        1.00                    0.00   \n",
              "4                        1.00                    0.00   \n",
              "...                       ...                     ...   \n",
              "68385                    1.00                    0.00   \n",
              "68386                    1.00                    0.00   \n",
              "68387                    0.96                    0.01   \n",
              "68388                    0.12                    0.06   \n",
              "68389                    0.30                    0.03   \n",
              "\n",
              "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
              "0                             0.17                         0.00   \n",
              "1                             0.88                         0.00   \n",
              "2                             0.03                         0.04   \n",
              "3                             0.00                         0.00   \n",
              "4                             0.12                         0.03   \n",
              "...                            ...                          ...   \n",
              "68385                         1.00                         1.00   \n",
              "68386                         0.33                         0.04   \n",
              "68387                         0.01                         0.00   \n",
              "68388                         0.00                         0.00   \n",
              "68389                         0.30                         0.00   \n",
              "\n",
              "       dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate  \\\n",
              "0                      0.00                      0.00                  0.05   \n",
              "1                      0.00                      0.00                  0.00   \n",
              "2                      0.03                      0.01                  0.00   \n",
              "3                      0.00                      0.00                  0.00   \n",
              "4                      0.00                      0.00                  0.00   \n",
              "...                     ...                       ...                   ...   \n",
              "68385                  0.00                      0.00                  0.00   \n",
              "68386                  0.33                      0.00                  0.00   \n",
              "68387                  0.00                      0.00                  0.00   \n",
              "68388                  0.72                      0.00                  0.01   \n",
              "68389                  0.00                      0.00                  0.00   \n",
              "\n",
              "       dst_host_srv_rerror_rate  \n",
              "0                          0.00  \n",
              "1                          0.00  \n",
              "2                          0.01  \n",
              "3                          0.00  \n",
              "4                          0.00  \n",
              "...                         ...  \n",
              "68385                      0.00  \n",
              "68386                      0.00  \n",
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          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df_train.drop(['protocol_type','service','flag'],axis=1)\n",
        "\n",
        "mutual_info = mutual_info_classif(X_train, y_train)\n",
        "mutual_info = pd.Series(mutual_info)\n",
        "mutual_info.index = X_train.columns\n",
        "mutual_info.sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uv7vfte7hCCM",
        "outputId": "2975e47f-f26e-43d6-deba-093af6ac22eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "src_bytes                      0.052826\n",
              "dst_bytes                      0.034020\n",
              "dst_host_srv_count             0.026586\n",
              "hot                            0.023560\n",
              "dst_host_same_src_port_rate    0.019043\n",
              "logged_in                      0.016514\n",
              "dst_host_srv_diff_host_rate    0.014549\n",
              "srv_count                      0.014419\n",
              "dst_host_count                 0.013559\n",
              "count                          0.012916\n",
              "same_srv_rate                  0.010734\n",
              "duration                       0.010323\n",
              "is_guest_login                 0.010146\n",
              "dst_host_same_srv_rate         0.008821\n",
              "srv_diff_host_rate             0.005804\n",
              "dst_host_diff_srv_rate         0.005475\n",
              "dst_host_rerror_rate           0.004420\n",
              "dst_host_srv_serror_rate       0.003267\n",
              "num_failed_logins              0.002696\n",
              "dst_host_serror_rate           0.002360\n",
              "dst_host_srv_rerror_rate       0.002204\n",
              "root_shell                     0.001301\n",
              "num_file_creations             0.001203\n",
              "srv_serror_rate                0.000694\n",
              "num_shells                     0.000688\n",
              "diff_srv_rate                  0.000677\n",
              "su_attempted                   0.000635\n",
              "srv_rerror_rate                0.000459\n",
              "serror_rate                    0.000319\n",
              "is_host_login                  0.000267\n",
              "num_root                       0.000104\n",
              "rerror_rate                    0.000033\n",
              "num_compromised                0.000005\n",
              "wrong_fragment                 0.000000\n",
              "land                           0.000000\n",
              "urgent                         0.000000\n",
              "num_access_files               0.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mutual_info.sort_values(ascending=False).plot.bar(figsize=(20, 8));"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 543
        },
        "id": "0L9Abr5LhfzI",
        "outputId": "a31270bc-034d-4c7a-f1c1-423e01f04ee5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x576 with 1 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sel_five_cols = SelectKBest(mutual_info_classif, k=20)\n",
        "sel_five_cols.fit(X_train, y_train)\n",
        "X_train.columns[sel_five_cols.get_support()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xRsWYO_Uhf2_",
        "outputId": "1b985f73-7881-4e14-9a24-e896bd2f70b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['duration', 'src_bytes', 'dst_bytes', 'hot', 'num_failed_logins',\n",
              "       'logged_in', 'is_guest_login', 'count', 'srv_count', 'same_srv_rate',\n",
              "       'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count',\n",
              "       'dst_host_same_srv_rate', 'dst_host_diff_srv_rate',\n",
              "       'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate',\n",
              "       'dst_host_serror_rate', 'dst_host_srv_serror_rate',\n",
              "       'dst_host_rerror_rate'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = df_train[['srv_count','hot', 'logged_in', 'is_guest_login', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count',\n",
        "       'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate']]"
      ],
      "metadata": {
        "id": "8fXu152thoJt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler2 = MinMaxScaler()\n",
        "df_minmax = scaler2.fit_transform(X_train)\n",
        "X_train = pd.DataFrame(df_minmax, columns=(X_train.columns))\n",
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "vaN6gf32hoL2",
        "outputId": "efa6b22e-1880-4a24-827b-b2113e1007ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       srv_count  hot  logged_in  is_guest_login  srv_diff_host_rate  \\\n",
              "0       0.003914  0.0        0.0             0.0                0.00   \n",
              "1       0.001957  0.0        0.0             0.0                0.00   \n",
              "2       0.009785  0.0        1.0             0.0                0.00   \n",
              "3       0.062622  0.0        1.0             0.0                0.09   \n",
              "4       0.013699  0.0        1.0             0.0                0.43   \n",
              "...          ...  ...        ...             ...                 ...   \n",
              "68385   0.001957  0.0        1.0             0.0                0.00   \n",
              "68386   0.021526  0.0        1.0             0.0                0.18   \n",
              "68387   0.003914  0.0        0.0             0.0                0.00   \n",
              "68388   0.001957  0.0        1.0             0.0                0.00   \n",
              "68389   0.001957  0.0        1.0             0.0                0.00   \n",
              "\n",
              "       dst_host_count  dst_host_srv_count  dst_host_same_src_port_rate  \\\n",
              "0            0.588235            0.098039                         0.17   \n",
              "1            1.000000            0.003922                         0.88   \n",
              "2            0.117647            1.000000                         0.03   \n",
              "3            1.000000            1.000000                         0.00   \n",
              "4            0.031373            0.858824                         0.12   \n",
              "...               ...                 ...                          ...   \n",
              "68385        0.003922            0.007843                         1.00   \n",
              "68386        0.011765            1.000000                         0.33   \n",
              "68387        1.000000            0.956863                         0.01   \n",
              "68388        1.000000            0.117647                         0.00   \n",
              "68389        1.000000            0.301961                         0.30   \n",
              "\n",
              "       dst_host_srv_diff_host_rate  \n",
              "0                             0.00  \n",
              "1                             0.00  \n",
              "2                             0.04  \n",
              "3                             0.00  \n",
              "4                             0.03  \n",
              "...                            ...  \n",
              "68385                         1.00  \n",
              "68386                         0.04  \n",
              "68387                         0.00  \n",
              "68388                         0.00  \n",
              "68389                         0.00  \n",
              "\n",
              "[68390 rows x 9 columns]"
            ],
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          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ohe2 = OneHotEncoder(handle_unknown='ignore')\n",
        "label_enc = ohe2.fit_transform(df_train.iloc[:,1:4])\n",
        "label_enc.toarray()\n",
        "new_labels = ohe2.get_feature_names_out(['protocol_type', 'service', 'flag'])\n",
        "df_enc = pd.DataFrame(data=label_enc.toarray(), columns=new_labels)\n",
        "X_train = pd.concat([X_train, df_enc], axis=1)\n",
        "#df_train = df_train.drop(['protocol_type','service','flag'], axis=1)\n",
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
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        "id": "7bibtLlDhoOa",
        "outputId": "b91c02c8-9a4f-49ed-e27e-3b23f9be458c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       srv_count  hot  logged_in  is_guest_login  srv_diff_host_rate  \\\n",
              "0       0.003914  0.0        0.0             0.0                0.00   \n",
              "1       0.001957  0.0        0.0             0.0                0.00   \n",
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              "4       0.013699  0.0        1.0             0.0                0.43   \n",
              "...          ...  ...        ...             ...                 ...   \n",
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              "68389   0.001957  0.0        1.0             0.0                0.00   \n",
              "\n",
              "       dst_host_count  dst_host_srv_count  dst_host_same_src_port_rate  \\\n",
              "0            0.588235            0.098039                         0.17   \n",
              "1            1.000000            0.003922                         0.88   \n",
              "2            0.117647            1.000000                         0.03   \n",
              "3            1.000000            1.000000                         0.00   \n",
              "4            0.031373            0.858824                         0.12   \n",
              "...               ...                 ...                          ...   \n",
              "68385        0.003922            0.007843                         1.00   \n",
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              "68388        1.000000            0.117647                         0.00   \n",
              "68389        1.000000            0.301961                         0.30   \n",
              "\n",
              "       dst_host_srv_diff_host_rate  protocol_type_icmp  ...  flag_OTH  \\\n",
              "0                             0.00                 0.0  ...       0.0   \n",
              "1                             0.00                 0.0  ...       0.0   \n",
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              "68387                         0.00                 0.0  ...       0.0   \n",
              "68388                         0.00                 0.0  ...       0.0   \n",
              "68389                         0.00                 0.0  ...       0.0   \n",
              "\n",
              "       flag_REJ  flag_RSTO  flag_RSTR  flag_S0  flag_S1  flag_S2  flag_S3  \\\n",
              "0           0.0        0.0        0.0      0.0      0.0      0.0      0.0   \n",
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              "68389       0.0        0.0        0.0      0.0      0.0      0.0      0.0   \n",
              "\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.013699</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>68386</th>\n",
              "      <td>0.021526</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>68388</th>\n",
              "      <td>0.001957</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <th>68389</th>\n",
              "      <td>0.001957</td>\n",
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              "<p>68390 rows × 49 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = df_test[(df_test['label']=='normal') | (df_test['label']=='guess_passwd') | (df_test['label']=='ftp_write')\n",
        "                    | (df_test['label']=='imap') | (df_test['label']=='xsnoop') | (df_test['label']=='phf')\n",
        "                    | (df_test['label']=='multihop') | (df_test['label']=='warezmaster') \n",
        "                    | (df_test['label']=='xlock')\n",
        "                    | (df_test['label']=='snmpguess') | (df_test['label']=='snmpgetattack') \n",
        "                    | (df_test['label']=='httptunnel')\n",
        "                    | (df_test['label']=='sendmail') | (df_test['label']=='named') \n",
        "                    | (df_test['label']=='spy') \n",
        "                    | (df_test['label']=='warezclient') | (df_test['label']=='buffer_overflow') \n",
        "                    | (df_test['label']=='loadmodule') | (df_test['label']=='rootkit')\n",
        "                    | (df_test['label']=='perl') | (df_test['label']=='xterm') \n",
        "                    | (df_test['label']=='sqlattack')\n",
        "                    | (df_test['label']=='ps')]\n",
        "\n",
        "y_test = np.array([0 if x=='normal' else 1 for x in df_test['label']])\n",
        "df_test = df_test.drop(['label'],axis=1)\n",
        "df_test = df_test.reset_index().drop(['index'], axis=1)\n",
        "df_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "LGHH09uKhoRQ",
        "outputId": "7a4ad9cf-b24c-44b5-d080-9f86f407a610"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       duration protocol_type   service flag  src_bytes  dst_bytes  land  \\\n",
              "0             2           tcp  ftp_data   SF      12983          0     0   \n",
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              "3             0           tcp    telnet   SF        129        174     0   \n",
              "4             0           tcp      http   SF        327        467     0   \n",
              "...         ...           ...       ...  ...        ...        ...   ...   \n",
              "12658         0           tcp      http   SF        274       1623     0   \n",
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              "12661         0           tcp      http   SF        317        938     0   \n",
              "12662         0           udp  domain_u   SF         42         42     0   \n",
              "\n",
              "       wrong_fragment  urgent  hot  ...  dst_host_count  dst_host_srv_count  \\\n",
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              "0                        0.61                    0.04   \n",
              "1                        1.00                    0.00   \n",
              "2                        0.11                    0.72   \n",
              "3                        1.00                    0.00   \n",
              "4                        1.00                    0.00   \n",
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              "12658                    1.00                    0.00   \n",
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              "12662                    0.99                    0.01   \n",
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              "       dst_host_same_src_port_rate  dst_host_srv_diff_host_rate  \\\n",
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              "1                             0.01                         0.03   \n",
              "2                             0.00                         0.00   \n",
              "3                             0.00                         0.00   \n",
              "4                             0.01                         0.03   \n",
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              "12660                         0.01                         0.01   \n",
              "12661                         0.01                         0.01   \n",
              "12662                         0.00                         0.00   \n",
              "\n",
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              "      <td>0.72</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>telnet</td>\n",
              "      <td>SF</td>\n",
              "      <td>129</td>\n",
              "      <td>174</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>327</td>\n",
              "      <td>467</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>151</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12658</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>274</td>\n",
              "      <td>1623</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>92</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12659</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>280</td>\n",
              "      <td>6087</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>5</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12660</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>smtp</td>\n",
              "      <td>SF</td>\n",
              "      <td>794</td>\n",
              "      <td>333</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>100</td>\n",
              "      <td>141</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12661</th>\n",
              "      <td>0</td>\n",
              "      <td>tcp</td>\n",
              "      <td>http</td>\n",
              "      <td>SF</td>\n",
              "      <td>317</td>\n",
              "      <td>938</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>197</td>\n",
              "      <td>255</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12662</th>\n",
              "      <td>0</td>\n",
              "      <td>udp</td>\n",
              "      <td>domain_u</td>\n",
              "      <td>SF</td>\n",
              "      <td>42</td>\n",
              "      <td>42</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>255</td>\n",
              "      <td>252</td>\n",
              "      <td>0.99</td>\n",
              "      <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>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12663 rows × 40 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = df_test[['srv_count','hot', 'logged_in', 'is_guest_login', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count',\n",
        "       'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate']]"
      ],
      "metadata": {
        "id": "4oLZg4M-iB9E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_minmax = scaler2.transform(X_test)\n",
        "X_test = pd.DataFrame(df_minmax, columns=(X_test.columns))\n",
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "QD78QwNHiB_V",
        "outputId": "bc7bf40a-65bc-4e21-8560-ba5da3d3ca37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       srv_count  hot  logged_in  is_guest_login  srv_diff_host_rate  \\\n",
              "0       0.001957  0.0        0.0             0.0                0.00   \n",
              "1       0.007828  0.0        1.0             0.0                0.00   \n",
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              "3       0.001957  0.0        0.0             0.0                0.00   \n",
              "4       0.091977  0.0        1.0             0.0                0.04   \n",
              "...          ...  ...        ...             ...                 ...   \n",
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              "12662   0.011742  0.0        0.0             0.0                0.33   \n",
              "\n",
              "       dst_host_count  dst_host_srv_count  dst_host_same_src_port_rate  \\\n",
              "0            0.525490            0.337255                         0.61   \n",
              "1            0.607843            1.000000                         0.01   \n",
              "2            1.000000            0.109804                         0.00   \n",
              "3            1.000000            1.000000                         0.00   \n",
              "4            0.592157            1.000000                         0.01   \n",
              "...               ...                 ...                          ...   \n",
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              "12660        0.392157            0.552941                         0.01   \n",
              "12661        0.772549            1.000000                         0.01   \n",
              "12662        1.000000            0.988235                         0.00   \n",
              "\n",
              "       dst_host_srv_diff_host_rate  \n",
              "0                             0.02  \n",
              "1                             0.03  \n",
              "2                             0.00  \n",
              "3                             0.00  \n",
              "4                             0.03  \n",
              "...                            ...  \n",
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              "12659                         0.04  \n",
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              "12661                         0.01  \n",
              "12662                         0.00  \n",
              "\n",
              "[12663 rows x 9 columns]"
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              "      <th>srv_count</th>\n",
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          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_enc = ohe2.transform(df_test.iloc[:,1:4])\n",
        "label_enc.toarray()\n",
        "new_labels = ohe2.get_feature_names_out(['protocol_type', 'service', 'flag'])\n",
        "df_enc = pd.DataFrame(data=label_enc.toarray(), columns=new_labels)\n",
        "X_test = pd.concat([X_test, df_enc], axis=1)\n",
        "X_test"
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      "metadata": {
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          "height": 483
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        "id": "0uQDIMsjiCB9",
        "outputId": "f52057f0-8349-457c-d543-39090399460e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       srv_count  hot  logged_in  is_guest_login  srv_diff_host_rate  \\\n",
              "0       0.001957  0.0        0.0             0.0                0.00   \n",
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              "\n",
              "       dst_host_count  dst_host_srv_count  dst_host_same_src_port_rate  \\\n",
              "0            0.525490            0.337255                         0.61   \n",
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              "       dst_host_srv_diff_host_rate  protocol_type_icmp  ...  flag_OTH  \\\n",
              "0                             0.02                 0.0  ...       0.0   \n",
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              "12662                         0.00                 0.0  ...       0.0   \n",
              "\n",
              "       flag_REJ  flag_RSTO  flag_RSTR  flag_S0  flag_S1  flag_S2  flag_S3  \\\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12663 rows × 49 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdc5e81e-7b50-4a6a-99bf-3c10d538ca17')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fdc5e81e-7b50-4a6a-99bf-3c10d538ca17 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-fdc5e81e-7b50-4a6a-99bf-3c10d538ca17');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pw12HJztiCEu",
        "outputId": "428e8648-9762-4c8f-f3f5-3a1c8cc1ea82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(68390, 49)\n",
            "(68390,)\n",
            "(12663, 49)\n",
            "(12663,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.under_sampling import RandomUnderSampler as under_sam\n",
        "from sklearn.feature_selection import SelectKBest, chi2, f_classif, mutual_info_classif\n",
        "sm = under_sam(sampling_strategy=1)\n",
        "X_train, y_train = sm.fit_resample(X_train,y_train)\n",
        "\n",
        "# pca_r2l_u2r = PCA(n_components=0.95)\n",
        "# X_train_r2l_u2r = pca_r2l_u2r.fit_transform(X_train)\n",
        "# X_test_r2l_u2r = pca_r2l_u2r.transform(X_test)\n",
        "\n",
        "kpca_r2l_u2r = KernelPCA(n_components=2, kernel=\"rbf\")\n",
        "X_train_r2l_u2r = kpca_r2l_u2r.fit_transform(X_train)\n",
        "X_test_r2l_u2r = kpca_r2l_u2r.transform(X_test)\n",
        "\n",
        "r2l_u2r_classifier = SVC(C=0.1, gamma=0.01, kernel='rbf')\n",
        "r2l_u2r_classifier.fit(X_train_r2l_u2r, y_train)\n",
        "predicted = r2l_u2r_classifier.predict(X_test_r2l_u2r)\n",
        "print(confusion_matrix(y_test,predicted))\n",
        "print('Accuracy = ', accuracy_score(y_test,predicted))\n",
        "print('F1 Score = ', f1_score(y_test,predicted))\n",
        "print('Precision = ', precision_score(y_test,predicted))\n",
        "print('Recall = ', recall_score(y_test,predicted))\n",
        "print(X_train_r2l_u2r.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p3TGCcy9iCJF",
        "outputId": "7cef050f-6324-4f72-f0e7-18665089cc53"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[8813  898]\n",
            " [1563 1389]]\n",
            "Accuracy =  0.8056542683408355\n",
            "F1 Score =  0.5302538652414582\n",
            "Precision =  0.6073458679492786\n",
            "Recall =  0.47052845528455284\n",
            "(2094, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test1 = copy.deepcopy(df_test_original)\n",
        "df_test2 = copy.deepcopy(df_test_original)\n",
        "y_test_real = np.array([0 if x=='normal' else 1 for x in df_test1['label']])"
      ],
      "metadata": {
        "id": "pL0pL4NYiUZ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = df_test1[['logged_in', 'count', 'serror_rate', 'rerror_rate', 'same_srv_rate',\n",
        "       'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_srv_count', 'dst_host_same_srv_rate',\n",
        "       'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate',\n",
        "       'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
        "       'dst_host_srv_serror_rate', 'dst_host_srv_rerror_rate']]\n",
        "\n",
        "df_minmax = scaler1.transform(X_test)\n",
        "X_test = pd.DataFrame(df_minmax, columns=(X_test.columns))\n",
        "label_enc = ohe.transform(df_test1.iloc[:,1:4])\n",
        "label_enc.toarray()\n",
        "new_labels = ohe.get_feature_names_out(['protocol_type', 'service', 'flag'])\n",
        "df_enc = pd.DataFrame(data=label_enc.toarray(), columns=new_labels)\n",
        "X_test = pd.concat([X_test, df_enc], axis=1)\n",
        "\n",
        "X_test_layer1 = pca_dos_probe.transform(X_test)\n",
        "\n",
        "# X_test_layer1 = kpca_dos_probe.transform(X_test)\n",
        "print(X_test_layer1.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W2W6dLuNiUcv",
        "outputId": "e8c73cf8-baf3-4da5-bd2e-476ea9bb43bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(22544, 29)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = df_test2[['srv_count','hot', 'logged_in', 'is_guest_login', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count',\n",
        "       'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate']]\n",
        "\n",
        "df_minmax = scaler2.transform(X_test)\n",
        "X_test = pd.DataFrame(df_minmax, columns=(X_test.columns))\n",
        "label_enc = ohe2.transform(df_test2.iloc[:,1:4])\n",
        "label_enc.toarray()\n",
        "new_labels = ohe2.get_feature_names_out(['protocol_type', 'service', 'flag'])\n",
        "df_enc = pd.DataFrame(data=label_enc.toarray(), columns=new_labels)\n",
        "X_test = pd.concat([X_test, df_enc], axis=1)\n",
        "\n",
        "# X_test_layer2 = pca_r2l_u2r.transform(X_test)\n",
        "\n",
        "X_test_layer2 = kpca_r2l_u2r.transform(X_test)\n",
        "print(X_test_layer2.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k1eznozziZs2",
        "outputId": "f5cbde80-7ac0-46dc-dae7-935a4943eb96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(22544, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier1 = dos_probe_classifier\n",
        "classifier2 = r2l_u2r_classifier"
      ],
      "metadata": {
        "id": "QAbrEUuriZvb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result = []\n",
        "\n",
        "for i in range(22544):\n",
        "    layer1 = classifier1.predict(X_test_layer1[i].reshape(1, -1))[0]\n",
        "    if layer1 == 1:\n",
        "        result.append(layer1)\n",
        "    else:\n",
        "        layer2 = classifier2.predict(X_test_layer2[i].reshape(1, -1))[0]\n",
        "        if layer2 == 1:\n",
        "            result.append(layer2)\n",
        "        else:\n",
        "            result.append(0)\n",
        "            \n",
        "result = np.array(result)\n",
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MV-FkH5oiZ6r",
        "outputId": "59af88a9-80ec-4601-9a62-9962aa2106e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 0, 0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(confusion_matrix(y_test_real,result))\n",
        "print('Accuracy = ', accuracy_score(y_test_real,result))\n",
        "print('F1 Score = ', f1_score(y_test_real,result))\n",
        "print('Precision = ', precision_score(y_test_real,result))\n",
        "print('Recall = ', recall_score(y_test_real,result))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xohzvJk6ij0N",
        "outputId": "cadfcdc3-7737-49f8-ee2a-7304048e6e63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 8014  1697]\n",
            " [  863 11970]]\n",
            "Accuracy =  0.886444286728176\n",
            "F1 Score =  0.9033962264150944\n",
            "Precision =  0.8758322967732495\n",
            "Recall =  0.932751500038962\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('KDDTest+.txt', sep=\",\", header=None)\n",
        "df_test = df_test[df_test.columns[:-1]]\n",
        "df_test.columns = titles.to_list()\n",
        "y_test = df_test['label']\n",
        "df_test = df_test.drop(['num_outbound_cmds'],axis=1)\n",
        "df_test_original = df_test"
      ],
      "metadata": {
        "id": "RmNSw5Hyij2r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_attack = []\n",
        "for i in df_test_original['label'].value_counts().index.tolist()[1:]:\n",
        "    if i not in df_train_original['label'].value_counts().index.tolist()[1:]:\n",
        "        new_attack.append(i)\n",
        "        \n",
        "new_attack.sort()\n",
        "new_attack"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_JUL8MBUij5Z",
        "outputId": "cbc3cd8e-a8aa-4199-ba20-0638c3e69222"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apache2',\n",
              " 'httptunnel',\n",
              " 'mailbomb',\n",
              " 'mscan',\n",
              " 'named',\n",
              " 'processtable',\n",
              " 'ps',\n",
              " 'saint',\n",
              " 'sendmail',\n",
              " 'snmpgetattack',\n",
              " 'snmpguess',\n",
              " 'sqlattack',\n",
              " 'udpstorm',\n",
              " 'worm',\n",
              " 'xlock',\n",
              " 'xsnoop',\n",
              " 'xterm']"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index_of_new_attacks = []\n",
        "\n",
        "for i in range(len(df_test_original)):\n",
        "    if df_test_original['label'][i] in new_attack:\n",
        "        index_of_new_attacks.append(df_test_original.index[i])"
      ],
      "metadata": {
        "id": "64_MKeCuiwP2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(index_of_new_attacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-XljASxiwSi",
        "outputId": "ff7ffdc0-e583-4da2-fc86-e16e6b4c7f23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3750"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_attack.append('normal')\n",
        "new_attack"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AZQ9setvi5n2",
        "outputId": "c53287ec-c9f6-4d41-91aa-af62fddf9512"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['apache2',\n",
              " 'httptunnel',\n",
              " 'mailbomb',\n",
              " 'mscan',\n",
              " 'named',\n",
              " 'processtable',\n",
              " 'ps',\n",
              " 'saint',\n",
              " 'sendmail',\n",
              " 'snmpgetattack',\n",
              " 'snmpguess',\n",
              " 'sqlattack',\n",
              " 'udpstorm',\n",
              " 'worm',\n",
              " 'xlock',\n",
              " 'xsnoop',\n",
              " 'xterm',\n",
              " 'normal']"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index_of_old_attacks = []\n",
        "\n",
        "for i in range(len(df_test_original)):\n",
        "    if df_test_original['label'][i] not in new_attack:\n",
        "        index_of_old_attacks.append(df_test_original.index[i])"
      ],
      "metadata": {
        "id": "6Qn3Mkv7i5qp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(index_of_old_attacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jhUTAaZi5tY",
        "outputId": "a764cf49-0986-4c4f-cbbe-e29265084f2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9083"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(result[index_of_new_attacks].shape[0])\n",
        "print(result[index_of_new_attacks].sum())\n",
        "print(result[index_of_new_attacks].sum()/result[index_of_new_attacks].shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkJdwDONi5wP",
        "outputId": "401531dd-729a-46c6-98b9-fc3ca4b76fd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3750\n",
            "3223\n",
            "0.8594666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(result[index_of_old_attacks].shape[0])\n",
        "print(result[index_of_old_attacks].sum())\n",
        "print(result[index_of_old_attacks].sum()/result[index_of_old_attacks].shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08QOeYeBi5zG",
        "outputId": "d41b420c-81e3-4f11-f279-b198db20a693"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9083\n",
            "8747\n",
            "0.9630078168006165\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test = pd.read_csv('KDDTest+.txt', sep=\",\", header=None)\n",
        "df_test = df_test[df_test.columns[:-1]]\n",
        "df_test.columns = titles.to_list()\n",
        "y_test = df_test['label']\n",
        "df_test = df_test.drop(['num_outbound_cmds'],axis=1)\n",
        "df_test_original = df_test\n",
        "df = df_test_original\n",
        "\n",
        "dos_index = df.index[(df['label'] == 'apache2') | (df['label']=='back') \n",
        "                     | (df['label'] == 'land') | (df['label'] == 'mailbomb') \n",
        "                     | (df['label'] == 'neptune') | (df['label'] == 'pod') \n",
        "                     | (df['label'] == 'processtable') | (df['label'] == 'smurf') \n",
        "                     | (df['label'] == 'teardrop') | (df['label'] == 'udpstorm') \n",
        "                     | (df['label'] == 'worm')].tolist()\n",
        "\n",
        "probe_index = df.index[(df['label'] == 'ipsweep') | (df['label']=='mscan') \n",
        "                       | (df['label']=='nmap') | (df['label'] == 'portsweep') \n",
        "                       | (df['label'] == 'saint') | (df['label'] == 'satan')].tolist()\n",
        "\n",
        "r2l_index = df.index[(df['label'] == 'ftp_write') | (df['label']=='guess_passwd') \n",
        "                     | (df['label'] == 'httptunnel') | (df['label'] == 'imap') \n",
        "                     | (df['label'] == 'multihop') | (df['label'] == 'named') \n",
        "                     | (df['label'] == 'phf') | (df['label'] == 'sendmail') \n",
        "                     | (df['label'] == 'snmpgetattack') | (df['label'] == 'snmpguess') \n",
        "                     | (df['label'] == 'warezmaster') | (df['label'] == 'xlock') \n",
        "                     | (df['label'] == 'xsnoop')].tolist()\n",
        "\n",
        "u2r_index = df.index[(df['label'] == 'buffer_overflow') | (df['label']=='loadmodule') \n",
        "                       | (df['label']=='perl') | (df['label'] == 'ps') \n",
        "                       | (df['label'] == 'rootkit') | (df['label'] == 'sqlattack')\n",
        "                       | (df['label'] == 'xterm')].tolist()\n",
        "\n",
        "# print(\"full sample:\", result[dos_index].shape[0])\n",
        "# print(\"detected sample:\", result[dos_index].sum())\n",
        "print(\"dos:\", result[dos_index].sum()/result[dos_index].shape[0])\n",
        "\n",
        "print(\"probe:\",result[probe_index].sum()/result[probe_index].shape[0])\n",
        "\n",
        "print(\"r2l:\",result[r2l_index].sum()/result[r2l_index].shape[0])\n",
        "\n",
        "print(\"u2r:\",result[u2r_index].sum()/result[u2r_index].shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EtrRqkcFjGRz",
        "outputId": "02db0d75-25fa-419b-94bf-460a02e2b523"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dos: 0.8871313672922252\n",
            "probe: 0.9958694754233788\n",
            "r2l: 0.9989601386481802\n",
            "u2r: 0.8805970149253731\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dectsion Tree model\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "# instantiate the model\n",
        "tree = DecisionTreeClassifier(max_depth = 5)\n",
        "# fit the model\n",
        "tree.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "X_c9aEJQjGUt",
        "outputId": "00a13939-a867-4761-bc02-e0aeff1d303b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_tree = tree.predict(X_test)\n",
        "y_train_tree = tree.predict(X_train)"
      ],
      "metadata": {
        "id": "Bxp1limWmm1P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc_train_tree = accuracy_score(y_train,y_train_tree)\n",
        "acc_test_tree = accuracy_score(y_test,y_test_tree)\n",
        "print(\"Decision Tree: Accuracy on training Data: {:.3f}\".format(acc_train_tree))\n",
        "print(\"Decision Tree: Accuracy on test Data: {:.3f}\".format(acc_test_tree))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvJCAdVRmm3S",
        "outputId": "1e954829-64ec-4e32-dc9e-90aa9c2c94d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree: Accuracy on training Data: 0.947\n",
            "Decision Tree: Accuracy on test Data: 0.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(43,43))\n",
        "\n",
        "n_features = X_train.shape[1]\n",
        "\n",
        "plt.barh(range(n_features), tree.feature_importances_, align='center')\n",
        "plt.yticks(np.arange(n_features), X_train.columns)\n",
        "plt.xlabel(\"Feature importance\")\n",
        "\n",
        "plt.ylabel(\"Feature\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "id": "ldwRSq4Omm6g",
        "outputId": "ccbc8fa4-c8f3-416a-a6aa-840eced77ea1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3096x3096 with 1 Axes>"
            ],
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Loading Data**"
      ],
      "metadata": {
        "id": "VfQnl0arWKkU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.random.seed(100)\n",
        "\n",
        "dataset_root = r'/content/drive/MyDrive/archive'\n",
        "train_file = os.path.join(dataset_root, 'KDDTrain+.txt')\n",
        "test_file = os.path.join(dataset_root, 'KDDTest+.txt')"
      ],
      "metadata": {
        "id": "1OQmSX4HIRCz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "header_names = ['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment',\n",
        "'urgent', 'hot', 'num_failed_logins', 'logged_in', 'num_compromised', 'root_shell', 'su_attempted',\n",
        "'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds',\n",
        "'is_host_login', 'is_guest_login', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate',\n",
        "'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate',\n",
        "'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate',\n",
        "'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate',\n",
        "'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'attack_type',\n",
        "'success_pred']"
      ],
      "metadata": {
        "id": "dCfR8l_SKoeX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(header_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zy3w7kUqPD-8",
        "outputId": "8e17d31a-5af5-4a0f-8f15-cdd622eb44b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Preprocessing**"
      ],
      "metadata": {
        "id": "5o7-a6AGV_Y4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting header into array\n",
        "\n",
        "col_names = np.array(header_names)\n",
        "\n",
        "#No retationship b/w nuserics\n",
        "\n",
        "nominal_idx = [1, 2, 3]\n",
        "\n",
        "#binaryb values\n",
        "\n",
        "binary_idx = [6, 11, 13, 14, 20, 21]\n",
        "#difference between two sets.\n",
        "\n",
        "numeric_idx = list(set(range(41)).difference(nominal_idx).difference(binary_idx))\n",
        "nominal_cols = col_names[nominal_idx].tolist()\n",
        "binary_cols = col_names[binary_idx].tolist()\n",
        "numeric_cols = col_names[numeric_idx].tolist()"
      ],
      "metadata": {
        "id": "eRlhw5TEPdpj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(nominal_cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LwWyYd3RryX",
        "outputId": "cb771777-dc33-4618-d949-aebbb9be60c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['protocol_type', 'service', 'flag']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(binary_cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KAYXUHZNRr0y",
        "outputId": "d83e0e4d-e831-410d-afbb-afb108a5360a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['land', 'logged_in', 'root_shell', 'su_attempted', 'is_host_login', 'is_guest_login']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(numeric_cols)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ibrK1QGR0pE",
        "outputId": "a2a021c3-d023-4608-bae2-01cd9732612b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['duration', 'src_bytes', 'dst_bytes', 'wrong_fragment', 'urgent', 'hot', 'num_failed_logins', 'num_compromised', 'num_root', 'num_file_creations', 'num_shells', 'num_access_files', 'num_outbound_cmds', 'count', 'srv_count', 'serror_rate', 'srv_serror_rate', 'rerror_rate', 'srv_rerror_rate', 'same_srv_rate', 'diff_srv_rate', 'srv_diff_host_rate', 'dst_host_count', 'dst_host_srv_count', 'dst_host_same_srv_rate', 'dst_host_diff_srv_rate', 'dst_host_same_src_port_rate', 'dst_host_srv_diff_host_rate', 'dst_host_serror_rate', 'dst_host_srv_serror_rate', 'dst_host_rerror_rate', 'dst_host_srv_rerror_rate']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "category = defaultdict(list)\n",
        "category['begin'].append('normal')\n",
        "\n",
        "with open(r'/content/drive/MyDrive/archive/training_attack_types.txt', 'r') as f:\n",
        "  for line in f.readlines():\n",
        "    attack, cat = line.strip().split(' ')\n",
        "    category[cat].append(attack)\n",
        "\n",
        "attack_mapping = dict((v,k) for k in category for v in category[k])\n",
        "train_df = pd.read_csv(train_file, names=header_names)\n",
        "\n",
        "train_df['attack_category'] = train_df['attack_type'] \\\n",
        "  .map(lambda x: attack_mapping[x])\n",
        "train_df.drop(['success_pred'],axis=1, inplace=True)\n",
        "\n",
        "test_df = pd.read_csv(test_file, names=header_names)\n",
        "test_df['attack_category'] = test_df['attack_type'] \\\n",
        "  .map(lambda x: attack_mapping[x])\n",
        "test_df.drop(['success_pred'],axis=1, inplace=True)\n",
        "\n",
        "train_attack_types = train_df['attack_type'].value_counts()\n",
        "train_attack_cats = train_df['attack_category'].value_counts()\n",
        "\n",
        "test_attack_types = test_df['attack_type'].value_counts()\n",
        "test_attack_cats = test_df['attack_category'].value_counts()\n",
        "\n",
        "train_attack_types.plot(kind= 'barh', figsize=(20,10), fontsize=20)\n",
        "\n",
        "train_attack_cats.plot(kind= 'barh', figsize=(20,10), fontsize=30)\n",
        "\n",
        "test_attack_types.plot(kind='barh', figsize=(20,10), fontsize=15)\n",
        "\n",
        "test_attack_cats.plot(kind='barh', figsize=(20,10), fontsize=30)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "NRQp-yWKR55L",
        "outputId": "409f2733-59a7-49f3-8405-4a5c1efb9132"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-71-b96a367f0473>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mcategory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'begin'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'normal'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'/content/drive/MyDrive/archive/training_attack_types.txt'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mattack\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m' '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/drive/MyDrive/archive/training_attack_types.txt'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(category)"
      ],
      "metadata": {
        "id": "ovA4efULU_NQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "metadata": {
        "id": "-0BlyP9XkvEb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df[binary_cols].describe().transpose()"
      ],
      "metadata": {
        "id": "AYWRgDszk221"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.groupby(['su_attempted']).size()"
      ],
      "metadata": {
        "id": "NFdBCji-k25j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df['su_attempted'].replace(2,0,inplace=True)\n",
        "test_df['su_attempted'].replace(2,0,inplace=True)\n",
        "train_df.groupby(['su_attempted']).size()"
      ],
      "metadata": {
        "id": "QwhBDFeKk2_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.groupby(['num_outbound_cmds']).size()"
      ],
      "metadata": {
        "id": "nODlpLmXlnBN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df.drop('num_outbound_cmds', axis=1, inplace=True)\n",
        "test_df.drop('num_outbound_cmds', axis=1, inplace=True)\n",
        "#numeric_cols.remove('num_outbound_cmds')\n",
        "#Data Preparation\n",
        "train_Y = train_df['attack_category']\n",
        "train_x_raw = train_df.drop(['attack_category','attack_type'], axis=1)\n",
        "test_Y = test_df['attack_category']\n",
        "test_x_raw = test_df.drop(['attack_category','attack_type'], axis=1)\n",
        "\n",
        "combined_df_raw = pd.concat([train_x_raw, test_x_raw])\n",
        "combined_df = pd.get_dummies(combined_df_raw, columns=nominal_cols, drop_first=True)\n",
        "\n",
        "train_x = combined_df[:len(train_x_raw)]\n",
        "test_x = combined_df[len(train_x_raw):]"
      ],
      "metadata": {
        "id": "E5JdCUSclnDq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = train_x_raw\n",
        "x_test = test_x_raw"
      ],
      "metadata": {
        "id": "_SBEdhktlnGO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Training**"
      ],
      "metadata": {
        "id": "1JcC9RfwtKwq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating holders to store the model performance results\n",
        "ML_Model = []\n",
        "acc_train = []\n",
        "acc_test = []\n",
        "#function to call for storing the results\n",
        "def storeResults(model, a,b):\n",
        "  ML_Model.append(model)\n",
        "  acc_train.append(round(a, 3))\n",
        "  acc_test.append(round(b, 3))"
      ],
      "metadata": {
        "id": "4cPcvCFgsOdk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#importing packoges\n",
        "from sklearn.metrics import accuracy_score\n",
        "X_train=train_x\n",
        "y_train=train_Y\n",
        "X_test=test_x\n",
        "y_test=test_Y"
      ],
      "metadata": {
        "id": "fBGULZ6usOhG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "outputId": "cf716bce-01d1-49ca-e4db-b6a0a6b700f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-90-fde8d03f48c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#importing packoges\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmetrics\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_Y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'train_x' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. Decision Tree Classifier**"
      ],
      "metadata": {
        "id": "Vzi_TPUjtwBu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dectsion Tree model\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "# instantiate the model\n",
        "tree = DecisionTreeClassifier(max_depth = 5)\n",
        "# fit the model\n",
        "tree.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Z4qtfcxiuCAE",
        "outputId": "7a8310d3-a102-4290-d96f-d5ca3d1336fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5)"
            ],
            "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>DecisionTreeClassifier(max_depth=5)</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\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting the target value from the model for the somples\n",
        "y_test_tree = tree.predict(X_test)\n",
        "y_train_tree = tree.predict(X_train)"
      ],
      "metadata": {
        "id": "seJj_iCruS8v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#computing the accuracy of the model performance\n",
        "acc_train_tree = accuracy_score(y_train,y_train_tree)\n",
        "acc_test_tree = accuracy_score(y_test,y_test_tree)\n",
        "print(\"Decision Tree: Accuracy on training Data: {:.3f}\".format(acc_train_tree))\n",
        "print(\"Decision Tree: Accuracy on test Data: {:.3f}\".format(acc_test_tree))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WJ_UPE0PuS_1",
        "outputId": "e22c1bb6-40bb-4b23-b3e8-d274b9f672a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree: Accuracy on training Data: 0.947\n",
            "Decision Tree: Accuracy on test Data: 0.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the feature improtance in the model\n",
        "plt.figure(figsize=(43,43))\n",
        "\n",
        "n_features = X_train.shape[1]\n",
        "\n",
        "plt.barh(range(n_features), tree.feature_importances_, align='center')\n",
        "plt.yticks(np.arange(n_features), X_train.columns)\n",
        "plt.xlabel(\"Feature importance\")\n",
        "\n",
        "plt.ylabel(\"Feature\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 956
        },
        "id": "m7SCupzuuTED",
        "outputId": "02f3e3e5-caef-4ab0-e329-0aca8334579a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3096x3096 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#storing the resuits. The delow mentioned order of porameter possing {s (sportant.\n",
        "#Coution: Execute only once to owoid duplications.\n",
        "storeResults('Decision Tree', acc_train_tree, acc_test_tree)"
      ],
      "metadata": {
        "id": "_Vj6hl4avbQ0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Random Forest Classifier**"
      ],
      "metadata": {
        "id": "Ot7UKwLkvkzF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Randow Forest model\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "# instontiote the model\n",
        "\n",
        "forest = RandomForestClassifier(max_depth=5)\n",
        "\n",
        "# fit the model\n",
        "\n",
        "forest.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "DRmJUVVPwfO1",
        "outputId": "5fe8b762-9ba7-40a8-b79e-7fa131444673"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5)"
            ],
            "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>RandomForestClassifier(max_depth=5)</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\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting the target volue from the model for the somples\n",
        "y_test_forest = forest.predict(X_test)\n",
        "y_train_forest = forest.predict(X_train)"
      ],
      "metadata": {
        "id": "PJSiv_ldx1UR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#computing the occuracy of the model performonce\n",
        "\n",
        "acc_train_forest = accuracy_score(y_train,y_train_forest)\n",
        "\n",
        "acc_test_forest = accuracy_score(y_test,y_test_forest)\n",
        "\n",
        "print(\"Random forest: Accuracy on training Data: {:.3f}\".format(acc_train_forest))\n",
        "print(\"Random forest: Accuracy om test Dota: {:.3f}\".format(acc_test_forest))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aQH0D4Rx1X5",
        "outputId": "f59fcff9-c227-4eb4-e9dc-282f185d5ffd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random forest: Accuracy on training Data: 0.975\n",
            "Random forest: Accuracy om test Dota: 0.000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking the feature {mprotance in the model\n",
        "plt.figure(figsize=(45,45))\n",
        "\n",
        "n_features = X_train.shape[1]\n",
        "\n",
        "plt.barh(range(n_features), forest.feature_importances_, align='center')\n",
        "plt.yticks(np.arange(n_features), X_train.columns)\n",
        "\n",
        "plt.xlabel(\"Feature importance\")\n",
        "\n",
        "plt.ylabel(\"Feature\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        },
        "id": "sYIlXG-ax1bT",
        "outputId": "d54b46d1-38d8-4582-9f3d-30b71d21c263"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3240x3240 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Multi Layer Perceptron**"
      ],
      "metadata": {
        "id": "OO1F_g5tz7Mz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "mlp = MLPClassifier(hidden_layer_sizes=(10,10,10), activation='relu', solver='adam', max_iter=1500)\n",
        "mlp.fit(X_train,y_train)\n",
        "\n",
        "y_train_mlp = mlp.predict(X_train)\n",
        "\n",
        "y_test_mlp = mlp.predict(X_test)"
      ],
      "metadata": {
        "id": "2VHQ54ki0AqC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc_train_mlp = accuracy_score(y_train,y_train_mlp)\n",
        "\n",
        "acc_test_mlp = accuracy_score(y_test,y_test_mlp)\n",
        "\n",
        "print(\"MLP: Accuracy on training Data: {:.5f}\".format(acc_train_mlp))\n",
        "print(\"MLP: Accuracy on test Data: {:.5f}\".format(acc_test_mlp))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmiWP3LK0NE0",
        "outputId": "4b6db80b-4b23-4e04-ee45-9340cf08d586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MLP: Accuracy on training Data: 0.99045\n",
            "MLP: Accuracy on test Data: 0.00000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. Logistic Regression**"
      ],
      "metadata": {
        "id": "9dpR6ftG0aCQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "regressor = LogisticRegression(solver='lbfgs', max_iter=100)\n",
        "regressor.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "IH5-UnJH0fKf",
        "outputId": "02396ce1-321c-4791-a22f-a24f0c9e2295"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 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-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 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-8 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-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 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-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 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-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 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-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicting the target value from the model for the samples\n",
        "y_test_regressor = regressor.predict(X_test)\n",
        "y_train_regressor = regressor.predict(X_train)"
      ],
      "metadata": {
        "id": "0RrkkV-L07lL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#computing the accuracy of the model performance\n",
        "\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "acc_train_regressor = accuracy_score(y_train,y_train_regressor)\n",
        "\n",
        "acc_test_regressor = accuracy_score(y_test,y_test_regressor)\n",
        "\n",
        "print(\"Random forest: Accuracy on training Data: {:.5f}\".format(acc_train_regressor))\n",
        "print(\"Random forest: Accuracy on test Data: {:.5f}\".format(acc_test_regressor))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YnJDFzrk1AEN",
        "outputId": "d3e6fc2e-fc35-4017-c22a-b5251296048c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random forest: Accuracy on training Data: 0.96132\n",
            "Random forest: Accuracy on test Data: 0.00000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5. Naive Bayes**"
      ],
      "metadata": {
        "id": "iC9bxcx21V9r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "naive = GaussianNB()\n",
        "naive.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "E-X-7PY91lrI",
        "outputId": "3351ff29-1c63-4c25-9748-a8441d06b7b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 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-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 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-9 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-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 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-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 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-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 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-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_nb = naive.predict(X_test)\n",
        "y_train_nb = naive.predict(X_train)"
      ],
      "metadata": {
        "id": "dSHamrY81uQ8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "acc_train_nb = accuracy_score(y_train,y_train_nb)\n",
        "\n",
        "acc_test_nb = accuracy_score(y_test,y_test_nb)\n",
        "\n",
        "print(\"Naive Bayes: Accuracy on training Data: {:.5f}\".format(acc_train_nb))\n",
        "print(\"Naive Bayes: Accuracy on test Data: {:.5f}\".format(acc_test_nb))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BcWTFKh10ZD",
        "outputId": "737bfd69-0338-401c-b74d-3b75cdef43bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Naive Bayes: Accuracy on training Data: 0.94174\n",
            "Naive Bayes: Accuracy on test Data: 0.00000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "storeResults('NaiveBayes', acc_train_nb, acc_test_nb)"
      ],
      "metadata": {
        "id": "4zlrUL7q18Nb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Comparison of Models**"
      ],
      "metadata": {
        "id": "m6inMZtu2M2p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#creating dataframe\n",
        "\n",
        "results = pd.DataFrame({ 'ML Model': ML_Model,'Train Accuracy': acc_train, 'Test Accuracy': acc_test})\n",
        "\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "Pfj5GKap2W0n",
        "outputId": "18aa225e-a859-4e96-b6e6-7a0e4606c31f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ML Model  Train Accuracy  Test Accuracy\n",
              "0  Decision Tree           0.947            0.0\n",
              "1     NaiveBayes           0.942            0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c198c0b2-3da9-47e3-9613-9f0c15082d3e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ML Model</th>\n",
              "      <th>Train Accuracy</th>\n",
              "      <th>Test Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.947</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaiveBayes</td>\n",
              "      <td>0.942</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c198c0b2-3da9-47e3-9613-9f0c15082d3e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c198c0b2-3da9-47e3-9613-9f0c15082d3e 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-c198c0b2-3da9-47e3-9613-9f0c15082d3e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Sorting the datafram on accuracy\n",
        "results.sort_values(by=[ 'Test Accuracy', 'Train Accuracy'], ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "QIvVjcPZ2lDl",
        "outputId": "3877f999-b5a4-4ff5-ba4d-1774a5773be9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        ML Model  Train Accuracy  Test Accuracy\n",
              "0  Decision Tree           0.947            0.0\n",
              "1     NaiveBayes           0.942            0.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3f9f1405-3b39-436e-8e54-4723dcd8e4b4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ML Model</th>\n",
              "      <th>Train Accuracy</th>\n",
              "      <th>Test Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.947</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>NaiveBayes</td>\n",
              "      <td>0.942</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3f9f1405-3b39-436e-8e54-4723dcd8e4b4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3f9f1405-3b39-436e-8e54-4723dcd8e4b4 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-3f9f1405-3b39-436e-8e54-4723dcd8e4b4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    }
  ]
}