cybersécurité.ipynb

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      "cell_type": "markdown",
      "id": "47275a5c",
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        "id": "47275a5c"
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      "source": [
        "<H1><div align=\"center\" style=\"color:#006400\" > Projet IA et Cybersécurité   </div></H1>\n",
        "    <H1> <div align=\"center\" style=\"color:navy\" >  Système de détection d’intrusion   </div> </H1>\n",
        "\n",
        "  <h6> <div align=\"right\">  réalisé par: Eya Besbes </div></h6>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "97d9e633",
      "metadata": {
        "id": "97d9e633"
      },
      "source": [
        "<h3 style=\"color:maroon\"> 1. Ouvrir le jeu de données CICIDS2017_sample.CSV</h3>\n",
        "</br>  <h4> Importation des bibliothèques </h4>"
      ]
    },
    {
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      "execution_count": 1,
      "id": "debc3dde",
      "metadata": {
        "id": "debc3dde"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report, confusion_matrix,accuracy_score,recall_score,f1_score,precision_score,ConfusionMatrixDisplay\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "10dc5668",
      "metadata": {
        "id": "10dc5668"
      },
      "source": [
        "<h4> importation du jeu de données <h4>"
      ]
    },
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              "       Flow Duration  Total Fwd Packets  Total Backward Packets  \\\n",
              "0       5.416666e-07           0.000000                0.000003   \n",
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              "       Total Length of Fwd Packets  Total Length of Bwd Packets  \\\n",
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              "       Fwd Packet Length Max  Fwd Packet Length Min  Fwd Packet Length Mean  \\\n",
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              "26798               0.000000               0.000000  ...   \n",
              "26799               0.000000               0.000000  ...   \n",
              "\n",
              "       min_seg_size_forward  Active Mean  Active Std  Active Max  Active Min  \\\n",
              "0                       1.0          0.0         0.0         0.0         0.0   \n",
              "1                       1.0          0.0         0.0         0.0         0.0   \n",
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              "26799                   1.0          0.0         0.0         0.0         0.0   \n",
              "\n",
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              "26798        0.0       0.0       0.0       0.0      6  \n",
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              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5f7ceaff-071f-4d80-9e86-64a26116ebf6 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-5f7ceaff-071f-4d80-9e86-64a26116ebf6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "data=pd.read_csv(\"/content/sample_data/CICIDS2017_sample_km.csv\")\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "af4e9aca",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "af4e9aca",
        "outputId": "20996b93-ce2a-4872-8ce3-b0c654aaeb8e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    18225\n",
              "3     3042\n",
              "6     2180\n",
              "1     1966\n",
              "5     1255\n",
              "2       96\n",
              "4       36\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "data.Label.value_counts()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c6597889",
      "metadata": {
        "id": "c6597889"
      },
      "source": [
        "\n",
        "<h3 style=\"color:maroon\"> 2.Diviser les instances du jeu de données en une partie pour l’entrainement et une partie pour le test avec une proportion de 80% et 20%.</h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "e608c3bd",
      "metadata": {
        "colab": {
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          "height": 713
        },
        "id": "e608c3bd",
        "outputId": "dd2c7257-3ccb-47d8-fac9-410eb66a4821"
      },
      "outputs": [
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              "       Flow Duration  Total Fwd Packets  Total Backward Packets  \\\n",
              "8535    1.963833e-04           0.000000                0.000003   \n",
              "3586    5.389920e-02           0.000014                0.000000   \n",
              "4755    5.075779e-01           0.000059                0.000041   \n",
              "3137    2.690083e-04           0.000014                0.000003   \n",
              "18342   1.416667e-07           0.000009                0.000000   \n",
              "...              ...                ...                     ...   \n",
              "10955   1.766667e-06           0.000005                0.000000   \n",
              "17289   2.040000e-04           0.000005                0.000007   \n",
              "5192    2.467838e-01           0.000023                0.000021   \n",
              "12172   5.602166e-04           0.000000                0.000003   \n",
              "235     1.558333e-06           0.000005                0.000007   \n",
              "\n",
              "       Total Length of Fwd Packets  Total Length of Bwd Packets  \\\n",
              "8535                      0.000004                 2.700422e-07   \n",
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              "235                       0.000004                 2.410546e-07   \n",
              "\n",
              "       Fwd Packet Length Max  Fwd Packet Length Min  Fwd Packet Length Mean  \\\n",
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              "3586                0.000242               0.002581                0.001010   \n",
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              "18342               0.001853               0.000000                0.004320   \n",
              "...                      ...                    ...                     ...   \n",
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              "17289               0.001853               0.019785                0.007743   \n",
              "5192                0.016559               0.000000                0.012204   \n",
              "12172               0.001773               0.018925                0.007406   \n",
              "235                 0.001128               0.012043                0.004713   \n",
              "\n",
              "       Fwd Packet Length Std  Bwd Packet Length Max  ...  act_data_pkt_fwd  \\\n",
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              "3586                0.000000               0.000000  ...          0.000014   \n",
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              "12172               0.000000               0.005530  ...          0.000000   \n",
              "235                 0.000000               0.004045  ...          0.000005   \n",
              "\n",
              "       min_seg_size_forward  Active Mean  Active Std  Active Max  Active Min  \\\n",
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              "3586                    1.0     0.000036    0.000000    0.000036    0.000036   \n",
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          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "x=data.drop(['Label'],axis=1)\n",
        "y=data['Label']\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,train_size=0.8,test_size=0.2,random_state=1)\n",
        "x_train\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "b141d15b",
      "metadata": {
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          "height": 713
        },
        "id": "b141d15b",
        "outputId": "0281395b-b203-45f2-8a58-ccdbc5b06c19"
      },
      "outputs": [
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          "output_type": "execute_result",
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              "       Flow Duration  Total Fwd Packets  Total Backward Packets  \\\n",
              "25086   6.749999e-07           0.000000                0.000003   \n",
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              "       min_seg_size_forward  Active Mean  Active Std  Active Max  Active Min  \\\n",
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              "      <th>24117</th>\n",
              "      <td>8.334774e-03</td>\n",
              "      <td>0.000009</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.746192e-08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000307</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000</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>11205</th>\n",
              "      <td>1.333333e-07</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.302326e-07</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000242</td>\n",
              "      <td>0.002581</td>\n",
              "      <td>0.001010</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000005</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",
              "  </tbody>\n",
              "</table>\n",
              "<p>5360 rows × 77 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d4e989b0-0af7-4017-bc2b-349269c64331')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      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-d4e989b0-0af7-4017-bc2b-349269c64331 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-d4e989b0-0af7-4017-bc2b-349269c64331');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "x_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "f4895520",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f4895520",
        "outputId": "46e2da1a-941a-46b4-ad41-6fbe97a1dbd0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    14542\n",
              "3     2450\n",
              "6     1761\n",
              "1     1607\n",
              "5      986\n",
              "2       66\n",
              "4       28\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "pd.Series(y_train).value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5bd550f5",
      "metadata": {
        "id": "5bd550f5"
      },
      "source": [
        "\n",
        "<h3 style=\"color:maroon\"> 3. Utiliser SMOTE pour équilibrer les classes non balancées avec une stratégie d’échantillonnage égale à {2:1000, 4:1000}. </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "13f47bea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13f47bea",
        "outputId": "993f36eb-0a77-4c76-d161-00b82ceae22b"
      },
      "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: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (0.10.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.22.4)\n",
            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.10.1)\n",
            "Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.2)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.1.0)\n"
          ]
        }
      ],
      "source": [
        "!pip3 install imbalanced-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "f0673570",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0673570",
        "outputId": "0fb2d51f-acd9-47cf-cb20-f1da6571afc3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/imblearn/over_sampling/_smote/base.py:336: FutureWarning: The parameter `n_jobs` has been deprecated in 0.10 and will be removed in 0.12. You can pass an nearest neighbors estimator where `n_jobs` is already set instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    14542\n",
            "3     2450\n",
            "6     1761\n",
            "1     1607\n",
            "4     1000\n",
            "2     1000\n",
            "5      986\n",
            "Name: Label, dtype: int64\n",
            "0    3683\n",
            "3     592\n",
            "6     419\n",
            "1     359\n",
            "5     269\n",
            "2      30\n",
            "4       8\n",
            "Name: Label, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "smote=SMOTE(n_jobs=-1,sampling_strategy={2:1000, 4:1000})\n",
        "x_train, y_train=smote.fit_resample(x_train, y_train)\n",
        "print(pd.Series(y_train).value_counts())\n",
        "print(pd.Series(y_test).value_counts())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e9cc18f3",
      "metadata": {
        "id": "e9cc18f3"
      },
      "source": [
        "<h2 style=\"color:navy\"> Partie 1 : implémentation du premier classificateur  </h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "36386646",
      "metadata": {
        "id": "36386646"
      },
      "source": [
        "<h3 style=\"color:maroon\"> 4. Décrire brièvement le fonctionnement du classificateur 1. </h3>\n",
        "\n",
        "L’analyse discriminante linéaire (LDA ou ADL) est une technique sous contrainte (canonique) qui divise une matrice de réponse en groupes en fonction d’un facteur en trouvant la combinaison de variables qui donne la meilleure séparation possible entre les groupes. Le regroupement est effectué en maximisant la dispersion entre les groupes par rapport à la dispersion à l’intérieur des groupes. Cela vous permet de déterminer dans quelle mesure votre ensemble de variables indépendantes explique un regroupement a priori.La LDA compile des fonctions discriminantes à partir de descripteurs centrés-réduits. Les coefficients obtenus quantifient la contribution relative des variables explicatives sur la discrimination des objets. Les fonctions d’identification peuvent être générées à partir des descripteurs originaux pour classifier de nouvelles données dans des groupes pré-définis. Il existe plusieurs applications utiles de cette capacité à prédire les regroupements, comme la prédiction de l’appartenance d’une espèce de poisson à un groupe selon sa morphologie. On pourrait aussi déterminer si un nouvel article concerne un écosystème terrestre, marin ou d’eau douce selon une classification existante d’articles dans ces biomes effectuée à partir de mots clés de résumés."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "052325fc",
      "metadata": {
        "id": "052325fc"
      },
      "source": [
        "<h4> Importation des bibliothèques </h4>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "5010dcbe",
      "metadata": {
        "id": "5010dcbe"
      },
      "outputs": [],
      "source": [
        "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
        "import time"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dac23d84",
      "metadata": {
        "id": "dac23d84"
      },
      "source": [
        "<h3 style=\"color:maroon\">5. Entrainer le modèle en utilisant le premier classificateur en passant les données d’entrainement</h3>  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "d7116b27",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7116b27",
        "outputId": "b8e9a43d-65cc-46dd-8acb-3fd23146e82e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 798 ms, sys: 203 ms, total: 1 s\n",
            "Wall time: 1.22 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "clf = LinearDiscriminantAnalysis()\n",
        "start_time = time.time()\n",
        "clf.fit(x_train, y_train)\n",
        "end_time = time.time()\n",
        " #Calculer le temps pris par le classificateur pour s'entraîner\n",
        "training_time = end_time-start_time"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5d369a9a",
      "metadata": {
        "id": "5d369a9a"
      },
      "source": [
        "<h3 style=\"color:maroon\">6. Faire la prédiction (test) en utilisant le premier classificateur en passant les données de test</h3>  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "245dbc55",
      "metadata": {
        "id": "245dbc55"
      },
      "outputs": [],
      "source": [
        "start_time = time.time()\n",
        "y_pred = clf.predict(x_test)\n",
        "end_time = time.time()\n",
        "\n",
        "# Calculer le temps pris par le classificateur pour prédire les étiquettes\n",
        "prediction_time = end_time-start_time\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7d440f12",
      "metadata": {
        "id": "7d440f12"
      },
      "source": [
        "<h3 style=\"color:maroon\">7. Donner le temps pris par le premier classificateur pour construire le modèle. </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "3b11357c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3b11357c",
        "outputId": "8ab94db3-cc31-4ad8-ca4d-a7234223639f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "temps pris pour l'entrainement du modele est :   1.2207691669464111\n",
            "\n",
            "\n",
            "temps pris pour prédire les classes  est :  0.03592967987060547\n"
          ]
        }
      ],
      "source": [
        "print(\"\\ntemps pris pour l'entrainement du modele est :  \", training_time)\n",
        "print(\"\\n\\ntemps pris pour prédire les classes  est : \", prediction_time)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7d88dff6",
      "metadata": {
        "id": "7d88dff6"
      },
      "source": [
        "<h3 style=\"color:maroon\">8. Donner les statistiques données par le classificateur (acuracy, precision, recall et f1-score) et interpréter les résultats.  </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "2345c266",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2345c266",
        "outputId": "0c01447c-febd-4d84-ab6a-20c675d5fbb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Accuracy of LDA Classifier  0.8434701492537313\n",
            "\n",
            " \n",
            "Presicion of LDA Classifier  0.8626359551380055\n",
            "\n",
            "\n",
            " Recall of LDA Classifier  0.8434701492537313\n",
            "\n",
            "\n",
            " f1 score of LDA Classifier  0.8454748403213556\n",
            "\n",
            "\n",
            " f1 score for each class of LDA Classifier \n",
            " [0.89969937 0.50814332 0.32167832 0.79482072 0.30769231 0.76571429\n",
            " 0.8284153 ]\n"
          ]
        }
      ],
      "source": [
        "accuracy1 = accuracy_score(y_test, y_pred)\n",
        "print(\"\\n\\nAccuracy of LDA Classifier \", accuracy1)\n",
        "precision1=precision_score(y_test, y_pred, average='weighted')\n",
        "print(\"\\n \\nPresicion of LDA Classifier \",precision1)\n",
        "recall1=recall_score(y_test, y_pred, average='weighted')\n",
        "print(\"\\n\\n Recall of LDA Classifier \", recall_score(y_test, y_pred, average='weighted'))\n",
        "fscore1=f1_score(y_test, y_pred, average='weighted')\n",
        "print(\"\\n\\n f1 score of LDA Classifier \",fscore1)\n",
        "print(\"\\n\\n f1 score for each class of LDA Classifier \\n\",f1_score(y_test, y_pred, average=None))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "80fa3785",
      "metadata": {
        "id": "80fa3785"
      },
      "source": [
        "On peut dire que  notre modèle est performant la plupart des temps avec un score de 0.84 mais si on prend le f1-score pour chaque classe on constate qu'il a des difficultés à prédire correctement les données de la classe 2 , 4 et même aussi la classe 1. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5a1efff5",
      "metadata": {
        "id": "5a1efff5"
      },
      "source": [
        "<h3 style=\"color:maroon\">9. Donner la précision détaillée pour chaque classe d’attaque (Detailed Accuracy By Class, precision, recall et f1-score) et interpréter les résultats </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "9aa5d17e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9aa5d17e",
        "outputId": "9a610b9e-9e22-43dc-a5b3-261253a937b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "la précision détaillée pour chaque classe d’attaque (Detailed Accuracy By Class, precision, recall et f1-score) \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.89      0.90      3683\n",
            "           1       0.61      0.43      0.51       359\n",
            "           2       0.20      0.77      0.32        30\n",
            "           3       0.97      0.67      0.79       592\n",
            "           4       0.22      0.50      0.31         8\n",
            "           5       0.62      1.00      0.77       269\n",
            "           6       0.76      0.90      0.83       419\n",
            "\n",
            "    accuracy                           0.84      5360\n",
            "   macro avg       0.61      0.74      0.63      5360\n",
            "weighted avg       0.86      0.84      0.85      5360\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"\\n\\nla précision détaillée pour chaque classe d’attaque (Detailed Accuracy By Class, precision, recall et f1-score) \\n\\n\")\n",
        "print(classification_report(y_test,y_pred))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "eb8b0eba",
      "metadata": {
        "id": "eb8b0eba"
      },
      "source": [
        "On constate que les métriques de la classe 1 sont très faibles par rapport aux autres classe"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f126d27",
      "metadata": {
        "id": "0f126d27"
      },
      "source": [
        "<h3 style=\"color:maroon\">10.  Donner et interpréter les résultats de la matrice de confusion donnée par le classificateur (confusion matrix) </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "6dcc7e3b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "6dcc7e3b",
        "outputId": "39e347b8-0651-4714-8b34-437426a4f391"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'la matrice de confusion ')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Affichage de la matrice de confusion\n",
        "\n",
        "cm\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "disp.plot()\n",
        "plt.title(\"la matrice de confusion \")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b33c9baf",
      "metadata": {
        "id": "b33c9baf"
      },
      "source": [
        "En interprétant ces résultats, nous pouvons conclure que notre modèle est assez précis pour identifier les classes : 0 ,3,5 et 6 , mais pas aussi précis pour identifier les classes 1 , 2 , 4 . "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "75967691",
      "metadata": {
        "id": "75967691"
      },
      "source": [
        "<h2 style=\"color:navy\"> Partie 2 : implémentation du deuxième classificateur  </h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "00730a5d",
      "metadata": {
        "id": "00730a5d"
      },
      "source": [
        "<h3 style=\"color:maroon\"> 11. Décrire brièvement le fonctionnement du classificateur 2. </h3>\n",
        "\n",
        "\n",
        "Le modèle GaussianProcessClassifier (classifieur de processus gaussien) est un algorithme d'apprentissage automatique supervisé utilisé pour la classification de données. Il est basé sur les processus gaussiens, également appelés processus de régression gaussienne, qui sont des modèles probabilistes flexibles.\n",
        "Il convient de noter que l'estimation des processus gaussiens peut être computationnellement intensive, en particulier pour des ensembles de données volumineux. Des approximations et des méthodes d'optimisation efficaces sont souvent utilisées pour rendre l'apprentissage et l'inférence réalisables dans des délais raisonnables.\n",
        "\n",
        "En résumé, le modèle GaussianProcessClassifier utilise des processus gaussiens pour modéliser les relations entre les caractéristiques et les étiquettes de classe, ce qui lui permet de fournir des prédictions probabilistes pour la classification."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "95dac42d",
      "metadata": {
        "id": "95dac42d"
      },
      "source": [
        "<h3 style=\"color:maroon\"> 12.  Entrainer le modèle en utilisant le deuxième classificateur en passant les données d’entrainement</h3>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6c441f86",
      "metadata": {
        "id": "6c441f86"
      },
      "source": [
        "<h4> Importation des bibliothèques </h4>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "2b31fd7f",
      "metadata": {
        "id": "2b31fd7f"
      },
      "outputs": [],
      "source": [
        "from sklearn.gaussian_process import GaussianProcessClassifier\n",
        "from sklearn.gaussian_process.kernels import RBF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "9426482d",
      "metadata": {
        "scrolled": false,
        "id": "9426482d"
      },
      "outputs": [],
      "source": [
        "gpc = GaussianProcessClassifier()\n",
        "start_time = time.time()\n",
        "gpc.fit(x_train, y_train)\n",
        "end_time = time.time()\n",
        " #Calculer le temps pris par le classificateur pour s'entraîner\n",
        "training_time = end_time-start_time"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a3826a7f",
      "metadata": {
        "id": "a3826a7f"
      },
      "source": [
        "<h3 style=\"color:maroon\">13.  Faire la prédiction en utilisant le deuxième classificateur en passant les données de test.   </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "b0ea28dc",
      "metadata": {
        "id": "b0ea28dc"
      },
      "outputs": [],
      "source": [
        "start_time = time.time()\n",
        "y_pred = gpc.predict(X_test)\n",
        "end_time = time.time()\n",
        "\n",
        "# Calculer le temps pris par le classificateur pour prédire les étiquettes\n",
        "prediction_time = end_time-start_time\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6c0cea87",
      "metadata": {
        "id": "6c0cea87"
      },
      "source": [
        "<h3 style=\"color:maroon\"> 14.  Donner le temps pris par le deuxième classificateur pour construire le modèle. </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "0fd423d9",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0fd423d9",
        "outputId": "1439951a-42c9-41fa-ad49-5be997630439"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "temps pris pour l'entrainement du modele est   1539.7552497386932\n",
            "\n",
            "\n",
            "temps pris pour prédire les classes  est   158.01563048362732\n"
          ]
        }
      ],
      "source": [
        "print(\"temps pris pour l'entrainement du modele est  \", training_time)\n",
        "print(\"\\n\\ntemps pris pour prédire les classes  est  \", prediction_time)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b4649d63",
      "metadata": {
        "id": "b4649d63"
      },
      "source": [
        "<h3 style=\"color:maroon\"> 15.  Donner les statistiques données par le classificateur (acuracy, precision, recall et f1-score) et interpréter les résultats.  </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "5fbd061b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5fbd061b",
        "outputId": "7ea2ceba-4ca5-48e1-84c0-689ebf15c910"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "Accuracy of Guassian process classifier   0.832723470987977\n",
            "\n",
            " \n",
            "Presicion of Guassian pôrcess classifier   0.8463844095634628\n",
            "\n",
            "\n",
            " Recall of Guassian process classifier  0.832723470987977\n",
            "\n",
            "\n",
            " f1 score of Guassian process classifier   0.8227899161328385\n",
            "\n",
            "\n",
            " f1 score for each class of Guassian porcess classifier \n",
            " [0.89160426 0.57446809 0.43809524 0.75903614 0.875      0.65625\n",
            " 0.85106383]\n"
          ]
        }
      ],
      "source": [
        "accuracy2 = accuracy_score(y_test, y_pred)\n",
        "print(\"\\n\\nAccuracy of Guassian process classifier  \", accuracy2)\n",
        "precision2=precision_score(y_test, y_pred, average='weighted')\n",
        "print(\"\\n \\nPresicion of Guassian pôrcess classifier  \",precision2)\n",
        "recall2=recall_score(y_test, y_pred, average='weighted')\n",
        "print(\"\\n\\n Recall of Guassian process classifier \", recall2)\n",
        "fscore2=f1_score(y_test, y_pred, average='weighted')\n",
        "print(\"\\n\\n f1 score of Guassian process classifier  \",fscore2)\n",
        "print(\"\\n\\n f1 score for each class of Guassian porcess classifier \\n\",f1_score(y_test, y_pred, average=None))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "51f4b2d6",
      "metadata": {
        "id": "51f4b2d6"
      },
      "source": [
        "<h3 style=\"color:maroon\"> 16.  Donner la précision détaillée pour chaque classe d’attaque (Detailed Accuracy By Class, precision, recall et f1-score) et interpréter les résultats. </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "e42d4b91",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e42d4b91",
        "outputId": "61f0781a-59ac-49f1-e76c-92e78969ea72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "la précision détaillée pour chaque classe d’attaque (Detailed Accuracy By Class, precision, recall et f1-score) \n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.95      0.89      1192\n",
            "           1       0.54      0.62      0.57       131\n",
            "           2       1.00      0.28      0.44        82\n",
            "           3       0.96      0.63      0.76       201\n",
            "           4       0.90      0.85      0.88        82\n",
            "           5       0.89      0.52      0.66        81\n",
            "           6       0.87      0.83      0.85       144\n",
            "\n",
            "    accuracy                           0.83      1913\n",
            "   macro avg       0.86      0.67      0.72      1913\n",
            "weighted avg       0.85      0.83      0.82      1913\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"\\n\\nla précision détaillée pour chaque classe d’attaque (Detailed Accuracy By Class, precision, recall et f1-score) \\n\\n\")\n",
        "print(classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "57554e46",
      "metadata": {
        "id": "57554e46"
      },
      "source": [
        "<h3 style=\"color:maroon\"> 17.  Donner et interpréter les résultats de la matrice de confusion donnée par le classificateur (confusion matrix) </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "id": "2a871c34",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "2a871c34",
        "outputId": "916e5d0f-832f-418b-cdd9-e88943857dc0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'la matrice de confusion ')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cm1 = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "# Affichage de la matrice de confusion\n",
        "\n",
        "cm1\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm1)\n",
        "disp.plot()\n",
        "plt.title(\"la matrice de confusion \")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b2ee0cb5",
      "metadata": {
        "id": "b2ee0cb5"
      },
      "source": [
        "En interprétant ces résultats, nous pouvons conclure que notre modèle est précis lors de l'identification des classes : 0 ,2,3,4,5 et 6  , mais pas aussi précis pour identifier la classe 1. "
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5319af91",
      "metadata": {
        "id": "5319af91"
      },
      "source": [
        "<h2 style=\"color:navy\"> Partie 3 : comparaison des résultats  </h2>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "71e0cf07",
      "metadata": {
        "id": "71e0cf07"
      },
      "source": [
        "<h3 style=\"color:maroon\"> 18.  Comparer les résultats trouvés par les deux classificateurs sous forme d’un tableau? Que peut-on conclure. </h3>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "id": "84313a05",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84313a05",
        "outputId": "91beffc5-c91c-4d72-c600-47f96c237cc7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "*******   Accuracy  Precision Recall    F1-score   \n",
            "\n",
            "Classifier1 0.843     0.863     0.843     0.845      \n",
            "\n",
            "classifier2 0.833     0.846     0.833     0.823      \n",
            "\n"
          ]
        }
      ],
      "source": [
        "metrics_names = ['Accuracy', 'Precision', 'Recall', 'F1-score']\n",
        "clf1_metrics =[accuracy1,precision1,recall1,fscore1]\n",
        "clf2_metrics =[accuracy2,precision2,recall2,fscore2]\n",
        "# Affichage des résultats sous forme de tableau\n",
        "print('\\n{:<10s}{:<10s}{:<10s}{:<10s}{:<10s}'.format('*******   ', *metrics_names),'\\n')\n",
        "print('{:<10s}{:<10.3f}{:<10.3f}{:<10.3f}{:<10.3f}'.format('Classifier1 ', *clf1_metrics),'\\n')\n",
        "print('{:<10s}{:<10.3f}{:<10.3f}{:<10.3f}{:<10.3f}'.format('classifier2 ', *clf2_metrics),'\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "id": "1f36f18f",
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "1f36f18f",
        "outputId": "f0df4882-4a9f-422e-ac8c-9f400dbaec13"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Classifier  Accuracy  Precision    Recall  F1-score\n",
              "0   LinearDiscriminantAnalysis  0.843470   0.862636  0.843470  0.845475\n",
              "1  Guassian process Classifier  0.832723   0.846384  0.832723  0.822790"
            ],
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              "      <th>Classifier</th>\n",
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              "      <td>LinearDiscriminantAnalysis</td>\n",
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              "      <td>Guassian process Classifier</td>\n",
              "      <td>0.832723</td>\n",
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              "      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-cb230fd3-20a2-4c28-abec-0b6c32346ab0 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-cb230fd3-20a2-4c28-abec-0b6c32346ab0');\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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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "\n",
        "df = pd.DataFrame({'Classifier': ['LinearDiscriminantAnalysis', 'Guassian process Classifier'],\n",
        "        'Accuracy': [accuracy1, accuracy2],\n",
        "        'Precision': [precision1, precision2],\n",
        "        'Recall': [recall1, recall2],\n",
        "        'F1-score': [fscore1, fscore2]})\n",
        "\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d38e9337",
      "metadata": {
        "id": "d38e9337"
      },
      "source": [
        "</br>\n",
        "\n",
        "En comparant les résultats des deux classificateurs, nous pouvons conclure que :\n",
        "\n",
        "* Le classifier 1 donne de meilleurs résultats que le classifier1 pour toutes les métriques (accuracy, precision, recall et f1-score). \n",
        "</br>\n",
        "\n",
        "* Le classifier 1 a une précision plus élevée que le classifier1, ce qui signifie qu'il a une plus grande capacité à identifier correctement les exemples positifs.\n",
        "</br>\n",
        "* Le classifier 1 a également un rappel plus élevé que le classifier1, ce qui signifie qu'il a une plus grande capacité à identifier tous les exemples positifs.\n",
        "</br>\n",
        "* Enfin, le f1-score est également plus élevé pour le classifier1, ce qui indique une meilleure performance globale pour la classification des classes. \n",
        "</br>\n",
        "* Le temps d'execution du premier classifieur est tres petit par rapport au deuxième classifieur ce qui signifie que le premier classifieur est beaucoup plus rapide que le deuxième classifieur.\n",
        " </br>\n",
        "\n",
        "Ces résultats suggèrent que le classificateur Linear Discriminant Analysis est  meilleur que le classifieur Gaussian Process Classifier pour un Système de détection d'intrusion étudiée"
      ]
    }
  ],
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