TP1_VE.ipynb

{
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.14",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "e5091fbd"
      ],
      "name": "notebook8c2d8e543d",
      "include_colab_link": true
    },
    "kaggle": {
      "accelerator": "gpu",
      "dataSources": [
        {
          "sourceId": 9566759,
          "sourceType": "datasetVersion",
          "datasetId": 5830723
        }
      ],
      "dockerImageVersionId": 30787,
      "isInternetEnabled": true,
      "language": "python",
      "sourceType": "notebook",
      "isGpuEnabled": true
    }
  },
  "nbformat_minor": 5,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Ramyrahmeni/DataMining/blob/main/TP1_VE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'titanic:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F5830723%2F9566759%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20241007%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20241007T190106Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "id": "0-QCDPHmW1Up"
      },
      "cell_type": "code",
      "outputs": [],
      "execution_count": null,
      "id": "0-QCDPHmW1Up"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<table>\n",
        "    <tr>\n",
        "        <td style='width:10%;'>\n",
        "            <img src='img/intro-to-programming.svg'/>\n",
        "        </td>\n",
        "        <td style='width:90%;text-align:left;'>\n",
        "            <div style='font-family:Inter,sans-serif;font-size: 22px;font-weight: 600;line-height: 24px;'>\n",
        "                Lab 1 : Préparation de données\n",
        "            </div>\n",
        "            <span>visualisation de données, Imputation des valeurs manquantes.</span>\n",
        "        </td>\n",
        "        <td ></td>\n",
        "    </tr>\n",
        "</table>"
      ],
      "metadata": {
        "id": "1b74003d"
      },
      "id": "1b74003d"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<div style='font-family:Inter,sans-serif;font-size: 14px;font-weight: 600;line-height: 24px;'>\n",
        "Importer les bibliothèques:</br></div>\n",
        "1. Pandas pour la manipulation et l'analyse des données</br>\n",
        "2. Numpy pour manipuler des matrices ou tableaux multidimensionnels</br>\n",
        "3. matplotlib pour tracer et visualiser des données sous forme de graphiques</br>\n",
        "4. seaborn pour la visualisation</br></br>\n"
      ],
      "metadata": {
        "id": "b2b2da16"
      },
      "id": "b2b2da16"
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from IPython.display import Image"
      ],
      "metadata": {
        "id": "7cfa3052",
        "execution": {
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        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "7cfa3052"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<div style='font-family:Inter,sans-serif;font-size: 14px;font-weight: 600;line-height: 24px;'>\n",
        "Importer le jeu de données Titanic:</br></div>\n",
        "1. Afficher les 5 premières ligne du jeu de données</br>\n",
        "2. Afficher les dimension de la DataFrame</br>\n",
        "3. Vérifier le type de chaque colonne</br>\n",
        "4. afficher les noms des colonnes de la DataFrame</br></br>\n"
      ],
      "metadata": {
        "id": "7ed3a1bf"
      },
      "id": "7ed3a1bf"
    },
    {
      "cell_type": "code",
      "source": [
        "#importer et lire un fichier, ici de type csv\n",
        "df=pd.read_csv('/kaggle/input/titanic/titanic.csv')"
      ],
      "metadata": {
        "id": "bd5a8e31",
        "execution": {
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      },
      "execution_count": null,
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      "id": "bd5a8e31"
    },
    {
      "cell_type": "code",
      "source": [
        "#afficher un aperçu, par défaut les 5 premières lignes du jeu de données.\n",
        "df[:5]"
      ],
      "metadata": {
        "scrolled": true,
        "id": "37adfee6",
        "execution": {
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      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 3,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   pclass  survived                                             name     sex  \\\n0       1         1                    Allen, Miss. Elisabeth Walton  female   \n1       1         1                   Allison, Master. Hudson Trevor    male   \n2       1         0                     Allison, Miss. Helen Loraine  female   \n3       1         0             Allison, Mr. Hudson Joshua Creighton    male   \n4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n\n       age  sibsp  parch  ticket      fare    cabin embarked  \n0  29.0000      0      0   24160  211.3375       B5        S  \n1   0.9167      1      2  113781  151.5500  C22 C26        S  \n2   2.0000      1      2  113781  151.5500  C22 C26        S  \n3  30.0000      1      2  113781  151.5500  C22 C26        S  \n4  25.0000      1      2  113781  151.5500  C22 C26        S  ",
            "text/html": "<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>pclass</th>\n      <th>survived</th>\n      <th>name</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>ticket</th>\n      <th>fare</th>\n      <th>cabin</th>\n      <th>embarked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allen, Miss. Elisabeth Walton</td>\n      <td>female</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24160</td>\n      <td>211.3375</td>\n      <td>B5</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allison, Master. Hudson Trevor</td>\n      <td>male</td>\n      <td>0.9167</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Miss. Helen Loraine</td>\n      <td>female</td>\n      <td>2.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mr. Hudson Joshua Creighton</td>\n      <td>male</td>\n      <td>30.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n      <td>female</td>\n      <td>25.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>S</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "id": "37adfee6"
    },
    {
      "cell_type": "code",
      "source": [
        "# afficher la dimension d'un dataframe\n",
        "print(\"Dimensions:\", df.shape)"
      ],
      "metadata": {
        "id": "e471f445",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:11.201227Z",
          "iopub.execute_input": "2024-10-07T18:30:11.202Z",
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        },
        "trusted": true,
        "outputId": "bec12cb8-3f63-47fe-8ece-a031a8b171a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Dimensions: (1309, 11)\n",
          "output_type": "stream"
        }
      ],
      "id": "e471f445"
    },
    {
      "cell_type": "code",
      "source": [
        "# Affichage du type de chaque colonne\n",
        "for i in df.columns:\n",
        "  print(df[i].dtype)"
      ],
      "metadata": {
        "id": "99c9e716",
        "execution": {
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        "trusted": true,
        "outputId": "094734f9-1122-46aa-9d42-2a514638f6d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "int64\nint64\nobject\nobject\nfloat64\nint64\nint64\nobject\nfloat64\nobject\nobject\n",
          "output_type": "stream"
        }
      ],
      "id": "99c9e716"
    },
    {
      "cell_type": "code",
      "source": [
        "# déterminer le nombre d'occurrences de chaque modalité d'une variable, ici la variable cible\n",
        "occurrences_survived = df['survived'].value_counts()\n",
        "print(\"Occurrences de la variable 'survived':\\n\", occurrences_survived)"
      ],
      "metadata": {
        "id": "17e7d51a",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:11.219331Z",
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      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Occurrences de la variable 'survived':\n survived\n0    809\n1    500\nName: count, dtype: int64\n",
          "output_type": "stream"
        }
      ],
      "id": "17e7d51a"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### VARIABLE DESCRIPTIONS\n",
        "Pclass : Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd) ;\n",
        "survival : Survival (0 = No; 1 = Yes);\n",
        "name : Name ;\n",
        "sex : Sex;\n",
        "age : Age;\n",
        "sibsp : Number of Siblings/Spouses Aboard;\n",
        "parch : Number of Parents/Children Aboard;\n",
        "ticket : Ticket Number;\n",
        "fare : Passenger Fare (British pound);\n",
        "cabin : Cabin;\n",
        "embarked : Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)"
      ],
      "metadata": {
        "id": "65ee77a8"
      },
      "id": "65ee77a8"
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "fig, axes = plt.subplots(2, 2, figsize=(15, 10))\n",
        "\n",
        "# 1. Histogramme de la répartition des passagers selon la classe (pclass)\n",
        "sns.countplot(x='pclass', data=df, palette='viridis', ax=axes[0, 0])\n",
        "axes[0, 0].set_title(\"Répartition des Passagers par Classe\")\n",
        "axes[0, 0].set_xlabel(\"Classe (1 = 1st; 2 = 2nd; 3 = 3rd)\")\n",
        "axes[0, 0].set_ylabel(\"Nombre de Passagers\")\n",
        "\n",
        "# 2. Diagramme en barres du taux de survie par classe et sexe\n",
        "sns.barplot(x='pclass', y='survived', hue='sex', data=df, palette='muted', ax=axes[0, 1])\n",
        "axes[0, 1].set_title(\"Taux de Survie par Classe et Sexe\")\n",
        "axes[0, 1].set_xlabel(\"Classe\")\n",
        "axes[0, 1].set_ylabel(\"Taux de Survie\")\n",
        "\n",
        "# 3. Boxplot de l'âge par classe et survie\n",
        "sns.boxplot(x='pclass', y='age', hue='survived', data=df, palette='Set2', ax=axes[1, 0])\n",
        "axes[1, 0].set_title(\"Répartition de l'Âge par Classe et Survie\")\n",
        "axes[1, 0].set_xlabel(\"Classe\")\n",
        "axes[1, 0].set_ylabel(\"Âge\")\n",
        "\n",
        "# 4. Répartition des passagers selon le port d'embarquement par classe\n",
        "sns.countplot(x='embarked', hue='pclass', data=df, palette='Set1', ax=axes[1, 1])\n",
        "axes[1, 1].set_title(\"Répartition des Passagers selon le Port d'Embarquement par Classe\")\n",
        "axes[1, 1].set_xlabel(\"Port d'Embarquement (C = Cherbourg; Q = Queenstown; S = Southampton)\")\n",
        "axes[1, 1].set_ylabel(\"Nombre de Passagers\")\n",
        "\n",
        "# Ajuster l'espacement entre les sous-graphiques pour éviter le chevauchement\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "scrolled": true,
        "id": "5bc74201",
        "execution": {
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x1000 with 4 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "id": "5bc74201"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Etape 1 : Visualisation du jeu de données"
      ],
      "metadata": {
        "id": "dfe7a9ca"
      },
      "id": "dfe7a9ca"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Distribution de la variable cible (les classes sont bien équilibrées!?) Survival (0 = No; 1 = Yes)"
      ],
      "metadata": {
        "id": "9ed263f2"
      },
      "id": "9ed263f2"
    },
    {
      "cell_type": "code",
      "source": [
        "survived = df['survived'].eq(1).sum()\n",
        "notsurvived = df['survived'].eq(0).sum()"
      ],
      "metadata": {
        "id": "75ca13c8",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:12.700543Z",
          "iopub.execute_input": "2024-10-07T18:30:12.700825Z",
          "iopub.status.idle": "2024-10-07T18:30:12.706287Z",
          "shell.execute_reply.started": "2024-10-07T18:30:12.700795Z",
          "shell.execute_reply": "2024-10-07T18:30:12.705403Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "75ca13c8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Relation entre pclass et survived or not"
      ],
      "metadata": {
        "id": "e5091fbd"
      },
      "id": "e5091fbd"
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(['pclass','survived'])['survived'].count()"
      ],
      "metadata": {
        "id": "8916be95",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:12.707698Z",
          "iopub.execute_input": "2024-10-07T18:30:12.708334Z",
          "iopub.status.idle": "2024-10-07T18:30:12.723174Z",
          "shell.execute_reply.started": "2024-10-07T18:30:12.708287Z",
          "shell.execute_reply": "2024-10-07T18:30:12.722238Z"
        },
        "trusted": true,
        "outputId": "a7074cd6-67ca-42e1-8865-8f5c106b17e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 9,
          "output_type": "execute_result",
          "data": {
            "text/plain": "pclass  survived\n1       0           123\n        1           200\n2       0           158\n        1           119\n3       0           528\n        1           181\nName: survived, dtype: int64"
          },
          "metadata": {}
        }
      ],
      "id": "8916be95"
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_df = df[['pclass', 'survived']].groupby('pclass')\n",
        "\n",
        "for key, item in grouped_df:\n",
        "    print(grouped_df.get_group(key), \"\\n\\n\")"
      ],
      "metadata": {
        "id": "f9936e28",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:12.726164Z",
          "iopub.execute_input": "2024-10-07T18:30:12.726544Z",
          "iopub.status.idle": "2024-10-07T18:30:12.740926Z",
          "shell.execute_reply.started": "2024-10-07T18:30:12.726511Z",
          "shell.execute_reply": "2024-10-07T18:30:12.739979Z"
        },
        "trusted": true,
        "outputId": "1b0279e0-58f9-4d95-986c-8fcbaf2f746d"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "     pclass  survived\n0         1         1\n1         1         1\n2         1         0\n3         1         0\n4         1         0\n..      ...       ...\n318       1         0\n319       1         1\n320       1         1\n321       1         0\n322       1         1\n\n[323 rows x 2 columns] \n\n\n     pclass  survived\n323       2         0\n324       2         1\n325       2         0\n326       2         0\n327       2         0\n..      ...       ...\n595       2         0\n596       2         1\n597       2         1\n598       2         1\n599       2         0\n\n[277 rows x 2 columns] \n\n\n      pclass  survived\n600        3         0\n601        3         0\n602        3         0\n603        3         1\n604        3         1\n...      ...       ...\n1304       3         0\n1305       3         0\n1306       3         0\n1307       3         0\n1308       3         0\n\n[709 rows x 2 columns] \n\n\n",
          "output_type": "stream"
        }
      ],
      "id": "f9936e28"
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_df.count()"
      ],
      "metadata": {
        "id": "2ea2e233",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:12.742387Z",
          "iopub.execute_input": "2024-10-07T18:30:12.742689Z",
          "iopub.status.idle": "2024-10-07T18:30:12.75255Z",
          "shell.execute_reply.started": "2024-10-07T18:30:12.742658Z",
          "shell.execute_reply": "2024-10-07T18:30:12.751688Z"
        },
        "trusted": true,
        "outputId": "16e19b31-8d66-44a6-cc6c-7d22660eb677"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 11,
          "output_type": "execute_result",
          "data": {
            "text/plain": "        survived\npclass          \n1            323\n2            277\n3            709",
            "text/html": "<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>survived</th>\n    </tr>\n    <tr>\n      <th>pclass</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>323</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>277</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>709</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "id": "2ea2e233"
    },
    {
      "cell_type": "code",
      "source": [
        "plt = df[['pclass', 'survived']].groupby('pclass').mean().survived.plot(kind='bar')\n",
        "plt.set_xlabel('Pclass')\n",
        "plt.set_ylabel('Survival Probability')"
      ],
      "metadata": {
        "id": "dc453a4e",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:12.753838Z",
          "iopub.execute_input": "2024-10-07T18:30:12.754203Z",
          "iopub.status.idle": "2024-10-07T18:30:13.047253Z",
          "shell.execute_reply.started": "2024-10-07T18:30:12.754161Z",
          "shell.execute_reply": "2024-10-07T18:30:13.046253Z"
        },
        "trusted": true,
        "outputId": "b19b0be0-8b40-4d6e-96de-7f445b8d7f52"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 12,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0, 0.5, 'Survival Probability')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "id": "dc453a4e"
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Les passagers de la classe 1 ont plus de chance pour survivre plus que 60%,\n",
        "Les passagers de la classe 3 ont moins de chance pour survivre moins que 30%,*"
      ],
      "metadata": {
        "id": "f60f0307"
      },
      "id": "f60f0307"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Distribution de la variable sexe"
      ],
      "metadata": {
        "id": "a806d46d"
      },
      "id": "a806d46d"
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "fig = px.histogram(df, x='sex', title='Distribution du Sexe des Passagers', color='sex',\n",
        "                   labels={'sex': 'Sexe'},\n",
        "                   category_orders={'sex': ['male', 'female']},  # Ordre d'affichage\n",
        "                   color_discrete_sequence=['#636EFA', '#EF553B'])  # Couleurs personnalisées\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "1801ae5c",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:13.048498Z",
          "iopub.execute_input": "2024-10-07T18:30:13.048822Z",
          "iopub.status.idle": "2024-10-07T18:30:15.177215Z",
          "shell.execute_reply.started": "2024-10-07T18:30:13.048787Z",
          "shell.execute_reply": "2024-10-07T18:30:15.176409Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "1801ae5c"
    },
    {
      "cell_type": "code",
      "source": [
        "# Personnalisation de l'apparence\n",
        "fig.update_layout(\n",
        "    xaxis_title=\"Sexe\",\n",
        "    yaxis_title=\"Nombre de Passagers\",\n",
        "    template='plotly_white'\n",
        ")\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "0551a1c3",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:15.181596Z",
          "iopub.execute_input": "2024-10-07T18:30:15.182073Z",
          "iopub.status.idle": "2024-10-07T18:30:15.393426Z",
          "shell.execute_reply.started": "2024-10-07T18:30:15.18204Z",
          "shell.execute_reply": "2024-10-07T18:30:15.392537Z"
        },
        "trusted": true,
        "outputId": "8dda5600-6b85-48fb-cc7c-599f702c3da1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": "        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-2.32.0.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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          "metadata": {}
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      ],
      "id": "0551a1c3"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Distribution de la variable sibsp (nbr de frère et soeur/ conjoint à board)\n",
        "\n",
        "> Add blockquote\n",
        "\n"
      ],
      "metadata": {
        "id": "1fb5c7e6"
      },
      "id": "1fb5c7e6"
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.histogram(df, x='sibsp', title='Distribution du Nombre de Frères/Sœurs ou Conjoints à Bord',\n",
        "                   labels={'sibsp': 'Nombre de Frères/Sœurs ou Conjoints à Bord'},\n",
        "                   color_discrete_sequence=['#FFA07A'])\n",
        "\n",
        "fig.update_layout(\n",
        "    xaxis_title=\"Nombre de Frères/Sœurs ou Conjoints à Bord\",\n",
        "    yaxis_title=\"Nombre de Passagers\",\n",
        "    template='plotly_white'\n",
        ")"
      ],
      "metadata": {
        "id": "5545b8c2",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:15.394552Z",
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          "iopub.status.idle": "2024-10-07T18:30:15.484716Z",
          "shell.execute_reply.started": "2024-10-07T18:30:15.394834Z",
          "shell.execute_reply": "2024-10-07T18:30:15.483812Z"
        },
        "trusted": true,
        "outputId": "f240c1e5-2922-4925-cfdb-79dd1f57b31a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "id": "5545b8c2"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Distribution de la variable Parch - Children/Parents"
      ],
      "metadata": {
        "id": "ec371c10"
      },
      "id": "ec371c10"
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.histogram(df, x='parch', title=' Distribution de nombre Parents/Fils à Bord',\n",
        "                   labels={'parch': 'Nombre Parents/Fils à Bord'},\n",
        "                   color_discrete_sequence=['#FFA07A'])\n",
        "\n",
        "fig.update_layout(\n",
        "    xaxis_title=\"Nombre Parents/Fils à Bord\",\n",
        "    yaxis_title=\"Nombre de Passagers\",\n",
        "    template='plotly_white'\n",
        ")"
      ],
      "metadata": {
        "id": "620e57a3",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:15.485886Z",
          "iopub.execute_input": "2024-10-07T18:30:15.486231Z",
          "iopub.status.idle": "2024-10-07T18:30:15.571877Z",
          "shell.execute_reply.started": "2024-10-07T18:30:15.486197Z",
          "shell.execute_reply": "2024-10-07T18:30:15.57093Z"
        },
        "trusted": true,
        "outputId": "4437d4b6-18a9-4381-d6ee-38f1af643980"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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        "<table>\n",
        "    <tr>\n",
        "        <td style='width:5%;'>\n",
        "            <img src='img/natural-language-processing-guide.svg'/>\n",
        "        </td>\n",
        "        <td style='width:90%;text-align:left;'>\n",
        "            <div style='font-family:Inter,sans-serif;font-size: 16px;font-weight: 600;line-height: 24px;'>\n",
        "                Etape 2: Transformation\n",
        "            </div>\n",
        "            <span>Transformation des valeur qualitatives.</span>\n",
        "        </td>\n",
        "        <td ></td>\n",
        "    </tr>\n",
        "</table>"
      ],
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        "id": "c54197a3"
      },
      "id": "c54197a3"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Convertir les variables qualitatives en quantitatives"
      ],
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        "id": "da5705c3"
      },
      "id": "da5705c3"
    },
    {
      "cell_type": "code",
      "source": [
        "df['sex'] = df['sex'].map({'male':0, 'female':1})\n",
        "df['embarked'] = df['embarked'].map({'C':0, 'Q':1, 'S':2})"
      ],
      "metadata": {
        "id": "0ae66083",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:15.573072Z",
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      "execution_count": null,
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      "id": "0ae66083"
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Garder seulement le titre de chaque passager (les caractères qui précèdent le .)*"
      ],
      "metadata": {
        "id": "d57546a6"
      },
      "id": "d57546a6"
    },
    {
      "cell_type": "code",
      "source": [
        "tt = df.name.str.extract('( [A-Za-z]+)\\.', expand=False)"
      ],
      "metadata": {
        "id": "2e6b84d7",
        "execution": {
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      "execution_count": null,
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      "cell_type": "code",
      "source": [
        "df['title'] = df.name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n"
      ],
      "metadata": {
        "id": "fc554982",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:15.594459Z",
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          "shell.execute_reply.started": "2024-10-07T18:30:15.595208Z",
          "shell.execute_reply": "2024-10-07T18:30:15.604172Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "fc554982"
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "4911d0b6",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:15.606061Z",
          "iopub.execute_input": "2024-10-07T18:30:15.606432Z",
          "iopub.status.idle": "2024-10-07T18:30:15.626317Z",
          "shell.execute_reply.started": "2024-10-07T18:30:15.606389Z",
          "shell.execute_reply": "2024-10-07T18:30:15.625395Z"
        },
        "trusted": true,
        "outputId": "dfaf5b34-aa29-4a2d-9afa-9a3a6150d469"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 20,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   pclass  survived                                             name  sex  \\\n0       1         1                    Allen, Miss. Elisabeth Walton    1   \n1       1         1                   Allison, Master. Hudson Trevor    0   \n2       1         0                     Allison, Miss. Helen Loraine    1   \n3       1         0             Allison, Mr. Hudson Joshua Creighton    0   \n4       1         0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)    1   \n\n       age  sibsp  parch  ticket      fare    cabin  embarked   title  \n0  29.0000      0      0   24160  211.3375       B5       2.0    Miss  \n1   0.9167      1      2  113781  151.5500  C22 C26       2.0  Master  \n2   2.0000      1      2  113781  151.5500  C22 C26       2.0    Miss  \n3  30.0000      1      2  113781  151.5500  C22 C26       2.0      Mr  \n4  25.0000      1      2  113781  151.5500  C22 C26       2.0     Mrs  ",
            "text/html": "<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>pclass</th>\n      <th>survived</th>\n      <th>name</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>ticket</th>\n      <th>fare</th>\n      <th>cabin</th>\n      <th>embarked</th>\n      <th>title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allen, Miss. Elisabeth Walton</td>\n      <td>1</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24160</td>\n      <td>211.3375</td>\n      <td>B5</td>\n      <td>2.0</td>\n      <td>Miss</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>Allison, Master. Hudson Trevor</td>\n      <td>0</td>\n      <td>0.9167</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>Master</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Miss. Helen Loraine</td>\n      <td>1</td>\n      <td>2.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>Miss</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mr. Hudson Joshua Creighton</td>\n      <td>0</td>\n      <td>30.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>Mr</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n      <td>1</td>\n      <td>25.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>Mrs</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "id": "4911d0b6"
    },
    {
      "cell_type": "code",
      "source": [
        "# supprimer la colonne \"name\"\n",
        "df=df.drop('name',axis=1)"
      ],
      "metadata": {
        "id": "9d73fbf9",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:15.627853Z",
          "iopub.execute_input": "2024-10-07T18:30:15.628167Z",
          "iopub.status.idle": "2024-10-07T18:30:15.636883Z",
          "shell.execute_reply.started": "2024-10-07T18:30:15.628135Z",
          "shell.execute_reply": "2024-10-07T18:30:15.63607Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "9d73fbf9"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "80ad09a7"
      },
      "execution_count": null,
      "outputs": [],
      "id": "80ad09a7"
    },
    {
      "cell_type": "code",
      "source": [
        "df.title.value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "id": "475be65b",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:15.637948Z",
          "iopub.execute_input": "2024-10-07T18:30:15.638265Z",
          "iopub.status.idle": "2024-10-07T18:30:16.133602Z",
          "shell.execute_reply.started": "2024-10-07T18:30:15.638233Z",
          "shell.execute_reply": "2024-10-07T18:30:16.132615Z"
        },
        "trusted": true,
        "outputId": "e7ecc079-e1c0-445a-a50d-2a3fadd4d983"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 22,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<Axes: xlabel='title'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "id": "475be65b"
    },
    {
      "cell_type": "code",
      "source": [
        "df['title'] = df['title'].replace(['Dr', 'Rev', 'Col', 'Major', 'Countess', 'Sir', 'Jonkheer', 'Lady', 'Capt', 'Don', 'Dona'], 'Others')\n",
        "df['title'] = df['title'].replace('Ms', 'Miss')\n",
        "df['title'] = df['title'].replace('Mme', 'Mrs')\n",
        "df['title'] = df['title'].replace('Mlle', 'Miss')"
      ],
      "metadata": {
        "id": "127ad5ce",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:16.135043Z",
          "iopub.execute_input": "2024-10-07T18:30:16.135468Z",
          "iopub.status.idle": "2024-10-07T18:30:16.148283Z",
          "shell.execute_reply.started": "2024-10-07T18:30:16.135422Z",
          "shell.execute_reply": "2024-10-07T18:30:16.147193Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "127ad5ce"
    },
    {
      "cell_type": "code",
      "source": [
        "plt = df.title.value_counts().sort_index().plot(kind='bar')\n",
        "plt.set_xlabel('Title')\n",
        "plt.set_ylabel('Passenger count')"
      ],
      "metadata": {
        "id": "26e25204",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:16.15002Z",
          "iopub.execute_input": "2024-10-07T18:30:16.150322Z",
          "iopub.status.idle": "2024-10-07T18:30:16.461535Z",
          "shell.execute_reply.started": "2024-10-07T18:30:16.150291Z",
          "shell.execute_reply": "2024-10-07T18:30:16.460618Z"
        },
        "trusted": true,
        "outputId": "b1b92f16-961c-4410-c5cd-bc941a60436c"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 24,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0, 0.5, 'Passenger count')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "id": "26e25204"
    },
    {
      "cell_type": "code",
      "source": [
        "plt = df[['title', 'survived']].groupby('title').mean().survived.plot(kind='bar')\n",
        "plt.set_xlabel('Title')\n",
        "plt.set_ylabel('Survival Probability')"
      ],
      "metadata": {
        "id": "af26fc84",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:16.462518Z",
          "iopub.execute_input": "2024-10-07T18:30:16.462826Z",
          "iopub.status.idle": "2024-10-07T18:30:16.720082Z",
          "shell.execute_reply.started": "2024-10-07T18:30:16.462795Z",
          "shell.execute_reply": "2024-10-07T18:30:16.719097Z"
        },
        "trusted": true,
        "outputId": "3b843e17-8d19-40ca-9dcc-46fa13176596"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 25,
          "output_type": "execute_result",
          "data": {
            "text/plain": "Text(0, 0.5, 'Survival Probability')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "id": "af26fc84"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Convertir la variable \"title\" en variable quantitative\n",
        "\n",
        "> Add blockquote\n",
        "\n"
      ],
      "metadata": {
        "id": "b6e16d92"
      },
      "id": "b6e16d92"
    },
    {
      "cell_type": "code",
      "source": [
        "l = list(df['title'].values)\n",
        "\n",
        "unique_titles = list(set(l))\n",
        "n=len(unique_titles)\n",
        "m={}\n",
        "k=0\n",
        "for i in unique_titles:\n",
        "  m[i]=k\n",
        "  k+=1\n"
      ],
      "metadata": {
        "id": "66168c3d",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:16.721925Z",
          "iopub.execute_input": "2024-10-07T18:30:16.722402Z",
          "iopub.status.idle": "2024-10-07T18:30:16.727718Z",
          "shell.execute_reply.started": "2024-10-07T18:30:16.722333Z",
          "shell.execute_reply": "2024-10-07T18:30:16.726833Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "66168c3d"
    },
    {
      "cell_type": "code",
      "source": [
        "df['title']=df['title'].map(m)"
      ],
      "metadata": {
        "id": "17boQ9aQHHcD",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:16.729077Z",
          "iopub.execute_input": "2024-10-07T18:30:16.729529Z",
          "iopub.status.idle": "2024-10-07T18:30:16.739286Z",
          "shell.execute_reply.started": "2024-10-07T18:30:16.729484Z",
          "shell.execute_reply": "2024-10-07T18:30:16.738395Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "17boQ9aQHHcD"
    },
    {
      "cell_type": "code",
      "source": [
        "df[:5]"
      ],
      "metadata": {
        "id": "ERtBA7HdHJAk",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:16.740501Z",
          "iopub.execute_input": "2024-10-07T18:30:16.740808Z",
          "iopub.status.idle": "2024-10-07T18:30:16.761371Z",
          "shell.execute_reply.started": "2024-10-07T18:30:16.740776Z",
          "shell.execute_reply": "2024-10-07T18:30:16.76022Z"
        },
        "trusted": true,
        "outputId": "19743175-9ba4-4629-ab6c-9ed6affca145"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 28,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   pclass  survived  sex      age  sibsp  parch  ticket      fare    cabin  \\\n0       1         1    1  29.0000      0      0   24160  211.3375       B5   \n1       1         1    0   0.9167      1      2  113781  151.5500  C22 C26   \n2       1         0    1   2.0000      1      2  113781  151.5500  C22 C26   \n3       1         0    0  30.0000      1      2  113781  151.5500  C22 C26   \n4       1         0    1  25.0000      1      2  113781  151.5500  C22 C26   \n\n   embarked  title  \n0       2.0      0  \n1       2.0      2  \n2       2.0      0  \n3       2.0      4  \n4       2.0      1  ",
            "text/html": "<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>pclass</th>\n      <th>survived</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>ticket</th>\n      <th>fare</th>\n      <th>cabin</th>\n      <th>embarked</th>\n      <th>title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24160</td>\n      <td>211.3375</td>\n      <td>B5</td>\n      <td>2.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.9167</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>25.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "id": "ERtBA7HdHJAk"
    },
    {
      "cell_type": "code",
      "source": [
        "# one hot encode title feature\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "\n",
        "# Créer l'objet OneHotEncoder\n",
        "encoder = OneHotEncoder(drop='first', sparse_output=False)\n",
        "\n",
        "# Appliquer l'encodage One-Hot sur la colonne 'title'\n",
        "title_encoded = encoder.fit_transform(df[['title']])\n",
        "\n",
        "# Créer un DataFrame avec les nouvelles colonnes encodées\n",
        "title_encoded_df = pd.DataFrame(title_encoded, columns=encoder.get_feature_names_out(['title']))\n",
        "\n",
        "# Ajouter ces nouvelles colonnes au DataFrame original\n",
        "title_encoded_df.head()"
      ],
      "metadata": {
        "id": "o9S6iEmrYx85",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:16.762809Z",
          "iopub.execute_input": "2024-10-07T18:30:16.76377Z",
          "iopub.status.idle": "2024-10-07T18:30:16.971313Z",
          "shell.execute_reply.started": "2024-10-07T18:30:16.76373Z",
          "shell.execute_reply": "2024-10-07T18:30:16.970312Z"
        },
        "trusted": true,
        "outputId": "90626418-47a6-4702-9855-3a656f9dbd4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 29,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   title_1  title_2  title_3  title_4\n0      0.0      0.0      0.0      0.0\n1      0.0      1.0      0.0      0.0\n2      0.0      0.0      0.0      0.0\n3      0.0      0.0      0.0      1.0\n4      1.0      0.0      0.0      0.0",
            "text/html": "<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>title_1</th>\n      <th>title_2</th>\n      <th>title_3</th>\n      <th>title_4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\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>1</th>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\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>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "id": "o9S6iEmrYx85"
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([df, title_encoded_df], axis=1)\n",
        "\n",
        "\n",
        "# Afficher les premières lignes du nouveau DataFrame pour vérifier\n",
        "df.head()"
      ],
      "metadata": {
        "id": "uPU0EZoQY3Pm",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:16.972706Z",
          "iopub.execute_input": "2024-10-07T18:30:16.973394Z",
          "iopub.status.idle": "2024-10-07T18:30:16.995889Z",
          "shell.execute_reply.started": "2024-10-07T18:30:16.973321Z",
          "shell.execute_reply": "2024-10-07T18:30:16.994911Z"
        },
        "trusted": true,
        "outputId": "07cf8163-e47e-45f1-f8dc-1f797ec01903"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 30,
          "output_type": "execute_result",
          "data": {
            "text/plain": "   pclass  survived  sex      age  sibsp  parch  ticket      fare    cabin  \\\n0       1         1    1  29.0000      0      0   24160  211.3375       B5   \n1       1         1    0   0.9167      1      2  113781  151.5500  C22 C26   \n2       1         0    1   2.0000      1      2  113781  151.5500  C22 C26   \n3       1         0    0  30.0000      1      2  113781  151.5500  C22 C26   \n4       1         0    1  25.0000      1      2  113781  151.5500  C22 C26   \n\n   embarked  title  title_1  title_2  title_3  title_4  \n0       2.0      0      0.0      0.0      0.0      0.0  \n1       2.0      2      0.0      1.0      0.0      0.0  \n2       2.0      0      0.0      0.0      0.0      0.0  \n3       2.0      4      0.0      0.0      0.0      1.0  \n4       2.0      1      1.0      0.0      0.0      0.0  ",
            "text/html": "<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>pclass</th>\n      <th>survived</th>\n      <th>sex</th>\n      <th>age</th>\n      <th>sibsp</th>\n      <th>parch</th>\n      <th>ticket</th>\n      <th>fare</th>\n      <th>cabin</th>\n      <th>embarked</th>\n      <th>title</th>\n      <th>title_1</th>\n      <th>title_2</th>\n      <th>title_3</th>\n      <th>title_4</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>29.0000</td>\n      <td>0</td>\n      <td>0</td>\n      <td>24160</td>\n      <td>211.3375</td>\n      <td>B5</td>\n      <td>2.0</td>\n      <td>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>1</th>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0.9167</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>2</td>\n      <td>0.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>2.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>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</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>4</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n      <td>25.0000</td>\n      <td>1</td>\n      <td>2</td>\n      <td>113781</td>\n      <td>151.5500</td>\n      <td>C22 C26</td>\n      <td>2.0</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "id": "uPU0EZoQY3Pm"
    },
    {
      "cell_type": "code",
      "source": [
        "df=df.drop('title',axis=1)"
      ],
      "metadata": {
        "id": "yk9ZHcDTZNVT",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:16.997215Z",
          "iopub.execute_input": "2024-10-07T18:30:16.997628Z",
          "iopub.status.idle": "2024-10-07T18:30:17.003259Z",
          "shell.execute_reply.started": "2024-10-07T18:30:16.997583Z",
          "shell.execute_reply": "2024-10-07T18:30:17.002274Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "yk9ZHcDTZNVT"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<table>\n",
        "    <tr>\n",
        "        <td style='width:5%;'>\n",
        "            <img src='img/intro-to-game-ai-and-reinforcement-learning.svg'/>\n",
        "        </td>\n",
        "        <td style='width:90%;text-align:left;'>\n",
        "            <div style='font-family:Inter,sans-serif;font-size: 16px;font-weight: 600;line-height: 24px;'>\n",
        "                Etape 3: Données manquantes\n",
        "            </div>\n",
        "            <span>Les techniques d'imputation des valeurs manquantes.</span>\n",
        "        </td>\n",
        "        <td ></td>\n",
        "    </tr>\n",
        "</table>"
      ],
      "metadata": {
        "id": "216307ce"
      },
      "id": "216307ce"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Vérifier que la colonne age contient des valeurs manquantes:</br>\n",
        "<span style='font-size: 13px;'>On peut calculer le nombre de valeurs manquantes au niveau de chaque attribut: ça nous aide à comparer le nombre de valeurs manquante par rapport à la taille du jeu de données</span>"
      ],
      "metadata": {
        "id": "900883e6"
      },
      "id": "900883e6"
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "scrolled": true,
        "id": "a1009e09",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.004706Z",
          "iopub.execute_input": "2024-10-07T18:30:17.005027Z",
          "iopub.status.idle": "2024-10-07T18:30:17.02187Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.004995Z",
          "shell.execute_reply": "2024-10-07T18:30:17.02088Z"
        },
        "trusted": true,
        "outputId": "f079a06b-239c-4ea6-ccb6-d944623734a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1309 entries, 0 to 1308\nData columns (total 14 columns):\n #   Column    Non-Null Count  Dtype  \n---  ------    --------------  -----  \n 0   pclass    1309 non-null   int64  \n 1   survived  1309 non-null   int64  \n 2   sex       1309 non-null   int64  \n 3   age       1046 non-null   float64\n 4   sibsp     1309 non-null   int64  \n 5   parch     1309 non-null   int64  \n 6   ticket    1309 non-null   object \n 7   fare      1308 non-null   float64\n 8   cabin     295 non-null    object \n 9   embarked  1307 non-null   float64\n 10  title_1   1309 non-null   float64\n 11  title_2   1309 non-null   float64\n 12  title_3   1309 non-null   float64\n 13  title_4   1309 non-null   float64\ndtypes: float64(7), int64(5), object(2)\nmemory usage: 143.3+ KB\n",
          "output_type": "stream"
        }
      ],
      "id": "a1009e09"
    },
    {
      "cell_type": "code",
      "source": [
        "# utiliser la méthode isnull()\n",
        "nbr_null_values = df['age'].isnull().sum()\n",
        "print(\"Le nombre de valeurs manquante au niveau de l'attribut age: \",nbr_null_values)\n",
        "print(\"Le nombre total de valeurs:\",len(df))"
      ],
      "metadata": {
        "id": "255be548",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.023034Z",
          "iopub.execute_input": "2024-10-07T18:30:17.023334Z",
          "iopub.status.idle": "2024-10-07T18:30:17.033968Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.023292Z",
          "shell.execute_reply": "2024-10-07T18:30:17.033001Z"
        },
        "trusted": true,
        "outputId": "359867e5-ca75-4773-ca09-b46587c6f072"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Le nombre de valeurs manquante au niveau de l'attribut age:  263\nLe nombre total de valeurs: 1309\n",
          "output_type": "stream"
        }
      ],
      "id": "255be548"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stratégie d'imputation:<br>\n",
        "### 1. La colonne cabin contient 1014 valeurs manquante d'un totale de 1309 valeurs ==> supprimer la colonne"
      ],
      "metadata": {
        "id": "e40750ad"
      },
      "id": "e40750ad"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Utiliser la méthode drop()\n",
        "syntaxe1 : data_frame = data_frame.drop([\"nom_colonne\", axis=1])\n",
        "syntaxe2 : data_frame = data_frame.drop(columns=['nom_colonne'])"
      ],
      "metadata": {
        "id": "66d68aca"
      },
      "id": "66d68aca"
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(columns=['cabin'])"
      ],
      "metadata": {
        "id": "85ca63ac",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.035444Z",
          "iopub.execute_input": "2024-10-07T18:30:17.036177Z",
          "iopub.status.idle": "2024-10-07T18:30:17.045099Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.036128Z",
          "shell.execute_reply": "2024-10-07T18:30:17.044232Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "85ca63ac"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Remplacer les valeurs manquantes de la colonne embarked par la valeur la plus redendante\n",
        "* la méthode fillna() remplace les valeurs NaN par ce q'on lui demande*"
      ],
      "metadata": {
        "id": "76235901"
      },
      "id": "76235901"
    },
    {
      "cell_type": "code",
      "source": [
        "most_frequent_embarked = df['embarked'].value_counts().idxmax()\n",
        "df['embarked'] = df['embarked'].fillna(most_frequent_embarked)"
      ],
      "metadata": {
        "id": "4d2c5e6a",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.046491Z",
          "iopub.execute_input": "2024-10-07T18:30:17.046842Z",
          "iopub.status.idle": "2024-10-07T18:30:17.057957Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.046793Z",
          "shell.execute_reply": "2024-10-07T18:30:17.057032Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "4d2c5e6a"
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "9mTPz3vKJhI-",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.05931Z",
          "iopub.execute_input": "2024-10-07T18:30:17.059709Z",
          "iopub.status.idle": "2024-10-07T18:30:17.073202Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.059672Z",
          "shell.execute_reply": "2024-10-07T18:30:17.072254Z"
        },
        "trusted": true,
        "outputId": "9b8c3377-f505-47f4-8a6b-99bc1296f5e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1309 entries, 0 to 1308\nData columns (total 13 columns):\n #   Column    Non-Null Count  Dtype  \n---  ------    --------------  -----  \n 0   pclass    1309 non-null   int64  \n 1   survived  1309 non-null   int64  \n 2   sex       1309 non-null   int64  \n 3   age       1046 non-null   float64\n 4   sibsp     1309 non-null   int64  \n 5   parch     1309 non-null   int64  \n 6   ticket    1309 non-null   object \n 7   fare      1308 non-null   float64\n 8   embarked  1309 non-null   float64\n 9   title_1   1309 non-null   float64\n 10  title_2   1309 non-null   float64\n 11  title_3   1309 non-null   float64\n 12  title_4   1309 non-null   float64\ndtypes: float64(7), int64(5), object(1)\nmemory usage: 133.1+ KB\n",
          "output_type": "stream"
        }
      ],
      "id": "9mTPz3vKJhI-"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Remplacer les valeurs manquantes de la colonne fare par la valeur moyenne"
      ],
      "metadata": {
        "id": "8a4cf3c5"
      },
      "id": "8a4cf3c5"
    },
    {
      "cell_type": "code",
      "source": [
        "df['fare']=df['fare'].fillna(df['fare'].mean())"
      ],
      "metadata": {
        "id": "0ec7a480",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.074342Z",
          "iopub.execute_input": "2024-10-07T18:30:17.074683Z",
          "iopub.status.idle": "2024-10-07T18:30:17.082986Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.07465Z",
          "shell.execute_reply": "2024-10-07T18:30:17.082071Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "0ec7a480"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Remplacer les valeurs manquantes de la colonne age par la valeur médiane"
      ],
      "metadata": {
        "id": "6c323aaf"
      },
      "id": "6c323aaf"
    },
    {
      "cell_type": "code",
      "source": [
        "df['age']=df['age'].fillna(df['age'].median())"
      ],
      "metadata": {
        "id": "54bfffef",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.084065Z",
          "iopub.execute_input": "2024-10-07T18:30:17.084378Z",
          "iopub.status.idle": "2024-10-07T18:30:17.096103Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.084322Z",
          "shell.execute_reply": "2024-10-07T18:30:17.095142Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "54bfffef"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Afficher la matrice de corrélation ('pclass', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'embarked', 'fare')"
      ],
      "metadata": {
        "id": "12dd1c37"
      },
      "id": "12dd1c37"
    },
    {
      "cell_type": "code",
      "source": [
        "columns_of_interest = ['pclass', 'sex', 'age', 'sibsp', 'parch', 'fare']\n",
        "\n",
        "# Calculer la matrice de corrélation\n",
        "correlation_matrix = df[columns_of_interest].corr()\n",
        "\n",
        "# Convertir la matrice de corrélation en DataFrame pour Plotly\n",
        "corr_df = pd.DataFrame(correlation_matrix)\n",
        "\n",
        "# Créer un heatmap avec Plotly\n",
        "fig = px.imshow(corr_df,\n",
        "                color_continuous_scale='RdBu',\n",
        "                title='Matrice de Corrélation',\n",
        "                labels=dict(x='Variables', y='Variables', color='Corrélation'),\n",
        "                x=columns_of_interest,\n",
        "                y=columns_of_interest)\n",
        "\n",
        "# Afficher le graphique\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "4b4c421e",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.097152Z",
          "iopub.execute_input": "2024-10-07T18:30:17.097499Z",
          "iopub.status.idle": "2024-10-07T18:30:17.200021Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.097464Z",
          "shell.execute_reply": "2024-10-07T18:30:17.199125Z"
        },
        "trusted": true,
        "outputId": "d68aeac8-ca0a-4be1-d680-903d272f6b9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ],
      "id": "4b4c421e"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<table>\n",
        "    <tr>\n",
        "        <td style='width:5%;'>\n",
        "            <img src='img/feature-engineering.svg'/>\n",
        "        </td>\n",
        "        <td style='width:90%;text-align:left;'>\n",
        "            <div style='font-family:Inter,sans-serif;font-size: 16px;font-weight: 600;line-height: 24px;'>\n",
        "                Etape 4: Ingénierie des attributs\n",
        "            </div>\n",
        "            <span>Génerer des attributs à travers des combinaisons entre les caractéristiques.</span>\n",
        "        </td>\n",
        "        <td ></td>\n",
        "    </tr>\n",
        "</table>"
      ],
      "metadata": {
        "id": "b78a869a"
      },
      "id": "b78a869a"
    },
    {
      "cell_type": "code",
      "source": [
        "df['familysize'] = df.sibsp + df.parch + 1"
      ],
      "metadata": {
        "id": "edec8d74",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.201264Z",
          "iopub.execute_input": "2024-10-07T18:30:17.201599Z",
          "iopub.status.idle": "2024-10-07T18:30:17.20754Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.201567Z",
          "shell.execute_reply": "2024-10-07T18:30:17.206417Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "edec8d74"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modélisation\n"
      ],
      "metadata": {
        "id": "6b2a11fe"
      },
      "id": "6b2a11fe"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Splitting dataset into train and test sets 80%  :20%\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "cf013f1c"
      },
      "id": "cf013f1c"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tanrIuKMVgKE"
      },
      "execution_count": null,
      "outputs": [],
      "id": "tanrIuKMVgKE"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Supposons que df soit votre DataFrame principal et que 'target' soit la colonne cible\n",
        "X = df.drop(['survived','ticket'], axis=1)  # Sélectionne toutes les colonnes sauf la cible\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "TWGchGA1T10h",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.215024Z",
          "iopub.execute_input": "2024-10-07T18:30:17.215501Z",
          "iopub.status.idle": "2024-10-07T18:30:17.308844Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.215454Z",
          "shell.execute_reply": "2024-10-07T18:30:17.308032Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "TWGchGA1T10h"
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['survived']  # Colonne cible\n",
        "\n",
        "# Diviser le jeu de données en ensembles d'entraînement (80%) et de test (20%)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
        "\n",
        "# Afficher les tailles des ensembles d'entraînement et de test\n",
        "print(f\"Taille de X_train: {X_train.shape}\")\n",
        "print(f\"Taille de X_test: {X_test.shape}\")\n",
        "print(f\"Taille de y_train: {y_train.shape}\")\n",
        "print(f\"Taille de y_test: {y_test.shape}\")"
      ],
      "metadata": {
        "id": "YOpbGC3_cex5",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.310013Z",
          "iopub.execute_input": "2024-10-07T18:30:17.310333Z",
          "iopub.status.idle": "2024-10-07T18:30:17.321267Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.310299Z",
          "shell.execute_reply": "2024-10-07T18:30:17.320249Z"
        },
        "trusted": true,
        "outputId": "35d74b30-f7de-4db9-c77f-ae27cefc070b"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Taille de X_train: (1047, 12)\nTaille de X_test: (262, 12)\nTaille de y_train: (1047,)\nTaille de y_test: (262,)\n",
          "output_type": "stream"
        }
      ],
      "id": "YOpbGC3_cex5"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Feature scaling"
      ],
      "metadata": {
        "id": "c3960ba9"
      },
      "id": "c3960ba9"
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "LO2kAULNXgAW",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.322589Z",
          "iopub.execute_input": "2024-10-07T18:30:17.32295Z",
          "iopub.status.idle": "2024-10-07T18:30:17.335934Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.322915Z",
          "shell.execute_reply": "2024-10-07T18:30:17.335033Z"
        },
        "trusted": true,
        "outputId": "156d7819-585c-43a1-aebc-d8e68f686a67"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1309 entries, 0 to 1308\nData columns (total 14 columns):\n #   Column      Non-Null Count  Dtype  \n---  ------      --------------  -----  \n 0   pclass      1309 non-null   int64  \n 1   survived    1309 non-null   int64  \n 2   sex         1309 non-null   int64  \n 3   age         1309 non-null   float64\n 4   sibsp       1309 non-null   int64  \n 5   parch       1309 non-null   int64  \n 6   ticket      1309 non-null   object \n 7   fare        1309 non-null   float64\n 8   embarked    1309 non-null   float64\n 9   title_1     1309 non-null   float64\n 10  title_2     1309 non-null   float64\n 11  title_3     1309 non-null   float64\n 12  title_4     1309 non-null   float64\n 13  familysize  1309 non-null   int64  \ndtypes: float64(7), int64(6), object(1)\nmemory usage: 143.3+ KB\n",
          "output_type": "stream"
        }
      ],
      "id": "LO2kAULNXgAW"
    },
    {
      "cell_type": "code",
      "source": [
        "# Affiche le nombre de valeurs NaN par colonne\n",
        "print(df.isnull().sum())"
      ],
      "metadata": {
        "id": "fMb-7cYucy-e",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.337054Z",
          "iopub.execute_input": "2024-10-07T18:30:17.337414Z",
          "iopub.status.idle": "2024-10-07T18:30:17.347966Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.337346Z",
          "shell.execute_reply": "2024-10-07T18:30:17.346895Z"
        },
        "trusted": true,
        "outputId": "eddc82c8-8977-4e74-c4de-051418af334f"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "pclass        0\nsurvived      0\nsex           0\nage           0\nsibsp         0\nparch         0\nticket        0\nfare          0\nembarked      0\ntitle_1       0\ntitle_2       0\ntitle_3       0\ntitle_4       0\nfamilysize    0\ndtype: int64\n",
          "output_type": "stream"
        }
      ],
      "id": "fMb-7cYucy-e"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "imputer = SimpleImputer(strategy='mean')\n",
        "\n",
        "X_train = imputer.fit_transform(X_train)\n",
        "X_test=imputer.transform(X_test)"
      ],
      "metadata": {
        "id": "hBfSuT0wfF_A",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.349123Z",
          "iopub.execute_input": "2024-10-07T18:30:17.349447Z",
          "iopub.status.idle": "2024-10-07T18:30:17.500206Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.349411Z",
          "shell.execute_reply": "2024-10-07T18:30:17.49941Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "hBfSuT0wfF_A"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "\n",
        "#Standardisation\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Appliquer la standardisation sur l'ensemble d'entraînement\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "\n",
        "# Appliquer la même transformation sur l'ensemble de test\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "print(\"Aperçu des données standardisées :\")\n",
        "print(f\"X_train_scaled:\\n{X_train_scaled[:5]}\")\n",
        "print(f\"X_test_scaled:\\n{X_test_scaled[:5]}\")"
      ],
      "metadata": {
        "id": "ysZRIL3YUigm",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.501253Z",
          "iopub.execute_input": "2024-10-07T18:30:17.501566Z",
          "iopub.status.idle": "2024-10-07T18:30:17.512026Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.501534Z",
          "shell.execute_reply": "2024-10-07T18:30:17.511055Z"
        },
        "trusted": true,
        "outputId": "1216a9ed-1044-416b-80dc-e0e898fdd8cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Aperçu des données standardisées :\nX_train_scaled:\n[[ 0.84035898 -0.73927586 -0.9565684  -0.4959643  -0.44243194 -0.49557686\n   0.61403205 -0.41527389 -0.22394296 -0.15957829  0.85641328 -0.57116167]\n [-0.35509734 -0.73927586  0.52955095 -0.4959643  -0.44243194 -0.44526407\n   0.61403205 -0.41527389 -0.22394296 -0.15957829  0.85641328 -0.57116167]\n [-1.55055366  1.35267503 -0.8783516  -0.4959643   1.79537599  0.89070441\n   0.61403205 -0.41527389 -0.22394296 -0.15957829 -1.16766055  0.67598383]\n [-1.55055366 -0.73927586  1.38993584  0.45683317 -0.44243194  3.74762906\n  -1.86817473 -0.41527389 -0.22394296  6.26651664 -1.16766055  0.05241108]\n [-1.55055366 -0.73927586 -0.09618352 -0.4959643  -0.44243194  0.17103954\n   0.61403205 -0.41527389 -0.22394296 -0.15957829  0.85641328 -0.57116167]]\nX_test_scaled:\n[[ 0.84035898 -0.73927586  0.45133414 -0.4959643  -0.44243194 -0.51046861\n   0.61403205 -0.41527389 -0.22394296 -0.15957829  0.85641328 -0.57116167]\n [ 0.84035898 -0.73927586 -0.72191798  0.45683317  0.67647203 -0.34399515\n  -1.86817473 -0.41527389 -0.22394296 -0.15957829  0.85641328  0.67598383]\n [ 0.84035898 -0.73927586 -0.09618352 -0.4959643  -0.44243194 -0.49557686\n   0.61403205 -0.41527389 -0.22394296 -0.15957829  0.85641328 -0.57116167]\n [ 0.84035898 -0.73927586 -0.09618352 -0.4959643  -0.44243194 -0.49259774\n   0.61403205 -0.41527389 -0.22394296 -0.15957829  0.85641328 -0.57116167]\n [ 0.84035898 -0.73927586 -0.09618352 -0.4959643  -0.44243194 -0.49839369\n  -0.62707134 -0.41527389 -0.22394296 -0.15957829  0.85641328 -0.57116167]]\n",
          "output_type": "stream"
        }
      ],
      "id": "ysZRIL3YUigm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. k Nearest Neighbors"
      ],
      "metadata": {
        "id": "436e0c44"
      },
      "id": "436e0c44"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.1.Entrainer et tester le modèle"
      ],
      "metadata": {
        "id": "77459550"
      },
      "id": "77459550"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred = knn.predict(X_test_scaled)\n"
      ],
      "metadata": {
        "id": "7e9efe70",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.513519Z",
          "iopub.execute_input": "2024-10-07T18:30:17.514303Z",
          "iopub.status.idle": "2024-10-07T18:30:17.546551Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.514267Z",
          "shell.execute_reply": "2024-10-07T18:30:17.545593Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "7e9efe70"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.2. Afficher la matrice de confusion et les métriques"
      ],
      "metadata": {
        "id": "1ddd7b2e"
      },
      "id": "1ddd7b2e"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "\n",
        "report = classification_report(y_test, y_pred)\n",
        "print(\"Classification Report:\\n\", report)\n",
        "\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n"
      ],
      "metadata": {
        "id": "VMx8cOx5iFjt",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.54767Z",
          "iopub.execute_input": "2024-10-07T18:30:17.547991Z",
          "iopub.status.idle": "2024-10-07T18:30:17.566568Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.547958Z",
          "shell.execute_reply": "2024-10-07T18:30:17.565599Z"
        },
        "trusted": true,
        "outputId": "5627fc11-8533-4308-9c91-6cd52e4fd252"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.7709923664122137\nClassification Report:\n               precision    recall  f1-score   support\n\n           0       0.75      0.88      0.81       144\n           1       0.81      0.64      0.72       118\n\n    accuracy                           0.77       262\n   macro avg       0.78      0.76      0.76       262\nweighted avg       0.78      0.77      0.77       262\n\nConfusion Matrix:\n [[126  18]\n [ 42  76]]\n",
          "output_type": "stream"
        }
      ],
      "id": "VMx8cOx5iFjt"
    },
    {
      "cell_type": "code",
      "source": [
        "Image('metrics.png')"
      ],
      "metadata": {
        "id": "b78453d6",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:17.567806Z",
          "iopub.execute_input": "2024-10-07T18:30:17.568115Z",
          "iopub.status.idle": "2024-10-07T18:30:18.193338Z",
          "shell.execute_reply.started": "2024-10-07T18:30:17.568082Z",
          "shell.execute_reply": "2024-10-07T18:30:18.192403Z"
        },
        "trusted": true,
        "outputId": "5d7d4baf-7c01-4a19-e165-4c0656048232"
      },
      "execution_count": null,
      "outputs": [
        {
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1045\u001b[0m, in \u001b[0;36mImage._data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m   1044\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1045\u001b[0m     b64_data \u001b[38;5;241m=\u001b[39m \u001b[43mb2a_base64\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mascii\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1046\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
            "\u001b[0;31mTypeError\u001b[0m: a bytes-like object is required, not 'str'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/formatters.py:974\u001b[0m, in \u001b[0;36mMimeBundleFormatter.__call__\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    971\u001b[0m     method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 974\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    975\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    976\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1035\u001b[0m, in \u001b[0;36mImage._repr_mimebundle_\u001b[0;34m(self, include, exclude)\u001b[0m\n\u001b[1;32m   1033\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed:\n\u001b[1;32m   1034\u001b[0m     mimetype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mimetype\n\u001b[0;32m-> 1035\u001b[0m     data, metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_and_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43malways_both\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1036\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m metadata:\n\u001b[1;32m   1037\u001b[0m         metadata \u001b[38;5;241m=\u001b[39m {mimetype: metadata}\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1047\u001b[0m, in \u001b[0;36mImage._data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m   1045\u001b[0m     b64_data \u001b[38;5;241m=\u001b[39m b2a_base64(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mascii\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1046\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1047\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m   1048\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo such file or directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1049\u001b[0m md \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m   1050\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetadata:\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: No such file or directory: 'metrics.png'"
          ],
          "ename": "FileNotFoundError",
          "evalue": "No such file or directory: 'metrics.png'",
          "output_type": "error"
        },
        {
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1045\u001b[0m, in \u001b[0;36mImage._data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m   1044\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1045\u001b[0m     b64_data \u001b[38;5;241m=\u001b[39m \u001b[43mb2a_base64\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mascii\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1046\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
            "\u001b[0;31mTypeError\u001b[0m: a bytes-like object is required, not 'str'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/formatters.py:344\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    342\u001b[0m     method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m    343\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 344\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    345\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1067\u001b[0m, in \u001b[0;36mImage._repr_png_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1065\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_repr_png_\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1066\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_FMT_PNG:\n\u001b[0;32m-> 1067\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_and_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1047\u001b[0m, in \u001b[0;36mImage._data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m   1045\u001b[0m     b64_data \u001b[38;5;241m=\u001b[39m b2a_base64(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mascii\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1046\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1047\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m   1048\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo such file or directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1049\u001b[0m md \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m   1050\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetadata:\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: No such file or directory: 'metrics.png'"
          ],
          "ename": "FileNotFoundError",
          "evalue": "No such file or directory: 'metrics.png'",
          "output_type": "error"
        },
        {
          "execution_count": 49,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {}
        }
      ],
      "id": "b78453d6"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3.3 Appliquer GridSearch"
      ],
      "metadata": {
        "id": "f56d5918"
      },
      "id": "f56d5918"
    },
    {
      "cell_type": "code",
      "source": [
        "Image('grid.png')"
      ],
      "metadata": {
        "id": "12e58eda",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:30:18.194709Z",
          "iopub.execute_input": "2024-10-07T18:30:18.195117Z",
          "iopub.status.idle": "2024-10-07T18:30:18.311244Z",
          "shell.execute_reply.started": "2024-10-07T18:30:18.195072Z",
          "shell.execute_reply": "2024-10-07T18:30:18.310317Z"
        },
        "trusted": true,
        "outputId": "3a845785-978e-4916-9315-f245fec4c331"
      },
      "execution_count": null,
      "outputs": [
        {
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1045\u001b[0m, in \u001b[0;36mImage._data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m   1044\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1045\u001b[0m     b64_data \u001b[38;5;241m=\u001b[39m \u001b[43mb2a_base64\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mascii\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1046\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
            "\u001b[0;31mTypeError\u001b[0m: a bytes-like object is required, not 'str'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/formatters.py:974\u001b[0m, in \u001b[0;36mMimeBundleFormatter.__call__\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    971\u001b[0m     method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m    973\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 974\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    975\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    976\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1035\u001b[0m, in \u001b[0;36mImage._repr_mimebundle_\u001b[0;34m(self, include, exclude)\u001b[0m\n\u001b[1;32m   1033\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed:\n\u001b[1;32m   1034\u001b[0m     mimetype \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mimetype\n\u001b[0;32m-> 1035\u001b[0m     data, metadata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_and_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43malways_both\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1036\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m metadata:\n\u001b[1;32m   1037\u001b[0m         metadata \u001b[38;5;241m=\u001b[39m {mimetype: metadata}\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1047\u001b[0m, in \u001b[0;36mImage._data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m   1045\u001b[0m     b64_data \u001b[38;5;241m=\u001b[39m b2a_base64(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mascii\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1046\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1047\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m   1048\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo such file or directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1049\u001b[0m md \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m   1050\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetadata:\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: No such file or directory: 'grid.png'"
          ],
          "ename": "FileNotFoundError",
          "evalue": "No such file or directory: 'grid.png'",
          "output_type": "error"
        },
        {
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1045\u001b[0m, in \u001b[0;36mImage._data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m   1044\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1045\u001b[0m     b64_data \u001b[38;5;241m=\u001b[39m \u001b[43mb2a_base64\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mascii\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1046\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
            "\u001b[0;31mTypeError\u001b[0m: a bytes-like object is required, not 'str'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/formatters.py:344\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    342\u001b[0m     method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n\u001b[1;32m    343\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 344\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    345\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    346\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1067\u001b[0m, in \u001b[0;36mImage._repr_png_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1065\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_repr_png_\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m   1066\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_FMT_PNG:\n\u001b[0;32m-> 1067\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_and_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/IPython/core/display.py:1047\u001b[0m, in \u001b[0;36mImage._data_and_metadata\u001b[0;34m(self, always_both)\u001b[0m\n\u001b[1;32m   1045\u001b[0m     b64_data \u001b[38;5;241m=\u001b[39m b2a_base64(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata, newline\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mascii\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1046\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1047\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\n\u001b[1;32m   1048\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo such file or directory: \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata)) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m   1049\u001b[0m md \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m   1050\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetadata:\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: No such file or directory: 'grid.png'"
          ],
          "ename": "FileNotFoundError",
          "evalue": "No such file or directory: 'grid.png'",
          "output_type": "error"
        },
        {
          "execution_count": 50,
          "output_type": "execute_result",
          "data": {
            "text/plain": "<IPython.core.display.Image object>"
          },
          "metadata": {}
        }
      ],
      "id": "12e58eda"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "pipeline = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('knn', KNeighborsClassifier())\n",
        "])\n",
        "param_grid = {\n",
        "    'knn__n_neighbors': [3, 5, 7, 9, 11, 13, 15],\n",
        "    'knn__weights': ['uniform', 'distance'],\n",
        "    'knn__metric': ['euclidean', 'manhattan', 'minkowski', 'chebyshev'],\n",
        "}\n",
        "\n"
      ],
      "metadata": {
        "id": "-J4wMcFCdsXB",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:17.804899Z",
          "iopub.execute_input": "2024-10-07T18:33:17.80597Z",
          "iopub.status.idle": "2024-10-07T18:33:17.811823Z",
          "shell.execute_reply.started": "2024-10-07T18:33:17.805925Z",
          "shell.execute_reply": "2024-10-07T18:33:17.810923Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "-J4wMcFCdsXB"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1)"
      ],
      "metadata": {
        "id": "8hbOWY1FeAWg",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:20.365098Z",
          "iopub.execute_input": "2024-10-07T18:33:20.365509Z",
          "iopub.status.idle": "2024-10-07T18:33:20.370701Z",
          "shell.execute_reply.started": "2024-10-07T18:33:20.36547Z",
          "shell.execute_reply": "2024-10-07T18:33:20.369648Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "8hbOWY1FeAWg"
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "gE-hvL29eml0",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:22.079116Z",
          "iopub.execute_input": "2024-10-07T18:33:22.079562Z",
          "iopub.status.idle": "2024-10-07T18:33:25.464586Z",
          "shell.execute_reply.started": "2024-10-07T18:33:22.07952Z",
          "shell.execute_reply": "2024-10-07T18:33:25.463433Z"
        },
        "trusted": true,
        "outputId": "844808e7-7828-48e1-b156-171330628138"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 56,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n                                       ('knn', KNeighborsClassifier())]),\n             n_jobs=-1,\n             param_grid={'knn__metric': ['euclidean', 'manhattan', 'minkowski',\n                                         'chebyshev'],\n                         'knn__n_neighbors': [3, 5, 7, 9, 11, 13, 15],\n                         'knn__weights': ['uniform', 'distance']},\n             scoring='accuracy')",
            "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>GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                                       (&#x27;knn&#x27;, KNeighborsClassifier())]),\n             n_jobs=-1,\n             param_grid={&#x27;knn__metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;, &#x27;minkowski&#x27;,\n                                         &#x27;chebyshev&#x27;],\n                         &#x27;knn__n_neighbors&#x27;: [3, 5, 7, 9, 11, 13, 15],\n                         &#x27;knn__weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                                       (&#x27;knn&#x27;, KNeighborsClassifier())]),\n             n_jobs=-1,\n             param_grid={&#x27;knn__metric&#x27;: [&#x27;euclidean&#x27;, &#x27;manhattan&#x27;, &#x27;minkowski&#x27;,\n                                         &#x27;chebyshev&#x27;],\n                         &#x27;knn__n_neighbors&#x27;: [3, 5, 7, 9, 11, 13, 15],\n                         &#x27;knn__weights&#x27;: [&#x27;uniform&#x27;, &#x27;distance&#x27;]},\n             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()), (&#x27;knn&#x27;, KNeighborsClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "id": "gE-hvL29eml0"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best parameters found: \", grid_search.best_params_)\n",
        "print(\"Best cross-validation accuracy score: \", grid_search.best_score_)"
      ],
      "metadata": {
        "id": "lmHcNJ-5fmrT",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:30.504377Z",
          "iopub.execute_input": "2024-10-07T18:33:30.505433Z",
          "iopub.status.idle": "2024-10-07T18:33:30.510935Z",
          "shell.execute_reply.started": "2024-10-07T18:33:30.505385Z",
          "shell.execute_reply": "2024-10-07T18:33:30.509854Z"
        },
        "trusted": true,
        "outputId": "a8a27b89-5a20-47eb-c8ba-b501301e4156"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Best parameters found:  {'knn__metric': 'euclidean', 'knn__n_neighbors': 13, 'knn__weights': 'uniform'}\nBest cross-validation accuracy score:  0.8175393028024607\n",
          "output_type": "stream"
        }
      ],
      "id": "lmHcNJ-5fmrT"
    },
    {
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=19,algorithm='auto',leaf_size=10,p=3,metric='minkowski',weights='uniform')\n",
        "knn.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred = knn.predict(X_test_scaled)\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "\n",
        "report = classification_report(y_test, y_pred)\n",
        "print(\"Classification Report:\\n\", report)\n",
        "\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ],
      "metadata": {
        "id": "d-Ns_gRYK2By",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:32.764217Z",
          "iopub.execute_input": "2024-10-07T18:33:32.764646Z",
          "iopub.status.idle": "2024-10-07T18:33:32.844028Z",
          "shell.execute_reply.started": "2024-10-07T18:33:32.764608Z",
          "shell.execute_reply": "2024-10-07T18:33:32.842913Z"
        },
        "trusted": true,
        "outputId": "6e942804-8461-4b28-bf94-ddf01b7e9204"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.7786259541984732\nClassification Report:\n               precision    recall  f1-score   support\n\n           0       0.75      0.90      0.82       144\n           1       0.84      0.63      0.72       118\n\n    accuracy                           0.78       262\n   macro avg       0.79      0.76      0.77       262\nweighted avg       0.79      0.78      0.77       262\n\nConfusion Matrix:\n [[130  14]\n [ 44  74]]\n",
          "output_type": "stream"
        }
      ],
      "id": "d-Ns_gRYK2By"
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0].shape"
      ],
      "metadata": {
        "id": "6J4qdGG3Imsm",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:03.247313Z",
          "iopub.status.idle": "2024-10-07T18:33:03.247813Z",
          "shell.execute_reply.started": "2024-10-07T18:33:03.247568Z",
          "shell.execute_reply": "2024-10-07T18:33:03.247592Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "6J4qdGG3Imsm"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Decison Tree"
      ],
      "metadata": {
        "id": "87e4d19a"
      },
      "id": "87e4d19a"
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "jwoDHdDETNr8"
      },
      "id": "jwoDHdDETNr8"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4.1 Appliquer GridSearch"
      ],
      "metadata": {
        "id": "mULvV5iFTOMf"
      },
      "id": "mULvV5iFTOMf"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "pipeline = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('clf', DecisionTreeClassifier())\n",
        "])\n",
        "\n",
        "param_grid = {\n",
        "    'clf__max_depth': [3, 5, 7, 9],\n",
        "    'clf__criterion': ['gini', 'entropy'],\n",
        "    'clf__min_samples_split': [2, 4, 6, 8, 10],\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "K8-e94BmdywR",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:34:12.505293Z",
          "iopub.execute_input": "2024-10-07T18:34:12.506207Z",
          "iopub.status.idle": "2024-10-07T18:34:12.969249Z",
          "shell.execute_reply.started": "2024-10-07T18:34:12.506165Z",
          "shell.execute_reply": "2024-10-07T18:34:12.968304Z"
        },
        "trusted": true,
        "outputId": "3a220eab-b8bd-46b5-d176-5f321556185c"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 59,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n                                       ('clf', DecisionTreeClassifier())]),\n             n_jobs=-1,\n             param_grid={'clf__criterion': ['gini', 'entropy'],\n                         'clf__max_depth': [3, 5, 7, 9],\n                         'clf__min_samples_split': [2, 4, 6, 8, 10]},\n             scoring='accuracy')",
            "text/html": "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                                       (&#x27;clf&#x27;, DecisionTreeClassifier())]),\n             n_jobs=-1,\n             param_grid={&#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;clf__max_depth&#x27;: [3, 5, 7, 9],\n                         &#x27;clf__min_samples_split&#x27;: [2, 4, 6, 8, 10]},\n             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                                       (&#x27;clf&#x27;, DecisionTreeClassifier())]),\n             n_jobs=-1,\n             param_grid={&#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;clf__max_depth&#x27;: [3, 5, 7, 9],\n                         &#x27;clf__min_samples_split&#x27;: [2, 4, 6, 8, 10]},\n             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                (&#x27;clf&#x27;, DecisionTreeClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><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\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "id": "K8-e94BmdywR"
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best parameters found: \", grid_search.best_params_)\n",
        "print(\"Best cross-validation accuracy score: \", grid_search.best_score_)"
      ],
      "metadata": {
        "id": "bmesKDwzSH8H",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:38:21.830518Z",
          "iopub.execute_input": "2024-10-07T18:38:21.831272Z",
          "iopub.status.idle": "2024-10-07T18:38:21.836964Z",
          "shell.execute_reply.started": "2024-10-07T18:38:21.83123Z",
          "shell.execute_reply": "2024-10-07T18:38:21.835814Z"
        },
        "trusted": true,
        "outputId": "0fedccd7-1871-41a0-ed58-1cb156663c26"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Best parameters found:  {'clf__criterion': 'gini', 'clf__max_depth': 3, 'clf__min_samples_split': 6}\nBest cross-validation accuracy score:  0.8223103212576897\n",
          "output_type": "stream"
        }
      ],
      "id": "bmesKDwzSH8H"
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "WmAy27cHTWNZ"
      },
      "id": "WmAy27cHTWNZ"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4.2 Entreiner et tester le modèle"
      ],
      "metadata": {
        "id": "319739a5"
      },
      "id": "319739a5"
    },
    {
      "cell_type": "code",
      "source": [
        "clf = grid_search.best_estimator_\n",
        "\n",
        "clf.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test_scaled)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "86XNVe9qSLVb",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:38:24.275242Z",
          "iopub.execute_input": "2024-10-07T18:38:24.27645Z",
          "iopub.status.idle": "2024-10-07T18:38:24.285368Z",
          "shell.execute_reply.started": "2024-10-07T18:38:24.276395Z",
          "shell.execute_reply": "2024-10-07T18:38:24.284404Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "86XNVe9qSLVb"
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "zHN1PlMWTtaE"
      },
      "id": "zHN1PlMWTtaE"
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4.3 Afficher les résultats"
      ],
      "metadata": {
        "id": "3b129222"
      },
      "id": "3b129222"
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "\n",
        "report = classification_report(y_test, y_pred)\n",
        "print(\"Classification Report:\\n\", report)\n",
        "\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ],
      "metadata": {
        "id": "XJgZB4VFTCH2",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:38:27.804059Z",
          "iopub.execute_input": "2024-10-07T18:38:27.804489Z",
          "iopub.status.idle": "2024-10-07T18:38:27.825017Z",
          "shell.execute_reply.started": "2024-10-07T18:38:27.804449Z",
          "shell.execute_reply": "2024-10-07T18:38:27.823711Z"
        },
        "trusted": true,
        "outputId": "3a1ad6fd-721a-4fd2-9aa0-fbcc88abd616"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.7786259541984732\nClassification Report:\n               precision    recall  f1-score   support\n\n           0       0.77      0.86      0.81       144\n           1       0.80      0.68      0.73       118\n\n    accuracy                           0.78       262\n   macro avg       0.78      0.77      0.77       262\nweighted avg       0.78      0.78      0.78       262\n\nConfusion Matrix:\n [[124  20]\n [ 38  80]]\n",
          "output_type": "stream"
        }
      ],
      "id": "XJgZB4VFTCH2"
    },
    {
      "cell_type": "code",
      "source": [
        "Image('decision.png')"
      ],
      "metadata": {
        "id": "cc473efb",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:03.256336Z",
          "iopub.status.idle": "2024-10-07T18:33:03.256823Z",
          "shell.execute_reply.started": "2024-10-07T18:33:03.25658Z",
          "shell.execute_reply": "2024-10-07T18:33:03.256604Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "cc473efb"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 5. Random Forest"
      ],
      "metadata": {
        "id": "b6dab464"
      },
      "id": "b6dab464"
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "pipeline = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('clf', RandomForestClassifier())\n",
        "])\n",
        "\n",
        "param_grid = {\n",
        "    'clf__n_estimators': [50,100,150],\n",
        "    'clf__max_depth': [3, 5, 7, 9, 11],\n",
        "    'clf__criterion': ['gini', 'entropy']\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "u3vAf3ycUkGA",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:39:09.505046Z",
          "iopub.execute_input": "2024-10-07T18:39:09.505946Z",
          "iopub.status.idle": "2024-10-07T18:39:23.241466Z",
          "shell.execute_reply.started": "2024-10-07T18:39:09.505906Z",
          "shell.execute_reply": "2024-10-07T18:39:23.240483Z"
        },
        "trusted": true,
        "outputId": "d6f89d0e-5a20-4763-dfe6-e36c79d451ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "execution_count": 63,
          "output_type": "execute_result",
          "data": {
            "text/plain": "GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n                                       ('clf', RandomForestClassifier())]),\n             n_jobs=-1,\n             param_grid={'clf__criterion': ['gini', 'entropy'],\n                         'clf__max_depth': [3, 5, 7, 9, 11],\n                         'clf__n_estimators': [50, 100, 150]},\n             scoring='accuracy')",
            "text/html": "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                                       (&#x27;clf&#x27;, RandomForestClassifier())]),\n             n_jobs=-1,\n             param_grid={&#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;clf__max_depth&#x27;: [3, 5, 7, 9, 11],\n                         &#x27;clf__n_estimators&#x27;: [50, 100, 150]},\n             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5,\n             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                                       (&#x27;clf&#x27;, RandomForestClassifier())]),\n             n_jobs=-1,\n             param_grid={&#x27;clf__criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n                         &#x27;clf__max_depth&#x27;: [3, 5, 7, 9, 11],\n                         &#x27;clf__n_estimators&#x27;: [50, 100, 150]},\n             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n                (&#x27;clf&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
          },
          "metadata": {}
        }
      ],
      "id": "u3vAf3ycUkGA"
    },
    {
      "cell_type": "code",
      "source": [
        "clf = grid_search.best_estimator_\n",
        "\n",
        "clf.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred = clf.predict(X_test_scaled)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-10-07T18:41:51.025323Z",
          "iopub.execute_input": "2024-10-07T18:41:51.026286Z",
          "iopub.status.idle": "2024-10-07T18:41:51.251927Z",
          "shell.execute_reply.started": "2024-10-07T18:41:51.026242Z",
          "shell.execute_reply": "2024-10-07T18:41:51.25094Z"
        },
        "trusted": true,
        "id": "OeyJMcdfW1WB"
      },
      "execution_count": null,
      "outputs": [],
      "id": "OeyJMcdfW1WB"
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "\n",
        "report = classification_report(y_test, y_pred)\n",
        "print(\"Classification Report:\\n\", report)\n",
        "\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2024-10-07T18:42:07.944287Z",
          "iopub.execute_input": "2024-10-07T18:42:07.945203Z",
          "iopub.status.idle": "2024-10-07T18:42:07.963309Z",
          "shell.execute_reply.started": "2024-10-07T18:42:07.945162Z",
          "shell.execute_reply": "2024-10-07T18:42:07.962171Z"
        },
        "trusted": true,
        "id": "9L5Db0l2W1WC",
        "outputId": "71c56444-272a-494c-9949-d76a23bd824c"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.7862595419847328\nClassification Report:\n               precision    recall  f1-score   support\n\n           0       0.77      0.88      0.82       144\n           1       0.82      0.68      0.74       118\n\n    accuracy                           0.79       262\n   macro avg       0.79      0.78      0.78       262\nweighted avg       0.79      0.79      0.78       262\n\nConfusion Matrix:\n [[126  18]\n [ 38  80]]\n",
          "output_type": "stream"
        }
      ],
      "id": "9L5Db0l2W1WC"
    },
    {
      "cell_type": "code",
      "source": [
        "Image('random_forest.png')"
      ],
      "metadata": {
        "id": "2e1e3186",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:03.261112Z",
          "iopub.status.idle": "2024-10-07T18:33:03.261674Z",
          "shell.execute_reply.started": "2024-10-07T18:33:03.26141Z",
          "shell.execute_reply": "2024-10-07T18:33:03.261435Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "2e1e3186"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 6. Support vector Machine"
      ],
      "metadata": {
        "id": "d0c4ff20"
      },
      "id": "d0c4ff20"
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "\n",
        "pipeline = Pipeline([\n",
        "    ('scaler', StandardScaler()),\n",
        "    ('svm', SVC())\n",
        "])\n",
        "\n",
        "param_grid = {\n",
        "    'svm__kernel': ['linear', 'rbf', 'poly'],\n",
        "    'svm__C': [0.1, 1, 10, 100],\n",
        "    'svm__gamma': ['scale', 'auto'],\n",
        "    'svm__degree': [2, 3, 4]\n",
        "}\n",
        "\n",
        "grid_search = GridSearchCV(estimator=pipeline, param_grid=param_grid, cv=5, scoring='accuracy', n_jobs=-1)\n",
        "grid_search.fit(X_train, y_train)\n",
        "\n",
        "svm = grid_search.best_estimator_\n",
        "\n",
        "svm.fit(X_train_scaled, y_train)\n",
        "\n",
        "y_pred = svm.predict(X_test_scaled)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "print(f\"Accuracy: {accuracy}\")\n",
        "\n",
        "report = classification_report(y_test, y_pred)\n",
        "print(\"Classification Report:\\n\", report)\n",
        "\n",
        "conf_matrix = confusion_matrix(y_test, y_pred)\n",
        "print(\"Confusion Matrix:\\n\", conf_matrix)\n"
      ],
      "metadata": {
        "id": "66ad45b2",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:46:10.405332Z",
          "iopub.execute_input": "2024-10-07T18:46:10.405888Z",
          "iopub.status.idle": "2024-10-07T18:46:32.179727Z",
          "shell.execute_reply.started": "2024-10-07T18:46:10.405838Z",
          "shell.execute_reply": "2024-10-07T18:46:32.17851Z"
        },
        "trusted": true,
        "outputId": "1f7dd3e3-9b82-464a-eb01-f26f45550a8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "text": "Accuracy: 0.7824427480916031\nClassification Report:\n               precision    recall  f1-score   support\n\n           0       0.77      0.86      0.81       144\n           1       0.80      0.69      0.74       118\n\n    accuracy                           0.78       262\n   macro avg       0.79      0.77      0.78       262\nweighted avg       0.78      0.78      0.78       262\n\nConfusion Matrix:\n [[124  20]\n [ 37  81]]\n",
          "output_type": "stream"
        }
      ],
      "id": "66ad45b2"
    },
    {
      "cell_type": "code",
      "source": [
        "Image('kernel_svm.png')"
      ],
      "metadata": {
        "id": "3da413a2",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:03.264988Z",
          "iopub.status.idle": "2024-10-07T18:33:03.265534Z",
          "shell.execute_reply.started": "2024-10-07T18:33:03.265233Z",
          "shell.execute_reply": "2024-10-07T18:33:03.265259Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "3da413a2"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7. Receiver Operating Characteristic (ROC) Curve"
      ],
      "metadata": {
        "id": "7e3e3c3a"
      },
      "id": "7e3e3c3a"
    },
    {
      "cell_type": "code",
      "source": [
        "Image(filename='roc.png')"
      ],
      "metadata": {
        "id": "93a858a5",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:33:03.266921Z",
          "iopub.status.idle": "2024-10-07T18:33:03.267457Z",
          "shell.execute_reply.started": "2024-10-07T18:33:03.267157Z",
          "shell.execute_reply": "2024-10-07T18:33:03.267182Z"
        },
        "trusted": true
      },
      "execution_count": null,
      "outputs": [],
      "id": "93a858a5"
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "import numpy as np\n",
        "\n",
        "confusion_matrices = {\n",
        "    'svm': np.array([[124, 20], [37, 81]]),\n",
        "    'random_forest': np.array([[126, 18], [38, 80]]),\n",
        "    'decision_tree': np.array([[124, 20], [38, 80]]),\n",
        "    'knn': np.array([[130, 14], [44, 74]])\n",
        "}\n",
        "\n",
        "roc_data = {}\n",
        "for model_name, cm in confusion_matrices.items():\n",
        "    tp = cm[1, 1]  # True Positives\n",
        "    fn = cm[1, 0]  # False Negatives\n",
        "    fp = cm[0, 1]  # False Positives\n",
        "    tn = cm[0, 0]  # True Negatives\n",
        "\n",
        "    tpr = tp / (tp + fn)  # True Positive Rate\n",
        "    fpr = fp / (fp + tn)  # False Positive Rate\n",
        "    roc_data[model_name] = (fpr, tpr)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "for model_name, (fpr, tpr) in roc_data.items():\n",
        "    plt.plot([0, fpr, 1], [0, tpr, 1], marker='o', linestyle='-', label=f'{model_name} (AUC = {round(auc([0, fpr, 1], [0, tpr, 1]), 2)})')\n",
        "\n",
        "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random Classifier (AUC = 0.5)')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curves')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "315595f4",
        "execution": {
          "iopub.status.busy": "2024-10-07T18:58:41.245017Z",
          "iopub.execute_input": "2024-10-07T18:58:41.245896Z",
          "iopub.status.idle": "2024-10-07T18:58:41.682456Z",
          "shell.execute_reply.started": "2024-10-07T18:58:41.245851Z",
          "shell.execute_reply": "2024-10-07T18:58:41.681491Z"
        },
        "trusted": true,
        "outputId": "2d4224fe-0de5-4ce3-ca38-362cd776c107"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1000x800 with 1 Axes>",
            "image/png": 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"
          },
          "metadata": {}
        }
      ],
      "id": "315595f4"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u4sZyresW1WH"
      },
      "execution_count": null,
      "outputs": [],
      "id": "u4sZyresW1WH"
    }
  ]
}