Tuto_Machine_Learning.ipynb

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    "colab": {
      "name": "Tuto_Machine Learning.ipynb",
      "provenance": [],
      "collapsed_sections": []
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrG0EKYUXRoY"
      },
      "source": [
        "# <center> **COMMENT CONSTRUIRE, EVALUER ET SELECTIONNER EFFICACEMENT UN MODELE DE MACHINE LEARNING ?**\r\n",
        "\r\n",
        "Par [Josué AFOUDA](https://afouda-datascience.com/)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NA7-K4HUVkkw"
      },
      "source": [
        "Regardez la [vidéo YouTube](https://youtu.be/idPDzWybHMw) pour davantages d'explications sur ce tutoriel."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2yuemD1fzJC"
      },
      "source": [
        "# <font color=red> Objectifs - Etapes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nJybgtoHf64S"
      },
      "source": [
        "* **Analyse exploratoire des données** ;\r\n",
        "\r\n",
        "* **Gestion des valeurs manquantes** ;\r\n",
        "\r\n",
        "* **Gestion des valeurs aberrantes (*outliers*)** ;\r\n",
        "\r\n",
        "* **Autres étapes possibles de prétraitement des données** ;\r\n",
        "\r\n",
        "* **Division de l'ensemble des données (données d'entraînement, de validation et de test)** ;\r\n",
        "\r\n",
        "* **Construction de plusieurs modèles à partir de différents algorithmes de Machine Learning** ;\r\n",
        "\r\n",
        "* **Comparaison des performances des algorithmes** ;\r\n",
        "\r\n",
        "* **Sélection et Evaluation du modèle final**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJPImZ5za2DY"
      },
      "source": [
        "# <font color=red> Importation des librairies et des données"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNPlTwMfbBbv"
      },
      "source": [
        "# Librairies et fonctions\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ArM0SHvwOld-",
        "outputId": "60dc71cf-ea7a-4e22-b738-9f1c3b738cac"
      },
      "source": [
        "sns.get_dataset_names()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['anagrams',\n",
              " 'anscombe',\n",
              " 'attention',\n",
              " 'brain_networks',\n",
              " 'car_crashes',\n",
              " 'diamonds',\n",
              " 'dots',\n",
              " 'exercise',\n",
              " 'flights',\n",
              " 'fmri',\n",
              " 'gammas',\n",
              " 'geyser',\n",
              " 'iris',\n",
              " 'mpg',\n",
              " 'penguins',\n",
              " 'planets',\n",
              " 'tips',\n",
              " 'titanic']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "dTxAJu7FO5fl",
        "outputId": "35508ea3-642c-441a-cad0-9498f96616b0"
      },
      "source": [
        "df = sns.load_dataset('titanic')\r\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>embarked</th>\n",
              "      <th>class</th>\n",
              "      <th>who</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>deck</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alive</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>C</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>S</td>\n",
              "      <td>First</td>\n",
              "      <td>woman</td>\n",
              "      <td>False</td>\n",
              "      <td>C</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>yes</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>S</td>\n",
              "      <td>Third</td>\n",
              "      <td>man</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>no</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass     sex   age  ...  deck  embark_town  alive  alone\n",
              "0         0       3    male  22.0  ...   NaN  Southampton     no  False\n",
              "1         1       1  female  38.0  ...     C    Cherbourg    yes  False\n",
              "2         1       3  female  26.0  ...   NaN  Southampton    yes   True\n",
              "3         1       1  female  35.0  ...     C  Southampton    yes  False\n",
              "4         0       3    male  35.0  ...   NaN  Southampton     no   True\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
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          "execution_count": 3
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        "id": "7d52rNsoQIzc",
        "outputId": "7d959db4-aded-4b14-8950-f3f44283de21"
      },
      "source": [
        "df = df.drop(['alive', 'who', 'embarked', 'class', 'deck'], axis = 1)\r\n",
        "df.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>survived</th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>False</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass     sex   age  ...     fare  adult_male  embark_town  alone\n",
              "0         0       3    male  22.0  ...   7.2500        True  Southampton  False\n",
              "1         1       1  female  38.0  ...  71.2833       False    Cherbourg  False\n",
              "2         1       3  female  26.0  ...   7.9250       False  Southampton   True\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HCRJr_KbQtZb",
        "outputId": "bfd5f397-cfe9-41b7-ddd0-11adaa9b15f7"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 10 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   survived     891 non-null    int64  \n",
            " 1   pclass       891 non-null    int64  \n",
            " 2   sex          891 non-null    object \n",
            " 3   age          714 non-null    float64\n",
            " 4   sibsp        891 non-null    int64  \n",
            " 5   parch        891 non-null    int64  \n",
            " 6   fare         891 non-null    float64\n",
            " 7   adult_male   891 non-null    bool   \n",
            " 8   embark_town  889 non-null    object \n",
            " 9   alone        891 non-null    bool   \n",
            "dtypes: bool(2), float64(2), int64(4), object(2)\n",
            "memory usage: 57.6+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3X8Z1fFb6or"
      },
      "source": [
        "# <font color=red> Visualisation des données"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "NdWAU5iTOkgl",
        "outputId": "df47df7c-7152-49b1-e345-980145e95c67"
      },
      "source": [
        "sns.countplot(x = 'survived', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEGCAYAAACKB4k+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAPQUlEQVR4nO3dfbDmZV3H8fcHFqR84MHdNtyllpLJoRTFE5HaVJAFZC5jgjgaK+7M1gw1OmZG/ZEPQ42OlmEatRPqQiUgZmxmGrNApgPq2UQeMzeC2A3cI0+KZLn27Y9z7cVhObvcZ9nfuc9y3q+Ze+7rd/2u3+/+3szO+XD9nu5UFZIkARww7gIkSQuHoSBJ6gwFSVJnKEiSOkNBktQtGXcBT8TSpUtr1apV4y5DkvYrmzdv/npVLZtt3X4dCqtWrWJycnLcZUjSfiXJnbtb5+EjSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUrdf39G8L7zwty4edwlagDa/++xxlyCNhTMFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkbNBSS3JHkpiQ3JJlsfUckuSrJV9v74a0/Sd6XZEuSG5McP2RtkqTHmo+Zws9W1fOraqItnwdsqqpjgE1tGeBU4Jj2WgdcOA+1SZJmGMfho9XAhtbeAJw+o//imnY9cFiSI8dQnyQtWkOHQgH/mGRzknWtb3lV3d3a9wDLW3sFcNeMbbe2vkdJsi7JZJLJqampoeqWpEVp6J/jfElVbUvyfcBVSf515sqqqiQ1lx1W1XpgPcDExMSctpUk7dmgM4Wq2tbetwMfB04AvrbzsFB7396GbwOOmrH5ytYnSZong4VCkqcmefrONvDzwM3ARmBNG7YGuLK1NwJnt6uQTgQenHGYSZI0D4Y8fLQc+HiSnZ/z11X1qSRfBC5Psha4Ezizjf8kcBqwBXgYOGfA2iRJsxgsFKrqduC4WfrvBU6epb+Ac4eqR5L0+LyjWZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEnd4KGQ5MAkX0ryibZ8dJLPJ9mS5LIkB7f+p7TlLW39qqFrkyQ92nzMFN4A3DZj+V3Ae6vq2cD9wNrWvxa4v/W/t42TJM2jQUMhyUrgF4G/aMsBTgKuaEM2AKe39uq2TFt/chsvSZonQ88U/hh4C/B/bfmZwANVtaMtbwVWtPYK4C6Atv7BNv5RkqxLMplkcmpqasjaJWnRGSwUkrwM2F5Vm/flfqtqfVVNVNXEsmXL9uWuJWnRWzLgvl8MvDzJacAhwDOAC4DDkixps4GVwLY2fhtwFLA1yRLgUODeAeuTJO1isJlCVf1OVa2sqlXAWcDVVfUa4BrglW3YGuDK1t7Ylmnrr66qGqo+SdJjjeM+hd8G3pRkC9PnDC5q/RcBz2z9bwLOG0NtkrSoDXn4qKuqa4FrW/t24IRZxnwbOGM+6pEkzc47miVJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpm5cf2ZE0d//5jueOuwQtQD/wezcNun9nCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1I0UCkk2jdInSdq/7fGO5iSHAN8LLE1yOJC26hnAioFrkyTNs8d7zMWvAm8EngVs5pFQ+Abw/gHrkiSNwR4PH1XVBVV1NPDmqvqhqjq6vY6rqj2GQpJDknwhyZeT3JLk7a3/6CSfT7IlyWVJDm79T2nLW9r6VfvoO0qSRjTSA/Gq6k+SvAhYNXObqrp4D5v9D3BSVT2U5CDgs0n+AXgT8N6qujTJnwFrgQvb+/1V9ewkZwHvAl61N19KkrR3Rj3RfAnwHuAlwI+318SetqlpD7XFg9qrgJOAK1r/BuD01l7dlmnrT06y83CVJGkejPro7Ang2Kqquew8yYFMn4t4NvAB4N+BB6pqRxuylUdOWK8A7gKoqh1JHgSeCXx9Lp8pSdp7o96ncDPw/XPdeVV9t6qeD6wETgCeM9d97CrJuiSTSSanpqae6O4kSTOMOlNYCtya5AtMnysAoKpePsrGVfVAkmuAnwQOS7KkzRZWAtvasG3AUcDWJEuAQ4F7Z9nXemA9wMTExJxmLpKkPRs1FN421x0nWQZ8pwXC9wAvZfrk8TXAK4FLgTXAlW2TjW35urb+6rkerpIkPTGjXn30T3ux7yOBDe28wgHA5VX1iSS3ApcmOR/4EnBRG38RcEmSLcB9wFl78ZmSpCdgpFBI8k2mrxwCOJjpK4m+VVXP2N02VXUj8IJZ+m9n+vzCrv3fBs4YpR5J0jBGnSk8fWe7XSa6GjhxqKIkSeMx56ektvsP/hb4hQHqkSSN0aiHj14xY/EApu9b+PYgFUmSxmbUq49+aUZ7B3AH04eQJElPIqOeUzhn6EIkSeM36rOPVib5eJLt7fWxJCuHLk6SNL9GPdH8IaZvLntWe/1d65MkPYmMGgrLqupDVbWjvT4MLBuwLknSGIwaCvcmeW2SA9vrtczyXCJJ0v5t1FB4PXAmcA9wN9PPJnrdQDVJksZk1EtS3wGsqar7AZIcwfSP7rx+qMIkSfNv1JnC83YGAkBV3ccszzWSJO3fRg2FA5IcvnOhzRRGnWVIkvYTo/5h/0PguiQfbctnAL8/TEmSpHEZ9Y7mi5NMAie1rldU1a3DlSVJGoeRDwG1EDAIJOlJbM6PzpYkPXkZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJ3WChkOSoJNckuTXJLUne0PqPSHJVkq+298Nbf5K8L8mWJDcmOX6o2iRJsxtyprAD+M2qOhY4ETg3ybHAecCmqjoG2NSWAU4FjmmvdcCFA9YmSZrFYKFQVXdX1b+09jeB24AVwGpgQxu2ATi9tVcDF9e064HDkhw5VH2SpMeal3MKSVYBLwA+DyyvqrvbqnuA5a29ArhrxmZbW9+u+1qXZDLJ5NTU1GA1S9JiNHgoJHka8DHgjVX1jZnrqqqAmsv+qmp9VU1U1cSyZcv2YaWSpEFDIclBTAfCX1XV37Tur+08LNTet7f+bcBRMzZf2fokSfNkyKuPAlwE3FZVfzRj1UZgTWuvAa6c0X92uwrpRODBGYeZJEnzYMmA+34x8CvATUluaH2/C7wTuDzJWuBO4My27pPAacAW4GHgnAFrkyTNYrBQqKrPAtnN6pNnGV/AuUPVI0l6fN7RLEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqRusFBI8sEk25PcPKPviCRXJflqez+89SfJ+5JsSXJjkuOHqkuStHtDzhQ+DJyyS995wKaqOgbY1JYBTgWOaa91wIUD1iVJ2o3BQqGqPgPct0v3amBDa28ATp/Rf3FNux44LMmRQ9UmSZrdfJ9TWF5Vd7f2PcDy1l4B3DVj3NbW9xhJ1iWZTDI5NTU1XKWStAiN7URzVRVQe7Hd+qqaqKqJZcuWDVCZJC1e8x0KX9t5WKi9b2/924CjZoxb2fokSfNovkNhI7CmtdcAV87oP7tdhXQi8OCMw0ySpHmyZKgdJ/kI8DPA0iRbgbcC7wQuT7IWuBM4sw3/JHAasAV4GDhnqLokSbs3WChU1at3s+rkWcYWcO5QtUiSRuMdzZKkzlCQJHWGgiSpMxQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1hoIkqTMUJEmdoSBJ6gwFSVJnKEiSOkNBktQZCpKkzlCQJHWGgiSpMxQkSZ2hIEnqFlQoJDklyVeSbEly3rjrkaTFZsGEQpIDgQ8ApwLHAq9Ocux4q5KkxWXBhAJwArClqm6vqv8FLgVWj7kmSVpUloy7gBlWAHfNWN4K/MSug5KsA9a1xYeSfGUealsslgJfH3cRC0Hes2bcJejR/Le501uzL/byg7tbsZBCYSRVtR5YP+46noySTFbVxLjrkHblv835s5AOH20DjpqxvLL1SZLmyUIKhS8CxyQ5OsnBwFnAxjHXJEmLyoI5fFRVO5L8OvBp4EDgg1V1y5jLWmw8LKeFyn+b8yRVNe4aJEkLxEI6fCRJGjNDQZLUGQry8SJasJJ8MMn2JDePu5bFwlBY5Hy8iBa4DwOnjLuIxcRQkI8X0YJVVZ8B7ht3HYuJoaDZHi+yYky1SBozQ0GS1BkK8vEikjpDQT5eRFJnKCxyVbUD2Pl4kduAy328iBaKJB8BrgN+JMnWJGvHXdOTnY+5kCR1zhQkSZ2hIEnqDAVJUmcoSJI6Q0GS1BkK0kCSvHxfPXU2yUP7Yj/S4/GSVOkJSLKk3esx9Oc8VFVPG/pzJGcKEpDkqUn+PsmXk9yc5FVJ7kiytK2fSHJta78tySVJPgdckuT6JD86Y1/XtvGvS/L+JIcmuTPJATM+664kByX54SSfSrI5yT8neU4bc3SS65LclOT8+f8vosXKUJCmnQL8V1UdV1U/BnzqccYfC/xcVb0auAw4EyDJkcCRVTW5c2BVPQjcAPx063oZ8Omq+g7TP0j/G1X1QuDNwJ+2MRcAF1bVc4G798UXlEZhKEjTbgJemuRdSX6q/SHfk41V9d+tfTnwytY+E7hilvGXAa9q7bOAy5I8DXgR8NEkNwB/DhzZxrwY+EhrXzLnbyPtpSXjLkBaCKrq35IcD5wGnJ9kE7CDR/7H6ZBdNvnWjG23Jbk3yfOY/sP/a7N8xEbgD5IcAbwQuBp4KvBAVT1/d2Xt9ReS9pIzBQlI8izg4ar6S+DdwPHAHUz/AQf45cfZxWXAW4BDq+rGXVdW1UNMP5H2AuATVfXdqvoG8B9Jzmg1JMlxbZPPMT2jAHjNXn8xaY4MBWnac4EvtMM4bwXOB94OXJBkEvju42x/BdN/xC/fw5jLgNe2951eA6xN8mXgFh75KdQ3AOcmuQl/CU/zyEtSJUmdMwVJUmcoSJI6Q0GS1BkKkqTOUJAkdYaCJKkzFCRJ3f8DThe6X9gR+9IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t3auxJNWRZpM",
        "outputId": "11d08756-9326-4143-f7b9-ffc708c1bc4d"
      },
      "source": [
        "df['survived'].value_counts(normalize = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.616162\n",
              "1    0.383838\n",
              "Name: survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "hNLQmEwsR2ZG",
        "outputId": "361f6677-c155-4e63-bf45-9d6a047b46f4"
      },
      "source": [
        "sns.countplot(x = 'pclass', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "PHEVFwNXSG_l",
        "outputId": "e6a37769-c260-4c97-998e-27b4dfe70a4b"
      },
      "source": [
        "sns.countplot(x = 'sex', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "hsZ7Xzh2UG5w",
        "outputId": "62d21ff9-029f-49ed-ee00-2e223e42e4c0"
      },
      "source": [
        "sns.boxplot(y = 'age', data = df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Hs1JRxDpUMms",
        "outputId": "ab688d5d-7f2f-4ddf-f0a5-d6531e1a2453"
      },
      "source": [
        "sns.distplot(df['age'], kde=False, bins=20);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "z9u45WLqSPNh",
        "outputId": "405b2605-fa82-4860-c98d-bbc65a2f0199"
      },
      "source": [
        "sns.countplot(x = 'sibsp', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "J-BHyGvBSU8s",
        "outputId": "939736da-529e-41bf-842a-bd89e9047989"
      },
      "source": [
        "sns.countplot(x = 'parch', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "Ry9tzyhiSg2v",
        "outputId": "0ff61aeb-9f60-45a9-8f7d-b4f6e45a9106"
      },
      "source": [
        "sns.boxplot(y = 'fare', data = df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "kVXDC-WaT1Wi",
        "outputId": "c5729781-1a46-4590-f15b-6db7f087d8ed"
      },
      "source": [
        "sns.distplot(df['fare'], kde=False);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "9T_baZDyTPZv",
        "outputId": "d3353168-d546-41a5-c764-f64d9174a9d5"
      },
      "source": [
        "sns.countplot(x = 'adult_male', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "DzVU0wadTUWH",
        "outputId": "f9e40f0f-193d-4197-c6b7-8f0329ad7eee"
      },
      "source": [
        "pd.crosstab(df['sex'], df['adult_male'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>adult_male</th>\n",
              "      <th>False</th>\n",
              "      <th>True</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>female</th>\n",
              "      <td>314</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>male</th>\n",
              "      <td>40</td>\n",
              "      <td>537</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "adult_male  False  True \n",
              "sex                     \n",
              "female        314      0\n",
              "male           40    537"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "id": "ss-NWPVETizN",
        "outputId": "523b9114-8bbe-43b1-a44e-8d9b76cb13c1"
      },
      "source": [
        "sns.countplot(x = 'embark_town', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "clqma5IFTnn7",
        "outputId": "19922270-2401-421a-c9e9-81ca7d2a685b"
      },
      "source": [
        "sns.countplot(x = 'alone', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "R_w9jo4pTzH0",
        "outputId": "3ad7e058-45ca-489f-ef33-ce58767c8cd9"
      },
      "source": [
        "# Relation entre âge et variable cible\r\n",
        "\r\n",
        "sns.boxplot(x = 'survived', y = 'age', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "AXjhN3H6UzkF",
        "outputId": "ed8dae07-b7d9-4506-9210-c7fbb86c4ac9"
      },
      "source": [
        "sns.boxplot(x = 'survived', y = 'age', hue = 'sex', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "TSWSKtvyVAzr",
        "outputId": "661ed6e0-dae4-4303-dc8a-82463b22386a"
      },
      "source": [
        "sns.boxplot(x = 'survived', y = 'fare', data = df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "jK_YEjC_VRrr",
        "outputId": "18b26616-e990-473f-c29a-04034e154f89"
      },
      "source": [
        "sns.boxplot(x = 'survived', y = 'fare', hue = 'pclass', data = df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "5p46dxdFV3kB",
        "outputId": "e956fcc1-17dc-43f9-e108-8a19be15a81d"
      },
      "source": [
        "sns.boxplot(x = 'survived', y = 'fare', hue = 'sex', data = df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "qppHNoQyWFRc",
        "outputId": "80fde46a-5516-49d6-d657-bfd9b3e7dde7"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "      <th>pclass</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>714.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>891.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.308642</td>\n",
              "      <td>29.699118</td>\n",
              "      <td>0.523008</td>\n",
              "      <td>0.381594</td>\n",
              "      <td>32.204208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.836071</td>\n",
              "      <td>14.526497</td>\n",
              "      <td>1.102743</td>\n",
              "      <td>0.806057</td>\n",
              "      <td>49.693429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.125000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.910400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>512.329200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         survived      pclass         age       sibsp       parch        fare\n",
              "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
              "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
              "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
              "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
              "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
              "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
              "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
              "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EeIGQfe5WzF6",
        "outputId": "f9fcff39-6966-40f5-96e5-a6ffccc1bcd5"
      },
      "source": [
        "df['fare'].quantile(0.99)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "249.00622000000033"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ASIJnLjScv9B"
      },
      "source": [
        "# <font color=red> Nettoyage des données"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M22Rvc_HXQwx",
        "outputId": "f536b368-59ab-480b-bcbd-2a26476d6f36"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived         0\n",
              "pclass           0\n",
              "sex              0\n",
              "age            177\n",
              "sibsp            0\n",
              "parch            0\n",
              "fare             0\n",
              "adult_male       0\n",
              "embark_town      2\n",
              "alone            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "en-Hi9YSXnK-"
      },
      "source": [
        "df.fillna(value={'age':df['age'].median()}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xy-2AcFgYBG-",
        "outputId": "534b2094-e387-4228-a0b1-aad9a5a27739"
      },
      "source": [
        "df['age'].std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13.019696550973194"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NpGFNTwRYcFj",
        "outputId": "b3d42995-fde5-4e3e-f0d6-682c791066f1"
      },
      "source": [
        "df['embark_town'].value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Southampton    0.724409\n",
              "Cherbourg      0.188976\n",
              "Queenstown     0.086614\n",
              "Name: embark_town, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qhlw0jeWYoj1"
      },
      "source": [
        "df.fillna(value={'embark_town':'Southampton'}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWEY3zsNY57j",
        "outputId": "456b54d1-5b04-48fc-c1b9-5fcdcd5a563a"
      },
      "source": [
        "df.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "survived       0\n",
              "pclass         0\n",
              "sex            0\n",
              "age            0\n",
              "sibsp          0\n",
              "parch          0\n",
              "fare           0\n",
              "adult_male     0\n",
              "embark_town    0\n",
              "alone          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4ok9HHHZJ7p"
      },
      "source": [
        "# valeur < Q1 - 1,5*IQR ou valeur > Q3 + 1,5*IQR\r\n",
        "\r\n",
        "def finding_outliers(data, variable_name):\r\n",
        "  iqr = data[variable_name].quantile(0.75) - data[variable_name].quantile(0.25)\r\n",
        "  lower = data[variable_name].quantile(0.25) - 1.5 * iqr\r\n",
        "  upper = data[variable_name].quantile(0.75) + 1.5 * iqr \r\n",
        "  return data[(data[variable_name] < lower) | (data[variable_name] > upper)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "_QirLS7ga0jE",
        "outputId": "e353bdfb-2c94-4f8f-98a2-648005dd5de4"
      },
      "source": [
        "finding_outliers(df, 'fare').sort_values('fare')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>336</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>29.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>66.6000</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>66.6000</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>369</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>69.3000</td>\n",
              "      <td>False</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>641</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>69.3000</td>\n",
              "      <td>False</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>28.0</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>69.5500</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>19.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>263.0000</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>341</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>24.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>263.0000</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>679</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>36.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>512.3292</td>\n",
              "      <td>True</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>737</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>512.3292</td>\n",
              "      <td>True</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>512.3292</td>\n",
              "      <td>False</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>116 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     survived  pclass     sex   age  ...      fare  adult_male  embark_town  alone\n",
              "336         0       1    male  29.0  ...   66.6000        True  Southampton  False\n",
              "151         1       1  female  22.0  ...   66.6000       False  Southampton  False\n",
              "369         1       1  female  24.0  ...   69.3000       False    Cherbourg   True\n",
              "641         1       1  female  24.0  ...   69.3000       False    Cherbourg   True\n",
              "846         0       3    male  28.0  ...   69.5500        True  Southampton  False\n",
              "..        ...     ...     ...   ...  ...       ...         ...          ...    ...\n",
              "27          0       1    male  19.0  ...  263.0000        True  Southampton  False\n",
              "341         1       1  female  24.0  ...  263.0000       False  Southampton  False\n",
              "679         1       1    male  36.0  ...  512.3292        True    Cherbourg  False\n",
              "737         1       1    male  35.0  ...  512.3292        True    Cherbourg   True\n",
              "258         1       1  female  35.0  ...  512.3292       False    Cherbourg   True\n",
              "\n",
              "[116 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "13agipIQbcoj",
        "outputId": "b645bcb9-2874-4e7e-e684-597d110078c5"
      },
      "source": [
        "iqr_fare = df['fare'].quantile(0.75) - df['fare'].quantile(0.25)\r\n",
        "df['fare'].quantile(0.75) + 1.5* iqr_fare"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65.6344"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ou6oIoycbTR"
      },
      "source": [
        "df.loc[(finding_outliers(df, 'fare').index, 'fare')] = df['fare'].quantile(0.75) + 1.5 * iqr_fare"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "yVEuIxN8dKHp",
        "outputId": "4d840402-f40d-4952-90f3-b5630e9f5abc"
      },
      "source": [
        "sns.boxplot(y = 'fare', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zs7RV3FVdRcx",
        "outputId": "506438b1-639e-44b7-daab-9da8155cb53a"
      },
      "source": [
        "df['age'].quantile(0.25) - 1.5 * (df['age'].quantile(0.75) - df['age'].quantile(0.25))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xlsugt1NdjGs",
        "outputId": "232c0c6b-c241-4bf7-e9c6-850f6b6f09a7"
      },
      "source": [
        "df['age'].quantile(0.75) + 1.5 * (df['age'].quantile(0.75) - df['age'].quantile(0.25))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "54.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "Fb11JeHAdttx",
        "outputId": "fc500db7-6586-46a6-dc1f-35b94c2e114e"
      },
      "source": [
        "finding_outliers(df, 'age').sort_values('age')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>803</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8.5167</td>\n",
              "      <td>False</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>755</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14.5000</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>469</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>0.75</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>19.2583</td>\n",
              "      <td>False</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>644</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>0.75</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>19.2583</td>\n",
              "      <td>False</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>831</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>male</td>\n",
              "      <td>0.83</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>18.7500</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>70.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>True</td>\n",
              "      <td>Queenstown</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>71.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>34.6542</td>\n",
              "      <td>True</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>71.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>49.5042</td>\n",
              "      <td>True</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>851</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>74.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.7750</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>630</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>male</td>\n",
              "      <td>80.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>66 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     survived  pclass     sex    age  ...     fare  adult_male  embark_town  alone\n",
              "803         1       3    male   0.42  ...   8.5167       False    Cherbourg  False\n",
              "755         1       2    male   0.67  ...  14.5000       False  Southampton  False\n",
              "469         1       3  female   0.75  ...  19.2583       False    Cherbourg  False\n",
              "644         1       3  female   0.75  ...  19.2583       False    Cherbourg  False\n",
              "831         1       2    male   0.83  ...  18.7500       False  Southampton  False\n",
              "..        ...     ...     ...    ...  ...      ...         ...          ...    ...\n",
              "116         0       3    male  70.50  ...   7.7500        True   Queenstown   True\n",
              "96          0       1    male  71.00  ...  34.6542        True    Cherbourg   True\n",
              "493         0       1    male  71.00  ...  49.5042        True    Cherbourg   True\n",
              "851         0       3    male  74.00  ...   7.7750        True  Southampton   True\n",
              "630         1       1    male  80.00  ...  30.0000        True  Southampton   True\n",
              "\n",
              "[66 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TBHL__jhd5eI"
      },
      "source": [
        "df.loc[df['age'] < df['age'].quantile(0.25) - 1.5 * (df['age'].quantile(0.75) - df['age'].quantile(0.25)), \r\n",
        "       'age'] = df['age'].quantile(0.25) - 1.5 * (df['age'].quantile(0.75) - df['age'].quantile(0.25))\r\n",
        "\r\n",
        "df.loc[df['age'] > df['age'].quantile(0.75) + 1.5 * (df['age'].quantile(0.75) - df['age'].quantile(0.25)), \r\n",
        "       'age'] = df['age'].quantile(0.55) + 1.5 * (df['age'].quantile(0.75) - df['age'].quantile(0.25))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "gYkARHuDevdo",
        "outputId": "4938b6ab-ddfb-4d5c-b6fb-0e4deb3f1ed2"
      },
      "source": [
        "sns.boxplot(y = 'age', data=df);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "qtok5hoefSmP",
        "outputId": "05e50a1a-4c2c-4e28-f0d7-3b166ae5db27"
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alone</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>male</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>True</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>female</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>65.6344</td>\n",
              "      <td>False</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>False</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass     sex   age  ...     fare  adult_male  embark_town  alone\n",
              "0         0       3    male  22.0  ...   7.2500        True  Southampton  False\n",
              "1         1       1  female  38.0  ...  65.6344       False    Cherbourg  False\n",
              "2         1       3  female  26.0  ...   7.9250       False  Southampton   True\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j57-ly4Jc5va"
      },
      "source": [
        "# <font color=red> Préparation des données"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dfgk67LKfV8N"
      },
      "source": [
        "df['sex'].replace({'female':0, 'male':1}, inplace = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kiG5HY7yfvxa"
      },
      "source": [
        "df['alone'] = df['alone'].astype('int')\r\n",
        "df['adult_male'] = df['adult_male'].astype('int')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zudBlx5Eglm_",
        "outputId": "4c608bc1-2b0c-4544-ab37-4315d480065c"
      },
      "source": [
        "df['embark_town'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Southampton', 'Cherbourg', 'Queenstown'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "5xIujvmNgLXQ",
        "outputId": "636cc78e-5388-4491-96b1-e6e920b0d3f4"
      },
      "source": [
        "embark_dummies = pd.get_dummies(df['embark_town'], drop_first=True)\r\n",
        "embark_dummies"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Queenstown</th>\n",
              "      <th>Southampton</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     Queenstown  Southampton\n",
              "0             0            1\n",
              "1             0            0\n",
              "2             0            1\n",
              "3             0            1\n",
              "4             0            1\n",
              "..          ...          ...\n",
              "886           0            1\n",
              "887           0            1\n",
              "888           0            1\n",
              "889           0            0\n",
              "890           1            0\n",
              "\n",
              "[891 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "Zii20bnChHUH",
        "outputId": "c4329d3d-a8f8-4516-cc9a-bab21a86d4b7"
      },
      "source": [
        "df = pd.concat([df, embark_dummies], axis=1)\r\n",
        "df.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>embark_town</th>\n",
              "      <th>alone</th>\n",
              "      <th>Queenstown</th>\n",
              "      <th>Southampton</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>65.6344</td>\n",
              "      <td>0</td>\n",
              "      <td>Cherbourg</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>Southampton</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  sex   age  ...  embark_town  alone  Queenstown  Southampton\n",
              "0         0       3    1  22.0  ...  Southampton      0           0            1\n",
              "1         1       1    0  38.0  ...    Cherbourg      0           0            0\n",
              "2         1       3    0  26.0  ...  Southampton      1           0            1\n",
              "\n",
              "[3 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3cx2ktBfhUDv"
      },
      "source": [
        "del df['embark_town']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "VZfky9tGhYe3",
        "outputId": "7b36c664-2872-41d1-e39c-683a97724e66"
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>sibsp</th>\n",
              "      <th>parch</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>alone</th>\n",
              "      <th>Queenstown</th>\n",
              "      <th>Southampton</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>65.6344</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  sex   age  ...  adult_male  alone  Queenstown  Southampton\n",
              "0         0       3    1  22.0  ...           1      0           0            1\n",
              "1         1       1    0  38.0  ...           0      0           0            0\n",
              "2         1       3    0  26.0  ...           0      1           0            1\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "ojomneCBhlTH",
        "outputId": "197f2d12-0188-4c53-ff6e-de95323da154"
      },
      "source": [
        "df['family'] = df['sibsp'] + df['parch']\r\n",
        "\r\n",
        "df.drop(['sibsp', 'parch'], axis=1, inplace=True)\r\n",
        "\r\n",
        "df.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>alone</th>\n",
              "      <th>Queenstown</th>\n",
              "      <th>Southampton</th>\n",
              "      <th>family</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>65.6344</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   survived  pclass  sex   age  ...  alone  Queenstown  Southampton  family\n",
              "0         0       3    1  22.0  ...      0           0            1       1\n",
              "1         1       1    0  38.0  ...      0           0            0       1\n",
              "2         1       3    0  26.0  ...      1           0            1       0\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZjX1QiKh-7n"
      },
      "source": [
        "# Division des données 60% train, 20% validation, 20% test\r\n",
        "\r\n",
        "from sklearn.model_selection import train_test_split\r\n",
        "seed = 111\r\n",
        "X = df.drop('survived', axis=1)\r\n",
        "y = df['survived']\r\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, \r\n",
        "                                                    random_state = seed, stratify=y)\r\n",
        "\r\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size = 0.5, \r\n",
        "                                                    random_state = seed, stratify=y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5hz6shwnkD4i",
        "outputId": "54ae0be9-5d1f-4e3f-b801-cf6f88d6d2f7"
      },
      "source": [
        "y.value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.616162\n",
              "1    0.383838\n",
              "Name: survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LbaakAd-kI6v",
        "outputId": "c9211005-0783-4458-ab1b-21368c8ffd37"
      },
      "source": [
        "y_train.value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.616105\n",
              "1    0.383895\n",
              "Name: survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XoprWqa7kM2X",
        "outputId": "3d231347-fafe-490a-868c-e1e7e865e0d6"
      },
      "source": [
        "y_val.value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.617978\n",
              "1    0.382022\n",
              "Name: survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2Fq8e5nkPWv",
        "outputId": "4792bb81-58fc-40ea-da88-0ae4b98fddfb"
      },
      "source": [
        "y_test.value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.614525\n",
              "1    0.385475\n",
              "Name: survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LdqsN0-tkd_t"
      },
      "source": [
        "# Normalisation\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "pHzwTWWAlKFO",
        "outputId": "0f2afbf9-71e9-4f1a-d4b6-c60056e77847"
      },
      "source": [
        "# Méthode de sur-échantillonnage (upsampling)\r\n",
        "\r\n",
        "from sklearn.utils import resample\r\n",
        "\r\n",
        "X2 = X_train\r\n",
        "X2['survived'] = y_train.values\r\n",
        "X2.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "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>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>alone</th>\n",
              "      <th>Queenstown</th>\n",
              "      <th>Southampton</th>\n",
              "      <th>family</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>692</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>56.4958</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>51.0</td>\n",
              "      <td>12.5250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     pclass  sex   age     fare  ...  Queenstown  Southampton  family  survived\n",
              "692       3    1  28.0  56.4958  ...           0            1       0         1\n",
              "150       2    1  51.0  12.5250  ...           0            1       0         0\n",
              "886       2    1  27.0  13.0000  ...           0            1       0         0\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "irSD_-C8mPqX",
        "outputId": "4bc1c939-3601-4356-b2be-360ce7ff52f7"
      },
      "source": [
        "minority = X2[X2.survived == 1]\r\n",
        "majority = X2[X2.survived == 0]\r\n",
        "\r\n",
        "minority_upsampled = resample(minority, replace=True, n_samples = len(majority))\r\n",
        "\r\n",
        "minority_upsampled"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>alone</th>\n",
              "      <th>Queenstown</th>\n",
              "      <th>Southampton</th>\n",
              "      <th>family</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>322</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>12.3500</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>37.0</td>\n",
              "      <td>52.5542</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>7.8792</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>65.6344</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>20.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>615</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>65.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>7.8792</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>329 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     pclass  sex   age     fare  ...  Queenstown  Southampton  family  survived\n",
              "133       2    0  29.0  26.0000  ...           0            1       1         1\n",
              "322       2    0  30.0  12.3500  ...           1            0       0         1\n",
              "248       1    1  37.0  52.5542  ...           0            1       2         1\n",
              "821       3    1  27.0   8.6625  ...           0            1       0         1\n",
              "28        3    0  28.0   7.8792  ...           1            0       0         1\n",
              "..      ...  ...   ...      ...  ...         ...          ...     ...       ...\n",
              "306       1    0  28.0  65.6344  ...           0            0       0         1\n",
              "279       3    0  35.0  20.2500  ...           0            1       2         1\n",
              "615       2    0  24.0  65.0000  ...           0            1       3         1\n",
              "28        3    0  28.0   7.8792  ...           1            0       0         1\n",
              "443       2    0  28.0  13.0000  ...           0            1       0         1\n",
              "\n",
              "[329 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-dKCPLQ0nJgW",
        "outputId": "fd666ff3-193a-4923-be44-36b928873d92"
      },
      "source": [
        "majority.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(329, 10)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "I7UBjSxRnQxe",
        "outputId": "aa39f623-44b2-4f20-dabc-d3325426fbb5"
      },
      "source": [
        "upsampled = pd.concat([majority, minority_upsampled])\r\n",
        "upsampled"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>alone</th>\n",
              "      <th>Queenstown</th>\n",
              "      <th>Southampton</th>\n",
              "      <th>family</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>51.0</td>\n",
              "      <td>12.5250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>27.0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>42.0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>249</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>54.0</td>\n",
              "      <td>26.0000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>464</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>306</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>65.6344</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>279</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>20.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>615</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>65.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>7.8792</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>658 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     pclass  sex   age     fare  ...  Queenstown  Southampton  family  survived\n",
              "150       2    1  51.0  12.5250  ...           0            1       0         0\n",
              "886       2    1  27.0  13.0000  ...           0            1       0         0\n",
              "149       2    1  42.0  13.0000  ...           0            1       0         0\n",
              "249       2    1  54.0  26.0000  ...           0            1       1         0\n",
              "464       3    1  28.0   8.0500  ...           0            1       0         0\n",
              "..      ...  ...   ...      ...  ...         ...          ...     ...       ...\n",
              "306       1    0  28.0  65.6344  ...           0            0       0         1\n",
              "279       3    0  35.0  20.2500  ...           0            1       2         1\n",
              "615       2    0  24.0  65.0000  ...           0            1       3         1\n",
              "28        3    0  28.0   7.8792  ...           1            0       0         1\n",
              "443       2    0  28.0  13.0000  ...           0            1       0         1\n",
              "\n",
              "[658 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dovvCuzFngNO",
        "outputId": "26cfb375-ac08-45a8-896b-da39d69f3409"
      },
      "source": [
        "upsampled['survived'].value_counts(normalize = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.5\n",
              "0    0.5\n",
              "Name: survived, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVqSIhpnnpLu"
      },
      "source": [
        "X_train_up = upsampled.drop('survived', axis=1)\r\n",
        "y_train_up = upsampled['survived']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "s2TA4A5KoFhi",
        "outputId": "2ea63177-d81b-487e-c035-5b2378ac33e2"
      },
      "source": [
        "# Méthode de sous-échantillonnage\r\n",
        "\r\n",
        "majority_downsampled = resample(majority, replace=False, n_samples = len(minority))\r\n",
        "\r\n",
        "majority_downsampled"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>19.0</td>\n",
              "      <td>36.7500</td>\n",
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              "      <td>0</td>\n",
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              "      <th>482</th>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>563</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>721</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>17.0</td>\n",
              "      <td>7.0542</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>30.0</td>\n",
              "      <td>16.1000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>27.9000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>613</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>451</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>19.9667</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>205 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     pclass  sex   age     fare  ...  Queenstown  Southampton  family  survived\n",
              "145       2    1  19.0  36.7500  ...           0            1       2         0\n",
              "482       3    1  50.0   8.0500  ...           0            1       0         0\n",
              "563       3    1  28.0   8.0500  ...           0            1       0         0\n",
              "721       3    1  17.0   7.0542  ...           0            1       1         0\n",
              "253       3    1  30.0  16.1000  ...           0            1       1         0\n",
              "..      ...  ...   ...      ...  ...         ...          ...     ...       ...\n",
              "45        3    1  28.0   8.0500  ...           0            1       0         0\n",
              "63        3    1   4.0  27.9000  ...           0            1       5         0\n",
              "613       3    1  28.0   7.7500  ...           1            0       0         0\n",
              "24        3    0   8.0  21.0750  ...           0            1       4         0\n",
              "451       3    1  28.0  19.9667  ...           0            1       1         0\n",
              "\n",
              "[205 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "opioRTgHoqCn",
        "outputId": "743e5bcb-f6e2-4d19-937b-42026da95242"
      },
      "source": [
        "downsampled = pd.concat([minority, majority_downsampled])\r\n",
        "downsampled"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pclass</th>\n",
              "      <th>sex</th>\n",
              "      <th>age</th>\n",
              "      <th>fare</th>\n",
              "      <th>adult_male</th>\n",
              "      <th>alone</th>\n",
              "      <th>Queenstown</th>\n",
              "      <th>Southampton</th>\n",
              "      <th>family</th>\n",
              "      <th>survived</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>692</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>56.4958</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>65.6344</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>788</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>20.5750</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>609</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>65.6344</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>7.7333</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4.0</td>\n",
              "      <td>27.9000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>613</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>21.0750</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>451</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>28.0</td>\n",
              "      <td>19.9667</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>410 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     pclass  sex   age     fare  ...  Queenstown  Southampton  family  survived\n",
              "692       3    1  28.0  56.4958  ...           0            1       0         1\n",
              "151       1    0  22.0  65.6344  ...           0            1       1         1\n",
              "788       3    1   2.5  20.5750  ...           0            1       3         1\n",
              "609       1    0  40.0  65.6344  ...           0            1       0         1\n",
              "697       3    0  28.0   7.7333  ...           1            0       0         1\n",
              "..      ...  ...   ...      ...  ...         ...          ...     ...       ...\n",
              "45        3    1  28.0   8.0500  ...           0            1       0         0\n",
              "63        3    1   4.0  27.9000  ...           0            1       5         0\n",
              "613       3    1  28.0   7.7500  ...           1            0       0         0\n",
              "24        3    0   8.0  21.0750  ...           0            1       4         0\n",
              "451       3    1  28.0  19.9667  ...           0            1       1         0\n",
              "\n",
              "[410 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZptTOCB7oy_v"
      },
      "source": [
        "X_train_down = downsampled.drop(['survived'], axis=1)\r\n",
        "y_train_down = downsampled['survived']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T-0eY9xOdA4S"
      },
      "source": [
        "# <font color=red> Modélisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZm__tH2pEuG",
        "outputId": "bd463c37-e303-448b-cf48-3572a09ca9b2"
      },
      "source": [
        "# Sélection de variables importantes\r\n",
        "\r\n",
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "\r\n",
        "from sklearn.metrics import accuracy_score\r\n",
        "\r\n",
        "rf = RandomForestClassifier(random_state = seed)\r\n",
        "\r\n",
        "rf.fit(X_train_up, y_train_up)\r\n",
        "\r\n",
        "accuracy_score(y_val, rf.predict(X_val))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7640449438202247"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8Nyiu8DqRzD",
        "outputId": "b3910d00-d090-4a1d-cb41-7b92a5e27e41"
      },
      "source": [
        "print(X_train_up.columns)\r\n",
        "print(rf.feature_importances_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['pclass', 'sex', 'age', 'fare', 'adult_male', 'alone', 'Queenstown',\n",
            "       'Southampton', 'family'],\n",
            "      dtype='object')\n",
            "[0.07850644 0.11030756 0.2464464  0.23998226 0.20318128 0.01199968\n",
            " 0.0077422  0.02236603 0.07946815]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFF4O2vtqYZc",
        "outputId": "ef3c61ad-c663-408a-831d-2c594d8d8da9"
      },
      "source": [
        "vars_imp = pd.Series(rf.feature_importances_, index = X_train_up.columns).sort_values(ascending=False)\r\n",
        "vars_imp"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age            0.246446\n",
              "fare           0.239982\n",
              "adult_male     0.203181\n",
              "sex            0.110308\n",
              "family         0.079468\n",
              "pclass         0.078506\n",
              "Southampton    0.022366\n",
              "alone          0.012000\n",
              "Queenstown     0.007742\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "0r1vAyaEqxxh",
        "outputId": "26699b4c-0757-445d-ad9c-358251dd22ad"
      },
      "source": [
        "sns.barplot(x=vars_imp, y=vars_imp.index)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFkb-3QLrh0X"
      },
      "source": [
        "vars_non_imp = ['Queenstown']\r\n",
        "\r\n",
        "X_train_up = X_train_up.drop(vars_non_imp, axis=1)\r\n",
        "X_val = X_val.drop(vars_non_imp, axis=1)\r\n",
        "X_test = X_test.drop(vars_non_imp, axis=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ppXerL1sI1_",
        "outputId": "b713902d-c000-43d9-ade7-624b438d4a5e"
      },
      "source": [
        "# Régression logistique\r\n",
        "\r\n",
        "from sklearn.linear_model import LogisticRegression\r\n",
        "\r\n",
        "from sklearn.model_selection import GridSearchCV\r\n",
        "\r\n",
        "lr = LogisticRegression(random_state= seed, max_iter=500)\r\n",
        "\r\n",
        "lr_hyp = {'C':[0.001, 0.01, 0.1, 1, 10, 100]}\r\n",
        "\r\n",
        "lr_cv = GridSearchCV(lr, lr_hyp, cv = 5)\r\n",
        "\r\n",
        "lr_cv.fit(X_train_up, y_train_up)\r\n",
        "\r\n",
        "print(lr_cv.best_score_)\r\n",
        "print(lr_cv.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8282442748091603\n",
            "LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=111, solver='lbfgs', tol=0.0001, verbose=0,\n",
            "                   warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vvjzYS7nuJVG",
        "outputId": "abb63b67-e660-4271-984f-5678209a2795"
      },
      "source": [
        "# Forêt aléatoire\r\n",
        "\r\n",
        "RF = RandomForestClassifier(random_state=seed)\r\n",
        "\r\n",
        "RF_hyp = {'n_estimators':[5, 10, 20, 50, 100, 200],\r\n",
        "          'max_depth':[None, 2, 5, 10, 15, 20]}\r\n",
        "\r\n",
        "RF_cv = GridSearchCV(RF, RF_hyp, cv = 5)\r\n",
        "\r\n",
        "RF_cv.fit(X_train_up, y_train_up)\r\n",
        "\r\n",
        "print(RF_cv.best_score_)\r\n",
        "\r\n",
        "print(RF_cv.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8860282211427248\n",
            "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=None, max_features='auto',\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
            "                       n_jobs=None, oob_score=False, random_state=111,\n",
            "                       verbose=0, warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vk-kDtBXvZA8",
        "outputId": "69c2249f-2f98-4919-88a7-53ec37057b38"
      },
      "source": [
        "# SVM \r\n",
        "\r\n",
        "from sklearn.svm import SVC\r\n",
        "\r\n",
        "sv = SVC(random_state = seed)\r\n",
        "\r\n",
        "sv_hyp = {'C':[0.1, 1, 10], 'kernel':['linear', 'rbf']}\r\n",
        "\r\n",
        "svm_cv = GridSearchCV(sv, sv_hyp, cv = 5)\r\n",
        "\r\n",
        "svm_cv.fit(X_train_up, y_train_up)\r\n",
        "\r\n",
        "print(svm_cv.best_score_)\r\n",
        "\r\n",
        "print(svm_cv.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8130927596576452\n",
            "SVC(C=10, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
            "    max_iter=-1, probability=False, random_state=111, shrinking=True, tol=0.001,\n",
            "    verbose=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHHyUi8SwyHn",
        "outputId": "a01b3fac-98cb-45a1-a0c3-47f27d222ba2"
      },
      "source": [
        "# Bagging Classifier\r\n",
        "\r\n",
        "from sklearn.ensemble import BaggingClassifier\r\n",
        "\r\n",
        "bc = BaggingClassifier(random_state=seed)\r\n",
        "\r\n",
        "bc_hyp = {'n_estimators':[5, 10, 20, 50, 100, 200]}\r\n",
        "\r\n",
        "bc_cv = GridSearchCV(bc, bc_hyp, cv = 5)\r\n",
        "\r\n",
        "bc_cv.fit(X_train_up, y_train_up)\r\n",
        "\r\n",
        "print(bc_cv.best_score_)\r\n",
        "\r\n",
        "print(bc_cv.best_estimator_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8784293314827666\n",
            "BaggingClassifier(base_estimator=None, bootstrap=True, bootstrap_features=False,\n",
            "                  max_features=1.0, max_samples=1.0, n_estimators=100,\n",
            "                  n_jobs=None, oob_score=False, random_state=111, verbose=0,\n",
            "                  warm_start=False)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IL6xkfpYyBFa"
      },
      "source": [
        "# Evaluation des performances et choix du modèle\r\n",
        "\r\n",
        "def model_evaluation(model, features, labels):\r\n",
        "  pred = model.predict(features)\r\n",
        "  score = accuracy_score(y_val, pred)\r\n",
        "  print('Score global du modèle :', round(score, 3))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KZHJFqc3zYtj",
        "outputId": "65998e0e-d597-4d07-8b55-61c35f6f1072"
      },
      "source": [
        "models = [lr_cv.best_estimator_, RF_cv.best_estimator_, \r\n",
        "          svm_cv.best_estimator_, bc_cv.best_estimator_]\r\n",
        "\r\n",
        "for model in models:\r\n",
        "  print('Modèle ' + str(model))\r\n",
        "  model_evaluation(model, X_val, y_val)\r\n",
        "  print('-'*90)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Modèle LogisticRegression(C=1, class_weight=None, dual=False, fit_intercept=True,\n",
            "                   intercept_scaling=1, l1_ratio=None, max_iter=500,\n",
            "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
            "                   random_state=111, solver='lbfgs', tol=0.0001, verbose=0,\n",
            "                   warm_start=False)\n",
            "Score global du modèle : 0.747\n",
            "------------------------------------------------------------------------------------------\n",
            "Modèle RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
            "                       criterion='gini', max_depth=None, max_features='auto',\n",
            "                       max_leaf_nodes=None, max_samples=None,\n",
            "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
            "                       min_samples_leaf=1, min_samples_split=2,\n",
            "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
            "                       n_jobs=None, oob_score=False, random_state=111,\n",
            "                       verbose=0, warm_start=False)\n",
            "Score global du modèle : 0.781\n",
            "------------------------------------------------------------------------------------------\n",
            "Modèle SVC(C=10, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
            "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
            "    max_iter=-1, probability=False, random_state=111, shrinking=True, tol=0.001,\n",
            "    verbose=False)\n",
            "Score global du modèle : 0.775\n",
            "------------------------------------------------------------------------------------------\n",
            "Modèle BaggingClassifier(base_estimator=None, bootstrap=True, bootstrap_features=False,\n",
            "                  max_features=1.0, max_samples=1.0, n_estimators=100,\n",
            "                  n_jobs=None, oob_score=False, random_state=111, verbose=0,\n",
            "                  warm_start=False)\n",
            "Score global du modèle : 0.775\n",
            "------------------------------------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjN9prxUz32f",
        "outputId": "dc80cee7-99b6-45b2-dd89-52bd2cb3e649"
      },
      "source": [
        "# Performance du meilleur modèle sur les données de test\r\n",
        "\r\n",
        "accuracy_score(y_test, RF_cv.best_estimator_.predict(X_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8044692737430168"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    }
  ]
}