From ba82622e2a177f6ed60180d9e8a39b4265fc99cf Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?F=C3=A9lix=20MARQUET?=
 <72651575+BreizhHardware@users.noreply.github.com>
Date: Thu, 4 Sep 2025 09:39:59 +0200
Subject: [PATCH] Obisidian vault auto-backup: 04-09-2025 09:39:59 on
 macbook-air-de-felix. 2 files edited

---
 ISEN/IA/CIPA4/TP/TP1/.gitignore   |    2 +
 ISEN/IA/CIPA4/TP/TP1/tp1_IA.ipynb | 1263 ++++++++---------------------
 2 files changed, 329 insertions(+), 936 deletions(-)
 create mode 100644 ISEN/IA/CIPA4/TP/TP1/.gitignore

diff --git a/ISEN/IA/CIPA4/TP/TP1/.gitignore b/ISEN/IA/CIPA4/TP/TP1/.gitignore
new file mode 100644
index 0000000..1435b77
--- /dev/null
+++ b/ISEN/IA/CIPA4/TP/TP1/.gitignore
@@ -0,0 +1,2 @@
+.venv
+.venv/*
\ No newline at end of file
diff --git a/ISEN/IA/CIPA4/TP/TP1/tp1_IA.ipynb b/ISEN/IA/CIPA4/TP/TP1/tp1_IA.ipynb
index bf95fb3..81ae419 100644
--- a/ISEN/IA/CIPA4/TP/TP1/tp1_IA.ipynb
+++ b/ISEN/IA/CIPA4/TP/TP1/tp1_IA.ipynb
@@ -1,22 +1,8 @@
 {
-  "nbformat": 4,
-  "nbformat_minor": 0,
-  "metadata": {
-    "colab": {
-      "provenance": []
-    },
-    "kernelspec": {
-      "name": "python3",
-      "display_name": "Python 3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -26,10 +12,14 @@
       },
       "outputs": [
         {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": [
-            "Drive already mounted at /content/gdrive/; to attempt to forcibly remount, call drive.mount(\"/content/gdrive/\", force_remount=True).\n"
+          "ename": "ModuleNotFoundError",
+          "evalue": "No module named 'google'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mgoogle\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mcolab\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m drive\n\u001b[32m      2\u001b[39m drive.mount(\u001b[33m\"\u001b[39m\u001b[33m/content/gdrive/\u001b[39m\u001b[33m\"\u001b[39m)\n",
+            "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'google'"
           ]
         }
       ],
@@ -40,28 +30,25 @@
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "# I- Préaparation des données"
-      ],
       "metadata": {
         "id": "jJ1vWq32jmmi"
-      }
+      },
+      "source": [
+        "# I- Préaparation des données"
+      ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### 1- Informations sur les données"
-      ],
       "metadata": {
         "id": "8Nt-INjzjs0e"
-      }
+      },
+      "source": [
+        "### 1- Informations sur les données"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import os\n",
-        "print(os.getcwd())"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -69,36 +56,23 @@
         "id": "okURABh7jv19",
         "outputId": "626134ad-f3a3-489a-9b27-2cabe127dac9"
       },
-      "execution_count": 20,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
-            "/content\n"
+            "/Volumes/SSD/Nextcloud/Documents/ISEN/Cours/Obsidian Vault/ISEN/IA/CIPA4/TP/TP1\n"
           ]
         }
+      ],
+      "source": [
+        "import os\n",
+        "print(os.getcwd())"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import pandas as pd\n",
-        "\n",
-        "file_path = '/content/gdrive/MyDrive/tp_IA/TP1/housing.csv'\n",
-        "\n",
-        "try:\n",
-        "    housing_df = pd.read_csv(file_path)\n",
-        "\n",
-        "    print(housing_df[\"longitude\"])\n",
-        "\n",
-        "    display(housing_df.head())\n",
-        "\n",
-        "except FileNotFoundError:\n",
-        "    print(f\"Error: The file was not found at {file_path}. Please check the path and try again.\")\n",
-        "except Exception as e:\n",
-        "    print(f\"An error occurred: {e}\")"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -107,12 +81,18 @@
         "id": "qdu_D1BUjyMu",
         "outputId": "d88f8177-402d-4242-a43e-4194947c713e"
       },
-      "execution_count": 21,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
+            "Requirement already satisfied: pandas in ./.venv/lib/python3.13/site-packages (2.3.2)\n",
+            "Requirement already satisfied: numpy>=1.26.0 in ./.venv/lib/python3.13/site-packages (from pandas) (2.3.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in ./.venv/lib/python3.13/site-packages (from pandas) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in ./.venv/lib/python3.13/site-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in ./.venv/lib/python3.13/site-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
+            "Note: you may need to restart the kernel to use updated packages.\n",
             "0       -122.23\n",
             "1       -122.22\n",
             "2       -122.24\n",
@@ -128,27 +108,9 @@
           ]
         },
         {
-          "output_type": "display_data",
           "data": {
-            "text/plain": [
-              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-              "0    -122.23     37.88                41.0        880.0           129.0   \n",
-              "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
-              "2    -122.24     37.85                52.0       1467.0           190.0   \n",
-              "3    -122.25     37.85                52.0       1274.0           235.0   \n",
-              "4    -122.25     37.85                52.0       1627.0           280.0   \n",
-              "\n",
-              "   population  households  median_income  median_house_value ocean_proximity  \n",
-              "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
-              "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
-              "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
-              "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
-              "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
-            ],
             "text/html": [
-              "\n",
-              "  <div id=\"df-b7352e39-3fbc-487b-835b-96ce227e46a8\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -246,241 +208,60 @@
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
-              "</div>\n",
-              "    <div class=\"colab-df-buttons\">\n",
-              "\n",
-              "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b7352e39-3fbc-487b-835b-96ce227e46a8')\"\n",
-              "            title=\"Convert this dataframe to an interactive table.\"\n",
-              "            style=\"display:none;\">\n",
-              "\n",
-              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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-              "  </svg>\n",
-              "    </button>\n",
-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
-              "      cursor: pointer;\n",
-              "      display: none;\n",
-              "      fill: #1967D2;\n",
-              "      height: 32px;\n",
-              "      padding: 0 0 0 0;\n",
-              "      width: 32px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert:hover {\n",
-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "      fill: #174EA6;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
-              "      background-color: #434B5C;\n",
-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-              "      fill: #FFFFFF;\n",
-              "    }\n",
-              "  </style>\n",
-              "\n",
-              "    <script>\n",
-              "      const buttonEl =\n",
-              "        document.querySelector('#df-b7352e39-3fbc-487b-835b-96ce227e46a8 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
-              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "\n",
-              "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-b7352e39-3fbc-487b-835b-96ce227e46a8');\n",
-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
-              "        const docLink = document.createElement('div');\n",
-              "        docLink.innerHTML = docLinkHtml;\n",
-              "        element.appendChild(docLink);\n",
-              "      }\n",
-              "    </script>\n",
-              "  </div>\n",
-              "\n",
-              "\n",
-              "    <div id=\"df-1730227e-e965-4791-a8f8-e85e88528344\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1730227e-e965-4791-a8f8-e85e88528344')\"\n",
-              "                title=\"Suggest charts\"\n",
-              "                style=\"display:none;\">\n",
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-              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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-              "    </g>\n",
-              "</svg>\n",
-              "      </button>\n",
-              "\n",
-              "<style>\n",
-              "  .colab-df-quickchart {\n",
-              "      --bg-color: #E8F0FE;\n",
-              "      --fill-color: #1967D2;\n",
-              "      --hover-bg-color: #E2EBFA;\n",
-              "      --hover-fill-color: #174EA6;\n",
-              "      --disabled-fill-color: #AAA;\n",
-              "      --disabled-bg-color: #DDD;\n",
-              "  }\n",
-              "\n",
-              "  [theme=dark] .colab-df-quickchart {\n",
-              "      --bg-color: #3B4455;\n",
-              "      --fill-color: #D2E3FC;\n",
-              "      --hover-bg-color: #434B5C;\n",
-              "      --hover-fill-color: #FFFFFF;\n",
-              "      --disabled-bg-color: #3B4455;\n",
-              "      --disabled-fill-color: #666;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart {\n",
-              "    background-color: var(--bg-color);\n",
-              "    border: none;\n",
-              "    border-radius: 50%;\n",
-              "    cursor: pointer;\n",
-              "    display: none;\n",
-              "    fill: var(--fill-color);\n",
-              "    height: 32px;\n",
-              "    padding: 0;\n",
-              "    width: 32px;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart:hover {\n",
-              "    background-color: var(--hover-bg-color);\n",
-              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
-              "  .colab-df-quickchart-complete:disabled:hover {\n",
-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
-              "    }\n",
-              "    20% {\n",
-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
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-              "      border-color: transparent;\n",
-              "      border-left-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    40% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-top-color: var(--fill-color);\n",
-              "    }\n",
-              "    60% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "    }\n",
-              "    80% {\n",
-              "      border-color: transparent;\n",
-              "      border-right-color: var(--fill-color);\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "    90% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "      <script>\n",
-              "        async function quickchart(key) {\n",
-              "          const quickchartButtonEl =\n",
-              "            document.querySelector('#' + key + ' button');\n",
-              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "          try {\n",
-              "            const charts = await google.colab.kernel.invokeFunction(\n",
-              "                'suggestCharts', [key], {});\n",
-              "          } catch (error) {\n",
-              "            console.error('Error during call to suggestCharts:', error);\n",
-              "          }\n",
-              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "        }\n",
-              "        (() => {\n",
-              "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-1730227e-e965-4791-a8f8-e85e88528344 button');\n",
-              "          quickchartButtonEl.style.display =\n",
-              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "        })();\n",
-              "      </script>\n",
-              "    </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
+              "</div>"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"    print(f\\\"An error occurred: {e}\\\")\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.013038404810404884,\n        \"min\": -122.25,\n        \"max\": -122.22,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          -122.22,\n          -122.25,\n          -122.23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0130384048104057,\n        \"min\": 37.85,\n        \"max\": 37.88,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          37.88,\n          37.86,\n          37.85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.501851724856113,\n        \"min\": 21.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          41.0,\n          21.0,\n          52.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2603.0181136519204,\n        \"min\": 880.0,\n        \"max\": 7099.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7099.0,\n          1627.0,\n          1467.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 405.24128614937547,\n        \"min\": 129.0,\n        \"max\": 1106.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1106.0,\n          280.0,\n          190.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 862.3365352343596,\n        \"min\": 322.0,\n        \"max\": 2401.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2401.0,\n          565.0,\n          496.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 424.49346284719155,\n        \"min\": 126.0,\n        \"max\": 1138.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1138.0,\n          259.0,\n          177.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9218775476080674,\n        \"min\": 3.8462,\n        \"max\": 8.3252,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          8.3014,\n          3.8462,\n          7.2574\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47089.73348830932,\n        \"min\": 341300.0,\n        \"max\": 452600.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          358500.0,\n          342200.0,\n          352100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"NEAR BAY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "0    -122.23     37.88                41.0        880.0           129.0   \n",
+              "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
+              "2    -122.24     37.85                52.0       1467.0           190.0   \n",
+              "3    -122.25     37.85                52.0       1274.0           235.0   \n",
+              "4    -122.25     37.85                52.0       1627.0           280.0   \n",
+              "\n",
+              "   population  households  median_income  median_house_value ocean_proximity  \n",
+              "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
+              "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
+              "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
+              "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
+              "4       565.0       259.0         3.8462            342200.0        NEAR BAY  "
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "%pip install pandas\n",
+        "import pandas as pd\n",
+        "\n",
+        "file_path = 'housing.csv'\n",
+        "\n",
+        "try:\n",
+        "    housing_df = pd.read_csv(file_path)\n",
+        "\n",
+        "    print(housing_df[\"longitude\"])\n",
+        "\n",
+        "    display(housing_df.head())\n",
+        "\n",
+        "except FileNotFoundError:\n",
+        "    print(f\"Error: The file was not found at {file_path}. Please check the path and try again.\")\n",
+        "except Exception as e:\n",
+        "    print(f\"An error occurred: {e}\")"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "iP8M2zH6lilY"
+      },
       "source": [
         "\n",
         "C'est un problème de régression."
-      ],
-      "metadata": {
-        "id": "iP8M2zH6lilY"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "housing_df.info()"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -488,11 +269,10 @@
         "id": "a5RYDxJPkPbd",
         "outputId": "dc5fef59-6282-4ac8-9629-831299ef7c80"
       },
-      "execution_count": 22,
       "outputs": [
         {
-          "output_type": "stream",
           "name": "stdout",
+          "output_type": "stream",
           "text": [
             "<class 'pandas.core.frame.DataFrame'>\n",
             "RangeIndex: 20640 entries, 0 to 20639\n",
@@ -513,36 +293,35 @@
             "memory usage: 1.6+ MB\n"
           ]
         }
+      ],
+      "source": [
+        "housing_df.info()"
       ]
     },
     {
       "cell_type": "markdown",
+      "metadata": {
+        "id": "2q-IrOprlk14"
+      },
       "source": [
         "Il y a moins de total_bedrooms que le reste\n",
         "\n",
         "L'attribut ocean_proximity n'est pas du même type que le reste des attributs"
-      ],
-      "metadata": {
-        "id": "2q-IrOprlk14"
-      }
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "housing_df[\"ocean_proximity\"].value_counts()"
-      ],
+      "execution_count": null,
       "metadata": {
-        "id": "22qBGmYzk9Wk",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 272
         },
+        "id": "22qBGmYzk9Wk",
         "outputId": "5e7d2eb0-3696-4e9c-b1f3-86b013b98f07"
       },
-      "execution_count": 23,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "ocean_proximity\n",
@@ -552,69 +331,20 @@
               "NEAR BAY      2290\n",
               "ISLAND           5\n",
               "Name: count, dtype: int64"
-            ],
-            "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>count</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>ocean_proximity</th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>&lt;1H OCEAN</th>\n",
-              "      <td>9136</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>INLAND</th>\n",
-              "      <td>6551</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NEAR OCEAN</th>\n",
-              "      <td>2658</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>NEAR BAY</th>\n",
-              "      <td>2290</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>ISLAND</th>\n",
-              "      <td>5</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div><br><label><b>dtype:</b> int64</label>"
             ]
           },
+          "execution_count": 6,
           "metadata": {},
-          "execution_count": 23
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "housing_df[\"ocean_proximity\"].value_counts()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "housing_df.describe()"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -623,46 +353,11 @@
         "id": "u2bjxKUblby8",
         "outputId": "ae57c719-a2c4-432a-9247-ba2d196431f5"
       },
-      "execution_count": 24,
       "outputs": [
         {
-          "output_type": "execute_result",
           "data": {
-            "text/plain": [
-              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
-              "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
-              "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
-              "std        2.003532      2.135952           12.585558   2181.615252   \n",
-              "min     -124.350000     32.540000            1.000000      2.000000   \n",
-              "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
-              "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
-              "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
-              "max     -114.310000     41.950000           52.000000  39320.000000   \n",
-              "\n",
-              "       total_bedrooms    population    households  median_income  \\\n",
-              "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
-              "mean       537.870553   1425.476744    499.539680       3.870671   \n",
-              "std        421.385070   1132.462122    382.329753       1.899822   \n",
-              "min          1.000000      3.000000      1.000000       0.499900   \n",
-              "25%        296.000000    787.000000    280.000000       2.563400   \n",
-              "50%        435.000000   1166.000000    409.000000       3.534800   \n",
-              "75%        647.000000   1725.000000    605.000000       4.743250   \n",
-              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
-              "\n",
-              "       median_house_value  \n",
-              "count        20640.000000  \n",
-              "mean        206855.816909  \n",
-              "std         115395.615874  \n",
-              "min          14999.000000  \n",
-              "25%         119600.000000  \n",
-              "50%         179700.000000  \n",
-              "75%         264725.000000  \n",
-              "max         500001.000000  "
-            ],
             "text/html": [
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-              "  <div id=\"df-d17367e8-3a17-4d32-b285-9cc9e3132dc9\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -790,234 +485,52 @@
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               "</table>\n",
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-              "  </svg>\n",
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-              "\n",
-              "  <style>\n",
-              "    .colab-df-container {\n",
-              "      display:flex;\n",
-              "      gap: 12px;\n",
-              "    }\n",
-              "\n",
-              "    .colab-df-convert {\n",
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-              "      background-color: #E2EBFA;\n",
-              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert {\n",
-              "      background-color: #3B4455;\n",
-              "      fill: #D2E3FC;\n",
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-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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-              "        document.querySelector('#df-d17367e8-3a17-4d32-b285-9cc9e3132dc9 button.colab-df-convert');\n",
-              "      buttonEl.style.display =\n",
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-              "        const dataTable =\n",
-              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-              "                                                    [key], {});\n",
-              "        if (!dataTable) return;\n",
-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
-              "        element.innerHTML = '';\n",
-              "        dataTable['output_type'] = 'display_data';\n",
-              "        await google.colab.output.renderOutput(dataTable, element);\n",
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-              "        element.appendChild(docLink);\n",
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-              "\n",
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-              "    fill: var(--button-hover-fill-color);\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-quickchart-complete:disabled,\n",
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-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
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-              "\n",
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-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
-              "\n",
-              "  @keyframes spin {\n",
-              "    0% {\n",
-              "      border-color: transparent;\n",
-              "      border-bottom-color: var(--fill-color);\n",
-              "      border-left-color: var(--fill-color);\n",
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-              "      border-bottom-color: var(--fill-color);\n",
-              "    }\n",
-              "  }\n",
-              "</style>\n",
-              "\n",
-              "      <script>\n",
-              "        async function quickchart(key) {\n",
-              "          const quickchartButtonEl =\n",
-              "            document.querySelector('#' + key + ' button');\n",
-              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
-              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
-              "          try {\n",
-              "            const charts = await google.colab.kernel.invokeFunction(\n",
-              "                'suggestCharts', [key], {});\n",
-              "          } catch (error) {\n",
-              "            console.error('Error during call to suggestCharts:', error);\n",
-              "          }\n",
-              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
-              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
-              "        }\n",
-              "        (() => {\n",
-              "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-f0b4ee8c-55b1-4be2-857c-64d444a2b682 button');\n",
-              "          quickchartButtonEl.style.display =\n",
-              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-              "        })();\n",
-              "      </script>\n",
-              "    </div>\n",
-              "\n",
-              "    </div>\n",
-              "  </div>\n"
+              "</div>"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"housing_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7333.554670164394,\n        \"min\": -124.35,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -119.56970445736432,\n          -118.49,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7286.333552413666,\n        \"min\": 2.1359523974571117,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.63186143410853,\n          34.26,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7288.35672120143,\n        \"min\": 1.0,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.639486434108527,\n          29.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13944.990983306392,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2635.7630813953488,\n          2127.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7106.427031043755,\n        \"min\": 1.0,\n        \"max\": 20433.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          537.8705525375618,\n          435.0,\n          20433.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13192.258841737372,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1425.4767441860465,\n          1166.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7167.532601135343,\n        \"min\": 1.0,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          499.5396802325581,\n          409.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7295.721435853639,\n        \"min\": 0.4999,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.8706710029069766,\n          3.5347999999999997,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156160.28379826449,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          206855.81690891474,\n          179700.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
+              "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
+              "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
+              "std        2.003532      2.135952           12.585558   2181.615252   \n",
+              "min     -124.350000     32.540000            1.000000      2.000000   \n",
+              "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
+              "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
+              "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
+              "max     -114.310000     41.950000           52.000000  39320.000000   \n",
+              "\n",
+              "       total_bedrooms    population    households  median_income  \\\n",
+              "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
+              "mean       537.870553   1425.476744    499.539680       3.870671   \n",
+              "std        421.385070   1132.462122    382.329753       1.899822   \n",
+              "min          1.000000      3.000000      1.000000       0.499900   \n",
+              "25%        296.000000    787.000000    280.000000       2.563400   \n",
+              "50%        435.000000   1166.000000    409.000000       3.534800   \n",
+              "75%        647.000000   1725.000000    605.000000       4.743250   \n",
+              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
+              "\n",
+              "       median_house_value  \n",
+              "count        20640.000000  \n",
+              "mean        206855.816909  \n",
+              "std         115395.615874  \n",
+              "min          14999.000000  \n",
+              "25%         119600.000000  \n",
+              "50%         179700.000000  \n",
+              "75%         264725.000000  \n",
+              "max         500001.000000  "
+            ]
           },
+          "execution_count": 7,
           "metadata": {},
-          "execution_count": 24
+          "output_type": "execute_result"
         }
+      ],
+      "source": [
+        "housing_df.describe()"
       ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "housing_df.hist(bins=50, figsize=(20,15))\n",
-        "plt.show()"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1026,39 +539,75 @@
         "id": "NhsKT26ZlsJy",
         "outputId": "5f11719a-5cef-427c-b7c5-668d6075761a"
       },
-      "execution_count": 25,
       "outputs": [
         {
-          "output_type": "display_data",
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting matplotlib\n",
+            "  Downloading matplotlib-3.10.6-cp313-cp313-macosx_11_0_arm64.whl.metadata (11 kB)\n",
+            "Collecting contourpy>=1.0.1 (from matplotlib)\n",
+            "  Downloading contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl.metadata (5.5 kB)\n",
+            "Collecting cycler>=0.10 (from matplotlib)\n",
+            "  Downloading cycler-0.12.1-py3-none-any.whl.metadata (3.8 kB)\n",
+            "Collecting fonttools>=4.22.0 (from matplotlib)\n",
+            "  Downloading fonttools-4.59.2-cp313-cp313-macosx_10_13_universal2.whl.metadata (109 kB)\n",
+            "Collecting kiwisolver>=1.3.1 (from matplotlib)\n",
+            "  Downloading kiwisolver-1.4.9-cp313-cp313-macosx_11_0_arm64.whl.metadata (6.3 kB)\n",
+            "Requirement already satisfied: numpy>=1.23 in ./.venv/lib/python3.13/site-packages (from matplotlib) (2.3.2)\n",
+            "Requirement already satisfied: packaging>=20.0 in ./.venv/lib/python3.13/site-packages (from matplotlib) (25.0)\n",
+            "Collecting pillow>=8 (from matplotlib)\n",
+            "  Downloading pillow-11.3.0-cp313-cp313-macosx_11_0_arm64.whl.metadata (9.0 kB)\n",
+            "Collecting pyparsing>=2.3.1 (from matplotlib)\n",
+            "  Downloading pyparsing-3.2.3-py3-none-any.whl.metadata (5.0 kB)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in ./.venv/lib/python3.13/site-packages (from matplotlib) (2.9.0.post0)\n",
+            "Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.13/site-packages (from python-dateutil>=2.7->matplotlib) (1.17.0)\n",
+            "Downloading matplotlib-3.10.6-cp313-cp313-macosx_11_0_arm64.whl (8.1 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.1/8.1 MB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m  \u001b[33m0:00:04\u001b[0m eta \u001b[36m0:00:01\u001b[0mm\n",
+            "\u001b[?25hDownloading contourpy-1.3.3-cp313-cp313-macosx_11_0_arm64.whl (274 kB)\n",
+            "Downloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
+            "Downloading fonttools-4.59.2-cp313-cp313-macosx_10_13_universal2.whl (2.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m  \u001b[33m0:00:02\u001b[0m eta \u001b[36m0:00:01\u001b[0m0m\n",
+            "\u001b[?25hDownloading kiwisolver-1.4.9-cp313-cp313-macosx_11_0_arm64.whl (64 kB)\n",
+            "Downloading pillow-11.3.0-cp313-cp313-macosx_11_0_arm64.whl (4.7 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m  \u001b[33m0:00:04\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
+            "\u001b[?25hDownloading pyparsing-3.2.3-py3-none-any.whl (111 kB)\n",
+            "Installing collected packages: pyparsing, pillow, kiwisolver, fonttools, cycler, contourpy, matplotlib\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7/7\u001b[0m [matplotlib]7\u001b[0m [matplotlib]\n",
+            "\u001b[1A\u001b[2KSuccessfully installed contourpy-1.3.3 cycler-0.12.1 fonttools-4.59.2 kiwisolver-1.4.9 matplotlib-3.10.6 pillow-11.3.0 pyparsing-3.2.3\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        },
+        {
           "data": {
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             "text/plain": [
               "<Figure size 2000x1500 with 9 Axes>"
-            ],
-            "image/png": 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AAIiAQxUAAAAAAAAAAAAAAAARcKgCAAAAAAAAAAAAAAAgAg5VAAAAAAAAAAAAAAAARMChCgAAAAAAAAAAAAAAgAg4VAEAAAAAAAAAAAAAABABhyoAAAAAAAAAAAAAAAAi4FAFAAAAAAAAAAAAAABABByqAAAAAAAAAAAAAAAAiIBDFQAAAAAAAAAAAAAAABH+Bb8HAtDwnxhRAAAAAElFTkSuQmCC\n"
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "%pip install matplotlib\n",
+        "import matplotlib.pyplot as plt\n",
+        "housing_df.hist(bins=50, figsize=(20,15))\n",
+        "plt.show()"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### 2- Répartition des données"
-      ],
       "metadata": {
         "id": "_gsAkFD2l-5m"
-      }
+      },
+      "source": [
+        "### 2- Répartition des données"
+      ]
     },
     {
       "cell_type": "code",
-      "source": [
-        "from sklearn.model_selection import train_test_split\n",
-        "\n",
-        "train_set, test_set = train_test_split(housing_df, test_size=0.2)\n",
-        "\n",
-        "# Display the first instances of the test set\n",
-        "display(test_set.head())"
-      ],
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1067,37 +616,36 @@
         "id": "5siNg-u5mQ98",
         "outputId": "7e0cd52a-49a5-4a7e-dc01-8ca2f89b7937"
       },
-      "execution_count": 26,
       "outputs": [
         {
-          "output_type": "display_data",
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting scikit-learn\n",
+            "  Downloading scikit_learn-1.7.1-cp313-cp313-macosx_12_0_arm64.whl.metadata (11 kB)\n",
+            "Requirement already satisfied: numpy>=1.22.0 in ./.venv/lib/python3.13/site-packages (from scikit-learn) (2.3.2)\n",
+            "Collecting scipy>=1.8.0 (from scikit-learn)\n",
+            "  Downloading scipy-1.16.1-cp313-cp313-macosx_14_0_arm64.whl.metadata (61 kB)\n",
+            "Collecting joblib>=1.2.0 (from scikit-learn)\n",
+            "  Downloading joblib-1.5.2-py3-none-any.whl.metadata (5.6 kB)\n",
+            "Collecting threadpoolctl>=3.1.0 (from scikit-learn)\n",
+            "  Downloading threadpoolctl-3.6.0-py3-none-any.whl.metadata (13 kB)\n",
+            "Downloading scikit_learn-1.7.1-cp313-cp313-macosx_12_0_arm64.whl (8.6 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m  \u001b[33m0:00:03\u001b[0mm0:00:01\u001b[0m00:01\u001b[0m\n",
+            "\u001b[?25hDownloading joblib-1.5.2-py3-none-any.whl (308 kB)\n",
+            "Downloading scipy-1.16.1-cp313-cp313-macosx_14_0_arm64.whl (20.8 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.8/20.8 MB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m  \u001b[33m0:00:18\u001b[0mm0:00:01\u001b[0m00:01\u001b[0m\n",
+            "\u001b[?25hDownloading threadpoolctl-3.6.0-py3-none-any.whl (18 kB)\n",
+            "Installing collected packages: threadpoolctl, scipy, joblib, scikit-learn\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4/4\u001b[0m [scikit-learn][0m [scikit-learn]\n",
+            "\u001b[1A\u001b[2KSuccessfully installed joblib-1.5.2 scikit-learn-1.7.1 scipy-1.16.1 threadpoolctl-3.6.0\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        },
+        {
           "data": {
-            "text/plain": [
-              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
-              "5847     -118.30     34.18                 5.0       5492.0          1549.0   \n",
-              "20498    -118.70     34.29                25.0       1678.0           252.0   \n",
-              "19136    -122.69     38.30                30.0       3919.0           743.0   \n",
-              "16442    -121.29     38.14                34.0       1500.0           337.0   \n",
-              "18788    -122.45     40.46                16.0       2734.0           501.0   \n",
-              "\n",
-              "       population  households  median_income  median_house_value  \\\n",
-              "5847       2997.0      1405.0         3.3205            172100.0   \n",
-              "20498       862.0       268.0         6.1834            229800.0   \n",
-              "19136      1693.0       693.0         3.3827            292100.0   \n",
-              "16442       674.0       282.0         2.5150            110800.0   \n",
-              "18788      1413.0       484.0         2.8085            105700.0   \n",
-              "\n",
-              "      ocean_proximity  \n",
-              "5847        <1H OCEAN  \n",
-              "20498       <1H OCEAN  \n",
-              "19136       <1H OCEAN  \n",
-              "16442          INLAND  \n",
-              "18788          INLAND  "
-            ],
             "text/html": [
-              "\n",
-              "  <div id=\"df-6bca8ba7-3e9c-4ea1-bfea-e1721777f74a\" class=\"colab-df-container\">\n",
-              "    <div>\n",
+              "<div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
               "        vertical-align: middle;\n",
@@ -1129,300 +677,143 @@
               "  </thead>\n",
               "  <tbody>\n",
               "    <tr>\n",
-              "      <th>5847</th>\n",
-              "      <td>-118.30</td>\n",
-              "      <td>34.18</td>\n",
-              "      <td>5.0</td>\n",
-              "      <td>5492.0</td>\n",
-              "      <td>1549.0</td>\n",
-              "      <td>2997.0</td>\n",
-              "      <td>1405.0</td>\n",
-              "      <td>3.3205</td>\n",
-              "      <td>172100.0</td>\n",
+              "      <th>8803</th>\n",
+              "      <td>-118.40</td>\n",
+              "      <td>33.78</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>5005.0</td>\n",
+              "      <td>776.0</td>\n",
+              "      <td>2357.0</td>\n",
+              "      <td>790.0</td>\n",
+              "      <td>8.5421</td>\n",
+              "      <td>500001.0</td>\n",
+              "      <td>NEAR OCEAN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>18072</th>\n",
+              "      <td>-122.00</td>\n",
+              "      <td>37.28</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>3133.0</td>\n",
+              "      <td>541.0</td>\n",
+              "      <td>1449.0</td>\n",
+              "      <td>555.0</td>\n",
+              "      <td>5.7295</td>\n",
+              "      <td>346100.0</td>\n",
               "      <td>&lt;1H OCEAN</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>20498</th>\n",
-              "      <td>-118.70</td>\n",
-              "      <td>34.29</td>\n",
-              "      <td>25.0</td>\n",
-              "      <td>1678.0</td>\n",
-              "      <td>252.0</td>\n",
-              "      <td>862.0</td>\n",
-              "      <td>268.0</td>\n",
-              "      <td>6.1834</td>\n",
-              "      <td>229800.0</td>\n",
+              "      <th>7879</th>\n",
+              "      <td>-118.11</td>\n",
+              "      <td>33.87</td>\n",
+              "      <td>33.0</td>\n",
+              "      <td>1379.0</td>\n",
+              "      <td>254.0</td>\n",
+              "      <td>795.0</td>\n",
+              "      <td>297.0</td>\n",
+              "      <td>4.6713</td>\n",
+              "      <td>231800.0</td>\n",
               "      <td>&lt;1H OCEAN</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>19136</th>\n",
-              "      <td>-122.69</td>\n",
-              "      <td>38.30</td>\n",
-              "      <td>30.0</td>\n",
-              "      <td>3919.0</td>\n",
-              "      <td>743.0</td>\n",
-              "      <td>1693.0</td>\n",
-              "      <td>693.0</td>\n",
-              "      <td>3.3827</td>\n",
-              "      <td>292100.0</td>\n",
+              "      <th>17940</th>\n",
+              "      <td>-121.94</td>\n",
+              "      <td>37.33</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>1893.0</td>\n",
+              "      <td>359.0</td>\n",
+              "      <td>797.0</td>\n",
+              "      <td>360.0</td>\n",
+              "      <td>3.6818</td>\n",
+              "      <td>257600.0</td>\n",
               "      <td>&lt;1H OCEAN</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>16442</th>\n",
-              "      <td>-121.29</td>\n",
-              "      <td>38.14</td>\n",
-              "      <td>34.0</td>\n",
-              "      <td>1500.0</td>\n",
-              "      <td>337.0</td>\n",
-              "      <td>674.0</td>\n",
-              "      <td>282.0</td>\n",
-              "      <td>2.5150</td>\n",
-              "      <td>110800.0</td>\n",
-              "      <td>INLAND</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>18788</th>\n",
-              "      <td>-122.45</td>\n",
-              "      <td>40.46</td>\n",
-              "      <td>16.0</td>\n",
-              "      <td>2734.0</td>\n",
-              "      <td>501.0</td>\n",
-              "      <td>1413.0</td>\n",
-              "      <td>484.0</td>\n",
-              "      <td>2.8085</td>\n",
-              "      <td>105700.0</td>\n",
-              "      <td>INLAND</td>\n",
+              "      <th>17812</th>\n",
+              "      <td>-121.86</td>\n",
+              "      <td>37.39</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>1777.0</td>\n",
+              "      <td>328.0</td>\n",
+              "      <td>1235.0</td>\n",
+              "      <td>329.0</td>\n",
+              "      <td>5.4225</td>\n",
+              "      <td>258100.0</td>\n",
+              "      <td>&lt;1H OCEAN</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
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-              "  </svg>\n",
-              "    </button>\n",
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-              "      background-color: #E8F0FE;\n",
-              "      border: none;\n",
-              "      border-radius: 50%;\n",
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-              "      height: 32px;\n",
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-              "      width: 32px;\n",
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-              "\n",
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-              "\n",
-              "    .colab-df-buttons div {\n",
-              "      margin-bottom: 4px;\n",
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-              "    [theme=dark] .colab-df-convert {\n",
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-              "    }\n",
-              "\n",
-              "    [theme=dark] .colab-df-convert:hover {\n",
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-              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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-              "\n",
-              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-              "          + ' to learn more about interactive tables.';\n",
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-              "    fill: var(--button-hover-fill-color);\n",
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-              "  .colab-df-quickchart-complete:disabled,\n",
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-              "    background-color: var(--disabled-bg-color);\n",
-              "    fill: var(--disabled-fill-color);\n",
-              "    box-shadow: none;\n",
-              "  }\n",
-              "\n",
-              "  .colab-df-spinner {\n",
-              "    border: 2px solid var(--fill-color);\n",
-              "    border-color: transparent;\n",
-              "    border-bottom-color: var(--fill-color);\n",
-              "    animation:\n",
-              "      spin 1s steps(1) infinite;\n",
-              "  }\n",
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-              "  @keyframes spin {\n",
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-              "      border-bottom-color: var(--fill-color);\n",
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-              "          const quickchartButtonEl =\n",
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-              "            const charts = await google.colab.kernel.invokeFunction(\n",
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-              "    </div>\n",
-              "\n",
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-              "  </div>\n"
+              "</div>"
             ],
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "dataframe",
-              "summary": "{\n  \"name\": \"display(test_set\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.069403295638625,\n        \"min\": -122.69,\n        \"max\": -118.3,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -118.7,\n          -122.45,\n          -122.69\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7491053090050954,\n        \"min\": 34.18,\n        \"max\": 40.46,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          34.29,\n          40.46,\n          38.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.640446726822816,\n        \"min\": 5.0,\n        \"max\": 34.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          25.0,\n          16.0,\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1665.998139254663,\n        \"min\": 1500.0,\n        \"max\": 5492.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1678.0,\n          2734.0,\n          3919.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 522.4794732810084,\n        \"min\": 252.0,\n        \"max\": 1549.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          252.0,\n          501.0,\n          743.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 918.0526673345054,\n        \"min\": 674.0,\n        \"max\": 2997.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          862.0,\n          1413.0,\n          1693.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 468.5064567324553,\n        \"min\": 268.0,\n        \"max\": 1405.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          268.0,\n          484.0,\n          693.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4657902261237792,\n        \"min\": 2.515,\n        \"max\": 6.1834,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          6.1834,\n          2.8085,\n          3.3827\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 79680.51832160732,\n        \"min\": 105700.0,\n        \"max\": 292100.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          229800.0,\n          105700.0,\n          292100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"INLAND\",\n          \"<1H OCEAN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
-            }
+            "text/plain": [
+              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+              "8803     -118.40     33.78                26.0       5005.0           776.0   \n",
+              "18072    -122.00     37.28                35.0       3133.0           541.0   \n",
+              "7879     -118.11     33.87                33.0       1379.0           254.0   \n",
+              "17940    -121.94     37.33                36.0       1893.0           359.0   \n",
+              "17812    -121.86     37.39                17.0       1777.0           328.0   \n",
+              "\n",
+              "       population  households  median_income  median_house_value  \\\n",
+              "8803       2357.0       790.0         8.5421            500001.0   \n",
+              "18072      1449.0       555.0         5.7295            346100.0   \n",
+              "7879        795.0       297.0         4.6713            231800.0   \n",
+              "17940       797.0       360.0         3.6818            257600.0   \n",
+              "17812      1235.0       329.0         5.4225            258100.0   \n",
+              "\n",
+              "      ocean_proximity  \n",
+              "8803       NEAR OCEAN  \n",
+              "18072       <1H OCEAN  \n",
+              "7879        <1H OCEAN  \n",
+              "17940       <1H OCEAN  \n",
+              "17812       <1H OCEAN  "
+            ]
           },
-          "metadata": {}
+          "metadata": {},
+          "output_type": "display_data"
         }
+      ],
+      "source": [
+        "%pip install scikit-learn\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "train_set, test_set = train_test_split(housing_df, test_size=0.2)\n",
+        "\n",
+        "# Display the first instances of the test set\n",
+        "display(test_set.head())"
       ]
     },
     {
       "cell_type": "markdown",
-      "source": [
-        "### 3- Découverte et visualisation des données"
-      ],
       "metadata": {
         "id": "Zca0rERPmxH8"
-      }
+      },
+      "source": [
+        "### 3- Découverte et visualisation des données"
+      ]
     }
-  ]
-}
\ No newline at end of file
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": ".venv",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.13.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}