californiaHousing.ipynb

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  "nbformat_minor": 0,
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    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Import des bibliotèques"
      ],
      "metadata": {
        "id": "aky6aY23Zpqj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "OxxjHyZ1WLs1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt # pr la visualisation\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.datasets import fetch_california_housing # le dataset\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# On charge notre dataset"
      ],
      "metadata": {
        "id": "UzYQR-ltc7YA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "housing = fetch_california_housing() #chargement du dataset\n",
        "\n",
        "X = pd.DataFrame(housing.data, columns=housing.feature_names)\n",
        "# dataframe des features\n",
        "y = pd.Series(housing.target)\n",
        "# Series prix de maison (target)"
      ],
      "metadata": {
        "id": "VthzmeImWlg0"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# On vérifie notre dataset\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "d7wQnLXqfThY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Features\")\n",
        "print(X.head()) # première ligne dataframe\n",
        "print(\"Cible\")\n",
        "print(y.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tjyp0R-9bjOY",
        "outputId": "2f97f740-fa07-422a-8427-ae66cc499ac9"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features\n",
            "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
            "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
            "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
            "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
            "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
            "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
            "\n",
            "   Longitude  \n",
            "0    -122.23  \n",
            "1    -122.22  \n",
            "2    -122.24  \n",
            "3    -122.25  \n",
            "4    -122.25  \n",
            "Cible\n",
            "0    4.526\n",
            "1    3.585\n",
            "2    3.521\n",
            "3    3.413\n",
            "4    3.422\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# On vérifie les valeurs null\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DFrALFTIfY-k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"feature\")\n",
        "print(X.isnull().sum()) # compte le nombre de valeur null par colonne\n",
        "print(\"Cible\")\n",
        "print(y.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YBVUs07pfdp2",
        "outputId": "7d3c1599-fecf-40fb-d126-c70625f1ebda"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "feature\n",
            "MedInc        0\n",
            "HouseAge      0\n",
            "AveRooms      0\n",
            "AveBedrms     0\n",
            "Population    0\n",
            "AveOccup      0\n",
            "Latitude      0\n",
            "Longitude     0\n",
            "dtype: int64\n",
            "Cible\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# On sépare les données d'entrainement et de test"
      ],
      "metadata": {
        "id": "IiztCD1Bh0WW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "70% d'entrainemente et 30% de test"
      ],
      "metadata": {
        "id": "SAY1XEysmrgE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)\n",
        "# separation donnée de train et de test"
      ],
      "metadata": {
        "id": "XAPXGBYWgQH7"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Regression linéaire"
      ],
      "metadata": {
        "id": "f6GkORxumydJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "# import model regression\n",
        "LinearRegression = LinearRegression()\n",
        "LinearRegression.fit(X_train, y_train)\n",
        "#entraiment du modèle sur les donnée de train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "1wX4SRleiAMi",
        "outputId": "d0eec220-59e6-4314-b41f-700e25d047e7"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
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              "  color: var(--sklearn-color-icon);\n",
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              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
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              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
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              "#sk-container-id-4 div.sk-label label {\n",
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              "  color: var(--sklearn-color-text-on-default-background);\n",
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              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
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              "\n",
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              "#sk-container-id-4 div.sk-estimator {\n",
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              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
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              "\n",
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              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
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              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
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              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c = LinearRegression.intercept_\n",
        "print(c) # ordonné à l'origine (intercept)\n",
        "m = LinearRegression.coef_\n",
        "print(m) # coef de regression (pente) pour chaque caractéristique"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5DElTQVsiT7h",
        "outputId": "e6775ed9-58dd-4ecb-8f9f-c729853b8a75"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-35.377436957084534\n",
            "[ 4.46902467e-01  9.71217165e-03 -1.18273456e-01  6.50457964e-01\n",
            " -2.75083021e-06 -6.06902748e-03 -4.06167164e-01 -4.17012517e-01]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Prédiction\n",
        "\n"
      ],
      "metadata": {
        "id": "2IEs3HtnnXm2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train = LinearRegression.predict(X_train)\n",
        "# prédiction sur les donnée d'entrainement\n",
        "print(y_pred_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ujU9J4WitqK",
        "outputId": "3426af78-1607-419a-9cef-9260ac0f1a5b"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.86761762 1.67376236 0.88142115 ... 2.49785365 3.13103974 0.48382883]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# visualisation des prédiction sur les données d'entrainement\n",
        "plt.scatter(y_train, y_pred_train)\n",
        "plt.xlabel(\"Valeurs réelles\")\n",
        "plt.ylabel(\"Valeurs prédites\")\n",
        "plt.title(\"Comparaison des valeurs réelles et prédites\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "IQabvWHei6P2",
        "outputId": "ca300fdb-04bf-4a37-a801-561e30779714"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "r2_score(y_train, y_pred_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94K8ll1ljFmo",
        "outputId": "a6b4ebb8-114a-4fca-99ba-32f30ec6b1ce"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6095551980896425"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train = LinearRegression.predict(X_test)\n"
      ],
      "metadata": {
        "id": "8-Q8V2gEkglC"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(y_test, y_pred_train)\n",
        "plt.xlabel(\"Valeurs réelles\")\n",
        "plt.ylabel(\"Valeurs prédites\")\n",
        "plt.title(\"Comparaison des valeurs réelles et prédites\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "QnlzT3USlGxW",
        "outputId": "1c98c88d-0db7-46b6-a295-b644cd247fb6"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Comparaison des valeurs réelles et prédites')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(r2_score(y_test, y_pred_train))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWm-QlJClNMJ",
        "outputId": "13df2aab-c2d1-495d-d01d-b1cfaa2e26ac"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6012789831678069\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pz7DLYZelkOO"
      },
      "execution_count": 57,
      "outputs": []
    }
  ]
}