california_housing.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMZpMLTg8tCY2b5l4/EQcRB",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/boyila/mlproject/blob/main/california_housing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "_K307F4yIpvC"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import fetch_california_housing\n",
        "from sklearn.linear_model import LinearRegression\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score, mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = fetch_california_housing()"
      ],
      "metadata": {
        "id": "uypaBUHyI2AP"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset.DESCR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W-7fMQgEJFcz",
        "outputId": "2cb6bd38-96a5-4cc4-eec5-4e46176a5bf8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ".. _california_housing_dataset:\n",
            "\n",
            "California Housing dataset\n",
            "--------------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 20640\n",
            "\n",
            "    :Number of Attributes: 8 numeric, predictive attributes and the target\n",
            "\n",
            "    :Attribute Information:\n",
            "        - MedInc        median income in block group\n",
            "        - HouseAge      median house age in block group\n",
            "        - AveRooms      average number of rooms per household\n",
            "        - AveBedrms     average number of bedrooms per household\n",
            "        - Population    block group population\n",
            "        - AveOccup      average number of household members\n",
            "        - Latitude      block group latitude\n",
            "        - Longitude     block group longitude\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "\n",
            "This dataset was obtained from the StatLib repository.\n",
            "https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\n",
            "\n",
            "The target variable is the median house value for California districts,\n",
            "expressed in hundreds of thousands of dollars ($100,000).\n",
            "\n",
            "This dataset was derived from the 1990 U.S. census, using one row per census\n",
            "block group. A block group is the smallest geographical unit for which the U.S.\n",
            "Census Bureau publishes sample data (a block group typically has a population\n",
            "of 600 to 3,000 people).\n",
            "\n",
            "A household is a group of people residing within a home. Since the average\n",
            "number of rooms and bedrooms in this dataset are provided per household, these\n",
            "columns may take surprisingly large values for block groups with few households\n",
            "and many empty houses, such as vacation resorts.\n",
            "\n",
            "It can be downloaded/loaded using the\n",
            ":func:`sklearn.datasets.fetch_california_housing` function.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\n",
            "      Statistics and Probability Letters, 33 (1997) 291-297\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjUXnUS3JDcU",
        "outputId": "0ab89b8b-57c4-40e0-f969-f7160ee3f9a6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'data': array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
            "          37.88      , -122.23      ],\n",
            "       [   8.3014    ,   21.        ,    6.23813708, ...,    2.10984183,\n",
            "          37.86      , -122.22      ],\n",
            "       [   7.2574    ,   52.        ,    8.28813559, ...,    2.80225989,\n",
            "          37.85      , -122.24      ],\n",
            "       ...,\n",
            "       [   1.7       ,   17.        ,    5.20554273, ...,    2.3256351 ,\n",
            "          39.43      , -121.22      ],\n",
            "       [   1.8672    ,   18.        ,    5.32951289, ...,    2.12320917,\n",
            "          39.43      , -121.32      ],\n",
            "       [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
            "          39.37      , -121.24      ]]), 'target': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894]), 'frame': None, 'target_names': ['MedHouseVal'], 'feature_names': ['MedInc', 'HouseAge', 'AveRooms', 'AveBedrms', 'Population', 'AveOccup', 'Latitude', 'Longitude'], 'DESCR': '.. _california_housing_dataset:\\n\\nCalifornia Housing dataset\\n--------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 20640\\n\\n    :Number of Attributes: 8 numeric, predictive attributes and the target\\n\\n    :Attribute Information:\\n        - MedInc        median income in block group\\n        - HouseAge      median house age in block group\\n        - AveRooms      average number of rooms per household\\n        - AveBedrms     average number of bedrooms per household\\n        - Population    block group population\\n        - AveOccup      average number of household members\\n        - Latitude      block group latitude\\n        - Longitude     block group longitude\\n\\n    :Missing Attribute Values: None\\n\\nThis dataset was obtained from the StatLib repository.\\nhttps://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html\\n\\nThe target variable is the median house value for California districts,\\nexpressed in hundreds of thousands of dollars ($100,000).\\n\\nThis dataset was derived from the 1990 U.S. census, using one row per census\\nblock group. A block group is the smallest geographical unit for which the U.S.\\nCensus Bureau publishes sample data (a block group typically has a population\\nof 600 to 3,000 people).\\n\\nA household is a group of people residing within a home. Since the average\\nnumber of rooms and bedrooms in this dataset are provided per household, these\\ncolumns may take surprisingly large values for block groups with few households\\nand many empty houses, such as vacation resorts.\\n\\nIt can be downloaded/loaded using the\\n:func:`sklearn.datasets.fetch_california_housing` function.\\n\\n.. topic:: References\\n\\n    - Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,\\n      Statistics and Probability Letters, 33 (1997) 291-297\\n'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Simple Linear Regressor : X_1 to Y\n",
        "x = dataset['data'][..., 0:1]\n",
        "y = dataset['target']"
      ],
      "metadata": {
        "id": "SjaiqcIQJPoL"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x[..., 0], y)\n",
        "plt.xlabel('MedInc')\n",
        "plt.ylabel('California Housing')\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "kbnS-JAGJaMJ",
        "outputId": "400d30d2-9e95-4c98-87c6-d052908dd5aa"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'California Housing')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "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": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20)"
      ],
      "metadata": {
        "id": "VlW1YYGqMuCs"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x_train.shape, x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MDndQHFUMutl",
        "outputId": "51f581cb-e352-4ebf-fda0-09f53642702a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(16512, 1) (4128, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()"
      ],
      "metadata": {
        "id": "NOgn28RJMw2i"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "TYqxATCQMzNd",
        "outputId": "9a591f99-d8cf-4925-d1f7-f18b24b85295"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "5q73EEhUM2iL"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(x[..., 0], y)\n",
        "plt.plot(x_test, y_pred, color='red')\n",
        "plt.xlabel('')\n",
        "plt.ylabel('')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "is4Le-GAM5Z-",
        "outputId": "64a32a30-a603-4097-b508-4e4909bc83fc"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "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": [
        "# y = wx + b\n",
        "\n",
        "w = model.coef_\n",
        "b = model.intercept_\n",
        "print(w, b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1nOiS-7ONzWv",
        "outputId": "2a77fff8-1fd1-4caa-e452-637b59747d47"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.41856568] 0.4469138700113431\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred[0], w*x_test[0] + b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dMg0mwNareVg",
        "outputId": "123c6d49-e1fc-452c-956a-94be06e0129a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.9856403008514354 [2.9856403]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_s = r2_score(y_test, y_pred)\n",
        "mse_err = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "print(r2_s, mse_err)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FNFQ19aCrh5x",
        "outputId": "157279b9-0d36-4c8f-b912-385a0b6be4be"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.46240511386770433 0.6885474152671339\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multi-linear Regression"
      ],
      "metadata": {
        "id": "7CBVEBE-sW4w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = dataset['data']\n",
        "y = dataset['target']"
      ],
      "metadata": {
        "id": "e7-cm67psSMJ"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h3423cd7scth",
        "outputId": "6b5085da-cee7-4fad-98c7-2fc7b904aa0b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20640, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.20)"
      ],
      "metadata": {
        "id": "-T7voI8Isfs_"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LinearRegression()\n",
        "model.fit(x_train, y_train)\n",
        "y_pred = model.predict(x_test)"
      ],
      "metadata": {
        "id": "RbZ9Ryv_sf4K"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "w = model.coef_\n",
        "b = model.intercept_\n",
        "print(w, b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vceZBRtpsf7t",
        "outputId": "be6e70d8-379a-4a22-ef42-01f7e8979ee0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 4.35431321e-01  9.47748332e-03 -1.08047558e-01  6.50801572e-01\n",
            " -3.68860673e-06 -3.41374500e-03 -4.21888223e-01 -4.33860008e-01] -36.84385488208722\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "r2_s = r2_score(y_test, y_pred)\n",
        "mse_err = mean_squared_error(y_test, y_pred)\n",
        "\n",
        "print(r2_s, mse_err)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7B1YT6Qsuj5",
        "outputId": "61de5cb3-1d04-465b-b31a-c98390d56cf1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6148931797358704 0.5137072385545574\n"
          ]
        }
      ]
    }
  ]
}