Linear_regression.ipynb

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        "<a href=\"https://colab.research.google.com/github/Harshika-123/harshika/blob/master/Linear_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "IHICZhcMHtUO"
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      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import fetch_california_housing"
      ],
      "metadata": {
        "id": "NGgR4-l3H0Z-"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
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        "id": "PRDChE1yH3lS"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "source": [
        "df = fetch_california_housing()"
      ],
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        "id": "bUs4zoT-H6VZ"
      },
      "execution_count": 4,
      "outputs": []
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        "df"
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              "{'data': array([[   8.3252    ,   41.        ,    6.98412698, ...,    2.55555556,\n",
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              "           39.43      , -121.22      ],\n",
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              "        [   2.3886    ,   16.        ,    5.25471698, ...,    2.61698113,\n",
              "           39.37      , -121.24      ]]),\n",
              " 'target': array([4.526, 3.585, 3.521, ..., 0.923, 0.847, 0.894]),\n",
              " 'frame': None,\n",
              " 'target_names': ['MedHouseVal'],\n",
              " 'feature_names': ['MedInc',\n",
              "  'HouseAge',\n",
              "  'AveRooms',\n",
              "  'AveBedrms',\n",
              "  'Population',\n",
              "  'AveOccup',\n",
              "  'Latitude',\n",
              "  'Longitude'],\n",
              " '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'}"
            ]
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          "execution_count": 5
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        "dataset = pd.DataFrame(df.data)"
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              "            0     1         2         3       4         5      6       7\n",
              "0      8.3252  41.0  6.984127  1.023810   322.0  2.555556  37.88 -122.23\n",
              "1      8.3014  21.0  6.238137  0.971880  2401.0  2.109842  37.86 -122.22\n",
              "2      7.2574  52.0  8.288136  1.073446   496.0  2.802260  37.85 -122.24\n",
              "3      5.6431  52.0  5.817352  1.073059   558.0  2.547945  37.85 -122.25\n",
              "4      3.8462  52.0  6.281853  1.081081   565.0  2.181467  37.85 -122.25\n",
              "...       ...   ...       ...       ...     ...       ...    ...     ...\n",
              "20635  1.5603  25.0  5.045455  1.133333   845.0  2.560606  39.48 -121.09\n",
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              "20638  1.8672  18.0  5.329513  1.171920   741.0  2.123209  39.43 -121.32\n",
              "20639  2.3886  16.0  5.254717  1.162264  1387.0  2.616981  39.37 -121.24\n",
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            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.columns=df.feature_names"
      ],
      "metadata": {
        "id": "xISNg9HNIKwn"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BBMwMDPvIPfE",
        "outputId": "2839a7d1-4de9-42f9-8706-5c961f7dd930"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   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  "
            ],
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              "type": "dataframe",
              "variable_name": "dataset",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8998217179452688,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 12928,\n        \"samples\": [\n          5.0286,\n          2.0433,\n          6.1228\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.58555761211165,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          35.0,\n          25.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4741731394243187,\n        \"min\": 0.8461538461538461,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 19392,\n        \"samples\": [\n          6.111269614835948,\n          5.912820512820513,\n          5.7924528301886795\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.473910856795466,\n        \"min\": 0.3333333333333333,\n        \"max\": 34.06666666666667,\n        \"num_unique_values\": 14233,\n        \"samples\": [\n          0.9906542056074766,\n          1.112099644128114,\n          1.0398230088495575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1132.462121765341,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3888,\n        \"samples\": [\n          4169.0,\n          636.0,\n          3367.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.386049562213618,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 18841,\n        \"samples\": [\n          2.6939799331103678,\n          3.559375,\n          3.297082228116711\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1359523974571153,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 862,\n        \"samples\": [\n          33.7,\n          34.41,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0035317235025882,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 844,\n        \"samples\": [\n          -118.63,\n          -119.86,\n          -121.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#independent Feature and dependent feature\n",
        "X=dataset\n",
        "y=df.target"
      ],
      "metadata": {
        "id": "BMbWlExQISdg"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train anf test split\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "HrHbRareIWl-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.30, random_state=42)"
      ],
      "metadata": {
        "id": "xqMAtWNQI3Cq"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "TtxnHnVvI612",
        "outputId": "6bb3f7f3-1ea0-4bfa-9ca9-12774158129c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
              "7061   4.1312      35.0  5.882353   0.975490      1218.0  2.985294     33.93   \n",
              "14689  2.8631      20.0  4.401210   1.076613       999.0  2.014113     32.79   \n",
              "17323  4.2026      24.0  5.617544   0.989474       731.0  2.564912     34.59   \n",
              "10056  3.1094      14.0  5.869565   1.094203       302.0  2.188406     39.26   \n",
              "15750  3.3068      52.0  4.801205   1.066265      1526.0  2.298193     37.77   \n",
              "...       ...       ...       ...        ...         ...       ...       ...   \n",
              "11284  6.3700      35.0  6.129032   0.926267       658.0  3.032258     33.78   \n",
              "11964  3.0500      33.0  6.868597   1.269488      1753.0  3.904232     34.02   \n",
              "5390   2.9344      36.0  3.986717   1.079696      1756.0  3.332068     34.03   \n",
              "860    5.7192      15.0  6.395349   1.067979      1777.0  3.178891     37.58   \n",
              "15795  2.5755      52.0  3.402576   1.058776      2619.0  2.108696     37.77   \n",
              "\n",
              "       Longitude  \n",
              "7061     -118.02  \n",
              "14689    -117.09  \n",
              "17323    -120.14  \n",
              "10056    -121.00  \n",
              "15750    -122.45  \n",
              "...          ...  \n",
              "11284    -117.96  \n",
              "11964    -117.43  \n",
              "5390     -118.38  \n",
              "860      -121.96  \n",
              "15795    -122.42  \n",
              "\n",
              "[14448 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-07c46448-d310-47e0-80c4-6e00b1a199ff\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MedInc</th>\n",
              "      <th>HouseAge</th>\n",
              "      <th>AveRooms</th>\n",
              "      <th>AveBedrms</th>\n",
              "      <th>Population</th>\n",
              "      <th>AveOccup</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>7061</th>\n",
              "      <td>4.1312</td>\n",
              "      <td>35.0</td>\n",
              "      <td>5.882353</td>\n",
              "      <td>0.975490</td>\n",
              "      <td>1218.0</td>\n",
              "      <td>2.985294</td>\n",
              "      <td>33.93</td>\n",
              "      <td>-118.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14689</th>\n",
              "      <td>2.8631</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4.401210</td>\n",
              "      <td>1.076613</td>\n",
              "      <td>999.0</td>\n",
              "      <td>2.014113</td>\n",
              "      <td>32.79</td>\n",
              "      <td>-117.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17323</th>\n",
              "      <td>4.2026</td>\n",
              "      <td>24.0</td>\n",
              "      <td>5.617544</td>\n",
              "      <td>0.989474</td>\n",
              "      <td>731.0</td>\n",
              "      <td>2.564912</td>\n",
              "      <td>34.59</td>\n",
              "      <td>-120.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10056</th>\n",
              "      <td>3.1094</td>\n",
              "      <td>14.0</td>\n",
              "      <td>5.869565</td>\n",
              "      <td>1.094203</td>\n",
              "      <td>302.0</td>\n",
              "      <td>2.188406</td>\n",
              "      <td>39.26</td>\n",
              "      <td>-121.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15750</th>\n",
              "      <td>3.3068</td>\n",
              "      <td>52.0</td>\n",
              "      <td>4.801205</td>\n",
              "      <td>1.066265</td>\n",
              "      <td>1526.0</td>\n",
              "      <td>2.298193</td>\n",
              "      <td>37.77</td>\n",
              "      <td>-122.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11284</th>\n",
              "      <td>6.3700</td>\n",
              "      <td>35.0</td>\n",
              "      <td>6.129032</td>\n",
              "      <td>0.926267</td>\n",
              "      <td>658.0</td>\n",
              "      <td>3.032258</td>\n",
              "      <td>33.78</td>\n",
              "      <td>-117.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11964</th>\n",
              "      <td>3.0500</td>\n",
              "      <td>33.0</td>\n",
              "      <td>6.868597</td>\n",
              "      <td>1.269488</td>\n",
              "      <td>1753.0</td>\n",
              "      <td>3.904232</td>\n",
              "      <td>34.02</td>\n",
              "      <td>-117.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5390</th>\n",
              "      <td>2.9344</td>\n",
              "      <td>36.0</td>\n",
              "      <td>3.986717</td>\n",
              "      <td>1.079696</td>\n",
              "      <td>1756.0</td>\n",
              "      <td>3.332068</td>\n",
              "      <td>34.03</td>\n",
              "      <td>-118.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>5.7192</td>\n",
              "      <td>15.0</td>\n",
              "      <td>6.395349</td>\n",
              "      <td>1.067979</td>\n",
              "      <td>1777.0</td>\n",
              "      <td>3.178891</td>\n",
              "      <td>37.58</td>\n",
              "      <td>-121.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15795</th>\n",
              "      <td>2.5755</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3.402576</td>\n",
              "      <td>1.058776</td>\n",
              "      <td>2619.0</td>\n",
              "      <td>2.108696</td>\n",
              "      <td>37.77</td>\n",
              "      <td>-122.42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14448 rows × 8 columns</p>\n",
              "</div>\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "  @keyframes spin {\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-3cf21144-bf88-48ad-ab36-50ef271517b0 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
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              "\n",
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              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8005f88d-4dfb-45e6-ab6c-f24c8b239fd1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_train');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 14448,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.904908402172071,\n        \"min\": 0.4999,\n        \"max\": 15.0001,\n        \"num_unique_values\": 9851,\n        \"samples\": [\n          5.5519,\n          3.6336,\n          2.9386\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HouseAge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.6136336116366,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          26.0,\n          17.0,\n          50.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.4535692914090212,\n        \"min\": 0.8888888888888888,\n        \"max\": 141.9090909090909,\n        \"num_unique_values\": 13802,\n        \"samples\": [\n          5.909937888198757,\n          4.741176470588235,\n          3.838763575605681\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.44749798279181857,\n        \"min\": 0.3333333333333333,\n        \"max\": 25.636363636363637,\n        \"num_unique_values\": 10758,\n        \"samples\": [\n          1.4240062353858145,\n          1.106019766397125,\n          1.1244725738396624\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1140.225189746138,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3495,\n        \"samples\": [\n          133.0,\n          2497.0,\n          1920.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.373635574417113,\n        \"min\": 0.6923076923076923,\n        \"max\": 1243.3333333333333,\n        \"num_unique_values\": 13479,\n        \"samples\": [\n          4.219354838709678,\n          3.490806223479491,\n          2.9565217391304346\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1357421829670686,\n        \"min\": 32.55,\n        \"max\": 41.95,\n        \"num_unique_values\": 826,\n        \"samples\": [\n          34.36,\n          37.88,\n          37.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0029302123758073,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 815,\n        \"samples\": [\n          -117.18,\n          -119.39,\n          -121.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#standardizig the data set\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X_train =scaler.fit_transform(X_train)"
      ],
      "metadata": {
        "id": "HbYDQLxpI-tP"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test=scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "mCFUfSfbJEuD"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U4fvcUBbJIMZ",
        "outputId": "f9c94075-d8a4-454f-df4d-82ca71561327"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.13350629,  0.50935748,  0.18106017, ..., -0.01082519,\n",
              "        -0.80568191,  0.78093406],\n",
              "       [-0.53221805, -0.67987313, -0.42262953, ..., -0.08931585,\n",
              "        -1.33947268,  1.24526986],\n",
              "       [ 0.1709897 , -0.36274497,  0.07312833, ..., -0.04480037,\n",
              "        -0.49664515, -0.27755183],\n",
              "       ...,\n",
              "       [-0.49478713,  0.58863952, -0.59156984, ...,  0.01720102,\n",
              "        -0.75885816,  0.60119118],\n",
              "       [ 0.96717102, -1.07628333,  0.39014889, ...,  0.00482125,\n",
              "         0.90338501, -1.18625198],\n",
              "       [-0.68320166,  1.85715216, -0.82965604, ..., -0.0816717 ,\n",
              "         0.99235014, -1.41592345]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#to inverse\n",
        "scaler.inverse_transform(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-spp9fJJLI6",
        "outputId": "d7ab5abc-31cb-4d07-ffa9-65feaafcd7bc"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[   4.1312    ,   35.        ,    5.88235294, ...,    2.98529412,\n",
              "          33.93      , -118.02      ],\n",
              "       [   2.8631    ,   20.        ,    4.40120968, ...,    2.0141129 ,\n",
              "          32.79      , -117.09      ],\n",
              "       [   4.2026    ,   24.        ,    5.61754386, ...,    2.56491228,\n",
              "          34.59      , -120.14      ],\n",
              "       ...,\n",
              "       [   2.9344    ,   36.        ,    3.98671727, ...,    3.33206831,\n",
              "          34.03      , -118.38      ],\n",
              "       [   5.7192    ,   15.        ,    6.39534884, ...,    3.17889088,\n",
              "          37.58      , -121.96      ],\n",
              "       [   2.5755    ,   52.        ,    3.40257649, ...,    2.10869565,\n",
              "          37.77      , -122.42      ]])"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import linearRegression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "#cross validation\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "NekQMSubJO5O"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "regression = LinearRegression()\n",
        "regression.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "j-gkCtxHJTIA",
        "outputId": "90dd110e-34ae-4b6c-96e5-e7308fad0ace"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse = cross_val_score(regression,X_train,y_train,scoring='neg_mean_squared_error',cv=5)"
      ],
      "metadata": {
        "id": "35X0HE7_JW4F"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#final mean\n",
        "np.mean(mse)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6BBgn4H5JbOX",
        "outputId": "4ac3814f-d2c3-426b-bd65-b9fe1eff2b3d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.5268253746355748"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#predicton of test data\n",
        "reg_pred=regression.predict(X_test)"
      ],
      "metadata": {
        "id": "8lheHyAHJenD"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reg_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YiJWIJ-hJhoD",
        "outputId": "379775a2-82ca-4cc7-c994-72e4d092a3df"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.72604907, 1.76743383, 2.71092161, ..., 2.07465531, 1.57371395,\n",
              "       1.82744133])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.displot(reg_pred-y_test,kind='kde')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "-KFkZwNZJlMV",
        "outputId": "04979d4d-2c43-4d79-fb12-1abc9abefd64"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7dcb2bc726b0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "1Ra_Gm27Jpqa"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score = r2_score(reg_pred,y_test)"
      ],
      "metadata": {
        "id": "TWPZcZ5jJtgY"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YB3PzNzzJw-F",
        "outputId": "3d78b87c-9f0c-4f5c-ef78-50eed6334952"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3451339380943961"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zZ-6ZsqlJ4a1"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}