House_Price_Prediction.ipynb

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      "source": [
        "Importing dependencies"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import sklearn.datasets\n",
        "from sklearn.model_selection import train_test_split\n",
        "from xgboost import XGBRegressor\n",
        "from sklearn import metrics"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "------------------------------------------------------------------\n",
        "Importing dataset"
      ],
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      "cell_type": "code",
      "source": [
        "house_price_dataset = sklearn.datasets.fetch_california_housing() #instance of dataset\n"
      ],
      "metadata": {
        "id": "F68FDAtfm23Y"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(house_price_dataset)\n",
        "#data - 1st 13 columns\n",
        "#target - 14th column"
      ],
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          "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": [
        "#loading dataset in pandas dataframe\n",
        "house_price_df1 = pd.DataFrame(house_price_dataset.data)"
      ],
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      "source": [
        "house_price_df1.head()"
      ],
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      "execution_count": 8,
      "outputs": [
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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",
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              "summary": "{\n  \"name\": \"house_price_df1\",\n  \"rows\": 20640,\n  \"fields\": [\n    {\n      \"column\": 0,\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\": 1,\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\": 2,\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\": 3,\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\": 4,\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\": 5,\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\": 6,\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\": 7,\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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#so include the  feature names as columns\n",
        "house_price_df = pd.DataFrame(house_price_dataset.data, columns=house_price_dataset.feature_names)"
      ],
      "metadata": {
        "id": "dKgPGzTyoUB4"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ob0r35CdoqIC",
        "outputId": "4bb084ff-07ec-4274-a792-0ae7fd0c3492"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  Longitude\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"
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              "  @keyframes spin {\n",
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              "    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",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "house_price_df",
              "summary": "{\n  \"name\": \"house_price_df\",\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "G9Diaficorz7",
        "outputId": "c0af26b7-d606-43c1-9d72-f2cbc4f999f4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  Longitude\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"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "house_price_df",
              "summary": "{\n  \"name\": \"house_price_df\",\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#add the target columns to df\n",
        "house_price_df['Price'] = house_price_dataset.target"
      ],
      "metadata": {
        "id": "DX2-VUYVowJ8"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LecOITVZo_1C",
        "outputId": "4698ab26-28e2-4806-8e3a-fb9c85b79ae8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  Longitude  Price\n",
              "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88    -122.23  4.526\n",
              "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86    -122.22  3.585\n",
              "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85    -122.24  3.521\n",
              "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85    -122.25  3.413\n",
              "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85    -122.25  3.422"
            ],
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              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "house_price_df",
              "summary": "{\n  \"name\": \"house_price_df\",\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      \"column\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1539561587441387,\n        \"min\": 0.14999,\n        \"max\": 5.00001,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          1.943,\n          3.79,\n          2.301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S9hdw6sapCms",
        "outputId": "88ac3c4f-090a-4faf-f613-1da5694cfeae"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20640, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "house_price_df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "0t1YeMuTpHKt",
        "outputId": "64d8075c-4750-438e-8365-928dac6a6937"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MedInc        0\n",
              "HouseAge      0\n",
              "AveRooms      0\n",
              "AveBedrms     0\n",
              "Population    0\n",
              "AveOccup      0\n",
              "Latitude      0\n",
              "Longitude     0\n",
              "Price         0\n",
              "dtype: int64"
            ],
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MedInc</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HouseAge</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AveRooms</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AveBedrms</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Population</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AveOccup</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latitude</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Longitude</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Price</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
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    {
      "cell_type": "code",
      "source": [
        "house_price_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "nY5JOSc0pOnE",
        "outputId": "f4e62832-dbd5-4046-ac4b-b6e0fa395258"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             MedInc      HouseAge      AveRooms  ...      Latitude     Longitude         Price\n",
              "count  20640.000000  20640.000000  20640.000000  ...  20640.000000  20640.000000  20640.000000\n",
              "mean       3.870671     28.639486      5.429000  ...     35.631861   -119.569704      2.068558\n",
              "std        1.899822     12.585558      2.474173  ...      2.135952      2.003532      1.153956\n",
              "min        0.499900      1.000000      0.846154  ...     32.540000   -124.350000      0.149990\n",
              "25%        2.563400     18.000000      4.440716  ...     33.930000   -121.800000      1.196000\n",
              "50%        3.534800     29.000000      5.229129  ...     34.260000   -118.490000      1.797000\n",
              "75%        4.743250     37.000000      6.052381  ...     37.710000   -118.010000      2.647250\n",
              "max       15.000100     52.000000    141.909091  ...     41.950000   -114.310000      5.000010\n",
              "\n",
              "[8 rows x 9 columns]"
            ],
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              "\n",
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              "<style scoped>\n",
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              "    <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",
              "      <th>Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "      <td>20640.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.870671</td>\n",
              "      <td>28.639486</td>\n",
              "      <td>5.429000</td>\n",
              "      <td>1.096675</td>\n",
              "      <td>1425.476744</td>\n",
              "      <td>3.070655</td>\n",
              "      <td>35.631861</td>\n",
              "      <td>-119.569704</td>\n",
              "      <td>2.068558</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.899822</td>\n",
              "      <td>12.585558</td>\n",
              "      <td>2.474173</td>\n",
              "      <td>0.473911</td>\n",
              "      <td>1132.462122</td>\n",
              "      <td>10.386050</td>\n",
              "      <td>2.135952</td>\n",
              "      <td>2.003532</td>\n",
              "      <td>1.153956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.499900</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.846154</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.692308</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>-124.350000</td>\n",
              "      <td>0.149990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.563400</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>4.440716</td>\n",
              "      <td>1.006079</td>\n",
              "      <td>787.000000</td>\n",
              "      <td>2.429741</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>-121.800000</td>\n",
              "      <td>1.196000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.534800</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>5.229129</td>\n",
              "      <td>1.048780</td>\n",
              "      <td>1166.000000</td>\n",
              "      <td>2.818116</td>\n",
              "      <td>34.260000</td>\n",
              "      <td>-118.490000</td>\n",
              "      <td>1.797000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.743250</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>6.052381</td>\n",
              "      <td>1.099526</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>3.282261</td>\n",
              "      <td>37.710000</td>\n",
              "      <td>-118.010000</td>\n",
              "      <td>2.647250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.000100</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>141.909091</td>\n",
              "      <td>34.066667</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>1243.333333</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>-114.310000</td>\n",
              "      <td>5.000010</td>\n",
              "    </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-92cce55a-d372-4a1f-a936-5b71ce262093')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </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-92cce55a-d372-4a1f-a936-5b71ce262093 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-92cce55a-d372-4a1f-a936-5b71ce262093');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c9643366-4fe1-41f2-9a7e-9ec74d3b101c')\"\n",
              "            title=\"Suggest charts\"\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",
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              "    20% {\n",
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              "      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-c9643366-4fe1-41f2-9a7e-9ec74d3b101c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"house_price_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"MedInc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7295.7214358536385,\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\": \"HouseAge\",\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\": \"AveRooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7289.098256602403,\n        \"min\": 0.8461538461538461,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.428999742190376,\n          5.229128787878788,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"AveBedrms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7295.375141615938,\n        \"min\": 0.3333333333333333,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.096675149606208,\n          1.048780487804878,\n          20640.0\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\": \"AveOccup\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7246.392673190071,\n        \"min\": 0.6923076923076923,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0706551594363742,\n          2.818115654360196,\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\": 2.1359523974571153,\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\": \"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\": \"Price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7296.634369861169,\n        \"min\": 0.14999,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.068558169089147,\n          1.797,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---------------------------------------------------------------------\n",
        "\n",
        "Understanding correlation"
      ],
      "metadata": {
        "id": "w3t3iNR2plm0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation = house_price_df.corr()"
      ],
      "metadata": {
        "id": "mmE3DhRLpZnO"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "constructing heatmap for correlation"
      ],
      "metadata": {
        "id": "6483vL_mpvzZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7,7))\n",
        "sns.heatmap(correlation,cbar=True, square=True, fmt='.1f', annot=True,annot_kws={'size':8}, cmap='Reds')  # cstfaac t-true"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "id": "YPmIu0S2pt0v",
        "outputId": "85c98aa0-3f8f-4d11-9c4f-bd8c5c4f24c2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting data and target\n",
        "X = house_price_df.drop(['Price'],axis = 1)\n",
        "Y = house_price_df['Price']"
      ],
      "metadata": {
        "id": "YXIENirUqR6R"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mnsfw-G5rCeg",
        "outputId": "6d661b21-ce74-4585-b6cb-d528c3edd505"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  Longitude\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",
            "20636  2.5568      18.0  6.114035   1.315789       356.0  3.122807     39.49    -121.21\n",
            "20637  1.7000      17.0  5.205543   1.120092      1007.0  2.325635     39.43    -121.22\n",
            "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",
            "\n",
            "[20640 rows x 8 columns]\n",
            "0        4.526\n",
            "1        3.585\n",
            "2        3.521\n",
            "3        3.413\n",
            "4        3.422\n",
            "         ...  \n",
            "20635    0.781\n",
            "20636    0.771\n",
            "20637    0.923\n",
            "20638    0.847\n",
            "20639    0.894\n",
            "Name: Price, Length: 20640, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.2,random_state=2)"
      ],
      "metadata": {
        "id": "n3DEfTWNrEzo"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape,X_train.shape,X_test.shape)\n",
        "print(Y.shape,Y_train.shape,Y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I1YoGwlarYpl",
        "outputId": "b8badcc6-3ec5-49c5-f742-c94d82cbbb91"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20640, 8) (16512, 8) (4128, 8)\n",
            "(20640,) (16512,) (4128,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------------------------------------------------------------\n",
        "Model training - xgboost regressor ensemble based decision tree model"
      ],
      "metadata": {
        "id": "MD1Q4C_krroo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#loading the model\n",
        "model = XGBRegressor()\n",
        "model.fit(X_train,Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "fcB_CR_Orpcy",
        "outputId": "d67de9ff-f4ce-4201-f758-9fb126ae8e5d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)"
            ],
            "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>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</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\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "-------------------------------------------------------------------\n",
        "Predictions onTest Data"
      ],
      "metadata": {
        "id": "Nrtlj9XouvGV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#evaluation we can;t find accuracy score for regression problems rather we use r square error or mean absolute error\n",
        "#prediction\n",
        "predictions = model.predict(X_train)\n",
        "\n"
      ],
      "metadata": {
        "id": "EBltUosgsOHs"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ftx_DKEmswlr",
        "outputId": "a619fa2b-bac7-42e0-8920-b06547e51326"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.5523039 3.0850039 0.5835302 ... 1.9204227 1.952873  0.6768683]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#R Squared error\n",
        "score1 = metrics.r2_score(Y_train, predictions) #Y_train- actual price, predictions-predicted price\n",
        "\n",
        "# mean absoulute error\n",
        "score2 = metrics.mean_absolute_error(Y_train,predictions)"
      ],
      "metadata": {
        "id": "RovoobAlsz9G"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"R squared error: {score1}\")\n",
        "print(f\"Mean absolute error: {score2}\")  #small the error high is accuracy must not be 5or 10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7sbAO2hYtVj1",
        "outputId": "a414ff06-84cd-461f-a8a0-e0a4d0376435"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error: 0.943650140819218\n",
            "Mean absolute error: 0.1933648700612105\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "--------------------------------------------------------------------\n",
        "Visualizing the actual prices and the predicted prices"
      ],
      "metadata": {
        "id": "6MUbYKC9ufTd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(Y_train,predictions)\n",
        "plt.xlabel(\"Actual Prices\")\n",
        "plt.ylabel(\"Predicted Prices\")\n",
        "plt.title(\"Actual Price v/s Precited Prices\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "jWUFy3TDuxQN",
        "outputId": "728e2892-fa70-46ad-80b2-e3d94bdf16ad"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "--------------------------------------------------------------\n",
        "Predictions on test data"
      ],
      "metadata": {
        "id": "AnxhHv--vBdG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# now testing\n",
        "test_predictions = model.predict(X_test)"
      ],
      "metadata": {
        "id": "xnBQs1jctqVk"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(test_predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DssiNcXZt5ZE",
        "outputId": "67ee6ca5-d101-413f-fe49-55dd5b0929be"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2.8649795  1.790346   0.92074925 ... 1.5385513  0.92647874 2.043316  ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_score1 = metrics.r2_score(Y_test, test_predictions)\n",
        "test_score2 = metrics.mean_absolute_error(Y_test, test_predictions)\n",
        "print(f\"R squared error: {test_score1}\")\n",
        "print(f\"Mean absolute error: {test_score2}\")\n",
        "# not that increase in error"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCZWckclt9X7",
        "outputId": "91e1c901-a5c9-49f3-82f2-c5689609a027"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R squared error: 0.8338000331788725\n",
            "Mean absolute error: 0.3108631800268186\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "aYoXEE0buVf2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}