LinearRegression_CaseStudy_LunarTech.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy.stats as stats\n",
        "import statsmodels.api as sm\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split"
      ]
    },
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      "cell_type": "markdown",
      "source": [
        "# Loading California Housing Data\n",
        "\n"
      ],
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      "source": [
        "data = pd.read_csv(\"housing.csv\")"
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      "outputs": []
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          "data": {
            "text/plain": [
              "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
              "       'total_bedrooms', 'population', 'households', 'median_income',\n",
              "       'median_house_value', 'ocean_proximity'],\n",
              "      dtype='object')"
            ]
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        "data.head(10)"
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              "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "0    -122.23     37.88                41.0        880.0           129.0   \n",
              "1    -122.22     37.86                21.0       7099.0          1106.0   \n",
              "2    -122.24     37.85                52.0       1467.0           190.0   \n",
              "3    -122.25     37.85                52.0       1274.0           235.0   \n",
              "4    -122.25     37.85                52.0       1627.0           280.0   \n",
              "5    -122.25     37.85                52.0        919.0           213.0   \n",
              "6    -122.25     37.84                52.0       2535.0           489.0   \n",
              "7    -122.25     37.84                52.0       3104.0           687.0   \n",
              "8    -122.26     37.84                42.0       2555.0           665.0   \n",
              "9    -122.25     37.84                52.0       3549.0           707.0   \n",
              "\n",
              "   population  households  median_income  median_house_value ocean_proximity  \n",
              "0       322.0       126.0         8.3252            452600.0        NEAR BAY  \n",
              "1      2401.0      1138.0         8.3014            358500.0        NEAR BAY  \n",
              "2       496.0       177.0         7.2574            352100.0        NEAR BAY  \n",
              "3       558.0       219.0         5.6431            341300.0        NEAR BAY  \n",
              "4       565.0       259.0         3.8462            342200.0        NEAR BAY  \n",
              "5       413.0       193.0         4.0368            269700.0        NEAR BAY  \n",
              "6      1094.0       514.0         3.6591            299200.0        NEAR BAY  \n",
              "7      1157.0       647.0         3.1200            241400.0        NEAR BAY  \n",
              "8      1206.0       595.0         2.0804            226700.0        NEAR BAY  \n",
              "9      1551.0       714.0         3.6912            261100.0        NEAR BAY  "
            ],
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              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
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              "      <th>0</th>\n",
              "      <td>-122.23</td>\n",
              "      <td>37.88</td>\n",
              "      <td>41.0</td>\n",
              "      <td>880.0</td>\n",
              "      <td>129.0</td>\n",
              "      <td>322.0</td>\n",
              "      <td>126.0</td>\n",
              "      <td>8.3252</td>\n",
              "      <td>452600.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-122.22</td>\n",
              "      <td>37.86</td>\n",
              "      <td>21.0</td>\n",
              "      <td>7099.0</td>\n",
              "      <td>1106.0</td>\n",
              "      <td>2401.0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>8.3014</td>\n",
              "      <td>358500.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-122.24</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1467.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>496.0</td>\n",
              "      <td>177.0</td>\n",
              "      <td>7.2574</td>\n",
              "      <td>352100.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1274.0</td>\n",
              "      <td>235.0</td>\n",
              "      <td>558.0</td>\n",
              "      <td>219.0</td>\n",
              "      <td>5.6431</td>\n",
              "      <td>341300.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1627.0</td>\n",
              "      <td>280.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>259.0</td>\n",
              "      <td>3.8462</td>\n",
              "      <td>342200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>919.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>413.0</td>\n",
              "      <td>193.0</td>\n",
              "      <td>4.0368</td>\n",
              "      <td>269700.0</td>\n",
              "      <td>NEAR BAY</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.84</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2535.0</td>\n",
              "      <td>489.0</td>\n",
              "      <td>1094.0</td>\n",
              "      <td>514.0</td>\n",
              "      <td>3.6591</td>\n",
              "      <td>299200.0</td>\n",
              "      <td>NEAR BAY</td>\n",
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              "      <th>8</th>\n",
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              "      <td>2555.0</td>\n",
              "      <td>665.0</td>\n",
              "      <td>1206.0</td>\n",
              "      <td>595.0</td>\n",
              "      <td>2.0804</td>\n",
              "      <td>226700.0</td>\n",
              "      <td>NEAR BAY</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
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              "      <td>714.0</td>\n",
              "      <td>3.6912</td>\n",
              "      <td>261100.0</td>\n",
              "      <td>NEAR BAY</td>\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 20640,\n  \"fields\": [\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\": \"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\": \"housing_median_age\",\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\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2181.615251582795,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 5926,\n        \"samples\": [\n          699.0,\n          1544.0,\n          3966.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 421.3850700740323,\n        \"min\": 1.0,\n        \"max\": 6445.0,\n        \"num_unique_values\": 1923,\n        \"samples\": [\n          1538.0,\n          1298.0,\n          1578.0\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\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 382.32975283161073,\n        \"min\": 1.0,\n        \"max\": 6082.0,\n        \"num_unique_values\": 1815,\n        \"samples\": [\n          21.0,\n          750.0,\n          1447.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\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\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115395.61587441387,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 3842,\n        \"samples\": [\n          194300.0,\n          379000.0,\n          230100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"<1H OCEAN\",\n          \"ISLAND\",\n          \"INLAND\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rz75TtHIbOq9",
        "outputId": "8d167a7f-2ac6-44ef-bf69-27764b57e038"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20640 entries, 0 to 20639\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   longitude           20640 non-null  float64\n",
            " 1   latitude            20640 non-null  float64\n",
            " 2   housing_median_age  20640 non-null  float64\n",
            " 3   total_rooms         20640 non-null  float64\n",
            " 4   total_bedrooms      20433 non-null  float64\n",
            " 5   population          20640 non-null  float64\n",
            " 6   households          20640 non-null  float64\n",
            " 7   median_income       20640 non-null  float64\n",
            " 8   median_house_value  20640 non-null  float64\n",
            " 9   ocean_proximity     20640 non-null  object \n",
            "dtypes: float64(9), object(1)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"ocean_proximity\"].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ycYE6eX6G-tJ",
        "outputId": "19e99277-4902-40af-e0d9-0ae479873021"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['NEAR BAY', '<1H OCEAN', 'INLAND', 'NEAR OCEAN', 'ISLAND'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Missing Data Analysis"
      ],
      "metadata": {
        "id": "ECqkQVsdZzfE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "missing_values = data.isnull().sum()\n",
        "\n",
        "# Calculate the percentage of missing data in each column\n",
        "missing_percentage = (missing_values / len(data)) * 100\n",
        "\n",
        "# Display the missing data statistics\n",
        "print(\"Missing Values in Each Column:\\n\", missing_values)\n",
        "print(\"\\nPercentage of Missing Data:\\n\", missing_percentage)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WotldcjZ55_",
        "outputId": "4d712b52-45cc-49da-b066-319c54b412bc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing Values in Each Column:\n",
            " longitude               0\n",
            "latitude                0\n",
            "housing_median_age      0\n",
            "total_rooms             0\n",
            "total_bedrooms        207\n",
            "population              0\n",
            "households              0\n",
            "median_income           0\n",
            "median_house_value      0\n",
            "ocean_proximity         0\n",
            "dtype: int64\n",
            "\n",
            "Percentage of Missing Data:\n",
            " longitude             0.000000\n",
            "latitude              0.000000\n",
            "housing_median_age    0.000000\n",
            "total_rooms           0.000000\n",
            "total_bedrooms        1.002907\n",
            "population            0.000000\n",
            "households            0.000000\n",
            "median_income         0.000000\n",
            "median_house_value    0.000000\n",
            "ocean_proximity       0.000000\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove rows with missing values\n",
        "data_cleaned = data.dropna()\n",
        "\n",
        "# Verify that missing values have been removed\n",
        "print(\"\\nMissing values in each column after removal:\")\n",
        "print(data_cleaned.isnull().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7pJCcvQaZ6-",
        "outputId": "16c20a9e-3314-4718-8b5c-910a6da415d1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values in each column after removal:\n",
            "longitude             0\n",
            "latitude              0\n",
            "housing_median_age    0\n",
            "total_rooms           0\n",
            "total_bedrooms        0\n",
            "population            0\n",
            "households            0\n",
            "median_income         0\n",
            "median_house_value    0\n",
            "ocean_proximity       0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Exploration and Visualization"
      ],
      "metadata": {
        "id": "7wnfKP0Qc0SI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "G_gIhfx4dC_i",
        "outputId": "532fd18a-b534-4376-c4bc-5bb6860ba350"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          longitude      latitude  housing_median_age   total_rooms  \\\n",
              "count  20640.000000  20640.000000        20640.000000  20640.000000   \n",
              "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
              "std        2.003532      2.135952           12.585558   2181.615252   \n",
              "min     -124.350000     32.540000            1.000000      2.000000   \n",
              "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
              "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
              "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
              "max     -114.310000     41.950000           52.000000  39320.000000   \n",
              "\n",
              "       total_bedrooms    population    households  median_income  \\\n",
              "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
              "mean       537.870553   1425.476744    499.539680       3.870671   \n",
              "std        421.385070   1132.462122    382.329753       1.899822   \n",
              "min          1.000000      3.000000      1.000000       0.499900   \n",
              "25%        296.000000    787.000000    280.000000       2.563400   \n",
              "50%        435.000000   1166.000000    409.000000       3.534800   \n",
              "75%        647.000000   1725.000000    605.000000       4.743250   \n",
              "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
              "\n",
              "       median_house_value  \n",
              "count        20640.000000  \n",
              "mean        206855.816909  \n",
              "std         115395.615874  \n",
              "min          14999.000000  \n",
              "25%         119600.000000  \n",
              "50%         179700.000000  \n",
              "75%         264725.000000  \n",
              "max         500001.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15b781da-1bd2-4bc5-a3d2-29abe4e79dd8\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</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>20433.000000</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>-119.569704</td>\n",
              "      <td>35.631861</td>\n",
              "      <td>28.639486</td>\n",
              "      <td>2635.763081</td>\n",
              "      <td>537.870553</td>\n",
              "      <td>1425.476744</td>\n",
              "      <td>499.539680</td>\n",
              "      <td>3.870671</td>\n",
              "      <td>206855.816909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.003532</td>\n",
              "      <td>2.135952</td>\n",
              "      <td>12.585558</td>\n",
              "      <td>2181.615252</td>\n",
              "      <td>421.385070</td>\n",
              "      <td>1132.462122</td>\n",
              "      <td>382.329753</td>\n",
              "      <td>1.899822</td>\n",
              "      <td>115395.615874</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.350000</td>\n",
              "      <td>32.540000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>14999.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.800000</td>\n",
              "      <td>33.930000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1447.750000</td>\n",
              "      <td>296.000000</td>\n",
              "      <td>787.000000</td>\n",
              "      <td>280.000000</td>\n",
              "      <td>2.563400</td>\n",
              "      <td>119600.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.490000</td>\n",
              "      <td>34.260000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2127.000000</td>\n",
              "      <td>435.000000</td>\n",
              "      <td>1166.000000</td>\n",
              "      <td>409.000000</td>\n",
              "      <td>3.534800</td>\n",
              "      <td>179700.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.010000</td>\n",
              "      <td>37.710000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3148.000000</td>\n",
              "      <td>647.000000</td>\n",
              "      <td>1725.000000</td>\n",
              "      <td>605.000000</td>\n",
              "      <td>4.743250</td>\n",
              "      <td>264725.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.310000</td>\n",
              "      <td>41.950000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>39320.000000</td>\n",
              "      <td>6445.000000</td>\n",
              "      <td>35682.000000</td>\n",
              "      <td>6082.000000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f5169816-1740-4f5b-93eb-c84aa5c80925 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\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7333.554670164394,\n        \"min\": -124.35,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -119.56970445736432,\n          -118.49,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7286.333552413666,\n        \"min\": 2.1359523974571153,\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\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7288.35672120143,\n        \"min\": 1.0,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.639486434108527,\n          29.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13944.990983306392,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2635.7630813953488,\n          2127.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_bedrooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7106.427031043753,\n        \"min\": 1.0,\n        \"max\": 20433.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          537.8705525375618,\n          435.0,\n          20433.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13192.258841737372,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1425.4767441860465,\n          1166.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7167.532601135343,\n        \"min\": 1.0,\n        \"max\": 20640.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          499.5396802325581,\n          409.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7295.7214358536385,\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\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 156160.28379826446,\n        \"min\": 14999.0,\n        \"max\": 500001.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          206855.81690891474,\n          179700.0,\n          20640.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(style=\"whitegrid\")\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(data_cleaned['median_house_value'], color='forestgreen', kde=True)\n",
        "plt.title('Distribution of Median House Values')\n",
        "plt.xlabel('Median House Value')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "1HqOHTSncxwL",
        "outputId": "b905e5d5-bf09-40ff-910b-fe1f9ff66470"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Using InterQualtile Range to Remove Outliers\n",
        "\n",
        "> Indented block\n",
        "\n"
      ],
      "metadata": {
        "id": "Y9hAjP6mfayq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming 'data' is your DataFrame and 'median_house_value' is the column of interest\n",
        "Q1 = data_cleaned['median_house_value'].quantile(0.25)\n",
        "print(Q1)\n",
        "Q3 = data_cleaned['median_house_value'].quantile(0.75)\n",
        "print(Q3)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "\n",
        "# Define the bounds for the outliers\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "# Remove outliers\n",
        "data_no_outliers_1 = data_cleaned[(data_cleaned['median_house_value'] >= lower_bound) & (data_cleaned['median_house_value'] <= upper_bound)]\n",
        "\n",
        "# Check the shape of the data before and after removal of outliers\n",
        "print(\"Original data shape:\", data_cleaned.shape)\n",
        "print(\"New data shape without outliers:\", data_no_outliers_1.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ovtiFOb5fq0O",
        "outputId": "e1cd5551-3e88-406f-c17a-5da168562233"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "119500.0\n",
            "264700.0\n",
            "Original data shape: (20433, 10)\n",
            "New data shape without outliers: (19369, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BoxPlot for Outlier Detection"
      ],
      "metadata": {
        "id": "dNUVut-MdVT7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Outliers in Median Income\n"
      ],
      "metadata": {
        "id": "APG6KKA4hDBy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x=data_no_outliers_1['median_income'], color='purple')\n",
        "plt.title('Outlier Analysis in Median Income')\n",
        "plt.xlabel('Median Income')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "TJQ3_XENdVDA",
        "outputId": "2ac84554-56c5-4222-fbc1-acff6cc9b41a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate Q1 and Q3\n",
        "Q1 = data_no_outliers_1['median_income'].quantile(0.25)\n",
        "Q3 = data_no_outliers_1['median_income'].quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "# Define the bounds for the outliers\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "# Remove outliers\n",
        "data_no_outliers_2 = data_no_outliers_1[(data_no_outliers_1['median_income'] >= lower_bound) & (data_no_outliers_1['median_income'] <= upper_bound)]\n",
        "\n",
        "# Check the shape of the data before and after the removal of outliers\n",
        "print(\"Original data shape:\", data_no_outliers_1.shape)\n",
        "print(\"Data shape without outliers:\", data_no_outliers_2.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5GABcoP9fZYU",
        "outputId": "b73b0d68-1d22-4764-82fe-10fc412ab888"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original data shape: (19369, 10)\n",
            "Data shape without outliers: (19026, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data_no_outliers_2"
      ],
      "metadata": {
        "id": "qN68jVXplYXn"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation HeatMap"
      ],
      "metadata": {
        "id": "gLlgJUKeiHsX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(data.corr(), annot=True, cmap='Greens')\n",
        "plt.title('Correlation Heatmap of Housing Data')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 907
        },
        "id": "lpxjwEx3fXGw",
        "outputId": "5e5fbec1-5a62-46e7-de56-8dfaf4cec683"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-eb40ac2f2192>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(data.corr(), annot=True, cmap='Greens')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(\"total_bedrooms\", axis = 1)\n",
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uv25R5k0TsNV",
        "outputId": "13ee09fb-f561-47fa-93fe-51b55ed414a9"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
              "       'population', 'households', 'median_income', 'median_house_value',\n",
              "       'ocean_proximity'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Unique value count for categorical data\n",
        "for column in ['ocean_proximity']:  # Add other categorical columns if any\n",
        "    print(f\"Unique values in {column}:\", data[column].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DSjr9BAOb2Bw",
        "outputId": "dbbf7cd3-fe45-4238-8352-7ca291e82441"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in ocean_proximity: ['NEAR BAY' '<1H OCEAN' 'INLAND' 'NEAR OCEAN' 'ISLAND']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# String Data Categorization to Dummy Variables"
      ],
      "metadata": {
        "id": "3R5MhzxZq27x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ocean_proximity_dummies = pd.get_dummies(data['ocean_proximity'], prefix='ocean_proximity')\n",
        "data = pd.concat([data.drop(\"ocean_proximity\", axis =1), ocean_proximity_dummies], axis=1)\n",
        "ocean_proximity_dummies\n"
      ],
      "metadata": {
        "id": "-gNSi7Mzq0Fe",
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          "height": 423
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        "outputId": "a3d50a38-08c8-4871-9d0f-90fb8aef3f98"
      },
      "execution_count": 29,
      "outputs": [
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ocean_proximity_dummies",
              "summary": "{\n  \"name\": \"ocean_proximity_dummies\",\n  \"rows\": 19026,\n  \"fields\": [\n    {\n      \"column\": \"ocean_proximity_<1H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_ISLAND\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "yUxdZO01lkS6",
        "outputId": "8beda696-973b-400d-8016-5d322e4459a5"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
              "       'population', 'households', 'median_income', 'median_house_value',\n",
              "       'ocean_proximity_<1H OCEAN', 'ocean_proximity_INLAND',\n",
              "       'ocean_proximity_ISLAND', 'ocean_proximity_NEAR BAY',\n",
              "       'ocean_proximity_NEAR OCEAN'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.drop(\"ocean_proximity_ISLAND\", axis = 1)\n",
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KWxtczi2WMvs",
        "outputId": "7c7f38c8-23a7-4873-9390-48bbabcd655b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
              "       'population', 'households', 'median_income', 'median_house_value',\n",
              "       'ocean_proximity_<1H OCEAN', 'ocean_proximity_INLAND',\n",
              "       'ocean_proximity_NEAR BAY', 'ocean_proximity_NEAR OCEAN'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "WFNOW9qCraJ5",
        "outputId": "8dbfd602-1be2-41ea-b014-0659f93c7284"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    longitude  latitude  housing_median_age  total_rooms  population  \\\n",
              "2     -122.24     37.85                52.0       1467.0       496.0   \n",
              "3     -122.25     37.85                52.0       1274.0       558.0   \n",
              "4     -122.25     37.85                52.0       1627.0       565.0   \n",
              "5     -122.25     37.85                52.0        919.0       413.0   \n",
              "6     -122.25     37.84                52.0       2535.0      1094.0   \n",
              "7     -122.25     37.84                52.0       3104.0      1157.0   \n",
              "8     -122.26     37.84                42.0       2555.0      1206.0   \n",
              "9     -122.25     37.84                52.0       3549.0      1551.0   \n",
              "10    -122.26     37.85                52.0       2202.0       910.0   \n",
              "11    -122.26     37.85                52.0       3503.0      1504.0   \n",
              "\n",
              "    households  median_income  median_house_value  ocean_proximity_<1H OCEAN  \\\n",
              "2        177.0         7.2574            352100.0                          0   \n",
              "3        219.0         5.6431            341300.0                          0   \n",
              "4        259.0         3.8462            342200.0                          0   \n",
              "5        193.0         4.0368            269700.0                          0   \n",
              "6        514.0         3.6591            299200.0                          0   \n",
              "7        647.0         3.1200            241400.0                          0   \n",
              "8        595.0         2.0804            226700.0                          0   \n",
              "9        714.0         3.6912            261100.0                          0   \n",
              "10       402.0         3.2031            281500.0                          0   \n",
              "11       734.0         3.2705            241800.0                          0   \n",
              "\n",
              "    ocean_proximity_INLAND  ocean_proximity_NEAR BAY  \\\n",
              "2                        0                         1   \n",
              "3                        0                         1   \n",
              "4                        0                         1   \n",
              "5                        0                         1   \n",
              "6                        0                         1   \n",
              "7                        0                         1   \n",
              "8                        0                         1   \n",
              "9                        0                         1   \n",
              "10                       0                         1   \n",
              "11                       0                         1   \n",
              "\n",
              "    ocean_proximity_NEAR OCEAN  \n",
              "2                            0  \n",
              "3                            0  \n",
              "4                            0  \n",
              "5                            0  \n",
              "6                            0  \n",
              "7                            0  \n",
              "8                            0  \n",
              "9                            0  \n",
              "10                           0  \n",
              "11                           0  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
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              "      <td>37.85</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1627.0</td>\n",
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              "      <th>5</th>\n",
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              "      <th>6</th>\n",
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              "      <td>37.84</td>\n",
              "      <td>52.0</td>\n",
              "      <td>2535.0</td>\n",
              "      <td>1094.0</td>\n",
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              "      <td>3.6591</td>\n",
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              "      <th>7</th>\n",
              "      <td>-122.25</td>\n",
              "      <td>37.84</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3104.0</td>\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 19026,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0054401763687055,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 842,\n        \"samples\": [\n          -118.64,\n          -119.9,\n          -121.15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1547025544732805,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 861,\n        \"samples\": [\n          33.53,\n          34.52,\n          38.24\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.470392429744395,\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\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2161.7739469111116,\n        \"min\": 2.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 5700,\n        \"samples\": [\n          1450.0,\n          6462.0,\n          4629.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1141.842312031855,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3836,\n        \"samples\": [\n          472.0,\n          1326.0,\n          2469.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 382.85341761252994,\n        \"min\": 2.0,\n        \"max\": 6082.0,\n        \"num_unique_values\": 1779,\n        \"samples\": [\n          1005.0,\n          724.0,\n          42.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4149545294642145,\n        \"min\": 0.4999,\n        \"max\": 7.6473,\n        \"num_unique_values\": 11681,\n        \"samples\": [\n          3.4551,\n          3.1884,\n          4.6029\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 92355.5801777101,\n        \"min\": 14999.0,\n        \"max\": 482200.0,\n        \"num_unique_values\": 3667,\n        \"samples\": [\n          439800.0,\n          350600.0,\n          472800.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_<1H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting the Data into Train/Test"
      ],
      "metadata": {
        "id": "qLpRt850llY6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define your features (independent variables) and target (dependent variable)\n",
        "features = ['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
        "       'population', 'households', 'median_income',\n",
        "       'ocean_proximity_<1H OCEAN', 'ocean_proximity_INLAND',\n",
        "       'ocean_proximity_NEAR BAY', 'ocean_proximity_NEAR OCEAN']\n",
        "target = [\"median_house_value\"]\n",
        "\n",
        "X = data[features]\n",
        "y = data[target]\n",
        "\n",
        "\n",
        "# Split the data into a training set and a testing set\n",
        "# test_size specifies the proportion of the data to be included in the test split\n",
        "# random_state ensures reproducibility of your split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1111)\n",
        "\n",
        "# Check the size of the splits\n",
        "print(f'Training set size: {X_train.shape[0]} samples')\n",
        "print(f'Test set size: {X_test.shape[0]} samples')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcjvoe8vlrqQ",
        "outputId": "78d76c7b-27b5-44b7-b964-416725a197f5"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: 15220 samples\n",
            "Test set size: 3806 samples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training"
      ],
      "metadata": {
        "id": "V4hThI-RtcFK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "BaSb2bl4YIod",
        "outputId": "edb5c9c8-5573-45bd-e0ba-89d040ca2159"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  population  \\\n",
              "15578    -116.90     33.03                11.0       3213.0      1975.0   \n",
              "1656     -121.93     37.89                13.0       2085.0       852.0   \n",
              "9190     -118.35     34.32                52.0        102.0        54.0   \n",
              "11839    -120.95     39.93                26.0       2023.0       922.0   \n",
              "12267    -116.91     33.75                13.0      10886.0      4266.0   \n",
              "...          ...       ...                 ...          ...         ...   \n",
              "15355    -117.32     33.25                 7.0       8206.0      4399.0   \n",
              "9608     -120.51     37.29                20.0       4927.0      4205.0   \n",
              "12606    -121.49     38.50                32.0       2364.0      1331.0   \n",
              "2907     -119.03     35.37                52.0       1503.0       554.0   \n",
              "18139    -122.01     37.34                31.0       3080.0      1493.0   \n",
              "\n",
              "       households  median_income  ocean_proximity_<1H OCEAN  \\\n",
              "15578       579.0         3.4750                          1   \n",
              "1656        264.0         7.3445                          0   \n",
              "9190         32.0         1.9875                          1   \n",
              "11839       365.0         2.8125                          0   \n",
              "12267      1955.0         2.3169                          0   \n",
              "...           ...            ...                        ...   \n",
              "15355      1423.0         3.6301                          1   \n",
              "9608       1009.0         1.7679                          0   \n",
              "12606       449.0         3.3190                          0   \n",
              "2907        277.0         1.6786                          0   \n",
              "18139       582.0         6.3052                          1   \n",
              "\n",
              "       ocean_proximity_INLAND  ocean_proximity_NEAR BAY  \\\n",
              "15578                       0                         0   \n",
              "1656                        1                         0   \n",
              "9190                        0                         0   \n",
              "11839                       1                         0   \n",
              "12267                       1                         0   \n",
              "...                       ...                       ...   \n",
              "15355                       0                         0   \n",
              "9608                        1                         0   \n",
              "12606                       1                         0   \n",
              "2907                        1                         0   \n",
              "18139                       0                         0   \n",
              "\n",
              "       ocean_proximity_NEAR OCEAN  \n",
              "15578                           0  \n",
              "1656                            0  \n",
              "9190                            0  \n",
              "11839                           0  \n",
              "12267                           0  \n",
              "...                           ...  \n",
              "15355                           0  \n",
              "9608                            0  \n",
              "12606                           0  \n",
              "2907                            0  \n",
              "18139                           0  \n",
              "\n",
              "[15220 rows x 11 columns]"
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              "      <th></th>\n",
              "      <th>longitude</th>\n",
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              "      <td>10886.0</td>\n",
              "      <td>4266.0</td>\n",
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              "      <td>2.3169</td>\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 15220,\n  \"fields\": [\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.008340644148896,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 821,\n        \"samples\": [\n          -121.63,\n          -118.37,\n          -118.38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1572770818170186,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 841,\n        \"samples\": [\n          39.45,\n          38.18,\n          37.3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.486671084695747,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          34.0,\n          48.0,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2129.1022165259847,\n        \"min\": 6.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 5244,\n        \"samples\": [\n          1563.0,\n          2029.0,\n          1130.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1118.4418616825437,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3572,\n        \"samples\": [\n          1435.0,\n          3288.0,\n          1504.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 379.5877066322556,\n        \"min\": 2.0,\n        \"max\": 5358.0,\n        \"num_unique_values\": 1673,\n        \"samples\": [\n          196.0,\n          798.0,\n          42.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4159103853106683,\n        \"min\": 0.4999,\n        \"max\": 7.6473,\n        \"num_unique_values\": 9840,\n        \"samples\": [\n          2.314,\n          4.3818,\n          1.0481\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_<1H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding a constant to the predictors because statsmodels' OLS doesn't include it by default\n",
        "X_train_const = sm.add_constant(X_train)\n",
        "X_train_const\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "jIGBYYu5n3rw",
        "outputId": "c262db06-1f69-4537-b83a-6632b5110ccf"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       const  longitude  latitude  housing_median_age  total_rooms  \\\n",
              "15578    1.0    -116.90     33.03                11.0       3213.0   \n",
              "1656     1.0    -121.93     37.89                13.0       2085.0   \n",
              "9190     1.0    -118.35     34.32                52.0        102.0   \n",
              "11839    1.0    -120.95     39.93                26.0       2023.0   \n",
              "12267    1.0    -116.91     33.75                13.0      10886.0   \n",
              "...      ...        ...       ...                 ...          ...   \n",
              "15355    1.0    -117.32     33.25                 7.0       8206.0   \n",
              "9608     1.0    -120.51     37.29                20.0       4927.0   \n",
              "12606    1.0    -121.49     38.50                32.0       2364.0   \n",
              "2907     1.0    -119.03     35.37                52.0       1503.0   \n",
              "18139    1.0    -122.01     37.34                31.0       3080.0   \n",
              "\n",
              "       population  households  median_income  ocean_proximity_<1H OCEAN  \\\n",
              "15578      1975.0       579.0         3.4750                          1   \n",
              "1656        852.0       264.0         7.3445                          0   \n",
              "9190         54.0        32.0         1.9875                          1   \n",
              "11839       922.0       365.0         2.8125                          0   \n",
              "12267      4266.0      1955.0         2.3169                          0   \n",
              "...           ...         ...            ...                        ...   \n",
              "15355      4399.0      1423.0         3.6301                          1   \n",
              "9608       4205.0      1009.0         1.7679                          0   \n",
              "12606      1331.0       449.0         3.3190                          0   \n",
              "2907        554.0       277.0         1.6786                          0   \n",
              "18139      1493.0       582.0         6.3052                          1   \n",
              "\n",
              "       ocean_proximity_INLAND  ocean_proximity_NEAR BAY  \\\n",
              "15578                       0                         0   \n",
              "1656                        1                         0   \n",
              "9190                        0                         0   \n",
              "11839                       1                         0   \n",
              "12267                       1                         0   \n",
              "...                       ...                       ...   \n",
              "15355                       0                         0   \n",
              "9608                        1                         0   \n",
              "12606                       1                         0   \n",
              "2907                        1                         0   \n",
              "18139                       0                         0   \n",
              "\n",
              "       ocean_proximity_NEAR OCEAN  \n",
              "15578                           0  \n",
              "1656                            0  \n",
              "9190                            0  \n",
              "11839                           0  \n",
              "12267                           0  \n",
              "...                           ...  \n",
              "15355                           0  \n",
              "9608                            0  \n",
              "12606                           0  \n",
              "2907                            0  \n",
              "18139                           0  \n",
              "\n",
              "[15220 rows x 12 columns]"
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>const</th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>ocean_proximity_&lt;1H OCEAN</th>\n",
              "      <th>ocean_proximity_INLAND</th>\n",
              "      <th>ocean_proximity_NEAR BAY</th>\n",
              "      <th>ocean_proximity_NEAR OCEAN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>15578</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-116.90</td>\n",
              "      <td>33.03</td>\n",
              "      <td>11.0</td>\n",
              "      <td>3213.0</td>\n",
              "      <td>1975.0</td>\n",
              "      <td>579.0</td>\n",
              "      <td>3.4750</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1656</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-121.93</td>\n",
              "      <td>37.89</td>\n",
              "      <td>13.0</td>\n",
              "      <td>2085.0</td>\n",
              "      <td>852.0</td>\n",
              "      <td>264.0</td>\n",
              "      <td>7.3445</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>9190</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-118.35</td>\n",
              "      <td>34.32</td>\n",
              "      <td>52.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>1.9875</td>\n",
              "      <td>1</td>\n",
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              "      <th>11839</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-120.95</td>\n",
              "      <td>39.93</td>\n",
              "      <td>26.0</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>922.0</td>\n",
              "      <td>365.0</td>\n",
              "      <td>2.8125</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12267</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-116.91</td>\n",
              "      <td>33.75</td>\n",
              "      <td>13.0</td>\n",
              "      <td>10886.0</td>\n",
              "      <td>4266.0</td>\n",
              "      <td>1955.0</td>\n",
              "      <td>2.3169</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15355</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-117.32</td>\n",
              "      <td>33.25</td>\n",
              "      <td>7.0</td>\n",
              "      <td>8206.0</td>\n",
              "      <td>4399.0</td>\n",
              "      <td>1423.0</td>\n",
              "      <td>3.6301</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9608</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-120.51</td>\n",
              "      <td>37.29</td>\n",
              "      <td>20.0</td>\n",
              "      <td>4927.0</td>\n",
              "      <td>4205.0</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>1.7679</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>12606</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-121.49</td>\n",
              "      <td>38.50</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2364.0</td>\n",
              "      <td>1331.0</td>\n",
              "      <td>449.0</td>\n",
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              "      <th>2907</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-119.03</td>\n",
              "      <td>35.37</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1503.0</td>\n",
              "      <td>554.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>1.6786</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <th>18139</th>\n",
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              "      <td>37.34</td>\n",
              "      <td>31.0</td>\n",
              "      <td>3080.0</td>\n",
              "      <td>1493.0</td>\n",
              "      <td>582.0</td>\n",
              "      <td>6.3052</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>15220 rows × 12 columns</p>\n",
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              "                                                    [key], {});\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      border-bottom-color: var(--fill-color);\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",
              "      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",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_9d40507e-ba6d-426d-bac8-3d36b587e64b\">\n",
              "    <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_const')\"\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_9d40507e-ba6d-426d-bac8-3d36b587e64b 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_const');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train_const",
              "summary": "{\n  \"name\": \"X_train_const\",\n  \"rows\": 15220,\n  \"fields\": [\n    {\n      \"column\": \"const\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.008340644148896,\n        \"min\": -124.35,\n        \"max\": -114.31,\n        \"num_unique_values\": 821,\n        \"samples\": [\n          -121.63\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1572770818170186,\n        \"min\": 32.54,\n        \"max\": 41.95,\n        \"num_unique_values\": 841,\n        \"samples\": [\n          39.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.486671084695747,\n        \"min\": 1.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 52,\n        \"samples\": [\n          34.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2129.1022165259847,\n        \"min\": 6.0,\n        \"max\": 39320.0,\n        \"num_unique_values\": 5244,\n        \"samples\": [\n          1563.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1118.4418616825437,\n        \"min\": 3.0,\n        \"max\": 35682.0,\n        \"num_unique_values\": 3572,\n        \"samples\": [\n          1435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 379.5877066322556,\n        \"min\": 2.0,\n        \"max\": 5358.0,\n        \"num_unique_values\": 1673,\n        \"samples\": [\n          196.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4159103853106683,\n        \"min\": 0.4999,\n        \"max\": 7.6473,\n        \"num_unique_values\": 9840,\n        \"samples\": [\n          2.314\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_<1H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the OLS model\n",
        "model_fitted = sm.OLS(y_train, X_train_const).fit()\n",
        "\n",
        "# Printing Summary\n",
        "print(model_fitted.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KFTGezidYFfr",
        "outputId": "0d1f2170-35b2-4998-c494-ce253029e679"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:     median_house_value   R-squared:                       0.588\n",
            "Model:                            OLS   Adj. R-squared:                  0.588\n",
            "Method:                 Least Squares   F-statistic:                     1973.\n",
            "Date:                Wed, 06 Mar 2024   Prob (F-statistic):               0.00\n",
            "Time:                        07:24:43   Log-Likelihood:            -1.8879e+05\n",
            "No. Observations:               15220   AIC:                         3.776e+05\n",
            "Df Residuals:                   15208   BIC:                         3.777e+05\n",
            "Df Model:                          11                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================================\n",
            "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "----------------------------------------------------------------------------------------------\n",
            "const                      -1.747e+06   9.16e+04    -19.073      0.000   -1.93e+06   -1.57e+06\n",
            "longitude                  -2.268e+04    997.843    -22.728      0.000   -2.46e+04   -2.07e+04\n",
            "latitude                   -2.109e+04    981.562    -21.490      0.000    -2.3e+04   -1.92e+04\n",
            "housing_median_age           846.4636     44.561     18.996      0.000     759.118     933.809\n",
            "total_rooms                   -2.6698      0.716     -3.728      0.000      -4.073      -1.266\n",
            "population                   -33.1937      1.050    -31.620      0.000     -35.251     -31.136\n",
            "households                   124.3609      4.388     28.343      0.000     115.761     132.961\n",
            "median_income               3.562e+04    424.453     83.928      0.000    3.48e+04    3.65e+04\n",
            "ocean_proximity_<1H OCEAN    -1.7e+05   2.95e+04     -5.755      0.000   -2.28e+05   -1.12e+05\n",
            "ocean_proximity_INLAND     -2.108e+05   2.96e+04     -7.123      0.000   -2.69e+05   -1.53e+05\n",
            "ocean_proximity_NEAR BAY   -1.779e+05   2.96e+04     -6.015      0.000   -2.36e+05    -1.2e+05\n",
            "ocean_proximity_NEAR OCEAN -1.689e+05   2.95e+04     -5.716      0.000   -2.27e+05   -1.11e+05\n",
            "==============================================================================\n",
            "Omnibus:                     3434.427   Durbin-Watson:                   2.004\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            10645.381\n",
            "Skew:                           1.157   Prob(JB):                         0.00\n",
            "Kurtosis:                       6.381   Cond. No.                     7.61e+05\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large, 7.61e+05. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prediction/Testing"
      ],
      "metadata": {
        "id": "gIiHsJNwtWxV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding a constant to the test predictors\n",
        "X_test_const = sm.add_constant(X_test)\n",
        "\n",
        "# Making predictions on the test set\n",
        "test_predictions = model_fitted.predict(X_test_const)\n",
        "test_predictions"
      ],
      "metadata": {
        "id": "E8BakjzJtWC8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3db79290-a2ba-46c7-a187-beb4e0a38eaf"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7322     153414.201880\n",
              "465      239312.044180\n",
              "12768     70054.781175\n",
              "2617     189568.171319\n",
              "13351    184267.795032\n",
              "             ...      \n",
              "11691    171568.742378\n",
              "1454     215893.783794\n",
              "7360     129935.765388\n",
              "19118    232846.312809\n",
              "13084    207120.213485\n",
              "Length: 3806, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test_const"
      ],
      "metadata": {
        "id": "-WLNGDXhtnwB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "53745d39-ae58-4c0d-936b-faf8244dd187"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       const  longitude  latitude  housing_median_age  total_rooms  \\\n",
              "7322     1.0    -118.17     33.98                27.0       1871.0   \n",
              "465      1.0    -122.28     37.86                52.0        938.0   \n",
              "12768    1.0    -121.43     38.61                40.0       1134.0   \n",
              "2617     1.0    -124.11     40.93                25.0       2392.0   \n",
              "13351    1.0    -117.62     34.02                 9.0       4265.0   \n",
              "...      ...        ...       ...                 ...          ...   \n",
              "11691    1.0    -117.99     33.87                17.0       2334.0   \n",
              "1454     1.0    -121.96     37.96                28.0       1838.0   \n",
              "7360     1.0    -118.19     33.96                28.0       3507.0   \n",
              "19118    1.0    -122.65     38.23                52.0       1923.0   \n",
              "13084    1.0    -121.36     38.57                26.0       1793.0   \n",
              "\n",
              "       population  households  median_income  ocean_proximity_<1H OCEAN  \\\n",
              "7322       2542.0       581.0         2.8427                          1   \n",
              "465         393.0       189.0         3.8594                          0   \n",
              "12768       675.0       249.0         1.3696                          0   \n",
              "2617       1298.0       461.0         3.5076                          0   \n",
              "13351      2280.0       589.0         5.5632                          0   \n",
              "...           ...         ...            ...                        ...   \n",
              "11691      1662.0       535.0         3.0147                          1   \n",
              "1454        899.0       270.0         5.2145                          0   \n",
              "7360       3740.0       970.0         2.0162                          1   \n",
              "19118       910.0       345.0         3.4500                          1   \n",
              "13084       653.0       235.0         5.6485                          0   \n",
              "\n",
              "       ocean_proximity_INLAND  ocean_proximity_NEAR BAY  \\\n",
              "7322                        0                         0   \n",
              "465                         0                         1   \n",
              "12768                       1                         0   \n",
              "2617                        0                         0   \n",
              "13351                       1                         0   \n",
              "...                       ...                       ...   \n",
              "11691                       0                         0   \n",
              "1454                        1                         0   \n",
              "7360                        0                         0   \n",
              "19118                       0                         0   \n",
              "13084                       1                         0   \n",
              "\n",
              "       ocean_proximity_NEAR OCEAN  \n",
              "7322                            0  \n",
              "465                             0  \n",
              "12768                           0  \n",
              "2617                            1  \n",
              "13351                           0  \n",
              "...                           ...  \n",
              "11691                           0  \n",
              "1454                            0  \n",
              "7360                            0  \n",
              "19118                           0  \n",
              "13084                           0  \n",
              "\n",
              "[3806 rows x 12 columns]"
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              "      <th>const</th>\n",
              "      <th>longitude</th>\n",
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              "<p>3806 rows × 12 columns</p>\n",
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              "    <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_e9f032f2-becb-4cf3-9369-5016383cc8ea 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_test_const');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_test_const",
              "summary": "{\n  \"name\": \"X_test_const\",\n  \"rows\": 3806,\n  \"fields\": [\n    {\n      \"column\": \"const\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 1.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.9939755127423933,\n        \"min\": -124.25,\n        \"max\": -114.58,\n        \"num_unique_values\": 639,\n        \"samples\": [\n          -117.72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1446399039750883,\n        \"min\": 32.55,\n        \"max\": 41.95,\n        \"num_unique_values\": 635,\n        \"samples\": [\n          36.47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"housing_median_age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.405648674927416,\n        \"min\": 2.0,\n        \"max\": 52.0,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          46.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_rooms\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2287.5911297862335,\n        \"min\": 2.0,\n        \"max\": 32627.0,\n        \"num_unique_values\": 2601,\n        \"samples\": [\n          1233.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"population\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1230.8164945011315,\n        \"min\": 6.0,\n        \"max\": 28566.0,\n        \"num_unique_values\": 2086,\n        \"samples\": [\n          4748.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"households\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 395.64932945063845,\n        \"min\": 2.0,\n        \"max\": 6082.0,\n        \"num_unique_values\": 1134,\n        \"samples\": [\n          1435.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"median_income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4110225441178341,\n        \"min\": 0.536,\n        \"max\": 7.642,\n        \"num_unique_values\": 3215,\n        \"samples\": [\n          3.0139\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_<1H OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_INLAND\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR BAY\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ocean_proximity_NEAR OCEAN\",\n      \"properties\": {\n        \"dtype\": \"uint8\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking OLS Assumtions"
      ],
      "metadata": {
        "id": "2rskgovfkhab"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Assumption 1: Linearity"
      ],
      "metadata": {
        "id": "IBs0xv9KkkHD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot for observed vs predicted values on test data\n",
        "plt.scatter(y_test, test_predictions, color = \"forestgreen\")\n",
        "plt.xlabel('Observed Values')\n",
        "plt.ylabel('Predicted Values')\n",
        "plt.title('Observed vs Predicted Values on Test Data')\n",
        "plt.plot(y_test, y_test, color='darkred')  # line for perfect prediction (true values)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "rqYFy6nfs4Ic",
        "outputId": "dc66cfd5-0c86-4ff4-e061-d29fc268f18f"
      },
      "execution_count": 40,
      "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": [
        "**Positive Linear Relationship:**\n",
        "The red line (which represents a perfect prediction line) and the distribution of the data points suggest there's a positive linear relationship between the observed and predicted values. This means that as the actual values increase, the predicted values also increase, which is a good sign for linearity."
      ],
      "metadata": {
        "id": "gWRAS9Ceur36"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Assumption 2: Random Sample"
      ],
      "metadata": {
        "id": "_-A1hukrkwUl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the mean of the residuals\n",
        "mean_residuals = np.mean(model_fitted.resid)\n",
        "\n",
        "print(f\"The mean of the residuals is {np.round(mean_residuals,2)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hP-qFMA7u5hj",
        "outputId": "45688352-da27-4fd8-9fc8-d9181dd81ccd"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The mean of the residuals is 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While we cannot directly observe the true errors in the model, we can work with the residuals, which are the differences between the observed values and the predicted values from the model. If the model is well-fitted, the residuals should be randomly scattered around zero without any systematic patterns."
      ],
      "metadata": {
        "id": "dvcjBQyBu4DR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the residuals\n",
        "plt.scatter(model_fitted.fittedvalues, model_fitted.resid, color = \"forestgreen\")\n",
        "plt.axhline(y=0, color='red', linestyle='--')\n",
        "plt.xlabel('Fitted Values')\n",
        "plt.ylabel('Residuals')\n",
        "plt.title('Residuals vs Fitted Values')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "hzaYpfiQs4pP",
        "outputId": "1d702441-dab7-4680-db34-14b3edf81db4"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Pzw8JCQk4cuSI2CNckWHDhmHnzp2YNGkShg0bhsePH2P//v1o06YN8vLyxLhu3bph5MiR2Lx5M27cuCF+/SQnJ+PEiRNYsGABBgwYgG+++QZffPEF+vbtCzc3N+Tl5eGrr76CnZ2d+EsIUXVg8kZElerfvz/c3Nzw+eefY8SIEWjTpg0OHTqEDRs24JtvvkFmZiacnZ3Rrl07la0axo4dizNnzuD8+fMoLi7GM888g1mzZmHSpEkVPu+jjz5Co0aNcOTIEZw+fRrdu3fHli1b0KtXL5W4Ro0aYcOGDVixYgX+/e9/o0WLFpg9ezbu3Lmjkrx5eHhg/fr1WLt2LT755BM0adIEo0ePhrOzs8atNsoaOXIkjhw5gh07diA/Px8uLi4YO3Yspk2bplPb9enTB9bW1sjLy9N4QkVV26gqfHx88M4772D//v348ccfoVAocPr0adja2mLu3LlYvHgx1q5di8LCQrz88svo1KkTmjRpggMHDiAqKgrfffcdvvjiCzg5OaFNmzZ49913xbJtbGywc+dOLFu2TNyWZciQIQgKCsKbb76pVz2V23D4+PjgueeeU7n31ltvoaCgAEeOHEF0dDTatWuHzZs3Y/Xq1ZWW27p1a3zyySdYv349Pv74Y7Rp0wYrV67E0aNHxSRUaenSpejQoQP279+PNWvWwNzcHM8++yzCwsLQuXNnAKVJ3vXr1xEdHY1Hjx7B3t4ePj4+WLVqlU7JJFFVyYSqzvAkIiIiohrHOW9EREREEsLkjYiIiEhCmLwRERERSQiTNyIiIiIJYfJGREREJCFM3oiIiIgkhPu81TFXrlyBIAiwsLAwdVWIiIhIRyUlJZDJZPDz86s0lj1vdYwgCFU+nLk+EgQBxcXFbLMaxnaveWxz02C7m4YU212fn9/seatjlD1uHTt2NHFNpCE/Px83btxAmzZtYGtra+rq1Bts95rHNjcNtrtpSLHdr1+/rnMse96IiIiIJITJGxEREZGEMHkjIiIikhAmb0REREQSwuSNiIiISEKYvBERERFJCJM3IiIiIglh8kZEREQkIUzeiIiIiCSEJyyQTuQKOS6nXEZ6XjqaNWyGri26wtzM3NTVIiIiqneYvFGlYhJisPTMUqTmporXXOxcsLjPYoR4hpiwZkRERPUPh02pQjEJMQg/HK6SuAFAWm4awg+HIyYhxkQ1IyIiqp+YvJFWcoUcS88shQBB7Z7y2rKzyyBXyGu6akRERPUWkzfS6nLKZbUet7IECHiQ8wCXUy7XYK2IiIjqNyZvpFV6XrpR44iIiMhwTN5Iq2YNmxk1joiIiAzH5I206tqiK1zsXCCDTON9GWRwtXdF1xZda7hmRERE9ReTN9LK3Mwci/ssBgC1BE75elHwIu73RkREVIOYvFGFQjxDEBUWheZ2zVWuu9i7ICosivu8ERER1TBu0kuVCvEMQd82fXnCAhERUS3A5I10Ym5mDn83f1NXg4iIqN7jsCkRERGRhDB5IyIiIpIQJm9EREREEsLkjYiIiEhCmLwRERERSUidSN6++eYb/Otf/0LHjh3RvXt3vPnmmygsLBTvnzlzBmFhYejYsSNCQkJw6NAhtTKKi4vxySefoGfPnvD19cUbb7yBxMREtbjbt2/jjTfegK+vL3r27ImVK1eiuLhYLe7AgQMICQlBx44dERYWhrNnz6rF5OTkYP78+ejWrRv8/Pwwc+ZMpKfznFAiIiLSTvLJ22effYZly5YhNDQU27dvx9KlS9GiRQvI5XIAwE8//YTp06fD19cXW7duxcCBA7FgwQKcOHFCpZzly5fjwIEDiIiIQGRkJIqLizFhwgTk5OSIMVlZWRg/fjxKSkoQGRmJiIgIfPXVV1ixYoVKWceOHcOiRYswcOBAbN26Fb6+vpg+fTquXr2qEjdr1iycP38e77//PlatWoWkpCRMnjwZT58+rZ7GIiIiIsmT9D5viYmJ2LBhAzZu3IhevXqJ10NC/tn1/7PPPoOPjw+WLl0KAPD398e9e/ewfv16DBgwAACQmpqKgwcPYsmSJRg2bBgAoGPHjggODsb+/fsxefJkAMD+/fuRl5eHDRs2wMnJCQAgl8vxwQcfYOrUqWjevPQUgvXr12PQoEGYNWuW+MyEhARERUVh69atAIArV67g3Llz2L59OwIDAwEA7u7uCA0NxcmTJxEaGlpNrUZERERSJumet6+//hotWrRQSdzKKi4uxqVLl8QkTSk0NBS3b99GSkoKAODcuXNQKBQqcU5OTujZsydiY2PFa7GxsQgICBATNwAYOHAgFAoFzp8/DwC4d+8ekpOTMXDgQLVnXrhwQRxijY2NhYODA3r27CnGeHh4wNvbW+WZRERERGVJuuftl19+gaenJzZu3Ijdu3cjJycHHTp0wLx589CpUyfcvXsXJSUl8PDwUHlf69atAZT23LVo0QKJiYlo3LgxHB0d1eIOHjwovk5MTMSrr76qEuPg4ICmTZuK8+OUH93d3dXKKikpwb1799C6dWskJibC3d0dMpnqge8eHh4a59rpQxAE5OfnG1RGfVFQUKDykWoG273msc1Ng+1uGlJsd0EQ1HICbSSdvD18+BC//vorEhISsGTJEtjY2GDTpk2YOHEiTp48iaysLAClCVZZytfK+9nZ2bC3t1cr38HBQYxRxpUvCwAcHR3FOEOf6ejoiF9//VWHz167kpIS3Lhxw6Ay6pvk5GRTV6FeYrvXPLa5abDdTUNq7W5paalTnKSTN2UP07p169C2bVsAQKdOndCnTx/s2bNHnEtW31hYWKBNmzamroYkFBQUIDk5Ga1atYKNjY2pq1NvsN1rHtvcNNjupiHFdr9165bOsZJO3hwcHODk5CQmbkDpXLV27drh1q1bGDRoEACorBgFSnu9AIjDpA4ODsjNzVUrPzs7W2Uo1cHBQa0soLQ3TRmn/JiTk4OmTZtW+MzU1NQKy6oqmUwGW1tbg8qob2xsbNhmJsB2r3lsc9Ngu5uGlNpd1yFTQOILFirqXSoqKoKbmxssLCzU5pApXyvnwnl4eODRo0cqQ6TKuLLz5TTNR8vJycHDhw9Vyir7jLJlWVhYoGXLlmJcUlISBEFQiUtKSlKbo0dERESkJOnkLTg4GJmZmSrzu548eYLffvsN7du3h6WlJbp3746YmBiV90VHR6N169Zo0aIFACAwMBBmZmY4efKkGJOVlYVz584hKChIvBYUFIS4uDixFw0ATpw4ATMzM3HVaMuWLdGqVSu1feSio6MREBAgjmcHBQUhKysLFy5cEGOSkpLw+++/qzyTiIiIqCxJD5v27dsXHTt2xMyZMxEREQErKyts2bIFlpaWeO211wAAb7/9NsaNG4f3338fAwcOxKVLl3D06FGsWbNGLMfFxQXDhg3DypUrYWZmhubNm2Pz5s2wt7fHqFGjxLhRo0Zh9+7dCA8Px9SpU5GWloaVK1di1KhR4h5vADBjxgy8++67cHNzQ/fu3REdHY1r165hz549Yoyfnx8CAwMxf/58vPfee7CyssKaNWvg5eWF/v3710DrERERkRRJOnkzMzPDli1b8PHHH2Px4sUoKSlBly5dsHfvXnG+WZcuXRAZGYm1a9fi4MGDeOaZZ7B8+XK1fdgWLlyIhg0bYvXq1cjLy0Pnzp2xY8cOlRWhjo6O2LVrF5YtW4bw8HA0bNgQw4YNQ0REhEpZgwcPRkFBAbZu3YotW7bA3d0dGzZsgJ+fn0rc2rVrxbo/ffoUgYGBWLhwIRo0kPRfCxEREVUjmVB+0hVJ2vXr1wGUnhBBlcvPz8eNGzfg7e0tmUmtdQHbveaxzU2D7W4aUmx3fX5+S3rOGxEREVF9w+SNiIiISEKYvBERERFJCJM3IiIiIglh8kZEREQkIUzeiIiIiCSEyRsRERGRhDB5IyIiIpIQJm9EREREEsLkjYiIiEhCmLwRERERSQiTNyIiIiIJYfJGREREJCFM3oiIiIgkhMkbERERkYQweSMiIiKSECZvRERERBLC5I2IiIhIQpi8EREREUkIkzciIiIiCWHyRkRERCQhTN6IiIiIJITJGxEREZGEMHkjIiIikhAmb0REREQSwuSNiIiISEKYvBERERFJCJM3IiIiIglh8kZEREQkIUzeiIiIiCSEyRsRERGRhDB5IyIiIpIQJm9EREREEsLkjYiIiEhCmLwRERERSQiTNyIiIiIJYfJGREREJCFM3oiIiIgkhMkbERERkYQweSMiIiKSECZvRERERBLC5I2IiIhIQpi8EREREUkIkzciIiIiCWHyRkRERCQhTN6IiIiIJITJGxEREZGEMHkjIiIikhAmb0REREQSwuSNiIiISEKYvBERERFJCJM3IiIiIglh8kZEREQkIUzeiIiIiCSEyRsRERGRhDB5IyIiIpIQJm9EREREEsLkjYiIiEhC6lTylpeXh6CgIHh5eeH69esq9w4cOICQkBB07NgRYWFhOHv2rNr7c3JyMH/+fHTr1g1+fn6YOXMm0tPT1eLi4+MxcuRI+Pj4IDg4GFu2bIEgCCoxgiBgy5Yt6N27N3x8fDBy5EhcvXpVray0tDTMmDEDfn5+6NatGxYsWIDc3FzDGoKIiIjqrDqVvG3cuBFyuVzt+rFjx7Bo0SIMHDgQW7duha+vL6ZPn66WTM2aNQvnz5/H+++/j1WrViEpKQmTJ0/G06dPxZg7d+5g0qRJaNq0KTZv3ozx48dj/fr1+Pzzz1XK2rp1K9avX48JEyZg8+bNaNq0KSZOnIh79+6JMSUlJXjzzTeRnJyM1atX4/3338e5c+cwZ84c4zYMERER1RkNTF0BY7l9+zb27duH9957D0uWLFG5t379egwaNAizZs0CAPj7+yMhIQFRUVHYunUrAODKlSs4d+4ctm/fjsDAQACAu7s7QkNDcfLkSYSGhgIAtm/fjkaNGuHTTz+FpaUlAgICkJGRgU2bNmHs2LGwtLREUVERNm/ejIkTJ2LChAkAgBdeeAEDBgzA9u3b8f777wMAYmJi8OeffyI6OhoeHh4AAAcHB0yaNAnXrl2Dj49PNbcaERERSU2d6Xlbvnw5Ro0aBXd3d5Xr9+7dQ3JyMgYOHKhyPTQ0FBcuXEBxcTEAIDY2Fg4ODujZs6cY4+HhAW9vb8TGxorXYmNj8dJLL8HS0lKlrOzsbFy5cgVA6bBqbm6uyjMtLS3Rr18/tbK8vLzExA0AevbsCScnJ/zwww+GNAcRERHVUXUieTtx4gQSEhIQHh6udi8xMREA1JK61q1bo6SkRBzGTExMhLu7O2QymUqch4eHWEZ+fj4ePHigkmwpY2QymRin/Fg+rnXr1vjrr79QWFgoxpWPkclkcHd3F8sgIiIiKkvyw6YFBQVYsWIFIiIiYGdnp3Y/KysLQOlwZFnK18r72dnZsLe3V3u/o6Mjfv31VwClCxo0lWVpaQkbGxuVsiwtLWFlZaX2TEEQkJWVBWtr6wqfqSyrKgRBQH5+fpXfX58UFBSofKSawXaveWxz02C7m4YU210QBLUOJG0kn7x99tlnaNy4MV599VVTV6XWKCkpwY0bN0xdDUlJTk42dRXqJbZ7zWObmwbb3TSk1u5lp2RVRNLJ2/379/H5558jKipK7BVT9jjl5+cjLy8Pjo6OAEp7zZo2bSq+Nzs7GwDE+w4ODkhNTVV7RlZWlhij7CVTPkupuLgYBQUFKmUVFxejqKhIpfctOzsbMplMJU7TtiBZWVlwdXXVtzlEFhYWaNOmTZXfX58UFBQgOTkZrVq1go2NjamrU2+w3Wse29w02O6mIcV2v3Xrls6xkk7eUlJSUFJSgilTpqjdGzduHDp16oTVq1cDUJ9flpiYCAsLC7Rs2RJA6fy0CxcuqHVbJiUlwdPTEwBga2sLV1dXtfloSUlJEARBLF/5MSkpCW3btlV55jPPPANra2sxLiEhQaUsQRCQlJSksnBCXzKZDLa2tlV+f31kY2PDNjMBtnvNY5ubBtvdNKTU7roOmQISX7Dg7e2N//znPyp/5s2bBwD44IMPsGTJErRs2RKtWrXCiRMnVN4bHR2NgIAAsYsyKCgIWVlZuHDhghiTlJSE33//HUFBQeK1oKAgnD59GiUlJSplOTg4wM/PDwDQuXNn2NnZ4fjx42JMSUkJTp48qVbWzZs3Vbp1L1y4gMzMTPTq1csILURERER1jaR73hwcHNC9e3eN99q3b4/27dsDAGbMmIF3330Xbm5u6N69O6Kjo3Ht2jXs2bNHjPfz80NgYCDmz5+P9957D1ZWVlizZg28vLzQv39/MW7SpEk4cuQI5syZg9GjRyMhIQHbt29HRESEmAhaWVlh6tSpiIyMhLOzMzw9PfHFF18gMzMTkyZNEssKCQnB5s2bMWPGDMyePRsFBQVYuXKleCoDERERUXmSTt50NXjwYBQUFGDr1q3YsmUL3N3dsWHDBrGnTGnt2rX4+OOPsXjxYjx9+hSBgYFYuHAhGjT4p5mee+45bN++HStWrMCUKVPg7OyMmTNnYuLEiSplTZ48GYIg4PPPP0dGRga8vb2xfft2cZgWKJ2btm3bNixfvhyzZ89GgwYN0K9fP8yfP796G4SIiIgkSyaUP5STJE15pmvHjh1NXBNpyM/Px40bN+Dt7S2ZeRF1Adu95rHNTYPtbhpSbHd9fn5Les4bERERUX1TL4ZNiYiodpMr5Licchnpeelo1rAZurboCnMzc1NXi6hWYvJGREQmFZMQg6VnliI195+9Nl3sXLC4z2KEeIaYsGZEtROHTYmIyGRiEmIQfjhcJXEDgLTcNIQfDkdMQoyJakZUezF5IyIik5Ar5Fh6ZikEqK+bU15bdnYZ5Ap5TVeNqFZj8kZEVAvJFXJcvHsRh28cxsW7F+tkAnM55bJaj1tZAgQ8yHmAyymXa7BWRLUf57wREdUy9WUOWHpeulHjiOoL9rwREdUi9WkOWLOGzYwaR1RfMHkjIqol6sMcsLLDwQpBgeYNm0MGzQdyyyCDq70rurboWsO1JKrdOGxKRFRL6DMHzN/NvwZrZhyahoOdrJ0gQIAMMpWkVZnQjew4Esf+OGbQ3m/cQ47qGiZvRES1RF2eA6YcDi7fq5hVmAUAcLR2RGZhpnhdmdStjVsrXqvKvL/6Mn+Q6hcOmxIR1RJ1dQ5YZcPBMshgY2GD3cN2Y82gNZjVYxaeFD5RSeYA/ef91af5g1S/MHkjIqolurboChc7lzo3B0zX4WAzMzMM8hqE/df2a40DdJv3Vx/mD1L9xeSNiKiWMDczx+I+iwFALYFTvl4UvEhy87X0GQ6u6t5v5ffFu3TvEveQozqLc96IiGqREM8QRIVFqc/TsnfBouBFkpynpc9wcFXm/Wma1+Zo5ah3OURSweSNiKiWCfEMQd82fevMCknlcHBabprGYUwZZHCxd0HXFl117glTJoRaF0IUZelVDpGUMHkjIqqFzM3MJbkdiCbK4eDww+FatwRRDgfrk+hVNK+tMuXLqSuJMtUPTN6IiKja6TocrE+id/HuxQrntWlTtpxTt06p1cnZxhlDvYeib5u+TOSoVmLyRkRENULX4WBdE72qzldTlgNA45BrRkEGdsTvwI74HdwTjmolJm9ERFRjdB0O1iXRq8p8tYYWDbGi/wp0a9kNwduCKx1yTc1NRfjhcESFRTGBo1qDyRsREdVKlSV6lc2P0ySvJA/jD42Hs40zMgoydK7LsrPL0LdNXw6hUq3Afd6IiEiSKtoXrzL6JG7cE45qGyZvREQkWcr5cc3tmlf7s+LuxombAPNkBjIlDpsSEUkMt7ZQpZwfd+nuJUw/Ol087N7Yoi5Gif/PhQxkSkzeiIgkRNNpArUhkTB1QmluZo4erXrg4/4fI/xwOABUaf83XSkPt+dCBjIFDpsSEUmE8jSB8nubKROJmIQYk9UraEsQxnw1BhHHIjDmqzEI2hJkkvpUZRi1kXUjvZ/Dw+3JlJi8ERFJQEWnCZgykaiNCWWIZwhip8Ri97DdcLTWfsapDDK42rsi7q047B2xF+P9xsPOwk7n53AhA5kKkzciIgm4nHK5wtMETJFI1NaEElAdRpX9/V9ZZU9ZsGxgiazCLMT8GYPcklwxpqFlQ52excPtqaYxeSMikgBdE4SaTCRqKqGUK+S4ePdilVZ6ahtGdbF3Eeeraes9zCvO0+kZPNyeahoXLBARSYCuCUJNJhI1kVBWtkBDl4USFZ3WYKzD7YlqEpM3IqqTTL360dgqO03AFIlEdSeUyh6x8p+vcj7dm13exJGbR3RaeavttIbKeg+1KTvsKuWvK5ImDpsSUZ1Tm1Y/GktFpwmYKpFQJpTaTjdQLghQJpT6DH9WNp9OgICtP201eKGErr2CjlaqCx/KDrsS1TQmb0RUp9TG1Y/Gosv8rZqkT0Kpb0Jd1R4xfRdK6NoraCZT/XGpUCj0rhuRsTB5I6I6ozavfjQW5TYYe0fsxZpBa7B3xF78MPkHk/UAGbIgQFtCLVfIEXc3rsp10mehRGW9h0pPCp+ovE7PS5f8LwMkXZzzRkR1hj6rHzXNf5IKbfO3TKWqCwIECJBBhmVnl6Fvm75iD135BQpVpcuQqLL3MPxwOGSQqdSz/OvK6k5UU5i8EVGdURu306gvqrogoGxCnVWYpXGBQlXpOiSq7D0snzQ2smmEjIIMre9T1n3N+TUIfC6w0kUxdW0RDZkOkzciqjNq43Ya9Z2uifL5O+ex9+peoyRuVVl5q6n38OSfJ7Hryq5K3/vZpc/w2aXPKjxj9tTtU/jk/CcqyaGjlSPeeOENTPOfxiSO9FItc96Ki4uRn59fHUUTEWml7+pHqn66JsobL21EVlGWwc8zZOWtsvcwzDsMWYVZOiVuZaXmpmqcB3fp4SXMjpmt1gOZVZSFtXFr0W1jN86dI70YlLwdO3YMH330kcq1DRs2oHPnzujatSvCw8ORl6fbDtVERIaqjdtp1EX6bPmh64IAYzHGylvlPL2qECBg/sn5iLsTB7lCDrlCjp1/7qywRzGzMJOLH0gvBg2bfv7552jXrp34Oj4+Hhs2bEDv3r3h4eGBPXv2YNOmTZgzZ47BFSUi0oW2+Usu9i5YFLyI+3IZqLITD8qraEGAMYX7h6OHWw+jzCOr6jYlSpmFmRh7YCxc7FzwqvereFz0uNL3CBCw8LuFKHhaABc7F86HowoZlLzdu3cPL7/8svj66NGjaNKkCTZs2IAGDRpAEAScPHmSyRsR1aiKVj9S1VV24oG2Hi9tCbUxtWncxmgrcI21oCU1NxVRl6N0js8oyMCc6NKflxUlxEQGJW/FxcWwsrISX58/fx5BQUFo0KC02NatW2Pfvn2G1ZCIqApq23YaUqfvlh/llU+obz2+haiLuic2lTHmIpTasKClsoSY6jeD5ry1aNECcXGlGylev34dd+7cwYsvvijef/z4MWxtbQ2rIRERmZw+W35oU3ZBQA+3Hjo/u/zpBmVVxyKUmp6np0ld2VSaqodBydvIkSNx/PhxDBkyBJMmTYKLiwuCg4PF+/Hx8WjTpo3BlSQiItMy9h56uiRITtZO2D1sN9YNXgfZ3/+VVV2LUCpa+FKTlAnxpbuXTFYHqp0MSt7Gjh2LpUuXws3NDS+99BK2b98Oa2trAEBmZiYePnyIsLAwo1SUiIhqnnJl6a3Ht3SK13XIsbKVwTLI8FH/j9CjVQ+EeoVWy5muFa2a1XbslylMPzqdK1FJhUwQhOpZ+kMmcf36dQBAx44dTVwTacjPz8eNGzfg7e3NIf4axHaveVVpc32OqlJujPvD5B/06gXT9AxXe1eNK4ONeUKBrqtm5Qo5Lt29hOlHpyOr0PB96KpKBhnnv+lBit9j9Pn5zRMWiIhIjbaVpZoYMnypz8pgYy1Cif4jGjOOzFC7rmmRgLmZOczMzEyauCnxHFVS0it5GzdunN4PkMlk2LVLv12qiYjIdCpaWaqJoXvoGZKU6dsbd/yP43jn6Dsa72lbNavrPL7q3Meu7IIQrqImvZK3qoywclSWiEhadN2k1pgb41aFvhsGxyTEYPqR6RWWqSlJamLbRKf69G3dF9/d/k6Pz0B/xtqDjqRNr+Rt9+7d1VUPIiKqJXRNEIy5Ma6+9N0wWN8jr1TaQMc+iOpO3IDasQcdmV61HExPRETSpWuCYKpEorINg5VHTRU/LRav63vkVdnP7VHBI8MqbCTKY7OIjLZgITc3F7m5uVAoFGr3nnnmGWM9hoiIqplyD7a03DSNCZJyZWlNJRLl57Vdunep0kQsoyADfpF+mNJtCqYHTNdruLH8pr9VSVJlkKGhZUPkFufq/V5tCp8W4tStU1xxSoYnb/v27cPOnTtx7949rTE3btww9DFERFRDKjpMvro2xtVGn+1KyiuUF2L9hfXYcnkLpnSdovP7RnYciWN/HBMXQFSWzGoiQEBucS6mvjAVW37eYpSFDJmFmZh2eBo2hm1kAlfPGTRs+sUXX4ib9M6aNQuCIGD8+PGYMmUKmjRpgrZt2+LDDz80Vl2JiKiGaNuk1tCNcfWhnNdm6GH2hU9Lkzgrc6sK42SQwcnaCWvj1iLiWATGfDUGQVuCcOrWqSqfuLDz6k6jr0CdfWw2CooLjFomSYtByduePXsQGBiIbdu2YcSIEQCAXr16ISIiAtHR0cjLy0NmZqYx6klERDUsxDMEsVNisXfEXqwZtAZ7R+zFD5N/qJHETd/tSnRRJC+q8L4AAZmFmSrXUnNTMe3wNACo0okLlT2zKgrlhei4viNWfL/C6GWTNBg0bHr37l289tprAAALCwsAQElJCQDA3t4ew4YNw759+zBx4kQDq0lERKZgrI1x9aXvAgNDOFo5ouhpEQrlhVpjFpxcgEvTLokbCqfmpuLDsx/iScGTatvbrSICBGz9aSsAYG7vuQCMewIF1W4GJW/29vaQy0vPgrOzs4ONjQ1SU//5x9awYUM8elQ7VukQEZF01MR+ZpbmliiWFyOrqPLTE54UPsGle5fQ47keYjJr08AG4YfDq7uaFdr+83YEPheI75O+x7c3vkVGQYZ4z8XOBaN8RuG5Rs8xmatjDErenn/+edy8eVN83alTJ3zxxRfo1asXFAoFvvzyS7Rq1crQOhIRUTWrzl6bqpRdE9uQFMuLKw8q4+K9i+jxXA/xtXJe4LyT80x2fJZCUGD8ofEa76XmpmJt3FrxdUUbGJO0GJS8hYWFYf/+/SguLoalpSVmzJiBN954A7179y4tvEEDREZGGqOeRERUTfQ9qaAmyq7KCs/qdj/rPg7fOKySgIZ4hsDe0h5jD441dfUqpW0DY5IegxYsvPrqqzhw4AAsLS0BAC+88AKOHTuGuXPnYsGCBTh8+LCYyFWH48eP4+2330ZQUBB8fX0xdOhQHDx4UO1IrgMHDiAkJAQdO3ZEWFgYzp49q1ZWTk4O5s+fj27dusHPzw8zZ85Eerp6t318fDxGjhwJHx8fBAcHY8uWLWrPEwQBW7ZsQe/eveHj44ORI0fi6tWramWlpaVhxowZ8PPzQ7du3bBgwQLk5hpvTyAiospoW9Gp/EEfkxBjkrKV25UAuq3wrGwlqTH898Z/VVahHv/jOC7evYiH+Q/RyLpRtT/fUNo2MCbpkQkSPnx05MiRePbZZ9G3b180atQIcXFx2LZtG8LDwzF9eun5dceOHcOcOXPw1ltvwd/fH9HR0Th06BD27t0LX19fsaxJkybh1q1beO+992BlZYW1a9fCzMwMhw4dQoMGpR2Ud+7cwb/+9S/07NkTY8aMwR9//IFVq1YhIiICkyZNEsvasmUL1q9fj3fffRdeXl7Yu3cv4uLi8O2336Jly5YAShd2vPLKKwCAiIgIFBYW4pNPPkHbtm2xefPmKrfJ9evXAQAdO3aschn1SX5+Pm7cuAFvb2/Y2tqaujr1Btu95mlqc7lCjqAtQRUuDHCydkLk4Eh0d+uu1zBqZWUrN/r9YfIPFZarqefO1d4VC3ovQCObRkjPS8edJ3dUhgepcs42zhjnN67OzoeT4vcYfX5+G+2EBVP47LPP4OzsLL4OCAhAZmYmduzYgWnTpsHMzAzr16/HoEGDMGvWLACAv78/EhISEBUVha1bS1fqXLlyBefOncP27dsRGBgIAHB3d0doaChOnjyJ0NBQAMD27dvRqFEjfPrpp7C0tERAQAAyMjKwadMmjB07FpaWligqKsLmzZsxceJETJgwAUBpj+SAAQOwfft2vP/++wCAmJgY/Pnnn4iOjoaHhwcAwMHBAZMmTcK1a9fg4+NTAy1IRPWZLis6MwszMfbgWL2HUSsrW9MB8JqEeIaIKzw1zZlTJomkn4yCDM6HkzCDhk3btm0Lb2/vSv9Ul7KJm5K3tzdyc3ORn5+Pe/fuITk5GQMHDlSJCQ0NxYULF1BcXNptHBsbCwcHB/Ts2VOM8fDwgLe3N2JjY8VrsbGxeOmll8RhYmVZ2dnZuHLlCoDSYdXc3FyVZ1paWqJfv35qZXl5eYmJGwD07NkTTk5O+OGHH6raJEREOtNnRae+w6i6lq1LnHK7kjDvMPi7+av0ENXkliJ1mTGGyanmGNTzFh4eDplMdS6CXC7H/fv3cerUKbi7uyM4ONigCurr559/RvPmzWFnZ4eff/4ZQGkvWlmtW7dGSUkJ7t27h9atWyMxMRHu7u5qn4uHhwcSExMBlHbBPnjwQCXZUsbIZDIkJiaie/fuYnz5uNatW2PXrl0oLCyEtbU1EhMT1WJkMhnc3d3FMqpKEATk5+cbVEZ9UVBQoPKRagbbveZpanPHBo46v1+5aGBezDxYwhJdnulS4TCbrmU7NnA06PtVypOUKr+X/qH8+1343UJ0d+kOywaWlbyjdpPi9xhBENTyEG0MSt5mzJih9V56ejpGjhxZo1uF/PTTT4iOjsZ7770HAMjKKl267eDgoBKnfK28n52dDXt7e7XyHB0d8euvvwIoXdCgqSxLS0vY2NiolGVpaQkrK9XJsw4ODhAEAVlZWbC2tq7wmcqyqqqkpITnyeopOTnZ1FWol9juNa9sm9sKtmhs1RiPix7r/P6soiy8efhNNLZqjAnPT0D3pt01xulStrOlM2yzbHEju+rfr/Kf8BdVY8ooyMCL21/E1LZTEdAswNTVMZjUvseUHdmrSLXNeWvWrBlGjRqFjRs3YvDgwdX1GFFqaioiIiLQvXt3jBs3rtqfV5tZWFigTZs2pq6GJBQUFCA5ORmtWrWCjY2NqatTb7Dda562Nl9otRCzY2YDgF5bcmQUZeDTXz/FpyGfom/rvhpjKitbLpPjgdUDlffLFXLEP4jHw/yHaGrbFJ2ad8Ivab+Irzu7dlbp8fNUeGLTn5uQnpdea7YUkbp8eT7W/LYGmRaZmN1jtqmrUyVS/B5z69YtnWOrdcGCjY0NUlKqv0s7OzsbkydPhpOTEyIjI2FmVjqVz9GxtNs+JycHTZs2VYkve9/BwUHlZAilrKwsMUbZS6bsgVMqLi5GQUGBSlnFxcUoKipS6X3Lzs6GTCZTidO0LUhWVhZcXV2r0Ar/kMlkklldU1vY2NiwzUyA7W4c+myCW77NwzqGwcrKSm1FZ2UECJBBhpVxKzGo/SCNz1OWPf/kfLUzQwEguygbs2Nmi/uOaVpZaiYzg0JQiK81Taxf8tIShB8OhwwyJnBGtOPqDnRu2RmhXqGmrkqVSel7jK5DpoCBCxYqkpCQgN27d1f7sGlhYSGmTp2KnJwcbNu2TWUoUjmnrPwcssTERFhYWIjbdnh4eCApKUltv7akpCSxDFtbW7i6uqqVpXyfMk75MSkpSe2ZzzzzDKytrcW48mUJgqDyTCKiysQkxCBoSxDGfDVGZQ8yfSaeh3iG4OybZ/Xeq6zsilFt+rbpC+sG1lrfDwDLzi7D8T+Oa9wTrmziBmieWK886UDfQ+Opcu9Gv4u4O3GQK+SmrgqVYVDy1qdPH7z00ktqf7p164ahQ4fi0aNHmDdvnrHqqubp06eYNWsWEhMTsW3bNjRvrvoPt2XLlmjVqhVOnDihcj06OhoBAQHi2HJQUBCysrJw4cIFMSYpKQm///47goL+WYIeFBSE06dPo6SkRKUsBwcH+Pn5AQA6d+4MOzs7HD9+XIwpKSnByZMn1cq6efOmynj8hQsXkJmZiV69ehnQKkRUXxhzg934v+LxpPBJleoRdzcOh28cxsW7F9V+yOu6Zcii7xbp1GtWNuEr+6wQzxDETonFgt4LqvQ5kGZF8iKMPTBW718IqHoZNGzarVs3jd18jo6OaNmyJQYNGgQnJydDHlGhDz74AGfPnsXcuXORm5urcopBu3btxCO73n33Xbi5uaF79+6Ijo7GtWvXsGfPHjHWz88PgYGBmD9/vrhJ75o1a+Dl5YX+/fuLcZMmTcKRI0cwZ84cjB49GgkJCdi+fTsiIiLERNDKygpTp05FZGQknJ2d4enpiS+++AKZmZkqG/mGhIRg8+bNmDFjBmbPno2CggKsXLlSPJWBiKgicoUcS88s1ZjwKIc0l51dhmCPYMT/FY+UJynIf5IPT4WnxvIMOQg+6mKU+P/lhzV1LVefxFHbHnHmZuYY33k8tl3ehrS8NJ3Lo8rxaK3aRdInLPTp0wf379/XeO/06dNo0aIFgNLjsbZu3Yq//voL7u7umD17ttoWJjk5Ofj444/x3Xff4enTpwgMDMTChQvVevPi4+OxYsUK3LhxA87OzhgzZgwmT56sksQqj8fat28fMjIy4O3tjXnz5om9c0ppaWlYvnw5zp07hwYNGqBfv36YP38+7OzsqtwmPGFBP1LchbsuYLsb7uLdixjz1ZhK4xpZN1JJjJo3bI4lLy1R+wGsa3mVUR5lpfwhb6xyNfk09FMMbTdU7XpMQgymHZ5WLc+s75xtnHF+6vlav5WIFL/H6PPzW9LJG6lj8qYfKf4DrwvY7oY7fOMwIo5F6P2+8smVki5HZenzDOXRV3KFHD029dDasyaDDI1sGiGjIEPv5zSyboQP+3+olohG/xGNGUe0b2VFhtHW7rWJFL/HVNvxWBs2bNC7MjKZDOHh4Xq/j4iItGvWsFmV3ld2SLVvm77iKlHlQfDG6LFSDmtuvLgR+6/trzBxA4AP+n6AD89+iLTcNL1Wiz4pfKI2lBeTEMPErZo9KXyCaYenYWPYxlqdwNVlBidvyuHC8h14MplM3C2YyRsRkXF1bdEVLnYueic8gPY5YyGeIdgYtlHr1h5KAzwH4ETCCa33lSo7LN7F3gWLghchxDME5jLzKm/3oUxEAWDpmaV6vZeq7t3j7yIlMwWN7RrDxc6lzh1uX5vplbzdvHlT5XVaWhqmTJmC559/HuPHjxePoUpMTMSuXbtw+/ZtbN682Xi1JSIiAP/0lBmyv5mmxQTKg+A3XtyIHfE7kFX4z4kvrvauWBS8CI7WjjolbxVxtnHGmUlnxLlTyu0+qrLfXNntSnjOac3JL8nHR7Efia95uH3NMWirkA8++ADPPfccVq1ahY4dO8LOzg52dnbw8fHB6tWr4ebmhqVL+VsQEVF10La/mbONs07v1zb0am5mjhk9ZuDytMvYO2Iv1gxag70j9uKHyT8gxDNE7PVTDntWRUZBBuL/ile5ptzuI9xf/9GauLtxBieUZJjU3FQebl9DDEreLl68CH9/f633/f39VfZOIyIi41ImPGWTrPNTz1eYXMkgg6u9K7q26Fph2eZm5vB380eYdxj83fzV5scpyypftq5O3Tql8Zk93HroXIZS1MUo7L66W+/3kXEJENT24CPjMyh5s7KyUtlbrbwrV66oHdBORETGVT7JsmxgWWlytSh4EYDSLUK0bbBbEW29fo1sGmGGv24LBnbE79DYS2OMnj0yncpO3SDDGZS8DRkyBEeOHMHy5cuRnJwMhUIBhUKB5ORkLFu2DEePHsWQIUOMVVciItKRtuSquV1zRIWVbqprjGO1FgQvUBmmzSjIwO4ruveALTi5QC1prKhnj6TBkE2fqXIGnbDw7rvv4smTJ9izZw/27t0rHgivUCggCAIGDRqEd9991ygVJSIi/SgXH1xOuVx6wsLDfLzs/zLi/opD+OFwtUUOuu6iL1fIcTnlMk7dOoUd8TvU7mcWZepcxyeFT3Dp3iX0eE51qLSqCxiodtj3yz442zjDDGZ4VPAIzRo242pUIzIoebO0tMS///1vTJo0CbGxseJpB88++yyCgoLQtm1bo1SSiIiqRjmkmt8kHzee3gCACo/VAoD5J+fD3tIe3d26q/2wjUmIMXpCdejXQ+jeUv1ZIZ4hUAgKTD8y3WjPoppxOeUyxh8cr3KNq1GNx6DkTalt27ZM1IiIJCD+QXyliVdmYSbGHhyr9sM2JiFGY4+dof5747+4eO+i2g92uUKO5WeXG/VZZDo8H9V4DJrzRkRE0vIw/6HOsam5qZh2eBoi4yJR/LRYa4+dMZR9lnIO3OWUyxwyrUOUXztcjWo4vXre2rZtCzMzM1y9ehWWlpZo27atyoHsmshkMvz+++8GVZKIiIyjqW1Tvd+zNm4tdsXv0nrMlTGtjVuL/1z5D4Z6D4WtpTTOpCTdKTdV3hW/C+M7j+ccuCrSK3kLDw+HTCZDgwYNVF4TEZE0dHbtXKVjtWoicVPKKMjQuBCC6o4Pv/8Q23/azjlwVaRX8jZjxowKXxMRUe1mjGO1dDXYazB+TP4RWUVZlQdTvcM5cFXHOW9ERPWMtj3gjK1pw6ZM3Egr4e//OAdOfwYlbxcuXMC2bdtUrh08eBC9e/dGjx498NFHH0Eu518IEZEpyBVy8QSFy/cvQyEoVO4LQvX1ugGle30RVeZBzgNsvLjR1NWQFIO2ComMjMQzzzwjvv7jjz+wZMkSeHl5wc3NDbt370aTJk0wZcoUgytKRES607QfW2OrxlhotRBWVlYGbflhZW6FInlRpXG6xBABpQtVPJt4cvhURwb1vN2+fRsdOnQQX3/77bews7PD3r17sXbtWgwfPhzffvutwZUkIiLdKfdjK7/NxuOix5gdMxvzT843aK4bkzKqDhw+1Z1ByVtBQQHs7OzE1z/++CMCAwNhY2MDAOjYsSP++usvw2pIRFSHlB3K1PcweF3Lr2g/NgECMgszjfpMImPggfa6M2jY1NXVFdevX8ewYcNw584d/Pnnn5g4caJ4PysrC5aWlgZXkoioLtA0lGnsI4OkuLGttbk1CuWFpq4G1QJpuWmmroIkGJS8DRkyBFFRUUhLS8OtW7fg6OiIl156Sbz/22+/oVWrVobWkYhI8rQdLWXs7RLS89INLqOmMXEjpUXfLYJ1A2vOfauEQcOmb731FqZMmYLU1FS4uroiKioKDg4OAIDMzEz873//Q58+fYxSUSIiqapoKNPYRwY1a9jM4DKITCWvJA/TDk9DTEKMqatSqxnU89agQQNEREQgIiJC7Z6TkxPOnz9vSPFERHVCZUOZyiODLqdchr+bv0HPyijIgJnMTG1bECIpmX9yPuyt7NG9ZXceoaWB0TbpTU9Px82bN5Gfn2+sIomI6gRdhzINHfKMSYjBzCMzmbiR5GUWZmLsgbEI2hLEXjgNDE7eTp06hQEDBqBXr154+eWX8csvvwAAMjIy8K9//QvfffedwZUkIpIyXYcyDRnyrGyVKQDIoNtZ1LrGEVU35ZxQJnCqDErezpw5gxkzZqBRo0YIDw9X2a3b2dkZzZs3x9dff21wJYmIpKxri65wsXPRmhTJIIOrvSu6tuha5WfosspU173dXm73cpXrQWRMxp4TWlcYlLxFRUWhS5cu+OKLLzBmzBi1+76+vrhx44YhjyAikjzlYfCAeq+W8vWi4EUGze3RdcjV0cqx0p61r3+vHb90sweQANU5oVTKoOTtzz//xMCBA7Xeb9KkCR4/fmzII4iI6gRth8G72LsYZZuQJrZNdIrzb1G6IEIKiZEhp0BQ3SPFbXCqi0GrTW1sbFBQUKD1/r179+Dk5GTII4iI6owQzxD0bdMXl1MuIz0vHc0aNkPXFl2Ns5pOxzznf/f/h8ghkVh+drnkNvOl+o3b4PzDoJ637t2747///S+ePn2qdu/hw4f46quvEBgYaMgjiIjqFHMzc/i7+SPMOwz+bv5G2wbhUcEjneKeFD5BI5tGiJ0SiwW9Fxjl2UTVyRhzQusag3reZs2ahZEjR2LYsGEYMGAAZDIZzp07h4sXL+LLL7+EIAgIDw83Vl2JiEgLfXol4u7GIT0vnWecUq1nrDmhdY1ByZuHhwf27duHDz/8EOvWrYMgCNi+fTsAoFu3bliyZAmeffZZo1SUiIi069qiKxpZN8KTwieVxkZdjKqBGhEZTiaTYdILk3hcVjkGJW8A8Pzzz2Pnzp3IysrCnTt3IAgCWrZsCTs7O3zzzTeYNm0aYmK4PwsRUXUyNzPHWL+xWH9hvamrQmQ0CkGBbT9tg98zfkzgyqhS8lZcXIwzZ87g7t27cHR0RO/evdG8eXP4+PigoKAAe/bswa5du/Do0SO4ubkZu85ERKTB9dTrpq4CUbVYdnYZ+rbpy6HTv+mdvKWlpWHcuHG4e/euuCmvlZUVNm3aBAsLC8yZMwdpaWnw8fHBokWL0L9/f6NXmoiIVBU/Lcb3Sd+buhpERld2n7euLbpWz2ptidE7eVu7di1SUlLw5ptvokuXLkhJSUFUVBQWLVqEJ0+e4Pnnn8e///1vdOvWrTrqS0REGuy5uof7olGdtuV/WzDjyAxkFGSI11zsXLC4z+J6N6Sqd/J2/vx5vPLKK5gzZ454rUmTJnjnnXfQu3dvbNy4EWZmRjvvnoiIdHA3866pq0BUrX5I/kHtmvLsU2NsdC0lemdZjx8/RqdOnVSu+fr6AgBeffVVJm5ERCbg5sT5xVT/1NezT/XOtORyOaysrFSuWVpaAgDs7OyMUysiItJLMzvuPk/1U308+7RKq03v37+P3377TXydk5MDALhz5w4cHBzU4tu3b1/F6hERUWViEmIw6+gsnWLNZGZQCIrqrRCRCdSns0+rlLytW7cO69atU7v+wQcfqLwWBAEymQw3btyoWu2IiKhCxU+LsfC7hTovVlAICszrNQ/rL6xHXnFeNdeOqObUp7NP9U7ePv744+qoBxER6SkmIQYLTi7Q6VSFsq78dYWJG9UZMsjgYu9Sr84+1Tt5e/nll6ujHkREpIeYhBiEHw6v0vYgJ/48UQ01IjKd+nb2KZeGEhFJjFwhx9IzS7mvG9V7TtZO9W6bEMAIZ5sSEVHNupxyGam5qaauBpHJRQ6JRI/nepi6GjWOyRsRkcTUp1V1RJoo57l1b9nd1FUxCQ6bEhFJTH1aVUekiQCh3s1zK4vJGxGRxHRt0RUudi6QQWbqqhCZhJO1E/q26WvqapgMkzciIokxNzPH4j6LAYAJHNVLmYWZ9epEhfKYvBERSVCIZwiiwqLQ3K65qatCZBL1ee4nkzciIokK8QxB7JRYhPuHm7oqRDWuPs/9ZPJGRCRh5mbm6OFW/7ZKoPpLBhlc7V3r1YkK5TF5IyKSOC5goPpC+TVen1eaAkzeiIgkjwsYqL5wsXeplycqlMfkjYioDuACBqrrGlk3woLeC+p94gbwhAUiojojxDMEfdv0xeWUy0jPS8etx7cQdTHK1NUiMorMwkzMODIDUTL2vLHnjYioDjE3M4e/mz/CvMO4kIHqFAECAGDZ2WWQK+Qmro1pMXkjIqqjuJCB6hoBAh7kPKjXG/QCTN6IiOqsyhYyyCDD5C6T4WTtVMM1IzJMfd6gF2DyZnK3b9/GG2+8AV9fX/Ts2RMrV65EcXGxqatFRHWEciFD+Q1NXe1dERUWhbm95+J/0/6HWT1mwdHa0US1JNJPfd6gF+CCBZPKysrC+PHj0apVK0RGRiItLQ0rVqxAYWEhFi9ebOrqEVEdEeIZgh7P9MA3F7+BbVNbtGjUAl1bdBX3yTI3M8eMHjMwzX8aZhyZgZg/Y0xcYyLNZJDBxd6lXm/QCzB5M6n9+/cjLy8PGzZsgJOTEwBALpfjgw8+wNSpU9G8OZf8E5FxmJuZo32j9vB+3hu2trZaY173fZ3JG9VK3KD3H0zeTCg2NhYBAQFi4gYAAwcOxJIlS3D+/Hm88sorVStYEIC8PM33zM0Ba+t/XmuLAwAzM8DGpmqx+fml9dBEJgPK/vDQJ7agAFAotNejYcOqxRYWaq+Dplh5BSudbG1L6w0ARUXA06fGibWxKW1nACguBkpKjBNrbV36daFvbElJabw2VlZAgwaaY/PzYVZQUPo1JQiqsU+flraFNpaWgIWF/rFyeenfnTYWFqXx+sYqFKVfa8aIbdCgtC2A0nbJzzdOrHm5H3Ra/i2f+vMUlsd+rHLNplj7vyGFDCiyMKtSrHWxQusyCgFAoWXVYq1KFDCr4J9yQRVjLUsUMDdWrIVM/Hdv+VSAuUJ7sD6xhQ1kEMxKYy2eCmhgpNiiBjIoqhDbQC7AQq49tthcBrm57rFNHV2wuM9ihHi8VPHPI+W/N6D0e0RlsbXpe4SuBDIZf39/4d///rfa9cDAQI3XdXHt2jXh2n//Kwil387V/jwNCRHy8vLEPwpbW+2xL76oGtukifbYzp1VYuVublpj5d7eqrHe3tpj3dxUYp927qw1VtGkiWrsiy9qj7W1FfLy8oRHjx4JP/30k1DUt6/WWAFQKbfk5Zcrjk1P/yd2zJiKY5OTxdjiKVMqjM3//fd/Yt95p+LYy5f/iZ0/v8LYgthYMbZo+fKKY48f/yf2008rjj10SIwt3LSpwtjC3bv/id29u+LYTZvE2IJDhyqMLfr0039ijx+vOHb58n9iY2MrjC2eP1+Mzb98ueLYd975J/b33yuOnTLln6+15OQKY0vGjPknNj294tiXXxa/1h89elRh7Jm2NoLHvz3EP3kWMq2xFzysVWIfNTTTGvtLCyuV2HuNGmiN/aO5hUrsH80ttMbea9RAJfaXFlZaYx81NFOJveBhrTU2z0KmEnumrU2F7VY29phPwwpj23/YSow9+IJdhbFdljwnxv4nwKHC2BfntRRjt/RyrDA2ZE4LMXZtP6cKY4fOfEaM/XiQc4Wxo99yFWMX/6txhbETJ7qIsf83ommFseGvNxNO3zyt8/cI5dd71r59FcbWpu8Rv/zyi3Dt2jWdftaz582EsrOz4eDgoHbd0dERWVlZ1fLM3Nxc3LpxQ3ztq1BAW+dzfl4eEsrE+jx9CgstsYWFhbhZJrZDSQmstMQWFRXhRpnYdkVFsNESW1JSohLbtrAQDbXEPn36VCXWMy8P9lpiFQqFSmxBQQEq+r2nbKxHdjYaVRD7xx9/QPF3L+RzWVloUkHsn3/+iaePHgEAWj55goqm4N66dQvFf/euPJuRAZcKYhMTE1H492/rrg8f4pkKYpOSkpD/d+9m8/R0tKgg9s6dO8j9uy2apqbCrYLYe/fuIfvv2MYPHqBVBbEp9+8j8+9Yp/v30bqC2AcPHuDx37EO9+7h+QpiU1NT8fDvWLs7d+BVQWx6ejrS/o61TUqCdwWxDx8+xIO/Y60TE9G+gtjHGRm4/3es5V9/oWMFsU+ePMG9v2MbPHmCThXEZmZl4c7fsWYFBfCrIDYnOxvJyckAgOTkZDSuIJaotrp++zoccx11+x7x99d7Wno61H/K/qPWfY/QsQdOJgiCoFMkGV379u3xzjvvYMqUKSrXBw8eDD8/PyxbtkzvMq9fvw5BoUCbZ7T8uOawqUpsQUEBkpOT0crFBTYV/aPhsKl6rAHDpgUFBbh79y7c3NxgY2PDYVOlahw2LRCE0q/1Vq1gU+7fxc9//Yy3j70NAJDLgOIqDoVy2FTHWA6bAtB/2HTzy9vRvUV3nf7dFzx9Wvr13qIFbMwq2FijFn2PuHXrFmQyGTp2rOhXvFLseTMhBwcH5OTkqF3PysqCo6NjlcuVmZnBtmlT3YK1TFyub7E2jRppncRdU3WQXKwh8vOhePgQNk2a6N7uxmCvrS/WwFg7u+qJbaitn7kKsX8ndzY2Nmpt/uRRsUryUZa264bGFlZTbNkE0ZixxdUV20AG6LiJsj6xJQ1kKDFx7FNzGZ6aGy/WvIG57t8vlF/v9vb6fY8x4fcImUy3tgKYvJmUh4cHEhMTVa7l5OTg4cOH8PDwMFGtiKi+qe97ZpE0TPx6IgZ5DcJLbV5Cs4bNVLa7qW+4Sa8JBQUFIS4uDtnZ2eK1EydOwMzMDD179jRhzYioPuExWiQFAgQc/eMoIo5FYMxXYxC0JQgxCfVzWxsmbyY0atQoNGzYEOHh4Th37hwOHTqElStXYtSoUdzjjYhqTGXHaBHVRmm5aQg/HF4vEzgmbybk6OiIXbt2wdzcHOHh4Vi9ejWGDRuGuXPnmrpqRFTPKI/Ram7HXxxJGgSULnBYdnYZ5IoKFpHVQZzzZmKtW7fGzp07TV0NIiKEeIagb5u+uJxyGel56bj1+BaiLkaZulpEWgkQ8CDnAS6nXIa/m7+pq1Nj2PNGREQiczNz+Lv5I8w7DD3ceuj0Hg61kqml56Wbugo1iskbERFpVNlCBhlkcLV3xbrB62q4ZkSq6tuKaSZvRESkUUULGcoeEj6o7SBsDNsIF7uKzv0gMj7lLxBdW3Q1dVVqFJM3IiLSSttCBhd7F0SFRSHEM0SMi50Si70j9qJ3q94mqCnVV4uCF9W7/d64YIGIiCpUfiGDtg1SlfPl/N38Ef1HNCKOReCpooIj34gM9E6Pd8RfIOoTJm9ERFQpZWKmc7zMHE1smyA1N7Uaa0X13XONnjN1FUyCyRsRERlVTEIMwg+Hi/twEVWX+rZQQYnJGxERGY1cIcfSM0uZuFG1kkEGF3sXnRcqyBXySof9pYTJGxERGc3llMscKqVqVXalsy4JWExCDJaeWarydeli54LFfRZLdr4cV5sSEZHR1LfNUqnmlV/pXJFTt08h/HC42i8UUj8XlT1vRERkNPV1DhIZpqFlQ+QV52m8J4MMjWwaYUHwArjYueg85KkQFFhxboXGIXwBAmSQYdnZZejbpq/khlDZ80ZEREZT2akMRJpM7jIZsr//K0v5enm/5fhXu3/B381f50TrRuYNpOWlab1f9lxUqWHyRkRERlPZqQwyyLB+8Hos6L0AY33H4tX2r5qimlSLOFk7waqBFfo/3x+2lrYq9/QZIi3vSfETneKkONTPYVMiIjIq5akMapPE7V2wKHiR2g/il1q/hPkn5yOzMLOGa0q1QWZhJj6J/UTlmq2FLaZ0nYJp/tOqPKTZyLKRTnFSHOpn8kZEREan66kMZWMv3buEi/cuAijd5Hfb5W3If5pf01WnWiC/JB9r49bCs4lnlVeEejt5o3nD5kjPS9c4703f7UZqEyZvRERULfQ5lcHczBw9nuuBHs/1ULm+/sL66qgaSYQhCwrMZGaYGzgXs2NmQwaZSgKn73YjtQ3nvBERUa0jV8ixK36XqatBJqZcUCBXyHHx7kUcvnEYF+9ehFwh1+n9fVv3RVRYFJrbNVe5bshcutqAPW9ERFTrXE65jKyiLFNXg2qB7259hznRc6q8ya4+Q/hSweSNiIhqHSmuAKTqsTN+p9o15Sa7uvae6TOELwUcNiUiolpHiisAqeYo568tO7tM5yHUuoTJGxER1Trc7JcqI+VNdg3F5I2IiGqdijb7JSqrPg6xM3kjIqJaSbnZb/mVgq72rpjcZTJc7FxMVDOqTerjEDsXLBARUa1V0UrB/wv6P1xOuYxtl7fhbNJZU1eVqqD8/mv6vleqm+waiskbERHVatpWCiqv+7v54/gfx7Hou0V4UqjbeZZUOwgQsKD3AjjbOuPDsx/iScETnZI5XTbZVQgKXL5/GVlPs+rE9iBlMXkjIiLJG+g1EAIEzDgyw9RVIT01adgEYd5hsGlgg/DD4Tr1xmk7J1fp1O1TWH5hOR4XPf7nPXrsDVfbMXkjIiLJkyvk+PDsh6auBlWBcs6aco7j0jNLVTbkdbV3xfze8+Fs46zTJrsxCTGYHTNbLQHUd2+42ozJGxERSd7llMsqP/DJdAY8PwAn/jyhU6yrvavKnDVDT0OQK+RYemapxp47AQJkkBl0XmptweSNiIgkrz5uF1EbzeoxC11bdNU5edM0Z82Q0xAqS+LL7g0n5RMXmLwREZHk1cftImobJ2snTPOfBqB0fllabprWuWtmMjOsH7ze6MOXuibxUk/2uc8bERFJHk9kML0JnSfA3Mxcpw2Wx/qNRSObRkY/2krXJF7qyT6TNyIikryyCQPVvLK9boD2DZbNZKVpx674XRjz1RgEbQlCTEKM0epRWRIvg0xtnp0UMXkjIqI6QZkw8OSFmvdR/4/U5q6FeIYgdkos9o7Yizc6vwGgdO+1spQrQI2VwFWWxAsQKtwbTiqYvBERUZ2hTBhm9Zhl6qoQSpOpri264njCcY33lXPilp1dZrQh1BDPEHwa8insGtip3XOydjLKM0yNyRsREdUp5mbmmNFjBhO4GlRR8qXPClBjyn2aq3YtqzDLqD19psLkjYiI6qRp/tM4hGqAGf66n1ZRUfJV0ytA5Qo5VpxbofFedfT0mQKTNyIiqpOU859kf/9nDPVlNeusHrPg0dhDr/doS75qegXo5ZTLSMtL03q/unr6ahKTNyIiqrO0rXrUx6vtX8Xq0NWY1WMWHK0djVi72uu5Rs/pnUxpi6/pFaD1Ya83btJLRER1Wvkjl+48uYMvfvlCpXfGzsIOCiiQX5IvXnO1dxUPP49JiMG6uHWVHpheVyiPpapss12lipIvZQ+opkPnlQmdMVeA1oe93pi8ERFRnVf+yKVp/tPUzs8EoPFMTblCjvkn59ebxE2ZiJVNuioig6zS5EvbofMu9i5igmwsXVt0RfOGzbUOncogg4u9i6T3emPyRkRE9Y628zM1Xdt4cSMyCzNroFa1w2CvwWIipi3pUirbO1kZQw+d15W5mTnmBs5FRExEjfT0mQKTNyIiIi3kCjl2/LzD1NWoUUf/OIr/C/o/lQROmXSl5abhcf5jONs6w8XORe/ky5BD5/XRt3VfzOkwB3uS9qj0wDWyaYSh3kPhaO0IuUIu2QSOyRsREZEWl1MuI6soy9TVqFHKlZhlk6yaSrqU5Aq5wT103Zt2x9jAsfgt4zecunUK//39v8goyMCO+B3YEb8DLnYuWNxnsVGHbGsKkzciIiItTt06ZeoqmIQpV2LGJMSoz42rYqJlbmaOrMIs7IzfqTZnUXk0V1RYlOQSOG4VQkREpIFcIcd/f/+vTrHWDawB1J194Ey1EjMmIQbhh8PV5tdpOgNVrpDj4t2LOHzjMC7evahx0125Qo6lZ5ZqXGwi5Q172fNGRESkweWUy3hS+KTSOJsGNoifHo+ziWe1TuyXippYiVl+SLTzM50R/1c8UnNT8eHZD7UmWjLIsOzsMvRt0xenbp3SqXcu/kG8zkdz1eSwsKGYvBEREWmg69DhKJ9RsGxgqbaa8ueUn7Hnlz3VXEvjEiBgfu/51TaRX9OQqJnMDApBoVPdHuQ8wMaLGzXuuVd2GPTFFi8CAB7mP9SpXrr+XRtjLp4xMHkjIiLSQNehw75t+or/X3Zif/z9+GqpV3X78OyHMJeZG30emHJItHzSpUviVtaOn3dU2jt3fMxxAEBT26Y6lanL37Ux5+IZinPeiIiINKjsWCeg4pMF3JzcqqtqRtH1Gc311jS/zFAVzT3TV0Wrf5W9c/EPShPnzq6djXI0lz5z8WoCkzciIiINlCcMAOoLEZSH3Ve02evrvq/DTKbfj9mZATOrVtkquPyX5oPZq2Mi/+WUywbPBZRBBidrJ51ilcOllf0dApVv2FsbFz0weSMiItJC28H2LvYualtMlF/9aG5mjvGdxuv0HGUP0PSA6ZjcZbJRP4eqKDuR3xgM3XpEmWhN6DxBp/iyw6X6/B1qUlniaey20gXnvBEREVVAl2OdtM2Heq/newhrGYajKUe1zu0q2wMEAEduHqnGz0Y/xtrvzdCtR5RnoPZt0xf7r+1HWm6axp4w5WrZzq6dkZCdIF435GguXdugJvfGY/JGRERUiYpOGNA2ET8tNw2zY2ZjdofZuDzwMr5O+Brnks8h/q945BTniHEu9i5Y0HsBHK0dsS5uXa3aaqR80lXV1ZbK+YPaki5NnG2csTB4IZrbNVd5zuI+ixF+OFzvc0urekqErolnTe6Nx+SNiIioiiqbDyWDDDv/3ImxgWMxsctETOwyUS0ByijIwPKzy42WtL3R+Q30bdMXGQUZ+PDsh1UqV9N+b4astlTOPdOUdGl6NgAs77dcY7nKYVC1uvzdOxfiGYL8/HydP9fKVJZ41sTeeOUxeSMiIqoiXeZDPS56jPgH8ej1fC8Aqj1AMQkxmHlkplFWYbrau4rJi1LI8yG4nHIZcXfjEHUxSqdyNPVgVdS7qOsRU9qSrvL7vJVNwioqq6rDoPqqKPHUddGDsTF5IyIiqiJd5zlp2iy2KttnyCBDI5tGWBC8oHSYTgAeFTzSmrwoE8WuLbri0K+HdBq2LJ886dK7qDz5oLIERlPSpTxhQd8krKrDoFWhS29fTWLyRkREVEW6znPStFmsvttnVDacWJHKeo8ECOJwa/nkSZ/VluWTKW1z5MrHSeFoqprs7asMkzciIqIq0mU+lLOVMzq7dla7p+/qREN7earae1TV1Za16UQCY6nJ3r6KSDZ5k8vl+Pzzz/H999/j1q1bEAQBXl5eeOedd9ClSxeV2OLiYqxZswaHDx9GXl4e/Pz8sGjRInh4eKjE3b59G8uXL8eVK1fQsGFDDB06FLNmzYKlpaVK3IEDB7Bt2zb89ddfcHd3R0REBIKDg1VicnJy8PHHH+PUqVMoKSnBiy++iIULF6JZM9Xf0uLj4/HJJ5/gxo0baNy4MUaPHo3JkydDJtO+ozcREdUOusyHmvD8BI29M7r22oX7h6OHWw+j9PJUpfeoKqstjTFHjrST7Ca9hYWF2LJlC9q3b49PPvkEq1atgqOjI8aNG4cLFy6oxC5fvhwHDhxAREQEIiMjUVxcjAkTJiAn55+l2llZWRg/fjxKSkoQGRmJiIgIfPXVV1ixYoVKWceOHcOiRYswcOBAbN26Fb6+vpg+fTquXr2qEjdr1iycP38e77//PlatWoWkpCRMnjwZT58+FWPu3LmDSZMmoWnTpti8eTPGjx+P9evX4/PPPzd+gxERUbWoaBPYT0M+Rfem3TW+r7Ljt5Qb977T4x34u/kbbXhO2XsU5h2mU7m61lO52rI2nkhQ10i2583a2hqnTp2Co6OjeK1nz54YPHgwdu3ahYCAAABAamoqDh48iCVLlmDYsGEAgI4dOyI4OBj79+/H5MmlO1nv378feXl52LBhA5ycnACU9u598MEHmDp1Kpo3L/1HuX79egwaNAizZs0CAPj7+yMhIQFRUVHYunUrAODKlSs4d+4ctm/fjsDAQACAu7s7QkNDcfLkSYSGhgIAtm/fjkaNGuHTTz+FpaUlAgICkJGRgU2bNmHs2LFqPX5ERFQ7aevRKioswo0bNzS+pzauYtRE33oaMkeOdCPZnjdzc3OVxE15zcvLC+np/4y7nzt3DgqFAgMGDBCvOTk5oWfPnoiNjRWvxcbGIiAgQEzcAGDgwIFQKBQ4f/48AODevXtITk7GwIEDVZ4bGhqKCxcuoLi4WCzLwcEBPXv2FGM8PDzg7e2t9syXXnpJJUkLDQ1FdnY2rly5UpVmISIiE9G3Rwsw/OimmqJPPWvjiQR1jWR73jR5+vQpfvnlF7zwwgvitcTERDRu3Fgt0WvdujUOHjyoEvfqq6+qxDg4OKBp06ZITEwUY4DSXrTyZZWUlODevXto3bo1EhMT4e7urjZvzcPDQywjPz8fDx48UJt35+HhAZlMhsTERHTvrrmrnYiI6o7atIqxIrrWszaeSFDX1Knkbdu2bUhLS8OECRPEa9nZ2bC3t1eLdXBwQFZWlkqcg4ODWpyjo6MYp/xYPk75Wnlf2zMdHR3x66+/AoA43658WZaWlrCxsVGpm74EQTDq7tJ1WUFBgcpHqhls95rHNjcNfdrdp4kP0KT0/4sKi6qzWgaprJ7tndujecPmSM9L17oCt7ldc7R3bl9tP6uk+PUuCILOixVrVfKWk5OjMuSpTcuWLdXmg50/fx6RkZGYNm0aOnToUF1VlISSkhKtcyxIs+TkZFNXoV5iu9c8trlp1Ld2f939daz+dbXGewIEjGk1Bgl/JGi8b0xSa3dd57rXquTtxIkTWLhwYaVx0dHRaN26tfj6t99+w4wZMzB48GBMnz5dJdbBwQG5ublqZWRnZ6sMpTo4OKisPlXKysoS45Qfc3Jy0LTpPxsuZmdnq9x3cHBAaqr6ZM2yZSl75so/s7i4GAUFBWrDvPqwsLBAmzZtqvz++qSgoADJyclo1aoVbGxsTF2deoPtXvPY5qZRX9vd29sbLZ5tgRXnViAtL0287mLngvd6voe+rftW6/Ol2O63bt3SObZWJW/Dhw/H8OHD9XrPnTt3MHnyZPj5+WH58uVq9z08PPDo0SOVxAkonb9Wdr5Z2floSjk5OXj48KEYp/xY/r2JiYmwsLBAy5YtxbgLFy6odYEmJSXB09MTAGBrawtXV1e1ZyYlJUEQBLW5cPqQyWSwtbWt8vvrIxsbG7aZCbDdax7b3DTqY7uHdQzDoPaDTDqXT0rtrs/+rpJdbQoA6enpmDhxIlxdXbF+/XpYWFioxQQGBsLMzAwnT54Ur2VlZeHcuXMICgoSrwUFBSEuLk7sRQNKewLNzMzEVaMtW7ZEq1atcOLECZVnREdHIyAgQOzuDAoKQlZWlsp+c0lJSfj999/Vnnn69GmUlJSolOXg4AA/P7+qNgsREVGtUJUVuFS5WtXzpo/CwkJMnjwZT548wYIFC/Dnn3+K9ywtLdGuXTsAgIuLC4YNG4aVK1fCzMwMzZs3x+bNm2Fvb49Ro0aJ7xk1ahR2796N8PBwTJ06FWlpaVi5ciVGjRol7vEGADNmzMC7774LNzc3dO/eHdHR0bh27Rr27Nkjxvj5+SEwMBDz58/He++9BysrK6xZswZeXl7o37+/GDdp0iQcOXIEc+bMwejRo5GQkIDt27cjIiKCe7wREVGdpu3cU6qcZJO3R48e4ebNmwCAt99+W+Xes88+izNnzoivFy5ciIYNG2L16tXIy8tD586dsWPHDpUVoY6Ojti1axeWLVuG8PBwNGzYEMOGDUNERIRK2YMHD0ZBQQG2bt2KLVu2wN3dHRs2bFDrKVu7di0+/vhjLF68GE+fPkVgYCAWLlyIBg3+afLnnnsO27dvx4oVKzBlyhQ4Oztj5syZmDhxotHaiYiIqLapi+ee1iSZIAjq63hJsq5fvw6g9BQJqlx+fj5u3LgBb29vycyLqAvY7jWPbW4abHd12s49VZ7WYIzNiaXY7vr8/Jb0nDciIiKqGXKFHBfvXsThG4dx8e7FKp1NynNPjUOyw6ZERERUM4w1zMlzT42DPW9ERESklXKYs3zSlZabhvDD4YhJiNG5LJ57ahxM3oiIiEgjYw9z8txT42DyRkRERBrpM8ypi64tusLFzkVcnFCeDDK42ruia4uuVapvfcHkjYiIiDQy9jCnuZk5FvdZDABqCZzy9aLgRdzvrRJM3oiIiEij6hjmDPEMQVRYFJrbNVe57mLvYpRtQuoDrjYlIiIijZTDnGm5aRrnvckgg4u9i97DnCGeIejbpi9PWKgiJm9ERESkkXKYM/xwOGSQqSRwhg5zKs89Jf1x2JSIiIi04jBn7cOeNyIiIqoQhzlrFyZvREREVCkOc9YeHDYlIiIikhAmb0REREQSwuSNiIiISEKYvBERERFJCJM3IiIiIglh8kZEREQkIUzeiIiIiCSEyRsRERGRhHCTXiIiIjIpuULO0xv0wOSNiIiITCYmIQZLzyxFam6qeM3FzgWL+yzmualacNiUiIiITCImIQbhh8NVEjcASMtNQ/jhcMQkxJioZrUbkzciIiKqcXKFHEvPLIUAQe2e8tqys8sgV8hrumq1HpM3IiIiqnGXUy6r9biVJUDAg5wHuJxyuQZrJQ1M3oiIiKjGpeelGzWuPmHyRkRERDWuWcNmRo2rT5i8ERERUY3r2qIrXOxcIINM430ZZHC1d0XXFl1ruGa1H5M3IiIiqnHmZuZY3GcxAKglcMrXi4IXcb83DZi8ERERkUmEeIYgKiwKze2aq1x3sXdBVFgU93nTgpv0EhERkcmEeIagb5u+PGFBD0zeiIiIyKTMzczh7+Zv6mpIBodNiYiIiCSEyRsRERGRhDB5IyIiIpIQJm9EREREEsLkjYiIiEhCmLwRERERSQiTNyIiIiIJYfJGREREJCFM3oiIiIgkRCYIgmDqSpDxxMfHQxAEWFpamroqkiAIAkpKSmBhYQGZTFb5G8go2O41j21uGmx305BiuxcXF0Mmk6Fz586VxvJ4rDpGKl+ktYVMJmOiawJs95rHNjcNtrtpSLHdZTKZzj/D2fNGREREJCGc80ZEREQkIUzeiIiIiCSEyRsRERGRhDB5IyIiIpIQJm9EREREEsLkjYiIiEhCmLwRERERSQiTNyIiIiIJYfJGREREJCFM3oiIiIgkhMkbERERkYQweSMiIiKSECZvJEm//vorvL294efnp3avuLgYn3zyCXr27AlfX1+88cYbSExMVIu7ffs23njjDfj6+qJnz55YuXIliouL1eIOHDiAkJAQdOzYEWFhYTh79qxaTE5ODubPn49u3brBz88PM2fORHp6ulpcfHw8Ro4cCR8fHwQHB2PLli0QBKGKrVD95HI5tm7dijFjxqB79+7o1q0bxo4di59++kktlu1ee+na5vXJ8ePH8fbbbyMoKAi+vr4YOnQoDh48qPZ1UdNfh4IgYMuWLejduzd8fHwwcuRIXL16Va2stLQ0zJgxA35+fujWrRsWLFiA3NxcwxqlhuXl5SEoKAheXl64fv26yj22eyUEIolRKBTCiBEjhB49egi+vr5q9xctWiS88MILwoEDB4TY2FjhtddeE1588UUhOztbjMnMzBR69uwpjBkzRoiNjRUOHDggvPDCC8IHH3ygUtbRo0cFLy8vYc2aNcKFCxeERYsWCe3atROuXLmiEjdx4kQhKChIOHbsmHDq1Clh8ODBQlhYmFBSUiLGJCcnC76+vkJ4eLgQFxcn7NixQ2jfvr2wbds24zaQEeXm5gpdunQRPvzwQ+Hs2bPCDz/8IISHhwve3t5CXFycSizbvXbStc3rmxEjRggRERHCsWPHhLi4OGHVqlVC27ZthcjISDHGFF+HmzdvFtq3by/s2LFDiIuLE8LDwwU/Pz/h7t27YkxxcbEwePBgYfDgwcLp06eFY8eOCUFBQcKUKVOqp7GqycqVK4UePXoInp6ewrVr18TrbPfKMXkjyTlw4IDQr18/YfXq1WrJ24MHDwRvb29h//794rUnT54Ivr6+wpYtW8RrmzZtEnx9fYUnT56I1/bv3y94e3sLqamp4rX+/fsLs2fPVnnGyJEjhTfffFN8HR8fL3h6ego//vijeO327duCl5eXcOzYMfHaokWLhODgYKGoqEi8tnr1aqFLly4q12qTp0+fCpmZmWrXBgwYIEydOlW8xnavvXRt8/rm8ePHatcWLlwodO7cWZDL5YIg1PzXYWFhodC5c2dh9erVYkxRUZEQHBwsLFmyRLx25MgRwcvLS7h9+7Z47ccffxQ8PT2FX375Rd+mMIlbt24Jvr6+whdffKGWvLHdK8dhU5KU7OxsrF69GvPmzYOFhYXa/XPnzkGhUGDAgAHiNScnJ/Ts2ROxsbHitdjYWAQEBMDJyUm8NnDgQCgUCpw/fx4AcO/ePSQnJ2PgwIEqzwgNDcWFCxfEYafY2Fg4ODigZ8+eYoyHhwe8vb3VnvnSSy/B0tJSpazs7GxcuXKlii1SvczNzeHo6Kh2zcvLS2V4gu1ee+nS5vWRs7Oz2jVvb2/k5uYiPz/fJF+H8fHxyM3NVXmmpaUl+vXrp1aWl5cXPDw8xGs9e/aEk5MTfvjhh6o2SY1avnw5Ro0aBXd3d5XrbHfdMHkjSVm7di3at2+P4OBgjfcTExPRuHFjtYSjdevWKvOvEhMTVf4BAoCDgwOaNm0qxik/lv/m0rp1a5SUlODevXtinLu7O2QymUqch4eHWEZ+fj4ePHig9kwPDw/IZDKNc8Nqq6dPn+KXX35R+VzY7rWXLm1OpX7++Wc0b94cdnZ2Jvk6VH4sH9e6dWv89ddfKCwsFOPKx8hkMri7u0vi7/TEiRNISEhAeHi42j22u26YvJFk3LhxAwcPHsS8efO0xmRnZ8Pe3l7tuoODA7KyslTiHBwc1OIcHR3FOOXH8nHK18r72p5ZtqycnByNZVlaWsLGxkalbrXdtm3bkJaWhgkTJojX2O61ly5tTsBPP/2E6OhoTJw4EYBpvg6zs7NhaWkJKysrtWcKgqDXM2urgoICrFixAhEREbCzs1O7z3bXTYMafRpRGTk5ORpXBpXXsmVLWFhY4IMPPsBrr72G1q1b10Dt6i592r3sUAMAnD9/HpGRkZg2bRo6dOhQXVUkqlGpqamIiIhA9+7dMW7cOFNXp0777LPP0LhxY7z66qumroqkMXkjkzlx4gQWLlxYaVx0dDRu3ryJxMRErF69GtnZ2QCAoqIiAKW/DVlZWcHKygoODg4al21nZ2erDOk5ODiIv5WVlZWVJcYpP+bk5KBp06YqZZW97+DggNTU1ArLUv62Vv6ZxcXFKCgoUBturE76tHvZRPm3337DjBkzMHjwYEyfPl0llu1ee+nS5vVZdnY2Jk+eDCcnJ0RGRsLMrHRAyhRfhw4ODiguLkZRUZFKL1B2djZkMplKnKZ/b1lZWXB1da1CK9SM+/fv4/PPP0dUVJTYFvn5+eLHvLw8truOmLyRyQwfPhzDhw/XKTY6OhpZWVno06eP2r2uXbti8uTJePfdd+Hh4YFHjx6p/WAqP1eh7LwIpZycHDx8+FCMU34s/97ExERYWFigZcuWYtyFCxcgCILK/IukpCR4enoCAGxtbeHq6qr2zKSkJAiCoDaPojrp0+5Kd+7cweTJk+Hn54fly5er3We71166tHl9VVhYiKlTpyInJwdffvmlypCYKb4OlR+TkpLQtm1blWc+88wzsLa2FuMSEhJUyhIEAUlJSSoT+GublJQUlJSUYMqUKWr3xo0bh06dOmH16tUA2O6V4Zw3koSXX34Z//nPf1T+vPzyy7CyssJ//vMfjBw5EgAQGBgIMzMznDx5UnxvVlYWzp07h6CgIPFaUFAQ4uLixN/mgNIeKTMzM/EfYcuWLdGqVSucOHFCpS7R0dEICAgQhxSDgoKQlZWFCxcuiDFJSUn4/fff1Z55+vRplJSUqJTl4OCgcbPh2iI9PR0TJ06Eq6sr1q9fr3GVL9u99tKlzeujp0+fYtasWUhMTMS2bdvQvHlzlfum+Drs3Lkz7OzscPz4cTGmpKQEJ0+eVCvr5s2bSE5OFq9duHABmZmZ6NWrlwGtUr28vb3Vvo8r5zB/8MEHWLJkCdtdVzW6MQmREa1fv17rJr1dunQRDh48KPz444/C66+/rnWz2Ndff1348ccfhYMHDwpdunRR27hUua/PunXrhIsXLwqLFy8W2rVrJ8THx6vETZw4UejVq5cQHR0tnD59usLNImfMmCHExcUJO3furPWbxRYUFAhhYWGCn5+fcPr0aeHKlSvin99++00llu1eO+na5vXNwoULBU9PT+Hzzz9X+bq+cuWKuP+XKb4ON2/eLHTo0EHYuXOnEBcXJ8yYMaPCzWLPnDkjHDt2TOjVq5fkNukVBEG4ePGi2j5vbPfKMXkjydKWvBUVFQkrVqwQAgICBB8fH2HChAnCrVu31OJu3boljB8/XvDx8RECAgKEFStWaNy09auvvhL69esntG/fXvxHW152drYwb948oUuXLoKvr68wffp0jRug/vzzz8Lw4cOFDh06CEFBQcLmzZsFhUJRxRaofvfu3RM8PT01/gkODlaJZbvXXrq2eX0SHBys9Wv73r17YlxNfx0qFAph06ZNQlBQkNChQwdh+PDhakmLIAhCamqqMH36dMHX11fo0qWLMG/ePCEnJ8cILVOzNCVvgsB2r4xMEOrRAX9EREREEsc5b0REREQSwuSNiIiISEKYvBERERFJCJM3IiIiIglh8kZEREQkIUzeiIiIiCSEyRsRERGRhDB5IyIiIpIQJm9EVK+lpKTAy8sLX3/9tUnr8fXXX8PLywspKSk1+txLly7By8sLly5dqtHnElHVMXkjojpNmRRp+rNq1SqN7/nhhx8QGRmpdr2goACRkZEmS3SGDBmC3r17o6KDcUaNGoUePXrg6dOnNVgzIqpJDUxdASKimjBz5ky0aNFC5ZqnpyeeffZZXLt2DQ0a/PPt8IcffsDevXsxY8YMlfiCggJs2LAB06dPR/fu3Wuk3mUNGTIEq1evxk8//YSuXbuq3U9JScHVq1fx+uuvq3w+RFS38F83EdULQUFB6Nixo8Z7VlZWNVybqhkyZAg+/fRTHDlyRGPyduzYMQiCgCFDhpigdkRUUzhsSkT1Wvk5b3PnzsXevXsBQGWINSUlBQEBAQCADRs2iNfLDq/evn0bM2fORLdu3dCxY0e88sorOH36tNoz//zzT4wbNw4+Pj4ICgrCxo0boVAoKq2rq6srunbtipiYGJSUlKjdP3r0KNzc3NCpUyfcv38f77//PkJCQuDj44Pu3btj5syZOs2p69OnD+bOnat2fezYsRg7dqzKteLiYqxfvx79+vVDhw4d0KtXL6xcuRLFxcUqcefPn8fo0aPRpUsX+Pn5ISQkBJ9++mmldSEidex5I6J6ITc3FxkZGSrXnJ2d1eJGjhyJ9PR0nD9/HitXrlSJff/99/H++++jX79+6NevH4DSBA8oTchGjx6N5s2bY/LkybC1tcXx48cRHh6OyMhIMf7hw4cYN24c5HI5pkyZAhsbG3z11Vc69/4NGTIEixYtwrlz5xAcHCxe/+OPP5CQkIDw8HAAwPXr13HlyhUMGjQILi4uuH//Pr744guMGzcOx44dg42NjR6tp5lCocDbb7+Nn3/+GSNGjEDr1q2RkJCAXbt2ITk5GRs3bhTbZurUqfDy8sLMmTNhaWmJO3fuID4+3uA6ENVHTN6IqF6YMGGC2rU//vhD7Zqfnx9atWqF8+fPY+jQoSr3QkJC8P7778PLy0vt3ocffghXV1ccOnQIlpaWAIDXXnsNo0ePxqpVq8TkbevWrcjIyMCBAwfg4+MDAHj55ZfRv39/nT6PkJAQLFu2DEePHlVJ3o4ePQoA4pBp7969MWDAAJX3BgcHY+TIkYiJicG//vUvnZ5XkSNHjiAuLg67d+9Gly5dxOvPP/88lixZgvj4eHTu3Bnnz59HSUkJtm7dqjFhJiL9cNiUiOqFxYsXY8eOHSp/jCUzMxMXL17EwIEDxR6+jIwMPHnyBIGBgUhOTkZaWhqA0sUQvr6+YuIGlPbq6TpPzdHREb169cKZM2eQn58PABAEAceOHUOHDh3g7u4OALC2thbfU1JSgidPnsDNzQ0ODg74/fffjfJ5nzhxAq1bt4aHh4f4OWdkZMDf3x8AxFW5Dg4OAIDTp0/rNDxMRBVjzxsR1Qs+Pj5aFywY6u7duxAEAevWrcO6des0xjx+/BjNmzfHX3/9hU6dOqndVyZduhgyZAi+++47nD59GkOGDEF8fDzu37+PcePGiTGFhYXYvHkzvv76a6SlpalsL5KTk6PHZ6fdnTt3cPv2bXEuYHmPHz8GAISGhuLAgQNYuHAhVq9ejYCAAPTr1w8DBgyAmRn7EIj0xeSNiMhAyt6kiRMn4sUXX9QY4+bmZrTnBQcHw97eHkePHsWQIUNw9OhRmJubY9CgQWLMsmXL8PXXX2P8+PHw9fWFvb09ZDIZIiIiKtwnriJyuRzm5ubia4VCAU9PT8ybN09jvIuLC4DSXsC9e/fi0qVL+P777/Hjjz8iOjoaX375JT7//HOVMomockzeiIjKkclkel1v2bIlAMDCwgI9evSosOxnnnkGd+7cUbuelJSkc/0sLS0REhKCb7/9Fo8ePcKJEyfg7++Ppk2bijHKeW1lV40WFRXp1Ovm6OiI7Oxstet//fWX+LkCpQnpzZs3ERAQoLVtlMzMzBAQEICAgADMmzcPmzZtwpo1a3Dp0qVK24yIVLG/moioHOVKzPIJjLbrjRs3Rrdu3fDll18iPT1drbyyq1x79eqFq1ev4tq1ayr3jxw5olcdhwwZgpKSEixevBgZGRlqc+Y09Wbt3r0bcrm80rJbtmyJX375RWW7j7Nnz+LBgwcqcQMHDkRaWhq++uortTIKCwvFOXmZmZlq9729vQFAbUsRIqoce96IiMpp3749AGD58uUIDAwUhyStra3Rpk0bHD9+HK1atYKTkxOef/55eHp6YsmSJXjttdcwZMgQjBgxAi1btsSjR49w9epVpKam4vDhwwCAN998E99++y3efPNNjBs3Ttwq5JlnntG4+lWbbt26wcXFBadPn4a1tbW4mlWpd+/e+Pbbb2FnZ4c2bdrg6tWriIuLg5OTU6VlDx8+HDExMXjzzTcxcOBA3L17F0eOHFEb+h06dCiOHz+OJUuW4NKlS+jcuTPkcjkSExNx4sQJbNu2DR07dkRUVBR++ukn9OrVC88++yweP36Mffv2wcXFBS+88ILOnzMRlWLyRkRUTv/+/TF27FgcO3YMhw8fhiAI4nyy5cuXY9myZfj4449RUlKC6dOnw9PTE23atMGhQ4ewYcMGfPPNN8jMzISzszPatWsn7r0GAM2aNcN//vMfLF++HFu2bIGTkxNGjRqFZs2aYcGCBTrX0czMDIMGDcL27dsRHBwMOzs7lfsLFiyAmZkZjhw5gqKiInTu3Bk7duzAm2++WWnZL774IubOnYsdO3bgo48+QocOHbBp0yZ88sknanWIiorCzp078e233+K7776DjY0NWrRogbFjx4qLMPr06YP79+/j0KFDePLkCRo1aoRu3bphxowZsLe31/lzJqJSMqGqM1eJiIiIqMZxzhsRERGRhDB5IyIiIpIQJm9EREREEsLkjYiIiEhCmLwRERERSQiTNyIiIiIJYfJGREREJCFM3oiIiIgkhMkbERERkYQweSMiIiKSECZvRERERBLC5I2IiIhIQv4fj5Glt2SIv9oAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this plot, we don't see any discernible patterns. The residuals are thus randomly distributed around the horizontal line at zero, with no clear shape or trend. If there's a pattern, or if the residuals show a systematic deviation from zero, it could suggest issues such as model misspecification, non-linearity, or omitted variable bias."
      ],
      "metadata": {
        "id": "v_sSa8YrvcHl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Assumtion 3: Exogeneity"
      ],
      "metadata": {
        "id": "smwiQmSek8gj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the residuals\n",
        "residuals = model_fitted.resid\n",
        "\n",
        "# Check for correlation between residuals and each predictor\n",
        "for column in X_train.columns:\n",
        "    corr_coefficient = np.corrcoef(X_train[column], residuals)[0, 1]\n",
        "    print(f'Correlation between residuals and {column}: {np.round(corr_coefficient,2)}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BjJtMySLs5W2",
        "outputId": "9dcacba9-a6d7-477f-a030-1017a5adf278"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation between residuals and longitude: -0.0\n",
            "Correlation between residuals and latitude: 0.0\n",
            "Correlation between residuals and housing_median_age: -0.0\n",
            "Correlation between residuals and total_rooms: 0.0\n",
            "Correlation between residuals and population: 0.0\n",
            "Correlation between residuals and households: 0.0\n",
            "Correlation between residuals and median_income: 0.0\n",
            "Correlation between residuals and ocean_proximity_<1H OCEAN: -0.0\n",
            "Correlation between residuals and ocean_proximity_INLAND: 0.0\n",
            "Correlation between residuals and ocean_proximity_NEAR BAY: -0.0\n",
            "Correlation between residuals and ocean_proximity_NEAR OCEAN: 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Durbin-Wu-Hausman Test:**\n",
        "For a more formal statistical test, use the **Durbin-Wu-Hausman** test. This involves comparing your model with one that includes an instrumental variable. This test checks whether the coefficients of the model change significantly when the potentially endogenous variables are instrumented. This test is a more advanced, econometrical approach and requires identification of suitable instruments, which is not always straightforward."
      ],
      "metadata": {
        "id": "s5H-F9D_wLKO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Assumtion 4: Homoskedasticty"
      ],
      "metadata": {
        "id": "VJDTW0W8lAnD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the residuals\n",
        "plt.scatter(model_fitted.fittedvalues, model_fitted.resid, color = \"forestgreen\")\n",
        "plt.axhline(y=0, color='red', linestyle='--')\n",
        "plt.xlabel('Fitted Values')\n",
        "plt.ylabel('Residuals')\n",
        "plt.title('Residuals vs Fitted Values')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "tX4zPXk6wusr",
        "outputId": "f20768ca-d150-4c3e-afed-91916a8d21ee"
      },
      "execution_count": 44,
      "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": [
        "**Random Scatter:** If plot shows a random scatter of residuals around the horizontal line at zero, it supports the homoscedasticity assumption.\n",
        "\n",
        "**Pattern or Shape:** If residuals display a pattern (such as a curve) or form a funnel shape where the spread increases with fitted values, this would suggest heteroscedasticity, meaning variance of residuals changes with the level of the independent variables."
      ],
      "metadata": {
        "id": "kbgyuRolxWbq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train/Test/Evaluation with Sklearn\n",
        "\n"
      ],
      "metadata": {
        "id": "1qiQayKYxr-x"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Scaling the Data"
      ],
      "metadata": {
        "id": "ZNQ6_d23zYwr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "# Initialize the StandardScaler\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Fit the scaler on the training data and transform it\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "\n",
        "# Apply the same transformation to the test data\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "fEfCSiQOzVCp"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "\n",
        "# Create and fit the model\n",
        "lr = LinearRegression()\n",
        "lr.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Make predictions on the scaled test data\n",
        "y_pred = lr.predict(X_test_scaled)\n",
        "\n",
        "# Calculate MSE and RMSE\n",
        "mse = mean_squared_error(y_test, y_pred)\n",
        "rmse = sqrt(mse)\n",
        "\n",
        "# Output the performance metrics\n",
        "print(f'RMSE on Test Set: {rmse}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J3nUb3t_woTG",
        "outputId": "8b99bc2e-00c0-476a-9372-b4857cf896c0"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "RMSE on Test Set: 59405.88869441674\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred)\n",
        "y_test"
      ],
      "metadata": {
        "id": "AAYG8yMckgOF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "11f7ca5f-f42c-46da-b41e-34d2867a15ca"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[153414.2018804 ]\n",
            " [239312.04418031]\n",
            " [ 70054.78117497]\n",
            " ...\n",
            " [129935.7653883 ]\n",
            " [232846.31280863]\n",
            " [207120.21348536]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       median_house_value\n",
              "7322             164400.0\n",
              "465              196400.0\n",
              "12768             65200.0\n",
              "2617              73600.0\n",
              "13351            195100.0\n",
              "...                   ...\n",
              "11691            217000.0\n",
              "1454             229200.0\n",
              "7360             142000.0\n",
              "19118            200600.0\n",
              "13084            129500.0\n",
              "\n",
              "[3806 rows x 1 columns]"
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              "      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-12bf83f8-06d2-406f-8bd3-80174fd6a763 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-12bf83f8-06d2-406f-8bd3-80174fd6a763');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-1028a80b-ed28-425c-80dc-f6bf9fb5ffb5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1028a80b-ed28-425c-80dc-f6bf9fb5ffb5')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1028a80b-ed28-425c-80dc-f6bf9fb5ffb5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b179df99-dc72-409d-a970-cc3c14338440\">\n",
              "    <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('y_test')\"\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_b179df99-dc72-409d-a970-cc3c14338440 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('y_test');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "y_test",
              "summary": "{\n  \"name\": \"y_test\",\n  \"rows\": 3806,\n  \"fields\": [\n    {\n      \"column\": \"median_house_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 94237.8101977866,\n        \"min\": 17500.0,\n        \"max\": 482200.0,\n        \"num_unique_values\": 2108,\n        \"samples\": [\n          126300.0,\n          88700.0,\n          87600.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    }
  ]
}