Boston_house.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Boston_house.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "qlZbq6LE_jXy",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Boston House Price Prediction with Linear regression"
      ]
    },
    {
      "metadata": {
        "id": "Pe7DKuL2_o1M",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Import Boston Data from sklearn\n",
        "from sklearn.datasets import load_boston\n",
        "\n",
        "#Assign Dataset to Variable\n",
        "boston = load_boston()\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gvWaBzx8AGTG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "20c41be7-bb0e-4774-e67f-34611e8c60c4"
      },
      "cell_type": "code",
      "source": [
        "# Verify that the data imported\n",
        "# Shape tells you ho many rows and columns are in your data\n",
        "print(boston.data.shape)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(506, 13)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "x1Y2utnvGLXv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1108
        },
        "outputId": "6c44b867-dded-4b1d-b949-9b95d674e80a"
      },
      "cell_type": "code",
      "source": [
        "# take a quick peak at what our boston Data looks like\n",
        "boston"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'DESCR': \".. _boston_dataset:\\n\\nBoston house prices dataset\\n---------------------------\\n\\n**Data Set Characteristics:**  \\n\\n    :Number of Instances: 506 \\n\\n    :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\\n\\n    :Attribute Information (in order):\\n        - CRIM     per capita crime rate by town\\n        - ZN       proportion of residential land zoned for lots over 25,000 sq.ft.\\n        - INDUS    proportion of non-retail business acres per town\\n        - CHAS     Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\\n        - NOX      nitric oxides concentration (parts per 10 million)\\n        - RM       average number of rooms per dwelling\\n        - AGE      proportion of owner-occupied units built prior to 1940\\n        - DIS      weighted distances to five Boston employment centres\\n        - RAD      index of accessibility to radial highways\\n        - TAX      full-value property-tax rate per $10,000\\n        - PTRATIO  pupil-teacher ratio by town\\n        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\\n        - LSTAT    % lower status of the population\\n        - MEDV     Median value of owner-occupied homes in $1000's\\n\\n    :Missing Attribute Values: None\\n\\n    :Creator: Harrison, D. and Rubinfeld, D.L.\\n\\nThis is a copy of UCI ML housing dataset.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/housing/\\n\\n\\nThis dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\\n\\nThe Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\\nprices and the demand for clean air', J. Environ. Economics & Management,\\nvol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\\n...', Wiley, 1980.   N.B. Various transformations are used in the table on\\npages 244-261 of the latter.\\n\\nThe Boston house-price data has been used in many machine learning papers that address regression\\nproblems.   \\n     \\n.. topic:: References\\n\\n   - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\\n   - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\\n\",\n",
              " 'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
              "         4.9800e+00],\n",
              "        [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
              "         9.1400e+00],\n",
              "        [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
              "         4.0300e+00],\n",
              "        ...,\n",
              "        [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
              "         5.6400e+00],\n",
              "        [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
              "         6.4800e+00],\n",
              "        [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
              "         7.8800e+00]]),\n",
              " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n",
              "        'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7'),\n",
              " 'filename': '/usr/local/lib/python3.6/dist-packages/sklearn/datasets/data/boston_house_prices.csv',\n",
              " 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
              "        18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
              "        15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
              "        13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
              "        21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
              "        35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
              "        19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
              "        20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
              "        23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
              "        33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
              "        21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
              "        20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
              "        23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
              "        15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
              "        17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
              "        25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
              "        23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
              "        32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
              "        34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
              "        20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
              "        26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
              "        31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
              "        22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
              "        42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
              "        36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
              "        32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
              "        20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
              "        20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
              "        22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
              "        21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
              "        19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
              "        32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
              "        18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
              "        16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
              "        13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
              "         7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
              "        12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
              "        27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
              "         8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
              "         9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
              "        10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
              "        15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
              "        19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
              "        29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
              "        20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
              "        23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "YflCf-jGGGOT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# import pandas | Note that usually when writing code you will keep your imports at the top\n",
        "# For this workshop I'm importing them when we use them to make it more clear what we're using them for\n",
        "import pandas as pd "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ED2X2MsKGaUh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# We won't go too deep into Pandas or visualizing our data\n",
        "# but lets take a quick look at it in a dataframe\n",
        "# Create a DataFrome from Pandas | Excel looking table\n",
        "df = pd.DataFrame(boston.data, columns=boston.feature_names)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ktdAHwybG410",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1969
        },
        "outputId": "661b9929-1e5e-4a8e-bf77-719d55d43799"
      },
      "cell_type": "code",
      "source": [
        "#print out the DataFrame for easier to read data\n",
        "df\n",
        "\n",
        "## Print just the last 5 rows \n",
        "# df.tail()\n",
        "\n",
        "## prints the first 5 rows\n",
        "# df.head()\n",
        "\n",
        "## Change settings to display all of our rows. \n",
        "# pd.options.display.max_rows = 200"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        vertical-align: middle;\n",
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              "        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>CRIM</th>\n",
              "      <th>ZN</th>\n",
              "      <th>INDUS</th>\n",
              "      <th>CHAS</th>\n",
              "      <th>NOX</th>\n",
              "      <th>RM</th>\n",
              "      <th>AGE</th>\n",
              "      <th>DIS</th>\n",
              "      <th>RAD</th>\n",
              "      <th>TAX</th>\n",
              "      <th>PTRATIO</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.00632</td>\n",
              "      <td>18.0</td>\n",
              "      <td>2.31</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.575</td>\n",
              "      <td>65.2</td>\n",
              "      <td>4.0900</td>\n",
              "      <td>1.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>15.3</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.02731</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>6.421</td>\n",
              "      <td>78.9</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.02729</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.07</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.469</td>\n",
              "      <td>7.185</td>\n",
              "      <td>61.1</td>\n",
              "      <td>4.9671</td>\n",
              "      <td>2.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>17.8</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.03237</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.998</td>\n",
              "      <td>45.8</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06905</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>7.147</td>\n",
              "      <td>54.2</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.02985</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.18</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.458</td>\n",
              "      <td>6.430</td>\n",
              "      <td>58.7</td>\n",
              "      <td>6.0622</td>\n",
              "      <td>3.0</td>\n",
              "      <td>222.0</td>\n",
              "      <td>18.7</td>\n",
              "      <td>394.12</td>\n",
              "      <td>5.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.08829</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>6.012</td>\n",
              "      <td>66.6</td>\n",
              "      <td>5.5605</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>395.60</td>\n",
              "      <td>12.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.14455</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>6.172</td>\n",
              "      <td>96.1</td>\n",
              "      <td>5.9505</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>19.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.21124</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>5.631</td>\n",
              "      <td>100.0</td>\n",
              "      <td>6.0821</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>386.63</td>\n",
              "      <td>29.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.17004</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>6.004</td>\n",
              "      <td>85.9</td>\n",
              "      <td>6.5921</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>386.71</td>\n",
              "      <td>17.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.22489</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>6.377</td>\n",
              "      <td>94.3</td>\n",
              "      <td>6.3467</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>392.52</td>\n",
              "      <td>20.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.11747</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>6.009</td>\n",
              "      <td>82.9</td>\n",
              "      <td>6.2267</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>13.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.09378</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.87</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.524</td>\n",
              "      <td>5.889</td>\n",
              "      <td>39.0</td>\n",
              "      <td>5.4509</td>\n",
              "      <td>5.0</td>\n",
              "      <td>311.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>390.50</td>\n",
              "      <td>15.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.62976</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.949</td>\n",
              "      <td>61.8</td>\n",
              "      <td>4.7075</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>8.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.63796</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.096</td>\n",
              "      <td>84.5</td>\n",
              "      <td>4.4619</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>380.02</td>\n",
              "      <td>10.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.62739</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.834</td>\n",
              "      <td>56.5</td>\n",
              "      <td>4.4986</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>395.62</td>\n",
              "      <td>8.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1.05393</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.935</td>\n",
              "      <td>29.3</td>\n",
              "      <td>4.4986</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>386.85</td>\n",
              "      <td>6.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.78420</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.990</td>\n",
              "      <td>81.7</td>\n",
              "      <td>4.2579</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>386.75</td>\n",
              "      <td>14.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.80271</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.456</td>\n",
              "      <td>36.6</td>\n",
              "      <td>3.7965</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>288.99</td>\n",
              "      <td>11.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.72580</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.727</td>\n",
              "      <td>69.5</td>\n",
              "      <td>3.7965</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>390.95</td>\n",
              "      <td>11.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1.25179</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.570</td>\n",
              "      <td>98.1</td>\n",
              "      <td>3.7979</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>376.57</td>\n",
              "      <td>21.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>0.85204</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.965</td>\n",
              "      <td>89.2</td>\n",
              "      <td>4.0123</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>392.53</td>\n",
              "      <td>13.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1.23247</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.142</td>\n",
              "      <td>91.7</td>\n",
              "      <td>3.9769</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>18.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>0.98843</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.813</td>\n",
              "      <td>100.0</td>\n",
              "      <td>4.0952</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>394.54</td>\n",
              "      <td>19.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>0.75026</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.924</td>\n",
              "      <td>94.1</td>\n",
              "      <td>4.3996</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>394.33</td>\n",
              "      <td>16.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>0.84054</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.599</td>\n",
              "      <td>85.7</td>\n",
              "      <td>4.4546</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>303.42</td>\n",
              "      <td>16.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>0.67191</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>5.813</td>\n",
              "      <td>90.3</td>\n",
              "      <td>4.6820</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>376.88</td>\n",
              "      <td>14.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>0.95577</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.047</td>\n",
              "      <td>88.8</td>\n",
              "      <td>4.4534</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>306.38</td>\n",
              "      <td>17.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>0.77299</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.495</td>\n",
              "      <td>94.4</td>\n",
              "      <td>4.4547</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>387.94</td>\n",
              "      <td>12.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1.00245</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.538</td>\n",
              "      <td>6.674</td>\n",
              "      <td>87.3</td>\n",
              "      <td>4.2390</td>\n",
              "      <td>4.0</td>\n",
              "      <td>307.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>380.23</td>\n",
              "      <td>11.98</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>476</th>\n",
              "      <td>4.87141</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.614</td>\n",
              "      <td>6.484</td>\n",
              "      <td>93.6</td>\n",
              "      <td>2.3053</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>396.21</td>\n",
              "      <td>18.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>15.02340</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.614</td>\n",
              "      <td>5.304</td>\n",
              "      <td>97.3</td>\n",
              "      <td>2.1007</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>349.48</td>\n",
              "      <td>24.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>478</th>\n",
              "      <td>10.23300</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.614</td>\n",
              "      <td>6.185</td>\n",
              "      <td>96.7</td>\n",
              "      <td>2.1705</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>379.70</td>\n",
              "      <td>18.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479</th>\n",
              "      <td>14.33370</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.614</td>\n",
              "      <td>6.229</td>\n",
              "      <td>88.0</td>\n",
              "      <td>1.9512</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>383.32</td>\n",
              "      <td>13.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>480</th>\n",
              "      <td>5.82401</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.532</td>\n",
              "      <td>6.242</td>\n",
              "      <td>64.7</td>\n",
              "      <td>3.4242</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>10.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>481</th>\n",
              "      <td>5.70818</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.532</td>\n",
              "      <td>6.750</td>\n",
              "      <td>74.9</td>\n",
              "      <td>3.3317</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>393.07</td>\n",
              "      <td>7.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>482</th>\n",
              "      <td>5.73116</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.532</td>\n",
              "      <td>7.061</td>\n",
              "      <td>77.0</td>\n",
              "      <td>3.4106</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>395.28</td>\n",
              "      <td>7.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>483</th>\n",
              "      <td>2.81838</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.532</td>\n",
              "      <td>5.762</td>\n",
              "      <td>40.3</td>\n",
              "      <td>4.0983</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>392.92</td>\n",
              "      <td>10.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>484</th>\n",
              "      <td>2.37857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.583</td>\n",
              "      <td>5.871</td>\n",
              "      <td>41.9</td>\n",
              "      <td>3.7240</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>370.73</td>\n",
              "      <td>13.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>485</th>\n",
              "      <td>3.67367</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.583</td>\n",
              "      <td>6.312</td>\n",
              "      <td>51.9</td>\n",
              "      <td>3.9917</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>388.62</td>\n",
              "      <td>10.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486</th>\n",
              "      <td>5.69175</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.583</td>\n",
              "      <td>6.114</td>\n",
              "      <td>79.8</td>\n",
              "      <td>3.5459</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>392.68</td>\n",
              "      <td>14.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487</th>\n",
              "      <td>4.83567</td>\n",
              "      <td>0.0</td>\n",
              "      <td>18.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.583</td>\n",
              "      <td>5.905</td>\n",
              "      <td>53.2</td>\n",
              "      <td>3.1523</td>\n",
              "      <td>24.0</td>\n",
              "      <td>666.0</td>\n",
              "      <td>20.2</td>\n",
              "      <td>388.22</td>\n",
              "      <td>11.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>488</th>\n",
              "      <td>0.15086</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.74</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.609</td>\n",
              "      <td>5.454</td>\n",
              "      <td>92.7</td>\n",
              "      <td>1.8209</td>\n",
              "      <td>4.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>20.1</td>\n",
              "      <td>395.09</td>\n",
              "      <td>18.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>489</th>\n",
              "      <td>0.18337</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.74</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.609</td>\n",
              "      <td>5.414</td>\n",
              "      <td>98.3</td>\n",
              "      <td>1.7554</td>\n",
              "      <td>4.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>20.1</td>\n",
              "      <td>344.05</td>\n",
              "      <td>23.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>490</th>\n",
              "      <td>0.20746</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.74</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.609</td>\n",
              "      <td>5.093</td>\n",
              "      <td>98.0</td>\n",
              "      <td>1.8226</td>\n",
              "      <td>4.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>20.1</td>\n",
              "      <td>318.43</td>\n",
              "      <td>29.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>491</th>\n",
              "      <td>0.10574</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.74</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.609</td>\n",
              "      <td>5.983</td>\n",
              "      <td>98.8</td>\n",
              "      <td>1.8681</td>\n",
              "      <td>4.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>20.1</td>\n",
              "      <td>390.11</td>\n",
              "      <td>18.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>492</th>\n",
              "      <td>0.11132</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.74</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.609</td>\n",
              "      <td>5.983</td>\n",
              "      <td>83.5</td>\n",
              "      <td>2.1099</td>\n",
              "      <td>4.0</td>\n",
              "      <td>711.0</td>\n",
              "      <td>20.1</td>\n",
              "      <td>396.90</td>\n",
              "      <td>13.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>0.17331</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.585</td>\n",
              "      <td>5.707</td>\n",
              "      <td>54.0</td>\n",
              "      <td>2.3817</td>\n",
              "      <td>6.0</td>\n",
              "      <td>391.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>12.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>0.27957</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.585</td>\n",
              "      <td>5.926</td>\n",
              "      <td>42.6</td>\n",
              "      <td>2.3817</td>\n",
              "      <td>6.0</td>\n",
              "      <td>391.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>13.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>495</th>\n",
              "      <td>0.17899</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.585</td>\n",
              "      <td>5.670</td>\n",
              "      <td>28.8</td>\n",
              "      <td>2.7986</td>\n",
              "      <td>6.0</td>\n",
              "      <td>391.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>393.29</td>\n",
              "      <td>17.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>496</th>\n",
              "      <td>0.28960</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.585</td>\n",
              "      <td>5.390</td>\n",
              "      <td>72.9</td>\n",
              "      <td>2.7986</td>\n",
              "      <td>6.0</td>\n",
              "      <td>391.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>21.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>497</th>\n",
              "      <td>0.26838</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.585</td>\n",
              "      <td>5.794</td>\n",
              "      <td>70.6</td>\n",
              "      <td>2.8927</td>\n",
              "      <td>6.0</td>\n",
              "      <td>391.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>14.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>498</th>\n",
              "      <td>0.23912</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.585</td>\n",
              "      <td>6.019</td>\n",
              "      <td>65.3</td>\n",
              "      <td>2.4091</td>\n",
              "      <td>6.0</td>\n",
              "      <td>391.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>12.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>499</th>\n",
              "      <td>0.17783</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.585</td>\n",
              "      <td>5.569</td>\n",
              "      <td>73.5</td>\n",
              "      <td>2.3999</td>\n",
              "      <td>6.0</td>\n",
              "      <td>391.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>395.77</td>\n",
              "      <td>15.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>500</th>\n",
              "      <td>0.22438</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.69</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.585</td>\n",
              "      <td>6.027</td>\n",
              "      <td>79.7</td>\n",
              "      <td>2.4982</td>\n",
              "      <td>6.0</td>\n",
              "      <td>391.0</td>\n",
              "      <td>19.2</td>\n",
              "      <td>396.90</td>\n",
              "      <td>14.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>0.06263</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.593</td>\n",
              "      <td>69.1</td>\n",
              "      <td>2.4786</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>391.99</td>\n",
              "      <td>9.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>502</th>\n",
              "      <td>0.04527</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.120</td>\n",
              "      <td>76.7</td>\n",
              "      <td>2.2875</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>0.06076</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.976</td>\n",
              "      <td>91.0</td>\n",
              "      <td>2.1675</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>504</th>\n",
              "      <td>0.10959</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.794</td>\n",
              "      <td>89.3</td>\n",
              "      <td>2.3889</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>393.45</td>\n",
              "      <td>6.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>505</th>\n",
              "      <td>0.04741</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11.93</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.573</td>\n",
              "      <td>6.030</td>\n",
              "      <td>80.8</td>\n",
              "      <td>2.5050</td>\n",
              "      <td>1.0</td>\n",
              "      <td>273.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>396.90</td>\n",
              "      <td>7.88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>506 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         CRIM    ZN  INDUS  CHAS    NOX     RM    AGE     DIS   RAD    TAX  \\\n",
              "0     0.00632  18.0   2.31   0.0  0.538  6.575   65.2  4.0900   1.0  296.0   \n",
              "1     0.02731   0.0   7.07   0.0  0.469  6.421   78.9  4.9671   2.0  242.0   \n",
              "2     0.02729   0.0   7.07   0.0  0.469  7.185   61.1  4.9671   2.0  242.0   \n",
              "3     0.03237   0.0   2.18   0.0  0.458  6.998   45.8  6.0622   3.0  222.0   \n",
              "4     0.06905   0.0   2.18   0.0  0.458  7.147   54.2  6.0622   3.0  222.0   \n",
              "5     0.02985   0.0   2.18   0.0  0.458  6.430   58.7  6.0622   3.0  222.0   \n",
              "6     0.08829  12.5   7.87   0.0  0.524  6.012   66.6  5.5605   5.0  311.0   \n",
              "7     0.14455  12.5   7.87   0.0  0.524  6.172   96.1  5.9505   5.0  311.0   \n",
              "8     0.21124  12.5   7.87   0.0  0.524  5.631  100.0  6.0821   5.0  311.0   \n",
              "9     0.17004  12.5   7.87   0.0  0.524  6.004   85.9  6.5921   5.0  311.0   \n",
              "10    0.22489  12.5   7.87   0.0  0.524  6.377   94.3  6.3467   5.0  311.0   \n",
              "11    0.11747  12.5   7.87   0.0  0.524  6.009   82.9  6.2267   5.0  311.0   \n",
              "12    0.09378  12.5   7.87   0.0  0.524  5.889   39.0  5.4509   5.0  311.0   \n",
              "13    0.62976   0.0   8.14   0.0  0.538  5.949   61.8  4.7075   4.0  307.0   \n",
              "14    0.63796   0.0   8.14   0.0  0.538  6.096   84.5  4.4619   4.0  307.0   \n",
              "15    0.62739   0.0   8.14   0.0  0.538  5.834   56.5  4.4986   4.0  307.0   \n",
              "16    1.05393   0.0   8.14   0.0  0.538  5.935   29.3  4.4986   4.0  307.0   \n",
              "17    0.78420   0.0   8.14   0.0  0.538  5.990   81.7  4.2579   4.0  307.0   \n",
              "18    0.80271   0.0   8.14   0.0  0.538  5.456   36.6  3.7965   4.0  307.0   \n",
              "19    0.72580   0.0   8.14   0.0  0.538  5.727   69.5  3.7965   4.0  307.0   \n",
              "20    1.25179   0.0   8.14   0.0  0.538  5.570   98.1  3.7979   4.0  307.0   \n",
              "21    0.85204   0.0   8.14   0.0  0.538  5.965   89.2  4.0123   4.0  307.0   \n",
              "22    1.23247   0.0   8.14   0.0  0.538  6.142   91.7  3.9769   4.0  307.0   \n",
              "23    0.98843   0.0   8.14   0.0  0.538  5.813  100.0  4.0952   4.0  307.0   \n",
              "24    0.75026   0.0   8.14   0.0  0.538  5.924   94.1  4.3996   4.0  307.0   \n",
              "25    0.84054   0.0   8.14   0.0  0.538  5.599   85.7  4.4546   4.0  307.0   \n",
              "26    0.67191   0.0   8.14   0.0  0.538  5.813   90.3  4.6820   4.0  307.0   \n",
              "27    0.95577   0.0   8.14   0.0  0.538  6.047   88.8  4.4534   4.0  307.0   \n",
              "28    0.77299   0.0   8.14   0.0  0.538  6.495   94.4  4.4547   4.0  307.0   \n",
              "29    1.00245   0.0   8.14   0.0  0.538  6.674   87.3  4.2390   4.0  307.0   \n",
              "..        ...   ...    ...   ...    ...    ...    ...     ...   ...    ...   \n",
              "476   4.87141   0.0  18.10   0.0  0.614  6.484   93.6  2.3053  24.0  666.0   \n",
              "477  15.02340   0.0  18.10   0.0  0.614  5.304   97.3  2.1007  24.0  666.0   \n",
              "478  10.23300   0.0  18.10   0.0  0.614  6.185   96.7  2.1705  24.0  666.0   \n",
              "479  14.33370   0.0  18.10   0.0  0.614  6.229   88.0  1.9512  24.0  666.0   \n",
              "480   5.82401   0.0  18.10   0.0  0.532  6.242   64.7  3.4242  24.0  666.0   \n",
              "481   5.70818   0.0  18.10   0.0  0.532  6.750   74.9  3.3317  24.0  666.0   \n",
              "482   5.73116   0.0  18.10   0.0  0.532  7.061   77.0  3.4106  24.0  666.0   \n",
              "483   2.81838   0.0  18.10   0.0  0.532  5.762   40.3  4.0983  24.0  666.0   \n",
              "484   2.37857   0.0  18.10   0.0  0.583  5.871   41.9  3.7240  24.0  666.0   \n",
              "485   3.67367   0.0  18.10   0.0  0.583  6.312   51.9  3.9917  24.0  666.0   \n",
              "486   5.69175   0.0  18.10   0.0  0.583  6.114   79.8  3.5459  24.0  666.0   \n",
              "487   4.83567   0.0  18.10   0.0  0.583  5.905   53.2  3.1523  24.0  666.0   \n",
              "488   0.15086   0.0  27.74   0.0  0.609  5.454   92.7  1.8209   4.0  711.0   \n",
              "489   0.18337   0.0  27.74   0.0  0.609  5.414   98.3  1.7554   4.0  711.0   \n",
              "490   0.20746   0.0  27.74   0.0  0.609  5.093   98.0  1.8226   4.0  711.0   \n",
              "491   0.10574   0.0  27.74   0.0  0.609  5.983   98.8  1.8681   4.0  711.0   \n",
              "492   0.11132   0.0  27.74   0.0  0.609  5.983   83.5  2.1099   4.0  711.0   \n",
              "493   0.17331   0.0   9.69   0.0  0.585  5.707   54.0  2.3817   6.0  391.0   \n",
              "494   0.27957   0.0   9.69   0.0  0.585  5.926   42.6  2.3817   6.0  391.0   \n",
              "495   0.17899   0.0   9.69   0.0  0.585  5.670   28.8  2.7986   6.0  391.0   \n",
              "496   0.28960   0.0   9.69   0.0  0.585  5.390   72.9  2.7986   6.0  391.0   \n",
              "497   0.26838   0.0   9.69   0.0  0.585  5.794   70.6  2.8927   6.0  391.0   \n",
              "498   0.23912   0.0   9.69   0.0  0.585  6.019   65.3  2.4091   6.0  391.0   \n",
              "499   0.17783   0.0   9.69   0.0  0.585  5.569   73.5  2.3999   6.0  391.0   \n",
              "500   0.22438   0.0   9.69   0.0  0.585  6.027   79.7  2.4982   6.0  391.0   \n",
              "501   0.06263   0.0  11.93   0.0  0.573  6.593   69.1  2.4786   1.0  273.0   \n",
              "502   0.04527   0.0  11.93   0.0  0.573  6.120   76.7  2.2875   1.0  273.0   \n",
              "503   0.06076   0.0  11.93   0.0  0.573  6.976   91.0  2.1675   1.0  273.0   \n",
              "504   0.10959   0.0  11.93   0.0  0.573  6.794   89.3  2.3889   1.0  273.0   \n",
              "505   0.04741   0.0  11.93   0.0  0.573  6.030   80.8  2.5050   1.0  273.0   \n",
              "\n",
              "     PTRATIO       B  LSTAT  \n",
              "0       15.3  396.90   4.98  \n",
              "1       17.8  396.90   9.14  \n",
              "2       17.8  392.83   4.03  \n",
              "3       18.7  394.63   2.94  \n",
              "4       18.7  396.90   5.33  \n",
              "5       18.7  394.12   5.21  \n",
              "6       15.2  395.60  12.43  \n",
              "7       15.2  396.90  19.15  \n",
              "8       15.2  386.63  29.93  \n",
              "9       15.2  386.71  17.10  \n",
              "10      15.2  392.52  20.45  \n",
              "11      15.2  396.90  13.27  \n",
              "12      15.2  390.50  15.71  \n",
              "13      21.0  396.90   8.26  \n",
              "14      21.0  380.02  10.26  \n",
              "15      21.0  395.62   8.47  \n",
              "16      21.0  386.85   6.58  \n",
              "17      21.0  386.75  14.67  \n",
              "18      21.0  288.99  11.69  \n",
              "19      21.0  390.95  11.28  \n",
              "20      21.0  376.57  21.02  \n",
              "21      21.0  392.53  13.83  \n",
              "22      21.0  396.90  18.72  \n",
              "23      21.0  394.54  19.88  \n",
              "24      21.0  394.33  16.30  \n",
              "25      21.0  303.42  16.51  \n",
              "26      21.0  376.88  14.81  \n",
              "27      21.0  306.38  17.28  \n",
              "28      21.0  387.94  12.80  \n",
              "29      21.0  380.23  11.98  \n",
              "..       ...     ...    ...  \n",
              "476     20.2  396.21  18.68  \n",
              "477     20.2  349.48  24.91  \n",
              "478     20.2  379.70  18.03  \n",
              "479     20.2  383.32  13.11  \n",
              "480     20.2  396.90  10.74  \n",
              "481     20.2  393.07   7.74  \n",
              "482     20.2  395.28   7.01  \n",
              "483     20.2  392.92  10.42  \n",
              "484     20.2  370.73  13.34  \n",
              "485     20.2  388.62  10.58  \n",
              "486     20.2  392.68  14.98  \n",
              "487     20.2  388.22  11.45  \n",
              "488     20.1  395.09  18.06  \n",
              "489     20.1  344.05  23.97  \n",
              "490     20.1  318.43  29.68  \n",
              "491     20.1  390.11  18.07  \n",
              "492     20.1  396.90  13.35  \n",
              "493     19.2  396.90  12.01  \n",
              "494     19.2  396.90  13.59  \n",
              "495     19.2  393.29  17.60  \n",
              "496     19.2  396.90  21.14  \n",
              "497     19.2  396.90  14.10  \n",
              "498     19.2  396.90  12.92  \n",
              "499     19.2  395.77  15.10  \n",
              "500     19.2  396.90  14.33  \n",
              "501     21.0  391.99   9.67  \n",
              "502     21.0  396.90   9.08  \n",
              "503     21.0  396.90   5.64  \n",
              "504     21.0  393.45   6.48  \n",
              "505     21.0  396.90   7.88  \n",
              "\n",
              "[506 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "metadata": {
        "id": "k9FQ1lVN4tI2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d984fe66-1b7e-44d0-97f4-f9382d4f4280"
      },
      "cell_type": "code",
      "source": [
        "boston.keys()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "metadata": {
        "id": "RFvfMwrmOGpJ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x = boston.data\n",
        "y = boston.target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1p1GInlvGoKM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn import linear_model\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jCCyqTV3JLCw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "reg = linear_model.LinearRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Sfg4WNbiJPzs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "81a1b7d4-65eb-4512-f386-b820f079d078"
      },
      "cell_type": "code",
      "source": [
        "reg.fit(x, y)\n",
        "\n",
        "test_house = [0.80271, 0.0, 8.14, 0.0, 0.538, 5.456, 36.6, 3.7965, 4.0, 307.0 ,21.0, 288.99, 11.69]\n",
        "\n",
        "result = reg.predict([test_house])\n",
        "\n",
        "print(result)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[16.17801106]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "FChtSswMJReM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Train Test Split"
      ]
    },
    {
      "metadata": {
        "id": "2l9MufzPJHSt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UVyMqp96JTNK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(x, y, \n",
        "                                                    test_size=0.1,\n",
        "                                                    random_state=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OClxHo0COYKp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "reg = linear_model.LinearRegression()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "06BxUghpOXPk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "8d5dc330-84ac-47e0-aa58-c13f8b1b458e"
      },
      "cell_type": "code",
      "source": [
        "reg.fit(x_train, y_train)"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
              "         normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 144
        }
      ]
    },
    {
      "metadata": {
        "id": "-tcFs6mAOesi",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_pred = reg.predict(x_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8IxQrl6gOg6P",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "63953b96-7ff2-4006-97d5-9fd5acb1d703"
      },
      "cell_type": "code",
      "source": [
        "print(\"Test Predictions: \\n {}\".format(y_pred))"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test Predictions: \n",
            " [11.46030778 26.80269335 17.43478925 17.5563101  37.39156424 25.07675556\n",
            " 31.05825852 20.30845531 19.66757374 22.82655375 28.47083056 28.53331605\n",
            " 18.72883256 33.11375161 21.34282974 15.20554693 21.57309275 10.92841589\n",
            " 11.69603405 13.54311508  5.07126801 17.40464043 20.69379268 22.72981238\n",
            " 16.4634139  20.42666271 17.53377349 14.22644356 21.56292745 17.33136115\n",
            " 14.28888479 23.92829804 34.31523522 22.03799035 17.47895779 20.20386005\n",
            " 30.70896335 35.21599528 24.07063567 24.51445184 36.77425366 33.15265201\n",
            " 19.67545976 31.93505104 33.55222906 25.59147737 40.59239607 17.99555017\n",
            " 19.92780188 23.65319423 33.48950986]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r8JyJvcFOjF7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d3630e9b-ad59-440d-eed7-57a07ae57eaf"
      },
      "cell_type": "code",
      "source": [
        "# Print accuracy\n",
        "reg.score(x_test, y_test)"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7926083526613206"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "metadata": {
        "id": "GfA-aNFhP-eh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "outputId": "469a4c29-39ba-4560-bf86-bd793dc4c1f3"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "predicted = reg.predict(x_test)\n",
        "\n",
        "expected = y_test\n",
        "\n",
        "plt.figure(figsize=(8, 5))\n",
        "plt.scatter(expected, predicted)\n",
        "plt.xlabel('Price ($1000s)')\n",
        "plt.ylabel('Predicted price ($1000s)')\n",
        "plt.tight_layout()"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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aCaAp+Qw03333nV544YV61zZu3KiMjIwGv27kyJEaOXLkWddXr159jiUCpwTy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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "u08rdhYk5-PR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Back to Github repo: https://github.com/sagecodes/intro-machine-learning"
      ]
    },
    {
      "metadata": {
        "id": "iSFQz5fr6BcL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}