Housing_price.ipynb

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  "metadata": {
    "colab": {
      "name": "Housing price.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Qxr2427/MachineLearning-Opensource-Projects/blob/master/Housing_price.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9vOtCKjiIF72",
        "colab_type": "text"
      },
      "source": [
        "**Homework practise for regression problem: Housing price prodiction**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DFpnvM7wIFVf",
        "colab_type": "text"
      },
      "source": [
        "This notebook builds a model to predict the median price of homes in a Boston suburb during the mid-1970s. To do this, we'll provide the model with some data points about the suburb, such as the crime rate and the local property tax rate."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yh7Xkb-IX-x",
        "colab_type": "code",
        "outputId": "9ad9ec42-f3e7-4dca-acd2-8f104e273a1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Let's get started by importing tensorflow and keras\n",
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "import pathlib\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.14.0-rc1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BAnoVPxDI3_m",
        "colab_type": "text"
      },
      "source": [
        "## The Boston Housing Prices dataset\n",
        "\n",
        "This [dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html) is accessible directly in TensorFlow. Download and shuffle the training set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aG0uzrHwKBek",
        "colab_type": "code",
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        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        }
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-2ce23e4f-8897-4b4a-a462-d360d22d0349\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-2ce23e4f-8897-4b4a-a462-d360d22d0349\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving original to original\n",
            "User uploaded file \"original\" with length 51256 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pzzh4pP7KHMP",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "Let's parse this data by using pandas\n",
        "Find the indices of odd and even rows and pass it  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dEloK7FOHaXB",
        "colab_type": "code",
        "outputId": "d6d7856d-80c1-43bc-aa6d-1f2eaeb0e519",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "!head {fn}\n",
        "dataset_path=fn\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n",
            " prices and the demand for clean air', J. Environ. Economics & Management,\n",
            " vol.5, 81-102, 1978.   Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n",
            " ...', Wiley, 1980.   N.B. Various transformations are used in the table on\n",
            " pages 244-261 of the latter.\n",
            "\n",
            " Variables 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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "McazAw4VHeIv",
        "colab_type": "text"
      },
      "source": [
        "**Data Cleaning**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WShO-MHn0Vup",
        "colab_type": "code",
        "outputId": "d62e5e9c-7961-4c43-e839-a1327829f9a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "columnNames = [\"CRIM\",\"ZN\",\"INDUS\",\"CHAS\",\"NOX\",\"RM\",\"AGE\",\"DIS\",\"RAD\",\"TAX\",\"PTRATIO\",\"B\",\"LSTAT\",\"MEDV\"]\n",
        "\n",
        "rawDataSet = pd.read_csv(fn,names = columnNames,na_values = \"?\",comment = \"\\t\",sep = \" \",skipinitialspace = True,skiprows = 21)\n",
        "rawDataSet = pd.DataFrame(rawDataSet.values.reshape(506, 28), columns = ['CRIM',\"ZN\",\"INDUS\",\"CHAS\",\"NOX\",\"RM\",\"AGE\",\"DIS\",\"RAD\",\"TAX\",\"PTRATIO\",'B','LSTAT','MEDV',\n",
        "                                                                         'B','LSTAT','MEDV','s','s','s','s','s','s','s','s','s','s','s'])\n",
        "dataSet = rawDataSet.copy()\n",
        "\n",
        "dataSet.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>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",
              "      <th>MEDV</th>\n",
              "      <th>B</th>\n",
              "      <th>LSTAT</th>\n",
              "      <th>MEDV</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
              "      <th>s</th>\n",
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              "  </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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>396.90</td>\n",
              "      <td>4.98</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>396.90</td>\n",
              "      <td>9.14</td>\n",
              "      <td>21.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>392.83</td>\n",
              "      <td>4.03</td>\n",
              "      <td>34.7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>394.63</td>\n",
              "      <td>2.94</td>\n",
              "      <td>33.4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>396.90</td>\n",
              "      <td>5.33</td>\n",
              "      <td>36.2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE  ...   s   s   s   s   s   s   s\n",
              "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "di-VskzsIdZG",
        "colab_type": "code",
        "outputId": "a7043fac-be8b-4358-80ac-9adac8cba9d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "dataSet.tail()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>502</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>9.08</td>\n",
              "      <td>20.6</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <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",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>5.64</td>\n",
              "      <td>23.9</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>393.45</td>\n",
              "      <td>6.48</td>\n",
              "      <td>22.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>505</th>\n",
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              "      <td>0.573</td>\n",
              "      <td>6.030</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>7.88</td>\n",
              "      <td>11.9</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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            "text/plain": [
              "        CRIM   ZN  INDUS  CHAS    NOX     RM   AGE  ...   s   s   s   s   s   s   s\n",
              "501  0.06263  0.0  11.93   0.0  0.573  6.593  69.1  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "502  0.04527  0.0  11.93   0.0  0.573  6.120  76.7  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "503  0.06076  0.0  11.93   0.0  0.573  6.976  91.0  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "504  0.10959  0.0  11.93   0.0  0.573  6.794  89.3  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "505  0.04741  0.0  11.93   0.0  0.573  6.030  80.8  ... NaN NaN NaN NaN NaN NaN NaN\n",
              "\n",
              "[5 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5oLq66NbjHX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "  dataSet.isna().sum()\n",
        "  dataSet = dataSet.dropna(axis='columns')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G-1-NXRuK6dd",
        "colab_type": "code",
        "outputId": "7f74071a-fd33-4296-9d33-ffb3c8a3f479",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
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      },
      "source": [
        "dataSet.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "data": {
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              "      <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",
              "      <td>33.4</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",
              "      <td>36.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      CRIM    ZN  INDUS  CHAS    NOX  ...    TAX  PTRATIO       B  LSTAT  MEDV\n",
              "0  0.00632  18.0   2.31   0.0  0.538  ...  296.0     15.3  396.90   4.98  24.0\n",
              "1  0.02731   0.0   7.07   0.0  0.469  ...  242.0     17.8  396.90   9.14  21.6\n",
              "2  0.02729   0.0   7.07   0.0  0.469  ...  242.0     17.8  392.83   4.03  34.7\n",
              "3  0.03237   0.0   2.18   0.0  0.458  ...  222.0     18.7  394.63   2.94  33.4\n",
              "4  0.06905   0.0   2.18   0.0  0.458  ...  222.0     18.7  396.90   5.33  36.2\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rc3uwVYnLfUV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataSet = dataSet.sample(frac=0.8,random_state=0)\n",
        "test_dataSet = dataSet.drop(train_dataSet.index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p4JTlYDQULKn",
        "colab_type": "code",
        "outputId": "e562bf0f-1b47-47dd-ea02-cb55b8890d8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "train_stats = train_dataSet.describe()\n",
        "train_stats.pop(\"MEDV\")\n",
        "train_stats = train_stats.transpose()\n",
        "train_stats"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CRIM</th>\n",
              "      <td>405.0</td>\n",
              "      <td>3.670339</td>\n",
              "      <td>8.977954</td>\n",
              "      <td>0.00632</td>\n",
              "      <td>0.08265</td>\n",
              "      <td>0.26838</td>\n",
              "      <td>3.47428</td>\n",
              "      <td>88.9762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ZN</th>\n",
              "      <td>405.0</td>\n",
              "      <td>11.067901</td>\n",
              "      <td>22.688396</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>12.50000</td>\n",
              "      <td>95.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>INDUS</th>\n",
              "      <td>405.0</td>\n",
              "      <td>11.097951</td>\n",
              "      <td>6.699157</td>\n",
              "      <td>0.74000</td>\n",
              "      <td>5.32000</td>\n",
              "      <td>9.69000</td>\n",
              "      <td>18.10000</td>\n",
              "      <td>27.7400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHAS</th>\n",
              "      <td>405.0</td>\n",
              "      <td>0.069136</td>\n",
              "      <td>0.253999</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>1.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NOX</th>\n",
              "      <td>405.0</td>\n",
              "      <td>0.553098</td>\n",
              "      <td>0.115564</td>\n",
              "      <td>0.38500</td>\n",
              "      <td>0.44900</td>\n",
              "      <td>0.53800</td>\n",
              "      <td>0.62400</td>\n",
              "      <td>0.8710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RM</th>\n",
              "      <td>405.0</td>\n",
              "      <td>6.269768</td>\n",
              "      <td>0.690552</td>\n",
              "      <td>3.56100</td>\n",
              "      <td>5.87800</td>\n",
              "      <td>6.19300</td>\n",
              "      <td>6.61900</td>\n",
              "      <td>8.7800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>405.0</td>\n",
              "      <td>68.241975</td>\n",
              "      <td>27.908527</td>\n",
              "      <td>2.90000</td>\n",
              "      <td>45.00000</td>\n",
              "      <td>76.50000</td>\n",
              "      <td>94.10000</td>\n",
              "      <td>100.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DIS</th>\n",
              "      <td>405.0</td>\n",
              "      <td>3.834098</td>\n",
              "      <td>2.162173</td>\n",
              "      <td>1.12960</td>\n",
              "      <td>2.07880</td>\n",
              "      <td>3.27210</td>\n",
              "      <td>5.21460</td>\n",
              "      <td>12.1265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RAD</th>\n",
              "      <td>405.0</td>\n",
              "      <td>9.474074</td>\n",
              "      <td>8.618564</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>4.00000</td>\n",
              "      <td>5.00000</td>\n",
              "      <td>24.00000</td>\n",
              "      <td>24.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TAX</th>\n",
              "      <td>405.0</td>\n",
              "      <td>405.839506</td>\n",
              "      <td>164.057951</td>\n",
              "      <td>187.00000</td>\n",
              "      <td>284.00000</td>\n",
              "      <td>334.00000</td>\n",
              "      <td>666.00000</td>\n",
              "      <td>711.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PTRATIO</th>\n",
              "      <td>405.0</td>\n",
              "      <td>18.437037</td>\n",
              "      <td>2.141275</td>\n",
              "      <td>12.60000</td>\n",
              "      <td>17.30000</td>\n",
              "      <td>18.80000</td>\n",
              "      <td>20.20000</td>\n",
              "      <td>22.0000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>405.0</td>\n",
              "      <td>356.276444</td>\n",
              "      <td>91.298597</td>\n",
              "      <td>2.60000</td>\n",
              "      <td>375.21000</td>\n",
              "      <td>391.23000</td>\n",
              "      <td>396.24000</td>\n",
              "      <td>396.9000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LSTAT</th>\n",
              "      <td>405.0</td>\n",
              "      <td>12.638914</td>\n",
              "      <td>7.090281</td>\n",
              "      <td>1.73000</td>\n",
              "      <td>7.19000</td>\n",
              "      <td>11.32000</td>\n",
              "      <td>16.65000</td>\n",
              "      <td>37.9700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         count        mean         std  ...        50%        75%       max\n",
              "CRIM     405.0    3.670339    8.977954  ...    0.26838    3.47428   88.9762\n",
              "ZN       405.0   11.067901   22.688396  ...    0.00000   12.50000   95.0000\n",
              "INDUS    405.0   11.097951    6.699157  ...    9.69000   18.10000   27.7400\n",
              "CHAS     405.0    0.069136    0.253999  ...    0.00000    0.00000    1.0000\n",
              "NOX      405.0    0.553098    0.115564  ...    0.53800    0.62400    0.8710\n",
              "RM       405.0    6.269768    0.690552  ...    6.19300    6.61900    8.7800\n",
              "AGE      405.0   68.241975   27.908527  ...   76.50000   94.10000  100.0000\n",
              "DIS      405.0    3.834098    2.162173  ...    3.27210    5.21460   12.1265\n",
              "RAD      405.0    9.474074    8.618564  ...    5.00000   24.00000   24.0000\n",
              "TAX      405.0  405.839506  164.057951  ...  334.00000  666.00000  711.0000\n",
              "PTRATIO  405.0   18.437037    2.141275  ...   18.80000   20.20000   22.0000\n",
              "B        405.0  356.276444   91.298597  ...  391.23000  396.24000  396.9000\n",
              "LSTAT    405.0   12.638914    7.090281  ...   11.32000   16.65000   37.9700\n",
              "\n",
              "[13 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Pgy-wnVLqxT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_labels = train_dataSet.pop('MEDV')\n",
        "test_labels = test_dataSet.pop('MEDV')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RzLw0MGkUH4O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def norm(x):\n",
        "  return (x - train_stats['mean']) / train_stats['std']\n",
        "normed_train_data = norm(train_dataSet)\n",
        "normed_test_data = norm(test_dataSet)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZm-hRMUUcu4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def build_model():\n",
        "  model = keras.Sequential([\n",
        "    layers.Dense(64, activation=tf.nn.relu, input_shape=[len(train_dataSet.keys())]),\n",
        "    layers.Dense(64, activation=tf.nn.relu),\n",
        "    layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  optimizer = tf.keras.optimizers.RMSprop(0.001)\n",
        "\n",
        "  model.compile(loss='mean_squared_error',\n",
        "                optimizer=optimizer,\n",
        "                metrics=['mean_absolute_error', 'mean_squared_error'])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fsJJUGrPUhc2",
        "colab_type": "code",
        "outputId": "44d751b6-6114-467f-98e8-a0d78594503a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "model = build_model()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0620 00:00:14.518736 140103267010432 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8mf_LYrbUnd0",
        "colab_type": "code",
        "outputId": "d476d063-4824-4dfc-8d78-b815d7548b25",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 64)                896       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 5,121\n",
            "Trainable params: 5,121\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FpW5ycN4YHi6",
        "colab": {}
      },
      "source": [
        "def plot_history(history):\n",
        "  hist = pd.DataFrame(history.history)\n",
        "  hist['epoch'] = history.epoch\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Mean Abs Error [MEDV]')\n",
        "  plt.plot(hist['epoch'], hist['mean_absolute_error'],\n",
        "           label='Train Error')\n",
        "  plt.plot(hist['epoch'], hist['val_mean_absolute_error'],\n",
        "           label = 'Val Error')\n",
        "  plt.ylim([0,5])\n",
        "  plt.legend()\n",
        "\n",
        "  plt.figure()\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Mean Square Error [$MEDV^2$]')\n",
        "  plt.plot(hist['epoch'], hist['mean_squared_error'],\n",
        "           label='Train Error')\n",
        "  plt.plot(hist['epoch'], hist['val_mean_squared_error'],\n",
        "           label = 'Val Error')\n",
        "  plt.ylim([0,20])\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "class PrintDot(keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epoch, logs):\n",
        "    if epoch % 100 == 0: print('')\n",
        "    print('.', end='')\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBHWHBBtVgSD",
        "colab_type": "code",
        "outputId": "50369a12-e8f6-4309-c6f8-07e84c13df36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        }
      },
      "source": [
        "model = build_model()\n",
        "EPOCHS=500\n",
        "# The patience parameter is the amount of epochs to check for improvement\n",
        "early_stop = keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)\n",
        "\n",
        "history = model.fit(normed_train_data, train_labels, epochs=EPOCHS,\n",
        "                    validation_split = 0.2, verbose=0, callbacks=[early_stop, PrintDot()])\n",
        "\n",
        "plot_history(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "....................................................................................................\n",
            "......."
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kr9H2WaXULuC/wAFgtNZ6rNb6Ta21JKs7WxpuD5i0znn9OnBu32j+/esOdmXmNWPBhBCi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\nERwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gBfAJ8LmIdPPmhiIyF1gC9BCRVBG5BviXiPwmIr8Co4DbHccmiMhnAMaYIuBm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\nERFtSxKJiIi2JYlERETbfgfGZyA363kPBgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIak-0X8VjZM",
        "colab_type": "code",
        "outputId": "32cd00c7-c57e-4948-dda2-712e9bfefb94",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "loss, mae, mse = model.evaluate(normed_test_data, test_labels, verbose=0)\n",
        "mse=mae*mae\n",
        "print(\"Testing set Mean Abs Error: {:5.2f} MEDV\".format(mae))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Testing set Mean Abs Error:  2.36 MEDV\n",
            "Testing set Mean Sq Error:  5.59 MEDV\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dgi1JqP6Vleh",
        "colab_type": "code",
        "outputId": "ff1684c0-1652-49e0-a062-cf092cc87765",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "test_predictions = model.predict(normed_test_data).flatten()\n",
        "\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel('True Values [MEDV]')\n",
        "plt.ylabel('Predictions [MEDV]')\n",
        "plt.axis('equal')\n",
        "plt.axis('square')\n",
        "plt.xlim([0,plt.xlim()[1]])\n",
        "plt.ylim([0,plt.ylim()[1]])\n",
        "_ = plt.plot([-100, 100], [-100, 100])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YcRwnjoZKZXccp76oawEpJeW9wvf/jqQtkh7P2neYpPslPR0+H1olW46W9ICk\nJyStk3RJje0ZKek3kh4N7fliuH+SpIfDz2xxGCivCpLaJa2WdE8d2LJR0lpJayT1hvtq8lmF9x4j\n6Q5JT0l6UtJplbCnbgWklJT3FPgu8M6cffOBFWY2GVgRbleDQeByMzseOBX4TPj7qJU9u4CzzOwk\nYBrwTkmnAl8CvmpmxwEvAXOqZA/AJcCTWdu1tAXg7WY2LWu6tFafFcDXgPvMbCpwEsHvqXx7zKwu\nH8BpwPKs7SuAK2pgx0Tg8azt9cAR4esjgPU1+v3cBZxTD/YABwGPAKcQJCd15PsMU7bhqPBLcBZw\nD6Ba2RLebyNweM6+mnxWwGuBPxDGPCtpT916IJSQ8l4lxpvZc+HrzcD4ahsgaSIwHXi4lvaEQ4Y1\nwBbgfuD3QL+ZDYaHVPMzuw74PDAUbo+toS0ABvxE0qowqxpq91lNArYCN4VDvBskjaqEPfUsIHWP\nBdJd1WksSQcDPwQuNbNttbTHzPaa2TSC//4nA1Orde9sJL0X2GJmq2px/wKcYWZvJhiCf0bSmdlv\nVvmz6gDeDFxvZtOBV8gZrpRqTz0LSKKU9xrwvKQjAMLnLdW6saROAvG4xcyW1NqeDGbWDzxAMEwY\nIymTX1Stz2wG8H5JGwmqu88iGPPXwhYAzKwvfN4C3EkgsLX6rDYBm8zs4XD7DgJBKdueehaQek15\nXwbMDl/PJohFpI4kATcCT5rZV+rAnnGSxoSvuwjiMU8SCMkF1bTHzK4ws6PMbCLB38lKM/twLWwB\nkDRK0iGZ18C5wOPU6LMys83AnyRl1nM9G3iiIvZUK6hUYvDn3cBvCcbW/6sG978VeA7YQ6DicwjG\n1iuAp4GfAodVyZYzCFzMx4A14ePdNbTnRGB1aM/jwD+G+48BfgP8DvgB8Joqf2ZvA+6ppS3hfR8N\nH+syf7u1+qzCe08DesPPaylwaCXs8UxUx3FKpp6HMI7j1DkuII7jlIwLiOM4JeMC4jhOybiAOI5T\nMi4gjuOUjAtIHSJpbFgGvkbSZkl9WdsVKUmXdIikF8PU+Oz990j6YMR575C0tBI2FLj+zZL+IOl/\nhNtXSbKw/idzzP8M900Ltzdllc6vkfTVnGs9Kum3khZJOjJ87z8kzcm59wWS7g5/N2sk7c4kyzn5\ncQGpQ8zsRQvKwKcB3yIoSZ8WPnZDkJkqqeTPz8y2EyQR7VvUK+wHcSpwb3k/QdlcZmY3ZG2vJcgw\nzfBBhpftA7w163d0Wc61TiKo01kLrAxLAm7NuSbh9q1mtj383Ve9LKDRcAFpICQdp6Ch0C0EGY5H\nS+rPev8iSTeEr8dLWiKpV0E14rNVAAAC7UlEQVTjn1PzXDL3S/RB4F4z2ynpVEkPhdWbv5I0OY89\nV0m6NGv7KUlHha9nh/ddI+mbktokdYT/+ddKelzS5xL+6EuAD4TXfSNBmf6fE54LgJkNmdm/heed\nS1A9fIKk14XXPYQgi7Uq6eXNggtI4zGVwCM5nujisK8D11jQzOZC4IY8x/wIOCWrE9VFBKICwX/4\nt1pQvfkvwFVJDZT0JoIv/Onhf/KO8Np/SdAj4wQzexPwvYSX7Ac2S5oKXExQMJfLL7KGMFHC9Agw\n1cz2EKR0/7dw/3nAT83slYQ2OdTxynROQX5vZr0JjnsHMCWowQPgUEldZjaQ2WFmuyTdC5yvoA3g\nXxDURACMAb4n6dgSbHwH8BagN7x/F0Fvl+WhTV8nGCb9pIhrLiYQofcBfwV8Kuf9t1pQFRyHsl7f\nSiCM3wiv/e0i7HFwAWlEsv9DDjH8CzEy67WAkzMxkwhuBeYRfMnvtP0NeP6VoIPXNyUdB9yX59xB\nhnuxmfuLYL2f/5N7gqQTCXtkEAyZ5uYeU4BlBF7Rg2a2I0sYi2Ua+2M8vwDeENr0FuD8Ui/aqvgQ\npoExsyHgJUmTw4DqB7Le/inBlxSAzIxFHlYQeB5/y/7hCwRt8DJDpI8VOHcjwbAESSezv3/LT4EL\nJR0evjdW0gRJ4wja6v0A+EeCnhSJMLMdwBeAq5Oek00YdL6MoAL1/vCaQwRVut8jqOCNE1snBxeQ\nxucLBEODBwlaDmT4DDBD0mOSngD+Jt/JZraXIEg5Gvhl1ltfAq6V9AjDvZxsfgCMV9C1fi6wIbzm\nWuCLwE8lPUYwVBlPIDA/V9AG8SbgH4r5Qc3s+2a2psDb2TGQm7L2f1XSowT9P6cRNILek/X+rQRN\nhrPF00mIl/M7dYWkm4E7zCy1XJMibNkEvClhbKUlcQ/EqTf6gasziWS1IJNIRuB5DcUd38q4B+I4\nTsm4B+I4Tsm4gDiOUzIuII7jlIwLiOM4JeMC4jhOyfx/bmatOs3f+gQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YRf-jJ4cVn54",
        "colab_type": "code",
        "outputId": "0dbeff63-6563-406b-e669-e5fec83813ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "error = test_predictions - test_labels\n",
        "\n",
        "plt.hist(error, bins = 25)\n",
        "plt.xlabel(\"Prediction Error [MEDV]\")\n",
        "_ = plt.ylabel(\"Count\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}